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The purpose of this paper is to test a catering theory by examining impacts of minority shareholders’ pressures on earnings management (EM), and attempt to answer: what is the role of minority shareholders participation (MSP) in corporate governance? and does MSP serve as an external monitor to managers, or does it put excessive pressure on them?

Using a novel online voting data set in China’s stock market, the author constructs the measure of MSP, and regress the EM on MSP. To address the endogeneity, the author introduces propensity score matching and difference-in-difference methods, instrumental variables, and Heckman estimation to show that the results are robust to different specifications and alternative measures.

The author documents that: MSP plays limited role in external monitoring; and firms facing high MSP levels tend to manage earnings more actively. In addition, information asymmetry, proposals’ importance, managerial incentives, and CEO financial expertise significantly affect firms’ catering behaviors.

This paper contributes to different strands of the literature. First, the finding significantly supports the catering hypothesis from a new perspective of EM. Second, the author contributes to a hotly debated issue in corporate governance: whether minority shareholders should be granted increased participation in corporate decisions? The results also provide timely empirical evidence for government regulators who are concerned about the costs and benefits of granting minority shareholders direct control over corporate decisions.

Social loafing in knowledge contribution (namely, knowledge contribution loafing [KCL]) usually happens in group context, especially in the mobile collaboration tasks. KCL shows dynamic features over time. However, most previous studies are based on static assumption, that is, KCL will not change over time. This paper aims to reveal the dynamics of KCL in mobile collaboration and analyze how network centrality influences KCL states considering the current loafing state.

This study is based on empirical study design. Real mobile collaboration behavioral data related to knowledge contribution were collected to investigate the dynamic relationship between network centrality and KCL. In total, 4,127 chat contents were collected through Slack (a mobile collaboration APP). The text data were first analyzed using the text analysis method and then analyzed by a machine learning method called hidden Markov model.

First, the results reveal the inner structure of KCL, showing that it has three states (low, medium and high). Second, it is found that network centrality positively influences individuals involved in medium and high loafing state, while it has a negative influence on individuals with low loafing state.

The limitations are related to the single machine learning method and no subdivision of social network. First, this paper only uses one kind of text classification model (TF-IDF) to divide chat contents, which may not be superior to other classification models. This paper considers the eigenvector centrality, and not further divides the social network into advice network and expressive network.

This study helps companies infer tendency of different KCL and dynamically re-organize a mobile collaborative team for better knowledge contribution.

First, previous studies based on static assumptions regarding KCL as static and the relationship between loafing reducing mechanisms and team members KCL does not change over time. This study relaxes static assumptions and allows KCL to change during the process of collaboration. Second, this study allows the impact of network centrality to be different when members are in different KCL states.

Criminal elements in today's technology‐driven society are using every means available at their disposal to launder the proceeds from their illegal activities. While many anti‐money laundering (AML) solutions have been in place for some time within the financial community, they face the challenge to adapt to the ever‐changing risk and methods in relation to money laundering (ML). This research seeks to focus on ML control and prevention, which aim to automate the monitoring and diagnosing of ML schemes in order to report suspicious activities to banks.

The research adopted the technology of intelligent agents to provide a more adaptive, flexible, and knowledge‐based solution for AML.

Based on the analysis of monitoring, diagnosing, and reporting of ML activities occurring in electronic transactions, several types of intelligent agents are proposed and a multi‐agent framework is presented for AML. Furthermore, business knowledge such as business rules and strategies are extracted from AML practice, and applied to the design of individual agents to make them act autonomously and collaboratively to fulfil the goal of ML detection.

The proposed multi‐agent framework is a stand‐alone system, which can be integrated by banks to combat ML. Although it is a uni‐bank framework at present, it can be extended to multi‐bank application in the future.

The research explores the approach of applying an intelligent agent for knowledge‐based AML in an electronic transaction environment for banks. By separating business logic from the business model, such a business‐rules approach can enhance the flexibility and adaptability of the agent‐based AML system.

This paper aims to study the effects of the processing parameters in the reciprocating magnetorheological polishing (RMRP) on abrasive particle trajectory by the simulation analysis, which provides a basis for the machining uniformity of the workpiece.

The principle of the RMRP method is discussed, and a series of simulation analysis of the abrasive particle trajectory are performed to evaluate the effects of the workpiece’s rotational speed, the eccentric wheel’s rotational speed, the eccentricity and the frame gap on abrasive particle trajectory by using the RMRP method.

The processing parameters have a significant influence on the abrasive particle trajectory, and then the machining uniformity of the workpiece is affected. Under certain experimental conditions, the height difference of workpiece measuring points varies between 4 and 11 µm, and the height difference of equal radial measuring points is less than 1.5 µm by optimizing processing parameters.

