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The Effects of Network Externality and Flow Experience on Mobile SNS Continuance

Published: 01 April 2017 Publication History

Abstract

Facilitating users' continuance usage and retaining them are crucial to the success of mobile social networking sites SNS. Drawing on both perspectives of network externality and flow, this research examined the factors affecting mobile SNS continuance usage. Network externality includes two factors: referent network size and perceived complementarity, which represent direct externality and indirect externality, respectively. The results indicated that both factors of network externality have significant effects on flow, which further affects satisfaction and continuance usage. The results imply that service providers need to deliver a positive network externality and an engaging experience in order to facilitate users' continuance usage.

References

[1]
Anderson, E. W., & Sullivan, M. W. 1993. The antecedents and consequences of customer satisfaction for firms. Marketing Science, 122, 125-143.
[2]
Anderson, J. C., & Gerbing, D. W. 1988. Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 1033, 411-423.
[3]
Baker, R. K., & White, K. M. 2010. Predicting adolescents use of social networking sites from an extended theory of planned behaviour perspective. Computers in Human Behavior, 266, 1591-1597.
[4]
Bhattacherjee, A. 2001. Understanding information systems continuance: An expectation-confirmation model. Management Information Systems Quarterly, 253, 351-370.
[5]
Chen, R., & Sharma, S. K. 2013. Self-disclosure at social networking sites: An exploration through relational capitals. Information Systems Frontiers, 152, 269-278.
[6]
CNNIC. 2012. A Report on Internet Users' Social Network Site Usage in China, China Internet Network Information Center.
[7]
CNNIC. 2016. 38th Statistical Survey Report on the Internet Development in China, China Internet Network Information Center.
[8]
Csikszentmihalyi, M. 1975. Beyond Boredom and Anxiety. San Francisco, CA: Jossey-Bass.
[9]
DeLone, W. H., & McLean, E. R. 2003. The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 194, 9-30.
[10]
Finneran, C. M., & Zhang, P. 2005. Flow in Computer-Mediated Environments: Promises and Challenges. Communications of the Association for Information Systems, 15, 82-101.
[11]
Gefen, D. 2002. Customer loyalty in e-commerce. Journal of the AIS, 3, 27-51.
[12]
Gefen, D., Straub, D. W., & Boudreau, M. C. 2000. Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 47, 1-70.
[13]
Goel, L., Johnson, N., Junglas, I., & Ives, B. 2013. Predicting users return to virtual worlds: A social perspective. Information Systems Journal, 231, 35-63.
[14]
Guo, Y. M., & Poole, M. S. 2009. Antecedents of flow in online shopping: A test of alternative models. Information Systems Journal, 194, 369-390.
[15]
Ha, I., Yoon, Y., & Choi, M. 2007. Determinants of adoption of mobile games under mobile broadband wireless access environment. Information & Management, 443, 276-286.
[16]
Hausman, A. V., & Siekpe, J. S. 2009. The effect of web interface features on consumer online purchase intentions. Journal of Business Research, 621, 5-13.
[17]
Ho, L.-A., & Kuo, T.-H. 2010. How can one amplify the effect of e-learning? An examination of high-tech employees computer attitude and flow experience. Computers in Human Behavior, 261, 23-31.
[18]
Hoffman, D. L., & Novak, T. P. 1996. Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 603, 50-68.
[19]
Hoffman, D. L., & Novak, T. P. 2009. Flow online: Lessons learned and future prospects. Journal of Interactive Marketing, 231, 23-34.
[20]
Jung, Y., Perez-Mira, B., & Wiley-Patton, S. 2009. Consumer adoption of mobile TV: Examining psychological flow and media content. Computers in Human Behavior, 251, 123-129.
[21]
Katz, M. L., & Shapiro, C. 1985. Network externalities, competition, and compatibility. The American Economic Review, 753, 424-440.
[22]
Kim, H.-W., Gupta, S., & Koh, J. 2011. Investigating the intention to purchase digital items in social networking communities: A customer value perspective. Information & Management, 486, 228-234.
[23]
