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The “metaverse” is the new buzzword. With the phenomenal growth of the metaverse comes accounting, taxation and jurisdictional challenges, which business and governments have yet to fully address. This paper aims to highlight and rationalise the lack of regulatory framework and multiplicity of jurisdictions on metaverse transactions. This paper addresses some of the complications with respect to accounting and taxation in virtual environments.

This study relies on secondary data and emerging literature to understand the multiplicity of jurisdiction and complexity of the accounting transactions. The concept of the metaverse is rapidly evolving, and this study uses extant literature to provide the foundation for understanding the key challenges relating to accounting and taxation.

Concepts of revenue recognition and deferment are challenged by the transactions in the metaverse. There are novel applications, underpinned by emerging technologies and blockchain supporting new crypto assets, such as non-fungible tokens and other decentralised finance (DeFi) tools; however, the caveats of anonymity and jurisdictional issues persist. The paper suggests that the industry must adapt to the unique reporting requirements of these assets and develop new standards for evaluating their value for financial reporting purposes. The paper emphasises the need for a case-based approach in the absence of standardised regulations for the accounting industry in the metaverse.

This paper adds original contributions to extant literature of the metaverse and advances ongoing debates into the accounting and taxation issues pertinent to the metaverse and DeFi.

The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.

The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.

Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.

The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.

The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

Despite the unique features and potential applications in various industries, widespread blockchain adoption is hindered for several reasons. One of them is the lack of government regulations regarding blockchain and cryptocurrencies. However, a deliberate preliminary analysis of the policy initiatives by various jurisdictions proved otherwise, and a lack of sound academic literature on the policy initiatives on blockchain worldwide was evident. Addressing this gap, this study aims to summarize the policy initiatives of jurisdictions around the world, assessing if governments do not enact many regulations.

A systematic literature review was adopted in this study, in which the authors shortlisted a set of research papers and policy reports using several selection criteria and a screening process.

It was found that numerous policy initiatives have been enacted by governments worldwide, and blockchain applications are also being piloted or practiced successfully in several nations. It was also evident that governments are reluctant to accept cryptocurrencies as legal tender while embracing their underlying technology, blockchain.

To the best of the authors’ knowledge, this paper appears to be one of the first attempts to summarize the blockchain policy initiatives contributing to the body of knowledge on blockchain adoption.

Corporate digital transformation (CDT) has challenged traditional tax administration systems. This study examines the impact of CDT on tax avoidance behavior and tests whether tax authorities can identify this behavior.

Using data on listed companies on the Shanghai and Shenzhen Stock Exchanges from 2008 to 2020, this study applies the Heckman two-stage and cross-section models.

The results show that the higher the degree of CDT, the more aggressive the tax avoidance behavior. The CDT's impact on corporate tax avoidance is more significant under strong government tax efforts.

This study expands research on the economic consequences of CDT and the factors influencing corporate tax avoidance behavior. Moreover, it has important implications for governments to monitor tax avoidance behavior under the CDT, improve digital tax systems, and pay more attention to the tax administration of digital assets.

This study aims to examine foreign direct investment (FDI) factors and develops a rational framework for FDI inflow in Western European countries such as France, Germany, the Netherlands, Switzerland, Belgium and Austria.

Data for this study were collected from the World development indicators (WDI) database from 1995 to 2018. Factors such as economic growth, pollution, trade, domestic capital investment, gross value-added and the financial stability of the country that influence FDI decisions were selected through empirical literature. A framework was developed using interpretable machine learning (IML), decision trees and three-stage least squares simultaneous equation methods for FDI inflow in Western Europe.

The findings of this study show that there is a difference between the most important and trusted factors for FDI inflow. Additionally, this study shows that machine learning (ML) models can perform better than conventional linear regression models.

This research has several limitations. Ideally, classification accuracies should be higher, and the current scope of this research is limited to examining the performance of FDI determinants within Western Europe.

Through this framework, the national government can understand how investors make their capital allocation decisions in their country. The framework developed in this study can help policymakers better understand the rationality of FDI inflows.

An IML framework has not been developed in prior studies to analyze FDI inflows. Additionally, the author demonstrates the applicability of the IML framework for estimating FDI inflows in Western Europe.

This study aims to assess the efficiency of managing working capital in 1,388 Indian manufacturing firms from 2008 to 2019 and investigate the effects of firm-specific and macro-level determinants on working capital management (WCM) efficiency.

The current study accommodates a slack-based measure (SBM) in data envelopment analysis (DEA) for computing WCM efficiency. Further, we implement a panel data fixed-effects model that controls for heterogeneity across firms in determining the relationships of selected variables with WCM efficiency.

