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Key drivers shaping adoption of BNPL (Buy Now, Pay Later) payments by consumers
No full textResearch background: BNPL (Buy Now, Pay Later) is a rapidly growing area of fintech that is changing the way consumers manage their finances and make purchases online. The rationale for undertaking research of the factors underlying the adoption of this payment method is the innovation of the subject matter and the practical importance of the findings for BNPL providers and regulatory institutions. Our research provides a better understanding of consumer needs and behavior, which can lead to more effective marketing strategies and regulatory action to protect consumers, and promote responsible lending and borrowing. In addition, the limited number of investigations on what makes people choose BNPL and the lack of studies relating to the reasons why consumers in Central and Eastern Europe (CEE) opt for deferred payments also indicate the need for such research.
Purpose of the article: To identify and assess the factors that influence consumers\u27 adoption of BNPL payments in Poland.
Methods: Critical analysis of the source literature, Technology Acceptance Model (TAM), Partial Least Square-Structural Equation Modelling (PLS-SEM). Empirical data is from a survey conducted in August 2024 using the CAWI method on a sample of 350 Poles.
Findings & value added: The study identifies factors influencing the adoption of BNPL payments by consumers in Poland. These include perceived usefulness (PU), risk (PR), and personal innovation (PI). Perceived trust (PT), however, does not have a statistically significant effect on adoption attitudes (ATT). Similarly, perceived ease of use (PEOU) does not directly influence attitudes (ATT). The paper fills a gap in the literature, as most of the research on BNPL to date has focused on the Anglo-Saxon and Asian markets, while the CEE context has not yet been explored. This is the first research study to present, based on the TAM model, the identification of factors of BNPL adoption by Polish consumers, where the digital payments market is the fastest growing in CEE. Beyond its national relevance, the study offers a new conceptual contribution to global fintech literature by demonstrating how the determinants of BNPL adoption evolve with the digital maturity of a market. Unlike results from Anglo-Saxon and Asian contexts—where perceived ease of use and trust are key drivers—our findings indicate that in post-emerging markets such as Poland, these classical TAM constructs lose explanatory power in favor of perceived usefulness, perceived risk, and personal innovativeness. This maturity-dependent shift in adoption mechanisms can inform cross-country comparisons and theoretical modelling of fintech adoption in diverse economies. Hence, the study provides a transferable analytical framework valuable for international researchers and practitioners seeking to understand how fintech adoption differs between mature, emerging, and developing markets
Agricultural eco-efficiency vs. efficiency in the EU-27: Dynamics and pathways towards sustainable agriculture
No full textResearch background: The agricultural sector plays a crucial role in ensuring food security and increasing economic growth. Due to natural resource constraints, agricultural policies need to focus on improving efficiency. However, agriculture contributes significantly to environmental degradation, making sustainable practices that balance efficiency and eco-efficiency essential, but challenging for policymakers, scientists, and farmers.
Purpose of the article: The main objective of this paper is to evaluate agricultural efficiency and eco-efficiency and their trends between 2015 and 2022 in the EU-27 member states. Part of the main objective is to verify the research hypothesis that “All agriculturally eco-efficient countries are not necessarily agriculturally efficient”.
Methods: The agricultural efficiency and eco-efficiency are calculated using an input-oriented Data envelopment analysis (DEA) model, assuming constant returns to scale (CRS). The assessment of eco-efficiency also considers undesirable output.
Findings & value added: An assessment of agricultural efficiency and eco-efficiency in EU Member States for 2015 and 2022 reveals significant trends and differences between countries. In 2015, 13 EU countries were agriculturally efficient, increasing to 16 by 2022. In terms of agricultural eco-efficiency, 19 countries were eco-efficient in 2015, rising to 21 by 2022. The study confirms that eco-efficiency in the agricultural sector does not necessarily guarantee agricultural efficiency. The article introduces a universally applicable framework to distinguish agricultural efficiency from agricultural eco-efficiency, enabling international comparisons and supporting research on sustainable agriculture. The findings offer an empirical basis for Member States to prioritize CAP objectives for 2023–2027, particularly in terms of enhancing competitiveness and environmental sustainability. The results highlight the need for integrated policy approaches, as eco-efficiency can coexist with high productivity, but targeted interventions are required to simultaneously achieve economic and environmental objectives. These insights are valuable for countries seeking to improve food security, economic performance, and environmental sustainability within evolving agricultural policy frameworks
AI and green credit: A new catalyst for green innovation in Chinese enterprises
No full textResearch background: China has invested heavily in special credit funds to promote green transformation in enterprises. While green loans have financial characteristics, their pricing is not fully market-driven. This unique environmental regulation has a significant impact on the behavior of enterprises in green innovation, and the rapid integration of artificial intelligence (AI) adds complexity to the process.
