簡易檢索 / 詳目顯示

研究生: 林忠禎
Zhong-Zhen Lin
論文名稱: 央行數位貨幣的互動設計與使用者體驗研究
A Study on the Interaction Design and User Experience of Central Bank Digital Currency
指導教授: 陳建雄
Chien-Hsiung Chen
口試委員: 吳志富
許言
宋同正
柯志祥
陳建雄
學位類別: 博士
Doctor
系所名稱: 設計學院 - 設計系
Department of Design
論文出版年: 2023
畢業學年度: 112
語文別: 中文
論文頁數: 119
中文關鍵詞: 中央銀行數位貨幣消費者採用預測多模態介面視覺回饋圖形隱喻使用者體驗人機互動
外文關鍵詞: Central bank digital currency, Consumer adoption forecasting, Multi-modal interface, Visual feedback, Graphical metaphor, User experience, Human-computer interaction
相關次數: 點閱:39下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 世界各國的中央銀行普遍認為中央銀行數位貨幣 (Central Bank Digital Currency, CBDC) 是貨幣發展的未來方向。在加快發展CBDC的問題上,各國中央銀行之間也已經達成共識。然而,目前許多國家的CBDC都還停留在概念驗證和試點階段,其發展速度其實並沒有預期中那麼快。現有研究更多的是針對CBDC宏觀經濟影響以及技術可行性展開的,鮮有和CBDC相關的消費者行為科學研究。此外,也沒有科學研究試圖設計一個有效的CBDC使用者介面。因此,本研究的目的是通過三個研究議題,以量化的研究方法來預測CBDC的使用性,並對央行數位貨幣的介面互動設計展開研究。
    研究議題一通過文獻的整理與回顧構建了一個創新的概念模型。隨後,該模型被用於預測影響CBDC的使用性的重要因素。研究結果表明:(1)影響使用意圖的最重要變數是感知有用性。(2)個人創新性被證實對CBDC的感知易用性影響最大。(3)本研究還發現獎勵機制是感知有用性最主要的預測因子。
    研究議題二的實驗是一個3(動態頁面互動)X4(多模態操作回饋)的混合因子設計,共創建了12個CBDC的原型應用程式,以評估研究變數對使用者的任務績效以及主觀評價的影響。研究結果顯示:(1)動態頁面互動和多模態操作回饋影響了CBDC使用者的任務績效。(2)具有動態頁面互動和多模態操作回饋的CBDC介面在使用者使用性方面的評價更高。(3)觸覺回饋可能與CBDC的使用者推薦指數有關。(4)儘管動態頁面互動比非動態互動有更高的使用者偏好,但它們可能帶給使用者的安全感更少。
    研究議題三的實驗是一個2(圖形隱喻)×3(手勢類型)×2(視覺回饋)的混合因子設計,共創建了12個CBDC原型應用程式,並根據使用者的任務績效和一系列的主觀評價進行了評估。研究結果顯示:(1)圖形隱喻的使用者介面設計可以提高CBDC的介面使用性和主觀評價,還可能喚起使用者曾經使用傳統紙幣的體驗感。(2)在CBDC使用者介面上使用錢幣移動的動畫提示可能有助於喚起使用者使用現金的體驗。(3)本研究發現揮動型手勢可能會讓使用者花更多的時間來完成CBDC的操作任務。(4)錢幣移動的動畫提示可能有更快的表現和更高的合理性。
    上述發現可能可以給各國央行及CBDC相關研究人員提供一定程度上的參考,以加快CBDC的採用速度,並提高CBDC的介面使用性。此外,本研究將填補現有研究的空白,為準備發行CBDC的各國央行提供理論支援,並且對CBDC的未來發展有積極意義。


    Central banks worldwide widely acknowledge that Central Bank Digital Currency (CBDC) represents the future trajectory of monetary development. Consensus among central banks across nations has been achieved regarding expediting CBDC development. However, many countries' CBDC initiatives remain in the conceptual validation and pilot phase, with actual progress not meeting initial expectations. Existing research predominantly focuses on the macroeconomic impacts and technical feasibility of CBDCs, neglecting consumer behavior science linked to CBDCs. Additionally, there is a dearth of scientific research attempting to design an effective CBDC user interface. Hence, this study aims to explore CBDC usability through three research issues, utilizing quantitative research methods, and delve into interface interaction design for central bank digital currencies.
    Issue one of the study constructs an innovative conceptual model through literature review and synthesis. Subsequently, this model is employed to forecast crucial factors influencing CBDC usability. Findings indicate that: (1) The most significant variable affecting usage intent is perceived usefulness. (2) Individual innovativeness significantly impacts the perceived ease of use of CBDCs. (3) Reward mechanisms emerge as the primary predictive factor for perceived usefulness.
    Issue two involves an experiment employing a 3 (dynamic page interaction) × 4 (multi-modal feedback) mixed factorial design, creating 12 prototype CBDC applications. The evaluation assesses the impact of research variables on user task performance and subjective evaluations. Results show that: (1) Dynamic page interaction and multi-modal feedback affect CBDC users' task performance. (2) CBDC user interfaces with dynamic page interaction and multi-modal feedback receive higher usability ratings from users. (3) Tactile feedback may be associated with the CBDC user recommendation index. (4) Despite dynamic page interaction being more preferred by users than non-dynamic interaction, it might offer users less sense of security.
    Issue three experiment employs a 2 (graphical metaphor) × 3 (gesture type) × 2 (visual feedback) mixed factorial design, creating 12 CBDC prototype applications. Evaluation based on user task performance and a series of subjective evaluations reveals that: (1) User interface designs employing graphical metaphors enhance CBDC interface usability and subjective evaluations, potentially evoking users' experiences with traditional cash use. (2) Animation prompts of coin movement on the CBDC user interface might evoke users' experiences with cash usage. (3) The study finds that waving gestures may result in users spending more time completing CBDC operational tasks. (4) Animation prompts of coin movement might display quicker performance and higher plausibility.
    These findings potentially provide insights to central banks globally and CBDC researchers, expediting CBDC adoption and enhancing CBDC interface usability. Furthermore, this study fills research gaps, offering theoretical supports to central banks preparing to issue CBDCs and holds positive implications for the future development of CBDCs.

