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| 研究生： |
楊鈺翎 Yang, Yu-Ling |
|---|---|
| 論文名稱： |
社交機器人保健品推薦中的說服設計：訊息框架、推薦解釋透明度與擬人化程度對使用者反應之影響 Persuasive Design in Social Robot-Based Dietary Supplement Recommendation: Effects of Message Framing, Recommendation Explanation Transparency, and Anthropomorphism on User Responses |
| 指導教授： | 簡士鎰 |
| 口試委員: |
林斯寅
康藝晃 |
| 學位類別： |
碩士
Master |
| 系所名稱： |
商學院 - 資訊管理學系 Department of Management Information System |
| 論文出版年： | 2026 |
| 畢業學年度： | 114 |
| 語文別： | 英文 |
| 論文頁數： | 194 |
| 中文關鍵詞： | Pepper 、社交型機器人 、保健品推薦 、訊息框架 、解釋透明度 、擬人化 |
| 外文關鍵詞： | Pepper, social robot, dietary supplement recommendation, message framing, recommendation explanation transparency, anthropomorphism |
| 相關次數： | 點閱：34 下載：0 |
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隨著人工智慧與社交型機器人逐漸應用於健康相關決策，機器人正從資訊傳遞工具轉變為可能影響使用者理解、評價與決策的推薦代理人。過去研究多分別探討訊息框架、推薦解釋透明度與擬人化程度，較少在同一具身推薦互動中整合檢驗三者的作用。本研究以社交人型機器人 Pepper 作為推薦代理人，在保健品推薦情境中探討三項說服設計因素對使用者反應的影響。經三輪前導研究修正刺激與互動設計後，正式研究招募60位大學生與研究生，採用2 × 2 × 2混合因子設計：訊息框架（獲益與損失）為受試者內因子，推薦解釋透明度（高與低）及擬人化程度（高與低）為受試者間因子，每位受試者完成兩輪推薦任務。本研究以感知推薦品質為主要評價結果，理解程度為過程相關構念，購買意願為後續行為意圖，二元接受決策則作為補充決策指標，並結合問卷、系統紀錄與半結構式訪談進行分析。操弄檢核顯示，框架操弄僅獲部分支持，主要反映於損失導向線索的感知；透明度操弄未形成顯著主觀差異；高擬人化則使 Pepper 被知覺為更具人性化與外向性。三項實驗因素均未對感知推薦品質產生顯著主效果，預設的二因子交互作用亦未獲支持，但理解程度與感知推薦品質呈顯著正向關聯，感知推薦品質亦與購買意願呈強烈正向關聯。系統紀錄與訪談資料進一步顯示，受試者主要依據產品是否符合需求、推薦理由是否清楚，以及安全性、劑型與便利性等實際考量評估推薦，並透過追問、比較與產品切換補充資訊。透明度感知多在整體互動中逐步形成；損失導向語句雖較容易被注意，卻常被理解為一般健康提醒；高擬人化則提升了 Pepper 的人性化與外向性印象，但未改善推薦評價。整體而言，使用者反應與推薦適配性、理由清楚度、互動流暢度及選擇空間較為密切，未呈現由單一說服設計因素所主導的模式。
As AI-driven social robots increasingly serve as recommendation agents in health-related decision-making, message framing, recommendation explanation transparency, and anthropomorphism have each been studied as persuasive design factors, yet few studies examine them together in the same interaction. After three rounds of pilot testing, 60 participants completed a 2 × 2 × 2 mixed-design experiment. Framing was manipulated within subjects, while transparency and anthropomorphism were manipulated between subjects. Manipulation checks showed partial support for framing, no significant perceived difference in transparency, and stronger human-like and extraverted impressions under High Anthropomorphism. None of the three factors significantly affected perceived recommendation quality, and the hypothesized two-way interactions were unsupported. However, understandability was positively associated with perceived recommendation quality, which was strongly associated with purchase intention. Behavioral logs and interviews showed that participants focused mainly on need–product fit, recommendation rationale, and practical concerns. Transparency developed across the interaction, loss-oriented statements were often interpreted as general health reminders, and stronger anthropomorphic impressions did not improve recommendation evaluation. Overall, user responses were more closely related to recommendation fit, clear reasoning, interaction fluency, and room for choice than to any single persuasive design factor.
