Citation
Service innovation for knowledge-intensive services in the digital age : the case of academic libraries

Material Information

Title:
Service innovation for knowledge-intensive services in the digital age : the case of academic libraries
Creator:
Yeh, Shea-Tinn
Place of Publication:
Denver, CO
Publisher:
University of Colorado Denver
Publication Date:
Language:
English

Thesis/Dissertation Information

Degree:
Doctorate ( Doctor of philosophy)
Degree Grantor:
University of Colorado Denver
Degree Divisions:
Department of Computer Science and Engineering, CU Denver
Degree Disciplines:
Computer science
Committee Chair:
Karimi, Jahangir
Committee Members:
Ramirez, Ronald
Oh, Onook
Ra, Ilkyeun
Stansbury, Mary

Notes

Abstract:
The pervasiveness of digital technology, along with unprecedented computing power, has altered innovation techniques in all industries, including those that provide knowledge-intensive services. Extant research on innovation has concentrated on information and communications technology in service innovation along the dimensions of technology related interfaces, delivery, and infrastructure. The immateriality and lack of physical form of new digital technologies, however, present unique challenges and research opportunities. What roles do digital technologies play in service innovation and how may digital technologies interact with critical resources for service innovation in knowledge-intensive service organizations? Based on the service-dominant logic perspective, this research theorizes that digital technologies, as operand and operant resources, integrate with intellectual capital to build digital platform capabilities essential for service innovation within knowledge-intensive service providers. This study presents a new integrative framework for service innovation in the digital age and validates the framework in the context of academic libraries, the type of organization whose central purpose is the delivery of knowledge-intensive services. For the validation, a survey instrument was developed and administered to library administrators at both doctoral universities and master’s colleges and universities in the United States. Structural equation modeling results support that knowledge-intensive service resources positively contribute to service innovation, an academic library’s ability to build digital platform capabilities is enhanced by the integration of knowledge-intensive service resources and digital technologies, and digital platform capabilities positively contribute to service innovation. The contributions of this research are manyfold owing to the fact that it is the first study to recognize that service-dominant logic’s intangible resources and intellectual capital are analogous; propose that intellectual capital and digital technologies are critical knowledge-intensive service resources; and propose and validate an integrative model of knowledge-intensive service resources that builds digital platform capabilities for service innovation. This research advances extant theories on service innovation and informs service providers about the way in which intangible resources are transformed by digital technologies for service innovation. This study also applies the Management of Information Systems discipline to the understanding of service innovation in the Library and Information Science research field.
Restriction:
Embargo ended 08/01/2019

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University of Colorado Denver
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Auraria Library
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Copyright Shea-Tinn Yeah. Permission granted to University of Colorado Denver to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.

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Full Text
SERVICE INNOVATION FOR KNOWLEDGE-INTENSIVE SERVICES IN THE
DIGITAL AGE: THE CASE OF ACADEMIC LIBRARIES
by
SHEA-TINN YEH
B.A., Cheng-Kung University, 1983 M.L.S., University of Maryland, 1985 B.S., Franklin University, 1995 M.S.E., Wright State University, 2009
A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment Of the requirements for the degree of Doctor of Philosophy
Computer Science and Information Systems 2018


©2018
SHEA-TINN YEH ALL RIGHTS RESERVED
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This thesis for the Doctor of Philosophy degree by Shea-Tinn Yeh
Has been approved for the
Computer Science and Information Systems Program
by
Jahangir Karimi, Chair Ronald Ramirez, Advisor Onook Oh Ilkyeun Ra Mary Stansbury
Date: July 28, 2018
111


Yeh, Shea-Tinn (Ph.D., Computer Science and Information Systems)
Service Innovation for Knowledge-Intensive Services in the Digital Age: The Case of Academic Libraries
Thesis directed by Associate Professor Ronald Ramirez
ABSTRACT
The pervasiveness of digital technology, along with unprecedented computing power, has altered innovation techniques in all industries, including those that provide knowledge-intensive services. Extant research on innovation has concentrated on information and communications technology in service innovation along the dimensions of technology related interfaces, delivery, and infrastructure. The immateriality and lack of physical form of new digital technologies, however, present unique challenges and research opportunities. What roles do digital technologies play in service innovation and how may digital technologies interact with critical resources for service innovation in knowledge-intensive service organizations? Based on the service-dominant logic perspective, this research theorizes that digital technologies, as operand and operant resources, integrate with intellectual capital to build digital platform capabilities essential for service innovation within knowledge-intensive service providers. This study presents a new integrative framework for service innovation in the digital age and validates the framework in the context of academic libraries, the type of organization whose central purpose is the delivery of knowledge-intensive services.
For the validation, a survey instrument was developed and administered to library administrators at both doctoral universities and master’s colleges and universities in the United States. Structural equation modeling results support that knowledge-intensive service resources positively contribute to service innovation, an academic library’s ability to build digital platform
IV


capabilities is enhanced by the integration of knowledge-intensive service resources and digital technologies, and digital platform capabilities positively contribute to service innovation.
The contributions of this research are manyfold owing to the fact that it is the first study to recognize that service-dominant logic’s intangible resources and intellectual capital are analogous; propose that intellectual capital and digital technologies are critical knowledge-intensive service resources; and propose and validate an integrative model of knowledge-intensive service resources that builds digital platform capabilities for service innovation.
This research advances extant theories on service innovation and informs service providers about the way in which intangible resources are transformed by digital technologies for service innovation. This study also applies the Management of Information Systems discipline to the understanding of service innovation in the Library and Information Science research field.
Approved: Ronald Ramirez
v


ACKNOWLEDGEMENTS
My doctoral adviser, Professor Ronald Ramirez, has supported and encouraged me whenever I was about to give up. His review and comments have guided me as I developed an academic voice to accompany my understanding. I sincerely appreciate his confidence in me; without it I would not be where I am in this journey today. I appreciate Professor Jahangir Karimi’s support as my committee chairperson. His knowledge, always prompt review, and insightful suggestions have been invaluable. I am also grateful for Professors Onook Oh, Ilkyeun Ra, and Mary Stansbury for their respective area expertise that has strengthened this dissertation research. In addition, special acknowledgement must go to Professor Zhiping Walter for her guidance at the beginning of my doctoral pursuit.
I would like to especially thank all the library administrators who participated in the survey research. And above all, I am grateful to my husband, Larry Owens, for his outstanding encouragement and to my son, Perry Owens, for his consideration and independence that enabled me to pursue my dreams.
The doctoral journey has been arduous as I am guessing it should be. It has tested my comprehension as a scholar, my mastery of English as a second language speaker, and my perseverance as a professional, wife, and mother. As I am about to close this chapter of my life with the receipt of my doctoral degree, I feel the overwhelming sense of humility described by the Chinese phrase which translates into English as “With respect to learning, the
sky’s the limit.” I look forward to contributing further to my beloved disciplines with the skills I have acquired along the way as well as my newly articulated academic voice.
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DEDICATION
For my twin sister, Dr. Sissi Yeh-Fleury, and my little sister, Stella Yeh, for their unconditional love and for my mother in Taiwan and my father in Heaven, both of whom worked hard throughout their lives to provide me with the best education I could ever have received.
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TABLE OF CONTENTS
CHAPTER! INTRODUCTION....................................................... 1
1.1 Knowledge-Intensive Services...........................................1
1.2 Academic Libraries as Knowledge-Intensive Services in the Digital Age.2
1.3 Service Innovation in the Digital Age.................................4
1.4 Theoretical Overview...................................................6
1.5 Research Objectives, Scope, and Design.................................8
1.6 Significance of the Research..........................................10
CHAPTER I! LITERATURE REVIEW................................................13
2.1 Knowledge-Intensive Services and Innovation...........................13
2.2 Service Innovation Process in IS Research.............................16
2.2.1 Outcome-based Service Innovation..................................16
2.2.2 Service-Activity-Based Innovation.................................19
2.2.3 Service-Dominant Logic Perspectives...............................20
2.3 The Dual Roles of Digital Technology as an Operand and an Operant Resource 25
2.4 Academic Library and Innovation.......................................28
CHAPTER III. CONCEPTUAL MODEL AND HYPOTHESES DEVELOPMENT....................35
3.1 Knowledge-Intensive Services Resources................................35
3.1.1 Intellectual Capital..............................................36
3.1.2 Digital Operant and Operand Technologies..........................38
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3.2 Digital Platform Capabilities
41


3.3 Research Model....................................................44
3.4 Hypotheses Development............................................45
CHAPTER IV. RESARCH DESIGN..............................................52
4.1 Measurement Model.................................................52
4.2 Data Collection Procedures........................................56
4.2.1 Survey Instrument.............................................56
4.2.2 Content Validity and Face Validity............................56
4.2.3 Sampling Process..............................................56
4.2.4 Pilot Test....................................................57
4.3 Main Data Collection..............................................57
CHAPTER V. DATA ANALYSIS AND RESULTS....................................59
5.1 Descriptive Statistics............................................59
5.2 Measurement Model.................................................63
5.3 Structural Model..................................................72
CHAPTEER VI. FINDINGS AND DISCUSSION....................................77
6.1 Findings..........................................................77
6.2 Research Implications.............................................79
6.3 Practice Implications and Recommendations.........................82
6.4 Limitations.......................................................86
6.5 Future Research...................................................87
REFERENCES..............................................................89
APPENDIX A. CONSTRUCT DEFINITION.......................................115
APPENDIX B. CONSTRUCT OPERATIONALIZATION...............................116
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APPENDIX C. SOLICITATION E-MAI I............................. 120
APPENDIX D. SURVEY ITEMS......................................121
APPENDIX E. INDICATORS DEESCRIPTIVEE STATISTICS...............124
APPENDIX F. ABBREVIATIONS.....................................125
x


