Journal of Business Economics and Management

Exploring distance learning in higher education: satisfaction and insights from Mexico, Saudi Arabia, Romania, Turkey

Miltiadis D. Lytras — 2025-02-03

Education, notably higher education, faced a significant challenge during the last period. Our data exploratory study aims to provide insights into the key factors that define students’ Distance Learning (DL) in the current period. Based on the main findings, we justify our bold proposition for the current era of distance and blended learning in Higher Education. Our research study aims to understand cultural and national differences in four countries: Mexico, Saudi Arabia, Turkey and Romania. It contributes to the theory of DL with a model of six hermeneutic factors for the satisfaction of using the DL method. It investigated and confirmed the capacity of the components to explain 60% of the DL satisfaction variance. Our research study also emphasized the interpretation of the essential findings and the drafting of bold propositions for the DL practice, emphasising academic environments. We identified significant areas of improvement, and we suggested the orchestration of combined efforts. Our research promotes the strategic deployment of DL in the current context as a resilient strategy of institutions for high-impact training and targeting of huge audiences, with emphasis on the deployment of new tools and teaching methods customized for a new, unique value proposition of the DL.

Multifractal analysis of Bitcoin price dynamics

Cristian Bucur — 2025-02-03

This research employs Multifractal Detrended Fluctuation Analysis (MFDFA) to investigate multifractal properties in financial variables, including Bitcoin prices and economic indicators. Spanning 2019–2022, the analysis reveals multifractal scaling not only in Bitcoin prices, but also in economic indicators such as inflation rates and energy commodity prices. The non-linear singularity spectra unveil the multifaceted nature of scaling properties. Temporal analysis exposes intriguing trends in multifractality with implications for market efficiency. Furthermore, correlation analysis unveils connections among multifractal properties. For instance, a positive correlation between oil prices and Bitcoin suggests similar market forces. The log-log plot of fluctuation function F_q versus lag size demonstrates a power-law relationship, characteristic of multifractal systems. The empirical data’s alignment in log-log space suggests self-similarity in the Bitcoin time series, supporting multifractality. The calculated Hurst exponents values suggest varying degrees of multifractality across the years, with 2021 exhibiting the highest degree and 2022 the lowest. Furthermore, an asymmetry index (0.5767) deviating from 0.5 indicates that the multifractal nature of the Bitcoin market is not symmetric. This research enhances risk assessment and portfolio optimization in finance. It challenges the Efficient Market Hypothesis (EMH), emphasizing the significance of MFDFA in comprehending financial market and economic factor’s relationships.

Bibliometric analysis of digital financial reporting: a comprehensive review of research trends and emerging topics

Darmawati Darmawati — 2025-02-11

Digital Financial Reporting (DFR) has gained significant research attention amid the digital transformation. This study comprehensively reviews DFR research, identifies trends, and highlights emerging topics. Key trends include advancements in sustainability reporting and improved financial reporting quality while emerging topics like XBRL and International Financial Reporting Standards (IFRS) reflect evolving research interests. Utilizing bibliometric methods, the study quantitatively analyzes DFR literature from Scopus, Emerald, Google Scholar, OpenAlex, Crossref, and SAGE. The research involved data sourcing, screening, eligibility selection, and bibliometric analysis. Findings show a dynamic increase in annual publications in DFR, with noticeable peaks and shifts in research focus over time. A notable rise post-2016 culminated in a peak in 2023, indicating sustained scholarly interest and field evolution. This study contributed into how digitalization enhances financial reporting quality, addressing gaps from previous bibliometric analyses. It emphasizes systematic trend analysis, identifying research gaps, and exploring factors driving the digital transformation of financial reporting. These insights guide researchers in developing new variables and strategies to advance DFR solutions, enhancing the accuracy, transparency, and accessibility of financial information through digital innovation.

