What Drives Intention to Use Facebook: An Empirical Study of Vietnamese Users
Main Article Content
Abstract
This study aims to investigate users’ attitude and intention to use of a social media site in the context of the Facebook platform. A questionnaire survey was conducted to collect data from 134 users of Facebook in Vietnam. Collected data were analyzed by using hierarchical regression analysis. This study points out that trustworthiness and perceived usefulness are the direct predictors of intention to use social media sites such as Facebook. Specifically, trustworthiness is found to have the greatest impact on intention to use Facebook, which is followed by perceived usefulness among Vietnamese users. These findings provide evidence about the value of trust and perceived usefulness that can be considered as direct predictors of behavioural intention to use a product or technology. Moreover, remarkable points are recommended for Facebook developers, business managers and the Vietnamese Government in integrating Facebook as well as other online social media platforms and business strategies. As a result, the study can be helpful for future researchers, managers, practitioners and educators in the area of the Vietnamese social media community.
Keywords:
Social media, Facebook, Technology Acceptance Model (TAM), Intention to use
References
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[33] Pantano, Eleonora, and Constantinos Vasilios Priporas, The effect of mobile retailing on consumers' purchasing experiences: A dynamic perspective, Computers in human behavior, 2016, pp. 548-555.
[1] P. Ozturk, N. Andalibi, A. Forte, Sensitive Self-disclosures, Responses, and Social Support on Instagram: the case of depression, Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing, 2017.
[2] A. Rapp, L.S. Beitelspacher, D. Grewal, D.E. Hughes, Understanding social media effects across seller, retailer, and consumer interactions, Journal of the Academy of Marketing Science, 2013, pp. 547-566.
[3] A. Crowe, The social media manifesto: A comprehensive review of the impact of social media on emergency management, Journal of business continuity and emergency planning, 2011, pp. 409-420.
[4] S. Diffley, J. Kearns, W. Bennett, P. Kawalek, Consumer behaviour in social networking sites: implications for marketers, Irish Journal of Management, 2011.
[5] S. Fogel, Issues in measurement of word of mouth in social media marketing, International Journal of Integrated Marketing Communications 2(2) (2010) 54-60.
[6] Statista, Social Media - Statistics and Facts, Statista, 2020.
[7] Asia Plus, Vietnamese social media and online ads” Asia Plus Inc., 2019.
[8] S. Dunphy, P.A. Herbig, Acceptance of innovations: the customer is the key, The Journal of High Technology Management Research 6(2) (1995) 193-209.
[9] F.D. Davis, Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS quarterly, 1989, pp. 319-340.
[10] S. Zhang, J. Zhao, W. Tan, Extending TAM for online learning systems: An intrinsic motivation perspective, Tsinghua Science and Technology 13(3) (2008) 312-317.
[11] M.J. Reinders, R. Frambach, M. Kleijnen, Mandatory use of technology-based self-service: does expertise help or hurt?, European Journal of Marketing 49 (2015) 190-211.
[12] L. Zhou, L. Dai, D. Zhang, Online shopping acceptance model-A critical survey of consumer factors in online shopping, Journal of Electronic commerce research, 2007.
[13] M. Fishbein, I. Ajzen, Predicting and changing behavior: The reasoned action approach, Taylor & Francis, 2011.
[14] L.A. Hussein, A.S. Baharudin, K. Jayaraman, B2B e-commerce technology factors with mediating effect perceived usefulness in Jordanian manufacturing SMES, Journal of Engineering Science and Technology 14(1) (2019) 411-429.
[15] A.A. Hamid, F.Z.A. Razak, A.A. Bakar, W.S.W. Abdullah, The effects of perceived usefulness and perceived ease of use on continuance intention to use e-government, Procedia Economics and Finance 35 (2016) 644-649.
[16] A. Kashada, E. Ehtiwsh, H. Nakkas, The role of technology acceptance model (TAM) towards information systems implementation success: A Meta-Analysis, The International Journal of Engineering and Science (IJES) 9(1) (2020)
30-36.
