ANTECEDENTS OF MOBILE APP USAGE AMONG SMARTPHONE USERS. Abstract. Introduction

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1 ANTECEDENTS OF MOBILE APP USAGE AMONG SMARTPHONE USERS Sang Chon Kim, University of Oklahoma, Norman, OK Doyle Yoon, University of Oklahoma, Norman, OK Abstract Although mobile apps are already an influential medium in the new media industry as a whole, these apps have received little academic attention within the communication and marketing literature. The current study develops and tests a hypothesized model to explain antecedents affecting app usage among smartphone users. The analysis of the structural equation model determined a final model with four significant factors (perceived informative and entertaining usefulness, perceived ease of use, and user review). Cost-effectiveness, a key variable of this study due to the particularity of 99-cent app price, had no influence on app usage. More implications are discussed. Introduction As information technology continues to evolve, mobile phones get smarter and smarter. Technological advancements have enabled mobile devices to add more advanced computing ability and broader data connectivity by wireless services, such as Wi-Fi, 3G, and 4G, and have led the advent of current so-called smartphones (Middleton 2010). In particular, 3G represents the third generation of wireless service, providing higher data speeds, always-on data access, and greater voice capacity (Okazaki and Barwise 2011). More recently 4G based on the LTE (long term evolution) system is an upgraded successor of the 3G standards (Patel 2011). This mobile ultrabroadband Internet access is considered one of the features that distinguish smartphones from previous feature phones. Moreover, a unique characteristic of smartphones is their ability to download and run tens of thousands of applications (apps), which vary from informative to entertaining (Middleton 2010). According to a report from a mobile analytics firm (Newark-French 2011), more than 85,000 mobile apps has been released across all major devices, including Android, iphone, Blackberry, and Windows phone. The report also finds that smartphone users in the U.S. spend more time using mobile apps than using the Web on a computer or mobile device. Specifically, users spend 81 minutes a day using mobile apps while spending 74 minutes a day using the Web on a computer or mobile device. This marks a significant change from 2010, when users spent 43 minutes a day using mobile apps and 64 minutes a day using the Web. The growing popularity of mobile apps is explained by the rise in smartphone ownership. A Nielsen study (Tode 2011) reported that 55 percent of those who purchased a new mobile phone in the past three months as of May 2011 bought a smartphone, reaching to a total of 74.6 million current subscribers in the U.S. Because mobile apps are an integral part of the smartphone experience, the growing base of smartphone users leads to more apps being developed to serve a wider and wider range of consumer needs. From a marketing perspective, this growth of smartphone ownership and mobile app consumption may create new mobile marketing opportunities beyond the traditional mobile marketing strategies (e.g. SMS advertising). A recent study by InMobi proves the potential market value of mobile apps, by reporting that in-application advertisements grew 100 percent in 90 days, reaching 50 million people in the U.S. (Johnson 2011). Although smartphone apps already represent an important part of mobile marketing strategies, these apps have received little academic attention within the communication and marketing literature. The purpose of the current study is (1) to identify a variety of factors affecting smartphone app usage from behavioral theories, uses and gratification theory, previous empirical studies, and the contexts of the current study, and (2) to test a hypothesized model extended from the technology acceptance model (TAM), which predicts how the factors are associated with the usage of technology (i.e. the usage of smartphone apps). This study has practical implications for mobile marketing practitioners, including app developers, app advertisers, and app providers. This study also expects to make an academic contribution to the field of mobile communication research by suggesting a conceptual model for the acceptance of mobile apps. 72

