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3721 items in total found

Journal Articles | 2018

When the big one came: A natural experiment on demand shock and market structure in India's Influenza Vaccine markets

Arzi Adbi, Chirantan Chatterjee, Matej Drev, and Anant Mishra

Production and Operations Management

This study examines the relationship between exogenous demand shock and market structure in India's influenza vaccine markets. Using a novel dataset of detailed purchasing information for vaccines in India, and exploiting the 2009–10 global H1N1 pandemic as an exogenous demand shock, we provide evidence of heterogeneous responses to the shock by domestic and multinational vaccine manufacturers in the influenza vaccine market relative to our control group of all other vaccine markets. We find that such a shock results in a reversal of the market structure for influenza vaccines in India, with a decline in the market share of multinational vaccine manufacturers and significant gains in the market share of domestic vaccine manufacturers. This reversal of the market structure is driven by increased efforts at new product introduction among domestic vaccine manufacturers, the effects of which persist even after the pandemic has ended. Our results remain robust to the use of alternative controls, synthetic control method, coarsened exact matching method, and other relevant estimation methodologies. These results provide new evidence on the role of a pandemic-induced demand shock in the context of an emerging economy by creating differential incentives for domestic and multinational vaccine manufacturers to bring new products to market. We also conduct additional analysis to explore the impact of targeted policy instruments on the new product introduction efforts of domestic vaccine manufacturers. Finally, we discuss the implications of our findings and offer insights into the role of policy on pandemic preparedness in emerging markets facing adverse welfare effects from pandemics.

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Journal Articles | 2018

Preferences-based learning of multinomial logit model

Manish Aggarwal

Knowledge and Information Systyems

We learn the parameters of the popular multinomial logit model to gain insights about a DM’s decision process. We accomplish this objective through the recent algorithmic advances in the emerging field of preference learning. The empirical evaluation of the proposed approach is performed on a set of 12 publicly available benchmark datasets. First experimental results suggest that our approach is not only intuitively appealing, but also competitive to state-of-the-art preference learning methods in terms of the prediction accuracy.

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Journal Articles | 2018

Modelling subjective utility through entropy

Manish Aggarwal

Journal of the Operational Research Society

We introduce a novel entropy framework for the computation of utility on the basis of an agent’s subjective evaluation of the granularised information source values. A concept of evaluating agent as an information gain function of this entropy framework is presented, which takes as its arguments both an information source value and the agent’s evaluation of the same. A method to model the agent’s perceived utility values is proposed. Based on these values, several new measures are designed for the evaluation of the information source values, perceived utilities, and the evaluating agent. A real application is included.

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Journal Articles | 2018

Learning of a decision-maker's preference zone with an evolutionary approach

Manish Aggarwal

IEEE Transactions on Neural Networks and Learning Systems

A new evolutionary-learning algorithm is proposed to learn a decision maker (DM)'s best solution on a conflicting multiobjective space. Given the exemplary pairwise comparisons of solutions by a DM, we learn an ideal point (for the DM) that is used to evolve toward a better set of solutions. The process is repeated to get the DM's best solution. The comparison of solutions in pairs facilitates the process of eliciting training information for the proposed learning model. Experimental study on standard multiobjective data sets shows that the proposed method accurately identifies a DM's preferred zone in relatively a few generations and with a small number of preferences. Besides, it is found to be robust to inconsistencies in the preference statements. The results obtained are validated through a variant of the established NSGA-2 algorithm.

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Journal Articles | 2018

Learning attitudinal decision model through pair-wise preferences

Manish Aggarwal

Kybernetes

Purpose

This paper aims to learn a decision-maker’s (DM’s) decision model that is characterized in terms of the attitudinal character and the attributes weight vector, both of which are specific to the DM. The authors take the learning information in the form of the exemplary preferences, given by a DM. The learning approach is formalized by bringing together the recent research in the choice models and machine learning. The study is validated on a set of 12 benchmark data sets.

Design/methodology/approach

The study includes emerging preference learning algorithms.

Findings

Learning of a DM’s attitudinal choice model.

