Athens University of Economics and Business

Background As digital healthcare services handle increasingly more sensitive health data, robust access control methods are required. Especially in emergency conditions, where the patient’s health situation is in peril, different healthcare providers associated with critical cases may need to be granted permission to acquire access to Electronic Health Records (EHRs) of patients. The research objective of our work is to develop a proactive access control method that can grant emergency clinicians access to sensitive health data, guaranteeing the integrity and security of the data, and generating trust without the need for a trusted third party. Methods To enable proactivity, we apply Long Short Term Memory (LSTM) Neural Networks (NNs) that utilize patient’s recent health history to prognose the next two-hour health metrics values. Fuzzy logic is used to evaluate the severity of the patient’s health state. These techniques are incorporated in a private and permissioned Hyperledger-Fabric blockchain network, capable of securing patient’s sensitive information in the blockchain network. Results Integrating this predictive mechanism within the blockchain network proved to be a robust tool to enhance the performance of the access control mechanism. Furthermore, our blockchain network can record the history of who and when had access to a specific patient’s sensitive EHRs, guaranteeing the integrity and security of the data. Conclusions Our proposed mechanism informs proactively the emergency team about patients’ critical situations by combining fuzzy and predictive techniques, and it exploits the distributed data of the blockchain network, guaranteeing the integrity and security of the data, and enhancing the users’ trust to the mechanism.

Background: Gallbladder cancer (GBC) is rare but aggressive. The extent of surgical intervention for different GBC stages is non-uniform, ranging from cholecystectomy alone to extended resections including major hepatectomy, resection of adjacent organs and routine extrahepatic bile duct resection (EBDR). Robust evidence here is lacking, however, and survival benefit poorly defined. This study assesses factors associated with recurrence-free survival (RFS), overall survival (OS) and morbidity and mortality following GBC surgery in high income countries (HIC) and low and middle income countries (LMIC). Methods: The multicentre, retrospective Operative Management of Gallbladder Cancer (OMEGA) cohort study included all patients who underwent GBC resection across 133 centres between 1st January 2010 and 31st December 2020. Regression analyses assessed factors associated with OS, RFS and morbidity. Findings: On multivariable analysis of all 3676 patients, wedge resection and segment IVb/V resection failed to improve RFS (HR 1.04 [0.84-1.29], p = 0.711 and HR 1.18 [0.95-1.46], p = 0.13 respectively) or OS (HR 0.96 [0.79-1.17], p = 0.67 and HR 1.48 [1.16-1.88], p = 0.49 respectively), while major hepatectomy was associated with worse RFS (HR 1.33 [1.02-1.74], p = 0.037) and OS (HR 1.26 [1.03-1.53], p = 0.022). Furthermore, EBDR (OR 2.86 [2.3-3.52], p < 0.0010), resection of additional organs (OR 2.22 [1.62-3.02], p < 0.0010) and major hepatectomy (OR 3.81 [2.55-5.73], p < 0.0010) were all associated with increased morbidity and mortality. Compared to LMIC, patients in HIC were associated with poorer RFS (HR 1.18 [1.02-1.37], p = 0.031) but not OS (HR 1.05 [0.91-1.22], p = 0.48). Adjuvant and neoadjuvant treatments were infrequently used. Interpretation: In this large, multicentre analysis of GBC surgical outcomes, liver resection was not conclusively associated with improved survival, and extended resections were associated with greater morbidity and mortality without oncological benefit. Aggressive upfront resections do not benefit higher stage GBC, and international collaborations are needed to develop evidence-based neoadjuvant and adjuvant treatment strategies to minimise surgical morbidity and prioritise prognostic benefit. Funding: Cambridge Hepatopancreatobiliary Department Research Fund.

