İçerik ve Görüntüleme Davranışının Dijital TV Platformu Satın Alma Niyeti Üzerindeki Etkisi: Sakarya Örneği

Öz

Bu çalışmanın amacı, bireylerin dijital TV platformu satın alma niyetlerini açıklamada uyumluluk, algılanan davranışsal kontrol, yakın çevrenin etkisi ve teknolojiyi takip etme eğiliminin rolünü ortaya koymak ve bu değişkenlerin içerik ve görüntüleme davranışı üzerinden nasıl bir etki oluşturduğunu incelemektir. Çalışma, dijitalleşmenin hız kazandığı medya pazarında tüketici davranışlarını bütüncül bir model çerçevesinde ele alması bakımından önem taşımaktadır. Araştırmada nicel araştırma yöntemi tercih edilmiştir. Veri toplama aracı olarak da anket kullanılmıştır. Veriler yapısal eşitlik modellemesi kapsamında analiz edilmiştir. Araştırmanın örneklemi Sakarya ilinde yaşayan 18 yaş üstü 408 katılımcıdan oluşmaktadır. Analizler soncunda uyumluluk, algılanan davranışsal kontrol, yakın çevre ve benlik ve teknolojiyi takip etme eğiliminin içerik ve görüntüleme davranışı üzerinde pozitif etkisi olduğu tespit edilmiştir. İçerik ve görüntüleme davranışı üzerinde en fazla etkiye sahip faktör algılanan davranışsal kontroldür. Ayrıca içerik ve görüntüleme davranışının da dijital TV platformu satın alma niyeti üzerinde pozitif etkisi bulunmaktadır. Elde edilen sonuçlar doğrultusunda dijital TV platformlarının, kullanıcı deneyimini kişiselleştiren içerik stratejilerine, sosyal etkileşimi artıran kampanyalara ve teknolojik yenilik vurgusuna odaklanmaları önerilmektedir.

Anahtar Kelimeler

• Uyumluluk
• Algılanan Davranışsal Kontrol
• Yakın Çevre
• Teknolojiyi Takip Etme
• İçerik ve Görüntüleme Davranışı

Effect of Content and Viewing Behavior on Digital TV Platform Purchase Intention: The Example of Sakarya

Öz

The aim of this study is to reveal the role of compability, perceived behavioral control, near environment, and following technology in explaining individuals' intentions to purchase digital TV platforms, and to examine how these variables affect content and viewing behavior. The study is significant because it examines consumer behavior in the rapidly digitizing media market within a holistic model framework. Quantitative research method was preferred in the research. A questionnaire was also used as the data collection tool. The data were analyzed using structural equation modeling. The sample of the research consists of 408 participants over the age of 18 living in Sakarya province. As a result of the analyses, it was determined that compatibility, perceived behavioral control, near environment and self, and following technology have a positive effect on content and viewing behavior. The factor that has the most effect on content and viewing behavior is perceived behavioral control. In addition, content and viewing behavior also have a positive effect on digital TV platform purchase intention. Based on these findings, it is recommended that digital TV platforms focus on content strategies that personalize user experience, campaigns that increase social interaction, and an emphasis on technological innovation.

