# ComFeel: Productivity is a Matter of the Senses Too

Marios Constantinides  Nokia Bell LabsCambridgeUK marios.constantinides@nokia-
bell-labs.com ,  Sanja Šćepanović  Nokia Bell LabsCambridgeUK
<EMAIL_ADDRESS>,  Daniele Quercia  Nokia Bell
LabsCambridgeUK<EMAIL_ADDRESS>,  Hongwei Li  Nokia Bell
LabsCambridgeUK<EMAIL_ADDRESS>,  Ugo Sassi  Nokia Bell
LabsCambridgeUK<EMAIL_ADDRESS>and  Michael Eggleston  Nokia
Bell LabsMurray HillUS<EMAIL_ADDRESS>
(2020)

###### Abstract.

Indoor environmental quality has been found to impact employees’ productivity
in the long run, yet it is unclear its meeting-level impact in the short term.
We studied the relationship between sensorial pleasantness of a meeting’s room
and the meeting’s productivity. By administering a 28-item questionnaire to
363 online participants, we indeed found that three factors captured 62% of
people’s experience of meetings: (a) _productivity_ ; (b) _psychological
safety_ ; and (c) _room pleasantness_. To measure room pleasantness, we
developed and deployed ComFeel, an indoor environmental sensing
infrastructure, which captures light, temperature, and gas resistance readings
through miniaturized and unobtrusive devices we built and named ‘ _Geckos_ ’.
Across 29 real-world meetings, using _ComFeel_ , we collected 1373 minutes of
readings. For each of these meetings, we also collected whether each
participant felt the meeting to have been productive, the setting to be
psychologically safe, and the meeting room to be pleasant. As one expects, we
found that, on average, the probability of a meeting being productive
increased by 35% for each standard deviation increase in the psychological
safety participants experienced. Importantly, that probability increased by as
much as 25% for each increase in room pleasantness, confirming the significant
short-term impact of the indoor environment on meetings’ productivity.

productivity, meetings, environmental conditions, perceptions

††copyright: rightsretained††journal: IMWUT††journalyear: 2020††journalvolume:
4††journalnumber: 4††doi: 10.1145/3432234††ccs: Human-centered computing
Ubiquitous and mobile devices††ccs: Human-centered computing Computer
supported cooperative work††ccs: Human-centered computing Empirical studies in
ubiquitous and mobile computing††ccs: Human-centered computing User
studies††ccs: Human-centered computing Empirical studies in ubiquitous and
mobile computing††ccs: Human-centered computing User studies

##  1\. Introduction

A prolonged exposure to poor indoor environmental conditions has been found to
impact employees’ cognitive functions (Allen et al., 2016) and decision-making
(MacNaughton et al., 2015), have a direct effect on workers’ performance and
productivity (Fisk, 2000; Mujan et al., 2019), and eventually lead to
increased levels of absenteeism (Hantani et al., 2009; Wyon, 2004; Snow,
2018). Health-related problems such as asthma, headaches, throat irritation,
respiration, and allergies (Mitchell et al., 2007; Mendell et al., 2003) have
reportedly been associated with a phenomenon often referred to as the Sick
Building Syndrome (SBS) (Redlich et al., 1997; Burge, 2004).

Traditionally, companies and organizations have resorted to sensors through
which the environmental conditions could be sensed (Alavi et al., 2017; Wyon,
2004; Zhong et al., 2020; Hantani et al., 2009), and even adapted accordingly
(Huizenga et al., 2006; Bader et al., 2019) to meet recommended
standards111https://www.ashrae.org/technical-
resources/bookstore/standards-62-1-62-2, thus increasing their employees’
productivity and well-being. Sensing devices are often being deployed in
spaces to sense indoor environmental quality (IEQ). To circumvent shortcomings
of commercial devices such as costs or flexibility of platforms in terms of
data acquisition, IEQ devices based on open-source electronic designs were
made available (Karami et al., 2018). For example, Ali et al. (2019) proposed
Elemental, an open-source device that combines IEQ readings with energy usage
and HVAC operation sensors. A recent review of studies on indoor comfort (Song
et al., 2019) identified the application of machine learning to IEQ factors
other than thermal comfort as one of the research gaps. Our work partly fills
this gap. Additionally, as we shall see in §2, while previous studies
highlighted the impact of the environmental conditions on people’s
productivity in the long run, it is unclear to which extent that impact
translates in the short term at a meeting-level.

