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Analysis of factors influencing adoption and usage of knowledge management systems and investigation of wiki technology as an innovative alternative to traditional systems

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Title:
Analysis of factors influencing adoption and usage of knowledge management systems and investigation of wiki technology as an innovative alternative to traditional systems
Creator:
Hester, Andrea J
Place of Publication:
Denver, Colo.
Publisher:
University of Colorado Denver
Publication Date:
Language:
English
Physical Description:
xv, 174 leaves : ; 28 cm.

Thesis/Dissertation Information

Degree:
Doctorate ( Doctor of Philosophy)
Degree Grantor:
University of Colorado Denver
Degree Divisions:
Department of Computer Science and Engineering
Degree Disciplines:
Computer Science and Information Systems
Committee Chair:
Scott, Judy
Committee Members:
Bryant, Peter
Gregg, Dawn
Ra, Ilkyeun
Stevens, Ellen

Subjects

Subjects / Keywords:
Knowledge management ( lcsh )
Management information systems ( lcsh )
Wikis (Computer science) ( lcsh )
Knowledge management ( fast )
Management information systems ( fast )
Wikis (Computer science) ( fast )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis (Ph. D.)--University of Colorado Denver, 2009.
Bibliography:
Includes bibliographical references (leaves 156-174).
Statement of Responsibility:
by Andrea J. Hester.

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Source Institution:
University of Colorado Denver
Holding Location:
Auraria Library
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All applicable rights reserved by the source institution and holding location.
Resource Identifier:
435428940 ( OCLC )
ocn435428940

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Full Text
ANALYSIS OF FACTORS INFLUENCING ADOPTION AND USAGE OF
KNOWLEDGE MANAGEMENT SYSTEMS AND INVESTIGATION OF
WIKI TECHNOLOGY AS AN INNOVATIVE ALTERNATIVE TO
TRADITIONAL SYSTEMS
by
Andrea J. Hester
B.S., Illinois State University, 1993
M.S., Southern Illinois University Edwardsville, 2004
A Dissertation Submitted to the
University of Colorado Denver
in Partial Fulfillment
of the Requirements for the Degree of
Doctor of Philosophy
Computer Science and Information Systems
2009


by Andrea J. Hester
All Rights Reserved.


This Dissertation for the Doctor of Philosophy
Degree by
Andrea J. Hester
has been approved by
Date


Hester, Andrea J. (Ph.D., Computer Science and Information Systems)
Analysis of Factors Influencing Adoption and Usage of Knowledge Management
Systems and Investigation of Wiki Technology as an Innovative Alternative to
Traditional Systems
Dissertation directed by Associate Professor Judy Scott
ABSTRACT
Knowledge management strives for effective capture and application of
organizational knowledge, a valuable resource imperative in sustaining an
organization. In an effort to better achieve knowledge management initiatives,
factors influencing increased adoption and usage of various technologies
implemented as knowledge management systems are of considerable interest.
Advances in technology have fostered new approaches to knowledge management in
the form of web-based collaborative technologies supporting environments of social
computing. Wiki technology is an emerging trend providing an effective knowledge
management system with benefits of improved communication and collaboration,
work processes, and knowledge sharing. Implementation of technological solutions
are often deemed organizational innovations subject to potential problems of
resistance as well as obstacles including organizational cultures lacking an
environment conducive to effective knowledge creation and sharing. With
Innovation Diffusion Theory (IDT) as a foundation, this research examines factors
influencing adoption and usage of knowledge management systems, with further


attention given to Wiki technology. The original IDT model is expanded to include
an additional independent variable, Reciprocity Expectation, and a moderating
variable, Personal Innovativeness in IT (PUT). Results indicate that some factors are
important in determining Adoption while others are important for Usage.
Voluntariness, Result Demonstrability, Visibility and Reciprocity Expectation were
found to be important factors having a significant positive impact on Adoption, while
a counter-intuitive result is presented for Ease of Use due to a significant negative
path. Relative Advantage, Trialability, and Visibility were found to be important
factors having a significant positive impact on Usage. Preliminary analysis of Wiki
technology indicated a different set of influential factors compared to traditional
KMS. Also, the moderating effect of PUT was much stronger for Wiki technology-
based systems. Post Hoc analysis provides further understanding of the results and
avenues for future research. Implications for researchers and practitioners are
discussed in the conclusion. This research contributes theoretical and empirical
support for an extended IDT model, and a theoretical contribution to the growing
body of research of Wiki technology.
This abstract accurately represents the contents of the candidates dissertation. I
recommend its publication.
Signed
udy Scott


DEDICATION
I dedicate this dissertation to my wonderful son, Rudy, who is the light of my
life and inspires me every moment of every day. I also dedicate this to my family
who have continually supported and believed in me.
I would like to acknowledge my Mother, the wind beneath my wings. From a
very young age, she taught me that women are all powerful. She has supported every
aspect of my life and guided me throughout with her wisdom and words of
encouragement. She challenged me to both set and achieve my goals, and she
inspired me to follow my dreams. She has devoted her life first to her family, and
second to a career in education where she has exhibited compassion and dedication.
She has always shown unwavering strength and is the true heart and soul of our
family. To Mom, I can only hope to be as good of a mother, and person, as you.
I am truly blessed to have not only my Mother, but generations of role models
in my family with numerous admirable qualities, only a few of which are intelligence,
creativity, benevolence, diligence and perseverance. The hard-earned success of my
grandparents and great-grandparents has paved the way for my success, and for that I
am eternally grateful. To my beloved Grandpa and Grandad, thank you and I miss
you.


ACKNOWLEDGEMENT
My thanks to my advisor, Judy E. Scott, not only for her support of my
dissertation, but also for inspiring and motivating me throughout my program. I also
wish to thank all members of my committee for their valuable participation and
insights. To all of the professors and fellow students I have had the pleasure of
working with in my program, thank you for not only challenging me but also
encouraging and supporting me.
I would like to say a special thank you to Dr. Jo Ellen Moore, Dr. Susan
Yager, and Dr. Doug Bock for rekindling my desire to pursue higher education, and
providing motivation and guidance throughout my journey.


TABLE OF CONTENTS
Figures...................................................................xiii
Tables....................................................................xiv
Chapter
1. Introduction..........................................................1
1.1 Research Problem & Scope..............................................2
1.2 Topic Importance......................................................4
1.3 Research Questions....................................................7
1.4 Research Approach.....................................................8
1.5 Contribution of Research..............................................9
1.6 Outline of the Dissertation...........................................9
2. Literature Review....................................................11
2.1 Knowledge Management.................................................11
2.1.1 Knowledge............................................................11
2.1.2 Knowledge Management.................................................12
2.1.3 Knowledge Processes..................................................13
2.1.4 Knowledge Management Systems.........................................15
2.2 Combining Technological and Social Aspects of Knowledge..............16
2.2.1 Collaboration and Collaborative Technologies.........................16
2.2.2 Conversational Knowledge Management..................................19
viii


21
23
23
25
26
28
30
34
35
37
41
41
43
46
50
50
53
59
59
61
62
Conversational Technologies.....................................
Wiki Technology.................................................
History.........................................................
The Wiki Way..................................................
Design Principles of Wikis......................................
Wiki Characteristics............................................
Trends in Wiki Technology.......................................
Research of Wiki Technology.....................................
How is Wiki Technology Unique?..................................
Why Research Wiki Technology?...................................
Adoption and Acceptance of Information Technology...............
Beyond TAM......................................................
Technology as Innovation........................................
Innovation Theories.............................................
Conceptual Framework............................................
Triangular Relationship of Underlying Fields of Study in Organizations
New Model.......................................................
Hypotheses......................................................
Voluntariness...................................................
Relative Advantage..............................................
Compatibility...................................................
IX


3.3.4 Ease Of Use...........................................................63
3.3.5 Result Demonstrability................................................64
3.3.6 Trialability..........................................................65
3.3.7 Visibility............................................................66
3.3.8 Image.................................................................67
3.3.9 Reciprocity Expectation...............................................68
3.3.10 PUT...................................................................70
4. Research Methodology..................................................72
4.1 Sample................................................................72
4.2 Research Design.......................................................74
4.3 Data Collection.......................................................74
4.4 Measures..............................................................76
4.4.1 Dependent Variables...................................................78
4.4.2 Independent Variables.................................................81
4.4.3 Moderating Variable...................................................82
4.4.4 Demographics and Descriptives.........................................82
4.5 Power and Sample Size.................................................84
5. Research Analysis and Results.........................................85
5.1 Measurement Model.....................................................85
5.1.1 Multicollinearity.....................................................88
5.1.2 Reliability...........................................................88
x


.89
.93
.93
.97
100
103
105
107
108
108
110
115
117
122
122
123
125
125
127
130
132
Validity..............................................
Structural Model......................................
Baseline Model (Without PUT)..........................
PUT as Direct Effect..................................
PUT as Moderating Effect..............................
Model Comparison......................................
Hypotheses Results....................................
Discussion............................................
Research Questions 1 and 2............................
Factors Indicating Positive Relationships.............
Negative Relationship between Ease of Use and Adoption
Factors Lacking Significance..........................
Research Questions 3 and 4............................
Post Hoc Analysis.....................................
Analyzing Level of Experience.........................
Experience as a Moderator.............................
Conclusion............................................
Limitations...........................................
Implications for Theory...............................
Implications for Practice.............................
Contributions.........................................
xi


Appendix
A Exploratory Study Examining Wikis for Project Management .............136
B Image of Survey.......................................................144
C Modifications Resulting from Pilot Studies............................145
D Sources for Survey Participants.......................................148
E Survey Items .........................................................150
F Factor Loadings and Cross-Loadings ...................................153
G Significance of Outer Loadings for Competing Models...................154
Bibliography................................................................156
Xll


LIST OF FIGURES
Figure
1. Knowledge Pyramid...................................................11
2. Triangular Relationship of Underlying Fields of Study...............51
3. Process of Technology Innovation Adoption and Acceptance............53
4. Research Model: Factors Influencing KMS Adoption and Usage..........58
5. Original Measurement Model..........................................87
6. Results of PLS Analysis for Baseline Model and Adoption.............95
7. Results of PLS Analysis for Baseline Model and Usage................96
8. Results of PLS Analysis with PUT as Direct Effect on Adoption.......98
9. Results of PLS Analysis with PUT as Direct Effect on Usage..........99
10. Results of PLS Analysis with Moderating Effect of PUT on Adoption.101
11. Results of PLS Analysis with Moderating Effect of PIIT on Usage...102
12. Chart of Adoption Level............................................112
13. Chart of Level of Expertise........................................113
14. Chart of Average Response to PIIT..................................114
15. Social Capital via Wiki Usage......................................138
16. Survey Image.......................................................144
xm


17
18
21
22
28
44
47
48
49
73
73
75
77
79
80
81
83
83
LIST OF TABLES
Categories of Collaborative technologies........................
CSCW work context matrix........................................
Description of community of practice types......................
Characteristics of conversational technologies..................
Comparison of activities performed with different technologies ....
Categories of contextual factors................................
Perspectives of innovation research.............................
Rogers attributes of innovation adoption........................
Moore and Benbasats attributes of IT innovation adoption.......
Descriptive statistics of the respondents.......................
Profile of organizations according to type and size.............
Summary of responses for each phase of data collection..........
Definitions of the constructs...................................
Adoption level and adoption lag.................................
Frequency of use................................................
Level of expertise and totals of KMS utilized and tasks performed
Types of KMS utilized...........................................
Types of tasks performed........................................
xiv


19. Internal consistency indicated by Cronbachs alpha.......................89
20. Composite reliability and average variance extracted (AVE)...............91
21. Correlation between constructs...........................................92
22. Direct effect, interaction effect and total effect......................104
23. Model comparison of effect size.........................................105
24. Tests of hypotheses HI through H9.......................................106
25. Test of hypotheses H10a and H1 Ob.......................................106
26. Comparison of research models for wikis and other KMS..................119
27. Effect size for model comparison for wiki data set......................121
28. Effect size for model comparison for non-wiki data set..................121
29. Model comparison of effect size.........................................124
30. Pilot study constructs, survey items and means..........................139
31. Feedback or advice and action regarding pilot studies...................145
32. Original and modified versions of survey items..........................146
33. Source type and name for survey participants............................148
34. Survey item definitions, means, standard deviations.....................150
35. Factor loadings and cross-loadings......................................153
36. Significance of outer loadings for competing models.....................154
xv


1. Introduction
The research presented in this dissertation involves examination of factors
influencing the adoption and usage of knowledge management systems, with further
attention given to Wiki technology. The proposed research model is comprised of
independent variables including the constructs of Innovation Diffusion Theory
(Voluntariness, Relative Advantage, Compatibility, Ease of Use, Result
Demonstrability, Trialability, Visibility, and Image) with the addition of Reciprocity
Expectation. Interaction between the independent variables and the moderating
variable of Personal Innovativeness in IT is hypothesized to predict the two
dependent variables, Adoption and Usage. The survey method is used to obtain data
from respondents using a variety of knowledge management systems. Analysis of the
data is two-fold with an examination of knowledge management systems in general
followed by analysis of the specific case of Wiki technology.
Given the role of knowledge as a valuable resource imperative in sustaining
an organization, attempts to better achieve knowledge management initiatives of
effective capture and application of organizational knowledge are of considerable
interest. Advances in technology have fostered new approaches to knowledge
management in the form of web-based collaborative tools supporting environments of
social computing with Wiki technology emerging as an effective alternative to
1


traditional systems. Examination of factors enabling increased adoption and usage of
various technologies fostering improved work processes in knowledge management
provides valuable theoretical and practical implications, as well as avenues for future
research.
1.1 Research Problem & Scope
Strategic management is a formidable challenge for todays organizations,
involving the process of formulating, implementing, and evaluating organizational
changes with the focus of achieving organizational objectives (Greenberg, 2002).
One of the most important goals of strategic management is to obtain competitive
advantage, a goal that constantly evolves and requires re-evaluation. Due to
continuous changes in the marketplace and technology, organizations must adapt or
suffer the consequences (Greenberg, 2002). Overcoming resistance to change is a
well established management concept in organizational behavior with numerous
potential causes for resistance (Dent & Goldberg, 1999). An extreme organizational
change can break down relationships resulting in loss of trust and willingness to share
(Armistead & Meakins, 2007). Implementation of a new technology is viewed as
such a change (Markus, 1983).
Organizational culture, defined as attitudes, experiences, beliefs and values of
an organization (Turban, McLean, & Wetherbe, 2002), is of further importance to the
business environment. While organizational culture is centered around the human
2


and social aspects, more physical aspects are also of concern. Organizational
environment and business infrastructure are major factors in all aspects of an
organization, particularly in areas of decision making, strategy formation, and
performance. Traditional business infrastructure is that of a top-down hierarchy
where there is a strict chain of command. The new paradigm is an environment that
is more distributed and flexible, often relying on group efforts. With increases of
uncertainty and a constantly changing environment, the new paradigm is more likely
to succeed (Pallot, Ruland, Traykov, & Kristensen, 2006; Stenmark, 2003).
As organizations strive to maximize resource acquisition and utilization, one
of the most valuable resources is intellectual capital, comprised of organizational
knowledge residing in both individuals or in the collective actions of a group. The
importance of intellectual capital has motivated the field of knowledge management,
which has in turn facilitated the development of a wide variety of Knowledge
Management Systems (KMS). Despite the development of systems allowing for
increased capabilities to support organizational knowledge, adoption of KMS remains
enigmatic (Wagner & Bolloju, 2005). Often times, KMS such as expert systems or
decision support systems may be too complex and expensive for organizations to use
(Raman, 2006). Even when KMS are in place, studies show that the majority of
knowledge relevant to the organization is not represented in the systems (Frappaolo &
Wilson, 2003). With the capture of organizational knowledge continuing to be a
problem for current KMS, new solutions need to be analyzed (Wagner, 2006). New
3


technologies should also consider the emerging trend of social computing, which
allows for individual users to belong and contribute to a group collective.
1.2 Topic Importance
Managing knowledge and keeping pace with information technology (IT) are
critical in sustaining competitive advantage, rendering a relentless challenge for
organizations. Although implementing innovative technological solutions can be
attractive, a change due to innovation can cause a disruption in normal activities that
are routine or even habitual (Lyytinen & Rose, 2003; Ram & Sheth, 1989).
Disruptions may cause a decrease in productivity, hindering organizational
performance. Another potential problem, resistance, is related to inter-organizational
balance of power whereby resistance to technology implementation occurs if users
perceive that usage of the system will result in loss of power (Markus, 1983).
In order to avoid these and other problems, efforts made to encourage or assist
users in acceptance of new technologies are paramount for management. Thus, user
acceptance of IT innovations is a crucial area of interest receiving much attention
(Agarwal & Prasad, 1997). IT implementation research, while extensive, tends to
focus on either technological or social aspects. A goal for new research is to strive to
bring together technological and social aspects providing a more effective framework
for successful implementation processes.
4


Social aspects contribute to the larger area of organizational culture, a factor
which may have a negative impact on business success when stem hierarchies and
closed-mindedness are present. However, traditional hierarchies still exist, often
symptomatic of cultural hurdles such as stringent channels of power and
communication, lack of trust and cohesion, and situations of knowledge acquisition
bottleneck effect. When employees are required to adhere strictly to proper
communication channels for endorsement or verification, time is wasted. Bottleneck
effect is an example of such latency, occurring in information systems when updates
or maintenance operations are delayed by centrally managed entry (Bean & Hott,
2005). This delay prevents content of the system from being current, which can be
crucial, particularly for customer service or help desk applications. Furthermore, an
environment of strict policies and procedures accompanied by top-down information
dissemination is not suitable for sharing processes (Bean & Hott, 2005; Stenmark,
2003; Wagner, 2004).
Obstacles also occur when upper management is reluctant to empower
knowledge workers. Without empowerment, workers lack autonomy and inter-
organizational network connections (Stenmark, 2003). Individuals contributing
knowledge experience a loss of ownership of knowledge resulting in feelings of loss
of power and potential for replacement (Kankanhalli, Tan, & Wei, 2005). An
environment lacking favorable group conditions, positive norms, and trust does not
provide for a social structure conducive to knowledge sharing and collaboration.
5


When users view knowledge as power, they may be reluctant to engage in knowledge
sharing processes (Kankanhalli et al., 2005). Such knowledge hoarding is a natural
reaction, often in response to feelings of threat, and occurs more often than not in
organizations (Bock, Zmud, Kim, & Lee, 2005). Nonetheless, effective knowledge
sharing cannot be forced upon individuals; environments fostering knowledge sharing
behaviors are more likely to motivate individuals to engage in actual practice of such
behaviors (Bock et al., 2005).
New trends in technology support a more collaborative business environment.
Conversational technologies such as discussion forums and weblogs have been used
as means for users to share and create knowledge through questions and answers.
Most conversational technologies provide one-to-many communication where a
single person or entity serves as the expert answering questions or posting
information. Wiki technology is an emerging trend featuring the unique
characteristics of open editing and an environment of social computing and sharing of
collective wisdom, improving upon previous methods of conversational technologies
by providing many-to-many communication with current knowledge and history
(Wagner, 2004). Furthermore, the addition of knowledge representation and
maintenance features of Wiki technology allows for more effective knowledge
sharing (Wagner, 2006).
Wiki technology utilization is growing at a dramatic rate, with empirical
evidence indicating that this technology is sustainable (Majchrzak, Wagner, & Yates,
6


2006). A wiki can take advantage of a pool of experts with any user being able to
provide pertinent information. Wiki technology can provide benefits of improved
work processes, improved communication and collaboration, and improved
knowledge sharing, thus it would be advantageous to continue to study environments
surrounding Wiki technology, particularly patterns and behavior of users. With
scholarly research of Wiki technology still in its infancy, this study marks an
important step forward in a theoretical understanding of wiki adoption and usage.
1.3 Research Questions
While knowledge management systems are attractive solutions for effective
creation, storage, and access to intellectual capital, successful implementation and
utilization is not always a guarantee. Introduction of technological solutions to the
work environment are deemed organizational innovations with potential problems of
disruption and resistance. Furthermore, knowledge management must continually
strive to overcome problems of ineffective knowledge capture and sharing, and
knowledge acquisition bottlenecks. These conditions and phenomena motivate the
following research questions:
(1) What are the factors which influence knowledge management system adoption
and usage?
(2) Does Personal Innovativeness in IT moderate the relationship between various
factors and adoption and usage of knowledge management systems?
7


(3) Is there a different set of factors which influence adoption and usage of Wiki
technology-based systems?
(4) Does Personal Innovativeness in IT moderate the relationship between various
factors and adoption and usage of Wiki technology-based systems, and is this
effect more evident in the case of wikis compared to other knowledge
management systems?
1.4 Research Approach
The research presented in this dissertation involves a causal study based on a
new model developed with a foundation in prior research of diffusion of innovations
and knowledge sharing. The independent variables are comprised of the constructs of
Innovation Diffusion Theory (Voluntariness, Relative Advantage, Compatibility,
Ease of Use, Result Demonstrability, Trialability, Visibility, and Image) with the
addition of Reciprocity Expectation. Interaction between the independent variables
and the moderating variable of Personal Innovativeness in IT is hypothesized to
predict the two dependent variables, Adoption and Usage.
The proposed research model improves upon previous conceptualizations of
usage by measuring the construct along four dimensions: frequency, total number of
tasks, total number of systems utilized, and level of expertise. The survey method for
data collection is used to test the proposed research model. The unit of analysis is at
the individual level and behavior level as users perceptions of themselves as well as
8


perceptions of using the technology are considered. Partial Least Squares (PLS) is
used to examine the hypotheses with a two-stage analysis performed using
confirmatory factor analysis to assess the measurement model followed by
examination of the structural model.
1.5 Contribution of Research
With organized and usable knowledge being a key ingredient to organizational
success, ensuring productive creation and sharing of knowledge can be deemed
advantageous for organizations. The contribution of this research will include
identification of factors facilitating adoption and usage of knowledge management
systems, with an additional comparative analysis of Wiki technology vs. traditional
KMS. Insight into the adoption and usage of wikis used in knowledge management
will provide an important contribution to the developing body of research involving
Wiki technology. The results of this research can be used by management to better
focus on important factors which may increase adoption and usage of knowledge
management systems. Additionally, researchers may utilize the proposed model for
further examination of knowledge management systems as well as other types of
information technology innovations.
1.6 Outline of the Dissertation
Chapter 2 of the dissertation presents the literature review focusing on
Knowledge Management, Combining Technological and Social Aspects of
9


Knowledge, Wiki Technology, and Adoption and Acceptance of Information
Technology. Chapter 3 of the dissertation presents the conceptual framework
including the research model and hypotheses. The research methodology is presented
in Chapter 4, and the research analysis and results are presented in Chapter 5.
Chapter 6 provides the discussion followed by the conclusion with limitations,
implications and contributions in Chapter 7.
10


2. Literature Review
2.1 Knowledge Management
2.1.1 Knowledge
When viewed as intellectual capital, knowledge is a crucial element of todays
organizations. The classic knowledge pyramid (depicted in Figure 1) provides a
common perspective of knowledge, distinguishing between data, information and
knowledge, with knowledge residing at the top of the pyramid. While data can be
described as raw facts, and information as data with the addition of useful
descriptives, knowledge refers to processed information which is organized,
structured, and ready for application.
Figure 1: Knowledge Pyramid
11


Additional perspectives of knowledge include knowledge as a state of mind, object,
process, access to information, and capability (Alavi & Leidner, 2001). Knowledge
management literature commonly distinguishes between two types of knowledge:
tacit knowledge and explicit knowledge. Tacit knowledge is codified knowledge that
is transmittable in a formal, systematic language (Nonaka, 1994). Explicit
knowledge, on the other hand, is more difficult to formalize and communicate as it is
characterized by action, commitment and involvement in a specific context.
2.1.2 Knowledge Management
Knowledge management is a key practice used in organizations seeking to
harness knowledge as a resource for sustained competitive advantage (Kankanhalli et
al., 2005). Common knowledge management initiatives include the following
(Turban et al., 2002):
Sharing knowledge and best practices
Instilling responsibility for sharing knowledge
Capturing and reusing best practices
Embedding knowledge in products, services, and processes
Producing knowledge as a product
Driving knowledge generation for innovation
Mapping networks of experts
Building and mining customer knowledge bases
12


Understanding and measuring the value of knowledge
Leveraging intellectual aspects
Benefits to knowledge management include idea generation and innovation,
better customer experiences, and consistency in good practices. An important
initiative in organizations is to maintain alignment between business and information
technology objectives. A recent study found that four factors influenced short-term
alignment: shared domain knowledge, IT implementation success, communication
between IT and business executives, and connections between IT and business
planning (Reich & Benbasat, 2000). Of these factors, only one was found to
influence long-term alignment: shared domain knowledge. A direct, positive
relationship exists between communication and knowledge sharing (Joshi, Sarker, &
Sarker, 2007). Knowledge management seeks to maximize this relationship by use of
information technology as a tool for capturing and storing knowledge. However,
having the knowledge in place is only the first step in effectively utilizing the
valuable knowledge resource.
2.1.3 Knowledge Processes
A framework for analysis of the role of information technology in knowledge
management defines four distinct knowledge processes: (1) creation, (2)
storage/retrieval, (3) transfer, and (4) application or reuse (Alavi & Leidner, 2001).
13


Referring to the aforementioned types of knowledge, tacit and explicit, there are
different methods of knowledge conversion or creation (Nonaka, 1994):
Socialization: conversion of tacit knowledge to tacit knowledge
Combination: conversion of explicit knowledge to explicit knowledge
Externalization: conversion of tacit knowledge to explicit knowledge
Internalization: conversion of explicit knowledge to tacit knowledge
Although knowledge creation may sometimes be difficult, knowledge transfer, which
encompasses knowledge sharing, receives the most attention in the knowledge
management literature (Alavi & Leidner, 2001). For example, the network model of
knowledge management systems emphasizes connections among people to facilitate
knowledge exchange or sharing (Kankanhalli et al., 2005). While many factors affect
the organizational environment, creating an environment allowing members of the
organization to engage in various forms of socializing and collaboration may foster
increased knowledge sharing. Once knowledge has been created and stored in the
organization, and then shared and transferred, members are then able to apply the
knowledge to work processes. The knowledge application may then aid the
organization in achieving knowledge management initiatives. In addition to
individuals within the organization, another important element required for achieving
effective knowledge management comes in the form of a knowledge management
system (KMS).
14


2.1.4 Knowledge Management Systems
The main goal of information systems (IS) is to process data into information
or knowledge. We can then examine the classification of information systems, which
may be by organizational levels, major functional areas, support provided, or IS
architecture. Analysis of information systems evolution indicates the progression of
IS according to the support provided. The development of systems to support users
and management in various tasks and decision making began as simple transaction
processing systems and evolved into highly specialized expert systems. Common
classifications according to support provided include Knowledge Management
Systems, Decision Support Systems, Group Support Systems, and Intelligent Support
Systems or Expert Systems. A system based on Wiki technology can be interpreted
as both a knowledge management system and a group support system.
Knowledge management systems were developed to provide a technological
solution to support the knowledge processes of creation, storage/retrieval, transfer and
application. The objectives of a KMS involve creating knowledge repositories,
improving knowledge access, enhancing the knowledge environment, and managing
knowledge as an asset (Turban et al., 2002). The three most prevalent applications of
KMS include: (1) the coding and sharing of best practices, (2) the creation of
corporate knowledge directories, and (3) the creation of knowledge networks (Alavi
& Leidner, 2001). While the content of a KMS is the knowledge itself, an overall
KMS also includes processes, goals, strategies and culture (King, 2007). Thus,
15


although KMS can provide great benefits when effective, the technological aspects
should not be over emphasized while neglecting the social aspects (Butler, 2003).
2.2 Combining Technological and Social Aspects of Knowledge
2.2.1 Collaboration and Collaborative Technologies
Collaboration is a key process in almost any organizational environment.
Collaboration can provide benefits in the form of deeper resource pools, a variety of
domain knowledge, and multiple viewpoints (Mohtashami, Marlowe, Kirova, &
Deek, 2006). Collaboration has evolved from routine forms, like a face-to-face
conversation, into web-based applications increasing speed and efficiency of
collaborating. Thus, with the evolution of information systems, computer-aided
collaboration has become commonplace.
After the emergence of collaborative technologies in the 1970s, such systems
experienced a surge of growth through the 1980s and 1990s with the arrival of
applications such as e-commerce, distance education, electronic publishing, digital
libraries, and virtual communities (Kling, 2000). These applications are examples of
the expansion of computerized systems to various types of organizations, aiding in
establishment of the information superhighway, commonplace in society and
organizations by the year 2000. Web-based collaboration tools can provide even
further benefits such as easy accessibility of material, up-to-date versions, hyper
16


