THE NEXT CLICK: FACTORS DRIVING OTT PLATFORM ADOPTION

Nilrajsinh Vaghela; Kerav Pandya

doi:10.29121/shodhkosh.v5.i1.2024.3195

Authors

Nilrajsinh Vaghela Research Scholar, Gujarat Technological University, Gujarat, India
Dr. Kerav Pandya Director and Professor, CKSVIM- Vadodara, Gujarat Technological University, Gujarat, India

DOI:

https://doi.org/10.29121/shodhkosh.v5.i1.2024.3195

Keywords:

OTT Adoption, UTAUT, Intention to Adopt

Abstract [English]

In India, OTT services have grown exponentially, changing the entertainment and media scene. OTT adoption in India is examined in this article, including its drivers, trends, and consequences. This research synthesises academic literature, industry reports, and empirical investigations to identify OTT adoption drivers in India. These include inexpensive cellphones, increased internet connectivity, rising disposable incomes, and customer desires for on-demand entertainment. Researcher have use variables of UTAUT model which is technology adoption model to identify the intention to adopt OTT platform.

References

Antonopoulos, A., Kartsakli, E., Perillo, C., & Verikoukis, C. ( (2017). ). Shedding Light on the Internet: Stakeholders and Network Neutrality. IEEE Communications Magazine. DOI: https://doi.org/10.1109/MCOM.2017.1600417

Baizal, Z., Widyantoro, D., & Maulidevi, N. . ((2016). ). Factors Influencing User’s Adoption of Conversational Recommender System Based on Product Functional Requirements. . TELKOMNIKA Telecommunication Computing Electronics and Control. DOI: https://doi.org/10.12928/telkomnika.v14i4.4234

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

Chang, E. C., Chen, L. S., & Chou, Y. H. . ((2019).). Enjoyment, social interaction, and consumption intention in virtual reality social media: Facebook vs. Tinder. . Computers in Human Behavior,. , 92, 51-61.

Chang, P., & Chang, H. . ((2020).). Exploring the Factors Influencing Continuance Usage of Over-the-Top Services:. The Interactivity, Consumption Value, and Satisfaction Perspectives. Int. J. Technol. Hum. Interact.. DOI: https://doi.org/10.4018/IJTHI.2020100108

Chen, C. . ((2019).). Evaluating the efficiency change and productivity progress of the top global telecom operators since OTT's prevalence. Telecommunications Policy. . DOI: https://doi.org/10.1016/j.telpol.2019.01.004

Chen, J. V., Chen, S. C., & Yen, D. C. ( (2015). ). Technological Forecasting and Social Change,. The acceptance and diffusion of the innovative smart TV: Insights from perceived value and risk factors. , 92, 271-282.

Chen, J. Y., & Chang, C. T. ( (2020).). Investigating the determinants of satisfaction and continuance intention for OTT video services. Journal of Retailing and Consumer Services, 53,, 101997. .

Chen, W., Chen, C., & Chen, W. ( (2019).). Drivers of Mobile Payment Acceptance in China: . An Empirical Investigation. . DOI: https://doi.org/10.3390/info10120384

Chen, Y. H., Hsieh, P. H., & Liao, C. H. ( (2020).). Understanding the continued usage intention of OTT streaming services: The roles of perceived enjoyment, social interaction, and flow experience. . Telematics and Informatics, 50,, 101401. .

Chiu, C. M., Wang, E. T., Fang, Y. H., & Huang, H. Y. ( (2014). ). Understanding customers' repeat purchase intentions in B2C e-commerce: The roles of utilitarian value, hedonic value and perceived risk. Information Systems Journal, , 24(1), 85-114. . DOI: https://doi.org/10.1111/j.1365-2575.2012.00407.x

Chong, A. Y. L., & Chan, F. T. S. . ((2012). ). Structural equation modeling for multi-stage analysis on radio frequency identification (RFID) diffusion: Insights from a case study in the healthcare industry., . Expert Systems with Applications, 39(10), 8984-8992. DOI: https://doi.org/10.1016/j.eswa.2012.01.201

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. . DOI: https://doi.org/10.1287/mnsc.35.8.982

Deng, Z., Mo, X., Liu, S., & Zhang, H. (. (2014). ). Understanding the antecedents of mobile video acceptance: The roles of perceived value, flow, and ubiquity. Journal of Broadcasting & Electronic Media,, 58(1), 103-120. .

Gefen, D., Karahanna, E., & Straub, D. W. . ((2003). ). Trust and TAM in online shopping: . An integrated model. MIS Quarterly,, 27(1), 51-90. . DOI: https://doi.org/10.2307/30036519

Holdack, E., Lurie-Stoyanov, K., & Fromme, H. . ((2020).). The role of perceived enjoyment and perceived informativeness in assessing the acceptance of AR wearables. Journal of Retailing and Consumer Services.

Hsieh, J. P. A., & Cheng, P. F. . ((2019). ). The effects of user satisfaction and switching costs on OTT TV continuation: Incorporating the perspective of perceived enjoyment. . Telematics and Informatics, 46, , 101243.