Regime complexes entail a variety of institutions with a degree of overlap in terms of thematic issues and participating actors. The EU is such an actor engaging with other governmental and non-governmental entities in the formation and evolution of regime complexes. In this article, we examine the role of the EU in the international transport regime complex, and more specifically in two of its core international organizations, namely ICAO and IMO. Our actor-based approach focuses on how the EU navigates between these two constitutive components of the global transport regime complex, advancing climate change mitigation measures. Our empirical material shows how the EU’s active engagement in ICAO contributed to the organization’s shift vis-à-vis the role of the aviation industry in greenhouse gas emissions. Besides the EU learning process that occurred and led to a more engaging and less conflictual EU approach in IMO, the ICAO achievement increased pressure and created a more conducive environment for the respective recognition of the maritime industry’s share in climate deterioration. In this respect, the EU benefited from the structure of the transport regime complex to pursue its own preferences.

In large distributed systems, ensuring the efficient utilization of the available resources is a very challenging task. Given limited information regarding the state of the system and no centralized control over the outcome, decentralized scheduling mechanisms are unable to enforce optimal utilization. To better understand such systems, some classic papers that introduced game theoretic models used the “price of anarchy” measure to evaluate the system’s performance. The paper “Resource-Aware Cost-Sharing Methods for Scheduling Games” by Christodoulou, Gkatzelis, and Sgouritsa overcomes some of the overly pessimistic results shown in this prior work by enhancing the scheduling mechanisms with access to some additional information regarding the state of the system: a “resource-aware” mechanism knows what machines are available in the system and uses this information to carefully incentivize the users toward more efficient Nash equilibrium outcomes.

Metaphors and storytelling are important communication tools that play a significant role in leadership and organizational life. Leaders have used metaphors and storytelling to enhance their written and verbal communication from ancient times, since Aristotle, to the modern age. In the present research, we focus on the use of storytelling and metaphors by leaders in times of crisis. We perform a qualitative analysis of the public statements and addresses of the leaders of two different countries in the context of recent worldwide crises: The prime minister of Greece during the COVID-19 health crisis and the president of Ukraine during the outbreak of the conflict with Russia in 2022. Based on existing evidence, their effectiveness in convincing their subordinates and conveying their intended meaning either nationally or internationally during the aforementioned crises has been widely recognized. Our analysis reveals that both leaders have consistently utilized metaphors and storytelling in their efforts to be more convincing and empowering. We also find that the higher the intensity of the crisis, the more pronounced the use of metaphors and stories. We accordingly provide an analysis of the types and frequency of use of the aforementioned communication tools. Reflecting on our findings, we provide specific insight for practice by leaders, discuss theoretical implications, and suggest directions for future research.

Prolonged Grief Disorder (PGD) has become a subject of increased interest among both researchers and practitioners, owing both to its recent inclusion in the DSM-5-TR and the growing evidence of widespread complications of bereavement in the context of the COVID-19 pandemic. From a set of 467 studies obtained from the Scopus database during the period 2009 to 2022, the present research provides bibliographic data on the most influential authors on the subject, most relevant journals based on the number of documents published, a keyword analysis of the focus of this work, and an overall characterization of the scientific literature on PGD. The Biblioshiny application along with VOSviewer software was used for the analysis and visual depiction of the results. Both the scientific and applied implications of this analysis are discussed.

We provide a novel panel model to decompose total factor productivity (TFP) growth in the Greek industry at the firm level while we tackle the contribution of R&D. We, therefore, opt for parametric methodology that provides statistical inference and would validate the results. Our modeling departs from prior strong assumptions such as error terms across firms being independent. In fact, we provide a novel limited information maximum likelihood (LIML) estimation method that adequately deals with the issue of endogeneity and model misspecification. We demonstrate that our model detects variability in terms of TFP growth components across industries and firms. Our results show that R&D would enhance TFP of Greek firms, albeit the crisis has had a detrimental impact. Financial ratios such as liquidity and solvency ratios also affect TFP as we demonstrate that both would enhance TFP. The solvency ratio is important as it provides an estimate of whether the firm can cope with debt. We also note variability across small versus medium and large firms and report that small firms are more productive and spend more of their revenues on R&D. In terms of policy, our evidence warrants higher R&D spending to enhance TFP growth, though R&D funding is a concern.

