An Examination of Mobile Application Use Intention through the Unified Theory of Acceptance and Use Technology Model

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1 An Examination of Mobile Application Use Intention through the Unified Theory of Acceptance and Use Technology Model Mu-Cheng Wu, Assistant Professor, Physical Education Office, National Chin-Yi University of Technology, Taiwan ABSTRACT This study aims to explore the causal relationships related to use intention for mobile application by investigating users in Northern, Central, and Southern Taiwan with purposive sampling, which selects 400 users as research subjects for a questionnaire survey. In total, 390 copies were collected with a return rate reaching 97.5%; among them, 370 copies were valid, reaching an effective rate of 94.8%. The data acquired was archived with the table coding of SPSS AMOS 20.0 was then adopted to analyze the causal relationship between variables. Research results are shown below: 1. performance expectancy did not significantly influence use intention; 2. effort expectancy did not significantly influence use intention; 3. social influence significantly influenced use intention; 4. benefits significantly influenced user behavior; 5. system quality significantly influenced use intention; 6. information quality significantly influenced use intention; 7. service quality significantly influenced use intention; 8. use intention significantly influenced user s behavior; 9. there was a significant interference effect for gender on seven dimensions of use intention, including performance expectancy, effort expectancy, social influence, benefits, system quality, information quality, and service quality; 10. there was a significant interference effect caused by age in social influence on use intention; and 11. there was a significant interference effect caused by experience in benefits and system quality on use intention. Keywords: Unified Theory of Acceptance and Use of Technology (UTAUT), Information Systems Success Model, mobile application INTRODUCTION With the advancement of information technology in recent years, most relevant research theories on utilization and acceptance of new information technology have focused on factors that raise use intention and acceptance level regarding new information technology and products. Hence, scholars in different research fields have proposed different perspectives and influential factors. Therefore, many variables generated in this manner were able to comprehensively explain the acceptance levels of new information technology. In 1985, Ajzen added perceived behavioral control to the Theory of Reasoned Action (TRA), and proposed the planned behavior theory (TPB), while in 1989, Davis integrated TRA and TPB into a new framework referred to as the Technology Acceptance Model (TAM) with the purpose of discussing the acceptance of new technology among users, describing the acceptance behavior of users towards new information technology, and analyzing the influence of new information technology on the majority of users. Davis thought that perceived usefulness and perceived ease of use of technology influenced users attitude and willingness to use that technology. In his theory, perceived usefulness referred to the fact that users subjectively perceive the system as useful to improve work performance. Perceived ease of use was the result of higher user confidence towards self-efficacy and control, and a more active and 110 The Journal of International Management Studies, Volume 11 Number 1, February, 2016

