Going Beyond Mere Relevance: Research on Topics of Managerial Importance
(A2020-64580)

AUTHORS

Michael Haenlein, ESCP Europe

ABSTRACT

Empirical and conceptual work published in leading marketing journals usually focuses on topics of relevance to some to marketing stakeholders such as managers. However, it is undisputed that some topics, despite relevant, are clearly of higher importance than others. This can be because they impact more marketing stakeholders, stakeholders of higher statue or lead to larger shifts in beliefs. The purpose of this special session is to focus on such topics of high managerial importance and to provide insights not only into the type of questions managers truly care about but also into the factors influencing the larger visibility of academic marketing research.

In the first presentation (Identifying market structure: A deep network representation learning of social engagement), Yi Yang, Kunpeng Zhang, and P.K. Kannan present a deep learning method to identify product-market boundaries beyond the traditionally used SIC/ NAICS logic. They then apply this method to user-brand engagement data generated on social media platforms for over five thousand brands to construct a large-scale heterogeneous user-brand network based on user engagement on brands’ social media public fan pages.

The second talk by Samuel Stäbler and Michael Haenlein (How do media channels report about academic research? An empirical study of 27,000 marketing articles) studies how different media channels report about academic research. Anecdotal evidence suggest that media coverage of academic research varies strongly. In response the authors develop a broad conceptual model of the drivers of media citations and investigate how more than 27,000 marketing articles are published on social media platforms (i.e., Facebook and Twitter), traditional news media, peer-review journals, video platforms (i.e., YouTube), and online encyclopedia (i.e., Wikipedia).

The third paper (Predicting the Likelihood and Duration of Brand Crises on Social Media), co-authored by Kelly Hewett, Meike Eilert, Koen Pauwels, and Raoul V. Kübler, looks into media crises, or well-publicized events such as product recalls and scandals involving company employees. The authors apply machine learning to a comprehensive database representing 16 firms across the time period 2010-2015 in order to identify (a) whether a firm can identify a potential brand crisis early, i.e. before major damage is done, (b) whether the likely duration and severity of an event can be predicted, and (c) whether social media can provide greater predictive ability than other metrics firms typically track.

In the final paper, John Hulland (Bibliometric analysis: What aspects of marketing research papers maximize their impact, and what are reporting best practices?), uses bibliometric techniques and specifically citation analysis to describe key emergent themes, constructs and methodologies observed in the Journal of the Academy of Marketing Science (JAMS). By focusing on three common bibliometric indicators – total number of publications, total number of citations and the h-index), Hulland provides an understanding what elements of individual research articles drive high citation within marketing.