PREDICTING FACTORS THAT INFLUENCE ATTITUDE TO USE AND ITS IMPLICATIONS ON CONTINUANCE INTENTION TO USE SVOD: STUDY ON NETFLIX USERS OF INDONESIA [PREDIKSI FAKTOR-FAKTOR YANG MEMPENGARUHI SIKAP PENGGUNAAN DAN IMPLIKASINYA TERHADAP KEBERLANJUTAN NIAT PENGGUNAAN SVOD: STUDI PADA PENGGUNA NETFLIX di INDONESIA]
Abstract
The increasing number of internet users in Indonesia, especially mobile internet users, has changed consumer habits and behavior in consuming entertainment. Internet penetration leads to the increasing consumption of streaming video on demand (SVOD) services in Indonesia, which is increasingly popular. Netflix is one of the largest SVOD service providers in the world that has a customer subscription system. The tight competition in the SVOD industry caused Netflix to experience a significant reduction in the number of global subscribers. Therefore, the researcher's goal is to predict which factors influence the consumer's decision-making process to continue to use Netflix in view of the model of consumer attitudes toward technology adoption. The research data come from non-probability sampling with judgmental sampling techniques of 237 Netflix’s subscribers across Indonesia that have subscribed Netflix for more than three months, and having an intensity of use for 2-5 hours or more per day. This research was conducted with a quantitative descriptive method using Structural Equation Modelling method. This study indicate that there’s a positive relationship between perceived system quality and perceived enjoyment of attitude to use. Furthermore, there is a negative influence between the perceived price level and attitude to use. This study also reaffirms that the SVOD attitude to use is a predictor of continuance intention to use SVOD. This research also proved that perceived ease of use, perceived content quality, customization, and perceived psychological risk did not affect the attitude to use SVOD.
Abstrak dalam Bahasa Indonesia. Dengan semakin meningkatnya jumlah pengguna internet di Indonesia, khususnya dari sisi pengguna internet seluler, telah mengubah kebiasaan dan perilaku konsumen dalam mengonsumsi hiburan. Penetrasi internet juga berdampak pada meningkatnya konsumsi layanan Streaming Video on Demand (SVOD) di Indonesia yang semakin populer. Netflix merupakan salah satu penyedia layanan SVOD terbesar di dunia yang memiliki sistem layanan pelanggan. Persaingan ketat di industri SVOD menyebabkan Netflix mengalami penurunan jumlah pelanggan global yang signifikan. Oleh karena itu, tujuan peneliti ini adalah untuk memprediksi faktor-faktor apa saja yang mempengaruhi proses pengambilan keputusan konsumen untuk terus menggunakan Netflix yang dilihat dari sisi model sikap konsumen terhadap adopsi suatu teknologi. Teknik pengambilan sampel dalam penelitian ini menggunakan non-probability sampling dengan teknik judgemental sampling terhadap 237 pelanggan Netflix di seluruh Indonesia yang telah berlangganan Netflix selama lebih dari tiga bulan, dan intensitas penggunaan selama 2-5 jam atau lebih per hari nya. Analisis Penelitian ini menggunakan metode deskriptif kuantitatif dengan metode Structural Equation Modeling (SEM). Hasil penelitian ini menunjukkan bahwa terhadap pengaruh positif antara kualitas sistem yang dirasakan (Perceived System Quality) dan kenikmatan yang dirasakan (Perceived Enjoyment) terhadap sikap untuk menggunakan (Attitude to Use). Selain itu, ada pengaruh negatif antara persepsi tingkat harga (Perceived Price Level) dan sikap penggunaan (Attitude to Use) SVOD. Penelitian ini juga menegaskan kembali bahwa sikap penggunaan SVOD merupakan prediktor niat untuk terus menggunakan SVOD (Continuance Intention to Use). Penelitian ini juga membuktikan bahwa persepsi kemudahan penggunaan (Perceived Ease of Use), persepsi kualitas konten (Perceived Content Quality), pengaturan ulang (Customization), dan persepsi risiko psikologis (Perceived Psychological Risk) tidak berpengaruh terhadap sikap penggunaan SVOD.
Keywords
DOI: http://dx.doi.org/10.19166/derema.v15i2.2541
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Aguete, M. R. (2019). Competing in a crowded streaming market. OMDIA Technology. https://technology.informa.com/618123/competing-in-a-crowded-streaming-market
Ajzen, I. (1985). From intentions to actions: A Theory of Planned Behavior. In J. Kuhl & J. Beckman (Eds.), Action-control: From cognition to behavior. Springer.
