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.
The article aims to evaluate for what amount of tax base or tax liability of a self-employed person the flat tax regime is advantageous. This evaluation is carried out in the legislative conditions of the Czech Republic valid in 2023. While the world's literature often understands the flat tax as the existence of a single tax rate, the concept of a flat tax in the Czech Republic is different. It combines payments for social security contributions and personal income tax as one payment. Since the flat tax amount is fixed, the research question is formulated, considering the literature, in the form of what tax base the entry into the flat tax regime is advantageous. Literature often mentions that taxpayers with a high tax base can benefit from the flat tax regime. However, it is a research question whether this will also apply in the conditions of the Czech Republic, where this payment also includes the aforementioned social security contributions. The different conceptual approaches to the flat tax in the Czech Republic create the broad potential that will be developed in this study.
Amidst the ongoing artificial intelligence (AI) technology revolution, businesses are increasingly embracing AI and environmental social and governance principles. This integration leads to sustainable business operations, improved efficiency, and enduring value creation to address global challenges. Therefore, this study explores that how AI technology enhance firm ESG performance through firm life cycle stages. By analyzing a sample of Chinese A-share listed firms from 2010-2020, the study primary findings reveal that AI technology significantly improves firm ESG performance, emphasizing the importance of technological advancements in ESG initiatives. Furthermore, the study reveals that the impact of AI on ESG is more pronounced during the growth and mature stages of the firm cycle compared to the introduction, decline and shakeout stages. Additionally, the study investigates this impact through two channels: AI enhances green innovation and firm performance, which in turn enhance ESG performance. Moreover, heterogeneity analysis highlighting a more pronounced effect in non-SOEs compared to SOEs, and in low bank concentration and robustness analysis through 2SLS and PSM.
Based on the theory of enterprise life cycle and the theory ofM&A synergy, this paper selects the successful cases ofM&A ofA-share listed companies in Shanghai and Shenzhen from 2000 to 2020 as the research object, and takes their financial data from 2000 to 2023 as the research sample, and empirically examines the association between M&A and the financial performance of enterprises in the growth, maturity and recession stages. The results show thatM&A in the growth stage can help to enhance the market position and competitiveness of enterprises, that is, to enhance the development ability. The implementation of mergers and acquisitions by mature enterprises can significantly improve the profitability, operation and debt repayment ability of enterprises, but may suppresses the development ability. During the recession period, enterprises can improve their solvency and stabilize their financial status through asset restructuring and optimization. Therefore, it is necessary to accurately identify the life cycle of the enterprise before implementing M&A activities, and continuously monitor and evaluate it in order to seize the opportunity to grow rapidly in the wave ofM&A.
With the rapid expansion of digital innovation ecosystems (DIEs), the quantitative assessment of system adaptability and collaborative evolutionary capacity, as well as the forward-looking prediction of future development trajectories, has become increasingly important. Nevertheless, existing studies still lack an integrated analytical framework capable of simultaneously capturing multidimensional ecological elements and supporting reliable trend forecasting. To address this limitation, this study develops an ecology-inspired evaluation and prediction framework specifically designed for DIEs. The framework conceptualizes niche structure across four dimensions, including digital innovation communities, resources, environment, and demand, and applies the method based on the removal effects of criteria (MEREC) for objective weighting. This approach is combined with an improved measurement of alternatives and ranking according to compromise solution (MARCOS) method using the Heronian mean (HM) operator to derive composite measures of niche fitness. In addition, enhanced grey prediction models are introduced to strengthen the framework's capacity to forecast evolutionary trends under weak-information conditions. The framework is empirically applied to provincial-level DIEs data from China covering the period from 2015 to 2021. The results indicate that overall niche fitness exhibits a fluctuating upward trend, accompanied by pronounced regional differentiation. Persistent imbalances are observed across niche dimensions, with resources, communities, and demand niches emerging as the primary constraints on the evolutionary process of DIEs. Forecasting results further suggest that niche fitness is likely to continue improving in the coming years, although structural misalignments may remain difficult to fully resolve in the short term. Overall, the proposed evaluation and prediction framework enables the systematic identification ofstructural constraints and latent development potential within DIEs, while also facilitating the analysis of their future evolutionary trajectories. As such, it provides a generalizable and scalable methodological tool for comparative analysis, dynamic monitoring, and cross-regional research on DIEs.
