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Record W4296990316 · doi:10.31234/osf.io/gv5bz

Goal Progress Velocity as a Determinant of Shortcut Behaviors

2022· preprint· en· W4296990316 on OpenAlexafffund
Vincent Phan, Midori Nishioka, James W. Beck, Abigail A. Scholer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFeelingFrustrationPsychologyReferentWork (physics)Quality (philosophy)Social psychologyComputer scienceScale (ratio)Engineering

Abstract

fetched live from OpenAlex

Employees often have a great deal of work to accomplish within stringent deadlines. Therefore, employees may engage in shortcut behaviors, which involve eschewing standard procedures during goal pursuit to save time. However, shortcuts can lead to negative consequences such as poor-quality work, accidents, and even large-scale disasters. Despite these implications, few studies have investigated the antecedents of shortcut behaviors. In this research, we propose that employees engage in shortcut behaviors to regulate their velocity (i.e., rate of progress). Specifically, we predict that when individuals experience slower-than-referent velocity, they will (a) believe that the goal is unlikely to be met via standard procedures and (b) experience feelings of frustration. In turn, we expect these psychological states to be related to the perceived utility of shortcuts, especially when shortcuts are perceived as viable means to achieve the goal. Finally, we predict that the perceived utility of shortcuts will be positively related to actual shortcut behaviors. We tested these predictions using a laboratory experiment in which we manipulated velocity and unobtrusively observed shortcuts (Study 1, N = 147), as well as a daily diary study in which employees reported their velocity and shortcut behaviors over 5 consecutive workdays (Study 2, N = 395). Both studies provided support for our predictions. In sum, this research provides evidence to suggest that the experience of slow progress can lead to shortcuts not only by casting doubt on employees’ perceived likelihood of meeting the goal but also by producing feelings of frustration.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.278
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes2
Has abstractyes

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