Exploring social media dialogic communication strategies in the oil and gas industry: practitioner, organization and audience perspectives
Bibliographic record
Abstract
Purpose This study aims to investigate social media dialogic communication in the Canadian oil and gas (O&G) industry. It explores the perspectives of communication practitioners, actual company practices and stakeholder engagement, aiming to identify gaps between practitioner expectations, organizational strategies and audience interaction. Design/methodology/approach A mixed-method approach was used, involving 21 interviews with communication practitioners and a content analysis of 1,347 social media posts from O&G companies. The interviews provided qualitative insights into practitioners' views on dialogic communication, while the content analysis assessed actual practices and audience engagement of dialogic communication on Facebook. Findings Findings reveal diverse views among practitioners. Junior practitioners favor social media for dialog, emphasizing transparency and responsiveness, while senior practitioners express concerns due to the industry’s controversial nature. The content analysis showed companies prioritize information richness and personalization, but actual dialogic interaction (e.g. responding to audiences) is rare. Audience engagement (likes and shares) increases with dialogic content, though comments tend to decrease as messages become more dialogic. Originality/value This study extends the dialogic communication framework by incorporating industry-specific challenges. It proposes additional context-specific principles, such as personalization and third-party endorsement, to better fit social media and industry contexts. The research offers a comprehensive view of dialogic communication from the perspectives of practitioners, organizations and the public, providing insights for improving engagement strategies in controversial industries like O&G.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".