Journal of Business Economics and Management

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

Miltiadis D. Lytras, Andreea Claudia Serban, Stamatios Ntanos — Mon, 03 Feb 2025 10:23:06 +0200

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, Bogdan-George Tudorică, Adela Bâra, Simona-Vasilica Oprea — Mon, 03 Feb 2025 10:37:25 +0200

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, Elis Mediawati, Andi Ratna Sari Dewi — Tue, 11 Feb 2025 00:00:00 +0200

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, Xueru Zu, Min Chen, Jinzhi Li, Guihua Shen, Shan Wang — Tue, 18 Feb 2025 00:00:00 +0200

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, Jari Kaivo-oja — Wed, 19 Feb 2025 00:00:00 +0200

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

Wed, 19 Feb 2025 00:00:00 +0200

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 — Thu, 20 Feb 2025 00:00:00 +0200

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.

The influence of internal marketing on job satisfaction in the Romanian public sector and the mediating role of empowerment

Wed, 12 Mar 2025 00:00:00 +0200

The public sector has made significant innovations in its contemporary operations. Beyond digitisation, enhancing the processes involving public servants is necessary to provide effective service to citizens. This study examines the connection between internal marketing strategies and job satisfaction among public sector employees in Romania. Through quantitative analysis and a questionnaire-based approach, the research explores various internal marketing dimensions such as communication, training, rewards, and empowerment. Using robust statistical methods, particularly partial least squares structural equation modelling (PLS-SEM), the study explores the complex relationships between these dimensions and job satisfaction. Aligned with the principles of new public management (NPM), this research pioneers the investigation of internal marketing in the public sector. The findings reveal nuanced dynamics, demonstrating that while internal communication indirectly influences job satisfaction by empowering employees, training has a direct impact. Additionally, fair reward systems directly contribute to satisfaction, while empowerment emerges as a significant standalone factor. The study emphasises the importance of strategic programme design in communication, training and rewards, recognizing empowerment as a key driver of job satisfaction among public sector employees.

Exploring justice perceptions in online banking recovery: gender moderation and behavioral outcomes

Attia Abdelkader Ali, Andreia Gabriela Andrei, Felipe Ruiz-Moreno, Luigi Zingone — Thu, 20 Mar 2025 00:00:00 +0200

The study addresses the recovery from service failures in online banking. It focuses on the three dimensions of perceived recovery justice – namely, distributive justice (DJ), procedural justice (PJ), and interactional justice (IJ) – and investigates their impact on post-recovery satisfaction (PRS), the moderating effect of gender, and further, the influences of PRS on customer trust (CT), affective commitment (AFFC), and customers’ behavioral intentions (CBI). The study uses partial least squares structural equation modelling to examine the data collected in Egypt from 445 respondents who experienced a service failure with online banking. The results show that the three dimensions of perceived recovery justice – DJ, PJ, IJ – exert positive influences on PRS, and gender moderates the effects of PJ and IJ on PRS: procedural justice makes women exhibit higher levels of PRS. In contrast, interactional justice makes men encounter higher levels of PRS. The results also show that PRS positively influences CBI through its direct and indirect effects (via CT and AFFC). Furthermore, PRS mediates the positive effects of DJ, PJ, and IJ on customers’ behavioral intentions. The study outcomes have significant theoretical and practical implications for online banking.

Assessment of causal relationship amid enablers of successful transition of management succession in family-owned businesses – a study of the South Asian Nations

Wed, 26 Mar 2025 00:00:00 +0200

The present study was done to evaluate the causal relationship amid the enablers of successful transition of management succession in family-owned business in South Asian Nations. This was an empirical study where owners of family-run business across various South Asian countries were interviewed. In this study we used the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach to examine the causal relationship among the twelve variables which were identified by examining the existing literature. The findings of the research demonstrated that formal education, well defined succession plan, early affiliation in business etc. formed the cause group and shared vision for future, active involvement of the successor/s in the succession process, tacit knowledge transfer, building trust and credibility in successors and competence over gender etc. formed the effect group.

Is fraud detection feasible without training data? Testing an expert-based approach

Serdar Benligiray, Ahmet Onay, Fatma Yaşli Şen — Wed, 26 Mar 2025 00:00:00 +0200

We aim to derive a fraud detection approach applicable to conditions where historical fraud data is absent, inadequate, or outdated for making predictions. To this end, we propose a new approach to fraud detection based on expert opinion, enabling tailored tools for various conditions of economic/institutional environments. For this, we determined the relative importance of common fraud indicators based on a widely used model in the literature. We then used this information to formulate a scoring alternative to conventional versions, which uses either the original coefficients or the coefficients obtained from training the model. Finally, these scoring alternatives were compared by their detection performances. The design of this research demanded a multifaceted dataset consisting of expert opinions, financial statement data of non-financial companies in the Istanbul Stock Exchange, and local regulatory authority’s notifications on fraudulent companies. The analysis of the detection performances indicates that the proposed alternative scoring method poses a feasible alternative with competitive performance and fewer data requirements. This research’s approach sidesteps the training data requirement and provides financial analysts, auditors, and regulatory bodies a versatile classifier for various use cases regarding financial data, such as detecting fraudulent financial activity, as demonstrated in this study.

The influence of AI on price forecasting. The view of the academic community

Alexandra-Cristina-Daniela Ciuverca, Simona‑Vasilica Oprea — Thu, 03 Apr 2025 00:00:00 +0300

In the context of the impressive development of Big Data, AI algorithms have proven their efficiency in processing and analyzing large volumes of data. Price prediction was no exception. In the modern economic fields, the need for advanced prediction models, with increased efficiency, has become more and more important. Thus, the interest in the potential of AI solutions in terms of price prediction for all industries has also grown progressively. The present study aims to capture, by using several Natural Language Processing techniques, the feeling that the academic community has in relation to the subject of price prediction and the way in which opinions have evolved over the years. For this purpose, the abstracts of the works indexed in the Clarivate WoS that addressed this topic are included in the current analysis. The scores obtained after the analysis reveal a slightly positive attitude towards the subject, but nevertheless quite reserved. The main topics existing in these articles are also extracted by means of Latent Dirichlet Allocation. Our analysis makes contributions to the formulation of the position that specialists in the scientific community have in relation to price prediction and AI evolution. Further, it provides new research directions for future studies.