The proposed study aims to investigate the digital transformation of Greek small and medium enterprises (SMEs), with a particular focus on their digital maturity and the strategic and organizational factors contributing to digital transformation. The research issue of digital transformation has been attracting considerable interest among academics and business practicians since COVID-19 accelerated the procedure of implementing Industry 4.0 principles all over global economies. Quantitative research on 147 Greek SMEs revealed the most important issues on how these businesses implement digital transformation, factors accelerating or decelerating the process, barriers and expected outcomes. The results indicate that digital maturity, digital transformation strategy and digital business models are strongly interconnected, while the main barriers of successfully implementing them is a lack of funding, cultural issues and the management’s engagement with the whole procedure. Greek SMEs are regarding digital transformation as a part of their customers’ satisfaction rather than as a holistic procedure of reshaping their operation. In almost 20% of Greek SMEs, there is no person who is responsible for the digital transformation procedure, while key drivers are suppliers and customers rather than competitors and the government. The results can be valuable for stakeholders who are enabled to the digital transformation process from both the business and academic points of view, while there exist aspects that can contribute to policy makers/motivation developers on the state’s level as well.

This study rigorously investigates the intricate dynamics between Corporate Social Responsibility (CSR), quantified through Environmental, Social, and Governance (ESG) scores, and financial performance (FP), measured via the return on assets (ROA) and return on equity (ROE), within the UK banking sector. Our analysis is based on a comprehensive dataset from Bloomberg. This research encapsulates data from 32 banks publicly listed on the London Stock Exchange over a six-year span from 2017 to 2022. Employing panel data regression models while controlling leverage and bank size, we delve into the relationship between banks’ CSR engagements, as reflected in their ESG scores, and their financial outcomes. Our findings indicate a negative correlation between the ESG score and both the ROA and ROE, suggesting that elevated CSR commitments may inversely impact short-term financial returns. This finding not only challenges prevailing narratives within the sector but also fosters a crucial discourse on the balance between ethical banking practices and profitability. The implications of this research study are manifold, extending to policymakers, banking executives, and investors, suggesting a revaluation of CSR strategies in alignment with long-term value creation and sustainable banking. This study not only enriches academic discourse on CSR within the financial sector but also serves as a beacon for future inquiries into the evolving landscape of responsible banking, advocating for a nuanced understanding of CSR’s role in shaping the financial and ethical contours of the banking industry.

Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar’s test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.

In the current dynamic business landscape, digital transformation is recognized as a critical driver of entrepreneurship, innovation, and growth, particularly among small and medium-sized enterprises (SMEs). This study aims to investigate the key factors influencing digital transformation, focusing on their relevance in shaping strategic decisions and fostering innovation. Using a robust methodological approach, data were collected through an online survey, with Likert-scale questions assessing multiple dimensions of digital maturity across companies in various sectors of the Greek economy. The survey, conducted in the first semester of 2024, involved 156 companies from sectors such as retail, communication, technology, and public services, with significant representation from established organizations employing over 250 individuals and reporting annual turnovers exceeding EUR 50 million. The questionnaire items, adapted from existing validated scales, captured aspects such as digital skills, management intensity, business processes, innovation performance, departmental agility, and digital vision. By analyzing the responses, this study identifies critical drivers of digital transformation and highlights their role in guiding strategic decisions, emphasizing the evolving nature of digital entrepreneurship. The findings contribute to the broader discourse on digital transformation, offering actionable insights for organizations aiming to enhance their digital maturity and competitiveness in a rapidly changing global economy.

Artificial Intelligence (AI) has revolutionized the way organizations face decision-making issues. One of these crucial elements is the implementation of organizational changes. There has been a wide-spread adoption of AI techniques in the private sector, whereas in the public sector their use has been recently extended. One of the greatest challenges that European governments have to face is the implementation of a wide variety of European Union (EU) funding programs which have evolved in the context of the EU long-term budget. In the current study, the Balanced Scorecard (BSC) and Artificial Neural Networks (ANNs) are intertwined with forecasting the outcomes of a co-financed EU program by means of its impact on the non-financial measures of the government body that materialized it. The predictive accuracy of the present model advanced in this research study takes into account all the complexities of the business environment, within which the provided dataset is produced. The outcomes of the study showed that the measures taken to enhance customer satisfaction allows for further improvement. The utilization of the proposed model could facilitate the decision-making process and initiate changes to the administrational issues of the available funding programs.

The performance measurement of a great variety of enterprises is a highly complicated issue, especially taking into account that performance has a great many aspects and many variables which may, at times, be highly inconsistent with each other. The use of analytics and advanced machine learning promotes the decision-making process for each and every organizational structure. This paper combines the Balanced Scorecard and predictive analytics in order to assess the performance of a co-financed European Union program, which addressed 4071 Greek Small and Medium-sized Enterprises (SMEs) that requested funding. The application of predictive analytics tools and metrics in the available dataset of all addressed SMEs reveal the M5 Model Tree regressor to be an overall best prediction model for estimating the effect of the evaluation of companies’ funding proposals on their financial results after the finalization of the co-financed program.