Digital technologies and sustainability performance in Dubai’s retail sector: A mixed-methods analysis of AI, IoT, and blockchain adoption

Purpose: This study examines the extent to which digital technologies - including artificial intelligence (AI), Internet of Things (IoT), blockchain, and predictive analytics - contribute to sustainability outcomes in Dubai's retail sector. Methodology: A mixed methods approach was employed. Quantitative data were collected from 100 retail professionals in Dubai's retail industry and analysed through Pearson's correlation, Chi-square, Friedman, and Spearman tests. Comparative case studies of five international retailers (IKEA, Inditex/Zara, Tesco, Carrefour, and Unilever) contextualised survey findings. Results: The analysis reveals a strong positive correlation between technology adoption and sustainability performance (r = 0.823, p < 0.001). Integration complexity (84%) and workforce skill gaps (79%) constitute the most significant barriers. Case evidence substantiates the effectiveness of phased implementation approaches, employee training investments, and blockchain-enabled traceability systems. Contribution: The research extends the Technology-Organisation-Environment framework to Gulf economies, demonstrating that technology adoption is driven by consumer and competitive forces rather than regulatory compliance. Practical implications address retailer and policy responses facilitating digital transformation aligned with UN Sustainable Development Goals (SDG 9, SDG 12, SDG 17). Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation and Infrastructure; SDG 12: Responsible Consumption and Production; SDG 17: Partnerships for the Goals

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Publication Details

Journal
Economics Management and Sustainability
Published
2025-12-04
DOI
https://doi.org/10.14254/jems.2025.10-2.8
Primary Topic
Organizational and Employee Performance
Type
article
Field-Weighted Citation Impact
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article

Digital technologies and sustainability performance in Dubai’s retail sector: A mixed-methods analysis of AI, IoT, and blockchain adoption

Sunil Patel, Anjali Pala
Economics Management and Sustainability
Organizational and Employee Performance
article

Digital technologies and sustainability performance in Dubai’s retail sector: A mixed-methods analysis of AI, IoT, and blockchain adoption

Sunil Patel, Anjali Pala
article en

Abstract

Purpose: This study examines the extent to which digital technologies - including artificial intelligence (AI), Internet of Things (IoT), blockchain, and predictive analytics - contribute to sustainability outcomes in Dubai's retail sector. Methodology: A mixed methods approach was employed. Quantitative data were collected from 100 retail professionals in Dubai's retail industry and analysed through Pearson's correlation, Chi-square, Friedman, and Spearman tests. Comparative case studies of five international retailers (IKEA, Inditex/Zara, Tesco, Carrefour, and Unilever) contextualised survey findings. Results: The analysis reveals a strong positive correlation between technology adoption and sustainability performance (r = 0.823, p < 0.001). Integration complexity (84%) and workforce skill gaps (79%) constitute the most significant barriers. Case evidence substantiates the effectiveness of phased implementation approaches, employee training investments, and blockchain-enabled traceability systems. Contribution: The research extends the Technology-Organisation-Environment framework to Gulf economies, demonstrating that technology adoption is driven by consumer and competitive forces rather than regulatory compliance. Practical implications address retailer and policy responses facilitating digital transformation aligned with UN Sustainable Development Goals (SDG 9, SDG 12, SDG 17). Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation and Infrastructure; SDG 12: Responsible Consumption and Production; SDG 17: Partnerships for the Goals

Economics Management and SustainabilityVol. 10(2)
Karnavati University
Openalex Percentile: Top 79%
Organizational and Employee Performance
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