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İşletmeler Arası (B2B) Pazarda Yapay Zekâ Kaygısı ve Okuryazarlığının Yapay Zeka Teknolojilerinin Kullanılma Niyeti Üzerindeki Etkisi

Year 2025, Volume: 8 Issue: 3, 399 - 425, 31.07.2025
https://doi.org/10.33723/rs.1708096

Abstract

Bu çalışma, teknoloji kabul modeli çerçevesinde, işletmeler arası pazarlarda yapay zekâ teknolojilerinin benimsenmesine etki eden yapay zekâ okuryazarlığı ve yapay zekâya yönelik kaygı düzeylerini incelemeyi amaçlamaktadır. Türkiye’de çeşitli sektörlerde faaliyet gösteren 407 karar vericiye uygulanan çevrim içi anket aracılığıyla elde edilen veriler, yapay zekâ okuryazarlığının özellikle uygulamaya yönelik bilgi düzeyinin kaygıyı azaltıcı ve kullanım niyetini artırıcı etkiler yarattığını ortaya koymuştur. Ayrıca, yapay zekâya yönelik kaygının kullanım niyetini olumsuz yönde etkilediği belirlenmiştir. Bulgular, yapay zekâ okuryazarlığının yalnızca teknik bilgi ile sınırlı kalmayıp etik farkındalık ve eleştirel değerlendirme yetkinliklerini de içerecek şekilde ele alınması gerektiğini göstermektedir. Çalışma, bu bağlamda literatüre katkı sunmakta ve gelecekteki araştırmalar için yönlendirici bir çerçeve önermektedir.

