Author Identifier (ORCID)
Sanjit K. Roy: https://orcid.org/0000-0003-4932-2222
Abstract
The use of Generative Artificial Intelligence (GAI) to transform vast amounts of textual content for strategic insights have been a rapidly growing trend for business researchers. Although various studies have identified applications of GAI in business operations, aspects related to strategic support are yet to be fully developed in the relevant literature. This paper introduces a new GAI-enabled social listening solution that integrates advanced text analytics techniques: (a) Long Short-Term Memory (LSTM)–a specialized form of Recurrent Neural Network (RNN) adept at capturing long-term dependencies through its memory function over time, and (b) Google BERT (Bidirectional Encoder Representations from Transformers). Our solution artifact as a method that utilises bi-directional context to derive a more nuanced understanding from large text datasets to transform user-generated content into actionable insights into customer experiences and strategic recommendations to managers in a context of phygital retailer’s decision support.
Document Type
Journal Article
Date of Publication
1-1-2025
Publication Title
Journal of Strategic Marketing
Publisher
Taylor & Francis
School
School of Business and Law
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
Dahish, Z., Miah, S. J., Pandit, A., & Roy, S. K. (2025). Enhancing phygital customer experience through generative AI: A social listening method for strategic retail decision-making. Journal of Strategic Marketing. Advance online publication. https://doi.org/10.1080/0965254X.2025.2540267