機器人理財顧問採用意願探討-無形價值綑綁與信任移轉觀點

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：6

、訪客IP：3.145.2.74

姓名

林妤芳(Yu-Fang Lin) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

機器人理財顧問採用意願探討-無形價值綑綁與信任移轉觀點
(Exploring the Adoption Intention of Robo-Advisor: Perspectives on Intangible Value Binding and Trust Transfer)

相關論文

★ 二氧化鈦技術生命週期之研究	★ 整體後勤業參與同步工程於產品開發績效關係之研究—以中科院為例
★ 筆記型電腦之IFA/PIFA天線技術生命週期分析	★ 國籍航空公司經營績效分析－以資料包絡分析方法分析
★ 從專利分析看3D IC技術與市場發展	★ 影響企業導入電子發票系統成效之因素探討
★ 影響企業導入數位學習成功因素之探討-以個案公司為例	★ 產品生命週期管理系統導入成功要素之探討--以S科技公司為例--
★ 組織創新能力影響因素研究	★ 製造業閒置資產轉售平台製造業閒置資產轉售平台-以廣達電腦股份有限公司為例
★ 供應商先行者優勢探討-以宸鴻科技為例	★ 團隊領導者創新特質與開放式創新專案關係之研究
★ 從商業生態系統談樞紐者策略-以Apple 與Nokia 為例	★ 個人電腦的競爭與發展策略-以台灣電子產業為例
★ 應用兩階段資料包絡分析法評估高級職業學校之經營績效	★ ERP導入的促進因素:使用者觀點

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

科技的發展帶來了許多新興的金融服務，其中機器人理財顧問作為一種創新的投資工具，正逐漸成為金融領域的一個熱門話題。這些智能化系統融合了演算法和人工智慧技術，為投資者提供了一種全新的投資方式，帶來了更個性化、智慧化的投資服務，吸引了越來越多人的關注和使用。然而，在金融科技領域，投資者對於機器人理財顧問的採用意願往往受到信任的影響。本研究運用信任移轉理論及無形價值綑綁概念，透過銀行信任，並整合信任移轉之認知與情感層面，深入探討對機器人理財顧問的信任如何影響其採用意願。本研究採用問卷調查法，共計回收有效問卷 412份，以線性結構方程式進行研究假說之分析。經由實證分析結果發現，聲譽及熟悉度皆有助於銀行與顧客之間無形價值綑綁效果，其中熟悉度的效用更為顯著。此外，本研究強調無形價值綑綁的作用，並證實顧客與銀行之間形成的緊密連結，並不會直接促使其採用銀行延伸的機器人理財服務。另一方面，本研究強調了情感信任在信任移轉中的重要性，並指出其對於促進採用意願的重要作用。這個發現突顯了投資者對於機器人理財顧問的採用行為偏向感性決策的趨勢。本研究的研究結果能夠提供學術與企業對於未來機器人理財顧問服務的參考方向與建議。

摘要(英)

The advancement of technology has introduced numerous emerging financial services, among which robo-advisors have become a prominent topic within the financial sector. These intelligent systems, integrating algorithms and artificial intelligence, offer investors a novel investment approach by providing more personalized and intelligent services, thereby garnering increasing attention and utilization. However, in fintech, investors′ willingness to adopt robo-advisors is often influenced by their level of trust. This study employs trust transfer theory and the concept of intangible value binding, examining how trust in banks, combined with the cognitive and affective dimensions of trust transfer, influences trust in robo-advisors and subsequently their adoption intention. This study collected 412 valid responses using a questionnaire survey method and analyzed the research hypotheses through structural equation modeling. Empirical analysis revealed that reputation and familiarity contribute to the effect of intangible value binding between banks and customers, with familiarity demonstrating a more significant impact. Additionally, this study emphasizes the role of intangible value binding and confirms that the close connection formed between customers and banks does not directly promote the adoption of the bank′s extended robo-advisory services. On the other hand, this study highlights the importance of affective trust in trust transfer, underscoring its significant role in fostering adoption intention. These findings illuminate the tendency of investors to make emotionally driven decisions regarding the adoption of robo-advisors. The results of this study provide valuable insights and recommendations for academia and enterprises concerning the future development of robo-advisory services.