References

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  • Balcioğlu, Y. S., and Artar, M. (2024). Artificial Intelligence in Employee Recruitment. Global Business and Organizational Excellence, 43(5), s.56-66.
  • Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, s.351-370.
  • Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., ... and Amodei, D. (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. arXiv preprint arXiv:1802.07228.
  • Bryman, A., and Cramer, D. (2012). Quantitative Data Analysis with IBM SPSS 17, 18 & 19: A Guide for Social Scientists. Routledge.
  • Brynjolfsson, E., and McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. WW Norton & company.
  • Carleton, R. N., Gosselin, P., and Asmundson, G. J. (2010). The Intolerance of Uncertainty Index: Replication and Extension with an English Sample. Psychological Assessment, 22(2), s.396.
  • Chiu, T. K., and Chai, C. S. (2020). Sustainable Curriculum Planning for Artificial Intelligence Education: a Self-Determination Theory Perspective. Sustainability, 12(14), No: 5568.
  • Choi, Y. J., Baek, J. H., Park, H. S., Shim, W. H., Kim, T. Y., Shong, Y. K., and Lee, J. H. (2017). A Computer-Aided Diagnosis System Using Artificial Intelligence for the Diagnosis and Characterization of Thyroid Nodules on Ultrasound: Initial Clinical Assessment. Thyroid, 27(4), s.546-552.
  • Civelek, M. E. (2009). İnternet Çağı Dinamikleri. Mustafa Emre Civelek.
  • Cooper, R. G. (2024). The AI Transformation of Product Innovation. Industrial Marketing Management, 119, s.62-74.
  • Cox, M., and Ellsworth, D. (1997, October). Application-Controlled Demand Paging for Out-of-Core Visualization. In Proceedings. Visualization'97 IEEE. (Cat. No. 97CB36155), s.235-244
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  • Davenport, T. H., and Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review, 96(1), s.108-116.
  • Davis, F. D. (1989). Technology Acceptance Model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205(219), s.5.
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  • Fishbein, M. and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading.
  • Frey, C. B. and Osborne, M. A. (2017). The Future of Employment: How Susceptible are Jobs to Computerisation? Technological Forecasting and Social Change, 114, s.254-280.
  • Gefen, D., & Straub, D. W. (2000). The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption. Journal of the association for Information Systems, 1(1), s.8.
  • Ha, J. G., Page, T., and Thorsteinsson, G. (2011). A Study on Technophobia and Mobile Device Design. International Journal of Contents, 7(2), s.17-25.
  • Haenlein, M., and Kaplan, A. (2019). A Brief History of Artificial Intelligence: On The Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), s.5-14.
  • Haenlein, M., Kaplan, A., Tan, C. W., and Zhang, P. (2019). Artificial Intelligence (AI) and Management Analytics. Journal of Management Analytics, 6(4), s.341-343.
  • Hand, C., Bohn, C., Tannir, S., Ulrich, M., Saniei, S., Girod-Hoffman, M., ... & Forsythe, B. (2025). American Academy of Orthopedic Surgery OrthoInfo Provides More Readable Information Regarding Rotator Cuff Injury than ChatGPT. Journal of ISAKOS, No: 100841
  • Harari, Y. N. (2018). 21 Lessons for The 21st Century:'Truly Mind-Expanding... Ultra-Topical'Guardian. Random House.
  • Huang, M. H., and Rust, R. T. (2021). A Strategic Framework for Artificial Intelligence in Marketing. Journal of the Academy of Marketing Science, 49, s.30-50.
  • Jarek, K., and Mazurek, G. (2019). Marketing and Artificial Intelligence. Central European Business Review, 8(2)
