The purpose of this paper is to provide an integrated group decision support system (GDSS) that will select the appropriate human resource (HR) capabilities for a firm by using existing decision algorithms and information technology (IT) software systems.
The proposed GDSS is constructed by taking advantage of the characteristics of some existing analytical and mathematical methods, including electronic focus groups, value chain, HR scorecard, synergy analysis, gap analysis, analytic hierarchy process based on genetic algorithms (GA‐AHP), similarity measures, fuzzy set theory, and fuzzy mathematics programming. A case study is performed to test and evaluate the performance and usability of the GDSS and to identify whether or not it achieved its designed purpose.
The results show that the proposed GDSS can create a flexible and user‐friendly environment that aids managers and other relevant staff members in evaluating all relevant factors in selecting a firm's HR capabilities.
HR capabilities have a significant effect on business performance in the long term. However, not every firm can easily develop suitable HR capability strategies due to lacking of the adapted support tool. The proposed GDSS is proposed to provide a complete procedure to support managers using a strategy‐oriented perspective to decide the right HR capability to be developed. As the result of using the proposed GDSS, tasks are simplified and the time for HR capability analysis can be significantly reduced.
Few studies have discussed the application of IT to the selection of HR capabilities in facilitating managers in the strategic formulation process. This paper particularly focuses on the question of how firms can actually identify HR capabilities. Thus, the model‐developing nature‐oriented support system is provided for managers in solving such decision‐making problems.
Lin, C. and Hsu, M. (2010), "Holistic decision system for human resource capability identification", Industrial Management & Data Systems, Vol. 110 No. 2, pp. 230-248. https://doi.org/10.1108/02635571011020322Download as .RIS
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