Drawing upon critical and technology studies, this conceptual provocation introduces the concept of “Progress without Consent”, referring to the introduction of AI technological innovation into society without collective societal deliberation, legitimacy and consent. The paper problematises the introduction of structurally transformative AI technology into society, the strategy of depolitisation of this technology, robotic automation of society without social mandate, and the manner in which technological innovation moves faster than democratic institutions can process its implications on societal structures. Ultimately, the authors argue, the development and deployment of AI does not belong to isolated labs and the private sphere of technological innovation but requires a transfer to the public sphere, representing the space of rational communication, collective contestation and democratic consent.
Software metrics play a central role in assessing and managing the quality of software systems providing quantitative insights into attributes such as complexity, reliability, rigidity, modifiability and maintainability. Among these, maintainability is particularly critical, as it directly influences the ease of system evolution, long-term sustainability, and overall cost effectiveness. Despite the widespread use of metric-based maintainability measurement algorithms, capturing a value that reflects the maintainability situation of software source code remains a challenging task, especially in the presence of design deficiencies such as code smells. To measure changes in maintainability, this study experimentaly characterises the relationship between code smells and maintainability. Four commonly studied code smells—Long Method, Long Parameter List, Feature Envy, and Refused Bequest—were systematically injected into open-source codebases derived from Apache Commons Lang. The modified systems were then evaluated using SonarQube, enabling a quantitative assessment of how these injected design deficiencies affect maintainability metrics. To have a comparative result set the Microsoft Maintainability Index algorithm was implemented into a tool to evaluate the same code base. The results from these approaches were combined into a structured dataset capturing the impact of different code smells across multiple measurement techniques. The analysis reveals notable discrepancies among existing maintainability algorithm, particularly regarding their sensitivity to specific types of code smells, which contrasts with expectations derived from prior literature, inconsistent maintainability degradation, ranking inconsistency, little or no reaction to some code smell. To address these limitations, we proposes a novel maintainability evaluation algorithm designed to better reflect the nuanced effects of structural and object-oriented design issues, bearing in mind not just the presence of code smell but the distribution of these smells. The proposed algorithm demonstrates improved alignment with observed patterns of maintainability degradation compared to traditional indices. This work contributes (i) A reproducible methodology for systematically evaluating maintainability degradation, (ii) a comparative analysis of widely used maintainability metrics, and (iii) a novel algorithm that enhances the accuracy of maintainability assessment in the presence of code smells, also the proposed algorithm showed high sensitivity and low sensitivity to some particular smells, it also showed that the maintainability value are sometimes clustered within a very short range of numbers irrespective of the number of smell injected. Keywords: Code-smell, Maintainability index, Software-quality-metric, Feature envy, SonarQube.
AI systems depend on human judgment, yet many annotation workflows are either designed for data-science specialists or managed through external commercial platforms. The first approach may demand more technical skills than relevant stakeholders possess. The second can require organizations to transfer data, expertise, and governance to an outside provider. Both can limit the involvement of people who understand what data means in its real-world context. Human-in-the-loop approaches introduce human judgment. Stakeholder-in-the-loop annotation focuses on selecting and organizing people whose contextual knowledge fits the AI application. This paper presents Classifact, a transparent and secure platform for stakeholder-in-the-loop data annotation and validation. Using an interdisciplinary design-science approach, we developed Classifact with practitioners and researchers from AI and data, operational practice, UX and engagement, privacy and governance, security, and lived-experience or project settings. The platform enables non-specialists to set up missions, supports cooperation among data scientists, business or context experts, annotators, and data protection officers, and preserves data governance through role-based access, audit trails, and controlled or local deployment. Its game-like interface supports rapid and deliberate annotation tasks. Classifact supports the selection and organization of stakeholder groups for annotating AI inputs or validating AI outputs. Formative feedback and a proof-of-concept pilot assessed whether non-specialists could set up and run an annotation mission, complete the end-to-end workflow, and sustain annotation activity. In a comparison with Labelbox on the same task, Classifact produced more annotations and required less mission-setup time under the documented study conditions. The workshops provide an operational comparison under differing conditions, rather than a controlled causal comparison. The results demonstrate feasibility and practical potential. Annotation quality, stakeholder representation, inclusion, fairness, and downstream AI performance form later evaluation stages. Classifact contributes a practical design for human-centred, transparent, secure, and ethically governed stakeholder-in-the-loop data annotation.
