Citation
Consumer evaluation of digital products : empirical study in the context of mobile applications

Material Information

Title:
Consumer evaluation of digital products : empirical study in the context of mobile applications
Creator:
Hazarika, Bidyut Bikash
Place of Publication:
Denver, CO
Publisher:
University of Colorado Denver
Publication Date:
Language:
English

Thesis/Dissertation Information

Degree:
Doctorate ( Doctor of philosophy)
Degree Grantor:
University of Colorado Denver
Degree Divisions:
Department of Computer Science and Engineering, CU Denver
Degree Disciplines:
Computer science and information systems
Committee Chair:
Karimi, Jahangir
Committee Members:
Khuntia, Jiban
Parthasarathy, Madhavan
Ra, Ilkyeun

Notes

Abstract:
Mobile applications (apps) are emerging as useful digital products transforming business models in healthcare, supply chains, travel and other sectors. Consumer evaluation reflects on apps sustainability, and is a key determinant for the success of app-centric and app-enabled businesses. In this context, the essay in this dissertation focus on the important research question: how technology frustration with apps influence the consumer evaluation shift of mobile apps in contingent with sustenance of, and passion for, the apps. We collect data from the Android market and applied text mining technique to code necessary variables for this study, including different app characteristics, features and evaluations. The empirical analysis of the study use econometric analyses to draw insights. The results of the analyses provide insights into highly interactive IT enabled consumer involvement and interaction issues with apps. The dissertation contributes to research in identifying technological, market externalities and internal factors associated with digital product success; along with managerial implications to effectively design, develop and manage successful apps.

Record Information

Source Institution:
University of Colorado Denver
Holding Location:
Auraria Library
Rights Management:
Copyright Bidyut Bikash Hazarika. Permission granted to University of Colorado Denver to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.

Downloads

This item has the following downloads:


Full Text
CONSUMER EVALUATION OF DIGITAL PRODUCTS: EMPIRICAL STUDY IN THE
CONTEXT OF MOBILE APPLICATIONS by
BIDYUT BIKASH HAZARIKA B.E., University of Mumbai, India 2005 M.B.A., University of Toledo, Ohio, 2007
A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy
Computer Science and Information Systems Program
2016


©2016
BIDYUT BIKASH HAZARIKA
ALL RIGHTS RESERVED


This thesis for the Doctor of Philosophy degree by
Bidyut Bikash Hazarika has been approved for the
Computer Science and Information Systems Program
by
Jahangir Karimi, Chair Jiban Khuntia, Advisor Madhavan Parthasarathy Ilkyuen Ra
Date: 06/24/2016


Hazarika, Bidyut Bikash (PhD, Computer Science and Information Systems)
Consumer Evaluation of Digital Products: Empirical Studies in the Context of Mobile Applications
Thesis directed by Assistant Professor Jiban Khuntia
ABSTRACT
Mobile applications (apps) are emerging as useful digital products transforming business models in healthcare, supply chains, travel and other sectors. Consumer evaluation reflects on apps sustainability, and is a key determinant for the success of app-centric and app-enabled businesses. In this context, the essay in this dissertation focus on the important research question: how technology frustration with apps influence the consumer evaluation shift of mobile apps in contingent with sustenance of, and passion for, the apps. We collect data from the Android market and applied text mining technique to code necessary variables for this study, including different app characteristics, features and evaluations. The empirical analysis of the study use econometric analyses to draw insights. The results of the analyses provide insights into highly interactive IT enabled consumer involvement and interaction issues with apps. The dissertation contributes to research in identifying technological, market externalities and internal factors associated with digital product success; along with managerial implications to effectively design, develop and manage successful apps.
The form and content of this abstract are approved. I recommend its publication.
Approved: Jiban Khuntia
IV


DEDICATION
I dedicate this thesis to my parents Renu and Ganesh. I hope that this achievement will complete the dream that you had for me all those many years ago when you chose to give me the best education you could and whose words of encouragement and push for tenacity ring in my ears. My brother Partha, has never left my side and very special.
I also dedicate this dissertation to my many friends and my extended family who have supported me throughout the process. I will always appreciate all they have done.
v


ACKNOWLEDGEMENT
Firstly, I would like to express my sincere gratitude to my advisor Prof. Jiban Khuntia for the continuous support of my Ph.D study and related research, for his patience, motivation, and immense knowledge. His guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better advisor and mentor for my Ph.D study.
Besides my advisor, I would like to thank the rest of my thesis committee: Prof. Jahangir Karimi, Prof. Madhavan Parthasarathy, and Dr. Ilkyuen Ra, for their insightful comments and encouragement, but also for the hard question which incented me to widen my research from various perspectives.
My sincere thanks also goes to the Jake Jabs Center for Entrepreneurship, who provided me an opportunity to join their team as a program assistant, and who gave access to my research data and facilities. Without they precious support it would not be possible to conduct this research.
vi


TABLE OF CONTENTS
CHAPTER
I. DISSERTATION OVERVIEW..............................................1
Introduction and Review of the Literature...........................1
Consumer Evaluation and Response....................................2
Objective of the Study..............................................8
II. TECHNOLOGY FRUSTRATION AND CONSUMER EVALUATION SHIFT
FOR MOBILE APPS: AN EXPLORATORY STUDY...............................9
Abstract............................................................9
Introduction.......................................................10
Prior Literature...................................................12
Theoretical Framework..............................................14
Hypotheses Development.............................................17
Method.............................................................22
Data and Variables..............................................22
Estimation Models...............................................27
Results............................................................28
Discussion.........................................................32
Theoretical Contributions..........................................33
Managerial Implications............................................37
Limitations and Future Research....................................36
Conclusion.........................................................36
III. EPILOGUE...........................................................37
Vll


REFERENCES


LIST OF TABLES
TABLE
1. Description of variables............................................................23
2. Descriptive statistics..............................................................25
3. Correlation amongst key variables...................................................26
4. Panel estimation random effect models...............................................29
5. GMM estimation results..............................................................31
IX


CHAPTER I
DISSERTATION OVERVIEW Introduction and Review of the Literature
Online consumer reviews and ratings, a key form of online user-generated content, are now widely available for many products. An online reviewer provides a qualitative assessment (online review) of his or her product experience, which informs and influences his or her quantitative evaluation (online product rating). The online reviewer is usually preceded by reviewers who have already rated the product. The average online product rating is prominently displayed (e.g., www.epinions.com;www.tripadvisor.com) to convey consensus information about the online reviewer community's product evaluations. We first provide the motivation for this research.
Given the growth of online review websites, scholars have examined demand consequences of online product ratings (Bickart and Schindler 2001). High online product ratings increase the online market shares of books (Chevalier and Mayzlin 2006), offline sales of television shows (Godes and Mayzlin 2004), sales of toiletry products (Moe and Trusov 2011), and sales of video games (Zhu and Zhang 2010). There is limited and mixed empirical evidence on social influence in online ratings. Schlosser (2005) reports that reviewers, motivated by a need to be perceived as discriminating, decrease their online product ratings after reading others' online reviews. Other research reports that when others' online ratings are at the lower end of the rating scale, reviewers tend to increase their online product rating (Moe and Trusov, 2011). A possible reason for the mixed evidence is that past research has overlooked the contingent nature of social influence effects in the online ratings context.
1


Insights into social influence effects in online product ratings have high managerial relevance. A comScore Inc. survey (2007) reports that 24% of consumers use online consumer reviews before purchasing a product. With respect to hotels, the empirical context for this article, online consumer reviews influenced choices for most consumers (87%). High online product ratings also translate into price In the comScore study, consumers were willing to pay more for a product with an "excellent" rating (5) than for one with a "good" rating (4); the premiums were 99% for legal services, 38% for hotels, and 20% for real estate agents. Managers find it useful to decompose online reviews to learn how their products' characteristics affect their online product ratings. In particular, insights into social influence effects in online product ratings provide actionable insights to managers, including how to use online product ratings as a communications element.
Therefore, consumer evaluation of products in a market is a predictor of the success or failure of a product. Also product feature is a pre-cursor to product success in the market. While good product appeal spells success for any given product, technology frustration with digital products leads to shift in evaluation of a product in digital markets, mostly for the worse.
Consumer Evaluation and Response
Businesses continue to create and develop new products in order to ensure continued profitability and brand survival. This is done with the hope that the new product will be of value to both the company and consumers (Belen, Vazquez, & Iglesias, 2001). In essence, a product that doesn’t meet customers’ needs or is of little value to them would be a major failure in the market. However, it is important to note that besides a business’ effort to try and meet customer needs, the success of a product is largely dependent on customer’
2


evaluation, which subsequently determines customer response. In fact, Rindova (2007) pointed out that researchers acknowledge that innovation is important for value creation, but equally warn on the uncertainties regarding the ability of a product to deliver valuable functionality. Rindova noted that this is more so because consumers find it hard to recognize the value of new products. In that case, customers’ evaluation is usually based on available cues and perceptions on the products value.
The development on new products is more so important for the success of technology intensive industries, such as those that produce digital products (Prins & Verhoef, 2007). Considering the importance of consumer evaluation and response on the market success of products, a lot of research has been made towards the determiners of consumer response. Several of these have shown that an individual’s pre-existing attitude about a product, beliefs on the ability to use the product and their evaluation of a product’s usefulness, influence the individual’s purchasing decisions (Morgan-Thomas & Veloutsou, 2013). This is as proposed by the technology acceptance model (Davis, 1989; Venkatesh et al. 2003).
A similar research, Osman et al. (2012), investigate the digital content usage behaviour of consumers and factors that influenced their purchases, specifically, the role of smartphone design, perfomance and price. With continued advances in technology, mobile phones now come with advanced features that allow more consumption and exchange of digital content (May & Hearn, 2005). The study revealed that trends in community (35.6%) was the leading customer influencer when it came to purchasing smartphones. This result implied that marketing and promotion, or rather availing product-related information to consumers was important in influencing consumers’ decisions. Besides marketing, social influence from peers, relatives, etc, seemed to be important in influencing purchasing
3


decisions. The results, therefore, further undeline the importance of information availability and social influence in the digital market. The influence of consumer needs and software came closely second and third at 34.4% and 33.1%, respectively, while other factors like price, hardware, and signal reception, were of less significance. When it came to specifications, a smartphone’s design (56%) seemed to be the most popular determiner of success, beating price (30.2%).
While social influence determines consumption, a consumer’s evaluation at a personal level is important in determining the market success of digital products. The visual appearance of a product goes a long way in determining a consumer’s choice or evaluation and subsequently the success of a new product. This is because, sometimes, the appearance/ package is the only information/communication accessible to a consumer at the point of purchase (Schoolmans & Robben, 1997). In that case, a product’s design is crucial because it communicates to the consumer, stimulates and attracts attention, and creates a lasting impact on the consumer (Bloch, 1995). Besides, unique designs help new products stand out from the competition, hence giving them a better chance to succeed.
Bertini et al. (2008) studies the impact of add-on features on consumer product evaluation. In an attempt to make their product unique/ outstanding, many companies add extra features to their products. However, the added benefit of these features can only be felt if the product is used together with a corresponding base good (Guiltinam, 1987). The tendency to produce add-on features is very common in electronic and digital products companies. The study by Bertini investigated the link between perceived product utility and add-on type. The research revealed that adding a new feature, different from and in addition to the expected addons made digital products more appealing to customers. However, those
4


add-ons that served as an upgrade to existing features had a negative impact on product evaluation. However, when information on individual digital products, their judgement on the individual products, the negative evaluation waned. This tendency can be explained through the concept of experience by Hoch (1989).
Despite the above information, a further analysis of literature reveals ambiguity over what determines/ a consumer uses to evaluate a product’s ease of use. Existing literature suggests that perceived ease of use is tied to appearance. While several researchers have previously focused on the role of packaging and appearance on consumers’ choices (Bloch, 1995; Garber, 1995; Veryzer, 1993; Veryzer, 1995), there exists a gap in describing ways in which appearance affects consumption. Moreover, different researchers have varied on their description or evaluation of appearance parameters. For example, Bloch (1995) considered the ease of use as a quantifier of appearance, while Lobach (1976) considered it to be an aesthetic function. Interestingly, Veryzer (1995) described the ease of use as a communicative function of appearance.
Existing literature reveals an influence of brand relationships on the consumption of digital products. Companies continue to invest heavily in brand development. That is, to create an image that can be linked to particular associations. It is important to note that there is no particular standard of measuring brand image (Dobni & Zinkhan, 1990). However, it is generally defined as perceptions about a product brought about by a set of associations that customers attribute to the product/ brand (Rio et al. 2001). According to Rio et al., a positive influence is elicited by brand associations on consumers, where their preferences, willingness to pay higher prices for particular brands, choice, and willingness to recommend products to others, are influenced. Theories on brand influence reveal a positive relationship between
5


“consumer behavior, self-image and the product image (Rio, Vazquez, & Iglesias, 2001 p. 3). What this means is that the use of particular brands can be used to enrich self-image. Additionally, particular brands give consumers an assurance of a particular set of associations, e.g. performance, appearance, status, etc.
Therefore, a high evaluation of a particular brand by a customer increases the likelihood of purchasing its products (Graef, 1996; Hogg et al. 2000). The same concept would also apply to new products launched by companies whose brands are held in high regard. Similar views by (Long & Schiffman, 2000) indicate that consumers interested in social identification will want to own products that are highly regarded by people within their social groups. This can be viewed as an extension of the findings of Pandit, Karpen, & Josiassen (2008) and Osman et al. (2012), that social groups influence consumer response to digital products.
Theories on consumer culture explore ways in which consumers make meaning of symbolic cues encoded in brands, advertisements, products, etc. to reveal their personal and social situations, as well as identity and lifestyle goals (Grayson and Martinec 2004; Holt 2002; Kozinets 2001, 2002);. In that case, markets provide consumers with a wide array of products from which to make choices and subsequently claim their individual and collective identities (Murray 2002; Schau and Gilly 2003). Consumers are, therefore, identity seekers who acquire products that enable them achieve their lifestyle goals.
While social influence, perception and brand relationships may determine consumption, it is important to note that consumers have varying consumption cultures. The influence of consumer culture on consumer evaluation at a personal level is important in
6


determining the market success of digital products. The role of personal values in influencing consumer choice has been researched previously, with results showing a direct relationship between personal values and consumption. According to Wiedmann and Hennigs (2007) theory showed that individual and social related values affected consumer choice, more so where consumers buy to impress and maintain their social image (Eagly and Chaiken 1993; Weidmann and Hennigs 2007).
While such research are important in helping us understand the importance of customer evaluation on the market success of products-specifically digital products and in shaping the technology acceptance model (Davis, 1989; Venkatesh et al. 2003), their limitation lies in the fact that the researchers focus on particular items at a time. For example, Osman et al. (2012) investigated the influence of smartphone design, perfomance and price. These factors were investigated independently, with little regard to their interaction. While it is true that pre-existing attitudes influence consumer response, the degree to which those attitudes influence purchasing decisions is dependent on a number of factors. Similarly, research on the impact of product design in influencing consumer choice failed to investigate or explicitly explain the various ways in which product appearance influences consumers, as well as the specific implications of that in product design. According to Creusen & Schormans (2005), consumers consider product in terms of communication of aesthetic value, symbolism, and functionality, in addition to ergonomic product information, its ability to draw attention, as well as categorization.
7


Objective of the Study
The essay in this dissertation focus on the research question that explores how technology frustration and hedonic and utilitarian and hedonic appeal have an impact on the consumer evaluation shift and ratings shift of an app respectively.
The essay of this dissertation address the research question: how technology frustration negatively influences the consumer evaluation shift of mobile app products in markets? In this essay, I explore how technology frustration negatively influences the evaluation of mobile app products in markets. Specifically, I examine the influence of technology frustration on the expectation from the product. The theoretical framework and hypotheses for this study are grounded in the marketing literature, with the positioning that understanding product expectation is a much explored area in the marketing field. I develop several hypotheses predicting the relationship of the elements of technology frustration on consumer evaluation shift. To test the hypotheses, I conducted empirical analysis using panel data. The dataset contains data from the android app store for 3 months.
8


CHAPTER II
TECHNOLOGY FRUSTRATION AND CONSUMER EVALUATION SHIFT FOR MOBILE APPS: AN EXPLORATORY STUDY
Abstract
Consumer evaluation of products in a market is a predictor of the success or failure of a product. For digital products, the usage of a product is often determined by the technology robustness associated with the design of the product. Bad designs, implementation and integration issues lead to frustration on the part of consumers using the product. In this study, I explore how technology frustration negatively influences the evaluation of mobile app products in markets. Theoretically, I argue that technology frustration disconfirms the expectation from the product. I hypothesize that technology frustration has a negative impact on consumer evaluation shift over time. In addition, I argue that the influence of technology frustration on consumer evaluation shift is moderated by price, age of the app, consumer passion and market sustenance. Finally, I contend that the negative effect of technology frustration is high for new and high priced products because of the higher expectations and hence more stringent evaluations for new and high priced products by consumers. I propose to empirically analysis the model using panel data on 87,000 apps for a period of 3 months, consisting of 20,000 panels. I plan on using random effect panel estimation for empirical analysis. In addition, I plan on using generalized method of moments (GMM) to address endogeneity issues. The implication of this study will inform apps developers to be careful in app design to sustain in the app markets. The study is important first step in coining consumer evaluation shift as a market factor, and further identifying and conceptualizing the role of technology frustration associated with apps.
9


