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The advent of artificial intelligence (AI) in the accounting landscape marks a significant shift, promising gains in efficiency and accuracy but also eliciting concerns about job displacement (JD) and broader socio-economic implications. This study aims to provide an in-depth understanding of how AI’s integration in accounting contributes to JD, reshapes decision-making processes and reverberates across economic and social dimensions. It also offers evidence-based policy recommendations to mitigate adverse outcomes.

Leveraging a cross-sectional survey disseminated through Facebook, this research used snowball sampling to target a diverse cohort of accounting professionals. The collected data were subjected to meticulous analysis through descriptive and regression models, facilitated by SmartPLS 4 software.

The analysis revealed a significant correlation between AI’s increasing role in accounting and a heightened rate of JD. This study found that this displacement is not isolated; it has tangible repercussions on decision-making paradigms, economic well-being, professional work dynamics and social structures. These insights corroborate existing frameworks, including, but not limited to, theories of technological unemployment and behavioural adjustments.

Although providing valuable insights, this study acknowledges limitations such as the restricted sample size, the cross-sectional nature of the survey and the inherent biases of self-reported data. Future research could aim to extend these initial findings by adopting a longitudinal approach and potentially integrating external data sources.

As AI technology becomes increasingly ingrained in accounting practices, there is an urgent need for coordinated action among stakeholders. Policy recommendations include focused efforts on talent retention, investment in upskilling programs and the establishment of support mechanisms for those adversely affected by AI adoption.

By synthesising a range of theoretical perspectives, this study offers a comprehensive exploration of AI’s multi-dimensional impacts on the accounting profession. It stands out for its nuanced examination of JD and its economic and social implications, thereby contributing to both academic discourse and policy formulation. This work serves as an urgent call to action, highlighting the need for strategies that both exploit AI’s potential benefits and protect the workforce from its disruptive impact.

Increased trapped heat in the atmosphere leads to global warming and economic activity is the primary culprit. This study proposes the nonlinear impact of economic activity on cooling degree days to develop a climate Kuznets curve (CKC). Further, this study explores the moderating role of higher education and renewable energy in diminishing the climate-altering effects of economic activity.

All the selected BRICS economies range from 1992 to 2020. The CKC analysis uses a distribution and outlier robust panel quantile autoregressive distributed lagged model.

Results confirmed a U-shaped CKC, controlling for population density, renewable energy, tertiary education enrollment and innovation. The moderating role of renewable energy and education can be exploited to tackle the progressively expanding climate challenges. Hence, education and renewable energy intervention can help in reducing CKC-based global warming.

This study highlighted the incorporation of climate change mitigating curriculum in education, so that the upcoming economic agents are well equipped to reduce global warming which must be addressed globally.

This study is instrumental in developing the climate change-based economic activity Kuznets curve and assessing the potential of higher education and renewable energy policy intervention.

This study aims to develop a methodology for predicting the resilience of individual firms to economic crisis, using historical government data to optimize one of the most important and costly interventions that governments undertake, the huge economic stimulus programs that governments implement for mitigating the consequences of economic crises, by making them more focused on the less resilient and more vulnerable firms to the crisis, which have the highest need for government assistance and support.

The authors are leveraging existing firm-level data for economic crisis periods from government agencies having competencies/responsibilities in the domain of economy, such as Ministries of Finance and Statistical Authorities, to construct prediction models of the resilience of individual firms to the economic crisis based on firms’ characteristics (such as human resources, technology, strategies, processes and structure), using artificial intelligence (AI) techniques from the area of machine learning (ML).

The methodology has been applied using data from the Greek Ministry of Finance and Statistical Authority about 363 firms for the Greek economic crisis period 2009–2014 and has provided a satisfactory prediction of a measure of the resilience of individual firms to an economic crisis.

The authors’ study opens up new research directions concerning the exploitation of AI/ML in government for a critical government activity/intervention of high importance that mobilizes/spends huge financial resources. The main limitation is that the abovementioned first application of the proposed methodology has been based on a rather small data set from a single national context (Greece), so it is necessary to proceed to further application of this methodology using larger data sets and different national contexts.

The proposed methodology enables government agencies responsible for the implementation of such economic stimulus programs to proceed to radical transformations of them by predicting the resilience to economic crisis of the firms applying for government assistance and then directing/focusing the scarce available financial resources to/on the ones predicted to be more vulnerable, increasing substantially the effectiveness of these programs and the economic/social value they generate.

