Sürdürülebilir Kalkınma Amaçları

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Araştırmacılar(9)

• Aşkın, Burak
• Ayberkin, Doruk
• Calapoğlu, Alperen
• Çelik, Pelin
• Esmer, Yusuf
• Göktaş, Bora
• Kökhan, Serhan
• Özden, Didem Güleryüz
• Yirik, Emirhan

• MakaleAçık Erişim2026/r/bayburt/14207
  Perception of Organizational Sycophancy in Universities: A Research on Academicians

  Aribas, Ayse Nihan; Esmer, Yusuf; Yüksel, Muhammet

  Today, it is seen that unethical behaviors are diversifying in universities, which are accepted as value-based organizations. Sycophancy, which is an unethical behavior in universities where the hierarchical structure is evident, emerges as an important issue in academic circles. The purpose of this study is to examine the perception of organizational sycophancy in universities. In the study, 36 academicians were interviewed within the scope of qualitative research method. The answers given by the academics were processed in depth within the scope of the themes determined in accordance with the content analysis technique. The results of the study showed that sycophantic behavior is quite common in universities, that it leads to various negative consequences in the academic environment, and that there are many factors that lead to sycophantic behavior. In conclusion, organizational sycophancy is a phenomenon intertwined with academic success, cooperation and organizational culture in universities. The role of sycophantic behaviors, the attitudes of academics towards managers and the place of these attitudes in organizational structures is a topic that requires more research in academic circles. In this context, studies on sycophancy in universities provide important information for academic success and improvement of the work environment.

  İncele
• MakaleAçık Erişim2026/r/bayburt/15930
  Review of E-Commerce Literature: Inferences, Trends and Recommendations from Bibliometric Analysis

  Esmer, Yusuf

  Today, with the acceleration of digitalization, interest in e-commerce is increasing and the number of scientific studies on e-commerce is increasing. In this study, bibliometric analysis technique was used to determine the structure and trends of e-commerce literature. As a result of the search conducted in the Web of Science (WoS) database on April 9, 2025, a total of 15.670 data from the 1973-2025 time period were analyzed using VOSviewer and R Studio Bibliometrix software. The results reveal that e-commerce research focuses on technical infrastructure, managerial and social contexts. The prominence of China, the USA and the UK in scientific collaboration networks shows that e-commerce has become an interdisciplinary research area on a global scale. The link established with Sustainable Development Goals (SDGs) reveals the potential of e-commerce to produce ethical, environmental and social benefits. In line with these results, recommendations have been developed for increasing interdisciplinary integration, encouraging regionally focused research, expanding open access policies and shifting towards data-based methods. This study is expected to provide a strategic perspective to academia and policy makers by systematically presenting the current state of e-commerce literature.

  İncele
• MakaleAçık Erişim2026/r/bayburt/15923
  Role of artificial intelligence in the process of innovation management: dimensions, themes and examples

  Esmer, Yusuf

  This study aims to discuss the role of artificial intelligence (AI) in innovation management in a conceptual framework. In line with the purpose of the research, the conceptual analysis technique was used within the scope of qualitative research methods. In this context, the effects of AI on innovation management are explained based on relevant scientific research, reports and sectoral examples. How AI is integrated with innovation management processes, its application areas, and its potential future effects are examined. It has been concluded that AI affects important elements of organizations such as knowledge management, organizational flexibility, organizational innovation, decision-making, sustainable growth, organizational culture and strategic human resources management and that AI makes innovation management processes more efficient and effective with the opportunities it offers in areas such as big data analysis, machine learning and automation. The role of AI in innovation management stands out as an important element for modern organizations and reveals that research in this field should be examined in depth. This study examines the role of AI in innovation management processes within a conceptual framework and recommends that future studies conduct empirical research to expand on this topic. This study provides important in terms of revealing the role of AI in innovation management based on the relevant literature and with examples and will contribute to the literature.

