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Architecting Agility: Unraveling the Impact of AI Capability on Organizational Change and Competitive Advantage

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Business Modeling and Software Design (BMSD 2023)

Abstract

With the progressing developments of Artificial Intelligence (AI) in business and society, understanding the role of AI in enabling organizational change and agility has become an increasingly relevant area of inquiry. However, despite this interest, the specific effects of an AI architecture capability that helps firms design and deploy AI technologies in the organization on firms’ dynamic change capability remains an underexplored area in the literature. To address this gap, we built upon the dynamic capabilities view and conducted an extensive survey of senior business and IT managers in the Netherlands. Based on a sample of 168 final respondents, we tested a research model with associated hypotheses using a composite-based structural equation modeling (SEM) approach. The analyses confirm the hypotheses and show that AI architecture positively enables the firm’s change capability. Furthermore, the firm’s change capability positively impacts operational agility. Hence, our work extends the current knowledge base of dynamic capabilities while offering various implications for practice. These practical recommendations could facilitate managers overcoming change capability and agility challenges.

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Notes

  1. 1.

    These are students who are studying in addition to their daily jobs and are, therefore, actually professionals.

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We want to thank all the respondents and organizations contributing to this research. This is much appreciated.

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Correspondence to Rogier van de Wetering .

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van de Wetering, R., de Weerd-Nederhof, P., Bagheri, S., Bons, R. (2023). Architecting Agility: Unraveling the Impact of AI Capability on Organizational Change and Competitive Advantage. In: Shishkov, B. (eds) Business Modeling and Software Design. BMSD 2023. Lecture Notes in Business Information Processing, vol 483. Springer, Cham. https://doi.org/10.1007/978-3-031-36757-1_12

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  • DOI: https://doi.org/10.1007/978-3-031-36757-1_12

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