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Evaluating the Effect of Artificial Intelligence on Perceived Business Performance of Turkish Firms in Technology Development Zones in Türkiye

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  • Salih Caner

    (Girne American University)

Abstract

Artificial intelligence (AI) has become integrated into many areas of business and daily life; however, its strategic impact on organizations in Türkiye has not been thoroughly studied. This study addresses this gap by empirically assessing the effect of AI adoption on perceived business performance. Additionally, using previously developed AI business strategy perspectives, the differences in strategic approaches between AI-adopting and non-adopting companies were examined.The research surveyed firms located in Technology Development Zones (TDZs) in Türkiye, which are generally more technology-oriented, validating previously constructed AI business strategy perspectives and examining AI’s effect on perceived business performance. The study found a statistically significant, albeit low-level, positive correlation between AI implementation and perceived business performance. Furthermore, it revealed differences in strategic approaches between AI-adopting and non-adopting companies based on previously constructed AI business strategy perspectives.The study concludes that AI adoption shows promise in enhancing business performance, but its effect is modest in TDZs in Türkiye. This research contributes to the growing body of knowledge on AI in business by providing empirical evidence of AI’s effect on perceived business performance in an emerging economy context and highlighting strategic differences between AI adopters and non-adopters. These findings have important implications for managers considering AI adop t ion and policymakers shaping AI-related regulations.

Suggested Citation

  • Salih Caner, 2024. "Evaluating the Effect of Artificial Intelligence on Perceived Business Performance of Turkish Firms in Technology Development Zones in Türkiye," Istanbul Business Research, Istanbul University Business School, vol. 53(3), pages 299-325, December.
    • Handle: RePEc:ist:ibsibr:v:53:y:2024:i:3:p:299-325
    DOI: 10.26650/ibr.2024.53.1359058
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    References listed on IDEAS

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    1. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
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