Search Results (310)

Background: Digital transformation is reshaping healthcare operations, with loyalty programs increasingly used to strengthen patient engagement and streamline administrative workflows. However, fragmented information systems and manual verification routines continue to create bottlenecks, inconsistencies, and extended lead times. Methods: This study applies a mixed-methods approach within the Business Process Management (BPM) lifecycle to redesign the eligibility verification process for a loyalty program at Casa de Saúde São Mateus Hospital. Quantitative time measurements were collected during peak periods, while qualitative insights from staff observations and discussions supported process discovery and bottleneck identification. The proposed solution integrates a centralized SQL database, automated verification routines, and a dedicated administrative interface synchronized with the MedicineOne system. Results: The redesigned process reduced eligibility verification time by approximately 80% and improved Flow Efficiency by around 11.7%. Manual interventions, data fragmentation, and discount-application errors decreased substantially. The centralized database improved data reliability, while automated checks enhanced consistency and reduced staff workload. The system also enabled more accurate beneficiary management and improved coordination across administrative activities. Conclusions: Integrating Healthcare 4.0 principles with BPM enhances internal logistics, reduces lead times, and improves operational reliability. The proposed model offers a replicable framework for modernizing healthcare service delivery. Full article

With the growing global popularity of Android apps, ensuring their quality and reliability has become increasingly important, as low-quality apps can lead to poor user experiences and potential business losses. A common approach to testing Android apps involves automatically generating event sequences that interact with the app’s graphical user interface (GUI) to detect crashes. To support this, we developed ACE (Android Crawler), a tool that systematically generates events to test Android apps by automatically exploring their GUIs. However, ACE’s original heuristic-driven exploration can be inefficient in complex application states. To address this, we extend ACE with a deep reinforcement learning-based crawling strategy, called Reinforcement Learning Strategy (RLS), which tightly integrates with ACE’s GUI exploration process by learning to intelligently select GUI components and interaction actions. RLS leverages the Proximal Policy Optimization (PPO) algorithm for stable and efficient learning and incorporates an action mask to filter invalid actions, thereby reducing training time. We evaluate RLS on 15 real-world Android apps and compare its performance against the original ACE and three state-of-the-art Android testing tools. Results show that RLS improves code coverage by an average of 2.1% over ACE’s Nearest unvisited event First Search (NFS) strategy and outperforms all three baseline tools in terms of code coverage. Paired t-test analyses further confirm that these improvements are statistically significant, demonstrating its effectiveness in enhancing automated Android GUI testing. Full article

Digital transformation is crucial for small- and medium-sized enterprises (SMEs), as it enhances organizational efficiency, productivity, and competitiveness by enabling process automation, cost reduction, and faster decision-making. It also allows for SMEs to leverage emerging technologies, improve customer engagement, and access new markets, thereby fostering innovation and sustainable growth. The proposed study aims to reveal the most significant aspects regarding the digital transformation in the SME business environment. Even though the concept of digital transformation has gained much research interest, SMEs still face significant obstacles, including limited financial resources, a shortage of skilled personnel, and resistance to change within organizational culture. A systematic literature review on the digital transformation of SMEs was conducted to reveal the enablers and obstacles encountered by these businesses in their pursuit of digital maturity. This review underscores the importance of human resources and digital maturity, emphasizing the need for a digitally skilled workforce and a culture of continuous learning. The results can enforce future research on this subject and could focus on the relationship between digital transformation and organizational performance, the role of digital entrepreneurship, and the long-term effects of digital transformation, providing valuable insights to help SMEs navigate the complexities of digital transformation and achieve sustainable growth. Full article

The increasing digitization of critical infrastructure and the increasing use of Industrial Internet of Things (IIoT) systems are leading to a significant increase in the exposure of operating systems to cyber threats. The integration of information (IT) and operational (OT) layers, characteristic of today’s industrial environments, results in an increase in the complexity of system architecture and the number of security events that require ongoing analysis. Under such conditions, classic approaches to monitoring and responding to incidents prove insufficient, especially in the context of systems with high reliability and business continuity requirements. The aim of this article is to analyze the possibilities of using Large Language Models (LLMs) in the protection of industrial IoT systems operating in critical infrastructure. The paper analyzes the architecture of industrial automation systems and identifies classes of cyber threat scenarios characteristic of IIoT environments, including availability disruptions, degradation of system operation, manipulation of process data, and supply-chain-based attacks. On this basis, the potential roles of large language models in security monitoring processes are examined, particularly with respect to incident interpretation, correlation of heterogeneous data sources, and contextual analysis under operational constraints. The experimental evaluation demonstrates that, when compared to a rule-based baseline, the LLM-based approach provides consistently improved classification of incident impact and attack vectors across IT, DMZ, and OT segments, while maintaining a low rate of unsupported responses. These results indicate that large language models can complement existing industrial IoT security mechanisms by enhancing context-aware analysis and decision support rather than replacing established detection and monitoring systems. Full article

