Drivers of green innovation and green acquisition: empirical evidence from the food and beverage industry
Bibliographic record
Abstract
Purpose Green innovation and green acquisition are key green marketing strategies. This paper aims to explore and compare the drivers of green acquisition and green innovation strategies firms adopt. Moreover, the moderating role of top management team (TMT) sustainability commitment is investigated. Design/methodology/approach The research model used secondary data based on 1,565 firm-year observations in the beverage and food industry in the US. The two-stage control function approach was used for data analysis. Findings Media attention motivates firms to pursue both green innovation and green acquisition. The TMT sustainability commitment plays a pivotal moderating role. It strengthens the link between environmental regulation stringency and green innovation but weakens the impact of media attention on green acquisition. Practical implications Managers can leverage the study’s findings to guide sustainable marketing decisions in response to environmental regulations and media scrutiny. Policymakers and investors can encourage firms to adopt more sustainable practices, helping align corporate strategies with Sustainable Development Goals 9 and 12. Originality/value Though green innovation determinants are extensively studied, most studies rely on surveys or qualitative methods rather than secondary data. Also, as an alternative to developing in-house green technologies or products, the drivers of green acquisition remain unclear despite its growing prevalence. This study addresses both gaps in the sustainable marketing literature.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".