Research Article
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Sosyal Medya Uygulamalarını Kullanmaya Yönelik Niyetin Belirleyicileri

Year 2019, Volume: 21 Issue: 3, 686 - 702, 30.09.2019
https://doi.org/10.32709/akusosbil.478170

Abstract

Bu çalışmanın amacı, akıllı telefon kullanıcılarının sosyal medya uygulamalarına yönelik tutumlarını ve niyetlerini, yaygın olarak kullanılan üç model; kullanımlar ve doyumlar teorisinin psikolojik faktörleri (eğlence, sosyalleşme ve bilgi), yeniliğin yayılımı kuramı ve teknoloji kabul modeli bakış açısıyla incelemektir. Bu araştırma hedefi çerçevesinde, bu üç model bir araştırma modeli altında birleştirilmiştir. Araştırma örnekleri Uşak İl’inden rasgele örneklem tekniği ile elde edilmiştir. Araştırma modelinin test edilmesinde kullanılan veriler anket yolu ile toplanmıştır. Toplanan geçerli anket sayısı 527 dir. Araştırma modeli ile araştırma hipotezleri yapısal eşitlik modellemesi ile test edilmiştir. Sonuçlar göstermektedir ki, kullanıcıların algılanan kullanım kolaylığı; karmaşıklık, bağıl avantaj, gözlemlenebilirlik ve denenebilirlik tarafından etkilenmektedir. Algılanan fayda ise; uygunluk, bağıl avantaj, gözlemlenebilirlik, denenebilirlik ve algılanan kullanım kolaylığı tarafından etkilenirken, algılanan fayda ile tutum sosyal medya uygulaması kullanım niyeti üzerinde etkilidir. Diğer taraftan sosyalleşme, eğlence, algılanan kullanım kolaylığı ve algılanan fayda tutumun belirleyicisidir. Nicel sonuçlar bütünleşik yaklaşımın desteklendiğini göstermiştir. Yeniliğin yayılımı kuramı ve psikolojik motivasyonlar bağlamında teknoloji kabul modelinin sosyal medya uygulamaları karar vericilerine yardımcı olabileceğini göstermektedir.