Ajzen, I., & Fishbein, M. (1977). Attitude-Behavior Relations: A Theoretical Analysis and Review of Empirical Research. Psychological Bulletin, 84(5), 888–918. https://doi.org/10.1037/0033-2909.84.5.888
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. In Internet Research, 25(5). https://doi.org/10.1108/IntR-05-2014-0146
Aladwani, A. M. (2006). An empirical test of the link between web site quality and forward enterprise integration with web consumers. Business Process Management Journal, 12(2), 178-190. https://doi.org/10.1108/14637150610657521
Alsajjan, B., & Dennis, C. (2010). Internet banking acceptance model: Cross-market examination. Journal of Business Research, 63(9-10), 957-963. https://doi.org/10.1016/j.jbusres.2008.12.014
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
Baek, T., & Morimoto, M. (2012). Stay away from me. Journal of Advertising, 41(1), 59–76. https://doi.org/10.2753/JOA0091-3367410105
Bagozzi, R. P. (1992). The Self-Regulation of Attitudes, Intentions, and Behavior. Social Psychology Quarterly, 55(2), 178. https://doi.org/10.2307/2786945
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
Bashir, I., & Madhavaiah, C. (2015). Consumer attitude and behavioral intention towards Internet banking adoption in India. Journal of Indian Business Research, 7(1), 67–102. https://doi.org/10.1108/JIBR-02-2014-0013
Bettman, J. R. (1973). Perceived Risk and Its Components: A Model and Empirical Test. Journal of Marketing Research, 10(2), 184-190. https://doi.org/10.2307/3149824
Bhattacherjee, A. (2011). Qarterly CONTINUANCE: MIS Quarterly, 25(3), 351-370.
Bhukya, R., & Singh, S. (2015). The effect of perceived risk dimensions on purchase intention: An empirical evidence from the Indian private label market. American Journal of Business, 30(4), 218-230. https://doi.org/10.1108/AJB-10-2014-0055
Boulay, J. (2018). SVOD in Asia Pacific. https://dataxis.com/wp-content/uploads/2018/03/SVOD-in-Asia-Pacific--the-gold-rush.pdf
Breckler, S. J. (1984). Empirical validation of affect, behavior, and cognition as distinct components of attitude. Journal of Personality and Social Psychology, 47(6), 1191–1205. https://doi.org/10.1037/0022-3514.47.6.1191
Castañeda, J. A., Muñoz-Leiva, F., & Luque, T. (2007). Web Acceptance Model (WAM): Moderating effects of user experience. Information and Management, 44(4), 384–396. https://doi.org/10.1016/j.im.2007.02.003
Cenfetelli, R. T., Benbasat, I., & Al-Natour, S. (2009). Addressing the What and How of Online Services: Positioning Supporting-Services Functionality and Service Quality for Business-to-Consumer Success. Information Systems Research, 19(2).
Chang, C. C., Hung, S. W., Cheng, M. J., & Wu, C. Y. (2015). Exploring the intention to continue using social networking sites: The case of Facebook. Technological Forecasting and Social Change, 95, 48–56. https://doi.org/10.1016/j.techfore.2014.03.012
Chang, C. C. (2013). Exploring the determinants of e-learning systems continuance intention in academic libraries. Library Management, 34(1), 40–55. https://doi.org/10.1108/01435121311298261
Chen, H., Rong, W., Ma, X., Qu, Y., & Xiong, Z. (2017). An Extended Technology Acceptance Model for Mobile Social Gaming Service Popularity Analysis. Mobile Information Systems, 2017. https://doi.org/10.1155/2017/3906953
Chen, L., Meservy, T. O., & Gillenson, M. (2012). Understanding Information Systems Continuance for Information-Oriented Mobile Applications. Communications of the Association for Information Systems, 30. https://doi.org/10.17705/1cais.03009
Chen, M. Y., & Teng, C. I. (2013). A comprehensive model of the effects of online store image on purchase intention in an e-commerce environment. Electronic Commerce Research, 13(1), 1–23. https://doi.org/10.1007/s10660-013-9104-5
Cheong, J., & Park, M. C. (2005). Mobile internet acceptance in Korea. Internet Research, 15(2), 125-140. https://doi.org/10.1108/10662240510590324
Chiu, C. M., Chiu, C. S., & Chang, H. C. (2007). Examining the integrated influence of fairness and quality on learners' satisfaction and Web-based continuous learning intention. Information Systems Journal, 17(3), 271–287. https://doi.org/10.1111/j.1365-2575.2007.00238.x
Chou, J. S., & Hong, J. H. (2013). Assessing the impact of quality determinants and user characteristics on successful enterprise resource planning project implementation. Journal of Manufacturing Systems, 32(4), 792-800. https://doi.org/10.1016/j.jmsy.2013.04.014
cnbctv18.com. (2019). Netflix's subscriber growth falls off a cliff. Https://Www.Cnbctv18.Com/. https://www.cnbctv18.com/technology/netflixs-subscriber-growth-falls-off-a-cliff-4020711.htm
Cox, S. R., Rutner, P. S., & Dick, G. (2012). Information Technology Customization: How Is It Defined and How Are Customization Decisions Made? SAIS 2012 Proceedings, 1, 49-54.
