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The Effect of Digital Transformation on Startups’ Innovation Performance

Document Type : Research Paper

Authors

1 Associate Professor, Department of System Management and decision Science, College of Management, University of Tehran, Tehran, Iran

2 PhD Student of Industrial Management, Alborz Campus, University of Tehran, Tehran, Iran

Abstract

Digital transformation has been widely recognized as a critical driver of high-quality development in firms and a fundamental enabler of innovation-oriented strategies. Despite its growing significance, prior research has not sufficiently clarified the mechanisms through which digital transformation influences innovation performance, and existing findings remain inconclusive. While some studies suggest that digital transformation fosters innovation, others report adverse effects and fail to specify the role of big data capabilities, the Internet of Things (IoT), and organizational agility in this relationship.Building on the dynamic capabilities theory and systems engineering theory, this study adopts a strategy–behavior–performance framework to systematically examine how digital transformation enhances startups’ innovation performance through the development of big data capabilities, IoT, and organizational agility. Empirical evidence was collected from 203 questionnaires completed by CEOs, senior and middle managers, and IT specialists across 15 Iranian startups. The results reveal the chain-mediating effects of big data capabilities, IoT, and organizational agility, highlighting their pivotal role in shaping the outcomes of digital transformation.The findings indicate that digital transformation significantly improves startups’ innovation performance, with the three mediators big data capabilities, IoT, and organizational agility serving as crucial mechanisms in this process. This research not only provides empirical validation for the theoretical model of digital transformation but also offers actionable insights for startups seeking to craft effective strategies and optimize resource allocation in the digital era.

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References
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Business Intelligence Management Studies
Volume 15, Issue 56
June 2026
Pages 113-134
Files
History
  • Receive Date: 18 September 2025
  • Revise Date: 21 December 2025
  • Accept Date: 10 March 2026
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