Abstract

ABSTRACT:

During the last half century, the theory-in-isolation (TiI) approach—within which scholars develop theories by first formulating quantitative models meant to approximate stakeholders’ behavior and then testing those models with data acquired for that purpose—has remained the gold standard for publishable marketing scholarship. Unfortunately, the almost universal adoption of this approach has blinded marketing scholars to a viable alternative: the empirical-then-theoretical (EtT) approach, which suggests theories based on observed empirical regularities. However, increasingly pervasive data collection efforts, powerful computer hardware, and sophisticated software-implemented algorithms are fostering a ‘big data’ (i.e., data sets that are massive, complex, yet quickly replenished from various sources) era far more amenable to the EtT approach. Traditionally, marketing theories emerged from managerial experience and/or scholarly activity in marketing and related disciplines (e.g., economics). Big data represents a complementary source. Nonetheless, revelatory big-data-derived scholarship requires multi-disciplinary research teams, knowledgeable industry experts, and specialized computing capabilities. In addition, big data is prone to biases that multi-disciplinary specialists can mitigate substantially. Thus, marketing scholars contemplating a contemporary EtT approach—which relies on big-data-related analytical tools such as data mining, cognitive computing, neural networks, and artificial intelligence—must have access to skills that extend beyond traditional graduate training in businessm .Essentially, we argue big data is more compatible with an empirical-then-theoretical (EtT) approach than a theoretical-in-isolation (TiI) approach to marketing theory development. Our exposition proceeds as follows. First, we introduce both approaches and big data. Then, we discuss three well-established sources of marketing theory and suggest big data as a fourth source. After presenting an abridged set of published marketing-related big data studies, we close with issues posed by using big data to develop marketing theory and a brief look forward.

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Additional Information

ISSN
1548-2278
Print ISSN
0022-037X
Launched on MUSE
2019-03-02
Open Access
No
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