Rights statement: The final, definitive version of this article has been published in the Journal, Journal of Travel Research, 55 (6), 2016, © SAGE Publications Ltd, 2016 by SAGE Publications Ltd at the Journal of Travel Research page: http://jtr.sagepub.com/ on SAGE Journals Online: http://online.sagepub.com/
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Unobserved heterogeneity in hospitality and tourism research
AU - Assaf, A. George
AU - Oh, Haemoon
AU - Tsionas, Efthymios
PY - 2016/7
Y1 - 2016/7
N2 - Despite the growing complexity of structural equation model (SEM) applications in tourism, it is surprising that most applications have estimated these models without accounting for unobserved heterogeneity. In this article, we aim to discuss the concept of unobserved heterogeneity in more detail, highlighting its serious threats to the validity and reliability of SEMs. We describe a Bayesian finite mixture modeling framework for estimating SEMs while accounting for unobserved heterogeneity. We provide a comprehensive description of this model, and provide guidance on its estimation using the WinBUGS software. We illustrate the importance of unobserved heterogeneity and the finite mixture modeling framework using a didactic application on brand equity where heterogeneity is likely to play an important role because of the differences in how consumers perceive the different dimensions of brand equity. We compare between various models and illustrate the differences between the standard and heterogeneous SEM and discuss the implications for research and practice.
AB - Despite the growing complexity of structural equation model (SEM) applications in tourism, it is surprising that most applications have estimated these models without accounting for unobserved heterogeneity. In this article, we aim to discuss the concept of unobserved heterogeneity in more detail, highlighting its serious threats to the validity and reliability of SEMs. We describe a Bayesian finite mixture modeling framework for estimating SEMs while accounting for unobserved heterogeneity. We provide a comprehensive description of this model, and provide guidance on its estimation using the WinBUGS software. We illustrate the importance of unobserved heterogeneity and the finite mixture modeling framework using a didactic application on brand equity where heterogeneity is likely to play an important role because of the differences in how consumers perceive the different dimensions of brand equity. We compare between various models and illustrate the differences between the standard and heterogeneous SEM and discuss the implications for research and practice.
KW - unobserved heterogeneity
KW - SEM
KW - finite mixture model
KW - Bayesian
U2 - 10.1177/0047287515588593
DO - 10.1177/0047287515588593
M3 - Journal article
VL - 55
SP - 774
EP - 788
JO - Journal of Travel Research
JF - Journal of Travel Research
SN - 0047-2875
IS - 6
ER -