Although study has shown that electronic performance monitoring (EPM) may have a positive impact on employee performance, it has also been suggested that it may lead to employee stress and dissatisfaction, which may inhibit performance. This study aims to provide a balanced perspective on this conflicting issue by using social information theory as an overarching framework. In addition, SIP theory was further refined by integrating attribution theory to reveal the conditions under which monitoring is most effective.
First, the authors conducted a series of confirmatory factor analyses (CFAs) using Mplus 8.3 software to assess the measurement validity of the model. A path model was then developed using maximum likelihood to test all hypotheses. Specifically, EPM was used as the independent variable, control attribution and feedback attribution as moderators, employees’ work goal progress and perceived privacy violation as mediators, and employees’ task performance as the outcome variable. Demographic variables (gender, age, education, tenure in current organization), LMX, positive affect and negative affect were entered into the model as control variables. Parametric bootstrap was used to test the mediator and the moderated mediator (5,000 repetitions, forming a 95% confidence interval) and construct the full path model.
The authors propose that EPM improves task performance by stimulating employees’ perception of work goal progress. Correspondingly, EPM will also stimulate employees’ perception of privacy violation, which will have an adverse impact on task performance. In addition, the authors consider employees’ different attributions of organizational implementation of EPM as moderators in the model and propose that feedback attributions strengthen the positive path of EPM-work goal progress-task performance and weaken the negative path of EPM-perceived privacy violation-task performance, while control attributions strengthen the negative path and weaken the positive path. The results supported most of the authors’ hypotheses.
First, all variables were self-reported, which may lead to common method bias. However, some research suggests that self-reporting is not only an appropriate method for exploring issues within the realm of personal experience, but in some situations it is even superior to the evaluation of others. Nevertheless, the authors encourage future research to adopt multi-source data. Second, despite the use of a time-lagged design, causality could not be established. Therefore, future research is encouraged to use experiments to manipulate EPM and attributions to establish causal relationships between the variables. Third, the study was conducted in one country. In the future, this study can be replicated in other countries to solve relatively limited universal problems.
First, the research shows that EPM practice can effectively improve employees’ task performance, and the implementation of EPM is of great significance to both individual employees and organizations. However, although these technologies have significant advantages in improving work efficiency and optimizing performance management, the authors must also be wary of their potential adverse effects. Therefore, when introducing these advanced technologies, companies should carefully evaluate their potential negative effects to ensure that the application of technology will not have a negative impact on the well-being of employees. Second, the findings reveal that EPM practices do not always achieve the expected results. Therefore, if the company’s goal is to promote employees to make feedback attributions, it should formulate reasonable monitoring policies, explain the purpose of monitoring, and make monitoring more transparent to protect employee privacy and reduce the negative impact caused by privacy violation perception.
With the continuous advancement of technology, EPM technology is also developing continuously, and more and more advanced technologies are being applied to employees’ performance management. For example, artificial intelligence and big data analysis technologies enable companies to monitor employees’ work performance in real time, generate detailed performance reports and provide personalized feedback. The study helps provide a theoretical basis for companies to balance efficiency and employee welfare, optimize management strategies and enhance the fairness of the work environment and employee satisfaction when implementing EPM.
First, innovation in theoretical perspective: social information processing (SIP) theory is systematically introduced into the field of e-performance monitoring research for the first time, providing a more balanced perspective on the contradictory views of EPM on employee performance. Second, research paradigm innovation: expanding the application scenarios and explanatory effectiveness of SIP theory. Most of the previous studies on SIP have focused on the effects and influence of leaders as information sources on employee behavior, while there is still a theoretical gap in the information transfer mechanism of human resource management practices. This study extends contextual cueing research from leadership behavior to HRTS by introducing SIP theory. Third, theoretical integration innovation: coupling SIP theory and attribution theory to build a comprehensive analysis model.
