César Zamudio

Two-Sided Matching in the Job Market for Assistant Professors in Marketing

In the job market for entry-level assistant professors in marketing, hiring departments and job candidates jointly determine the final market outcome - who matches with whom. In this work, we investigate the effects of research field, research productivity and ranking status on these matching outcomes. This is accomplished by estimating a structural two-sided matching model that uncovers the joint productivity, or matching value, of the matches between departments and candidates. Our results show that a match between a candidate trained in a particular research field and a department with similarly trained faculty does not always generate the highest value. Moreover, publications in top marketing journals are most valuable in matches that involve top-ranked departments. However, this effect is moderated by the candidate’s field of research as well as the ranking of his or her degree-granting department. Finally, matches between top-ranked hiring departments and candidates from top-ranked degree-granting departments generate especially high matching value, suggesting that academic stratification exists within marketing academia. These insights are useful for candidates and departments in improving their matching outcomes in the entry-level job market.

Review Richness: How Online Consumer Review Information Content Shapes Persuasion Through Review Richness: An Abstract

Online reviews are a critical electronic word-of-mouth source: firms with helpful reviews are thought of as providing better value, and consumers trust and use reviews to make purchase decisions. Accordingly, research has explored how review features, such as length and valence, influence how persuasive a review is. However, an implicit assumption in the literature is that review length can be used to proxy for how much information is in a review. Yet, two reviews of equal length may contain different amounts of information. To relax this assumption, we conceptualize and propose a new measure of review information content termed “review richness” and an ancillary review complexity measure, constructed based on Shannon’s entropy. These measures rely on constructing a lexicon that represents the distribution of words consumers most often use in a category, which reveals words that are often repeated (and thus carry little new content) and words that are more uncommon, providing review readers with more information. Results indicate that review richness is a significant predictor of review helpfulness, particularly for purchases with high expected risk. In terms of predictive ability, adding review richness to a helpfulness model is equivalent to half the predictive ability of review length. Therefore, review richness is a metric that should be included in predictive models of review helpfulness to identify which reviews are most persuasive to review readers.