Mindful Sustainable Consumption and Sustainability Chatbots in Fast Fashion Retailing During and After the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic and ecological crisis are paving the way for new consumption models based on customers’ conscious choices and the subsequent integration of sustainable policies into retailers’ business strategies. As a consequence, the current consumer trends suggest that more people are becoming aware of their consumption standards and their repercussion on the environment and society. Statistics demonstrate that, in their purchasing processes, these “mindful customers” now search for a sustainable, self-sufficient way of living in harmony with nature. This paper argues that artificial intelligence (AI) is able to facilitate this process in the marketplace. More specifically, mindfulness with the support of AI technologies could be a plausible way to activate sustainable consumption patterns for avoiding overconsumption. The life-changing ability of mindful consumption is reviewed in this paper across domains of sustainability. Using a comprehensive literature review, the paper first outlines the theoretical and conceptual foundations of the mindful sustainable consumption (MSC) approach that fills the literature gap that almost always separates mindful consumption from sustainability. Second, the new conceptual approach is applied through a strategic framework in the field of fast fashion retailing through the use of AI-powered chatbots. In particular, the study defines a new category of chatbots, named sustainability chatbots (SC), which could convey mindful and sustainable consumption choices. The paper highlights that the MSC approach combined with the support of SC could enable marketing managers to create the appropriate context for embedding sustainability into consumer behaviour and fast fashion retailers’ strategies from a value co-creation perspective.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".