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The purpose of this paper is to focus on finding the optimal maintenance interval and the minimum maintenance cost for redundant system, considering environment factors.

The authors propose a decision model with environment-based preventive maintenance for the repairable redundant system. Referring to the k-out-of-n model and Proportional Hazard Model, the reliability analysis is completed for the redundant system affected by internal and external issues. Meanwhile, the maintenance cost for the redundant system is divided into two categories: the fixed maintenance cost involving whole system replacement at the time of system failure, and the cost to replace failure components when the system still functions.

Upon the required reliability analysis, an optimal maintenance interval that minimizes the average maintenance cost per unit time is identified. The simulation results indicate that the optimal maintenance interval with consideration of environmental factors is significantly shorter than that without consideration of these factors, with the maintenance cost increase within 10 percent.

The redundant systems have widely been used in industries including the aero craft control system and warship power system. The model could be applied in the more real case considering the types of components and the operation environment, and help production managers better maintain machines by increasing the safety and reliability of the redundant model with the more frequent inspection.

Previous research of redundant system always focuses on internal degradation, while ignoring the reliability analysis for a redundant system with various multiple components under the influence of environment. However, this work could fill the theoretical gap, i.e. simultaneously consider both environmental and internal factors for a redundant system with non-homogeneous components. Meanwhile, the proposed superior model increases the reliability and safety of the k-out-of-n model with reasonable cost. Production managers could benefit a lot from this as well.

Multi-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

In this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.

In order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.

In order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.

Risk perception is an essential factor affecting how individuals evaluate risk, make decisions and behave. The impact of risk perception on customer purchase behavior has been widely studied; however, the association has been debated. Therefore, the purpose of this paper is to examine the relationship between risk perception and customer purchase behavior and to examine factors that could moderate it.

This study conducted a meta-analysis of this relationship and examined factors that could moderate it. Six databases were comprehensively searched. Two reviewers independently selected the studies for inclusion, extracted data and assessed quality. Pearson's r was used as the effect estimate. A total of 33 studies were included in the meta-analysis.

The results revealed a negative relationship between risk perception and customer purchase behavior. The geographical region, purchase channel and country development level affected the relationship. The correlation between perceived risk and purchase behavior in European consumers was the highest, followed by the correlation in American consumers; the weakest correlation was found in Asian consumers. For consumers in developed countries, perceived risk had a stronger negative influence on customer purchase behavior than that for consumers in developing countries. The perceived risk of online purchase channels had a stronger negative impact on customer purchase behavior than that of offline purchase channels.

Risk perception is a useful context in which to explain barriers to customer purchase behavior. In addition, reducing consumers’ risk perception and perfecting the market transaction process with respect to buying behavior should be further studied.

The findings of this review indicate a direct negative relationship between risk perception and customer purchase behavior. To the best of the authors’ knowledge, this review is the first to meta-analytically summarize the impact of risk perception on customer purchase behavior in social sciences research, and it also illuminates new perspectives for future studies.

Mindfulness is the human ability to be fully present, aware of where we are and what we're doing. This study explores on the impact of mindfulness on different elements of knowledge management, knowledge creation, knowledge sharing and knowledge hiding.

The authors undertake an inductive reasoning approach whereby they try to generate themes from specific observations and conceptualize the theory of mindfulness and knowledge transfer.

This study finds out that mindfulness critically facilitates an open environment and enhances the clarity of thought which aids in effective knowledge creation. Such a realistic understanding about the present scenario encourages employees to share knowledge and equips them to collaborate and effectively work in teams. Surprisingly, this study also finds that mindfulness increases the result orientation to the extent that employees tried to hide knowledge from their co-workers targeting certain self-desired outcomes. In other words, similar to knowledge creation and knowledge sharing, mindfulness increases agenda-based knowledge hiding.

The theory of mindfulness and knowledge transfer states that mindfulness increases knowledge creation, knowledge sharing and agenda-based knowledge hiding.

This study aims to evaluate the effect of workplace ostracism on organization-based self-esteem (OBSE), organizational silence and knowledge sharing.

The statistical population of this research includes the employees of University of Isfahan in Iran. The structural equation modeling method is used to analyze the data and the conceptual model using 130 participants.

The empirical results suggest that ostracism and organizational silence influence knowledge sharing, negatively. Ostracism influences organizational silence, positively; and ostracism influences OBSE, negatively. Furthermore, organization-based self-esteem influences knowledge sharing, positively. In addition, the indirect relationship from ostracism to knowledge sharing through organizational silence was confirmed; however, the mediating role of OBSE in the relationship between workplace ostracism and knowledge sharing was not confirmed.

This research is among the first to evaluate the relationship between workplace ostracism, knowledge sharing, organizational silence and OBSE simultaneously.

The purpose of this research study is to determine the ways in which employees’ personal power-expert and referent power influences their knowledge sharing and hiding behaviour. There are hardly any studies that have investigated the effects of employee power and expectations regarding the consequences of divulging knowledge. In this study, the authors investigate whether expected gains and losses in employee personal power influence employees’ willingness to participate in knowledge transfer.

The authors adopted a two-wave survey design and collected critical data from 288 employees of knowledge-intensive industries identified through online techno-groups, such as Stack Exchange. In the first wave, out of the total, 192 knowledge workers attended the follow-up survey. The authors apply polynomial regression followed by surface response analysis to establish the effects of any discrepancy between the current levels of employees’ personal power and their expected levels if they divulge their unique critical knowledge.

The authors find out that employees having relatively strong personal power are more likely to share knowledge, and the expected losses in power are categorically associated with a reduced intention to share knowledge. The authors also observed an increased knowledge hiding with expected losses in power. Surprisingly, the authors find that these established negative outcomes are also specifically associated with the expected gains in personal power.

The most significant contribution of this study is to establish that power plays an important but complex role in determining the employees’ participation in knowledge transfer activities. The authors specifically conclude that the optimal scenario for knowledge sharing is one in which the employees’ contributions are fairly valued and their reputation is not expected to change because of knowledge sharing.

To the best of the authors’ knowledge, this study is one of the first comprehensive studies that link power to both sharing and hiding of knowledge. This study is also unique in terms of its investigation of the effects of any discrepancy between current levels of employees’ personal power and their expected levels if they share or hide their unique critical knowledge. Thus, this research study is a unique contribution in terms of what and why of an untouched area in the entire knowledge management literature with a special focus on knowledge sharing and hiding.