INVESTIGATING THE FACTORS INFLUENCING ADOPTION INTENTIONS OF CHATGPT FOR SPORT EVENTS

Metin Argan; Halime Dinç

doi:10.33689/spormetre.1606845

Research Article

INVESTIGATING THE FACTORS INFLUENCING ADOPTION INTENTIONS OF CHATGPT FOR SPORT EVENTS

Year 2025, Volume: 23 Issue: 2, 77 - 97, 30.06.2025

Metin Argan , Halime Dinç

https://doi.org/10.33689/spormetre.1606845

Abstract

The growing interest in artificial intelligence tools like ChatGPT has led to numerous studies examining its role in enhancing information access, decision-making, and task efficiency across various domains. This study investigates the key factors influencing the adoption of ChatGPT as a tool for engaging with and learning about sports events, which encompass diverse content, formats, and objectives. Specifically, the research explores the effects of perceived ease of use, perceived usefulness, attitude, and subjective norms on behavioral intention and word-of-mouth (WOM) as mechanisms for technology dissemination. Adopting a quantitative approach, the study employs survey research to test a conceptual framework linking these six variables. The findings reveal that perceived ease of use and perceived usefulness positively influence users' attitudes toward ChatGPT, which in turn shape their behavioral intention to use the technology for learning about sports events. Additionally, subjective norms significantly impact behavioral intention and directly contribute to word-of-mouth sharing. Behavioral intention further emerges as a crucial factor, strongly driving word-of-mouth recommendations. These results offer valuable insights for future research on ChatGPT, specifically within the context of sports events. Practical implications suggest that event organizers and technology developers should prioritize improving ChatGPT's usability and perceived benefits to enhance adoption and encourage positive dissemination through user recommendations.

Keywords

Sport events, technology acceptance model, theory of planned behaviour, word of mouth, ChatGPT

