Browsing by Author "Sariyer, Gorkem"

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Citation - WoS: 11
Citation - Scopus: 16
A hybrid Bayesian approach for assessment of industry 4.0 technologies towards achieving decarbonization in manufacturing industry
(Elsevier Ltd, 2024-04) Devesh Kumar; Gunjan Soni; Fauzia Jabeen; Neeraj Kumar Tiwari; Gorkem Sariyer; Bharti Ramtiyal; Ramtiyal, Bharti; Jabeen, Fauzia; Soni, Gunjan; Kumar Tiwari, Neeraj; Sariyer, Gorkem; Kumar, Devesh; Tiwari, Neeraj Kumar
Since the 1st Industrial Revolution the Earth's atmosphere has warmed due to human activities like deforestation burning fossil fuels for energy generation and livestock raising. Without preventative measures the Earth's atmosphere would warm by 2 °C before the next Industrial Revolution. Thus it has become crucial to move toward a low-carbon economy. Reaching carbon neutrality means cutting our carbon footprint to zero. Innovative research methods and technologies can play a significant role in supporting the economy in its carbon reduction efforts. Industry 4.0 (I4.0) technologies hold great potential for decarbonizing the economy. However there is a need to explore and utilize this potential effectively. This study aims to address this by developing a methodology that identifies relevant attributes and critical measures from existing literature mapping them with I4.0 technologies. Using a MCDM approach each measure is prioritized based on importance. To better understand the interrelationships between these attributes and I4.0 technologies the Bayesian Network (BN) method is employed. This approach enables the exploration of dependencies and influences among variables. By implementing this four-stage strategy economies can make informed decisions and prioritize actions contributing to carbon neutrality while leveraging the benefits of I4.0 technologies. This approach offers a comprehensive framework for guiding economies on their path towards carbon neutrality considering the potential of I4.0 technologies and the importance of various attributes identified through literature. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 20
Citation - Scopus: 26
An analysis of Emergency Medical Services demand: Time of day- day of the week- and location in the city
(ELSEVIER, 2017-06) Gorkem Sariyer; Mustafa Gokalp Ataman; Serhat Akay; Turhan Sofuoglu; Zeynep Sofuoglu; Ataman, Mustafa Gokalp; Sariyer, Gorkem; Sofuoglu, Turhan; Akay, Serhat; Sofuoglu, Zeynep
Objective: Effective planning of Emergency Medical Services (EMS) which is highly dependent on the analysis of past data trends is important in reducing response time. Thus we aimed to analyze demand for these services based on time and location trends to inform planning for an effective EMS. Materials and methods: Data for this retrospective study were obtained from the Izmir EMS 112 system. All calls reaching these services during first six months of 2013 were descriptively analyzed based on time and location trends as a heat-map form. Results: The analyses showed that demand for EMS varied within different time periods of day and according to day of the week. For the night period demand was higher at the weekend compared to weekdays whereas for daytime hours demand was higher during the week. For weekdays a statistically significant relation was observed between the call distribution of morning and evening periods. It was also observed that the percentage of demand changed according to location. Among 30 locations the five most frequent destinations for ambulances which are also correlated with high population densities accounted for 55.66% of the total. Conclusion: The results of this study shed valuable light on the areas of call center planning and optimal ambulance locations of Izmir which can also be served as an archetype for other cities. Copyright (C) 2016 The Emergency Medicine Association of Turkey. Production and hosting by Elsevier B.V. on behalf of the Owner. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Citation - WoS: 10
Citation - Scopus: 13
Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations
(SPRINGER, 2022-09-15) Gorkem Sariyer; Mustafa Gokalp Ataman; Sachin Kumar Mangla; Yigit Kazancoglu; Manoj Dora; Ataman, Mustafa Gokalp; Dora, Manoj; Sariyer, Gorkem; Mangla, Sachin Kumar; Kazancoglu, Yigit