Telecom network fraud continues to evolve, and its textual expressions have become increasingly concealed, making automated detection more challenging. When combined with mainstream prompting strategies, large language models (LLMs) often exhibit unstable performance when handling diverse fraud texts, particularly for long-tail categories and confusing cases where consistent detection is difficult to maintain. To address this limitation, this study proposes a Multi-Role, Multi-Layer (MRML) prompting strategy. The strategy constructs three expert roles—text analysis, business process analysis, and security analysis—and adopts a conditional hierarchical reasoning mechanism to achieve a structured detection process that transitions from rapid binary screening to deep multi-class classification. This design systematically organizes the LLM’s inference steps and enhances its ability to distinguish different types of telecom fraud. Experiments conducted on two public datasets show that the proposed framework significantly outperforms mainstream prompting strategies and surpasses deep learning baselines such as BERT, TextCNN, and Transformer in terms of precision, recall, and F1-score, demonstrating superior performance and robustness. Overall, the results indicate that the proposed prompting strategy provides an effective and practically applicable solution for telecom fraud text detection in real-world scenarios. Full article

Contemporary business process modeling lacks a systematic framework for converting unstructured requirements into structured models. Traditional manual approaches fail to support integrated lifecycle management from requirements elicitation to iterative model refinement. The gap severely limits the efficiency and accuracy of the alignment between requirements and business process modeling and often leads to costly rework and implementation errors in complex software projects. Therefore, this paper aims to establish a coherent modeling framework from requirements extraction to business process model verification. The framework maintains the traceability and consistency of the unstructured requirements through three tasks: (1) automatic generation of a structured requirements model from textual input to a set of designed prompts of hyperparameter-optimized large language models (LLMs); (2) establishment of a modeling routine to handle the iterative requirements via two sets of formalized mapping rules, a merging algorithm, and a toolkit; (3) detection of the obtained CBPMN model by a static flow error verification algorithm and reachability verification using CPN tools 4.0. A total of 15 sets of comparative experiments with three state-of-the-art automated modeling approaches demonstrate the superiority of our method in generating higher-quality requirements models, while an additional case study with two-step verification proves its validity. Full article

Large Language Models (LLMs) are emerging as powerful tools for automating business process modeling, promising to streamline the translation of textual process descriptions into Business Process Model and Notation (BPMN) diagrams. However, the extent to which these Al systems can produce high-quality BPMN models has not yet been rigorously evaluated. This paper presents an early evaluation of five LLM-powered BPMN generation tools that automatically convert textual process descriptions into BPMN models. To assess the external quality of these Al-generated models, we introduce a novel structured evaluation framework that scores each BPMN diagram across three key process model quality dimensions: clarity, correctness, and completeness, covering both accuracy and diagram understandability. Using this framework, we conducted experiments where each tool was tasked with modeling the same set of textual process scenarios, and the resulting diagrams were systematically scored based on the criteria. This approach provides a consistent and repeatable evaluation procedure and offers a new lens for comparing LLM-based modeling capabilities. Given the focused scope of the study, the results should be interpreted as an exploratory benchmark that surfaces initial observations about tool performance rather than definitive conclusions. Our findings reveal that while current LLM-based tools can produce BPMN diagrams that capture the main elements of a process description, they often exhibit errors such as missing steps, inconsistent logic, or modeling rule violations, highlighting limitations in achieving fully correct and complete models. The clarity and readability of the generated diagrams also vary, indicating that these Al models are still maturing in generating easily interpretable process flows. We conclude that although LLMs show promise in automating BPMN modeling, significant improvements are needed for them to consistently generate both syntactically and semantically valid process models. Full article

