This study examines the impact of e-government mobile applications on corporate reputation through user comments. Today, when digitalisation is accelerating, public services offered through mobile applications directly affect user experiences and shape the reputation of institutions. 2000 user comments from the Google Play Store were analysed using artificial intelligence methods, text mining, and sentiment analysis techniques. It was determined that 45% of the comments were positive, 15% were negative, and 40% were neutral. Positive comments indicate that the application has a positive user perception in general. However, some users were dissatisfied due to technical problems. As a result of text mining, the most frequently mentioned words and phrases of users were analysed, and feedback was categorised through sentiment analysis. In this process, WordNet was used to extract word frequencies, TextBlob was applied to classify user comments into positive, negative, and neutral categories, and Seaborn visualisations such as word clouds were employed to illustrate the findings. The findings reveal the importance of mobile applications for the sustainability of digital public services. It is emphasised that the technical performance of the application should be improved to increase user satisfaction and strengthen institutional reputation.

Entropy is an important criteria weighting measure used in decision making. There are different forms of entropy that are used to measure the inter criterion contrast intensity. In this study, we defined various entropy and diversity measures as criteria weighting approach in MCDM. We compared the approaches in terms of the rank reversal phenomenon by conducting a simulation study according to the framework we established. In addition, we compared these weighting approaches in terms of their dispersion characterization in an illustrative case. The Gini-Simpson index is the foremost index among these approaches, which is more persistent to rank reversal, less sensitive to distribution of domain and outputs more acceptable weightings.

This study examines the electric motor selection process of a research and development (R&D)-focused automatic guided vehicle (AGV) manufacturer within the framework of the Technology Management Model, evaluating the impact of technological decision-making on strategic competitiveness. As one of the core activities of technology management, technology selection was analyzed using multi-criteria decision-making (MCDM) methods. The technical and sustainability-based criteria identified in the study were weighted using the FUCOM (Full Consistency Method), and alternative motor manufacturers were evaluated through the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) approach. The findings indicate that sustainability-oriented criteria play a decisive role in technology selection, revealing that companies prioritize not only technical performance but also social and environmental sustainability dimensions. The results show that suppliers with strong sustainability practices and high technical competence achieve higher competitive positions, demonstrating the growing importance of integrating sustainability into technological choices. Overall, the study emphasizes that technology selection should not be limited to technical compatibility but should also incorporate sustainability, governance, and integration factors within a holistic technology management perspective. By highlighting the strategic value of combining sustainability with technological capability, this research contributes to the literature by underlining that integrating sustainability principles into technology management enhances long-term competitiveness and organizational adaptability.

Today, the performance of universities is evaluated not only based on their academic outputs but also on their collaboration, intellectual property production, and economic and social contributions. In this context, the Entrepreneurial and Innovative University Index (EIUI), developed by the Scientific and Technological Research Council of Türkiye (TÜBİTAK), evaluates universities in Türkiye according to four dimensions and 23 indicators. The EIUI methodology is based on subjective weights determined by expert opinions and policy priorities; however, in multi-criteria decision-making (MCDM) problems, results are often sensitive to the weighting approach employed. This study uses objective weighting methods such as CRITIC (Criterion Importance Through Correlation of Criteria), SD (Standard Deviation), CILOS (Criterion Impact Loss of Significance), and LOPCOW (Logarithmic Percentage Change Objective Weighting). Based on these weights, university rankings were re-established through the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ARAS (Additive Ratio Assessment) methods and compared with the original TÜBİTAK ranking. Ranking consistency was examined using Spearman's rank correlation analysis, and it was found that all correlations were statistically significant (p-value < 0.05), and that the highest correlation was observed between the TÜBİTAK ranking and the LOPCOW–ARAS method (ρ=0.985). The findings were supported by visualization tools such as heatmaps and radar charts. The highest variation in criterion weights among the methods was observed for Net Sales Revenue of Companies Owned by Students/Graduates, Number of BİGG Companies, Net Sales Revenue of Companies Owned by Academics, and Number of Faculty Members/Students with Mobility. In the ranking results, Middle East Technical University and Istanbul Technical University frequently occupy the top positions. In general, universities in the top and bottom ranks exhibit consistent positions across different methods, while universities in the middle ranks are more sensitive to methodological choices. This highlights the importance of considering alternative weighting and ranking approaches in university performance evaluations.

This study investigates factors influencing Z generation consumers’ willingness to pay when switching from free to paid applications (apps). These factors include personal characteristics, product characteristics and availability, and perceived performances of the service providers. The study employs an exploratory approach to assess a structural model that organizes these variables within the framework of a push-pull-mooring (PPM) framework. In this empirical study, the SmartPLS was used for the purpose of model testing and moderator analysis. The survey results, which included 239 respondents, identified price value of premium apps, dissatisfaction with free apps, perceived performance risk of free apps, price-quality inference, positive reputation of apps, and free mentality as the factors most influencing consumers’ switching intention. A comparison of hedonic (pleasure-oriented) and utilitarian (productivity-oriented) apps showed significant differences in switching intentions, influenced by security and privacy related concerns. The study identified two factors that were found to differ between groups in terms of their impact on the intention to transition to paid apps: perceived security risks associated with free apps and consumers’ privacy concerns. The study’s original contribution lies in its formulation of a comparative model and subsequent findings, which address salient aspects that mobile apps developers should consider when formulating their pricing strategies.

