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Chalta hai (it is fine or it is acceptable) is an Indian cultural phenomenon that influences attitude towards work and business and diachronically adversely affects both. The purpose of this study is to explore its impact on the sustainability of business firms operating in India.

The research has firstly undertaken a theoretical study towards the development of appropriate hypotheses and a corresponding conceptual model, with emphasis on the effects of chalta hai culture as a moderator of the predictor-sustainability linkages. The model has been validated statistically through partial least square- structural equation modelling analysis of usable feedbacks from 349 respondents.

The research has concluded that the cultural notion of chalta hai impacts adversely the sustainability of business firms operating in India, with its effects being dominant.

The research has scholarly and executive implications, as well as socio-cultural implications. The sample, however, allows for conclusions to be drawn reliably but with limited generalizability. Additionally, only three predictors have been considered, bestowing upon future research the task of building on the present model through additional pertinent predictors and boundary conditions that will enhance its explanative power.

The research has provided a scientifically developed model that guides Indian firm managers through appropriate steps that dissuade stakeholders from exhibiting the behavioural traits and attitudes of chalta hai culture, highlighting along the way its detrimental effects on Indian business sustainability.

There is little research on the business impacts of chalta hai and regarding the sustainability perspective/focus. In addition, this is in sharp contrast to the spread and impact of the phenomenon. This research and its findings, therefore, are valuable with regard to both their wider context (“chalta hai” business effects) and their specific focus (sustainability).

The authors target the interrelationships between non-fungible tokens (NFTs), decentralized finance (DeFi) and carbon allowances (CA) markets during 2021–2023. The recent shift of crypto and DeFi miners from China (the People's Republic of China, PRC) green hydro energy to dirty fuel energies elsewhere induces investments in carbon offsetting instruments; this is a backdrop to the authors’ investigation.

The quantile vector autoregression (VAR) approach is employed to examine extreme-quantile-connectedness and spillovers among the NFT Index (NFTI), DeFi Pulse Index (DPI), KraneShares Global Carbon Strategy ETF price (KRBN) and the Solactive Carbon Emission Allowances Rolling Futures Total Return Index (SOLCARBT).

At bull markets, DPI is the only consistent net shock transmitter as NFTI transmits innovations only at the most extreme quantile. At bear markets, KRBN and SOLCARBT are net shock transmitters, while NFTI is the only consistent net shock receiver. The receiver-transmitter roles change as a function of the market conditions. The increases in the relative tail dependence correspond to the stress events, which make systemic connectedness augment, turning market-specific idiosyncratic considerations less relevant.

The shift of digital asset miners from the PRC has resulted in excessive fuel energy consumption and aggravated environmental consequences regarding NFTs and DeFi mining. Although there exist numerous studies dedicated to CA trading and its role in carbon print reduction, the direct nexus between NFT, DeFi and CA has never been addressed in the literature. The originality of the authors’ research consists in bridging this void. Results are valuable for portfolio managers in bull and bear markets, as the authors show that connectedness is more intense under such conditions.

Like the cross-country convergence or divergence analysis in incomes to address the global phenomenon, the same analysis is also required to be done in the case of a group of states within a national territory. Further, it is also required to see whether convergence or divergence in incomes of the states is attributable to the convergence or divergence in their allocations of bank credits. Thus, this chapter aims at examining whether the selected major states in India are converging or diverging in the allocations of bank credit, and if so, what will be the magnitudes of decreases or increases in the level of disparities and inequalities in credit allocations. This study concludes that there is a clear diverging tendency of credit allocations of the states of India during the post-reform period so far as the absolute convergence hypothesis of the neoclassical theory is concerned. Further, in terms of the framework of σ convergence, the study observes that all phases of the Indian economy have produced converging paths of the inter-state credit allocations, and the path becomes diverging during the post-reform phase. Based on the quantifications of the magnitudes of disparities and inequalities in terms of CV, C4 concentration, HHI and Gini values, this study thus reveals that there are significant increases in the levels of disparities and inequalities in the allocations of credit to the states from the pre-reform to the post-reform phases. Therefore, the persistence of divergence in income or rising income inequality during the phase of the major reform program in India may be due to the persistence of divergence and rising inequality in the allocation of bank credit.

Recalling that the introductory chapter (Chapter 1) wanted to carry out similar types of analysis for the major states in India. Thus, the present chapter tries to examine the trends of a bank branch, deposit, credit, the credit–deposit ratio, sectoral shares of credit, magnitudes of banking transactions, credit concentration, etc., for the selected 15 states and Delhi as the only union territory for the period 1972–2019. The study period covers the pre-reform period from 1972 to 1992 and the post-reform period 1993–2019. The observations show that the branch, deposit and credit did not grow significantly during the post-reform period. As a result, the credit–deposit ratio did not increase significantly during the reform period. But, the magnitude of banking transactions increased in most of the states during the reform period. Regarding the sector-wise share of credit, AP, Maharashtra, UP and TN are the leading states in agricultural credit, WB, Gujarat and Maharashtra are in industrial credit and Kerala, Assam and Delhi are in the service sector. On the other hand, the study finds rising magnitudes credit concentrations of the states during the post-reform period in contrast to the declining concentration in the pre-reform period. Maharashtra is the state which holds around 25 per cent of all states’ credit throughout the entire period of 1972–2019. Hence, there are the notions of rising disparity and inequality in credit as well as incomes of the states and all India levels.

