AI Awareness, Facilitating Factors, Perceived Risk, Attitude Toward AI, Behavior Toward AI, AI Application and Ethical Concerns: A Study of Higher Education Institutions
DOI:
https://doi.org/10.62843/jssr.v5i1.529Keywords:
Artificial Intelligence, AI Adoption, AI Awareness, Facilitating Factors, Perceived Risk, Attitude Toward AI, Behavior Toward AI, AI Application, Ethical ConcernsAbstract
AI is one of the most significant trends in the modern world, and it changes industries and redesigns people's lives and occupations. This research aims to establish attitudes and behavior toward AI using 439 finalized samples of instructors in academic institutions in Multan, Pakistan. Key variables like AI awareness level, facilitating factors, perceived risk, and ethical concerns were tested for the application of AI. The results show that AI awareness, the facilitating factors, and perceived risks impact attitudes towards AI. A positive attitude predicts AI-related behavior, which is a key determinant of AI application in educational institutions. However, ethical concern plays a non-significant role in the relationship between behavior and AI application, suggesting that it may not have a direct influence on the adoption of AI from this perspective. This paper highlights the necessity of an awareness campaign and the establishment of conducive conditions for AI use. Some of the limitations include the cross-sectional study design and the geographical location of the participants, which may affect the generalization of the findings. Future research will need to include longitudinal designs, different populations, and other social psychological factors like trust, emotions, and norms to investigate AI adoption patterns further.
References
Ahmad, I., & Hayee, R. (2024). Impact of Green Human Resource Management (GHRM) on Employee Eco-Friendly Behavior and Environmental Performance of Hospitality Industry with Mediating Role of Environmental Consciousness. Journal of Asian Development Studies, 13(3), 1167–1181.
Ahmad, S., Zulkurnain, N., & Khairushalimi, F. (2016). Assessing the validity and reliability of a measurement model in Structural Equation Modeling (SEM). British Journal of Mathematics & Computer Science, 15(3), 1–8.
Ahmed, S. K. (2024). How to choose a sampling technique and determine sample size for research: A simplified guide for researchers. Oral Oncology Reports, 12, 100662. https://doi.org/10.1016/j.oor.2024.100662
Aldosari, S. A. M. (2020). The future of higher education in the light of artificial intelligence transformations. International Journal of Higher Education, 9(3), 145–151.
Ali, M. A., Ahmed, Z., Ahmad, I., & Hayee, R. (2024). Exploring the Role of Social Comparison: Unraveling the Influence of Malicious Envy and Materialism on Impulse Buying, with Promotional Campaigns as a Moderator. Journal of Asian Development Studies, 13(3), 1538–1555.
Alzghoul, A., Aboalganam, K. M., & Al-Kasasbeh, O. (2024). Nexus among green marketing practice, leadership commitment, environmental consciousness, and environmental performance in Jordanian pharmaceutical sector. Cogent Business and Management, 11(1). https://doi.org/10.1080/23311975.2023.2292308
Ansari, M. A. A., Sajid, M., Khan, S. N., Antohi, V. M., Fortea, C., & Zlati, M. L. (2024). Unveiling the effect of renewable energy and financial inclusion towards sustainable environment: Does interaction of digital finance and institutional quality matter? Sustainable Futures, 7, 100196. https://doi.org/https://doi.org/10.1016/j.sftr.2024.100196
Anwar, N., Nik Mahmood, N. H., Yusliza, M. Y., Ramayah, T., Noor Faezah, J., & Khalid, W. (2020). Green Human Resource Management for organisational citizenship behaviour towards the environment and environmental performance on a university campus. Journal of Cleaner Production, 256, 120401. https://doi.org/https://doi.org/10.1016/j.jclepro.2020.120401
Atalla, A. D. G., El-Ashry, A. M., & Mohamed Sobhi Mohamed, S. (2024). The moderating role of ethical awareness in the relationship between nurses’ artificial intelligence perceptions, attitudes, and innovative work behavior: a cross-sectional study. BMC Nursing, 23(1), 488. https://doi.org/10.1186/s12912-024-02143-0
Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K. F., & Qin, J. (2021). Perceptions of and Behavioral Intentions towards Learning Artificial Intelligence in Primary School Students. Educational Technology and Society, 24(3).
