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This study aims to combine two fraud-related streams of the literature on guanxi and overconfidence into an integrated framework, which is the fraud triangle, to interpret the mechanism of fraud commission and detection.

A bivariate probit model with Partial Observability (POBi Probit) is applied. Moreover, the POBi Probit model is adjusted to the Chinese context. The China-specific POBi Probit model is constructed using data of Chinese A-share listed companies from 2008 to 2014, with a total of 15,109 firm-year observations.

Overconfidence induces fraud commission and worsens fraud detection; overconfidence mediates the relationship between fraud and guanxi; the “white side” of guanxi comes from alumni networks, while the “dark side” is derived from relatives-based networks; overconfidence induces fraud commission in accounting and disclosure and benefits the detection of disclosure frauds. Guanxi suppresses fraud commission in management and disclosure, however, it worsens fraud detection given fraud in management and disclosure; overconfidence induces fraud commission in both state-owned enterprises (SOE) and non-SOEs, and benefits fraud detection in SOEs. Guanxi suppresses fraud commission and worsens fraud detection in SOEs and city-owned firms.

There are two drawbacks of the partial observable bivariate probit (POBi-Probit) method that must be mentioned here. On one hand, the ex ante variable selection is one of the most difficult parts of applying the POBi-Probit model and different variables are included in different studies. On the other hand, the POBi-Probit model might not converge if too many variables are included. Thus, many widely accepted factors can be included in the model. Thus, this study initially sets the POBi-Probit model based mainly on Khanna et al. (2015) and then adjusts the model for the Chinese context (e. g. considering government ownership) according to Yiu et al. (2018) and Zhang (2018) and the local study of Meng et al. (2019). Considering the observability of fraud, on one hand, the observability of fraud commission is a widely accepted limitation, especially when accounting opacity comes across with regulatory efficiency (Yiu et al. (2018). On the other hand, the observability of relationships is another obstacle to this study. Future studies can go further by revealing the presently unobservable relationships using Big Data technology.

This paper theoretically and practically contributes to the literature on both corporate fraud and corporate governance. Theoretically, by introducing integrated principal-agent resource-reliance theory (IPRT) and upper echelon theory (UET), this paper broadens the framework of fraud triangle theory (FTT) and testifies the availability of the broaden FTT in the transitional and emerging-market context of China. Practically, this paper provides evidence that guanxi and overconfidence are two of the factors affecting corporate fraud. Thus, this paper provides a governance approach opposing corporate fraud in China, which may help the other emerging economies in transition.

This study aims to explore whether, how and when leaders' artificial intelligence (AI) symbolization (i.e. the demonstration of leaders' acceptance of and support for AI by engaging in AI-related behaviors and/or displaying objects that reflect their affinity for AI) affects employee job crafting behaviors.

The authors conducted two studies (i.e. an experiment and a multi-wave field survey) with samples from different contexts (i.e. United States and China) to test our theoretical model. The authors used ordinary least squares (OLS) and hierarchical linear modeling (HLM) to test the hypotheses.

Leaders' AI symbolization is positively related to employee change readiness and, in turn, promotes employee job crafting. Moreover, employee-attributed impression management motives moderate the positive indirect effect of leaders' AI symbolization on employee job crafting via change readiness, such that this indirect effect is stronger when employee-attributed impression management motives are low (vs high).

Leaders should engage in AI symbolization to promote employee job crafting and avoid behaviors that may lead employees to attribute their AI symbolization to impression management.

By introducing the concept of leaders' AI symbolization, this study breaks new ground by illustrating how leaders' AI symbolization positively influences employees' change readiness, as well as job crafting in the workplace. Further, integrating AI as a novel and timely context for evaluating job crafting contributes to the literature where empirical research is relatively scant, particularly regarding the factors that prompt employees to engage in job crafting.

This paper aims to study both the mechanical properties and the corrosion behavior of the synthesized in situ (TiC-TiB₂) particulates/AZ91 magnesium matrix composite and compare the results with that of the conventional AZ91D alloy.

Scanning electron microscope (SEM) equipped with energy dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) were used to study the surface morphology and crystalline structure. Mechanical compression tests were used to investigate the mechanical performance according to ASTM E9-89a. The corrosion behavior of the synthesized magnesium alloy was examined using both electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization techniques in dilute Harrison solutions.

The microstructure of the Mg composite showed a uniform distribution of reinforcing phases. Also, the reinforcing phases were formed without residual intermediate phases. The addition of titanium and boron carbides not only enhanced the mechanical properties of the matrix but also improve its corrosion behavior.

This is the first time that magnesium matrix composite has been to synthesized with TiC and TiB₂ particulates starting from starting from Ti and B carbides powder without adding aluminium using practical and low-cost technique (in situ reactive infiltration technique). This paper studies the corrosion behavior of synthesized Mg matrix in dilute Harrison solution and compares the results with that of conventional AZ91D.

The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.

The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.

The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.

The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.

The COVID-19 outbreak has been effectively controlled in China, but the resulting public psychological crisis is a latent, persistent and torturous disaster. This crisis affects not only the individual’s health but also social stability. This study aims to reveal the structure and situation of public psychological crisis during the remission period of the COVID-19 epidemic.

A questionnaire survey of the psychological crisis was conducted from February to May 2021. Data was collected from 1,056 respondents from different provinces in China.

The findings reveal the dangerous risks of public psychological crises. The specific results are as follows: first, a hierarchical state structure of psychological crisis is constructed and characterized as a significant gradient decline, namely, anxious state>panic state>depressive state>hostile state. Second, 43.939% of respondents reported an anxious state, 46.780% reported a panic state, 40.909% reported a depressive state, 28.883% reported a hostile state and 22.254% of the respondents had four psychological crisis states. Third, those with 56–65 years of age, equal or below high school and PhD, with monthly family incomes of ¥50,000–¥100,000 and living in the county and city areas have relatively high levels of psychological crisis.

This study enriches the research on the evolution of the psychological crisis in terms of structure and periods. This study also provides substantial evidence for hierarchical intervention and differentiated intervention of psychological crisis.

The authors investigate the extent to which online talk can influence contemporaneous and future stock trading, especially when market news is unpresented.

The authors propose an improved sentiment formula incorporating online hype, neutral sentiment and poster reputation. In addition, they conduct event study, OLS regression analyses and probit models.

First, investors tend to be more talkative in relation to firms that are (1) smaller size, (2) more growth-like, (3) with lower prices and higher short interests and (4) of higher beta. Second, the bullish tone of investors positively affects the abnormal returns of small-capitalization stocks. However, online talk has little impact on large-capitalization stocks, except that more postings boost trading liquidity. Third, online talk predicts the presence of future news regardless of firm size, with stronger predictive power found for small-capitalization stocks.

It is of interest to practitioners and researchers to study online talk so as to better understand the trading psychology of retail investors and the effects on the stock market. Furthermore, policymakers are interested in tracking activities on stock message boards in order to prevent security fraud and protect investors' interests.

The results are robust and suggest that online talk has significant impacts on stock trading exploiting an information asymmetry. This study of stock message board posting activities helps researchers to understand whether message contents contain valuable and unique content compared with information available via more traditional media channels.