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The literature review stated that financial inclusion (FI) influences economic growth through different channels. Hence, this paper aims to investigate the underlying process of FI in Egypt theoretically, and to derive some policy implications for promoting the process and achieving more improvement in different financial and economic aspects, that is basically through discussing the opinions of FI's main stockholders in Egypt.

The analysis used secondary data from the Global Findex and FAS Database, namely, automated teller machines, outstanding deposits and loans with commercial banks, debit and credit cards ownership. The research particularly used scientific methods as method of deduction, methods of graphical and tabular representation of data, comparative analysis and synthesis of partial knowledge. The paper is also based on a descriptive approach in addition to in-depth interviews with the main stakeholders of the financial inclusion process in Egypt.

The analyzed results of interviews revealed that new FI vision should have a deep understanding of the financial lives of the poor and low-income groups, including how they acquire, manage and use their money. However, the impact is becoming more prominent for the efficiency of the banking system and hence economic growth rather a regulatory and sound institutional framework enhances it. This finding supported the fact that Egypt can design an appropriate FI strategy, but the main challenge is how to implement it with the required speed and outreach capacity, especially in underprivileged communities.

The result of this study has interesting implications for Egypt's ability to attain effective FI initiatives that promote sound financial choices and behavior which in turn help to stimulate financial and economic growth.

The study contributes to the literature by assessing the FI level in Egypt, its implications and how it should be enhanced for better performance and results in the future. It addresses the deep fact of this process through inclusive surveys and interviews that help in determining the road ahead.

The present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.

The rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

The purpose of the paper is to propose and demonstrate a novel approach for addressing the challenges of path planning and obstacle avoidance in the context of mobile robots (MR). The specific objectives and purposes outlined in the paper include: introducing a new methodology that combines Q-learning with dynamic reward to improve the efficiency of path planning and obstacle avoidance. Enhancing the navigation of MR through unfamiliar environments by reducing blind exploration and accelerating the convergence to optimal solutions and demonstrating through simulation results that the proposed method, dynamic reward-enhanced Q-learning (DRQL), outperforms existing approaches in terms of achieving convergence to an optimal action strategy more efficiently, requiring less time and improving path exploration with fewer steps and higher average rewards.

The design adopted in this paper to achieve its purposes involves the following key components: (1) Combination of Q-learning and dynamic reward: the paper’s design integrates Q-learning, a popular reinforcement learning technique, with dynamic reward mechanisms. This combination forms the foundation of the approach. Q-learning is used to learn and update the robot’s action-value function, while dynamic rewards are introduced to guide the robot’s actions effectively. (2) Data accumulation during navigation: when a MR navigates through an unfamiliar environment, it accumulates experience data. This data collection is a crucial part of the design, as it enables the robot to learn from its interactions with the environment. (3) Dynamic reward integration: dynamic reward mechanisms are integrated into the Q-learning process. These mechanisms provide feedback to the robot based on its actions, guiding it to make decisions that lead to better outcomes. Dynamic rewards help reduce blind exploration, which can be time-consuming and inefficient and promote faster convergence to optimal solutions. (4) Simulation-based evaluation: to assess the effectiveness of the proposed approach, the design includes a simulation-based evaluation. This evaluation uses simulated environments and scenarios to test the performance of the DRQL method. (5) Performance metrics: the design incorporates performance metrics to measure the success of the approach. These metrics likely include measures of convergence speed, exploration efficiency, the number of steps taken and the average rewards obtained during the robot’s navigation.

The findings of the paper can be summarized as follows: (1) Efficient path planning and obstacle avoidance: the paper’s proposed approach, DRQL, leads to more efficient path planning and obstacle avoidance for MR. This is achieved through the combination of Q-learning and dynamic reward mechanisms, which guide the robot’s actions effectively. (2) Faster convergence to optimal solutions: DRQL accelerates the convergence of the MR to optimal action strategies. Dynamic rewards help reduce the need for blind exploration, which typically consumes time and this results in a quicker attainment of optimal solutions. (3) Reduced exploration time: the integration of dynamic reward mechanisms significantly reduces the time required for exploration during navigation. This reduction in exploration time contributes to more efficient and quicker path planning. (4) Improved path exploration: the results from the simulations indicate that the DRQL method leads to improved path exploration in unknown environments. The robot takes fewer steps to reach its destination, which is a crucial indicator of efficiency. (5) Higher average rewards: the paper’s findings reveal that MR using DRQL receive higher average rewards during their navigation. This suggests that the proposed approach results in better decision-making and more successful navigation.

The paper’s originality stems from its unique combination of Q-learning and dynamic rewards, its focus on efficiency and speed in MR navigation and its ability to enhance path exploration and average rewards. These original contributions have the potential to advance the field of mobile robotics by addressing critical challenges in path planning and obstacle avoidance.

The purpose of this paper is to investigate the way technology affects the provision of prehospital emergency care, upgrading the quality of services offered and significantly reducing the risk of premature termination of the patients.

