The proximal partially-parallel splitting method (PPSM), originally proposed in Wang, Desai, and He (2017), is a hybrid mechanism that inherits the nice properties of both Gauss-Seidel and Jacobian substitution procedures for solving the multiple-block convex minimisation problem, whose objective function is the sum of m individual (separable) functions without any shared variables, subject to a linear coupling constraint. In this paper, we extend this work and present some linearised versions of the PPSM, which fully utilise the separable structure and result in subproblems that either have closed-form solutions or are relatively easy to solve as compared to their original nonlinear versions. Global convergence of these linearised methods under the projection contraction algorithmic framework is proven, and furthermore, detailed remarks that serve to clarify the interconnections between these linearised variants are highlighted. Finally, the worstcase O(1=t) convergence rate of these methods under ergodic conditions is also established. (c) 2024 Published by Elsevier Ltd on behalf of Indian Institute of Management Bangalore. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/)

This paper presents a comprehensive review of various statistical, machine learning, and deep learning methods for quantile regression in time series, focusing on their application in predicting dengue outbreaks. Given the increasing global incidence of dengue, the ability to forecast higher quantiles of cases is crucial for effective public health responses. The study emphasizes the limitations of traditional regression models, which typically predict average outcomes, and instead highlights the importance of probabilistic forecasting methods that account for uncertainty, offering more detailed predictions for extreme events. Methods such as quantile autoregression (QAR), quantile neural networks, penalized quantile regression and hybrid models are evaluated through simulations and are applied to a real-world dataset from a dengue-prone region. The study also discusses the potential of recent advances, including deep reinforcement learning, in quantile regression forecasts under various scenarios. The performances of these models are assessed using metrics like root mean squared error and continuous ranked probability score, with QAR consistently outperforming other models in real-life scenario and LASSO-based quantile regression providing excellent results in various simulation settings.

This paper explores and compares two participatory management approaches-the Company Democracy Model and Holacracy-for their application within the Indian Ministry of Education. It emphasizes the need for innovative organizational techniques in the management of the public sector, particularly in light of the dynamic demands posed by the New Education Policy (NEP) 2020. The study evaluates how these approaches enhance employee engagement and improve the quality of deliverables. Lewin's Field Force Analysis is utilized to examine the organization's strategy. The study employs Kotter's Change Model to assess the applicability of Holacracy-a decentralized, project-oriented system, characterized by its dynamic and self-organizing structures. This model is analyzed for its potential to meet the Ministry's shifting priorities and to foster adaptability through autonomous teams. Conversely, the Company Democracy Model, which emphasizes employee-centric growth and decision-making within a tiered, spiral framework, is evaluated using the ADKAR Change Model. This model's compatibility with the Ministry's hierarchical structure and its potential to enhance participatory governance are key areas of focus. The study contributes novel insights by integrating change management theories with a refined presentation of the CDM pyramid and by introducing specific performance metrics for both models. By combining theoretical frameworks with practical applications, this paper offers a sustainable governance model suited to dynamic organizational environments.

