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Record W4294636318 · doi:10.5267/j.uscm.2022.7.002

The role of employee communication skills on sustainable export performance

2022· article· en· W4294636318 on OpenAlexvenueno aff
Sudarat Pimonratanakan, Tanawat Wisedsin, Mano Prachayapipat, Prumsub Wetsukum, Kittisak Jermsittiparsert

Bibliographic record

VenueUncertain Supply Chain Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessExport performanceMarketingLogistic regressionAttractionIndustrial organizationComputer science

Abstract

fetched live from OpenAlex

The objective of this study is to examine the role of employee communication skills in sustainable export performance. Furthermore, this study also examined the mediating role of client understanding, client satisfaction and client attraction. Therefore, the relationship between employee communication skills, client understanding, client satisfaction, attraction and sustainable export performance was examined. Population of the study is logistic companies of Thailand. The logistic company employees involved in export activities were selected as respondents. Therefore, data were collected from the export related employees of Thai logistic companies. Finally, data were analyzed and found that communication skills are playing a major role in export performance. Export performance is majorly influenced by the communication skills of employees working in logistic companies. Furthermore, communication skills have a positive role in client understanding. Client understanding has a positive effect on client satisfaction and client attraction. This whole mechanism increases the sustainable export performance.

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)0.006
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.007
GPT teacher head0.212
Teacher spread0.206 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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