References

Adiwardana, D., Luong, M. T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., ... & Le, Q. V. (2020). Towards a human-like open-domain chatbot. arXiv preprint arXiv:2001.09977.
Ahadzadeh, A. S., Wu, S. L., & Sijia, X. (2024). Exploring academic intentions for ChatGPT: A perspective from the theory of planned behavior. Chiang Mai University Journal of Agriculture and Science Research, 11(2), e2024016. https://doi.org/10.12982/CMUJASR.2024.016
Ahmad, A., AlMallah, M. M., & AbedRabbo, M. (2022). Does eWOM influence entrepreneurial firms’ rate of diffusion of innovation? Journal of Research in Marketing and Entrepreneurship, 24(1), 92–111.
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action-control: From cognition to behavior (pp. 11–39). Springer-Verlag.
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
Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665–683. https://doi.org/10.1111/j.1559-1816.2002.tb00236.x
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.
Ali, F., Yasar, B., Ali, L., & Dogan, S. (2023). Antecedents and consequences of travelers' trust towards personalized travel recommendations offered by ChatGPT. International Journal of Hospitality Management, 114, 103588. https://doi.org/10.1016/j.ijhm.2023.103588
Argan, M., Gürbüz, B., Dursun, M. T., Dinç, H., & Tokay Argan, M. (2024). Usage intention of ChatGPT for health in Turkey: An extended technology acceptance model. International Journal of Human–Computer Interaction, 1–13.
Aziz, S., Afaq, Z., Muhammad, L., & Khan, B. (2020). The role of media, word of mouth, and subjective norms in determining attitude and intentions to purchase family takaful schemes. Journal of Islamic Business and Management, 10(1).
Bano, N., & Siddiqui, S. (2024). Consumers' intention towards the use of smart technologies in tourism and hospitality (T&H) industry: a deeper insight into the integration of TAM, TPB and trust. Journal of Hospitality and Tourism Insights, 7(3), 1412-1434.
Baskara, R. (2023). Exploring the implications of ChatGPT for language learning in higher education. Indonesian Journal of English Language Teaching and Applied Linguistics, 7(2), 343–358.
Beh, P. K., Ganesan, Y., Iranmanesh, M., & Foroughi, B. (2021). Using smartwatches for fitness and health monitoring: The UTAUT2 combined with threat appraisal as moderators. Behavior & Information Technology, 40(3), 282–299.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., ... & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv preprint arXiv:2303.12712.
Budhathoki, T., Zirar, A., Njoya, E. T., & Timsina, A. (2024). ChatGPT adoption and anxiety: a cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT). Studies in Higher Education, 1–16. https://doi.org/10.1080/03075079.2024.2333937
Cain, M. K., Zhang, Z., & Yuan, K. H. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49, 1716–1735.
Carvalho, I., & Ivanov, S. (2024). ChatGPT for tourism: Applications, benefits, and risks. Tourism Review, 79(2), 290–303.
Cascella, M., Montomoli, J., Bellini, V., & Bignami, E. (2023). Evaluating the feasibility of ChatGPT in healthcare: An analysis of multiple clinical and research scenarios. Journal of Medical Systems, 47(1), 33.
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(38), 1–25.
Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39(9), 1727–1739.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation). Massachusetts Institute of Technology.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies, 45(1), 19–45. https://doi.org/10.1006/ijhc.1996.0040
Del Giudice, M., Scuotto, V., & Maggioni, V. (2023). Factors influencing the adoption of artificial intelligence: An extended TAM perspective. Technological Forecasting and Social Change, 191, 122447. https://doi.org/10.1016/j.techfore.2023.122447
Dinç, T., et al. (2024). A comprehensive model of ChatGPT acceptance in events using technology acceptance model, theory of planned behavior, and word of mouth. International Journal of Event and Festival Management.
Dowling, M., & Lucey, B. (2023). ChatGPT for (finance) research: The Bananarama Conjecture. Finance Research Letters, 53, 103662.
Ferine, K. F., Nadriati, S., Arif, M., Muharmi, Y., & Kusuma, C. (2024). Socialization of the use of ChatGPT in increasing village community resources. Journal of Human and Education (JAHE), 4(3), 271–277.
Forbes. (2023). What is ChatGPT? A review of the AI in its own words. Forbes. Retrieved from https://www.forbes.com/advisor/business/software/what-is-chatgpt/ (accessed 15 December 2024).
