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Journals in business logistics, operations management, supply chain management, and business strategy have initiated ongoing calls for Big Data research and its impact on…
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.
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.
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.
This study provides a central grounding of the term, dimensions, and issues related to Big Data in supply chain research.
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.
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.
This paper aims to investigate how perceptions of employee authenticity and customer–employee rapport influence customers’ interactional justice assessments and related…
This paper aims to investigate how perceptions of employee authenticity and customer–employee rapport influence customers’ interactional justice assessments and related service evaluations, and how customers’ need for uniqueness impacts these relationships.
A multi-method, three-study design is used to test the research model. Specifically, structural equation modeling provides tests of the main hypotheses, and two supplemental experimental studies tease out conditional effects providing insightful managerial contributions.
Results indicate that customers’ perceptions of employee authenticity affect customers’ interactional justice evaluations, particularly when customers identify high levels of customer–employee rapport. Additionally, the aforementioned relationships are contingent upon customers’ need for uniqueness, such that customers with higher levels of need for uniqueness experience lower levels of customer–employee rapport and, consequently, provide poorer interactional justice assessments. Finally, conditional effects are found given the type of provider and frequency of visit.
This research extends prior efforts to understand how customer–employee dynamics influence customers’ service encounter evaluations. In particular, it furthers understanding of authentic FLE–customer encounters, explores drivers of interactional justice and explicates how consumers’ varying levels of need for uniqueness have differential effects on service outcomes.