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Record W2612491650 · doi:10.1093/jcr/ucx062

Social-Recognition versus Financial Incentives? Exploring the Effects of Creativity-Contingent External Rewards on Creative Performance

2017· article· en· W2612491650 on OpenAlexaff
Ravi Mehta, Darren W. Dahl, Rui Zhu

Bibliographic record

VenueJournal of Consumer Research · 2017
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCreativityNormativeIncentiveOriginalityContingencyPsychologyContext (archaeology)Social psychologyEconomicsMicroeconomicsPolitical scienceEpistemology

Abstract

fetched live from OpenAlex

Abstract The present work examines the role of creativity-contingent monetary versus social-recognition rewards on creative performance and provides new insights into the underlying motivational processes through which these rewards affect consumer creativity. A series of five studies demonstrate that within the context of creativity contingency, monetary rewards induce a performance focus, while social-recognition rewards induce a normative focus. Such performance (normative) focus in turn enhances (attenuates) approach motivation to be original and hence leads to higher (lower) originality in a creative task. Thus, this work not only advances the current understanding of how and why two types of widely used creativity-contingent external rewards may have contrasting effects on creative performance, but it also offers important practical insights to managers who utilize reward systems in cultivating consumer creativity in their innovation platforms.

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)0.022
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.324
GPT teacher head0.497
Teacher spread0.173 · 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

Citations64
Published2017
Admission routes1
Has abstractyes

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