Does Strategic Change Enhance the Relationship between Firms’ Resources and SMEs Performance in Pakistan?
Bibliographic record
Abstract
There are approximately 3.2 million SMEs in Pakistan. It is believed that more than 90% of the economic establishments are SMEs. They contribute 40% of the economic growth and create 70% of Pakistan’s overall employment opportunities. Despite substantial presence and contribution, 95% of SMEs fail within the first five years. Out of the remaining 5%, 25% of the SMEs survive up to four more years, adversely impacting economic growth, employment, and living standards. Previous studies indicated SMEs’ low performance as a significant cause and provoked entrepreneurs to shut down their businesses. Therefore, this study aims to examine the performance of SMEs in Pakistan. Based on the problem, the study contextualized the research model that investigates the relationship between financial capital availability (FCA) and innovative work behavior (IWB), which is believed to be crucial for enhancing small and medium-sized businesses’ performance through accelerated strategic change (SC). In addition, the moderating role of Government support (GS) on SMEs’ performance was also considered. The quantitative, cross-sectional research design was considered appropriate for this research. Data was collected through a structured questionnaire to 340 SMEs in the Pakistan manufacturing sector. The hypothesized relationships were tested through structural equation modeling (SEM) using Smart-PLS 4. Results showed a positive link between FCA, IWB, and SMEs’ performance. Furthermore, FCA and IWB are the key drivers to achieving an optimum level of SME performance, which translates the SC process within the SMEs in Pakistan. Additionally, this research discovered that SC partially mediates the relationship between FCA and IWB on SMEs’ performance. Moreover, GS strengthens the relationship between SC and SMEs’ performance. The present findings offer valuable insight to SME owners, policymakers, and first-line managers to understand the radical change in the process. The study also outlined policy interventions to uplift the diminishing SMEs’ 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 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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".