Annually, countries allocate hundreds of millions of dollars to subsidize fossil fuels, often at the expense of public health and environmental sustainability. This undermines progress toward Sustainable Development Goals (SDG) 3 (Good Health and Well-Being) and 13 (Climate Action). Despite this, the impact of fossil fuel subsidies (FFS) on social protection expenditure, including health, remains poorly quantified. This study aimed to determine whether FFS crowd out health expenditure globally, using panel data from 126 countries covering the period 2015-2019. An instrumental variable approach, relying on countries' exposure to international energy trade and fluctuations in crude oil price, was employed to capture exogenous variation in FFS and estimate a causal relationship. The analyses revealed that in 2019, 17 countries spent more than five percent of GDP on FFS, with FFS expenditure exceeding health expenditure in 15 of those countries. Specifically, a 1% increase in FFS per capita, driven by rising international oil prices and weighted by countries' exposure to international energy trade, led to a 0.05% (95% CI -0.08% to -0.02%) decrease in domestic health expenditure per capita. These findings underscore the detrimental impact of FFS on health expenditure, presenting another reason to eliminate FFS to achieve SDG3 in addition to avoiding further dangerous climate heating.

India launched the National Health Mission (NHM) in 2005 to reduce maternal and child mortality by providing equitable, affordable, and quality healthcare services. Studies evaluating the impact of NHM primarily focused on the uptake of maternal and child health (MCH) services, revealing a dearth of evidence on related health outcomes. Using annual time-series data, we estimated the population-level (intention-to-treat effect) impact of the NHM on MCH outcomes such as infant mortality rate (IMR), stillbirth rate, neonatal mortality rate (NMR), and maternal mortality ratio (MMR). Our results exhibit strong consensus on the positive impact on IMR in the post-NHM period, with a significant reduction in its rate and regional inequity. Conversely, our estimates show a moderate impact on stillbirth rate and an insignificant impact on NMR and MMR. Our findings inform policy decisions for strengthening the public health system to amplify the positive gains in MCH outcomes.

Connected vehicles present a wide range of applications that have the potential to revolutionize the transportation landscape, offering benefits to drivers, passengers, and transportation systems while enabling innovative business models for automakers. Despite these opportunities, consumer acceptance of Connected Vehicle Technologies (CVT) has progressed at a slower pace than anticipated, creating obstacles for automakers in monetizing the associated data. As a result, it becomes essential to determine the factors influencing consumers' behavioural intention to use CVT, particularly in emerging markets like India. This study addresses this gap by gathering data from various metropolitan cities across India to identify the factors influencing intention to use CVT. Using a multi-method approach incorporating Structural Equation Modelling (SEM) and Fuzzy-Set Qualitative Comparative Analysis (fsQCA), the study reveals that perceived usefulness, perceived ease of use, and brand consciousness serve as core factors driving the intention to use CVT. Meanwhile, perceived enjoyment and social influence function as complementary factors, further supporting this intention. Perceived risks and technology readiness of consumers were not found to influence intention to use CVT. The findings provide valuable insights into consumer behavior concerning advanced transportation technology and offer actionable recommendations to stakeholders to promote the acceptance of CVT.

This study examines how technology-organization-environment (TOE) factors influence innovation capability, which drives blockchain and enterprise metaverse adoption, ultimately impacting competitive advantage. This study uses the data from 258 managers to understand blockchain integration and enterprise metaverse to achieve competitive advantage. The findings of this study reveal that blockchain traceability, blockchain transparency, technology readiness, the threat of data ownership, and competitive pressure are significantly associated with innovation capability. Moreover, the findings suggest that innovation capability is positively associated with blockchain integration and enterprise metaverse adoption. Furthermore, blockchain integration is positively associated with a competitive advantage. Finally, the findings of this study reveal that technostress moderates the relationship between innovation capability and enterprise metaverse adoption. This study enriches the existing literature pertaining to blockchain, enterprise metaverse, and technology adoption. The research also enriches the TOE and innovation capability theory. Furthermore, this study will also be helpful to organizations that intend to adopt blockchain and enterprise metaverse in the future.