Quality tools have a well-established place in business management. They help diagnose, analyze, and solve quality problems. In manufacturing companies, they are also used in process and product improvement projects. However, only the proper selection of quality tools can bring tangible benefits to an organization. Given their diverse content and methodologies, supporting the selection of these tools becomes a crucial issue. A literature review indicates only a few solutions in this area, implemented as decision support systems. Additionally, the challenges of Quality 4.0 and the demands of modern business reveal a research gap in automating the process of selecting quality tools. This is particularly true for less experienced company employees participating in improvement programs. Our previous research shows how machine learning using neural network models supports the development of an expert system in this area. The aim of this paper is to present the results of research conducted in which classifiers in the form of decision trees were developed. At the same time, attempts were made to demonstrate that decision tree classifiers (on an extended Excellence Toolbox dataset) can automatically recommend qualitative tools with an accuracy better than neural networks, while offering interpretable rules. The decision-tree models achieve strong classification performance, with the best tree reporting 96.75% effectiveness. In contrast, the neural network from previous studies achieved 94.87%. Full article

In the era of digital transformation, transaction-intensive e-business applications—such as high-frequency trading (HFT), e-monetary services and decentralized marketplaces—require infrastructures that are not only fast and secure but also sustainable. Current solutions often prioritize short-term performance over long-term resilience, leading to inefficiencies in energy use and system reliability. This paper introduces a conceptual framework for sustainable transaction processing, leveraging energy-efficient hardware accelerators, real-time communication protocols inspired by industrial automation and lightweight authentication mechanisms. By integrating associative memory-based matching engines and optimized network architectures, the proposed approach ensures predictable latency, robust security and scalability without compromising sustainability. The framework aligns with the United Nations Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure) by reducing resource consumption, enhancing operational resilience and supporting future-ready digital ecosystems. Full article

Digitalization, including the implementation of artificial intelligence (AI) applications, is one of the key enablers of business agility in contemporary enterprises. Micro and small enterprises (MSEs) are increasingly expected to adopt scalable and cost-effective AI tools as part of their digital transformation. This study investigates the implementation of an AI-powered chatbot in a Slovak micro-enterprise operating an e-commerce platform, aiming to assess its effectiveness in automating customer service processes. Using a mixed-method case study approach, the research combines quantitative data on service performance (e.g., number of inquiries handled, response time, and automation rate) with qualitative insights from employee and customer feedback. The findings show that the chatbot significantly reduced staff workload and improved response speed and customer satisfaction. However, challenges were identified in handling ambiguous queries and maintaining empathetic communication in complex situations, underscoring the need for regular updates and human oversight. The study contributes to the limited empirical literature on AI integration in micro-enterprises and provides practical recommendations for MSEs seeking to enhance their operational efficiency through AI-driven tools without large-scale investments. These results offer a nuanced perspective on how even resource-constrained businesses can benefit from AI adoption when implementation is carefully aligned with their specific needs and capabilities. Full article

In today’s rapidly evolving digital landscape, businesses are constantly under pressure to produce high-quality, engaging content for various marketing channels, including blog posts, social media updates, and email campaigns. However, the traditional manual content generation process is often time-consuming, resource-intensive, and inconsistent in maintaining the desired messaging and tone. As a result, the content production process can become a bottleneck, delay marketing campaigns, and reduce organizational agility. Furthermore, manual content generation introduces the risk of inconsistencies in tone, style, and messaging across different platforms and pieces of content. These inconsistencies can confuse the audience and dilute the message. We propose a hybrid approach for content generation based on the integration of Markov Chains with Semantic Technology (CGMCST). Based on the probabilistic nature of Markov chains, this approach allows an automated system to predict sequences of words and phrases, thereby generating coherent and contextually accurate content. Moreover, the application of semantic technology ensures that the generated content is semantically rich and maintains a consistent tone and style. Consistency across all marketing materials strengthens the message and enhances audience engagement. Automated content generation can scale effortlessly to meet increasing demands. The algorithm obtained an entropy of 9.6896 for the stationary distribution, indicating that the model can accurately predict the next word in sequences and generate coherent, contextually appropriate content that supports the efficacy of this novel CGMCST approach. The simulation was executed for a fixed time of 10,000 cycles, considering the weights based on the top three topics. These weights are determined both by the global document index and by term. The stationary distribution of the Markov chain for the top keywords, by stationary probability, includes a stationary distribution of “people” with a 0.004398 stationary distribution. Full article