LIST OF TABLES
Table 1. New Product Development and New Service Development Activities................17
Table 2. Studies Appling Operand and Operant Resources from S-D Logic..................23
Table 3. Similarities and Differences in Perspectives in Innovation Research...........25
Table 4. Library Journals Ranking and Impact Factor....................................29
Table 5. The Dual Roles of Digital Technology Investigated in Library Literature.......32
Table 6. Measurement of Constructs.....................................................55
Table 7. Descriptive Statistics........................................................59
Table 8. Comparison of Population Value and Responder Value............................62
Table 9. Nonresponse Bias T-Test Results...............................................62
Table 10. Psychometric Properties for First-Order Constructs...........................65
Table 11. Loadings, AVE, and CR for Second-Order Constructs............................68
Table 12. Loading and Cross-Loading....................................................69
Table 13. Intercorrelations and VAVE of Latent Variables for First-Order Constructs....71
Table 14. Intercorrelations and VAVE for Second-Order Constructs.......................71
Table 15. Systematic Evaluation of the Constructs......................................72
Table 16. Analysis of the Indirect Effects.............................................75
Table 17. Hypotheses Summary...........................................................75
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LIST OF FIGURES
Figure 1. Classifying Operant Digital Technology - Adapted from Day (1994).........41
Figure 2. Inner Research Model.....................................................45
Figure 3. Measurement Model........................................................52
Figure 4. Factor Loadings..........................................................72
Figure 5. Testing of the Hypothesized Path Model with Control Variables.........75
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CHAPTER I. INTRODUCTION
1.1 Knowledge-Intensive Services
Among the three core sectors of world economies—primary (raw materials), secondary (manufacturing), and tertiary (services)—the service sector has experienced the largest increase in productivity output and total employment over the past several decades (Soubbotina & Sheram, 2000). Rising per capita incomes have driven the demand for services, especially knowledge services (Bryson et al., 2004, p. 8), resulting in significant growth in the knowledge-intensive services industry. In 2012, for example, this industry produced 22% of gross domestic product (GDP) in the United States and 20% in New Zealand (Hill, 2014; Ministry of Business, Innovation & Employment, 2014).
Knowledge-intensive services industries include business, finance, information technology, education, and health services with service activities comprised of research and development (R&D), consulting, accounting, information services, legal services, and marketing related services (Hill, 2014; OECD, 2006). Knowledge-intensive service providers act as knowledge integrators or transferors (Bessant & Rush, 1995), scouring the environment for relevant knowledge, and participating in ongoing knowledge developing networks (Tether & Hipp, 2010). They provide customized solutions for clients, developed from the intangible knowledge of employees, social interaction between employees (Larsen, 2001), and provider-user interactions (Tether & Hipp, 2010) that occur as part of the service process. The output of such a process is information, supporting the notion of information as a service (Hayes 2003, p. 159). Information is easily transported and distributed with technologies in this digital age, thus further contributing to the creative and innovative nature of knowledge-intensive service activities.
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1.2 Academic Libraries as Knowledge-Intensive Services in the Digital Age
Service innovation is a critical component of organizational sustainability and a source of competitive advantage to both for-profit and nonprofit organizations (Bigelow et al. 1996; Lay Hong et al., 2016; Noorani, 2014; Parris et al., 2016; Salunke et al., 2013). This research investigates service innovation by identifying the organizational setting for the investigation and why it fits within the context of the broader study. Specifically, this research defines academic libraries as providers of knowledge-intensive services.
Before the digital age, publishers gathered, edited, printed, and marketed the knowledge contributions of scholars in tangible, physical, paper-based products. Libraries as the information consumer purchased or collected discrete information in the form of monographs and periodicals; and they in turn as information providers provided access to these published materials (Womack, 2002). Furthermore, librarians compiled, classified, and created bibliographic knowledge from the printed and distributed knowledge through consultation and reference services. The competitive advantage of libraries is thus determined, in part, by the quality and quantity of their collected information and librarians’ qualification as knowledge creators and transferors.
In the digital age the Internet, digitization, and digital technologies have revolutionized how knowledge is obtained, shared, and retained. Digital technologies have enabled a digital-based networked economy and contributed to the current Information Age and knowledge-based service economy (Castells, 2009, p. 162). Also, information is in the form of digital “bits” in the digital age versus analog or physical entities made up of “atoms” in the non-digital era. When information in bits is delivered through a digital network, a vast number of them can be transmitted at lightning speed, shared across greater distance, and accessed immediately.
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Information can also be replicated perfectly and retrieved from anywhere via ubiquitous mobile broadband networks and smartphone technologies (Tapscott, 2014, p. 116), thereby contributing to a world of “knowledge without boundaries” and “information without borders.” Although academic libraries still purchase physically published information, most acquisitions today are in the form of electronic books, articles, and digital files. In addition to curating these purchased information goods, academic libraries are preserving public information goods produced by their community in the digital repositories. These repositories, known as digital libraries, host research outputs, makerspace creations, special collections, and archives that collectively may be otherwise inaccessible without a personal visit to the library. Librarians, also known as “cyberians,” or information professionals, apply their expertise to vast, diverse digital information to create and transfer knowledge. Librarians may not know the answer to a specific question from a user, but they know where and how to find it; through consultation services, reference librarians create digital wayfinding and transfer what they find to their users. Instructional librarians, as part of a teaching team, identify programs to assist curriculum development; through library instructions at the course level, librarians contribute to knowledge creation and student learning. As subject specialists, moreover, librarians design complex searches in databases to perform systematic reviews of specific literatures for faculty and researchers; they critically analyze multiple studies to transform the findings into meaningful answers. In these endeavors, librarians play the role of information and knowledge intermediaries in the use of digital technologies. Therefore, academic libraries create, integrate, transform, and transfer information and knowledge as noteworthy knowledge-intensive service providers in the digital age.
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1.3 Service Innovation in the Digital Age
Traditionally, technologies have served as tools to innovate the service delivery process (Barras, 1990). However, advances in information and communication technology (ICT), especially digital technology, have altered service innovation at its core. Consistent with the evolution of technology-enabled product innovation (Tallon, 2010), service innovation is transforming; its input, process, and output are digital in nature. Digitization makes non-digital artifacts digitized into bits of data that are “programmable, addressable, sensible, communicable, memorable, traceable, and associable” (Yoo, 2010; Yoo et al., 2010), and digital data are openly available via the Web for exploration, experimentation, and innovation. Actors from upstream and downstream sources can collaborate and communicate with digital tools, exchanging immediate feedback throughout the innovation process. The prevalent use of social media, moreover, has recently created a socio-technical structure that enables organizations to form strategic actions from information-based analytics (Heath et al., 2014). The openness of the data also offers generative and unbounded opportunities resulting in service innovation which may or may not have been originally intended. Underlying the changes in the service innovation process described above is the phenomenon of digitalization, defined as “the encoding of analog information into digital format and the subsequent reconfiguration of socio-technical context of production and consumption of the product and services” (Yoo, 2012) prevalent in the knowledge economy.
Service innovation as transformed by technology has posed challenges not only to profit-oriented businesses but also to nonprofit higher education institutions (Danjuma & Rasli, 2012). The challenges are compounded when service innovation is viewed as a strategic necessity for staying relevant and for attracting and retaining large pools of students (Danjuma & Rasli, 2012;
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Yeh & Ramirez, 2016). Because of globalization and the recognition of knowledge as an economic driver, higher education institutions have changed from being teaching-and-research universities to teaching, research, and economic development enterprises that stimulate employment and productivity from knowledge resources (Etzkowitz, 2003). Many universities, meanwhile, are experiencing difficulties from changing funding systems and increasing pressure from competition (Bettis et al., 2005; Holbrook, 2004). Despite the strategic urgency in examining service innovation, there is a dearth of research on service innovations in higher education institutions, especially regarding higher education libraries in the digital age.
Existing literature on innovation in academic libraries centers on the aspects of innovation diffusion and adoption (Bieraugel & Neill, 2017; Raynard, 2017; Torres-Perez et al., 2016), the critical role of organizational leadership (Jantz, 2012; Jantz, 2015), and aspects of innovative services (Aharony, 2009; Chua & Goh, 2010; Letnikova & Xu, 2017). Much of this literature can be classified as descriptive research derived from observational data, in part owing to the fact that librarianship is a practical field (Audunson, 2017). Thus, less focus is given to identify researchable problems (Hjorland, 2000). However, for the last two decades, the goal and natural progression in the Library and Information Science (LIS) field have been to transform librarianship from a practice-related and to some extent a vocational field into an academic interdisciplinary discipline (Audunson, 2007). One way to accomplish this transformation would be by applying research methodology from a specific reference discipline (Keen, 1980). Farkas and Dobrai (2012) see similarities between higher education and business services, given the commonalities of knowledge-intensive services within each industry. Therefore, this research project, set within the Management of Information Systems (MIS) discipline, furthers the understanding of knowledge-intensive service innovation in the context of higher education
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libraries by means of theories developed in MIS.
1.4 Theoretical Overview
This research project first examines the characteristics of knowledge-intensive services and what service innovation means to the knowledge-intensive service sector, known in literature and in practice as “knowledge-intensive business services” (Hipp, 1999; Miles et al., 1995; Muller & Zenker, 2001; Wegrzyn, 2010), “knowledge-intensive industries” (Liao et al., 2007), and “knowledge-intensive firms” (von Nordenflycht, 2010). The objective of knowledge-intensive service organizations is to integrate internal and external knowledge to serve customers better; the interaction between service providers and customers can thus be recognized as the key to knowledge integration. Secondly, this study examines the role of digital technology in service innovation and introduces the perspective of service-dominant (S-D) logic as a relevant theoretical lens for examining this type of innovation. When goods were fundamental to economic exchange, dominant logic focused on tangible resources, such as machinery and raw materials (Vargo & Lusch, 2004). They were operand resources requiring other resources to act on them to product benefits (Constantin & Lusch, 1994, p. 145). As the paradigm shifted from goods to service in economic exchanges, however, the logic also shifted to intangibles, highlighting the application of knowledge and skills as units of exchange, knowledge as the fundamental source of competitive advantage, and value co-creation with customers (Vargo & Lusch, 2004). They are the operant resources acting on operand or other operant resources to produce further effects (Constantin & Lusch, 1994, p. 145).
The S-D logic framework has been researched across scholarly disciplines because of its shared focus on intangible resources that every organization is sure to possess. Recent updates to the logic emphasize the concept of institution, institutional arrangement, and technology as
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additional operant resources that provide a wider configuration for service innovation (Vargo & Lusch, 2016). The term institution does not mean an organization; instead, in this context the term comprises the humanly devised rules, norms, and beliefs of an organization (Scott, 2001, p. 12). Institutional arrangement refers to a set of interrelated institutions—alliances and partnerships—critical for a collaboration to take place (Vargo & Lusch, 2016). As to digital technologies, they are viewed as non-material entities in the form of operating systems, software codes, and application software programs (apps) capable of initiating service innovations (Eaton etal.,2011).
To further define digital technologies, Ibem and Laryea (2014) and Pullen (2009, p. 18) categorize them as stand-alone, integrated, or web-based tools that use microprocessors to produce, store, process, and communicate data and information between human beings and electronic systems. Known examples include social media, online games, multimedia, productivity applications, cloud computing, and mobile devices (State Government of Victoria, Australia, 2017). These examples reflect the materiality of technological objects. Faulkner and Runde (2011, p. 2) suggest, however, attention be given to the non-material technological objects that have no intrinsic physical being but are inundating this digital world. Examples include computational algorithms, software programs, or Web pages. The immateriality of digital technology objects offers generativity that has altered the core of service innovation (Eaton et al., 2011), and their diffusion and adoption have affected organizational design, decision-making, and communication (Orlikowski & Scott, 2008). Through the openness of the Internet and the connectedness of computers, generativity is embedded within a platform that triggers innovation by distributing code and content to wider audiences (Eaton et al., 2011). Apple successfully exemplifies generativity by offering an iPhone platform where Apple developers, external
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application developers, and iPhone users co-create and interact to form a digital ecosystem. Innovation from Apple’s platform continues long after the phone’s introduction. Generativity is thus “a technology’s overall capacity to produce unprompted change driven by large, varied, and uncoordinated audiences” (Zittrain, 2006).
The aforementioned terms of operand and operant resources, digitization, digitalization, generativity, platform, and digital ecosystem can be applied to explain the phenomenon of a digital library, one of the most complex service innovations produced by academic libraries. A digital library is an electronic, not a physical, library. It consists of a collection of objects in digital formats, along with services that organize, store, index, and retrieve those objects over a network to meet the information needs of a given user population (MacCall et al., 1999). In the case of the Open Music Library (https://openmusiclibrary.org/), the digitization process is first applied to convert printed music scores to digital-format objects. This step requires digitization equipment, ICT, and human knowledge and skills in the digitization effort. Software applications are developed and used to index and make the objects searchable and accessible online. The object data opening through the Web application programming interface (API) offer crowdsourcing and interactive opportunities for both curator and users. The discoveries, tagging, and sharing of the objects thus bring about unpredicted socio-technical results that create the digitalization phenomenon described in section 1.3, above.
1.5 Research Objectives, Scope, and Design
This study examines the influence of digital technologies on innovation in knowledge-intensive service organizations, specifically in academic libraries within higher education institutions. The results of this research are intended to inform the broader classifications of knowledge-intensive service organizations. Similar to academic libraries, they employ
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knowledge as an essential asset for servicing their customers and users, either for profit or not.
All such institutions are facing challenges from the application of digital technologies. While the limitations of applying research results from nonprofit organizations to for-profit organizations is acknowledged, this research presents the first step in developing an integrative model applicable to both. This model depicts how knowledge and other intangible resources from inside and outside the nonprofit knowledge-intensive service organization interact with digital technologies to create service innovation.
Higher education institutions as social service providers do contribute to a knowledge-based economy by providing services that are essential for economic competitiveness (Bryson et al., 2004, p. 120). With the advent of new digital technologies, higher education institutions have been challenged to redefine their student constituents and pedagogy. Academic libraries, at the heart of these institutions, must now articulate their contributions to institutional missions and goals. In this digital age, Google Scholar, Wikipedia, and Open Web are the first stop for users (student and faculty) seeking information; in this they replace human labor (reference librarians) who wait to answer questions through in-person consultation. eBooks and eJoumals by being downloaded from the Web thus replace the physical assets on library shelves and reduce the need for users to visit physical libraries. In short, digital technologies have disrupted and altered the process of information search and retrieval and have challenged the vitality and validity of library organizations. To maintain relevancy and continue to add value to their home institutions, academic libraries must look to digital-based service innovation as a strategic response to this disruptive impact (Yeh & Ramirez, 2016).
Although service innovation research extends back over 40 years, a consolidated view of service innovation has yet to be recognized (Howells, 2010, p. 68). Since the introduction of S-D
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logic a decade ago, service innovation has been viewed as an interactive venture between and among producers, consumers, and collaborators in a joint sphere. This venture especially aligns with innovation in the digital age where interaction is manifested and integrated by the generative characteristics of digital technology (Nambisan et al., 2017). Despite the acknowledgement of the new service-centric paradigm, extant research is still weak in capturing many varieties of service innovation with digital technologies (Amara et al., 2009). This leaves open a critical research gap with regard to knowledge-intensive service organizations in both the for-profit and nonprofit spheres. To gain in-depth insight into service innovation, the S-D logic framework is applied to examine the interactivity between resources and the initiator role that digital technology plays in knowledge-intensive service innovation. An integrative model is presented to address four research questions in the specific case of this dissertation—academic libraries:
1. What are the critical resources for service innovation?
2. How do digital technologies interact with other resources to build digital platform capabilities for service innovation?
3. How do digital platform capabilities contribute to service innovation?
4. Do digital platform capabilities mediate the impact of resources on service innovation?
This model was empirically validated using structural equation modeling to analyze primary
survey data. The targeted survey population is comprised of academic library administrators at higher education institutions in the United States. These administrators are responsible for advancing the institutions strategically and for keeping the institutions relevant in the digital age.
1.6 Significance of the Research
This study makes several contributions to Information Systems (IS) research, LIS
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research, and the practice of librarianship. First, the S-D logic-based model contributes to the understanding of digital service innovation in an environment of operand and operant resources and emerging digital platform capabilities. To date, there have been few quantitative studies that have taken this approach to examine service innovation. Despite the emphasis on the dual roles of digital technology as an operand and an operant resource by Nambisan (2013), up to now there does not exist any quantitative study on the dual-resource roles. This research also adds knowledge to the S-D logic research stream by proposing and empirically validating an integrative model that augments the logic and the dual roles as they are applied in knowledge-intensive service sector.
Second, this study adds to extant digital innovation research. Digital technology has changed how information is processed and delivered to a different and interconnected level. With ubiquitous computing, digital devices and technologies have been embedded in users’ physical and social environments as a fixture in their everyday movement and interactions (Lyytinen & Yoo, 2010; MacDonald, 2012). Unlike recent work focusing materiality by digital technologies (Yoo, 2010; Yoo et al., 2012), this research examines digital technologies in their broader immaterial form, analogues to services as immaterial goods, to create a digital edge transcending the traditional mindset of digital substitution and distribution.
Third, this study contributes to the understanding of service innovation in the public and nonprofit service sectors. Innovation research has traditionally focused on new products in industrial organizations and the manufacturing sector (Bigliardi et al., 2011; Gauvin & Sinha, 1993; Ning & Li, 2016) and has thereby paid less attention to the private and public sectors (Miles, 2005; Mulgan, 2008; Potts & Kastelle, 2010). However, in the present digital age all sectors are equally invested in digital technologies and need to apply them to create advantage,
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the knowledge-intensive services sector being no exception.
Fourth, this study contributes to the LIS discipline by first recognizing academic libraries as knowledge-intensive service organizations. Based on the characteristics of knowledge-intensive services in various technical and disciplinary literatures, academic libraries represent the knowledge sector by applying their intangible knowledge assets to service faculty and students. They also seek users’ feedback to enrich their knowledge base to continually improve core services. Although service innovation has always been a staple of academic libraries’ strategic plans, libraries are cautious when adopting new technologies. Often, they apply the technologies to improve process efficiency before attempting to create any innovations. Through the application of research from the MIS discipline, this study conducts a novel examination in LIS by using a unique theoretical lens and a core assumption that service innovation in academic libraries can be as dynamic and productive as in the business sector. For practice of librarianship, the findings here will assist library administrators in the development of digital methods for building digital platform capabilities that increase service offerings through co-creation with users.
Lastly, the dissertation research model will serve as a basis for future empirical studies of service innovation in all types of organizations, for-profit and nonprofit alike.
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CHAPTER II. LITERATURE REVIEW
2.1 Knowledge-Intensive Services and Innovation
Miles et al. (1995, p. 18) define knowledge-intensive services as “services that involve economic activities which are intended to create, accumulate, or disseminate knowledge.” Hipp (1999) characterizes the services “by the ability to receive information from outside the company and to transform this information together with firm-specific knowledge into useful services for customers.” Muller and Zenker (2001) describe knowledge-intensive services as “firms performing, mainly for other firms, services encompassing a high intellectual value-added.” These definitions assume a twofold role that knowledge-intensive services play as the intermediaries of knowledge: (1) they contribute to economic growth with their internal knowledge base, and (2) they acquire external knowledge to enhance their internal knowledge base and to further contribute to economic growth. To fulfill the first role of applying their internal knowledge base, a process in which knowledge constitutes the main input and output is usually in place within the provider organization (Gallouj, 2002, p. 2). The process focuses on providers being contributors to strengthen innovation capabilities (Wood et al., 1993) or enhance added value (O’Farrell & Moffat, 1995), all for the benefit of others, i.e., their customers. The service offered has the knowledge capacity to respond to specific questions, problems, or needs, and the process demonstrates the concept of “knowledge push,” where internal knowledge drives and pushes innovation. It is similar to the “technology push” philosophy where innovation starts with a phase of fundamental research and development within a back-office base before moving toward a systematic dissemination (Barras, 1986; de Hertog, 2000; Rubalcaba et al., 2012). For over two decades, however, the focus of knowledge-intensive services has evolved from contributing innovation as a contributor to producing innovation as co-producers (Muller &
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Doloreux, 2009), naturally fulfilling the second role of acquiring external knowledge to enhance internal knowledge. Examples of the second role include knowledge-intensive service organizations as consulting firms and the recent open innovation movement by knowledge-intensive services. When technical consulting services provide project-by-project service, they often call forth co-production from their customers as partners in order to understand the effect of industry-specific factors (Doloreux & Shearmur, 2010). The knowledge they gain with each project is then filtered and internalized to become local knowledge to be applied in subsequent projects. Unlike the traditional model of R&D activity-based innovations that are distributed by the organization, open innovation combines the “inflows” and “outflows” of knowledge to accelerate innovation to be distributed (Chesbrough et al., 2006). The Lego Company exemplifies open innovation activities by engaging its users with an online “Create&Share” site (https://www.lego.com/en-us/createandshare) where community members offer their design and ideas for new products. This process demonstrates the concept of “knowledge pull” whereby external knowledge drives and contributes to innovation. It is similar to the “technological demand-pull” approach where users articulate their needs to influence the innovation trajectory (Barras, 1986; deHertog, 2010; Gallouj & Weinsteien, 1997). Reflecting on the above-described distinct set of activities and knowledge characteristics, knowledge-intensive services provide a type of service that no other service provider supplies (Muller & Zenker, 2001); they are considered an “innovator” and “bridge for innovation” through their roles as knowledge repertoires and intermediaries in the knowledge-based economy (Czarnitzki & Spielkamp, 2003).
What constitutes knowledge? Research views the formation of knowledge as being multidimensional including know-what, know-how, know-why, and know-who (Lundvall & Johnson, 1994), and declarative, procedural, and causal (Cohen & Bacdayan, 1994). The best-known
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categorical view of knowledge is that of the tacit dimension (Polanyi, 1962, p. 92) versus the explicit dimension of knowledge (Nonaka, 1991, p. 11). Tacit knowledge is the subjective insights and intuitions people carry that are highly personal, hard to formalize, and difficult to communicate to others, whereas explicit knowledge can be communicated directly or in a manual that is formal, systematic, codified, and readily transmitted (Nonaka, 1991, p. 59; Sun et al., 2005). In the context of knowledge-intensive services, tacit knowledge resides with an organization’s personnel as distinct knowledge, while codified knowledge resides in the organization’s manual, documents, or databases as collective knowledge. Codified knowledge can be easily transmitted to other employees through documentation in the form of digital or print; however, tacit knowledge is subjective and difficult to articulate. Oftentimes, the knowledge source is unwilling to share the tacit knowledge rooted in their experience.
How does the tacit-explicit dichotomy contribute to the knowledge-intensive service innovation discussion? It helps in the understanding of types of knowledge applied in the service innovation process to identify methods that enhance knowledge creation and acquisition.
While product innovation also involves knowledge, the output of knowledge is mainly in the codified form that accommodates a tangible product (Muller & Doloreux, 2009), such as a product description brochure or a user manual. However, when the output is a knowledge-based service product intended to solve a customer’s problems, tacit knowledge is likely to be present (Ritala et al., 2013). The reason is that problem solving is an interactive intentional process requiring producers’ tacit knowledge to understand the problem domain. Moreover, the customer’s perceptions are also often tacit. To maximize the benefits for customer knowledge, researchers suggest that knowledge-intensive service organizations strive to interact socially with knowledge sources (Muller & Doloreux, 2009) to create social and dynamic processes that are
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effective in obtaining the customers’ tacit knowledge (Strambach, 2008). Furthermore, organizations should consider the use of information technology applications to facilitate the sharing, reuse, and transfer of both tacit and explicit knowledge within their research teams who are most often the contributors of breakthrough innovations (Kleis et al., 2012).
2.2 Service Innovation Process in IS Research
Academic research has come to have an increasing focus on service innovation because of the growth of service organizations over the past decades (Dotzel et al., 2013; Synder et al., 2016). Service innovation research appears in several research disciplines with significant contributions from marketing, service management, business, the social sciences, engineering, health care, and operations (Witell et al., 2016). This section reviews the body of scholarly research on service innovation processes through the lens of innovation outcomes, service activities, and innovation dynamics.
2.2.1 Outcome-based Service Innovation
Based on the types of innovation outcomes—product or service—two opposite viewpoints are suggested in Ordanini and Parasuraman (2011): a residual view where service is a result of product innovation and a dichotomous view in which service innovation is distinct from product innovation. In the residual view, service innovation is assumed to be fundamentally the same as manufacturing product innovation and emphasizes a sectoral taxonomy where service industries are considered the leftover sectors that do not produce raw materials and tangible artifacts (Miles, 2008). These residual industries in supplier-dominated sectors (Pavitt, 1984), scale-intensive sectors, physical network sectors, and science-based sectors (Soete & Miozzo, 1989) receive technological impetus assimilated from manufacturing for the service innovation process (Barras, 1990; Djellal et al., 2013).
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Being a residue of product innovation, new service development (NSD) has the same underlying dimensions as new product development (NPD) (Djellal et al., 2013; Nijssen et al., 2006). Booz, Allen, and Hamilton (1982) divide the new product development process into seven stages which are sequential and derived from studies of the development of consumer goods and industrial products. Krishnan and Ulrich (2001), who define new product development as “the transformation of a market opportunity into a product available for sale,” propose a five-stage framework. Models of NSD are similar. Bowers (1989) proposes a normative model that includes eight activity stages, while Scheuing and Johnson (1989) suggest a systematic model of 15 sequential steps. Bullinger et al. (2003) formulate a six-stage service innovation development process. Table 1 summarizes the NPD and NSD stage models in their linear progression.
Table 1. New Product Development and New Service Development Activities
Study Process Stage Activities
Booz, Allen, and Hamilton (1982) NPD 7 Strategy development, idea generation, screening & evaluation, business analysis, development, testing, communication
Krishnan and Ulrich (2001) NPD 5 Concept development, supply-chain design, product design, testing, launch
Bowers (1989) NSD 8 Business strategy, new-service strategy, idea generation, concept development, business analysis, service development, marketing, commercialization
Scheuing and Johnson (1989) NSD 15 New strategy, idea generation, idea screening, concept development, concept testing, business analysis, project authorization, top management commitment, development of operational details, personnel training, service delivery process and system, service testing, marketing, launch, postlaunch review
Bullinger et al. (2003) NSD 6 Idea generation, analysis, concept development, implementation, marketing, review
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However, in the digital age, the common linear sequence that NPD and NSD processes share has been revoked with the rapid and disruptive changes in ICT and digital technologies. Recent research has suggested that the NSD process lacks linear progression, is of ad-hoc nature, and contains a small number of processing stages (Athanassopoulou & Johne, 2004; Menor & Roth, 2008; Vermeulen, 2004). Most interestingly, empirical evidence rejects the staged-model proposal and supports the “one model does not fit all” idea in the NSD process. For example, Toivonen and Tuominen (2009) examine nine individual innovation processes in three knowledge-intensive services organizations and find that the traditional staging activities of idea generation, development, and marketing were mixed and matched into three innovation models for various stages. They include the R&D model (idea -> development -> marketing), the rapid-application model (idea -> marketing <-> development), and the practice-driven model (change in practice -> idea -> development), emphasizing the bidirectional process between marketing and development. Martovoy and Mention (2016) identify NSD processes as also including main patterns and sub-patterns. For example, problem driven, proactivity driven, market driven, and strategy driven are an NSD process’s main patterns, while “frugalness/consecutiveness” is the sub-pattern to the problem driven pattern. NSD has thus departed from the residual view of NDP.
Based on the dichotomous view, the distinctive intangibility and interactivity characteristics of services call for concepts and models unique to service innovation (Miles, 2008). Intangibility is reflective of service as a non-tangible artifact and the need to produce and consume the service at the same time (Coombs & Miles, 2000). Interactivity emphasizes the multiplicity of actors involved in service innovation, including both producers and clients (Miles, 2008). The interactivity perspective especially contradicts the goods-dominant (G-D) logic in the
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manufacturing context, where goods are the unit of exchange and the customer is secondary, to be viewed as a possible value receiver or destroyer at different times (Vargo & Lusch, 2004).
The generalization about services in G-D logic has its limitations because the distinction between products and services is not clear-cut and remains problematic (Miles, 2008). For example, a valued brand produced by a manufacturing firm as a product may be part of a service exchange, or, a specialized supplier may have high interactivity with clients during the production processes for the valued brand.
2.2.2 Service-Activity-Based Innovation
Researchers provide insights from the perspective of service activities that support the intangibility and interactivity service characteristics. In their views, service innovation is seldom limited to a change in the service product characteristics (Miles, 2008) and is less centralized and standardized (de Hertog, 2010); thus, service innovation is better thought of in terms of dimensions for a wide variety of angles. Den Hertog (2000) proposes four dimensions of service innovation including service concept, client interface, service delivery system, and technology.
In his perspective, the service concept dimension relates to the intangibility characteristic in service products with emphasis on the value offering created by the service provider, whereas client interface and service delivery relate to the interactivity characteristic in service processes and products shared between producers and customers. Many innovations involve some combination of these four dimensions (Miles, 2008), with the technological dimension playing an enabling role to all the other dimensions (Barrett et al., 2015). For example, an automatic teller machine (technology dimension) enables a new client interface in the banking industry, and the mobile boarding pass mechanism (technology dimension) delivers a new check-in system (service delivery system dimension) in the airline industry. Through the lens of dimensions, the
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characteristic of creation and consumption at the same time for service innovation is also prominent. Both Sundbo (1994) and Gallouj and Weinstein (1997) describe the possibility of services created out of standard elements with modules combined or recombined for individual customers, all at the same moment of consumption. In the case of a boarding pass, for example, it can be delivered by a combination or recombination of technologies in many ways by a passenger. The pass can be downloaded from a mobile device, a personal tablet, a gate agent’s computer, or an airport kiosk connected to the airline’s electronic network. Although dimension-based service innovation literature separates the artifact of product and service, it does not address or resolve the age-old debate of whether service innovation is different from product innovation.
Recently, researchers have argued that focusing on the distinction of innovation output is no longer relevant because products have been recognized as mechanisms for delivering services (Lusch & Nambisan, 2015; Orlikowski & Scott, 2015), and services are demanded that add value to products (Wegrzyn, 2010). It has been especially evident in the last decade that products and their related services have been packaged collectively as a service, giving rise to the concept of servitization in which products and services function side by side (Rust, 1998). In other words, services have been interwoven into the physical production of products (Bryson et al., 2004, p.
2), and the two are no longer separable in the collective function of the innovation. Thus, Preissl (2000, p. 126) suggests the alternative view that the boundaries between product and service innovation should be based on innovation dynamics rather than narrowly defined innovation output characteristics and dimensions.
2.2.3 Service-Dominant Logic Perspectives
Focusing on innovation dynamics with a synthesis approach and an integrative view of
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goods and services, S-D logic has emerged as a foundation for understanding service innovation in general (Barrett et al., 2015). This logic examines the intangible resources of human beings and technology as providing a telescopic view of their roles in a set of foundational premises (FPs) in service innovation. These FPs emphasize a continuous value-creation process as opposed to the product-centric focus in product innovation where processes and outputs are finite (Lusch et al., 2008). Three relevant FPs related to knowledge-intensive service innovations from the logic are: (1) service as a process, (2) value co-creation through actor-generated institution and institutional arrangements, and (3) operant resources as the source of strategic benefits.
When service is conceptualized as a process rather than a unit of output, service innovation is the result of the application of resources for the benefit of itself or for other organizations (Lusch et al., 2008; Vargo & Lusch, 2004). This conceptualization supports knowledge-intensive service providers’ characteristics of contributing their internal knowledge and acquiring external knowledge to enhance their internal knowledge. According to Vargo & Lusch (2016), institutions are not buildings but rules, norms, and practices established by human beings, while institutional arrangements resemble the various assemblages of those rules, norms, and practices that govern a process. Such arrangements facilitate value co-creation and resource integration by actors from partnership who collaborate within the arrangements.
The emerging S-D logic also promotes the important distinction between operand and operant resources (Vargo & Lusch, 2004), a distinction first conceptualized by Constantin and Lusch (1994, p. 143) to separate an organization’s physical and cultural resources. The basic distinction is that an operand resource is “a physical or tangible thing to be operated or used,” while an operant resource is “mostly a cultural or intangible skill that is applied to the operation or use of the physical resource” (Constantin & Lusch, 1994, p. 149). Such a disaggregation
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makes possible more relevant analyses for “resource management” in addition to “asset management” (Constantin & Lusch, 1994, p. 141). Researchers have since 1994 further defined characteristics of operand and operant resources in their fields of study, among them marketing, service science, and health care with commonalities. In general, they view operand resources as finite, tangible, static, and inert. Some examples would include goods, products, natural resources, raw materials, and equipment, whereas operant resources are invisible, intangible, and dynamic, including organizational processes, employees’ knowledge and skills, organization’s competences, and information technology (deLeon & Chatterjeen 2017; Higa & Davidson, 2017; Lusch et al., 2008; Vargo & Akaka, 2009; Vargo & Lusch, 2004). However, the resource list has been evolving and transforming over time. For example, Vargo and Lusch (2004) originally categorize customers as tangible operand resources to be segmented and acted on with marketing strategies. But recently, customers have come to be viewed as operant resources who could change marketing practices with their voices and co-create a service offering as part of the marketing dialog (Lusch et al., 2006; Lusch et al., 2008; Ordanini & Parasuraman, 2011; Raddats & Burton, 2014; Vargo & Akaka, 2009; Vargo & Lusch, 2014). Table 2 lists studies applying operand and operant resources from the S-D logic perspective with definition and resource examples.
In the context of knowledge-intensive services, the emphasis is on intangible resources, such as the knowledge and skills retained by people. As operant resources, they can operate on other operand or operant resources to create innovation. Furthermore, all actors including service providers, customers, and other collaborators are considered operant resources. The process of co-creation is also essential to source and integrate ideas from all actors. These operant resources and the outcome of their integration become the competencies and capabilities fundamental to
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knowledge-intensive services’ strategic benefit.
Table 2. Studies Appling Operand and Operant Resources from S-D Logic
Study Discipline Operand Definition Operand Examples Operant Definition Operant Examples
Vargo and Lusch (2004) Marketing management Finite Products; markets; nature resources; customers Invisible and intangible; can convert operand resources into outputs at a low cost Core competences; organizational processes; technology
Lusch et al. (2006) Marketing management Tangible; static; finite; depletable Dynamic; non- depletable; replenishable, replicable Customers
Lusch et al. (2008) Service Science Static; inert Natural resources; assets Intangible; produce effects; capable of acting on operand resources and other operant resources Knowledge and skills; computers; robots; customers
Vargo and Akaka (2009) Service science Must be acted on to be beneficial Natural resources; goods; money Act upon other resources to crate benefit Knowledge and skills; underlying source of value; customers, suppliers, stakeholders
Ordanini and Parasuraman (2011) Service Science Enhance cocreation opportunity; Service employees; firm’s competences
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Table 2. cont’d
Study Discipline Operant Definition Operand Examples Operant Definition Operant Examples
Raddats and Burton (2014) Manufacturing Financial; physical; legal Cash; raw materials; goods; plant; patents Human; organizational informational Skill and knowledge of employees; competences, culture, knowledge of customers, relationships with suppliers and customers
Vargo and Lusch (2014) Entrepreneurship Act on other resources to create value All actors (individuals, households, firms, nations)
deLeon and Chattrjee (2017) B2B Tangible Core product; service concept Intangible Instrumental service; interpersonal service; value mindset
Higa and Davidson (2017) Healthcare Tangible; finite; static Natural resources; equipment; goods Intangible; infinite; dynamic Information and knowledge; information technology
S-D logic leverages previous research investigating service innovation (Ordanini & Prarsuraman, 2011). An earlier foundational study is the resource-based approach to innovation that attempts to look at how firms innovate in terms of their amalgam of resources—knowledge, competencies, relationships, collaboration, and technology (Barney, 1991; Wernerfelt, 1984).
But S-D logic and the resource-based approach diverge with respect to the outcome, based on the conceptualization of the word “service.” In the resource-based approach to innovation, there will
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be a finite outcome which comprises goods or services; therefore, an innovation process is internal and targets segmented market users (from internal to external). On the other hand, S-D logic service innovation outcome is continuous and can benefit someone else as well as the self (from internal to external and from external back to internal) (Vargo & Lusch, 2016). S-D logic’s premise positions the beneficiary (user alone or producer and user together) to determine the value of an innovation brought about by an integration of resources (Mele et al., 2014). Value co-creation is therefore intensely emphasized in S-D logic. Table 3 summarizes the similarities and differences between G-D logic, the resource-based approach, and S-D logic in the innovation research referenced in this research.
Table 3. Similarities and Differences in Perspectives in Innovation Research
Perspective What is the Purpose of Innovation Outcome? Who contributes to Innovative Ideas? Who carries out Innovative Ideas?
Goods- dominant Logic A product or a service Producer Producer
Resource- based Approach A product or a service Producer and customer Producer
Service- dominant Logic Make self or someone else better off Producer and customer Producer and customer
2.3 The Dual Roles of Digital Technology as an Operand and an Operant Resource
Traditionally, IT is a material artifact (Orlikowsky & Iacono, 2001) viewed as an operand resource to facilitate technological service innovation (Lusch & Nambisan, 2015) and to enhance the efficiency and effectiveness of service deliveries (den Hertog, 2000). The scope of the artifact is largely limited to devices and formats that are unique to a product or service (Tilson et
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al., 2010). However, within S-D logic, technologies, especially digital technologies, are not limited to forms; they are conceptualized as operant resources of “capabilities” that trigger or initiate a change (Akaka & Vargo, 2014; Lusch & Nambisan, 2015; Nambisan, 2013).
How do digital technologies, being physical entities, trigger or initiate a change? Two research lenses can be identified to explain this conceptualization: resourcing and generativity. Resourcing is explained by Vargo and Lusch’s (2004) statement that “resources are not; they become”—they become operant resources through the application of the resources. To illustrate the concept, Lusch et al. (2008) describe how computers can do “resourcing” to become operant resources. For example, computers are embedded in robots to accomplish tasks with knowledge and skills as human beings do. Another example is from Akaka and Vargo (2014); they describe how the use of an X-ray machine triggers changes through the operation by X-ray specialists. When an X-ray machine is newly introduced into an organization, the protocol of machine use has to be established; based on the machine’s purpose and whom the machine is used on, current institutional rules are often modified. Digital technologies, through resourcing, become operant resources that initiate actions and changes in use protocols.
Although Nambisan (2013) acknowledges the technology’s materiality as an operand resource, he also supports the conceptualization of digital technology as an operant resource through its immateriality. The operant aspect in Nambisan’s view is not reflected in the application of digital technologies but is explicated by the generativity unleashed with the design of digital components. It is because “digital components of a service platform may seek out and pursue unique resource-integration opportunities on their own and in the process engage with (or act upon) other actors (both animate and inanimate) in the network in value co-creation” (Lusch & Nambisan, 2015). Abundant instances exist to illustrate a digital component’s generativity in
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varying degrees. For example, an HTTP cookie, as a digital component, stored on the computer by means of the Web browser while the user is browsing, tracks the user’s Web browsing habits when the cookie is not blocked. The tracked data files are used to create such positive innovation as personalized Web content. Another example is an app on smartphone platforms that can grow a network of external developers to design games, dictionaries, or other tools to enhance the value of the platform (Boudreau, 2012). The component is not limited to and can be of more than a single unit. In the case of a digital control system, formed of many digital components, it has the ability to span local decision units without human intervention and stimulate additional digital applications when included in a complex system (Lee & Bemete, 2012). A complex system, such as Amazon demonstrates through a collage of digital components—websites, cookies, tags, blogs, and ranking systems—can provide a platform that initiates a successful digital marketing ecosystem.
Such dual roles for digital technologies have transformed knowledge-intensive services with digitization efforts and produced subsequent digitalization phenomenon. Digitization equipment digitizes artifacts from their non-digital format or creates artifacts directly in digital form known as “bom-digital” artifacts. As such, books, music, codes and statues, and maps, just to name a few, are increasingly available in the digital format. The digitalization of these artifacts not only offer innovative products, but it also changes the way organizations function and individuals interact with each other by way of socio-technical processes (Yoo, 2012).
Digitization is now a dominant business practice. In the case of U.S. academic libraries, the digitization of artifacts is reflected in their budgets, with 75% of budget expenditures currently in the form of electronic journal subscription, and 57% of book purchases in the form of e-books in 2014 (National Center for Education Statistics, 2014). In the new digital era, the
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services that libraries offer have been re-oriented around electronic resources, digital libraries and, eBook repositories rather than printed physical materials. Digital technologies, as operant resources, have transformed academic libraries. This core digital-based change, enhanced with librarians’ intangible knowledge, library management practices, and digital technology applications, fuels further changes within library organizations and their alliances.
Although S-D logic focuses on the operant aspect of digital technology, this research supports the view that digital technology is of both an operand and an operant resource owing to the fact that a digital innovation will not happen without a physical digital device present. This assertion is evident in countless practical digital innovation examples: Facebook must be supported by networks, and digital libraries must be accessed through devices. Therefore, digital technology’s dual role as both an operand and an operant resource cannot be underestimated, and these resources must coexist to produce effects.
2.4 Academic Library and Innovation
As knowledge-intensive service providers, academic libraries have been offering traditional services that focus on collecting and exchanging knowledge (Casali et al., 2017). A climate of declining budgets and increasing collection costs, however, has challenged the status quo, redirecting leadership to consider the potential benefit of innovation. Although there has been an uptick in research on innovation in academic libraries, opportunities for significant research remain (Brundy, 2015).
In the Library Science field, there is no professionally accepted tiered list of journals as there is in other academic disciplines. Therefore, specific criteria must be applied to select the top journals for this review. First, Nixon (2014) provides three tiers of ranked journals based on expert opinions, surveys, acceptance and circulation rates, impact factors, and h-indexes. Eleven
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journals are on the top tier, in an alphabetical order: College & Research Libraries, Information Technology and Libraries, The Journal of Academic Librarianship, Journal of American Society for Information Science and Technology, Journal of Documentation, Library & Information Science Research, Library Journal, Library Quarterly, Library Resources & Technical Services, Library Trends, and Reference & User Services Quarterly. Applying these journals to Thomson Reuters’ 2014 Journal Citation Reports, the top three journals in the Library Science field with the most impact factors emerge as The Journal of the American Society for Information Science and Technology, College & Research Libraries, and Library & Information Science Research. Table 4 lists ranked library journals and their impact factors.
Table 4. Library Journals Ranking and Impact Factor
Rank Full Journal Title Impact Factor
1 Journal of the American Society for Information Science and Technology 1.846
2 College & Research Libraries 1.206
3 Library & Information Science Research 1.153
4 Journal of Documentation 0.833
5 Library Quarterly 0.500
6 Library Journal 0.465
7 Library Resources & Technical Services 0.452
8 The Journal of Academic Librarianship 0.448
9 Library Trends 0.386
10 Reference & User Services Quarterly 0.231
11 Information Technologies and Libraries 0.075
A literature review of articles was performed in the top three library journals in addition to The Journal of Academic Librarianship given its relevancy to inform the library community of technological innovations in academic libraries. The keywords “academic libraries,”
“innovation,” and “technology” were searched in individual journals, with retrieved articles individually reviewed with a focus on the roles of technology in the libraries. Technologies refer
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to electronic tools, systems, devices, data, applications, games, webpages, algorithms, software programs, and digitized images.
The review synthesizes two views guided by Nambisan’s (2013) framework which separates technology’s role into that of an operand and an operant resource. The operand role is notably focused upon in prior studies, serving as a tool to facilitate teaching and research as in the adoption of eBooks and eReaders (D’Ambra et al., 2013; Dougherty, 2009; Martin & Quan-Haase, 2013), the diffusion of eJoumals (Brennan et al., 2002; Olle & Borrego, 2010), and the utilization of web-based learning through mobile and desktop websites (Beagle, 2000; Leo et al., 2016; Torres-Perez et al., 2016). Tools that enhance information retrieval and facilitate information service delivery are also researched. Examples are the implementation of Web 2.0 functionalities (Aharony, 2009; Chua & Goh, 2010; Kim & Abbas, 2010; Redden, 2010), the provision of digital reference services (Gibbs et al., 2015; White, 2001), and the deployment of the digital library for special collections (Nov & Ye, 2008; Oguz, 2016).
However, digital technology is also viewed as an operant resource, albeit in less quantity. Travica (1999) describes how academic libraries have been transformed from a physical space to a virtual library with the advancement of ICT and how their services are altered from a print to an electronic format by means of digitalization (Higa et al., 2005). Crawford and Rice (1997) perceive automation as a change agent for academic libraries, with the concept further pronounced as the means by which libraries acquire, organize, and provide access to information (Warnken, 2004). Acknowledging how libraries function similarly to organizations in the business sector, Shapiro and Long (1994) describe the application of technology to drive a business-reengineering process in the library setting. Yeh and Walter (2016) propose service innovation as a response to business-oriented disruptive innovation experienced in academic
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libraries. Noted from this review, most of these studies were published in the late 1990s and early 2000s when ICT was dominant in society. Publications were silent on digital technologies in academic libraries, however, until Yeh and Walter’s work appeared in 2016. Such a knowledge gap and the incommensurate understanding of digital technologies as operant resources need further exploration.
It is also worth noting that research on digital technologies as operand resources in academic libraries focuses mainly on the aspects of adoption and diffusion of technological tools. This trend is understandable, especially when applying the lens of Barras’s (1986) reverse-product cycle in which technology’s traditional cycle is reversed. In Barras’s cycle, an organization adopts a technology to increase the efficiency of its service delivery; once the service quality has been improved, the quality and adoption open the door for future service innovation. Evidence shows that academic libraries have been at the receiving end of technological development, usually after a lengthy period of R&D efforts. The internal concerns by the libraries are thus how well the technology can be adopted and how wide its derived services can be diffused. The most prominent example is the library management system (LMS) that automates a library’s back-end process to integrate with the front-end service delivery. As described by Barras, innovation occurs when processes are efficient and products are widely adopted. With LMS adoption, eBooks and eJournals are distributed outside library walls, and virtual references are provided by means of digital technologies. Since 2010, academic libraries have been migrating to the library service platform (LSP), the next generation of LMS with software as a service (SaaS) and an open data model. Service innovation will surly follow and may inevitably change the organizational structure. It is therefore imperative that academic libraries think proactively about how these digital technologies may trigger change in libraries
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and prepare accordingly. The present research should help fill the gap. Table 5 illustrates the dual roles of digital technology investigated in the academic library literature.
Table 5. The Dual Roles of Digital Technology Investigated in Library Literature
Citation Operand Role Operant Role Context Method
Shapiro and Long (1994) Technology transforms the library Through the concept of reengineering Case study
Crawford and Technology as a Automation is a Empirical
Rice(1997) change agent change agent within organizations secondary data
Travica (1999) Digital technology contributes to organizational change Academic library is organized as virtual library Empirical survey
Beagle (2000) Digital technology as a tool to facilitate learning Web-based learning Conceptual
White (2001) Digital technology enhances information retrieval Diffusion of digital reference services Case studies
Brennan et al. (2002) Digital technology as a tool to facilitate teaching and research Adoption of e Journals Qualitative
Warnken (2004) Technology contributes to organizational change Technology alters means of how libraries function Conceptual
Higa et al. (2005) Digital technology causes reorganization of the library Present an approach to guide the transition of services from print-based to electronic based Case study
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Table 5 cont’d
Citation Operand Role Operant Role Context
Nov and Ye Digital Users’
(2008) technology as a perception of
tool for searching and retrieving information digital libraries
Aharony (2009) Digital technology as facilitating information-service delivery Use of Web 2.0
Chua and Goh Digital Web 2.0
(2010) technology as enhances library
facilitating service quality
service delivery and delivery
Dougherty Digital Utilization of e-
(2010) technology as facilitating service delivery Readers
Kim and Abbas Digital Adoption of
(2010) technology as facilitating information-service delivery Library Web 2.0
Olle and Digital Adoption of
Borrego (2010) technology as a tool to facilitate research e Journals
Redden (2010) Digital Utilization of
technology as social
facilitating bookmarking, a
service delivery Web 2.0 tool
D’Ambra et al. Digital Adoption of e-
(2013) technology as a books on e-
tool to facilitate readers and other
teaching and research mobile devices
Martin and Digital Adoption of e-
Quan-Haase technology as a books by
(2013) tool to facilitate teaching and research historians
Method
Empirical
Technology
acceptance
model
Empirical
Empirical secondary data
Conceptual
Empirical
Qualitative
Empirical secondary data
Empirical test task-technology fit model
Qualitative
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Table 5 cont’d
Citation Operand Role Operant Role Context Method
Gibbs et al. (2015) Digital technology as enhancing information retrieval Diffusion of digital reference services Case study
Yeh and Walter (2015) Digital technology provides opportunities for changes Drive service innovation to meet institutional goals Conceptual
Leo et al. (2016) Digital technology as a tool to facilitate learning Flip classrooms Case study
Oguz (2016) Digital technology as a tool in digital libraries Adoption of digital libraries Case study
Torres-Perez et Digital Adoption of Empirical
al. (2016) technology as a tool to facilitate research and learning mobile website secondary data
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CHAPTER III. CONCEPTUAL MODEL AND HYPOTHESES DEVELOPMENT
3.1 Knowledge-Intensive Services Resources
In the strategic management literature regarding the resource-based approach, a firm holds tangible and intangible semi-fixed assets, such as brand names, in-house knowledge of technology, skilled personnel, trade contacts, machinery, efficient procedures, and capital (Caves, 1980; Wemerfelt, 1984). These resources are a firm’s attributes, applied by its top management to determine the firm’s value creating strategies (Barney, 1991; Caves, 1980). In this resource-based approach, they are sources of competitiveness only if they are valuable, rare, or cannot be replicated (Barney, 1991). Besides, the competitiveness does not occur or continue, unless these attributes are used effectively and efficiently (Hunt & Moran, 1996). Are these attributes applicable to knowledge-intensive service organizations? What are the resources for knowledge-intensive service organizations in this digital age? If rareness is hard to find, how do resources stay competitive for knowledge-intensive service organizations?
The firm’s attributes that are listed in Barney (1991) and Wemerfelt (1984) remain applicable as the resources for knowledge-intensive services; however, digital technology has become a prominent addition to every resource list. To a considerable extent the literature has examined technology, such as an infrastructure or a device, as a distinct, stand-alone resource (Tippins & Sohi, 2003). Meanwhile, research has demonstrated that technology alone does not lead to competitive benefits. Rather, it is technology in combination with complements that leads to maximum benefit. The interrelatedness of human assets, technology infrastructure assets, and relationship assets is what creates capabilities as a source of advantage (Ross et al., 1996).
Instead of rarity, it is the sophisticated technology infrastructure, the quality of human capital, and high-value relationships that comprise the sources of competitiveness (Ravichandran &
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Lertwongsatien, 2005). High-quality human assets can be found in a staff with technical skills, business understanding, and a problem solving orientation. A sophisticated technology asset includes a well-defined architecture of network and platform, data-and-core application sophistication as well as good platform standards. Additionally, a valuable relationship asset is reflected in a shared and trusted partnership between the technology unit, line managers, and external providers or partners (Ravichandran & Lertwongsatien, 2005; Ross et al., 1996).
The core assumptions of S-D logic are consistent with the competitive implications of technology and its complements. The logic collectively emphasizes digital technology competence along with human capital, institutional rules, and broader organizational relationships (Vargo & Lusch, 2016). An organization’s operant resources act upon the digital technology infrastructure to create synergistic benefits. In the extant literature, institutional rules are analogous to organizational capital, while relationship is analogous to social capital. Human capital, organizational capital, and social capital are collectively denoted as intellectual capital, a key factor for obtaining competitive advantage in the knowledge economy and for innovation and economic growth (Dean & Kretschmer, 2007; Hayton, 2005; Reina et al., 2011; Yaseen et al., 2016). The present research asserts that intellectual capital and digital technologies in combination represent knowledge-intensive service resources, and the interdependence of these elements expands the competitive advantage of the service.
3.1.1 Intellectual Capital
Early studies of this concept include Edvinson and Malone (1997, p. 34), who held that the hidden dynamic factors underlying a company’s visible buildings and products are very valuable. Those factors are the human capital inherent in the employees’ knowledge, skill, and experience; the structural capital of the company’s infrastructure that supports its human capital;
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and the customer capital the company generates through positive relationships with its customers. Together, these elements are termed intellectual capital. Marchand et al. (1996) visually divided intellectual capital into what is in the heads of employees (“human capital”) and what is left in the organization when they go home (“structural capital”). Issac et al. (2010) propose that intellectual capital consists of human capital, organizational capital, and relationship capital, where relationship capital is broadened to include other stakeholders in addition to customers. Issac’s organizational capital refers to the same structural capital as found in Edvinson and Malone but with the infrastructural concept expanded to include a higher-level goal of creating value for the company. Since the 2000s, the term “social capital” has been introduced to replace “relationship capital” to reflect a network of increasing social interactions (Hsu & Sabherwal, 2011; Subramaniam & Youndtm 2005; Youndt et al., 2004). This research views intellectual capital as including human capital, organizational capital, and social capital.
Specifically, human capital is defined as the capabilities embedded in employees and not owned by the organization (Hsu & Fang, 2009). Consequently, this capital does not stay with the organization when employees leave. Organizational capital is the knowledge and codified experience residing within databases, manuals, culture, structures, and processes, and remains in the organization when employees leave (Chen & Shih, 2009; Edvinson & Malone, 1997, p. 35; Issac et al., 2010). Social capital is the knowledge embedded within networks of relationships and interactions amongst employees and stakeholders. As a result, the knowledge may or may not stay in the organization when employees leave (Nahapiet & Ghoshal, 1998; Subramaniam & Youndt, 2005). Because intellectual capital looks beyond the mere financial health of a firm, it is not confined to for-profit enterprises; the concept is thus applicable to nonprofit organizations as well. With knowledge becoming a critical resource in the knowledge economy, intellectual
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capital is an indispensable asset in knowledge-intensive service organizations (Stewart, 1997, p. 67). Understanding these various capitals alone as “assets,” however, is not as valuable as understanding the capabilities from which these capitals may develop (Stewart, 1997, p. 55). For example, human capital is the capability of an individual providing knowledge solutions to customers-implying the action of providing solutions. Organizational capital is the support mechanism that allows the sharing, transforming, and transporting of knowledge-implying the action of providing support. Social capital is the willingness of the customers to share plans and expertise with the producer-implying the action of knowledge transfer.
A successful financial sector serves as a practical example where skilled employees are in a better position to respond to customers’ financial queries and are competent to provide advice to customers. An efficient organizational structure which enables these employees to excel thereby creates value and innovation for customers. Aided by prevalent media technologies, banking activities are increasingly social through growing interactions between employees and customers (Chahal & Bakshi, 2015). In this example of a financial organization as a knowledge-intensive service provider in this digital age, all three capitals—human, organizational, and social—are needed to contribute to organizations’ successful edge. They should therefore be viewed as a synergistic, integrated set for intellectual capital (Yaseen et al., 2016). Thus, based on extent literature, intellectual capital is a higher-level abstraction defined and operationalized by first-order human capital, organizational capital, and social capital.
3.1.2 Digital Operant and Operand Technologies
When digital technologies are viewed as operant resources, they become material artifacts of practical instantiation and significance rather than of physical forms (Leonardi,
2010). Without form they may be further conceptualized to exhibit three capabilities: inside-out
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capability, outside-in capability, and spanning capability. Inside-out capability is internally focused and deployed from inside the firm in response to market requirements and opportunities (Day, 1994; Wade & Hulland, 2004). The focus of inside-out capability is on internal technology architecture of applications and services (Cai et al., 2016). Outside-in capability is an externally oriented capability that anticipates market requirements, understands competitors, and builds external relationships (Day, 1994; Wade & Hulland, 2004). Outside-in capability not only helps an organization acquire external knowledge from partners but also assists them in assimilating internal knowledge (Tippins & Sohi, 2003). Spanning capability is the competence that integrates and coordinates all the capabilities inside and outside an organization (Cai et al., 2016; Day, 1994; Wade & Hulland, 2004).
Consider the transformation of knowledge-intensive legal service where legal research is intensely engaged in this digital age. Machine learning has now been applied to a massive amount of client history, briefings, and reports. Robot lawyers have been hired in the United Kingdom for years, and online legal services have provided basic legal advice and forms for decades. Mobile devices are used to track billable hours or prepare for a trial. It has thus become necessary to convert a law firm’s capabilities from a rigid paper-based system to mobile-friendly, responsive services. This transition requires an inside-out capability to apply digital technologies, an outside-in capability for understanding the evolving requirements of customer needs, and a spanning capability to strategically integrate the firm’s capabilities to innovate costcutting measures or to satisfy customers’ growing needs. Figure 1, adapted from Day (1994), illustrates how knowledge-intensive services can apply the capabilities of digital technology to fulfill their roles. At one end of the spectrum are those capabilities that are deployed from the inside out activated by opportunities and market requirements. They are reflected in appropriate
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and flexible data and network architecture. At the other end of the spectrum are those capabilities that bring in external knowledge and disseminate internal knowledge. They are reflected in digital technologies that link the provider with users, vendors, and other libraries. Spanning capabilities are needed to integrate and coordinate all aspects of digital technology. They are reflected in teams with blended expertise, a good relationship with technology personnel, a nurturing climate for digital projects, and appropriate workflows leveraging digital technologies. Thus, based on extent literature, digital technology as an operant resource is a higher-level abstraction defined and operationalized by the three first-order capabilities: inside-out, outside-in, and spanning capabilities.
The role digital technology plays as an operand resource cannot be discounted. The infusion of digital infrastructure and devices—the Internet, cloud computing, mobile computing, digitization equipment, and 3D printing—into knowledge-intensive service organizations has transformed the way these organizations provide service innovation. Digital operand technologies are essential parts of knowledge-intensive services in this digital age. The bundle of intellectual capital, digital operant resources, and digital operand resources are therefore conceptualized as knowledge-intensive service resources. Although resources alone can improve an organization’s operation, it is the combination of resources by way of interaction, sometimes complex and with added time required, that builds the organization’s capacity for greater outputs (Amit & Schoemaker, 1993; Karimi et al., 2007). This research proposes that the interaction of knowledge-intensive service resources build digital platform capabilities that provide opportunities for service innovation. Appendix A lists the definitions for the dimensions for intellectual capital and operant digital technology from the prior literature.
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External Focus
Internal Focus
• Digllal technology linking with users
• Digital technology linking with vendors
â–  Digital technology linking with other libraries
• Leverages external digital technology
resources
• Teams with blended digital and non-digital technology expertise
• Management has good relationships with digital technology personnel
• A climate nurturing digital technology projects
• Workflows leverage digital technologies
• Appropriate data architecture
• Appropriate network architecture
• Flexible data architecture
â–  Flexible network architecture
Figure 1. Classifying Operant Digital Technology - Adapted from Day (1994)
