簡易檢索 / 詳目顯示

研究生: 謝怡悅
Hsieh, Yi-Yueh
論文名稱: 以技術接受模式與創新擴散理論探討影響區塊鏈技術接受與擴散之因素
Exploring the Factors Affecting the Acceptance and Diffusion of Blockchain Technology by Technology Acceptance Model and Innovation Diffusion Theory
指導教授: 黃啟祐
Huang, Chi-Yo
學位類別: 碩士
Master
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2019
畢業學年度: 108
語文別: 英文
論文頁數: 145
中文關鍵詞: 區塊鏈技術科技接受模式(TAM)創新擴散理論(IDT)整合科技接受模型(UTAUT)金融科技偏最小平方結構方程模型(PLS-SEM)決策實驗室分析基礎之網路層級分析法(DANP)
英文關鍵詞: Blockchain technology, Technology acceptance model (TAM), Innovation diffusion theory (IDT), Unified Theory of Acceptance and Use of Technology (UTAUT), Fintech, DEMATEL-based ANP model (DNP model), PLS-SEM
DOI URL: http://doi.org/10.6345/NTNU201901157
論文種類: 學術論文
相關次數: 點閱:403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
  • 區塊鏈(Blockchain)是金融科技的關鍵技術,以去中介化(Disintermediation)的交易機制為基礎,具有一致(Consensus)、來源可溯(Provenance)、不可更改(Immutability)與永久保存(Finality)等特質,為信賴度極高的金融技術。因此,區塊鏈徹底顛覆傳統銀行所使用於支付清算和信用管理的基礎資訊技術。雖然,區塊鏈之新興應用日增,唯傳統銀行對區塊鏈技術仍處於探索階段,亦少有業者或學者、專家研究影響區塊鏈技術接受與擴散之關鍵要素。為解決此影響未來金融界發展之議題,加速傳統銀行接受區塊鏈,並且普及此技術,本研究擬結合科技接受模式(TAM)、創新擴散理論(IDT)及整合科技接受模型(UTAUT),定義研究模型,探討影響金融業使用導入並普及區塊鏈之關鍵要素。本研究擬邀集專家,導入基於網決策實驗室分析法 (Decision Making Trial and Evaluation Laboratory,DEMATEL) 之分析網路流程 (DEMATE based Analytic Network Process,DANP),推衍關鍵要素,建構影響關係圖,並定義加速技術擴散與接受之策略,並進而邀請銀行從業人員,以偏最小平方法之結構方程模式(Partial Least Square Structural Equation Modelling) 技術驗證影響關係之顯著性。本研究將以我國銀行產業實證分析模式之可行性,透過決策實驗室分析法了解,易於使用為影響採用區塊練行為意圖之最重要因素;而透過結構方程模型得知,與銀行業人員使用並接受區塊鏈科技關聯度最高之因素為努力期望,而採用區塊鏈所產生的風險,與其採用區塊鏈技術的行為意圖為負相關。研究結果將可作為定義促成銀行業導入並擴散區塊鏈技術資訊策略之用,所定義之分析模式亦可作為分析各新興科技技術接受與擴散關鍵成功要素與策略之用。

    Blockchain is a key technology of financial technology (Fintech). Based on the disintermediation trading mechanism, the blockchain is characterized of the feature including consensus, provenance, immutability and finality. Thus, the blockchain can become a trusted Fintech. Therefore, blockchain has become the technique which can revolutionize and transform the underlying technologies of bank payment clearing and credit information systems. Although the applications of the blockchain is increasing rapidly, traditional banks are still exploring the unknown future. Further, very limited scholars or managers tried to explore the factors influencing the acceptance and diffusion of blockchain technology. Therefore, this study aims to propose an integrated research model based on Technology Acceptance Model (TAM), Innovation Diffusion Theory (IDT), and Unified Theory of Acceptance and Use of Technology (UTAUT) the factors influencing banks' future usage of blockchain technology will be derived cased on experts by using the DNP method. The Influence Relation Map (IRM) can thus be defined and defining strategies for accelerating technology diffusion and acceptance. Bank employees will be invited further to verify the significance of the influence relationship by using the PLS Structural Equation Model. Empirical analysis results based on Taiwanese experts and respondents have verified the feasibility of the analytic process and theoretic frameworks. Based on the results being derived by DEMATEL, “Perceived Ease of Use” will be the most influential factor on the behavioral intention for adopting blockchain technology. Further, based on the confirmatory factor analysis by the PLS-SEM method, “Effort” is the aspect with the highest correlation with the behavioral intention to adopt blockchain technology. Conversely, the “Risk” aspect is the negatively correlated with the adoption of the blockchain technology. The research results will serve as the basis for defining strategies to diffuse and promote the adoption of the blockchain in the banking industry. The analysis model can also be used as a key success factor in the analysis of the acceptance and diffusion of emerging technologies.