Davis, F. D. (1987). 1 user acceptance TAM Davis.pdf.
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/10.5962/bhl.title.33621
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
Delone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Management, 3(1), 60–95. https://doi.org/10.5267/j.uscm.2014.12.002
Delone, W. H., Mclean, E. R. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9-30. ttps://doi.org/10.1080/07421222.2003.11045748
Detiknet. (2016). Finally! Netflix Officially Arrives in Indonesia. https://inet.detik.com/consumer/d-3112516/finally-netflix-resmi-hadir-di-indonesia
Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of Price, Brand, and Store Information on Buyers' Product Evaluations. Journal of Marketing Research, 28(3), 307-319. https://doi.org/10.1177/002224379102800305
Doll, W. J., Hendrickson, A., & Deng, X. (1998). Using Davis's perceived usefulness and ease-of-use instruments for decision making: A confirmatory and multigroup invariance analysis. Decision Sciences, 29(4), 839–869. https://doi.org/10.1111/j.1540-5915.1998.tb00879.x
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory and research. Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Errors. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
Gan, C., Wee, H. Y., Ozanne, L., Kao, T.-H. (2008). Consumers' purchasing behavior towards green products in New Zealand. Innovative Marketing, 4(1), 93–102.
Gao, J., Zhang, C., Wang, K., & Ba, S. (2012). Understanding online purchase decision making: The effects of unconscious thought, information quality, and information quantity. Decision Support Systems, 53(4), 772-781. https://doi.org/10.1016/j.dss.2012.05.011
Gilmore, J. H., & Pine, B. J. (2000). Markets of One: Creating Customer-Unique Value through Mass Customization. Boston, MA: Harvard Business School Press.
Govind, N., & Balachandran, A. (2016). Optimizing Content Quality Control at Netflix with Predictive Modeling. https://netflixtechblog.com/optimizing-content-quality-control-at-netflix-with-predictive-modeling-712281658ab9
Ha, I., Yoon, Y., & Choi, M. (2007). Determinants of adoption of mobile games under the wireless broadband wireless access environment. Information and Management, 44(3), 276–286. https://doi.org/10.1016/j.im.2007.01.001
Haines, M. N. (2009). Understanding enterprise system customization: An exploration of implementation realities and the key influence factors. Information Systems Management, 26(2), 182–198. https://doi.org/10.1080/10580530902797581
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate Data Analysis (7th ed.). Prentice Hall.
Heijden, H. van der. (2004). User Acceptance of Hedonic Information Systems. MIS Quarterly, 28(4), 695–704. https://doi.org/10.2307/25148660
Heijden, H. van der, Verhagen, T., & Creemers, M. (2003). Understanding online purchase intentions: Contributions from technology and trust perspectives. European Journal of Information Systems, 12(1), 41–48. https://doi.org/10.1057/palgrave.ejis.3000445
Hong, S., Kim, J., & Lee, H. (2008). Antecedents of Use-Continuance in Information Systems: Toward an Inegrative View. Journal of Computer Information Systems, 61–73.
Hsu, P. F., Yen, H. J. R., & Chung, J. C. (2015). Assessing ERP post-implementation success at the individual level: Revisiting the role of service quality. Information and Management, 52(8), 925–942. https://doi.org/10.1016/j.im.2015.06.009
Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the Determinants of Microcomputer Usage via a Structural Equation Model. Journal of Management Information Systems, 11(4), 87–114. https://doi.org/10.1080/07421222.1995.11518061
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. Proceedings of the 5th International Conference on Computing and Informatics, ICOCI 2015, 125, 206–215.
