Indian Institute of Management Tiruchirappalli

About: Indian Institute of Management Tiruchirappalli is a education organization based out in Tiruchchirappalli, India. It is known for research contribution in the topics: Emerging markets & Context (language use). The organization has 47 authors who have published 105 publications receiving 1694 citations. The organization is also known as: IIM Trichy.

Playing games, receiving gifts, creating experiences and building brands

Abstract: Purpose – The purpose of this paper is to extend the understanding the role of consumer experiences, instantiated through gift giving and game play, in communication of brand values. Design/methodology/approach – The paper is based on in-depth phenomenological interviews of marketing managers and various channel intermediaries involved in the execution of a mass brand promotion program in rural India. Findings – The study reveals the employment of innovative game designs and gift choices, their design rooted deep in the village populace’s context and life experiences. It shows how the consumer’s experience created through games and gifts shapes their perceptions about the brand leading to favorable consideration and purchase outcomes for it. Research limitations/implications – This work is derived primarily from practice. It is hoped that industry practitioners will benefit from this stream of research and will use games and gifts in innovative ways to engage customers and create brand experiences. Origin...

Semantic dilution of inequality: a smoke-screen for philanthrocapitalism

Abstract: A recent trend in policy responses the rising public resentments with inequality is to prod the wealthy into spending a fraction of their profits on projects that promote social welfare. Legitimati...

Is health care a luxury? The debate revisited with new evidence from emerging economies

Abstract: This paper studies the long-run relationship between health care expenditure and income using a panel data set of emerging economies over the period 1995–2012. The results show that expenditure on health care and income are non-stationary and cointegrated. After controlling for cross-sectional dependence and unobserved heterogeneity among different countries, we find that the income elasticity of health care is less than 1, indicating that health care is a necessity and not a luxury. Government expenditure and out-of-pocket expenditure turn out to be important determinants of health care expenditure. Among non-monetary factors, results show that old age dependency and female education seem to have significant bearings on health care expenditures. Policy recommendations suggest that government should increase spending on health care in emerging economies since higher incomes may not automatically translate into higher health care spending by the people of these countries.

Mathematical model to mitigate planning fallacy and to determine realistic delivery time

Abstract: Planning fallacy is the tendency to underestimate the duration of a task due to the optimistic bias of individuals. We design a mechanism from the principal's perspective (an original equipment manufacturer (OEM)) to mitigate the optimistic bias of agents (a contract manufacturer (CM) and a supplier) in a serial supply chain. The OEM determines the deadline of agents by explicitly factoring the agent's planning fallacy in the model through the cost under-estimation factor. Further, we prove that threshold based incentives are better than lump-sum bonus to motivate the supplier and the CM to mitigate procrastination of task.

Public Policy and Regulatory Challenges of Artificial Intelligence (AI)

Abstract: Artificial Intelligence (AI) usage is rapidly expanding in our society. Private sector has already taken the leap of faith in using AI for efficiency and for generating better value for the customers and shareholders. The promise of AI is quite alluring for the governments as well. It promises to be the breakthrough technology which can catapult public sector to hitherto unseen efficiency and productivity. It has the potential to truly transform the public service delivery and the way government interfaces with citizens – from a demand driven model to a predictive model of public service delivery. However, there are a large number of pitfalls and blind-spots associated with AI, which make its adoption in government particularly challenging. For successful adoption of AI in public sector, governments must understand these challenges clearly and lay down regulatory public policies to ensure that the possible adverse impacts (such as exclusion, bias etc.) of AI are mitigated. This paper attempts to systematically explore these challenges with a view to enable public policy makers to respond to them.