有別於傳統以職務為基礎的薪酬給付（job-based pay），越來越多薪酬專家認為職能（competency）更能代表員工在職務上的勝任能力。但職能間的關係複雜，導致職能為基礎的薪酬給付缺乏較正式而有系統的評價過程，並存在各企業部門間標準不一的問題。本研究目的為發展以職能為基礎的薪酬給付大數據分析架構，整合逐步迴歸分析（Stepwise Regression）、隨機森林（Random Forests）（Breiman, 2001）與貝氏網路（Bayesian Network）（Friedman et al., 1997）方法，消除求職者在背景資訊（如：學歷、年資、產業及公司規模等）的薪資影響，進行資料平準化，並發掘各職能在不同職務之下，求職者獲得相對高薪的機率大小，進而推估各項職能的加值性；本研究以台灣某指標性人力銀行網站為實證對象，從網站中收集之履歷資料與求職行為中萃取有意義的知識與樣型，並訂定效度檢驗指標以驗證研究結果之有效性。研究結果可提供求職者了解各職務在勞動市場上重視的職能為何，並針對個人現況與能力，快速進行生涯規劃及擬定職能提昇策略；對於政府單位與教育訓練相關機構，可以作為籌備課程的決策支援依據；學校單位亦可根據分析結果了解職場需求，調整授課內容與新增系所事宜；企業更能藉由求職者具備的職能，找到適才適所的人才，進而提昇各方對職能認知的共通性。

Compensation experts advocate that competency-based pay can represent employee's job capabilities better than job-based pay. However, competencies exist complex relationships among each other. This leads to that competency-based pay have no formal and systematic evaluation process, and there is no common standard among different corporate departments. The study aims to develop a big data analytics framework for competency-based pay. The framework integrates stepwise regression, Random Forests, and Bayesian Network method to construct a competency analytics model. The model can explore that how competencies influence probabilities of employee gain high pay level and further give job seekers specific advice for competencies enhancing. The study cooperates with a Taiwanese indicative Job Bank Web site for empirical research. To validate the result, this framework extracts latent knowledge and patterns from huge data about job seekers and sets the validation index. The results assist various types of job seekers to understand what competencies labor market need actually. In the meantime, the enterprise can recruit suitable candidates based on the results. On the other hand, this study also provides the decision-making reference for government agencies to organize education and training course and finally enhances the commonality of all parties.

表目錄 v
圖目錄 vi
符號定義 1
第一章 緒論 2
1.1 研究背景、動機與重要性 2
1.2 研究目的 3
1.3 論文結構 4
第二章 文獻回顧 6
2.1 勞動經濟 6
2.2 資料挖礦工具 6
2.2.1 隨機森林（Random Forests） 7
2.2.2 貝氏網路（Bayesian Network） 9
2.3 資料挖礦於人力資源管理的應用 12
2.4 職能為基礎的薪酬給付（competency-based pay） 13
第三章 研究架構 16
3.1 問題定義 18
3.2 資料準備 18
3.2.1 資料收集與檢視 18
3.2.2 資料轉換 19
3.2.3 資料清理 20
3.2.4 消除背景資訊 20
3.2.5 重要變數篩選與排序 21
3.2.6 資料分割 23
3.3 模型建構 23
3.3.1 職能分析模型 23
3.3.2 模型效度檢驗 23
3.4 結果評估與解釋 24
第四章 實證研究 26
符號設定 27
4.1 問題定義 28
4.2 資料準備 29
4.2.1 資料收集與檢視 29
4.2.2 資料轉換 34
4.2.3 資料清理 35
4.2.4 消除背景資訊 36
4.2.5 重要變數篩選與排序 37
4.2.6 資料分割 39
4.3 模型建構 39
4.3.1 職能分析模型 39
4.3.2 效度檢驗 43
4.4 結果評估與解釋 43
4.5 情境應用 46
4.5.1 案例一：品管品保類職務 47
4.5.2 案例二：幼教從業人員 50
4.6 綜合討論 54
4.6.1 以實做型（Realistic）為主的職務 55
4.6.2 以研究型（Investigative）為主的職務 56
4.6.3 以藝術型（Artistic）為主的職務 57
4.6.4 以社交型（Social）為主的職務 58
4.6.5 以企業型（Enterprising）為主的職務 59
4.6.6 以常規型（Conventional）為主的職務 60
4.6.7 實務應用 63
第五章 結論與後續研究方向 65
5.1 研究貢獻 65
5.2 研究限制與未來研究方向 66
參考文獻 68
附錄 72

丁志達（2013），薪酬管理，揚志文化，台北。
全球華人股份有限公司（2016），Holland職務分類對照，2016年5月30日，取自http://www.jobwiki.com.tw/Holland/Holland.aspx.
