How do business-to-business companies use Twitter? A case study of global forest and paper product companies' tweets
Bibliographic record
Abstract
The objective of this study is to find out how business-to-business companies are using social media through a case study of global forest and paper product companies' use of Twitter. Previous social media research has placed focus on business-to-consumer types of organizations and this study aims to add to research of business-to-business types of organizations' use of social media through study of Twitter content. The intention of this study is to answer two main research questions and two sub questions. The first main research question: (1) How do forest and paper product companies use Twitter?, is answered by two sub questions: (1a) What different types of content do forest and paper product companies publish on Twitter?, and, (1b) Do forest and paper product companies use elements that support interaction on Twitter? The second research question asks: (2) Are there differences between North American and Northern European companies? This research project uses a quantitative content analysis to study the content of tweets and find differences between the chosen companies. This study looks at the 713 tweets published by 16 forest and paper product companies from Canada, Finland, Sweden and the United States of America. Dialogic principles are used to study the use of interaction supporting elements. The main use of Twitter as a communication channel is defined through three main categories composed of fifteen individual tweet types. The main findings of this study are: (1) forest and paper product companies use Twitter to inform the public, (2) forest and paper product companies use interaction supporting elements in their tweets and (3) North American forest and paper product companies use Twitter to build rapport and interact with the public and (4) Northern European forest and paper product companies use Twitter to inform the public. These findings implicate that (1) the nationality of a company affects the use of social media, (2) Twitter is not used for mobilization of audiences and (3) business-to-business companies use information messages to both inform and spread a positive image of themselves to the public.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".