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題名 運用AI預測業務人員之非財務及財務績效：以P公司為例
Applying AI to Predict Sales Persons’ Non-Financial and Financial Performance: A Case Study of P Company作者 劉睿
Liu, Rui貢獻者 吳安妮
Wu, An-Ne
劉睿
Liu, Rui關鍵詞 績效評估
人工智慧
財務績效
非財務績效
機器學習
決策樹
資料包絡分析
Performance Evaluation
Artificial Intelligence
Financial Performance
Non-Financial Performance
Machine Learning
Decision Tree
Data Envelopment Analysis日期 2021 上傳時間 3-Jan-2022 16:04:27 (UTC+8) 摘要 本研究運用人工智慧預測業務人員之非財務與財務績效。現如今，愈來愈多的研究機構將人工智慧應用於會計領域，然管理會計領域之人工智慧研究卻少之又少。茲以人工智慧在管理會計領域的研究尚處於萌芽期，吾人該如何將人工智慧應用於本領域尚不清楚。本研究通過將人工智慧與管理會計相結合，展示了管理會計研究該如何開始運用這一有效工具。本研究採用個案研究法，設計了一套基於機器學習之決策樹與資料包絡分析的人工智慧系統。由此產生的系統可以利用非財務績效指標來預測業務人員的財務績效，評估業務人員月度非財務與財務目標的完成度，分析標竿員工的行為資料。管理者可以利用這些結果來瞭解內部人員的績效表現，挖掘潛在人才，並藉此幫助員工提高對自身績效的認識，從而優化整體業務效率。
This study applies artificial intelligence (AI) to predict the non-financial and financial performance of salespeople. Although an increasingly robust body of research employs AI in accounting, the research on artificial intelligence in the field of management accounting is less developed. Due to the nascent state of AI research in management accounting, it is unclear how AI should be applied in the field. By combining management accounting with artificial intelligence, the following paper demonstrates how management accounting research can begin incorporating this efficacious tool.Using the case study method, this research designs an AI system based on machine learning decision tree and data envelopment analysis. The resultant system can use non-financial performance indicators to predict the financial performance salespeople, evaluate the degree to which salespeople completed non-financial and financial monthly goals, and analyze the behavioral data of high-performing staff members. Managers can use these results to understand the performance of internal personnel, identify potential talents, and promote the awareness of employees regarding their own performance, thus optimizing overall business efficiency.參考文獻 一、中文部分出版圖書吳安妮，2019，企業策略的終極答案：用「作業價值管理AVM」破除成本迷思，掌握正確因果資訊，做對決策賺到「管理財」。台北市：臉譜出版。張紹勳，2000，研究方法。台中市：滄海書局。翻譯作品塚本邦尊、山田典一、大澤文孝，2019，東京大学のデータサイエンティ スト育成講座 Pythonで手を動かして学ぶデータ分析，中譯名：東京大學資料科學家養成全書：使用Python動手學習資料分析。莊永裕譯（2020）。台北市：臉譜出版。Provost, F., and T. Fawcett. 2013. Data Science for Business: What you need to know about data mining and data-analytic thinking，中譯名：資料科學的商業運用。陳亦苓譯（2016）。台北市：碁峰資訊股份有限公司。Raschka, S., and V. Mirjalili. 2017. Python Machine Learning: Machine Learning and Deep Learning with Python. Scikit-Learn, and TensorFlow (2nd ed.)，中譯名：Python機器學習。劉立民、吳建華譯（2020）。新北市：博碩文化股份有限公司。期刊論文田耕銘、洪嘉馨、黃政仁、張深閔，2019，普祺樂實業有限公司-創造價值的契機。管理評論，第38卷第1期，頁45-46。吳安妮，2019，進入全自動化及AI預測之作業價值管理（AVM）。人文與社會科學簡訊，第20卷第3期，頁89-92。楊春、鄧紅，2005，基於DEA模型的企業員工績效考評研究。價值工程，第6期，頁96-98。網際網路張彥文，2018，APP+AVM，翻轉大客戶不是好客戶的魔咒。哈佛商業評論（全球繁體中文版），7月號。https://www.hbrtaiwan.com/article_content_AR0008078.html公文報告中華民國經濟部，2015，行政院生產力4.0發展方案：生產力4.0-商業服務業。行政院第3476次院會報告。二、英文部分出版圖書Asimov, I. 1942. I, robot.Kaplan, R. S., and A. A. Atkinson. 1998. Advanced Management Accounting (3rd ed.). Hoboken, USA: 3 Prentice-Hall.Korstanje, J. 2021. Advanced Forecasting with Python. Maisons Alfort, France: Apress.期刊論文Abdel-Maksoud, A., F. Cerbioni, F. Ricceri, and S. Velayutham. 2010. Employee morale, non-financial performance measures, deployment of innovative managerial practices and shop-floor involvement in Italian manufacturing firms. The British Accounting Review 42 (March): 36-55.Banker, R. D., A. Charnes, and W. W. Cooper. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science 30 (9): 1078-1092.Banker, R. D., G. Potter, and D. Srinivasan. 