  • Jobin, A., Ienca, M., and Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. Nature Machine Intelligence, 1(9), s.389-399.
  • Kim, H. W., and Kankanhalli, A. (2009). Investigating User Resistance to Information Systems Implementation: A Status Quo Bias Perspective. MIS Quarterly, s.567-582.
  • Kim, J., and Forsythe, S. (2008). Adoption of Virtual Try-on Technology for Online Apparel Shopping. Journal of Interactive Marketing, 22(2), s.45-59.
  • Kocabıyık, T., Karaatlı, M., Özsoy, M., and Özer, M. F. (2024). Cryptocurrency Portfolio Management: A Clustering-Based Association ATpproach. Ekonomika, 103(1), s.25–43.
  • Laupichler, M. C., Aster, A., Haverkamp, N., and Raupach, T. (2023). Development of The “Scale for the Assessment of Non-Experts’ AI Literacy”–An Exploratory Factor Analysis. Computers in Human Behavior Reports, 12, No: 100338.
  • Long, D., and Magerko, B. (2020, April). What is AI literacy? Competencies and Design Considerations. In Proceedings of The 2020 CHI Conference on Human Factors in Computing Systems, s.1-16).
  • Lu, J.; Yu, C.S.; Liu, C. and Yao, J.E. (2003). Technology Acceptance Model for Wireless Internet, Internet Research: Electronic Networking Applications and Policy, 13(3): s.206-222.
  • Martin, K., and Shilton, K. (2016). Putting Mobile Application Privacy in Context: An Empirical Study of User Privacy Expectations for Mobile Devices. The Information Society, 32(3), s.200-216.
  • Moreno-Guerrero, A. J., López-Belmonte, J., Marín-Marín, J. A., & Soler-Costa, R. (2020). Scientific Development of Educational Artificial Intelligence in Web of Science. Future Internet, 12(8), No: 124.
  • O'neil, C. (2017). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
  • Passig, D. (2011). The Impact of Immersive Virtual Reality on Educators’ Awareness of The Cognitive Experiences of Pupils with Dyslexia. Teachers College Record, 113(1), s.181-204.
  • Powell, A., Bagilhole, B., and Dainty, A. (2009). How Women Engineers Do and Undo Gender: Consequences for Gender Equality. Gender, Work and Organization, 16(4), s.411-428.
  • Russell, S. J., and Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson.
  • Turing, A. M. (1950). Mind. Mind, 59(236), s.433-460.
  • Venkatesh, V., and Davis, F. D. (2000). A Theoretical Extension of The Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), s.186-204.
  • Venkatesh, V., Thong, J. Y., and Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending The Unified Theory of Acceptance and Use of Technology. MIS Quarterly, s.157-178.
  • Wang, B., Rau, P. L. P., and Yuan, T. (2023). Measuring User Competence in Using Artificial Intelligence: Validity and Reliability of Artificial Intelligence Literacy Scale. Behaviour and Information Technology, 42(9), s.1324-1337.
  • Wu, H., and Dong, Z. (2025). What Motivates Second Language Majors to Use Generative AI for Informal Learning? Insights from the Theory of Planned Behavior. IEEE Access.
  • Yılmaz, F. G. K., and Yılmaz, R. (2023). Yapay Zekâ Okuryazarlığı Ölçeğinin Türkçeye Uyarlanması. Bilgi ve İletişim Teknolojileri Dergisi, 5(2), s.172-190
  • Yilmaz, R., and Yilmaz, F. G. K. (2023). Augmented Intelligence in Programming Learning: Examining Student Views on The Use of ChatGPT for Programming Learning. Computers in Human Behavior: Artificial Humans, 1(2), No: 100005.
  • Zhou, M., Hu, Q., Hong, X., Song, X., and Zhou, Y. (2025). Evaluating ChatGPT-4o’s Web-Enhanced Responses in Patient Education: Ankle Stabilization Surgery as a Case Study. Available at SSRN 5135847.

