Over-the-Top (OTT) video streaming services’ continuance adoption during and after Covid-19 pandemic: Mediation analysis combining two research frameworks

M. Hena ,Nadia Sha ,P. Lekshmi ,Shiny Salam

doi:10.24294/jipd9059

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Over-the-Top (OTT) video streaming services’ continuance adoption during and after Covid-19 pandemic: Mediation analysis combining two research frameworks

M. Hena

Nadia Sha

P. Lekshmi

Shiny Salam

Journal of Infrastructure Policy and Development 2024, 8(13), 9059; https://doi.org/10.24294/jipd9059

Submitted：10 Sept 2024

Accepted：12 Dec 2024

Published：07 Nov 2024

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Abstract

During and after the Covid-19 outbreak, people’s precautionary measures of not visiting public venues like cinema halls or multiplexes were replaced by watching treasured videos or films in private settings. People are able to watch their favourite video contents on a variety of internet-connected gadgets thanks to advanced technologies. As a result, it appears that the Covid-19 outbreak has had a substantial impact on people’s inclination to continue using video streaming services. This study attempted to establish an integrated framework that describes how people change their health behaviours during pandemic conditions using the health belief model (HBM), as well as the mediating effect of HBM constructs over ECM constructs such as continuous intention to subscribe to OTT video streaming services among subscribers. The study looked at the impact of three perceived constructs, susceptibility, severity, and self-efficacy, on the confirmation/adoption of over-the-top (OTT) video streaming services during the lethal pandemic (Covid-19). The study focused on new OTT video streaming service subscribers, and 473 valid replies were collected. Path analysis and multivariate analytical methods, such as structural equation modelling (SEM), were used to estimate construct linkages in the integrated framework. Perceived severity has been identified as the most influential factor in confirmation/adoption, followed by perceived susceptibility. The results also showed that satisfied users/subscribers are more likely to use OTT video streaming services. The mediators, confirmation/adoption, perceived usefulness, and satisfaction were used to validate the influence of perceived susceptibility on continuance intention. Furthermore, contactless entertainment enhances security for users/subscribers by allowing them to be amused across several internet-based venues while adhering to social distance norms.

Keywords

References

Ajzen, I. (1991). The theory of planned behavior. In: Van Lange P, Kruglanski A, Higgins E (editors). Handbook of Theories of Social Psychology, Sage Publications, 1, pp. 438–459. https://doi.org/10.4135/9781446249215.n22

Ambalov I. A. (2018). A meta-analysis of IT continuance: an evaluation of the expectation-confirmation model. Telematics and Informatics, 35 (6): 1561–1571. https://doi.org/10.1016/j.tele.2018.03.016

Anderson J. C., Gerbing D. W. (1988). Structural equation modeling in practice: a review and recommended Two-Step approach. Psychological Bulletin, 103 (3): 411–423. https://doi.org/10.1037/0033-2909.103.3.411

Bandura A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2): 191–215. https://doi.org/10.1037/0033-295X.84.2.191

Bandura A. (1997). Self-Efficacy: The Exercise of Control, W H Freeman/Times Books/Henry Holt and Co, pp. 604.

Banerjee A., Rappoport P. N., Alleman J. (2014). Forecasting Video Cord-Cutting: The Bypass of Traditional Pay Television. In: Alleman, J., Ní-Shúilleabháin Á., Rappoport P. (editors). Demand for Communications Services – Insights and Perspectives. The Economics of Information, Communication, and Entertainment, Springer. 59–82. https://doi.org/10.1007/978-1-4614-7993-2_4

Becker M. H., Maiman L. A. (1980). Strategies for enhancing patient compliance. Journal of Community Health, 6, 113–135. https://doi.org/10.1007/BF01318980

Bhattacherjee A. (2001). Understanding information systems continuance: an expectation confirmation model, MIS Quarterly, 25 (3): 351–370. https://doi.org/10.2307/3250921

