PurposeAs the world comes to terms with climate change as an accepted scientific reality and focuses on mitigation, the major challenge emerges in the financing of climate change mitigation or green sustainable finance. This study aims to systematically map, decode and interpret the evolving landscape of green finance research by identifying core themes, emerging trends and theoretical underpinnings.Design/methodology/approachTo address this objective, the study adopts a systems thinking approach. We use multiple analytical methods, such as traditional bibliometric analysis and more advanced text mining techniques, such as Latent Dirichlet Allocation (LDA), BERTopic modelling, association rule mining and knowledge graph construction.FindingsThe analysis identifies six major thematic clusters in the green finance literature: (1) Sustainable Finance & Environment, (2) Energy & Sustainable Economic Growth, (3) Green Enterprises, (4) Green Finance & Investment, (5) Green Banking & Financial Institutions and (6) Green Market Dynamics & Risk Management. The study highlights green finance's growing complexity and interdisciplinarity, including regulatory innovation, ESG integration, climate risk transmission, cross-market spillovers, blue finance, localized financial models and digital technologies.Originality/valueThis study offers a novel scholarly contribution by combining bibliometrics with natural language processing and network-based methods to uncover both latent structures and semantic relationships in green finance research. The research improves methodological sophistication and establishes the groundwork for future studies to employ sophisticated, computational methods in theory development and literary analysis. The results highlight the importance of establishing lucid, inclusive and adaptable financial instruments that promote long-term sustainability within the financial system.

PurposeThe mechanism through which corporate social responsibility (CSR) leads to customer engagement (CE) with a brand is unclear, with most studies proposing a direct relationship with limited focus on intervening variables. To fill the gap, the current research suggests a pathway from CSR perceptions to CE, including the mediating role of trust, company-consumer identification (CCI) and satisfaction, and the moderating roles of customer values and CSR-brand image fit.Design/methodology/approachThe study proposes a model connecting CSR perceptions to CE through thirteen hypotheses. To validate them, data were collected in two phases from 596 randomly selected respondents, who were customers of the products/services of a firm and were aware of the various CSR activities. The data was analysed using covariance-based structural equation modelling (CBSEM).FindingsThe analysis of the results revealed that all the study hypotheses were supported, including the mediation and the moderation ones. The findings validate the direct relationship between CSR perceptions and trust and CCI, as well as the sequential build-up to CE, with the mediating role of satisfaction and the intervening roles of customer values and brand image-CSR fit.Originality/valueThe work offers a unique mechanism through which a customer can engage with a firm through its CSR efforts. The findings guide managers in enhancing their efforts to market CSR effectively and develop positive connections with consumers.

Using a collective household model and a recent structural estimation methodology, we estimate how intrahousehold resource shares of individual members vary in India by exogenously varying soil texture. In general, we find that relative to men, women and children have substantially lower access to consumption resources within Indian households. However, the gender gap in resource shares and consequently relative female poverty is found to be substantially lower in households in clayey relative to loamy soil regions. Children's resource shares are also modestly higher in clayey soil regions, but significant differences in child poverty are not found across soil textures. Because clayey soil has been associated with higher relative female employment and overall gender norms favorable to women, our findings suggest that soil texture also plays an important role in facilitating improvement of relative female bargaining power within households.

This paper examines, within a panel data setting, the spatial impacts on prices and on wages, of India's trade liberalization in edible oils. Starting from near-autarkic policies that prohibited the import of edible oils, imports surged to meet most of the domestic demand following trade liberalization in the 1990s. While the domestic oils sector provides negligible employment, it uses domestically grown nontraded oilseeds, which occupy 14% of cultivated land and are next in importance only to the cereal grains of rice and wheat. These oilseeds are grown in the dryland arid regions where farm incomes are low and precarious. To examine spatial effects, the paper constructs geographically varying exposure to trade shocks that depend on the cultivated area planted with oilseeds. Consistent with a model of spatial price competition, the paper finds greater price impacts in the high oilseed growing regions. On the other hand, spatial impacts on wages are not significant, suggesting labor reallocation. While we do find significantly greater cropping pattern and production responses in the high oilseed growing regions; however, such evidence does not extend to labor reallocation outside agriculture.

Influencer marketing has rapidly become a cornerstone of modern advertising, yet sponsored posts often underperform compared to organic content in terms of consumer engagement. This study investigates how brand-imposed language constraints affect engagement in sponsored influencer content. Drawing on Media Naturalness Theory and the control paradox, we hypothesize that language formality disrupts the authenticity of influencer communication, leading to lower engagement. Using a dataset of 1.4 million Instagram posts, including around 200,000 sponsored postings, our analysis reveals that formal language reduces engagement, while informal language features, specifically higher use of netspeak and emojis, enhance engagement. To further validate these findings, we conducted a controlled experiment examining the effect of language style on engagement and perceived authenticity. These findings suggest that informal language fosters a more genuine connection between influencers and their audiences, increasing interaction in sponsored content. The paper provides practical recommendations for brands, urging them to co-create flexible language strategies with influencers, allowing for more authentic expression and boosting consumer engagement. The current study also uses the latest agentic AI models to replicate similar results through additional experiments, contributing to the theoretical understanding of influencer marketing and the practical strategies for optimizing influencer collaborations.