linking, independence of platform and application and content markup (Leuf &
Cunningham, 2001).
Computer-aided collaboration can be categorized into three different models:
E-mail Exchange, Shared folder/file access, and Interactive content update/access
(Leuf & Cunningham, 2001). E-mail exchange provides direct exchanges of
communication between two or more persons. Shared folder/file access provides
users with a repository for documents and information located on a server.
Interactive content involves pages or documents which are authored collectively by a
group. A more modem version of the E-mail exchange model would include message
boards and blogs. Collaboration technologies can be categorized into three groups:
electronic communication tools, electronic conferencing tools, collaborative
management tools. Examples of collaborative technologies are listed in Table 1.
Table 1: Categories of Collaborative technologies
Electronic Communication Tools Electronic Conferencing Tools Collaborative Management Tools
Synchronous conferencing E-mail Faxing Voice mail Discussion forums Blogs Wikis Web publishing Version control Internet forums Online chat Instant messaging Telephony Video conferencing Web conferencing Application sharing Electronic meeting systems Electronic calendars Project management systems Workflow systems Knowledge management systems Prediction markets Extranet Social software systems Online spreadsheets
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Collaboration software packages range from elaborate and expensive options to
lightweight, simplistic solutions. Requirements of organizations vary and most
systems can be tailored to the subsequent needs. Collaborative technologies utilized
in the work environment have been shown to enhance mobile working processes
(Alarcon, Guerrero, Ochoa, & Pino, 2006) and improve group performance (P.A.
Pavlou, Dimoka, & Housel, 2008).
Computer-supported collaborative work (CSCW), a design-oriented concept
focusing on the characteristics of groups and a computer system adequate to support
group work, utilizes collaborative technologies. Visualization of the concept is
demonstrated by a matrix describing work contexts along two dimensions: time and
place (see Table 2) (Grudin, 1994). Examples of relevant technologies are listed in
the appropriate section of the matrix.
Table 2: CSCW work context matrix
Synchronous Asynchronous
Same Place Face-to-face Interactions Decision rooms, single display groupware, shared table, wall displays Continuous Tasks Team rooms, large displays, shift work groupware, project management
Different Place Remote Interactions Internet forums, online chat, instant messaging, video- and web-conferencing Communication & Coordination E-mail, discussion forums, blogs, wikis, workflow systems, document or content management systems
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The quadrant indicated by Asynchronous, Different place requires the highest level of
collaboration. Web-based technologies are prevalent in this quadrant, allowing for
collaboration to occur at any location and at any time, thereby providing improved
support of organizational initiatives in globalization. The movement toward
globalization has led to geographical expansion of organizations facilitating increased
effectiveness particularly in the area of customer support by enabling 24x7 assistance.
2.2.2 Conversational Knowledge Management
One area where collaborative technologies have evolved is within
conversational knowledge management, an attractive option for organizations in need
of a system which is informal as well as quick and easy. One example entails users
creating and sharing knowledge by way of a question and answer dialog, as in the
case of a frequently asked questions (FAQs) application. Conversational
technologies are best suited for ad-hoc or repetitive tasks such as an emergency
response system or a help desk. Benefits to conversational knowledge management
systems include the following (Wagner, 2004; Wagner & Bolloju, 2005):
users able to quickly and easily publish knowledge
knowledge effectively and securely shared with other members of the
community
economically and technologically undemanding
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A distinct advantage of these benefits is that they occur at numerous stages of
knowledge management process beginning with knowledge creation and ending with
knowledge reuse (Alavi & Leidner, 2001; Wagner & Bolloju, 2005). Conversational
knowledge management leverages collective wisdom by harnessing communal
knowledge and social capital of groups thereby satisfying needs of daily knowledge
queries and gaining access to a diverse group of experts, with further benefits of
incremental knowledge refinement mechanisms.
Another distinction of conversational knowledge management systems is the
lack of formal knowledge representations. The systems forego a highly structured
database, knowledge interpretation and formal structure rules (Wagner & Bolloju,
2005). This attribute aids in the quickness of knowledge exchange and extraction
since users are not bogged down by formality. Nonetheless, a conversational
knowledge management system accommodates contextualization and search
capabilities. The systems most often utilize plain text and have the ability to build
relationships to other content in the repository (Wagner & Bolloju, 2005).
Conversational technologies can be utilized in numerous applications in the
work environment. The main Community of Practice (CoP) types for conversational
knowledge management were identified by a study by the American Productivity and
Quality Center in 2000 (APQC, 2000). These basic types and their requirements are
summarized in Table 3.
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Table 3: Description of community of practice types
Community Type Function Requirements
Help Communities Support each other on everyday problems and share ideas on an ad hoc basis Connect people and enable spontaneous exchange
Best Practice Communities Develop, validate and share best practices Process support for idea validation and refinement
Knowledge Stewarding Communities Maintain a body of knowledge for day-to-day use as well as the community around it Document management; community management; enlisting of experts
Innovation Communities Seek breakthrough ideas Bring together individuals with multiple perspectives; identify new trends
These CoPs can be served efficiently and effectively with conversational knowledge
management systems. Unlike other conversational technologies which may only be
able to fit one CoP, Wiki technology can serve all of the above-mentioned CoPs due
to its incremental development of knowledge and community or collective approach
(Wagner & Bolloju, 2005).
2.2.3 Conversational Technologies
Conversational technologies can be categorized according to the
communication model, and whether they have a knowledge repository. A list of
conversational technologies and their properties are summarized in Table 4 (Wagner,
2004). The original table was adapted to also include the CSCW work context. Wiki
technology improves upon previous types of conversational technologies by
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providing many-to-many communication with current knowledge and history
(Wagner, 2004).
Table 4: Characteristics of conversational technologies
Technology Communication Knowledge Repository Work Context
E-mail 1-to-l, 1-to-many, person-to-person Archives possible Asynchronous, different place
Discussion forum Many-to-many in web based forums, repeated 1 to-many in list servers Central repository Asynchronous, different place
Internet chat 1-to-l, many-to-many None Synchronous, different place
Video / web conference 1-to-l, 1-to-many Local repository Synchronous, different place
Group Decision Support Systems Many-to-many Possible Asynchronous, same place
Static and DB backed web pages 1 -to-many, approaching many-to-many, dialog between web pages through hyperlinks Archives Asynchronous, different place
Blog 1 -to-many, can approach many-to-many (similar to web pages) Local repository Asynchronous, different place
Wiki Many-to-many Yes, current knowledge and history Asynchronous, different place
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2.3 Wiki Technology
2.3.1 History
Wiki technology is the system and concept of collaborative websites
maintained by users who are allowed access. A website based on Wiki technology
(referred to as a wiki) is different from other websites in that content can be
created, modified and updated by any user via a web browser. In general, websites
available on the World Wide Web are static and can only be modified or updated by
the webmaster, whereas wiki websites are dynamic and allow users to participate on-
line. The word Wikiwiki means fast or quick in Hawaiian (Leuf & Cunningham,
2001). The Father of Wiki technology, Ward Cunningham, coined this word after
remembering a trip to Hawaii where a shuttle at the airport was called Wiki Wiki,
meaning really quick. Cunningham wanted to create an alternative to static web
pages with the added advantage of quick and easy maintenance. When Cunningham
developed the first wiki website, he decided to call the technology behind the website
WikiWikiWeb. The home of the first wiki resides at c2.com, installed on March 25,
1995 and still running today. From the home page, you can navigate to the original
wiki, the Portland Pattern Repositorys Wiki, also referred to as Wards Wiki. The
primary function of the wiki is a collection of people, projects and patterns involved
in software development.
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A few years after the arrival of the original wiki, Jimmy Wales and Larry
Sanger began their Nupedia encyclopedia project. When the Nupedia project failed
to take off, Wales and Sanger decided to complement the project with Wiki
technology (Wagner & Prasamphanich, 2007). The project evolved into what is now
known as Wikipedia, a free content encyclopedia that can be accessed and edited by
anyone. Since its inception on January 15, 2001, Wikipedia has grown to almost
three million articles1 in the English language. Besides the mere increase in the
number of articles (pages), the site has also expanded to include versions in literally
hundreds of different languages. Currently the English version is the only version
containing over one million articles, although other versions are quickly approaching
the one million mark. Eleven different language versions contain over one hundred
thousand articles. In addition to the growth of Wikipedia itself, there is now a family
of so-called sister sites to the first bom which include Wiktionary, Wikibooks,
Wikiversity, Wikinews, Wikispecies, Commons, Wikiquote, Wikisource, and Meta-
Wiki. These sites are hosted by the Wikimedia Foundation.
Since beginning my research of wiki technology, the number of wiki sites has
grown immensely. Wikipedia also continues to grow and may or may not currently
be the largest wiki. Other large wikis include WikiWikiWeb, Ward Cunninghams
first-ever wiki; Wikitravel, an open content world-wide travel guide; and Memory
1 http://en.wikipedia.Org/wiki/Wikipedia:Size of Wikipedia, retrieved on 03/23/2009
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Alpha, an encyclopedic reference for all things related to Star Trek, the science
fiction television series and movies.
2.3.2 The Wiki Way
In order to maintain the conceptual design behind Wiki technology, Leuf and
Cunningham describe the essence of Wiki, providing a definition as well as a
guideline of sorts for all wiki users to adhere to (Leuf & Cunningham, 2001):
A wiki invites all users to edit any page or to create new pages within the wiki
Web site, using only a plain-vanilla Web browser without any extra add-ons.
Wiki promotes meaningful topic associations between different pages by
making page link creation almost intuitively easy and by showing whether an
intended target page exists or not.
A wiki is not a carefully crafted site for casual visitors. Instead, it seeks to
involve the visitor in an ongoing process of creation and collaboration that
constantly changes the Web site landscape.
The Wiki Way, providing the overall concept and purpose of Wiki technology
(Leuf & Cunningham, 2001), includes the essence of Wiki and main purposes of
using wikis: a collaboration space and a way to organize and cross-link knowledge.
Additionally, a wiki can be described as inherently democratic every user has
exactly the same capabilities as any other user (Leuf & Cunningham, 2001).
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The Wiki Way has led to a unique culture surrounding wiki users. A wiki
community provides interaction, sharing, and understanding. Surprisingly, wiki
culture is often characterized by a sense of politeness among users. Following wiki
etiquette is often the norm of guidelines such as assume good faith and remember
the golden rule Many wikis also emphasize maintaining a neutral point of view
(NPOV) (Aronson, 2002). Most notable is the self-healing aspect of wiki culture
which entails users monitoring the content and quickly correcting others mistakes
(Balias, 2006; Holloway, Bozicevic, & Bomer, 2007; Long, 2006; Viegas,
Wattenberg, & Dave, 2004).
2.3.3 Design Principles of Wikis
Cunningham originally created Wiki technology to be open-source. However,
with its continuing growth, there are now commercial wiki products available for
purchase. Cunningham also proposed the following wiki design principles to be
followed (Leuf & Cunningham, 2001):
Open Should a page be found to be incomplete or poorly organized, any
reader can edit it as they see fit.
Incremental Pages can cite other pages, including pages that have not been
written yet.
2 http://en.wikipedia.org/wiki/Wiki_etiquette, retrieved on 03/23/2009.
26


Organic The structure and text content of the site is open to editing and
evolution.
Mundane A small number of (irregular) text conventions will provide
access to the most useful page markup.
Universal The mechanisms of editing and organizing are the same as those
of writing so that any writer is automatically an editor and organizer.
Overt The formatted (and printed) output will suggest the input required to
reproduce it.
Unified Page names will be drawn from a flat space so that no additional
context is required to interpret them.
Precise Pages will be titled with sufficient precision to avoid most name
clashes, typically by forming noun phrases.
Tolerant Interpretable (even if undesirable) behavior is preferred to error
messages.
Observable Activity within the site can be watched and reviewed by any
other visitor to the site.
Convergent Duplication can be discouraged or removed by finding and
citing similar or related content.
A final principle that Cunningham proposed was trust. This is at the core of wiki.
Trust the people, trust the process, enable trust-building.
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2.3.4 Wiki Characteristics
A website based on Wiki Technology is different from other websites because
the content can be created, modified and updated automatically by anyone through
the Internet. Alternatively, a wiki being used in an organizational setting may utilize
an intranet. A user may choose to simply observe, by reading and following links, or
participate by writing and editing, or both. The activities (see Table 5) available to
the user of Wiki technology vary significantly from static pages or databases (Leuf &
Cunningham, 2001).
Table 5: Comparison of activities performed with different technologies
Activity Wiki Static Page Database
Content publishing Anyone or member of specific group Web master or delegated FTP/publishing Database contributors
Updating material Anyone (group), anytime Scheduled or when there is time by person with access Database updaters
Browsing Free-form structure, topics, search, back links Site structure and navigation as defined by Web master(s) Query transactions or generated site structure
Following site links, cross- referencing Anyone who can edit can also create cross-links and create topic pages for searches Difficult to do effectively, hard to change or update, broken links due to change a problem Depends on query and serving engine, often only search is possible
Commenting or reviewing Anyone or any member of specific group, on any (open) page By way of e-mail, feedback form, or guestbook page Only if comment pages/fields implemented
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The language used in the creating and editing process is usually a simplified
markup language which is similar to HTML, although extensive knowledge of HTML
is not required. This language varies slightly between various wiki software, but is
usually referred to as the wiki markup language, or WikiML. As Wiki technology
continues to evolve, most wikis are now equipped with WYSIWYG formatting. As
creating and editing take place, revisions are tracked by history and recent changes
pages. When errors or misinformation are uncovered, additional revisions can be
made, or the wiki can be restored to a previous version listed on the history page. In
most cases, monitoring by users, or sometimes moderators, keeps the content of wikis
current and valid.
When creating a wiki, a single page, referred to as a wiki page, links to
other wiki pages via hyperlinks. These links are created during the authoring or
editing process most often by typing the text to serve as the link in camel case,
commonly referred to as WikiWords, however, again the different versions of wiki
software vary. Camel case is created by capitalizing each word of a phrase and
eliminating spaces between the phrase, for example: CamelCase. When a link is
typed within a wiki page, the new page for that link is automatically created. This
feature allows for the distinct advantage of wikis never containing broken links.
When a new link is created, an empty page is generated, allowing for the
development of the page. Navigation of the wiki is not restricted to inter-linked wiki
pages; external links may also be created.
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Additional features of most wikis include file uploading, a Sandbox which
serves as a practice area for new users to experiment with creating and editing pages,
and searching and indexing capabilities. Individual wikis may also choose whether or
not to limit access to registered users. In their book, The Wiki Way: Quick
Collaboration on the Web, Leuf and Cunningham describe six different types of wikis
categorized by the permissions allowed (2001):
Fully open, meaning that anyone has full access to the Wiki;
Lockable, with restricted editing for some or all pages;
Gated, with some public pages (that may be locked), but other pages
restricted to authorized users;
Members-only, where access is limited to registered users;
Firewalled, where access is restricted to a range of specific IP addresses; and
Personal, where access is limited to a specific computer or private site.
Thus, the level of security can be managed to fit the particular needs of the wiki.
2.3.5 Trends in Wiki Technology
A wiki may be implemented as server software or client software.
Implementations deviating from Cunninghams original Wiki technology are referred
to as Wiki Clones. These applications may be written in a different language and/or
may be customized to fit a particular functionality. Wiki Clones are essentially
different versions of wiki software combined with the wiki engine which implements
30


the Wiki technology. If you choose not to set up your own server, you can still
implement a wiki with the help of a Wiki Farm, which is a server or collection of
servers that provides wiki hosting, or a group of wikis hosted on such servers.
2.3.5.1 Personal Wikis
Wikis can serve as a free-form database without the headache of
implementing and maintaining database software. This can be advantageous for both
personal use and shared use. An individual may use a wiki for a notebook or journal,
an address book, or as a database for videos, books or documents. Cunningham
refers to Wiki technology as the simplest online database that could possibly work
(Leuf & Cunningham, 2001). Benefits to the individual include its free-form nature
and its inter-linking ability.
2.3.5.2 Community Wikis
A shared or multi-user application may also entail simple database functions
as well as shared bulletin board spaces, collaborative FAQs and project management.
Shared benefits again include the inter-linking, but more importantly stem from the
ability for multiple users to collaborate easily regardless of physical location.
Community wikis may also be centered around hobbies or shared interests, covering
topics from quilting to everything there is to know about Star Trek.
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2.3.5.3 Organizational Wikis
As wiki technology grows out of its infancy, its use is going beyond
Wikipedia.org and the Web. One of the most common ways in which wiki
technology is being used is as a collaborative tool. The key advantage of using a wiki
as opposed to other current means, such as a blog, is of course the ability to create
and modify content on-line. The collaboration may take the form of a simple
communication forum, or a complex project management area. Another possibility is
for the wiki to serve as a collaborative authoring tool, with the benefit being that the
collaboration can occur among many users simultaneously as opposed to passing a
document from one person to the next.
Wikis are also gaining popularity as a knowledge or content management tool.
Again, an easily recognizable advantage over previous technologies is the capability
for multiple users to collaborate and contribute simultaneously as the technology
allows for real-time editing. Other advantages to using a wiki for knowledge
management include:
Ability to create a knowledge base of linked and categorized content
Content and taxonomy can be reorganized
Ability to upload documents and manage document versions
A wiki can also be used for so-called conversational knowledge creation in which
case the process of creating the knowledge takes the form of a question and answer
dialog. Using a wiki to store, edit and access organizational knowledge can be an
32


effective organizational knowledge management initiative (Hasan & Pfaff, 2006;
Raman, 2006).
Additional application areas for organizational wikis include groupware, help
systems, product trouble-shooting systems, communities for best practices, software
development, e-leaming, project management, ad-hoc collaboration, technical
support, customer relationship management, resource management, research and
development, emergency response systems(Majchrzak et al., 2006; Raman, 2006;
Wagner, 2004). Applications may be limited to those involving ad-hoc, dynamic and
informal knowledge. Additionally, the architecture may be limited by systems based
on open source development requiring constant maintenance.
The wiki is also subject to an unconventional knowledge sharing and creation
paradigm (Wagner, 2004). Nonetheless, tasks fitting these limitations do exist and
can benefit from Wiki technology. Common applications include: to-do list/task
management, document management, meeting management, brainstorming, project
planning, new product development, software version management, customer
relationship frequently asked questions, recruiting management, competition tracking,
distributed marketing intelligence, best-in-class employee engagement practices, help
systems, technical support, emergency response coordination for distributed
locations.
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2.3.6 Research of Wiki Technology
Being the first widespread application of Wiki technology, early research has
focused on Wikipedia. Wikipedia has experienced unprecedented growth on the
Internet. Preliminary criticisms of Wikipedia focused on issues of trust and
vandalism; these opinions have now been overturned. Research on the issue of trust
has shown that Wikipedia does in fact contain quality material comparable, if not
superior, to traditional encyclopedias (Emigh & Herring, 2005; Lih, 2004; McMullin,
2005). The issue of vandalism has been put to rest by the realization of Wikipedia as
a self-healing community utilizing the history and rollback features (Balias, 2006;
Holloway et al., 2007; McMullin, 2005; Schaffert, 2006; Viegas et al., 2004). In
addition to addressing criticism, research has also used Wikipedia for experiments
involving history flow and information visualization (Biuk-Aghai, 2005; Gilbert &
Karahalios, 2006; Holloway et al., 2007; Viegas et al., 2004), text mining (D. J.
Cohen, 2006; Davidov, Gabrilovich, & Markovitch, 2004), link analysis (Adafre &
DeRijke, 2005; Grangier & Bengio, 2005), and question answering tasks (Jijkoun et
al., 2004; Lita, Hunt, & Nyberg, 2004).
Wiki technology research has provided informative articles less scientific in
nature, with numerous works providing history and descriptions of Wiki technology
along with features and advantages and disadvantages (Aronson, 2002; Bean & Hott,
2005; Long, 2006; Wagner, 2004; Wei, Maust, Barrick, Cuddihy, & Spyridakis,
2005). Research that goes beyond fairly simple explanation drives classification of
34


Wiki technology as social software where users engage in community computing
(Chawner & Lewis, 2006; Gonzalez-Reinhart, 2005; Hasan & Pfaff, 2006; Khan,
2005a; Lih, 2004). More involved studies highlight applications for Wiki technology
including customer resource management (Wagner & Majchrzak, 2007), on-line news
services (Balias, 2006; Lih, 2004), on-line communities (Milberry, 2006; VanDeursen
& Visser, 2002), software development (Louridas, 2006), and learning tools in
educational environments (Bennett & Watson, 2006; McMullin, 2005; Mindel &
Verma, 2006; Raman, Ryan, & Olfman, 2005; Wang et al., 2005). The wiki
application receiving the most attention is collaborative authoring (Bennett &
Watson, 2006; DePedro et al., 2006; Desilets, Paquet, & Vinson, 2005; Emigh &
Herring, 2005; Wei et al., 2005; Zhang, 2006). Task-technology compatibility was a
major factor in adoption of an innovative material requirements planning system
based on Wiki technology (Cooper & Zmud, 1990). Wiki technology is also a good
fit for emergency preparedness efforts dependent on cross-unit collaboration (Raman,
2006).
2.3.7 How is Wiki Technology Unique?
Wiki technology is unique in that it utilizes a many-to-many communication
system and it contains both a knowledge repository and a knowledge catalog. As a
conversational management tool, wikis provide speed and ease with so-called single-
click publication allowing modifications to be realized instantaneously (Wagner &
35


Bolloju, 2005). The wiki also has the option of being available company-wide, or
components can be set up and restricted for specific groups of users. The level of
security measures is flexible and configurable to meet the specific needs of the
organization. Versions and histories can be recorded with the option of utilizing
rollback mechanisms. Most importantly, wikis allow for an environment of open
editing, social computing, and collective wisdom.
2.3.7.1 Open Editing
Prior to development of Wiki technology, conversational technologies such as
discussion forums and weblogs were used as means for users to share and create
knowledge through questions and answers. The drawback to these methods is the
organization of the information, most often in chronological order. Wiki technology
allows for open editing which can be done anywhere in the text allowing for
information which is more incremental than chronological. Content can be modified
at any area thus allowing the subject material to develop more constructively as
opposed to a listing of postings in chronological order. By utilizing internet
technologies, modifications will materialize at the speed of the network, most often
instantaneously.
2.3.1.2 Social Computing
Due to its collaborative nature, engaging in Wiki technology has been called
socially inclusive interactive community computing (Khan, 2005b). Engaging in
36


social interaction such as collaboration can provide for more effective work processes
in areas of creativity and innovation (Pallot et al., 2006). Social computing provides
a technical outlet for individual users to belong and contribute to a group collective.
Such voluntary group participation provides benefits to the individual as well as the
group (Gonzalez-Reinhart, 2005). Wiki technology is deemed effective social
software allowing users to engage in community computing (Chawner & Lewis,
2006; Gonzalez-Reinhart, 2005; Hasan & Pfaff, 2006; Khan, 2005a; Lih, 2004).
2.3.7.3 Collective Wisdom
Wikis can take advantage of collective wisdom to create an effective source of
knowledge (Bean & Hott, 2005; Fuchs-Kittowski & Kohler, 2005; Hasan & Pfaff,
2006; Raman, 2006; Wagner, 2006). Collective wisdom is a form of man/machine
symbiotic computing occurring when collective human computing power is harnessed
by a community of human and computer resources (Khan, 2005a). Wiki technology
is able to support and record processes of communication in the form of conversation
thereby attaining collective wisdom.
2.3.8 Why Research Wild Technology?
Wiki technology presents a shift from standard collaboration and knowledge
management tools thus motivating the approach of viewing the technology as an
innovation warranting special consideration. Wiki technology differs significantly
from traditional knowledge management systems which have an element of
37


centralized control and a formal rule structure. In the case of wikis, on the other
hand, custodianship of the knowledge is distributed among its users. While this
openness may invite the potential for vandalism, the community is often quick to
monitor and correct any misinformation. This joint ownership and open access can
be seen as an innovative approach to knowledge management. Furthermore, the
knowledge creation process is informal in nature, lacking a strict set of rules, also
representing a shift from the traditional paradigm, which involves more rigid and
centralized control.
Wiki technology improves upon previous methods of conversational
technologies by providing an enhanced mode of communication along with up-to-
date knowledge as well as the history and revisions to the content (Wagner, 2004).
Wikis also allow for strong linking of relevant concepts providing for an effectively
inter-connected knowledge source with the addition of knowledge representation and
maintenance features (Wagner, 2006). Using a wiki to store, edit and access
organizational knowledge can be an effective organizational knowledge management
initiative (Hasan & Pfaff, 2006; Raman, 2006).
Certain characteristics of Wiki technology can satisfy specific requirements of
knowledge management. First and foremost, Wiki technology is best suited for
knowledge which is ad-hoc, dynamic, and decentralized (Wagner, 2004). Wikis also
facilitate searching and filtering which is made possible by linking and indexing
38


capabilities. Finally, due to the revision and history features, errors can be minimized
and recovery or roll-back functions can be performed, allowing for quality assurance.
2.3.8.1 Benefits of Wiki Technology and The Wiki Way
Wikis allow for collaborative authoring and knowledge management by
incorporating mechanisms for communicating within the system. Unification of
multiple functions into a single tool combined with the ability to access the tool via
simple internet technologies provides an innovative product for organizations.
Although the extent of benefits realized by an organization may vary, the key benefits
recognized are improved work processes, improved communication and
collaboration, and improved knowledge sharing and reuse.
2.3.8.1.1 Improved Work Processes
Respondents to a survey regarding organizational wiki usage reported that
wikis made their work easier (Majchrzak et al., 2006). The specific processes
indicated were:
Need or use of information of immediate relevance to work,
Need or use of updated knowledge,
Ability to disseminate knowledge.
Wiki usage at Aperture Technologies allowed for improved work processes in the
form of faster work completion (Gonzalez-Reinhart, 2005). Curtin University
Library implemented a wiki largely to serve as a knowledge base for frequently asked
39


questions and other references (Wiebrands, 2006). The wiki allowed for increased
efficiency and effectiveness of day to day tasks such as maintenance of the
knowledge.
2.3.8.1.2 Improved Communication and Collaboration
Benefits of improved communication and collaboration may be experienced in
any scenario, however, a distinct advantage among conversational technologies,
including wikis, is the ability to support communication and collaboration among
users at different times and different places (Bean & Hott, 2005; Wagner & Bolloju,
2005). Far-flung employees, who are members of virtual groups dispersed
geographically (Bean & Hott, 2005; Wei et al., 2005), as well as telecommuters can
embrace this benefit. Enhanced communication was also cited as a key benefit in an
emergency response system developed to integrate seven member colleges of
Claremont University Consortium with the central coordinating entity. The units,
originally using telephone and radio as its means of communication, found that the
wiki supports cross-unit collaboration more effectively (Raman, 2006).
2.3.8.1.3 Improved Knowledge Sharing and Reuse
Use of information technology as a knowledge management system leads to
more effective knowledge creation, storage, transfer, and application in organizations
(Alavi & Leidner, 2001). By using wikis, organizations can take advantage of
collective wisdom to create an effective source for knowledge sharing (Bean & Hott,
40


2005; Hasan & Pfaff, 2006; Raman, 2006; Wagner & Majchrzak, 2007; Wiebrands,
2006). As a conversational management tool, wikis provide speed and ease with so-
called single-click publication allowing modifications to be realized
instantaneously (Wagner & Bolloju, 2005; Wagner & Prasamphanich, 2007). The
addition of searching capabilities allows for quick and easy access to knowledge for
reuse. In a survey of corporate wiki users, wikis were indicated as an effective means
of knowledge sharing which led to increased knowledge reuse (Majchrzak et al.,
2006).
2.4 Adoption and Acceptance of Information Technology
2.4.1 Beyond TAM
Acceptance and adoption research specific to the context of information
systems has a theoretical basis stemming from two more general areas: research in
psychology and research in organizational behavior. Research in psychology tends to
have a model-centric approach, with numerous studies based on Theory of Planned
Behavior (TPB) and/or Theory of Reasoned Action (TRA) (Venkatesh, Davis, &
Morris, 2007). Research in organizational behavior is more outcome-centric focusing
largely on job-related outcomes such as job satisfaction. One of the earliest
acceptance models developed for use specifically in IS is Davis Technology
Acceptance Model (TAM) (Davis, 1989). Largely hased on TPB and TRA,
antecedents of the TAM model, perceived ease of use and perceived usefulness, are
41


posited to predict intentions to use a particular technology, in turn predicting actual
usage behavior.
Studies incorporating TAM are quite numerous with many proving TAMs
predictive validity in IS use contexts (Lee, Kozar, & Larsen, 2003; Venkatesh et al.,
2007). However, there is debate within the IS field as to whether the use of TAM has
been exhausted, and that broadening and deepening the research through development
of more context-specific models with greater richness is needed. One suggestion is
that future research should challenge the basic tenets of intention (Bagozzi, 2007;
Schwarz & Chin, 2007; Venkatesh et al., 2007; Venkatesh, Morris, Davis, & Davis,
2003), providing more investigation into outcomes of technology use, including both
individual and organizational outcomes. Another criticism of TAM involves the
limitations of self-reported use compared to actual use (Straub & Burton-Jones, 2007;
Venkatesh et al., 2003). Further possible flaws lie in the lack of attention to changes
in importance of factors over time and other potential moderating factors such as age,
gender, and voluntariness (Venkatesh et al., 2007).
An expanded version of TAM, the Unified Theory of Acceptance and Use of
Technology (UTAUT), provides four core constructs related to intention and usage
with the addition of four moderators (Venkatesh et al., 2003). Developed by closely
examining eight different models of technology acceptance, then formulating a
unified model, the core constructs (performance expectancy, effort expectancy, social
influence, facilitating conditions) and moderating factors (gender, age, experience,
42


voluntariness) constitute a step forward from the original TAM model, however
UTAUT still focuses largely on intention to use, thus may be more appropriate for
pre-adoptive environments.
The purpose of this research is not to hash out the debate over TAM, however
the main points of contention are worth summarizing. As mentioned above, focus on
intention to use and/or self-reported use are problematic. Studies are often specific to
a single point in time as opposed to longitudinal. Also, there may be further
moderating factors worth consideration such as group, social and cultural aspects
(Bagozzi, 2007), as well as facilitating conditions such as user involvement and
participation, resource availability and training (Schwarz & Chin, 2007).
2.4.2 Technology as Innovation
Competing models of TAM are often approached with the view that
introducing a new technology constitutes an organizational innovation. To be
classified as innovative, any idea, artifact or behavior must be considered new or
novel, if not by definition, at least by perception (Lyytinen & Rose, 2003).
Innovations are described as disruptive when they cause dramatic changes in the
architecture of work process (Sherif, Zmud, & Browne, 2006). Innovation
implementation may also cause a shift in the balance of power within the organization
leading to resistance by users (Lapointe & Rivard, 2005). The specific case of
technological innovation involves advances in computing capability consisting not
43


only of a technological component, but also potentially new business processes and
organizational structure (Lyytinen & Rose, 2003). The goal for innovation research
should be to provide management with interventions to foster more successful
adoption of technology innovation.
2.4.2.1 Contextual Factors
The degree to which a technology introduced to an organization is perceived
as new and novel may occur at different levels, namely individual, group, or
organization. Similarly, certain components of the implementation may be viewed as
innovative, such as the task, process or technology itself. This variety in viewpoints
describes the contextual factors influencing technology adoption and diffusion and
can be categorized according to the level of analysis (see Table 6).
Table 6: Categories of contextual factors
Category Characteristics
Environmental Uncertainty, Inter-organizational Dependence
Organizational Culture, Specialization, Hierarchical Structure, Organizational Innovativeness in IT, Resource Availability, Management Support
User/Individual Job Tenure, Job Profile, Education, Adopter Categories, Personal Innovativeness in IT, Resistance to Change
Task Task Uncertainty, Autonomy, Responsibility of Person Performing the Task, Task Variety
Technology Complexity, Platform, Hardware/Software, Design/Interface
Social Trust, Reciprocity Expectation, Enjoyment in Helping Others, Pro- Sharing Norms
Motivational Economic incentives, Social/psychological incentives
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2A.2.2 Stages of Adoption
Approaches to IT innovation research also differ in the way the specific stages
of adoption are addressed. Acceptance of technology is best viewed as a progression
in which perceptions related to the technology occur simultaneously throughout the
process of adoption (Schwarz & Chin, 2007). One approach is to focus on a three
stage model of adoption: Initiation, Adoption, Implementation. Alternatively, an
expanded model involves the six stages of implementation: Initiation, Adoption,
Adaptation, Acceptance, Routinization, Infusion (Cooper & Zmud, 1990). Rogers
Diffusion of Innovation Theory (1995) has served as the foundation of IT innovation
research (Agarwal & Prasad, 1997; Beatty, Shim, & Jones, 2001; Dedrick & West,
2004; Fichman, 2001,2004; Fiol & O'Connor, 2003; Kraut, Rice, Cool, & Fish, 1998;
Lim, Choi, & Park, 2003; Moore & Benbasat, 1991). Rogers five stage model
consisting of Knowledge, Persuasion, Decision, Implementation and Confirmation,
has taken on a variety of reinventions with some indicating that certain constructs
predict adoption, while others predict diffusion (Agarwal & Prasad, 1997). To
distinguish between the two terms, adoption pertains to initial use of an innovation,
whereas diffusion pertains to continued or routinized use. For the purposes of this
research, we will assume that implementation of a knowledge management system is
in place. We will then consider the notion of adoption as the initial use of the
technology by the users.
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2.4.3 Innovation Theories
2.4.3.1 Research Streams of IT Innovation
Fichman provides an analysis of the evolution of IT innovation research. The
bulk of the research involves what Fichman describes as the dominant paradigm,
characterized by the desire to explain innovation using economic-rationalistic
models, whereby organizations that have a greater quantity of the Right Stuff are
expected to exhibit a greater quantity of innovation(Fichman, 2004). The Right
Stuff is described as greater innovation-related needs and abilities, as well as the
earliness and effectiveness of adoption. The substantial research in this area has
provided valuable insight and guidance for promoting effective innovation; however,
breakthroughs of future research must step away from the dominant paradigm.
New perspectives on IT innovation research involve departures from the
standard conceptualizations of independent variables commonly studied in the
dominant paradigm. Fichman describes seven key perspectives in the emerging
research, summarized in Table 7 (Fichman, 2004).
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Table 7: Perspectives of innovation research
Perspective Central Concept
Innovation Configurations An innovation configuration is a specific combination of factors that are collectively sufficient to produce a particular innovation-related outcome.
Social Contagion Social contagion exists when organizations feel social pressure to adopt an innovation that increases in proportion to the extent of prior adoptions.
Management Fashion Management fashion waves are relatively transitory collective beliefs, disseminated by the discourse of management-knowledge entrepreneurs, that a management technique resides at the forefront of rational management progress.
Mindfulness An organization innovates mindfully to the extent that it attends to the innovation with reasoning grounded in its own facts and specifics.
Technology Destiny Technology destiny is the ultimate disposition of a technology at the point it is no longer considered to be something new among most members of its target adoption community.
Quality of Innovation The quality of innovation is the extent to which an organization has adopted the right innovation, at the right time and in the right way.
Performance Impacts Performance impacts capture the effect an innovation has on business process measures (e.g., inventory turns), firm level measures (e.g., productivity and accounting profit based), and market-based measures (e.g., capitalization, Tobins Q).
2.4.3.2 Rogers Diffusion of Innovation Theory
Diffusion of Innovation Theory was originally developed with a more general
concept of organizational innovation, in other words, not the specific case of
technology innovation. This work categorized individuals into five basic types:
innovators, early adopters, early majority, late majority, and laggards (Rogers &
47


Allbritton, 1995). Rogers also proposed a basic model for the diffusion of
innovations, as well as core constructs. Rogers five stage model consists of
knowledge, persuasion, decision, implementation and confirmation. The core
constructs affecting innovation diffusion include relative advantage, compatibility,
complexity, observability and trialability (see Table 8). As information systems
began to grow and develop, Rogers theory was adapted to the specific case of
technology innovation.
Table 8: Rogers attributes of innovation adoption
Construct Definition
Relative Advantage The degree to which an innovation is perceived as being better than its predecessor
Compatibility The degree to which an innovation is being consistent with the existing values, needs, and past experiences of potential adopters
Complexity The degree to which an innovation is perceived as being difficult to use
Observability The degree to which the results of an innovation are observable to others
Trialability The degree to which an innovation may be experimented with before adoption
2.4.3.3 Innovation Diffusion Theory
Moore and Benbasat draw on Rogers Theory as well as Technology
Acceptance Model to develop Innovation Diffusion Theory (IDT) consisting of a
model comprised of eight constructs focusing on users perceptions of adopting an
48


information technology innovation (see Table 9): voluntariness, image, relative
advantage, compatibility, ease of use, result demonstrability, trialability, and visibility
(Moore & Benbasat, 1991).
Table 9: Moore and Benbasats attributes of IT innovation adoption
Construct Definition
Voluntariness The degree to which use of the innovation is perceived as being voluntary, or of free will
Image The degree to which use of the innovation is perceived to enhance ones image or status within the organization
Relative Advantage The degree to which using the innovation is perceived as being better than using its predecessor
Compatibility The degree to which using the innovation is being consistent with the existing values and past experiences of potential adopters
Ease Of Use The degree to which the innovation is easy to learn and use
Result Demonstrability The degree to which the results of using an innovation are observable to others
Trialability The degree to which an innovation may be experimented with before adoption
Visibility The degree to which using the innovation is visible within the organization
An important difference between Rogers theory and the model developed by Moore
and Benbasat is the level of analysis. Rogers attributes of innovation adoption are
defined such that the focus in on the innovation itself. Moore and Benbasat
transformed the original definitions to describe the behavior of using the innovation,
and labeled the behaviors Perceived Characteristics of Innovating (PCI) (1991).
Thus the level of analysis is at the behavior level.
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3. Conceptual Framework
3.1 Triangular Relationship of Underlying Fields of Study in Organizations
The latest trend in technology involves systems and applications
encompassing higher levels of social interaction and collaboration. With
globalization of organizations also gaining in popularity, the ability to work more
effectively and efficiently with the aid of technology provides considerable benefit.
Even before technology became prevalent in the work environment, organizations
were faced with an on-going challenge to balance the management of a variety of
resources including both physical resources and human resources. Todays
organizations are faced with the additional obstacle of managing technological
resources. The perpetual balancing act brings together three fields of study: strategic
management, organizational theory and information systems. This dilemma can be
visualized as a triangular relationship, depicted in Figure 2.
50


