İlgi alanları

Sürdürülebilir Kalkınma Amaçları

• 3
• 2
• 2
• 1
• 1
• 1

• MakaleAçık Erişim2026/r/gumushane/13727
  Association Between Health Literacy and Mediterranean Diet Adherence Among Adult Women in Türkiye

  Çam, Alper Veli; Şik, Elif

  Objective: Health literacy is widely recognized as an important determinant of informed health-related decision-making. However, evidence regarding whether health literacy translates into healthy eating behaviors, particularly adherence to the Mediterranean diet, remains limited. This study aimed to examine the association between health literacy levels and adherence to the Mediterranean diet among adult women living in Türkiye. Methods: This cross-sectional study was conducted with 400 women aged 18 years and older residing in Türkiye. Data were collected using a sociodemographic information form, the Turkish Health Literacy Scale–Short Form, and the Mediterranean Diet Quality Index (KIDMED). Health literacy levels were categorized as inadequate, problematic–limited, adequate, and excellent, while Mediterranean diet adherence was classified as low, moderate, or optimal. Ordinal logistic regression analysis was performed to examine the association between health literacy and adherence to the Mediterranean diet, adjusting for sociodemographic variables (age, education, income, and occupation). Findings: Among the participants, 25.3% had inadequate health literacy, 31.8% had problematic–limited health literacy, 34.0% had adequate health literacy, and 9.0% had excellent health literacy. Mediterranean diet adherence was low in 34.5% of participants, moderate in 52.3%, and optimal in 13.3%. The ordinal logistic regression analysis revealed that, after adjusting for sociodemographic variables, one dimension of health literacy (Cognitive Health Literacy) was significantly associated with Mediterranean diet adherence, whereas no significant association was observed for the other dimension. Conclusion: The findings suggest that certain dimensions of health literacy may be associated with adherence to the Mediterranean diet when sociodemographic factors are taken into account. These results highlight the importance of considering both individual knowledge and contextual factors in understanding dietary behavior. Future research should further explore the role of nutrition-specific literacy and structural determinants in shaping healthy eating practices.

  • 
  • 
  İncele
• MakaleAçık Erişim2026/r/gumushane/13717
  Toward Sustainable Financial Behavior: The Role of Financial Socialization and FinTech in Enhancing Green Financial Literacy

  Soydaş, Şafak Sönmez; Özbek, Adem; Çam, Alper Veli; Radulescu, Magdalena; Alofaysan, Hind

  This study examines how financial literacy and financial socialization influence green financial literacy through the mediating role of FinTech usage within a sustainability-oriented framework. Green financial literacy is treated as a proxy for sustainable financial behavior rather than a direct behavioral measure. Based on financial socialization theory, the study proposes an integrated model linking individual competencies, social learning processes, and digital financial technologies. Data were collected from 539 individuals using a structured questionnaire and analyzed with structural equation modeling (SEM) employing a robust weighted least squares estimator. The findings show that financial literacy (β = 0.76, p < 0.001) and financial socialization (β = 0.23, p < 0.001) significantly increase FinTech usage. While financial literacy does not directly affect green financial literacy (β = −0.13, p > 0.05), FinTech usage has a strong positive impact (β = 0.88, p < 0.001), indicating a full mediation effect. Bootstrapping results further confirm the robustness of the mediation effects. Financial socialization also has a direct positive effect (β = 0.20, p < 0.01). The results suggest that financial knowledge alone is insufficient to support sustainability-oriented financial awareness unless it is supported by digital financial technologies. The study highlights the key role of FinTech in facilitating the integration of financial knowledge and social learning into sustainability-oriented financial awareness and decision-making and offers practical implications for promoting sustainable finance.

