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Üretken Yapay Zekâ Kabulünü Etkileyen Faktörler: Enerji Sektörü Çalışanları Üzerine Bir Araştırma

Yıl 2025, Cilt: 13 Sayı: 2, 304 - 325

Vildan Ateş , Elçin Söğüt

https://doi.org/10.22139/jobs.1679464

Öz

Üretken yapay zekâ (ÜYZ) yazılı metin, görüntü, ses gibi değişik veri türlerini işleyerek orijinal içerik oluşturmaya yarayan yapay zekâ (YZ) teknolojisidir. Bu çalışmada enerji sektörü çalışanlarının üretken yapay zekâ kabulünü etkileyen faktörlerin ortaya çıkarılması amaçlanmıştır. Bu amaç doğrultusunda nicel araştırma yöntemi kullanılmış olup, araştırmanın deseni tarama desenidir. Çalışmanın veri toplama aracı Yılmaz, Yılmaz ve Ceylan tarafından 2023 yılında Türkçe geçerliği ve güvenirliği çalışılmış olan Üretken Yapay Zekâ Kabul Ölçeği (ÜYZK) olup ölçek iki bölümden oluşmaktadır. Ölçekte, ÜYZ kabul davranışları performans beklentisi, çaba beklentisi, kolaylaştırıcı koşullar ve sosyal etki faktörleri bağlamında incelenmiştir. Ölçeğin birinci bölümünde kişisel bilgiler için yedi soru, ikinci bölümde katılımcıların üretken yapay zekâ kabulünü etkileyen faktörleri belirlemeye yönelik dört faktör için 20 madde bulunmaktadır. Araştırmanın çalışma grubunu, enerji sektöründe çalışan 234 katılımcı oluşturmaktadır. Veriler 5-15 Ocak 2025 tarihleri arasında toplanmıştır. Veriler ile IBM SPSS 22 programı kullanılarak açıklayıcı faktör analizi (AFA) gerçekleştirilmiştir. AFA sonucu elde edilen üç faktörlü ve 18 maddeli yeni ölçek doğrulayıcı faktör analizi (DFA) ile doğrulanmıştır. DFA için LISREL 8.80 programı kullanılmıştır. Analiz sonuçları değerlendirildiğinde ÜYZK ölçeği enerji sektörü çalışanları için 18 madde ile performans beklentisi, çaba beklentisi ve sosyal etki olmak üzere üç faktörlü yapısının geçerli ve güvenilir olduğu görülmüştür. Enerji sektörü çalışanlarının ÜYZ kabulünü etkileyen faktörler performans beklentisi, çaba beklentisi ve sosyal etki şeklinde sıralanmıştır. Sonuç bölümünde çalışma bulguları alanyazın ışığında tartışılmış ve ÜYZ uygulama tasarımcıları ve geliştiricilerine yönelik öneriler sunulmuştur.

Anahtar Kelimeler

Üretken Yapay Zekâ Enerji Sektörü Kullanıcı Kabulü Performans Beklentisi Çaba Beklentisi

