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Self-Efficacy’s Influence on Persistence: Moderating Effect of Performance Feedback Ambiguity

2015· article· en· W2615752158 on OpenAlexaboutno aff
Leah R. Halper, Jeffrey B. Vancouver

Bibliographic record

VenueAcademy of Management Proceedings · 2015
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsnot available
Fundersnot available
KeywordsSelf-efficacyPsychologyContext (archaeology)Persistence (discontinuity)AmbiguityAffect (linguistics)Task (project management)Social psychologyNegative feedbackCognitive psychologyDevelopmental psychologyComputer scienceCommunication

Abstract

fetched live from OpenAlex

The present investigation was designed to test the predictions of a control theory based view of self-regulation on the complex effects of self-efficacy on persistence (Vancouver, 2008). In particular, self-efficacy was expected to positively affect performance in a context where performance feedback never indicated goal achievement. In such a context, goal choice (i.e., whether to abandon the goal or not) is relevant and high self-efficacy individuals were expected to hold out longer. In contrast, when no performance feedback is available, those with high self-efficacy are more likely to believe they achieved the goal more readily, and thus persist less, than those with low self-efficacy. Participants (87 females and 67 males) were manipulated to have either high or low self-efficacy for a task involving hand grip strength by using a confederate who claimed to have overworked their hand (high manipulated self-efficacy) or to be a tennis player (low manipulated self- efficacy). Feedback ambiguity was manipulated by the presence or absence of a physical barrier between the participant and the confederate as they performed. Performance was assessed by length of time an individual could grip at 50% of their maximum grip strength. The results supported the hypothesized interaction of self-efficacy and feedback condition. Moreover, self-efficacy was found to negatively relate to self-reported effort regardless of feedback condition.​

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.304
Teacher spread0.264 · 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 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

Citations1
Published2015
Admission routes1
Has abstractyes

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