Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry

Chien, Chen–Fu; Chen, Lifei

doi:10.1016/j.eswa.2006.09.003

Cited by 371 publications

(248 citation statements)

References 29 publications

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“…This is comparable to the step "Data preparation" in Chien and Chen (2008)'s framework for personnel selection. The manipulation of data refers to the cleaning of data, merging connected data, transforming data into interpretable attributes and dealing with missing values.…”

Section: The Ifr 2 Frameworkmentioning

confidence: 77%

A Framework for Internal Fraud Risk Reduction at IT Integrating Business Processes: The IFR² Framework

Jans¹,

Lybaert²,

Vanhoof³

2009

IJDAR

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Abstract. Fraud is a million dollar business and it is increasing every year. Both internal and external fraud present a substantial cost to our economy worldwide. A review of the academic literature learns that the academic community only addresses external fraud and how to detect this type of fraud. Little or no effort to our knowledge has been put in investigating how to prevent ánd to detect internal fraud, which we call 'internal fraud risk reduction'. Taking together the urge for research in internal fraud and the lack of it in academic literature, research to reduce internal fraud risk is pivotal. Only after having a framework in which to implement empirical research, this topic can further be investigated. In this paper we present the IFR² framework, deduced from both the academic literature and from current business practices, where the core of this framework suggests to use a data mining approach.

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Section: The Ifr 2 Frameworkmentioning

confidence: 77%

A Framework for Internal Fraud Risk Reduction at IT Integrating Business Processes: The IFR² Framework

Jans¹,

Lybaert²,

Vanhoof³

2009

IJDAR

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“…The loss of manufacturing volume is due to the manufacturing process requirement including the setting of tool parameter and recipe. Yield volume (YV) that is the portion of material volume used in effective wafers for manufacturing showed as Equation (6). The loss of yield volume is due to the material volume of effective wafers which are influenced by rework and scrap.…”

Section: Problem Definitionmentioning

confidence: 99%

“…In particular, we propose overall usage effectiveness (OUE) indices to measure indirect material usage performance and drive effective improvement directions. Data mining is employed to analyze the big data from different perspectives in the semiconductor industry such as improving a specific cleaning process, [3] yield enhancement, [10,13,15] personnel selection and human capital improvement [ , 5,6] and demand forecast. [7] However, little research has been done to employ data mining for material tracing mechanism and thus reducing material usage.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

A data mining approach for analyzing semiconductor MES and FDC data to enhance overall usage effectiveness (OUE)

Chien¹,

Diaz²,

Lan³

2014

IJCIS

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Wafer fabrication is a complex and lengthy process that involves hundreds of process steps with monitoring numerous process parameters at the same time for yield enhancement. Big data is automatically collected during manufacturing processes in modern wafer fabrication facility. Thus, potential useful information can be extracted from big data to enhance decision quality and enhance operational effectiveness. This study aims to develop a data mining framework that integrates FDC and MES data to enhance the overall usage effectiveness (OUE) for cost reduction. We validated this approach with an empirical study in a semiconductor company in Taiwan. The results demonstrated the practical viability of this approach. The extracted information and knowledge is helpful to engineers for identifying the major tools factors affecting indirect material usage effectiveness and identify specific periods of time when a functional tool has abnormal usage of material.

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“…For instance, data mining and machine learning classification models are constructed on the basis of historical data exactly with the purpose of learning the distinctive elements of different classes, such as good/bad debtor in credit/insurance scoring systems [3,10,27] or good/bad worker in personnel selection [6]. When applied for automatic decision making, DSS can potentially guarantee less arbitrary decisions, but still they can be discriminating in the social, negative sense.…”

Section: Icail-2009mentioning

confidence: 99%

Integrating induction and deduction for finding evidence of discrimination

2010

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Automatic Decision Support Systems (DSS) are widely adopted for screening purposes in socially sensitive tasks, including access to credit, mortgage, insurance, labor market and other benefits. While less arbitrary decisions can potentially be guaranteed, automatic DSS can still be discriminating in the socially negative sense of resulting in unfair or unequal treatment of people. We present a reference model for finding (prima facie) evidence of discrimination in automatic DSS which is driven by a few key legal concepts. First, frequent classification rules are extracted from the set of decisions taken by the DSS over an input pool dataset. Key legal concepts are then used to drive the analysis of the set of classification rules, with the aim of discovering patterns of discrimination. We present an implementation, called LP2DD, of the overall reference model integrating induction, through data mining classification rule extraction, and deduction, through a computational logic implementation of the analytical tools.

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Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry

Cited by 371 publications

References 29 publications

A Framework for Internal Fraud Risk Reduction at IT Integrating Business Processes: The IFR² Framework

A Framework for Internal Fraud Risk Reduction at IT Integrating Business Processes: The IFR² Framework

A data mining approach for analyzing semiconductor MES and FDC data to enhance overall usage effectiveness (OUE)

Integrating induction and deduction for finding evidence of discrimination

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