Purpose of the article: This study aims to empirically investigate whether China\u27s green credit policy, as a unique environmental regulatory instrument, has led to the "Porter Effect". The study examines the impact of the green credit policy on firms\u27 green innovation in two different periods (2007–2012 and 2012–2020), while also assessing the heterogeneous impact on different types of firms. Particular attention is paid to how the integration of artificial intelligence (AI) and fintech has influenced the impact of the policy on corporate green innovation, especially by changing the transmission mechanisms related to operational and agency costs.
Methods: The Causal Forest method is applied to observational data from 1,510 listed companies in China between 2007 and 2020. This approach integrates the Neyman-Rubin framework with classical econometric techniques and machine learning to capture complex causal relationships and analyze the long-term effects of policy interventions over time, overcoming the limitations of dealing with nonexperimental data.
Findings & value added: The role of green credit policy in stimulating green innovation in enterprises is quite limited. However, the application of AI technology appears to play a significant role in amplifying the effects of green credit. The study suggests that while the classic "Porter hypothesis" may not be fully applicable in terms of corporate operating costs and innovation outcomes, the interplay of green credit policy and AI technology does indeed help reduce agency costs
Can machine learning bring ESG ratings closer to small and medium-sized enterprises?
No full textResearch background: Environmental, Social, and Governance (ESG) principles provide an important framework for companies to create value for stakeholders. Companies aim to enhance their performance in ESG as it has gained importance in investment analysis, with ESG ratings often used for this purpose. However, there is no objective way to calculate ESG scores, and small and medium-sized enterprises (SMEs) struggle to access ratings provided by score providers.
Purpose of the article: The main goal of this paper is to investigate whether the application of an artificial neural network (ANN) and feature selection techniques can make it possible to identify and prioritize the key features affecting companies’ ESG scores. Determining a set of key features from among the wide range of non-financial data reported by companies and then used by rating ESG score providers would benefit SMEs. It would allow them to report the most relevant non-financial data, that is considered in calculating ESG scores.
Methods: A feedforward ANN was employed to predict corporate ESG ratings based on environmental, social and governance data reported by companies to Refinitiv. Specifically, ESG data from 1,194 companies was analysed in this study across seven diverse sectors: Banking Services, Diversified Retail, Telecommunications Services, Metals and Mining, Oil and Gas, Software and IT Services, and Specialty Retailers. The companies represented 61 countries from six continents. The data encompassed the period from 2017 to 2021. Sectoral representation varied, with company numbers ranging from 74 to 260. A sequential forward feature selection process was next implemented to identify the minimal feature subset of 186 parameters used by Refinitiv for accurate ESG score prediction. The ANN model was trained iteratively to predict the ESG score, starting with an empty feature set and progressively adding features that most enhanced the model’s performance. The experiment was performed repeatedly for each sector.
Findings & value added: This paper proposes using machine learning (ML) to bring ESG ratings closer to SMEs. The study shows that ANN can accurately predict ESG scores using Refinitiv data, while retaining ESG ranking consistency. Furthermore, feature selection can infer ESG scores with acceptable accuracy using a minimal subset of key company attributes. Specifically, a neural network can accurately predict a company’s ESG score and determine its ranking using only around 13% of reported parameters. This approach may simplify access to ESG assessment for SMEs, allowing them to evaluate their performance with fewer parameters
Navigating the social media market: AI and the challenge of fake news dissemination in the business environment
No full textResearch background: Social media plays a crucial role today in enhancing or limiting how fake news is spread. Whether devised by man or developed by artificial intelligence, it has the power to rapidly change consumers’ minds, encouraging them to adopt new behaviors, perceive situations differently, or even act in total opposition to what might be expected. The new dynamics of communication highlights the need for an organizational response adapted to new AI technologies and to the dissemination of fake news within social media networks.
