Multilevel Structural Equation Modelling in Marketing and Management Research

Johannes Hirschmann; Bernhard Swoboda

doi:10.15358/0344-1369-2017-3-50

Contents
- Chapter Expand | CollapseAuthorsPage
- 1–2 Titelei/Inhaltsverzeichnis 1–2
- 2–3 Editorial Bernhard Swoboda Bernhard Swoboda 2–3
- 4–98 Research Articles 4–98
  - 4–16 On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations Edward E. Rigdon, Marko Sarstedt, Christian M. Ringle Edward E. Rigdon, Marko Sarstedt, Christian M. Ringle 4–16
  - 17–31 Functional Flexibility, Latent Heterogeneity and Endogeneity in Aggregate Market Response Models Harald Hruschka Harald Hruschka 17–31
  - 32–49 Multinomial Logit Models in Marketing - From Fundamentals to State-of-the-Art Ossama Elshiewy, Daniel Guhl, Yasemin Boztug Ossama Elshiewy, Daniel Guhl, Yasemin Boztug 32–49
  - 50–75 Multilevel Structural Equation Modelling in Marketing and Management Research Johannes Hirschmann, Bernhard Swoboda Johannes Hirschmann, Bernhard Swoboda 50–75
  - 76–98 Conducting Mediation Analysis in Marketing Research Carsten L. Demming, Steffen Jahn, Yasemin Boztug Carsten L. Demming, Steffen Jahn, Yasemin Boztug 76–98
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Marketing ZFP , page 50 - 75

Multilevel Structural Equation Modelling in Marketing and Management Research

Autoren

Johannes Hirschmann Bernhard Swoboda

DOI
- doi.org/10.15358/0344-1369-2017-3-50
- ISSN: 0344-1369
- ISSN online: 0344-1369
Zusammenfassung

Marketing research often involves two levels (e.g., international customers nested within countries or cultures). Multilevel modelling (MLM) allows one to accurately model lower-level (level 1) effects and the surrounding (level 2) context in addition to various interrelations between the levels. Hierarchical linear modelling (HLM) is the most prevalent methodology for MLM. However, HLM does not handle latent constructs and, compared to multilevel structural equation modelling (MSEM), has several disadvantages. For example, HLM does not account for measurement errors. MSEM is increasingly discussed in methodological papers and textbooks but is seldom applied. A systematic, nontechnical guide for conducting an MSEM analysis is lacking, and a systematic review of current and future applications would benefit future research. Our first research objective is to introduce scholars to MSEM and its advantages relative to HLM and to provide novel insights into the topics and flaws of MSEM/HLM use in extant studies. This manuscript contributes to the literature by providing a review of 527 marketing and management studies in which HLM and MSEM are applied. Four categories of multilevel hypotheses found in studies published in 22 leading journals over 20 years are discussed and differentiated into five characteristic research fields (general marketing, international marketing, international management, general management, and human resource management). Our second research objective is to address the requirements, options, and challenges of MSEM. This manuscript contributes to the literature by systematically discussing the sampling, measurement, and methodological requirements, options, and challenges of MSEM-based studies. In doing so, it provides a nontechnical explanation and a systematic step-by-step procedure for designing and conducting a cross-sectional MSEM study that tests hypotheses across levels. Additionally, our empirical study presents the results for three frequently employed types of MSEM models: cross-level effects, cross-level interactions, and cross-level effects and interactions. The latter is presented for the first time in business research. The manuscript reveals that MSEM is seldom employed in marketing and management research, although various possible applications exist. It provides a systematic illustration and interpretation of MSEM that scholars can consult to conduct future MSEM-based studies. Finally, promising directions and major challenges for future MSEM research are examined and are organized as follows: conceptualization issues (sample size, position of outcome variables, and control variables), methodological shortcomings (level-specific preliminary tests and their implementation in software), and general advice (multi-item scales, computation times, and model convergence).

Abstract

Marketing research often involves two levels (e.g., international customers nested within countries or cultures). Multilevel modelling (MLM) allows one to accurately model lower-level (level 1) effects and the surrounding (level 2) context in addition to various interrelations between the levels. Hierarchical linear modelling (HLM) is the most prevalent methodology for MLM. However, HLM does not handle latent constructs and, compared to multilevel structural equation modelling (MSEM), has several disadvantages. For example, HLM does not account for measurement errors. MSEM is increasingly discussed in methodological papers and textbooks but is seldom applied. A systematic, nontechnical guide for conducting an MSEM analysis is lacking, and a systematic review of current and future applications would benefit future research. Our first research objective is to introduce scholars to MSEM and its advantages relative to HLM and to provide novel insights into the topics and flaws of MSEM/HLM use in extant studies. This manuscript contributes to the literature by providing a review of 527 marketing and management studies in which HLM and MSEM are applied. Four categories of multilevel hypotheses found in studies published in 22 leading journals over 20 years are discussed and differentiated into five characteristic research fields (general marketing, international marketing, international management, general management, and human resource management). Our second research objective is to address the requirements, options, and challenges of MSEM. This manuscript contributes to the literature by systematically discussing the sampling, measurement, and methodological requirements, options, and challenges of MSEM-based studies. In doing so, it provides a nontechnical explanation and a systematic step-by-step procedure for designing and conducting a cross-sectional MSEM study that tests hypotheses across levels. Additionally, our empirical study presents the results for three frequently employed types of MSEM models: cross-level effects, cross-level interactions, and cross-level effects and interactions. The latter is presented for the first time in business research. The manuscript reveals that MSEM is seldom employed in marketing and management research, although various possible applications exist. It provides a systematic illustration and interpretation of MSEM that scholars can consult to conduct future MSEM-based studies. Finally, promising directions and major challenges for future MSEM research are examined and are organized as follows: conceptualization issues (sample size, position of outcome variables, and control variables), methodological shortcomings (level-specific preliminary tests and their implementation in software), and general advice (multi-item scales, computation times, and model convergence).
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