Drivers and Outcomes of Green Acquisitions
Bibliographic record
Abstract
Reporting on the growing number of green initiatives across various industries in media is at odds with only sporadic academic research on green acquisition strategy in the marketing discipline. This presents a unique opportunity for me to identify and empirically examine different factors that can impact a firm’s value when adopting the green acquisition strategy and explore drivers of adopting green strategies, namely, green acquisition and green innovation. In this thesis, I explore these questions through two studies. In the first study, I analyze 182 green acquisition announcements using the event study method to see how the stock market reacts. The study reveals that the stock market responds positively to announcements of green acquisitions. Additionally, acquirers with stronger marketing capability but limited innovation capability experience better stock performance. However, the stock market return−green acquisition relationship, influenced by the two capabilities mentioned above, is moderated by industry environmental sensitivity. The results enhance our understanding of how marketing and innovation capabilities impact investor behavior in the context of green acquisitions. These findings broaden our existing knowledge of the marketing−finance interface, green marketing, and corporate sustainability. The second study examines external and internal drivers of corporate green strategies (i.e., green innovation and green acquisition). Using a sample of 1565 firm-year observations from the food and beverage industries, I show that firms under greater media attention are more likely to adopt both green acquisition and green innovation strategies. However, with the presence of the top management team’s commitment toward sustainability, media attention’s positive effects on firms’ likelihood of adopting green acquisition will be weakened. Moreover, firms with top management teams committed to sustainability are more likely to engage in green innovations under higher environmental regulation stringency. This study fills the gap in the green marketing literature by providing insights into why and how firms react to social and environmental challenges proactively. Notably, my findings show when and why firms adopt green acquisition or green innovation strategies.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 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".