Embrace or resist? Drivers of artificial intelligence writing software adoption in academic and non-academic contexts

Stavros Papakonstantinidis; Piotr Kwiatek; Filomachi Spathopoulou

doi:10.30935/cedtech/14250

Stavros Papakonstantinidis ¹ ^* , Piotr Kwiatek ², Filomachi Spathopoulou ³

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CONT ED TECHNOLOGY, Volume 16, Issue 2, Article No: ep495

https://doi.org/10.30935/cedtech/14250

Submitted: 03 November 2023, Published Online: 20 February 2024

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Abstract

This research investigates the perspectives of using artificial intelligence writing software (AIWS) in professional contexts, focusing on academic and non-academic writers. These two groups, while standing to gain increased productivity through the adoption of AIWS, also express concerns regarding the widespread implementation of this technology. Notably, artificial intelligence (AI) writing tech’s impact on content creation has been profound, with its swift grammatically accurate content generation. This adoption, however, remains controversial. The study employs a quantitative approach, combining technology acceptance model and new computer game attitude scale. This approach allows us to discern implications of using AI-powered writing tools while accounting for possible differences in different domains of use. Through a survey of 219 participants, spanning academia and business, the study explores attitudes and willingness to use AIWS. Findings yield insights into non-academic writers’ readiness and implications of AIWS adoption. Business, non-academic professionals view AIWS as a tool for efficiency and content quality, while writers in academic contexts express concerns about biases, manipulation, and job displacement. The study contributes to AIWS understanding, benefiting developers, educational institutions, and content creators, and elucidates differing attitudes and age dynamics between academics and professionals. The research underscores the multifaceted influence of AIWS, providing a foundation for future exploration in this emerging domain, as well as practical applications for industries and educational institutions.

Keywords: artificial intelligence, chatbots, ChatGPT, education, educational technologies

