Business Ethics: A European Review, EarlyView.

As a common organizational phenomenon, employee silence behavior has various negative implications for organizations, making it critical to understand what factors can reduce employee silence. Drawing upon self-verification theory, this study explores the inhibiting effect of moral identity on silence via felt obligation towards organization. Meanwhile, we also examine the moderating effect of corporate social responsibility perception. We collected three waves of data with a two-month interval from 402 Chinese employees. Results indicated that moral identity positively predicted felt obligation towards (...) organization, which in turn negatively predicted silence behavior. The negative indirect effect of moral identity on silence behavior via felt obligation towards organization was moderated by corporate social responsibility perception. Specifically, this indirect effect was stronger when employees perceived a higher level of corporate social responsibility. By integrating self-verification theory with silence behavior research, our findings provide important insights into the prevention of employee silence behavior in organizations. (shrink)

In order to solve the problem of unsmooth and inefficient human-computer interaction process in the information age, a method for human-computer interaction intention prediction based on electroencephalograph signals and eye movement signals is proposed. This approach is different from previous methods where researchers predict using data from human-computer interaction and a single physiological signal. This method uses the eye movements and EEG signals that clearly characterized the interaction intention as the prediction basis. In addition, this approach is not only tested (...) with multiple human-computer interaction intentions, but also takes into account the operator in different cognitive states. The experimental results show that this method has some advantages over the methods proposed by other researchers. In Experiment 1, using the eye movement signal fixation point abscissa Position X, fixation point ordinate Position Y, and saccade amplitude to judge the interaction intention, the accuracy reached 92%, In experiment 2, only relying on the pupil diameter, pupil size and fixed time, fixed time of eye movement signals can not achieve higher accuracy of the operator’s cognitive state, so EEG signals are added. The cognitive state was identified separately by combining the screened EEG parameters Rα/β with the eye movement signal pupil diameter and fixation time, with an accuracy of 91.67%. The experimental combination of eye movement and EEG signal features can be used to predict the operator’s interaction intention and cognitive state. (shrink)