This study explores the impact of content topic, media form, and conversation principle on brand information value (emotion, reputation, and relationship value). Data were collected from 14 brands through a web crawler and processed with coding and sentiment analysis. Empirical results from non-parametric tests and multiple linear regression (MLR) show that content containing community-building topics greatly influences brand information value. Compared to content that merely provides an information-providing topic, content with an action-encouraging topic has a greater effect on brand information value. There were significant differences in reputation value among the three communication modalities, but no notable variances in relationship value. The high modality was stronger for emotion value than the moderate or low modalities. For conversation features, post frequency, readability, relevance, and brand response positively influence brand information value. This study utilizes the 5W communication model as its foundational framework, extends the conversation principle to this model, and enriches content marketing theories by taking a more comprehensive brand perspective. Additionally, this study finds the relationship between the driving factors and brand information value by leveraging big data from online platforms, offering insights and a research foundation for future studies on online brand value.
This research study investigates the impact of macro and micro-level indicators of the stock price crash risk. We used the daily stock prices of 15 top-performing stocks listed in the S&P 500 index. We choose these specific companies based on their trading volume. We used data ranging from Jan 2010 to Dec 2022 for all the companies. In the first step, we calculated the monthly series of stock price crash risk using the negative skewness approach. In a similar pattern, we use monthly data of macro indicators, which are exchange rate, interest rate, and economic policy uncertainty. In addition, we use trading volume and short selling as micro-level determinants of individual stock price crash risk (SPCR). We deploy four different models to forecast the stock price crash risk and the impact of individual determinants on the SPCR. These models include linear regression, support vector regression, a single-layer perceptron model, and a multilayer perceptron model. The findings suggest that both the micro (firm) level and macro-level potential predictors are highly significant. The overall accuracy of machine learning models improved significantly when macro-level indicators were incorporated. Furthermore, machine learning models, especially SLP and MLP, outperform linear regression.
Promoting deep industrial transformation and upgrading serves as the micro-level foundation for improving the institutional framework of new quality productive forces and advancing a high-standard socialist market economy. Enhancing government data governance mechanisms represents a critical pathway to achieving this objective. Drawing on panel data from 2011 to 2021 concerning the establishment of government big data administration agencies and Chinese A-share listed firms, this paper employs a multi-period difference-in-differences (DID) approach to systematically examine the impact of government data governance on firm upgrading, along with its underlying mechanisms. The empirical results reveal that government data governance significantly facilitates firm upgrading, and this effect remains robust across a series of sensitivity checks. Heterogeneity analyses indicate that the positive effects are more pronounced among firms in heavily regulated industries, high-tech sectors, enterprises with higher levels of data utilization, and regions with higher administrative levels of data governance institutions. Mechanism tests further suggest that government data governance improves the quality of public service delivery and empowers corporate digital governance, both of which play key intermediary roles in promoting firm upgrading. These findings confirm the enabling role of government data governance in advancing firm upgrading and offer policy implications for stimulating innovation among market entities and strengthening the micro-foundations of China’s economic recovery and growth momentum.
The Lithuanian laser industry is considered one of the country's most appreciated manufacturing sectors due to its high value-added output, growing potential significance, and the wide range of future applications of laser technologies. Despite considerable global potential and increasing export volumes, the laser industry faces challenges such as intensifying global competition, short product life cycles, high production costs, demand for highly skilled employees, dependence on global supply chains, standards, and regulations. The core aim of the research is to identify the main economic and non-economic determinants of laser export development in Lithuania in the context of rapid technological progress and geopolitical uncertainty. To empirically assess the impact of selected factors on the export results of the Lithuanian laser industry, correlation analysis, the Granger causality test, regression model, and the autoregressive distributed lag (ARDL) model were used. The results indicate that the export performance of the Lithuanian laser industry is positively affected by investment in patents and licenses. However, after controlling for other selected factors, interest rates, the number of military conflicts, and R&D expenditure were found to have a negative effect. Strategic support for intellectual property acquisition and mitigation of external economic risks are key insights for policymakers and stakeholders.
