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Recently, the concept of financial technology (FinTech) has attracted extensive attention from international organisations and regulators, in particular, how to achieve a “win–win” situation between financial institutions' FinTech innovation and effective regulation has become a hot topic. This study purposes to explore the evolutionary game relationship between FinTech innovation and regulation by constructing both static and dynamic earmarking game models.

A simulation experiment was conducted using primary data obtained from a commercial bank in China.

The results of the theoretical analysis of evolutionary game models were consistent with the corresponding simulation results, proving the validity of the proposed evolutionary game models. It was also found that the dynamic earmarking game model was more stable and effective than the static earmarking game model in promoting FinTech innovation and regulation. Furthermore, when the regulators utilised a dynamic earmarking mechanism, the evolutionary path of financial institutions and regulators' behaviour strategies took the shape of a spiral and eventually converged to a central point, indicating the existence of an evolutionary stable strategy and Nash equilibrium. Finally, because the behaviour strategies of financial institutions were mainly influenced by the regulators' policies, the regulators were inspired to adjust the corresponding regulation policies on FinTech innovation.

This study bridges the knowledge gap in the existing literature on financial innovation and regulation, in particular by establishing evolutionary game models from the perspective of financial earmarking policies. Also, the case study for simulation experiments can gain a more intuitive insight into FinTech innovation and financial earmarking policies.

This study uses a systematic review to explore the potential causes of previous findings related to e-government acceptance research. By identifying the most frequently used, best, promising or worst factors that affect the acceptance of e-government, this research presents a research agenda for e-government researchers.

Through conducting a systematic review following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) procedure, this research first selected 109 papers. Subsequently, this research analyzed the predictors and linkages of e-government acceptance by adopting a weight-analysis method proposed by Jeyaraj et al. (2006).

The results first revealed the five most frequently used predictors and five best predictors of e-government acceptance at a comprehensive level. Furthermore, this study summarized the best predictors affecting the acceptance of e-government from the perspectives of adopter types and e-government stages. The results also illustrated the promising and the worst predictors influencing e-government acceptance.

The contribution of this research is twofold. First, this study identified the linkages between e-government acceptance at the individual and organizational levels and between different e-government development stages. Second, this research provided a research direction that could offer useful insights for future e-government studies.

Retaining customers is very important for the survival of e-commerce sellers. The purpose of this paper is to investigate the roles of computer-mediated communication (CMC) tools, interactivity, trust and perceived effectiveness of e-commerce institutional mechanisms (PEEIM) in influencing customer’s repurchase intention in the Chinese online e-commerce marketplace.

The research model is empirically tested using survey data analyzed with partial least squares structural equation modeling.

This study confirms the positive relation between customer satisfaction and trust in the seller, which further contributes to repurchase intention. Results also support the positive influences of effective use of an instant messenger and feedback system on customer perceived interactivity, which helps enhance trust in the seller. PEEIM demonstrates interesting results regarding its moderating effects.

In the future, researchers can extend the study to other e-commerce platforms and take trust transfer effects and product categories into consideration.

This study highlights the importance to manage trust, PEEIM, interactivity and CMC tools in e-commerce platforms, assisting practitioners to develop appropriate business strategies and processes to retain customers.

This study extends previous investigations by integrating trust and PEEIM with interactivity and testing the model in the context of the Chinese online marketplace.

The purpose of this paper is to focus on the operation strategy of high-performance alliance portfolios by analyzing the effect of alliance portfolios on the performance of focal firms, using post-structuralism of social network theory and contingency theory. In detail, this paper refines alliance portfolios into three dimensions, and studies the moderating role of context on the relation between alliance portfolios and firm performance.

The empirical study was carried out with second-hand data gathered from Internal Revenue Service. In total, this paper gathered data from 506 focal firms in Zhejiang Province from 2001 to 2010 as the sample to test the hypotheses.

