YAPAY ZEKA ÖZ-YETERLİLİK ÖLÇEĞİNİN TÜRKÇE’YE UYARLANMASI: GEÇERLİLİLİK VE GÜVENİRLİK ÇALIŞMASI

Yıl 2024, Cilt: 9 Sayı: 1, 135 - 148, 27.06.2024

Umut Uyan , Sait Uğur Gültekin

https://doi.org/10.54452/jrb.1415212

Öz

Son dönemde geliştirilen ve geleneksel iş yapma pratiklerimize meydan okuyan yapay zeka (YZ) teknolojileri, otonom araçlardan tıbbi teşhise kadar birçok alanda kullanılmaya başlanmıştır. Bahsedilen teknolojiler hızlı ve bağlama uyarlanabilir çıktılar sunabilmesi bakımından kullanıcılarına etkinliklerini arttırmayı vadetmektedir. Diğer taraftan insan-benzeri etkileşim deneyimi sunabilen bu teknolojiler makine-insan ilişkisini benzersiz bir boyuta taşımaktadır. Ancak bahsedilen teknolojilerin bireysel düzeyde benimsenmesi ve kullanımına yönelik bilimsel çabaya ihtiyaç duyulmaktadır. Bu bağlamda Wang ve Chuang (2023) dört boyuttan oluşan yapay zeka öz yeterlilik (YZÖY) ölçeğini oluşturmuşlardır. Mevcut çalışmanın amacı yabancı dilde oluşturulan ve yazında öncü nitelikte olan bu ölçüm aracının Türkçeye uyarlanmasıdır. Uyarlama çalışması için Munzur Üniversitesinde görev yapan 156 akademik ve idari personelden online anketler vasıtasıyla veri toplanmıştır. Keşifsel ve doğrulayıcı analizlerin sonucunda elde edilen bulgular orijinal ölçeğin Türkçe formunun geçerli ve güvenilir olduğunu göstermektedir. Kültürel doğrulaması yapılan ölçüm aracının ileride bu alanda gerçekleştirilecek Türkçe çalışmalara katkı sunması beklenmektedir.

Anahtar Kelimeler

Yapay zeka, öz-yeterlilik, yapay zeka öz-yeterliliği, ölçek uyarlaması

TURKISH ADAPTATION OF ARTIFICIAL INTELLIGENCE SELF-EFFICACY SCALE: VALIDITY AND RELIABILITY STUDY

Öz

The integration of artificial intelligence (AI) into numerous fields, from autonomous vehicles to medical diagnosis, has challenged our traditional business practices. Through providing fast, context-specific outputs, these technologies promise to enhance users' effectiveness. The machine-human interaction has been elevated with these technologies, which can simulate human actors. Yet, to fully understand how these technologies are adopted and used at the individual level, further scientific research is required. In this context, Wang and Chuang (2023) created the Artificial Intelligence Self-Efficacy Scale (AISE) consisting of four dimensions. The main purpose of the study is to adapt the above-mentioned pioneering instrument in a foreign language into Turkish. The data were collected from 156 academic and administrative staff working at Munzur University through online questionnaires. The findings obtained through exploratory and confirmatory analyses show that the Turkish form of the original scale is valid and reliable. The culturally validated measurement instrument is expected to contribute to future studies in this field.

Anahtar Kelimeler

Artificial intelligence, self-efficacy, artificial intelligence self-efficacy, scale adaptation

