Bon Appétit for Apps: Young American Consumers’ Acceptance of Mobile Applications

Spring 2013 Journal of Computer Information Systems 85

Bon AppétIt for AppS: Young AmerICAn ConSumerS’ ACCeptAnCe of moBIle ApplICAtIonS

HongweI ‘CHrIS’ YAngAppalachian State University

Boone, NC 28608

ABStrACt

The study integrated the Theory of Planned Behavior, the Technology Acceptance Model, and the Uses and Gratification Theory to predict young American consumers’ mobile apps attitudes, intent and use. The model was tested by a web survey of 555 American college students in winter, 2011. SEM results show that young American consumers’ attitudes and intent predict their use of mobile applications. Perceived enjoyment, usefulness, ease of use and subjective norm emerge as significant predictors of their mobile apps attitudes. Perceived behavioral control, usefulness, and mobile Internet use predict their intent to use mobile applications. Their use of mobile applications is determined by perceived usefulness, intent to use, mobile Internet use, income and gender. Implications for academia and industry are discussed. Keywords: Mobile applications adoption; TPB; TAM; Uses and gratifications; Mobile marketing; Mobile information systems

IntroDuCtIon

The use of mobile application is growing exponentially because wireless subscribers can use mobile applications for any conceivable purposes. Mobile apps can be defined as “end-user software applications that are designed for a mobile device operating system and which extend that device’s capabilities” [68]. According to a new survey by the Pew Research Center’s Internet & American Life Project, wireless subscribers who have downloaded an app to their phone nearly doubled from 22% in September 2009 to 38% in August 2011 and now half of U.S. adult cell phone users (50%) have apps on their phones, compared to 43% in May 2010 [68]. The rapid increase of U.S. smartphone ownership facilitates a wider use of mobile applications. ComScore [19] reports that 82.2 million peoplein the U.S. owned smartphones and 40.6% of mobile subscrib-ers had used downloaded apps by July 2011. Similarly, a 2011 Nielsen survey [61] reveals that 44% of U.S. mobile sub-scribers own a smartphone and the majority of these smart-phone owners (63%) have downloaded apps on their devices. American consumers use mobile apps for a variety of purposes including playing games, receiving updates about news, weather, sports or stocks, social networking, navigating, and mobile learning [19, 62, 68]. As a result, the business of mobile applications is flourishing. Gartner, a market research firm, forecasts that 17.7 billion mobile applications will be downloaded from apps stores worldwide in 2011 and over 185 billion apps will be downloaded by the end of 2014. Accordingly, worldwide mobile application store revenue reached $5.2 billion in 2010 and $7.3 billion in 2011, both from end users buying applications and applications themselves

generating advertising revenue for their developers [26, 70]. Research and Markets expects the apps market to increase to $14.6 billion by the end of 2012 and to reach $36.7 billion by 2015 [70]. International Data Corporation (IDC) predicts that worldwide mobile apps revenues will surpass $35 billion in 2014 after the number of downloaded apps increases from 10.9 billion in 2010 to 76.9 billion in 2014 [36]. Similarly, Canalys [10] announces that app store direct revenue will hit $14.1 billion in 2012 and reach $36.7 billion by 2015. Corporate America has also realized the huge potentials of using mobile apps for marketing purposes. U.S. marketers know that these small downloadable programs not only can expand the functionality of mobile devices but also can carry advertisements or are advertisements themselves. So, they create customized mobile applications for promoting their brand as well as adding sustainable value to mobile devices. Besides, American companies utilize mobile apps to engage consumers in two-way interactions that increase consumer loyalty and overall brand engagement [13]. Actually, big companies such as UPS, Wells Fargo and Cerner Corporation began to adopt mobile applications for customer relationship management before Apple opened its app store in 2005 [60]. Small and mid-size businesses are also riding the tide to develop mobile apps to gain new customers, strengthen relationships with existing ones, target consumers geographically, and even to reinvent their business models [21]. In 2011, a new IBM study of more than 1,700 chief marketing officers shows that 80% of respondents plan to use mobile applications more extensively in the next three to five years [35]. Young American adults are heavy users of smartphones, more likely to download apps to their phone than any other group. However, at present, most studies are descriptive in naturesuch as [19, 62, 68]. Inadequate research has focused on psychological motives behind young consumers’ acceptance of mobile applications except [45, 81]. Almost no similar study can be located in the United States. Consequently, the limited understanding of young consumers’ acceptance of mobile apps prevents interactive marketers from developing effective strategies to promote mobile apps, engage consumers withapps, and advertise within apps. This survey study tries to construct and test a conceptual model to predict young consumers’ attitudes toward mobile apps, intent to use and actual use, based on previous studies and personal interviews. It also examines important demographic and behavioral variables behind consumer acceptance of mobileapps.

