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The purpose of this paper is to calculate the financial inclusion index and analyze its dynamics in developing countries.

The authors use the two-stage principal component analysis (PCA) method and consider financial technology innovations to improve the accuracy of the financial inclusion index.

The authors found a downward trend in the financial inclusion index in most developing countries over the study period. The authors also found that a high financial inclusion index is linked to high scores in the Doing Business and high business climate regulation ranking. In addition, the authors observed that the rates of low financial inclusion in developing countries are due to low utilization of and unequal access to financial services.

The analysis suggests that policymakers in developing countries could invest in digital infrastructure to extend access to financial services in remote areas. They could also encourage financial innovation, particularly in financial technologies, by adopting flexible regulatory frameworks. Promoting the financial inclusion of marginalized groups through targeted initiatives tailored to their needs is another solution. They could also encourage the use of financial services by raising awareness and educating populations through training programs. Finally, to improve the business climate, governments could simplify administrative procedures and promote transparency and legal stability.

Unlike previous studies, the use of the two-stage PCA method and the consideration of financial technology (Fintech) innovations such as mobile money in the determinants of the financial inclusion index improve the accuracy of the index.

Waste generated from electrical and electronic equipment, collectively known as E-waste, remains a persistent environmental, economic and social problem. Sustainable E-waste management (EWM) has numerous benefits, such as preventing electronic waste from entering landfills, reducing the need for virgin materials by recovering valuable materials from recycling and lowering greenhouse gas emissions. Circular economy (CE) practices are considered the initial steps toward sustainable EWM, but some hurdles have been reported in the adoption of these practices. Therefore, the current study aims to identify the common CE practices, sustainability of the EWM process and the challenges in EWM, and to develop a conceptual framework for effective EWM.

Very few studies have proposed frameworks that acknowledge the challenges and CE practices of EWM. To fill this gap, a systematic literature review (SLR) was performed, and 169 research articles were explored.

A total of seven challenges in the adoption of effective EWM were identified: rules and policy, infrastructure, consumer behaviour, informal sectors, community culture, technology and economy. Eight common CE practices were also found for effective EWM: reuse, recycle, remanufacturing, refurbishment, repair, reduce, recover and repurpose.

A conceptual framework guiding sustainable EWM was proposed, which includes solutions for the identified challenges, and CE practices with sustainable benefits.

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.

This paper explores how financial technology (FinTech) organisations address poverty-related challenges when providing digital financial services. Employing the conceptual foundation of the liability of poorness (i.e. literacy gaps, a scarcity mindset, intense non-business pressures and a lack of financial slack), this paper explores the innovative strategies that FinTechs use to address these liabilities and promote entrepreneurship.

The paper uses detailed case data collected from three FinTech organisations operating in one South Asian country.

FinTech organisations' innovative strategies reflect a combination of “high touch” (human) vs “low touch” (digital) solutions. All the organisations simplified internal systems or procedures to accommodate customers. The degree to which the three organisations adopted each of the identified strategies shows an emerging typology of FinTechs; that is, innovators with high digital interactions, a mix of digital-human interactions and high human interactions.

The paper develops a typology which categorises FinTech innovative strategies. The typology highlights strategies pro-poor FinTechs use and explains the types of entrepreneurial support innovative organisations provide for their customers. Both the typology and the innovative strategies contribute to enhanced financial inclusion and entrepreneurial promotion amongst the poor.

The originality of the paper comes from its focus on FinTechs' innovative pro-poor strategies. Existing studies typically address the technology-side of innovations. In contrast, this paper combines innovative strategies with the liability of poorness to identify issues associated with financial inclusion.

The Central Bank of Nigeria (CBN) recently relaunched Nigeria’s cashless policy initiative which seeks to reduce financial crime and tax avoidance, decrease cash dependency, advance the adoption of digital financial services (DFS), decrease the risks to the payment system and foster financial inclusion. This study aims to identify the unique challenges of going cashless in Nigeria, particularly in terms of infrastructural, exclusionary and cost implications of the policy on the average citizens.

The author applies a doctrinal research methodology to identify and reflect on key challenges of the cashless policy from the economic, regulatory and transactional perspectives.

The cashless policy initiative in Nigeria heralds value for financial integrity, financial policy regulation and user convenience. The mode of introduction, however, ushers in significant challenges and hardly considers Nigeria’s inadequate payment infrastructure, persistent financial exclusion, low levels of financial and digital literacy and capability, high cost of using DFS and pervasive proclivity for cash. As Nigerians adjust albeit inconveniently to the policy, the CBN can ameliorate the hardship by strengthening the payment infrastructure, particularly for digital payments, fostering consumer trust by safeguarding user funds and enabling consumer preferences.

