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• MakaleAçık Erişim2026/r/bayburt/15907
  The Transformation of Local Independent Retail in City Centers and Its Effects on the City's Macroform: The Case of Ankara

  Yıldız, Seçil Gül Meydan; Şengün, Hayriye; Kara, Adnan; Ozden, Erdemalp

  The unplanned growth of city centers threatens the local fabric of cities, while local retail has become an important component of preserving city identity and sustainable consumption. This research aims to examine the effect of shopping motivations on customer satisfaction and to determine how factors such as customer proximity, relationships with local suppliers, and store atmosphere shape consumers' perception of Urban Attachment. Data obtained using systematic and stratified sampling methods in the districts of Altındağ, Çankaya, Etimesgut, Gölbaşı, Keçiören, Mamak, Pursaklar, and Sincan in Ankara were analyzed. Statistical analysis methods such as linear regression, logistic regression, ANOVA, and independent samples t-test were used in the study. Regression analyses show a positive and statistically significant relationship between shopping motivations and customer satisfaction. Furthermore, proximity to the customer and relationships with local suppliers were found to have a significant and positive effect on customer satisfaction. Shopping motivations, community connection, and store atmosphere factor scores showed significant differences across different visit frequency groups. This study uniquely highlights the critical role local retailers play in strengthening consumers' emotional connection to the city. The findings emphasize local businesses' importance in preserving city identity and creating a sustainable consumption ecosystem.

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  AtıfOpenAlex1

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• Kitap BölümüYalnız künye2024/r/bayburt/13080
  A hybrid method for determining the export sector and product for Turkey: Bayesian econometrics and multi-criteria decision analysis

  Ozden, Erdemalp; Yaprakli, Sevda

  Developing economies need to support strategic sectors that will transform their productive structures to ensure development. Therefore, the ability of these strategic sectors to produce exportable products is essential. This study follows a different path to determine strategically important export sector and products for Turkey, one of the countries struggling with the middle-income trap for a while. This different path is a hybrid method by combining the results of Bayesian econometrics with the analytical hierarchy process (AHP) method, one of the multi-criteria decision making (MCDM) methods. As a result, the export sector that should be prioritized is transportation, and export products are cars and vehicle parts. By applying the hybrid method in this study to other countries, strategic sectors and goods can be determined.

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• MakaleAçık Erişim2022/r/bayburt/16630
  An analysis on the relationship between income distribution and human development with panel data and machine learning

  Ozden, Erdemalp; Gültekin, Ömer Faruk; Bayramoglu, Turgut

  The aim of this study is to examine the effects of socioeconomic indicators such as human development,\rurbanization and female employment rate on income distribution in MIST countries, with both econometric\rand machine learning methods, and to predict the performance of these variables in income distribution\restimation.. Although there are many studies on growth in the economics literature, the income distribution\rhas not found the place it should be. To fill this void, the intensity of studies between growth and income\rdistribution has increased recently. For this purpose, we employed two separate analyzes using the panel data\rmethod and the Support Vector Regression method, which is one of the machine learning methods, by reaching\rthe Gini coefficients, HDI, urbanization, and female labor force participation data of the MIST countries for\rthe years 1990-2019. As a result, all determinants in the random-effects model have a statistically significant\reffect on income inequality. In the model, while HDI and urbanization are significant at 5%, female labor force\rparticipation is significant at 0.1%. Signs of all explanatory variables are negative, and therefore they have a\rdecreasing effect on income inequality. As HDI, urbanization, and female labor force participation values of\rMIST countries have improved since 1990, the Gini coefficients have improved. In other words, the income\rdistribution of these countries has become fairer. Unlike other studies in the literature, the Support Vector\rRegression model was used in this study, and SVR produced a more suitable model for predicting income\rinequality.

