The influence of design functionalities in the adoption in m-commerce

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The influence of design functionalities

in the adoption in m-commerce

By

Bart-Jan van Hilten

This thesis is submitted in fulfillment of the requirements of the

thesis for the MSc Business Administration

Specialization Strategy & innovation

Rijksuniversiteit Groningen

Faculty Economics and Business

15 August, 2013

Groningen

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Acknowledgements

I have finished this thesis to graduate in the Master's program Business Administration with the

specialization Strategy & Innovation. I have worked really hard for a long time to complete this research. It was a big journey and very time consuming, but I am proud with the result. Right now, I am really happy I am able to end a long period of studying and I am ready to work within a company.

First of all, I really want to thank Eric Lim. He always wanted to help me to direct me in the right directions and provide valuable feedback. He was also really helpful when he moved to Sydney. I will memorize the long and useful meetings in his office and the meeting on Skype. The meetings were very pleasant and after the meetings I was refilled with energy to continue with the thesis and fully equipped with new insights. He also direct me in the right order to complete several tasks, which finally result in many and professional results. I wish him all the luck in his new job and living.

I am also really thankful to the people who have filled in my survey. The survey took about half an hour and was, as my father said, an intellectual challenge. I appreciate they have spent the effort and time to complete my survey, which finally helped me to complete my thesis.

I also want to thank my parents who provided with enough financial possibilities to study full time. My family as well as my girlfriend Liesbeth supported me to study in my own way and to take the

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Abstract

Since the first launch of the smartphone in 2008, the use of smartphones became common in people’s daily activities. Because of this, m-commerce has made major growth rates. In this paper, a theoretical framework is proposed to investigate key drivers of user behavior in the adoption of m-commerce and specifically app stores. This study examines the role of four object-based beliefs - Information, System, Service, and Hedonic Quality - that are predictors in adoption decisions and also examined the role of dimensions that influence the object-based beliefs. Based on an extant literature review, this study constructs, refines, and tests a taxonomy of design functionalities in app stores that play an important role in the adoption m-commerce applications and finally this study proposed a new adoption model. This model can be a valuable tool in providing knowledge in the designing and evaluation process of app stores and can be useful for managers because the model provides knowledge on how managers can increase several quality predictors. The proposed model is empirically tested through an online survey among 96 app store users. Empirical evidence shows the importance of the object-based beliefs and supported several design functionalities that influence dimensions.

Keywords: m-commerce, adoption model, information quality, system quality, service quality, hedonic

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1 Introduction

1.1 Background information

Since the introduction of the first smartphone of Apple in 2008, the mobile app industry has made major growth rates. Nowadays smartphone users spend on average about two hours a day using apps . The two major players in the app industry, Apple and Google, offer more than 700.000 apps each. According to the Wall Street Journal it is estimated that the total global revenue from App Stores is expected to rise with 62% to $25 billion in 2013 (Lessin & Ante, 2013). A research from Reuters found that the total revenue of the app stores in the first quarter of 2013 was 2.3 billion and that the Apple’s iOS App store has got a market share of 74% (1.6 billion), where Android’s Google Play-store has got a market share of 18%. Important to note is that 70% of all smartphones possess the Android software, so the iOS App Store generates with a significant lower number of devices, a larger amount of revenue. Moreover, the total number of application downloads is dominated by Android. About 90% of the total app downloads is downloaded via the Google Play Store (Yang, 2013).

Furthermore, Empson showed that the market for paid application downloads reached €8 billion in 2012, representing an increase of 27% compared with 2011, but in the meantime the average revenue per paid apps decreased by 27% from €26.720 in 2011 to €19.560 in 2012 (Empson, 2013).

1.2 Relevancy

The use of smartphones is becoming more and more important in people’s daily life. In a study conducted by Microsoft Tag, mobile device usage was estimated to overtake desktop Internet users by 2014. 86% of mobile Internet users are using their devices while watching TV (Richmond, 2010). Furthermore, they revealed that children aged 7-16 are more likely to own a mobile device than a book. Besides, a study of Pew Research Center shows that the percentage of adult mobile phone users who have downloaded an app to their device nearly doubled in the years 2011-2012 (Purcell, 2011). Because research in the fields of smartphones and their belongings apps in the m-commerce is still young in comparison with desktop computing, research in m-commerce can add real value.

1.3 Statement of the problem

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model variants, and model extensions of the TAM model (Taylor & Todd, 1995; Agarwal & Prasad, 1997;

Gefen & Straub, 1997; Venkatesh, et al., 2003).

Although the TAM model has been widely applied in the literature there has also been criticism. First, in most studies, actual use is not measured (Bouwman & Wijngaert, 2009). Most studies focus on behavioral intention. And in most cases, where actual use is measured, it is not measured on the basis of log-data, but on the basis of self-reports, which means that what is measured is not the actual use, but rather self-reported use. Secondly, in many cases the research population consists of students, which may affect the outcome. Student-based studies offer stronger support for a TAM-based approach than studies involving other kinds of populations (Schepers & Wetzels, 2007). A third and more fundamental criticism is that it is unclear to what extent TAM actually provides insight into (intended) user behavior. Other concepts may explain the correlations between the core concepts. In TAM-based studies, the focus is on concepts such as self-efficacy (Mao, et al., 2005) computer experience (Kleijnen, et al., 2004), subjective norms (Venkatesh & Davis, 2000; Legris, et al., 2003), hedonistic values ( (Van der Heijden,

2003), perceived enjoyment (Cyr, et al., 2006) and personal innovativeness (Lu, et al., 2005).

Another criticism is that TAM treats technology as a black box. However, it is important to take specific characteristics of technology in account, which may influence the intention of people to use a technology (Legris, et al., 2003; Lu, et al., 2003; Amberg, et al., 2004; Benbasat & Barki, 2007). The technology acceptance literature provides sound predictions of usage by linking behaviors to attitudes and beliefs (ease of use and usefulness) that are consistent in time, target, and context with the behavior of interest (system usage) (Wixom & Todd, 2005). Despite its predictive ability, TAM provides only limited guidance about how to influence usage through design and implementation (Taylor & Todd,

1995; Venkatesh, et al., 2003). For example, designers receive feedback regarding ease of use and

usefulness in a general sense, but they do not receive actionable feedback about important aspects of the Information Technology artifact itself (e.g. flexibility, completeness of information, and information currency). Such guidance was a core objective in the development of TAM, but one that has received limited attention (Davis, et al., 1989).