PurposeAs artificial intelligence (AI) continues to make inroads into several industries, it has taken over tasks previously performed by humans. However, given that individuals frequently have their self-esteem, identity and feelings of self-worth wrapped up in financial matters, will there be a difference in their satisfaction when their credit applications are processed and approved through AI versus humans?Design/methodology/approachThis work uses five studies, including a field study, three online experiments and one laboratory study, to underline the difference in customer satisfaction when credit application processing occurs via AI versus humans.FindingsThe authors find that customers are more satisfied when credit application processing is performed through an AI algorithm rather than by humans. This effect is explained by reduced embarrassment. Furthermore, the authors show that for emotionally intelligent individuals, credit application processing through humans will mitigate the impact of embarrassment, leading to higher customer satisfaction. Finally, the authors identify an individual's relationship with the financial organisation as the boundary condition stating that for first-time customers (vs continuous customers), credit application processing through humans causes less embarrassment.Research limitations/implicationsThis research makes significant contributions in the realm of consumer psychology and credit application processing. First, it advances the existing literature on AI versus human interactions by investigating their comparative impact on customer satisfaction within financial processes such as credit approval. In addition, it identifies credit application processing (whether by AI or humans) as an unexplored antecedent of embarrassment. Moreover, this study enhances the body of work on emotional intelligence by demonstrating its role as a coping mechanism for dealing with embarrassment. Finally, it uncovers a novel driver of embarrassment: the nature of individuals' relationships with financial organisations, differentiating between continuous customers and first-time applicants.Practical implicationsThis study suggests deploying AI for credit approval and adopting strategies to reduce customer embarrassment to boost consumer satisfaction. In addition, managers should consider customers' emotional intelligence levels and use humans for first-time credit applications to minimise embarrassment.Originality/valueArguably, to the best of the authors' knowledge, this study is the first to identify AI versus human processing as a novel factor influencing customer embarrassment in financial service satisfaction. It also provides a new aspect of emotional intelligence as a coping mechanism for embarrassment. Furthermore, it uncovers a unique driver of embarrassment: the nature of individuals' relationships with financial organisations, distinguishing between continuous customers and first-time applicants.

Prior research shows a strong association between entrepreneurship and agentic-masculine traits such as positivity, competence, and creativity, and practice focuses on sharpening pitches to emphasize these entrepreneurial traits, even in the context of small-scale entrepreneurship. But do such pitches help women entrepreneurs? We rely on attribution, gender and role expectation, and expectancy violation theories to predict that women entrepreneurs, and especially women who display entrepreneurial traits in their pitches, fit the role expectations of entrepreneurs in the microlending context, and benefit from the fastest funding speeds. We find support for these predictions in a dataset involving 600,000 entrepreneurial pitches. Our research on gender and crowd-financed microlending shows how, unlike in the commercial entrepreneurship context, displays of entrepreneurial traits, like positivity, provide women a funding advantage in microlending.

PurposeIn the turbulence of a business-to-business (B2B) environment, employees often face role conflict as the result of changes in task allocations due to work requirements. This research uses organizational role theory to investigate the impact of role conflict on active lurking in B2B online communities. Active lurking, defined as the behavior of seeking information and acquiring knowledge without contributing, contrasts with posters who actively share knowledge. By focusing on these dynamics, this study aims to deepen the understanding of knowledge management (KM) practices and behaviors within B2B contexts.Design/methodology/approachThis research conducts two experimental studies with a behavioral outcome to examine the impact of role conflict on active lurking and the mediating role of knowledge self-efficacy in this regard.FindingsThe results also indicate the moderating role of transformational leadership, such that when transformational leadership is low, role conflict decreases knowledge self-efficacy and active lurking. In contrast, this effect is attenuated in a high transformational leadership environment. The findings contribute to the KM literature by demonstrating how role conflict influences knowledge-sharing behaviors in B2B contexts and by capturing active lurking using a behavioral measure.Practical implicationsThe implications of this study offer strategies to address role conflict and mitigate its negative impact. By integrating the concepts of posters and lurkers into the research framework, this paper offers a fresh perspective on organizational role conflict and KM. It provides insights into lurkers and active lurking behavior in B2B online communities, thereby extending the KM literature.Originality/valueThe findings of this research are novel as they first show the moderating role of transformational leadership, wherein when transformational leadership is low, role conflict decreases knowledge self-efficacy and active lurking and second it extends the literature on online knowledge sharing in a B2B context by capturing the role of active lurking through a behavioral measure.