The 2024 Global Risks Report identifies misinformation and disinformation under the technology category as the foremost short-term global risk, followed closely by the risk of extreme weather events categorized under environmental concerns. In a longer-term perspective, the prominence of extreme weather events escalates to the top position, with misinformation and disinformation remaining significant, and adverse outcomes from AI technologies emerging as the seventh most critical risk. This delineation underscores the preeminent challenges posed by environmental and technological factors to global stability. These risks are not confined to the realm of environmental scientists or technologists; rather, they impact humanity as a whole. Recognizing that each individual holds inherent responsibilities, it is crucial to approach these issues through a multidisciplinary academic lens. This study, therefore, concurrently addresses climate change and technological impacts, investigating the interconnections and sub-indicators of these issues on a national scale. Countries were assessed based on these dimensions, compared, and visually represented, culminating in a comprehensive ranking. To facilitate these analyses, methodologies such as exploratory data analysis, principal component analysis, multidimensional scaling, and the TOPSIS were employed. The findings reveal a negative correlation between energy consumption and technology metrics, and a positive correlation between renewable energy indicators and technology. This study provides a nuanced understanding of how countries align with these global risks, offering a ranked evaluation starting from the most to the least affected.

In this study, we address the problem of assigning correlated Stock Keeping Units (SKUs) to storage locations under uncertain SKUs correlation conditions. The objective is to allocate SKUs within the forward picking area of a warehouse to minimize the total picking distance. To quantify the correlation between SKUs, we employ the joint distribution concept, enabling a more systematic representation of their correlations. The problem is formulated as a Quadratic Assignment Problem (QAP), which becomes computationally intractable at large scales due to its complexity. To mitigate this challenge, the QAP model is linearized, and a robust counterpart is developed to effectively handle uncertainty. The robust model was evaluated through various small-scale scenarios. While it yielded optimal results within an efficient time frame for small-scale problems, the solution time increased significantly as the problem size expanded.

This study aims to explore the Grey Fuzzy Transportation Problem, which describes the decision-making processes under uncertainty in the transportation problem, which is an especially important study problem for the logistics sector and academic studies. Comprehensive analyses and suggestions are made to contribute to the effective solution of the Grey Fuzzy Transportation Problem and better control of transportation problems which contain uncertainty. In the research, four different optimization methods for the Grey Fuzzy Transportation Problem (GFTP), the Closed Path Method, Interval Optimization, Robust Optimization and Interval Optimization with Penalty Function, are comparatively analyzed. The analyses are done on a total of 40 test problems with four different problem sizes, small, medium, large and extra-large. The results revealed that Interval Optimization and Robust Optimization performed the best in terms of solution quality and computation time. In particular, extensive analyses on the Interval Optimization with Penalty Function method verified that this is an effective and consistent solution approach for GFTP.

In this study, the impact of mobile applications developed by the Ministry of Health of the Republic of Turkey as part of its digitalization strategy on corporate reputation is analysed by using artificial intelligence methods through user comments. Within the scope of the research, the last 300 user comments of MHRS, Hayat Eve Sığar and eNabız applications on Google Play were analysed, and sentiment analysis and text mining techniques were applied. The findings reveal that MHRS and eNabız applications are generally perceived positively by users, which has a positive impact on the corporate reputation of the Ministry of Health. 81% of MHRS users and 73% of eNabız users made positive comments about the applications. However, for the Hayat Eve Sığar application, the positive comment rate remained at 51 percent, and more technical problems were reported. This shows that the application offers complex user experiences and needs to be improved. In conclusion, it is emphasized that the mobile applications of the Ministry of Health have strengthened its corporate reputation in general, but user satisfaction and sustainability of technical performance are critical to maintaining this reputation.

Milk is among the products of strategic importance for countries due to its nutritional value and being a priority foodstuff. Feed raw materials are one of the most important input items in the dairy cattle sector. Ensuring the balance of milk/feed parity is of great importance for producers to maintain their activities and profitability. In countries like Turkey, where inflationary effects are observed, the prices of feed raw materials are not stable. In an environment of high price fluctuations, forecasting feed raw material prices for producers is of vital importance for future planning. In this study, price forecasting of 43 feed raw materials, which are used extensively in the ration preparation process in the dairy cattle sector, was carried out. The performances of 11 methods based on Time Series, Statistics and Grey System Theory are compared. After the comparison using model success criteria, it was found that the DGM (1,1) method forecasts more effectively than Exponential Smoothing and Regression models as well as other Grey Forecasting models. Based on MAD, MSE and MAPE values, it is concluded that Grey Forecasting methods can be a good alternative for price forecasting of feed ingredients.