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Yapay Zeka Tutum ve Değişime Hazır Olma: İki Ölçek Uyarlama Çalışması

Year 2024, Volume: 8 Issue: 2, 137 - 167, 31.12.2024
https://doi.org/10.35342/econder.1544898

Abstract

Bu araştırmanın amacı, Nickell ve Pinto (1986) tarafından geliştirilen ve bilgisayarlara yönelik tutumları ve inançları ölçmeye yönelik olan “Bilgisayar Tutum” ölçeğinin, Durndell ve Haag (2002) tarafından “İnternet Tutum Ölçeği” ismiyle uyarlanan versiyonunu temel alarak, Yapay Zeka Tutum Ölçeği olarak Türkçe’ye ve Türk kültürüne uyarlanmasıdır. Ayrıca, Rafferty ve Minbashian (2019) tarafından geliştirilen “Değişime Hazır Olma” ölçeğinin de Türkçe’ye ve Türk kültürüne uyarlanması amaçlanmaktadır. Bu doğrultuda, her iki ölçek için geçerlik ve güvenirlik çalışmaları yapılarak literatüre kazandırılması ve çalışanların yapay zeka tutumları ile değişime hazır olma durumları arasındaki ilişkinin incelenmesi hedeflenmektedir. Ölçek uyarlama sürecinde açımlayıcı ve doğrulayıcı faktör analizlerini ayrı ayrı gerçekleştirmek ve değişkenler arasındaki ilişkinin incelenmesi amacıyla üç farklı örneklemden toplam 741 kamu ve özel sektör çalışanına ulaşılmış, kriteri sağlamayan katılımcıların çıkarılmasıyla, analizler 693 katılımcı üzerinden gerçekleştirilmiştir. Ölçeklerin dilsel eşdeğerliği sağlandıktan sonra AFA ve DFA yapılmış, Cronbach's Alpha iç tutarlılık katsayısı ile madde-toplam korelasyonu analizleri gerçekleştirilmiştir. Analiz sonuçlarına göre, "Yapay Zeka Tutum" ve "Değişime Hazır Olma" ölçeklerinin Türk kültürüne uygun, geçerli ve güvenilir ölçüm araçları olduğu tespit edilmiştir. Son olarak, kolerasyon analizi neticesinde çalışanların, yapay zeka tutumları ile değişime hazır olmaları arasında pozitif, anlamlı bir ilişki bulunmuştur (r = 0,572; p < 0,001).

