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    An In-Depth Case Study of Volkswagen's AI Integration
    (CEUR-WS, 2024) Erdoğan, Ali Mert; Hiziroglu, Ourania Areta; Hiziroglu, Abdulkadir
    As Artificial Intelligence (AI) technologies have become increasingly integral to business operations and many firms aspire to generate business value with that, understanding the factors that facilitate or hinder successful implementation is crucial for organizations across industries. Using Volkswagen Group (VW) as a case study, the goal of this study is to comprehensively examine the AI implementations in a holistic manner, including enablers and inhibitors, utilization in terms of automation and augmentation, process-level impacts, and broader firm-level outcomes. This work not only contributes to the understanding of AI adoption within a major automotive player, but also serves as a resource for organizations by navigating through the complexities of AI implementation, offering practical insights and lessons learned from the case. © 2023 Copyright for this paper by its authors.
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    Artificial Intelligence-based Health Data
    (CRC Press, 2024) Akarsu, Kamil; Hiziroglu, Ourania Areta; Er, Orhan
    This book chapter provides a comprehensive examination of the importance of health data, the use of health data with artificial intelligence, and synthetic data generation. First, it focuses on the importance of health data, privacy and security, and the combined use of artificial intelligence with health data. Secondly, it focuses on the generation and purpose of synthetic data. Different methods for generating synthetic data, such as parametric modelling, Generative Adversarial Networks (GAN), and Variational Autoencoders (VAE), are discussed, and examples of applications and uses of synthetic data are provided. The benefits and challenges of synthetic data generation are also discussed. The chapter then discusses topics related to the application of artificial intelligence to health data, examining disease diagnosis and treatment recommendations, as well as applications of artificial intelligence in the field of personalized medicine. It then examines privacy and security legislation, ethical principles for the use and sharing of synthetic data, and different approaches to applications in the private and public sectors. Finally, it looks to the future, providing information on emerging technologies and applications of artificial intelligence-based health data analysis and management, and the future of anonymization and synthetic data generation techniques. This section provides a comprehensive view of the future potential and role of health data and artificial intelligence. © 2025 Mustafa Berktas, Abdulkadir Hiziroglu, Ahmet Emin Erbaycu, Orhan Er and Sezer Bozkus Kahyaoglu.
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    Digitalization for enhancing reading habits: the improved hybrid book recommendation system with genre-oriented profiles
    (Emerald Group Publishing Ltd, 2024) Dogan, Onur; Yalcin, Emre; Hiziroglu, Ourania Areta
    PurposeReading habit plays a pivotal role in individuals' personal and academic growth, making it essential to encourage among campus users. University libraries serve as valuable platforms to promote reading by providing access to a diverse range of books and resources. Recommending books through personalized systems not only helps campus users discover new materials but also enhances their engagement and satisfaction with the library's offerings, contributing to a holistic learning experience.Design/methodology/approachThis study presents a web-based solution, the Web-Based Hybrid Intelligent Book Recommender System (W_HybridBook), as a solution that addresses challenges like cold start issues and limited scalability by factoring in user preferences and item similarities in generating book recommendations. The paper improves the traditional hybrid system using Genre-Oriented Profiles (GOPs) instead of original rating profiles of users when determining similarities between individuals. Consumption-based genre profiles (W_HybridBook-CBP) are created by assessing whether an item has received any ratings in the dataset, and vote-based genre profiles (W_HybridBook-VBP) are generated by considering the genre categories based on the magnitude of the user's rating.FindingsThe comparative results indicated that users are quite satisfied with the recommendations generated by W\_HybridBook-VBP profiling, with an average rating of 4.0633 and a precision value of 0.7988. W\_HybridBook-VBP is also the fastest way with respect to the algorithm and recommendation run time.Originality/valueThe proposed W\_HybridBook has been then enhanced by adopting two user profiling strategies to boost the similarity calculation process in the recommendation generation phase. This system provides ranking-based recommendations by mainly integrating well-known collaborative and content-based filtering strategies. A dataset has been collected by considering the preferences of both users and academics at Izmir Bakircay University, which is one of the universities with the highest number of books per student. More importantly, this dataset has been released and become publicly available for future research in the recommender system field.