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  İncele
• MakaleYalnız künye2026Scopus SJRQ2/r/bayburt/14320
  Analyzing IPO Performance on Borsa Istanbul: A Hybrid Econometric and Machine Learning Framework (2013-2025)

  Ayberkin, Doruk

  This study examines the determinants of the performance of initial public offerings (IPOs) conducted by 237 firms across 38 sectors on the Istanbul Stock Exchange between 2013 and 2025, using a hybrid analytical framework. To analyze the impact of sector characteristics, firm-level financial indicators, supply structure, and market timing on the returns of USD-denominated IPOs, this study combines heteroskedasticity-resistant ordinary least squares (OLS) regression with tree-based ensemble machine learning algorithms (random forest, XGBoost) and benchmark models (support vector regression, artificial neural network). The distributional characteristics of returns from IPOs highlight the use of nonlinear modelling techniques characterized by high positive skewness (5.12) and excess kurtosis (45.24). Our findings reveal significant sectoral heterogeneity. According to the findings, the Financial Services and Fisheries sectors showed statistically significant superior performance, while the Metal Mining, Insurance, and Construction sectors underperformed. Transportation and Storage exhibited the highest point estimate but did not reach conventional significance levels. The XGBoost machine learning model, with an R 2 of 0.869 and a mean absolute error (MAE) of 17.87, achieved superior single-split prediction accuracy compared to the OLS method, which yielded an R 2 of 0.220. However, cross-validation analyses indicated that all models exhibited limited out-of-sample generalizability (best CV R 2 = 0.04). The Diebold-Mariano test, conducted to compare prediction performance, confirmed that the artificial neural network (ANN) model provided a statistically significant improvement over the OLS method (DM = 2.121, p = 0.034). The SHAP value analysis used in the models reveals how each feature affects the final predictions, the relative importance of each feature, and the model's dependence on feature interactions. It identifies the IPO price (USD), the USD/TRY exchange rate, and the size of the supply as the most effective estimators with economically significant nonlinear interactions. The study aims to demonstrate the comparative advantages and limitations of ensemble learning under abnormal financial data conditions by presenting detailed sectoral evidence across 38 sectors. In this way, it aims to contribute to the IPO literature in emerging market economies by offering viable policy recommendations for investors, issuers, and regulators in Turkish capital markets.

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  İncele
• MakaleYalnız künye2026Scopus SJRQ2/r/bayburt/11185
  Depth-dependent predictability of soil temperature under Mediterranean climatic conditions: a frequency-based perspective

  Guleryuz, Didem; Yıldırım, Ümit; Güler, Cüneyt

  Soil-temperature predictability is inherently depth-dependent because atmospheric forcing is progressively attenuated and transformed into smoother, lower-frequency thermal dynamics with increasing depth. Under Mediterranean climatic conditions, where pronounced seasonality and strong surface forcing coexist, this depth-dependent transformation provides an informative basis for evaluating model behaviour. This study examines soil-temperature prediction at depths of 5, 10, 20, 50, and 100 cm using a multivariate dataset comprising 5,285 retained daily observations from the Mersin meteorological station on Türkiye’s eastern Mediterranean coast. Precipitation, air temperature, relative humidity, wind speed, and evaporation were used as predictors. Support vector regression, random forest, and LightGBM were evaluated using R², RMSE, and MAE. All three models performed strongly in the near-surface layers, with test R² values ranging from 0.948 to 0.954 at depths of 5–20 cm. Predictive performance declined with increasing depth as short-period variability weakened and the thermal signal became smoother and more seasonally structured. RF and LightGBM showed a greater numerical decline at 50–100 cm, whereas SVR achieved test R² values of 0.923 at 50 cm and 0.847 at 100 cm; however, inter-model differences remained modest and were interpreted descriptively. Lomb–Scargle spectral analysis, applied using the actual observation dates, indicated that short-period variability became relatively less pronounced with depth while seasonal-scale and low-frequency components remained prominent. Overall, the findings suggest that soil-temperature prediction should be interpreted in relation to depth-dependent temporal signal characteristics rather than as a search for a universally superior algorithm.