2 Theoretical Background: Technology Acceptance Model (TAM) According to Fishbein and Ajzen (1975), behavioral intention is predicted based on multiattribute models. Davis s (1989) technology acceptance model (TAM) is a multiattribute model that predicts users intentions to use a technology, based on two key determinants perceived usefulness and perceived ease of use. usefulness is defined in the model as the degree to which a person believes that using a particular system would enhance his or her job performance (p. 320). Also, perceived ease of use refers to the degree to which a person believes that using a particular system would be free of effort (p. 320). Higher perceived usefulness or perceived ease of use leads to more positive attitudes toward using a piece of technology. According to TAM, these attitudes affect behavioral intentions which in turn influence actual behavior. In comparison with other behavioral theories such as the theory of reasoned action (Fishbein and Ajzen 1975) and the theory of planned behavior (Ajzen 1991), TAM generally explains more variance in behavioral intention, especially in the technology context (Gentry and Calantone 2002). More specifically, a study by Gentry and Calantone (2002) compared the three behavioral theories and revealed that TAM was superior to the other two in predicting the intention to use shop-bots in online shopping. A recent study from the mobile marketing field (Okazaki and Barwise 2011) that reviewed the literature on mobile advertising from 1993 to 2010 showed that TAM has been the model most widely applied in recent studies. TAM explained use of the mobile Internet (Fogelgren-Pedersen, Anderson, and Jelbo 2003), acceptance of mobile SMS advertising (Zhang and Mao 2008), and behavioral intention to use mobile services (Nysveen et al. 2005). In the current study, the intention to use mobile apps is defined as the intention for smartphone users to download mobile apps on their device. In accordance with TAM, the perceived usefulness of mobile apps may increase users intentions to download them because it would enhance users job performance or help them achieve goals (Davis, 1989). Also, the perceived ease of use of mobile apps may increase users intentions to download them because technology users aim to minimize their behavioral effort (Venkatesh 2000). Proposed Model and Concept Explication The traditional TAM has been recognized to have weaknesses including the inability to explain other possible factors besides usefulness and ease of use (Mathieson 1991; Moon and Kim 2001; Venkatesh 2000). In the context of the current study, TAM is too simple (1) to reflect the various needs of diverse individuals, (2) to predict the information systems of the networked society, and (3) to account for the influence of practical marketing components. As a medium, mobile services are highly personalized (Pagani 2004). This implies that determinants affecting the use of mobile services may vary across different individuals. Non-traditional factors, such as fashion (Nysveen, Pedersen, and Thorbjørnsen 2005) and sociality (Zhang and Mao 2008), may affect use of personalized mobile services because different individuals have different motivations and attitudes. Especially when it comes to using mobile apps, users are likely to be influenced by a variety of factors. Users select and download apps individually from the app store installed on their mobile device. Users probably have particular reasons or needs for each app they download and use. Users are fragmented, but share information about apps through the networked information systems (e.g., user review sections in the app store). Users intentions to download and use apps are likely influenced by the cost of each app, as well. The traditional TAM is still predictive, but is not detailed enough to explain all the factors that affect app users behavior. Therefore, the proposed model for this study is TAM-related and adds app user reviews and costeffectiveness to the traditional TAM factors of perceived usefulness and perceived ease of use (See Figure 1). Additionally, uses and gratification theory is used to further specify the TAM variable of perceived usefulness. The concept of user reviews is derived from social psychology, while the cost-effectiveness factor comes from the marketing perspective. 73

3 FIGURE 1 The Hypothesized Research Model Informative Entertaining Social Ease of Use H1 H2 H3 H4 Attitude Toward App Usage H5 User Review H6 Behavioral Intention to Use Mobile Apps H7 Cost- Effectiveness Informative, Entertaining, and Social As expected in the traditional TAM, perceived ease of use is still a predictive variable in this study. However, perceived usefulness is revised to reflect individual needs of app users and of the various functions of apps (e.g. game, music, news, navigation, social networking, etc.). Other potential motivations for using technology have been often derived through the application of uses and gratification theory. This approach proposes that audiences consciously select and use certain media and contents to gratify specific needs (Katz, Haas, and Gurevitch 1973). According to Katz et al. (1973), there are three categories of needs: (1) needs related to strengthening information, knowledge and understanding, (2) needs related to strengthening aesthetic, pleasurable and emotional experience, and (3) needs related to strengthening contact with family, friends, and the world. These constructs perceived information, perceived entertainment, and perceived sociality have been added to TAM as a way of extending the model (Bauer, Barnes, Reichardt, and Neumann 2005; Nysveen et al. 2005; Zhang and Mao 2008). In the studies by Bauer et al. (2005) and Zhang and Mao (2008), these three constructs were defined as another antecedent affecting perceived usefulness. However, Nysveen et