Evaluating teachers and educational work is now mandatory in the Greek educational system. The current study aims at investigating the attitudes and perceptions of principals of Piraeus Secondary Education units regarding the contributions of this evaluation law to the orderly functioning of schools in terms of administrative and educational aspects. For this qualitative research, a semi-structured interview was chosen as the results collection method. The interview questions included respondents’ demographic information and interview questions corresponding to three thematic axes according to the research questions. The convenience sample consisted of twelve (12) principals of secondary school units from the greater Piraeus area. All interviewed principals recognized the need for the evaluation process in education and agreed on the importance of their roles and responsibilities for the effectiveness of the proper functioning of their units. Nine respondents underlined the need for training teachers on new technologies, six considered internal evaluation reports as a means of weaknesses identification and improvement and five believed the evaluation imposed by the Ministry of Education creates negative feelings. As concluded, evaluation is of great educational importance, as it contributes to teachers’ professional development and schools’ educational quality improvement. However, when implemented suddenly or imposed by the state, it causes pressure, stress, fear, nervousness, and insecurity.

The goal of this paper is to build and compare methods for the prediction of the final outcomes of basketball games. In this study, we analyzed data from four different European tournaments: Euroleague, Eurocup, Greek Basket League and Spanish Liga ACB. The data-set consists of information collected from box scores of 5214 games for the period of 2013-2018. The predictions obtained by our implemented methods and models were compared with a “vanilla” model using only the team-name information of each game. In our analysis, we have included new performance indicators constructed by using historical statistics, key performance indicators and measurements from three rating systems (Elo, PageRank, pi-rating). For these three rating systems and every tournament under consideration, we tune the rating system parameters using specific training data-sets. These new game features are improving our predictions efficiently and can be easily obtained in any basketball league. Our predictions were obtained by implementing three different statistics and machine learning algorithms: logistic regression, random forest, and extreme gradient boosting trees. Moreover, we report predictions based on the combination of these algorithms (ensemble learning). We evaluate our predictions using three predictive measures: Brier Score, accuracy and F 1-score. In addition, we evaluate the performance of our algorithms with three different prediction scenarios (full-season, mid-season, and play-offs predictive evaluation). For the mid-season and the play-offs scenarios, we further explore whether incorporating additional results from previous seasons in the learning data-set enhances the predictive performance of the implemented models and algorithms. Concerning the results, there is no clear winner between the machine learning algorithms since they provide identical predictions with small differences. However, models with predictors suggested in this paper out-perform the “vanilla” model by 3-5% in terms of accuracy. Another conclusion from our results for the play-offs scenarios is that it is not necessary to embed outcomes from previous seasons in our training data-set. Using data from the current season, most of the time, leads to efficient, accurate parameter learning and well-behaved prediction models. Moreover, the Greek league is the least balanced tournament in terms of competitiveness since all our models achieve high predictive accuracy (78%, on the best-performing model). The second less balanced league is the Spanish one with accuracy reaching 72% while for the two European tournaments the prediction accuracy is considerably lower (about 69% ). Finally, we present the most important features by counting the percentage of appearance in every machine learning algorithm for every one of the three analyses. From this analysis, we may conclude that the best predictors are the rating systems (pi-rating, PageRank, and ELO) and the current form performance indicators (e.g., the two most frequent ones are the game score of Hollinger and the floor impact counter).

