This study explores the complex interactions between renewable energy production, innovation, economic growth, institutional quality, economic globalization, and CO2 emissions in OECD countries and emerging economies from 1996 to 2021. Results from Driscoll-Kraay standard error and feasible generalized least square reveal distinct trends: renewable energy production leads to increased CO2 emissions in emerging economies but significantly reduces emissions in OECD countries. Besides, residential and non-residential innovation, along with total innovation, show similar effects. Notably, technology-moderated renewable energy production effectively lowers CO2 emissions in both country groups. Similarly, economic growth enhances environmental quality in both sets of countries. However, institutional quality needs improvement in emerging economies, while current levels suffice in OECD nations to maintain environmental quality. Moreover, the study emphasizes the importance of considering globalization's impact on CO2 emissions, advocating for international agreements to leverage globalization for environmental benefits. Overall, these findings provide valuable insights for shaping renewable energy policies, fostering innovation, promoting economic growth, enhancing institutional quality, and harnessing globalization efforts to reduce CO2 emissions and enhance environmental quality.

Companies sign prior contracts for working capital demand loans with banks to mitigate their working capital shortfall. In order to honor the contract, banks also undertake lines of credit agreements in the interbank market to protect themselves from liquidity mismatch. The paper proposed a mathematical model to assess the per-formance of banks in a probabilistic environment, where corporates exercise their options for working capital loans and banks face liquidity problems, therefore resort to lines of credit in the interbank market. The paper through simulation examined the effect of expected income and expenses, at different probabilities for companies exercising their options for different quantities of working capital demand loans, and banks approaching the interbank market for lines of credit. It is found that as the probability of exercising working capital demand loans increases, the net-interest income for banks also increases. The model is further validated with unique primary data for a bank having overdraft contracts with 55 companies in an interbank market of 22 banks. The paper uses the network theory to examine the stability of banks in an interbank market. This model will help banks with policy formulations to maximize net-interest income by considering the number of corporates signing overdraft contracts, overdraft interest rate, the probability of corporates exercising overdraft contracts, and the number of banks to be considered in an inter-bank market.

The adverse effects of traditional vehicles on the environment increase the demand for clean vehicles, such as electric vehicles (EVs). The correct positioning of the charging points for such vehicles certainly promotes the acceptance and spread of EVs. Indeed, selecting optimal locations for electric vehicle charging stations (EVCS) is crucial for shaping a sustainable future. This study introduces an integrated methodology under a double hierarchy linguistic context with a criteria importance through an inter -criteria correlation (CRITIC) technique for experts ' reliability determination, an attitudinal Cronbach ' s method for criteria weight estimation, and a novel multi -criteria technique considering the compromise ranking of alternatives from distance to ideal solution (CRADIS) formulation for optimal EVCS location selection. Based on the results, criteria such as service capability, ecological impact, land cost, and traffic density are the most crucial, with Manapparai, India, as the optimal location for a new EVCS construction. Further, a crucial finding is that the social dimension is substantial compared to the economy and environment dimensions for EVCS location selection. The novelty of the paper is that (i) uncertainty and expression of choices in natural language form for locations for EVCS are modeled effectively using a double hierarchy structure, (ii) experts ' weights are obtained methodically by considering hesitation and interactions among experts, (iii) interdependencies among criteria and importance of experts are considered during criteria weight determination, and (iv) locations are ranked by not only considering criteria type but also resembles closely to the human -centric decision process. A detailed sensitivity analysis is further conducted to prove the proposed approach ' s effectiveness and stability. In the context of sustainable transportation, the work could contribute to the relevant literature through a powerful decision -making tool.

The need to constantly adapt to the changes in the business environment and selling situations places enormous demands on salespeople. Applying the Job Demands-Resources theory, this study examines the negative impact of adaptive selling on salespeople. Using survey data from 384 sales professionals in different industries in an emerging market, we find that adaptive selling directly increases cognitive anxiety among salespeople. Furthermore, our research suggests that cognitive anxiety mediates the relationship between adaptive selling and emotional exhaustion. However, our study did not find support for the direct impact of adaptive selling on emotional exhaustion, suggesting that in some instances, the need to practice adaptive selling by itself might motivate salespeople to get more involved. Our study also demonstrates that salesperson perseverance reduces the negative impact of adaptive selling on cognitive anxiety, suggesting a moderating effect. The study concludes by providing theoretical and practical implications.