lIterAture reVIew

Many recent studies have been conducted to understand the motivations behind consumers’ adoption of various mobile services such as text messaging service, multimedia messaging

86 Journal of Computer Information Systems Spring 2013

service, mobile instant messaging service, mobile Internet ser-vice, mobile entertainment services, mobile games, mobile commerce and financial services. They have identified the following psychological factors influencing consumers’ accep-tance of mobile services: perceived usefulness [22, 31, 32, 41, 49, 53, 54, 57, 60, 64, 65, 69, 71, 79, 80, 84, 85], ease of use[12, 41, 42, 49, 53, 54, 57, 60, 64, 65, 69, 79, 80], relative advantages [12, 42, 71], perceived ubiquity [51], expressive-ness [64, 79], enjoyment [31, 41, 54, 64, 65, 79, 85], behavioral control [46, 64, 79], social or normative influences [32, 40,46, 64, 71, 79, 85], technology self-efficacy [84], innovativeness [29, 38, 53, 65], trust [22, 51], social risk [71], communication intensity [29], users’ experiences [69], content relevancy [66], perceptions of price [49, 65, 66], and cultural values [55]. Nysveen and associates constructed and tested a very comprehensivemodel to predict consumers’ use of mobile services in Norway in 2005, integrating three perspectives of information systems research, uses and gratification research and domestication research [64]. Recently, Vatanparast attempted to test Nysveen and colleagues’ (2005) adoption model in a U.S. case study of a mobile service (or an app?) but the method and results are questionable [79]. So far, no study specifically examined the demographic, behavioral and psychological factors predicting consumers’ acceptance of mobile apps in the U.S. Therefore, no evidence indicates that the drivers of adoption of these mobile services also apply to mobile applications. Previous studies of mobile applications mostly focus on technological aspects of developing mobile applications for various purposes. For example, many studies address the problem of improving the usability and visibility of mobile apps [6, 24, 77, 87]. Other studies proposed advanced or new designs of mobile applications [39, 45, 72, 75, 78]. Some mobile marketing research has also summarized the successful strategies of using mobile applications for businesses, based on personal interviews and case studies [13, 27, 76]. Only a few studies examined the demographic, psychological and behavioral factors influencing consumers’ adoption of mobile applications in Asia and Europe [45, 81]. No research that investigates American consumers’ adoption of mobile apps can be retrieved from EBSCO databases. To fill the research gap and deepen our understanding of consumer acceptance of mobile applications in the United States, this survey study

attempts to integrate the Theory of Planned Behavior, Technology Acceptance Model and Uses and Gratifications Theory to predict young American consumers’ attitudes toward mobile apps, intent to use and actual use.

tHeoretICAl frAmeworK

Ajzen’s Theory of Planned Behavior (the TPB) [1] argues that consumers’ intentions to perform different kinds of behaviors can be predicted with high accuracy from attitudes toward the behavior (multiplicative products of belief strength and outcome evaluation), subjective norms (multiplicative composites of n normative belief–motivation interactions), and perceived behavioral control (a composite sum of perceived control over performing a behavior, ease or difficulty of performing a behavior, and likelihood of performing a behavior if sufficiently motivated). The TPB Model [1, 2] is shown as follows: The TPB has been applied and validated directly by previous studies about consumer adoption of mobile services [32, 38, 46, 52, 55, 64, 86]. Therefore, the TPB will be adopted in this study to investigate young American consumers’ attitudes toward mobile apps, intent to use and actual use. In this model, attitude is a person’s overall evaluation of performing the behavior and subjective norm concerns the person’s perception of the expectations of important others about the specific behavior. Perceived behavioral control denotes a subjective degree of control over the performance of a behavior and should be read as “perceived control over the performance of a behavior” [2]. In the case of mobile apps, attitude is defined as a consumer’s overall evaluation of the desirability of mobile apps while subjective norm refers to the person’s perception of the expectations of important others about using mobile apps. For this study, perceived behavioral control comes from a consumer’s sense of being in control of using mobiles apps and having sufficient means and resources to use them. It follows that cell phone users are more likely to embrace the use of mobile applications if the behavior is approved and expected by the people who are important to them. A consumer is more willing to try to use mobile apps if it is within his means, resources and control. Thus, these hypotheses are proposed based on the TPB and previous studies of mobile services:


H1: Young American consumers’ attitudes toward mobile apps positively predict (a) their intent to use mobile apps and (b) the number of mobile apps they have used.

