SOCIAL MEDIA INNOVATION: DRIVING BEHAVIOURAL INTENTION IN MALAYSIA’S CORPORATE SECTOR
DOI:
https://doi.org/10.55197/qjssh.v7i2.1203Keywords:
social media innovation, behavioral intention, Malaysia, corporate sectorAbstract
With the continual growth of social media integration in corporate environments, many employees lack social media literacy skills necessary for effective and responsible platform use. Additionally, the rapid evolution of social media innovation presents notable challenges for corporate employees, many of whom struggle with adapting to new tools and features. These issues have posed significant challenges regarding employees behavioural intention to use social media, that could impact their professional efficiency and business performance. Therefore, this study aimed to investigate the relationship between social media innovation characteristics (relative advantage, compatibility, complexity, trialability, and observability) and behavioural intention to use social media among Malaysian corporate workers. A quantitative method was adopted, employing both paper and online surveys distributed to Malaysian corporate employees who use social media in their professional roles. A total of 156 responses were collected, and the findings indicated that Malaysian corporate employees exhibited favourable perceptions regarding all the factors affecting their behavioural intention to use social media, as reflected in the mean score. However, out of the five (5) hypotheses examined, only two (2) were supported, indicating that the compatibility and complexity of social media significantly influence the behavioural intention of Malaysian corporate employees to use social media. This study has identified certain limitations that indicate future research to consider increasing the sample size and potentially introducing a new independent variable for further exploration.
References
[1] Abbasi, G.A., Rahim, N.F.A., Wu, H., Iranmanesh, M., Keong, B.N.C. (2022): Determinants of SME’s social Media Marketing adoption: Competitive industry as a moderator. – SAGE Open 12(1): 20p.
[2] Agarwal, R., Prasad, J. (1997): The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. – Decision Sciences 28(3): 557-582.
[3] Al-Kfairy, M., Mustafa, D., Kshetri, N., Insiew, M., Alfandi, O. (2024): Ethical Challenges and Solutions of Generative AI: an interdisciplinary perspective. – Informatics 11(3): 29.
[4] Almogren, A.S., Al-Rahmi, W.M., Dahri, N.A. (2024): Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective. – Heliyon 10(11): 19p.
[5] Ambarwati, R., Harja, Y.D., Thamrin, S. (2020): The role of Facilitating conditions and user Habits: A case of Indonesian online learning platform. – Journal of Asian Finance Economics and Business 7(10): 481-489.
[6] Bashir, T., Zhongfu, T., Sadiq, B., Niaz, U., Anjum, F., Mahmood, H. (2022): An assessment of influential factors developing the intention to use social media sites: A technology acceptance model based approach. – Frontiers in Psychology 13: 13p.
[7] Chille, F.J., Mollel, A.B. (2024): Factors Influencing Adoption of The Diffusion of Instagram Social Media Innovations among Selected Football Clubs in Tanzania. – Indonesian Journal of Social Research (IJSR) 6(1): 32-43.
[8] Chua, P.Y., Rezaei, S., Gu, M., Oh, Y., Jambulingam, M. (2018): Elucidating social networking apps decisions. – Nankai Business Review International 9(2): 118-142.
[9] Davis, F.D. (1989): Perceived usefulness, perceived ease of use, and user acceptance of information technology. – MIS Quarterly 13(3): 319-340.
[10] Deci, E.L., Ryan, R.M. (2013): Intrinsic motivation and self-determination in human behavior. – Springer Science & Business Media 6p.
[11] Dwivedi, Y.K., Ismagilova, E., Hughes, D.L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., Krishen, A.S., Kumar, V. (2021): Setting the future of digital and social media marketing research: Perspectives and research propositions. – International Journal of Information Management 59: 37p.
[12] Emma, L. (2024). User-centered design to enhance accessibility and usability in digital systems. – Lafoke Akintola University of Technology 21p.
[13] Eslami, P., Najafabadi, M., Gharehgozli, A. (2024): Exploring the journey of influencers in shaping social media engagement success. – Online Social Networks and Media 41: 11p.
[14] Farzin, M., Sadeghi, M., Kharkeshi, F.Y., Ruholahpur, H., Fattahi, M. (2021): Extending UTAUT2 in M-banking adoption and actual use behavior: Does WOM communication matter? – Asian Journal of Economics and Banking 5(2): 136-157.
[15] Handoyo, S. (2024): Purchasing in the digital age: A meta-analytical perspective on trust, risk, security, and e-WOM in e-commerce. – Heliyon 10(8): 23p.
[16] Hausman, A. (2024): Innovation: adoption and diffusion in the age of social media. – MKT Maven 15p.
[17] Hui, E., Singh, S., Lin, P.K.F., Dillon, D. (2024): Social Media Influence on Emerging Adults’ Prosocial Behavior: A Systematic Review. – Basic and Applied Social Psychology 46(4): 239-265.
