2024_Early Access

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    Digital payments as an indicator of financial inclusion in Euro Area countries
    (2024-01-23) Petrikova, Tamara; Kocisova, Kristina
    The process of digitisation in the financial sector is developing through the systematic introduction of computer systems, the establishment of Internet connectivity and the use and ownership of various information and communication devices. Information and communication technologies can increase the desired degree of financial inclusion in a country by increasing the availability of various financial services. This study examines the individual attributes that can affect financial inclusion in the Euro Area countries in 2021. Our analysis applies a probit model to data from the World Bank Global Findex database, focusing on digital payments as a proxy for financial inclusion. The main finding highlights that higher income, higher education, female gender, and younger age groups are associated with an increased propensity to engage in digital payments. Notably, our expectation of a non-linear relationship between age and digital payments is confirmed, as evidenced by the application of the Robin Hood algorithm. Specifically, we observe a positive correlation between age and digital payment usage. However, this trend reverses beyond a specific breakpoint, approximately around the age of 40, leading to a subsequent decline in digital payment activity. Furthermore, our research shows that individuals who utilised alternative payment methods alongside cash before the COVID-19 pandemic are likelier to engage in higher digital payments. Additionally, a tendency for higher adoption of digital payments coincides with countries that achieved a higher Digital Access Index (DAI), an indicator assessing the degree of digitalisation in a country. Furthermore, it is associated with countries among the Euro Area’s founding members.
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    The influence of the COVID-19 pandemic on managerial functions: Theory verified by Delphi method
    (2024-01-23) Noskova, Marta; Kutlak, Jiri
    The aim of the article is to analyse the performance of managerial functions in the context of the COVID-19 pandemic. The first part of the article focuses on a systematic literature review (SLR) aimed at identifying the most frequently researched managerial functions in the context of changes due to the pandemic and the difficulties in performing these functions. A total of 211 articles from the Web of Science database were analysed, 18 of which were relevant to the present research. Based on the SLR conducted, two research questions were identified and answered by conducting a three-round Delphi survey among the experts interviewed (a total of 23 company managers). The results show that during the COVID-19 pandemic, the managerial function of planning has the highest importance and was performed the most often, followed by the function of leading. The managers gave minor importance to the organising function, which was statistically confirmed by Friedmann ANOVA followed by post hoc analysis – the Bonferroni-Dunn test. The results also confirmed a satisfactory level of expert agreement on the data obtained (Kendall W ~ 0.7–0.84), confirming the relevance of the findings. Also, several internal barriers that affected the work of managers were identified. The results are somewhat unusual, as most of the constraints faced by managers were imposed by the external environment, such as government regulations or sanitary measures. This discrepancy suggests that companies should emphasise improving their crisis management in the future. The results obtained thus provide the basis for further research in the area analysed. At the same time, it is possible to move away from the COVID-19 situation and transform the issue into managerial management in crises.
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    Optimization of inventory cost control for SMEs in supply chain transformation: A case study and discussion
    (2024-01-23) Zheng, Xiaosong; Chen, Yilin
    With the continuous transformation of supply chains in various industries in China, the strategic landscape, industrial structure, industry rules, business models, and management logic have all changed dramatically, and the consumer market has become more demanding regarding pre-sales quality and after-sales service. Primarily for distribution companies whose primary business model is “buy and sell products and earn a profit margin,” the supply chain transformation has placed higher demands on inventory cost control. In this study, we propose an integrated approach for optimization of inventory cost control of internal supply chain management. The integrated approach includes an improved ABC inventory classification method, spare parts demand forecasting, and an adapted inventory management method. We then select a small and medium-sized home appliance distribution company as the case study because the company is at its early stage of inventory transformation due to the supply chain transformation. Using the case study and field research methods, we analyzed the specific impact of supply chain transformation on the company’s inventory cost control and demonstrated the efficiency of the integrated approach. This study finds that the case company can control inventory costs more efficiently and effectively after implementing the improved ABC inventory classification method. The proposed different demand forecasting plans can help improve the accuracy of spare parts demand forecasting. Finally, different inventory management methods based on different classifications of spare parts can help determine the appropriate spare parts ordering point and procurement quantity.
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    A comparative analysis of multivariate approaches for data analysis in management sciences
    (2024-01-23) Ahmed, Rizwan Raheem; Streimikiene, Dalia; Streimikis, Justas; Siksnelyte-Butkiene, Indre
    The researchers use the SEM-based multivariate approach to analyze the data in different fields, including management sciences and economics. Partial least square structural equation modeling (PLS-SEM) and covariance-based structural equation modeling (CB-SEM) are powerful data analysis techniques. This paper aims to compare both models, their efficiencies and deficiencies, methodologies, procedures, and how to employ the models. The outcomes of this paper exhibited that the PLS-SEM is a technique that combines the strengths of structural equation modeling and partial least squares. It is imperative to know that the PLS-SEM is a powerful technique that can handle measurement error at the highest levels, trim and unbalanced datasets, and latent variables. It is beneficial for analyzing relationships among latent constructs that may not be candidly witnessed and might not be applied in situations where traditional SEM would be infeasible. However, the CB-SEM approach is a procedure that pools the strengths of both structural equation modeling and confirmatory factor analysis. The CB-SEM is a dominant multivariate technique that can grip multiple groups and indicators; it is beneficial for analyzing relationships among latent variables and multiple manifest variables, which can be directly observed. The paper concluded that the PLS-SEM is a more suitable technique for analyzing relations among latent constructs, generally for a small dataset, and the measurement error is high. However, the CB-SEM is suitable for analyzing compound latent and manifest constructs, mainly when the goal is to generalize results to specific population subgroups. The PLS-SEM and CB-SEM have specific efficiencies and deficiencies that determine which technique to use depending on resource availability, the research question, the dataset, and the available time.