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題名 從大數據創造價值 : 金融產業的多個案研究
Generating Value from Big Data: A Multiple Case Study in Financial Industry
作者 蔡佑晟
Tsai, Yu-Chen
貢獻者 尚孝純
Shang, Shari S. C.
蔡佑晟
Tsai, Yu-Chen
關鍵詞 大數據
商業價值
數據導向決策
Big data
Business value
Data-driven decision-making
日期 2018
上傳時間 27-Jul-2018 11:37:40 (UTC+8)
摘要 隨著社群和分析技術的快速發展，大數據已經成為許多產業中的熱門議題。眾多知名跨國公司都從大數據應用中獲得了巨大的價值，如: Google、Walmart、和Amazon等。然而除了這些具有代表性的成功例子之外，在大數據上進行大量的投入並不一定能帶來實質的收益，商業決策者們仍然對大數據科技的回報持懷疑態度。

本研究對數據導向決策的案例和大數據應用進行了系統化的文獻回顧，並歸納出大數據可能創造的效益，以及一些可能影響大數據創造價值的相關關鍵因素。之後，此研究將與金融公司的高階資訊主管進行深度的訪談與了解。最後，本研究透過對金融產業的跨個案橫向分析，期望能為大數據的應用提供相關的發現與見解。
With rapid advances in social and analytics technology, Big Data has become a popular subject in many industries. Numerous well-known multinational companies, such as Google, Walmart, and Amazon, reported deriving enormous value from Big Data applications. However, except for these emblematic examples, there is no promise that large investments in Big Data can result in material benefits. Business decision-makers remain doubtful as to returns from Big Data technologies.

Performing a systematic literature review of data-driven decision-making cases and Big Data applications, this study identified several possible benefits that may be generated from Big Data, and identified several Big Data-related key factors that may affect value creation. Then, this study conducted in-depth interviews with senior IT managers from selected financial companies. Finally, by a cross-sectional analysis of financial industry, this study intends to provide insights into Big Data implementation.
參考文獻 Aloysius, J. A., Hoehle, H., Goodarzi, S., & Venkatesh, V. (2016). Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes. Annals of operations research, 1-27.
Barr, M. S., Koziara, B., Flood, M. D., Hero, A., & Jagadish, H. V. (2018). Big Data in Finance: Highlights from the Big Data in Finance Conference Hosted at the University of Michigan October 27-28, 2016.
Baxter, P., & Jack, S. (2008). Qualitative case study methodology: Study design and implementation for novice researchers. The qualitative report, 13(4), 544-559.
Beath, C., Becerra-Fernandez, I., Ross, J., & Short, J. (2012). Finding value in the information explosion. MIT Sloan Management Review, 53(4), 18.
Bertino, E., & Ferrari, E. (2018). Big Data Security and Privacy. In A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years (pp. 425-439). Springer, Cham.
Black, B. L., Cowens-Alvarado, R., Gershman, S., & Weir, H. K. (2005). Using data to motivate action: the need for high quality, an effective presentation, and an action context for decision-making. Cancer Causes and Control, 16, 15-25.
Blazquez, D., & Domenech, J. (2017). Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change.
Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in numbers: How does data-driven decisionmaking affect firm performance?.
Bughin, J. (2016). Big data, Big bang?. Journal of Big Data, 3(1), 2.
Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey quarterly, 56(1), 75-86.
Cai, L., & Zhu, Y. (2015). The challenges of data quality and data quality assessment in the big data era. Data Science Journal, 14.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 1165-1188.
Chen, H. M., Kazman, R., Haziyev, S., & Hrytsay, O. (2015, May). Big data system development: An embedded case study with a global outsourcing firm. In Proceedings of the First International Workshop on BIG Data Software Engineering (pp. 44-50). IEEE Press.
Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L. (2017). Disease prediction by machine learning over big data from healthcare communities. IEEE Access, 5, 8869-8879.
Das, N., Das, L., Rautaray, S. S., & Pandey, M. (2018). Big Data Analytics for Medical Applications.
Davenport, T. H., & Dyché, J. (2013). Big data in big companies. International Institute for Analytics, 3.
