Comparative assessment of structural equation modeling and multiple regression research methodologies

E-commerce context

Research output: Contribution to journalArticle

66 Citations (Scopus)

Abstract

Structural equation modeling (SEM) is a powerful statistical technique that establishes measurement models and structural models. On the other hand, multiple regression (MR) is considered a sophisticated and well-developed modeling approach to data analysis with a history of more than 100 years. This paper empirically compares SEM and MR by testing a model of commitment in a B-to-C e-commerce travel context, shedding light on applications of these two popular methods in tourism research. The findings indicate that only two significant relationships are justified by MR. In comparison, SEM results reveal more statistically significant relationships after the "best-fitting" measurement model with model D being the "best-fitting" model. The findings support some key empirical limitations of MR as a widely used statistical technique in the tourism research.

Original languageEnglish
Pages (from-to)314-324
Number of pages11
JournalTourism Management
Volume31
Issue number3
DOIs
Publication statusPublished - Jun 2010

Fingerprint

Electronic commerce
commerce
multiple regression
regression
methodology
modeling
Tourism
tourism
electronic commerce
electronic business
structural model
data analysis
travel
Structural equation modeling
Multiple regression
commitment
History
history
Testing
Measurement model

Keywords

  • E-commerce research
  • Multiple regression
  • Online commitment
  • Structural equation modeling (SEM)
  • Tourism research

ASJC Scopus subject areas

  • Development
  • Transportation
  • Tourism, Leisure and Hospitality Management
  • Strategy and Management

Cite this

Comparative assessment of structural equation modeling and multiple regression research methodologies : E-commerce context. / Nusair, Khaldoon; Hua, Nan.

In: Tourism Management, Vol. 31, No. 3, 06.2010, p. 314-324.

Research output: Contribution to journalArticle

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