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  AtıfOpenAlex4·TR Dizin6

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• MakaleAçık Erişim2022/r/bayburt/15762
  Forecasting of Export Volume Using Artificial Intelligence Based Algorithms

  Ozden, Erdemalp

  Technological breakthroughs have transformed communication and taken transportation, health, and commerce to an unprecedented level. In this way, sudden developments have rapidly affected all countries. In this context, analysis methods are changing compared to the past, and annual analyses fail to catch the trend even for macroeconomic indicators. In this paper, new artificial intelligence-based estimation methods were used to see the future trend of export volume, and their estimation performances were compared by adding them to the classical econometric method. Historical quarterly data from 2013 to 2021 were used in the training and testing phases of the models. For this purpose, the variables of gross domestic product, foreign direct investment, and dollar exchange rate, which affect the export volume, were determined as inputs in estimating the export volume. According to the analysis results, support vector machine model was determined as the best method for predicting export volume in Turkey. This study can provide an essential basis for policymakers to export estimation and formulate their export-enhancing policies effectively.

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• MakaleAçık Erişim2022/r/bayburt/15757
  The Effect of Foreign Trade Volume of Seafood: An Analysis on the Gravity Model Method of Turkey

  Ozden, Erdemalp; Gültekin, Ömer Faruk; Bayramoglu, Turgut

  Seafood is a vital food resource and, more importantly, a common subject in many areas such as the environment, employment, and foreign trade in sectoral terms. In this study, we investigated the effect of Turkey's seafood production and its trade with these products on Turkey’s foreign in line with the predictions of the gravity model, which is a theory of international economics. As a result, using 1990-2019 data with 12 countries where Turkey has the highest seafood trade, trading partners with higher revenues showed a higher demand and greater import chances, and an increase in production capacity by Turkey could create additional trade volumes. Besides, the geographical distance is negative and statistically significant in the models following the theoretical expectation, and the shipping cost was the critical factor determining Turkey's bilateral trade performance. The exchange rate has a positive and significant effect on trade, and seafood exports have a positive and statistically significant effect on trade, while imports have a negative and significant effect. As a result of the study, if Turkey's production increases overall the model variables, its trade will increase following the gravity model.

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  AtıfOpenAlex1

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• MakaleAçık Erişim2022/r/bayburt/14305
  The Dynamics Affecting the Export-Import Ratio in Turkey: A Hybrid Model Proposal with Econometrics and Machine Learning Approach

  Özden, Erdemalp

  The indicators related to foreign trade are conventionally measured in a currency or as the ratio of the country's gross domestic products. The ratio of exports to imports, alternatively, provides more useful results when comparing the foreign trade performance of economies both over time and with other countries as a unit-free indicator. In this study, the macroeconomics and financial determinants affecting this ratio are examined both econometrically and using the machine learning method. In this context, the autoregressive distributed lag model method was first used to investigate the relationship between normalized gross domestic products, exchange rate, consumer price index, producer price index, crude oil and Turkey's ratio of exports to imports rate between 2010-2021, monthly. Long-term analysis showed that the 1% depreciation of the Turkish Liras against the US dollar increased the ratio of exports to imports rate by 0.7 points. In addition, a 1% increase in consumer price index will increase ratio of exports to imports by 1.9 points, while a 1% increase in producer price index will cause a -0.8 point decrease on the ratio of exports to imports. Then, the pattern between the variables was analyzed with quadratic support vector machine, a machine learning method. Finally, the novel ARDL-SVM hybrid method was developed, and the pattern between the variables was examined. The findings revealed that although the econometric method provided a broader scope for interpreting the relationships between variables, the developed ARDL-SVM method successfully captured patterns between variables.