Social media have been developed as a vital source of information for millennials worldwide, including in India. The present study identifies the influence of the various social media traits on the perceived usefulness of social media by Indian millennials and its subsequent impact on their online accommodation booking decisions. A conceptual model based upon the Stimulus-Organism-Response model was proposed for the study that was empirically verified using sample data from 476 Indian millennial respondents with prior social media experience. The study reveals that Indian millennials consider the usefulness of social media as an essential factor in their online booking decision for hotel products and services. The usefulness of social media primarily depends upon various characteristics such as information quality, source credibility, visual presentation, perceived ease of use, perceived enjoyment, and perceived curiosity fulfilment. Further, the study results indicate that the relationships among these factors are moderated by gender. The present research work extends the extant literature by examining the influence of social media on the online booking decisions of Indian millennials. In addition, this study will give practical suggestions to marketers for addressing new markets through social media activities.

The need for effective training of cyber security personnel working in critical infrastructures and in the corporate has brought attention to the evolution of Cyber Ranges (CRs) as learning and training tools. Although CRs have been organized for many years, there is a lack of standards and common methodologies that facilitate their development and optimize their effectiveness. Aiming at strengthening cyber security education and research that utilize well designed CRs, we first analyze the CRs domain to identify key characteristics, strengths and fundamental weaknesses, and based on these outcomes we propose the Cyber Range Design Framework (CRDF), which includes the CR Architecture and the CR Life-Cycle. The CR Architecture presents the main components of CRDF compliant CRs, whereas the CR Life-Cycle presents the development phases of such approaches and the activities these phases embrace. CRDF builds on the Conceptual Framework for eLearning and Training (COFELET) and on the Exercise Life-Cycle. COFELET is particularly elaborated for the development of cyber security educational approaches, by adopting its design considerations that were based on widely adopted educational theories and approaches (e.g., scenario-based, reuse of elements). CRDF envisages the elaboration of CRs which optimize their impact, mitigate their weaknesses, and minimize their preparation and running costs. Under this prism, a preliminary appreciation of the CRDF approaches effectiveness is presented along with the expected outcomes of such approaches.

We study the problem of intervention effects generating various types of outliers in an integer-valued autoregressive model with Poisson innovations. We concentrate on outliers which enter the dynamics and can be seen as effects of extraordinary events. Weconsider three different scenarios, namely the detection of an intervention effect of a known type at a known time, the detection of an intervention effect of unknown type at a known time andthe detection of an intervention effect when both the type and the time are unknown. We develop \(F\) -tests and score tests for the first scenario. For the second and third scenarios we rely on the maximum of the different $F$-type or score statistics. The usefulness of the proposed approach is illustrated using monthly data on human brucellosis infections in Greece.

The European Green Deal (EGD) is the growth strategy for Europe, covering multiple domains, and aiming to an equitable, carbon neutral European Union by 2050. The UN Agenda 2030, with its 17 Sustainable Development Goals (SDGs) set the bases for a global sustainability transition. However, the integration of the SDGs into the EGD is an overlooked issue in the literature, although it is particularly important, given Europe’s slow progress to achieve the sustainability targets. In this paper, 22 central policies and strategies published during 2020–21 to support the EGD's implementation are assessed on how they align with Agenda’s 2030 aspirations, using novel text-mining methodologies: one human-based and one machine-learning-based. The results outline an alignment of EGD policies to the main SDGs themes relevant to Food, Land, Oceans, Energy, but also a strong indication that the progress towards sustainability passes through "Peace, Justice, and Strong Institutions" (SDG16) and international "Partnerships for the Goals" (SDG17). We further explain the underlying policy mechanisms of the established ‘necessary transformations’ to build a sustainable Europe, along with the relevance of valuing the natural capital and integrating it into future investment and financial decisions.