References

Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429. https://doi.org/10.30935/cedtech/13152
Alneyadi, S., & Wardat, Y. (2023). ChatGPT: Revolutionizing student achievement in the electronic magnetism unit for eleventh-grade students in Emirates schools. Contemporary Educational Technology, 15(4), ep448. https://doi.org/10.30935/cedtech/13417
Anson, C., & Straume, I. (2022). Amazement and trepidation: Implications of AI-based natural language production for the teaching of writing. Journal of Academic Writing, 12(1), 1-9. https://doi.org/10.18552/joaw.v12i1.820
Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI. University of Rhode Island. https://digitalcommons.uri.edu/cba_facpubs/548?utm_source=digitalcommons.uri.edu%2Fcba_facpubs%2F548&utm_medium=PDF&utm_campaign=PDFCoverPages
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94. https://doi.org/10.1007/bf02723327
Biermann, O., Ma, N. F., & Yoon, D. (2022). From tool to companion: Storywriters want AI writers to respect their personal values and writing strategies. In Proceedings of the Designing Interactive Systems Conference (pp. 1209-1227). https://doi.org/10.1145/3532106.3533506
Bryman, A. (2016). Social research methods. Oxford University Press.
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 7, 3-11.
Campolo, A., & Crawford, K. (2020). Enchanted determinism: Power without responsibility in artificial intelligence. Engaging Science, Technology, and Society, 6, 1. https://doi.org/10.17351/ests2020.277
Chen, C., Fu, J., & Lyu, L. (2023). A pathway towards responsible AI generated content. ArXiv. https://doi.org/10.48550/arXiv.2303.01325
Cheng, L., Varshney, K. R., & Liu, H. (2021). Socially responsible AI algorithms: Issues, purposes, and challenges. Journal of Artificial Intelligence Research, 71, 1137-1181. https://doi.org/10.1613/jair.1.12814
Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-336). Lawrence Erlbaum Associates Publishers.
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189-217. https://doi.org/10.1287/isre.14.2.189.16018
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum.
Coley, C. W., Thomas, D. A., Lummiss, J. A. M., Jaworski, J. N., Breen, C. P., Schultz, V., Hart, T., Fishman, J. S., Rogers, L., Gao, H., Hicklin, R. W., Plehiers, P. P., Byington, J., Piotti, J. S., Green, W. H., Hart, A. J., Jamison, T. F., & Jensen, K. F. (2019). A robotic platform for flow synthesis of organic compounds informed by AI planning. Science, 365(6453), eaax1566. https://doi.org/10.1126/science.aax1566
Colleoni, E., & Corsaro, D. (2022). Critical issues in artificial intelligence algorithms and their implications for digital marketing. In R. Llamas, & R. Belk (Eds.), The Routledge handbook of digital consumption. Routledge. https://doi.org/10.4324/9781003317524-16
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International. https://doi.org/10.1080/14703297.2023.2190148
Dargham, M., Hachimi, H., & Boutalline, M. (2022). How AI is automating writing: The rise of robot writers. In Proceedings of the 8^th International Conference on Optimization and Applications. https://doi.org/10.1109/icoa55659.2022.9934723
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
De Witte, M. (2023). How will ChatGPT change the way we think and work? Stanford News. https://news.stanford.edu/2023/02/13/will-chatgpt-change-way-think-work/
Dharmadhikari, S. (2023). AI writing assistant software market report 2023. Cognitive Market Research. https://www.cognitivemarketresearch.com/ai-writing-assistant-software-market-report
Dhiman, Dr. B. (2023). Does artificial intelligence help journalists: A boon or bane? SSRN. https://doi.org/10.2139/ssrn.4401194
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., & Carter, L. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71(0268-4012), 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Else, H. (2023). Abstracts written by ChatGPT fool scientists. Nature. https://doi.org/10.1038/d41586-023-00056-7
Gkinko, L., & Elbanna, A. (2022). The appropriation of conversational AI in the workplace: A taxonomy of AI chatbot users. International Journal of Information Management, 69, 102568. https://doi.org/10.1016/j.ijinfomgt.2022.102568
Gülen, K. (2023). Artificial intelligence in accounting explained. Dataconomy. https://dataconomy.com/2023/05/09/artificial-intelligence-in-accounting/
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
Halaweh, M. (2023). ChatGPT in education: Strategies for responsible implementation. Contemporary Educational Technology, 15(2), ep421. https://doi.org/10.30935/cedtech/13036
Haleem, A., Javaid, M., & Singh, R. P. (2023). An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 2(4), 100089. https://doi.org/10.1016/j.tbench.2023.100089
Heikkilä, M. (2023). Why detecting AI-generated text is so difficult (and what to do about it). MIT Technology Review. https://www.technologyreview.com/2023/02/07/1067928/why-detecting-ai-generated-text-is-so-difficult-and-what-to-do-about-it/
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(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Horn, B., Clark, C., Strom, O., Chao, H., Stahl, A. J., Harteveld, C., & Smith, G. (2016). Design insights into the creation and evaluation of a computer science educational game. In Proceedings of the 47^th ACM Technical Symposium on Computing Science Education. ACM. https://doi.org/10.1145/2839509.2844656
Huang, J., & Tan, M. (2023). The role of ChatGPT in scientific communication: Writing better scientific review articles. American Journal of Cancer Research, 13(4), 1148-1154.
Hutson, M. (2022). Could AI help you to write your next paper? Nature, 611(7934), 192-193. https://doi.org/10.1038/d41586-022-03479-w
Illia, L., Colleoni, E., & Zyglidopoulos, S. (2022). Ethical implications of text generation in the age of artificial intelligence. Business Ethics, the Environment & Responsibility, 32(1), 201-210. https://doi.org/10.1111/beer.12479
Ippolito, D., Yuan, A., Coenen, A., & Burnam, S. (2022). Creative writing with an AI-powered writing assistant: Perspectives from professional writers. ArXiv. https://doi.org/10.48550/arxiv.2211.05030