Research materials include the national regulator’s policy documents and newspaper articles that have not been published in formal reports but non-the-less adequately mirror the policy intention of the CBN and the lived experiences of Nigerians.

This study identifies the practical steps and regulatory measures that the CBN can take to improve acceptance and meaningful and sustainable adoption of the cashless policy by the majority of Nigerians.

The recommendations that are proffered provide some rich insights to inform regulatory direction for the CBN to seamlessly phase-in the cashless policy and consequently drive down financial exclusion in Nigeria.

This study contributes to the policy discussion around the introduction of the cashless Nigeria project. The doctrinal research method highlights the policy intentions of the regulator in juxtaposition with lived experiences of Nigerians. This study offers recommendations to bolster financial inclusion, stability and integrity.

The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.

This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.

Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.

The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.

This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

This study aims to deepen the understanding of the challenges and implications entailed by deploying mobile clinics in conflict zones to reach populations affected by violence and cut off from health-care services.

This research combines an integrated literature review and an instrumental case study. The literature review comprises two targeted reviews to provide insights: one on conflict zones and one on mobile clinics. The case study describes the process and challenges faced throughout a mobile clinic deployment during and after the Iraq War. The data was gathered using mixed methods over a two-year period (2017–2018).

Armed conflicts directly impact the populations’ health and access to health care. Mobile clinic deployments are often used and recommended to provide health-care access to vulnerable populations cut off from health-care services. However, there is a dearth of peer-reviewed literature documenting decision support tools for mobile clinic deployments.

This study highlights the gaps in the literature and provides direction for future research to support the development of valuable insights and decision support tools for practitioners.

This study aims to convey the understanding of the ecosystem – how “hundi” works on the border trade between Myanmar and northern Thailand, which is an informal transfer system and is widely used as an alternative banking system. Even though the role of hundi is unable to declare the sources of money under the standard settlement of formal banking system, a failure to operate of its official mechanism are carrying using hundi, as a financial platform across the border between Thailand and Myanmar. This study surveys the best practice mechanism for the regional and international cooperation.

This paper draws on relevant literature, open-source reporting, and interviews with more than 30 interviewees on the border between Thailand and Myanmar. Interviewees includes border-trader, money changer, money transfer operators, business leaders, hundi operators, immigrant labors, government officials and commercial banking staffs.

This study provides a unique insight of hundi system, which work as the alternative mode of formal banking. It is an informal fund transfer payment platform used on the border between Thailand and Myanmar in the past five decades. It insists that hundi plays a significant role in both substitution and complementary on the trade and payment across the border of Myanmar–Thailand. Even though confronting with the barriers of financing of terrorism (anti money laundering AML/combating the financing of terrorism CFT) risk, the competition with the expanding and modernizing formal banking sector, and the introduction of Fintech and mobile money services. In the short term, these are unlikely to eliminate the hundi system completely, but may instead push hundi operators towards adopting these networks and technologies in their own operations.

This paper will be a useful source for academics, development professionals, policymakers, law enforcement agencies and business actors who are seeking to understand Myanmar’s informal payment system, hundi.

This is the latest work for border trade payment or trade financing role of hundi which has hidden under the informal market of the border for several decades. It has few research of hundi on border trade and payment, particularly after the military coup in 2021 which made hundi return to be on the spotlight and simultaneous mechanism of border trade and payment ecosystem of Myanmar. This paper will be a useful source for academics, development professionals, policymakers, law enforcement agencies and business actors who are eager to understand Myanmar’s informal payment system, hundi, especially during the hardship.

This study aims to reveal how mobile app stickiness is formed and how the stickiness formation process differs for apps of different social levels.

This study proposed and validated a stickiness formation model following the cognitive–affective–conative framework. Data were collected from surveys of 1,240 mobile app users and analyzed using structural equation modeling. Multigroup analysis was applied to contrast the stickiness formation process among apps of different social levels.

This study revealed a causal link between cognitive, affective and conative factors. It found partial mediation effects of trust in the association between perceptions and satisfaction, and the full mediation role of satisfaction and personal investment (PI) in the effects of subjective norm (SN) on stickiness. The multigroup analysis results suggested that social media affordances benefit stickiness through increased PI and strengthened effects of SN on PI. However, it damages stickiness through increased perceived privacy risk (PPR), decreased trust and strengthened effects of PPR on trust.

This study contributes to both stickiness scholars and practitioners, as it builds a model to understand the stickiness formation process and reveals the effects of the “go social” strategy. The novelty of this study is that it examined social influences, considered privacy issues and revealed two mediation mechanisms. The findings can guide the improvement of mobile app stickiness and the application of the “go social” strategy.