Effect of E-Marketing Adoption Strategy on Export Performance of SMEs
Bibliographic record
Abstract
Purpose - The purpose of this study is to analyze the effect of e-marketing adoption strategy on export performance of SMEs in Pakistan. The mediating effect of marketing activities on the relationship between e-marketing adoption and export performance is also investigated. Design/methodology/approach – Data was collected from 169 SMEs from four sectors, namely textile, leather, medical and surgical goods and services. The five constructs namely e-marketing budget, e-marketing tools, pre sales activities, after sales activities and export performance linked through eight hypotheses were tested using structural equation modeling in AMOS version 5. Findings - This study finds the positive impact of allocation of e-marketing resources for marketing activities and confirms that mere adoption of e-marketing tools is not sufficient for improving marketing activities. Similarly, SMEs export performance is positively influenced by allocation of e-marketing budget, adoption of e-marketing tools and after sales activities, but pre sales activities does not produce significant effects on export performance of SMEs. Research limitations/implications-- The data collected for this study was cross sectional in nature, whereas longitudinal approach is more suitable for such a study. Moreover, the data was collected from four sectors from Pakistan (a developing country), therefore, care should be taken while generalizing the results of the study. Practical implications – The study points out that SME sector needs to be facilitated by providing IT infrastructure, training employees and resources for utilizing e-marketing at its full potential. Key Words: E-Marketing, Adoption Strategy, Export Performance, SMEs
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".