高文彬（2012），人力資源發展：職能基礎觀點，雙葉書廊，台北。
莊慧玲、蔡勝弘（2006），電子“薪”貴嗎？－電子製造業與傳統製造業受僱員工薪資報酬差異之研究，管理學報，23卷1期，頁1-22.
彭百崇（2000），勞動經濟學，彭百崇，台中.
簡禎富、許嘉裕（2014），資料挖礦與大數據分析，前程文化，台北。
Azmi, I. A. G., Ahmad, Z. A., and Zainuddin, Y. (2009), “Competency-based pay and service quality: an empirical study of Malaysian public organisations,” Asian Academy of Management Journal, Vol. 14, No. 1, pp. 21-36.
Bapna, R., Langer, N., Mehra, A., Gopal, R., and Gupta, A. (2013), “Human capital investments and employee performance: an analysis of IT services industry,” Management Science, Vol. 59, No. 3, pp. 641-658.
Becker, G. S. (2009), Human capital: A theoretical and empirical analysis, with special reference to education, University of Chicago press.
Berger, J. O. (2013), Statistical decision theory and Bayesian analysis, Springer Science & Business Media.
Blundell, R., Dearden, L., Meghir, C., and Sianesi, B. (1999), “Human capital investment: the returns from education and training to the individual, the firm and the economy,” Fiscal studies, Vol. 20, No. 1, pp. 1-23.
Breiman, L. (2001), “Random forests,” Machine learning, Vol. 45, No. 1, pp. 5-32.
Catano, V. (1998), “Appendix 1: Competencies: A review of the literature and bibliography,” Canadian Council of Human Resources Associations.
Chen, K.-K., Chen, M.-Y., Wu, H.-J., and Lee, Y.-L. (2007), “Constructing a Web-based Employee Training Expert System with Data Mining Approach,” IEEE International Conference on Enterprise Computing.
Chen, L.-F. and Chien, C.-F. (2011), “Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries,” Flexible services and manufacturing journal, Vol. 23, No. 3, pp. 263-289.
Chien, C. F., Chen, S. L., & Lin, Y. S. (2002). “Using Bayesian network for fault location on distribution feeder,” IEEE Transactions on Power Delivery, Vol. 17, No. 3, pp. 785-793.
Chien, C.-F. and Chen, L.-F. (2007), “Using rough set theory to recruit and retain high-potential talents for semiconductor manufacturing,” IEEE Transactions on Semiconductor Manufacturing, Vol. 20, No. 4, pp. 528-541.
Chien, C.-F. and Chen, L.-F. (2008), “Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry,” Expert Systems with applications, Vol. 34, No. 1, pp. 280-290.
Cho, V. and Ngai, E. W. (2003), “Data mining for selection of insurance sales agents,” Expert systems, Vol. 20, No. 3, pp. 123-132.
Cooper, L. G. (2000), “Strategic marketing planning for radically new products,” Journal of marketing, Vol. 64, No. 1, pp. 1-16.
Dainty, A. R., Cheng, M.-I., and Moore, D. R. (2005), “Competency-based model for predicting construction project managers’ performance,” Journal of Management in Engineering, Vol. 21, No. 1, pp. 2-9.
Dejaeger, K., Verbraken, T., and Baesens, B. (2013), “Toward comprehensible software fault prediction models using bayesian network classifiers,” IEEE Transactions on Software Engineering, Vol. 39, No. 2, pp. 237-257.
Dessler, G. (2013), Human resource management, Pearson.
Draganidis, F. and Mentzas, G. (2006), “Competency based management: a review of systems and approaches,” Information Management & Computer Security, Vol. 14, No. 1, pp. 51-64.
Fanelli, G., Dantone, M., Gall, J., Fossati, A., and Van Gool, L. (2013), “Random forests for real time 3d face analysis,” International Journal of Computer Vision, Vol. 101, No. 3, pp. 437-458.
Friedman, N., Geiger, D., and Goldszmidt, M. (1997), “Bayesian network classifiers,” Machine learning, Vol. 29, No. 2-3, pp. 131-163.
Genuer, R., Poggi, J.-M., and Tuleau-Malot, C. (2010), “Variable selection using random forests,” Pattern Recognition Letters, Vol. 31, No. 14, pp. 2225-2236.
Holland, J. L. (1997), Making vocational choices: A theory of vocational personalities and work environments, Psychological Assessment Resources.
Hon, A. H. (2012), “When competency-based pay relates to creative performance: The moderating role of employee psychological need,” International Journal of Hospitality Management, Vol. 31, No. 1, pp. 130-138.
Huang, M.-J., Tsou, Y.-L., and Lee, S.-C. (2006), “Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge,” Knowledge-Based Systems, Vol. 19, No. 6, pp. 396-403.
Juan, L. (2009), “Early warning model research of state-owned enterprises' human capital risks based on improved neural network,” Proceedings of BioMedical Information Engineering.
Kelemenis, A., Ergazakis, K., and Askounis, D. (2011), “Support managers’ selection using an extension of fuzzy TOPSIS,” Expert Systems with Applications, Vol. 38, No. 3, pp. 2774-2782.
Lawler, E. E. (1994), “From job‐based to competency‐based organizations,” Journal of organizational behavior, Vol. 15, No. 1, pp. 3-15.
Leu, S.-S. and Chang, C.-M. (2013), “Bayesian-network-based safety risk assessment for steel construction projects,” Accident Analysis & Prevention, Vol. 54, pp. 122-133.
Lindner, C., Bromiley, P. A., Ionita, M. C., and Cootes, T. F. (2015), “Robust and Accurate Shape Model Matching using Random Forest Regression-Voting,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 9, pp. 1862-1874.
Madadgar, S. and Moradkhani, H. (2014), “Spatio-temporal drought forecasting within Bayesian networks,” Journal of Hydrology, Vol. 512, pp. 134-146.
McClelland, D. C. (1973), “Testing for competence rather than for intelligence,” American psychologist, Vol. 28, No. 1, pp. 1.
Mirabile, R. J. (1997), “Everything you wanted to know about competency modeling,” Training & Development, Vol. 51, No. 8, pp. 73-78.
Patil, S. S. and Chougale, P. N. (2013), “Empirical Model for Improving Human Resource Development Competencies of Employees in Hospitality Industries-a Study,” A Journal of research articles in management science and allied areas, Vol. 6, No. 1, pp. 1.
Schultz, T. W. (1961), “Investment in human capital,” The American economic review, pp. 1-17.
Shaw, M. J., Subramaniam, C., Tan, G. W., and Welge, M. E. (2001), “Knowledge management and data mining for marketing,” Decision support systems, Vol. 31, No. 1, pp. 127-137.
Spencer, L. M. and Spencer, P. S. M. (2008), Competence at Work models for superior performance, John Wiley & Sons.
Strohmeier, S. and Piazza, F. (2013), “Domain driven data mining in human resource management: A review of current research,” Expert Systems with Applications, Vol. 40, No. 7, pp. 2410-2420.
Thissen-Roe, A. (2005), Adaptive selection of personality items to inform a neural network predicting job performance, University of Washington, Doctoral dissertation.
Ulrich, D., Brockbank, W., Yeung, A. K., and Lake, D. G. (1995), “Human resource competencies: An empirical assessment,” Human Resource Management, Vol. 34, No. 4, pp. 473-495.
Zhao, X. (2008), “An empirical study of data mining in performance evaluation of HRM,” Proceedings of International Symposium on Intelligent Information Technology Application Workshops.