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Substantial pressure from civil society and investors has forced governments around the world to take climate neutrality initiatives. Several countries have pledged their nationally determined contributions towards net-zero. However, there exist various obstacles to achieving the same and the agriculture sector is one of them. Thus, this study identifies and models the critical barriers to achieving climate neutrality in the agriculture food supply chain (AFSC).

Sixteen barriers are identified through a literature survey and are validated by the questionnaire survey. Furthermore, the interactions amongst the barriers are estimated through the application of the “weighted influence non-linear gauge system (WINGS)” method which considers the both intensity of influence and the strength of the barrier. To mitigate these barriers, a framework based on green, resilient and inclusive development (GRID) is proposed.

The obtained results reveal that lack of collaboration amongst AFSC stakeholders, lack of information and education awareness, and lack of technical expertise obtained a higher rank (amongst the top five) in three indicators of the WINGS method and thus are the most significant barriers.

This paper is the first attempt in modelling the climate neutrality barriers for the Indian AFSC. Additionally, the mitigating strategies are prepared using the GRID framework.

The study evaluates small marginal farmers’ (SMFs) potential behavior, attitude and trust in the adoption of innovative emerging technologies.

The study employed an agile multi-factor approach to conceptualize a digital marketplace to connect a supply chain ecosystem for stakeholders.

The empirical findings suggest that most SMFs are willing to embrace innovative technologies. Nonetheless, they lack the necessary technological oriented education, training and funds to innovate. However, their reluctance to adapt changes is attributable to their fear of losing past customs and practices; they were threatened by the reaction of intermediaries (arthyias) to the adoption of technologies, which could result in them suffering huge losses.

This innovative disintermediation business model has a significant potential to reduce information asymmetry, cost and hoarding – and can thus increase the SMFs’ profit margins. Agricultural technological innovations have a profound potential to impact their supply chain logistics positively by reducing the wastage of perishable food and thus enhancing the consumer experience.

This study aims to investigate the factors that impact the solo travel intentions of millennial women in rural and urban areas. By exploring these factors, this research also sheds light on the similarities and differences in travel behaviors and motivations of women in different geographical contexts within India.

ChatGPT is an advanced artificial intelligence (AI) form that can generate human-like text based on large amounts of data. This paper aims to empirically examine the ChatGPT adoption level among Indian individuals by considering the key factors in determining individuals’ attitudes and intentions toward newly emerged AI tools.

This paper used “partial least square structural equation modeling” (PLS-SEM) to investigate the relation among several latent factors by applying a representative sample of 351 individuals.

This study found that trialability, performance expectancy and personal innovativeness significantly influence individuals' attitudes, while compatibility and effort expectancy do not significantly impact attitudes. Additionally, trialability, performance expectancy, effort expectancy, personal innovativeness and attitude significantly influence behavioral intentions. However, compatibility has an insignificant impact on behavioral intention. Moreover, the research highlights that attitude and behavioral intention directly correlate with actual use. Specifically, the absence of compatibility makes people hesitate to use technology that does not meet their specific needs.

These unique findings provide valuable insights for technology service providers and government entities. They can use this information to shape their policies, deliver timely and relevant updates and enhance their strategies to boost the adoption of ChatGPT.

This paper is one of the pioneering attempts to exhibit the research stream to understand the individual acceptance of ChatGPT in an emerging country. Moreover, it gained significant attention from individuals for delivering a unique experience and promising solutions.

Financial technology (FinTech) is experiencing transformation because artificial intelligence has become the new norm to enrich the experiences of individuals in this modern era of technological advancement. The article utilizes the stimuli-organism-response (SOR) framework to investigate how individual attitudes and behavioral intentions influence the adoption of FinTech, particularly in mobile banking.

433 respondents participated in the self-administered survey to answer questions related to demographic profiles and items to assess the variables adopted in the conceptual framework. The study applied “partial least squares structural equation modeling” PLS-SEM to analyze the data.

A structural equation model indicates that perceived usefulness and ease of use significantly affect attitude and behavioral intention. Moreover, the outcomes show that perceived value and social influence significantly influence, while perceived risks and performance expectancy insignificantly affect behavioral intention. Further, the outcomes also confirm that attitude and behavioral intention substantially influence mobile banking adoption.