Kim, N. J., & Kim, M. K. (2022). Teacher’s perceptions of using an artificial intelligence-based educational tool for scientific writing. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.755914
Laker, B. (2023). AI at the crossroads: Navigating job displacement, ethical concerns, and the future of work. Forbes. https://www.forbes.com/sites/benjaminlaker/2023/05/09/ai-at-the-crossroads-navigating-job-displacement-ethical-concerns-and-the-future-of-work/?sh=4639cc62391c
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114-121. https://doi.org/10.1037//0021-9010.86.1.114
Liu, E. Z.-F., Lee, C.-Y., & Chen, J.-H. (2013). Developing a new computer game attitude scale for Taiwanese early adolescents. Educational Technology & Society, 16(1), 183-193.
Liu, I-Fan. (2020). The impact of extrinsic motivation, intrinsic motivation, and social self-efficacy on English competition participation intentions of pre-college learners: Differences between high school and vocational students in Taiwan. Learning and Motivation, 72, 101675. https://doi.org/10.1016/j.lmot.2020.101675
Lussier, B., Chaker, N. N., Hartmann, N. N., & Rangarajan, D. (2022). Lone wolf tendency and ethical behaviors in sales: Examining the roles of perceived supervisor support and salesperson self-efficacy. Industrial Marketing Management, 104, 304-316. https://doi.org/10.1016/j.indmarman.2022.04.015
Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90(90), 46-60. https://doi.org/10.1016/j.futures.2017.03.006
Malatji, W. R., Eck, R. V., & Zuva, T. (2020). Understanding the usage, modifications, limitations and criticisms of technology acceptance model (TAM). Advances in Science, Technology and Engineering Systems Journal, 5(6), 113-117. https://doi.org/10.25046/aj050612
Mhlanga, D. (2023). Open AI in education, the responsible and ethical use of ChatGPT towards lifelong learning. SSRN. https://doi.org/10.2139/ssrn.4354422
Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167, 209-234. https://doi.org/10.1007/s10551-019-04407-1
Nakazawa, E., Udagawa, M., & Akabayashi, A. (2022). Does the use of AI to create academic research papers undermine researcher originality? AI, 3(3), 702-706. https://doi.org/10.3390/ai3030040
Nathan, L. F. (2018). Creativity, the arts, and the future of work. In J. W. Cook (Eds.), Sustainability, human well-being, and the future of education (pp. 283-310). Palgrave Macmillan. https://doi.org/10.1007/978-3-319-78580-6_9
Nazari, N., Shabbir, M. S., & Setiawan, R. (2021). Application of artificial intelligence powered digital writing assistant in higher education: Randomized controlled trial. Heliyon, 7(5), E07014. https://doi.org/10.1016/j.heliyon.2021.e07014
Patel, S. B., & Lam, K. (2023). ChatGPT: The future of discharge summaries? The Lancet Digital Health, 5(3), E107-E108. https://doi.org/10.1016/s2589-7500(23)00021-3
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. https://doi.org/10.1037/0021-9010.88.5.879
Poole, S. (2019). The rise of robot authors: Is the writing on the wall for human novelists? The Guardian. https://www.theguardian.com/books/2019/mar/25/the-rise-of-robot-authors-is-the-writing-on-the-wall-for-human-novelists
Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12, 22. https://doi.org/10.1186/s41039-017-0062-8
Qadir, J. (2023). Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. In Proceedings of the 2023 IEEE Global Engineering Education Conference. https://doi.org/10.1109/educon54358.2023.10125121
Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332-344. https://doi.org/10.1016/j.ijresmar.2009.08.001
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). A critical look at the use of PLS-SEM in “MIS Quarterly.” MIS Quarterly, 36(1), iii. https://doi.org/10.2307/41410402
Roscoe, J. T. (1975). Fundamental research statistics for the behavioral sciences. Dryden Press Harcourt Brace Jovanovich.
Salvagno, M., Taccone, F. S., & Gerli, A. G. (2023). Can artificial intelligence help for scientific writing? Critical Care, 27, 75. https://doi.org/10.1186/s13054-023-04380-2
Sands, S., Campbell, C. L., Plangger, K., & Ferraro, C. (2022). Unreal influence: Leveraging AI in influencer marketing. European Journal of Marketing, 56(6), 1721-1747. https://doi.org/10.1108/ejm-12-2019-0949
Satariano, A., & Mozur, P. (2023). The people onscreen are fake. The disinformation is real. The New York Times. https://www.nytimes.com/2023/02/07/technology/artificial-intelligence-training-deepfake.html
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner-instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18, 54. https://doi.org/10.1186/s41239-021-00292-9
Shing, T. (2022). AI content writing tools: Are they productive or pointless? Spiceworks. https://www.spiceworks.com/marketing/ai-in-marketing/guest-article/ai-content-writing-tools/
Simmering, M. J., Fuller, C. M., Richardson, H. A., Ocal, Y., & Atinc, G. M. (2014). Marker variable choice, reporting, and interpretation in the detection of common method variance. Organizational Research Methods, 18(3), 473-511. https://doi.org/10.1177/1094428114560023
Sumakul, D. T. Y. G., Hamied, F. A., & Sukyadi, D. (2022). Artificial intelligence in EFL classrooms: Friend or foe? LEARN Journal: Language Education and Acquisition Research Network, 15(1), 232-256.
Tang, Y. (2021). A robot wrote this?: An empirical study of AI’s applications in writing practices. In Proceedings of the 39^th ACM International Conference on Design of Communication. https://doi.org/10.1145/3472714.3475817
The Authors Guild. (2023). Survey reveals 90 percent of writers believe authors should be compensated for the use of their books in training generative AI. The Authors Guild. https://authorsguild.org/news/ai-survey-90-percent-of-writers-believe-authors-should-be-compensated-for-ai-training-use/
Wagner, G., Lukyanenko, R., & Paré, G. (2021). Artificial intelligence and the conduct of literature reviews. Journal of Information Technology, 37(2), 026839622110482. https://doi.org/10.1177/02683962211048201
Wu, S-Y., & Yang, K-K. (2022). The effectiveness of teacher support for students’ learning of artificial intelligence popular science activities. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.868623

