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PurposeThe influence of security practices is increasingly common in the supply chain management and logistics literature. However, an under-researched area exists within the logistics service provider (LSP) selection process. The purpose of this paper is to introduce a security capability into the LSP selection process. Specifically, this research seeks to understand partner willingness to compensate and collaborate with service providers that possess a security capability.Design/methodology/approachAdaptive choice modeling is adopted to assess the influence of a security capability in the LSP selection process. This study represents the first use of this method in supply chain management and logistics research. Cluster analysis is also performed to uncover specific buyer segments along with traditional regression-based significance testing and counting analysis.FindingsThe findings indicate that security can have an important influence on the LSP selection process. In particular, the findings note a willingness to pay for a security capability in LSP selection. Applying segmentation techniques to the findings, three LSP buying segments are determined, each placing different importance and value on LSP capabilities.Practical implicationsThis research notes an ongoing provider deficiency in security offerings. Partner firms sometimes maintain a cost focus, but others show a willingness to pay higher prices for access to partners with a security capability. Key practitioner findings include the need to include security with other traditional selection variables. The study walks the researcher and manager through the development of segments based on LSP capabilities.Originality/valueThis manuscript investigates logistic service provider selection. The authors detail an advanced form of conjoint analysis, adaptive conjoint modeling, for first time consideration. Additionally, this is the first study to integrate security into the LSP selection process. This is also the first study to identify a willingness to pay for a security capability.

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.

ABSTRACTIn this research, we test the curvilinear relationships between service climate perceptions and two employee performance outcomes. Specifically, we propose that while service climate can be beneficial, high levels can actually be detrimental to customer satisfaction and sales performance. Additionally, we propose that a global assessment of employee experience that captures knowledge, skills, and abilities, or KSAs, moderates these curvilinear relationships by providing a means to balance outcome goals. We test our theory using data obtained from 312 employees in a service setting, which we pair with their managers' assessments of their sales performance as well as satisfaction ratings from their customers. Our results reveal two things: (1) an inverted U‐shaped relationship between service climate and sales performance and (2) the level of experience moderates the relationship. These findings suggest that more experienced employees are better able to adjust behaviors to achieve high levels of performance than less experienced employees. Based on these results, we offer theoretical implications and applications for managerial practice.

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.