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This study aims to investigate the wetting behavior of AgCuTi and AgCu filler metals on selective laser melting (SLMed) Ti/TiB₂, and to analyze the microstructure and fracture characteristics of SLMed Ti/TiB₂/AgCuTi or AgCu alloy/SLMed Ti/TiB₂ brazed joints. The wetting behavior of AgCuTi and AgCu filler metals on the selective laser melted (SLMed) Ti/TiB₂ has been studied. The analysis of microstructures and fracture characteristics in vacuum-brazed SLMed Ti/TiB₂ substrate, using AgCuTi and AgCu filler metals, has been conducted to elucidate the influence of brazing temperature and alloy composition on the shear strength of the brazed joints.

Brazing SLMed-Ti/TiB₂ in a vacuum using AgCuTi and AgCu filler metals, this study aims to explore the optimal parameters for brazed joints at various brazing temperatures (800°C−950°C).

The findings suggest that elevated brazing temperatures lead to a more extensive diffusion region in the joint as a result of the partial melting of the filler metal. The joint composition changes from distinct Ti₂Cu layer/TiCu layer/filler metal to a-Ti (ss) + ß-Ti (ss)/TiCu. As the brazing temperature increases, the fracture mode shifts from brittle cleavage to ductile fracture, mainly attributed to a decrease in the CuTi within the brazed joint. This change in fracture behavior indicates an improvement in the ductility and toughness of the joint.

The originality of this study lies in the comprehensive analysis of the microstructure and shear strength of vacuum brazing SLMed Ti/TiB₂ using AgCuTi and AgCu filler metals.

This paper aims to propose an incremental graph indexing method based on probabilistic inferences in Bayesian network (BN) for approximate nearest neighbor search (ANNS) that adds unindexed queries into the graph index incrementally.

This paper first uses the attention mechanism based graph convolutional network to embed a social network into the low-dimensional vector space, which could improve the efficiency of graph index construction. To add the unindexed queries into the graph index incrementally, this study proposes to learn the rule-based BN from social interactions. Thus, the dependency relations of unindexed queries and their neighbors are represented, and the probabilistic inferences in BN are then performed.

Experimental results demonstrate that the proposed method improves the search precision by at least 5% and search efficiency by 10% compared to the state-of-the-art methods.

This paper proposes a novel method to construct the incremental graph index based on probabilistic inferences in BN, such that both indexed and unindexed queries in ANNS could be addressed efficiently.

Drawing upon socio-technical system theory, this study intends to investigate the effects of the congruence and incongruence between artificial intelligence (AI) and explorative learning on supply chain resilience as well as the moderating role of organizational inertia.

Using survey data collected from 170 Chinese manufacturing firms, we performed polynomial regression and response surface analyses to test our hypotheses.

We find that the congruence between AI and explorative learning enhances firms’ supply chain resilience, while the incongruence between these two factors impairs their supply chain resilience. In addition, compared with low–low congruence, high–high congruence between AI and explorative learning improves supply chain resilience to a greater extent. Moreover, organizational inertia attenuates the positive influence of the congruence between AI and explorative learning on supply chain resilience, while it aggravates the negative influence of the incongruence between these two factors on supply chain resilience.

Our study expands the literature on supply chain resilience by demonstrating that the congruence between a firm’s AI (i.e. technical aspect) and explorative learning (i.e. social aspect) boosts its supply chain resilience. More importantly, our study sheds new light on the role of organizational inertia in moderating the congruent effect of AI and explorative learning, thereby extending the boundary condition for socio-technical system theory in the supply chain resilience literature.

Live Streaming Marketing has emerged as a transformative medium, facilitating real-time product promotion and brand messaging and reshaping consumer engagement. However, knowledge of the impact of Store Atmospheric cues within live streaming contexts remains scarce. This research delves into the dynamic interplay between streamers and viewers across diverse live streaming platforms, with a focus on the impact of distinct atmospheric cues. It also seeks to explore prosocial behavior and integrate elements of social comparison theory.