2 positive attitude towards the technology when it was easy to operate and use. The research field that examines the technology acceptance behavior of users produced several theoretical models derived from information systems, psychology, and sociology. This led to difficulty for researchers in choosing an appropriate research model. Inevitably, researchers were forced to select a few dimensions from these models to create new ones, or select one of the existing models, for relevant studies. As a result, Venkatesh et al. (2003) combined various theories, including TRA, TPB, TAM, Social Cognitive Theory (SCT), Model of Personal Computer Utilization (MPCU), Motivational Model (MM), Innovation Diffusion Theory (IDT), and the integrated TAM and TPB (i.e. C-TAM-TPV) to develop the Unified Theory of Acceptance and Use of Technology (UTAUT), which contained four main dimensions. This theory was proved to have an explanatory power of more than 70% after empirical analysis, more effective than any previous models. As demonstrated in the above discussion, UTAUT serves as a behavioral measurement model for managers prior to introducing a new technology. This model can be modified and evaluated based on the use of information systems, system interface, factors from the surrounding environment, and professional organizations and technologies. Businesses are required to spend huge amounts of money and time introducing information systems; thus, scholars in the relevant fields of information management have long studied relevant issues. In the meanwhile, they have continuously sought relevant factors concerning positive influences in informational system success models. The purpose is to improve the effectiveness that the information system can bring to users. In 1992, DeLone and McLean reviewed literature relevant to information systems. After compiling 180 articles from seven key information system journals, they produced an Information System Success Model designed to positively influence businesses. This model included six dimensions: System Quality, Information Quality, Use, User Satisfaction, Individual Impact, and Organizational Impact. With the advancement of technology, an information system brings influences not only to internal information personnel, but also to the whole business organization. Pitt et al. (1995) argued that the Information System Success Model of DeLone and McLean (1992) did not thoughtfully consider the role of services. They therefore proposed the addition of a Service Quality dimension to the two original dimensions: Information Quality and System Quality, which jointly influence use and user satisfaction in the information system. In 2003 DeLone& McLean modified the information system success model proposed in 1992 and, taking the suggestion of Pitt et al. (1995), added service quality to create a new and more comprehensive information system success model that consisted of six dimensions: system quality, information quality, service quality, use, user satisfaction, and net benefits. Among these, system quality, information quality, and service quality influence use and user satisfaction, while use and user satisfaction are interdependent and have an influence on net benefits. When the information system generates performance and benefits, it also affects use intention and user satisfaction. To sum up the above literature review, the purpose of the UTAUT model lies in the acceptance of users towards new information technology, the explanation of acceptance behavior for new information technology demonstrated by users, and analysis of the influence of new information technology on the majority of users. Additionally, three dimensions: system quality, information quality, and service quality in the informational system success model directly influence user satisfaction and willingness. Therefore, the combination of UTAUT model and informational system success model facilitates a discussion on acceptance willingness and the use of new information technology perceived by users. This study examines user willingness and use on the part of mobile application program users, and takes the UTAUT The Journal of International Management Studies, Volume 11 Number 1, February,

3 model as its main structure, as well as the addition of information quality, system quality, and service quality from the informational system success model. Its research results can serve as a reference for the development and design of future mobile application programs conducted by the Sports Administration of the Ministry of Education. RESEARCH METHOD Research hypothesis Through an examination of previous literature we find that Venkatesh et al. (2003) integrated TRA, TPB, TAM, SCT, MPCU, MM, IDT, and C-TAM-TPB to produce the UTAUT that unifies four dimensions including performance expectancy, effort expectancy, social influence, and facilitating conditions. Upon empirical analysis, this theory demonstrated an explanatory power of more than 70%. As a result, this study uses the performance expectancy, effort expectancy, social influence, and facilitating conditions of UTAUT, as well as the system quality, information quality, and service quality of the informational system success model to propose its research hypotheses: Hypothesis 1: Performance expectancy significantly influences use intention, while moderating variables include gender and age. Hypothesis 2: Effort expectancy significantly influences use intention, while moderating variables include gender and age. Hypothesis 3: Social influence significantly influences use intention, while moderating variables include gender, age and experience. Hypothesis 4: Facilitating conditions significantly influence use intention, while moderating variables include age and experience. Hypothesis 5: System quality significantly influences use intention, while moderating variables include gender, age and experience. Hypothesis 6: Information quality significantly influences use intention, while moderating variables include gender, age and experience. Hypothesis 7: Service quality significantly influences use intention, while moderating variables include gender, age and experience. Hypothesis 8: Use intention significantly influences user behavior. Research tool This study adopts UTAUT of Venkateshet al. (2003) as its main analysis structure and introduces dimensions from the informational system success model including system quality, information quality, and service quality to the structure for analysis. For questionnaire contents, this study mainly referred to questions proposed by Cheng (2008) about the use of on-line shopping websites by consumers. Necessary modifications were made according to research needs. The scale consists of five factor dimensions: performance expectancy, effort expectancy, social influence, facilitating conditions, and use intention, with a total of 18 questions. Additionally, the three dimensions of system quality, information quality, and service quality, with 11 total questions, were also modified according to research needs. This study used Likert s seven-point scale to distribute points from 1 to 7 according to answers ranging from highly disagree to highly agree. 112 The Journal of International Management Studies, Volume 11 Number 1, February, 2016