To the best of the authors’ knowledge, this study is the first application of AI/ML in government that leverages existing data for economic crisis periods to optimize and increase the effectiveness of the largest and most important and costly economic intervention that governments repeatedly have to make: the economic stimulus programs for mitigating the consequences of economic crises.

This study aims to assess the efficiency of Brazil, Russia, India, China, South Africa (BRICS) countries in achieving sustainable development by analyzing their ability to convert resources and technological innovations into sustainable outcomes.

Using data envelopment analysis (DEA), the study evaluates the economic, environmental and social efficiency of BRICS countries over the period 2010–2018. It ranks these countries based on their sustainable development performance and compares them to the period 2000–2007.

The study reveals varied efficiency levels among BRICS countries. Russia and South Africa lead in certain sustainable development aspects. South Africa excels in environmental sustainability, whereas Brazil is efficient in resource utilization for sustainable growth. China and India, despite economic growth, face challenges such as pollution and lower quality of life.

The study’s findings are constrained by the DEA methodology and the selection of variables. It highlights the need for more nuanced research incorporating recent global events such as the COVID-19 pandemic and geopolitical shifts.

Insights from this study can inform targeted and effective sustainability strategies in BRICS nations, focusing on areas such as industrial quality improvement, employment conditions and environmental policies.

The study underscores the importance of balancing economic growth with social and environmental considerations, highlighting the need for policies addressing inequality, poverty and environmental degradation.

This research provides a unique comparative analysis of BRICS countries’ sustainable development efficiency, challenging conventional perceptions and offering a new perspective on their progress.

The authors construct an index of economic policy uncertainty (EPU) for Greece using textual analysis and analyze its role in the 10-year Greek economic crisis.

To identify the causal relationship between various measures of economic activity and EPU in Greece, the authors use a sophisticated “shock-based” structural vector autoregressive identification scheme. Additionally, the authors use two additional models to ensure the robustness of the results.

EPU is negatively associated with domestic economic activity and economic sentiment, and positively with bond credit spreads. EPU is also estimated to have prolonged the crisis even in periods when macroeconomic imbalances were cured. The results are robust across various model specifications and different proxies of economic activity.

Brunnermeier (2017) observed that uncertainty may be central to understanding the evolution of the Greek crisis. Yet little attention has been paid to policy uncertainty in the existing long and growing literature on the Greek crisis. The authors attempt to fill this gap.

The paper provides a detailed historical account of Douglass C. North's early intellectual contributions and analytical developments in pursuing a Grand Theory for why some countries are rich and others poor.

The author approaches the discussion using a theoretical and historical reconstruction based on published and unpublished materials.

The systematic, continuous and profound attempt to answer the Smithian social coordination problem shaped North's journey from being a young serious Marxist to becoming one of the founders of New Institutional Economics. In the process, he was converted in the early 1950s into a rigid neoclassical economist, being one of the leaders in promoting New Economic History. The success of the cliometric revolution exposed the frailties of the movement itself, namely, the limitations of neoclassical economic theory to explain economic growth and social change. Incorporating transaction costs, the institutional framework in which property rights and contracts are measured, defined and enforced assumes a prominent role in explaining economic performance.

In the early 1970s, North adopted a naive theory of institutions and property rights still grounded in neoclassical assumptions. Institutional and organizational analysis is modeled as a social maximizing efficient equilibrium outcome. However, the increasing tension between the neoclassical theoretical apparatus and its failure to account for contrasting political and institutional structures, diverging economic paths and social change propelled the modification of its assumptions and progressive conceptual innovation. In the later 1970s and early 1980s, North abandoned the efficiency view and gradually became more critical of the objective rationality postulate. In this intellectual movement, North's avant-garde research program contributed significantly to the creation of New Institutional Economics.

Although the significance of technological progress in economic development is well-established in theory and policy, it has remained challenging to agree upon shared priorities for strategies and policies. This paper aims to develop a model of how policymakers can develop effective and easy to communicate strategies for science, technology and economic development.