Kaynakça

  • Aburbeian, A. M., Owda, A. Y., & Owda, M. (2022). A technology acceptance model survey of the metaverse prospects. AI, 3(2), 285–302.
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  • Albayati, H. (2024). Investigating undergraduate students' perceptions and awareness of using ChatGPT as a regular assistance tool: A user acceptance perspective study. Computers and Education: Artificial Intelligence, 6, 100203.
  • Aldraiweesh, A. A., & Alturki, U. (2025). The influence of social support theory on AI acceptance: Examining educational support and perceived usefulness using SEM analysis. IEEE Access, 13, 18366–18385. https://doi.org/10.1109/ACCESS.2025.3534099
  • Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024). Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective. Heliyon, 10(11), e31887.
  • Al-Qaysi, N., Al-Emran, M., Al-Sharafi, M. A., Iranmanesh, M., Ahmad, A., & Mahmoud, M. A. (2024). Determinants of ChatGPT use and its impact on learning performance: An ıntegrated model of BRT and TPB. International Journal of Human-Computer Interaction, 41(9), 5462–5474. https://doi.org/10.1080/10447318.2024.2361210
  • Ayyoub, A., Khlaif, Z. N., Hamamra, B., Bensalem, E., Mitwally, M., Fteiha, A., Joma, A., Bsharat, T., Khaldi, M., & Sanmugam, M. (2024). Drivers of acceptance of generative AI through the lens of the Extended Unified Theory of acceptance and use of technology.
  • Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246.
  • Bollen, K. A. (1986). Sample size and Bentler and Bonett’s non-normed fit index. Psychometrika, 51, 375–377.
  • Bozkurt, A. (2023). ChatGPT, üretken yapay zekâ ve algoritmik paradigma değişikliği. Alanyazın, 4(1), 63-72.
  • Brachten, F., Kissmer, T., & Stieglitz, S. (2021). The acceptance of chatbots in an enterprise context–a survey study. International Journal of Information Management, 60, 102375.
  • Büyüköztürk, Ş. (2002). Faktör analizi: Temel kavramlar ve ölçek geliştirmede kullanımı. Kuram ve Uygulamada Eğitim Yönetimi, 32, 470-483.
  • Cheng, E.W.L. (2001). SEM being more effective than multiple regression in parsimonious model testing for management development research. Journal of Management Development, 20(7), 650-667.
  • Chin, W. W., & Todd, P. A. (1995). On the use, usefulness, and ease of use of structural equation modeling in MIS research: A note of caution. MISQuarterly, 19(2), 237-246.
  • Choi, J., Park, J., & Suh, J. (2023). Evaluating the current state of ChatGPT and its disruptive potential: An empirical study of Korean users. Asia Pacific Journal of Information Systems, 33(4), 1058-1092.
  • Choudhury, A., & Shamszare, H. (2023). Investigating the impact of user trust on the adoption and use of ChatGPT: Survey analysis. Journal of Medical Internet Research, 25, e47184.
  • Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE Publications.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
  • Dekkal, M., Arcand, M., Prom Tep, S., Rajaobelina, L., & Ricard, L. (2024). Factors affecting user trust and intention in adopting chatbots: The moderating role of technology anxiety in insurtech. Journal of Financial Services Marketing, 29(3), 699-728.
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  • Duong, C. D., Vu, T. N., & Ngo, T. V. N. (2023). Applying a modified technology acceptance model to explain higher education students’ usage of ChatGPT: A serial multiple mediation model with knowledge sharing as a moderator. The International Journal of Management Education, 21(3), 100883.
  • Eksail, F. A. A., & Afari, E. (2020). Factors affecting trainee teachers’ intention to use technology: A structural equation modeling approach. Education and Information Technologies, 25(4), 2681-2697.
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Factors Affecting the Adoption of Generative Artificial Intelligence: A Study of Energy Sector Employees

Yıl 2025, Cilt: 13 Sayı: 2, 304 - 325

Vildan Ateş , Elçin Söğüt

https://doi.org/10.22139/jobs.1679464

Öz

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.

Anahtar Kelimeler

Generative Artificial Intelligence Enerji Sector User Acceptance Performance Expectancy Effort Expectancy

