The amount of data produced and communicated over the Internet is significantly increasing, thereby creating challenges for the organizations that would like to reap the benefits from analyzing this massive influx of big data. This is because big data can provide unique insights into, inter alia, market trends, customer buying patterns, and maintenance cycles, as well as into ways of lowering costs and enabling more targeted business decisions. Realizing the importance of big data business analytics (BDBA), we review and classify the literature on the application of BDBA on logistics and supply chain management (LSCM) -that we define as supply chain analytics (SCA), based on the nature of analytics (descriptive, predictive, prescriptive) and the focus of the LSCM (strategy and operations). To assess the extent to which SCA is applied within LSCM, we propose a maturity framework of SCA, based on four capability levels, that is, functional, process-based, collaborative, agile SCA, and sustainable SCA. We highlight the role of SCA in LSCM and denote the use of methodologies and techniques to collect, disseminate, analyze, and use big data driven information. Furthermore, we stress the need for managers to understand BDBA and SCA as strategic assets that should be integrated across business activities to enable integrated enterprise business analytics. Finally, we outline the limitations of our study and future research directions.

Scholars acknowledge the importance of big data and predictive analytics (BDPA) in achieving business value and firm performance. However, the impact of BDPA assimilation on supply chain (SCP) and organizational performance (OP) has not been thoroughly investigated. To address this gap, this paper draws on resource-based view. It conceptualizes assimilation as a three stage process (acceptance, routinization, and assimilation) and identifies the influence of resources (connectivity and information sharing) under the mediation effect of top management commitment on big data assimilation (capability), SCP and OP. The findings suggest that connectivity and information sharing under the mediation effect of top management commitment are positively related to BDPA acceptance, which is positively related to BDPA assimilation under the mediation effect of BDPA routinization, and positively related to SCP and OP. Limitations and future research directions are provided.

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The importance of big data and predictive analytics has been at the forefront of research for operations and manufacturing management. The literature has reported the influence of big data and predictive analytics for improved supply chain and operational performance, but there has been a paucity of literature regarding the role of external institutional pressures on the resources of the organization to build big data capability. To address this gap, this paper draws on the resource‐based view of the firm, institutional theory and organizational culture to develop and test a model that describes the importance of resources for building capabilities, skills and big data culture and subsequently improving cost and operational performance. We test our research hypotheses using 195 surveys, gathered using a pre‐tested questionnaire. Our contribution lies in providing insights regarding the role of external pressures on the selection of resources under the moderating effect of big data culture and their utilization for capability building, and how this capability affects cost and operational performance.

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The purpose of this paper is to propose and test a theoretical framework to explain resilience in supply chain networks for sustainability using unstructured Big Data, based upon 36,422 items gathered in the form of tweets, news, Facebook, WordPress, Instagram, Googleþ, and YouTube, and structured data, via responses from 205 managers involved in disaster relief activities in the aftermath of Nepal earthquake in 2015. The paper uses Big Data analysis, followed by a survey which was analyzed using content analysis and confirmatory factor analysis (CFA). The results of the analysis suggest that swift trust, information sharing and publiceprivate partnership are critical enablers of resilience in supply chain networks. The current study used cross-sectional data. However the hypotheses of the study can be tested using longitudinal data to attempt to establish causality. The article advances the literature on resilience in disaster supply chain networks for sustainability in that (i) it suggests the use of Big Data analysis to propose and test particular frameworks in the context of resilient supply chains that enable sustainability; (ii) it argues that swift trust, public private partnerships, and quality information sharing link to resilience in supply chain networks; and (iii) it uses the context of Nepal, at the moment of the disaster relief activities to provide contemporaneous perceptions of the phenomenon as it takes place.

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