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  İncele
• MakaleYayıncıda açık erişim2026Scopus SJRQ2/r/bayburt/10955
  A pilot pre–post study of a scaffolded AI chatbot for financial literacy education among university students

  Ayberkin, Doruk; Yirik, Emirhan

  This pilot study examines pre- to post-test changes in university students’ financial literacy knowledge following a one-week student-directed learning period using a pedagogically scaffolded GPT‑4o Application Programming Interface (API) chatbot. Guided by constructivist learning theory [29], the immediate feedback model [10], and self-determination theory [24], the study employed a single-group pre-test–post-test quantitative design with 32 undergraduate students at a Turkish public university. The primary outcome was financial literacy knowledge, measured with 32 factual statements rated on a six-point agreement scale and keyed against correct responses, across six subscales (Basic Economics and Finance, Individual Banking, Retirement and Insurance, Financial Statements, Investment, and Tax and Legislation). Five objective multiple-choice items served as a limited, complementary performance check. Paired-samples t-tests were used for normally distributed difference scores, and Wilcoxon signed-rank tests for non-normal difference scores; six subscale comparisons were interpreted using Holm-adjusted p values. The overall score increased from pre-test to post-test, with a large standardized mean change (d = 1.09, 95% CI [0.64, 1.52]), and all six exploratory subscale comparisons remained statistically significant after Holm adjustment. Objective item performance also increased on several items, including the compound-interest item (3.1% to 81.2%). However, because the design included no control or comparison group, identical objective items were repeated, and chatbot interaction logs were not systematically recorded, the observed changes cannot be causally attributed to the chatbot. The findings therefore provide preliminary, uncontrolled evidence that warrants testing in controlled studies rather than evidence of intervention effectiveness.

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  İncele
• MakaleAçık Erişim2026Scopus SJRQ4/r/bayburt/10924
  Experience-Based Decentralized Marketing and Advertising (Ex-Mad): A Blockchain Framework for Transparent and Fair Digital Campaigns

  Ayberkin, Doruk; Özen, Üstün

  The contemporary digital advertising ecosystem faces critical technical and operational challenges, primarily driven by centralized intermediaries, data opacity, and privacy vulnerabilities. To address these systemic issues, this paper proposes "Ex-Mad," a decentralized architecture leveraging blockchain technology and the Interplanetary File System (IPFS). The proposed framework integrates three core architectural layers: the user entities (advertisers and end-users), the blockchain ledger for immutable transaction recording, and IPFS for distributed content storage. Unlike traditional centralized models, Ex-Mad utilizes smart contracts to automate campaign management and enforce trustless interactions, ensuring verifiable reward distribution based on user contribution without third-party validation. Furthermore, the integration of IPFS optimizes on-chain storage costs by handling large media files off-chain while maintaining data integrity through cryptographic hashes. The system design enhances auditability through a tamper-proof ledger, eliminates single points of failure, and provides a technically robust alternative to current ad-tech intermediaries. This study demonstrates how the proposed architecture improves system efficiency, reliability, and cost-effectiveness in the digital advertising domain.

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  İncele
• MakaleAçık Erişim2025/r/bayburt/16232
  TarBIoT KDS: Akıllı Tarım Uygulamaları için Blokzincir ve IoT teknolojileri ile Gerçek zamanlı Karar Destek Sistemi Tasarımı