Kaynakça

  • Aburbeian, A. M., Owda, A. Y., & Owda, M. (2022). A technology acceptance model survey of the metaverse prospects. AI, 3(2), 285–302.
  • Aharony, N. (2024, June). Generative artificial intelligence (GenAI) chatbots usage and acceptance: An exploratory study. 15th Information Behaviour Conference (ISIC 2024), Aalborg, Danimarka.
  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
  • Albayati, H. (2024). Investigating undergraduate students' perceptions and awareness of using ChatGPT as a regular assistance tool: A user acceptance perspective study. Computers and Education: Artificial Intelligence, 6, 100203.
  • Aldraiweesh, A. A., & Alturki, U. (2025). The influence of social support theory on AI acceptance: Examining educational support and perceived usefulness using SEM analysis. IEEE Access, 13, 18366–18385. https://doi.org/10.1109/ACCESS.2025.3534099
  • Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024). Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective. Heliyon, 10(11), e31887.
  • Al-Qaysi, N., Al-Emran, M., Al-Sharafi, M. A., Iranmanesh, M., Ahmad, A., & Mahmoud, M. A. (2024). Determinants of ChatGPT use and its impact on learning performance: An ıntegrated model of BRT and TPB. International Journal of Human-Computer Interaction, 41(9), 5462–5474. https://doi.org/10.1080/10447318.2024.2361210
  • Ayyoub, A., Khlaif, Z. N., Hamamra, B., Bensalem, E., Mitwally, M., Fteiha, A., Joma, A., Bsharat, T., Khaldi, M., & Sanmugam, M. (2024). Drivers of acceptance of generative AI through the lens of the Extended Unified Theory of acceptance and use of technology.
  • Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246.
  • Bollen, K. A. (1986). Sample size and Bentler and Bonett’s non-normed fit index. Psychometrika, 51, 375–377.
  • Bozkurt, A. (2023). ChatGPT, üretken yapay zekâ ve algoritmik paradigma değişikliği. Alanyazın, 4(1), 63-72.
  • Brachten, F., Kissmer, T., & Stieglitz, S. (2021). The acceptance of chatbots in an enterprise context–a survey study. International Journal of Information Management, 60, 102375.
  • Büyüköztürk, Ş. (2002). Faktör analizi: Temel kavramlar ve ölçek geliştirmede kullanımı. Kuram ve Uygulamada Eğitim Yönetimi, 32, 470-483.
  • Cheng, E.W.L. (2001). SEM being more effective than multiple regression in parsimonious model testing for management development research. Journal of Management Development, 20(7), 650-667.
  • Chin, W. W., & Todd, P. A. (1995). On the use, usefulness, and ease of use of structural equation modeling in MIS research: A note of caution. MISQuarterly, 19(2), 237-246.
  • Choi, J., Park, J., & Suh, J. (2023). Evaluating the current state of ChatGPT and its disruptive potential: An empirical study of Korean users. Asia Pacific Journal of Information Systems, 33(4), 1058-1092.
  • Choudhury, A., & Shamszare, H. (2023). Investigating the impact of user trust on the adoption and use of ChatGPT: Survey analysis. Journal of Medical Internet Research, 25, e47184.
  • Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE Publications.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
  • Dekkal, M., Arcand, M., Prom Tep, S., Rajaobelina, L., & Ricard, L. (2024). Factors affecting user trust and intention in adopting chatbots: The moderating role of technology anxiety in insurtech. Journal of Financial Services Marketing, 29(3), 699-728.
  • Deloitte (2024). Dijital tüketici trendleri 2023. https://www.deloitte.com/tr/tr/Industries/consumer/research/dijital-tuketici-trendleri-2023.html
  • Dharmadhikari, S. (2025). Global artificial intelligence in energy market report 2025. https://www.cognitivemarketresearch.com/artificial-intelligence-in-energy-market-report?srsltid=AfmBOorY44oTVnT9sxV6_ERGXHxhpY_RdV6P6wut-v71geX8jywlLzZd#tab_toc
  • Du, L., & Lv, B. (2024). Factors influencing students’ acceptance and use generative artificial intelligence in elementary education: An expansion of the UTAUT model. Education and Information Technologies, 1-20.
  • Duong, C. D., Vu, T. N., & Ngo, T. V. N. (2023). Applying a modified technology acceptance model to explain higher education students’ usage of ChatGPT: A serial multiple mediation model with knowledge sharing as a moderator. The International Journal of Management Education, 21(3), 100883.
