OWA operators in human resource management

We develop a new approach that uses the ordered weighted averaging (OWA) operator in different methods for the selection of human resources. The objective of this new model is to manipulate the neutrality of the old methods, so the decision maker can select human resources according to his degree of optimism or pessimism. In order to develop this model, first, a short revision of the OWA operators is introduced. Next, we briefly explain the general model for the selection of human resources and suggest three new indexes for the selection of human resources that use the OWA operator and the hybrid average in the Hamming distance, in the adequacy coefficient and in the index of maximum and minimum level. The main advantage of this method is that it is more complete than the previous ones so the decision maker gets a better understanding of the decision problem. The work ends with an illustrative example that shows the results obtained by using different types of aggregation operators in the new approaches.

Comparing the Apple iPad and non-Apple camp tablet PCs: a multicriteria decision analysis

This study mainly evaluates the performances of Tablet PCs such as the Apple iPad based on a benefits, opportunity, costs, and risks (BOCR) conceptual framework with qualitative and quantitative criteria. We apply four methods, namely, the multiple-criteria decision-making (MCDM) tools (grey relational analysis (GRA), the technique for order performance by similarity to ideal solution (TOPSIS), the VlseKriterijumska Optimizacija I Kompromisno (VIKOR) method, and fuzzy approach) to evaluate and select the tablet PCs’ rankings and then construct a tablet PCs evaluation performance model under an analytic hierarchy process (AHP). The empirical results reveal that a firm's revenue growth, capacity for profitability, product design and product function are highly important evaluation indexes. This indicates that Tablet PC companies should channel more efforts into their product innovation for creating revenue growth and maintaining customer loyalty. Finally, fuzzy AHP also leads to the same findings