Introduction
Mobile applications (apps) are digital innovations using internet and mobile platforms. The popularity of mobile apps has resulted in a new entrepreneurship model, where app developers can appeal to and reach mass consumers relatively inexpensively. Apps are also becoming useful in several unprecedented ways, such as transforming healthcare delivery, enabling supply chains, and leveraging crowd sourcing concepts (Ghose and Han 2014). Revenue from app sales is projected to increase from $10.3 billion in 2013 to $25.2 billion by 2017 (IDC 2013). Both the Apple market has and the Android market offer over 1.5 million unique apps apiece (Statistica, 2015), while the Android market offers over 40,000 apps. These apps include a wide variety of applications, ranging from work, play and everything in between.
While the surge in the number of apps is exponential, not all developers are able to find success in this cutthroat market space. Indeed, a number of apps and associated ventures have failed in a relatively short period of time. Studies show a considerable skew in download rate of apps; for example, only 5 apps account for about 15% of all downloads, while over 50% of available apps achieve fewer than 500 downloads apiece (Aitken and Gauntlett 2013).
Prior research has suggested that managing business models for consumer oriented digital products needs a good customer value proposition, a solid revenue model, and the availability of key resources and processes to generate revenue (Johnson et al. 2008). One such resource and process specific to apps is innovative technology, involving design, smooth functioning, and integration of the product in the market space. Specific to digital business models, Bhardwaj et al. (2013) note that the scope, scale, speed, and sources of business value creation and capture are the four major components of digital business
10


strategy, and all of these components can be influenced by consumer evaluation of the app. Thus, focusing on consumer evaluation of an app, and monitoring the progression, or shift, in the evaluation over time, would provide invaluable information regarding the net scope and scale of the app’s usage, the speed at which it is proliferating, and whether consumers are likely to continue their patronage of the app in the immediate future. While consumers evaluate the apps on a continual basis, the extent to which technology remains a driving factor in this evaluation remains an unexplored question for both academics and practitioners alike.
Given the aforementioned, our goal in this paper is to focus on consumer frustration with technology associated with apps in a market. At individual level, technology frustration is the emotional response of a negative computing experience with the technology (Bessiere et al. 2006). Technology frustration originates when a consumer faces a technological obstacle in the process of trying to achieve a desired function. Such obstacles may include crashing, network congestion, poor interfaces, confusing design, unnecessary complexity, and usage problems. Failure may be intermittent, periodic, random or frequent. Such frustration could lead to personal dissatisfaction and loss of self-efficacy, may disrupt workplaces, stifle work efficiency, and reduce the work efficiency of an individual (Lazar et al. 2006). Technology frustration may also lead to elevated levels of anxiety and anger on the part of the individual (Wilfong 2006). The outcome of the technology frustration, in turn, may result in a segment of the customers staying away from the technology (Lenhart 2003), initiating negative word-of-mouth influence regarding their experience and, ultimately, discontinuing usage of the app. Taken cumulatively, we posit that these individual
11


technology frustration events aggregate to create a highly adverse overall response for the product in the market (Guchait and Namasivayam, 2012).
The conceptual model in this study proposes that technology frustration negatively influences consumer evaluation of a product in digital markets. In addition to the main effects, we hypothesize that market externalities, such as consumer passion for, and sustenance of, the product in question temper this negative effect. Furthermore, we hypothesize that the negative effect of technology frustration is high for new and high priced products because of the greater expectations (and hence more stringent evaluations) that new and high priced products warrant. To test our hypotheses, I plan on using dataset that tracks cell phone apps in the android market, tracking more than 80,000 apps comprising of 20,000 panels apps for a period of 3 months. This study contributes to the information systems and consumer research literature in providing a new dimension of technology frustration associated with consumer evaluation of products. We discuss managerial implications and contributions of the study.
Prior Literature
Prior literature on apps has mainly focused on apps intake (Aleem et al. 2014; Han, 2014), growth and diffusion (Khalid et al. 2014; Gerlich et al. 2015) issues. Apps markets are information technology enabled markets based on the mobile technology developments. Instead of being freely available products, apps are part of the iTunes, Android, Windows, or Blackberry marketplaces from where consumers can download them, use them, and subsequently review them. Being digital products apps provide granularity in terms of usage and adoption, and have a trialability option that refers that a buyer can try it prior to purchase (Hui and Chau, 2002; Phang and Kankanhalli, 2010; Zwass, 2003). The app digital market
12


place offers both free and paid apps, and have lock-in mechanisms for consumers (Garg and Telang, 2012; Huang and Hung, 2014; Liu et al. 2014). Further, the digital app marketplaces provide a platform for sharing of information through ratings and reviews that inform and influence other consumers regarding an app’s use and net effect. (Yin et al. 2012; Huang and Korfiatis, 2015)
Software issues play a major role in consumers’ evaluation of a digital product, such as an app. These include the crashing of software, unclear displays, unnecessary pop-ups, and a confusing user interface (Preece et al. 2002). For a non-technical or naive user, software issues could become highly frustrating (Shneiderman, 2000; Cummings and Kraut, 2002) leading, in turn, to behaviors such initiating negative interpersonal influence regarding the product, resisting the product and, ultimately, intending to discontinue using it (Beaudry and Pinsonneault, 2010; Stein et al., 2015). In fact, a few prior studies have highlighted the impact of software or technology issues with apps usage. For example, a study conducted by Pew in 2003 reported that 42% of the consumers in the United States did not use technology due to software issues. Some other researchers note that users waste one-third to one-half of productive time due to a frustrating experience with technology (Ceaparu et al. 2004). Others have found that frustration with technology might lead to low level of job satisfaction (Scheirer et al. 2002). As much as any product can cause dissatisfaction amongst consumers, the end result is the reduction of the product usage in the market (Park et al. 2012; Bolton, 2011; Keaveney and Parthasarathy, 2001). The bottom line is that when a product does not meet consumer expectations, the resultant evaluation of the product becomes very low (Brown et al. 2014; Yang et al. 2013). Although expectation and confirmation of a product seems as an individual issue, an accumulation of negative evaluations could have a dual
13


macro-level negative effect: 1) dissuading prospective consumers from adopting the app (due to others’ negative experiences), and 2) influencing existing consumers to discontinue using the app (due, in part, to the frustration experienced by other users which is likely to sooner or later be encountered by existing users). Thus, consistent and large-scale negative evaluations of a product may eventually wipe it out entirely from the market (Mudambi and Schuff, 2010; Negash et al. 2003). Since existing research on the negative shift in consumer evaluation, and its subsequent impact on the eventual success or failure of a product (such as an app) is sparse in the information systems and marketing literatures, this study tries to address this gap in the context of technology frustration and consumer evaluation shift in the apps marketplace.
Theoretical Framework
The notion of technology frustration is often a result of actual experience with a product falling short of expectations. The Expectation confirmation theory (ECT) and prior literature on consumer evaluations for this study. ECT posits that post-purchase or postadoption satisfaction is a function of expectations, perceived performance, and disconfirmation of beliefs associated with a product (Oliver 1977; Oliver 1980). Applying to the digital products context, existing research has established that the confirmation or disconfirmation of consumer expectations is a highly influential factor in determining whether consumers continue or discontinue a product (Bhattacherjee 2001; Brown et al. 2012). When the product confirms to its objectives or intended goals of use, consumer evaluations increase (Aaker and Keller 1990; Dabholkar 1996). Thus, if an app suffers from technical issues, such as crashing, poor response rate, incorrect formatting, etc., it may fall below reasonable consumer expectations. Furthermore, if the same app once worked well in
14


a previous version, but a newer version is plagued with bugs and other technical issues, a consumer may use the previous version of the app as an expectation anchor, against which the new version of the app falls short leading, quite naturally, to frustration and disenchantment.
Indeed, research on the discontinuance of innovations (e.g., Keaveney and Parthasarathy, 2001; Parthasarathy and Bhattacherjee, 1998; Parthasarathy, 1995) identifies disenchantment as a key reason for the termination of a tech-related service. These scholars found that disenchantment could result from a variety of sources including technical issues with the product itself and poor customer service and support. Further, it was found that unnecessary complexity of a product could lead to some less technically savvy consumers being unable to use a product to its intended capacity, leading to underutilization and, eventually, to discontinuance. Thus progressively complex interfaces may lead to frustration resulting from a consumer’s inability to use an app to its full potential.
However, frustration with a product is often tempered by passion. Passion refers to a consumer’s involvement with and interest in a particular product, in our case, an app. Existing research confirms that highly involved consumers are likely to be heavy users of the product, have considerable product knowhow and are also likely to be opinion leaders (e.g. Norman and Russell, 2006; Venkataraman 1988). Furthermore, passionate, involved, consumers are known to have strong emotions, both positive and negative, regarding a product (Norman and Russel 2006), and are likely to tell others about their experiences. This is a double-edged sword in that highly passionate consumers who have a negative experience with a product are more likely to initiate negative word-of-mouth influence regarding that product, and also more likely to hence be disenchanted with the product themselves.
15


Other studies lend credence to this contention. For example, Richins and Bloch (1991) found that while passionate, high-involvement consumers were more satisfied with a product soon after adoption, their opinions were more likely to decline after adoption than their lower-involvement counterparts. This would suggest that passionate consumers were also likely to be pickier, and small issues were more likely to annoy or frustrate them than less-passionate consumers. Furthermore, it would indicate that, in the present study’s context, high sustenance apps, or apps that have been in the market for a longer time, have probably gone through more updates and beta versions, and therefore had greater chance of having some aspect of their system frustrate passionate consumers, leading to a progressive decline in their evaluations. Highly involved consumers are likely to be knowledgeable about their product category and thus set higher, more cognitive expectations regarding the product’s performance. However, as per contrast theory (Krogaonkar and Moschis 1982), a consumer whose expectations are negatively disconfirmed will amplify that difference, and thus initiate more negative interpersonal influence than the difference between expectations and actual product performance would warrant. The longer the app has sustained itself, the greater is the opportunity for this contrast effect to have manifested itself.
Based on the aforementioned premises, we propose a conceptual model (see Figure 1) for this study that presents three sets of relationships: (1) technology frustration has a direct impact on consumer evaluation shift (i.e., the negative shift in consumer evaluation of the app); thus, the greater the technology frustration felt by the user, the greater will be the evaluation shift for the product, (2) two market externalities, i.e., consumer passion and market sustenance, moderate the relationship of technology frustration on consumer evaluation shift, and (3) two internal factors, e.g., age of the app and price of the app, both
16


discussed in the next section, also moderate the association of technology frustration on consumer evaluation shift.
Figure 1: Conceptual Model Hypotheses Development
Technology frustration deals with a set of emotional responses as a result of negative experiences with technology use. In the apps context, an app is always expected to perform or provide a specific functionality. We argue that technology frustration will have a negative impact on consumer evaluation shift for three reasons.
First, an underlying mechanism associated with technology frustration is the crashing of an app. When an app crashes or is not well integrated, the app would not meet the user expectations. Existing studies note that the initial feelings when a user look at a product might be one indicator of the quality of a product (Tractinsky, 2004). But, broader digital product quality is influenced by many sources such as functioning of the product, reviews and technical features. From the expectation confirmation theory, it is known that consumers
17


have initial expectations from a product, and once they use the product they form perceptions about the product. After using it for a certain period of time, the user compares the performance with the expected performance and see if the expectations are met, which lead to their decision about continued usage of the product or discontinue it. When extrapolated to a market context, the app would lose a set of existing customers and will not be able to get more customers. Thus, we argue that the frustration felt by users while using the app would negative influences customer evaluation shift.
In addition, confusing design and usage problems lead consumers to discontinue the app. Prior studies note that technology frustration leads to loss of the users’ self-efficacy and subsequent personal dissatisfaction (Lazar et al. 2006). A frustrating experience with technology can also leave a user feeling frustrated toward the technology and its makers (Klein et al., 2002). If being used in a work place, the slow functionalities or issues may lead to underutilization and lower employee productivity. If a user is not satisfied by the app, it would result dissatisfaction and finally would lead to negative word-of-mouth as well as bad consumer evaluation initiated by the dissatisfied consumers will lead to fewer new consumers to adopt it, or positively review it, leading to a fewer number of positive ratings and a greater number of negative ratings. As a result, the overall consumer evaluation will decrease. At a consumer or market level, shared understanding of technology frustrations felt at an individual level aggregates for a product or service to create a highly adverse response for the product in the market (Guchait and Namasivayam 2012). Thus, we hypothesize:
HI: Consumer technology frustration is negatively associated with consumer
evaluation shift for cellphone apps.
18


Prior research shows that emotions have a huge impact on consumer decision making (Ding et al. 2005, Darke et al. 2006). Especially frustration is a negative emotion experienced by individuals when their goals are not met or missed (Colman, 2001). This negative experience might lead to a product avoidance or bad consumer evaluation. In the context of app use, consumers try to find an app that they plan to use for a particular task and gets frustrated if the app is not able to fulfil the task and become frustrated leading to bad consumer evaluation. In particular, the ineffectiveness of proper functioning of digital products could leave consumers frustrated and impact attitude toward the product evaluation (Campbell & Wright 2008, Su 2008). We argue that market factors such as consumer passion and market sustenance of the app until the focal time are two moderating factors on the relationship between customer technology frustration and consumer evaluation shift for several reasons.
Consumer passion indicates the liking of for an app due to its appeal, function or on the attribute that meets a specific consumer need. For example, a fitness app would have a good appeal for the consumer segment that want to stay fit, and hence, would have generated a niche of passionate consumers. These passionate consumers would then derive higher benefits, be beta testers, and help the app developer work towards the improvement of the app by providing feedback and, in general, help in mitigating the aspects related to technology frustration issues. Moreover, these satisfied consumers will generate positive word-of-mouth influence, resulting in higher consumer evaluations. But, if the apps with high consumer passion starts having technical issues like glitches, the evaluation shift for such apps would be much higher. These passionate, satisfied consumers will therefore serve
19


to amplify the direct, negative effect that technology frustration would garner among general, non-involved adopters of the app.
H2a: The negative effect of consumer technology frustration on consumer evaluation shift is stronger for apps with high consumer passion.
In addition to consumer passion, apps that have enjoyed sustained success in a market are more likely to command consumer support as a reflection of the apps’ good usage and functionality. These apps are reviewed by consumers, have been evaluated and established through high download volumes in the market in the last many months. Thus, the scope of sustained apps to the idiosyncrasies associated with technology issues will be viewed as a failure for the particular app. These apps have stood the test of time, and have commanded extensive positive support by consumers and are therefore more susceptible to consumer frustrations when technical glitches starts happening maybe after an update. Consumers will not be forgiving with technology glitches associated with these apps because they either are passionate about them, or because they have sustained positive reviews, indicating that any glitches are not anomalies and not the usual norm. Based on these arguments, we hypothesize:
H2b: The negative effect of consumer technology frustration on consumer evaluation shift is stronger for apps with high consumer sustenance.
We contend that internal market factors related to the app, such as age of the app and pricing of the app are prone to negative consumer evaluations. For example, new apps that are introduced to the market recently will be evaluated less stringently than old apps, as they are not yet consumer-tested in the market. One of the reasons might be because the app has
20


been recently introduced to the app market. Because of its newness, the expectations are not that high versus apps that are already in the market and has withstood the test of times. Users of the new apps are willing to oversee any technical failures in the app and would expect the technical failures to be taken care of in the subsequent new version releases, which is not the case with older apps. This is also perhaps due to the newness, and therefore greater interest and involvement (and hence, scrutiny) of old apps. Old apps may have gone through a lot of versioning and updates that would have resulted in a degree of robustness in terms of usability and functional integration in the app platform. Crashing or technological problems with old apps would be dealt with skepticism and would force the users to give lower consumer evaluation.
H2c: The negative effect of consumer technology frustration on consumer evaluation shift is stronger for old apps.
Similarly, price of an app is sensitive to consumer evaluation as consumers have higher expectations for more expensive apps, and hence perform more stringent evaluations for products that command a price premium. When a relatively expensive product suffers from technological troubles or issues, consumers may find themselves being less tolerant of such mishaps, as their expectations have been negatively disconfirmed, leading to dissatisfaction with the app. Existing studies mention that price-stringent evaluations are taken very seriously by consumers even to the point of returning or discontinuing a product (Jones and Suh 2000; Parasuraman et al. 1994).
In sum, expectations are a direct function of price. Expectations associated with free apps is likely to be low, leading to positive disconfirmation of expectation even for relatively
21


mediocre apps. As price of an app increases, so do expectations. Expensive apps are held to a higher standard, and technological blips and frustrations are progressively less tolerable. Thus, we argue that compared to the free or low-cost apps, the negative influence of technology frustration on consumer evaluation will be higher for more expensive apps.
Based on these arguments, we hypothesize:
H2d: The negative effect of consumer technology frustration on consumer
evaluation shift is higher for pricier apps than for low priced or free apps.
Method
Data and Variables. The data for this study comes from a secondary source. A consulting company engaged in tracking app businesses in android market collected the data. Our units of analysis are based on the tracking information about the apps for two and a half months from 13 October 2014 to 1 January 2015. For the analysis purposes the first week is the focal reference week, and the second week is used as the reference for the increase in ratings and reviews to calculate the variables used in this study. We created a panel data set consisting of 20,007 panels and 87,192 observations.
We could not consider apps that were only available for one panel for our analysis, which did not have any usage at all in terms of clicks or zero downloads in the market until the focal week. On close scrutiny, we also found apps that were in a different language than English, had illegible names, or were duplicates in the market. We excluded these apps. In addition, we excluded apps as there were no change in ratings or number of people who rated or reviewed the apps in the focal week and the subsequent week.
22


