Akademik Veri Yönetim Sistemi
Journal of Career Assessment, 2025 (SSCI, Scopus)
We investigated the predictive value of measuring vocational interests towards complex data-driven and people-driven tasks in social and enterprising areas, using the Vertical Social Interests Scale-Data Complexity Levels (VSIS-DCL) and the Vertical Social Interests Scale-People Complexity Levels (VSIS-PCL). Through a qualitative and two quantitative studies involving three independent college student and employed samples (Total N = 829), we developed and validated the VSIS-DCL and further validated the VSIS-PCL. Findings indicated that interests in increasingly complex data-driven and people-driven tasks predict occupational commitment, vocational self-efficacy, satisfaction, and persistence intentions. The VSIS-DCL and VSIS-PCL scales demonstrated incremental validity over traditional interest measures, grit, and the job’s motivating potential, highlighting their utility in career counseling and academic advising. Additionally, we found that interests in complex tasks are more predictive of job satisfaction and occupational commitment in higher-complexity jobs. Our study contributes to the literature by providing robust tools for measuring vocational interests in complex social and enterprising occupations. Results have implications for the age of AI in the workplace, where human talent will be increasingly sought for complex planning, creation, and oversight roles.