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1.4 Purpose

Because the application market is booming business and the literature does not contain much research on the design functions that influence the adoption of an application store, the main focus of this research is to gain insight in these design functionalities. Hereby, an App Store is defined as “a platform where users can search for and download applications on their device (e.g. mobile phone, tablet)”. Common examples of application stores are:

- iTunes App Store on a iPhone; - iTunes App Store on an iPad; - iTunes Store on a MacBook; - Google Play Store on a tablet; - Google Play Store on the computer; - Blackberry World on a Blackberry;

- Windows Phone Store on a Windows phone.

But, as Wong already notes, “The winner of any comparison between Android and iOS is essentially the user.” Platforms have different consumer approaches and business models, they can obtain open, or gated app distribution portals and can differ in the openness of technology (e.g. access to source codes). In this research I will develop a framework that can be used as a technology adoption model in the m-commerce. This model is based on the framework of Wixom & Todd (Wixom & Todd, 2005). Their model considered a more comprehensive view on Information Quality, System Quality, and Service Quality that influence the adoption in an e-commerce context. I will transform their model to an m-commerce context. Furthermore, I will add Hedonic Quality to it to ensure a holistic framework. Finally, I will add different kind of design functionalities to it, based on a literature review. This model is first tested among the iOS and the Google Play App Stores and is finally tested among customers through a survey. Considering the aforementioned, the research goals are:

 Add to the growing literature on technology adoption models in m-commerce;  Re-evaluate the adoption models in the Information Technology literature;  Specify design functionalities in the App Stores;

 Develop a model for the adoption of App Stores;

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1.5 Significance of the study

This research will add to the growing field of Quality dimensions in mobile context. Several authors already wrote and published in the field of the mobile context (Kim & Han, 2009; Zhou & Lu, 2011; Pitt, et al., 2010; Lee, et al., 2009; Venkatesh, et al., 2012; Benbunan-Fich & Benbunan, 2007; Lee, et al.,

2007). This study goes further and describes many dimensions and measures how they influence the

technology adoption simultaneously. Besides, in my recollection, not one study defines and measures the design functionalities which influence the dimensions in technology adoption. From a managerial perspective this thesis can add value too. Managers can decide on which dimensions they have to focus to increase the Information, System, Service , and Hedonic Quality that will increase the value of the mobile service and finally result in a higher use of the mobile service. Besides, this research may also be useful for developers because it will give insights in the design functionalities that serve as enablers and inhibitors of intention to use.

1.6 Research question

Main question:

How can an App Store be designed to increase the adoption of App Stores?

1.7 Application area: iOS and Google Play App Store

The design functionalities that are formulated in the Theoretical Background are first coded, based on their presence, in the two largest App Stores: iOS from Apple and Google Play Store from Google. As an article by Reuters showed, these two App Stores were the largest in the first quarter of 2013. Based on the revenue, Apple has got a market share of 74% (1.6 billion) and Google has got a market share of 18%

(Yang, 2013). After that a survey was held among users of an App Store to empirically test the research

model.

1.8 Outline of the current research

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2 Theoretical backgrounds

A widely applied model to study the technology adaption is the TAM model proposed by Davis (Davis,

1989). Researchers have extended the TAM model in three primary ways to provide greater

understanding and explanatory power and additional points of managerial leverage in its application. The first approach involves introducing factors from related models, such as subjective norm, perceived behavioral control, and self-efficacy (Taylor & Todd, 1995; Mathieson, et al., 2001; Xu, et al., 2012; Lee,

et al., 2009). A second approach involves introducing additional or alternative belief factors to the

model. Most often this includes adding key related factors from the diffusion of innovation literature, such as trialability, compatibility, visibility, or result demonstrability (Agarwal & Prasad, 1997;

Karahanna, et al., 1999; Plouffe, et al., 2001; Dickinger, et al., 2008). A third approach has been to

examine external variables, which are antecedents to or that moderate the influence of ease of use and usefulness within the TAM, such as personality traits and demographic characteristics (Gefen & Straub,

1997; Venkatesh, 2000; Bouwman & Wijngaert, 2009; Lin & Bhattacherjee, 2010).

2.1 Model of Wixom and Todd

Wixom & Todd tried to integrate the user satisfaction literature in the technology acceptance literature

(Wixom & Todd, 2005). They stated that user satisfaction needs to be recognized as an object-based

attitude (Ajzen & Fishbein, 1980) whereby it serves as an external variable with the influences on intention and behavior that are fully mediated by behavioral beliefs and attitudes. For example, one’s satisfaction with the reliability of a system does not directly impact whether one will use the system. However, beliefs about reliability certainly will affect one’s attitude toward the system, which will shape behavioral beliefs about using the system (e.g. ease of use). It is the system behavioral belief (ease of use) that directly influences attitude toward system usage.

For accurate prediction, beliefs and attitudes must be specified in a manner that is consistent in time, target, and context with the behavior of interest. This is often referred as the “correspondence principle” and is at the core power of models such as TAM where beliefs and attitudes about a specific behavior (e.g., the use of an e-mail system), in a particular context (e.g., work), at a particular point in time (e.g., over the next month) are found to be predictive of intention and behavior (Wixom & Todd,

2005). Wixom & Todd proposed a model, which was fully consistent with several and widely, applied

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(the target behavior of interest) is driven by behavioral intention, intention is determined by attitude towards use and usefulness, and usefulness is a function of ease of use. Usefulness and ease of use are both assessments of the consequences of using a system to accomplish some task. Their model is presented in figure 1.

Figure 1: Research model by Wixom & Todd (2005)

2.1.1 Behavioral beliefs

To measure the behavioral beliefs Use and Usefulness, many models use the instrumental Perceived Usefulness (PU) and Perceived Ease Of Use (PEOU). PU is defined as “the degree to which a person

believes that using a particular system would enhance his or her job performance” (Davis, et al., 1989).

Within an e-service context, PU more specifically refers to the degree to which a customer believes a website helps them achieve their shopping goals (Cenfetelli, et al., 2009). PEOU is defined as “the degree to which a person believes that using a particular system would be free of effort” (Davis, et al.,

1989). Accordingly, I propose:

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H1d: PU will have a positive effect on user’s intention to use App Stores.

Next to PU and PEOU, XU et al. proposed that Perceived Enjoyment (PE) is also a behavioral belief (Xu, et

al., Working paper). PE is defined as “the extent to which the activity of using a technology is perceived

to be enjoyable in its own right, apart from any performance consequences that may be anticipated and is considered to be an intrinsic source of motivation” (Kim & Han, 2009; Davis & Bagozzi, 1992;

Al-Gahtani & King, 1999). PE is important for the evaluation of e-services (Bauer, et al., 2006; Xu, et al.,

Working paper) and can be measured to which extent customers find the IT-based service to be

enjoyable, fun and pleasant to use. PE has been found to be a significant antecedent of attitude toward using technology-based services (Dabholkar & Bagozzi, 2002) and is important in an online context where it can have a significant impact on attitude and intention toward intention to use a website

(Wakefield, et al., 2011), online shopping (Xu, et al., Working paper), mobile internet services (Lee, et al.,

2007; Yang, et al., 2012) and other systems (Lin & Bhattacherjee, 2010; Dickinger, et al., 2008).