2000. An empirical investigation of an incentive plan that includes nonfinancial performance measures. The accounting review 75 (1): 65-92.Charbuty, B., and A. Abdulazeez. 2021. Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends 2 (01): 20-28.Charnes, A., and W. W. Cooper. 1984. The non-Archimedean CCR ratio for efficiency analysis: a rejoinder to Boyd and Fare. European Journal of Operational Research 15 (3): 333-334.Charnes, A., W. W. Cooper, and B. Golany, L. Seiford, and J. Stutz. 1985. Foundations of DEA for Pareto-Koopmans efficiency empirical production functions. Journal of Econometrics 30 (1): 91-107.Charnes, A., W. W. Cooper, and E. Rhodes. 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2 (6): 429-444.Charnes, A., W. W. Cooper, and E. Rhodes. 1979. Short communication: measuring the efficiency of decision making units. European Journal of Operational Research 3: 339.Chow, C. W., and W. A. Van Der Stede. 2006. The use and usefulness of nonfinancial performance measures. Management accounting quarterly 7 (3): 1-8.Donthu, N., and B. Yoo. 1998. Retail productivity assessment using data envelopment analysis. Journal of retailing 74 (1): 89-105.Eisenhardt, K. M. 1989. Building theories from case study research. Academy of management review 14 (4): 532-550.Hoque, Z., L. Mia, and M. Alam. 2001. Market competition, computer-aided manufacturing and use of multiple performance measures: an empirical study. British Accounting Review 33 (1): 23-45.Janaki, M., and M. M. J. Clifford. 2021. A study on the scope of artificial intelligence in accounting. Dogo Rangsang Research Journal 11 (5): 1-8.Kao, C. 2009. Efficiency decomposition in network data envelopment analysis: a relational model. European journal of operational research 192 (3): 949-962.Lau, C. M., and M. Sholihin. 2005. Financial and nonfinancial performance measures: how do they affect job satisfaction?. The British Accounting Review 37 (4): 389-413.Le Guyader, L. P. 2020. Artificial intelligence in accounting: GAAP`s “FAS133”. Journal of Corporate Accounting & Finance 31 (3): 185-189.Leitner-Hanetseder, S., O. M. Lehner, C. Eisl, and C. Forstenlechner. 2021. A profession in transition: actors, tasks and roles in AI-based accounting. Journal of Applied Accounting Research 22 (3): 539-556.Manoharan, T. R., C. Muralidharan, and S. G. Deshmukh. 2009. Employee performance appraisal using data envelopment analysis: a case study. Research and Practice in Human Resource Management 17 (1): 92-111.Prentice, C., S. Dominique Lopes, and X. Wang. 2019. Emotional intelligence or artificial intelligence-an employee perspective. Journal of Hospitality Marketing & Management 29 (4): 377-403.Prentice, C., S. Weaven, and I. A. Wong. 2020. Linking AI quality performance and customer engagement: the moderating effect of AI preference. International Journal of Hospitality Management 90: 102629.Said, A. A., H. R. HassabElnaby, and B. Wier. 2003. An empirical investigation of the performance consequences of nonfinancial measures. Journal of management accounting research 15 (1): 193-223.Scapens, R. W. 1990. Researching management accounting practice: the role of case study methods. The British Accounting Review 22 (3): 259-281.Shearer, C. 2000. The CRISP-DM model: the new blueprint for data mining. Journal of data warehousing 5 (4): 13-22.Sutton, S. G., M. Holt, and V. Arnold. 