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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 description of “blockchain banking”, the determination of “the sub-processes” of “blockchain banking” as a “business process”, and the assessment of “maturity level” in Parsian Bank.

Theoretical sources on “blockchain banking” were initially investigated. Then the “sub-processes” of “blockchain banking” as a “business process” were extracted by Parsian Bank's experts through the “Delphi method”. Next, the “sequence” of the “sub-processes” was determined by means of the “AHP”. Eventually, Parsian Bank's maturity levels for all the sub-processes as well as the overall maturity level were specified on the basis of the “CMMI” V1.3 in order for Business Process Management (BPM).

Blockchain banking’ combines traditional banking with cryptocurrencies, which can be provided by merging “hybrid e-wallet” with “bank account” and “bank card” – all together as “crypto bank account”. Plus, “hybrid e-wallet” is a form of mobile e-wallet on blockchain that supports both cryptocurrencies and traditional currencies in the same platform by which the purchase and sale of cryptocurrencies are possible. Besides, “Blockchain banking service” can also be offered within the framework of “open banking” aligned with “open innovation” through a FinTech (or a beta bank) in collaboration with a licensed bank via “open API”, which is called “blockchain banking based on FinTech”. At last, the eight sub-processes of “blockchain banking” were determined and Parsian Bank's “maturity level” was specified.

This is the very first practical guide to “blockchain banking service”.

The objective of this study is to investigate the impact of the COVID-19 pandemic on the consumer payment behavior in Italy by correlating financial literacy with digital payment awareness, examining media anxiety and financial security, and including a gender analysis.

Consumers’ attitudes toward cashless payments were investigated using an online survey conducted from November 2021 to February 2022 on a sample of 836 Italian citizens by considering the behavioral characteristics and aspects of financial literacy. Structural equation modeling (SEM) was used to test the hypotheses and to determine whether the model was invariant by gender.

The analysis showed that the fear of contracting COVID-19 and the level of financial literacy had a direct influence on the payment behavior of Italians, which was completely different in its weighting. Fear due to the spread of news regarding the pandemic in the media indirectly influenced consumers’ noncash attitude. The preliminary results of the gender multigroup analysis showed that cashless payment was the same in the male and female subpopulations.

This research is noteworthy because of its interconnected examination. It examined the effects of the COVID-19 pandemic on people’s payment choices, assessed their knowledge, and considered the influence of media-induced anxiety. By combining these factors, the study offered an analysis from a gender perspective, providing understanding of how financial behaviors were shaped during the pandemic.

This study explores whether targeted microcredit can effectively alleviate households’ multidimensional relative poverty (MdRP) in rural China in the new era following the poverty elimination campaign and discusses it from a gendered perspective.

This study applies a fixed-effects model, propensity score matching (PSM) and two-stage instrumental variable method to two-period panel data collected from 611 households in rural western China in 2018 and 2021 to explore the effects, mechanisms and heterogenous performance of targeted microcredit on households’ MdRP in the new era.

(i) Targeted microcredit can alleviate MdRP among rural households in the new era, mainly by reducing income and opportunity inequality. (ii) Targeted microcredit can promote women’s empowerment, mainly by enhancing their social participation, thereby helping alleviate households’ MdRP. The effect of the targeted microcredit on MdRP is more significant in medium-educated women households and non-left-behind women households. (iii) The MdRP alleviation effect is stronger in villages with a high degree of digitalization.

Learn from the experience of targeted microcredit. Accurately identify poor groups and integrate loan design into financial health and women empowerment. Particularly, pay attention to less-educated and left-behind women households and strengthen coordination between targeted microcredit and digital village strategies.

This study clarifies the effect of targeted microcredit on women’s empowerment and households’ MdRP alleviation in the new era. It also explores its various effects on households with different female characteristics and regional digitalization levels, providing ideas for optimizing microcredit.

Traditional classification algorithms always have an incorrect prediction. As the misclassification rate increases, the usefulness of the learning model decreases. This paper presents the development of a wisdom framework that reduces the error rate to less than 3% without human intervention.

The proposed WisdomModel consists of four stages: build a classifier, isolate the misclassified instances, construct an automated knowledge base for the misclassified instances and rectify incorrect prediction. This approach will identify misclassified instances by comparing them against the knowledge base. If an instance is close to a rule in the knowledge base by a certain threshold, then this instance is considered misclassified.

The authors have evaluated the WisdomModel using different measures such as accuracy, recall, precision, f-measure, receiver operating characteristics (ROC) curve, area under the curve (AUC) and error rate with various data sets to prove its ability to generalize without human involvement. The results of the proposed model minimize the number of misclassified instances by at least 70% and increase the accuracy of the model minimally by 7%.

This research focuses on defining wisdom in practical applications. Despite of the development in information system, there is still no framework or algorithm that can be used to extract wisdom from data. This research will build a general wisdom framework that can be used in any domain to reach wisdom.

As many businesses faced economic disruption due to the Covid-19 pandemic and sought financial relief, existing bank relationships became critical to getting a loan. This study examines factors associated with the development of personal relationships of rural small businesses with community bank representatives.

We applied a mixed-method approach. We employed descriptive statistics, principal factor analysis and logistic regression for data analysis. We distributed an online survey to rural small businesses in five states in the United States. Key informant interviews with community bank representatives supplemented the survey results.