To explore the short term impact of indoor environmental quality, we looked at
one of the most common daily activities at work: _meetings_. More
specifically, we studied the relationship between the sensorial pleasantness
of a meeting’s room and the meeting’s productivity. In so doing, we made four
main contributions:

  * •

We operationalized the concept of ‘sensorial pleasantness’ and ‘productivity’
by developing an online 28-item questionnaire, grounded on literature in the
fields of Management and Organizational Science, and administering it to 363
participants (§3). Based on factor analysis, we indeed found that three
factors captured 62% of people’s experience of meetings: (a) _productivity_
(i.e., whether a meeting has been productive); (b) _psychological safety_
(i.e., whether participants felt listened to); and (c) _room pleasantness_
(i.e., whether the room was felt to be pleasing to the senses).

  * •

We developed an indoor environmental sensing infrastructure, called _ComFeel_
, which captures light, temperature, and gas resistance readings through
miniaturized and unobtrusive devices that we built and named ‘Geckos’(§4).

  * •

To measure room pleasantness, we deployed ComFeel across 29 real-world
meetings held at a corporate setting, and collected 1373 minutes of
environmental readings (§5). For each of these meeting, we also collected
whether each participant felt the meeting to have been productive, the setting
to be psychologically safe, and the meeting room to be pleasant.

  * •

Using the collected data, we studied the interplay between the sensorial
pleasantness and productivity (§6, §7). We found that, on average, the
probability of a meeting being productive increased by 35% for each standard
deviation increase in the psychological safety participants experienced.
Importantly, the very same probability increased by as much as 25% for each
standard deviation increase in room pleasantness. These results suggest that
indoor environmental conditions do matter not only in the long run (as
previous work found), but also in the short term so much so that significant
differences in productivity were observed even within the constrained temporal
space of individual meetings.

##  2\. Related Work

Next, we surveyed various lines of research that our work draws upon, and we
grouped them into three main areas: _i)_ indoor environmental sensing, _ii)_
the effects of IEQ on productivity and performance, and _iii)_ productivity
and meetings.

###  2.1. Indoor Environmental Sensing: Reality and Perception

A prolonged exposure to poor indoor environmental conditions has been found to
impact people’s health (Sundell, 2004), cognitive functions (Allen et al.,
2016) and decision-making (MacNaughton et al., 2015), and eventually lead to
increased levels of absenteeism (Hantani et al., 2009; Wyon, 2004; Snow,
2018). SBS is a term often used in Occupational Safety and
Health222www.cdc.gov/niosh to describe health-related problems associated with
poor “indoor air quality”. Symptoms such as watering eyes, headaches,
dizziness, mental fogginess, respiration, and allergies, just to name a few,
make up the SBS symptoms list. Report after
report333https://buildingevidence.forhealth.org/ has shown that the amount of
‘good’ air and, more broadly, good indoor environmental conditions can reduce
SBS symptoms, cut absenteeism, and even put on hold the transmission of
infectious diseases (Allen, 2017). The most prominent indoor environmental
properties identified in previous works include the ventilation rate, the
particle concentration, the amount of carbon dioxide and volatile organic
compounds, the temperature, the floor-coverings and used supply air filters,
the computational equipment, the lighting conditions, and the noise
distraction and occupancy rate (Wyon, 2004; Allen et al., 2016; MacNaughton et
al., 2015; Sbar et al., 2012; Jin et al., 2016; Jin et al., 2014). Links
between the actual indoor environmental conditions and people’s perceptions
about it have also been reported in studies (De Giuli et al., 2014; Kang et
al., 2017). For example, studies have shown differences between doctors’ and
patients’ perceptions of the environmental conditions at the same hospital (De
Giuli et al., 2013), and gender differences when reporting perceived
environmental quality (female employees consistently report lower levels (Kim
et al., 2013)).

