Adoption of Artificial Intelligence in retail: Examining the impact of technological and organizational factors on customer retention and loyalty

https://doi.org/10.35912/amor.v6i3.2548
Published: Feb 27, 2025

Abstract:

Purpose: This study investigates the factors influencing retail firms' intentions to adopt Artificial Intelligence (AI) to enhance customer retention and loyalty in Dhaka, Bangladesh. The research focuses on examining how perceived usefulness, perceived ease of use, competitive pressure, technological readiness, and organizational innovativeness influence retail entrepreneurs’ adoption of AI as a strategic tool for customer engagement.

Research Methodology: A quantitative research design was employed, incorporating a hypothetical-deductive approach. The study utilized a cross-sectional design, drawing a sample of 250 retail firms through stratified random sampling in Dhaka. Data were collected using structured questionnaires and analyzed using statistical techniques to assess the relationships between the variables.

Results: The study identified that all five factors perceived usefulness, perceived ease of use, competitive pressure, technological readiness, and organizational innovativeness positively and significantly influence retail entrepreneurs' intentions to adopt AI. These findings emphasize the crucial role of both technological and organizational dynamics in driving AI adoption decisions within the retail sector.

Limitations: The research is geographically confined to retail firms in Dhaka, which may limit the generalizability of the findings to other regions or countries. Furthermore, the study's cross-sectional design restricts the ability to monitor AI adoption trends over time, indicating that future research could benefit from employing longitudinal designs and encompassing a broader geographical scope.

Contribution: This study provides valuable insights for retail managers and entrepreneurs seeking to leverage AI to enhance customer loyalty. It underscores the importance of fostering technological readiness and cultivating a culture of innovation within retail firms. The research contributes to the expanding body of knowledge on AI adoption in emerging markets, particularly concerning customer retention strategies in the retail sector.

Keywords:
1. artificial intelligence
2. customer retention
3. retail industry
4. technological readiness
5. innovation
Authors:
1 . Ismoth Zerine
Trine University, United States
2 . Younis Ali Biswas
Lincoln University College, Malaysia
3 . Md Zulkernain Doha
Grand Canyon University, Arizona, United States
4 . Humayra Mehreen Meghla
Glendale Community College, United States
5 . Mohammad Rashed Hasan Polas
Asia Pacific University of Technology and Innovation (APU), Technology Park Malaysia
https://orcid.org/0000-0002-6080-1075
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How to Cite
Zerine , I. ., Biswas , Y. A. ., Doha, M. Z., Meghla , H. M. ., & Polas, M. R. H. (2025). Adoption of Artificial Intelligence in retail: Examining the impact of technological and organizational factors on customer retention and loyalty. Annals of Management and Organization Research, 6(3), 287–302. https://doi.org/10.35912/amor.v6i3.2548
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Issue & Section
Vol. 6 No. 3 (2025): February
Articles
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References

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    Ali Abbasi, G., Abdul Rahim, N. F., Wu, H., Iranmanesh, M., & Keong, B. N. C. (2022). Determinants of SME’s social media marketing adoption: competitive industry as a moderator. Sage Open, 12(1), 21582440211067220.

    Almasria, N. A., Alhatabat, Z., Ershaid, D., Ibrahim, A., & Ahmed, S. (2024). The Mediating Impact of Organizational Innovation on the Relationship Between Fintech Innovations and Sustainability Performance. Sustainability, 16(22), 10044.

    Amoako, D. K., Zakuan, M. N., Okyere-Kwakye, E., & Tetteh, F. K. (2023). Effect of Training and Reward on Social Sustainability in Ghana’s Cocoa Supply Chain: The Role of Green Buyer-Supplier Relationship. Journal of International Food & Agribusiness Marketing, 35(2), 212-243.

