ComFeel: Productivity is a Matter of the Senses Too
Hello everyone. I am Marios Constantinides and I will present
our work on the impact of indoor environmental conditions on meetings productivity,
which was done in collaboration with my colleagues from Bell Labs: Sanja, Daniele, Hongwei, Ugo, and Michael.
Meetings are one of the most common practices where people come together
to discuss work matters, to brainstorm ideas, or to make decisions.
In a sense, it’s where culture forms and grows.
And yet, one in every three employees find meetings unproductive or ineffective,
despite the technological advancements of communications tools.
So, we started asking questions related to people’s meeting experience,
and set out to understand the factors that make up that experience.
We surveyed more than 50 scientific articles from the areas of Organizational and Management Science,
and identified recurring aspects that concern meetings experience.
And using these themes, we developed a questionnaire which allowed us to understand what influences people’s experience.
We deployed our questionnaire in a crowdsourcing platform,
and obtained a total of 363 responses for a set of 28 questions.
To give you an idea, we asked crowd workers to think of their last meeting and answer questions related to
the meeting structure, inclusion, diversity, participation among others.
Having this data, we run a Principal Component Analysis on the responses
and identified three components that cover most of our survey’s variance.
These three components capture meeting’s execution, the psychological safety, and the physical comfort,
and we paraphrased them into three questions that one could ask to capture psychological experience in a meeting.
So, the psychological safety is about whether people felt listened and motivated to contribute.
The execution is about getting things done, and concerned a meeting’s agenda, structure, purpose, and action items.
And, finally the physical comfort is about the room’s pleasantness in terms of air and crowdedness levels.
Having these insights from the online survey, we then developed a system called ComFeel
to understand the interplay between these factors and meetings productivity.
In the core of our system are these miniaturized devices, called Geckos
which were developed at Bell Labs,
and capture indoor environmental properties like temperature, light and gas resistance.
The rest of our sensing infrastructure consists of a Raspberry Pi
which receives the data from the Gecko devices
and transmits them to our backend server for further analyses.
So having this system in place, we conducted a one-month study in our offices in Cambridge in the UK,
where we placed these devices in meeting rooms
and we unobtrusively measured environmental conditions,
while people had their regular meetings.
As part of the study, we also asked participants at the end of each meeting
the three questions we identified in the online survey,
and the type of meeting and its duration.
In total we collected more than 13 hundred minutes of environmental data,
across 29 unique meetings,
and 364 self-reports that we used to study the interplay between productivity and psychological safety and physical comfort.
To study that, we formulated three hypotheses based on previous literature.
First, we hypothesized that meeting productivity depends on the meeting type and duration.
Second, we hypothesized that it also depends on people’s psychological safety,
and finally, that is dependent on the environmental conditions.
To capture the environmental aspect, we developed two metrics.
The first one captures what our Gecko devices are able to capture,
and the second one captures the gap between what people felt about the environment and what the actual environmental conditions were.
So, from the simple formula, you can see that
the higher its value, the more the sensors overestimate what people felt about the environment.
To test our hypotheses, we developed three logistic regression models,
and used the self-reported execution as our outcome variable for meetings productivity.
In the first model, we included meeting type and duration as predictors,
and the model performed slightly better than tossing a coin.
In the second model, we built on top of the first model
by adding the self-reported psychological safety.
In that model, we saw a significant improvement in performance from an AUC of 55 to 82.
Finally, in the last model when we included the environmental aspect the performance increased further.
By looking at the beta coefficients of our best performing model, we see that productivity increased by 35%
every one standard deviation increase in psychological safety.
And this corroborates previous findings that showed the importance of inclusiveness, and comfort
in sharing and contributing during meetings.
In addition to this, we also found that productivity increased by 25%
every one standard deviation increase in the metric that captures the environmental conditions.
This suggests that environmental conditions also impact meetings’ productivity,
not only in the long run, but also short-term even within the constrained temporal space of individual meetings.
Therefore, having such infrastructure and analyses in place,
one could use them to sense indoor environmental conditions in any office and design real-time interventions.
For example, from simple interventions like opening a window or taking a break,
to more sophisticated ones like automated IOT based interventions like adjusting the room ventilation.
And with that, I’d to end my talk and invite questions. Thank you.