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Decoding excellence: Mapping the demand for psychological traits of operations and supply chain professionals through text mining

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  • S. Di Luozzo
  • A. Fronzetti Colladon
  • M. M. Schiraldi

Abstract

The current study proposes an innovative methodology for the profiling of psychological traits of Operations Management (OM) and Supply Chain Management (SCM) professionals. We use innovative methods and tools of text mining and social network analysis to map the demand for relevant skills from a set of job descriptions, with a focus on psychological characteristics. The proposed approach aims to evaluate the market demand for specific traits by combining relevant psychological constructs, text mining techniques, and an innovative measure, namely, the Semantic Brand Score. We apply the proposed methodology to a dataset of job descriptions for OM and SCM professionals, with the objective of providing a mapping of their relevant required skills, including psychological characteristics. In addition, the analysis is then detailed by considering the region of the organization that issues the job description, its organizational size, and the seniority level of the open position in order to understand their nuances. Finally, topic modeling is used to examine key components and their relative significance in job descriptions. By employing a novel methodology and considering contextual factors, we provide an innovative understanding of the attitudinal traits that differentiate professionals. This research contributes to talent management, recruitment practices, and professional development initiatives, since it provides new figures and perspectives to improve the effectiveness and success of Operations Management and Supply Chain Management professionals.

