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Record W2034249128 · doi:10.1080/08959285.2011.580807

The Effect of Commitment to a Learning Goal, Self-Efficacy, and the Interaction Between Learning Goal Difficulty and Commitment on Performance in a Business Simulation

2011· article· en· W2034249128 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHuman Performance · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of TorontoWestern University
Fundersnot available
KeywordsPsychologyModerationTask (project management)Goal orientationSet (abstract data type)Goal settingSelf-efficacySocial psychologyOrder (exchange)Applied psychologyKnowledge managementComputer scienceManagementBusiness

Abstract

fetched live from OpenAlex

The effect of commitment to a learning goal, self-efficacy, and the interaction between learning goal difficulty and goal commitment with performance was investigated using a highly complex business simulation. Participants (n = 128) needed to acquire knowledge in order to perform the task effectively. The correlation between commitment to the learning goal and performance was positive and significant (r = .47, p < .001). Commitment was also a moderator of the learning goal–task performance effect. The relationship between self-efficacy and performance was partially mediated by commitment to the learning goal. Performance was a partial mediator of the relationship between goal commitment and self-efficacy. Seventy-five percent of the participants self-set a performance goal. The correlation between self-set performance goals and performance was positive and significant (r = .31, p < .001).

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.

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 score0.014
Threshold uncertainty score0.598

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 studies0.0010.000
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 head0.020
GPT teacher head0.257
Teacher spread0.237 · 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