The paper presents the development of the eEKAB, a pilot emergency medical information system that simulates the main services offered by the Greek National Instant Aid Centre (EKAB). The eEKAB was developed on an agile system methodology. From a technical perspective, the features and the technology were mainly chosen to provide reliable and user-friendly interfaces that will attract many users. eEKAB is based on three important pillars for offering health care to the patients: the “On-time Incident Reporting”, the “On-time Arrival at the Incident” and “Transfer to the Health Center”. According to the literature review, the emergency medical services (EMS) systems that combine all the features are very few.

It reduces the total time of the EMS procedures and it allows for an easier management of EMS, by providing a better allocation of human resources and a better geographical distribution of ambulances. The evaluation displayed that it is a very helpful application for the ambulance drivers as it reduces the ambulance response time to arrive in the patient's location and contributes significantly to the general performance of the prehospital medical care system. Also, the survey verified the importance of implementing eEKAB on a larger scale beyond the pilot usage. It is worth mentioning that the younger ambulance drivers had a more positive view for the purpose of the application.

The paper clearly identifies implications for further research. Regarding interoperability, the mobile app cooperates with the Operational Center of EKAB, while further collaboration could be achieved with other operational ambulance handling center, mainly, of the private sector. The system can evolve to include better communications among the EKAB departments. Particularly, the ambulance crew as well as the doctors should be informed with more incident features such as the emergency signal so that they know whether to open the siren, the patient's name, etc. The authors are currently working on implementing some features to provide effective medical health services to the patient in the ambulance.

eEKAB will have very significant implications in case of its enforcement, such as the reduction of the total time of EMS procedures with a corresponding reduction of the operating costs of an accident management system and an ambulance fleet handling system while in parallel informing in time the doctors/clinics. It will provide better distribution of ambulances as well as of total human resources. It will greatly assist ambulance drivers, while reducing ambulance response time to reach the patient's location. In other words, the authors will have a better performance of the whole prehospital care system.

Providing emergency care before the hospital is of great importance for upgrading the quality of health services provided at the accident site, thus significantly reducing the risk of premature death of patients. This in itself has a significant social implication.

The paper demonstrates a solid understanding in the field of the EMS systems and the corresponding medical services offered. It proposes the development of an effective, feasible and innovative EMS information system that will improve the existing emergency health care system in Greece (EKAB). An in depth literature review and presentation of the adopted new technologies and the respective architecture take place. An evaluation and statistical validation were conducted for proving the high applicability of eEKAB in case of real-life running.

Corporate social responsibility (CSR) activities of companies have been reported to generate favorable consumer attitudes towards the business, which in turn, encourages positive consumer behavior. But the application of this conclusion in the mobile commerce domain remains limited. Drawing on social exchange theory, the authors examined the mediating role of trusting beliefs and satisfaction in the relationship between CSR and purchase intention in mobile commerce.

Using nonprobability convenience sampling, the authors administered a cross-sectional survey with 314 students from Keimyung University in Korea. Initially, SPSS-Amos was utilized to run confirmatory factor analysis. The proposed hypotheses were tested using structural equation modeling.

This study showed that two components of trusting beliefs, namely benevolence and competence, had a positive effect on the association between CSR and the purchase intention on mobile shopping applications. This relationship was partially mediated by trusting beliefs. Further findings demonstrated that the impact of CSR on purchase intention was also partially mediated by satisfaction.

The authors' contribution includes extending the social exchange theory to the mobile commerce setting by establishing mechanisms that explain the ways CSR influences purchase intention in the mobile commerce framework. The authors integrated trusting beliefs and satisfaction in the CRS chain link with purchase intention. Additionally, the authors examined the individual effects of three trusting beliefs components. Based on the results, the authors proposed suggestions for the mobile shopping application business on the methods they can implement to boost the outcomes of their CSR activities.

:過去的研究結果、均顯示公司的企業社會責任行為會產生對公司良好的消費者態度,而這消費者態度,則進而促進積極的消費行為; 唯這結論的應用、在移動商務的領域內仍是有限的。我們利用社會交換理論、探討了信任的信念和滿意在移動商務裡的企業社會責任與購買意圖之間的關係上的中介角色。

:透過使用非機率便利抽樣的研究法,我們對來自南韓啟明大學314名學生進行了一個橫向調查研究。最初,我們使用結構方程模型 (SPSS-AMOS) 來進行驗證性因素分析。又用結構方程模型 (SEM) 來為提出的假設進行測試。

:研究結果顯示,信任的信念的兩個組成部分,即仁愛和能力,均於移動購物應用上對企業社會責任與購買意圖之間的關聯有積極正面的影響。這關聯部分上為信任的信念所調節。研究結果更表明了企業社會責任對購買意圖的影響、亦是部分上為滿意所調節的。

:本研究的貢獻、包括藉著建立一個可解釋企業社會責任、如何在移動商務的框架裡影響購買意圖的機制,去把社會交換理論擴展至移動商務的環境。我們在企業社會責任與購買意圖一連串的關聯裡、融入了信任的信念和滿意。而且,我們探討了三個信任的信念的組成部分各自所帶來的影響。我們根據研究的結果,為移動購物應用之商業機構,建議有效的方法,去增強其企業社會責任行為的效果。

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

Mouth segmentation is one of the challenging tasks of development in lip reading applications due to illumination, low chromatic contrast and complex mouth appearance. Recently, deep learning methods effectively solved mouth segmentation problems with state-of-the-art performances. This study presents a modified Mobile DeepLabV3 based technique with a comprehensive evaluation based on mouth datasets.