The article provides insights for practitioners to improve and assess the quality of mobile banking services by using proposed antecedents that may increase the actual use of FinTech services, which serves as a valuable resource for stakeholders.

The new research model adds to the existing literature by offering empirical evidence of mobile banking adoption by considering three theories. Further, the study builds upon the S-O-R framework that incorporates FinTech attributes to explain the antecedents of the actual use of FinTech towards mobile banking adoption.

This study aims to examine the impact and contribution of priority and non-priority sectors, as well as their sub-sectors, on the gross non-performing assets of public, private and foreign sector banks.

The Reserve Bank of India's database on the Indian economy is used to retrieve data over 13 years (2008–2021). Public sector (12), private sector (22) and foreign sector (44) banks are represented in the sample. Two-way ANOVA, multiple regression and panel regression statistical techniques are used in SPSS and EViews to examine the data. Further, the results are also validated by using robustness testing by applying the fully modified ordinary least square (FMOLS) and dynamic least square (DOLS) regression.

The results showed that, for private and foreign banks, the non-priority sector makes up the majority of the total gross non-performing assets, although both the priority and non-priority sectors are substantial for public sector banks. The largest contributors to the total gross non-performing assets in public, private and foreign banks are industries, agriculture and micro and small businesses. The FMOLS displays robustness results that are qualitatively similar to the baseline result.

Based on the study's findings about the patterns of non-performing assets originating from these specific industries, banks might improve the way in which these advanced loans are managed.

There has not been much research done on the subject of sub-sector-specific non-performing assets and how they affect total gross non-performing assets across the three sector banks. The study's primary focus will be on the issue of non-performing assets in the priority’s and non-priority’s sub-sectors, namely, agricultural, micro and small businesses, food credit, industries, services, retail loans and other priority and non-priority sectors.

COVID-19 has affected the economies adversely from all sides. The sudden halt in production has impacted both the supply and demand sides. It calls for analysis to quantify the impact of the reduction in economic activity on the economy-wide variables so that appropriate steps can be taken. This study aims to evaluate the sensitivity of various sectors of the Indian economy to this dual shock.

The eight-sector open economy general equilibrium Global Trade Analysis Project (GTAP) model has been simulated to evaluate the sector-specific effects of a fall in economic activity due to COVID-19. This model uses an economy-wide accounting framework to quantify the impact of a shock on the given equilibrium economy and report the post-simulation new equilibrium values.

The empirical results state that welfare for the Indian economy falls to the tune of 7.70% due to output shock. Because of demand–supply linkages, it also impacts the inter- and intra-industry flows, demand for factors of production and imports. There is a momentous fall in the demand for factor endowments from all sectors. Among those, the trade-hotel-transport and manufacturing sectors are in the first two positions from the top. The study recommends an immediate revival of the manufacturing and trade-hotel-transport sectors to get the Indian economy back on track.

The present study has modified the existing GTAP model accounting framework through unemployment and output closures to account for the impact of change in sectoral output due to COVID-19 on the level of employment and other macroeconomic variables.

The use of technology in 4th industrial revolution is at its peak. Industries are trying to reduce the consumption of resources by effectively utilizing information and technology to connect different functioning agents of the manufacturing industry. Without digitization “Industry 4.0” will be a virtual reality. The present survey-based study explores the factual status of digital manufacturing in the Northern India.

After an extensive literature review, a questionnaire was designed to gather different viewpoints of Indian industrial practitioners. The first half contains questions related to north Indian demographic factors which may affect digitalization of India. The latter half includes the queries concerned with various operational factors (or drivers) driving the digital revolution without ignoring Indian constraints.

The focus of this survey was to understand the current level of digital revolution under the ongoing push by the Indian government focused upon digital movement. The analysis included non-parametric testing of the various demographic and functional factors impacting the digital echoes, specifically in Northern India. Findings such as technological upgradations were independent of type of industry, the turnover or the location. About 10 key operational factors were thoughtfully grouped into three major categories—internal Research and Development (R&D), the capability of the supply chain and the capacity to adapt to the market. These factors were then examined to understand how they contribute to digital manufacturing, utilizing an appropriate ordinal logistic regression. The resulting predictive analysis provides seldom-seen insights and valuable suggestions for the most effective deployment of digitalization in Indian industries.

