Vol. 14 No. 1 (2026): Business & Management Studies: An International Journal
Articles

Predicting thematic trends in sustainable marketing literature using BERTopic and deep learning

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  • İbrahim Budak
İbrahim Budak
Dr., Kastamonu University, Kastamonu, Türkiye

Published 2026-03-25

Keywords

  • Sustainable Marketing, BERTopic, LSTM, GRU, Research Trend Prediction
  • Sürdürülebilir Pazarlama, BERTopic, LSTM, GRU, Araştırma Trendi Tahmini

How to Cite

Predicting thematic trends in sustainable marketing literature using BERTopic and deep learning. (2026). Business & Management Studies: An International Journal, 14(1), 113-128. https://doi.org/10.15295/bmij.v14i1.2688

Copyright (c) 2026 İbrahim Budak

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Predicting thematic trends in sustainable marketing literature using BERTopic and deep learning. (2026). Business & Management Studies: An International Journal, 14(1), 113-128. https://doi.org/10.15295/bmij.v14i1.2688

Abstract

This study aims to map academic trends in sustainable marketing both retrospectively and to develop future projections. To this end, the study presents thematic trend forecasts extending to 2030 by creating a quantitative map of the sustainable marketing literature around five main themes. Only English articles, conference papers, book chapters, and related document types were selected using the query "sustainable" AND "marketing" in the Elsevier Scopus database; abstracts of 17,747 records covering the period 1980–2025 were analysed. After preprocessing, BERTopic was applied using paraphrase-multilingual-MiniLM-L12-v2 embeddings. In line with prior BERTopic practice, the outlier/noise cluster (Topic = −1), accounting for 4.4% of documents, was excluded, and the remaining 209 topics were consolidated into five higher-level themes based on content similarity. The resulting themes cluster around axes such as green marketing, sustainable branding, marine and food-focused sustainability, green supply chain marketing, and AI-supported sustainability applications, quantitatively revealing the current backbone structure of the sustainable marketing literature. Annual publication counts were calculated for each theme and converted into time series, which were then forecasted for the 2026–2030 period using LSTM and GRU models. During the test period, LSTM demonstrated clearly superior performance compared to GRU, with lower errors (MAE and RMSE) and higher R² values. The findings clearly show that themes such as sustainable branding, green supply chain marketing, and AI-supported sustainability applications will experience a strong upward trend in the coming years. In contrast, areas such as sustainable packaging marketing will follow a relatively balanced, mature course. By combining topic modelling with deep learning-based time series forecasting, the study offers a unique methodological contribution to sustainable marketing research. It produces concrete, foresight-based insights for both the academic agenda and brand strategies.

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