References

  • Armenakis, A. A., Harris, S. G., & Mossholder, K. W. (1993). Creating readiness for organizational change. Human relations, 46(6), 681–703.
  • Armenakis, A. A., Harris, S. G., & Mossholder, K. W. (1993). Creating readiness for organizational change. Human relations, 46(6), 681–703.
  • Balcı, A. (2013). Sosyal Bilimlerde Araştırma: Yöntem, Teknik ve İlkeler (10 ed.). Ankara: Pegem Yayıncılık.
  • Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International journal of Research in Marketing, 13(2), 139-161.
  • Bolander, T. (2019). What do we loose when machines take the decisions? Journal of Management and Governance, 23(4), 849-867.
  • Brislin, R. W., Lonner, W. J., & R.M., T. (1973). Cross Cultural Research Methods. New York: John Wiley-SonsPub
  • Brusaca, L. A., Moriguchi, C. S., Barbieri, D. F., Stevens, M. L., & Oliveira, A. B. (2022). Brazilian version of need for recovery scale: Assessment of structural validity, criterion validity, and internal consistency. Braz J Phys Ther, 26(6), 100465.
  • Büyüköztürk, Ş., Kılıç-Çakmak. E., Akgün. Ö. E., Karadeniz. Ş., & Demirel. F. (2011). Bilimsel araştırma yöntemleri (8. Baskı).
  • Chen, Z. (2023). Collaboration among recruiters and artificial intelligence: removing human prejudices in employment. Cognition, Technology & Work, 25(1), 135-149.
  • Costello, A. B., & Osborne, J. W. (2005). Best Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most From Your Analysis. Practical Assessment, Research & Evaluation, 101531-7714, 1-9.
  • Cunningham, G. K. (1998). Assessment in the classroom: Constructing and interpreting tests. London: Falmer Press.
  • Çelik, H. E. (2010). The Turkish Version of the Computer Attitude Scale. World Applied Sciences Journal 11, 1439-1431 1445.
  • Çöllü, E. F., & Öztürk, Y. E. (2006). Örgütlerde inançlar-tutumlar tutumların ölçüm yöntemleri ve uygulama örnekleri bu yöntemlerin değerlendirilmesi. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 9(1-2).
  • Dewey, M., & Wilkens, U. (2019). The Bionic Radiologist: avoiding blurry pictures and providing greater insights. NPJ Digital Medicine, 2(1), 65.
  • Durmuş, B., Yurtkoru, E. S., & Çinko, M. (2013). Sosyal bilimlerde SPSS’le veri analizi (5, Ed.). İstanbul: Beta Basım Yayım.
  • Durndell, A., & Haag, Z. (2002). Computer self efficacy, computer anxiety, attitudes towards the Internet and reported experience with the Internet, by gender, in an East European sample. Computers in Human Behavior, 18, 521–535.
  • Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Harcourt brace Jovanovich college publishers.
  • Elbanna, A., Dwivedi, Y., Bunker, D., & Wastell, D. (2020). The Search for Smartness in Working, Living and Organising: Beyond the ‘Technomagic’ Editorial for Special Issue of Information Systems Frontiers. Information Systems Frontiers, 22, 275-280.
  • Fabrigar, L. R., Wegener, D. T., Maccallum, R., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272-299.
  • Field, A. (2009). Discovering Statistics Using SPSS. SAGE Publications.
  • Garland, K. J., & Noyes, J. M. (2008). Computer attitude scales: How relevant today? Computers in Human Behavior, 24(2), 563-575.
  • Güvenç, B. (1972). İnsan ve Kültür: Antropolojiye Giriş Türk Sosyal Bilimler Derneği Yayınları. Ankara: Ayyıldız Matbaası.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis: A Global Perspective. Pearson Education.
  • Harrison, A. W., & Rainer, J. R. K. (1992). An Examınatıon Of The Factor Structures And Concurrent Valıdıtıes For The Computer Attıtude Scale, The Computer Anxıety Ratıng Scale, And The Computer Self-Effıcacy Scale. Educatıonal And Psychologıcal Measurement, 52, 735-745.
  • 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.
  • Irimia-Diéguez, A., Velicia-Martín, F., & Aguayo-Camacho, M. (2023). Predicting FinTech innovation adoption: the mediator role of social norms and attitudes. Financial Innovation, 9(1), 36. Karakaya, İ. (2012). Bilimsel araştırma yöntemleri. In A. Tanrıöğen (Ed.). Ankara: Anı.
  • LaLomia, M. J., & Sidowski, J. B. (1991). Measurements of computer attitudes: A review. International Journal of Human‐Computer Interaction, 3(2), 171-197.
  • Lui, A. K., Lee, M. C., & Ngai, E. W. (2022). Impact of artificial intelligence investment on firm value. Annals of Operations Research, 308(1), 373-388.
  • McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., & Darzi, A. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89-94.
  • Meske, C., Bunde, E., Schneider, J., & Gersch, M. (2022). Explainable artificial intelligence: objectives, stakeholders, and future research opportunities. Information Systems Management, 39(1), 53-63.
  • Moric Milovanovic, B., Bubas, Z., & Cvjetkovic, M. (2022). Employee Readiness for Organizational Change in the SME Internalization Process: The Case of a Medium-Sized Construction Company. Social Sciences, 11(3), 131.
  • Nickell, G. S., & Pinto, J. N. (1986). The Computer Attitude Scale. Computers in Human Behavior, 2, 301-306.
  • Nickell, G. S., & Seado, P. C. (1986). The impact of attitudes and experience on small business computer use. American Journal of Small Business, 10, 37-48.
  • Obilor, E. I. (2023). Convenience and purposive sampling techniques: Are they the same. International Journal of Innovative Social & Science Education Research, 11(1), 1-7.
  • Omar, M. H. (1992). Attitudes of College Students Towards Computers: A Comparative Study in the United States and the Middle. Comprrters in Human Behavior, 8, 249-251.
  • Pallathadka, H., Ramirez-Asís, E., Loli-Poma, T. P., Kaliyaperumal, K., Ventayen, R. J. M., & Naved, M. (2021). Applications of artificial intelligence in business management, e-commerce and finance. Materials Today: Proceedings.
  • Parker, C., Scott, S., & Geddes, A. (2019). Snowball sampling. SAGE research methods foundations.
  • Pinto, J. N., Calvillo, M., & Nickell, G. S. (1985). Concurrent validity study of the computer attitudes scale. Paper presented at the Annual Meeting of the Midwestern Psychological Association, Chicago IL.
  • Rafferty, A. E., & Minbashian, A. (2019). Cognitive beliefs and positive emotions about change: Relationships with employee change readiness and change-supportive behaviors. Human relations, 72(10), 1623-1650.
  • Rainer Jr, R. K., & Miller, M. D. (1996). An assessment of the psychometric properties of the computer attitude scale. Computers in Human Behavior, 12(1), 93-105.
  • Roberts, K. (2014). Convenience sampling through Facebook (Vol. 1). SAGE Publications London. Saunders, M., Lewis, P., & Thornhill, A. Research Methods for Business Students (6th ed.). Harlow: Pearson Education Ltd
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of psychological research online, 8(2), 23-74.
  • Sharma, S., Verma, K., & Hardaha, P. (2022). Implementation of Artificial Intelligence in Agriculture. Journal of Computational and Cognitive Engineering, 2(2), 155-162.
  • Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H. S., Li, M., Sariyska, R., Stavrou, M., Becker, B., & Montag, C. (2021). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. KI-Künstliche intelligenz, 35(1), 109-118.
  • Stanley, D. S., & Aggarwal, V. (2019). Impact of disruptive technology on human resource management practices. International Journal of Business Continuity and Risk Management, 9(4), 350-361.
  • Stone, B. M. (2021). The ethical use of fit indices in structural equation modeling: Recommendations for psychologists. Frontiers in psychology, 12, 783226.
  • Suseno, Y., Chang, C., Hudik, M., & Fang, E. S. (2022). Beliefs, anxiety and change readiness for artificial intelligence adoption among human resource managers: the moderating role of high-performance work systems. The International Journal of Human Resource Management, 33(6), 1209-1236.
  • Tabachnick B. G., & Fidell, L. S. (2001). Using multivariate statistics (4. Baskı). MA: Allyn and Bacon. Tuncer, M. (2012). Investigation of effects of computer anxiety and internet attitudes on computer self-efficacy. The Journal of Academic Social Science Studies, 5(4), 205-222.
  • WEF. (2023). The Future of Jobs Report 2023. https://www.weforum.org/publications/the-future-of-jobs-report-2023/digest/
  • Winkel, M., Nickell, G., Pinto, J., Novak, D., Contrares, L., & Seado, P. (1985). Attitude toward computers: A cross-cultural comparison Paper presented at the meeting of the Southwestern Psychological Association, Austin, TX.

