Understanding continuance intention of subscription video-on-demand based over-the-top (OTT) platforms: A multiple moderation approach

Document Type : Research Paper

Authors

1 VIT Business School, Vellore Institute of Technology, Vellore, India 2. VIT Business School, Vellore Institute of Technology, Vellore, India

2 VIT Business School, Vellore Institute of Technology, Vellore, India

3 Kingston Engineering College, Vellore, India

4 GITAM School of Business School, Visakhapatnam, India

Abstract

OTT (over-the-top) platforms that stream media directly to viewers via the Internet, bypassing cable, broadcast, and satellite television platforms, are one of the fastest-growing platforms in India. This study investigates the variables influencing continuance intention to subscribe to video-on-demand streaming media services. Utilizing the expectation confirmation model (ECM) and adding habit and content availability, the moderating effects of those variables are examined. SmartPLS was used for Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the results of the measurement and structural models. The findings show that perceived usefulness and expectation had a significant impact on satisfaction and that the effect of satisfaction was significant on continuance intention to subscribe. Research establishes that habit and content availability moderate the association between satisfaction and continuance intention. The study provides valuable insights for service providers, marketers, and practitioners to strengthen continuance intentions.

Keywords

Main Subjects


Aigbefo, Q. A., Blount, Y., & Marrone, M. (2020). The influence of hardiness and habit on security behaviour intention. Behaviour & Information Technology, 1-20. https://doi.org/10.1080/0144929x.2020.1856928
Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust. Technology in Society, 55, 100-110. https://doi.org/10.1016/j.techsoc.2018.06.007
Al-Debei, M. M., Akroush, M. N., & Ashouri, M. I. (2015). Consumer attitudes towards online shopping: The effects of trust, perceived benefits, and perceived web quality. Internet Research, 25(5), 707–733.
Amoroso, D., & Lim, R. (2017). The mediating effects of habit on continuance intention. International Journal of Information Management, 37(6), 693-702. https://doi.org/10.1016/j.ijinfomgt.2017.05.003
Ashfaq, M., Yun, J., Waheed, A., Khan, M. S., & Farrukh, M. (2019). Customers’ expectation, satisfaction, and repurchase intention of used products online: Empirical evidence from China. SAGE Open, 9(2), 215824401984621. https://doi.org/10.1177/2158244019846212
Avkiran, N. K. (2018). Rise of the partial least squares structural equation modeling: An application in banking. Partial Least Squares Structural Equation Modeling, 1-29. https://doi.org/10.1007/978-3-319-71691-6_1
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351. https://doi.org/10.2307/3250921
Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28(2), 229. https://doi.org/10.2307/25148634
Brown, S.A & Venkatesh,V (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399. https://doi.org/10.2307/25148690
Camilleri, M. A., & Falzon, L. (2020). Understanding motivations to use online streaming services: Integrating the technology acceptance model (TAM) and the uses and gratifications theory (UGT). Spanish Journal of Marketing - ESIC, 25(2), 217-238. https://doi.org/10.1108/sjme-04-2020-0074
Chakraborty, D., Siddiqui, M., Siddiqui, A., Paul, J., Dash, G., & Mas, F. D. (2023). Watching is valuable: Consumer views – Content consumption on OTT platforms. Journal of Retailing and Consumer Services, 70, 103148. https://doi.org/10.1016/j.jretconser.2022.103148
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. https://doi.org/10.4324/9780203771587
