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Internal auditor selection using a TOPSIS/non-linear programming model

Author

Listed:
  • Konstantinos Petridis

    (University of Macedonia)

  • Georgios Drogalas

    (University of Macedonia)

  • Eleni Zografidou

    (University of Macedonia)

Abstract

One of the most challenging problems in personnel selection is the multi-attribute nature of the candidates. This problem is magnified during the procedure of selection of sophisticated personnel, such as internal auditors. By definition, an internal auditor must combine a selection of analytical and non-analytical skills, corresponding to specific cognitive and behavioral attributes. In this paper, a framework for internal auditors’ selection using TOPSIS technique is proposed, integrating behavioral and cognitive skills. AHP technique has been used to determine the weights of each criterion. By prioritizing the latter skills, the proposed framework can identify employable and potentially employable candidates. Besides considering the desirable skills in the process of personnel selection, the expected performance is also taken into account. To examine what would be the ideal importance of cognitive and behavioral skills that maximizes candidates’ performance, a non-linear programming method is applied. A real-life application is demonstrated to a sample of internal auditors from the Greek branch of a multi-national company.

Suggested Citation

  • Konstantinos Petridis & Georgios Drogalas & Eleni Zografidou, 2021. "Internal auditor selection using a TOPSIS/non-linear programming model," Annals of Operations Research, Springer, vol. 296(1), pages 513-539, January.
    • Handle: RePEc:spr:annopr:v:296:y:2021:i:1:d:10.1007_s10479-019-03307-x
    DOI: 10.1007/s10479-019-03307-x
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    2. Tsai, Pei-Hsuan & Kao, Ya-Ling & Kuo, Szu-Yu, 2023. "Exploring the critical factors influencing the outlying island talent recruitment and selection evaluation model: Empirical evidence from Penghu, Taiwan," Evaluation and Program Planning, Elsevier, vol. 99(C).
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    5. Seyed Hossein Razavi Hajiagha & Jalil Heidary-Dahooie & Ieva Meidutė-Kavaliauskienė & Kannan Govindan, 2022. "A new dynamic multi-attribute decision making method based on Markov chain and linear assignment," Annals of Operations Research, Springer, vol. 315(1), pages 159-191, August.

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