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The purpose of this paper is to examine the influence of transparency on consumers' judgment and decision-making.

This study uses an experimental research design in which participants' negative emotions dynamically change driven by group emotional interactions when they are experiencing a group complaint.

The experimental results show that compared with opaque products, transparent products make consumers rely more on emotions to make judgments and decisions (Experiment 1). It is precise because transparency increases the influence of emotion on consumers' judgment and decision-making that positive emotion makes consumers' evaluation and willingness to pay higher, while negative emotion makes consumers' evaluation and willingness to pay lower (Experiments 2 and 3). Transparency will also affect consumers' subsequent judgment and decision-making methods, so they are more inclined to choose the option with the dominant emotional dimension (Experiment 4).

Previous studies mainly focus on the impact of transparent packaging on consumers and discuss the impact of transparent packaging on consumer product evaluation and consumption quantity. This study proves that product-related transparent elements can also affect consumers' decision-making methods, making them more dependent on emotions to make decisions, enriching the research on the influencing factors of consumer decision-making methods.

The purpose of this paper is to explore the two effects (flow effect and resonance effect) during a group complaint based on the emotional contagion theory.

This study uses an experimental research design in which participants’ negative emotions dynamically change driven by group emotional interactions when they are experiencing a group complaint.

Flow effect and resonance effect can occur during the process of group emotional contagion. Specifically, when group customers’ negative emotional similarity is low in a group complaint, group emotional contagion leads to flow effect (i.e. negative emotions flow from customers with higher levels of negative emotions to those with lower levels of negative emotions). By contrast, when group customers’ negative emotional similarity is high in a group complaint, group emotional contagion leads to resonance effect (i.e. group customers’ negative emotions increase significantly).

Most of the previous research studies the process of emotional contagion from one with higher levels of emotional displays to the other with lower levels of emotional displays, which is named as the “flow effect” of emotional contagion. However, when two individuals with the same levels of negative emotional displays interact with each other, the flow effect of emotional contagion is very likely not to occur. It is interesting to find that both individuals’ negative emotions increase significantly during the process of emotional contagion. The authors propose the “resonance effect” of emotional contagion to explain this phenomenon.

This paper aims to empirically investigate time–frequency linkages between Euramerican mature and Asian emerging crude oil futures markets in terms of correlation and risk spillovers.

With daily data, the authors first undertake the MODWT method to decompose yield series into four different timescales, and then use the R-Vine Copula-CoVaR to analyze correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets.

The empirical results are as follows: (a) short-term trading is the primary driver of price volatility in crude oil futures markets. (b) The crude oil futures markets exhibit certain regional aggregation characteristics, with the Indian crude oil futures market playing an important role in connecting Euramerican mature and Asian emerging crude oil futures markets. What’s more, Oman crude oil serves as a bridge to link Asian emerging crude oil futures markets. (c) There are significant tail correlations among different futures markets, making them susceptible to “same fall but different rise” scenarios. The volatility behavior of the Indian and Euramerican markets is highly correlated in extreme incidents. (d) Those markets exhibit asymmetric bidirectional risk spillovers. Specifically, the Euramerican mature crude oil futures markets demonstrate significant risk spillovers in the extreme short term, with a relatively larger spillover effect observed on the Indian crude oil futures market. Compared with India and Japan in Asian emerging crude oil futures markets, China's crude oil futures market places more emphasis on changes in market fundamentals and prefers to hold long-term positions rather than short-term technical factors.

The MODWT model is utilized to capture the multiscale coordinated motion characteristics of the data in the time–frequency perspective. What’s more, compared to traditional methods, the R-Vine Copula model exhibits greater flexibility and higher measurement accuracy, enabling it to more accurately capture correlation structures among multiple markets. The proposed methodology can provide evidence for whether crude oil futures markets exhibit integration characteristics and can deepen our understanding of connections among crude oil futures prices.

The purpose of this paper is to target on designing a short-term load prediction framework that can accurately predict the cooling load of office buildings.

A feature selection scheme and stacking ensemble model to fulfill cooling load prediction task was proposed. Firstly, the abnormal data were identified by the data density estimation algorithm. Secondly, the crucial input features were clarified from three aspects (i.e. historical load information, time information and meteorological information). Thirdly, the stacking ensemble model combined long short-term memory network and light gradient boosting machine was utilized to predict the cooling load. Finally, the proposed framework performances by predicting cooling load of office buildings were verified with indicators.

The identified input features can improve the prediction performance. The prediction accuracy of the proposed model is preferable to the existing ones. The stacking ensemble model is robust to weather forecasting errors.

The stacking ensemble model was used to fulfill cooling load prediction task which can overcome the shortcomings of deep learning models. The input features of the model, which are less focused on in most studies, are taken as an important step in this paper.

This study aims to explore the emotion-based mediator of information security fatigue in the relationship between employees’ information security–related stress (SRS) and information security policy (ISP) compliance intention and the effects of psychological capital (PsyCap) on relieving SRS and promoting compliance.

The authors tested a series of hypotheses by applying partial least squares–based structural equation modeling to survey data from 488 employees in Chinese enterprises.

The results suggest that the relationship between SRS and ISP compliance intention is fully mediated by information security fatigue. Employees’ SRS promotes their information security fatigue, which reduces their intention to follow ISPs. In addition, employees with high PsyCap may experience low levels of SRS and information security fatigue, which promotes their willingness to comply with ISPs.

This study extends knowledge by introducing information security fatigue and PsyCap to the field of information security management, and it calls attention to the effects on information security behaviors of employee emotions and positive psychological resources in an organization. The authors reveal the emotion-based mediating effect of information security fatigue and the positive influence of PsyCap in information security management.

Surrogate-assisted evolutionary algorithms (SAEAs) are the most popular algorithms used to solve design optimization problems of expensive and complex engineering systems. However, it is difficult for fixed surrogate models to maintain their accuracy and efficiency in the face of different issues. Therefore, the selection of an appropriate surrogate model remains a significant challenge. This paper aims to propose a dynamic adaptive hybrid surrogate-assisted particle swarm optimization algorithm (AHSM-PSO) to address this issue.

A dynamic adaptive hybrid selection method (AHSM) is proposed. This method can identify multiple ensemble models formed by integrating different numbers of excellent individual surrogate models. Then, according to the minimum root-mean-square error, the best suitable surrogate model is dynamically selected in each generation and is used to assist PSO.

Experimental studies on commonly used benchmark problems, and two real-world design optimization problems demonstrate that, compared with existing algorithms, the proposed algorithm achieves better performance.

The main contribution of this work is the proposal of a dynamic adaptive hybrid selection method (AHSM). This method uses the advantages of different surrogate models and eliminates the shortcomings of experience selection. Furthermore, the empirical results of the comparison of the proposed algorithm (AHSM-PSO) with existing algorithms on commonly used benchmark problems, and two real-world design optimization problems demonstrate its competitiveness.