The country-specific Industry 4.0 literature is quite limited. The survey mainly focuses on the National Capital Region. The number of demographic and functional factors can further be incorporated. Moreover, an addition of factors related to ecology, environment and society can make the study more insightful.

The present work provides valuable insights about the current status of digitization and expects to facilitate public or private policymakers to implement digital technologies in India with less efforts and the least resistance. It empowers India towards Industry 4.0 based tools and techniques and creates new socio-economic dimensions for the sustainable development.

The quantitative nature of the study and its statistical predictions (data-based) are novel. The clubbing of similar success factors to avoid inter-collinearity and complexity is seldom seen. The predictive analytics provided in this study is quite elusive as it provides directions with logic. It will help the Indian Government and industrial strategists to plan and perform their interventions accordingly.

The purpose of this paper is to investigate millennials’ purchase behaviours towards green housing in India. This paper also examines the mediating effect of purchase intention between determinants of buying green housing and purchase behaviour in the real estate industry.

A cross-sectional research design was applied to collect data from 393 rural and 388 urban millennials. This study used “partial least squares structural equation modelling” to verify the framed hypotheses.

The outcomes indicate that attitude, environmental concern and green trust substantially influence the purchase intention and purchase behaviour towards green housing in rural and urban studies. However, perceived risk has an insignificant effect on purchase intention and purchase behaviour towards green housing in both studies. Likewise, innovativeness insignificantly impacts the purchase intention in study rural while substantially impacting the purchase behaviour in both studies. Additionally, a favourable relationship between purchase intention and purchase behaviour towards green housing in both rural and urban contexts.

This study provides fruitful evidence for practitioners, marketers and academicians about the drivers of purchase behaviour toward green housing. The results of this study also enable regulatory bodies to design appropriate strategies and tactics to foster the sustainable growth of nations.

This paper is a preliminary attempt to explore the decision to buy green housing in India. Furthermore, the authors targeted a specific age group, especially millennials, to gain a valuable understanding of how different factors affect green housing decisions in different areas, that is, rural and urban areas.

The study attempts to estimate farm subsidies the governments can save by transitioning to a millet-based production system, replacing GHG emission-intensive crops.

It updates a 131 × 131 commodity input–output (IO) table of the year 2015–16 into 2021–22 using the RAS procedure and simulates the economy-wide impacts of replacing rice and wheat with pearl millet and sorghum using consumption and production approaches. It then quantifies fertilizer, electricity and credit subsidy expenses the government can save through this intervention. It also estimates the potential reduction in GHG emissions that the transition could bring about. India is taken as a case.

Results show pearl millet expansion brings greater benefits to the government. It is estimated that when households return to their pearl millet consumption rates that prevailed in the early-reform period, this could save the Indian government Rs. 622 crores (USD 75 m). The savings shall be reinvested in agriculture to finance climate adaptation/mitigation efforts, contributing to a sustainable food system. Net GHG emissions also decline by 3.3–3.6 MMT CO2e.

Indian government has been actively aiming to bring down paddy areas since 2013–14 through the Crop Diversification Program and promoting millets (and pulses and oilseeds) on these farms. The prime reason is to check rapidly declining groundwater irrigation in Green Revolution states. Regulations in the past in these states have not brought the intended results. Meanwhile, electricity and fertilizers are heavily subsidized for agriculture. A slight shift in the cropping system can help conserve these resources. Meanwhile, GHG emissions could also be brought down and subsidies could well be saved. The results of the study indicate the same.

A less warm society is what governments and nongovernment organizations across the world are aiming for at present. Financial implications affect actions against climate change to a greater extent, apart from technological innovations. The effects of policy strategies discussed in the study, taking a large country as a case, when implemented appropriately around the regions, could help move a step closer to action against climate change.

The paper addresses a key but rarely explored research issue – that how a climate-sensitive crop choice will help reduce the government’s fiscal burden to finance climate adaption/mitigation. It also offers a mechanism to estimate the benefits within an economy-wide framework.