[17] A.M. Elkaseh, K.W. Wong, C.C. Fung, Perceived ease of use and perceived usefulness of social media for e-learning in Libyan higher education: A structural equation modeling analysis, International Journal of Information and Education Technology 6(3) (2016) 192.
[18] G. Nayanajith, K.A. Damunupola, Effects of Subjective Norms and Security on Online Banking Adoption: Multilevel Linear Model Analysis, Asian Journal of Multidisciplinary Studies 2(1) (2019) 9-16.
[19] D. Suleman, I. Zuniarti, E.D.S. Sabil, V.A. Yanti, I.H. Susilowati, I. Sari, A.S. Lestiningsih, Decision Model Based on Technology Acceptance Model (TAM) for Online Shop Consumers in Indonesia, 2019.
[20] D.A. Schmidt, E. Baran, A.D. Thompson,
P. Mishra, M.J. Koehler, T.S. Shin, Technological pedagogical content knowledge (TPACK) the development and validation of an assessment instrument for preservice teachers", Journal of research on Technology in Education, 2009, p. 123-149.
[21] C. Volkle, P. Planing, Digital Automation of Customer Contact Processes-an Empirical Research on Customer Acceptance of different Chatbot Use-cases, Digitalen Wandel gestalten, Springer Gabler, Wiesbaden, 2019, pp. 217-229.
[22] F. Qiao, Conceptualizing Interactivity on Social Media and Exploring the Effects of Interactivity on Consumers’ Engagement with Online Social-Interactions, Online Journal of Communication and Media Technologies, 2019.
[23] H. Nysveen, P.E. Pedersen, H. Thorbornsen, Intention to use mobile services: antecedents and cross-service comparison, Journal of the Academy of Marketing Science 33 (2005) 330-346.
[24] T. Childers, C. Carr, J. Peck, S. Carson, Hedonic and utilitarian motivations for online retail shopping behavior, Journal of Retailing 77(4) (2001) 511-535.
[25] R. Rupak, et al., Technology acceptance model (TAM) and social media usage: an empirical study on Facebook, Journal of Enterprise Information Management, 2014.
[26] H. Lou, W. Luo, D. Strong, Perceived critical mass effect on groupware acceptance, European Journal of Information Systems 9 (2000) 91-103.
[27] C. Dwyer, S.R. Hiltz, K. Passerini, Trust and privacy concern within social networking sites: a comparison of Facebook and MySpace, Proceedings of AMCIS, 2007.
[28] J.F. Hair, W.C. Black, B.J. Babin, R.E. Anderson, Multivariate data analysis: Pearson new international edition, Pearson Higher Ed, 2013.
[29] T.K. Nguyen, The Effect of Perceived E-Service Quality on Customer Satisfaction and Loyalty in the E-Commerce Business-A Case of B2C Online Shopping in Vietnam, 2013 (in Vietnamese).
[30] R.J. Cohen, R.J. Cohen, D.K. Smith, M.E. Swerdlik, Psychological testing and assessment, 1992.
[31] Liao, Chechen, Chuang-Chun Liu, Kuanchin Chen, Examining the impact of privacy, trust and risk perceptions beyond monetary transactions: An integrated model, Electronic Commerce Research and Applications, 2011, pp. 702-715.
[32] A.U. Rehman, A. Jiang, A. Rehman, A. Paul, Weighted based trustworthiness ranking in social internet of things by using soft set theory, IEEE 5th International Conference on Computer and Communications (ICCC), 2019.
[33] Pantano, Eleonora, and Constantinos Vasilios Priporas, The effect of mobile retailing on consumers' purchasing experiences: A dynamic perspective, Computers in human behavior, 2016, pp. 548-555.
[1] P. Ozturk, N. Andalibi, A. Forte, Sensitive Self-disclosures, Responses, and Social Support on Instagram: the case of depression, Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing, 2017.