    中文摘要 IV Abstract VI 誌 謝 VIII 目 錄 X 圖目錄 XV 表目錄 XVII 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 3 1.3 研究範圍與限制 4 1.4 重要名詞的解釋 5 1.5 研究規劃與流程 6 第二章 文獻探討 8 2.1 CBDC的發展 8 2.1.1 CBDC的定義及研究現狀 8 2.1.2 CBDC的優勢與挑戰 10 2.2 新技術採納研究的相關理論 11 2.2.1 新技術採納研究的發展 11 2.2.2 移動支付採納相關的變數 12 2.3 CBDC的介面互動設計 14 2.3.1 多模態操作回饋 14 2.3.2 動態頁面互動 15 2.3.3 圖形隱喻 16 2.3.4 視覺回饋 17 2.3.5 手勢互動 18 2.4 小結 19 第三章 研究議題一:預測中央銀行數位貨幣消費者採納的決定因素:基於數位人民幣的分析 20 3.1 研究載體與研究目的 20 3.2 研究概念模型 20 3.3 研究變數與假設 22 3.3.1 個人創新性 22 3.3.2 主觀規範 23 3.3.3 個人熟練度 23 3.3.4 獎勵機制 24 3.3.5 感知有用性 24 3.3.6 感知易用性 25 3.3.7 感知安全性 26 3.3.8 使用意向 26 3.4 測量項目 26 3.5 受訪者邀請與數據收集 27 3.6 數據分析和結果 29 3.6.1 信效度分析 29 3.6.2 結構模型和假設檢驗 32 3.7 對研究議題一結果的討論 35 3.7.1 研究議題一結果概要 35 3.7.2 研究議題一的理論貢獻 35 3.7.3 研究議題一的實際意義 36 第四章 研究議題二:評估動態頁面互動和多模態操作回饋對中央銀行數位貨幣使用者體驗的影響 38 4.1 研究目的 38 4.2 研究架構 39 4.3 儀器和原型 40 4.4 研究議題二的參與者 41 4.5 實驗環境和流程 42 4.6 結果與分析 42 4.6.1 任務績效 43 4.6.2 SUS 44 4.6.3 特殊性感受程度 46 4.6.4 安全性感受程度 48 4.6.5 净推薦指數NPS 51 4.6.6 使用者偏好 52 4.7 對研究議題二結果的討論 53 第五章 研究議題三:圖形隱喻、手勢類型和視覺回饋對中央銀行數位貨幣使用者介面設計的影響 56 5.1 研究目標 56 5.2 研究議題三的變數與架構 56 5.3 儀器和原型 61 5.4 研究議題三的參與者 61 5.5 實驗的參與者操作任務 62 5.6 實驗的參與者操作任務 63 5.7 研究議題三的結果與分析 64 5.7.1 任務績效 64 5.7.2 SUS 68 5.7.3 合理性程度 69 5.7.4 安全性感受程度 72 5.7.5 與傳統紙幣支付體驗相似性程度 73 5.8 對研究議題三結果的討論 76 5.8.1 研究議題三結果概要 76 5.8.2 研究議題三的貢獻 77 第六章 結論與建議 79 6.1 結論 79 6.1.1 研究議題一的結論 79 6.1.2 研究議題二的結論 79 6.1.3 研究議題三的結論 80 6.2 未來研究的建議 81 英文參考文獻 82 中文參考文獻 109 附錄A 研究議題一之問卷結構 110 附錄B 研究議題二之問卷 112 附錄C 研究議題三之問卷 116

    英文參考文獻
    1. Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238-256.
    2. Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215.
    3. Agarwal, R., Ahuja, M., Carter, P. E., & Gans, M. (1998). Early and late adopters of IT innovations: extensions to innovation diffusion theory. In Proceedings of the DIGIT Conference.
    4. Agur, I., Ari, A., & Dell’Ariccia, G. (2022). Designing central bank digital currencies. Journal of Monetary Economics, 125, 62-79.
    5. Agur, M. I., Ari, M. A., & Dell'Ariccia, M. G. (2019). Designing central bank digital currencies. International Monetary Fund.
    6. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
    7. Ajzen, I., & Fishbein, M. (1973). Attitudinal and normative variables as predictors of specific behavior. Journal of Personality and Social Psychology, 27(1), 41.
    8. Alfar, A. J., Kumpamool, C., Nguyen, D. T., & Ahmed, R. (2023). The determinants of issuing central bank digital currencies. Research in International Business and Finance, 101884.
    9. Alfonso, V., Boar, C., Jon Frost, J., Gambacorta, L., & Liu, J. (2021). E-commerce in the pandemic and beyond. BIS Bulletin, 36.
    10. Al-Okaily, M., Alalwan, A. A., Al-Fraihat, D., Alkhwaldi, A. F., Rehman, S. U., & Al-Okaily, A. (2022). Investigating antecedents of mobile payment systems’ decision-making: A mediated model. Global Knowledge, Memory and Communication.
    11. Al-Okaily, M., Lutfi, A., Alsaad, A., Taamneh, A., & Alsyouf, A. (2020). The determinants of digital payment systems’ acceptance under cultural orientation differences: The case of uncertainty avoidance. Technology in Society, 63, 101367.
    12. Al-Okaily, M., Rahman, M. S. A., Ali, A., Abu-Shanab, E., & Masa'deh, R. E. (2023). An empirical investigation on acceptance of mobile payment system services in Jordan: extending UTAUT2 model with security and privacy. International Journal of Business Information Systems, 42(1), 123-152.
    13. Alonso, S. L. N. (2019). Activities and operations with cryptocurrencies and their taxation implications: The Spanish case. Laws, 8, 1.
    14. Alonso, S. L. N., Jorge-Vazquez, J., & Forradellas, R. F. R. (2021). Central Banks Digital Currency: Detection of Optimal Countries for the Implementation of a CBDC and the Implication for Payment Industry Open Innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 1-21.
    15. Al-Qudah, A. A., Al-Okaily, M., Alqudah, G., & Ghazlat, A. (2022). Mobile payment adoption in the time of the COVID-19 pandemic. Electronic Commerce Research, 1-25.
    16. Al-Saedi, K., & Al-Emran, M. (2021). A systematic review of mobile payment studies from the lens of the UTAUT model. Recent Advances in Technology Acceptance Models and Theories, 79-106.
    17. Alty, J. L., Knott, R. P., Anderson, B., & Smyth, M. (2000). A framework for engineering metaphor at the user interface. Interacting with Computers, 13(2), 301-322.
    18. Alves-Oliveira, P., Lupetti, M. L., Luria, M., Löffler, D., Gamboa, M., Albaugh, L., ... & Lockton, D. (2021). Collection of Metaphors for Human-Robot Interaction. In Designing Interactive Systems Conference 2021 (pp. 1366-1379).
    19. Amoroso, D. L., & Magnier-Watanabe, R. (2012). Building a research model for mobile wallet consumer adoption: the case of mobile Suica in Japan. Journal of Theoretical and Applied Electronic Commerce Research, 7(1), 94-110.
    20. An, S., Eck, T., & Yim, H. (2023). Understanding Consumers’ Acceptance Intention to Use Mobile Food Delivery Applications through an Extended Technology Acceptance Model. Sustainability, 15(1), 832.
    21. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411.
    22. Andolfatto, D. (2021). Assessing the impact of central bank digital currency on private banks. The Economic Journal, 131(634), 525-540.
    23. Angelos, De. (2021). Central bank digital currencies: Evolution or revolution? EPRS: European Parliamentary Research Service. Belgium.
    24. Arciuch, A., & Donigiewicz, A. M. (2018). Quality study of user activity using mobile device. Tap, double tap, flick gestures. Przegląd Teleinformatyczny, 6(3-4).
    25. Armelius, H., Guibourg, G., Johansson, S., & Schmalholz, J. (2020). E-krona design models: pros, cons and trade-offs. Sveriges Riksbank Economic Review, 2, 80-96.
    26. Auer, R., & Böhme, R. (2020). The technology of retail central bank digital currency. BIS Quarterly Review, March.
    27. Auer, R., Boar, C., Cornelli, G., Frost, J., Holden, H., & Wehrli, A. (2021). CBDCs beyond borders: results from a survey of central banks. BIS Papers.
    28. Auer, R., Cornelli, G., & Frost, J. (2020). Covid-19, cash, and the future of payments (No. 3). Bank for International Settlements.