Ding, G., Wu, Q., Wang, J., & Yao, Y. D. (2014). Big spectrum data: The new resource for cognitive wireless networking. arXiv preprint arXiv:1404.6508.
Emani, C. K., Cullot, N., & Nicolle, C. (2015). Understandable big data: a survey. Computer science review, 17, 70-81.
Evans, L., & Kitchin, R. (2018). A smart place to work? Big data systems, labour, control and modern retail stores. New Technology, Work and Employment, 33(1), 44-57.
Fang, B., & Zhang, P. (2016). Big data in finance. In Big Data Concepts, Theories, and Applications (pp. 391-412). Springer, Cham.
Fouad, M. M., Oweis, N. E., Gaber, T., Ahmed, M., & Snasel, V. (2015). Data mining and fusion techniques for WSNs as a source of the big data. Procedia Computer Science, 65, 778-786.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
Gregor, S., Martin, M., Fernandez, W., Stern, S., & Vitale, M. (2006). The transformational dimension in the realization of business value from information technology. The Journal of Strategic Information Systems, 15(3), 249-270.
Grover, V., Chiang, R. H., Liang, T. P., & Zhang, D. (2018). Creating Strategic Business Value from Big Data Analytics: A Research Framework. Journal of Management Information Systems, 35(2), 388-423.
How big data analytics yields big gains. (2017, Jul 12). Electronics for You, Retrieved from https://search.proquest.com/docview/1918006741?accountid=10067
Ikemoto, G. S., & Marsh, J. A. (2007). chapter 5 Cutting Through the “Data‐Driven” Mantra: Different Conceptions of Data‐Driven Decision Making. Yearbook of the National Society for the Study of Education, 106(1), 105-131.
Intel IT center (2012). Peer Research: Big Data Analytics. Intel’s IT Manager Survey on How Organizations Are Using Big Data.
Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. (2014). Big data and its technical challenges. Communications of the ACM, 57(7), 86-94.
Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338-345.
Kabir, N., & Carayannis, E. (2013, January). Big data, tacit knowledge and organizational competitiveness. In Proceedings of the 10th International Conference on Intellectual Capital, Knowledge Management and Organisational Learning: ICICKM (p. 220).
Katal, A., Wazid, M., & Goudar, R. H. (2013, August). Big data: issues, challenges, tools and good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference on (pp. 404-409). IEEE.
Kitchin, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3(1), 2053951716631130.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT sloan management review, 52(2), 21.
Li, S., & Gao, J. (2016). Security and Privacy for Big Data. In Big Data Concepts, Theories, and Applications (pp. 281-313). Springer, Cham.
Lohr, S. (2012). The age of big data. New York Times, 11(2012).
Madden, S. (2012). From databases to big data. IEEE Internet Computing, 16(3), 4-6.
Mandinach, E.B., Honey, M., & Light, D. (2006). A theoretical framework for data-driven decision making. Paper presented at the annual meeting of the American Educational Research Association, San Francisco.
Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education.
McAfee, A., Brynjolfsson, E., & Davenport, T. H. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68.
Mikalef, P., Framnes, V., Danielsen, F., Krogstie, J., & Olsen, D. H. (2017). Big data analytics capability: antecedents and business value. In Proceedings of the 21st Pacific Asia conference on information systems (PACIS).
Mirani, R., & Lederer, A. L. (1998). An instrument for assessing the organizational benefits of IS projects. Decision Sciences, 29(4), 803-838.
Neff, G. (2013). Why big data won`t cure us. Big data, 1(3), 117-123.
Ong, K. L., De Silva, D., Boo, Y. L., Lim, E. H., Bodi, F., Alahakoon, D., & Leao, S. (2016). Big data applications in engineering and science. In Big Data Concepts, Theories, and Applications (pp. 315-351). Springer, Cham.
Prabhakar, S., & Maves, L. (2017). Big Data Analytics and Visualization: Finance. In Big Data and Visual Analytics (pp. 219-229). Springer, Cham.
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59.
Ren, S. J., Fosso Wamba, S., Akter, S., Dubey, R., & Childe, S. J. (2017). Modelling quality dynamics, business value and firm performance in a big data analytics environment. International Journal of Production Research, 55(17), 5011-5026.