3.2 Digital Platform Capabilities
There is no consensus on exactly what constitutes a digital platform, but there are common elements offered in the literature. An early effort described “the digital platform” as an “intermediation activity linked with the ‘assembly’ of content and services onto a coherent technical and commercial access platform” (Meyer, 2000). Evans (2008) considers web-based business as a platform on which other businesses rely to produce complementary products. Gawer (2009, p. 2) thinks of Microsoft Windows as a platform of building blocks with which other firms can develop complementary products, technologies, and services. Shelanski (2013) describes digital platforms as “products or services through which end users and a wide variety of complementary products, services, or information can interact.” Lusch and Nambisan (2015) define a digital service platform as “a modular structure that comprises tangible and intangible
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components (resources) and facilitates the interaction of actors and resources (or resources bundles).” These definitions support the S-D logic perspective by providing a venue for service exchange and value co-creation for service innovation.
Most importantly, the dual roles of digital technology as an operand and an operant resource are validated in a digital service platform through two concepts: convergence and generativity. Yoo et al. (2012) state that when digital components, especially software-based components with digital capabilities, are embedded in objects, their affordances create innovations characterized by convergence. Convergence is attributed to the programmability and re-programmability of digital technology that contribute to data homogenization and system interoperability. In reality, digital convergence has blurred the boundaries between many types of service providers, including content suppliers, advertising agencies, telecommunication and TV operators, computing companies, and device manufacturers. They have relied on homogenized data and interoperable systems to create such bundled service innovations as Spotify, Netflix, Sling TV, and Hulu.
The affordances of digital capabilities also produce innovations of generativity (Lusch & Nambisan, 2017; Tilson et al., 2010; Yoo et al., 2012). Generativity is attributed to digitalization, “a sociotechnical process of applying digitizing techniques to broader social and institutional contexts that render digital technologies infrastructural” (Tilson et al., 2010). Digitalization materializes when the process converts analog information into digital bits that can be shared by many technologies. This sharing, moreover, removes tight coupling requirements between information types, storage demands, and transmission methods. Without the tight coupling, digital infrastructure is capable of leveraging or being leveraged across a range of tasks, adapting to many different tasks, and being accessible and appeasing to many different audiences
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(Zittrain, 2010, p. 14). From the theoretical lens, convergence and generativity seem to imply an opposite force by digital capabilities. In fact, convergence complements and contributes to generativity. As an example, take Google Maps, an infrastructural service for geolocation information, upon which various applications have been built by developers for tours, restaurants, or library locations (Palfrey & Gasser, 2010, p. 111). Without the digitization of maps to ensure data homogenization (convergence), various applications would not be possible (generativity). For another example consider GibHub platform, a development platform with social-networking-like functions for open source software projects, upon which developers can create, co-create, and share unlimited innovative projects. Without the use of the global standards—extended markup language (XML) for data homogenization and system interoperability (convergence)—co-creation would not be possible (generativity).
The concepts of building blocks and digitalization have taken the digital platform to the next level with its conceptualization as part of a whole for service innovation. The whole is a service ecosystem where loosely coupled social and economic actors connect by sharing institutional logics and mutual value creation through service exchange (Lusch & Nambisan, 2015). The platform is the core element in this ecosystem, filling the role as an engine to drive service innovation. Google, for example, is a service ecosystem. It has a multi-sided digital platform serving people who search the Web, businesses who reach searchers, and developers who use API for mash-up projects and other complementary products (Evans, 2008). The example of Google demonstrates a unique platform economy where benefits to the customers are not provided by the owner of the platform, Google, but by independent third-party developers that co-evolve within the platform to create innovation (Baghbadorani & Harandi, 2012). The
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generativity of digital technologies has thus been shown to unleash and will continuously unleash unexpected service innovation opportunities.
Digital platform capabilities have mainly been linked to servitization where manufacturers successfully acquired it to engage co-creation with customers for advanced service offerings (Cenamor et al., 2017; Lenka et al., 2017; Parida et al., 2015; Ronnberg Sjodin et al., 2016). In servitization, digital platform capabilities have reflected a joint sphere where provider and customer co-create value through direct interaction (Vargo et al., 2008) as opposed to a provider sphere where providers dominate value creation without interaction with customers (Gronroos & Voima, 2013). In servitization, digital platform capabilities have analyzed and transformed digital data into knowledge (Coreynen et al., 2017). Digital applications have also improved the management of both endogenous and exogenous knowledge to further increase digital platform capabilities (Coreynen et al., 2017; Sher & Lee, 2004). From these arguments for value co-creation, data analytics, and knowledge linking capabilities offered in digital technology platforms, this research asserts that digital technologies are the building blocks that form a foundation of platform capabilities for developing service innovation.
3.3 Research Model
Based on the above review and discussion, a research model is proposed which is represented in Figure 2. Within the S-D logic framework, this model recognizes the existence of digital technologies as both operand and operant resources. Digital technologies and intangible intellectual capital are critical resources for knowledge-intensive services. They form knowledge-intensive service resources and build digital platform capabilities that foster cocreation and analytics capabilities, furthering service innovation outcomes.
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Figure 2. Inner Research Model
3.4 Hypotheses Development
Knowledge-intensive services are recognized as highly active in innovation (Catey, 2012; Corrocher et al., 2009; Hipp et al., 2015; Srivastava & Gnyawali, 2011) and their knowledge-based resources (e.g., knowledge and experience of employees, alliance and partner resources, and technologies) are critical to generating innovations in the digital age. These resources mirroring the foundational premises in S-D logic include intellectual capital and digital technologies collectively. When the components of intellectual capital are applied strategically, they lead an organization to better performance and more innovations (Agostini & Nosella, 2017; Roos et al., 2001). Strategies are two-faceted, encompassing how an organization exists within its environment and how well an organization uses its intellectual capital (Roos et al., 2001). To use it well, intellectual capital must be viewed as more than a stock of knowledge (Hsu &
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Sabherwal, 2011); it is an operant resource with actions.
For example, human capital drives strategic renewal from brainstorming, daydreaming, or process re-engineering alike (Bonds, 1998); organizational capital, with codified knowledge mandates procedures and rules (Nelson & Winter, 1982, p. 103); and social capital intensifies unbound interactions between individuals and groups (Subramaniam & Youndtm, 2005). In addition, the presence of technology enhances the practical application of knowledge (Lusch & Nambisan, 2015) because it is viewed as a subset of knowledge that acts as know-how and provides information (Capon & Glazer, 1987). The know-how capability coupled with ubiquity makes digital technology a powerful resource to knowledge-intensive services in the digital age. An organization with appropriate data and network architecture, data processing capability, as well as familiarity with social media and communication technology can respond to ever-changing environmental requirements, manage external relationships, and blend non-digital and digital expertise (Wade & Hulland, 2004).
Academic libraries are in a unique position to stimulate service innovation through the strategic possessing and deploying of knowledge-intensive service resources. Librarians have been utilizing intellectual capital in providing instructional services to faculty and students, although traditionally in the form of face-to-face consultation with printed materials. Digital technologies have meantime exploited and expedited innovative instructional service deliveries that are more fitting for millennials and incoming generation Zs. Libraries which recognize critical knowledge-intensive services resources are innovating. For example, “Library DIY” (https://library.pdx.edu/diy/) is a system of learning objects designed to give students the quick answers for point-of-need support. Students can drill down from the objects to the specific piece of information they are looking for rather than having to skim through a long tutorial before
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finding the answers. “Guide on the Side” (http://tutorials.lib.umd.edu) creates interactive learning tutorials for students on the use of research literature databases and searching techniques. The instructional pane contains text, pictures, links, and interactive questions on the side of browser where the online resource is the focus of the tutorial. These innovations utilize the work of library employees who are skilled in their functions to develop new ideas and collaborate with others to share ideas. They also utilize databases, the Internet, and websites— digital technologies—to disseminate innovations. Without these traits in the employees and presence of digital technologies, an academic library would be performing in the old way. The following hypothesis is therefore proposed:
Hypothesis 1: The possession of critical knowledge-intensive service resources has a positive association with service innovation outcomes in an academic library.
Resources are required to build capabilities; therefore, the lack of resources harms an organization’s ability to build capabilities for performance and innovation (Karimi et al., 2007). However, the mere presence of resources does not guarantee an organization’s ability to build capabilities (Barney et al., 2001). It is rather the integration and configuration of resources that improve digitalization to create platform capabilities for an organization in this digital age (Coreynen et al., 2017). Such platforms conflate physical and digital technology elements. With appropriate and flexible hardware, server, network, databases, mobile devices, and so forth, an organization can codify tacit knowledge amongst employees, users, vendors, and collaborators. This codified knowledge may further assist an organization in anticipating and evaluating opportunities, acquiring more external knowledge, assimilating external and internal knowledge, and integrating and coordinating knowledge capabilities via digital technologies.
Academic libraries are in a unique position to stimulate digital platform capabilities as
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many of their collections are already in the digital format. With the advancement and prevalence of digitization technologies, academic libraries have directed efforts to digitalize their special collections to preserve and fulfil their service mission. “Library as Platform” has been promoted by David Weinberger (2012). Weinberger’s vision explains that a library is more than a portal that users go through occasionally, but a ubiquitous and persistent platform infrastructure of capabilities that serves more users, serves them better, and better fulfills the library’s value and mission. Unlike traditional libraries, a library’s electronic platform provides access to everything it can, including some treasures yet to become available. The library platform will enable social knowledge networks to emerge and flourish, supporting idea sharing and peer collaboration. To accomplish this vision, libraries would need to digitize their hidden treasures, open their digital content including metadata about the content, provide end-user tools, especially social tools for exploring data and content, and open APIs for developers to create applications. Weinberger compares his vision to that of Facebook, where innovative apps make Facebook ever more valuable to its users. The following hypothesis is therefore proposed:
Hypothesis 2: The greater the ability to integrate knowledge-intensive service resources, the more able an academic library will be to build digital platform capabilities.
Recent studies emphasized the growing role of digital platform capabilities for increasing new service offerings in the manufacturing industry (Cenamor et al., 2017; Lenka et al., 2017; Lerch & Gotsch, 2015; Parida et al., 2015; Ronnberg Sjodin et al., 2016) and the shared economy (Frey et al., 2017). Such a platform provides a unique space where community users and providers interact and co-create, contributing to the capabilities for customization; hence, it is ideal for knowledge-intensive services in which a high level of customization is often demanded as an output (Zeithaml, 1981). Customization is aided when co-creation serves as a
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form of dialogue between providers and users—two equal problem solvers—who engage in learning and communication (Prahalad & Ramaswamy, 2004). Within the platform, often a virtual space (e.g., blogs, wikis, Zoom, Jabber, GoToMeeting, Skype for Business), communication happens vertically and horizontally with ease; thus, development and customization are enhanced. Project CORA stands for Community of Online Research Assignment and is an exemplary library digital platform where librarians and faculty collaborate and co-create assignments that support information literacy on campus (CORA, 2018). The assignments are built on a platform, backed by a content management system that promotes the pedagogical practices of a specific institution; however, the assignments are not isolated entities but are shared with other institutions for adaptation and experimentation. The assignments are also enhanced by users’ continuous feedback to build a community of practice in new and interesting ways.
Although digital platform generates vast amounts of digital data, digital platform capabilities also provide analytical capability by means of sophisticated technological applications that develop rules, logics, and algorithms to transform these available data into predictive insights (Iyer 2011; Lenka et al., 2017). Predictive analytics has been proved successful at Merck & Co., Inc. Boulton (2017) points out that instead of having engineers spending their effort in finding, accessing, and ingesting data to evaluate project success, Merck created MANTIS (Manufacturing and Analytics Intelligence), a data warehousing system to crunch data in both structured and unstructured systems including text, video, and social media. A paradigm shift has thus occurred in data usage from collecting and reporting to modeling and visualizing in Merck. MANTIS has helped Merck decrease the time and cost of overall IT analytics projects by 45 percent, while increasing the tangible business outcomes that include a
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20 percent reduction in average lead time, and a 50 percent reduction in average inventory carrying costs (Boulton, 2017). Predictive analytics does not need to be established in a scale as large as MANTIS; small-context work can be done with equal efficiency. For example, Dr. Pepper’s MyDPS is an app equipped with machine learning and other analytics tools to funnel recommendations and operation scorecards to the sales staff. Those staff load the MyDPS app onto their iPads; the metrics in turn tell them what offers to make to retailers and what stores they should be paying a visit (Boulton, 2017). It is assumed that predictive analytics will give libraries the ability to approach opportunities, mitigate risks, and foresee user behaviors in a way that was not possible before the digital age. Using data to make decisions and inform process change have long been successfully practiced in the libraries, evident in studies by Daneshgar and Parirokh (2012), Kirkwood (2016), and Veldof (1999). These studies describe libraries’ data-driven decision-making processes and innovative use of “descriptive data” in various formats, including tracking sheets, interview scripts, and database records to support the tasks of processes change, collection-development policies, user-experience enhancement, and knowledge. Therefore, the following hypothesis is proposed:
Hypothesis 3: Digital platform capabilities have a positive association with service innovation outcomes in an academic library.
Service innovation is a complex phenomenon that could only be achieved with a variety of capabilities (Agarwal & Selen, 2009; den Hertog et al., 2010). For an organization these capabilities are obtained by transforming current resources into a valuable bundle for capabilitybuilding (Hunt & Morgan, 1996) and by reconfiguring the organization’s resources to maintain such capabilities (Fiol, 2001). In other words, resources are a pre-requisite to the existence of capabilities, and resources alone may not necessarily be directly related to service innovation.
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Especially when resources are deemed mostly valuable but not rare, how may they provide more service innovation? Karimi and Walter (2015) suggest that organizational resources, such as financial and human capital, be aligned effectively to build digital platform capabilities. Digital platform capabilities in turn deliver innovation and value by connecting resources and the network effects between them (Gartner Executive Programs, 2016). In the academic library context, as digitalization moves from a trend to a library’s core competency, it is expected that service innovation will depend less on the rigidity of possessing knowledge-intensive resources and more on intellectual capital integrating with digital technologies to create digital platform capabilities. The more digital platform capabilities, the more the libraries can co-create value with users and apply analytics to foresee opportunities for innovation. The following hypothesis is therefore proposed:
Hypothesis 4: The impact of knowledge-intensive service resources on service innovation outcomes is mediated by the academic library’s ability to build digital platform capabilities.
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CHAPTER IV. RESARCH DESIGN
4.1 Measurement Model
Figure 3. Measurement Model
This research proposes fourteen constructs: ten first-order constructs, three second-order constructs, and one third-order construct. In total, these constructs were measured by 43 items adapted from prior research.
Third-order formative construct knowledge-intensive service resources (KISR) are formed by operand digital technology (ODDT), operant digital technology (OTDT), and intellectual capital (IC). ODDT is a first-order reflective construct measured by five items adapted from Karimi et al. (2007) assessing whether hardware, network, server and database technologies, digitization technologies, and mobile and digital devices are in place as resources. OTDT is a second-order construct reflected by three first-order reflective constructs: inside-out capability (IOC), outside-in capability (OIC), and spanning capability (SPC). IOC was measured
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by four items assessing digital technologies meeting external opportunities. OIC was measured by four items assessing the library’s response to evolving external-user requirements. SPC was measured by four items that satisfy the opportunities and requirements identified by IOC and OIC. The measurement items for IOC, OIC, and SPC are adapted from Bharadwaj et al. (1999), Cai et al. (2016), and Zhang et al. (2008). IC is a second-order construct reflected by three first-order constructs: human capital (HC), organizational capital (OC), and social capital (SC). HC was measured by four items representing library employees’ capabilities. OC was measured by four items assessing codified knowledge and experience residing in various channels within the library. SC was measured by four items representing capabilities embedded in various network of relationships with the library. The measurement items for HC, SC, and OC are adapted from Chen et al. (2014), Hsu and Sabherwal (2011), and Subramaniam and Youndt (2005).
Digital platform capabilities (DPC) are a second-order construct reflected by two first-order reflective constructs: co-creation capability (CC) and analytics capability (AC). CC was measured by five items, adapted from Hsieh and Hsieh (2015), which affirm the interaction between users and employees. AC was measured by five items, adapted from Gupta and Georg (2016), which assess the library’s available data and analysis mechanism for decision-making.
First-order reflect construct service innovation outcomes (SIO), adapted from Wang et al. (2010) and Yen et al. (2012), is measured by four items assessing the library’s ability to adopt or create service innovation as well as its perception of its service innovation performance. Often the outcome of a service innovation is “not defined by what firms produced as output but how firms can better serve” (Lusch et al., 2008), or how customers become better off when using a product or service (Skalen et al., 2015). Also, the initiation of a service innovation often reflects an organization’s perceived readiness to innovate through an assessment of the various resources
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it possesses (Wang et al., 2010). Combining resource valuation and service innovation to help library users better at doing so, this research assesses the library’s perceived readiness to provide service innovation. Additionally, intellectual capital as the knowledge-intensive service resource integrating with digital technologies creates digital platform capabilities that presumably enable academic libraries to serve the needs of their valued network of users. Table 6 summarizes measurements of the fourteen latent constructs in this study. Appendix B lists constructs’ operationalization and their sources.
For the control variables, a number of demographic variables, namely the library’s total expenditures (EXP) and the library’s total full-time equivalent (FTE) of both professional and support staff, are important factors which may affect the amount of innovation. EXP reflects an organization’s budget, whereas FTE is broadly similar to budget reflections of an organization’s size. Budget and size are often used as control variables, Bharadwaj (2000) and Saldanha et al. (2017) find that both account for the abundance of resources devoting to various technologies to produce more innovation in an organization.
EXP was measured with five variables reflecting a library’s expenditures of less than $500,000, between $500,000 and 1 million, between 1 million and 10 million, between 10 million and 20 million, and more than 20 million. FTE was measured with four variables reflecting a library’s FTE of less than 100, between 100 and 250, between 250 and 500, and more than 500. They are control variables for KISR, DPC, and SIO and are established based on the Association of Research Libraries statistical measures (https://www.arlstatistics.org).
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Table 6. Measurement of Constructs
Latent Construct Order Latent Construct Type Sub-Construct or Dimension Number of Items
Knowledge-intensive service resources (KISR) Third Formative Digital operand technologies (DOD) Digital operant technologies (DOT) Intellectual capital (IC)
Operant digital technology (OTDT) Second Reflective Inside-out capability (IOC) Outside-in capability (OIC) Spanning capability (SPC)
Intellectual capital (IC) Second Reflective Human capital (HC) Organizational capital (OC) Social capital (SC)
Digital platform capabilities (DPC) Second Reflective Co-creation capability (CC) Analytics capability (SC)
Service innovation outcomes (SIO) First Reflective 4
Operand digital technology (ODDT) First Reflective 5
Inside-out capability (IOC) First Reflective 4
Outside-in capability (OIC) First Reflective 4
Spanning capability (SPC) First Reflective 4
Human capital (HC) First Reflective 4
Organizational capital (OC) First Reflective 4
Social capital (SC) First Reflective 4
Co-creation capability (CC) First Reflective 5
Analytics capability First (AC) Total measurement items 43 Total constructs 14 Reflective 5
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4.2 Data Collection Procedures
4.2.1 Survey Instrument
Data were collected using a carefully developed self-reporting survey instrument based on guidelines and exemplars in the literature from Straub (1989) and Sethi and King (1991). Past literature was reviewed to specify a set of items that ensured content and face validity and to achieve minimal overlap between constructs, as suggested in Cronbach (1971) and Kerlinger (1986, p. 19). Items associated with these constructs use a seven-item Likert type scale where respondents were asked to state their agreement with a given statement on a scale that ranged from “strongly agree” to “strongly disagree” with its midpoint anchored as “neutral.”
4.2.2 Content Validity and Face Validity
Content validity is “an assessment of how well a set of scale items matches with the relevant content domain of the construct that it is trying to measure.” Face validity refers to whether an indicator seems to be a reasonable measure of its underlying construct “on its face” (Bhattacherjee, 2012). Five academic library administrators evaluated both the content validity and face validity of the construct measures, after which they were excluded from the official survey research. Their feedback contributed to the re-arrangement of measurement items, wording clarification, and modification of question types.
4.2.3 Sampling Process
The target population for this study is academic libraries in the United States. The sampling frame includes academic libraries in doctoral universities and master’s colleges and universities according to the Carnegie Classification of Institutions of Higher Education. In total, there are 975 academic libraries in the frame (328 doctoral universities and 647 master’s colleges and universities). The samples are library administrators in these academic libraries including
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deans, associate deans, assistant deans, university librarians, associate university librarians, assistant university librarians as well as directors, associate directors, assistant directors, and heads of information technology. To collect email addresses of the samples, each library’s website was searched, visited, and the administrators’ contact information manually collected. Some libraries’ websites do not provide contact information and some administrative positions are vacant at the time of the collection.
4.2.4 Pilot Test
Pilot testing helps detect potential problems in research design and instrumentation and to ensure that the measurements are reliable and valid (Bhattacherjee, 2012). Twelve deans and directors from the target population were recruited for pilot testing; they were also excluded from the official survey research. Data collected from a pilot test of deans and directors in academic libraries were used for instrument validation and refinement only. Only minor wording changes were required from the pilot testing.
4.3 Main Data Collection
An institutional review board (IRB) application was submitted and approved by the University of Colorado, Denver in November 2017. The survey was developed in Qualtrics to collect empirical data for the proposed knowledge-intensive service resources dimensions, digital platform capabilities, and the effect of building digital platform capabilities for service innovation outcomes. The data collection for confirmatory analysis and hypotheses testing began in December 2017. To improve response rate, the solicitation e-mail addressed each recipient individually and was sent individually. The e-mail stated that respondents could leave the survey at any time and that their responses would be completely anonymous and confidential. No specific incentive was provided to participants for completing the survey beyond promising them
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a copy of the aggregated results if they expressed interest in receiving the results. On the survey, respondents were instructed to answer each question as a representative of the institution as opposed to basing replies on purely personal views. In total 1,313 emails were sent to academic library administrators (728 of the emails to the doctoral universities and 585 emails to the master’s colleges and universities). The survey remained open for 55 days. Ten days from the first-sent email, 186 data points were collected; within 30 days from the first-send email, up to 245 data points were collected. An additional 16 responses were collected between 30 and 55 days, yielding a total of 261 responses. Among them, 251 were usable responses. The response rate was 19.1% for the entire sample population. Specifically, 178 responses with the rate of 24.5% were received from the doctoral universities, whereas 73 responses for the rate of 12.5% were received from the master’s colleges and universities. The rates in this study are within and above the typical external survey response rate of 10% - 15% (Fryrear, 2015). Such a response rate without a reminder being sent signified unusual interest in this topic amongst library administrators. Appendix C displays the solicitation e-mail, and Appendix D lists the survey questionnaire.
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CHAPTER V. DATA ANALYSIS AND RESULTS
5.1 Descriptive Statistics
Descriptive statistics listed in Table 7 shows that 54.2% of the top administrators from the sample libraries responded to the survey; they held the title of deans, university librarians, or directors. This was followed by more than a 27% response by associate deans, associate university librarians, or associate directors. The majority of responding libraries (49.4%) had annual expenditures of between $1 to $10 million, and the majority of responding libraries had a total FTE of less than 100 (65.3%). The most of the responding libraries (47.8%) were public doctoral institution. Institutions in the Northeast (26.3%), the Southeast (23.1%), and the Midwest (23.1%) were more responsive than those in the West (15.5%) and Southwest (12%). Table 7. Descriptive Statistics
Demographic Descriptive Statistics
Number Percent of of total responses
Position in the library
Dean, University Librarian, or Director 136 54.2%
Associate Dean, Associate University Librarian, or 68 27.0%
Associate Director
Assistant Dean, Assistant University Librarian, or 20 8.0%
Assistant Director
Head of Technology Unit 16 6.4%
Other 11 4.4%
Grand Total 251 100%
Library total expenditures
< $500,000 15 6.0%
$500,0000 - $1,000,000 20 8.0%
$1,000,000 - $10,000,000 124 49.4%
$10,000,000 - $20,000,000 53 21.1%
>$20,000,000 39 15.5%
Grand Total 251 100%
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Table 7 cont’d
Demographic Descriptive Statistics Number Percept of of total responses
Total FTE (both professional and support staff)
< 100 164 65.3%
100 -250 62 24.7%
250 - 500 17 6.8%
> 500 8 3.2%
Grand Total 251 100%
Library geographic region
Northeast 66 26.3%
Southeast 58 23.1%
Midwest 58 23.1%
Southwest 30 12.0%
West 39 15.5%
Grand Total 251 100%
Type of institution
Public Doctoral University 120 47.8%
Private Doctoral University 56 22.3%
Public Master’s College or University 37 14.7%
Private Master’s College or University 38 15.1%
Grand Total 251 100%
To test for potential nonresponse bias, two techniques applied in prior research were followed (i.e., Mani et al., 2010; Welch & Barlau, 2013): (1) comparing respondents to the population in response rate based on background characteristics, and (2) comparing early responders to late responders based on background characteristics. The demographic characteristics of position, geographic region, and type of institution were known for both the respondents and the population and were therefore used to compare response rates. As summarized in Table 8, there were some differences in response rate in each demographic characteristic, with heads of IT unit responding the most at 30.8% comparing with the population, whereas deans, university librarians, and directors responded the least at 15.2%. The
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rates of the other position categories were closely distributed between 26.3% and 27.5%. This distribution was not unexpected because the survey topic is related to technology, which naturally attracts the leaders in IT units. Also, deans, university librarians, and directors of an academic library might lack the time to respond to a survey. With respect to the geographic region, the Southwest had the highest responder rate at 25.2% comparing with the population, whereas the rates of other regions were closely distributed between 17.6% and 19.8%. As to the type of institution, public doctoral universities had the highest responder rate at 25.4% comparing with the population, followed by private doctoral universities (21.9%), public master’s colleges and universities (16.2%), and private master’s colleges and universities (10.6%).
To compare early with late responders, the sample was divided into half according to response date/time to compare the demographics of the two groups, which included the position of the responder, the library’s total EXP, its total FTE, and the type of the library’s parent institution. T-test were performed on two sets of data collected for the doctoral universities separated from the master’s colleges and universities, with no significant differences found between early and late respondents. The results are summarized in Table 9, below. All values are greater than 0.05, indicating no significant bias. Given the lack of extent empirical statistics in academic library research, the statistical findings in this study can serve as reference points for future research.
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Table 8. Comparison of Population Value and Responder Value
Demographic Population Value Responder Value Percentage
Position in the library
Dean, University Librarian, or Director 894 136 15.2%
Associate Dean, Associate University 251 68 27.1%
Librarian, or Associate Director
Assistant Dean, Assistant University 76 20 26.3%
Librarian, or Assistant Director
Head of Technology Unit 52 16 30.8%
Other 40 11 27.5%
Grand Total 1313 251
Library geographic region
Northeast 376 66 17.6%
Southeast 318 58 18.2%
Midwest 303 58 19.1%
Southwest 119 30 25.2%
West 197 39 19.8%
Grand Total 1313 251
Type of institution
Public Doctoral University 472 120 25.4%
Private Doctoral University 256 56 21.9%
Public Master’s College or University 228 37 16.2%
Private Master’s College or University 357 38 10.6%
Grand Total 1313 251
Table 9. Nonresponse Bias T-Test Results
Classification Position Total Expenditures Total FTE Geographic Region Type of Institution
Doctoral Universities 0.279 0.147 0.064 0.683 0.345
Master’s 0.669 Colleges and Universities Not Significant if p > 0.05 0.591 0.365 0.321 0.506
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5.2 Measurement Model
The method of partial least squares (PLS) supported by SmartPLS version 3 was used to test the measurement and path models. This analytical approach is generally recommended for predictive research models where the emphasis is on theory development (Joreskog & Wold, 1982). Given that there have been very few empirical studies in this research context, the focus was on theory development. In addition, the ability of PLS to model formative and reflective constructs (Rai et al., 2006) makes it appropriate that this research contain both construct types. Another feature of SmartPLS is that when the number of missing value is relatively small (i.e., less than 5% missing value per indicator), SmartPLS 3 uses mean value replacement instead of case-wise or pair-wise deletion to treat the missing values when running the PLS-SEM algorithm (Hair et al., 2017). For reflective constructs in this research, psychometric properties including all first-order and second-order constructs were assessed by examining internal consistency, convergent validity, and discriminant validity. SmartPLS calculates means and standard deviations for measurement items, factor loading, t-statistics, cross-loading, average variance extracted (AVE), Cronbach’s alphas, and composite reliability scores. Internal consistency was evaluated by examining Cronbach’s alpha and composite reliability score. Based on Nunnally’s (1978, p. 55) guidelines, a score of 0.70 or above for both Cronbach alpha and composite reliability indicate a strong internal consistency for exploratory research. For first-order reflective constructs, as seen in Table 10, Cronbach’s alpha ranges from 0.808 to 0.912 and composite reliability ranges from 0.875 to 0.938, showed strong internal consistency. For second-order reflective constructs, as seen in Tables 11, Cronbach’s alpha ranges from 0.903 to 0.924 and composite reliability ranges from 0.920 to 0.935, showed strong internal consistency.
Convergent validity was verified with the outer loading of indicators and AVE. At a
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minimum, the standardized outer loading of all indicators should be 0.70 or higher to signify the commonality of an item by the indicators. An AVE value of 0.50 or higher indicates that, on average, the construct explains more than half of the variance of its indicators (Hair et al. 2017). For first-order reflective constructs, as seen in Table 10, standardized outer loading ranges from 0.721 to 0.922 and AVE ranges from 0.637 to 0.792, showing strong convergent validity. For second-order reflective constructs, as seen in Tables 11, standardized outer loading ranges from 0.773 to 0.907 and AVE ranges from 0.502 to 0.546, establishing convergent validity.
To demonstrate discriminant validity, researchers have traditionally relied on crossloadings and the Fomell-Larcker criterion approaches (Fornell & Larcker, 1981). Specifically, an indicator’s outer loading on the associated construct should be greater than any of its correlation on other constructs; and the square root of each construct’s AVE should be greater than its highest correlation with any other construct (Hair et al., 2017). As shown in Table 12, all loadings for the first-order constructs are greater than all cross-loadings. Table 13 and Table 14 show that the square root of AVE of a construct is greater than its correlations with other constructs for first-order and second-order constructs respectively. Therefore, discriminate validities are established for the constructs. Table 15 summarizes the systematic evaluation stages performed for the reflective and formative constructs in this research. Figure 4 displays the factor loadings in the visual format.
Construct reliability of the formative third-order construct KISR was assessed by examining indicator multicollinearity and all path coefficients from the sub-constructs to KISR. High levels of multicollinearity in a formative measure is a problem because the influence of each indicator on the latent construct cannot be distinctly determined (Diamantopoulos &
Siguaw, 2006). The variance inflation factor (VIF) values for ODDT, OTDT, and IC as
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predictors of KISR were calculated to be 1.88, 2.98, and 1.72 respectively; they are well below the threshold value of 5.0 specified in Hair et al. (2011), indicating the lack of multicollinearity for the formative construct KISR. Next, all path coefficients by means of bootstrapping procedure with 1,000 subsamples for 251 cases were assessed. The results show that they are sizable, significant, and with the right sign for a formative construct coefficients: ODDT -> KISR (P = 0.19, t = 9.216, one-tailedp < 0.001), OTDT -> KISR (P = 0.52, t = 23.129, one-tailed p < 0.001), and IC -> KISR (P = 0.45, t = 16.085, one-tailedp < 0.001).
Common method variance is the variance attributed to the measurement method rather than to the constructs the measures represent (Podsakoff et al., 2003). It is a concern for the measurement method of Likert-type scales in an SEM study. When two or more predictors measure the same underlying construct, or a facet of such construct, they are said to be collinear (Kline 2005, p. 56). The value of 36.93% (less than 50%) from the Harman one-factor test indicates Factor 1 did not explain most of the variance; therefore, common-method bias is unlikely to be a concern in this study.
Table 10. Psychometric Properties for First-Order Constructs
Construct Item Mean Std dev. Loading t-stats Alpha CR AVE
Operand Digital Technology (ODDT) 0.893 0.922 0.702
ODDTi 2.311 1.204 0.865 26.320
ODDT2 2.203 1.127 0.838 28.016
ODDT3 2.320 1.278 0.897 55.134
ODDT4 2.422 1.393 0.783 25.076
ODDT5 2.888 1.443 0.802 26.356
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Table 10. cont’d
Construct Item Mean Std dev. Loading t-stats Alpha CR AVE
Operant Digital Technology (OTDT)
Inside-out Capability (IOC) 0.907 0.935 0.782
IOCi 3.363 1.592 0.853 47.198
IOC2 2.622 1.341 0.853 41.415
IOC3 3.364 1.442 0.922 91.507
IOC4 3.357 1.461 0.907 69.751
Outside-in Capability (IOC) 0.830 0.888 0.665
OICi 2.143 1.050 0.747 16.061
OIC2 2.184 1.102 0.850 32.580
OIC3 2.303 1.292 0.874 38.487
OIC4 2.343 1.154 0.786 22.889
Spanning Capability (SPC) 0.863 0.907 0.710
SPCi 2.530 1.404 0.795 24.103
SPC2 2.028 1.148 0.813 22.497
SPC3 2.502 1.426 0.894 52.513
SPC4 2.769 1.398 0.864 44.339
Intellectual Capital (IC)
Human Capital (HC) 0.858 0.904 0.703
HCi 1.964 0.848 0.759 18.616
hc2 2.610 1.107 0.823 33.905
hc3 2.219 0.917 0.864 43.108
hc4 2.530 1.151 0.900 71.713
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Table 10. cont’d
Construct Item Mean Std dev. Loading t-stats Alpha CR AVE
Organizational Capital (OC) 0.808 0.875 0.637
O p 3.363 1.397 0.812 34.403
OC2 2.248 1.044 0.721 19.439
OC3 3.080 1.288 0.869 36.462
O O -U 2.596 1.217 0.785 23.053
Social Capital (SC) 0.899 0.930 0.769
SCi 2.296 1.217 0.884 44.818
sc2 2.344 1.117 0.917 81.009
sc3 2.592 1.184 0.905 63.503
sc4 2.876 1.294 0.795 27.050
Digital Platform Capabilities (DCP)
Co-creation Capability (CC) 0.882 0.914 0.679
O p 3.285 1.360 0.835 36.508
CC2 3.520 1.398 0.815 30.378
cc3 2.832 1.202 0.852 41.761
0 0 -U 2.444 1.127 0.795 24.501
CC5 2.920 1.244 0.823 35.224
Analytics Capability (AC) 0.893 0.922 0.702
ACi 3.227 1.518 0.853 42.253
ac2 2.804 1.391 0.777 23.233
ac3 3.084 1.444 0.865 54.060
ac4 3.514 1.585 0.876 48.495
AC5 4.000 1.678 0.815 33.774
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Table 10. cont’d
Construct Item Mean Std dev. Loading t-stats Alpha CR AVE
Service Innovation Outcomes (SIO) 0.912 0.938 0.792
SIOi 3.127 1.425 0.912 83.910
SI02 3.068 1.439 0.891 61.737
SI03 3.149 1.405 0.895 64.422
SI04 2.844 1.349 0.861 41.372
Note: All t-statistics for loading are higher than 10, indicating high significance
Table 11. Loadings, AYE, and CRfor Second-Order Constructs
Construct Operant Digital Technologies (OTDT) Intellectual Capital (IC) Digital Platform Capabilities (DPC)
Alpha 0.924 0.908 0.903
CR 0.935 0.923 0.920
AVE 0.546 0.502 0.536
Inside-out Capability (IOC) 0.858
Outside-in Capability (OIC) 0.850
Spanning Capability (SPC) 0.907
Human Capital (HC) 0.870
Organizational Capital (OC) 0.773
Social Capital (SC) 0.883
Co-creation Capability (CC) 0.888
Analytics Capability (AC) 0.874
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Table 12. Loading and Cross-Loading
AC CC HC IOC OC ODDT OIC SC SIO SPC
ACi 0.853 0.464 0.231 0.316 0.388 0.195 0.279 0.358 0.424 0.350
ac2 0.777 0.418 0.304 0.444 0.397 0.291 0.347 0.356 0.400 0.412
ac3 0.865 0.510 0.324 0.356 0.409 0.277 0.346 0.442 0.529 0.477
ac4 0.876 0.475 0.311 0.375 0.434 0.215 0.328 0.381 0.464 0.418
AC5 0.815 0.442 0.225 0.367 0.360 0.294 0.339 0.330 0.444 0.420
o p 0.487 0.835 0.361 0.357 0.370 0.336 0.454 0.414 0.617 0.574
cc2 0.455 0.815 0.351 0.351 0.346 0.330 0.491 0.345 0.521 0.527
cc3 0.484 0.852 0.521 0.400 0.494 0.314 0.440 0.506 0.652 0.591
o o -U 0.422 0.795 0.367 0.356 0.396 0.208 0.423 0.405 0.475 0.493
CC5 0.425 0.823 0.452 0.356 0.430 0.205 0.465 0.470 0.547 0.564
HCi 0.239 0.409 0.759 0.282 0.358 0.225 0.203 0.448 0.373 0.326
hc2 0.285 0.395 0.823 0.373 0.476 0.240 0.385 0.577 0.461 0.462
hc3 0.263 0.503 0.864 0.348 0.426 0.207 0.310 0.517 0.444 0.427
hc4 0.331 0.429 0.900 0.387 0.477 0.257 0.417 0.659 0.563 0.517
IOCi 0.481 0.324 0.398 0.853 0.428 0.538 0.531 0.377 0.454 0.599
IOC2 0.321 0.410 0.314 0.853 0.375 0.618 0.489 0.310 0.418 0.539
IOC3 0.393 0.397 0.395 0.922 0.363 0.614 0.472 0.384 0.518 0.606
IOC4 0.362 0.312 0.367 0.907 0.329 0.626 0.484 0.366 0.520 0.584
o p 0.330 0.465 0.327 0.316 0.812 0.252 0.338 0.360 0.320 0.376
oc2 0.346 0.359 0.571 0.312 0.721 0.178 0.365 0.451 0.507 0.472
oc3 0.439 0.422 0.352 0.357 0.869 0.277 0.315 0.388 0.395 0.449
o o -U 0.395 0.241 0.384 0.357 0.785 0.312 0.356 0.437 0.457 0.435
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Table 12. cont’d
AC CC HC IOC OC ODDT OIC SC SIO SPC
ODDTi 0.362 0.178 0.214 0.569 0.217 0.865 0.360 0.234 0.331 0.443
ODDT2 0.212 0.275 0.174 0.556 0.224 0.838 0.361 0.212 0.336 0.437
ODDT3 0.212 0.381 0.229 0.614 0.261 0.897 0.415 0.229 0.457 0.491
ODDT4 0.287 0.334 0.316 0.517 0.308 0.783 0.440 0.234 0.423 0.540
ODDT5 0.310 0.541 0.222 0.574 0.312 0.802 0.474 0.223 0.458 0.525
OICi 0.385 0.441 0.364 0.451 0.404 0.370 0.747 0.365 0.473 0.524
OIC2 0.323 0.418 0.308 0.445 0.376 0.386 0.850 0.278 0.389 0.588
OIC3 0.330 0.407 0.306 0.438 0.367 0.410 0.874 0.363 0.416 0.597
OIC4 0.240 0.450 0.331 0.489 0.269 0.437 0.786 0.310 0.381 0.587
SCi 0.414 0.450 0.564 0.317 0.449 0.176 0.312 0.884 0.561 0.473
sc2 0.348 0.453 0.616 0.325 0.478 0.241 0.312 0.917 0.548 0.474
sc3 0.430 0.494 0.584 0.381 0.489 0.240 0.352 0.905 0.546 0.537
sc4 0.377 0.424 0.559 0.410 0.394 0.299 0.448 0.796 0.495 0.514
SIOi 0.503 0.620 0.534 0.539 0.492 0.483 0.482 0.617 0.912 0.675
SI02 0.464 0.554 0.452 0.513 0.461 0.447 0.460 0.562 0.891 0.632
SI03 0.487 0.641 0.490 0.473 0.478 0.423 0.450 0.502 0.895 0.598
SI04 0.472 0.622 0.497 0.393 0.468 0.358 0.411 0.496 0.861 0.571
SPCi 0.339 0.524 0.544 0.454 0.465 0.362 0.573 0.570 0.570 0.795
SPC2 0.351 0.534 0.381 0.496 0.418 0.497 0.601 0.369 0.465 0.813
SPC3 0.433 0.608 0.458 0.624 0.445 0.574 0.598 0.441 0.641 0.894
SPC4 0.535 0.579 0.391 0.629 0.518 0.522 0.606 0.541 0.662 0.864
Notes: AC = analytics capability; CC = co-creation capability; HC = human capital; IOC = Inside-out capability; OC = organizational capital; ODDT = operand digital technologies; OIC = outside-in capability; SC = social capital; SIO = service innovation outcomes; SPC = spanning capability
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Table 13. Intercorrelations and VAVE of Latent Variables for First-Order Constructs
1 2 3 4 5 6 7 8 9 10
Analytics Capability (AC) 0.838
Cocraetion Capability (CC) 0.552 0.824
Human Capital (HC) 0.334 0.499 0.838
Inside-out Capability (IOC) 0.441 0.442 0.418 0.884
Outside-in Capability (OIC) 0.475 0.495 0.521 0.422 0.798
Operand Digital Technology 0.302 0.340 0.278 0.677 0.318 0.838
(ODDT)
Outside-in Capability (OIC) 0.390 0.551 0.400 0.559 0.433 0.492 0.815
Social Capital (SC) 0.447 0.520 0.663 0.407 0.518 0.271 0.403 0.877
Service Innovation Outcomes 0.541 0.685 0.555 0.541 0.534 0.483 0.507 0.613 0.890
(SIO)
Spanning Capability (SPC) 0.496 0.668 0.523 0.659 0.548 0.585 0.705 0.569 0.697 0.842
Notes: Diagonal values (in bold font) are square roots of AVEs. Off-diagonal values are corrections.
Table 14. Intercorrelations and VAVE for Second-Order Constructs
1 2 3
Digital Platform Capabilities (DPC) 0.732
Intellectual Capital (IC) 0.620 0.709
Operant Digital Technology (OTDT) 0.652 0.622 0.739
Notes: Diagonal values (in bold font) are square root of AVEs. Off diagonal values are correlations.
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Table 15. Systematic Evaluation of the Constructs
Reflective Construct Evaluation Criteria
Evaluation Criterion a Criterion b
Internal consistency Convergent validity Discriminant validity Cronbach’s alpha > 0.70 Outer loading > 0.70 Cross loading - highest on the associated construct Composite reliability > 0.70 Average variance extracted > 0.50 Fomell-Larcker - square root of average variance extracted > highest correlation
Formative Construct Evaluation Criteria
Composite reliability Multicollinearity test <5.0 Path coefficients - significant with right sign
IOC1
IOC2
IOC3
IOC4
SPC1
SPC2
SPC3
SPC4
OC1
OC2
OC3
OC4
5101
5102
5103
5104
Figure 4. Factor Loadings
5.3 Structural Model
KISR affects SIO directly as specified in Hi and affects DCP directly as specified in H2.
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DCP contributes to SIO positively as specified in H3. In addition, DCP mediates the relationship between KISR and SIO as specified in H4. The bootstrap procedure with 1,000 subsamples for 251 cases was performed to test significance. The results are shown in Figure 5. All path coefficients were positive, indicating positive relationships between the predictor and the dependent variable hypothesized. Based on Cohen’s (1977, p. 83) classification of path coefficients (strong = 0.50, medium = 0.30, small = 0.10), there is a strong and significant impact of KISR on SIO (P = 0.51, t = 8.128, one-tailed p < 0.001), indicating that Hi is supported. KISR also has a strong and significant effect on DCP (P = 0.69, t = 17.543, one-tailedp < 0.001), demonstrating that H2 is supported. The direct effort of DCP on SIO is medium in size (P = 0.34, t = 5.227) and statistically significant (one-tailedp < 0.001), therefore H3 is supported.
To test the mediating effect of DPC on SIO (H4), Hair et al. (2017) suggest the approach of bootstrapping the sampling distribution of the indirect effect. Prior testing often uses Sobel’s (1982) test, which assumes a normal distribution that is not consistent with the nonparametric PLS-SEM method. However, bootstrapping makes no assumptions about the shape of the variables’ distribution and is thus more suited for the PLS-SEM method. The direct effect from KISR to SIO was pronounced as 0.52 and significant (t = 8.39,/) < 0.001), and the indirect effect was also pronounced as 0.24 and significant (t = 5.23 ,p< 0.001). Neither of the 95% confidence intervals included zero. Since the direct and indirect effects were both positive, and the sign of their product was also positive (i.e., 0.52 x 0.24 = 0.1248), the conclusion is that DPC partially mediates the relationship between KISR and SIO. In other words, DPC represents complementary mediation of the relationship from KISR to SIO. H4 is therefore partially supported.
Innovation varies across firm size; this is because large firms have an alleged advantage
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in innovation (Rogers, 2004). In the context of academic libraries, larger ones may inherit larger foundational funds from their parent institutions or have a larger fiscal budget. They may also have more human resources at their disposal. On the other hand, larger academic libraries may be less flexible in directing their funds and resources for service innovation. To better explain the differential impact of KISR on building DPC, this study minimizes the confounding effects by having the library’s EXP as a proxy for the library’s financial resources. The coefficient for the path from EXP to KISR, DCP, and SIO are all small and not significant—EXP to KISR (P = 0.000, t = 0.047, p > 0.05), EXP to DCP (P = -0.019, t = 0.334, p > 0.05), and EXP to SIO (P = 0.037, t = 0.826,p > 0.05). In addition, the library’s FTE is a proxy for the library’s human resources. The coefficient for the path from FTE to KISR, DCP, and SIO are all small and not significant—FTE to KISR (P = 0.003, t = 1.269,p> 0.05), FTE to DCP (P = 0.038, t = 0.597,/?
> 0.05), and FTE to SIO (P = -0.012, t = 0.269, p > 0.05). These coefficients indicate that neither the size of fiscal budget nor the number of employees affected the libraries’ ability to innovate.
R2 value is a measure of the model’s predictive power because it represents the exogenous latent variables’ combined effects on the endogenous latent variable. That is, it denotes “the amount of variance in the endogenous constructs explained by all of the exogenous constructs linked to it” (Hair et al., 2017). Therefore, the higher the value, the higher the level of the model’s predictive accuracy. DPC explains 48% variance, while SIO explains 62% of variance. They are both considered moderate, based on the rule of thumb where R2 values of 0.75, 0.50, or 0.25 be respectively described as substantial, moderate, or weak (Hair et al., 2011). Table 16 displays the direct- and indirect-effects analyses for H4. Figure 5 displays the testing results of the hypothesized path model with the control variable in the graph, while Table 17 summarizes the results in the tabular format.
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Table 16. Analysis of the Indirect Effects
Direct Effect 95% Cl of Direct Effect t Value P< 0.05? Indirect Effect 95% Cl of Indirect Effect t Value P< 0.05?
KISR 0.520 [0.401,0.645] 8.385 Yes 0.239 [0.149,0.325] 5.230 Yes
->
SIO
FTE. EXP: control ‘•■aiablis
Figure 5. Testing of the Hypothesized Path Model with Control Variables
Table 17. Hypotheses Summary
Hypothesis Position
Hypothesis 1: The possession of critical knowledge-intensive service Supported
resources has a positive association with service innovation outcomes in an academic library.
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Table 17. cont’d
Hypothesis Position
Hypothesis 2: The greater the ability to integrate knowledge-intensive services resources, the more able an academic library will be to build digital platform capabilities. Supported
Hypothesis 3: Digital platform capabilities have a positive association with service innovation outcomes in an academic library. Supported
Hypothesis 4: The impact of knowledge-intensive service resources on service innovation outcomes is mediated by the academic library’s ability to build digital platform capabilities. Partially Supported; DPC represents complementary mediation from KISR -> SIO
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CHAPTEER VI. FINDINGS AND DISCUSSION
6.1 Findings
This research examines four research questions: (1) What are the critical resources for service innovation? (2) How do digital technologies interact with other resources to build digital platform capabilities for service innovation? (3) How do digital platform capabilities contribute to service innovation? and (4) Do digital platform capabilities mediate the impact of resources on service innovation? Based on the S-D logic perspective, this research hypothesizes that intellectual capital and digital technologies are considered essential knowledge-intensive service resources and are critical to service innovation in academic libraries (Hi). Digital technologies serve as tangible operand resources; they are also operant resources displaying multidimensional inside-out, outside-in, and spanning capabilities. When these digital technology resources are integrated, applied, and made use of by intangible human, organizational, and social resources, they build digital platform capabilities (H2). The platform provides an environment where co-creation between library staff and users happens fluidly, and the platform provides data analytics that can be gauged to develop service innovation in academic libraries (H3). This research also hypothesizes that the effect of knowledge-intensive service resources on innovation is mediated by the academic library having a digital platform with capabilities, that is, the more digital platform capabilities, the more the service innovation outcomes (H4). Through empirical research, using the survey sample results from library administrators, the data support Hi and found a direct association between the possessions of knowledge-intensive services to service innovation. The data support H2 through finding a direct association between knowledge-intensive service resources and digital platform capabilities. The data support H3 through finding a direct association between digital platform capabilities and service innovation. However, the
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data found that digital platform capabilities present complementary mediation effect from knowledge-intensive service resources to service innovation outcomes. This finding is not surprising because the concept of a digital platform is new to academic libraries. It has only been recently promoted by the Institute of Museum and Library Services (https://www.imls.gov/) and described in one research article by Weinberger in 2012. Although many special collections in academic libraries have been digitized and the data made available through digital library platforms, a tremendous amount of treasures are still hidden behind library walls due to copyright clearance difficulties and slow digitization efforts. In addition, academic libraries are often short of developers to fully enable digital platform capabilities with APIs and other addons. Academic libraries also tend to be intimidated by data security concerns, which lead them to stay away from exploring and experimenting with hosted data solution opportunities.
The survey sample data show that operand digital technology was considered the least important knowledge-intensive service resource, whereas operant digital technology was more important than intellectual capital. This is understandable because most academic libraries do not maintain their own servers or networks; instead, they leverage operant digital technology’s outside-in capability, such as external digital technology resources, that the parent institution or vendors may offer. Furthermore, academic libraries regard digital technology’s spanning capability as more important than inside-out and outside-in capabilities, with administrators consider the library’s management having a good working relationship with their digital technology personnel as the most critical capability.
The survey sample data indicate that academic libraries consider social capital as the most important intellectual capital. This is also understandable based on the evidence that an increasing number of consortia has been established in the nation and more and more coalitions,
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conferences, and unconferences have been convened by the libraries. Academic libraries are known to be collaborators; in a time of dwindling finance, administrators are capitalizing on each other’s resources and employees and collaborating with each other to solve problems (Yeh & Walter, 2016). Library administrators view library employees as more assertive in providing answers and feedback to users rather than the other way around. Administrators also believe that although usage data of all kinds are collected, those data are in less consistent and visible forms and libraries are less likely to have data visualization mechanisms and other means to produce systemic reporting. This analytics capability deficiency is understandable because most of the library data have been embedded in an integrated library system which is mainly capable of operational reporting rather than analytical reporting. Only in the last few years have library system vendors and developers begun incorporating advanced business analytics suites into their next generation library services platform, replacing the legacy integrated library systems. Library administrators perceive that their service innovation meets the institution’s mission more than they meet the users’ needs. This perception reflects equally on their less favorable evaluation of receiving feedback and ideas from their users; therefore, they may not be keen to understand whether and how they meet the users’ needs. Appendix E lists the survey’s descriptive statistics.
6.2 Research Implications
This study contributes to the S-D logic perspective by identifying, operationalizing, and measuring key premises for creating knowledge-intensive service resources and building digital platform capabilities to increase service innovation outcomes. There are five contributions by this research to IS literature as the first study to: (1) recognize the analogousness between S-D logic’s intangible resources and intellectual capital, (2) recognize intellectual capital and digital technologies as the critical resources for knowledge-intensive service organizations, (3) propose
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the bundle of these critical resources as knowledge-intensive service resources to build digital platform capabilities, (4) present an integrative model of knowledge-intensive service resources that builds digital platform capabilities for service innovation, and (5) empirically validate the model in the context of academic libraries. This research is also the first study that applies the MIS discipline to an understanding of service innovation phenomenon in the LIS field.
Although S-D logic has received a lot of attention since the first decade of this century, the intangible resources including employees’ knowledge and competencies (Shaw et al., 2011), organizational procedures (Barqawi et al., 2016), and social activities (Blasco-Arcas et al., 2014; Edvardsson et al., 2011) have been studied as isolated elements and mainly in the for-profit sectors. By consolidating them into intellectual capital and operationalizing and empirically validating that combined factor in academic libraries, this research contributes to the applicability of S-D logic and its relevance to the nonprofit organizational context. While S-D logic emphasizes digital technology as an operant resource, this research reminds the reader and advocates for digital technology’s dual roles as both an operand and an operant resource. As operand resources, digital technologies act as facilitators, whereas as operant resources, they act as initiators for service innovation (Nambisan, 2013). By bundling digital technologies with intellectual capital as knowledge-intensive service resources to build digital platform capabilities, this study contributes to both strategic management and the IS literature on platform research.
In a resource-based approach, rare knowledge embedded in employees within a business organization is trumpeted as competitive advantage; however, the knowledge in a knowledge-intensive service in the digital age has no border and is not constrained within the organization. In fact, the unbound knowledge is considered to give a competitive advantage and can be
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enriched further with external users’ knowledge through value co-creation. Co-creation is uniquely assisted by digital platform capabilities which unlock the collective wisdom in users’ communities by means of digital technologies (Karimi & Walter, 2015). Exemplary co-creation through digital platform capabilities can be seen in the newspaper industry, where citizen journalism is provided through crowdsourcing (Karimi & Walter, 2015). Similarly, in academic libraries, crowdsourcing functionality is offered in a digital library, representing successful cocreation of and by means of digital platforms. Besides co-creation, this research extends and validates digital platform capabilities to include analytics capability, which has become possible owing to big data affordance. As digital data becomes a daily routine, they will be increasingly seen as valuable assets for digital transformations when explored and analyzed to support strategic directions.
This research has constructed a novel research framework that demonstrates how the S-D-logic perspective is applicable to investigate digital service innovation in knowledge-intensive services. Aside from tangible materials, intangible resources interact with each other and at times are bundled together to create service innovation. Digital technologies strengthen the bond to streamline processes and relationships to create new opportunities (Sambamurthy et al., 2003). This research recognizes academic libraries as knowledge-intensive service organizations and the roles knowledge plays in academic libraries. This research contributes to the LIS field as the first study examining service innovation in academic libraries through the lens of the MIS reference discipline.
The LIS field applies the practice and perspective of knowledge and information to answer questions related to the activities of target groups (Lugya, 2014), and the discipline educates academic librarians to successfully lead library organizations in this digital age. The MIS
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discipline instills an understanding of technology uses in an organization, the way people interact with the technology, and the way the technology works with the organization’s business processes (Oinas-Kukkonen, 2010). As important entities in the knowledge economy, libraries are facing the same digital interruption as other organizations. The synergy between LIS and MIS disciplines is apparent. By applying the theoretical model development rigor in MIS, this research delineates future research avenues for LIS scholars attempting to contribute to theory development. By empirically validating the model of service innovation in the digital age, this research helps close the gap of innovation research in the LIS field, which generally uses case-study research methodology.
6.3 Practice Implications and Recommendations
Academic libraries are at a crossroads with dwindling budgetary support from their parent institutions. In the meantime, academic libraries are expected to service and meet the institution’s mission. The advancement in digital technologies and the diffusion of mobile devices contribute to a digital society that has brought both excitement and challenge to academic libraries. The excitement is with the unprecedented speed and method of information dissemination that enables academic libraries as knowledge-intensive service providers to help expand knowledge and encourage rapid new discoveries. At the same time, this very excitement also brings challenges that force academic libraries to fulfill users’ needs that are drastically different from traditional needs. A fundamental question is thus how academic libraries can maintain their relevance (Campbell, 2017) and overcome disruption from digital technologies. Yeh and Walter (2016) suggest that academic libraries adapt and innovate rather than run away from the challenge when encountering technological disruption. Rein (2007) urges academic libraries to adjust to and accept the fact of techno-based tools as the information resources of
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choice. Both approaches are plausible. This study recommends adaption and adjustment by examining how a complex academic library creates service innovation in this digital age. The research model and results here will help library administrators view digital technologies in service innovation affirmatively and in a different light.
Aside from printed, electronic, and digital materials as knowledge assets in an academic library, other knowledge assets reside in employees and user communities as well as within the library’s rules and procedures. In addition, knowledge assets are generated from interactive and social activities. Such knowledge is termed intellectual capital, taking the form of tacit or explicit knowledge that should be captured and preserved as current or historical organizational knowledge and disseminated. Library administrators should invest in digital technologies, especially considering the application of social technologies that boosts desirability of documenting such knowledge. This widespread social usage of digital technology presents a great opportunity for libraries, as argued by Kwanya et al. (2015, p. 4) for information that is ultimately a conversation sought and used in a social, active, contextual, personalized, and connected environment.
Intellectual capital and digital technologies are identified as the critical resources for knowledge-intensive service resources in the digital age. Their dimensional content of human capital, organizational capital, social capital, operand digital technology, and operant digital technology collectively provide a roadmap for library administrators to assess their resource needs. Owning and having access to these resources, however, are just the first step; recognizing and applying these resources, especially the digital technology resource in a new, more agile frame is the critical second step. Technologies are more than infrastructure or processors; they can initiate and start up a process or project. Therefore, instead of inquiring what we can do to a
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piece of digital technology, a better question would be, “What can this piece of digital technology do for us?” Such a different mindset enables library administrators to actively find and put to use digital technologies capable of providing innovative services and experiment with them.
Businesses change their strategies to fit the digital platform economy and utilize digital platform capabilities (Kabakova et al., 2016). So should academic libraries. What are academic libraries’ digital strategies? To answer this question, academic libraries must come to full realization that most library collections are being accessed online, and, as a historical change, library services now need to be delivered online (O’Donnell, 2011). In other words, library administrators should strive to build and recognize digital platform capabilities as the mainstream approach to servicing library users. Digital platforms are where actions transpire among library employees, users, and between library employees and users to co-create value leading to service innovation. The digital platform is also where usage data of all sorts exist to be harvested and analyzed to create a better understanding of emerging realities.
Building, nurturing, and maintaining digital platform capabilities are admittedly difficult tasks. How do academic libraries accomplish these tasks? Library administrators should invest in acquiring individuals with diverse talents, enhancing employees’ knowledge repertoire, establishing proper knowledge management mechanisms to collect and make accessible institutional knowledge as well as encourage knowledge sharing within the library and with other libraries. Library administrators should develop or acquire the libraries’ bounty access to digital technology infrastructure and carefully plan to sustain such an investment. These technologies in the forms of network, hardware, database, mobile, scanners, software, and applications are part of knowledge sharing and exploitation.
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Digital platform capabilities also leverage digitalization derived from large-scale digitization effort (Cenamor et al., 2017). Academic libraries have devoted considerable work to digitization in the past few decades. Many notable projects are the ultimate result of applying employee expertise, institutional policies and procedures, digital infrastructure, digital equipment, and application software to digitize collections. These collections are then made available on a digital platform, becoming a digital library whereby users can access the content electronically. Exemplary digital libraries range from national, state, to individual libraries including HathiTrust (national), DigitalNC (state), and ScholarSpace (individual library). Although the content is centrally hosted, when it is considered to be in the public domain, the content is giving back to the users to slice, use, and reuse in whatever way they find most beneficial. At the institutional level, a digital library is an institutional repository where users can self-deposit content exemplifying co-creation capability. When crowdsourcing is applied to such digital libraries, it is a form of uberization, a term put forward by Andro and Saleh (2017). Crowdsourcing also depicts the form of digitalization by Yoo (2012), when the encoding of information into a digital format results in the reconfiguration of production and the use of a product or service. This reconfiguration in the socio-technical context is amplified by digital technologies and social-media capabilities. Library administrators should continuously invest in digitization efforts to preserve and make available the knowledge from the library’s special collections and to fulfill the library’s service mission through the concept of Library as Platform. After all, Levine-Clark (2014) points out that it is a library’s special collections that help distinguish it from others. In other words, providing digital platform capabilities for a library’s special collections should be considered a strategic direction for the library to take to gain a competitive advantage in the digital age.
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In addition to tending to library’s collections, library administrators should enhance the value of the co-creation process with users and promote a data-driven culture in the library. Academic libraries and their employees have been known to be service-oriented and user-friendly. The survey data show that library employees are less than optimally active in responding to users’ suggestions. Is this behavior simply because the suggestion cannot be followed? Value co-creation is a process that sources inputs into a continuous feedback process; as such, a hard-to-follow suggestion may turn into a can-do project. As to the data-driven culture, the quest is more than to manage the data well but to understand how data manage the library organization within the context of a mission that is value-based. Does everything need to be measured? How do the numbers support objectives? Most library assessment is performed by a select few in current practice; however, cultivating data literacy in the library to promote ongoing discussions about the metrics among library employees will result in further input to improve the data metrics. The end goal should be understood by all library employees about what the library is accomplishing and where it is heading.
6.4 Limitations
The sample population in this research includes academic libraries in all doctorate granting universities and master’s colleges and universities in the United States based on the Carnegie Classification of Institutions of Higher Education. The breadth of the sample suggests that the findings are generalizable to many other academic libraries.
However, similar to any empirical research, there are specific strengths and limitations to this study. First, the survey questionnaire was directed at top library administrators because they have the most knowledge for answering questions about the library’s personnel, procedures, alliances, and service innovation status. Still, they may not be familiar with the state of the
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library’s digital technology infrastructure. The responses might have been different if technology managers were the respondents. In retrospect, the part of questionnaire on digital technologies should have been cross-validated specifically with technology managers to increase confidence in the final results.
Second, this study is the first attempt to conceptualize and operationalize the proposed constructs—knowledge-intensive service resources and digital platform capabilities—for the knowledge-intensive service sector. Although the focal constructs and sub-constructs are adapted from previous research and subsequently validated in the context of academic libraries, further adaptation is needed for them to be used in other sectors. Furthermore, all measures are self-reported and thus subject to response bias. Email reminders should have been used to further minimize nonresponse bias. Lastly, the survey research applied in the study is cross-sectional in nature. It is applied to capture a snapshot of an academic library’s current state with respect to its knowledge-intensive service resources, digital platform capabilities, and service innovation outcomes; therefore, the internal validity of this study is difficult to determine.
6.5 Future Research
This research has investigated phenomena with a technology focus. There are, nevertheless, other factors contributing to service innovation including but not limited to organizational leadership and innovation culture. Future studies might be expanded to consider these other factors. Additionally, forms of service innovation vary and include conceptual, administrative, or radical innovation; therefore, future studies could be framed to validate the research model based on types of service innovation.
Because the construct knowledge-intensive service is conceptualized the first time and the nature of its relationships with the other constructs investigated is exploratory, future studies may
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apply qualitative methods, such as the Delphi and case-study approaches, to confirm or refine the constructs and their relationships. In addition, a mechanism may be built-in to probe the respondents who select “disagree” as their response to obtain content data for further analysis. At the very least, this research model can be adapted to validate findings in other knowledge-intensive service contexts—engineering consulting firms, legal firms, financial firms, any and all for-profit knowledge-intensive service sectors to extend the model’s generalizability.
To contribute further to the LIS discipline, the model can be adapted and validated in diverse types of libraries, including those at community college and public libraries, as well as for different educational levels, for example, in undergraduate and associate-degree programs. Although digital technologies are embraced by all types of libraries, different missions will likely require different perspectives in integrating digital technologies to fulfill a library’s mission. Finally, the research model used here can also be validated in other countries to gain a global perspective on the questions of how digital technologies are being applied to and changing academic libraries and other for- and nonprofit organizations.
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Full Text

