Antecedents of knowledge management: The case of professional employees in Indonesia

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This study aims to evaluate and test the impact of knowledge, trust, unity, and self-efficacy on knowledge management of employees in Indonesian businesses in the education sector. This empirical study also examines the relationship between knowledge management among employees in Indonesian education sector businesses and conformity through knowledge self-efficacy, trust, and unity. A quantitative causality technique was employed in the study using a sample of workers from Indonesian companies involved in the education sector (at least 35 years old and engaged). The primary data consisted of 220 replies from respondents with varying educational backgrounds. Partial Least Squares Structural Equation Modelling software was used to analyze the data. The Likert scale is used to determine each answer’s value. Empirical evidence shows that knowledge self-efficacy does not affect the knowledge management of employees in these companies (T Statistics = 0.992); on the other hand, knowledge self-efficacy influences conformity, harmony, and confidence, which in turn affects the knowledge management of employees in these companies. The indirect effect demonstrates that agreement can moderate the influence of knowledge self-efficacy on the knowledge management of employees in these companies (T Statistics = 5,959); conversely, conformity mediates the strong impact of trust on the knowledge management of employees in these businesses. The empirical results show that employees need to have a high level of knowledge for diverse expertise to function well. Conversely, trust will make people more eager to share their information. Lack of confidence might impede the dissemination of knowledge.

Acknowledgment
We acknowledge the partial publication funding provided by Indonesia school of Economic (STIESIA), Surabaya, Indonesia

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    • Figure 1. Measurement model
    • Table 1. Variables and indicators
    • Table 2. Questionnaire distribution
    • Table 3. Respondent descriptions, including gender and age
    • Table 4. Cross loading
    • Table 5. Result of AVE
    • Table 6. R-square
    • Table 7. Composite reliability
    • Table 8. Hypothesis testing results
    • Conceptualization
      Nur Laily, Hindah Mustika, Sukma Irdiana
    • Data curation
      Nur Laily, Hindah Mustika, Sukma Irdiana
    • Formal Analysis
      Nur Laily, Hindah Mustika, Sukma Irdiana, Rusdiyanto Rusdiyanto, Marto Silalahi
    • Funding acquisition
      Nur Laily, Hindah Mustika, Sukma Irdiana, Rusdiyanto Rusdiyanto
    • Investigation
      Nur Laily, Hindah Mustika, Sukma Irdiana
    • Methodology
      Nur Laily, Hindah Mustika, Sukma Irdiana, Rusdiyanto Rusdiyanto, Marto Silalahi
    • Project administration
      Nur Laily, Hindah Mustika, Sukma Irdiana
    • Resources
      Nur Laily, Hindah Mustika, Sukma Irdiana, Rusdiyanto Rusdiyanto, Marto Silalahi
    • Software
      Nur Laily, Hindah Mustika, Sukma Irdiana
    • Supervision
      Nur Laily, Hindah Mustika, Sukma Irdiana
    • Validation
      Nur Laily, Hindah Mustika, Sukma Irdiana, Rusdiyanto Rusdiyanto, Marto Silalahi
    • Writing – original draft
      Nur Laily, Hindah Mustika, Sukma Irdiana
    • Writing – review & editing
      Nur Laily, Hindah Mustika, Sukma Irdiana, Rusdiyanto Rusdiyanto, Marto Silalahi