Purpose of this article: This paper aims to reveal, by means of bibliometric analysis and a systematic literature review, the generative capabilities of artificial intelligence in the creation and spread of fake news in the business environment, acknowledging the role of previous research in predicting accurately the constant developments in contemporary society.
Methods: The analysis is based on a PRISMA flowchart to examine how artificial intelligence technologies contribute to the creation of fake news whilst also highlighting potential artificial intelligence regulations and standards for limiting the dissemination of false information. Initially, the database included over 3,400 highly cited articles retrieved from Scopus and Web of Science, published in the last years, from which a total of 203 were selected for inclusion in the analysis. The bibliometric analysis follows research directions related to detection methods and strategies, legislation and policies governing artificial intelligence technologies used in the creation and dissemination of fake news connected to the business environment. Fake news typologies relating to the advancement of artificial intelligence new technologies are also explored.
Findings & value added: By analysing important phrases, including false information, misinformation, disinformation, mal-information, and deepfakes, this research investigates the categorization of fake news linked to the business environment and social media concepts. It underscores the need for better truth comprehension and the significance of fact-checking in preventing the spread of false information, with governance and institutional implications in terms of the economics of artificial intelligence-generated fake news in the social media market. While previous studies have examined the fake news phenomenon from several angles, there is still a research gap, as the literature concentrates more on how fake news is consumed rather than how it is created. This research aims to bridge the gap by providing a comprehensive examination of fake news research from the perspectives of fake news typology, creation, detection, and regulatory means
Adapting to digital transformation: Determinants of training motivation in response to digital automation among workers in six EU countries
No full textResearch background: The increasing automation of work tasks is transforming labour markets, creating both challenges and opportunities for workers. Reskilling and upskilling through training are essential for maintaining employability in the rapidly changing digital economy. While automation may complement certain job roles, it substitutes others, leading to skills mismatches and heightened concerns about job security. Previous studies have provided inconsistent findings regarding the influence of automation on workers\u27 training motivations, lacking detailed distinctions between task complementarity and substitution effects, as well as differentiations in types of job insecurity.
Purpose of the article: This article examines the key determinants of workers\u27 motivation to participate in training in response to automation. It specifically addresses the gaps in literature by clearly distinguishing between the complementarity and substitution effects of automation on job tasks, differentiating general fear of job loss from specific technological unemployment fears, and exploring the role of previous training experiences, formal education levels, and structural barriers in shaping training decisions. The study contributes to existing theories by clarifying how task-specific automation perceptions distinctly affect training motivations.
Methods: The study uses quantitative survey data collected from over 6,000 respondents across six European Union countries (Austria, Czechia, Germany, Hungary, Poland, and Slovakia). Multivariate logistic regression analysis is employed to assess the relationships between workers\u27 training motivations and factors such as automation exposure, general job loss fear, specific technological unemployment fear, prior training participation, and education.
Findings & value added: The study provides empirical evidence enriching workforce adaptation and lifelong learning theories by highlighting how nuanced perceptions of automation distinctly shape training motivations. Results indicate that workers previously engaged in training, those experiencing complementarity or partial substitution of tasks due to automation, and individuals expressing general fear of job loss show higher motivation for training. Conversely, extensive substitution of tasks and specific fears of technological unemployment decrease training willingness. Formal education levels overall do not significantly influence training participation, but notably workers with vocational education exhibit lower training motivation. These findings offer a detailed theoretical understanding of motivational factors and present critical implications for policymakers and organizational leaders. To effectively support lifelong learning in the digital economy, fostering positive training experiences and proactively addressing structural and perceptual barriers are essential
The impact of green finance on ESG: Evidence from the Chinese enterprises
No full textResearch background: Existing works highlight the multifaceted nature of ESG and the wide range of factors that can influence and predict ESG standing. However, there is currently a lack of research on the impact of green finance pilots on corporate ESG. How to improve the ESG level of enterprises through green finance policies is an urgent topic to be studied.
Purpose of the article: Using samples of 1300 listed companies, this study constructs a multi-period difference-in-differences model to examine the impact and mechanisms of the green finance reform and innovation pilot zone (GFPZ) pilot policies on corporate ESG levels.