###  2.2. Long and Short-term Effects of IEQ on Productivity

The effects of IEQ factors on productivity, performance, and absenteeism were
studied at real-world workplaces (Niemelä et al., 2002; Clements-Croome and
Baizhan, 2000; Hantani et al., 2009; Wyon, 2004; Snow, 2018), or in controlled
laboratory environments (Bako-Biro et al., 2004; Wargocki et al., 2002; Ishii
et al., 2018). Neidell (2017) highlighted the importance of IEQ effects:
_‘Although the damage per individual is small when compared to more extreme
events, such as mortality and hospitalizations, the effects are more
widespread and may thus represent an important cost to society.’_ In addition
to IEQ, another important concept is the perceived environmental quality (PEQ)
(Fanger, 1988): in some cases, even when employees could not consciously
perceive poor IEQ conditions their productivity and performance were
negatively impacted (Wyon, 2004; Seppänen and Fisk, 2006). While the short-
term effect of environmental quality on productivity in both experimental
(Wyon, 2004) and real workplace (Niemelä et al., 2002) settings was studied,
it is yet unclear the short-term effect of indoor environments on a recurring
activity within a constrained temporal space such as a _work meeting_.

###  2.3. Productivity and Meetings

In the workplace, performance is a multi-faceted construct mostly
characterized by ‘how well workers and employees perform their tasks, the
initiative they take and the resourcefulness they show in solving problems’
(Viswesvaran and Ones, 2000; Rothmann and Coetzer, 2003). Simply put, how
productive they are. Traditionally, companies and organizations assess their
workforce’s productivity using various methods including self-reports, and
peer- and supervisors-ratings (Iaffaldano and Muchinsky, 1985; Barrick and
Mount, 1991). However, these methods are not always administered frequently,
thus making it challenging for organizations to obtain a snapshot of their
workforce’s productivity in the short term, and are often debatable due to
potential biases that self-assessments may entail (Sartori et al., 2017; Fan
et al., 2006). More recently, it has been shown that job performance and
productivity might well be assessed using passive data collected from workers’
smartphones and wearable devices (Mirjafari et al., 2019), and workers’
cognitive states can be successfully determined (Schaule et al., 2018).
Generally speaking, measuring productivity, however, can be a complex,
challenging task depending on the work activity one looks into.

One of the most common daily activities at work is meetings. It is one of the
most common practices in which organizations mobilize their individual members
to work together, share ideas, brainstorm solutions, and solve problems. While
people allocate significant amount of their work time in attending meetings,
and organizations devote notable large amounts of resources to facilitate them
(MacNaughton et al., 2015), people still tend to feel disconnected and
perceive them as unproductive (Romano and Nunamaker, 2001). Reports suggest
that one in every three employees, across cities and industry sectors,
considers meetings ineffective and unproductive (Russel, 2015). However,
ineffective meetings are bad for morale and productivity, negatively impact
employees’ health (Leslie A. Perlow, 2017), and are directly linked to
organizations’ wasted time and resources (Kauffeld and Lehmann-Willenbrock,
2012; Rogelberg et al., 2012). Therefore, it is paramount for organizations to
understand how their employees experience meetings as an attempt to boost
their productivity, and design tools to support it (Aseniero et al., 2020).