PurposeThis research focuses on developing and testing a conceptual model that explores customer behavioural responses (engagement, experience and recommendation) towards generative artificial intelligence (AI)-enabled chatbots. It highlights the significant influence of anthropomorphic characteristics in enhancing perceptions of competence and warmth, further enhancing perceived authenticity. In addition, this study aims to investigate how the need for social interactions moderates these relationships.Design/methodology/approachThis study used a self-administered questionnaire distributed on Prolific Academic to gather data from 282 eligible participants worldwide. This study uses a structural equation modelling approach to answer the research questions.FindingsThe findings reveal that anthropomorphic characteristics of generative AI-enabled chatbots are positively associated with perceived competence. Moreover, the findings show that the perceived competence and warmth of generative AI-enabled chatbots are significantly associated with perceived authenticity. Furthermore, the results highlight that the perceived authenticity of generative AI-enabled chatbots is positively associated with customer engagement, experience and recommendation. Finally, the results illustrate that the impact of anthropomorphic characteristics on perceived warmth is significantly moderated by the need for social interaction.Originality/valueThis study enriches the generative AI literature and guides organizations in understanding consumer interactions for leveraging generative AI-enabled chatbots. Furthermore, this study contributes to the social response theory literature as this study investigates how user behavioural intentions towards generative AI-enabled chatbots are influenced by their perceived level of anthropomorphic characteristics.

In this study, we use mandatory gender quota rule for corporate boards introduced in the year 2015 in India as an identification strategy to explore the relationship between women on board and accounting conservatism. Our results based on the difference-in-differences regression model, using firms that appointed women for the first time after the law as the treatment group, support the conjecture that women directors improve governance by adhering to accounting principles, resulting in improved accounting conservatism.

This study aims to comprehensively analyze the current body of literature on adaptive selling behavior. Accordingly, it employs a hybrid review methodology that integrates systematic literature review, lexicometric analysis, and meta-analysis. This multifaceted approach facilitates both thematic and theoretical syntheses while revealing empirical inconsistencies through quantitative validation. The study further advances the field by developing a conceptual framework based on the antecedents-decisions-outcomes model and proposing a future research agenda based on theory-context-characteristics-method framework, encompassing emerging phenomena, such as technology-driven adaptive selling, gamification, and corporate social responsibility alignment. This novel triangulation of methods and the resulting integrated framework offer rich insights, charting a future roadmap for adaptive selling research.

ESG campaigns on rewards-based crowdfunding platforms face challenges due to their immeasurable rewards to investors. In this study, we examine the role of community activism and social ties in campaign success. Using community activism and social ties at county-level data in the US, we show that, those ESG campaigns (compared to propensity score matched non-ESG campaigns) launched in the counties with strong social ties and counties where there is higher civic engagement, show higher success rate. Our results highlight the role of quality of community engagement and the strength of social ties in ESG campaign success.

Connected vehicles play a pivotal role in shaping the future of transportation, contributing to safer, more efficient, and sustainable mobility. However, the widespread adoption of connected vehicles faces uncertainty due to varying levels of consumer readiness in emerging markets. This study examines consumers' technology readiness towards Connected Vehicle Technology, investigating the influence of readiness dimensions on their intention to use, identifying distinct consumer segments, and exploring how these segments differ in their usage intention. Technology Readiness Index (TRI2.0) and relevant statistical tools are utilised to achieve the research goals. The findings reveal that technology motivators are more significant than inhibitor dimensions, and users are divided into four segments based on their readiness levels, which ultimately impact their intention to use Connected Vehicle Technology. These insights offer valuable guidance for automakers, technology providers, and policymakers to facilitate the widespread adoption of connected vehicles and plan the future of transportation systems.

