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The purpose of this paper is to examine when and how organizations build big data analytics capability (BDAC) to improve supply chain agility (SCA) and gain competitive advantage.

The authors grounded the theoretical framework in two perspectives: the dynamic capabilities view and contingency theory. To test the research hypotheses, the authors gathered 173 usable responses using a pre-tested questionnaire.

The results suggest that BDAC has a positive and significant effect on SCA and competitive advantage. Further, the results support the hypothesis that organizational flexibility (OF) has a positive and significant moderation effect on the path joining BDAC and SCA. However, contrary to the belief, the authors found no support for the moderation effect of OF on the path joining BDAC and competitive advantage.

The study makes some useful contributions to the literature on BDAC, SCA, OF, and competitive advantage. Moreover, the results may further motivate future scholars to replicate the findings using longitudinal data.

Technological developments have made it possible for organizations to use enterprise resource planning (ERP) services without indulging in heavy investments like IT infrastructure, trained manpower for implementation and maintenance and updating the systems regularly to maintain business competitiveness. Plug and play model offered by cloud ERP has led to a constant creation of large data sets which are structured, semi-structured and unstructured by nature. Thus, there has been a need to analyze such complex data sets and the purpose of this paper is to focus on how cloud ERP and big data predictive analytics (BDPA) will impact the performance of a firm.

A dynamic capability view (DCV) theory-based model was developed and the authors have collected data by using an online questionnaire from India. Thereafter, the authors have analyzed it by employing structural equation modeling.

SEM analysis of 231 respondents showcases that the use of DCV theory to define the relationships of cloud ERP and BDPA has been the right move. Out of the 13 hypotheses empirically tested, only 7 hypotheses were supported by the data.

The study showcases cross-sectional data from India. It would be interesting for this study to see if the country-level differences would influence these relationships between cloud ERP and financial performance, BDPA and financial performance and cloud ERP and BDPA.

This study empirically tests the relationship of cloud ERP and BDPA through a model based on DCV theory.

The purpose of this paper is to develop a theoretical model to explain the impact of big data and predictive analytics (BDPA) on sustainable business development goal of the organization.

The authors have developed the theoretical model using resource-based view logic and contingency theory. The model was further tested using partial least squares-structural equation modeling (PLS-SEM) following Peng and Lai (2012) arguments. The authors gathered 205 responses using survey-based instrument for PLS-SEM.

The statistical results suggest that out of four research hypotheses, the authors found support for three hypotheses (H1-H3) and the authors did not find support for H4. Although the authors did not find support for H4 (moderating role of supply base complexity (SBC)), however, in future the relationship between BDPA, SBC and sustainable supply chain performance measures remain interesting research questions for further studies.

This study makes some original contribution to the operations and supply chain management literature. The authors provide theory-driven and empirically proven results which extend previous studies which have focused on single performance measures (i.e. economic or environmental). Hence, by studying the impact of BDPA on three performance measures the authors have attempted to answer some of the unresolved questions. The authors also offer numerous guidance to the practitioners and policy makers, based on empirical results.

The purpose of this paper is to examine how firms can develop business risk resilience from supply chain disruption events, by developing big data analytics (BDA) capabilities within their organization. The authors test whether BDA mediates the impact of institutional response to supply chain disruption events, and information technology infrastructure capabilities (ITICs), on firm’s ability to develop risk resilience from supply chain disruption events.

The study is based on survey data collected from 225 firms, spread across several sectors in the USA and Europe. The respondents are primarily senior and middle management professionals who have experience within the information technology (IT) and supply chain domain. Validity and reliability analyses were performed using SPSS and AMOS; and covariance-based structural equation modeling was used to test the hypothesis.

The analysis reveals two significant findings. First, the authors observe that institutional experience with managing supply chain disruption events has a negative impact on firm’s ability to develop business risk resilience. However, if the organizations adopt BDA capabilities, it enables them to effectively utilize resident firm knowledge and develop supply chain risk resilience capacity. The results further suggest that BDA positively adds to an organization’s existing IT capabilities. The analysis shows that BDA mediates the impact of ITIC on the organization’s ability to develop risk resilience to supply chain disruption events.

This study is one of the few works that empirically validate the important role that BDA capabilities play in enabling firms develop business risk resilience from supply chain disruption events. The study further provides a counterpoint to the existing perspective within the supply chain risk management literature that institutional experience of managing past supply chain disruption events prepares the organization to deal with future disruption events. This paper adds to our understanding of how, by adopting BDA capabilities, firms can develop supply chain risk resilience from disruption events.

Sustainable supply chain management (SSCM) has attracted considerable interest among academics and practitioners. The purpose of this paper is to present a critical review of the literature, to identify missing links, to argue for the use of world class SSCM (WCSSCM) through a framework, and suggest further research directions.

