Exploring Students’ Continuence Intention Toward Artificial Intelligence
https://doi.org/10.51574/jrip.v4i3.2027
Keywords:
Artificial Intelligence (AI), Marketing Research, IPMA, Technology Acceptance Model (TAM)Abstract
Various forms of Artificial intelligence (AI), including machine-driven AI, cognitive AI, and emotional AI, collaborate to provide numerous advantages in marketing research and help marketers develop effective strategies. This study applied the Technology Acceptance Model (TAM) to explore the aspects impelling students' ongoing intention to make advantages of AI in marketing research. To meet the study's objectives, this research used quantitative methods to identify AI applications in marketing research. A questionnaire survey was distributed to 200 student respondents to collect data. The collected data is examined using Importance Performance Map Analysis (IPMA). The study assesses the significance of AI in marketing research among students, revealing that attitude significantly impacts the committed to keep utilizing AI. Positive attitudes are strongly linked to continued usage, with perceived usefulness being the most critical factor, suggesting users find AI enhances their experiences. Acceptance of AI is also influenced by perceived ease of use, system, information, and service quality, as well as attitude. The results indicate that efficient system operation, improved information quality, and high-quality systems and services foster positive perceptions and preferences for AI. The results of this study offer valuable insights for educators in higher education, guiding them on effective strategies to encourage their students to utilize AI in a responsible manner, particularly within the realm of marketing research.
Downloads
References
Al-Debei, M. M., Akroush, M. N., & Ashouri, M. I. (2015). Consumer attitudes towards online shopping: The effects of trust, perceived benefits, and perceived web quality. Internet Research, 25(5), 707-733. doi:https://doi.org/10.1108/IntR-05-2014-0146
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 Integrated Model of BRT and TPB. International Journal of Human–Computer Interaction, 1-13. doi:https://doi.org/10.1080/10447318.2024.2361210
Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473. doi:https://doi.org/10.1016/j.tele.2020.101473
Azzahra, T. R., & Kusumawati, N. (2023). The Impact of Mobile Service Quality, Perceived Value, Perceived Usefulness, Perceived Ease of Use, Customer Satisfaction Towards Continuance Intention to Use MyTelkomsel App. Journal of Consumer Studies and Applied Marketing, 1(1), 46-60. doi:https://doi.org/10.58229/jcsam.v1i1.74
Baker-Eveleth, L., & Stone, R. W. (2020). User's perceptions of perceived usefulness, satisfaction, and intentions of mobile application. International Journal of Mobile Communications, 18(1), 1-18. doi:https://doi.org/10.1504/IJMC.2020.104431
Cai, Q., Lin, Y., & Yu, Z. (2023). Factors influencing learner attitudes towards ChatGPT-assisted language learning in higher education. International Journal of Human–Computer Interaction, 1-15. doi:https://doi.org/10.1080/10447318.2023.2261725
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42. doi:https://doi.org/10.1007/s11747-019-00696-0
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., . . . Ahuja, M. (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, 102642. doi:https://doi.org/10.1016/j.ijinfomgt.2023.102642
Estrada, J., Paheding, S., Yang, X., & Niyaz, Q. (2022). Deep-learning-incorporated augmented reality application for engineering lab training. Applied Sciences, 12(10), 5159. doi:https://doi.org/10.3390/app12105159
Farhi, F., Jeljeli, R., Aburezeq, I., Dweikat, F. F., Al-shami, S. A., & Slamene, R. (2023). Analyzing the students' views, concerns, and perceived ethics about chat GPT usage. Computers and Education: Artificial Intelligence, 100180. doi:https://doi.org/10.1016/j.caeai.2023.100180
Foroughi, B., Iranmanesh, M., & Hyun, S. S. (2019). Understanding the determinants of mobile banking continuance usage intention. Journal of Enterprise Information Management, 32(6), 1015-1033. doi:http://dx.doi.org/10.1108/JEIM-10-2018-0237
Friedrich, S., Antes, G., Behr, S., Binder, H., Brannath, W., Dumpert, F., . . . Leitgöb, H. (2022). Is there a role for statistics in artificial intelligence? Advances in Data Analysis and Classification, 16(4), 823-846. doi:https://doi.org/10.1007/s11634-021-00455-6
Ghasemaghaei, M., & Hassanein, K. (2019). Dynamic model of online information quality perceptions and impacts: a literature review. Behaviour & Information Technology, 38(3), 302-317. doi:https://doi.org/10.1080/0144929X.2018.1531928
Guayta, J. L. E. (2021). Artificial Intelligence Empowered Augmented Reality Application For Electrical Engineering Lab Education. Purdue University.