    29. Awang Abu Bakar, N. S., Yahya, N., Khairuddin, I. E., Zainal Abidin, A. F., Mohamad Zain, J., Idris, N. B., & Engku Ali, E. R. A. (2022). The Central Bank Digital Currency in Malaysia: A Literature Review. In Innovation of Businesses, and Digitalization during Covid-19 Pandemic: Proceedings of The International Conference on Business and Technology (ICBT 2021) (pp. 307-317). Cham: Springer International Publishing.
    30. Aydin, G., & Burnaz, S. (2016). Adoption of mobile payment systems: a study on mobile wallets. Journal of Business Economics and Finance, 5(1), 73-92.
    31. Aziz, N. A. A., Batmaz, F., Stone, R., & Chung, P. W. H. (2013). Selection of touch gestures for children's applications. In 2013 Science and Information Conference (pp. 721-726).
    32. Baecker, R., & Small, I. (1990). Animation at the interface. The Art of Human-Computer Interface Design, 251, 267.
    33. Baehre, S., O’Dwyer, M., O’Malley, L., & Lee, N. (2022). The use of Net Promoter Score (NPS) to predict sales growth: insights from an empirical investigation. Journal of the Academy of Marketing Science, 1-18.
    34. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 74-94.
    35. Bakhshian, S., & Lee, Y. A. (2023). Influence of extrinsic and intrinsic attributes on consumers’ attitude and intention of using wearable technology. International Journal of Human–Computer Interaction, 39(3), 562-574.
    36. Bangor, A., Kortum, P., & Miller, J. (2009). Determining what individual SUS scores mean: Adding an adjective rating scale. Journal of Usability Studies, 4(3), 114-123.
    37. Bank for International Settlements (2020). Central Bank Digital Currencies: Foundational Principles and Core Features. BIS Papers.
    38. Barfield, L. (1993). The User Interfaces: Concepts & Design. Addison-Welsey. Wokingham, UK (pp. 99-116).
    39. Barontini, C., & Holden, H.(2019) Proceeding with caution—A Survey on Central Bank Digital Currency. BIS Papers.
    40. Bauer, H. H., Reichardt, T., Barnes, S. J., & Neumann, M. M. (2005). Driving consumer acceptance of mobile marketing: A theoretical framework and empirical study. Journal of Electronic Commerce Research, 6(3), 181.
    41. Bhattacherjee, A. (2001). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201-214.
    42. Bhatti, T. (1970). Exploring factors influencing the adoption of mobile commerce. The Journal of Internet Banking and Commerce, 12(3), 1-13.
    43. Bindseil, U. (2019). Central bank digital currency: Financial system implications and control. International Journal of Political Economy, 48(4), 303-335.
    44. Blackwell, A. F. (2006). The reification of metaphor as a design tool. ACM Transactions on Computer-Human Interaction (TOCHI), 13(4), 490-530.
    45. Boar, C., & Wehrli, A. (2021). Ready, steady, go? Results of the third BIS survey on central bank digital currency. BIS Papers.
    46. Boar, C., Holden, H., & Wadsworth, A. (2020). Impending arrival–a sequel to the survey on central bank digital currency. BIS Paper, (107).
    47. Board of Governors of The Federal Reserve System(2022). Money and Payments: The U.S. Dollar in the Age of Digital Transformation. Washington D.C.: The Federal Reserve.
    48. Böser, F., & Gersbach, H. (2020). Monetary policy with a central bank digital currency: The short and the long term. Centre for Economic Policy Research.
    49. Bossu, W., Itatani, M., Margulis, C., Rossi, A., Weenink, H., & Yoshinaga, A. (2020). Legal aspects of central bank digital currency: Central bank and monetary law considerations. IMF Working Paper.
    50. Bouch, A., Kuchinsky, A., & Bhatti, N. (2000). Quality is in the eye of the beholder: Meeting users' requirements for internet quality of service. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 297-304).
    51. Branaghan, R. J., & Sanchez, C. A. (2009). Feedback preferences and impressions of waiting. Human factors, 51(4), 528-538.
    52. Brewster, S. A., & Brown, L. M. (2004). Tactons: Structured tactile messages for non-visual information display. In Proceedings of the Fifth Conference on Australasian User Interface. Australian Computer Society (pp. 15-23).
    53. Brewster, S., Chohan, F., & Brown, L. (2007). Tactile feedback for mobile interactions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 159-162).
    54. Brooke, J. (1996). Sus: a “quick and dirty’usability. Usability Evaluation in Industry, 189(3).
    55. Browne, M. W. (1993). Alternative ways of assessing model fit. Testing Structural Equation Models, 136-162.
    56. Brunnermeier, M. K., & Niepelt, D. (2019). On the equivalence of private and public money. Journal of Monetary Economics, 106, 27-41.
    57. Brunnermeier, M., & Landau, J. P. (2022). The digital euro: policy implications and perspectives. Data, Digitalization, Decentialized Finance and Central Bank Digital Currencies, 63.
    58. Burdea, G., Richard, P., & Coiffet, P. (1996). Multimodal virtual reality: Input‐output devices, system integration, and human factors. International Journal of Human‐Computer Interaction, 8(1), 5-24.
    59. Burke, J. L., Prewett, M. S., Gray, A. A., Yang, L., Stilson, F. R., Coovert, M. D., ... & Redden, E. (2006). Comparing the effects of visual-auditory and visual-tactile feedback on user performance: a meta-analysis. In Proceedings of the 8th International Conference on Multimodal Interfaces (pp. 108-117).
    60. Byrne, B. M. (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming.
    61. Carapella, F., & Flemming, J. (2020). Central bank digital currency: A literature review. FEDS Notes.
    62. Carroll, J. M. (1997). Human-computer interaction: psychology as a science of design. Annual Review of Psychology, 48(1), 61-83.
    63. Chalbi, A., Ritchie, J., Park, D., Choi, J., Roussel, N., Elmqvist, N., & Chevalier, F. (2019). Common fate for animated transitions in visualization. IEEE Transactions on Visualization and Computer Graphics, 26(1), 386-396.
    64. Cham, T. H., Cheah, J. H., Cheng, B. L., & Lim, X. J. (2022). I Am too old for this! Barriers contributing to the non-adoption of mobile payment. International Journal of Bank Marketing, 40(5), 1017-1050.
    65. Chan, G. (2022). The mobile Silk Road: digital economy and the e-yuan. In China’s Digital Silk Road (pp. 66-90). Edward Elgar Publishing.
    66. Chapman, P., Selvarajah, S., & Webster, J. (1999). Engagement in multimedia training systems. In Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. HICSS-32. Abstracts and CD-ROM of Full Papers (pp. 9-pp).
    67. Chen, C. H., & Li, S. (2020). The effect of visual feedback types on the wait indicator interface of a mobile application. Displays, 61, 101928.
    68. Chen, L. D. (2008). A model of consumer acceptance of mobile payment. International Journal of Mobile Communications, 6(1), 32-52.
    69. Chevalier, F., Riche, N. H., Plaisant, C., Chalbi, A., & Hurter, C. (2016, June). Animations 25 years later: New roles and opportunities. In Proceedings of the International Working Conference on Advanced Visual Interfaces (pp. 280-287).
    70. Choi, K. J., Henry, R., Lehar, A., Reardon, J., & Safavi-Naini, R. (2021). A Proposal for a Canadian CBDC. Available at SSRN 3786426.
    71. Chong, A. Y. L. (2013). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40(4), 1240-1247.
    72. Claeys, G., Demertzis, M., & Efstathiou, K. (2018). Cryptocurrencies and monetary policy (No. 2018/10). Bruegel Policy Contribution.