Chedrawi, C., & Howayeck, P. (2019). Artificial Intelligence a Disruptive Innovation in Higher Education Accreditation Programs: Expert Systems and AACSB BT - ICT for a Better Life and a Better World: The Impact of Information and Communication Technologies on Organizations and Society (Y. Baghdadi & A. Harfouche (eds.); pp. 115–129). Springer International Publishing. https://doi.org/10.1007/978-3-030-10737-6_8
Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. Statistical Strategies for Small Sample Research, 1(1), 307–341.
Choi, K. S. (2020). Opportunities for higher education of artificial intelligence in Korea. International Journal of Engineering Research and Technology, 13(11), 3428–3430.
Dieguez, T., Loureiro, P., & Ferreira, I. (2021). Entrepreneurship and Leadership in Higher Education to Develop the Needed 21st Century Skills. Proceedings of the 17th European Conference on Management, Leadership and Governance, ECMLG 2021.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Sage Publications Sage CA: Los Angeles, CA.
Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277–304. https://doi.org/10.1080/15228053.2023.2233814
Gerlich, M. (2023). Perceptions and Acceptance of Artificial Intelligence: A Multi-Dimensional Study. Social Sciences, 12(9). https://doi.org/10.3390/socsci12090502
Grimus, M. (2020). Emerging Technologies: Impacting Learning, Pedagogy and Curriculum Development. https://doi.org/10.1007/978-981-15-0618-5_8
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.
Hair, J. F., 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. 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.
Hair, J., Risher, J., Sarstedt, M., & Ringle, C. (2018). When to use and how to report the results of PLS-SEM. European Business Review, 31. https://doi.org/10.1108/EBR-11-2018-0203
Hajam, K. B., & Gahir, S. (2024). Unveiling the Attitudes of University Students Toward Artificial Intelligence. Journal of Educational Technology Systems, 52(3), 335–345. https://doi.org/10.1177/00472395231225920
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
Hermanto, Y. B., & Srimulyani, V. A. (2022). The Effects of Organizational Justice on Employee Performance Using Dimension of Organizational Citizenship Behavior as Mediation. In Sustainability (Vol. 14, Issue 20). https://doi.org/10.3390/su142013322
Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. In Computers and Education: Artificial Intelligence (Vol. 1). https://doi.org/10.1016/j.caeai.2020.100001
Hwang, J., Kim, J. (Sunny), Joo, K.-H., & Choe, J. Y. (Jacey). (2024). An integrated model of artificially intelligent (AI) facial recognition technology adoption based on perceived risk theory and extended TPB: a comparative analysis of US and South Korea. Journal of Hospitality Marketing & Management, 33(8), 1071–1099. https://doi.org/10.1080/19368623.2024.2379269
Jain, R., Garg, N., & Khera, S. N. (2022). Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model. Frontiers in Psychology, 13(June). https://doi.org/10.3389/fpsyg.2022.893691
Katsantonis, A., & Katsantonis, I. G. (2024). University Students’ Attitudes toward Artificial Intelligence: An Exploratory Study of the Cognitive, Emotional, and Behavioural Dimensions of AI Attitudes. In Education Sciences (Vol. 14, Issue 9). https://doi.org/10.3390/educsci14090988
Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence. International Journal of Human–Computer Interaction, 40(2), 497–514. https://doi.org/10.1080/10447318.2022.2151730
Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77. https://doi.org/10.1016/j.tele.2022.101925
Klein, U., Depping, J., Wohlfahrt, L., & Fassbender, P. (2024). Application of artificial intelligence: risk perception and trust in the work context with different impact levels and task types. AI & SOCIETY, 39(5), 2445–2456. https://doi.org/10.1007/s00146-023-01699-w
Kock, N. (2017). Common Method Bias: A Full Collinearity Assessment Method for PLS-SEM BT - Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications (H. Latan & R. Noonan (eds.); pp. 245–257). Springer International Publishing. https://doi.org/10.1007/978-3-319-64069-3_11
Koenig, P. D. (2024). Attitudes toward artificial intelligence: combining three theoretical perspectives on technology acceptance. AI & SOCIETY. https://doi.org/10.1007/s00146-024-01987-z
Leung, S.-O. (2011). A comparison of psychometric properties and normality in 4-, 5-, 6-, and 11-point Likert scales. Journal of Social Service Research, 37(4), 412–421.
