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The literature on knowledge management in sustainable supply chain (KMSSC) has witnessed significant growth in the past two decades. However, a scientometric review that consolidates the primary trends and clusters within this topic has been notably absent. This paper aims to scrutinize recent advancements and identify the intellectual underpinnings of KMSSC research conducted between 2002 and 2022.

The present review employs a scientometric analysis approach via visualization maps of prolific contributions, co-citation, co-occurrence and thematic networks to examine a total of 114 articles and conference papers on KMSSC.

Emerging research frontiers and hotspots are revealed and a state-of-the-art framework of KMSSC research structure is developed.

The review provides significant implications that guide KMSSC research and better inform sustainability decisions in the supply chain context.

To the best of the authors' knowledge, this is the first review to thoroughly synthesize the intersected domain of KMSSC using scientometric analysis.

The study aims to devise a comprehensive evaluation model (CEM) for evaluating spatial equity in the layout of elderly service facilities (ESFs) to address the inequity in the layout of ESFs within city center communities characterized by limited land resources and a dense elderly population.

The CEM incorporates a suite of analytical tools, including accessibility assessment, Lorenz curve and Gini coefficient evaluations and spatial autocorrelation analysis. Utilizing this model, the study scrutinized the distributional equity of three distinct categories of ESFs in the city center of Xi’an and proposed targeted optimization strategies.

The findings reveal that (1) there are disparities in ESFs’ accessibility among different categories and communities, manifesting a distinct center (high) and periphery (low) distribution pattern; (2) there exists inequality in ESFs distribution, with nearly 50% of older adults accessing only 18% of elderly services, and these inequalities are more pronounced in urban areas with lower accessibility, and (3) approximately 14.7% of communities experience a supply-demand disequilibrium, with demand surpassing supply as a predominant issue in the ongoing development of ESFs.

The CEM formulated in this study offers policymakers, urban planners and service providers a scientific foundation and guidance for decision-making or policy amendment by promptly assessing and pinpointing areas of spatial inequity in ESFs and identifying deficiencies in their development.

Infrastructure financialization plays a critical role in infrastructure development and urban growth around the world. However, on the one hand, the existing research on the infrastructure financialization focuses on qualitative and lacks quantitative country-specific studies. On the other hand, the spatial heterogeneity and influencing factors of infrastructure financialization are ignored. This study takes China as a typical case to identify and analyze the spatial characteristics, development process and impact factors of infrastructure financialization.

To assess the development and characteristics of infrastructure financialization in China, this study constructs an evaluation index of infrastructure financialization based on the infrastructure financialization ratio (IFR). This study then analyzes the evolution process and spatial pattern of China's infrastructure financialization through the spatial analysis method. Furthermore, this study identifies and quantitatively analyzes the influencing factors of infrastructure financialization based on the spatial Dubin model. Finally, this study offers a policy suggestion as a governance response.

The results demonstrate that infrastructure financialization effectively promotes the development of infrastructure in China. Second, there are significant spatial differences in China’s infrastructure financialization. Third, many factors affect infrastructure financialization, with government participation having the greatest impact. In addition, over-financialization of infrastructure has the potential to lead to government debt risks, which is a critical challenge the Chinese Government must address. Finally, this study suggests that infrastructure financialization requires more detailed, tailored,and place-specific policy interventions by the government.

This study not only contributes to enriching the knowledge body of global financialization theory but also helps optimize infrastructure investment and financing policies in China and provides peer reference for other developing countries.

Ensuring the safety of structures is important. However, when a structure possesses both an implicit performance function and an extremely small failure probability, traditional methods struggle to conduct a reliability analysis. Therefore, this paper proposes a reliability analysis method aimed at enhancing the efficiency of rare event analysis, using the widely recognized Relevant Vector Machine (RVM).

Drawing from the principles of importance sampling (IS), this paper employs Harris Hawks Optimization (HHO) to ascertain the optimal design point. This approach not only guarantees precision but also facilitates the RVM in approximating the limit state surface. When the U learning function, designed for Kriging, is applied to RVM, it results in sample clustering in the design of experiment (DoE). Therefore, this paper proposes a FU learning function, which is more suitable for RVM.

Three numerical examples and two engineering problem demonstrate the effectiveness of the proposed method.