Hawkes process (HP) is a point process with a conditionally dependent intensity function. This paper defines the generalized fractional Hawkes process (GFHP) by time-changing the HP with an inverse L & eacute;vy subordinator. This definition encompasses all potential (inverse Levy) time changes as specific instances. We also explore the distributional characteristics and the governing difference-differential equation of the one-dimensional distribution for the GFHP. Furthermore, we focus on the specific tempered fractional Hawkes process (TFHP), which is derived by time-changing the Hawkes process (HP) using an inverse-tempered stable subordinator. Our results generalize the fractional Hawkes process introduced in [20] to a tempered version, which exhibits semi-heavy-tailed decay. We derive the mean, the variance, covariance and the governing fractional difference-differential equations of the TFHP. Finally, we present simulated sample paths of the HP and the TFHP. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AItraining, and similar technologies

While migration is a largely discussed form of labor mobility, internal return migration of workers has not received much attention. Individuals return to their native or previous place either for reasons related to work, distress, or family. Not only are they returning to a place that is familiar to them in terms of language, culture, and society, but also, they may have acquired some skills before returning. Therefore, labor market outcomes for return migrants are likely to differ from those of nonmigrants, who are otherwise similar to each other in most aspects. Against this context, the paper compares the earnings of internal return migrants with nonmigrants in India using the Periodic Labor Force Survey 2020-21. To this end, the study performs ordinary least square estimation by sector (rural and urban), gender, age cohorts, and employment activity status (self-employed, regular salaried, casual labor). As part of robustness analysis, it also uses the propensity score matching method. Both the exercises suggest that internal return migrants, on average, experience an earnings penalty in rural areas but a premium in urban areas. Also, a regular salaried internal return migrant experiences an earnings premium irrespective of the location (rural or urban). The earning pattern for men largely imitates the overall pattern. However, the earnings differential between female internal return migrants and nonmigrants is not significant as most of them migrate because of reasons other than work, like marriage. The findings of this study have significant implications, as what individuals do and how much they earn upon their return have direct consequences on the employment and income distribution of any region.

Computer experiments with quantitative and qualitative inputs are widely used to study many scientific and engineering processes. Much of the existing work has focused on design and modeling or process optimization for such experiments. This paper proposes an adaptive design approach for estimating a contour from computer experiments with quantitative and qualitative inputs. A new criterion is introduced to search for the follow-up inputs. The key features of the proposed criterion are (a) the criterion yields adaptive search regions; and (b) it is region-based cooperative in that for each stage of the sequential procedure, the candidate points in the design space is divided into two disjoint groups using confidence bounds, and within each group, an acquisition function is used to select a candidate point. Among the two selected points, a point that is closer to the contour level with the higher uncertainty or that has higher uncertainty when the distance between its prediction and the contour level is within a threshold is chosen. The proposed approach provides empirically more accurate contour estimation than existing approaches as illustrated in numerical examples and a real application. Theoretical justification of the proposed adaptive search region is given.

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.

Objective: Negative social media ties can be defined as virtual relationships between two individuals (who may or may not be from the same social circle) who engage with each other, resulting in adverse outcomes for at least one individual. Research indicates that teenagers' social media usage adversely affects them through self-deprecating comparisons, cyberbullying, and participation in high-risk activities. Individuals with negative social media encounters are more prone to social isolation (Primack et al., 2019). The present study develops and validates a scale to measure negative social media ties among individuals in an online social media. Method: The established process of scale construction involving item generation exploratory and confirmatory factor analyses was used to construct the scale. In addition, regression analysis was done to test for the scale's nomological validity. Results: The results indicate that the developed negative social media tie scale comprising 11 items is a single-factor construct that satisfies all psychometric properties. Regression analysis supported the nomological validity of the developed scale. Conclusion: Recently, researchers have shifted their focus to negative ties. In addition, there has been a surge in the usage of social media platforms; this scale can be utilized to assess the prevalence, antecedents, and consequences of negative social media ties and contribute to the existing and evolving body of knowledge in this domain.

