Maike Debus, professeure assistante


Université de Neuchâtel
Institut de Psychologie du Travail et des Organisations
2e étage, bureau E220
Rue Emile-Argand 11
CH - 2000 Neuchâtel
Tél. + 41 32 718 1390
E-mail : maike.debus@unine.ch

News Archive

December 2022: New article on reactions to perceived overqualification and formal work arrangements accepted

If individuals feel they have more skills and qualifications than are required for their jobs, they perceive themselves to be overqualified. Extant research has primarily focused on lower satisfaction and wellbeing that individuals experience in such a situation. In the present study, we complement this perspective by examining how individuals can adaptively and proactively deal with being overqualified. In a sample of 453 employees who we surveyed across three points in time we show that overqualified employees are angrier about their situation, which hampers their performance and satisfaction. Moreover, our study highlights the demotivating role of a temporary employment contract and long job tenure for overqualified employees to actively reorganize their work – such that overqualified employees are particularly less likely to actively change their work if they are only employed on a temporary basis or have been working in their jobs for a relatively long time. Taken together, this study points to the (de)motivating role of formal work arrangements in the context of perceived overqualification.


Debus, M. E., Körner, B., Wang, M., & Kleinmann, M. (in press). Reacting to perceived overqualification: Uniting strain-based and self-regulatory adjustment reactions and the moderating role of formal work arrangements. Journal of Business and Psychology. PDF


November 2022: New article on handling missing data for sleep monitoring systems published

Sensor-based sleep monitoring systems (e.g., wristbands) can be used to track sleep behavior on a daily basis and provide feedback to their users. To provide useful feedback, sleep monitoring systems must be able to recognize whether an individual is sleeping or awake. Existing approaches to infer sleep-wake phases typically assume continuous streams of data, yet this is rarely the case in reality. We use regression- and interpolation-based imputation strategies to mitigate the errors that might be caused by incomplete data. To evaluate our approach, we use a data set that includes physiological, behavioral, and self-report sleep monitoring data. Our results show that the presence of missing sensor data degrades the balanced accuracy of the classifier on average by 10-35 percentage points for detecting sleep and wake depending on the missing data rate. The imputation strategies explored in this work increase the performance of the classifier by 4-30 percentage points.These results open up new opportunities to improve the robustness of sleep monitoring systems against missing data.


Gashi, S., Alecci, L., Gjoreski, M., Di lascio, E., Mehrotra, A., Musolesi, M., Debus, M. E., Gasparini, F., & Santini, S. (2022). Handling missing data for sleep monitoring systems. 10th International Conference on Affective Computing and Intelligent Interaction (ACII), 1-8. https://doi.org/10.1109/ACII55700.2022.9953832 PDF


November 2022: Interview with Deutschlandfunk (with podcast) about work stress and meaningfulness,  https://www.deutschlandfunkkultur.de/beruflicher-stress-kuendigen-jobwechsel-beratung-100.html


September 2022: Interview with Augsburger Allgemeine on «Quiet quitting», https://www.augsburger-allgemeine.de/wirtschaft/arbeit-wie-das-phaenomen-quiet-quitting-die-arbeitswelt-veraendern-kann-id63851321.html


March 2022: New article on sleep stage and sleep quality recognition using wearables accepted

Sleep is a vital process for maintaining good health and well-being. While automatic detection of sleep (e.g., polysomnography, accelerators, wrist-worn devices) has been studied extensively, there is a lack of studies exploring how population and personalized models influence the performance of sleep detection. In this paper, we address this challenge by investigating the recognition of sleep/wake stages and high/low sleep quality with a focus on investigating the impact of personalized models. To evaluate our approach, we use a dataset of physiological signals and self-reports about sleep/wake times and sleep quality score. The dataset contains 6557 hours of data collected using wristbands over one month. Our results show that personalized models perform significantly better than population models for sleep quality recognition, and comparably good for sleep stage detection.


Gashi, S., Alecci, L., Di Lascio, E., Debus, M. E., Gasparini, F., & Santini, S. (2022). The role of model personalization for sleep stage and sleep quality recognition using wearables. IEEE Pervasive Computing. Advance online publication. https://doi.org/doi : 10.1109/MPRV.2022.3164334 PDF


October 2021: New article on automatic recognition of flow experiences at work

Flow is a positive affective state that occurs when individuals feel optimally challenged at work. To measure flow experiences, researchers typically rely on self-report data collected through surveys. In the present research, we use physiological data, collected using wrist-worn devices, combined with context information, obtained through self-reports, to automatically distinguish between low and high levels of flow. We investigate the role of the type of activity that participants were engaged in for flow perceptions and in its automatic recognition. Our results show that using raw blood volume pulse, electrodermal activity and the type of activity as input to a sensor-based late fusion approach can accurately predict flow experiences at work.


Di Lascio, E., Gashi, S., Debus, M. E., & Santini, S. (2021). Automatic recognition of flow during work activities using context and physiological signals. 9th International Conference on Affective Computing and Intelligent Interaction (ACII), 1-8. https://doi.org/10.1109/ACII52823.2021.9597434


October 2021 : Interview on work experiences during the COVID-19 pandemic with HR Today, 11/2021, available here.


July 2021: New article on self-promotion climate in work groups accepted

Self-promotion refers to behaviors used to appear competent and capable (e.g., highlighting one’s talents and making others aware of one’s accomplishments). We demonstrated that work group self-promotion climate – referring to the shared perception of the occurrence of self-promotion in the work group – shapes the relationships between individuals’ supervisor-focused self-promotion and supervisor ratings of both job performance and promotability. These relationships were positive when self-promotion climate was low. Moreover, we demonstrated that self-promotion climate negatively relates to supervisor-rated work group performance via impaired work group cohesion. Taken together, our findings highlight that high self-promotion climate is not necessarily a good thing, and can actually impair goup cohesion and performance.

Gross, C., Debus, M. E., Ingold, P. V., & Kleinmann, M. (2021):  Too much self-promotion! How self-promotion climate relates to employees’ supervisor-focused self-promotion effectiveness and their work group’s performance. Journal of Organizational Behavior, 42, 1042-1059. doi: 10.1002/job.2547


June 2021: Talk at Wiley Industry Days on work after the COVID-19 pandemic

In June 2021, I gave an interview at the Wiley Industry Days, a virtual congress and meeting point for Safety & Security, Civil Engineering, Healthcare & Hygiene and for Automation, Machine Vision & Photonics. In this interview, I talked about what working life might look like after the COVID-19 pandemic, how teams can be successfully managed and led virtually, and how prevalent remote work might be in the future. You can watch the complete interview here (in German): https://www.wileyindustrynews.com/news/wie-sieht-die-arbeitswelt-nach-corona-aus-wie-arbeiten-wir-morgen-0


March 2021: New article on frontline workers and COVID-19

Which employees are most at risk of getting infected and suffering from COVID-19? In this article we argue that the burden from the global COVID-19 pandemic falls heavily on often marginalized groups working in frontline occupations (e.g., bus drivers, gricery store workers, janitors) who already face serious pre-existing health and socioeconomic disparities. To protect these vulnerable workers, we pose potential interventions at the national, community, and organizational levels.


Debus, M. E., Unger, D., & Probst, T. M. (2021). Dirty work on the COVID-19 frontlines: Exacerbating the situation of marginalized groups in marginalized professions. Industrial and Organizational Psychology: Perspectives on Science and Practice, 14, 144-148. doi:10.1017/iop.2021.33


February 2021: Interview on procrastination at work with Bilan, 2/2021, available here