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Improving innovation performance through learning capability and adaptive capability: The moderating role of big data analytics

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  • Szu-Yu Kuo

Abstract

Organizations’ learning capability (LC) and adaptive capability (AC) are very important for addressing complex challenges and performance, particularly in situations of environmental dynamism (ED). Drawing on the theory of the resource-based view (RBV) and an uncertainty perspective, this study theorised and examined how big data analytics (BD) improve organisations’ innovation performance (IP). Based on a sample of 228 respondents in Taiwan, structural equation modelling was used to investigate the effects of LC and AC on IP as well as the moderating effects of ED and BD among these major dimensions in the context of container shipping. These findings show that LC had a positive influence on both AC and IP. Additionally, both AC and BD had a positive influence on IP. Although ED negatively moderated the effect of LC, AC, and IP, we found that BD had a positively moderating effect on these dimensions, and thus improved performance.

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  • Szu-Yu Kuo, 2024. "Improving innovation performance through learning capability and adaptive capability: The moderating role of big data analytics," Knowledge Management Research & Practice, Taylor & Francis Journals, vol. 22(4), pages 364-376, July.
    • Handle: RePEc:taf:tkmrxx:v:22:y:2024:i:4:p:364-376
    DOI: 10.1080/14778238.2023.2212182
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