  • Eksail, F. A. A., & Afari, E. (2020). Factors affecting trainee teachers’ intention to use technology: A structural equation modeling approach. Education and Information Technologies, 25(4), 2681-2697.
  • EY Work Reimagined. (2024). EY Work Reimagined Survey 2024-GenAI adoption at work. https://www.ey.com/en_cy/newsroom/2024/11/news-release-ey-reimagined-survey-2024.
  • Faruk, L. I. D., Rohan, R., Ninrutsirikun, U., & Pal, D. (2023, December). University students’ acceptance and usage of generative AI (ChatGPT) from a psycho-technical perspective. 13th International Conference on Advances in Information Technology, Bangkok, Tayland.
  • Fishbein, M., & Ajzen, I. (2010). Predicting and Changing Behavior: The Reasoned Action Approach. New York: Psychology Press.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  • Gao, B., Xie, H., Yu, S., Wang, Y., Zuo, W., & Zeng, W. (2023, August). Exploring user acceptance of AI image generator: Unveiling influential factors in embracing an artistic AIGC Software. International Conference on AI-Generated Content, Shanghai, Çin.
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  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. The MIT Press.
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  • Gupta, V. (2024). An empirical evaluation of a generative artificial intelligence technology adoption model from entrepreneurs’ perspectives. Systems, 12(3), 103.
  • Gupta, V., & Yang, H. (2024). Generative artificial intelligence (AI) technology adoption model for entrepreneurs: Case of ChatGPT. Internet Reference Services Quarterly, 28(2), 223-242.
  • Huy, L. V., Nguyen, H. T., Vo-Thanh, T., Thinh, N. H. T., & Thi Thu Dung, T. (2024). Generative AI, why, how, and outcomes: A user adoption study. AIS Transactions on Human-Computer Interaction, 16(1), 1-27.
  • Kang, S., Choi, Y., & Kim, B. (2024). Impact of motivation factors for using generative AI services on continuous use intention: Mediating trust and acceptance attitude. Social Sciences, 13(9), 475.
  • Kanont, K., Pingmuang, P., Simasathien, T., Wisnuwong, S., Wiwatsiripong, B., Poonpirome, K., ... ,& Khlaisang, J. (2024). Generative-AI, a learning assistant? Factors influencing higher-ed students' technology acceptance. Electronic Journal of e-Learning, 22(6), 18-33.
  • Koponen, K. (2023). Acceptance of generative AI in knowledge work. [Yayınlanmamış Yüksek lisans tezi, Tampere University].
  • Kwarteng, M. A., Ntsiful, A., Diego, L. F. P., & Novák, P. (2024). Extending UTAUT with competitive pressure for SMEs digitalization adoption in two European nations: A multi-group analysis. Aslib Journal of Information Management, 76(5), 842-868.
  • Lee, S., Jones-Jang, S. M., Chung, M., Kim, N. & Choi, J. (2024). Who is using ChatGPT and why? Extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Information Research an International Electronic Journal, 29(1), 54-72.
  • Lee, M., & Park, J. S. (2022). Do parasocial relationships and the quality of communication with AI shopping chatbots determine middle‐aged women consumers’ continuance usage intentions? Journal of Consumer Behaviour, 21(4), 842–854.
  • Lu, H., He, L., Yu, H., Pan, T., & Fu, K. (2024). A study on teachers’ willingness to use generative AI technology and its influencing factors: Based on an integrated model. Sustainability, 16(16), 7216.
  • Ma, J., Wang, P., Li, B., Wang, T., Pang, X. S., & Wang, D. (2025). Exploring user adoption of ChatGPT: A technology acceptance model perspective. International Journal of Human–Computer Interaction, 41(2), 1431-1445.
  • Ma, M. (2025). Exploring the acceptance of generative artificial intelligence for language learning among EFL postgraduate students: An extended TAM approach. International Journal of Applied Linguistics. 35(1), 91-108.
  • Ma, X., & Huo, Y. (2023). Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework. Technology in Society, 75, 102362.
  • McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334–359. https://doi.org/10.1287/isre.13.3.334.81
  • Menon, D., & Shilpa, K. (2023). Chatting with ChatGPT: Analyzing the factors ınfluencing users' ıntention to use the open AI's ChatGPT using the UTAUT model. Heliyon, 9(11), e20962.
  • Mustafa, A. S., & Garcia, M. B. (2021, November). Theories integrated with Technology Acceptance Model (TAM) in online learning acceptance and continuance intention: A systematic review. In 2021 1st Conference on Online Teaching for Mobile Education (OT4ME), 68-72.