Daneji, A. A., Ayub, A. F. M., & Khambari, M. N. M. (2019). The effects of perceived usefulness, confirmation and satisfaction on continuance intention in using massive open online course (MOOC). Knowledge Management & E-Learning, 11(2), 201–214. https://doi.org/10.34105/j.kmel.2019.11.010
Dasgupta, S., & Grover, P. (2019). Understanding adoption factors of over-the-top video services among millennial consumers. International Journal of Computer Engineering & Technology, 10(1). https://doi.org/10.34218/ijcet.10.1.2019.008
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
Eren, B. A. (2021). Determinants of customer satisfaction in chatbot use: Evidence from a banking application in Turkey. International Journal of Bank Marketing, 39(2), 294-311. https://doi.org/10.1108/ijbm-02-2020-0056
Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. University of Akron Press.
Franque, F. B., Oliveira, T., & Tam, C. (2021). Understanding the factors of mobile payment continuance intention: Empirical test in an African context. Heliyon, 7(8), e07807. https://doi.org/10.1016/j.heliyon.2021.e07807
Friederich, F., Palau-Saumell, R., Matute, J., & Meyer, J.-H. (2023). Digital natives and streaming TV platforms: an integrated perspective to explain continuance usage of over-the-top services. Online Information Review, https://doi.org/10.1108/OIR-03-2022-0133
Gefen, D., Straub, D., & Boudreau, M. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4. https://doi.org/10.17705/1cais.00407
Golalizadeh, F., Ranjbarian, B., & Ansari, A. (2023). An Evaluation and Analysis of Perceived Online Service Quality Dimensions Impacts on Online Purchasing Behavior of Luxury Cosmetic Products by Women. Iranian Journal of Management Studies. https://doi.org/10.22059/IJMS.2023.337578.674908
Gupta, S. (2023). The impact of E-wom on users’ attitudes toward over-the-top (OTT) streaming video content and its subscription intention. – Young Indians perspective. International Journal of Professional Business Review, 8(2), e01046. https://doi.org/10.26668/businessreview/2023.v8i2.1046
Gupta, G., & Singharia, K. (2021). Consumption of OTT media streaming in COVID-19 lockdown: Insights from PLS analysis. Vision: The Journal of Business Perspective, 25(1), 36-46. https://doi.org/10.1177/0972262921989118
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1-12. https://doi.org/10.1016/j.lrp.2013.01.001
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442-458. https://doi.org/10.1108/imds-04-2016-0130
Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W.,
Jr., D J, K., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on ronkko and evermann (2013). Organizational Research Methods, 17(2), 182–209. https://doi.org/10.1177/1094428114526928
Hsiao, C., Chang, J., & Tang, K. (2016). Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives. Telematics Informatics, 33, 342-355. https://doi.org/10.1016/j.tele.2015.08.014
Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037/1082-989X.3.4.424
Hu, T., Stafford, T. F., Kettinger, W. J., Zhang, X. “., & Dai, H. (2017). Formation and effect of social media usage habit. Journal of Computer Information Systems, 58(4), 334-343. https://doi.org/10.1080/08874417.2016.1261378
IBEF. (2022, February 23). Indian OTT market to touch $13-15 bn in 10 years: Deloitte. Retrieved from https://www.ibef.org/news/indian-ott-market-to-touch-13-15-bn-in-10-years-deloitte
Indrawati. (2014). The use of modified unified theory of acceptance and use of technology to predict the behavioural intention toward website. Applied Mechanics and Materials, 568-570, 1586-1592. https://doi.org/10.4028/www.scientific.net/amm.568-570.1586
Indrawati, & Haryoto, K.S. (2015). The use of modified theory of acceptance and use of technology 2 to predict prospective users' intention in adopting TV Streaming.
Jha, L. (2020, October 22). India is the world’s fastest growing OTT market: PwC report. Retrieved from https://www.livemint.com/news/india/india-is-the-world-s-fastest-growing-ott-market-pwc-report-11603355739242.html