  • 
  İncele
• MakaleAçık Erişim2026/r/gumushane/13607
  The effect of behavioral factors related to cashlessness on the perception of green finance

  Özbek, Adem; Çam, Alper Veli; Soydaş, Şafak Sönmez; Barut, Abdulkadir; Sharabati, Abdel-Aziz Ahmad

  This study aims to examine the effects of behavioral factors shaping cashless payment behaviors in Turkey on green finance perception. The study focuses on subconscious factors such as functional benefit, economic benefit, psychological benefit, attitude toward technology, and perceived behavioral control. Data were collected from 520 participants aged 18 and older via an online survey and analyzed using Structural Equation Modeling (SEM). The findings reveal that functional benefit, perceived behavioral control, and attitudes toward technology significantly and positively influence perceptions of green finance, while economic and psychological benefits do not have a significant effect on behavioral control. The results reveal that behavioral factors such as ease of use and attitudes toward technology play a more significant role in individuals’ adoption of cashless payment systems than rational economic gain expectations. The study contributes to the literature by showing that cashless payment behaviors contribute to the development of sustainable finance and environmental awareness. It also provides practical recommendations for policymakers and financial institutions on improving digital payment infrastructure and user experience to promote green finance.

  • 
  • 
  İncele
• MakaleAçık Erişim2026/r/gumushane/13436
  Prioritization of Internal and External Corporate Sustainability Dimensions: Evidence from Sustainability Auditors Using AHP

  Wang, Yufu; Yeşilçelebi, Gül; Tosunoğlu Kusan, Büşra; Çam, Alper Veli; Dumitru, Mihaela

  Corporate sustainability has traditionally been evaluated through environmental, social, and economic dimensions. However, increasing sustainability challenges, stakeholder expectations, and regulatory requirements indicate that sustainability extends beyond conventional performance measures and requires an integrated perspective that considers both internal organizational capabilities and external institutional conditions. This study develops an integrated internal–external sustainability framework and prioritizes sustainability dimensions from the perspective of licensed sustainability auditors in Türkiye. The proposed framework comprises governance, ethical, behavioral, cultural, environmental, social, economic, legal, and technological dimensions. The Analytic Hierarchy Process (AHP) was applied to pairwise comparison data collected from 15 licensed sustainability auditors to determine the relative importance of these dimensions. The results indicate that external sustainability dimensions received substantially higher priority (70.97%) than internal dimensions (29.03%), demonstrating that sustainability assessments are primarily shaped by environmental challenges, regulatory requirements, stakeholder expectations, technological developments, and market conditions. Among the nine corporate sustainability dimensions, environmental sustainability received the highest overall priority, followed by technological, ethical, social, economic, legal, governance, cultural, and behavioral dimensions. These findings suggest that although internal organizational capabilities remain important, corporate sustainability is predominantly influenced by external institutional and environmental factors. By proposing an integrated internal–external sustainability framework, this study extends conventional sustainability assessment approaches and contributes to the sustainability governance and assurance literature. The findings also provide practical implications for sustainability reporting, assurance, governance, and strategic decision-making by identifying the sustainability dimensions that deserve the greatest managerial and regulatory attention.

  İncele
• MakaleAçık Erişim2026/r/gumushane/12260
  Financial literacy and psychological vulnerability to financial exploitation: the roles of financial anxiety, self-confidence, and behavior

  Çam, Alper Veli; Soydaş, Şafak Sönmez; Özbek, Adem; Çam, Handan

  Background: Financial exploitation constitutes a significant psychological vulnerability shaped by cognitive resources, emotional regulation, and financial decision-making behaviors. While financial literacy is widely recognized as a protective factor, limited empirical evidence disentangles the distinct psychological roles of financial knowledge, attitudes, and behaviors in reducing vulnerability to financial exploitation. Methods: This cross-sectional study analyzed survey data from 565 adults in Türkiye. Financial literacy was conceptualized as a multidimensional construct comprising financial knowledge, financial attitude, and financial behavior. Vulnerability to financial exploitation was assessed through three psychosocial indicators: self-confidence, financial anxiety, and financial satisfaction. Structural Equation Modeling (SEM) was employed to examine the relationships among cognitive, behavioral, and emotional components. Results: Financial knowledge was significantly associated with lower financial anxiety and higher financial satisfaction but showed no direct effect on self-confidence. Financial behavior emerged as the most robust protective factor, significantly enhancing self-confidence and financial satisfaction while substantially reducing financial anxiety. Financial attitude was positively related to financial satisfaction but did not significantly influence anxiety or self-confidence. Financial anxiety demonstrated a strong negative association with both self-confidence and financial satisfaction. Conclusions: Financial exploitation vulnerability is driven by interconnected psychological mechanisms rather than cognitive factors alone. The findings highlight the central role of behavior-based financial competencies and emotional regulation in strengthening financial resilience. Interventions aimed at preventing financial exploitation should prioritize behavioral skill development and psychological risk screening alongside traditional financial education approaches.

