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Prosocial Motivation as a Double-Edged Sword on Creativity

2018· article· en· W2856626210 on OpenAlexaff
Yeun Joon Kim, Ji Sok Choi

Bibliographic record

VenueAcademy of Management Proceedings · 2018
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProsocial behaviorCreativityNoveltyPsychologyTask (project management)Social psychologyCognitive psychology

Abstract

fetched live from OpenAlex

The current research develops the theoretical account of how prosocial motivation relates to creativity and how prosocial impact of task affects this relationship. I theorize that prosocial motivation has conflicting influences on employee creativity such that it bolsters the usefulness but hinders the novelty dimension of creativity. In an effort to search for a way of increasing prosocial employees’ creativity, I investigate the roles of prosocial impact of task. Specifically, I hypothesize that prosocial employees are likely to show higher levels of creativity when they work on tasks that can benefit others. This is because such tasks meet the needs of prosocial employees and thus increase their intrinsic motivation on the tasks, which then complements the weakness of prosocial employees (i.e., novelty). A mix of three laboratory and quasi-field experiments supported the hypotheses. Study 1 (a laboratory experiment) showed that prosocial motivation increases usefulness while decreasing novelty of ideas. Study 2 (a quasi-field experiment) found that prosocial impact of task enables prosocial individuals to be creative in idea generation by increasing not only novelty but also usefulness. Study 3 (a quasi-field experiment) showed that intrinsic motivation mediates the interactive effects that prosocial motivation and prosocial impact of task have on creativity. Theoretical and practical implications, as well as directions for future research, are discussed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.082
GPT teacher head0.397
Teacher spread0.315 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2018
Admission routes1
Has abstractyes

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