Kim, H. W., Xu, Y., & Koh, J. 2004. A comparison of online trust building factors between potential customers and repeat customers. Journal of the Association for Information Systems, 510, 392-420.
[24]
Koufaris, M. 2002. Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 132, 205-223.
[25]
Ku, Y.-C., Chen, R., & Zhang, H. 2013. Why do users continue using social networking sites? An exploratory study of members in the United States and Taiwan. Information & Management, 507, 571-581.
[26]
Lankton, N. K., McKnight, D. H., & Thatcher, J. B. 2012. The moderating effects of privacy restrictiveness and experience on trusting beliefs and habit: An empirical test of intention to continue using a social networking website. IEEE Transactions on Engineering Management, 594, 654-665.
[27]
Lee, K. C., Kang, I. W., & McKnight, D. H. 2007. Transfer from offline trust to key online perceptions: An empirical study. IEEE Transactions on Engineering Management, 544, 729-741.
[28]
Lin, C.-P., & Bhattacherjee, A. 2008. Elucidating individual intention to use interactive information technologies: The role of network externalities. International Journal of Electronic Commerce, 131, 85-108.
[29]
Lin, K.-Y., & Lu, H.-P. 2011. Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in Human Behavior, 273, 1152-1161.
[30]
Malhotra, N. K., Kim, S. S., & Patil, A. 2006. Common method variance in IS research: A comparison of alternative approaches and a reanalysis of past research. Management Science, 5212, 1865-1883.
[31]
Nunnally, J. C. 1978. Psychometric theory. New York: McGraw-Hill.
[32]
OCass, A., & Carlson, J. 2010. Examining the effects of website induced flow in professional sporting team websites. Internet Research, 202, 115-134.
[33]
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. 2003. Common method biases in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology, 885, 879-903. 14516251.
[34]
Reichheld, F. F., & Schefter, P. 2000. E-loyalty: Your secret weapon on the Web. Harvard Business Review, 784, 105-113.
[35]
Strader, T. J., Ramaswami, S. N., & Houle, P. A. 2007. Perceived network externalities and communication technology acceptance. European Journal of Information Systems, 161, 54-65.
[36]
Straub, D., Boudreau, M.-C., & Gefen, D. 2004. Validation guidelines for IS positivist research. Communications of the Association for Information Systems, 131, 380-427.
[37]
Wattal, S., Racherla, P., & Mandviwalla, M. 2010. Network externalities and technology use: A quantitative analysis of intraorganizational blogs. Journal of Management Information Systems, 271, 145-173.
[38]
Xu, F., Michael, K., & Chen, X. 2013. Factors affecting privacy disclosure on social network sites: An integrated model. Electronic Commerce Research, 132, 151-168.
[39]
Xu, H., Teo, H.-H., Tan, B. C. Y., & Agarwal, R. 2012. Effects of individual self-protection, industry self-regulation, and government regulation on privacy concerns: A study of location-based services. Information Systems Research, 234, 1342-1363.
[40]
Zaman, M., Anandarajan, M., & Dai, Q. 2010. Experiencing flow with instant messaging and its facilitating role on creative behaviors. Computers in Human Behavior, 265, 1009-1018.
[41]
Zhang, Z. 2010. Feeling the sense of community in social networking usage. IEEE Transactions on Engineering Management, 572, 225-239.
[42]
Zhao, L., Lu, Y. B., & Gupta, S. 2012. Disclosure intention of location-related information in location-based social network services. International Journal of Electronic Commerce, 164, 53-89.
[43]
Zhou, T. 2013. An empirical examination of the determinants of mobile purchase. Personal and Ubiquitous Computing, 171, 187-195.

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Information & Contributors

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Published In

cover image International Journal of Technology and Human Interaction
International Journal of Technology and Human Interaction  Volume 13, Issue 2
April 2017
100 pages
ISSN:1548-3908
EISSN:1548-3916
Issue’s Table of Contents

Publisher

IGI Global

United States

Publication History

Published: 01 April 2017

Author Tags

  1. Continuance Usage
  2. Flow
  3. Mobile SNS
  4. Network Externality
  5. System Quality

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