Challenges with big data analytics in service supply chains in the UAE

Abstract

Purpose The purpose of this paper is to study the challenges associated with big data analytics (BDA) in service supply chains in the United Arab Emirates (UAE). Design/methodology/approach A comprehensive questionnaire has been developed based on semi-structured interviews with different administrators and IT experts. In the second phase, data (n=164) are collected from procurement, operations, administration and customer service staff in the UAE. In the third phase, responses are examined using principal component analysis to identify eight major challenges for big data. A structural model is developed to examine the significance of these dimensions to the notion of big data challenges in supply chains. Findings The statistical model shows 66 percent variance of response to BDA, which is caused by technical, cultural, ethical, operational, tactical, procedural, functional and organizational challenges. These are positively correlated measurement challenges with BDA in service supply chains. Research limitations/implications Service supply chain professionals and stakeholders believe that catering to the challenges with BDA must be a multi-faceted approach and not limited to specific practices. Practical implications The challenges with BDA should be taken into planning and implementation from a holistic perspective. The framework in this paper can have both theoretical and practical implications. Originality/value The contribution of this paper is to advance the understanding of BDA in service sector by viewing it from the perspective of different stakeholders.

Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

“…Perceived benefits, technology complexity, data quality, IT infrastructure/capabilities, financial readiness, and top management support Khan [68] Propose a framework to address challenges in employing BDA for service supply chains.…”

“…Employing diffusion of innovation (DOI) theory and the technology-organizationenvironment (TOE) framework, Lai et al [67] found that perceived benefits and top management support can significantly influence the adoption intention of BDA, and environmental factors and supply chain connectivity can significantly moderate the direct relationships between driving factors and the adoption intention. Khan [68] employed multiple theories/views (i.e., stakeholder theory, resource-based view, transaction cost economics, and systems theory) to explore challenges in adopting BDA and demonstrated that technical, cultural, ethical, operational, tactical, procedural, functional, and organizational factors have a significant impact on BDA adoption and the highest and lowest impacts come from ethical and technical factors.…”

Alignment capabilities of big data’s value creation in the context of service delivery processes

“…Thus, the paper adds to the body of knowledge of big data in supply chain context with empirical founded research which is deemed in shortage (Kache and Seuring, 2017). In a service context, challenges of the value creation of big data include technical, cultural, ethical, operational, tactical, procedural, functional and organizational challenges (Khan, 2019). By establishing strategic alignment capabilities, a company can improve its utilization of big data and thus mitigate or overcome these challenges.…”

“…Investigating the diverse challenges of the value creation of big data is thus needed. Service supply chains are considered to develop efficient ways to use all the data that comes from different channels (Cohen, 2018; Khan, 2019; Opresnik and Taisch, 2015; Zhong et al , 2016). Big data affects several extant practices why it is paramount to uncover how these practices are related to each other for achieving a higher level of alignment and thus a higher performance-related value created from big data.…”

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

“…Extant literature has studied ‘Data Science Strategy’ in the contexts of dynamic market places, organizational and dynamic capabilities (Knabke & Olbrich, 2018 ), innovations (Mikalef et al 2018 , 2019a ), and new product development processes (Johnson et al 2017 ). Studies have been conducted in the context of but not limited to healthcare (Chen and Banerjee 2020 ; Kamble et al 2019 ; Kemppainen et al 2019 ; Li et al 2021 ; Newlands et al 2020 ; Ramnath et al 2020 ; Yang et al 2015 ; Wang and Hajli 2017 ), B2B (Hallikainen et al 2020 ), construction (Ahmed et al 2018 ; Ram et al 2019 ; Sang et al 2020 ), supply chain management (Ali et al 2020 ; Arunachalam et al 2018 ; Brinch et al 2018 ; Dubey et al 2019a ; Khan 2019 ; Lai et al 2018 ; Lamba and Singh 2018 ; Mandal 2019 ; Singh and Singh 2019 ; Wang et al 2018c ), manufacturing (Popovič et al 2018 ; Verma 2017 ), consumer goods (Rialti et al 2018 ); e-commerce (Behl et al 2019 ; Wamba et al 2017 ), telecommunications (Saldžiūnas and Skyrius 2017 ; Walker and Brown 2019 ), banking and financial services (Lee et al 2017 ; Lautenbach et al 2017 ; Gregory 2011 ), automotive (Dremel et al 2017 ), and airlines (Holland et al 2020 ).…”