Rui Wang

This research investigates firms’ bribery motivations in China. Based on resource dependence theory and anomie theory, we identify resource conditions as firms’ proactive motivation to bribe and firms’ perceived institutional environment as their passive motivation to bribe. We use the data from 2002 World Business Environment Survey, collected by the World Bank, to investigate firms’ bribery in the world’s largest emerging market, China. We employ a multi-level logistic model to test our hypotheses. The results show that unsatisfactory general and task environmental conditions may trigger firms to bribe in order to compete for better resources and opportunities; institutional conditions such as the security expenditure and anomie climate may make firms perceive bribery as a common phenomenon and thus induce firms to bribe. This research provides some insights to understand business bribery behaviors in emerging market. It also discusses some managerial implications and guidelines for policy-making from the findings

This research explores how consumers respond to Corporate Social Responsibility (CSR) in China with a multiproduct, comparative survey. Empirical results conclude that (1) Chinese consumers, who show a high level of awareness and trust of CSR, are more likely to transform a good CSR record into positive corporate evaluation, product association, and purchase intention; (2) Consumer responses to CSR vary across different product categories. Those firms selling experience products (vs. search and credence products) are more likely to gain consumers' positive product associations and purchase support through CSR practices; and (3) The relationships between consumer demographics and their CSR responses are not linear, and those consumers with a middle level of age and income would respond to CSR more positively. Managerial implications are provided

Based on the power basis theory, this study examined the relationship between high-level talents’ perceived overqualification and withdrawal behavior and the mediating role of sense of power. We also analyze the boundary effects of protected values and being trusted. The hypotheses of this study were tested through questionnaires gathered across three phases over 3 months from 371 high-level talents from 6 enterprises, 5 governments, and 13 universities in China. Hierarchical regression analyses and bootstrapping appraisals showed that: POQ has a positive relationship with withdrawal behavior; sense of power mediates the relationship between overqualification and withdrawal behavior, with high POQ resulting in low perceived power, which then reinforces withdrawal behavior; the negative relationship between POQ and sense of power is stronger for high-level talents with high protected value ; the negative relationship between sense of power and withdrawal behavior is stronger for high-level talents with highly being trusted ; moderated-mediation analyses reveal that the mediating effects of sense of power are stronger for employees with high protected values or being trusted. At the end of this study, the theoretical and practical implications are discussed.

To have enough financial literacy, an investor must be able to make intelligent investment choices, and on the other hand, the heuristic bias, the framing effect, cognitive illusions, and herd mentality are all variables that contribute to the formation of behavioral biases, also known as illogical conduct, in the decision-making process. The current research looks specifically at behavioral biases and financial literacy influence investment choices, particularly on stock market investment. For the research, a representative sample of 450 individual investors was evaluated. A structured questionnaire was designed using the Likert’s scale method to elicit the research variables, and the data acquired were analyzed using the SEM method. According to the findings, there was a statistically significant link between heuristic bias and the development of behavioral bias in decision-making. Nevertheless, cognitive illusions, the herd mentality, and the framing effect all have a deleterious impact on behavioral biases. In addition, investors often adhere to heuristic biases rather than other irrational strategies when making investment judgments. Therefore, individual investors’ financial literacy level greatly influences the choices made about investments in the stock market.

To cultivate children’s imagination, observation, thinking ability, and aesthetic consciousness, the questionnaire survey is adopted to analyze the design strategies and principles of children’s picture books based on augmented reality. Primarily, the related concepts and theories are expounded for the research content. Children in preschool aged 4–5 years are invited as primary participants in this work, and the psychological characteristics of the invited children are analyzed in depth. Then, a study is carried out on the existing AR children’s picture books. The problems existing in the design of AR children’s picture books are found, and then, related solutions are put forward based on the results of the questionnaire survey. Besides, a design is made on the strategies and interactive design principles of AR children’s picture books on mobile terminals that are more in line with the needs of children. The results show that 41.07% of parents do not understand AR technology, and 37.5% of preschool children indicate that they do not operate mobile devices independently. However, they need the assistance of parents to use this kind of picture book. A total of 44.64% of parents believe that the main problem of AR picture books in the current market is the lack of interesting interaction. Given the above problems, five principles are proposed for the design of AR children’s picture books based on mobile terminals, namely, easy operation principle, interesting principle, guiding principle, timely feedback principle, and safety principle. A set of universally applicable design methods are proposed for AR children’s picture books based on mobile terminals, which provides certain theoretical guidance for the development of related types of products.

