Measuring Consumer Perceived Warm-Glow for Technology Adoption Modeling
Bibliographic record
Abstract
In this paper, we adapt and validate two constructs—perceived extrinsic warm-glow (PEWG) and perceived intrinsic warm-glow (PIWG) —to measure the two dimensions of consumer perceived warm-glow (i.e., extrinsic and intrinsic) for use with the practice of technology adoption modeling. Taking an experimental approach, participants were exposed to one of four vignettes designed to simulate either the absence or the presence of warm-glow (specifically, extrinsic warm-glow, intrinsic warm-glow, and concurrently extrinsic and intrinsic warm-glow). The results revealed that both constructs measured their respective forms of warm-glow with two caveats. Firstly, singularly trying to evoke extrinsic warm-glow led to only a slight increase in consumer perception of extrinsic warm-glow. We attributed this finding to individuals not being attracted to technology products that overtly target and seek to satisfy their vanity, instead preferring technology that does so in a more subtle way. The second is that singularly trying to evoke intrinsic warm-glow also resulted in the manifestation of extrinsic warm-glow. Thus, warm-glow appears as a blend of extrinsic and intrinsic dimensions. This finding serves to reinforce what has already been reported in existing literature regarding warm-glow and the idea of impure altruism.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".