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SERVICE INNOVATION F OR KNOWLEDGE INTENSIVE SERVICES IN THE DIGITAL AGE : THE CASE OF ACADEMIC LIBRARIES by SHEA TINN YEH B.A., Cheng Kung University, 1983 M.L.S., University of Maryland, 1985 B.S., Franklin University, 1995 M.S.E., Wright State University, 2009 A thesis submitted to the Faculty of the Graduate School of the Univ ersity of Colorado in partial fulfillment Of the requirements for the degree of Doctor of Philosophy Computer Science and Information Systems 2018

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ii © 2018 SHEA TINN YEH ALL RIGHTS RESERVED

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iii This thesis for the Doctor of Philosophy degree by Shea Tinn Y eh Has been approved for the Computer Science and Information Systems Program by Jahangir Karimi, Chair Ronald Ramirez, Advisor On o ok Oh IIkyeun Ra Mary Stansbury Date: July 28 , 2018

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iv Yeh, Shea Tinn (Ph.D., Computer Science and Information Systems) Service Innovation for Knowledge Intensive Services in the Digital Age: The Case of Academic Libraries Thesis directed by Associate Professor Ronal d Ramirez ABSTRACT T he pervasiveness of digital technology, along with unprecedented computing power, h as altered innovation techniques in all industries , including those that provide knowledge intensive services. Extant research on innovation has concentr ated on information and communications technology in service innovation alo ng the dimensions of technology related interfaces, delivery, and infrastructure. The immateriality and lack of physical form of new digital technol ogies , however, present unique ch allenges and research opportunities. W hat roles do digital technologies play in serv ice innovation and how may digital technolog ies interact with critical resources for service innovation in knowledge intensive service organization s ? Based on the service d ominant logic perspective, this research theorizes that digital technologies, as operand and operant resources, integrate with intellectual capital to build digital platform capabilities essential for service innovation within knowledge intensive service p roviders. This study presents a new integrative framework for service innovation in the digital age and validates the framework in the context of academic libraries , the type of organization whose central purpose is the delivery of knowledge intensive serv ices. For the validation , a survey instrument was developed and administered to library administrators at both d in the United States . Structural equation modeling results support that knowledge in tensive service resources platform

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v capabilities is enhanced by the integration of knowledge intensive service resources and digital technologies, and digital platfo rm capabilities positively contribute to service innovation. The contributions of this research are many f old owing to the fact that it is the first study to recognize th at service dominant logic s intangible resources and intellectual capital are analogou s; propose that intellectual capital and digital technologies are critical knowledge intensive service resources ; and propose and validate an integrative model of knowledge intensive service resources that builds digital platform capabilities for service i nnovation. This research advances extant theories on service innovation and informs service providers about the way in which intangible resources are t ransformed by digital technologies for service innovation. This study also applies the M anagement of I nfo rmation S ystems discipline to the understanding of service innovation in the L ibrary and I nformation S cience research field . Approved: Ronald Ramirez

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vi ACKNOWL E DG E MENT S My doctoral adviser, Professor Ronald Ramirez , has supported and encouraged me whenever I was about to give up . His review and comments have guided me as I develop ed an academic voice to accompany my understanding . I sincerely appreciate his confidence in me; without it I would not be where I am in this journey today . I appreciate Professor J ahangir Karimi support as my committee chairperson. His knowledge, always prompt review, and insightful suggestions have been invaluable . I am also grateful for Professors Onook Oh, Ilkyeun Ra, and Mary Stan sbury for their respect ive area expert ise that has strengthen ed this dissertation research . In addition, s pecial acknowledgement must go to Professor Zhiping Walter for her guidance at the beginning of my doctoral pursuit. I would like to especially thank all the library administrators who participate d in the survey research. And above all , I am grateful to my husband , Larry Owens , for his outstanding encouragement and to my son, Perry Owens , for his consideration and independence that enabled me to pursue my dreams. The doctoral journey has been ardu ous as I am guessing it should be . It has tested my comprehension as a scholar, my mastery of English as a s econd l anguage speaker, and my perseverance as a professional, wife, an d mother. As I am about to close this chapter of my life with the receipt of my doctoral degree, I feel the overwhelming sense of humility described by the Chinese phrase , which translates in to English as sky I look forward to contribu ting further to my beloved disciplines with the skills I have acquired along the way a s well as my newly articulated academic voice .

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vii DEDICATION For my twin sister , Dr. Sissi Yeh Fleury , and my little sister , Stella Yeh , for their unconditional love and f or my mother in Taiwan and my father in Heaven , both of who m worked hard throughout their lives to provide me with the best education I could ever have received .