Joo, Y. J., Park, S., & Shin, E. K. (2017). Students' expectation, satisfaction, and continuance intention to use digital textbooks. Computers in Human Behavior, 69, 83-90. https://doi.org/10.1016/j.chb.2016.12.025
Kim, B. (2011). Understanding antecedents of continuance intention in social-networking services. Cyberpsychology, Behavior, and Social Networking, 14(4), 199-205. https://doi.org/10.1089/cyber.2010.0009
Koul, S., Ambekar, S. S., & Hudnurkar, M. (2020). Determination and ranking of factors that are important in selecting an over-the-top video platform service among millennial consumers. International Journal of Innovation Science, 13(1), 53-66. https://doi.org/10.1108/ijis-09-2020-0174
Keržič, D., Alex, J. K., Pamela Balbontín Alvarado, R., Bezerra, D. D., Cheraghi, M., Dobrowolska, B., … Aristovnik, A. (2021). Academic student satisfaction and perceived performance in the E-lEarning environment during the COVID-19 pandemic: Evidence across ten countries. PLOS ONE, 16(10), e0258807. https://doi.org/10.1371/journal.pone.0258807
Kwon, Y., Park, J., & Son, J. (2020). Accurately or accidentally? Recommendation agent and search experience in over-the-top (OTT) services. Internet Research, 31(2), 562-586. https://doi.org/10.1108/intr-03-2020-0127
Lankton, N. K., Wilson, E. V., & Mao, E. (2010). Antecedents and determinants of information technology habit. Information & Management, 47(5-6), 300-307. https://doi.org/10.1016/j.im.2010.06.004
Le, T. T., Pham, H. M., Chu, N. H., Nguyen, D. K., & Ngo, H. M. (2020). Factors affecting users’ continuance intention towards mobile banking In Vietnam. American Journal of Multidisciplinary Research & Development (AJMRD)2(4), 42-51.
Lee, J. S., & Cho, J. (2021). Determinants of continuance intention for over-the-top services. Social Behavior and Personality: An International Journal, 49(12), 1–13. https://doi.org/10.2224/sbp.10566
Limayem, M., & Hirt, S. (2003). Force of habit and information systems usage: Theory and initial validation. Journal of the Association for Information Systems, 4(1), 65-97. https://doi.org/10.17705/1jais.00030
Malewar, S., & Bajaj, S. (2020). Acceptance of OTT video streaming platforms in India during covid -19: Extending utaut2 with content availability. Journal of Content Community and Communication, 12, 89-106. https://doi.org/10.31620/jccc.12.20/09
Morgeson, F. V. (2012). Expectations, disconfirmation, and citizen satisfaction with the US federal government: Testing and expanding the model. Journal of Public Administration Research and Theory23(2), 289-305. https://doi.org/10.1093/jopart/mus012
Müller, R., Van der Merwe, M., & Bevan-Dye, A. L. (2020). Influence of perceived usefulness and ease of use on Generation Y students’ attitude towards streaming services in South Africa. Polish Journal of Management Studies, 21(1), 224-235. https://doi.org/10.17512/pjms.2020.21.1.17
Nagaraj, 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, 101534. https://doi.org/10.1016/j.techsoc.2021.101534
Nguyen, P. M. B., Do, Y. T., & Wu, W. Y. (2021). Technology Acceptance Model and Factors Affecting Acceptance of Social Media: An Empirical Study in Vietnam. The Journal of Asian Finance, Economics and Business, 8(6), 1091–1099. https://doi.org/10.13106/JAFEB.2021.VOL8.NO6.1091
Ngah, A. H., Abdul Rashid, R., Ariffin, N. A., Ibrahim, F., Abu Osman, N. A., Kamalrulzaman, N. I., Harun, N. O. (2021). Fostering students’ attitude towards online learning: The mediation effect of satisfaction and perceived performance. Proceedings of International Conference on Emerging Technologies and Intelligent Systems, 290-302. https://doi.org/10.1007/978-3-030-82616-1_26
Nikou, S. A. (2021). Web-based videoconferencing in online teaching during the COVID-19 pandemic: University students’ perspectives. 2021 International Conference on Advanced Learning Technologies (ICALT). https://doi.org/10.1109/icalt52272.2021.00137
