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Record W3122514184 · doi:10.1093/jcr/ucz042

People Rely Less on Consumer Reviews for Experiential than Material Purchases

2019· article· en· W3122514184 on OpenAlex
Hengchen Dai, Cindy Chan, Cassie Mogilner

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Consumer Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsUniversity of Toronto
FundersMinistry of Education, India
KeywordsExperiential learningMarketingQuality (philosophy)Consumer behaviourAdvertisingPsychologyBusiness

Abstract

fetched live from OpenAlex

Abstract An increasingly prevalent form of social influence occurs online where consumers read reviews written by other consumers. Do people rely on consumer reviews differently when making experiential purchases (events to live through) versus when making material purchases (objects to keep)? Though people often use consumer reviews both when making experiential and material purchases, an analysis of more than six million reviews on Amazon.com and four laboratory experiments reveal that people are less likely to rely on consumer reviews for experiential purchases than for material purchases. This effect is driven by beliefs that reviews are less reflective of the purchase’s objective quality for experiences than for material goods. These findings not only indicate how different types of purchases are influenced by word of mouth, but also illuminate the psychological processes underlying shoppers’ reliance on consumer reviews. Furthermore, as one of the first investigations into how people choose among various experiential and material purchase options, these findings suggest that people are less receptive to being told what to do than what to have.

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.

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)0.008
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.147
GPT teacher head0.449
Teacher spread0.302 · 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