Yazar "Delen, Dursun" seçeneğine göre listele
Listeleniyor 1 - 20 / 20
Sayfa Başına Sonuç
Sıralama seçenekleri
Yayın A critical analysis of COVID-19 research literature: Text mining approach(Elsevier, 2021) Delen, Dursun; Delen, Dursun; Delen, Dursun; Zengul, Ferhat D.; Zengul, Ayşe G.; Mugavero, Michael J.; Oner, Nurettin; Özaydın, Bünyamin; Delen, Dursun; Willig, James H.; Kennedy, Kierstin C.; Cimino, James; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme BölümüObjective: Among the stakeholders of COVID-19 research, clinicians particularly experience difficulty keeping up with the deluge of SARS-CoV-2 literature while performing their much needed clinical duties. By revealing major topics, this study proposes a text-mining approach as an alternative to navigating large volumes of COVID-19 literature. Materials and methods: We obtained 85,268 references from the NIH COVID-19 Portfolio as of November 21. After the exclusion based on inadequate abstracts, 65,262 articles remained in the final corpus. We utilized natural language processing to curate and generate the term list. We applied topic modeling analyses and multiple correspondence analyses to reveal the major topics and the associations among topics, journal countries, and publication sources. Results: In our text mining analyses of NIH’s COVID-19 Portfolio, we discovered two sets of eleven major research topics by analyzing abstracts and titles of the articles separately. The eleven major areas of COVID-19 research based on abstracts included the following topics: 1) Public Health, 2) Patient Care & Outcomes, 3) Epidemiologic Modeling, 4) Diagnosis and Complications, 5) Mechanism of Disease, 6) Health System Response, 7) Pandemic Control, 8) Protection/Prevention, 9) Mental/Behavioral Health, 10) Detection/Testing, 11) Treatment Options. Further analyses revealed that five (2,3,4,5, and 9) of the eleven abstract-based topics showed a significant correlation (ranked from moderate to weak) with title-based topics. Conclusion: By offering up the more dynamic, scalable, and responsive categorization of published literature, our study provides valuable insights to the stakeholders of COVID-19 research, particularly clinicians.Yayın A probabilistic data analytics methodology based on Bayesian Belief network for predicting and understanding breast cancer survival(Elsevier, 2022) Delen, Dursun; Delen, Dursun; Dağ, Aslı Z.; Akcam, Zümrüt; Kibis, Eyyub; Şimşek, Serhat; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme BölümüUnderstanding breast cancer survival has proven to be a challenging problem for practitioners and researchers. Identifying the factors affecting cancer progression, their interrelationships, and their influence on patients’ long-term survival helps make timely treatment decisions. The current study addresses this problem by proposing a Tree-Augmented Bayesian Belief Network (TAN)-based data analytics methodology comprising of four steps: data acquisition and preprocessing, variable selection via Genetic Algorithm (GA), data balancing with synthetic minority over-sampling and random undersampling methods, and finally the development of the TAN model to determine the probabilistic inter-conditional dependency structure among breast cancer-related variables along with the posterior survival probabilities The proposed model is compared to well-known machine learning models. A what-if analysis has also been conducted to verify the associations among the variables in the TAN model. The relative importance of each variable has been investigated via sensitivity analysis. Finally, a decision support tool is developed to further explore the conditional dependency structure among the cancer-related factors. The results produced by the proposed methodology, namely the patientspecific posterior survival probabilities and the conditional relationships among the variables, can be used by healthcare professionals and physicians to improve the decision-making process in planning and managing breast cancer treatments. Our generic methodology can also accommodate other types of cancer and be applied to manage various medical procedures.Yayın A text-mining based cyber-risk assessment and mitigation framework for critical analysis of online hacker forums(Elsevier, 2022) Delen, Dursun; Delen, Dursun; Biswas, Baidyanath; Mukhopadhyay, Arunabha; Bhattacharjee, Sudip; Kumar, Ajay; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme BölümüOnline hacker communities are meeting spots for aspiring and seasoned cybercriminals where they engage in technical discussions, share exploits and relevant hacking tools to be used in launching cyber-attacks on business organizations. Sometimes, the affected organizations can detect these attacks in advance, with the help of cyberthreat intelligence derived from the explicit and implicit features of hacker communication in these forums. Herein, we proposed a novel text-mining based cyber-risk assessment and mitigation framework, which performs the following critical tasks. (i) Cyber-risk Assessment - to identify hacker expertise (i.e., newbie, beginner, intermediate, and advanced) using explicit and implicit features applying various classification algorithms. Among these features, cybersecurity keywords, sharing of attachments, and sentiments emerged as significant. Further, we found that expert hackers demonstrate leadership in the online forums that eventually serve as communities of practice. Consequently, novice hackers gradually develop their cyber-attack skills through prolonged observations, interactions, and external influences in this social learning process. (ii) Cyber-risk mitigation - computes financial impact for every {hacker expertise, attack-type} combination, and then by ranking them on a {likelihood, impact} decision-matrix to prioritize mitigation strategies in affected organizations. Through these novel recommendations, our framework can guide managers to decide on appropriate cybersecurity controls using an {expected loss, probability, attack-type, hacker expertise} metric against financial losses due to cyber-attacks.Yayın An analytic approach to assessing organizational citizenship behavior(Elsevier, 2017) Tatoğlu, Ekrem; Zaim, Selim; Delen, Dursun; Tatoğlu, Ekrem; Zaim, Selim; Delen, Dursun; Arda, Özlem