隨著近年來數據對於企業組織的重要性驟增，商業分析成為各產業目前投資的核心，對其投入的成本逐年增加，主要的技術包含利用資料倉儲（Data warehouse）、報告、資料的視覺化、線上分析處理（OLAP）、預測模型和統計分析等分析企業內外部的結構化及非結構化的資料，讓組織創新並保持競爭力。台灣企業目前也跟隨這波潮流，逐漸開發商業分析的相關技術，但根據調查發現台灣組織運用商業分析的能力仍在探索的階段，在導入時，整個企業組織中勢必有相當多的因素會影響到數位轉型的優劣。先前研究文獻較沒有針對影響商業分析的因素及商業分析的技術流程進行衡量，因此本研究以Leavitt的鑽石模式為基礎，並參考商業分析相關之文獻後建立一個框架，目的在於探討影響商業分析能力的因素，包括組織面、科技面、資料處理流程面與人力面，並以描述性、預測性和規範性分析作為衡量商業分析能力的變數，最後產生資訊品質和組織敏捷性的衡量。研究方法採用線上問卷和實體問卷方法進行調查，以台灣的企業為研究對象，並用統計分析軟體對蒐集的資料分析來驗證假說。本研究顯示人力、科技和資料處理流程有顯著正向影響，組織則無顯著影響，此模型可以作為台灣企業開始數位化轉型或改善商業分析流程的依據。

With the sudden increase in the importance of data to business organizations in recent years, business analysis has become the core of current investment in various industries, and the cost of investing in it has increased year by year. Taiwanese companies are also following this trend and gradually developing related technologies for business analysis. However, according to the survey, it is found that the ability of Taiwan organizations to use business analysis is still at the stage of exploration. During the introduction, there are bound to be a lot of factors in the entire enterprise organization. The advantages and disadvantages of digital transformation. The previous research literature did not measure the factors affecting business analysis and the technical part of business analysis. Therefore, this study is based on Leavitt's diamond model and refers to the literature related to business analysis to establish a framework to explore the impact of business analysis capabilities. Factors, including organization, technology, data processing flow, and manpower, and use descriptive, predictive, and normative analysis as variables to measure business analysis capabilities, and ultimately produce a measure of information quality and organizational agility. The research method used online questionnaires and physical questionnaires to conduct surveys, using Taiwanese companies as the research object, and using statistical analysis software to analyze the collected data to verify the hypothesis. This study shows that People, Technology, and Data Management have a significant positive impact, but Organizations have no significant impact. This model can be used as a basis for Taiwanese companies to start digital transformation or improve business analysis.

論文審定書 i
摘要 ii
Abstract iii
List of Figures vi
List of Tables vi
Chapter 1 Introduction 1
1.1 Research background 1
1.2 Research Motivation and Research Purposes 3
1.3 Research Process 5
Chapter 2 Literature Review 7
2.1 Business Analytics (BA) 7
2.2 Business Analytics Capability 9
2.3 Affecting Factors of Business Analytics 11
Chapter 3 Research Model and Hypotheses 18
3.1 Research Model 18
3.2 BA capability 19
3.3 Organization(Organizational Maturity) 23
3.4 Technology(Technology Maturity) 25
3.5 Data (Data Management Maturity) 27
3.6 People（Personnel Maturity） 29
3.7 Information Quality 31
3.8 Firm Agility 32
3.9 Operational Definition 34
3.10 Research Design 37
3.10.1 Questionnaire Design 38
3.10.2 Data Collection 38
Chapter 4 Data Analytics 40
4.1 Demographic Statistics 40
4.2 Measurement Validation 42
4.2.1 Common Method Bias 42
4.2.2 Reliability and Validity 45
4.3 Hypothesis Testing 56
4.3.1 Affecting Factors of Business Analytics 58
4.3.2 Value after business analytics 59
4.4 Discussions 60
4.4.1 Affecting Factors of Business Analytics 61
4.4.2 Value after business analytics 64
Chapter 5 Conclusion and Implication 65
5.1 Academic Implication 65
5.2 Practical Implication 66
5.3 Limitation 68
5.4 Future Directions 68
Chapter 6 Reference 70
Appendix A - Questionnaire 75

Agarwal, R. (2002). How Should Firms Organize Their IT Function? University of Minnesota MIS Quarterly Executive, 1(1), 158–162. Retrieved from
Ashrafi, A., Zare Ravasan, A., Trkman, P., & Afshari, S. (2019). The role of business analytics capabilities in bolstering firms’ agility and performance. International Journal of Information Management, 47(July 2018), 1–15.
