Influence of knowledge hiding on innovation climate: The moderating role of artificial intelligence adoption

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Type of the article: Research Article

In emerging digital economies, knowledge hiding can disrupt organizational knowledge flows that support innovation, yet empirical evidence on how artificial intelligence adoption shapes these effects remains limited. This study examines how knowledge hiding influences knowledge integration capability and innovation climate in digital firms and tests the moderating role of artificial intelligence adoption. Data were collected in May 2025 through a questionnaire survey of 145 firms operating in Thailand’s New S-Curve digital sectors. Respondents included senior executives, middle managers, and knowledge management specialists involved in artificial intelligence implementation, knowledge management, and innovation activities. A total of 426 responses were obtained and aggregated to the firm level. The data were analyzed using partial least squares structural equation modeling. Results show that knowledge hiding significantly reduces knowledge integration capability (β = −0.503, p < 0.001) and innovation climate (β = −0.339, p < 0.001), while knowledge integration capability positively affects innovation climate (β = 0.337, p < 0.001). Artificial intelligence adoption weakens the negative effects of knowledge hiding on knowledge integration capability (interaction β = 0. 359, p < 0.001) and innovation climate (interaction β = 0. 500, p < 0.001), indicating a buffering mechanism through improved access to organizational knowledge. These findings suggest that digital firms should address knowledge hiding while strengthening knowledge integration practices and implementing artificial intelligence in ways that complement collaborative knowledge processes.

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    • Figure 1. Results of the structural model with moderation effects
    • Figure 2. Moderating effect of artificial intelligence adoption
    • Table 1. Sample characteristics
    • Table 2. Measurement model and structural model assessment
    • Table 3. Predictor assessment
    • Table A1. Constructs and measurement items
    • Conceptualization
      Kritsakorn Jiraphanumes
    • Data curation
      Kritsakorn Jiraphanumes
    • Formal Analysis
      Kritsakorn Jiraphanumes
    • Investigation
      Kritsakorn Jiraphanumes
    • Methodology
      Kritsakorn Jiraphanumes
    • Project administration
      Kritsakorn Jiraphanumes
    • Validation
      Kritsakorn Jiraphanumes
    • Visualization
      Kritsakorn Jiraphanumes
    • Writing – original draft
      Kritsakorn Jiraphanumes
    • Writing – review & editing
      Kritsakorn Jiraphanumes