Investigating the effect of knowledge management systems on university performance: The interplay of intellectual and human capital

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This study examines the impact of knowledge management systems on the performance of universities while considering the mediating role of intellectual capital and the moderating effect of human capital. In today’s knowledge-driven economy, improving university performance through effective knowledge management is essential. The study collected data through an online survey targeting academic and administrative staff at 18 accredited private universities in Jordan. These participants were selected for their involvement in knowledge-related activities within their institutions. The survey was conducted via email between July and September 2024, yielding 273 valid responses out of 384 invitations, ensuring a relevant and representative sample for the analysis. The study analyzed the data using structural equation modeling, focusing on partial least squares. The results show that knowledge management systems have a significant direct effect on university performance (beta = 0.317, p < 0.001) and a strong effect on intellectual capital (beta = 0.714, p < 0.001). Intellectual capital also significantly affects university performance (beta = 0.310, p < 0.001) and mediates the relationship between knowledge management systems and performance (beta = 0.221, p < 0.001). Additionally, human capital positively moderates this relationship (beta = 0.104, t = 2.201, p = 0.006). These findings highlight the need for universities to invest in both intellectual and human capital to fully realize the benefits of knowledge management systems and enhance institutional performance. The study provides valuable evidence that strengthening knowledge management systems, along with intellectual and human capital, is key to driving meaningful performance improvements in universities.

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    • Figure 1. Measurement model
    • Figure 2. Structural model
    • Table 1. Respondent characteristics
    • Table 2. Sources of measurement scales
    • Table 3. Measurement model assessment
    • Table 4. Discriminant validity results
    • Table 5. HTMT criteria
    • Table 6. Structural model assessment
    • Table A1. The study questionnaire
    • Conceptualization
      Amro Alzghoul
    • Investigation
      Amro Alzghoul, Khaled M. Aboalganam
    • Methodology
      Amro Alzghoul
    • Resources
      Amro Alzghoul
    • Supervision
      Amro Alzghoul
    • Validation
      Amro Alzghoul, Khaled M. Aboalganam
    • Writing – original draft
      Amro Alzghoul, Khaled M. Aboalganam
    • Writing – review & editing
      Amro Alzghoul, Khaled M. Aboalganam
    • Data curation
      Khaled M. Aboalganam
    • Formal Analysis
      Khaled M. Aboalganam
    • Funding acquisition
      Khaled M. Aboalganam
    • Project administration
      Khaled M. Aboalganam
    • Software
      Khaled M. Aboalganam
    • Visualization
      Khaled M. Aboalganam