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Record W3002706339 · doi:10.1177/0022243719889336

Inaction Traps in Consumer Response to Product Malfunctions

2020· article· en· W3002706339 on OpenAlexaff
Neil Brigden, Gerald Häubl

Bibliographic record

VenueJournal of Marketing Research · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsProduct (mathematics)Consumption (sociology)PhenomenonVulnerability (computing)Action (physics)Psychological interventionTrap (plumbing)BusinessDownstream (manufacturing)Risk analysis (engineering)MarketingEconomicsPsychologySocial psychologyMicroeconomicsComputer scienceComputer securitySociologyEngineering

Abstract

fetched live from OpenAlex

The authors develop and test a theory of consumer inaction traps in the domain of decisions to either address or endure product malfunctions. According to this theory, the magnitude of product malfunctions can have a paradoxical effect on consumption experience. In particular, the less severe a product malfunction is, the more inclined consumers are to defer the initial decision about whether to take corrective action. Subsequent opportunities for corrective action are devalued relative to previously forgone ones. This dynamic tends to trap consumers in a state of inaction, resulting in their enduring smaller malfunctions longer than larger ones. A consequence of these inaction traps is that minor product malfunctions may result in less enjoyable overall consumption experiences than more severe defects. Evidence from eight experiments and a survey provides support for this theorizing by demonstrating the inaction-trap phenomenon, examining its downstream consequences, shedding light on the psychological dynamics of inaction, and identifying boundary conditions that suggest interventions for counteracting consumers’ vulnerability to suffering disproportionately from relatively minor product malfunctions.

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)0.107
metaresearch head score (Gemma)0.173
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1070.173
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.467
GPT teacher head0.530
Teacher spread0.062 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations7
Published2020
Admission routes1
Has abstractyes

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