  • 
  İncele
• Kitap BölümüYalnız künye2025/r/gumushane/14247
  An analysis of factors affecting the market value of airline companies: An application in Borsa, Istanbul

  Soydas, Safak Sonmez; Cam, Alper Veli

  The market value of firms is mainly affected by financial factors. Financial factors such as sales, profitability, changes in equity, debt structure, competitiveness, interest rates, and inflation affect the market value of firms positively or negatively. Additionally, even if the financial data of firms are investable, some nonfinancial factors can significantly affect the market value of firms. Many nonfinancial factors, such as political and social events, natural disasters, technological developments, government policies, investor confidence or distrust (behavioral finance), managerial changes, sectoral developments, and social media, can change the market value of firms by having a greater impact on their stock prices. Determining the level and timing of the impact of these financial or nonfinancial factors will help institutional or individual investors in their investment decisions. The primary objective of this study is to identify the financial and nonfinancial factors affecting the market value of firms operating in the aviation sector. In this context, the factors affecting the market value of the firms were analyzed using the fundamental analysis data of three firms whose stocks are traded in Borsa Istanbul, one of the emerging markets. In the study, abnormal declines in stock prices of these firms were identified, and the importance levels of the factors that caused them were tried to be revealed. In this direction, a fundamental analysis was performed using financial data obtained from the financial statements of airline companies from the years 2014-2023. The events in the world and Turkey during the relevant periods that affected the capital markets were analyzed and compared. The results of the study are important for both domestic and foreign investors who will invest based on the market value of companies operating in the aviation sector.

  İncele
• MakaleAçık Erişim2025/r/gumushane/12519
  Efficiency analysis using the machine learning algorithms: model development and verification

  Soydaş, Şafak Sönmez; Kalkan, Yusuf; Çam, Alper Veli; Barut, Abdulkadir

  Recently, machine learning (ML) algorithms have been employed intensively in the field of finance as in all sectors. The issues such as financial distress prediction, bank credit risk calculation, etc., have been analyzed using ML algorithms. This study aimed to determine firm performance with the data envelopment analysis (DEA) method, sensitivity analysis, and ML algorithms and analyze the efficiency of companies via artificial neural networks (ANNs), support vector machines (SVMs), and logistic regression (LR) classification algorithms. In the study, first, 10 financial ratios were categorized into two parts, such as output and input, and efficiency scores were determined in MS Excel software. The obtained scores were included in the ML algorithm as a categorical dependent variable. Secondly, the data were extracted and included in the analysis software as 80% training and 20% test data, and the accuracy of ML algorithms was tested. Lastly, a comparative analysis of the estimation and classification algorithms of active and inactive companies was conducted. As a result of the analysis, the best classification prediction was seen as the ANN algorithm. SVM and LR algorithms also made an acceptable level of classification prediction. It was expected that the study would have contributed to the literature in terms of testing the companies whose efficiency scores were determined by the DEA method with ML techniques and determining which technique was more successful.

  İncele
• MakaleYalnız künye2024/r/gumushane/20035
  Türkiye’de coğrafi bilgi sistemi tabanlı yatırım teşvik politikalarının oluşturulması