References

  • Agarwal, R. (2000). Individual Acceptance of Information Technologies, (Ed: R. W. Zmud), Framing the Domains of It Management: Projecting the Future Through the Past: (pp. 85-104).
  • Agarwal, R. and Prasad, J. (1999). Are Individual Differences Germane to the Acceptance of New Information Technologies? Decision Sciences, 30(2): 361-391.
  • Bajaj, A., and Nidumolu, S. R. (1998). A Feedback Model to Understand Information System Usage, Information and Management, 33(4): 213-224.
  • Baker, R. K., and White, K. M. (2010). Predicting Adolescents’ Use of Social Networking Sites from an Extended Theory of Planned Behaviour Perspective, Computers in Human Behavior, 26(6): 1591-1597.
  • Boyd, D. M., and Ellison, N. B. (2007). Social Network Sites: Definition, History, and Scholarship, Journal of Computer‐Mediated Communication, 13(1): 210-230.
  • Bradford, M., and Florin, J. (2003). Examining the Role of Innovation Diffusion Factors on the Implementation Success of Enterprise Resource Planning Systems, International Journal of Accounting Information Systems, 4(3): 205-225.
  • Brandtzæg, P. B., and Heim, J. (2009). Why People Use Social Networking Sites, Paper presented at The International Conference on Online Communities and Social Computing.
  • Cao, Y. and Hong, P. (2011). Antecedents and Consequences of Social Media Utilization in College Teaching: A Proposed Model with Mixed-Methods Investigation, On the Horizon, 19(4): 297-306.
  • Carr, C. T., and Hayes, R. A. (2015). Social Media: Defining, Developing, and Divining, Atlantic Journal of Communication, 23(1): 46-65.
  • Chang, H. C. (2010). A New Perspective on Twitter Hashtag Use: Diffusion of Innovation Theory, Proceedings of The American Society for Information Science and Technology, 47(1): 1-4.
  • Chang, Y. P. and Zhu, D. H. (2011). Understanding Social Networking Sites Adoption in China: A Comparison of Pre-Adoption and Post-Adoption, Computers in Human Behavior, 27(5): 1840-1848.
  • Chen, L.-d., Gillenson, M. L., and Sherrell, D. L. (2002). Enticing Online Consumers: An Extended Technology Acceptance Perspective, Information and Management, 39(8): 705-719.
  • Chung, C., and Austria, K. (2010). Social Media Gratification and Attitude toward Social Media Marketing Messages: A Study of the Effect of Social Media Marketing Messages on Online Shopping Value, Paper presented at the Northeast Business & Economics Association.
  • comSCORE. (2018). Global Digital Future in Focus Retrieved from https://www.comscore.com/Insights/Presentations-and-Whitepapers/2018/Global-Digital-Future-in-Focus-2018, (Access: 02.10.2018)
  • Cronbach, L. J., and Shavelson, R. J. (2004). My Current Thoughts on Coefficient Alpha and Successor Procedures, Educational and Psychological Measurement, 64(3): 391-418.
  • Davis, F. D. (1985). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results, Massachusetts Institute of Technology.
  • Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly, 319-340.
  • Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models, Management Science, 35(8): 982-1003.
  • Dolan, R., Conduit, J., Fahy, J., and Goodman, S. (2016). Social Media Engagement Behaviour: A Uses and Gratifications Perspective, Journal of Strategic Marketing, 24(3-4): 261-277.
  • Ducoffe, R. H. (1996). Advertising Value and Advertising on the Web-Blog@ Management, Journal of Advertising Research, 21.
  • Ellison, N., Steinfield, C., and Lampe, C. (2006). Spatially Bounded Online Social Networks and Social Capital, International Communication Association, 36(1-37).
  • Fishbein, M., and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Reading, MA: Addison-Wesley.
  • Fornell, C., and Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error, Journal of Marketing Research, 39-50.
