Your body tells more than words – predicting perceived meeting productivity through body signals

ABSTRACT

The productivity of work meetings is mostly assessed through post-hoc questionnaires. These questionnaires are impractical as they require additional time after the meeting has ended. Thus, measuring meeting productivity in a non-intrusive manner is of practical and theoretical importance. Extending research on physiological arousal and the healthy physiological variability thesis to the context of work meetings, we take a novel approach and investigate whether physiological arousal and the variability in implicit body signals of meeting participants (heart rate, arm movements, and speech intensity) can be accurate predictors of perceived meeting productivity. In a preliminary field study, we used smartwatches and tracked the body signals of 16 team members in 26 team meetings. The perceived meeting productivity was assessed at the end of the meetings. Partly supporting our assumptions, multilevel analysis showed that the variance in arm acceleration was a significant predictor of perceived meeting productivity. Further, using a random forest classifier, we accurately predicted perceived meeting productivity in roughly 60% of the cases with body signals. This study adds to previous work on meeting effectiveness by tapping into the potential of wearables to provide valid information about perceived meeting productivity. Cultivating our findings, we discuss lessons learned for future research.

KEYWORDS:

• Meeting productivity
• body signals
• wearables
• machine learning
• physical arousal variability

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The datasets and code used for this publication can be accessed on Harvard Dataverse via the link: https://doi.org/10.7910/DVN/63ESXL

Notes

1. Perceived meeting effectiveness, productivity, and satisfaction are used interchangeably in the meeting literature.

2. We assume, the reason for the high correlation between mic_avg and micv_ar lies in the nature of speaking. mic_var increases with more airtime within the meeting. As we pause, lower and raise our voice while we speak, mic_var increases. Thus, the more we speak, the higher are mica_vg and mic_var..

Additional information

Funding

This work was supported by Signify N.V. under Grant number MIT 024826-00013.