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Globally, the landscape of corporate carbon disclosures (CCD) is continually evolving as societal, environmental and regulatory expectations change over time. The goal of this study is to examine the challenges faced by Indian firms’ corporate carbon reporting (CCR). The literature recognized the hurdles to reaching net zero emissions and decarbonization, which are equally applicable to carbon disclosure (CD).

The scope 3 emission disclosure barriers (S3EDBs) identified from the literature were ranked, and their relationships were discovered using the “Grey-based decision-making trial and evaluation laboratory” (Grey- DEMATEL) technique.

The key findings are the S3EDBs, the most prominent barriers, their interrelationships and important insights for managers of organizations in prioritizing the action area for scope 3 CD. Eight S3EDBs were categorized in terms of cause and effect, threshold value is calculated as 0.78. “Quality, and reliability of data,” “Government policies and statutory requirement on emission disclosure” and “Traceability and managing supply chain partners” are the most prominent S3EDBs.

The results will help industry people in countries with emerging economies that have significant scope 3 carbon footprints. The managers can plan to deal with top S3EDBs as a step towards decarbonization and ultimately fighting climate change (CC).

This study is one of the first to rank these barriers to CD so that industry practitioners can prioritize their actions. The core contribution of this research is to detect the most significant S3EDBs and their interdependencies.

Creditor rights reduce or increase agency problems in corporations, affecting financial decisions. This study examines the impact of India’s Insolvency and Bankruptcy Code of 2016 (hereinafter referred to as the Code) on firms’ investment and investment sensitivity based on their health, considering this Code as more creditor-friendly than pre-existing bankruptcy laws.

This study uses a quasi-natural experiment that employs the difference-in-differences (DID) and propensity score matching difference-in-differences (PSM DiD) approach by considering the Code as treatment and categorizing the firms into distressed (treated group) and healthy (control group) firms. For the purpose of analysis, a fixed-effect regression model is used.

This study finds that distressed firms reduce their investment after the Code, but healthy firms do not observe any change. It shows that the reduction in investment of the distressed group is significantly greater than that of the healthy group due to agency conflict and the liquidation bias hypothesis. However, the reduction in investment is not followed by the change in investment sensitivity.

This study adds to the existing studies on the impact of the Insolvency and Bankruptcy Code, 2016 on investment. No study explores the relationship between this Code and investment based on the financial health of the firms. Also, none of the studies explores the impact of the Code on investment sensitivity. The results show that this Code has provided stronger protection to the creditors, which hurts the internal stakeholders’ interests. The study has implications for policymakers and academicians.

This study primarily examines the link between carbon and financial performance in the Asia-Pacific region. In addition, the study also explores how the economic impact of carbon performance varies in carbon-intensive and non-carbon-intensive industries.

This study takes a sample of 1,539 non-financial firms from 13 Asia-Pacific countries from 2014 to 2021. It employs a firm-fixed effect panel regression model to examine the objective.

The findings indicate that carbon performance improvement enhances accounting-based and market-based financial performance. The positive impact of carbon abatement stems from increased operational efficiency, energy efficiency and lower production costs. Further, the stock market participants also reward the firm for carbon efficiency. However, the carbon intensity of industrial sectors presents a conflicting picture for this association.

This study adds insights to the literature by providing a contemporary reflection on the nexus between carbon emissions and economic outcomes in the understudied Asia-Pacific region. It also unveils the nuanced difference in the carbon-financial performance relationship attributed to industries' carbon sensitivity.

The purpose of this study is to identify Offshore Wind Energy (OWE) as a key technology that could drive countries toward achieving climate goals. However, there are multiple challenges that this sector faces.

This study aims to identify the challenges faced by the sector globally by systematically reviewing the existing literature in global context and portraying it in the Indian context. Factors are identified using content analysis.

Results suggest high levelized cost of energy as the most discussed challenge for the growth of OWE. Insufficient financial support and policy, initial capital and inadequate technology formed the second, third and fourth most discussed challenges respectively.

To reduce the cost of OWE, the distribution companies in India could adopt feed-in tariffs (FiTs) in the early stages of development and make OWE procurement mandatory. The renewable purchase obligation (RPO) in India is specific to solar and non-solar; policy should accommodate offshore wind-specific RPO targets for each state to reach the 2030 target of 30 GW from OWE.

To the best of the authors’ knowledge, this is the first attempt to study the challenges of OWE development from a global perspective and portray these major challenges in the Indian context and uses content analysis from the existing literature to ascertain the major roadblocks for the development of OWE.

The study identifies the unexplored gap in literature that includes futuristic challenges for OWE from climate change. Future studies can explore the possibilities of forecasting based on climate change scenarios and rank the challenges based on their relevance caused by possible damages.

Smart manufacturing (SM) capitalizes on big data analytics (BDA) advancements by enhancing current capabilities such as defect identification and enabling supporting capabilities such as preventive maintenance (PM). The previous literature fails to investigate the comprehensive associations between SM, BDA and PM. Therefore, this study aims to investigate the relationship among SM, BDA and PM.

The present research implements a multi-analytical PLS-SEM-ANN approach to investigate the relationships among BDA, PM and SM.

This investigation indicates that BDA is an effective digital technology that positively affects the operations of SM and PM. Furthermore, the results suggest that PM has a positive influence on SM and that it also positively mediates the relationship between BDA and SM, where PM cannot be treated as an auxiliary practice and plays an important role in SM as a primary operation. Furthermore, implementing the BDA enhances the performance of SM and PM.

