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

NCUIR > School of Management at National Central University > enterprise resource planning of Optics and Photonics > Electronic Thesis & Dissertation > Item 987654321/94851

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/94851

Title:	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
Authors:	徐仁風;Hsu, Jen-Feng
Contributors:	會計研究所企業資源規劃會計碩士在職專班
Keywords:	商業智慧;視覺化資料報表工具;分析層級程序法;Business Intelligence;Visualization Data Reporting Tools;Analytic Hierarchy Process
Date:	2024-07-19
Issue Date:	2024-10-09 15:33:54 (UTC+8)
Publisher:	國立中央大學
Abstract:	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。 ;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.
Appears in Collections:	[enterprise resource planning of Optics and Photonics] Electronic Thesis & Dissertation

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