The rapid aging of the global population warrants multidisciplinary research on factors influencing the quality of life of older adults, with the goal of creating old-age-friendly cities and communities. We investigate whether lack of car ownership or carlessness is associated with reduced life satisfaction and increased depression - and hence degraded quality of life - among older adults and analyze whether depression mediates the carlessness-life satisfaction relationship. We use nationally representative data comprising more than 31,000 persons aged 60 years or more from India, a country experiencing rapid population aging as well as car adoption. We employ OLS regression along with mediation analysis using Structural Equation Modeling techniques (SEM and GSEM) to analyze the associations and mechanisms. We find that carlessness is associated with lower life satisfaction (measured using the SWLS) and higher levels of depression (measured using the CES-D scale) and that depression partially mediates the carlessness-life satisfaction relationship. Carlessness-related life satisfaction degradation is greatest among the oldest age cohort and women. Women are most vulnerable to carlessness-induced depression. Depression amplifies life dissatisfaction the most among relatively younger cohorts, men, and urban residents. Our study underscores the need for policy action to delink the car ownership and accessibility advantage connection for simultaneously addressing life satisfaction declines and mental health disorders among carless older adults. Since structural transformations in land use and transportation systems take time, policymakers should urgently recognize and address carlessness-induced depressive symptoms using medical or social support interventions to enable carless older adults to lead relatively more satisfying lives. Preventing transportationrelated degradations in older adults' quality of life is imperative for promoting transportation equity.

PurposeOrganizations seek to increase their innovation ambidexterity through knowledge management practices. This study aims to examine how training and development (T&D) impacts an organization's dynamic capability, a key antecedent to innovation ambidexterity. Based on the knowledge-based view and dynamic capability theory, this study also investigates the mediating role of the organizational climate of trust (OT) in the relationship between training and development and dynamic capability.Design/methodology/approachThis study develops and validates a conceptual model using the partial least squares-structural equation modeling technique, considering data gathered from a field study of 362 survey responses from a firm in an emerging economy.FindingsThis study reveals that T&D directly impacts dynamic capabilities' dimensions of sensing, seizing and reconfiguring, with organizational trust mediating these relationships.Practical implicationsThe findings highlight the importance of providing robust T&D programs that enable desired competency building and urge managers to recognize the critical role of a climate of trust in enabling dynamic capabilities. The findings suggest that practitioners must adopt this dual approach to build organizational dynamic capability strategically.Originality/valueResearch on human resource management initiatives like T&D and collaborative relationships is crucial for enhancing innovation within firms. However, most research primarily focuses on knowledge dissemination motivations, neglecting the varying effects of formal and informal knowledge-sharing mechanisms on dynamic capability creation. Despite increased research in trust and training, the role of T&D and the organizational climate of trust in influencing dynamic capability is not yet established. Thus, the proposed research model is unique and extends the KBV and dynamic capability theory literature.

In an era where digital platforms are reshaping healthcare delivery, we have also seen the rise of online platforms for mental health consumption. While the literature on consumer behaviour in an online context is rich, mental health consumption presents a unique context requiring attention to personal health-related dynamics alongside the larger aspect of online consumption. This motivates the current study to conduct a multi-method study for understanding the phenomenon of online mental health consumption. We combine a systematic review of 105 articles (2014-2024) with topic modelling of 168,040 user reviews from mental health applications. We theorise how the logic of choice and care are at work in online mental health consumption. Our findings reveal a complex and dynamic interplay of 'choice'-related enablers and 'care'-related inhibitors, shaping online mental health consumption behaviour. While online platforms offer 'choice' for consuming mental health services by overcoming traditional barriers related to stigma and accessibility, their uptake at the same time is challenged by the emerging care-related factors such as trust and privacy concerns. An analysis of user reviews further reveals that consumer experiences focus on the service delivery quality, personalised user interfaces and technical platform reliability, which collectively demonstrate how users navigate between autonomous choice making and professional care expectations. This apparent tension between the 'logics' in mental health consumption online also informs the larger online consumption behaviour literature about attending to the constantly evolving, often competing logic in online platforms.