Jacoby, J., & Kaplan, L. B. (1972). The Components of Perceived Risk. Proceedings of the Annual Conference of the Association for Consumer Research, 10, 382-393.
Jacoby, J., Olson, J. C., & Haddock, R. A. (1971). Price, brand name, and product composition characteristics as determinants of perceived quality. Journal of Applied Psychology, 55(6), 570–579. https://doi.org/10.1037/h0032045
JAKPAT Survey. (2019). What Indonesian Viewers Say About National TV and Digital Content? – JAKPAT Survey Report. https://blog.jakpat.net/what-indonesian-viewers-say-about-national-tv-and-digital-content-jakpat-survey-report/
Johnson, K. L., & Misic, M. M. (1999). Benchmarking: A tool for Web site evaluation and improvement. Internet Research, 9(5), 383–392. https://doi.org/10.1108/10662249910297787
Kalyanaraman, S., & Sundar, S. (2006). The psychological appeal of personalized content in web portals: Does customization affect attitudes and behavior? Journal of Communication, 56(1), 110–132. https://doi.org/10.1111/j.1460-2466.2006.00006.x
Klein, R. (2007). Customization and real time information access in integrated eBusiness supply chain relationships. Journal of Operations Management, 25(6), 1366–1381. https://doi.org/10.1016/j.jom.2007.03.001
Kobsa, A., Koenemann, J., & Pohl, W. (2001). Personalised hypermedia presentation techniques for improving online customer relationships. The Knowledge Engineering Review, 16(2), 111–155. https://doi.org/10.1017/s0269888901000108
Koivisto, M. (2008). Development of quality expectations in mobile information systems. Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering, 336–341. https://doi.org/10.1007/978-1-4020-8735-6_63
Kulkarni, U. R., Ravindran, S., & Freeze, R. (2006). A knowledge management success model: Theoretical development and empirical validation. Journal of Management Information Systems, 23(3), 309–347. https://doi.org/10.2753/MIS0742-1222230311
Lai, V. S., & Li, H. (2005). Technology acceptance model for internet banking: An invariance analysis. Information and Management, 42(2), 373–386. https://doi.org/10.1016/j.im.2004.01.007
Lederer, A. L, Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). Technology acceptance model and the World Wide Web. Decision Support Systems, 29(3), 269–282. https://doi.org/10.1016/S0167-9236(00)00076-2
Lee, B. C., Yoon, J. O., & Lee, I. (2009). Learners' acceptance of e-learning in South Korea: Theories and results. Computers and Education, 53(4), 1320–1329. https://doi.org/10.1016/j.compedu.2009.06.014
Lee, K. C., & Chung, N. (2009). Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean's model perspective. Interacting with Computers, 21(5–6), 385–392. https://doi.org/10.1016/j.intcom.2009.06.004
Lee, M.-C., & Tsai, T.-R. (2010). What Drives People to Continue to Play Online Games? An Extension of Technology Model and Theory of Planned Behavior. International Journal of Human-Computer Interaction, 26(6), 601–620. https://doi.org/10.1080/10447311003781318
Lee, M. C. (2010). Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers and Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002
Leon, S. (2018). Service mobile apps: a millennial generation perspective. Industrial Management and Data Systems, 118(9), 1837–1860. https://doi.org/10.1108/IMDS-10-2017-0479
Li, Y.-H., & Huang, J.-W. (2009). Applying Theory of Perceived Risk and Technology Acceptance Model in the Online Shopping Channel. World Academy of Science, Engineering and Technology, 53(1), 919–925. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.193.6343&rep=rep1&type=pdf
Liao, C., Lin, H. N., & Liu, Y. P. (2010). Predicting the use of pirated software: A contingency model integrating perceived risk with the theory of planned behavior. Journal of Business Ethics, 91(2), 237–252. https://doi.org/10.1007/s10551-009-0081-5
Liaw, S. S., & Huang, H. M. (2007). Investigating motivation, enjoyment, usefulness toward video on demand. 15th International Conference on Computers in Education: Supporting Learning Flow through Integrative Technologies, ICCE 2007, January 2007, 355–358.
Lim, K. B., Yeo, S. F., Goh, M. L., & Gan, J. A. X. (2018). A study on consumer adoption of ride-hailing apps in Malaysia. Journal of Fundamental and Applied Sciences ISSN, 10(6S), 1132–1142. https://doi.org/http://dx.doi.org/10.4314/jfas.v10i6s.74 1.