Attitude Towards Artificial Intelligence and Change Readiness: Adaptation Studies of Two Scales

Year 2024, Volume: 8 Issue: 2, 137 - 167, 31.12.2024
https://doi.org/10.35342/econder.1544898

Abstract

The aim of this study is to adapt the "Artificial Intelligence Attitude Scale" to Turkish and Turkish culture, based on the version of the " Internet Attitude Scale " adapted by Durndell and Haag (2002) to measure attitudes toward and beliefs about internet, which was developed by Nickell and Pinto (1986) as the " Computer Attitude Scale (CAS)." In addition, the study aims to adapt the "Change Readiness" scale, developed by Rafferty and Minbashian (2019), to Turkish and Turkish culture. In this context, validity and reliability studies will be conducted for both scales, contributing to the literature by examining the relationship between employees' attitudes toward artificial intelligence and their readiness for change. During the scale adaptation process, exploratory (EFA) and confirmatory (CFA) factor analyses were conducted separately. A total of 741 public and private sector employees were reached from three different samples to examine the relationship between variables. After excluding participants who did not meet the criteria, the analyses were conducted with 693 participants. Following the linguistic equivalence of the scales, EFA and CFA were performed, and Cronbach's Alpha internal consistency coefficients and item-total correlation analyses were conducted. The analysis results indicated that the "Artificial Intelligence Attitude" and "Readiness for Change" scales are valid and reliable measurement tools, appropriate for Turkish culture. Finally, as a result of the correlation analysis, a positive and significant relationship was found between employees' attitudes toward artificial intelligence and their readiness for change (r = 0.572; p < 0.001).

