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The purpose of this study is to theoretically discuss and empirically test the mediating effect of emotional exhaustion (EE) and the moderating effects of perceived coworker support (PCS) and positive affectivity (PA) in the relationship between abusive supervision (AS) and knowledge hiding (KH).

The authors adopted a three-wave survey to collect data from eight high-tech companies in north China. The final sample of 209 available responses was analyzed with hierarchical regression analyses and bias-corrected bootstrapping methods.

AS positively affected employees’ KH. EE fully mediated this positive relationship. PCS and PA attenuated the relationship between AS and EE, respectively, and the mediating effects of EE.

First, the study contributes to the knowledge management and leadership literature by examining the relationship between the following two dysfunctional workplace behaviors: AS and KH. Second, the authors explicitly tested the underlying psychological mechanism in the above relationship. Third, this study specified two ways – obtaining support from coworkers and generating positive emotions – to attenuate the toxic effects of AS on victims.

The purpose of this paper is to report the investigation of a low temperature application process for three‐layer polyethylene coating (3LPE) at 185‐195°C to meet the requirement of the coating temperature for X80 longitudinally welded pipe.

Plant coating and laboratory tests were performed to determine the properties of 3LPE coating coated at 185‐195°C.

The properties of the 3LPE coating, which was applied by using a suitable low temperature cured epoxy powder at temperature of 185‐195°C, were nearly the same as those of a 3LPE coating that had been applied at the normal coating temperature (200‐230°C).

Recommended acceptance criteria for 3LPE coatings applied at 185‐195°C are proposed. The research results met the requirements for external anti‐corrosion properties for X80 longitudinally welded pipe.

The purpose of this paper is to improve the robustness of the traditional Bhattacharyya metric for the effect of histogram quantization in the histogram-based visual tracking. However, the traditional Bhattacharyya metric neglects the correlation of crossing-bin and is not robust for the effect of histogram quantization.

In this paper, the authors propose a visual tracking method via crossing-bin histogram Bhattacharyya similarity in the particle filter.

A crossing-bin matrix is introduced into the traditional Bhattacharyya similarity for measuring the reference histogram and the candidate histogram, and the basic tasks of measure such as maximum similarity of self and the triangle inequality are proven. The authors use the proposed measure in the particle filter visual tracking framework and address a model update strategy based on the crossing-bin histogram Bhattacharyya similarity to improve the robustness of visual tracking.

In the experiments using the famous challenging benchmark sequences, precision of the proposed method increases by 12.8 per cent comparing the traditional Bhattacharyya similarity and the cost time decreases by 38 times comparing the incremental Bhattacharyya similarity. The experimental results show that the proposed method can track the object robustly and rapidly under illumination change and occlusion.

Drawing on social information processing theory, this study constructs a multilevel moderated mediation model. This model seeks to delve into the intricate and previously overlooked interplay between supervisor bottom-line mentality (BLM) and knowledge hiding. Within this context, we introduce self-interest as a mediating factor and incorporate performance climate as a team-level moderating variable.

The time-lagged data involve 336 employees nested in 42 teams from 23 automobile sales companies in five regions of China. The analysis was meticulously executed using Hierarchical Linear Modeling, complemented by bias-corrected bootstrapping techniques.

The findings reveal that self-interest acts as a full mediator in the positive link between supervisor BLM and knowledge hiding. Furthermore, the performance climate plays a moderating role in both the relationship between supervisor BLM and self-interest, and the entire mediation process. Notably, these relationships are intensified in environments with a high performance climate compared to those with a low one.

This research stands as one of the pioneering efforts to integrate supervisor BLM into the discourse on knowledge hiding, elucidating the underlying psychological mechanisms and delineating the boundary conditions that shape the “supervisor BLM–knowledge hiding” relationship. Further, our insights provide organizations with critical guidance on strategies to curtail knowledge hiding among their employees.

This study aims to investigate countries without national property insurance and see how experience affects behavior toward higher-risk flood prone property.

Using a unique data set that captures the flood experiences of homeowners that search for new housing, the authors examine the premiums or discounts of such experience on homes at risk. The authors use hedonic property modeling to estimate the effects of experience on values.

