IDEAS home Printed from https://ideas.repec.org/a/ist/ibsibr/v53y2024i3p299-325.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/95214EA59B8F4747AE061397A8D0EA87
    Download Restriction: no
    File URL: https://iupress.istanbul.edu.tr/en/journal/ibr/article/evaluating-the-effect-of-artificial-intelligence-on-perceived-business-performance-of-turkish-firms-in-technology-development-zones-in-turkiye
    Download Restriction: no
    File URL: https://libkey.io/10.26650/ibr.2024.53.1359058?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Robertson, Jeandri & Ferreira, Caitlin & Botha, Elsamari & Oosthuizen, Kim, 2024. "Game changers: A generative AI prompt protocol to enhance human-AI knowledge co-construction," Business Horizons, Elsevier, vol. 67(5), pages 499-510.
    2. Bavaresco, Rodrigo Simon & Nesi, Luan Carlos & Victória Barbosa, Jorge Luis & Antunes, Rodolfo Stoffel & da Rosa Righi, Rodrigo & da Costa, Cristiano André & Vanzin, Mariangela & Dornelles, Daniel & J, 2023. "Machine learning-based automation of accounting services: An exploratory case study," International Journal of Accounting Information Systems, Elsevier, vol. 49(C).
    3. Fernandez Martinez, Roberto & Lostado Lorza, Ruben & Santos Delgado, Ana Alexandra & Piedra, Nelson, 2021. "Use of classification trees and rule-based models to optimize the funding assignment to research projects: A case study of UTPL," Journal of Informetrics, Elsevier, vol. 15(1).
    4. Ayat Sami ODEIBAT, 2021. "The Effect Of Technology Evolution On The Future Of Jobs," Network Intelligence Studies, Romanian Foundation for Business Intelligence, Editorial Department, issue 17, pages 57-67, June.
    5. Neubert, Mitchell J. & Montañez, George D., 2020. "Virtue as a framework for the design and use of artificial intelligence," Business Horizons, Elsevier, vol. 63(2), pages 195-204.
    6. Li, Chia-Ying & Zhang, Jin-Ting, 2023. "Chatbots or me? Consumers’ switching between human agents and conversational agents," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    7. Alina Köchling & Marius Claus Wehner, 2020. "Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 795-848, November.
    8. Kamoonpuri, Sana Zehra & Sengar, Anita, 2023. "Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    9. Eliza Nichifor & Adrian Trifan & Elena Mihaela Nechifor, 2021. "Artificial Intelligence in Electronic Commerce: Basic Chatbots and Consumer Journey," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 1-87, February.
    10. Arias-Pérez, José & Vélez-Jaramillo, Juan, 2022. "Ignoring the three-way interaction of digital orientation, Not-invented-here syndrome and employee's artificial intelligence awareness in digital innovation performance: A recipe for failure," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    11. Chenyu Gu & Yu Zhang & Linhao Zeng, 2024. "Exploring the mechanism of sustained consumer trust in AI chatbots after service failures: a perspective based on attribution and CASA theories," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    12. Jingwen Dong & Siti Nurulain Mohd Rum & Khairul Azhar Kasmiran & Teh Noranis Mohd Aris & Raihani Mohamed, 2022. "Artificial Intelligence in Adaptive and Intelligent Educational System: A Review," Future Internet, MDPI, vol. 14(9), pages 1-11, August.
    13. Chen Yang & Jing Hu, 2022. "When do consumers prefer AI-enabled customer service? The interaction effect of brand personality and service provision type on brand attitudes and purchase intentions," Journal of Brand Management, Palgrave Macmillan, vol. 29(2), pages 167-189, March.
    14. Shamim, Saqib & Yang, Yumei & Ul Zia, Najam & Khan, Zaheer & Shariq, Syed Muhammad, 2023. "Mechanisms of cognitive trust development in artificial intelligence among front line employees: An empirical examination from a developing economy," Journal of Business Research, Elsevier, vol. 167(C).
    15. Jarrahi, Mohammad Hossein & Askay, David & Eshraghi, Ali & Smith, Preston, 2023. "Artificial intelligence and knowledge management: A partnership between human and AI," Business Horizons, Elsevier, vol. 66(1), pages 87-99.
    16. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    17. Cristian-Mihai Vidu & Florina Pinzaru & Andreea Mitan, 2022. "What managers of SMEs in the CEE region should know about challenges of artificial intelligence’s adoption? – an introductive discussion," Nowoczesne Systemy Zarządzania. Modern Management Systems, Military University of Technology, Faculty of Security, Logistics and Management, Institute of Organization and Management, issue 1, pages 63-76.
    18. Nick Drydakis, 2022. "Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic," Information Systems Frontiers, Springer, vol. 24(4), pages 1223-1247, August.
    19. Makarius, Erin E. & Mukherjee, Debmalya & Fox, Joseph D. & Fox, Alexa K., 2020. "Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization," Journal of Business Research, Elsevier, vol. 120(C), pages 262-273.
    20. Feng, Cai (Mitsu) & Botha, Elsamari & Pitt, Leyland, 2024. "From HAL to GenAI: Optimizing chatbot impacts with CARE," Business Horizons, Elsevier, vol. 67(5), pages 537-548.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ist:ibsibr:v:53:y:2024:i:3:p:299-325. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Istanbul University Press Operational Team (Ertuğrul YAŞAR) (email available below). General contact details of provider: https://edirc.repec.org/data/isisttr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.