English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113303/144284 (79%)
Visitors : 50798407      Online Users : 781
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/145904


    Title: 拆解隱私悖論:以隱私計算理論探討電商平台推薦系統之數位助推效果
    Debunking the Mystery of Privacy Paradox: Examining the Effectiveness of Digital Nudging in E-commerce Recommender System from a Privacy Calculus Perspective
    Authors: 朱翊榕
    Chu, I-Jung
    Contributors: 林芝璇
    Lin, Jhih-Syuan
    朱翊榕
    Chu, I-Jung
    Keywords: 數位助推
    隱私計算
    隱私疑慮
    揭露意願
    隱私犬儒主義
    電商推薦系統
    個人化服務
    Digital nudging
    Privacy calculus
    Privacy concern
    Disclosure intention
    Privacy cynicism
    E-commerce recommender system
    Personalized service
    Date: 2023
    Issue Date: 2023-07-06 16:56:29 (UTC+8)
    Abstract: 近年來,學者們將推薦系統視為一種數位助推,強調它內嵌多種助推機制來影響用戶的決策。現今許多研究致力於研究推薦系統中不同數位助推的效果,以了解有助於優化用戶介面的關鍵要素。然而此類個人化服務需要收集大量用戶數據,進而引發了人們對隱私揭露的疑慮。因此,本研究旨在深入了解兩種數位助推(揭露助推和社會認同助推)如何在電子商務推薦系統的情境下影響用戶的隱私風險感知、隱私疑慮和隱私決策,並探討隱私犬儒主義在用戶隱私決策過程中的作用。
    本研究採用線上實驗法蒐集了264份有效樣本。研究結果顯示揭露助推會直接顯著提升用戶提供個人數據以進行交易的意願,其中隱私風險感知亦扮演著中介的角色,即揭露助推的使用能有效降低隱私風險感知,並接續正向影響用戶的隱私決策;揭露助推和社會認同助推存在交互作用以顯著降低隱私風險感知和提升用戶提供個人數據以進行交易的意願,其中社會認同助推獨立存在時不具效果,但對揭露助推有加乘效果,意即兩者同時存在時影響用戶隱私感知與隱私決策的效果最強。另一方面,本研究發現隱私風險感知會正向影響隱私疑慮,進而對用戶提供個人數據以進行交易的意願產生負面影響,且隱私犬儒主義分別調節隱私風險感知和隱私疑慮對用戶隱私決策的直接影響,即隱私犬儒主義能減弱隱私風險感知和隱私疑慮對用戶隱私決策的負面作用。然而,隱私犬儒主義對於隱私風險感知透過隱私疑慮對用戶隱私決策產生的間接影響不具有顯著的調節效果。本研究進一步探討研究結果的理論與實務面意涵,並提出研究限制與建議,供未來相關研究參考。
    Recommender systems are considered a form of digital nudging, which inherently embed several nudging mechanisms to influence users’ decision-making. In light of that, extant research has been devoted to examining the effectiveness of different digital nudges in recommender systems in order to identify crucial elements that could contribute to the optimization of user interfaces. However, the fact that a considerable amount of user data has to be collected for such personalized services has drawn universal concern about the disclosure of privacy. The purpose of this study is, therefore, to provide insights into how digital nudges (disclosure nudges and social-proof nudges) might influence users’ privacy risk perceptions, privacy concerns and privacy decisions in the context of an E-commerce recommender system, as well as to investigate the moderating role of privacy cynicism in users’ privacy decision-making processes.
    The results of the online experiment (N = 264) revealed that (1) a disclosure nudge was effective in increasing willingness to provide personal data to proceed with the transaction, and this effect was significantly mediated by perceived privacy risk; (2) the interaction effect of a disclosure nudge and a social-proof nudge was significant in decreasing perceived privacy risk and increasing willingness to provide personal data to proceed with the transaction, while a social-proof nudge only functioned as a complement to a disclosure nudge; (3) perceived privacy risks positively affected privacy concern, which in turn negatively affected willingness to provide personal data to proceed with the transaction; (4) privacy cynicism served as a significant moderator in attenuating the direct effects of perceived privacy risk and privacy concern on willingness to provide personal data to proceed with the transaction, but it did not moderate the indirect effect of perceived privacy risk on willingness to provide personal data to proceed with the transaction via privacy concern. This study provides numerous theoretical and practical implications, and puts forward some limitations and directions for future research.
    Reference: Acquisti, A. (2004, May). Privacy in electronic commerce and the economics of immediate gratification. In Proceedings of the 5th ACM conference on Electronic commerce (pp. 21-29).
    Acquisti, A., & Gross, R. (2006, June). Imagined communities: Awareness, information sharing, and privacy on the Facebook. In International workshop on privacy enhancing technologies (pp. 36-58). Springer, Berlin, Heidelberg.
    Acquisti, A., & Grossklags, J. (2003, May). Losses, gains, and hyperbolic discounting: An experimental approach to information security attitudes and behavior. In 2nd Annual Workshop on Economics and Information Security-WEIS (Vol. 3, pp. 1-27).
    Acquisti, A., Adjerid, I., Balebako, R., Brandimarte, L., Cranor, L. F., Komanduri, S., ... & Wilson, S. (2017). Nudges for privacy and security: Understanding and assisting users’ choices online. ACM Computing Surveys (CSUR), 50(3), 1-41.
    Acquisti, A., Gritzalis, S., Lambrinoudakis, C., & di Vimercati, S. (2007). What can behavioral economics teach us about privacy?. In Digital privacy (pp. 385-400). Auerbach Publications.
    Adjerid, I., Acquisti, A., Brandimarte, L., & Loewenstein, G. (2013, July). Sleights of privacy: Framing, disclosures, and the limits of transparency. In Proceedings of the ninth symposium on usable privacy and security (pp. 1-11).
    Aguinis, H., & Bradley, K. J. (2014). Best practice recommendations for designing and implementing experimental vignette methodology studies. Organizational research methods, 17(4), 351-371.
    Aguirre, E., Mahr, D., Grewal, D., De Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of retailing, 91(1), 34-49.
    Ajzen, I. (1980). Understanding attitudes and predicting social behavior. Englewood cliffs.