PurposeThis study aims to revisit the properties of 24-item version of mindfulness scale proposed by Bohlmeijer et al. (2011) in an Indian context to add to the existing global knowledge base on mindfulness.Design/methodology/approachA questionnaire was administered to 531 adult employees working in the IT/ITES sector in India. Their responses were analysed using exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and validity statistics to validate the Indian adaptation of the five-facet mindfulness questionnaire (FFMQ).FindingsThe findings confirmed that the Indian version of the 24-item short form of the FFMQ (denoted as FFMQ-SF) matches the findings of Bohlmeijer et al. (2011). It can, therefore, provide valuable insights to both employees and management on the benefits of mindfulness in the workplace.Research limitations/implicationsThis paper also presents the limitations of this work along with scholarly and practical implications. It enhances the global understanding of mindfulness, with applications in education, health and well-being, workplaces, social justice, spirituality and personal growth.Originality/valueThis study justifies and presents a unique instrument for assessing employee mindfulness and is beneficial for both management and employees in navigating the evolving hybrid work environment. It promotes present-moment awareness in a non-judgemental manner, facilitating perspective shifts, improved self-regulation and experiential acceptance. Additionally, the study affirms the five-dimensional structure underlying the mindfulness construct.

PurposeFinancial inclusion is acknowledged as a critical facilitator of the United Nations Sustainable Development Goals agenda for 2030. Therefore, this study aims to examine the asymmetric role of overall globalization on financial inclusion by controlling economic growth, urbanization and population for the selected South Asian countries.Design/methodology/approachApplying the nonlinear autoregressive distributed lag approach to cointegration explores the impact of overall globalization on financial inclusion in the presence of additional variables like economic growth, urbanization and population in the designed financial inclusion function.FindingsThe estimated econometric outcomes show that increasing overall globalization fosters financial inclusion while decreasing overall globalization reduces financial inclusion. Furthermore, a positive (negative) change in economic growth leads to an increase (decrease) in financial inclusion while varying short-run findings. Moreover, both positive and negative changes increase financial inclusion in the long run in connection with urbanization. Although the short-run results are not significant, the study finds that an increase (decrease) in population leads to a decrease (increase) in financial inclusion. Finally, to support the promotion of financial inclusivity throughout South Asia, several policies pertaining to financial inclusion are suggested.Originality/valueTo the best of the authors' knowledge, this is the first study to examine the asymmetries related to overall globalization on financial inclusion by controlling economic growth, urbanization and population.

Hydrogen is the new age alternative energy source to combat energy demand and climate change. Storage of hydrogen is vital for a nation's growth. Works of literature provide different methods for storing the produced hydrogen, and the rational selection of a viable method is crucial for promoting sustainability and green practices. Typically, hydrogen storage is associated with diverse sustainable and circular economy (SCE) criteria. As a result, the authors consider the situation a multi-criteria decision-making (MCDM) problem. Studies infer that previous models for hydrogen storage method (HSM) selection (i) do not consider preferences in the natural language form; (ii) weights of experts are not methodically determined; (iii) hesitation of experts during criteria weight assessment is not effectively explored; and (iv) three-stage solution of a suitable selection of HSM is unexplored. Driven by these gaps, in this paper, authors put forward a new integrated framework, which considers double hierarchy linguistic information for rating, criteria importance through inter-criteria correlation (CRITIC) for expert weight calculation, evidence-based Bayesian method for criteria weight estimation, and combined compromise solution (CoCoSo) for ranking HSMs. The applicability of the developed framework is testified by using a case example of HSM selection in India. Sensitivity and comparative analysis reveal the merits and limitations of the developed framework.

This study analyzes the time-varying dependence between U.S. leveraged loan and debt markets within a highly linked financial system using a quantile-based time-varying connectedness framework to determine the hedging benefits of leveraged loans for financial investors at various quantiles. Based on daily closing price data from November 28, 2008 to October 3, 2023, the evidence demonstrates considerable (moderate) spillovers across the leveraged loan and debt markets for severe (normal) occurrences, with additional results indicating symmetric interaction. In terms of risk spillover, we also affirm the dominance of short-term fixed-income instruments over leveraged loans and long-term bonds. These findings indicate that no hedging or diversification occurred among the investigated markets.