關鍵字(中)

★ 機器人理財顧問
★ 無形價值綑綁
★ 信任移轉
★ 採用意願

關鍵字(英)

★ Robo-Advisors
★ Intangible Value Binding
★ Trust Transfer
★ Adoption Intention

論文目次

目錄
摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vi
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 研究流程 5
第二章文獻探討 6
2.1 機器人理財顧問 (Robo-Advisor) 6
2.2 銀行信任 8
2.3 信任移轉 10
2.4 行為反應(採用意願) 12
第三章研究方法 14
3.1 研究架構 14
3.2 研究假設 16
3.3 操作型定義與問項設計 20
3.4 研究對象與資料蒐集 24
3.5 統計方法分析 27
3.5.1 樣本資料分析 28
3.5.2 信度檢定 28
3.5.3 效度檢定 28
3.5.4 假設檢定 29
第四章資料分析與研究討論 33
4.1 樣本基本資料分析 33
4.2 研究構面敘述性統計分析 35
4.3 測量模型之信效度分析 37
4.3.1 信度分析 37
4.3.2 效度分析 39
4.4 結構模型與路徑分析 43
4.4.1 研究模型與模型配適度檢定 43
4.4.2 路徑分析 44
4.4.3 實證討論 46
第五章結論與建議 49
5.1 結論 49
5.2 研究意涵 49
5.2.1 學術意涵 49
5.2.2 實務建議 51
5.3 研究限制與建議 52
參考文獻 54
附錄問卷 61