    Alamdari, P. M., Navimipour, N. J., Hosseinzadeh, M., Safaei, A. A., & Darwesh, A. (2020). A systematic study on the recommender systems in the E-commerce. IEEE Access, 8, 115694-115716.
    Albashrawi, M., & Motiwalla, L. (2019). Privacy and personalization in continued usage intention of mobile banking: An integrative perspective. Information Systems Frontiers, 21(5), 1031-1043.
    Almada, S. J., Zonderman, A. B., Shekelle, R. B., Dyer, A. R., Daviglus, M. L., Costa, P. T., & Stamler, J. (1991). Neuroticism and cynicism and risk of death in middle-aged men: the Western Electric Study. Psychosomatic Medicine.
    Altman, I. (1975). The environment and social behavior: privacy, personal space, territory, and crowding.
    Atienza, M., Cantero, J. L., & Escera, C. (2001). Auditory information processing during human sleep as revealed by event-related brain potentials. Clinical neurophysiology, 112(11), 2031-2045.
    Awad, N. F., & Krishnan, M. S. (2006). The personalization privacy paradox: an empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS quarterly, 13-28.
    Babula, E., Mrzygłód, U., & Poszewiecki, A. (2017). CONSUMERS’NEED OF PRIVACY PROTECTION–EXPERIMENTAL RESULTS. Economics & Sociology, 10(2), 74.
    Baker, W. E. (1999). When can affective conditioning and mere exposure directly influence brand choice?. Journal of advertising, 28(4), 31-46.
    Balebako, R. (2014). Mitigating the Risks of Smartphone Data Sharing: Identifying Opportunities and Evaluating Notice (Doctoral dissertation, Carnegie Mellon University).
    Bandara, R., Fernando, M., & Akter, S. (2020). Explicating the privacy paradox: A qualitative inquiry of online shopping consumers. Journal of Retailing and Consumer Services, 52, 101947.
    Bansal, G., & Gefen, D. (2010). The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decision support systems, 49(2), 138-150.
    Barnes, S. B. (2006). A privacy paradox: Social networking in the United States. First Monday.
    Barth, S., & De Jong, M. D. (2017). The privacy paradox–Investigating discrepancies between expressed privacy concerns and actual online behavior–A systematic literature review. Telematics and informatics, 34(7), 1038-1058.
    Bartosiak, M. (2022). Not So Digital After All? A Look at the Nature of Digital Nudging through the Prism of the Digital Object Concept. In Proceedings of the 55th Hawaii International Conference on System Sciences.
    Bauer, C. (2020, March). Multi-Method Evaluation: Leveraging Multiple Methods to Answer What You Were Looking For. In Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (pp. 472-474).
    Bearden, W. O., & Rose, R. L. (1990). Attention to social comparison information: An individual difference factor affecting consumer conformity. Journal of Consumer Research, 16(4), 461-471.
    Becker, G. S., & Murphy, K. M. (1988). A theory of rational addiction. Journal of political Economy, 96(4), 675-700.
    Belanger, F., Hiller, J. S., & Smith, W. J. (2002). Trustworthiness in electronic commerce: the role of privacy, security, and site attributes. The journal of strategic Information Systems, 11(3-4), 245-270.
    Bennett, C. J., Grant, R. A., & Bennett, C. J. (Eds.). (1999). Visions of privacy: Policy choices for the digital age (Vol. 6). University of Toronto Press.
    Benson, V., Saridakis, G., & Tennakoon, H. (2015). Information disclosure of social media users: does control over personal information, user awareness and security notices matter?. Information Technology & People.
    Bergram, K., Djokovic, M., Bezençon, V., & Holzer, A. (2022, April). The Digital Landscape of Nudging: A Systematic Literature Review of Empirical Research on Digital Nudges. In CHI Conference on Human Factors in Computing Systems (pp. 1-16).
    Betzing, J. H., Tietz, M., vom Brocke, J., & Becker, J. (2020). The impact of transparency on mobile privacy decision making. Electronic Markets, 30(3), 607-625.
    Beyari, H. (2021). RECENT E-COMMERCE TRENDS AND LEARNINGS FOR E-COMMERCE SYSTEM DEVELOPMENT FROM A QUALITY PERSPECTIVE. International Journal for Quality Research, 15(3).
    Blanca, M. J., Alarcón, R., Arnau, J., Bono, R., & Bendayan, R. (2018). Effect of variance ratio on ANOVA robustness: Might 1.5 be the limit?. Behavior Research Methods, 50, 937-962.
    Bol, N., Dienlin, T., Kruikemeier, S., Sax, M., Boerman, S. C., Strycharz, J., ... & De Vreese, C. H. (2018). Understanding the effects of personalization as a privacy calculus: Analyzing self-disclosure across health, news, and commerce contexts. Journal of Computer-Mediated Communication, 23(6), 370-388.
    Bollen, D., Knijnenburg, B. P., Willemsen, M. C., & Graus, M. (2010, September). Understanding choice overload in recommender systems. In Proceedings of the fourth ACM conference on Recommender systems (pp. 63-70).
    Bothos, E., Apostolou, D., & Mentzas, G. (2015). Recommender systems for nudging commuters towards eco-friendly decisions. Intelligent Decision Technologies, 9(3), 295-306.
    Bovens, L. (2009). The ethics of nudge. In Preference change (pp. 207-219). Springer, Dordrecht.
    Buck, C., Horbel, C., Germelmann, C. C., & Eymann, T. (2014, June). The Unconscious App Consumer: Discovering and Comparing the Information-seeking Patterns among Mobile Application Consumers. In ECIS.
    Busemeyer, J. R., Wang, Z., & Townsend, J. T. (2006). Quantum dynamics of human decision-making. Journal of Mathematical Psychology, 50(3), 220-241.
    Chang, D., Krupka, E. L., Adar, E., & Acquisti, A. (2016, May). Engineering information disclosure: Norm shaping designs. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 587-597).
    Chellappa, R. K., & Sin, R. G. (2005). Personalization versus privacy: An empirical examination of the online consumer’s dilemma. Information technology and management, 6(2), 181-202.
    Chen, L., & Liu, H. W. (2015). A review of privacy protection in e-commerce. Journal of.
