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.

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 addresses the challenges employers face in screening the large number of resumes received for job positions. We aim to ensure fair evaluation of candidates, reduce bias, and increase the efficiency of the candidate evaluation process by automating the resume screening process. The proposed system uses NLP techniques to extract the relevant competencies from resumes, focusing on the key skills required for specific positions. The competency sets taken for the positions were used. A case study was conducted for 123 job positions. The extracted competencies are matched to predefined skill sets associated with various job positions using Jaccard Similarity. This method provides a similarity score that helps rank candidates by comparing the presence or absence of words in the candidate's resume to the required competencies. This NLP-based system offers significant benefits such as saving time and resources, increasing accuracy in candidate selection, and reducing bias by focusing only on competencies. The system's integration with LinkedIn increases its usefulness by allowing seamless import and analysis of resumes. Overall, this study demonstrates the transformative potential of NLP in optimizing the resume screening process by providing a scalable, efficient, and unbiased solution for large organizations.

This study aims to examine the opinions and thoughts of students studying at Manisa Celal Bayar University Faculty of Economics and Administrative Sciences in the 2022-2023 academic year regarding distance and face-to-face education systems during and after the Covid-19 pandemic process, by factor analysis.The findings obtained as a result of the factor analysis were examined and interpretations were made. The data used in the study was collected by survey technique. Analyzes were carried out using the SPSS-22 statistical package program and results were obtained using explanatory factor analysis, one of the multivariate statistical techniques.The number of students who will participate in the survey was determined by taking into account their departments using the stratified sampling technique, provided that they took courses with the distance education model during the Covid-19 pandemic and continued their education with the face-to-face education model after the pandemic. In this way, 342 of the 4518 students studying at the faculty were determined as the sample and the study was conducted with 342 students who fully responded to the survey. The findings obtained as a result of the explanatory factor analysis applied Accordingly, it has been observed that the effects of the Covid-19 pandemic process and subsequent education systems on students are explained by two factors. The first of these two factors is named "Positive Effects of Face-to-Face Education" and the second is called "Negative Effects of Face-to-Face Education". These named factors explain 69% of the total variance.

Population forecasting plays a significant role in determining demography, economics, and agriculture policies for developing countries. In this study, we employ the five different grey prediction models to estimate the population of Türkiye until 2050 using the 2007 to 2022 address-based data. These models are given as the grey standard (GM (1,1)), grey time-varying dynamic (GM (1,1) t), grey Gompertz (GGM), grey Verhulst (GVM), and grey exponential (EXGM (1,1). The comparison of grey models is evaluated by mean absolute percentage error (MAPE), regression coefficient (R2), variance ratio (C), and probability of error (P). The GGM and GM (1,1) t are identified as the most suitable models for predicting the period 2007-2022. For the future population forecasts from 2023 to 2050, the five models are compared with the projection values of the Turkish Statistical Institute published in 2018. The GGM is determined to be the most compatible based on the MAPE value of 0.68116 and the C value of 0.05218, and the Grey Verhulst model is provided the most compatible R2 value of 0.99818. According to the GGM, the population of Türkiye is projected to reach 105,948,975 by 2050, 106,877,632 based on the GM (1,1) t, and 102,591,471 based on the GVM.

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.

The occurrence of imperfect products that are studied at a specific rate during production holds a significant and comprehensive position in the literature. The fundamental premises underlying the research on the EOQ and EPQ models are as follows: there exists a specific percentage of imperfect production; that the defective rate of production is in line with a certain probability distribution; and that defective products are in themselves defective, repairable (rework), and defective (selling at a low price) etc. The studies of EPQ models that consider the inspection rate and production rate hold significant significance in the literature. This work presents the development of a novel EPQ model to handle the issue of reduced production time to eliminate backorders. The model's functioning is described by including uncertainty modeling, specifically using grey values to represent production rate, costs and prices.

Foreign direct investment (FDI) is a desirable form of capital inflows to emerging and developing countries. Such investments are less susceptible to crises and sudden breakdowns. Therefore, foreign direct investment between the political risks of using annual data for the years 1984-2019 which might influence Turkey has been tested. For this purpose, Fourier-based unit root and cointegration analysis are used. As a result of the Fourier unit root analysis, it is concluded that the FDI variable is difference stationary and the political risk variable is level stationary. For this reason, Yılancı et al. (2020), a fractional-frequency Fourier bootstrap autoregressive distributed lag (ARDL) boundary test was applied to the literature. This test, which is in nonlinear form, captures smooth transitional changes in long-term relationships. As a result of the analysis, it is seen that there is a cointegration relationship between foreign direct investments and political risk and the variables come to equilibrium in the long run. In addition, it is seen that the calculated long-term coefficient is positive and significant and the error mechanism works correctly in the short term. Therefore, as political stability is achieved foreign direct investment inflows will increase and the economies of the country will gain an advantage in reaching high levels of financial development. As a result, it is concluded that the Weak Partisan model is valid in the Turkish economy.

This paper reviews the autonomous vehicle (AV) trials around the world and proposes a socio-technical transition (multi-level perspective) approach for facilitating the adoption of AVs in countries. Built on a comprehensive literature review and a review of ongoing AV trials and tests around the world, this paper utilizes both theoretical and empirical approaches in understanding the dynamics of AV adoption. Three AV transition models (government support, industry push and public transport oriented) are proposed to help countries introduce and adopt AVs as part of their transport systems. The findings and insights are also aimed to help Turkey’s efforts to introduce AVs.

Increasing environmental awareness in today's society and stricter environmental regulations have forced manufacturing firms to take necessary actions for the recovery of end-of-life (EOL) products through different options (e.g., recycling, remanufacturing,). Disassembly is regarded as a critical operation in EOL treatment of used products since all product recovery options require the disassembly of EOL products at certain levels. This critical operation is generally carried out by forming disassembly lines in product recovery facilities. Miscellaneous methodologies based on heuristics, metaheuristics and mathematical programming have been proposed for the balancing of disassembly lines. Majority of those methodologies assume that disassembly line parameters are deterministic by ignoring the fact that a disassembly line involves great deal of uncertainty mainly due to uncertain conditions of arriving EOL products. Considering this high level of uncertainty, simulation modeling can be an effective tool for the modeling of disassembly lines. In this study, a simulation-based disassembly line balancing methodology is proposed for the explicit consideration of stochastic parameters. First, simulation model of a disassembly line is constructed. Since the disassembly line balancing problem has a combinatorial nature, two commonly used metaheuristics (i.e., genetic algorithms (GAs) and simulated annealing (SA)) are integrated with the simulation model in order to balance the disassembly line. The disassembly sequence and task assignments proposed by GA are compared with the sequence and task assignments proposed by SA. This comparison indicates that GA outperforms SA in four of eight performance measures while both algorithms have the same value for line efficiency measure.