4 Research subject This study aims to investigate the causal relationship of use intention of app isports Go among users in Northern, Central, and Southern Taiwan. Utilizing purposive sampling, 400 users were randomly selected as the research samples for the questionnaire survey. In total, 390 copies were returned with a return rate of 97.5%; among them, 370 copies were effective, thus producing an effective rate of 94.8%. Information processing and analysis Data acquired from effective copies of questionnaires in this study was archived with the table coding of SPSS 20.0, and AMOS 20.0 was adopted to analyze the relationship between variables. RESEARCH RESULT Offending Estimate This study used seven indices, including χ 2 (Chi-square), χ 2,degree of freedom, GFI, AGFI, RMSEA, CFI and PCFI to examine goodness of fit. Bagozzi and Yi (1988) suggested that offending estimate should be conducted before test of goodness of fit. Discrepancy function value is between 0.02 and 0.12 while standardized coefficient is between 0.43 and 0.94 not exceeding the standardized value of Thus, this study has no offending estimate (Hair, Anderson, Tatham & Black, 1998), and overall goodness of fit can be examined. Analysis of measurement model This study used CFA for questionnaire reliability and validity, and also referred to modification indices (MI) to delete questions (Chen, 2007). This study deleted questions from Effort 4, Facilitating 1, Facilitating 2, Intention 4, and Behavior 1 in the UTAUT Scale, as well as System 1, Information 1, and Information 2 in the Informational Success Model. In the end, the reliability and validity of the study was re-examined. (1)Test of convergent validity Convergent validity can be measured from composition reliability (CR) and average variance extracted (AVE) for dimension. It is suggested CR value should be higher than 0.7 (Bagozzi and Yi, 1988) while AVE should be higher than 0.5 (Fornell and Larcker 1981) to indicate convergent validity. This study conducted convergent validity testing of respective dimensions of the UTAUT model, as well as the informational success model, including performance expectancy, effort expectancy, social influence, facilitating conditions, use intention, user behavior, system quality, information quality, and service quality. Factor loading for all dimensions lies between , CR between , and AVE between , (as shown in Table 1 and 2) which meets the standards of Bagozzi and Yi (1988), Fornell & Larcker (1981), and Hair, Anderson, Tatham, & Black (1998) and shows convergent validity for this study. The Journal of International Management Studies, Volume 11 Number 1, February,

5 Dimension Performance Expectancy Effort Expectancy Social Influence Facilitating Conditions Use Intention User Behavior Table 1: UTAUT Model Confirmatory Analysis Index Standardized Non Standardized C.R. S.E. Loading Loading (t-value) P SMC C.R. AVE Performance Performance *** 0.80 Performance *** 0.72 Effort Effort *** 0.79 Effort *** 0.69 Effort *** 0.63 Influence Influence *** 0.84 Influence *** 0.58 Facilitating Facilitating *** 0.61 Intention Intention *** 0.77 Intention *** 0.71 Behavior Behavior *** 0.75 Dimension System Quality Information Quality Service Quality Table 2: Informational Success Model Confirmatory Analysis Index Standardized Non Standardized C.R. S.E. Loading Loading (t-value) P SMC C.R. AVE System System *** 0.76 System *** 0.70 Information Information *** 0.81 Service Service *** 0.75 Service *** 0.73 (2)Test of discriminant validity Wu (2009) pointed out discriminant validity tests the relevance and significant difference between two different dimensions. This study adopted bootstrapped 95% confidence interval for its test of discriminant validity of the model. Hancock & Nevitt (1999) suggested bootstrapping should be conducted at least 250 times when testing the coefficient of the structural equation model. Therefore, this study conducted 1,000 instances of bootstrapping. According to Table 3 and 4, confidence interval of relevant coefficient does not include 1, and this indicates the discriminant validity of this study (Chang, 2011; Hsu, 2011; Torkzadeh, Koufteros & Pflughoeft, 2003). 114 The Journal of International Management Studies, Volume 11 Number 1, February, 2016