We conducted semi-structured interviews with 14 streamers and 26 viewers. Participants who were active on streaming platforms and had experience of multiple live streaming sessions were purposively identified. The thematic coding approach and NVivo 12 software were employed to gain a nuanced understanding of live streaming dynamics.

Our findings highlight the significant role of emerging atmospheric cues in shaping immersive streaming experiences and fostering prosocial behavior. Additionally, we observed three formats of upward social comparisons between streamers and viewers, wherein viewers compared themselves with streamers and peers, and streamers engaged in comparisons with more experienced counterparts. This finding contributes to a sense of digital community and positive interactions because of live streaming adoptions.

By extending the application of social comparison theory, this study provides valuable insights for practitioners and scholars, enriching the understanding of both streamers’ and viewers’ psychological behavior and the dynamics of virtual retail settings.

Drawing on power approach-inhibition theory, this study develops a conditional indirect effect model to explore how team vertical leader position and expert power indirectly impact members’ shared leadership through vertical leader’s empowering behaviors.

Multi-source data was collected using a field survey research design. The final sample includes 944 employees in 164 teams from 14 companies in China.

This study found that the interaction of team vertical leader position power and expert power was positively related to their empowering behaviors, which in turn were positively associated with shared leadership. Moreover, our post hoc-analysis revealed the moderating effect of team power distance orientation on the relationship between vertical leader empowering behaviors and shared leadership.

This study sheds light on shared leadership literature by examining vertical leader position and expert power as antecedents. We also offer new directions for exploring how power functions by discussing leadership through the lens of power approach-inhibition theory.

This chapter critically evaluates whether football can attain recognition as a national sport in China. Article No. 11, released by the Chinese government in 2015, aimed to develop a new national strategy centralised on the sport of football to foster consumption and enhance national soft power. Consequently, this also means encouraging Chinese football fans to support the national football team. Comparing the significance of local football clubs and the national football team to Chinese football fans is deemed meaningless and unable to generate useful information to comprehend Chinese people's attitudes towards local and national communities. Through literature comparisons with established Chinese national sports such as Chinese martial arts, badminton and table tennis, the discussion reveals that football currently falls short of meeting the general criteria of invention and popularity to be considered a Chinese national sport. In the specific Chinese context, it also proves that football fails to meet the criterion of politics, hindering its identification as a national sport. Consequently, the chapter rebuts the assumption and advocates for the validity of comparing how fans assess their fandom for local and national football teams.

This study explores key dimensions of mobile commerce activities (MCAs), evaluates their impact on firm performance and examines the role of mobile commerce performance as a mediator and the role of industry competitive intensity as a moderator.

The qualitative research identified 21 principal retailers’ MCAs. A survey involving 172 retail executives was then conducted to examine the structure of MCAs and their impacts on firm performance.

Our findings reveal that the MCAs comprise four dimensions: guidance, connection, in-store conversion and relation. These dimensions jointly impact firm performance through mobile commerce performance, moderated by industry competition.

This study provides a foundational understanding of MCAs. Future research should continue to explore how these dimensions interact.

Retailers can enhance their management of MCA investments by focussing on four key areas: guidance, contact, in-store conversion and relation. By customizing activities and prioritizing those that strengthen customer relationship management within one area, retailers can effectively align their MCA strategies with their specific business context.

The study’s originality lies in identifying and empirically testing the dimensionality of MCAs, emphasizing the role of customer-centric mobile performance and expanding the understanding of MCA value creation.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

This paper tries to retrospect the mounting application of machine learning (ML) and artificial intelligence (AI) in the human resource management area. The document applies bibliometric analysis and uses relational techniques to explore dimensions of documents in the field. The results highlight publication trends, most impactful authors, countries and institutes in the research area. The science mapping along with co-citation and bibliometric coupling analysis revealed major developments in the field. The thematic mapping and trend analysis highlighted the past and emerging trends towards significant and impactful research in the areas of robotics, big data, AI and data analytics. This paper sets the base for future researchers by coordinating and combining various past researches to help in understanding the evolution of ML and AI in human resource management and expansion of knowledgebase.