MétaCan
Menu
Back to cohort
Record W4404142697 · doi:10.1108/jabs-01-2024-0032

Sustainability tweeting triumphs during the COP events: analysing environmental, social and governance (ESG) communication on twitter

2024· article· en· W4404142697 on OpenAlex
Amr ElAlfy, John M. Quigley, Leilei Tang, Youssef Al Hariri, Olaf Weber

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Asia Business Studies · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsYork UniversityUniversity of Waterloo
Fundersnot available
KeywordsSustainabilityCorporate governanceSocial mediaBusinessPublic relationsEnvironmental communicationCorporate social responsibilitySociologyPolitical scienceEcology

Abstract

fetched live from OpenAlex

Purpose With the recent conclusion of the United Nations Conference of the Parties (COP) 28 in the United Arab Emirates, this study aims to investigate the tweeting behaviour of firms surrounding COP events. The authors analyse the environmental, social and governance (ESG) tweets from the COP 26 and COP 27 events, aiming to deepen the understanding of the complex relationships between social media communication, industry characteristics and financial performance. This timely analysis is critical for assessing how the latest global discussions on climate change are influencing corporate communication strategies on sustainability, offering fresh insights into the evolving dynamics of ESG engagement in the context of these pivotal international meetings. Design/methodology/approach In this study, the authors embrace a grounded theory approach to gain insights into the ESG and sustainability initiatives presented by companies on social media, with an intensified focus on climate change discourse. Leveraging advanced social media analytics, this study expands its scope by conducting a thorough examination of ESG-related tweets from Standard and Poor’s (S&P) 500 companies. In addition, the authors explore the relationships between such communication efforts and financial performance, applying an advanced cumulative abnormal returns (CARs) model. This methodological enhancement enables a more sophisticated understanding of how ESG communication on Twitter correlates with, and potentially influences, a firm’s market valuation and financial health, offering invaluable insights into the strategic importance of digital sustainability discourse. Findings The research findings introduce four novel distinct groups – Unengaged, Catalysts, Cautious and Shapers – based on firms’ proactive or reactive sustainability communication patterns. The results explore the potential impact of COP event locations on tweeting behaviour, proposing that conferences held in different regions, such as Asia versus Europe, may elicit varied reactions from S&P 500 firms. Despite no significant inter-industry differences in tweeting habits, the authors discover a significant link between firms’ financial metrics, specifically CARs, and their categorised communication styles. The results challenge the simplistic view that higher social media engagement leads to positive financial outcomes, suggesting instead that lower financial performance may drive firms to adopt more extreme communication patterns, possibly as a strategic move to enhance corporate legitimacy. Originality/value This study offers new insights into how companies use social media during significant climate change events, namely, COP events. By classifying firms according to their ESG communication approaches, the results reveal uncharted correlations between how companies communicate on social media, namely, Twitter, and the correlation to financial performance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.270
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it