H2: Young American consumers’ intent to use mobile apps positively predicts the number of mobile apps they have used.

H3: Young American consumers’ subjective norm positively predicts (a) their attitudes toward mobile apps and (b) intent to use mobile apps.

H4: Young American consumers’ perceived behavioral control positively predicts their intent to use mobile apps.

The Technology Acceptance Model (TAM) demonstrates that an individual’s intention to adopt and use a new information technology is determined by both perceived usefulness (PU) and perceived ease of use (EOU). According to Davis [20], PU refers to the individual’s subjective assessment of the utilities offered by the technology. For the purpose of this research, PU is operationalized as wireless subscribers’ beliefs that mobile apps provide timely, exclusive and customized information, constantly connect them with friends anywhere anytime, and improve their efficiency. EOU refers to the cognitive effort that the individual puts forward in learning the technology [20]. In this study, ease of use (EOU) is defined as the degree to which a consumer believes it easy to learn and use mobile apps. The TAM has been validated and expanded by many studies on adoption of mobile services [32, 37, 53, 54, 56, 57, 64, 69, 74, 80, 84]. Accordingly, the current research incorporates the Technology Acceptance Model to examine young American consumers’ adoption of mobile apps by testing the following hypotheses:

H5: Young American consumers’ perceived usefulness (perceived utilities of receiving updated information, connecting with friends and improving efficiency) of mobile apps positively predicts (a) their attitudes toward mobile apps and (b) intent to use mobile apps.

H6: Young American consumers’ perceived ease of use positively predicts (a) their attitudes toward mobile apps and (b) intent to use mobile apps.

Drawing upon the Uses and Gratifications Theory of communication, the current study treats mobile apps as a medium that fulfills young consumers’ communicative, informational, social, and entertainment needs. The Uses and Gratifications Theory posits that the use of media and technology is determined by the individual user’s needs or motivations [8, 50]. Consumers’ utilitarian motives of using mobile services are very similar to perceived usefulness in the TAM [48, 64]. On the other hand, the Uses and Gratifications research has highlighted consumers’ hedonistic motives of using new communication technologies — the need for entertainment, pleasure or enjoyment [34, 74]. In this line of research, previous studies have identified the following motives behind consumers’ use of mobile services: entertainment/pleasure/enjoyment/passing time, social interaction/sociability, immediate access, fashion/status/expressiveness, and time management [3, 7, 14, 44, 47, 64, 82]. As the operational definition of perceived usefulness has covered sociability, immediate access and time management, only two distinctive constructs of perceived enjoyment and expressiveness are integrated into the conceptual model of this study. Therefore,

H7: Young American consumers’ perceived enjoyment positively predicts (a) their attitudes toward mobile apps and (b) intent to use mobile apps.

H8: Young American consumers’ perceived expressiveness positively predicts (a) their attitudes toward mobile apps and (b) intent to use mobile apps.

Integrating the TPB, TAM and Uses and Gratifications Theory, the proposed research model and hypotheses are summarized in Figure 1.

metHoD

The principal investigator worked closely with a class of 10 undergraduate students to design the web survey questionnaire on Surveymonkey.com after conducting the extensive review of literature and 100 personal interviews. Six new questions were created and added to Nysveen and colleagues’ scale of perceived usefulness to better measure the construct [63]. The question about the use of mobile apps was developed especially for this study. Most items are adapted from previous studies of mobile marketing research [4, 15, 64, 83]. Five demographic questions about their gender, age, race, SES and personal income are also included. Important measures are presented in Appendix I. A pi-lot survey of 155 respondents in a convenience sample tested the reliability and validity of the original survey questionnaire. As a result, an interval level scale was adopted to measure the use of mobile apps instead of an open-ended question, in addition to some minor changes in working of questions. Three teams of these undergraduate students were instructed to send an email notice to 2900 randomly selected college stu-dents to ask for their participation in the web survey at a mid-sized public university in Southeast America in November 2011. Online survey is an appropriate research method frequently ad-opted by mobile marketing scholars such as [4, 30]. An incentive was offered to boost the response rate that one respondent would receive a $100 online gift certificate of Amazon.com in a random drawing. As previous research indicates, cash and non-cash in-centives can significantly increase the response rates of both mail surveys and Web-based surveys [17, 23]. After three e-mailings, 294 completed questionnaires were collected in a week. The principal investigator repeated the same web survey pro-cedure and completed the second wave of data collection in De-cember 2011. An additional 306 completed questionnaires were collected in a week, resulting in a response rate of 20.7%. After deleting respondents aged over 35, the sample size decreased to 555. The focus of this study is young American consumers aged 18-35 as they are most frequent users of mobile apps [19, 62, 68]. Then, the survey data were subject to statistical analyses includ-ing Pearson correlation, multiple regression analyses and struc-tural equation modeling with SPSS-19 and Amos-19.