The construction sector faces growing challenges in integrating sustainability, risk management, and regulatory compliance, in line with initiatives such as the European Green Deal, the Corporate Sustainability Reporting Directive, and international building standards. However, the systematic adoption of ESG metrics in decision-making remains limited due to fragmented data, the lack of predictive tools, and reliance on static reporting. This study proposes and illustrates a digital framework, based on simulated data, that combines Artificial Intelligence, Process Mining, and Robotic Process Automation to enhance ESG risk assessment in sustainable construction management. The model, formalized through Business Process Model and Notation, integrates Machine Learning for risk weighting and classification, and leverages Web Scraping and Business Intelligence for dynamic data acquisition. A simulated case study involving 100 synthetic construction projects is used to demonstrate the internal logic and quantitative feasibility of the framework, showing how automated data integration and predictive modeling can improve the consistency of ESG risk identification and classification. While the results are illustrative rather than empirical, they confirm the analytical coherence and reproducibility of the proposed workflow. From a scientific perspective, it contributes an integrated methodology that bridges predictive analytics and process management for ESG evaluation. From a practical standpoint, it offers a structured and reproducible workflow to anticipate, classify, and mitigate ESG risks, supporting the construction sector’s transition toward data-driven and sustainability-first management practices. Full article

Digital maturity is increasingly recognised as a determinant of competitiveness for small and medium-sized enterprises (SMEs), yet empirical evidence from advanced economies remains limited. Here, we evaluate a sample of Luxembourgish manufacturing SMEs across six dimensions of the Digital Maturity Assessment Tool (DMAT)—Digital Business Strategy (DBS), Digital Readiness (DR), Human-Centric Digitalisation (HCD), Data Governance/Connectedness (DG), Automation and AI (AAI), and Green Digitalisation (GD)—to quantify their overall maturity. To avoid compositional artefacts, given that we rely on the EU’s DMAT, we introduce leave-one-out correlation (LOOC) to assess the association between DMA score and each focal dimension; within-firm disparities were tested via repeated-measures ANOVA; sample profiles were examined using Principal Component Analysis (PCA) followed by hierarchical clustering (HCPC). Respectively, the results converged across methods: HCD (r = 0.717) and DBS (r = 0.652) exhibited the strongest links to maturity, DG/AAI/GD were moderate contributors (r ≈ 0.50–0.58), and DR was weak (r = 0.298). The ANOVA analysis indicated substantial between-dimension differences (partial η² ≈ 0.41), with DG and DBS leading and AAI and GD lagging. PCA–HCPC revealed two coherent cluster profiles—Leaders and Laggards—arrayed along a general maturity axis, with the most significant gaps in DBS and HCD. Practically, firms that prioritise DBS and HCD exhibit a higher DMA score, which creates a foundation for industrialising and automatising manufacturing processes. Given the small, single-country, cross-sectional design, longitudinal and adequately powered studies with objective performance outcomes are warranted to validate and generalise these findings. Full article

Micro, small and medium enterprises (MSMEs) contribute significantly to Indonesia’s economic growth. In an increasingly digitalised era, MSMEs face challenges and opportunities that affect their performance. Technology adoption will have an impact on operational efficiency and ease of transactions, providing added value for consumers. Meanwhile, good financial management depends on the level of financial literacy and inclusion of MSME players. This study aims to examine the factors that influence the sustainable performance of MSMEs from the aspects of technology adoption and financial knowledge. The independent variables include automation, digital payments, financial inclusion and financial literacy, and the dependent variable is MSME performance. This study uses primary data in the form of questionnaires, and data processing uses SEM-PLS. Statistical test results show that the variables of business automation and financial literacy have a positive effect, while the variables of digital payments and financial inclusion have no effect. The results of the study show that financial literacy is an important key to MSME performance and the importance of business automation that affects efficiency through technology. The results of this study are expected to provide useful recommendations for MSME actors and policymakers in formulating strategies to improve the competitiveness of MSMEs. Full article

This study conducts a bibliometric analysis of the scientific literature on digital, automated, and AI-assisted accounting systems. The data include documents listed in the Web of Science and Scopus databases. The analysis identifies the main authors, countries/territories, sources, and thematic trends. The results reveal that the scientific output within this research field has increased since 2018, emphasising the integration of artificial intelligence (AI), robotic process automation, and blockchain technologies in accounting. The findings also suggest that automation enhances efficiency, accuracy, and reliability while also raising concerns about ethics, cybersecurity, and job displacement. This study evaluates the accounting research from early discussions on information systems and automation to current topics such as digital transformation, sustainability, and intelligent decision-making. Furthermore, it contributes to the understanding of the scientific development of digital accounting and addresses future research directions involving AI and machine learning for predictive analytics and fraud detection, blockchain for secure and transparent accounting systems, sustainability through the integration of ESG reporting, and interdisciplinary collaboration between accounting, computer science, and business management to develop intelligent financial systems. The findings provide insights for academics and practitioners aiming to understand the ongoing digital transformation of accounting systems. Full article