Grounded in dynamic capabilities this study mainly aims to model emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics (BDA) technologies. Since government may impose some restrictions and prohibitions in coping with emergencies to protect the functioning of EDs it also aims to investigate how such policies affect ED operations. The proposed model is designed by collecting big data from multiple sources and implementing BDA to transform it into action for providing efficient responses to emergencies. The model is validated in modeling the daily number of patients the average daily length of stay (LOS) and daily numbers of laboratory tests and radiologic imaging tests ordered. It is applied in a case study representing a large-scale ED. The data set covers a seven-month period which collectively means the periods before COVID-19 and during COVID-19 and includes data from 238152 patients. Comparing statistics on daily patient volumes average LOS and resource usage both before and during the COVID-19 pandemic we found that patient characteristics and demographics changed in COVID-19. While 18.92% and 27.22% of the patients required laboratory and radiologic imaging tests before-COVID-19 study period these percentages were increased to 31.52% and 39.46% during-COVID-19 study period. By analyzing the effects of policy-based variables in the model we concluded that policies might cause sharp decreases in patient volumes. While the total number of patients arriving before-COVID-19 was 158347 it decreased to 79805 during-COVID-19. On the other hand while the average daily LOS was 117.53 min before-COVID-19 this value was calculated to be 16503 min during-COVID-19 study period. We finally showed that the model had a prediction accuracy of between 80 to 95%. While proposing an efficient model for sustainable operations management in EDs for dynamically changing environments caused by emergencies it empirically investigates the impact of different policies on ED operations.
Citation - WoS: 17
Citation - Scopus: 22
Clustering of firms based on environmental social and governance ratings: Evidence from BIST sustainability index
(Borsa Istanbul Anonim Sirketi, 2022-12) Gorkem Sariyer; Dilvin Taşkın; Taskin, Dilvin; Sariyer, Gorkem
In this paper companies listed on the Borsa Istanbul (BIST) Sustainability Index are analyzed by performing a cluster analysis based on their environmental social and governance (ESG) scores. The results prove that firms with higher ESG ratings do not necessarily perform well in all ESG aspects. The outcomes of the cluster analysis reveal that firms with higher environmental and social scores are the cluster with the most prominent firms in terms of size but with low profitability. However the group that scored poorly in environmental and social practices but the highest governance pillar was the highest performing in terms of the return on assets. This paper highlights the significance of forming clusters and linking sustainability practices with performance characteristics. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 7
Citation - Scopus: 7
Data-driven decision making for modelling covid-19 and its implications: A cross-country study
(Elsevier Inc., 2023-12) Gorkem Sariyer; Sachin Kumar Kumar Mangla; Yigit Kazancoglu; Vranda Jain; Mustafa Gökalp Ataman; Ataman, Mustafa Gokalp; Sariyer, Gorkem; Jain, Vranda; Mangla, Sachin Kumar; Kazancoglu, Yigit
Grounded in big data analytics capabilities this study aims to model the COVID-19 spread globally by considering various factors such as demographic cultural health system economic technological and policy-based. Classified values on each country's case death and recovery numbers (per 1000000 population) were used to represent COVID-19 spread. Data sets also included 29 input variables for the corresponding six factors containing data from 159 countries. The proposed model used a Multilayer Perceptron algorithm. The results show that each of the pre-mentioned factors significantly affects disease spread. Urban population median age life expectancy numbers of medical doctors and nursing personnel current health expenditure as a % of GDP international health regulations capacity score continent literacy rate governmental response stringency index testing policy internet usage % human development index and GDP per capita were identified as significant. Taking early measures and adopting open public testing policies were recommended to policymakers in fighting pandemic diseases since the created scenarios on policy-based factors revealed their importance. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 8
Citation - Scopus: 12
Do past ESG scores efficiently predict future ESG performance?
