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Record W2522875718 · doi:10.5539/ijms.v8n5p69

The Relationship between Coordination Mechanisms and Communication Efficiency in Projects Involving Marketing Managers: Quantitative Findings from Moroccan SMEs

2016· article· en· W2522875718 on OpenAlexvenueno aff
Youssef Saida, Younes Kohail, Hakima Fasly, Rachid Bouthanoute

Bibliographic record

VenueInternational Journal of Marketing Studies · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessProject managementKnowledge managementMarketingProcess managementMarketing communicationComputer scienceManagementEconomics

Abstract

fetched live from OpenAlex

<p>Project coordination is recognized as one of the most important aspects that should be integrated effectively in project management to guarantee project success. In fact, coordination provides organization the ability to integrate heterogeneous activities for achieving specific targets. In project, when teams are built according to cross-functional approach, coordination is required to enhance communication between all project stakeholders. This article is in search of the relationship between specific project coordination mechanisms, adopted by marketing managers to integrate other functional activities, and project communication efficiency. Methodologically, our research was based on a quantitative questionnaire distributed to 107 functional managers involved in cross-funtional project team in Moroccan SMEs. An appropriate statistical analysis was deployed to examine data collected. Findings show the existence of a positive correlation between project coordination mechanisms and project communication efficiency. Besides, the importance given to marketing managers’ participation in project meetings was found as a factor impacting the amount of time devoted to. Therefore some insights are emphasized to develop research in this field.</p>

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)0.024
metaresearch head score (Gemma)0.060
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.060
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.148
GPT teacher head0.406
Teacher spread0.258 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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