PurposeGrounded in training engagement theory and the theory of planned behavior, this study aims to propose a sequential mediation model, contributing to a nuanced understanding of the systematic process of managerial transfer design that drives training transfer (TT).Design/methodology/approachBased on the research framework, data was collected from 263 managerial executives over different timelines. The first questionnaire (T1), after training completion, was administered in two Indian industrial organizations, and 410 managers responded. The second online questionnaire (T2) was sent to the respondents after 12 weeks of training, and only 263 usable responses were received. Structural equation modeling (SEM) was used to examine the direct and indirect impact of the managerial transfer design on TT through the two mediators, motivation to transfer (MtT) and volition to transfer (VtT).FindingsThe results based on SEM bootstrap analysis suggested a significant mediation effect of MtT and VtT in the association between managerial transfer design and TT in sequential order.Practical implicationsHuman resource development personnel in organizations should consider the conditions associated with managerial transfer design, MtT and VtT while designing and implementing positive TT strategies.Originality/valueThis study stands out from existing studies by using a time-lagged design to empirically examine a sequential mediation mechanism, where MtT and VtT enhance TT through managerial transfer design.

PurposeArtificial intelligence (AI) has brought major disruptions in the new generation human resources (HR) ecosystems. The research community as well as chief human resources officers (CHROs) have been taking strong initiatives to examine the use of AI in human resource management (HRM) function, including harmonious human-machine collaboration. The AI-HRM area is under-researched, and this study addresses an important research gap regarding the benefits and challenges of AI applications in HRM.Design/methodology/approachThe study adopts a qualitative research methodology (abductive case research) and collects data from multiple sources in three Indian companies. These organizations span diverse sectors and were at different stages of AI adoption in HRM at the time of the study. The multi-data-sources strategy helps triangulation and establishes credibility of the research.FindingsThe findings provide a clear view about the benefits of AI applications in HRM, higher productivity, recruitment efficiency, adaptive learning and high-quality HR decisions. The study also underpins key challenges, including a lack of human touch, employees' loss of control over jobs and the fear of losing jobs to AI.Originality/valueThe research provides a theoretical contribution to the growing AI-HRM literature in the context of the theory of cost economics in the context of recruitment efficiency as well as leveraging adaptive learning from the context of the multi-level organizational learning framework to improve the performance of the HR function. The research also provides significant managerial insights for CHROs recommending that they embrace humanized AI in the HRM function and institutionalize AI ethics.

Fertilizer is a resource-intensive, hard-to-abate industry that provides crucial support to sustain agricultural production. This study estimates the technical efficiency (TE) of the Indian fertilizer-producing firms over the years 2009 to 2019. We use the input distance function under the production function farmwork to find potential input reduction to produce a certain output level. We further examine potential determinants of TE, such as age, ownership, and innovation. We use Greene's (2005) 'true fixed effect' and 'true random effect' to isolate inefficiency effectively from fixed effects. The results demonstrate a notable scope for improvement in TE, with a potential increase of up to 30% for half of the firms. There are substantial differences across firms where the bottom 25 percent of samples have less than 60 percent TE. TE slightly declined, particularly in the case of state-owned firms (SOEs), whereas privately owned firms have greater TE than SOEs. Better financial performance and R&D activity positively influence TE, while experienced firms have better TE. Hence, the results support resource-based theory, which argues that firms with better resources and capabilities achieve greater efficiency. Effective government policies should be implemented to boost firms' capacity for innovation and TE to meet the country's future fertilizer needs.

With the surge of domestic tourism in India and the influence of social media on young tourists, this paper aims to address the research question on how social return - responses received on social media sharing - of recent trip details can influence decision-making for short-term future travels. The paper develops a multi-model framework to build a predictive machine learning model that establishes a relationship between a traveler's social return, various social media usage, trip-related factors, and her future trip-planning behavior. The primary data was collected via a survey from Indian tourists. The imbalance in the data was addressed using a robust oversampling method, and the reliability of the predictive model was ensured by applying a Monte Carlo cross-validation technique. The results suggest at least 75% accuracy and provide crucial practical implications for the domestic tourism sector in India, with future research directions concerning young travelers and social media.

To achieve net zero targets, countries worldwide are focusing on scaling renewable energy. While India has made substantial strides in solar power capacity, the adoption of Floating Solar PV (FPV) remains limited. Despite apparent benefits, scaling FPV technologies presents several challenges including identifying suitable reservoirs for FPV implementation, and higher than ground-based solar PV costs. This study addresses these challenges and provides insights into the technological, financial and other related aspects for its at-scale adoption. To address the technological aspect of FPV adoption, this study develops a novel selection framework based on specific necessary and conducive conditions to select reservoirs suitable for FPV deployment in India. Applying this framework to a comprehensive dataset of 130 reservoirs in India, 17 reservoirs were identified. We also developed a reservoir ranking framework, to evaluate and rank the 17 identified reservoirs. The analysis revealed that while Almatti reservoir is the most suitable reservoir, Maharashtra emerged as the state with the maximum number of reservoirs, followed by Odisha. To address the financial aspect of FPV adoption, we carried out a levelized cost based economic assessment and found that LCOE for FPV systems ranges from INR 3.16-3.80/kWh which is much lower than the national average. Among the 17 reservoirs, FPV at the Tungabhadra reservoir has the least LCOE of INR 3.16/kWh. For reducing LCOE of FPV vis-a`-vis ground-based PV systems, we suggest policies such as interest rate subsidy, capital expenditure subsidy and Generation Based Incentives. The study's framework and insights could be useful for countries with high FPV potential looking to scale up FPV technology.