Rindler, A., McLowry, S., & Hillard, R. (2013). Big Data Definition. MIKE2. 0, the open source methodology for Information Development.
Russom, P. (2014). TDWI Best Practices Report: Big Data Analytics (Best Practices)(pp. 1–35). The Data Warehouse Institute (TDWI). Retrieved from on, 15.
Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano, P. (2012). Analytics: the real-world use of big data: How innovative enterprises extract value from uncertain data, Executive Report. IBM Institute for Business Value and Said Business School at the University of Oxford.
Shah, S., Horne, A., & Capellá, J. (2012). Good data won`t guarantee good decisions. Harvard Business Review, 90(4).
Shim, J. P., French, A. M., Guo, C., & Jablonski, J. (2015). Big Data and Analytics: Issues, Solutions, and ROI. CAIS, 37, 39.
Simon, P. (2013). Too big to ignore: The business case for big data (Vol. 72). John Wiley & Sons.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286.
Srivastava, R. (2018). Big Data Retail Analysis and Product Distribution (BREAD) Model for Sales Prediction. Indian Journal of Computer Science, 3(1), 7-16.
Swan, M. (2013). The quantified self: Fundamental disruption in big data science and biological discovery. Big Data, 1(2), 85-99.
Tankard, C. (2012). Big data security. Network security, 2012(7), 5-8.
Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.
Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017). A manufacturing big data solution for active preventive maintenance. IEEE Transactions on Industrial Informatics, 13(4), 2039-2047.
Xu, L., & Shi, W. (2016). Security Theories and Practices for Big Data. In Big Data Concepts, Theories, and Applications (pp. 157-192). Springer, Cham.
Zhang, Y., Ren, S., Liu, Y., & Si, S. (2017). A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. Journal of Cleaner Production, 142, 626-641.
Zhong, R. Y., Newman, S. T., Huang, G. Q., & Lan, S. (2016). Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers & Industrial Engineering, 101, 572-591.
描述 碩士
國立政治大學
資訊管理學系
105356002
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105356002
資料類型 thesis
dc.contributor.advisor 尚孝純zh_TW
dc.contributor.advisor Shang, Shari S. C.en_US
dc.contributor.author (Authors) 蔡佑晟zh_TW
dc.contributor.author (Authors) Tsai, Yu-Chenen_US
dc.creator (作者) 蔡佑晟zh_TW
dc.creator (作者) Tsai, Yu-Chenen_US
dc.date (日期) 2018en_US
dc.date.accessioned 27-Jul-2018 11:37:40 (UTC+8)-
dc.date.available 27-Jul-2018 11:37:40 (UTC+8)-
dc.date.issued (上傳時間) 27-Jul-2018 11:37:40 (UTC+8)-
dc.identifier (Other Identifiers) G0105356002en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/118937-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 105356002zh_TW
dc.description.abstract (摘要) 隨著社群和分析技術的快速發展，大數據已經成為許多產業中的熱門議題。眾多知名跨國公司都從大數據應用中獲得了巨大的價值，如: Google、Walmart、和Amazon等。然而除了這些具有代表性的成功例子之外，在大數據上進行大量的投入並不一定能帶來實質的收益，商業決策者們仍然對大數據科技的回報持懷疑態度。

本研究對數據導向決策的案例和大數據應用進行了系統化的文獻回顧，並歸納出大數據可能創造的效益，以及一些可能影響大數據創造價值的相關關鍵因素。之後，此研究將與金融公司的高階資訊主管進行深度的訪談與了解。最後，本研究透過對金融產業的跨個案橫向分析，期望能為大數據的應用提供相關的發現與見解。		zh_TW
dc.description.abstract (摘要) With rapid advances in social and analytics technology, Big Data has become a popular subject in many industries. Numerous well-known multinational companies, such as Google, Walmart, and Amazon, reported deriving enormous value from Big Data applications. However, except for these emblematic examples, there is no promise that large investments in Big Data can result in material benefits. Business decision-makers remain doubtful as to returns from Big Data technologies.