    Chen, S., Wu, Y., Deng, F., & Zhi, K. (2023). How does ad relevance affect consumers` attitudes toward personalized advertisements and social media platforms? The role of information co-ownership, vulnerability, and privacy cynicism. Journal of Retailing and Consumer Services, 73, 103336.
    Choe, E. K., Jung, J., Lee, B., & Fisher, K. (2013, September). Nudging people away from privacy-invasive mobile apps through visual framing. In IFIP Conference on Human-Computer Interaction (pp. 74-91). Springer, Berlin, Heidelberg.
    Choi, H., Park, J., & Jung, Y. (2018). The role of privacy fatigue in online privacy behavior. Computers in Human Behavior, 81, 42-51.
    Cialdini, R. B., & Cialdini, R. B. (2007). Influence: The psychology of persuasion (Vol. 55, p. 339). New York: Collins.
    Cialdini, R. B., & Garde, N. (1987). Influence (Vol. 3). Port Harcourt: A. Michel.
    Cialdini, R. B., & Trost, M. R. (1998). Social influence: Social norms, conformity and compliance.
    Cremonesi, P., Garzotto, F., & Turrin, R. (2012). Investigating the persuasion potential of recommender systems from a quality perspective: An empirical study. ACM Transactions on Interactive Intelligent Systems (TiiS), 2(2), 1-41.
    Cremonesi, P., Garzotto, F., & Turrin, R. (2013). User-centric vs. system-centric evaluation of recommender systems. In Human-Computer Interaction–INTERACT 2013: 14th IFIP TC 13 International Conference, Cape Town, South Africa, September 2-6, 2013, Proceedings, Part III 14 (pp. 334-351). Springer Berlin Heidelberg.
    Culnan, M. J., & Armstrong, P. K. (1999). Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organization science, 10(1), 104-115.
    Culnan, M. J., & Bies, R. J. (2003). Consumer privacy: Balancing economic and justice considerations. Journal of social issues, 59(2), 323-342.
    Danesi, M. (2011). What`s in a brand name? A note on the onomastics of brand naming. Names, 59(3), 175-185.
    Darley, W. K., & Lim, J. S. (1993). Assessing demand artifacts in consumer research: an alternative perspective. Journal of Consumer Research, 20(3), 489-495.
    Davies, S. G. (1997). Re-engineering the right to privacy: How privacy has been transformed from a right to a commodity. Technology and Privacy: The New Landscape, Ed. P. Agre, E. and M. Rotenberg.
    De Mooij, M., & Hofstede, G. (2011). Cross-cultural consumer behavior: A review of research findings. Journal of international consumer marketing, 23(3-4), 181-192.
    Dean Jr, J. W., Brandes, P., & Dharwadkar, R. (1998). Organizational cynicism. Academy of Management review, 23(2), 341-352.
    Demarque, C., Charalambides, L., Hilton, D. J., & Waroquier, L. (2015). Nudging sustainable consumption: The use of descriptive norms to promote a minority behavior in a realistic online shopping environment. Journal of Environmental Psychology, 43, 166-174.
    Dennis, A. R., Yuan, L., Feng, X., Webb, E., & Hsieh, C. J. (2020). Digital nudging: Numeric and semantic priming in e-commerce. Journal of management information systems, 37(1), 39-65.
    Deuker, A. (2009, September). Addressing the privacy paradox by expanded privacy awareness–the example of context-aware services. In IFIP PrimeLife International Summer School on Privacy and Identity Management for Life (pp. 275-283). Springer, Berlin, Heidelberg.
    Dhingra, S., Gupta, S., & Bhatt, R. (2020). A study of relationship among service quality of E-commerce websites, customer satisfaction, and purchase intention. International Journal of E-Business Research (IJEBR), 16(3), 42-59.
    Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information systems research, 17(1), 61-80.
    Dinev, T., Bellotto, M., Hart, P., Russo, V., Serra, I., & Colautti, C. (2006). Privacy calculus model in e-commerce–a study of Italy and the United States. European Journal of Information Systems, 15(4), 389-402.
    Dolan, P., Hallsworth, M., Halpern, D., King, D., Metcalfe, R., & Vlaev, I. (2012). Influencing behaviour: The mindspace way. Journal of economic psychology, 33(1), 264-277.
    Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of consumer research, 21(1), 119-134.
    Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., ... & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168.
    Engelen, B. (2019). Nudging and rationality: What is there to worry?. Rationality and Society, 31(2), 204-232.
    Ermakova, T., Fabian, B., & Zarnekow, R. (2014). Acceptance of health clouds-a privacy calculus perspective.
    Eslami, M., Krishna Kumaran, S. R., Sandvig, C., & Karahalios, K. (2018, April). Communicating algorithmic process in online behavioral advertising. In Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1-13).
    Ferketich, S. (1991). Focus on psychometrics. Aspects of item analysis. Research in nursing & health, 14(2), 165-168.
    Field, A. (2013). Discovering statistics using IBM SPSS statistics. sage.
    Fife, E., & Orjuela, J. (2012). The privacy calculus: Mobile apps and user perceptions of privacy and security. International Journal of Engineering Business Management, 4(Godište 2012), 4-11.
    Flender, C., & Müller, G. (2012, June). Type indeterminacy in privacy decisions: the privacy paradox revisited. In International Symposium on Quantum Interaction (pp. 148-159). Springer, Berlin, Heidelberg.
    Franzoi, S., & vom Brocke, J. (2022). Sustainability by Default? Nudging Carbon Offsetting Behavior in E-Commerce.
    Furedi, F. (2011). Defending moral autonomy against an army of nudgers. Spiked.[Online]. Retrieved on, 5(09), 2018.
    Ge, Y., Xu, S., Liu, S., Fu, Z., Sun, F., & Zhang, Y. (2020, July). Learning personalized risk preferences for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 409-418).
    Gena, C., Grillo, P., Lieto, A., Mattutino, C., & Vernero, F. (2019). When personalization is not an option: An in-the-wild study on persuasive news recommendation. Information, 10(10), 300.
    George, D., & Mallery, M. (2003). Using SPSS for Windows step by step: a simple guide and reference.
    Gerber, N., Gerber, P., & Volkamer, M. (2018). Explaining the privacy paradox: A systematic review of literature investigating privacy attitude and behavior. Computers & security, 77, 226-261.