    Table of Contents 摘要 i Abstract iii Table of Contents v List of Table vii List of Figure ix Chapter 1 Introduction 1 1.1 Research Backgrounds and Motivations 1 1.2 Research Purposes 3 1.3 Research Framework 4 1.4 Research Limitations 5 1.5 Thesis Structure 5 Chapter 2 Literature Review 7 2.1 Fintech 7 2.2 Blockchain 10 2.3 Innovation Diffusion Theory 13 2.4 Technology Acceptance Model 15 2.5 The Integration of TAM and IDT 18 2.6 Research model and development of hypotheses 19 Chapter 3 Research Method 33 3.1 Modified Delphi Method 33 3.2 DANP 36 3.3 Partial Least Squares-Structural Equation Model (PLS-SEM) 42 3.4 Sample and Measures 59 Chapter 4 Empirical Study 61 4.1 Background and Related Factors 62 4.2 Factors Determination for Blockchain Technology Acceptance by Modified Delphi Method 63 4.3 Constructing the Causal Relationship between Dimensions and Criteria by DEMATEL 64 4.4 Derive the Influence Weights by DNP 70 4.5 The Empirical Study Result of PLS Method 70 Chapter 5 Discussion 89 5.1 Managerial Implication 89 5.2 DNP and PLS Results Comparisons 92 Chapter 6 Conclusion 95 Appendix 97 References 137

    Abdi, H. (2007). Partial least squares regression. Encyclopedia of measurement and statistics, 2, 740-744.
    Agarwal, R. (2000). Individual acceptance of information technologies. Framing the domains of IT management: Projecting the future through the past, 85-104.
    Agarwal, R., & Karahanna, E. (1998). On the multi-dimensional nature of compatibility beliefs in technology acceptance. Paper presented at the Proceedings of the 19th annual international conference on information systems.
    Agarwal, R. (2000). Individual acceptance of information technologies. Framing the domains of IT management: Projecting the future through the past, 85-104.
    Agarwal, R., & Karahanna, E. (1998). On the multi-dimensional nature of compatibility beliefs in technology acceptance. Paper presented at the Proceedings of the 19th annual international conference on information systems.
    Ahangama, S., & Poo, D. C. C. (2016). Credibility of Algorithm Based Decentralized Computer Networks Governing Personal Finances: The Case of Cryptocurrency. Paper presented at the International Conference on HCI in Business, Government and Organizations.
    Albers, S. (2010). PLS and success factor studies in marketing. In Handbook of partial least squares (pp. 409-425): Springer.
    Ashraf, A. R., Thongpapanl, N., & Auh, S. (2014). The application of the technology acceptance model under different cultural contexts: The case of online shopping adoption. Journal of International Marketing, 22(3), 68-93.
    Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213-238.
    Bartlett, P. A., Julien, D. M., & Baines, T. S. (2007). Improving supply chain performance through improved visibility. The International Journal of Logistics Management, 18(2), 294-313.
