Bibliographic record
Abstract
This paper develops a framework which suggests that the decision to diversify is influenced by four factors, viz., environmental, organizational, performance, and ownership. These factors influence the deployment of resources over a period thereby creating ‘specific assets’ that, in turn, along with these factors will determine the extent of diversification. The environmental factors like the rate of industrial growth and the level of industry concentration encourage the use of the enterprise's specific assets. The organizational variables such as top management attitudes, size of enterprise, age, etc., will determine the extent to which an enterprise shall use the specific assets within environmental constraints. Past performance representing organizational slack will also motivate and constrain the management's willingness to undertake diversification because this will determine the availability of resources to be allocated and invested over a period. Using this framework, this paper seeks to investigate the diversification trend in the pre-liberalization phase in India. Specifically, it addresses the following issues: What types of diversification strategy have the Indian enterprises been pursuing in the past? Did these strategies change over a period of time? What was the impact of these strategies on the performance of enterprises? To test the model, the author develops two measures of diversification: Wrigley's qualitative measure of product-market diversification and the entropy measure. The performance is measured by cash flow and return on assets (ROA), return on equity (ROE), and growth of sales (GRS). The empirical analysis is based on 336 private firms listed on the Bombay Stock Exchange of which more than 80 per cent are more than 25 years old with low marginal foreign equity. The extent of diversification of the firms at two different points of time-1977 and 1988-is measured. The results of the study indicate the following: Enterprises in India in the pre-liberalization period are dominated by single and dominant business categories. They have moved from specialization (single product market) to diversification. Performance-wise, specialized enterprises are found to be far ahead of diversified enterprises in terms of ROA and GRS. This paper explains the variation in performance among firms classified under different diversification categories using multiple regression technique. Both related and unrelated diversified categories show negative relationship with ROA. The impact of variables like industry, foreign equity, etc. on the firms' performance is measured through multiple regression method. The relationship between diversification and performance variables is isolated by controlling other variables. The impact of diversification on four industry groups is also analysed separately with ROA as a dependent variable. The results show that the performance is superior for firms in the food, chemical, and engineering industries. Similarly, the impact of foreign equity on performance is found to be positive and significant while capital intensity, R&D, technology import, etc. have negative relationship with ROA. The main conclusions of the paper are: Corporate strategy and its relationship with performance cannot be understood merely by relating diversification level to ROA. Diversification strategy in combination with industry, foreign equity, and firm- specific variables explains the performance significantly. To study diversification and its impact on performance, an industry focus study would be appropriate. Firm-specific variables are equally important along with industry-specific vari- ables to explain the variation in the performance of firms.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".