(ELSEVIER, 2025-02) Dilvin Taskin; Gorkem Sariyer; Ece Acar; Efe Caglar Cagli; Taskin, Dilvin; Sariyer, Gorkem; Acar, Ece; Cagli, Efe Caglar
Given the effects of Environmental Social and Governance (ESG) scores on financial performance and stock returns the prediction of future ESG scores is highly crucial. ESG scores are calculated using an enormous number of variables related to the sustainability practices of firms, thus it is impractical for investors to come up with predictions of ESG performance. This paper aims to fill this gap by using only the past score-based and rating-based ESG performance as the determinant of future ESG performance using four machine learning-based algorithms, decision tree (DT) random-forest (RF) k-nearest neighbor (KNN) and logistic regression (LR). The proposed model is validated in BIST sustainability index companies. The results suggest that past ESG grade-based and numerical scores can be used as a determinant of future ESG performance. The results prove that a simple indicator could serve to predict future ESG scores rather than complex data alternatives. Using data from BIST sustainability index companies in Turkey the findings demonstrate that past ESG grades and scores are reliable predictors of future ESG performance offering a simple yet effective alternative to complex data-driven methods. This study not only contributes to advancing sustainable finance practices but also provides practical tools for emerging markets like Turkey to align corporate strategies with global sustainability standards. The methodological contributions also have broader relevance for international financial markets.
Citation - WoS: 5
Citation - Scopus: 8
Does ambulance utilization differ between urban and rural regions: a study of 112 services in a populated city- Izmir
(SPRINGER HEIDELBERG, 2017-04-24) Gorkem Sariyer; M. Gokalp Ataman; Turhan Sofuoglu; Zeynep Sofuoglu; Ataman, M. Gokalp; Sariyer, Gorkem; Sofuoglu, Turhan; Sofuoglu, Zeynep
Objective Emergency Medical Services (EMS) play an important role in health care systems especially when well planned and well managed. The goal of this research was to characterize ambulance utilization rates and investigate associated factors. Such an analysis could make a contribution to operational planning of these services. Materials and methods The data for this study were taken from the Izmir emergency ambulance service known as the 112 service because of its call number. Total emergency demand made during 2013 was analyzed and the data were categorized according to four sub-categories: gender age rural-urban and reason for the call. For each category an analysis was made in terms of the absolute number of calls and a relative measure. Hypothesis testing and correlation analysis were used to investigate the differences between the demand for each category and to compare demand across categories. Results Although demand rates from males and females were very similar a significant difference was observed in the daily utilization of these services by gender. The absolute number of calls from rural regions was less than for urban regions but the rural regions had a higher proportion of calls (i.e. calls per 1000 people). Similarly the absolute number of calls generated by the elderly was less than that generated by the young but the elderly had a higher value in terms of relative measures. A medical condition was the most frequent reason for calls. A significant and positive relation was observed between male-female and elderly-young citizens and there was a significant but negative relation between rural-urban demand. Conclusion This study confirms that gender age and rural-urban distinctions are major factors that affect demand for these services and should therefore to be taken into consideration in operations management. It also highlights the need for a specific focus on rural regions and elderly citizens.
