Institutional Repository of Academic Research University of Macedonia
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Systematicity and generalizability in business process redesign methodologies: A systematic literature review
No full textContext: Business Process Management (BPM) plays a central role in helping organizations improve efficiency and service delivery, particularly in environments with rising demands and limited resources. Within this field, Business Process Redesign (BPR) has emerged as a way to rethink and restructure processes in response to continuous change. However, many existing BPR methodologies fall short—they lack methodological rigor and are often too narrowly tailored to specific industries or use cases. Objectives: This study explores whether BPR methodologies are both systematically structured and broadly applicable across domains. It addresses three key questions: whether current approaches are methodologically grounded, whether they can be applied across diverse contexts, and what core elements are necessary to support both structure and generalizability in BPR design. Methods: A systematic literature review (SLR) was conducted, applying an eight-step protocol to assess sixty-four primary BPR methodologies drawn from academic databases. Each methodology was evaluated against two sets of criteria: five indicators of systematic design (e.g., defined phases, interdependencies, evaluation checkpoints), and five indicators of generalizability (e.g., cross-domain adaptability, notation flexibility, heuristic support). A concept-centric synthesis was used to analyze the findings. Results: Of the methodologies reviewed, thirty-eight demonstrated systematic features, while only eight met broader applicability standards. Only one methodology satisfied all ten criteria, revealing a notable gap in the field and a need for more balanced, reusable frameworks. Conclusion: The study highlights a significant gap in the current BPR methodologies and presents the BPR Application Framework —a structured yet adaptable methodology that combines phase-based design with heuristic integration and notation-aware modeling. Compared with established references like BPM CBOK and Lean Six Sigma, it offers a clearer, more actionable path for practitioners and researchers seeking both rigor and flexibility in BPR.24810339
Variable Neighborhood Programming for Job Shop Scheduling Problems
No full textThe Job Shop Scheduling Problem is a classic combinatorial optimization problem and one of the most well-studied scheduling problems. Several methodologies, both exact and metaheuristic, have already been proposed for the solution of this computationally difficult problem. This work presents for the first time a solution approach based on Variable Neighborhood Programming for the Job Shop Scheduling Problem. Variable Neighborhood Programming is a recent methodology which constitutes a combination of Genetic Programming and Variable Neighborhood Search. In addition, some encouraging comparative computational results are also shown against the state-of-the-art Gurobi optimization solver using medium- and large-scale benchmark instances. The findings of this work have a plethora of modern applications in Manufacturing-as-a-Service online platforms. All experimental evaluations were performed on the Google Cloud Platform.16256120133Variable Neighborhood Searc
Are They AI-Competent? Future Teachers’ Readiness to Use Conversational Agents as Learning Assistants
No full textThis study examines the perceptions of student teachers regarding artificial intelligence technologies particularly their knowledge, their willingness to use them, any concerns and perceived benefits and challenges in relation to the Digital Competence Framework for Citizens (DigComp) 2.2 digital competence framework. Our mixed-methods research, involving 372 undergraduate student teachers, revealed correlations among these aspects, indicating that frequent AI users have a stronger intention to use AI, while infrequent users express greater concerns about it. Student teachers acknowledge AI’s time-saving benefits as well as the convenience and academic enhancement it provides, but also voice concerns about its misuse and reliability and the potential impact on skill development and learning. These concerns are in agreement with digital competence areas of information literacy and safe technology use. Reflecting on these perceptions, it is essential to maximise the educational benefits of effective and responsible AI integration into higher education and foster the digital competencies of future teachers.16231832
A Pattern-Oriented Ontology and Workflow Modeling Approach for the Sui Move Programming Language
No full textSmart contracts are vulnerable to critical, design-level Business Logic Flaws (BLFs) that conventional analysis tools often fail to detect. To address this semantic gap, this study introduces a novel ontological framework that formally models the link between high-level architectural intent and low-level Sui Move code. The methodology employs a rigorous Linked Open Terms (LOT) approach to construct a comprehensive ontology, integrated with a library of secure design patterns and process-aware Object-Centric Dynamic Condition Response (OC-DCR) graphs. Qualitative validation was conducted on four canonical security patterns (Access Control, Circuit Breaker, Time Incentivization, Escapability) drawn from the official Sui Framework, confirming the framework’s representational adequacy and logical consistency. Ultimately, this work contributes the first machine-readable semantic layer for Sui Move, decoupling reasoning from raw code availability, and providing the essential semantic foundation for the future development of pattern-aware auditing tools.171
Constraint Programming and Simulated Annealing Approaches for Parallel-Machine Scheduling with Conflict Constraints, Server Setups, and Flexible Maintenance
No full textThis paper addresses a parallel-machine scheduling problem where jobs require setups performed by a single server and must respect conflict constraints that prevent certain jobs from running simultaneously. This type of problem can find applications in logistics and transport operations, particularly when scheduling vehicle fleets that share limited resources. The server is also subject to a fixed-duration maintenance activity that must be scheduled alongside the jobs. The objective is to minimize the makespan. We develop both constraint programming and simulated annealing approaches to solve this problem. Experimental results demonstrate that the constraint programming model, executed on the Minizinc solver, successfully obtains optimal solutions for small instances with 10 jobs. For larger problems with 15 jobs, simulated annealing is a good alternative since it allows to obtain solutions at 2.3\% of the optimum on average despite a fixed calculation time of 10 seconds.1302310Proceedings of the 15th International Conference on Operations Research and Enterprise System
Strategic capacity investment with common ownership
No full textWe study how common ownership affects the magnitude and dynamics of investments in a duopoly. Followers exhibit less aggressive timing and quantity reactions because they internalize their effects on leaders. Leaders are therefore more likely to opt for a deterrence strategy, but their own internalization of followers softens their decisions. If firm roles are exogenous, high common ownership links lead to a relatively efficient staged investment outcome. Conversely, if firm roles are endogenous, high common ownership drives the winner of the preemption race to concede a “follower monopoly.” Our numerical analysis finds that common ownership is generally detrimental to consumer surplus and welfare
Renewable Energy Sources: An Investigation of Service Firms Supporting Photovoltaic Parks.
