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PurposeThe purpose of this paper is to extend existing and motivate future sustainable supply chain management (SCM) and logistics research by examining a structure-conduct-performance framework linking resource commitment to sustainable SCM, reverse logistics, and operational performance. A sustainable reverse logistics capability is investigated as mediating the performance benefits associated with resource commitments to sustainable SCM.Design/methodology/approachSurvey methods and structural equation modeling were used to collect and analyze data from 180 supply chain professionals.FindingsThe results of a mediated model suggest that resource commitments may be used to develop a sustainable reverse logistics capability, reducing the environmental impact of reverse logistics activities. A strong sustainable reverse logistics capability results from resources committed specifically to sustainable reverse logistics and a commitment to the sustainability of the supply chain.Research limitations/implicationsThis study applied a purposefully general sampling procedure. Specific industries may have additional constraints (e.g. risk, transparency, governance factors) that directly impact reverse logistics. These constraints are limitations of the study as well as opportunities for future research. Resource commitment is critical to the success of an overall firm strategy to build a sustainable supply chain, especially when considering reverse logistics.Practical implicationsAs managers examine the benefits of sustainable SCM, they must consider the resources required. For firms engaging in sustainable SCM, developing a sustainable reverse logistics capability is a key success factor for improved performance.Originality/valueGiven the growing acceptance and importance of sustainable SCM, this research provides insights to managers and academics regarding the key mediating role of a sustainable reverse logistics capability when integrated into existing and future supply chain research frameworks and processes.

Purpose– The purpose of this paper is to explore the influence of collaboration and information technology (IT) on the reverse logistics competency of firms. Through collaboration firms can improve their ability to handle returns, but this research introduces IT as providing a moderating influence over the impact of collaboration in the advancement of a reverse logistics competency.Design/methodology/approach– A survey was administered to employees involved with supply chain relationships. Empirical evidence from 267 respondents is analyzed with structural equation modeling.Findings– Support is found for the positive moderating influence of an IT competency on the relationship between collaboration and a reverse logistics competency. Additional benefits for logistics performance are also realized.Research limitations/implications– This research provides theoretical implications for the development of a reverse logistics competency through an application of resource-based theory/resource-based view of the firm. The study is limited to the selected research questions and sample of predominantly US firms.Practical implications– This research assists managers as they attempt to develop a reverse logistics competency to address the growing problem of returns through collaboration with supply chain members and the development of an IT competency.Originality/value– The framework developed in this research provides insights regarding the handling of product returns. Specifically, the moderating influence of an IT competency is addressed as it enhances the impact of collaboration on the development of a reverse logistics competency.

Purpose
Journals in business logistics, operations management, supply chain management, and business strategy have initiated ongoing calls for Big Data research and its impact on research and practice. Currently, no extant research has defined the concept fully. The purpose of this paper is to develop an industry grounded definition of Big Data by canvassing supply chain managers across six nations. The supply chain setting defines Big Data as inclusive of four dimensions: volume, velocity, variety, and veracity. The study further extracts multiple concepts that are important to the future of supply chain relationship strategy and performance. These outcomes provide a starting point and extend a call for theoretically grounded and paradigm-breaking research on managing business-to-business relationships in the age of Big Data.


Design/methodology/approach
A native categories qualitative method commonly employed in sociology allows each executive respondent to provide rich, specific data. This approach reduces interviewer bias while examining 27 companies across six industrialized and industrializing nations. This is the first study in supply chain management and logistics (SCMLs) to use the native category approach.


Findings
This study defines Big Data by developing four supporting dimensions that inform and ground future SCMLs research; details ten key success factors/issues; and discusses extensive opportunities for future research.


Research limitations/implications
This study provides a central grounding of the term, dimensions, and issues related to Big Data in supply chain research.


Practical implications
Supply chain managers are provided with a peer-specific definition and unified dimensions of Big Data. The authors detail key success factors for strategic consideration. Finally, this study notes differences in relational priorities concerning these success factors across different markets, and points to future complexity in managing supply chain and logistics relationships.


Originality/value
There is currently no central grounding of the term, dimensions, and issues related to Big Data in supply chain research. For the first time, the authors address subjects related to how supply chain partners employ Big Data across the supply chain, uncover Big Data's potential to influence supply chain performance, and detail the obstacles to developing Big Data's potential. In addition, the study introduces the native category qualitative interview approach to SCMLs researchers.