Information Systems
Strategic Management * ~ Organizational Theory
Figure 2: Triangular Relationship of Underlying Fields of Study
Strategic management is a complex discipline involving the relentless pursuit
of an organizations long-term objectives, with one of the primary objectives being
sustainable competitive advantage. Pursuing these goals entails adaptation and
change which can be seen as obstacles met with resistance by members of the
organization. Overcoming these hurdles can be done successfully by incorporating
lessons learned from organizational theory. Imperative to this process is proper
consideration of the social behavior within the organization. Technology
implementation is often a key element of the strategic plan, helping organizations to
utilize their resources more efficiently. Introducing such technologies can be deemed
innovation, yet another hurdle to overcome. In order to ensure the best chance of
51


success, an organization must focus on all three of these intertwined areas on a
continual basis.
A crucial component of an overall strategic plan involves knowledge
management. Intellectual capital can be one of the most valuable resources in an
organization. Harnessing knowledge accompanied by sharing knowledge are
imperative processes in any and all operational areas of an organizations day-to-day
activities. While knowledge management systems are attractive solutions to meeting
organizational needs, successful implementation and utilization is not always a
guarantee. Balancing the triangular relationship of underlying fields of study in
organizations is essential for effective adoption and usage of knowledge management
systems.
While legacy systems can still be used effectively, todays organizations often
seek new technologies to resolve issues in knowledge management. Wiki technology
is one example of an innovative option for an effective knowledge management
system. Regardless of technological platform, the introduction of a new system can
produce wavering results depending on the overall approach of the implementation.
As mentioned previously, the process of technology implementation is a multi-stage
process beginning with the decision to implement and evolving from initial
implementation to widespread acceptance and diffusion of the technology. This
project involves analysis of only a portion of the extensive process. The stages
examined in this analysis are depicted in Figure 3. It is assumed that the technology
52


has been implemented and users are at some level of adoption. The focus is to
examine the factors influencing the adoption level and subsequent usage level.
Figure 3: Process of Technology Innovation Adoption and Acceptance
3.2 New Model
With an extensive body of research in the area of technology acceptance and
adoption to draw upon, areas for improvement are of interest. Two of the most
commonly used models in prior research are Davis Technology Acceptance Model
(TAM) (Davis, 1989) and Moore and Benbasats model of Innovation Diffusion
Theory (IDT) (Moore & Benbasat, 1991). Although TAM does include an element of
user behavior, bringing together organizational theory and information systems
theory, the model ignores social factors and the model is often criticized for focusing
on the intention to use as opposed to actual usage. TAM, as well as other models
based on intention, is more effective for situations prior to adoption, serving as a tool
53


to help predict whether a technology may or may not be adopted by users (Davis,
Bagozzi, & Warshaw, 1989; Straub, Limayem, & Karahanna, 1995; Venkatesh,
Brown, Maruping, & Bala, 2008).
Innovation Diffusion Theory focuses largely on technological aspects, but
does incorporate user behaviors and social factors associated with the technology.
However, the way the dependent variable is defined and operationalized varies among
studies with many continuing to focus on behavioral intention (Agarwal & Prasad,
1997; Karahanna, Straub Jr., & Chervany, 1999) and others on actual usage (Agarwal
& Prasad, 1997; Compeau & Higgins, 1995; Igbaria, Pavri, & Huff, 1989; Limayem
& Hirt, 2003; Venkatesh et al., 2008). Measuring actual usage in subjective
(duration, frequency, intensity) or objective (system logs) terms may provide more
explanatory power than intention (Limayem & Hirt, 2003; Limayem, Hirt, & Cheung,
2007; Plouffe, Hulland, & Vandenbosch, 2001; Venkatesh et al., 2008). Studies of
IDT focusing on adoption tend to simply target either adopters or non-adopters for
evaluation (Lu, Quan, & Cao, 2009) or model adoption as a dichotomous variable
(Cooper & Zmud, 1990), thus ignoring a variable level of adoption.
This research study encompasses a wide range of respondents employed in a
variety of organizations and engaged in technology usage at various stages of
adoption. As pre-adoption stages are not the focus of the study, measuring behavioral
intentions to adopt or use a technology are inappropriate. Furthermore, issues have
been raised as to the assumption of intentions as a reliable predictor of actual
54


behavior, as the relationship is often found to be more complex (Limayem et al.,
2007). Therefore, the dependent variable is measured according to adoption level and
subjective measures of usage level. Adoption is measured as length of individual
adoption indicating the approximate stage in which the respondent is engaged. This
measure also allows for comparison of early adopters and late adopters.
Usage measures which are subjective or self-reported are often criticized as
potentially inaccurate measures of actual usage. However, this issue can be improved
by incorporating multiple dimensions of usage, allowing for a richer assessment of
the extent of usage. Venkatesh et al. (2008) employ three components of system use:
duration, frequency, and intensity. These three measures reflect the variations
according to unit of time: hours of daily use, weekly or monthly use, or years of use.
Agarwal and Prasad (1997) incorporate a frequency measure to better ascertain the
outcomes of acceptance behavior. Similarly, Compeau et al. focus on use intensity as
a more appropriate measure to explain behavior patterns after adoption has taken
place (Compeau, Meister, & Higgins, 2007). Frequency and intensity measures may
also be utilized in studies comparing intentions and actual usage (Limayem & Hirt,
2003). The dimensions utilized in this study are similar to those of Igbaria et al.
(1989) which include inclusion of computer analysis in decision making, actual daily
use of microcomputers, frequency of use, number of packages used, level of
sophistication of usage.
55


The model presented in this research is based upon Innovation Diffusion
Theory (IDT). The IDT model may provide more richness over TAM having shown
that the perceived characteristics of innovating (PCI) can have a significant effect on
intentions after controlling for effects of Usefulness or Ease of Use (Plouffe et al.,
2001). The decision to use IDT as a basis is also motivated by preliminary
exploratory research into Wiki technology diffusion (Hester & Scott, 2008), which
suggests a model for Wiki diffusion encompassing three constructs drawn from IDT,
Compatibility, Relative Advantage, Complexity, along with Organizational Culture
and a moderating factor of Critical Mass. As the study of critical mass requires a
longitudinal study, the construct was not included in this research.
The proposed new model expands IDT to include a construct motivated by the
increasing level of social interaction involved in work processes. Another
exploratory study was conducted to examine importance of social capital factors in
adoption and usage of wikis as a project management tool. A description of the study
is described in detail in Appendix A. The preliminary research indicates that social
capital factors are important to usage of Wiki technology (Hester & Scott, 2007).
Reciprocity Expectation reflects users perceptions of how the social environment
affects technology usage by examining the degree to which contributing knowledge
will result in the reciprocal action of receiving knowledge. As this study focuses on
knowledge management systems as the technology, Reciprocity Expectation was
included as an independent variable for the model.
56


Another addition to the model is a moderating factor, Personal Innovativeness
in IT (PUT). This factor is posited to have a moderating effect because it measures a
users perceptions about their comfort level with information technology in general as
opposed to the specific knowledge management system (KMS) used most frequently.
The independent variables each refer specifically to use of the KMS, whereas PUT is
more broad in scope. PIIT was found affect the relationship between compatibility
and usage intentions of the World Wide Web (Agarwal & Prasad, 1998b), as well as
Ease of Use and Usefulness of other Internet technologies (Lewis, Agarwal, &
Sambamurthy, 2003).
Finally, the proposed model presents an improved method of measuring the
dependent variables. Adoption is measured as length of adoption (Choudhury &
Karahanna, 2008; Eder & Igbaria, 2000) with an additional calculation of adoption
lag to be used for further analysis of early vs. late adopters. The measurement of
usage is greatly improved by incorporating more than one dimension of usage
(Igbaria et al., 1989). Usage is operationalized by measuring the construct along four
dimensions: frequency, total number of tasks, total number of systems utilized, and
level of expertise. As depicted in Figure 4, the eight constructs proposed by
Innovation Diffusion Theory with the addition of Reciprocity Expectation are
postulated to affect both Adoption and Usage, with Personal Innovativeness in IT
moderating the relationship.
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Figure 4: Research Model: Factors Influencing KMS Adoption and Usage
58


3.3 Hypotheses
The research model prompts the following hypotheses, each described as two
relationships with the independent and moderating variables affecting (a) adoption
and (b) usage.
3.3.1 Voluntariness
Voluntariness is defined as the degree to which the use of the KMS is
perceived as being voluntary (Moore & Benbasat, 1991). Adoption is not always a
choice for the user as organizations mandate use of a technology in some instances.
However, in drawing from a diversity of respondents in various organizations, no
assumption was made for this study as to whether use of the knowledge management
system was voluntary. Thus the construct is measured using a scale which allows for
variable levels of Voluntariness.
If use of the system is not mandated by the organization, Voluntariness may
be viewed as a form of social influence through compliance processes
(Bandyopadhyay & Fraccastoro, 2007; Karahanna et al., 1999). In a cross-cultural
study using the UTAUT model, the effect of social influence was found to be stronger
for older men with experience and less income when usage was voluntary
(Bandyopadhyay & Fraccastoro, 2007). Perceptions of Voluntariness also vary over
time. A study focusing on World Wide Web usage indicated that Voluntariness was a
factor in the early stages of usage, however insignificant regarding intentions of
59


future usage (Agarwal & Prasad, 1997). This contradicted findings by Karahanna et
al. that potential adopters perceived initial use as voluntary, but continued use as
more mandatory (Karahanna et al., 1999). The longitudinal study focusing on pre-
adoption and post-adoption beliefs found that although the organizational level
decision to initially adopt a technology was not an important factor, continued use
may be influenced by social norms, with users being pressured not to abandon the
technology, thus perceptions were different depending on the adoption stage.
Although the UTAUT model posits that Voluntariness has a moderating effect
on factors affecting behavioral intention and use behavior (Venkatesh et al., 2003),
this research follows Moore and Benbasats modeling of the construct as having a
direct effect (Moore & Benbasat, 1991). The influence of Voluntariness is posited to
be positive, thus if use of the KMS is perceived to be more voluntary as opposed to
mandatory, there will be an increased level of adoption and usage of the KMS.
Hla. The perceived voluntariness of the KMS will be positively
related to the adoption level in an organization.
Hlb. The perceived voluntariness of the KMS will be positively
related to the usage level in an organization.
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3.3.2 Relative Advantage
Relative Advantage is defined as the degree to which using the KMS is
perceived as being better than using its predecessor (Moore & Benbasat, 1991).
Rogers conceptualization of Relative Advantage is very similar to the perceived
usefulness construct from TAM (Moore & Benbasat, 1991), however the idea differs
in that it refers to a predecessor. Relative Advantage can often take on a variety of
dimensions depending on the environment and technology studied. For instance, in a
study involving adoption of new computing architectures, Relative Advantage was
considered a combination of better software quality, lower costs, better acceptance,
and more backward compatibility (Bajaj, 2000). Compared to proprietary operating
systems, the open source software Linux was perceived as having increased Relative
Advantage in terms of cost and reliability (Dedrick & West, 2004).
Relative Advantage is often found as the best predictor of adoption and usage
(Agarwal & Prasad, 1997, 1998a; Compeau & Higgins, 1995; Karahanna et al., 1999;
Moore & Benbasat, 1991; Plouffe et al., 2001; Tornatzky & Klein, 1982). Wi-Fi
technology adoption among University faculty members indicated Relative
Advantage as of particular importance to early adopters (Lu et al., 2009). Similarly
Relative Advantage and Demonstrability were significant predictors of Internet usage
intention, when other factors were not significant (Plouffe et al., 2001), while
Relative Advantage alone was a significant predictor of intentions to use a
knowledge-based product configuration and ordering system (Agarwal & Prasad,
61


1998a). Consumers indicated increased adoption of electronic marketing channels
with increased perceptions of Relative Advantage over traditional marketing channels
(Choudhury & Karahanna, 2008). Given its proven performance as an important
factor, increased perceptions of Relative Advantage are posited to increase levels of
adoption and usage of the KMS.
H2a. The perceived relative advantage of the KMS will be
positively related to the adoption level in an organization.
H2b. The perceived relative advantage of the KMS will be
positively related to the usage level in an organization.
3.3.3 Compatibility
Compatibility is defined as the degree to which use of the KMS is compatible
with, or requires change, in one's job (Moore & Benbasat, 1991). The perception of
Compatibility may be influenced by compatibility of the study technology with
current technologies as well as skills and tasks (Dedrick & West, 2004) or
compatibility with needs, beliefs and values (Lu et al., 2009). Using yet another
interpretation, Compatibility was an important factor in determining use intensity of a
hospital computer system in terms of compatibility with prior experience and prior
values (Compeau & Higgins, 1995).
Compatibility was found to have a significant impact on merchant adoption of
a smart card-based payment system (Plouffe et al., 2001) and consumers intentions
62


in web-based shopping (Van Slyke, France, & Christie, 2004). Task-technology
compatibility was a major factor in explaining material requirements planning
adoption behaviors (Cooper & Zmud, 1990). A study analyzing adoption of
methodologies for improvement of software development processes found that
compatibility of the methodology with how developers perform their work had a
positive influence on adoption intentions (Riemenschneider, Hardgrave, & Davis,
2002). Compatibility is one of the perceived characteristics of innovating which has
r
been consistently related to adoption (Tomatzky & Klein, 1982). Compatibility is
posited to have a positive influence on both adoption and usage of the KMS.
H3a. The perceived compatibility of the KMS will be positively
related to the adoption level in an organization.
H3b. The perceived compatibility of the KMS will be positively
related to the usage level in an organization.
3.3.4 Ease Of Use
As a component of both TAM and IDT, Ease of Use is extensively researched
and continues to prove to be a relevant factor. Ease of Use is defined as the degree to
which the KMS is easy to leam and use (Moore & Benbasat, 1991). Perceived lack
of Ease of Use can present a considerable obstacle to technology adoption and usage.
If using a technology requires a tremendous effort on the part of the user, usage will
be seriously affected, and in fact, adoption may not occur at all. As described in
63


Appendix A, preliminary research indicated that the absence of Ease of Use had a
negative impact on perceptions of Wiki technology resulting in decreased adoption
and usage (Hester & Scott, 2007).
A research study evaluating of the impact of TAM indicates that the extensive
amount of studies investigating TAM and its many variants have found Ease of Use
as an important determinant of use (Benbasat & Barki, 2007). In a meta-analysis of
TAM research, replications of the model reported Ease of Use as having a significant
impact on word processors, graphics, spreadsheets, e-mail, voice mail, text editors
and GDSS (Lee et al., 2003). Substantial empirical evidence of the importance of
Ease of Use as a component of IDT also exists (Compeau et al., 2007), lending
further support to the expectation that Ease of Use will also be an important factor in
this model.
H4a. The perceived ease of use of the KMS will be positively
related to the adoption level in an organization.
H4b. The perceived ease of use of the KMS will be positively
related to the usage level in an organization.
3.3.5 Result Demonstrability
Result Demonstrability is defined as the degree to which the results of using
the KMS are observable to others (Moore & Benbasat, 1991). In a study comparing
pre- and post-adoption beliefs pertaining to the Windows operating system, Result
64


Demonstrability and Image were the only important factors prior to adoption
(Karahanna et al., 1999). Agarwal and Prasad (1997) also found Result
Demonstrability to be an important factor in World Wide Web adoption, however,
contrary to Karahanna et al.s findings, the effect was significant for intentions of
future use as opposed to initial use. Result Demonstrability had a significant indirect
effect on behavioral intention to use PDAs (Yi, Jackson, Park, & Probst, 2006) and
significant direct effect on adoption of groupware applications (Van Slyke, Lou, &
Day, 2002). Increased perceptions of Result Demonstrability are posited to increase
adoption and usage levels of the KMS.
H5a. The perceived result demonstrability of the KMS will be
positively related to the adoption level in an organization.
H5b. The perceived result demonstrability of the KMS will be
positively related to the usage level in an organization.
3.3.6 Trialability
Trialability is defined as the degree to which it is possible to try using the
KMS (Moore & Benbasat, 1991). A certain degree of Trialability also exists in the
presence of others use of a technology as users are able to experiment with the
technology vicariously (Compeau et al., 2007). Trialability may be more important
for early adopters as the ability to try the technology will decrease levels of
uncertainty. However, as users gain experience, the importance of Trialability will
65


most likely decline. Trialability was found to affect adoption of Wi-Fi technology
among University faculty (Lu et al., 2009). Faculty who did not own a laptop were
unable to experiment with Wi-Fi technology leading to a lesser rate of adoption.
Trialability was also an important factor in the early stages of adoption of the World
Wide Web (Agarwal & Prasad, 1997; Limayem et al., 2007), and Windows
technology (Karahanna et al., 1999). Increased perceptions of Trialability may also
increase adoption and usage levels of the KMS.
H6a. The perceived trialability of the KMS will be positively
related to the adoption level in an organization.
H6b. The perceived trialability of the KMS will be positively
related to the usage level in an organization.
3.3.7 Visibility
Visibility is defined as the degree to which using the KMS is visible within
the organization (Moore & Benbasat, 1991). Visibility refers to the ability to see
others use of the technology. Observation of others use can influence a greater
sense of usability (Compeau & Higgins, 1995). Similar to the notion of Visibility,
opinions of coworkers and supervisors had a positive impact on developers intentions
to adopt methodologies intended to improve software development processes
(Riemenschneider et al., 2002). Visibility may also be viewed as a normative
pressure from peers being most influential in the early stages of adoption when users
66


may tend to comply with others views (Venkatesh & Morris, 2000). Visibility was
also found to influence pre-adoptive behaviors in a longitudinal examination of
Windows technology implementation (Karahanna et al., 1999). In situations where
use of the technology is not mandatory, Visibility may serve as a mechanism to
motivate users to adopt in order to achieve a sense of belonging. Increased
perceptions of Visibility are posited to increase levels of adoption and usage of the
KMS.
H7a. The perceived visibility of the KMS will be positively
related to the adoption level in an organization.
H7b. The perceived visibility of the KMS will be positively
related to the usage level in an organization.
3.3.8 Image
Image is defined as the degree to which the use of the KMS enhances one's
image or status within the organization (Moore & Benbasat, 1991). While Image is
most often associated with organizational identity, a users image also plays an
important role. Perceptions of Image incorporate into the model an aspect of social
influence. Drawing on Image, subjective norms and social factors, social influence is
an important construct in the UTAUT model (Venkatesh et al., 2003). Users may be
more willing to share or contribute knowledge when they feel that it will strengthen
their image. Olivera et al. posit that users enhance their image by demonstrating their
67


proficiency while meeting requests for knowledge in their area of expertise (Olivera,
Goodman, & Tan, 2008).
Image can be of particular importance as users gain experience, meanwhile
placing lesser emphasis on factors such as usefulness, ease-of-use and result
demonstrability (Karahanna et al., 1999). Image may be considered equivalent to the
idea of reputation enhancement which was found to be a significant predictor of
knowledge contribution in electronic networks of practice (Wasko & Faraj, 2005).
Image was an important predictor of merchant adoption of a smart card-based
payment system (Plouffe et al., 2001). As perceptions of Image increase, levels of
adoption and usage of the KMS may also increase.
H8a. A users perceived image will be positively related to the
adoption level in an organization.
H8b. A users perceived image will be positively related to the
usage level in an organization.
3.3.9 Reciprocity Expectation
Defined as the degree to which use of the KMS for knowledge contribution
will lead to future requests for knowledge being met, Reciprocity Expectation is an
important construct from Social Capital Theory. Consideration of Social Capital
Theory research may help to explain how group characteristics, norms, and trust may
influence knowledge sharing and collaboration. With knowledge sharing, users may
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experience reciprocal benefits, whereby contributing knowledge may lead to
receiving knowledge from others when requested (Kankanhalli et al., 2005).
Knowledge sharing is a crucial process for effective use of knowledge management
systems, motivating the inclusion of the construct in this model.
The pilot study described in Appendix A indicated that Reciprocity
Expectation positively influenced usage of Wiki technology (Hester & Scott, 2007).
Reciprocity Expectation has also been found to positively influence attitude toward
knowledge sharing (Bock et al., 2005), knowledge contribution to electronic
knowledge repositories (Kankanhalli et al., 2005) and electronic networks of practice
(Wasko & Faraj, 2005). As users perceive their knowledge sharing behavior as
resulting in reciprocal actions by others, they will be more willing to utilize
knowledge management systems. Therefore, a positive relationship between
Reciprocity Expectation and Adoption and Usage is expected.
H9a. A users perceived reciprocity expectation will be positively
related to the adoption level in an organization.
H9b. A users perceived reciprocity expectation will be positively
related to the usage level in an organization.
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3.3.10 PUT
A more recent development in the stream of IT innovation research is the
notion of Personal Innovativeness in IT (PUT). Defined as the willingness of an
individual to try out any new information technology (Agarwal & Prasad, 1998b),
PUT may be viewed as a moderating factor affecting the relationship between
perceptions of an innovation and intentions to use the innovation. PUT may be used
as a preliminary predictor of adopter categories (Agarwal, Ahuja, Carter, & Gans,
1998) identified as innovators, early adopters, early majority, late majority, and
laggards (Rogers, 1995). At an organizational level, innovativeness as a component
of organizational climate has been shown to affect intention to share knowledge
(Bock et al., 2005). An alternative view at the user level considers users propensity
to innovate composed of technology cognizance, ability explore, and intention to
explore a technology, as an important factor in acquisition and conversion of
knowledge (Nambisan, Agarwal, & Tanniru, 1999).
PUT is expected to be present at a higher level for innovators and early
adopters, which can have an impact on the factors influencing adoption. Higher
levels of PUT may diminish factors involving subjective norms and Ease of Use, and
place more focus on facilitating conditions (Agarwal et al., 1998). PUT was found to
have a significant moderating effect on the relationship between compatibility and
usage intentions of the World Wide Web (Agarwal & Prasad, 1998b). One study
indicated that PUT had a direct effect on Ease of Use and Usefulness of the
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technology (Lewis et al., 2003), while another study indicated a direct effect on Ease
of Use, Result Demonstrability, Perceived Behavioral Control and Subjective Norm
(Yi et al., 2006). The varied findings of PIITs effect at direct, indirect and
moderating levels lends support to the inclusion of PUT as a moderator of the
relationship between the proposed independent and dependent variables in this study.
HlOa. A users perceived PUT will have a moderating effect on
the adoption level in an organization.
HI Ob. A users perceived PUT will have a moderating effect on
the usage level in an organization.
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4. Research Methodology
The research presented in this project can be described as quantitative,
positivist research involving a causal study. The survey method for data collection
was used to test the proposed research model. The unit of analysis is at the individual
level and behavior level as users perceptions of themselves as well as perceptions of
use of the technology are considered.
4.1 Sample
The theoretical population comprises of any and all employees of business
organizations. The study population involves individuals engaging in usage of
knowledge management systems in an organizational setting. The sampling frame
consists of respondents to an on-line survey. The usable sample size was 129,
consisting of 86 females and 43 males. Further details regarding the respondents are
given in Table 10 with a profile of the organizations given in Table 11.
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Table 10: Descriptive statistics of the respondents
Age Frequency (%) Job Profile Frequency (%)
20-29 10(8) Administrative 7(5)
30-39 48 (37) Clerical 2(2)
40-49 34 (26) Technical 28(22)
50-59 33 (26) Supervisory 5(4)
60 or Over 4(3) Middle Management 43 (33)
Education Level Frequency (%) Top Management 10(8)
Some College 9(7) Executive 14(11)
Associate's Degree 4(3) Consultant 4(3)
Bachelor's Degree 41 (32) Education/Training 5(4)
Master's Degree 62 (48) Research 4(3)
Doctorate 13 (10) Other 7(5)
Table 11: Profile of organizations according to type and size
Type Frequency (%) Size Frequency (%)
Communications/Media 5(4) 1-50 32 (25)
Consulting/Professional Services 10(8) 51-200 17(13)
Education 19(15) 201-500 11(9)
Finance/Accounting 14(11) 501-1000 10(8)
Government 10(8) 1001-2000 8(6)
Healthcare 6(5) Over 2000 51 (40)
Information Technology 14(11)
Insurance 3(2)
Manufacturing 10(8)
Marketing 5(4)
Non-profit 3(2)
Research 3(2)
Service Industry 12(9)
Other 15(12)
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4.2 Research Design
An on-line survey was composed for data collection. An image of the survey as
it appeared on-line is given in Appendix B. Before the final survey was deployed,
two pilot studies were performed. Only very minor changes were made to the
original versions of the measures, results of which are described in Appendix C.
Some questions stood out as having awkward wording or outdated terminology, and
some of the questions were rephrased to have a negative slant in order to prevent the
survey from having a positive bias. The questions measuring the constructs were
randomized in the final version.
4.3 Data Collection
Purposive sampling was used to target individuals engaging in utilization of
knowledge management systems. The respondents were located by accessing on-line
groups and communities and contacting individuals employed in various
organizations known to utilize knowledge management systems. The data was
collected in two phases. Phase I of the collection spanned three weeks and resulted in
100 respondents. Phase II of the collection also spanned three weeks and resulted in
34 respondents. Three unusable responses were eliminated immediately, and two
additional responses were removed as outliers.
Two methods were used to invite potential respondents to participate in the
survey. In each case, a brief description of the research was given and a link was
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provided to access the survey. First, e-mails were sent directly to individuals known
to work in organizations utilizing knowledge management systems, and e-mails were
sent to applicable e-mail lists. In each scenarios, an e-mail was sent to a recipient
who was asked to forward the email to additional potential respondents. Second,
messages were posted in the appropriate areas of on-line groups and communities.
Four sources were used for the postings: various wiki communities, Yahoo! Groups,
Linkedln Groups, and Facebook Groups. Further details regarding the sources of
survey participants are given in Appendix D. A table summarizing responses
obtained for each phase is given below. A raffle was offered in exchange for
completing the survey. One winner was randomly selected in each phase of the data
collection.
Table 12: Summary of responses for each phase of data collection
Source Phase I Phase II
Number of Messages or Postings Number of Responses Received Number of Messages or Postings Number of Responses Received
E-mail 10 19 6 15
Wiki Communities 4 28
Yahoo! Groups 5 24 5 7
Linkedln and Facebook Groups 8 29 21 12
Total 27 100 32 34
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4.4 Measures
The survey measures were derived from previously published studies. The
dependent variables include Adoption and Usage. The independent variables include
Voluntariness, Image, Relative Advantage, Compatibility, Ease of Use, Result
Demonstrability, Trialability, Visibility, and Reciprocity Expectation. Also included
is the moderating variable personal innovativeness in IT (PUT). The formal construct
definitions and sources are given in Table 13 below. The actual items used in the
survey are given in Appendix E, along with the corresponding means and standard
deviations. All items were measured using a seven-point Likert scale, from strongly
disagree to strongly agree.
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Table 13: Definitions of the constructs
Construct Definition Source
Voluntariness The degree to which the use of the KMS is perceived as being voluntary (Moore & Benbasat, 1991)
Relative Advantage The degree to which using the KMS is perceived as being better than using its predecessor (Moore & Benbasat, 1991)
Compatibility The degree to which use of the KMS is compatible with, or requires change, in one's job (Moore & Benbasat, 1991)
Ease of Use The degree to which the KMS is easy to learn and use (Moore & Benbasat, 1991)
Result Demonstrability The degree to which the results of using the KMS are observable to others (Moore & Benbasat, 1991)
T rialability The degree to which it is possible to try using the KMS (Moore & Benbasat, 1991)
Visibility The degree to which using the KMS is visible within the organization (Moore & Benbasat, 1991)
Image The degree to which the use of the KMS enhances one's image or status within the organization (Moore & Benbasat, 1991)
Reciprocity Expectation The expectation of knowledge contributors that their current contribution will lead to their future request for knowledge being met The degree to which use of the KMS for knowledge contribution will lead to future requests for knowledge being met (Wasko & Faraj, 2005) Modified to emphasize behavior level
PUT The willingness of an individual to try out any new information technology (Agarwal & Prasad, 1998b)
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4.4.1 Dependent Variables
4.4.1.1 Adoption
Users were asked: (1) How long has the KMS been in place at your
organization, and (2) How long have you engaged in usage of the KMS? The
questions were measured as follows: less than 6 months, 6-12 months, 1-2 years, 2-3
years, 3-4 years, 4-5 years and greater than 5 years. Level of adoption was assessed
as the length of time the user has engaged in usage of the KMS (Choudhury &
Karahanna, 2008). In order to identify early adopters vs. late adopters, the adoption
lag was calculated as the difference between individual adoption and organization
adoption. A positive result indicates that the individual has used the technology
longer than the organization, while a negative result indicates that the organization
has had the technology in place for a period longer than the individual has used that
technology. Although it was not used in the primary analysis, adoption lag was used
to provide group categorization similar to Rogers adopter categories (1995). A
summary of results for responses to length of adoption and the calculated adoption
lag are given in Table 14.
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Table 14: Adoption level and adoption lag
Adoption Level Adoption Lag
Years Frequency (%) Years Frequency (%)
Less than 6 months 9(7) 5 2(2)
6-12 months 13 (10) 4 0(0)
1 -2 years 23(18) 3 2(2)
2-3 years 21(16) 2 5(4)
3-4 years 18(14) 1 5(4)
4-5 years 8(6) 0 93 (72)
Greater than 5 years 37 (29) -1 11(9)
-2 6(5)
-3 5(4)
4.4.1.2 Usage
Based largely on the work of Igbaria et al. (1989), the level of usage was
measured along four dimensions. First, respondents were asked to choose all KMS
utilized as part of their job. A calculation was done to indicate the total number of
KMS utilized. Second, respondents were asked what types of tasks are performed
within the KMS. A calculation was then done to indicate the total number of tasks
performed. Third, respondents were asked to indicate how often they (a) retrieve
and/or read content available on the KMS, (b) modify and/or update content available
on the KMS, and (c) add brand new content to the KMS. Frequency was measured as
follows: several times a day, about once a day, a few times a week, a few times a
month, once a month, or less than once a month (Davis et al., 1989; Limayem & Hirt,
2003; Venkatesh et al., 2008). Finally, users were asked to rate their personal level of
79


expertise with the KMS on a scale from 1 (novice) to 7 (expert). A summary of the
frequency responses are given in Table 15. A summary of results for the calculated
total number of KMS utilized and total number of tasks performed, along with a
summary of responses for level of expertise are given in Table 16.
Table 15: Frequency of use
Retrieve and/or Read Modify and/or Update Add New Content
Frequency (%) Frequency (%) Frequency (%)
Less than once a month 0 11(9) 17(13)
Once a month 2(2) 5(4) 10(8)
Few times a month 4(3) 15(21) 34 (26)
Few times a week 31(24) 46 (36) 36 (28)
Once a day 25 (19) 25 (19) 23 (18)
Several times a day 67 (52) 27 (21) 9(7)
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Table 16: Level of expertise and totals of KMS utilized and tasks performed
KMS Utilized Tasks Performed Level of Expertise
Frequency (%) Frequency (%) Frequency (%)
1 27 (21) 2(2) 1 (novice) 0
2 38 (29) 3(2) 2 5(4)
3 37 (29) 12(9) 3 6(5)
4 18(14) 11(9) 4 12(9)
5 7(5) 22(17) 5 38 (29)
6 2(2) 15(12) 6 37 (29)
7 16(12) 7 (expert) 31 (24)
8 9(7)
9 16(12)
10 or more 23(18)
4.4.2 Independent Variables
The independent variables consist of the eight constructs proposed by Moore
and Benbasat with the addition of Reciprocity Expectation. The unit of analysis for
the independent variables is considered as the behavior level, or the use of the
technology. Moore and Benbasat referred to the constructs as measuring perceived
characteristics of innovating or PCI (Moore & Benbasat, 1991). Thus, the
characteristics of innovating refer to the characteristics of a users process or
behavior. Upon adding the construct Reciprocity Expectation, the definition was
modified slightly to parallel those of the other eight constructs. Formal definitions of
the constructs along with sources are given in Table 13 above.
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4.4.3 Moderating Variable
PUT is defined as the willingness of an individual to try out any new
information technology (Agarwal & Prasad, 1998b). The unit of analysis for PUT is
the individual level as this construct measures users perceptions of themselves. This
variable is postulated as having a moderating affect because it measures personal
innovativeness in general as opposed to measuring a characteristic of the specific
technology utilized as the knowledge management system, as is the case with the nine
independent variables. Therefore, PUT is seen to have an interaction effect and is
classified as a moderating variable.
4.4.4 Demographics and Descriptives
Respondents were asked to indicate their gender, age, education level and job
profile (see Table 10 above). In order to indicate the breadth of companies
represented, respondents were also asked to indicate the type and size of their
organization (see Table 11 above). Respondents were then asked first which KMS
they use as part of their daily job, with the option of choosing more than one, and
second, selecting only one, which of the KMS indicated was used most frequently.
Respondents were also asked which types of tasks are performed within the KMS
with the option of choosing more than one, with a summary of responses regarding
types of KMS utilized and tasks performed are given in Table 17 and Table 18.
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Table 17: Types of KMS utilized
Type of KMS Utilized Chosen as one or more KMS used most frequently
Frequency (%) Frequency (%)
Message board, discussion forum, blog 92(71) 11(9)
Document or content management system 9(7) 5(4)
Group Decision Support System (GDSS) 19(15) 2(2)
Microsoft SharePoint 47 (36) 28(22)
Dynamic Database 23 (18) 3(2)
Proprietary KMS 6(5) 4(3)
Web-based system or Intranet 86 (67) 38 (29)
Wiki technology based system 66 (51) 36 (28)
Social networking application 5(4) 0
Other 2(2) 2(2)
Table 18: Types of tasks performed
Types of Tasks Performed Frequency (%)
Searching for content or requesting information 108 (84)
Reading existing content 112(87)
Retrieving existing content 102(79)
Adding to existing content 110(85)
Making comments on existing content 79 (61)
Making small corrections in factual inaccuracies 63 (49)
Integrating ideas into existing content 68(53)
Reorganizing content 68 (53)
Editing others' grammar or spelling 31 (24)
Rewriting whole paragraphs 25(19)
Adding brand new content 84 (65)
Rolling-back others' writing 14(11)
Structuring or Organizing Pages 3(2)
Other 10(8)
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4.5 Power and Sample Size
A priori statistical power analysis can be calculated using various heuristics.
Used as a long-time standard for structural equation modeling using LISREL, the first
indicates that sample size should be at least five times the number of indicators. The
initial proposed model is comprised of twelve variables, with the nine independent
variables and one moderating variable involving three indicators each for a total of 30
indicators. Seven indicators are used for the dependent variables: one for adoption
and six for usage. Thus, there are 37 indicators, 37 x 5 = 185, is the required sample
size. An alternative method states that, when using partial least square (PLS), the
sample size is independent of the number of indicators when the model is reflective
(Chin, Marcolin, & Newsted, 2003). The heuristic requires ten times the construct
with the largest number of structural paths, which would be either of the two
dependent variables, resulting in ten paths. This method indicates 10 x 10 = 100 as
an adequate sample size. As this research utilizes PLS as opposed to LISREL, the
second heuristic will be considered adequate. The usable sample size of 129 exceeds
the sample size of 100 deemed adequate by these power calculations. Although it
cannot be calculated a priori, yet another method suggests that the ratio between the
number of observations and the number of independent variables needs to be within
the range of 5 to 30 (Kanawattanachai & Yoo, 2007). Counting the moderating
variable as an independent variable, this gives 129 to 10, or 12.9, which is within the
acceptable range.
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5. Research Analysis and Results
The partial least squares (PLS) method was used to examine the hypotheses,
as it is recommended for complex models focusing on prediction, and allows for
minimal demands on measurement scales, sample size, and residual distribution (Chin
et al., 2003). A two-stage analysis was performed using confirmatory factor analysis
to assess the measurement model followed by examination of the structural
relationships. The PLS method allows for simultaneous analysis of the measurement
and structural models, and allows each indicator to vary in how much it contributes to
the composite score of the latent variable (Chin et al., 2003). PLS also allows for
latent variable modeling of interaction effects, necessary for the proposed model as it
includes a moderating variable. Path modeling and analysis was performed using
SmartPLS (Ringle, Wende, & Will, 2005).
5.1 Measurement Model
The measurement model is depicted in Figure 5 (EOU: Ease of Use; IMG:
Image; RAD: Relative Advantage; RCX: Reciprocity Expectation; RDM: Result
Demonstrability; TRL: Trialability; VIS: Visibility; VOL: Voluntariness). The latent
variables representing both the independent variables and the moderating variable in
the research model each have three indicators. The latent variable representing the
dependent variable adoption has only one indicator. The latent variable representing
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Full Text

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ANALYSIS OF FACTORS INFLUENCING ADOPTION AND USAGE OF KNOWLEDGE MANAGEMENT SYSTEMS AND INVESTIGATION OF WIKI TECHNOLOGY AS AN INNOVATIVE ALTERNATIVE TO TRADITIONAL SYSTEMS by Andrea J. Hester B.S., Illinois State University, 1993 M.S., Southern Illinois University Edwardsville, 2004 A Dissertation Submitted to the University of Colorado Denver in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Computer Science and Information Systems 2009

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by Andrea J. Hester All Rights Reserved.