Ayaz; Delen, Dursun; Tatoğlu, Ekrem; Zaim, Selim; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme BölümüThis study examines the organizational citizenship behavior (OCB) of employees by designing and developing an analytic network process (ANP) methodology. The viability of the proposed methodology is demonstrated via the sales representatives of Beko, a brand name controlled by Koç Group. We first develop a conceptual framework based on qualitative research methods – in-depth interviews and focus group sessions. We employ the principles of ANP methodology to examine and discover the inter-relationships among the OCBs. This process results in a descriptive model that encapsulates the findings from both qualitative and analytics methods. Necessity, altruism, departmental, compliance, and independence are the underlying dimensions of OCBs found to be the most influential/important. The key novelty of this study resides in designing and developing a prescriptive analytics (i.e. ANP) methodology to evaluate the OCBs, which is rare in the area of organizational behavior (a managerial field of study that have been dominated by traditional statistical methods), and thus serves as a useful contribution/augmentation to the business/managerial research methods, and also extends the reach/coverage of analytics-based decision support systems research and practice into a new direction.Yayın An ANP and fuzzy TOPSIS-based SWOT analysis for Turkey's energy planning(Elsevier, 2018) Zaim, Selim; Demirel, Ömer Fahrettin; Delen, Dursun; Ervural, Beyzanur Çayır; Aydın, Zeynep; Yönetim Bilimleri Fakültesi, İşletme BölümüEnergy planning involves a perpetual process of reevaluating alternative energy strategies. Authorities responsible for energy planning and management have to adjust their strategies according to new and improved alternative solutions based on the sustainability criteria. In this study, we propose an integrated hybrid methodology for the analysis of Turkey’s energy sector using Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, Analytic Network Process (ANP) process, and weighted fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) to formulate and holistically analyze the energy strategy alternatives and priorities. The methodology proposed in this study allowed identifying the relevant criteria and sub-criteria using a SWOT analysis. Then, ANP approach, which is one of the popular multi-criteria decision making (MCDM) methods, is employed to determine the weights of each SWOT factors and sub-factors. Finally, fuzzy TOPSIS methodology is conducted to prioritize alternative energy strategies. We discuss the obtained results for the development of long-range alternative energy strategies. The results showed that turning the country into an energy hub and an energy terminal by effectively using the geo-strategic position within the framework of the regional cooperation is the most important priority. On the other hand, using the nuclear energy technologies within the energy supply strategies found to be the least favored priority.Yayın 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 BölümüTimely 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.Yayın 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 BölümüOne 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.Yayın An integrated approach for lean production using simulation and data envelopment analysis(Springer, 2021) Zaim, Selim; Delen, Dursun; Zaim, Selim; Delen, Dursun; Buyuksaatci Kiris, Sinem; Eryarsoy, Enes; Zaim, Selim; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme BölümüAccording to the extant literature, improving the leanness of a production system boosts a company’s productivity and competitiveness. However, such an endeavor usually involves managing multiple, potentially conflicting objectives. This study proposes a framework that analyzes lean production methods using simulation and data envelopment analysis (DEA) to accommodate the underlying multi-objective decision-making problem. The proposed framework can help identify the most efficient solution alternative by (i) considering the most common lean production methods for assembly line balancing, such as single minute exchange of dies (SMED) and multi-machine set-up reduction (MMSUR), (ii) creating and simulating various alternative assembly line configuration options via discrete-event simulation modeling, and (iii) formulating and applying DEA to identify the best alternative assembly system configuration for the multi-objective decision making. In this study, we demonstrate the viability and superiority of the proposed framework with an application case on an automotive spare parts production system. The results show that the suggested framework substantially improves the existing system by increasing efficiency while concurrently decreasing work-in-process (WIP).Yayın 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 BölümüChanges 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.Yayın Assessing the supply chain performance: A causal analysis(Springer US, 2019) Tatoğlu, Ekrem; Zaim, Selim; Delen, Dursun; Tatoğlu, Ekrem; Zaim, Selim; Delen, Dursun; Bayraktar, Erkan; Sarı, Kazım; Tatoğlu, Ekrem; Zaim, Selim; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme BölümüMeasuring the performance-related factors of a unit within a supply-chain is a challenging problem, mainly because of the complex interactions among the members governed by the supply chain strategy employed. Synergistic use of discrete-event simulation and structural equation modeling allows researchers and practitioners to analyze causal relationships between order-fulfillment characteristics of a supply-chain and retailers’ performance metrics. In this study, we model, simulate, and analyze a two-level supply-chain with seasonal linear demand, and using the information therein, develop a causal model to measure the links/relationships among the order-fulfillment factors and the retailer’s performance. According to the findings, of all the order-fulfillment characteristics of a supply-chain, the forecast inaccuracy was found to be the most important in mitigating the bullwhip effect. Concerning the total inventory cost and fill-rate as performance indicators of retailers, the desired service level had the highest