reSultS

The descriptive statistics of 555 respondents are reported in Table 1. The majority of the sample is female (67.6%) and white (88.6%) with the average age of 21.61. Their family annual in-come distribution skews toward lower income brackets and only 20% of their families earns over $100,000 annually. More obvi-ously, their monthly personal income falls below $600 (71.4%). Their average time spent on mobile phone calling is 72.62 min-utes and 96.4% of them used text messaging. However, 43.2% of


fIgure 1. the Integrated research model of mobile Apps use

tABle 1. Descriptive Statistics (n = 555)

Male Female Mean SDGender 32.4% 67.6% Age 21.61 3.52

White Hispanics American Indians Asian Black Racially Mixed OtherRace 88.6% 2% 0.4% 3.6% 3.2% 1.8% 0.4%

Family Annual Income (SES) % Personal Monthly Income %Don’t know 20.5% Prefer not to tell 0.7% 0.7%< $40,000 24.9% < $200 34.6% 35.3%$40,001 - $60,000 8.8% $201 – $400 21.6% 56.9%$60,001 - $80,000 13.5% $401 – $600 14.4% 71.4%$80,001 - $100,000 12.3% $601 - $800 8.3% 79.6%> $100,000 20.0% $801 - $1000 6.8% 86.5% $1001 - $1200 3.4% 89.9% $1201 - $1400 2.3% 92.3% > $1400 7.7% 100%

(Minutes) Mean SD Median Mode RangePhone calls 72.62 95.55 45.00 60.00 0-900

(Frequency 1-6) Mean SD Median Mode RangeMobile Web Use 3.02 1.75 3.00 1.00 0-6

No. 0 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 100+Texting 1.4% 12.4% 16.8% 13.7% 8.3% 8.6% 9.9% 4.3% 4.0% 2.3% 4.7% 13.5%

No. 0 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 100+Apps 22.9% 35% 16.2% 9.5% 6.3% 2.9% 3.2% 0.9% 0.7% 0.4% 0.5% 1.4%


them never or seldom surf the mobile web while 22.9% has not used any mobile app. Table 2 presents the Cronbach coefficients (α) of seven adapt-ed multi-item scales and the results of exploratory factor analyses (principle axis factoring with varimax rotation). A liberal mini-mum requirement for scale reliability is 0.60 [16, 67] but some scholars recommended a stricter minimum requirement of 0.70 [63]. In addition, the extracted variance of each construct should exceed the 0.50 recommended level [25]. So, the performance of all scales is very satisfactory considering their high Cronbach co-efficients. The variance inflation factors (VIF) of six independent vari-ables are also shown in Table 2 to detect multi-collinearity. The Pearson correlation matrix in Table 3 demonstrates that three in-dependent variables are highly correlated: perceived enjoyment, perceived usefulness and expressiveness. However, only one VIF is higher than 4: perceived usefulness (4.63) and others are in the range of 1-4. A commonly used rule of thumb is that a VIF of 10