Lin, H. F. (2007). The role of online and offline features in sustaining virtual communities: An empirical study. Internet Research, 17(2), 119–138. https://doi.org/10.1108/10662240710736997
Liñán, F., & Chen, Y.-W. (2009). Development and Cross-Cultural Application of a Specific Instrument to Measure Entrepreneurial Intentions. Entrepreneurship Theory and Practice, 593–617. https://doi.org/10.1111/j.1540-6520.2009.00318.x
Liou, D. K., Hsu, L. C., & Chih, W. H. (2015). Understanding broadband television users' continuance intention to use. Industrial Management and Data Systems, 115(2), 210–234. https://doi.org/10.1108/IMDS-07-2014-0223
Liu, C., & Arnett, K. P. (2000). Exploring the factors associated with Web site success in the context of electronic commerce. Information and Management, 38(1), 23–33. https://doi.org/10.1016/S0378-7206(00)00049-5
Lu, J. (2014). Are Personal Innovativeness and Social Influence Critical to Internet Research Article information? Internet Research, 24(2), 134–159. https://doi.org/10.1108/IntR-05-2012-0100
Maciaszek, L. A., & Owoc, M. L. (2001). Designing Application Authorizations. Informing Science. https://doi.org/10.28945/2409
Maholtra, N. K. (2010). Marketing Research (4th ed.). Pearson.
McFarland, D. J., & Hamilton, D. (2006). Adding contextual specificity to the technology acceptance model. Computers in Human Behavior, 22(3), 427–447. https://doi.org/10.1016/j.chb.2004.09.009
Meier, D. (2019). Netflix loses 1.1 million US subscribers to Disney Plus. Www.Tvbeurope.Com. https://www.tvbeurope.com/business/netflix-loses-1-1-million-us- subscribers-to-disney-plus
Mirabito, M. M. A., & Morgenstern, B. (2014). New Communication Technology: Applications, Policy, and Impact. Focal Press.
Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information and Management, 38(4), 217–230. https://doi.org/10.1016/S0378-7206(00)00061-6
Nabavi, A., Taghavi-Fard, M.T., Hanafizadeh, P., & Taghva, M. R. (2016). Information Technology Continuance Intention: A Systematic Literature Review. International Journal of E-Business Research, 12(1), 58–95. https://doi.org/10.4018/IJEBR.2016010104
Nelson, R. R., Todd, P. A., & Wixom, B. H. (2005). Antecedents of information and system quality: An empirical examination within the context of data warehousing. Journal of Management Information Systems, 21(4), 199–235. https://doi.org/10.1080/07421222.2005.11045823
Ng, E. H., & Kwahk, K. Y. (2010). Examining the determinants of mobile internet service continuance: A customer relationship development perspective. International Journal of Mobile Communications, 8(2), 210–229. https://doi.org/10.1504/IJMC.2010.031448
Ngai, E. W. T., Poon, J. K. L., & Chan, Y. H. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers and Education, 48(2), 250–267. https://doi.org/10.1016/j.compedu.2004.11.007
Nguyen, D. (2015). Understanding Perceived Enjoyment and Continuance Intention in Mobile Games. ICFAI Journal of Systems, 58. http://epub.lib.aalto.fi/fi/ethesis/pdf/14000/hse_ethesis_14000.pdf
Park, H. S. (2000). Relationships among attitudes and subjective norms: Testing the theory of reasoned action across cultures. Communication Studies, 51(2), 162–175. https://doi.org/10.1080/10510970009388516
PCmag.com. (2020). Video-on-demand. Pcmag.Com. https://www.pcmag.com/encyclopedia/term/video-on-demand
Peng, L., Wang, H., He, X., Guo, D., & Lin, Y. (2014). Exploring factors affecting the user adoption of call-taxi App. Proceedings of the 25th Australasian Conference on Information Systems, ACIS 2014.