This paper aims at linking the work presented in Dauzère‐Pérès et al. (1998) and more recently in Kasapidis et al. (2021) on the multi‐resource flexible job‐shop scheduling problem with non‐linear routes or equivalently with arbitrary precedence graphs. In particular, we present a Mixed Integer Linear Programming model and a Constraint Programming model, to formulate the problem. We also compare the theorems introduced in Dauzère‐Pérès et al. (1998) and Kasapidis et al. (2021), and propose a new theorem extension. Computational experiments were conducted to assess the efficiency and effectiveness of all propositions. Lastly, the proposed MIP and CP models are tested on benchmark problems of the literature and comparisons are made with state‐of‐the‐art algorithms. This article is protected by copyright. All rights reserved

In today’s era, humanity has been overwhelmed by technological revolutions that have changed and will continue to change how business operations are performed, directly or indirectly. At the same time, the processes within the supply chain are quite complex, and as technology and processes evolve, they become more and more challenging. Traceability has become a critical issue in the food industry to ensure safety, quality, and compliance with regulations. The adoption of blockchain technology in the food supply chain has gained significant attention as a potential solution to improve traceability. This paper presents the development of a distributed application for table olives’ traceability on the Ethereum network. The paper also presents a methodological framework, which can help anyone aiming to implement an Ethereum decentralized application and demonstrates the practical use of the developed application by a Greek table olives producer. The application significantly improved the producer’s product traceability by providing a secure, transparent, and efficient solution for tracking and tracing the products in the supply chain. The app reduced the time, increased the accuracy and reliability of data, improved supply chain efficiency, and helped the producer comply with international regulations and standards.

Energy conservation in public buildings is an important means towards reducing CO2 emissions worldwide and tackling climate change. In this context, employee behaviour has been recognised as a highly impactful factor that needs to be studied more thoroughly. In this study, we propose and investigate a behavioural model that can be utilised in energy-saving interventions in the workplace. Employing a questionnaire (N = 119 employees in three workplaces in EU countries), we identified two types of energy consumption behaviour at work: personal and collective actions. We further investigated the effect of six factors on employee willingness, as well as self-reported energy-saving habits and behaviour. We found that an employee’s profile (i.e., i. personal energy-saving norms, ii. emotional exhaustion/burnout, iii. collective energy-saving responsibility and efficacy, iv. awareness of energy wastage and knowledge of solution, v. personal comfort/comfort levels, vi. age, vii. gender, and viii. having children) determines energy-saving habits and behaviour, as well as affects willingness to alter it and to conserve energy at work. Employee willingness in turn directly affects energy-saving habits and behaviour at work. The proposed behavioural model can provide guidance towards applying energy conservation initiatives in the workplace. Behavioural interventions should accordingly primarily focus on improving personal energy-saving norms at work and be designed to be easy to follow and not overly demanding, time consuming, or pressuring. Moreover, to motivate collective energy-saving behaviours, interventions should focus on increasing employees’ collective energy-saving responsibility and efficacy, while respecting their personal comfort/comfort levels and their emotional exhaustion/burnout levels. Practical advice towards specific types of interventions is provided accordingly.

Monitoring, incentive alignment, and social controls are used to minimize the agency costs to headquarters (HQ) resulting from subsidiaries' opportunistic behaviors by aligning subsidiaries' behaviors and interests with those of the HQ. Subsidiaries' motivation to comply with these controls, however, is contingent on the social context that links the subsidiary to the HQ. In this context, we propose to identify procedural justice as a motivational contingency that shapes the conditions under which agency‐driven controls can effectively minimize agency costs. Our results show that monitoring and social control reduce agency costs when procedural justice is high, whereas the use of incentive alignment mechanisms can have the opposite effect. The headquarters (HQ) of multinational corporations use control mechanisms to ensure the alignment of their subsidiaries with the organization's interests and goals. However, these mechanisms do not always provide value to the corporation since subsidiaries may exhibit varying levels of motivation to comply with such controls, resulting in behaviors that range from resistance to compliance and ceremonial compliance to genuine compliance. We argue that the procedural justice applied by the HQ influences subsidiaries' motivation to comply with the controls implemented by the HQ. We find that in subsidiaries that operate in such a climate of fairness, monitoring based on rules, processes and procedures as well as social control can provide value, whereas the use of incentive alignment can lead to the opposite results.