Journal of Intelligent Marketing Management

Journal of Intelligent Marketing Management

Social benefits of artificial intelligence for students with emphasis on marketing activities

Document Type : The scientific research paper

Authors
Yazdan Shirmohammadi 1
Mohammad Ali Sharif 2
1 Associate Professor, Department of Business Management, Payame Noor University, Tehran, Iran,
2 Master of Science in Tourism Management, Payam Noor University, Tehran, Iran
JABM.3.2.15564.351256.325745154
Abstract
The purpose of this research is to investigate the variables (self-confidence, communication, anxiety, social benefits, social good, attitude towards education and people's preparation) on the behavioral intention of tourism students towards the use of artificial intelligence.

Method: The current research is classified as a descriptive survey research in terms of its practical purpose and in terms of its research method, and the statistical population is tourism students across the country. Statistical analyzes were also performed using spss and Amos software, and the regression method was also used in the hypothesis path analysis test, and it also displays the mean, standard deviation, skewness, and kurtosis.

Findings and Conclusion: The results of the research showed that all the factors and variables of the research have a positive and significant effect on the behavioral intention, except the perceived usefulness, which hypothesis was rejected, and the result showed us that the variables have an important and significant effect on the behavioral intention of the people. And by controlling these variables in the education system of the country, especially tourism, a suitable platform for optimal education can be provided.
Keywords
Artificial intelligence
confidence
communication
anxiety
social benefits
tourism