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  1. Afrin, S., Sehreen, F., Polas, M. R. H., & Sharin, R. (2020). Corporate Social Responsibility (CSR) practices of financial institution in Bangladesh: the case of United Commercial Bank. Journal of Sustainable Tourism and Entrepreneurship, 2(2), 69-82.
  2. Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence: Harvard Business Pres.
  3. Aktar, S. (2023). Analyzing the Use of Artificial Intelligence and MachineLearning in Customer Support Systems. PMIS Review, 2(1), 97-120.
  4. Al-Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alamri, M. M., Aljarboa, N. A., Alturki, U., & Aljeraiwi, A. A. (2019). Integrating technology acceptance model with innovation diffusion theory: An empirical investigation on students’ intention to use E-learning systems. IEEE access, 7, 26797-26809.
  5. Al Shamsi, J. H., Al-Emran, M., & Shaalan, K. (2022). Understanding key drivers affecting students’ use of artificial intelligence-based voice assistants. Education and Information Technologies, 27(6), 8071-8091.
  6. Alfadda, H. A., & Mahdi, H. S. (2021). Measuring students’ use of zoom application in language course based on the technology acceptance model (TAM). Journal of Psycholinguistic Research, 50(4), 883-900.
  7. Ali Abbasi, G., Abdul Rahim, N. F., Wu, H., Iranmanesh, M., & Keong, B. N. C. (2022). Determinants of SME’s social media marketing adoption: competitive industry as a moderator. Sage Open, 12(1), 21582440211067220.
  8. Almasria, N. A., Alhatabat, Z., Ershaid, D., Ibrahim, A., & Ahmed, S. (2024). The Mediating Impact of Organizational Innovation on the Relationship Between Fintech Innovations and Sustainability Performance. Sustainability, 16(22), 10044.
  9. Amoako, D. K., Zakuan, M. N., Okyere-Kwakye, E., & Tetteh, F. K. (2023). Effect of Training and Reward on Social Sustainability in Ghana’s Cocoa Supply Chain: The Role of Green Buyer-Supplier Relationship. Journal of International Food & Agribusiness Marketing, 35(2), 212-243.
  10. Andaregie, A., Shimura, H., Chikasada, M., Sasaki, S., Alamnie Mulugeta, M., Worku, A., . . . Takagi, I. (2024). Assessing eating habits and preferences for bakery products among urban dwellers in Ethiopia. Cogent Business & Management, 11(1), 2375621.
  11. Anh, N. T. M., Hoa, L. T. K., Thao, L. P., Nhi, D. A., Long, N. T., Truc, N. T., & Ngoc Xuan, V. (2024). The effect of technology readiness on adopting artificial intelligence in accounting and auditing in Vietnam. Journal of Risk and Financial Management, 17(1), 27.
  12. Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of marketing research, 14(3), 396-402.
  13. Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163, 120420.
  14. Calantone, R. J., Cavusgil, S. T., & Zhao, Y. (2002). Learning orientation, firm innovation capability, and firm performance. Industrial marketing management, 31(6), 515-524.
  15. Chen, Y., Hu, Y., Zhou, S., & Yang, S. (2023). Investigating the determinants of performance of artificial intelligence adoption in hospitality industry during COVID-19. International Journal of Contemporary Hospitality Management, 35(8), 2868-2889.
  16. Cho, K. A., & Seo, Y. H. (2024). Dual mediating effects of anxiety to use and acceptance attitude of artificial intelligence technology on the relationship between nursing students’ perception of and intention to use them: a descriptive study. BMC nursing, 23(1), 212.
  17. Choudhary, S., Kaushik, N., Sivathanu, B., & Rana, N. P. (2024). Assessing Factors Influencing Customers’ Adoption of AI-Based Voice Assistants. Journal of Computer Information Systems, 1-18.
  18. Dabbous, A., Aoun Barakat, K., & Merhej Sayegh, M. (2022). Enabling organizational use of artificial intelligence: an employee perspective. Journal of Asia Business Studies, 16(2), 245-266.
  19. Damerji, H., & Salimi, A. (2021). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Accounting Education, 30(2), 107-130.
  20. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review, 96(1), 108-116.
  21. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
  22. Falebita, O. S., & Kok, P. J. (2024). Artificial Intelligence Tools Usage: A Structural Equation Modeling of Undergraduates’ Technological Readiness, Self-Efficacy and Attitudes. Journal for STEM Education Research, 1-26.
  23. Flavián, C., Pérez-Rueda, A., Belanche, D., & Casaló, L. V. (2022). Intention to use analytical artificial intelligence (AI) in services–the effect of technology readiness and awareness. Journal of Service Management, 33(2), 293-320.
  24. Giroux, M., Kim, J., Lee, J. C., & Park, J. (2022). Artificial intelligence and declined guilt: Retailing morality comparison between human and AI. Journal of business ethics, 178(4), 1027-1041.