Accordingly, I propose:

H1e: PE will have a positive effect on user’s attitude to use App Stores.

Furthermore, scholars have established the principle that PE positively influences PEOU, because users with higher PE underestimate the difficulty associated with the technologies, resulting in a decreasing cognitive burden and an increase in PEOU (Agarwal & Karahanna, 2000; Venkatesh & Davis, 2000;

Cheng, 2011). So:

H1f: A user’s PE positively influences that individual’s PEOU.

2.1.2 Object-based attitudes and beliefs

Wixom & Todd also stated that object-based attitudes (e.g. attitudes about a system) can be predictive of behavioral dispositions by influencing the way in which information is perceived and judged (Wixom

& Todd, 2005). Xu et al. asserts that beliefs about objects (in their case system quality, information

quality, and service quality) are linked to attitudes towards an object (in their case system satisfaction, information satisfaction, and service satisfaction) (Xu, et al., Working paper).

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Service quality, in particular, is widely supported in the marketing literature as a determinant of service satisfaction (Xu, et al., Working paper; DeLone & McLean, 2003).

In this research, system quality is defined as “the extent to which the technical operations of the system

add value to the consumer’s transactional activities”, information quality is defined as “the extent to which information provided on the system adds value to the consumer’s transactional activities”, and

service quality is defined as “the extent to which the service provided by the App Store adds value to the consumer’s transactional activities”. Accordingly, I suppose:

H2a: A user’s perceived information quality influences that user’s information satisfaction. H2b: A user’s perceived system quality influences that user’s system satisfaction.

H2c: A user’s perceived service quality influences that user’s service satisfaction.

Satisfaction with the information produced by the system will influence perceptions of usefulness. That is, the higher the overall satisfaction with the information, the more likely one will find the application of that information useful in enhancing work performance. A similar effect is anticipated in terms of system and service satisfaction. System satisfaction represents a degree of favorableness with respect to the system and the mechanics of interaction. The more satisfied one is with the system itself, the more likely one is to find the system to be easy to use. The significant positive relationship between service satisfaction and PE has been recognized in the service literature (Bauer, et al., 2006; Xu, et al., Working

paper). In other words, satisfaction (e.g. information, system, and service) influences behavioral beliefs

(e.g. PU, PEOU, and PE) and satisfaction represents an object-based attitude that serves an external variable shaping behavioral beliefs (Wixom & Todd, 2005). So, I propose:

H3a: A user’s information satisfaction will positively influence PU. H3b: A user’s system satisfaction will positively influence PEOU. H3c: A user’s service satisfaction will positively influence PE. H3d: A user’s service satisfaction will positively influence PU.

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Because information is produced by a system, problems with the system’s actual quality can degrade the actual quality of the information it produces. For example, if a program does not operate reliably, this can create problems for the formatting, currency, accuracy, or completeness of data. So, when customers evaluate information quality, they will not only access the relevant information quality elements in their mind, but they will also access and draw their perception of system quality (Xu, et al.,

Working paper). So, I propose:

H3e: A user’s system satisfaction will positively influence a user’s information satisfaction.

H2d: A user’s perceived system quality positively influence that user’s perceived information quality. The perceived service quality is a consumer’s overall evaluation of the service provision. So the evaluation includes also a consideration of both content and delivery (Tan, et al., 2013; Grönroos, et al.,

2000; Teo, et al., 2008). So, when a customer perceives a higher quality of what is offered (i.e. content)

and a higher quality of how it is offered (i.e. delivery) in a system, the customer’s perceived service quality will also be higher (Grönroos, et al., 2000). This is also empirically confirmed (Xu, et al., Working

paper; Tan, et al., 2013). Besides, in the evaluation of the service quality, customers will also access

relevant system quality elements in their mind (dimensions). Accordingly, I propose:

H2e: A user’s perceived information quality positively influences that user’s perceived service quality. H2f: A user’s perceived system quality positively influences that user’s perceived service quality.

2.1.3 Dimensions

Wixom & Todd; Xu et al., and Delone & McLean conducted an extensive literature research about dimensions (key antecedents), which influence the object-based beliefs information quality, system quality (Wixom & Todd, 2005; Xu, et al., Working paper; DeLone & McLean, 2003) and service quality

(Xu, et al., Working paper; DeLone & McLean, 2003). These dimensions are derived from a

decomposition and integration of factors from the user satisfaction literature.

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According to Xu et al. system quality is shaped by four dimensions: reliability refers to the dependability of system operation, flexibility refers to the way the system adapts to changing demands of the user,

accessibility refers to the ease how information can be accessed or extracted from the system, and timeliness refers to the degree to which the system offers timely responses to requests for information

or action (Xu, et al., Working paper). It is important to note that each of these factors reflects perceptions of the system itself and the way it delivers information.

According to DeLone & McLean service quality is shaped by five dimensions: tangibility reflects the appearance of mobile interface such as layout and graphic design; reliability means that service providers keep promises and present on-time services to users; responsiveness means that service providers offer prompt responses to users’ inquires; assurance means that service providers build users’ confidence by providing professional services to them; and empathy means that service providers present personalized products or services to users (DeLone & McLean, 2003).

Because of the lack of specific online service dimensions, Zeithaml et al. developed the e-SERVQUAL scale to measure the E-service quality and revealed seven dimension of electronic service quality: efficiency, reliability, fulfillment, privacy, responsiveness, compensation and contact (Zeithaml, et al.,

2002). Many of these dimensions are kind of the same of the dimensions specified by DeLone & McLean.

Compensation is the only dimension, which cannot be assigned to the dimensions of DeLone & McLean.

So compensation is added as a dimension of service quality and defined as the degree to which the system compensates customers for problems.

Consistent with the literature I propose the dimensions have a positively relationship with the object-based beliefs:

H4a: A user’s perceived information completeness positively influences that user’s perceived information quality.

H4b: A user’s perceived information accuracy positively influences that user’s perceived information quality.

H4c: A user’s perceived information currency positively influences that user’s perceived information quality.

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H5c: A user’s perceived system reliability positively influences that user’s perceived system quality. H5d: A user’s perceived system flexibility positively influences that user’s perceived system quality.

