درخت حوزه‌های تخصصی

The purpose of the article is to perform stress-testing technologies of the financial stability of an insurance company based on the constructed mathematical model of the insurance company's activity, which would meet the established requirements (adequate reproduction of the main parameters of the insurance company's functioning; taking into account the stochastic nature of insurance processes; flexible management of model parameters describing company's behaviour; the ability to influence the intensity of flows; suitability for algorithmization and construction of computational simulation model. The relevance of this study is due to the need to address the problem of changes and complications, the growing variety of strategies and products implemented by insurance companies. There is a need for innovative methods to assess and monitor the vulnerability of these institutions to various types of risks. One of these methods, which is gaining widespread recognition both among regulators and financial corporations, is stress testing. It has been established that stress testing as a risk management tool is used both to assess the insurance company's readiness for a crisis situation, and to develop a plan of adequate measures to counteract and eliminate its negative impact. The development and application of the proposed mathematical and simulation model of stress testing of the financial stability of the insurance company allows to solve issues of ensuring sufficiency of capital level, control of financial stability and solvency, reliability of efficiency of activities, taking into account the probabilistic nature of insurance activities, various typical insurance risks and time horizons.

Purpose: Start-up businesses have attracted considerable attention regarding the new approach in the modern economy The present study was conducted to investigate the factors affecting the improvement of customers’ online shopping experience in e-commerce start-ups in the healthcare sector.Method: This is an applied research conducted as a descriptive-survey. The target population includes all customers who used the electronic start-up services in the healthcare sector; out of which a sample of 384 individuals was selected using the Morgan table. A self-administered questionnaire was developed to collect the data which were analyzed using partial least squares and Smart-PLS software.Findings: All research hypotheses were confirmed, and it was proved that factors such as customer respect, customer enjoyment, importance of time, information security, convenience, perceived experience, valuable experience, and perceived image experience have a positive effect on improving customers’ online shopping experience in e-commerce start-ups.Conclusion: Applying the proposed model helps e-start-ups increase their performance   by eliminating their shortcomings and boosting their strengths.

The term "clickbait" refers to content specifically designed to capture readers' attention, often through misleading headlines, leading to frustration among social media users. In this study, titled "Mushakkal," which translates to "variety" in Arabic, we utilized a Convolutional Neural Network (CNN)—a deep learning approach—to detect clickbait within an Arabic dataset. We compared three optimizers: RMSprop, Adam, and Adadelta, evaluating various parameter settings to determine the most effective combination for detecting clickbait in Arabic content. Our findings revealed that the CNN model performed best when both pre-processing and Word2Vec techniques were applied. The Adam optimizer outperformed the others, achieving a Macro-F1 score of 77%. The RMSprop optimizer closely followed, attaining a Macro-F1 score of 76%. In contrast, Adadelta proved to be the least effective for classifying Arabic text.

This study aims to develop an adoption model tailored for service-oriented organizations and then evaluate its effectiveness within the specific context of Tehran Municipality, Iran's foremost service-oriented institution. Utilizing a mixed-method research approach integrating qualitative and quantitative methodologies, this study delineated the dimensions, categories, and indicators pertinent to the adoption of Electronic Human Resource Management (EHRM) systems in service-oriented organizations. Qualitative methodologies were employed to identify and develop the adoption model, which was subsequently evaluated within Tehran Municipality using a quantitative approach. In the qualitative segment of this study, in-depth interviews were conducted using a snowball sampling technique until theoretical saturation was achieved. For the quantitative phase, a sample of 310 experts affiliated with Tehran Municipality's EHRM system was surveyed. Structural equation modeling and Smart PLS 4.0 software were employed for data analysis. Ultimately, this research extracted five dimensions, 14 categories, and 94 indicators for the proposed adoption model. Notably, experts accorded the highest priority to the technological dimension in the adoption model, with specific emphasis on “adaptive architecture, security and privacy of employees, trialability and reliability, organizational citizenship behavior, organizational dynamic capabilities, digital Leadership Policy and Actions, cloud computing, etc…”, as pivotal factors in EHRM adoption. The organizational dimension assumed the second-highest priority, while the individual dimension was assigned a third-place ranking. Micro and macro-environmental factors followed in subsequent priority order.

This paper presents the design, simulation and realization of a patch antenna for IOT applications. The patch antenna consists of a radiating element printed on one face of a dielectric substrate, when the ground plane is placed on the other face. In this work, two techniques are used to design a miniaturized patch antenna: the set-up of slots on the radiating element and the use of defective ground plane. Also, the slot’s radius and Length of inset point effects on the performances of the antenna is illustrated. All the simulated results are performed with FEKO, a solver based on a Moments Method and measurement is made using Vector Network Analyzer Anritsu MS2026C. The propose antenna resonates in three frequency bands 3.91, 4.86 and 5.16GHz for different characteristics such as radiation pattern, gain, return loss, which makes it suitable for many wireless communication applications such as IoT applications.