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  AtıfWoS1·OpenAlex5·TR Dizin1

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• MakaleYalnız künye2022Scopus SJRQ2/r/bayburt/13241
  Optimized Machine Learning Algorithms for Investigating the Relationship Between Economic Development and Human Capital

  Ozden, Erdemalp; Guleryuz, Didem

  In Economic Development, human capital was previously seen as production factors but gradually evolved into endogenous growth theories. Most of the previous studies have examined the relationships between economic development and human capital via econometric models. Since this relationship is usually nonlinear and machine learning (ML) models can resolve it better, this study investigates the relationships by employing ML methods to provide a new perspective. For this purpose, the optimized ML methods, namely Bayesian Tuned Support Vector Machine and Bayesian Tuned Gaussian Process Regression (BT-GPR), were performed to develop the prediction model for economic development. The hyperparameters have been optimized with the Bayes method by using different kernel functions to increase SVM and GPR methods' predictive performance. The Multiple Linear Regression model has been employed to make a comparison as an econometric model. The performance of the models is evaluated using three statistical metrics, namely, the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The BT-GPR with the exponential kernel model has superior prediction ability with the highest accuracy (R2: 0.9727, RMSE: 0.4022, MAE: 0.3728 in the testing phase). The study shows that the BT-GPR model increases the accuracy of R2 6.4%, RMSE 10.7%, and MAE 1% compared with other developed models.

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  AtıfScopus27·WoS18·OpenAlex30

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• MakaleAçık Erişim2021/r/bayburt/14335
  Clustering framework to cope with COVID-19 for cities in Turkey

  Guleryuz, Didem; Ozden, Erdemalp

  Introduction: This article is the product of the research Clustering Framework to Cope with COVID-19 for Cities in Turkey, developed at Bayburt University in 2021. Problem: Turkey's risk map, presented in January 2021, to take local decisions in tackling the COVID-19 pandemic, was based on confirmed cases only. Health, socio-economic and environmental indicators are also important for management decisions of COVID-19. The risk map to be designed by adding these indicators will support more effective decisions. Objective: The research aims to propose a clustering scheme to design a risk map of cities for Turkey. Methodology: The unsupervised clustering algorithm suggested dividing the cities of Turkey into clusters, considering health, socio-economic, environmental indicators, and the spread pattern of COVID-19. Results: We found that cities are clustered into five groups while megacity Istanbul alone formed a cluster, three of Turkey's largest cities formed another cluster. Other clusters consist of 19, 26, and 32 cities, respectively. The most important determinants which have predictive power are identified. Conclusion: The suggested clustering method can be a decision support system for policymakers to determine the differences and similarities of cities in quarantine decisions and normalization phases for the following periods of the pandemic. Originality: To the best of our knowledge, this study differs from previous studies because countries were grouped in previous studies by only considering the confirmed cases. In this study, cities were clustered in terms of the health, socio-economic, and environmental indicators to make decisions locally. Limitations: The distribution of confirmed cases by age could be added, especially to make decisions about education, but this data is not officially announced.

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  AtıfWoS1·OpenAlex1

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• Kitap BölümüYalnız künye2021/r/bayburt/13523
  An Unsupervised Learning Framework to Analyze the Economic Impact of Covid-19: An Application on the Most Affected Countries

  Özden, Erdemalp

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• MakaleAçık Erişim2021Scopus SJRQ1/r/bayburt/12071
  The effect of sustainable development on economics complexity in oecd countries; [El efecto del desarrollo sostenible en la complejidad económica de los países de la ocde]

  Yaprakli, Sevda; Özden, Erdemalp

  Economic complexity showing a holistic measure of countries' economic productive power and characteristics has become a new tool for understanding the dynamics of the economy. Examining the relationship between sustainable development and this new tool is vital in determining new policies. By applying panel data of OECD countries covering different development levels from 1996 to 2017 to a data-driven dynamic econometric model, the research provides fresh insight between sustainable development and economic complexity. The results indicate that economic complexity is significantly affected by sustainable developments’ economic indicators such as GDP, FDI, R&D expenditure, social indicators such as human development, income inequality, and environmental indicators such as production-based CO2 emissions, renewable energy consumption, and greenhouse gas. The research, consequently, suggests that switching to technology and knowledge-based production processes, expanding qualified production factor capacity, raising social living standards, and making investments in the green economy will foster economic complexity while ensuring stable sustainability.

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  AtıfScopus8·WoS3·OpenAlex7

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