In the paper the authors have undertaken an extensive review of literature and classified articles using a novel classification scheme.

Through the extensive review and identification of research gaps, the paper identifies significant differences between definitions and methodologies in the SSCM literature; and argues for “WCSSCM.” This term is elaborated on via a theoretical framework in which 18 dimensions are classified under six constructs of SSCM. Furthermore, a list of potential research directions for WCSSCM is discussed.

The research is an attempt to critically review literature, argue for WCSSCM, and develop a theoretical framework.

The paper offers a new approach to SSCM literature, arguing for WCSSCM through a framework, and providing further research directions.

Big data-driven supply chain analytics capability (SCAC) is now emerging as the next frontier of supply chain transformation. Yet, very few studies have been directed to identify its dimensions, subdimensions and model their holistic impact on supply chain agility (SCAG) and firm performance (FPER). Therefore, to fill this gap, the purpose of this paper is to develop and validate a dynamic SCAC model and assess both its direct and indirect impact on FPER using analytics-driven SCAG as a mediator.

The study draws on the emerging literature on big data, the resource-based view and the dynamic capability theory to develop a multi-dimensional, hierarchical SCAC model. Then, the model is tested using data collected from supply chain analytics professionals, managers and mid-level manager in the USA. The study uses the partial least squares-based structural equation modeling to prove the research model.

The findings of the study identify supply chain management (i.e. planning, investment, coordination and control), supply chain technology (i.e. connectivity, compatibility and modularity) and supply chain talent (i.e. technology management knowledge, technical knowledge, relational knowledge and business knowledge) as the significant antecedents of a dynamic SCAC model. The study also identifies analytics-driven SCAG as the significant mediator between overall SCAC and FPER. Based on these key findings, the paper discusses their implications for theory, methods and practice. Finally, limitations and future research directions are presented.

The study fills an important gap in supply chain management research by estimating the significance of various dimensions and subdimensions of a dynamic SCAC model and their overall effects on SCAG and FPER.

This study critically analyzes the micro-, meso- and macro-level factors that influence the female academics to engage in academic entrepreneurship (AE). The extant literature, which seeks to understand the female academics engagement in AE mostly revolves around a gender-comparative lens, where women entrepreneurs are understood only in comparison with men. This study examines the association between informal academic entrepreneurship (IAE) and the level of asymmetry between the micro-, meso- and macro-level factors (5M framework). In this study, the author conducted three interviews with female academics in Bangladesh.

Humanitarian services usually perform in the face of uncertainty in which mobilization of resources in an efficient and effective manner is a big challenge. Sharing timely and correct information among logistics partners and workers is a key to drive rapid response logistics effectively. The purpose of this paper is to understand how coordinated effort effects resources management (RM).

This study uses quantitative research methodology and collected data from 82 humanitarian workers dealing with logistical activities from a densely populated city of Pakistan. Data were then statistically analyzed through partial least squares–structural equation modeling.

The results suggest that the success of humanitarian supply network depends upon the level of trust among the partners, which accelerates commitment through strong coordination. Information sharing reduces behavioral uncertainty and enhances swift trust (ST). ST then helps to improve coordination and commitment from all stakeholders in order to manage resources to lead effective relief operations.

The study guides the practitioners and relief operations’ policy makers to lay emphasis on distributing right and timely information flow among the partners, which can lead to effective, efficient and swift humanitarian relief operations.

This study on RM during humanitarian logistics is well timed in the context of developing country with high uncertain events, improper infrastructure and very limited resources.

The purpose of this paper is to examine how managers orchestrate, bundle and leverage resources from big data for value creation in emerging economies.

The authors grounded the theoretical framework in two perspectives: the resource management and entrepreneurial orientation (EO). The study utilizes an inductive, multiple-case research design to understand the process of creating value from big data.

The findings suggest that EO is vital through which companies based in emerging economies can create value through big data by bundling and orchestrating resources thus improving performance.

This is one of the first studies to have integrated resource orchestration theory and EO in the context of big data and explicate the utility of such theoretical integration in understanding the value creation strategies through big data in the context of emerging economies.

The purpose of this paper is to examine when and how organizations create agility, adaptability, and alignment as distinct supply chain properties to gain sustainable competitive advantage.

The current study utilizes the resource-based view (RBV) under the moderating effect of top management commitment (TMC). To test the research hypotheses, the authors gathered 351 usable responses using a pre-tested questionnaire.

The statistical analyses suggest that information sharing and supply chain connectivity resources influence supply chain visibility capability, which, under the moderating effect of TMC, enhance supply chain agility, adaptability, and alignment (SCAAA).

The contribution lies in: providing a holistic study of the antecedents of agility, adaptability, and alignment; investigating the moderating role of TMC on SCAAA; following the RBV and addressing calls for investigating the role of resources in supply chain management, and for empirical studies with implications for supply chain design.