Effective service design in strategic customer setting
(American Scientific Publishers order@aspbs.com, 2015-12-01) Gorkem Sariyer; Sariyer, Gorkem
This paper theoretically analyzes interactive decision making in the service sector in order to evaluate and interpret the optimal actions of the actors involved. The actors of this sector are the strategic customers and the service providers. The decisions of these actors are interactive since they affect each other. The service provider decides on the service design service rate and the price whereas the strategic customer decides whether to receive the service or not. The optimal actions for the service provider and the strategic customer are respectively the ones which maximize their profit and utility functions. Both the short term and long term decisions of the service provider are analyzed. While price is the main decision parameter on both service rate is the decision parameter on only the long term. The analyses emphasize the value of wait time in strategic customers' decision-making which will be the service provider's main criteria for the design of the system. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 11
Citation - Scopus: 10
Factors Relating to Decision Delay in the Emergency Department: Effects of Diagnostic Tests and Consultations
(Dove Medical Press Ltd, 2023-04) Mustafa Gökalp Ataman; Gorkem Sariyer; Caner Saǧlam; Arif Karagöz; Erden Erol Ünlüer; Ataman, Mustafa Gokalp; Sariyer, Gorkem; Karagoz, Arif; Unluer, Erden Erol; Saglam, Caner
Purpose: The purpose of this study is to investigate the factors increasing waiting time (WT) and length of stay (LOS) in patients which may cause delays in decision-making in the emergency departments (ED). Patients and Methods: Patients who arrived at a training hospital in the central region of Izmir City Turkey during the first quarter of 2020 were retrospectively analyzed. WT and LOS were the outcome variables of the study and gender age arrival type triage level determined based on the clinical acuity diagnosis encoded based on International Classification of Diseases-10 (ICD-10) the existence of diagnostic tests or consultation status were the identified factors. The significance of the differences in WT and LOS values based on each level of these factors was analyzed using independent sample t-tests and ANOVA. Results: While patients for which no diagnostic testing or consultation was requested had a significantly higher WT in EDs their LOS values were substantially lower than those for which at least one diagnostic test or consultation was ordered (p≤0.001). Besides elderly and red zone patients and those who arrived by ambulance had significantly lower WT and higher LOS values than other levels for all groups of patients for which laboratory-type or imaging-type diagnostic test or consultation was requested (p≤0.001 for each comparison). Conclusion: Besides ordering diagnostic tests or consultation in EDs different factors may extend patients’ WT and LOS values and cause significant decision-making delays. Understanding the patient characteristics associated with longer waiting times and LOS values and thus delayed decisions will enable practitioners to improve operations management in EDs. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 4
How machine learning facilitates decision making in emergency departments: Modelling diagnostic test orders
(WILEY, 2021-10-21) Gorkem Sariyer; Mustafa Gokalp Ataman; Ataman, Mustafa Gokalp; Sariyer, Gorkem
Objectives Since emergency departments (EDs) are responsible for providing initial care for patients who may need urgent medical care they are highly sensitive to increased patient delays. A key factor that increases patient delays is ordering diagnostic tests. Therefore understanding the factors increasing diagnostic test orders and proposing efficient models may facilitate decision making in EDs. Methods Month and week of the year day of the week and daily numbers of patients encoded based on 21 different ICD-10 codes were used as input variables. Daily test frequencies of patients requiring tests from laboratory and imaging services were modelled separately by linear regression models. Although significance of the input variables was identified based on these models obtained forecasts and residuals were further processed by machine learning techniques to obtain hybrid models. Results Day of the week and number of patients with ICD-10 codes of 'A00-B99' 'I00-I99' 'J00-J99' 'M00-M99' and 'R00-R99' were significant in both test types. In addition to these although daily patient frequencies with 'H60-H95' 'N00-N99' and 'O00-O9A' were significant for laboratory services 'L00-L99' 'S00-T88' and 'Z00-Z99' were significant for imaging services. Although prediction accuracies of regression models were respectively as 93.658% and 95.028% for laboratory and imaging services modelling they increased to 99.997% and 99.995% with the machine learning-integrated hybrid model. Conclusion The significant factors identified here can predict increases in use of laboratory and imaging services. This could enable these services to be prepared in advance to reduce ED patient delays thereby reducing ED overcrowding. The proposed model may also be efficiently used for decision making.