Municipalities are facing increasingly complex, interconnected challenges in areas like housing, climate adaptation, mobility, and social policy. Local Digital Twins (LDTs) are seen as a promising tool to make this complexity more understandable and support decision-making. At the same time, both literature and practice show that few initiatives get past the pilot phase, even though getting through that phase is essential for successful long-term adoption. This paper presents a research-in-progress study on the development and application of an implementation method for LDT technology within the municipality of Veenendaal, based on human values rather than driven by technological possibilities. Based on literature, case studies, and an initial stakeholder analysis, both opportunities and bottlenecks are identified. The first results show that stakeholders broadly recognize the added value of LDTs, but that fragmented data, limited organizational maturity, and lack of governance are important obstacles. This research contributes to understanding the conditions for successful implementation of LDTs as policy and management tools in municipal organizations.
This paper evaluates the technical, economic, and environmental feasibility of residential prosumer implementation in Sarajevo, Bosnia and Herzegovina, through the integration of photovoltaic (PV) generation and battery energy storage. A simulation‑based assessment was performed in HOMER Pro using a measured 12‑month household load profile with an average daily consumption of 11.25 kWh and a peak demand of 2.85 kW. Six system configurations were investigated, consisting of 3 kW, 4 kW, and 5 kW PV installations, each analyzed with and without a 5.04 kWh lithium‑ion battery, over a 25‑year project lifetime under a zero‑credit export scheme. Among the analyzed configurations, the 3 kW PV system without battery storage achieved the lowest Net Present Cost (NPC) and Levelized Cost of Energy (LCOE) of 0.137 BAM/kWh, representing a 28% reduction compared to the grid tariff, with a simple payback period of 17 years and the highest Internal Rate of Return (IRR) of 3.8%. The 4 kW PV system provided the most balanced operational performance, generating 4,954 kWh annually with imports and exports nearly equal, yielding an LCOE of 0.139 BAM/kWh and a 20‑year payback. The 5 kW PV system generates highest total renewable energy and full annual carbon neutrality, but its surplus generation provided limited additional financial benefit under the current net‑metering framework, which only offsets future bills rather than generating revenue. Battery storage integration substantially enhanced energy autonomy by increasing self‑consumption and renewable fraction (from 52% to 61% for the 3 kW system, and from 57% to 68% for the 4 kW system), but it raised LCOE to 0.198–0.202 BAM/kWh due to higher capital investment. The environmental assessment demonstrated that PV adoption significantly reduces greenhouse gas emissions, with the 3 kW system lowering annual CO₂ emissions by ~76%, the 4 kW system by ~92%, and the 5 kW system achieving net‑negative emissions. The results demonstrate that under zero‑sellback regulatory frameworks, PV sizing must prioritize direct self‑consumption matching. Moderate‑capacity PV installations therefore represent the most economically viable investments, while battery storage serves primarily as a driver of energy independence rather than immediate financial gain.
Digitalization has shifted Business Process Management (BPM) from a technical discipline toward a stronger emphasis on culture, behaviour and organizational knowledge. Yet little is known about which cultural values support process implementation in SMEs. This study examines how BPM culture influences process implementation success in Dutch service-oriented SMEs, drawing on six values: customer orientation, excellence, responsibility, teamwork, innovation, and results orientation. A qualitative multiple case study was conducted across four SMEs, based on thirteen semi-structured interviews and the Organizational Culture Assessment Instrument (OCAI). While implementation success was assessed through employee acceptance, actual use and structural embedding. The findings show that teamwork and responsibility are most strongly associated with implementation success, whereas customer orientation and results orientation act mainly as motivators when made concrete for employees. The study further identifies communication, leadership & decision-making and organizational culture as contextual conditions that shape how BPM values translate into sustainable process behaviour.
