Pride or Guilt? Impacts of Consumers’ Socially Influenced Recycling Behaviors on Closed-Loop Supply Chains
Bibliographic record
Abstract
Problem definition: Social influenced emotions of pride and guilt have been identified by the environmental psychology (EP) literature as crucial drivers impacting recycling behavior, but they have mostly been overlooked in operations management (OM) research. In contrast, EP studies often ignore firms’ operational decisions. We analyze the impacts of both social influence and firms’ operational decisions to provide a comprehensive understanding of consumers’ recycling behaviors, which is essential for realizing remanufacturing’s full potential. Methodology/results: We consider a closed-loop supply chain consisting of a manufacturer selling a single product to a consumer community. Consumers’ recycling behavior depends on both the recycling reward offered by the manufacturer, as well as intrinsic and socially influenced pride (guilt) from recycling (not recycling). We develop an evolutionary game to model consumers’ recycling behavior and characterize the resulting equilibrium recycling rate, which is then integrated into the manufacturer’s decision problem. We characterize the manufacturer’s optimal strategy and the equilibrium recycling rate in four distinct regions defined by both the product’s overall difficulty of remanufacturing and the underlying strengths of consumers’ socially influenced pride and guilt. We show that in settings where the product has a moderately high difficulty of remanufacturing and consumers have stronger socially influenced pride than guilt, the manufacturer optimally induces an interior recycling rate. In such scenarios, there exist win-win pathways in using social influence–based interventions to increase both the manufacturer’s profit and the recycling rate. However, misalignment may occur when consumers substantially care for the product’s recyclability. Managerial implications: This study bridges sustainable OM and EP literature by analyzing how consumers’ socially influenced emotions of pride and guilt affect a manufacturer’s optimal decisions, profits, and the resulting recycling rate. We provide important insights for designing effective and efficient social influence–based interventions to improve recycling rates. Funding: W. Chen was supported by the National Natural Science Foundation of China [Grant 71902017], and C.-L. Tseng was supported by the University of New South Wales UNOVA Knowledge Hub. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0721 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".