2016. “The reports of my death are greatly exaggerated”- artificial intelligence research in accounting. International Journal of Accounting Information Systems 22: 60-73.Yin, R. K. 1994. Discovering the future of the case study. Method in evaluation research. Evaluation practice 15 (3): 283-290.雜誌報刊Eccles, R. G. 1991. The performance measurement manifesto. Harvard business review 69 (1): 131-137.Kaplan, R. S., D. P. Norton. (1992, January-February). The balanced scorecard; measures that drive performance. Harvard Business Review, 71-79.Kaplan, R. S., D. P. Norton. (1996, January-February). Using the balanced scorecard as a strategic management systems. Harvard Business Review , 75-85.網際網路Brynjolfsson, E., and A. Mcafee. (2017, July 18). How AI Fits into Your Science Team. Harvard Business Review.https://hbr.org/cover-story/2017/07/the-business-of-artificial-intelligenceButler, S. (1863, June 13). Darwin among the machines. Te Herenga Waka-Victoria University of Wellington.http://nzetc.victoria.ac.nz/tm/scholarly/tei-ButFir-t1-g1-t1-g1-t4-body.html 描述 碩士
國立政治大學
會計學系
108353046資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108353046 資料類型 thesis dc.contributor.advisor 吳安妮 zh_TW dc.contributor.advisor Wu, An-Ne en_US dc.contributor.author (Authors) 劉睿 zh_TW dc.contributor.author (Authors) Liu, Rui en_US dc.creator (作者) 劉睿 zh_TW dc.creator (作者) Liu, Rui en_US dc.date (日期) 2021 en_US dc.date.accessioned 3-Jan-2022 16:04:27 (UTC+8) - dc.date.available 3-Jan-2022 16:04:27 (UTC+8) - dc.date.issued (上傳時間) 3-Jan-2022 16:04:27 (UTC+8) - dc.identifier (Other Identifiers) G0108353046 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/138370 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 會計學系 zh_TW dc.description (描述) 108353046 zh_TW dc.description.abstract (摘要) 本研究運用人工智慧預測業務人員之非財務與財務績效。現如今，愈來愈多的研究機構將人工智慧應用於會計領域，然管理會計領域之人工智慧研究卻少之又少。茲以人工智慧在管理會計領域的研究尚處於萌芽期，吾人該如何將人工智慧應用於本領域尚不清楚。本研究通過將人工智慧與管理會計相結合，展示了管理會計研究該如何開始運用這一有效工具。本研究採用個案研究法，設計了一套基於機器學習之決策樹與資料包絡分析的人工智慧系統。由此產生的系統可以利用非財務績效指標來預測業務人員的財務績效，評估業務人員月度非財務與財務目標的完成度，分析標竿員工的行為資料。管理者可以利用這些結果來瞭解內部人員的績效表現，挖掘潛在人才，並藉此幫助員工提高對自身績效的認識，從而優化整體業務效率。 zh_TW dc.description.abstract (摘要) This study applies artificial intelligence (AI) to predict the non-financial and financial performance of salespeople. Although an increasingly robust body of research employs AI in accounting, the research on artificial intelligence in the field of management accounting is less developed. Due to the nascent state of AI research in management accounting, it is unclear how AI should be applied in the field. By combining management accounting with artificial intelligence, the following paper demonstrates how management accounting research can begin incorporating this efficacious tool.Using the case study method, this research designs an AI system based on machine learning decision tree and data envelopment analysis. The resultant system can use non-financial performance indicators to predict the financial performance salespeople, evaluate the degree to which salespeople completed non-financial and financial monthly goals, and analyze the behavioral data of high-performing staff members. Managers can use these results to understand the performance of internal personnel, identify potential talents, and promote the awareness of employees regarding their own performance, thus optimizing overall business efficiency. en_US dc.description.tableofcontents 第壹章 緒論 1第一節 研究動機及目的 1第二節 研究問題 3第三節 研究架構 5第貳章 文獻探討 7第一節 非財務性績效指標之相關文獻 7第二節 AI技術與其在會計領域應用之相關文獻 15第三節 決策樹模型之相關文獻 24第四節 DEA分析法之相關文獻 35第五節 本研究之研究延伸內容 41第參章 研究方法 46第一節 個案研究法 46第二節 研究流程 47第肆章 個案公司介紹 49第一節 產業介紹 49第二節 個案公司介紹 51第伍章 運用AI預測業務人員之非財務與財務績效 54第一節 資料前處理及各項詳細說明 54第二節 利用AI技術對非財務與財務績效進行預測 59第三節 