The nexus between boundary-spanning leadership, proactive behavior, corporate governance, and employee performance through work engagement
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DOIhttp://dx.doi.org/10.21511/ppm.24(3).2026.26
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Article InfoVolume 24 2026, Issue #3, pp. 406–421
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Type of the article: Research Article
Abstract
Employee performance is a key factor determining a company’s sustainability and competitiveness. Therefore, this study aims to explore the role of work engagement in mediating the influence of boundary-spanning leadership, proactive behavior, and corporate governance on employee performance. The study used a survey method via a Likert-scale questionnaire, involving 485 participants from companies in Indonesia engaged in trade, services, finance, and investment in 2025. Data analysis used structural equation modeling supported by descriptive and correlational analysis. The results showed that boundary-spanning leadership, proactive behavior, corporate governance, and work engagement were associated with employee performance (β = 0.242, 0.160, 0.186, 0.276; p < 0.05). Then, boundary-spanning leadership, proactive behavior, and corporate governance also correlated with employee work engagement (β = 0.227, 0.414, 0.160; p < 0.05). In addition, boundary-spanning leadership, proactive behavior, and corporate governance are linked to employee performance through work engagement (β = 0.063, 0.114, 0.044; p < 0.05). With these findings, this study offers a new empirical model of the relationship between boundary-spanning leadership, proactive behavior, corporate governance, and employee performance, mediated by work engagement. This model contributes to organizational behavior and human resource management research and provides practitioners with insights into how boundary-spanning leadership, proactive behavior, and corporate governance can improve employee performance, with work engagement as the crucial mediating factor.
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JEL Classification (Paper profile tab)D23, D91, M12, M14
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References61
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Tables6
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Figures0
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- Table 1. Participant profile
- Table 2. Descriptive, skewness, kurtosis, and correlation analysis
- Table 3. Result of the measurement model
- Table 4. Results of HTMT, Fornell–Larcker, and VIF
- Table 5. Hypothesis testing results
- Table A1. Variables, indicators, and items
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