H6a: A user’s perceived service responsiveness positively influences that user’s perceived service quality.

H6b: A user’s perceived service assurance positively influences that user’s perceived service quality. H6c: A user’s perceived service empathy positively influences that user’s perceived service quality. H6d: A user’s perceived service reliability positively influences that user’s perceived service quality. H6e: A user’s perceived service tangibles positively influence that user’s perceived service quality. H6f: A user’s perceived service compensation positively influences that user’s perceived service quality.

2.2 Extending the model

2.2.1 Adding hedonic quality

In the information systems literature, researchers pay an increasing attention on the distinction between utilitarian and hedonic technologies and their value (Kim & Han, 2009; Xu, et al., 2012;

Venkatesh, et al., 2012; Lin & Bhattacherjee, 2010). Utilitarian technologies aim to provide instrumental

(goal-oriented) value to the users (e.g. information for performing a task), while hedonic technologies mainly provide experiential value to user (e.g. joy or fun). However modern complex technologies are neither purely utilitarian nor purely hedonic (Van der Heijden, 2002). A good example is a smartphone, which might provide utilitarian value with the phone function (i.e. the convenience of making and receiving calls) but the smartphone might also provide hedonic value with the mp3 player (i.e. fun of listening to music). It is also possible that a single function of a technology might provide both a utilitarian and hedonic value. This depends on a user’s purpose of the function. For example, the phone function of a smartphone can be used for either goal-oriented purposes (e.g. connecting business contacts) or experiential purposes (e.g. chatting for fun).

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enjoyment) turned out to be more salient for the intention to use experiential mobile services (high hedonic value) than for the intention to use goal-oriented services (low hedonic value) (Nysveen, et al.,

2005). Besides, Fang et al. concluded that intrinsic motivation (i.e. perceived playfulness) affect the

intention to use mobile devices for gaming tasks (high hedonic value), but not affect transactional or general tasks (low hedonic value) (Fang, et al., 2005).

Lin & Bhattacherjee proposed that technical quality of a hedonic system positively influence the perceived enjoyment (Lin & Bhattacherjee, 2010). Hereby refers technical quality to the technological sophistication and the availability of enhanced features in a given hedonic system, such as high-resolution displays, greater audio/video quality (e.g. surround sound) and high-definition programming, that have greatly improved the technical quality of systems (Grönroos, et al., 2000). Improved technological features and capabilities embedded in hedonic systems tend to enhance users’ perceived enjoyment by providing them with greater opportunities for deriving enjoyment or excitement from the usage experience, while lack of technical quality engender disappointment, lowered enjoyment perceptions and eventually less usage intentions. Furthermore, Deng et al. proposed that a higher perceived hedonic performance of mobile Internet services, the more a user is satisfied with mobile internet services (Deng, et al., 2010).

In conclusion, although many authors perceive the hedonic quality as a behavioral belief which directly influences the attitude and the intention to use a system (Kim & Han, 2009; Xu, et al., 2012; Fang, et al.,

2005; Van der Heijden, 2004), Lin & Bhattacherjee and Deng et al. perceive the hedonic aspects of a

system as an object-based belief which influence the satisfaction of a system (Lin & Bhattacherjee, 2010;

Deng, et al., 2010). Because of this, I perceive the hedonic quality and hedonic satisfaction as

object-based beliefs and attitudes. Deng et al. found that, the more the users feel the experience with an information technology, the more likely they will perceive hedonic performance of the service, be satisfied with the technology, and in turn intend to continue the technology usage. So to be consistent, I propose:

H2g: A user’s perceived hedonic quality positively influences that user’s hedonic satisfaction. H2h: A user’s perceived hedonic quality positively influences that user’s service quality.

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2.2.2 Dimensions of hedonic quality

Playfulness

Tan & Chou studied the perceived playfulness in the context of Mobile Information and Entertainment Services (Tan & Chou, 2008). They supported prior research with the confirmation of the importance of intrinsic and extrinsic service attributes to perceived playfulness and their findings also suggested that Perceived Usefulness, Ease of Use, and Personalization were the three most important Mobile Service Quality aspects that influenced Perceived Playfulness.

Mahatanankoon studied the personality traits (individual playfulness, personal innovativeness, and optimum stimulation level) in mobile-commerce intention and concluded that the personality traits play a significant role in how individuals interact with their mobile phones (Mahatanankoon, 2007). In other words, an individual’s intrinsic motivation to interact in a complex and playful fashion with information technology can be mediated through an individual’s tendency to search for new stimuli. Mahatanakoon further noted: “As consumers tend to favor a certain mobile activity over others, marketers need to analyze different consumer segments and motivations in order to reap the revenue of m-commerce applications.”

Furthermore, Schwarz et al. studied the influence of the perceived playfulness in a virtual world. They supported the hypothesis that higher degrees of playfulness will lead to a more positive attitude toward the virtual world (Schwarz, et al., 2012).

Therefore, playfulness is considered as one of the dimension of hedonic quality and defined “as a person’s tendency to interact spontaneously, inventively, and imaginatively with the technology”. Aesthetic cues

Baker, Levy, and Grewal studied the aesthetic cues such as store layout, color scheme, lighting, music, and odor and concluded these cues influence shopper responses and decision (Baker, et al., 1992). Termed atmospheric variables, they are defined by McKinney as stimuli, which result in a number of responses including enjoyment, purchase and satisfaction and may include color, graphics, layout, and design (McKinney, 2004). They also influence outcome behaviors of satisfaction and purchase intention

(Wang, et al., 2010). Rose et al. studied the role of aesthetic cues on Internet shopping websites on the

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Social presence

Wakefield et al. studied the influence of website socialness on enjoyment (Wakefield, et al., 2011). Social presence (or website socialness) is defined as “the phenomenon of users treating technology or technology interfaces (such as websites) as social actors. So, the user perceives that the interface exhibits life-like attributes associated with personality or emotion.” According to Wakefield et al. website socialness has the strongest total effect on behavioral intentions toward the website compared with Perceived Ease Of Use, Perceived Usefulness, and Enjoyment. Furthermore, Gefen & Straub concluded that social presence affects consumers trust and that trust has a strong effect on purchase intentions (Gefen & Straub, 2003).

Firms that excel in installing high degrees of social presence in their websites may prosper more than those that do not. Since social presence plays an important role in users’ emotional feeling about a technology or technology interface, social presence could play a role in the hedonic quality. So, social presence is also considered as one of the dimension of hedonic quality.

