Bibliometric analysis of honeycomb model: research trend in social media marketing

Nurafni Rubiyanti; Lim Ying San; Arry Widodo; Lim Ying Zhee; Anita Silvianita; Ng Tuan Hock

doi:10.1117/12.3011830

19 December 2023 Bibliometric analysis of honeycomb model: research trend in social media marketing

Nurafni Rubiyanti, Lim Ying San, Arry Widodo, Lim Ying Zhee, Anita Silvianita, Ng Tuan Hock

Proceedings Volume 12936, International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 2023); 129361O (2023) https://doi.org/10.1117/12.3011830
Event: International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 2023), 2023, Istanbul, Turkey

Abstract

The honeycomb model serves as a comprehensive framework for understanding social media engagement. It encompasses seven dimensions, including identity, conversations, sharing, presence, relationships, reputation and groups. This study utilised a bibliometric analysis to examine keywords related to the honeycomb model, revealing distinct clusters that highlight practical aspects, theoretical frameworks, mathematical and computational aspects as well as a broad range of topics. The analysis showcased the multidimensional nature of the honeycomb model's research landscape and its significance across diverse sectors. The findings provided valuable insights for businesses and researchers seeking to enhance social media engagement and develop effective marketing strategies.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nurafni Rubiyanti, Lim Ying San, Arry Widodo, Lim Ying Zhee, Anita Silvianita, and Ng Tuan Hock "Bibliometric analysis of honeycomb model: research trend in social media marketing", Proc. SPIE 12936, International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 2023), 129361O (19 December 2023); https://doi.org/10.1117/12.3011830

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KEYWORDS

Web 2.0 technologies

Analytical research

Quantum modeling

Analytic models

Visualization

Quantum spin

Mathematical modeling

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