Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287960 
Year of Publication: 
2023
Citation: 
[Journal:] International Journal of Consumer Studies [ISSN:] 1470-6431 [Volume:] 47 [Issue:] 4 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2023 [Pages:] 1596-1608
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
Picking one ‘winner’ model for researching a certain phenomenon while discarding the rest implies a confidence that may misrepresent the evidence. Multimodel inference allows researchers to more accurately represent their uncertainty about which model is ‘best’. But multimodel inference, with Akaike weights—weights reflecting the relative probability of each candidate model—and bootstrapping, can also be used to quantify model selection uncertainty, in the form of empirical variation in parameter estimates across models, while minimizing bias from dubious assumptions. This paper describes this approach. Results from a simulation example and an empirical study on the impact of perceived brand environmental responsibility on customer loyalty illustrate and provide support for our proposed approach.
Subjects: 
Akaike weights
bootstrapping
information criteria
model selection
uncertainty
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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