Intellectual capital in small and medium enterprises in Pakistan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – The purpose of this paper is to evaluate the links between intellectual capital sub-components and organizational performance in small and medium enterprises (SMEs) operating in the electrical and electronics manufacturing sector in Pakistan. Design/methodology/approach – Data were collected through structured questionnaires from a sample of 247 respondents from Pakistani SMEs in Gujranwala and Gujarat. Several tests were used to examine the reliability and validity of the research instrument. Finally, multiple regression analysis was used to test the proposed research hypotheses. Findings – The findings of this study demonstrate that the overall regression model of intellectual capital shows goodness of fit while one component of intellectual capital – namely human capital – appeared insignificant. Subsequently, six out of seven research hypotheses was accepted. Practical implications – This study will provide a valuable framework for entrepreneurs, executives, managers and policy makers in managing intellectual capital within the Pakistani context. Originality/value – To the best knowledge of the authors, this is the first empirical study that has been conducted on SMEs operating in the electrical and electronics manufacturing sector in Pakistan.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it