Categorizing corporate social responsibility (CSR) initiatives in B2B markets: the why, when and how
Bibliographic record
Abstract
Purpose This paper aims to address the questions of why, when and how business-to-business (B2B) firms engage in sustainability initiatives. The authors believe that this is the first attempt to address all three questions in a single paper, and one of the earliest to focus on these in B2B markets. Design/methodology/approach The sustainability initiatives of B2B firms throughout the value/supply chain were examined. Input data came from external sources and the firms themselves. Two conceptual frameworks were developed, illustrating why firms partake in sustainability initiatives and when and how they may do so. Findings This paper provides two conceptual frameworks that address why, when and how firms get involved in sustainability initiatives, and how they can better communicate their involvement to stakeholders. Research limitations/implications To obtain a broader perspective of B2B firms’ involvement in sustainability initiatives, a variety of third-party sources were used, augmented with data from firm websites. Examples of firms the authors selected were constrained by the collection of firms described in student research papers. Practical implications This paper suggests useful guidelines for firms considering starting or expanding sustainability initiatives by providing frameworks that address why, when and how firms do so, with examples of firms illustrating engagement in each area. It also provides communication guidelines, necessary for enhancing stakeholder relations. Social implications Integrating environmental sustainability within a firm’s strategy can improve corporate image and increase efficiency, while contributing to a better world environment. Originality/value A review of the corporate social responsibility (CSR) literature indicated that most research has focused on business-to-consumer markets. This paper addresses CSR in B2B markets, examining players at all levels of the value/supply chain: manufacturers, channel intermediaries and end-users.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".