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Record W1555212126 · doi:10.1177/174183051100900103

Does Supervisory Coaching Behaviour Reduce Salespeople's Lies?

2011· article· en· W1555212126 on OpenAlexaff
Anne Mathieu, Claudio Pousa

Bibliographic record

VenueInternational journal of evidence based coaching and mentoring · 2011
Typearticle
Languageen
FieldPsychology
TopicCoaching Methods and Impact
Canadian institutionsLakehead UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsCoachingPsychologyBusinessNeurosciencePsychotherapist

Abstract

fetched live from OpenAlex

Lying is pervading organizations and has a high economic and social toll. Lying among salespeople can be particularly dangerous, as they are at the boundaries of the organization and can affect many organizational outcomes. Traditionally, control systems have been used to reduce information asymmetry and the possibility of opportunistic behaviour (such as lying), and lately it was suggested that managers’ attitudes and role modeling could further reduce them. Building on the social exchange framework, we analyse relational exchanges between managers and subordinates, and we propose that managerial coaching can reduce subordinates’ lies. We present empirical evidence supporting our hypothesis and discuss significant theoretical and managerial implications.

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)0.001
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.256
GPT teacher head0.418
Teacher spread0.162 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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