    Beck, R., Czepluch, J. S., Lollike, N., & Malone, S. (2016). Blockchain-the Gateway to Trust-Free Cryptographic Transactions. Paper presented at the ECIS.
    Bengisu, M., & Nekhili, R. (2006). Forecasting emerging technologies with the aid of science and technology databases. Technological Forecasting and Social Change, 73(7), 835-844.
    Biswas, K., Muthukkumarasamy, V., & Tan, W. L. (2017). Blockchain Based Wine Supply Chain Traceability System. Paper presented at the Future Technologies Conference.
    Bontis, N. (1998). Intellectual capital: an exploratory study that develops measures and models. Management decision, 36(2), 63-76.
    Brown, S. A., Dennis, A. R., & Venkatesh, V. (2010). Predicting collaboration technology use: Integrating technology adoption and collaboration research. Journal of Management Information Systems, 27(2), 9-54.
    Bugembe, J. (2010). Perceived usefulness, perceived ease of use, attitude and actual usage of anew financial management system: A case of Uganda National Examinations Board. Makerere University,
    Byrne, B. M. (2013). Structural equation modeling with EQS: Basic concepts, applications, and programming: Routledge.
    Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
    Chin, W. W. (2010). How to write up and report PLS analyses. In Handbook of partial least squares (pp. 655-690): Springer.
    Christensen, J. L. (1997). Financing innovation. TSER Project Report: Innovation Systems and Europe, 3(3).
    Custer, R. L., Scarcella, J. A., & Stewart, B. R. (1999). The Modified Delphi Technique--A Rotational Modification. Journal of vocational and technical education, 15(2), 50-58.
    Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458-467.
    Davidson, S., De Filippi, P., & Potts, J. (2016). Economics of blockchain.
    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
    Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: three experiments. International Journal of Human-Computer Studies, 45(1), 19-45.
    Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: a predictive validity perspective. Journal of the academy of marketing science, 40(3), 434-449.
    Dijkstra, T. K., & Henseler, J. (2015a). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational statistics & data analysis, 81, 10-23.
    Dijkstra, T. K., & Henseler, J. (2015b). Consistent partial least squares path modeling. MIS quarterly, 39(2).
    Douceur, J. R. (2002). The sybil attack. Paper presented at the International workshop on peer-to-peer systems.
    Erdem, T., & Swait, J. (2004). Brand credibility, brand consideration, and choice. Journal of consumer research, 31(1), 191-198.
    Featherman, M., & Fuller, M. (2003). Applying TAM to e-services adoption: the moderating role of perceived risk. Paper presented at the System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on.
    Ferro, E., Loukis, E. N., Charalabidis, Y., & Osella, M. (2013). Policy making 2.0: From theory to practice. Government Information Quarterly, 30(4), 359-368.
    Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research.
    Folkinshteyn, D., & Lennon, M. (2016). Braving Bitcoin: A technology acceptance model (TAM) analysis. Journal of Information Technology Case and Application Research, 18(4), 220-249.
    Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. In: SAGE Publications Sage CA: Los Angeles, CA.
    Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega, 28(6), 725-737.
    Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 7.
    Gipp, B., Meuschke, N., & Gernandt, A. (2015). Decentralized trusted timestamping using the crypto currency bitcoin. arXiv preprint arXiv:1502.04015.
    Glaser, F. (2017). Pervasive decentralisation of digital infrastructures: a framework for blockchain enabled system and use case analysis.
    Glaser, F., & Bezzenberger, L. (2015). Beyond cryptocurrencies-a taxonomy of decentralized consensus systems.
    Grandori, A., & Soda, G. (1995). Inter-firm networks: antecedents, mechanisms and forms. Organization studies, 16(2), 183-214.
    Granovetter, M. (1978). Threshold models of collective behavior. American journal of sociology, 83(6), 1420-1443.
    Green, K., Armstrong, J., & Graefe, A. (2007). Method to Elicit Forecasts form Groups: Delphi and Prediction Markets compared. In.