[18] Iyyani, V. (2024): LinkedIn vs Instagram: Which Platform is Better for Professional Networking in 2025? – Millennial Minds Web Portal 11p.
[19] Jalo, H., Pirkkalainen, H. (2024): Effect of user resistance on the organizational adoption of extended reality technologies: A mixed methods study. – International Journal of Information Management 75: 26p.
[20] Jilani, M.M.A.K., Moniruzzaman, M., Dey, M., Alam, E., Uddin, M.A. (2022): Strengthening the trialability for the intention to use of mHealth apps amidst pandemic: A Cross-Sectional study. – International Journal of Environmental Research and Public Health 19(5): 16p.
[21] Liu, D., Son, S., Cao, J. (2023): The determinants of public acceptance of telemedicine apps: an innovation diffusion perspective. – Frontiers in Public Health 11: 15p.
[22] Maqbool, B., Herold, S. (2023): Potential effectiveness and efficiency issues in usability evaluation within digital health: A systematic literature review. – Journal of Systems and Software, 208: 26p.
[23] Menon, D., Shilpa, K. (2023): “Chatting with ChatGPT”: Analyzing the factors influencing users’ intention to Use the Open AI’s ChatGPT using the UTAUT model. – Heliyon 9(11): 19p.
[24] Moore, G.C., Benbasat, I. (1991): Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. – Information Systems Research 2: 173-191.
[25] Nurmi, J., Knittle, K., Ginchev, T., Khattak, F., Helf, C., Zwickl, P., Castellano-Tejedor, C., Lusilla-Palacios, P., Costa-Requena, J., Ravaja, N., Haukkala, A. (2019): Engaging users in the behavior change process with digitalized motivational interviewing and gamification: development and feasibility testing of the Precious app. – JMIR Mhealth and Uhealth 8(1):30p.
[26] Nuseira, M.T., Aljumahb, A. (2020): Digital marketing adoption influenced by relative advantage and competitive industry: a UAE tourism case study. – Marketing 11(2): 23-37.
[27] Rahardjo, J.S., Sunuantari, M. (2021): Acceptance and behavior of social media use: causality analysis of media needs, media choice and intervening characteristics of social media innovations. – South Asian Journal of Social Studies and Economics 10(4): 36-58.
[28] Rogers, E. (2003). Diffusion of Innovations. – Free Press 576p.
[29] Scharlach, R., Hallinan, B. (2023): The value affordances of social media engagement features. – Journal of Computer-Mediated Communication 28(6): 11p.
[30] Susilawaty, F.T., Jaya, A., Sudirman, F.A., Halika, L.O.H. (2023): Exploring the impact of Instagram like visibility on self-confidence. – Jurnal Kajian Komunikasi 11(2): 180-194.
[31] Taherdoost, H., Mohamed, N., Madanchian, M. (2024): Navigating Technology Adoption/Acceptance Models. – Procedia Computer Science 237: 833-840.
[32] Tandoc Jr, E.C., Yee, A.Z., Ong, J., Lee, J.C.B., Xu, D., Han, Z., Matthew, C.C.H., Ng, J.S.H.Y., Lim, C.M., Cheng, L.R.J., Cayabyab, M.Y. (2021): Developing a Perceived Social Media Literacy Scale: Evidence from Singapore. – International Journal of Communication 15: 22p.
[33] Venkatesh, V., Bala, H. (2008): Technology Acceptance Model 3 and a research Agenda on interventions. – Decision Sciences 39(2): 273-315.
[34] Venkatesh, V., Davis, F.D. (2000): A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. – Management Science 46(2): 186-204.
[35] Venkatesh, V., Thong, J.Y., Xu, X. (2012): Consumer acceptance and use of information technology: Extending the Unified Theory of Acceptance and Use of Technology1. – MIS Quarterly 36(1): 157-178.
[36] Vuchkovski, D., Zalaznik, M., Mitręga, M., Pfajfar, G. (2023): A look at the future of work: The digital transformation of teams from conventional to virtual. – Journal of Business Research 163: 17p.
[37] Vukušić Rukavina, T., Viskić, J., Machala Poplašen, L., Relić, D., Marelić, M., Jokic, D., Sedak, K. (2021): Dangers and benefits of social media on e-professionalism of health care professionals: scoping review. – Journal of Medical Internet Research 23(11): 25p.
[38] Yi, M.Y., Jackson, J.D., Park, J.S., Probst, J.C. (2006): Understanding information technology acceptance by individual professionals: Toward an integrative view. – Information & Management 43(3): 350-363.
[39] Zhao, Y., Von Delft, S., Morgan-Thomas, A., Buck, T. (2019): The evolution of platform business models: Exploring competitive battles in the world of platforms. – Long Range Planning 53(4): 24p.
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