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Record W2004958194 · doi:10.3917/riges.394.0156

Vos vendeurs savent-ils gérer les objections de leurs clients ? Attention : c’est une arme à double tranchant !

2015· article· fr· W2004958194 on OpenAlexaffvenue
Bruno Lussier, Jean‐François Ouellet, Haithem Guizani

Bibliographic record

VenueGestion · 2015
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsHumanitiesPhilosophyPolitical scienceArt

Abstract

fetched live from OpenAlex

Les entreprises dépensent des sommes colossales à former leurs vendeurs à gérer efficacement les objections soulevées par leurs clients potentiels ou à argumenter avec ceux-ci. Or, nos recherches démontrent qu’il s’agit là d’une arme à double tranchant. En gros, leurs aptitudes à gérer les objections ne joueront en faveur des vendeurs que si ces derniers sont perçus par leurs clients comme techniquement compétents, capables de faire autre chose que de réciter ce qu’ils ont lu dans le manuel de vente et, surtout, comme ayant d’abord à cœur de trouver une vraie solution et non pas seulement de leur vendre quelque chose à tout prix.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.092
GPT teacher head0.275
Teacher spread0.183 · 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.

Study designNot applicable
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
Published2015
Admission routes2
Has abstractyes

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