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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/136841
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/136841


    Title: ⽤⼾感知外的回饋揭露意願因素
    Factors influencing feedback disclosure intention beyond users’ perception
    Authors: 楊昇祐
    Yang, Sheng-You
    Contributors: 林怡伶
    Lin, Yi-Ling
    楊昇祐
    Yang, Sheng-You
    Keywords: 推薦系統
    餐廳推薦
    情境
    產品參與
    回饋
    揭露意願
    Recommender system
    Restaurant recommendation
    Context
    Product involvement
    Feedback
    Disclosure intention
    Date: 2021
    Issue Date: 2021-09-02 15:49:21 (UTC+8)
    Abstract: 如今,推薦系統被普遍用於解決各種網站和APP中資訊過載的問題。且許多研究都集中在如何提高推薦準確性。「回饋」是提高推薦準確性和滿足用戶需求的方法之一。然而,用戶往往不願意主動提供回饋。以往的研究表明,回饋資訊揭露的意願受到感知利益、風險評估和應對評估等因素的影響。儘管如此,這些因素只考慮到了用戶本身。而「用戶與環境的關係(情境)」和「用戶與產品的關係(產品參與)」並沒有被考量過。本研究以餐廳推薦系統為例,旨在討論用戶本身外其他影響回饋揭露意願的因素。在實驗的第一部分,透過問卷對104名參與者進行了前測;在第二部分,透過餐廳推薦APP對67名參與者進行了實地研究。結果表明,活動情境、時間和參與度,是要求用戶提供回饋時,需要考慮的基本因素。
    Recommender systems are commonly used to solve information overload problems in various websites and APPs nowadays. Many studies focus on the issue of increasing recommendation accuracy. “Feedback” is one of the methods to enhance recommendation accuracy and fulfill users’ needs. However, users are often not willing to provide feedback actively. Previous study demonstrates that feedback information disclosure intention is influenced by factors from the perspectives of perceived benefits, risk appraisal, and coping appraisal. Nonetheless, these factors only take users themselves into account. The “user-environment relationship (context)” and “user-product relationship (product involvement)” have not been considered. This study takes the restaurant recommender system as an example and aims to discuss the factors that influencing feedback disclosure intention. A pretest study was conducted with 104 participants by questionnaire in the experiment first section and a field study was conducted with 67 participants by restaurant recommendation APP in the second section. The results indicating that activity context, timing, and involvement are essential factors to be considered when requesting the user to provide feedback.
    Reference: Adomavicius, G., & Tuzhilin, A. (2011). Context-aware recommender systems. In Recommender Systems Handbook.
    Aljukhadar, M., Senecal, S., & Daoust, C. E. (2012). Using recommendation agents to cope with information overload. International Journal of Electronic Commerce.
    Amatriain, X., Pujol, J. M., Tintarev, N., & Oliver, N. (2009). Rate it again: Increasing recommendation accuracy by user re-rating. RecSys’09 - Proceedings of the 3rd ACM Conference on Recommender Systems.
    Baum, D., & Spann, M. (2014). The interplay between online consumer reviews and recommender systems: An experimental analysis. In International Journal of Electronic Commerce (Vol. 19).
    Betzalel, N. D., Shapira, B., & Rokach, L. (2015). “Please, not now!” A model for timing recommendations. RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems, 297–300.
    Boeckelman, C. (2018). Everything you need to know about survey response rates. Retrieved from https://www.getfeedback.com/resources/online-surveys/better-online-survey-response-rates/
    Campbell, J., DiPietro, R. B., & Remar, D. (2014). Local foods in a university setting: Price consciousness, product involvement, price/quality inference and consumer’s willingness-to-pay. International Journal of Hospitality Management, 42, 39–49.
    Chamberlain, L. (2016). GeoMarketing 101: What Is Geofencing? Retrieved from https://geomarketing.com/geomarketing-101-what-is-geofencing
    Chen, G., & Kotz, D. (2000). A survey of context-aware mobile computing research. Dartmouth Computer Science Technical Report TR2000-381, 1–16.
    De Pessemier, T., Courtois, C., Vanhecke, K., Van Damme, K., Martens, L., & De Marez, L. (2016). A user-centric evaluation of context-aware recommendations for a mobile news service. Multimedia Tools and Applications, 75(6), 3323–3351.
    Dey, A. K., Abowd, G. D., & Salber, D. (2001). A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human-Computer Interaction.
    Fischer, C. S. (1982). What do we mean by “friend”? An inductive study. Social Networks, 3(4), 287–306.
    Ha, J., & Jang, S. (2012). Consumer dining value: Does it vary across different restaurant segments? Journal of Foodservice Business Research, 15(2), 123–142.
    Ilgen, D., Fisher, C., & Taylor, M. (1979). Consequence of feedback on behavior in organizations. Journal of Applied Psychology, 64(4), 349–371.
    Jacoby, J., Speller, D. E., & Berning, C. K. (1974). Brand choice behavior as a function of information load: Replication and extension. Journal of Consumer Research.
