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China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various major challenges. One of the most important challenges is its impact on the social and economic development of arid and semi-arid regions. By simulating the carbon price trends under different economic development and energy consumption levels, this study aims to help the government can plan ahead to formulate various countermeasures to promote the integration of arid and semi-arid regions into the national carbon market.

To achieve this goal, this paper builds a back propagation neural network model, takes the third phase of the European Union Emissions Trading System (EU ETS) as the research object and uses the mean impact value method to screen out the important driving variables of European Union Allowance (EUA) price, including economic development (Stoxx600, Stoxx50, FTSE, CAC40 and DAX), black energy (coal and Brent), clean energy (gas, PV Crystalox Solar and Nordex) and carbon price alternatives Certification Emission Reduction (CER). Finally, this paper sets up six scenarios by combining the above variables to simulate the impact of different economic development and energy consumption levels on carbon price trends.

Under the control of the unchanged CER price level, economic development, black energy and clean energy development will all have a certain impact on the EUA price trends. When economic development, black energy consumption and clean energy development are on the rise, the EUA price level will increase. When the three types of variables show a downward trend, except for the sluggish development of clean energy, which will cause the EUA price to rise sharply, the EUA price trend will also decline accordingly in the remaining scenarios.

On the one hand, this paper incorporates driving factors of carbon price into the construction of carbon price prediction system, which not only has higher prediction accuracy but also can simulate the long-term price trend. On the other hand, this paper uses scenario simulation to show the size, direction and duration of the impact of economic development, black energy consumption and clean energy development on carbon prices in a more intuitive way.

The authors aimed to examine how the level of digitalization in Poland and Ukraine affects the contribution of small and medium-sized enterprises (SMEs) to the countries’ gross domestic product (GDP).

The study involved a comparative analysis and statistical modeling of the impact of key economic factors on the contribution of SMEs to Poland’s and Ukraine’s GDP in the 2010–2020 period. The authors used principles of the theory of economic growth and calculated the coefficient of digital competitiveness as a composite indicator consisting of a number of global indices.

The study revealed significant differences between both countries, which can be attributed to a higher level of digitalization in Polish SMEs. The authors used the Polish experience to recommend how to reform Ukraine’s digital economy in postwar recovery.

The contribution of SMEs to Poland’s GDP is higher than that of Ukraine’s because of the higher entrepreneurship rate in the Polish micro and small enterprises (MSEs) sector. The authors found that a unit change in the integrated coefficient of digital competitiveness is related to the greatest change in the contribution of SMEs to the country’s GDP when the other factors in the model equation remain fixed.

This study aims to examine the impact and contribution of priority and non-priority sectors, as well as their sub-sectors, on the gross non-performing assets of public, private and foreign sector banks.

The Reserve Bank of India's database on the Indian economy is used to retrieve data over 13 years (2008–2021). Public sector (12), private sector (22) and foreign sector (44) banks are represented in the sample. Two-way ANOVA, multiple regression and panel regression statistical techniques are used in SPSS and EViews to examine the data. Further, the results are also validated by using robustness testing by applying the fully modified ordinary least square (FMOLS) and dynamic least square (DOLS) regression.

The results showed that, for private and foreign banks, the non-priority sector makes up the majority of the total gross non-performing assets, although both the priority and non-priority sectors are substantial for public sector banks. The largest contributors to the total gross non-performing assets in public, private and foreign banks are industries, agriculture and micro and small businesses. The FMOLS displays robustness results that are qualitatively similar to the baseline result.

Based on the study's findings about the patterns of non-performing assets originating from these specific industries, banks might improve the way in which these advanced loans are managed.

There has not been much research done on the subject of sub-sector-specific non-performing assets and how they affect total gross non-performing assets across the three sector banks. The study's primary focus will be on the issue of non-performing assets in the priority’s and non-priority’s sub-sectors, namely, agricultural, micro and small businesses, food credit, industries, services, retail loans and other priority and non-priority sectors.

This study aims to present the path-to-purchase of omni micro-resellers in affordable fashion shopping centers and differentiates them from the existing knowledge of end-user’s purchase journey. Furthermore, the study aims to explore the omnichannel readiness for Micro and Small Enterprises (MSEs) merchants to provide a seamless experience for the micro-resellers.

This study adopted an ethnographic approach to gain deep insights into the unexplored omni journey of micro-resellers, using multiple techniques and respondent types in various locations.

