Assessing Factors Affecting Intention to Adopt AI and ML: The Case of the Jordanian Retail Industry

  • Mohammed I. Alghamdi Department of Computer Science, Al-Baha University, Al-Baha City, Kingdom of Saudi Arabia
Keywords: Artificial Intelligence, Machine Learning, AI, ML, Jordan, Retail Industry, Factors, Adoption Intention

Abstract

Aim: The aim of this research is to evaluate the factors that affect the adoption intention of AI and ML in the context of Jordan’s retail industry

Method: For this research paper, primary data was collected with the help of surveying different retail companies that are operational in Jordan with a sample of 400 participants. The survey questionnaire was based on a Likert scale where five points ranging from strongly agree to strongly disagree were provided to the participants. Structural Equation Modelling (SEM) used to analyse the impact and significance of the different factors on the adoption of AI and ML in Jordanian retail sector.

Results: It has been concluded from this research paper that communication, government regulations, market structure, and technological infrastructure are important factors that influence the adoption of AI and ML in the retail industry of Jordan. However, the results of this research have pointed out that managerial support and vendor relationship do not have a significant influence on the adoption of AI and ML.

Limitations: The scope of the research is restricted to the context of the retail industry only. This research has been carried out in the context of Jordan thus it cannot be applied on to other geographical backgrounds. Due to the time and scope limitations, there are restricted factors considered in the framework.

References

Alarie, B., Niblett, A., and Yoon, A. How artificial intelligence will affect the practice of law. University of Toronto Law Journal 68 (2018), 106-124.

Bhatt, B., and Singh, A. Power sector reforms and technology adoption in the indian electricity distribution sector. Energy 215 (2021), ID 118797.

Brynjolfsson, E., Rock, D., and Syverson, C. Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. National Bureau of Economic Research (2017), working paper 24001.

Chen, H., 2019. Success Factors Impacting Artificial Intelligence Adoption|Perspective From the Telecom Industry in China, Dissertation thesis, Old Dominion University.

Das, S., Dey, A., Pal, A., and Roy, N. Applications of artificial intelligence in machine learning: review and prospect. International Journal of Computer Applications (2015), 31-41.

Diez-Olivan, A., Ser, J. D., Galar, D., and Sierra, B. Data fusion and machine learning for industrial prognosis: Trends and perspectives towards industry 4.0. Information Fusion 50 (2019), 92-111.

dos Santos, B., Steiner, M., Fenerich, A., and Lima, R. Data mining and machine learning techniques applied to public health problems: A bibliometric analysis from 2009 to 2018. Computers & Industrial Engineering 138 (2019), ID 106120.

ELSamen, A., and Hiyasat, R. Beyond the random location of shopping malls: A gis perspective in amman, jordan. Journal of Retailing and Consumer Services 34 (2017), 30-37.

Hair, J., Ringle, C., and Sarstedt, M. Pls-sem: Indeed a silver bullet. Journal of Marketing theory and Practice 19 (2011), 139-152.

Hamid, M. A., Sami, W., and Sidek, M. Discriminant validity assessment: Use of fornell & larcker criterion versus htmt criterion. Journal of Physics: Conference Series 890 (2017), ID 012163.

Henseler, J., Ringle, C., and Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science 43 (2015), 115-135.

Kruse, L., Wunderlich, N., and Beck, R. Artificial intelligence for the financial services industry: What challenges organizations to succeed. In Proceedings of the 52nd Hawaii International Conference on System Sciences (2019).

Moberg, F., and Blomberg, E., 2019. Artificial Intelligence Adoption{Is it more than just hype?, Master thesis, Lund University.

Nejati, M., Rabiei, S., and Jabbour, C. Envisioning the invisible: Understanding the synergy between green human resource management and green supply chain management in manufacturing rms in iran in light of the moderating effect of employees' resistance to change. Journal of Cleaner Production 168 (2017), 163-172.

Panch, T., Szolovits, P., and Atun, R. Artificial intelligence, machine learning and health systems. Journal of global health 8 (2018), ID 020303.

Ringle, C., Silva, D. D., and Bido, D. Structural equation modeling with the smartpls. Brazilian Journal Of Marketing 13 (2015).

Simard, P., and et. al. Machine teaching: A new paradigm for building machine learning systems. arXiv preprint (2017), arXiv:1707.06742.

Tambe, P., Cappelli, P., and Yakubovich, V. Artificial intelligence in human resources management: Challenges and a path forward. California Management Review 61 (2019), 15-42.

Thrall, J., and et. al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. Journal of the American College of Radiology 15 (2018), 504-508.

Vaio, A. D., Palladino, R., Hassan, R., and Escobar, O. Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research 121 (2020), 283-314.

Wong, K. Partial least squares structural equation modeling (pls-sem) techniques using smart-pls. Marketing Bulletin 24 (2013), 1-32.

Published
2020-12-21
How to Cite
[1]
AlghamdiM. 2020. Assessing Factors Affecting Intention to Adopt AI and ML: The Case of the Jordanian Retail Industry. MENDEL. 26, 2 (Dec. 2020), 39-44. DOI:https://doi.org/10.13164/mendel.2020.2.039.
Section
Articles