Bhattacherjee A., Perols J., Sanford C. (2008). Information technology continuance: a theoretic extension and empirical test. Journal of Computer Information Systems, 49 (1): 17–26. https://doi.org/10.1080/08874417.2008.11645302

Boonsiritomachai W., Pitchayadejanant K. (2017). Determinants affecting mobile banking adoption by generation Y based on the unified theory of acceptance and use of technology model modified by the technology acceptance model concept. Kasetsart Journal of Social Sciences, 40(2):349–358. https://doi.org/10.1016/j.kjss.2017.10.005

Byrne B. M. (2016). Structural equation modeling with Amos: Basic Concepts, Applications and Programming, 3rd ed. Routledge, pp 460. https://doi.org/10.4324/9781315757421

Center for Financial Inclusion (2020). Center for Financial Inclusion Remittances and financial Inclusion:”Sending money home” in the COVID Era. https://www.centerforfinancialinclusion.org/remittances-and-financial-inclusion-sending-money-home-in-the-covid-era/

Champion V. L. (1984). Instrument development for health belief model constructs. ANS Advances in Nursing Science, 6(3):73–85. https://doi.org/10.1097/00012272-198404000-00011

Champion V., Skinner C. S. (2008). The Health Belief Model. In: Glanz K, Rimer B, Viswanath K (editors). Health behavior and health education. 4. San Francisco, CA: Jossey-Bass; 2008. pp. 45–65.

Chang S. L., Harding N., Zachreson C. (2020). Modelling transmission and control of the COVID-19 pandemic in Australia. Nature Communications, 11, 5710. https://doi.org/10.1038/s41467-020-19393-6

Chen S. C., Yen D. C., Hwang M. I. (2012). Factors influencing the continuance intention to the usage of Web 2.0: An empirical study. Computers in Human Behavior, 28 (3): 933–941. https://doi.org/10.1016/j.chb.2011.12.014

Chih C. S., Yen C. D., Hwang M., (2012), “Factors influencing the continuance intention to the usage of Web 2.0: An empirical study”, Computers in Human Behavior, Vol.28 (3) pp.933–941. https://doi.org/10.1016/j.chb.2011.12.014

Chinazzi M., Davis J. T., Ajelli M. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science, 368(6489):395–400. https://doi.org/10.1126/science.aba9757

Chiu W., Cho H., Chi C. G. (2021). Consumers’ continuance intention to use fitness and health apps: an integration of the expectation–confirmation model and investment model. Information Technology and People, 34(3): 978–998. https://doi.org/10.1108/ITP-09-2019-0463

Cronshaw S. (2021). Web workouts and consumer well-being: The role of digital-physical activity during the UK COVID-19 lockdown. Journal of Consumer Affairs, 56 (1): 449–464. https://doi.org/10.1111/joca.12375

Daragmeh A., Sági J., Zéman Z. (2021). Continuous Intention to Use E-Wallet in the Context of the COVID-19 Pandemic: Integrating the Health Belief Model (HBM) and Technology Continuous Theory (TCT). Journal of Open Innovation: Technology, Market and Complexity, 7(2): 132. https://doi.org/10.3390/joitmc7020132

Davis F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3): 319–340. https://doi.org/10.2307/249008

DeDonno M. A., Longo J., Levy, X. (2022). Perceived Susceptibility and Severity of COVID-19 on Prevention Practices, Early in the Pandemic in the State of Florida. Journal of Community Health 47, 627–634 (2022). https://doi.org/10.1007/s10900-022-01090-8

Donthu N., Gustafsson A. (2020). Effects of Covid-19 on business and research. Journal of Business Research, 117: 284–289. https://doi.org/10.1016/j.jbusres.2020.06.008

Dou K., Yu P., Deng N. et al (2017). Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test. JMIR mHealth and uHealth, 5(12): e177. https://doi.org/10.2196/mhealth.7886

Dutta S. S. (2021). India seeing stabilization of Covid second wave, says Centre”21st May 2021, The New Indian Express, https://www.newindianexpress.com/nation/2021/May/27/india-seeing-stabilisation-of-covid-second-wave-says-centre-2308392.html

