Decoding excellence: Mapping the demand for psychological traits of operations and supply chain professionals through text mining
<|reference_start|>Decoding excellence: Mapping the demand for psychological traits of operations and supply chain professionals through text mining: 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.<|reference_end|>
@article{di luozzo2024decoding,
    title={Decoding excellence: Mapping the demand for psychological traits of
  operations and supply chain professionals through text mining},
    author={S. Di Luozzo, A. Fronzetti Colladon, M. M. Schiraldi},
    journal={arXiv preprint arXiv:2403.17546},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17546},
    primaryClass={cs.CL cs.SI econ.GN physics.soc-ph q-fin.EC}
}