THE EFFECT OF ARTIFICIAL INTELLIGENCE ANXIETY AND LITERACY ON THE INTENTION TO USE ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN THE BUSINESS-TO-BUSINESS (B2B) MARKET

Year 2025, Volume: 8 Issue: 3, 399 - 425, 31.07.2025
https://doi.org/10.33723/rs.1708096

Abstract

This research aims to examine the levels of AI literacy and anxiety towards AI that affect the adoption of AI technologies in business-to-business markets within the framework of the technology acceptance model. The data obtained through an online questionnaire administered to 407 decision-makers operating in various sectors in Turkey revealed AI literacy, efficient knowledge, reduced anxiety, and increased intention to use. In addition, it was determined that anxiety toward artificial intelligence negatively affected the intention to use it. The findings suggest that artificial intelligence literacy should not be limited to technical knowledge but should include ethical awareness and critical evaluation competencies. In this context, the study contributes to the literature and suggests a guiding framework for future research.

References

  • Accenture (2020). GDPR and AI Solutions: Cudia, C. P., and Legaspi, J. L. R. (2024). Strategic Management of Technological Frontiers in Banking: Challenges and Strategies for Cloud Adoption, Big Data Analytics, and AI Integration. Library Progress International, 44(3), s.10173-10192.
  • Acemoglu, D., Autor, D., Hazell, J., & Restrepo, P. (2022). Artificial Intelligence and Jobs: Evidence from Online Vacancies. Journal of Labor Economics, 40(S1), s.293-340.
  • AI Adoption and Business Impact Study: Cooper, R. G. (2024). Overcoming Roadblocks to AI Adoption in Innovation. Research-Technology Management, 67(5), s.23-29.
  • Akkaya, B., Özkan, A., & Özkan, H. (2021). Yapay Zekâ Kaygı (YZK) Ölçeği: Türkçeye Uyarlama, Geçerlik ve Güvenirlik Çalışması. Alanya Akademik Bakış, 5(2), s.1125-1146.
  • Balcioğlu, Y. S., and Artar, M. (2024). Artificial Intelligence in Employee Recruitment. Global Business and Organizational Excellence, 43(5), s.56-66.
  • Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, s.351-370.
  • Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., ... and Amodei, D. (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. arXiv preprint arXiv:1802.07228.
  • Bryman, A., and Cramer, D. (2012). Quantitative Data Analysis with IBM SPSS 17, 18 & 19: A Guide for Social Scientists. Routledge.
  • Brynjolfsson, E., and McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. WW Norton & company.
  • Carleton, R. N., Gosselin, P., and Asmundson, G. J. (2010). The Intolerance of Uncertainty Index: Replication and Extension with an English Sample. Psychological Assessment, 22(2), s.396.
  • Chiu, T. K., and Chai, C. S. (2020). Sustainable Curriculum Planning for Artificial Intelligence Education: a Self-Determination Theory Perspective. Sustainability, 12(14), No: 5568.
  • Choi, Y. J., Baek, J. H., Park, H. S., Shim, W. H., Kim, T. Y., Shong, Y. K., and Lee, J. H. (2017). A Computer-Aided Diagnosis System Using Artificial Intelligence for the Diagnosis and Characterization of Thyroid Nodules on Ultrasound: Initial Clinical Assessment. Thyroid, 27(4), s.546-552.
  • Civelek, M. E. (2009). İnternet Çağı Dinamikleri. Mustafa Emre Civelek.
  • Cooper, R. G. (2024). The AI Transformation of Product Innovation. Industrial Marketing Management, 119, s.62-74.
  • Cox, M., and Ellsworth, D. (1997, October). Application-Controlled Demand Paging for Out-of-Core Visualization. In Proceedings. Visualization'97 IEEE. (Cat. No. 97CB36155), s.235-244
  • Dai, Y., Chai, C. S., Lin, P. Y., Jong, M. S. Y., Guo, Y., and Qin, J. (2020). Promoting Students’ Well-Being by Developing Their Readiness for the Artificial Intelligence Age. Sustainability, 12(16), No: 6597.
  • Davenport, T. H., and Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review, 96(1), s.108-116.
  • Davis, F. D. (1989). Technology Acceptance Model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205(219), s.5.
  • Durmuş, B., Ekizler, H., and Bolelli, M. (2024). Behind the Youtube Addiction and Online Stickiness. Does Fear of Missing Out Count?. Istanbul Business Research, 53(2), s.137-159.
  • Eastin, M. S. (2002). Diffusion of E-Commerce: an Analysis of The Adoption of Four E-Commerce Activities. Telematics and Informatics, 19(3), s.251-267.
  • European Commission (2022). Artificial Intelligence and The Law: Addressing Transparency and Accountability in The EU: Tzimas, T. (2023). Algorithmic Transparency and Explainability Under EU Law. European Public Law, 29(4).
  • Fishbein, M. and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading.
  • Frey, C. B. and Osborne, M. A. (2017). The Future of Employment: How Susceptible are Jobs to Computerisation? Technological Forecasting and Social Change, 114, s.254-280.
  • Gefen, D., & Straub, D. W. (2000). The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption. Journal of the association for Information Systems, 1(1), s.8.
  • Ha, J. G., Page, T., and Thorsteinsson, G. (2011). A Study on Technophobia and Mobile Device Design. International Journal of Contents, 7(2), s.17-25.
  • Haenlein, M., and Kaplan, A. (2019). A Brief History of Artificial Intelligence: On The Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), s.5-14.