References

  • Armenakis, A. A., Harris, S. G., & Mossholder, K. W. (1993). Creating readiness for organizational change. Human relations, 46(6), 681–703.
  • Armenakis, A. A., Harris, S. G., & Mossholder, K. W. (1993). Creating readiness for organizational change. Human relations, 46(6), 681–703.
  • Balcı, A. (2013). Sosyal Bilimlerde Araştırma: Yöntem, Teknik ve İlkeler (10 ed.). Ankara: Pegem Yayıncılık.
  • Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International journal of Research in Marketing, 13(2), 139-161.
  • Bolander, T. (2019). What do we loose when machines take the decisions? Journal of Management and Governance, 23(4), 849-867.
  • Brislin, R. W., Lonner, W. J., & R.M., T. (1973). Cross Cultural Research Methods. New York: John Wiley-SonsPub
  • Brusaca, L. A., Moriguchi, C. S., Barbieri, D. F., Stevens, M. L., & Oliveira, A. B. (2022). Brazilian version of need for recovery scale: Assessment of structural validity, criterion validity, and internal consistency. Braz J Phys Ther, 26(6), 100465.
  • Büyüköztürk, Ş., Kılıç-Çakmak. E., Akgün. Ö. E., Karadeniz. Ş., & Demirel. F. (2011). Bilimsel araştırma yöntemleri (8. Baskı).
  • Chen, Z. (2023). Collaboration among recruiters and artificial intelligence: removing human prejudices in employment. Cognition, Technology & Work, 25(1), 135-149.
  • Costello, A. B., & Osborne, J. W. (2005). Best Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most From Your Analysis. Practical Assessment, Research & Evaluation, 101531-7714, 1-9.
  • Cunningham, G. K. (1998). Assessment in the classroom: Constructing and interpreting tests. London: Falmer Press.
  • Çelik, H. E. (2010). The Turkish Version of the Computer Attitude Scale. World Applied Sciences Journal 11, 1439-1431 1445.
  • Çöllü, E. F., & Öztürk, Y. E. (2006). Örgütlerde inançlar-tutumlar tutumların ölçüm yöntemleri ve uygulama örnekleri bu yöntemlerin değerlendirilmesi. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 9(1-2).
  • Dewey, M., & Wilkens, U. (2019). The Bionic Radiologist: avoiding blurry pictures and providing greater insights. NPJ Digital Medicine, 2(1), 65.
  • Durmuş, B., Yurtkoru, E. S., & Çinko, M. (2013). Sosyal bilimlerde SPSS’le veri analizi (5, Ed.). İstanbul: Beta Basım Yayım.
  • Durndell, A., & Haag, Z. (2002). Computer self efficacy, computer anxiety, attitudes towards the Internet and reported experience with the Internet, by gender, in an East European sample. Computers in Human Behavior, 18, 521–535.
  • Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Harcourt brace Jovanovich college publishers.
  • Elbanna, A., Dwivedi, Y., Bunker, D., & Wastell, D. (2020). The Search for Smartness in Working, Living and Organising: Beyond the ‘Technomagic’ Editorial for Special Issue of Information Systems Frontiers. Information Systems Frontiers, 22, 275-280.
  • Fabrigar, L. R., Wegener, D. T., Maccallum, R., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272-299.
  • Field, A. (2009). Discovering Statistics Using SPSS. SAGE Publications.
  • Garland, K. J., & Noyes, J. M. (2008). Computer attitude scales: How relevant today? Computers in Human Behavior, 24(2), 563-575.
  • Güvenç, B. (1972). İnsan ve Kültür: Antropolojiye Giriş Türk Sosyal Bilimler Derneği Yayınları. Ankara: Ayyıldız Matbaası.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis: A Global Perspective. Pearson Education.
  • Harrison, A. W., & Rainer, J. R. K. (1992). An Examınatıon Of The Factor Structures And Concurrent Valıdıtıes For The Computer Attıtude Scale, The Computer Anxıety Ratıng Scale, And The Computer Self-Effıcacy Scale. Educatıonal And Psychologıcal Measurement, 52, 735-745.
  • 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.
  • Irimia-Diéguez, A., Velicia-Martín, F., & Aguayo-Camacho, M. (2023). Predicting FinTech innovation adoption: the mediator role of social norms and attitudes. Financial Innovation, 9(1), 36. Karakaya, İ. (2012). Bilimsel araştırma yöntemleri. In A. Tanrıöğen (Ed.). Ankara: Anı.
  • LaLomia, M. J., & Sidowski, J. B. (1991). Measurements of computer attitudes: A review. International Journal of Human‐Computer Interaction, 3(2), 171-197.
  • Lui, A. K., Lee, M. C., & Ngai, E. W. (2022). Impact of artificial intelligence investment on firm value. Annals of Operations Research, 308(1), 373-388.
  • McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., & Darzi, A. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89-94.