Anahtar Kelimeler

• Compatibility
• Perceived Bahavioral Control
• Following Technology
• Content and Viewing Behavior
• Near Environment

Kaynakça

1. Abadi, T. W., Refisca, S. F., Febriana, P. & Sobirov, B. (2025). Media usage motivations and netflix video viewer satisfaction in East Java. The Journal of Society and Media, 9(2), 504-538. https://doi.org/10.26740/jsm.v9n2.p504-538
2. Agarwal, R. & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215. https://doi.org/10.1287/isre.9.2.204
3. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
4. Ajzen, I. (2002). Perceived behavioral control, self‐efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665-683. https://doi.org/10.1111/j.1559-1816.2002.tb00236.x
5. Ajzen, I. & Madden, T. J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology, 22(5), 453-474. https://doi.org/10.1016/0022-1031(86)90045-4
6. Amoah, F., Radder, L. & van Eyk, M. (2016). Perceived experience value, satisfaction and behavioural intentions: A guesthouse experience. African Journal of Economic and Management Studies, 7(3), 419-433. https://doi.org/10.1108/AJEMS-10-2015-0121
7. Aparício, M., Figueiredo, P., Raposo, F., De Matos, D. M., Ribeiro, R. & Marujo, L. (2016). Summarization of films and documentaries based on subtitles and scripts. Pattern Recognition Letters, 73, 7-12. https://doi.org/10.1016/j.patrec.2015.12.016
8. Ariansyah, K. & Wardahnia, W. (2022). Managing sustainable adoption of digital terrestrial television: Demand-side and supply-side approaches. Digital Policy Regulation and Governance, 24(3), 229-245. https://doi.org/10.1108/dprg-06-2021-0077

9. Aslan, A. (2025). Türkiye’de en çok izlenen platform belli oldu! 15 Nisan 2025 tarihinde https://shiftdelete.net/turkiye-en-cok-izlenen-platform-belli-oldu adresinden alınmıştır.
10. Atkin, D. J., Hunt, D. S. & Lin, C. A. (2018). Diffusion theory in the new media environment: Toward an integrated technology adoption model. Mass Communication and Society, 18(5), 623-650. http://dx.doi.org/10.1080/15205436.2015.1066014
11. Baker, V., Mulwa, S., Khanyile, D., Arnold, G., Cousens, S., Cawood, C. & Birdthistle, I. (2025). Evaluating reaction videos of young people watching edutainment media (MTV Shuga): Qualitative observational study. Journal of Medical Internet Research (JMIR) Formative Research, 9(1), e55275. https://doi.org/10.2196/55275
12. Bambini, R., Cremonesi, P. & Turrin, R. (2010). A recommender system for an IPTV service provider: A real large-scale production environment. In Recommender systems handbook (pp. 299-331). Boston, MA: Springer US.
13. Bassul, C., Corish, C. & Kearney, J. (2021). Associations between home environment, children’s and parents’ characteristics and children’s tv screen time behavior. International Journal of Environmental Research and Public Health, 18(4), 1589. https://doi.org/10.3390/ijerph18041589
14. Bearden W. O. & Etzel, M. J. (1982). Reference group influence on product and brand purchase decisions. Journal of Consumer Research, 9(2), 183-194. https://doi.org/10.1086/208911
15. Behare, N. & Jeet, D. (2024). The art and science of user engagement: Personalization and recommendations in the OTT era. In The rise of Over-the-Top (OTT) media and ımplications for media consumption and production (pp. 130-159). Idea Group Inc. (IGI) Global Scientific Publishing.
16. Beullens, K. & Schepers, A. (2013). Display of alcohol use on Facebook: A content analysis. Cyberpsychology, Behavior, and Social Networking, 16(7), 497-503. https://doi.org/10.1089/cyber.2013.0044