or more is evidence of severe multi-collinearity [18, 43]. Even if there is moderate multi-collinearity, good measure reliability (Cronbach alph > 0.7), a model whose explanatory power is high (R2 is high), and a large sample size (N = 555) can effectively protect the present study against the harmful effects of multicol-linearity in structural equation modeling [28, 63]. Therefore, mul-ticollinearity will not be an issue for the current research. The maximum likelihood method of structural equation mod-eling was employed to fit the research model of Figure 1 to the survey data and test the hypotheses with AMOS-19. The fitness indices of the proposed model and re-specified models are shown in Table 4. Figure 2 and 3 display the standardized path estimates of all variables in the proposed model and re-specified model. Significant results of 2 chi-square statistics imply that those two models were not ideal but if the normed chi-square (the model chi-square divided by the degree of freedom) is in the 2:1 or 3:1 range, the model can be considered acceptable [11]. In addition, the chi-square or likelihood ratio test is very sensitive to sample size (i.e. large samples) by assuming that the model fits perfectly in the population. Thus, other fitness indexes were developed to address this problem [9]. The RMSEA of a good model should be equal to or smaller than the recommended cutoff value of .06. Its Tucker-Lewis index (TLI) or non-normed fit index (NNFI) should be higher than .95, the widely accepted cutoff for a good model fit. By convention, its comparative fit index (CFI) should be equal to or greater than .90 to accept the model, indicating that 90% of the covariation in the data can be reproduced by the given model [9, 33, 73]. Based on these criteria, the re-specified model has achieved a better fit than the original model as the normed chi-square falls between 2:1 and 3:1 range [11], the RMSEA is smaller than the recommended cutoff value of .06 [33], and the CFI exceeds the conventional standard of .90. The TLI is a little below .95 probably because it penalized the complexity of the tested model. Marsh, Hau and Wen argue that the cutoff value of .95 for the TLI is too stringent for hypothesis testing [58]. There-

fore, the re-specified model is deemed most satisfactory. The path estimates connecting mobile apps attitudes with mobile apps intent and use in Figure 3 supported H1b and H2 but rejected H1a. Young American consumers’ mobile apps attitudes and intent strongly predicted their actual use of mobile apps but their attitudes did not predict their intent to use mobile apps.

tABle 3. Correlation of motives to mobile Apps Attitudes, Intent and use

Apps Apps Apps Subjective Perceived Ease Perceived Expressiveness Use Attitudes Intent Norm Control Usefulness of Use Enjoyment

Apps Use — Apps Attitudes .44** — Apps Intent .54** .56** — Subjective Norm .22** .49** .32** — Control .50** .61** .72** .38** — Usefulness .55** .74** .72** .57** .78** — Ease of Use .39** .63** .51** .35** .62** .60** — Enjoyment .44** .82** .56** .52** .61** .77** .63** — Expressiveness .41** .64** .54** .65** .59** .81** .51** .73* —

Note. N = 555. * p < 0.05. ** p < 0.01.

tABle 2.Construct reliability, Variance explained and VIf (n = 555)

VarianceConstruct Cronbach α explained VIf

Attitude toward mobile apps .836 65.2% N/ASubjective norm .746 51.6% 1.81Perceived behavioral control .811 51.6% 3.00Perceived usefulness .950 68.3% 4.63Ease of use .816 62.9% 2.04Perceived enjoyment .887 66.5% 3.38Perceived expressiveness .766 52.8% 3.16

Note. Principal axis factoring with varimax rotation. VIF = Vari-ance Inflation Factor.

tABle 4.fit Indices for the proposed and re-specified models

model χ2(df) normed χ2 RMSEA TLI(NNFI) CFI

ProposedModel 1356.9 (371)* 3.66 0.069 0.912 0.925

Re-specifiedModel 1045.7 (372)* 2.81 0.057 0.940 0.949

Note. RMSEA: root mean square error of approximation, TLI: the Tucker-Lewis index or NNFI: non-normed fit index, CFI: comparative fit index. * p < .01.


Note: N = 555. Significance of the path estimates are shown in parentheses (critical ratio). *p < .05, **p < .01, ns = not significant. Model fit: χ2 = 1045.7, df = 372, p = .000; RMSEA= 0.057; TLI=0.940; CFI= 0.949.

fIgure 3. the re-specified omnibus model with Standardized path estimates

fIgure 2. the proposed omnibus model with Standardized path estimates

Note: N = 555. Significance of the path estimates are shown in parentheses (critical ratio). *p < .05, **p < .01, ns = not significant. Model fit: χ2 = 1356.9, df = 371, p = .000; RMSEA= 0.069; TLI=0.912; CFI= 0.925.


