Dursun Delen

İş dünyasının giderek karmaşıklaşan ve dinamik hale gelen yapısı, farklı disiplinlerden gelen bireylerin aynı örgütsel çatı altında aynı amaçlar doğrultusunda etkin ve verimli çalışmalarını zorunlu hale getirmiştir. Bu sebeple de, işletmenin tüm işlevlerini bütüncül bir bakış açısı ile değerlendirebilecek ve bu hususları faaliyet gösterilen ekosistemin diğer dinamikleri ile uyumlu yönetebilecek bireylere duyulan ihtiyaç artmıştır. Ayrıca, teknoloji alanında yaşanan baş döndürücü gelişmeler rekabetin sahasını genişletmiş ve özellikle üretim, dağıtım, pazarlama ve finans alanlarında entegre bilgi birikimine sahip, yönetsel becerisi yüksek insan kaynağına önemli ölçüde bir talep doğurmuştur.

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Dursun Delen

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Sağlık Analitiği, Karar Destek Sistemleri, Sağlık Analitiği, İş Zekası, İş Analitiği

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Listeleniyor 1 - 10 / 20

Crafting performance-based cryptocurrency mining strategies using a hybrid analytics approach
(Elsevier, 2021) Chlyeh, Dounia; Hacıoğlu, Ümit; Tatoğlu, Ekrem; Yılmaz, Mustafa Kemal; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü
Crafting and executing the best cryptocurrency mining strategy is vital to succeeding in cryptocurrency market investments. This study aims to identify the best cryptocurrency mining strategy based on service providers’ performance for cryptocurrency mining using a hybrid analytics approach, which integrates the Analytic Hierarchy Process (AHP) and Fuzzy-TOPSIS techniques, along with sensitivity analysis. The results show that hosted mining is the overall best cryptocurrency mining strategy, followed by home mining and cloud mining, based on both total cost of operations and cryptocurrency payout criteria. The empirical findings also suggest that the critical features of the highest performing service providers (i.e., hosted mining strategies and cloud mining) were their flexibility of contracts and the superior efficiency in terms of the daily payout. Finally, of the three location alternatives for home mining, Turkey ranks first compared to the U.S. and Europe.
Development of a sustainable corporate social responsibility index for performance evaluation of the energy industry: A hybrid decision-making methodology
(Elsevier, 2023) Hacıoğlu, Ümit; Yılmaz, Mustafa Kemal; Delen, Dursun; Hacıoğlu, Ümit; Yılmaz, Mustafa Kemal; Delen, Dursun; Dinçer, Hasan; Yüksel, Serhat; Hacıoğlu, Ümit; Yılmaz, Mustafa Kemal; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme Bölümü
The ever-increasing pressure from stakeholders and policymakers on energy companies to achieve Sustainable Development Goals (SDGs) and Corporate Social Responsibility (CSR) mission requires them to reinvent their policies and practices. This study aims to examine the performance of alternative business models for the oil and gas industry by employing a hybrid business analytics methodology under a fuzzy environment resulting in a generalizable model named “Sustainable Development Goals-oriented CSR Index.” The proposed methodology employs a hybrid framework that utilizes bipolar Q-rung Orthopair Fuzzy (q-ROF), Multi Stepwise Weight Assessment Ratio Analysis (M-SWARA), and Elimination and Choice Translating Reality (ELECTRE) methods. The findings show that (i) the proposed model is reliable and consistent throughout the similar fuzzy set value ranges, (ii) clean energy is the most important SDG-oriented CSR Index factor for the sustainable energy industry in emerging economies, (iii) drilling is the best alternative energy sourcing for the oil and gas industry, and (iv) clean energy projects have the highest priority for energy investors. The results also highlight that global warming can be managed with effective energy practices for long-term sustainability. Finally, the findings suggest that energy companies should have the essential technological infrastructure and capable workforce to increase investment efficiency.
Business analytics and firm performance: The mediating role of business process performance
(Elsevier, 2019) Aydıner, Arafat Salih; Tatoğlu, Ekrem; Bayraktar, Erkan; Zaim, Selim; Delen, Dursun; Tatoğlu, Ekrem; Zaim, Selim; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü
Due to the rapidly increasing popularity of business analytics (BA), investigation of the antecedents/determinants of the adoption of BA and the subsequent impact of the same to the firm performance has become an important research topic. Drawing on the fundamentals of the resource-based view (RBV), this study proposes a model that examines the effects of the BA adoption on business process performance (BPER) and the mediating role that BPER plays in the relationship between the adoption of BA and firm performance (FP). Based on the data collected from 204 medium- to high-level business executives in various industries, the results of this empirical study indicate that the adoption of BA positively influences BPER. There is also positive relationship between BPER and FP. Finally, the results show that BPER fully mediates the relationship between BA adoption and FP.
Optimizing sustainable industry investment selection: A golden cut-enhanced quantum spherical fuzzy decision-making approach
