Evonik Digital Research

Because smart technology is capable of collecting, analyzing and forwarding sensitive information,e.g., when machine learning procedures are employed or in order to provide certain services, the psychological construct that is especially relevant is privacy.

A preliminary understanding based on scholarly theories regarding privacy-related decision-making can be driven from, e.g., privacy calculus theory. This theory describes the trade-off between perceived risks and anticipated benefits of providing personal information. The theory is therefore well suited to provide a basis for our research.

According to the dimensions of privacy specified by Burgoon, smart electronic surveillance at work expands the extent to which private information can be collected. In addition to psychological privacy (related to one’s thoughts and feelings), this kind of tracking affects informational privacy describing the individual’s right to control the disclosure about the self. Furthermore, social privacy, related to regulation and restriction of social relationships and interactions is affected by smart surveillance systems, as employee communication can be identified as well as particularly physical privacy, encompassing the accessibility of individual’s physical space including surveillance and physical contact.

Within the framework of electronic surveillance at work the communication privacy management theory (CPM) serves as another theoretical foundation. The five principles of CPM state that individuals believe to have a right to control their private information (1), that they decide whether to disclose private information based on personal privacy rules (2), that people with access to someone’s private information become co-owners of that information (3) and have to negotiate new privacy rules regarding information disclosure (4) leading to privacy boundary turbulences when the rules are unclear or not mutually agreeable (5) . Boundary turbulences means that a violation of an individual’s privacy rules results in a conflict regarding the tension between disclosure and concealment of private information.

According to CPM, smart electronic surveillance at work compromises the privacy of employees by accessing private information notwithstanding individual privacy rules with an incalculable number of co-owners without leaving any space for negotiations of one’s privacy, increasing the likelihood of boundary turbulences.

The information limit shows parallels to CPM regarding individual’s possibility to determine what information to disclose. Accordingly, people accept a certain amount of their private information being collected. However, when this limit is reached the willingness to accept data tracking declines. In the workplace with smart electronic monitoring, employees are exposed to a frequent, obscure surveillance that in the long term might exceed their information limit leading to a decreasing acceptance of a privacy-invading system.

Therefore, the present work aims at investigating the necessary preconditions for employees accepting the deployment of smart technology that captures their data at the workplace.

In order to guarantee state-of-the-art working conditions and ensure job-satisfaction, employers have the responsibility to protect the privacy of their workforce. In the process of creating smart technology,privacy can be implemented by design. However, the privacy preserving approach requires more research on how users perceive privacy and what value they attach to their personal data. Moreover, factors influencing users’ acceptance of and intention to deploy smart technology need to be understood. Evonik as an employer needs to know under what conditions tracking technology is accepted and which measures can enhance trust in the technology and the company which deploys it (e.g., what needs to be communicated about the nature of the data that are tracked and how these are stored).

A particular focus of our work is put on the investigation of how people are affected by IoT systems tracking their data and how Evonik in particular can be prepared for the application of a multi-stage emergency recognition system at the workplace, introduced in chapter 2. In order to examine these research questions, we conducted two first empirical studies. Both studies aimed at providing a first understanding of basic psychological mechanisms when confronted with technology which tracks personal data.