    73. Cobanoglu, C., Yang, W., Shatskikh, A., & Agarwal, A. (2015). Are consumers ready for mobile payment? An examination of consumer acceptance of mobile payment technology in restaurant industry. Hospitality Review, 31(4), 6.
    74. Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Academic Press.
    75. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 189-211.
    76. Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 145-158.
    77. Conlon, T., & McGee, R. (2020). Safe haven or risky hazard? Bitcoin during the COVID-19 bear market. Finance Research Letters, 35, 101607.
    78. Cooper, A., Reimann, R., Cronin, D., & Noessel, C. (2014). About face: the essentials of interaction design. John Wiley & Sons.
    79. Cunha, P. R., Melo, P., & Sebastião, H. (2021). From Bitcoin to Central Bank Digital Currencies: Making Sense of the Digital Money Revolution. Future Internet, 13(7), 165.
    80. Dahlberg, T., Guo, J., & Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, 14(5), 265-284.
    81. Dahlberg, T., Mallat, N., Ondrus, J., & Zmijewska, A. (2008). Past, present and future of mobile payments research: A literature review. Electronic Commerce Research and Applications, 7(2), 165-181.
    82. Darko, A. P., Liang, D., Xu, Z., Agbodah, K., & Obiora, S. (2023). A novel multi-attribute decision-making for ranking mobile payment services using online consumer reviews. Expert Systems with Applications, 213, 119262.
    83. Davies, G. (2010). History of money. University of Wales Press.
    84. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.
    85. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
    86. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of Applied Social Psychology, 22(14), 1111-1132.
    87. De Angeli, A., Sutcliffe, A., & Hartmann, J. (2006). Interaction, usability and aesthetics: what influences users' preferences?. In Proceedings of the 6th conference on Designing Interactive systems (pp. 271-280).
    88. De Luna, I. R., Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2019). Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied. Technological Forecasting and Social Change, 146, 931-944.
    89. de Sena Abrahão, R., Moriguchi, S. N., & Andrade, D. F. (2016). Intention of adoption of mobile payment: An analysis in the light of the Unified Theory of Acceptance and Use of Technology (UTAUT). RAI Revista de Administração e Inovação, 13(3), 221-230.
    90. De Smet, C., Bourgonjon, J., De Wever, B., Schellens, T., & Valcke, M. (2012). Researching instructional use and the technology acceptation of learning management systems by secondary school teachers. Computers & Education, 58(2), 688-696.
    91. Dey, D., Habibovic, A., Pfleging, B., Martens, M., & Terken, J. (2020). Color and animation preferences for a light band eHMI in interactions between automated vehicles and pedestrians. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-13).
    92. Dong, L., Zhang, J., Huang, L., & Liu, Y. (2021). Social influence on endorsement in social Q&A community: Moderating effects of temporal and spatial factors. International Journal of Information Management, 61, 102396.
    93. Dourish, P. (2001). Seeking a foundation for context-aware computing. Human–Computer Interaction, 16(2-4), 229-241.
    94. Dynan, K. E. (2000). Habit formation in consumer preferences: Evidence from panel data. American Economic Review, 90(3), 391-406.
    95. Fang, F., Ventre, C., Basios, M., Kanthan, L., Martinez-Rego, D., Wu, F., & Li, L. (2022). Cryptocurrency trading: a comprehensive survey. Financial Innovation, 8(1), 1-59.
    96. Fang, Y. M., Chun, L., & Chu, B. C. (2019). Older adults’ usability and emotional reactions toward text, diagram, image, and animation interfaces for displaying health information. Applied Sciences, 9(6), 1058.
    97. Fernández-Villaverde, J., Sanches, D., Schilling, L., & Uhlig, H. (2021). Central bank digital currency: Central banking for all?. Review of Economic Dynamics, 41, 225-242.
    98. Ferrari, M., Mehl, A., & Stracca, L. (2022). Central bank digital currency in an open economy. Journal of Monetary Economics.
    99. Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research.
    100. Fisher, D. (2010). Animation for Visualization: Opportunities and Drawbacks. Beautiful visualization, 19, 329-352.
    101. Fisk, A. D., Czaja, S. J., Rogers, W. A., Charness, N., & Sharit, J. (2009). Designing for Older Adults: Principles and Creative Human Factors Approaches Second Edition. In Designing for Older Adults: Principles and Creative Human Factors Approaches, Second Edition (pp. 1-232).
    102. Forceville, C. (1994). Pictorial metaphor in advertisements. Metaphor and Symbol, 9(1), 1-29.
    103. Forceville, C. (2002). The identification of target and source in pictorial metaphors. Journal of Pragmatics, 34 (1), 1-14.
    104. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
    105. Gao, L., & Waechter, K. A. (2017). Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation. Information Systems Frontiers, 19, 525-548.
    106. Gao, S., Moe, S. P., & Krogstie, J. (2010). An empirical test of the mobile services acceptance model. In 2010 Ninth International Conference on Mobile Business and 2010 Ninth Global Mobility Roundtable (ICMB-GMR) (pp. 168-175).
    107. García-Peñalvo, F. J., García-Holgado, A., Vázquez-Ingelmo, A., & Seoane-Pardo, A. M. (2018). Usability test of WYRED Platform. In International Conference on Learning and Collaboration Technologies (pp. 73-84).
    108. Garratt, R. J., & Van Oordt, M. R. (2021). Privacy as a public good: a case for electronic cash. Journal of Political Economy, 129(7), 2157-2180.
    109. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 51-90.
    110. Gibbs, J. K., Gillies, M., & Pan, X. (2022). A comparison of the effects of haptic and visual feedback on presence in virtual reality. International Journal of Human-Computer Studies, 157, 102717.
    111. Gonzalez, C. (1996). Does animation in user interfaces improve decision making?. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 27-34).
    112. Greenberg, S., Carpendale, S., Marquardt, N., & Buxton, B. (2011). Sketching user experiences: The workbook. Elsevier.
    113. Grisaffe, D. B. (2007). Questions about the ultimate question: conceptual considerations in evaluating Reichheld's net promoter score (NPS). The Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 20, 36-53.
    114. Guo, L., Lu, Z., & Yao, L. (2021). Human-machine interaction sensing technology based on hand gesture recognition: A review. IEEE Transactions on Human-Machine Systems.
    115. Hair, J. F. (1998). Multivariate data analysis (5th ed...). Prentice-Hall International.
    116. Hajazi, M. A., Chan, S. S., Ya’kob, S. A., Siali, F., & Latip, H. A. (2021). Usage Intention of Qr Mobile Payment System Among Millennials in Malaysia. Int. J. Acad. Res. Bus. Soc. Sci., 11(1), 645-661.
    117. Harris, M. A., Brookshire, R., & Chin, A. G. (2016). Identifying factors influencing consumers’ intent to install mobile applications. International Journal of Information Management, 36(3), 441-450.
    118. Hassenzahl, M., & Tractinsky, N. (2006). User experience-a research agenda. Behaviour & Information Technology, 25(2), 91-97.
    119. Hekkert, P., & Cila, N. (2015). Handle with care! Why and how designers make use of product metaphors. Design Studies, 40, 196-217.
    120. Hirschheim, R. (2007). Introduction to the special issue on" quo vadis TAM-issues and reflections on technology acceptance research". Journal of the Association for Information Systems, 8(4), 9.