(Elsevier, 2023) Dinçer, Hasan; Yüksel, Serhat; Sonko, Mariama; Hacıoğlu, Ümit; Yılmaz, Mustafa Kemal; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü
This study aims to rank sustainable industry alternatives in emerging markets based on the directional impact relations of the environmental, social, and governance (ESG) index components for a socially and environmentally conscious investment strategy. To achieve this goal, we employ a golden cut-enhanced quantum spherical fuzzy decision-making approach. Specifically, we first use a quantum spherical fuzzy DEMATEL technique to identify the impact-relation directions and the weights of the ESG criteria set. Second, we employ the extended TOPSIS with the quantum spherical fuzzy sets to rank the industry alternatives concerning their directional ESG performances. The findings show that (i) H20 Emissions, Innovation, Community Investment, Gender Equity, Human Rights, and CSR Strategy are the main influencing factors based on their impact-relations directional scores, (ii) Resource Usage, Product Responsibility, and Shareholders’ Rights are the set of criteria under the influence of remaining ESG, (iii) Innovation is the strongest ESG performance criterion, whereas Human Rights is the weakest, (iv) technology and communication are the best-performing industries based on the directional ESG index performance scores, whereas real estate and basic materials industries are the worst performing. The study provides valuable and actionable insights for companies that aim to make socially responsible investments.
An interactive decision support system for real-time ambulance relocation with priority guidelines
(Elsevier, 2022) Delen, Dursun; Delen, Dursun; Hajiali, Mahdi; Teimoury, Ebrahim; Rabiee, Meysam; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme Bölümü
Changes in demand patterns and unexpected events are the two primary sources of delays in healthcare emergency operations. To mitigate such delays, researchers proposed the movement of idle ambulances between emergency bases as one of the effective ways to improve the areal coverage of future demands. In this study, we have developed a model-driven decision support system that simultaneously seeks to maximize demand coverage while minimizing travel time by optimally relocating emergency response vehicles. The developed mathematical model partitions and prioritizes demand into four categories and continuously updates them over time. Furthermore, it dynamically calculates the number of coverages in different regions based on the current location of idle ambulances. Also, we developed a real-time risk assessment DSS for recommended relocations, which could be utilized as a reference by the EMS user while implementing suggested relocation decisions. A real case study is used to validate the proposed DSS, and its final output is compared to the existing operational policy. The findings show that the average workload added to each ambulance due to relocations has significantly improved the response time and coverage ratio. Compared to the existing operational policy, the developed decision support system decreased the time to respond to calls, which was deemed to be more than to offsets the increase in travel time due to relocation. Furthermore, the system also reduced the total working time of all ambulances by about 9% per shift.
The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review
(Elsevier, 2022) Delen, Dursun; Delen, Dursun; Yalçın, Ahmet Selçuk; Kılıç, Hüseyin Selçuk; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme Bölümü
Business analytics (BA) systems are considered significant investments for enterprises because they have the potential to considerably improve firms’ performance. With the value offered by BA, companies are able to discover the hidden information in the data, improve decision-making processes, and support strategic planning. On the other hand, because there are multiple criteria and multiple alternatives involved in most decision- making situations, multi-criteria decision-making (MCDM) methods play an important role in BA practices. Providing inputs to the components of descriptive or predictive analytics or being used as a decision-making tool for evaluating the alternatives within prescriptive analytics exemplify the roles. Therefore, the use of hidden information discovered by business analytics and the need for utilizing the right MCDM method for optimal decision-making made these two concepts inseparable. In this paper, in order to review the use of MCDM methods in BA, the subject of BA is investigated from a taxonomical perspective (descriptive, predictive, and prescriptive), and its connection with MCDM techniques is revealed. Similarly, MCDM methods are studied using two main categories, multi-attribute decision making (MADM) and multi-objective decision making (MODM) methods. Furthermore, tabular and graphical analyses are also performed within the proposed review meth-odology. To the best of our knowledge, this review is the first attempt that holistically considers the use of MCDM methods in BA.
An explanatory analytics framework for early detection of chronic risk factors in pandemics