    Javad Taghipourian, M., Author, C., & Heidarzadeh Hanzaee, K. (2012). The effects of brand credibility and prestige on consumers purchase intention in low and high product involvement. Journal of Basic and Applied Scientific Research, 2(2), 1281–1291.
    Jawaheer, G., Szomszor, M., & Kostkova, P. (2010). Comparison of implicit and explicit feedback from an online music recommendation service. Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec 2010, Held at the 4th ACM Conference on Recommender Systems, RecSys 2010, 47–51.
    Kim, M. S., & Kim, S. (2018). Factors influencing willingness to provide personal information for personalized recommendations. Computers in Human Behavior, 88, 143–152.
    Ladki, S. M., & Nomami, M. Z. A. (1996). Consumer involvement in restaurant selection: A measure of satisfaction/dissatisfaction (Part II). Journal of Nutrition in Recipe & Menu Development, 2(1), 15–32.
    Lastovicka, J. L. (1979). Questioning the concept of involvement defined product classes. Advances in Consumer Research, 6, 174–179.
    Lewis, J. R. (1995). IBM computer usability satisfaction questionnaires: Psychometric evaluation and instructions for use. International Journal of Human‐Computer Interaction, 7(1), 57–78.
    Liang, Y.-P. (2012). The relationship between consumer product involvement, product knowledge and impulsive buying behavior. Procedia - Social and Behavioral Sciences, 57, 325–330.
    Lommatzsch, A. (2014). Real-time news recommendation using context-aware ensembles. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8416 LNCS, 51–62.
    Martin, C. L. (1998). Relationship marketing: A high-involvement product attribute approach. Journal of Product & Brand Management, 7(1), 6–26.
    McKechnie, J. L. (1983). Webster’s new twentieth century dictionary of the English language.
    Michaelidou, N., & Dibb, S. (2006). Product involvement: an application in clothing. Journal of Consumer Behaviour.
    Najafian, S., Wörndl, W., & Braunhofer, M. (2016). Context-aware user interaction for mobile recommender systems. CEUR Workshop Proceedings, 1618.
    O’Cass, A. (2000). An assessment of consumers product, purchase decision, advertising and consumption involvement in fashion clothing. Journal of Economic Psychology.
    Prendergast, G. P., Tsang, A. S. L., & Chan, C. N. W. (2010). The interactive influence of country of origin of brand and product involvement on purchase intention. Journal of Consumer Marketing.
    Quester, P., & Lin Lim, A. (2003). Product involvement/brand loyalty: Is there a link? Journal of Product & Brand Management.
    Rennison, C. M., & Welchans, S. (2000). Intimate partner violence. In U.S. Department of Justice, Office of Justice Programs, Bureau of Justice Statistics.
    Roetzel, P. G. (2019). Information overload in the information age: a review of the literature from business administration, business psychology, and related disciplines with a bibliometric approach and framework development. Business Research.
    S. O’Dea. (2020). Number of smartphone users worldwide from 2016 to 2021. Retrieved from https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/
    Schilit, B., Adams, N., & Want, R. (1995). Context-aware computing applications. Mobile Computing Systems and Applications - Workshop Proceedings.
    Schmidt, A., Beigl, M., & Gellersen, H. W. (1999). There is more to context than location. Computers and Graphics (Pergamon).
    Soucek, R., & Moser, K. (2010). Coping with information overload in email communication: Evaluation of a training intervention. Computers in Human Behavior.
    Subject definitions. (2020). Retrieved from U.S. Department of Commerce, Bureau of the Census website: https://www.census.gov/programs-surveys/cps/technical-documentation/subject-definitions.html#family
    Te’eni-Harari, T., & Hornik, J. (2010). Factors influencing product involvement among young consumers. Journal of Consumer Marketing, 27(6), 499–506.
    Traylor, M. B. (1981). Product involvement and brand commitment. Journal of Advertising Research, 21(6), 51–56.
    Urquhart, L. M., Ker, J. S., & Rees, C. E. (2018). Exploring the influence of context on feedback at medical school: A video-ethnography study. Advances in Health Sciences Education, 23, 159–186.
    Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12(3), 341–352.
    Zeng, J., Li, F., Liu, H., Wen, J., & Hirokawa, S. (2016). A restaurant recommender system based on user preference and location in mobile environment. 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 55–60.
    Zhao, X., Anma, F., Ninomiya, T., & Okamoto, T. (2008). Personalized Adaptive Content System for Context-Aware Mobile Learning. IJCSNS International Journal of Computer Science and Network Security.
    Zimmermann, A., Lorenz, A., & Oppermann, R. (2007). An operational definition of context. International and Interdisciplinary Conference on Modeling and Using Context, 4635, 558–571.
    Description: 碩士
    國立政治大學
    資訊管理學系
    108356009
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108356009
    Data Type: thesis
    DOI: 10.6814/NCCU202101309
    Appears in Collections:[資訊管理學系] 學位論文

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