    121. Hoang, Y. H., Ngo, V. M., & Vu, N. B. (2023). Central bank digital currency: A systematic literature review using text mining approach. Research in International Business and Finance, 101889.
    122. Hong, W., Thong, J. Y., & Tam, K. Y. (2007). How do Web users respond to non‐banner‐ads animation? The effects of task type and user experience. Journal of the American Society for Information Science and Technology, 58(10), 1467-1482.
    123. Hsiao, C. H., & Yang, C. (2011). The intellectual development of the technology acceptance model: A co-citation analysis. International Journal of Information Management, 31(2), 128-136.
    124. Hsiao, C. H., Tang, K. Y., & Liu, J. S. (2015). Citation-based analysis of literature: a case study of technology acceptance research. Scientometrics, 105, 1091-1110.
    125. Hu, M., Bian, P., & Zhang, N. (2014). The research of human-machine interactions unity on IOS and android smartphone platform. In 2014 9th International Conference on Computer Science & Education (pp. 640-643). IEEE.
    126. Huber, J. (2019). Digital currency. Design principles to support a shift from bankmoney to central bank digital currency. Real-World Economics Review, (88), 76-90.
    127. Hudson, S. E., & Stasko, J. T. (1993, December). Animation support in a user interface toolkit: Flexible, robust, and reusable abstractions. In Proceedings of the 6th annual ACM symposium on User Interface Software and Technology (pp. 57-67).
    128. Jeans, E. D. (2015). Funny money or the fall of fiat: Bitcoin and forward-facing virtual currency regulation. Colo. Tech. LJ, 13, 99.
    129. Jung, H., & Jeong, D. (2021). Blockchain Implementation Method for Interoperability between CBDCs. Future Internet, 13(5), 133.
    130. Jung, H., Wiltse, H., Wiberg, M., & Stolterman, E. (2017). Metaphors, materialities, and affordances: Hybrid morphologies in the design of interactive artifacts. Design Studies, 53, 24-46.
    131. Kalinic, Z., & Marinkovic, V. (2016). Determinants of users’ intention to adopt m-commerce: an empirical analysis. Information Systems and e-Business Management, 14, 367-387.
    132. Kalinić, Z., Liébana-Cabanillas, F. J., Muñoz-Leiva, F., & Marinković, V. (2020). The moderating impact of gender on the acceptance of peer-to-peer mobile payment systems. International Journal of Bank Marketing, 38(1), 138-158.
    133. Kalinic, Z., Marinkovic, V., Molinillo, S., & Liébana-Cabanillas, F. (2019). A multi-analytical approach to peer-to-peer mobile payment acceptance prediction. Journal of Retailing and Consumer Services, 49, 143-153.
    134. Kanchanatanee, K., Suwanno, N., & Jarernvongrayab, A. (2014). Effects of attitude toward using, perceived usefulness, perceived ease of use and perceived compatibility on intention to use E-marketing. Journal of Management Research, 6(3), 1.
    135. Karam, M. (2006). A framework for research and design of gesture-based human-computer interactions (Doctoral dissertation, University of Southampton).
    136. Kelly, N. M. (2018). “Works like Magic”: Metaphor, Meaning, and the GUI in Snow Crash. Science Fiction Studies, 45(1), 69-90.
    137. Khoa, B. T. (2020). The role of mobile skillfulness and user innovation toward electronic wallet acceptance in the digital transformation era. In 2020 International Conference on Information Technology Systems and Innovation (ICITSI) (pp. 30-37).
    138. Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310-322.
    139. Kim, S., & Park, S. Y. (2019). The reciprocal impact of both visual and verbal metaphors in advertisements: the moderating role of need for cognition. Journal of Visual Literacy, 38(4), 305-323.
    140. Kim, Y. J., & Han, J. (2014). Why smartphone advertising attracts customers: A model of Web advertising, flow, and personalization. Computers in Human Behavior, 33, 256-269.
    141. Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.
    142. Krishnaswamy, N., & Pustejovsky, J. (2018). An evaluation framework for multimodal interaction. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).
    143. Kumar, J. A., Bervell, B., Annamalai, N., & Osman, S. (2020). Behavioral intention to use mobile learning: Evaluating the role of self-efficacy, subjective norm, and WhatsApp use habit. IEEE Access, 8, 208058-208074.
    144. Kumhof, M., & Noone, C. (2018). Central bank digital currencies—design principles and balance sheet implications. Bank of England Staff Working Paper, No. 725.
    145. Kuroki, Y., & Ishihara, M. (2015). Manipulating Animation Speed of Progress Bars to Shorten Time Perception. In HCI International 2015-Posters’ Extended Abstracts: International Conference, HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015. Proceedings, Part II 17 (pp. 670-673). Springer International Publishing.
    146. Laboure, M., H.‐P. Müller, M., Heinz, G., Singh, S., & Köhling, S. (2021). Cryptocurrencies and cbdc: The route ahead. Global Policy, 12(5), 663-676.
    147. Lai, J. Y. (2009). How reward, computer self‐efficacy, and perceived power security affect knowledge management systems success: An empirical investigation in high‐tech companies. Journal of the American Society for Information Science and Technology, 60(2), 332-347.
    148. Lallemand, C., Gronier, G., & Koenig, V. (2015). User experience: A concept without consensus? Exploring practitioners’ perspectives through an international survey. Computers in Human Behavior, 43, 35-48.
    149. Landoni, M. (1997). The Visual Book system: A study of the use of visual rhetoric in the design of electronic books (Doctoral dissertation, University of Strathclyde).
    150. Landoni, M., & Gibb, F. (2000). The role of visual rhetoric in the design and production of electronic books: the visual book. The electronic library.
    151. Laukkanen, T., Sinkkonen, S., Kivijärvi, M., & Laukkanen, P. (2007). Innovation resistance among mature consumers. Journal of Consumer Marketing.
    152. Law, E. L. C., Vermeeren, A. P., Hassenzahl, M., & Blythe, M. (2007, September). Towards a UX manifesto. In Proceedings of HCI 2007 The 21st British HCI Group Annual Conference University of Lancaster, UK 21 (pp. 1-2).
    153. Lee, Y., Son, B., Park, S., Lee, J., & Jang, H. (2021). A Survey on Security and Privacy in Blockchain-based Central Bank Digital Currencies. J. Internet Serv. Inf. Secur., 11(3), 16-29.
    154. Leong, L. Y., Hew, J. J., Wong, L. W., & Lin, B. (2022). The past and beyond of mobile payment research: A development of the mobile payment framework. Internet Research.
    155. Leong, L. Y., Hew, T. S., Tan, G. W. H., & Ooi, K. B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14), 5604-5620.
    156. Lewis, J. R. (2018). The system usability scale: past, present, and future. International Journal of Human–Computer Interaction, 34(7), 577-590.
    157. Li, D., Ge, X., Ma, Q., Mehra, B., Liu, J., Han, T., & Liu, C. (2022). Evaluating Three Touch Gestures for Moving Objects across Folded Screens. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 6(3), 1-28.
    158. Li, H., & Chen, C. H. (2021). Effects of affordance state and operation mode on a smart washing machine touch sensitive user interface design. IEEE Sensors Journal, 21(19), 21956-21967.
    159. Li, H., Liu, Y., & Heikkilä, J. (2014). Understanding the factors driving NFC-enabled mobile payment adoption: An empirical investigation.