Performing a systematic literature review of data-driven decision-making cases and Big Data applications, this study identified several possible benefits that may be generated from Big Data, and identified several Big Data-related key factors that may affect value creation. Then, this study conducted in-depth interviews with senior IT managers from selected financial companies. Finally, by a cross-sectional analysis of financial industry, this study intends to provide insights into Big Data implementation.		en_US
dc.description.tableofcontents 謝誌 I
英文摘要 II
中文摘要 III
目次 IV
圖目錄 VI
表目錄 VI
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機與研究目的 5
第二章 文獻回顧 7
第一節 測量大數據可能帶來的好處 7
第二節 影響大數據的關鍵要素 10
第三章 研究方法 18
第一節 研究設計與流程 18
第二節 資料收集 19
第四章 研究結果 23
第一節 個案研究 23
第二節 跨個案分析-大數據帶來的好處 29
第三節 跨個案分析-使大數據能創造商業價值的關鍵要素 34
第五章 結論 41
第一節 結果摘要 41
第二節 研究貢獻 41
第三節 研究限制與未來研究 42
參考文獻 43
附錄A: 半結構化問卷 48		zh_TW
dc.format.extent 1139376 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105356002en_US
dc.subject (關鍵詞) 大數據zh_TW
dc.subject (關鍵詞) 商業價值zh_TW
dc.subject (關鍵詞) 數據導向決策zh_TW
dc.subject (關鍵詞) Big dataen_US
dc.subject (關鍵詞) Business valueen_US
dc.subject (關鍵詞) Data-driven decision-makingen_US
dc.title (題名) 從大數據創造價值 : 金融產業的多個案研究zh_TW
dc.title (題名) Generating Value from Big Data: A Multiple Case Study in Financial Industryen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Aloysius, J. A., Hoehle, H., Goodarzi, S., & Venkatesh, V. (2016). Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes. Annals of operations research, 1-27.
Barr, M. S., Koziara, B., Flood, M. D., Hero, A., & Jagadish, H. V. (2018). Big Data in Finance: Highlights from the Big Data in Finance Conference Hosted at the University of Michigan October 27-28, 2016.
Baxter, P., & Jack, S. (2008). Qualitative case study methodology: Study design and implementation for novice researchers. The qualitative report, 13(4), 544-559.
Beath, C., Becerra-Fernandez, I., Ross, J., & Short, J. (2012). Finding value in the information explosion. MIT Sloan Management Review, 53(4), 18.
Bertino, E., & Ferrari, E. (2018). Big Data Security and Privacy. In A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years (pp. 425-439). Springer, Cham.
Black, B. L., Cowens-Alvarado, R., Gershman, S., & Weir, H. K. (2005). Using data to motivate action: the need for high quality, an effective presentation, and an action context for decision-making. Cancer Causes and Control, 16, 15-25.
Blazquez, D., & Domenech, J. (2017). Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change.
Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in numbers: How does data-driven decisionmaking affect firm performance?.
Bughin, J. (2016). Big data, Big bang?. Journal of Big Data, 3(1), 2.
Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey quarterly, 56(1), 75-86.
Cai, L., & Zhu, Y. (2015). The challenges of data quality and data quality assessment in the big data era. Data Science Journal, 14.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 1165-1188.
Chen, H. M., Kazman, R., Haziyev, S., & Hrytsay, O. (2015, May). Big data system development: An embedded case study with a global outsourcing firm. In Proceedings of the First International Workshop on BIG Data Software Engineering (pp. 44-50). IEEE Press.
Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L. (2017). Disease prediction by machine learning over big data from healthcare communities. IEEE Access, 5, 8869-8879.
Das, N., Das, L., Rautaray, S. S., & Pandey, M. (2018). Big Data Analytics for Medical Applications.
Davenport, T. H., & Dyché, J. (2013). Big data in big companies. International Institute for Analytics, 3.
Ding, G., Wu, Q., Wang, J., & Yao, Y. D. (2014). Big spectrum data: The new resource for cognitive wireless networking. arXiv preprint arXiv:1404.6508.
Emani, C. K., Cullot, N., & Nicolle, C. (2015). Understandable big data: a survey. Computer science review, 17, 70-81.