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This Dissertation for the Doctor of Philosophy Degree by Andrea J. Hester has been approved by Date r

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Hester, Andrea J. (Ph.D., Computer Science and Information Systems) Analysis of factors Influencing Adoption and Usage of Knowledge Management Systems and Investigation of Wiki Technology as an Innovative Alternative to Traditional Systems Dissertation directed by Associate Professor Judy Scott ABSTRACT Knowledge management strives for effective capture and application of organizational knowledge, a valuable resource imperative in sustaining an organization. In an effort to better achieve knowledge management initiatives, factors influencing increased adoption and usage of various technologies implemented as knowledge management systems are of considerable interest. Advances in technology have fostered new approaches to knowledge management in the form of web-based collaborative technologies supporting environments of social computing. Wiki technology is an emerging trend providing an effective knowledge management system with benefits of improved communication and collaboration, work processes, and knowledge sharing. Implementation oftechnological solutions are often deemed organizational innovations subject to potential problems of resistance as well as obstacles including organizational cultures lacking an environment conducive to effective knowledge creation and sharing. With Innovation Diffusion Theory (IDT) as a foundation, this research examines factors influencing adoption and usage of knowledge management systems, with further

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attention given to Wik.i technology. The original IDT model is expanded to include an additional independent variable, Reciprocity Expectation, and a moderating variable, Personal Innovativeness in IT (PIIT). Results indicate that some factors are important in determining Adoption while others are important for Usage. Voluntariness, Result Demonstrability, Visibility and Reciprocity Expectation were found to be important factors having a significant positive impact on Adoption, while a counter-intuitive result is presented for Ease of Use due to a significant negative path. Relative Advantage, Trialability, and Visibility were found to be important factors having a significant positive impact on Usage. Preliminary analysis ofWik.i technology indicated a different set of influential factors compared to traditional KMS. Also, the moderating effect of PIIT was much stronger for Wik.i technology based systems. Post Hoc analysis provides further understanding of the results and avenues for future research. Implications for researchers and practitioners are discussed in the conclusion. This research contributes theoretical and empirical support for an extended IDT model, and a theoretical contribution to the growing body of research of Wik.i technology. This abstract accurately represents the contents of the candidate's dissertation. I recommend its publication. Signed

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DEDICATION I dedicate this dissertation to my wonderful son, Rudy, who is the light of my life and inspires me every moment of every day. I also dedicate this to my family who have continually supported and believed in me. I would like to acknowledge my Mother, the wind beneath my wings. From a very young age, she taught me that women are all powerful. She has supported every aspect of my life and guided me throughout with her wisdom and words of encouragement. She challenged me to both set and achieve my goals, and she inspired me to follow my dreams. She has devoted her life first to her family, and second to a career in education where she has exhibited compassion and dedication. She has always shown unwavering strength and is the true heart and soul of our family. To Mom, I can only hope to be as good of a mother, and person, as you. I am truly blessed to have not only my Mother, but generations of role models in my family with numerous admirable qualities, only a few of which are intelligence, creativity, benevolence, diligence and perseverance. The hard-earned success of my grandparents and great-grandparents has paved the way for my success, and for that I am eternally grateful. To my beloved Grandpa and Grandad, thank you and I miss you.

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ACKNOWLEDGEMENT My thanks to my advisor, Judy E. Scott, not only for her support of my dissertation, but also for inspiring and motivating me throughout my program. I also wish to thank all members of my committee for their valuable participation and insights. To all ofthe professors and fellow students I have had the pleasure of working with in my program, thank you for not only challenging me but also encouraging and supporting me. I would like to say a special thank you to Dr. Jo Ellen Moore, Dr. Susan Yager, and Dr. Doug Bock for rekindling my desire to pursue higher education, and providing motivation and guidance throughout my journey.

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TABLE OF CONTENTS Figures ........................................................................................................................ xiii Tables ......................................................................................................................... xiv Chapter I. Introduction ....................................................................................................... I I.I Research Problem & Scope .............................................................................. 2 I.2 Topic Importance .............................................................................................. 4 I.3 Research Questions ........................................................................................... 7 I.4 Research Approach ........................................................................................... 8 I.5 Contribution of Research .................................................................................. 9 1.6 Outline of the Dissertation ................................................................................ 9 2. Literature Review ............................................................................................ II 2.I Knowledge Management ................................................................................ II 2.I.I Knowledge ...................................................................................................... II 2.I.2 Knowledge Management ................................................................................ I2 2.I.3 Knowledge Processes ...................................................................................... I3 2.1.4 Knowledge Management Systems .................................................................. I5 2.2 Combining Technological and Social Aspects of Knowledge ....................... I6 2.2.1 Collaboration and Collaborative Technologies .............................................. 16 2.2.2 Conversational Knowledge Management ....................................................... I9. Vlll

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2.2.3 Conversational Technologies .......................................................................... 21 2.3 Wiki Technology ............................................................................................ 23 2.3.1 History ............................................................................................................. 23 2.3.2 "The Wiki Way" ............................................................................................. 25 2.3.3 Design Principles of Wikis ............................................................................. 26 2.3.4 Wiki Characteristics ........................................................................................ 28 2.3.5 Trends in Wiki Technology ............................................................................ 30 2.3.6 Research of Wiki Technology ........................................................................ 34 2.3.7 How is Wiki Technology Unique? ................................................................. 35 2.3.8 Why Research Wiki Technology? .................................................................. 37 2.4 Adoption and Acceptance of Information Technology .................................. 41 2.4. 1 Beyond TAM .................................................................................................. 41 2.4.2 Technology as Innovation ............................................................................... 43 2.4.3 Innovation Theories ........................................................................................ 46 3. Conceptual Framework ................................................................................... 50 3.1 Triangular Relationship ofUnderlying Fields ofStudy in Organizations ...... 50 3.2 New Model. ..................................................................................................... 53 3.3 Hypotheses ...................................................................................................... 59 3.3.1 Voluntariness .................................................................................................. 59 3.3.2 Relative Advantage ......................................................................................... 61 3.3.3 Compatibility .................................................................................................. 62 IX

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3.3.4 Ease Of Use ..................................................................................................... 63 3.3.5 Result Demonstrability ................................................................................... 64 3.3.6 Trialability ....................................................................................................... 65 3.3.7 Vtstbthty ......................................................................................................... 66 3.3.8 Image ............................................................................................................... 67 3.3.9 Reciprocity Expectation .................................................................................. 68 3.3.1 0 PIIT ................................................................................................................. 70 4. Research Methodology ................................................................................... 72 4.1 Sample ............................................................................................................. 72 4.2 Research Design .............................................................................................. 74 4.3 Data Collection ............................................................................................... 74 4.4 Measures ......................................................................................................... 76 4.4.1 Dependent Variables ....................................................................................... 78 4.4.2 Independent Variables .................................................................................... 81 4.4.3 Moderating Variable ....................................................................................... 82 4.4.4 Demographics and Descriptives ..................................................................... 82 4.5 Power and Sample Size ................................................................................... 84 5. Research Analysis and Results ....................................................................... 85 5.1 Measurement Model ....................................................................................... 85 5.1.1 Multicollinearity ............................................................................................. 88 5.1.2 Reliability ........................................................................................................ 88 X

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5.1.3 Validity ........................................................................................................... 89 5.2 Structural Model ............................................................................................. 93 5.2.1 Baseline Model (Without PIIT) ...................................................................... 93 5.2.2 PUT as Direct Effect ....................................................................................... 97 5.2.3 PUT as Moderating Effect ............................................................................ 100 5.2.4 Model Comparison ........................................................................................ 103 5.2.5 Hypotheses Results ....................................................................................... 105 6. Discussion ..................................................................................................... 107 6.1 Research Questions 1 and 2 .......................................................................... 1 08 6.1.1 Factors Indicating Positive Relationships ..................................................... 108 6.1.2 Negative Relationship between Ease of Use and Adoption .......................... 110 6.1.3 Factors Lacking Significance ........................................................................ 115 6.2 Research Questions 3 and 4 .......................................................................... 117 6.3 Post Hoc Analysis ......................................................................................... 122 6.3.1 Analyzing Level of Experience .................................................................... 122 6.3.2 Experience as a Moderator ............................................................................ 123 7. Conclusion .................................................................................................... 125 7.1 Limitations .................................................................................................... 125 7.2 Implications for Theory ................................................................................ 127 7.3 Implications for Practice ............................................................................... 130 7.4 Contributions ................................................................................................. 132 Xl

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Appendix A Exploratory Study Examining Wikis for Project Management ................... 136 8 Image of Survey ........................................................................................... 144 C Modifications Resulting from Pilot Studies ................................................. 145 D Sources for Survey Participants ................................................................... 148 E Survey Items ................................................................................................ 150 F Factor Loadings and Cross-Loadings .......................................................... 153 G Significance of Outer Loadings for Competing Models .............................. 154 Bibliography .............................................................................................................. 156 Xll

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LIST OF FIGURES Figure 1. Knowledge Pyramid ........................................................................................ 11 2. Triangular Relationship of Underlying Fields of Study ................................. 51 3. Process ofTechnology Innovation Adoption and Acceptance ....................... 53 4. Research Model: Factors Influencing KMS Adoption and Usage .................. 58 5. Original Measurement Model ......................................................................... 87 6. Results ofPLS Analysis for Baseline Model and Adoption ........................... 95 7. Results ofPLS Analysis for Baseline Model and Usage ................................ 96 8. Results ofPLS Analysis with PIIT as Direct Effect on Adoption .................. 98 9. Results of PLS Analysis with PIIT as Direct Effect on Usage ....................... 99 10. Results ofPLS Analysis with Moderating Effect ofPIIT on Adoption ....... 101 11. Results ofPLS Analysis with Moderating Effect ofPIIT on Usage ............ 102 12. Chart of Adoption Level ............................................................................... 112 13. Chart of Level of Expertise ........................................................................... 113 14. Chart of Average Response to PIIT .............................................................. 114 15. Social Capital via Wiki Usage ...................................................................... 138 16. Survey Image ................................................................................................ 144 Xlll

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LIST OF TABLES Table I. Categories of Collaborative technologies ....................................................... I7 2. CSCW work context matrix ............................................................................ I8 3. Description of community of practice types ................................................... 2I 4. Characteristics of conversational technologies ............................................... 22 5. Comparison of activities performed with different technologies ................... 28 6. Categories of contextual factors ...................................................................... 44 7. Perspectives of innovation research ................................................................ 47 8. Rogers' attributes of innovation adoption ................................... .' ................... 48 9. Moore and Benbasat's attributes ofiT innovation adoption .......................... 49 I 0. Descriptive statistics of the respondents ......................................................... 73 II. Profile of organizations according to type and size ........................................ 73 I2. Summary of responses for each phase of data collection ............................... 75 13. Definitions of the constructs ........................................................................... 77 I4. Adoption level and adoption lag ..................................................................... 79 15. Frequency of use ............................................................................................. 80 I6. Level of expertise and totals of KMS utilized and tasks performed ............... 8I I7. Types ofKMS utilized .................................................................................... 83 I8. Types oftasks performed ................................................................................ 83 XIV

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19. Internal consistency indicated by Cronbach's alpha ....................................... 89 20. Composite reliability and average variance extracted (AVE) ........................ 91 21. Correlation between constructs ....................................................................... 92 22. Direct effect, interaction effect and total effect ............................................ I 04 23. Model comparison of effect size ................................................................... 105 24. Tests ofhypotheses HI through H9 .............................................................. 106 25. Test of hypotheses HIOa and HIOb .............................................................. 106 26. Comparison of research models for wikis and other KMS ........................... 119 27. Effect size for model comparison for wiki data set ...................................... 121 28. Effect size for model comparison for non-wiki data set.. ............................. 121 29. Model comparison of effect size ................................................................... 124 30. Pilot study constructs, survey items and means ............................................ 139 31. Feedback or advice and action regarding pilot studies ................................. 145 32. Original and modified versions of survey items ........................................... 146 33. Source type and name for survey participants .............................................. 148 34. Survey item definitions, means, standard deviations .................................... 150 35. Factor loadings and cross-loadings ............................................................... 153 36. Significance of outer loadings for competing models .................................. 154 XV

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1. Introduction The research presented in this dissertation involves examination of factors influencing the adoption and usage of knowledge management systems, with further attention given to Wiki technology. The proposed research model is comprised of independent variables including the constructs of Innovation Diffusion Theory (Voluntariness, Relative Advantage, Compatibility, Ease of Use, Result Demonstrability, Trialability, Visibility, and Image) with the addition of Reciprocity Expectation. Interaction between the independent variables and the moderating variable ofPersonal Innovativeness in IT is hypothesized to predict the two dependent variables, Adoption and Usage. The survey method is used to obtain data from respondents using a variety of knowledge management systems. Analysis of the data is two-fold with an examination of knowledge management systems in general followed by analysis of the specific case of Wiki technology. Given the role of knowledge as a valuable resource imperative in sustaining an organization, attempts to better achieve knowledge management initiatives of effective capture and application of organizational knowledge are of considerable interest. Advances in technology have fostered new approaches to knowledge management in the form of web-based collaborative tools supporting environments of social computing with Wiki technology emerging as an effective alternative to 1

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traditional systems. Examination of factors enabling increased adoption and usage of various technologies fostering improved work processes in knowledge management provides valuable theoretical and practical implications, as well as avenues for future research. 1.1 Research Problem & Scope Strategic management is a formidable challenge for today's organizations, involving the process of formulating, implementing, and evaluating organizational changes with the focus of achieving organizational objectives (Greenberg, 2002). One ofthe most important goals of strategic management is to obtain competitive advantage, a goal that constantly evolves and requires re-evaluation. Due to continuous changes in the marketplace and technology, organizations must adapt or suffer the consequences (Greenberg, 2002). Overcoming resistance to change is a well established management concept in organizational behavior with numerous potential causes for resistance (Dent & Goldberg, 1999). An extreme organizational change can break down relationships resulting in loss of trust and willingness to share (Armistead & Meakins, 2007). Implementation of a new technology is viewed as such a change (Markus, 1983). Organizational culture, defined as attitudes, experiences, beliefs and values of an organization (Turban, McLean, & Wetherbe, 2002), is of further importance to the business environment. While organizational culture is centered around the human 2

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and social aspects, more physical aspects are also of concern. Organizational environment and business infrastructure are major factors in all aspects of an organization, particularly in areas of decision making, strategy formation, and performance. Traditional business infrastructure is that of a top-down hierarchy where there is a strict chain of command. The new paradigm is an environment that is more distributed and flexible, often relying on group efforts. With increases of uncertainty and a constantly changing environment, the new paradigm is more likely to succeed (Pallot, Ruland, Traykov, & Kristensen, 2006; Stenmark, 2003). As organizations strive to maximize resource acquisition and utilization, one of the most valuable resources is intellectual capital, comprised of organizational knowledge residing in both individuals or in the collective actions of a group. The importance of intellectual capital has motivated the field of knowledge management, which has in tum facilitated the development of a wide variety of Knowledge Management Systems (KMS). Despite the development of systems allowing for increased capabilities to support organizational knowledge, adoption of KMS remains enigmatic (Wagner & Bolloju, 2005). Often times, KMS such as expert systems or decision support systems may be too complex and expensive for organizations to use (Raman, 2006). Even when KMS are in place, studies show that the majority of knowledge relevant to the organization is not represented in the systems (Frappaolo & Wilson, 2003). With the capture of organizational knowledge continuing to be a problem for current KMS, new solutions need to be analyzed (Wagner, 2006). New 3

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technologies should also consider the emerging trend of social computing, which allows for individual users to belong and contribute to a group collective. 1.2 Topic Importance Managing knowledge and keeping pace with information technology (IT) are critical in sustaining competitive advantage, rendering a relentless challenge for organizations. Although implementing innovative technological solutions can be attractive, a change due to innovation can cause a disruption in normal activities that are routine or even habitual (Lyytinen & Rose, 2003; Ram & Sheth, 1989). Disruptions may cause a decrease in productivity, hindering organizational performance. Another potential problem, resistance, is related to inter-organizational balance of power whereby resistance to technology implementation occurs if users perceive that usage of the system will result in loss of power (Markus, 1983). In order to avoid these and other problems, efforts made to encourage or assist users in acceptance of new technologies are paramount for management. Thus, user acceptance of IT innovations is a crucial area of interest receiving much attention (Agarwal & Prasad, 1997). IT implementation research, while extensive, tends to focus on either technological or social aspects. A goal for new research is to strive to bring together technological and social aspects providing a more effective framework for successful implementation processes. 4

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Social aspects contribute to the larger area of organizational culture, a factor which may have a negative impact on business success when stem hierarchies and closed-mindedness are present. However, traditional hierarchies still exist, often symptomatic of cultural hurdles such as stringent channels of power and communication, lack of trust and cohesion, and situations of knowledge acquisition bottleneck effect. When employees are required to adhere strictly to proper communication channels for endorsement or verification, time is wasted. Bottleneck effect is an example of such latency, occurring in information systems when updates or maintenance operations are delayed by centrally managed entry (Bean & Hott, 2005). This delay prevents content of the system from being current, which can be crucial, particularly for customer service or help desk applications. Furthermore, an environment of strict policies and procedures accompanied by top-down information dissemination is not suitable for sharing processes (Bean & Hott, 2005; Stenmark, 2003; Wagner, 2004). Obstacles also occur when upper management is reluctant to empower knowledge workers. Without empowerment, workers lack autonomy and inter organizational network connections (Stenmark, 2003). Individuals contributing knowledge experience a loss of ownership of knowledge resulting in feelings of loss of power and potential for replacement (Kank.anhalli, Tan, & Wei, 2005). An environment lacking favorable group conditions, positive norms, and trust does not provide for a social structure conducive to knowledge sharing and collaboration. 5

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When users view knowledge as power, they may be reluctant to engage in knowledge sharing processes (Kankanhalli et al., 2005). Such knowledge hoarding is a natural reaction, often in response to feelings of threat, and occurs more often than not in organizations (Bock, Zmud, Kim, & Lee, 2005). Nonetheless, effective knowledge sharing cannot be forced upon individuals; environments fostering knowledge sharing behaviors are more likely to motivate individuals to engage in actual practice of such behaviors (Bock et al., 2005). New trends in technology support a more collaborative business environment. Conversational technologies such as discussion forums and weblogs have been used as means for users to share and create knowledge through questions and answers. Most conversational technologies provide one-to-many communication where a single person or entity serves as the expert answering questions or posting information. Wiki technology is an emerging trend featuring the unique characteristics of open editing and an environment of social computing and sharing of collective wisdom, improving upon previous methods of conversational technologies by providing many-to-many communication with current knowledge and history (Wagner, 2004). Furthermore, the addition of knowledge representation and maintenance features of Wiki technology allows for more effective knowledge sharing (Wagner, 2006). Wiki technology utilization is growing at a dramatic rate, with empirical evidence indicating that this technology is sustainable (Majchrzak, Wagner, & Yates, 6

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2006). A wiki can take advantage of a pool of experts with any user being able to provide pertinent information. Wiki technology can provide benefits of improved work processes, improved communication and collaboration, and improved knowledge sharing, thus it would be advantageous to continue to study environments surrounding Wiki technology, particularly patterns and behavior of users. With scholarly research of Wiki technology still in its infancy, this study marks an important step forward in a theoretical understanding of wiki adoption and usage. 1.3 Research Questions While knowledge management systems are attractive solutions for effective creation, storage, and access to intellectual capital, successful implementation and utilization is not always a guarantee. Introduction of technological solutions to the work environment are deemed organizational innovations with potential problems of disruption and resistance. Furthermore, knowledge management must continually strive to overcome problems of ineffective knowledge capture and sharing, and knowledge acquisition bottlenecks. These conditions and phenomena motivate the following research questions: (1) What are the factors which influence knowledge management system adoption and usage? (2) Does Personal Innovativeness in IT moderate the relationship between various factors and adoption and usage of knowledge management systems? 7

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(3) Is there a different set of factors which influence adoption and usage of Wiki technology-based systems? (4) Does Personal Innovativeness in IT moderate the relationship between various factors and adoption and usage of Wiki technology-based systems, and is this effect more evident in the case of wikis compared to other knowledge management systems? 1.4 Research Approach The research presented in this dissertation involves a causal study based on a new model developed with a foundation in prior research of diffusion of innovations and knowledge sharing. The independent variables are comprised of the constructs of Innovation Diffusion Theory (Voluntariness, Relative Advantage, Compatibility, Ease of Use, Result Demonstrability, Trialability, Visibility, and Image) with the addition of Reciprocity Expectation. Interaction between the independent variables and the moderating variable of Personal Innovativeness in IT is hypothesized to predict the two dependent variables, Adoption and Usage. The proposed research model improves upon previous conceptualizations of usage by measuring the construct along four dimensions: frequency, total number of tasks, total number of systems utilized, and level of expertise. The survey method for data collection is used to test the proposed research model. The unit of analysis is at the individual level and behavior level as users' perceptions ofthemselves as well as 8

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perceptions of using the technology are considered. Partial Least Squares (PLS) is used to examine the hypotheses with a two-stage analysis performed using confirmatory factor analysis to assess the measurement model followed by examination of the structural model. 1.5 Contribution of Research With organized and usable knowledge being a key ingredient to organizational success, ensuring productive creation and sharing of knowledge can be deemed advantageous for organizations. The contribution of this research will include identification of factors facilitating adoption and usage of knowledge management systems, with an additional comparative analysis of Wiki technology vs. traditional KMS. Insight into the adoption and usage ofwikis used in knowledge management will provide an important contribution to the developing body of research involving Wiki technology. The results ofthis research can be used by management to better focus on important factors which may increase adoption and usage of knowledge management systems. Additionally, researchers may utilize the proposed model for further examination of knowledge management systems as well as other types of information technology innovations. 1.6 Outline of the Dissertation Chapter 2 ofthe dissertation presents the literature review focusing on Knowledge Management, Combining Technological and Social Aspects of 9

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Knowledge, Wiki Technology, and Adoption and Acceptance of Information Technology. Chapter 3 of the dissertation presents the conceptual framework including the research model and hypotheses. The research methodology is presented in Chapter 4, and the research analysis and results are presented in Chapter 5. Chapter 6 provides the discussion followed by the conclusion with limitations, implications and contributions in Chapter 7. 10

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2. Literature Review 2.1 Knowledge Management 2.1.1 Knowledge When viewed as intellectual capital, knowledge is a crucial element oftoday's organizations. The classic knowledge pyramid (depicted in Figure 1) provides a common perspective of knowledge, distinguishing between data, information and knowledge, with knowledge residing at the top of the pyramid. While data can be described as raw facts, and information as data with the addition of useful descriptives, knowledge refers to processed information which is organized, structured, and ready for application. Knowledge Information Data Figure 1: Knowledge Pyramid 11

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Additional perspectives of knowledge include knowledge as a state of mind, object, process, access to information, and capability (Alavi & Leidner, 2001). Knowledge management literature commonly distinguishes between two types ofknowledge: tacit knowledge and explicit knowledge. Tacit knowledge is codified knowledge that is transmittable in a formal, systematic language (Nonaka, 1994). Explicit knowledge, on the other hand, is more difficult to formalize and communicate as it is characterized by action, commitment and involvement in a specific context. 2.1.2 Knowledge Management Knowledge management is a key practice used in organizations seeking to harness knowledge as a resource for sustained competitive advantage (Kankanhalli et al., 2005). Common knowledge management initiatives include the following (Turban et al., 2002): Sharing knowledge and best practices Instilling responsibility for sharing knowledge Capturing and reusing best practices Embedding knowledge in products, services, and processes Producing knowledge as a product Driving knowledge generation for innovation Mapping networks of experts Building and mining customer knowledge bases 12

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Understanding and measuring the value of knowledge Leveraging intellectual aspects Benefits to knowledge management include idea generation and innovation, better customer experiences, and consistency in good practices. An important initiative in organizations is to maintain alignment between business and information technology objectives. A recent study found that four factors influenced short-term alignment: shared domain knowledge, IT implementation success, communication between IT and business executives, and connections between IT and business planning (Reich & Benbasat, 2000). Of these factors, only one was found to influence long-term alignment: shared domain knowledge. A direct, positive relationship exists between communication and knowledge sharing (Joshi, Sarker, & Sarker, 2007). Knowledge management seeks to maximize this relationship by use of information technology as a tool for capturing and storing knowledge. However, having the knowledge in place is only the first step in effectively utilizing the valuable knowledge resource. 2.1.3 Knowledge Processes A framework for analysis of the role of information technology in knowledge management defines four distinct knowledge processes: ( l) creation, (2) storage/retrieval, (3) transfer, and ( 4) application or reuse (Alavi & Leidner, 200 I). 13

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Referring to the aforementioned types of knowledge, tacit and explicit, there are different methods of knowledge conversion or creation (Nonaka, 1994): Socialization: conversion oftacit knowledge to tacit knowledge Combination: conversion of explicit knowledge to explicit knowledge Externalization: conversion of tacit knowledge to explicit knowledge Internalization: conversion of explicit knowledge to tacit knowledge Although knowledge creation may sometimes be difficult, knowledge transfer, which encompasses knowledge sharing, receives the most attention in the knowledge management literature (Alavi & Leidner, 2001). For example, the network model of knowledge management systems emphasizes connections among people to facilitate knowledge exchange or sharing (Kankanhalli et al., 2005). While many factors affect the organizational environment, creating an environment allowing members ofthe organization to engage in various fonns of socializing and collaboration may foster increased knowledge sharing. Once knowledge has been created and stored in the organization, and then shared and transferred, members are then able to apply the knowledge to work processes. The knowledge application may then aid the organization in achieving knowledge management initiatives. In addition to individuals within the organization, another important element required for achieving effective knowledge management comes in the form of a knowledge management system (KMS ). 14

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2.1.4 Knowledge Management Systems The main goal of information systems (IS) is to process data into information or knowledge. We can then examine the classification of information systems, which may be by organizational levels, major functional areas, support provided, or IS architecture. Analysis of information systems evolution indicates the progression of IS according to the support provided. The development of systems to support users and management in various tasks and decision making began as simple transaction processing systems and evolved into highly specialized expert systems. Common classifications according to support provided include Knowledge Management Systems, Decision Support Systems, Group Support Systems, and Intelligent Support Systems or Expert Systems. A system based on Wiki technology can be interpreted as both a knowledge management system and a group support system. Knowledge management systems were developed to provide a technological solution to support the knowledge processes of creation, storage/retrieval, transfer and application. The objectives of a KMS involve creating knowledge repositories, improving knowledge access, enhancing the knowledge environment, and managing knowledge as an asset (Turban et al., 2002). The three most prevalent applications of KMS include: ( 1) the coding and sharing of best practices, (2) the creation of corporate knowledge directories, and (3) the creation of knowledge networks (Alavi & Leidner, 200 I). While the content of a KMS is the knowledge itself, an overall KMS also includes processes, goals, strategies and culture (King, 2007). Thus, 15

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although KMS can provide great benefits when effective, the technological aspects should not be over emphasized while neglecting the social aspects (Butler, 2003). 2.2 Combining Technological and Social Aspects of Knowledge 2.2.1 Collaboration and Collaborative Technologies Collaboration is a key process in almost any organizational environment. Collaboration can provide benefits in the form of deeper resource pools, a variety of domain knowledge, and multiple viewpoints (Mohtashami, Marlowe, Kirova, & Deek, 2006). Collaboration has evolved from routine forms, like a face-to-face conversation, into web-based applications increasing speed and efficiency of collaborating. Thus, with the evolution of information systems, computer-aided collaboration has become commonplace. After the emergence of collaborative technologies in the 1970s, such systems experienced a surge of growth through the 1980s and 1990s with the arrival of applications such as e-commerce, distance education, electronic publishing, digital libraries, and virtual communities (Kling, 2000). These applications are examples of the expansion of computerized systems to various types of organizations, aiding in establishment of the "information superhighway", commonplace in society and organizations by the year 2000. Web-based collaboration tools can provide even further benefits such as easy accessibility of material, up-to-date versions, hyper 16

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linking, independence of platform and application and content markup (Leuf & Cunningham, 2001 ). Computer-aided collaboration can be categorized into three different models: E-mail Exchange, Shared folder/file access, and Interactive content update/access (Leuf & Cunningham, 2001 ). E-mail exchange provides direct exchanges of communication between two or more persons. Shared folder/file access provides users with a repository for documents and information located on a server. Interactive content involves pages or documents which are authored collectively by a group. A more modem version of the E-mail exchange model would include message boards and blogs. Collaboration technologies can be categorized into three groups: electronic communication tools, electronic conferencing tools, collaborative management tools. Examples of collaborative technologies are listed in Table 1. Table l: Categories of Collaborative technologies Electronic Electronic Collaborative Communication Tools Conferencing Tools Management Tools Synchronous conferencing Internet forums Electronic calendars E-mail Online chat Project management Faxing Instant messaging systems Voice mail Telephony Workflow systems Discussion forums Video conferencing Knowledge management Blogs Web conferencing systems Wikis Application sharing Prediction markets Web publishing Electronic meeting systems Extranet Version control Social software systems Online sp_readsheets 17

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Collaboration software packages range from elaborate and expensive options to lightweight, simplistic solutions. Requirements of organizations vary and most systems can be tailored to the subsequent needs. Collaborative technologies utilized in the work environment have been shown to enhance mobile working processes (Alarcon, Guerrero, Ochoa, & Pino, 2006) and improve group performance (P.A. Pavlou, Dimoka, & Housel, 2008). Computer-supported collaborative work (CSCW), a design-oriented concept focusing on the characteristics of groups and a computer system adequate to support group work, utilizes collaborative technologies. Visualization of the concept is demonstrated by a matrix describing work contexts along two dimensions: time and place (see Table 2) (Grudin, 1994). Examples of relevant technologies are listed in the appropriate section of the matrix. Table 2: CSCW work context matrix Synchronous Face-to-face Interactions Continuous Tasks Same Place Decision rooms, single display Team rooms, large displays, shift groupware, shared table, wall work groupware, project displays management Remote Interactions Communication & Coordination Different Internet forums, online chat, E-mail, discussion forums, blogs, Place instant messaging, videoand wikis, workflow systems, web-conferencing document or content management systems 18

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The quadrant indicated by Asynchronous, Different place requires the highest level of collaboration. Web-based technologies are prevalent in this quadrant, allowing for collaboration to occur at any location and at any time, thereby providing improved support of organizational initiatives in globalization. The movement toward globalization has led to geographical expansion of organizations facilitating increased effectiveness particularly in the area of customer support by enabling 24x7 assistance. 2.2.2 Conversational Knowledge Management One area where collaborative technologies have evolved is within conversational knowledge management, an attractive option for organizations in need of a system which is informal as well as quick and easy. One example entails users creating and sharing knowledge by way of a question and answer dialog, as in the case of a frequently asked questions (F AQs) application. Conversational technologies are best suited for ad-hoc or repetitive tasks such as an emergency response system or a help desk. Benefits to conversational knowledge management systems include the following (Wagner, 2004; Wagner & Bolloju, 2005): users able to quickly and easily publish knowledge knowledge effectively and securely shared with other members of the community economically and technologically undemanding 19

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A distinct advantage of these benefits is that they occur at numerous stages of knowledge management process beginning with knowledge creation and ending with knowledge reuse (Alavi & Leidner, 2001; Wagner & Bolloju, 2005). Conversational knowledge management leverages collective wisdom by harnessing communal knowledge and social capital of groups thereby satisfying needs of daily knowledge queries and gaining access to a diverse group of experts, with further benefits of incremental knowledge refinement mechanisms. Another distinction of conversational knowledge management systems is the lack of formal knowledge representations. The systems forego a highly structured database, knowledge interpretation and formal structure rules (Wagner & Bolloju, 2005). This attribute aids in the quickness of knowledge exchange and extraction since users are not bogged down by formality. Nonetheless, a conversational knowledge management system accommodates contextualization and search capabilities. The systems most often utilize plain text and have the ability to build relationships to other content in the repository (Wagner & Bolloju, 2005). Conversational technologies can be utilized in numerous applications in the work environment. The main Community of Practice (CoP) types for conversational knowledge management were identified by a study by the American Productivity and Quality Center in 2000 (APQC, 2000). These basic types and their requirements are summarized in Table 3. 20