Citation - WoS: 12
Citation - Scopus: 12
Leveraging explainable artificial intelligence in understanding public transportation usage rates for sustainable development
(PERGAMON-ELSEVIER SCIENCE LTD, 2024-09) Gorkem Sariyer; Sachin Kumar Mangla; Mert Erkan Sozen; Guo Li; Yigit Kazancoglu; Sariyer, Gorkem; Sozen, Mert Erkan; Li, Guo; Mangla, Sachin Kumar; Kazancoglu, Yigit
Public transportation usage prediction is valuable for the sustainable development of transportation systems particularly in crowded megacities. Machine learning technologies are of great interest for predicting public transportation usage. While these technologies outperform many other techniques they suffer from limited interpretability. Explainable artificial intelligence (XAI) tools and techniques that offer post -hoc explanations of the obtained predictions are gaining popularity. This paper proposes an advanced tree-based ensemble algorithm for public transportation usage rate prediction. We aim to explain the predictions both with the most widely used technique of XAI Shapley additive explanation (SHAP) and in the light of the rules presented. To predict the total public transportation usage the proposed model combines all types of public transportation categorized as ferry railway and bus unlike most existing studies focusing on a single kind of public transport. Besides the sort of transportation the day of the week whether the day is special and the daily ratio of passenger types were identified as model features for predicting the daily usage of each type of public transportation. We tested the proposed model using an open data set from Izmir City Turkey. While the model had superior prediction performance the explanations showed that the type of public transportation weekday and the ratio of full-fare passengers have the highest SHAP values and the model features have many interactions. We also validated our results using an online data set showing Google search trends.
Citation - WoS: 1
Citation - Scopus: 1
Machine Learning Implementations for Multi-class Cardiovascular Risk Prediction in Family Health Units
(Ram Arti Publishers, 2023-12-01) Mert Erkan Sözen; Gorkem Sariyer; Mustafa Sözen; Gaurav Kumar Badhotiya; Lokesh Vijavargy; Sariyer, Gorkem; Sozen, Mert Erkan; Vijavargy, Lokesh; Badhotiya, Gaurav Kumar
Cardiovascular disease (CVD) risk prediction plays a significant role in clinical research since it is the key to primary prevention. As family health units follow up on a specific group of patients particularly in the middle-aged and elderly groups CVD risk prediction has additional importance for them. In a retrospectively collected data set from a family health unit in Turkey in 2018 we evaluated the CVD risk levels of patients based on SCORE-Turkey. By identifying additional CVD risk factors for SCORE-Turkey and grouping the study patients into 3-classes "low risk" "moderate risk" and "high risk" patients we proposed a machine learning implemented early warning system for CVD risk prediction in family health units. Body mass index diastolic blood pressures serum glucose creatinine urea uric acid levels and HbA1c were significant additional CVD risk factors to SCORE-Turkey. All of the five implemented algorithms k-nearest neighbour (KNN) random forest (RF) decision tree (DT) logistic regression (LR) and support vector machines (SVM) had high prediction performances for both the K4 and K5 partitioning protocols. With 89.7% and 92.1% accuracies for K4 and K5 protocols KNN outperformed the other algorithms. For the five ML algorithms while for the "low risk" category precision and recall measures varied between 95% to 100% "moderate risk" and "high risk" categories these measures varied between 60% to 92%. Machine learning-based algorithms can be used in CVD risk prediction by enhancing prediction performances and combining various risk factors having complex relationships. © 2023 Elsevier B.V. All rights reserved.
Machine Learning-Driven Clustering Based Environmental, Social, and Governance Performance Prediction Model
(Springer International Publishing AG, 2025-10-14) Sarioglu, Mert; Sariyer, Gorkem; Ramtiyal, Bharti
This paper integrates machine learning (ML) to propose a cluster-based ESG performance prediction model. Using data from 1235 companies across 8 sectors, we first clustered companies using their environmental, social, and governance performances and predicted their grouping using financial parameters such weighted average cost of capital (WACC), systematic risk, and market risk premium. The k-means ++ algorithm was used to cluster companies, revealing notable differences in their ESG pillar scores. To predict ESG performance group, we employed Decision Tree, Random Forest, and XGBoost comparatively. Among these the best-performing algorithm, Random Forest, had 97.57% accuracy. In the next stage, feature engineering was applied to identify key financial parameters that influence ESG prediction, where weighted average cost of capital equity and market risk premium emerging as the most significant factors. These findings demonstrate the significance of financial parameters in predicting ESG performance and present implications for investors, analysts and companies to ensure alignment with sustainability and ESG goals. The proposed model demonstrates the effective integration of ML and financial data for ESG analysis and prediction.