  Ayberkin, Doruk

  Bu çalışma, tarım sektöründeki küresel zorlukları ele almak için yenilikçi bir yaklaşım sunmaktadır. Araştırma, blokzincir ve Nesnelerin İnterneti (IoT) teknolojilerini entegre ederek, tarımsal verilerin toplanması, güvenli bir şekilde depolanması ve analiz edilmesi için kapsamlı bir sistem önermektedir. TarBIoT olarak adlandırılan bu sistem, çeşitli sensörlerden elde edilen toprak ve iklim verilerini gerçek zamanlı olarak işleyerek, çiftçilere ve diğer paydaşlara yönelik bir karar destek mekanizması sunmaktadır. Sistemin temel bileşenleri arasında IoT sensör ağı, blokzincir altyapısı, veri işleme ve analiz modülü ile karar destek sistemi bulunmaktadır. Bu entegre yapı, tarımsal üretimde verimliliği artırma, kaynakları optimize etme ve çevresel sürdürebilirliği destekleme potansiyeli taşımaktadır. Blokzincir teknolojisinin sağladığı şeffaflık ve güvenilirlik, verilerin tüm paydaşlar arasında güvenli bir şekilde paylaşılmasına olanak tanırken, karar destek sistemi, bu verileri anlamlı ve uygulanabilir bilgilere dönüştürmektedir. Çalışma, önerilen sistemin simülasyonunu gerçekleştirerek, su seviyesi, nem oranı, toprak pH değeri gibi kritik tarımsal parametrelerin izlenmesi ve analiz edilmesindeki etkinliğini değerlendirmektedir. Sonuçlar, TarBIoT sisteminin, akıllı tarım uygulamalarının geliştirilmesinde, gıda güvenliğinin artırılmasında ve sürdürülebilir tarım pratiklerinin yaygınlaştırılmasında önemli bir rol oynayabileceğini göstermektedir. Bu araştırma, tarım sektöründeki dijital dönüşümü hızlandırma ve veri odaklı tarım uygulamalarını yaygınlaştırma potansiyeli taşımaktadır.

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  İncele
• MakaleAçık Erişim2025/r/bayburt/16189
  Artificial Intelligence and the Metaverse in Sport: Emerging Trends and Future Directions from a Bibliometric Analysis

  Esmer, Yusuf

  In recent years, artificial intelligence (AI) and metaverse technologies have found applications in areas such as athlete performance analysis, fan engagement and virtual event management, leading to a growing volume of research in these areas. The aim of this study is to reveal the bibliometric profile of scientific research on AI and metaverse in sports and to increase the knowledge at the intersection of these disciplines by analyzing scientific production and trends in this field. For this purpose, 255 publications on AI and metaverse in sports between 1992-2025 in the Web of Science (WoS) core collection in the period of October 2024 were examined by bibliometric analysis technique. As a result of the analysis, it was concluded that scientific studies on AI and metaverse in sports have increased over the years and attracted more and more attention. Especially the high scientific production in 2022 and the high citation rate in 2013 revealed the potential and importance of these fields. It is possible to say that this study is important in terms of identifying new research/application areas, emphasizing international collaborations, providing important information/data for academia/the sports industry, encouraging interdisciplinary approaches, guiding future studies and contributing to the relevant literature. In future studies, it is considered necessary to conduct more research on AI and metaverse in sports and to develop practical projects. In this context, it is recommended to conduct research to explain the effects of AI and metaverse technologies on sports and how these technologies can be used in sports organizations.

  AtıfOpenAlex2·TR Dizin1

  İncele
• MakaleAçık Erişim2025/r/bayburt/16124
  The Effectiveness of Advanced Machine Learning Models in IQ Prediction in the Context of Education, Health, and Socioeconomic Indicators

  Sebetci, Özel; Ayberkin, Doruk

  This study investigates the effectiveness of advanced machine learning models in predicting IQ levels using a diverse set of socioeconomic and health indicators from global databases such as WHO, the World Bank, and United Nations organizations. The research employs various algorithms, including Linear Regression, Random Forest, Gradient Boosting, Support Vector Machines, Ridge and Lasso Regressions, XGBoost, LightGBM, and a Stacking Regressor to capture both linear and non-linear relationships. Evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²) reveal that LightGBM and Stacking Regressor models excel in accuracy and generalization. The study highlights the trade-off between model interpretability and predictive power, emphasizing that simpler models offer greater transparency. In contrast, more complex models successfully capture intricate interactions among education, health, and economic factors. The findings provide valuable insights for policymakers and researchers, suggesting that machine learning approaches can significantly enhance understanding of the determinants of IQ and aid in developing targeted strategies in education and social policy.

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