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Table 3: Description of community of practice types Community Type Function Requirements Support each other on Connect people and enable Help Communities everyday problems and spontaneous exchange share ideas on an ad hoc basis Best Practice Develop, validate and Process support for idea Communities share best practices validation and refinement Maintain a body of Document management; Knowledge Stewarding knowledge for day-to-day community management; Communities use as well as the enlisting of experts community around it Seek breakthrough ideas Bring together individuals Innovation Communities with multiple perspectives; identify new trends These CoPs can be served efficiently and effectively with conversational knowledge management systems. Unlike other conversational technologies which may only be able to fit one CoP, Wiki technology can serve all of the above-mentioned CoPs due to its incremental development of knowledge and community or collective approach (Wagner & Bolloju, 2005). 2.2.3 Conversational Technologies Conversational technologies can be categorized according to the communication model, and whether they have a knowledge repository. A list of conversational technologies and their properties are summarized in Table 4 (Wagner, 2004). The original table was adapted to also include the CSCW work context. Wiki technology improves upon previous types of conversational technologies by 21

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providing many-to-many communication with current knowledge and history (Wagner, 2004). Table 4: Characteristics of conversational technologies Technology Communication Knowledge Work Repository Context E-mail I-to-I, I-to-many, Archives possible Asynchronous, person-to-Q_erson different place Many-to-many in web Central repository Asynchronous, Discussion forum based forums, repeated 1-different place to-many in list servers Internet chat 1-to-1, many-to-many None Synchronous, different place Video I web 1-to-I, I-to-many Local repository Synchronous, conference different place Group Decision Many-to-many Possible Asynchronous, Support Systems same place I-to-many, approaching Archives Asynchronous, Static and DB many-to-many, "dialog" different place backed web pages between web pages through hyperlinks I-to-many, can approach Local repository Asynchronous, Blog many-to-many (similar to different place web pages) Many-to-many Yes, current Asynchronous, Wiki knowledge and different place history 22

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2.3 Wiki Technology 2.3.1 History Wiki technology is the system and concept of collaborative websites maintained by users who are allowed access. A website based on Wiki technology (referred to as a "wiki") is different from other websites in that content can be created, modified and updated by any user via a web browser. In general, websites available on the World Wide Web are static and can only be modified or updated by the webmaster, whereas wiki websites are dynamic and allow users to participate on line. The word "Wikiwiki" means fast or quick in Hawaiian (Leuf & Cunningham, 2001). The Father ofWiki technology, Ward Cunningham, coined this word after remembering a trip to Hawaii where a shuttle at the airport was called "Wiki Wiki", meaning really quick. Cunningham wanted to create an alternative to static web pages with the added advantage of quick and easy maintenance. When Cunningham developed the first wiki website, he decided to call the technology behind the website WikiWikiWeb. The home of the first wiki resides at c2.com, installed on March 25, 1995 and still running today. From the home page, you can navigate to the original wiki, the Portland Pattern Repository's Wiki, also referred to as Ward's Wiki. The primary function of the wiki is a collection of people, projects and patterns involved in software development. 23

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A few years after the arrival of the original wiki, Jimmy Wales and Larry Sanger began their Nupedia encyclopedia project. When the Nupedia project failed to take off, Wales and Sanger decided to complement the project with Wiki technology (Wagner & Prasarnphanich, 2007). The project evolved into what is now known as Wikipedia, a free content encyclopedia that can be accessed and edited by anyone. Since its inception on January 15, 2001, Wikipedia has grown to almost three million articles 1 in the English language. Besides the mere increase in the number of articles (pages), the site has also expanded to include versions in literally hundreds of different languages. Currently the English version is the only version containing over one million articles, although other versions are quickly approaching the one million mark. Eleven different language versions contain over one hundred thousand articles. In addition to the growth of Wikipedia itself, there is now a family of so-called sister sites to the first born which include Wiktionary, Wikibooks, Wikiversity, Wikinews, Wikispecies, Commons, Wikiquote, Wikisource, and Meta Wiki. These sites are hosted by the Wikimedia Foundation. Since beginning my research of wiki technology, the number of wiki sites has grown immensely. Wikipedia also continues to grow and may or may not currently be the largest wiki. Other large wikis include WikiWikiWeb, Ward Cunningham's first-ever wiki; Wikitravel, an open content world-wide travel guide; and Memory 1 http://en.wikipedia.org/wiki!Wikipedia:Size_of_ Wikipedia, retrieved on 03/23/2009 24

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Alpha, an encyclopedic reference for all things related to Star Trek, the science fiction television series and movies. 2.3.2 "The Wiki Way" In order to maintain the conceptual design behind Wiki technology, Leuf and Cunningham describe the essence of Wiki, providing a definition as well as a guideline of sorts for all wiki users to adhere to (Leuf & Cunningham, 200 I): A wiki invites all users to edit any page or to create new pages within the wiki Web site, using only a plain-vanilla Web browser without any extra add-ons. Wiki promotes meaningful topic associations between different pages by making page link creation almost intuitively easy and by showing whether an intended target page exists or not. A wiki is not a carefully crafted site for casual visitors. Instead, it seeks to involve the visitor in an ongoing process of creation and collaboration that constantly changes the Web site landscape. "The Wiki Way", providing the overall concept and purpose of Wiki technology (Leuf & Cunningham, 200 I), includes the essence of Wiki and main purposes of using wikis: a collaboration space and a way to organize and cross-link knowledge. Additionally, a wiki can be described as "inherently democratic-every user has exactly the same capabilities as any other user" (Leuf & Cunningham, 200 I). 25

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The Wiki Way has led to a unique culture surrounding wiki users. A wiki community provides interaction, sharing, and understanding. Surprisingly, wiki culture is often characterized by a sense of politeness among users. Following wiki etiquette is often the norm of guidelines such as "assume good faith" and "remember the golden rule"2 Many wikis also emphasize maintaining a neutral point of view (NPOV) (Aronson, 2002). Most notable is the self-healing aspect ofwiki culture which entails users monitoring the content and quickly correcting others' mistakes (Ballas, 2006; Holloway, Bozicevic, & Borner, 2007; Long, 2006; Viegas, Wattenberg, & Dave, 2004). 2.3.3 Design Principles of Wikis Cunningham originally created Wiki technology to be open-source. However, with its continuing growth, there are now commercial wiki products available for purchase. Cunningham also proposed the following wiki design principles to be followed (Leuf & Cunningham, 2001): Open Should a page be found to be incomplete or poorly organized, any reader can edit it as they see fit. IncrementalPages can cite other pages, including pages that have not been written yet. 2 http://en.wikipedia.org/wiki!Wiki_etiquette, retrieved on 03/23/2009. 26

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Organic -The structure and text content of the site is open to editing and evolution. Mundane-A small number of (irregular) text conventions will provide access to the most useful page markup. Universal-The mechanisms of editing and organizing are the same as those of writing so that any writer is automatically an editor and organizer. OvertThe formatted (and printed) output will suggest the input required to reproduce it. UnifiedPage names will be drawn from a flat space so that no additional context is required to interpret them. Precise Pages will be titled with sufficient precision to avoid most name clashes, typically by forming noun phrases. Tolerant-Interpretable (even if undesirable) behavior is preferred to error messages. Observable -Activity within the site can be watched and reviewed by any other visitor to the site. Convergent-Duplication can be discouraged or removed by finding and citing similar or related content. A final principle that Cunningham proposed was trust. "'This is at the core of wiki. Trust the people, trust the process, enable trust-building." 27

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2.3.4 Wiki Characteristics A website based on Wiki Technology is different from other websites because the content can be created, modified and updated automatically by anyone through the Internet. Alternatively, a wiki being used in an organizational setting may utilize an intranet. A user may choose to simply observe, by reading and following links, or participate by writing and editing, or both. The activities (see Table 5) available to the user of Wiki technology vary significantly from static pages or databases (Leuf & Cunningham, 200 1 ). Table 5: Comparison of activities performed with different technologies Activity Wiki Static Pa2e Database Content Anyone or Web master or Database publishing member of delegated contributors specific group FTP/publishing Anyone (group), Scheduled or when Database updaters Updating material anytime there is time by person with access Free-form Site structure and Query transactions Browsing structure, topics, navigation as or generated site search, back links defined by Web structure master(s) Anyone who can Difficult to do Depends on query Following site edit can also effectively, hard to and serving engine, links, crosscreate cross-links change or update, often only search is referencing and create topic broken links due to possible e_ages for searches change a problem Anyone or any By way of e-mail, Only if comment Commenting or member of feedback form, or pages/fields reviewing specific group, on guestbook page implemented any (open) page 28

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The language used in the creating and editing process is usually a simplified markup language which is similar to HTML, although extensive knowledge of HTML is not required. This language varies slightly between various wiki software, but is usually referred to as the wiki markup language, or WikiML. As Wiki technology continues to evolve, most wikis are now equipped with WYSIWYG formatting. As creating and editing take place, revisions are tracked by history and recent changes pages. When errors or misinformation are uncovered, additional revisions can be made, or the wiki can be restored to a previous version listed on the history page. In most cases, monitoring by users, or sometimes moderators, keeps the content of wikis current and valid. When creating a wiki, a single page, referred to as a "wiki page", links to other wiki pages via hyperlinks. These links are created during the authoring or editing process most often by typing the text to serve as the link in camel case, commonly referred to as "WikiWords", however, again the different versions ofwiki software vary. Camel case is created by capitalizing each word of a phrase and eliminating spaces between the phrase, for example: CamelCase. When a link is typed within a wiki page, the new page for that link is automatically created. This feature allows for the distinct advantage ofwikis never containing "broken links". When a new link is created, an "empty" page is generated, allowing for the development of the page. Navigation ofthe wiki is not restricted to inter-linked wiki pages; external links may also be created. 29

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Additional features of most wikis include file uploading, a "Sandbox" which serves as a practice area for new users to experiment with creating and editing pages, and searching and indexing capabilities. Individual wikis may also choose whether or not to limit access to registered users. In their book, The Wiki Way: Quick Collaboration on the Web, Leuf and Cunningham describe six different types of wikis categorized by the pennissions allowed (2001): Fully open, meaning that anyone has full access to the Wiki; Lockable, with restricted editing for some or all pages; Gated, with some public pages (that may be locked), but other pages restricted to authorized users; Members-only, where access is limited to registered users; Firewalled, where access is restricted to a range of specific IP addresses; and Personal, where access is limited to a specific computer or private site. Thus, the level of security can be managed to fit the particular needs of the wiki. 2.3.5 Trends in Wiki Technology A wiki may be implemented as server software or client software. Implementations deviating from Cunningham's original Wiki technology are referred to as Wiki Clones. These applications may be written in a different language and/or may be customized to fit a particular functionality. Wiki Clones are essentially different versions of wiki software combined with the wiki engine which implements 30

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the Wiki technology. If you choose not to set up your own server, you can still implement a wiki with the help of a Wiki Farm, which is a server or collection of servers that provides wiki hosting, or a group of wikis hosted on such servers. 2.3.5.1 Personal Wikis Wikis can serve as a free-form database without the headache of implementing and maintaining database software. This can be advantageous for both personal use and shared use. An individual may use a wiki for a notebook or journal, an address book, or as a database for videos, books or documents. Cunningham refers to Wiki technology as "the simplest online database that could possibly work" (Leuf & Cunningham, 2001). Benefits to the individual include its free-form nature and its inter-linking ability. 2.3.5.2 Community Wikis A shared or multi-user application may also entail simple database functions as well as shared bulletin board spaces, collaborative F AQs and project management. Shared benefits again include the inter-linking, but more importantly stem from the ability for multiple users to collaborate easily regardless of physical location. Community wikis may also be centered around hobbies or shared interests, covering topics from quilting to everything there is to know about Star Trek. 31

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2.3.5.3 Organizational Wikis As wiki technology grows out of its infancy, its use is going beyond Wikipedia.org and the Web. One of the most common ways in which wiki technology is being used is as a collaborative tool. The key advantage of using a wiki as opposed to other current means, such as a blog, is of course the ability to create and modify content on-line. The collaboration may take the form of a simple communication forum, or a complex project management area. Another possibility is for the wiki to serve as a collaborative authoring tool, with the benefit being that the collaboration can occur among many users simultaneously as opposed to passing a document from one person to the next. Wikis are also gaining popularity as a knowledge or content management tool. Again, an easily recognizable advantage over previous technologies is the capability for multiple users to collaborate and contribute simultaneously as the technology allows for real-time editing. Other advantages to using a wiki for knowledge management include: Ability to create a knowledge base of linked and categorized content Content and taxonomy can be reorganized Ability to upload documents and manage document versions A wiki can also be used for so-called conversational knowledge creation in which case the process of creating the knowledge takes the form of a question and answer dialog. Using a wiki to store, edit and access organizational knowledge can be an 32

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effective organizational knowledge management initiative (Hasan & Pfaff, 2006; Raman, 2006). Additional application areas for organizational wikis include groupware, help systems, product trouble-shooting systems, communities for best practices, software development, e-learning, project management, ad-hoc collaboration, technical support, customer relationship management, resource management, research and development, emergency response systems(Majchrzak et al., 2006; Raman, 2006; Wagner, 2004). Applications may be limited to those involving ad-hoc, dynamic and informal knowledge. Additionally, the architecture may be limited by systems based on open source development requiring constant maintenance. The wiki is also subject to an unconventional knowledge sharing and creation paradigm (Wagner, 2004). Nonetheless, tasks fitting these limitations do exist and can benefit from Wiki technology. Common applications include: to-do list/task management, document management, meeting management, brainstorming, project planning, new product development, software version management, customer relationship frequently asked questions, recruiting management, competition tracking, distributed marketing intelligence, best-in-class employee engagement practices, help systems, technical support, emergency response coordination for distributed locations. 33

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2.3.6 Research of Wiki Technology Being the first widespread application of Wiki technology, early research has focused on Wikipedia. Wikipedia has experienced unprecedented growth on the Internet. Preliminary criticisms of Wikipedia focused on issues oftrust and vandalism; these opinions have now been overturned. Research on the issue of trust has shown that Wikipedia does in fact contain quality material comparable, if not superior, to traditional encyclopedias (Emigh & Herring, 2005; Lih, 2004; McMullin, 2005). The issue of vandalism has been put to rest by the realization ofWikipedia as a self-healing community utilizing the history and rollback features (Ballas, 2006; Holloway et al., 2007; McMullin, 2005; Schaffert, 2006; Viegas et al., 2004). In addition to addressing criticism, research has also used Wikipedia for experiments involving history flow and information visualization (Biuk-Aghai, 2005; Gilbert & Karahalios, 2006; Holloway et al., 2007; Viegas et al., 2004), text mining (D. J. Cohen, 2006; Davidov, Gabrilovich, & Markovitch, 2004), link analysis (Adafre & DeRijke, 2005; Grangier & Bengio, 2005), and question answering tasks (Jijkoun et al., 2004; Lita, Hunt, & Nyberg, 2004). Wiki technology research has provided informative articles less scientific in nature, with numerous works providing history and descriptions of Wiki technology along with features and advantages and disadvantages (Aronson, 2002; Bean & Hott, 2005; Long, 2006; Wagner, 2004; Wei, Maust, Barrick, Cuddihy, & Spyridakis, 2005). Research that goes beyond fairly simple explanation drives classification of 34

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Wiki technology as social software where users engage in community computing (Chawner & Lewis, 2006; Gonzalez-Reinhart, 2005; Hasan & Pfaff, 2006; Khan, 2005a; Lih, 2004). More involved studies highlight applications for Wiki technology including customer resource management (Wagner & Majchrzak, 2007), on-line news services (Ballas, 2006; Lih, 2004), on-line communities (Milberry, 2006; VanDeursen & Visser, 2002), software development (Louridas, 2006), and learning tools in educational environments (Bennett & Watson, 2006; McMullin, 2005; Mindel & Verma, 2006; Raman, Ryan, & Olfman, 2005; Wang et al., 2005). The wiki application receiving the most attention is collaborative authoring (Bennett & Watson, 2006; DePedro et al., 2006; Desilets, Paquet, & Vinson, 2005; Emigh & Herring, 2005; Wei et al., 2005; Zhang, 2006). Task-technology compatibility was a major factor in adoption of an innovative material requirements planning system based on Wiki technology (Cooper & Zmud, 1990). Wiki technology is also a good fit for emergency preparedness efforts dependent on cross-unit collaboration (Raman, 2006). 2.3.7 How is Wiki Technology Unique? Wiki technology is unique in that it utilizes a many-to-many communication system and it contains both a knowledge repository and a knowledge catalog. As a conversational management tool, wikis provide speed and ease with so-called "single click publication" allowing modifications to be realized instantaneously (Wagner & 35

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Bolloju, 2005). The wiki also has the option of being available company-wide, or components can be set up and restricted for specific groups of users. The level of security measures is flexible and configurable to meet the specific needs ofthe organization. Versions and histories can be recorded with the option of utilizing rollback mechanisms. Most importantly, wikis allow for an environment of open editing, social computing, and collective wisdom. 2.3. 7.1 Open Editing Prior to development of Wiki technology, conversational technologies such as discussion forums and weblogs were used as means for users to share and create knowledge through questions and answers. The drawback to these methods is the organization of the information, most often in chronological order. Wiki technology allows for open editing which can be done anywhere in the text allowing for information which is more incremental than chronological. Content can be modified at any area thus allowing the subject material to develop more constructively as opposed to a listing of postings in chronological order. By utilizing internet technologies, modifications will materialize at the speed of the network, most often instantaneously. 2.3.7.2 Social Computing Due to its collaborative nature, engaging in Wiki technology has been called "socially inclusive interactive community computing" (Khan, 2005b). Engaging in 36

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social interaction such as collaboration can provide for more effective work processes in areas of creativity and innovation (Pallot et al., 2006). Social computing provides a technical outlet for individual users to belong and contribute to a group collective. Such voluntary group participation provides benefits to the individual as well as the group (Gonzalez-Reinhart, 2005). Wiki technology is deemed effective social software allowing users to engage in community computing (Chawner & Lewis, 2006; Gonzalez-Reinhart, 2005; Hasan & Pfaff, 2006; Khan, 2005a; Lih, 2004). 2.3. 7.3 Collective Wisdom Wikis can take advantage of collective wisdom to create an effective source of knowledge (Bean & Hott, 2005; Fuchs-Kittowski & Kohler, 2005; Hasan & Pfaff, 2006; Raman, 2006; Wagner, 2006). Collective wisdom is a form of man/machine symbiotic computing occurring when collective human computing power is harnessed by a community of human and computer resources (Khan, 2005a). Wiki technology is able to support and record processes of communication in the form of conversation thereby attaining collective wisdom. 2.3.8 Why Research Wiki Technology? Wiki technology presents a shift from standard collaboration and knowledge management tools thus motivating the approach of viewing the technology as an innovation warranting special consideration. Wiki technology differs significantly from traditional knowledge management systems which have an element of 37

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centralized control and a formal rule structure. In the case of wikis, on the other hand, custodianship of the knowledge is distributed among its users. While this openness may invite the potential for vandalism, the community is often quick to monitor and correct any misinformation. This joint ownership and open access can be seen as an innovative approach to knowledge management. Furthermore, the knowledge creation process is informal in nature, lacking a strict set of rules, also representing a shift from the traditional paradigm, which involves more rigid and centralized control. Wiki technology improves upon previous methods of conversational technologies by providing an enhanced mode of communication along with up-to date knowledge as well as the history and revisions to the content (Wagner, 2004). Wikis also allow for strong linking of relevant concepts providing for an effectively inter-connected knowledge source with the addition of knowledge representation and maintenance features (Wagner, 2006). Using a wiki to store, edit and access organizational knowledge can be an effective organizational knowledge management initiative (Hasan & Pfaff, 2006; Raman, 2006). Certain characteristics of Wiki technology can satisfy specific requirements of knowledge management. First and foremost, Wiki technology is best suited for knowledge which is ad-hoc, dynamic, and decentralized (Wagner, 2004). Wikis also facilitate searching and filtering which is made possible by linking and indexing 38

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capabilities. Finally, due to the revision and history features, errors can be minimized and recovery or roll-back functions can be performed, allowing for quality assurance. 2.3.8.1 Benefits ofWiki Technology and The Wiki Way Wikis allow for collaborative authoring and knowledge management by incorporating mechanisms for communicating within the system. Unification of multiple functions into a single tool combined with the ability to access the tool via simple internet technologies provides an innovative product for organizations. Although the extent of benefits realized by an organization may vary, the key benefits recognized are improved work processes, improved communication and collaboration, and improved knowledge sharing and reuse. 2.3.8.1.1 Improved Work Processes Respondents to a survey regarding organizational wiki usage reported that wikis made their work easier (Majchrzak et at., 2006). The specific processes indicated were: Need or use of information of immediate relevance to work, Need or use of updated knowledge, Ability to disseminate knowledge. Wiki usage at Aperture Technologies allowed for improved work processes in the form of faster work completion (GonzalezReinhart, 2005). Curtin University Library implemented a wiki largely to serve as a knowledge base for frequently asked 39

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questions and other references (Wiebrands, 2006). The wiki allowed for increased efficiency and effectiveness of day to day tasks such as maintenance of the knowledge. 2.3.8.1.2 Improved Communication and Collaboration Benefits of improved communication and collaboration may be experienced in any scenario, however, a distinct advantage among conversational technologies, including wikis, is the ability to support communication and collaboration among users at different times and different places (Bean & Hott, 2005; Wagner & Bolloju, 2005). Far-flung employees, who are members of virtual groups dispersed geographically (Bean & Hott, 2005; Wei et al., 2005), as well as telecommuters can embrace this benefit. Enhanced communication was also cited as a key benefit in an emergency response system developed to integrate seven member colleges of Claremont University Consortium with the central coordinating entity. The units, originally using telephone and radio as its means of communication, found that the wiki supports cross-unit collaboration more effectively (Raman, 2006). 2.3.8.1.3 Improved Knowledge Sharing and Reuse Use of information technology as a knowledge management system leads to more effective knowledge creation, storage, transfer, and application in organizations (Alavi & Leidner, 2001 ). By using wikis, organizations can take advantage of collective wisdom to create an effective source for knowledge sharing (Bean & Hott, 40

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2005; Hasan & Pfaff, 2006; Raman, 2006; Wagner & Majchrzak, 2007; Wiebrands, 2006). As a conversational management tool, wikis provide speed and ease with so called "single-click publication" allowing modifications to be realized instantaneously (Wagner & Bolloju, 2005; Wagner & Prasamphanich, 2007). The addition of searching capabilities allows for quick and easy access to knowledge for reuse. In a survey of corporate wiki users, wikis were indicated as an effective means of knowledge sharing which led to increased knowledge reuse (Majchrzak et al., 2006). 2.4 Adoption and Acceptance of Information Technology 2.4.1 Beyond TAM Acceptance and adoption research specific to the context of information systems has a theoretical basis stemming from two more general areas: research in psychology and research in organizational behavior. Research in psychology tends to have a model-centric approach, with numerous studies based on Theory of Planned Behavior (TPB) and/or Theory of Reasoned Action (TRA) (Venkatesh, Davis, & Morris, 2007). Research in organizational behavior is more outcome-centric focusing largely on job-related outcomes such as job satisfaction. One of the earliest acceptance models developed for use specifically in IS is Davis' Technology Acceptance Model (TAM) (Davis, 1989). Largely based on TPB and TRA, antecedents ofthe TAM model, perceived ease of use and perceived usefulness, are 41

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posited to predict intentions to use a particular technology, in tum predicting actual usage behavior. Studies incorporating TAM are quite numerous with many proving TAM's predictive validity in IS use contexts (Lee, Kozar, & Larsen, 2003; Venkatesh et al., 2007). However, there is debate within the IS field as to whether the use ofT AM has been exhausted, and that broadening and deepening the research through development of more context-specific models with greater richness is needed. One suggestion is that future research should challenge the basic tenets of intention (Bagozzi, 2007; Schwarz & Chin, 2007; Venkatesh et al., 2007; Venkatesh, Morris, Davis, & Davis, 2003), providing more investigation into outcomes of technology use, including both individual and organizational outcomes. Another criticism ofT AM involves the limitations of self-reported use compared to actual use (Straub & Burton-Jones, 2007; Venkatesh et al., 2003 ). Further possible flaws lie in the lack of attention to changes in importance of factors over time and other potential moderating factors such as age, gender, and voluntariness (Venkatesh et al., 2007). An expanded version ofT AM, the Unified Theory of Acceptance and Use of Technology (UT AUT), provides four core constructs related to intention and usage with the addition of four moderators (V enkatesh et al., 2003 ). Developed by closely examining eight different models of technology acceptance, then formulating a unified model, the core constructs (performance expectancy, effort expectancy, social influence, facilitating conditions) and moderating factors (gender, age, experience, 42

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voluntariness) constitute a step forward from the original TAM model, however UTAUT still focuses largely on intention to use, thus may be more appropriate for pre-adoptive environments. The purpose ofthis research is not to hash out the debate over TAM, however the main points of contention are worth summarizing. As mentioned above, focus on intention to use and/or self-reported use are problematic. Studies are often specific to a single point in time as opposed to longitudinal. Also, there may be further moderating factors worth consideration such as group, social and cultural aspects (Bagozzi, 2007), as well as facilitating conditions such as user involvement and participation, resource availability and training (Schwarz & Chin, 2007). 2.4.2 Technology as Innovation Competing models ofT AM are often approached with the view that introducing a new technology constitutes an organizational innovation. To be classified as innovative, any idea, artifact or behavior must be considered new or novel, if not by definition, at least by perception (Lyytinen & Rose, 2003). Innovations are described as disruptive when they cause dramatic changes in the architecture ofwork process (Sherif, Zmud, & Browne, 2006). Innovation implementation may also cause a shift in the balance of power within the organization leading to resistance by users (Lapointe & Rivard, 2005). The specific case of technological innovation involves advances in computing capability consisting not 43

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only of a technological component, but also potentially new business processes and organizational structure (Lyytinen & Rose, 2003). The goal for innovation research should be to provide management with interventions to foster more successful adoption of technology innovation. 2.4.2.1 Contextual Factors The degree to which a technology introduced to an organization is perceived as new and novel may occur at different levels, namely individual, group, or organization. Similarly, certain components of the implementation may be viewed as innovative, such as the task, process or technology itself. This variety in viewpoints describes the contextual factors influencing technology adoption and diffusion and can be categorized according to the level of analysis (see Table 6). Table 6: Categories of contextual factors Category Characteristics Environmental Uncertainty, Inter-organizational Dependence Organizational Culture, Specialization, Hierarchical Structure, Organizational Innovativeness in IT, Resource Availability, Management Support User/Individual Job Tenure, Job Profile, Education, Adopter Categories, Personal Innovativeness in IT, Resistance to Change Task Task Uncertainty, Autonomy, Responsibility of Person Performing the Task, Task Variety Technology Complexity_, Platform, Hardware/Software, Design/Interface Social Trust, Reciprocity Expectation, Enjoyment in Helping Others, ProSharing Norms Motivational Economic incentives, Social/psychological incentives 44

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2.4.2.2 Stages of Adoption Approaches to IT innovation research also differ in the way the specific stages of adoption are addressed. Acceptance of technology is best viewed as a progression in which perceptions related to the technology occur simultaneously throughout the process of adoption (Schwarz & Chin, 2007). One approach is to focus on a three stage model of adoption: Initiation, Adoption, Implementation. Alternatively, an expanded model involves the six stages of implementation: Initiation, Adoption, Adaptation, Acceptance, Routinization, Infusion (Cooper & Zmud, 1990). Rogers' Diffusion of Innovation Theory ( 1995) has served as the foundation of IT innovation research (Agarwal & Prasad, 1997; Beatty, Shim, & Jones, 2001; Dedrick & West, 2004; Fichman, 2001, 2004; Fiol & O'Connor, 2003; Kraut, Rice, Cool, & Fish, 1998; Lim, Choi, & Park, 2003; Moore & Benbasat, 1991). Rogers' five stage model consisting of Knowledge, Persuasion, Decision, Implementation and Confirmation, has taken on a variety of reinventions with some indicating that certain constructs predict "adoption", while others predict "diffusion" (Agarwal & Prasad, 1997). To distinguish between the two terms, adoption pertains to initial use of an innovation, whereas diffusion pertains to continued or routinized use. For the purposes of this research, we will assume that implementation of a knowledge management system is in place. We will then consider the notion of adoption as the initial use of the technology by the users. 45

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2.4.3 Innovation Theories 2.4.3.1 Research Streams of IT Innovation Fichman provides an analysis of the evolution of IT innovation research. The bulk of the research involves what Fichman describes as the dominant paradigm, characterized by "the desire to explain innovation using economic-rationalistic models, whereby organizations that have a greater quantity of 'the Right Stuff are expected to exhibit a greater quantity of innovation"(Fichman, 2004). "The Right Stuff' is described as greater innovation-related needs and abilities, as well as the earliness and effectiveness of adoption. The substantial research in this area has provided valuable insight and guidance for promoting effective innovation; however, breakthroughs of future research must step away from the dominant paradigm. New perspectives on IT innovation research involve departures from the standard conceptualizations of independent variables commonly studied in the dominant paradigm. Fichman describes seven key perspectives in the emerging research, summarized in Table 7 (Fichman, 2004). 46

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Table 7: Perspectives of innovation research Perspective Central Concept Innovation An innovation configuration is a specific combination of Configurations factors that are collectively sufficient to produce a particular innovation-related outcome. Social contagion exists when organizations feel social Social Contagion pressure to adopt an innovation that increases in proportion to the extent of prior adoptions. Management fashion waves are relatively transitory Management collective beliefs, disseminated by the discourse of management-knowledge entrepreneurs, that a management Fashion technique resides at the forefront of rational management progress. An organization innovates mindfully to the extent that it Mindfulness attends to the innovation with reasoning grounded in its own facts and sQ_ecifics. Technology Technology destiny is the ultimate disposition of a technology at the point it is no longer considered to be something new Destiny among most members of its target adoption community. Quality of The quality of innovation is the extent to which an organization has adopted the "right" innovation, at the "right" Innovation time and in the "right" way. Performance impacts capture the effect an innovation has on Performance business process measures (e.g., inventory turns), firm level Impacts measures (e.g., productivity and accounting profit based), and market-based measures (e.g., capitalization, Tobin's Q). 2.4.3.2 Rogers' Diffusion of Innovation Theory Diffusion of Innovation Theory was originally developed with a more general concept of organizational innovation, in other words, not the specific case of technology innovation. This work categorized individuals into five basic types: innovators, early adopters, early majority, late majority, and laggards (Rogers & 47

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Allbritton, 1995). Rogers also proposed a basic model for the diffusion of innovations, as well as core constructs. Rogers' five stage model consists of knowledge, persuasion, decision, implementation and confirmation. The core constructs affecting innovation diffusion include relative advantage, compatibility, complexity, observability and trialability (see Table 8). As information systems began to grow and develop, Rogers' theory was adapted to the specific case of technology innovation. Table 8: Rogers' attributes of innovation adoption Construct Definition Relative The degree to which an innovation is perceived as being better than Advantage its predecessor Compatibility The degree to which an innovation is being consistent with the existing values, needs, and past experiences of potential adopters Complexity The degree to which an innovation is perceived as being difficult to use Observability The degree to which the results of an innovation are observable to others Trialability The degree to which an innovation may be experimented with before adoption 2.4.3.3 Innovation Diffusion Theory Moore and Benbasat draw on Rogers' Theory as well as Technology Acceptance Model to develop Innovation Diffusion Theory (IDT) consisting of a model comprised of eight constructs focusing on users' perceptions of adopting an 48

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information technology innovation (see Table 9): voluntariness, image, relative advantage, compatibility, ease of use, result demonstrability, trialability, and visibility (Moore & Benbasat, 1991 ). Table 9: Moore and Benbasat's attributes of IT innovation adoption Construct Definition Voluntariness The degree to which use of the innovation is perceived as being voluntary, or of free will Image The degree to which use of the innovation is perceived to enhance one's image or status within the organization Relative The degree to which using the innovation is perceived as being Advantage better than using its predecessor Compatibility The degree to which using the innovation is being consistent with the existif!g values and _l)_ast ex_I>_eriences of g_otential adopters Ease Of Use The degree to which the innovation is easy to learn and use Result The degree to which the results of using an innovation are Demonstrability observable to others Trialability The degree to which an innovation may be experimented with before adoption Visibility The degree to which using the innovation is visible within the organization An important difference between Rogers' theory and the model developed by Moore and Benbasat is the level of analysis. Rogers' attributes of innovation adoption are defined such that the focus in on the innovation itself. Moore and Benbasat transformed the original definitions to describe the behavior of using the innovation, and labeled the behaviors "Perceived Characteristics oflnnovating (PCI)" ( 1991 ). Thus the level of analysis is at the behavior level. 49

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3. Conceptual Framework 3.1 Triangular Relationship of Underlying Fields of Study in Organizations The latest trend in technology involves systems and applications encompassing higher levels of social interaction and collaboration. With globalization of organizations also gaining in popularity, the ability to work more effectively and efficiently with the aid of technology provides considerable benefit. Even before technology became prevalent in the work environment, organizations were faced with an on-going challenge to balance the management of a variety of resources including both physical resources and human resources. Today's organizations are faced with the additional obstacle of managing technological resources. The perpetual balancing act brings together three fields of study: strategic management, organizational theory and information systems. This dilemma can be visualized as a triangular relationship, depicted in Figure 2. 50

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Information Systems Strategic Management Organizational Theory Figure 2: Triangular Relationship of Underlying Fields of Study Strategic management is a complex discipline involving the relentless pursuit of an organization's long-term objectives, with one of the primary objectives being sustainable competitive advantage. Pursuing these goals entails adaptation and change which can be seen as obstacles met with resistance by members of the organization. Overcoming these hurdles can be done successfully by incorporating lessons learned from organizational theory. Imperative to this process is proper consideration of the social behavior within the organization. Technology implementation is often a key element of the strategic plan, helping organizations to utilize their resources more efficiently. Introducing such technologies can be deemed innovation, yet another hurdle to overcome. In order to ensure the best chance of 51