摘要 1
Abstract 2
Table of Contents 3
Tables 7
Figures 9
Chapter 1 INTRODUCTION 10
Chapter 2 RELATED WORK 16
2.1 Social Robots in Persuasive Recommendation Contexts 16
2.2 Message Framing in Health Communication and Recommendation 18
2.3 Recommendation Explanation Transparency and Understandability 20
2.4 Anthropomorphism and Multimodal Robot Personality 25
2.5 Proposed Interaction Effects 28
2.5.1 Transparency as a Moderator of Message Framing 29
2.5.2 Anthropomorphism as a Moderator of Recommendation Explanation Transparency 30
2.6 Perceived Recommendation Quality and Purchase Intention 32
2.7 Research Model and Hypotheses 35
Chapter 3 PILOT STUDY 39
3.1 Purpose and Overall Pilot Design 39
3.2 Initial Pilot Materials, Procedure, and Measures 41
3.3 Pilot Round 1: Initial Test of Framing and Transparency 45
3.4 System and Stimulus Revisions after Pilot Round 1 47
3.4.1 Recommendation Content, QA, and Tablet Revisions 48
3.4.2 System Backend and Generation Control 50
3.5 Pilot Round 2: Revised Framing and Transparency Test 53
3.6 Pilot Round 3: Final Pretest Including Anthropomorphism 56
3.7 Final Design Decisions and Scope of Pilot Evidence 61
Chapter 4 MAIN STUDY DESIGN AND METHOD 65
4.1 Overview of the Main Study 65
4.2 Participants and Condition Assignment 66
4.3 Experimental Design and Counterbalancing 68
4.4 Recommendation Tasks, Personas, and Product Pool 69
4.5 Manipulation Design 71
4.5.1 Message Framing Manipulation 72
4.5.2 Recommendation Explanation Transparency Manipulation 73
4.5.3 Anthropomorphism Manipulation 74
4.6 System Implementation and Interaction Design 77
4.6.1 System Architecture and Controlled Content Generation 78
4.6.2 Interaction Functions and Behavioral Logging 80
4.7 Experimental Procedure 82
4.8 Measurement Instruments 84
4.8.1 Pre-Interaction Measures 84
4.8.2 Post-Recommendation Measures 85
4.8.3 Post-Interaction Measures 87
4.8.4 Semi-Structured Interview 88
4.9 Data Preparation and Analysis Strategy 91
4.9.1 Questionnaire Analysis 91
4.9.2 Behavioral-Log Analysis 92
4.9.3 Qualitative Data Preparation and Thematic Analysis 93
Chapter 5 RESULTS 95
5.1 Overview of Analyses 95
5.2 Sample Characteristics and Descriptive Statistics 95
5.3 Reliability Analysis 98
5.4 Manipulation Checks 100
5.5 Hypothesis Testing 103
5.5.1 Main Effects on Perceived Recommendation Quality 103
5.5.2 Transparency, Understandability, and Perceived Recommendation Quality 105
5.5.3 Interaction Effects 106
5.5.4 Relationship between Recommendation Quality and Purchase Intention 107
5.5.5 Summary of Hypothesis Testing 108
5.6 Supplementary Analyses 110
5.6.1 Experimental Effects on Supplementary Outcomes 110
5.6.2 Three-Way Interaction Effects 112
5.6.3 Descriptive Pattern of the Significant Three-Way Interactions 113
5.7 Behavioral Log Analysis 114
5.7.1 Overall Interaction Patterns and Decision Pathways 114
5.7.2 Experimental-Condition Differences in Behavioral Indicators 116
5.7.3 Associations between Behavioral Indicators and User Evaluations 118
5.7.4 Descriptive Categorization of Acceptance and Rejection Reasons 120
5.8 Exploratory Qualitative Findings 122
5.8.1 Participants Evaluated Recommendations through Fit, Rationale, and Practical Considerations 124
5.8.2 Participants Used QA, Comparison, and Product Switching to Supplement Recommendation Information 125
5.8.3 Anthropomorphic Cues and Interaction Performance Jointly Shaped Participants’ Impressions of Pepper 126
5.8.4 Loss-Oriented Cues Were More Noticeable but Were Often Interpreted as General Health Reminders 128
Chapter 6 GENERAL DISCUSSION 130
6.1 Perceived Recommendation Quality as the Central Evaluative Link 130
6.2 Discussion of the Three Design Factors 132
6.2.1 Message Framing: More Noticeable Loss Cues without a Change in Recommendation Evaluation 132
6.2.2 Recommendation Explanation Transparency: Transparency as a User-Experienced Interaction Property 134
6.2.3 Anthropomorphism: Stronger Social Impressions without Better Recommendation Evaluation 136
6.3 Interaction-Level Interpretation of User Responses 138
6.4 Theoretical and Practical Implications 139
6.4.1 Theoretical Implications 140
6.4.2 Practical Implications 141
6.5 Limitations 142
6.5.1 Manipulation Validity and the Complexity of Live Interaction 142