In this paper, we begin by modeling the parking garage revenue management problem as a deterministic network revenue management system with fixed capacity. For a given hourly pricing structure, our model uses a network linear programming approach, incorporating factors such as arrival time, departure time, and length of stay to maximize revenue. We assume that vehicle demand increases during the day, peaks at midday, fluctuates in the evening, and declines later. In many practical problems, obtaining data on the hourly length of stay (LOS) is often difficult, especially in countries where parking revenue management is underdeveloped. However, data on average hourly cars arrivals are available, enabling vehicle demand modeling using a nonhomogeneous Poisson process with variable mean arrival rates. Finally, we study how revenue changes with demand fluctuations. We assume a fixed length of stay distribution throughout this study. By adjusting demand, we observe that the system operates below capacity early in the day, reaching full capacity as arrival rates increase. When capacity reaches its limit, the service provider can increase prices to regulate demand. Therefore, if car arrival rates are predictable, the model reveals opportunities to charge higher prices as the facility approaches full capacity. Using this model, we derive bid or shadow prices and examine how these prices fluctuate in response to car arrival rates at the parking facility.

The indirect productivity gains related to information technology (IT), known as IT externalities, in inter-firm contexts have been extensively studied. However, the impact of IT investments within a business unit (BU) of a multibusiness firm on the productivity of other BUs remains unclear. Additionally, the conditions that facilitate such intra-firm externalities are not well understood. Research on resource externalities within multibusiness firms typically focuses on capacity-sharing benefits, where unused capacity in one unit can be utilized by another. IT resources, however, often lack capacity-sharing potential due to their full utilization or contractual limitations. Despite this, IT resources can generate non-rivalrous intangibles, such as internally developed applications, expertise, and consulting know-how, which can be shared within the firm to create externalities. This study investigates whether IT centralization (ITC), as a vertical coordination mechanism, is effective in harnessing IT externality potential arising from IT portfolio similarities (ITPSs), a form of horizontal coordination, across BUs. Utilizing data from 8,374 unique units within 866 firms from 2005 to 2020, we find that BUs must meet two conditions-higher ITPS and higher levels of ITC-to realize greater intra-firm IT externality benefits. Furthermore, these benefits accrue from IT investments made by units with a sufficient number of IT employees. Interestingly, BUs with limited access to IT employees gain more from pooled IT investments. Our findings suggest that concurrent vertical and horizontal coordination, along with access to human talent for creating knowledge, code, and expertise from digital resources, are crucial for maximizing digital resource externalities.

The paper challenges the antagonistic binary that Hindutva and Muslims are always in opposition to each other and identifies a new group of Muslims whom we call Hindutva Muslims. In this paper, firstly, we define what we mean by Hindutva Muslims. Secondly, we explore the case of Hindutva Pop superstar Shahnaaz Akhtar as a Hindutva Muslim. Methodologically, we performed critical textual analysis of her songs, through which we found that she openly supported the policies and candidates of the BJP, celebrated the demolition of Babri Masjid and favoured the broader imagination of Hindu Rashtra (Hindu Nation). Through her performance of Hindutva Muslimness, we look at how some Muslims do not see Muslim identity and Hindutva in an antagonistic binary but perform both of these simultaneously.

While earlier studies have examined how AI social chatbots provide emotional support or alleviate loneliness, their direct implications on user wellbeing remain underexplored. Drawing on the theory of emotional intelligence, this research investigates the dual impact of emotionally intelligent (EI) social chatbots on users' wellbeing, enhancing psychological wellbeing while potentially deteriorating social wellbeing (i.e., real-world human-to-human connection). Using a mixed-method design, first, we conducted a netnographic analysis of user-generated data from Reddit, YouTube, and Trustpilot to identify how users experience emotional intelligence within chatbot interactions. Building on these insights, two experimental studies (N = 167; N = 350) confirm that high-EI chatbots positively influence users' psychological wellbeing but simultaneously decrease their social wellbeing. Furthermore, the study found that perceived closeness mediates both the relationships and the chatbot interaction mode (text vs. augmented reality), and also moderates these mediating effects. These findings highlight a wellbeing trade-off growing in chatbots and offer implications for the ethical design of AI companions that maximize emotional benefits without compromising users' real-world connections.