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success, an organization must focus on all three of these intertwined areas on a continual basis. A crucial component of an overall strategic plan involves knowledge management. Intellectual capital can be one of the most valuable resources in an organization. Harnessing knowledge accompanied by sharing knowledge are imperative processes in any and all operational areas of an organizations day-to-day activities. While knowledge management systems are attractive solutions to meeting organizational needs, successful implementation and utilization is not always a guarantee. Balancing the triangular relationship of underlying fields of study in organizations is essential for effective adoption and usage of knowledge management systems. While legacy systems can still be used effectively, today's organizations often seek new technologies to resolve issues in knowledge management. Wiki technology is one example of an innovative option for an effective knowledge management system. Regardless of technological platform, the introduction of a new system can produce wavering results depending on the overall approach of the implementation. As mentioned previously, the process oftechnology implementation is a multi-stage process beginning with the decision to implement and evolving from initial implementation to widespread acceptance and diffusion of the technology. This project involves analysis of only a portion of the extensive process. The stages examined in this analysis are depicted in Figure 3. It is assumed that the technology 52

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has been implemented and users are at some level of adoption. The focus is to examine the factors influencing the adoption level and subsequent usage level. Innovation Technology Organization Various Various Adopted and r-) Determines _____.. Factors __. Factors Implemented Usage as Affect User's Affect User's by Mandatory or Level of Level of Organization Voluntary Adoption Usage Figure 3: Process of Technology Innovation Adoption and Acceptance 3.2 New Model With an extensive body of research in the area ofteclmology acceptance and adoption to draw upon, areas for improvement are of interest. Two ofthe most commonly used models in prior research are Davis' Teclmology Acceptance Model (TAM) (Davis, 1989) and Moore and Benbasat's model of Innovation Diffusion Theory (lOT) (Moore & Benbasat, 1991). Although TAM does include an element of user behavior, bringing together organizational theory and information systems theory, the model ignores social factors and the model is often criticized for focusing on the intention to use as opposed to actual usage. TAM, as well as other models based on intention, is more effective for situations prior to adoption, serving as a tool 53

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to help predict whether a technology may or may not be adopted by users (Davis, Bagozzi, & Warshaw, 1989; Straub, Limayem, & Karahanna, 1995; Venkatesh, Brown, Maruping, & Bala, 2008). Innovation Diffusion Theory focuses largely on technological aspects, but does incorporate user behaviors and social factors associated with the technology. However, the way the dependent variable is defined and operationalized varies among studies with many continuing to focus on behavioral intention (Agarwal & Prasad, 1997; Karahanna, Straub Jr., & Chervany, 1999) and others on actual usage (Agarwal & Prasad, 1997; Compeau & Higgins, 1995; lgbaria, Pavri, & Huff, 1989; Limayem & Hirt, 2003; Venkatesh et al., 2008). Measuring actual usage in subjective (duration, frequency, intensity) or objective (system logs) terms may provide more explanatory power than intention (Limayem & Hirt, 2003; Limayem, Hirt, & Cheung, 2007; Plouffe, Hulland, & Vandenbosch, 2001; Venkatesh et al., 2008). Studies of IDT focusing on adoption tend to simply target either adopters or non-adopters for evaluation (Lu, Quan, & Cao, 2009) or model adoption as a dichotomous variable (Cooper & Zmud, 1990), thus ignoring a variable level of adoption. This research study encompasses a wide range of respondents employed in a variety of organizations and engaged in technology usage at various stages of adoption. As pre-adoption stages are not the focus of the study, measuring behavioral intentions to adopt or use a technology are inappropriate. Furthermore, issues have been raised as to the assumption of intentions as a reliable predictor of actual 54

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behavior, as the relationship is often found to be more complex (Limayem et al., 2007). Therefore, the dependent variable is measured according to adoption level and subjective measures of usage level. Adoption is measured as length of individual adoption indicating the approximate stage in which the respondent is engaged. This measure also allows for comparison of early adopters and late adopters. Usage measures which are subjective or self-reported are often criticized as potentially inaccurate measures of actual usage. However, this issue can be improved by incorporating multiple dimensions of usage, allowing for a richer assessment of the extent of usage. Venkatesh et al. (2008) employ three components of system use: duration, frequency, and intensity. These three measures reflect the variations according to unit of time: hours of daily use, weekly or monthly use, or years of use. Agarwal and Prasad ( 1997) incorporate a frequency measure to better ascertain the outcomes of acceptance behavior. Similarly, Compeau et al. focus on use intensity as a more appropriate measure to explain behavior patterns after adoption has taken place (Compeau, Meister, & Higgins, 2007). Frequency and intensity measures may also be utilized in studies comparing intentions and actual usage (Limayem & Hirt, 2003). The dimensions utilized in this study are similar to those oflgbaria et al. (1989) which include inclusion of computer analysis in decision making, actual daily use of microcomputers, frequency of use, number of packages used, level of sophistication of usage. 55

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The model presented in this research is based upon Innovation Diffusion Theory (IDT). The JOT model may provide more richness over TAM having shown that the perceived characteristics of innovating (PCI) can have a significant effect on intentions after controlling for effects of Usefulness or Ease of Use (Plouffe et al., 2001 ). The decision to use IDT as a basis is also motivated by preliminary exploratory research into Wiki technology diffusion (Hester & Scott, 2008), which suggests a model for Wiki diffusion encompassing three constructs drawn from IDT, Compatibility, Relative Advantage, Complexity, along with Organizational Culture and a moderating factor of Critical Mass. As the study of critical mass requires a longitudinal study, the construct was not included in this research. The proposed new model expands JOT to include a construct motivated by the increasing level of social interaction involved in work processes. Another exploratory study was conducted to examine importance of social capital factors in adoption and usage ofwikis as a project management tool. A description ofthe study is described in detail in Appendix A. The preliminary research indicates that social capital factors are important to usage of Wiki technology (Hester & Scott, 2007). Reciprocity Expectation reflects user's perceptions of how the social environment affects technology usage by examining the degree to which contributing knowledge will result in the reciprocal action of receiving knowledge. As this study focuses on knowledge management systems as the technology, Reciprocity Expectation was included as an independent variable for the model. 56

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Another addition to the model is a moderating factor, Personal Innovativeness in .IT (PIIT). This factor is posited to have a moderating effect because it measures a user's perceptions about their comfort level with information technology in general as opposed to the specific knowledge management system (KMS) used most frequently. The independent variables each refer specifically to use of the KMS, whereas PIIT is more broad in scope. PIIT was found affect the relationship between compatibility and usage intentions of the World Wide Web (Agarwal & Prasad, 1998b), as well as Ease of Use and Usefulness of other Internet technologies (Lewis, Agarwal, & Sambamurthy, 2003). Finally, the proposed model presents an improved method of measuring the dependent variables. Adoption is measured as length of adoption (Choudhury & Karahanna, 2008; Eder & lgbaria, 2000) with an additional calculation of adoption lag to be used for further analysis of early vs. late adopters. The measurement of usage is greatly improved by incorporating more than one dimension of usage (lgbaria et al., 1989). Usage is operationalized by measuring the construct along four dimensions: frequency, total number of tasks, total number of systems utilized, and level of expertise. As depicted in Figure 4, the eight constructs proposed by Innovation Diffusion Theory with the addition of Reciprocity Expectation are postulated to affect both Adoption and Usage, with Personal Innovativeness in IT moderating the relationship. 57

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Voluntariness Hl Relative H2 Advantage Compatibility H3 Ha Adoption Ease of Use H4 Result H5 Demonstrability Trialability H6 Visibility H7 Hb Usage .. Image H8 H 10 Reciprocity H9 PIIT Expectation Figure 4: Research Model: Factors Influencing KMS Adoption and Usage 58

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3.3 Hypotheses The research model prompts the following hypotheses, each described as two relationships with the independent and moderating variables affecting (a) adoption and (b) usage. 3.3.1 Voluntariness Voluntariness is defined as the degree to which the use ofthe KMS is perceived as being voluntary (Moore & Benbasat, 1991 ). Adoption is not always a choice for the user as organizations mandate use of a technology in some instances. However, in drawing from a diversity of respondents in various organizations, no assumption was made for this study as to whether use of the knowledge management system was voluntary. Thus the construct is measured using a scale which allows for variable levels of Voluntariness. If use ofthe system is not mandated by the organization, Voluntariness may be viewed as a form of social influence through compliance processes (Bandyopadhyay & Fraccastoro, 2007; Karahanna et al., 1999). In a cross-cultural study using the UTAUT model, the effect of social influence was found to be stronger for older men with experience and less income when usage was voluntary (Bandyopadhyay & Fraccastoro, 2007). Perceptions of Voluritariness also vary over time. A study focusing on World Wide Web usage indicated that Voluntariness was a factor in the early stages of usage, however insignificant regarding intentions of 59

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future usage (Agarwal & Prasad, 1997). This contradicted findings by Karahanna et al. that potential adopters perceived initial use as voluntary, but continued use as more mandatory (Karahanna et al., 1999). The longitudinal study focusing on pre adoption and post-adoption beliefs found that although the organizational level decision to initially adopt a technology was not an important factor, continued use may be influenced by social norms, with users being pressured not to abandon the technology, thus perceptions were different depending on the adoption stage. Although the UTAUT model posits that Voluntariness has a moderating effect on factors affecting behavioral intention and use behavior (Venkatesh et al., 2003), this research follows Moore and Benbasat's modeling of the construct as having a direct effect (Moore & Benbasat, 1991 ). The influence of Voluntariness is posited to be positive, thus if use ofthe KMS is perceived to be more voluntary as opposed to mandatory, there will be an increased level of adoption and usage ofthe KMS. Hla. The perceived voluntariness of the KMS will be positively related to the adoption level in an organization. Hl b. The perceived voluntariness of the KMS will be positively related to the usage level in an organization. 60

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3.3.2 Relative Advantage Relative Advantage is defined as the degree to which using the KMS is perceived as being better than using its predecessor (Moore & Benbasat, 1991 ). Rogers conceptualization of Relative Advantage is very similar to the perceived usefulness construct from TAM (Moore & Benbasat, 1991 ), however the idea differs in that it refers to a predecessor. Relative Advantage can often take on a variety of dimensions depending on the environment and technology studied. For instance, in a study involving adoption of new computing architectures, Relative Advantage was considered a combination of better software quality, lower costs, better acceptance, and more backward compatibility (Bajaj, 2000). Compared to proprietary operating systems, the open source software Linux was perceived as having increased Relative Advantage in terms of cost and reliability (Dedrick & West, 2004 ). Relative Advantage is often found as the best predictor of adoption and usage (Agarwal & Prasad, 1997, 1998a; Compeau & Higgins, 1995; Karahanna et al., 1999; Moore & Benbasat, 1991; Plouffe et al., 2001; Tornatzky & Klein, 1982). Wi-Fi technology adoption among University faculty members indicated Relative Advantage as of particular importance to early adopters (Lu et al., 2009). Similarly Relative Advantage and Demonstrability were significant predictors of Internet usage intention, when other factors were not significant (Plouffe et al., 2001 ), while Relative Advantage alone was a significant predictor of intentions to use a knowledge-based product configuration and ordering system (Agarwal & Prasad, 61

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1998a). Consumers indicated increased adoption of electronic marketing channels with increased perceptions of Relative Advantage over traditional marketing channels (Choudhury & Karahanna, 2008). Given its proven performance as an important factor, increased perceptions of Relative Advantage are posited to increase levels of adoption and usage of the KMS. H2a. The perceived relative advantage of the KMS will be positively related to the adoption level in an organization. H2b. The perceived relative advantage of the KMS will be positively related to the usage level in an organization. 3.3.3 Compatibility Compatibility is defined as the degree to which use of the KMS is compatible with, or requires change, in one's job (Moore & Benbasat, 1991 ). The perception of Compatibility may be influenced by compatibility of the study technology with current technologies as well as skills and tasks (Dedrick & West, 2004) or compatibility with needs, beliefs and values (Lu et al., 2009). Using yet another interpretation, Compatibility was an important factor in determining use intensity of a hospital computer system in terms of compatibility with prior experience and prior values (Compeau & Higgins, 1995). Compatibility was found to have a significant impact on merchant adoption of a smart card-based payment system (Plouffe et al., 2001) and consumer's intentions 62

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in web-based shopping (VanSlyke, France, & Christie, 2004). Task-technology compatibility was a major factor in explaining material requirements planning adoption behaviors (Cooper & Zmud, 1990). A study analyzing adoption of methodologies for improvement of software development processes found that compatibility of the methodology with how developers perform their work had a positive influence on adoption intentions (Riemenschneider, Hardgrave, & Davis, 2002). Compatibility is one ofthe perceived characteristics of innovating which has been consistently related to adoption (Tomatzky & Klein, 1982). Compatibility is posited to have a positive influence on both adoption and usage of the KMS. H3a. The perceived compatibility of the KMS will be positively related to the adoption level in an organization. H3b. The perceived compatibility of the KMS will be positively related to the usage level in an organization. 3.3.4 Ease Of Use As a component of both TAM and IDT, Ease of Use is extensively researched and continues to prove to be a relevant factor. Ease of Use is defined as the degree to which the KMS is easy to learn and use (Moore & Benbasat, 1991). Perceived lack of Ease of Use can present a considerable obstacle to technology adoption and usage. If using a technology requires a tremendous effort on the part ofthe user, usage will be seriously affected, and in fact, adoption may not occur at all. As described in 63

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Appendix A, preliminary research indicated that the absence of Ease of Use had a negative impact on perceptions of Wiki technology resulting in decreased adoption and usage (Hester & Scott, 2007). A research study evaluating ofthe impact ofT AM indicates that the extensive amount of studies investigating TAM and its many variants have found Ease ofUse as an important determinant of use (Benbasat & Barki, 2007). In a meta-analysis of TAM research, replications of the model reported Ease of Use as having a significant impact on word processors, graphics, spreadsheets, e-mail, voice mail, text editors and GDSS (Lee et al., 2003). Substantial empirical evidence ofthe importance of Ease of Use as a component of IDT also exists (Compeau et al., 2007), lending further support to the expectation that Ease of Use will also be an important factor in this model. H4a. The perceived ease of use of the KMS will be positively related to the adoption level in an organization. H4b. The perceived ease of use of the KMS will be positively related to the usage level in an organization. 3.3.5 Result Demonstrability Result Demonstrability is defined as the degree to which the results of using the KMS are observable to others (Moore & Benbasat, 1991 ). In a study comparing preand post-adoption beliefs pertaining to the Windows operating system, Result 64

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Demonstrability and Image were the only important factors prior to adoption (Karahanna et al., 1999). Agarwal and Prasad ( 1997) also found Result Demonstrability to be an important factor in World Wide Web adoption, however, contrary to Karahanna et al. 's findings, the effect was significant for intentions of future use as opposed to initial use. Result Demonstrability had a significant indirect effect on behavioral intention to use PDAs (Yi, Jackson, Park, & Probst, 2006) and significant direct effect on adoption of groupware applications (VanSlyke, Lou, & Day, 2002). Increased perceptions of Result Demonstrability are posited to increase adoption and usage levels of the KMS. USa. The perceived result demonstrability of the KMS will be positively related to the adoption level in an organization. HSb. The perceived result demonstrability of the KMS will be positively related to the usage level in an organization. 3.3.6 Trialability Trialability is defined as the degree to which it is possible to try using the KMS (Moore & Benbasat, 1991 ). A certain degree of Trialability also exists in the presence of others' use of a technology as users are able to experiment with the technology vicariously (Compeau et al., 2007). Trialability may be more important for early adopters as the ability to try the technology will decrease levels of uncertainty. However, as users gain experience, the importance ofTrialability will 65

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most likely decline. Trialability was found to affect adoption of Wi-Fi technology among University faculty (Lu et al., 2009). Faculty who did not own a laptop were unable to experiment with Wi-Fi technology leading to a lesser rate of adoption. Trialability was also an important factor in the early stages of adoption of the World Wide Web (Agarwal & Prasad, 1997; Limayem et al., 2007), and Windows technology (Karahanna et al., 1999). Increased perceptions ofTrialability may also increase adoption and usage levels ofthe KMS. H6a. The perceived trialability of the KMS will be positively related to the adoption level in an organization. H6b. The perceived trialability of the KMS will be positively related to the usage level in an organization. 3.3. 7 Visibility Visibility is defined as the degree to which using the KMS is visible within the organization (Moore & Benbasat, 1991 ). Visibility refers to the ability to see other's use ofthe technology. Observation of other's use can influence a greater sense of usability (Compeau & Higgins, 1995). Similar to the notion of Visibility, opinions of coworkers and supervisors had a positive impact on developers intentions to adopt methodologies intended to improve software development processes (Riemenschneider et al., 2002). Visibility may also be viewed as a normative pressure from peers being most influential in the early stages of adoption when users 66

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may tend to comply with others' views (Venkatesh & Morris, 2000). Visibility was also found to influence pre-adoptive behaviors in a longitudinal examination of Windows technology implementation (Karahanna et al., 1999). In situations where use of the technology is not mandatory, Visibility may serve as a mechanism to motivate users to adopt in order to achieve a sense of belonging. Increased perceptions of Visibility are posited to increase levels of adoption and usage of the KMS. H7a. The perceived visibility of the KMS will be positively related to the adoption level in an organization. H7b. The perceived visibility of the KMS will be positively related to the usage level in an organization. 3.3.8 Image Image is defined as the degree to which the use of the KMS enhances one's image or status within the organization (Moore & Benbasat, 1991 ). While Image is most often associated with organizational identity, a user's image also plays an important role. Perceptions of Image incorporate into the model an aspect of social influence. Drawing on Image, subjective norms and social factors, social influence is an important construct in the UTAUT model (Venkatesh et al., 2003). Users may be more willing to share or contribute knowledge when they feel that it will strengthen their image. Olivera et al. posit that users enhance their image by demonstrating their 67

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proficiency while meeting requests for knowledge in their area of expertise (Olivera, Goodman, & Tan, 2008). Image can be of particular importance as users gain experience, meanwhile placing lesser emphasis on factors such as usefulness, ease-of-use and result demonstrability (Karahanna et al., 1999). Image may be considered equivalent to the idea of reputation enhancement which was found to be a significant predictor of knowledge contribution in electronic networks of practice (Wasko & Faraj, 2005). Image was an important predictor of merchant adoption of a smart card-based payment system (Plouffe et al., 2001 ). As perceptions oflmage increase, levels of adoption and usage of the KMS may also increase. H8a. A user's perceived image will be positively related to the adoption level in an organization. H8b. A user's perceived image will be positively related to the usage level in an organization. 3.3.9 Reciprocity Expectation Defined as the degree to which use of the KMS for knowledge contribution will lead to future requests for knowledge being met, Reciprocity Expectation is an important construct from Social Capital Theory. Consideration of Social Capital Theory research may help to explain how group characteristics, norms, and trust may influence knowledge sharing and collaboration. With knowledge sharing, users may 68

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experience reciprocal benefits, whereby contributing knowledge may lead to receiving knowledge from others when requested (Kankanhalli et al., 2005). Knowledge sharing is a crucial process for effective use of knowledge management systems, motivating the inclusion of the construct in this model. The pilot study described in Appendix A indicated that Reciprocity Expectation positively influenced usage of Wiki technology (Hester & Scott, 2007). Reciprocity Expectation has also been found to positively influence attitude toward knowledge sharing (Bock et al., 2005), knowledge contribution to electronic knowledge repositories (Kankanhalli et al., 2005) and electronic networks of practice (Wasko & Faraj, 2005). As users perceive their knowledge sharing behavior as resulting in reciprocal actions by others, they will be more willing to utilize knowledge management systems. Therefore, a positive relationship between Reciprocity Expectation and Adoption and Usage is expected. H9a. A user's perceived reciprocity expectation will be positively related to the adoption level in an organization. H9b. A user's perceived reciprocity expectation will be positively related to the usage level in an organization. 69

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3.3.10 PIIT A more recent development in the stream of IT innovation research is the notion of Personal Innovativeness in IT (PIIT). Defined as the willingness of an individual to try out any new information technology (Agarwal & Prasad, 1998b ), PIIT may be viewed as a moderating factor affecting the relationship between perceptions of an innovation and intentions to use the innovation. PIIT may be used as a preliminary predictor of adopter categories (Agarwal, Ahuja, Carter, & Gans, 1998) identified as innovators, early adopters, early majority, late majority, and laggards (Rogers, 1995). At an organizational level, innovativeness as a component of organizational climate has been shown to affect intention to share knowledge (Bock et al., 2005). An alternative view at the user level considers user's propensity to innovate composed of technology cognizance, ability explore, and intention to explore a technology, as an important factor in acquisition and conversion of knowledge (Nambisan, Agarwal, & Tanniru, 1999). PIIT is expected to be present at a higher level for innovators and early adopters, which can have an impact on the factors influencing adoption. Higher levels of PIIT may diminish factors involving subjective norms and Ease of Use, and place more focus on facilitating conditions (Agarwal et al., 1998). PIIT was found to have a significant moderating effect on the relationship between compatibility and usage intentions of the World Wide Web (Agarwal & Prasad, 1998b). One study indicated that PIIT had a direct effect on Ease of Use and Usefulness of the 70

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technology (Lewis et al., 2003), while another study indicated a direct effect on Ease of Use, Result Demonstrability, Perceived Behavioral Control and Subjective Norm (Yi et al., 2006). The varied findings of PIIT' s effect at direct, indirect and moderating levels lends support to the inclusion of PIIT as a moderator of the relationship between the proposed independent and dependent variables in this study. HlOa. A user's perceived PIIT will have a moderating effect on the adoption level in an organization. HIOb. A user's perceived PIIT will have a moderating effect on the usage level in an organization. 71

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4. Research Methodology The research presented in this project can be described as quantitative, positivist research involving a causal study. The survey method for data collection was used to test the proposed research model. The unit of analysis is at the individual level and behavior level as users' perceptions of themselves as well as perceptions of use of the technology are considered. 4.1 Sample The theoretical population comprises of any and all employees of business organizations. The study population involves individuals engaging in usage of knowledge management systems in an organizational setting. The sampling frame consists of respondents to an on-line survey. The usable sample size was 129, consisting of 86 females and 43 males. Further details regarding the respondents are given in Table 10 with a profile ofthe organizations given in Table 11. 72

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Table 10: Descriptive statistics ofthe respondents Age Frequency(%) Job Profile Frequency (%) 20-29 10 (8) Administrative 7 (5) 30-39 48 (37) Clerical 2 (2) 40-49 34 (26) Technical 28 (22) 50-59 33 (26) Supervisory 5 (4) 60 or Over 4 (3) Middle Management 43 (33) Education Level Frequency(%) Top Management 10 (8) Some College 9 (7) Executive 14 (II) Associate's Degree 4 (3) Consultant 4 (3) Bachelor's Degree 41 (32) Education/Training 5 (4) Master's Degree 62 (48) Research 4 (3) Doctorate 13 (I 0) Other 7 (5) Table II: Profile of organizations according to type and size Type Frequency Size Frequency (%) (%) Communications/Media 5 (4) 1-50 32 (25) Consulting/Professional Services 10 (8) 51-200 17 (13) Education 19 (15) 201-500 II (9) Finance/ Accounting 14 (II) 501-1000 10 (8) Government 10 (8) 1001-2000 8 (6) Healthcare 6 (5) Over 2000 51 ( 40) Information Technology 14(11) Insurance 3 (2) Manufacturing 10 (8) Marketing 5 (4) Non-grofit 3 (2) Research 3 (2) Service Industry 12 (9) Other 15 (12) 73

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4.2 Research Design An on-line survey was composed for data collection. An image of the survey as it appeared on-line is given in Appendix B. Before the final survey was deployed, two pilot studies were performed. Only very minor changes were made to the original versions of the measures, results of which are described in Appendix C. Some questions stood out as having awkward wording or outdated terminology, and some of the questions were rephrased to have a negative slant in order to prevent the survey from having a positive bias. The questions measuring the constructs were randomized in the final version. 4.3 Data Collection Purposive sampling was used to target individuals engaging in utilization of knowledge management systems. The respondents were located by accessing on-line groups and communities and contacting individuals employed in various organizations known to utilize knowledge management systems. The data was collected in two phases. Phase I of the collection spanned three weeks and resulted in 1 00 respondents. Phase II of the collection also spanned three weeks and resulted in 34 respondents. Three unusable responses were eliminated immediately, and two additional responses were removed as outliers. Two methods were used to invite potential respondents to participate in the survey. In each case, a brief description of the research was given and a link was 74

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provided to access the survey. First, e-mails were sent directly to individuals known to work in organizations utilizing knowledge management systems, and e-mails were sent to applicable e-mail lists. In each scenarios, an e-mail was sent to a recipient who was asked to forward the email to additional potential respondents. Second, messages were posted in the appropriate areas of on-line groups and communities. Four sources were used for the postings: various wiki communities, Yahoo! Groups, Linkedln Groups, and Facebook Groups. Further details regarding the sources of survey participants are given in Appendix D. A table summarizing responses obtained for each phase is given below. A raffle was offered in exchange for completing the survey. One winner was randomly selected in each phase of the data collection. Table 12: Summary of responses for each phase of data collection Phase I Phase II Source Number of Number of Number of Number of Messages Responses Messages Responses or Postings Received or Postings Received E-mail 10 19 6 15 Wiki Communities 4 28 Yahoo! Groups 5 24 5 7 Linkedln and Facebook 8 29 21 12 Group_s Total 27 100 32 34 75

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4.4 Measures The survey measures were derived from previously published studies. The dependent variables include Adoption and Usage. The independent variables include Voluntariness, Image, Relative Advantage, Compatibility, Ease of Use, Result Demonstrability, Trialability, Visibility, and Reciprocity Expectation. Also included is the moderating variable personal innovativeness in IT (PIIT). The formal construct definitions and sources are given in Table 13 below. The actual items used in the survey are given in Appendix E, along with the corresponding means and standard deviations. All items were measured using a seven-point Likert scale, from "strongly disagree" to "strongly agree". 76

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Table 13: Definitions of the constructs Construct Definition Source Voluntariness The degree to which the use of the KMS is (Moore & perceived as being voluntary Benbasat, 1991) Relative The degree to which using the KMS is (Moore & Advantage perceived as being better than using its Benbasat, 1 991) predecessor The degree to which use of the KMS is (Moore & Compatibility compatible with, or requires change, in one's Benbasat, 1991) job Ease of Use The degree to which the KMS is easy to learn (Moore & and use Benbasat, 1 991 ) Result The degree to which the results of using the (Moore & Demonstrability KMS are observable to others Benbasat, 1991) Trialability The degree to which it is possible to try using (Moore & the KMS Benbasat, 1 991) Visibility The degree to which using the KMS is visible (Moore & within the organization Benbasat, 1991) The degree to which the use of the KMS (Moore & Image enhances one's image or status within the Benbasat, 1991) organization The expectation of knowledge contributors (Wasko & Faraj, that their current contribution will lead to their 2005) Reciprocity future request for knowledge being met Expectation The degree to which use of the KMS for Modified to knowledge contribution will lead to future emphasize requests for knowledge being met behavior level PIIT The willingness of an individual to try out any (Agarwal & new information technology Prasad, 1998b) 77

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4.4.1 Dependent Variables 4.4. t.l Adoption Users were asked: (I) How long has the KMS been in place at your organization, and (2) How long have you engaged in usage of the KMS? The questions were measured as follows: less than 6 months, 6-12 months, 1-2 years, 2-3 years, 3-4 years, 4-5 years and greater than 5 years. Level of adoption was assessed as the length of time the user has engaged in usage of the KMS (Choudhury & Karahanna, 2008). In order to identify early adopters vs. late adopters, the adoption lag was calculated as the difference between individual adoption and organization adoption. A positive result indicates that the individual has used the technology longer than the organization, while a negative result indicates that the organization has had the technology in place for a period longer than the individual has used that technology. Although it was not used in the primary analysis, adoption lag was used to provide group categorization similar to Rogers' adopter categories (1995). A summary of results for responses to length of adoption and the calculated adoption lag are given in Table 14. 78

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Table 14: Adoption level and adoption lag Adoption Level Adoption Lag Years Frequency(%) Years Frequency(%) Less than 6 months 9 (7) 5 2 (2) 6-12 months 13 (1 0) 4 0 (0) 1-2 years 23 (18) 3 2 (2) 2-3 years 21 (16) 2 5 (4) 3-4 years 18 (14) I 5 (4) 4-5 years 8 (6) 0 93 (72) Greater than 5 years 37 (29) -I 11 (9) -2 6 (5) -3 5 (4) 4.4.1.2 Usage Based largely on the work of lgbaria et al. ( 1989), the level of usage was measured along four dimensions. First, respondents were asked to choose all KMS utilized as part of their job. A calculation was done to indicate the total number of KMS utilized. Second, respondents were asked what types of tasks are performed within the KMS. A calculation was then done to indicate the total number of tasks performed. Third, respondents were asked to indicate how often they (a) retrieve and/or read content available on the KMS, (b) modify and/or update content available on the KMS, and (c) add brand new content to the KMS. Frequency was measured as follows: several times a day, about once a day, a few times a week, a few times a month, once a month, or less than once a month (Davis et al., 1989; Limayem & Hirt, 2003; Venkatesh et al., 2008). Finally, users were asked to rate their personal level of 79

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expertise with the KMS on a scale from 1 (novice) to 7 (expert). A summary ofthe frequency responses are given in Table 15. A summary of results for the calculated total number ofKMS utilized and total number of tasks performed, along with a summary of responses for level of expertise are given in Table 16. Table 15: Frequency of use Retrieve Modify Add New and/or Read and/or Content Update Frequency Frequency Frequency (%) (%) (%) Less than once a month 0 11 (9) 17 (13) Once a month 2 (2) 5 (4) 10 (8) Few times a month 4 (3) 15 (21) 34 (26) Few times a week 31 (24) 46 (36) 36 (28) Once 25 (19) 25 (19) 23 ( 18) Several times a day 67 (52) 27 (21) 9 (7) 80

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Table 16: Level of expertise and totals of KMS utilized and tasks performed KMS Tasks Level of Expertise Utilized Performed Frequency Frequency Frequency (%) (%) (%) 1 27 (21) 2 (2) 1 (novice) 0 2 38 (29) 3 (2) 2 5 (4) 3 37 (29) 12 (9) 3 6 (5) 4 18 (14) 11 (9) 4 12 (9) 5 7 (5) 22 (17) 5 38 (29) 6 2 (2) 15 (12) 6 37 (29) 7 16 (12) 7 (expert) 31 (24) 8 9 (7) 9 16 (12) 10 or more 23 (18) 4.4.2 Independent Variables The independent variables consist of the eight constructs proposed by Moore and Benbasat with the addition of Reciprocity Expectation. The unit of analysis for the independent variables is considered as the behavior level, or the use of the technology. Moore and Benbasat referred to the constructs as measuring "perceived characteristics of innovating" or PCI (Moore & Benbasat, 1991 ). Thus, the characteristics of innovating refer to the characteristics of a user's process or behavior. Upon adding the construct Reciprocity Expectation, the definition was modified slightly to parallel those of the other eight constructs. Formal definitions of the constructs along with sources are given in Table 13 above. 81

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4.4.3 Moderating Variable PUT is defined as the willingness of an individual to try out any new information technology (Agarwal & Prasad, 1998b ). The unit of analysis for PIIT is the individual level as this construct measures user's perceptions ofthemselves. This variable is postulated as having a moderating affect because it measures personal innovativeness in general as opposed to measuring a characteristic of the specific technology utilized as the knowledge management system, as is the case with the nine independent variables. Therefore, PIIT is seen to have an interaction effect and is classified as a moderating variable. 4.4.4 Demographics and Descriptives Respondents were asked to indicate their gender, age, education level and job profile (see Table to above). In order to indicate the breadth of companies represented, respondents were also asked to indicate the type and size of their organization (see Table 11 above). Respondents were then asked first which KMS they use as part of their daily job, with the option of choosing more than one, and second, selecting only one, which of the KMS indicated was used most frequently. Respondents were also asked which types of tasks are performed within the KMS with the option of choosing more than one, with a summary of responses regarding types of KMS utilized and tasks performed are given in Table 17 and Table 18. 82

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Table 17: Types of KMS utilized Chosen as one KMS used most Type of KMS Utilized or more frequently Frequency(%) Frequency(%) Message board, discussion forum, blog 92 (71) 11 (9) Document or content management system 9 (7) 5 (4) Group Decision Support System (GDSS) 19 (15) 2 (2) Microsoft SharePoint 47 (36) 28 (22) Dynamic Database 23 (18) 3 (2) Proprietary KMS 6 (5) 4 (3) Web-based system or Intranet 86 (67) 38 (29) Wiki technology based system 66 (51) 36 (28) Social networking application 5 (4) 0 Other 2 (2) 2 (2) Table 18: Types oftasks performed T_ypes of Tasks Performed Frequency_(%) Searching for content or requesting information 108 (84) Reading existing content 112 (87) Retrieving existing content 102 (79) Adding to existing content 110 (85) Making comments on existing content 79 (61) Making small corrections in factual inaccuracies 63 (49) Integrating ideas into existing content 68 (53) Reorganizing content 68 (53) Editing others' grammar or spelling 31 (24) Rewriting whole paragraphs 25 (19) Adding brand new content 84 (65) Rolling-back others' writing 14 (11) Structuring or Organizing Pages 3 (2) Other 10 (8) 83