The results highlight that manufacturing firms operate at around 50 percent efficiency, which is constant throughout the study period. Furthermore, among the selected variables, yield, earnings, age, size, ability to create internal resources, interest rate and gross domestic product (GDP) significantly affect WCM efficiency.

Instead of the traditional models used for assessing efficiency, the SBM-DEA model is unit-invariant and monotone for slacks, implying that it can handle zero and negative data, which overcomes the incapability of prior DEA models. Hence, this provides accurate efficiency scores for robust analysis. Additionally, this paper provides a holistic working capital model recognizing firm-specific and macro-level determinants for a more explicit estimation of the relationship between WCM efficiency and the selected determinants.

Most research on tax evasion focused on microeconomic variables revolving around perceptions and decisions of individual taxpayers. However, a new wave of research is now investigating the role of macroeconomic variables in inducing tax evasion. This study adds to the limited studies in this new direction of research. Previous studies found that inflation, low gross domestic product (GDP) growth and gross fixed capital formation causes recession, increases unemployment, raise interest rates, hurts both domestic and foreign direct investments. This study examined the relationship between these variables and estimated tax evasion in Sub-Saharan Africa.

The study adopts a correlation research design with 2,300 data points collected from 23 countries in Sub-Saharan Africa. Specifically, tax to GDP ratio, gross fixed capital formation per GDP and the GDP annual growth report from each country for the period 2011–2020 was retrieved. Generalised least square regression technique was employed to analyse the data due to the presence of heteroskedasticity in the model and random effect was utilized based on the Hausman test. To avoid misspecification and biased result; therefore, all relevant test was conducted including the multicollinearity test.

The results indicate that GDP annual growth and gross fixed capital formation have a significant negative impact on estimated tax evasion in Sub-Saharan Africa. The findings further indicate a negative but insignificant relationship between inflation and estimated tax evasion in Sub-Saharan Africa. The study concludes that both GDP annual growth rate and gross fixed capital formation negatively influence estimated tax evasion and the policy implications in the African continent were discussed.

The new findings on the effects of GDP annual growth, growth fixed capital formation and inflation on estimated tax evasion provide novel knowledge that is currently lacking in the current literature, specifically Sub-Saharan African continent.

This study aims to address three research questions pertaining to climate neutrality within the supply chain of metal and mining industry: (1) How can an organization implement practices related to climate neutrality in the supply chain? (2) How do members of the supply chain adopt different measures and essential processes to assist an organization in responding to climate change-related concerns? (3) How can the SAP-LAP framework assist in analyzing and proposing solutions to attain climate neutrality?

To address the proposed research questions concerning climate neutrality, this study employs a case study approach utilizing the SAP-LAP (situation, actor, process–learning, action, performance) framework. Within the SAP-LAP framework, adopting a natural resource-based perspective, the study thoroughly examines the intricacies and interactions among existing situations, pertinent actors and processes that impact climate initiatives within a metal and mining company.

The study's findings suggest that organizations can achieve the objective of climate neutrality by prioritizing resources and capabilities that lead to reduced GHG emissions, lower energy consumption and optimal resource utilization. The study further proposes key elements that significantly influence the pursuit of climate neutrality within enterprises.

This study is one of the earliest contributions to the development of a holistic understanding of climate neutrality in the supply chain of the metal and mining industry.

The study will assist practitioners and policymakers in comprehending the present circumstances, actors and processes involved in enterprises' supply networks in order to attain climate neutrality in supply chains, as well as in taking the right steps to enhance performance.

This study presents a climate neutrality model and provides valuable insights into emission management, contributing to the achievement of the climate neutrality objective.

The learning outcomes of this study are to identify and discuss ways in which energy consumption in a residential educational institute can be reduced by improving demand-side energy management for sustainable development; summarise the challenges that an institute faces in transitioning to a more environmentally friendly mode of operations concerning energy management; illustrate the difference between operating expense and capital expenditure methods used for solar rooftop projects from the perspective of Goa Institute of Management (GIM); and analyse different project proposals for solar rooftop power generation energy using capital budgeting techniques.

Dr Ajit Parulekar, director at GIM, was evaluating the steps taken over the past few years for sustainable energy management to understand their impact and consider ways in which to take the environmental sustainability agenda forward. One of the projects that he was considering was the rooftop solar power plant. GIM had received proposals from several different vendors and evaluated three proposals out of these. He needed to decide on the capacity of the rooftop solar power generation and the type of contract that he should get into for the implementation of the project. This case study describes the differences and highlights the advantages and disadvantages of all the mentioned models with respect to GIM.

This case study is suitable for post-graduate level management students, as well as for undergraduate-level finance and management students.

Teaching notes are available for educators only.

CSS4: Environmental management.