Purpose The purpose of this paper is to investigate, from a thorough review of the literature, the role of metaverse-based quality 4.0 (MV-based Q4.0) in achieving manufacturing resilience (MFGRES). Based on a categorization of MV-based Q4.0 enabler technologies and MFGRES antecedents, the paper provides a conceptual framework depicting the relationship between both areas while exploring existing knowledge in current literature.Design/methodology/approach The paper is structured as a comprehensive systematic literature review (SLR) at the intersection of MV-based Q4.0 and MFGRES fields. From the Scopus database up to 2023, a final sample of 182 papers is selected based on the inclusion/exclusion criteria that shape the knowledge base of the research.Findings In light of the classification of reviewed papers, the findings show that artificial intelligence is especially well-suited to enhancing MFGRES. Transparency and flexibility are the resilience enablers that gain most from the implementation of MV-based Q4.0. Through analysis and synthesis of the literature, the study reveals the lack of an integrated approach combining both MV-based Q4.0 and MFGRES. This is particularly clear during disruptions.Practical implications This study has a significant impact on managers and businesses. It also advances knowledge of the importance of MV-based Q4.0 in achieving MFGRES and gaining its full rewards.Originality/value This paper makes significant recommendations for academics, particularly those who are interested in the metaverse concept within MFGRES. The study also helps managers by illuminating a key area to concentrate on for the improvement of MFGRES within their organizations. In light of this, future research directions are suggested.

PurposeThe purpose of this paper is to develop a framework for the assessment of service quality of bus fleet services based on the service quality influencing factors. The paper also tries to evolve a quantitative measure for fleet service quality in the form of a fleet service quality index.Design/methodology/approachA graph theoretical approach is employed in this paper for bus fleet service quality assessment. Modelling of fleet service quality factors and their interrelations with due attention towards their structure is achieved through graph theory. A directed graph (digraph) of the service quality is developed, where its nodes represent factors influencing the quality while its edges show the degree of interrelationships. A matrix, which is equivalent to the digraph, is established that will generate a service quality function that will result in the development of a fleet service quality index (FSQI).FindingsAttaining customer satisfaction through assurance of quality is the cornerstone of the existence and survival of any business organization, and bus fleet services are no exception to this. Several influential factors are there for the bus fleet service quality. This research paper has identified factors such as fleet management practices, operational characteristics, safety and reliability features, travel comfort, bus maintenance and environmental concerns that affect fleet service quality. Every factor is composed of distinct sub-factors. Furthermore, these factors are linked with one another. A higher value of the fleet service quality index indicates the adequate performance of the bus fleet service organization.Practical implicationsThe methodology is useful for not only evaluating but also for comparison of service quality of different fleet agencies or organizations. The perceptions would be useful to the fleet service managers to create procedures and arrangements for improving the service quality.Originality/valueThe paper identifies various service quality factors of the bus fleet and an evaluation scheme for those factors has been developed. Based on these, a framework had been developed for the assessment of the service quality of different fleet service providers.

This study investigates the influence of Russia-Ukraine war and associated economic sanctions sentiments on the returns of cryptocurrencies, NFTs, and DeFi assets. We analyse daily returns of twelve blockchain-based assets by employing quantile-on-quantile regression (QQR) and an asymmetric time-varying parameter vector autoregression (TVP-VAR) connectedness approach. The QQR reveals that the war sentiment has varying effects on the returns of digital assets, with negative (positive) impacts in bullish (bearish) markets. Notably, there is a heterogeneous effect observed in normal market conditions. Results from the TVP-VAR-based asymmetric connectedness approach demonstrate a time-varying influence of war sentiment, particularly heightened post-invasion. The war sentiment emerges as a significant transmitter (receiver) of price shocks in bullish (bearish) market conditions. These findings offer extensive implications for investors and policymakers when modelling market behavior during geopolitical events.