The role of PM in the context of BDA and SM has been ignored in past research, and this study offers novelty by examining this relationship.

The aim of this study is to evaluate Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM).

Ten different BDA drivers in FSC are examined for transition to CE; these are Supply Chains (SC) Visibility, Operations Efficiency, Information Management and Technology, Collaborations between SC partners, Data-driven innovation, Demand management and Production Planning, Talent Management, Organizational Commitment, Management Team Capability and Governmental Incentive. An interpretive structural modelling (ISM) methodology is used to indicate the relationships between identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with a number of experts from industry and academia.

The results show that Information Management and Technology, Governmental Incentive and Management Team Capability drivers are classified as independent factors; Organizational Commitment and Operations Efficiency are categorized as dependent factors. SC Visibility, Data-driven innovation, Demand management and Production Planning, Talent Management and Collaborations between SC partners can be classified as linkage factors. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSC transition from linearity to CE and SOM. In addition, Operations Efficiency, Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM.

The interactions between these drivers will provide benefits to both industry and academia in prioritizing and understanding these drivers more thoroughly when implementing BDA based on a range of factors. This study will provide valuable insights. The results from this study will help in drawing up regulations to prevent food fraud, implementing laws concerning government incentives, reducing food loss and waste, increasing tracing and traceability, providing training activities to improve knowledge about BDA and focusing more on data analytics.

The main contribution of the study is to analyze BDA drivers in the context of FSC for transition to CE and SOM. This study is unique in examining these BDA drivers based on FSC. We hope to find sustainable solutions to minimize losses or other negative impacts on these SC.

Current research in the field of behavioural finance has attempted to discover behavioural biases and their characteristics in individual investors’ irrational decision-making. This study aims to find out how biases in information based on knowledge affect decisions about investments.

In step one, through existing research and consultation with specialists, 13 relevant items covering major aspects of bias were determined. In the second step, multiple linear regression and artificial neural network were used to analyse the data of 337 retail investors.

The investment choice was heavily impacted by regret aversion, followed by loss aversion, overconfidence and the Barnum effect. It was observed that the Barnum effect has a statistically significant negative link with investing choices. The research also found that investors’ fear of making mistakes and their tendency to be too sure of themselves were the most significant factors in their decisions about where to put their money.

This research contributes to the expansion of the knowledge base in behavioural finance theory by highlighting the significance of cognitive psychological traits in how leading investors end up making irrational decisions. Portfolio managers, financial institutions and investors in developing markets may all significantly benefit from the information offered.

This research is a one-of-a-kind study, as it analyses the emotional biases along with the cognitive biases of investor decision-making. Investor decisions generally consider the shadowy side of knowledge management.

Industries have been the most significant contributor to carbon emissions since the beginning of the Industrial Revolution. The transition to Industry 5.0 (I5.0) marks a pivotal moment in the industrial revolution, which aims to reconcile productivity with environmental responsibility. As concerns about the decline of environmental quality increase and the demand for sustainable industrial methods intensifies, experts recognize the shift toward the I5.0 transition as a crucial turning point.

This review study explores the convergence of green technological advancements with the evolving landscape of I5.0, thereby presenting a roadmap toward carbon neutrality. Through an extensive analysis of literature spanning from 2012 to 2024, sourced from the Scopus database, the research study unravels the transformative potential of green technological innovations, artificial intelligence, green supply chain management and the metaverse.

The findings underscore the urgent imperative of integrating green technologies into the fabric of I5.0, highlighting the opportunities and challenges inherent in this endeavor. Furthermore, the study provides insights tailored for policymakers, regulators, researchers and environmental stakeholders, fostering informed decision-making toward a carbon-neutral future.

This review serves as a call to action, urging collective efforts to harness innovation for the betterment of industry and the environment.

The purpose of this study is to investigate the impact of psychological ownership (PO) on residents’ destination advocacy (DA) behaviour in the context of emerging tourist destinations and to assess the role of attitude as a mediator in the relationship.

A quantitative methodology was used and primary data was collected via an online survey to a sample of 333 residents from emerging tourist destinations in India. This study used the partial least squares (PLS) method to test the hypotheses.

The results indicate that residents’ knowledge about their hometown positively influences PO which in turn affects DA behaviour. Furthermore, PO influences attitude which in turn significantly influences DA behaviour. Additionally, the findings reveal the mediating role of attitude between PO and residents’ advocacy behavioural outcomes.

This study advances the concept that residents are important stakeholders who can promote a destination. Local authorities should prioritise residents over tourists and incorporate their image, identity, personality, style and values into destination promotion. They can also improve destination services to boost residents' positive attitudes.

The uniqueness of the study lies in associating PO and outcome as DA behaviour. The model suggests that enhancing PO of their hometown among the residents can have significant advantages for tourism development.

As the Metaverse emerges as a prominent paradigm, Artificial Intelligence (AI) plays a pivotal role in shaping its landscape. This chapter explores the convergence of AI and the Metaverse, exploring the symbiotic relationship between these two transformative technologies. From virtual assistants to immersive experiences, AI-driven innovations are redefining how individuals interact and navigate within the Metaverse. This chapter also assesses the challenges and prospects brought about by AI in the Metaverse which is impacting both businesses and society on a broader scale.