Advances in machine learning for healthcare are abundant, yet most validated models remain confined to research notebooks and never reach secure, usable clinical software. This paper addresses that deployment gap by presenting a unified, security-hardened software platform that operationalizes two complementary streams of doctoral research inside a single, role-based hospital information system. The first stream contributes a clinical-prediction capability: an ultra-hybrid ensemble that couples a quantum-inspired feature transformation, particle-swarm feature selection, and calibrated soft voting for cancer-outcome prediction (96.41% accuracy, AUC-ROC 0.983 on TCGA-BRCA), survival stratification, multi-cancer generalization, and pharmacogenomic drug-response classification (89.31% mean accuracy across 25 compounds). The second stream contributes a sequential-security capability built on a bidirectional gated recurrent unit (Bi-GRU) for network intrusion detection (94.95–98.40% accuracy across three datasets, including a purpose-built healthcare benchmark) and an attention-enhanced variant for electroencephalography (EEG) biometric identification (96.68% accuracy). The platform follows a three-tier architecture (Flutter client, ASP.NET Core gatekeeper, and an isolated model-serving service) in which security is embedded architecturally through role-based access control, audit logging, an intrusion-detection monitor, and a biometric access gate, aligned with HIPAA and GDPR obligations. A remote elderly-care module illustrates the platform in an aging-in-place scenario. The contribution is the engineering translation of research-grade AI into one deployed, compliant system.
This paper presents a dual-layered AI framework for real-time landslide risk assessment developed under the GeoNetSee project within the Interreg Danube Region Programme. The first layer employs a fuzzy logic model, inspired by the Slovenian MASPREM system, which integrates Landslide Susceptibility Maps (LSS) with high-resolution precipitation forecasts from the Open-Meteo API to generate a Predicted Landslide Hazard (PLSH) score on a 0–5 scale, updated every 6–12 hours. The second layer focuses on real-time ground displacement detection by fusing low-cost dual-frequency GNSS receivers with MEMS accelerometers and applying machine learning regression algorithms, including Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Convolutional Neural Network – Long Short-Term Memory (CNN-LSTM). Validation at the Curine Njive pilot site in Bosnia and Herzegovina demonstrated that SVR achieved a Mean Absolute Error (MAE) as low as 0.0018 m across four GNSS nodes, while the CNN-LSTM achieved the highest overall predictive accuracy across all evaluated nodes with a MAPE of only 3.99% for Node 41. The fuzzy model successfully identified critical hazard levels during intense rainfall events. Results confirm the viability of integrating low-cost sensor networks with AI-driven analytics for transboundary geohazard early warning systems.
Fuzzy inference systems have demonstrated considerable promise for EEG-based cognitive state monitoring in neurodegenerative conditions. However, two design decisions significantly influence system performance and clinical applicability: the choice of inference architecture (Takagi-Sugeno vs Mamdani) and the method of rule and membership function generation (manual expert-driven vs data-driven automated). This paper presents a comparative analysis of both dimensions in the context of an EEG-based Alzheimer’s disease monitoring system operating on the ds004504 OpenNeuro dataset (88 subjects: 36 AD, 23 FTD, 29 HC). A Takagi-Sugeno system, implemented as a hybrid FSM-Fuzzy architecture, is compared against a Mamdani equivalent across four axes: inference transparency, output interpretability, computational complexity, and suitability for FPGA deployment. The limitations of manual membership function tuning are then examined through failure case analysis, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) proof-of-concept is presented, demonstrating automated rule and MF generation from the delta/alpha ratio (DAR) biomarker. ANFIS training on 88-subject DAR data (70 training, 18 test) achieved minimal training RMSE of 0.392 and validation RMSE of 0.391. Data-driven MF centers differ substantially from manually designed centers — shifts of 2.754σ, 0.455σ, and 2.055σ for LOW, MEDIUM, and HIGH MFs respectively — quantifying the representational gap that expert tuning introduces. These findings motivate a hybrid design strategy: ANFIS for offline MF calibration, TS for online FPGA inference, and Mamdani for clinical output presentation.
