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.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Orca: Enhancing Role-Playing Abilities of Large Language Models by Integrating Personality Traits
Large language models has catalyzed the development of personalized dialogue systems, numerous role-playing conversational agents have emerged. While previous research predominantly focused on enhancing the model's capab…
PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents
Psychological measurement is essential for mental health, self-understanding, and personal development. Traditional methods, such as self-report scales and psychologist interviews, often face challenges with engagement a…
MoME: Estimating Psychological Traits from Gait with Multi-Stage Mixture of Movement Experts
Gait encodes rich biometric and behavioural information, yet leveraging the manner of walking to infer psychological traits remains a challenging and underexplored problem. We introduce a hierarchical Multi-Stage Mixture…
Gender PredictionLimited Ability of LLMs to Simulate Human Psychological Behaviours: a Psychometric Analysis
The humanlike responses of large language models (LLMs) have prompted social scientists to investigate whether LLMs can be used to simulate human participants in experiments, opinion polls and surveys. Of central interes…
Multiple-choiceQuestion AnsweringPsyMo: A Dataset for Estimating Self-Reported Psychological Traits from Gait
Psychological trait estimation from external factors such as movement and appearance is a challenging and long-standing problem in psychology, and is principally based on the psychological theory of embodiment. To date, …
Gait Recognition