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.
In global healthcare logistics, ensuring the timely delivery of medical commodities is critical, particularly in low- and middle-income countries characterized by infrastructural limitations and operational uncertainties. This research introduces an advanced, data-driven predictive framework designed to forecast delivery delays by synthesizing granular, internal shipment-level data from the USAID Global Health Supply Chain Program (GHSC-PSM) with external country-level logistics capabilities indicators derived from the World Bank’s Logistics Performance Index (LPI). Rather than relying on retrospective trend analyses, this study employs machine learning algorithms such as Random Forest, XGBoost, Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP) to detect shipment delays proactively. A distinctive methodological innovation lies in explicitly integrating country logistics capabilities such as customs efficiency, infrastructure quality, and timeliness with internal shipment metadata, enabling a more comprehensive and precise prediction of delays. Empirical validation demonstrates that this integrative approach significantly enhances predictive performance, revealing systemic inefficiencies and enabling targeted managerial interventions. Consequently, logistics managers can leverage these insights to strategically optimize healthcare logistics and supply chains. This study contributes to a rigorously validated and scalable predictive tool, facilitating a strategic shift from reactive to anticipatory logistics management within global health supply chains.
Cybercrime has evolved to be a menacing challenge to all the countries of the globe. Consequently, it has become necessary for effective laws and policies to be enacted and implemented globally. This paper aims to review the cybercrime legislation of three jurisdictions: Nigeria, South Africa, and the United Kingdom (UK), to examine how each country has conceptualised and legislated cybercrime. The paper begins with a discussion on the definitional considerations necessary for legislating cybercrime. It draws from the Council of Europe Convention on Cybercrime (Budapest Convention) as the primary international convention on cybercrimes, to analyse some of the specific provisions in the laws of the respective countries. It then proceeds to unpack some of the specific provisions in the respective laws and discusses the offence relating to cyberforgery. It considers the offence from a different perspective in each jurisdiction. It examines the crime of cyberforgery by laying the descriptive foundation of the cybercrime in the South African Cybercrimes Act; then briefly considers the role of data insecurity from the UK perspective and finally comments on the challenge it poses for e-commerce from a Nigerian lens. Ultimately, the findings reveal that each country has unique strengths and weaknesses, resulting in varied experiences of cybercrime.
How come Open Science is a well-shared vision among research communities, while the prerequisite practice of research data management (RDM) is lagging? This research sheds light on RDM adoption in the Dutch context of universities of applied sciences, by studying influencing technological, organizational, and environmental factors using the TOE-framework. A survey was sent out to researchers of universities of applied sciences in the Netherlands. The analyses thereof showed no significant relation between the influencing factors and the intention to comply with the RDM guidelines (p-value of ≤ .10 and a 90% confidence level). Results did show a significant influence of the factor Management Support towards compliance with a p-value of 0.078. This research contributes towards the knowledge on RDM adoption with the new insight that the factors used in this research do not seem to significantly influence RDM adoption in the Dutch context of universities of applied sciences. The research does show that the respondents have a positive attitude in their intention to change, increase or invest time and effort towards RDM compliance. More research is advised to uncover factors that do significantly influence RDM adoption among universities of applied sciences in the Netherland for stakeholders in Open Science and RDM to enhance their strategies.
Legacy software is becoming increasingly common, and many companies nowadays are facing the challenges associated with this phenomenon. In certain circumstances, re-engineering is the only logical way to deal with legacy software. Such projects, by their very nature, are subject to a wide variety of risks. The aim of this study was to begin building the basis of a risk framework that will support future re-engineering projects within Agile (Scrum) environments. An interpretive case study approach has been followed, where the case study was the first phase of a re-engineering process, with the method of analysis being inductive and reflexive Thematic Analysis. The dataset comprises a list of different risks that occurred during the re-engineering process. The risks observed were themed around people, processes, and technology. While technical and procedural risks are discussed in the literature, it was found that the presence of risks in social situations relating to re-engineering has been overlooked. Although these risks do not necessarily have a higher individual impact, they were found to outnumber those encountered in other aspects of the project by a significant factor. Furthermore, the social risks were often either underestimated or not even recognised. It has also been found that Scrum is an appropriate approach to re-engineering projects. Since many of the re-engineering tasks in the case study were unknown at the beginning, the flexibility brought by Scrum was an important factor in the timely and successful mitigation of emerging risks. The first contribution of this study is a comprehensive analysis of identified risks associated with one particular re-engineering project. The potential impact of those risks over a given development phase of the project, along with their actual impact, have been analysed. The second contribution discusses a proposed methodology for managing and mitigating risks in software re-engineering. It is intended that the identified risk categories form the basis of further research into different types of re-engineering projects in order to produce a more generalised framework. It is anticipated that the results presented here will help future project teams to prioritise areas of re-engineering and put adequate risk mitigation into place.
