Influence of knowledge hiding on innovation climate: The moderating role of artificial intelligence adoption
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Received November 30, 2025;Accepted March 17, 2026;Published March 30, 2026
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Author(s)Kritsakorn JiraphanumesLink to ORCID Index: https://orcid.org/0000-0002-2400-7085
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DOIhttp://dx.doi.org/10.21511/kpm.10(1).2026.11
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Article InfoVolume 10 2026, Issue #1, pp. 155-169
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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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JEL Classification (Paper profile tab)O33, M15, M10
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References50
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Tables4
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Figures2
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- Figure 1. Results of the structural model with moderation effects
- Figure 2. Moderating effect of artificial intelligence adoption
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- Table 1. Sample characteristics
- Table 2. Measurement model and structural model assessment
- Table 3. Predictor assessment
- Table A1. Constructs and measurement items
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Conceptualization
Kritsakorn Jiraphanumes
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Data curation
Kritsakorn Jiraphanumes
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Formal Analysis
Kritsakorn Jiraphanumes
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Investigation
Kritsakorn Jiraphanumes
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Methodology
Kritsakorn Jiraphanumes
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Project administration
Kritsakorn Jiraphanumes
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Validation
Kritsakorn Jiraphanumes
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Visualization
Kritsakorn Jiraphanumes
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Writing – original draft
Kritsakorn Jiraphanumes
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Writing – review & editing
Kritsakorn Jiraphanumes
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Conceptualization
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Fintech in the eyes of Millennials and Generation Z (the financial behavior and Fintech perception)
Mohannad A. M. Abu Daqar
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Samer Arqawi ,
Sharif Abu Karsh
doi: http://dx.doi.org/10.21511/bbs.15(3).2020.03
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The resource-based view: a tool of key competency for competitive advantage
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Knowledge management technology: human-computer interaction & cultural perspective on pattern of retrieval, organization, use, and sharing of information and knowledge
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