參考文獻

Ahn, M., Zhu, H., Hartikainen, K., Ponte, H., Gupta, A., Levine, S., & Kumar, V. (2020, May). Robel: Robotics benchmarks for learning with low-cost robots. In Conference on robot learning (pp. 1300-1313). PMLR.
Alipour, H., Olya, H. G. T., Maleki, P., & Dalir, S. (2020). Behavioral responses of 3S tourism visitors: Evidence from a Mediterranean Island destination. Tourism Management Perspectives, 33,100624.
Alsabah, H., Capponi, A., Ruiz Lacedelli, O., & Stern, M. (2021). Robo-advising: Learning investors’ risk preferences via portfolio choices. Journal of Financial Econometrics, 19(2), 369–392.
Aw, E. C. X., Leong, L. Y., Hew, J. J., Rana, N. P., Tan, T. M., & Jee, T. W. (2023). Counteracting dark sides of robo-advisors: justice, privacy and intrusion considerations. International Journal of Bank Marketing, 42(1), 133-151.
Badrinarayanan, V., Becerra, E. P., Kim, C. H., & Madhavaram, S. (2012). Transference and congruence effects on purchase intentions in online stores of multi-channel retailers: initial evidence from the US and South Korea. Journal of the Academy of Marketing Science, 40, 539-557.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
Baker, T., & Dellaert, B. (2017). Regulating robo advice across the financial services industry. Iowa L. Rev., 103, 713.
Baker, T. and Benedict, D. (2018), “Regulating robo advice across the financial services industry”, Iowa Law Review, 103(2), 713-750.
Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411-1430.
Bhatia, A., Chandani, A., Atiq, R., Mehta, M., & Divekar, R. (2021). Artificial intelligence in financial services: a qualitative research to discover robo-advisory services. Qualitative Research in Financial Markets, 13(5), 632-654.
Bollen, K. A. (1989b). A new incremental fit index for general structural equation models. Sociological Methods and Research, 17(3), 303-316.
Bolger, N., & Laurenceau, J. P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. Guilford press.
Brenner, L., & Meyll, T. (2020). Robo-advisors: a substitute for human financial advice?. Journal of Behavioral and Experimental Finance, 25, 100275.
Browne, M. W., Cudeck, R., Bollen, K. A., & Long, J. S. (1993). Testing structural equation models (Vol. 154). Sage.
Callahan, S. P., & Ledgerwood, A. (2016). On the psychological function of flags and logos: Group identity symbols increase perceived entitativity. Journal of Personality and Social Psychology, 110(4), 528.
Campbell, J. Y., Ramadorai, T., & Ranish, B. (2014). Getting better or feeling better? How equity investors respond to investment experience (No. w20000). National Bureau of Economic Research.
Campbell, D. T. (1958). Common fate, similarity, and other indices of the status of aggregates of persons as social entities. Behavioral science, 3(1), 14.
Cao, X., Yu, L., Liu, Z., Gong, M., & Adeel, L. (2018). Understanding mobile payment users’ continuance intention: a trust transfer perspective. Internet Research, 28(2), 456-476.
Chai, J. C. Y., & Dibb, S. (2014). How consumer acculturation influence interpersonal trust. Journal of Marketing Management, 30(1-2), 60-89.
Cheng, G., Cherian, J., Sial, M. S., Mentel, G., Wan, P., Álvarez-Otero, S., & Saleem, U. (2021). The Relationship between CSR Communication on Social Media, Purchase Intention, and E-WOM in the Banking Sector of an Emerging Economy. Journal of Theoretical and Applied Electronic Commerce Research, 16(4), 1025-1041.
Cheng, X., Guo, F., Chen, J., Li, K., Zhang, Y., & Gao, P. (2019). Exploring the trust influencing mechanism of robo-advisor service: A mixed method approach. Sustainability, 11(18), 4917.
Chiou, J. S., & Shen, C. C. (2012). The antecedents of online financial service adoption: the impact of physical banking services on Internet banking acceptance. Behaviour & Information Technology, 31(9), 859-871.
Chou, S. Y., Lin, C. W., Chen, Y. C., & Chiou, J. S. (2023). The complementary effects of bank intangible value binding in customer robo-advisory adoption. International Journal of Bank Marketing, 41(4), 971-988.
Craney, T. A., & Surles, J. G. (2002). Model-Dependent Variance Inflation Factor Cutoff Values. Quality Engineering, 14(3), 391-403.
D’Hondt, C., De Winne, R., Ghysels, E., & Raymond, S. (2020). Artificial intelligence alter egos: Who might benefit from robo-investing?. Journal of Empirical Finance, 59, 278-299.
Doney, P. M., & Cannon, J. P. (1997). An examination of the nature of trust in buyer–seller relationships. Journal of marketing, 61(2), 35-51.
Fatma, M., Khan, I., & Rahman, Z. (2018). CSR and consumer behavioral responses: The role of customer-company identification. Asia Pacific Journal of Marketing and Logistics, 30, 460-477.
Fisch, J. E., Laboure, M., & Turner, J. A. (2019). The Emergence of the Robo-advisor. The disruptive impact of FinTech on retirement systems, 13, 13-37.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of marketing research, 382-388.
Frankenfield, J. Retrieved from What Is a Robo-Advisor? 28 March 2020.
Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega, 28(6), 725-737.
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS quarterly, 51-90.
Gennaioli, N., Shleifer, A., & Vishny, R. (2015). Money doctors. The Journal of Finance, 70(1), 91-114.
Gong, X., Zhang, K. Z., Chen, C., Cheung, C. M., & Lee, M. K. (2020). What drives trust transfer from web to mobile payment services? The dual effects of perceived entitativity. Information & Management, 57(7), 103250.
Greiner, M. E., & Wang, H. (2010). Building consumer-to-consumer trust in e-finance marketplaces: An empirical analysis. International Journal of Electronic Commerce, 15(2), 105-136.
Guilford, J. P. (1965). Fundamental Statistics in psychology and education 4th Ed.
Hair, Jr., J. F. et al. (1998). Multivariate Data Analysis with Readings. Englewood Cliffs, NJ: Prentice-Hall.
Hildebrand, C., & Bergner, A. (2021). Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making. Journal of the Academy of Marketing Science, 49(4), 659-676.
Hohenberger, C., Lee, C., & Coughlin, J. F. (2019). Acceptance of robo‐advisors: Effects of financial experience, affective reactions, and self‐enhancement motives. Financial Planning Review, 2(2), e1047.
Hong, I. B., & Cho, H. (2011). The impact of consumer trust on attitudinal loyalty and purchase intentions in B2C e-marketplaces: Intermediary trust vs. seller trust. International journal of information management, 31(5), 469-479.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.
James, E. Bailey & Sammy W. Pearson. (1983). Development of a Tool for Measuring and Analyzing Computer User Satisfaction. Management Science, Vol. 29(5), 530-545.
Ji, M. (2017). Are robots good fiduciaries: regulating robo-advisors under the investment advisers act of 1940. Colum. L. Rev., 117, 1543.
Johnson, D., & Grayson, K. (2005). Cognitive and affective trust in service relationships. Journal of Business Research, 58(4), 500-507.
Jung, D., Dorner, V., Weinhardt, C. and Pusmaz, H. (2018), “Designing a robo-advisor for risk-averse, low-budget consumers”, Electronic Markets, Vol. 28 No. 3, pp. 367-380.
Kenny, D. A. (2005). Cross‐lagged panel design. Encyclopedia of statistics in behavioral science.
Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision support systems, 44(2), 544-564.
Kim, G., Shin, B., & Lee, H. G. (2009). Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal, 19(3), 283-311.
Kim, H. W., Xu, Y., & Koh, J. (2004). A comparison of online trust building factors between potential customers and repeat customers. Journal of the association for information systems, 5(10), 13.
Komiak, S. Y., & Benbasat, I. (2006). The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS quarterly, 941-960.
Komiak, S. X., & Benbasat, I. (2004). Understanding customer trust in agent-mediated electronic commerce, web-mediated electronic commerce, and traditional commerce. Information technology and management, 5, 181-207.
Kwon, D., Jeong, P., & Chung, D. (2022). An empirical study of factors influencing the intention to use robo-advisors. Journal of Information & Knowledge Management, 21(03), 2250039.
Lee, J. C., & Wang, J. (2022). From offline to online: understanding users′ switching intentions from traditional wealth management services to mobile wealth management applications. International Journal of Bank Marketing, 41(2), 369-394.
Lee, K. C., Lee, S., & Hwang, Y. (2014). The impact of hyperlink affordance, psychological reactance, and perceived business tie on trust transfer. Computers in Human Behavior, 30, 110-120.
Lee, S., Choi, J., Ngo-Ye, T. L., & Cummings, M. (2018). Modeling trust in the adoption decision process of robo-advisors: an agent-based simulation approach.
Lewis, J. D., & Weigert, A. (1985). Trust as a social reality. Social Forces, 63(4), 967-985.
Manwaring, K. C., Larsen, R., Graham, C. R., Henrie, C. R., & Halverson, L. R. (2017). Investigating student engagement in blended learning settings using experience sampling and structural equation modeling. The Internet and Higher Education, 35, 21-33.
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of management review, 20(3), 709-734.
Moysidou, K., & Hausberg, J. P. (2020). In crowdfunding we trust: A trust-building model in lending crowdfunding. Journal of Small Business Management, 58(3), 511-543.
Mudd, S., Pashev, K., & Valev, N. T. (2010). The effect of loss experiences in a banking crisis on future expectations and behavior. The Be Journal of Macroeconomics, 10(1), article 32.
Mulaik, S. A., James, L. R., Altine, J. V., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430-44
Nguyen, Y. T. H., Tapanainen, T., & Nguyen, H. T. T. (2022). Reputation and its consequences in Fintech services: the case of mobile banking. International Journal of Bank Marketing, 40(7), 1364-1397.
Ngo-Ye, T. L., Choi, J. J., & Cummings, M. (2018). MODELING THE ROBO-ADVISOR ECOSYSTEM: INSIGHTS FROM A SIMULATION STUDY. Issues in Information Systems, 19(1).
Nunnally, J. C., & Bernstein, I. H. (1978). Psychometric theory.
