Corruption poses a significant challenge to economic development and governance worldwide, with its detrimental effects permeating various levels of society. In the context of Greece, where corruption has been a longstanding issue, the role of internal audit mechanisms within local government organizations (LGOs) emerges as paramount. This paper presents a comprehensive analysis of the internal control landscape within LGO revenue departments, focusing on factors influencing its effectiveness and proposing strategies for improvement. Drawing upon survey data and regression analyses, this study highlights the crucial role of robust internal control mechanisms in combating corruption and fostering economic development. The findings underscore the importance of competent personnel, legislative compliance, interdepartmental collaboration, and technology utilization in enhancing internal control practices. Despite existing legislation, gaps in internal control implementation persist, including understaffing, inadequate procedures, and limited access to information. This study emphasizes the transformative potential of effective internal audit measures in mitigating corruption at the local level, thereby contributing to broader economic growth and societal well-being. Recommendations for strengthening the internal control structures within LGOs include the formal establishment of internal audit functions, adherence to professional standards, and the promotion of information system utilization. By addressing the corruption and inefficiencies within LGOs, this research underscores the pivotal role of institutional effectiveness in promoting transparency, accountability, and sustainable economic progress.

We examine the extent to which basic factors, such as the structure, complexity, and usefulness of a cost system, affect the design of cost systems and the resulting satisfaction and help companies make the right decisions. Moreover, we examine the relationship between the structure and complexity of cost systems with (a) a company’s demographic data, such as the volume of its activities, the number of years it has been operating, its sector, its size, and the gender, age, level of training, and position of its employees; and (b) information concerning production and competition, such as the number of products that a company produces, the number of a company’s production lines, the level of competition, and the extent to which competition affects a company’s pricing policy. Empirical research was conducted via a questionnaire in which a sample of 114 industrial companies in Greece took part. The findings revealed that the structure and the usefulness of a cost system, but not its complexity, significantly affect the satisfaction users get from the system when they are called to make fast and correct decisions. The results point out a positive correlation between the satisfaction a user gets from a cost system and the range of information (R), the calculation of deviations (CS), the provision of accurate information (CS), the quality of information (CS), the number of cost pools (C), the number of allocation bases (C), and the cost information (U). Companies that produce more goods and have a complex production process have cost systems that not only have a more detailed structure and provide more detailed information with the calculation of deviations as well as accurate information but also have more cost pools and cost allocation bases. The more competition affects a company’s pricing policy, the more a company seeks systems that categorize costs based on behavior (structure) and more cost allocation bases (complexity). The larger a company is, with a long (>20 years) and international presence, the higher the probability a company will have a system with a more detailed cost information structure.

The coronavirus pandemic resulted in an exponential increase in the use of e-banking and resulted in the establishment of telework in the banking sector. This chapter attempts to capture the impact digital transformation had on the Greek banking system during the COVID-19 pandemic, comparing the views and attitudes of bank employees and bank customers. Based on the relevant technology acceptance model (TAM) literature, a questionnaire was appropriately structured to provide awareness, satisfaction, trust, and quality evidence. The regression analysis results obtained were critically evaluated against existing previous literature, focusing on the statistical differences observed between bank employees and bank customers.

Blockchain technology has become a game changer in sectors like accounting and auditing. Its usage is still restricted due to a lack of insight into what drives people to adopt it for financial services like accounting and auditing. This research delves into the factors that influence the adoption of blockchain systems in accounting and auditing services by utilizing an enhanced edition of the Theory of Planned Behavior. In this study, alongside the previously established elements like Attitude, subjective norm, and Perceived Behavioral Control, self-perception and personal moral values are included to reflect how identity and ethics impact decision-making processes. Data were gathered via an online survey (N = 751) conducted on the Prolific platform, and the hypotheses were tested using Structural Equation Modeling. The hypotheses were examined through the Structural Equation Modeling method. The findings indicate that each of the five predictors plays a significant role in influencing Behavioral Intention, with personal moral values being the influential factor followed by subjective norm and Perceived Behavioral Control. Attitude plays an important role in shaping adoption choices and showcases the complexity involved in such decisions. As such, it is crucial to take into account ethical factors when encouraging the use of blockchain technology. This study adds to the existing knowledge of the Theory of Planned Behavior framework, offering insights for companies aiming to boost the implementation of blockchain systems in professional settings. Future research avenues and real-world implications are explored with an emphasis placed on developing targeted strategies that align technological adoption with personal values and organizational objectives.

This study assesses the effect of the COVID-19 pandemic on tourists' preferences and behaviour, drawing on a structured questionnaire completed by 257 residents of the Greek island of Kefalonia, recruited through convenience and snowball sampling. Behaviour was measured across nine constructs (general risks, price, quality, hygiene risks, general attitudes towards travel, travel mode, destination, food and activities) on five-point Likert scales, and analysed with descriptive statistics, non-parametric group-difference tests (Mann-Whitney U, Kruskal-Wallis) and multiple linear regression. Mean scores clustered close to the scale midpoint, indicating moderate and somewhat ambivalent rather than uniformly strong behavioural shifts. Significant differences emerged for a small number of dimensions: men reported more cautious general travel attitudes, while older respondents and those with children scored higher on destination-related concerns. The findings offer practical implications for destination managers and tourism businesses seeking to attract visitors and to strengthen sustainability and resilience in the post-pandemic era.

The growing use of artificial intelligence (AI) in Environmental, Social, and Governance (ESG) audits has created new opportunities to enhance corporate governance through faster and more consistent assurance processes. However, concerns remain regarding accountability, transparency, and the role of human oversight in AI-assisted auditing. Prior research has predominantly examined AI adoption in traditional financial auditing, leaving its role in the more qualitative and judgment-intensive context of ESG assurance comparatively underexplored, particularly with respect to how AI decision-making authority should be balanced against human oversight. This study examines the effects of AI automation and AI decision making on audit efficiency, accountability, and transparency, while assessing the mediating role of human oversight. A quantitative research design was adopted, and data were collected from 150 professionals involved in ESG audit-related functions (including ESG auditors, compliance officers, sustainability managers, and AI governance analysts) across private, public, and non-governmental organizations, recruited through purposive and snowball sampling via professional networks, audit forums, and LinkedIn ESG groups. The data were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings indicate that AI automation significantly improves audit efficiency, accountability, and transparency. Similarly, AI decision making within governance structures exerts positive effects on all three outcome dimensions. Furthermore, human oversight partially mediates these relationships, strengthening the overall impact of AI on ESG audit outcomes. These results suggest that AI enhances ESG auditing effectiveness, but its benefits are maximized when supported by strong governance and human supervision. The study contributes to the emerging literature on AI-enabled ESG assurance by demonstrating the importance of balancing technological efficiency with accountability and transparency in corporate governance.