Findings reveal that the journey of omni micro-reseller is not the same as the end-user's purchase journey. The new value needed in every path-to-purchase is an essential knowledge that helps MSE merchants in serving micro-resellers. MSE merchants' readiness is assessed by their ability to provide the best customer experience for their buyers, consisting of six omnichannel experience dimensions: researching, purchase-payment, shipping, omnichannel testing, return goods experience and relationship building.

Using the Engel-Kollat-Blackwell (EKB) decision-making model, this study develops the path-to-purchase of omnichannel micro-resellers. The new readiness dimensions developed in this study are set as a potential measurement tool.

It provides new insights to benefit MSE merchants and the institutions responsible for enhancing merchant quality.

This study focuses on micro-resellers in the MSEs environment, the prominent buyers of affordable fashion in developing countries, which is a novelty of the study. Moreover, unlike previous studies that have focused on large and medium merchants, this study concentrates on MSE merchants. To the best of the authors’ knowledge, this study is one of the first studies to highlight ways to measure MSE merchants omnichannel readiness.

The purpose of this study is to explore the status and potential of industrial symbiosis in fostering sustainable entrepreneurship among micro and small enterprises (MSEs) in Uganda.

This study is cross-sectional and qualitative in nature. It involved conducting in-depth interviews with MSEs owners in Kampala, and data were analyzed using Atlasi Ti.

The results indicate that industrial symbiosis is a phenomenon that, notably, all MSE owners interviewed were practicing unknowingly, including recycling waste into sellable products, as well as sharing utilities, specialized machines and personnel for the smooth running of their businesses. In addition, participants expressed optimism that industrial symbiosis would be a major tool for business growth, innovation, employment opportunities, diversification and environmental protection for the benefit of the current and the future generations.

This study offers initial evidence on the status and potential of industrial symbiosis in fostering sustainable entrepreneurship among MSEs in Uganda.

The purpose of this study is to better understand technology adoption and working from home (WFH) behaviour of micro and small enterprises (MSE) with 4 to 49 employees during the first (2020) and second (2021) COVID-19 lockdowns in Switzerland.

This study uses two data sets gathered using computer-assisted telephone interviewing surveys conducted with 503 managing directors of Swiss MSEs after the first and 506 MDs after the second COVID-19 lockdown period.

The study revealed that during the COVID-19 pandemic, WFH arrangements are related to the adoption of technology by Swiss industry groups. Furthermore, industry characteristics and technology adoption strategies are also associated with the long-term prospect of WFH. The overall result confirms the predominant role of technology pioneers.

The study focuses on MSEs in Switzerland during a specific period. The data set includes mainly quantitative data. Future studies could investigate larger enterprises in international contexts, integrating employees’ viewpoints founded on long-term gathered qualitative data. The implications of this study include predictions about future WFH behaviour in Swiss MSEs.

To the best of the authors’ knowledge, this is the first study collecting data in Swiss MSEs after the two COVID-19 lockdowns in 2020 and 2021. As a result, this study offers a unique perspective on a specific business segment, which accounts for around 70% of global employment.

The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent any immature withdrawals. This study provides a comprehensive approach toward the prediction of student academic performance through the lens of the knowledge, attitudes and behavioral skills (KAB) model. The purpose of this paper is to aim to improve the modeling accuracy of students’ performance by introducing two methodologies based on variable selection and dimensionality reduction.

The performance of the proposed methodologies was evaluated using a real data set of ten critical-to-success factors on both attitude and skill-related behaviors of 320 first-year students. The study used two models. In the first model, exploratory factor analysis is used. The second model uses regression model selection. Ridge regression is used as a second step in each model. The efficiency of each model is discussed in the Results section of this paper.

The two methods were powerful in providing small mean-squared errors and hence, in improving the prediction of student performance. The results show that the quality of both methods is sensitive to the size of the reduced model and to the magnitude of the penalization parameter.

First, the survey could have been conducted in two parts; students needed more time than expected to complete it. Second, if the study is to be carried out for second-year students, grades of general engineering courses can be included in the model for better estimation of students’ grade point averages. Third, the study only applies to first-year and second-year students because factors covered are those that are essential for students’ survival through the first few years of study.

The study proposes that vulnerable students could be identified as early as possible in the academic year. These students could be encouraged to engage more in their learning process. Carrying out such measurement at the beginning of the college year can provide professional and college administration with valuable insight on students perception of their own skills and attitudes toward engineering.

This study employs the KAB model as a comprehensive approach to the study of success predictors. The implementation of two new methodologies to improve the prediction accuracy of student success.