  • Folorunso, O., Vincent, R. O., Adekoya, A. F., and Ogunde, A. O. (2010). Diffusion of Innovation in Social Networking Sites among University Students, International Journal of Computer Science and Security, 4(3): 361-372.
  • Freeman, C. (1974). The Economics of Industrial Innovation, Harmondsworth: Penguin.
  • Hair, J. F., Black, W. C., Babin, B. J., and Anderson, R. E. (2014). Multivariate Data Analysis, Harlow, Essex: Pearson Education Limited.
  • Hardgrave, B. C., Davis, F. D., and Riemenschneider, C. K. (2003). Investigating Determinants of Software Developers' Intentions to Follow Methodologies, Journal of Management Information Systems, 20(1): 123-151.
  • Hu, L. t., and Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives, Structural Equation Modeling: A Multidisciplinary Journal, 6(1): 1-55.
  • Hu, P. J., Chau, P. Y., Sheng, O. R. L., and Tam, K. Y. (1999). Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology, Journal of Management Information Systems, 16(2): 91-112.
  • Jackson, C. M., Chow, S., and Leitch, R. A. (1997). Toward an Understanding of the Behavioral Intention to Use an Information System, Decision Sciences, 28(2): 357-389.
  • Jung, T., Youn, H., and McClung, S. (2007). Motivations and Self-Presentation Strategies on Korean-based Cyworld Weblog Format Personal Homepages, CyberPsychology & Behavior, 10(1): 24-31.
  • Kaplan, A. M., and Haenlein, M. (2010). Users of the World, Unite! The Challenges and Opportunities of Social Media, Business Horizons, 53(1): 59-68.
  • Karahanna, E., Straub, D. W., and Chervany, N. L. (1999). Information Technology Adoption across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs, MIS Quarterly, 183-213.
  • Katz, E. (1974). Utilization of Mass Communication by the Individual. (Ed: J. G. Blumler), The Uses of Mass Communications: Current Perspectives on Gratifications Research, (pp. 19-32), Beverly Hills: SAGE Publications.
  • Kietzmann, J. H., Hermkens, K., McCarthy, I. P., and Silvestre, B. S. (2011). Social Media? Get Serious! Understanding the Functional Building Blocks of Social Media, Business Horizons, 54(3): 241-251.
  • Kim, Y., Sohn, D., and Choi, S. M. (2011). Cultural Difference in Motivations for Using Social Network Sites: A Comparative Study of American and Korean College Students, Computers in Human Behavior, 27(1): 365-372.
  • Ko, H., Cho, C.-H., and Roberts, M. S. (2005). Internet Uses and Gratifications: A Structural Equation Model of Interactive Advertising, Journal of Advertising, 34(2): 57-70.
  • Krisanic, K. (2008). Motivations and Impression Management: Predictors of Social Networking Site Use and User Behavior, University of Missouri, Columbia.
  • Kyun Choi, Y., Kim, J., and McMillan, S. J. (2009). Motivators for the Intention to Use Mobile Tv: A Comparison of South Korean Males and Females, International Journal of Advertising, 28(1): 147-167.
  • Lee, Y.-H., Hsieh, Y.-C., and Hsu, C.-N. (2011). Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees' Intentions to Use E-Learning Systems, Journal of Educational Technology and Society, 14(4).
  • Legris, P., Ingham, J., and Collerette, P. (2003). Why Do People Use Information Technology? A Critical Review of the Technology Acceptance Model, Information & Management, 40(3): 191-204.
  • Lien, C. H., and Cao, Y. (2014). Examining Wechat Users’ Motivations, Trust, Attitudes, and Positive Word-of-Mouth: Evidence from China, Computers in Human Behavior, 41: 104-111.
  • Lomax, R. G., and Schumacker, R. E. (2010). A Beginner's Guide to Structural Equation Modeling, New York: Taylor and Francis Group.
  • Lucas Jr, H. C., and Spitler, V. (1999). Technology Use and Performance: A Field Study of Broker Workstations, Decision Sciences, 30(2): 291-311.
  • Luo, X. (2002). Uses and Gratifications Theory and E-Consumer Behaviors: A Structural Equation Modeling Study, Journal of Interactive Advertising, 2(2): 34-41.