Media Vividness

User interface elements influence the experience of consumers with a retailer. These elements can determine the level of interface involvement (i.e. the ability of a user interface to facilitate user involvement with the information/products presented through the interface) (Griffith, et al., 2001). Higher interface involvement increases user involvement with the retailer's product offering or other presented information. Increased consumers involvement with the retailer's product offering results that the consumers will more actively process the information (Andrews & Shimp, 1990; Petty &

Cacioppo, 1979), which will lead to a more positive consumer response (attitude, product evaluation,

and shopping intentions) (Andrews & Shimp, 1990). To explore the potential for increasing user involvement, Griffith et al. examined the content-presentation options on the Web. They founded that employing a media-vivid content-presentation interface (e.g. animated graphical images and graphical user interface) in a Web-based context can stimulate higher levels of consumer involvement with a retailer’s product offering than a static content presentation (Griffith, et al., 2001). Media vividness is defined as “the representational richness of a mediated environment as defined by its formal features”

(Steuer, 1992). The findings of Griffith et al. are consistent with Morrison & Vogel, who indicated that a

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influences consumers attitudes toward services and behavioral intent to use the service by making service more real and experiential (Koernig, 2003). In conclusion, media vividness can influence the experience of the consumers with a retailer and determine a higher involvement and is therefore also considered as a dimension of hedonic quality.

Because of media vividness and Tangibles, I will drop the dimension format as a dimension of information quality, because they specify the same relationship. Format, Tangibles, and Media vividness all deal with the lay out of the technology. Format deals more with the structure the content is presented. Tangibles deal more with whether the technology is presented in a clear and proper fashion. Media vividness describes whether the user interface elements influence the experience and involvement of consumers.

Socialness

Lin & Bhattecherjee tested and supported the positive relationship between the interaction quality and the perception of a user’s social image and the positive relationship between the technical quality and the perceived enjoyment in hedonic systems (Lin & Bhattacherjee, 2010). Hereby refers interaction quality to the extent to which a system allows individual users to cultivate, foster and maintain online relationships which each other. Technical quality refers to the technological sophistication and the availability of enhanced features in a given hedonic system, such as high-resolution displays, greater audio/video quality (e.g. surround sound) and high-definition programming. Social image refers to the respect and admiration that one expects to derive from referent others as a result of his or her behavior. And perceived enjoyment refers to the user’s excitement and happiness derived from the technology.

Furthermore, social norm (degree to which an individual perceives that important others believe he or she should use the new system) has a positive effect on perceived usefulness and perceived enjoyment

(Venkatesh, et al., 2003; Venkatesh, et al., 2011; Dickinger, et al., 2008). In addition, many other authors

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In this research, five dimensions shape the hedonic quality of a system: perceived playfulness, aesthetics cues, social presence, media vividness, and socialness and therefore I propose:

H7a: A user’s perceived playfulness positively influences that user’s perceived hedonic quality. H7b: A user’s perceived aesthetic cues positively influence that user’s perceived hedonic quality. H7c: A user’s perceived social presence positively influences that user’s perceived hedonic quality. H7d: A user’s perceived media vividness positively influences that user’s perceived hedonic quality. H7e: A user’s perceived socialness positively influences that user’s perceived hedonic quality.

2.2.3 Design functionalities of quality dimensions

After an extensive literature review, it can be concluded that the technology adoption literature lacks of antecedents that influences the dimensions. This literature research consisted of an evaluation of all the articles for the last five years. A summary of the useful articles is presented in Appendix A. These articles were adapted from the following MIS journals, Marketing journals and Human Computer Interaction:

- European Journal of Information Systems - Information Systems Journal

- Information Systems Research - Journal of AIS

- Journal of Information Technology - Journal of MIS

- Journal of Strategic Information Systems - MIS Quarterly

- International Journal of Electronic Commerce - Journal of Marketing

- Journal of Marketing Research - Journal of Consumer Research - Journal of Retailing

- Managing Service Quality

- Journal of the Academy of Marketing Science - Marketing Science

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- ACM Transactions on Computer-Human Interaction - International Journal of Human-Computer Interaction

In this research, I specify and test design features that could influence the dimension of the object-based beliefs.

Kim et al. discussed that due the absence of salespeople to answer shoppers’ questions, it is especially critical for online retailers to have necessary information available on the web site (Kim, et al., 2009). The ability to find necessary information on a website, especially about price and quality, positively influenced satisfaction with the online shopping experience and the product purchased. Since information reduces perceived risk in purchasing (Kim & Lennon, 2000), online retailers could easily provide information to reduce online shoppers perceived risks. Barnes & Vidgen asserted that web site information is critical in affecting perceived web site quality (Barnes & Vidgen, 2002). Kim & Lim also tested the information quality (using the attributes with, depth and update of information) and founded a positive correlation between information quality and satisfaction. (Kim & Lim, 2001). Furthermore, according to Kim et al., in the context of online retailing, prices, product descriptions, and specifications are part of non-commercial information (Kim, et al., 2006). Wolfinbarger & Gilly and Janda et al. considered the opportunity to see pictures of products as a part of the product information

(Wolfinbarger & Gilly, 2003; Janda, et al., 2002). According to Deng & Poole, information needs to be

reachable and findable (Deng & Poole, 2010). By allowing the user to customize his/her own personal interface, the user is able to perfectly customize his/her own preferences in accessing the information. To test the antecedents that influence the completeness of the information, I propose:

H8: App description (a), App pictures (b), App specifications (c), Update description (d), Total number of downloads (e), Trend of downloads (f), List of in-app purchases (g), and Customizability of amount of information (h) are positively related to Completeness of information.

In order to test the accuracy of the information, I use the possibility to add and read comments about the application. In this way, users can inform each other about the accuracy of the information. So:

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As Kim & Lim tested, the update of information positively influences the satisfaction (Kim & Lim, 2001). Update date and a notification of updates inform a user about the information currency. Besides, Cenfetelli & Schwarz investigated the inhibitors of technology usage intentions. One of the inhibitors resulting from their result was the ‘intrusiveness’ aspect (Cenfetelli & Schwarz, 2011). This aspect concerns all the tasks that the system performs that were not requested or, expected to create a task interruption. Cenfetelli & Schwarz also found that ‘irrelevant request for information’ was an inhibitor. They found that when a user is confronted with a request for information that is irrelevant, the user’s technology usage intention will decrease. In this research, advertisements are seen as an irrelevant request for information, and so negatively influence the currency of information. Accordingly, I propose:

H10: Update date (a) and update message (b) are positively related to the Currency of information. H10c: Advertisements are negatively related to the Currency of information.

