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Do Bored Employees Job Craft When Demands and Resources are Low?

2017· article· en· W2765621916 on OpenAlex
Patricia L. Baratta, Jeffrey S. Spence

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

VenueAcademy of Management Proceedings · 2017
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBoredomCraftWorkloadTask (project management)Job designVariety (cybernetics)Work (physics)Job attitudeJob analysisPsychologyJob performanceJob satisfactionSocial psychologyComputer scienceEngineeringManagementEconomics

Abstract

fetched live from OpenAlex

Organizational researchers tout job crafting given its potential to enhance employee well-being through the creation of positive work environments. Previous research suggests that job crafting is most likely to occur in enriched environments replete with resources and challenging demands. The purpose of the current study was to examine if and why individuals job craft in unenriched work environments – work contexts low in demands and resources. We suggest that unenriched environments may only generate job crafting behaviors insofar as these environments are subjectively experienced as unpleasant by employees. Specifically, we offer state boredom as a mechanism through which unenriched environments can generate job crafting. Using a daily diary design, we found that low levels of job demands (i.e., workload) and job resources (i.e., task variety and co- worker social support) were associated with greater state boredom. In addition, and contrary to what we hypothesized, the results of our multilevel regressions indicated that bored individuals were less likely to job craft.

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)0.000
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 score0.260
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.042
GPT teacher head0.286
Teacher spread0.244 · 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