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821 items in total found

Journal Articles | 2024

Effect of crisis colocation on online prosocial behavior

Dhruven Zala Swanand J. Deodhar Mani Subramani

In this study, we examine how a project owner’s colocation with a crisis influences the chances of their project securing requisite funding. Our study draws upon and extends several streams of work, particularly the importance of owners’ location and the role of crisis in online prosocial behavior, namely online donations. Further, we project and empirically test an important theoretical tension. On the one hand, the altruism effect predicts that beneficiaries colocated with a crisis will likely attract more donations. On the other hand, the bystander effect indicates that donors may perceive lower importance of their contribution as the responsibility of aiding the affected gets distributed. Thus, the effect of crisis colocation on the beneficiary’s project is equivocal, requiring empirical assessment. We address this tension empirically using the occurrence of a hurricane as the external crisis coupled with coarsened exact matching. Drawing on a donation platform dataset that facilitates schools in the US to seek funds, we find empirical support for the bystander effect. Additionally, we find that the baseline effect is contingent on the racial makeup of the beneficiary’s location and the extent to which a crisis occurs abruptly. Our study has implications for the theory and practice of managing online prosocial behavior.

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Journal Articles | 2024

When you hop, What do you hope? Evolution of success parameters for expatriates across assignments

"Prantika Ray Sunil Maheshwari "

International assignments are not just opportunities for career advancement but also for personal growth and exploration. This paper, by capturing the changing expectations and success parameters across the assignments, is a timely and relevant resource for individuals navigating the complexities of international careers. In addition, the paper aims to help organizations build policies for enabling successful assignments for international assignees and managers.

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Journal Articles | 2024

What explains rice exports? An analysis of major rice-exporting countries

Poornima Varma

This study examines the drivers of rice trade. The analysis uses the standard comparative advantage model, the Heckscher–Ohlin–Vanek (HOV) framework, supplemented with a gravity-type equation. Using the Poisson pseudo-maximum likelihood (PPML) estimation for data from 2002 to 2020, the analysis broadly confirms HOV model predictions. Results indicate that arable land, along with GDP, distance, precipitation and crop season temperature, significantly influences rice trade dynamics. The results showed that the precipitation play a key role in influencing the rice trade rather than the blue water availability. However, agricultural water stress discouraged exports and encouraged imports.

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Journal Articles | 2024

What happens when parents find violence acceptable? A case of violent-humorous commercials targeted at children

Akshaya Vijayalakshmi Russell N.Laczniak

We examine the influence of violent–humorous commercials on children and whether parental mediation can temper children’s aggressive responses to violent–humorous ads. We find that (a) violent–humorous ads lead to higher levels of aggressive affect in children, and (b) violent ads lead to higher levels of aggressive cognition and aggressive affect in children (Study 1). We also find that active parental mediation does not have the intended effect of reducing children’s aggressive responses after they view violent–humorous commercials (Study 1). This effect, which is contradictory to general expectations, occurs because parents are less likely to perceive the violent–humorous (vs. solely violent) ad as violent (Studies 2A and 2B) and, consequently, they show less interest in critically mediating the ad (Study 3). Through this study, for the first time, we show (a) the impact of violent–humorous ads on children (vs. adults); (b) the impact of violent–humorous ads on aggression (beyond attitudes toward ads); and (c) the effect of parents’ violent–humorous ad beliefs on parental mediation. The findings of our study suggest that the humor in a violent–humorous ad appears to trivialize the violence in the ad, with not-so-trivial consequences.

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Journal Articles | 2024

Bayesian predictive inference for nonprobability samples with spatial poststratification

"Dhiman Bhadra, Balgobin Nandram"

Non-probability sampling involves selecting samples from a population in which the probability of selection is unknown and some population units may have zero selection probabilities. This differentiates it from probability sampling where selection is governed by a probability model and every population unit has a non-zero chance of being selected. Nonprobability samples usually suffer from selection bias and hence may not represent the target population accurately. An important problem that arises in this context is the prediction of responses corresponding to non-sampled units, which should ideally have been sampled. In this article, we propose three modeling frameworks to address this issue. We use propensity scores to balance the sampled and non-sampled units and a Bayesian estimation scheme for parameter inference and prediction. We incorporate a spatial poststratification scheme to assess the predictive ability of our models on a simulated dataset. In addition, we perform model selection routines to identify the optimal model having the best predictive ability.

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Journal Articles | 2024

Interpretable classifier models for decision support using high utility gain patterns

Srikumar Krishnamoorthy

Ensemble models such as gradient boosting and random forests are proven to offer the best predictive performance on a wide variety of supervised learning problems. The high performance of these black box models, however, comes at a cost of model interpretability. They are also inadequate to meet regulatory demands and explainability needs of organizations. The model interpretability in high performance black-box models is achieved with the help of post-hoc explainable models such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). This paper presents an alternate intrinsic classifier model that extracts a class of higher order patterns and embeds them into an interpretable learning model. More specifically, the proposed model extracts novel High Utility Gain (HUG) patterns that capture higher order interactions, transforms the model input data into a new space, and applies interpretable classifier methods on the transformed space. We conduct rigorous experiments on forty benchmark binary and multi-class classification datasets to evaluate the proposed model against the state-of-the-art ensemble and interpretable classifier models. The proposed model was comprehensively assessed on three key dimensions: 1) quality of predictions using classifier measures such as accuracy, F1 , AUC, H-measure, and logistic loss, 2) computational performance on large and high-dimensional data, and 3) interpretability aspects. The HUG-based learning model was found to deliver performance comparable to that of the state-of-the-art ensemble models. Our model was also found to achieve 2-40% (45%) prediction quality (interpretability) improvements with significantly lower computational requirements over other interpretable classifier models. Furthermore, we present case studies in finance and healthcare domains and generate one- and two-dimensional HUG profiles to illustrate the interpretability aspects of our HUG models. The proposed solution offers an alternate approach to build high performance and transparent machine learning classifier models. We hope that our ML solution help organizations meet their growing regulatory and explainability needs.