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viii TABLE OF CONTENTS CHAPTER I. INTRODUCT ION ................................ ................................ ................................ .... 1 1.1 K NOWLEDGE I NTENSIVE S ERVICES ................................ ................................ ........................ 1 1.2 A CADEMIC L IBRARIES AS K NOWLEDGE I NTENSIVE S ERVICES IN THE D IGITAL A GE .............. 2 1.3 S ERVICE I NNOVATION IN THE D IGITAL A GE ................................ ................................ ........... 4 1.4 T HEORETICAL O VERVIEW ................................ ................................ ................................ ...... 6 1.5 R ESEARCH O BJECTIVES , S COPE , AND D ESIGN ................................ ................................ ........ 8 1.6 S IGNIFICANCE OF THE R ESEARCH ................................ ................................ ......................... 10 CHAPTER II. LITERATU RE REVIEW ................................ ................................ ..................... 13 2.1 K NOWLEDGE I NTENSIVE S ERVICES AND I NNOVATION ................................ ......................... 13 2.2 S ERVICE I NNOVATION P ROCESS IN IS R ESEARCH ................................ ................................ . 16 2.2.1 Outcome based Ser vice Innovation ................................ ................................ ............. 1 6 2.2.2 Service Activity Based Innovation ................................ ................................ ............... 19 2.2.3 Service Dominant Logic Perspectives ................................ ................................ ......... 20 2.3 T HE D UAL R OLES OF D IGITAL T ECHNOLOGY AS AN O PERAND AND AN O PERANT R ESOURCE ................................ ................................ ................................ ................................ ................... 25 2.4 A CADEMIC L IBRARY AND I NNOVATION ................................ ................................ ............... 28 CHAPTER III. CONCEPT UAL MODEL AND HYPOTH ESES DEVELOPMENT ................. 35 3.1 K NOWLEDGE I NTENSIVE S ERVICES R ESOURCES ................................ ................................ .. 35 3.1.1 Intellectual Capital ................................ ................................ ................................ ...... 36 3.1.2 Digital Operant and Operand Technologies ................................ ............................... 38 3.2 D IGITAL P LATFORM C APABILITIES ................................ ................................ ....................... 41

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ix 3.3 R ESEARCH M ODEL ................................ ................................ ................................ ............... 44 3.4 H YPOTHESES D EVELOPMENT ................................ ................................ ............................... 45 CHAPTER I V. RESARCH DESIGN ................................ ................................ ........................... 52 4.1 M EASUREMENT M ODEL ................................ ................................ ................................ ....... 52 4.2 D ATA C OLLECTION P ROCEDURES ................................ ................................ ........................ 56 4.2.1 Survey Instrument ................................ ................................ ................................ ........ 56 4.2.2 Content Validity and Face Validity ................................ ................................ .............. 56 4.2.3 Sampling Process ................................ ................................ ................................ ......... 56 4.2.4 Pilot Test ................................ ................................ ................................ ...................... 57 4.3 M AIN D ATA C OLLECTION ................................ ................................ ................................ .... 57 CHAPTER V. DATA ANAL YSIS AND RE SULTS ................................ ................................ .. 59 5.1 D ESCRIPTIVE S TATISTICS ................................ ................................ ................................ ..... 59 5.2 M EASUREMENT M ODEL ................................ ................................ ................................ ....... 63 5.3 S TRUCTURAL M ODEL ................................ ................................ ................................ ........... 72 CHAPTEER VI. FINDING S AND DISCUSSION ................................ ................................ ...... 77 6.1 F INDINGS ................................ ................................ ................................ ............................. 77 6.2 R ESEARCH I MPLICATIONS ................................ ................................ ................................ .... 79 6.3 P RACTICE I MPLICATIONS AND R ECOMMENDATIONS ................................ ............................ 82 6.4 L IMITATIONS ................................ ................................ ................................ ........................ 86 6.5 F UTURE R ESEARCH ................................ ................................ ................................ .............. 87 REFERENCES ................................ ................................ ................................ ............................. 89 APPENDIX A. CONSTRUC T DEFINITION ................................ ................................ ........... 115 APPENDIX B. CONSTRUC T OPERATIONALIZATION ................................ ...................... 116

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x APPENDIX C. SOLICITA TION E MAIL ................................ ................................ ................ 120 APPENDIX D. SURVEY I TEMS ................................ ................................ .............................. 121 APPENDIX E. INDICATO RS DEESCRIPTIVEE STA TISTICS ................................ ............ 124 APPENDIX F. ABBREVIA TIONS ................................ ................................ ........................... 125

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xi LIST OF TABLE S Table 1. New Product Development and New Service Development Activities ......................... 17 Table 2. Studies Appling Operand and Operant Resources from S D Logic ............................... 23 Table 3. Similarities and Differences in Perspectives in Innovation Research ............................ 25 Table 4. Library Journals Ranking and Impact Factor ................................ ................................ . 29 Table 5. The Dual Roles of Digital Technolog y Investigated in Library Literature .................... 32 Table 6. Measurement of Constructs ................................ ................................ ............................ 55 Table 7. Descriptive Statistics ................................ ................................ ................................ ....... 59 Table 8. Comparison of Population Value and Responder Value ................................ ................ 62 Table 9. Nonresponse Bias T Test Results ................................ ................................ ................... 62 Table 10. Psychometric Properties for First Order Constructs ................................ ..................... 65 Table 11. Loadings, AVE, and CR for Second Order Constructs ................................ ................ 68 Table 12. Loading and Cross Loading ................................ ................................ .......................... 69 Table 13. Intercorrelations and of Latent Variables for First Order Constructs ................ 71 Table 14. Intercorrelat ions and for Second Order Constructs ................................ ........... 71 Table 15. Systematic Evaluation of the Constructs ................................ ................................ ...... 72 Table 16. Analysis of the Indirect Effects ................................ ................................ .................... 75 Table 17. Hypotheses Summary ................................ ................................ ................................ ... 75

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xii LIST OF FIGURES Figure 1. Classifying Operant Digital Technology Adapted from Day (1994) .......................... 41 Figure 2. Inner Research Model ................................ ................................ ................................ .... 45 Fi gure 3. Measurement Model ................................ ................................ ................................ ...... 52 Figure 4. Factor Loadings ................................ ................................ ................................ ............. 72 Figure 5. Testing of the Hypothesized Path Model with Control Variables ................................ . 75

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1 CHAPTER I . INTRODUCTION 1.1 K nowledge Intensive Services Among the three core sectors of world economies primary (raw materials), secondary (manufact uring), and tertiary (services) the service sector has experienced the largest increase in productivity output and total employment ove r the past several decades (Soubbotina & Sheram , 2000). Rising per capita incomes have driven the demand for services, especially knowledge services (Bryson et al. , 2004 , p. 8 ) , resulting in significant growth in the knowledge intensive services industr y . In 2012 , for example, this industry produced 22% of gross domestic product (GDP) in the United States and 20% in New Zealand ( Hill , 2014 ; Ministry of Business, Innovation & Employment , 2014). Knowledge intensive services industr ies include business, finan ce, information technology, education, and health services with service activities comprised of research and development (R&D), consulting, accounting, information services, legal services, and marketing related services ( Hill , 2014 ; OECD , 2006). Knowledge intensive servic e provider s act as knowledge integrator s or transfer o r s (Bessant & Rush , 1995), scouring the environment for re levant knowledge , and participating in ongoing knowledge developing ne tworks (Tether & Hipp , 2010). They provide customized solu tions for clients, developed from the intangible knowledge of employees, social interaction between employees (Larsen , 2001), and provider user interactions (Tether & Hipp , 2010) that occur as part of the service process . The output of such a process is in formation , supporting the notion of information as a service (Hayes 2003, p. 159). Information is easily transported and distributed with technologies in this digital age , thus further contributing to the creative and innovative nature of knowledge intensi ve service activities.

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2 1.2 Academic Libraries as Knowledge Intensive Services in the Digital Age Service innovation is a critical component of organizational sustainability and a source of competitive advantage to both for profit and nonprofit organizatio ns (Bigelow et al. 1996; Lay Hong et al., 2016 ; Noorani, 2014; Parris et al., 2016 ; Salunke et al., 2013 ). This research investigates service innovation by identifying the organizational setting for the investigation and why it fits within the context of t he broader study. Specifically, this research defines academic libraries as provider s of knowledge intensive services. Before the digital age, publishers gathered, edited, printed , and market ed the knowledge contributions of scholars in tangible, physical , paper based products. Libraries as the information consumer purchased or collected discrete information in the form of monographs and periodicals; and they in turn as information providers provided access to these published materials (Womack , 2002). Furt hermore, librarians compiled, classified, and created bibliographic knowledge from t he printed and distributed knowledge through consultation and reference services. The competitive advantage of libraries is thus determined , in part, by the quality and qua ntity of their collected information an creators and transfer o rs . In the digital age the Internet, digi tization, and digital technologies have revolutionized how knowledge is obtained, shared, and retained. Digital technologies have enabled a digital based networked economy and contributed to the current Information Age and knowledge based service economy ( Castells , 2009 , p. 162 ). Also , in the digital age versus analog or physical entit ies made up of the non digital era. When information in bits is delivered through a digital network, a vast number of them can be transmitted at lightning speed, shared across greater distance, and accessed immediately.

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3 Informatio n can also be replicated perfectly and retrieved from anywhere via ubiquitous mobile broadband networks and smartpho ne technologies (Tapscott , 2014 , p. 116 ) , thereby contributing Although academic libraries still purchase physically published information, most acquisition s today are in the form of electronic books, articles, and digital files . In addition to curating the se purchased information good s , academic libraries are pres erving public information good s produced by their community in the digital repositories . The se repositories , known as digital libraries , host research output s , m akerspace creations , special collections , and archives that collectively may be otherwise inacc essible without a personal visit to the library . L ibrarians, also known as cyberians , or information professionals , apply their expertise to vast , diver se digital information to create and transfer knowledge. L ibrarians may not know the answer to a speci fic question from a user , but they know where and how to find it ; through consultation service s , reference librarians create digital wayfinding and transfer what they find to their users. Instructional librarians, as part of a teaching team, identify progr ams to assist curriculum development; t hrough library instructions at the course level, librarians contribute to knowledge creation and student learning . As subject specialists, moreover, librarians design complex searches in databases to perform systemati c review s of specific literature s for faculty and researchers ; t hey critically analyze multiple studies to transform the findings into meaningful answers . In these endeavor s , librarians play the role of information and knowledge intermediaries in the use o f digital technologi es . Therefore, a cademic libraries create, integrate, transform, and tran sfer information and knowledge as noteworthy knowledge intensive service provider s in th e digital age .

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4 1.3 Service Innovation in the Digital Age Traditionally, tech nologies have served as tools to innovate the service delivery process (Barras , 1990). However, advances in information and communication technology (ICT) , especially digital technology, have altered service innovation at its core. Consistent with the evol ution of technology enabled product innovation (Tallon , 2010), service innovation is transforming ; its input, process, and output are d igital in nature. Digitization makes non digital artifacts digitized into bits of data that are e, sensible, communicable, memorable, traceable, and associable (Yoo, 2010; Yoo et al., 2010 ), and digital data are openly available via the Web for exploration , experimentation , and innovation. Actors from upstream and downstream sources can collaborate and communicate with digital tools, exchanging immediate feedback throughout the innovation process. The prevalent use of social media , moreover, has recently created a socio technical structure that enables organization s to form strategic actions from inf ormation based analytics (Heath et al. , 2014). The openness of the data also offers generative and unbounded opportunities resulting in service innovation which may or may not have been originally intended. U nderlying the change s in the service innovation process described above is the phenomenon of digitalization , defined as information into digital format and the subsequent reconfiguration of socio technical context of , 2 012) prevalent in the knowledge economy. S ervice innovation as transformed by technology has pos ed challenges not only to profit oriented businesses but also to nonprofit higher education institutions ( Danjuma & Rasli, 2012) . The challenges are compounded when service innovation is viewed as a strategic necessity for staying relevant and for attracting and retaining large pools of students (Danjuma & Rasli , 2012 ;

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5 Yeh & Ramirez, 2016 ). Because of globalization and the recognition of knowledge as an economic driver, higher education institutions have changed from being teaching and research universit ies to teaching, research, and economic development enterprise s that stimulate employment and productivity from knowledge resources (Etzkowitz , 2003). Many univers ities , meanwhile, are experiencing difficulties from changing funding systems and increasing pressure from competition (Bettis et al. , 2005 ; Holbrook , 2004 ) . Despite the strategic urgency in examining service innovation , there is a dearth of research on se rvice innovation s in higher education institution s , especially regarding higher education libraries in the digital age. Existing l iterature on innovation in academic libraries centers on the aspects of innovation diffusion and adoption ( Bierauge l & Neill , 2017; Raynard , 2017 ; Torres Pérez et al. , 2016 ), the critical role of organizational leadership (Jantz , 2012; Jantz , 2015 ), and aspects of innovative services (Aharony , 2009; Chua & Goh , 2010; Letnikova & Xu , 2017). Much of th is literature can be classifi ed as descripti ve research derived from observation al data , in part owing to the fact that librarianship is a practical field (Audunson , 2017 ). Thus , less focus is given to identify researchable problems (Hjørland , 2000). However, for the last two decades, the goal and natural progression in the L ibrary and I nformation S cience (LIS) field ha ve been to transform librarianship from a practice related and to some extent a vocational field into an academic interdisciplinary discipline (Audunson , 2007). One way to accomplish this transformation would be by applying research methodology from a specific reference discipline (Keen, 1980) . Farkas and D obrai (2012) see similarities between high er education and business services, given the commonalities of knowledge in tensive services within each industry. Therefore, t his research project , set within the M anagement of I nformation S ystem s (MIS) discipline, further s the understanding of knowledge intensive service innovation in the context of higher education

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6 librari es by means of theories developed in MIS . 1.4 Theoretical Overview This research project first examin e s the characteristics of knowledge intensive services and what service innovation means to the knowledge intensive service sector , known in literature and in practice as , 1999; Miles et al. , 1995 ; Muller & Zenker , 2001; Wegrzyn , dge et al. , 2007), intensive , 2010). The objective of kno wledge intensive service organization s is to integrate i nternal and external knowledge to serve customers better ; t he i nteraction between service providers and cu stomers can thus be recognized as the key to knowledge integration . Secondly, this study exami ne s the role of digital technolog y in service innovation and introduces the perspective of service d ominant (S D) logic a s a relevant theoretical lens for examining th is type of innovation . When goods were fundamental to economic exchange , dominant logic f ocused on tangible resources , such as machinery and raw materials (Vargo & Lusch, 2004). They were operan d resources requiring other resources to act on them to product benefits ( Constantin & Lusch , 1994 , p. 14 5 ). As the paradigm shift ed from goods to serv ice in economic exchange s , however, the logic also shift ed to intangibles , highlighting the application of knowledge and skills as unit s of exchange, knowledge as the fundamental source of competitive advantage, and value co creation with customers (Vargo & Lusch , 2004). They are the operant resources acting on operand or other operant resources to produce further effects (Constantin & Lusch , 1994 , p. 145 ). The S D logic framework has been researched across scholarly disciplines because of its shared fo cus on intangible resources that every organization is sure to possess . Recent updates to the logic emphasize the concept of i nstitution , institutional arra ngement, and technology as

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7 additional operant resources that provide a wider configuration for service i nnovation (Vargo & Lusch , 2016 ). The term institution does not mean an organization ; i nstead , in this context the term comprises the humanly devised rules, norms, and beliefs of an organization (Scott , 2001 , p. 12 ). Institutional arrangement refer s to a se t of interrelated institutions alliances and partnership s critical for a collaboration to take place (Vargo & Lusch , 2016 ) . As to digital technolog ies , they are viewed as non materia l entities in the form of operating systems , software code s , and app l icati on software program s (app s ) capable of initiating service innovation s ( Eaton et al., 2011 ). To further define digital technolog ies , Ibem and Laryea (2014) and Pullen ( 2009 , p. 18) categorize them as stand alone, integrated, or web based tools that use mi croprocessors to produce, store, process, and communicate data and information between human beings and electronic systems . K nown examples include social media, online games, multimedia, productivity applications, cloud computing, and mobile devices (State Governme nt of Victoria, Australia , 2017). Th ese examples reflect the materiality of technological objects . Faulkner and Runde (2011 , p. 2 ) suggest , however, attention be given to the non material t echnological objects that have no intrinsic physical being but are inundating this digital world . Examples include computational algorithm s , software programs, or W eb pages . T h e immateriality of digital technology objects offer s generativity that has altered the core of service innovation ( Eaton et al. , 2011), an d their diffusion and adoption have affected organizational design, decision making, and communication (Orlikowski & Scott , 2008 ). Through the openness of the Internet and the connectedness of computers, generativity is embedded within a platform that trig ger s innovation by distributing code and content to wider audience s ( Eaton et al. , 2011 ) . Apple successfully exemplifies generativity by offering an iPhone platform where Apple developers, external

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8 app lication developers, and iPhone users co create and int eract to form a digital ecosystem . continues long after the phone . Generativity is thus train , 2006). The aforementioned terms of operand and operant resources, digitization, digitalization, generativity, platform, and digital ecosystem can be applied to explain the phenomenon of a digital libra ry, one of the most complex service innovation s produced by academic libraries. A digital library is an electronic, not a physical , library. It consists of a collection of objects in digital formats, along with services that organize, store, index, and retrieve those objects over a network to meet the information needs of a given user population ( MacCall et al. , 1999). In the case of the Open Music Library (https://openmusiclibrary.org/) , the d igitization process is first applied to convert printed music scores to digital format objects . This step requ ires digitization equipment, ICT, and human knowledge and skill s in the digitization effort. Software applications are developed and used to index and make the objects searchable and accessible online. The object data open ing through the Web application pr ogramming interface (API) offer crowdsourcing and interactive opportunit ies for both curator and users. The discoveries , tagging, and sharing of the objects thus bring about unpredicted socio technical results that create the digitalization phen omenon desc ribed in section 1.3 , above . 1.5 Research Objectives, Scope, and Design T h is study examine s the influence of digital technologies on innovation in knowledge intensive service organizations , specifically in academic libraries within higher education insti tution s . The results of this research are intended to inform the broader classifications of knowledge intensive service organizations . Similar to academic libraries , they employ

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9 knowledge as an essential asset for servic ing their customers and users , eithe r for profit or not . All such institutions are facing challenges from the application of digital technologies . While the limitations of applying research results from nonprofit organizations to for profit organizations is acknowledged, this research presen ts the first step in developing a n integrative model applicable to both . Th is model depicts how knowledge and other intangible resources from inside and outside the nonprofit knowledge intensive service organization interact with digital technologies to cr eate service innovation. H igher education institutions as social service providers do contribute to a knowledge based economy by providing services that are essential for economic competitiveness (Bryson et al. , 2004 , p. 120 ). With the advent of new digit al technologies, higher education institutions have been challenged to redefine their student constituents and pedagogy . Academic libraries, at the heart of these institutions, must now articulate their contributions to institutional mission s and goals. In this digital age, Google Scholar, Wikipedia, and O pen Web are the first stop for users (student and faculty) seeking information ; in this they replac e human labor ( reference librarians ) who wait to answer questions through in person consultation . eBooks a nd eJournals by being downloaded from the Web thus replac e the physical assets on library shelves and reduc e the need for users to visit physical libraries. In short, digital technologies have disrupted and altered the process of information search and ret rieval and have challenged the vitality and validity of library organizations. To maintain relevancy and continue to add value to their home institution s , academic libraries must look to digital based service innovation as a strategic response to this disr uptive impact (Yeh & Ramirez , 20 16 ). Although service innovation research extends back over 40 years , a consolidated view of service innovation has yet to be r ecognized (Howells , 2010 , p. 68 ). Since the introduction of S D

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10 logic a decade ago, service innov ation has been viewed as an interactive venture b etween and among producers, consumers , and collaborators in a joint sphere. This venture e specially aligns with innovation in the digital age where interaction is manifested and integrated by the generative characteristic s of digital technology (Nambisan et al. , 2017). Despite the acknowledgement of the new service centric paradigm, extant research is still weak in capturing many varieties of service innovation with digital technologies (Amara et al. , 2009) . This leaves open a critical research gap with regard to knowledge intens ive service organizations in both the for profit and nonprofit spheres . To gain in depth insight into service innovation , the S D logic framework is applied to examine the interac tivit y between resources and the initiator role that digital technology plays in knowledge intensive service innovation . An integrative model is presented to address four research questions in the specific case of this dissertation academic libraries : 1. What are the critical resources for service innovation? 2. How do digital technologies interact with other resources to b uild digital platform capabilities for service innovation? 3. How do digital platform capabilities contribute to service innovation? 4. Do digital platf orm capabilities mediate the impact of resources on service innovation ? Th is model was empirically validated using structural equation model ing to analyze primary survey data . The targeted survey population is comprised of academic library administrators a t higher education institutions in the United States. The se administrators are responsible for advancing the institutions strategically and for keeping the institutions relevant in the digital age. 1.6 Significance of the Research This study makes several contributions to Information Systems (IS) research, LIS

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11 research, and the pra ctice of librarianship . First, the S D logic based model contributes to the understanding of digital service innovation in an environment of operand and operant resources and eme rging digital platform capabilities. To date, there have been few quantitative studies that have taken this approach to examine service innovation. D espite the emphasis on the dual roles of digital technology as an operand and an operant resource by Nambis an ( 2013 ) , up to now there does not exist any quantitative study on the dual resource roles. This research also adds knowledge to the S D logic research stream by proposing and empirically validating an integrative model that augments the logic and the dua l roles as they are applied in knowledge intensive service sector . Second, this study adds to extant digital innovation research . D igital technology has change d how information is process ed and delive red to a different and interconnected level . W ith ubiqu itous computing, digital devices and and social environments as a fixture in their everyday movement and interactions ( Lyytinen & Yoo, 2010; M a cDonald , 2012). Unlike recent work focusing materiality by dig ital technologies (Yoo, 2010; Yoo et al., 2012), t his research examines digital technologies in their broader immaterial form , analogues to services as immaterial goods, to create a digital edge transcending the traditional mindset of digital substitution and distribution. Third, this study contributes to the understanding of service innovation in the public and nonprofit service sectors. Innovation research has tradit ionally focused on new products in industrial organizations and the manufacturing sector (Bigliard i et al. , 2011 ; Gauvin & Sinha , 1993 ; Ning & Li , 2016 ) and has thereby paid less attention to the private and public sectors (Miles , 2005; Mulgan , 200 8 ; Potts & Kastelle , 2010). However, in the present digital age all sectors are equally invested in digital technologies and need to apply the m to create advantage ,

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12 the k nowledge intensive servic es sector being no exception . Fourth, this study contributes to the LIS discipline by first recognizing academic libraries as knowledge intensive service organizations. Based on the characteristics of knowledge intensive services in various technical and disciplinary literature s , academic libraries represent the knowledge sector by applying their intangible knowledge assets to service faculty and students. continu al ly improve core services. Although s ervice innovation has always been a staple o f academic libraries strategic plans , libraries are cautious when adopting new technologies. Often, they apply the technologies to improve process efficiency before attempting to create any innovations . T hrough the application of research from the MIS discipline , this study conducts a novel examination in LIS by using a unique theoretical lens and a core assumption that service innovation in academic libraries can be as dynamic and produc tive as in the business sector. For practice of librarianship , the findings here will assist library administrators in the development of digital methods for building dig ital plat form capabilities that increase service offe rings through co creation with users. Lastly, t he dissertation research model will serve as a basis for future empirical studies of service innovation in all types of organizations , for profit and nonpr ofit alike .

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13 CHAPTER II . LITERATURE REVIEW 2.1 Knowledge Intensive Services and Innovation Mi les et al. (1995, p. 18) define knowledge intensive services economic activities which are intended to create, accumulate, or dissemi (1999) characterizes the services and to transform this information together with firm specific knowledge into useful services for scribe knowledge intensive services performing, mainly for other firms, services encompassing a high intellectual value These definitions assume a twofold role that knowledge intensive services play as the intermediaries of knowledge: (1) they contribute to economic growth with their internal knowledge base, and (2) they acquire external knowledge to enhance their internal knowledge base and to furthe r contribute to economic growth. To fulfill the first role of applying thei r internal know ledge base , a process in which k nowledge constitutes the main input and output is usually in place within the provider organization ( Gallouj , 2002 , p. 2 ). The p rocess focus es on providers being contributors to strengthe n innovation capabilities ( Wood et al . , 1993 ) or enhanc e added value ( & Moffat , 1995 ) , all for the benefit of others , i.e. , their customers . The service offered h as the knowledge capacity to respond to specifi c questions, problems, or needs , and the process demonstrates the concept of knowledge push , where inter nal knowledge drives and pushes innovation. It is similar to the technology push philosophy where innovation starts with a phase of fundamental research and development within a back office base before moving toward a syst ematic dissemination (Barras , 1986; de Hertog , 2000; Rubalcaba et al. , 2012) . For over two decades , however, the focus of knowledge intensive services has evolved from contribut ing innovation as a contributor to producing innovation as co producers (Muller &

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14 Doloreux , 200 9 ), naturally fulfilling the second r o le of acquiring external knowledge to enhance internal knowledge. Examples of th e second role include knowledge intensive service organizations as consulting firms and the recent open innovation movemen t by knowledge intensive services . W hen technical consulting services provide project by project servi ce, they often call forth co production from their customers as partners in order to understand the effect of industry specific factors (Doloreux & Shearm ur , 2010 ) . The knowledge they gain with each project is then filtered and internalized to become local knowledge to be applied in subsequent projects . Unlike the traditional model of R&D activity based innovation s that are distributed by the organization, open innovation combines the inflows and outflows of knowledge to accelerate innovation to be distributed (Chesbrough et al. , 200 6 ). The Lego Company exemplifies open innovation activities by engaging its users with an online Creat site (https ://www.lego.com/en us/createandshare) where community members offer their design and ideas for new product s . This process demonstrates the concept of knowledge pull where by external knowledge drives and contributes to in novation. It is similar to the te chnological demand pull approach where users articulate their needs to influence the innovation trajectory (Barras , 1986; de Hertog, 2010 ; Gallouj & Weinsteien , 1997 ) . Reflecting on the above described distinct set of activities and knowledge character ist ics , knowledge intensive services provide a type of service that no other service provider supplies (Muller & Zenker , 2001 ); t hey are considered a repertoires and intermediaries in t he knowledge based economy (Czarnitzki & Spielkamp , 2003) . What constitutes knowledge? Research views the form ation of knowledge as being multi dimensional including know what, know how, know why, and know who (Lundvall & Johnson , 1994) , and declarative, p rocedural, and causal (Cohen & Bacdayan , 1994) . The best known

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15 categorical view of knowledge is that of the tacit dimension (Polanyi, 1962, p. 92) versus the explicit dimension of knowledge (Nonaka, 1991, p. 11 ). Tacit knowledge is the subjective insights and intuitio ns people carry that are highly personal, hard to formalize, and dif ficult to communicate to others, w hereas explicit knowledge can be communicated directly or in a manual that is formal, systematic , codified, and readily transmitted ( Nonaka , 1991 , p. 59 ; Sun et al. , 2005). In the context of knowledge intensive services, tacit knowledge resides with an as distinct knowledge, while codified knowledge resides in the , or databases as collec tive knowledge . C odified knowledge can be easily transmitted to other employees through documentation in the form of digital or print ; however, t acit knowledge is subjective and difficult to articulate . Oftentimes, the knowledge source is unwilling to shar e the tacit knowledge rooted in their experience . How does th e ta cit explicit dichotomy contribute to the k nowledge intensive service innovation discussion ? It helps in the understanding of types of knowledge applied in the service innovation process to i dentify methods that enhance knowledge creation and acquisition. While product innovation also involves knowledge, the output of knowledge is mainly in the codified form that accommodate s a tangible product (Muller & Do loreux , 2009) , such as a product des cription brochure or a user manual . Ho wever, when the output is a knowledge based service product intended to solve a , tacit knowledge is likely to be present (Ritala et al., 2013) . The reason is that p roblem so lving is an interactive i ntentional process acit knowledge to understand the problem domain . Moreover, the also often tacit . To maximize the benefits for customer knowledge, r esearchers suggest that knowledge intensive service organ ization s strive to interact socially with knowledge sources (Muller & Doloreux , 2009) to create social and dynamic processes that are

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16 effective in obtain ing the (Strambach , 2008) . Furthermore , organizations should consider the us e of information technology applications to facilitate the sharing, reuse, and transfer of both tacit and explicit knowledge within their research teams who are most often the contributors of breakthrough innovations ( Kleis et al. , 2012) . 2.2 Service Innov ation Process in IS Research Academic research has come to have an increasing focus on service innovation because of the growth of service organizations over the past decades ( Dotzel et al. , 2013 ; Synder et al. , 201 6 ). Service innovation research appears i n se ver al research disciplines with significant contributions from marketing, service management , business, the social science s , engineering, health care, and operations ( W itell et al. , 2016). This section reviews the body of scholarly research on service innovation process es through the lens of innovation outcome s , service activities, and innovation dynamic s . 2.2.1 Outcome based Service Innovation Based on the types of innovation outcome s product or service two opposite viewpoints are suggested in Ordani ni and Parasuraman (2011 ): a residual view where service is a result of product innovation and a dichotomous view in which service innovation is distinct from product innovation. In the residual view, service innovation is assumed to be fundamentally the s ame as manufacturing product innovation and emphasizes a sectoral taxonomy where service industries are considered the leftover sectors that do not produce raw materials and tangible artifacts (Miles , 2008) . These residual industries in supplier dominated sectors (Pavitt , 1984), scale intensive sectors, physical network sectors, and science based sectors (Soete & Miozzo , 1989) receive technological impetus assimilated from manufacturing for the service innovation process (Barras , 1990; Djellal et al. , 2013) .

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17 Being a residue of product innovation, new service development (NSD) has the same underlying dimensions as new product development (NPD) (Djellal et al. , 2013 ; Nijssen et al. , 2006 ). Booz, Allen, and Hamilton (1982) divide the new pr oduct development pr ocess into seven stage s which are sequential and derived from studies of the development of consumer goods and industrial products. Krishnan and Ulrich (2001) , who define new transformation of a market opportunity into a product available for sale , five stage framework. M odels of NSD are similar . B owers (1989) proposes a normative model that includes eight activity stage s , while S c heuing and Johnson (1989) suggest a systematic model of 15 sequential st eps . Bullinger et al. (2003) formulate a six stage service innovation development process . Table 1 summarizes the NPD and NSD stage models in their linear progression . Table 1 . New Product Development and New Service Development Activities Study Pr ocess Stage Activities Booz, Allen, and Hamilton (1982) NPD 7 Strategy development, idea generation, screening & evaluation , business analysis, development, testing, communication Krishnan and Ulrich (2001) NPD 5 Concept development, supply chain desi gn, product design, testing, launch Bowers (1989) NSD 8 Business strategy, new service strategy, idea generation, concept development, business analysis, service development, marketing, commercialization S c heuing and Johnson (1989) NSD 15 New strategy , idea generation, idea screening, concept development, concept testing, business analysis, project authorization, top management commitment, development of operational details , personnel training, service delivery process and system , service testing, mark eting, launch, post launch review Bullinger et al. (2003) NSD 6 Idea generation, analysis, concept development, im plementation, marketing, review

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18 However, in the digital age, the common linear sequence that N P D and N S D processes share has been revok ed with the rapid and disruptive changes in ICT and digital technologies . Re cent research has suggested that the NSD process lacks linear progression, is of ad hoc nature , and contains a small number of processing stages (Athanassopoulou & Johne , 2004 ; Men or & Roth , 2008 ; Vermeulen , 2004 ). Most interestingly, empirical evidence rejects the staged model proposal and s upport s the one model does not fit all idea in the NSD process . For example, Toivonen and Tuominen (2009) examine nine individual innovation processes in three knowledge intensive services organizations and find that the traditional staging activities of idea generation, development, and marketing were mixed and match ed into three innovation models for various stages . They include the R&D model (idea > development > marketing), the rapid application model (idea > marketing < > development), and the practice driven model ( change in pr actice > idea > development), emphasizing t he bi direction al process between marketing and development. Martov oy and Mention (2016) ide ntify NSD processes as also includ ing main pattern s and sub patterns . For example, problem driven, proactivity driven, market driven, and strategy driven are a n main pattern s , while frugalness / consecutiveness is the sub pattern to the problem driven pattern . NSD has thus departed from the residual view of NDP. Based on the dichotomous view, th e distinctive intangibility and interactivity characteristics of services call for concepts and models unique to service innov ation (Miles, 2008) . Intangibility is reflective of service as a non tangible artifact and the need to produce and consume the service at the same time (Coombs & Miles , 2000) . I nteractivity emphasizes the multiplicity of actors involved in service innovati on , including both producers and clients (Miles , 2008). The interactivity perspective especially contradicts the goods dominant (G D) logic in the

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19 manufacturing context, where goods are the unit of exchange and the customer is secondary , to be viewed as a possible value receiver or destroyer at different time s (Vargo & Lusch , 2004). The generalization about services in G D logic has its limitations because the distinction between products and services is not clear cut and remains problematic (Miles , 2008). For example, a valued brand produced by a manufacturing firm as a product may be p art of a service exchange, or, a specialized s upplier may have high interactivity with clients during the production processes for the valued brand. 2. 2.2 Service Activity B ased Innovation R esearchers provide insights from the perspective of service activities that support the intangi bility and interactivity service characteristics . In their views, service innovation is seldom limited to a change in the service product chara cteristics (Miles , 2008) and is less centralized and standardized (de Hertog , 2010) ; thus, service innovation is better thought of in terms of dimensions for a wide variety of angles . D en Hertog (20 0 0 ) proposes four dimensions of service innovation includi ng service concept, client interface, service delivery system, and technology . In his perspective , t he service concept dimension relates to the intangibility characteristic in service products with emphasis on the value offering created by the service prov ider , whereas client interface and service delivery relate to the interactivity characteristic in service processes and products shared between producers and customer s . M any innovations involve some combination of th e se four dimensions (Miles , 2008) , with t he technological dimension play ing an enabling role to all the other dimensions (Barrett et al. , 2015) . For example, an automatic teller machine (technology dimension) enable s a new client interface in the banking industry, and the mobile boarding pass me chanism (technology dimension) deliver s a new check in system (service delivery system dimension) in the airline industry. Through the lens of dimension s , the

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20 characteristic of creation and consumption at the same time for service innovation is also promin ent . Both Sundbo (1994) and Gallouj and Weinstein (1997) describe the possibility of services c reated out of standard elements with modules combined or recombined for individual customers , all at the same moment of consumption. In the case of a boarding pa ss, for example, it can be delivered by a combination or recombination of technologies in many ways by a passenger. The pass can be downloaded from a mobile device , a personal tablet , a gate computer, or an airport kiosk connected to the air electronic network . Although d imension based service innovation literature separates the artifact of product and service, it does not address or resolve the age old debate of whether service innovation is different from product innovation. Recently, resea rchers have argued that focusing on the distinction of innovation output is no longer relevant because products have been recognized as mechanisms for delivering services (Lusch & Nambisan , 2015; Orlikowski & Scott , 2015), and services are demanded that ad d value to products (Wegrzyn , 2010). It has been especially evident in the last decade that products and their related services have been packaged collectively as a service, giving rise to the concept of servitization in which products and services functio n side by side (Rust , 1998). In other words, services have been interwoven into the physical production of products (Bryson et al. , 2004 , p. 2 ) , and the two are no longer separable in the collective function of the innovation . Thus, Preissl (2000 , p. 126 ) suggests the alternative view that the boundaries between product and service innovation should be based on innovation dynamics rather than narrowly defined innovation output characteristics and dimensions. 2. 2.3 Service Dominant Logic Perspectives Focus ing on innovation dynamics with a synthesis approach and an integrative view of

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21 goods and services, S D logic has emerged as a foundation for understanding service innovation in general (Barrett et al. , 2015). Th is logic examines the intangible resources o f human being s and technology as provid ing a telescopic view of their roles in a set of foundational premises (FPs) in service innovation . These FPs emphasize a continu ous value creation process as opposed to the product centric focus in product innovation where process es and outputs are finite (Lusch et al. , 2008). Three relevant FPs related to knowledge intensive service innovation s from the logic are : (1) service as a process, (2) value co creation through actor generated institution and institutional arr angements, and (3) operant resources as the source of strategic benefit s . When service is conceptualized as a process rather than a unit of output , service innovation is the result of the application of resources for the benefit of itself or for other orga nizations ( Lusch et al. , 2008 ; Vargo & Lusch , 2004). This conceptualization supports knowledge intensive service knowledge to enhance their internal knowledge. Accor ding to Vargo & Lusch (2016), institutions are not buildings but rules, norms, and practices established by human being s , while i nstitutional arrangements resemble the various assemblage s of those rules, norms, and practices that govern a process . Such arr angements facilitate value co creation and resource integration by actors from partnership who collaborate within the arrangements . The emerging S D logic also promotes the important d istinction between operand and operant resources (Vargo & Lusch , 2004 ) , a distinction first conceptualized by Co n stantin and Lusch (1994 , p . 143 ) to separate and cultur al resources. The basi c distinction is that an operand resource is a physical or tangible thing to be operated or used , while an operant resource is mostly a cultural or intangible skill that is applied to the operation or use of the physical resource ( Constantin & Lusch , 1994, p. 149) . Such a disaggregation

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22 makes possible more relevant analyses for resource management in additi on to management ( Constantin & Lusch , 1994, p. 141). R esearchers have since 1994 further defined characteristics of operand and operant resou rces in their fi eld s of stud y, among them marketing, service science, and health care with commonalities. I n general, they view operand resources as finite, tangible, static, and inert . Some examples would include goods, products, natur al resources, raw materials, and equipment, whereas operant resource s are invisible, intangible, and dynamic , including organiz competences, and information technology ( deLeon & Chatt e rjee n 2017; Higa & Davidson , 2017 ; Lusch et al. , 2008 ; Vargo & Akaka , 2009 ; Vargo & Lusch , 2004). However, the resource list has been evolving and transforming over time. For example, Vargo and Lusch (2004 ) originally categorize customers as tangible operand resources to be segmented and acted on with marketing strategies . But recently , customers have come to be viewed as operant resour ces who could change marketing practices with their voices and co create a service offering as part of the marketing dialog ( Lusch et al. , 2006 ; Lusch et al. , 2008 ; Ordanini & Parasuraman , 2011 ; Raddats & Burton , 2014 ; Vargo & Akaka , 2009 ; Vargo & Lusch , 2 014). Table 2 lists studies applying operand and operant resources from the S D logic perspective with definition and resource examples . In the context of knowledge intensive services, the emphasis is on intangible resources , such as the knowledge and skil ls retained by people . As operant resources , they can o perate on other operand or operant resources to create innovation . Furthermore, all actor s including service provider s , customer s , and other collaborators are considered operant resource s. T he process of co creation is also essential to s ource and integrate ideas from all actors . These operant resources and the outcome of their integration become the competencies and capabilities fundamental to

