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Record W1592783732 · doi:10.17705/1cais.02227

An Empirical Investigation of E-mail Use versus Face-to-Face Meetings: Integrating the Napoleon Effect Perspective

2008· article· en· W1592783732 on OpenAlexaboutno aff
Henri Isaac, Michel Kalika, Nabila Boukef Charki

Bibliographic record

VenueCommunications of the Association for Information Systems · 2008
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsFace-to-facePerspective (graphical)Quarter (Canadian coin)Face (sociological concept)Sample (material)PopulationEmpirical researchInformation and Communications TechnologyAdvertisingMarketingPublic relationsBusinessPsychologySociologyPolitical scienceComputer scienceHistoryDemographySocial scienceLaw

Abstract

fetched live from OpenAlex

As the range of ICT applications in business organizations grows ever larger and takes up an increasing amount of time, the question arises as to whether this could have an impact on meetings. This paper explores the extent to which the use of ICTs replaces face-to-face interactions. The data was gathered by telephone interviews from a sample population of 2,500 company managers questioned over a five-year period between 2001 and 2005. The results indicate that substitution of face-to-face interactions by e-mail only occurs in a few organizations (< 15 percent of cases), while a quarter of the sample population felt that ICT use had led to an improvement in meetings. This appears to confirm the superposition effect of different media or the so-called “Napoleon effect.”

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)0.013
metaresearch head score (Gemma)0.063
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 score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.063
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0020.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.059
GPT teacher head0.364
Teacher spread0.305 · 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

Citations15
Published2008
Admission routes1
Has abstractyes

Explore more

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