    Guo, Y., & Liang, C. (2016). Blockchain application and outlook in the banking industry. Financial Innovation, 2(1), 24.
    Höök, K., & Löwgren, J. (2012). Strong concepts: Intermediate-level knowledge in interaction design research. ACM Transactions on Computer-Human Interaction (TOCHI), 19(3), 23.
    Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
    Hemmadi, M. (2015). FinTech is both friend and FOE. Can Bus, 88(6), 10-11.
    Henseler, J. (2015). Is the whole more than the sum of its parts. On the interplay of marketing and design research: Initial lecture: Universiteit Twente.
    Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., . . . Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational research methods, 17(2), 182-209.
    Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems, 116(1), 2-20.
    Henseler, J., Hubona, G., & Ray, P. A. (2017). Partial Least Squares Path Modeling: Updated Guidelines. In H. Latan & R. Noonan (Eds.), Partial least squares path modeling: Basic concepts, methodological issues and applications (pp. 19-39): Springer.
    Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (pp. 277-319): Emerald Group Publishing Limited.
    Huang, C.-Y., & Kao, Y.-S. (2015). UTAUT2 based predictions of factors influencing the technology acceptance of phablets by DNP. Mathematical Problems in Engineering, 2015.
    Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of Management Information Systems, 11(4), 87-114.
    Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language: Scientific Software International.
    Jarvenpaa, S. L., Tractinsky, N., & Saarinen, L. (1999). Consumer trust in an Internet store: A cross-cultural validation. Journal of Computer-Mediated Communication, 5(2), JCMC526.
    Jeppsson, A., & Olsson, O. (2017). Blockchains as a solution for traceability and transparency.
    Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis (Vol. 5): Prentice hall Upper Saddle River, NJ.
    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), 1-13.
    Kano, Y., & Nakajima, T. (2018). A novel approach to solve a mining work centralization problem in blockchain technologies. International Journal of Pervasive Computing and Communications, 14(1), 15-32.
    Kesharwani, A., & Singh Bisht, S. (2012). The impact of trust and perceived risk on internet banking adoption in India: An extension of technology acceptance model. International Journal of Bank Marketing, 30(4), 303-322.
    Ketterlinus, R. D., Bookstein, F. L., Sampson, P. D., & Lamb, M. E. (1989). Partial least squares analysis in developmental psychopathology. Development and Psychopathology, 1(4), 351-371.
    Kim, H.-Y. (2013). Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restorative dentistry & endodontics, 38(1), 52-54.
    Kshetri, N. (2018). 1 Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80-89.
    Lamarque, M. (2016). The blockchain revolution: new opportunities in equity markets. Massachusetts Institute of Technology,
    Lamming, R. C., Caldwell, N. D., Harrison, D. A., & Phillips, W. (2001). Transparency in supply relationships: concept and practice. Journal of Supply Chain Management, 37(3), 4-10.
    Landis, R. S., Beal, D. J., & Tesluk, P. E. (2000). A comparison of approaches to forming composite measures in structural equation models. Organizational research methods, 3(2), 186-207.
    Lee, C. C., & Ou-Yang, C. (2009). A neural networks approach for forecasting the supplier’s bid prices in supplier selection negotiation process. Expert systems with Applications, 36(2), 2961-2970.
    Liébana-Cabanillas, F., Marinković, V., & Kalinić, Z. (2017). A SEM-neural network approach for predicting antecedents of m-commerce acceptance. International Journal of Information Management, 37(2), 14-24.
    Lohmöller, J.-B. (2013). Latent variable path modeling with partial least squares: Springer Science & Business Media.
    Lu, Q., & Xu, X. (2017). Adaptable blockchain-based systems: a case study for product traceability. IEEE Software, 34(6), 21-27.
    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.
    Ludwig, B. G. (1994). Internationalizing Extension: An exploration of the characteristics evident in a state university Extension system that achieves internationalization. The Ohio State University,
    Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81-95.
    Maraun, M. D., & Halpin, P. F. (2008). Manifest and latent variates.
