Reshaping Loyalty Programs for Sustainability: Harnessing the Power of Mobile Marketing
Bibliographic record
Abstract
With technologies embedded throughout much of business and society, new pathways are opening to address mounting externalities like climate change. Today, there is an opportunity to leverage ICTs to address sustainability issues head-on through digital marketing and other business activities that go beyond traditional corporate social responsibility (CSR). Across four studies, we instigated how mobile promotions administered via retail loyalty programs impacted the consumption of sustainable products. Multi-level mixed-effect Tobit regression models were used. Data were included for weekly purchases of 21 brands of plant-based beverages made across 242 stores located in Quebec, Canada between 2015 and 2016. Overall, mobile promotions had a positive direct impact on demand (B=0.232, p<0.0001) but increased their price sensitivity (B=-0.898, p<0.0001). Mobile promotions that awarded loyalty points were the most effective at generating demand directly. Advertisements with everyday low pricing increased price sensitivities the most (B=-0.702, p<0.0001). Implications for theory and practice are discussed.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".