Over the Top services (OTT) is a significant internet industry. It delivers video, audio, and other media such as voice and chat services and is transmitted to different platforms or devices through the internet. The services are delivered via the internet by a service provider that is not accountable for the signal’s transmission to the end-user, and users will log on to the OTT services using the open internet protocol or public internet. When COVID-19 hit Malaysia in early 2020, the number of new users for OTT services increased to 14.1 million users. The numbers keep increasing as most users depend on OTT services for the latest information including entertainment. This study used Technology Acceptance Model (TAM) as the outline and take into account the external variables (personal innovativeness and social influence) on users’ attitudes towards OTT video streaming. Thus, this study aimed to identify the influence of external variables by examining the perceived usefulness and ease of use towards users’ attitudes. There were 352 respondents who have been selected among Klang’s citizens via an online survey. The findings supported the idea that personal innovativeness and social influence have an impact on Klang’s citizens to stream OTT video streaming. The hypotheses showed a positive and significant effect on users’ attitudes, perceived usefulness, and ease of use (H1a, H1b, H1c, H2a, H2b, H2c). Meanwhile, perceived usefulness and ease of use showed insignificant relationships on users’ attitudes toward OTT video streaming (H3a, H3b, and H4). The findings also acknowledged our understanding of OTT video streaming can be used as part of an educational approach to create awareness and give information.
Agarwal, R., & Karahana, G. (2000). Time flies when you are having fun: cognitive assumption and belief about information on teaching usage. MiS Quartely, 24(4), 665-694
Ajzen, I. (1991). The theory of planned behavior, Organizational Behavior and Human Decision Processes, 50(2),179-211, http://do.org/10.1016/0749-5978(91)90020-T.
Bentler, P. M., & Bonnet, D. G. (1980). Significance tests and goodness-of-fit in the analysis of covariance structure. Psychological Bulletin, 88(3), 588–606.
Bollen, K. A. (1990). Overall fit in covariance structure models: Two types of sample size effects. Psychological Bulletin, 107(2), 256–259.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Ph.D, Management, Massachusetts Institute of Technology.
Duane, A., O’Reilly, P., and Andreev, P. (2014). Realising M-payments: modelling consumers’ willingness to M-pay using smart phones, Behaviour & Information Technology, Vol. 33 No. 4, pp. 318-334.
Farahat, T. (2012). Applying the Technology Acceptance Model to Online Learning in the Egyptian Universities. Procedia-Social and Behavioral Sciences, 64, 95–104.
Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50(2), 229–238. https://doi.org/10.1037/0022-3514.50.2.229
Fornell & Larcker. (1981), Evaluating structural equation models with unobservable
variables and measurement error. Journal of Marketing Research, 48, 39–50.
Goh, G. (2020). On-demand video disrupting TV in South-east Asia, study finds. https://www.businesstimes.com.sg/asean-business/on-demand-video-disrupting-tv-in-south-east-asia-study-finds
Geurin, A. N., & Burch, L. M. (2017). User-generated branding via social media: An examination of six running brands, Sport Management Review, Elsevier, vol. 20(3), pages 273-284. DOI: 10.1016/j.smr.2016.09.001
Hair, J. M., Black, W., Babin, B., and Anderson, R. (2010), Multivariate data analysis, 7th ed., Prentice-Hall Inc, Upper Saddle River, NJ.
Johari, N., Mustaffha, N., Ripain, N., Zulkifli, A., & Ahmad, N. W. (2015). Students’ Acceptance of Online Learning in KUIS. First International Conference on Economics and Banking, 326–335.
Joreskog, K. G., & Sorbom, D. (1996). LISREL8 User’s reference Guide. SSI.
Khasawneh, M. (2015). A Mobile Banking Adoption Model in the Jordanian Market: An Integration of TAM with Perceived Risks and Perceived Benefits. Journal of Internet Banking and Commerce, vol. 20, no. 3, pp. 1-15, Oct. 3, 2015.
Krosnick, J. A., & Petty, R. E. (1995). Attitude strength: An overview. In R. E. Petty & J. A. Krosnick (Eds.), Attitude strength: Antecedents and consequences (pp. 1–24). Lawrence Erlbaum Associates, Inc.
Krejcie, R. V., & Morgan D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurements, 30, 607-10
Kim, S. W. (2003). Exploring factors influencing personal digital assistant (PDA) adoption. Unpublished Ph.D thesis, University of Florida, Gainesville, FL.
Kulviwat, S., Bruner, G. C., & Al-Shurian, O. (2009). The role of social influence on adoption of
high-tech innovations: The moderating effect of public/private consumption. Journal of Business Research 62(7):706-712
Lazim, M. C. S. L., Ismail, N. D., Tazilah, K. M. D. A. (2021). Technology acceptance model (TAM) towards online learning during COVID-19 pandemic: Accounting students perspective. International Journal of Business, Economics and Law, Vol. 24, Issue 1 (April)
Li, S-C. S. (2004). Exploring the factors influencing the adoption of interactive cable television services in Taiwan. Journal of Broadcasting &Electronic Media, 48(3), 466-483.
Lim, F. W., Fakhrorazi, A., Asmat, N., Talib, A. B. (2019). Behavioural Intention towards Using Electronic Wallet: A Conceptual Framework in the Light of the Unified Theory of Acceptance and Use of Technology (UTAUT), Imperial Journal of Interdisciplinary Research vol. 5, no. 1, pp. 79-86, 2019.
Lee, Y., & Kozar, K. (2008). An empirical investigation of anti-spyware software adoption: A multitheoretical perspective. Information & Management, 45(2), 109-119.