    Marcoulides, G. A., & Saunders, C. (2006). Editor's comments: PLS: a silver bullet? MIS quarterly, iii-ix.
    Markus, M. L. (1987). Toward a “critical mass” theory of interactive media: Universal access, interdependence and diffusion. Communication research, 14(5), 491-511.
    Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of management review, 20(3), 709-734.
    McDonald, R. (1999). Test theory: a unified treatment. Lawrence Earlbaum Associates. Inc., Mahwah, NJ, 142-145.
    Meesapawong, P., Rezgui, Y., & Li, H. (2014). Planning innovation orientation in public research and development organizations: using a combined Delphi and analytic hierarchy process approach. Technological Forecasting and Social Change, 87, 245-256.
    Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222.
    Murry Jr, J. W., & Hammons, J. O. (1995). Delphi: A versatile methodology for conducting qualitative research. The Review of Higher Education, 18(4), 423-436.
    Mutahar, A. M., Daud, N. M., Ramayah, T., Isaac, O., & Alrajawy, I. (2017). Integration of Innovation Diffusion Theory (IDT) and Technology Acceptance Model (TAM) to Understand Mobile Banking Acceptance in Yemen: The Moderating Effect of Income. International Journal of Soft Computing, 12(3), 164-177.
    Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
    Nelson, M., Sen, R., & Subramaniam, C. (2006). Understanding open source software: A research classification framework. Communications of the Association for Information Systems, 17(1), 12.
    Notheisen, B., Cholewa, J. B., & Shanmugam, A. P. (2017). Trading Real-World Assets on Blockchain. Business & Information Systems Engineering, 59(6), 425-440.
    Nunnally, J. (1978). Psychometric methods. In: New York: McGraw-Hill.
    Oh, J., & Yoon, S.-J. (2014). Validation of haptic enabling technology acceptance model (HE-TAM): Integration of IDT and TAM. Telematics and Informatics, 31(4), 585-596.
    Putzer, G. J., & Park, Y. (2012). Are physicians likely to adopt emerging mobile technologies? Attitudes and innovation factors affecting smartphone use in the Southeastern United States. Perspectives in health information management/AHIMA, American Health Information Management Association, 9(Spring).
    Rahi, S., Ghani, M. A., & Alnaser, F. M. (2017). The influence of e-customer services and perceived value on brand loyalty of banks and internet banking adoption: a structural equation model (SEM). The Journal of Internet Banking and Commerce, 22(1), 1-18.
    Reyna, A., Martín, C., Chen, J., Soler, E., & Díaz, M. (2018). On blockchain and its integration with IoT. Challenges and opportunities. Future Generation Computer Systems.
    Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long range planning, 45(5-6), 341-358.
    Rigdon, E. E. (2014). Rethinking partial least squares path modeling: breaking chains and forging ahead. Long range planning, 47(3), 161-167.
    Rindskopf, D. (1984). Using phantom and imaginary latent variables to parameterize constraints in linear structural models. Psychometrika, 49(1), 37-47.
    Risius, M., & Spohrer, K. (2017). A blockchain research framework. Business & Information Systems Engineering, 59(6), 385-409.
    Rogers, E. M. (1995). Lessons for guidelines from the diffusion of innovations. Joint Commission Journal on Quality and Patient Safety, 21(7), 324-328.
    Rossouw, A., Hacker, M., & de Vries, M. J. (2011). Concepts and contexts in engineering and technology education: An international and interdisciplinary Delphi study. International Journal of Technology and Design Education, 21(4), 409-424.
    Saaty, T., & Vargas, L. (2006). Decision making with the analytic network process: Economic, Political, Social and Technological Applications with Benefits, Opportunities, Costs and Risks. New York, NY, U.S.A.: Springer.
    Sarstedt, M., Ringle, C. M., Henseler, J., & Hair, J. F. (2014). On the emancipation of PLS-SEM: A commentary on Rigdon (2012). Long range planning, 47(3), 154-160.