Originality/value

Preferences-based learning of a DM’s attitudinal decision model.

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Journal Articles | 2018

Hesitant information sets and application in group decision making

Manish Aggarwal

Applied Soft Computing

The recent information set theory provides a useful mechanism to represent an agent’s perceived information values. However, often a decision-maker (DM) considers multiple evaluations for the same information source value. To this end, we extend the recent information set as hesitant information set (HIS). It gives the multiple perceived information values, corresponding to an information source value. In the context of multi-attribute decision making, HIS represents a set of different possible subjective utilities that an agent may perceive as an evaluation of an alternative-attribute pair. The basic operations, and properties of HIS are investigated. A few information measures based on HIS are presented. Besides many illustrative examples, a real application in group multi attribute decision making problem is included.

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Journal Articles | 2018

Generalized attitudinal Choquet Integral

Manish Aggarwal

International Journal of Intelligent Systems

Attitudinal Choquet integral (ACI) extends Choquet integral (CI) through a consideration of a decision-maker's (DM's) attitudinal character. In this paper, we generalize ACI, and the resulting operator is termed as generalized ACI (GACI). GACI adds to the efficacy of the ACI in the representation of a DM's unique and complex attitudinal character. It also generates a vast range of exponential ACI operators, such as harmonic ACI, ACI, quadratic ACI, to name a few. We further present induced GACI to consider additional information that may be associated with the arguments of aggregation. The special cases of the proposed operators are investigated. The usefulness of the proposed operators in modelling human decision behavior is shown through a case study.

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Journal Articles | 2018

Decision aiding model with entropy-based subjective utility. Information Sciences

Manish Aggarwal

Information Sciences

An entropy-based method is presented to model a decision-maker’s (DM’s) subjective utility for a criterion value. The proposed method considers distribution of all the values that the criterion takes for the given set of alternatives. Based on the utility so modeled, and and the DM’s attitudinal character, a multi criteria decision aiding (MCDA) approach is developed to find the best alternative. The proposed method and the approach are applied in a real car selection case-study.

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Journal Articles | 2018

Constitution, Supreme Court and Regulation of Coal Sector in India

M P Ram Mohan and Shashikant Yadav

NUJS Law Review

The paper maps four decades of coal sector litigation before the Supreme Court of India and draws a narrative on the constitutional contestation and the legal position as it stands today. Coal is one of the most important minerals from an economic perspective, accounting for over sixty percent of India’s energy requirement. The Constitution of India empowers both the Centre and states with legislative powers relating to regulation and control over mines and minerals, including coal. The coal sector has witnessed highly contested and protracted litigation with respect to law-making powers between the Centre and state governments, and this has impacted business and society in many ways. Through a mapping of judicial decisions of Supreme Court, the contested nature of governance of Indian coal sector is detailed in the paper. The Court has consistently maintained a greater responsibility of regulating mines and mineral development on the Union government. However, advocating sustainable use of coal resources, the Court emphasised that the regulatory power vested with Centre and states must have its basis on public interest and coal must be treated as a material resource of the community.

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Journal Articles | 2018

Investigating the impact of workforce racial diversity on the organizational corporate social responsibility performance: An institutional logics perspective

Amalesh Sharma, Aditya Christopher Moses, Sourav Bikash Borah, and Anirban Adhikary

Journal of Business Research

Racial diversity is considered an integral part of the business world. The extant literature has focused on the effect of racial diversity on a firm's financial performance and presented mixed findings. Building on the institutional logics lens and using a sample of 204 firms belonging to 9 industries and spread across 21 countries for a period of six years, we explore the impact of workforce racial diversity on the Corporate Social Responsibility Performance (CSRP) of a firm. In addition, we also investigate the contingency effects of a firm's absorptive capacity and slack resources on the proposed relationship. Using a seemingly unrelated regression model and accounting for endogeneity, we find that racial diversity has an inverted U-shaped relationship with a firm's financial and social performance and has a U-shaped relationship with its environmental performance. We also find significant moderating effects. Thus, we contribute to the theory and practice in the field.

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