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Under the background of low consumer financial knowledge and accumulated credit card liabilities, this study investigates the relationship between financial knowledge and responsible credit card behavior using data from the 2019 China Household Finance Survey (CHFS). From the perspective of consumer economic well-being, this study defines accruing credit card debt to buy houses and cars when loans with lower interest rates are available as irresponsible credit card behavior.

This study uses probit regressions to examine the association between financial knowledge and responsible credit card behavior because the dependent variable is a dummy variable. To alleviate endogeneity problems, this study uses instrument variables and Heckman’s two-step estimation. Furthermore, to explore the potential mediators in this process, this study follows the stepwise regression method. Finally, this study introduces interaction terms to examine whether this association differs in different groups.

The results indicate that financial knowledge is conducive to increasing the probability of responsible credit card behavior. Mediating analyses reveal that the roles of financial knowledge occur by increasing the degree of concern for financial and economic information and the propensity to plan. Moderating analyses show that the effects of financial knowledge on responsible credit card behavior are stronger among risk-averse consumers and in regions with favorable digital access.

This study measures responsible credit card behavior from the perspective of the consumer’s well-being, which enriches practical implications for consumer finance. Furthermore, this study explores the potential mediators influencing the process of financial knowledge that affects responsible credit card behavior and identifies moderators to conduct heterogeneous analyses, which helps comprehensively understand the nexus between financial knowledge and credit card behavior. By achieving these contributions, this study helps to curb the adverse effects of irresponsible credit card behavior on consumers’ well-being and the economic system and helps policymakers promote financial knowledge to fully prevent irresponsible credit card behavior.

Financial inclusion can be proxied by banking status. The purpose of this study is to investigate the potential effects of financial capability on the financial fragility of US adults with various banking statuses during the COVID-19 pandemic.

This study utilized the 2021 National Financial Capability Study (NFCS) dataset to investigate the relationship between financial capability and financial fragility among consumers with different banking statuses. The analysis controlled for employment shocks, health shocks and other consumer characteristics. Banking statuses included fully banked, under-banked (utilizing both banking and alternative financial services) and unbanked individuals. Logistic regression analyses were conducted on both the entire sample and subsamples based on banking statuses.

The results showed that financial capability was negatively associated with financial fragility. The magnitude of the potential negative effect of financial capability was the greatest among the fully banked group, followed by the underbanked and unbanked groups. Respondents who were underbanked or unbanked were more likely to experience financial fragility than those who were fully banked. Additionally, respondents who were laid off or furloughed during the pandemic were more likely to experience financial fragility than those without employment shocks. The effect size of financial capability factors was greater than that of COVID-19 shock factors. These results suggest that higher levels of both financial capability and financial inclusion may be effective in reducing the risk of financial fragility.

This study represents one of the first attempts to examine the potential effects of financial capability on financial fragility among consumers with various banking statuses during the COVID-19 pandemic. Furthermore, this study offers new evidence to determine whether COVID-19 shocks, as measured by health and employment status, are associated with financial fragility. Additionally, the effect size of financial capability factors is greater than that of COVID-19 shock factors. The results from the 2021 NFCS dataset provide valuable insights for banking professionals and public policymakers on how to enhance consumer financial wellbeing.

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

To endure and cope in the rapidly changing environment, it is required of firms to gain a deeper acquisition of knowledge on market dynamics and subsequently concentrate on corporations' capacity to create, restructure and integrate their internal and external competences. Hence, the objective of this study is to investigate the influence of eco-dynamic capability (EDC) on the sustainability performance of small and medium-sized enterprises (SMEs).

Structured questionnaires were used to obtain primary data. The data were solicited from 500 employees and owner-managers of SMEs. The study’s hypotheses were tested using standard multiple regression through IBM SPSS Statistics (version 24).

The study revealed that EDC has a substantial positive effect on the economic, social and environmental sustainability performance dimensions.

The focus of this study is on EDC. Thus, although dynamic capability has been the subject of substantial study, little is known regarding the effect of EDC on the economic sustainability performance (ESP) (financial), environmental sustainability performance (ENSP) and social sustainability performance (SSP) of SMEs, predominantly amongst SMEs in emerging economies.

The purpose of this study is to delve into the mechanism of Si3N4 nanowires (NWs) in Sn-based solder, thereby furnishing a theoretical foundation for the expeditious design and practical implementation of innovative lead-free solder materials in the electronic packaging industry.

This study investigates the effect of adding Si₃N₄ NWs to Sn58Bi solder in various mass fractions (0, 0.1, 0.2, 0.4, 0.6 and 0.8 Wt.%) for modifying the solder and joining the Cu substrate. Meanwhile, the melting characteristics and wettability of solder, as well as the microstructure, interfacial intermetallic compound (IMC) and mechanical properties of joint were evaluated.

