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The purpose of this paper is to examine the causal relationship between economic growth and foreign direct investment (FDI) in context of India.

Using Toda‐Yamamoto granger causality technique, authors tried to examine the causal link between GDP per capita (proxy for economic growth) and FDI. The data is tested for stationarity using Augmented Dickey‐Fuller test and Phillips Peron test. Authors also examined the co integration properties using Johansen test to identify long run relationship between the two variables.

It was found that GDP per capita and FDI are integrated in long run. There also exist a bidirectional between FDI and growth in post liberalization period, i.e. post 1991. There is also evidence of FDI led growth in the pre‐liberalization period, i.e. pre 1991.

There are many factors which contribute to FDI and GDP per capita. A comprehensive study can be done to explore other determinants of FDI and GDP per capita.

The findings reveal that economic growth measured by GDP per capita has become one of the important determinants of FDI after liberalization. The evidence of FDI led growth in both the periods signifies that policy makers should ensure a minimum level of economic growth to maintain India as an attractive destination for FDI. The policy should lay emphasis on business facilitation measures like improving the conditions for doing business in India, expanding the role of investment promotion agencies (IPAs) and providing single window for foreign investment.

There exists cross‐sectional studies on examining relationship between FDI and growth. However, there is a need to have country level study to identify FDI and growth nexus as it is sensitive to country specific factors which are unobservable in time series analysis of group of countries.

Innovation is the key to sustained competitive edge, especially for organizations functioning in a fast changing environment. The purpose of this paper is to understand select factors affecting innovation in Indian pharmaceutical organizations.

The empirical analysis first tries to find relationships between the variables on a one‐to‐one basis, by conducting a step‐wise regression analysis. Thereafter, the analysis is taken a step further to find out the effect of interaction between the variables.

The findings are interesting in the sense that while the one‐on‐one analysis did not yield significant results, the interaction analysis yielded significant results.

An organizations' performance should be looked at as a function of the interaction effect of many variables and not as straight linear equations. This would give a more realistic understanding of the entire domain of organizational functioning.

This paper presents a study done in an Indian environment to understand the functioning in original as compared to adapting the frameworks developed elsewhere. This assumes relevance specifically in the post WTO era wherein the impetus of survival of the pharmaceutical industry is innovation.