6.5.2 Integrated Anthropomorphism Design and Interaction Fluency 143
6.5.3 Sample Size and Exploratory Interaction Effects 143
6.5.4 Persona-Based Tasks, Limited Acceptance Variation, and External Validity 144
6.5.5 Scope of the Behavioral and Qualitative Evidence 144
6.6 Future Research 145
References 147
Appendix A Anthropomorphism Manipulation Features 155
Appendix B Recommendation Task Scenarios and Family Member Personas 158
Appendix C Product Information Table 161
Appendix D Experimental Manipulation Materials 165
Appendix E Prompt Design and System Instructions 169
Appendix F Example Interaction Scripts 180
Appendix G Pre-interaction Questionnaire 185
Appendix H Post-recommendation Questionnaire 188
Appendix I Post-interaction Questionnaire 190
Appendix J Supplementary Descriptive Statistics and Reliability Results 192
Ainiwaer, A., Zhang, S., Ainiwaer, X., & Ma, F. (2021). Effects of message framing on cancer prevention and detection behaviors, intentions, and attitudes: Systematic review and meta-analysis. Journal of Medical Internet Research, 23(9), e27634. https://doi.org/10.2196/27634
Aly, A., & Tapus, A. (2013). A model for synthesizing a combined verbal and nonverbal behavior based on personality traits in human–robot interaction. In 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (pp. 325–332). IEEE. https://doi.org/10.1109/HRI.2013.6483606
Arlinghaus, C. S., Straßmann, C., & Dix, A. (2025). Increased morality through social communication or decision situation worsens the acceptance of robo-advisors. Computers in Human Behavior: Artificial Humans, 5, Article 100173. https://doi.org/10.1016/j.chbah.2025.100173
Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International Journal of Social Robotics, 1(1), 71–81. https://doi.org/10.1007/s12369-008-0001-3
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
Binder, A., Naderer, B., & Matthes, J. (2020). The effects of gain- and loss-framed nutritional messages on children’s healthy eating behaviour. Public Health Nutrition, 23(10), 1726–1734. https://doi.org/10.1017/S1368980019004683
Boos, A., Schauer, T., Arndt, E., & Bengler, K. (2024). (Un-)persuasive robots: Exploring the effect of anthropomorphic cues on the foot-in-the-door effect across three experimental studies. Computers in Human Behavior: Artificial Humans, 2(1), Article 100061. https://doi.org/10.1016/j.chbah.2024.100061
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Cesario, J., Corker, K. S., & Jelinek, S. (2013). A self-regulatory framework for message framing. Journal of Experimental Social Psychology, 49(2), 238–249. https://doi.org/10.1016/j.jesp.2012.10.014
Chen, C.-L. (2021). How implicit cues influence human–humanoid robot interaction: Combining personality traits and non-verbal communication for developing robot librarian [Master’s thesis, National Chengchi University]. https://doi.org/10.6814/NCCU202100384
Chester, D. S., & Lasko, E. N. (2021). Construct validation of experimental manipulations in social psychology: Current practices and recommendations for the future. Perspectives on Psychological Science, 16(2), 377–395. https://doi.org/10.1177/1745691620950684
Chung, H., Kang, H., & Jun, S. (2023). Verbal anthropomorphism design of social robots: Investigating users’ privacy perception. Computers in Human Behavior, 142, Article 107640. https://doi.org/10.1016/j.chb.2022.107640
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Damholdt, M. F., Quick, O. S., Seibt, J., Vestergaard, C., & Hansen, M. (2023). A scoping review of HRI research on ‘anthropomorphism’: Contributions to the method debate in HRI. International Journal of Social Robotics, 15(7), 1203–1226. https://doi.org/10.1007/s12369-023-01014-z
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/10.2307/249008
De Souza, L. S., & Manzato, M. G. (2023). A user study on explanations with different levels of detail in recommender systems. In Proceedings of the 29th Brazilian Symposium on Multimedia and the Web (pp. 208–212). Association for Computing Machinery. https://doi.org/10.1145/3617023.3617051
Ejelöv, E., & Luke, T. J. (2020). “Rarely safe to assume”: Evaluating the use and interpretation of manipulation checks in experimental social psychology. Journal of Experimental Social Psychology, 87, Article 103937. https://doi.org/10.1016/j.jesp.2019.103937
Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864–886. https://doi.org/10.1037/0033-295X.114.4.864
Esterwood, C., & Robert, L. P. (2021). A systematic review of human and robot personality in health care human–robot interaction. Frontiers in Robotics and AI, 8, Article 748246. https://doi.org/10.3389/frobt.2021.748246
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146
Gallagher, K. M., & Updegraff, J. A. (2012). Health message framing effects on attitudes, intentions, and behavior: A meta-analytic review. Annals of Behavioral Medicine, 43(1), 101–116. https://doi.org/10.1007/s12160-011-9308-7
Gedikli, F., Jannach, D., & Ge, M. (2014). How should I explain? A comparison of different explanation types for recommender systems. International Journal of Human-Computer Studies, 72(4), 367–382. https://doi.org/10.1016/j.ijhcs.2013.12.007
Ghazali, A. S., Ham, J., Barakova, E., & Markopoulos, P. (2018). The influence of social cues in persuasive social robots on psychological reactance and compliance. Computers in Human Behavior, 87, 58–65. https://doi.org/10.1016/j.chb.2018.05.016
Ghazali, A. S., Ham, J., Barakova, E. I., & Markopoulos, P. (2020). Persuasive robots acceptance model (PRAM): Roles of social responses within the acceptance model of persuasive robots. International Journal of Social Robotics, 12(5), 1075–1092. https://doi.org/10.1007/s12369-019-00611-1
Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity, and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020
Guesmi, M., Chatti, M. A., Vorgerd, L., Ngo, T., Joarder, S. A., Ain, Q. U., & Muslim, A. (2022). Explaining user models with different levels of detail for transparent recommendation: A user study. In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (pp. 175–183). Association for Computing Machinery. https://doi.org/10.1145/3511047.3537685
Guesmi, M., Chatti, M. A., Joarder, S., Ain, Q. U., Alatrash, R., Siepmann, C., & Vahidi, T. (2024). Interactive explanation with varying level of details in an explainable scientific literature recommender system. International Journal of Human–Computer Interaction, 40(22), 7248–7269. https://doi.org/10.1080/10447318.2023.2262797
Hauser, D. J., Ellsworth, P. C., & Gonzalez, R. (2018). Are manipulation checks necessary? Frontiers in Psychology, 9, Article 998. https://doi.org/10.3389/fpsyg.2018.00998
Huang, M., & Ki, E.-J. (2024). Examining the effect of anthropomorphic design cues on healthcare chatbots acceptance and organization–public relationships: Trust in a warm human vs. a competent machine. International Journal of Human–Computer Interaction, 40(24), 8781–8793. https://doi.org/10.1080/10447318.2023.2290378
John, O. P., Donahue, E. M., & Kentle, R. L. (1991). The Big Five Inventory—Versions 4a and 54 [Technical report]. University of California, Berkeley, Institute of Personality and Social Research.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292. https://doi.org/10.2307/1914185
Knijnenburg, B. P., Bostandjiev, S., O’Donovan, J., & Kobsa, A. (2012). Inspectability and control in social recommenders. In Proceedings of the 6th ACM Conference on Recommender Systems (pp. 43–50). Association for Computing Machinery. https://doi.org/10.1145/2365952.2365966
Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4–5), 441–504. https://doi.org/10.1007/s11257-011-9118-4
Liang, K.-Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), 13–22. https://doi.org/10.2307/2336267
Liu, B., Tetteroo, D., & Markopoulos, P. (2022). A systematic review of experimental work on persuasive social robots. International Journal of Social Robotics, 14(6), 1339–1378. https://doi.org/10.1007/s12369-022-00870-5
Lu, W.-I. (2023). An exploration of multimodal communication for developing extrovert, ambivert, and introvert robot [Master’s thesis, National Chengchi University]. National Chengchi University Theses and Dissertations System. https://thesis.lib.nccu.edu.tw/thesis/detail/9b05718143cda9a23df1fb29c63d1a39/
Mitova, E., Blassnig, S., Strikovic, E., Urman, A., de Vreese, C., & Esser, F. (2024). Exploring users’ desire for transparency and control in news recommender systems: A five-nation study. Journalism, 25(10), 2001–2021. https://doi.org/10.1177/14648849231222099