This paper examines the asymmetric returns spillovers and time-frequency causality between the carbon emissions market and the energy market. To this end, we apply the time-varying asymmetry spillovers and Granger causality over the spectrum approaches. This research uses daily price indices of natural gas, gasoline, gas oil, heating oil, crude oil, coal, petroleum, kerosene, propane, and diesel to denote the energy market and the European Union Emissions Trading System (i.e., certificate prices for CO2 emissions) to represent the carbon market. Using historical time-series data from May 18, 2011, to September 23, 2020, the study reveals interesting and convincing empirical results showing that the carbon and energy markets are dynamically and asymmetrically connected. Further results show that the carbon market predominantly explains positive or negative returns in the energy market. Regarding volatility transmission, the study demonstrates that the carbon market is a primary net receiver of good or bad volatility transmitted from the energy market, such as crude oil, petroleum, heating oil, diesel, and kerosene.

This paper explores the asymmetric relationship between clean and dirty energy markets. The study uses the time -varying and frequency -domain spillover approaches, while accounting for asymmetries. We use natural gas, gasoline, gas oil, heating oil, crude oil, coal, petroleum, kerosene, propane, and diesel to denote dirty energy markets and wind, solar and clean energy markets to denote clean energy markets. We use daily data running from May 18, 2011, to August 12, 2020. According to the results obtained, good news in fluctuations in global energy market indices increases the integration of international energy markets in the long run compared to bad news. Our result show that transmission of good and bad volatilities in global energy market indices are dispersed with different time -varying intensities. Empirical evidence further reveals that good news increases integration of international energy markets in the long run compared to bad news. Additionally, markets transmit more bad volatility on average than good volatility during global events. According to the results of the research, we foresee that portfolio managers and investors may experience difficulties in diversifying opportunities in financial volatility periods in the short term. Overall, our findings reveal asymmetric risk effects in investment opportunities between clean and dirty energy. As a result of this information, investors can diversify their investments in the clean energy sector in the long term by using the asymmetry in good and bad fluctuations.

Lean management is a well-known manufacturing and service supply chain management philosophy. The various industry 4.0 technologies have also received enough attention because of their capabilities and initiated a debate among practitioners and academicians on integrating both paradigms. Despite the potential benefits estimated in many research studies and reports, the implementation status could be more encouraging in organisations. Therefore, in this study, all potential barriers are identified and analysed. These barriers were identified from existing literature and finalised with the help of experts. Further, 'Interpretive Structure Modeling (ISM)' was used to reveal their contextual relationship. Finally, the 'Analytic Network Process (ANP)' ranked the key barriers based on their importance. The results revealed that 'lack of skilled and experienced workers' and 'lack of government policies' are the most critical barriers. Based on the study's outcomes, policymakers could make a more effective strategy to address these critical barriers. Additionally, organizations can adapt the proposed hybrid approach to address the barriers more effectively.

Remote Working has gained exponential popularity across the world, especially after COVID-19. Despite carrying important implications for individual and organizational functioning and the growing interest of the scientific community over the years, very few retrospective studies have been made to synthesize and understand the intellectual narrative of this field. This study delves into the dynamic landscape of remote working literature through bibliometric analysis. Utilizing the SCOPUS database, we examined 745 articles published in reputed journals in management, psychology, and social sciences from 2001 to 2023. The study followed descriptive analysis and science mapping techniques to uncover valuable insights for scholars by identifying the most seminal contributions and collaborative networks. The analysis revealed that effectiveness, work-life balance, adoption factors, and challenges associated with RW are the major themes within the research. These findings collectively contribute to RW's holistic development and help develop a structural map of RW literature. Future research directions in the fields of academics and HRD are suggested.

PurposeThis study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management.Design/methodology/approachWe use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmaz's connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management.FindingsRegarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors.Practical implicationsThere is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies.Originality/valueThe study's major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate.