Eikenberry S. E., Mancuso M., Iboi E., et al (2020). To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic. Infectious Disease Modelling, 5: 293–308. https://doi.org/10.1016/j.idm.2020.04.001

El-Toukhy S. (2015). Parsing Susceptibility and Severity Dimensions of Health Risk Perceptions. Journal of Health Communication, 20(5): 499–511. https://doi.org/10.1080/10810730.2014.989342

Emara N., Zhang Y. (2021). The non-linear impact of digitization on remittances inflow: Evidence from the BRICS. Telecommunications Policy, 45(4). https://doi.org/10.1016/j.telpol.2021.102112

Fagerjord A., Kueng L. (2019). Mapping the core actors and flows in streaming video services: what Netflix can tell us about these new media networks. Journal of Media Business Studies, 16 (3): 166–181. https://doi.org/10.1080/16522354.2019.1684717

Fishbein M., Ajzen I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley, pp.520. https://people.umass.edu/aizen/f&a1975.html

Fornell C., Larcker D. F. (1981). Structural equation models with unobservable variables and measurement error: algebra and statistics. Journal of Marketing Research, 18(3) :382–388. https://doi.org/10.1177/002224378101800313

Foroughi B., Iranmanesh M., Hyun S. S. (2019). Understanding the determinants of mobile banking continuance usage intention. Journal of Enterprise Information Management, 32(6): 1015–1033. https://doi.org/10.1108/JEIM-10-2018-0237

Gaube S., Lermer E., Fischer P. (2019). The Concept of Risk Perception in Health-Related Behavior Theory and Behavior Change. Raue M., Streicher B., Lermer E. (editors) Perceived Safety. Risk Engineering. Springer, Cham. pp 101–118. https://doi.org/10.1007/978-3-030-11456-5_7

Ghosh A., Nundy S., Mallick T. K. (2020). How India is dealing with COVID-19 pandemic. Sensors International, 1. https://doi.org/10.1016/j.sintl.2020.100021

Glanz K., Rimer B. K., Viswanath K. (Eds.). (2008). Health behavior and health education: Theory, research, and practice (4th ed.). Jossey-Bass. https://psycnet.apa.org/record/2008-17146-000

Gupta, G., Singharia, K. (2021). Consumption of OTT Media Streaming in COVID-19 Lockdown: Insights from PLS Analysis. Vision, 25(1), 36–46. https://doi.org/10.1177/0972262921989118

Hair J. F., Black W. C., Babin B. J., Anderson R. E. (2014). Multivariate Data Analysis. 7th Edition, Pearson Education, Upper Saddle River.

Herrmann A., Hall, Proietto A. (2018). Using the Health Belief Model to explore why women decide for or against the removal of their ovaries to reduce their risk of developing cancer. BMC Women’s Health, 18, 184. https://doi.org/10.1186/s12905-018-0673-2

Janz N. K., Becker M. H. (1984). The Health Belief Model: A decade later. Health Education and Behaviour, 11(1): 1–47 https://doi.org/10.1177/109019818401100101

Karahanna E., Straub D. W., Chervany NL (1999) Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs. MIS Quarterly, 23(2): 183–21. https://doi.org/10.2307/249751

Kim J., Park H. A. (2012). Development of a health information technology acceptance model using consumers’ health behavior intention. Journal of Medical Internet Research, 14(5): e133. https://doi.org/10.2196/jmir.2143

Kim M.S., Kim E., Hwang S.et al (2017). Willingness to pay for over-the-top services in China and Korea. Telecommunications Policy, 41(3):197–207. https://doi.org/10.1016/j.telpol.2016.12.011

Kline R. B. (2011). Principles and practice of structural equation modelling. (3rd ed.). Guilford Press.

