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Journals / Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi / 2025 / Sayı: YZ (AI) ve Gelecek Perspektifler Özel Sayı

Artificial Intelligence Adaptivance Scale (AIAS): Validity and reliability study

Yapay Zekâya Uyum Ölçeği (YZYÖ): Geçerlik ve güvenirlik çalışması

Hasan Saltuk Durak(DÜZCE ÜNİVERSİTESİ, SOSYAL BİLİMLER MESLEK YÜKSEKOKULU)0000-0003-1994-0011
Mehmet Fatih Kanoğlu(YOZGAT BOZOK ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ, SAĞLIK YÖNETİMİ BÖLÜMÜ, SAĞLIK YÖNETİMİ PR.)0000-0001-6551-7332
Pages
242–259
Publication Date
2025-12-07
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In conclusion, the developed Artificial Intelligence Adaptation Scale provides a valid and reliable measurement tool to assess employees’ attitudes and adaptation levels toward AI technologies, contributing both to academic research and practical applications in organizational settings.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of this study is to develop a valid and reliable scale to measure employees’ adaptation levels to artificial intelligence (AI) technologies.
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Key Findings & Data:Initially, a 20-item pool was generated through literature review, expert opinions, and a pilot study.
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Conclusion & Impact:In conclusion, the developed Artificial Intelligence Adaptation Scale provides a valid and reliable measurement tool to assess employees’ attitudes and adaptation levels toward AI technologies, contributing both to academic research and practical applications in organizational settings.
🔬Design:Lab & Field Experiment
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Zekâ · Ölçek Geliştirme · Dijital Dönüşüm

Abstract

The aim of this study is to develop a valid and reliable scale to measure employees’ adaptation levels to artificial intelligence (AI) technologies. The scale development process was carried out systematically by following the main steps suggested in the literature. Initially, a 20-item pool was generated through literature review, expert opinions, and a pilot study. After content validity assessments and preliminary analyses, the number of items was reduced to 14. Based on the results of Exploratory Factor Analysis (EFA), two items with similar loadings and two items forming a single factor were removed, and the scale was finalized with 10 items. The resulting one-dimensional structure explained 54.84% of the total variance, with factor loadings ranging between 0.615 and 0.787. Confirmatory Factor Analysis (CFA) confirmed the single-factor structure with strong fit indices (χ²/sd=2.93; RMSEA= 0.058; CFI= 0.975; GFI= 0.962). Reliability analyses also revealed high internal consistency (Cronbach’s Alpha=0.906; CR=0.922; AVE=0.543). In conclusion, the developed Artificial Intelligence Adaptation Scale provides a valid and reliable measurement tool to assess employees’ attitudes and adaptation levels toward AI technologies, contributing both to academic research and practical applications in organizational settings.

Özet

Bu araştırmanın amacı, çalışanların yapay zekâ teknolojilerine uyum düzeylerini ölçebilecek geçerli ve güvenilir bir ölçek geliştirmektir. Çalışma, ölçek geliştirme sürecinde izlenmesi gereken aşamaları dikkate alarak yürütülmüştür. Öncelikle literatür taraması, uzman görüşleri ve pilot çalışma (ön uygulama) sonucunda 20 maddelik madde havuzu oluşturulmuş; yapılan içerik geçerliliği değerlendirmeleri ve ön analizler sonucunda bu sayı 14’e düşürülmüştür. Açıklayıcı Faktör Analizi (AFA) ile benzer yüklenme gösteren iki madde ve diğer tek başına kalan iki madde çıkarılarak ölçek 10 maddeye indirgenmiştir. Elde edilen yapı, toplam varyansın %54,84’ünü açıklamış ve faktör yükleri 0,615 ile 0,787 arasında değişmiştir. Daha sonra uygulanan Doğrulayıcı Faktör Analizi (DFA) sonucunda, modelin tek boyutlu yapısının güçlü bir uyum sergilediği görülmüştür (χ²/sd=2,93; RMSEA= 0,058; CFI= 0,975; GFI= 0,962). Ayrıca, ölçeğin güvenilirliği Cronbach’s Alpha=0,906; CR= 0,922 ve AVE= 0,543 değerleri ile yüksek bulunmuştur. Sonuç olarak geliştirilen Yapay Zekâya Uyum Ölçeği, çalışanların yapay zekâ teknolojilerine yönelik tutumlarını ve uyum düzeylerini ölçmede kullanılabilecek güvenilir ve geçerli bir araç olarak literatüre kazandırılmıştır.