Table 1 provides a description of variables we used in this study. Table 2 provides the descriptive statistics and table 3 pair-wise correlation amongst the key variables. The dependent variable in our model is the consumer evaluation shift (CES) in the week’s span. This variable is calculated as the net change in the product of average rating and raters in that week, per original rater in the beginning of the week (see the calculation description in Table 1). Using both average ratings per week and the total number of raters provides both a measure of score, and a measure of popularity in a single variable that assesses overall rating impact. By measuring the change in overall impact score between the two time periods, an index is developed based on the benchmark measure of the first week. This index is our dependent variable, CES. Aggregate measures of this kind have been used before, one famous one that uses a combination of two variables is Gross Rating Points, which combines two very different variables, frequency and reach, into an aggregate measure of overall size of an advertising campaign (Curran, 1999). Similarly, by combining two important app rating measures, namely average rating score and total number of raters, CES truly measures the shift in an app’s overall impact rating between two time periods, both in terms of quality and quantity.
Table 1: Description of Variables
Variable Description and Operationalization References
Dependent Variables
Consumer Evaluation Shift (CES) The change in the net consumer rating of the app, considering both the total raters who rated the app and the ratings that the app received in the span of the week. The CES index is calculated as follows: CES = (R2N2-R1N1)/N1. Where, R2= Average rating of the app in week 2 Rl= Average rating of the app in week 1 N2= Total number of raters of the app in week 2 Nl= Total number of raters of the app in week 1 (Perdikaki and Swaminathan 2013; Ulkii, Dimofte and Schmidt 2012)
23


Table 1 cont’d
Independent Variables
Consumer Technology Frustration (CTF) Frustration felt by the consumers for the app in the market. This variable is coded by mining the text reviews of each app in the weeks' time. The index measures the percentage of reviewers that discuss technical issues, frustrating problems, challenges in and crashing of the app within the focal week. (Bessiere et al. 2006; Guchait and Namasivayam 2012)
Consumer Passion (CP) A powerful and persistent urge by the consumers to use or buy or interact with the app. This variable is coded by mining the text reviews of each app in the panels' time. The index measures the percentage of reviewers that discuss admiration, devotion for and exaltation for the app within the focal weeks. (Belk, Ger and Askegaard 2003; Denegri-Knott and Molesworth 2013)
Variable Description and Operationalization References
Market Sustenance (MS) To what extent consumers have sustained the app in the market. The variables is operationalized by measuring the total download volume of the app until the focal week. This was a range variable with unequal ranges. We coded download volume as a continuous variable by taking the mid-point of the range provided. (E.g. if the download range is 10,000-50,000, we used the midpoint 30,000 as the number of downloads). We then divided the number of downloads 1,000,000. (Lee and Raghu 2014)
Age of the app (AGE) How long the app has existed in the Android market since its release. The variable was coded as the difference between the focal week and the release date of the app.
Price of the app (PRICE) The price of the app in US$.
Control Variables
Gameapp Whether the app is an application app. The variable is coded as l=game app, 0=not a game app.

AppLASTUPD When the app was last updated
Avg RP Average rating of all the apps that was published by a particular publisher
TotalRP Total Ratings for the Publisher that includes all the apps created by the particular publisher
Avg PUBLRTNG Average reviews of all the apps published by a publisher
SumRP Total reviews of a publisher that includes all the apps created by that publisher
Avg PP The average passionate users of a publisher that includes all the apps published by the publisher
Avg PP Passionate users of a publisher that includes all the apps published by the publisher in percentage
24


Table 2. Descriptive Statistics
Variable Obs Mean Std. Dev. Min Max
CES 87192 4.55 2.91 -7.74 18.23
CTF 87192 6.65 8.16 0 100
CP 87192 0.57 0.12 0.00 1.00
MS 87192 20.71 27.27 0.01 30
AGE 87192 2.09 1.07 0.41 6.58
PRICE 87192 0.97 0.70 0.40 6.58
AppLASTUPD 87192 3.88 0.83 0 5
Avg RP 87192 4.46 5.22 0 37.30
TotalRP 87192 9.58 33.88 0 77.59
Avg PUBLRTNG 87192 17.66 49.13 0 54.97
SumRP 87192 0.55 0.19 0 1
Avg PP 87192 0.00 0.03 -1 0.75
Avg PP 87192 0.34 1.90 0 10
Avg EP 87192 1.69 4.59 0 20
AvgCP 87192 4.47 12.74 0 17.5
AvgAP 87192 2.75 8.55 0.41 11.55
Avg PrP 87192 2.52 8.56 0.41 11.55
Avg RDP 87192 1.70 8.59 0.40 11.55
PubDeb 87192 0.02 0.14 0 1
Age LRP 87192 0.02 0.15 0 1
AgeLUP 87192 0.03 0.18 0 1
CTrivia 87192 0.04 0.19 0 1
C Communication 87192 0.03 0.16 0 1
CEducational 87192 0.01 0.12 0 1
C Entertainment 87192 0.03 0.16 0 1
CFamily 87192 0.02 0.13 0 1
CFinance 87192 0.03 0.17 0 1
C Health & Fitness 87192 0.03 0.18 0 1
C_Lifestyle 87192 0.03 0.17 0 1
C_Media & Video 87192 0.03 0.17 0 1
C_News & Mag. 87192 0.02 0.13 0 1
CPersonalization 87192 0.03 0.18 0 1
CProductivity 87192 0.03 0.17 0 1
CShopping 87192 0.02 0.15 0 1
CSimulation 87192 0.03 0.18 0 1
C_Social 87192 0.03 0.17 0 1
25


Table 3. Correlation amongst Key Variables
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 CES 1
2 CTF 0.10 1
3 CP -0.10 -0.28 1
4 MS 0.12 0.03 -0.05 1
5 AGE -0.06 -0.18 0.13 -0.04 1
6 PRICE 0.12 0.11 0.04 -0.02 0.44 1
7 App_LASTU 0.01 0.00 -0.01 0.00 0.00 0.00 1
8 Avg_RP 0.01 0.01 0.00 0.00 0.00 -0.01 0.03 1
9 Total_RP 0.01 0.00 0.00 0.00 0.00 -0.01 0.10 0.28 1
10 Avg_PUBLR 0.00 0.01 0.00 0.00 0.00 -0.01 0.06 0.58 0.38 1
11 Sum_RP 0.01 0.00 -0.01 0.00 0.01 0.00 0.86 0.04 0.09 0.06 1
12 Avg_PP -0.01 0.00 0.00 -0.01 0.00 0.00 0.00 0.00 0.02 0.01 0.00 1
13 Avg_PP 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.02 0.05 0.04 0.07 -0.01 1
14 Avg_EP 0.00 -0.01 0.01 -0.01 0.00 0.01 -0.02 -0.02 -0.07 -0.04 0.02 0.01 -0.03 1
15 Avg_CP 0.01 -0.01 0.01 0.00 0.00 -0.01 0.07 0.05 0.09 0.07 0.04 0.02 0.03 0.01 1
16 Avg_AP 0.00 0.01 0.00 -0.01 0.01 0.01 0.09 0.07 0.16 0.07 0.03 0.07 -0.01 0.08 0.24 1
The focal independent variable in this study is consumer technology frustration (CTF) which indicates the frustration in using the app. This variable is coded by mining the text reviews of each app in the weeks’ time. The second independent variable is consumer passion (CP), for the app in the market that reflects the appeal of the app to the consumers as a powerful and persistent product, and is measured by text mining the reviews as well. Appendix A provides the coding scheme for CTF and CP variables. The CTF index measures the percentage of reviewers that discuss technical issues, frustrating problems, challenges in usage, and crashing of the app. Thus, CTF provides an aggregate measure of consumer frustration with the app in the market. The reviews were coded using a hermeneutic coding process to code the customer technology frustration by the consulting firm. We looked for words like “crash”, “frustrating”, “issues” etc. to find the percentage of apps that showed users frustration while operating an app.
26


The third independent variable is market sustenance that is operationalized as to what extent the app is sustained in the market through downloads until now from its release date. The download volume varies from 0 to 3,000,000, with average download of 27,000 for the apps in our sample. The third dependent variable is the price of the app in the market. The price of the apps varies from free to 7 US$. The final dependent variable is the age of the app since its release date. The average app in our sample is 2.5 years old, with the maximum age being 7 years and minimum is one year.
Estimation Models. We will be using panel data for our analysis. To decide between fixed or random effects, we will run a Hausman test where the null hypothesis is that the preferred model is random effect vs. the alternative of fixed effect (Green, 2008). The Hausman test tests whether the unique errors are correlated with regressors, the null hypothesis is they are not. The Hausman test wasn’t significant and hence we used a random effect model. We used the panel data estimation with random effect to model the consumer rating shift model because the CES is a continuous variable.
CESi = pxit+ a + uit + Bit (1)
Where, Where, CESi is the dependent variable, X; is a set of explanatory variables, P is a vector of parameters, u is the between-entity error and s are within entity error associated with each observation.
More specifically, to test the direct effects, we specified the equation:
CES = PnCTF + P12CP + P13MS + P14AGE+ Pi5Price+ a + ua + Sit (1)
To test the interaction hypotheses we specify:
27


CES2 — P21CTF+ P22CP+ P23MS+ P24AGE+ P25Price+ P26 CTFx CP+a + uit + Sit
CES3 — P31CTF+ P32CP+ P33MS+ P34AGE+ P35Price+ P36 CTFxMS+a + Uit + Bit
CES4 = P41CTF+ P42CP+ P43MS+ P44AGE+ P45Price+ P46 CTFx AGE+a + Uit + Sit
CESs = P51CTF+ P52CP+ P53MS+ P54AGE+ p55Price+ p56 CTFx PRlCE+a + uit + sit
CES6 = PeiCTF+ P62CP+ p63MS+ p64AGE+ p65Price+ p66 CTFx PRICE+ p6? CTF x AGE + p68 CTFx MS+ p69 CTFx CP + a + uit + eit
Results
We first tested the direct relationship between consumer technology frustrations on consumer evaluation shift and reported the results (see Column 1 of Table 4). We then included one interaction term each to test their individual interaction effects on consumer evaluation shift (Column 2-5, Table 4), and finally, tested for joint effects of all interaction terms in a model (Column 6 of Table 4).
We find support for Hi as the coefficient for consumer technology frustration is negative and significant in the direct effect model (Column 1 of Table 4, P21 = - 0.07, p < 0.01). This result shows that with approximately for every 1% increase in CTF, the customer evaluation decreases by 0.07% in a week’s time. In other words, if an app has 100 consumers in the first week, and 1 consumer feels that the app is bad due to technical issues; then in second week, the app is devaluated or rated bad by approximately 2 consumer. This multiplying effect would lead to devaluation of around 100% in around 7-8 weeks losing complete stock of consumers in the market.
28


Table 4: Panel Estimation Random Effect Models
VARIABLES (1) (2) (3) (4) (5) (6)
Direct Effect Model Consumer Passion Interaction Market Sustenance Interaction Age Interaction Price Interaction All Interaction Terms
CES CES CES CES CES CES
CTF -0 07*** (0.002) -0.04** (0.009) -0.06** (0.002) -0.06* (0.006) -0.09** (0.004) -0.06** (0.010)
CP 1.876*** (0.231) 1.903*** (0.256) 1 879*** (0.230) 1.876*** (0.231) 1 874*** (0.231) 1 901*** (0.256)
MS 0.011*** (0.002) 0.011*** (0.002) 0.007*** (0.002) 0.011*** (0.002) 0.011*** (0.002) 0.007*** (0.002)
AGE -0.193*** (0.034) -0.193*** (0.034) -0.193*** (0.034) -0.193*** (0.038) -0.192*** (0.034) -0.193*** (0.038)
PRICE 0.267*** (0.050) 0.268*** (0.050) 0.270*** (0.049) 0.267*** (0.050) 0.248*** (0.056) 0.247*** (0.056)
CTF x CP -0.03*** (0.014) -0.03*** (0.014)
CTF x MS -0.02*** (0.000) -0.01*** (0.000)
CTF x AGE -0.05** (0.002) -0.04** (0.002)
CTF x PRICE -0.02** (0.003) -0.03** (0.003)
Controls Included Included Included Included Included Included
Constant 1.281*** (0.356) 1.248*** (0.381) 1.158*** (0.358) 1 277*** (0.368) 1.313*** (0.359) ^ ^64** * (0.392)
Observations 87,192 87,192 87,192 87,192 87,192 87,192
Number of Panels 20,007 20,007 20,007 20,007 20,007 20,007
R-squared 0.46 0.45 0.45 0.44 0.43 0.43
F Stat 15.85*** 16.36*** 16.48*** 16.78*** 16.89*** 17 23***
(1) Significance levels: ***p < 0.01, **p < 0.05, *p < 0.10
(2) Standard errors in parentheses
(2) Models have controls
(3) Models with each interaction term added individually in the model produce similar results.
(4) Detailed results of the models including results of controls are given in Appendix B
29