This research investigates the integration of stakeholders' values into the digital frameworks of Collective Management Organizations (CMOs) within the Dutch music copyright system. Utilizing Q methodology, the study captures diverse perspectives from composers, lyricists, publishers, and CMO representatives on values, value tensions, norms, and system requirements. A pilot study with four experts tested data collection methods and refined the study design for a larger, follow-up study involving 30 participants. Preliminary findings, based on factor analysis of participant rankings of 30 statements, reveal two distinct perspectives: one focused on "Fairness and Accountability," emphasizing trust-building and equitable treatment, and the other on "Technological Efficiency and Transparency," prioritizing clear information, verification mechanisms, and advanced IT systems. Qualitative insights from participant interviews provide nuanced understanding, highlighting the importance of transparency in royalty processes, balanced application of technology, and equitable royalty distribution in the digital age. This research contributes to the modernization of copyright management systems offering a conceptual model adaptable to other creative (Intellectual Property) industries.
As organizations increasingly adopt artificial intelligence (AI) technologies to optimize their supply chain operations, they face a growing set of ethical challenges that demand careful consideration. This study explores the key ethical dilemmas and potential solutions in implementing AI within the supply chain context, drawing insights from in-depth interviews with a diverse group of experts. The thematic analysis of the interview data revealed four primary ethical dilemmas: algorithmic bias and discrimination, lack of transparency and explainability, privacy and data ethics issues, and the socioeconomic impacts of AI-driven automation. To address the dilemmas, this study identified various mitigation strategies, including techniques for bias detection and correction, methods for improving the interpretability of AI-driven decisions, comprehensive data governance frameworks, and approaches to responsible automation that prioritize human-AI collaboration. The findings of this research are grounded in various philosophical principles, such as fairness and distributive justice, and the ethical treatment of workers in the face of technological change. By contextualizing the practical challenges within these broader philosophical considerations, the study provides a holistic understanding of the ethical implications of AI adoption in supply chain management. The proposed ethical AI governance framework for supply chain organizations offers a systematic approach to navigating the complex terrain of responsible innovation, fostering a culture of ethical decision-making, and ensuring the long-term sustainability of AI-powered supply chain operations. This research contributes to the growing body of literature on the ethical dimensions of emerging technologies and their implementation within operational settings.
As AI technologies evolve, organizations must continuously adapt their governance structures to address emerging challenges related to transparency, ethics, and regulatory compliance. Given the rapid pace of AI advancements, traditional governance approaches may not be sufficient to ensure responsible AI oversight while maintaining competitiveness. This research examines AI corporate governance through the lens of Dynamic Capability (DC) Theory, focusing on how organizations can develop the capacity to sense technological and regulatory shifts, seize opportunities, and reconfigure governance mechanisms to align with AI-driven transformation. Through a literature review and the development of an AI Corporate Governance Framework grounded in DC Theory, this study explores how dynamic capabilities influence governance adaptability in the context of AI.