We examine whether earnings from parent-only financial statements are incrementally informative to those from consolidated financial statements. We use a unique mandate in India that requires firms to provide both consolidated and parent-level financial statements, since currently neither US GAAP nor IFRS mandates this level of disaggregation. While disaggregation provides additional information, it also imposes costs, raising the empirical question of whether its benefits outweigh the costs. Our analyses reveal that disaggregated quarterly earnings components inform investors, with investors placing more weight on parent-level unexpected earnings than on subsidiaries' unexpected earnings. We do not find evidence of mispricing associated with disaggregation; rather, the higher weight on the parent's earnings reflects higher persistence, consistent with semi-strong market efficiency. Moreover, parent earnings provide incremental informativeness, especially in the context of poor earnings quality and high mergers and acquisitions intensity. Our results endure when we examine annual parent- and subsidiary-level earnings, where available, in 98 countries around the world. Our results contribute to the literature on disaggregation in accounting and earnings informativeness in equity markets, offering insights that may influence regulatory considerations on the usefulness of financial statement disaggregation. La valeur informative des r & eacute;sultats consolid & eacute;s et ceux de la soci & eacute;t & eacute; m & egrave;re pour les investisseurs : donn & eacute;es provenant de l'IndeCette & eacute;tude vise & agrave; d & eacute;terminer si les r & eacute;sultats provenant des & eacute;tats financiers de la soci & eacute;t & eacute; m & egrave;re sont plus informatifs que ceux des & eacute;tats financiers consolid & eacute;s. Les auteurs s'appuient sur une exigence propre & agrave; l'Inde imposant aux entreprises de pr & eacute;senter & agrave; la fois des & eacute;tats financiers consolid & eacute;s et des & eacute;tats financiers de la soci & eacute;t & eacute; m & egrave;re, & eacute;tant donn & eacute; qu'aucune exigence & eacute;quivalente n'est demand & eacute;e & agrave; ce jour par les principes comptables g & eacute;n & eacute;ralement reconnus aux & Eacute;tats-Unis ou les normes internationales d'information financi & egrave;re. Bien que la d & eacute;sagr & eacute;gation des & eacute;tats financiers puisse fournir des informations suppl & eacute;mentaires, elle engendre & eacute;galement des co & ucirc;ts, exigeant une analyse empirique pour & eacute;valuer si les avantages l'emportent sur les co & ucirc;ts. Les analyses r & eacute;alis & eacute;es par les auteurs indiquent que les donn & eacute;es d & eacute;sagr & eacute;g & eacute;es des r & eacute;sultats trimestriels informent les investisseurs, qui accordent une plus grande importance aux r & eacute;sultats inattendus de la soci & eacute;t & eacute; m & egrave;re par rapport & agrave; ceux des filiales. Les r & eacute;sultats ne livrent aucune donn & eacute;e relative & agrave; une erreur d'& eacute;valuation li & eacute;e & agrave; la d & eacute;sagr & eacute;gation; au contraire, l'importance attribu & eacute;e aux r & eacute;sultats de la soci & eacute;t & eacute; m & egrave;re refl & egrave;te une plus grande persistance, ce qui est conforme aux crit & egrave;res d'efficience semi-forte du march & eacute;. Par ailleurs, les r & eacute;sultats de la soci & eacute;t & eacute; m & egrave;re apportent une valeur informative ajout & eacute;e, en particulier dans un contexte caract & eacute;ris & eacute; par une faible qualit & eacute; des r & eacute;sultats et des op & eacute;rations fr & eacute;quentes de fusions et acquisitions. Les auteurs confirment leurs r & eacute;sultats en examinant, lorsqu'ils sont disponibles, les r & eacute;sultats annuels au niveau de la soci & eacute;t & eacute; m & egrave;re et des filiales dans 98 pays & agrave; l'& eacute;chelle mondiale. Ces conclusions enrichissent la litt & eacute;rature sur la d & eacute;sagr & eacute;gation en comptabilit & eacute; et la valeur informative des r & eacute;sultats sur les march & eacute;s des actions, offrant des perspectives susceptibles d'influer sur les consid & eacute;rations r & eacute;glementaires relatives & agrave; l'utilit & eacute; de la d & eacute;sagr & eacute;gation des & eacute;tats financiers.