With increasing food budgets, consumers look for healthier and better food products to add to their diets. For some consumers, imported food products provide an entry into global food consumption trends. As the market for imported products increases, locally produced alternatives appear at lower prices due to lower transportation costs and the lack of import duties. Even though the perceived utility is higher for imported products, these products also come at a higher cost to the environment due to transport emissions. For an environmentally conscious consumer, production and transportation emissions reduce the overall willingness to pay. However, it is not clear if the final price the consumer is willing to pay would be higher for an imported product or a domestic product. This paper provides a utility-based model to understand how consumers navigate these tradeoffs while factoring in the emissions of the products. More specifically, two firms providing domestic vs. imported products with carbon footprints compete in the end-market in price competition. The model provides market outcomes based on consumer characteristics and firms' optimal pricing. Both products can jointly exist in the market when there is a considerable difference in the carbon footprint. When the carbon footprint of both products is similar, exclusive product markets emerge. In a joint market, a higher emissions (either transportation or manufacturing) lead to lower prices and increased market participation, even under consumer environmental preferences.

The present work investigates the previous research on cryptocurrency since its inception and provides an overview of its journey as a discipline. The study utilizes the Scopus database to collect the documents, resulting in 2,533 research papers published between 2008 and 2021. A bibliometric analysis is conducted, followed by the network analysis using VOSviewer. The study provides a more comprehensive overview of cryptocurrency research and its significant growth since its inception. It also gives us an understanding of how cryptocurrency research emerged as a domain across various dimensions. Further, it also provides an understanding of the possible future research areas for cryptocurrency research. It will provide a base for scholars and researchers to identify the current gaps in cryptocurrency research across various domains within business, management, and accounting and select the most appropriate field for their study.

Agriculture financing in developing countries is dominated by informal lending. One challenge in the expansion of institutional (formal) credit is the lack of reliable data on the historical performance of farmers. Due to the absence of data, financial institutions face uncertainties that obstruct the decision-making process, leading to sub-optimal credit disbursal. Based on the theoretical lens of uncertainty reduction, this study focuses on achieving two key research objectives: identifying uncertainties in institutional crop credit management processes and examining how a data-driven digital transformation for social innovation based on satellite imagery analytics could alleviate these hindrances. We longitudinally study a satellite imagery analytics firm and complement the case data with stakeholder interviews. The results capture state space, option, and ethical uncertainties institutional lenders face in expanding crop credit and explain how data-driven digital transformation can reduce these uncertainties. Adopting such a data-driven digital transformation promises to make different stakeholder groups interact and collaborate to achieve the common objective of financial inclusion of small-scale economic actors. Further, we show that satellite imagery in crop credit management can significantly reduce the uncertainties caused by the lack of independent data sources.

Synthetic Aperture Radar (SAR) imagery is indispensable for earth observation, offering the ability to capture data under challenging conditions such as cloud cover and darkness. However, its grayscale format and speckle noise hinder interpretability and pose significant challenges for traditional processing methods. This study introduces an innovative framework for SAR image colorization, leveraging an Attention-Based WGAN-GP (Wasserstein GAN with Gradient Penalty). The model incorporates multi-head self-attention mechanisms to enhance feature extraction, capture long-range dependencies, and dynamically suppress noise through a novel variance-based attention adjustment mechanism. Extensive evaluations on Sentinel-1 and Sentinel-2 datasets across diverse terrains, including agriculture, urban areas, barren land, and grasslands, demonstrate the model's superiority over existing approaches. It achieves an LPIPS score of 0.27, SSIM of 0.76, and an average inference time of 0.22 s, showcasing its ability to preserve spatial coherence and perceptual quality even in complex, noisy environments. This capability enables real-time applications in disaster management, flood monitoring, and urban planning, providing actionable insights and advancing the state-of-the-art in SAR image processing.