al. (2005) categorized the three constructs into utilitarian vs. hedonic motives, which influence attitude toward use and intention to use. More specifically, information was viewed as a utilitarian motive related to usefulness; thus, perceived information was integrated into the construct of perceived usefulness. The two other concepts of entertainment and sociality were defined as perceived enjoyment, a hedonic motive (Nysveen et al. 2005). The current study attempts to operationally define the abstract, inclusive construct of perceived usefulness as three specific, independent dimensions perceived informative usefulness, perceived entertaining usefulness, and perceived social usefulness. Each construct refers to some of the key motivations to use the Web, such as research, communicating, and entertainment (Yoon, Cropp, and Cameron, 2002). This implies that each construct could not be integrated into one construct of perceived usefulness because each one is uniquely important, independently influencing attitude and behavioral intention. Based on TAM and the uses and gratification approach, therefore, four hypotheses are suggested. H1: The perceived informative usefulness of mobile apps positively affects attitudes toward using mobile apps. H2: The perceived entertaining usefulness of mobile apps positively affects attitudes toward using mobile apps. H3: The perceived social usefulness of mobile apps positively affects attitudes toward using mobile apps. H4: The perceived ease of use of mobile apps positively affects attitudes toward using mobile apps. 74

4 As mentioned earlier, the basic assumption of TAM is that antecedents predict attitude toward behavior and, in turn, attitude predicts behavioral intention. That is, the above four antecedents would lead to the overall positive attitude toward mobile app usage that would have a positive effect on intention to use mobile apps. Thus, an additional following hypothesis is suggested: H5: Attitude toward using mobile apps has a mediating effect on intention to use mobile apps. Product Reviews by App Users The potential of product reviews by app users as another factor affecting behavioral intention is theoretically explained by the theory of reasoned action (TRA). This theory basically proposes that behavior is determined by behavioral intention, which is assumed to be a function of the individual s attitude toward the act and subjective norms (Fishbein and Ajzen 1975). Fishbein and Ajzen (1975) defined subjective norm as the person s perception that most people who are important to him think he should or should not perform the behavior in question (p. 302). In research on mobile service use, TRA has been used mainly as a way to extend TAM and include normative influences (Hung, Ku, and Chang 2003; Teo and Pok 2003). Because of the importance of social influences on media use, TRA posits that subjective norms have a direct effect on behavioral intention (Fishbein and Ajzen 1975). Subjective norms in the current study, accordingly, can be defined as user reviews about mobile apps. In general, user reviews refer to original, first-hand opinions written by users about the quality of products based on personal experiences with the products (Benlian, Titah, and Hess 2012). Little research has been conducted on user reviews regarding mobile apps; however, the positive role of consumer reviews in the context of Internetbased electronic commerce has been investigated in a number of studies. Basically, consumers perceive product reviews to be very helpful in performing their shopping tasks (Pan and Zhang 2011) and have more affective trust in consumer-generated product reviews than in system-filtered recommendations by product providers (Benlian et al. 2012). Such positive perceptions toward product reviews are likely to enhance consideration about product, quality of choice (Gupta and Harris 2009), and purchase intention (Park, Lee, and Han 2007). Many studies have found that favorable consumer reviews even lead to actual purchases (Chevalier and Mayzlin 2006; Duan, Gu, and Whinston 2008; Zhu and Zhang 2010). Moreover, Clemons, Gao, and Hitt (2006) argued that consumer reviews could make new products introductions more successful. If so, user reviews could be especially important in the mobile app market with hundreds of new apps being introduced daily. The current study defines user review more broadly than other studies have defined it (e.g. Benlian et al. 2012). In this study, specified acquaintances and unspecified users are included in the meaning of user. Accordingly, personal recommendations and anonymous written reviews are both treated as user reviews. Based on the assumption of TRA and previous literature, the following hypothesis is postulated. H6: Positive user reviews of mobile apps have a direct positive influence on intention to use mobile apps. Cost-effectiveness of Mobile Apps Another influential factor on mobile app usage is cost-effectiveness. Price is a traditional primary variable that has an influence on decision-making (Zeithaml 1988). The price of mobile apps has a very narrow price barrier, starting at 99 