The vehicle routing problem with time windows is a well-researched problem in literature. We study the 2-index flow formulation for the problem and propose a relatively little-used approach of polar duality/local cuts to compute new general valid inequalities for the problem. Our method of applying polar duality is quite distinct from and complimentary to an earlier attempt of applying the same idea to this problem and produces significantly better results on instances with tight time windows. On almost all 25-customer Solomon instances with tight time windows, our approach is capable of producing very strong lower bounds that are close to 100% of the optimal solution within reasonable computing time. On larger instances too the lower bounds are significantly better than those reported in the literature. We also present a new version of the previously proposed k-path inequalities that are easy to compute as well as effective. These inequalities also lead to a substantial improvement in lower bounds and solution times for some classes of instances. Computational tests performed on benchmark instances indicate significant improvement in computing time and decrease in the number of nodes in the branch-and-bound tree as compared to extant methods that employ the flow formulation for the problem.

The global discourse is nearly unanimous that dietary transitions are crucial to achieve sustainability goals. In this context, healthy dietary recommendations offer demand-side solutions towards minimizing environmental impacts from food production. However, these guidelines have also faced some criticism for their blanket approach and limited consideration of regional preferences. Using a validated food-economy-environment integrated modelling framework, we compare between two types of healthy diets-the globally recommended EAT-Lancet diets and Indian government's National Institute of Nutrition (NIN) local diets-by examining their impacts on agricultural production, agricultural commodity prices, food expenditures, trade impacts, Greenhouse gas (GHG) emissions and water withdrawals. Our results suggest that the adoption of regional recommendations (NIN diets) lead to better outcomes for select economic and environmental indicators. When India shifts to NIN diet, its domestic demand for cereal crops decreases, leading to a 36 % reduction in cereal crop production by 2050 and change in demand for sugars and animal-sourced foods (ASFs). This has the potential to reduce commodity prices of food by upto 24 % by 2050. A shift to the NIN diet in India reduces methane (CH4) emissions by 36 % and N2O by 35 % compared to business-as-usual, performing better than the EAT-Lancet diet, which reduces CH4 emissions by 13 %. Water withdrawals reduce almost by the same value under both the dietary scenarios primarily due to lesser dependence on cereal crops and livestock products. These findings remain consistent in our sensitivity analysis, with varying global trade scenarios, offering greater benefits of food systems transformation through liberal trade policies. Our analysis underscores the pivotal role of regional inclusivity in global assessments, enhancing our comprehension of how food systems can be reimagined to align with both food security and environmental sustainability.

The correctness of predictions rendered by an AI/ML model is key to its acceptability. To foster researchers' and practitioners' confidence in the model, it is necessary to render an intuitive understanding of the workings of a model. In this work, we attempt to explain a model's working by providing some insights into the quality of data. While doing this, it is essential to consider that revealing the training data to the users is not feasible for logistical and security reasons. However, sharing some interpretable parameters of the training data and correlating them with the model's performance can be helpful in this regard. To this end, we propose a new measure based on Euclidean Minimum Spanning Tree (EMST) for quantifying the intrinsic separation (or overlaps) between the data classes. For experiments, we use datasets from diverse domains such as finance, medical, and marketing. We use state-of-the-art measure known as Davies Bouldin Index (DBI) to validate our approach on four different datasets from aforementioned domains. The experimental results of this study establish the viability of the proposed approach in explaining the working and efficiency of a classifier. Firstly, the proposed measure of class- overlap quantification has shown a better correlation with the classification performance as compared to DBI scores. Secondly, the results on multi-class datasets demonstrate that the proposed measure can be used to determine the feature importance so as to learn a better classification model.

We introduce a linear programming-based approach for hyperparameter tuning of machine learning models. The approach finetunes continuous hyperparameters and model parameters through a linear program, enhancing model generalization in the vicinity of an initial model. The proposed method converts hyperparameter optimization into a bilevel program and identifies a descent direction to improve validation loss. The results demonstrate improvements in most cases across regression, machine learning, and deep learning tasks, with test performance enhancements ranging from 0.3% to 28.1%.