Li, W., & Zheng, X. (2024). Social Media Use and Attitudes toward AI: The Mediating Roles of Perceived AI Fairness and Threat. Human Behavior and Emerging Technologies, 2024. https://doi.org/10.1155/2024/3448083
Li, X., Jiang, M. Y., Jong, M. S., Zhang, X., & Chai, C. (2022). Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study. In International Journal of Environmental Research and Public Health (Vol. 19, Issue 14). https://doi.org/10.3390/ijerph19148733
Maheshwari, G. (2024). Factors influencing students’ intention to adopt and use ChatGPT in higher education: A study in the Vietnamese context. Education and Information Technologies, 29(10), 12167–12195. https://doi.org/10.1007/s10639-023-12333-z
Marshall, G. (2005). The purpose, design and administration of a questionnaire for data collection. Radiography, 11(2), 131–136.
Méndez-Suárez, M., Delbello, L., de Vega de Unceta, A., & Ortega Larrea, A. L. (2024). Factors Affecting Consumers’ Attitudes Towards Artificial Intelligence. Journal of Promotion Management, 30(7), 1141–1158. https://doi.org/10.1080/10496491.2024.2367203
Minkevics, V., & Kampars, J. (2021). Artificial intelligence and big data driven IS security management solution with applications in higher education organizations. 2021 17th International Conference on Network and Service Management (CNSM), 340–344. https://doi.org/10.23919/CNSM52442.2021.9615575
Mishra, R. (2019). Usage of data analytics and artificial intelligence in ensuring quality assurance at higher education institutions. 2019 Amity International Conference on Artificial Intelligence (AICAI), 1022–1025.
Mize, T. D., & Manago, B. (2022). The past, present, and future of experimental methods in the social sciences. Social Science Research, 108, 102799. https://doi.org/https://doi.org/10.1016/j.ssresearch.2022.102799
Murray, P. (1999). Fundamental issues in questionnaire design. Accident and Emergency Nursing, 7(3), 148–153. https://doi.org/https://doi.org/10.1016/S0965-2302(99)80074-5
O’Shaughnessy, M. R., Schiff, D. S., Varshney, L. R., Rozell, C. J., & Davenport, M. A. (2023). What governs attitudes toward artificial intelligence adoption and governance? Science and Public Policy, 50(2), 161–176. https://doi.org/10.1093/scipol/scac056
Oliveira, A., Feyzi Behnagh, R., Ni, L., Mohsinah, A. A., Burgess, K. J., & Guo, L. (2019). Emerging technologies as pedagogical tools for teaching and learning science: A literature review. Human Behavior and Emerging Technologies, 1(2). https://doi.org/10.1002/hbe2.141
Oprea, S. V., & Bâra, A. (2024). Profiling public perception of emerging technologies: Gene editing, brain chips and exoskeletons. A data-analytics framework. Heliyon, 10(22). https://doi.org/10.1016/j.heliyon.2024.e40268
Rahiman, H. U., & Kodikal, R. (2024). Revolutionizing education: Artificial intelligence empowered learning in higher education. Cogent Education, 11(1), 2293431. https://doi.org/10.1080/2331186X.2023.2293431
Rahman, I., Reynolds, D., & Svaren, S. (2012). How “ green” are North American hotels? An exploration of low-cost adoption practices. International Journal of Hospitality Management, 31(3). https://doi.org/10.1016/j.ijhm.2011.09.008
Rana, M. M., Siddiqee, M. S., Sakib, M. N., & Ahamed, M. R. (2024). Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework. Heliyon, 10(18). https://doi.org/10.1016/j.heliyon.2024.e37569
Rolstad, S., Adler, J., & Rydén, A. (2011). Response Burden and Questionnaire Length: Is Shorter Better? A Review and Meta-analysis. Value in Health, 14(8), 1101–1108. https://doi.org/https://doi.org/10.1016/j.jval.2011.06.003
