Search results

This study aims to investigate how small talk interaction affects knowledge sharing, examining the mediating role of interpersonal trust (affect- and cognition-based trust) and the moderating role of perceived similarity among the mechanisms of small talk and knowledge sharing.

This research conducts complementary studies and collects multi-culture and multi-wave data to test research hypotheses and adopts structural equation modeling to validate the whole conceptual model.

The research findings first reveal two trust mechanisms linking small talk and knowledge sharing. Meanwhile, the perceived similarity between employees, specifically, strengthens the affective pathway of trust rather than the cognitive pathway of trust.

This study combines Interaction Ritual Theory and constructs a dual-facilitating pathway approach that aims to reveal the impact of small talk on knowledge sharing, describing how and when small talk could generate a positive effect on knowledge sharing. This research provides intriguing and dynamic insights into understanding knowledge sharing processes.

The purpose of this study is to examine the relationships among knowledge attributes (complexity and implicitness), interpersonal distrust, knowledge hiding (KH) and team efficacy and second, to explore a new dimension of KH.

Data for this research were collected from more than 940 employees working in manufacturing, information technology (IT), finance and the purification industry. Structural equation modeling was used to test hypothesized relationships.

First, the research confirmed the existence of bullying hiding behaviors in the knowledge economy era based on “knowledge power.” Second, the findings suggest that knowledge attributes are an important predictor of KH behaviors in organizations. The findings implicate the mediating effect of interpersonal distrust and the moderating role of team efficacy, while team efficacy negatively moderated the relationships between interpersonal distrust with evasive hiding and playing dumb, but positively moderated the relationship between interpersonal distrust with rationalized hiding and bullying hiding.

This is the first study to propose bullying hiding, a behavior that has emerged in organizational knowledge transfer, and it is more detrimental to knowledge sharing than other KH behaviors. The results of research on the different regulating effects of team efficacy on KH behaviors enrich the boundary conditions of KH research.

The flow state of wet steam will affect the thermodynamic and aerodynamic characteristics of steam turbine. The purpose of this study is to effectively control the wetness losses caused by wet steam condensation, and hence a cascade of 600 MW steam turbine was taken as the research object.

The influence of blade surface roughness on the condensation characteristics was analyzed, and the dehumidification mechanism and wetness control effect were obtained.

With the increase of blade surface roughness, the peak nucleation rate decreases gradually. According to the Mach number distribution on the blade surface, there is a sensitive region for the influence of roughness on the aerodynamic performance of cascade. The sensitive region of nucleation rate roughness should be between 50 and 150 µm.

The increase of blade surface roughness will increase the dynamic loss in cascade, but it can reduce the thermodynamic loss caused by condensation to a certain extent.

This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.

This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.

The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.

These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.

This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.