  25. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis: Cengage.
  26. Haque, A., Akther, N., Khan, I., Agarwal, K., & Uddin, N. (2024). Artificial Intelligence in Retail Marketing: Research Agenda Based on Bibliometric Reflection and Content Analysis (2000–2023). Paper presented at the Informatics.
  27. Hawlader, M. R., Rana, M. M., Kalam, A., & Polas, M. R. H. (2022). Empowering workers’ involvement: Unveiling the dynamics of communication, recognition, productivity, and decision-making in the RMG sector. Journal of Sustainable Tourism and Entrepreneurship, 3(3), 195-215.
  28. Hoffmann, V. E., Belussi, F., Molina-Morales, F. X., & Vieira Pires, D. (2023). Clusters under pressure: the impact of a crisis in Italian industrial districts. Entrepreneurship & Regional Development, 35(3-4), 424-443.
  29. Hult, G. T. M., Hurley, R. F., & Knight, G. A. (2004). Innovativeness: Its antecedents and impact on business performance. Industrial marketing management, 33(5), 429-438.
  30. Islam, K., Polas, M. R. H., Parvin, K., & Akter, T. (2023). Decoding Demographics on Generation Z's Post-Pandemic Shopping Trends: E-Commerce Evolution 4.0, the Digital Shopper's Dilemma, and Tailoring Strategies. International Journal of Business, Management and Economics, 4(4), 360-380.
  31. Islam, M. T., Jabber, M. A., & Sakib, M. N. (2023). Application of revised theory of planned behavior model to assess the readiness of circular economy in the RMG sector of Bangladesh. Journal of cleaner production, 420, 138428.
  32. Jafar, R. M. S., Sun, Y., Niu, B., Hussain, S., Zhu, J., Gu, M., . . . Yang, Y. (2024). Revealing the secrets of metaverse technology adoption for sustainable performance via dual-stage SEM-ANN analysis. International Journal of Human–Computer Interaction, 1-18.
  33. Karim, R. A., Sobhani, F. A., Rabiul, M. K., Lepee, N. J., Kabir, M. R., & Chowdhury, M. A. M. (2022). Linking fintech payment services and customer loyalty intention in the hospitality industry: The mediating role of customer experience and attitude. Sustainability, 14(24), 16481.
  34. Kashive, N., Powale, L., & Kashive, K. (2020). Understanding user perception toward artificial intelligence (AI) enabled e-learning. The International Journal of Information and Learning Technology, 38(1), 1-19.
  35. Lopes, J. M., Silva, L. F., & Massano-Cardoso, I. (2024). AI meets the shopper: psychosocial factors in ease of use and their effect on E-Commerce purchase intention. Behavioral Sciences, 14(7), 616.
  36. Malodia, S., Islam, N., Kaur, P., & Dhir, A. (2021). Why do people use Artificial Intelligence (AI)-enabled voice assistants? IEEE Transactions on Engineering Management, 71, 491-505.
  37. Martínez-Plumed, F., Gómez, E., & Hernández-Orallo, J. (2021). Futures of artificial intelligence through technology readiness levels. Telematics and Informatics, 58, 101525.
  38. Mirhadian, N., Azizan, O., & Shahriari, M. (2024). The impact of green culture on employee organizational commitment: The mediating role of green identity. Journal of Human Behavior in the Social Environment, 34(6), 906-925.
  39. Misra, S., Katz, B., Roberts, P., Carney, M., & Valdivia, I. (2024). Toward a person-environment fit framework for artificial intelligence implementation in the public sector. Government Information Quarterly, 41(3), 101962.
  40. Mondal, M. S. A., Akter, N., & Polas, M. R. H. (2023). Factors influencing the environmental accounting disclosure practices for sustainable development: A systematic literature review. International Journal of Financial, Accounting, and Management, 5(2), 195-213.
  41. Mpuon, J. A., Ndem, S. E., Obo, E. B., Edema, A. J. M., Lebo, M. P., Eneh, S. I., . . . Mbu-Ogar, G. B. (2024). Paradigmatic impact of leagility supply chain integration on fragmented operational performance of construction businesses. International Journal of Business Process Integration and Management, 11(4), 259-286.
  42. Mustafa, A. S., & Garcia, M. B. (2021). Theories integrated with technology acceptance model (TAM) in online learning acceptance and continuance intention: A systematic review. Paper presented at the 2021 1st Conference on online teaching for mobile education (OT4ME).
  43. Mveku, B., Mutero, T. T., & Masinire, S. (2024). The effectiveness of growth strategies used by Zimbabwean microfinance institutions to improve company performance. Journal of Sustainable Tourism and Entrepreneurship, 6(1), 65-77.
  44. Natasia, S. R., Wiranti, Y. T., & Parastika, A. (2022). Acceptance analysis of NUADU as e-learning platform using the Technology Acceptance Model (TAM) approach. Procedia Computer Science, 197, 512-520.
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