Huang, H. C., Chuang, S. H., Chen, C. J., & Chen, Y. L. . ((2010). ). Applying the UTAUT to website stickiness: A case study of an online travel portal. Tourism Management, 31(6),, 861-865. .

Jiang, Y., & Lau, A. ( (2021). ). Roles of consumer trust and risks on continuance intention in the sharing economy: An empirical investigation. Electron. Commer. Res. Appl. DOI: https://doi.org/10.1016/j.elerap.2021.101050

Keating, B. W., & Hargittai, E. . ((2019). ). Social media engagement theory: Exploring the influence of user engagement on risk perception and information disclosure on Facebook. . Journal of Computer-Mediated Communication,, 24(2), 61-77. .

Kim, D. J., & Ko, E. ( (2012)). . Impacts of luxury fashion brand's social media marketing on customer relationship and purchase intention. Journal of Global Fashion Marketing, 3(3), , 158-171. .

Kim, J., Kim, J., & Nam, C. . ((2016).). An integrated model for the impact of perceived product, service, and purchase risks on consumer purchase decisions. . Journal of Business Research,, 69(11), 4568-4574. .

Lee, H. J., & Lee, Y. J. ( (2016).). A study of user satisfaction with long-term evolution-based mobile Internet services: A case of the Korean experience. Information Development,, 32(2), 245-261. .

Lee, J. H., & Choi, Y. J. ( (2020).). Influence of the perceived risk of media platforms on the adoption of over-the-top services. . Journal of Theoretical and Applied Electronic Commerce Research, , 15(4), 141-155. .

Lian, J. W., & Lin, C. P. ( (2008). ). Effects of consumer characteristics on their acceptance of online shopping: Comparisons among different product types. Computers in Human Behavior,, 24(1), 48-65. . DOI: https://doi.org/10.1016/j.chb.2007.01.002

Lim, H., & Dubinsky, A. J. ( (2004). ). Consumers' perceived risk: Sources versus consequences. . Electronic Commerce Research and Applications,, 3(1), 82-91. . DOI: https://doi.org/10.1016/S1567-4223(03)00025-5

Liu, C., Lu, Y., Yu, C. S., & Wang, B. ( (2016).). Exploring the impact of social media on perceived enjoyment and exercise intentions: Evidence from Chinese male users of WeChat. . Computers in Human Behavior, 56,, 30-38. .

Moon, J. W., Kim, Y. G., & Yi, M. Y. . ((2001).). An integrative model of web usage and satisfaction: A study of the voluntary individual use of the Internet. . International Journal of Electronic Commerce, 5(3),, 85-108.

Negahban, A., & Chung, C. ( (2014). ). Discovering determinants of users perception of mobile device functionality fit. . Comput. Hum. Behav.. DOI: https://doi.org/10.1016/j.chb.2014.02.020

Oliveira, T., Faria, M., Thomas, M., & Popovič, A. . ((2014). ). Extending the understanding of mobile banking adoption: . When UTAUT meets TTF and ITM. Int. J. Inf. Manag.. . DOI: https://doi.org/10.1037/t47662-000

Park, C., & Kim, Y. G. . ((2003).). Identifying key factors affecting consumer purchase behavior in an online shopping context. . International Journal of Retail & Distribution Management,, 31(1), 16-29. . DOI: https://doi.org/10.1108/09590550310457818

Reyes-Mercado, P., & , R. ( (2017). ). Adoption of renewable energy technologies in Mexico: The role of cognitive factors and innovation attributes. . International Journal of Energy Sector Management. DOI: https://doi.org/10.1108/IJESM-02-2017-0001

Shafer, D., Carbonara, C., & Korpi, M. ( (2019). ). Factors Affecting Enjoyment of Virtual Reality Games: A Comparison Involving Consumer-Grade Virtual Reality Technology.. Games for health journal. DOI: https://doi.org/10.1089/g4h.2017.0190

Shin, D. H., Lee, S. E., & Lee, H. ( (2019). ). Understanding the determinants of perceived enjoyment and usage intention of over-the-top media service. Telematics and Informatics, 41,, 84-95.

Silva, H., Ratnadiwakara, D., & Zainudeen, A. ( (2009). ). Social Influence in Mobile Phone Adoption: Evidence from the Bottom of Pyramid in Emerging Asia. Information Technologies and International.

Song, Y., & Kong, S. . ((2017). .). Investigating Students’ Acceptance of a Statistics Learning Platform Using Technology Acceptance Model. Journal of Educational Computing Research. Journal of Educational Computing Research. DOI: https://doi.org/10.1177/0735633116688320

Taylor, S., & Todd, P. A. . ((1995).). Understanding information technology usage: A test of competing models. . Information Systems Research, 6(2), , 144-176. . DOI: https://doi.org/10.1287/isre.6.2.144

Teo, T. S. H., & Lim, V. K. G. ( (1998).). Gender differences in Internet usage and task preferences. . Behavior & Information Technology,, 17(3), 160-167. .

Teo, T. S. H., Lim, V. K. G., & Lai, R. Y. C. . ((1999).). Intrinsic and extrinsic motivation in Internet usage. Omega, 27(1),. (, 25-37. DOI: https://doi.org/10.1016/S0305-0483(98)00028-0