Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».