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Cite This Article

Durak, H. S., Kanoğlu, M. F., *, H. S. D. (2025). Artificial Intelligence Adaptivance Scale (AIAS): Validity and reliability study. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, Sayı: YZ (AI) ve Gelecek Perspektifler Özel Sayı, 242–259. https://doi.org/10.30783/nevsosbilen.1769303

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References (54)

  1. Akbaş, A. (2024). Yapay zekâ ile toplumsal dönüşüm: sosyolojik perspektif. Uluslararası Beşerî Bilimler ve Eğitim Dergisi 10(22). https://doi.org/10.59304/ijhe.1477203
  2. Akkaya, B., Özkan, A., & Özkan, H. (2021). Yapay zekâ kaygı (YZK) ölçeği: Türkçeye uyarlama, geçerlik ve güvenirlik çalışması. Alanya Akademik Bakış, 5(2), 1125-1146.
  3. Almeida, F., Junça Silva, A., Lopes, S. L., & Braz, I. (2025). Understanding recruiters' acceptance of artificial intelligence: Insights from the Technology Acceptance Model. Applied Sciences, 15(2), 746. https://www.mdpi.com/2076-3417/15/2/746
  4. Bashir, K. (2023). The impact of artificial intelligence on employee performance and satisfaction: a study from academic sector. Available at SSRN 5200144.
  5. Behl, A., Chavan, M., Jain, K., & Sharma, I. (2022). The role of organizational culture and voluntariness in the adoption of artificial intelligence for disaster relief operations. International Journal of Manpower, 43(2), 569–592. https://www.emerald.com/ijm/article/43/2/569/305781
  6. Bhatt, M., & Shah, P. (2023). Acceptance of artificial intelligence in human resource practices by employees. In The adoption and effect of ARtificial Intelligence on human resources management, part B (13-30). Emerald Publishing Limited.
  7. Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford publications.
  8. Byrne, B. M. (2013). Structural equation modeling with Mplus: Basic concepts, applications, and programming. Routledge.
  9. Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis. Psychology press.
  10. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
  11. DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications. Sage publications.
  12. Field, A. (2024). Discovering statistics using IBM SPSS statistics. Sage publications limited.
  13. George, D., & Mallery, P. (2024). IBM SPSS statistics 29 step by step: A simple guide and reference. Routledge.
  14. Ghadi, I., Alwi, N. H., Bakar, K. A., & Talib, O. (2012). Construct Validity Examination of Critical Thinking Dispositions for Undergraduate Students in University Putra Malaysia. Higher Education Studies, 2(2), 138-145.
  15. Hair Jr, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. In Multivariate data analysis (785-785).
  16. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.
  17. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks, CA: Sage Publications.
  18. Haji-Othman, Y., & Yusuff, M. S. S. (2022). Assessing reliability and validity of attitude construct using partial least squares structural equation modeling. Int J Acad Res Bus Soc Sci, 12(5), 378-385.
  19. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.
  20. Jerez-Jerez, M. J. (2025). A study of employee attitudes towards AI, its effect on sustainable development goals and non-financial performance in independent hotels. International Journal of Hospitality Management, 124, 103987.
  21. Ibrahim, F., Münscher, J. C., Daseking, M., & Telle, N. T. (2025). The technology acceptance model and adopter type analysis in the context of artificial intelligence. Frontiers in Artificial Intelligence, 7, 1496518.
  22. Ikromov, A. (2024). Exploring the Impact of Artificial Intelligence Adoption on Workplace Job Satisfaction: A Study of German Employees in Manufacturing and Finance & Insurance Sectors.
  23. İnce, H., İmamoğlu, S. E., & İmamoğlu, S. Z. (2021). Yapay Zekâ Uygulamalarının Karar Verme Üzerine Etkileri: Kavramsal Bir Çalışma. International Review of Economics and Management, 9(1), 50-63. https://doi.org/10.18825/iremjournal.866432
  24. Kaiser, H. F. (1974). An index of factorial simplicity. psychometrika, 39(1), 31-36.
  25. Karabağ, M. (2021). Ahlaki değerlerin kodlanabilmesi bağlamında yapay zekâ etiğine kuramsal bir bakış. TRT Akademi, 6(13), 748-767. https://doi.org/10.37679/trta.954641
  26. Karataş, M., Güre, M. D. P., & Başcıllar, M. (2022). Dijital çağda sosyal algoritmalar: Yapay zekâ ve sosyal hizmet. Sosyal Politika Çalışmaları Dergisi 22(56). https://doi.org/10.21560/spcd.vi.1081060
  27. Khan, F. A., Khan, N. A., & Aslam, A. (2024). Adoption of artificial intelligence in human resource management: an application of TOE-TAM model. Research and review: human resource and labour management, 5, 22-36.
  28. Kim, B. J., & Lee, J. (2024). The mental health implications of artificial intelligence adoption: the crucial role of self-efficacy. Humanities and Social Sciences Communications, 11(1), 1-15.
  29. Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications.
  30. Liias, E. (2025). From resistance to resilience: understanding employee perceptions and supporting technological adaptation process of artificial intelligence.