cents. Its distribution is likely to be very positively skewed. A recent report has shown that app prices average 2.43 dollars in North America and 3.86 dollars in Europe (Lendino 2010, March). Because the price of downloading a mobile app is typically low, price is less likely to constrain behavioral intention or actual behavior. A study on adoption of mobile TV services found a significant inverse relationship between adoption and cost (Pagani 2011). The theory of planned behavior (TPB), a successor of TRA, provides a rationale for why the price variable is relevant to the current study. According to TPB, behavioral intentions are affected by three primary factors: attitude toward the behavior, subjective norms, and perceived behavioral control (Ajzen 2008). In other words, TPB extends TRA by perceived behavioral control as another determinant of behavioral intention. Ajzen (1991) defined perceived behavioral control as people s perception of the ease or difficulty of performing the behavior of interest (p. 183). behavioral control is also referred to as self-efficacy in relation to the behavior (Ajzen 2008, p. 537). If individuals have sufficient control over a behavior, they are expected to act on their behavioral intentions when the opportunity occurs. On the other hand, when control over the behavior is limited by individuals economic situation, personal experience, or skill level, their behavioral intentions may decline. Therefore, perceived behavioral control would directly influence behavioral intentions only if there are no behavioral limitations. 75

5 In the context of this study, low price of mobile apps theoretically refers to higher level of perceived behavioral control. Individuals should have little difficulty performing the behavior (i.e. app downloading). However, this study focuses on cost-effectiveness instead of cost as an antecedent of behavioral intention. Costeffectiveness is a better reflection of the way behavioral intent varies when the price differences are small (Pagani 2004). Cost-effectiveness, which refers to what consumers conclude when evaluating benefits against costs, should be considered as an alternative antecedent of TAM, which directly influences behavioral intention (Pagani 2004). From a marketing perspective, no matter how new technology is perceived in terms of usefulness or ease of use, the technology will not be accepted if the costs outweigh the benefits. Therefore, TAM s core constructs of perceived usefulness and perceived ease of use are insufficient to explain purchasing behavior from a marketing viewpoint. In the case of mobile apps, users are likely to download and use apps only if they believe the apps are worth the money. Thus, this study suggests the following hypothesis. H7: The perceived cost-effectiveness of mobile apps has a direct positive influence on intention to use mobile apps. In summary, based on these hypotheses, a proposed model extended from TAM is formulated (See Figure 1). The four integrated constructs from TAM and uses and gratification perceived informative (H1), entertaining (H2), social usefulness (H3), and perceived ease of use (H4) predict more positive attitudes toward using mobile apps, which mediates intention to use mobile apps (H5). User reviews (H6) and cost-effectiveness (H7) directly affect intention to use mobile apps. Method Procedures Data were collected online from 257 undergraduates enrolled in several advertising classes in a large southern university on a volunteer-basis. College students were deemed appropriate for the current study because they represent a significant segment of the mobile population with new media skills (Yang, Zhou, and Liu 2010). All respondents were current smartphone users, who were classified by the investigator s prior question. A survey was conducted. Respondents who agreed to participate in the study were given the Web site address ( directing them to log onto the first page of the study site with general instructions. The participants were asked to fill out the questionnaire about behavioral intentions to use mobile apps. Some of the survey items were adapted from previous studies (Bauer et al. 2005; Davis 1989; Nysveen 2005; Yang, Zhou, and Liu 2010), and others were self-created. Measurement Each of the model s eight constructs perceived informative usefulness, perceived entertaining usefulness, perceived social usefulness, perceived ease of use, user reviews, perceived cost-effectiveness, attitudes toward app usage, and intentions to use apps consisted of three items measured with 7-point Likert scales, ranging from 1 (strongly disagree) to 7 (strongly agree). Intention to use apps was measured as a dependent variable, and attitude toward app usage was measured as a mediating variable. The rest were measured as independent variables. As for measurement of all variables, Factor analysis and Cronbach s alpha showed that three items of each variable created an internally consistent scale. One component was extracted from all three items for each variable. The statements of item, mean, standard deviation, and Cronbach s alpha are displayed in Table 1. Analysis Structural Equation Modeling (SEM) with AMOS 5 was employed to test the hypotheses in the proposed model. To assess the fit of the proposed model, the current study used (1) chi square statistic, specifically the ratio of chi square value to degree of freedom, (2) the goodness-of-fit index (GFI), (3) the adjusted GFI (AGFI), (4) the normed fit index (NFI), (5) the comparative fit index (CFI), and (6) the root mean square error of approximation (RMSEA). 76

