The objective of this research is to assess the current state of e-banking in Saudi Arabia. The banking industry is rapidly evolving to use e-banking as an efficient and appropriate tool for customer satisfaction. Traditional banks recommend online banking as a particular service to their customers in order to provide them with faster and better service. As a result of the rapid advancement of technology, banks have used e-banking and mobile banking to both accumulate users and conduct banking transactions. Nonetheless, the primary challenge with electronic banking is satisfying customers who use Internet banking. Thus, the current study seeks to determine what factors affect e-payment adoption with e-banking services. mobile banking, e-wallets, and e-banking, as well as the mediating role of customer trust, can drive e-payment adoption. We distributed the survey online and offline to a total of 336 participants. A convenience sampling technique was used; structure equation modeling (SEM), convergence and discriminant validity; and model fitness were achieved through Smart PLS 3. The findings have shown that mobile banking, e-banking, and e-wallets are three significant independent variables that mediate the role of customer trust in influencing e-payment adoption when using Internet banking services. They should emphasize trust-building activities, specifically in relation to the new ways of e-payment such as e-banking, m-payments, NFC, and e-proximity, which will further help reduce consumer perceptions of risk. The system developers should design user-friendly applications and e-payment apps to enhance consumers’ belief in using them for payment purposes over any Internet-enabled device. They should promptly respond to consumers in cases of failed e-payment transactions and be able to promptly demonstrate transparency in settling claims for such failed transactions. Future studies could benefit from implementing probability sampling to facilitate comparisons with non-probability sampling studies. This study selected responses from only Saudi Arabian adopters of mobile payment technology. We need to conduct research on non-adopters and analyze the results using the model we proposed in this study. Due to time and resource constraints, in depth research using a mixed-methods approach could not be conducted. Future studies can utilize a mixed-methods approach for further understanding.

This study investigates the impact of digital payment infrastructure accessibility on the social influence of microenterprises in Barranquilla, Colombia, while examining the mediating roles of financial inclusion, digital literacy, social support networks, and collaboration with social innovation initiatives. Employing a mixed-methods approach, the study analyzes data from a sample of 25 microenterprises operating in various sectors. The findings, based on statistical techniques such as multiple regression, path analysis, and structural equation modeling (SEM), provide strong evidence for the positive influence of digital payment infrastructure accessibility on the social relationship of microenterprises. The results also highlight the crucial roles played by financial inclusion and social support networks in mediating this relationship. The study contributes to the growing body of literature on the factors driving the social effect of microenterprises and offers valuable insights for policymakers and practitioners aiming to foster inclusive economic development in the region. The findings suggest that investing in the development and expansion of digital payment systems, alongside efforts to promote financial inclusion and strengthen social support networks, can have far-reaching benefits for microenterprises and their communities.

This research explores the interactions within supply chains in the manufacturing sector, with a special emphasis on the distinctive obstacles encountered by the mosquito coil industry. The study is motivated by the need to comprehensively understand and address the multifaceted challenges encountered by manufacturers in their supply chain processes. The mosquito coil industry holds significant importance in Malaysia, primarily due to the country’s tropical climate, which is conducive to mosquito proliferation and the transmission of mosquito-borne diseases. Nowadays, there are growing complexities and disruptions experienced by the mosquito coil sector’s supply chain, prompting an in-depth investigation. The main objective is to identify the challenges and resilience strategies employed by manufacturers in this sector, providing an understanding that contributes to the broader discourse on supply chain dynamics. Employing a qualitative case study methodology, this research engages in extensive data collection through interviews, document analysis, and direct observations within the selected mosquito coil manufacturing entity. This methodology allows for an immersive exploration of the challenges faced, revealing insights into the factors influencing the supply chain dynamics. The study reveals a wide array of challenges, from obtaining raw materials to managing distribution logistics, underscoring the unique complexities specific to the sector. As a result, the research identifies and analyzes resilience strategies implemented by the mosquito coil manufacturer to mitigate challenges, such as procurement challenges faced in financial related issues, logistical complexities occurred from recent years’ worldwide pandemic, production disruptions from company’s human resource-related issues, global factors from the company’s competitors and market challenges, and technology integration from rapid technological advancements. Thus, implications of this study extend beyond the mosquito coil sector, contributing valuable knowledge to the academic community, practitioners, and policymakers involved in supply chain management. The research not only addresses the identified challenges but also serves as a foundation for enhancing the overall understanding of manufacturing supply chain dynamics, thereby fostering informed decision-making for improved industry resilience.