The system is the technical functioning of the App store and determines the way the service will be delivered. In the e-service literature, researchers agreed that assuring links functions correctly and availability of all pages contribute to an increased overall quality perception (Santos, 2003).

A widely applied theory to measure the e-service quality is the E-RecS-QUAL. This model connects efficiency (which is almost the same as system accessibility) to customers’ ease of web site access, simplicity of using the site, ease of finding information, and fast check-out with minimal effort. According to Wolfingbarger & Gilly and Parasuraman et al. organized categories and search functions are helpful in access the information easily (Wolfinbarger & Gilly, 2003; Parasuraman, et al., 2005). Evidence suggests the importance of efficiency in online retailing. Sitemaps significantly improve the efficiency of navigation and easy navigation is a critical factor in online retailing. They also suggests that easy to read and clear content attract customers to shop more and buy more (Kim, et al., 2006).

Switching cost is the cost incurred when users switch from one service provider to another and includes learning cost, sunk cost, and continuance cost. Zhou & Lu tested the relationship of switching barriers on the postadoption of using mobile services and concluded that switching costs positively influence the continuance using of a system (Zhou & Lu, 2011). One explanation is that systems can increase the value of the relationship which will lead to increase switching costs and users will be locked into their

relationship with the system and will continue accessing it. To test the antecedents which influence the

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H11: Sitemaps (a), Organized categories (b), Switching costs (c), Search functions (d), Download status/tracking (e), and Recommendation agents (f) are positively related to the Accessibility of the system.

Many researchers considered user’s perception of website load speed as an antecedent which influences the efficiency of a website (Wolfinbarger & Gilly, 2003; Janda, et al., 2002). Wells et al. conducted research towards the perception of product quality and online purchase intentions (Wells, et

al., 2011). They founded that download delay had a negative effect on the perception of quality and

purchase intention. Furthermore, Hoxmeier & DiCesare operationalized the loading speed and found that an application should load completely within twelve seconds, before a user’s attention is likely to be drawn away and the application would be closed (Hoxmeier & DiCesare, 2000). This is also relevant in an App Store, because timeliness refers to timely responses to request of information and action. Accordingly, I propose:

H12: Site load speed is positively related to the Timeliness of the system.

Non-correctly functioning websites such as missing links, non-functioning buttons, or error messages, leads to user frustration and exiting. Consequently, a valuable opportunity to build customer loyalty is lost (Wachter, 2002). Many researchers agreed that structurally failing technical aspects of a system possibly hinder customer’s shopping intentions and measured the influence of the system availability, in example: Wolfinbarger & Gilly, 2003; Parasuraman, et al., 2005. Therefore, I propose:

H13: Site loading without errors is positively related to the Reliability of the system.

According to Tan & Chou a mobile phone is a highly personalized device and is expected that its usage is highly relevant to the user’s context. A system is considered context aware if it can extract, interpret, and use context information and adapts its functionality to the current context of use (Tan & Chou,

2008). Tan & Chou found a positive relationship between the context awareness and users Mobile

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adapt these preferences depends on the flexibility of the system (the way the system adapts to changing demands of the user). Because of this, I propose:

H14: User preference settings (a) and a User’s context adaption (b) are positively related to the Flexibility of the system.

When a customer has a problem, a process to contact a customer agent is vital. The Responsiveness dimension of E-RecS-QUAL reflects a website’s willingness to help customers and provide prompt service when consumers have questions and/or problems through the site (Kim, et al., 2006). According to Kohler et al. the usability (in their case virtual worlds) can be increased by providing individual support to users (Kohler, et al., 2011). However, the lack of interpersonal interaction has been considered a key defining characteristic of e-business. Palmer included the availability of FAQ and a Feedback channel in his responsiveness measures as part of his website performance study. These functionalities can provide the service to help customers and at the same time they decrease the need for helpdesk labor (Palmer, 2002). An Instant help library can also provide service to customers to help them understand the terminology on the website and explain it in a clear language (Kim, et al., 2006). According to Wolfinbarger & Gilly, a specific method of collecting feedback is done through the use of website surveys (Wolfinbarger & Gilly, 2003). This will lead to handle problems of users more effectively. According to Parasuraman et al. the availability to contact the system to get assistance positively influences the service quality. Getting in contact with a helpdesk will also fulfill the goal to give consumers assistance with completing their transactions (Parasuraman, et al., 2005). Wolfingbarger & Gilly and Janda et al. tested the availability of a telephone number, or the opportunity to chat with a live online customer service representative (Wolfinbarger & Gilly, 2003; Janda, et al., 2002). To improve service quality, Xu et al. recommended website designers to consider employing live help technology

(Xu, et al., 2011). Furthermore, Janda et al. tested the availability to contact an e-mail address (Janda, et

al., 2002). Therefore, I propose:

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The issue of privacy has been a critical issue in online retailing. Because of perceptions of risk related to misuse of personal information, many people are still unwilling to buy products online. Due to heightened concern with privacy, online retailers are becoming more aware of the importance of providing consumer privacy policies (Kim, et al., 2006). Besides, Wells et al. and Liang & Xue founded that the privacy quality can be increased by showing a privacy policy (Wells, et al., 2011; Liang & Xue,

2010). The privacy dimension has been shown to have a strong impact on intention to purchase,

satisfaction, and overall site quality. The Privacy dimension in E-RecS-QUAL is related to the protection of personal information associated with risk perceptions of online shopping. Service attributes that facilitate improvement of consumer trust and safety in relation to the online shopping process were integrated in this dimension. Making online service attributes available that reduce consumers perceived risks (e.g. explicit privacy and security policy) and increase confidence (e.g. business ethnics) is critical for success of online retailing, especially for retailers without strong brand names (McKnight, et

al., 1998). The assurance of an online service can be increased by showing an encryption symbol that the

website uses to protect confidential information. It can be recognized as a padlock picture (Douglas, et

al., 2003). Acquisiti et al. also determined in their work whether the prominent display of privacy

information and symbols influence the privacy and security perception of users of retailing websites

(Acquisiti et al, 2011). They stated that a row of symbols indicating the quality of privacy on a website,

or the picture of a ‘lock’, can improve the privacy quality on a website. To enhance perceived trust of a consumer, Pavlou & Gefen found evidence for their proposition that perceived effectiveness of IT-enabled institutional mechanism stimulate trust in an electronic shopping environment. So in other words, buyers may feel safer in a shopping environment where security is guaranteed by an identified party (Pavlou & Gefen, 2004). Accordingly, I propose:

H16: Encryption system (a), Mailing list prevention (b), Mailing list withdrawal (c), Personal info protection (d), Privacy policy (e), and Third party verification (f) are positively related to the Assurance of a service.