Munnukka, J., Talvitie-Lamberg, K., & Maity, D. (2022). Anthropomorphism and social presence in human–virtual service assistant interactions: The role of dialog length and attitudes. Computers in Human Behavior, 135, Article 107343. https://doi.org/10.1016/j.chb.2022.107343
Nocella, G., & Kennedy, O. (2012). Food health claims—What consumers understand. Food Policy, 37(5), 571–580. https://doi.org/10.1016/j.foodpol.2012.06.001
Nomura, T., Suzuki, T., Kanda, T., & Kato, K. (2006). Measurement of negative attitudes toward robots. Interaction Studies, 7(3), 437–454. https://doi.org/10.1075/is.7.3.14nom
Olatunji, S. A., Falcon, V., Ramesh, A., & Rogers, W. A. (2025). Considerations for designing socially assistive robots for older adults. Frontiers in Robotics and AI, 12, Article 1622206. https://doi.org/10.3389/frobt.2025.1622206
Petty, R. E., & Cacioppo, J. T. (1986). Communication and persuasion: Central and peripheral routes to attitude change. Springer-Verlag. https://doi.org/10.1007/978-1-4612-4964-1
Pu, P., & Chen, L. (2007). Trust-inspiring explanation interfaces for recommender systems. Knowledge-Based Systems, 20(6), 542–556. https://doi.org/10.1016/j.knosys.2007.04.004
Rothman, A. J., & Salovey, P. (1997). Shaping perceptions to motivate healthy behavior: The role of message framing. Psychological Bulletin, 121(1), 3–19. https://doi.org/10.1037/0033-2909.121.1.3
Salem, M., Eyssel, F., Rohlfing, K., Kopp, S., & Joublin, F. (2013). To err is human(-like): Effects of robot gesture on perceived anthropomorphism and likability. International Journal of Social Robotics, 5(3), 313–323. https://doi.org/10.1007/s12369-013-0196-9
Saunderson, S., & Nejat, G. (2019). How robots influence humans: A survey of nonverbal communication in social human–robot interaction. International Journal of Social Robotics, 11(4), 575–608. https://doi.org/10.1007/s12369-019-00523-0
Sherman, D. K., Mann, T., & Updegraff, J. A. (2006). Approach/avoidance motivation, message framing, and health behavior: Understanding the congruency effect. Motivation and Emotion, 30(2), 164–168. https://doi.org/10.1007/s11031-006-9001-5
Shin, Y. H., Im, J., Jung, S. E., & Severt, K. (2018). The theory of planned behavior and the norm activation model approach to consumer behavior regarding organic menus. International Journal of Hospitality Management, 69, 21–29. https://doi.org/10.1016/j.ijhm.2017.10.011
Steinhauser, J., & Hamm, U. (2018). Consumer and product-specific characteristics influencing the effect of nutrition, health and risk-reduction claims on consumer behaviour. Appetite, 127, 303–323. https://doi.org/10.1016/j.appet.2018.05.012
Thepsoonthorn, C., Ogawa, K., & Miyake, Y. (2018). The relationship between robot’s nonverbal behaviour and human’s likability based on human’s personality. Scientific Reports, 8(1), Article 8435. https://doi.org/10.1038/s41598-018-25314-x
Ulhøi, J. P., & Nørskov, S. (2022). The emergence of social robots: Adding physicality and agency to technology. Journal of Engineering and Technology Management, 65, Article 101703. https://doi.org/10.1016/j.jengtecman.2022.101703
Vidal, G., Machín, L., Aschemann-Witzel, J., & Ares, G. (2019). Does message framing matter for promoting the use of nutritional warnings in decision making? Public Health Nutrition, 22(16), 3025–3034. https://doi.org/10.1017/S1368980019002507
Wang, W., & Benbasat, I. (2016). Empirical assessment of alternative designs for enhancing different types of trusting beliefs in online recommendation agents. Journal of Management Information Systems, 33(3), 744–775. https://doi.org/10.1080/07421222.2016.1243949
Wardatzky, K., Inel, O., Rossetto, L., & Bernstein, A. (2025). Whom do explanations serve? A systematic literature survey of user characteristics in explainable recommender systems evaluation. ACM Transactions on Recommender Systems, 3(4), Article 49. https://doi.org/10.1145/3716394
Zhou, T. (2012). Examining location-based services usage from the perspectives of unified theory of acceptance and use of technology and privacy risk. Journal of Electronic Commerce Research, 13(2), 135–156.