    160. Li, S., & Chen, C. H. (2019). The effects of visual feedback designs on long wait time of mobile application user interface. Interacting with Computers, 31(1), 1-12.
    161. Li, S., Chen, C. H., & Lin, Z. (2022). Evaluating the impact of wait indicators on user visual imagery and speed perception in mobile application interfaces. International Journal of Industrial Ergonomics, 88, 103280.
    162. Liébana-Cabanillas, F., García-Maroto, I., Muñoz-Leiva, F., & Ramos-de-Luna, I. (2020). Mobile payment adoption in the age of digital transformation: The case of Apple Pay. Sustainability, 12(13), 5443.
    163. Liébana-Cabanillas, F., Marinkovic, V., de Luna, I. R., & Kalinic, Z. (2018). Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach. Technological Forecasting and Social Change, 129, 117-130.
    164. Liébana-Cabanillas, F., Molinillo, S., & Japutra, A. (2021). Exploring the determinants of intention to use P2P mobile payment in Spain. Information Systems Management, 38(2), 165-180.
    165. Liébana-Cabanillas, F., Ramos de Luna, I., & Montoro-Ríos, F. (2017). Intention to use new mobile payment systems: a comparative analysis of SMS and NFC payments. Economic Research-Ekonomska Istraživanja, 30(1), 892-910.
    166. Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). Antecedents of the adoption of the new mobile payment systems: The moderating effect of age. Computers in Human Behavior, 35, 464-478.
    167. Liu, J., & Serletis, A. (2019). Volatility in the cryptocurrency market. Open Economies Review, 30(4), 779-811.
    168. Lloyd, M. (2022). The Future of Money: Central Bank Digital Currencies. Atlantic Economic Journal, 1-14.
    169. Lohr, S. (2012). For impatient web users, an eye blink is just too long to wait. New York Times, (February 29).
    170. Lu, H. P., & Su, P. Y. J. (2009). Factors affecting purchase intention on mobile shopping web sites. Internet research, 19(4), 442-458.
    171. Lu, J. (2014). Are personal innovativeness and social influence critical to continue with mobile commerce?. Internet research, 24(2), 134-159.
    172. Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245-268.
    173. Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873-891.
    174. Luo, Z. Z. (2022). PBoC: Cumulative digital RMB transactions amounted to 100.4 billion yuan as of Aug. 31. People's Daily.
    175. Lwoga, E. T., & Lwoga, N. B. (2017). User acceptance of mobile payment: the effects of user‐centric security, system characteristics and gender. The Electronic Journal of Information Systems in Developing Countries, 81(1), 1-24.
    176. Lymperopoulos, C., & Chaniotakis, I. E. (2005). Factors affecting acceptance of the internet as a marketing‐intelligence tool among employees of Greek bank branches. International Journal of Bank Marketing, 23(6), 484-505.
    177. Malhotra, Y., & Galletta, D. F. (1999). Extending the technology acceptance model to account for social influence: Theoretical bases and empirical validation. In Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers. IEEE.
    178. Malik, A. N. A., & Annuar, S. N. S. (2021). The effect of perceived usefulness, perceived ease of use, reward, and perceived risk toward e-wallet usage intention. In Eurasian Business and Economics Perspectives: Proceedings of the 30th Eurasia Business and Economics Society Conference (pp. 115-130). Springer International Publishing.
    179. Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14, 81-95.
    180. Marcus, A. (1998). Metaphor design in user interfaces. ACM SIGDOC Asterisk Journal of Computer Documentation, 22(2), 43-57.
    181. McNeill, D. (1992). Hand and Mind1. Advances in Visual Semiotics, 351.
    182. Mikhaylov, A. (2021). Development of Friedrich von Hayek’s theory of private money and economic implications for digital currencies. Terra Economicus, 19(1), 53-62.
    183. Mitra, S., & Acharya, T. (2007). Gesture recognition: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(3), 311-324.
    184. Molina, L. M., Lloréns-Montes, J., & Ruiz-Moreno, A. (2007). Relationship between quality management practices and knowledge transfer. Journal of Operations Management, 25(3), 682-701.
    185. Możdżyński, D., & Cellary, W. (2022). Determinants of the acceptance of mobile payment systems by e-merchants. Journal of Electronic Commerce in Organizations (JECO), 20(1), 1-23.
    186. Muñoz, F. (2008). La adopción de una innovación basada en la Web [Tesis Doctoral]. Departamento de Comercialización e Investigación de Mercados, Universidad de Granada.
    187. Myers, B. A. (1985). The importance of percent-done progress indicators for computer-human interfaces. ACM SIGCHI Bulletin, 16(4), 11-17.
    188. Myers, B., Hollan, J., Cruz, I., Bryson, S., Bulterman, D., Catarci, T., ... & Ioannidis, Y. (1996). Strategic directions in human-computer interaction. ACM Computing Surveys (CSUR), 28(4), 794-809.
    189. Nah, F. F. H. (2004). A study on tolerable waiting time: how long are web users willing to wait?. Behaviour & Information Technology, 23(3), 153-163.
    190. Namisango, F., Kang, K., & Rehman, J. (2023). Examining the relationship between sociomaterial practices enacted in the organizational use of social media and the emerging role of organizational generativity. International Journal of Information Management, 71, 102643.
    191. Náñez Alonso, S. L., Echarte Fernández, M. Á., Sanz Bas, D., & Kaczmarek, J. (2020). Reasons fostering or discouraging the implementation of central bank-backed digital currency: A review. Economies, 8(2), 41.
    192. Natakusumah, K., Maulina, E., Muftiadi, A., & Purnomo, M. (2023). Integrating religiosity into a technology acceptance model for the adoption of mobile payment technology. International Journal of Data and Network Science, 7(1), 305-312.
    193. Neale, D. C., & Carroll, J. M. (1997). The role of metaphors in user interface design. In Handbook of Human-Computer Interaction (pp. 441-462). North-Holland.
    194. Nielsen, J. (1994). 10 Usability heuristics for user interface design.
    195. Niepelt, D. (2018). Reserves for all? Central Bank Digital Currency, deposits, and their (non)-equivalence.
    196. Norman, D., Miller, J., & Henderson, A. (1995). What you see, some of what's in the future, and how we go about doing it: HI at Apple Computer. In Conference Companion on Human Factors in Computing Systems (p. 155).
    197. Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404-414.
    198. Omarini, A. E. (2018). Fintech and the future of the payment landscape: the mobile wallet ecosystem. A challenge for retail banks?
    199. Ooi, K. B., & Tan, G. W. H. (2016). Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Systems with Applications, 59, 33-46.
    200. Ottenheimer, H. J., & Pine, J. M. (2018). The anthropology of language: an introduction to linguistic anthropology. Cengage Learning.
    201. Ozcelik, E., Arslan-Ari, I., & Cagiltay, K. (2010). Why does signaling enhance multimedia learning? Evidence from eye movements. Computers in Human Behavior, 26(1), 110-117.
    202. Ozili, P. K. (2022). Central bank digital currency research around the World: a review of literature. Journal of Money Laundering Control.
    203. Palash, M. A. S., Talukder, M. S., Islam, A. N., & Bao, Y. (2022). Positive and negative valences, personal innovativeness and intention to use facial recognition for payments. Industrial Management & Data Systems.
    204. Park, E., & Kim, K. J. (2014). An integrated adoption model of mobile cloud services: exploration of key determinants and extension of technology acceptance model. Telematics and Informatics, 31(3), 376-385.