Empirical analysis of the impact of technology import on industrial innovation in China: a technology expenditure perspective

Bingqiang Li — 2025-02-18

Promoting steady industrial innovation through technology import is an important initiative to accelerate innovation-driven and high-quality economic development in China. In this study, a model of the impact of technology import on industrial innovation is constructed from the perspective of technology expenditure, and empirical analysis is made by employing panel data from 2011 to 2019 in China. There exists obvious regional and industrial differences in the level of technological innovation and the intensity of technology import in China. The econometric results indicate that technology import and industrial innovation are complementary generally, but in the eastern region, as well as the labor- and the technology-intensive industries appear to be substitute. There are obvious regional differences when considering the impact of the interaction items, and the acceleration of technology digestion and absorption is conducive to the improvement of innovation, while that on various industries fail the significant test except for the technology-intensive industry.

The six-stage model of profitable growth and entrepreneurship in Finland: a Delphi study

Yrjö Myllylä — 2025-02-19

Our research has focused on addressing the following research questions for the growth strategies of SMEs: (1) What are the stages of profitable growth, and what factors contribute to these growth stages? (2) Which critical factors must be addressed for an organization to progress to the next stages of growth? (3) What is the importance of management in identifying and addressing critical growth factors? We have utilized the Delphi method and emphasized the role of company managers who have experienced profitable growth process as experts. Based on our findings, we have developed a Six-Stage Model of Profitable Growth (SSMPG), which we explain in detail in the article. The SSMPG model is compared to the prevailing Death Valley and Startup growth company development models. The article identifies the most crucial factors for the profitable growth of SMEs at different stages of growth within the SSMPG model. This model emphasizes sales, profitability, the individual characteristics of the entrepreneur, and leadership, in contrast to the debt-driven growth models emphasized in the other approaches. Further research could explore developing a start-up business culture using the new phasing model in Europe and elsewhere. In the future, it is important to consider profitability at both the company level and within clusters and regions.

How can data manipulation matter in predicting the failure risk? Evidence from Romanian companies

Isabella Lucuţ Capraș — 2025-02-19

Recent fraud scandals have raised concerns about the reliability of financial data disclosed in financial statements. The main purpose of this article is to investigate how financial data manipulation affects company failure risk. The research sample comprises 63 non-financial Romanian companies listed on the Bucharest Stock Exchange between 2015 and 2020. Three types of statistical methods were used to determine and consolidate the results. The results partially support the strand in literature according to which there is a correlation between manipulated data and failure risk. More specifically, the findings indicate that there is no statistically significant correlation between the Beneish Model and the Altman Z-score. However, after a more in-depth investigation taking into account the specific elements that indicate the existence of customized data in financial data, it was discovered that, among the eight Beneish model component variables, days' sales in a receivable index, sales growth index, and total accruals to total assets have a significant impact on the measurement of bankruptcy risk. This study constitutes an important contribution to the body of knowledge because it focuses not only on the relationship between the risk of failure and financial statement manipulation, but it examines also the significance of financial manipulation indicators in predicting the likelihood of bankruptcy. Our findings are valuable to decision makers seeking a deeper understanding of the reality behind financial data presented in financial statements.

Decision support algorithm development for assortment optimization in the retail chain

Olga Iurasova — 2025-02-20

As the consumer market changes rapidly, retail networks require a system to optimize the quantity and assortment of goods. The authors develop and test theoretical and practical assortment optimization and distribution principles. The study aims to improve retail assortment management by creating a decision support system for optimizing commodity composition, quantity, and location. The system’s primary objective is to enhance the trading margin obtained from the sale while considering constraints related to commodity resources and shelf space. This entails optimizing the procurement and inventory management processes to maximize the profit margin. By generating freight invoices, distributing, and redistributing commodities within the network under inbound logistics orders, the system optimizes the allocation of commodities using information from the company’s existing software. The authors present an optimization method for commodities that relies on mathematical modeling and the calculation of the consolidated profitability ratio. It ensures the necessary accuracy and provides assortment management within time and cost limits, without substantial investments in equipment and updating qualifications of employees. The research outcomes are applicable to apparel retail. The practical outcome is maximizing the return on investment for goods sold per day. The algorithm’s benefits and effectiveness were calculated based on real data after implementation.