To be or not to be stressed: Designing autonomy to reduce stress at work

BACKGROUND

Many organizations are undertaking efforts to reduce the stress of (oftentimes overworked) employees. Information Technology (IT) (e.g., smartphones) has the potential to be a key instrument for reducing stress. One design-relevant factor considered to reduce stress is the concept of autonomy. Unfortunately, little research exists using autonomy as a characteristic of technology design.

OBJECTIVE

Against this background, this study aimed to investigate specific autonomy-related design options with the potential to prevent stress.

METHODS

In a factorial survey, this experimental study tested three design options in an overwork scenario: 1) autonomy (no intervention by design), 2) nudge ("nudging" by design), and 3) enforcement (hard stop by design). 51 participants (mean age 38 years, 50% women, mean work experience 18 years) from the Netherlands, United Kingdom, United States of America, and Germany participated in the experiment for 330 seconds on average. To test our hypothesis, we used a two-step approach. First, a multiple linear regression was applied. Second, we carried out a one-way ANCOVA comparing the effects of our design options.

RESULTS

Our results indicate that autonomy can be manipulated through technology design and is negatively correlated with stress. Additionally, the design options autonomy and nudge were associated with lower levels of perceived stress than was enforcement.

CONCLUSION

The study proposes a careful use of IT and policies that limit the perceived autonomy of employees. Overall, this study offers a set of design recommendations arguing that organizations should implement technology that helps employees prevent overwork and maintain their autonomy.

Multigroup Analysis in Information Systems Research using PLS-PM

Heterogeneity is a pertinent issue in Information Systems (IS) research because human behavior often differs across groups. In the partial least squares path modeling (PLS-PM) context, several approaches have been proposed to investigate potential group differences. Despite the availability of numerous approaches, literature that compares their efficacy is sparse. Consequently, IS researchers lack guidance on which approach is best suited to detect group differences. We address this issue by presenting the results of an extensive Monte Carlo simulation study that juxtaposes the various approaches' behavior under numerous conditions. In doing so, we first provide an overview on existing approaches proposed for multigroup analysis (MGA) in the PLS-PM context. Moreover, we derive important implications for applied research: Firstly, we show that the omnibus test of group differences (OTG) and approaches based on the comparison of confidence intervals are not recommendable for MGA. Secondly, we provide detailed information as to which approaches are suitable for comparing one specific path coefficient and which are recommended if the complete structural model is compared across groups. Finally, we show that approaches which are designed to compare a single parameter require an adjustment for multiple comparisons when used to compare more than two groups.