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題名 ⽤⼾感知外的回饋揭露意願因素
Factors influencing feedback disclosure intention beyond users’ perception
作者 楊昇祐
Yang, Sheng-You
貢獻者 林怡伶
Lin, Yi-Ling
楊昇祐
Yang, Sheng-You
關鍵詞 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
日期 2021
上傳時間 2-Sep-2021 15:49:21 (UTC+8)
摘要 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.
參考文獻 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.
描述 碩士
國立政治大學
資訊管理學系
108356009
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108356009
資料類型 thesis
dc.contributor.advisor 林怡伶zh_TW
dc.contributor.advisor Lin, Yi-Lingen_US
dc.contributor.author (Authors) 楊昇祐zh_TW
dc.contributor.author (Authors) Yang, Sheng-Youen_US
dc.creator (作者) 楊昇祐zh_TW
dc.creator (作者) Yang, Sheng-Youen_US
dc.date (日期) 2021en_US
dc.date.accessioned 2-Sep-2021 15:49:21 (UTC+8)-
dc.date.available 2-Sep-2021 15:49:21 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2021 15:49:21 (UTC+8)-
dc.identifier (Other Identifiers) G0108356009en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136841-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 108356009zh_TW
dc.description.abstract (摘要) 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。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents CHAPTER 1 INTRODUCTION 1
CHAPTER 2 LITERATURE REVIEW AND RESEARCH HYPOTHESES 3
2-1 FEEDBACK 3
2-2 CONTEXT AND TIMING 3
2-3 PRODUCT INVOLVEMENT 4
CHAPTER 3 METHODOLOGY 7
3-1 FIRST SECTION - SURVEY QUESTIONNAIRE 7
3-1-1 Questionnaire Design 7
3-1-2 Data Collection 9
3-1-3 Pretest Analysis 10
3-2 SECOND SECTION - FIELD STUDY BY AN APP 20
3-2-1 Dataset 20
3-2-2 Recommendation Algorithm and Post-filtering 21
3-2-3 Design and Procedure 22
3-2-4 Hypotheses and Metrics 26
3-2-5 Participants 26
CHAPTER 4 ANALYSIS AND RESULT 29
4-1 ANALYSIS OF PII SCORES DISTRIBUTION 29
4-2 DESCRIPTIVE STATISTICS 30
4-3 LOG DATA ANALYSIS 32
4-4 FEEDBACK ACCEPTED RATE 40
CHAPTER 5 DISCUSSION AND CONCLUSION 42
5-1 DISCUSSION 42
5-1-1 Factors Influencing Feedback Disclosure Intention 42
5-1-2 Activity Context, Involvement, and Timing 44
5-2 IMPLICATIONS 44
5-2-1 Theoretical Implications 44
5-2-2 Practical Implications 45
5-3 LIMITATIONS AND FUTURE WORK 46
REFERENCE 47
APPENDIX A - EXAMPLE OF THE PRETEST QUESTIONNAIRE 50
APPENDIX B - PILOT STUDY QUESTIONNAIRE 52
APPENDIX C – POST-QUESTIONNAIRE 53		zh_TW
dc.format.extent 1922324 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108356009en_US
dc.subject (關鍵詞) 推薦系統zh_TW
dc.subject (關鍵詞) 餐廳推薦zh_TW
dc.subject (關鍵詞) 情境zh_TW
dc.subject (關鍵詞) 產品參與zh_TW
dc.subject (關鍵詞) 回饋zh_TW
dc.subject (關鍵詞) 揭露意願zh_TW
dc.subject (關鍵詞) Recommender systemen_US
dc.subject (關鍵詞) Restaurant recommendationen_US
dc.subject (關鍵詞) Contexten_US
dc.subject (關鍵詞) Product involvementen_US
dc.subject (關鍵詞) Feedbacken_US
dc.subject (關鍵詞) Disclosure intentionen_US
dc.title (題名) ⽤⼾感知外的回饋揭露意願因素zh_TW
dc.title (題名) Factors influencing feedback disclosure intention beyond users’ perceptionen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 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.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202101309en_US