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Record W3048135584 · doi:10.1108/pr-08-2019-0461

When are employees idea champions? When they achieve progress at, find meaning in, and identify with work

2020· article· en· W3048135584 on OpenAlexaff
Dirk De Clercq

Bibliographic record

VenuePersonnel Review · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsBrock University
Fundersnot available
KeywordsOriginalityExtant taxonValue (mathematics)Public relationsWork (physics)Meaning (existential)Career developmentIdentification (biology)Human resource managementCareer managementSociologyPsychologyMarketingBusinessManagementSocial psychologyPolitical scienceCreativityEconomics

Abstract

fetched live from OpenAlex

Purpose Drawing from conservation of resources (COR) theory, this study investigates the relationship between employees' perceived career progress and their championing behavior and particularly how this relationship might be invigorated by two critical personal resources at the job (work meaningfulness) and employer (organizational identification) levels. Design/methodology/approach Quantitative data were collected from a survey administered to 245 employees in an organization that operates in the oil industry. Findings Beliefs about organizational support for career development are more likely to stimulate idea championing when employees find their job activities meaningful and strongly identify with the successes and failures of their employing organization. Practical implications This study offers organizations deeper insights into the personal circumstances in which positive career-related energy is more likely to be directed toward the active mobilization of support for novel ideas. Originality/value As a contribution to extant championing research, this research details how employees' perceived career progress spurs their relentless efforts to push novel ideas, based on their access to complementary personal resources.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.886

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.259
Teacher spread0.223 · 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.

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

Citations19
Published2020
Admission routes1
Has abstractyes

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