How consumer opinions are affected by marketers: an empirical examination by deep learning approach
Journal of Research in Interactive Marketing
ISSN: 2040-7122
Article publication date: 7 December 2021
Issue publication date: 6 December 2022
Abstract
Purpose
The natural language processing (NLP) technique enables machines to understand human language. This paper seeks to harness its power to recognise the interaction between marketers and consumers. Hence, this study aims to enhance the conceptual and future development of deep learning in interactive marketing.
Design/methodology/approach
This study measures cognitive responses by using actual user postings. Following a typical NLP analysis pipeline with tailored neural network (NN) models, it presents a stylised quantitative method to manifest the underlying relation.
Findings
Based on consumer-generated content (CGC) and marketer-generated content (MGC) in the tourism industry, the results reveal that marketers and consumers interact in a subtle way. This study explores beyond simple positive and negative framing, and reveals that they do not resemble each other, not even in abstract form: CGC may complement MGC, but they are incongruent. It validates and supplements preceding findings in the framing effect literature and underpins some marketing wisdom in practice.
Research limitations/implications
This research inherits a fundamental limitation of NN model that result interpretability is low. Also, the study may capture the partial phenomenon exhibited by active reviewers; lurker-consumers may behave differently.
Originality/value
This research is among the first to explore the interactive aspect of the framing effect with state-of-the-art deep learning language model. It reveals research opportunities by using NLP-extracted latent features to assess textual opinions. It also demonstrates the accessibility of deep learning tools. Practitioners could use the described blueprint to foster their marketing initiatives.
Keywords
Acknowledgements
The author thanks the four anonymous reviewers, the associate editor and the editor for their careful evaluations, many insightful comments and suggestions. They helped improve this manuscript very much. The author also thanks a SAS@ data analytics professional Mr. Kar-Ming Iao for his assistance in the data collection phase.
Citation
Yu, B. (2022), "How consumer opinions are affected by marketers: an empirical examination by deep learning approach", Journal of Research in Interactive Marketing, Vol. 16 No. 4, pp. 601-614. https://doi.org/10.1108/JRIM-04-2021-0106
Publisher
:Emerald Publishing Limited
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