The authors find that such experiences play a strong role in convincing buyers of the real risks imposed by climate change and sea level rise and the authors expect these demand-side behavioral changes to persist. This finding is unlike more developed markets where insurance may be subsidized and negative effects on value dissipate within a few years.

The world is starting to pay more attention to climate risk and the results in developed countries have been biased by the extensive insurance provided by the government or emergency funding.

Providing market transparency on climate risks will result in permanent market effects, if not otherwise subsidized.

The governments should encourage market disclosure.

No one has ever had a data set like this before where the authors get to observe the behavior of those already experiencing property losses from flooding.

Content‐based image retrieval (CBIR) is an important research area for automatically retrieving images of user interest from a large database. Due to many potential applications, facial image retrieval has received much attention in recent years. Similar to face recognition, finding appropriate image representation is a vital step for a successful facial image retrieval system. Recently, many efficient image feature descriptors have been proposed and some of them have been applied to face recognition. It is valuable to have comparative studies of different feature descriptors in facial image retrieval. And more importantly, how to fuse multiple features is a significant task which can have a substantial impact on the overall performance of the CBIR system. The purpose of this paper is to propose an efficient face image retrieval strategy.

In this paper, three different feature description methods have been investigated for facial image retrieval, including local binary pattern, curvelet transform and pyramid histogram of oriented gradient. The problem of large dimensionalities of the extracted features is addressed by employing a manifold learning method called spectral regression. A decision level fusion scheme fuzzy aggregation is applied by combining the distance metrics from the respective dimension reduced feature spaces.

Empirical evaluations on several face databases illustrate that dimension reduced features are more efficient for facial retrieval and the fuzzy aggregation fusion scheme can offer much enhanced performance. A 98 per cent rank 1 retrieval accuracy was obtained for the AR faces and 91 per cent for the FERET faces, showing that the method is robust against different variations like pose and occlusion.

The proposed method for facial image retrieval has a promising potential of designing a real‐world system for many applications, particularly in forensics and biometrics.

Compared with the fierce price competition in 1998, the well‐order price competition is witnessed in Chinese iron and steel industry now and the pricing behaviors of steel firms also follow the certain rules. Based on the methods of collecting the secondary data and interviewing, this paper examines the pricing behaviors of firms to explain the how Chinese steel firms make their pricing decisions and maintain the well‐order competitive relationship among them. The authors found out that (1) most Chinese steel companies adopt a kind of strategic perspective in their pricing decision making, in which understanding of the market trend and the close attention to their competitors are both important; (2) there obviously exists price leader and followers in Chinese iron and steel industry, and the relationship between price leader and followers is relatively stable and the factor behind this phenomenon is the existence of a kind of informal platform of communication among competitors, government and trade associations.

Suggests that misunderstandings frequently occur when trying to understand Chinese language and culture, and so gives the implied meaning of various Chinese expressions and sayings such as greetings, thanks, respect, age, congratulations and taboo subjects.

Driven by the rapid expansion of online retail and the surge in livestream commerce, the impact of different livestream mode on brand and platform performance has become a critical issue. This paper analyzes the impact of artificial intelligence (AI) and key opinion leader (KOL) livestream on the profitability of brands and the platform, incorporating the effects of horizontal interactions to identify the optimal livestream mode.

This paper develops a model of a platform supply chain involving two brands and a platform, where each brand independently decides whether to utilize KOL or AI livestream. Applying Stackelberg game approach, the study derives equilibria for various livestream scenarios, identifying the optimal livestream mode for both parties. Additionally, the model is extended to incorporate asymmetric market potential and network externality to evaluate their impact on a brand’s choice of livestream mode.

Several interesting and important results are derived in this paper. Firstly, it is found that AI livestream enables brands to leverage network externality and mitigate the market disadvantage, thereby gaining a competitive advantage. Secondly, while KOL livestream promotes trust, the medium KOL commission rates could cause brands to be trapped in a prisoner’s dilemma, and excessively high commission rates may render them less profitable. Thirdly, the KOL commission rate, network externality intensity, horizontal interactions and market disadvantage are critical determinants influencing a brand’s choice of livestream mode.

This study is the first to investigate the effects of horizontal interactions, asymmetric market potential and asymmetric network externality on livestream mode selection by brands within a platform supply chain. The research provides valuable insights into optimizing livestream strategies to enhance brand profitability.