4 results
Search Results
Now showing 1 - 4 of 4
Article Consumer Behavior and Sustainability: What Role Does Eco-Friendly Packaging Play in Emerging Markets(Emerald Publishing, 2025) Güngördü Belbağ, A.Purpose – This study aims to offer a systematic review of eco-friendly packaged products from the consumer perspective in emerging markets. Design/methodology/approach – This study analyzed 47 Web of Science and SCOPUS articles using the PRISMA and theory-context-characteristics-methodology (TCCM) framework. Findings – This systematic review shows that prior research was mainly quantitative and relied heavily on the theory of planned behavior. This study categorized the articles by personal, product, price, place, promotion and external factors. Research primarily focused on personal factors, especially values and attitudes. Place-related factors were the least studied. Few studies explored mediators and moderators of purchase intention. Originality/value – This study shifts the focus of eco-friendly packaging research toward the consumer perspective, which was underexplored in the existing literature, particularly in emerging markets. While prior studies have primarily centered on manufacturers, this review synthesizes prior research showing that consumer behavior, knowledge and perception play a critical role in advancing sustainability and circular economy goals. This systematic review is the first to address this gap in emerging markets and offers implications for policymakers and businesses aiming to promote sustainable consumption in diverse socioeconomic settings. © 2025 Emerald Publishing LimitedArticle If Europe Lived the Same Lifestyle: Insights Into Cardiovascular Risk From the European Social Survey(KeAi Communications Co., 2025) Valko, M.; Walker, M.D.; Htoon, April; Dumlao, Jocelyn; Lane, H.; Lee, Isabella G.; Bursová, J.Background: Cardiovascular disease remains the leading cause of mortality across the European region. Despite marked regional variations, cross-national differences in underlying risk factors have received comparatively little attention. Objective: To use European Social Survey, a unique cross-European dataset, to examine regional patterns in prevalence and lifestyle risks. Methods: This study employs clustering analysis and nested logistic modelling. Counterfactual analysis was conducted to illustrate how lifestyle modifications could reduce risk. Results: The prevalence of heart problems was highest in Latvia (25.6 %, 95 % CI: 23.0 to 28.2), Lithuania (17.6 %, 95 % CI: 15.5 to 19.7), and Bulgaria (14.9 %, 95 % CI: 13.4 to 19.4). Regionally, heart problems were higher in Northern and Eastern Europe (15 % and 11.9 %) than Western and Southern Europe (10.8 % and 9.5 %). Among the risk factors, modelling emphasised the importance of modifiable factors including education, body mass index and physical activity. Conclusion: The results underline that cardiovascular disease is influenced by interrelated socioeconomic, environmental and lifestyle determinants. Public policy interventions could be targeted at those countries where greatest reductions are obtainable and concentrate on interventions on those lifestyle traits identified. The study utilised a social science dataset, thereby illustrating how multidisciplinary resources can benefit epidemiological research. © 2025Article Citation - WoS: 1Citation - Scopus: 1How Do Real and Monetary Integrations Affect Inflation Dynamics?(Elsevier, 2023) Saygili, HulyaThis paper examines the significance of real and monetary integrations for the inflationary dynamics of an emerging country, Turkey. The analysis accounts for 2-digit items of CPI inflation, which can be broadly categorized as tradable versus non-tradable and goods versus services. We find that a fall in the inflation gap between partner countries is mainly related to real integration whereas the co-movement of inflation is prominently driven by monetary policy co-movements. The product-type analysis shows that inflation gap in tradable items between trade partners shrinks and becomes more correlated with the (de)convergence and co-movement of real integration.Article Performance Investigation of ML Algorithms for Potato Blight Classification: The Role of Hyperparameter Tuning(Springer, 2026) Saeed, Sadia; Rehman, Hafiz Zia Ur; Hussain, Muhammad Ureed; Khan, Muhammad Umer; Saeed, Muhammad TallalPotato is the world's fourth most important food crop, consumed by over a billion people. Early and late blight diseases can reduce yields by up to 40%, leading to severe economic and food security challenges. While manual detection methods are prone to error, automated, image-based machine learning (ML) offers a promising alternative, though its performance depends strongly on proper optimization. This study investigates the role of hyperparameter tuning in improving ML algorithms for potato blight classification. We utilized two datasets: the PlantVillage dataset (500 images per class) and a region-specific Potato Leaf Dataset (PLD) from Pakistan (1628 early blight, 1424 late blight, 1020 healthy). All images were resized to 256 & times; 256 pixels and augmented. Features were extracted using the Bag-of-Features (BoF) technique, and four classic ML models-Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), and Random Forest (RF)-were trained. Hyperparameters were optimized via grid search with 5-fold cross-validation. This tuning led to measurable improvements; for instance, SVM accuracy increased from 93.0% to 95.9% on PlantVillage and from 85.0% to 87.0% on PLD. Evaluation using precision, recall, F1-score, and specificity confirmed SVM as the best-performing model. A confusion matrix analysis revealed that most misclassifications occurred between the two blight types due to visual similarity. To translate our findings into practice, we developed a MATLAB Graphical User Interface (GUI) that enables farmers to classify a leaf image in under three seconds and receive precautionary recommendations. This study demonstrates that systematic hyperparameter optimization is crucial for maximizing ML performance and is a key step in building accessible, real-time tools for precision agriculture. Future work will focus on extending the system to mobile and web platforms.