6 Table 3: UTAUT Model Bootstrapped 95% Confidence Interval of Relevant Coefficient Parameter Estimate Bias-corrected Percentile method Lower Bound Upper Bound Lower Bound Upper Bound Performance Expectancy < - > Effort Expectancy Performance Expectancy < - > Social Influence Performance Expectancy < - > Facilitating Conditions Performance Expectancy < - > Use Intention Performance Expectancy < - > User Behavior Effort Expectancy < - > Social Influence Effort Expectancy < - > Facilitating Conditions Effort Expectancy < - > Use Intention Effort Expectancy < - > User Behavior Social Influence < - > Facilitating Conditions Social Influence < - > Use Intention Social Influence < - > User Behavior Facilitating Conditions < - > Use Intention Facilitating Conditions < - > User Behavior Use Intention < - > User Behavior Table 4: Informational Success Model -Bootstrapped 95% Confidence Interval of Relevant Coefficient Parameter Estimated Bias-corrected Percentile method Lower Upper Lower Upper System Quality < - > Information Quality System Quality < - > Service Quality Information Quality < - > Service Quality (3)Structural Model Analysis This study took the suggestions of Bagozzi and Yi (1988), Wu (2009), Chen (2007), Hsu (2010), and Hair et al. (1998) as reference and used seven indices, including χ2 test, the ratio of χ2 to degree of free freedom, GFI, AGFI, root mean square error of approximation (RMSEA), comparative fit index (CFI), and PCFI for the determination of goodness of fit for this study s overall model. As shown in Table 5, Bagozzi& Yi (1988) suggested the ratio of χ2 and degree of freedom should be smaller than 3. This study, after modification, achieves a ratio of Hair et al. (1998) also advised that the closer GFI and AGFI values are to 1, the better. The values for this study were 0.90 and 0.86 respectively after modification. Browne and Cudeck (1993) pointed out a better value of RMSEA is between 0.05 and 0.08, and the modified value of this study is CFI tolerance standard should be higher than 0.90, and this study modifies the value to 0.96, while the modified value of PCFI is 0.79 in comparison to tolerance, which is higher than The research results of this study show the overall goodness of fit falls within the range of standard acceptance. Table 5: Goodness of Fit Analysis of Overall Model Index Tolerance Scope Modification Model Determination of Goodness of Fit χ 2 (Chi-square) The smaller the better Ratio of χ 2 to Degree of Freedom < Pass GFI > Pass AGFI > Pass RMSEA < Pass CFI > Pass PCFI > Pass The Journal of International Management Studies, Volume 11 Number 1, February,

7 Diag 1: UTAUT Model 116 The Journal of International Management Studies, Volume 11 Number 1, February, 2016

8 Table 6: Confirmatory Result of Research Hypothesis Hypothesis Path Relationship Path Value Does hypothesis stand? 1 Performance Expectancy Use Intention No 2 Effort Expectancy No 3 Social Influence Use Intention Yes 4 Facilitating Conditions User Behavior No 5 System Quality Use Intention Yes 6 Information Quality Yes 7 Service Quality Use Intention Yes 8 Use Intention User Behavior Yes *p <0.05 (5) Influence of moderating variable Based on the Chi-square Test proposed by Bentler and Bonnet (1980), the moderating variable of this study s research model was tested. When the degree of freedom for the two models are 1 and α=0.05, chi-square value reaches 3.84, which indicates the existence of a moderating effect. From Tables 8 and 9, we discover that the CMIN values for this research with respect to the social influence of ages on use intention, facilitating conditions on user behavior, and experience of system quality on behavior and intention are , , and respectively, and all higher than the standardized value of Thus, there is a moderating effect. Table 7: Confirmatory Result of Moderating Effect of Gender on Research Hypothesis Model DF CMIN P NFI IFI RFI TL1 Does the hypothesis Delta-1 Delta-2 rho-1 rho2 stand? Mode1 Number 2 Performance Expectancy No Mode1 Number 2 Effort Expectancy No Mode1 Number 2 Social Influence No Mode1 Number 2 System Quality Use Intention No Mode1 Number 2 Information Quality No Mode1 Number 2 Service Quality Use Intention No Table 8: Confirmatory Results for Moderating Effect of Age on Research Hypothesis Model DF CMIN P NFI IFI RFI TL1 Delta-1 Delta-2 rho-1 rho2 Does the hypothesis stand? Mode1 Number 2 Performance Expectancy No Mode1 Number 2 Effort Expectancy No Mode1 Number 2 Social Influence Yes Mode1 Number 2 Facilitating Conditions User Behavior No Mode1 Number 2 System Quality No Mode1 Number 2 Information Quality Use Intention No Mode1 Number 2 Service Quality No The Journal of International Management Studies, Volume 11 Number 1, February,