6 Construct Informative Entertaining Social Ease of Use User Review Cost- Effectiveness Attitude Intention TABLE 1 Summary of Measures Measures Using mobile apps improves my information-seeking performance. Using mobile apps makes it easier to seek information. I find mobile apps useful in seeking information. Using mobile apps improves my entertaining performance. Using mobile apps makes me playful. I find mobile apps useful in having fun. Using mobile apps improves my social performance. Using mobile apps makes it easier to communicate with people. I find mobile apps useful in having social relationships. Learning to download mobile apps is easy for me. My interaction with mobile apps is clear and understandable. I find mobile apps easy to use. I am interested in mobile apps used by people close to me. What mobile apps to download is affected by users reviews of the apps. I am willing to download mobile apps recommended by people close to me. I find the overall price of mobile apps inexpensive. Mobile apps deserve the current price range (starting from 99 cents). I find mobile apps cost-effective. Overall I find using mobile apps positive. Overall I feel favorable toward mobile apps. Overall I am satisfied with mobile apps provided by my smartphone. My general intention to use mobile apps is high. I will continue to search mobile apps that I am interested in. I will continue to use mobile apps in the future. Results Mean / SD Cronbach s Alpha 6.41 / / / / / / / / Descriptive Statistics Only 257 participants were valid for the data analysis out of 309 responses from the survey. Of these 257 participants, 200 were female (77.8%) and 57 were male (22.2%). The average age of the participants was (SD = 2.029) and ranged from 14 to 34. With respect to the use of mobile apps, participants used mobile apps ranged from 1 to 18 hours per day, with an average of 3.67 hours (SD = The numbers of mobile apps participants downloaded was on average (SD = ), ranging from 2 to 150. The duration for using the mobile apps was months (SD = ), and ranged from 1 to 84. Analysis of Structural Equation Modeling Prior to model testing, analyses assessed if the scales achieved satisfactory levels of reliability and whether factor loadings significantly related to their corresponding constructs. The proposed model was next tested and retested with AMOS 5. The measurement model including the latent constructs and their respective observed variables was first analyzed, and then the structural model with the hypothesized relationships was tested and refined through a series of tests in an attempt to better explain the data. A final, modified model was determined as a result. Measurement model evaluation. A confirmatory factor analysis of the full measurement model showed that all of the indicators significantly loaded on their corresponding latent construct (p <.01). Scale items for measuring latent variables used in the current study were adapted from the literature. Thus, the measured variables were expected to load on only one factor and no covariance was expected among the error terms of observed variables. Table 2 presents correlations of the latent constructs in the measurement model. The highest correlation coefficient among these variables was.775 between attitudes toward the mobile apps and intention to download. All other correlation coefficients were below the recommended threshold of.70 (Tabachnik and Fidell, 2001), which suggest 77

7 no significant multicollinearity issues. Overall, the results indicated that the scales assessed what they were intended to measure and were reliable. TABLE 2 Correlation for Latent Constructs PIU 1.00 PEU PSU PEOU REVIEWS COST , ATTITUDE , INTENTION Note: All correlation coefficients are statistically significant at p <.001. Structural model evaluation. The structural model was assessed using maximum likelihood. Five out of seven hypothesized relationships among eight latent variables were statistically significant in the directions hypothesized. In examining the effects of the four latent constructs of TAM on attitude, perceived informative usefulness (PIU), perceived entertaining usefulness (PEU), and perceived ease of use (PEOU) were positively associated with attitudes toward the mobile apps, lending support for H1, H2, and H4. However, no significant association was found between perceived social usefulness (PSU) and attitudes toward the mobile apps, not supporting for H3. As expected, both attitudes toward the mobile apps and user reviews were positively associated with usage intention, confirming H5 and H6. However, no statistically significant association was found between cost effectiveness and usage intention. Thus, H7 was not supported. The parameter estimates for the proposed structural model are reported in Table 3. TABLE 3 Analysis of Structural Models Proposed Model Final Model Relationship From To Unstd. Std. Unstd. Std. H1: PIU ATTITUDE H2: PEU ATTITUDE H3: PSU ATTITUDE.397***.238*** ***.318*** ***.199*** -.229***.235*** - H4: PEOU ATTITUDE.511***.522***.551***.499*** H6: REVIEWS INTENTION.302***.335***.355***.300*** H7: COST INTENTION ***.723***.760***.656*** H5: ATTITUDE INTENTION Goodness-of-fit indices 2 (d.f.) Goodness of Fit Index (GFI) Adjusted GFI (AGFI) Normed Fit Index (NFI) Comparative Fit Index (CFI) RMSEA (245) (119) Note: * p <.05; ** p <.01; *** p <