ABSTRACT The demand for cybersecurity talent appears to be constantly growing[1], but the statistics can be illusory. According to Wages and Employment Trends in ONET On-Line[2] cybersecurity job openings are ranked as having a Bright Outlook, and on May 1 2025 Cyberseek listed 450,000 US job openings out of a workforce of 1.25 million. However, most entry-level cybersecurity jobs require several years of experience resulting in a mismatch between supply and demand. Despite a “hot job market”, recently minted cybersecurity college graduates without experience have a hard time securing their first “entry-level” because they don’t meet the minimum requirements[3]. According to multiple hiring managers, “You have to be able to tell your story.” If an applicant cannot explicitly explain to the hiring manager the value they bring to an organization, they don’t get hired. Cybersecurity is a socio-technological team-oriented workplace, and competitive hires need to be able to articulate examples of experiences they have had while working in comparable environments. Students are not able to compete effectively for entry level jobs if they are only able to talk about what they have learned in class. Our interview-based research demonstrates how students can acquire relevant and requisite experiences through participating in cybersecurity competitions. Students who distinguish themselves by developing a track record outside of basic classroom activities earn more and have more employment choices upon graduation. To summarize our findings: what students do outside the classroom is as important as what they do in the classroom. Participating in competitions provides students with an opportunity to understand the relevance and purpose of what they need to learn in the classroom, and to market themselves more effectively in the job market. Key words: competency, employability, competitions, cybersecurity education. [1] Retrieved from: Cybersecurity Supply And Demand Heat Map, www.cyberseek.org/heatmap/html [2] Retrieved from: ONET On-Line www.onetonline.org [3] Retrieved from: Sayegh, E., The Cybersecurity Crisis Companies Can't fill roles, Forbes, 2025-02-05
As artificial intelligence (AI) rapidly transforms industries and societies, its applications present both immense opportunities and significant ethical challenges. This paper explores the concept of Responsible AI, emphasizing the importance of integrating ethical considerations into AI development and deployment. It examines a range of AI applications, from healthcare and finance to autonomous systems, highlighting their potential to drive innovation while also raising concerns about bias, privacy, accountability, and job displacement. The paper evaluates the ethical principles that should guide AI design, including transparency, fairness, privacy protection, and human oversight. Furthermore, it critically analyzes the current landscape of AI governance, identifying gaps in regulation and the need for robust, global frameworks to ensure AI technologies are developed and used responsibly.
This paper explores the integration of sustainability into traditional Enterprise Architecture (EA), proposing a novel Sustainable Enterprise Architecture (SEA) model, and uses the fashion industry to illustrate the efficacy of the proposed model. The SEA framework systematically embeds environmental, social, and economic sustainability principles into corporate strategy and operational practices. This research also addresses the significant intention-action gaps among consumers and organizations in sustainability practice. Through vivid case examples, such as MUD Jeans' leasing model and Pact's affordable circular apparel, this research highlights practical pathways for operationalizing sustainability. By leveraging Circular Economy (CE) and Product Life Cycle (PLC) concepts, SEA fosters collaborative stakeholder engagement, promoting substantial advancements in sustainable practices within the fashion industry.
Detecting sarcasm in text remains a critical yet challenging task in natural language processing (NLP). Despite significant advances through deep learning, particularly the use of transformer-based architecture like BERT, sarcasm detection models still face challenges in achieving high accuracy. A major limitation lies in their insufficient incorporation of contextual awareness, including conversational history, social inter- actions, emotional cues, and cultural factors. To address this, this paper proposes the BERT with Meta-Feature Logistic Regression Fusion (BMLRF) model, which in- tegrates sentence-level embeddings from a pre-trained transformer with meta-features capturing social, emotional, and cultural contexts. The model also leverages multi-turn dialogue history and social interaction embeddings to enhance contextual understanding. This fusion approach aims to improve sarcasm detection robustness across domains such as social media and political discourse, emphasizing the critical role of emotion, social dynamics, and cultural context in sarcastic communication. Keywords: Natural Language Processing (NLP), BMLRF model, BERT, Multi-turn dialogue, Meta-features.
ABSTRACT The rapid advancement of Artificial Intelligence (AI) is transforming the global workforce, presenting both opportunities and challenges for leadership development, particularly for women. As AI automates routine tasks and redefines skill requirements, there is a growing demand for uniquely human capabilities such as emotional intelligence, creativity, and strategic thinking, qualities that are inherently strong and often highly associated with women. Research indicates that women typically score higher in emotional intelligence, particularly in areas such as empathy and relationship management, which are critical for effective leadership (Goleman, 2020). Furthermore, studies by McKinsey & Company (2022) highlight that gender-diverse leadership teams, benefiting from women's creativity and strategic thinking, drive higher innovation and improved business performance. This alignment between women's natural strengths and the evolving needs of the AI-driven workplace presents a pivotal opportunity to bridge gender gaps in leadership roles. This shift offers a pivotal opportunity to bridge gender gaps in leadership roles by equipping women with the necessary skills to thrive in an AI-driven world. This study explores the Skill Evolution in the Age of AI, focusing on how targeted education, mentorship, and policy interventions can empower women for leadership positions. Utilizing text analytics techniques, the research employs topic modeling on training materials, leadership programs, and professional development content. The analysis aims to identify existing and emerging skill gaps, evaluate the effectiveness of current programs, and propose actionable strategies for inclusive leadership development. A key aspect of this research is examining how AI technologies can be leveraged to reduce the gender gap in leadership roles while addressing the biases in hiring and promotion processes, while also addressing the risks of perpetuating inequalities if gender considerations are overlooked. The findings highlight essential competencies that women need to lead effectively across various disciplines especially in AI-integrated workplaces. Moreover, the study provides practical recommendations for organizations, educational institutions, and policymakers to create environments that support women's advancement into leadership roles.Ultimately, this research underscores the potential of AI as a catalyst for gender equity in leadership, advocating for systemic change that leads to more diverse, innovative, and resilient organizations in the digital era.