  • Nagy, S., & Hajdú, N. (2021). Consumer acceptance of the use of artificial ıntelligence in online shopping: Evidence from Hungary. Amfiteatru Economic, 23(56), 155-173.
  • Norizan, A. R., & Zamri, M. F. M. (2024, Ağustos). Visual generative AI tools' acceptance by multimedia and animation university students. 2024 International Visualization, Informatics and Technology Conference (IVIT), Kuala Lumpur, Malezya.
  • Patterson, A., Frydenberg, M., & Basma, L. (2024). Examining generative artificial intelligence adoption in academia: a UTAUT perspective. Issues in Information Systems, 25(3), 238-251.
  • Rath, S. P., Tripathy, R., & Jain, N. K. (2023, Aralık). Assessing the factors influencing the adoption of Generative Artificial Intelligence (GENAI) in the manufacturing sector. The International Working Conference on Transfer and Diffusion of IT, İsviçre.
  • Russell, S. J. & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.), Pearson Publishing.
  • Sallam, M., Salim, N., Barakat, M., Al-Mahzoum, K., Al-Tammemi, A. B., Malaeb, D. ..., & Hallit, S. (2023). Validation of a Technology Acceptance Model-Based Scale TAME-ChatGPT on health students' attitudes and usage of ChatGPT in Jordan. JMIR Medical Education, 9:e48254.
  • Schreibelmayr, S., & Mara, M. (2022). Robot voices in daily life: Vocal human-likeness and application context as determinants of user acceptance. Frontiers in Psychology, 13, 787499.
  • Singh, J. P. (2022). Quantifying healthcare consumers' perspectives: An empirical study of the drivers and barriers to adopting generative AI in personalized healthcare. ResearchBerg Review of Science and Technology, 2(1), 171-193
  • Skjuve, M., Brandtzæg, P. B., & Følstad, A. (2024). Why do people use ChatGPT? Exploring user motivations for generative conversational AI. First Monday, 29(1).
  • 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.
  • Stoeckli, E., Dremel, C., Uebernickel, F., & Brenner, W. (2020). How affordances of chatbots cross the chasm between social and traditional enterprise systems. Electronic Markets, 30(2), 369–403.
  • Streiner, D. L. (2013). A guide for the statistically perplexed: Selected readings for clinical researchers (First Edition). University of Toronto Press.
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  • Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
  • Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology (UTAUT2). MIS Quarterly, 36(1), 157–178.
  • Wang, X., Lin, X., & Shao, B. (2022). How does artificial intelligence create business agility? Evidence from chatbots. International Journal of Information Management, 66, 102535.
  • Weizheng, W. A. N. G., Hong, Q. I. A. O., Xiaojun, L. I., & Jingjing, W. A. N. G. (2024). User willingness to use generative artificial intelligence based on AIDUA framework. Journal of Library & Information Science in Agriculture, 36(2).
  • Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The Unified Theory of Acceptance and Use of Technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443-488.
  • Wu, L., Li, J., Qi, J., Kong, D., & Li, X. (2021). The role of opinion leaders in the sustainable development of corporate-led consumer advice networks: Evidence from a Chinese travel content community. Sustainability, 13(19), 11128.
  • Xia, Y., & Chen, Y. (2024). Driving factors of generative AI adoption in new product development teams from a UTAUT perspective. International Journal of Human–Computer Interaction, 1-22.
  • Yang, L., & Wang, J. (2024). Factors influencing initial public acceptance of integrating the ChatGPT-type model with government services. Kybernetes, 53(11), 4948-4975.
  • 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.
  • Yoon, T. H., & Wang, S. (2023). Examining users' intention to use generative artificial intelligence for travel information search: An exploratory study of ChatGPT. 관광연구논총, 35(4), 197-221.
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  • Zou, M., & Huang, L. (2023). To use or not to use? Understanding doctoral students’ acceptance of ChatGPT in writing through technology acceptance model. Frontiers in Psychology, 14, 1259531.
Toplam 78 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme , İş Sistemleri (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Vildan Ateş 0000-0002-8855-8556