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Journal Articles | 2024

AI as an inventor debate under the Patent Law: A post-DABUS comparative analysis

Sarvanan A Deva, Prasad M

Artificial Intelligence (AI) has gained momentum during the last decade, achieving narrow intelligence to general intelligence. The Gen AIs can generate original content and create patentable inventions. The key challenge is whether AI machines can be considered as "inventors" under the existing patent laws; this question has been challenged before various domestic courts. In this context, this paper analyses the interplay between AI and existing patent law frameworks; more specifically, we look at a post-DABUS case from a comparative perspective. The paper explores this question predominantly from the viewpoint of Australia, the United Kingdom, and the United States. The limitations of the existing patent regime and the need to adopt a flexible one that could adapt to the challenges posed by AI are highlighted. The crucial factors shaping the future direction of patent law in the context of AI debate, such as the evolving need for a sui generis law and convergence for a global standard, also form a part of this paper.

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Journal Articles | 2024

𝛿- perturbation of bilevel optimization problems: An error bound analysis

Margarita Antoniou, Ankur Sinha, Gregor Papa

In this paper, we analyze a perturbed formulation of bilevel optimization problems, which we refer to as -perturbed formulation. The -perturbed formulation allows to handle the lower level optimization problem efficiently when there are multiple lower level optimal solutions. By using an appropriate perturbation strategy for the optimistic or pessimistic formulation, one can ensure that the optimization problem at the lower level contains only a single (approximate) optimal solution for any given decision at the upper level. The optimistic or the pessimistic bilevel optimal solution can then be efficiently searched for by algorithms that rely on solving the lower level optimization problem multiple times during the solution search procedure. The -perturbed formulation is arrived at by adding the upper level objective function to the lower level objective function after multiplying the upper level objective by a small positive/negative . We provide a proof that the -perturbed formulation is approximately equivalent to the original optimistic or pessimistic formulation and give an error bound for the approximation. We apply this scheme to a class of algorithms that attempts to solve optimistic and pessimistic variants of bilevel optimization problems by repeatedly solving the lower level optimization problem.

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Journal Articles | 2024

The strongest link: Service-profit chain as a conduit for enabling entrepreneurial orientation and multidimensional service performance

Gurjeet Kaur Sahi Rupali Mahajan Anand Kumar Jaiswal Pankaj C. Patel

The paucity of literature to integrate the duality of customer and employee strategic orientations of an organization with its service operational inputs for achieving superior returns has provided an impetus to examine the significance of internal processes and behaviors as a mediating mechanism in the entrepreneurial orientation–firm performance relationship. Grounded in dynamic capabilities theory, in this article, we aim to extend the previous literature on operations management by investigating the role of the service-profit chain (SPC) in relating entrepreneurial orientation with performance—new products, subscribers, and revenues—in the telecom service industry. The study draws on archival performance data provided by the Telecom Regulatory Authority of India, combined with the primary data collected from 179 managers working in private telecom organizations in India. The findings of our study show that entrepreneurial orientation positively influences new product success only if internal and external SPCs are strong. However, entrepreneurial orientation is negatively and nonsignificantly related to gross revenue. Subscriber base, another important factor that helps in establishing the positive and significant link between entrepreneurial orientation and gross revenue, is higher under stronger internal SPC. Thus, the managers must emphasize more on employee job conditions to pursue strong customer orientation in improving service operations. The findings have implications for strategic and operations’ considerations in a service context.

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Journal Articles | 2024

Mitigation of non-CO2 greenhouse gases from Indian agriculture sector

Omkar Patange Pallav Purohit Vidhee Avashia Zbigniew Klimont Amit Garg

The Indian agriculture sector is driven by small and marginal farmers and employs two-thirds of the Indian work force. Agriculture also accounts for around a quarter of the total greenhouse gas emissions, mainly in the form of methane (CH4) and nitrous oxide (N2O). Hence, agriculture is an important sector for India’s transition to net-zero emissions and for the achievement of the sustainable development goals. So far, very few studies have assessed the future trajectories for CH4 and N2O emissions from the agriculture sector. Moreover, assessment of CH4 and N2O mitigation potential at a subnational (state) level is missing but is important owing to the regional diversity in India. To fill this gap, we focus on methane and nitrous oxide emissions from the agricultural activities using 23 sub-regions in India. We use the GAINS modelling framework which has been widely applied for assessing the mitigation strategies for non-CO2 emissions and multiple air pollutants at regional and global scales. We analyze a current policy and a sustainable agriculture scenario using different combinations of structural interventions and technological control measures to inform the Indian and global climate policy debates. Our results suggest that a combination of sustainable agricultural practices and maximum feasible control measures could reduce the CH4 and N2O emissions by about 6% and 19% by 2030 and 27% and 40% by 2050 when compared to the current policies scenario with limited technological interventions. At a sub-national level, highest mitigation potential is observed in Uttar Pradesh, followed by, Madhya Pradesh, Rajasthan, Gujarat, Maharashtra, Andhra Pradesh, and Telangana. The mitigation of agricultural CH4 and N2O also has co-benefits in terms of reduced local pollution, improved health, and livelihood opportunities for the local communities.

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