Purpose: This study extends the current understanding of customer engagement by examining the impact of relationship bonding and relationship quality on customer engagement in value creation for online taxis.Method: A quantitative research design was used to validate the hypotheses proposed in this research. Customer engagement has become an important metric for measuring the quality of relationships between brands and their customers. Despite this, there has been limited research into how relationship bonds affect the effectiveness of building such a relationship in online taxi services. A survey was conducted using the convenience method among 600 users of online transportation services in the city of Urmia, Iran to test the theoretical model. Structural equation modelling in software Amos 23 were used in this study to test the research hypotheses. Findings: Findings showed that relationship bonding (financial, social, and structural) positively affects the quality of online relationships. Moreover, it was found that the quality of online relationships had a positive impact on all four aspects of customer engagement value (lifetime value, influence value, customer knowledge value, and customer referral value).Conclusion: In order to demonstrate the promotion of customer engagement through relationship bonds and online relationship quality, the study adds new data to the literature on online taxis services. Online taxi services are able to offer open innovation structures to help them   increase customer engagement, gather innovative ideas and integrate them into their operations. In this regard, in order to enrich ideas, customers who have successfully come up with new ideas should be rewarded.

Today, users generate various data increasingly using the Internet when choosing a product or service. This leads to the generation of data about the purchases and services of various consumers. In addition, consumers often leave feedback about the purchase. At the same time, consumers discuss their attitudes about goods and services on social networks, messengers, thematic sites, etc. This leads to the emergence of large volumes of data that contain useful information about various manufacturers of goods and services. Such information can be useful to both ordinary users and large companies. However, it is practically impossible to use this information due to the fact that it is located in different places, that is, it has a raw, unstructured character. At the same time, depending on the target group of users, not the entire data set is needed, but a specific target sample. To solve this problem, it is necessary to have a tool for structuring information arrays and their further analysis depending on the set goal. This can be done with the help of various frameworks that use methods of machine learning and work with data. This work is devoted to elucidating the problem of creating means for evaluating consumer preferences based on the analysis of large volumes of data for its further use by the target audience.  The goal of the development of big data analysis systems is obtaining new, previously unknown information. The methodology of application of algorithms of work with large data sets and methods of machine learning is used, namely the pandas library for operations on a data set and logistic regression for information classification As a result, a system was built that allows the analysis of lexical information, translate it into numerical format and create on this basis the necessary statistical samples. The originality of the work lies in the use of specialized libraries of data processing and machine learning to create data analysis systems. The practical value of the work lies in the possibility of creating data analysis systems built using specialized machine learning libraries.

Smart packaging plays a pivotal role in food distribution by integrating innovative technologies to enhance product safety, quality, and efficiency throughout the supply chain. Leveraging smart packaging minimizes waste, reduces spoilage, and enables real-time monitoring. However, producers face a few challenges with smart packaging, including high initial investment costs, compatibility issues with existing infrastructure, and regulatory hurdles concerning data privacy and safety standards. Additionally, ensuring widespread adoption and addressing consumer acceptance pose significant challenges and the need for continuous innovation to keep pace with evolving market demands and technological advancements. Therefore, this study aims to identify the current landscape of smart packaging within food distribution, outline the main themes within this field, highlight potential areas for further study, and provide a roadmap for future research endeavors. To fulfill this aim, a comprehensive bibliometric analysis was undertaken, utilizing data extracted from the Scopus database. A total of 1,094 documents relevant to the chosen keywords were successfully gathered for analysis. Frequency analysis was conducted using Microsoft Excel, while citation metrics and further analysis were performed utilizing R package software. The outcomes of this investigation are presented using standard bibliometric metrics, encompassing publication year, authorship, subject area, document type, source title, citation analysis, keyword analysis, active institutional participation, geographic distribution, and language. The analysis revealed a significant increase in publications concerning smart packaging within food distribution since 1990. This upward trend underlines the crucial role of technology in the food industry, potentially impacting the environment, economy, and societal dynamics.

In today’s world, there is a notable focus on incorporating disabled people within the community and the workplace. Advancements in artificial intelligence (AI) have the potential to significantly impact empowering individuals with disabilities to enhance inclusivity and autonomy for this demographic in the era of Society 5.0. This paper aims to offer a bibliometric analysis of the increasing number of publications addressing the potential impact of AI on disabled individuals and their future employment. To conduct this analysis, Scopus served as the bibliographic source, using Disability, Employment, and Future Workforce as the search terms, yielding 203 publications on the subject of study from 1973 to 2024. The analysis was conducted using VOS viewer. The results indicate that as Society 5.0 evolves, advancements in AI have the potential to significantly empower individuals with disabilities, enhancing inclusivity and autonomy for this demographic.

This paper aims to conceptualize the success factors of a digital transformation (DT) strategy and analyze its impact on a company's economic performance. We explore the concepts that affect the field of DT definition and the key drivers that lead to successful DT. Through these key drivers considered as success factors, we propose a research framework linking these drivers to the DT strategy and then corporate economic performance in emerging markets. To test the research model empirically and provide a contextualized interpretation of the results, we adopted a sequential explanatory design. Initially, we performed a quantitative study through a survey among companies listed on the Casablanca Stock Exchange in Morocco. We then analyzed the collected data using the structural equation method. Next, to explain the results, we conducted a qualitative analysis through interviews with semi-structured questions. The findings show that in an emerging economy context such as Morocco, placing the customer at the core of the DT strategy, aligning the organization with the DT strategy, adopting a value system imbued with DT values, and establishing an operational roadmap to drive the change can enhance the company’s digital transformation. These drivers contribute to 59.5% of the implementation of the DT strategy. Driving a DT strategy has a significant impact on companies' economic performance, contributing to 21.5% of their commercial and financial outcomes. This study highlights that the maintenance of a "phygital" business model, which mixes digital and physical business models, and the lack of human resources involvement in the DT process are specific to the emerging market context studied.