This study compared Romanian college students’ off-task use of digital devices in online courses with their digital distractions in face-to-face courses. Students taking both online and face-to-face courses completed surveys assessing off-task use of technology in each delivery mode, extent to which such use constitutes a distraction, and instructor policies for curbing use of digital devices in class. Off-task use of digital devices was significantly greater in online than face-to-face courses. Students were twice as likely to state that they were distracted by multitasking in online than in face-to-face classes. They also indicated that instructors in online classes were less likely than their face-to-face instructors to have a policy on the use of digital devices for off-task purposes during class. Study participants were more lenient toward the use of digital devices in online than in in-person classes and were less bothered by multitasking and classmates’ off-task use of digital devices in online than in face-to-face classes. The results of this study have implications for instructors in online classes. By improving instructional design and pedagogical methods and creating opportunities for more interaction during the class, online instructors might be able to increase student motivation and lower multitasking behaviors.

The aim of this cross-sectional study was to investigate the factors associated with trust in AI algorithms used in the e-commerce industry in Romania. The motivation for conducting this analysis arose from the observation of a research gap in the Romanian context regarding this specific topic. The researchers utilized a non-probability convenience sample of 486 college students enrolled at a public university in Romania, who participated in a web-based survey focusing on their attitudes towards AI in e-commerce. The findings obtained from an ordinal logistic model indicated that trust in AI is significantly influenced by factors such as transparency, familiarity with other AI technologies, perceived usefulness of AI recommenders, and the students’ field of study. To ensure widespread acceptance and adoption by consumers, it is crucial for e-commerce companies to prioritize building trust in these new technologies. This study makes significant contributions to our understanding of how young consumers in Romania perceive and evaluate AI algorithms utilized in the e-commerce sector. The findings provide valuable guidance for e-commerce practitioners in Romania seeking to effectively leverage AI technologies while building trust among their target audience.

This study offers a comprehensive insight into the realms of economic fraud and risk management, underscoring the necessity of adaptability to evolving technologies and shifts in financial market dynamics. Through the application of bibliometric methodologies, this study meticulously maps the relevant literature, delineating influential works, notable authors, collaborative networks, and emerging trends. It reviews key research contributions within the field, alongside reputable journals and institutions engaged in academic research. The examination highlights the logical, conceptual, and social interconnections that define the landscape of economic fraud and associated risks, elucidating how these findings inform the understanding, mitigating, and combating of the risk of fraud. Our bibliometric analysis methodology is grounded in the utilization of the Scopus database, employing rigorous filtering and extraction processes to obtain a substantial corpus of pertinent articles. Through a fusion of performance analysis and science mapping, our investigation elucidates central themes and visually represents the interrelationships between studies. Our research outcomes underscore the frequency of paper publications across diverse regions, with particular emphasis on the predominant scientific output from the US and China. Additionally, trends in academic citations are identified, indicative of the significant impact of papers on academic research and the formulation of public policies. By means of bibliometric analysis, this study not only consolidates existing knowledge but also catalyzes the exploration of future research trajectories, emphasizing the imperative of addressing these issues with heightened scientific rigor.

Digital technologies have transformed business models quickly, making sustainable, inventive performance essential for the survival of businesses, particularly in emerging markets. However, a lot of English educational institutions struggle to make use of digital platform capability (DPC) due to a lack of funding, resources, and experience that stifles prospects for advanced innovation. After the COVID-19 pandemic, this study suggests a framework to investigate how English educational institutions might improve their innovation performance using DPC. The study examines the relationship between digital innovation (DI) and innovation performance (IP), as well as the moderating effects of digital technology (DT). This study uses a quantitative methodology and questionnaire survey with 435 respondents from institutions that support DPC. The analysis examines the hypotheses using correlations, regression analyses, and 5000 bootstraps. The results demonstrate a positive relationship between DPC and IP, with DI acting as a mediator. Additionally, the outcomes show that the development of digital technology strengthens the positive influence of DPC on innovation performance. Through filling research gaps, this study adds to the body of knowledge. It also has significant management implications for English educational institutions looking to improve their innovation performance by fostering digital platform ability and digital innovation.

Sanctions should improve business compliance and mitigate the risks of non-conformity. This premise motivated our research and led to very interesting results for the trinomen business performance—transport infrastructure and local development strategies—public control. We used a customized Brunswik lens model to illustrate the decision-making process based on the interactions between the analysis of sanctions in the transport sector and the projected financial judgement, as we have traditionally understood and experienced them. We clustered 186,671 cases of noncompliance sanctioned by the Romanian authorities and created a chromatic map with accents on the risks of nonconformities. We employed principal component analysis to find patterns and correlations between faulty behavior in transport activities and the evolution of financial indicators, such as exports and imports. The ROC curve was used to investigate the credibility of a possible connection between transport sanctions and the development of regional exports and imports. We found multiple challenges that interfere with the projection of a trustworthy financial judgement in transport and offer insights and recommendations for integrated local governance practices and strategies aimed at mitigating the risks of noncompliance and promoting sustainable development in transport.

Based on traditional market theory, this study aims to investigate whether conventional market investment slopes affect the unconventional Bitcoin market, considering both normal conditions and crises. This study examines three main characteristics of the economy-intensive blockchain system, namely reliability, investment slopes, financial and accounting aspects that ultimately determine the confidence in the choice to invest in cryptocurrency. The analysis focuses on the study of the Bitcoin (BTC) investment slopes during January 2014–April 2023, considering the specifics of blockchain technology and the inferences of ethics, reliability and real-world data on investment Tassets in the context of conventional regulated markets. Using an econometric model that incorporates reliability analysis techniques, factorial comparisons and multinomial regression using economic crisis periods as a dummy variable, this study reveals important findings for practical and academic purposes. The results of this study show that the investment slopes of Bitcoin (BTC) are mostly predictable for downward trends, when statistically significant correlations with the investment slopes of conventional stock markets are observable. The moderate or high increase in performance slopes pose several challenges for predictive analysis, as they are influenced by other factors than conventional regulated market performance inferences. The results of this study are of intense interest to researchers and investors alike, as they demonstrate that investment slopes analysis sheds light on the intricacies of investment decisions, allowing a comprehensive assessment of both conventional markets and Bitcoin transactions.