A business owner’s trust in a banker was positively associated with the establishment of a business–bank relationship. However, an analysis of individual trust’s components revealed that the nature of trust is complex, and a failure of one or more components may lead to decreased trustworthiness in a banker. Small businesses that preferred personal communication with a bank were more inclined to relationship banking.

Due to the relatively small sample size and cross-sectional data, our results may not be conclusive but should be viewed as preliminary and as suggestions for future research. Bankers should be aware of the importance of trust for small business owners and of the actions that lead to increased trustworthiness.

The study extends the existing knowledge on the business–bank relationship by focusing mainly on social (instead of economic) factors associated with the establishment of the business–bank relationship in times of crisis and high uncertainty.

The purpose of the study is to identify cybersecurity threats that hinder the adoption of digital banking and provide sustainable strategies to combat cybersecurity risks in the banking industry.

Systematic literature review guidelines were used to conduct a quantitative synthesis of empirical evidence regarding the impact of cybersecurity threats and risks on the adoption of digital banking.

A total of 84 studies were initially examined, and after applying the selection and eligibility criteria for this systematic review, 58 studies were included. These selected articles consistently identified identity theft, malware attacks, phishing and vishing as significant cybersecurity threats that hinder the adoption of digital banking.

With the country’s banking sector being new in this area, this study contributes to the scant literature on cyber security, which is mostly in need due to the myriad breaches that the industry has already suffered thus far.

Traditional payment systems based on cash and bank cards are being replaced by new innovative formats. This research analyzes the success factors in the adoption by customers of Bizum, a peer-to-peer (P2P) mobile payment system widely used in Spain. This study proposes a theoretical framework based on the Stimulus–Organism–Response (S-O-R) model and includes the analysis of the moderating effect of perceived risk and the mediating effect of perceived trust.

To achieve the proposed objectives, an online questionnaire was administered to 701 Spanish smartphone users, potential users of the proposed P2P payment systems.

The results show that perceived usefulness is the most important predictor of intention to use. Additionally, a medium predictive relevance performance of the proposed model is found.

This research contributes to a more holistic understanding of the adoption of P2P payment systems and provides new business opportunities that companies can exploit through the use of this technology.

以現金和銀行卡為基礎的傳統支付系統、正被嶄新而又富創意的模式取代。本研究擬分析驅使消費者使用Bizum的成功因素 (Bizum 是一個在西班牙得到廣泛使用的P2P移動支付系統) 。本研究以刺激-有機體-反應模型為基礎, 建議了一個概念框架;研究亦擬分析感知風險的調節作用, 以及感知信任的中介效應。

為達研究目標, 研究人員處理了701名西班牙智慧型手機使用者回答的網上問卷, 這些使用者是本研究提出的P2P支付系統的潛在用戶。

研究結果顯示、感知有用性是消費者會否使用P2P支付系統最重要的預測因素。研究結果亦顯示、本研究建議的概念框架的預測能力水平屬中等。

本研究使我們對驅使消費者使用P2P支付系統的因素更全面地了解;研究也使企業可透過使用這技術, 為自己創造新的商機。

Drawing upon a political economy approach, this article aims to analyze the transformations in the labor market within the context of contemporary capitalism, focusing on the phenomenon of financialization.

Financialization is defined as a distinct wealth pattern marked by a growing proportion of financial assets in capitalist wealth. Within financial markets, corporate performance is continuously assessed, in a process that disciplines management to achieve expected financial results, with consequences throughout corporate management.

We find that this phenomenon has implications for labor management, resulting in the intensification of labor processes and the adoption of insecure forms of employment, leading to the fractalization of work. These two mechanisms, added to the indebtedness of workers, constitute three elements for disciplining labor in contemporary capitalism.

We argue that these forms of discipline constitute a subsumption of labor to finance, resulting in an increase in labor exploitation. This formulation of the relationship between financialization and changes in the realm of labor also contributes to understanding the unrealizing potential of social free time in contemporary capitalism.

This paper aims to investigate whether professional data analysts’ trust of black-box systems is increased by explainability artifacts.

The study was developed in two phases. First a black-box prediction model was estimated using artificial neural networks, and local explainability artifacts were estimated using local interpretable model-agnostic explanations (LIME) algorithms. In the second phase, the model and explainability outcomes were presented to a sample of data analysts from the financial market and their trust of the models was measured. Finally, interviews were conducted in order to understand their perceptions regarding black-box models.

The data suggest that users’ trust of black-box systems is high and explainability artifacts do not influence this behavior. The interviews reveal that the nature and complexity of the problem a black-box model addresses influences the users’ perceptions, trust being reduced in situations that represent a threat (e.g. autonomous cars). Concerns about the models’ ethics were also mentioned by the interviewees.

The study considered a small sample of professional analysts from the financial market, which traditionally employs data analysis techniques for credit and risk analysis. Research with personnel in other sectors might reveal different perceptions.

Other studies regarding trust in black-box models and explainability artifacts have focused on ordinary users, with little or no knowledge of data analysis. The present research focuses on expert users, which provides a different perspective and shows that, for them, trust is related to the quality of data and the nature of the problem being solved, as well as the practical consequences. Explanation of the algorithm mechanics itself is not significantly relevant.