17. Büyüköztürk, Ş. (2020). Sosyal bilimler için veri analizi el kitabı (27. Baskı). Pegem Akademi.
18. Canatan, E. Ç., Toker, A. & Çoşkun, A. (2025). Measuring motives for YouTube video consumption: An updated uses and gratifications scale and the impact of past view counts on users’ viewing decisions. Business & Management Studies: An International Journal, 13(2), 456-477. https://doi.org/10.15295/bmij.v13i2.2480
19. Castro, D., Rigby, J. M., Cabral, D. & Nisi, V. (2021). The binge-watcher’s journey: Investigating motivations, contexts, and affective states surrounding Netflix viewing. Convergence, 27(1), 3-20. https://doi.org/10.1177/1354856519890856
20. Cha, J. (2013). Predictors of television and online video platform use: A coexistence model of old and new video platforms. Telematics and Informatics, 30(4), 296-310. https://doi.org/10.1016/j.tele.2013.01.001
21. Chan-Olmsted, S. M. & Chang, B. H. (2006). Audience knowledge, perceptions and factors affecting the adoption intent of terrestrial digital television. New Media & Society, 8(5), 773-800. https://doi.org/10.1177/1461444806067588
22. Cialdini, R. B. & Goldstein, N. J. (2004). Social influence: Compliance and conformity. Annual Review of Psychology, 55(1), 591-621. https://doi.org/10.1146/annurev.psych.55.090902.142015
23. Cross, S. E., Bacon, P. L. & Morris, M. L. (2000). The relational-interdependent self-construal and relationships. Journal of Personality and Social Psychology, 78(4), 791-808. https://doi.org/10.1037/0022-3514.78.4.791
24. Cross, S. E., Hardin, E. E. & Gercek-Swing, B. (2011). The what, how, why, and where of self-construal. Personality and Social Psychology Review, 15(2), 142-179. https://doi.org/10.1177/1088868310373752
25. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems (MIS) Quarterly 13(3), 319-340. https://doi.org/10.2307/249008
26. Diwan, S., Nistane, M., Kapadiya, R. & Pahade, A. (2025). Exploring user choice in OTT streaming: Conjoint analysis of streaming service attributes. In EPJ Web of Conferences 2. International Conference on Advent Trends in Computational Intelligence and Communication Technologies (ICATCICT 2025, 341, 01029). Édition Diffusion Presse (EDP) Sciences. https://doi.org/10.1051/epjconf/202534101029
27. 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., Rahman, M. M., Raman, R., Rauschnabel, P. A., Rowley, J., Salo, J., Tran, G. A. & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168. https://doi.org/10.1016/j.ijinfomgt.2020.102168
28. Feng, K. K., Koo, X., Tan, L., Bruckman, A., McDonald, D. W. & Zhang, A. X. (2024, May). Mapping the design space of teachable social media feed experiences. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1-20). May 11–16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 20 pages. https://doi.org/10.1145/3613904.3642120 Fiske J. (2019) İletişim çalışmalarına giriş. Süleyman İrvan (Çev), Pharmakon Kitap.
29. George, D. & Mallery, P. (2010). SPSS for windows step by step: A simple guide and reference 17.0 update (10th ed.), Pearson.
30. Ghamkhari, S. M. & Pour Ali Ganji, L. (2023). Investigating the effect of perceived usefulness, ease of use, enjoyment, trust and social influence on intention to use with the mediating role of consumer attitude (Case of study: Household solar electricity customers in Yazd city). Journal of Intelligent Marketing Management, 4(2), 66-87.
31. Grover, P., Kar, A. K., Janssen, M., & Ilavarasan, P. V. (2019). Perceived usefulness, ease of use and user acceptance of blockchain technology for digital transactions–insights from user-generated content on Twitter. Enterprise Information Systems, 13(6), 771-800. https://doi.org/10.1080/17517575.2019.1599446
32. Guo, J., Li, Y., Xu, Y. & Zeng, K. (2021). How live streaming features impact consumers’ purchase intention in the context of cross-border E-commerce? A research based on SOR theory. Frontiers in Psychology, 12, 767876. https://doi.org/10.3389/fpsyg.2021.767876
33. Guste, R. & Ong, A. (2024). Machine learning decision system on the empirical analysis of the actual usage of interactive entertainment: a perspective of sustainable innovative technology. Computers, 13(6), 128. https://doi.org/10.3390/computers13060128