Gupta, P., Prashar, S., Vijay, T. S., & Parsad, C. (2021). Examining the influence of antecedents of continuous intention to use an informational app: the role of perceived usefulness and perceived ease of use. International Journal of Business Information Systems, 36(2), 270-287. doi:https://doi.org/10.1504/IJBIS.2021.112829
Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. doi:https://doi.org/10.1016/j.rmal.2022.100027
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. doi:https://doi.org/10.1108/EBR-11-2018-0203
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook: Springer Nature.
Hanjaya, S. M., Kenny, S. K., & Gunawan, S. F. (2019). Understanding factors influencing consumers online purchase intention via mobile app: perceived ease of use, perceived usefulness, system quality, information quality, and service quality. Marketing of Scientific and Research Organizations, 32(2), 175-205. doi:https://doi.org/10.2478/minib-2019-0035
Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30-50. doi:https://doi.org/10.1007/s11747-020-00749-9
Jo, H. (2023). Understanding the key antecedents of users’ continuance intention in the context of smart factory. Technology Analysis & Strategic Management, 35(2), 153-166. doi:https://doi.org/10.1080/09537325.2021.1970130
Karunananda, A., Silva, T., Handapangoda, D., & Sumanapala, S. (2021). Intelligent Personal Research Assistant. Paper presented at the 2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI).
Kasuma, J., Razak, F. L. A., Omar, N., Naim, A. S. A., Naimullah, B. S. S., & Darma, D. C. (2020). Attitude, Perceived Usefulness and Perceived Ease of Use towards Intention to Use M-Commerce: A Case of Grocery Shoppers in Kuching, Sarawak. Journal of Entrepreneurship & Business, 8(1). doi:10.17687/JEB.0801.06
Kieslich, K., Keller, B., & Starke, C. . (2022). Artificial intelligence ethics by design. Evaluating public perception on the importance of ethical design principles of artificial intelligence. . Big Data & Society, 9(1). doi:https://doi.org/10.1177/20539517221092956
Kronemann, B., Kizgin, H., Rana, N., & K. Dwivedi, Y. (2023). How AI encourages consumers to share their secrets? The role of anthropomorphism, personalisation, and privacy concerns and avenues for future research. Spanish Journal of Marketing-ESIC, 27(1), 3-19. doi:http://dx.doi.org/10.1108/SJME-10-2022-0213
Kumar, S. (2021). The Future of Decision Making: Augmented Intelligence. International Journal of Innovative Research in Computer Science & Technology, 9(3), 161-167.
