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887 items in total found

Journal Articles | 2024

India's pathway to net zero by 2070: Status, challenges, and way forward

Vaibhav Chaturvedi Arunabha Ghosh Amit Garg Vidhee Avashia Saritha Sudharmma Vishwanathan Dipti Gupta Nilesh Kumar Sinha Chandra Bhushan Srestha Banerjee Divya Datt Juhi Bansal Minal Pathak Subash Dhar Ajeet Kumar Singh Nayeem Khan Rajani Ranjan Rashmi Sh

The announcement of India’s 2070 net-zero target has demonstrated the power of a credible policy signal and changed the course of India’s climate debate. While the Government of India (GoI) has not specified whether this target refers to carbon-dioxide or all greenhouse gases, the announcement has been a watershed moment in India’s climate policy. From questions related to whether and at what pace should India decarbonize its economy, various actors in India are now aligned towards this target. An important contribution to inform India’s net-zero journey has come through various modelling assessments undertaken by India’s institutions and researchers. While a few economy-wide net-zero modelling assessments are available, a comprehensive and integrated picture woven collaboratively by India’s climate experts is conspicuously missing. It is critical to complement quantitative modelling-based assessments with insightful perspectives of experts on India’s climate policy. Together, modelling based quantitative assessments and insightful qualitative perspectives of climate experts would be an instrumental force that will ensure that the country achieves its net-zero target by understanding synergies and trade-offs, harnessing opportunities, and avoiding risks along the way. This collaborative article discusses various aspects of pathways towards India’s net-zero goal to address the gap in literature by looking at broad and inter-related dimensions of ‘national and sub-national perspectives’, ‘sectoral and technological transitions’, and ‘enablers’ needed for India’s transition. While the larger net-zero debate relates to all greenhouse gases, we focus on carbon dioxide in our current effort. The assessment aims to inform not just India’s policy makers and stakeholders, but various researchers, practitioners and governments around the world for them to be better aware of the various aspects of India’s net-zero debate. It weaves the perspectives of experts from 24 institutions across the three broad dimensions to give a comprehensive view of a roadmap towards India’s net-zero future.

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Journal Articles | 2024

Dynamic managerial capabilities: A critical synthesis and future directions

Somnath Baishya, Amit Karna, Diptiranjan Mahapatra, Satish Kumar, Debmalya Mukherjee

The literature on dynamic managerial capabilities (DMCs) has grown considerably and has evolved over the past two decades. Helfat and Martin (2015) reviewed this literature, which helped clarify the nomological network surrounding DMCs while synthesizing the empirical literature related to its impact on strategic change and firm performance. In this paper, we build on their work by applying bibliometric techniques to trace the evolution of this multidisciplinary construct. The analysis of 33 key journals and 188 articles spanning more than three decades (1989–2023) comprises distinct time periods and longitudinal trends that support meaningful visual representations of the bibliographic data. The findings reveal seven foundational themes for DMC research: upper echelons, cognitive biases, cognitive strategic groups, capability configurations, issue interpretation, individual & group characteristics, and market & network orientation. We also extend the DMC framework of Helfat & Martin (2015) by including political capital as the fourth underpinning. On the basis of the temporal and topic trend analysis, we conclude with recommendations for further research avenues that can shed light on the future of DMC literature. We also highlight practical implications for practicing managers and firms to strengthen competitive differentiation by building and leveraging DMCs.

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Journal Articles | 2024

Changing dynamics of secondhand tractor markets in Punjab: An institutional innovation perspective

Sukhpal Singh

Agricultural inputs and services are crucial for reducing production costs and improving efficiency in Indian agriculture, which is characterized by smallholders. However, since many farm inputs, especially farm machinery and equipment, are costly, they must be made more affordable for such small producers. Though there has been a recent spread of custom hiring centres, farmers still prefer individual ownership of such machines and equipment for various reasons. Here, the role of markets in facilitating such productive assets comes in, and secondhand tractor markets are one such platform. These markets, which can be treated as institutional innovation, result from the locally felt need as they are neither promoted by any stakeholder nor regulated by the state. This paper examines the organization, functioning, and dynamics of secondhand tractor markets in Punjab with the help of a primary interview survey of major stakeholders, i.e., secondhand tractor buying and selling farmers and the commission agents facilitating transactions between them. It tries to understand the nature and dynamics of this market in terms of participants, their motive for participation, and the implications thereof. It profiles the buyer and seller farmers and agents facilitating the transactions and understanding the exact nature of transactions. It examines the effectiveness of these markets for farmers in accessing tractors and other farm machinery, as well as challenges, if any, and explores possible regulatory or enabling policy provisions to promote such institutional innovations in the state and the country.

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Journal Articles | 2024

The consequences of universal basic income

Mohsen Mohaghegh

Universal Basic Income (UBI) has gained attention in both academic and policy circles. However, its implications are not fully understood. This paper develops a general equilibrium model with uninsured income risks to examine such implications. Multiple policy alternatives are considered under both deficit-expanding and deficit-neutral structures. If the UBI policy is not financed through additional taxation, its impact on measures of inequality is unclear. The consumption and income inequality decrease while wealth inequality rises. Deficit-neutral UBIs resolve this ambiguity as the higher marginal tax rates prevent the wealth inequality from rising, which leads to a more equal distribution of consumption and income. However, the aggregate effects are amplified. The income tax must be as high as 80% of the output to keep the deficit from expanding. The interest rate rises, and the output and capital-to-output ratio sharply fall as the precautionary saving motives are weakened.

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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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