運用層級分析程序法探討企業抉擇視覺化報表工具之關鍵因子

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姓名

徐仁風(Jen-Feng Hsu) 查詢紙本館藏

畢業系所

會計研究所企業資源規劃會計碩士在職專班

論文名稱

運用層級分析程序法探討企業抉擇視覺化報表工具之關鍵因子
(Utilizing Analytic Hierarchy Process (AHP) to Identify Critical Factors in the Selection of Enterprise Visualization Reporting Tools)

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摘要(中)

為了跟上客戶端需求急遽變化，許多企業早已著手導入商業智慧與視覺化工具等資訊系統去實踐各部門績效管理與業務成長變化，由於資料源可能來自各個不同的海內外分公司或資料散落於不同資料庫，此時，商業智慧和視覺化資料工具的運用更加凸顯其在企業中的重要性與價值。
因此本研究運用分析層級程序法(AHP)的理論方法建構出5個指標群(成本因素、有用性、易用性、資料風險管理、供應商服務品質與名聲以及20個項目指標(維護成本、員工訓練成本、開發報表成本、升級成本、系統建置成本、應用多樣性、工作效率、決策支援、儀表板設計、報表與使用者互動性、報表分享、資料源相容性、隱私管理、安全管理、授權管理、問題回覆速度、服務專業度、保證期、軟體市占率、廠商名聲)，去探討與分析企業在抉擇購買資料視覺化報表工具所考量指標的權重比，再透過9位業界數據領域專家的問卷調查收集與分析產生之結論，研究結果發現，企業在導入視覺化報表工具考量的指標群權重由大至小為: 資料風險管理(0.275)>易用性(0.270)>有用性(0.177)>成本因素(0.170)>供應商服務品質與名聲(0.107)之權重，其中以項目指標的資料安全管理占整體權重最高(0.125)由此得知企業成長下使內部資料量劇烈成長，同時也突顯了資料風險管理在企業中的重要性，而供應商服務品質與名聲並非主要關鍵的考量因素。
最後本研究在三位專家在公司有軟體採購權與使用過Tableau以及Power BI這兩套視覺化工具報表之數據專家比較其層級分析法產生之結果與實際抉擇是否一致，以驗證本研究之可用性，結果顯示三位專家的層級分析法結果與實際選擇方案皆相符，因此本研究之層級分析法評量模式可適用於企業採購與遴選視覺化資料報表工具方案之參考。

摘要(英)

To keep up with rapidly changing client dem&s, many enterprises have already begun implementing business intelligence & visualization tools, as well as other information systems, to manage performance across various departments & track business growth. Given that data sources might come from different domestic & international branches or be scattered across various databases, the use of business intelligence & data visualization tools becomes even more crucial in demonstrating their importance & value within the enterprise.

Therefore, this study employs the Analytic Hierarchy Process (AHP) to construct five indicator groups (cost factors, usefulness, ease of use, data risk management, & vendor service quality & reputation) & twenty sub-indicators (maintenance cost, employee training cost, report development cost, upgrade cost, system setup cost, departmental use cases, work efficiency, decision support, dashboard design, report & user interaction, report sharing, data source compatibility, privacy management, security management, authorization management, problem response speed, service professionalism, warranty period, software market share, vendor reputation). This framework aims to explore & analyze the weight of each indicator that enterprises consider when choosing to purchase data visualization tools. A survey was conducted with nine industry experts in the data field to gather & analyze their responses.

The study found that the weight of the indicator groups, from highest to lowest, is as follows: data risk management (0.275) > ease of use (0.270) > usefulness (0.177) > cost factors (0.170) > vendor service quality & reputation (0.107). Among the sub-indicators, data security management holds the highest overall weight (0.125), indicating that as enterprises grow & internal data volumes increase rapidly, data risk management becomes significantly important. Conversely, vendor service quality & reputation are not primary considerations.

In this study, we compared the results generated by the Analytic Hierarchy Process (AHP) with the actual decisions made by three experts who have the authority to procure software in their company and have experience using both Tableau and Power BI visualization tools. The aim was to validate the usability of our research model. The results showed that the AHP outcomes for all five experts aligned with their actual choices. Thus, our AHP evaluation model is applicable as a reference for enterprises in selecting and procuring data visualization reporting tools.

關鍵字(中)

★ 商業智慧
★ 視覺化資料報表工具
★ 分析層級程序法

關鍵字(英)

★ Business Intelligence
★ Visualization Data Reporting Tools
★ Analytic Hierarchy Process

論文目次

ABSTRACT
誌謝
目錄
圖目錄 List of Figures
表目錄 List of Tables
一、緒論 1
1-1 研究背景與動機 1
1-2 研究目的 2
1-3 研究流程 2
二、文獻探討 4
2-1 成本因素 4
2-2 有用性 5
2-3 易用性 6
2-4 資料風險管理 7
2-5 供應商服務品質與名聲 8
2-6 資料視覺化 12
三、研究架構、方法與設計 15
3-1 研究方法 15
四、研究結果與權重分析 23
4-1 研究結果 23
4-2 結果分析 28
五、結論與論述 36
5-1 結論 36
5-2 後續之研究建議 38
參考文獻 40
中文部分 40
英文部分 40

參考文獻

中文部分
5-5-1. 黃文德 (2006)，應用軟體銷售模式探討 ─ 以企業客戶為例.
5-5-2. 褚志鵬 (2009)，Analytic Hierarchy Process Theory 層級分析法(AHP)理論與實作.
5-5-3. 鄧振鴻、曾國雄 (1989)，中國統計學報「分析層級程序法（AHP）的內涵特性與應用（上）」.
5-5-4. 莊曉琪 (2008)，企業採用商業智慧系統考慮因素之研究.

