Bibliographic record
Abstract
Consumer preferences for social responsibility have both intrinsic and extrinsic components. The first essay of this thesis investigates whether extrinsic preferences, referred to as extrinsic warm-glow and cold-prickle, generate network effects among individuals when choosing eco-friendly versus regular products. The essay employs an experimental design that manipulates extrinsic preferences in sustainable purchase behaviour, by creating a scenario where participants' choices are observed by others. The findings suggest that among socially conscious participants, higher expectations of eco-friendly purchases result in weaker warm-glow feelings, indicating a negative network effect. The study does not find strong evidence of a network effect from experiencing extrinsic cold-prickle when choosing the regular product, suggesting that extrinsic warm-glow and cold-prickle can be asymmetric. Additionally, individuals who are more socially conscious demonstrate a more significant negative network effect induced by extrinsic warm-glow, compared to those who are intrinsically less concerned about social responsibility. Importantly, firms across various industries invest considerable resources in developing innovations to improve the social and environmental responsibility of their products, capitalizing on consumers' intrinsic and extrinsic preferences for social responsibility. In this context, the second essay examines how a firm's incentive to invest in environmental responsibility (ER) innovation is influenced by the quality of its products in a competitive market. More precisely, the essay investigates whether product quality and product ER are complements or substitutes, by utilizing a game-theoretic framework. The analysis reveals that when the magnitude of ER innovation is sufficiently large to grant a high-quality firm monopoly power over environmentally conscious buyers, quality and ER are complementary. This obtains because higher quality amplifies the benefits of having ER-induced monopoly power over environmentally conscious buyers. Conversely, when the expected ER advantage is medium, such that competition for environmentally conscious buyers is reduced but not eliminated, quality and ER become substitutes because they both act as levers to reduce competition. Finally, when the expected ER advantage has little effect on competition intensity, quality and ER are complements when the extrinsic benefits of ER are strong and are substitutes otherwise.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".