Priya, B., & Sharma, V. (2023). Exploring users′ adoption intentions of intelligent virtual assistants in financial services: An anthropomorphic perspectives and socio-psychological perspectives. Computers in Human Behavior, 148, 107912.
Roh, T., Park, B. I., & Xiao, S. S. (2023). Adoption of AI-enabled Robo-advisors in fintech: Simultaneous employment of UTAUT and the theory of reasoned action. Journal of Electronic Commerce Research, 24(1), 29-47.
Rossi, A. G., & Utkus, S. P. (2020). The needs and wants in financial advice: Human versus robo-advising. Available at SSRN 3759041.
Seiler, V., & Fanenbruck, K. M. (2021). Acceptance of digital investment solutions: The case of robo advisory in Germany. Research in International Business and Finance, 58, 101490.
Shanmuganathan, M. (2020). Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions. Journal of Behavioral and Experimental Finance, 27, 100297.
Sharma, S., Durand, R. M., & Gur-Arie, O. (1981). Identification and Analysis of Moderator Variables. Journal of Marketing Research, 18(3), 291-300.
Sharpe, D. L., Anderson, C., White, A., Galvan, S., & Siesta, M. (2007). Specific elements of communication that affect trust and commitment in the financial planning process. Journal of Financial Counseling and Planning, 18(1).
Sheng, S., Zhou, K. Z., & Li, J. J. (2011). The effects of business and political ties on firm performance: Evidence from China. Journal of Marketing, 75(1), 1-15.
Song, P., Xu, H., Techatassanasoontorn, A., & Zhang, C. (2011). The influence of product integration on online advertising effectiveness. Electronic Commerce Research and Applications, 10(3), 288-303.
Song, P., Zhang, C., Chen, W., & Huang, L. (2009). Understanding usage-transfer behavior between nonsubstitutable technologies: evidence from instant messenger and portal. IEEE Transactions on Engineering Management, 56(3), 412-424.
Sorescu, A., & Schreier, M. (2021). Innovation in the digital economy: A broader view of its scope, antecedents, and consequences. Journal of the Academy of Marketing Science, 49(4), 627-631.
Su, B.-C., Wu, L.-W., & Yen, Y.-C. (2021). Antecedents and Consequences of Trust and Loyalty in Physical Banks Affecting Mobile Payments. Sustainability, 13(22), 12368.
Sun, H. (2010). Sellers′ trust and continued use of online marketplaces. Journal of the Association for Information systems, 11(4), 2.
Stewart, K. J. (2003). Trust transfer on the world wide web. Organization science, 14(1), 5-17.
Stewart, K. J. (2006). How hypertext links influence consumer perceptions to build and degrade trust online. Journal of Management Information Systems, 23(1), 183-210.
Tang, Z., Hu, Y. U., & Smith, M. D. (2008). Gaining trust through online privacy protection: Self-regulation, mandatory standards, or caveat emptor. Journal of Management Information Systems, 24(4), 153-173.
Vijayasarathy, L. R., & Jones, J. M. (2000). Print and Internet catalog shopping: Assessing attitudes and intentions. Internet Research, 10(3), 191–202.
Walsh, G., & Beatty, S. E. (2007). Customer-based corporate reputation of a service firm: scale development and validation. Journal of the academy of marketing science, 35, 127-143.
Wang, N., Shen, X L., & Sun, Y. (2013). Transition of electronic word-of-mouth services from web to mobile context: A trust transfer perspective. Decision support systems, 54(3), 1394-1403.
Wexler, M. N., & Oberlander, J. (2021). Robo-advisors (RAs): the programmed self-service market for professional advice. Journal of Service Theory and Practice, 31(3), 351-365.
Wu, M., & Neill, S. (2021). Trust transfer and the intention to use app-enabled carpooling service. Asia Pacific journal of marketing and logistics, 33(6), 1498-1512.
Yang, Z., Zhou, C., & Jiang, L. (2011). When do formal control and trust matter? A contest-based analysis of the effects on marketing channel relationships in China. Industrial Marketing Management, 40(1), 86-96.
Yeh, H. C., Yu, M. C., Liu, C. H., & Huang, C. I. (2023). Robo-advisor based on unified theory of acceptance and use of technology. Asia Pacific Journal of Marketing and Logistics, 35(4), 962-979.
Yi, T. Z., Rom, N. A. M., Hassan, N. M., Samsurijan, M. S., & Ebekozien, A. (2023). The adoption of robo-advisory among millennials in the 21st century: Trust, usability and knowledge perception. Sustainability, 15(7), 6016.
Zhang, C. B., Li, Y. (2019). How social media usage influences B2B customer loyalty: roles of trust and purchase risk. Journal of Business & Industrial Marketing, JBIM-07-2018-0211.
Zhang, L., Pentina, I., & Fan, Y. (2021). Who do you choose? Comparing perceptions of human vs robo-advisor in the context of financial services. Journal of Services Marketing, 35(5), 634-646.
Zhang, K. Z., Cheung, C. M., & Lee, M. K. (2014). Examining the moderating effect of inconsistent reviews and its gender differences on consumers’ online shopping decision. International Journal of Information Management, 34(2), 89-98.

指導教授

洪秀婉(Shiu-Wan Hung)

審核日期

2024-6-25

推文