Breaking down barriers: prioritizing big data analytics integration in e-commerce sector

Purpose To remain competitive and meet customer expectations, e-commerce companies have started embracing big data analytics (BDA). However, the successful implementation of BDA in the e-commerce sector is often hindered by various barriers that challenge its adoption and utilisation, especially in regions such as India. This study fills this gap by identifying, analysing and prioritising critical barriers to BDA integration in the e-commerce sector. Design/methodology/approach A review of the extant literature and expert interviews led the authors to identify five barriers and 24 sub-barriers. This study uses a popular modelling method, interpretive structural modelling (ISM), to determine and organise the barriers in a hierarchical order. Findings The findings suggest that a lack of data sharing/siloed data, consultants/specialists, knowledge and skills, data complexity, data compatibility and data trialability, support and cooperation from top management and government support are major barriers with the highest driving power in the e-commerce industry for an emerging country like India. Originality/value The e-commerce sector in India is gaining traction, and the use of technology for business excellence is the prime driver. This study presents the critical drivers that can impact the use of big data analytics to win competition in an emerging market. This study demonstrates the ISM technique for analysing and synthesising the selected barriers to BDA and offers insightful consequences for practitioners and researchers.