9 Table 9: Confirmatory Results for Moderating Effect of Experience on Research Hypothesis Model DF CMIN P NFI IFI RFI TL1 Does the hypothesis Delta-1 Delta-2 rho-1 rho2 stand? Mode1 Number 2 Performance Expectancy No Mode1 Number 2 Effort Expectancy No Mode1 Number 2 Social Influence Yes Mode1 Number 2 System Quality Yes Mode1 Number 2 Information Quality No Mode1 Number 2 Service Quality No RESULTS AND DISCUSSION This study is based on UTAUT and adds three dimensions from the D&M Informational System Success Model: system quality, information quality, and service quality to discuss user attitude and behavioral intention towards the internal variables of popular mobile app isports Go. The research results are as follows: (1) No significant impact of performance expectancy on use intention As shown in the confirmatory results of this study, performance expectancy has no significant influence on use intention for popular mobile app, isportsgo. It is assumed that when promoting this mobile app, isports Go, the Sport Administration, MOE would like to provide users with an easy-to-understand interface and simple practical function design with the aim of enhancing the use intention of users towards it. However, the influence of performance expectancy on use intervention might have changed due to the interface s lack of memory function. This led to the unavailability of services such as the identification of users personal information, automatic detection of heart beats, and the improvement of interpersonal interaction. This research result corresponds with research findings of Chen (2012) and Chang (2012). (2) No significant impact for effort expectancy on use intention As shown in the confirmatoryresults of this study, effort expectancy has no significant impact on the popular mobile app, isportsgo. It is assumed this result might be due to users excessive expectations concerning function, interface, and easiness for the mobile app. Most users believed the mobile app, isports Go was supposed to help users improve exercise effectiveness, but there was no practical effect in this regard. Thus, users of the mobile app, isports Go did not feel it was effective. This research result matches research findings of previous scholars such as Chen (2012), Hsu (2011), Huang & Huang (2008), and Gu (2012). (3) A significant impact of social influence on behavioral intention The Confirmatory results of this study find social influences significantly impacted behavioral intention towards mobile app, isportsgo. It is assumed that, due to current trend of technology becoming part of our lives, the majority of people are familiar with, and rely on, the convenience of apps. They use various types of apps anytime and anywhere. Smart phones are integrated into people s lives. Therefore, apps downloaded by users also impact the assessment of users in the eye of their friends and families. When users find the app, isports Go, improves their images among friends and families, they demonstrate 118 The Journal of International Management Studies, Volume 11 Number 1, February, 2016

10 higher use intention towards the app. This research finding echoes those of Chen (2012), Hsu (2011), Huang & Huang (2008), Tsai (2011), Cheng (2008), and Gu (2012). (4) No significant influence for facilitating conditions on user behavior Based on confirmatoryresults of this study, facilitating conditions have no significant influence of user behavior of the popular mobile app, isportsgo. The emergence of information technology in Taiwan, as well as the great popularity of cell phones, may positively encourage the public s user behavior towards the app. However, it seems that only those who demonstrate high sports participation frequency will demonstrate more interest in operating and using the app. Accordingly, facilitating conditions of users influencing use intention for the app are impacted. This research result agrees with those of Huang (2008), Li (2007), Tsai (2011), and Shi & Chen (2009). (5) A significant influence of system quality on use intention According to the confirmatory results of this study, system quality has a significant influence on use intention. It is assumed that the use of the app, isports Go, not only possesses good reliability and is user friendly, but also has the effect of blocking commercials on the interface to avoid visual interruption and encourage users use intention towards the app. This research result corresponds with those proposed by Cheng (2008), Chen (2008), Chou & Lin (2011), and Tsai & Huang (2007). (6) A significant influence for information quality on use intention As indicated in confirmatory results of this study, information quality has a significant influence on use intention. It is probably because the Sport Administration, MOE currently promotes three regular excise modes (333, 7330, and 7615) of the mobile app,isports Go, to allow the public to find the exercise frequency and interval that fit their needs. The public can also enjoy sport micro-films announced by sports anchorwoman Wu Yi-Pei after exercise to understand the importance of exercise. This also encourages users use intention toward the mobile app, isportsgo. This research result matches the findings of Cheng (2008), Chen (2008), Chou & Lin (2011), and Tsai & Huang (2007). (7)A significant influence for service quality on use intention According to confirmatory results of this research, service quality has a significant influence on use intention with respect to use intention for the popular mobile application isports Go. It is estimated that, via Internet connection, the app s responsible personnel can provide real-time responses to problems on the Facebook Fan Page or dispense needed services within the promised time with the aim of effectively increasing the functions of the app, as well as service quality for users and use intention. This research result corresponds to those of previous researchers such as Cheng (2008) and Chen (2008). (8) A significant influence for user behavior on use intention As shown in research results, use intention has a significant influence on user behavior towards the popular app, isportsgo. It is possible that when the system quality, information quality, and service quality of the app reach a level of acceptance among users, and when they develop both curiosity and trust toward the app, they will then use it. This research analysis agrees with those of Cheng (2008), Hung (2008), and Chen (2008). (9) Influence of moderating variables including gender, age, and experience Moderating influence of gender This research discovers the gender of users has no significant moderating effect on the seven dimensions such as performance expectancy, effort expectancy, social influence, facilitating conditions, system quality, information quality, and service quality. It is possible that, as long as users, either the male or female, identify positive results with respect to performance expectancy, effort expectancy, social The Journal of International Management Studies, Volume 11 Number 1, February,

