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Record W2107239011 · doi:10.1509/jm.12.0051

Consumer Adoption of New Products: Independent versus Interdependent Self-Perspectives

2013· article· en· W2107239011 on OpenAlexaff
Zhenfeng Ma, Zhiyong Yang, Mehdi Mourali

Bibliographic record

VenueJournal of Marketing · 2013
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of CalgaryWilfrid Laurier University
Fundersnot available
KeywordsOptimal distinctiveness theoryInterdependenceMindsetProduct (mathematics)MarketingBusinessPerspective (graphical)ScarcityPsychologySocial psychologyMicroeconomicsEconomicsComputer scienceSociology

Abstract

fetched live from OpenAlex

In five studies, the authors examine the impact of an independent (vs. interdependent) mindset on consumer adoption of new products. Study 1 demonstrates that consumers in a predominantly independent (vs. interdependent) culture are more willing to adopt really new products, whereas consumers in a predominantly interdependent (vs. independent) culture are more willing to adopt incrementally new products. Studies 2 and 3 conceptually replicate these findings using situationally activated mindsets and demonstrate that this effect is driven by the perceived fit between the product's newness level and the optimal level of distinctiveness consumers want. Finally, Studies 4a and 4b show that the presence of distinctiveness-dampening cues (i.e., popularity cues) and distinctiveness-enhancing cues (i.e., scarcity cues) can reverse the effect of self-perspective such that the independent self becomes less willing to adopt really new products and more willing to adopt incrementally new products than does the interdependent self. These findings offer practical implications for managing innovation adoption in both domestic and international marketplaces.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.337
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations165
Published2013
Admission routes1
Has abstractyes

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