PurposeThis study uses India's dramatic monetary policy change in November 2016 (demonetization) as a natural experiment to investigate whether it changed the usage of cash and cashless payment systems among individual consumers. We investigate whether demonetization uniformly impacted individual consumers on both sides of the digital divide dimensions, such as (1) locale (urban vs rural), (2) gender, (3) education and (4) income.Design/methodology/approachWe use data from two waves collected in September 2016 (N = 305) and February 2017 (N = 303) from two districts in India to test our hypotheses. We control for various contextual factors, including individual factors, and use ANCOVA to test the changes before and after demonetization.FindingsResults show that cash and cashless system usage reduced after demonetization. The results also indicate that the impact of demonetization may not be uniform across the different social dimensions. Whereas cash usage was reduced across gender, income and education dimensions, it was reduced only for urban consumers and not rural consumers. Cashless payment system usage remained unchanged among urban, male, high-education and high-income groups but reduced among women, low-education, and low-income groups. Again, cashless payment system usage remained unchanged among rural consumers.Originality/valueThis study examines how monetary policy change may impact consumers differentially based on their position on either side of the digital divide. While cashless payment systems have been studied in different contexts, the impact of a monetary policy decision has not been investigated in the literature.

India is a leading global hot spot for extreme heat waves induced by climate change. The social demography of India is centered on its caste hierarchy rooted in endogamous occupational groups. We investigate the association between caste and climate inequality by studying occupational exposure during the 2019 and 2022 heat waves. We combine high spatiotemporal resolution heat stress information from satellite imagery with a large nationally and regionally representative labor force survey with rich socioeconomic and demographic information (n > 100,000 individuals). The slope of the heat stress dose-workhours curve corresponding to the marginalized caste groups is between 25% and 150% steeper than that for dominant caste groups for UTCI (Universal Thermal Climate Index) thresholds between 26 degrees C and 35 degrees C. Our models control for other economic-demographic confounders, including age, gender, education, and economic status, besides political-geographic controls and fixed effects. Our robust evidence for the association between caste identity and exposure to heat stress shows why adaptation and mitigation plans in India must account for the hierarchical social order characterized by the division of laborers along caste lines rather than the mere division of labor. Methodologically, our analysis demonstrates the utility of pairing satellite imagery and detailed demographic data.

We examine whether and how the corporate integrity culture affects a firm's dividend policy. Using a validated time-variant measure of firm-level integrity culture, we find that integrity culture increases firms' dividend payout: a one standard deviation increase in integrity culture is associated with an increase of 9.89 % of the mean dividend yield. We also find that integrity culture positively influences dividends through financial constraint and firm risk channels. Our study is robust to endogeneity tests, including an instrumental variable approach using state-level corruption convictions and organ donation as instrumental variables and difference-indifference methods employing the global financial crisis and the COVID pandemic as an exogenous shock.

Political representatives and their directives are discredited when there is an instance of rising mortality. However, there is limited empirical evidence linking public health outcomes to the quality of politicians. We investigate whether electing political leaders with higher levels of formal education affects child survival. Using an instrumental variable strategy exploiting quasi-experimental outcomes of close elections, we find that collegegraduate politicians lead to better child health outcomes, i.e., a reduction in neonatal, infant, and under-five mortality in the regions they are elected from. We explore the potential channels of graduate leaders' impact, drawing from early life and health infrastructure investments. We also find heterogeneous impacts of graduate leaders on child mortality across states with varying levels of institutional quality and based on the leaders' political affiliation.

Time-use statistics are recall intensive and sensitive to measurement error. This study uses a nationally representative time-use survey from India to investigate how self and proxy reporting impacts the reported time spent on various activities by men and women. Proxy informants tend to report higher time use for both men and women on employment activities (14 to 26 percent) and lower time use on production for self-consumption, unpaid domestic work, and care work (5 to 33 percent) as compared to self-reports. On average, women proxies differ more from self-reports when reporting about both men and women in their households as compared to men proxies. Investigating the mechanisms we find that the self-proxy differences are systematic and not attributable solely to random measurement error. Information asymmetry between the self and proxy respondents plays a key role-spouses and self-proxy respondents with similar characteristics have smaller reporting differences than non-spouses and other respondents. Gendered perception of what activities are classified as work influences the differences in reporting, which highlights asymmetric measurement error.