By employing the HHO algorithm, this paper innovatively applies RVM in IS reliability analysis, proposing a novel method termed RVM-HIS. The RVM-HIS demonstrates exceptional computational efficiency, making it eminently suitable for rare events reliability analysis with implicit performance function. Moreover, the computational efficiency of RVM-HIS has been significantly enhanced through the improvement of the U learning function.

As an emerging socio-technical paradigm, high-speed railways profoundly change individuals' lifestyle and allow for the shift toward a green transportation. Digital technologies open an opportunity window for the development of enterprises. This study aims to clarify the impact of firm digitalization on the innovation efficiency of the Chinese high-speed rail industry. In addition, human capital is the important non-physical capital of enterprises. The authors also elucidate the moderating role of human capital on the above relationship.

Based on the data of Chinese high-speed railway listed companies from 2015 to 2021, this study explores the impact of digital transformation on the innovation efficiency, and further clarifies the boundary role of human capital with two-way fixed effect regression models.

The empirical results indicate that digital transformation has a positive impact on the innovation efficiency of the Chinese high-speed railway enterprises. Furthermore, human capital significantly enhances the above relationship. In addition, digital transformation fosters the innovation efficiency of small- and medium-sized enterprises and private-owned enterprises, but the correlation coefficients between digital transformation and the innovation efficiency of large enterprises and state-owned enterprises are not significant.

This is one of the earliest studies to explore how digital technologies shape R&D activities. From the perspective of relative efficiency, this study evaluates the effectiveness of digital transformation and provides empirical evidence for the formulation and implementation of corporate digital strategies. Moreover, this study links human capital with digital transformation and identifies condition factors that affect the effectiveness of digital transformation, thereby supplementing existing knowledge.

This study aims to describe the m-learning experience of school students and teachers during the COVID-19 pandemic and explores the factors influencing the continuance intention of m-learning.

Semistructured interviews of 24 students and 09 teachers of schools in national capital territory (NCT) Delhi, India were conducted over 03 months and transcribed verbatim. A hermeneutic phenomenological design was used to interpret the text and bring out the “lived experiences” of m-learning.

The following 15 themes or factors influencing continuance intention emerged through the hermeneutic circle: (1) actual usage, (2) attitude, (3) context, (4) extrinsic motivation, (5) facilitating conditions, (6) intrinsic motivation, (7) perceived compatibility, (8) perceived content quality, (9) perceived mobile app quality, (10) perceived teaching quality, (11) perceived usefulness, (12) satisfaction, (13) self-efficacy, (14) self-management of learning and (15) social influence.

The study offers insightful recommendations for school administrators, mobile device developers and app designers. In addition, suggestions for effectively using m-learning during disasters such as COVID-19 have been provided. Several future research directions, including a nuanced understanding of m-assessment and online discussions, are suggested to enhance the literature on m-learning continuance.

The study enriches the literature on m-learning continuance. A qualitative approach has been used to identify relevant factors influencing m-learning continuance intention among secondary and higher secondary level (Grades 9 to 12) school students and teachers in India. In addition, a conceptual framework of the relationships among the factors has been proposed. Further, an analysis of the lived experiences of m-learning during the COVID-19 pandemic indicated several issues and challenges in using m-learning during disasters.

This study aims to investigate a unique approach to learning languages through self-directed online learning. Specifically, it explores the self-management abilities and skills learners need while learning a language outside traditional classroom settings when using mobile-assisted learning technology.

A mixed-methods approach was used in this study, including an online survey of 84 people and 10 semi-structured interviews.

Findings reveal the significant role of specific and well-defined learning goals in enhancing learners’ performance. These goals can be either self-initiated by the learners themselves or defined by the technological features of the learning platform. However, the presence of distractions in learners’ daily lives presents challenges to effective time management, affecting learners both physically and psychologically. A key aspect of self-directed language learning lies in the learners’ ability to seek out relevant human and material resources beyond the confines of a single mobile-assisted language learning (MALL) tool. The authenticity of these resources is crucial in ensuring meaningful and effective learning experiences.

Understanding how learners navigate and discover valuable resources is a central focus of this study. This research offers valuable insights into the field of self-directed language learning, revealing the pivotal role of self-management skills with mobile-assisted learning technology. The findings contribute to the broader field of language education and offer practical implications for educators and developers seeking to optimize self-directed language learning experiences through innovative and technologically driven approaches.