Income risks are not accurately captured by standard AR processes that are common in the literature. This paper proposes a simple stochastic process which matches several moments in the data including the cross-sectional distribution of income and the distribution income risk, and can be easily used in models with uninsurable income risk. Incorporating this process into an off-the-shelf OLG model leads to a rise in wealth concentration narrowing the gap between traditional models and the data. However, the right tail of the wealth distribution remains significantly thinner than the data.

Introduction: Clinical trials are critical for drug development and patient care; however, they often need more efficient trial design and patient enrolment processes. This research explores integrating machine learning (ML) techniques to address these challenges. Specifically, the study investigates ML models for two critical aspects: (1) streamlining clinical trial design parameters (like the site of drug action, type of Interventional/Observational model, etc) and (2) optimizing patient/volunteer enrolment for trials through efficient classification techniques. Methods: The study utilized two datasets: the first, with 55,000 samples (from ClinicalTrials.gov), was divided into five subsets (10,000-15,000 rows each) for model evaluation, focusing on trial parameter optimization. The second dataset targeted patient eligibility classification (from the UCI ML Repository). Five ML models-XGBoost, Random Forest, Support Vector Classifier (SVC), Logistic Regression, and Decision Tree-were applied to both datasets, alongside Artificial Neural Networks (ANN) for the second dataset. Model performance was evaluated using precision, recall, balanced accuracy, ROC-AUC, and weighted F1-score, with results averaged across k-fold cross-validation. Results: In the first phase, XGBoost and Random Forest emerged as the best-performing models across all five subsets, achieving an average balanced accuracy of 0.71 and an average ROC-AUC of 0.7. The second dataset analysis revealed that while SVC and ANN performed well, ANN was preferred for its scalability to larger datasets. ANN achieved a test accuracy of 0.73714, demonstrating its potential for real-world implementation in patient streamlining. Discussion: The study highlights the effectiveness of ML models in improving clinical trial workflows. XGBoost and Random Forest demonstrated robust performance for large clinical datasets in optimizing trial parameters, while ANN proved advantageous for patient eligibility classification due to its scalability. These findings underscore the potential of ML to enhance decision-making, reduce delays, and improve the accuracy of clinical trial outcomes. As ML technology continues to evolve, its integration into clinical research could drive innovation and improve patient care.

Waste minimisation and prevention are crucial for the circular economy (CE), sustainable development goals (SDGs) and environmental, social and governance (ESG) principles, focusing on waste elimination and resource efficiency. However, there are significant gaps in implementing effective waste minimisation strategies, mainly due to the lack of standardised waste prevention terminologies and indicators. This article introduces a novel simplified and comprehensive framework for assessing waste prevention and minimisation measures tailored to developing countries. The primary contribution of this study lies in proposing relevant indicators aligned with the SDGs, ESG standards, and CE principles, while addressing data scarcity through proxy indicators to enable effective assessment in resource-limited settings. Six key indicators were proposed: Zero Waste Index, Food Loss Index, Extended producer responsibility, Education and awareness programmes for waste minimisation, Waste prevention and Plastic Bag Reduction Ratio. Eleven countries were selected as case studies to demonstrate the framework's applicability. The findings reveal that while these countries are progressing in enacting legislation and recognising the importance of waste prevention, compliance in practice is lacking, as indicated by poor quantitative results in actual waste reduction and diversion. The framework evaluates the environmental, social and economic implications of waste prevention measures, showing wide variations among countries. Each country faces unique challenges, but strengthening policy frameworks, investing in infrastructure, promoting public awareness and fostering collaboration are key steps towards advancing sustainable waste management practices. The study highlights the necessity for tailored policies addressing specific weaknesses while ensuring economic viability. The integrated framework provides actionable insights and forward-thinking solutions that can be adapted, scaled and replicated to address developing nations' unique challenges.