    Gibson, J. J. (1977). The theory of affordances. Hilldale, USA, 1(2), 67-82.
    Goldstein, D. G., Johnson, E. J., Herrmann, A., & Heitmann, M. (2008). Nudge your customers toward better choices. Harvard Business Review, 86(12), 99-105.
    Gong, W. (2009). National culture and global diffusion of business‐to‐consumer e‐commerce. Cross cultural management: an international journal, 16(1), 83-101.
    Govani, T., & Pashley, H. (2005). Student awareness of the privacy implications when using Facebook. Unpublished paper presented at the “Privacy poster fair” at the Carnegie Mellon university school of library and information science, 9, 1-17.
    Grazioli, S., & Jarvenpaa, S. L. (2000). Perils of Internet fraud: An empirical investigation of deception and trust with experienced Internet consumers. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 30(4), 395-410.
    Gregor, S., & Lee‐Archer, B. (2016). The digital nudge in social security administration. International Social Security Review, 69(3-4), 63-83.
    Grossklags, J., & Acquisti, A. (2007, June). When 25 Cents is Too Much: An Experiment on Willingness-To-Sell and Willingness-To-Protect Personal Information. In WEIS.
    Gryz, J. (2013). Privacy as informational commodity.
    Guo, G., Zhang, J., Thalmann, D., & Yorke-Smith, N. (2014). Leveraging prior ratings for recommender systems in e-commerce. Electronic Commerce Research and Applications, 13(6), 440-455.
    Guo, X., Sun, Y., Yan, Z., & Wang, N. (2012). Privacy-personalization paradox in adoption of mobile health service: the mediating role of trust.
    Gutierrez, A., & Calle, T. (2017). Acceptance of mobile location-based advertising: a privacy calculus model.
    Gutierrez, A., O`Leary, S., Rana, N. P., Dwivedi, Y. K., & Calle, T. (2019). Using privacy calculus theory to explore entrepreneurial directions in mobile location-based advertising: Identifying intrusiveness as the critical risk factor. Computers in Human Behavior, 95, 295-306.
    Halpern, S. D., Ubel, P. A., & Asch, D. A. (2007). Harnessing the power of default options to improve health care. New England Journal of Medicine, 357(13), 1340-1344.
    Hansen, P. G., & Jespersen, A. M. (2013). Nudge and the manipulation of choice: A framework for the responsible use of the nudge approach to behaviour change in public policy. European Journal of Risk Regulation, 4(1), 3-28.
    Hauff, J. C., & Nilsson, J. (2021). Individual costs and societal benefits: the privacy calculus of contact-tracing apps. Journal of Consumer Marketing.
    Hausman, D. M., & Welch, B. (2010). Debate: To nudge or not to nudge. Journal of Political Philosophy, 18(1), 123-136.
    Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford publications.
    Henkel, C., Seidler, A. R., Kranz, J., & Fiedler, M. (2019). How to Nudge Pro-Environmental behaviour: an Experimental Study. In ECIS.
    Hoffmann, C. P., Lutz, C., & Ranzini, G. (2016). Privacy cynicism: A new approach to the privacy paradox. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 10(4).
    Holbrook, M. B., & Hirschman, E. C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of consumer research, 9(2), 132-140.
    Hrastović, D. (2022). PARADOX OR CYNICISM: THE QUESTION OF PRIVACY CONSIDERATION IN MOBILE APP USE (Doctoral dissertation, University of Zagreb. The Faculty of Political Science).
    Huang, Z., Yoon, S. Y., & Benyoucef, M. (2012). Adding social features to e-commerce. In Proceedings of the Conference on Information Systems Applied Research ISSN (Vol. 2167, p. 1508).
    Hui, K. L., Teo, H. H., & Lee, S. Y. T. (2007). The value of privacy assurance: An exploratory field experiment. Mis Quarterly, 19-33.
    Hummel, D., & Maedche, A. (2019). How effective is nudging? A quantitative review on the effect sizes and limits of empirical nudging studies. Journal of Behavioral and Experimental Economics, 80, 47-58.
    Hummel, D., Toreini, P., & Maedche, A. (2018). Improving digital nudging using attentive user interfaces: theory development and experiment design. In 13th International Conference on Design Science Research in Information Systems and Technology (DESRIST).
    Information Commission’s Office (2012). Guidance on the rules on use of cookies and similar technologies. Retrieved from https://ico.org.uk/for-organisations/guide-to-pecr/guidance-on-the-use-of-cookies-and-similar-technologies/
    International Trade Administration U.S. Department of Commerce (2022, November). Taiwan – eCommerce. Retrieved from https://www.trade.gov/knowledge-product/taiwan-ecommerce
    Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing?. Journal of personality and social psychology, 79(6), 995.
    Jeckmans, A. J., Beye, M., Erkin, Z., Hartel, P., Lagendijk, R. L., & Tang, Q. (2013). Privacy in recommender systems. In Social media retrieval (pp. 263-281). Springer, London.
    Jesse, M., & Jannach, D. (2021). Digital nudging with recommender systems: Survey and future directions. Computers in Human Behavior Reports, 3, 100052.
    Jia, H., Wisniewski, P. J., Xu, H., Rosson, M. B., & Carroll, J. M. (2015, February). Risk-taking as a learning process for shaping teen`s online information privacy behaviors. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (pp. 583-599).
    Jiang, Z., Heng, C. S., & Choi, B. C. (2013). Research note—privacy concerns and privacy-protective behavior in synchronous online social interactions. Information Systems Research, 24(3), 579-595.
    John, L. K., Acquisti, A., & Loewenstein, G. (2011). Strangers on a plane: Context-dependent willingness to divulge sensitive information. Journal of consumer research, 37(5), 858-873.
    Jozani, M., Ayaburi, E., Ko, M., & Choo, K. K. R. (2020). Privacy concerns and benefits of engagement with social media-enabled apps: A privacy calculus perspective. Computers in Human Behavior, 107, 106260.
    Kaaniche, N., Laurent, M., & Belguith, S. (2020). Privacy enhancing technologies for solving the privacy-personalization paradox: Taxonomy and survey. Journal of Network and Computer Applications, 171, 102807.
    Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-292.
