Intellectual Capital: A Case Study of Power Loom using AHP
Bibliographic record
Abstract
The purpose of this paper is to identify the critical success factors of IC influencing the performance of power loom textiles, to evaluate their impact on the organizational performance and to find out the effect of these factors on the organizational performance of small and medium-sized enterprises (SMEs) in Maharashtra using AHP. The methodology adopted is factors are identified through the literature survey and finalization of these factors is done by taking the opinion of Experts in the Indian context. By cognitive map the relation between these factors is determined and cause and effect diagram is prepared. Then these factors are arranged hierarchically and tree diagram is prepared. A questionnaire was designed and distributed among the experts; data is collected. By using Expert choice software data is filled to quantify by pair wise comparison of these factors and are prioritized. The weights demonstrate several key findings: local and global priority reveals there is a substantial effect of the Human capital on the organizational performance. The work related experience contributes 34.21%, which has a greater impact on performance. Operational procedures or practices contribute 52% in order to improve the operational performance and hence organizational performance. Overall, the results showed the central role of the human capital is important. The research is subject to the normal limitations of AHP. The study is using perceptual data provided by Experts which may not provide clear measures of impact factors. However, this can be overcome using more experts to collect data in future studies. Interestingly, the findings here may be generalisable outside Maharashtra, India. AHP as an innovative tool for quantification of IC factors impacting on performance and improving operational and organizational performance in today’s dynamic manufacturing environment. The finding suggests the notion that these critical success factors (CSFs) are to be studied carefully and improvement strategy should be developed. Moreover, the study emphasizes the need to link priority of factors to organizational performance and improvement. The study integrates the CSFs of performance and its quantification by using AHP and its effect on performance of power loom textiles. Very few studies have been performed to investigate and understand this issue. Therefore, the research can make a useful contribution. Keywords: Intellectual capital, RBV, Power looms, Performance, AHP, Maharashtra
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".