We find support for H2a which predicted that negative effect of consumer technology frustration on consumer evaluation shift is high for apps with high consumer passion. The interaction term of CTF x CP is negative and significant in the interaction effects model (refer to column 6 of Table 4, /?<59 = - 0.03, p < 0.01). The interaction hypotheses Fhb predicted that for the apps with high market sustenance, the technology frustration will have a higher effect than for the apps for which consumer passion is low. We find support for this hypothesis as the interaction term CTF x MS is significant and negative in the interaction effects model (Column 6 of Table 4,068 = - 0.01, p < 0.01). Similarly, The is also supported with the interaction term CTF x AGE being significant on CES in the interaction model (see Column 6 of Table 4; /?<57 = -0.04, p < 0.05). Finally, Fbd is also supported with the interaction term CTF x PRICE being significant on CES (Column 6 of Table 4; /C/, = -0.03, p < 0.05).
We tested for multicollinearity by computing variance inflation factors (VIFs) for all estimation models. The highest VTF was 2.0 in the direct-effect models, confirming that multicollinearity is not a serious concern. To reduce potential high multicollinearity issues due to the number of interaction terms in the models, all continuous variables were mean-centered by subtracting the corresponding variable mean from each value (Aiken and West 1991). The VTF of any individual variable in any of the interaction effect models was less than 7.0. Furthermore, mean VIFs in all the models were less than 5.0. Thus, we find that multicollinearity is not a serious concern in the estimation.
To investigate how a change in consumer technology frustration affects the customer evaluation shift, we employ a random-effects model to analyze our sample of panel. We also consider a more generic generalized-method-of-moments (GMM) approach. Table 5 presents
30


the GMM estimation results, which is similar to the panel estimation results. This validates the robustness of our results, specific to the sensitivity to endogeneity issues.
Table 5: GMM Estimation Results
VARIABLES (1) (2) (3) (4) (5) (6)
Direct Effect Model Consumer Passion Interaction Market Sustenance Interaction Age Interaction Price Interaction All Interaction Terms
CES CES CES CES CES CES
CTF -0.041** (0.122) -0.031** (0.123) -0.067** (0.122) -0.022*** (0.121) -0.074** (0.121) -0.067** (0.117)
CP 0.022*** (0.032) 0.020*** (0.032) 0.025*** (0.032) 0.020** (0.032) 0.009** (0.034) 0.003** (0.033)
MS 1.763** (3.308) 3.349* (3.623) 1.421** (3.303) 1.596* (3.323) 1.892* (3.277) 3.544** (3.499)
AGE -0.954** (0.478) -1.097** (0.494) -0.938** (0.476) -0.612** (0.563) -1.057** (0.481) -0.570* (0.534)
PRICE 0.513** (0.568) 0.427*** (0.573) 0.480*** (0.566) 0.506*** (0.569) 0.824** (0.634) 0.645** (0.626)
CTF X CP -0.160** (0.147) -0.237** (0.160)
CTF X MS -0.157** (0.159) -0.121** (0.156)
CTF X AGE -0.023*** (0.021) -0.047** (0.022)
CTF X PRICE -0.042*** (0.040) -0.042*** (0.040)
Constant 3.412** (4.461) 3.313** (4.452) -15.388** (19.781) 3.166** (4.465) 3.575** (4.402) -11.000** (19.318)
Observations 87,192 87,192 87,192 87,192 87,192 87,192
Number of panels 20,007 20,007 20,007 20,007 20,007 20,007
R-Squared 0.54 0.53 0.53 0.57 0.52 0.47
F Stat 12.48*** 13.26** 13.43*** 13.87*** 13.92** 14 34***
(1) Significance levels: ***p < 0.01, **p < 0.05, *p < 0.10
(2) Standard errors in parentheses
We plot the interaction effects. Figures in Appendix E provide the interaction graphs.
The results of the interaction terms are discernible in the graphs, providing validity to our results. Amongst other findings, we also find CP, MS, and PRICE have positive and significant direct effects on CES, while AGE has a negative significant direct effect. These
31


results were not hypothesized due to their intuitive nature, but are nonetheless noteworthy. The categorical control variables are not significant in our models.
Discussion
Our study will be among the first attempts to examine the effect of negative emotions specifically technology frustration in consumer evaluation shift. The impact of technology frustration is especially unescapable and long-lasting in online app reviews, whereby the negative feelings caused by technological issues are expressed by reviewers can influence the purchase decisions of thousands of consumers who have access to the reviews. Therefore, this paper will deepen our understanding of how technology frustration are interpreted and attributed in social settings.
One of the managerial implication of this study will be that irrespective of a great proposition that an app is useful for customers, the technical design and integration plays a highly valuable role in consumer acceptance of the app. Hence, developers should pay more attention to the technical aspect of the apps. Moreover, app developers should be careful in the design and development aspects of an app—which is often a forgotten aspect due to the low-cost development regime of apps developments. In addition, identifying passionate consumers and pricing of the apps play important role for an apps success. Finally, as much as an app’s release is important, taking feedbacks from consumers and tracking almost on every day basis is highly influential for an app’s success. While such feedbacks and market mechanisms such as release controls and exist strategy are seen in consumer oriented products such as movies or music products, apps markets need to implement such system.
In addition, app developers should also take into account the role price plays on consumer evaluation. If the users pay a price for the app, they are to evaluate the apps more
32


strictly compared to apps that are free. So, when developers set a price for the apps, they should make sure there are no technical issues with the app.
This study will take into account an important factor in addressing consumer evaluation shift in an app market. This study will contribute to research in identifying technological, market externalities and internal factors associated with digital product success.
We believe that this study will take an important first step in identifying and conceptualizing the role of technology frustration of modern mobile platform ecosystems and presents a rich empirical evidence to support our conceptualizations.
Theoretical Contributions
Our study is among the first attempts to examine the effect of negative emotions specifically technology frustration in consumer evaluation shift. The impact of technology frustration is especially unescapable and long-lasting in online app reviews, whereby the negative feelings caused by technological issues are expressed by reviewers can influence the purchase decisions of thousands of consumers who have access to the reviews. Therefore, this paper deepens our understanding of how technology frustration are interpreted and attributed in social settings.
Second, this study is novel in developing a comparative theoretical model to understand the differences in the frustrating experiences across segments of customers differentiated by passion. Third, this paper adds to the prior research that support from the information systems design and developers in mitigating concerns and therefore, sustaining the produce in market has implications. Previous research (e.g., Von Hippel and Katz, 2002)
33


suggests that users can participate in the innovation process by completing need related tasks (e.g., obtaining information about users’ unique needs) while leaving the solution-related tasks (e.g., implementing the innovation) to companies. Sustaining apps means indirectly a linkage between the users’s innovating needs and the provider’s response to meet the needs, which is a need- and solution- based participatory process. This study relates technical attribute to consumer emotions/appeal/response. From the information systems design perspective, usability aspects are often overlooked as much a technology functions well, and vice versa—but never together.
Mobile apps is one of the fastest growing digital platform in this decade. This study takes into account an important factor in addressing consumer evaluation shift in an app market. This study contributes to research in identifying technological, market externalities and internal factors associated with digital product success. Contextualizing to apps market, the study suggests the integral role of these three complementary factors, and thus, contribute to evaluate the components of a digital business strategy (Bharadwaj et al. 2013). We believe that this study takes an important first step in identifying and conceptualizing the role of technology frustration of modem mobile platform ecosystems and presents a rich empirical evidence to support our conceptualizations. Since the scope of the study was limited to identifying and ordering the underlying factors that impact consumer evaluation shift, no direct recommendations is provided on how to increase consumer evaluation for apps. Although the results give an indication of the importance of technology frustration as a value-adding component, more detailed research is needed on how improvements can be achieved in the various dimensions of customer evaluation.
34


Managerial Implications
One of the managerial implication of this study is that irrespective of a great proposition that an app is useful for customers, the technical design and integration plays a highly valuable role in consumer acceptance of the app. Hence, developers should pay more attention to the technical aspect of the apps. Moreover, app developers should be careful in the design and development aspects of an app—which is often a forgotten aspect due to the low-cost development regime of apps developments. In addition, identifying passionate consumers and pricing of the apps play important role for an apps success. Finally, as much as an app’s release is important, taking feedbacks from consumers and tracking almost on every day basis is highly influential for an app’s success. While such feedbacks and market mechanisms such as release controls and exist strategy are seen in consumer oriented products such as movies or music products, apps markets need to implement such system. In addition, app developers should also take into account the role price plays on consumer evaluation. If the users pay a price for the app, they are to evaluate the apps more strictly compared to apps that are free. So, when developers set a price for the apps, they should make sure there are no technical issues with the app.
This study informs apps developers on how to take into account users’ or customers’ view points and encourage them in the apps innovation and development. Perhaps rather than rushing an app to the market and then facing discontinuance due to technology issues, developers are better off in involving users and consumers early on in the development cycle and take inputs or identify problems (e.g., beta testing) Further, instead of treating all consumer identically, this study distinguishes two groups (i.e., passionate and dispassionate). The findings suggest that apps developers should differentiate their strategies in attracting
35


more passionate consumers, and developing or orienting their designs to convert more dispassionate customers into passionate ones.
Limitations and Future Research
Future research may explore nuances associated with technology frustration with a deeper lens. Another interesting aspect for the future research would be to include the “dead apps” and look for commonalities between the groups of apps and the business value associated with it. Possibly, exploring willingness to pay in the apps market can establish the nuances associated with establishing a business case for apps.
The next steps of this study is to generalize our findings by considering multiple platform ecosystems such as apple store and to investigate the dynamics of technology frustration and consumer evaluation shift along with the pricing, age, consumer passion and market sustenance of app. We believe that such investigations of mobile platform ecosystems have a rich potential to extend the research frontier of the app ecosystem and competitive strategy research stream and hope that other scholars will follow suit.
Conclusion
In conclusion, the objective of this study was to explore to what extent technology frustration influences consumer evaluation of apps. In addition, we posed the research question as to how market externalities such as passionate consumers and sustenance of the app in the market; and whether market internal factors such as pricing and age of the app moderate the influence of technology frustration on consumer evaluations. This study contributes to the research on the digital business strategy for apps markets.
36


CHAPTER IV
EPILOGUE
Digital products are creating value for businesses in several ways to conceive, structure, implement, offer, manage, and augment services and service capabilities. The impact and value potential of digital services varies from transforming business processes, to managing the complexities involved with the customer centric solutions, to enabling interfirm integration and providing unique solutions. However, these digital product developers face challenges to assimilate, implement and integrate the digital services. Developers also face difficulties in execution of strategies to be effective in the context of digital products.
This dissertation proposes consumer evaluation of products in a market as a predictor of the success or failure of a product. To look into this phenomenon, this dissertation focused on the role of technology frustration on consumer evaluation of digital products. In the essay, we explored how technology frustration influences the shift in valuation of a product in digital markets.
37


REFERENCES
Aaker, D. A., and Keller, K. L. 1990. "Consumer Evaluations of Brand Extensions," The Journal of Marketing), pp. 27-41.
Aitken, M., and Gauntlett, C. 2013. "Patient Apps for Improved Healthcare from Novelty to Mainstream," IMS Institute for Healthcare Informatics), pp. 1-65.
Aleem, U., Cavusoglu, H., & Benbasat, I. (2014). Uncovering Privacy Uncertainty: An Empirical Investigation in the Context of Mobile Apps.
Belk, R. W., Ger, G., and Askegaard, S. 2003. "The Fire of Desire: A Multisited Inquiry into Consumer Passion," Journal of consumer research (30:3), pp. 326-351.
Bessiere, K., Newhagen, J. E., Robinson, J. P., and Shneiderman, B. 2006. "A Model for Computer Frustration: The Role of Instrumental and Dispositional Factors on Incident, Session, and Post-Session Frustration and Mood," Computers in Human Behavior (22:6), pp. 941-961.
Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., and Venkatraman, N. 2013. "Digital Business Strategy: Toward aNext Generation of Insights," MIS Quarterly (37:2), pp. 471-482.
Bhattacheijee, A. 2001. "Understanding Information Systems Continuance: An Expectation-Confirmation Model," MIS quarterly), pp. 351-370.
Bowman, C., Ambrosini, V., 2000. Value creation versus value capture: Towards a coherent definition of value in strategy. British Journal of Management, 11: 1-15.
Brown, T. J., Churchill, G. A., & Peter, J. P. (1993). Improving the measurement of service quality. Journal of retailing, 69(1), 127-139.
Brown, S. A., Venkatesh, V., and Goyal, S. 2012. "Expectation Confirmation in Technology Use," Information Systems Research (23:2), pp. 474-487.
Ceaparu, I., Lazar, J., Bessiere, K., Robinson, J., & Shneiderman, B. (2004). Determining causes and severity of end-user frustration. International journal of human-computer interaction, 17(3), 333-356.
Cummings, J. N., & Kraut, R. (2002). Domesticating computers and the Internet. The Information Society, 18(3), 221-231.
Curran, C. M. (1999). Misplaced marketing-A best buy in advertising: schools selling students as media audiences. Journal of Consumer Marketing, 16(6), 534-536.
Dabholkar, P. A. 1996. "Consumer Evaluations of New Technology-Based Self-Service Options: An Investigation of Alternative Models of Service Quality," International Journal of research in Marketing (13:1), pp. 29-51.
Denegri-Knott, J., and Molesworth, M. 2013. "Redistributed Consumer Desire in Digital Virtual Worlds of Consumption," Journal of Marketing Management (29:13-14), pp. 1561-1579.
38


Garg, R., & Telang, R. (2012). Inferring app demand from publicly available data. MIS Quarterly, Forthcoming.
Gerlich, R. N., Drumheller, K., & Babb, J. (2015). App Consumption: An Exploratory Analysis of the Uses & Gratifications of Mobile Apps. Academy of Marketing Studies Journal, 19(1), 69.
Ghose, A., and Han, S. P. 2014. "Estimating Demand for Mobile Applications in the New Economy," Management Science (60:6), pp. 1470-1488.
Greene, William. Econometric Analysis. Fourth Edition. Upper Saddle River: Prentice-Hall (2000).
Guchait, P., and Namasivayam, K. 2012. "Customer Creation of Service Products: Role of Frustration in Customer Evaluations," Journal of services Marketing (26:3), pp. 216-224.
Han, S. P. Mobile App Analytics: A Multiple Discrete-Continuous Choice Framework (Doctoral dissertation, kaist).
Huang, E. Y., & Hung, K. L. (2014, January). Understanding Mobile Apps Purchase: The Effect of Free Trial and Online Consumer Review. In Academy of Management Proceedings (Vol. 2014, No. l,p. 13992). Academy of Management.
Huang, G. H., & Korfiatis, N. (2015). Trying before buying: The moderating role of online reviews in trial attitude formation toward mobile applications. International Journal of Electronic Commerce, 19(4), 77-111.
Hui, K. L., & Chau, P. Y. (2002). Classifying digital products. Communications of the ACM, 45(6), 73-79.
IDC. 2013. "Worldwide and U.S. Mobile Applications Download and Revenue 2013-2017 Forecast: The App as the Emerging Face of the Internet."
Johnson, M. W., Christensen, C. M., and Kagermann, H. 2008. "Reinventing Your Business Model," Harvard Business Review (86:12), pp. 50-59.
Jones, M. A., & Suh, J. (2000). Transaction-specific satisfaction and overall satisfaction: an empirical analysis. Journal of services Marketing, 14(2), 147-159.
Keaveney, S. M., & Parthasarathy, M. (2001). Customer switching behavior in online services: An exploratory study of the role of selected attitudinal, behavioral, and demographic factors. Journal of the academy of marketing science, 29(4), 374-390.
Khalid, M. S. Secondary Educational Institution Centered Diffusion of ICT in Rural Bangladesh (Doctoral dissertation, Videnbasen for Aalborg UniversitetVBN, Aalborg UniversitetAalborg University, Det Humanistiske FakultetThe Faculty of Humanities, Forskningsgruppen for TeknoantropologiThe Techno-Anthropology Research Group).
Klein, J., Moon, Y., & Picard, R. W. (2002). This computer responds to user frustration:: Theory, design, and results. Interacting with computers, 14(2), 119-140.
39


Korgaonkar, P. K., & Moschis, G. P. (1982). An experimental study of cognitive dissonance, product involvement, expectations, performance and consumer judgement of product performance. Journal of advertising, 11(3), 32-44.
Lazar, J., Jones, A., Hackley, M., and Shneiderman, B. 2006. "Severity and Impact of Computer User Frustration: A Comparison of Student and Workplace Users," Interacting with Computers (18:2), pp. 187-207.
Lee, G., and Raghu, T. 2014. "Determinants of Mobile Apps' Success: Evidence from the App Store Market," Journal of Management Information Systems (31:2), pp. 133-170.
Lenhart, A. 2003. The Ever-Shifting Internet Population: A New Look at Access and the Digital Divide. Pew Internet & American Life Project.
Lepak, D. P., Smith, K. G., and Taylor, M. S., 2007. Value creation and value capture: A multilevel perspective. Academy of Management Review, 32(1), 180-194.
Liu, Y., Deng, S., & Hu, F. (2014). E-Loyalty Building in Competitive E-Service Market of SNS: Resources, Habit, Satisfaction and Switching Costs. In Digital Services and Information Intelligence (pp. 79-90). Springer Berlin Heidelberg.
Mudambi, S. M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon.com. MIS quarterly, 34(1), 185-200.
Muller, R. M., Kijl, B., & Martens, J. K. (2011). A comparison of inter-organizational business models of mobile app stores: there is more than open vs. closed. Journal of theoretical and applied electronic commerce research, 6(2), 63-76.
Negash, S., Ryan, T., & Igbaria, M. (2003). Quality and effectiveness in web-based customer support systems. Information & Management, 40(8), 757-768.
Norman, A. T., & Russell, C. A. (2006). The pass-along effect: Investigating word-of-mouth effects on online survey procedures. Journal of Computer-Mediated Communication, 11(4), 1085-1103.
Oliver, R. L. 1977. "Effect of Expectation and Disconfirmation on Postexposure Product Evaluations: An Alternative Interpretation," Journal of applied psychology (62:4), p. 480.
Oliver, R. L. 1980. "A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions," Journal of marketing research), pp. 460-469.
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1994). Reassessment of expectations as a comparison standard in measuring service quality: implications for further research, the Journal of Marketing, 111-124.
Parthasarathy, M. (1995). The impact of discontinuance on the subsequent adoption of an innovation: Theoretical foundation and empirical analysis.
Parthasarathy, M., & Bhattacheijee, A. (1998). Understanding post-adoption behavior in the context of online services. Information systems research, 9(4), 362-379.
40