    205. Park, K., Seok, H., & Kim, K. (2019). The effects of graphic metaphor, gesture interaction, and form factor on e‐book performances and preferences. Human Factors and Ergonomics in Manufacturing & Service Industries, 29(6), 493-503.
    206. Parveen, F., & Sulaiman, A. (2008). Technology complexity, personal innovativeness and intention to use wireless internet using mobile devices in Malaysia. International Review of Business Research Papers, 4(5), 1-10.
    207. Pavlou, P. A. (2002). A theory of planned behavior perspective to the consumer adoption of electronic commerce. MIS Quarterly, 30(1), 115-143.
    208. PBC. (2021). THE PEOPLE’S BANK OF CHINA. Progress of Research & Development of E-CNY in China. Working Group on E-CNY Research and Development of the People’s Bank of China, July.
    209. Pham, T. T. T., & Ho, J. C. (2015). The effects of product-related, personal-related factors and attractiveness of alternatives on consumer adoption of NFC-based mobile payments. Technology in Society, 43, 159-172.
    210. Qian, Y. (2019). Central Bank Digital Currency: optimization of the currency system and its issuance design. China Economic Journal, 12(1), 1-15.
    211. Quek, F., McNeill, D., Bryll, R., Duncan, S., Ma, X. F., Kirbas, C., ... & Ansari, R. (2002). Multimodal human discourse: gesture and speech. ACM Transactions on Computer-Human Interaction (TOCHI), 9(3), 171-193.
    212. Rabaa’i, A. A. (2021). An Investigation into the acceptance of mobile wallets in the FinTech era: an empirical study from Kuwait. International Journal of Business Information Systems, 1(1), 1.
    213. Rahman, A. J. (2018). Deflationary policy under digital and fiat currency competition. Research in Economics, 72(2), 171-180.
    214. Ramos-de-Luna, I., Montoro-Ríos, F., & Liébana-Cabanillas, F. (2016). Determinants of the intention to use NFC technology as a payment system: an acceptance model approach. Information Systems and e-Business Management, 14, 293-314.
    215. Reichheld, F. (2011). The ultimate question 2.0 (revised and expanded edition): How net promoter companies thrive in a customer-driven world. Harvard Business Review Press.
    216. Reichheld, F. F. (2003). The one number you need to grow. Harvard Business Review, 81(12), 46-55.
    217. Richard, P., Burdea, G., Gomez, D., & Coiffet, P. (1994). A comparison of haptic, visual and auditive force feedback for deformable virtual objects. In Proceedings of the Internation Conference on Automation Technology (ICAT) (Vol. 49, p. 62).
    218. Rogers, E. M. (2010). Diffusion of innovations (5th ed.). The Free Press, New York.
    219. Rogoff, K. S. (2017). The curse of cash. In The Curse of Cash. Princeton University Press.
    220. Rosendahl-Kreitman, K. (1990). The challenge of interface design: Creating a quality experience for the user. Multimedia [user] Interface Design, 1-3.
    221. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67.
    222. Sahi, A. M., Khalid, H., Abbas, A. F., & Khatib, S. F. (2021). The evolving research of customer adoption of digital payment: Learning from content and statistical analysis of the literature. Journal of Open Innovation: Technology, Market, and Complexity, 7(4), 230.
    223. Salvendy, G., & Carayon, P. (1997). Data collection and evaluation of outcome measures. Handbook of Human Factors and Ergonomics, 1451-1470.
    224. Sanfilippo, F., Blažauskas, T., Girdžiūna, M., Janonis, A., Kiudys, E., & Salvietti, G. (2022). A multi-modal auditory-visual-tactile e-learning framework. In Intelligent Technologies and Applications: 4th International Conference, INTAP 2021, Grimstad, Norway, October 11–13, 2021, Revised Selected Papers (pp. 119-131). Cham: Springer International Publishing.
    225. Sauer, J., Sonderegger, A., & Schmutz, S. (2020). Usability, user experience and accessibility: towards an integrative model. Ergonomics, 63(10), 1207-1220.
    226. Schierz, P. G., Schilke, O., & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9(3), 209-216.
    227. Sethaput, V., & Innet, S. (2023). Blockchain application for central bank digital currencies (CBDC). Cluster Computing, 1-15.
    228. Shin, D. H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior, 25(6), 1343-1354.
    229. Shin, D. H. (2010). The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption. Interacting with Computers, 22(5), 428-438.
    230. Shneiderman, B., Plaisant, C., Cohen, M. S., Jacobs, S., Elmqvist, N., & Diakopoulos, N. (2016). Designing the User Interface: Strategies for Effective Human-Computer Interaction. Pearson.
    231. Silva, S. C., & Martins, C. C. (2016). Understanding Portuguese young consumers intention to use mobile commerce. Communication and New Media, 4(7), 106-131.
    232. Sinelnikova-Muryleva, E. V. (2020). Central bank digital currencies: Potential risks and benefits. Voprosy Ekonomiki, (4), 147-159.
    233. Singh, S. (2020). An integrated model combining ECM and UTAUT to explain users’ post-adoption behaviour towards mobile payment systems. Australasian Journal of Information Systems, 24.
    234. Slade, E. L., Williams, M. D., & Dwivedi, Y. K. (2013). Mobile payment adoption: Classification and review of the extant literature. The Marketing Review, 13(2), 167-190.
    235. Sleiman, K. A. A., Juanli, L., Lei, H., Liu, R., Ouyang, Y., & Rong, W. (2021). User trust levels and adoption of mobile payment systems in China: An empirical analysis. Sage Open, 11(4), 21582440211056599.
    236. Smelser, N. J., & Baltes, P. B. (Eds.). (2001). International encyclopedia of the social & behavioral sciences (Vol. 11). Amsterdam: Elsevier.
    237. Smith, A. L., & Chaparro, B. S. (2015). Smartphone text input method performance, usability, and preference with younger and older adults. Human Factors, 57(6), 1015-1028.
    238. Smith, P. A. (1996). Towards a practical measure of hypertext usability. Interacting with Computers, 8(4), 365-381.
    239. Solinas, M. (2021). The future of money: central bank digital currencies in the Eurosystem. Journal of Banking and Finance Law and Practice, 32(4), 254-256.
    240. Sun, H., Mao, H., Bai, X., Chen, Z., Hu, K., & Yu, W. (2017, December). Multi-blockchain model for central bank digital currency. In 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) (pp. 360-367). IEEE.
    241. Suwanaposee, P., Gutwin, C., & Cockburn, A. (2022). The influence of audio effects and attention on the perceived duration of interaction. International Journal of Human-Computer Studies, 159, 102756.
    242. Tahar, A., Riyadh, H. A., Sofyani, H., & Purnomo, W. E. (2020). Perceived ease of use, perceived usefulness, perceived security and intention to use e-filing: The role of technology readiness. The Journal of Asian Finance, Economics and Business, 7(9), 537-547.
    243. Tan, G. W. H., & Ooi, K. B. (2018). Gender and age: Do they really moderate mobile tourism shopping behavior?. Telematics and Informatics, 35(6), 1617-1642.
    244. Tan, G. W. H., Ooi, K. B., Leong, L. Y., & Lin, B. (2014). Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach. Computers in Human Behavior, 36, 198-213.
    245. Tew, H. T., Tan, G. W. H., Loh, X. M., Lee, V. H., Lim, W. L., & Ooi, K. B. (2022). Tapping the next purchase: embracing the wave of mobile payment. Journal of Computer Information Systems, 62(3), 527-535.
