DETERMINANTS AND OUTCOMES OF MOBILE APP USAGE INTENTION OF GEN Z: A CROSS CATEGORY ASSESSMENT

Yıl 2019, Cilt: 7 Sayı: 2, 239 - 265, 01.12.2019

Edin Güçlü Sözer

https://doi.org/10.14514/byk.m.26515393.2019.7/2.239-265

Öz

The objective of this study is to identify the determinants such as usefulness and ease of use as well as the behavioral outcomes of mobile app usage intention among Generation Z consumers. Three mobile app categories, entertainment, communication and networking, are included in the study. The results of the study confirmed that perceived ease of use plays an important role in determining the intention to use the app while perceived privacy, perceived security, perceived design and perceived compatibility are the factors which shape both perceived ease of use and usefulness depending on the mobile app category. Higher usage intention is found to be effective in generating willingness to pay in all mobile app categories. In contrast to the entertainment category, increasing usage intention is also found to lead increasing intention to engage into the WoM activity in communication and networking categories. Based on these findings, some practical implications are provided.

Anahtar Kelimeler

Mobile App, Usage Intention, Willingness to Pay, Word of Mouth, Generation Z.

Kaynakça

• Akgün, A. E., Ince, H., Imamoğlu, S., Keskin, H., and Kocoğlu, I. (2014). The mediator role of learning capability and business innovativeness between total quality management and financial performance. International Journal of Production Research, 52(3), 888-901.
• Albarracin, D., Johnson, B. T., Fishbein, M. and Muellerleile, P. A. (2001). Theories of reasoned action and planned behavior as models of condom use: A meta-analysis. Psychological Bulletin, 127, 142–161.
• Altuntuğ, N. (2012). Kuşaktan Kuşağa Tüketim Olgusu ve Geleceğin Tüketici Profili. Organizasyon ve Yönetim Bilimleri Dergisi, 4(1), 203-212.
• Anderson, J. and Gerbing, D. W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103(3), 411-423.
• Arndt, J. A. (1967). Word of Mouth Advertising. New York: Advertising Research Foundation.
• Arts, J. W. C., Frambach, R. T., and Bijmolt, T. H. A. (2011). Generalizations on consumer innovation adoption: A meta-analysis on drivers of intention and behavior. International Journal of Research in Marketing, 28, 134-144.
• Bagozzi, R. P., and Yi, Y. (1990). Assessing Method Variance in Multitrait-Multimethod Matrices: The Case of Self-Reported Affect and Perceptions at Work. Journal of Applied Psychology, 75(1), 547-560.
• Bansal, H.S. and Voyer, P.A. (2000). Word-of-mouth processes within a service purchase decision context. Journal of Service Research 3(2), 166-77.
• Barford, I. N. and Hester, P.T. (2011). Analysis of Generation Y Workforce Motivation Using Multiattribute Utility Theory. FT BELVOIR VA: Defense Acquisition University.
• Benítez, L.F. and Martinez, B.A. (2015). The effects mobile of applications as a marketing tool in airport infrastructure and airlines. International Journal of Leisure and Tourism Marketing, 4(3/4), 222-240.
• Belch. G. E. and Belch. M. A. (2003). Advertising and Promotion an Integrated Marketing Communications Perspective. New York: McGraw-Hill.
• Bellman, S., Potter, R. F., Treleaven-Hassard, S., Robinson, J. A. and Varan, D. (2011). The Effectiveness of Branded Mobile Phone Apps. Journal of Interactive Marketing, 25(4), 191- 200.
• Bone, P.F. (1995). Word-of-mouth effects on short-term and long-term product judgments. Journal of Business Research, 32(3), 213-223.
• Brown, J. J., and Reingen, P. H. (1987). Social ties and word-of-mouth referral behavior. Journal of Consumer Research, 14, 350–362.
• Byrne, B. M. (2010). Structural Equation Modeling with AMOS. New York: Routledge Taylor & Francis Group.
• Chan, H. (2019). Generation Z: The Disrupters of Tomorrow. MyCommerce. Accessed: 27.05. 2019. https://www.mycommerce.com/generation-z-the-disrupters-of-tomorrow/.
