The Impact of Mobile Technology on Consumers’ Charitable Behaviors: a Research Protocol
Bibliographic record
Abstract
Mobile sales have increased over the past decade. In today's online retail environment, the mobile channel has the added potential to bring greater value to the retail value chain. While researchers have examined a number of factors contributing to the success of mobile technology in the context of for-profit businesses, the benefits of the mobile channel remain largely untapped by organizations in the third sector – those outside the public and private sectors. Such organizations known as non-profits include voluntary and community organizations, cooperatives, and registered charities. Focusing specifically on charities, this article explores the impact of mobile technology on individuals’ charitable intentions. Because the design of mobile apps influences both usability and functionality, we believe that their successful implementation can help charities not only increase their visibility but also attract more donations. This research proposes the use of the color green in a mobile app as a way to improve user browsing time on the charity’s application. It is also proposed that the best time to target donors (existing and potential) is when they go to bed, otherwise known as “bedtime”. Accordingly, the use of the color green in the conceptualization of a charity’s mobile app significantly improves the user’s attention when navigating the app and ultimately positively affects their intention to donate. To illustrate this research protocol, we developed a conceptual framework for improving donation behavior; this framework will be tested through online studies. This research proposal has the potential to add much to the existing literature on multi-channel marketing and, in particular, on the impact of the mobile channel on consumers’ donation behaviors towards charitable organizations.
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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 | 0.068 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.076 | 0.016 |
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".