  • Meske, C., Bunde, E., Schneider, J., & Gersch, M. (2022). Explainable artificial intelligence: objectives, stakeholders, and future research opportunities. Information Systems Management, 39(1), 53-63.
  • Moric Milovanovic, B., Bubas, Z., & Cvjetkovic, M. (2022). Employee Readiness for Organizational Change in the SME Internalization Process: The Case of a Medium-Sized Construction Company. Social Sciences, 11(3), 131.
  • Nickell, G. S., & Pinto, J. N. (1986). The Computer Attitude Scale. Computers in Human Behavior, 2, 301-306.
  • Nickell, G. S., & Seado, P. C. (1986). The impact of attitudes and experience on small business computer use. American Journal of Small Business, 10, 37-48.
  • Obilor, E. I. (2023). Convenience and purposive sampling techniques: Are they the same. International Journal of Innovative Social & Science Education Research, 11(1), 1-7.
  • Omar, M. H. (1992). Attitudes of College Students Towards Computers: A Comparative Study in the United States and the Middle. Comprrters in Human Behavior, 8, 249-251.
  • Pallathadka, H., Ramirez-Asís, E., Loli-Poma, T. P., Kaliyaperumal, K., Ventayen, R. J. M., & Naved, M. (2021). Applications of artificial intelligence in business management, e-commerce and finance. Materials Today: Proceedings.
  • Parker, C., Scott, S., & Geddes, A. (2019). Snowball sampling. SAGE research methods foundations.
  • Pinto, J. N., Calvillo, M., & Nickell, G. S. (1985). Concurrent validity study of the computer attitudes scale. Paper presented at the Annual Meeting of the Midwestern Psychological Association, Chicago IL.
  • Rafferty, A. E., & Minbashian, A. (2019). Cognitive beliefs and positive emotions about change: Relationships with employee change readiness and change-supportive behaviors. Human relations, 72(10), 1623-1650.
  • Rainer Jr, R. K., & Miller, M. D. (1996). An assessment of the psychometric properties of the computer attitude scale. Computers in Human Behavior, 12(1), 93-105.
  • Roberts, K. (2014). Convenience sampling through Facebook (Vol. 1). SAGE Publications London. Saunders, M., Lewis, P., & Thornhill, A. Research Methods for Business Students (6th ed.). Harlow: Pearson Education Ltd
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of psychological research online, 8(2), 23-74.
  • Sharma, S., Verma, K., & Hardaha, P. (2022). Implementation of Artificial Intelligence in Agriculture. Journal of Computational and Cognitive Engineering, 2(2), 155-162.
  • Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H. S., Li, M., Sariyska, R., Stavrou, M., Becker, B., & Montag, C. (2021). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. KI-Künstliche intelligenz, 35(1), 109-118.
  • Stanley, D. S., & Aggarwal, V. (2019). Impact of disruptive technology on human resource management practices. International Journal of Business Continuity and Risk Management, 9(4), 350-361.
  • Stone, B. M. (2021). The ethical use of fit indices in structural equation modeling: Recommendations for psychologists. Frontiers in psychology, 12, 783226.
  • Suseno, Y., Chang, C., Hudik, M., & Fang, E. S. (2022). Beliefs, anxiety and change readiness for artificial intelligence adoption among human resource managers: the moderating role of high-performance work systems. The International Journal of Human Resource Management, 33(6), 1209-1236.
  • Tabachnick B. G., & Fidell, L. S. (2001). Using multivariate statistics (4. Baskı). MA: Allyn and Bacon. Tuncer, M. (2012). Investigation of effects of computer anxiety and internet attitudes on computer self-efficacy. The Journal of Academic Social Science Studies, 5(4), 205-222.
  • WEF. (2023). The Future of Jobs Report 2023. https://www.weforum.org/publications/the-future-of-jobs-report-2023/digest/
  • Winkel, M., Nickell, G., Pinto, J., Novak, D., Contrares, L., & Seado, P. (1985). Attitude toward computers: A cross-cultural comparison Paper presented at the meeting of the Southwestern Psychological Association, Austin, TX.
There are 50 citations in total.

Details

Primary Language Turkish
Subjects Business Administration
Journal Section Articles
Authors

Murat Çakan 0000-0003-3707-2545

Adnan Akın 0000-0003-4418-9856

Publication Date December 31, 2024
Submission Date September 6, 2024
Acceptance Date November 26, 2024
Published in Issue Year 2024 Volume: 8 Issue: 2

Cite

APA Çakan, M., & Akın, A. (2024). Yapay Zeka Tutum ve Değişime Hazır Olma: İki Ölçek Uyarlama Çalışması. Econder Uluslararası Akademik Dergi, 8(2), 137-167. https://doi.org/10.35342/econder.1544898

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