Disability Orientation and Regulatory Focus in the Assistive Technology Context: A Study of Deaf and Hard-of-Hearing Consumers
Bibliographic record
Abstract
With people with disabilities (PwDs) representing 15% of the United States population, the PwD market demonstrates significant potential as a lucrative target market for businesses. Yet, empirical data is lacking on consumer behaviour among PwDs considering assistive technology products to enhance accessibility. The purpose of this study is to understand the purchase decision process through the lens of a major theory of consumer behaviour, regulatory focus. 171 deaf and hard-of-hearing individuals primarily aged 18-29 were surveyed on two empirically tested scales that measure regulatory focus and disability orientation. This survey included the viewing of a fictional advertisement about an assistive technology product. The findings supported the evidence of a relationship between disability orientation and regulatory focus. A sense of exclusion, social model acceptance, and disability pride were statistically significant predictors of either or both regulatory focus orientations with regard to assistive technology products. Also, whether the subject did/did not have a second disability was partly determinative of prevention focus. Segmentation by disability identity and regulatory focus is suggested. The findings are an important contribution to the established literature on regulatory focus, and fill a major empirical gap in marketing literature for the PwD market. The limitations to this study include the continuing theoretical evolution of disability orientation, and the limitation of the sample to a single disability type (deafness) within a single age group. Similar studies on other disability types could better establish the findings of this study.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".