  31. Malik, N., Tripathi, S. N., Kar, A. K., & Gupta, S. (2022). Impact of artificial intelligence on employees working in industry 4.0 led organizations. International Journal of Manpower, 43(2), 334-354.
  32. Mogaji, E., Viglia, G., & Srivastava, P. (2024). Is it the end of the technology acceptance model in the era of generative artificial intelligence? International Journal of Contemporary Hospitality Management, 36(10), 3324–3343. https://www.emerald.com/ijchm/article/36/10/3324/1226004
  33. Mohr, S., & Kühl, R. (2021). Acceptance of artificial intelligence in German agriculture: An application of the technology acceptance model and the theory of planned behavior. Precision Agriculture, 22(6), 1630–1650. https://link.springer.com/article/10.1007/s11119-021-09814-x
  34. Morales-García, W. C., Sairitupa-Sanchez, L. Z., Morales-García, S. B., & Morales-García, M. (2024). Adaptation and Psychometric Properties of an Attitude toward Artificial Intelligence Scale (AIAS-4) among Peruvian Nurses. Behavioral Sciences, 14(6), 437.
  35. Möslein, F., & Bora Çınar, S. (2021). Robots in the Boardroom: Artificial Intelligence and Corporate Law. Istanbul Law Review, 79(2), 699-728. https://doi.org/10.26650/mecmua.2021.79.2.0004
  36. Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance model of artificial intelligence (AI)-based technologies in construction firms: Applying the Technology Acceptance Model (TAM) in combination with the Technology–Organisation–Environment (TOE) framework. Buildings, 12(2), 90.
  37. Na, S., Heo, S., Choi, W., Kim, C., & Whang, S. W. (2023). Artificial intelligence (AI)-based technology adoption in the construction industry: A cross-national perspective using the Technology Acceptance Model. Buildings, 13(10), 2518. https://www.mdpi.com/2075-5309/13/10/2518
  38. Orellana, O. (2025). Exploration of employee attitudes in AI adoption (Master’s thesis, University of Vaasa).
  39. Özkan, H., Özkan, A., & Akkaya, B. (2021). Yapay zekâ kaygı (yzk) ölçeği: türkçeye uyarlama, geçerlik ve güvenirlik çalışması. Alanya Akademik Bakış 5(2). https://doi.org/10.29023/alanyaakademik.833668
  40. Parasuraman, A. (2000). Technology Readiness Index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320.
  41. Park, J., Woo, S. E., & Kim, J. (2024). Attitudes towards artificial intelligence at work: Scale development and validation. Journal of Occupational and Organizational Psychology, 97(3), 920-951.
  42. Presbitero, A., & Teng-Calleja, M. (2023). Job attitudes and career behaviors relating to employees' perceived incorporation of artificial intelligence in the workplace: a career self-management perspective. Personnel Review, 52(4), 1169-1187.
  43. Ransbotham, S., Khodabandeh, S., Fehling, R., LaFountain, B., & Kiron, D. (2021). The Cultural Benefits of Artificial Intelligence in the Enterprise. MIT Sloan Management Review and Boston Consulting Group Report.
  44. Satici, S. A., Okur, S., Yilmaz, F. B., & Grassini, S. (2025). Psychometric properties and Turkish adaptation of the artificial intelligence attitude scale (AIAS-4): evidence for construct validity. BMC psychology, 13(1), 1-14.
  45. Stevens, A. F., & Stetson, P. (2023). Theory of trust and acceptance of artificial intelligence technology (TrAAIT): An instrument to assess clinician trust and acceptance of artificial intelligence. Journal of biomedical informatics, 148, 104550.
  46. Takıl, N., Erden, N. K., & Sarı, A. B. (2022). Farklı meslek grubu adaylarının YZteknolojisine yönelik kaygı seviyesinin incelenmesi. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 25(48), 343-353. https://doi.org/10.31795/baunsobed.1165386
  47. Tavakoli, S. S., Mozaffari, A., Danaei, A., & Rashidi, E. (2023). Explaining the effect of artificial intelligence on the technology acceptance model in media: A cloud computing approach. Electronic Library, 41(1), 1–18. https://www.emerald.com/el/article/41/1/1/36390
  48. Tavşancıl, E. (2014). Tutumların Ölçülmesi ve SPSS ile Veri Analizi. Nobel Akademik Yayıncılık. 5. Baskı. Ankara.
  49. Tutar, H., Eryüzlü, H., Mutlu, H. T., & Nam, S. (2024). Bilimsel analiz teknikleri (Nicel, nitel, karma yöntem ve ekonometrik analiz teknikleri), Kocaeli:Umuttepe Yayıncılık.
  50. Xu, G., Xue, M., & Zhao, J. (2023). The association between artificial intelligence awareness and employee depression: the mediating role of emotional exhaustion and the moderating role of perceived organizational support. International Journal of Environmental Research and Public Health, 20(6), 5147.
  51. Wang, Y. Y., & Chuang, Y. W. (2024). Artificial intelligence self-efficacy: Scale development and validation. Education and Information Technologies, 29(4), 4785-4808.
  52. Wang, Y. Y., & Wang, Y. S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619-634.
  53. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
  54. Ziegler, M., Kemper, C. J., & Kruyen, P. (2014). Short scales–Five misunderstandings and ways to overcome them. Journal of Individual Differences.