34. Gürbüz, S. & Şahin, F. (2017). Sosyal bilimlerde araştırma yöntemleri. Seçkin Yayıncılık.
35. Hair, J. F., Sarstedt, M., Hopkins, L. & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106-121. https://doi.org/10.1108/EBR-10-2013-0128
36. Hatamleh, I., Aissani, R. & Alduwairi, R. (2024). The role of social media motivation in enhancing social responsibility. Social Sciences, 13(8), 409. https://doi.org/10.3390/socsci13080409
37. Ho, C. M., Yeh, C. C., Wang, J. Y., Hu, R. H. & Lee, P. H. (2021). Curiosity in online video concept learning and short-term outcomes in blended medical education. Frontiers in Medicine, 8, 772956. https://doi.org/10.3389/fmed.2021.772956
38. Hoffman, D. L. & Novak, T. P. (2018). Consumer and object experience in the Internet of Things: An assemblage theory approach. Journal of Consumer Research, 44(6), 1178–1204. https://doi.org/10.1093/jcr/ucx105
39. Hoque, M. E., Susanto, P., Shah, N. U., Khatimah, H. & Mamun, A. A. (2024). Does perceived behavioral control mediate customers’ innovativeness and continuance intention of e-money? The moderating role of perceived risk and e-security. International Journal of Emerging Markets, 19(12), 4481-4502. https://doi.org/10.1108/IJOEM-06-2022-0914
40. Jiang, K., Zhou, D., Bao, X. & Mo, S. (2024). Self-created or collaborative? The effects of virtual influencers’ identity type and post type on purchase intentions. Asia Pacific Journal of Marketing and Logistics, 37(6), 1528-1549. https://doi.org/10.1108/apjml-07-2024-0980
41. Joo, T. M. & Teng, C. E. (2024). Behaviors and patterns of TV news viewing in Malaysia during and after COVID-19 pandemic. (Eds. Xin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi), In Proceedings of Ninth International Congress on Information and Communication Technology (pp. 23-36). Springer Nature Singapore.
42. Kalaycı, Ş. (2018). SPSS uygulamalı çok değişkenli istatistik değişkenleri. Asil Yayın Dağıtım.
43. Kania, R., Ramadhani, N. S., Wibisono, N. & Gunawan, A. I. (2022). Understanding behavioral intention to subscribe to Video-On-Demand Platforms during the COVID-19 pandemic: evidence from Indonesia. The Asian Journal of Technology Management, 15(2), 126-140. https://doi.org/10.12695/ajtm.2022.15.2.3
44. Karaca, Y. (2016). Referans grupları ile iletişimin satın alma kararları üzerine etkisi: Genç yetişkinlere yönelik bir araştırma. Galatasaray Üniversitesi İletişim Dergisi, 25, 209-231. https://doi.org/10.16878/gsuilet.283067
45. Karagöz, Y. (2019). SPSS-AMOS-META uygulamalı istatistiksel analizler (2. Baskı). Nobel.
46. Katz, E., Blumler, J. G. & Gurevitch, M. (1973). Uses and gratifications research. The Public Opinion Quarterly, 37(4), 509-523. https://www.jstor.org/stable/2747854
47. Kim, M. E., Cho, J. M., Yoo, J. J., Hong, J. W. & Kim, S. H. (2013, January). A Proposal of Semantic Analysis based Integrated Multi-Level Search System for Smart TV. In 2013 15th International Conference on Advanced Communications Technology (ICACT) (pp. 197-205). IEEE.
48. Koul, S., Ambekar, S. S. & Hudnurkar, M. (2021). 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
49. Küçükkurt, M., Murat Hazar, Ç., Çetin, M. & Topbaş, H. (2009). Kullanımlar ve doyumlar yaklaşımı perspektifinden üniversite öğrencilerinin medyaya bakışı. Selçuk İletişim, 6(1), 37-50. https://izlik.org/JA46XH64EU
50. Leeman, J., Wiecha, J., Vu, M., Blitstein, J., Allgood, S., Lee, S. & Merlo, C. (2018). School health implementation tools: a mixed methods evaluation of factors influencing their use. Implementation Science, 13(1), 48. https://doi.org/10.1186/s13012-018-0738-5
51. Lievrouw, L. A. (2006). New media design and development: Diffusion of innovations v social shaping of technology. (Eds. Leah A. Lievrouw, Sonia Livingstone), In The Handbook of New Media (pp. 246-265), Sage Publications.