  • Haenlein, M., Kaplan, A., Tan, C. W., and Zhang, P. (2019). Artificial Intelligence (AI) and Management Analytics. Journal of Management Analytics, 6(4), s.341-343.
  • Hand, C., Bohn, C., Tannir, S., Ulrich, M., Saniei, S., Girod-Hoffman, M., ... & Forsythe, B. (2025). American Academy of Orthopedic Surgery OrthoInfo Provides More Readable Information Regarding Rotator Cuff Injury than ChatGPT. Journal of ISAKOS, No: 100841
  • Harari, Y. N. (2018). 21 Lessons for The 21st Century:'Truly Mind-Expanding... Ultra-Topical'Guardian. Random House.
  • Huang, M. H., and Rust, R. T. (2021). A Strategic Framework for Artificial Intelligence in Marketing. Journal of the Academy of Marketing Science, 49, s.30-50.
  • Jarek, K., and Mazurek, G. (2019). Marketing and Artificial Intelligence. Central European Business Review, 8(2)
  • Jobin, A., Ienca, M., and Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. Nature Machine Intelligence, 1(9), s.389-399.
  • Kim, H. W., and Kankanhalli, A. (2009). Investigating User Resistance to Information Systems Implementation: A Status Quo Bias Perspective. MIS Quarterly, s.567-582.
  • Kim, J., and Forsythe, S. (2008). Adoption of Virtual Try-on Technology for Online Apparel Shopping. Journal of Interactive Marketing, 22(2), s.45-59.
  • Kocabıyık, T., Karaatlı, M., Özsoy, M., and Özer, M. F. (2024). Cryptocurrency Portfolio Management: A Clustering-Based Association ATpproach. Ekonomika, 103(1), s.25–43.
  • Laupichler, M. C., Aster, A., Haverkamp, N., and Raupach, T. (2023). Development of The “Scale for the Assessment of Non-Experts’ AI Literacy”–An Exploratory Factor Analysis. Computers in Human Behavior Reports, 12, No: 100338.
  • Long, D., and Magerko, B. (2020, April). What is AI literacy? Competencies and Design Considerations. In Proceedings of The 2020 CHI Conference on Human Factors in Computing Systems, s.1-16).
  • Lu, J.; Yu, C.S.; Liu, C. and Yao, J.E. (2003). Technology Acceptance Model for Wireless Internet, Internet Research: Electronic Networking Applications and Policy, 13(3): s.206-222.
  • Martin, K., and Shilton, K. (2016). Putting Mobile Application Privacy in Context: An Empirical Study of User Privacy Expectations for Mobile Devices. The Information Society, 32(3), s.200-216.
  • Moreno-Guerrero, A. J., López-Belmonte, J., Marín-Marín, J. A., & Soler-Costa, R. (2020). Scientific Development of Educational Artificial Intelligence in Web of Science. Future Internet, 12(8), No: 124.
  • O'neil, C. (2017). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
  • Passig, D. (2011). The Impact of Immersive Virtual Reality on Educators’ Awareness of The Cognitive Experiences of Pupils with Dyslexia. Teachers College Record, 113(1), s.181-204.
  • Powell, A., Bagilhole, B., and Dainty, A. (2009). How Women Engineers Do and Undo Gender: Consequences for Gender Equality. Gender, Work and Organization, 16(4), s.411-428.
  • Russell, S. J., and Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson.
  • Turing, A. M. (1950). Mind. Mind, 59(236), s.433-460.
  • Venkatesh, V., and Davis, F. D. (2000). A Theoretical Extension of The Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), s.186-204.
  • Venkatesh, V., Thong, J. Y., and Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending The Unified Theory of Acceptance and Use of Technology. MIS Quarterly, s.157-178.
  • Wang, B., Rau, P. L. P., and Yuan, T. (2023). Measuring User Competence in Using Artificial Intelligence: Validity and Reliability of Artificial Intelligence Literacy Scale. Behaviour and Information Technology, 42(9), s.1324-1337.
  • Wu, H., and Dong, Z. (2025). What Motivates Second Language Majors to Use Generative AI for Informal Learning? Insights from the Theory of Planned Behavior. IEEE Access.
  • Yılmaz, F. G. K., and Yılmaz, R. (2023). Yapay Zekâ Okuryazarlığı Ölçeğinin Türkçeye Uyarlanması. Bilgi ve İletişim Teknolojileri Dergisi, 5(2), s.172-190
  • Yilmaz, R., and Yilmaz, F. G. K. (2023). Augmented Intelligence in Programming Learning: Examining Student Views on The Use of ChatGPT for Programming Learning. Computers in Human Behavior: Artificial Humans, 1(2), No: 100005.
  • Zhou, M., Hu, Q., Hong, X., Song, X., and Zhou, Y. (2025). Evaluating ChatGPT-4o’s Web-Enhanced Responses in Patient Education: Ankle Stabilization Surgery as a Case Study. Available at SSRN 5135847.
There are 52 citations in total.

Details

Primary Language English
Subjects Consumer Behaviour
Journal Section Articles
Authors

Behlül Can Şengül 0009-0002-2035-1732

Ceyda Aysuna Türkyılmaz 0000-0002-9015-4980

Yusuf Ozan Yıldırım 0000-0002-0346-2660

Serdar Pirtini 0000-0002-9858-060X

Early Pub Date July 31, 2025
Publication Date July 31, 2025
Submission Date May 28, 2025
Acceptance Date July 11, 2025
Published in Issue Year 2025 Volume: 8 Issue: 3

Cite

APA Şengül, B. C., Aysuna Türkyılmaz, C., Yıldırım, Y. O., Pirtini, S. (2025). THE EFFECT OF ARTIFICIAL INTELLIGENCE ANXIETY AND LITERACY ON THE INTENTION TO USE ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN THE BUSINESS-TO-BUSINESS (B2B) MARKET. R&S - Research Studies Anatolia Journal, 8(3), 399-425. https://doi.org/10.33723/rs.1708096
R&S - Research Studies Anatolia Journal 

https://dergipark.org.tr/rs