Machine Learning-Driven Clustering Based Environmental, Social, and Governance Performance Prediction Model
(Springer Science and Business Media B.V., 2025-10-14) Sarioglu, Mert; Sariyer, Gorkem; Ramtiyal, Bharti
Citation - WoS: 14
Citation - Scopus: 17
Predicting cost of defects for segmented products and customers using ensemble learning
(Elsevier Ltd, 2022-09) Gorkem Sariyer; Sachin Kumar Kumar Mangla; Yigit Kazancoglu; Lei Xu; Ceren Ocal Tasar; Tasar, Ceren Ocal; Kumar Mangla, Sachin; Kazancoglu, Yigit; Sariyer, Gorkem; Xu, Lei; Mangla, Sachin Kumar; Ocal Tasar, Ceren
Due to technological advances Big Data Analytics (BDA) has become increasingly important over the last few years. This has led companies to evolve BDA capabilities (BDAC) to manage operations and make better decisions. In this study we propose a model Clustering Based Classifier Ensemble Method for Cost of Defect Prediction (CBCEM-CoD) incorporating clustering classification prediction and learning techniques of BDA for quality management in the manufacturing industry. CBCEM-CoD (1) is fact-driven as it is based on a fundamental problem of the manufacturing industry (2) integrates different BDA techniques in a specific way when an output of one technique is used as an input of another and (3) extracts insights from real-world big data and directly offers many implications for practice. In the first stage of the CBCEM-CoD k-means and agglomerative clustering techniques are used comparatively for segmenting customers and products. CoD values of each product and customer segment are predicted using ensemble learning techniques in the second stage. The model is tested using a case data set from the kitchenware industry. As a result 53 and 720 different types of customers and products in the train data set are segmented in optimal numbers of 4 and 20 clusters. Around 89% accuracy is obtained for CoD predictions in the test data set. These results have substantial business value since they inform managers how to prioritize their focus on specific products and customer types to reduce the cost of a defect. We also highlight the importance of developing BDAC in dynamically changing environments to create a competitive advantage. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 25
Citation - Scopus: 34
Predictive and prescriptive analytics for ESG performance evaluation: A case of Fortune 500 companies
(Elsevier Inc., 2024-08) Gorkem Sariyer; Sachin Kumar Kumar Mangla; Soumyadeb Chowdhury; Mert Erkan Sozen; Yigit Kazancoglu; Erkan Sozen, Mert; Kumar Mangla, Sachin; Kazancoglu, Yigit; Sariyer, Gorkem; Sozen, Mert Erkan; Chowdhury, Soumyadeb; Mangla, Sachin Kumar
Given the growing importance of organizations’ environmental social and governance (ESG) performance studies employing AI-based techniques to generate insights from ESG data for investors and managers are limited. To bridge this gap this study proposes an AI-based multi-stage ESG performance prediction system consolidating clustering for identifying patterns within ESG data association rule mining for uncovering meaningful relationships deep learning for predictive accuracy and prescriptive analytics for actionable insights. This study is grounded in the big data analytics capability view that has emerged from the dynamic capabilities theory. The model is validated using an ESG dataset of 470 Fortune listed 500 companies obtained from the Refinitiv database. The model offers practical guidance for decision-makers to maintain or enhance their ESG scores crucial in a business landscape where ESG metrics significantly affect investor choices and public image. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 9
Citation - Scopus: 8
Sizing capacity levels in emergency medical services dispatch centers: Using the newsvendor approach
(W B SAUNDERS CO-ELSEVIER INC, 2018-05) Gorkem Sariyer; Sariyer, Gorkem
Background: The increased volume in demand world wide in the present day has led to the need for the establishment of effective ambulance services. As call centers have become the primary contact point between patients and emergency service providers the planning of the call center has become a key task for administrators. Objectives: The aim of this study is to apply a widely used operations management method the news vendor model for optimizing the capacity level in EMS call centers with a minimum cost in order to efficiently meet the calls arriving. Methods: Real-life data from a call center for ambulance services in a major city in Turkey was used. We propose using the news vendor model for optimizing this call center's capacity level based on the forecasts of periodic call volumes via basic methods. Results: Ambulance service call volumes vary during the day and weekday call profiles are different from weekends. By separating the analysis into weekdays and weekends and illustrating shorter time intervals within the days call volume can be forecast. Taking not only the point forecast but also the variation of the forecast into account the capacity level of each period can be planned in a cost-effective way. Conclusions: This paper provides a basis for operation planning strategies of ambulance services by reconsidering the uncertainties of demand. The news vendor model which works well under parameter uncertainty can be used in planning the capacities of health care services especially when high service levels are required. (C) 2017 Elsevier Inc. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
The Power of Governments in Fight Against COVID-19: High-Performing Health Systems or Government Response Policies?