Elçin Söğüt 0009-0001-1502-6129

Erken Görünüm Tarihi 17 Temmuz 2025
Yayımlanma Tarihi
Gönderilme Tarihi 18 Nisan 2025
Kabul Tarihi 9 Temmuz 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 13 Sayı: 2

Kaynak Göster

APA Ateş, V., & Söğüt, E. (2025). Üretken Yapay Zekâ Kabulünü Etkileyen Faktörler: Enerji Sektörü Çalışanları Üzerine Bir Araştırma. İşletme Bilimi Dergisi, 13(2), 304-325. https://doi.org/10.22139/jobs.1679464
AMA Ateş V, Söğüt E. Üretken Yapay Zekâ Kabulünü Etkileyen Faktörler: Enerji Sektörü Çalışanları Üzerine Bir Araştırma. About the Journal. Temmuz 2025;13(2):304-325. doi:10.22139/jobs.1679464
Chicago Ateş, Vildan, ve Elçin Söğüt. “Üretken Yapay Zekâ Kabulünü Etkileyen Faktörler: Enerji Sektörü Çalışanları Üzerine Bir Araştırma”. İşletme Bilimi Dergisi 13, sy. 2 (Temmuz 2025): 304-25. https://doi.org/10.22139/jobs.1679464.
EndNote Ateş V, Söğüt E (01 Temmuz 2025) Üretken Yapay Zekâ Kabulünü Etkileyen Faktörler: Enerji Sektörü Çalışanları Üzerine Bir Araştırma. İşletme Bilimi Dergisi 13 2 304–325.
IEEE V. Ateş ve E. Söğüt, “Üretken Yapay Zekâ Kabulünü Etkileyen Faktörler: Enerji Sektörü Çalışanları Üzerine Bir Araştırma”, About the Journal, c. 13, sy. 2, ss. 304–325, 2025, doi: 10.22139/jobs.1679464.
ISNAD Ateş, Vildan - Söğüt, Elçin. “Üretken Yapay Zekâ Kabulünü Etkileyen Faktörler: Enerji Sektörü Çalışanları Üzerine Bir Araştırma”. İşletme Bilimi Dergisi 13/2 (Temmuz 2025), 304-325. https://doi.org/10.22139/jobs.1679464.
JAMA Ateş V, Söğüt E. Üretken Yapay Zekâ Kabulünü Etkileyen Faktörler: Enerji Sektörü Çalışanları Üzerine Bir Araştırma. About the Journal. 2025;13:304–325.
MLA Ateş, Vildan ve Elçin Söğüt. “Üretken Yapay Zekâ Kabulünü Etkileyen Faktörler: Enerji Sektörü Çalışanları Üzerine Bir Araştırma”. İşletme Bilimi Dergisi, c. 13, sy. 2, 2025, ss. 304-25, doi:10.22139/jobs.1679464.
Vancouver Ateş V, Söğüt E. Üretken Yapay Zekâ Kabulünü Etkileyen Faktörler: Enerji Sektörü Çalışanları Üzerine Bir Araştırma. About the Journal. 2025;13(2):304-25.

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