Lai A. C. K., Poon C. K. M., Cheung A. C. T. (2012). Effectiveness of facemasks to reduce exposure hazards for airborne infections among general populations. Journal of the Royal Society Interface, 9 (70): 938–948. https://doi.org/10.1098/rsif.2011.0537

Lee S., Lee S., Joo H., Nam Y. (2021). Examining Factors Influencing Early Paid Over-The-Top Video Streaming Market Growth: A Cross-Country Empirical Study. Sustainability,13(10): 5702. https://doi.org/10.3390/su13105702

Li, O., Qian, D. (2021). An analysis of the relationship between risk perceptions and willingness- to-pay for commodities during the COVID-19 pandemic. Journal of Consumer Affairs, 56 (1): 257–275. https://doi.org/10.1111/joca.12407

Melzner J., Heinze J., Fritsch T. (2014). Mobile health applications in workplace health promotion: an integrated conceptual adoption framework. Procedia Technology,16, 1374–1382. https://doi.org/10.1016/j.protcy.2014.10.155

Meng K. S., Leung L. (2021). Factors influencing TikTok engagement behaviors in China: An examination of gratifications sought, narcissism, and the Big Five personality traits. Telecommunications Policy, 45(7): 102172. https://doi.org/10.1016/j.telpol.2021.102172

Mouakket S. (2015). Factors influencing continuance intention to use social network sites: the facebook case. Computers in Human Behavior, 53: 102–110, https://doi.org/10.1016/j.chb.2015.06.045

Nagaraja S., Singh S., Yasa V. R. (2021). Factors affecting consumers’ willingness to subscribe to over-the-top (OTT) video streaming services in India. Technology in Society, 65. https://doi.org/10.1016/j.techsoc.2021.101534

Oghuma A. P., Libaque-Saenz C. F., Wong S. F.,Chang Y. (2016). An expectation-confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics, 33(1): 34–47, https://doi.org/10.1016/j.tele.2015.05.006

Oliver R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4): 460–469. https://doi.org/10.2307/3150499

Pai C., Bhaskar A., Rawoot V. (2020). Investigating the dynamics of COVID-19 pandemic in India under lockdown. Chaos, Solitons & Fractals, 138, 109988. https://doi.org/10.1016/j.chaos.2020.109988

Park E. A. (2018). Business strategies of Korean TV players in the age of Over-The-Top (OTT) video service. International Journal of Communication. 12: 4646–4667. https://ijoc.org/index.php/ijoc/article/view/6286

Patnaik R., Patra S.K., Mahapatra D. M., Baral S. K. (2024). Adoption and Challenges Underlying OTT Platform in India during Pandemic: A Critical Study of Socio-Economic and Technological Issues. FIIB Business Review, 13(3), 356–363. https://doi.org/10.1177/23197145221101676

Rahi S., Abd G. M. (2019). Integration of expectation confirmation theory and self-determination theory in internet banking continuance intention. Journal of Science and Technology Policy Management, 10(3): 533–550. https://doi.org/10.1108/JSTPM-06-2018-0057

Rahi S., Khan M. M., Alghizzawi M. (2021). Extension of technology continuance theory (TCT) with task technology fit (TTF) in the context of Internet banking user continuance intention. International Journal of Quality & Reliability Management, 38 (4): 986–1004. https://doi.org/10.1108/IJQRM-03-2020-0074

Ruiz-Real J. L., Nievas-Soriano B. J., Uribe-Toril, J. (2020). Has Covid-19 Gone Viral? An Overview of Research by Subject Area. Health Education & Behavior, 47(6): 861–869. https://doi.org/10.1177/1090198120958368

Sahu G., Gaur L., Singh G. (2021). Applying niche and gratification theory approach to examine the users’ indulgence towards over-the-top platforms and conventional TV. Telematics and Informatics, 65. https://doi.org/10.1016/j.tele.2021.101713

Sanson K., Steirer G. (2019). Hulu, streaming, and the contemporary television ecosystem. Media, Culture & Society, 41(8): 1210–1227. https://doi.org/10.1177/0163443718823144