8 The goodness-of-fit indices suggest that the model did not fit the data well; 2 = , d.f. = 245, p <.001, 2 /df ratio = 4.7; GFI =.693; AGFI =.624; NFI =.777; CFI =.814; RMSEA =.120. In order to refine the model and achieve parsimony, model revision was conducted as follows. First, the non-significant relationships were removed from the model. Chi-square values of revised models were re-estimated and compared to the proposed model. The paths (1) from PSU to attitudes towards the mobile apps and (2) cost to usage intention did not yield significant changes in chi-square of the model fit: (1) 2 difference = , d.f. = 1, p >.05; (2) 2 difference = 1.933, d.f. = 1, p >.05. As a result, both paths, which did not significantly contribute to the improvement of the model fit, were eliminated and subsequently the two latent constructs were removed. Then, based on modification indices (MIs) from statistical analysis, several paths which were not included in the initial model were added. Modification indices suggested the relationships among five constructs in the model [PIU, PEU, PEOU, User review, and Usage intention]. This modified model (See Figure 2) was chosen as the final model because it is more parsimonious without any non-significant causal linkages. Overall, this final model accounts for the data better than the originally proposed model: 2 = , d.f. = 119, p = <.001; GFI =.861; AGFI =.800; NFI = 903; CFI =.930; RMSEA =.095. Even though all indices were improved from the initial proposed model, a couple of indices such as GFI and RMSEA did not meet their threshold level. However, the chi-square difference tests showed that final model is significantly better than proposed model. Table 3 also reports parameter estimates for the revised model. FIGURE 2 The Final Research Model Informative.23 User Review.30 Entertaining.23 Attitude Toward App Usage.66 Behavioral Intention to Use Mobile Apps Ease of Use.50 Discussion This study was designed to examine the antecedents of mobile app usage among smart phone users. An extended TAM, which included the additional factors of user review and cost-effectiveness, was applied to predict people s intention to use mobile apps. Overall, the hypothesized research model did a fairly good job explaining significant associations between the independent variables and the dependent variable. The analysis result offers three primary implications in using mobile apps: (1) the theoretical support for TAM (i.e. usefulness and easiness of mobile apps), (2) the importance of app user reviews, and (3) implications of 99 cents. The Theoretical Support for TAM As expected, both informative and entertaining usefulness positively affected smartphone users attitudes toward mobile apps usage. Let us compare information seeking from apps and from the Web. Much like using search engines on the Web, smartphone users can type keywords into their mobile devices to search for certain kinds of apps. Then, users can see lists of apps related to their keywords and read descriptions of each app. For example, users who are interested in the stock market can find numerous stock-related apps. Users can also get sports-related apps or weather-related apps. This is a very personalized informative function of mobile apps. In addition to informative usefulness, there are many apps that gratify entertaining usefulness. Users can play almost all kinds of game apps (e.g. sports, puzzle, shooting games, and so on), can listen to music wherever and whenever they want, and can watch TV programs in their hands. In terms of perceived ease of use, users are likely to find it easy to search 79

9 for, download, and use mobile apps. As expected, users attitude toward mobile apps was positively related to their behavioral intention to use mobile apps. Based on the analysis, TAM seems to be theoretically supported in the contexts of mobile apps. Contrary to expectations, the social usefulness of mobile apps was not meaningful to users. There are two possible reasons for this finding. First, mobile devices already offer text messaging and services without requiring users to download additional apps. Text messaging has been available on mobile devices since the age of feature phone and is extremely popular. As the technology of newer phones has evolved, with developments such as Wi-Fi, 3G, and 4G, as well as larger screen sizes, has become more and more popular, too. Furthermore, social networking