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Goodman, L. A. (1961). Snowball sampling. The Annals of Mathematical Statistics, 32(1), 148–167. https://doi.org/10.1214/aoms/1177705148
Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169.
Hair, E., Halle, T., Terry-Humen, E., Lavelle, B., & Calkins, J. (2006). Children's school readiness in the ECLS-K: Predictions to academic, health, and social outcomes in first grade. Early Childhood Research Quarterly, 21(4), 431-454.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12.
Hair, J., Hult, G., Ringle, C., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equations Modeling (PLS-SEM). Sage, Los Angeles.
Harman, H. H. (1976). Modern factor analysis. University of Chicago Press.
Harrington, D. (2009). Confirmatory factor analysis. Oxford University Press.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135.
Hidayat, R., Hidayat, R. A., Suswanto, B., & Tholib, A. (2021, October). Green Technology E-toll Card: Level of acceptance and use of a technology. In 2021 IEEE 7th Information Technology International Seminar (ITIS) (pp. 1-6). IEEE.
Hu, Y. H. (2022). Effects and acceptance of precision education in an AI-supported smart learning environment. Education and Information Technologies, 27(2), 2013–2037.
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204.
Irawan, D. A., Adiputra, I., & Arshanty, R. P. (2021, August). E-Recruitment: Extending Technology Acceptance Model in the Indonesian Context. In 2021 International Conference on Information Management and Technology (ICIMTech) (Vol. 1, pp. 99-104). IEEE.
Jo, H., & Bang, Y. (2023). Analyzing ChatGPT adoption drivers with the TOEK framework. Scientific Reports, 13(1), 22606.
Jo, H., & Park, D. H. (2024). Effects of ChatGPT’s AI capabilities and human-like traits on spreading information in work environments. Scientific Reports, 14(1), 7806. https://doi.org/10.1038/s41598-024-57977-0
Jolly, C. (2024). Cross-cultural perspectives on digital media and well-being: A narrative review. IAHRW International Journal of Social Sciences Review, 12(1).
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
Kim, S., Kandampully, J., & Bilgihan, A. (2018). The influence of eWOM communications: An application of online social network framework. Computers in Human Behavior, 80, 243–254. https://doi.org/10.1016/j.chb.2017.11.015
Kobiella, C., Flores López, Y. S., Waltenberger, F., Draxler, F., & Schmidt, A. (2024). How the use of ChatGPT changes young professionals' perception of productivity and accomplishment. Proceedings of the CHI Conference on Human Factors in Computing Systems, 1–16.
Koul, S., & Eydgahi, A. (2017). A systematic review of technology adoption frameworks and their applications. Journal of Technology Management & Innovation, 12(4), 106–113.
Koul, S., & Eydgahi, A. (2018). Utilizing technology acceptance model (TAM) for driverless car technology adoption. Journal of technology management & innovation, 13(4), 37-46.
Koverola, M., Kunnari, A., Sundvall, J., & Laakasuo, M. (2022). General attitudes towards robots scale (GAToRS): A new instrument for social surveys. International Journal of Social Robotics, 14(7), 1559–1581. https://doi.org/10.1007/s12369-022-00880-3
Kozinets, R. V., De Valck, K., Wojnicki, A. C., & Wilner, S. J. S. (2010). Networked narratives: Understanding word-of-mouth marketing in online communities. Journal of Marketing, 74(2), 71–89. https://doi.org/10.1509/jm.74.2.71
Li, S., Han, R., Fu, T., Chen, M., & Zhang, Y. (2024). Tourists’ behavioural intentions to use ChatGPT for tour route planning: An extended TAM model including rational and emotional factors. Current Issues in Tourism, 1–17. https://doi.org/10.1080/13683500.2024.2355563
Lu, Y., Zhou, T., & Wang, B. (2009). Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25(1), 29–39. https://doi.org/10.1016/j.chb.2008.06.002
Lucey, P. (2023). The future of sports coverage: Part 1. Stats Perform. Available at: https://www.statsperform.com/resource/stats-performs-chief-scientist-on-chatgpts-role-in-the-future-of-sports-coverage-part-1/ (accessed 15 December 2024).
Ma, J., Wang, P., Li, B., Wang, T., Pang, X. S., & Wang, D. (2024). Exploring user adoption of ChatGPT: A technology acceptance model perspective. International Journal of Human–Computer Interaction, 1–15. https://doi.org/10.1080/10447318.2024.2314358
