Empowering MSMEs through Strategy Marketing and Digital Transformation: Reassessing Pathways to Distribution Financing
Bibliographic record
Abstract
Micro, Small and Medium Enterprises (MSMEs) are critical engines of economic growth and innovation, yet their potential is often constrained by limited access to financing. This dissertation explores how strategy marketing and digital transformation can empower MSMEs to overcome these barriers and secure financial resources more effectively. Utilizing a mixed-methods design, the study combines quantitative path analysis with qualitative insights from 399 MSME owners in Palembang, Indonesia, offering a nuanced understanding of how market oriented strategies and digital capabilities interact to influence capital access. Results demonstrate that well crafted Strategy marketing significantly facilitate the adoption of digital tools, which in turn act as a crucial mediator enhancing MSMEs’ credibility and appeal to financial institutions. Digital transformation is revealed not merely as a technological shift but as a dynamic capability that strengthens value communication, operational efficiency, and market responsiveness. The study also highlights contextual factors such as firm size, sector, and digital literacy that shape the extent to which marketing and digital initiatives translate into tangible financial outcomes. Theoretically, this research extends the resource-based view and dynamic marketing capabilities frameworks by showing how intangible assets, including marketing expertise and digital proficiency, drive concrete economic benefits. Practically, the findings offer actionable guidance for policymakers, financial institutions, and MSME support programs, suggesting that integrative strategies combining marketing insight and digital adoption can unlock broader access to capital. By reassessing the pathways to financing, this dissertation provides an evidence based framework for empowering MSMEs to navigate financial constraints, enhance competitiveness, and thrive in an increasingly digital economy.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".