Subjects

Chai, C. S., Lin, P., & Jong, M. S. (2020). Factors infuencing students’ behavioral intention to Continue artifcial intelligence learning (pp. 2020–2023). https://doi.org/10.1109/ ISET49818.2020.00040.
Chai, C. S., Wang, X., & Xu, C. (2020). An extended theory of planned behavior for modelling Chinese secondary school students’ intention to learn artifcial intelligence. Mathematics, 8(11), 1–18. https://doi.org/10.3390/math8112089.
Chiu, T. K. (2021). A holistic approach to the design of artifcial intelligence (AI) education for K-12 schools. TechTrends, 1–12. https://doi.org/10.1007/s11528-021-00637-1.
Dai, Y., Chai, C. S., Lin, P. Y., Jong, M. S. Y., Guo, Y., & Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artifcial intelligence age. Sustainability, 12 (16), 1–15. https://doi.org/10.3390/su12166597.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008.
Farbad, Ebrahim (2013) Structural equation modeling in questionnaire data using Amos22 software. Tehran. (In persian).
Han, X., Hu, F., Xiong, G., Liu, X., Gong, X., Niu, X., et al. (2018). Design of AI curriculum for primary and secondary schools in Qingdao. In 2018 Chinese + automation congress (pp. 4135–4140). CAC. https://doi.org/10.1109/  CAC.2018.8623310.
House Of Lords: Select committee on artifcial intelligence AI in the UK? Ready, willing and able.
Lee, I., & Perret, B. (2022). Preparing high school teachers to integrate AI methods into STEM classrooms. Association for the Advancement of Artifcial Intelligence. https://doi.org/10.1609/aaai.v36i11.21557
Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward elearning: An extension of the expectation-confrmation model. Computers & Education, 54 (2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002.
Mostofi, Shokofa. (1401). Performance of Artificial Intelligence in Learning and Education, 7th National Conference on New Approaches in Education and Research, Mahmoudabad. (In persian).
Nguyen, N. T., Chu, A. T., Tran, L. H., Pham, S. X., Nguyen, H. N., & Nguyen, V. T. (2022). Factors infuencing elementary teachers’ readiness in delivering sex education amidst Covid-19 pandemic. International Journal of Learning, Teaching and Educational Research, 21(2). https://doi.org/10.26803/ijlter.21.2.18.
Oxford Insights, and International Development Research Centre (IDRC). (2019). Government artifcial intelligence readiness index [Online] Available: https://www.oxfordinsights.com/ai-readiness2019.
Oxford Insights, and International Development Research Centre (IDRC). (2020). Government artifcial intelligence readiness index [Online] Available: https://www.oxfordinsights.com/government-ai-readiness-index-2020.
Pan, J., Ding, S., Wu, D., Yang, S., & Yang, J. (2019). Exploring behavioural intentions toward smart healthcare services among medical practitioners: A technology transfer perspective. International Journal of Production Research, 57(18), 5801–5820.https://doi.org/10.1080/00207543.2018.1550272.
Sanusi, I. T., & Olaleye, S. A. (2022). An insight into cultural competence and ethics in K- 12 artifcial intelligence education. In 2022 IEEE global engineering education conference (EDUCON) (pp. 790–794). IEEE. 10.1109/EDUCON52537.2022.9766818.
UNESCO. (2022). K-12 AI curricula A mapping of government-endorsed AI curricula. France: United Nations Educational, Scientifc and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000380602.
Wu, I. L., Li, J. Y., & Fu, C. Y. (2011). The adoption of mobile healthcare by hospital’s professionals: An integrative perspective. Decision Support Systems, 51 (3), 587–596. https://doi.org/10.1016/j.dss.2011.03.003.
Yang, X. (2019). Accelerated move for AI education in China. ECNU Review of Education, 2 (3), 347–352, 2019. https://doi.org/10.1177/2096531119878590.
Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward elearning: An extension of the expectation-confrmation model. Computers & Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002
Chai, C. S., Lin, P., & Jong, M. S. (2020). Factors infuencing students’ behavioral intention to Continue artifcial intelligence learning (pp. 2020–2023). https://doi.org/10.1109/ ISET49818.2020.00040
Katsarou, E. (2021). The effects of computer anxiety and self-effcacy on L2 learners’ self- perceived digital competence and satisfaction in higher education. Journal of Education and E-Learning Research, 8(2), 158–172. https://doi.org/10.20448/JOURNAL.509.2021.82.158.172
Rahman, S. (2020). Teenagers’ behavioural intention towards wearable technologies and intention to recommend others : An empirical study in Bangladesh. https://doi.org/10.1108/JSTPM-05-2020-0088.
Sanusi, I. T., Olaleye, S. A., Oyelere, S. S., & Dixon, R. A. (2022). Investigating learners’ competencies for artifcial intelligence education in an African K-12 setting.Computers and Education Open, 3, Article 100083.
Sanusi, I. T., & Oyelere, S. S. (2020). Pedagogies of machine learning in K-12 context. In 2020 IEEE Frontiers in education conference (FIE) (pp. 1–8). IEEE.
Folstad, K., Gialamas, V., Lavidas, K., & Komis, V. (2021). Teachers’ readiness to adopt mobile learning in classrooms: A study in Greece. Technology, Knowledge and Learning, 26(1), 53–77.
Hsieh, P.-J. (2015). Physicians’ acceptance of electronic medical records exchange: An extension of the decomposed TPB model with institutional trust and perceived risk. International Journal of Medical Informatics, 84(1), 1–14 (April  https://publications.parliament.uk/pa/ld201719/ldselect/ldai/100/100.pdf.  (Accessed 6 August 2022).
Pan, J., Ding, S., Wu, D., Yang, S., & Yang, J. (2019). Exploring behavioural intentions toward smart healthcare services among medical practitioners : A technology transfer perspective. International Journal of Production Research, 57(18), 5801–5820.https://doi.org/10.1080/00207543.2018.1550272
Darmansyah, F. B. A., Hendratmi, A., & Aziz, P. F. (2020). Factors determining behavioral intentions to use Islamic fnancial technology: Three competing models. Journal of Islamic Marketing, 12(4), 794–812. https://doi.org/10.1108/JIMA-12-2019-0252
Sugandini, D., Purwoko, Pambudi, A., Resmi, S., Reniati, M., & Kusumawati, R. A. (2018). The role of uncertainty, perceived ease of use, and perceived usefulness towards the technology adoption. International Journal of Civil Engineering &Technology, 9(4), 660–669.
Journal of Intelligent Marketing Management
Volume 6, Issue 1 - Serial Number 27
Spring 2025
Pages 189-209

  • Receive Date 30 August 2024
  • Revise Date 05 February 2025
  • Accept Date 07 February 2025