Total views : 462

Determinant Factors on Student Empowerment and Role of Social Media and eWOM Communication: Multivariate Analysis on LinkedIn usage


  • Information Technology, SRM University, Kattankulathur, Chennai - 603203, Tamil Nadu, India
  • Computer Science and Engineering, Hindustan University, Padur, Chennai - 602203, Tamil Nadu, India
  • Computer Science and Engineering, SRM University, Kattankulathur, Chennai -603203, Tamil Nadu, India


Background/Objectives: In recent times, there is phenomenal increase in usage of Social Networking Sites like Facebook, LinkedIn etc. by college students and young professionals. This study focuses on identifying key factors that influence LinkedIn usage and the role of eWOM communication in enhancing social connectivity and engagement of students in meaningful activities to improve their social and academic standings. A theoretical model on social networking by students is proposed and the results and recommendations of this study will be brought to practical use towards student empowerment. Methods/Statistical Analysis: A preliminary survey was conducted to understand how young university students use the Social Networking Site LinkedIn and the responses were used to frame a questionnaire. A second level survey was conducted among the same set of participants by collecting their responses in five point Likert Scale. Exploratory Factor Analysis was conducted using the LinkedIn Survey responses to identify the hidden factors associated with the indicator items in the data set. Subsequently, a theoretical model was constructed using Structural Equation Modeling principles, depicting the interrelationships between the latent constructs and indicator items constituting a measurement model and a structural model. Four Hypotheses were framed such that Social Media Usage and eWOM communication have significant positive effect on Student Empowerment. Finally, Confirmatory factor analysis was done to prove the hypotheses and to analyze how well the model fits into the theory. The software IBM SPSS, and AMOS 23 were used to perform multivariate statistical analysis on the LinkedIn Survey response items. Findings: The exploratory study on LinkedIn Usage Survey responses revealed three latent factors that accounted for 69.462 percent of the total variance. The three key factors explaining the eWOM behavior of students in LinkedIn usage were Expert Opinion Seeking, Networking with Professionals and Notification of Profile Changes. The latent factors and associated relationships were used to frame a theoretical model based on SEM techniques. Based on Confirmatory factor analysis done on this model using the data set revealed that the model supported the hypotheses H1, H2, H3 and H4 and all indicators in the model significantly loaded to their respective factors and the predicting variables had a significant positive effect on the predicted variable. The factor loadings were fair to excellent ranging from .634 to .853 and the test for model fitness showed good fitness result based on value of various fitness indices which were within accepted limits. Based on CFA, the important fitness indices and their values arrived at were: CMIN/df = 2.022, NFI = 0.824, TLI = 0.887, RMSEA = 0.098 and CFI = 0.901. Improvements/Applications: The accuracy of the predicting ability of the proposed theoretical model can be improved by augmenting this research study and statistical analysis to be extended to a larger target group belonging to different institutions to achieve good model fit as well as for testing the scalability of the model. As a future work, this model can be integrated with online learning systems also with the aim of improving student engagement in the current online learning scenarios.


Confirmatory Factor Analysis, eWOM Communication, Exploratory factor Analysis, Multivariate Analysis, Online Social Networking Sites (OSN), Principal Component Analysis, Social Networking Sites, Structural Equation Modeling (SEM), Student Empowerment, Uses and Gratification Theory (U&G)

Full Text:

 |  (PDF views: 442)