Evans, L., & Kitchin, R. (2018). A smart place to work? Big data systems, labour, control and modern retail stores. New Technology, Work and Employment, 33(1), 44-57.
Fang, B., & Zhang, P. (2016). Big data in finance. In Big Data Concepts, Theories, and Applications (pp. 391-412). Springer, Cham.
Fouad, M. M., Oweis, N. E., Gaber, T., Ahmed, M., & Snasel, V. (2015). Data mining and fusion techniques for WSNs as a source of the big data. Procedia Computer Science, 65, 778-786.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
Gregor, S., Martin, M., Fernandez, W., Stern, S., & Vitale, M. (2006). The transformational dimension in the realization of business value from information technology. The Journal of Strategic Information Systems, 15(3), 249-270.
Grover, V., Chiang, R. H., Liang, T. P., & Zhang, D. (2018). Creating Strategic Business Value from Big Data Analytics: A Research Framework. Journal of Management Information Systems, 35(2), 388-423.
How big data analytics yields big gains. (2017, Jul 12). Electronics for You, Retrieved from https://search.proquest.com/docview/1918006741?accountid=10067
Ikemoto, G. S., & Marsh, J. A. (2007). chapter 5 Cutting Through the “Data‐Driven” Mantra: Different Conceptions of Data‐Driven Decision Making. Yearbook of the National Society for the Study of Education, 106(1), 105-131.
Intel IT center (2012). Peer Research: Big Data Analytics. Intel’s IT Manager Survey on How Organizations Are Using Big Data.
Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. (2014). Big data and its technical challenges. Communications of the ACM, 57(7), 86-94.
Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338-345.
Kabir, N., & Carayannis, E. (2013, January). Big data, tacit knowledge and organizational competitiveness. In Proceedings of the 10th International Conference on Intellectual Capital, Knowledge Management and Organisational Learning: ICICKM (p. 220).
Katal, A., Wazid, M., & Goudar, R. H. (2013, August). Big data: issues, challenges, tools and good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference on (pp. 404-409). IEEE.
Kitchin, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3(1), 2053951716631130.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT sloan management review, 52(2), 21.
Li, S., & Gao, J. (2016). Security and Privacy for Big Data. In Big Data Concepts, Theories, and Applications (pp. 281-313). Springer, Cham.
Lohr, S. (2012). The age of big data. New York Times, 11(2012).
Madden, S. (2012). From databases to big data. IEEE Internet Computing, 16(3), 4-6.
Mandinach, E.B., Honey, M., & Light, D. (2006). A theoretical framework for data-driven decision making. Paper presented at the annual meeting of the American Educational Research Association, San Francisco.
Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education.
McAfee, A., Brynjolfsson, E., & Davenport, T. H. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68.
Mikalef, P., Framnes, V., Danielsen, F., Krogstie, J., & Olsen, D. H. (2017). Big data analytics capability: antecedents and business value. In Proceedings of the 21st Pacific Asia conference on information systems (PACIS).
Mirani, R., & Lederer, A. L. (1998). An instrument for assessing the organizational benefits of IS projects. Decision Sciences, 29(4), 803-838.
Neff, G. (2013). Why big data won`t cure us. Big data, 1(3), 117-123.
Ong, K. L., De Silva, D., Boo, Y. L., Lim, E. H., Bodi, F., Alahakoon, D., & Leao, S. (2016). Big data applications in engineering and science. In Big Data Concepts, Theories, and Applications (pp. 315-351). Springer, Cham.
Prabhakar, S., & Maves, L. (2017). Big Data Analytics and Visualization: Finance. In Big Data and Visual Analytics (pp. 219-229). Springer, Cham.
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59.
Ren, S. J., Fosso Wamba, S., Akter, S., Dubey, R., & Childe, S. J. (2017). Modelling quality dynamics, business value and firm performance in a big data analytics environment. International Journal of Production Research, 55(17), 5011-5026.
Rindler, A., McLowry, S., & Hillard, R. (2013). Big Data Definition. MIKE2. 0, the open source methodology for Information Development.
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dc.identifier.doi (DOI) 10.6814/THE.NCCU.MIS.006.2018.A05-