MALL is often ideal for individualized informal learning, but the existing literature focuses heavily on formal learning situations, underestimating the importance of MALL practices in various informal settings. Most research reports on MALL-based self-directed learning primarily sample traditional English-learning university students. Therefore, there is a need for research on how nontraditional older adult learners self-direct their language learning with mobile technology outside the classroom.

Government organizations often store large amounts of data and need to choose effective data governance service to achieve digital government. This paper aims to propose a novel multi-attribute group decision-making (MAGDM) method with multigranular uncertain linguistic variables for the selection of data governance service provider.

This paper presents a MAGDM method based on multigranular uncertain linguistic variables and minimum adjustment consensus. First, a novel transformation function is proposed to unify the multigranular uncertain linguistic variables. Then, the weights of the criteria are determined by building a linear programming model with positive and negative ideal solutions. To obtain the consensus opinion, a minimum adjustment consensus model with multigranular uncertain linguistic variables is established. Furthermore, the consensus opinion is aggregated to obtain the best data governance service provider. Finally, the proposed method is demonstrated by the application of the selection of data governance service provider.

The proposed consensus model with minimum adjustments could facilitate the consensus building and obtain a higher group consensus, while traditional consensus methods often need multiple rounds of modifications. Due to different backgrounds and professional fields, decision-makers (DMs) often provide multigranular uncertain linguistic variables. The proposed transformation function based on the positive ideal solution could help DMs understand each other and facilitate the interactions among DMs.

The minimum adjustment consensus-based MAGDM method with multigranular uncertain linguistic variables is proposed to achieve the group consensus. The application of the proposed method in the selection of data governance service provider is also investigated.

Prior studies have shown various mediating and moderating mechanisms regarding the effect of employees' perceived overqualification on intention to leave (ITL). Nonetheless, only a few empirical studies have shed light on the negative underlying processes that explain this relationship. Furthermore, less is known about the role of high-performance work systems (HPWSs) in the overqualification literature. Drawing upon relative deprivation theory (RDT), this research attempts to fill these gaps by examining the mediating role of work-related boredom and the moderating role of perceived HPWSs in the association between perceived overqualification and ITL.

Data from a sample of 188 employees working in a Greek manufacturing company were analyzed using the PROCESS macros for SPSS.

The results indicated that work-related boredom mediates the association between perceived overqualification and ITL. Moreover, HPWSs attenuated the relationship of perceived overqualification with both work-related boredom and ITL, such that their association was positive only when employees' perceptions of HPWSs were low.

This study adds to the existing literature regarding why and how perceived overqualification affects ITL. Even more, this is one of the first studies that examine the role of HPWSs in the literature of overqualification. Theoretical and practical implications were also considered.

The boom in live streaming has intensified competition among streamers for viewers' gifts, which makes it meaningful to study the factors that affect the viewers’ gifting behavior. Given the emotional attachment between streamers and viewers, the authors set out to elucidate a new driver on viewer gifting: expressions of the streamer. This research aims to explore the impact of streamer emotions on the viewer gifting behaviors, including free and paid gifting. The loyalty level of the viewers is also introduced as a moderating factor to investigate the heterogeneous effect of streamer emotions on gifting behavior.

The dataset the authors collected consists of two parts, including 1809.69 h of live streaming videos and 358,002 gift giving records. Combined with deep learning methods and regression analysis, the authors performed empirical tests on the 81,110 valid samples. Several robustness checks were also conducted to ensure the reliability of main results.

The empirical results show that streamer emotions do have effects on viewers' free and paid gifting behavior. The authors’ findings show that positive streamer expressions, such as happiness and surprise, have a positive influence on viewer gifting behavior. However, some negative expressions, like sadness, can also have a positive impact. Moreover, the authors discovered that higher viewer loyalty amplifies the positive effect of streamer emotions and reduces the negative effect.

This research contributes to the study about streamer emotions and viewers' consumption behavior, which extends the application of emotion as social information model (EASI model) in the live streaming setting. The authors carefully divide the gifting behavior into two types: free and paid, and study how these two types are affected by streamer emotions. Besides, these effects are analyzed within viewers of different loyalty levels. This study offers practical emotion management strategies for streamers and live streaming platforms to gain more economic profits.