  • Mathieson, K. (1991). Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior, Information Systems Research, 2(3): 173-191.
  • Moore, G. C., and Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation, Information Systems Research, 2(3): 192-222.
  • Park, N., Kee, K. F., and Valenzuela, S. (2009). Being Immersed in Social Networking Environment: Facebook Groups, Uses and Gratifications, and Social Outcomes, CyberPsychology and Behavior, 12(6): 729-733.
  • Pavlou, P. A. (2003). Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model, International Journal of Electronic Commerce, 7(3): 101-134.
  • Peslak, A., Ceccucci, W., and Sendall, P. (2010). An Empirical Study of Social Networking Behavior Using Diffusion of Innovation Theory, Conference on Applied Information Systems Research (CONISAR), Nashville, TN., 3(1526)
  • Peter, J. P. (1979). Reliability: A Review of Psychometric Basics and Recent Marketing Practices, Journal of Marketing Research, 6-17.
  • Rogers, E. M. (1983). Diffusion of Innovations (3 rd ed.), New York: The Free Press.
  • Rogers, E. M. (2010). Diffusion of Innovations (5th ed.), New York: Simon and Schuster.
  • Roy, S. K. (2009). Internet Uses and Gratifications: A Survey in the Indian Context, Computers in Human Behavior, 25(4): 878-886.
  • Russo, A., Watkins, J., Kelly, L., and Chan, S. (2008). Participatory Communication with Social Media, Curator: The Museum Journal, 51(1): 21-31.
  • Schaefer, C. D. (2008). Motivations and Usage Patterns on Social Network Sites, Paper presented at the ECIS.
  • Sonnenwald, D. H., Maglaughlin, K. L., and Whitton, M. C. (2001). Using Innovation Diffusion Theory to Guide Collaboration Technology Evaluation: Work in Progress, Paper presented at The Tenth IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, Cambridge, MA.
  • Stafford, T. F., and Gonier, D. (2004). "What Americans Like About Being Online", Communications of the ACM, 47(11), 107-112.
  • Surry, D. W., and Farquhar, J. D. (1997). Diffusion Theory and Instructional Technology, Paper presented at the Proceedings of the Annual Conference of the Association for Educational Communications and Technology.
  • Taylor, S., and Todd, P. (1995). Assessing It Usage: The Role of Prior Experience, MIS Quarterly, 561-570.
  • Taylor, S., and Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models, Information Systems Research, 6(2): 144-176.
  • Tidd, J., Bessant, J., and Pavitt, K. (1997). Managing Innovation: Integrating Technological, Market and Organizational Change, Chichester, England: John Wiley and Sons.
  • Tran, T. C. T., and Cheng, M. S. (2017). Adding Innovation Diffusion Theory to Technology Acceptance Model: Understanding Consumers' Intention to Use Biofuels in Viet Nam, International Review of Management and Business Research, 6(2): 595.
  • Van der Heijden, H. (2003). Factors Influencing the Usage of Websites: The Case of a Generic Portal in the Netherlands, Information and Management, 40(6): 541-549.
  • Venkatesh, V., and Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies, Management Science, 46(2): 186-204.
  • Weaver Lariscy, R., Tinkham, S. F., and Sweetser, K. D. (2011). Kids These Days: Examining Differences in Political Uses and Gratifications, Internet Political Participation, Political Information Efficacy, And Cynicism on The Basis of Age, American Behavioral Scientist, 55(6): 749-764.
  • Whiting, A., and Williams, D. (2013). Why People Use Social Media: A Uses and Gratifications Approach, Qualitative Market Research: An International Journal, 16(4): 362-369.
  • Wu, J.-H., and Wang, S.-C. (2005). What Drives Mobile Commerce? An Empirical Evaluation of the Revised Technology Acceptance Model, Information and Management, 42(5): 719-729.
  • Zarrella, D. (2009). The Social Media Marketing Book, Sebastopol: O'Reilly Media Inc.

