Globalization, technological advancements, and organizational changes have made work life often involve relocation and multiple careers. The modern 'boundaryless' career model emphasizes employability—defined as "an individual’s chance of a job in the internal and/or external labor market"—across various boundaries. Human and social capital theories highlight the importance of skills, knowledge, and networks in enhancing employability, underscoring the necessity of lifelong learning and adaptability to technology for maintaining competitiveness. The rise of artificial intelligence further impacts employability, as AI automates tasks and transforms industries, necessitating continuous skill acquisition. Lifelong learning is crucial for individuals to proactively upgrade their capabilities and navigate the evolving job market shaped by AI advancements.

Lifelong learning and adaptability to technology vary significantly across different career stages and generational cohort values, influencing how each generation remains competitive. This makes it essential to compare generational cohorts regarding the effects of AI competence on perceived employability. Integrating human and social capital theories with generational cohort theory, the current study examines generational differences in the patterns of effects of social and human capital variables on perceived employability, focusing on AI competence as a part of human capital.

The study was conducted through an online survey of 723 respondents aged 18-58 from generations X, Y, and Z. Our descriptive findings show that AI competence among "digital immigrants" (Gen X) was significantly lower compared to both generations of "digital natives" (Gen Y and Z). The employability of young career employees (Gen Z) was significantly lower compared to mid-career Gen Y. To address the study's aim, we ran three models of hierarchical regression separately for each generation. The first model included demographic variables (gender, locality, age, and religiosity). The second model included social capital variables (creating or maintaining professional relationships). The third model included human capital variables (education and AI competence). The findings show that for Generations X and Z, social capital significantly enhances employability, while human capital variables are nonsignificant. In contrast, for Gen Y, both education and AI competence positively affect employability, while social capital is nonsignificant. Demographic variables did not significantly affect employability among all three generations.

Our findings provide nuanced insights into the generational dynamics influencing employability. For Generations X and Z, the significant positive effect of social capital suggests that professional networks and relationships are crucial for career advancement, possibly compensating for lower AI competence in Gen X and lack of professional experience in Gen Z. In contrast, for Gen Y, the significant positive effects of both education and AI competence indicate that human capital is paramount, reflecting their need to leverage advanced skills and qualifications to navigate a competitive job market. These findings underscore the divergent pathways through which different generations enhance their employability, shaped by their distinct career stages and technological proficiencies.

Keywords: Lifelong learning, AI competence, employability, generational cohorts, human capital, social capital.