Abstract

The question how similar organizations are to each other is pivotal for organization studies, as it underlies issues like competition, classification, learning, benchmarking, identity, and status. Audience-based approaches to organizational similarity argue that it is the audiences of organizations who define how similar organizations are to each other. This paper agrees with the audience-based view but contends that not only audiences define organizations but, simultaneously, organizations define audiences. We argue that accounting for this duality results in a deeper understanding of the nature and effects of organizational similarity, as well as in a more accurate, theoretically grounded measurement approach.

The empirical setting of this paper is restaurant similarity, and our data come from a restaurant review dataset in which customers rate restaurants they have visited. We show that the proposed dual approach provides a classification of restaurants and reviewers that is superior to structural equivalence-based classification as it improves the predictive power of similarity-based decision models that predict reviewers’ ratings of restaurants. To further investigate the dual approach, we set up a formal simulation model and explore the conditions under which the dual organizations-audiences approach outperforms structural equivalence.