services like Facebook and Twitter started on the Web. Therefore, even if respondents use these services on their mobile devices, they may not think of them as mobile apps. The second reason social usefulness was not meaningful to users may be that most users think of mobile devices in personalized and individual contexts rather than social contexts. The link between entertaining and informative usefulness and mobile app usage may be evidence of egocentric tendencies among mobile users. A study on mobile service usage suggested the concept of self-expressiveness as a potential variable to motivate users, reporting that users sometimes perceive mobile services as a tool for expressing their image, emotion, status, and even fashion (Nysveen et al. 2005). In other words, people may use mobile services as a mediator of self-satisfaction through self-expressiveness rather than as a channel for social interactions. Unlike traditional mobile services, such as short message service (SMS), examined in Nysveen et al. s (2005) study, mobile app services are likely to be a much more personalized medium for egocentric self-expressiveness due to users thorough selective exposure. In contrast to the present study, Zhang and Mao (2008) did find the variable of sociality to be an influential factor on acceptance of mobile SMS advertising among Chinese consumers. The different findings may be due to the somewhat different media in each study. However, the differences may also be the result of different cultural contexts (i.e. higher individualism in the U.S. vs. higher collectivism in collectivism). Future research could compare motivations for app usage across cultures. Why User Reviews are Influential Consistent with the assumption of TRA, the analysis showed that user reviews had an influence on users app usage. Two points of view may explain this finding: (1) an information processing perspective at the psychological level and (2) an information system perspective at the sociological level. First, the human information processing theory assumes that people have limited cognitive capacity; thus, they tend to satisfice when they process information and make decisions (Simon, 1955). The theory argues that there are two stages to reducing cognitive burdens in the decision-making process. The first stage is to reduce the set of products to a manageable level and the second stage is to evaluate the reduced set of products in detail (Simon 1955). In the context of the current study, when app users search for apps they want, users can see lists of numerous apps related to their interests. For example, when a user searches for a GPS app, she or he encounters hundreds of GPS apps. This information overload may cause problems for choosing which app to use. However, user reviews may help users narrow their choices to just the best GPS apps available. Consequently, user reviews may enable app users to cope with app information overload by reducing their search costs and freeing them to evaluate a reduced set of apps more elaborately. This, in turn, may lead to more satisfaction with the decision made. If users follow this process, it is easy to see why user reviews would affect their intention to download apps. Second, in terms of a sociological perspective, Rogers s (2003) argument explains how information, especially related to innovation, is systematically diffused in the social network. He pointed out interpersonal diffusion networks are mostly homophilous (p. 307). Therefore, people who are relatively less innovative tend to seek information and advice about innovation from near peers who are more innovative (Rogers 2003). App users also may be networked through this systematic diffusion process and successively share information about new apps. Successors would follow app reviews of innovators and earlier successors, and the successors successors would be able to acquire greater numbers of, and more organized user reviews. This networked group created in the virtual space would reduce social distance toward each other (Simmel 1964) and feel homophilous (Rogers 2003). Consequently, the systematically accumulated reviews would be a subjective norm, which affects app usage based on credibility. Implications of 99 Cents Price is a traditional factor affecting decision-making in marketing, and fits with TPB as an antecedent of behavioral intention. The context of the current study (i.e. mobile apps fixed at low price mostly) and the reinterpretation of marketing-oriented TAM both encouraged the current study to investigate cost-effectiveness rather than price, itself. Contrary to the hypothesis, cost-effectiveness had no significant association with mobile app 80

