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Record W2990693425 · doi:10.15353/cjds.v8i5.566

Disability Orientation and Regulatory Focus in the Assistive Technology Context: A Study of Deaf and Hard-of-Hearing Consumers

2019· article· en· W2990693425 on OpenAlexvenueno aff
Michael Janger

Bibliographic record

VenueCanadian Journal of Disability Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsRegulatory focus theoryPrideContext (archaeology)PsychologyFocus groupMarketingEmpirical researchPopulationBusinessSocial psychologyMedicinePolitical scienceCreativityEnvironmental health

Abstract

fetched live from OpenAlex


 
 
 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.
 
 

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.339
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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