Lule, I., Omwansa, T., and Mwololo, T. (2012). Application of Technology Acceptance Model (TAM) in M-Banking Adoption in Kenya, International Journal of Computing and ICT Research, vol. 6, no. 1, pp. 31-43, 01/01 2012.
Matsuo, M., Minami, C., & Matsuyam, T. (?2018). Social influence on innovation resistance in internet banking services. Journal of Retailing and Consumer Services, 45:42-51 DOI:10.1016/j.jretconser.2018.08.005
Mohammadi, H. (2015). A study of mobile banking usage in Iran. International Journal of Bank Marketing, vol. 33, no. 6, pp. 733-759.
Media Partners Asia. (2020). Coronavirus Gives 60% Boost to Mobile Streaming in Southeast Asia. Variety. https://variety.com/2020/streaming/asia/coronavirus boost-mobile-streaming-southeast-asia-1234584706/
Ministry of Health (2021). Terkini negeri covid-19 di Malaysia sehingga 15042021. https://covid-19.moh.gov.my/terkini-negeri/2021/04/kemaskini-negeri-covid-19-di- malaysia-sehingga-15042021
Mulaik, S. A., James, L. R., Van A. J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-?t indices for structural equation models. Psychological Bulletin, 105(3), 430–445. http://doi/org/10.1037/0033-2909.105.3.430
Nielson. (2021). Top 5 streaming platforms in Malaysia. The Malaysian reserve Population-Hub.com. (2021). Population of Klang 2021.
https://populationhub.com/en/my/population-of-klang-6883.html
Raleting, T., and Nel, J. (2011). Determinants of low-income non-users' attitude towards WIG mobile phone banking: Evidence from South Africa, African Journal of Business Management, vol. 5, pp. 212-223, 02/01 2011.
Ramayah, T., & Ignatius, J. (2005). Impact of perceived usefulness, perceived ease of use and perceived enjoyment on intention to shop online. ICFAI Journal of Systems Management (IJSM), 3(3), 36-51.
Raleting, T., & Nel, J. (2011). Determinants of low-income non-users' attitude towards WIG mobile phone banking: Evidence from South Africa, African Journal of Business Management, vol. 5, pp. 212-223, 02/01 2011.
Ruiz-Mafe, C., Bigne-Alcañiz, E., Sanz-Blas, S., and Tronch, J. (2018). Does social climate influence positive eWOM? A study of heavy-users of online communities, BRQ Business Research Quarterly, Vol. 21, pp. 26-38.
Rogers, E. (2003). Diffusion of innovation, 5th Edition, New York: The Free Press.
Salancik, G. R., Pfeffer, J. (1978). A social information processing approach to job attitudes and task design. Administrative Science Quartely. 23, 224-253
Schierz, G. P., Gerhardt, Schilke, O., & Wirtz, B., W. (2010). Understanding consumer Acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, vol. 9, no. 3, pp. 209-216, 2010/05/01/ 2010.
Stafford, T. F., Stafford, M. R., & Schkade, L. L. (2004). Determining uses and gratifications for the Internet. Decision Science, 35 (2), 259-288.
Schillewaert, N. M., Ahearne, R., Frambach, & Moenaert, R. K. (2000). The acceptance of information technology in the sales force. ISBM Report 15- 2000.
Tan, G. W. H., Ooi, K. B., Chong, S. C., and Hew, T. S. (2014). NFC mobile credit card: the next frontier of mobile payment? Telematics and Informatics, Vol. 31 No. 2, pp. 292-307.
Tanaka, J. S., & Huba, G. J. (1985). A fit index for covariance structure models under arbitrary GLS estimation. British Journal of Mathematical and Statistical Psychology, 38, 197-201
Tjoksaputro, M., & Cokki, C. (2020). The Role of Social Influence Towards Purchase Intention With Value Perception as Mediator: A Study on Starbucks Coffee as an Environmentally Friendly Product. Proceedings of the 8th International Conference on Entrepreneurship and Business Management (ICEBM 2019) UNTAR
Thompson, R., Compeau, D., & Higgins, C. (2006). Intentions to use information technologies: An integrative model. Journal of Organizational & End User Computing, 18 (3) (2006), pp. 25-46
Wah, L. F., Fakhrorazi, A., & Islam, R. (2019). Consumers’ parsimony of mobile internet
banking usage in Malaysia, Humanities & Social Sciences Reviews, vol. 7, no. 1, pp. 239-248, 2019.
Wu, J. H., Wang, S. C. (2005). What drives mobile commerce? An empiral evaluation of the revised technology acceptance model. Information management. 42 (5), 719-729
Yunus, R. (2020). Increasing streaming subscribers signals viewing preferences.
https://themalaysianreserve.com/2020/08/03/increasing-streaming-subscribers-
signals-viewing-preferences/, para 16
Zhu, K., Kraemer, K. L., Gurbaxani, V., and Xu, S. X. (2006). Migration to open-standard interorganizational systems: Network effects, switching costs, and path dependency, MIS Quarterly (30:1), 515-539.
In-Text Citation: (Abdul Latiff et al., 2022)
To Cite this Article: Abdul Latiff, D. I., Ayub, M. S. M., Kamal, S., Zambri, W. A. A. M., & Abdul Latif, A. S. S. S. (2022). The Role of Personal Innovativeness and Social Influence Towards Over The Top (OTT) Video Streaming During the Covid-19 Pandemic. International Journal of Academic Research in Business and Social Sciences. 12(6), 2043– 2057.
Copyright: © 2022 The Author(s)
Published by Knowledge Words Publications (www.kwpublications.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