In this study, the optimal processing parameters can be obtained by the simulation analysis of abrasive particle trajectory, which can replace the experimental methods to obtain the reasonable processing parameters for the machining uniformity of the workpiece. It provides references for the selection of processing parameter values in magnetorheological polishing process.

This paper aims to analyze the significance of machining parameters (workpiece’s rotational speed, magnet coil current and working gap) on final Ra (surface roughness) and material removal rate (MRR) of workpiece in reciprocating magnetorheological polishing (RMRP) process.

The research is planned to analyze, model and predict the optimum machining parameters to anticipate final Ra and MRR by applying response surface methodology (RSM) and multiresponse optimization (desirability function approach). The experiments have been planned by design of experiments (DOE). Analysis of variance (ANOVA) is applied to determine the significances of machining parameters on RMRP performance characteristics.

Response surface plots for final Ra and MRR by RSM show that machining parameters are significant for the responses. The optimum machining parameters obtained are optimized by desirability function approach (DFA), and the optimum parametric combination has been validated by confirmatory experiments. The experimental results of the final Ra and MRR are deviated by 5.12% and 2.31% from the response results under the same optimization conditions, respectively.

In this study, the RMRP responses (final Ra and MRR) are improved at predicted input machining parameters condition obtained by RSM and DFA approach. Furthermore, the research results provide a reference for experimental design and optimization of MRP process.

The purpose of the study is to examine the impact of augmented reality (AR) on users' tour experience and psychological well-being in museums. The study utilizes the Information System (IS) Success Model and the Perceived Value Theory to investigate the relationships between AR quality, content quality, system quality, vividness, utilitarian value, hedonic value, tour experience, and psychological well-being.

The study adopts a quantitative research approach and surveys 286 visitors at the Wuhan Natural History Museum. The collected data is analyzed using PLS-SEM to test the proposed model.

The study finds that content quality, system quality, and vividness significantly influence utilitarian and hedonic value. Utilitarian and hedonic values mediate the relationship between AR quality and tour experience and psychological well-being, with hedonic value having a greater impact. Utilitarian value positively influences customers' hedonic value, while tour experience positively influences customers' psychological well-being.

This study contributes to the existing literature by applying the Information System Success Model and Perceived Value Theory to explore the impact of AR on users' tour experience and psychological well-being in museums. It provides empirical evidence supporting the importance of content quality and perceived value in promoting AR adoption and use. The findings add value to the field of augmented reality research and offer practical implications for AR service providers.

Concerning that limited explanation exists examining the function of corporate governance in trust processing within entrepreneurial network development, the purpose of this paper is to explore trust evolution and the role of corporate governance in an entrepreneurial network.

This paper makes an innovative exploration based on the case study of NVC Lighting Holding Limited.

It proposes that in the initial period of network relationship which is based on entrepreneur’s individual social network and embodies sole social network embeddness, entrepreneurial network relies more on affective trust than contractual trust. When stepping into extending period of network relationship which reflects separate embeddedness of social and market network, however, entrepreneurial network has an equal reliance on both affective trust and contractual trust. With further development, when ushering in the phase of maturity which undergoes superimposing embeddedness of both social and market network, entrepreneur network inclines to rely more heavily on affective trust than contractual trust. During the whole process, it can be found that the reliance of entrepreneurial network on trust has the tendency to transfer from affective trust to contractual trust. Furthermore, decreasing of equity ratio of founders and strengthening of controlling right heterogeneity in the corporate governance have facilitated the transfer process and the entrepreneurs’ authority has restraining effect on the evolution of the process.

Through case study, this paper presents the trust evolution process in different stages of entrepreneurial network. Another important theoretic contribution of this paper is that it reveals the function of corporate governance in trust processing within entrepreneurial network development.

Based on the knowledge-based view, this study investigates how firms' information technology (IT) capability broadens and deepens their knowledge base, which consequently improves digital innovation. By further drawing on the institutional theory perspective, this study examines how the relationships between IT capability and knowledge base are moderated by the institutional environments in which the firm operates.

This paper uses 170 samples of Chinese firms and an empirical test conducted by the authors following a hierarchical moderated regression analysis.

The results find that IT capability positively affects knowledge breadth and knowledge depth, which consequently improves digital innovation. Furthermore, the study reveals the negative moderating effects of enforcement inefficiency on IT capability–knowledge breadth relationship, and the negative moderating effects of government support on IT capability–knowledge depth relationship.

This research is one of the earliest attempts to explore the impact of the institutional environment of emerging economies on IT capability. It also clarifies the impact of knowledge breadth and knowledge depth on digital innovation.