Perdikaki, O., and Swaminathan, J. 2013. "Improving Valuation under Consumer Search: Implications for Pricing and Profits," Production and Operations Management (22:4), pp. 857-874.
Phang, C. W., Kankanhalli, A., Ramakrishnan, K., & Raman, K. S. (2010). Customers’ preference of online store visit strategies: an investigation of demographic variables. European Journal of Information Systems, 19(3), 344-358.
Preece, J. R., & Rogers, Y. (2007). SHARP (2002): Interaction Design: Beyond Human-Computer Interaction. Crawfordsville: John Wiley and Sons, Inc. Answers, com Technology.
Richins, M. L., & Bloch, P. H. (1991). Post-purchase product satisfaction: Incorporating the effects of involvement and time. Journal of Business Research, 23(2), 145-158.
Rogers, E. M. (2003). Elements of diffusion. Diffusion of innovations, 5, 1-38.
Scheier, M. F., Carver, C. S., & Bridges, M. W. (2001). Optimism, pessimism and psychological wellbeing. In E. C. Chang (Ed.), Optimism and pessimism. Implications for theory, research, and practice (pp. 189-216). Washington, DC: American Psychological Association.
Shneiderman, B. (2000). Creating creativity: user interfaces for supporting innovation. ACM Transactions on Computer-Human Interaction (TOCHI), 7(1), 114-138.
Stein, M. K., Newell, S., Wagner, E., & Galliers, R. D. (2015). Coping with information technology: mixed emotions, vacillation and non-conforming use patterns. Forthcoming MISQ.
Ulkii, S., Dimofte, C. V., and Schmidt, G. M. 2012. "Consumer Valuation of Modularly Upgradeable ProductsManagement Science (58:9), pp. 1761-1776.
Venkatraman, M. P. (1988). Investigating differences in the roles of enduring and instrumentally involved consumers in the diffusion process. Advances in Consumer Research, 15(1), 299-303.
Wilfong, J. D. 2006. "Computer Anxiety and Anger: The Impact of Computer Use, Computer Experience, and Self-Efficacy Beliefs," Computers in Human Behavior (22:6), pp. 1001-1011.
Yang, S., Lu, Y., & Chau, P. Y. (2013). Why do consumers adopt online channel? An empirical investigation of two channel extension mechanisms. Decision Support Systems, 54(2), 858-869.
Yin, P., Luo, P., Lee, W. C., & Wang, M. (2013, February). App recommendation: a contest between satisfaction and temptation. In Proceedings of the sixth ACM international conference on Web search and data mining (pp. 395-404). ACM.
Zwass, V. (2003). Electronic commerce and organizational innovation: aspects and opportunities. International Journal of Electronic Commerce, 7(3), 7-37.
41


Full Text

PAGE 1

CONSUMER EVALUATION OF DIGITAL PRODUCTS: EMPIRICAL STUD Y IN THE CONTEXT OF MOBILE APPLICATIONS by BIDYUT BIKASH HAZARIKA B.E., University of M umbai , I ndia 2005 M.B.A., U niversity of T oledo , O hio , 2007 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Computer Science and Information Systems Program 2016

PAGE 2

ii © 2016 BIDYUT BIKASH HAZARIKA ALL RIGHTS RESERVED

PAGE 3

iii This thesis for the Doctor of Philosophy degree by Bidyut Bikash Hazarika h as been approved for the Computer Sc ience and Information Systems Program by Jahangir Karimi , Chair Jiban Khuntia , Advisor Madhavan Parthasarathy Ilkyuen Ra Date: 0 6 / 2 4 /2016

PAGE 4

iv Hazarika , Bidyut Bikash ( PhD, Computer Science and Information Systems) Consumer Evaluation of Digital Products: Empirical Studies in the Context of Mobile Applications Thesis directed by Ass istant Professor Jiban Khuntia ABSTRACT M obile applications (apps) are emerging as useful digital products transforming business models in healthcare, supply chains, travel and other sectors. Consumer evaluation reflects on apps sustainability, and is a key determinant for the success of app cen tric and app enabled businesses. In this context, the essay in this dissertation focus on the important research question: how technology frustration with apps influence the consumer evaluation shift of mobile apps in contingent with sustenance of, and pa ssion for, the apps. We collect data from the Android market and applied text mining technique to code necessary variables for this study, including different app characteristics, features and evaluations. The empirical analysis of the study use economet ric analyses to draw insights. The results of the analyses provide insights into highly interactive IT enabled consumer involvement and interaction issues with apps. The dissertation contributes to research in identifying technological, market externalit ies and internal factors associated with digital product success; along with managerial implications to effectively design, develop and manage successful apps. The form and content of this abstract are approved. I recommend its publication. Approved: Jiba n Khuntia

PAGE 5

v DEDICATION I dedicate this thesis to my parents Renu and Ganesh. I hope that this achievement will complete the dream that you had for me all those many years ago when you chose to give me the best education you could and whose words of encoura gement and push for tenacity ring in my ears. My brother Partha, has never left my side and very special. I also dedicate this dissertation to my many fri ends and my extended family who have supported me throughout the process. I will alway s appreciate all they have done.

PAGE 6

vi ACKNOWLEDGEMENT F irstly, I would like to express my sincere gratitude to my advisor Prof. Jiban Khuntia for the continuous support of my Ph.D study and related research, for his patience, motivation, and immense knowledge. His guidance helped me in all the time of research and writing of this th e sis. I could not have imagined having a better advisor and men tor for my Ph.D study. Besides my advisor, I would like to thank the rest of my thesis committee: Prof. Jahangir Karimi , Prof. Madhavan Parthasarathy , and Dr. Ilkyuen Ra , for their insightful comments and encouragement, but also for the hard question which incented me to widen my research from various perspectives. My sincere thanks also goes to the Jake Jabs Center for Entrepreneurship, who provided me an opportunity to join their team as a program assistant, and who gave access to my research data and fac ilities. Without they precious support it would not be possible to conduct this research.

PAGE 7

vii TABLE OF CONTENTS CHAPTER I. Introduction and Review of the Literature Consumer Evaluation and Response Objective of the Study 8 II. TECHNOLOGY FRUSTRATION AND CONSUMER EVALUATION SHIFT FOR MOBILE APPS: AN EXPLORATORY STUDY 9 Abstract 9 Introduction 0 Pri or Literature 2 Theoretical Framework 4 Hypotheses Development 7 Method 2 Data and Variables 2 Estimation Models 7 Results 28 Discussion 2 Theoretical Contributions 3 Managerial Implications Limitations and Future Research 6 Conclusion 6 III. 37

PAGE 8

viii 38

PAGE 9

ix LIST OF TABLES TABLE 1. Description of variables 2 3 2. Descriptive statistics 2 5 3. Correlation amongst key variables 2 6 4. Panel estimation random effect models 29 5. GMM estimation results 3 1

PAGE 10

1 CHAPTER I DISSERTATION OVERVIEW Introduction and Review of the Literature Online consumer reviews and ratings, a key form of online user generated content, are now widely available for many products. An online reviewer provides a qualitative assessment (online review) of his or her product experience, which informs and influence s his or her quantitative evaluation (online product rating). The online reviewer is usually preceded by reviewers who have already rated the product. The average online product rating is prominently displayed (e.g., www.epinions.com; www.tripadvisor.com) to convey consensus information about the online reviewer community's product evaluations. We first provide the motivation for this research . Given the growth of online review websites, scholars have examined demand consequences of online product ratings (Bickart and Schindler 2001). High online product ratings increase the online market shares of books (Chevalier and Mayzlin 2006), offline sales of television shows (Godes and Mayzlin 2004), sales of toiletry products (Moe and Trusov 2011), and sales of vi deo games (Zhu and Zhang 2010). There is limited and mixed empirical evidence on social influence in online ratings. Schlosser (2005) reports that reviewers, motivated by a need to be perceived as discriminating, decrease their online product ratings after reading others' online reviews. Other research reports that when others' online ratings are at the lower end of the rating scale, reviewers tend to increase their online product rating (Moe and Trusov , 2011). A possible reason for the mixed evidence is th at past research has overlooked the contingent nature of social influence effects in the online ratings context.

PAGE 11

2 Insights into social influence effects in online product ratings have high managerial relevance. A comScore Inc. survey (2007) reports that 24% of consumers use online consumer reviews before purchasing a product. With respect to hotels, the empirical context for this article, online consumer reviews influenced choices for most consumers (87%). High online product ratings also translate into pric e In the comScore study, consumers were willing to pay more for a product with an "excellent" rating (5) than for one with a "good" rating (4); the premiums were 99% for legal services, 38% for hotels, and 20% for real estate agents. Managers find it usefu l to decompose online reviews to learn how their products' characteristics affect their online product ratings. In particular, insights into social influence effects in online product ratings provide actionable insights to managers, including how to use on line product ratings as a communications element. Therefore, consumer evaluation of products in a market is a predictor of the success or failure of a product. A lso product feature is a pre cursor to product success in the market. While good product appeal spells success for any given product, technology frustration with digital products leads to shift in e valuation of a product in digital markets, mostly for the worse. Consumer Evaluation and Response Businesses continue to create and develop new products in order to ensure continued profitability and brand survival. This is done with the hope that the new product will be of value to both the company and consumers (Belen, Vazquez, & Iglesias, 2001). In essence, a s or is of little value to them would be a major

PAGE 12

3 evaluation, which subsequently determines customer response. In fact, Rindova (2007) pointed out that researchers acknowledge that innovation is important for value creation, but equally warn on the uncertainties regarding the ability of a product to deliver valuable functionality. Rin dova noted that this is more so because consumers find it hard to recognize e valuation is usually based on available cues and perceptions on the products value. The development on new products is more so important for the success of technology intensive industries, such as those that produce digital products (Prins & Verhoef, 2007) . Considering the importance of consumer evaluation and response on the market success of products, a lot of research has been made towards the determiners of consumer response. existing attitude about a product, beliefs purchasing decisions (Morgan Thomas & Veloutsou, 2013) . This is as proposed by the technology acceptance model (Davis, 1989; Venkatesh et al. 2003) . A similar research, Os man et al. ( 2012) , investigate the digital content usage behaviour of consumers and factors that influenced their purchases, specifically, the role of smartphone design, perfomance and price. With continued advances in technology, mobile phones now come with advanced featu res that allow more consumption and exchange of digital content (May & Hearn, 2005) . The study revealed that trends in community (35.6%) was the leading customer influencer when it came to purchasing smartphones. This result implied that marketing and prom otion, or rather availing product related information to influence from peers, relatives, etc, seemed to be important in influencing purchasing

PAGE 13

4 decisions. The results, therefore, further undeline the importance of information availability and social influence in the digital market. The influence of consumer needs and software came closely second and third at 34.4% and 33.1%, respectively, while other factors like price, hardware, and signal reception, were of less significance. When it came to success, beating price (30.2%). ev aluation at a personal level is important in determining the market success of digital products. The visual and subsequently the success of a new product . This is be cause, sometimes, the appearance/ package is the only information/communication accessible to a consumer at the point of purchase (Schoormans & Robben, 1997) communicates to the consumer, stimulates a nd attracts attention, and creates a lasting impact on the consumer (Bloch, 1995). Besides, unique designs help new products stand out from the competition, hence giving them a better chance to succeed. Bertini et al. ( 2008) studies the impact of add on features on consumer product evaluation. In an attempt to make their product unique/ outstanding, many companies add extra features to their products. However, the added benefit of these features can only be felt if the product is used together with a corr esponding base go od ( Guiltinam , 1987) . The tendency to produce add on features is very common in electronic and digital products companies. The study by Bertini investigated the link between perceived product utility and add on type. The research revealed that adding a new feature, different from and in addition to the expected addons made digital products more appealing to customers. However, those

PAGE 14

5 add ons that served as an upgrade to existing features had a negative impact on product evaluation. However, when information on individual digital products, their judgement on the individual products, the negative evaluation waned. This tendency can be explained through the concept of experience by Hoch (1989). Despite the above information, a f urther analysis o f literature reveals ambiguity over suggests that perceived ease of use is tied to appearance. While several researchers have previously focused on the role of packag (Bloch, 1995; Garber, 1995 ; Veryzer, 1993; Veryzer, 1995), there exists a gap in describing ways in which appearance affects consumption. Moreover, different researchers have varied on their description or e valuatio n of appearance parameters. For example, Bloch (1995) considered the ease of use as a quantifier of appearance, while Lobach (1976) considered it to be an aesthetic function. Interestingly, Veryzer (1995) described the ease of use as a communicative functi on of appearance. Existing literature reveals an influence of brand relationships on the consumption of digital products. Companies continue to invest heavily in brand development. That is, to create an image that can be linked to particular associations. It is important to note that there is no particular standard of measuring brand image (Dobni & Zinkhan, 1990) . However, it is generally defined as perceptions about a product brought about by a set of associations that customers attribute to the product/ brand (Rio et al. 2001) . According to Rio et al. , a positive influence is elicited by brand associations on con sumers, where their preferences, willingness to pay higher prices for particular brands, choice, and willingness to recommend products to others, are influenced. Theories on brand influence reveal a positive relationship between

PAGE 15

6 behavior , self im age and the product image (Rio, Vazquez, & Iglesias, 2001 p. 3 ) . What this means is that the use of particular brands can be used to enrich self image . Additionally, particular brands give consumers an assurance of a particular set of associations, e.g. pe rformance, appearance, status, etc. Therefore, a high evaluation of a particular brand by a customer increases the likelihood of purchasing its products (Graef, 1996; Hogg et al. 2000) . Th e same concept would also apply to new products launched by companies whose brands are held in high regard. Similar views by (Long & Schiffman, 2000) indicate that consumers interested in social identification will want to own products that are highly regarded by people within their social groups. This can be viewed as an extension of the findings of Pandit, Karpen, & Josiassen ( 2008) and Os man et al. ( 2012) , that social groups influence consumer response to digital products. Theories on consumer culture explore ways i n which consumers make meaning of symbolic cues encoded in brands, advertisements, products, etc . to reveal their personal and social situations, as well as identity and lifestyle goals ( Grayson and Martinec 2004; Holt 2002; Kozinets 2001, 2002 ) ;. In that case, markets provide consumers with a wide array of products from which to make choices and subsequently claim their individual and collective identities ( Murray 2002; Schau and Gilly 2003). Consumers are, therefore, identity seekers who acquire products that enable them achieve their lifestyle goals. While social influence, perception and brand relationships may determine consumption, it is important to note that consumers have varying consumption cultures. The influence of consumer culture on consumer evaluation at a personal level is important in

PAGE 16

7 determining the market success of digital products. The role of personal values in influencing consumer choice has been research ed previously, with results showing a direct relationship between personal values and consumption. According to Wiedmann and Hennigs (2007) theory showed that individual and social related values affected consumer choice, more so where consumers buy to imp ress and maintain their social image (Eagly and Chaiken 1993; Weidmann and Hennigs 2007). While such research are important in helping us understand the importance of customer evaluation on the market success of products specifically digital products and i n shaping the technology acceptance model (Davis, 1989; Venkatesh et al. 2003) , their limitation lies in the fact that the researchers focus on particular items at a time. For example, Os man et al. ( 2012) investigated the influence of smartphone design, pe rfomance and price. These factors were investigated independently, with little regard to their interaction. While it is true that pre existing attitudes influence consumer response, the degree to which those attitudes influence purchasing decisions is depe ndent on a number of factors. Similarly, research on the impact of product design in influencing consumer choice failed to investigate or explicitly explain the various ways in which product appearance influences consumers, as well as the specific implica tions of that in product design. According to Creusen & Schormans ( 2005) , consumers consider product in terms of communication of aesthetic value, symbolism, and functionality, in addition to ergonomic product information, its ability to draw attention, as well as categorization.

PAGE 17

8 Objective of the S tudy The essay in this dissertation focus on the research question that explores how technology frustration and hedonic and utilitarian and hedonic appeal have an impact on the consumer evaluation shift and ratings shift of an app respectively . The essay of this dissertation address the research question: how technology frustration negatively influences the consumer e valuation shift of mobile app products in markets? In th is essay , I explore how technolog y frustration negatively influences the e valuation of mobile app products in markets. Specifically, I examine the influence of technology frustration on the expectation from the product. The theoretical framework and hypotheses for this study are grounded in the marketing literature, with the positioning that understanding product expectation is a much explored area in the marketing field. I develop several hypotheses predicting the relationship of the elements of technology frustration on consumer evaluati on shift. To test the hypotheses, I conduct ed empirical analysis using panel data. The dataset contains data from the android app store for 3 months. .

PAGE 18

9 CHAPTER I I TECHNOLOGY FRUSTRATION AND CONSUMER E VALUATION SHIFT FOR MOBILE APPS: AN EXPLORATORY STUDY A bstract Consumer evaluation of products in a market is a predictor of the success or failure of a product. For digital products, the usage of a product is often determined by the technology robustness associated with the design of the product. Bad d esigns, implementation and integration issues lead to frustration on the part of consumers using the product. In this study, I explore how technology frustration negatively influences the e valuation of mobile app products in markets. Theoretically, I argue that technology frustration disconfirms the expectation from the product. I hypothesize that technology frustration has a negative impact on consumer e valuation shift over time. In additi on, I argue that the influence of technology frustration on consumer e valuation shift is moderated by price, age of the app, consumer passion and market sustenance. Finally, I contend that the negative effect of technology frustration is high for new and h igh priced products because of the higher expectations and hence more stringent evaluations for new and high priced products by consumers. I propose to empirically analysis the model using panel data on 87,000 apps for a period of 3 months, consisting of 20,000 panels. I plan on using random effect panel estimation for empirical analysis. In addition, I plan on using generalized method of moments (GMM) to address endogeneity issues. The implication of this study will inform apps developers to be careful i n app design to sustain in the app markets. The study is important first step in coining consumer e valuation shift as a market factor, and further identifying and conceptualizing the role of technology frustration associated with apps.