This study aims to investigate the factors that influence behavioral intention (BI) and usage of e-wallets by extending the unified theory of acceptance and use of technology (UTAUT) with constructs, namely, mobile self-efficacy, perceived enjoyment and satisfaction.

This quantitative study used partial least squares structural equation modeling on a sample of 576 mobile e-wallet users surveyed online.

The key findings indicate that the model can explain 58.8% of the variance in behavioral intention and 53.8% in usage. Moreover, mobile self-efficacy has a significant influence on perceived enjoyment. Perceived enjoyment significantly affects satisfaction, effort expectancy and performance expectancy. Furthermore, effort expectancy significantly influences customer satisfaction in contrast to performance expectancy. In addition, although performance expectancy, social influence and satisfaction significantly impact consumers’ behavioral intention, effort expectancy and facilitating conditions condition have an insignificant influence on consumers’ behavioral intention. E-wallet stakeholders can use the findings of this study to make strategic decisions regarding the e-wallet ecosystem.

Although previous studies have independently addressed the impact of mobile self-efficacy, perceived enjoyment and satisfaction on consumers’ behavioral intention and usage behavior, the expanded framework with the possible relationships proposed in this study has never been adequately studied in previous research in the context of e-wallets in developing countries based on an empirical analysis. This study represents one of the first attempts to improve the UTAUT by empirically analyzing these relationships.

Este estudio tiene como objetivo investigar los factores que influyen en la intención de comportamiento y el uso de los monederos electrónicos mediante la ampliación de la teoría unificada de aceptación y uso de la tecnología (UTAUT) con los constructos de autoeficacia móvil, disfrute percibido y satisfacción.

Este estudio cuantitativo utilizó un modelo de ecuaciones estructurales de mínimos cuadrados parciales (PLS-SEM) sobre una muestra de 576 usuarios de monederos electrónicos móviles encuestados en línea.

Los principales resultados indican que el modelo puede explicar el 58,8% de la varianza en la intención de comportamiento y el 53,8% en el uso. Además, la autoeficacia en el uso del móvil influye significativamente en el disfrute percibido. El disfrute percibido afecta significativamente a la satisfacción, la expectativa de esfuerzo y la expectativa de rendimiento. Además, la expectativa de esfuerzo influye significativamente en la satisfacción del cliente, en contraste con la expectativa de rendimiento. Además, aunque la expectativa de rendimiento, la influencia social y la satisfacción influyen significativamente en la intención de comportamiento de los consumidores, la expectativa de esfuerzo y la condición de condiciones facilitadoras tienen una influencia insignificante en la intención de comportamiento de los consumidores. Las partes interesadas en los monederos electrónicos pueden utilizar las conclusiones de este estudio para tomar decisiones estratégicas en relación con el ecosistema de los monederos electrónicos.

Aunque estudios anteriores han abordado de forma independiente el impacto de la autoeficacia móvil, el disfrute percibido y la satisfacción en la intención de comportamiento y la conducta de uso de los consumidores, el marco ampliado con las posibles relaciones propuestas en este estudio nunca se ha estudiado adecuadamente en investigaciones anteriores en el contexto de los monederos electrónicos en los países en desarrollo sobre la base de un análisis empírico. Este estudio representa uno de los primeros intentos de mejorar el UTAUT analizando empíricamente estas relaciones.

本研究旨在通过扩展统 – 的技术接受和使用理论 (UTAUT), 研究影响电子钱包行为意图和使用的因素, 包括移动自我效能、感知乐趣和满意度等建构。

这项定量研究采用偏最小二乘法结构方程模型 (PLS-SEM), 对576名移动电子钱包用户进行了在线调查。

主要研究结果表明, 该模型可以解释58.8%的行为意图和53.8%的使用差异。此外, 移动自我效能感对感知到的享受有显著影响。感知到的享受显著影响满意度、努力期望和绩效期望。此外, 与绩效期望相比, 努力期望对顾客满意度有显著影响。此外, 尽管绩效期望、社会影响和满意度对消费者的行为意图有显著影响, 但努力期望和便利条件对消费者的行为意图没有显著影响。电子钱包的利益相关者可以利用本研究的结果来做出有关电子钱包生态系统的战略决策。

尽管之前的研究已经独立地探讨了移动自我效能、感知享受和满意度对消费者行为意图和使用行为的影响, 但本研究提出的具有可能关系的扩展框架在之前的研究中从未基于实证分析对发展中国家的电子钱包进行充分研究。本研究是通过实证分析这些关系来改进UTAUT的首次尝试之一。