The significant estimated path coefficients from subjective norm to perceived behavioral control and mobile apps attitudes in Figure 3 supported H3a and H3b, but H3c was rejected because no significant path coefficient could be estimated between sub-jective norm to mobile apps intent. H4 was strongly confirmed as behavioral control positively predicted mobile apps intent in Figure 3. H5a and H5b proposed that perceived usefulness would posi-tively predict young American consumers’ mobile apps attitudes and intent. They were both supported by two significant path estimates in Figure 3 from perceived usefulness to mobile apps attitudes and intent. H6a was also affirmed as ease of use sig-nificantly predicted mobile apps attitudes but H6b was rejected because ease of use did not influence their intent to use mobileapps. Figure 3 yielded the strongest predictor of young American consumers’ mobile apps attitudes: perceived enjoyment. So, H7a was supported. However, H7b was rejected as perceived enjoy-ment did not predict young American consumers’ intent to use mobile apps. Similarly, H8a and H8b were both rejected as per-ceived expressiveness did not positively predict young American consumers’ mobile apps attitudes and intent. As the revised research model did not fit the survey data per-fectly, three multiple regression procedures with “enter” method were conducted to examine what demographic, psychographic and behavioral factors influence young American consumers’ mo-bile apps attitude, intent and behavior. The results of three mul-tiple regressions replicated and complemented that of structural equation modeling procedures, as shown in Table 5.

DISCuSSIon AnD ImplICAtIonS

As one of the first, this study has attempted to construct an integrated model to examine what important demographic, psy-chographic and behavioral factors influence young American consumers’ mobile apps attitudes, intent and use. The findings have important implications for both theory building and profes-

sional practice. Theoretically, the current research has integrated the Theory of Planned Behavior, the Technology Acceptance Model, and the Uses and Gratifications Theory to predict young American consumers’ mobile apps attitudes, intent and usage.By incorporating the actual use of mobile apps and refiningperceived usefulness, this study has extended and validatedNysveen and associates’ (2005) model of intentions to use mo-bile services. The results have furthered our understanding ofdemographic, psychological and behavioral determinants of young American consumers’ mobile apps attitudes, intent and use so that wireless service providers, mobile marketing practi-tioners and apps developers can develop appropriate strategies to promote a wider use of mobile apps as a special mobile serviceand marketing platform. As postulated by the TPB, the present study has found that young American consumers actually download and use more mobile apps if they hold favorable attitudes toward mobile apps. In addition, their intention to use mobile apps will translateinto the actual use of mobile apps. Surprisingly, their attitudesdid not predict their intent to use in the survey data. The miss-ing link can be explained by the cost barrier for young Ameri-can college students. Even though most of them personally like mobile apps, the likelihood of using them in six months issmall as they cannot afford to buy a smartphone and to subscribe to a data plan. The model testing might produce a better fit ifwe ask them directly whether they intend to mobile apps in the near future. The current study also empirically supported the influenceof subjective norm on young American consumers’ attitudestoward mobile apps even though it did not predict their intentto use mobile apps. It suggests that young American consum-ers’ attitudes toward mobile apps can be shaped by the impor-tant people in their lives such as their parents, professors, idols and peers. On the other hand, subjective norm did not have a significant impact on their likelihood to use mobile apps insix months. The mixed result implies that the use of mobileapps has not yet become a social norm among important people

tABle 5. predictors of mobile Apps Attitudes, Intents and Behavior (n = 555)

mobile Apps Attitudes mobile Apps Intent mobile Apps use β β β

Subjective norm .057† ns nsBehavioral control ns .259*** nsPerceived usefulness .202*** .249*** .216*Ease of use .134*** ns nsPerceived enjoyment .562*** ns nsPerceived expressiveness ns ns nsGender a ns ns -.125***Race b ns ns nsAge ns ns nsSES (Family income) ns ns nsPersonal income ns ns .123**Texting ns ns nsMobile phoning ns ns nsMobile web surfing ns .404*** .330***Mobile apps attitudes ____ ns nsMobile apps intent ____ ____ .124*Total R2 .718 .665 .427

Multiple regression results. † p < .10, * p < .05, ** p < .01, *** p < .001, ns = not significant. a Gender: dummy-coded as 1 = male, 2 = female. b Race: dummy-coded as 1 = non-whites, 2 = white.