Determinants of Users’ Intention to Use Social Media Apps

Year 2019, Volume: 21 Issue: 3, 686 - 702, 30.09.2019
https://doi.org/10.32709/akusosbil.478170

Abstract

This
study examines to understand the smartphone users' attitudes and intention
towards social media apps with the perspective of three widely used models;
uses and gratification (U&G) theory's psychological motivations factors
(entertainment, sociality, and information), innovation diffusion theory (IDT)
and the technology acceptance model (TAM). Thus, in the framework of this
research, the proposed research model consists of three respective models. The
random sampling technique used to collect research samples from Usak province
in Turkey.  The data used in testing the
research model collected by the questionnaires. The numbers of the valid survey
collected were 527. The structural equation modeling conducted to analyze the
research assumptions and model. The outcomes indicate that the users' perceived
ease of use (PEOU) influenced by complexity, relative advantage, observability,
trialability. Perceived usefulness (PU) affected by compatibility, relative
advantage, observability, trialability, and PEOU, while with attitude together
influencing intention to use social media app. Another outcome showed that
attitude determined by sociality, entertainment, PEOU, and PU. Empirical
results also provided support for the integrative approach. The results show
that TAM in the extension of an innovation diffusion theory and psychological
motivations can help decision-makers in the social media app.

References

  • Agarwal, R. (2000). Individual Acceptance of Information Technologies, (Ed: R. W. Zmud), Framing the Domains of It Management: Projecting the Future Through the Past: (pp. 85-104).
  • Agarwal, R. and Prasad, J. (1999). Are Individual Differences Germane to the Acceptance of New Information Technologies? Decision Sciences, 30(2): 361-391.
  • Bajaj, A., and Nidumolu, S. R. (1998). A Feedback Model to Understand Information System Usage, Information and Management, 33(4): 213-224.
  • Baker, R. K., and White, K. M. (2010). Predicting Adolescents’ Use of Social Networking Sites from an Extended Theory of Planned Behaviour Perspective, Computers in Human Behavior, 26(6): 1591-1597.
  • Boyd, D. M., and Ellison, N. B. (2007). Social Network Sites: Definition, History, and Scholarship, Journal of Computer‐Mediated Communication, 13(1): 210-230.
  • Bradford, M., and Florin, J. (2003). Examining the Role of Innovation Diffusion Factors on the Implementation Success of Enterprise Resource Planning Systems, International Journal of Accounting Information Systems, 4(3): 205-225.
  • Brandtzæg, P. B., and Heim, J. (2009). Why People Use Social Networking Sites, Paper presented at The International Conference on Online Communities and Social Computing.
  • Cao, Y. and Hong, P. (2011). Antecedents and Consequences of Social Media Utilization in College Teaching: A Proposed Model with Mixed-Methods Investigation, On the Horizon, 19(4): 297-306.
  • Carr, C. T., and Hayes, R. A. (2015). Social Media: Defining, Developing, and Divining, Atlantic Journal of Communication, 23(1): 46-65.
  • Chang, H. C. (2010). A New Perspective on Twitter Hashtag Use: Diffusion of Innovation Theory, Proceedings of The American Society for Information Science and Technology, 47(1): 1-4.
  • Chang, Y. P. and Zhu, D. H. (2011). Understanding Social Networking Sites Adoption in China: A Comparison of Pre-Adoption and Post-Adoption, Computers in Human Behavior, 27(5): 1840-1848.
  • Chen, L.-d., Gillenson, M. L., and Sherrell, D. L. (2002). Enticing Online Consumers: An Extended Technology Acceptance Perspective, Information and Management, 39(8): 705-719.
  • Chung, C., and Austria, K. (2010). Social Media Gratification and Attitude toward Social Media Marketing Messages: A Study of the Effect of Social Media Marketing Messages on Online Shopping Value, Paper presented at the Northeast Business & Economics Association.
  • comSCORE. (2018). Global Digital Future in Focus Retrieved from https://www.comscore.com/Insights/Presentations-and-Whitepapers/2018/Global-Digital-Future-in-Focus-2018, (Access: 02.10.2018)
  • Cronbach, L. J., and Shavelson, R. J. (2004). My Current Thoughts on Coefficient Alpha and Successor Procedures, Educational and Psychological Measurement, 64(3): 391-418.
  • Davis, F. D. (1985). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results, Massachusetts Institute of Technology.
  • Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly, 319-340.
  • Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models, Management Science, 35(8): 982-1003.
  • Dolan, R., Conduit, J., Fahy, J., and Goodman, S. (2016). Social Media Engagement Behaviour: A Uses and Gratifications Perspective, Journal of Strategic Marketing, 24(3-4): 261-277.
  • Ducoffe, R. H. (1996). Advertising Value and Advertising on the Web-Blog@ Management, Journal of Advertising Research, 21.
  • Ellison, N., Steinfield, C., and Lampe, C. (2006). Spatially Bounded Online Social Networks and Social Capital, International Communication Association, 36(1-37).
  • Fishbein, M., and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Reading, MA: Addison-Wesley.
  • Fornell, C., and Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error, Journal of Marketing Research, 39-50.
  • Folorunso, O., Vincent, R. O., Adekoya, A. F., and Ogunde, A. O. (2010). Diffusion of Innovation in Social Networking Sites among University Students, International Journal of Computer Science and Security, 4(3): 361-372.
  • Freeman, C. (1974). The Economics of Industrial Innovation, Harmondsworth: Penguin.
  • Hair, J. F., Black, W. C., Babin, B. J., and Anderson, R. E. (2014). Multivariate Data Analysis, Harlow, Essex: Pearson Education Limited.
  • Hardgrave, B. C., Davis, F. D., and Riemenschneider, C. K. (2003). Investigating Determinants of Software Developers' Intentions to Follow Methodologies, Journal of Management Information Systems, 20(1): 123-151.
  • Hu, L. t., and Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives, Structural Equation Modeling: A Multidisciplinary Journal, 6(1): 1-55.
  • Hu, P. J., Chau, P. Y., Sheng, O. R. L., and Tam, K. Y. (1999). Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology, Journal of Management Information Systems, 16(2): 91-112.
  • Jackson, C. M., Chow, S., and Leitch, R. A. (1997). Toward an Understanding of the Behavioral Intention to Use an Information System, Decision Sciences, 28(2): 357-389.
  • Jung, T., Youn, H., and McClung, S. (2007). Motivations and Self-Presentation Strategies on Korean-based Cyworld Weblog Format Personal Homepages, CyberPsychology & Behavior, 10(1): 24-31.
  • Kaplan, A. M., and Haenlein, M. (2010). Users of the World, Unite! The Challenges and Opportunities of Social Media, Business Horizons, 53(1): 59-68.
  • Karahanna, E., Straub, D. W., and Chervany, N. L. (1999). Information Technology Adoption across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs, MIS Quarterly, 183-213.
  • Katz, E. (1974). Utilization of Mass Communication by the Individual. (Ed: J. G. Blumler), The Uses of Mass Communications: Current Perspectives on Gratifications Research, (pp. 19-32), Beverly Hills: SAGE Publications.
  • Kietzmann, J. H., Hermkens, K., McCarthy, I. P., and Silvestre, B. S. (2011). Social Media? Get Serious! Understanding the Functional Building Blocks of Social Media, Business Horizons, 54(3): 241-251.
  • Kim, Y., Sohn, D., and Choi, S. M. (2011). Cultural Difference in Motivations for Using Social Network Sites: A Comparative Study of American and Korean College Students, Computers in Human Behavior, 27(1): 365-372.
  • Ko, H., Cho, C.-H., and Roberts, M. S. (2005). Internet Uses and Gratifications: A Structural Equation Model of Interactive Advertising, Journal of Advertising, 34(2): 57-70.
  • Krisanic, K. (2008). Motivations and Impression Management: Predictors of Social Networking Site Use and User Behavior, University of Missouri, Columbia.
  • Kyun Choi, Y., Kim, J., and McMillan, S. J. (2009). Motivators for the Intention to Use Mobile Tv: A Comparison of South Korean Males and Females, International Journal of Advertising, 28(1): 147-167.
  • Lee, Y.-H., Hsieh, Y.-C., and Hsu, C.-N. (2011). Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees' Intentions to Use E-Learning Systems, Journal of Educational Technology and Society, 14(4).
  • Legris, P., Ingham, J., and Collerette, P. (2003). Why Do People Use Information Technology? A Critical Review of the Technology Acceptance Model, Information & Management, 40(3): 191-204.
  • Lien, C. H., and Cao, Y. (2014). Examining Wechat Users’ Motivations, Trust, Attitudes, and Positive Word-of-Mouth: Evidence from China, Computers in Human Behavior, 41: 104-111.
  • Lomax, R. G., and Schumacker, R. E. (2010). A Beginner's Guide to Structural Equation Modeling, New York: Taylor and Francis Group.
  • Lucas Jr, H. C., and Spitler, V. (1999). Technology Use and Performance: A Field Study of Broker Workstations, Decision Sciences, 30(2): 291-311.