PAGE 19

10 I ntroduction Mobil e applications (apps) are digital innovations using internet and mobile platforms. The popularity of mobile apps has resulted in a new entrepreneurship model, where app developers can appeal to and reach mass consumers relatively inexpensively. Apps are a lso becoming useful in several unprecedented ways, such as transforming healthcare delivery, enabling supply chains, and leveraging crowd sourcing concepts (Ghose and Han 2014) . Revenue from app sales is projected to increase from $10.3 billion in 2013 to $25.2 billion by 2017 (IDC 2013) . Both the Apple market has and the Android market offer over 1. 5 million unique apps apiece (Statistica , 2015), while the Android market offers over 40,000 apps. These apps include a wide variety of applications, ranging from work, play and everything in between. While the surge in the number of apps is exponential , not all developers are able to find success in this cutthroat market space. Indeed, a number of apps and associated ventures have failed in a relatively short period of time. Studies show a considerable skew in download rate of apps; for example, only 5 apps account for about 15% of all downloads, while over 50% of available apps achieve fewer than 500 downloads apiece (Aitken and Gauntlett 2013) . Prior research has suggested that managing business models for consumer oriented digital products needs a good customer value proposition, a solid revenue model, and the availability of key resources and processes to generate revenue (Johnson et al. 2008) . One such resource and process specifi c to apps is innovative technology, involving design, smooth functioning, and integration of the product in the market space. Specific to digital business models, Bhardwaj et al. (2013) note that the scope, scale, speed, and sources of business value creation and capture are the four major components of digital business

PAGE 20

11 strategy, and all of these components can be influenced by cons umer evaluat ion of the app. Thus, focusing on consumer evaluation of an app, and monitoring the progression, or shift, in the e valuation over time, would provide invaluable information regarding the net scope and liferating, and whether consumers are likely to continue their patronage of the app in the immediate future. While consumers evaluate the apps on a continual basis, the extent to which technology remains a driving factor in this evaluation remains an unexp lored question for both academics and practitioners alike. Given the aforementioned, our goal in this paper is to focus on consumer frustration with technology associated with apps in a market. At individual level, technology frustration is the emotiona l response of a negative computing experience with the technology (Bessière et al. 2006) . Technology frustrati on originates when a consumer faces a technological obstacle in the process of trying to achieve a desired function. Such obstacles may include crashing, network congestion, poor interfaces, confusing design, unnecessary complexity, and usage problems. Fa ilure may be intermittent, periodic, random or frequent. Such frustration could lead to personal dissatisfaction and loss of self efficacy, may disrupt workplaces, stifle work efficiency, and reduce the work efficiency of an individual (Lazar et al. 2006) . Technology frustration may also lead to elevated levels of anxiety and anger on the part of the individual (Wilfong 2006) . The outcome of the technology frustration, in turn, may result in a segment of the customers staying away from the technology (Lenhart 2003) , initiating negative word of mouth influence regarding their experience and, ultimately, discontinuing usage of the app. Taken cumulatively, we posit that these individual

PAGE 21

12 technology frustration events aggregate to create a hi ghly adverse overall response for the product in the market (Guchait and Namasivayam , 2012) . The conceptual model in this study proposes th at technology frustration negatively influences consumer e valuation of a product in digital markets. In addition to the main effects, we hypothesize that market externalities, such as consumer passion for, and sustenance of, the product in question temper this negative effect. Furthermore, we hypothesize that the negative effect of technology frustration is high for new and high priced products because of the greater expectations (and hence more stringent evaluations) that new and high priced products warr ant. To test our hypotheses, I plan on using dataset that tracks cell phone apps in the android market, tracking more than 80,000 apps comprising of 20,000 panels apps for a period of 3 months. This study contributes to the information systems and consume r research literature in providing a new dimension of technology frustration associated with consumer evaluation of products. We discuss managerial implications and contributions of the study. P rior Literature Prior literature on apps has mainly focus ed o n apps intake (Aleem et al. 2014; Han, 2014), growth and diffusion (Khal id et al. 2014; Gerlich et al. 2015) issues. Apps markets are information technology enabled markets based on the mobile technology developments. Instead of being freely available pro ducts, apps are part of the iTunes, Android, Windows, or Blackberry marketplaces from where consumers can download them, use them, and subsequently review them. Being digital products apps provide granularity in terms of usage and adoption, and have a tria lability option that refers that a buyer can try it prior to purchase (Hui and Chau, 2002; Phang and Kankanhalli, 2010; Zwass, 2003). The app digital market

PAGE 22

13 place offers both free and paid apps, and have lock in mechanisms for consumers (Garg and Telang, 2 012; Huang and Hung, 2014; Liu et al. 2014). Further, the digital app marketplaces provide a platform for sharing of information through ratings and reviews that inform and . 2012; Huang and Korfiatis, 2015) as an app. These include the crashing of software, unclear displays, unnecessary pop ups, and a confusing user interface (Preece et al. 2002 ). For a non technical or naïve user, software issues could become highly frustrating (Shneiderman, 2000; Cummings and Kraut, 2002) leading, in turn, to behaviors such initiating negative interpersonal influence regarding the product, resisting the product and, ultimately, intending to discontinue using it (Beaudry and Pinsonneault, 2010; Stein et al., 2015). In fact, a few prior studies have highlighted the impact of software or tec hnology issues with apps usage. For example, a study conducted by Pew in 2003 reported that 42% of the consumers in the United States did not use technology due to software issues. Some other researchers note that users waste one third to one half of productive time due to a frustrating experience with technology (Ceaparu et al. 2004). Others have found that frustration with technology might lead to low level of job satisfaction (Scheirer et al. 2002). As much as any product can cause dissatisfaction amongst consumers, the end result is the reduction of the product usage in the market (Park et al . 2012; Bolton, 2011; Keaveney and Parth a sarathy, 2001). The bottom line is that when a product does not meet consumer expectations, the resultant evaluation of the product becomes very low (Brown et al. 2014; Yang et al. 2013). Although expectation and confirmation of a product seems as an individual issue, an accumulation of negative evaluations could have a dual

PAGE 23

14 macro level negative effect: 1) dissuading p rospective consumers from adopting the app using the app (due, in part, to the frustration experienced by other users which is likely to sooner or later be encounte red by existing users). Thus, consistent and large scale negative evaluations of a product may eventually wipe it out entirely from the market (Mudambi and Schuff, 2010; Negash et al. 2003). Since existing research on the negative shift in consumer eval uation, and its subsequent impact on the eventual success or failure of a product (such as an app) is sparse in the information systems and marketing literatures, this study tries to address this gap in the context of technology frustration and consumer ev aluation shift in the apps marketplace. Theoretical Framework The notion of technology frustration is often a result of actual experience with a product falling short of expectations. The Expectation confirmation theory (ECT) and prior literature on consu mer evaluations for this study. ECT posits that post purchase or post adoption satisfaction is a function of expectations, perceived performance, and disconfirmation of beliefs associated with a product (Oliver 1977; Ol iver 1980) . Applying to the digital products context, existing research has established that the confirmation or disconfirmation of consumer expectations is a highly influential factor in determining whether consumers continue or discontinue a product (Bhattacherjee 2001; Brown et al. 2012) . When the product confirms to its objectives or intended goals of use, consumer evaluations increase (Aaker and Keller 1990; Dabholkar 1996) . Thus, if an app suffers from technical issues, such as crashing, poor response rate, incorrect formatting, etc., it may fall below reasonable consumer expectations. Furthermore, if the same app once worked well in

PAGE 24

15 a previous version, but a newer version is plagued with bugs and other technical issues, a consumer may use the previous version of the app as an expectation anchor, against which the new version of the app falls short leading, quite naturally, to frustration and disenchantment. Indeed, research on the discontinuance of innovations (e.g., Keaveney and Parthasarathy, 2001; Parthasarathy and Bhattacherjee, 1998; Parthasarathy , 1995) identifies disenchantment as a key reason for the termination of a te ch related service. These scholars found that disenchantment could result from a variety of sources including technical issues with the product itself and poor customer service and support. Further, it was found that unnecessary complexity of a product c ould lead to some less technically savvy consumers being unable to use a product to its intended capacity, leading to underutilization and, eventually, to discontinuance. Thus progressively complex interfaces may lead to frustration resulting from a consu However, frustration with a product is often tempered by passion. Passion refers to a Existing research confirms that highly involved consumers are likely to be heavy users of the product, have considerable product knowhow and are also likely to be opinion leaders (e.g. Norman and Russell, 2006; Venkataraman 1988). Furthermore, passionate, involved, consumers are k nown to have strong emotions, both positive and negative, regarding a product (Norman and Russel 2006), and are likely to tell others about their experiences. This is a double edged sword in that highly passionate consumers who have a negative experience with a product are more likely to initiate negative word of mouth influence regarding that product, and also more likely to hence be disenchanted with the product themselves.

PAGE 25

16 Other studies lend credence to this contention. For example, Richins and Bloch ( 1991) found that while passionate, high involvement consumers were more satisfied with a product soon after adoption, their opinions were more likely to decline after adoption than their lower involvement counterparts. This would suggest that passionate c onsumers were also likely to be pickier, and small issues were more likely to annoy or frustrate them than less context, high sustenance apps, or apps that have been in the market for a longer time, have probably gone through more updates and beta versions, and therefore had greater chance of having some aspect of their system frustrate passionate consumers, leading to a progressive decline in their evaluations. Highly invo lved consumers are likely to be knowledgeable about their product category and thus set higher, more cognitive expectations regarding the consumer whose expectations a re negatively disconfirmed will amplify that difference, and thus initiate more negative interpersonal influence than the difference between expectations and actual product performance would warrant. The longer the app has sustained itself, the greater is the opportunity for this contrast effect to have manifested itself. Based on the aforementioned premises, we propose a conceptual model (see Figure 1) for this study that presents three sets of relationships: (1) technology frustration has a direct impact on consumer evaluation shift (i.e., the negative shift in consumer e valuation of the app); thus, the greater the technology frustration felt by the user, the greater will be the evaluation shift for the product, (2) two market externalities, i.e., consumer passion and market sustenance, moderate the relationship of technol ogy frustration on consumer evaluation shift, and (3) two internal factors, e.g., age of the app and price of the app, both

PAGE 26

17 discussed in the next section, also moderate the association of technology frustration on consumer evaluation shift. Figure 1: Conceptual Model H y pothese s Development Technology frustration deals with a set of emotional responses as a result of negative experiences with technology use. In the apps context, an app is always expected to perform or provide a specific functionality. W e argue that technology frustration will have a negative impact on consumer e valuation shift for three reasons. First, an underlying mechanism associated with technology frustration is the crashing of an app. When an app crashes or is not well integrated, the app would not meet the user expectations. Existing studies note that the initial feelings when a user look at a product might be one indicator of the quality of a product (Tractinsky, 2004). But, broader digital product quality is influenced by many sources such as functioning of the product, reviews and technical features. From the expectation confirmation theory, it is known that consumers

PAGE 27

18 have initial expectations from a product, and once they use the product they form perceptions about the produc t. After using it for a certain period of time, the user compares the performance with the expected performance and see if the expectations are met, which lead to their decision about continued usage of the product or discontinue it. When extrapolated to a market context, the app would lose a set of existing customers and will not be able to get more customers. Thus, we argue that the frustration felt by users while using the app would negative influences customer evaluation shift. In addition, confusing design and usage problems lead consumers to discontinue the efficacy and subsequent personal dissatisfaction (Lazar et al. 2006) . A frustrating experience with technology can also leave a user feeling frustrated toward the technology and its makers (Klein et al., 2002). If being used in a work place, the slow functionalities or issues may lead to underutilization and lower employee productivity. If a user is not satisfied by the app, it would result dissatisfaction and finally would lead to negative word of mouth as well as bad consumer evaluation initiated by the dissatisfied consumers will lead to fewer new consumers to adopt it, or positively review it, leading to a fewer number of positive ratings and a greater number of negative ratings. As a result, the overall consumer e valuation will decrease. At a consumer or market level, shared understanding of technology frustrations felt at an individual level aggregates for a product or service to create a highly adverse response for the product in the market (Guchait and Namasivayam 2012) . Thus, we hypothesize: H1: Consumer technology frustration is negatively associated with consumer e valuation shift for cellphone apps.

PAGE 28

19 Prior research shows that emotions have a huge impact on consumer decision making (Ding et al. 2005, Darke et al. 2006). Especially frustration is a negative emotion experienced by individuals when their goals are not met or missed (Colman, 2001). This negative experience might lead to a product avoidance or bad consumer evaluation. In the context of app use, consumers try to find an app that they plan to use for a particular task and gets frustrated if the app is not able to fulfil the task and become frustrated leading to bad consumer evaluation. In particular, the ineffectiveness of proper funct ioning of digital products could leave consumers frustrated and impact attitude toward the product evaluation (Campbell & Wright 2008, Su 2008). We argue that market factors such as consumer passion and market sustenance of the app until the focal time are two moderating factors on the relationship between customer technology frustration and consumer e valuation shift for several reasons. Consumer passion indicates the liking of for an app due to its appeal, function or on the attribute that meets a specifi c consumer need. For example, a fitness app would have a good appeal for the consumer segment that want to stay fit, and hence, would have generated a niche of passionate consumers. These passionate consumers would then derive higher benefits, be beta te sters, and help the app developer work towards the improvement of the app by providing feedback and, in general, help in mitigating the aspects related to technology frustration issues. Moreover, these satisfied consumers will generate positive word of mo uth influence, resulting in higher consumer e valuations. But, if the apps with high consumer passion starts having technical issues like glitches, the evaluation shift for such apps would be much higher. These passionate, satisfied consumers will therefore serve

PAGE 29

20 to amplify the direct, negative effect that technology frustration would garner among general, non involved adopters of the app. H2a: The negative effect of consumer technology frustration on consumer e valuation shift is stronger for apps with hi gh consumer passion. In addition to consumer passion, apps that have enjoyed sustained success in a market functionality. These apps are reviewed by consumers, have be en evaluated and established through high download volumes in the market in the last many months. Thus, the scope of sustained apps to the idiosyncrasies associated with technology issues will be viewed as a failure for the particular app. These apps hav e stood the test of time, and have commanded extensive positive support by consumers and are therefore more susceptible to consumer frustrations when technical glitches starts happening maybe after an update. Consumers will not be forgiving with technology glitches associated with these apps because they either are passionate about them, or because they have sustained positive reviews, indicating that any glitches are not anomalies and not the usual norm. Based on these arguments, we hypothesize: H2b: The negative effect of consumer technology frustration on consumer e valuation shift is stronger for apps with high consumer sustenance. We contend that internal market factors related to the app, such as age of the app and pricing of the app are prone to negat ive consumer evaluations. For example, new apps that are introduced to the market recently will be evaluated less stringently than old apps, as they are not yet consumer tested in the market. One of the reasons might be because the app has

PAGE 30

21 been recently introduced to the app market. Because of its newness, the expectations are not that high versus apps that are already in the market and has withstood the test of times. Users of the new apps are willing to oversee any technical failures in the app and woul d expect the technical failures to be taken care of in the subsequent new version releases, which is not the case with older apps. This is also perhaps due to the newness, and therefore greater interest and involvement (and hence, scrutiny) of old apps. Old apps may have gone through a lot of versioning and updates that would have resulted in a degree of robustness in terms of usability and functional integration in the app platform. Crashing or technological problems with old apps would be dealt with ske pticism and would force the users to give lower consumer evaluation. H2c: The negative effect of consumer technology frustration on consumer e valuation shift is stronger for old apps. Similarly, price of an app is sensitive to consumer evaluation as cons umers have higher expectations for more expensive apps, and hence perform more stringent evaluations for products that command a price premium. When a relatively expensive product suffers from technological troubles or issues, consumers may find themselves being less tolerant of such mishaps, as their expectations have been negatively disconfirmed, leading to dissatisfaction with the app. Existing studies mention that price stringent evaluations are taken very seriously by consumers even to the point of ret urning or discontinuing a product (Jones and Suh 2000 ; Parasuraman et al. 1994 ) . In sum, expectations are a direct function of price. Expectations associated with free apps is likely to be low, leading to positive disconfirmation of expectation even for relati vely

PAGE 31

22 mediocre apps. As price of an app increases, so do expectations. Expensive apps are held to a higher standard, and technological blips and frustrations are progressively less tolerable. Thus, we argue that compared to the free or low cost apps, the n egative influence of technology frustration on consumer evaluation will be higher for more expensive apps. Based on these arguments, we hypothesize: H2d: The negative effect of consumer technology frustration on consumer e valuation shift is higher for pricier apps than for low priced or free apps. M ethod Data and Variables . The data for this study comes from a secondary source. A consulting company engaged in tracking app businesses in android market collected the data. Our units of analysis are based on the tracking information about the apps for two and a half months from 13 October 2014 to 1 January 2015. For the analysis purposes the first week is the focal reference week, and the second week is used as the reference for the increase in ratings and reviews to calculate the variables used in this study. We created a pane l data set consisting of 20,007 panels and 87,192 observations. We could not consider apps that were only available for one panel for our analysis, which did not have any usage at all in terms of clicks or zero downloads in the market until the focal week . On close scrutiny, we also found apps that were in a different language than English, had illegible names, or were duplicates in the market. We excluded these apps. In addition, we excluded apps as there were no change in ratings or number of people who rated or reviewed the apps in the focal week and the subsequent week.