News recommender systems are decision support systems that exploit user-article interactions over a short duration of time to discover users' interests and predict unseen news articles to generate a ranking of news articles that are relevant and interesting. In the news recommendation scenario, the relevance of articles decays quickly, and fresh articles are generated daily. Session based models are proposed using time-aware approaches to exploit interactions sequentially. Prior news recommender systems do not consider emotional information expressed in news articles within sessions for recommendations. Emotions play a key role in supporting decision-making and emotionally charged headlines can evoke curiosity or urgency, prompting users to click on certain articles. This paper presents an innovative decision support system for session based news recommendation, using expressed emotions from news articles, such as expressed in the title, abstract, and text, to improve user decision-making. We introduce a novel methodology that incorporates expressed emotions into three session based news recommendation models. Our results demonstrate that expressed emotion carries valuable information to improve session based news recommenders on various ranking metrics significantly and proved especially beneficial in scenarios with limited user interaction history, addressing the cold-start problem. The results show significant improvements in ranking metrics, emphasizing the utility of emotional features for dynamic decision-making support.

PurposeSmart farming (SF) holds immense potential in making farming viable and improving farmers' livelihoods. However, its adoption is still in the early stages, and the resulting impacts are underexplored. This study investigates the information systems/policy and implementation barriers to the adoption of SF and its implications for various socioeconomic aspects.Design/methodology/approachThis survey research used a structured questionnaire to collect data on farm details, farmer characteristics and usage of SF technologies from a sample of 197 farmers based in the State of Karnataka, India. Exploratory factor analysis with principal component extraction is used to validate the proposed questionnaire constructs. Results are analyzed using one-way and two-way ANOVAs and interaction plots.FindingsOur study throws light on the benefits and barriers of SF technologies. Advanced SF technologies displayed a significant positive effect on socioeconomic variables compared to startup SF technologies, which displayed no significant effect. Overcoming information systems/policy barriers for adopting startup and advanced SF technologies displayed significant positive effects on farmers' health and net income, whereas overcoming implementation barriers for adopting advanced SF technologies led to improvement in the farmers' gross income.Originality/valueAlthough the technical feasibility of SF has been explored in the literature, its adoption barriers and socioeconomic impacts have been underexplored. To the best of our knowledge, the interaction between different types of barriers and the level of SF adoption has not yet been investigated in the literature. Our study addresses those research gaps using data from a previously underexplored context, viz. developing nations.

PurposeThe present study examines the impact of sustainable transitions on the energy and environmental efficiency (EEE) of nations across the developed and developing world. It studies the temporal shift in the share of renewable sources in energy generation. It also analyses the shift in the efficiency frontier of nations using data envelopment analysis (DEA). Further, it studies the macro-level drivers of EEE in the countries.Design/methodology/approachAs the first step, we benchmark the EEE of the developed and developing nations using DEA. Subsequently, we look at the influence of institutional quality, human capital, R&D and knowledge systems on EEE, to develop a comprehensive understanding of the macro-level drivers of EEE.FindingsOur analyses reveal that a country's institutional quality, human capital and R&D are critical determinants of EEE. The results show that while human capital has a significant positive impact on EEE, R&D expenditure alone has no substantial impact. The findings also suggest that knowledge diffusion disperses best practices across nations and bridges EEE gaps.Practical implicationsAttempts to promote sustainable energy transitions and improve EEE have met with varying levels of success. The results of this study will provide a useful guideline for the governments to achieve the goal of EEE through sustainable energy transitions (SET).Originality/valueUnlike previous studies, we adopt a multi-factor EEE assessment. We also examine additional influences like the human capital of a nation and its knowledge management system to develop a comprehensive understanding of the macro-level drivers of EEE.

