This article examines how disruptive digital technologies are reshaping the accounting profession by integrating bibliometric analysis with a critical, theory-informed synthesis. Using a structured review of Web of Science publications, the study maps the intellectual structure and thematic evolution of research on artificial intelligence, big data analytics, blockchain, cloud computing, and robotic process automation in accounting. The findings reveal a shift from infrastructure-oriented digitalization toward analytics-driven and intelligence-based applications, accompanied by growing attention to decision processes, control mechanisms, and organizational adaptation. Rather than producing uniform benefits, technological impacts are economically contingent, depending on complementary investments in human capital, governance structures, and institutional frameworks. The study advances prior literature by explicitly linking accounting digitalization to efficiency, transaction costs, productivity, and resource allocation. By combining bibliometric evidence with analytical interpretation, the article clarifies when and under what conditions digital transformation in accounting generates sustainable economic value for organizations and institutions across contemporary professional contexts.
Industry4.0 applications have become a strategic issue for countries because of giving competitive potential. In this context, studies are realized on ranking and grouping countries to determine the usage levels of Industry4.0 technologies. However, containing different variables, indexes and techniques in these studies presents different results. The lack of an agreed technique for grouping countries based on Industry4.0 technologies leads to method confusion on the subject. Clustering is one of the most suitable options for sorting or grouping a dataset. The purpose of the study is to categorize the countries according to usage of Industry4.0 technologies by using machine learning techniques. The clustering algorithms were applied to the ICT Usage in Enterprises data compiled from Eurostat. In the study, the most appropriate clustering algorithm was specified, the distance values within and between clusters were calculated, and the level differences in Industry4.0 technologies of the countries were determined.
The scientific problem of estimating the size of the tax gap is the interpretation of the tax gap results. Many interpretations focus on a single number, ignoring the context of the methodology and data source used or the evolution of GDP. This paper examines the variability of tax gap estimates and the high dependence of the output on the chosen methodology, keeping the assessment of the shadow economy as the first step in estimating the tax gap. The study collects and deploys all the known methods used to estimate the total tax gap and identifies the spread of estimation using the case study of the Czech Republic's gap for the period of 1993-2012 evaluation. The paper analyzes data from official sources such as the OECD, the EU, the CSO, the Ministry of Finance and Tax Administration reports. The first part of the analysis estimates the size of the shadow economy using the most common estimation methods. Due to the variability of the methods used, the results show the range of different values with a surprisingly high variability between 2.2 per cent and 19.5 per cent for the year 2012. Based on the previous results of the shadow economy, the amount of total tax gap of the Czech Republic for the period 1993-2012 is determined. The range of the values is wide, based on the method of estimating the size of the shadow economy. The results are presented in the context of other previously realized estimates and compared with the results of relevant studies. The most important finding of the study is the confirmation of the wide range of the tax gap results, which theoretically eliminates any possible simplified approach to the interpretation of the size of the tax gap.
In recent years, growing awareness of sustainable development has brought increasing attention to corporate social responsibility (CSR) among investors, as well as a sustained influence on firms ' long-term operations. In developed economies, CSR initiatives enhance societal well-being and strengthen corporate brand image, thereby improving firms' financial performance (the governance effect). However, in China, some enterprises are reluctant to engage in CSR activities or undertake them primarily as symbolic greenwashing efforts to improve market performance (the window-dressing effect). Drawing on strategic management, stakeholder, and reputational capital theories, this study examines the divergent effects of conventional CSR practices on corporate financial performance in the Chinese context. Using a sample of Chinese listed firms from 2011 to 2019, we employ a stepwise generalized method of moments (GMM) approach and bootstrap testing. The results show that CSR strategy and behavior are not significantly associated with accounting-based performance indicators (indicating no governance effect), but are significantly associated with market-based performance indicators (supporting the window-dressing effect). Furthermore, corporate reputation does not mediate the relationship between CSR strategy, CSR behavior, and accounting performance; however, it partially mediates the relationship with market performance. These findings contribute to the existing literature by explaining the inconsistency between CSR implementation and performance outcomes in China and by providing insight into the drivers of corporate greenwashing behavior, thereby offering meaningful managerial implications.
This research provides an analysis of the effects of innovation, digitalization, and environmental taxation on economic growth, as well as the moderating effect of national cultural traits within the context of the European Union's member states. It addresses a need that arises as the significance of technological transformation has increased within the context of multiple nations with diverse cultures. The study analyzed a balanced panel dataset covering a time period of 2013 to 2022 for 27 EU member states, with a total of 270 observations. We employed a mixture of econometric analyses, including ordinary least squares (OLS) estimates, fixed-effects panel regressions, and a form of lagged variable modeling. Environmental tax revenues are found to have a negative effect on GDP per capita in the short run. Scientific output from research and digitalization engagement levels (with a measurement variable based on online purchasing) both show positive impacts on the rate of economic growth. To account for the potential effects of culture on the outcomes, countries are grouped into 3 clusters based upon Hofstede’s cultural dimensions. Results show that cultural differences play an important role in determining the effect of innovation, environmental policy, and environmental taxation on economic outcomes. In short, EU member state countries with improved rates of growth were observed in the context of low levels of uncertainty avoidance and high levels of individualism. The findings of this study, as discussed in the empirical section of this paper, provide several contributions to research and future policy considerations. For theoretical contributions, the study expanded upon macroeconomic models of empirical strategies by adding cultural elements, following the work of Hofstede. As for future policies, the results suggest that customizing innovation and environmental policy to the context of the national culture may leverage better performance of these outcomes.