This paper presents a novel approach to mouth segmentation by Mobile DeepLabV3 technique with integrating decode and auxiliary heads. Extensive data augmentation, online hard example mining (OHEM) and transfer learning have been applied. CelebAMask-HQ and the mouth dataset from 15 healthy subjects in the department of rehabilitation medicine, Ramathibodi hospital, are used in validation for mouth segmentation performance.

Extensive data augmentation, OHEM and transfer learning had been performed in this study. This technique achieved better performance on CelebAMask-HQ than existing segmentation techniques with a mean Jaccard similarity coefficient (JSC), mean classification accuracy and mean Dice similarity coefficient (DSC) of 0.8640, 93.34% and 0.9267, respectively. This technique also achieved better performance on the mouth dataset with a mean JSC, mean classification accuracy and mean DSC of 0.8834, 94.87% and 0.9367, respectively. The proposed technique achieved inference time usage per image of 48.12 ms.

The modified Mobile DeepLabV3 technique was developed with extensive data augmentation, OHEM and transfer learning. This technique gained better mouth segmentation performance than existing techniques. This makes it suitable for implementation in further lip-reading applications.

Despite the fact that chatbots have been largely adopted for the last few years, a comprehensive literature review research focusing on the intention of individuals to adopt chatbots is rather scarce. In this respect, the present paper attempts a literature review investigation of empirical studies focused on the specific issue in nine scientific databases during 2017-2021. Specifically, it aims to classify extant empirical studies which focus on the context of individuals' adoption intention toward chatbots.

The research is based on PRISMA methodology, which revealed a total of 39 empirical studies examining users' intention to adopt and utilize chatbots.

After a thorough investigation, distinct categorization criteria emerged, such as research field, applied theoretical models, research types, methods and statistical measures, factors affecting intention to adopt and further use chatbots, the countries/continents where these surveys took place as well as relevant research citations and year of publication. In addition, the paper highlights research gaps in the examined issue and proposes future research directions in such a promising information technology solution.

As far as the authors are concerned, there has not been any other comprehensive literature review research to focus on examining previous empirical studies of users' intentions to adopt and use chatbots on the aforementioned period. According to the authors' knowledge, the present paper is the first attempt in the field which demonstrates broad literature review data of relevant empirical studies.

This research work aims to arise from developing new communication channels for customer service in micro, small and medium enterprises (MSMEs), such as chatbots. In particular, the results of the usability testing of three chatbots implemented in MSMEs are presented.

The methodology employed includes participants, chatbot development platform, research methodology, software development methodology and usability test to contextualize the study's results.

Based on the results obtained from the System Usability Scale (SUS) and considering the accuracy of the chatbot's responses, it is concluded that the level of satisfaction in using chatbots is high; therefore, if the chatbot is well integrated with the communication systems/channels of the MSMEs, the client receives an excellent, fast and efficient service.

The paper analyzes chatbots for customer service and presents the usability testing results of three chatbots implemented in MSMEs.

This study aims to examine the influence of the derived attributes of embedded artificial intelligence-mobile smart speech recognition (AI-MSSR) technology, namely perceived usefulness, perceived ease of use (PEOU) and perceived enjoyment (PE) on consumer purchase intention (PI) through the chain relationships of attitudes to AI and consumer smart experience, with the moderating effect of consumer innovativeness and Generation (Gen) X and Gen Y in fashion retail.

The study employed a quantitative survey strategy, drawing a sample of 836 respondents from Sri Lanka and India representing Gen X and Gen Y. The data analysis was carried out using smart partial least squares structural equation modelling (PLS-SEM).

The findings show a positive relationship between the perceived attributes of MSSR and consumer PI via attitudes towards AI (AAI) and smart consumer experiences. In addition, consumer innovativeness and Generations X and Y have a moderating impact on the aforementioned relationship. The theoretical and managerial implications of the study are discussed with a note on the research limitations and further research directions.

To multiply the effects of embedded AI-MSSR and consumer PI in fashion retail marketing, managers can develop strategies that strengthen the links between awareness, knowledge of the derived attributes of embedded AI-MSSR and PI by encouraging innovative consumers, especially Gen Y consumers, to engage with embedded AI-MSSR.

This study advances the literature on embedded AI-MSSR and consumer PI in fashion retail marketing by providing an integrated view of the technology acceptance model (TAM), the diffusion of innovation (DOI) theory and the generational cohort perspective in predicting PI.