Learning from Data: An Empirics-First Approach to Relevant Knowledge Generation

Abstract

A “theory-first” paradigm tends to be the dominant approach in much academic marketing research. In this approach, a theory is borrowed, refined, or developed and then tested empirically. In this challenging-the-boundaries article, we make a case for an “empirics-first” approach. Empirics-first refers to research that (i) is grounded in (originates from) a real-world marketing phenomenon, problem, or observation, (ii) involves obtaining and analyzing data, and (iii) produces valid marketing-relevant insights without necessarily developing or testing theory. The empirics-first approach is not antagonistic to theory but rather can serve as a stepping-stone to theory. The approach lends itself well to today’s data-rich environment, which can surface novel research questions untethered to theory. The present paper describes the underlying principles of an empirics-first approach, which consists of exploring a domain purposefully without preconceptions. Using a rich set of published examples, the present paper offers guidance on how to implement empirics-first research and how it can lead to valuable knowledge development. Advice is also offered to authors on how to report EF research and to reviewers and to editorial teams on how to evaluate it. Our ultimate objective is to pave a way for empirics-first to enter the mainstream of academic marketing research.

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“…Rather, they have the potential to create structure and shape the understating of connections that may not have been identified by humans (Chen et al, 2021; Jha et al, 2022). Some academics have adopted the data first approach in research (Golder et al, 2022), where software such as DataRobot (Lee, 2020) or IBM SPSS Modeller (Wendler & Gröttrup, 2016) identifies the best predictive models of relationships within a dataset, with authors then seeking to explain the relationships identified. While these modelling tools are more supportive of research and need academics to drive the research process, they are advancing to being able to identify and explain relationships academics may have not initially considered (Golder et al, 2022).…”

“…Some academics have adopted the data first approach in research (Golder et al, 2022), where software such as DataRobot (Lee, 2020) or IBM SPSS Modeller (Wendler & Gröttrup, 2016) identifies the best predictive models of relationships within a dataset, with authors then seeking to explain the relationships identified. While these modelling tools are more supportive of research and need academics to drive the research process, they are advancing to being able to identify and explain relationships academics may have not initially considered (Golder et al, 2022). Thus, AI tools seem to be going beyond simply supporting academics and moving towards generating, directing and articulating ideas that advance knowledge in ways akin to the current collaboration process.…”

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“…First, a focus on the real world can bring attention to fresh topics that often cannot be found by scouring the literature. As quoted in Golder et al (2022), Nobel Prize Laureate Paul Krugman (2002) famously shared the following advice from his advisor: “Don’t reread the literature. Your head is already stuffed full of that material, and you’ll end up doing a small twiddle on someone else's model.” JM examples of such real-world-inspired topics include the use and misuse of genetic data in marketing (Daviet, Nave, and Wind 2022) and how GMO labeling policy affects consumer choice and willingness to pay (Kim, Kim, and Arora 2022).…”

“…First, a focus on the real world can bring attention to fresh topics that often cannot be found by scouring the literature. As quoted in Golder et al (2022), Nobel Prize Laureate Paul Krugman (2002) famously shared the following advice from his advisor: "Don't reread the literature. Your head is already stuffed full of that material, and you'll end up doing a small twiddle on someone else's model."…”

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“…Thus, communities built around marketing phenomena are more likely to foster effortless interaction across the academia-practice divide. Finally, phenomena-focused research approach might be particularly useful in today's data-rich environment (Golder et al, 2022).…”