in young respondents’ lives. When young American consumers find that these important people have widely accepted mobile apps as relevant and useful, they will be more likely to embrace the use of mobile apps. Subjective norm should have more influ-ence on their mobile apps attitudes and intent after a critical mass of cell phone users cannot live without downloading and using mobile apps in the near future. The time will be very near espe-cially when more wireless operators are offering great deals to encourage individuals and family to subscribe to a data plan. Correlated with subjective norm, perceived behavioral con-trol turned out to be a very significant predictor of youngAmerican consumers’ intent to use mobile apps. It is quite rea-sonable in the case of mobile apps. When their significant others think they should use mobile apps, they will feel free to down-load and use mobile apps. As they believe they have neces-sary means and resources to use mobile apps, they are morelikely to use mobile apps in six months. Without a data planand necessary means, young American consumers will have no control and instead they will think twice before downloading and using mobile apps. The results demonstrate that the Technology Acceptance Model should be incorporated to predict young American con-sumers’ mobile apps attitudes and intent as the perceived useful-ness significantly predicted young American consumers’ attitudes toward and intent to use mobile apps. The findings are consistent with previous studies on adoption of mobile services [13, 32, 57, 64, 69, 74, 80, 84]. They have important implications for wireless service providers, mobile apps developers, vendors, and mobile marketers. They suggest that young American consumers are more likely to prefer and to use mobile apps designed to improve efficiency, to connect people instantly and ubiquitously, and to provide timely, exclusive and personalized information. This also explains why the most popular downloaded apps across smart-phones in the U.S. are those developed by Facebook, Google Maps and the Weather Channel [61]. Marketers should also fit the branded mobile apps into young consumers’ lives as a useful and relevant tool. Indeed, Bellman and colleagues recently confirmed that mobile apps with an informational/user-centered style were more effective at inducing purchase intention of brands [5]. We should not dismiss ease of use as irrelevant for the adop-tion of mobile apps even though it predicted young American consumers’ mobile apps attitudes, not their intent to use. In other words, the easier a mobile app is to use, the more favorably it will be viewed by young American consumers ages 18-35. Perhaps for technology savvy young Millennials, it comes very naturalto download and use mobile apps. Even so, it has influencedtheir attitudes toward mobile apps. Probably, young Ameri-can consumers take it for granted that great technology implieseffortlessness. The present study also shows that the Uses and Gratifications theory can shed light on young American consumers’ acceptance of mobile applications as the results indicate that mobile apps can fulfill their informational, social and entertainment needs as com-munication media. Specifically, perceived enjoyment stands out as the most important predictor of young American consumers’ attitudes toward mobile apps. When young American consumers perceive mobile apps as a good source of fun and pleasure, they will form positive attitudes that lead to more downloading and use of mobile apps. Their association of mobile apps and entertain-ment can be explained by the fact that most of them enjoy using mobile games, the most popular category of apps, while music and entertainment apps are No. 5 and No. 7 in popularity for both

feature phone and smartphone users [61]. So, mobile marketers and apps developers should design mobile apps with both utili-tarian value and hedonistic value if their target users are young American consumers aged 18-35. Fun or naughty or joyful brands such as Axe and Snickers should consider developing humorous apps, and advertising in mobile games, music or entertainment apps. In other words, mobile apps can serve as a good advertising platform for humor or music appeals. On the other hand, perceived expressiveness did not influence young American consumers’ mobile apps attitudes and intent. The finding suggests that Nysveen and colleagues’ (2005) model should be revised to predict young American consumers’ adop-tion of mobile apps. It is possible that perceived expressiveness can influence consumers’ intent to use some kinds of mobile apps but not so many different kinds of mobile apps that are both goal-directed (such as navigation and banking apps) and experiential (such as music and entertainment apps), and both machine-in-teractive (such as mobile games) and person-interactive (such as social networking apps). More likely, young American consumers adopt mobile apps primarily for their various utilities, instead of expressing their identity and impressing their friends. Therefore, it is not advisable to use vanity appeals when marketing mobile apps to young American consumers. Five demographic variables (gender, age, race, SES, income) did not predict young American consumers’ mobile apps attitudes and intent even though gender and income seem to influence their actual use of mobile apps. Caution is advised when we try to gen-eralize the findings to general population as the sample consists of a quite homogeneous group of well-educated young white Americans. Therefore, more research is warranted to validate the study. On the other hand, the results indicate that very likely men use more mobile apps than women and people with a higher personal income use more mobile apps than those with a lower income. The findings are consistent with previous studies where more young men with higher incomes and educational levels than women downloaded apps to their mobile phones [68]. Finally, the current research shows that young American consumers’ mobile Internet usage strongly contributes to their mobile apps intent and actual use. It makes sense as wireless subscribers need to have a mobile Internet access before they can download any mobile app. At present, cost is still an inhibiting factor for the adoption of mobile apps among young college students. To overcome the cost barrier to the wider use of mobile apps, wireless service providers should offer affordable data plans as they can generate more revenues from selling downloadable apps and advertisements within apps. The popularization of mobile Internet will be a boon for the telecommunications industry, mobile commerce and marketing.