Malynov, A., & Prokhorov, I. (2023). Enhancing event selection with ChatGPT-powered chatbot assistant: An innovative approach to input data preparation. In Biologically Inspired Cognitive Architectures Meeting (pp. 588–594). Cham: Springer Nature Switzerland.
Masur, P. K., Bazarova, N. N., & DiFranzo, D. (2023). The impact of what others do, approve of, and expect you to do: An in-depth analysis of social norms and self-disclosure on social media. Social Media + Society, 9(1), 20563051231156401.
Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191. https://doi.org/10.1287/isre.2.3.173
Md Husin, M., Ismail, N., & Ab Rahman, A. (2016). The roles of mass media, word of mouth and subjective norm in family takaful purchase intention. Journal of Islamic Marketing, 7(1), 59–73. https://doi.org/10.1108/JIMA-03-2015-0020
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), 1816–1844.
N3XT Sports. (2023). Understanding the first steps to AI adoption in sports. N3XT Sports. Available at: https://www.n3xtsports.com (accessed 15 December 2024).
Naqvi, A. M. H., Jiang, Y., Miao, M., & Naqvi, M. H. (2020). The effect of social influence, trust, and entertainment value on social media use: Evidence from Pakistan. Cogent Business and Management, 7(1), 1–23.
Nguyen, H. T., & Chaudhuri, M. (2019). Making new products go viral and succeed. International Journal of Research in Marketing, 36(1), 39–62. https://doi.org/10.1016/j.ijresmar.2018.09.007
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.
Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., ... & Smith, E. M. (2020). Recipes for building an open-domain chatbot. arXiv preprint arXiv:2004.13637.
Rynarzewska, A. I. (2019). It’s not fake, it’s biased: Insights into morality of incentivized reviewers. Journal of Consumer Marketing, 36(3), 401–409. https://doi.org/10.1108/JCM-02-2018-2570
Sabah, N. M. (2016). Exploring students’ awareness and perceptions: Influencing factors and individual differences driving m-learning adoption. Computers in Human Behavior, 65, 522–533. https://doi.org/10.1016/j.chb.2016.09.009
Saif, N., Khan, S. U., Shaheen, I., ALotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024). ChatGPT: Validating technology acceptance model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154, 108097. https://doi.org/10.1016/j.chb.2023.108097
Salah, M., Alhalbusi, H., Ismail, M. M., & Abdelfattah, F. (2024). Chatting with ChatGPT: Decoding the mind of chatbot users and unveiling the intricate connections between user perception, trust, and stereotype perception on self-esteem and psychological well-being. Current Psychology, 43(9), 7843–7858.
Saputra, R., Nasution, M. I. P., & Dharma, B. (2023). The impact of using AI ChatGPT on marketing effectiveness: A case study on Instagram marketing. Indonesian Journal of Economics and Management, 3(3), 603–617.
Silva, O., Nunes, J., & Sousa, Á. (2024). University students’ perceptions of ChatGPT: Factors that affect their behavioural intention, usage and utility perceived. In ICERI2024 Proceedings (pp. 7427–7434). IATED.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489.
Skjuve, M., Følstad, A., & Brandtzaeg, P. B. (2023). The user experience of ChatGPT: Findings from a questionnaire study of early users. In Proceedings of the 5th International Conference on Conversational User Interfaces (pp. 1–10).
Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 1–14.
Tabachnick, B. G., & Fidell, L. S. (2012). Using multivariate statistics (6th ed.). Boston: Pearson.
Tibau, M., Siqueira, S. W. M., & Nunes, B. P. (2024). ChatGPT for chatting and searching: Repurposing search behavior. Library & Information Science Research, 46(4), 101331.
Umirov, S. (2024). Should we create better assignments instead of worrying about generative AI (such as ChatGPT)? American Journal of Language, Literacy and Learning in STEM Education, 2(1), 420–423.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31–46.
Zhong, Q., Ding, L., Liu, J., Du, B., & Tao, D. (2023). Can ChatGPT understand too? A comparative study on ChatGPT and fine-tuned BERT. arXiv preprint arXiv:2302.10198. https://doi.org/10.48550/arXiv.2302.10198
Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., ... & Sun, L. (2024). A comprehensive survey on pretrained foundation models: A history from BERT to ChatGPT. International Journal of Machine Learning and Cybernetics, 1–65.
Zijlmans, E., van der Ark, L. A., & Sijtsma, K. (2019). Item-score reliability as a selection tool in test construction. Frontiers in Psychology, 9, 2298.