Saihi, A., Ben-Daya, M., Hariga, M., & As’ad, R. (2024). A Structural equation modeling analysis of generative AI chatbots adoption among students and educators in higher education. Computers and Education: Artificial Intelligence, 7, 100274. https://doi.org/https://doi.org/10.1016/j.caeai.2024.100274
Sallam, M., Elsayed, W., Al-Shorbagy, M., Barakat, M., El Khatib, S., Ghach, W., Alwan, N., Hallit, S., & Malaeb, D. (2024). ChatGPT usage and attitudes are driven by perceptions of usefulness, ease of use, risks, and psycho-social impact: a study among university students in the UAE. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1414758
Scantamburlo, T., Cortés, A., Foffano, F., Barrué, C., Distefano, V., Pham, L., & Fabris, A. (2024). Artificial Intelligence across Europe: A Study on Awareness, Attitude and Trust. IEEE Transactions on Artificial Intelligence, 1–14. https://doi.org/10.1109/TAI.2024.3461633
Schwesig, R., Brich, I., Buder, J., Huff, M., & Said, N. (2023). Using artificial intelligence (AI)? Risk and opportunity perception of AI predict people’s willingness to use AI. Journal of Risk Research, 26(10), 1053–1084. https://doi.org/10.1080/13669877.2023.2249927
Shahzad, M. F., Xu, S., Lim, W. M., Yang, X., & Khan, Q. R. (2024). Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning. Heliyon, 10(8), e29523. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e29523
Shum, N.-Y. E., & Lau, H.-P. B. (2024). Perils, power and promises: Latent profile analysis on the attitudes towards artificial intelligence (AI) among middle-aged and older adults in Hong Kong. Computers in Human Behavior: Artificial Humans, 2(2), 100091. https://doi.org/https://doi.org/10.1016/j.chbah.2024.100091
van Twillert, A., Kreijns, K., Vermeulen, M., & Evers, A. (2020). Teachers’ beliefs to integrate Web 2.0 technology in their pedagogy and their influence on attitude, perceived norms, and perceived behavior control. International Journal of Educational Research Open, 1, 100014.
Wang, C., Wang, H., Li, Y., Dai, J., Gu, X., & Yu, T. (2024). Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy. International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2024.2383033
Waris, I., & Hameed, I. (2020). Promoting environmentally sustainable consumption behavior: an empirical evaluation of purchase intention of energy-efficient appliances. Energy Efficiency, 13(8), 1653–1664. https://doi.org/10.1007/s12053-020-09901-4
Wenge, M. (2021). Artificial Intelligence-Based Real-Time Communication and Ai-Multimedia Services in Higher Education. Journal of Multiple-Valued Logic & Soft Computing, 36.
Ahmad, I., & Hayee, R. (2024). Impact of Green Human Resource Management (GHRM) on Employee Eco-Friendly Behavior and Environmental Performance of Hospitality Industry with Mediating Role of Environmental Consciousness. Journal of Asian Development Studies, 13(3), 1167–1181.
Ahmad, S., Zulkurnain, N., & Khairushalimi, F. (2016). Assessing the validity and reliability of a measurement model in Structural Equation Modeling (SEM). British Journal of Mathematics & Computer Science, 15(3), 1–8.
Ahmed, S. K. (2024). How to choose a sampling technique and determine sample size for research: A simplified guide for researchers. Oral Oncology Reports, 12, 100662. https://doi.org/10.1016/j.oor.2024.100662
Aldosari, S. A. M. (2020). The future of higher education in the light of artificial intelligence transformations. International Journal of Higher Education, 9(3), 145–151.
Ali, M. A., Ahmed, Z., Ahmad, I., & Hayee, R. (2024). Exploring the Role of Social Comparison: Unraveling the Influence of Malicious Envy and Materialism on Impulse Buying, with Promotional Campaigns as a Moderator. Journal of Asian Development Studies, 13(3), 1538–1555.