Email spam detection and filtering are crucial security measures in all organizations. It is applied to filter unsolicited messages; most of the time, they comprise a large portion of harmful messages. Machine learning algorithms, specifically classification algorithms, are used to filter and detect if the email is spam or not spam. These algorithms entail training models on labelled data to predict whether an email is spam or not based on its features. In particular, traditional classification machine learning algorithms have been applied for decades but proved ineffective against fast-evolving spam emails. In this research, ensemble techniques by using the meta-learning approach are introduced to reduce the problem of misclassification of spam email and increase the performance of the combined model. This approach is based on combining different classification models to enhance the performance of detecting the spam emails by aggregating different algorithms to reduce false positives and false negative rates, and increase the accuracy of the combined model. The paper proposed ensemble techniques where various machine-learning algorithms are combined to improve the accuracy and strength of spam detection systems. Using different algorithms, it tries to create an appropriate systematic behaviour to increase the detection rates and reduce the number of misclassification cases. In this research, four machine learning algorithms were selected to build the meta-learning model; these algorithms have been chosen based on their proven effectiveness in spam detection systems, such as Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbours (KNN). The selected algorithms were applied individually on different datasets. Subsequently, an ensemble model was created using the stacking method to collect all the predictions of the models then aggregate and use them as input features for the final classifier that is based on the Logistic Regression algorithm. This study demonstrates the effectiveness of an ensemble approach for email spam detection by aggregating multiple weak machine learning algorithms to produce a strong machine learning model. The purpose of this research is to enhance the accuracy and robustness of the predictive model to detect spam emails. As a result, the proposed approach produced a better performance with 95.8% accuracy.
Cybersecurity challenges are common in Nigeria. Sharing cyber threat intelligence is essential in addressing the extensive challenges posed by cyber threats. It also helps in meeting regulatory compliance. There are a range of impediments that prevent cyber threat intelligence sharing. We hypothesise that we want to maximise this cyber threat intelligence sharing to resist malicious attackers. Therefore, this research investigates factors influencing threat intelligence sharing in Nigeria's cyber security practitioners. To achieve this aim, we conducted research interviews with 14 cyber security practitioners using a semi-structured, open-ended interview guide, which was recorded and transcribed. We analysed the data using an approach informed by grounded theory. We coded the data, organised the data into categories, and used constant comparison to check our code's consistency and accuracy. We developed memos from which our descriptive grounded theory emerged. After a detailed study, we found that cybersecurity practitioners in Nigeria are enthusiastic about collaborating to exchange and receive cyber threat intelligence. However, we discovered two impediments to sharing. Firstly, the existence of competing standardisation in cyber threat intelligence sharing and, secondly, the lack of practitioner's skills in data protection. These barriers inhibit cyber security practitioners from disseminating such cyber threat intelligence sharing inside Nigeria. Based on our findings, we conclude that overcoming these impediments will help cybersecurity practitioners share more cyber threat intelligence in Nigeria.
Cyber threat intelligence (CTI) is an actionable information or insight an organization uses to understand potential vulnerabilities it does have and threats it is facing. One important CTI for proactive cyber defense is exploit type with possible values system, web, network, website or Mobile. This study compares the performance of machine learning algorithms in predicating exploit types using form posts in the dark web, which is a semi- structured dataset collected from dark web. The study uses the CRISP data science approach. The results of the study show that machine learning algorithms which are function-based including support vector machine and deep-learning using artificial neural network are more accurate than those algorithms which are based on tree including Random Forest and Decision-Tree for CTI discovery from semi-structured dataset. Future research will include the use of high-performance computing and advanced deep-learning algorithms.
Organizations are increasingly confronted with the consequences of globalization, digitalization and disruptive (technological) developments. The constantly changing and dynamic social, economic, and political environment as well as the public opinion require public organizations to deliver effective and efficient services. These trends also apply to Dutch housing associations who are working together with suppliers in the process of property maintenance to increase quality and to reduce costs. Business Process Management (BPM) has the prospect of enhancing the performance of maintenance processes. Previous studies have confirmed a positive relationship between BPM-maturity and Process performance. However, in different contexts this relation is sometimes inconclusive. Especially, it is not yet clear which BPM-maturity dimensions add most value to performance within a specific environment. Therefore, in this research we focus on studying the influence of BPM-maturity on Process performance of maintenance processes at Dutch housing associations. Furthermore, we focus on intra- versus inter-organizational processes. The findings show that an increase in BPM-maturity leads to improved Process performance. This is specifically true within the inter-organizational context.
Healthcare organizations collaborate, share knowledge, and need to be accountable to each other. Therefore, healthcare organizations manage a dynamic information system landscape. Enterprise Architecture (EA) is a management tool for aligning these landscapes to the primary information needs that healthcare organizations have. EA is of value in some environments, but it seems to be not well suited to the dynamics of healthcare. Despite the publication of several systematic literature reviews on EA in healthcare, a systematic literature study comparing EA applicability at various levels of cooperation (intra, inter, and network collaboration) is lacking. Therefore, we posed the following research question: To what extent is EA researched within healthcare organizations in the context of intra, inter and network collaboration? A systematic literature review was used to select 94 scientific publications for evaluation. These studies make explicit the EA elements at three levels of collaboration in the context of healthcare. The findings show that EA is most frequently studied in relation to a single healthcare organization with a wide range of topics. IT governance and EA implementation are the subjects of the majority of EA network level studies (17 out of 94 studies), followed by building/developing EA, EA acceptance, EA issues and root causes, and EA modeling. Although numerous EA frameworks are discussed in studies at the intra- and interorganizational levels, they are rarely referenced in studies at the network level. Additionally, the EA benefits, success factors, and challenges are comparable at high level, but details differ per level. These findings demonstrate that EA is researched within the healthcare sector context. The majority of knowledge on EA is focused on a single healthcare organization, but little is known about EA in a networked healthcare environment. To learn more about how EA might be used in a healthcare network setting, a research agenda has been set up based on the results.
