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Based on sensory marketing theory and cognitive appraisal theory, this study investigates whether and how the background visual complexity of live-streaming affects consumers' purchase intention and reveals the underlying mechanisms through which background visual complexity influences consumers' purchase decisions.

The experiment was conducted with 180 college students, using eye-tracking technology to explore the impact mechanism of live background visual complexity on consumers' purchase intention, considering three types of background visual complexity (high vs medium vs low) and two levels of need for cognitive closure (high vs low).

Firstly, the background visual complexity of live-streaming positively influences consumers' purchase intention by eliciting positive emotions (pleasure and arousal), and the relationship between consumer emotions and purchase intention is nonlinear. Secondly, need for cognitive closure to significantly moderate the influence of background visual complexity on purchase intention.

The limited sample size makes it difficult to generalize to other consumer groups. Also, the study only focuses on one visual factor, lacking comprehensive analysis from multiple perspectives.

It is recommended that live e-commerce companies optimize the visual design of live-streaming backgrounds and identify consumer traits to match the visual complexity with consumers' level of need for cognitive closure, thereby stimulating positive emotions and facilitating more satisfactory shopping decisions.

This paper addresses an interesting and practical issue related to the effects of live background visual complexity on consumers' purchase intention.

Discusses an ongoing research work which attempts to formulate, develop and test mining equipment reliability assessment models based on genetic algorithms. Genetic algorithms are powerful and broadly applicable stochastic search techniques based on the principles of natural selection, heredity and genetics. The reason for selecting genetic algorithms is the fact that the reliability of mining equipment changes over time due to its dependence upon several covariates/factors (e.g. equipment age, the operating environment, number and quality of repairs). These factors combine to create a complex impact on an equipment’s reliability function. Gives an example of the application of genetic algorithms to capture the impact of these factors on time between failures of a piece of mining equipment.

The purpose of this study is to investigate the causal linkages between tourism and economic growth in the USA and determine how they respond to shocks in the system.

The study uses a variety of time series procedures, including the bounds test, Granger causality test, impulse response functions and generalized variance decomposition to analyze the relationship between monthly tourist arrivals (TA) to the USA, real gross domestic product (GDP) and real effective exchange rates.

Results suggest that GDP Granger causes TA in the USA in the long run, indicating the economy-driven tourism growth hypothesis. Additionally, a shock to GDP generates a positive and significant effect on TA that persists in the long-run, while exchange rate shocks only have a significant effect in the first six months.

Different tourism sectors may exert different degrees of influence on the economy. The use of aggregate data on TA in the analysis assumes homogeneity in the industry, thus, only represents the average relationship between tourism and GDP.

This study provides insight that shapes the investment, marketing, sustainability decisions of the public and private sectors aim at increasing tourist flows to drive economic development at the national, state and local levels.

Though several studies have examined the factors influencing the international tourist demand of the USA, this is the first to investigate the causal relationships between tourism, GDP and exchange rates for the USA. It is also the first in the US tourism literature to account for the nature of interactions between the three variables because of innovations in the system.

The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

As a counterproductive interpersonal work behavior, knowledge hiding inhibits team creativity, hampers collaboration and ultimately has a detrimental impact on organizational performance. Drawing upon the impression management perspective. This study aims to investigate how and when employees’ political skill affects their knowledge-hiding behavior in real work contexts.

The authors tested the hypotheses using data gathered from 266 employees in China using a time-lagged research design.

The results indicate that political skill positively influences knowledge hiding through the supplication strategy. Moreover, the positive effect of political skill on this strategy is stronger under higher levels of competition.

A cross-sectional design and the use of self-report questionnaires are the limitations of this study.

The authors contribute to the literature on the emergence of knowledge hiding by identifying an impression management perspective. The authors also contribute to the literature on political skill by exploring the potential negative effects of political skill in the interpersonal interaction. Moreover, the authors enrich the understanding of the literature in competitive climate by introducing the impression management theory and exploring its influence on knowledge floating.

Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities.

The authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data.

The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance.

First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms.

The purpose of this paper is to present an approach to reduce energy consumption in data centers. Subsequently, it reduces electricity bills and carbon dioxide footprints resulting from their use.

The authors present a mathematical model of the energy dissipation optimization problem. The authors formulate analytically the server selection problem and the supply air temperature as a non-linear programming, and propose an algorithm to solve it dynamically.

A simulation study on SimWare, using real workload traces, shows considerable savings for different data center sizes and utilization rates as compared to three other classic algorithms. The results prove that the proposed algorithm is efficient in handling the energy-performance trade-off, and that the proposed algorithm provides significant energy savings and maintains a relatively homogenous and stable thermal state at the different rack units in the data center.

The proposed algorithm ensures energy provisioning, performance optimization over existing state-of-the-art heuristics, and on-demand workload allocation.

The coal reserves overlain by buildings, water bodies and railways are estimated to be about 14 billion tons in China. Based on the concept of green mining, in order to save the unit energy, Double pumps in parallel forms of pumping are adopted in 0the paste filling of the pumping system. The optimal solution of the objective function could be obtained by taking the unit flow power of the pumping system as the objective function, using the quadratic polynomial by the least square method curve fitting and by the application of MATLAB. Thereby it is possible to reduce power consumption and increase the efficiency of the operating economy in the paste filling station.

The purpose of this paper is to test the effects of cigarette warning labels that used text-only or text-plus relevant graphics. The labels were framed in terms of either the negative consequences of smoking (loss frame) or the benefits of not smoking (gain frame). The role of smoking identity – the centrality of being a smoker to one's self-concept – in the effects of the warning labels was also examined.

An experiment was conducted online with 132 college smokers. Participants were randomly assigned to viewing either graphic or text-only labels that were either gain or loss framed. Smoking identity was measure prior to viewing. Message evaluations and smoking intentions were assessed after exposure as dependent measures.

A consistent interaction between graphics and framing emerged across a number of dependent measures. For graphic warning labels, the loss frame was more advantageous than the gain frame. For text-only warning labels, framing did not make any difference. This two-way interaction was further qualified by smoking identity for some, but not all, dependent measures.

Findings from this study support the use of graphic warning labels that focus on the negative health consequences of smoking.

Experimental research on graphic cigarette warning labels is limited and rarely considers the roles of framing and smoking identity. This study offered an initial test of the complex interaction among these message and audience variables as they jointly influence message reception and smoking intentions.

The purpose of this paper is to discuss the application of computational intelligence techniques to the field of industrial robot control.

The core ideas behind using neural computation, evolutionary computation, and fuzzy logic techniques are presented, along with a selection of specific real‐world applications.

Their practical advantages and disadvantages relative to more traditional approaches are made clear.

The reader will appreciate the power of computational intelligence techniques for industrial robot control, and hopefully be encouraged to explore further the possibility of using them to achieve improved performance in their own applications.