Alzghoul, A., Aboalganam, K. M., & Al-Kasasbeh, O. (2024). Nexus among green marketing practice, leadership commitment, environmental consciousness, and environmental performance in Jordanian pharmaceutical sector. Cogent Business and Management, 11(1). https://doi.org/10.1080/23311975.2023.2292308
Ansari, M. A. A., Sajid, M., Khan, S. N., Antohi, V. M., Fortea, C., & Zlati, M. L. (2024). Unveiling the effect of renewable energy and financial inclusion towards sustainable environment: Does interaction of digital finance and institutional quality matter? Sustainable Futures, 7, 100196. https://doi.org/https://doi.org/10.1016/j.sftr.2024.100196
Anwar, N., Nik Mahmood, N. H., Yusliza, M. Y., Ramayah, T., Noor Faezah, J., & Khalid, W. (2020). Green Human Resource Management for organisational citizenship behaviour towards the environment and environmental performance on a university campus. Journal of Cleaner Production, 256, 120401. https://doi.org/https://doi.org/10.1016/j.jclepro.2020.120401
Atalla, A. D. G., El-Ashry, A. M., & Mohamed Sobhi Mohamed, S. (2024). The moderating role of ethical awareness in the relationship between nurses’ artificial intelligence perceptions, attitudes, and innovative work behavior: a cross-sectional study. BMC Nursing, 23(1), 488. https://doi.org/10.1186/s12912-024-02143-0
Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K. F., & Qin, J. (2021). Perceptions of and Behavioral Intentions towards Learning Artificial Intelligence in Primary School Students. Educational Technology and Society, 24(3).
Chedrawi, C., & Howayeck, P. (2019). Artificial Intelligence a Disruptive Innovation in Higher Education Accreditation Programs: Expert Systems and AACSB BT - ICT for a Better Life and a Better World: The Impact of Information and Communication Technologies on Organizations and Society (Y. Baghdadi & A. Harfouche (eds.); pp. 115–129). Springer International Publishing. https://doi.org/10.1007/978-3-030-10737-6_8
Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. Statistical Strategies for Small Sample Research, 1(1), 307–341.
Choi, K. S. (2020). Opportunities for higher education of artificial intelligence in Korea. International Journal of Engineering Research and Technology, 13(11), 3428–3430.
Dieguez, T., Loureiro, P., & Ferreira, I. (2021). Entrepreneurship and Leadership in Higher Education to Develop the Needed 21st Century Skills. Proceedings of the 17th European Conference on Management, Leadership and Governance, ECMLG 2021.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Sage Publications Sage CA: Los Angeles, CA.
Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277–304. https://doi.org/10.1080/15228053.2023.2233814
Gerlich, M. (2023). Perceptions and Acceptance of Artificial Intelligence: A Multi-Dimensional Study. Social Sciences, 12(9). https://doi.org/10.3390/socsci12090502
Grimus, M. (2020). Emerging Technologies: Impacting Learning, Pedagogy and Curriculum Development. https://doi.org/10.1007/978-981-15-0618-5_8
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.
Hair, J. F., 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. 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.
Hair, J., Risher, J., Sarstedt, M., & Ringle, C. (2018). When to use and how to report the results of PLS-SEM. European Business Review, 31. https://doi.org/10.1108/EBR-11-2018-0203
Hajam, K. B., & Gahir, S. (2024). Unveiling the Attitudes of University Students Toward Artificial Intelligence. Journal of Educational Technology Systems, 52(3), 335–345. https://doi.org/10.1177/00472395231225920
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
Hermanto, Y. B., & Srimulyani, V. A. (2022). The Effects of Organizational Justice on Employee Performance Using Dimension of Organizational Citizenship Behavior as Mediation. In Sustainability (Vol. 14, Issue 20). https://doi.org/10.3390/su142013322
Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. In Computers and Education: Artificial Intelligence (Vol. 1). https://doi.org/10.1016/j.caeai.2020.100001
Hwang, J., Kim, J. (Sunny), Joo, K.-H., & Choe, J. Y. (Jacey). (2024). An integrated model of artificially intelligent (AI) facial recognition technology adoption based on perceived risk theory and extended TPB: a comparative analysis of US and South Korea. Journal of Hospitality Marketing & Management, 33(8), 1071–1099. https://doi.org/10.1080/19368623.2024.2379269
Jain, R., Garg, N., & Khera, S. N. (2022). Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model. Frontiers in Psychology, 13(June). https://doi.org/10.3389/fpsyg.2022.893691
Katsantonis, A., & Katsantonis, I. G. (2024). University Students’ Attitudes toward Artificial Intelligence: An Exploratory Study of the Cognitive, Emotional, and Behavioural Dimensions of AI Attitudes. In Education Sciences (Vol. 14, Issue 9). https://doi.org/10.3390/educsci14090988
Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence. International Journal of Human–Computer Interaction, 40(2), 497–514. https://doi.org/10.1080/10447318.2022.2151730
Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77. https://doi.org/10.1016/j.tele.2022.101925
Klein, U., Depping, J., Wohlfahrt, L., & Fassbender, P. (2024). Application of artificial intelligence: risk perception and trust in the work context with different impact levels and task types. AI & SOCIETY, 39(5), 2445–2456. https://doi.org/10.1007/s00146-023-01699-w
Kock, N. (2017). Common Method Bias: A Full Collinearity Assessment Method for PLS-SEM BT - Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications (H. Latan & R. Noonan (eds.); pp. 245–257). Springer International Publishing. https://doi.org/10.1007/978-3-319-64069-3_11
Koenig, P. D. (2024). Attitudes toward artificial intelligence: combining three theoretical perspectives on technology acceptance. AI & SOCIETY. https://doi.org/10.1007/s00146-024-01987-z
Leung, S.-O. (2011). A comparison of psychometric properties and normality in 4-, 5-, 6-, and 11-point Likert scales. Journal of Social Service Research, 37(4), 412–421.
Li, W., & Zheng, X. (2024). Social Media Use and Attitudes toward AI: The Mediating Roles of Perceived AI Fairness and Threat. Human Behavior and Emerging Technologies, 2024. https://doi.org/10.1155/2024/3448083
Li, X., Jiang, M. Y., Jong, M. S., Zhang, X., & Chai, C. (2022). Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study. In International Journal of Environmental Research and Public Health (Vol. 19, Issue 14). https://doi.org/10.3390/ijerph19148733
Maheshwari, G. (2024). Factors influencing students’ intention to adopt and use ChatGPT in higher education: A study in the Vietnamese context. Education and Information Technologies, 29(10), 12167–12195. https://doi.org/10.1007/s10639-023-12333-z
Marshall, G. (2005). The purpose, design and administration of a questionnaire for data collection. Radiography, 11(2), 131–136.
Méndez-Suárez, M., Delbello, L., de Vega de Unceta, A., & Ortega Larrea, A. L. (2024). Factors Affecting Consumers’ Attitudes Towards Artificial Intelligence. Journal of Promotion Management, 30(7), 1141–1158. https://doi.org/10.1080/10496491.2024.2367203
Minkevics, V., & Kampars, J. (2021). Artificial intelligence and big data driven IS security management solution with applications in higher education organizations. 2021 17th International Conference on Network and Service Management (CNSM), 340–344. https://doi.org/10.23919/CNSM52442.2021.9615575
Mishra, R. (2019). Usage of data analytics and artificial intelligence in ensuring quality assurance at higher education institutions. 2019 Amity International Conference on Artificial Intelligence (AICAI), 1022–1025.