The crystal plane spacing and lattice constant of Sn and Bi phase increase slightly. A minor variation in the Sn58Bi solder melting point was caused, while it does not impact its functionality. An appropriate Si₃N₄ NWs content (0.2∼0.4 Wt.%) significantly improves its wettability, and modifies the microstructure and interfacial IMC layer. The shear strength increases by up to 10.74% when adding 0.4 Wt.% Si₃N₄ NWs, and the failure mode observed is brittle fracture mainly. However, excessive Si₃N₄ will cause aggregation at the junction between the solder matrix and IMC layer, this will be detrimental to the joint.

The Si₃N₄ NWs were first used for the modification of lead-free solder materials. The relative properties of composite solder and joints were evaluated from different aspects, and the optimal ratio was obtained.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

The purpose of this paper is to examine the antecedents of customer satisfaction during mortgage purchases. Mortgage demand in the USA has reached an all-time high because of an increase in housing demand after COVID-19. Nonetheless, several customers are dissatisfied with their service providers. Customers who actively search the market gain more information about mortgage providers and use this information to define expectations for lenders. The only way there will be customer satisfaction is if lenders meet these expectations. Therefore, it is economically significant for mortgage lenders to discover the antecedents of mortgage satisfaction.

In this study, the partial least squares approach was used to test the hypothesis that satisfaction was influenced by objective knowledge, familiarity and search intensity among a sample of customers (n = 4,512) from the National Survey of Mortgage Originations who had purchased a mortgage in the USA between 2019 and 2020.

The results of structural modelling showed that familiarity (β = 0.23 and p = 0.01) with and knowledge (β = 0.16 and p = 0.01) of mortgages significantly affected consumer satisfaction during mortgage purchase. Search intensity (p = 0.01) mediated the relationship between knowledge, familiarity and satisfaction.

The primary implication is that mortgage service providers should prioritise educating customers about the mortgage buying process on their websites and in person. So managers must actively assist clients in having realistic expectations. Second, mortgage companies should establish a presence on third-party mortgage comparison websites to ensure that customers actively consider alternatives, thereby increasing customer satisfaction.

This study is unique in being an exploratory study to examine the antecedents of mortgage satisfaction using a public data set. This study uniquely examines the National Survey of Mortgage Originations data set with partial least squares approach to examine underlying customer attitudes.

This study aims to explore possible behavioural change venues, beyond the traditional approach to financial education, using the capability-opportunity-motivation behaviour theoretical framework of behavioural change.

The study included 45, semi-structured, in-depth interviews of young adults to explore which elements of financial behaviour formation should interventions target to be effective.

To strengthen capability, the study recommends behavioural education and training for boosting financial knowledge and skills, enablement of financial independence and modelling for empowering self-control and reducing impulsiveness. To boost motivation, gamification of modelling is advised for boosting responsible financial behaviour as part of the identity and inducing consideration of future consequences. Persuasion is advised for inducing positive emotions while incentivization and coercion are advised for empowering self-conscious intentions. To rise opportunity, the study proposes incentivization and coercion imposed by parents, and governmental efforts regarding restriction, enablement and environmental restructuring.

The study brings recommendations for developing efficient interventions for strengthening responsible financial behaviour that may help design type-specific education programmes to promote responsible financial behaviour.

The present study attempts to explore new venues in intervention design that break away from the traditional approach of financial education focused on knowledge and skills that is proven to be ineffective

Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.

The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.

Local social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.

The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.

Social risk management has recently come to the fore as a significant feature of project management. This prominence is particularly evident in urban construction projects that take place in cultural heritage and tourism historic sites. Accordingly, this study aims to adopt social network analysis (SNA) to investigate social risks in construction projects occurring in urban districts rife with historically and culturally significant tourism sites.

The present study analyzed a real case study in Iran as an emergent economy and a developing country. Primarily, the study reviewed previous literature on social risks and relevant stakeholders. Next, the judgments of experts through the content validity ratio analysis confirmed 12 social risks and 9 key stakeholders. Finally, SNA is used to determine the relations between the social risks and stakeholders as well as the significance of each risk.

The investigation demonstrated that the most important social risks in the construction projects of the case study are “Psychological disorders”, “Environmental pollution” and “Cultural conflicts”.

The findings could help policymakers, urban planners and project managers in developing countries with a rich cultural heritage to reduce social risks and improve the efficiency of their projects.

To the best of the authors’ knowledge, the present study is one of the first instances to investigate construction projects implemented in densely populated urban areas hosting cultural heritage and historic tourism sites.