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4.5 Power and Sample Size A priori statistical power analysis can be calculated using various heuristics. Used as a long-time standard for structural equation modeling using LISREL, the first indicates that sample size should be at least five times the number of indicators. The initial proposed model is comprised of twelve variables, with the nine independent variables and one moderating variable involving three indicators each for a total of 30 indicators. Seven indicators are used for the dependent variables: one for adoption and six for usage. Thus, there are 3 7 indicators, 3 7 x 5 = 185, is the required sample size. An alternative method states that, when using partial least square (PLS), the sample size is independent of the number of indicators when the model is reflective (Chin, Marcolin, & Newsted, 2003). The heuristic requires ten times the construct with the largest number of structural paths, which would be either of the two dependent variables, resulting in ten paths. This method indicates 10 x 10 = 100 as an adequate sample size. As this research utilizes PLS as opposed to LISREL, the second heuristic will be considered adequate. The usable sample size of 129 exceeds the sample size of 100 deemed adequate by these power calculations. Although it cannot be calculated a priori, yet another method suggests that the ratio between the number of observations and the number of independent variables needs to be within the range of 5 to 30 (Kanawattanachai & Y oo, 2007). Counting the moderating variable as an independent variable, this gives 129 to 10, or 12.9, which is within the acceptable range. 84

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5. Research Analysis and Results The partial least squares (PLS) method was used to examine the hypotheses, as it is recommended for complex models focusing on prediction, and allows for minimal demands on measurement scales, sample size, and residual distribution (Chin et al., 2003). A two-stage analysis was performed using confirmatory factor analysis to assess the measurement model followed by examination of the structural relationships. The PLS method allows for simultaneous analysis of the measurement and structural models, and allows each indicator to vary in how much it contributes to the composite score of the latent variable (Chin et al., 2003). PLS also allows for latent variable modeling of interaction effects, necessary for the proposed model as it includes a moderating variable. Path modeling and analysis was performed using SmartPLS (Ringle, Wende, & Will, 2005). 5.1 Measurement Model The measurement model is depicted in Figure 5 (EOU: Ease of Use; IMG: Image; RAD: Relative Advantage; RCX: Reciprocity Expectation; RDM: Result Demonstrability; TRL: Trialability; VIS: Visibility; VOL: Voluntariness). The latent variables representing both the independent variables and the moderating variable in the research model each have three indicators. The latent variable representing the dependent variable adoption has only one indicator. The latent variable representing 85

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the dependent variable usage has seven indicators. All indicators are modeled as reflective. Missing data was not an issue as the survey instrument prevented respondents from omitting answers to any of the questions. After completing the necessary calculations for totals and adoption lag, reverse coding was performed to correct the measurement items modified to impart a negative slant. The data set was then standardized as suggested for the PLS procedure. Standardization is recommended for reflective measures and is acceptable for Likert-scaled attitudinal items (Chin et al., 2003). The measurement model was then assessed for multicollinearity, reliability and validity. 86

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VOLUNI VOLUN2 VOLUN3 RELADVI RELADV2 RELADV3 CMPTI CMPT2 CMPT3 EOUI EOU2 EOU3 RSDMI RSDM2 RSDM3 TRIAL! TRJAL2 TRJAL3 VISIBI VISIB2 VISIB3 IMGI IMG2 IMG3 RCPRI RCPR2 RCPR3 Figure 5: Original Measurement Model ADOPLEN EXPRT FREQt FREQ2 FREQ3 TTASK TKMS 87

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5.1.1 Multicollinearity Multicollinearity was assessed for the independent variables using the variance inflation factor (VIF) value obtained from regression analysis performed with SPSS, as well as examination of correlations among the indicators. The initial analysis revealed VIF scores ranging from 1.5 to 4.3, suggesting moderate multicollinearity. Additionally, several indicators showed cross-loadings greater than .50. Upon thorough analysis and testing, the variable of Compatibility was removed from the model, as well as one indicator for Ease of Use (EOU2), one indicator for Relative Advantage (RELADV3), and one indicator for Result Demonstrability (RSDM2). Similar problems with Compatibility as a component of the Innovation Diffusion model have been reported and are described in Chapter 6. The revised model indicated more acceptable scores, with VIF scores below the threshold of 3 (1.4 to 2.9). 5.1.2 Reliability Reliability of the measurement model was assessed by examining internal consistency and indicator reliability. Internal consistency measures the reliability of a set of indicators, represented by Cronbach' s alpha. Indicator reliability is defined as the proportion of the indicator variance explained by the corresponding latent variable, and is represented by indicator loading, described as follows: fair (.45 .54), good (.55-.62), very good (.63.70), and excellent (.71 and higher) (Comrey, 88

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1973 ). On the initial run of the PLS algorithm, one variable showed a rather low score for Cronbach's alpha, Trialability. Complete removal ofTrialability did not improve the subsequent Cronbach's alpha scores, nor the R2 thus the variable was retained. Additionally, three indicators gave loading scores less than .55: TKMS (.37), TRIAL3 (.53), and VOLUN3 (.44). Only VOLUN3, was not significant at an alpha level of .01. However, the loadings for TKMS, TRIAL3, were in fact significant, therefore, only VOLUN3 was removed. Results for Cronbach's alpha for the modified model are given in Table 19. Table 19: Internal consistency indicated by Cronbach's alpha Variable Cronbach's alpha Ease of Use 0.58 Image 0.79 Reciprocity Expectation 0.58 Relative Advantag_e 0.61 Result Demonstrability 0.55 Trialability 0.46 Visibility 0.74 Voluntariness 0.59 5.1.3 Validity Validity of the measurement model was assessed by examining content validity, convergent validity and discriminant validity. Defined as assessment of the degree of correspondence between the items selected to constitute a summated scale 89

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and its conceptual definition (Hair, Anderson, Tatham, & Black, 1998), content validity was ensured by utilizing measurement items validated by existing research and pilot-testing the survey. Confirmatory factor analysis was performed to examine convergent and discriminant validities. Convergent validity was assessed by examining composite reliability and the average variance extracted (AVE). For composite reliability, a threshold of .50 is considered to indicate the majority of the variance accounted for by the construct, although values greater than .70 (Chin et al., 2003) or .80 (Fomell & Larcker, 1981) are considered more reliable. As shown in Table 20, composite reliability values range from .72 to .85. Values of .50 and greater are considered acceptable for AVE (Fornell & Larcker, 1981 ). As shown in Table 20, AVE values range from .47 to .72. Although the AVE for Trialability was below .50, the variable was retained for reasons given above. Therefore, results of the confirmatory factor analysis were deemed to indicate adequate convergent validity. 90

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Table 20: Composite reliability and average variance extracted (AVE) Construct Composite AVE Reliability Ease of Use 0.83 0.70 Image 0.85 0.66 Reciprocity Expectation 0.76 0.52 Relative Advantage 0.83 0.72 Result Demonstrability 0.82 0.69 Trialability 0.72 0.47 Visibility 0.85 0.66 Voluntariness 0.83 0.71 Convergent validity is further defined as the degree to which the operationalization is similar to other operationalizations to which it theoretically should be similar, whereas discriminant validity is defined as the degree to which the operationalization is not similar to other operationalizations that it theoretically should not be similar to (Trochim, 2001). This can be assessed by examining the correlation matrix, given in Table 21 (EOU: Ease of Use; IMG: Image; RAD: Relative Advantage; RCX: Reciprocity Expectation; RDM: Result Demonstrability; TRL: Trialability; VIS: Visibility; VOL: Voluntariness). 91

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Table 21: Correlation between constructs EOU IMG RAD RCX TRL VIS ROM VOL EOU 0.838 IMG 0.074 0.811 RAD 0.264 0.456 0.722 RCX 0.406 0.426 0.716 0.847 RDM 0.354 0.259 0.366 0.476 0.830 TRL 0.375 0.432 0.495 0.541 0.376 0.688 VIS 0.354 0.310 0.226 0.260 0.369 0.314 0.809 VOL 0.080 -0.005 0.094 0.096 0.135 0.183 -0.087 0.840 The bold and italic numbers in the diagonal of the table indicate the square root ofthe average variance extracted for each construct. This number should be greater than the values for the correlations between the given construct and each of the other constructs to indicate discriminant validity (Fomell & Larcker, 1981 ). One potential problem is indicated by the correlation between Relative Advantage and Reciprocity Expectation, as the correlation value is only slightly lower than the diagonal value for Relative Advantage. No action was taken and the issue is addressed in Chapter 6. Further validation is given by examining the factor loadings and crossloadings of the measures as given in Appendix F. Convergent and discriminant validity are indicated when each indicator loads higher on the corresponding construct than on the other constructs. Again a problem arises with Relative Advantage and Reciprocity Expectation, namely RELADV1 and RCPR3. However, all other measures indicate validity. 92

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5.2 Structural Model Assessment of the structural model involves examining the path coefficients and the R2 values. Path coefficients reflect the strengths of the relationships between the independent and dependent variables. Significance of the paths is determined by using a bootstrap resampling method (500 samples) (Chin, 1998). The R2 value indicates the predictive power of a model for the dependent variables. In order to examine the influence of PIIT as a moderating variable, three separate models were evaluated. This method of examining competing models is recommended by Chin and Marco lin (2003) for evaluation of interaction effects of moderating variables modeled in PLS. First, the baseline model includes only the nine independent variables and omits PIIT. Second, PIIT is added to the model as a direct effect. Finally, PIIT is modeled as a moderating effect on the relationship between each of the independent variables and each dependent variable. The results of the analyses are given individually, and then a model comparison is discussed. Results for outer loadings and corresponding t-values and p-values for each of the three models are given in Appendix G. 5.2.1 Baseline Model (Without PIIT) Results for the baseline model without incorporating PIIT are shown separately for Adoption ( Figure 6) and Usage (Figure 7) (EOU: Ease of Use; lMG: Image; RAD: Relative Advantage; RCX: Reciprocity Expectation; RDM: Result Demonstrability; 93

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TRL: Trialability; VIS: Visibility; VOL: Voluntariness). This model accounts for 16 percent ofthe variance in Adoption and 32.3 percent of the variance in Usage. All outer loadings were significant (p < .01 level). Eight paths were found significant as indicated in the following figures. 94

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.19* significant at 1 0 ** significant at .05 *** significant at .01 Figure 6: Results of PLS Analysis for Baseline Model and Adoption 95

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.23*** significant at .1 0 ** significant at .05 *** significant at .01 Figure 7: Results ofPLS Analysis for Baseline Model and Usage 96

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5.2.2 PUT as Direct Effect The second model involves the baseline model with PIIT added as an independent variable, thus representing a direct effect on the dependent variables. Results are again shown separately for Adoption (Figure 8) and Usage (Figure 9). This model accounts for 16.3 percent of the variance in Adoption and 32.7 percent of the variance in Usage. Outer loadings for the baseline model variables were significant at p < .01 level, however, outer loadings for PIIT were significant at p < .05 (PIIT1, PIIT2) and p < .10 (PIIT3). The same eight paths were again significant. This model results in slightly higher R2 values indicating slightly better performance than the baseline model. 97

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* significant at .1 0 ** significant at .05 significant at .01 Figure 8: Results of PLS Analysis with PIIT as Direct Effect on Adoption 98

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* significant at .1 0 ** significant at .05 *** significant at .01 Figure 9: Results of PLS Analysis with PIIT as Direct Effect on Usage 99

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5.2.3 PIIT as Moderating Effect The third model involves modeling PIIT as a moderating variable, thus representing an interaction effect between the independent and dependent variables. Results are again shown separately for Adoption (Figure 1 0) and Usage (Figure 11 ). This model accounts for 27.4 percent of the variance in Adoption and 39.3 percent of the variance in Usage. This indicates better performance than both the baseline model without PIIT and the baseline model with PIIT as a direct effect. 100

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-.04 significant at .1 0 ** significant at .05 *** significant at .01 Figure 10: Results ofPLS Analysis with Moderating Effect ofPIIT on Adoption 101

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-.05 significant at .1 0 significant at .05 *** significant at .01 Figure 11: Results ofPLS Analysis with Moderating Effect ofPIIT on Usage 102

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5.2.4 Model Comparison The results of PLS analysis provide standardized beta estimates of the constructs (Xi on the dependent variables (Yi{ For the baseline model, we interpret the results as the amount of influence of Xi on Yi when the moderator construct Z (PIIT) is equal to zero. For the direct effect model, the standardized beta estimate from Z to Yi is interpreted as the direct effect of Z on Yi when all Xi are equal to zero. The interaction model represents the moderating effect of Z, influencing both Xi and Yi. Thus, given the beta effect Bi for each Xi and the beta effect Mi3 for Z, an increase of one standard deviation in Z results in a beta change of Bi + Mi for the effect of Xi on Yi. Results for the direct, interaction, and total effects are given in Table 22. The only significant interaction relationship was that of Voluntariness and PIIT affecting Adoption. Therefore, for users who perceive themselves as having higher levels of personal innovativeness in information technology, a negative interaction may exist with Voluntariness. In other words, with an increase of one standard deviation in PIIT, the effect ofVoluntariness on Adoption will decrease. 3 i = I to 8, representing the eight independent variables of the baseline model 4 i = I representing Adoption and i = 2 representing Usage 103

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Table 22: Direct effect, interaction effect and total effect Hypothesis Direct Interaction Total Effect Effect Effect Voluntariness Adoption .180 -.184 -0.004 Voluntariness Usage -.005 .103 0.098 Relative Advantage Adoption -.119 .142 0.023 Relative Usage .296 .179 0.475 Ease of Use Adoption -.220 .143 -0.077 Ease of Usage -.024 .018 -0.006 Result Demonstrability Adoption .198 -.027 0.171 Result Usage .095 -.106 -0.011 Trialability Adoption .023 -.035 -0.012 Trialability Usage .206 -.168 0.038 Visibility Adoption .321 -.113 0.208 Visibility Usage .221 -.042 0.179 Image Adoption -.078 .071 -0.007 Image Usage -.034 .034 0.0 Reciprocity Expectation Adoption .134 .123 0.257 Reciprocity Usage -.092 -.184 -0.276 PIIT Adoption -.037 -.037 PIIT -.049 -.049 To examine the explanatory power of the baseline model, direct effect model, and moderating effect model, the three models were compared in terms of change in R2 The effect size5 :f, is a measure of the strength of the theoretical relationship found in an analysis (Chin et al., 2003 ). Values of .02, .15, and .35 are considered small, moderate and large effects, respectively (J. Cohen, 1988). Results for effect 5 f = [R2(interaction model)R2(main effects model))/[1R2(main effects model)] 104

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size are given in Table 23, indicating that PIIT has just less than a moderate effect on Adoption and between a small and moderate effect on Usage. Table 23: Model comparison of effect size Adoption Usage R-z-7 7 R-z-7 f Baseline Model (Without PIIT) .160 .136 .323 .103 Moderating Effect of PIIT .274 .393 Direct Effect of PIIT .163 .133 .327 .098 5.2.5 Hypotheses Results Hypotheses 1 through 9, which examine the relationship between the independent variables and dependent variables, are interpreted in terms of the baseline model. Results are based on the t-test with the corresponding t-values and pvalues given in Table 24. Hypotheses 1 Oa and 1 Ob examine the moderating relationship of PIIT. To test the significance of the effect size, a pseudo-F statistic can be computed using the formula F = f x (n-k-1) with 1, (n-k) degrees of freedom, where n equals the sample size and k equals the number of constructs (Chin et a!., 2003 ). For the interaction model, the sample size is 129 and the number of constructs is nine (8 independent variables and one moderating variable). Results for the F test are given in Table 25, indicating that the moderating effect of PIIT is in fact significant. 105

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Table 24: Tests of hypotheses HI through H9 Hypothesis ../ Path t-Value p-Value Coefficient Hla Voluntariness 7 Adoption "' .192* 1.802 .07 Hlb Voluntariness 7 Usage .019 0.209 .83 H2a Relative Advantage 7 Adoption -.173 1.123 .26 H2b Relative Advantage 7 Usage "' .305** 2.220 .03 HJa Compatibility_ 7 Adoption Not supported due to multicollinearity HJb Compatibility 7 Usage H4a Ease of Use 7 Adoption H4b Ease of Use 7 Usage H5a Result Demonstrability 7 Ado_ption H5b Result Demonstrability 7 Usage H6a Trialability 7 Adoption H6b Trialability 7 Usage H7a Visibility 7 Adoption H7b Visibility 7 Usage H8a Image 7 Adoption H8b Image 7 Usage H9a Reciprocity Expectation 7 Adoption H9b Reciprocity Expectation 7 Usage significant at .1 0 ** significant at .05 ***significant at .01 "' "' "' "' "' Table 25: Test of hypotheses HIOa and HIOb Hypothesis HlOa PUT moderates Adoption HlOb PIIT moderates Usage 106 -.211 ** 1.927 .05 -.022 0.261 .79 .159* 1.807 .07 .113 0.272 .27 .044 0.694 .69 .227*** 2.508 .01 .270*** 2.880 .00 .244*** 2.712 .01 -.080 .558 .56 -.037 .681 .68 .200* 1.62 .10 -.114 .93 .35 ../ Pseudo Fp-Value statistic "' 16.15 .0001 "' 12.30 .0006

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1. 6. Discussion This research study examined the factors influencing adoption and usage of knowledge management systems (KMS). The study extended the model of Innovation Diffusion Theory by including one additional independent variable, Reciprocity Expectation, and the inclusion of a moderating variable, Personal Innovativeness in IT (PIIT). The model also included an extended measure of Usage by incorporating four separate dimensions. The research analysis and results described in Chapter 5 provides answers to the first two research questions presented in this dissertation. The remaining two questions require further analysis. The research questions are reiterated below. (1) What are the factors which influence knowledge management system adoption and usage? (2) Does Personal Innovativeness in IT moderate the relationship between various factors and knowledge management systems? (3) Is there a different set of factors which influence adoption and usage of Wiki technology-based systems? (4) Does Personal Innovativeness in IT moderate the relationship between various factors and adoption and usage of Wiki technology-based systems, and is this 107

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effect more evident in the case of wikis compared to other knowledge management systems? 6.1 Research Questions 1 and 2 6.1.1 Factors Indicating Positive Relationships Results for the baseline model indicate that some of the factors analyzed are important in determining Adoption and others are important in determining Usage. Voluntariness, Result Demonstrability, Visibility and Reciprocity Expectation were found to be important factors having a positive effect on adoption level. Relative Advantage, Trialability, and Visibility were found to be important factors having a positive effect on usage level. The significant impact of Voluntariness on Adoption is consistent with the findings of Agarwal and Prasad ( 1997) that the construct is important in early stages, however insignificant for continued use. Thus, when use of a system is Voluntary during initial adoption by the organization, the system is more likely to be adopted. The positive influence of Result Demonstrability on Adoption as opposed to Usage supports findings of Karahanna et al. ( 1999). The ability to observe the results of using the KMS is more important when users make the initial decision to adopt the system as opposed to continue using the system. It is also important to the initial adoption phase for the user to have perceptions of Reciprocity Expectation, indicating 108

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the viewpoint that by adopting the system and sharing knowledge, those actions will be reciprocated by other users in the organization. The significant positive influence of Relative Advantage on Usage supports previous indications that the construct is consistently found to be an important factor. Relative Advantage is defined as "the degree to which using the KMS is perceived as being better than using its predecessor", which may explain why the construct is important for Usage as opposed to Adoption. When users have yet to adopt a system, they are less able to compare that system to the one they are accustomed to using. However, as users attain more continued use, they are able to see the advantages of using the new system compared to the system used previously. A similar interpretation may explain the significance ofTrialability on Usage. The more users are able to try using the system, the more experience they gain, resulting in higher levels of usage. Visibility is the only factor having a positive impact on both Adoption and Usage. This supports previous research indicating Visibility as important in early stages of adoption (Karahanna et al., 1999; Riemenschneider et al., 2002; Venkatesh & Morris, 2000), while the significant impact on Usage may be indicative of higher levels of normative pressure influencing users to continue to use the system. Although all of the constructs were hypothesized to have a positive influence on both adoption and usage, the results are consistent with previous findings suggesting that 109

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certain factors are important in the early stages of adoption while other factors are important for continued use. 6.1.2 Negative Relationship between Ease of Use and Adoption Although the negative relationship between Ease of Use and Adoption initially appears to be counter-intuitive, a plausible explanation may exist. Several studies have in fact indicated either a negative effect or lack of effect of Ease of Use on Usage. Complexity is a construct comparable to Ease of Use such that low Complexity corresponds to high Ease of Use. Low Complexity had a negative effect on consumer's intentions to use online shopping services (VanSlyke et al., 2004). In a study involving a hospital computer system, Ease of Use had no effect on use intensity with the moderating effect of Relative Advantage considered the source of the unexpected result (Compeau et al., 2007). Student intentions to use a University website were not affected by Ease of Use due to the moderating influence of perceived enjoyment (Sun & Zhang, 2006). Ease of Use had no direct effect on website navigability but instead directly influenced self-efficacy (Paul A. Pavlou & Fygenson, 2006). Finally, a study of adoption of Internet TV reported no effect of Ease of Use which was attributed simply to the fact that the technology successfully exhibited user-friendly design (Hsieh, Rai, & Keil, 2008). One ofthe more commonly cited explanations for unexpected results with Ease of Use is the confounding factor of experience (Compeau et al., 2007). The 110

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level of experience with a technology will vary from the initial adoption stages to continued usage. Studies have found that antecedents to usage differ in terms of experience of users and stage of adoption (Taylor & Todd, 1995). In a study of Wi-Fi technology adoption, complexity had an important influence on initial adoption decisions, but had no impact on intentions of future use of more experienced users (Lu eta!., 2009). Karahanna et al. confirmed findings of Taylor and Todd (1995) that experienced users are more concerned with usefulness and image (Karahanna et al., 1999). When considering the UTA UT model, experience as a moderator of behavioral expectations was found to have a negative impact on usage (V enkatesh et a!., 2008). Thompson et al. found that experience had some direct effect, but a much stronger moderating effect on PC utilization (Thompson, Higgins, & Howell, 1994 ). A different perspective of experience is that of habit, a sub-conscious, automatic response, which "moderates influence of intention such that its importance in determining behavior decreases as the behavior in question takes on a more habitual nature" (Limayem eta!., 2007). In a study involving continuance usage ofthe World Wide Web, habit was found to have a negative moderating effect. The sample obtained for this research may indicate not only more experienced users, but also a majority of users perceiving themselves as possessing a high level of personal innovativeness. If we compare the responses to length of individual adoption to the proposed distribution of types of adopters as posited by Rogers and 111

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Allbritton ( 1995), we would expect to find a semblance of a normal distribution. However, as depicted in Figure 12, the number of users indicating an adoption level of greater than 5 years represents the largest percentage of respondents. This may indicate that the sample contains an unusually large proportion of early adopters. Length of Adoption 40 JB 35 c CD 't:J 30 .C 8. 25 rn 20 It15 0 ... CD 10 .c E 5 ::::J z 0 co (/) (/) (/) (/) (/) Q) N L. L. L. L. ....... C(J) m m m m m..c ...... c Em ..c ....... Ia ..... c (/)Q N M v LO I I I I Q) ....... N M v LO ...J Figure 12: Chart of Adoption Level Experience level for the sample may also be ascertained by level of expertise. Survey participants were asked to indicate their level of expertise with the current 112

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technology utilized most as their knowledge management system with responses depicted in Figure 13. An overwhelming 82% of respondents indicated an expertise level of 5 or greater. r---------------------------Level of Expertise 40 ,--------------------------, l U) 35 r::::: r::::: 8. 25 SJ 20 0:: 0 15 ... 10 5 z --1 2 3 (N:r.Ace) Figure 13: Chart of Level of Expertise 4 5 6 7 Personal innovativeness may also have a an impact on the results for Ease of Use. For each respondent, an average of the three responses measuring PIIT was computed. Next, the total number of respondents for each the seven levels represented by the Likert scale were calculated. We can interpret these levels as the 113

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degree to which the respondent agrees with the perception of themselves as having a high level of personal innovativeness. Thus, respondents indicating an average score for PIIT of 7 strongly agree that they have a high level of personal innovativeness. The results are depicted in Figure 14. Similar to the results for level of expertise, the vast majority of respondents, 84.5%, gave an average response of 5 or greater for the PIIT indicators. Personallnnovativeness in IT 1 2 3 4 5 6 7 Average of 3 PIIT Indicators Figure 14: Chart of Average Response to PIIT 114

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6.1.3 Factors Lacking Significance As not all relationships were found to be significant, questions arise as to the performance of the IDT model as a basis. Despite its widespread use, the model is subject to criticism. In a thorough review of IDT, Compeau et al. (2007) analyze previous research incorporating IDT and provide a reconceptualization and extension of the model, raising several points of criticism. First, numerous studies reported that Relative Advantage and Compatibility were indistinguishable. This explains the issue of multicollinearity presented by this research. Compeau et al. suggest that Compatibility may be more of an indicator of Relative Advantage as opposed to having a direct effect on adoption or usage. Other studies indicated problems with the reliability of Result Demonstrability, Visibility and Trialability. The lowest scores for reliability for this study were those for Result Demonstrability and Trialability, however Visibility exhibited one of the highest scores for reliability. Additional issues found to be common among previous research studies included confusion among Trialability and Voluntariness as well as problems with negatively worded items. Therefore, despite the widespread use of IDT, the instrument developed by Moore and Benbasat may have drawbacks. Compeau et al. also suggest a possible issue with the deconstruction of Rogers' Innovation model which resulted in the IDT model. Rogers' original construct of observability is separated into visibility and result demonstrability. Compeau et al. take this one step further by separating result demonstrability into communicability and measurability. 115

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Further attention is also given to the construct of Compatibility which is broken down into three types of compatibility: prior experience, preferred work style and values. The reconceptualized model consists of revised variables and relationships among the variables as opposed to direct relationships with each independent variable and the dependent variable. In addition to possible flaws indicated by Compeau et al. other potential problems arise with the model proposed by this research. Adoption and Usage are considered separately although a more appropriate investigation would involve a longitudinal study following adoption of the technology from the early stages into continued use. Adoption level is only indicated by the length of adoption in terms of years, giving only one indicator. Also, although the measurement of usage may be an improvement upon intentions to use, actual system usage may provide a more substantial measure of usage. Yet another issue is raised by the addition of Reciprocity Expectation. Although previously validated measures were used for both the lOT constructs and Reciprocity Expectation, the resulting survey instrument was not subjected to further testing as a whole. This explains the problems of correlation among Relative Advantage and Reciprocity Expectation. There may be a relationship between the two variables which is in need of further examination. Thus, further analysis and testing of the measures is needed. 116

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6.2 Research Questions 3 and 4 Research questions 3 and 4 focus on the specific case of Wiki technology as a knowledge management system. In order to analyze whether there a different set of factors which influence adoption and usage of Wiki technology-based systems, the original data set was divided into two data sets, the first consisting of respondents who indicated wikis (sample size of 36) as the most frequently used KMS, and the second consisting of respondents indicating any KMS other than wikis (sample size of 93). In order to address the moderating role of Personal Innovativeness in IT, and whether the effect is more evident in the case of wikis compared to other KMS, the three models were analyzed and compared. The baseline model accounts for 38.6 percent of the variance in Adoption and 67.9 percent ofthe variance in Usage ofWiki technology-based systems, and 11.6 percent of the variance in Adoption and 29.9 percent of the variance in Usage of other KMS. All outer loadings were significant (p < .01 level) for the wiki data, however four indicators for the non-wiki data did not achieve significance at p < .01, although they were significant at either p < .05 or p <.I 0. No indicators were removed to allow for comparison of identical models. The explained variance is much higher for the wiki data than the non-wiki data. The relative high R2 obtained for the wiki data may be due to smaller sample stze. Although organizational usage of Wiki technology is growing, cases in which wikis are the primary knowledge management system in this study represent only one 117

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third of the total sample. However, as shown in Table 17, just over half of the respondents (66) do in fact use wikis for knowledge management tasks. Despite this limitation, the results are still indicative of a difference between the two data sets. Four paths were found significant for the wiki data and five paths were found significant for the non-wiki data. The two data sets only had one significant path in common, Visibility to Adoption. A comparison of the path coefficients for all three models is given in Table 26. 118

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Table 26: Comparison of research models for wikis and other KMS Relationship Voluntariness Adoption Voluntariness Relative Advantage Adoption Relative Advantage Usage Ease of Adoption Ease of Use Usage Result Demonstrability Result Demonstrability Trialability Adoption Trialability Usage Visibility Adoption Visibility Usage Image Ado_ption Image Usage Reciprocity Expectation Adoption Reciprocity Expectation Usage PIIT Adoption PIIT significant at .1 0 ** significant at .05 *** significant at .01 Baseline Model Wiki NonWiki 0.431 0.131 -0.034 0.046 0.011 -0.036 0.405 0.137 -0.215 -0.235 0.286 -0.121 0.024 0.068 0.089 0.302 0.237 0.137 0.116 0.286 0.405 0.236 0.177 0.212 -0.079 -0.175 0.204 -0.123 0.027 0.136 -0.025 -0.131 119 Direct Effect PIIT Wiki NonWiki 0.436 ... 0.124 -0.020 0.049 0.001 -0.041 0.372 0.139 -0.224 -0.216 0.257 -0.128 0.037 0.104 0.130 0.284 0.229 0.152 0.091 0.284 0.394 0.216 0.141 0.221 -0.087 -0.170 0.181 -0.125 0.037 0.119 0.011 -0.124 -0.078 -0.107 -0.227 0.045 Moderating Effect PIIT Wiki NonWiki .348 .136 .118 -.078 -.503 -.039 .527 .024 -.023 -.228 .260 -.033 -.030 .126 -.119 .091 .205 .121 -.183 -.043 .576 .232 .252 .155 -.141 -.162 .062 .049 .156 .121 .034 -.043 -.231 -.085 -.206 -.061

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The results indicate that there are in fact a different set of factors which affect adoption and usage levels of Wiki technology-based systems. Important factors for Adoption ofwikis include Voluntariness and Visibility, while Relative Advantage and Ease of Use are important for Usage ofwikis. Ease of Use has a negative influence on Adoption of non-wiki systems, whereas Visibility has a positive influence. Result Demonstrability, Trialability and Visibility all have a positive influence on Usage ofnon-wiki systems. One ofthe more interesting findings is the positive impact of Ease of Use on Usage of wikis, while the influence on Adoption of wikis, as well as both Adoption and Usage ofnon-wikis is negative. Wiki technology may be considered as more innovative, whereas the non-wiki systems are more traditional. It is possible that for the more innovative system Ease of Use is in fact important, while traditional systems may be more in line with basic computer skills, thus the experience level may be a confounding factor as discussed above. To analyze the effect of PIIT, the three models were compared. As noted previously, the explained variance, R2 is much higher for the wiki data than the non wiki data. This finding is even more evident when examining the effect size for the model comparison. As mentioned previously, values of .02, .15, and .35 are considered small, moderate and large effects, respectively (J. Cohen, 1988). The strength of the moderating relationship of PIIT compared to the baseline model for the wiki data is .491 (see Table 27), which exceeds the requirement for a large effect. On the other hand, the effect size for the non-wiki data is .143 (see Table 28), just shy 120

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of a moderate effect. These results indicate that PIIT does have a stronger impact on adoption and usage levels of Wiki technology-based systems than other KMS. This result is supported by the presumption that wikis are in fact a more innovative technology than traditional KMS. Thus, a person who perceives themselves as having a higher propensity to innovate will most likely be more inclined to adopt and use an innovative technology. Table 27: Effect size for model comparison for wiki data set Adoption Usage Rz r r Rz r r Baseline Model (Without PIIT) 0.386 .491 0.679 .445 Moderating Effect of PIIT 0.688 0.822 .487 .354 Direct Effect of PIIT 0.391 0.725 Table 28: Effect size for model comparison for non-wiki data set Adoption Usage R:l f f R:l f f Baseline Model (Without PIIT) 0.116 .143 0.299 .119 Moderating Effect of PIIT 0.242 0.382 Direct Effect of PIIT 0.125 .134 0.301 .116 121

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6.3 Post Hoc Analysis 6.3.1 Analyzing Level of Experience Analysis of the research results suggests that level of experience may be a significant confounding factor on adoption and usage levels. This motivates more in depth analysis in which the data set is divided into respondents with lower levels of experience and higher levels of experience. In order to make this distinction, careful consideration was given to responses for the survey question measuring level of expertise. Respondents indicating a level of expertise of 4 or less were considered as having lower levels of experience (sample size of 23 ). Respondents indicating a level of expertise of 5 or greater were considered as having higher levels of experience (sample size of 1 06). Although the sample size for respondents having lower levels of expertise is small, the results may still provide some insight into the difference between the two data sets. Future research in this area would require a larger sample size. However, the sample size for the more highly experienced respondents is at an acceptable level. Results for the baseline model indicate that for experienced users, Visibility is an important factor for Adoption (path coefficient of .26 significant at .01 ), and both Visibility (path coefficient of .30 significant at .0 I) and Result Demonstrability (path coefficient of .25 significant at .05) are important factors for Usage. These results are similar to those of the entire data set, however, there are in fact fewer significant 122

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constructs, indicating some factors may not be important for more experienced users. Results for users with lower experience indicated only one significant factor. Voluntariness had a significant negative impact on Usage, with a path coefficient of.54 significant at .I 0. Thus, although it was difficult to discern which factors are important positive influences on Adoption and Usage for this group, users with lower experience levels who perceive usage of the system as mandatory exhibit lower usage levels. 6.3.2 Experience as a Moderator A second approach for examining the effect of experience level involved modeling Level of Expertise as a moderator as opposed to PIIT. Results were similar to those ofthe baseline model, however Result Demonstrability was found to have a significant impact on Usage as opposed to Adoption. All other significant paths from the baseline model were maintained with the interaction model. Two significant interaction relationships were also found. First, the relationship of Voluntariness and Level of Expertise affecting Usage resulted in a path coefficient of .20 significant at .10. This result was similar to the significant relationship ofVoluntariness and PIIT, however the effect was on Usage instead of Adoption. Second, the relationship of Image and Level of Expertise affecting Adoption resulted in a path coefficient of .26 significant at .05. Contrary to the negative moderating effect of PIIT, Level of Expertise was found to have a positive moderating effect. Therefore, for users who 123