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    Implementation of Artificial Intelligence for the Healthcare Supply Chain: Prospects and Challenges
    (CRC Press, 2024) Hiziroglu, Ourania Areta
    The healthcare industry is a rapidly growing one that relies on an intricate and vital supply chain that encompasses the acquisition, allocation, and transportation of medical goods and services to patients. However, the healthcare supply chain can be complex and inefficient, as seen during the Covid-19 pandemic, and it may also encounter several obstacles, including unpredictable demand, inventory control, ensuring quality, and reducing costs. Artificial intelligence (AI) is a very promising technology that has the potential to enhance the efficiency, efficacy, and resilience of the health supply chain. Artificial intelligence (AI) has the capability to provide insights based on data, automate decision-making processes, and enhance the efficiency of workflows in various supply chain tasks. This study examines the applications of artificial intelligence (AI) in the health supply chain, while it also explores the advantages and difficulties associated with integrating AI into the health supply chain, along with the future research prospects. © 2025 Mustafa Berktas, Abdulkadir Hiziroglu, Ahmet Emin Erbaycu, Orhan Er and Sezer Bozkus Kahyaoglu.
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    Sustainability Performance Through a Business Process Mining Based Conceptual Framework Integrating GRI Metrics
    (Springer Science and Business Media Deutschland GmbH, 2025) Hiziroglu, Ourania Areta; Dogan, Onur
    In the global pursuit of sustainable business practices, organizations face the challenge of effectively measuring and enhancing their sustainability performance. Process mining has emerged as a pivotal tool in this endeavour, offering data-driven insights that enable the analysis and visualization of operational processes. This approach uncovers inefficiencies, bottlenecks, and deviations from intended workflows, facilitating the identification of key sustainability metrics such as resource utilization, energy consumption, and waste generation. However, despite the increasing pressure to adopt sustainable practices, many sectors lack comprehensive methods to assess and optimize sustainability across complex operational processes. This study addresses this gap by proposing a novel conceptual framework that leverages business process mining techniques to measure and improve sustainability performance, specifically incorporating Global Reporting Initiative (GRI) metrics. Our framework integrates process mining with GRI sustainability indicators to identify inefficiencies, bottlenecks, and improvement opportunities throughout the value chain. We present a detailed model that maps GRI metrics to specific process steps, enabling a standardized yet flexible approach to sustainability performance measurement. The proposed framework demonstrates the potential of process mining as a powerful tool for driving sustainable practices, offering a new approach to tackling pressing environmental challenges while aligning with globally recognized reporting standards. This framework provides a continuous monitoring and improvement methodology for organizations to improve their sustainability performance, identify improvement opportunities, and provide transparent reporting. It bridges the gap between high-level sustainability reporting and operational processes, promoting a culture of continuous improvement across diverse industries and operational environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
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    The Role of Digital Transformation for SMEs During a Health Crisis: Lessons Learnt from Covid-19 Epidemic
    (CEUR-WS, 2024) Hiziroglu, Ourania Areta; Aydin, Ali Emre; Van Isacker, Karel; Milis, George; Sirashki, Hristo; Tsoris, Panayiotis
    The global outbreak of COVID-19 posed significant challenges for small and medium-sized enterprises (SMEs) across the globe, compelling them to swiftly adapt to ensure business continuity and resilience. Considering this issue, this research investigates the role of digital transformation in enabling small and medium-sized enterprises (SMEs) to navigate the challenges posed by the COVID-19 pandemic and maintain business continuity. Through a qualitative study involving 50 case studies of SMEs, primarily micro-enterprises, from Bulgaria, Belgium, Cyprus, Greece, and Turkey, the findings reveal that digital transformation strategies such as increased ICT usage, new distribution channels, remote work implementation, and product diversification were crucial for SME resilience during the crisis. The study highlights the significance of digital transformation in crisis management and provides practical recommendations for SMEs and policymakers to enhance crisis preparedness through continuous digital transformation efforts. © 2023 Copyright for this paper by its authors.