• Chiu, C.C. & Yang, H.E. (2016). The Impact of Website Design Features on Behavioral Intentions. International Journal of Scientific and Technology Research Volume, 5(9), 71-78.
• Cho, N. and Park, S. (2001). Development of electronic commerce user – consumer satisfaction index (ECUSI) for internet shopping. Industrial Management and Data Systems, 101(8), 400–405.
• Crabbe M, Standing C, Standing S and Karjaluoto, H (2009). An adoption model for mobile banking in Ghana. International Journal of Mobile Communications, 7(5), 515–543.
• Dai, H., and Palvia, P.C. (2009). Mobile commerce adoption in China and the United States: a cross-cultural study. DATA BASE, 40, 43-61.
• Darden W.R. and Reynolds F.D. (1971). The Multidimensionality of Fashion Innovation, in SV - Proceedings of the Second Annual Conference of the Association for Consumer Research, eds. David M. Gardner, College Park, MD: Association for Consumer Research, 452-458.
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339.
• DeLone, W.H. and McLean, E.R. (1992). Information systems success: the quest for the dependent variable. Information Systems Research, 3(1), 60-95.
• Eastman, J. K., and Liu, J. (2012). The impact of generational cohorts on status consumption: an exploratory look at generational cohort and demographics on status consumption. Journal of Consumer Marketing, 29(2), 93-102.
• e-Marketer. (2018). More Millennials Gen Z Are Using Social Apps. Accessed: 05.06.2019. https://www.emarketer.com/content/more-millennials-gen-z-are-using-social-apps.
• Fornell, C. and Larcker, D. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1): 39-50.
• Gurau, C. (2012). A life‐stage analysis of consumer loyalty profile: comparing Generation X and Millennial consumers. Journal of Consumer Marketing, 29(2), 103-113.
• Hausenblas, H. A., Caron, A. V. and Mack, D. E. (1997). Application of the theories of reasoned action and planned behavior to exercise behavior: A meta-analysis. Journal of Sport and Exercise Psychology, 19, 36–51.
• Hewlett, S. A., Sherbin, L., and Sumberg, K. (2009). How Gen Y and Boomers will reshape your agenda. Harvard Business Review, 87(7-8), 71-6.
• Hur, H.J., Lee, H.K., and Choo, H.J. (2017). Understanding usage intention in innovative mobile app service: Comparison between millennial and mature consumers. Computers in Human Behavior, 73, 353-361.
• Jackson, V., Stoel, L. and Brantley, A. (2011). Mall attributes and shopping value: differences by gender and generational cohort. Journal of Retailing and Consumer Services, 18(1), 1-9.
• Kang, J.M., Mun, J.M., and Johnson, K.K.P. (2015). In-store mobile usage: Downloading and usage intention toward mobile location-based retail apps. Computers in Human Behavior, 46, 210-217.
• Kim, J. (2012). The Effect of Design Characteristics of Mobile Applications on User Retention: An Environmental Psychology Perspective. AMCIS 2012 Proceedings, 13, 1-9.
• Kim, S. and Garrison, G. (2009). Investigating mobile wireless technology adoption: an extension of the technology acceptance model. Information Systems Frontiers, 11(3), 323-333.
• Kim H. and Kim J. (2002). An empirical research on important factors of mobile internet usage. Journal of MIS Research, 12(3), 90-113.
• Koenig-Lewis, N., Marquet, M., Palmer, A. and Zhao, A.L. (2015). Enjoyment and social influence: predicting mobile payment adoption. The Service Industries Journal, 35(10), 1-18.
• Krishnan, K.S.T., and How, L.K. (2016). The Effect of Mobile Apps on Gen Z’s Intention to Download Apps in Malaysia. International Journal of Advanced and Multidisciplinary Social Science, 2(3), 51-60.
• Kumar, A., Adlakaha, A., and Mukherjee, K. (2018). The effect of perceived security and grievance redressal on continuance intention to use M-wallets in a developing country. International Journal of Bank Marketing, 36 (7), 1170-1189.