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perceive themselves as having a higher Level of Expertise, a positive interaction may exist with Voluntariness and Image. In other words, with an increase of one standard deviation in Level of Expertise, the effect of Voluntariness on Usage and Image on Adoption will increase. A comparison of effect size was also performed with results given in Table 29, indicating that Level of Expertise a moderate effect on Adoption and just less than a moderate effect on Usage. Therefore, compared to the moderating effect of PIIT which indicated an effect size of .136 for Adoption and .I 03 for Usage, the moderating effect of Level of Expertise has a slightly larger effect size for both dependent variables. Using the pseudo F-statistic, the effect size for Adoption was significant at p < .01 (F = 17.82) and the effect size for Usage was also significant at p < .01 (F = 13.62). Further analysis is required to determine whether both moderating factors are appropriate for inclusion in the model, or only one or the other is needed. At any rate, future research should include careful consideration of level of experience of users. Table 29: Model comparison of effect size Ado Usage Rz f Rz f Baseline Model .160 .15 .323 .114 Moderating Effect of Ex2_ertise .286 .401 124

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2. 7. Conclusion 7.1 Limitations Despite careful planning to ensure a well-designed and executed research project, the present study is not without limitations. First, the context of the study, although contributing a certain amount of benefit, also has drawbacks. The technology studied was knowledge management systems (KMS). The extension of the model was derived to specifically target KMS adoption and usage. Therefore, the model may not be applicable to all types of systems and technological contexts. The organizational context involved various characteristics and cultures. The type and size of organizations was measured indicating a variety of responses. Although this is recommended for generalizability, certain contexts may exhibit special circumstances warranting further consideration. Neither the organizational culture nor the geographic culture was measured. These factors may contribute to the constructs analyzed, particularly in the area of social influence and subjective norms. Future research may require a measurement of various cultural contexts. Second, the sample acquired for this study consisted of twice as many females as males. Previous research indicates that gender may have an impact on usage behavior (Bandyopadhyay & Fraccastoro, 2007; Venkatesh & Morris, 2000; Venkatesh et al., 2003). Also 90% of respondents had at least a bachelor's degree 125

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indicating a well-educated sample. And as discussed extensively, the experience level of the sample may also be unusually high. These factors may require more careful consideration in future research. Third, the research model presented by this study focuses on user perceptions and self-reported measures of adoption and usage. As these measures are subjective in nature, the results are not guaranteed to reflect the actual behavior patterns of the antecedents nor consequents. Fourth, this study looks at several different technologies and several different stages of adoption which leads to respondents having a highly varied set of "important" factors. Again this may support generalizability, however specific cases may have a different set of underlying factors. An alternative to this approach would be to focus on a specific technology and conduct a longitudinal study which would follow all stages of the implementation process. Finally, this study reveals important factors influencing the outcomes of adoption and usage of knowledge management systems. While knowledge management is a comer stone of overall strategic management, the results of this analysis do not indicate the impact of effective knowledge management system utilization on organizational performance or other potential outcomes, such as user satisfaction. 126

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7.2 Implications for Theory The implications of this research provide opportunities for further theoretical development and advancement. The research model proposes an extension to the IDT model by including the direct effect of Reciprocity Expectation and the moderating effect of PIIT. Reciprocity Expectation was deemed important as the technology studied was knowledge management systems. Reciprocity Expectation can be an important factor positively influencing knowledge sharing which is crucial for effective utilization of knowledge management systems. The construct did have a significant positive influence on adoption of KMS, thus may also be important in other studies. Further theoretical development may include other factors considered important depending on the technology studied. Certain factors beyond the core constructs proposed by Moore and Benbasat may be important indicators for specific types oftechnology. The addition of a moderating factor also motivates deeper investigation of various study environments. PIIT may play an important role in other scenarios, or alternative moderating factors may also be present. Several studies have examined the moderating influences of experience and perceived enjoyment (Paul A. Pavlou & Fygenson, 2006; Sun & Zhang, 2006; Taylor & Todd, 1995; Thompson et al., 1994; Venkatesh et al., 2008; Venkatesh et al., 2003). Additional studies involving different types of technologies and the effect of PIIT on adoption and usage may 127

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result in improved explanatory power of the lOT-based model, and perhaps other acceptance models as well. Alternatively, problems with the IDT model uncovered by this research also provide further avenues for theoretical development. Issues of multicollinearity among Relative Advantage and Compatibility echo problems encountered in other research studies utilizing IDT. Additional inter-relationships may be present between Relative Advantage, Compatibility, Ease of Use and Result Demonstrability. Re evaluation of the survey items may improve problematic issues, particularly with the potential for multicollinearity among Relative Advantage and Compatibility, and the diminished effectiveness of Ease of Use, although other constructs of the model may also benefit from reformulation which could provide increased reliability. Overall reconceptualization of the model involving consideration of the relationships between variables may further strengthen performance of the model. The reconfigured model proposed by Compeau et al. (2007) may be examined further, or alternative configurations may also be of interest. The significant negative effect of Ease of Use although seemingly problematic may shed light on the broader issue of acceptance behavior. This research proposed that perceived levels of personal innovativeness may play a moderating role, thereby changing the effects ofthe core perceived characteristics of innovating. The sample showed a large proportion of respondents with three different attributes possibly affecting the results: longer lengths of adoption, high levels of expertise, and high 128

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levels of personal innovativeness. Further examination is required to analyze these factors as well as other potential factors which may be diminishing the effect of Ease of Use. When TAM and other adoption and acceptance models were developed, technology in the workplace, although increasing steadily, was not as widespread as today. We may have reached a new era in computing in which users have achieved a level of technical savvy that leads to higher levels of general computer experience that can be translated to multiple systems and technologies. While Ease of Use has been shown by this study to be ineffective for continued use of knowledge management systems, other studies have shown similar results, giving way to the possibility that the phenomenon may now be applicable to technology in general. Thus, as members of organizations are gaining general computer experience, the role of Ease of Use may be diminishing. Finally, advances in deeper understanding ofthe conceptualization of usage can provide further theoretical contributions. Four dimensions of usage were utilized in this study providing a richer measure of usage. Additional dimensions may also be of interest such as duration in terms of hours. Also, the combination of subjective and objective measures can extend the robustness of usage level measurement. Another area receiving increased attention is the notion of habit. Studies indicate that habit, a subconscious, automatic behavior, may lead to a different set of antecedents than those predicted by models focusing on intentions (Limayem & Hirt, 2003; 129

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Limayem et al., 2007; Paul A. Pavlou & Fygenson, 2006). At any rate, further exploration of the usage measurement can potentially improve the results of studies analyzing adoption and usage behaviors. 7.3 Implications for Practice This study provides implications for management engaged in either preor post-implementation of information technology innovation, particularly knowledge management systems. Although several studies involving technology innovation adoption and acceptance have provided empirical evidence supporting the influence of various factors, situations of ineffective implementation still exist. Research findings should be translated into terms allowing management to apply the knowledge learned to the business environment. Furthermore, practitioners must keep in mind that certain factors are important in the early stages of adoption, however a different set of factors may be important for usage continuance. When implementation of a knowledge management system is initiated, a greater adoption level may be achieved by lending special consideration to Voluntariness, Result Demonstrability, Visibility and Reciprocity Expectation. During the initial stages of adoption, allowing the use of the system to be voluntary may increase the level of adoption. When technology use is mandated, there may be less likelihood that users will embrace the system implemented. Another important initiative should be demonstration of the potential tangible results of using the 130

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system. Information and training sessions may involve demonstrations of the system allowing users to visualize outcomes to system usage that may benefit work processes. Consideration of Visibility and Reciprocity Expectation will involve paying attention to the social environment ofthe organization. Measures taken to encourage an environment of positive social norms allowing for sharing of efficacious experiences with using the technology will heighten the visibility of the system. Pro sharing norms among groups may increase perceptions of reciprocity. When members of a group contribute and share knowledge with the expectation that other group members will reciprocate, usage levels will increase. Thus, as the social relationships among users strengthen and improve, positive outcomes will emerge. As the initial adoption stages are surpassed, management should focus on factors important for continued usage. Relative Advantage, Trialability and Visibility are important factors positively influencing increased usage. As users realize the advantages of using the newly implemented system compared to the previous technology, usage levels may increase. Continued training of the system may allow users to achieve full utilization of all tools and features of the technology, leading to realization of the advantages in improved quality and work processes. The ability to experiment with the system will also provide opportunities for increased usage. Trying different functions of the system will increase the level of experience with the technology, reducing uncertainty and building confidence with using the system. 131

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Visibility will continue to have importance beyond initial adoption, thus measures taken in early stages should be maintained throughout all phases of implementation. Getting users to share their adoption and use of the technology will positively influence other's behaviors. The proposed moderating effect of PIIT may provide further understanding of processes of adoption and usage of knowledge management systems, and possibly to broader categories of information technology. Management may benefit from a deeper understanding ofthe nature ofPIIT. Organization members who exhibit higher levels of PIIT may be targeted to serve as early adopters of technologies thereby serving as leaders in guiding additional members to adoption. 7.4 Contributions With organized and usable knowledge being a key ingredient to organizational success, ensuring productive creation and sharing of knowledge can be deemed advantageous for organizations. Successful implementation of knowledge management systems may provide considerable support for achievement of knowledge management initiatives. This research identifies factors facilitating increased adoption and usage of knowledge management systems. Further insight into the adoption and usage of wikis allows for consideration of Wiki technology based systems as an innovative alternative to traditional KMS. 132

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This research provides an important theoretical and empirical contribution to Innovation Diffusion Theory. Prior to analysis of factors influencing adoption and usage, the original lOT model was extended providing an improved means for examination of the technological context of knowledge management systems. The construct of Reciprocity Expectation was included due to the importance of knowledge sharing and contribution for effective utilization of KMS. Reciprocity Expectation did in fact have a significant positive impact on the adoption level of KMS. The model was further extended by the moderating factor of Personal Innovativeness in IT (PIIT). Results indicate that the explanatory power of the model is improved by the inclusion of PIIT as a moderator. The model also suggests a more robust measure of Usage by incorporating four dimensions of use. Results of the study support prior research indicating that some of the factors analyzed are important in determining Adoption and others are important in determining Usage. Voluntariness, Result Demonstrability, Visibility and Reciprocity Expectation were found to be important factors having a positive effect on adoption level. Relative Advantage, Trialability, and Visibility were found to be important factors having a positive effect on usage level. Absence of a significant impact of some ofthe factors can be explained by flaws with the original IDT model, as well as the confounding impact of level of experience. This research confirms previous criticisms of IDT indicating that modifications may be necessary. Substantial prior research suggests the importance 133

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of consideration of experience level, which is also supported by these findings. This also may explain the rather interesting finding of a significant negative effect of Ease of Use on Adoption. The sample for this study exhibited high levels of perceptions of personal innovativeness and expertise as well as suggestions of high levels of experience. Future research should include careful consideration of these factors. The analysis of Wiki technology-based knowledge management systems provides an important theoretical contribution to the growing body of research involving Wiki technology. With research of wikis in its infancy, factors underlying adoption and usage provide valuable insight for both researchers and practitioners showing an interest in this emerging technology. Findings of this study indicate that there are a different set of factors important for adoption and usage of wikis as opposed to more traditional KMS. Furthermore, as wikis are an innovative approach to knowledge management, attention to user's personal innovativeness is recommended. ln conclusion, consideration of the perceived characteristics of innovating along with Reciprocity Expectation and user's Personal Innovativeness in IT are important for organizations contemplating adoption of knowledge management systems. Continued consideration of the factors at various stages of adoption is recommended, as important factors may vary over time. An organization considering implementation of a Wiki technology-based system should first consider the degree of personal innovativeness among users, with higher levels recommended. Upon 134

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introducing wikis to the organization, allowing initial adoption by users on a voluntary basis, along with targeting of users who may serve as early adopters making their use of the wiki visible, may increase adoption level of Wiki technology based systems. As the stages move beyond initial adoption, increased usage of wikis may be facilitated by ensuring that the wiki is easy to use and provides recognizable advantages over previous technologies. Consideration ofthese recommendations may allow Wiki technology-based systems to serve as an effective alternative to traditional knowledge management systems. 135

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3. APPENDIX A Exploratory Study Examining Wikis for Project Management In order to perform exploratory research into Wiki technology and motivation behind wiki usage, a pilot study was conducted involving graduate students enrolled in a master's level course on Business Process Management. Students were required to complete a group project involving business process redesign. As groups were formed, students were encouraged to utilize a wiki to serve as a tool for communication and collaboration In order to facilitate speedy adoption of a wiki tool, a free web-based solution was implemented, Schtuff.com. Schtuff provided a simplified wiki environment that did not require any set-up other than the registration of the students as users. After the registration process, group wikis were created in the form of "spaces" and only the appurtenant members of each group were allowed access to their subsequent group space. A brief training session was held to introduce students how to use the wiki. Students were also encouraged to use the available help features. Throughout the semester, students were encouraged to use the wiki as an on-line meeting place. Most students followed a basic structure providing for different sections containing items such as important dates, goals, tasks, and documents. Although specific elements were not required, general wiki usage was counted as a small portion of project grades. 136

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Upon dissemination of previous research in Social Capital Theory and the various models it provides, a new model was proposed and examined with the pilot study (see Figure 1 ). This model provided five basic constructs contributing to social capital: Group Characteristics, General Norms, Trust, Personal & Collective Efficacy, and Reciprocity Expectation. We define Group Characteristics as characteristics of the cohesiveness of the group (Nahapiet and Ghoshal 1998; Bock, Zmud et al. 2005). General Norms are defined as characteristics of the members within the groups and the extent to which these characteristics are thought to be shared among the group members (Nahapiet and Ghoshal 1998; Bock, Zmud et al. 2005; Kankanhalli, Tan et al. 2005). Trust entails the reliance on the integrity, strength and ability of group members (Coleman, 1988; Nahapiet and Ghoshal 1998; Kankanhalli, Tan et al. 2005). Personal & Collective Efficacy involve the effectiveness of knowledge sharing as it exists on the individual level as well as the group level (Bock, Zmud et al. 2005; Kankanhalli, Tan et al. 2005; Wasko and Faraj 2005). And finally, Reciprocity Expectation describes anticipated reciprocal actions and relationships (Coleman, 1988; Kankanhalli, Tan et al. 2005; Wasko and Faraj 2005). 137

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Group Characteristics General Norms Trust Personal & Collective Reciprocity Figure 15: Social Capital via Wiki Usage Social Capital via Wiki Usage A survey was developed to examine the proposed model. The use of established validated questions provided for continued validity for the newly devised survey. Questions were modified only slightly to refer to the specific nature of the use ofwikis. Additional questions were added to record basic characteristics ofthe group project, as well as open-ended questions providing qualitative data. The survey was given to students at the end of the semester with nine out often students enrolled completing the survey. The survey consisted of forty-one questions: six questions applying to group characteristics, general norms, trust and reciprocity expectation, and eight questions applying to personal and collective efficacy. Questions were measured on a 5-point Likert scale. In addition to collecting information on the constructs, eight questions were composed to obtain data applying specifically to the 138

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tool provided by Schtuff.com. The final question was open-ended, asking for any additional comments or feedback. The constructs, survey items and corresponding means are provided in Table 30 below. Table 30: Pilot study constructs, survey items and means Construct Survey Item Mean Group Members in my group close ties with each other. 2.1 Characteristics Members in my group have a strong feeling of one team. 2.1 I find that my values and my group's values are very 2.1 similar. I feel a sense of belonging towards my group. 2.4 While working on the project, how many times per week 2.0 did your group meet face-to-face? Please comment on the extent to which your group needed to have a communication/collaboration tool in order to accomplish work toward the group project during non face-to-face times. General There is a norm of cooperation in my group. 2.0 Norms There is a norm of collaboration in my group. 2.1 There is a norm of teamwork in my group. 1.9 There is a norm of openness to conflicting views in my 2.2 group. Using a collaboration tool in the form of a wiki 3.4 Please describe how the wiki did or did not support the normsofyourgroup. 139

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Table 30 (Cont.) Construct Survey Item Mean Trust I believe that people in my group give credit for other's 1.9 knowledge where it is due. I believe that people in my group do not use unauthorized 2.2 knowledge. I believe that people in my group use other's knowledge 1.9 appropriately. I believe that people in my group share the best knowledge 2.0 that they have. I believe that the knowledge contributed to the wiki was 3.2 pertinent to my group and group project. To what extent, if any, did the wiki help the group to share knowledge? Personal and My own personal knowledge sharing through the wiki 3.2 Collective helps other members in the group solve problems. SelfMy own personal knowledge sharing through the wiki 3.2 Efficacy improves work processes in the group. My own personal knowledge sharing through the wiki 3.1 increases _Qroductivity in the group. My own personal knowledge sharing through the wiki 2.9 helps the group achieve its performance objectives. Sharing my knowledge through the wiki improves other 3.3 group members' recognition of me. I have confidence in my ability to provide valuable 3.0 knowledge to the wiki for my group. It doesn't really make any difference whether I add to the 3.3 knowledge others are likely to share throug_h the wiki. Most other group members are able to provide more 3.0 valuable knowledge to the wiki than I can. 140

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Table 30 (Cont.) Construct Survey Item Mean Reciprocity I know that other group members would help me, so it was 2.1 Expectation fair to help other group members. I trust that someone would help me if I were in a similar 2.1 situation. My knowledge sharing through the wiki strengthens the 3.0 ties between members in the group and myself. My knowledge sharing through the wiki will draw smooth 3.0 cooperation from outstanding members in the future. When I share my knowledge through wikis, I expect 3.1 somebody to respond when I'm in need. When I contribute knowledge to wikis, I expect to get back 3.1 knowledge when I need it. Schtuff.com Learning to use the wiki provided by Schtuff.com was easy 3.3 for me. My interaction with the wiki provided by Schtuff.com was 3.4 clear and understandable. It would be easy for me to become skillful at using the wiki 3.4 provided by Schtuff.com. I found the wiki provided by Schtuff.com easy to use. 3.1 Using the wiki provided by Schtuff.com enabled me to 3.6 accomplish my grou_Q project more effectively. Using the wiki provided by Schtuff.com improved my 3.8 performance in my group project. I found the wiki provided by Schtuff.com useful in my 3.7 group project. Throughout the semester, students expressed a lack of enthusiasm with the implementation of a wiki, specifically with the usability of Schtuff.com. A common concern expressed by the students was that groups met on class meeting days and therefore did not feel the need to fully utilize an on-line collaboration tool. Every student indicated that their group did in fact meet twice per week. Open-ended questions reaffirmed the sentiments expressed throughout the semester regarding the 141

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redundancy of an on-line collaboration tool. The open-ended question relating to Schtuff.com reiterated sentiments expressed throughout the semester that indicated an unfavorable opinion of the tool. Although the study was preliminary and exploratory, important implications were revealed. Factors enabling creation of social capital were shown as having a presence, yet additional factors were also uncovered. The findings indicated that the basic elements of favorable group conditions, positive norms, and trust were in place. Furthermore, the attitude toward knowledge sharing and collaboration was positive. However, the attitude toward the specific tool used was negative. Results of this pilot study were analyzed and interpreted in the context of its limitations. Social Capital Theory examines situations of volunteerism. The students were assigned to use the wiki as part of the project, thus their engagement in wiki usage was not completely voluntary. The sample size was small and the course project was not designed specifically for study of wiki usage. The tool chosen was out of convenience and proved to be unsatisfactory. Despite the presence of important factors related to Social Capital Theory, usage levels of the wiki were rather low. Three main elements were revealed as the sources of the decreased usage ofthe wiki. First, students used the tool reluctantly, with both perceived usefulness and perceived ease of use in question. Secondly, students communicated largely through email, rather than the wiki. Thirdly, the wiki was an element that did not have to be turned in, thus, students viewed use of the wiki 142

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as an additional burdensome step. The results of this pilot study motivated further research into factors underlying adoption and usage of Wiki technology with newly derived models extending the basic model of Social Capital Theory by including theories of technology adoption and acceptance. 143

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APPENDIX B Image of Survey r ------------:-o_ -file .Edit lliew Hi,itory Jlookmarks Iools l:ielp ( -'-=' : 6. How long has the 101S been In place at your organization? 7. How long have you engaged in U.illge of the KHS? For the following please indicate the extent to which you agree/disi!!lgree. B. Hy use of the lOtS is voluntary (as opposed to required by my superiors or job description). / Strongly Disagree _, Disagree _.. Somewhat Disagree Neutral / Somewhat Agree IJ. When I share my knowledge through the KMS, I expect somebody to respond when I'm In need. / Strongly Disagree Disagree .,. Somewhat Disagree 10. The results of using the KHS are apparent to me. _, Strongly Disi!lgree .,.. Disagree Somewhat Disl!gree Neutral Neutral 11. Using the KMS enables me to accompli5h tasks more quickly. .., Strongly "' Somewhat Disagree Neutral ""' Somewhat Agree _., Somewhat Agree Somewhat Agree "' Agree / Agree _, Agree 12. I was not pennltted to use the KHS on a trial basis long enough to 5ee what It could do. !' Strongly Disagree _, Disagree Somewhat Disagree Neutral 13. I think that using the KMS fits well with the way I like to work. "' Strongly Disagree Done ./ Disagree Figure 16: Survey Image _, Somewhat Dsagree Neutrol 144 c/ Somewhat Agree .,., Somewhat Agree !' Agree _J Agree ; Strongly -Agree "' Strongly Agree / Strongly Agree / Strongly Agree .., Strongly Agree / Strongly Agree

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4. APPENDIX C Modifications Resulting from Pilot Studies Table 31: Feedback or advice and action regarding pilot studies Feedback/ Advice Action Pilot l Reviewed by Doctoral Consortium Panel Add more descriptive questions Added job profile, level of education Questioned choice of KMS options Modified KMS options to be generic too specific. with the exception of Microsoft SharePoint (I asked for advice) Changed "a KMS" to "the KMS" to focus on the particular KMS chosen as most frequently used Incorporate some drop-down boxes Changed org type, org size, age to dropdown Pilot 2 Feedback Respondents don't like to provide income Income level removed level. Hard to choose just one KMS when Added question to "check all" also several are used. able to incorporate this into the multidimensional measurement of usage RSDM I: "consequences" -odd Changed to "outcomes" terminology-negative connotation-punishment RSDM3: ... may or may not be Rephrased question beneficial"confusing VOLN3: "compulsory" not always Rephrased to "mandatory" understood 145

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Table 31 (Cont.) Feedback/ Advice I Action Committee Feedback I Advice Most ofthe questions are "positively" Rephrased several questions to have a phrased causing possible bias. negative angle. VISIB I: "desks"out of date Rephrased to "computers" TRIAL2: "any applications" too Removed phrase specific Table 32: Original and modified versions of survey items Indicator Modified Survey Item Original Version CMPT3 Using the KMS does not fit into Using a PWS fits into my work (Comrn.) my work style. style. EOUl I believe that it is difficult to get I believe that it is easy to get a PWS (Comm.) the KMS to do what I want it to to do what I want it to do. do. IMG2 Using the KMS does not Using a PWS improves my image (Comm.) improve my image within the within the organization. organization. RADV3 Using the KMS makes it more Using a PWS makes it easier to do (Comrn.) difficult to do my job. my job. RCPR3 When I contribute knowledge to When I contribute knowledge to (Comm.) the KMS, I do not expect to get EKRs, I expect to get back back knowledge when I need it. knowledge when I need it. RSDMl I believe I could communicate I believe I could communicate to (Pilot 2) to others the outcomes of using others the consequences of using a the KMS. PWS. RSDM3 I would have difficulty I would have difficulty explaining (Pilot 2) explaining the advantages of why using_ a PWS may or may not using the KMS. he beneficial. TRIAL2 Before deciding whether to use Before deciding whether to use anv (Pilot 2) the KMS, I was able to properly PWS applications, I was able to try it out. properly try them out. TRIAL3 I was not permitted to use the I permitted to use a PWS on a (Comm.) KMS on a trial basis long trial basis long enough to see what it enough to see what it could do. could do. 146

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Table 32 (Cont.) Indicator Modified Survey Item Original Version VISIBl In my organization, one sees the In my organization, one sees PWS (Comm.) KMS on many computers. on many desks. VOLNl My use of the KMS is voluntary My use of a PWS is voluntary (as (Pilot 1) (as opposed to required) by my opposed to required) by my superiors or job description. superiors or job description. VOLN3 Although it might be helpful, Although it might be helpful, using a (Pilot 2) using the KMS is certainly not PWS is certainly not compulsory in mandatory in my job. my job. 147

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5. APPENDIX D. Sources for Survey Participants Table 33: Source type and name for survey participants Source Type Source Name E-mail lists: Brink Global Knowledge Net Intelligent Identity Information @valuenetworks Learning-org Tech Pitch (www.techpitch.com) Wiki communities: Twiki codev (www.twiki.org) Wikisym research (www.wikisym.org) Wikisym _general (www.wikisym.org) Media Wiki-enterprise ( www .mediawiki.ol'g) Yahoo! Grou_ps: act-km (groups. yahoo.com) klogs KM Best Practices kmciVirtual-Chapter KM-Forum KMTech KMTool meqmp ROinet sikmleaders Facebook Grouos: Digital Natives ( www.facebook.com) Girls in Tech Knowledge Management Knowledge Management for the Federal Government Knowledge Management in healthcare OpenSolaris 148

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Table 33 (Cont.) Source Type Source Name Linkedln Grou_ps: actKM Forum (www.linkedin.com) Agile Alfresco Connections Best Practice Transfer Center for eBusiness and Advanced IT (eBiziTPA) CKO (Chief Knowledge Officers) Forum Customer Service Innovation Group Eccellere Business Community e-MC ( e-marketing_ Communication) eMarketing_ Association Network Information Access & Search Professionals (IASP) Information, Knowledge and Content Manag_ement Specialists KM Practitioners Group Knowledge Management Knowledge Management Experts Knowledge Management Group of Philadelphia Knowledge Managers Legal IT Professionals Linked To Denver Ppt-Collaboration: People, Process & Technology SLA Knowledge Management Division The Braintrust: Knowledge Management Group The Institute for Knowledge and Innovation Tiki Wiki CMS/Groupware University of Colorado Denver Business School 149

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6. APPENDIX E Survey Items Table 34: Survey item definitions, means, standard deviations Indicator Survey Item Mean Stand. Dev. VOLNl My use ofthe KMS is voluntary (as opposed to 4.92 2.21 required) by my superiors or job description. VOLN2 My boss does not require me to use the KMS. 4.02 1.86 VOLN3 Although it might be helpful, using the KMS is 3.84 1.87 certainly not mandatory in my job. RADVl Using the KMS enables me to accomplish tasks more 5.65 1.20 quickly. RADV2 Using the KMS improves the quality of work I do. 5.60 1.20 RADV3 Using the KMS makes it more difficult to do my job. 5.98 1.32 CMPTl Using the KMS is compatible with all aspects of my 4.97 1.57 work. CMPT2 I think that using the KMS fits well with the way I like 5.84 1.28 to work. CMPT3 Using the KMS does not fit into my work style. 5.82 1.37 IMGl People in my organization who use the KMS have 3.63 1.62 more prestige than those who do not. IMG2 Using the KMS does not improve my image within the 4.40 1.66 organization. IMG3 People in my organization who use the KMS have a 4.15 1.55 high profile. EOUl I believe that it is difficult to get the KMS to do what I 4.94 1.41 want it to do. EOU2 Overall, I believe that the KMS is easy to use. 5.64 1.23 EOU3 Learning to operate the KMS is easy for me. 6.05 .87 150

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Table 34 (Cont.) Indicator Survey Item Mean Stand. Dev. RSDMl I believe I could communicate to others the outcomes 5.77 1.08 of using the KMS. RSDM2 The results of using the KMS are apparent to me. 5.81 1.09 RSDM3 I would have difficulty explaining the advantages of 5.34 1.66 using the KMS. VISIBl In my organization, one sees the KMS on many 5.38 1.57 computers. VISIB2 The KMS is not very visible in my organization. 4.94 1.75 VISIB3 It is easy for me to observe others using the KMS in 5.11 1.57 my firm. TRIAL I I know where I can go to satisfactorily try out various 5.30 1.46 uses ofthe KMS. TRIAL2 Before deciding whether to use the KMS, I was able to 4.80 1.68 properly try it out. TRIAL3 I was not permitted to use the KMS on a trial basis long 5.29 1.70 enough to see what it could do. When I share my knowledge through the KMS, I 4.87 1.39 RCPRl* believe that my queries for knowledge will be answered in future. When I share my knowledge through the KMS, I 5.14 1.55 RCPR2* expect somebody to respond when I'm in need in the future. RCPR3* When I contribute knowledge to the KMS, I do not 4.44 1.72 expect to get back knowledge when I need it. 151

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Table 34 (Cont.) Indicator Survey Item Mean Stand. Dev. PliTt** If I heard about a new information technology, I would 5.65 1.18 look for ways to experiment with it. PIIT2** Among my peers, I am usually the first to try out new 5.55 1.35 information technologies. PIIT3** In general, I am hesitant to try out new information 6.02 1.23 technologies. Notes: (I) All items adapted from Moore and Benbasat ( 1991) except for those indicated by (Kankanhalli et al., 2005) and** (Agarwal & Prasad, 1997). (2) All items measured on a seven-point Likert scale (Strongly Disagree, Disagree, Somewhat Disagree, Neutral, Somewhat Agree, Agree, Strongly Agree). 152

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7. APPENDIX F Factor Loadings and Cross-Loadings Table 35: Factor loadings and cross-loadings EOU IMG RCX RAD RDM TRL VIS VOL EOUl 0.85 0.12 0.28 0.37 0.29 0.31 0.29 0.09 EOU3 0.83 0.00 0.17 0.31 0.30 0.32 0.30 0.04 IMGl 0.08 0.80 0.28 0.21 0.05 0.33 0.23 -0.08 IMG2 0.06 0.92 0.48 0.49 0.33 0.42 0.32 0.03 IMG3 0.05 0.70 0.25 0.18 0.12 0.23 0.11 0.04 RCPRl 0.08 0.31 0.70 0.32 0.22 0.30 0.09 0.26 RCPR2 0.19 0.35 0.60 0.32 0.19 0.28 0.19 -0.13 RCPR3 0.28 0.36 0.85 0.77 0.34 0.46 0.22 0.03 RADVl 0.33 0.43 0.66 0.89 0.37 0.45 0.18 0.08 RADV2 0.37 0.27 0.55 0.80 0.45 0.47 0.27 0.08 RSDMl 0.35 0.25 0.31 0.39 0.87 0.33 0.34 0.10 RSDM3 0.22 0.17 0.30 0.41 0.79 0.29 0.27 0.13 TRIAL I 0.33 0.37 0.32 0.37 0.29 0.81 0.31 -0.01 TRIAL2 0.27 0.37 0.47 0.46 0.32 0.70 0.11 0.31 TRIAL3 0.13 0.09 0.25 0.30 0.14 0.53 0.20 0.17 VISIBl 0.35 0.17 0.18 0.22 0.26 0.19 0.85 -0.03 VISIB2 0.29 0.29 0.16 0.18 0.31 0.29 0.85 -0.15 VISI83 0.20 0.33 0.22 0.24 0.34 0.30 0.73 -0.02 VOLNl 0.14 0.11 0.21 0.24 0.20 0.21 0.03 0.81 VOLN2 0.00 -0.10 -0.03 -0.05 0.04 0.11 -0.16 0.87 153

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APPENDIXG Significance of Outer Loadings for Competing Models Table 36: Significance of outer loadings for competing models Baseline Model Direct Effect of PIIT Indirect Effect of PIIT Ld t _p Ld t _I)_ Ld t p EOUl 0.845 8.793 0.00 0.847 7.458 0.00 .846 7.345 0.00 EOU3 0.831 5.561 0.00 0.829 5.703 0.00 .830 4.753 0.00 IMGl 0.797 4.501 0.00 0.795 4.388 0.00 .794 3.914 0.00 IMG2 0.921 5.226 0.00 0.922 5.421 0.00 .923 5.974 0.00 IMG3 0.701 3.312 0.00 0.700 3.262 0.00 .699 2.903 0.00 PIITl 0.770 2.058 0.04 .775 1.907 0.06 PIIT2 0.769 2.045 0.04 .767 2.023 0.04 PIIT3 0.683 1.857 0.06 .681 1.797 0.07 RCPRl 0.695 5.152 0.00 0.695 4.626 0.00 .698 5.810 0.00 RCPR2 0.602 3.485 0.00 0.603 3.345 0.00 .606 3.559 0.00 RCPR3 0.847 7.566 0.00 0.847 8.571 0.00 .844 7.644 0.00 RADVl 0.890 20.761 0.00 0.890 19.292 0.00 .891 12.181 0.00 RADV2 0.801 7.682 0.00 0.800 6.885 0.00 .799 7.459 0.00 RSDMl 0.867 13.584 0.00 0.867 14.190 0.00 .867 15.407 0.00 RSDM3 0.791 8.838 0.00 0.791 8.433 0.00 .791 9.513 0.00 TRIAL I 0.810 12.039 0.00 0.811 12.049 0.00 .811 12.096 0.00 TRIAL2 0.699 7.234 0.00 0.698 6.888 0.00 .701 6.365 0.00 TRIAL3 0.527 3.054 0.00 0.525 3.407 0.00 .522 3.344 0.00 VISIBl 0.849 22.569 0.01 0.849 22.190 0.00 .849 21.931 0.00 VISIB2 0.846 19.242 0.00 0.846 18.661 0.00 .846 20.151 0.00 VISIB3 0.728 11.368 0.00 0.727 11.073 0.00 .727 10.714 0.00 VOLNl 0.806 2.771 0.00 0.804 2.737 0.01 .802 3.086 0.00 VOLN2 0.873 3.377 0.00 0.875 3.296 0.00 .876 2.656 0.01 154

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Table 36 (Cont.) Baseline Model Direct Effect of PIIT Indirect Effect of PIIT Ld t p Ld t p Ld T p EXPRT 0.614 6.682 0.00 0.608 6.462 0.00 0.602 6.235 0.00 FREQl 0.676 9.529 0.00 0.680 9.326 0.00 0.675 I 0.217 0.00 FREQ2 0.807 16.021 0.00 0.809 14.444 0.00 0.818 20.810 0.00 FREQ3 0.780 16.804 0.00 0.783 16.972 0.00 0.788 20.453 0.00 KMS 0.377 3.520 0.00 0.369 3.371 0.00 0.366 3.357 0.00 TASK 0.779 20.249 0.00 0.780 19.297 0.00 0.779 18.364 0.00 155

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