    Kang, H., & Oh, J. (2021). Communication privacy management for smart speaker use: Integrating the role of privacy self-efficacy and the multidimensional view. New Media & Society, 14614448211026611.
    Kardes, F. R., Posavac, S. S., & Cronley, M. L. (2004). Consumer inference: A review of processes, bases, and judgment contexts. Journal of consumer psychology, 14(3), 230-256.
    Karwatzki, S., Dytynko, O., Trenz, M., & Veit, D. (2017). Beyond the personalization–privacy paradox: Privacy valuation, transparency features, and service personalization. Journal of Management Information Systems, 34(2), 369-400.
    Kehr, F., Kowatsch, T., Wentzel, D., & Fleisch, E. (2015). Blissfully ignorant: the effects of general privacy concerns, general institutional trust, and affect in the privacy calculus. Information Systems Journal, 25(6), 607-635.
    Keith, M. J., Babb, J. S., Lowry, P. B., Furner, C. P., & Abdullat, A. (2015). The role of mobile‐computing self‐efficacy in consumer information disclosure. Information Systems Journal, 25(6), 637-667.
    Keith, M. J., Thompson, S. C., Hale, J., Lowry, P. B., & Greer, C. (2013). Information disclosure on mobile devices: Re-examining privacy calculus with actual user behavior. International journal of human-computer studies, 71(12), 1163-1173.
    Keller, K. L., Heckler, S. E., & Houston, M. J. (1998). The effects of brand name suggestiveness on advertising recall. Journal of marketing, 62(1), 48-57.
    Kelley, P. G., Cesca, L., Bresee, J., & Cranor, L. F. (2010, April). Standardizing privacy notices: an online study of the nutrition label approach. In Proceedings of the SIGCHI Conference on Human factors in Computing Systems (pp. 1573-1582).
    Kezer, M., Dienlin, T., & Baruh, L. (2022). Getting the privacy calculus right: Analyzing the relations between privacy concerns, expected benefits, and self-disclosure using response surface analysis. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 16(4).
    Kim, J., Gambino, A., Sundar, S. S., Rosson, M. B., Aritajati, C., Ge, J., & Fanning, C. (2018). Interface cues to promote disclosure and build community: An experimental test of crowd and connectivity cues in an online sexual health forum. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1-18.
    Klumpe, J., Koch, O. F., & Benlian, A. (2020). How pull vs. push information delivery and social proof affect information disclosure in location based services. Electronic Markets, 30(3), 569-586.
    Knijnenburg, B. P., & Kobsa, A. (2014). Increasing Sharing Tendency Without Reducing Satisfaction: Finding the Best Privacy-Settings User Interface for Social Networks. In ICIS.
    Knijnenburg, B., Raybourn, E., Cherry, D., Wilkinson, D., Sivakumar, S., & Sloan, H. (2017). Death to the privacy calculus?. Available at SSRN 2923806.
    Kokolakis, S. (2017). Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Computers & security, 64, 122-134.
    Komiak, S. Y., & Benbasat, I. (2006). The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS quarterly, 941-960.
    Kordzadeh, N. (2014). Communicating personal health information in virtual health communities: An integration of privacy calculus model and affective commitment. The University of Texas at San Antonio.
    Kordzadeh, N., Warren, J., & Seifi, A. (2016). Antecedents of privacy calculus components in virtual health communities. International Journal of Information Management, 36(5), 724-734.
    Krasnova, H., Spiekermann, S., Koroleva, K., & Hildebrand, T. (2010). Online social networks: Why we disclose. Journal of information technology, 25(2), 109-125.
    Krasnova, H., Veltri, N. F., & Günther, O. (2012). Self-disclosure and privacy calculus on social networking sites: the role of culture. Business & Information Systems Engineering, 4(3), 127-135.
    Lance, C. E., Butts, M. M., & Michels, L. C. (2006). The sources of four commonly reported cutoff criteria: What did they really say?. Organizational research methods, 9(2), 202-220.
    Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: A multidimensional developmental theory. Journal of social Issues, 33(3), 22-42.
    Lee, H., Lim, D., Kim, H., Zo, H., & Ciganek, A. P. (2015). Compensation paradox: the influence of monetary rewards on user behaviour. Behaviour & Information Technology, 34(1), 45-56.
    Lembcke, T. B., Engelbrecht, N., Brendel, A. B., Herrenkind, B., & Kolbe, L. M. (2019). Towards a unified understanding of digital nudging by addressing its analog roots.
    Lex, E., Kowald, D., Seitlinger, P., Tran, T. N. T., Felfernig, A., & Schedl, M. (2021). Psychology-informed recommender systems. Foundations and Trends® in Information Retrieval, 15(2), 134-242.
    Li, H., Sarathy, R., & Xu, H. (2010). Understanding situational online information disclosure as a privacy calculus. Journal of Computer Information Systems, 51(1), 62-71.
    Li, Y. M., Wu, C. T., & Lai, C. Y. (2013). A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship. Decision Support Systems, 55(3), 740-752.
    Liao, M., & Sundar, S. S. (2022). When e-commerce personalization systems show and tell: Investigating the relative persuasive appeal of content-based versus collaborative filtering. Journal of Advertising, 51(2), 256-267.
    Liebermann, Y., & Stashevsky, S. (2002). Perceived risks as barriers to Internet and e‐commerce usage. Qualitative Market Research: An International Journal.
    Lo, J. (2010). Privacy concern, locus of control, and salience in a trust-risk model of information disclosure on social networking sites.
    Lowry, P. B., Moody, G. D., & Chatterjee, S. (2017). Using IT design to prevent cyberbullying. Journal of management information systems, 34(3), 863-901.
    Lu, S., Chen, G. G., & Wang, K. (2020). Overt or covert? Effect of different digital nudging on consumers’ customization choices. Nankai Business Review International.
    Lutz, C., & Tamó-Larrieux, A. (2020). The robot privacy paradox: Understanding how privacy concerns shape intentions to use social robots. Human-Machine Communication, 1, 87-111.
    Lutz, C., Hoffmann, C. P., & Ranzini, G. (2020). Data capitalism and the user: An exploration of privacy cynicism in Germany. new media & society, 22(7), 1168-1187.
