Abstract
Purpose – The study explores the key barriers and enablers of technology adoption for managing social sustainability in supply chains. Design/methodology/approach – Overall, 46 in-depth semi-structured interviews with managers from 18 firms serving different roles in the supply chain are undertaken across four distinct industries (i.e. electronics, food and beverage, pharmaceuticals and fashion and apparel). Findings – The results reveal four main barriers as well as four main enablers of technology adoption for managing social sustainability in supply chains. Particularly, the results identified technological barriers (e.g. compromise on quality assurance) and enablers (e.g. interoperability), economic barriers (e.g. low perceived return on investment) and enablers (e.g. financial incentive/tax exemptions), organisational barriers (e.g. lack of adequate training) and enablers (e.g. commitment from senior management), environmental enablers (e.g. development of industrial standards) and cultural barriers (e.g. misalignment of culture). Research limitations/implications – The results are based on a sample size of 46 participants. Hence, its results should be treated with caution and not generalisable to a broader population. Practical implications – The study offers practical guidance for supply chain managers and policymakers to overcome the barriers and leverage enablers when adopting technology to enhance social sustainability, supporting long-term social compliance and impact. Originality/value – This study provides methodological originality through a multiple-case approach and contextual novelty by comparing barriers and enablers of technology adoption for social sustainability across four distinct industries, different supply chain roles and size of the organisation (large, medium, etc.), often overlooked in prior research.
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Butt, A. S., & Alghababsheh, M. (2025). Exploring the barriers and enablers of technology adoption for managing social sustainability in supply chains. Industrial Management and Data Systems. https://doi.org/10.1108/IMDS-01-2025-0014
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