英文部分
1. Al-Aqrabi, et al. (2013). Business Intelligence Security on the Clouds: Challenges, Solutions & Future Directions.
2. Albert L, et al. (1990). Information System Cost Estimating: A Management Perspective. 159-176.
3. Aparicio, Manuela, & Carlos J. Costa. (2014). Data visualization. Communication Design Quarterly Review.
4. Aparicio, Manuela, & Carlos J. Costa. (2014). Data Visualization.
5. Ardagna & Damiani. (2014). Business Intelligence meets Big Data: An Overview on Security & Privacy.
6. Arroyo & Pozzebon. (2010). Implementing a Three-Level Balanced Scorecard System at Chilquinta Energia.
7. Avraam et al. (2021). Privacy preserving data visualizations.
8. Baranwal & Vidyarthi. (2014). A framework for selection of best cloud service provider using ranked voting method.
9. Brown, Carol V., & Iris Vessey & Anne Powell. (2000). The ERP Purchase Decision: Influential Business & IT Factors. 1029-1032.
10. Cavaliere, Roberto. (2015). How to choose the right statistical software?—a method increasing the post-purchase satisfaction.
11. Daradkeh, M. (2017). A preliminary study of user acceptance & adoption of data visualization tools for decision support in business organisations.
12. Dimara, E., & Perin, C. (2019). What is Interaction for Data Visualization?
13. Ellram, Lisa M., & George A. Zsidisin. (2002). Factors That Drive Purchasing & Supply Management’s Use of Information Technology.
14. Friedman & Bitterer. (2011). Magic Quadrant for Data Quality Tools.
15. Friendly, Michael. (2017). A Brief History of Data Visualization.
16. Gefen, David. (2000). It Is Not Enough to Be Responsive: The Role of Cooperative Intentions in MRP II Adoption.
17. Han, X. Lin, & Yee-Yin Choong, & Gavriel Salvendy. (2010). A proposed index of usability: a method for comparing the relative usability of different software systems. 267-277.
18. Harb & Alhayajneh. (2019). Intention to use BI tools: Integrating technology acceptance model (TAM) & personality trait model.
19. Higginbotham, Nick, & Luke Nash, & Will Demeré. (2021). Making audits more effective through data visualization. Journal of Accountancy.
20. Horvath et al. (2009). Sharing Adverse Drug Event Data Using Business Intelligence Technology.
21. Islam & Jin. (2019). An Overview of Data Visualization.
22. Munoz, J. Mark. (2017). Global Business Intelligence. Routledge.
23. Olszak, M., & Ziemba. (2012). Critical Success Factors for Implementing Business Intelligence Systems in Small & Medium Enterprises on the Example of Upper Silesia, Pol&.
24. Olmsted. (2021). Secure Business Intelligence.
25. Reddy, C. S., Sangam, R. S., & Srinivasa Rao, B. (2018). A Survey on Business Intelligence Tools for Marketing, Financial, & Transportation Services.
26. Sadiku, Matthew N. O., Adebowale E. Shadare, Sarhan M. Musa, & Cajetan M. Akujuobi. (2016). Data Visualization.
27. Sahay & Ranjan. (2008). Business Intelligence Implementation Critical Success Factors.
28. Saaty. (1977, 1980, 1986). The Analytic Hierarchy Process (AHP).
29. Simon, A. (2014). Surveying Relevant Enterprise Data Management Technologies. Enterprise Business Intelligence and Data Management, 45–61.
30. Szwajlik, A. (2023). IT software purchase decisions in terms of business customer experience. Human Technology, 19(2), 207–219.
31. Syed Mohd Ali, Noopur Gupta, Gopal Krishna Nayak, & Rakesh Kumar Lenka. (2016). Big Data Visualization: Tools and Challenges.
32. Tervakari et al. (2014). Usefulness of Information Visualizations Based on Educational Data.
33. Treude, C., & Storey, M. (2010). Awareness 2.0: Staying aware of projects, developers & tasks using dashboards & feeds.
34. Zare, A. (2002). The Business Value of Business Intelligence & Analytics.
35. Zare, A. (2014). An Investigation of BI Implementation Critical Success Factors in Iranian Context.

網站部分
5-6-1. Indeed. (2023). Why are after-sales services important? https://www.indeed.com/career-advice/career-development/after-sales-service
5-6-2. Brother Australia. (2018). Why after-sales support should be part of your buying decision. https://www.brother.com.au/en/blog/why-after-sales-support-should-be-part-of-your-buying-decision
5-6-3. Cisco. (2018). Cisco Hardware & Software Warranty Information.
https://www.cisco.com/c/en/us/products/warranties/warranty-doc-c99-740957.html#_Toc520320030.
5-6-4. Simplilearn. (2024). 23 Best Data Visualization Tools for 2024.
https://www.simplilearn.com/data-visualization-tools-article
5-6-5. Forbes. (2024). The Best Data Visualization Tools Of 2024
https://www.forbes.com/advisor/business/software/best-data-visualization-tools/

指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2024-7-19

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