Scarinci, I. C., Pandya, V. N., Kim, Y. (2021). Factors Associated with Perceived Susceptibility to COVID-19 Among Urban and Rural Adults in Alabama. Journal of Community Health 46, 932–941 (2021). https://doi.org/10.1007/s10900-021-00976-3

Sheng J., Amankwah-Amoah J., Khan Z., Wang X. (2021). COVID-19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions. British Journal of Management, 32 (4): 1164–1183. https://doi.org/10.1111/1467-8551.12441

Sheth J. (2020). Impact of Covid-19 on consumer behavior: Will the old habits return or die? Journal of Business Research, 117: 280–283. https://doi.org/10.1016/j.jbusres.2020.05.059

Shiau W. L., Yuan Y., Pu X., et al (2020). Understanding fintech continuance: perspectives from self-efficacy and ECT-IS theories. Industrial Management & Data Systems, 120(9): 1659–1689. https://doi.org/10.1108/IMDS-02-2020-0069

Shin S., Park J. (2021). Factors affecting users’ satisfaction and dissatisfaction of OTT services in South Korea. Telecommunications Policy, 45(9): 102203. https://doi.org/10.1016/j.telpol.2021.102203

Sreelakshmi C. C., Prathap S. K. (2020). Continuance adoption of mobile-based payments in Covid-19 context: an integrated framework of health belief model and expectation confirmation model. International Journal of Pervasive Computing and Communications, 16(4): 351–369. https://doi.org/10.1108/IJPCC-06-2020-0069

Statista (2017). Netflix Surpasses Major Cable Providers in the U.S. Available online: https://www.statista.com/chart/9799/netflix-vs-cable-pay-tv-subscribers/

Susanto A., Chang Y., Ha Y. (2016). Determinants of continuance intention to use the smartphone banking services: An extension to the expectation-confirmation model. Industrial Management & Data Systems, 116(3): 508–525. https://doi.org/10.1108/IMDS-05-2015-0195

Talwar S., Dhir A., Khalil A., et al (2020). Point of adoption and beyond. Initial trust and mobile-payment continuation intention. Journal of Retailing and Consumer Services, 55. https://doi.org/10.1016/j.jretconser.2020.102086

Tzeng S. Y., Ho T. Y. (2022). Exploring the Effects of Product Knowledge, Trust, and Distrust in the Health Belief Model to Predict Attitude Toward Dietary Supplements. Sage Open, 12(1). https://doi.org/10.1177/21582440211068855

Venkatesh V., Morris M. G., Davis G. B., Davis F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27 (3): 425–478. https://doi.org/10.2307/30036540

Vogel E. A., Henriksen L., Schleicher N. C., Prochaska J. J. (2021). Perceived Susceptibility to and Seriousness of COVID-19: Associations of Risk Perceptions with Changes in Smoking Behavior. International Journal of Environmental Research and Public Health, 18(14):7621. https://doi.org/10.3390/ijerph18147621

Wei J., Vinnikova A., Lu L., Xu J. (2020). Understanding and predicting the adoption of fitness mobile apps: evidence from China. Health Communication, 36(8): 950–961. https://doi.org/10.1080/10410236.2020.1724637

World B. (2020). Global Economic Prospects. Washington, DC, https://doi.org/10.1596/978-1-4648-1553-9

World H. O. (2020). Director-General’s opening remarks at the media briefing on COVID-19-11 March 2020, https://www.who.int/director- general/speeches/detail/who-director-general-s-opening-remarks-at-the-media- briefing-on-covid-19-11 march 2020 [Online; accessed 21 March 2021].

Yeole, S. M., Saha L., Bhaisare C. (2022) A study on User Perspective on OTT platform in India, Journal of Positive School Psychology, 6 (3), 7351–7364. https://www.journalppw.com/index.php/jpsp/article/download/4604/3038/5226

Zhao Y., Ni Q., Zhou R. (2018). What factors influence the mobile health service adoption? A Meta-analysis and the moderating role of age. International Journal of Information Management, 43: 342–350. https://doi.org/10.1016/j.ijinfomgt.2017.08.006

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