Yazar "Pisirgen, Ali" seçeneğine göre listele

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    A clustering approach for classifying scholars based on publication performance using bibliometric data
    (Cairo Univ, Fac Computers & Information, 2024) Pisirgen, Ali; Peker, Serhat
    This study introduces a clustering framework that effectively evaluate scholars' publication performance by utilizing cluster analysis and bibliometric data. In order to capture the various aspects of scholars' publication characteristics, our proposed framework integrates four distinct features, namely APIR which represents Academic age, Productivity, Impact, and Recency. The proposed framework is implemented in a case study focusing on Turkish academia, utilizing a dataset comprising 13,070 scholars from 24 diverse academic divisions across 30 Turkish universities. Cluster analysis yields seven groups of scholars with diverse publishing characteristic based on APIR features and these obtained clusters are profiled as freshmen, stagnant impactful mids, rising stars, stagnant and non-prolific juniors, stagnant impactful seniors, super stars, currently active and prolific seniors. To enhance the cluster analysis results, additional cross analysis is performed based on scholars' certain demographics such as affiliating institutes, divisions, academic titles, and PhD qualification. Scholars in clusters with superior publication performance are often affiliated with top-ranked universities and have academic backgrounds in the fields of Medicine, Engineering, and Natural Sciences. Practically, generated scholar segments and analysis based on these scholar profiles can serve as useful input for policy makers during having decisions about recruitment, promotion, awarding and allocation of funds.
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    Business analytics in customer lifetime value: an overview analysis
    (Wiley Periodicals, Inc, 2025) Doğan, Onur; Hızıroğlu, Abdulkadir; Pisirgen, Ali; Seymen, Omer Faruk
    In customer-oriented systems, customer lifetime value (CLV) has been of significant importance for academia and marketing practitioners, especially within the scope of analytical modeling. CLV is a critical approach to managing and organizing a company's profitability. With the vast availability of consumer data, business analytics (BA) tools and approaches, alongside CLV models, have been applied to gain deeper insights into customer behaviors and decision-making processes. Despite the recognized importance of CLV, there is a noticeable gap in comprehensive analyses and reviews of BA techniques applied to CLV. This study aims to fill this gap by conducting a thorough survey of the state-of-the-art investigations on CLV models integrated with BA approaches, thereby contributing to a research agenda in this field. The review methodology consists of three main steps: identification of relevant studies, creating a coding plan, and ensuring coding reliability. First, relevant studies were identified using predefined keywords. Next, a coding plan-one of the study's significant contributions-was developed to evaluate these studies comprehensively. Finally, the coding plan's reliability was tested by three experts before being applied to the selected studies. Additionally, specific evaluation criteria in the coding plan were implemented to introduce new insights. This study presents exciting and valuable results from various perspectives, providing a crucial reference for academic researchers and marketing practitioners interested in the intersection of BA and CLV.
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    Examining hotel characteristics and facilities influencing customer satisfaction using decision tree analysis
    (Emerald Group Publishing Ltd, 2024) Pisirgen, Ali; Erdoğan, Ali Mert; Peker, Serhat
    PurposeThis study aims to identify the key hotel characteristics and facilities that significantly influence customer satisfaction based on Google review scores. By applying decision tree analysis, the research seeks to determine which aspects, such as service quality, hotel facilities and location, play pivotal roles in shaping customer experiences. The objective is to provide professional with practical recommendations to improve service quality and cultivate enduring customer loyalty.Design/methodology/approachThe research used a data set collected from Hotels.com, featuring various characteristics of 802 hotels in Izmir Province. Decision tree analysis was conducted using Classification and Regression Tree algorithm to explore the relationship between hotel characteristics and facilities with customer satisfaction.FindingsThe analysis revealed that the number of rooms is the primary factor influencing hotel ratings, with proximity to the airport and hotel classification also being significant. Additional factors such as public transportation distance and laundry services were important, while facilities such as ATMs, beach access and spas showed no significant impact on customer satisfaction. These findings emphasize the importance of core facilities and accessibility.Originality/valueThis study contributes to the literature by offering a novel approach, using decision tree analysis to assess hotel customer satisfaction with structured data. It provides practical implications for hotel managers, enabling them to make data-driven improvements to achieve customer satisfaction. The integration rules created by the decision tree model into hotel management systems can enhance operational efficiency and competitive advantage in the hospitality industry.
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    Health Big Data Modelling and Analytics
    (CRC Press, 2024) Hiziroglu, Abdulkadir; Pisirgen, Ali; Codal, Keziban Seckin
    This chapter delves into the multifaceted applications of health big data modelling and analytics across distinct categories, offering valuable insights into the transformative landscape of healthcare systems. The discussion encompasses health big data, health decision-making development, business process management for more efficient hospital operations, and analytics focused on medical diagnostics and disease/treatment monitoring. Health data modelling involves creating structured frameworks for organizing and managing healthcare data. These frameworks are crucial for efficient data storage, retrieval, and analysis within healthcare systems. Moreover, the concept of health big data extends beyond personal health records and includes vast amounts of information produced by the health sector. It explores opportunities and challenges associated with health big data analytics, highlighting its potential benefits in improving health services, early disease detection, and personalized medicine. Further exploration into artificial intelligence for health data modelling and analytics emphasizes the transformative impact of machine learning in healthcare, specifically in disease diagnosis, outcome prediction, and personalized treatment strategies. The selection of an appropriate AI model is referred crucial, considering factors such as accuracy, interpretability, scalability, and ethical considerations. Transparent and interpretable models, exemplified by decision trees, are recommended to foster trust among healthcare professionals. The chapter concludes by underscoring the paramount importance of addressing ethical and legal considerations, along with domain-specific requirements, to ensure the responsible and effective application of machine learning in healthcare, ultimately contributing to optimal patient care and the evolution of healthcare systems. © 2025 Mustafa Berktas, Abdulkadir Hiziroglu, Ahmet Emin Erbaycu, Orhan Er and Sezer Bozkus Kahyaoglu.