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23 knowledge intensive services strategic benefit. Table 2 . Studies Appling Operand and Operant Resources from S D Logic Study Discipline Operand Definition Operand Examples Operant Definition Operant Examples Vargo and Lusch (2004) Marketing management Finite Products; markets; nature resou rces; customers Invisible and intangible; can convert operand resources into outputs at a low cost Core competences; organizational processes; technology Lusch et al. (2006) Marketing management Tangible; static; finite; depletable Dynamic; non deplet able; replenishable, replicable Customers Lusch et al. (2008) Service Science Static; inert Natural resources; assets Intangible; produce effects; capable of acting on operand resources and other operant resources Knowledge and skills; computers; robots; customers Vargo and Akaka (2009) Service science Must be acted on to be beneficial Natural resources; goods; money A ct upon other resources to crate benefit Knowledge and skills; underlying source of value; customers, suppliers, stakeholders Ordanini a nd Parasuraman (2011) Service Science Enhance co creation opportunity; Service employees; competences

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24 Table 2. c Study Discipline Operant Definition Operand Examples Operant Definition Operant Examples Raddats and Burton (2014) Manufa cturing Financial; physical; legal Cash; raw materials; goods; plant; patents Human; organizational informational Skill and knowledge of employees; competences, culture, knowledge of customers, relationships with suppliers and customers Vargo and Lusch (2 014) Entrepreneurship Act on other resources to create value All actors (individuals, households, firms, nations) deLeon and Chattrjee (2017) B2B Tangible Core product; service concept Intangible Instrumental service; interpersonal service; value min dset Higa and Davidson (2017) Healthcare Tangible; finite; static Natural resources; equipment; goods Intangible; infinite; dynamic Information and knowledge; information technology S D logic leverages previous research investigating service innovation (Ordanini & Prarsuraman , 2011). An earlier foundational study is the resource based approach to innovation that attempts to look at how firms innovate in terms of their amalgam of resources knowledge, competencies, relationships, collaboration, and technol ogy (Barney , 1991 ; Wernerfelt, 1984 ) . But S D logic and the resource based approach diverge with respect to the outcome , based on the . In the resource based approach to innovation , there will

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25 be a finite outcome whi ch comprises goods or services ; therefore, an innovation process is internal and target s segmented market users (from internal to external). On the other hand, S D logic service innovation outcome is continuous and can benefit someone else as well as the s elf (from internal to external and from external back to internal) ( Vargo & Lusch , 2016 ) . S premise positions the beneficiary (user alone or producer and us e r together) to determine the value of an innovation brought about by an integration of re sourc e s (Mele et al. , 2014 ). Value co creation is therefore intensely emphasized in S D logi c . Table 3 summarizes the similarities and differences between G D logic, the resource based approach, and S D logic in the innovation research referenced in this r esearch. Table 3 . Similarities and Differences in Perspectives in Innovation Research Perspective What is the Purpose of Innovation Outcome ? Who contributes to Innovati ve Ideas ? Who carries out Innovati ve Id eas ? Goods dominant Logi c A product or a service Producer Producer Resource based Approach A pr oduct or a service Producer and customer Producer Service dominant Logic Make self or someone else better off Producer and customer Producer and customer 2. 3 The Dual Roles of Digital Technolog y as an Operand and an Operan t Resource Traditionally, IT is a material artifa ct (Orlikowsky & Iacono , 2001) viewed as an operand resource to facilitate technological service innovation (Lusch & Nambisan , 2015) and to enhance the efficienc y and effectiveness of service deliveries ( d en Hertog , 2000). The scope of the artifact is largely limited to devices and formats that are unique to a product or service (Tilson et

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26 al. , 2010). However, within S D logic, technologies , especially digital tec hnologies , are not limited to forms ; they are conceptualized as operant resource s that trigger or initiate a change ( Akaka & Vargo , 2014; Lusch & Nambisan , 2015 ; Nambisan , 2013 ). How do digital technologies , being physical entities , trig ger or initiate a change? Two research lens es can be identified to explain this conceptualization: resourcing and generativity. Resourcing is explained by Vargo and Lusch (2004) statement that resources are not ; they become they become operant resource s through the ap plication of the resources. To illustrate the concept , Lusch et al. (2008) describe how computers can do become operant resources. For example, computers are embedded in robots to accomplish tasks with knowledge a nd skills as human being s do . Another example is from Akaka and Vargo ( 2014) ; t hey describ e how the use of an X ray machine triggers changes through the operation by X ray specialist s . When an X ray machine is newly i ntroduced into an organization, t he pro tocol of machine use has to be established ; based on the machine who m the machine is used on, current institution al rules are often modified . Digital technologies, through resourcing, become operant resources that initiate actions and changes in use protocols . Although Nambisan (2013) as an operand resource , he also supports the conceptualization of digital technolog y as an operant resource through its im m ateriality . The operant aspect view is not reflected in the application of digital technologies but is explicated by the generativity unleashed with the design of digital component s . It is because and pursue unique resource integrat ion opportunities on their own and in the process engage with (or act upon) other actors (both animate and inanimate) in the network in value co creation (Lusch & Nambisan , 2015) . Abundant instances exist to illustrate a i n

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27 varying degrees. For example, an HTTP cookie, as a digital component, stored on the computer by means of the Web browser while the user is browsing , W eb browsing habits when the cookie is not blocked. The tracked data files are used to create such positive innovation as personalized W eb content. Another example is an a pp on smartphone platforms that can grow a network of external developers to design games, dictionaries, or other tools to enhance the value of the platform (Boudreau , 2012 ) . The component is not limited to and can be of more than a single unit . In the case of a digital control system , formed of many digital components , it has the ability to span local decision units without human intervention and stimulate additional digita l applications when included in a complex system (Lee & Bernete, 2012 ) . A complex system, such as Amazon demonstrates through a collage of digital components websites, cookies, tags, blogs, and ranking systems can provide a platform that initiates a succes sful digital marketing ecosystem . Such dual roles for digital technologies have transformed knowledge intensive services with digitization efforts and produced subsequent digitalization phenomenon . Digitization equipment digitizes artifacts from their non digital format or creates artifacts directly in digital form known as born digital artifacts. As such, books, music, codes and statues, and maps, just to name a few, are increasingly available in the digital form at . The digitalization of these artifacts not only offer innovative products, but it also changes the way organizations function and individuals interact with each other by way of socio technical processes (Yoo , 2012). Digitization is now a domina nt business practice. In the case of U.S. academic libraries , the digitization of artifacts is reflected in their budgets , with 75% of budget expenditure s currently in the form of electronic journal subscription , and 57% of book purchases in the form of e books in 2014 ( National Center for Education Stati stics , 2014 ) . In the new digital era , t he

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28 services that libraries offer have been re or ient e d around electronic resources , digital libraries and, eBook repositories rather than print ed physical materials. D igital technologies, as operant resources , have tr ansformed academic libraries . Th is core digital based change, enhanced with librarian intangible knowledge, library management practices, and digital technology application s , f uel s further changes within library organizations and their alliances. Altho ugh S D logic focus es on the operant aspect of digital technology, t his research supports the view that digital technology is of both an operand and an operant resource owing to the fact that a digital innovation will not happen without a physical digital device present . This assertion is e vident in countless practical digital innovation examples : Facebook must be supported by networks , and digital libraries must be accessed through devices . Therefore, digital both an operand an d a n operant resource cannot be underestimated , and t he se resources must coexist to produce effects. 2. 4 Academic Library and Innovation As knowledge intensive service providers , academic libraries have been offer ing traditional services that focus on colle cting and exchanging knowledge (Casali et al. , 2017). A climate of declining budgets and increasing collection costs , however, has challenge d the status quo, redirecting leaders hip to consider the p otential benefit of innovation . Although there has been an uptick in research on innovation in academic libraries, opportunities for significant research remain (Brundy , 2015). In the L ibrary S cience field, there is no professionally accepted tiered list of journals as there is in other academic disciplines. Ther efore, specific criteria must be applied to select the top journals for this review. First, Nixon (2014) provides three tiers of ranked journals based on expert opinion s, surveys, acceptance and circulation rates, impact factors, and h indexes. E leven

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29 jour nals are on the top tier , in an alphabetical order: College & Research Libraries, Information Technology and Libraries, The Jou rnal of Academic Librarianship , Journal of American Society for Information Science and Technology, Journal of Documentation , Lib rary & Information Science Research, Library Journal, Library Quarterly, Library Resources & Technical Services, Library Trends, and Reference & User Services Quarterly . Applying these journals to Thomson ee journals in the Library Science field with the most impact factors emerge as The Journal of the American Society for Information Science and Technology, College & Research Libraries , and Library & Information Science Research . Table 4 lists ranked libra ry journals and their impact factors. Table 4 . Library Journals Ranking and Impact Factor Rank Full Journal Title Impact Factor 1 Journal of the American Society for Information Science and Technology 1.846 2 College & Research Li braries 1.206 3 Library & Information Science Research 1.153 4 Journal of Documentation 0.833 5 Library Quarterly 0.500 6 Library Journal 0.465 7 Library Resources & Technical Services 0.452 8 The Journal of Academic Librarianship 0.448 9 Library T rends 0.386 10 Reference & User Services Quarterly 0.231 11 Information Technologies and Libraries 0.075 A literature review of articles was performed in the top three library journals in addition to The Journal of Academic Librarianship given its rele vancy to inform the library community of technological innovation s in academic libraries , with retrieved articles individually reviewed with a focus on the role s of technolog y in the libraries. Technologies refer

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30 to electronic tools, systems, devices, data, applications, games, webpages, algorithms, software programs, and digitized images . The review synthesizes two views guided by Nambisan (2013) frame work which separates technology s role into that of an operand and an operant resource. The operand role is notably focused upon in prior studies , serving as a tool to facilitate teaching and research as in the adoption of e B . , 2013; Dougherty , 20 09 ; Martin & Quan Haase , 2013), the diffusion of eJournals (Brennan et al. , 2002; Ollé & Borrego , 2010), and the utilization of web based learning through mobile and desktop websites (Beagle , 2000 ; Leo et al. , 2016; Torres Pérez et al . , 2016 ). Tools t hat enhance information retrieval and facilitate information service delivery are also researched. Examples are the implementation of Web 2.0 functionalities ( Aharony , 2009; Chua & Goh , 2010; Ki m & Abbas , 2010; Redden , 2010), the provision of digital reference services ( Gibbs et al. , 2015 ; White , 2001 ), and the deployment of the digital libra ry for special collections (Nov & Ye , 2008; Oguz , 2016). However, digital technology is also viewed as an operant resource , albeit in less quantity . Travica (1999) describe s how academic libraries have been transform ed from a physical space to a virtual library with the advancement of ICT and how their services are altered from a print to an electronic format by means of digitalization ( Higa et al. , 20 05). Crawford and Rice (1997) perceive automation as a change agent for academic libraries , with the concept further pronounced as t he means by which libraries acquire, organize, and provide access to information ( Warnken , 2004) . Acknowledging how librarie s function similarly to organizations in the business sector, Shapiro and Long (1994) describe the application of technology to drive a business reengineering process in the library setting . Yeh and Walter (201 6 ) propose service innovation as a response to business oriented disruptive innovation experienced in academic

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31 libraries . Noted from this review, m ost of these studies were published in the late 1990s and early 2000s when ICT was dominant in society . P ublications were silent on digital technologies in academic libraries , however, work appeared in 201 6 . Such a knowledge gap and the incommensurate understanding of digital technologies as operant resources need further exploration. It is also worth noting that research on digital t echnologies as operand resources in academic libraries focus es mainly on the aspect s of adoption and diffusion of technological tools. This trend is understandable , especially when applying the s (1986) reverse product cycle in which technol traditional cycle is reversed. cycle, an organization adopts a technology to increase the efficiency of its service delivery; once the service qu ality has been improved, the quality and adoption open the door for future service innovation . Evidence shows that a cademic libraries have been at the receiving end of technological development, u sually after a lengthy period of R&D efforts . The internal concerns by the libraries are thus how well the technology can be adopted and how wide its der ived services can be diffused. The most prominent example is the library management system ( LMS ) that end process to integrate with the front end service delivery. As described by Barras, innovation occurs when processes are effi cient and products are widely adopted . With LMS adoption , e B ooks and e J ournals are distributed outside library wall s , and virtual refer ences are provided by means of digital technologies . Since 2010, academic libraries have been migrating to the library se rvice platform (LSP) , the next generation of LMS with software as a service (SaaS) and an open data model . Service innovation will surly follow and may inevitably change the organizational structure. It is therefore imperative that academic libraries think proactively about how these digital technologies may trigger change in libraries

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32 and prepare accordingly . Th e present research should help f i ll the gap. Table 5 illustrates the dual roles of digital technology investigated in the academic library literatu re. Table 5 . The Dual Roles of Digital Technology Investigated in Library Literature Citation Operand Role Operant Role Context Method Shapiro and Long (1994) Technology transforms the library Through the concept of re engineerin g Case study Crawford and Rice (1997) Technology as a change agent Automation is a change agent within organizations Empirical secondary data Travica (1999) Digital technology contributes to organizational change Academic library is organized as virtua l library Empirical survey Beagle (2000) Digital technology as a tool to facilitate learning Web based learning Conceptual White (2001) Digital technology enhances information retrieval Diffusion of digital reference services Case studies Brennan et al. (2002) Digital technology as a tool to facilitate teaching and research Adoption of e J ournals Qualitative Warnken (2004) Technology contributes to organizational change Technology alters means of how libraries function Conceptual Higa et al. (2005 ) Digital technology causes reorganization of the library Present an approach to guide the transition of services from print based to electronic based Case study

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33 Citation Operand Role Operant Role Context Method Nov and Ye (2008) D igital technology as a tool for searching and retrieving information perception of digital libraries Empirical Technology acceptance model Aharony (2009) Digital technology as facilitat ing information service delivery Use of Web 2.0 Empirica l Chua and Goh (2010) Digital technology as facilitat ing service delivery Web 2.0 enhances library service quality and delivery Empirical secondary data Dougherty (2010) Digital technology as facilitat ing service delivery Utilization of e Readers C onceptual Kim and Abbas (2010) Digital technology as facilitat ing information service delivery Adoption of Library Web 2.0 Empirical Ollé and Borrego (2010) Digital technology as a tool to facilitate research Adoption of e J ournals Qualitative Redden (2010) Digital technology as facilitat ing service delivery Utilization of social bookmarking, a Web 2.0 tool Empirical secondary data (2013) Digital technology as a tool to facilitate teaching and research Adoption of e books on e reader s and other mobile devices Empirical test task technology fit model Martin and Quan Haase (2013) Digital technology as a tool to facilitate teaching and research Adoption of e books by historians Qualitative

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34 Citation Operand Role Op erant Role Context Method Gibbs et al. (2015) Digital technology as enhanc ing information retrieval Diffusion of digital reference services Case study Yeh and Walter (2015) Digital technology provides opportunities for changes Drive service innovati on to meet institutional goals Conceptual Leo et al. (2016) Digital technology as a tool to facilitate learning Flip classroom s Case study Oguz (2016) Digital technology as a tool in digital libraries Adoption of digital libraries Case study Torres Perez et al. (2016) Digital technology as a tool to facilitate research and learning Adoption of mobile website Empirical secondary data

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35 CHAPTER III . CONCEPTUAL MODEL AND HYPOTHESES DEVELOPM E NT 3.1 Knowledge Intensive Services Resources In the strategic management literature regarding the resource based approach , a firm holds tangible and intangible semi fixed assets , such as brand names, in house knowledge of technology, skilled personnel, trade contacts, machinery, efficient procedures, and ca pital ( Caves, 1980; Wernerfelt , 1984). These resources are a firm attributes , applied by its top creating strategies ( Barney , 1991 ; Caves , 1980 ). In this resource based approach, t hey are sources of competitivenes s only if they are valuabl e, rare, or cannot be replicated (Barney , 1991). Besides , t he competi ti veness does not occur or continue, unless these attributes are used effectively and efficiently (Hunt & Moran , 199 6 ). Are these attributes applicable to knowle dge intensive service organizations? What are the resources for knowledge intensive service organizations in this digital age? If rareness is hard to find, h ow do resources stay competitive for knowledge intensive service organizations ? The firm attribu tes that are listed in Barney (1991) and Wernerfelt (1984) remain applicable as the resources for knowledge intensive services ; however, digital technolog y ha s become a prominent addition to every resource list . To a considerable extent the literature has examined technology , such as an infrastructure or a device , as a distinct, stand alone resource (Tippins & Sohi , 2003). Meanwhile , research has demonstrate d that technology alone does not lead to competitive benefits . Rather, it is technology in combinatio n with complements that lead s to maximum benefit . The interrelatedness of human assets , technology infrastructure assets, and relationship assets is what crea te s capabilities as a source of advantage (Ross et al. , 1996) . Instead of rarity, it is the sophis ticated t echnology infrastructure, the quality of human capital , and high value relationship s that comprise the source s of competitiveness (Ravichandran &

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36 Lertwongsatien , 2005). High quality human asset s can be found in a staff with technical skills, busin ess understanding, and a problem s olving orientation . A sophisticated technology asset includes a well defined architecture of network and platform, data and core application sophistication as well as good platform standards . Additionally, a valuable relat ionship asset is reflected in a shared and trusted partnership between the technology unit, line managers, and external providers or partners (Ravichandran & Lertwongsatien , 2005 ; Ross et al. , 1996 ). The core assumptions of S D logic are consistent with t he competitive implications of technology and its complements. The logic collectively emphasizes digital technology competence along with human capital , institutional rules, and broader organizational relationships (Vargo & Lusch , 2016) operant resources act upon the digital technology infrastructur e to create synergistic benefit s . In the extant literature, institutional rules are analogous to organizational capital , while relationship is analogous to social capital. Human capital, organi zational capital, and social capital are collectively denoted as intellectual capital, a key factor for obtaining competitive advantage in the knowledge economy and for innovation and economic growth (Dean & Kretschmer , 2007; Hayton , 2005; Reina et al. , 20 11 ; Yaseen et al. , 2016 ). T he present research asserts that intellectual capital and digital technologies in combination represent knowledge intensive service resources , and the interdependence of these elements expands the c ompetitive advantage of the ser vice . 3.1.1 Intellectual Capital Early studies of this concept include Edvins on and Malone (1997 , p. 34 ) , who held that the hidden dynamic factors underlying a c visible buildings and p roducts are very valuable . Those factors are the human capita l inherent in and experience; the structural capital astructure t hat supports its human capital;

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37 and the customer capital the company generates through positive relationship s with its customers. Togethe r , the se elements are termed intellectual capital . Marchand et al. (199 6 ) visually divided intellectual capital into what is in the heads of employees ( human capital ) and what is left in the organization when they go home ( structural capital ) . Issac et al. (2010) propose that i ntellectual capital consists of human capital , organizational capital , and relationship capital , where relationship capital is broad ened to include other stakeholders in addition to customers . refers to the same structural capital as found in Edvinson and Malone but with the infrastructural concept expanded to include a higher level goal of creating value for the company. Since the 2000s, the term social capital has been introduced to replace relat ionship capital to reflect a network of increasing social interactions ( Hsu & Sabherwal , 2011 ; Subramaniam & Youndt m 2005 ; Youndt et al. , 2004). This research views intellectual capital as including human capital, organizational capital, and social capita l. Specifically , h uman capital is defined as the capabilities embedded in employees and not owned by the organization (Hsu & Fang , 2009 ) . Consequently, this capital does not stay with the organization when employees leave. O rganizational capital is the k nowledge and codified experience residing within databases, manuals, culture, structures, and processes , and remain s in the organization when employee s leave ( Chen & Shih , 2009 ; Edvinson & Malone , 1997 , p. 35 ; Issac et al. , 2010 ) . S ocial capital is the kno wledge embedded within networks of relationships and interactions among st employees and stakeholders . As a result, the knowledge may or may not stay in the organization when employees leave (Nahapiet & Ghoshal , 1998; Subramaniam & Youndt , 2005) . Because in tellectual capital looks beyond the mere financial health of a firm, it is not confined to for profit enterprises; the concept is thus applicable to non profit organizations as well . With know ledge becoming a critical resource in the knowledge economy, inte llectual

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38 capital is an indispensable asset in knowledge intensive service organizations (Stewart , 1997 , p. 6 7 ). U nderstanding the se various capitals alone as is not as valuable as understanding the capabilities from which these capitals may develop (Stewart , 1997 , p. 55 ) . Fo r example, human capital is the capability of an individual providing kn owledge solutions to customers implying the action of providing solu tions. Organizational capital is the support mechanism that allows the sharing, transforming, and transporting of knowledge i mplying the action of providing support. Social capital is the willingness of the customer s t o share plans and expertise with the producer implying the action of knowledge transfer . A successful financial sector serves as a practical example where skilled employees are in a better position to respond to customer financial queries and are compet ent to provide advice to customers . An e fficient organizational structure which enables these employees to excel thereby create s value and innovation for customers . Aided by prevalent media technologies, b anking activities are increasing ly social through g rowing interaction s between employees and customer s (Chahal & Bakshi , 201 5 ). In this example of a financial organization as a knowledge intensive service provider in this digital age, all three capitals human, organizational, and social are needed to contribute to organization successful edge . They should therefore be viewed as a synergistic, integrated set for intellectual capital (Yaseen et al. , 2016). Thus , based on extent literature, intell ectual capital is a higher level abstraction defined and operationalized by first order human capital, organizational capital, and social capital. 3.1.2 Digital Operant and Operand Technologies When digital technologies are viewed as operant resources, t hey become material artifacts of practical instantiation and significance rather than of physical forms (Leonardi , 2010). Without form t hey may be further conceptualized to exhibit three capabilities: inside out

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39 capability, outside in capability, and spann ing capability. I nside out capability is internally focused and deployed from inside the firm in response to market requirements and opportunities (Day , 1994; Wade & Hulland , 2004). The focus of inside out capability is on internal technology architecture of application s and services (Cai et al. , 2016). O utside in capability is an externally oriented capability that anticipates market requirements , understand s competitors, and build s external relationships (Day , 1994; Wade & Hulland , 2004). Outside in capab ility not only helps an organization acquire external knowledge fro m partners but also assists them in assimilat ing internal knowledge (Tippins & Sohi , 2003). S panning capability is the competence that integrate s and coordinates all the capabilities inside and outside an organization ( Cai et al. , 2016 ; Day , 1994; Wade & Hulland , 2004) . Consider the transformation of knowledge intensive legal service where legal research is intensely engaged in th is digital age. Machine learning has now been applied to a ma ssive amount of client history, briefings, and reports. Robot lawyers have been hired in the United Kingdom for years, and online legal services have provided basic legal advice and forms for decades. Mobile devices are used to track billable hours or prep are for a trial. It has thus become based system to mobile friendly , responsive services. This transition requires an inside out capability to apply digital technologi es, an outside in capab ility for understanding the evolving requirements of customer needs, and a spanning capability to strategically integrate the firm s capabilities to innovate cost cutting measure s growing needs. Figure 1 , adapted from Day (1994) , i llustrates how knowledge intensive services can apply the capabilities of digital technology to fulfill their roles. At one end of the spectrum are those capabilities that are deployed from the inside out activated by opportu nities and market requirements. T hey are reflected in appropriate

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40 and flexible data and network architecture . At the other end of the spectrum are those capabilities that bring in external knowledge and disseminate internal knowledge. They are reflected in digital technologies that link the provider with users, vendor s , and other libraries. Spanning capabilities are needed to integrate and coordinate all aspects of digital technolog y . T hey are reflected in teams with blended expertise, a good relationship with technology personnel, a nur turing climate for digital projects, and appropriate workflows leveraging digital technologies. Thus, based on extent literature, digital technology as an operant resource is a higher level abstraction defined and operationalized by the three first order c apabilities: inside out, outside in, and spanning capabilities . The role digital technology plays as an operand resource cannot be discounted. The infusion of digital infrastructure and devices the Internet, cloud computing, mobile computing, digitization equipment, an d 3D printing into knowledge intensive service organizations has transformed the way these organizations provide service innovation. Digital operand technologies are essential parts of knowledge intensive services in this digital age. The bundle of intelle ctual capital, digital operant resources, and digital operand resources are therefore conceptualized as knowledge intensive service resources. Although resources alone can improve teraction, sometimes complex and with added time required , that builds greater outputs (Amit & Schoemaker , 1993; Karimi et al. , 2007). This research proposes that the i nteraction of knowledge intensive service resources buil d digital platform capabilities that provide opportunities for service innovation. Appendix A lists the definitions for the dimensions for intellectual capital and operant digital technology from the prior literature.

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41 Figure 1 . Classifying Operant Digital Technology Adapted from Day (1994) 3.2 Digital Platform Capabilities There is no consensus on exactly what constitutes a digital platform, but there are common elements offered in the literature. An early effort described the digital platform an , 2000). Evans (2008) considers web based business as a platform on which other busines ses rely to produce complementary products. Gawer ( 2009, p. 2) thinks of Microsoft Windows as a platform of building block s with which other firms can develop complementary products, technologies, and services. Shelanski (2013) describes digital platform s define a digital e

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42 components (resources) and facilitates the interaction of actors and resources (or resources D logic perspective by providing a venue for service exchange and value co creation for service innovation. Most impo rtantly, the dual role s of digital technolog y as an operand and an operant resource are validated in a digital service platform through two concep ts : convergence and generativity. Yoo et al. (201 2 ) state that when digital components , especially software ba sed components with digital capabilities , are embedded in objects, their affordances create innovations characterized by convergence. C onvergence is attributed to the programmability and re programmability of digital technology that contribute to data homo genization and system interoperabili ty. In reality , digital convergence has blurred the boundaries between many types of service providers , including content supplier s, advertising agencies, telecom munication and TV operators, computing companies, and devi ce manufacturers . Th ey have relied on homogenized data and interoperable systems to create such bundled service innovations as Spotify, Net flix , Sling TV , and Hulu . The affordances of digital capabilities also produce innovations of generativity (Lusch & Nambisan, 2017; Tilson et al., 2010; Yoo et al., 2012). G enerativity is attributed to digitalization , techniques to broader social and institutional (Tilson et al. , 2010 ). D igitalization materializes when the process convert s analog information into digital bits that can be shared by many technologies . This sharing , moreover, remove s tight coupling require ments between information types, s torage deman ds, and transmission methods. Without the tight coupling, digital infrastructure is capable of leveraging or being leveraged across a range of tasks , ad apting to many different tasks , and being accessible and appeasing to many different audience s

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43 (Zittrain , 2010 , p. 14 ) . From the theoretical lens , convergence and generativity seem to imply an opposite force by digital capabilities . In fact , convergence complements and contributes to generativity . As an example , take Google Maps, an infrastructural service f or geolocation information, upon which various applications have been built by developers for tour s , restaurant s , or library locations (Palfrey & Gasser, 2010 , p. 111 ). Without the digitization of maps to ensure data homogenization (convergence), various a pplications would not be possible (generativity). For a nother example consider GibHub platform , a development platform with social networking like functions for open source software projects , upon which developers can create, co create, and share unlimited innovative projects. Without the use of the global standards extended markup language (XML) for data homogenization and system interoperability (convergence) co creation would not be possible (generativity). The concepts of building blocks and digitaliz ation have taken the digital platform to the next level with its conceptualization as part of a whole for service innovation. The whole is a service ecosystem where loosely coupled social and economic actors connect by sharing institutional logics and mutu al value creation through service exchange (Lusch & Nambisan , 2015). The platform is the core element in this ecosystem, filling the role as an engine to drive service innovation. Google, for example, is a service ecosystem. It has a multi sided digital pl atform serving people who search the Web, businesses who reach searchers, and developers who use API for mash up projects and other complementary products (Evans , 2008). The example of Google demonstrates a unique platform economy where benefits to the cus tomers are not provide d by the owner of the platform , Google, bu t by independent third part y developers that co evolve within the platform to create innovation (Baghbadorani & Harandi , 2012). The

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44 generativity of digital technologies ha s thus been shown to unleash a nd will continuously unleash unexpected service innovation opportunities. Digital platform capabilities ha ve mainly been linked to servit i zation where man ufacturers successfully acq uired it to engage co creation with customers for advanced servic e offerings (Cenamor et al. , 201 7 ; Lenka et al. , 2017 ; Parida et al. , 2015 ; R nnb er g Sj din et al. , 2016 ) . In servitization , digital platform capabilities have reflect ed a join t sphere where provide r and customer co create value through direct interaction (Vargo et al. , 2008) as opposed to a provider sphere where provider s domina te value creation without interaction with customers & Voima , 2013). In servitization , digital platform capabilities have analyze d and transformed digital data into knowledge (Coreynen et al. , 2017). D igital applications have also improved the m anagement of both endogenous and exogenous knowledge to further increase digital platform capabilities (Coreynen et al. , 2017 ; Sher & Lee , 2004 ) . From these arguments for value co creation, data analytics, and knowledge linking capabilities offered in digi tal technology platforms , this research asserts that digital technologies are the building blocks that form a foundation of platform capabilities for developing service innovation. 3. 3 Research Model Based on the above review and discussion , a research m odel is proposed which is represented in Figure 2 . Within the S D logic framework, t his model recognizes the existence of digital technologies as both operand and operant resources . Digital technologies and intangible intellectual capital are critical reso urces for knowledge intensive services. They form knowledge intensive service resources and build digital platform capabilities that foster co creation and analytics capabilitie s , f urthering service innovation outcomes .

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45 Figure 2 . Inner Research Model 3. 4 Hypotheses Development Knowledge intensive services are recognized as highly active in innovation ( Catey, 2012; Corrocher et al. , 200 9 ; Hipp et al. , 2015 ; Srivastava & Gnyawali, 2011 ) and their knowledge based resources (e. g., knowledge and experience of employees, alliance and partner resources, and technologies) are critical to generating innovations in th e digital age . T hese resources mirroring the foundational premises in S D logic include intellectual capital and digita l technologies collectively . When the components of intellectual capital are applied strategically, they lead an organization to better performance and more innovations ( Agostini & Nosella , 2017 ; Roos et al. , 2001 ). Strategies are two faceted , encompassing how an organization exists within its environment and how well an organization uses its intellectual capital (Roos et al. , 2001). To use it well, intellectual capital must be viewed as more than a stock of knowledge (Hsu &

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46 Sabherwal , 2011); it is an opera nt resource with actions. For example, h uman capital drives strategic renewal from brainstorming, daydreaming, or process re engineering alike (Bontis , 1998); organizational capital , with codified knowledge mandate s procedures and rules (Nelson & Winter , 1982 , p. 103 ); and social capital intensifies unbound interactions between individuals and groups (Subramaniam & Youndt m, 2005). In addition, the presence of technology enhances the practical application of knowledge (Lusch & Nambisan , 2015) because it is viewed as a subset of knowledge that acts as know how and provides information (Capon & Glazer , 1987). The know how capability coupled with ubiquity make s digital technology a powerful resource to knowledge intensive services in the digital age . An organiz ation with appropriate data and network architecture, data processing capability , as well as familiarity with social media and communication technology can respond to ever changing environmental requirements , manage external relationship s , and blend non di gital and digital expertise ( Wade & Hulland , 2004 ). Academic libraries are in a unique position to stimulate service innovation through the strategic possessing and deploying of knowledge intensive service resources. L ibrarians have been utilizing intelle ctual capital in providing instructional services to faculty and student s , although traditionally in the form of face to face consultation with printed materials. Digital technologies have meantime exploited and expedited innovative instructional service d eliveries that are more fitting for millennials and incoming generation Zs. Libraries which r ecognize critical knowledge intensive service s resources are innovating. For example, Library DIY ( https://library.pdx.edu/diy/ ) is a system of learning objects designed to give students the quick answers for point of need support. Students can drill down from the objects to the specific piece of information they are look ing for rather than having to skim through a long tutorial before

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47 find ing the answers. (http://tutorials.lib.umd.edu ) creates interactive learning tutorial s for students on the use of research literature databases and searching techniques . The instructional pane contain s text, pictures, links, and interactive questions on the si de of browser where the online resourc e is the focus of the tutorial. These innovations utilize the work of library employees who are skilled in their functions to develop new ideas and collaborate with others to share ideas . They also utilize databases, t he Internet, and website s digital technologies to disseminate innovation s . Without these traits in the employees and presence of digital technologies, an academic library w ould be performing in the old way . The follow ing hypothesis is therefore proposed: Hypothesis 1: The possession of critical k nowledge intensive service resources ha s a positive association with service innovation outcome s in an academic library . Resources are required to build capabilities ; therefore, the lack of resources harms an organ build capabilities for performance and innovation (Karimi et al. , 2007). However, the mere presence of resources does not guarantee ability to build capabilities (Barney et al. , 2001 ). It is rather the integration and configuration of resources that improve digitalization to create platform capabilities for an organization in this digital age (Coreynen et al. , 2017) . Such platforms conflate physical and digital technology elements . With appropriate and flexible hardwar e, server, network, databases, mobile devices , and so forth, an organization can codify tacit knowledge amongst employees, users, vendors , and collaborators. Th is codified knowledge may further assist an organization in anticipat ing and evaluating opportun ities, acquir ing more external knowledge , assimilating external and internal knowledge, and integrating and coordinating knowledge capabilities via digital technologies. Academic libraries are in a unique position to stimulate digital platform capabiliti e s as

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48 many of the ir collection s are already in the digital format. With the advancement and prevalence of digitization technologies, a cademic libraries have directed efforts to digitalize their special collections to preserve and fulfil their service missio has been promoted by David Weinberger (2012). Weinberger explains that a library is more than a portal that users go through occasionally, but a ubiquitous and persistent platform infrastructure of capabilities that serves more users, serves them better value and mission. Unlike traditional libraries, a electronic platform provide s access to everything it can, including some treasures yet to become available. The library platform will e nable social knowledge networks to emerge and flourish, supporting idea sharing and peer collaboration. To accomplish this vision, libraries would need to digitize their hidden treasure s , open their digital content includ ing metadata about the conte nt, provide end user tool s , especial ly social tool s for exploring data and content, and open APIs for developers to create applications . Weinberger compares his vision to that of Facebook , where innovative apps make Facebook ever more valuable to its user s. The following hypothesis is therefore proposed: Hypothesis 2: The greater the ability to integrate knowledge intensive service resources, the more able an academic library will be to build digital platform capabilities. Recent studies emphasized the g rowing role of digital platform capabilities for increasing new service offerings in the manufacturing industry ( Cenamor et al. , 201 7; Lenka et al. , 2017 ; Lerch & Gotsch , 2015; Parida et al. , er , 2016) and the shared economy (Fr ey et al. , 2017). Such a platform provide s a unique space where community users and providers interact and co create , contributing to the capabilities for customization; hence , it is ideal for knowledge intensive services in which a high level of customiza tion is often demanded as an output (Zeithaml , 1981). Customization is aided when co cr e ation serves as a

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49 form of dialo gue between providers and users two equal problem solvers who engage in learning and communication (Prahalad & Ramaswamy, 2004). Within t he platform, often a virtua l space (e.g. , blogs, wikis, Zoom, Jabber, GoToMeeting, Skype for Business ), communication happens vertically and horizonta lly with ease; thus, development and customization are e n hanced . Project CORA stands for Community of Onli ne Research Assignment and is an exemplary library digital platform where librarians and faculty collaborate and co create assignments that support information literacy on campus (CORA , 2018) . The assignments are built on a platform, backed by a content ma nagement system that promote s the pedagogical practices of a specific institution ; however, the assignments are not isolated entities but are shared with other institutions for adaptation and experimentation. The assignments are also enhanced by user con tinuous feedback to build a community of practice in new and interesting ways. A lthough digital platform generates vast amounts of digital data, digital pla tform capabilities also provide analytic al capability by means of sophisticated technological appli cations that develop rules, logics, and algorithms to transform these available data into predictive insights ( Iyer 2011; Lenka et al. , 2017). Predictive analytics has been prov ed successfu l at Merck & Co., Inc. Boulton (2017) points out that i nstead of ha ving engineers spending their effort in finding, accessing, and ingesting data to evaluate project success, Merck created MANTIS (Manufacturing and Analytics Intelligence), a data warehousing system to crunch data in both structured and unstructured system s including text, video, and social media . A paradigm shift has thus occur red in data us age from collecting and reporting to modeling and visualizing in Merck . MANTIS has helped Merck decrease the time and cost of overall IT analytics projects by 45 percen t, while increasing the tangible business outcomes that include a

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50 20 percent reduction in average lead time, and a 50 percent reduction in average inventory carrying costs (Boulton, 2017) . Predictive analytics does not need to be established in a scale as large as MANTIS ; small context work can be done with equal efficien cy . For example, Dr. recommendations and operation scorecard s to the sales staff. Those staff loa d the MyDPS app onto their iPads ; th e metrics in turn tell them what offers to make to retailers and what stores they should be paying a visit (Boulton , 2017) . It is assumed that predictive analytics will give libraries the ability to approach opportunitie s, mitigate risks, and foresee user behaviors in a way that was not possible before the digital age. Using data to make decision s and inform process change ha ve long been successfully practiced in the libraries, evident in studies by Daneshgar and Parirokh (2012), Kirkwoo d (2016) , and Veldof (1999) . These studies describe data driven decision making process es and innovative use of descriptive data in various formats , including tracking sheets, interview scripts, and database records to support the tasks of p rocess es change , collection development policies , user experience enhancement, and knowledge. Therefore, the following hypothesis is proposed: Hypothesis 3: Digital platform capabilities have a positive association with service innovation outcomes in an academic li brary. S ervice innovation is a complex phenomenon that could only be achieved with a variety o f capabilities ( Agarwal & Selen , 2009; den Hertog et al. , 2010 ). For an organization these capabilities are obtained by transform ing current resources into a valuable bundle for capability building (Hunt & Morgan , 1996 ) and by reconfiguring resources to maintain such capabilities (Fiol , 2001). In other words , resources are a pre requisite to the existence of capabilities , and r esource s alone may not necessarily be directly related to service innovation.