As artificial intelligence technology rapidly develops, enterprises pursuing intelligent transformation may face innovation resource allocation dilemmas. On one hand, the construction and maintenance of AI systems require substantial financial investment, and this high-cost pressure may crowd out resources traditionally allocated to innovation activities. On the other hand, excessive dependence on AI may lead enterprises to neglect talent cultivation and equipment updates, resulting in technological path lock-in. Based on resource allocation theory, principal-agent theory, and path dependence theory, this paper uses Chinese A-share listed companies from 2013-2023 as research samples and employs text analysis methods to construct an enterprise AI application intensity index to explore the impact of AI on enterprise innovation behavior and its mechanisms. The research finds that: (1) AI application has a significant crowding-out effect on enterprise innovation; (2) this crowding-out effect is primarily realized through two channels: reducing innovation talent investment intensity and cutting professional technical equipment configuration; (3) heterogeneity analysis shows that characteristics such as state ownership, high market competition, low financing constraints, high technology intensity, and high management shareholding can effectively mitigate AI's inhibitory effect on innovation. This research not only deepens theoretical understanding of the relationship between AI and enterprise innovation but also provides practical guidance for optimizing innovation resource allocation during enterprise digital transformation.
In the quest for a sustainable and innovative future, the integration of Artificial Intelligence (AI) with Digital Transformation has emerged as a pivotal strategy for businesses navigating the rapidly evolving digital economy. This research explores the dynamic relationship between AI adoption and corporate transformation, with a focus on publicly listed companies in China. Drawing on a multi-theoretical framework-comprising dynamic capabilities theory, the resource-based view, and the technological innovation systems perspective-the study employs robust panel econometric techniques, including Westerlund panel cointegration tests, dynamic panel OLS, and panel vector error correction models, to analyze 3,602 firm-year observations from 2010 to 2020. The results reveal significant long-term cointegration and a bidirectional causality between AI and DT, supporting a mutually reinforcing relationship. Moreover, technology-oriented businesses achieve greater levels of transformation. The findings demonstrate that AI integration significantly influences both current and future corporate transformation, underscoring a mutually beneficial relationship where AI and transformation drive each other. These insights are crucial for enterprises and policymakers aiming to harness the transformative potential of technological innovations.
This paper analyses tourism influence on growth and environment in the EU27 for the period 2009-2024, integrating tourism-specific factors (seasonality, tourist revenues, tourist expenditures per trip, international arrivals, the share of tourism in total exports) along with macroeconomic and institutional variables (economic freedom, state investments in education, unemployment rate). We have approached tourism from two angles: economic (in terms of resilience) and ecological (in terms of environmental sustainability), applying multiple linear regression (MLR) and structural equation models (SEM), based on Pearson correlations. Tourism simultaneously and antagonistically influences the environment's resilience and sustainability. Tourist expenditure per trip, revenues from international tourism, the marginal effect of tourist flows, or tourist attractiveness, the market's freedom supports resilience, but not the share of tourism in total exports. Environmental sustainability is positively supported by market freedom and negatively endorsed by the tourist expenditure per trip. Seasonality appears as a reliable solution for both environmental sustainability and resilience. The results indicate no statistically significant effects of unemployment and education, and reveal that tourism dependency entails risks to resilient growth. Nevertheless, tourism remains essential for European economic prosperity. Reducing green pressure involves a series of concessions on seasonality and directing tourism revenues toward technologies and practices that reduce the carbon footprint. The novelty lies in the integrated approach to resilience and sustainability, highlighting the ambivalent impact of tourism, and showing that seasonality is rather a structural policy tool than a constraint in the simultaneous achievement of environmental and resilience objectives, offering insights with direct implications for European tourism development policies.