52. Limayem, M., Hirt, S. G. & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. Management Information Systems (MIS) Quarterly, 31(4), 705–737. https://doi.org/10.2307/25148817
53. Lin, C. A. (2001). Audience attributes, media supplementation, and likely online service adoption. Mass Communication & Society, 4(1), 19-38. https://doi.org/10.1207/S15327825MCS0401_03
54. Liquin, E. G. & Lombrozo, T. (2024). Breaking down (and moving beyond) novelty as a trigger of curiosity. Behavioral and Brain Sciences, 47, e106. https://doi.org/10.1017/S0140525X23003333
55. Lo, M. C., Mohamad, A. A., Wan Ibrahim, W. H. & Thong, J. Z. (2024). Unveiling the dynamics of Malaysian viewers’ continuance usage intention of OTT platforms: insights from the stimulus-organism-response (SOR) paradigm. Global Knowledge, Memory and Communication. http://dx.doi.org/10.1108/GKMC-03-2024-0139
56. Lu, B., Fan, W. & Zhou, M. (2021). Social presence, trust, and social commerce purchase intention: An empirical research. Computers in Human Behavior, 56, 225–237. https://doi.org/10.1016/j.chb.2015.11.057
57. Hoffman, C. P., Lutz, C., Meckel, M. & Ranzini, G. (2015). Diversity by choice: Applying a social cognitive perspective to the role of public service media in the digital age. International Journal of Communication, 9, 1360-1381.
58. Manik, A. & Aristio, A. (2023). Analisis faktor intensi berlangganan dan keberlanjutan pengguna Over-The-Top (OTT) berdasarkan teori uses and gratification dengan structural equation modelling. Jurnal Sains Pemasaran Indonesia (Indonesian Journal of Marketing Science), 22(3), 187-227. https://doi.org/10.14710/jspi.v22i3.187-227
59. McNeill, L. & Venter, B. (2019). Identity, self‐concept and young women’s engagement with collaborative, sustainable fashion consumption models. International Journal of Consumer Studies, 43(4), 368-378. https://doi.org/10.1111/ijcs.12516
60. Menon, D. (2024). Uses and Gratifications of Over-the-Top (OTT) Video Streaming Platforms in India: Predictors of User’s Affinity, Satisfaction and Cord-Cutting. Journal of Creative Communications, 09732586241240392. https://doi.org/10.1177/09732586241240392
61. Napoli, P. M. (2011). Audience evolution: New technologies and the transformation of media audiences. Columbia University Press.
62. Nayanajith, D. G. & Damunupola, K. A. (2020). Impact of perceived behavioral control on e-learning adoption. Interdisciplinary Research in Education, 5(1-2), 1-14. https://doi.org/10.3126/ire.v5i1-2.34728
63. Nguyen, D. G., & Ha, M. T. (2022). What makes users continue to want to use the digital platform? Evidence from the ride-hailing service platform in Vietnam. Sage Open, 12(1), 21582440211069146. https://doi.org/10.1177/21582440211069146
64. Nsereka, B. G. (2021). Diffusion of innovation and globalization of new media in third world countries. International Journal of Research and Innovation in Social Science (IJRISS), 5(9), 273-279. https://doi.org/10.47772/IJRISS.2021.5904
65. Nunkoo, R. & Ramkissoon, H. (2010). Gendered theory of planned behaviour and residents’ support for tourism. Current Issues in Tourism, 13(6), 525-540. https://doi.org/10.1080/13683500903173967
66. Oh, Y., Yi, J. & Kim, J. (2023). What enhances or worsens the user-generated metaverse experience? An application of BERTopic to Roblox user eWOM. Internet Research, 34(5), 1800-1817. https://doi.org/10.1108/intr-03-2022-0178
67. Park, K. C. & Lee, S. (2022). Investigating consumer innovativeness for new media infusion: role of literacy in the context of OTT services in Korea. Korea Society for Internet Information (KSII) Transactions on Internet & Information Systems, 16(6). https://doi.org/10.3837/tiis.2022.06.009
68. Park, N., Kee, K. & Valenzuela, S. (2009). Being immersed in social networking environment: facebook groups, uses and gratifications, and social outcomes. Cyberpsychology & Behavior, 12(6), 729-733. https://doi.org/10.1089/cpb.2009.0003
69. Pavlou, P. A. & Gefen, D. (2004). Building effective online marketplaces with ınstitution-based trust. Information Systems Research, 15(1), 37-59. https://doi.org/10.1287/isre.1040.0015
70. Pekpazar, A., Coskun, M. & Gümüşsoy, Ç. (2023). Conceptualization and survey instrument development for Over-the-Top Platforms’ usability. Journal of Theoretical and Applied Electronic Commerce Research, 18(4), 1764-1796. https://doi.org/10.3390/jtaer18040089
71. Peleja, F., Dias, P., Martins, F. & Magalhães, J. (2013). A recommender system for the TV on the web: integrating unrated reviews and movie ratings. Multimedia Systems, 19(6), 543-558. https://doi.org/10.1007/s00530-013-0310-8
72. Prabhavathy, R. & Senthilkumar, S. (2025). User experiences in Over-The-Top (OTT) streaming media platform services. Qubahan Academic Journal, 5(2), 82-99. https://doi.org/10.48161/qaj.v5n2a1600 Prebensen, N. K., Woo, E. & Uysal, M. S. (2014). Experience value: Antecedents and consequences. Current Issues in Tourism, 17(10), 910-928. https://doi.org/10.1080/13683500.2013.770451
73. Rake, R. & Baul, S. (2017). Over-the-top (OTT) market outlook – 2025, Allied Market Research. 15 Nisan 2025 tarihinde https://www.alliedmarketresearch.com/over-the-topservices-market adresinden alınmıştır.
