Analysis determinants of social media acceptance in higher educational institutes of Pakistan

  • Abstract
  • Keywords
  • References
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  • Abstract

    Seen through the lens of the educational sector, social media grew to become a vital source of academic learning. The learning through social media occurs mainly through the collaborative approach to information sharing, where the web-based social networking sites provide the optimal platform for knowledge enhancing among colleagues, co-workers, and others. Developed economies have already recognized the significant value of learning through social media. However, developing economies such as Pakistan did not yet interpret future implications and real benefits of social media aided learning. This research focuses on determining significant factors through an integrated framework that features broadly recognized technology models such as Technology acceptance model (TAM) and Innovation diffusion theory (IDT). The subjects of the framework testing were students in higher education institutions that use social media, and the sample size was 350 students. Data analysis results, reached via SPSS software, were substantially in favour of extended model. Results reached through this study, in terms of factors with a significant influence on social media acceptance rate in Pakistani higher education institutions, are particularly crucial for students in the field of education, located in developing countries. This should assist the increasing acceptance and use of technological solutions, benefiting both faculty and students.

  • Keywords

    Innovation Diffusion Theory, Technology Acceptance Model, Learning and Social Media

  • References

      [1] The Express Tribune Report. (2013). Retrieved October 1, 2015, from

      [2] Fred D. Davis, Richard P. Bagozzii, Paul R. Warshaw. (1989). User Acceptance of Computer Technology: A Comparsion of Two Models. Management Science.

      [3] Davis, F. D., Bagozzi, R. P., &Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982–1003. Retrieved from

      [4] Rogers, E. M., Singhal, A., & Quinlan, M. M. (1995). Diffusion of Innovations Everett M. Rogers. New York.

      [5] Karahanna, E., W. Straub, D., & L. Chervany, N. (1999). Information technology adoption across time: A cross-sectional comparsion of Pre-adoption and Post adoption beliefs. MIS Quarterly, 23(2), 183–213.

      [6] Agarwal, R., Sambamurthy, V., & Stair, R. M. (2000). The evolving relationship between general and specific computer efficacy. Information Systems Research.

      [7] Davis, F. D. (1989). Perceived usefulness, Perceived ese of user and User acceptance of information technology. MIS Quarterly, 13(3), 319–340.

      [8] Al-rahimi, W. M., Othman, M. S., & Musa, M. A. (2013). Using TAM Model To Measure The Use Of Social Media For Collaborative Learning (Vol. 5, pp. 90–95). International Journal of Engineering Trends and Technology (IJETT).

      [9] Agarwal, R., & Prasad, J. (1999). Are Individual Differences Germane to the Acceptance of New Information Technologies? Decision Sciences, 30(2), 361–391. doi:10.1111/j.1540-5915.1999.tb01614.x

      [10] KKwon, S. J., Park, E., & Kim, K. J. (2014). What drives successful social networking services? A comparative analysis of user acceptance of Facebook and Twitter. The Social Science Journal, 1–11. doi:10.1016/j.soscij.2014.04.005

      [11] Chang, S.-C., & Tung, F.-C. (2007). An empirical investigation of students’ behavioural intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 070625111823003–??? doi:10.1111/j.1467-8535.2007.00742.x

      [12] HHardgrave, B., Davis, F. D., &Riemenschneider, C. K. (2003). Investigating Determinants of Software Developers’ Intentions to Follow Methodologies. Journal of Management Information Systems, 20(1), 123–151.

      [13] PPituch, K. a., & Lee, Y. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222–244. doi:10.1016/j.compedu.2004.10.007

      [14] SSu, L. T. (1994). The relevance of recall and precision in user evaluation. Journal of the American Society for Information Science, 45(3), 207–217. doi:10.1002/(SICI)1097-4571(199404)45:3<207::AID-ASI10>3.0.CO;2-1

      [15] BBandura, A. (1986). Social Foundations of Thought and Action: A Social-Cognitive View. Prentice Hall:.Englewood Cliffs, NJ.

      [16] KKoondhar, M. Y., Molok, N., Chandio, F., Rind, M. M., Raza, A., & Shah, A. (2015). A Conceptual Framework for Measuring the Acceptance of Pervasive Learning. Proceedings of the 5th International Conference on Computing & Informatics, (193), 97–103. Retrieved from <Go to ISI>://WOS:000359431400013

      [17] GGay, L. R., &Airasian, P. W. (2000). Educational Research, Competencies for Analysis and Application (6th ed.). Merill an Imprint of Prentice Hall.

      [18] SSekeran, U., & Bougie, R. (2016). Research Methods for Business: A Skill-Building Approach (6th ed.). U.k: John Wiley & Sons, Inc..

      [19] MMoore, G., &Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2, 192–222. doi:10.1287/isre.2.3.192

      [20] RRaza, A., Chandio, F. H., Koondhar, M. Y., Rind, M. M., & Shah, A. (2015). A Framework for the Analysis if Determinants of Social Media Acceptance in Higher Educational Institutes of Pakistan. In the 5th International conference on computing and informatic, ICOCI 2015 (pp. 104–111). Istanbul, Turkey: Universiti Utara Malaysia.




Article ID: 10050
DOI: 10.14419/ijet.v7i2.5.10050

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