Translating Human Capital Amid Varying Intentions to Stay: An Integrative Conceptual Review of the Immigrant Employment Attainment Process

• 成果类型: Article
• 署名作者: Hora, Snehal; Campion, Emily D.; Sajjadiani, Sima; Lee, Diana
• 署名单位: Drexel University; University of Iowa; University of British Columbia
• 刊物名称: JOURNAL OF APPLIED PSYCHOLOGY
• ISSN/ISSBN: 0021-9010
• DOI: 10.1037/apl0001343
• 发表日期: 2026
• 关键词: labor-market performance; socioemotional selectivity theory; social networks; Job-search; PSYCHOLOGICAL ADAPTATION; ACCULTURATION ATTITUDES; SKILLED IMMIGRANTS; CHINESE IMMIGRANTS; CAREER-DEVELOPMENT; self-employment

摘要

Immigrants face a unique challenge in translating their home country human capital to secure employment in their host country's labor market, potentially leading to underemployment. In this integrative conceptual review, we formalize a framework to explain the process of human capital translation for immigrants, specifically laborers and professionals. Synthesizing findings across disciplines, we explicitly model and consider the theoretical role of intended duration of stay, which refers to the amount of time an immigrant desires to stay in their host country. We derive this notion from socioemotional selectivity theory to theorize that an immigrant's intended duration of stay influences proximal social goals central to human capital translation, and in turn, employment speed (i.e., how quickly they attain a job) and quality (i.e., the extent to which the job aligns with their knowledge, skills, and abilities). We contribute to scholarship by (a) enhancing the conceptual accessibility and precision of the immigrant construct for future organizational psychology and management scholarship; (b) synthesizing and integrating multidisciplinary literature on the labor and professional immigrants' employment attainment process to advance a foundational framework that explains human capital translation and how underemployment may occur for these immigrants; and (c) generating a future research agenda and delineating practical implications for practitioners. We also develop and showcase a novel approach for using supervised machine learning, unsupervised machine learning, and large language models to conduct high quality, multidisciplinary systematic reviews more efficiently.