We study the impact of transparency about banks' costs on loan interest rates. The Indian Central Bank required banks to disclose a cost-based benchmark interest rate instead of the prime rate. The banks could price loans using any spread to the cost-based benchmark. We find that this change, which made banks' cost structures more transparent, lowers the interest rates charged and leads to increases in debtor firms' total borrowings and investments. We hypothesize that increased cost transparency reveals relationship rents to competitor banks and makes it difficult for incumbent banks to maintain high relationship rents because of increased threat of entry.

This paper examines the impact of Economic Policy Uncertainty (EPU) on corporate trade credit policies, utilizing an international dataset spanning 20 countries from 1999 to 2019. Our analysis reveals that firms adjust by increasing the provision of trade credit to their customers during periods of heightened EPU. By investigating firm-level dynamics, we identify the influence of inventory composition and financial constraints on trade credit financing in the context of rising EPU. We document that the market devalues firms that offer and accept trade credit during periods of rising EPU. At the country level, institutional characteristics such as governance quality and financial market efficiency significantly moderate the relationship between EPU and trade credit. Our findings highlight the role of trade credit as a mechanism for managing liquidity during uncertain times.

Motivated by the emergence of online content aggregators such as Google News, as a prominent source of web traffic for content publishers, this paper examines the impact of introducing a content aggregator platform on market equilibrium and welfare under two different content publishers' business models: (i)$$ (i) $$ free content with advertisements and (ii)$$ (ii) $$ subscription-based content with advertisements. A crucial feature of our framework is competition between content publishers and aggregator platforms in the advertising market. Using a game-theoretic model, our analysis yields interesting insights. First, we find that platform introduction is Pareto optimal when publishers charge subscription prices to the users. However, when content is free, publishers' profits strictly decrease, while both users and advertisers benefit. This finding contrasts with the general regulatory presumption that content aggregators make publishers worse off. Next, in the presence of an aggregator platform, we find that publishers display fewer advertisements under the subscription-based model for weak content differentiation and sufficiently large platform quality or strong content differentiation. Finally, we provide implications for social welfare.

Dominant online platforms are increasingly collecting user-specific data across multiple markets, raising red flags as regards privacy, as well as potential anti-competitive abuses. We analyze competition among two platforms, with one platform collecting data in market 1 where it is dominant, using it to improve its competitive edge in market 2 where it competes in both prices and advertisements with the other platform. Our analysis unearths a novel anti-competitive effect of competition-that increased market 2 competition may increase the dominant firm's market power in market 1, which in turn leads to several interesting results. First, that data collection by the dominant firm is increasing in market 2 competition. Second, efficiency of data collection is non-monotonic in the level of competition; being, from a welfare perspective, excessive (respectively low) whenever market competition is at an intermediate level (respectively weak). We then use this framework to examine several policy proposals-user control of data, blocking data-driven mergers, restricting data collection, and allowing data-sharing-deriving actionable prescriptions for managers and policymakers. For example, in markets with effective ad-targeting, we find that user control of data decreases data collection, thus improving privacy, if and only if competition is strong.

Amid the global crisis of climate change, urgent action is imperative. In this study, we develop two types of decarbonized indices, which render a dynamic hedging approach for passive investors. Focusing on long-term returns with minimal active trading and risk exposure, we create the decarbonized indices for NIFTY-50, a benchmark index for the Indian market. Proposed methodology relies on suitable optimization techniques to choose the portfolio weights that minimize the tracking error while significantly reducing carbon footprints. These indices are shown to perform better than existing benchmarks, especially during major climate events. They are likely to offer investors a buffer to adapt to climate policies and carbon pricing. Since these indices align with the net-zero objective and foster climate-resilient advancements, they also offer actionable pathways to address climate challenges while maintaining financial objectives.

