Abstract
INTRODUCTION: Personality assessments based on the Big Five framework remain widely used in employee selection and organizational research. However, debates persist regarding their predictive strength and practical relevance across dynamic workplace contexts. This study revisits these debates by examining how personality traits interact with individual capabilities and contextual factors in predicting job performance. METHODS: This integrative review followed PRISMA guidelines and analyzed peer-reviewed studies published between 1991 and 2024. Literature was searched across Web of Science, Scopus, ScienceDirect, and the Saudi Digital Library. A total of 315 records were identified, 125 duplicates were removed, and 190 studies were screened. After the eligibility assessment, 43 studies met the inclusion criteria and were analyzed using descriptive synthesis, thematic coding, and alignment with an integrative framework. RESULTS: The review found that conscientiousness consistently emerged as the strongest predictor of job performance across occupations. Other traits, such as extraversion, openness, agreeableness, and neuroticism, showed more context-dependent relationships with performance outcomes. Across the analyzed studies, three major themes emerged: (1) personality traits as behavioral resources, (2) capabilities such as learning agility, adaptability, and job crafting as mechanisms translating traits into behavior, and (3) contextual moderators including job design, leadership climate, and cultural environment. DISCUSSION: The study proposes the Trait-Capability-Context (TCC) model, which explains how personality traits interact with individual capabilities and organizational environments to influence performance outcomes. The model suggests that traits alone are insufficient predictors of job performance and must be considered alongside capability development and contextual conditions. This integrative framework provides a more realistic foundation for employee selection and talent development, highlighting the need for future research using longitudinal and multi-level designs that model how traits, capabilities, and contexts interact dynamically over time, across careers, and under dynamic conditions.