利用AI技術分析員工當期非財務與財務績效表現 75第四節 AI系統之預測準確度分析 88第五節 利用AI技術分析員工及其行為之表現 96第六節 決策樹判定結果與DEA分析結果之比較 107第七節 AI技術與員工績效評估結合後解決之管理問題 111第陸章 研究結論與展望 114第一節 研究結論 114第二節 研究限制 116第三節 研究建議 118第柒章 參考文獻 120 zh_TW dc.format.extent 5278847 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108353046 en_US dc.subject (關鍵詞) 績效評估 zh_TW dc.subject (關鍵詞) 人工智慧 zh_TW dc.subject (關鍵詞) 財務績效 zh_TW dc.subject (關鍵詞) 非財務績效 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 決策樹 zh_TW dc.subject (關鍵詞) 資料包絡分析 zh_TW dc.subject (關鍵詞) Performance Evaluation en_US dc.subject (關鍵詞) Artificial Intelligence en_US dc.subject (關鍵詞) Financial Performance en_US dc.subject (關鍵詞) Non-Financial Performance en_US dc.subject (關鍵詞) Machine Learning en_US dc.subject (關鍵詞) Decision Tree en_US dc.subject (關鍵詞) Data Envelopment Analysis en_US dc.title (題名) 運用AI預測業務人員之非財務及財務績效：以P公司為例 zh_TW dc.title (題名) Applying AI to Predict Sales Persons’ Non-Financial and Financial Performance: A Case Study of P Company en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 一、中文部分出版圖書吳安妮，2019，企業策略的終極答案：用「作業價值管理AVM」破除成本迷思，掌握正確因果資訊，做對決策賺到「管理財」。台北市：臉譜出版。張紹勳，2000，研究方法。台中市：滄海書局。翻譯作品塚本邦尊、山田典一、大澤文孝，2019，東京大学のデータサイエンティ スト育成講座 Pythonで手を動かして学ぶデータ分析，中譯名：東京大學資料科學家養成全書：使用Python動手學習資料分析。莊永裕譯（2020）。台北市：臉譜出版。Provost, F., and T. Fawcett. 2013. Data Science for Business: What you need to know about data mining and data-analytic thinking，中譯名：資料科學的商業運用。陳亦苓譯（2016）。台北市：碁峰資訊股份有限公司。Raschka, S., and V. Mirjalili. 2017. Python Machine Learning: Machine Learning and Deep Learning with Python. Scikit-Learn, and TensorFlow (2nd ed.)，中譯名：Python機器學習。劉立民、吳建華譯（2020）。新北市：博碩文化股份有限公司。期刊論文田耕銘、洪嘉馨、黃政仁、張深閔，2019，普祺樂實業有限公司-創造價值的契機。管理評論，第38卷第1期，頁45-46。吳安妮，2019，進入全自動化及AI預測之作業價值管理（AVM）。人文與社會科學簡訊，第20卷第3期，頁89-92。楊春、鄧紅，2005，基於DEA模型的企業員工績效考評研究。價值工程，第6期，頁96-98。網際網路張彥文，2018，APP+AVM，翻轉大客戶不是好客戶的魔咒。哈佛商業評論（全球繁體中文版），7月號。https://www.hbrtaiwan.com/article_content_AR0008078.html公文報告中華民國經濟部，2015，行政院生產力4.0發展方案：生產力4.0-商業服務業。行政院第3476次院會報告。二、英文部分出版圖書Asimov, I. 1942. I, robot.Kaplan, R. S., and A. A. Atkinson. 1998. Advanced Management Accounting (3rd ed.). Hoboken, USA: 3 Prentice-Hall.Korstanje, J. 2021. Advanced Forecasting with Python. Maisons Alfort, France: Apress.期刊論文Abdel-Maksoud, A., F. Cerbioni, F. Ricceri, and S. Velayutham. 2010. Employee morale, non-financial performance measures, deployment of innovative managerial practices and shop-floor involvement in Italian manufacturing firms. The British Accounting Review 42 (March): 36-55.Banker, R. D., A. Charnes, and W. W. Cooper. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science 30 (9): 1078-1092.Banker, R. D., G. Potter, and D. Srinivasan. 2000. An empirical investigation of an incentive plan that includes nonfinancial performance measures. The accounting review 75 (1): 65-92.Charbuty, B., and A. Abdulazeez. 2021. Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends 2 (01): 20-28.Charnes, A., and W. W. Cooper. 1984. The non-Archimedean CCR ratio for efficiency analysis: a rejoinder to Boyd and Fare. European Journal of Operational Research 15 (3): 333-334.Charnes, A., W. W. Cooper, and B. Golany, L. Seiford, and J. Stutz. 1985. Foundations of DEA for Pareto-Koopmans efficiency empirical production functions. Journal of Econometrics 30 (1): 91-107.Charnes, A., W. W. Cooper, and E. Rhodes. 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2 (6): 429-444.Charnes, A., W. W. Cooper, and E. Rhodes. 1979. Short communication: measuring the efficiency of decision making units. European Journal of Operational Research 3: 339.Chow, C. W., and W. A. Van Der Stede. 2006. The use and usefulness of nonfinancial performance measures. Management accounting quarterly 7 (3): 1-8.Donthu, N., and B. Yoo. 1998. Retail productivity assessment using data envelopment analysis. Journal of retailing 74 (1): 89-105.Eisenhardt, K. M. 1989. Building theories from case study research. Academy of management review 14 (4): 532-550.Hoque, Z., L. Mia, and M. Alam. 2001. Market competition, computer-aided manufacturing and use of multiple performance measures: an empirical study. British Accounting Review 33 (1): 23-45.Janaki, M., and M. M. J. Clifford. 2021. A study on the scope of artificial intelligence in accounting. Dogo Rangsang Research Journal 11 (5): 1-8.Kao, C. 2009. Efficiency decomposition in network data envelopment analysis: a relational model. European journal of operational research 192 (3): 949-962.Lau, C. M., and M. Sholihin. 2005. Financial and nonfinancial performance measures: how do they affect job satisfaction?. The British Accounting Review 37 (4): 389-413.Le Guyader, L. P. 2020. Artificial intelligence in accounting: GAAP`s “FAS133”. Journal of Corporate Accounting & Finance 31 (3): 185-189.Leitner-Hanetseder, S., O. M. Lehner, C. Eisl, and C. Forstenlechner. 2021. A profession in transition: actors, tasks and roles in AI-based accounting. Journal of Applied Accounting Research 22 (3): 539-556.Manoharan, T. R., C. Muralidharan, and S. G. Deshmukh. 2009. Employee performance appraisal using data envelopment analysis: a case study. Research and Practice in Human Resource Management 17 (1): 92-111.Prentice, C., S. Dominique Lopes, and X. Wang. 2019. Emotional intelligence or artificial intelligence-an employee perspective. Journal of Hospitality Marketing & Management 29 (4): 377-403.Prentice, C., S. Weaven, and I. A. Wong. 2020. Linking AI quality performance and customer engagement: the moderating effect of AI preference. International Journal of Hospitality Management 90: 102629.Said, A. A., H. R. HassabElnaby, and B. Wier. 2003. An empirical investigation of the performance consequences of nonfinancial measures. Journal of management accounting research 15 (1): 193-223.Scapens, R. W. 1990. Researching management accounting practice: the role of case study methods. The British Accounting Review 22 (3): 259-281.Shearer, C. 2000. The CRISP-DM model: the new blueprint for data mining. Journal of data warehousing 5 (4): 13-22.Sutton, S. G., M. Holt, and V. Arnold. 2016. “The reports of my death are greatly exaggerated”- artificial intelligence research in accounting. International Journal of Accounting Information Systems 22: 60-73.Yin, R. K. 1994. Discovering the future of the case study. Method in evaluation research. Evaluation practice 15 (3): 283-290.雜誌報刊Eccles, R. G. 1991. The performance measurement manifesto. Harvard business review 69 (1): 131-137.Kaplan, R. S., D. P. Norton. (1992, January-February). The balanced scorecard; measures that drive performance. Harvard Business Review, 71-79.Kaplan, R. S., D. P. Norton. (1996, January-February). Using the balanced scorecard as a strategic management systems. Harvard Business Review , 75-85.網際網路Brynjolfsson, E., and A. Mcafee. (2017, July 18). How AI Fits into Your Science Team. Harvard Business Review.https://hbr.org/cover-story/2017/07/the-business-of-artificial-intelligenceButler, S. (1863, June 13). Darwin among the machines. Te Herenga Waka-Victoria University of Wellington.http://nzetc.victoria.ac.nz/tm/scholarly/tei-ButFir-t1-g1-t1-g1-t4-body.html zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202101739 en_US