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the other hand, relevant other studies have extensively included personalization and customization in their metrics (Wolfinbarger & Gilly, 2003; Kim, et al., 2006; Lee, et al., 2009; Wakefield, et al., 2011; Tan

& Chou, 2008) and found a positive relationship between the empathy dimension and online service

quality. Wolfinbarger & Gilly included customer recognition, purchase suggestions, and personalized features measurements in their EtailQ model to measure personalization. Srinivasan et al. included the presence of purchase recommendations and customized items in their study (Srinivasan, et al., 2002). These features facilitate online shopping, and therewith electronic service quality. Wolfinbarger & Gilly and Devaraj et al. included customer recognition in a number of measures. When a system recognizes visitors, the system is able to reproduce previously saved user preferences when a user is visiting the system again (Wolfinbarger & Gilly, 2003; Devaraj, et al., 2002). Haubl & Trifts found that the use of comparison matrices cause shoppers to make more efficient and faster decisions (Häubl & Trifts, 2000). Furthermore, Devaraj et al. also included a comparison matrix in their study (Devaraj, et al., 2002). These matrices can be adapted to a customer’s application choices and a user’s specific demands that will results in an enhanced personalization. Because these features are adapted from a user’s input to a user’s specific preferences and are personal, I propose:

H17: Purchase recommendations (a), Customized items (b), Comparison matrix (c), and Customer recognition (d) are positively related to the Empathy of a service.

Service reliability describes the extent to which the service provider is accurate and dependable. It looks like the fulfillment dimension of E-RecS-QUAL which is defined as the extent to which the site’s promises about order delivery and item availability are fulfilled (Wolfinbarger & Gilly, 2003). According to them reliability/fulfillment ratings are the strongest predictor of customer satisfaction and quality, and the second strongest predictor of intentions to repurchase. Wolfinbarger & Gilly; Parasurman et al.; and Cenfetelli et al. included the facilitations of a correct processing of orders through the website in their measurement of fulfillment (Wolfinbarger & Gilly, 2003; Parasuraman, et al., 2005; Cenfetelli, et al.,

2009). After selecting a product, customers may find it convenient to be able to directly pay online. Tan

& Benbasat measured the availability of at least one mode of direct electronic payment (Tan &

Benbasat, 2009). Kim & Stoel and Janda et al. also included the possibility to complete transactions

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Accordingly, I propose:

H18: Ordering (a), Direct online payment (b), and Different payment methods (c) are positively related to the Service Reliability of a service.

The compensation dimension (derived from E-RecS-QUAL) deals with the financial consequences of actual problematic transactions. Before giving refunds to consumers when a problem occurred, companies can solve the problems by assisting customers through a resolution center. Furthermore, in online apparel retailing, the inability to touch and use the product prior to purchase is the biggest barrier to purchasing. Provision of return and exchange policies may make consumers more comfortable purchasing online because such policies reduce risk (Dubbs, 2001; Kim, et al., 2006). Besides Parasuraman et al. and Janda et al. measured the availability of clearly stated return policies

(Parasuraman, et al., 2005; Janda, et al., 2002). Therefore, I propose:

H19: Resolution center (a) and Refund/Return policies (b) are positively related to the Compensation of a service.

Tangibility reflects the appearance of mobile interfaces such as layout and graphic design and differs from Media vividness, because the last deals more with the pleasure it caused. The graphic quality (which represents the tangibles) captures how well various elements of the user interface are visually represented (e.g. text, icons, digital images, or backgrounds). According to Kim et al. researchers have found that graphic quality influence satisfaction of a retail website and the consumer perceptions of online shopping (Kim, et al., 2006). After a literature review Fassnacht & Koese included the text legibility and properly pictures in their metrics to measure the graphic quality (Fassnacht & Koese,

2006). Accordingly, I propose:

H20: Text visualization (a) and Image visualization (b) are positively related to the Tangibility of a service.

Hedonic value represents the self-fulfilling value of using a product or service (Yang, et al., 2012). It reflects the enjoyment, pleasure, and anxiety related to the use of a product/service (Holbrook &

Hirschman, 1982). In other words it is the experiential value to the user (e.g. joy or fun) (Xu, et al., 2012).

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technologies. Perceived playfulness is defined as “a person’s tendency to interact spontaneously, inventively, and imaginatively with the technology. Antecedent of perceived playfulness are speed, content, variety and focused attention (Fang, et al., 2005). Mahatanankoon stated that an individual’s intrinsic motivation to interact in a complex and playful fashion with Information Technology can be mediated through an individual’s tendency to search new stimuli (Mahatanankoon, 2007). A feature to enhance a user’s interaction with the technology and a user’s search for new stimuli is offering a free trial of an application. So, I propose:

H21: Free trial is positively related to the Perceived Playfulness of hedonic quality.

According to Baker et al., because of limitations on the individual’s cognitive resources in a traditional retail context, aesthetic cues (such as store layout, color scheme, lightening, music, and odor) influence shopper responses and decisions (Baker, et al., 1992) and web aesthetic similarly provide sensory stimuli. This supports the formation of experience impressions (Rose, et al., 2012). So Baker et al. mentioned music as one of the antecedents of the aesthetic cues. Because the other antecedents (e.g. store layout, color scheme, and lightening) are represented in other dimensions or are not applicable on a web based system (odor), I propose:

H22: Background music is positively related to the Aesthetic cues of hedonic quality.

Schwarz et al. found a positive relationship between social presence (in their definition, the degree of salience of the other people in the interaction) and the attitude toward the virtual world (Schwarz, et al.,

2012). Wakefield et al. described the social presence as the phenomenon of users treating technology or

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So, I propose:

H23: Informal language (a) and language nationalities (b) are positively related to the Social presence of hedonic quality.

According to Griffith et al. text-only presentations is characteristic of low media vividness, while multimedia presentations integrating animated graphical images, audio, as well as text are characteristics of high media vividness (Griffith, et al., 2001; Hoque & Lohse, 1999; Straker, et al., 1997).

Besides graphic styles of the web site (e.g. animation, print size, layout, and number of photos) influence online channel use and overall satisfaction by reducing security risk perceptions (Montoya-Weiss, et al.,

2003; Raney, et al., 2003). Accordingly, I propose:

H24: High-definition videos (a) and High-resolution animations (b) are positively related to Media vividness of hedonic quality.

