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The purpose of this manuscript is to provide a step-by-step primer on systematic and meta-analytic reviews across the service field, to systematically analyze the quality of meta-analytic reporting in the service domain, to provide detailed protocols authors may follow when conducting and reporting these analyses and to offer recommendations for future service meta-analyses.

Eligible frontline service-related meta-analyses published through May 2021 were identified for inclusion (k = 33) through a systematic search of Academic Search Complete, PsycINFO, Business Source Complete, Web of Science, Google Scholar and specific service journals using search terms related to service and meta-analyses.

An analysis of the existing meta-analyses within the service field, while often providing high-quality results, revealed that the quality of the reporting can be improved in several ways to enhance the replicability of published meta-analyses in the service domain.

This research employs a question-and-answer approach to provide a substantive guide for both properly conducting and properly reporting high-quality meta-analytic research in the service field for scholars at various levels of experience.

This work aggregates best practices from diverse disciplines to create a comprehensive checklist of protocols for conducting and reporting high-quality service meta-analyses while providing additional resources for further exploration.

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.

Employing a service-profit chain (S-PC) framework, this manuscript investigates the relationship between employee engagement (EE) and customer engagement (CE) within service contexts and explores how a mediating mechanism, service employee work performance (SEWP), links EE with CE.

Meta-analytic procedures ascertain the magnitude of the relationship between EE and SEWP (k = 102, ρ^ = 0.45) and between SEWP and three dimensions of CE: customer purchases (k = 42, ρ^ = 0.47), customer knowledge (k = 4, ρ^ = 0.33) and customer influence (k = 7, ρ^ = 0.42). The current meta-analysis reports an effect size for the EE-overall SEWP relationship nearly 1.50 times greater than related extant meta-analyses.

Results suggest SEWP, consisting of service employee task performance and contextual performance, serves as an important intervening mechanism between EE and CE by considering nine dimensions of SEWP. Such findings suggest that to maximize SEWP, service employees must go beyond simply being satisfied in their work roles; instead, service employees must feel energized, find fulfillment and meaning and be engrossed in their work to maximize the service they provide to customers.

This research extends previous meta-analytic efforts, bridges the multi-disciplinary gap between EE and CE research, provides an empirical link allowing for informed decision-making for managers and stakeholders, underscores the importance of service employees surpassing required job responsibilities to meet and exceed customer needs and suggests an agenda for future service research integrating EE and CE.

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.