(WALTER DE GRUYTER GMBH, 2022-10-17) Gorkem Sariyer; Mert Erkan Sozen; Mustafa Gokalp Ataman; Ataman, Mustafa Gokalp; Sariyer, Gorkem; Sozen, Mert Erkan
Due to the pandemic situation caused by COVID-19 disease there have been tremendous efforts worldwide to keep the spread of the virus under control and protect the functioning of health systems. Although governments take many actions in fighting this pandemic it is well known that health systems play an undeniable role in this fight. This study aimed to investigate the role of health systems and government responses in fighting COVID-19. By purposively sampling Finland Denmark the UK and Italy and analyzing their health systems' performances governments' stringency indexes and COVID-19 spread variables this study showed that high-performing health systems were the main power of states in managing pandemic environments. This study also measured relations between short and medium-term measures and COVID-19 case and death numbers in all study countries. It showed that medium-term measures had significant effects on death numbers.
Citation - WoS: 9
Use of derivatives financial stability and performance in Turkish banking sector
(AMER INST MATHEMATICAL SCIENCES-AIMS, 2020) Dilvin Taskin; Gorkem Sariyer; Taskin, Dilvin; Sariyer, Gorkem
Recent financial turmoil raised suspicions about the impact of derivatives usage on banking stability. Considering the period between 2007 and 2017 this paper analyzes the impact of derivatives on the financial stability and performance of the Turkish banking system. The stability of the banking is measured by considering the Z-index which shows the probability of and calculated for each bank. The second aim of this paper is to determine the impact of bank specific characteristics on the derivatives usage of banks. Panel regression models and factorial ANOVA analysis is adopted to perform the analysis. The results show that derivatives usage of banks decrease the profitability of banking system and increase the bank risk. The determinants of derivative usage also suggest that banks do not use derivatives to hedge their risks.
Citation - Scopus: 1
Use of machine learning for classifying manufacturing companies based on their digital transformation levels
(Inderscience Publishers, 2025) Ece Acar; Gorkem Sariyer; Sariyer, Gorkem; Acar, Ece
The transformative role of machine learning technology in promoting technological innovation leading sustainable growth is becoming increasingly significant in today’s business era. In this study we implemented machine learning technology to classify the companies according to their digital transformation levels. We used manufacturing companies in Borsa Istanbul (BIST) index as the sample. We constructed a digital transformation level index based on text analysis to measure the frequency of keywords related to digital transformation. We used the sampled companies’ financial sustainability corporate governance performance and research & development (R&D) expenditures to model their digitalisation levels. We observed that between the various machine learning algorithms with 82% accuracy Random Forest outperformed the others. We also showed that while R&D expenditure was the most important feature financial performance-related features were also significant. Thus we concluded that companies with higher financial performances especially those making more expenditures for R&D activities have higher digital transformation levels. © 2025 Elsevier B.V. All rights reserved.