MétaCan
Menu
Back to cohort
Record W6991163899

Exploring the possible negative effects of self-efficacy upon performance

2013· dissertation· en· W6991163899 on OpenAlexaboutno aff

Bibliographic record

VenueBangor University Research Portal (Bangor University) · 2013
Typedissertation
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsnot available
Fundersnot available
KeywordsReciprocalEmpirical researchDuration (music)CriticismOutcome (game theory)Empirical evidence
DOInot available

Abstract

fetched live from OpenAlex

The thesis contains five chapters (including three empirical chapters), which attempt
\nto further our knowledge of the reciprocal relationship between self-efficacy and
\nperformance. The thesis attempts to answer questions related to the possible negative effects
\nthat self-efficacy can have on subsequent performance by considering the limitations of
\nprevious research (e.g., Bandura & Lock, 2003; Vancouver, Thompson, Tischner, & Putka,
\n2002; Vancouver, Thompson, & Williams, 2001).
\nChapter 1 provides a general conceptual overview of the self-confidence and selfefficacy
\nliterature, the majority of which has typically supported the positive relationship
\nbetween efficacy beliefs and performance in a range of settings. The chapter then provides a
\ndetailed review of how and when self-efficacy may be negatively related to subsequent
\nperformance. Finally, the limitations and future directions that are offered form the basis of
\nthe ensuing three empirical chapters.
\nChapter 2 addresses the limitation that previous tests of the reciprocal relationship
\nbetween self-efficacy and performance tend to be of short duration (i.e., approx. 8–10 trials).
\nThis short duration may limit the mastery experiences that are an important source of selfefficacy
\nbeliefs. This chapter explores the reciprocal relationship between self-efficacy and
\nperformance in a longitudinal golf putting study where participants complete 40 trials of 20
\nputts each (800 putts in total). The results supported the positive effects of self-efficacy on
\nperformance in only one of the four putting sessions, where self-efficacy had a significant
\nalbeit weak positive reciprocal relationship with putting performance.
\nChapter 3 explores the criticism that mundane tasks (or tasks that remain static
\nthroughout testing) generally do not vary or intrude on attentional focus (Bandura & Locke,
\n2003). Two studies were conducted to examine the reciprocal relationship between selfefficacy
\nand performance using a complex task (car racing simulation). Participants were required to learn to race on a difficult computer racing track across trials where performance
\nwas assessed in relation to improvement on the preceding lap time (Study 1) and in relation to
\na baseline time (Study 2). The results supported the positive reciprocal effects of self-efficacy
\non performance over time (Bandura, 1997).
\nChapter 4 reports a golf putting study which examined the effects of feedback on the
\nreciprocal relationship between self-efficacy and performance. Previous tests of the
\nreciprocal relationship between self-efficacy and performance tend to ignore previous
\nperformances in the measurement of self-efficacy. Consequently, important information
\nregarding previous performances may be ignored. The current test provides a performance
\ndiary where participants have access to all previous performance results, upon which they can
\nbase their subsequent self-efficacy beliefs. Again, support was shown for the positive
\nreciprocal effects of self-efficacy on performance (Bandura, 1997).
\nChapter 5 provides a summary and integrated discussion of these findings.
\nFurthermore, methodological and conceptual limitations, implications, and future research
\ndirections for the study of the reciprocal relationship between self-efficacy and performance
\nare 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.227
Teacher spread0.198 · 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.

Study designNot applicable
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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueBangor University Research Portal (Bangor University)Same topicSports Dynamics and BiomechanicsFrench-language works237,207