This study empirically investigates the interdependency of select Asian emerging economies along with the financial stress index during the times of the global financial crisis, the Euro crisis and the COVID-19 period. Moreover, it inspects the long-memory effects of the different crises during the study period.

To address the objectives of the study, the authors apply different statistical tools, namely the adjusted correlation coefficient, fractionally integrated generalised autoregressive conditional heteroskedasticity (FIGARCH) model and wavelet coherence model, along with descriptive statistics.

Financial stress is having a prodigious effect on the economic growth of select economies. From the data analysis, it is found that the long-memory effect is noted in the gross domestic product (GDP) for India and Korea only, which implies that the volatility in the GDP series for these two nations demonstrates persistence and dependency on previous values over a lengthy period.

The study is unique of its kind to consider multi-segments within the period of the study to get a clear idea about the effects of the financial stress index on select Asian emerging economies by applying different econometric tools.

Meetings are conducted by increasingly age-diverse participant groups as the workforces in most industrialized economies are aging due to demographic change. There are at least three reasons why meetings constitute a particularly interesting environment to study intergenerational learning processes, defined as individuals’ joint construction of knowledge through an exchange of information with one or more individuals from different age groups. First, meetings allow us to observe a wide variety of interactions that may foster or inhibit intergenerational learning. Second, the interactions taking place in meetings reflect general organizational practices as well as social exchange and age norms. As such, meetings offer a view through the magnifying glass at the age-inclusive or age-discriminating organizational culture which is interwoven with the engagement of different generations in intergenerational learning processes. Third, organizational members use meetings as an arena for strategic interactions to negotiate their current and future status by positioning themselves in relation to their colleagues through social comparisons. This chapter particularly focuses on the latter topic and develops a conceptual model outlining the motivational and emotional coˇnsequences as well as antecedents that link social comparison processes in meetings to intergenerational learning outcomes of participants from different age groups.

Using 13 major Indian state-level data of the rural sector, covering the period 2004–2005 to 2011–2012 and by estimating a simultaneous-panel model employing Baltagi's Instrumental-Variable EC2SLS estimation method, this chapter contributes to the literature by establishing: (i) the simultaneous dependence between female labor force participation rate (FLFPR) and female health status as measured by female life expectancy (FLE), (ii) the negative impact of outdoor air pollution as measured by prevalence of SPM, SO₂, and NO₂ on FLE, and (iii) the interaction among different demographic factors in determining both FLFPR and FLE. The interaction effect of air pollution with (i) economic growth and (ii) poverty (POV) on FLE is negative implying that the partial effect of a change in growth (POV) depends on air pollution level. Thus reduction in air pollution will increase FLE and hence FLFPR, as the simultaneous positive dependence between FLFPR and FLE is supported. The interaction effect of women's political power and education on rural FLFPR is significant and nonlinear with positive marginal effect. Thus the partial effect of a change in women's political power on FLFPR will in turn depend on level of education and vice versa. The positive impact of other demographic factors like (i) education, (ii) female leader, (iii) POV, and (iv) urbanization on FLFPR and (a) education, (b) female household head, (c) female leader, (d) sex ratio, and (e) growth on FLE are apparent. However, the household size significantly and negatively affects FLFPR.

Circular supply chains (CSC) are particularly important for the agri-food sector, which faces strict requirements generated by increased food consumption as a consequence of world population growth, changes in lifestyle, development of consumer society and increasing health awareness. Recent disruptive factors have placed the vulnerability of agri-food supply chains in the spotlight. Therefore, the purpose of this paper was to identify the most manageable groups of risks in order to ensure the smooth operation of agri-food circular supply chains.

Seven main risk groups were evaluated in relation to nine criteria. To solve this multi-criteria decision making (MCDM) problem, a novel MCDM model, which integrates the best-worst method (BWM) and the COmprehensive distance-Based RAnking (COBRA) method in a grey environment, was developed.

Three risks were singled out, namely, product features risks, logistics risks and managerial risks. The obtained risks are those whose management would create the most positive effects for the stakeholders and help them achieve their primary goals regarding the circularity of agri-food supply chains.

This study investigates the main characteristics of the CSC in the agri-food sector, identifies, simultaneously explores and ranks all main risk groups associated with them and expands the possibilities for solving these kinds of problems by developing a novel MCDM model. It also identifies the most significant risks, both for individual stakeholders and for all stakeholder groups together.

The contributions of this chapter are to establish (a) simultaneous dependence between female labor force participation rate (FLFPR) and their health status measured by the life expectancy; (b) the roles of (i) air pollutants in explaining female life expectancy (FLE); and (ii) joint interactions of different explanatory variables in determining both FLFPR and FLE, by estimating a simultaneous panel model comprising equations of FLFPR and FLE, using Baltagi’s Instrumental-Variable EC2SLS method and 13 major Indian state-level data for urban sector, over 2004–2005 to 2011–2012. The air pollutants (measured by prevalence of SO₂ and NO₂) have significant negative impacts on FLE. The interaction effect of air pollutants with economic growth on FLE is negative implying that the partial effect of a change in growth depends on air pollution level. FLFPR can be improved by reducing air pollution through health, as FLE significantly affects FLFPR positively. The roles of other socioeconomic variables affecting FLFPR and FLE are also evident.