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Record W1986928642 · doi:10.4018/jgim.2010040103

Social Structures and Personal Values That Predict E-Mail Use

2010· article· en· W1986928642 on OpenAlexaffabout
Mark Peterson, Stephanie J. Thomason, Norm Althouse, Nicholas Athanassiou, Gudrun Curri, Robert Konopaske, Tomasz Lenartowicz, Mark Meckler, Mark E. Mendenhall, Andrew Mogaji, J.I.A. Rowney

Bibliographic record

VenueJournal of Global Information Management · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsDalhousie UniversityUniversity of Calgary
Fundersnot available
KeywordsCultural valuesValue (mathematics)Work (physics)Context (archaeology)Public relationsSocial psychologyPsychologySociologySocial mediaPolitical scienceGeographyComputer scienceSocial scienceLawEngineering

Abstract

fetched live from OpenAlex

This article extends communication and technology use theories about factors that predict e-mail use by explaining the reasons for cultural contingencies in the effects of managers’ personal values and the social structures (roles, rules and norms) that are most used in their work context. Results from a survey of 576 managers from Canada, the English-speaking Caribbean, Nigeria, and the United States indicate that e-mail use may support participative and lateral decision making, as it is positively associated with work contexts that show high reliance on staff specialists especially in the U.S., subordinates, and unwritten rules especially in Nigeria and Canada. The personal value of self-direction is positively related to e-mail use in Canada, while security is negatively related to e-mail use in the United States. The results have implications for further development of TAM and media characteristic theories as well as for training about media use in different cultural contexts.

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)0.002
metaresearch head score (Gemma)0.017
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.307
Teacher spread0.281 · 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

Citations10
Published2010
Admission routes2
Has abstractyes

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