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The purpose of this study is to investigate the phenomenon of knowledge transfer between employees and coworkers. That is, when and why employees engage in knowledge seeking or knowledge sabotage when confronted with coworkers with higher relative overqualification.

This study collected survey data from 315 employee-coworker pairs in East China at three-time points.

The results showed that when the cooperative goal interdependence between employee and coworker is high, the perception of coworker’s relative overqualification will cause benign envy of employees, which in turn promote employees to engage in knowledge seeking from coworker. However, when the competitive goal interdependence between employee and coworker is high, the perception of coworker’s relative overqualification will cause malicious envy of employees, which in turn promote employees to engage in knowledge sabotage toward coworker.

This research not only expands the theoretical perspective and outcomes of relative overqualification but also enriches the mechanism of knowledge seeking and knowledge sabotage. Meanwhile, this study also provides practical guidance for enterprises to reduce knowledge sabotage.

The relationship between perceived overqualification and knowledge sharing has always been a hot topic, but scholars have come to different conclusions on this issue. The purpose of this study is to integrate conflicting conclusions by considering the moderating role of rewards for knowledge sharing and the mediating role of intrinsic motivation in the relationship between perceived overqualification and knowledge sharing based on self-determination theory.

The authors collected three-wave survey data from 246 research and development employees in four companies in China.

The results showed that when rewards for knowledge sharing was higher, employees with perceived overqualification would have higher intrinsic motivation, which could promote their knowledge-sharing behavior. However, when rewards for knowledge sharing was lower, employees with perceived overqualification would have lower intrinsic motivation, thus inhibiting their knowledge-sharing behavior. This result supported the informational function rather than the controlling function of rewards for knowledge sharing.

By considering the important boundary condition of rewards for knowledge sharing, this study reconciles the contradictory conclusions on the relationship between perceived overqualification and knowledge-sharing behavior. At the same time, the authors tell organizations that they can increase the knowledge-sharing behavior of overqualified employees through rewards for knowledge sharing.

Three-dimensional (3D) food printing is an innovative technology used to customize food products through the integration of digital technology and food ingredients. The purpose of this study is to assess the current state of research in the field of 3D food printing, identify trending topics and identify promising future research directions.

This bibliometric review systematically evaluates the field of 3D food printing using data from published literature in the Web of Science database. After reference screening, 812 articles were included in the analysis.

The result reveals that research in 3D food printing primarily focuses on the optimization and characterization of mechanical and rheological properties of food inks and that post-printing processing, such as laser treatment, has emerged recently as an important consideration in 3D food printing. However, extant works lack animal and human studies that demonstrate the functionality of 3D-printed food.

This sophisticated bibliometric analysis uncovered the most studied current research topics and the leading figures in the area of 3D food printing, providing promising future research directions.

During the service condition of residential constructions with concrete structure, the influence of inside and exterior factors will result in different degrees of harm to concrete engineering. Therefore, it is very necessary to continue engineering work toward the concrete structure. Therefore, the purpose of this study is to evaluate the influence of the factors on the comprehensive performance of the concrete structure engineering.

Applying a mathematics method to establish the comprehensive evaluation model of the concrete structure engineering performance, qualitative and quantitative methods were adopted for reinforced concrete structural performance analysis.

The key to a safe construction project is to require all bidders to submit a written safety plan with their bids. The instruction to bidders must include guidelines for an acceptable safety plan and state clearly that the substance of the safety plan will be reviewed, and its adequacy will be a determining factor in who shall be selected as the contractor.

Based on the characteristics of concrete structure, applying the analytic hierarchy process to evaluate the concrete structure engineering comprehensive performance, an evaluation index system was built. The research combines quantitative method with qualitative method evaluation was made.

This research aims to identify the climate for innovation variables and to propose an innovative tool to quantitatively assess the degree of climate for innovation of construction firms.

14 climate-for-innovation variables were identified from a literature review and discussion with experienced practitioners. After that, a questionnaire survey was developed to collect data. Factor analysis was used to analyze data gathered from 157 completed responses. Then, fuzzy synthetic evaluation (FSE) was employed to assess the degree of climate for innovation in Vietnamese construction firms.

Climate-for-innovation variables were categorized into four factors by factor analysis. The FSE analysis shows leadership is the most critical category of four factors, followed by working culture, organization and employee commitment. The calculation also illustrates that the climate for innovation in Vietnamese construction firms is at a moderate level.

This research is one of the first integrated climate for innovation of construction firms in a comprehensive formulation. The formulation provides the decision-makers with a reliable tool to evaluate the degree of climate for innovation, thus having appropriate strategies to develop sustainable innovation performance within their organizations.

The purpose of this paper is to design an online-data driven adaptive control scheme based on fuzzy rules emulated network (FREN) for a class of unknown nonlinear discrete-time systems.

By using the input-output characteristic curve of controlled plant and the set of IF-THEN rules based on human knowledge inspiration, the adaptive controller is established by an adaptive FREN. The learning algorithm is established with convergence proof of the closed-loop system and controller’s parameters are directly designed by experimental data.

The convergence of tracking error is verified by the theoretical results and the experimental systems. The experimental systems and comparison results show that the proposed controller and its design procedure based on input-output data can achieve superior performance.

The theoretical aspect and experimental systems with the light-emitting diode (LED) current control and the robotic system prove that the proposed controller can be designed by using only input-output data of the controlled plants when the tracking error can be affirmed the convergence.

The proposed controller has been theoretically developed and used through experimental systems by using only input-output data of the controlled plant. The novel design procedure has been proposed by using the input-output characteristic curve for both positive and negative control directions.

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

The research data were limited to only one e-clothing store.

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.

The paper examines the effect of relative leader-member exchange (LMX) on follower's in-role performance, citizenship behaviour and cynicism via relational identification. Moreover, LMXSC (LMXSC) moderates the direct and mediating relationship.

Based on multi-level (individual and group level) model, dyadic data were collected from 298 employees working under 47 group managers in the banking sector in Pakistan.

The multi-level moderated mediation model tested in Mplus and HLM software showed the full support for direct, mediating and moderating hypothesized relationships; however, the moderated mediation hypothesis was partially supported. It reveals that relative LMX standing of followers predicted in-role performance, organizational citizenship behaviour at an individual level (OCB-individual) and cynicism. Relational identification with the leader mediated the relationship. Moreover, at high LMXSC, the relationship between relative LMX and relational identification and consequently the outcomes were stronger.

LMX has widely been studied at dyadic level, despite the suggested high and low LMX quality relationships that exist in a workgroup. This study not only investigates the role of relative LMX on employee performance through relational identification but also reports that subjective evaluation of LMXSC plays a major role in promoting employee performance.