No full text11251
Graph-Based Bitcoin Fraud Detection Using Variational Graph Autoencoders and Supervised Learning
No full textBitcoin is a decentralized cryptocurrency, which is rapidly growing and offering many advantages. Although its structure protects users from some types of fraud, it is not completely immune, while fraud detection in Bitcoin remains still relatively unexplored. In this paper, we use a graph to model Bitcoin transactions and benefit from the graph's structure to overcome the lack of informative transaction and user data. We utilize network analysis for feature extraction and model fraud detection as a classification problem using a Deep Neural Network as our classifier. Furthermore, we propose a novel approach that combines a Variational Graph Autoencoder (VGAE), for deriving appropriate node and graph embeddings, and supervised learning to detect fraudulent Bitcoin transactions. Our experimental results show that the proposed approach, while also affected by high class imbalance, similarly to using only the graph-based features for classification, performs significantly better in detecting high-risk areas in the graph.25781782
Towards an Ontology for Describing the Contextualization of Written Cultural Heritage in the Light of Semiotics
No full textSemiotics of culture is a valuable consideration, as it illuminates indiscernible aspects associated with social practices and processes of signification. Against this background, the goal of this paper is to delineate the cultural and social (contextual) dimensions of written cultural heritage from a semiotic point of view. Hence, an ontology defining a light vocabulary of high-level concepts is developed to describe these contextual dimensions. The proposed ontology, called Onto-Semiotics DCT (Describing the Contextualization of Text), is essentially a general theoretical conceptual model based on the semantic idea that a text penetrates into a communication circuit. This perspective makes a clear distinction between intra-textual and extra-textual semiotic context, as well as cultural micro-situation and social macro-situation. However, in this paper, we focus on the description of the situations that refer to the social and cultural dimensions of a text. Thus, context-aware information representation is applied. This description is crucial for the correct analysis and interpretation of texts, as according to a prevailing semiotic position, texts are influenced by the environment that produces them. As a result, knowing and recording their context reinforces text semantics, through the methodological transition from logo-centrism to culture-centrism, and even further to socio-centrism.2391 CCIS91104Advances in ICT Research in the Balkan
Students Collaboratively Prompting ChatGPT
No full textThis study investigated how undergraduate students collaborated when working with ChatGPT and what teamwork approaches they used, focusing on students’ preferences, conflict resolution, reliance on AI-generated content, and perceived learning outcomes. In a course on the Applications of Information Systems, 153 undergraduate students were organized into teams of 3. Team members worked together to create a report and a presentation on a specific data mining technique, exploiting ChatGPT, internet resources, and class materials. The findings revealed no strong preference for a single collaborative mode, though Modes #2, #4, and #5 were marginally favored due to clearer structures, role clarity, or increased individual autonomy. Students reasonably encountered initial disagreements (averaging 30.44%), which were eventually resolved—indicating constructive debates that improve critical thinking. Data also showed that students moderately modified ChatGPT’s responses (50% on average) and based nearly half (44%) of their overall output on AI-generated content, suggesting a balanced yet varied level of reliance on AI. Notably, a statistically significant relationship emerged between students’ perceived learning and actual performance, implying that self-assessment can complement objective academic measures. Students also employed a diverse mix of communication tools, from synchronous (phone calls) to asynchronous (Instagram) and collaborative platforms (Google Drive), valuing their ease of use but facing scheduling, technical, and engagement issues. Overall, these results reveal the need for flexible collaborative patterns, more supportive AI use policies, and versatile communication methods so that educators can apply collaborative learning effectively and maintain academic integrity.14515