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<p>This study offers a novel perspective on gender disparities in digital skills by focusing on manufacturing workers, a group underrepresented in existing gender-in-tech and digital divide literature. Using an original typology of digital competency levels (T1–T4), we reveal task-based and department-based digital stratification shaped by gendered patterns of skill acquisition and job assignment. Using multinomial logit regression, we explore associations among Non-Routine Task Intensity, demographics, and digital automation, focusing on gender-related patterns: women are more likely to cluster in T2 (management/sales) and T4 (low digital skills/administrative tasks), while men are overrepresented in T1 (technical/research skills) and T3 (operational/equipment competencies). These results are consistent with hypotheses suggesting gendered patterns of skill distribution (H1), a potential association between non-routine task intensity and digital skill levels (H2), and a correlation between departmental digital maturity and skill strata (H3). A greater male presence in high-skilled, non-routine-intensive roles (H4) and women’s greater concern about technological displacement (H5) are observed. Patterns related to targeted training (H6) and the differentiated effects of skill levels on organizational competitiveness (H7) underscore the importance of inclusive strategies to promote equity and adaptability in the digital labor market.</p>

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Gendered perspectives on digital skill stratification among manufacturing workers: implications for strategic human resource management

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月11日

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  • Ling Zhang,
  • Junzhou Xu

摘要

This study offers a novel perspective on gender disparities in digital skills by focusing on manufacturing workers, a group underrepresented in existing gender-in-tech and digital divide literature. Using an original typology of digital competency levels (T1–T4), we reveal task-based and department-based digital stratification shaped by gendered patterns of skill acquisition and job assignment. Using multinomial logit regression, we explore associations among Non-Routine Task Intensity, demographics, and digital automation, focusing on gender-related patterns: women are more likely to cluster in T2 (management/sales) and T4 (low digital skills/administrative tasks), while men are overrepresented in T1 (technical/research skills) and T3 (operational/equipment competencies). These results are consistent with hypotheses suggesting gendered patterns of skill distribution (H1), a potential association between non-routine task intensity and digital skill levels (H2), and a correlation between departmental digital maturity and skill strata (H3). A greater male presence in high-skilled, non-routine-intensive roles (H4) and women’s greater concern about technological displacement (H5) are observed. Patterns related to targeted training (H6) and the differentiated effects of skill levels on organizational competitiveness (H7) underscore the importance of inclusive strategies to promote equity and adaptability in the digital labor market.