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  • S. Di Luozzo & A. Fronzetti Colladon & M. M. Schiraldi, 2024. "Decoding excellence: Mapping the demand for psychological traits of operations and supply chain professionals through text mining," Papers 2403.17546, arXiv.org.
    • Handle: RePEc:arx:papers:2403.17546
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    1. McClain, John O. & Thomas, L. Joseph & Sox, Charles, 1992. ""On-the-fly" line balancing with very little WIP," International Journal of Production Economics, Elsevier, vol. 27(3), pages 283-289, October.
    2. Yufei Ren & Rachel Croson, 2013. "Overconfidence in Newsvendor Orders: An Experimental Study," Management Science, INFORMS, vol. 59(11), pages 2502-2517, November.
    3. Maurice E. Schweitzer & Gérard P. Cachon, 2000. "Decision Bias in the Newsvendor Problem with a Known Demand Distribution: Experimental Evidence," Management Science, INFORMS, vol. 46(3), pages 404-420, March.
    4. Weihua Liu & Shuqing Wang & DongLei Zhu & Di Wang & Xinran Shen, 2018. "Order allocation of logistics service supply chain with fairness concern and demand updating: model analysis and empirical examination," Annals of Operations Research, Springer, vol. 268(1), pages 177-213, September.
    5. Jiahua Zhang & Shu-Cherng Fang & Yifan Xu, 2018. "Inventory centralization with risk-averse newsvendors," Annals of Operations Research, Springer, vol. 268(1), pages 215-237, September.
    6. Zhimin Huang & Shaofu Du & Yugang Yu & Liang Liang, 2018. "Preface: Behavioral operations management in social networks," Annals of Operations Research, Springer, vol. 268(1), pages 1-4, September.
    7. Teck-Hua Ho & Noah Lim & Tony Haitao Cui, 2010. "Reference Dependence in Multilocation Newsvendor Models: A Structural Analysis," Management Science, INFORMS, vol. 56(11), pages 1891-1910, November.
    8. Sanjay T. Menon, 2012. "Human resource practices, supply chain performance, and wellbeing," International Journal of Manpower, Emerald Group Publishing Limited, vol. 33(7), pages 769-785, October.
    9. Jure Erjavec & Peter Trkman, 2020. "Behavioural operations management - identification of its research program," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 36(1), pages 42-71.
    10. Karen Donohue & Özalp Özer, 2020. "Behavioral Operations: Past, Present, and Future," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 191-202, January.
    11. Emily W. Choi & Özalp Özer & Yanchong Zheng, 2020. "Network Trust and Trust Behaviors Among Executives in Supply Chain Interactions," Management Science, INFORMS, vol. 66(12), pages 5823-5849, December.
    12. Papoutsoglou, Maria & Rigas, Emmanouil S. & Kapitsaki, Georgia M. & Angelis, Lefteris & Wachs, Johannes, 2022. "Online labour market analytics for the green economy: The case of electric vehicles," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    13. J. Erjavec & A. Popovič & P. Trkman, 2019. "The effect of personality traits and knowledge on the quality of decisions in supply chains," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 32(1), pages 2269-2292, January.
    14. Ancarani, A. & Di Mauro, C. & D'Urso, D., 2013. "A human experiment on inventory decisions under supply uncertainty," International Journal of Production Economics, Elsevier, vol. 142(1), pages 61-73.
    15. Bradley R. Staats & Francesca Gino, 2012. "Specialization and Variety in Repetitive Tasks: Evidence from a Japanese Bank," Management Science, INFORMS, vol. 58(6), pages 1141-1159, June.
    16. Christoph H. Loch & Yaozhong Wu, 2008. "Social Preferences and Supply Chain Performance: An Experimental Study," Management Science, INFORMS, vol. 54(11), pages 1835-1849, November.
    17. Elena Katok & Enno Siemsen, 2011. "Why Genius Leads to Adversity: Experimental Evidence on the Reputational Effects of Task Difficulty Choices," Management Science, INFORMS, vol. 57(6), pages 1042-1054, June.
    18. Wesley M. Cohen & Henry Sauermann & Paula Stephan, 2020. "Not in the Job Description: The Commercial Activities of Academic Scientists and Engineers," Management Science, INFORMS, vol. 66(9), pages 4108-4117, September.
    19. Zhang, Xiang & Chen, Rongqiu, 2013. "Asymmetric effects, regulatory focus, and attribute satisfaction—Mixed experimental evidence in airline overbooking recovery," International Journal of Production Economics, Elsevier, vol. 142(1), pages 27-36.
    20. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    21. Franco, L. Alberto & Hämäläinen, Raimo P., 2016. "Behavioural operational research: Returning to the roots of the OR profession," European Journal of Operational Research, Elsevier, vol. 249(3), pages 791-795.
    22. Wu, Diana Yan, 2013. "The impact of repeated interactions on supply chain contracts: A laboratory study," International Journal of Production Economics, Elsevier, vol. 142(1), pages 3-15.
    23. Phillip W. Witt & Timothy Baker & Nancy W. Ashley & MaryAnne Winniford, 2017. "Is personality a key element of Six Sigma project success?," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 27(4), pages 472-523.
    24. Basak Kalkanci & Kay-Yut Chen & Feryal Erhun, 2011. "Contract Complexity and Performance Under Asymmetric Demand Information: An Experimental Evaluation," Management Science, INFORMS, vol. 57(4), pages 689-704, April.
    25. Stephane Timmer & Lutz Kaufmann, 2019. "Do Managers’ Dark Personality Traits Help Firms in Coping with Adverse Supply Chain Events?," Journal of Supply Chain Management, Institute for Supply Management, vol. 55(4), pages 67-97, October.
    26. Ted Schwaba & Mijke Rhemtulla & Christopher J Hopwood & Wiebke Bleidorn, 2020. "A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-21, July.
    27. Azzurra Morreale & Jan Stoklasa & Mikael Collan & Giovanna Lo Nigro, 2018. "Uncertain outcome presentations bias decisions: experimental evidence from Finland and Italy," Annals of Operations Research, Springer, vol. 268(1), pages 259-272, September.
    28. Danny Samson & Pat Foley & Heng Soon Gan & Marianne Gloet, 2018. "Multi-stakeholder decision theory," Annals of Operations Research, Springer, vol. 268(1), pages 357-386, September.
    29. Yukun Zhao & Xiaobo Zhao & Zuo-Jun Max Shen, 2018. "The hot-versus-cold effect in a punishment game: a multi-round experimental study," Annals of Operations Research, Springer, vol. 268(1), pages 333-355, September.
    30. Margaret F. Shipley & Faiza Khoja & J. Brooke Shipley, 2018. "Investigating task and risk orientations in social behavior in networks: a fuzzy set-based model connecting natural and social sciences," Annals of Operations Research, Springer, vol. 268(1), pages 21-40, September.
    31. Lee, Peter K.C. & To, W.M. & Yu, Billy T.W., 2013. "Team attributes and performance of operational service teams: An empirical taxonomy development," International Journal of Production Economics, Elsevier, vol. 142(1), pages 51-60.
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