  • Luo, X. (2002). Uses and Gratifications Theory and E-Consumer Behaviors: A Structural Equation Modeling Study, Journal of Interactive Advertising, 2(2): 34-41.
  • Mathieson, K. (1991). Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior, Information Systems Research, 2(3): 173-191.
  • Moore, G. C., and Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation, Information Systems Research, 2(3): 192-222.
  • Park, N., Kee, K. F., and Valenzuela, S. (2009). Being Immersed in Social Networking Environment: Facebook Groups, Uses and Gratifications, and Social Outcomes, CyberPsychology and Behavior, 12(6): 729-733.
  • Pavlou, P. A. (2003). Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model, International Journal of Electronic Commerce, 7(3): 101-134.
  • Peslak, A., Ceccucci, W., and Sendall, P. (2010). An Empirical Study of Social Networking Behavior Using Diffusion of Innovation Theory, Conference on Applied Information Systems Research (CONISAR), Nashville, TN., 3(1526)
  • Peter, J. P. (1979). Reliability: A Review of Psychometric Basics and Recent Marketing Practices, Journal of Marketing Research, 6-17.
  • Rogers, E. M. (1983). Diffusion of Innovations (3 rd ed.), New York: The Free Press.
  • Rogers, E. M. (2010). Diffusion of Innovations (5th ed.), New York: Simon and Schuster.
  • Roy, S. K. (2009). Internet Uses and Gratifications: A Survey in the Indian Context, Computers in Human Behavior, 25(4): 878-886.
  • Russo, A., Watkins, J., Kelly, L., and Chan, S. (2008). Participatory Communication with Social Media, Curator: The Museum Journal, 51(1): 21-31.
  • Schaefer, C. D. (2008). Motivations and Usage Patterns on Social Network Sites, Paper presented at the ECIS.
  • Sonnenwald, D. H., Maglaughlin, K. L., and Whitton, M. C. (2001). Using Innovation Diffusion Theory to Guide Collaboration Technology Evaluation: Work in Progress, Paper presented at The Tenth IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, Cambridge, MA.
  • Stafford, T. F., and Gonier, D. (2004). "What Americans Like About Being Online", Communications of the ACM, 47(11), 107-112.
  • Surry, D. W., and Farquhar, J. D. (1997). Diffusion Theory and Instructional Technology, Paper presented at the Proceedings of the Annual Conference of the Association for Educational Communications and Technology.
  • Taylor, S., and Todd, P. (1995). Assessing It Usage: The Role of Prior Experience, MIS Quarterly, 561-570.
  • Taylor, S., and Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models, Information Systems Research, 6(2): 144-176.
  • Tidd, J., Bessant, J., and Pavitt, K. (1997). Managing Innovation: Integrating Technological, Market and Organizational Change, Chichester, England: John Wiley and Sons.
  • Tran, T. C. T., and Cheng, M. S. (2017). Adding Innovation Diffusion Theory to Technology Acceptance Model: Understanding Consumers' Intention to Use Biofuels in Viet Nam, International Review of Management and Business Research, 6(2): 595.
  • Van der Heijden, H. (2003). Factors Influencing the Usage of Websites: The Case of a Generic Portal in the Netherlands, Information and Management, 40(6): 541-549.
  • Venkatesh, V., and Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies, Management Science, 46(2): 186-204.
  • Weaver Lariscy, R., Tinkham, S. F., and Sweetser, K. D. (2011). Kids These Days: Examining Differences in Political Uses and Gratifications, Internet Political Participation, Political Information Efficacy, And Cynicism on The Basis of Age, American Behavioral Scientist, 55(6): 749-764.
  • Whiting, A., and Williams, D. (2013). Why People Use Social Media: A Uses and Gratifications Approach, Qualitative Market Research: An International Journal, 16(4): 362-369.
  • Wu, J.-H., and Wang, S.-C. (2005). What Drives Mobile Commerce? An Empirical Evaluation of the Revised Technology Acceptance Model, Information and Management, 42(5): 719-729.
  • Zarrella, D. (2009). The Social Media Marketing Book, Sebastopol: O'Reilly Media Inc.
There are 69 citations in total.

Details

Primary Language English
Journal Section Philosophy
Authors

Fatih Şahin 0000-0002-4760-4413

Alkan Alkaya This is me 0000-0002-9917-200X

Ercan Taşkın 0000-0001-8499-1013

Publication Date September 30, 2019
Submission Date November 3, 2018
Published in Issue Year 2019 Volume: 21 Issue: 3

Cite

APA Şahin, F., Alkaya, A., & Taşkın, E. (2019). Determinants of Users’ Intention to Use Social Media Apps. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 21(3), 686-702. https://doi.org/10.32709/akusosbil.478170

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