Stakeholder engagement in carbon reduction engineering: A perspective analysis of production optimization leveraging social-media interactions

Abstract

This study investigates the complex dynamics of stakeholder engagement on social media platforms within the context of carbon reduction engineering. To shed light on this underexplored phenomenon, we gather a unique dataset of 6,940 Facebook-verified page posts, and we employ advanced data mining techniques to analyze the factors influencing stakeholder engagement. The findings demonstrate the significant impact of post characteristics on stakeholder engagement rates. Factors such as post length, hashtags, vividness level, hyperlinks, and the inclusion of call-to-action (CTA) play essential roles in shaping engagement patterns. Specifically, we find that shorter posts without hashtags tend to have lower engagement, while posts with moderate character counts, low vividness, and no hyperlinks often generate higher engagement. Additionally, our topic modeling analysis identifies critical themes discussed in carbon reduction engineering, including collaborative efforts among stakeholders, the role of academic institutions, renewable energy adoption, AI technology, and climate change mitigation. This, in turn, highlights the diverse perspectives and concerns of stakeholders actively engaged in these discussions. Our results significantly expand the literature on stakeholder theory, social interaction management, and the application of data mining techniques in analyzing social media engagement.

Publication DOI: https://doi.org/10.1016/j.cie.2024.110807
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences
College of Business and Social Sciences > Aston Business School
Aston University (General)
Additional Information: Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( https://creativecommons.org/licenses/by/4.0/ ).
Uncontrolled Keywords: Carbon reduction engineering,CHAID decision tree,Facebook,Social interaction management,Social media,Stakeholder engagement,Topic modeling,General Computer Science,General Engineering
Publication ISSN: 1879-0550
Last Modified: 25 Apr 2025 07:12
Date Deposited: 23 Apr 2025 16:16
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 929X?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2024-12-12
Published Online Date: 2024-12-09
Accepted Date: 2024-12-01
Authors: Hathat, Zakaria El
Venkatesh, V. G.
Raja Sreedharan, V.
Zouadi, Tarik
Shi, Yangyan
Arunmozhi, Manimuthu (ORCID Profile 0000-0003-4909-4880)

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