By integrating insights from economic complexity, competitiveness and foresight literature, a replicable research framework for analysing the opportunities and challenges of technological revolutions for small catching-up countries is developed. The authors highlight key lessons from piloting this framework for informing the strategy and policies for bioeconomy in Estonia towards 2030–2050.

The integration of economic complexity research with traditional foresight methods establishes a solid analytical basis for a data-driven analysis of the opportunities for industrial upgrading. The increase in the importance of regional alliances in the global economy calls for further advancement of the analytical toolbox. Integration of complexity, global value chains and export potential assessment approaches offers valuable direction for further research, as it enables discussion of the opportunities of moving towards more knowledge-intensive economic activities along with the opportunities for winning international market share.

The research merges insights from the economic complexity, competitiveness and foresight literature in a novel way and illustrates the applicability and priority-setting in a real-life setting.

The relationships among institutions, entrepreneurship, and economic growth are hotly contested topics. The objective of this present study is to conduct a systematic literature review aimed at comprehensively assessing the relationships between institutional pillars, entrepreneurship and economic growth.

Specifically, a comprehensive analysis of 141 empirical publications was carried out using the PRISMA protocol. The reviewed publications were taken from the Web of Science, Scopus and Google Scholar databases. Thirty-three articles that met the eligibility criteria of quality, relevance and timeliness of the publications were included in the the study.

Three key lessons emerged from the review. First, it was discovered that entrepreneurship and economic growth are influenced by three institutional pillars at various levels, including the regulatory, cognitive and normative pillars. Second, according to the type of institutional quality, the institutional pillars in a causal framework have a good or negative impact on entrepreneurship. Third, novel enterprise creation, self-employment, citizen employment, poverty alleviation, radical innovation, formalization of the informal sector, promotion of competition in existing and new markets, Gross Domestic Product (GDP) growth and the emergence of new business models that significantly improve quality of life.

The study proposes a conceptual framework for further exploring this important relationship based on solid empirical evidence. By providing a theoretically grounded framework, the paper fills the gaps in the literature and helps to clarify the relationship between institutional foundations, entrepreneurship and economic progress.

While some countries have used debt to drive economic growth, the asymmetric effect on sub-Saharan African (SSA) countries has received little attention in the empirical literature. This paper therefore examines the asymmetric effect of external debts on economic growth.

The panel nonlinear autoregressive distributed lag (NARDL) approach was employed in the study for 29 sub-Saharan African countries from 1990 to 2021. The cross-sectional dependence test was used to determine the presence of cross-sectional dependence, while the second-generation panel unit root tests was used to examine the unit-root properties.

The empirical results show that external debt has an asymmetric effect on economic growth in both the short and long run. In the long run, a positive shock in external debts of 1% triggers an upturn in economic growth by 0.216% while a negative shock triggers 0.354% decline in economic growth. This implies that the negative shock of external debts has a much stronger impact on economic growth than the positive shock. In the short run, a positive shock in external debts by 1% triggers a decline in economic growth by 0.641%, while a negative shock of 1% triggers a fall in economic growth of 0.170%.

The paper used the NARDL model to examine the asymmetric impact of external debt on the economic growth of SSA countries, which has not been extensively studied. It is recommended that governments in the selected countries in sub-Saharan Africa should drive economic growth by promoting domestic revenue mobilization since external debts impede economic growth.

Countries worldwide aim to improve their comparative advantages by efficiently using scarce resources for economic growth and development. While many studies have been conducted to measure intellectual capital at the firm's level, measuring it at the national level has been under-examined. In addition, while the important role of national intellectual capital in economic growth has been theoretically recognized in literature, this important link has largely been ignored in empirical analyses.

This study uses the newly developed index of national intellectual capital from Vo and Tran's (2022) study to examine its effects on national economic growth in the long run. The dynamic common correlated effects technique and the pooled mean group estimation are used on the sample of 23 economies in the Asia–Pacific region from 2000 to 2020.

Findings from this study confirm the positive and significant contribution of the national intellectual capital to economic growth in the region. The authors also find that, as a feedback effect, economic growth will also enhance and improve the accumulation of national intellectual capital.

The findings of this paper provide valuable evidence and implications for policymakers in managing and improving national intellectual capital in the Asia–Pacific region.

To the best of the authors’ knowledge, this is the first empirical study to examine the impact of national intellectual capital on economic growth in the long run in the Asia–Pacific economies.