Changing Attitudes Toward Checkout Charity
Bibliographic record
Abstract
Retailers in the U.S. are increasingly asking customers to donate to charity at their sales registers. This practice, known as Checkout Charity, is a form of Cause-Related Marketing (CRM) providing many benefits to retailers (Giebelhausen, Lawrence, Chun, & Hsu, 2017) and raising billions of dollars for charities (Coleman & Peasley, 2015). Despite the perceived goodwill of this retail practice, research suggests an imbalance between retailers and consumers, as Checkout Charity offers fewer benefits to customers than traditional CRM (Krishna, 2011; Owens, 2016). Using equity theory’s impact on prosocial behavior (Ross & Kapitan, 2018), this paper explores whether customers perceive an imbalance in the Checkout Charity process. Open-ended survey results show that customers are aware of Checkout Charity’s drawbacks and hold mostly negative sentiments toward the practice. Attitudinal survey results show that most customers prefer donating elsewhere and not being asked to donate at checkout. However, some are happy with the process. A regression analysis of factors known to influence charitable giving found that those who donate frequently have a relatively more positive attitude toward Checkout Charity. Research implications and ways retailers might use the practice to build deeper customer relationships are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".