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The evolving world of research in marketing and the blending of theory and data

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  • Lehmann, Donald R.

Abstract

The world, and the way research is conducted in marketing, has changed over the last fifty years. For example, there has been an increased emphasis on theory and model testing. This paper briefly identifies some of the changes that have occurred. It then discusses the role of theory and presents a method for combining theory (intuition, expectations) with data to form a model. Finally, it identifies a number of concerns with the way research is typically conducted, most of which involve “formulaic” approaches that have developed. The purpose of the paper is not to present a correct approach. Rather it is to have all of us, but especially young scholars, think more critically (and imaginatively) about how to advance knowledge in marketing.

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  • Lehmann, Donald R., 2020. "The evolving world of research in marketing and the blending of theory and data," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 27-42.
    • Handle: RePEc:eee:ijrema:v:37:y:2020:i:1:p:27-42
    DOI: 10.1016/j.ijresmar.2019.12.001
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    References listed on IDEAS

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    Cited by:

    1. Justin Jia & Jia Li & Weixin Liu, 2023. "Expectation-based consumer purchase decisions: behavioral modeling and observations," Marketing Letters, Springer, vol. 34(3), pages 397-413, September.
    2. Wierenga, Berend, 2021. "The study of important marketing issues in an evolving field," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 18-28.
    3. Isaac Levi Henderson & Mark Avis & Wai Hong Kan Tsui & Thanh Ngo & Andrew Gilbey, 2023. "Compound Brands and the Multi-Creation of Brand Associations: Evidence from Airports and Shopping Malls," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    4. Islam, Towhidul & Meade, Nigel & Carson, Richard T. & Louviere, Jordan J. & Wang, Juan, 2022. "The usefulness of socio-demographic variables in predicting purchase decisions: Evidence from machine learning procedures," Journal of Business Research, Elsevier, vol. 151(C), pages 324-338.
    5. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    6. Malshe, Avinash & Hughes, Douglas E. & Good, Valerie & Friend, Scott B., 2022. "Marketing strategy implementation impediments and remedies: A multi-level theoretical framework within the sales-marketing interface," International Journal of Research in Marketing, Elsevier, vol. 39(3), pages 824-846.

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