Pertiwi, W. K. (2020). Penetrasi Internet di Indonesia Capai 64 Persen Artikel ini telah tayang di Kompas.com dengan judul “Penetrasi Internet di Indonesia Capai 64 Persen”. https://tekno.kompas.com/read/2020/02/20/14090017/penetrasi-internet-di-indonesia-capai-64-persen
Purnamaningsih, P., Erhan, T. P., & Rizkalla, N. (2019). Behavioral Intention Towards Application-Based Short- Distance Delivery Services Adoption In Indonesia. Review of Behavioral Aspect in Organizations & Society, 1(1), 77–86. https://doi.org/10.16309/j.cnki.issn.1007-1776.2003.03.004
Ramayah, T., Ahmad, N. H., & Lo, M. C. (2010). The role of quality factors in intention to continue using an e-learning system in Malaysia. Procedia - Social and Behavioral Sciences, 2(2), 5422–5426. https://doi.org/10.1016/j.sbspro.2010.03.885
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information and Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007
Schubert, P., & Selz, D. (1999). Web assessment - measuring the effectiveness of electronic commerce sites going beyond traditional marketing paradigms. Proceedings of the Hawaii International Conference on System Sciences, 00(c), 185. https://doi.org/10.1109/hicss.1999.772941
Seddon, P. B. (1997). A Respecification and Extension of the DeLone and McLean Model of IS Success. Information Systems Research, 8(3), 240–253. https://doi.org/10.1287/isre.8.3.240
Shiau, W. L., & Luo, M. M. (2010). Continuance intention of blog users: The impact of perceived enjoyment and user involvement. PACIS 2010 - 14th Pacific Asia Conference on Information Systems, 856–867.
Shih, H. P. (2004). An empirical study on predicting user acceptance of e-shopping on the Web. Information and Management, 41(3), 351–368. https://doi.org/10.1016/S0378-7206(03)00079-X
Shin, D.-H. (2012). 3DTV as a social platform for communication and interaction. Information Technology & People, 25(1), 55–80. https://doi.org/10.1108/09593841211204344
Shin, D.-H. (2009a). An empirical investigation of a modified technology acceptance model of IPTV. Behavior and Information Technology, 28(4), 361–372. https://doi.org/10.1080/01449290701814232
Shin, D.-H. (2009b). Determinants of customer acceptance of multi-service network: An implication for IP-based technologies. Information and Management, 46(1), 16–22. https://doi.org/10.1016/j.im.2008.05.004
Sundar, S. S., & Kim, J. (2005). Interactivity and Persuasion. Journal of Interactive Advertising, 5(2), 5–18. https://doi.org/10.1080/15252019.2005.10722097
Tehubijuluw, F. K., & Sari, D. P. (2017). Pengaruh Bauran Pemasaran, Pester Power, Dan Heritage Terhadap Keputusan Pembelian Biskuit Merek Roma Malkist. Kompetensi - Jurnal Manajemen Bisnis, 12(1), 41–47.
Teng, C. I. (2010). Customization, immersion satisfaction, and online gamer loyalty. Computers in Human Behavior, 26(6), 1547–1554. https://doi.org/10.1016/j.chb.2010.05.029
Ueltschy, L., Krampf, R. F., & Yannopoulos, P. (2018). A Cross-National Study Of Perceived Consumer Risk Towards Online (Internet) Purchasing. Multinational Business Review, 12(2), 59–82. https://doi.org/10.1108/1525383X200400010
Venkatesh, V. (2000). Determinants of perceived ease of use : integrating control , intrinsic motivation , acceptance model. Inorganic Chemistry Communications, 11(3), 319–340. https://doi.org/10.5962/bhl.title.33621
Verhagen, T., Meents, S., & Tan, YH (2006). Perceived risk and trust associated with purchasing at electronic marketplaces. European Journal of Information Systems, 15(6), 542–555. https://doi.org/10.1057/palgrave.ejis.3000644
Vijayasarathy, LR (2004). Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance model. Information and Management, 41(6), 747–762. https://doi.org/10.1016/j.im.2003.08.011
Wakefield, RL, & Whitten, D. (2006). Mobile computing: A user study on hedonic/utilitarian mobile device usage. European Journal of Information Systems, 15(3), 292–300. https://doi.org/10.1057/palgrave.ejis.3000619
Weniger, S. (2010). User adoption of IPTV: A research model. BLED Proceedings, 30, 154–165.
Wixom, BH, & Todd, PA (2005). A Theoretical Integration of User Satisfaction and Technology Acceptance. Information Systems Research, 16(1), 85–102.
Yoo, WS, Suh, KS, & Lee, MB (2001). Exploring the factors enhancing member participation in virtual communities. Pacific Asia Conference on Information Systems (PACIS), 551–570. https://doi.org/10.4018/jgim.2002070104
Zarrad, H., & Debabi, M. (2012). Online Purchasing Intention: Factors and Effects. International Business and Management, 4(41), 37–47. https://doi.org/10.3968/j.ibm.1923842820120401.2115
Zeithaml, VA (1988). of Consumer Perceptions A Means-End Value : Quality , and Model Synthesis of Evidence. Journal of Marketing, 52(3), 2–22.
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