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A Q&A primer and systematic review of meta-analytic reporting in organizational frontline service research
Journal of Service Management ( IF 10.6 ) Pub Date : 2023-11-24 , DOI: 10.1108/josm-07-2022-0229
Kristina K. Lindsey-Hall , Eric J. Michel , Sven Kepes , Ji (Miracle) Qi , Laurence G. Weinzimmer , Anthony R. Wheeler , Matthew R. Leon

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.



中文翻译:

组织一线服务研究中荟萃分析报告的问答入门和系统回顾

目的

本文的目的是为整个服务领域的系统和元分析评论提供逐步入门,系统地分析服务领域元分析报告的质量,提供作者在以下情况下可以遵循的详细协议:进行和报告这些分析,并为未来的服务元分析提供建议。

设计/方法论/途径

通过使用相关搜索词对Academic Search Complete、PsycINFO、Business Source Complete、Web of Science、Google Scholar 和特定服务期刊进行系统搜索,确定了截至 2021 年 5 月发表的符合条件的一线服务相关荟萃分析(k = 33)。服务和荟萃分析。

发现

对服务领域内现有荟萃分析的分析,虽然经常提供高质量的结果,但表明可以通过多种方式提高报告的质量,以增强服务领域中已发布荟萃分析的可复制性。

实际影响

本研究采用问答的方式,为不同经验水平的学者正确开展和正确报告服务领域的高质量元分析研究提供实质​​性指导。

原创性/价值

这项工作汇总了不同学科的最佳实践,创建了一个全面的协议清单,用于进行和报告高质量的服务元分析,同时为进一步探索提供额外的资源。

更新日期:2023-11-23
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