INVESTIGATING THE FACTORS INFLUENCING ADOPTION INTENTIONS OF CHATGPT FOR SPORT EVENTS

Year 2025, Volume: 23 Issue: 2, 77 - 97, 30.06.2025

Metin Argan , Halime Dinç

https://doi.org/10.33689/spormetre.1606845

Abstract

Keywords

Sport events, technology acceptance model, theory of planned behaviour, word of mouth

References

Adiwardana, D., Luong, M. T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., ... & Le, Q. V. (2020). Towards a human-like open-domain chatbot. arXiv preprint arXiv:2001.09977.
Ahadzadeh, A. S., Wu, S. L., & Sijia, X. (2024). Exploring academic intentions for ChatGPT: A perspective from the theory of planned behavior. Chiang Mai University Journal of Agriculture and Science Research, 11(2), e2024016. https://doi.org/10.12982/CMUJASR.2024.016
Ahmad, A., AlMallah, M. M., & AbedRabbo, M. (2022). Does eWOM influence entrepreneurial firms’ rate of diffusion of innovation? Journal of Research in Marketing and Entrepreneurship, 24(1), 92–111.
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action-control: From cognition to behavior (pp. 11–39). Springer-Verlag.
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
Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665–683. https://doi.org/10.1111/j.1559-1816.2002.tb00236.x
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.
Ali, F., Yasar, B., Ali, L., & Dogan, S. (2023). Antecedents and consequences of travelers' trust towards personalized travel recommendations offered by ChatGPT. International Journal of Hospitality Management, 114, 103588. https://doi.org/10.1016/j.ijhm.2023.103588
Argan, M., Gürbüz, B., Dursun, M. T., Dinç, H., & Tokay Argan, M. (2024). Usage intention of ChatGPT for health in Turkey: An extended technology acceptance model. International Journal of Human–Computer Interaction, 1–13.
Aziz, S., Afaq, Z., Muhammad, L., & Khan, B. (2020). The role of media, word of mouth, and subjective norms in determining attitude and intentions to purchase family takaful schemes. Journal of Islamic Business and Management, 10(1).
Bano, N., & Siddiqui, S. (2024). Consumers' intention towards the use of smart technologies in tourism and hospitality (T&H) industry: a deeper insight into the integration of TAM, TPB and trust. Journal of Hospitality and Tourism Insights, 7(3), 1412-1434.
Baskara, R. (2023). Exploring the implications of ChatGPT for language learning in higher education. Indonesian Journal of English Language Teaching and Applied Linguistics, 7(2), 343–358.
Beh, P. K., Ganesan, Y., Iranmanesh, M., & Foroughi, B. (2021). Using smartwatches for fitness and health monitoring: The UTAUT2 combined with threat appraisal as moderators. Behavior & Information Technology, 40(3), 282–299.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., ... & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv preprint arXiv:2303.12712.
Budhathoki, T., Zirar, A., Njoya, E. T., & Timsina, A. (2024). ChatGPT adoption and anxiety: a cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT). Studies in Higher Education, 1–16. https://doi.org/10.1080/03075079.2024.2333937
Cain, M. K., Zhang, Z., & Yuan, K. H. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49, 1716–1735.
Carvalho, I., & Ivanov, S. (2024). ChatGPT for tourism: Applications, benefits, and risks. Tourism Review, 79(2), 290–303.
Cascella, M., Montomoli, J., Bellini, V., & Bignami, E. (2023). Evaluating the feasibility of ChatGPT in healthcare: An analysis of multiple clinical and research scenarios. Journal of Medical Systems, 47(1), 33.
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(38), 1–25.
Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39(9), 1727–1739.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation). Massachusetts Institute of Technology.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies, 45(1), 19–45. https://doi.org/10.1006/ijhc.1996.0040
Del Giudice, M., Scuotto, V., & Maggioni, V. (2023). Factors influencing the adoption of artificial intelligence: An extended TAM perspective. Technological Forecasting and Social Change, 191, 122447. https://doi.org/10.1016/j.techfore.2023.122447
Dinç, T., et al. (2024). A comprehensive model of ChatGPT acceptance in events using technology acceptance model, theory of planned behavior, and word of mouth. International Journal of Event and Festival Management.
Dowling, M., & Lucey, B. (2023). ChatGPT for (finance) research: The Bananarama Conjecture. Finance Research Letters, 53, 103662.
Ferine, K. F., Nadriati, S., Arif, M., Muharmi, Y., & Kusuma, C. (2024). Socialization of the use of ChatGPT in increasing village community resources. Journal of Human and Education (JAHE), 4(3), 271–277.
Forbes. (2023). What is ChatGPT? A review of the AI in its own words. Forbes. Retrieved from https://www.forbes.com/advisor/business/software/what-is-chatgpt/ (accessed 15 December 2024).