Venkatesh et al. tested the social influence among mobile internet users and concluded that applications, such as mobile picture sharing among friends, may be a higher price value to consumers because of their social value and timeliness (Venkatesh, et al., 2012). Moreover, Lin & Bhattecherjee tested the interaction quality on socialness and mentioned capabilities such as forming online teams, communicating with team members, chatting privately with others, sharing functions (graphic, documents and emoticons) as antecedents of interaction quality (Lin & Bhattacherjee, 2010). Kohler et al. also found that interactions are important inhibitors and stated that having tools that foster the dialog and help users participate more effectively in interactions is an important determinant of usage and fosters informal sociability, which enables customers to build up relations with others and perceive themselves as members of a group (Kohler, et al., 2011). In the social media world consumers viewing a brand page are likely to see pictorial information about other people who have voluntarily affiliated with the brand. According to Bikhchandani et al. it is optimal for an individual, having observed the actions of those ahead of him, to follow the behavior of the preceding individual without regard to his own information (Bikhchandani, et al., 1992). This could lead to the rejection of more efficient technologies

(Duan, et al., 2009). “Mere virtual presence” refers to the passive exposure to a brand's supporters

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virtual presence is based on the supporters experience and is based on a social network, Mere virtual presence is considered as an antecedent of socialness. Kim et al. stated that direct contact services to friends, such as e-mail services, are akin to personal information sources such as word-of-mouth. Word-of-mouth has a significant influence on one’s purchase decision-making and a company’s reputation

(Kim, et al., 2006). This type of word-of-mouth communications has been extent with the

communications through social network sites (i.e. Facebook, of Twitter). Lee & Chen also recognized the importance of communications among users (Lee & Chen). They concluded that the great variety of people and users, and the ability of communications between them, is a design preference of high quality Information System design. Duan et al. have found that user rating have a significant impact on the adoption of less popular products. They also found that online user’s choice of software is heavily driven by change in download ranking. Besides Purcell et al. found in their research for Nielsen that 88% of the users who are downloading apps, said that user reviews and ratings are at least somewhat important, including 19% who said they are extremely important (Purcell, et al., 2010). Because ratings are peer-to-peer communications, they are seen as social antecedents (Duan, et al., 2009). Therefore, I propose:

H25: Possibility to form groups (a), Communicating methods (b), Chat function (c), Sharing functions (d), Mere virtual presence (e), Friendship development (f), Direct purchase communications (g), Social communications (h), and Rating (i) are positively related to Socialness of hedonic quality.

2.3 Final research model

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3 Methodology

This chapter will outline the methodology of the research. The dimensions of the object-based beliefs (information, system, service, and hedonic quality) will be operationalized and the test sample will be provided and discussed. Finally, the procedure of measuring will be explained and demonstrated.

3.1 Operationalization

After the process of determining appropriate dimension for this research, measurement items have to be imbedded into the particular dimension, in order to complete the framework for evaluating App Stores. This process is called operationalization. The measurement items are implemented as design functionalities in the App Store. The dimensions of the object based beliefs (information, system, service, and hedonic quality) are derived from the models of Wixom & Todd and McLean & Delone, and the E-S-QUAL and E-RecS-QUAL models (Wixom & Todd, 2005; DeLone & McLean, 2003; Parasuraman,

et al., 2005; Zeithaml, et al., 2002). These models evaluate the dimensions for retailing websites and are

measured by perceptual measurement items. Respondents rated the web site’s performance on each scale item using a 5-point scale (1 = strongly disagree, 5 = strongly agree). Using perceptual measurement items has two major disadvantages. Firstly, they lack univocality; conclusions drawn from the perceptual scales are not obvious because the results are anywhere between one and five, or between one and seven depending on the scale used. Secondly, due to the varying nature of businesses and social networks, the measures themselves may not fit all kind of companies or online communities. The original measurement items need to be replaced with appropriate objective measures in order to be applicable to measure the presence of the design functionalities in the App Stores. Parasuraman et al. stated that the main purpose of E-S-Qual and E-ReS-QUAL is to generate information for website designers rather than to measure service quality as experienced by customers (Parasuraman, et al.,

2005). This is congruent with the purpose of my research to determine important design functionalities

in an App Store.

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measurement items should not create too much overlap and need to cover as many relevant aspects as possible.

Table 1: Measurement items

Constructs

Definition

Authors

Measurement

Definition

Information Quality

Completeness Degree to which the

system provides all necessary information.

Wixom & Todd (2005); Lee, Shin & Lee (2008); Wolfinbarger & Gilly (2003) - App description - App pictures - App specifications - Update description - Total number of downloads - Trend of downloads - List of in-app purchases – Customizability of amount of information

- Description of the application is available. - App store shows screenshots from the application.

- Specifications of the application are available (e.g. version, size, and price).

- App store informs about the changes/new features of the application of the latest update. - The app store shows the total number of downloads of the application.

- The latest trend of the total downloads is available.

- A list is available of the best in-app purchases. - There are hidden sections on the app store, which can be extracted when the customer clicks on it.

Accuracy The user’s perception

that the information is correct.

Wixom & Todd (2005); Lee, Shin & Lee (2008)

- Comments - A customer can see comments/reviews about

an application.

Currency The user’s perception

of the degree to which the information is up to date.

Wixom & Todd (2005); Lee, Shin & Lee (2008); Rose, Clark, Samouel & Hair (2012)

- Update date - Update messages - Advertisements (-)

- App store shows a date when the application has been updated.

- App store send push notifications when an update is available.

- App store contains advertisements.

System Quality

Accessibility Extent to which

service content (i.e., information and functionalities) offered by an app store is easily available to consumers.

Wixom & Todd (2005); Lee, Shin & Lee (2008); Rosen & Purinton (2004); Kim et al. (2006), Tan & Benbasat (2009); Wolfinbarger & Gilly (2003); Srinivasan et al. (2003) – Sitemap – Organized categories – Switching costs – Download status/ tracking – Search function - Recommendation agents

- There is a sitemap of the app store available. - Products and information can be found in logical places.

- Switching cost is the cost incurred when users switch from one service provider to another and includes learning cost, sunk cost, and continuance cost.

- Customer can track the status of the download (e.g. percentage of completeness of download or whether the application is already in his possession)

- There is a search function available which functions without errors.

- A mechanism on the app store makes recommendations to the customer.  After an application has been selected (i.e. “Customers who bought this also bought…”);  After a search query has been performed (i.e. “You searched for application X, you may also be interested in application Y”);

 Browsing app categories (i.e. “You are currently browsing category X, you may also browse category Y”).

Timeliness Extent to which the

system offers timely responses to requests for information or

Wixom & Todd (2005); Lee, Shin & Lee (2008) Hoxmeier &

– Site load speed under ten seconds.

The influence of design functionalities in the adoption in m-commerce