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Aug 15, 2024

The analysis of urbanization drivers in Kazakhstan: A regional assessment from 2010–2022

Urbanization process affects global socio-economic development. Originally tied to modernization and industrialization, current urbanization policy is focused on productivity, economic activities, and environmental sustainability. This study examines impact of urbanization in various regions of Kazakhstan, focusing on environmental, social, labor, industrial, and economic indicators. The study aims to assess how different indicators influence urbanization trends in Kazakhstan, particularly regarding environmental emissions and pollution. It delves into regional development patterns and identifies key contributing factors. The research methodology is based on classical economic theories of urbanization and modern interpretations emphasizing sustainability and socio-economic impacts and includes two stages. Shannon entropy measures diversity and uncertainty in urbanization indicators, while cluster analysis identifies regional patterns. Data from 2010 to 2022 for 17 regions forms the basis of analysis. Regions are categorized into groups based on urbanization levels leaders, challenged, stable, and outliers. This classification reveals disparities in urban development and its impacts. Findings stress the importance of integrating environmental and social considerations into urban planning and policies. Targeted interventions based on regional characteristics and urbanization levels are recommended to enhance sustainability and socio-economic outcomes. Tailored urban policies accommodating specific regional needs are crucial. Effective management and policy-making demand a nuanced understanding of these impacts, emphasizing region-specific strategies over a uniform approach.

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Dec 18, 2024

Critical success factors in integrated tourism development projects: A thematic analysis of major tourism investors’ perspectives in the Red Sea, Egypt

Investment plays a critical role in the progress of the tourism industry. Understanding the perspectives of tourism real estate developers and major investors is essential, as their decisions and insights shape the success of tourism projects. This study analyzes the critical success factors for integrated tourism development projects from the viewpoints of real estate developers and investors in Egypt’s Red Sea region. By conducting thematic analysis of interviews with eight experts, the study identifies key factors influencing the success of integrated tourism development projects. The findings reveal that, according to the investors perspectives, the ranking of factor groups is as follows: regulatory and administrative factors, tourism development and urban planning factors, collaboration and market responsiveness factors, economic and financial factors, infrastructure and accessibility factors, and environmental factors. The results contribute to the literature on tourism investment and provide insights for future tourism development projects aiming to succeed in similar coastal environments.

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Sept 10, 2024

Harmonizing sentiments: Analyzing user reviews of Spotify through sentiment analysis

This research investigates the sentiment of user reviews on Spotify, with a particular focus on the Indonesian market, leveraging advanced sentiment analysis techniques. We employed three prominent classification models—Naïve Bayes, Support Vector Machine (SVM), and Random Forest—to analyze a dataset of 14,296 user reviews extracted from the Google Play Store and App Store. These findings reveal that the SVM model achieved the highest performance, with an F1-score of 0.875 and an accuracy of 0.874, outperforming Naïve Bayes and Random Forest, which scored accuracies of 0.857 and 0.856 respectively. These results highlight not only the significance of this research which offers valuable contributions to the broader academic discourse on digital marketing, sentiment analysis, and consumer behavior. Additionally, it also showcases the robustness and superior performance of SVM and Random Forest in various sentiment analysis contexts. This study not only provides valuable insights for Spotify’s future development strategies but also contributes to the broader academic discourse on sentiment analysis and machine learning model performance in digital marketing. By highlighting the efficacy of specific models, this research underscores the importance of model selection in sentiment analysis, paving the way for more accurate and effective sentiment analysis applications in the music streaming industry.

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Aug 21, 2024

Towards smarter and greener cities: Harnessing AI and green technology for urban sustainability

In the face of growing urban problems such as overcrowding and pollution, we urgently need innovative ideas to build smarter and greener cities. Current urban development strategies often fail to address these challenges, revealing a significant research gap in integrating advanced technologies. This study addresses these gaps by integrating green technologies and artificial intelligence (AI), studying its impact on achieving smart and sustainable habitats and identifying barriers to effective use of these technologies, considering local variations in infrastructural, cultural, and economic contexts. By analyzing how AI and green technologies can be combined, this study aims to provide a vision that can be used to improve urban development planning. The results emphasize the significance of environmental responsibility and technological innovation in the development of sustainable urban environments and provide practical recommendations for improving the overall quality of life in cities through planning and urban planning.

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Aug 28, 2024

Modelling Government-to-Government (G2G) usage among government employees in Kuwait: The contingent role of task interdependence

The purpose of this paper is to investigate employee performance using G2G system in Kuwait. The study is based on D&M model, and it proposed the moderating effect of task interdependence on employee performance. Data of 290 employees were collected from Kuwait ministries, and smart PLS was used to analyse the data. Results indicate that information quality, service quality, and employee training have positive influence on G2G usage. Information quality, G2G usage, and service quality have positive influence on employee satisfaction. G2G usage, employee satisfaction, and employee training have positive influence on employee performance. However, system quality and employee training have no relationship with employee satisfaction. System quality also has no relationship with G2G usage. The study also showed no moderating effect of task interdependence on the relationship between G2G usage and employee performance. To overcome research limitations, several recommendations have been given for future research on this area.

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Nov 28, 2023

The influence of priming methods, planting dates, and weed interference levels on the vegetative growth, yield, and yield components of AS71 hybrid corn

By carrying out a laboratory experiment, the influence of priming methods, including ZnSO4, BSN, and hydropriming was evaluated on the seed germination of hybrid AS71 corn. Then, the main and interaction effects of the priming methods, planting dates, and weed interference levels were surveyed on the vegetative growth traits, yield, and yield components of corn in a field experiment. Based on the lab experiment, although the maximum germination percentage (100%) was observed in the treated plots by hydropriming 22 h after treatment (HAT), the greatest seedling vigor index (122.99) was recorded with treated seeds by ZnSO4 (0.03 mg L–1) at 8 HAT. The greatest emergence index was observed in the treated plots by hydropriming on both planting dates of June 1 and 11. The interaction of planting dates and weed interference levels revealed that the highest emergence index (14%–17%) occurred in the weed-free plots on both planting dates. BSN recorded the greatest corn 1000-grain weight that was significantly higher than the control plots by 28%. Furthermore, BSN enhanced the corn grain yield compared with the control plots by 63% and 24.9% on the planting dates of June 1 and 11, respectively. BSN, as a nutri-priming approach, by displaying the highest positive effects in boosting the corn grain yield in both weedy and weed-free plots as well as both planting dates, could be a recommendable option for growers to improve the crop yield production.

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