PAGE 32

23 Table 1 provides a description of variables we used in this study. Table 2 provides the descriptive statistics and table 3 pair wise correlation amongst the key variables. The depende nt variable in our model is the consumer e valuation shift ( CES This variable is calculated as the net change in the product of average rating and raters in that week, per original rater in the beginning of the week (see the calculation description in Table 1). Using both average ratings per week and the total number of raters provides both a measure of score, and a measure of popularity in a single variable that assesses overall rating impact. By measuring the change in overall impact s core between the two time periods, an index is developed based on the benchmark measure of the first week. This index is our dependent variable, CES . Aggregate measures of this kind have been used before, one famous one that uses a combination of two vari ables is Gross Rating Points, which combines two very different variables, frequency and reach, into an aggregate measure of overall size of an advertising campaign (Curran, 1999). Similarly, by combining two important app rating measures, namely average rating score and total number of raters, CES truly measures and quantity . Ta ble 1: Description of Variables Variable Description and Operationalization Referenc es Dependent Variables Consumer Evaluation Shift (CES) The change in the net consumer rating of the app, considering both the total raters who rated the app and the ratings that the app received in the span of the week. The CES index is calculated as follows: CES = (R2N2 R1N1)/N1. Where, R2= Average rating of the app in week 2 R1= Average rating of the app in week 1 N2= Total number of raters of the app in week 2 N1= Total number of raters of the app in week 1 (Perdikaki and Swaminathan 2013; Ülkü, Dimofte and Schmidt 2012)

PAGE 33

24 Independent Variables Consumer Technology Frustration (CTF) Frustration felt by the consumers for the app in the market. This time. The index measures the percentage of reviewers that discuss technical issues, frustrating problems, challenges i n and crashing of the app within the focal week. (Bessière et al. 2006; Guchait and Namasivayam 2012) Consumer Passion (CP) A powerful and persistent urge by the consumers to use or buy or interact with the app. This variable is coded by mining the text reviews reviewers that discuss admiration, devotion for and exaltation for the app within the focal weeks. (Belk, Ger and Askegaard 2003; Denegri Knott and Molesworth 2013) Variable Description and Operationalization References Market Sustenance (MS) To what extent consumers have sustained the app in the market. The variables is operationalized by measuring the total download volume of the app until the focal week. This was a range variable with unequal ranges. We coded download volume as a continuou s variable by taking the mid point of the range provided. (E.g. if the download range is 10,000 50,000, we used the midpoint 30,000 as the number of downloads). We then divided the number of downloads 1,000,000. (Lee and Raghu 2014) Age of the app (AGE) How long the app has existed in the Android market since its release. The variable was coded as the difference between the focal week and the release date of the app. Price of the app (PRICE) The price of the app in US$. Control Variables Game_app Whether the app is an application app. The variable is coded as 1=game app, 0=not a game app. App_LASTUPD When the app was last updated Avg_RP Average rating of all the apps that was published by a particular publisher Total_RP Total Ratings for the Publisher that includes all the apps created by the particular publisher Avg_PUBLRTNG Average reviews of all the apps published by a publisher Sum_RP Total reviews of a publisher that includes all the apps created by that publisher Avg_PP The average passionate users of a publisher that includes all the apps published by the publisher Avg_PP Passionate users of a publisher that includes all the apps published by the publisher in percentage

PAGE 34

25 Table 2. Descriptive Statistics Variable Obs Mean Std. Dev. Min Max CES 87192 4.55 2.91 7.74 18.23 CTF 87192 6.65 8.16 0 100 CP 87192 0.57 0.12 0.00 1.00 MS 87192 20.71 27.27 0.01 30 AGE 87192 2.09 1.07 0.41 6.58 PRICE 87192 0.97 0.70 0.40 6.58 App_LASTUPD 87192 3.88 0.83 0 5 Avg_RP 87192 4.46 5.22 0 37.30 Total_RP 87192 9.58 33.88 0 77.59 Avg_PUBLRTNG 87192 17.66 49.13 0 54.97 Sum_RP 87192 0.55 0.19 0 1 Avg_PP 87192 0.00 0.03 1 0.75 Avg_PP 87192 0.34 1.90 0 10 Avg_EP 87192 1.69 4.59 0 20 Avg_CP 87192 4.47 12.74 0 17.5 Avg_AP 87192 2.75 8.55 0.41 11.55 Avg_PrP 87192 2.52 8.56 0.41 11.55 Avg_RDP 87192 1.70 8.59 0.40 11.55 Pub_Deb 87192 0.02 0.14 0 1 Age_LRP 87192 0.02 0.15 0 1 Age_LUP 87192 0.03 0.18 0 1 C_Trivia 87192 0.04 0.19 0 1 C_Communication 87192 0.03 0.16 0 1 C_Educational 87192 0.01 0.12 0 1 C_Entertainment 87192 0.03 0.16 0 1 C_Family 87192 0.02 0.13 0 1 C_Finance 87192 0.03 0.17 0 1 C_Health & Fitness 87192 0.03 0.18 0 1 C_Lifestyle 87192 0.03 0.17 0 1 C_Media & Video 87192 0.03 0.17 0 1 C_News & Mag. 87192 0.02 0.13 0 1 C_Personalization 87192 0.03 0.18 0 1 C_Productivity 87192 0.03 0.17 0 1 C_Shopping 87192 0.02 0.15 0 1 C_Simulation 87192 0.03 0.18 0 1 C_Social 87192 0.03 0.17 0 1

PAGE 35

26 Table 3. Correlation amongst Key Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 CES 1 2 CTF 0.10 1 3 CP 0.10 0.28 1 4 MS 0.12 0.03 0.05 1 5 AGE 0.06 0.18 0.13 0.04 1 6 PRICE 0.12 0.11 0.04 0.02 0.44 1 7 App_LASTU 0.01 0.00 0.01 0.00 0.00 0.00 1 8 Avg_RP 0.01 0.01 0.00 0.00 0.00 0.01 0.03 1 9 Total_RP 0.01 0.00 0.00 0.00 0.00 0.01 0.10 0.28 1 10 Avg_PUBLR 0.00 0.01 0.00 0.00 0.00 0.01 0.06 0.58 0.38 1 11 Sum_RP 0.01 0.00 0.01 0.00 0.01 0.00 0.86 0.04 0.09 0.06 1 12 Avg_PP 0.01 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.02 0.01 0.00 1 13 Avg_PP 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.02 0.05 0.04 0.07 0.01 1 14 Avg_EP 0.00 0.01 0.01 0.01 0.00 0.01 0.02 0.02 0.07 0.04 0.02 0.01 0.03 1 15 Avg_CP 0.01 0.01 0.01 0.00 0.00 0.01 0.07 0.05 0.09 0.07 0.04 0.02 0.03 0.01 1 16 Avg_AP 0.00 0.01 0.00 0.01 0.01 0.01 0.09 0.07 0.16 0.07 0.03 0.07 0.01 0.08 0.24 1 The focal independent variable in this study is consumer technology frustration (CTF) which indicates the frustration in using the app. This variable is coded by mining the text onsumer passion (CP), for the app in the market that reflects the appeal of the app to the consumers as a powerful and persistent product, and is measured by text mining the reviews as well. Appendix A provides the coding scheme for CTF and CP variables. T he CTF index measures the percentage of reviewers that discuss technical issues, frustrating problems, challenges in usage, and crashing of the app. Thus, CTF provides an aggregate measure of consumer frustration with the app in the market. The reviews we re coded using a hermeneutic coding process to code the customer technology frustration by the consulting firm. We looked for users frustration while operating an a pp.

PAGE 36

27 The third independent variable is market sustenance that is operationalized as to what extent the app is sustained in the market through downloads until now from its release date. The download volume varies from 0 to 3,000,000, with average download of 27,000 for the apps in our sample. The third dependent variable is the price of the app in the market. The price of the apps varies from free to 7 US$. The final dependent variable is the age of the app since its release date. The average app in ou r sample is 2.5 years old, with the maximum age being 7 years and minimum is one year . Estimation Models . We will be using panel data for our analysis. To decide between fixed or random effects, we will run a Hausman test where the null hypothesis is that the preferred model is random effect vs. the alternative of fixed effect (Green, 2008). The Hausman test tests whether the unique errors are correlated with regressors, the null sed a random effect model. We used the panel data estimation with random effect to model the consumer rating shift model because the C E S is a continuous variable. C E S i it it it (1) Where, Where, C E S i is the dependent variable, X i is a vector of parameters, u is the between with each observation. More specifically, to test the direct effects, we specified the equation: C ES 11 CTF + 12 CP 13 14 15 it it (1) To test the interaction hypotheses we specify:

PAGE 37

28 CES 2 21 CTF+ 22 23 24 25 26 it it CES 3 31 CTF+ 32 33 34 35 36 it it CES 4 41 CTF+ 42 43 44 45 46 it it CES 5 51 CTF+ 52 53 54 55 56 it it CES 61 62 63 64 65 66 67 CTF × 68 69 it it Results We first tested the direct relationship between consumer technology frustrations on consumer e valuation shift and reported the results (see Column 1 of Table 4 ). We then included one interaction term each to test their individual interaction effects on consumer e valuation shift (Column 2 5, Table 4 ), and finally, tested for joint effects of all interaction terms in a model (Column 6 of Table 4 ). We find support for H 1 as the coefficient for consumer technology frustration is negative and significant in the direct effect model (Column 1 of Table 4 , 21 = 0.07, p < 0.01). This result shows that with approximately for every 1% increase in CTF, the customer evalu consumers in the first week, and 1 consumer feels that the app is bad due to technical issues; then in second week, the app is devaluated or rated bad by approximately 2 consumer . This multiplying effect would lead to devaluation of around 100% in around 7 8 weeks losing complete stock of consumers in the market.

PAGE 38

29 Table 4: Panel Estimation Random Effect Models VARIABLES (1) (2) (3) (4) (5) (6) Direct Effect Model Consumer Passion Interaction Market Sustenance Interaction Age Interaction Price Interaction All Interaction Terms CES CES CES CES CES CES CTF 0.07*** (0.002) 0.04** (0.009) 0.06** (0.002) 0.06* (0.006) 0.09** (0.004) 0.06** (0.010) CP 1.876*** (0.231) 1.903*** (0.256) 1.879*** (0.230) 1.876*** (0.231) 1.874*** (0.231) 1.901*** (0.256) MS 0.011*** (0.002) 0.011*** (0.002) 0.007*** (0.002) 0.011*** (0.002) 0.011*** (0.002) 0.007*** (0.002) AGE 0.193*** (0.034) 0.193*** (0.034) 0.193*** (0.034) 0.193*** (0.038) 0.192*** (0.034) 0.193*** (0.038) PRICE 0.267*** (0.050) 0.268*** (0.050) 0.270*** (0.049) 0.267*** (0.050) 0.248*** (0.056) 0.247*** (0.056) CTF × CP 0.03*** (0.014) 0.03*** (0.014) CTF × MS 0.02*** (0.000) 0.01*** (0.000) CTF × AGE 0.05** (0.002) 0.04** (0.002) CTF × PRICE 0.02** (0.003) 0.03** (0.003) Controls Included Included Included Included Included Included Constant 1.281*** (0.356) 1.248*** (0.381) 1.158*** (0.358) 1.277*** (0.368) 1.313*** (0.359) 1.164*** (0.392) Observations 87,192 87,192 87,192 87,192 87,192 87,192 Number of Panels 20,007 20,007 20,007 20,007 20,007 20,007 R squared 0.46 0.45 0.45 0.44 0.43 0.43 F Stat 15.85*** 16.36*** 16.48*** 16.78*** 16.89*** 17.23*** (1) Significance levels: ***p < 0.01, **p < 0.05, *p < 0.10 (2) Standard errors in parentheses (2) Models have controls (3) Models with each interaction term added individually in the model produce similar results. (4) Detailed results of the models including results of controls are given in Appendix B

PAGE 39

30 We find support for H 2a which predicted that negative effect of consumer technology frustration on consumer e valuation shift is high for apps with high consumer passion. The interaction term of CTF × CP is neg ative and significant in the interaction effects model (refer to column 6 of Table 4 , 69 = 0.03, p < 0.01 ). The interaction hypotheses H 2b predicted that for the apps with high market sustenance , the technology frustration will have a higher effect tha n for the apps for which consumer passion is low. We find support for this hypothesis as the interaction term CTF × MS is significant and negative in the interaction effects model (Column 6 of Table 4 , 68 = 0.01, p < 0.01 ). Similarly, H 2c is also supported with the interaction term CTF × AGE being significant on C E S in the interaction model (see Column 6 of Table 4 ; 67 = 0.04, p < 0.05 ). Finally, H 2d is also supported with the interaction term CTF × PRICE being significant on C E S (Colum n 6 of Table 4 ; 66 = 0.03, p < 0.05 ). We tested for multicollinearity by computing variance inflation factors (VIFs) for all estimation models. The highest VIF was 2.0 in the direct effect models, confirming that multicollinearity is not a serious conc ern. To reduce potential high multicollinearity issues due to the number of interaction terms in the models, all continuous variables were mean centered by subtracting the corresponding variable mean from each value (Aiken and West 1991). The VIF of any individual variable in any of the interaction effect models was less than 7.0. Furthermore, mean VIFs in all the models were less than 5.0. Thus, we find that multicollinearity is not a serious concern in the estimation. To investigate how a change in c onsumer technology frustration affects the customer e valuation shift, we employ a random effects model to analyze our sample of panel. We also consider a more generic generalized method of moments (GMM) approach. Table 5 presents

PAGE 40

31 the GMM estimation results , which is similar to the panel estimation results. This validates the robustness of our results, specific to the sensitivity to endogeneity issues. Table 5: GMM Estimation Results VARIABLES (1) (2) (3) (4) (5) (6) Direct Effect Model Consumer Passion Interaction Market Sustenance Interaction Age Interaction Price Interaction All Interaction Terms CES CES CES CES CES CES CTF 0.041** (0.122) 0.031** (0.123) 0.067** (0.122) 0.022*** (0.121) 0.074** (0.121) 0.067** (0.117) CP 0.022*** (0.032) 0.020*** (0.032) 0.025*** (0.032) 0.020** (0.032) 0.009** (0.034) 0.003** (0.033) MS 1.763** (3.308) 3.349* (3.623) 1.421** (3.303) 1.596* (3.323) 1.892* (3.277) 3.544** (3.499) AGE 0.954** (0.478) 1.097** (0.494) 0.938** (0.476) 0.612** (0.563) 1.057** (0.481) 0.570* (0.534) PRICE 0.513** (0.568) 0.427*** (0.573) 0.480*** (0.566) 0.506*** (0.569) 0.824** (0.634) 0.645** (0.626) CTF X CP 0.160** (0.147) 0.237** (0.160) CTF X MS 0.157** (0.159) 0.121** (0.156) CTF X AGE 0.023*** (0.021) 0.047** (0.022) CTF X PRICE 0.042*** (0.040) 0.042*** (0.040) Constant 3.412** (4.461) 3.313** (4.452) 15.388** (19.781) 3.166** (4.465) 3.575** (4.402) 11.000** (19.318) Observations 87,192 87,192 87,192 87,192 87,192 87,192 Number of panels 20,007 20,007 20,007 20,007 20,007 20,007 R Squared 0.54 0.53 0.53 0.57 0.52 0.47 F Stat 12.48*** 13.26** 13.43*** 13.87*** 13.92** 14.34*** (1) Significance levels: ***p < 0.01, **p < 0.05, *p < 0.10 (2) Standard errors in parentheses We plot the interaction effects. Figures in Appendix E provide the interaction graphs. The results of the interaction terms are discernible in the graphs, providing validity to our results. Amongst other findings, we also find CP, MS, and PRICE have positive and significant direct effects on C E S, while AGE has a negative significant direct effect. These