Purpose - This study investigates the role of mindfulness in the relationship between entrepreneurial intention and behavior. Design/methodology/approach - To investigate the effect of mindfulness on entrepreneurial intention and behavior, we adopt a conceptual framework based on the theory of planned behavior and develop our hypothesis, anticipating that mindfulness has a moderating effect on the entrepreneurial intention-behavior relationship. We conduct an empirical study by administering a survey questionnaire with 329 respondents who attended a training program organized by one of the leading management institutes in India. Findings - We find a positive effect of entrepreneurial intention and mindfulness on entrepreneurial behavior. Further, mindfulness has a moderating effect on the entrepreneurial intention-behavior relationship. Research limitations/implications - The study has a few limitations. It was conducted among unemployed youth who participated in a government-sponsored training program for the promotion of entrepreneurship. Although not all the participants in the program were automatically eligible for government funding for starting a business, their entrepreneurial intention-behavior relationship may vary based on their conditions after the training concluded. This study emphasizes only the relationship between mindfulness and entrepreneurial intention behavior, considering EI and well-being implicit in mindfulness. Other contingent factors might also influence the entrepreneurship intention-behavior relationship, but our argument is that, ultimately, all emotional and rational factors can be subordinated to mindfulness. Hence, future research could be carried out to study the effect of mindfulness practice, entrepreneurial intention and the effectiveness of implementation behavior. Further longitudinal studies could be designed to understand how mindfulness training bridges the gap in the entrepreneurial intention-behavior relationship. Practical implications - Through this study, we offer empirical evidence on the role of mindfulness in moderating the intention-behavior relationship in entrepreneurship. Mindfulness makes people more aware of their internal and external environment when they pay attention with a purpose that helps them to regulate their emotions, cognition, novelty seeking and social contexts to sustain the ups and downs in starting a business. Originality/value - The findings of the study offer new insights into the nuanced association between entrepreneurial intention and behavior through the lens of mindfulness.

Generative Artificial Intelligence (GenAI) is a disruptive technology that has started to be used among students in management education. However, the question is whether the utilisation of GenAI in educational settings stimulates students to engage in learning activities and broaden their knowledge base. Hence, this study investigates student motivation and perception of using GenAI (ChatGPT) technology in management education. A random sampling technique was employed to survey 478 students from various educational institutions in the southern region of India. The outcomes revealed that GenAI presents both prospects and hurdles in the domain of management education. The disruptive nature of this technology brings forth numerous opportunities for acquiring knowledge and augmenting one's cognitive capacity. Nonetheless, a cautious and accountable approach is imperative for the successful integration of GenAI into the of management education. Consequently, this study provides pragmatic implications for students, educators, and educational institutions. The effectiveness of GenAI in practical settings can be heightened by arranging interactive training sessions led by AI experts, devising easily accessible online educational modules, embedding AI proficiencies into educators through collaborative endeavors or specialized training programs, and establishing systematic assessment protocols to ensure continual improvement.

This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system.

The pervasiveness of the Internet coupled with the advantages of cloud have almost made cloud services a necessity for organizations and individuals alike. However, it is a complex decision to choose a cloud vendor because of the large number of criteria and alternatives. Furthermore, the criteria in the case of cloud vendor selection are often interactive (synergic or opposing). To address this need, an approach is presented in this paper to select the most suitable cloud vendor while considering the nuances of human decision making, such as interactive criteria, and varying behavioural characteristics of the decision-makers (DMs). To this end, the recent Choquet logit models are deployed and generalized to add to their modelling abilities. Specifically, the following choice models are developed: generalized Choquet logit, generalized attitudinal Choquet logit, generalized Choquet mixed logit, and generalized attitudinal Choquet mixed logit models. The proposed generalized logit models have the Choquet logit, and the attitudinal Choquet logit models as their special cases. Thus the contributions of this work are two-fold: first an approach is developed to select suitable cloud vendor considering the human nuances and the actual practical aspects of the problem, and secondly a new class of powerful choice models is developed which may be useful in aiding any complex human decision-making, in which the cognitive power of the DM proves to be limited. The method is designed to consider the real nuances of the human decision making such as varying attitudes and varying degree of criteria interaction. The effectiveness of the proposed models in cloud vendor selection is tested through a real world case study from the Indian state of Tamil Nadu. In the case-study, there are 9 cloud vendors in the fray and 4 experts with different attitudes and different suggestions for the best cloud vendor. An approach is developed to solve this problem and a final ranking of the cloud vendors is determined considering experts suggestions. Apart from finding the best cloud vendor, the case study also helps in studying the role of an expert's attitudinal tendency on his preferences. Thus, concomitantly, the proposed approach also guides how to choose the best alternative based on the opinions of different experts with different attitudinal tendencies, without necessitating a forced consensus.