11 influence, facilitating conditions, system quality, information quality, and service quality, they will have stronger use intention. Gender itself is not a significant influencing factor. Moderating influence of age According to the results of this study s analysis, the age of users has no significant moderating effect on the six dimensions of performance expectancy, effort expectancy, facilitating conditions, system quality, information quality, and service quality. That is to say, in order to allow users of all age groups to accept and use apps within modern life, of which technology has become a part, interface design has become simplified to avoid the influence of age on the use of the app in terms of use intention related to performance expectancy, effort expectancy, facilitating conditions, system quality, information quality, and service quality. However, user age results in a moderating effect on use intention in terms of social influence. It is possible that a relatively influential relationship may result from the different ages of app users. The significant influence of the recognition towards the mobile app, isportsgo, introduced by Sport Administration, MOE further influences the use intention of users. Moderating influence of experience As shown in the results of this research s analysis, user experience has no significant moderating effect on the five dimensions of performance expectancy, effort expectancy, social influence, information quality, and service quality. That is to say, in terms of design, isports Go app needs to consider ease of interface operation as the key component in avoiding the influence of user experience on performance expectancy, effort expectancy, social influence, information quality, and service quality. But due to different user experience, significant moderating effects on use intention, including facilitating conditions and system quality, will result. It is assumed that differences in user experience may determine the attractiveness, reliability, privacy, ease of use, and safety of the mobile app, isportsgo, and this further influences use intention. REFERENCES Ajzen, I., (1985). From Intentions to Actions: A Theory of Planned Behavior, Action-Control: From Cognition to Behavior, Heidelberg: Springer. Browne, M. W. &Cudeck, R. (1993). Alternative ways of assessing model fit. In: Bollen, K. A. & Long, J. S. (Eds.) Testing Structural Equation Models. pp Beverly Hills, CA: Sage Bentler, P.M. and Bonnet, D.C. (1980). "Significance Tests and Goodness of Fit in the Analysis of Covariance Structures," Psychological Bulletin, 88 (3), Bagozzi, R. P., & Yi, Y. (1988). On the evaluation for structural equation models.journal of the Academy of Marketing Science, 16, Chou, H. J. & Lin, S. Y.( 2011). A Discussion of the Informational System Success Model of Digital Learning Platform- A Case Study of the MLM Industry. Chang, W. H. (2011). Self-help Guide for Paper Writing SEM. Kaohsiung: San Hsing. Chen, S.Y. (2007). Structural Equation Model-Amos Operation: Taipei City: Psychological Publication Co., Ltd. Chen, H. Y. (2012). A Discussion on the Acceptance of Employees towards E-Bills- A Case Study of TaiPower. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), The Journal of International Management Studies, Volume 11 Number 1, February, 2016

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