The growing popularity of fitness influencers on Instagram has created a competitive space where human and AI-generated influencers vie for user engagement. This study examines how agent type (human vs. AI), gender, and age influence perceptions of interpersonal attraction, credibility, and social presence. Using a 2 x 2 x 2 experimental design, 161 participants rated mock Instagram posts from influencers across these categories. Results showed that human influencers were rated significantly higher in social attraction, credibility, and social presence than AI influencers. Younger influencers were perceived as more physically attractive, while older influencers scored higher in character and social presence. Regardless of agent type, female influencers were rated higher in physical attraction. These findings highlight persistent demographic biases and a strong preference for human connection in digital fitness spaces, offering key insights for brands, marketers, and influencer strategy.

We present beta4dist, the first open-source Python package that implements a likelihood-based estimation framework for the four-parameter Beta distribution. This flexible distribution is widely used to model bounded, continuous data with diverse shapes, including skewed and heavy-tailed patterns. Such datasets are common in fields such as hydrology, environmental science, and reliability engineering. The software estimates location parameters via order statistics and computes shape parameters using marginal likelihood optimization, ensuring that all estimates adhere to natural parameter constraints. In addition to core estimation routines, beta4dist includes utilities for density evaluation, random sampling, cumulative distribution, quantiles, and model diagnostics. The package is fully tested, easy to integrate into standard Python workflows, and supports both research reproducibility and practical applications requiring shape-robust modeling tools.

The challenges associated with critical and abrupt pandemic outbreaks have long-term impacts, an example is recent pandemic that presented severe threats to the humanity and global supply chains. To effectively neutralize futuristic pandemics, the need is to develop and adopt proactive plans. Consequently, this study formulates and examines proactive supply chain strategies to neutralize the potential future pandemics. The study uncovers 19 strategies that are proactive, responsible, and resilient in nature. These strategies are ranked using the Grey-DEMATEL (Decision-Making Trial and Evaluation Laboratory) method of Multi-Criteria Decision Making (MCDM) area. The individual ranking is obtained based on the capability of each strategy in neutralizing the effects of future pandemics. The analysis exhibits that the top three ranked strategies are: Inventory policy planning and strategy, Optimizing shift management for employees and labor, and Prioritizing production scheduling agility. The key findings include policy amendments and optimal inventory levels, flexible shifts and operational preparations and implementation of agile manufacturing systems. The discussion on analysis helps establish theoretical and practical implications for the stakeholders. The strategic recommendations propounded in this manuscript are for the consideration of the stakeholders to effectively tackle future disruptions.

This work examines the perception of safety of travelers during the recovery and post-recovery period of a pandemic. It measures the effect of a hotel's perceived health safety service quality, referred to as PHS-SERVQUAL, on the hotel's brand image of being safe, customer satisfaction, and revisit intention. Data from 856 respondents (phase I = 417; phase II = 439) were empirically validated with structural equation modeling using AMOS21. The study finds that perceived reliability, tangibles, and responsiveness strongly impact a hotel's brand image of being safe, but perceived assurance and empathy do not. Next, apart from perceived tangibles, the rest of the dimensions significantly influence customer satisfaction. The hotel's brand image of being safe was positively associated with customer satisfaction and customer revisit intention, with customer satisfaction affecting customer revisit intentions.

Cross-border cold supply chains face critical challenges, including demand variability, quality degradation, and inefficiencies due to limited transparency. This study presents an analytical model comparing traditional and blockchain-enabled cold supply chains, emphasizing optimal order quantities, retail pricing, quality preservation, and profitability. A distinctive feature of this work is the integration of real-time data-sharing capabilities enabled by blockchain, which enhances demand estimation by incorporating additional data from random demand variations, an essential factor in improving forecasting accuracy and operational efficiency. A novel blockchain cost-sharing mechanism is introduced, redistributing implementation costs among stakeholders to lower financial barriers and encourage collaborative participation. Our findings reveal that blockchain adoption not only improves supply chain coordination and profitability but also fosters greater customer trust by enabling lower retail prices, in contrast to traditional models that rely on higher prices to offset transparency gaps. These insights offer a strategic framework for managers aiming to leverage blockchain technology for more resilient, efficient, and sustainable cross-border supply chain operations.