This study examines the impact of Environmental, Social, and Governance (ESG) performance on firms' financial performance, specifically focusing on the mediating role of institutional investors and the implications for business. Using data from 27,412 firm-year observations across 20 established and emerging economies over 14 years, the findings suggest that ESG performance favorably impacts firms' financial performance, and the effects differ by geographical region. In emerging markets, institutional investors have a significant impact on environmental(E) factors, highlighting their growing business relevance in international markets. In contrast, social(S) and governance(G) elements have a greater influence in developed markets, which is aided by institutional investors. Endogeneity issues are addressed through robustness checks and additional analysis using instrumental variables and Two-Stage Least Squares models, validating the results. The findings highlight the need for framing regional ESG policies and leveraging institutional investors for effective ESG implementation and achieving sustainable financial success across international markets.

PurposeThe study aims to unravel the complex dynamics between consumers and smart voice assistants (SVAs) in online transactions by identifying key values and motivations through the triangular theory of love (TTL) and the theory of consumption value (TCV), offering a nuanced view of digital consumer behavior.Design/methodology/approachThe study employed a quantitative approach. It analyzed data from 289 participants in the UK and US to explore relationships among perceived values, passion, intimacy, commitment, and engagement, employing structural equation modeling and PROCESS macro for hypothesis testing.FindingsThe study's findings suggest that perceived values such as symbolic, novelty, and anthropomorphic values foster passion for SVAs in consumers. In addition, the study establishes that passion positively impacts intimacy and commitment, further influencing consumer engagement. The moderation analysis of technostress reveals its influence on the relationship between social presence value and passion.Research limitations/implicationsThe study enhances understanding of SVAs adoption in terms of perceived consumer value and engagement literature. The practical implications offer actionable insights for SVAs marketers and companies on leveraging hedonic, anthropomorphic values and a user-centric approach, complemented by strategies to mitigate technostress to enhance engagement and foster a deep emotional connection with SVAs.Originality/valueThis study introduces a novel integrated framework combining the TTL and the TCV to explore consumer engagement with SVAs, offering unique insights into the motivations behind SVAs usage and the impact of technostress, thereby enriching both academic and practical understanding of SVAs engagement in the digital marketplace.

The present study investigates the propensity of organisations to adopt the Enterprise Metaverse. This research reveals that organisational readiness, facilitating conditions, performance expectancy, and normative and mimetic pressures are significantly associated with adopting the Enterprise Metaverse. Additionally, trust significantly mediates the relationship between organisational competency, organisational complexity, performance expectancy, effort expectancy, mimetic pressures and adopting the Enterprise Metaverse. Also, the findings reveal that trust and management support play a crucial mediating and moderating role in the present context. Finally, the Artificial Neural Network (ANN) results indicate normative pressures as the most important predictor of adopting the Enterprise Metaverse. The study's outcomes are set to enhance the Metaverse literature significantly. Moreover, it aims to deepen the comprehension of B2B technology adoption, drawing from the theoretical foundations of the Unified Theory of Acceptance and Use of Technology (UTAUT), the Technology-organisation-environment (TOE) framework, and institutional theory. This research offers organisations valuable insights into the determinants linked to integrating the Enterprise Metaverse.

This study examined how Bitcoin, energy prices, and geopolitical risk interact by examining the first four moments (mean, variance, skewness, and kurtosis) of their return distributions by using wavelet analysis. The findings reveal that the co-movement patterns of energy index, geopolitical risk index, and Bitcoin prices are time and frequency sensitive. During the turbulent period of 2020-2024, significant cross effect was observed at medium- and long-term time scales in the relationship between the energy index and the geopolitical risk index. Similarly, in the case of Bitcoin and the geopolitical risk index, significant cross-effects were detected at medium- and short-term time scales. From 2021 onwards, a strong coherence is observed at high and medium frequencies for all four moment pairs among Bitcoin, energy prices, and geopolitical risk. In terms of the Bitcoin-energy relationship, significant co-movement in mean and volatility is noted throughout most of the sample period and across different frequency bands. Moreover, cross-skewness and cross-kurtosis connections are more prominent at short- and medium-term horizons, especially during covid pandemic. These insights are valuable for investors and policymakers in risk management.