Citation

Papakonstantinidis, S., Kwiatek, P., & Spathopoulou, F. (2024). Embrace or resist? Drivers of artificial intelligence writing software adoption in academic and non-academic contexts. Contemporary Educational Technology, 16(2), ep495. https://doi.org/10.30935/cedtech/14250

Papakonstantinidis, S., Kwiatek, P., and Spathopoulou, F. (2024). Embrace or resist? Drivers of artificial intelligence writing software adoption in academic and non-academic contexts. Contemporary Educational Technology, 16(2), ep495. https://doi.org/10.30935/cedtech/14250

Papakonstantinidis S, Kwiatek P, Spathopoulou F. Embrace or resist? Drivers of artificial intelligence writing software adoption in academic and non-academic contexts. CONT ED TECHNOLOGY. 2024;16(2), ep495. https://doi.org/10.30935/cedtech/14250

Papakonstantinidis, Stavros, Piotr Kwiatek, and Filomachi Spathopoulou. "Embrace or resist? Drivers of artificial intelligence writing software adoption in academic and non-academic contexts". Contemporary Educational Technology 2024 16 no. 2 (2024): ep495. https://doi.org/10.30935/cedtech/14250

Papakonstantinidis, Stavros et al. "Embrace or resist? Drivers of artificial intelligence writing software adoption in academic and non-academic contexts". Contemporary Educational Technology, vol. 16, no. 2, 2024, ep495. https://doi.org/10.30935/cedtech/14250

Papakonstantinidis S, Kwiatek P, Spathopoulou F. Embrace or resist? Drivers of artificial intelligence writing software adoption in academic and non-academic contexts. CONT ED TECHNOLOGY. 2024;16(2):ep495. https://doi.org/10.30935/cedtech/14250