74. Ratchford, B. T. & Barnhart, M. (2012). Development and validation of the technology adoption propensity (TAP) index. Journal of Business Research, 65(8), 1209–1215. https://doi.org/10.1016/j.jbusres.2011.07.001
75. Rogers, E. M. (2003). Diffusion of innovations. Free Press.
76. Sadana, M. & Sharma, D. (2021). How over-the-top (OTT) platforms engage young consumers over traditional pay television service? An analysis of changing consumer preferences and gamification. Young Consumers, 22(3), 348-367. https://doi.org/10.1108/YC-10-2020-1231
77. Sahin, I. (2006). Detailed review of Rogers’ diffusion of innovations theory and educational technology-related studies based on Rogers’ theory. Turkish Online Journal of Educational Technology-TOJET, 5(2), 14-23.
78. Sakaki, M., Yagi, A. & Murayama, K. (2018). Curiosity in old age: A possible key to achieving adaptive aging. Neuroscience & Biobehavioral Reviews, 88, 106-116. https://doi.org/10.1016/j.neubiorev.2018.03.007
79. Schulz, H. M. (2015). Reference group influence in consumer role rehearsal narratives. Qualitative Market Research: An International Journal, 18(2), 210-229. https://doi.org/10.1108/QMR-02-2012-0009
80. Sekaran, U. (2003), Research methods for business: A skill building approach. John Wiley.
81. Shan, Q., Tian, Y., Chen, H., Lin, X. & Tian, Y. (2023). Reduction in the activity of VTA/SNc dopaminergic neurons underlies aging-related decline in novelty seeking. Communications Biology, 6(1), 1224. https://doi.org/10.1038/s42003-023-05571-x
82. Sharma, A., Sreen, N. & Baishya, K. (2023). Identifying gratification behind the consumption of the OTT platforms in an emerging market: a means-end theory approach. Marketing Intelligence & Planning, 41(5), 593-612. https://doi.org/10.1108/mip-12-2022-0558
83. Shittu, I., & Salisu, Y. (2023). Attitude, subjective norm, perceived behavioural control and digital ṣukūk adoption in Nigeria: the mediating effect of awareness. International Shari'ah Research Academy for Islamic Finance (ISRA) International Journal of Islamic Finance, 15(2), 4-19. https://doi.org/10.55188/ijif.v15i2.539
84. Simatupang, R. A., Sabarofek, M. S. & Bajari, M. (2022). A study on behavioral intentions: An theory planned behavior perspectives. Proceedings of the 3rd International Conference of Business, Accounting, and Economics, ICBAE 2022, 10-11 August, Purwokerto, Central Java, Indonesia. http://dx.doi.org/10.4108/eai.10-8-2022.2320837
85. Song, Z., Liu, C. & Shi, R. (2022). How do fresh live broadcast ımpact consumers’ purchase intention? Based on the SOR Theory. Sustainability, 14(21), 14382. https://doi.org/10.3390/su142114382
86. Soren, A. A. & Chakraborty, S. (2024). Beliefs, flow and habit in continuance of over-the-top (OTT) platforms. International Journal of Retail & Distribution Management, 52(2), 183-200. https://doi.org/10.1108/ijrdm-06-2023-0379
87. Stoneman, Z. & Brody, G. (1981). Peers as mediators of television food advertisements aimed at children. Developmental Psychology, 17(6), 853-858. https://doi.org/10.1037/0012-1649.17.6.853
88. Stoll, J. (2024). Traditional and OTT pay TV penetration rate in Europe 2021-2027. 15 Nisan 2025 tarihinde https://www.statista.com/statistics/1383172/pay-tv-penetration-europe/ adresinden alınmıştır.
