Title

Measuring the Effects of Time: Repeated Cross-Sectional Research in Operations and Supply Chain Management

Department

Business Administration

Document Type

Article

Publication Source

Supply Chain Management. Volume 25, Issue 1, Pages 122-138.

Publication Date

2020-1

Abstract

Purpose: Longitudinal investigations are often suggested but rarely used in operations and supply chain management (OSCM), mainly due to the difficulty of obtaining data. There is a silver lining in the form of existing large-scale and planned repeated cross-sectional (RCS) data sets, an approach commonly used in sociology and political sciences. This study aims to review all relevant RCS surveys with a focus on OSCM, as well as data and methods to motivate longitudinal research and to study trends at the plant, industry and geographic levels.

Design/methodology/approach: A comparison of RCS, panel and hybrid surveys is presented. Existing RCS data sets in the OSCM discipline and their features are discussed. In total, 30 years of Global Manufacturing Research Group data are used to explore the applicability of analytical methods at the plant and aggregate level and in the form of multilevel modeling.

Findings:RCS analysis is a viable alternative to overcome the confines associated with panel data. The structure of the existing data sets restricts quantitative analysis due to survey and sampling issues. Opportunities surrounding RCS analysis are illustrated, and survey design recommendations are provided.

Practical implications: The longitudinal aspect of RCS surveys can answer new and untested research questions through repeated random sampling in focused topic areas. Planned RCS surveys can benefit from the provided recommendations.

Originality/value: RCS research designs are generally overlooked in OSCM. This study provides an analysis of RCS data sets and future survey recommendations.

Keywords

performance management, research, operational performance, time, empirical study, panel data, global supply chain, longitudinal research, repeated cross-sectional data, survey design recommendations

DOI

10.1108/SCM-04-2019-0142

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