Structural Equation Model for Analyzing the Impact of Business Environment on Firm’s Growth
Bibliographic record
Abstract
The progress of any firm and the wealth it accumulates depends upon the business environment in which the firm functions. Firms of all kinds are strongly influenced by the business environment that they experience and a good business environment ensures their prosperity. In this paper, we have conducted a survey of the manufacturing firms in three different commercial strong Libyan cities to determine the influence of the business environment on the growth of the sales of the firms located in these three Libyan cities. The Structural Equation Model (SEM) method is used in this paper and the empirical variables are calculated for the purpose of the study. These results point to a strong correlation between the growth of the firm’s sales and the prevailing factors of corruption, crime, financing, infrastructure, business regulations and human capital. However, the research does not indicate any kind of relationship between the level of competition faced by the firm and its sales growth. To improve the Libyan business environment, it is necessary to frame effective policies and to enforce the Anti Corruption Law and also to finish the red-tape and bureaucratic hurdles. A legal system would have to be developed which provides sufficient finance for the businesses and a proper system of rules for the financial markets would also have to be developed.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".