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51 Especially when resources are deemed mostly valuable but not rare, how may they provide more service innovation ? Karimi and Walter (2015) suggest that organizational resources , such as financial and human capital, be aligned effectively to build digital platform capabilities. Digital platform capabilities in turn deliver innovation and value by connecting resources and the network effects between them ( Gartner Executive Programs , 2016). In the academic library i t is expected that service innovation will depend less on the rigidity of possessing knowledge intensive resources and more on intellectual capital integ rating with digital technologies to create digital platform capabilities. The more digital platform capabilities , the more the libraries can co create value with users and apply analytics to foresee opportunities for innovation . The following hypothesis is therefore proposed: Hypothesis 4: The impact of knowledge intensive service resources on service innovation outcomes is mediated by the academic platform capabilities.

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52 CHAPTER IV . RESARCH DESIGN 4.1 Measurement Mod el Figure 3 . Measurement Model This research proposes fourteen constructs: ten first order constructs, three second order constructs, and one third order construct . In total, t hese constructs were measured by 43 items adapted f rom prior research . Third order formative construct knowledge intensive service resources ( KIS R ) are formed by operand digital technology ( OD D T ), operant digital technolog y ( O TD T ), and intellectual capital (IC). OD DT is a first order reflective construct measured by five items adapted from Karimi et al. ( 2007) assessing whether hardware, network, server and database technologies, digitization technologies, and mobile and digital devices are in place as resources . OT DT is a second order construct reflected by three first order reflective constructs: inside out capability (IOC), outside in capability (OIC), and spanning capability (SPC). IOC was measured

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53 by four items assessing digital technologies meeting external opportunities . OIC was measured by four item s assessing the evolving external user requirements . SPC was measured by four items that satisf y the opportunities and requirements identified by IOC and OIC . The measurement items for IOC, OIC, and SPC are adapted from Bharadwaj et a l. (1999), Cai et al. (2016), and Zhang et al. ( 2008). IC is a second order construct reflected by three first order constructs: human capital (HC), organizational capital (O C), and social capital (SC). HC was measured by four items representing library em . OC was measured by four items assessing codified knowledge and experience residing in various channels within the library . SC was measured by four items representing capabilities embedded in various network of relationships with the library . The measurement items for HC, SC, and OC are adapted from Chen et al. (2014), Hsu and Sabherwal (2011), and Subramaniam and Youndt (2005). Di git al platform capabilities (DPC) are a second order construct reflected by two first order reflect ive co nstructs: co creation capability (CC) and analytics capability (AC). C C was measured by five items , adapted from Hsieh and Hsieh (2015 ), which affirm the interaction between users and employees . AC was measured by five items , adapted from Gupta and Georg ( 2016 ), which assess available data and analysis mechanism for decision making . First order reflect construct s ervice innovation outcom e s (SIO) , adapted from Wang et al. (2010) and Yen et al. (2012) , is measured by four items assessing the lib create service innovation as well as its perception o f its service innovation performance . Often t he outcome of a service innovation is but how firms can better serve , 2008) , or how customers become better off when using a product or service (Skålén et al. , 2015). Also, t he initiation of a service innovation often reflects

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54 it possesses (Wang et al. , 2010). Combining resource valuation and service innovation to help librar y users better at doing so provide service innovation. Additionally, intellectual capital as the k nowledge intensive service resource integrating with digital technologies create s digital platform capabilities that presumably enable academic libraries to serve the needs of their value d network of users. Table 6 summarizes measurement s of the fourteen l atent constructs in this study. operationalization and their sources. For the control variabl e s , a number of demographic variables, namely the expenditures (EXP) time equivalent (FTE ) of both professional and support staff , are important factors which may affect the amount of innovation . EXP reflects an ions of size. Budget and size are often used as control variables, Bharadwaj (2000) and Saldanha et al. (2017) find that both account for the abundance of resources devoting to various technologies to produce more innovation in an organization . E XP was measured with five variables reflecting a libr $500,000, between $500,000 and 1 million, between 1 million and 10 million, between 10 million and 20 million, and more than 20 million. FTE was measured with four variables en 100 and 250, between 250 and 500, and more than 500. They are control variable s for KISR, DPC , and SIO and are established based on the Association of Research Libraries statistic al measures ( https://www.arlstatistics.org ).

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55 Table 6 . Measurement of Constructs Latent Construct Order Latent Construct Type Sub Construct or Dimension Number of Items Knowledge intensive service resources (KISR) Third Formative Digital operand technologies (DOD) Digital operant technologies (D OT) Intellectual capital (IC) Operant digital technology ( OT DT ) Second Reflective Inside out capability (IOC) Outside in capability (OIC) Spanning capability (SPC) Intellectual capital (IC) Second Reflective Human capital (HC) Org anizational capital (OC) Social capital (SC) Digital platform capabilities (DPC) Second Reflective Co creation capability (CC) Analytics capability (SC) Service i nnovation o utcome s (SIO) First Reflective 4 Operand digital technolog y ( O D DT ) First Reflective 5 Inside out capability (IOC) First Reflective 4 Outside in capability (O I C) First Reflective 4 Spanning capability (S P C) First Reflective 4 Human capital (HC) First Reflective 4 Organizational capital (OC) First Reflecti ve 4 Social capital (SC) First Reflective 4 Co creation capability (CC) First Reflective 5 Analytics capability (AC) First Reflective 5 Total measurement items 4 3 Total constructs 14

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56 4.2 Data Collection Procedures 4.2.1 Survey Instrument Data w ere collected using a carefully developed self report ing survey instrument based on guidelines and exemplars in the literature from Straub (1989) and Sethi and King (1991). Past literature was reviewed to specify a set of items that ensured content and fac e validity and to achieve min imal overlap between constructs, as suggested in Cronbach (1971) and Kerlinger (1986 , p. 19 ). Items associated with these constructs use a seven item Likert type scale where respondents were asked to state their agreement with a given statement on a scale that ranged neutral 4.2. 2 Content Validity and Face Validity re levant content domain of the construct that it is trying to meas (Bhattacherjee , 2012). Five academic library administrators evalua ted both the content validity and face val idity of the construct measures, a fter which t hey were excluded from the official survey research. Their feedback contributed to the re arrangement of measurement items, wording clarification, and modification of q uestion types. 4.2. 3 Sampling P rocess The target population for this study is academic libraries in the United States . The sampling frame includes academic libraries in d octoral u niversities and m c ollege s and u niversities according to the Carnegi e Classification of Institutions of Higher Education. In total, there are 9 7 5 academic libraries in the frame (3 28 d octoral u niversities and 64 7 m c olleges and u niversities). The samples are library administrators in these academic libraries includi ng

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57 dean s , associate dean s , assistant dean s , university librarian s , associate university librarian s , assistant university librarian s a s well as director s , associate director s , assistant director s , and head s of information technology. To collect email addres website was searched , visited, and the admi are vacant at the time of the co llection. 4.2. 4 Pilot Test Pilot testing helps detect potential problems in research design and instrumentation and to ensure that the measurements are reliable and valid (Bhattacherjee , 2012). Twelve deans and directors from the target population were recruited for pilot testing ; they were also excluded from the official survey research. Data collected from a pilot test of deans and directors in academic libraries were used for instrument validation and refinement only. Only minor wording changes were required from the pilot testing. 4.3 Main Data C ollection An institutional review board (IRB) application was submitted and approved by the University of Colorado , Denver in November 2017. The survey was developed in Qualtrics to collect empirical data for the proposed knowledge intensive service resources dimensions, digital platform capabilities, and the effect of building digital platform capabilities for service innovation outcomes. The data collection for confirmatory analysis and hypotheses testing be gan in December 2017 . To improve response rate, the solicitation e mail addresse d each recipient individually and was sent individually . The e mail state d that respondents could leave the survey at any time and that their resp onses would be completely anon ymous and confidential. No specific incentive was provided to participants for completing the survey beyo nd promising them

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58 a copy of the aggregated results if they express ed interest in receiving the results . On the survey, respondents were instructed to a nswer each question as a representative of the institution as opposed to basing replies on purely personal views. In total 1, 313 emails were sent to academic library administrators (7 28 of the emails to the d octoral u niversiti es and 5 85 emails to the m aste c olleges and u niversities ) . T he survey remain ed open for 55 days. Ten days from the first sent email , 186 data points were collected; within 30 days from the first send email, up to 245 data points were collected. An a dditional 1 6 responses were collec ted between 30 and 55 days , yielding a total of 26 1 responses. Among them, 251 were usable responses. The response rate was 19 .1 % fo r the entire sample population. Specifically, 17 8 responses with the rate of 2 4 . 5 % were received from the d octoral u niversit ies, whereas 73 responses for the rate of 1 2. 5 % were received from the m c olleges and u niversities. The rate s in this study are within and above the typical external survey response rate of 10% 15% ( Fryrear , 2015 ). Such a response rate without a r eminder being sent signif ie d unusual interest in this topic amongst library administrators. Appendix C displays the solicitation e mail , and Appendix D lists the survey questionnaire.

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59 CHAPTER V . DATA ANALYSIS AND RESULTS 5.1 Descriptive Statistics Desc riptive statistics listed in Table 7 shows that 54 .2 % of the top administrator s from the sample libraries responded to the survey; they h e ld the title of deans, university librarians, or directors. This was followed by m ore than a 2 7 % response by associate deans, associate university librarian s , or associate director s. The majority of responding libraries (49 .4 %) ha d annual expenditures of between $1 to $ 10 million , and the majority of responding libraries ha d a total FTE of less than 100 (65 .3 %). The most of the responding libraries (4 7. 8%) were public doctoral institution . Institutions in the Northeast (26 .3 %), the Southeast (23 .1 %), and the Midwest (23 .1 %) were more responsive than th ose in the West (15.5%) and Southwest (12%). Table 7 . Descriptive Statistics Demographic Descriptive Statistics Number of responses Percent of total Position in the library D e an, University Librarian, or Director 136 54.2% Associate Dean, Associate U niversity Librarian, or Associate Director 68 27 .0 % Assistant Dean, Assistant University Librarian, or Assistant Director 20 8 .0 % Head of Technology Unit 16 6 .4 % Other 11 4 .4 % Grand Total 251 100% Library total expenditures < $500,000 15 6 .0 % $500,0000 $1,000,000 20 8 .0 % $1,000,000 $10,000,000 124 4 9 .4 % $10,000,000 $20,000,000 53 2 1 .1% >$20,000,000 39 15.5 % Grand Total 251 100%

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60 Demographic Descriptive Statistics Number of responses Percept of total Total FT E (both professional and support staff) < 100 164 65.3% 100 250 62 24.7 % 250 500 17 6.8% > 500 8 3.2% Grand Total 251 100% Library geographic region Northeast 66 2 6 .3 % Southeast 58 2 3 .1 % Mid west 58 2 3 .1 % Southwest 30 1 2 .0 % West 39 15.5 % Grand Total 251 100% Type of institution Public Doctoral University 120 47.8% Private Doctoral University 56 22.3 % iversity 37 14.7 % 38 15.1 % Grand Total 251 100% To test for potential nonresponse bias, two techniques applied in prior research were followed ( i.e . , Mani et al. , 2010; Wel ch & Barlau , 2013) : (1) comparing respondents to the population in response rate based on background characteristics , and (2) co mparing early responders to late responders based on background characteristics . The demographic characteristics of position, geo graphic region, and type of institution were known for both the responde nts and the population and were therefore used to compare response rate s . As summarized in Table 8, there were some difference s in response rate in each demographic characteristic , wit h heads of IT unit respond ing the most at 3 0.8 % comparing with the population , whereas deans, university librarian s, and directors responded the least at 15 .2 %. The

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61 rates of the other position categories we re closely distributed between 26 .3 % and 2 7.5 %. Th is distribution was not unexpected because the survey topic is related to technology , which naturally attracts the leaders in IT unit s . A lso, deans, university librarians, and directors of an academic library might lack the time to respond to a survey. Wit h respect to the geographic region, the Southwest had the highest responder rate at 25 .2 % comparing with the population , whereas the rate s of other regions were closely distributed between 1 7.6 % and 19.8 %. As to the type of institution, public doctoral uni versities ha d the highest respon der rate at 25 .4 % comparing with the population, followed by private doctoral universities (21.9%) , public (16.2%), and private ma s (10.6%) . To compare early with late responders, t he sample w as divided into half according to response date/time to compare the demographics of the two group s , which includ ed the position of the responder, the EXP , it s total FTE, and the type of t institution . T test were performed on two sets of data collected for the d octoral u niversities separated from the m c olleges and u niversities , with no significant differences found between early and late respondents. The results are summarized in Table 9 , below. All values are greater than 0.05 , indicating no significant bias. Given the lack of extent empirical statistics in academic library research, the statistical findings in this study can serve as reference points for future research.

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62 Tabl e 8 . Comparison of Population Value and Responder Value Demographic Population Value Responder Value Per centage Position in the library Dean, University Librarian, or Director 894 136 15 .2 % Associate Dean, Associate Universit y Librarian, or Associate Director 251 68 27 .1 % Assistant Dean, Assistant University Librarian, or Assistant Director 76 20 26 .3 % Head of Technology Unit 52 16 3 0.8 % Other 40 11 2 7.5 % Grand Total 1313 251 Library geographic region Nor theast 376 6 6 1 7. 6 % Southeast 318 5 8 18.2 % Midwest 303 58 19 .1 % Southwest 119 30 25 .2 % West 197 39 19.8 % Grand Total 1313 25 1 Type of institution Public Doctoral University 472 120 25 .4 % Privat e Doctoral University 256 56 2 1.9 % 2 28 37 16.2 % 3 57 38 1 0 .6 % Grand Total 1313 251 Table 9 . Nonr esponse Bias T Test Results Classification Position Total Expenditures Total FTE Geographic Region Type of Institution Doctoral Universities 0.279 0.147 0.064 0.683 0.345 Colleges and Universities 0.669 0.591 0.365 0.321 0.506 Not Significant if p > 0.05

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63 5.2 Measurement Model The me thod of p artial least squares (PLS) supported by SmartPLS version 3 was used to test the measurement and path models . This analytical approach is generally recommended for predictive research models where the emp hasis is on theory developmen t (J ö reskog & Wold , 1982). Given that there have been very few empirical studies in this research context, the f ocus was on theory development. In addition, the ability of PLS to model formative and reflective constructs (Rai e t al. , 2006) makes it appropriate that this research contain both construct types. Another feature of SmartPLS is that w hen the number of missing value is relatively small (i.e., less than 5% missing value per indicator), SmartPLS 3 uses mean value replace ment instead of case wise or pair wise deletion to treat the missing values when running the PLS SEM algorithm (Hair et al., 2017). For reflective constructs in this research, p sychometric properties including all first order and second order constructs we re assessed by examining internal consistency, convergent validity, and discriminant validity. SmartPLS calculat e s means and standard deviations for measurement items, factor loading, t statistics, cross loading, average variance s alphas, and composite reliability scores . Internal consistency was evaluated by examining score . Based on (1978 , p. 55 ) guidelines , a score of 0.70 or above for both Cronbach alpha and composite relia bility indicate a strong internal consist ency for exploratory research. For first order reflective constructs, as seen in Table 10 composite reliability ranges from 0.875 to 0.938, show ed strong internal con sistency. For second order reflective constructs, as seen in Tables 11 0.924 and composite reliability ranges from 0.920 to 0.935, show ed strong internal consistency. Convergent validity w as verified with the outer l oading of indicators and AVE . A t a

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64 minimum, the standardized outer loading of all indicators should be 0.70 or higher to signify the commonality of an item by the indicators. An AVE value of 0.50 or higher indicates that, on average, the construct explains more than half of the variance of its indicators (Hair et al. 2017). For first order reflective constructs, as seen in Table 1 0 , standardized outer loading ranges from 0. 721 to 0.92 2 and AVE ranges from 0.637 to 0.792, showing strong convergent validity. For second order reflective constructs, as seen in Tables 11 , standardized outer loading ranges from 0.773 to 0.907 and AVE ranges from 0.502 to 0.546, establishing convergent validity . To demonstrate discriminant validity , researchers have traditionally r elied on cross loadings and the Fornell Larcke r criterion approaches (Fornell & Larcker , 1981) . Specifically , an i associated construct should be greater than any of its correlation on other constructs; and the square root of highest correlation with any other construct (Hair et al. , 2017). As shown in Table 1 2 , all loadings for the first order constructs are greater than all cross loadings. Table 1 3 and Table 1 4 show that the sq uare root of AVE of a construct is greater than its correlations with other constructs for first order and second order constructs respectively. Therefore, discriminate validities are established for the constructs. Table 1 5 summarizes the systematic evalu ation stages performed for the reflective and formative constructs in this research. Figure 4 displays the factor loadings in the visual format. Construct reliability of the formative third order construct KISR was assessed by examining indicator multicoll inearity and all path coefficients from the sub constructs to KISR. High levels of multicollinearity in a formative measure is a problem because the influence of each indicator on the latent construct cannot be distinctly determined (Diamantopoulos & Sigua w , 2006). The variance inflation factor ( VIF ) values for ODDT, OTDT, and IC as

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65 predictors of KISR were calculated to be 1.88, 2.98, and 1.72 respectively; they are well below the threshold value of 5.0 specified in Hair et al. (2011), indicating the lack o f multicollinearity for the formative construct KISR. Next, all path coefficients by means of bootstrapping procedure with 1,000 subsamples for 251 cases were assessed. The results show that t hey are sizable, significant, and with the right sign for a form ative construct coefficients : ODDT > KISR ( = 0. 19 , t = 9.216 , one tailed p < 0.00 1), OTDT > KISR ( = 0. 52, t = 23.129 , one tailed p < 0.00 1), and IC > KISR ( = 0. 45 , t = 16.085 , one tailed p < 0.001). Common method variance is the variance attributed to the measurement method rather than to t he constructs the measures represent (Podsakoff et al. , 2003). It is a concern for the measurement method of Likert type scales in an SEM study. When two or more predictors measure the same underlying construct, or a facet of such construct, they are said to be collinear (Kline 2005, p. 56 ). The value of 36.93% (less than 50%) from the Harman one factor test indicates Factor 1 did not explain most of the variance ; therefore , common method bias is unlikely to be a concern in this study. Table 10 . Psychometric Properties for First Order Constructs Construct Item Mean Std dev. Loading t stats Alpha CR AVE Operand Digital Technology (ODDT) 0.893 0.922 0.702 ODDT 1 2.311 1.204 0.865 26.320 ODDT 2 2.203 1.127 0.838 28.016 ODDT 3 2.320 1.278 0.897 55.134 ODDT 4 2.422 1.393 0.783 25.076 ODDT 5 2.888 1.443 0.802 26.356

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66 Construct Item Mean Std dev. Loading t stats Alpha CR AVE Operant Digital Technology (OTDT) Inside out Capability ( IOC) 0.907 0.935 0.782 IOC 1 3.363 1.592 0.853 47.198 IOC 2 2.622 1.341 0.853 41.415 IOC 3 3.364 1.442 0.922 91.507 IOC 4 3.357 1.461 0.907 69.751 Outside in Capability (IOC) 0.830 0.888 0.665 OIC 1 2.143 1.050 0. 747 16.061 OIC 2 2.18 4 1.102 0.850 32.580 OIC 3 2.303 1.292 0.874 38.487 OIC 4 2.343 1.154 0.786 22.889 Spanning Capability (SPC) 0.863 0.907 0.710 SPC 1 2.530 1.404 0.795 24.103 SPC 2 2.028 1.148 0.813 22.497 SPC 3 2.502 1.426 0.894 52.513 SPC 4 2.769 1.398 0.864 44.339 Intellectual Capital (IC) Human Capital (HC) 0.858 0.904 0.703 HC 1 1.964 0.848 0.759 18.616 HC 2 2.610 1.107 0.823 33.905 HC 3 2.219 0.917 0.864 43.108 HC 4 2.530 1.151 0.900 71.713

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67 T Construct Item Mean Std dev. Loading t stats Alpha CR AVE Organizational Capital (OC) 0.808 0.875 0.637 OC 1 3.363 1.397 0.812 34.403 OC 2 2.248 1.044 0.721 19.439 OC 3 3.080 1.288 0.869 36.462 OC 4 2.596 1.217 0.785 23.05 3 Social Capital (SC) 0.899 0.930 0.769 SC 1 2.296 1.217 0.884 44.818 SC 2 2.344 1.117 0.917 81.009 SC 3 2.592 1.184 0.905 63.503 SC 4 2.876 1.294 0.795 27.050 Digital Platform Capabilities (DCP) Co creation Capability (CC) 0.882 0.91 4 0.679 CC 1 3.285 1.360 0.835 36.508 CC 2 3.520 1.398 0.815 30.378 CC 3 2.832 1.202 0.852 41.761 CC 4 2.444 1.127 0.795 24.501 CC 5 2.920 1.244 0.823 35.224 Analytics Capability (AC) 0.893 0.922 0.702 AC 1 3.227 1.518 0.853 42.253 AC 2 2.804 1.391 0.777 23.233 AC 3 3.084 1.444 0.865 54.060 AC 4 3.514 1.585 0.876 48.495 AC 5 4.000 1.678 0.815 33.774

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68 Construct Item Mean Std dev. Loading t stats Alpha CR AVE Service Innovation Outcomes (SIO) 0.912 0.938 0.792 SIO 1 3.127 1.425 0.912 83.910 SIO 2 3.068 1.439 0.891 61.737 SIO 3 3.149 1.405 0.895 64.422 SIO 4 2.844 1.349 0.861 41.372 Note: All t statistics for loading are higher than 10, indicating high significan ce Table 11 . Loadings, AV E , and CR for Second Order Constructs Construct Operant Digital Technologies ( OTD T) Intellectual Capital (IC) Digital Platform Capabilities (DPC) Alpha 0.924 0.908 0.903 CR 0.935 0.923 0.920 AVE 0.546 0 .502 0.536 Inside out Capability (IOC) 0.858 Outside in Capability (OIC) 0.850 Spanning Capability (SPC) 0.907 Human Capital (HC) 0.870 Organizational Capital (OC) 0.773 Social Capital (SC) 0.883 Co creation Capability (CC) 0.888 Anal ytics Capability (AC) 0.874

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69 Table 12 . Loading and Cross Loading AC CC HC IOC OC ODDT OIC SC SIO SPC AC 1 0.853 0.464 0.231 0.316 0.388 0.195 0.279 0.358 0.424 0.350 AC 2 0.777 0.418 0.304 0.444 0.397 0.291 0.347 0.356 0.400 0.412 AC 3 0.865 0.510 0.324 0.356 0.409 0.277 0.346 0.442 0.529 0.477 AC 4 0.876 0.475 0.311 0.375 0.434 0.215 0.328 0.381 0.464 0.418 AC 5 0.815 0.442 0.225 0.367 0.360 0.294 0.339 0.330 0.444 0.420 CC 1 0.487 0.835 0.361 0.357 0.370 0.336 0.454 0.414 0. 617 0.574 CC 2 0.455 0.815 0.351 0.351 0.346 0.330 0.491 0.345 0.521 0.527 CC 3 0.484 0.852 0.521 0.400 0.494 0.314 0.440 0.506 0.652 0.591 CC 4 0.422 0.795 0.367 0.356 0.396 0.208 0.423 0.405 0.475 0.493 CC 5 0.425 0.823 0.452 0.356 0.430 0.205 0.465 0.47 0 0.547 0.564 HC 1 0.239 0.409 0.759 0.282 0.358 0.225 0.203 0.448 0.373 0.326 HC 2 0.285 0.395 0.823 0.373 0.476 0.240 0.385 0.577 0.461 0.462 HC 3 0.263 0.503 0.864 0.348 0.426 0.207 0.310 0.517 0.444 0.427 HC 4 0.331 0.429 0.900 0.387 0.477 0.257 0.417 0.659 0.563 0.517 IOC 1 0.481 0.324 0.398 0.853 0.428 0.538 0.531 0.377 0.454 0.599 IOC 2 0.321 0.410 0.314 0.853 0.375 0.618 0.489 0.310 0.418 0.539 IOC 3 0.393 0.397 0.395 0.922 0.363 0.614 0.472 0.384 0.518 0.606 IOC 4 0.362 0.312 0.367 0.907 0.329 0.62 6 0.484 0.366 0.520 0.584 OC 1 0.330 0.465 0.327 0.316 0.812 0.252 0.338 0.360 0.320 0.376 OC 2 0.346 0.359 0.571 0.312 0.721 0.178 0.365 0.451 0.507 0.472 OC 3 0.439 0.422 0.352 0.357 0.869 0.277 0.315 0.388 0.395 0.449 OC 4 0.395 0.241 0.384 0.357 0.785 0.312 0.356 0.437 0.457 0.435

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70 AC CC HC IOC OC ODDT OIC SC SIO SPC ODDT 1 0.362 0.178 0.214 0.569 0.217 0.865 0.360 0.234 0.331 0.443 ODDT 2 0.212 0.275 0.174 0.556 0.224 0.838 0.361 0.212 0.336 0.437 ODDT 3 0.212 0.381 0.229 0.614 0.261 0.897 0.415 0.229 0.457 0.491 ODDT 4 0.287 0.334 0.316 0.517 0.308 0.783 0.440 0.234 0.423 0.540 ODDT 5 0.310 0.541 0.222 0.574 0.312 0.802 0.474 0.223 0.458 0.525 OIC 1 0.385 0.441 0.364 0.451 0.404 0.370 0 .747 0.365 0.473 0.524 OIC 2 0.323 0.418 0.308 0.445 0.376 0.386 0.850 0.278 0.389 0.588 OIC 3 0.330 0.407 0.306 0.438 0.367 0.410 0.874 0.363 0.416 0.597 OIC 4 0.240 0.450 0.331 0.489 0.269 0.437 0.786 0.310 0.381 0.587 SC 1 0.414 0.450 0.564 0.317 0.449 0.176 0.312 0.884 0.561 0.473 SC 2 0.348 0.453 0.616 0.325 0.478 0.241 0.312 0.917 0.548 0.474 SC 3 0.430 0.494 0.584 0.381 0.489 0.240 0.352 0.905 0.546 0.537 SC 4 0.377 0.424 0.559 0.410 0.394 0.299 0.448 0.796 0.495 0.514 SIO 1 0.503 0.620 0.534 0.539 0 .492 0.483 0.482 0.617 0.912 0.675 SIO 2 0.464 0.554 0.452 0.513 0.461 0.447 0.460 0.562 0.891 0.632 SIO 3 0.487 0.641 0.490 0.473 0.478 0.423 0.450 0.502 0.895 0.598 SIO 4 0.472 0.622 0.497 0.393 0.468 0.358 0.411 0.496 0.861 0.571 SPC 1 0.339 0.524 0.544 0.454 0.465 0.362 0.573 0.570 0.570 0.795 SPC 2 0.351 0.534 0.381 0.496 0.418 0.497 0.601 0.369 0.465 0.813 SPC 3 0.433 0.608 0.458 0.624 0.445 0.574 0.598 0.441 0.641 0.894 SPC 4 0.535 0.579 0.391 0.629 0.518 0.522 0.606 0.541 0.662 0.864 Notes: AC = an alytics capability; CC = co creation capability; HC = human capital; IOC = Inside out capability; OC = organizational capital; ODDT = operand digital technologies; OIC = outside in capability; SC = social capital; SIO = service innovation outcomes; SPC = s panning capability

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71 Table 13 . Intercorrelations and of Latent Variables for First Order Constructs 1 2 3 4 5 6 7 8 9 10 Analytics Capability (AC) 0.838 Cocraetion Capability (CC) 0.552 0.824 Human Capital (HC) 0.334 0.499 0.838 Inside out Capability (IOC) 0.441 0.442 0.4 18 0.884 Outside in Capability (OIC) 0.475 0.495 0.521 0.422 0.798 Operand Digital Technology (ODDT) 0.302 0.340 0.278 0.677 0.318 0.838 Outside in Capability (OIC) 0.390 0.551 0.400 0.559 0.433 0.492 0.815 Social Capital (SC) 0.447 0 .520 0.663 0.407 0.518 0.271 0.403 0.877 Service Innovation Outcomes (SIO) 0.541 0.685 0.555 0.541 0.534 0.483 0.507 0.613 0.890 Spanning Capability (SPC) 0.496 0.668 0.523 0.659 0.548 0.585 0.705 0.569 0.697 0.842 Notes: Diagonal values (in bold fon t) are square roots of AVEs. Off diagonal values are corrections. Table 14 . Intercorrelations and for Second Order Constructs 1 2 3 Digital Platform Capabilities (DPC) 0.732 Intellectual Capital (IC) 0.620 0.709 Operant Digital Technology (OTDT) 0.652 0.622 0.739 Notes: Diagonal values (in bold font) are square root of AVEs. Off diagonal values are correlations.

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72 Table 15 . Systematic Evaluation of the Constructs Reflective Construct Ev aluation Criteria Evaluation Criterion a Criterion b Internal consistency a lpha > 0.70 Composit e reliability > 0.70 Convergent validity Outer loading > 0.70 Average variance extracted > 0.50 Discriminant validity Cross loading highest on the associated construct Fornell Larcker square root of average variance extracted > highest correlation Formative Construct Evaluation Criteria Composite reliability Multicollinearity test < 5.0 Path coefficients significant with right sign Figure 4 . Factor Loadings 5.3 Structural Model KISR affects SIO directly as specified in H 1 and affects DCP directly as specified in H 2 .

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73 DCP contributes to SIO positively as specified in H 3 . In addition, DCP mediates the relati onship between KISR and SIO as specified in H 4 . The b ootstrap procedure with 1,000 subsamples for 251 cases was performed to test significance . The results are shown in Figure 5 . All path coefficients we re positive, indicating positive relationships betwee n the predictor and the dependent variable hypothesized . coefficients ( strong = 0.50, medium = 0.30, small = 0.10), there is a strong and significant impact t = 8.128, one tailed p < 0.001), indicating that H 1 is supported . KISR t = 17.543, one tailed p < 0.001), demonstrating that H 2 i s supported . t = 5.227) and statistically significant (one tailed p < 0.001), therefore H 3 is supported. To test the mediating effect of DPC on SIO (H 4 ) , Hair et al. (2017) suggest the approach of bootstrapping the sampling distribution of the indirect effect. Prior testing often uses (1982) test , which assumes a normal distribution that is not consistent with the nonparametric PLS SEM method. However, b ootstrapping makes no assumptions a bout the shape of t he and is thus more suited for the PLS SEM method . The direct effect from KISR to SIO was pronounced as 0.5 2 and significant ( t = 8.3 9 , p < 0.0 01 ) , and the indirect effect was also pronounced as 0. 24 and significa nt ( t = 5. 2 3 , p < 0.0 01 ). Neither of the 95% confidence intervals include d zero. Since the direct and indirect effects were both positive, and the sign of their product was also positive (i.e. , 0.52 x 0.24 = 0.1248), the conclusion is that DPC partially me diates the rel ationship between KISR and SIO . In other words, DPC represents complementary mediation of the relationship from KISR to SIO. H 4 is therefore partially supported. Innovation varies across firm size; this is because large firms have an alleged advantage

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74 in innovation (Rogers , 2004). In the context of academic libraries , larger ones may inherit larger foundational funds from their parent institutions or have a larger fiscal budget. They may also have more human resources at their disposal. On th e other hand, larger academic libraries may be less flexible in directing their funds and resources for service innovation. To better explain the differential impact of KIS R on building DPC, this study minimizes the confoundin g effects by having the librar EXP The coefficient for the path from EXP to KISR, DCP, and SIO are all small and not significant EXP to KISR ( = 0.00 0 , t = 0. 04 7 , p > 0.05 ), EXP to DCP ( = 0.019 , t = 0. 33 4 , p > 0.05 ), and EXP to S IO ( = 0.0 37 , t = 0.826 , p > 0.05 ). resources. The coefficient for the path from FTE to KISR, DCP, and SIO are all small and not sig nificant FTE to KISR ( = 0.00 3 , t = 1. 269 , p > 0.05 ), FT E to DCP ( = 0.0 38 , t = 0. 597 , p > 0.05 ), and FTE to SIO ( = 0.012 , t = 0. 26 9 , p > 0.05 ). These coefficients indicate that neither the size of fiscal budget nor the number of employees affect ed ability to innovate. R 2 value is a measure because it represents the . That is, i t denotes constructs Therefore, t he higher the value, the higher the level of the predictive accuracy . DPC explains 48% variance , whi le SIO explains 62% of variance. T hey are both co nsidered moderate , based on the rule of thumb where R 2 values of 0.75, 0.50, or 0.25 be respectively described as substantial, moderate, or weak (Hair et al., 2011) . Table 1 6 displays the direct and indirect effects analys e s for H 4 . Figure 5 displays the testing results of the hypothesized path model with the control variable in the graph, while T able 1 7 su mmarizes the results in the tabular format.