89. Sun, K. A. & Moon, J. (2024). Determinants of usefulness and ease of use for top service users using the technology acceptance model: the moderating role of content diversity. SAGE Open, 14(4). https://doi.org/10.1177/21582440241289995
90. Syamsuar, D. & Witarsyah, D. (2025). The role of perceived value and risk in shaping purchase ıntentions in live-streaming commerce: evidence from Indonesia. Journal of Theoretical and Applied Electronic Commerce Research, 20(4), 298. https://doi.org/10.3390/jtaer20040298
91. Şener, H. Y. & Tunçbilek, S. (2014). Genç tüketicilerin referans gruplara üyeliklerinin sembolik tüketime etkisi. Akademik Bakış Dergisi, 40, https://dergipark.org.tr/en/download/article-file/382689
92. Tavşancıl, E. (2002). Tutumların ölçülmesi ve SPSS ile veri analizi. Nobel Akademi Yayıncılık.
93. Tornatzky, L. G. & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 1, 28-45. https://doi.org/10.1109/TEM.1982.6447463
94. Türkiye İstatistik Kurumu (2025). 4 Mayıs 2025 tarihinde https://data.tuik.gov.tr/Bulten/Index?p=Adrese-Dayali-Nufus-Kayit-Sistemi-Sonuclari-2024-53783 adresinden alınmıştır.
95. Venkatesh, V., Thong, J. Y. L. & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. Management Information Systems (MIS) Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
96. Venkatesh, V. & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
97. Verma, S. & Yadav, S. K. (2023). The antecedents of consumer satisfaction toward OTT platforms during COVID-19 lockdown in India. Jindal Journal of Business Research, 12(1), 30-43. https://doi.org/10.1177/22786821231161853
98. Viswanathan, V., Malthouse, E., Masłowska, E., Hoornaert, S. & Poel, D. (2018). Dynamics between social media engagement, firm-generated content, and live and time-shifted tv viewing. Journal of Service Management, 29(3), 378-398. https://doi.org/10.1108/josm-09-2016-0241
99. Wu, J. H. & Wang, S. C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719-729. https://doi.org/10.1016/j.im.2004.07.001
100. Wu, T. (2023). An integrated model to assess EFL learners’ online learning behaviour. International Journal of Technology-Enhanced Education (IJTEE), 2(1), 1-17. https://doi.org/10.4018/ijtee.323453
101. Wu, Z., Zhou, K., Liu, Y., Zhang, M. & Ma, S. (2019). Does diversity affect user satisfaction in image search. ACM Transactions on Information Systems (TOIS), 37(3), 1-30. https://doi.org/10.1145/3320118
102. Yersel, B. (2019). Adoption factors to digital TV platforms in Turkey. Journal of Human Sciences, 16(2), 469-476. https://doi.org/10.14687/jhs.v16i2.5696
103. Yıldırım, Y. (2016). Tüketicinin satın alma karar sürecinde bilgi kaynakları ve güvenirlikleri: Referans grubu olarak yakın çevrenin etkisinin incelenmesi. Akademik Yaklaşımlar Dergisi, 7(1), 214-231. https://izlik.org/JA94UM33JA
104. Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22. https://doi.org/10.1177/002224298805200302
105. Zhu, P., Liu, Z., Li, X., Jiang, X. & Zhu, M. (2022). The influences of livestreaming on online purchase intention: examining platform characteristics and consumer psychology. Industrial Management & Data Systems, 123(3), 862-885. https://doi.org/10.1108/imds-07-2022-0430