Methods: Multi-period difference-in-differences (DID) estimation.
Findings & value added: (1) GFPZ is positively associated with the ESG of listed companies, indicating that the pilot policies effectively facilitate the improvement of ESG. This conclusion withstands robustness tests. (2) The pilot policies of GFPZ enhance ESG by promoting green technological innovation and strengthening the stock liquidity of enterprises. (3) From a theoretical perspective, this paper contributes to the literature on green finance and corporate governance by establishing a clear causal link between place-based green finance policies and firm-level environmental, social, and governance factors. This expands the understanding of policy-driven ESG in developing economies. Technically, the paper demonstrates how financial infrastructure reforms can lead to measurable sustainability outcomes through innovation and market responses. Furthermore, the paper provides targeted, actionable recommendations for optimizing GFPZ design, offering valuable insights for policymakers and ESG-oriented investors alike. The study deepens the understanding of the role of green finance in sustainable development and presents a replicable policy model for other economies seeking to improve their corporate ESG levels
Green innovation, ESG governance, and digital transformation: Evidence from China\u27s high-end manufacturing sector
No full textResearch background: Green technological innovation (GTI) is widely regarded as a pathway to reducing corporate carbon emissions; however, its effectiveness remains contested because of rebound effects, governance gaps, and complexities in the digital era. Although existing studies highlight the role of innovation, few investigate how ESG governance and digital transformation (DT) jointly influence firm-level carbon outcomes, particularly in emerging economies.
Purpose of the article: This study investigates how GTI, ESG performance, and DT interact to influence firm-level carbon emission intensity in China’s high-end equipment manufacturing sector. The study examines the mediating and moderating mechanisms that determine whether innovation generates sustainable environmental outcomes.
Methods: Drawing on Endogenous Growth Theory, Stakeholder Theory, and Dynamic Capabilities Theory, the paper develops an integrated conceptual framework. Drawing on an unbalanced panel dataset of 4,213 firm-year observations (2010–2020) from A-share listed high-end manufacturers, the study employs fixed-effects regression models to capture nonlinear innovation effects, mediation analysis to test ESG performance, and moderation analysis to assess the amplifying or constraining role of DT.
Findings & value added: The results reveal a U-shaped relationship between GTI (quality and quantity) and carbon emissions: while innovation reduces emissions at lower levels, it may generate rebound effects when excessive or misaligned. ESG performance mediates this relationship, particularly in state-owned enterprises, while DT moderates it as a double-edged capability—enhancing both the benefits and risks of innovation. The study advances strategic environmental management by reconceptualizing ESG and DT as core enablers of innovation, refining growth and innovation theories through the identification of nonlinear dynamics, and offering firm-level insights for carbon-intensive sectors in emerging and advanced economies alike
Model of relationships between corporate social responsibility, human resources management, and artificial intelligence
No full textResearch background: The research explores the interrelationship between human resource management (HRM), corporate social responsibility (CSR), and artificial intelligence (AI) in the modern business environment. It examines the potential of AI to optimise HR processes while ensuring ethical considerations and social responsibility are integrated into corporate strategies.
Purpose of the article: The aim of the article is to identify and quantify the causal relationships between human resource management, corporate social responsibility, and the perception of artificial intelligence within the company as key aspects of sustainable business development.
Methods: Research was conducted in the Czech business environment based on 451 responses from HR managers in medium- to large-sized companies. A uniquely designed questionnaire was created to capture the respondents\u27 subjective attitudes in September 2024. The hypotheses were evaluated using the application of structural equation modelling (SEM).
Findings & value added: The findings confirm that CSR activities exert a clear and positive impact on HRM, whereas AI, despite its significant potential to enhance HR processes, is not yet fully implemented or utilised at an optimal level. In addition, we have also analysed the relationship between AI and CSR, and empirical findings indicate that AI can significantly support CSR activities as these two domains had the potential to enhance the competitiveness of the organisation. Our results emphasise the necessity for policy makers and managers to enhance CSR focused HRM practices and to support guidelines to ensure the ethical deployment of AI as the ethical and social implications of implementing AI in HR and CSR present another key challenge, including data bias and privacy concerns