Kaynakça

• Akkaya, B., Özkan, A., & Özkan, H. (2021). Yapay zeka kaygı (YZK) ölçeği: Türkçeye uyarlama, geçerlik ve güvenirlik çalışması. Alanya Akademik Bakış, 5(2), 1125-1146.
• Al Mansoori, S., Salloum, S. A., & Shaalan, K. (2020). The impact of artificial intelligence and information technologies on the efficiency of knowledge management at modern organizations: a systematic review. In Recent Advances in Intelligent Systems and Smart Applications (pp.163-182). Springer.
• Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215.
• Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4(3), 359-373.
• Bayık, M. E., & Gürbüz, S. (2016). Ölçek uyarlamada metodoloji sorunu: Yönetim ve örgüt alanında uyarlanan ölçekler üzerinden bir araştırma. İş ve İnsan Dergisi, 3(1), 1-20.
• Betz, N. E. (2004). Contributions of self‐efficacy theory to career counseling: A personal perspective. The Career Development Quarterly, 52(4), 340-353.
• Brislin, R. W., Lonner, W. J. & Thorndike, R. M. (1973). Cross-cultural research methods. New York: John Wiley.
• Cao, D., Sun, Y., Goh, E., Wang, R., & Kuiavska, K. (2022). Adoption of smart voice assistants technology among Airbnb guests: A revised self-efficacy-based value adoption model (SVAM). International Journal of Hospitality Management, 101(2022), 1-9.
• Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K., & Qin, J. (2021). Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students. Educational Technology & Society, 24(3), 89-101.
• Compeau, D. R., & Higgins, C. A. (1995). Application of social cognitive theory to training for computer skills. Information Systems Research, 6(2), 118-143.
• Çelebi, C., Yilmaz, F., Demir, U., & Karakuş, F. (2023). Artificial Intelligence Literacy: An Adaptation Study. Instructional Technology and Lifelong Learning, 4(2), 291-306.
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly,13(3), 319-340.
• Deng, J., & Lin, Y. (2022). The benefits and challenges of ChatGPT: An overview. Frontiers in Computing and Intelligent Systems, 2(2), 81-83.
• Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48(2019), 63-71.
• French, R. (2001). “Negative capability”: managing the confusing uncertainties of change. Journal of Organizational Change Management, 14(5), 480-492.
• Grashof, N., & Kopka, A. (2023). Artificial intelligence and radical innovation: an opportunity for all companies?. Small Business Economics, 61(2), 771-797.
• Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. (2019). Multivariate data analysis (8th edn). Hampshire: Cengage Learning.
• Hemedoğlu, E., Koçak, M., Özkan, A., & Berberoğlugil, B. M. (2012). Psikolojik güçlendirmenin finansal olmayan performans üzerindeki etkileri. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 13(2), 87-105. Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343-367.
• Hong, J. W. (2022). I was born to love AI: the influence of social status on AI self-efficacy and intentions to use AI. International Journal of Communication, 16(2022), 172-191.
• Hubbart, J. A. (2023). Organizational Change: The challenge of change aversion. Administrative Sciences, 13(7), 162. Huffman, A. H., Whetten, J., & Huffman, W. H. (2013). Using technology in higher education: The influence of gender roles on technology self-efficacy. Computers in Human Behavior, 29(4), 1779-1786.
• Huo, W., Yuan, X., Li, X., Luo, W., Xie, J., & Shi, B. (2023). Increasing acceptance of medical AI: The role of medical staff participation in AI development. International Journal of Medical Informatics, 175(2023), 1-10.
• Hwang, Y., Lee, Y., & Shin, D. H. (2016). The role of goal awareness and information technology self-efficacy on job satisfaction of healthcare system users. Behaviour & Information Technology, 35(7), 548-558.
• Karakoç, F. Y., & Dönmez, L. (2014). Ölçek geliştirme çalışmalarında temel ilkeler. Tıp Eğitimi Dünyası, 13(40), 39-49.
• Kim, J., Kadkol, S., Solomon, I., Yeh, H., Soh, J. Y., Nguyen, T. M., ... & Ajilore, O. A. (2023). AI Anxiety: A Comprehensive Analysis of Psychological Factors and Interventions. SSRN, (Preprint).
• Kline, R. B. (2011). Principles and practice of structural equation modeling. (3rd edn). New York, NY: Guilford.