    Scott, D. G., Washer, B. A., & Wright, M. D. (2006). A Delphi Study to Identify Recommended Biotechnology Competencies for First-Year/Initially Certified Technology Education Teachers. Journal of Technology Education, 17(2), 43-55.
    Shiau, S., Huang, C.-Y., Yang, C.-L., & Juang, J.-N. (2018). A derivation of factors influencing the innovation diffusion of the OpenStreetMap in STEM education. Sustainability, 10(10), 3447.
    Småros, J., Lehtonen, J.-M., Appelqvist, P., & Holmström, J. (2003). The impact of increasing demand visibility on production and inventory control efficiency. International journal of physical distribution & logistics management, 33(4), 336-354.
    Swink, M., & Schoenherr, T. (2015). The effects of cross‐functional integration on profitability, process efficiency, and asset productivity. Journal of Business Logistics, 36(1), 69-87.
    Tenenhaus, M. (2008). Component-based structural equation modelling. Total quality management, 19(7-8), 871-886.
    Tzeng, G.-H., & Huang, C.-Y. (2012). Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing & logistics systems. Annals of Operations Research, 197(1), 159-190. doi:10.1007/s10479-010-0829-4
    Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS quarterly, 115-139.
    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.
    Wörner, D., Von Bomhard, T., Schreier, Y.-P., & Bilgeri, D. (2016). The Bitcoin ecosystem: disruption beyond financial services?
    Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206-215.
    Wang, Y.-S., Wang, Y.-M., Lin, H.-H., & Tang, T.-I. (2003). Determinants of user acceptance of Internet banking: an empirical study. International journal of service industry management, 14(5), 501-519.
    Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): a literature review. Journal of Enterprise Information Management, 28(3), 443-488.
    Winkler, J., Kuklinski, C. P. J.-W., & Moser, R. (2015). Decision making in emerging markets: The Delphi approach's contribution to coping with uncertainty and equivocality. Journal of Business Research, 68(5), 1118-1126.
    Wold, H. (1982). Soft modeling: The basic design and some extensions, systems under indirect observations. Causality. Structure. Prediction. Ed. KG Joreskog. H. Wold. Amsterdam: North Holland Publishing Company.
    Wu, J., Wang, G., & Shyy, W. (2005). Time‐dependent turbulent cavitating flow computations with interfacial transport and filter‐based models. International Journal for Numerical Methods in Fluids, 49(7), 739-761.
    Xu, X., Pautasso, C., Zhu, L., Gramoli, V., Ponomarev, A., Tran, A. B., & Chen, S. (2016). The blockchain as a software connector. Paper presented at the 2016 13th Working IEEE/IFIP Conference on Software Architecture (WICSA).
    Yang, C.-L., Huang, C.-Y., Kao, Y.-S., & Tasi, Y.-L. (2017). Disaster Recovery Site Evaluations and Selections for Information Systems of Academic Big Data. Eurasia Journal of Mathematics, Science and Technology Education, 13(8), 4553-4589.
    Yang, C.-L., Yuan, B. J., & Huang, C.-Y. (2015). Key determinant derivations for information technology disaster recovery site selection by the multi-criterion decision making method. Sustainability, 7(5), 6149-6188.
    Zavolokina, L., Dolata, M., & Schwabe, G. (2016). FinTech–What's in a Name?
    Zhang, N., Guo, X., & Chen, G. (2008). IDT-TAM integrated model for IT adoption. Tsinghua Science and Technology, 13(3), 306-311.
    Zhao, J. L., Fan, S., & Yan, J. (2016). Overview of business innovations and research opportunities in blockchain and introduction to the special issue. In: Springer.
    Zou, J., Ye, B., Qu, L., Wang, Y., Orgun, M. A., & Li, L. (2018). A Proof-of-Trust Consensus Protocol for Enhancing Accountability in Crowdsourcing Services. IEEE Transactions on Services Computing.

    無法下載圖示 本全文未授權公開
    QR CODE