Based on the empirical results, the authors find the positive effect of relational dimension (weak alliance portfolios) and partner dimension (the diversity of partners) on performance. The effect of the former will become weaker with the increasing environmental dynamic, while the effect of the latter will become stronger. However, the structural dimension (alliance portfolios size) and relational dimension (new partners) have the negative effect on performance. And the negative effect will become stronger under high environmental dynamic. Moreover, the negative effect of non-local partners on performance becomes stronger when the environmental dynamic is high.

The paper reveals that with the industry transformation caused by “internet +,” companies have been required go beyond traditional dyadic alliance management perspective. That is to say, individual alliance relationship should be seen as a part of a much broader picture of alliance portfolio. As such, the framework may help companies to manage their alliance portfolios by matching high-performance alliance portfolios to the external environment to produce a synergistic effect (Lea et al., 2006; Tritos et al., 2013; Keith et al., 2014) taking the characteristics of the configuration of alliance portfolios into consideration.

The paper presents a model that explains the effect of three dimensions of alliance portfolios on the performance of focal firms in different contexts through empirical study. This paper also integrates post-structuralism of social network theory and contingency theory to enable the understanding on the configuration of alliance portfolios.

Human resources have become a key issue in relation to the strong competition between service firms. Therefore, the purpose of this paper is to explore the relationship between high-performance human resource management (HRM) within this field to firm performance, making a useful attempt to explore the “black box” of enterprise human resources management effect on firm performance.

In order to validate the relationship between high-performance HRM and firm performance, Chinese service industry samples were collected. Structural equation modeling and regression are adopted to estimate the direct effect of high-performance HRM on firm performance and the mediating role of innovation.

The results show that the impacts of high-performance HRM on firm performance are significant. Moreover, innovation plays a partial mediating role between them. Training, work analysis and employee participation has a significantly positive impact on firm performance, while effects of profit sharing, employee development and performance evaluation on enterprise performance is not significant. The results strongly support the hypothesis that innovation holds intermediary variables between high-performance HRM and firm performance.

Studying the relationship between high-performance HRM and firm performance can help Chinese enterprises more reasonable and effective learning foreign advanced management ideas and methods. And then can help Chinese enterprises to establish a high-performance HRM system that is suitable for Chinese enterprises; the research can help enterprises to identify meaningful practice of human resources management, outstanding keys, and perfect the HRM system of enterprises; research on innovation and innovative thinking is conducive to develop employees’ innovation motive, promote employee’ innovative behavior, and improve firm performance.

This paper takes innovation as a mediating variable into the model and studies the intermediary role of innovation.

The aim of this research paper is to evaluate the effect of knowledge management on the adoption of the e-business in the supply chain of small and medium enterprises (SMEs) in Malaysia.

Questionnaires were distributed to 200 Malaysian SMEs with a response rate of 68 percent. Data were analyzed by employing multiple regression analysis.

The results showed that knowledge management processes such as knowledge acquisition and knowledge application are significant in affecting Malaysian SMEs' decision to adopt e-business in their supply chain. Knowledge dissemination was found not to be significant in affecting the adoption of e-business among Malaysian SMEs.

This study has addressed the previous lack of literature on the relationship between knowledge management processes and e-business adoption among Malaysian SMEs.

Although existing literature has shown a relationship between knowledge management processes and technology adoption, this study empirically verifies an adoption model based on knowledge management processes. SMEs can use this model to do a pre-test baseline measurement and reassessment of effect by any KM changes on e-business adoption periodically. Organizations planning to adopt e-business would also be able to apply strategies based on the findings from this research.

This research aimed to examine the current status of artificial intelligence's (AI's) integration into Chinese adult education, by analyzing the influences that AI has had on current adult education practices in China and by discussing the opportunities and challenges that adult education in China is faced with under the rapid AI development in the past 12 years.

This research employed systematic literature analysis. CNKI (China National Knowledge Infrastructure) Chinese Journals Full-text Database was used to collect scholarly publications on the use of AI in adult education in China that was published in the past decade. Data analysis included the following steps: identifying key words and phrases, detecting underlying meanings, searching for logical connections and relationships, collecting and connecting evidence to the research questions, and drawing logical and credible conclusions.