Understanding and solving the psychological health problems of college students have become a focus of social attention. Complex networks have become important tools to study the factors affecting psychological health, and the Gaussian graphical model is often used to estimate psychological networks. However, previous studies leave some gaps to overcome, including the following aspects. When studying networks of subpopulations, the estimation neglects the intrinsic relationships among subpopulations, leading to a large difference between the estimated network and the real network. Because of the high cost, previous psychological surveys often have a small sample size, and the psychological description is insufficient. Here, the intrinsic connections among multiple tasks are used, and multitask machine learning is applied to develop a multitask Gaussian graphical model. The psychological networks of the population and subpopulations are estimated based on psychological questionnaire data. This study is the first to apply a psychological network to such a large-scale college student psychological analysis, and we obtain some interesting results. The model presented here is a dynamic model based on complex networks which predicts individual behavior and provides insight into the intrinsic links among various symptoms.

We study a joint pricing and inventory control problem for perishables with positive lead time in a finite horizon periodic-review system. Unlike most studies considering a continuous density function of demand, in our paper the customer demand depends on the price of current period and arrives according to a homogeneous Poisson process. We consider both backlogging and lost-sales cases, and our goal is to find a simultaneously ordering and pricing policy to maximize the expected discounted profit over the planning horizon. When there is no fixed ordering cost involved, we design a deep reinforcement learning algorithm to obtain a near-optimal ordering policy and show that there are some monotonicity properties in the learned policy. We also show that our deep reinforcement learning algorithm achieves a better performance than tabular-based Q-learning algorithms. When a fixed ordering cost is involved, we show that our deep reinforcement learning algorithm is effective and efficient, under which the problem of “curse of dimension” is circumvented.

Aiming at the high false alarm rate when using single sensor to detect fire in aircraft cabin, a multisensor data fusion method is proposed to detect fire. First, the weights of multiple factors, that is, temperature, smoke concentration, CO concentration, and infrared ray intensity in the event of fire, were calculated by using the improved analytic hierarchy process method on each sensor node of wireless sensor network, and the probability of fire event in the cabin was evaluated by multivariable-weighted fusion method. Second, based on the mutual support among the evaluation data of fire probabilities of each node, the adaptive weight coefficient is assigned to each evaluation value, and the weighted fusion of all evaluation values of each node is conducted to obtain the fire probability. In the end, compared to the threshold of probability, the fire alarm is determined. Comparing the proposed algorithm to the grey fuzzy neural network fusion algorithm and fuzzy logic fusion algorithm in terms of the time consumption for fire detection and sending alarm and the accuracy of fire alarm perspectives, the experiments demonstrate that the proposed fire detection algorithm can detect the fire within 10s and reduce the false alarm rate to less than 0.5%, which verifies the superiority of the algorithm in promptness and accuracy. In the meanwhile, the fault tolerance of the algorithm is proved as well.

Relying on large-scale parallel corpora, neural machine translation has achieved great success in certain language pairs. However, the acquisition of high-quality parallel corpus is one of the main difficulties in machine translation research. In order to solve this problem, this paper proposes unsupervised domain adaptive neural network machine translation. This method can be trained using only two unrelated monolingual corpora and obtain a good translation result. This article first measures the matching degree of translation rules by adding relevant subject information to the translation rules and dynamically calculating the similarity between each translation rule and the document to be translated during the decoding process. Secondly, through the joint training of multiple training tasks, the source language can learn useful semantic and structural information from the monolingual corpus of a third language that is not parallel to the current two languages during the process of translation into the target language. Experimental results show that better results can be obtained than traditional statistical machine translation.

This paper proposes a new 5D chaotic system with the flux-controlled memristor. The dynamics analysis of the new system can also demonstrate the hyperchaotic characteristics. The design and analysis of adaptive synchronization for the new memristor-based chaotic system and its slave system are carried out. Furthermore, the modularized circuit designs method is used in the new chaotic system circuit implementation. The Multisim simulation and the physical experiments are conducted, compared, and matched with each other which can demonstrate the existence of the attractor for the new system.