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Record W3151597345 · doi:10.5267/j.uscm.2021.2.009

The mediating role of product planning and development on the relationship between markets strategies and export performance

2021· article· en· W3151597345 on OpenAlexvenueno aff
Ahmedia Musa Mohamed Ibrahim, Mohamed Salih Yousif Ali

Bibliographic record

VenueUncertain Supply Chain Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
FundersDeanship of Scientific Research, Prince Sattam bin Abdulaziz UniversityPrince Sattam bin Abdulaziz University
KeywordsExport performanceBusinessIndustrial organizationProduct (mathematics)Competition (biology)MarketingControl (management)Structural equation modelingEconomicsManagement

Abstract

fetched live from OpenAlex

To ensure competition and survival of business, understanding the indicators or drivers of export performance is important. Based on the organizational learning theory and strategic fit theory, this study aims to test the influence of market exploration strategies (MERS) and market exploitation strategies (METS) on SMEs’ export strategic performance, export financial performance (EFP), and export customer performance (ECP). This study confirms the leading mediating role of product planning and development (PPD) in the effects of MERS and METS on export performance outcomes. The authors collected questionnaire data electronically from 122 experienced SMEs that conduct international transactions in Saudi Arabia. Results from the Analysis of Moment Structures indicate that MERS and METS positively influence export performance; PPD mediates the relationship between MERS and METS in export performance dimensions; and number of sales and ownerships are control variables that influence EFP and ECP, respectively. This study contributes to the literature and society by proposing a framework that addresses the direct and indirect relationships between export marketing strategies and PPD, and their effects on SMEs’ 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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.032
GPT teacher head0.241
Teacher spread0.209 · 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.

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

Citations4
Published2021
Admission routes1
Has abstractyes

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