The findings indicated that AI has been gradually integrated into Chinese adult education through innovations and explorations and AI's influence is broad and profound. More specifically, the following five main themes were identified. The field's understanding of AI technology and AI's influence on adult education has evolved and become more comprehensive; AI challenges traditional Chinese adult education practices by helping to actualize personalized learning and precision education; AI transforms adult learning resource development; AI helps to turn learning environment into an open intelligent learning system; and lastly, AI urges the shift of adult educator's role in adult learning.

This study is not without limitations. Contextualized in China, this study shares the limitations with other single country studies. One such limitation is “cumulation” issue. This study should be replicated in other country contexts to further validate the generalizability of the five main themes identified in this research.

The five themes identified in this study can help understand the promises and challenges that AI brings to the field of adult education in China. These five themes can also serve as an integrated lens through which one can make sense of AI's integration into other countries' adult education practices.

This paper fulfills an identified need of understanding the current status of AI's integration into and influence on the field of adult education in China.

Lubricating oil leakage is a common issue in thermal power plant operation sites, requiring prompt equipment maintenance. The real-time detection of leakage occurrences could avoid disruptive consequences caused by the lack of timely maintenance. Currently, inspection operations are mostly carried out manually, resulting in time-consuming processes prone to health and safety hazards. To overcome such issues, this paper proposes a machine vision-based inspection system aimed at automating the oil leakage detection for improving the maintenance procedures.

The approach aims at developing a novel modular-structured automatic inspection system. The image acquisition module collects digital images along a predefined inspection path using a dual-light (i.e. ultraviolet and blue light) illumination system, deploying the fluorescence of the lubricating oil while suppressing unwanted background noise. The image processing module is designed to detect the oil leakage within the digital images minimizing detection errors. A case study is reported to validate the industrial suitability of the proposed inspection system.

On-site experimental results demonstrate the capabilities to complete the automatic inspection procedures of the tested industrial equipment by achieving an oil leakage detection accuracy up to 99.13%.

The proposed inspection system can be adopted in industrial context to detect lubricant leakage ensuring the equipment and the operators safety.

The proposed inspection system adopts a computer vision approach, which deploys the combination of two separate sources of light, to boost the detection capabilities, enabling the application for a variety of particularly hard-to-inspect industrial contexts.

Using computer technology to realize ferrographic intelligent fault diagnosis technology is fundamental research to inspect the operation of mechanical equipment. This study aims to effectively improve the technology of deep learning technology in the field of ferrographic image recognition.

This paper proposes a binocular image classification model to solve ferrographic image classification problems.

This paper creatively proposes a binocular model (BesNet model). The model presents a more extreme situation. On the one hand, the model is almost unable to identify cutting wear particles. On the other hand, the model can achieve 100% accuracy in identifying Chunky and Nonferrous wear particles. The BesNet model is a bionic model of the human eye, and the used training image is a specially processed parallax image. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The work presented in this thesis is original, except as acknowledged in the text. The material has not been submitted, either in whole or in part, for a degree at this or any other university. The BesNet model developed in this article is a brand new system for ferrographic image recognition. The BesNet model adopts a method of imitating the eyes to view ferrography images, and its image processing method is also unique. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0150/

The purpose of this paper is to investigate the effect of different soldering temperatures on the performance of chip-on-board (COB) light sources during vacuum reflow soldering.

First, the influence of the void ratio of the COB light source on the steady-state voltage, luminous flux, luminous efficiency and junction temperature has been explored at soldering temperatures of 250°C, 260°C, 270°C, 280°C and 290°C. The COB chip has also been tested for practical application and aging.

The results show that when the soldering temperature is 270°C, the void ratio of the soldering layer is only 5.1%, the junction temperature of the chip is only 76.52°C, and the luminous flux and luminous efficiency are the highest, and it has been observed that the luminous efficiency and average junction temperature of the chip are 107 lm/W and 72.3°C, respectively, which meets the requirements of street lights. After aging for 1,080 h, the light attenuation is 84.64% of the initial value, which indicates that it has higher reliability and longer life.

It can provide reference data for readers and people in this field and can be directly applied to practical engineering.