    246. Thomas, B. H., & Calder, P. (2001). Applying cartoon animation techniques to graphical user interfaces. ACM Transactions on Computer-Human Interaction (TOCHI), 8(3), 198-222.
    247. Triantafyllidis, E., Mcgreavy, C., Gu, J., & Li, Z. (2020). Study of multimodal interfaces and the improvements on teleoperation. IEEE Access, 8, 78213-78227.
    248. Tsai, C. Y. (2017). Effect of graphic simplification and graphic metaphor on the memory and identification of travel map. International Journal of Industrial Ergonomics, 61, 29-36.
    249. Türker, C., Altay, B. C., & Okumuş, A. (2022). Understanding user acceptance of QR code mobile payment systems in Turkey: An extended TAM. Technological Forecasting and Social Change, 184, 121968.
    250. Turrin, R. (2021). Cashless: China's digital currency revolution. Smashwords Edition.
    251. Upadhyay, N., Upadhyay, S., Abed, S. S., & Dwivedi, Y. K. (2022). Consumer adoption of mobile payment services during COVID-19: Extending meta-UTAUT with perceived severity and self-efficacy. International Journal of Bank Marketing.
    252. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315.
    253. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
    254. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478.
    255. Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376.
    256. Vigna, P., & Casey, M. J. (2016). The age of cryptocurrency: how bitcoin and the blockchain are challenging the global economic order. Macmillan.
    257. Villamor, C., Willis, D., & Wroblewski, L. (2010). Touch gesture reference guide. Touch Gesture Reference Guide.
    258. Vitense, H. S., Jacko, J. A., & Emery, V. K. (2003). Multimodal feedback: an assessment of performance and mental workload. Ergonomics, 46(1-3), 68-87.
    259. Wang, G., Tan, G. W. H., Yuan, Y., Ooi, K. B., & Dwivedi, Y. K. (2022). Revisiting TAM2 in behavioral targeting advertising: a deep learning-based dual-stage SEM-ANN analysis. Technological Forecasting and Social Change, 175, 121345.
    260. Wang, Y., Huang, Y., Li, J., & Zhang, J. (2021). The effect of mobile applications’ initial loading pages on users’ mental state and behavior. Displays, 68, 102007.
    261. Wang, Y., Lucey, B. M., Vigne, S. A., & Yarovaya, L. (2022). The effects of central bank digital currencies news on financial markets. Technological Forecasting and Social Change, 180, 121715.
    262. Wang, Y., Wang, S., Wang, J., Wei, J., & Wang, C. (2020). An empirical study of consumers’ intention to use ride-sharing services: using an extended technology acceptance model. Transportation, 47, 397-415.
    263. Wang, Z., Guan, Z., Hou, F., Li, B., & Zhou, W. (2019). What determines customers’ continuance intention of FinTech? Evidence from YuEbao. Industrial Management & Data Systems, 119(8), 1625-1637.
    264. Wei, G., Xinyan, Z., & Yue, M. (2011, May). Notice of Retraction: Literature review on consumer adoption behavior of mobile commerce services. In 2011 International Conference on e-Business and e-Government (ICEE) (pp. 1-5). IEEE.
    265. Weir, C. S., Anderson, J. N., & Jack, M. A. (2006). On the role of metaphor and language in design of third party payments in eBanking: Usability and quality. International Journal of Human-Computer Studies, 64(8), 770-784.
    266. Williams, M. D., Roderick, S., Davies, G. H., & Clement, M. (2017). Risk, trust, and compatibility as antecedents of mobile payment adoption.
    267. Williamson, S. (2022). Central bank digital currency: Welfare and policy implications. Journal of Political Economy, 130(11), 2829-2861.
    268. Wong, W. H., & Mo, W. Y. (2019). A study of consumer intention of mobile payment in Hong Kong, based on perceived risk, perceived trust, perceived security and Technological Acceptance Model. Journal of Advanced Management Science Vol, 7(2), 33-38.
    269. Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719-729.
    270. Xu, J. (2022). Developments and implications of central bank digital currency: The case of China e‐CNY. Asian Economic Policy Review, 17(2), 235-250.
    271. Yang, J., & Zhou, G. (2022). A study on the influence mechanism of CBDC on monetary policy: An analysis based on e-CNY. Plos One, 17(7), e0268471.
    272. Yang, K. C. (2005). Exploring factors affecting the adoption of mobile commerce in Singapore. Telematics and Informatics, 22(3), 257-277.
    273. Yang, L. I., Huang, J., Feng, T. I. A. N., Hong-An, W. A. N. G., & Guo-Zhong, D. A. I. (2019). Gesture interaction in virtual reality. Virtual Reality & Intelligent Hardware, 1(1), 84-112.
    274. Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 28(1), 129-142.
    275. Yatani, K., & Truong, K. N. (2009, October). SemFeel: a user interface with semantic tactile feedback for mobile touch-screen devices. In Proceedings of the 22nd Annual ACM Symposium on User Interface Software and Technology (pp. 111-120).
    276. Yi, M., Huang, Z., & Yu, Y. (2022). Creating a Sustainable E-Commerce Environment: The Impact of Product Configurator Interaction Design on Consumer Personalized Customization Experience. Sustainability, 14(23), 15903.
    277. Zams, B. M., Indrastuti, R., Pangersa, A. G., Hasniawati, N. A., Zahra, F. A., & Fauziah, I. A. (2015). Designing Central Bank Digital Currency for Indonesia: The Delphi–Analytic Network Process. Bulletin of Monetary Economics and Banking, 23(3), 413-440.
    278. Zarmpou, T., Saprikis, V., Markos, A., & Vlachopoulou, M. (2012). Modeling users’ acceptance of mobile services. Electronic Commerce Research, 12, 225-248.
    279. Zhang, L., Shao, Z., Zhang, J., & Li, X. (2022). The Situational Nature of Impulse Buying on Mobile Platforms: A Cross-Temporal Investigation. Electronic Commerce Research and Applications, 101204.
    280. Zhang, L., Zhu, J., & Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior, 28(5), 1902-1911.
    281. Zhang, Q., Khan, S., Cao, M., & Khan, S. U. (2023). Factors Determining Consumer Acceptance of NFC Mobile Payment: An Extended Mobile Technology Acceptance Model. Sustainability, 15(4), 3664.
    282. Zhou, T. (2011). The impact of privacy concern on user adoption of location‐based services. Industrial Management & Data Systems.
    283. Zuo, W., Mu, B., Fang, H., & Wan, Y. (2023). User Experience: A Bibliometric Review of the Literature. IEEE Access.

    中文參考文獻
    1. 游章雄,黃培華 & 陳彥如 (2009) 。 使用者導向之互動設計研究。 工業設計,(121), 189-194。
    2. 陳建雄 & 李莎莎 (2022) 。 行動設備使用者介面設計之時間知覺誤差與等待體驗研究。 設計學報 (Journal of Design) , 27(4) 。
    3. 呂佳臻 (2020) 。 購物網站瀏覽對使用者體驗影響之探討-以產品選單與介面複雜度為實驗因子。国立台湾师范大学。

    無法下載圖示 全文公開日期 2034/01/22 (校內網路)
    全文公開日期 2074/01/22 (校外網路)
    全文公開日期 2074/01/22 (國家圖書館:臺灣博碩士論文系統)
    QR CODE