(Elsevier, 2022) Delen, Dursun; Delen, Dursun; Davazdahemami, Behrooz; Zolbanin, Hamed M.; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme Bölümü
Timely decision-making in national and global health emergencies such as pandemics is critically important from various aspects. Especially, early identification of risk factors of contagious viral diseases can lead to efficient management of limited healthcare resources and saving lives by prioritizing at-risk patients. In this study, we propose a hybrid artificial intelligence (AI) framework to identify major chronic risk factors of novel, contagious diseases as early as possible at the time of pandemics. The proposed framework combines evolutionary search algorithms with machine learning and the novel explanatory AI (XAI) methods to detect the most critical risk factors, use them to predict patients at high risk of mortality, and analyze the risk factors at the individual level for each high-risk patient. The proposed framework was validated using data from a repository of electronic health records of early COVID-19 patients in the US. A chronological analysis of the chronic risk factors identified using our proposed approach revealed that those factors could have been identified months before they were determined by clinical studies and/or announced by the United States health officials.
Big data analytics capabilities and firm performance: An integrated MCDM approach
(Elsevier, 2020) Yasmin, Mariam; Kılıç, Hüseyin Selçuk; Tatoğlu, Ekrem; Zaim, Selim; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü
This study explores the interdependence of big data analytics (BDA) capabilities and the impact of these capabilities on firm performance using an integrated multicriteria decision-making (MCDM) methodology. Drawing on a rich data set obtained from selected case study firms in Pakistan, three MCDM tools, namely, intuitionistic fuzzy decision-making trial and evolution laboratory (IF-DEMATEL), analytic network process (ANP), and simple additive weighting (SAW), are employed to assess the relative importance of BDA capabilities and the relationship of these capabilities with the firm performance. The results show that BDA capabilities are interdependent, and infrastructure capabilities are the highest-ranked among all, followed by management and human resource capabilities, respectively. The SAW results indicate an association between BDA capabilities and firm performance. Moreover, BDA capabilities are more strongly related to operational performance than to market performance.
Can customer sentiment impact firm value? An integrated text mining approach
(Elsevier, 2022) Delen, Dursun; Delen, Dursun; Eachempati, Prajwal; Srivastava, Praveen Ranjan; Kumar, Ajay; Mu˜noz de Prat, Javier; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme Bölümü
Developing measures to capture customer sentiment and securing a positive customer experience is a strategic necessity to improve firm profitability and shareholder value. The paper considers the relationship between customer satisfaction, earnings, and firm value as these drives change in stock prices, customer, and investor sentiment. The present study investigates the impact of customer sentiment polarity on stock prices based on Indian automobile sector databased such as the Indian Nifty Auto SNE (Maruti Suzuki, Tata Motors, and Eicher). A top-down approach is adopted to construct a financial proxy-based sentiment index completed with sentiment extracted from automobile news and customer reviews. The paper uses a text mining approach to holistically measure customer sentiment’s impact on investor sentiment and stock prices. The study was initially performed at the overall individual stock from the Nifty Auto NSE but focused on the top three passenger vehicle manufacturing companies i.e., Maruti Suzuki, Tata Motors, and Eicher. It was found that the sentiment index was augmented with news and customer reviews allows predicting more accurately NIFTY AUTO stock price movements. This implies that customer sentiment is a major driver of investor sentiment which in turn impacts the stock market and the firm value. Thus, the present study is an integrated approach to holistically measure customer sentiment’s impact on investor sentiment and stock prices.
An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions
(Elsevier, 2022) Delen, Dursun; Delen, Dursun; Davazdahemami, Behrooz; Zolbanin, Hamed M.; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme Bölümü
One of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory- predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner. The proposed framework is showcased in studying emergency department (ED) readmissions of COVID-19 patients using ED visits from a real-world electronic health records database. After an exploratory feature selection phase using genetic algorithm, we develop and train a deep artificial neural network to predict early (i.e., 7-day) readmissions (AUC = 0.883). Lastly, a SHAP model is formulated to estimate additive Shapley values (i.e., importance scores) of the features and to interpret the magnitude and direction of their effects. The findings are mostly in line with those reported by lengthy and expensive clinical trial studies.

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