    Ma Y. (2022). Coronavirus impact on online purchases in Taiwan 2020. Statista. Retrieved from https://www.statista.com/statistics/1127293/taiwan-coronavirus-impact-on-online-purchases/
    Ma, X., Qin, Y., Chen, Z., & Cho, H. (2021). Perceived ephemerality, privacy calculus, and the privacy settings of an ephemeral social media site. Computers in Human Behavior, 124, 106928.
    Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users` information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information systems research, 15(4), 336-355.
    Marreiros, H., Tonin, M., Vlassopoulos, M., & Schraefel, M. C. (2017). “Now that you mention it”: A survey experiment on information, inattention and online privacy. Journal of Economic Behavior & Organization, 140, 1-17.
    Martin, K. (2020). Breaking the privacy paradox: the value of privacy and associated duty of firms. Business Ethics Quarterly, 30(1), 65-96.
    Marwick, A., & Hargittai, E. (2019). Nothing to hide, nothing to lose? Incentives and disincentives to sharing information with institutions online. Information, Communication & Society, 22(12), 1697-1713.
    Masur, P. K. (2018). Situational privacy and self-disclosure: Communication processes in online environments. Springer.
    Meng, F., Guo, X., Lai, K. H., & Zhao, X. (2018). A Personalization-Privacy Paradox in Usage of Mobile Health Services: A Game Theoretic Perspective. In Wuhan International Conference on E-Business. Association For Information Systems.
    Meske, C., Amojo, I., & Mohr, P. (2020). Digital Nudging to Increase Usage of Charity Features on E-Commerce Platforms. In Wirtschaftsinformatik (Zentrale Tracks) (pp. 1203-1218).
    Meske, C., Amojo, I., Poncette, A. S., & Balzer, F. (2019, July). The potential role of digital nudging in the digital transformation of the healthcare industry. In International Conference on Human-Computer Interaction (pp. 323-336). Springer, Cham.
    Metzger, M. J. (2006). Effects of site, vendor, and consumer characteristics on web site trust and disclosure. communication research, 33(3), 155-179.
    Metzger, M. J. (2007). Communication privacy management in electronic commerce. Journal of Computer-Mediated Communication, 12(2), 335-361.
    Metzger, M. J., & Flanagin, A. J. (2013). Credibility and trust of information in online environments: The use of cognitive heuristics. Journal of pragmatics, 59, 210-220.
    Miller, S. (2005). Experimental design and statistics. Routledge.
    Milne, G. R., & Culnan, M. J. (2002). Using the content of online privacy notices to inform public policy: A longitudinal analysis of the 1998-2001 US Web surveys. The Information Society, 18(5), 345-359.
    Milne, G. R., & Culnan, M. J. (2004). Strategies for reducing online privacy risks: Why consumers read (or don`t read) online privacy notices. Journal of interactive marketing, 18(3), 15-29.
    Mirsch, T., Lehrer, C., & Jung, R. (2017). Digital nudging: Altering user behavior in digital environments. Proceedings der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017), 634-648.
    Miyazaki, A. D., & Fernandez, A. (2001). Consumer perceptions of privacy and security risks for online shopping. Journal of Consumer affairs, 35(1), 27-44.
    Moloney, M., & Potì, V. (2013). A behavioral perspective on the privacy calculus model. SSRN Electronic Journal.
    Money101 (2022, September). 2022消費者網購支付行為調查. Retrieved from https://money101.events/content/202209sv-268
    Montgomery, A. L., & Smith, M. D. (2009). Prospects for Personalization on the Internet. Journal of Interactive Marketing, 23(2), 130-137.
    Mukherjee, S., Manjaly, J. A., & Nargundkar, M. (2013). Money makes you reveal more: Consequences of monetary cues on preferential disclosure of personal information. Frontiers in psychology, 4, 839.
    Mutimukwe, C., Kolkowska, E., & Grönlund, Å. (2020). Information privacy in e-service: Effect of organizational privacy assurances on individual privacy concerns, perceptions, trust and self-disclosure behavior. Government Information Quarterly, 37(1), 101413.
    Norberg, P. A., Horne, D. R., & Horne, D. A. (2007). The privacy paradox: Personal information disclosure intentions versus behaviors. Journal of consumer affairs, 41(1), 100-126.
    Nosko, A., Wood, E., Kenney, M., Archer, K., De Pasquale, D., Molema, S., & Zivcakova, L. (2012). Examining priming and gender as a means to reduce risk in a social networking context: Can stories change disclosure and privacy setting use when personal profiles are constructed?. Computers in Human Behavior, 28(6), 2067-2074.
    Osterwalder, A., & Pigneur, Y. (2002). An eBusiness model ontology for modeling eBusiness.
    Ozdemir, Z. D., Jeff Smith, H., & Benamati, J. H. (2017). Antecedents and outcomes of information privacy concerns in a peer context: An exploratory study. European Journal of Information Systems, 26(6), 642-660.
    Pavlou, P. A., Liang, H., & Xue, Y. (2007). Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS quarterly, 105-136.
    Peter, J. P., & Tarpey Sr, L. X. (1975). A comparative analysis of three consumer decision strategies. Journal of consumer research, 2(1), 29-37.
    Posey, C., Lowry, P. B., Roberts, T. L., & Ellis, T. S. (2010). Proposing the online community self-disclosure model: the case of working professionals in France and the UK who use online communities. European journal of information systems, 19(2), 181-195.
    Putra, A. A., Mahendra, R., Budi, I., & Munajat, Q. (2017, November). Two-steps graph-based collaborative filtering using user and item similarities: Case study of E-commerce recommender systems. In 2017 International Conference on Data and Software Engineering (ICoDSE) (pp. 1-6). IEEE.
    Rahman, M. S. (2019, June). Does Privacy Matters When We are Sick? An Extended Privacy Calculus Model for Healthcare Technology Adoption Behavior. In 2019 10th International Conference on Information and Communication Systems (ICICS) (pp. 41-46). IEEE.
    Reips, U. D. (2006). Web-Based Methods.
