The effect of lean tool on research culture and research performance in Indonesia’s higher education institutions

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Quality Assurance in higher education is a lean tool to improve the quality and performance of HEIs, including lecturer research performance. This study examines the effect of implementing Quality Assurance in higher education as a lean tool on the research culture and research performance of lecturers at Indonesia’s private HEIs. The data represent respondents’ perceptions of the research variable indicators. 184 questionnaires were suitable for processing. Data were collected from 184 lecturers from approximately 25 private HEIs in Jakarta, Indonesia. A 5-point Likert scale was used to measure indicators of research variables. Statistical data analysis was carried out using Structural Equation Modeling with the Smart-PLS ™ program. The results show that Quality Assurance as a lean tool has a significant impact on research performance (β = 0.643; p = 0.000) and research culture (β = 0.361; p = 0.000). Research culture affected research performance significantly (β = 0.281; p = 0.000), and research culture significantly mediates the effect of Quality Assurance as a lean tool on the research performance (β = 0.102; p = 0.010), and the effect is strong (Ѵ = 0.181). Research findings reveal that the successful implementation of Quality Assurance as a lean tool is determined more by organizational readiness than individual readiness; this is reflected in the existence of effective research centers. An effective research center will support the standardization of research processes through continuous improvement so that lecturers behave more actively in scientific activities and perform research more productively.

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    • Figure 1. Implementation of QA in HE as a lean tool to research performance (Model 1)
    • Figure 2. Implementation of QA in HE as a lean tool to research performance (Model 2)
    • Table 1. Indicators of QA as a lean tool
    • Table 2. Research culture indicators
    • Table 3. Research performance indicators
    • Table 4. Construct reliability and validity
    • Table 5. HTMT value in discriminant validity
    • Table 6. New HTMT value in discriminant validity
    • Table 8. Predictive relevance (Q2)
    • Table 9. Model fit
    • Conceptualization
      Farida Farida, Ahmad Badawi Saluy, Kasmir Kasmir, Lenny Christina Nawangsari
    • Data curation
      Farida Farida, Lenny Christina Nawangsari
    • Formal Analysis
      Farida Farida, Ahmad Badawi Saluy, Kasmir Kasmir, Lenny Christina Nawangsari
    • Funding acquisition
      Farida Farida
    • Investigation
      Farida Farida
    • Methodology
      Farida Farida, Ahmad Badawi Saluy, Lenny Christina Nawangsari
    • Project administration
      Farida Farida
    • Resources
      Farida Farida
    • Software
      Farida Farida
    • Validation
      Farida Farida, Ahmad Badawi Saluy, Kasmir Kasmir
    • Visualization
      Farida Farida
    • Writing – original draft
      Farida Farida
    • Supervision
      Ahmad Badawi Saluy, Kasmir Kasmir
    • Writing – review & editing
      Ahmad Badawi Saluy, Kasmir Kasmir, Lenny Christina Nawangsari