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All Journal Annals of Management and Organization Research
Doha, Md Zulkernain
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Economics, Econometrics & Finance

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Journal : Annals of Management and Organization Research

 1 
Adoption of Artificial Intelligence in retail: Examining the impact of technological and organizational factors on customer retention and loyalty Zerine , Ismoth; Biswas , Younis Ali; Doha, Md Zulkernain; Meghla , Humayra Mehreen; Polas, Mohammad Rashed Hasan
Annals of Management and Organization Research Vol. 6 No. 3 (2025): February
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v6i3.2548

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.
Data-Driven Sustainability: How Predictive Analytics ShapeSupply Chain Performance Zerine, Ismoth; Biswas , Younis Ali; Doha, Md Zulkernain; Meghla , Humayra Mehreen; Polas, Mohammad Rashed Hasan
Annals of Management and Organization Research Vol. 6 No. 4 (2025): May
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v6i4.2613

Abstract

Purpose: The integration of predictive analytics into supply chains has emerged as a critical  driver of sustainability in the manufacturing sector. This study explores the role of predictive analytics in enhancing sustainable supply chain performance, with a particular focus on manufacturing firms in Dhaka, Bangladesh. Research Methodology: This study adopts a positivist paradigm with a hypothetical deductive approach and employs a cross-sectional design. Data were collected from 211 manufacturing firms using stratified random sampling and structured questionnaire surveys.   Results: The findings revealed that supply chain transparency, predictive analytics accuracy, data integration, and organizational sustainability goals positively and significantly influenced sustainable supply chain performance. However, no significant relationship was found between technology readiness and sustainable supply chain performance, indicating the need for further investigation into factors beyond technological capabilities. Conclusions: This study concludes that while predictive analytics and aligned organizational practices are key drivers of sustainable supply chain performance, technology readiness alone is insufficient, highlighting the importance of integrated strategies beyond infrastructure capability. Limitations: This study is limited to manufacturing firms in Dhaka and adopts a cross-sectional design, which restricts the generalizability of the findings. Future research should explore longitudinal studies and incorporate other industries to provide a broader perspective on sustainable supply chain performance. Contribution: These results highlight the importance of fostering transparent practices, enhancing predictive analytics capabilities and aligning organizational goals with sustainability objectives. The practical implications include strategies for improved data integration and analytics adoption to drive sustainable outcomes
Adoption of Artificial Intelligence in retail: Examining the impact of technological and organizational factors on customer retention and loyalty Zerine , Ismoth; Biswas , Younis Ali; Doha, Md Zulkernain; Meghla , Humayra Mehreen; Polas, Mohammad Rashed Hasan
Annals of Management and Organization Research Vol. 6 No. 3 (2025): February
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v6i3.2548

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.
Data-Driven Sustainability: How Predictive Analytics ShapeSupply Chain Performance Zerine, Ismoth; Biswas , Younis Ali; Doha, Md Zulkernain; Meghla , Humayra Mehreen; Polas, Mohammad Rashed Hasan
Annals of Management and Organization Research Vol. 6 No. 4 (2025): May
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v6i4.2613

Abstract

Purpose: The integration of predictive analytics into supply chains has emerged as a critical  driver of sustainability in the manufacturing sector. This study explores the role of predictive analytics in enhancing sustainable supply chain performance, with a particular focus on manufacturing firms in Dhaka, Bangladesh. Research Methodology: This study adopts a positivist paradigm with a hypothetical deductive approach and employs a cross-sectional design. Data were collected from 211 manufacturing firms using stratified random sampling and structured questionnaire surveys.   Results: The findings revealed that supply chain transparency, predictive analytics accuracy, data integration, and organizational sustainability goals positively and significantly influenced sustainable supply chain performance. However, no significant relationship was found between technology readiness and sustainable supply chain performance, indicating the need for further investigation into factors beyond technological capabilities. Conclusions: This study concludes that while predictive analytics and aligned organizational practices are key drivers of sustainable supply chain performance, technology readiness alone is insufficient, highlighting the importance of integrated strategies beyond infrastructure capability. Limitations: This study is limited to manufacturing firms in Dhaka and adopts a cross-sectional design, which restricts the generalizability of the findings. Future research should explore longitudinal studies and incorporate other industries to provide a broader perspective on sustainable supply chain performance. Contribution: These results highlight the importance of fostering transparent practices, enhancing predictive analytics capabilities and aligning organizational goals with sustainability objectives. The practical implications include strategies for improved data integration and analytics adoption to drive sustainable outcomes
Co-Authors Biswas , Younis Ali Meghla , Humayra Mehreen Polas, Mohammad Rashed Hasan Zerine , Ismoth Zerine, Ismoth