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. doi:https://doi.org/10.3390/educsci13040410
Lund, B. D., & Wang, T. (2023). Chatting about ChatGPT: how may AI and GPT impact academia and libraries? Library Hi Tech News, 40(3), 26-29. doi:http://dx.doi.org/10.1108/LHTN-01-2023-0009
Machdar, N. M. (2016). The effect of information quality on perceived usefulness and perceived ease of use. Business and entrepreneurial review, 15(2), 131-146. doi:https://doi.org/10.25105/ber.v15i2.4630
Malgieri, G. (2023). In/acceptable marketing and consumers' privacy expectations: four tests from EU data protection law. Journal of Consumer Marketing, 40(2), 209-223. doi:http://dx.doi.org/10.1108/JCM-03-2021-4571
Montazemi, A. R., & Qahri-Saremi, H. (2015). Factors affecting adoption of online banking: A meta-analytic structural equation modeling study. Information & management, 52(2), 210-226. doi:https://doi.org/10.1016/j.im.2014.11.002
Moradi, M. (2021). Importance of internet of things (IoT) in marketing research and its ethical and data privacy challenges. doi:https://doi.org/10.21272/bel.5(1).22-30.2021
Musa, H. G., Fatmawati, I., Nuryakin, N., & Suyanto, M. (2024). Marketing research trends using technology acceptance model (TAM): a comprehensive review of researches (2002–2022). Cogent Business & Management, 11(1), 2329375. doi:https://doi.org/10.1080/23311975.2024.2329375
Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance model of artificial intelligence (AI)-based technologies in construction firms: Applying the Technology Acceptance Model (TAM) in combination with the Technology–Organisation–Environment (TOE) framework. Buildings, 12(2), 90. doi:https://doi.org/10.3390/buildings12020090
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). doi:https://doi.org/10.1016/j.heliyon.2021.e07014
Neumann, O., Guirguis, K., & Steiner, R. (2024). Exploring artificial intelligence adoption in public organizations: a comparative case study. Public Management Review, 26(1), 114-141. doi:https://doi.org/10.1080/14719037.2022.2048685
Priyadarshini, C., Sreejesh, S., & Anusree, M. (2017). Effect of information quality of employment website on attitude toward the website: A moderated mediation study. International Journal of Manpower, 38(5), 729-745. doi:https://doi.org/10.1108/IJM-12-2015-0235
Putra, G. A., Taniady, V., & Halmadiningrat, I. M. (2023). Tantangan Hukum: Keakuratan Informasi Layanan AI Chatbot Dan Pelindungan Hukum Terhadap Penggunanya. Jurnal Rechts Vinding: Media Pembinaan Hukum Nasional, 12(2). doi:http://dx.doi.org/10.33331/rechtsvinding.v12i2.1258
Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results the importance-performance map analysis. Industrial Management and Data Systems, 116(9), 1865–1886. doi:https://doi.org/10.1108/IMDS-10-2015-0449
Salleh, H. M. (2023). Errors of commission and omission in artificial intelligence: contextual biases and voids of ChatGPT as a research assistant. Digital Economy and Sustainable Development, 1(1), 14. doi:https://doi.org/10.1007/s44265-023-00015-0
Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics and Informatics, 47, 101324. doi:https://doi.org/10.1016/j.tele.2019.101324
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. doi:https://doi.org/10.1080/10494820.2023.2209881
Tam, C., Santos, D., & Oliveira, T. (2020). Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model. Information Systems Frontiers, 22, 243-257. doi:https://doi.org/10.1007/s10796-018-9864-5
Teeluckdharry, N. B., Teeroovengadum, V., & Seebaluck, A. K. (2022). A roadmap for the application of PLS-SEM and IPMA for effective service quality improvements. The TQM Journal. doi:https://doi.org/10.1108/TQM-11-2021-0340
Vanduhe, V. Z., Nat, M., & Hasan, H. F. (2020). Continuance intentions to use gamification for training in higher education: Integrating the technology acceptance model (TAM), social motivation, and task technology fit (TTF). Ieee Access, 8, 21473-21484. doi:https://doi.org/10.1109/ACCESS.2020.2966179
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. doi:https://doi.org/10.1016/j.jjimei.2020.100002
Weber, F. D., & Schütte, R. (2019). State-of-the-art and adoption of artificial intelligence in retailing. Digital Policy, Regulation and Governance, 21(3), 264-279. doi:https://doi.org/10.1108/DPRG-09-2018-0050
Yapp, E. H., & Yeap, J. A. (2023). Assessing the determinants of customer-perceived value and customer satisfaction in e-hailing services: An Importance-Performance Matrix Analysis (IPMA) Approach. Cogent Business & Management, 10(1), 2191808. doi:https://doi.org/10.1080/23311975.2023.2191808
Zhang, H., & Song, M. (2022). How Big Data Analytics, AI, and Social Media Marketing Research Boost Market Orientation. Research-Technology Management, 65(2), 64-70. doi:https://doi.org/10.1080/08956308.2022.2022907
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Adila Sosianika, Wahyu Rafdinal, Fatya Alty Amalia
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