In bilevel optimization, an upper level (UL) decision maker seeks to optimize an objective function while considering the optimal solutions of a lower level (LL) optimization problem. This hierarchical structure poses modeling and solution challenges, especially when the LL problem has multiple solutions. In such a case, the UL needs to make assumptions about the LL reaction. In the optimistic approach, the UL assumes that the LL reaction will be favorable, while in the pessimistic approach the opposite is true. In this study, we consider the case of a multiobjective bilevel optimization problem, where the UL has multiple objectives, while the LL has a single objective, but multiple optimal solutions for any given UL decision. Given that the LL can choose any solution from its optimal set, and in case the UL is not aware of the LL choice function, it leads to the possibility of two Pareto-optimal fronts at the UL, i.e. the optimistic and the pessimistic frontiers. To approximate both Pareto-optimal fronts, a d-perturbation approximation is proposed in this paper, where the LL objective is perturbed by a small d by utilizing the UL objectives at the LL. The perturbed reformulated bilevel problem is then solved via an extension of m-BLEAQ, an evolutionary bilevel algorithm that can deal with bilevel problems with multiple objectives at the UL and a single objective at the LL. The application of the m-BLEAQ algorithm to the reformulated bilevel problem leads to the identification of the optimistic and pessimistic frontiers for the multiobjective bilevel problem. In this proof-of-concept study, the proposed reformulation strategy is demonstrated on two test problems, showing the effectiveness of the proposal in accurately finding the optimistic and pessimistic frontiers.

We examine whether affirmative action incentivizes a disadvantaged religious minority group in India to obtain additional years of education. We study the implementation of quotas in government hiring and university admissions for backward-class Muslims in 2007 in the Indian state of Andhra Pradesh. Using a difference-in-differences approach that uses variation in exposure to the policy by age-cohort and social group, we find that these quotas increase educational attainment of the targeted population. Investigating the effects by gender, we find statistically significant and robust positive effects on the educational attainment of male members of the targeted Muslim groups, but not females.

This paper reports a study of how the Indian IT industry navigated the COVID-19 pandemic.Agility emerged as the crucial determining factor aiding the industry's successful survival. IT orga-nisations'agility, facilitated by the state's response to the pandemic and by both their anticipation ofthe lockdown and their technological capabilities, helped them overcome the crisis. The slowdownforced firms to downsize, reduce bench strength, freeze wages and intensify work, while deferringclient payments. When the economy recovered, high attrition, termed'The Great Resignation', forcedemployers to increase wages. Employers were unable to compel employees to return to the office,despite facing issues relating to organisational culture, data security and moonlighting. Remote work-ing helped employees maintain work-life balance and save on cost of living, forcing employers toprovide a hybrid option.

Gold plays an important role as a hedge and a safe haven for investors. This paper presents new evidence on gold's role for Indian households during the COVID-19 pandemic. By using panel and cross-sectional household surveys, we investigate the propensity of households to purchase, pledge or sell gold based on the district-level heterogeneity in COVID cases and economic impact proxied by night-time light activity. We find higher gold purchases of households in the more affected districts compared to other districts during the crisis. Importantly, households that are more directly affected by the shock are less likely to purchase gold and more likely to pledge or sell gold. The purchases are likely driven by an increased risk perception of households in response to an unexpected shock - a novel perspective of gold's safe haven property. We also find that relatively poor households that receive government transfers or have less access to formal credit display stronger gold purchasing behavior.

The existence, causes, and consequences of the accessibility advantage offered by personal motorized vehicles relative to alternative modes have been explored in the literature. We use data from a relatively understudied geographical context to estimate the magnitude and analyze the implications of the disparity in spatial range, specifically the 60-min travel range - i.e., the maximum distance that can be covered, on average, via the multimodal transportation network - between personal motorized vehicle owners and non-owners. A higher travel range within a specified time window may indicate greater accessibility to opportunities. We use nationally representative survey data comprising over 178,000 households across India to first examine whether and to what extent household vehicle ownership is associated with a relative 60-min travel range advantage. Using an experience- and perception-based measure of household-level travel range, we find that the 60-min travel range of vehicle-owning households is at least 10 % more than vehicle-less households. This travel range advantage is relatively greater in rural and low-density areas and locations with limited public transit services. Next, we analyze whether the 60-min travel range determines the aspiration of owning a household vehicle. In urban areas, a one-km lower 60-min travel range is associated with about 5 % higher odds of aspiring to own a car. Our analysis highlights that existing vehicle owners in India enjoy a potential spatial travel range advantage relative to non-owners, and that this advantage promotes latent demand for vehicle ownership in urban areas. Closing the gap can ensure equity in accessibility and reduce personal vehicle dependence.