  • Juha L. User factors in recommender systems: Case studies in e-Commerce, News Recommending and e-Learning. Dissertations in Interactive Technology No. 17. School of Information Sciences, Tampere University; 2014.
  • Cheung CMK, Thadani DR. The effectiveness of electronic Word-of-Mouth communication: A literature analysis. 23rd Bled eConference eTrust: Implications for the Individual, Enterprises and Society; Bled, Slovenia. 2010 Jun 20–23. p. 329–45.
  • Davis CJ, Kmetz K. Student engagement: The core model and inter-cohort analysis. Information Systems Education Journal. 2015; 13(3):4–14.
  • Kuester S, Thomsen J. Drivers of eWOM marketing for successful new product launch. IMU Research Insights 10; 2012.
  • Chu SC, Kim Y. Determinants of consumer engagement in electronic Word-of-Mouth (eWOM) in social networking sites. International Journal of Advertising. 2011; 30(1).
  • Kietzmann JH, Canhoto A. Bittersweet! Understanding and managing electronic word of mouth. Journal of Public Affairs. 2013; 13(2):146–59.
  • Nowak KL, McGloin R. The influence of peer reviews on source credibility and purchase intention. Societies. 2014; 4(4):689–705.
  • Karampiperis P, Koukourikos A, Stoitsis G. Collaborative filtering recommendation of educational content in social environments utilizing sentiment analysis techniques. Recommender Systems Handbook: A complete guide for Research Scientists and Practitioners. Berlin: Springer; 2010. p. 3–23.
  • Electronic Word-of-Mouth Marketting as important today as it ever was. University of Brighton Blog; 2015 Apr.
  • Escobar AE, Reyes P, Van Hilst M. Metrics for effectiveness of e-learning objects in software engineering education. IEEE SOUTHEASTCON; 2014 Mar. p. 1–5.
  • Churchill EF. Social networks and social networking. IEEE Internet Computing. 2005; 9(5):14–9.
  • King RB.The social underpinnings of motivation and achievement: Investigating the role of parents, teachers and peers on academic outcomes. The Asia-Pacific Education Researcher. 2013; 23(3):745–56.
  • Stafford TF, Stafford MR, Schkade LL. Determining uses and gratifications for the internet. Decision Sciences. 2004; 35(2):259–88.14. Basak E, Calisir F. Uses and gratifications of LinkedIn: An exploratory study. Proceedings of the World Congress on Engineering; 2014 Jul 2-4.
  • Jin L, Chen Y, Wang T, Hui P, Vasilakos AV. Understanding user behavior in online social networks: A survey. IEEE Communications Magazine. 2013; 51(9):144–50.
  • Johnson P, Yang Sn. Uses and gratifications of Twitter: An examination of user motives and satisfaction of Twitter use. Paper presented at the Annual Meeting of the Association for Education in Journalism and Mass Communication. Boston, MA: Sheraton Boston; 2009 Aug 5.
  • Zimmerman BJ. Self regulated learning and academic achievement: An overview. Educational Psychologist. 1990; 25(1):3–17.
  • IBM. SPSS version 19.
  • Field A. Discovering statistics using SPSS 4th Ed. Sage Publications; 2013.
  • Shawar BA. Evaluating quality of e-Learning trends used at AOU based on Participants’ Satisfaction. IEEE 3rd Int Conf on Future Internet of Things and Cloud; 2015 Aug. p. 545–52.
  • Gfrerer A, Pokrywka J. Traditional vs. electronic Word-of-Mouth: A study of WOM communication and its influence on young consumers within the Automobile Industry. [Master thesis]. Int Marketting and Brand Management. Lund University; 2012 May.
  • Shen H, et al. Knowledge sharing in the Online Social Network of yahoo! Answers and its implications. IEEE Transactions on Computers. 2015 Jun; 9(6):1715–28.
  • Xia F, et al. Socially-Aware Networking : A Survey. IEEE Systems Journal. 2015 Sep; 9(5):904–21.
  • Campbell WM, et al. Social Network Analysis with Content and graphs. Lincoln Laboratory Journal. 2013; 20(1):62–81.
  • Karnik M, et al. Uses and gratification of Facebook Media Sharing group. Proceedings of the Conference on Computer Supported Cooperative work (CSCW’13), ACM; Newyork, NY, USA. 2013. p. 821–6.
  • Quan-Hasse A, et al. Uses and gratification of Social Media: A comparison of Facebook and instant messaging. Bulletin of Science and Technology and Society. 2010; 30(5):350–61.
  • Meng-Hsiang H, et al. Determinants of continued use of Social Media: The perspectives of uses and gratification theory and perceived interactivity. Information Research. 2015; 20(2).
  • Vivekananthamoorthy N, Rajkumar R. The role of social networking sites and eWOM Communication in enhancing student engagement in current learning scenarios. International Journal of Applied Engineering Research. 2015 Jul; 10(69):237–41.
  • Kietzmann JH, McCarthy I. Social Media? Get Serious! Understanding the functional building blocks of Social Media. Business Horizons. 2011; 54:241–51.
  • Paul T, et al. The user behavior in Facebook and its development from 2009 until 2014. 2015. arXiv preprint arXiv:1505.04943.
  • Lopez M, Sicilia M. Determinants of e-WOM influence: The role of consumers’ internet expereince. Journal of Theoritical and Applied Electronic Commerce Research. 2014 Jan; 9(1):28–43.
  • Nielsen. The Nielson global trust in advertising Survey, 2012. Nielsen [Online] Available from:
  • Archambault A, Grudin J. A longitudinal study of Facebook, Linkedin and Twitter use. Proc SIGCHI Conf Human Factors in Computing Systems (CHI ‘12) ACM; NY, USA. 2012.
  • Storey MAD, Singer L, Cleary B, Filho FMF, Zagalsky A. The (R) Evolution of Social Media in Software Engineering. Proc of the International Conference on Software Engineering (ICSE’14), Track on the Future of Software Engineering (FOSE). ACM; 2014. p. 100–16.
  • Utz S. (in press). Is LinkedIn making you more successful? The informational benefits derived from public social media. New Media and Society. DOI: 10.1177/1461444815604143
  • Marlow J, et al. Activity traces and signals in software developer recruitment and hiring. Proc of 2013 Conf Comput Supported Cooperative work, CSCW ‘13. ACM; NY, USA. 2013. p. 145–56.
  • Jenkins H, Clinton K, Purushotma R, Robison AJ, Weigel M. Confronting the challenges of participatory culture: Media education for the 21st century. 2006. Available from:{7E45C7E0-A3E0-4B89-AC9C-807E1B0AE4E}/JENKINS_WHITE_PAPER.PDF
  • Son JE, Kim HW, Jang YJ. Investigating factors affecting electronic word-of-mouth in the open market context: A mixed methods approach. PACIS 2012 Proceedings. Paper 167. 2012. Available from:
  • Mendes-Filho L, Tan FB. User-generated content and consumer empowerment in the travel industry: A uses and gratifications and dual-process conceptualization. PACIS proceedings; 2009.
  • Conger JA, Kanungo RN. The empowerment process: Integrating theory and practice. Academy of Management Review. 1988; 13(3):471–82.
  • Anderson JC, Gerbing DW. Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin. 1988; 103(3):411–23.
  • Byrne BM. Structural equation modeling with AMOS: Basic concepts, applications and programming. New York: Routledge; 2010.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.