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75 Table 16 . Analysis of the Indirect Effects Direct Effect 95% CI of Direct Effect t Value p < 0.05? Indirect Effect 95% CI of Indirect Effect t Value P < 0.05? KISR > SIO 0. 52 0 [0. 401 ,0. 645 ] 8.385 Yes 0.239 [0.149,0.325] 5.230 Yes Figure 5 . Testing of the Hypothesized Path Model with Control Variables Table 17 . Hypotheses Summary H ypothesis Position Hypothesis 1: The possession of critical knowledge intensive service resources has a positive association with service innovation outcome s in an academic library. Supported

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76 Hypothesis Position Hypothesis 2 : The greater the ability to integrate knowledge intensive services resources, the more able an academic library will be to build digital platform capabilities. Supported Hypothesis 3: Digita l platform capabilities have a positive association with servic e innovation outcomes in an academic library. Supported Hypothesis 4: The impact of knowledge intensive service resources on service build digital platform capabilities. Partially Su pported ; DPC represents complementary mediation from KISR > SIO

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77 CHAPTEER V I . FINDINGS AND DISCUSSION 6. 1 Findings This research examines four research questions: (1) What are the critical resources for service innovation? (2) How do digital technolo gies interact with other resources to build digital platform capabilit ies for service innovation? (3) How do digital platform capabilities contribute to service innovation? and (4) Do digital platform capabilities mediate the impact of resources on service innovation ? Based on the S D logic perspective, this research hypothesizes that intellectual capital and digital technologies are considered essential knowledge intensive service resources and are critical to service innovation in academic libraries (H 1 ). Digital technologies serve as tangible operand resources; they are also operant resources displaying multi dimensional inside out, outside in, and spanning capabilities. When these digital technology resources are integrated, applied, and made use of by i ntangible human, organizational, and social resources, they build digital platform capabilities (H 2 ). The platform provides an environment where co creation between library staff and users happens fluidly, and the platform provides data analytics that can be gauged to develop service innovation in academic libraries (H 3 ). This research also hypothesize s that the effect of knowledge intensive service resources on innovation is mediated by the academic library having a digital platform with capabilities, that is, the more digital platform capabilities, the more the service innovation outcomes (H 4 ). Through empirical research, using the survey sample results from library administrators, the data support H 1 and found a direct association between the possessions of knowledge intensive services to service innovation. The data support H 2 through finding a direct association between knowledge intensive service resources and digital platform capabilities. The data support H 3 through finding a direct association betwee n digital platform capabilities and service innovation. However, the

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78 data found that digital platform capabilities present complementary mediation effect from knowledge intensive service resources to service innovation outcomes. This finding is not surpris ing because the concept of a digital platform is new to academic libraries. It has only been recently promoted by the Institute of Museum and Library Services (https://www.imls.gov/) and described in one research article by Weinberger in 2012. Although man y special collections in academic libraries have been digitized and the data made available through digital library platforms, a tremendous amount of treasures are still hidden behind library walls due to copyright clearance difficulti e s and slow digitizat ion efforts. In addition, academic libraries are often short of developers to fully enable digital platform capabilities with API s and other add ons. Academic libraries also tend to be intimidated by data security concerns , which lead them to stay away fro m exploring and experimenting with hosted data solution opportunities . The survey sample data show that operand digital technology was considered the least important knowledge intensive service resource, whereas operant digital technology was more impor tant than intellectual c apital . This is understandable because most academic libraries do not maintain their own servers or network s ; instead, they leverage operant outside in capability , such as external digital technology resources , that the parent institution or vendors m ay offer . Fu rthermore, a c apability as more important than inside out and outside in capabilities , with administrators ving a good working relationship with their digital technology personnel as the most critical capability . The survey sample data indicate that a cademic libraries consider social capital as the most important intellectual capital. This is also understandable base d on th e evidence that an increasing number of consortia has been established in the nation and more and more coalitions,

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79 conferences, and unconferences ha ve been convened by the libraries. Academic libraries are known to be collaborator s ; in a time of dwi ndling finance , administrators are capitalizing on each sources and employees a nd collaborating wit h each other to solve problems (Yeh & Walter , 2016) . Library administrators view library employees a s more assertive in providing answers and feed b ack to users rather than the other way around. Administrators also believe that although usage data of all kinds are collected, those data are in less consistent and visible forms and libraries are less likely to have data visualization mechanisms and othe r means to produce systemic reporting. This analytics capability deficiency is understandable because most of the library data have been embedded in an integrated library system which is mainly capable of operational reporting rather than analytical report ing . Only in the last few years have library system vendors and developers begun incorporating advanced business analytics suites into their next generation library services platform , replacing the legacy integrated library systems . Library administrators perceive that they meet This perception reflects equally on their less favorable evaluation of receiving feedback and ideas from the ir users ; therefore, they may not be ke en to understand whether and how they meet the Appendix E lists the survey descriptive statistics. 6.2 Research Impl ication s This study contributes to the S D logic perspective by identifying, operationalizing, and measuring key premises fo r creat ing knowledge intensive service resources and build ing digital platform capabilities to increase service innovation outcomes. There are five contributions by this research to IS literature as the first study to : (1) recognize the analogousness betwee n S D technologies as the critical resources for knowledge intensive service organizations , (3) propose

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80 the bundle of these critical resources as know ledge intensive service resources to build digital platform capabilities, (4) present an integrative model of knowledge intensive service resources that builds digital platform capabilities for service innovation, and (5) empir ically validate the model in the context of academic libraries. This research is also the first study that applies the MIS discipline to an understanding of service innovation phenomenon in the LIS field. Although S D logic has received a lot of attention since the first decade of thi s century, the intangible resources including competencies ( Shaw et al. , 2011), organizational procedures (Barqawi et al. , 2016 ) , and social activities (Blasco Arcas et al. , 2014 ; Edvardsson et al., 2011 ) have been studie d as isola ted elements and mainly in the for profit sectors . B y consolidating them into intellectual capital and operationaliz ing and empirically validating that combined factor in academic libraries , this research contributes to the applicability of S D logic and i ts relevance to the non profit organizational context . While S D logic emphasizes digital technolog y as an operant resource , this research reminds the reader and advocates for digital technology as both an operand and an operant resource. As op erand resources, digital technologies act as facilitators, whereas as operant resources, they act as initiators for serv ice innovation (Nambisan , 2013). By bundling digital technologies with intellectual capital as knowledge intensive service resources to build digital platform capabilities, this study contributes to both strategic management and the IS literature on platform research . In a resource based approach , rare k nowledge embedded in employees within a business organization is trumpeted as c ompetit ive advantage; however, the knowledge in a knowledge intensive service in the digital age has no border and is not cons trained within the organization . In fact, the unbound knowledge is considered to give a competitive advantage and can be

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81 enrich ed further with external knowledge through value co creation. Co creation is uniquely assisted by digital platform capabilities which unlock the collective wisdom in user communities by means of digital technologies (Karimi & Walter , 2015). Exemplar y co c reation through digital platform capabilities can be seen in the newspaper industry , where citizen journalism is provided through crowdsourcing (Karimi & Walter , 2015). Similarly, i n academic libraries, crowdsourcing functionality is offered in a digital l ibrary, representing successful co creation of and by means of digital platforms. Besides co creation , this research extends and v alidates digital platform capabilities to include analytics capability , which has become possible owing to big data affordance . As digital data becomes a daily routine , they will be increasingly seen as valuable assets for digital transformations when explored and analyzed to support strategic dir e ctions. Th is research has constructed a novel research framework that demonstrates how the S D logic perspective is applicable to investigate digital service innovation in knowledge intensive service s . Aside from tangible materials, intangible r esources interact with each other and at times are bundled together to create service innovat ion. Digital technologies strength en the bond to streamline processes and relationships to create new opportunities (Sa mbamurthy et al. , 2003). T his research recognizes academic libraries as knowledge intensive ser vice organizations and the role s knowledge plays in academic libraries. This research contributes to the LIS field as the first study examining service innovation in academic libraries through the lens of the MIS reference discipline. The LIS field applies the practice and perspective of knowledg e and information to answer questions related to the activities of target groups (Lugya , 2014 ), and the discipline educates academic librarians to successfully lead librar y organization s in this digital age. The MIS

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82 discipline instills an understanding of technology uses in an organization, the way people interact processes (Oinas Kukkonen , 2010). As i mportant entities in the knowledge economy , l i braries are facing the sa me digital interruption a s other organizations . The synergy between LIS and MIS disciplines is apparent . By applying the theoretical model development rigor in MIS, this research delineates future research avenues for LIS scholars att empting to contribute to theory development . By empirically validating the model of service innovation in the digital age, this research helps close the gap of innovation research in the LIS field , which generally uses case study research methodology. 6.3 Practice Implications and Recommendations Academic libraries are at a crossroad s with dwindling budget ary support f rom the ir parent institution s. I n the meantime, academic libraries are expected to service and meet the in digital technolog ies and the diffusion of mobile devices contribute to a digital society that has brought both e xcitement and challenge to academic libraries. The excitement is with the unprecedented spe ed and method of information dissemination that enables academic libra ries as knowledge intensive service provide r s to help expand knowledge and encourage rapid new discoveries. At the same time , th is very excitement also brings challenge s that force academic libraries to fulfill that are drastically different f rom traditional needs . A fundamental question is thus how academic libraries can maintain their relevance (Campbell , 2017) and overcome disruption from digital technologies . Yeh and Walter (2016) suggest that academic libraries adapt and innovate rather th an run away from the challenge when encountering technological disruption . Rein (2007) urges academic libraries to adjust to and accept t he fact of techno based tools as the information resources of

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83 choice. Both approaches are plausible. This study recomme nds adaption and adjustment by examin ing how a complex academic library create s service innovation in this digital age . Th e research model and results here will help library administrators view digital technologies in service innovation affirmatively and i n a different light. Aside from printed, electronic , and digital materials as knowledge assets in an academic library , other knowledge assets reside in employee s and user communities as well as within the library rules and procedures . In addition, knowle dge asse ts are generated from interactive and social activities . Such knowledge is termed intellectual capital , taking the form of tacit or explicit knowledge that should be captured and preserved as current or historical organizational knowledge and disse minate d . Library administrators should invest in digital technologies, especially considering the application of social technologies that boosts desirability of documenting such knowledge. This widespread social usage of digital technology presents a great opportunity for libraries , as argued by Kwanya et al. (2015 , p. 4 ) for information that is ultimately a conversation sought and used in a social , active, contextual, personalized, and connected environment . Intellectual capital and digital technologies a r e identified as the critical resources for knowledge intensive service resources in the digital age. Their dimensional content of human capital, organizational capital, social capital, operand digital technology, and operan t digital technology collectively provide a roadmap for library administrators to assess their resource needs. Owning and having access to these resources , however, are just the first step; recognizing and applying these resources, especially the digital technology resource in a new, more agile frame is the critical second step. Technologies are more than infrastructure or processors ; they can initi ate and start up a process or project. Therefore, instead of i nquiring what we can do to a

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84 piece of digital technology, a better question would be , W hat can this piece of digital technology do for Such a different mindset enables library administrators to actively find and put to use digital technologies capable of providing innovative service s and experiment with them . Business es cha nge their strateg ies to fit the digital platform economy and utilize digital platform capabilities (Kabakova et al. , 2016) . So should ac ademic libraries. What are academ ic libraries digital strateg ies ? To answer this question, academic libraries must come to full realization t hat most library collections are being accessed online , and , as a historical change, library services now need to be delivered online ( , 2011 ). In other words , library administrators should strive to build and recognize digit al platform capabilities as the mainstream approach to servicing library users. Digital platform s are where actions transpire among library employees , users, and between library employees and users to co create value leading to service innovation. The d igi tal platform is also where usage data of all sorts exist to be harvested and analyze d to create a better understanding of emerging realities . Building, nurturing, and maintaining digital platform capabilities are admittedly difficult tasks. How do academi c libraries accomplish th ese task s ? Library administrators should invest in acquiring individuals with diverse establishing proper knowledge management mechanism s to collect and make accessible institutio nal knowledge as well as encourag e knowledge sharing within the librar y and with other libraries . Library administrators should develop or acquire the librar bounty access to digital technology i nfrastructure and carefully plan to sustain such an inves tment. These technologies in the forms of network, hardware , database, mobile, scanners, software, and applications a re part of knowledge sharing and exploitation.

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85 Digital platform capabilities also leverage digitalization derived from large scale digitiz ation effort (Cenamor et al. , 201 7 ). Academic libraries have devoted considerable work to digitiz ation in the past few decades. Many notable projects are the ultimate result of applying employee expertise, institutional policies and procedures, digital inf rastructure, digital equipment , and application software to digitize collections. These collections are then made available on a digital platform , becoming a digital library whereby users can access the content electronically. Exemplar y digital libraries r ange from national, state, to individual librar ies including HathiTrust (national) , DigitalNC (state), and ScholarSpace (individual library ) . Although the content is centrally hosted, when it is considered to be in the public domain, the content is giving back to the users to slice, use, and reuse in whatever way they find most beneficial . At the institutional level, a digital library is an institutional repository where users can self deposit content exemplifying co creation capability. Whe n crowdsourcing is applied to such digital libraries, it is a form of uberization , a term put forward by Andro and Saleh ( 2017 ). Crowdsourcing also depicts the form of digitalization by Yoo (2012 ) , when the encoding of information into a digital format results in the reco nfiguration of production and the use of a product or service. This reconfiguration in the socio technical context is amplified by digital technologies and social media capabilities . Library administrators should continuously invest in digitization efforts to collections and to fulfil l service mission through the concept of Library as Platform . After all, Levine Clark ( 2014) ns that help distinguish it from other s . In other words, providing digital special collections should be considered a strategic direction for the library to take to gain a competitive advantage in the digital age.

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86 In a dministrators should enhance the value of the co creation process with users and promote a data driven culture in the library. Academic libraries and their employees have been known to be service orient ed and user friendly. The survey data show that library employees are less than optimally active in Is this behavior simply because the suggestion cannot be followed ? Value co creation is a process that source s inputs into a continuous feedback process; as such, a hard to follow suggestion may turn into a can do project. As to the data driven culture, t he quest is more than to manage the data well but to understand how data manage the library organization with in the context of a mission that is value based. Does everything need to be measured? How do the numbers support objectives? Most library assessment is performed by a select few in current practice; howeve r, cultivating data literacy in the library to promote ongoing di scussion s about the metrics among library employees will result in further input to improve the data metrics. The end goal should be understood by all library employees about what the library is accomplishing and where it is heading. 6. 4 Limitations The sample population in this research includes academic libraries in all d octorate granting universities and in the United States based on the Carnegie Classification of Institutions of Highe r Education. The breadth of the s ample suggests that the findings are generalizable to many other academic libraries. However, similar to any empirical research, t here are specific strengths and limitations to this study. F irst, the survey questionnaire was directed at top library admini strators because they have the most knowledge for answer ing s , alliances, and service innovation status . Still, they may not be familiar with the state of the

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87 digital technology infrastructure. T h e responses might have been different if technology managers were the respondents . In retrospect, the part of questionnaire on digital technologies should have been cross validated specifically with technology man a gers to increase confidence in the final r esults. Second, this study is the first attempt to conceptualize and operationalize the proposed construct s knowledge intensive service resources and digital platform capabilities for the knowledge intensive service sector . Alt hough the focal constructs a nd sub constructs are adapted from previous research and subsequently validated in the context of academic libraries, further adaptation is needed for them to be used in other sector s . Furthermore, all measures are self re ported and thus subject to respons e bias. Email reminders should have been used to further minimize nonresponse bias. Lastly, the survey research applied in the study is cross sectional in nature . It is applied s knowledge intensive service resources, digital platform capabilities, and servi ce innovation outcomes ; t herefore, the internal validity of this study is difficult to determine . 6. 5 Future Research T his research has investigate d phenomen a with a tech nology focus. There are , nevertheless , other factors contributing to service innovation including but not limited to organizational lea dership and innovation culture . F uture studies might be expanded to consider t hese other factors . Additionally, f or ms of service innovation vary and include conceptual, administrative, or radical innovation; therefore, f uture studies c ould be framed to validate th e research model based on type s of service innovation. Because the construct knowledge in tensive s ervice is conc eptualized the first time and the nature of its relationships with the other constructs investigated is exploratory , future studies may

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88 apply qualitative method s , such as the Delphi and case study approaches , to confirm or refine the constructs and their r elationships . In a ddition, a mechanism may be built in to probe the respondents who select A t the very least, this research m odel can be adapted to validate findings in other knowled ge intensive service context s engineering consulting firms, legal firms, financial firm s, any and all for profit knowledge intensive . To contribute further to the LIS discipline, t he model can be adap ted and validate d in diverse types of libraries , including those at c ommunity college and public libraries , as well as for different educational levels, for example, in undergraduate and associate degree program s . Although digital technologies are embraced by all types of l ibraries, different mission s will likely re quire different perspectives in integrat ing digital technologies to fulfill a library mission. Finally, the research model used here can also be validate d i n other countries to gain a global pe rspective on the questions of how digital technologies are being applied to and changing academic libraries and other for and nonprofit organizations .

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114 Yoo, Y. (2010) . Computing in everyday life: A call for research on experiential computing. M IS Quarter ly , 34 (2), 213 231 . Yoo, Y. (2012) . Digital m ateriality and the e mergence of an e volutionary s cience of the a rtificial . Materiality and Organizing , 11 , 134 154. Yoo, Y. , Henfridsson, O., & Lyytinen, K. (2010) . The new organizing logic of digital innovati on: An agenda for information systems research. Information Systems Research, 21 (4), 724 735 . Yoo, Y., Lyytinen, R. J. B., & Majchrzak, A. (2012). Organizing for innovation in the digitized world. Organization Science, 23 (5), 1398 1408. Youndt, M. A., S ubramaniam, M., & Snell, S. A. ( 2004 ) . Intellectual c apital p rofiles: An examination of i nvestments and r eturns . Journal of Management Studies , 41 ( 2), 335 361. Zeithaml, V. A. ( 1981 ) . How c onsumer e valuation p rocesses d iffer b etween goods and s ervices . I n J.A. Donnelly & W. R. George (Eds . ), Marketing of Services, American Marketing Association (pp. 186 90). Chicago, IL : American Marketing Association . Zhang, M., Sarker, S., & Sarker, S. ( 2008 ) . Unpacking the e ffect of IT c apability on the p erformance of e xport f ocused SMEs: A report from China. Information Systems Journal , 18 ( 4), 357 380. Zittrain, J. L. ( 2006 ) . The g enerative Internet. Harvard Law Review , 119 ( 7), 1974 2040. Zittrain, J. L. (2010). The future of the i nternet a nd how to stop it. New Hav en , CT : Yale University Press.

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115 A P P ENDIX A. CONSTRUCT DEFINI TION Construct Definitions Source Human capital Comprises all business capital embedded in employees and not owned by the organization. Hsu and Fang (2009) Organizational capital Knowledge and codified experience residing within databases, manuals, culture, systems, structures, and processes. Edvinson and Malone (1997 , p. 35 ); Chen and Shih (2009); Issac et al. (20 10 ) Social capital Knowledge embedded within networks of relationships and i nteractions among individuals or social units. Nahapiet and Ghoshal (1998); Youndt et al. (2004) Inside out capability Deployed from inside the firm in response to market requirements and opportunities and tend to be internally focused (e.g., technology development, cost controls). Day (1994) ; Wade and Hulland ( 2004 ); Cai et al. ( 2016 ) Outside in capability Externally oriented, placing an emphasis on anticipating market requirements, creating durable customer relationships, and understanding competitors . Day (1994); Tippins and Sohi ( 2003 ); Wade and Hulland ( 2004 ) Spanning capability Involve both internal and external analysis, are needed to integrate the firm's inside out and outside in capabilities. Day (1994) ; Wade and Hulland ( 2004 ); Cai et al. ( 20 16 ) Co creation capability As dialogue co creation, emphasize that dialogue refers to learning and communication between companies and customers two equal problem solvers rather than to merely listening to customers. Prahalad and Ramaswamy (2004) Analy tics capability The deployment of sophisticated technological applications that provide managers with information and the ability to plan and execute decisions. Iyer (2011) ; Lenka et al. ( 2017 )

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116 APPENDIX B . CONSTRUCT OPERATIONALIZATION Construct Indic ators Items Scale Source Adapted Intellectual Capital (IC) ( Reflective ) Human capital (HC) HC 1 Overall, o ur employees are skilled in their functions . HC 2 Overall, o ur employees are considered competent. HC 3 O verall, o ur employees are creative. HC 4 Our employees are considered experts in their jobs and functions. HC 5 Our employees develop new ideas and knowledge. Likert 1 7 1 = strongly disagree, 4 = neither agree nor disagree, 7 = strongly agree Hsu and Sabherwal (2011) ; Subramaniam and Youndt (2005 ) Organizational capital (OC) OC 1 Much of our collective knowledge is documented in manuals, intranet, or databases. OC 2 Much of our collective knowledge is embedded in our organizational culture . OC 3 Our library culture contains valuable ideas and wa ys of servicing users . OC 4 Our library uses documentation as a method to store organizational knowledge . OC 5 Our employees can effectively utilize manuals, intranet, or databases . Chen et al. (2014); Hsu and Sabherwal (2011) ; Subramaniam and Youndt (200 5 ) Social capital (SC) SC 1 Our employees are skilled at collaborating with each other to solve problems. SC 2 Our employees share information to l earn from each other . SC 3 Our employees exchange ideas from different areas of the library . SC 4 Our employee s apply knowledge from one areas to problems in different areas of the library . Subramaniam and Youndt (2005)

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117 SC 5 Our employees collaborate with other libraries to develop solutions . Operand Digital Technology (ODDT) (Reflective) ODDT 1 Our library has appropriate hardware infrastructure . ODDT 2 Our library has appropriate network infrastructure . ODDT 3 Our library has necessary servers and databases technologies . ODDT 4 Our library has appropriate scanners for digitization tasks. ODDT 5 Our library ha s the necessary mobile devices. ODDT 6 Overall, our library has appropriate digital devices. Karimi et al. (2007) Operant Digital Technology (OTDT) (Reflective) Inside out capability (IOC) IOC 1 Our library has appropriate data architecture (i.e., polic ies, rules or standards that govern which data is collected and how it is stored, arranged, and used) . IOC 2 Our library has appropriate network architecture (i.e., how computers or servers are organized in a system and tasks are allocated between these co mputers or servers) . IOC 3 Our library has appropriate data processing capability . IOC 4 architectures are flexible . IOC 5 architectures are flexible . Likert 1 7 1 = strongly disagree, 4 = neither agree nor disagree , 7 = strongly agree Bharadwaj et al. (1999); Cai et al. (2016) Outside in capability (OIC) OIC 1 Our library uses digital technologies that link us with our users (e.g., SMS, social media, blog, or RSS) . OIC 2 Our library uses digital technologies that link us with our vendors (e.g., WebEx, GoToMeeting, or Adobe Connect) . OIC 3 Our library uses digital technologies that link us with other libraries when collaborating (e.g., Skype, Google Hangout, or Wiki) . Bharadwaj et al. (1999); Cai et al. (2016); Zhang et al. (2008 )

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118 OIC 4 Our library leverages external digital techn ology resources (e.g., enterprise technologies that the institution offers) . Spanning capability (SPC) SPC 1 Our library has teams with blended digital and non digital tech nology expertise . SPC 2 a good working relationship with their digital technology personnel . SPC 3 There is a climate that nurtures digital technology projects . SPC 4 Our library restructures workflow processes to leverage digital technology opportunities . Digital Platform Capability (DPC) (Reflective) Co creation capability (CC) CC 1 Users are actively engaged in providing information for service innovation . CC 2 Users actively give suggestions via various approaches. CC 3 Users give lots of feedback for the new ideas. CC 4 Our employees explain the ideas in a meaningful way to users . CC 5 Our employees offer users as much information as they need . CC 6 Our employ ees actively provide information to reply to . CC 7 Our employees assert continuous communication with users . Likert 1 7 1 = strongly disagree, 4 = neither agree nor disagree, 7 = strongly agree Hsieh and Hsieh (2015) Analytics capabil ity (AC) AC 1 Our employees collect usage data. AC 2 Our library makes thee collected data consistent, visible, and easily accessible for further analysis . AC 3 Our library keeps the collected data in appropriate data storage . AC 4 Our employees identify tr ends from the data that lead to insights . Gupta and Georg (2016)

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119 AC 5 Our employees predict patterns of library use from the data . AC 6 Our library has systematic and comprehensive reporting from the data . AC 5 Our library has data visualizations that allow for easy interpretation of the data . Service Innovation Outcomes (SIO) (Reflective) SIO 1 Our institution possesses all the necessary conditions for adopting service innovation. SIO 2 Our institution is well prepared for adopting service innovation. S IO 3 Our institution possesses all the necessary conditions for creating service innovation. SIO 4 Our institution is well prepared for creating service innovation. SIO 5 Compare to other libraries, our service innovation is perceived to eeds. SIO 6 Compare to other libraries, we are perceived to produce more successful service innovation . SIO 7 Compare to other libraries, our service innovation is perceived to mission. SIO 8 Compare to other libraries, we are perceived to have better overall service innovation performance . Likert 1 7 1 = strongly disagree, 4 = neither agree nor disagree, 7 = strongly agree Wang et al. (2010); Yen et al. (2012 )

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120 APP E NDIX C . S OLICITATION E MAIL Dear Dean : My name is She ila Yeh, a graduate student at the University of Colorado Denver Business School in the process of completing my PhD dissertation titled Service Innovation for Knowledge Intensive Services in the Digital Age: The Case of Academic Libraries . As part of thi s work I examine how digital technologies integrate with other intangible resources, such as skills, knowledge, organizational procedures, and collaboration within and with other libraries to create service innovation. This investigation proposes a digita l service innovation model which requires empirical validation, thus the reason for me to write to you today. As an Assistant University Librarian for the Library Information Technology at the University of I am inviting my academic lib rary administrative colleagues to participate in an online survey. Your participation not only assists me with my dissertation endeavors, but will also help fellow library administrators apply digital technologies more effectively to meet their institutio This survey requires only 15 20 minutes of your valuable time to complete and is mobile friendly. Your responses are recorded as you progress, making it possible to complete in one or more sessions. There is no compensation for participati ng, nor is there any known risk. Participation is strictly voluntary and you may opt out at any time. Responses to this online questionnaire are anonymous and your contact information remains confidential. It will not be used in the data analysis or for any other purposes. I appreciate your interest and thank you for providing your insights when completing this survey. If you have any questions concerning this research study or would like to have a copy of the study results, please e mail me at: sheil a.yeh@ucdenver.edu, or sheila.yeh@hawaii.edu. This research has been approved by the Colorado Multiple Institutional Review Board at the University of Colorado Denver | Anschutz Medical Campus, protocol #17 0153. This survey will be available until Jan uary 31, 2018 and may be accessed online via this link: https://ucdenver.co1.qualtrics.com/jfe/form/SV_51I378cS8xEnK5f . Sincerely, Sheila Yeh Ph.D. Candidate University of Colorado | Business School

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121 APP E NDIX D . SURVEY ITEMS Strongly agree = 1 Agr ee = 2 Somewhat agree = 3 Neutral = 4 Somewhat disagree = 5 Disagree = 6 Strongly disagree = 7 Please indicate how much you agree or disagree with each of the following statements as they apply to your library by selecting a single option for each st atement. Operand Digital Technology (ODDT) ODDT1. Our library has appropriate hardware infrastructure. ODDT2. Our library has appropriate network infrastructure . ODDT3. Our library has necessary servers and databases technologies. ODDT4. Our librar y has appropriate scanners for digitization tasks. ODDT5. Our library has the necessary mobile devices. *Overall, our library has appropriate digital devices. Operant Digital Technology (OTDT) A. Inside out Capability (IOC) IOC1. Our library has approp riate data architecture (i . e., policies, rules or standards that govern which data is collected and how it is stored, arranged, and used). IOC2. Our library has appropriate network architecture (i . e., how computers or servers are organized in a system and tasks are allocated between these computers or servers). is flexible. is flexible. *Our library has appropriate data processing capability. B. Outside in Capability (OIC) OIC1. Our library used digital technologies that link us with our users (e.g., SMS, social media, blog, or RSS). OIC2. Our library use s digital technologies that link us with our vendors (e.g., WebEx, GoToMeeting, or Adobe Connect). OIC3. Our library use s digital technologies that link us with other libraries when collaborating (e.g., Skype, Google Hangout, or Wiki). OIC4. Our library leverages external digital technology resources (e.g., enterprise technologies that the institution offers). C. Spanning Capability (SPC) SPC1. Our library has teams with blended digital and non digital technology expertise. technology personnel. SPC3. There is a climate that nurtures digita l technology projects. SPC4. Our library restructures workflow processes to leverage digital technology opportunities. Intellectual Capital (IC) A. Human Capital (HC) HC1. Overall, our employees are skilled in their functions. HC2. Overall, our employe es are creative.

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122 HC3. Overall, our employees are considered experts in their jobs and functions. HC4. Overall, our employees develop new ideas and knowledge. *Overall, our employees are considered competent. B. Organizational Capita l (OC) OC1. Much of our collective knowledge is documented in manuals, intranet s , or databases. OC2. Our library culture contains valuable ideas and ways of servicing users. OC3. Our library uses documentation as a method to store organizational knowled ge. OC4. Our employees can effectively utilize manuals, intranet, or databases. *Much of our collective knowledge is embedded in our organizational culture. C. Social Capital (SC) SC1. Our employees are skilled at collaborating with each other to solve problems. SC2. Our employees share information to learn from one another. SC3. Our employees exchange ideas from different areas of the library. SC4. Our employees collaborate with other libraries to develop solutions. *Our employees apply knowledge fr om one areas to problems in different areas of the library. Service Innovation Outcomes (SIO) SIO1. Our library possesses all the necessary conditions for adopting service innovation. SIO2. Our library possesses all the necessary conditions for creati ng service innovation. SIO3. Compared with needs. SIO4. Compared with other libraries, our service innovation is perceived to successfully meet *Our library is well prepared for adopting service innovation. *Our library is well prepared for creating service innovation. *Compared with other libraries, we are perceived to produce more successful service innovation. *Compared with other libraries, we a re perceived to have better overall service innovation performance. The following questions are to be evaluated in the context of a digital platform. A digital platform is defined as a way of thinking about all the digital tools, services, infrastructur e, and human efforts libraries use to provide services. Please indicate how much you agree or disagree with each of the following statements as they apply to your library by selecting a single option for each statement. Digital Platform Capabilities (DP C) A. Co creation Capability (CC) CC1. Users are actively engaged in providing information for service innovation. CC2. Users give lots of feedback on the new ideas. CC3. Our employees explain the se ideas in a meaningful way to users. CC4. Our employees CC5. Our employees assert continuous communication with users.

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123 *Users actively give suggestions via various approaches. *Our employees offer users as much information as they need. B. Analytics Capability (AC) AC1. Our library makes the collected data consistent, visible , and easily accessible for further analysis. AC2. Our library keeps the collected data in appropriate data storage. AC3. Our employees identify trends from the data that lead to insights. AC4. Our library has systemic and comprehensive reporting from the data. AC5. Our library has data visualizations that allow for easy interpretation of the data. *Our library collects usage data. *Our employees pred ict patterns of library use from the data. Excluded items are indicated by an asterisk ; therefore no factor loading can be reported for them.

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124 APPENDI X E . INDICATORS DEESCRIPTIVEE STATISTICS Indicator Mean Medium Min Max Std dev. HC1 1.96 2 1 6 0.85 HC2 2.61 3 1 6 1.11 HC3 2.22 2 1 6 0.92 HC4 2.53 2 1 6 1.15 OC1 3.36 3 1 7 1.40 OC2 2.25 2 1 6 1.04 OC3 3.08 3 1 7 1.29 OC4 2.60 2 1 7 1.22 SC1 2.30 2 1 6 1.12 SC2 2.34 2 1 7 1.18 SC3 2.59 2 1 7 1.29 SC4 2.88 3 1 7 1.46 ODDT1 2.31 2 1 7 1.20 O DDT2 2.20 2 1 7 1.13 ODDT3 2.32 2 1 7 1.28 ODDT4 2.42 2 1 7 1.39 ODDT5 2.89 3 1 7 1.44 IOC1 3.36 3 1 7 1.59 IOC2 2.62 2 1 7 1.34 IOC3 3.36 3 1 7 1.44 IOC4 3.36 3 1 7 1.46 OIC1 2.14 2 1 7 1.05 OIC2 2.18 2 1 7 1.10 OIC3 2.30 2 1 7 1.29 OIC4 2.34 2 1 7 1.15 SPC1 2.53 2 1 7 1.4 0 SPC2 2.03 2 1 7 1. 15 SPC3 2.50 2 1 7 1. 43 SPC4 2.77 3 1 7 1.4 0 CC1 3.29 3 1 7 1.36 CC2 3.52 3 1 7 1.40 CC3 2.83 3 1 7 1.20 CC4 2.44 2 1 7 1.13 CC5 2.92 3 1 7 1.24 AC1 3.23 3 1 7 1.52 AC2 2.80 3 1 7 1.39 AC3 3.08 3 1 7 1.44 AC4 3.51 3 1 7 1.59 AC5 4.00 3 1 7 1.69 SIO1 3.13 3 1 7 1.43 SIO2 3.07 3 1 7 1.44 SIO3 3.15 3 1 7 1.41 SIO4 2.84 3 1 7 1.35

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1 25 A PP E NDIX F . ABBREVIATIONS AC Analytics capability CC Co creation c apability D P C Digital platform capabilit ies HC Human capital IC Intellectual capital IOC Inside out capability KISR Knowledge intensive service resources OC Organizational capital OD DT Operand Digital Technology O IC Outside in capability O T D T Operant Digital Technology SC Soc ial capital SIO Service innovation outcomes SPC Spanning capability