Üretken Yapay Zekâ Kabulünü Etkileyen Faktörler: Enerji Sektörü Çalışanları Üzerine Bir Araştırma
Factors Affecting the Adoption of Generative Artificial Intelligence: A Study of Energy Sector Employees

Summary/Abstract: Generative artificial intelligence (GAI) is an artificial intelligence (AI) technology that analyzes various data types to create original content, including written text, images, and sound. This study aims to identify the factors influencing employees' acceptance of generative artificial intelligence in the energy sector. A quantitative research method was used, and the research design adopted a screening approach. The data collection tool used in the study was the Generative Artificial Intelligence Acceptance Scale (GAI Acceptance Scale), whose validity and reliability were examined in Turkish by Yılmaz, Yılmaz, and Ceylan in 2023; the scale consists of two parts. The scale assessed GAI acceptance behaviors in terms of performance expectations, effort expectations, facilitating conditions, and social impact factors. The first part of the scale comprises seven questions for personal information, and the second part includes 20 items to assess the factors affecting participants' acceptance of generative artificial intelligence. The study group consists of 234 participants working in the energy sector. Data were collected between January 5 and 15, 2025. Exploratory factor analysis (EFA) was conducted on the data using the IBM SPSS 22 program. The new scale, consisting of three factors and 18 items derived from the EFA, was validated through confirmatory factor analysis (CFA). The LISREL 8.80 program was used for the CFA. Upon evaluating the analysis results, it was found that the GAI scale, comprising 18 items and a three-factor structure namely, performance expectation, effort expectation, and social impact was valid and reliable for employees in the energy sector. The factors influencing energy sector employees' acceptance of GAI usage include performance expectation, effort expectation, and social impact. In conclusion, the study presents its findings based on the existing literature and offers recommendations for GAI application designers and developers.

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