Stripped of Agency: The Paradoxical Effect of Employee Monitoring on Deviance

Organizations have long sought to mitigate risks associated with unsupervised employee conduct (e.g., employee deviance) through employee monitoring, an approach consistent with traditional theorizing. Yet the effectiveness of employee monitoring as a deviance deterrent has been called into question by emerging evidence suggesting that monitored employees may actually engage in higher levels of deviance. To address this critical tension and shed light on why and when monitoring leads to deviance, we draw upon social cognitive theory to examine the self-regulatory consequences of employee monitoring. We theorize that monitoring paradoxically creates conditions for more (not less) deviance by diminishing employees’ sense of agency, thereby facilitating moral disengagement via displacement of responsibility. Integrating fairness heuristic theory, we further argue that overall justice provides a powerful heuristic that mitigates the potential loss of sense of agency associated with monitoring. Accordingly, we suggest that employee perceptions of high justice will attenuate displacement of responsibility and, in turn, deviance. Across a field study and an experimental study, we find converging support for our predictions and rule out alternative explanations. This research provides important theoretical and practical insights into how monitoring can be used effectively without also promoting unintended consequences.

E-PANOPTICON OF FACE-TO-DISPLAY WORKERS: FROM THE OFFICE TO THE HOME

Electronic Monitoring (EM) is becoming prevalent, enabling varied and pervasive monitoring of workplaces. The research design was a set of e-mail surveys. Quantitative data were analyzed using cross-tabulation of data, descriptive and chi-square tests statistics. The study provides an overview of e-worker monitoring in five countries. Twenty percent of respondents believe that their organization uses employee monitoring software to track their activities. Almost half of the e-workers believe that their activities are not being tracked by software. Nearby 1/10 of the face-to-display workers surveyed would trust their employer more using EM. Four-fifths of e-workers state that EM affects their productivity. Presented data emphasizes that companies using face-to-display workers monitoring software can negatively affect morale and productivity instead of producing better work. Further, employees are often unfamiliar with whether or not there is monitoring software tracking their activities. The study recommends that organizations should inform its employees before implementation of EM system to facilitate their positive attitudes

Economic and legal aspects of employee monitoring, with particular emphasis on remote work

Purpose – The purpose of this article is to identify and indicate the basic issues of employee moni-toring that arise in the field of economic and legal sciences from the perspective of employers, such as the factors motivating employers to use various forms of employee monitoring, current conditions of such activities resulting from state law and the European Union law, as well as potential threats to employers using broadly understood monitoring. Research method – Dogmatic and statistical methods, the analysis of legal acts and research results, as well as literature studies and observations of practice were used to prepare the article.Results – The possibility of using modern technologies to monitor employees is an important tool in the pursuit of effective organization of working time, although there is a lack of studies showing whether the monitoring methods offered on the market actually increase work efficiency. The actual needs of employers with regard to various forms of monitoring are not reflected in the provisions of the Labor Code. Employers should approach employee monitoring with caution, because inapprop-riate actions in this area may have negative effects.Originality / value / implications / recommendations – The article concerns issues of significant impor-tance from the perspective of not only Poland, but also other European Union countries, current in the realities of the pandemic and based on the latest legal regulations.

Big Data in the workplace: Privacy Due Diligence as a human rights-based approach to employee privacy protection

Data-driven technologies have come to pervade almost every aspect of business life, extending to employee monitoring and algorithmic management. How can employee privacy be protected in the age of datafication? This article surveys the potential and shortcomings of a number of legal and technical solutions to show the advantages of human rights-based approaches in addressing corporate responsibility to respect privacy and strengthen human agency. Based on this notion, we develop a process-oriented model of Privacy Due Diligence to complement existing frameworks for safeguarding employee privacy in an era of Big Data surveillance.

The Implications of Digital Employee Monitoring and People Analytics for Power Relations in the Workplace

Jeremy Bentham’s panopticon prison project was based on three central assumptions: the omnipresence of the “watcher”; the universal visibility of objects of surveillance; and the assumption, by the “watched,” that they are under constant observation. While the metaphor of the panopticon, following Michel Foucault’s work, was often applied to workplace and workplace surveillance to highlight the “disciplining” power of the supervisor’s “gaze,” this paper argues that it is only with the recent advent of digital employee monitoring technology that the workplace is becoming truly “panoptic.” With modern electronic means of surveillance, the supervisor is always “looking”—even when not physically present or not actually watching employees—as all worker actions and movements may now be recorded and analyzed (in real time or at any time in the future). This paper argues that the modern workplace approximates Bentham’s panoptic prison much more than the “traditional” workplace ever did and examines the implications of this fundamental historical change in the paradigm of employee monitoring for power relations in the modern workplace.

Classifying Behaviours in Videos with Recurrent Neural Networks

This article describes how the human activity recognition in videos is a very attractive topic among researchers due to vast possible applications. This article considers the analysis of behaviors and activities in videos obtained with low-cost RGB cameras. To do this, a system is developed where a video is input, and produces as output the possible activities happening in the video. This information could be used in many applications such as video surveillance, disabled person assistance, as a home assistant, employee monitoring, etc. The developed system makes use of the successful techniques of Deep Learning. In particular, convolutional neural networks are used to detect features in the video images, meanwhile Recurrent Neural Networks are used to analyze these features and predict the possible activity in the video.