    Research and Markets. (2022, July). Taiwan B2C Ecommerce Market Opportunities Databook - 100+ KPIs on Ecommerce Verticals (Shopping, Travel, Food Service, Media & Entertainment, Technology), Market Share by Key Players, Sales Channel Analysis, Payment Instrument, Consumer Demographics - Q2 2022 Update. Retrieved from https://www.researchandmarkets.com/reports/5648290/taiwan-b2c-ecommerce-market-opportunities
    Rizzo, M. J., & Whitman, D. G. (2008). Little brother is watching you: New paternalism on the slippery slopes. NYU Law School, Public Law Research Paper, (08-12), 08-15.
    Röthlisberger, M. (2020). Digital Nudging Decoy Effect and Social Norms Nudge in E-commerce.
    Sah, Y. J., & Peng, W. (2015). Effects of visual and linguistic anthropomorphic cues on social perception, self-awareness, and information disclosure in a health website. Computers in Human Behavior, 45, 392-401.
    Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web (pp. 291-324). Springer, Berlin, Heidelberg.
    Schafer, J. B., Konstan, J., & Riedl, J. (1999, November). Recommender systems in e-commerce. In Proceedings of the 1st ACM conference on Electronic commerce (pp. 158-166).
    Schär, A., & Stanoevska-Slabeva, K. (2019). Application of digital nudging in customer journeys–A systematic literature review.
    Schaub, F., Balebako, R., Durity, A. L., & Cranor, L. F. (2015). A design space for effective privacy notices. In Eleventh symposium on usable privacy and security (SOUPS 2015) (pp. 1-17).
    Schneider, D., Klumpe, J., Adam, M., & Benlian, A. (2020). Nudging users into digital service solutions. Electronic Markets, 30(4), 863-881.
    Schneider, D., Lins, S., Grupp, T., Benlian, A., & Sunyaev, A. (2017). Nudging users into online verification: The case of carsharing platforms.
    Selinger, E., & Whyte, K. P. (2010). Competence and trust in choice architecture. Knowledge, Technology & Policy, 23(3), 461-482.
    Sen, A. (1988). Freedom of choice: concept and content. European economic review, 32(2-3), 269-294.
    Sheehan, K. B., & Hoy, M. G. (1999). Flaming, complaining, abstaining: How online users respond to privacy concerns. Journal of advertising, 28(3), 37-51.
    Simon, H. A. (1955). A behavioral model of rational choice. The quarterly journal of economics, 69(1), 99-118.
    Sivapalan, S., Sadeghian, A., Rahnama, H., & Madni, A. M. (2014, August). Recommender systems in e-commerce. In 2014 World Automation Congress (WAC) (pp. 179-184). IEEE.
    Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: an interdisciplinary review. MIS quarterly, 989-1015.
    Solove, D. J. (2015). The meaning and value of privacy. Social dimensions of privacy: Interdisciplinary perspectives, 71-81.
    Spottswood, E. L., & Hancock, J. T. (2017). Should I share that? Prompting social norms that influence privacy behaviors on a social networking site. Journal of Computer-Mediated Communication, 22(2), 55-70.
    Sumner, C., Byers, A., & Shearing, M. (2011). Determining personality traits & privacy concerns from facebook activity. Black Hat Briefings, 11(7), 197-221.
    Sun, Y., Drivas, M., Liao, M., & Sundar, S. S. (2023, April). When Recommender Systems Snoop into Social Media, Users Trust them Less for Health Advice. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-14).
    Sun, Y., Fang, S., & Hwang, Y. (2019). Investigating privacy and information disclosure behavior in social electronic commerce. Sustainability, 11(12), 3311.
    Sun, Y., Wang, N., Shen, X. L., & Zhang, J. X. (2015). Location information disclosure in location-based social network services: Privacy calculus, benefit structure, and gender differences. Computers in Human Behavior, 52, 278-292.
    Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility (pp. 73-100). Cambridge, MA: MacArthur Foundation Digital Media and Learning Initiative.
    Sundar, S. S., Jia, H., Waddell, T. F., & Huang, Y. (2015). Toward a theory of interactive media effects (TIME) four models for explaining how interface features affect user psychology. The handbook of the psychology of communication technology, 47-86.
    Sundar, S. S., Kim, J., & Gambino, A. (2017). Using theory of interactive media effects (TIME) to analyze digital advertising. In Digital advertising (pp. 86-109). Routledge.
    Sundar, S. S., Kim, J., Rosson, M. B., & Molina, M. D. (2020, April). Online privacy heuristics that predict information disclosure. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-12).
    Sunstein, C. R. (2014). Nudging: a very short guide. Journal of Consumer Policy, 37(4), 583-588.
    Swaminathan, V., Lepkowska-White, E., & Rao, B. P. (1999). Browsers or buyers in cyberspace? An investigation of factors influencing electronic exchange. Journal of computer-mediated communication, 5(2), JCMC523.
    Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven & London: Yale University Press.
    Thaler, R. H., Sunstein, C. R., & Balz, J. P. (2013). Choice architecture (Vol. 2013). Princeton, NJ: Princeton University Press.
    Tsai, J. Y., Egelman, S., Cranor, L., & Acquisti, A. (2011). The effect of online privacy information on purchasing behavior: An experimental study. Information systems research, 22(2), 254-268.
    Tufekci, Z. (2008). Grooming, gossip, Facebook and MySpace: What can we learn about these sites from those who won`t assimilate?. Information, Communication & Society, 11(4), 544-564.
    Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.
    Tversky, A., & Kahneman, D. (1985). The framing of decisions and the psychology of choice. In Behavioral decision making (pp. 25-41). Springer, Boston, MA.
    UNCTAD. (2022). COVID-19 has changed online shopping forever, survey shows. Retrieved from https://unctad.org/news/covid-19-has-changed-online-shopping-forever-survey-shows.
    Van Eerde, W., & Thierry, H. (1996). Vroom`s expectancy models and work-related criteria: A meta-analysis. Journal of applied psychology, 81(5), 575.
    Van Herpen, E., Pieters, R., & Zeelenberg, M. (2009). When demand accelerates demand: Trailing the bandwagon. Journal of Consumer Psychology, 19(3), 302-312.
    van Ooijen, I., Segijn, C. M., & Opree, S. J. (2022). Privacy Cynicism and its Role in Privacy Decision-Making. Communication Research, 00936502211060984.
