The Indian health care market is $40 billion (Rs 1,94,246 crore).
This market is growing at a compounded annual growth rate of 15 per cent.
It will grow to $80 billion (Rs 3,88,492 crore) by 2013.
This growth will create 3.5 million additional jobs, including 700,000 paramedical ones.
The health care industry accounts for 5.2 per cent of the country’s GDP.
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Some say the root cause of the global financial crisis was a few regional financiers selling risky mortgages to poor people. How can that be? The subprime mortgage market is a fraction of the US mortgage market, which is a fraction of the US credit market, which is a fraction of the global credit market. How can defaults in a sub-sub-submarket destroy banks on two continents and send several countries to the brink of bankruptcy?
In his landmark analysis of the Three Mile Island nuclear accident, sociologist Charles Perrow argued that that meltdown was not caused by any particular component or operator failure. Rather, it was caused by a number of small component failures that interacted in unpredicted ways. Those failures ranged from a pressure-relief valve that didn’t reseat properly to a meter that gave deceptive information to some perceptual errors by operators. If a system has high interactive complexity, then by definition it is hard to predict the impact of a particular action on other elements within the system. If the system is tightly coupled, to use Perrow’s terminology, an action at one point propagates rapidly throughout. Perrow argued that systems with high interactive complexity and tight coupling are more prone to systems accidents. The financial sector meltdown in the fall of 2008 was a systems accident. However, rather than failures interacting, the byproducts of innovations interacted. With the possible exception of some regulatory decisions — and the definite exception of illegal activities — many of the factors leading up to the financial crisis involved innovations to the system, rather than existing system components that failed.
Innovating our way to a meltdown
By Peter Cebon MIT Sloan
Management Review, Winter Issue, 2009
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This study sheds insight on how consumers perceive and relate to family and non-family stores. Using a critical incident approach, the authors show that — compared to non-family businesses — consumers evaluate family businesses better in terms of service, frontline employee benevolence and problem-solving orientation, and worse in terms of selection and price/value. Results further indicate higher consumer trust in family business management policies and practices, frontline employee trust and satisfaction but no differences in loyalty. Examining an integrative loyalty framework, the study finally shows differential effects in how image elements influence customer loyalty directly as well as indirectly through trust and satisfaction. Implications focus on advancing customer relationship management in retailing and on successfully positioning family-owned and -operated businesses.
Consumer loyalty to family versus non-family business: The roles of store image, trust and satisfaction
By Ulrich R Ortha and Mark T Green
Journal of Retailing and Consumer Services
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Historically, neither the creators nor the distributors of cultural products such as books or movies have used analytics — data, statistics, predictive modeling — to determine the likely success of their offerings. Instead, companies relied on the brilliance of tastemakers to predict and shape what people would buy. But the balance between art and science is shifting. Today companies have unprecedented access to data and sophisticated technology that allows even the best-known experts to weigh factors and consider evidence that was unobtainable just a few years ago. And with increased cost and risk associated, it has never been more important to get these decisions right.
In this article, the authors discuss different approaches used to make predictions, the contexts in which these predictions are applied and the barriers to more extensive use, including the problem of decision making pre-creation.
What people want (and how to predict it)
By Thomas H Davenport and Jeanne G Harris MIT Sloan
Management Review, Winter Issue, 2009
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