  Çam, Alper Veli; Taşdemir, Seda Sağlamyürek

  Bu çalışmanın temel amacı, Türkiye’nin yürürlükteki yatırım teşvik politikaları değerlendirilerek, Coğrafi Bilgi Sistemleri tabanlı farklı yatırım teşvik haritaları oluşturmaktır. Çalışmanın diğer amacı ise yatırım teşvik politikalarının oluşturulmasında Coğrafi Bilgi Sistemlerinin kullanılabilirliğini ispatlamaktır. ArcGIS 10.7.kullanılan çalışmada, mekânsal oto korelasyon için Moran’s I yöntemi, mekânsal analizler için ise Coğrafi Ağırlıklı Regresyon analizi kullanılmıştır. Analizler sonucunda Coğrafi Ağırlıklı Regresyon yöntemine göre oluşturulan enerji, tarım ve ihracat teşvik bölgelerinin mevcut teşvik bölgelerinden farklılık gösterdiği tespit edilmiştir. Türkiye’de ulusal kalkınmanın yaşanması, verimliliğin arttırılması, rekabet üstünlüğünün sağlanması ve bölgelerarası gelir eşitsizliğinin giderilerek bölgesel kalkınmanın sürdürülebilmesi için yatırım teşvik sisteminin illerin coğrafi özelliklerine göre yeniden düzenlenmesi gerektiği sonucuna varılmıştır. Çalışmada yatırım teşvik bölgeleri farklı bir yöntemle (Coğrafi Bilgi Sistemleri) yeniden oluşturulmuştur. Çalışmanın, ulusal ve bölgesel kalkınma politikalarının yeniden oluşturulması ve farklı yöntemlerin kullanılabilirliğini ispatlama açısından literatüre önemli bir katkı sağlaması beklenmektedir.

  İncele
• MakaleAçık Erişim2024/r/gumushane/14534
  Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers

  Cam, Handan; Cam, Alper Veli; Demirel, Ugur; Ahmed, Sana

  This paper presents a sentiment analysis combining the lexicon-based and machine learning (ML)-based approaches in Turkish to investigate the public mood for the prediction of stock market behavior in BIST30, Borsa Istanbul. Our main motivation behind this study is to apply sentiment analysis to financial-related tweets in Turkish. We import 17189 tweets posted as "#Borsaistanbul, #Bist, #Bist30, #Bist100″ on Twitter between November 7, 2022, and November 15, 2022, via a MAXQDA 2020, a qualitative data analysis program. For the lexicon-based side, we use a multilingual sentiment offered by the Orange program to label the polarities of the 17189 samples as positive, negative, and neutral labels. Neutral labels are discarded for the machine learning experiments. For the machine learning side, we select 9076 data as positive and negative to implement the classification problem with six different supervised machine learning classifiers conducted in Python 3.6 with the sklearn library. In experiments, 80 % of the selected data is used for the training phase and the rest is used for the testing and validation phase. Results of the experiments show that the Support Vector Machine and Multilayer Perceptron classifier perform better than other classifiers with 0.89 and 0.88 accuracy and AUC values of 0.8729 and 0.8647 respectively. Other classifiers obtain approximately a 78,5 % accuracy rate. It is possible to increase sentiment analysis accuracy with parameter optimization on a larger, cleaner, and more balanced dataset by changing the pre-processing steps. This work can be expanded in the future to develop better sentiment analysis using deep learning approaches.

  İncele
• MakaleAçık Erişim2022/r/gumushane/17239
  Covid-19 Pandemisi Süresince Uzaktan Çalışma Alanları Kullanımının İncelenmesi: Katılım Bankası Çalışanları Üzerinde Bir Araştırma

  Çam, Alper Veli; Daştan, Abdulkadir

  Covid-19 sürecinde özellikle bankalar, alternatif çalışma alanlarını aktif olarak kullanmaya başlamıştır. Bu çalışmada, Covid-19 sürecinde uzaktan çalışma alanlarının katılım bankaları çalışanları üzerindeki etkileri araştırılmıştır. Türkiye genelindeki Katılım Bankalarında ofis veya şubede çalışan 411 örneklemden elde edilen veriler kullanılmıştır. Modelde oluşturulan toplam 19 hipotez test edilmiştir ve Teknoloji Kabul Modeli kullanılmıştır. Bu modelin test edilebilmesi için Yapısal Eşitlik Modelinden yararlanılmıştır. Çalışma sonucunda, çalışanların uzaktan çalışma sistemine uyumlulukları, zaman, algılanan davranışsal kontrolü ve algılanan kullanım kolaylıkları faktörlerinin, algılanan faydayı olumlu etkilediği tespit edilmiştir. Ayrıca çalışanların uzaktan çalışma sistemi için kolaylaştırıcı koşullar, endişe, güven ve öz yeterlilik faktörlerinin, algılanan faydayı olumsuz etkilediği tespit edilmiştir.

  • 
  • 
  İncele