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Goodman, L. A. (1961). Snowball sampling. The Annals of Mathematical Statistics, 32(1), 148–167. https://doi.org/10.1214/aoms/1177705148
Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169.
Hair, E., Halle, T., Terry-Humen, E., Lavelle, B., & Calkins, J. (2006). Children's school readiness in the ECLS-K: Predictions to academic, health, and social outcomes in first grade. Early Childhood Research Quarterly, 21(4), 431-454.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12.
Hair, J., Hult, G., Ringle, C., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equations Modeling (PLS-SEM). Sage, Los Angeles.
Harman, H. H. (1976). Modern factor analysis. University of Chicago Press.
Harrington, D. (2009). Confirmatory factor analysis. Oxford University Press.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135.
Hidayat, R., Hidayat, R. A., Suswanto, B., & Tholib, A. (2021, October). Green Technology E-toll Card: Level of acceptance and use of a technology. In 2021 IEEE 7th Information Technology International Seminar (ITIS) (pp. 1-6). IEEE.
Hu, Y. H. (2022). Effects and acceptance of precision education in an AI-supported smart learning environment. Education and Information Technologies, 27(2), 2013–2037.
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204.
Irawan, D. A., Adiputra, I., & Arshanty, R. P. (2021, August). E-Recruitment: Extending Technology Acceptance Model in the Indonesian Context. In 2021 International Conference on Information Management and Technology (ICIMTech) (Vol. 1, pp. 99-104). IEEE.
Jo, H., & Bang, Y. (2023). Analyzing ChatGPT adoption drivers with the TOEK framework. Scientific Reports, 13(1), 22606.
Jo, H., & Park, D. H. (2024). Effects of ChatGPT’s AI capabilities and human-like traits on spreading information in work environments. Scientific Reports, 14(1), 7806. https://doi.org/10.1038/s41598-024-57977-0
Jolly, C. (2024). Cross-cultural perspectives on digital media and well-being: A narrative review. IAHRW International Journal of Social Sciences Review, 12(1).
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
Kim, S., Kandampully, J., & Bilgihan, A. (2018). The influence of eWOM communications: An application of online social network framework. Computers in Human Behavior, 80, 243–254. https://doi.org/10.1016/j.chb.2017.11.015
Kobiella, C., Flores López, Y. S., Waltenberger, F., Draxler, F., & Schmidt, A. (2024). How the use of ChatGPT changes young professionals' perception of productivity and accomplishment. Proceedings of the CHI Conference on Human Factors in Computing Systems, 1–16.
Koul, S., & Eydgahi, A. (2017). A systematic review of technology adoption frameworks and their applications. Journal of Technology Management & Innovation, 12(4), 106–113.
Koul, S., & Eydgahi, A. (2018). Utilizing technology acceptance model (TAM) for driverless car technology adoption. Journal of technology management & innovation, 13(4), 37-46.
Koverola, M., Kunnari, A., Sundvall, J., & Laakasuo, M. (2022). General attitudes towards robots scale (GAToRS): A new instrument for social surveys. International Journal of Social Robotics, 14(7), 1559–1581. https://doi.org/10.1007/s12369-022-00880-3
Kozinets, R. V., De Valck, K., Wojnicki, A. C., & Wilner, S. J. S. (2010). Networked narratives: Understanding word-of-mouth marketing in online communities. Journal of Marketing, 74(2), 71–89. https://doi.org/10.1509/jm.74.2.71
Li, S., Han, R., Fu, T., Chen, M., & Zhang, Y. (2024). Tourists’ behavioural intentions to use ChatGPT for tour route planning: An extended TAM model including rational and emotional factors. Current Issues in Tourism, 1–17. https://doi.org/10.1080/13683500.2024.2355563
Lu, Y., Zhou, T., & Wang, B. (2009). Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25(1), 29–39. https://doi.org/10.1016/j.chb.2008.06.002
Lucey, P. (2023). The future of sports coverage: Part 1. Stats Perform. Available at: https://www.statsperform.com/resource/stats-performs-chief-scientist-on-chatgpts-role-in-the-future-of-sports-coverage-part-1/ (accessed 15 December 2024).
Ma, J., Wang, P., Li, B., Wang, T., Pang, X. S., & Wang, D. (2024). Exploring user adoption of ChatGPT: A technology acceptance model perspective. International Journal of Human–Computer Interaction, 1–15. https://doi.org/10.1080/10447318.2024.2314358
Malynov, A., & Prokhorov, I. (2023). Enhancing event selection with ChatGPT-powered chatbot assistant: An innovative approach to input data preparation. In Biologically Inspired Cognitive Architectures Meeting (pp. 588–594). Cham: Springer Nature Switzerland.
Masur, P. K., Bazarova, N. N., & DiFranzo, D. (2023). The impact of what others do, approve of, and expect you to do: An in-depth analysis of social norms and self-disclosure on social media. Social Media + Society, 9(1), 20563051231156401.
Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191. https://doi.org/10.1287/isre.2.3.173
Md Husin, M., Ismail, N., & Ab Rahman, A. (2016). The roles of mass media, word of mouth and subjective norm in family takaful purchase intention. Journal of Islamic Marketing, 7(1), 59–73. https://doi.org/10.1108/JIMA-03-2015-0020
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), 1816–1844.
N3XT Sports. (2023). Understanding the first steps to AI adoption in sports. N3XT Sports. Available at: https://www.n3xtsports.com (accessed 15 December 2024).
Naqvi, A. M. H., Jiang, Y., Miao, M., & Naqvi, M. H. (2020). The effect of social influence, trust, and entertainment value on social media use: Evidence from Pakistan. Cogent Business and Management, 7(1), 1–23.
Nguyen, H. T., & Chaudhuri, M. (2019). Making new products go viral and succeed. International Journal of Research in Marketing, 36(1), 39–62. https://doi.org/10.1016/j.ijresmar.2018.09.007
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.
Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., ... & Smith, E. M. (2020). Recipes for building an open-domain chatbot. arXiv preprint arXiv:2004.13637.
Rynarzewska, A. I. (2019). It’s not fake, it’s biased: Insights into morality of incentivized reviewers. Journal of Consumer Marketing, 36(3), 401–409. https://doi.org/10.1108/JCM-02-2018-2570
Sabah, N. M. (2016). Exploring students’ awareness and perceptions: Influencing factors and individual differences driving m-learning adoption. Computers in Human Behavior, 65, 522–533. https://doi.org/10.1016/j.chb.2016.09.009
Saif, N., Khan, S. U., Shaheen, I., ALotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024). ChatGPT: Validating technology acceptance model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154, 108097. https://doi.org/10.1016/j.chb.2023.108097
Salah, M., Alhalbusi, H., Ismail, M. M., & Abdelfattah, F. (2024). Chatting with ChatGPT: Decoding the mind of chatbot users and unveiling the intricate connections between user perception, trust, and stereotype perception on self-esteem and psychological well-being. Current Psychology, 43(9), 7843–7858.
Saputra, R., Nasution, M. I. P., & Dharma, B. (2023). The impact of using AI ChatGPT on marketing effectiveness: A case study on Instagram marketing. Indonesian Journal of Economics and Management, 3(3), 603–617.
Silva, O., Nunes, J., & Sousa, Á. (2024). University students’ perceptions of ChatGPT: Factors that affect their behavioural intention, usage and utility perceived. In ICERI2024 Proceedings (pp. 7427–7434). IATED.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489.
Skjuve, M., Følstad, A., & Brandtzaeg, P. B. (2023). The user experience of ChatGPT: Findings from a questionnaire study of early users. In Proceedings of the 5th International Conference on Conversational User Interfaces (pp. 1–10).
Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 1–14.
Tabachnick, B. G., & Fidell, L. S. (2012). Using multivariate statistics (6th ed.). Boston: Pearson.
Tibau, M., Siqueira, S. W. M., & Nunes, B. P. (2024). ChatGPT for chatting and searching: Repurposing search behavior. Library & Information Science Research, 46(4), 101331.
Umirov, S. (2024). Should we create better assignments instead of worrying about generative AI (such as ChatGPT)? American Journal of Language, Literacy and Learning in STEM Education, 2(1), 420–423.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31–46.
Zhong, Q., Ding, L., Liu, J., Du, B., & Tao, D. (2023). Can ChatGPT understand too? A comparative study on ChatGPT and fine-tuned BERT. arXiv preprint arXiv:2302.10198. https://doi.org/10.48550/arXiv.2302.10198
Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., ... & Sun, L. (2024). A comprehensive survey on pretrained foundation models: A history from BERT to ChatGPT. International Journal of Machine Learning and Cybernetics, 1–65.
Zijlmans, E., van der Ark, L. A., & Sijtsma, K. (2019). Item-score reliability as a selection tool in test construction. Frontiers in Psychology, 9, 2298.

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Primary Language	English
Subjects	Sports and Recreation
Journal Section	Research Article
Authors	Metin Argan 0000-0002-9570-0469 Halime Dinç 0000-0002-2391-5508
Early Pub Date	June 24, 2025
Publication Date	June 30, 2025
Submission Date	December 24, 2024
Acceptance Date	May 2, 2025
Published in Issue	Year 2025 Volume: 23 Issue: 2

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APA	Argan, M., & Dinç, H. (2025). INVESTIGATING THE FACTORS INFLUENCING ADOPTION INTENTIONS OF CHATGPT FOR SPORT EVENTS. SPORMETRE Beden Eğitimi Ve Spor Bilimleri Dergisi, 23(2), 77-97. https://doi.org/10.33689/spormetre.1606845

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