10 usage. Despite no significant result, more elaborated discussions are demanded because of the particularity of app cost. Four possible rationales are considered: (1) price resistance, (2) price consciousness, (3) willingness to pay, and (4) free apps. Price Resistance. There are two economics terms that have been used mainly in the trade market. Price resistance refers to the price level at which selling is thought to be strong enough to prevent the price from rising further. Price support is defined as the price level at which demand is thought to be strong enough to prevent the price from declining further (Thomsett 2003). Since the emergence of mobile apps, most apps have cost 99 cents. This price of 99 cents may have created a Maginot line of price resistance, keeping prices from rising much higher. Thomsett (2003) explains that the longer time, the stronger resistance. Thus, to the resistors 99 cents is a not-socheap price. This price will not be cost-effective for resistors, either mathematically or psychologically. Price Consciousness. The economic meaning of 99 cents may vary according to app users price consciousness, or the degree to which the consumer focuses exclusively on paying low prices (Lichtenstein, Ridgway, and Netemeyer 1993). In other words, every single person has different price-related cognitions. Therefore, consumers who have high price consciousness tend to purchase products at low prices (Lichtenstein et al. 1993). These consumers might perceive a 99-cent app as not-so-cheap. Regardless of quality of the app, the app to them is not worth 99 cents due to their natural tendency to seek lower prices. Cost-effectiveness can be more exactly measured by dividing subjects into two groups ㅡ high and low price consciousness group. Willingness to Pay. The meaning of 99 cents may also vary according to app users willingness to pay, which refers to the maximum cost a consumer is willing to pay for product (Cameron and James 1987). Consumers tend to evaluate the price offered by product providers according to their personal situations; thus, validity of the price will be different for different consumers (Cameron and James 1987). App users who place a value of 99 cents on an app may perceive more cost-effectiveness than those who estimate it to be worth a lower price. The betweengroup comparison will be more effective for exact measurement of cost-effectiveness. Free apps. Despite the low price of mobile apps, users may hesitate to download paid apps because free versions of apps are provided. The free versions are divided into free apps with paid items and free sample versions of paid apps. Both formats require users to view some type of in-app advertising. In these cases, the service provider offers an incentive the free app as a reward for viewing an advertisement (e.g., a full-screen video ad, a banner ad, or a skip ad). Users may be willing to accept irritating advertising in order to avoid paying for apps. Several studies on the effectiveness of incentives in accepting mobile advertising have been conducted already (Barwise and Strong 2002; Hanley, Becker, and Martinsen 2006; Nittala 2011). However, free apps may be an obstacle against downloading paid apps if users want to save 99 cents and are satisfied with free apps despite their limitations. In other words, free apps may negatively affect the cost-effectiveness of paid apps. Conclusion A mobile analytics firm (Newark-French 2011) reported that smartphone users in the U.S. spend more time using mobile apps than using the Web on a computer or mobile device. This implies the dramatically rapid growth of the mobile app market. Mobile apps are already an influential medium not just in the mobile industry, but also in the new media industry as a whole. Nonetheless, these apps have received little academic attention within the communication and marketing literature. This study, based on TAM, proposed a hypothesized model with six factors affecting attitude toward app usage and intention to use apps (perceived informative, entertaining, and social usefulness, perceived ease of use, user review, cost-effectiveness). The analysis of the structural equation model determined a final model with four significant factors (perceived informative and entertaining usefulness, perceived ease of use, and user review). Following the basic assumptions of TAM, the final model identified both perceived usefulness and perceived ease of use for antecedents of app usage. In terms of perceived usefulness, the model showed that app users tended to use apps mainly with informative and entertaining needs while they did not use apps as a communication tool (i.e. social usefulness). User reviews, a social psychological factor, played a very important role in using apps. The hypothesized model expected an influence of cost-effectiveness on app usage, but the final model did not reveal it. Due to the particularity of app price fixed at 99 cents mostly, this study highlighted costeffectiveness as a differential variable from other technology acceptance studies. However, the perceptions about app price were likely to vary (1) according to individual economic tendencies and (2) because of free apps. These individual differences were not likely to lead an exact measurement of cost-effectiveness. For mobile app practitioners, the findings imply that the appropriate combination of information and entertainment (e.g. information-related apps with entertaining components or vise versa) is likely to be the best fit 81

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