Applying Decomposed Theory of Planned Behaviour towards a Comprehensive Understanding of Social Network Usage in Saudi Arabia

Автор: Waleed A. Al-Ghaith

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 5 Vol. 8, 2016 года.

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This study examines the individuals' participation intentions and behaviour on Social Networking Sites. For this purpose, the Decomposed Theory of Planned Behaviour is utilized. Data collected from a survey of 1100 participants and distilled to 657 usable sets has been analysed to assess the predictive power of Decomposed Theory of Planned Behaviour' model via structural equation modelling. The results show that attitude and subjective norm have significant effect on the participation intention of adopters. Further, the results show that participation intention has significant effect on participation behaviour. However, the study findings also show that perceived behavioural control has no significant effect on participation intention or behaviour of adopters. The model adopted in this study explains 47% of the variance in "Participation Intentions" and 36% of the variance in "Participation Behaviour". Participation of behavioural intention in the model' explanatory power was the highest amongst the constructs (able to explain 14.6% of usage behaviour). While, "attitude" explain around 9% of SNSs usage behaviour.

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Adoption, Saudi Arabia, Social networking sites, Decomposed Theory of Planned Behaviour, DTPB, Usage

Короткий адрес: https://sciup.org/15012486

IDR: 15012486

Список литературы Applying Decomposed Theory of Planned Behaviour towards a Comprehensive Understanding of Social Network Usage in Saudi Arabia

  • Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Processes, 50(2), 179-211.
  • Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behaviour. Englewood Cliffs, NJ: Prentice-Hall.
  • Al-Debei, M., Al-Lozi, E., & Papazafeiropoulou, A. (2013). Why people keep coming back to Facebook: Explaining and predicting continuance participation from an extended theory of planned behaviour perspective. Decision Support Systems, 55(1), 43-54.
  • Alghaith, W., Sanzogni, L., Sandhu, K. (2010). Factors Influencing the Adoption and Usage of Online Services in Saudi Arabia. Electronic Journal of Information Systems in Developing Countries (EJISDC). 40(1), 1-32.
  • Alotaibi, M. (2015). Mobile Computing Trends in Saudi Arabia: An Exploratory Study. I.J. Information Technology and Computer Science, 01, 21-32.
  • Arab Social Media Report. (2014). Citizen Engagement and Public Services in the Arab World: The Potential of Social Media. Mohammed bin Rashid School of government, 1(6). Retrieved from Arab Social Media Report Website: http://www.arabsocialmediareport.com/
  • 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, 26, 1591–1597.
  • Chen, A., Lu, Y., Chau, P. Y., & Gupta, S. (2014). Classifying, Measuring, and Predicting Users’ Overall Active Behavior on Social Networking Sites. Journal Of Management Information Systems, 31(3), 213-253.
  • Chennamaneni, A., Teng, J. T., & Raja, M. (2012). A unified model of knowledge sharing behaviours: theoretical development and empirical test. Behaviour & Information Technology, 31(11), 1097-1115.
  • Cheung, C. & Lee, M. (2010). A theoretical model of intentional social action in online social networks, Decision Support Systems, 49(1), 24–30.
  • Choi, G., & Chung, H. (2013). Applying the Technology Acceptance Model to Social Networking Sites (SNS): Impact of Subjective Norm and Social Capital on the Acceptance of SNS. International Journal Of Human-Computer Interaction, 29(10), 619-628.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982-1003
  • De Guinea, A., & Markus, M. (2009). Why break the habit of a lifetime? rethinking the roles of intention, habit, and emotion in continuing information technology use. MIS Quarterly, 33(3), 433.
  • Dholakia, U., Bagozzi, R., & Pearo, L. (2004). A social influence model of consumer participation in network- and small-group-based virtual communities. International Journal of Research in Marketing, 21(3), 241-263.
  • Ellison, N.B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook ‘‘friends’’: social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, (12), 1143–1168.
  • Gnyawali, D., Fan, W., & Penner, J. (2010). Competitive actions and dynamics in the digital age: an empirical investigation of social networking firms. Information Systems Research. 21(3), 594–613.
  • Hernandez, M. & Mazzon, J. (2007). Adoption of internet banking: proposition and implementation of an integrated methodology approach. The International Journal of Bank Marketing, 25(2), 72-88.
  • Kim, S., & Malhotra, N. K. (2005). A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Postadoption Phenomena. Management Science, 51(5), 741-755.
  • 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, 50(7), 571-581.
  • Ma, Q., & Liu, L. (2004). The technology acceptance model: A meta-analysis of empirical findings. Journal of Organizational and End User Computing, 16(1), 59-72.
  • Matute, H., Vadillo, M.A., Vegas, S. & Blanco, F. (2007). Illusion of control in Internet users and college students. Cyberpsychology & Behavior, 10, 176–181.
  • Montesarchio, C. (2009). Factors influencing the unethical
  • behavioral intention of college business students: Theory of planned behavior. Ph.D. dissertation, Lynn University, United States -- Florida.
  • Moore, C., & Benbasat, I. (2001). Development of an instrument to measure the perception of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.
  • Novak, T., Hoffman, D., & Yung, Y. (2000). Measuring the customer experience in online environments: A structural modelling approach. Marketing Science, 19(1), 22-42.
  • Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free Press.
  • Sidorova, A., Evangelopoulos, N., Valacich, J. S., & Ramakrishnan, T. (2008). Uncovering the intellectual core of the information systems discipline. MIS Quarterly, 32(3), 467-A20.
  • Taylor, S., & Todd, P.A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176.
  • Xu, C., Ryan, S., Prybutok, V., & Wen, C. (2012). It is not for fun: An examination of social network site usage. Information and Management, 49(5), 210–217.
  • Zhang, H., Lu, Y., Gupta, S., & Zhao, L. (2014). What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences, Information and Management, 51(8), 1017–103.
  • Zhang, J. (2009). Exploring Drivers in the Adoption of Mobile Commerce in China. Journal of American Academy of Business, Cambridge, 15(1), 64-69.
  • Zhou, T. (2011). Understanding online community user participation: a social influence perspective. Internet Research, 21(1), 67–81.
  • Zikmund, W. G. (2003). Business research methods (7th ed.). Cincinnati, OH: Thomson.
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