• Lallmahamood, M. (2007). An Examination of Individual’s Perceived Security and Privacy of the Internet in Malaysia and the Influence of This on Their Intention to Use ECommerce: Using an Extension of the Technology Acceptance Model. Journal of Internet Banking and Commerce, 12(3), 1-26.
• Lee, S. (2009). Mobile internet services from consumers' perspectives. International Journal of Human-Computer Interaction, 25(5), 390-413.
• Lee, I., Choi, B., Kim, J. and Hong, S-J. (2007). Culture-Technology Fit: Effects of Cultural Characteristics on the Post-Adoption Beliefs of Mobile Internet Users. International Journal of Electronic Commerce, 11(4), 11-51.
• Looney, C.A., Jessup, L.M., and Valacich, J.S. (2004). Emerging Business Models for Mobile Brokerage Services. Communications of the ACM, 47(6), 71-77.
• Lyons, S. T., Duxbury, L., and Higgins, C. (2007). An empirical assessment of generational differences in basic human values. Psychological reports,101(2), 339-352.
• Martin, C. A., and Turley, L. W. (2004). Malls and consumption motivation: an exploratory examination of older Generation Y consumers. International Journal of Retail & Distribution Management, 32(10), 464-475.
• McCrindle, M. and Wolfinger, E. (2010). The ABC of XYZ: Understanding the Global Generations. Sydney: University of New South Wales Press.
• Meuter, M. L., Bitner, M. J., Ostrom, A. L., and Brown, S. W. (2005). Choosing among alternative service delivery modes: An investigation of customer trial of self-service technologies. Journal of Marketing, 69(2), 61–83.
• Miyazaki, A.D. and Fernandez, A. (2001). Consumer perceptions of privacy and security risks for online shopping. Journal of Consumer Affairs, 35(1), 27-44.
• Natarajan, T., Balasubramanian, S.A. and Kasilingam, D.L. (2017). Understanding the intention to use mobile shopping applications and its influence on price sensitivity. Journal of Retailing and Consumer Services, 37, 8-22.
• Nikkhah, H.R., Balapour, A., and Sabherwal, R. (2018). Mobile Applications Security: Role of Privacy. Proceedings of Twenty-fourth Americas Conference on Information Systems, New Orleans, 1-5.
• Norum, P.S. (2003). Examination of generational differences in household apparel expenditures. Family and Consumer Sciences Research Journal, 32(1), 52-75.
• Porter, C. E., and Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999–1007.
• Queitzsch, S. (2015). Five generation juggle. Johnston & Goldsmith, Think Paper. Accessed: 25.05.2019.https://static1.squarespace.com/static/5859fc13197aeabb8cf959f0/t/58a50 de1d482e94691deda4e/1487212012446/Five+Generation+Juggle.pdf
• Reisenwitz, T. and Iyer, R. (2007). A comparison of younger and older baby boomers: investigating the viability of cohort segmentation. Journal of Consumer Marketing, 24(4), 202-213.
• Rogers, E. M. (1962). Diffusion of Innovations, New York: The Free Press.
• Rohm, A.J., Gao, T., and Sultan, F. (2012). Brand in the hand: A cross-market investigation of consumer acceptance of mobile marketing. Business Horizons, 55, 485-493.
• Roy, S. and Moorthi, Y.L.R. (2017). Technology readiness, perceived ubiquity and Mcommerce adoption: The moderating role of privacy. Journal of Research in Interactive Marketing, 11(3), 268-295.
• Sarker, S. and Wells, J. D. (2003). Understanding mobile handheld device use and adoption. Communications of the ACM, 46, 35–40.
• Sathye, M. (1999). Adoption of Internet banking by Australian consumers: an empirical investigation. International Journal of Bank Marketing, 17(7), 324-34.
• Schlossberg, M. (2016). Teen Generation Z is being called 'millennials on steroids,' and that could be terrifying for retailers. Retrieved 13.06.2019, Business Insider UK https://www.businessinsider.in/retail/teen-generation-z-is-being-called-millennialson-steroids-and-that-could-be-terrifying-for-retailers/slidelist/53565468.cms.