Oliver, R. L. (1993). Cognitive, affective, and attribute bases of the satisfaction response. Journal of Consumer Research, 20(3), 418. https://doi.org/10.1086/209358
Pereira, R., & Tam, C. (2021). Impact of enjoyment on the usage continuance intention of video-on-demand services. Information & Management, 58(7), 103501. https://doi.org/10.1016/j.im.2021.103501
Periaiya, S., & Nandukrishna, A. T. (2023). What drives user stickiness and satisfaction in OTT video streaming platforms? A mixed-method exploration. International Journal of Human–Computer Interaction, 1-17. https://doi.org/10.1080/10447318.2022.2160224
Philip, A.V, & Zakkariya, K.A. (2023). Exploring the dimensions of cognitive absorption in a hedonic systems context. Iranian Journal of Management Studies, 16, 515–534. https://doi.org/10.22059/IJMS.2022.326000.674610
Premkumar, G., & Bhattacherjee, A. (2008). Explaining information technology usage: A test of competing models. Omega, 36(1), 64-75. https://doi.org/10.1016/j.omega.2005.12.002
Ringle, C., Da Silva, D., & Bido, D. (2015). Structural equation modeling with the SmartPLS. Bido, D., da Silva, D., & Ringle, C.(2014). Structural Equation Modeling with the Smartpls. Brazilian Journal of Marketing13(2). https://doi.org/10.5585/remark.v13i2.2717
Rouibah, K., Al-Qirim, N., Hwang, Y., & Pouri, S. G. (2021). The determinants of eWOM in social commerce. Journal of Global Information Management, 29(3), 75-102. https://doi.org/10.4018/jgim.2021050104
Rubenking, B., & Bracken, C. C. (2021). Binge watching and serial viewing: Comparing new media viewing habits in 2015 and 2020. Addictive Behaviors Reports, 14, 100356. https://doi.org/10.1016/j.abrep.2021.100356
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons.
Sharma, K., & Lulandala, E. E. (2023). OTT platforms resilience to COVID-19 – a study of business strategies and consumer media consumption in India. International Journal of Organizational Analysis, 31(1), 63-90. https://doi.org/10.1108/ijoa-06-2021-2816
Singh, S., Singh, N., Kalinić, Z., & Liébana-Cabanillas, F. J. (2021). Assessing determinants influencing continued use of live streaming services: An extended perceived value theory of streaming addiction. Expert Systems with Applications, 168, 114241. https://doi.org/10.1016/j.eswa.2020.114241
Spreng, R. A., MacKenzie, S. B., & Olshavsky, R. W. (1996). A reexamination of the determinants of consumer satisfaction. Journal of Marketing, 60(3), 15. https://doi.org/10.2307/1251839
Statista. (2023, April). Video-on-Demand - India | Statista market forecast. Retrieved from https://www.statista.com/outlook/dmo/digital-media/video-on-demand/india
Sun, W., Liu, H., & Wen, N. (2022). What motivates people to continuously engage in online task-oriented check-ins? The role of perceived social presence. Aslib Journal of Information Management, 75(2), 390–406. https://doi.org/10.1108/ajim-05-2022-0252
Sundaravel, E., & Elangovan, N. (2020). Emergence and future of over-the-top (OTT) video services in India: An Analytical research. International Journal of Business Management and Social Research, 8(2), 489-499. https://doi.org/10.18801/ijbmsr.080220.50
Thong, J. Y., Hong, S., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810. https://doi.org/10.1016/j.ijhcs.2006.05.001
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. https://doi.org/10.2307/30036540
Zhang, W., Zhang, W., Wang, C., & Daim, T. U. (2021). What drives continuance intention of disruptive technological innovation? The case of e-business microcredit in China. Technology Analysis & Strategic Management, 34(8), 905–918. https://doi.org/10.1080/09537325.2021.1932798
Zolotov, M. N., Oliveira, T., & Casteleyn, S. (2018). Continued intention to use online participatory budgeting. Proceedings of the 11th International Conference on Theory too Practice of Electronic Governance. https://doi.org/10.1145/3209415.3209461