Catastrophes, like earthquakes, bring sudden and severe damage, causing fatalities, injuries, and property loss. This often triggers a rapid increase in insurance claims. These claims can encompass various types, such as life insurance claims for deaths, health insurance claims for injuries, and general insurance claims for property damage. For insurers offering multiple types of coverage, this surge in claims can pose a risk of financial losses or bankruptcy. One option for insurers is to transfer some of these risks to reinsurance companies. Reinsurance companies will assess the potential losses due to a catastrophe event, then issue catastrophe reinsurance contracts to insurance companies. This study aims to construct a valuation model for catastrophe reinsurance contracts that can cover claim losses arising from two types of insurance products. Valuation in this study is done using the Fundamental Theorem of Asset Pricing, which is the expected present value of the number of claims that occur during the reinsurance coverage period. The number of catastrophe events during the reinsurance coverage period is assumed to follow a Poisson process. Each impact of a catastrophe event, such as the number of fatalities and injuries that cause claims, is represented as random variables, and modeled using Peaks Over Threshold (POT). This study uses Clayton, Gumbel, and Frank copulas to describe various dependence characteristics between random variables. The parameters of the POT model and copula are estimated using Inference Functions for Margins method. After estimating the model parameters, Monte Carlo simulations are performed to obtain numerical solutions for the expected value of catastrophe reinsurance based on the Fundamental Theorem of Asset Pricing. The expected reinsurance value based on Monte Carlo simulations using Indonesian earthquake data from 1979–2021 is Rp 10,296,819,838.

Surveys are one of the most important tasks to be executed to get valued information. One of the main problems is how the data about many different persons can be processed to give good information about their environment. Modelling environments through Artificial Neural Networks (ANNs) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in dealing with sets of data with some noise, but they are fundamentally surjective mathematical functions, and they aren’t able to give different results for the same input. So, if an ANN is trained using data where samples with the same input configuration has different outputs, which can be the case of survey data, it can be a major problem for the success of modelling the environment. The environment used to demonstrate the study is a strategic environment that is used to predict the impact of the applied strategies to an organization financial result, but the conclusions are not limited to this type of environment. Therefore, is necessary to adjust, eliminate invalid and inconsistent data. This permits one to maximize the probability of success and precision in modeling the desired environment. This study demonstrates, describes and evaluates each step of a process to prepare data for use, to improve the performance and precision of the ANNs used to obtain the model. This is, to improve the model quality. As a result of the studied process, it is possible to see a significant improvement both in the possibility of building a model as in its accuracy.

This study aims to identify the risk factors causing the delay in the completion schedule and to determine an optimization strategy for more accurate completion schedule prediction. A validated questionnaire has been used to calculate a risk rating using the analytical hierarchy process (AHP) method, and a Monte Carlo simulation on @RISK 8.2 software was employed to obtain a more accurate prediction of project completion schedules. The study revealed that the dominant risk factors causing project delays are coordination with stakeholders and changes in the scope of work/design review. In addition, the project completion date was determined with a confidence level of 95%. All data used in this study were obtained directly from the case study of the Double-Double Track Development Project (Package A). The key result of this study is the optimization of a risk-based schedule forecast with a 95% confidence level, applicable directly to the scheduling of the Double-Double Track Development Project (Package A). This paper demonstrates the application of Monte Carlo Simulation using @RISK 8.2 software as a project management tool for predicting risk-based-project completion schedules.