ConCluSIon

This survey study demonstrates that it is appropriate to integrate the Theory of Planned Behavior, the Technology Acceptance Model, and the Uses and Gratification Theory to predict young American consumers’ mobile apps attitudes, intent and use. Perceived enjoyment, usefulness, subjective norm, and ease of use serve as significant predictors of young American consumers’ attitudes toward mobile apps. Their intent to use mobile apps is determined by perceived behavioral control, usefulness and mobile internet use. Their actual use of mobile apps can be predicted by perceived usefulness, mobile internet use, mobile apps intent, personal income, and gender.


lImItAtIonS AnD future reSeArCH

The study’s external validity should be strengthened by future research as the survey data was collected from a random sample of college students at a mid-sized Southeastern public university. As those participants are mostly white and female, the sample is not very representative of the U.S. college student population. Further research should use a random sample from general population. Future studies should emulate to construct a more comprehensive model to predict American consumers’ mobile apps attitudes, intent and use by including more influence factors such as pricing, consumer innovativeness, technology experience or self-efficacy, trust, risk, and mobile mavenism. The model should also be validated across different demographic groups and categories of mobile apps. Additionally, it will be worthwhile to test such a model in a different cultural context.

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AppenDIX I.web Survey on the use of mobile Applications (Selected measures)

Cell phone usage 1. How much time do you spend talking on your cell phone on a typical day? __________hours _________minutes 2. How many text messages do you send with your cell phone on a typical day? (1)0 (2) 1-10 (3) 11-20 (4) 21-30 (5) 31-40 (6) 41-50 (7) 51-60 (8) 61-70 (9) 71-80 (10) 81-90 (11)

91-100 (12) More than 100 3. How often do you use your cell phone to surf the web? (1) Never. (2) Seldom. (3) Sometimes. (4) Often. (5) Very Often. (6) Always.

Use of mobile apps How many mobile applications have you ever used on your cell phone? (Such as Facebook app, games, ringtones, GPS, etc.) (1) 0 (2) 1-10 (3) 11-20 (4) 21-30 (5) 31-40 (6) 41-50 (7) 51-60 (8) 61-70 (9) 71-80 (10) 81-90 (11) 91-100 (12) More than 100

Intent to use mobile apps1 How likely will you use mobile applications within the next 6 months? (1) Very unlikely. (2) Unlikely. (3) Don’t Know. (4) Likely. (5) Very Likely.

Perceived Usefulness2, 3, 4 1. Using mobile applications saves me a lot of time. 2

2. Using mobile applications improves my efficiency. 2

3. Mobile applications are useful to me. 2

4. I stay connected with my friends with mobile applications. 3

5. I communicate with my friends anywhere via mobile applications. 3

6. I communicate with my friends anytime via mobile applications. 3

7. I receive timely information via mobile applications. 4

8. I receive exclusive information via mobile applications. 4

9. I received customized news to my liking via mobile applications. 4

Subjective norm5 1. People important to me think I should use mobile applications. 2. It is expected that people like me use mobile applications. 3. People I look up to expect me to use mobile applications.

Perceived control6 1. I feel free to use mobile application when I like to. 2. To use mobile applications is entirely within my control. 3. I have the necessary means and resources to use mobile applications.

Ease of use7 1. Learning to use a mobile application is easy to me. 2. It is easy to make a mobile application to do what I want it to. 3. It is easy to use mobile applications.

Perceived enjoyment8 1. I find mobile applications entertaining. 2. I find mobile applications exciting. 3. Using mobile applications gives me pleasure.

Perceived expressiveness9 1. I often talk to others about mobile applications. 2. Using mobile applications is part of how I express my personality. 3. Other people are often impressed by the way I use mobile applications.

Attitude toward mobile apps10 1. My attitude toward passing along electronic messages is positive. 2. Generally, I think it is good to pass along electronic messages to friends or relatives. 3. I honestly don’t like passing along electronic messages to friends or relatives.

1 , 2 Adapted from [64]. All response options ranged from 1, “strongly disagree” to 5, “strongly agree” if not provided. 3 Developed based on 100 personal interviews by students. 4 Adapted from [4, 15].5, 6, 7, 8, 9 Adapted from [64].

10 Adapted from [83].

Bon Appétit for Apps: Young American Consumers’ Acceptance of Mobile Applications

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