Considering Different Motivations in Design for Consumer-Behavior Change
Bibliographic record
Abstract
Much existing work aims to understand how to change human behavior through product-design interventions. Given the diversity of individuals and their motivations, solutions that address different motives are surprisingly rare. We aim to develop and validate a framework that clearly identifies and targets different types of behavioral motives in users. We present a behavior model comprising egoistic, sociocultural and altruistic motives, and apply the model to sustainable behavior. We confirmed the explanatory power of the behavior model by categorizing user comments about an international environmental agreement from multiple news sources. We next developed concepts, each intended to target a single motive type, and elicited evaluations from online respondents who self-assessed their motivation type after evaluating the concepts. We present and discuss correlation results between motive types and preference for products that target these types for two iterations of the experiment. Deviations from our expected results are mainly due to unexpected perceptions, both positive and negative, of our concepts. Despite this, the main value of this work lies in the explicit consideration of a manageable number of different types of motives. A proposed design tool incorporates the three types of motives from the model with the different levels of persuasion others have proposed to change user behavior.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".