priority, followed by the lead-time and forecast inaccuracy, respectively. To reduce the total inventory cost, the bullwhip effect seems to have the lowest priority for the retailers, as it does not appear to have a significant impact on the fill rate. Although seasonality (to some extent) influences the retailer’s performance, it does not seem to have a significant impact on the ranking of the factors affecting retailers’ supply-chain performance; except for the case where the backorder cost is overestimated.Yayın 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 BölümüThis 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.Yayın Business analytics adoption and technological intensity: An efficiency analysis(Springer, 2023) Tatoğlu, Ekrem; Delen, Dursun; Bayraktar, Erkan; Aydıner, Arafat Salih; Yönetim Bilimleri Fakültesi, İşletme BölümüDespite the overwhelming popularity of business analytics (BA) as an evidence-based decision support mechanism, the impact of its adoption on organizational performance has received scant attention from the research community. This study aims to unfold the adoption efficiencies of BA and its applications by proposing a data envelopment analysis (DEA) methodology to holistically assess the underlying factors with respect to the level of achievement regarding organizational performance, operational performance, and financial performance. Furthermore, the study unveils the firm-level and sectoral-level discrepancies in BA adoption efficiency in different industry settings. Relying on survey data obtained from 204 executives in various industries, this study provides empirical support for the cross-industry differences in BA adoption efficiencies. The results show that the firms in low-tech industries seem to achieve the highest efficiency from adopting BA regarding its influence on firm performance.Yayın 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 BölümüDue 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.Yayın 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 BölümüDeveloping 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.Yayın 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 BölümüCrafting 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.Yayın Deep-learning-based short-term electricity load forecasting: A real case application(Elsevier, 2022) Delen, Dursun; Delen, Dursun; Yazıcı, İbrahim; Beyca, Ömer Faruk; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme BölümüThe rising popularity of deep learning can largely be attributed to the big data phenomenon, the surge in the development of new and novel deep neural network architectures, and the advent of powerful computational innovations. However, the application of deep neural networks is rare for time series problems when compared to other application areas. Short-term load forecasting, a typical and difficult time series problem, is considered as the application domain in this study. One-dimensional Convolutional Neural Networks (CNNs) use is rare in time series forecasting problems when compared to Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU), and the efficiency of CNN has been rather remarkable for pattern extraction. Hence, a new method that uses one-dimensional CNNs based on Video Pixel Networks (VPNs) in this study, in which the gating mechanism of Multiplicative Units of the VPNs is modified in some sense, for short term load forecasting. Specifically, the proposed one-dimensional CNNs, LSTM and GRU variants are applied to real-world electricity load data for 1-hour-ahead and 24-hour-ahead prediction tasks which they are the main concerns for the electricity provider firms for short term load forecasting. Statistical tests were conducted to spot the significance of the performance differences in analyses for which ten ensemble predictions of each method were experimented. According to the results of the comparative analyses, the proposed one-dimensional CNN model yielded the best result in total with 2.21% mean absolute percentage error for 24-h ahead predicitions. On the other hand, not a noteworthy difference between the methods was spotted even the proposed one-dimensional CNN method yielded the best results with approximately 1% mean absolute percentage error for 1-h ahead predictions.Yayın Developing a hybrid analytics approach to measure the efficiency of deposit banks(Elsevier, 2019) Dinçer, Hasan; Hacıoğlu, Ümit; Tatoğlu, Ekrem; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme BölümüThis study aims at analyzing the efficiency of deposit banks using contemporary analytics-based decision-making techniques within a fuzzy environment. Specifically, a hybrid analytic model drawing on a fuzzy analytical network process and data envelopment analysis was developed and applied to the assessment of Turkish deposit banks quoted on Borsa Istanbul. The findings revealed that; (i) the efficiency results for banking activity vary for competitiveness and for the adoption of new technologies before and after the financial recession; (ii) the majority of deposit banks operating primarily with non-interest based factors found to be less-efficient; (iii) the ownership and capital structure of banks do not significantly contribute to their banking performance, as they were technically inefficient during the same period; and (iv) the inputs of the banking activities could be reduced while a constant level of output is maintained by adopting and properly using the most efficient technology to boost the technical efficiency.Yayın Development of a sustainable corporate social responsibility index for performance evaluation of the energy industry: A hybrid decision-making methodology(Elsevier, 2023) Dinçer, Hasan; Yüksel, Serhat; Hacıoğlu, Ümit; Yılmaz, Mustafa Kemal; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme BölümüThe 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.Yayın 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 BölümüThis 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.Yayın The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review(Elsevier, 2022) Delen, Dursun; Yalçın, Ahmet Selçuk; Kılıç, Hüseyin Selçuk; Yönetim Bilimleri Fakültesi, İşletme Bölümü; Yönetim Bilimleri Fakültesi, İşletme BölümüBusiness 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.
