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.

翻译：本研究提出了一种创新方法，用于构建运营管理（OM）与供应链管理（SCM）专业人才的心理特质画像。我们采用文本挖掘与社会网络分析的前沿方法及工具，基于职位描述数据集，重点聚焦心理特征维度，系统梳理相关技能的市场需求。该研究路径通过整合心理学构念、文本挖掘技术及创新性测量指标——语义品牌得分（Semantic Brand Score），旨在评估特定心理特质的市场价值。我们将该方法应用于OM与SCM专业人才的职位描述数据集，旨在绘制其所需核心技能（含心理特征）的全景图谱。在此基础上，进一步从发布职位的组织所属区域、组织规模及职位资历层级三个维度进行精细化分析，以揭示差异化特征。最后，采用主题建模技术解析职位描述中的关键要素及其相对重要性。通过引入创新方法论并统筹情境因素，本研究为区分专业人才的态度特质提供了全新视角。研究成果可服务于人才管理、招聘实践及职业发展体系建设，为提升运营管理与供应链管理专业人才的工作效能与职业成功提供量化依据与创新视角。