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Aron M. Levin
Researcher at University of Iowa
Publications - 12
Citations - 976
Aron M. Levin is an academic researcher from University of Iowa. The author has contributed to research in topics: Product (category theory) & Brand equity. The author has an hindex of 8, co-authored 12 publications receiving 942 citations. Previous affiliations of Aron M. Levin include Washburn University.
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Journal ArticleDOI
Consumer evaluation of multi-product bundles: An information integration analysis
TL;DR: In this paper, consumers' reactions to multi-product bundles were obtained by systematically varying the attributes of both the primary product and the tie-in product (e.g., VCRs) and respondents were given written product descriptions as well as the opportunity to inspect the actual products before responding.
Journal Article
Product Category Dependent Consumer Preferences for Online and Offline Shopping Features and Their Influence on Multi-Channel Retail Alliances.
TL;DR: This paper addresses the question of how to combine online and offline services in the most complementary way for different product classes by confirming the operation of both assimilation and complementarity effects and found that the images of both brands could be improved with such alliances.
Journal ArticleDOI
Modeling the Role of Brand Alliances in the Assimilation of Product Evaluations
Irwin P. Levin,Aron M. Levin +1 more
TL;DR: This paper examined the extent to which evaluations of two restaurants are assimilated by virtue of their dual-branding strategic linkage and found that the degree of linkage between the target and the context as operationally defined by varying the shared features of the dual brands played a key role.
Journal Article
Theoretical and Empirical Linkages Between Consumers' Responses to Different Branding Strategies
Journal Article
A multi-attribute analysis of preferences for online and offline shopping: differences across products, consumers, and shopping stages
TL;DR: In this paper, a multipart survey was administered to assess how product attribute evaluations drive differences in online/offline shopping preferences between products, between consumers, and between stages of the shopping experience.