    Van Slyke, C., Shim, J. T., Johnson, R., & Jiang, J. J. (2006). Concern for information privacy and online consumer purchasing. Journal of the Association for Information Systems, 7(6), 1.
    Wakil, K., Alyari, F., Ghasvari, M., Lesani, Z., & Rajabion, L. (2019). A new model for assessing the role of customer behavior history, product classification, and prices on the success of the recommender systems in e-commerce. Kybernetes.
    Wang, L., Hu, H. H., Yan, J., & Mei, M. Q. (2019). Privacy calculus or heuristic cues? The dual process of privacy decision making on Chinese social media. Journal of Enterprise Information Management.
    Wang, T., Duong, T. D., & Chen, C. C. (2016). Intention to disclose personal information via mobile applications: A privacy calculus perspective. International journal of information management, 36(4), 531-542.
    Wang, Y. Y., Luse, A., Townsend, A. M., & Mennecke, B. E. (2015). Understanding the moderating roles of types of recommender systems and products on customer behavioral intention to use recommender systems. Information Systems and e-Business Management, 13(4), 769-799.
    Warren, S. D., & Louis, D. (1890). Brandeis, The Right to Privacy, 4 Harv. L. rev, 193(10.2307), 1321160.
    Wason, P. C., & Evans, J. S. B. (1974). Dual processes in reasoning?. Cognition, 3(2), 141-154.
    Weinmann, M., Schneider, C., & Brocke, J. V. (2016). Digital nudging. Business & Information Systems Engineering, 58(6), 433-436.
    Westin, A. F. (1967). Privacy and freedom Atheneum. New York, 7, 431-453.
    Wilkinson, N., & Klaes, M. (2017). An introduction to behavioral economics. Bloomsbury Publishing.
    Wilkinson, T. M. (2013). Nudging and manipulation. Political Studies, 61(2), 341-355.
    Wilson, D., & Valacich, J. S. (2012). Unpacking the privacy paradox: Irrational decision-making within the privacy calculus.
    Wu, J., Ping, L., Wang, H., Lin, Z., & Zhang, Q. (2008, December). Research on improved collaborative filtering-based mobile e-commerce personalized recommender system. In 2008 International Conference on MultiMedia and Information Technology (pp. 143-146). IEEE.
    Xhafa, F. (2019). Lecture Notes on Data Engineering and Communications Technologies.
    Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS quarterly, 137-209.
    Xie, E., Teo, H. H., & Wan, W. (2006). Volunteering personal information on the internet: Effects of reputation, privacy notices, and rewards on online consumer behavior. Marketing letters, 17(1), 61-74.
    Xie, W., Chen, C., & Sithipolvanichgul, J. (2022). Understanding e-commerce customer behaviors to use drone delivery services: A privacy calculus view. Cogent Business & Management, 9(1), 2102791.
    Xu, F., Michael, K., & Chen, X. (2013). Factors affecting privacy disclosure on social network sites: an integrated model. Electronic Commerce Research, 13(2), 151-168.
    Xu, H., Dinev, T., Smith, H. J., & Hart, P. (2008). Examining the formation of individual`s privacy concerns: Toward an integrative view.
    Xu, H., Dinev, T., Smith, J., & Hart, P. (2011). Information privacy concerns: Linking individual perceptions with institutional privacy assurances. Journal of the Association for Information Systems, 12(12), 1.
    Xu, H., Luo, X. R., Carroll, J. M., & Rosson, M. B. (2011). The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decision support systems, 51(1), 42-52.
    Xu, H., Teo, H. H., Tan, B. C., & Agarwal, R. (2009). The role of push-pull technology in privacy calculus: the case of location-based services. Journal of management information systems, 26(3), 135-174.
    Youn, S. (2009). Determinants of online privacy concern and its influence on privacy protection behaviors among young adolescents. Journal of Consumer affairs, 43(3), 389-418.
    Yu, L., Li, H., He, W., Wang, F. K., & Jiao, S. (2020). A meta-analysis to explore privacy cognition and information disclosure of internet users. International Journal of Information Management, 51, 102015.
    Zafeiropoulou, A. M., Millard, D. E., Webber, C., & O`Hara, K. (2013, May). Unpicking the privacy paradox: can structuration theory help to explain location-based privacy decisions?. In Proceedings of the 5th Annual ACM Web Science Conference (pp. 463-472).
    Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of consumer research, 12(3), 341-352.
    Zaichkowsky, J. L. (1994). The personal involvement inventory: Reduction, revision, and application to advertising. Journal of advertising, 23(4), 59-70.
    Zangerle, E., & Bauer, C. (2022). Evaluating Recommender Systems: Survey and Framework. ACM Computing Surveys (CSUR).
    Zeithaml, V. A. (2002). Service excellence in electronic channels. Managing Service Quality: An International Journal.
    Zhang, B., & Xu, H. (2016, February). Privacy nudges for mobile applications: effects on the creepiness emotion and privacy attitudes. In Proceedings of the 19th ACM conference on computer-supported cooperative work & social computing (pp. 1676-1690).
    Zhang, B., Wang, N., & Jin, H. (2014). Privacy concerns in online recommender systems: influences of control and user data input. In 10th Symposium On Usable Privacy and Security (SOUPS 2014) (pp. 159-173).
    Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), 52(1), 1-38.
    Zhang, Y., & Chen, X. (2020). Explainable recommendation: A survey and new perspectives. Foundations and Trends® in Information Retrieval, 14(1), 1-101.
    Zhu, H., Ou, C. X., van den Heuvel, W. J. A., & Liu, H. (2017). Privacy calculus and its utility for personalization services in e-commerce: An analysis of consumer decision-making. Information & Management, 54(4), 427-437.
    Zhu, M., Wu, C., Huang, S., Zheng, K., Young, S. D., Yan, X., & Yuan, Q. (2021). Privacy paradox in mHealth applications: An integrated elaboration likelihood model incorporating privacy calculus and privacy fatigue. Telematics and Informatics, 61, 101601.
    Description: 碩士
    國立政治大學
    國際傳播英語碩士學位學程(IMICS)
    109461001
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109461001
    Data Type: thesis
    Appears in Collections:[國際傳播英語碩士學程] 學位論文

    Files in This Item:

    File Description SizeFormat
    100101.pdf7130KbAdobe PDF2171View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback