傳記式資料非線性甄選模式之建構-運用類神經網路並以櫃檯行員為例

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林俊宏(Chun-Hung Lin) 查詢紙本館藏

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人力資源管理研究所

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傳記式資料非線性甄選模式之建構-運用類神經網路並以櫃檯行員為例
(The Construction of non-linear model of biodata: Using neural network and Bank teller as an example)

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摘要(中)

傳記式資料過去多用來預測員工的任務績效，也獲得了很多不錯的結果，然而預測員工的任務績效於對今日的組織而言，已經無法滿足組織利害關係人多樣的需求，因為好的員工除了自己份內工作需要有好表現之外(任務績效)，也要展現組織公民行為(情境績效)，更要隨組織策略的更迭而有隨之改變(適應性績效)，因此就一個好的甄選工具而言，這三種效標都要加以關心，才能補足理論的缺口與實務的需求。另外，員工離職對組織有相當負面的影響，學者建議應多以有效甄選的方式來降低離職率，因為運用甄選的方式來降低離職率對企業的成本是最低的，他們也建議傳記式資料為可運用的工具之一。經運用個案公司櫃檯人員之傳記式資料及工作表現資料分析，並控制共同方法變異的問題後，發現傳記式資料與員工之任務績效、適應績效、情境績效與離職傾向都有顯著的關係，證明傳記式資料對員工行為擁有良好的預測力，是優秀的甄選工具。
企業人員甄選過去多以線性模式去呈現預測值與效標的關係。這些模式假設預測值上極端的分數總是最佳的。然而許多的研究已發現人格變數與工作績效是曲線性的關係，也發現能力與人格特質間或能力間有所謂互動效果；假若資料型態是非線性的，且變數與變數間有某種程度的互動效果，則以傳統線性模型加以預測與解釋可能並不適合，必須採用新的數學模式方能展現出甄選工具的解釋力與預測力，本研究認為類神經網路是可能的途徑之一，經與迴歸模式比較後發現，於六種工作指標中，類神經網路於五種工作指標之準確率較迴歸模式來的佳，證明類神經網路是未來可考慮採用的工具之一。
從1990年代起，許多的學者提出運用專家系統於人力資源管理之決策上，然而到目前為止，多僅是概念性的陳述，國內外真正應用專家系統於人員決策上仍屬少見，在這少數的研究中，絕大多是開發出雛形系統供研究使用，實作上成功的專家系統則更少。本研究運用利用傳記式資料及員工的績效表現，訓練類神經網路，建立智慧型決策支援系統，協助管理者判斷候選人適任程度，系統架構、介面與資料分析方式可提供研究者及人資工作者參考。

摘要(英)

In the past, the biodata have been used to predict task performance. However, the selection instrument that merely predicts task performance is not enough to meet the organization stakeholders’ needs. The employee should be evaluated by whether he/she performs his/her job well (task performance), exhibits organizational citizenship behaviors (contextual performance), and is ready to make a change after the organizational strategy altered. The selection instrument should be built with an emphasis on these three criteria. Since higher turnover rate has a negative effect on organization, scholars have suggested that organizations should reduce costs due to the turnover through selection. Researchers have recommended the use of biodata as an alternative for selection. By controlling the common method variance, this study found that biodata has a significant relationship with task performance, adaptive performance, contextual performance and employee intention for turnover. This research demonstrated that the biodata can be a good selection tool to predict employees’ work behaviors.
Decisions made by organization on employee selection most often used the linear model to demonstrate the relationship between assessed factors and criteria. The linear mode assumes that candidates who have extremely high scores in the independent variables are the best ones. However, relevant research has found a curve linear relationship between personality and job performance. It also discovered that there are interactions among personal traits. Those data that demonstrate non-linear and interaction relationships between the variables are not adequate to use linear approach to interpret selection model. In other words, using a non-linear method such as neural network can be a possible solution. After comparing the result of regression and neural network approach; the neural network outperforms regression approach model in 5 predicting criteria. The neural network approach could be a good statistic method in future selection studies.
Since 1990, many scholars have proposed that expert systems could be used in human resource management decision making. However, most of the expert system research has simply revealed conceptual framework and only few studies developed the prototype system. Among these few, little research has developed expert systems which can be applied to the actual environment. This study used biodata and employee performance data to train the neural network and use the trained neural network to build the intelligent decision support system. This system can assist HR to evaluate the candidate potential. The architecture, user interface and data analysis method of the system could be a good reference for researchers and HR practitioners.

關鍵字(中)

★ 傳記式資料
★ 甄選
★ 類神經網路

關鍵字(英)

★ Neural Network
★ Selection
★ Biodata

論文目次

目錄
中文摘要 ………………………………………………………………………… I
英文摘要 ………………………………………………………………………… II
致謝辭 …………………………………………………………………………… III
目錄 …………………………………………………………………………… IV
表目錄 ………………………………………………………………………… VI
圖目錄 ………………………………………………………………………… VII
第一章緒論 …………………………………………………………………… 1
第一節研究動機………………………………………………………………… 1
第二節研究目的 ………………………………………………………………… 3
第三節論文架構 ……………… ……………………………………………… 4
第二章文獻探討 ……………………………………………………………… 5
第一節傳記式資料 …………………………………………………………… 5
第二節櫃檯行員之特質 ……………………………………………………… 14
第三節傳記式資料與績效、離職率的關係 ………………………………… 16
第四節智慧型決策支援系統與類神經網路 ………………………………… 21
第五節研究假設與研究架構 ………………………………………………… 32
第三章研究方法 ………………………………………………………………… 33
第一節個案公司簡介 ………………………………………………………… 33
第二節衡量 …………………………………………………………………… 34
第三節統計分析方式 ………………………………………………………… 46
第四章研究結果 ………………………………………………………………… 49
第一節樣本 …………………………………………………………………… 49
第二節傳記式問卷之信度 …………………………………………………… 49
第三節傳記式問卷與績效指標及離職傾向的關係 ………………………… 51
第四節負向題影響力之檢驗 ………………………………………………… 57
第五節類神經網路分析結果 ………………………………………………… 58
第五章系統建構與展示………………………………………………………… 64
第一節系統架構 ……………………………………………………………… 64
第二節智慧型決策支援專家輔助系統之平台設計 ………………………… 65
第三節系統功能介紹 ………………………………………………………… 66
第六章結論與建議 …………………………………………………………… 86
第一節研究結果 ……………………………………………………………… 86
第二節意涵 …………………………………………………………………… 89
第三節研究限制 ……………………………………………………………… 92
第四節後續研究建議 ………………………………………………………… 94
參考文獻 ………………………………………………………………………… 96
附錄一、問卷 …………………………………………………………………… 102
附錄二、規則萃取結果 ………………………………………………………… 115

參考文獻

參考文獻
中文部分
1.周鵬程(2006)，類神經網路入門，台北：全華科技圖書股份有限公司。
2.陳彰儀、張裕隆、王榮春、李文銓（2001），「傳記式問卷預測大陸外派適應」，應用心理研究，第十期，頁135-166。
3.梁定澎(2002)，決策支援系統與企業智慧，台北：智勝文化事業有限公司。
4.劉繼升(1995)，以傳記式問卷甄選壽險推銷員之信度與效度研究，國立政治大學心理學研究所碩士論文。
5.羅華強（2005），類神經網路：MATLAB的應用，台北：高立圖書有限公司。
英文部分
1.Asher, J.J. (1972) The biographical item: Can it be improved? Personnel Psychology. 25, 251-69.
2.Barrick, M. R., & Mount, M. K. (1991). The Big Five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 44, 1-26.
3.Borman, W. C., & Motowidlo, S. J. (1993). Expanding the criterion domain to include elements of contextual performance. In N. Schmitt & W. C. Borman (Eds, Personnel selection in organizations (pp. 71-98). San Francisco: Jossey-Bass.
4.Boritz, J. & Kennedy, D., 1995. Effectiveness of Neural Network Types for Prediction of Business Failure, Expert syst. 9, 504-512.
5.Brown, S.H. (1978) Long-term validity of personal history item scoring procedure. Journal of Applied Psychology 63, 673-6.
6.Byun, D. H., & Sun, E. H. (1994). Human resource management expert systems technology. Expert Systems.
7.Campbell, J. P. (1990). Modeling the performance prediction problem in industrial and organizational psychology. In M. D. Dunnette & L. M. Hungh (Eds.), Handbook of industrial and organizational psychology (Vol. 1, 2nd ed., pp. 687-732). Palo Alto, CA: Consulting Psychologist Press.
8.Cattell, H. B. (1989). The 16PF: Personality in depth. Champaign, IL: Institute for Personality and Ability Testing.
9.Caspi, A., Bem, D.J., & Elder, G.H. (1989). Continuities and consequences of interactional styles across the life course. Journal of Personality, 57, 375-406.
10.Campion, M. A., Pursell, E. D., & Brown, B. K. (1988). Structured interviewing: Raising the psychometric properties of the employment interview. Personnel Psychology, 41, 25-42.
11.Chen, J.C., Heh, J.S., Chang, M., (2006), Designing a Decompositional Rule Extraction Algorithm for Neural Networks”, 3th International Symposium on Neural Networks (ISNN 2006), Chengdu, China.
12.Chen, J.C., Heh, J.S., Lee, S.C., Chang, M., (2006), Designing a Decompositional Rule Extraction Algorithm for Neural Networks with Bound Decomposition Tree and Lattice Cube, Neural Networks (submitted).
13.Chu, P. C., (1990). Developing expert systems for human resources planning and management. Human Resource Planning. 13(3), 159-178.
14.Cook, M. (1998) Personnel Selection. Chichester, West Sussex: John Wiley & Sons.
15.Cortina, J. M., Goldstein, N. B., Payne, S. C., Davison, H. K., & Gilliland, S. W. (2000). Te incremental validity of interview scores over and above cognitive ability and conscientiousness scores. Personnel Psychology, 53, 325-352.
16.Dean, M. A. (2004). An Assessment of Biodata Predictive Ability across Multiple Performance Criteria. Applied H.R.M. Research, 9(1), 1-12.
17.Dean, M. A., & Russell, C. (2005). An Examination of Biodata Theory-Based Constructs in a Field Context. International Journal of Selection and Assessment, 13(2), 139-149.
18.Deadrick, D., Bennett, N. & Russell, C. (1997). Using hierarchical linear modeling to examine dynamic performance criteria over time. Journal of Management, 23.
19.Dean, M. A., Russell, C. J., & Muchinsky, P. M. (1999). Life experiences and performance prediction: Toward a theory of biodata. In G. Ferris (Ed.), Research in personnel and human resources management (pp.245–281). Greenwich, CT: JAI Press.
20.Drakeley, RJ, Herriot, P. and Jones, A. (1988) Biographical data, training success
and turnover. Journal of Occupational Psychology 61, 145–52.
21.Eberhardt, Bruce J. and Muchinsky, Paul M. (1982). Biodata determinants of vocational typology: an integration of two paradigms. Journal of Applied Psychology. 67(6), 714 - 27.
22.England, G.W. (1971) Development and Use of Weighted Application Blanks. International Relations Centre, University of Minnesota.
23.Goldsmith, W. M. (1922) The Catlin mark: the inheritance of an unusual opening in the parietal bones. Journal of Hered., 13, 69–71.
24.Golembiewski, R. T., Billingsley K. and Munzenrider, R. (1970) Electoral choice and individual characteristics: Towards a biodata approach. Unpublished manuscript, Institute for Behavioral Research, University of Georgia.
25.Glennon, J.R., Albright, L.E., & Owens, W.A. (1966). A catalog of life history items. Greensboro, NC: The Richardson Foundation..
26.Greenlaw, P. S. & Valonis W. R. (1994). Applications of expert systems in human resource management. Human Resource Planning, 17(1), 27-42.
27.Hunter, J. E., & Hunter, R. F. (1984). Validity and utility of alternative predictors ofjob performance. Psychological Bulletin, 96, 72-98.
28.Hirsch, E.D., Jr. (1993). The Core Knowledge Curriculum--What's behind Its Success? Educational Leadership, 50(8), 23-30.
29.Karas, M., West, J. (1999) ,Construct-oriented Biodata Development for Selection to a Differentiated Performance Domain ,International Journal of Selection and Assessment, 7(2), 86-96.
30.Klimoski, R.J. (1973) A biographical data analysis of career patterns in engineering. Journal of Vocational Behaviour 3, 103-13.
31.Lautenschlager, G. J. and G. S. Shaffer (1987). Reexamining the Component Stability of Owen's Biographical Questionnaire. Journal of Applied Psychology. 72(1), 149-152.
32.LaHuis, D. M., Martin, N. R., & Avis, J. M. (2005). Investigating Nonlinear Conscientiousness-Job Performance Relations for Clerical Employees. Human Performance, 18(3), 199-212.
33.Lievens, F., & Harris, M.M. (2003). Research on Internet Recruiting and Testing: Current Status and Future Directions. In C.L. Cooper & I.T. Robertson (Eds.) International Review of Industrial and Organizational Psychology, 16, 131-165.
34.Lindsey, E. H., Holmes, V., & McCall, M.W. (1987). Key events in excutives’ lives (Technical Report 32). Greenboro, NC: Center for Creative Leadership.
35.Mitchell, T.W. (1986) Secialized job analysis for developing rationally oriented biodata prediction systems. Paper presented at the meeting of the Society for Individual and Organizational Psychology, Chicago, IL.
36.Mitchell, Terry W. and Klimoski, Richard J. (1982), Is it rational to be empirical? A test of methods for scoring biographical data, Journal of Applied Psychology, 67, 411-481 .
37.Mobley, W. H. (1977). Intermediate linkages in the relationship between job satisfaction and employee turnover. Journal of applied psychology, 62, 237-240.
38.Muchinsky, P. M. (2003) Psychology applied to work (7th Ed.). Belmont, CA: Wadsworth.
39.Mumford, M. D. (1999). Construct Validity and Background Data: Issues, Abuses, and Future Directions. Human Resource Management Review. 9(2), 117-145.
40.Mumford, M. D., & Owens, W. A. (1984) Individuality in a developmental context: Some empirical and theoretical considerations. Human Development 27, 84-108.
41.Mumford, M. D., & Owens, W. A. (1987). Methodological review: Principles,
procedures, and findings in the application of background data measures.
Applied Psychological Measurement, 11, 1-31.
42.Mael, F. A. (1991). A conceptual rationale for the domain and attributes of biodata items. Personnel Psychology, 44, 763-792.
43.Matteson, M.G. (1978) An alternative approach using biographical data for predicting job success. Journal of Occupational Psychology 51, 155-62.
44.Matteson, M.G., Osborn, H.G. & Sparks, C.P. (1969) A Computer-based Methodology for Constructing Homogeneous Keys with Applications to Biographical Data. Report 1. Houston, TX: University of Houston Personnel Psychology Services Center.
45.McEvoy, G. M. & Cascio, W. F. (1985). Strategies for reducing employee turnover: A meta-analysis. Journal of Applied Psychology, 70, 342-353.
46.Mumford, M.D., Stokes, G.S., & Owens, W.A. (1990). Patterns of life history: The ecology of human individuality. Hillsdale, NJ: Lawrence Erlbaum.
47.Nickels, B.J. (1994) The nature of biodata. In G.S. Stokes, M.D. Mumford and W.A. Owens (eds) Biodata Handbook: Theory, Research and Use of Biographical Information in Selection and Performance Prediction. Palo Alto, Calif.: CPP Books, 1-16.
48.Ones, D. S., Viswesvaran, C., & Reiss, A. D. (1996). Role of social desirability in personality testing for personnel selection: The red herring. Journal of Applied Psychology, 81, 660-679.
49.Owens, W.A. (1971) A quasi-actuarial prospect for individual assessment. American Psychologist 26, 992-9.
50.Owens, W.A. (1976) Background data. In M.D. Dunnette (ed.) Handbook of Industrial and Organizational Psychology. Chicago, IL.: Rand McNally, pp. 609-46.
51.Owens, W. A. & Schoenfeldt, L. F. (1979). "Toward a Classification of Persons." Journal of Applied Psychology Monograph 65(5), 569-607.
52.Pulakos, E. D., Arad, S., Donovan, M. A., & Plamondon, K. E. (2000). Adaptability in the workplace: Development of a taxonomy of adaptive performance. Journal of Applied Psychology. 85, 612-624.
53.Reilly, R. R. & Chao, G. R. (1982). Validity and fairness of some alternative employee selection procedures. Personnel Psychology,35, 1-62.
54.Roberts, H. (1992). Expert systems and the personnel department. Personnel Management. 20(6), 52-55.
55.Robie, C., & Ryan, A. M. (1999). Effects of nonlinearity and heteroscedasticity on the validity of conscientiousness in predicting overall job performance. International Journal of Selection and Assessment, 7, 157-169.
56.Rothstein, H. R., Schmidt, F. L., Erwin, F. W., Owens, W. A., & Sparks, P. P. (1990). Biographical data in employment selection: Can validities be made generalizable? Journal of Applied Psychology, 75, 175-184.
57.Rychlak, Joseph F. (1982). Some psychotherapeutic implications of logical phenomenology. Psychotherapy: Theory, Research and Practice, 19 (3), 259-265.
58.Russell, C.J. (1990) Selecting top corporate leaders: An example of biographical information. Journal of Management 16, 73-86.
59.Russell, C. J., Mattson, J., Devlin, S. E., & Atwater, D. (1990). Predictive validity of biodata items generated from retrospective life experience essays. Journal of Applied Psychology, 75, 569-580.
60.Russell, C. J., & Domm, D. R., (1995). On the construct validity of biographical information: Evaluation of a theory based method of item generation. Presented at the fifth annual meetings of the Society of Industrial and Organizational Psychology, Miami Beach , FL.
61.Shafer A. B. (2000). Relation of the Big Five to Biodata and aspects of the self. Personality and Individual Difference. 28, 1017-1035.
62.Schmidt, F. L. & Hunter, J. E. (1998). The Validity and Utility of Selection methods in personnel psychology: Practical and Theoretical Implications of 85 Years of Research Findings. Psychological Bulletin, 124, 262-274.
63.Schoenfeldt, L.F. (1992) Biographical data as the new frontier in employee selection research. Paper presented at the annual meeting of the American Psychological Association New Orleans, LA.
64.Schoenfeldt, L. F. (1999) From Dust Bowl Empiricism to Rational Constructs in Biographical Data. Human Resouce Management Review. 9(2), 147-167.
65.Schwab, Donald P. and Richard L. Oliver (1974), Predicting Tenure with Biographical Data: Exhuming Buried Evidence, Personnel Psychology, 27 (1), 125-128.
66.Smith, W. J., Albright, L. E., Glennon, J. R., & Owens, W. A. (1961) . The prediction of research competence and creativity from personal history . Journal of Applied Psychology, 45(1), 59-62.
67.Snell, A. F., Sydell, E. J., Lueke, S. B. (1999). Towards A Theory of Applicant Faking: Integrating Studies of Deception. Human Resource Management Review, 9(2), 219-242.
68.Spencer, L. M. & Spencer, S. M. (1993). Competence at work: model for superior performance. John Wiley & Sons: New York.
69.Stokes, G. S., & Cooper, L. A. (1994). Selection using biodata: Old notions revisited. In G.S. Stokes, M. C. Mumford, & W. A. Owens (Eds.), The biodata handbook: theory, research and applications. Palo Alto, CA: Consulting Psychologists Press.
70.Stokes, G. S., & Reddy, S. (1992). Use of background data in organizational decisions. In C. L. Cooper & I. T. Robertson (Eds.), International review of industrial and organizational psychology. London: Wiley. Pp. 285-322.
71.Stone, D. L., & Jones, G. E. (1994). Percived fairness of biodata as a function of the purpose of the request for information and gender of the applicant. Paper presented at the annual meeting of the Society for Industrial and Organizational Psychology, Nashville, TN.
72.Strimbu, J. L., Schoenfeldt, L. F., & Sims, O. S., Jr. (1973). Sex differences in college student drug use. Journal of College Student Personnel, 14, 507-510.
73.Tett, R. P. (1998). Is conscientiousness always positively related to job performance? Industrial-Organizational Psychologist, 36, 24-49.
74.Tesluk, P. E., & Jacobs, R. R. (1998). Toward an integrated model of work experience. Personnel Psychology, 51, 321-355.
75.Thayer, P.W. (1977). Somethings old; Somethings new. Personnel Psychology, 30, 513-524.
76.Van Scotter, J. R., & Motowidlo, S. J. (1996). Interpersonal facilitation and job dedication as separate facets of contextual performance. Journal of Applied Psychology, 81, 525-531.
77.Vasilopoulos, N. L., Usala, P. D., & Leaman, J. A. (1999). Demonstrating a curvilinear relationship between personality and performance in training. Poster presented at the 14th Annual Conference of the Society for industrial and Organizational Psychology, Atlanta, GA.
78.Wernimont, P.F. (1962) Reevaluation of a weighted application blank for office personnel. Journal of Applied Psychology 46, 417-19.
79.Wernimont, P.F. and Campbell, J.P. (1968). Signs, samples, and criteria. Journal of Applied Psychology, 52, 372-376.

指導教授

鄭晉昌(Jihn-Chang Jehng)

審核日期

2006-7-22

推文