PAGE 41

32 results were not hypothesized due to their intuitive nature, but are nonetheless noteworthy. The catego rical control variables are not significant in our models. Discussion Our study will be among the first attempts to examine the effect of negative emotions specifically technology frustration in consumer evaluation shift. The impact of technology frustration is especially unescapable and long lasting in online app reviews, whereby the negative feelings caused by technological issues are expressed by reviewers can influence the purchase decisions of thousands of consumers who have access to the revi ews. Therefore, this paper will deepen our understanding of how technology frustration are interpreted and attributed in social settings. One of the managerial implication of this study will be that irrespective of a great proposition that an app is useful for customers, the technical design and integration plays a highly valuable role in consumer acceptance of the app. Hence, developers should pay more attention to the technical aspect of the apps. Moreover, app developers should be careful in the design and development aspects of an app which is often a forgotten aspect due to the low cost development regime of apps developments. In addition, identifying passionate consumers and pricing of the apps play important role for an apps success. Finally, as m uch mechanisms such as release controls and exist strategy are seen in cons umer oriented products such as movies or music products, apps markets need to implement such system. In addition, app developers should also take into account the role price plays on consumer evaluation. If the users pay a price for the app, they are to e valuate the apps more

PAGE 42

33 strictly compared to apps that are free. So, when developers set a price for the apps, they should make sure there are no technical issues with the app. This study will take into account an important factor in addressing consumer eval uation shift in an app market. This study will contribute to research in identifying technological, market externalities and internal factors associated with digital product success. We believe that this study will take an important first step in identify ing and conceptualizing the role of technology frustration of modern mobile platform ecosystems and presents a rich empirical evidence to support our conceptualizations. Theoretical Contributions Our study is among the first attempts to examine the effect of negative emotions specifically technology frustration in consumer evaluation shift. The impact of technology frustration is especially unescapable and long lasting in online app reviews, whereby the negative feelings caused by technological issues are expressed by reviewers can influence the purchase decisions of thousands of consumers who have access to the reviews. Therefore, this paper deepens our understanding of how technology frustration are interpreted and attributed in social settings. Second, t his study is novel in developing a comparative theoretical model to understand the differences in the frustrating experiences across segments of customers differentiated by passion. Third, this paper adds to the prior research that support from the inform ation systems design and developers in mitigating concerns and therefore, sustaining the produce in market has im plications. Previous research (e.g., V on Hippel and Katz , 2002)

PAGE 43

34 suggests that users can participate in the innovation process by completing need related tasks related tasks (e.g., implementing the innovation) to companies. Sustaining app s means indirectly a linkage between the which is a need and solution based participatory process. This study relates technical attribute to consumer emotions/appeal/response. From the information systems design perspective, usability aspects are often overlooked as much a technology functions well, and vice versa but never together. Mobile apps is one of the fastest growing digital platform in this decade. This study takes into account an important factor in addressing consumer evaluation shift in an app market. This study contributes to research in identifying technological, market externalities and internal factors associated with digital product success. Contextualizing to app s market, the study suggests the integral role of these three complementary factors, and thus, contribute to evaluate the components of a digital business strategy (Bharadwaj et al. 2013) . We believe that this study takes an important first step in identifying and conceptualizing the role of technology frustration of modern mobile platform ecosystems and presents a rich empirical evidence to support our conceptualizations. Since the scope of the study was limited to identifying and ordering the underlying factors that impact consumer evaluation shift, no direct recommendations is provided on how to increase consumer evaluation for apps. Although the r esults give an indication of the importance of technology frustration as a value adding component, more detailed research is needed on how improvements can be achieved in the various dimensions of customer evaluation.

PAGE 44

35 Managerial Implications One of the man agerial implication of this study is that irrespective of a great proposition that an app is useful for customers, the technical design and integration plays a highly valuable role in consumer acceptance of the app. Hence, developers should pay more atten tion to the technical aspect of the apps. Moreover, app developers should be careful in the design and development aspects of an app which is often a forgotten aspect due to the low cost development regime of apps developments. In addition, identifying pa ssionate consumers and pricing of the apps play important role for an apps success. Finally, as much such feedbacks and market mechanisms such as release controls and exist strategy are seen in consumer oriented products such as movies or music products, apps markets need to implement such system. In addition, app developers should also take into account the role price plays on consumer evaluation. If the users pay a price for the app, they are to evaluate the apps more strictly compared to apps that are free. So, when developers set a price for the apps, they should make sure there are no technical issue s with the app. view points and encourage them in the apps innovation and development. Perhaps rather than rushing an app to the market and then facing discontinuance due t o technology issues, developers are better off in involving users and consumers early on in the development cycle and take inputs or identify problems (e.g., beta testing) Further, instead of treating all consumer identically, this study distinguishes two groups (i.e., passionate and dispassionate). The findings suggest that apps developers should differentiate their strategies in attracting

PAGE 45

36 more passionate consumers, and developing or orienting their designs to convert more dispassionate customers into p assionate ones . Limitations and Future Research Future research may explore nuances associated with technology frustration with a ue associated with it. Possibly, exploring willingness to pay in the apps market can establish the nuances associated with establishing a business case for apps. The next steps of this study is to generalize our findings by considering multiple platform ecosystems such as apple store and to investigate the dynamics of technology frustration and consumer evaluation shift along with the pricing, age, consumer passion and market sustenance of app. We believe that such investigations of mobile platform ecosy stems have a rich potential to extend the research frontier of the app ecosystem and competitive strategy research stream and hope that other scholars will follow suit. Conclusion In conclusion, the objective of this study was to explore to what extent technology frustration influences consumer evaluation of apps. In addition, we posed the research question as to how market externalities such as passionate consumers and sustenance of the app in the market; and whether market internal factors such as pricing and age of the app moderate the influence of technology frustration on consumer evaluations. This study contributes to the research on the digital business strategy for apps mar kets.

PAGE 46

37 CHAPTER I V EPILOGUE Digital products are creating value for businesses in several ways to conceive, structure, implement, offer, manage, and augment services and service capabilities. The impact and value potential of digital services varies from transforming business processes, to managing the complexities involved with the customer centric solutions, to enabling inter firm integration and providing unique solutions. However, these digital product developers face challenges to assimilate, implemen t and integrate the digital services. Developers also face difficulties in execution of strategies to be effective in the context of digital products. This dissertation proposes c onsumer evalu ation of products in a market as a predictor of the success or failure of a product. To look into this phenomenon, this dissertation focus ed on the role of technology frustration on consumer evaluation of digital products. In the essay , we explore d how technology frustration influences the shift in valuation of a product in digital markets .

PAGE 47

38 REFERENCES Aaker, D. A., and Keller, K. L. 1990. "Consumer Evaluations of Brand Extensions," The Journal of Marketing ), pp. 27 41. Aitken, M., and Gauntlett, C. 2013. "Patient Apps for Improved Healthcare from Novelty to Mainstream," IMS Institute for Healthcare Informatics ), pp. 1 65. Aleem, U., Cavusoglu, H., & Benbasat, I. (2014). Uncovering Privacy Uncertainty: An Empirical Inves tigation in the Context of Mobile Apps. Belk, R. W., Ger, G., and Askegaard, S. 2003. "The Fire of Desire: A Multisited Inquiry into Consumer Passion," Journal of consumer research (30:3), pp. 326 351. Bessière, K., Newhagen, J. E., Robinson, J. P., and Shneiderman, B. 2006. "A Model for Computer Frustration: The Role of Instrumental and Dispositional Factors on Incident, Session, and Post Session Frustration and Mood," Computers in Human Behavior (22:6), pp. 941 961. Bharadwaj, A., El Sawy, O. A., Pavlo u, P. A., and Venkatraman, N. 2013. "Digital Business Strategy: Toward a Next Generation of Insights," MIS Quarterly (37:2), pp. 471 482. Bhattacherjee, A. 2001. "Understanding Information Systems Continuance: An Expectation Confirmation Model," MIS quart erly ), pp. 351 370. Bowman, C., Ambrosini, V., 2000. Value creation versus value capture: Towards a coherent definition of value in strategy. British Journal of Management , 11: 1 15. Brown, T. J., Churchill, G. A., & Peter, J. P. (1993). Improving the measurement of service quality. Journal of retailing, 69(1), 127 139. Brown, S. A., Venkatesh, V., and Goyal, S. 2012. "Expectation Confirmation in Technology Use," Information Systems Research (23:2), pp. 474 487. Ceaparu, I., Lazar, J., Bessiere, K., R obinson, J., & Shneiderman, B. (2004). Determining causes and severity of end user frustration. International journal of human computer interaction, 17(3), 333 356. Cummings, J. N., & Kraut, R. (2002). Domesticating computers and the Internet. The Informa tion Society, 18(3), 221 231. Curran, C. M. (1999). Misplaced marketing A best buy in advertising: schools selling students as media audiences. Journal of Consumer Marketing, 16(6), 534 536. Dabholkar, P. A. 1996. "Consumer Evaluations of New Technology Based Self Service Options: An Investigation of Alternative Models of Service Quality," International Journal of research in Marketing (13:1), pp. 29 51. Denegri Knott, J., and Molesworth, M. 2013. "Redistributed Consumer Desire in Digital Virtual Worlds of Consumption," Journal of Marketing Management (29:13 14), pp. 1561 1579.

PAGE 48

39 Garg, R., & Telang, R. (2012). Inferring app demand from publicly available data. MIS Quarterly, Forthcoming. Gerlic h, R. N., Drumheller, K., & Babb, J. (2015). App Consumption: An Exploratory Analysis of the Uses & Gratifications of Mobile Apps. Academy of Marketing Studies Journal, 19(1), 69. Ghose, A., and Han, S. P. 2014. "Estimating Demand for Mobile Applications in the New Economy," Management Science (60:6), pp. 1470 1488. Greene, William. Econometric Analysis. Fourth Edition. Upper Saddle River: Prentice Hall (2000). Guchait, P., and Namasivayam, K. 2012. "Customer Creation of Service Products: Role of Frustr ation in Customer Evaluations," Journal of services Marketing (26:3), pp. 216 224. Han, S. P. Mobile App Analytics: A Multiple Discrete Continuous Choice Framework (Doctoral dissertation, kaist). Huang, E. Y., & Hung, K. L. (2014, January). Understanding Mobile Apps Purchase: The Effect of Free Trial and Online Consumer Review. In Academy of Management Proceedings (Vol. 2014, No. 1, p. 13992). Academy of Management. Huang, G. H., & Korfiatis, N. (2015). Trying before buying: The moderating role of online reviews in trial attitude formation toward mobile applications. International Journal of Electronic Commerce, 19(4), 77 111. Hui, K. L., & Chau, P. Y. (2002). Classifying digital products. Communications of the ACM, 45(6), 73 79. IDC. 2013. "Worldwide a nd U.S. Mobile Applications Download and Revenue 2013 2017 Forecast: The App as the Emerging Face of the Internet." Johnson, M. W., Christensen, C. M., and Kagermann, H. 2008. "Reinventing Your Business Model," Harvard Business Review (86:12), pp. 50 59. Jones, M. A., & Suh, J. (2000). Transaction specific satisfaction and overall satisfaction: an empirical analysis. Journal of services Marketing, 14(2), 147 159. Keaveney, S. M., & Parthasarathy, M. (2001). Customer switching behavior in online services: An exploratory study of the role of selected attitudinal, behavioral, and demographic factors. Journal of the academy of marketing science, 29(4), 374 390. Khalid, M. S. Secondary Educational Institution Centered Diffusion of ICT in Rural Bangladesh (Doc toral dissertation, Videnbasen for Aalborg UniversitetVBN, Aalborg UniversitetAalborg University, Det Humanistiske FakultetThe Faculty of Humanities, Forskningsgruppen for TeknoantropologiThe Techno Anthropology Research Group). Klein, J., Moon, Y., & Pic ard, R. W. (2002). This computer responds to user frustration:: Theory, design, and results. Interacting with computers, 14(2), 119 140.

PAGE 49

40 Korgaonkar, P. K., & Moschis, G. P. (1982). An experimental study of cognitive dissonance, product involvement, expecta tions, performance and consumer judgement of product performance. Journal of advertising, 11(3), 32 44. Lazar, J., Jones, A., Hackley, M., and Shneiderman, B. 2006. "Severity and Impact of Computer User Frustration: A Comparison of Student and Workplace U sers," Interacting with Computers (18:2), pp. 187 207. Lee, G., and Raghu, T. 2014. "Determinants of Mobile Apps' Success: Evidence from the App Store Market," Journal of Management Information Systems (31:2), pp. 133 170. Lenhart, A. 2003. The Ever Shif ting Internet Population: A New Look at Access and the Digital Divide . Pew Internet & American Life Project. Lepak, D. P., Smith, K. G., and Taylor, M. S., 2007. Value creation and value capture: A multilevel perspective. Academy of Management Review, 32( 1), 180 194. Liu, Y., Deng, S., & Hu, F. (2014). E Loyalty Building in Competitive E Service Market of SNS: Resources, Habit, Satisfaction and Switching Costs. In Digital Services and Information Intelligence (pp. 79 90). Springer Berlin Heidelberg. Muda mbi, S. M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon. com. MIS quarterly, 34(1), 185 200. Müller, R. M., Kijl, B., & Martens, J. K. (2011). A comparison of inter organizational business models of mobile app st ores: there is more than open vs. closed. Journal of theoretical and applied electronic commerce research, 6(2), 63 76. Negash, S., Ryan, T., & Igbaria, M. (2003). Quality and effectiveness in web based customer support systems. Information & Management, 40(8), 757 768. Norman, A. T., & Russell, C. A. (2006). The pass along effect: Investigating word of mouth effects on online survey procedures. Journal of Computer Mediated Communication, 11(4), 1085 1103. Oliver, R. L. 1977. "Effect of Expectation and Disconfirmation on Postexposure Product Evaluations: An Alternative Interpretation," Journal of applied psychology (62:4), p. 480. Oliver, R. L. 1980. "A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions," Journal of marketing research ), pp. 460 469. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1994). Reassessment of expectations as a comparison standard in measuring service quality: implications for further research. the Jour nal of Marketing, 111 124. Parthasarathy, M. (1995). The impact of discontinuance on the subsequent adoption of an innovation: Theoretical foundation and empirical analysis. Parthasarathy, M., & Bhattacherjee, A. (1998). Understanding post adoption behavior in the context of online services. Information systems research, 9(4), 362 379.

PAGE 50

41 Perdikaki, O., and Swaminathan, J. 2013. "Improving Valuation under Consumer Search: Implications for Pricing and Profits," Production and Operations Management (22:4) , pp. 857 874. online store visit strategies: an investigation of demographic variables. European Journal of Information Systems, 19(3), 344 358. Preece, J. R., & Rogers, Y. (2007). SHARP (2002): Interaction Design: Beyond Human Computer Interaction. Crawfordsville: John Wiley and Sons, Inc. Answers. com Technology. Richins, M. L., & Bloch, P. H. (1991). Post purchase product satisfaction: Incorporating the e ffects of involvement and time. Journal of Business Research, 23(2), 145 158. Rogers, E. M. (2003). Elements of diffusion. Diffusion of innovations, 5, 1 38. Scheier, M. F., Carver, C. S., & Bridges, M. W. (2001). Optimism, pessimism and psychological we ll being. In E. C. Chang (Ed.), Optimism and pessimism. Implications for theory, research, and practice (pp. 189 216). Washington, DC: American Psychological Association. Shneiderman, B. (2000). Creating creativity: user interfaces for supporting innovati on. ACM Transactions on Computer Human Interaction (TOCHI), 7(1), 114 138. Stein, M. K., Newell, S., Wagner, E., & Galliers, R. D. (2015). Coping with information technology: mixed emotions, vacillation and non conformin g use patterns. Forthcoming MIS Q. Ülkü, S., Dimofte, C. V., and Schmidt, G. M. 2012. "Consumer Valuation of Modularly Upgradeable Products," Management Science (58:9), pp. 1761 1776. Venkatraman, M. P. (1988). Investigating differences in the roles of enduring and instrumentally involved consumers in the diffusion process. Advances in Consumer Research, 15(1), 299 303. Wilfong, J. D. 2006. "Computer Anxiety and Anger: The Impact of Computer Use, Computer Experience, and Self Efficacy Beliefs," Computers in Human Behavior (22:6), pp. 1001 1011. Yang, S., Lu, Y., & Chau, P. Y. (2013). Why do consumers adopt online channel? An empirical investigation of two channel extension mechanisms. Decision Support Systems, 54(2), 858 869. Yin, P., Luo, P., Lee, W. C., & Wang, M. ( 2013, February). App recommendation: a contest between satisfaction and temptation. In Proceedings of the sixth ACM international conference on Web search and data mining (pp. 395 404). ACM. Zwass, V. (2003). Electronic commerce and organizational innovati on: aspects and opportunities. International Journal of Electronic Commerce, 7(3), 7 37.