• Sheeran, P., Abraham, C. and Orbell, S. (1999). Psychosocial correlates of heterosexual condom use: A meta-analysis. Psychological Bulletin, 125, 90–132.
• Shih, H.P. (2004). Extended technology acceptance model of Internet utilization behavior. Information & Management, 41, 719-729.
• Shin, D. H. (2010). The Effects of Trust, Security and Privacy in Social Networking: A SecurityBased Approach to Understand the Pattern of Adoption. Interacting with Computers, 22(5), 428-438.
• Siamagka, N.T., Christodoulides, C., Michaelidou, N., and Valvi, A. (2015). Determinants of social media adoption by B2B organizations. Industrial Marketing Management, 51, 89-99.
• Siti, A., Azizah, O., and Siti, H. (2016). Influence of personal values on generation Z’s purchase intention toward natural beauty products. e-Journal of Economics and Management Science, 2, 1-11.
• Smith, H. J., Dinev, T., and Xu, H. 2011. Information Privacy Research: An Interdisciplinary Review. MIS Quarterly, 35(4), 989-1016.
• Spears, N. and Singh, S. N. (2004). Measuring Attitude toward the Brand and Purchase Intentions. Journal of Current Issues & Research in Advertising, 26(2), 53-66.
• Statista (2019). Mobile phone internet user penetration worldwide from 2014 to 2019. Accessed: 17.05.2017. https://www.statista.com/statistics/284202/mobile-phoneinternet-user-penetration-worldwide/.
• Statista (2019). Worldwide mobile app revenues in 2015, 2016 and 2020. Accessed: 19.05.2017. https://www.statista.com/statistics/269025/worldwide-mobile-app-revenue-forecast/.
• Stocchi, L., Michaelidou, N., and Micevski, M. (2019). Drivers and outcomes of branded mobile app usage intention. Journal of Product & Brand Management, 28(1), 28-49.
• Sultan, F. and Rohm, A.J. (2005). The Coming Era of "Brand in the Hand" Marketing. The growing popularity of cell phones and other hand-held mobile devices has opened up new marketing possibilities. MIT Sloan Management Review, 47(1), 83-95.
• Thibaut, J. W. and Kelley, H.H. (1959). The Social Psychology of Groups. New York: John Wiley & Sons, Inc.
• Tojib, D. and Tsarenko, Y. (2012). Post-adoption modeling of advanced mobile service use. Journal of Business Research, 65(7), 922-928.
• Tornatzky, L. G. and Klein, K. J. (1982). Innovation Characteristics and Innovation AdoptionImplementation: A Meta-Analysis of Findings. IEEE Transactions on Engineering Management, 29(1), 28-45.
• Varnalı, K. and Toker, A.G. (2010). Mobile marketing research: The state of the art. International Journal of Information Management, 30(2), 144-151.
• Venkatesh, V., Davis, F. D. and Morris, M. G. (2007). Dead or alive? The development, trajectory and future of technology adoption research. Journal of the Association for Information Systems, 8(4), 267–286.
• Viswanathan, V, Hollebeek, L.D., Malthouse, E.C., Maslowska, E., Kim, S.J., and Xie, W. (2017). The dynamics of consumer engagement with mobile technologies. Service Science, 9(1), 36-49.
• Watson, R.T., Pitt, L.F., Berthon, P., and Zinkhan, G.M. (2002). U-Commerce: Expanding the Universe of Marketing. Journal of the Academy of Marketing Science, 30(4), 333-347.
• Werner, P. (2004). Reasoned action and planned behaviour. In: S.J. Peterson and T.S. Bredow, eds. Middle range theories: application to nursing research. Lippincott: Williams and Wilkins,125–147.
• Woo, J.H. and Wang, S.C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42, 719-729.
• Wu, C.W. (2014). The study of service innovation for digservice on loyalty. Journal of Business Research, 67(5), 819-824.
• Wu, J.H. and Wang, S.C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42, 719-729.
• Xu, C., Peak, D. and Prybutok, V. (2015). A customer value, satisfaction, and loyalty perspective of mobile application recommendations. Decision Support Systems, 79, 171-183.
• Yang, H. (2013). Bon Appétit for Apps: Young American Consumers' Acceptance of Mobile Applications. Journal of Computer Information Systems, 53(3), 85-96.