• Knowles, B., & Hanson, V. L. (2018). The wisdom of older technology (non) users. Communications of the ACM, 61(3), 72-77.
• Korteling, J. H., van de Boer-Visschedijk, G. C., Blankendaal, R. A., Boonekamp, R. C., & Eikelboom, A. R. (2021). Human-versus artificial intelligence. Frontiers in Artificial Intelligence, 4(2021), 1-13.
• Laver, K., George, S., Ratcliffe, J., & Crotty, M. (2012). Measuring technology self-efficacy: reliability and construct validity of a modified computer self-efficacy scale in a clinical rehabilitation setting. Disability and Rehabilitation, 34(3), 220-227.
• Lee, J. H., Kim, J. H., Kim, Y. H., Song, Y. M., & Gim, G. Y. (2021, February). Factors affecting the intention to use artificial intelligence-based recruitment system: a structural equation modeling (SEM) approach. In International Conference on Intelligence Science (pp. 111-124). Cham: Springer International Publishing.
• Li, J., & Huang, J. S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63(2020), 1-10.
• Liu, K., & Tao, D. (2022). The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services. Computers in Human Behavior, 127(2022), 1-11.
• Morales-Rodríguez, F. M., & Pérez-Mármol, J. M. (2019). The role of anxiety, coping strategies, and emotional intelligence on general perceived self-efficacy in university students. Frontiers in Psychology, 10(2019), 1-9.
• Mozahem, N. A., Boulad, F. M., & Ghanem, C. M. (2021). Secondary school students and self-efficacy in mathematics: Gender and age differences. International Journal of School & Educational Psychology, 9(1), 142-152.
• Pelau, C., Dabija, D. C., & Ene, I. (2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior, 122(2021), 1-9.
• Rahmawati, R. N. (2019). Self-efficacy and use of e-learning: A theoretical review technology acceptance model (TAM). American Journal of Humanities and Social Sciences Research, 3(5), 41-55.
• Ropp, M. M. (1999). Exploring individual characteristics associated with learning to use computers in preservice teacher preparation. Journal of Research on Computing in Education, 31(4), 402-424.
• Salvagno, M., Taccone, F. S., & Gerli, A. G. (2023). Can artificial intelligence help for scientific writing?. Critical Care, 27(1), 1-5.
• Sarker, I. H. (2022). Ai-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3(2), 158.
• Song, S. Y., & Kim, Y. K. (2022). Factors influencing consumers’ intention to adopt fashion robot advisors: psychological network analysis. Clothing and Textiles Research Journal, 40(1), 3-18.
• Van Dis, E. A., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023). ChatGPT: five priorities for research. Nature, 614(7947), 224-226.
• Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924.
• Wang, Y. Y., & Chuang, Y. W. (2023). Artificial intelligence self-efficacy: Scale development and validation. Education and Information Technologies, 1-24.
• Wang, Y., Liu, C., & Tu, Y. F. (2021). Factors affecting the adoption of AI-based applications in higher education. Educational Technology & Society, 24(3), 116-129.
• 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.
• Weger, K., Easley, T., Branham, N., Tenhundfeld, N., & Mesmer, B. (2022). Individual Differences in the Acceptance and Adoption of AI-enabled Autonomous Systems. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 66, No. 1, pp. 241-245). Sage CA: Los Angeles, CA: SAGE Publications.
• Wolff, J., Pauling, J., Keck, A., & Baumbach, J. (2020). The economic impact of artificial intelligence in health care: systematic review. Journal of Medical Internet Research, 22(2), 509-516.
• Worthington, R. L., & Whittaker, T. A. (2006). Scale development research: A content analysis and recommendations for best practices. The counseling psychologist, 34(6), 806-838.
• Xu, W., Dainoff, M. J., Ge, L., & Gao, Z. (2023). Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI. International Journal of Human–Computer Interaction, 39(3), 494-518.
• Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2023). Generative Artificial Intelligence Acceptance Scale: A Validity and Reliability Study. International Journal of Human–Computer Interaction, 1-13.