There has been a substantial body of research examining the relationship between resource growth and resource finance; limited attention has been devoted to the resource-energy interface. This gap has motivated the current study, which examines the relationship between resources, finance, and energy in the context of China. However, China has undertaken extensive efforts to diversify its energy portfolio; coal remains a dominant source of energy to meet national demand. China has been actively pursuing alternative pathways for achieving its target of carbon neutrality by 2060. Drawing attention towards an extensive literature review, this research has posited that natural resources, eco-financing, fintech, and financial inclusion are key determinants of energy efficiency. Using annual data spanning 2000 to 2023, the study employs the Quantile Autoregressive Distributed Lag (QARDL) model to examine the heterogeneous impacts of these determinants across the distribution of energy efficiency. Empirical findings reveal that fintech consistently enhances energy efficiency in both the short-and long-run across multiple quantiles. On the other hand, financial inclusion and natural resource availability negatively affect energy efficiency, suggesting the presence of structural and behavioural inefficiencies. Moreover, carbon finance is found to have no statistically significant effect on energy efficiency across all quantiles and time horizons. Based on these insights, the study proposes targeted policy recommendations to advance China's transition toward a more sustainable, energy-efficient economy.
This research has examined the role of carbon finance, globalization, and digital trade for shaping sustainable management of total natural resources (TNR) in Thailand over the period of 2000 to 2022. For the analysis of these relationships and capturing potential asymmetries, the research employs a Non-linearAutoregressive Distributed Lag (NARDL) model, which allows both short-run and long-run effects to be determined across different quantiles. The empirical outcomes reveals that globalization has a negative and significant effect on TNR in both short and long run, which suggests an increased economic integration would worsen natural resource depletion. Digital trade also reveals generally negative relationship with TNR, although its long-run impact is significantly adverse, the short-run effects are statistically insignificant, highlighting complex temporal dynamics. On contrast, carbon finance shows a positive and significant impacts on natural resource management across time horizons, underscoring its potential as a sustainable financial mechanism. Moreover, the economic growth is found to have a positive relationship with TNR, that likely to reflect increased extraction and utilization of finite resources rather than sustainable use, signalling a potential trade-off which challenges long-term environmental objectives. The findings of study offer crucial insights for policy implications for Thailand, focusing the need to refine globalization and digital trade policies for mitigating their environmental impacts, while simultaneously leveraging carbon finance to promote sustainable resource management. These insights contribute towards development of integrated strategies for balancing economic development with environmental sustainability in context of Thailands' evolving digital economy.
With response towards the global responsibility in addressing the climate change, China has implemented a wider range of policy measures that aimed at limiting the emissions of growth and promotion of renewable energy consumption. These measures include the regulation of energy parameters, with increased funding for research and development, the digitalisation of financial services, and expansion of environmental taxation and energy efficiency policies. In this context, current study determines the impact of carbon policy, natural resource rents, financial technology (fintech), energy tariffs and green bonds on renewable energy consumption in China for the period of 2000-2023. With the use of Dynamic Auto-regressive Distributed Lag (Dynamic ARDL) approach, the outcomes indicate that carbon policy, green bonds, and energy tariffs exerts a significant and positive effect on renewable consumption of energy in long-run, where as fintech development and natural resource rents do not show statistically significant impact on renewable energy consumption, however carbon-policy, energy tariffs, natural resource rents and green bonds do not show significant effects. These outcomes offer significant policy implications for China, suggesting that strengthening green bond markets, enhancement of carbon policy frameworks, optimisation of energy tariff structures, and supporting fintech development could play a crucial role in promoting the renewable energy consumption.
This research has addressed the crucial significance of natural resource efficiency for achieving sustainable development, specifically between contemporary environmental challenges. However, previous research has highlighted the relevance of eco-innovation, recycling of waste management, youth education, and digital finance for sustainability, their combined impact on natural resource efficiency remains undiscovered. The study focused on China and Korea over the period of2000 to 2022, employing advanced panel data econometric techniques, which include Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS), for empirically testing these variables on natural resource efficiency. The outcomes shows that eco-innovation, recycling, youth education, and digital finance have significantly contributed towards enhancing the efficient use of natural resources. The outcome underscores the synergistic role of technological innovation, educational advancement, sustainable waste practices, and digital financial platforms for promoting sustainable developments. The policy implications focus on the significance of integrating these factors for fostering responsible resource sustainability in resource-abundant economies.
