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Record W4385196957 · doi:10.2308/jmar-2021-044

Mitigating the Demotivating Effects of Frequent Unfavorable Feedback about Goal Progress

2023· article· en· W4385196957 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

VenueJournal of Management Accounting Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsWilfrid Laurier UniversityUniversity of Waterloo
Fundersnot available
KeywordsExpectancy theoryLife expectancyNegative feedbackGoal pursuitComputer sciencePsychologyRisk analysis (engineering)Social psychologyEngineeringMedicine

Abstract

fetched live from OpenAlex

ABSTRACT Performance goals are used pervasively by organizations to motivate individual effort, and feedback about goal progress is often available on a highly frequent basis. While feedback can be beneficial, there is evidence that frequent unfavorable feedback can be demotivating. We use expectancy theory to predict that compared to infrequent feedback, frequent unfavorable feedback about goal progress will reduce effort by negatively impacting individuals’ expectancy of goal attainment. We also predict that these negative effects will be mitigated when accompanied by a goal attainability reminder that bolsters the expectancy of goal attainment. Results from two experiments support both predictions and also show that a goal attainability reminder does not reduce the effort when early frequent feedback is favorable. These findings have practical implications as we demonstrate that a simple and readily implementable reminder about the attainability of assigned goals can mitigate the negative motivational effects of frequent unfavorable performance feedback.

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)0.011
metaresearch head score (Gemma)0.001
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 score0.349
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.055
GPT teacher head0.409
Teacher spread0.354 · 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