Mize, T. D., & Manago, B. (2022). The past, present, and future of experimental methods in the social sciences. Social Science Research, 108, 102799. https://doi.org/https://doi.org/10.1016/j.ssresearch.2022.102799
Murray, P. (1999). Fundamental issues in questionnaire design. Accident and Emergency Nursing, 7(3), 148–153. https://doi.org/https://doi.org/10.1016/S0965-2302(99)80074-5
O’Shaughnessy, M. R., Schiff, D. S., Varshney, L. R., Rozell, C. J., & Davenport, M. A. (2023). What governs attitudes toward artificial intelligence adoption and governance? Science and Public Policy, 50(2), 161–176. https://doi.org/10.1093/scipol/scac056
Oliveira, A., Feyzi Behnagh, R., Ni, L., Mohsinah, A. A., Burgess, K. J., & Guo, L. (2019). Emerging technologies as pedagogical tools for teaching and learning science: A literature review. Human Behavior and Emerging Technologies, 1(2). https://doi.org/10.1002/hbe2.141
Oprea, S. V., & Bâra, A. (2024). Profiling public perception of emerging technologies: Gene editing, brain chips and exoskeletons. A data-analytics framework. Heliyon, 10(22). https://doi.org/10.1016/j.heliyon.2024.e40268
Rahiman, H. U., & Kodikal, R. (2024). Revolutionizing education: Artificial intelligence empowered learning in higher education. Cogent Education, 11(1), 2293431. https://doi.org/10.1080/2331186X.2023.2293431
Rahman, I., Reynolds, D., & Svaren, S. (2012). How “ green” are North American hotels? An exploration of low-cost adoption practices. International Journal of Hospitality Management, 31(3). https://doi.org/10.1016/j.ijhm.2011.09.008
Rana, M. M., Siddiqee, M. S., Sakib, M. N., & Ahamed, M. R. (2024). Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework. Heliyon, 10(18). https://doi.org/10.1016/j.heliyon.2024.e37569
Rolstad, S., Adler, J., & Rydén, A. (2011). Response Burden and Questionnaire Length: Is Shorter Better? A Review and Meta-analysis. Value in Health, 14(8), 1101–1108. https://doi.org/https://doi.org/10.1016/j.jval.2011.06.003
Saihi, A., Ben-Daya, M., Hariga, M., & As’ad, R. (2024). A Structural equation modeling analysis of generative AI chatbots adoption among students and educators in higher education. Computers and Education: Artificial Intelligence, 7, 100274. https://doi.org/https://doi.org/10.1016/j.caeai.2024.100274
Sallam, M., Elsayed, W., Al-Shorbagy, M., Barakat, M., El Khatib, S., Ghach, W., Alwan, N., Hallit, S., & Malaeb, D. (2024). ChatGPT usage and attitudes are driven by perceptions of usefulness, ease of use, risks, and psycho-social impact: a study among university students in the UAE. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1414758
Scantamburlo, T., Cortés, A., Foffano, F., Barrué, C., Distefano, V., Pham, L., & Fabris, A. (2024). Artificial Intelligence across Europe: A Study on Awareness, Attitude and Trust. IEEE Transactions on Artificial Intelligence, 1–14. https://doi.org/10.1109/TAI.2024.3461633
Schwesig, R., Brich, I., Buder, J., Huff, M., & Said, N. (2023). Using artificial intelligence (AI)? Risk and opportunity perception of AI predict people’s willingness to use AI. Journal of Risk Research, 26(10), 1053–1084. https://doi.org/10.1080/13669877.2023.2249927
Shahzad, M. F., Xu, S., Lim, W. M., Yang, X., & Khan, Q. R. (2024). Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning. Heliyon, 10(8), e29523. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e29523
Shum, N.-Y. E., & Lau, H.-P. B. (2024). Perils, power and promises: Latent profile analysis on the attitudes towards artificial intelligence (AI) among middle-aged and older adults in Hong Kong. Computers in Human Behavior: Artificial Humans, 2(2), 100091. https://doi.org/https://doi.org/10.1016/j.chbah.2024.100091
van Twillert, A., Kreijns, K., Vermeulen, M., & Evers, A. (2020). Teachers’ beliefs to integrate Web 2.0 technology in their pedagogy and their influence on attitude, perceived norms, and perceived behavior control. International Journal of Educational Research Open, 1, 100014.
Wang, C., Wang, H., Li, Y., Dai, J., Gu, X., & Yu, T. (2024). Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy. International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2024.2383033
Waris, I., & Hameed, I. (2020). Promoting environmentally sustainable consumption behavior: an empirical evaluation of purchase intention of energy-efficient appliances. Energy Efficiency, 13(8), 1653–1664. https://doi.org/10.1007/s12053-020-09901-4
Wenge, M. (2021). Artificial Intelligence-Based Real-Time Communication and Ai-Multimedia Services in Higher Education. Journal of Multiple-Valued Logic & Soft Computing, 36.
Yoosomboon, S., Amornkitpinyo, T., Sopapradit, S., Amornkitpinyo, P., & Kinhom, R. (2021). A discriminant analysis of actual use of cloud technology for vocational and technical education. Journal of Theoretical and Applied Information Technology, 99(21).
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