Study the Effect of Culture on Customer Loyalty on the Target Markets for Successful Export
Bibliographic record
Abstract
Export deals with a wide range of environmental factors, customers and competitors that are different with the domestic market. That’s why market research and export promotion require management plans and appropriate procedures to their target markets and audiences. Exporter before entering a foreign market requires that by doing the necessary research on the marketrealizethe type of information required and how to collect it from a country other than their country and study about the cultural dimensions.In fact, differences in the environment, cultural, legal, political, economic, financial, geographic, multinational markets, free trade zones and economic agreements include the level of economic development and the risks and major exporter that they should do an investigation to consider the conditions of satisfaction and thus increase customer loyalty.This applied research was done aims to determine the effect of culture on customer loyalty at target markets for successful export using a descriptive method by a questionnaire that its validity and reliability was calculated. To analyze the issue of structural equations and correlation test was used. Based on the results, this study found a relationship between the cultural dimension, cultural beliefs and cultural values and traditions with customer loyalty at target market.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".