Bibliographic record
Abstract
Based on an analysis of prior literature on consumer behaviour and reverse logistics, this study proposes a model for the design of returns policies that includes considerations for costs, logistics requirements, and consumer behaviour. The case study investigations yielded several important findings. Product characteristic concerns seem to have a low level of importance in the decision-making process of return policy establishment. Practitioners that are responsible for creating effective return policies seem to not place great importance on either product characteristics or supply chain optimization. Using case analysis, this study explored the decision-making process of return policy creation and found that customer satisfaction and organization-specific concerns have a high level of importance in the returns creation process. The results indicate that the current models and frameworks for return policies need to be re-examined, in order to reflect the practical realities of the environment and constraints in which organizations operate. A review of the literature suggests that retailers consider a product's return policy a source of competitive advantage that can increase customer satisfaction and overall profitability. However, the existing research into returns policies focuses mainly on optimizing product flows and minimizing the financial cost of returns, rather than examining the inter-relationships between multiple constructs such as customer satisfaction, product characteristics, logistic constraints and consumer behaviour. This is problematic because it creates a disconnect between the considerations that the practitioners take into account and the considerations that are included in the current models for returns policy establishment. For retail organizations, the returns process can have a significant impact on costs and customer satisfaction due to the unique logistics costs and customer interactions in the returns process. Based on an analysis of prior literature on consumer behaviour and reverse logistics, this study proposes a research framework for the design of returns policies for retailers that considers the impacts of a specific return policy on costs, logistics requirements, and consumer behaviour. The study uses the proposed framework to identify, highlight, and catalog the different influences and considerations that retail and manufacturing organizations face during the creation of a return policy in the retail environment. The case study investigations yielded several important findings. First, product characteristic concerns seem to have a low level of importance in the decision-making process of return policy establishment. The study finds that practitioners that are responsible for creating effective return policies do not place great importance on either product characteristics or supply chain optimization. Second, this study found that most of the current models on return policy creation do not include customer satisfaction and organizational concerns. Using case analysis, this study explored the decision-making process of return policy creation in three retail organizations and found that customer satisfaction and organization-specific concerns actually have a high level of importance in the returns creation process. By using current models on return policy establishment and using empirical results, this study proposes a tentative theory by outlining the propositions for the design of a returns policy in retail organizations. The results of this study are based on organizational data as well as interviews conducted with persons who are directly involved in the returns process for their organization. The results indicate that the current models and frameworks for return policies need to be re-examined, in order to reflect the practical realities of the environment and constraints in which organizations operate.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".