Within the software engineering context, agile approaches encourage customer collaboration, iterative development, and flexibility. However, the mass exodus of highly skilled professionals, known as the “brain drain” or "Japa Syndrome," has emerged as a significant challenge, especially in Nigeria. This phenomenon has particularly impacted agile software development practitioners by undermining project continuity, knowledge transfer, and team dynamics. This study empirically examines the effect of brain drain, “Japa Syndrome,” on agile practitioners developing healthcare information systems software in Nigeria. It employed a qualitative, multi-method approach to gather empirical data from 13 agile practitioners in Nigeria’s healthcare information systems sector. The study used semi-structured, open-ended interview questions and snowball sampling from our network of professional experts. The collected data were analysed using a grounded theory-based approach, including open coding, constant comparison, memoing, and reaching theoretical saturation. The study identified 24 codes and organised them into five memos, which cover the sudden loss of agile team members, increased technical debt, delays in decision-making, psychological effects on the agile team, and organisational coping mechanisms. The contribution of this research is a detailed analysis of these five memos. We recommend urgent systemic policy reforms and the development of a privacy and secure-by-design culture. These measures are vital to minimise reputational damage and to maintain the viability and competitiveness of Nigeria's healthcare information systems software development.
The Scaled Agile Framework (SAFe) has gained significant traction in recent years as a strategy to mitigate the risks associated with traditional front-end planning, which often results in downstream development challenges. The adoption of SAFe is not common for Small- and Medium Enterprises (SMEs), as its implementation is often perceived as complex, time-consuming and resource-intensive. However, the application of SAFe can add value for SMEs as well, when adapted and scaled down for use in those organisations. This qualitative multiple case study was undertaken to deepen our understanding of the applicability of the seven core competencies of SAFe in business software implementation projects at SMEs in The Netherlands, as well as detecting challenges they face when adopting this framework. Data, collected via document analysis and semi-structured interviews with key project stakeholders, was analysed using flexible pattern matching and thematic analysis. Cross-case analysis identified key themes and relationships related to project success and challenges. The results reveal that SAFe uniquely offers support in dealing with resistance, stakeholder-focused agility boosting bottom-up innovation, and a continuous learning culture enabling organisations making strategically aligned decisions faster. Resource limitations in SMEs require careful consideration for successful SAFe implementation.
The beverage industry faces growing scrutiny as demands for transparency and accountability intensify. In today’s digital landscape, companies must prioritize enhanced traceability to ensure product safety, comply with regulations, maintain consumer trust, and safeguard brand reputation. This research investigates blockchain technology as a potential solution to these challenges, emphasizing its capacity to decentralize data, improve traceability, and accelerate response times during safety recalls. The study traces the evolution of food safety regulations and analyzes current traceability practices and technological innovations within the beverage sector. Using the Coca-Cola and BODYARMOR Sport Water recall as a case study, this study examines drivers, benefits, and challenges of the adoption of blockchain in beverage supply chains. Key findings highlight the importance of integrating blockchain with legacy systems, ensuring accurate data input, and addressing concerns around scalability and privacy. This research bridges academic theory and industry application, offering practical strategies for strengthening supply chain integrity through blockchain adoption in the beverage industry. Several promising future research directions related to the adoption of blockchain in the food and beverage industry are also suggested.