Generative AI (GenAI) is disrupting global IT management and challenging established practice. The increasing use of GenAI technology is redefining localization, transforming existing workforce roles, outsourcing strategy, and team dynamics. Simultaneously, GenAI's security complexities have prompted the rethinking of existing risk frameworks to meet a new set of challenges from GenAI enhanced cyber threats. This article explores these complex and converging factors, providing a roadmap to address GenAI's significant impact on global IT management. We advocate the responsible adoption of GenAI and importance of building resilient, value-driven, globally consistent IT ecosystems able to adapt to the significant challenges and opportunities from the use of GenAI.

PurposeDue to land-locked topography, poor connectivity, struggling market conditions and other region-specific factors, the arts and crafts industry in Kashmir is facing several challenges. However, irrespective of all odds, artisan entrepreneurs continue their entrepreneurial activities. To find the answer, this study aims to ascertain key determinants of the continuity of entrepreneurial endeavour among artisan entrepreneurs in this unique context.Design/methodology/approachA qualitative methodology was employed to explore the determinants of the continuity of entrepreneurial endeavour. The sample comprises semi-structured interviews with 23 artisan entrepreneurs across five districts in Kashmir, India. Thematic analysis was subsequently used to determine the main ideas in the transcripts from the interviews conducted.FindingsThe following themes emerged from the data set: (a) job-related determinants, (b) functional determinants, (c) socio-cultural determinants and (d) emotional and religious determinants as the main drivers of the continuity of entrepreneurial activities among artisan entrepreneurs.Originality/valueThe paper explores how artisan entrepreneurs in Kashmir navigate challenges to sustain their craft, addressing a gap in research on their resilience in a constrained context. This paper is the first, to the best of the authors' knowledge, to address the determinants of the continuity of entrepreneurial endeavour among artisan entrepreneurs in the context of Kashmir, hence paving the way for further scholarly exploration.

Drawing from institutional theory and behaviour risk agency perspective, we argue that various institutional investors' risk propensity and perception towards emerging market firms' R&D investments are contingent on the institutional context. We examine a longitudinal sample of 11,359 firm-year observations of Indian firms for a period of 20 years (2005-2024) to test the proposed hypothesis. Our findings suggest that pressure-sensitive institutional investors' (PSIIs) ownership negatively impacts R&D investment decisions. The ownership by pressure-resistant institutional investors (PRIIs - mutual funds and foreign institutional investors) positively affects the R&D investment decisions of firms. We also find that firms' export intensity negatively moderates the association between mutual funds (MFs) ownership and R&D investment. In contrast, it positively moderates the association between foreign institutional investors (FIIs) ownership and firms' R&D investment. Our findings have important implications for policymakers in strengthening the environment to protect institutional investors and encourage R&D investments.

Despite extensive research on the influence of religion on pro-environmental behavior, little attention has been paid to the role of religious epics-that is, narratives that embody the core beliefs and moral values of religious traditions-as a mechanism for promoting such behavior. Using Hinduism as a case, this research seeks to bridge this gap by contrasting the orientations of idealism in the Ramayana with realism in the Mahabharata, offering a better understanding of how these distinct orientations in religious epics can shape pro-environmental behavior. Utilizing belief congruence theory as a theoretical framework, this four-study research examines altruism as a mediator and materialism as a moderator in understanding the role of religious epics in shaping pro-environmental behavior. The findings suggest that the idealistic orientation of the Ramayana fosters stronger pro-environmental behavior through heightened altruistic values, whereas the realistic orientation of the Mahabharata shows a less pronounced effect, which is further attenuated by materialism. These insights not only contribute to the theoretical discourse on consumer behavior, ethics, religion, and sustainability, but also offer practical implications for leveraging religious epics in fostering pro-environmental behavior in a materialistic world.