Cepa, K., & Schildt, H. (2019). Technological embeddedness of inter-organizational collaboration processes. Research in the Sociology of Organizations, Vol. 64, pp. 91–115.
Cosic, R., Shanks, G., & Maynard, S. (2015). A business analytics capability framework. Australasian Journal of Information Systems, 19, S5–S19.
Davenport, T. H., & Harris, J. G. (2017). Competing on Analytics, Updated, with a New Introduction: The New Science of Winning. September 19, p. 320. Retrieved from
Dove, R. (2002). Response Ability: The Language, Structure, and Culture of the Agile Enterprise (Google eBook) (Vol. 1, p. 368). Vol. 1, p. 368. Retrieved from
Espinosa, J. A., & Armour, F. (2016). The big data analytics gold rush: A research framework for coordination and governance. Proceedings of the Annual Hawaii International Conference on System Sciences, 2016-March, 1112–1121.
Fink, L., Yogev, N., & Even, A. (2017). Business intelligence and organizational learning: An empirical investigation of value creation processes. Information and Management, 54(1), 38–56.
Fornell, C., & Larcker, D. F. (1981). SEM with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research, Vol. 18, pp. 382–388.
Frazzetto, D., Nielsen, T. D., Pedersen, T. B., & Šikšnys, L. (2019). Prescriptive analytics: a survey of emerging trends and technologies. VLDB Journal.
Ganguly, A., Nilchiani, R., & Farr, J. V. (2009). Evaluating agility in corporate enterprises. International Journal of Production Economics, 118(2), 410–423.
Gartner, & Hagerty, J. (2016). 2017 Planning Guide for Data Management and Analytics. Gartner, (October 2015), 1–27. Retrieved from
Information, M. (2011). Q rMIS ON INFORMATION AND FIRM PERFORMANCE : AN. Management Information Systems, 24(1), 169–196.
Information, M. (2013). Qiarterjy Specifying Formative Constructs Information Systems Research1. 31(4), 623–656.
Kamioka, T., Luo, X., & Tapanainen, T. (2016). Association for Information Systems AIS Electronic Library (AISeL) AN EMPIRICAL INVESTIGATION OF DATA GOVERNANCE: THE ROLE OF ACCOUNTABILITIES. Retrieved from
Kitchens, B., Dobolyi, D., Li, J., & Abbasi, A. (2018). Advanced Customer Analytics: Strategic Value Through Integration of Relationship-Oriented Big Data. Journal of Management Information Systems, 35(2), 540–574.
Krumeich, J., Werth, D., & Loos, P. (2016). Prescriptive Control of Business Processes: New Potentials Through Predictive Analytics of Big Data in the Process Manufacturing Industry. Business and Information Systems Engineering, 58(4), 261–280.
Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), 387–394.
Laursen, G. H. N., & Thorlund, J. (2010). Business Analytics for Managers: Taking Business Intelligence Beyond Reporting. Depth Report, p. 273. Retrieved from
Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50(April 2019), 57–70.
Li, S., & Lin, B. (2006). Accessing information sharing and information quality in supply chain management. Decision Support Systems, 42(3), 1641–1656.
Lismont, J., Vanthienen, J., Baesens, B., & Lemahieu, W. (2017). Defining analytics maturity indicators: A survey approach. International Journal of Information Management, 37(3), 114–124.
Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. Journal of Strategic Information Systems, 24(3), 149–157.
Lu, Y., & Ramamurthy, K. (2011). Understanding the link between information technology capability and organizational agility: An empirical examination. MIS Quarterly: Management Information Systems, 35(4), 931–954.
McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, Vol. 90, p. 4.
Nam, D., Lee, J., & Lee, H. (2019). Business analytics use in CRM: A nomological net from IT competence to CRM performance. International Journal of Information Management, 45(96), 233–245.
Nasir, M. A., & Morgan, J. (2017). Article information :
Negash, S. (2017). Business Intelligence в экономике. Концепт, 13(2).
Nerur, S., Mahapatra, R., & Mangalaraj, G. (2005). Challenges of migrating to agile methodologies. Communications of the ACM, 48(5), 72–78.
O’Neill, M., & Brabazon, A. (2019). Business analytics capability, organisational value and competitive advantage. Journal of Business Analytics, 00(00), 1–14.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. Journal of Applied Psychology, 88(5), 879–903.
Popovič, A., Hackney, R., Coelho, P. S., & Jaklič, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54(1), 729–739.
Radovilsky, Z., Hegde, V., Acharya, A., & Uma, U. (2018). Skills {Requirements} of {Business} {Data} {Analytics} and {Data} {Science} {Jobs}: {A} {Comparative} {Analytics}. Journal of Supply Chain and Operations Management, 16(1), 82.
Rouhani, S., Ashrafi, A., Zare Ravasan, A., & Afshari, S. (2016). The impact model of business intelligence on decision support and organizational benefits. Journal of Enterprise Information Management, 29(1), 19–50. https://doi.org/10.1108/JEIM-12-2014-0126
Scott, R. W. (1992). The Subject is Organizations. Organizations: Rational, Natural, and Open Systems, (January 1998), 3–26.
Shen, C. C., Chang, R. E., Hsu, C. J., & Chang, I. C. (2017a). How business intelligence maturity enabling hospital agility. Telematics and Informatics, 34(1), 450–456.
Shen, C. C., Chang, R. E., Hsu, C. J., & Chang, I. C. (2017b). How business intelligence maturity enabling hospital agility. Telematics and Informatics, 34(1), 450–456. https://doi.org/10.1016/j.tele.2016.06.009
Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly: Management Information Systems, 35(3), 553–572.
Someh, I., Wixom, B., Songhori, M. J., & Shanks, G. (2018). Emergence of data and non-data team networks: An agent-based model. International Conference on Information Systems 2018, ICIS 2018, (December).
Streiner, D. L., & Norman, G. R. (2008). Health Measurement Scales: A practical guide to their development and use. Health Measurement Scales: A Practical Guide to Their Development and Use, pp. 1–452.
Sun, S., Cegielski, C. G., Jia, L., & Hall, D. J. (2018). Understanding the Factors Affecting the Organizational Adoption of Big Data. Journal of Computer Information Systems, Vol. 58, pp. 193–203. https://doi.org/10.1080/08874417.2016.1222891
Swafford, P. M., Ghosh, S., & Murthy, N. (2008). Achieving supply chain agility through IT integration and flexibility. International Journal of Production Economics, 116(2), 288–297.
Teece, D. J., Peteratd, M., & Leih, S. (2016). Dynamic Capabilities and Organizational Agility. California Management Review, 58(4), 13–35.
Viaene, S., & Van Den Bunder, A. (2011). The secrets to managing business analytics projects. MIT Sloan Management Review, Vol. 53, pp. 65–69.
Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626–639.
Wade, M., & Hulland, J. (2004). Review: The resource-based view and information systems research: Review, extension, and suggestions for future research. MIS Quarterly: Management Information Systems, 28(1), 107–142.
Whitelock, V. (2018). Business analytics and firm performance: role of structured financial statement data. Journal of Business Analytics, 1(2), 81–92.