Traditionally, a meeting’s performance and perceived experience has been
evaluated through standardized questionnaires, or ad-hoc post meeting surveys.
For example, the Meeting Effectiveness Index (MEI)444MEI:
http://www.meetingmetrics.com/EPI-Report-demo-data.pdf is a six-item
questionnaire that focuses on a meeting’s overall performance results. In the
fields of Management and Organizational Science, meetings’ productivity has
been assessed by probing its agenda, structure, and purpose (Lent, 2015;
Cohen, 2016; Schwarz, 2016). Inclusiveness (Axtell, 2016, 2017), dominance
(Romano and Nunamaker, 2001), peripheral activities (Niemantsverdriet and
Erickson, 2017), and psychological safety (Grenny, 2017; Axtell, 2018) are
aspects that have also been found to influence meetings’ experience and
productivity. The physical comfort of the workplace environment is an
additional one that makes up the list (Alavi et al., 2017). For example, a
recent survey555https://futureworkplace.com/ found that light and outdoor
views were among the most popular perks employees craved for (Meister, 2018).
Furthermore, studies have shown that stuffy and stale air offices reduce
employees’ productivity (Allen et al., 2016; Allen, 2017; Tom Y. Chang and
Neidell, 2016).

##  3\. Online Survey

Table 1. Demographics of the Online Survey Participants (_Note: Five participants did not disclose their age.)_ | Range | #answers
---|---|---
Age | 18-24 years old | 3
25-34 years old | 122
35-44 years old | 84
45-54 years old | 50
| 55-64 years old | 19
| 65+ years old | 5
Gender | Female | 154
Male | 132
Prefer not to say | 2
Education | High school graduate | 45
Bachelor’s degree | 151
| Master’s degree | 63
| Ph.D. or medical degree | 29

Figure 1. Scree plot showing our PCA components and their eigenvalues in
descending order.

As stated in the previous section, existing questionnaires used to evaluate
meetings focus primarily on performance, whereas the perceived experience of
attendees has received little attention. We reviewed previous works from HCI
communities, and articles in the fields of Management and Organizational
Science relating to ‘meetings’ (Axtell, 2016, 2017; Romano and Nunamaker,
2001; Niemantsverdriet and Erickson, 2017; Grenny, 2017; Axtell, 2018; Tom Y.
Chang and Neidell, 2016; Allen, 2017; Meister, 2018), and we identified themes
concerning people such as their psychological experience (i.e., hierarchy and
cultural balance, personality and gender diversity, and contribution and
broad/participation), and the meetings’ structure and content (i.e., agenda,
environment, technology used, action points, time, and multitasking). Based on
this review, we developed a questionnaire666http://bit.ly/psycho-meetings
consisting of 28 questions that cover the wide range of themes we identified,
and an additional set of three questions relating to demographics (age,
gender, and education levels).

###  3.1. Participants and Recruitment

We distributed the survey via social media (i.e., Facebook and Twitter on
targeted lists of knowledge workers. We specified ‘job meeting’ and ‘project
management’ as interests, and targeted a range of sectors from administrative
to sale to legal services to business and finance), and as an Amazon
Mechanical Turk (AMT) task. We ensured highly reputable AMT workers by
targeting workers with 95% HIT approval rate, and at least 100 approved HITs.
We received a total of 363 responses from both sources. We applied quality
checks in the form of trap questions. For example, one question prompted
participants to report whether they attended the meeting remotely or in-
person, while a subsequent question asked them to state their perceived air
quality of the room where the meeting took place. We rejected those who
reported contradicting answers (e.g., attended the meeting remotely and
answered the air quality question, while having the option to skip the air
quality question), thus ensuring data quality. As the survey questions were
not mandatory, we carried out further filtering of empty responses. We also
eliminated those whose last meeting was held ‘more than a week ago’ to remove
potential recall bias. The final dataset we analyzed consisted of 288
responses (Table 1).

Table 2. Rotated Component Matrix for the 3 derived principal components of
the psychological experience during meetings:
_Psychological safety_ ; _Productivity_ ; and _Pleasantness_.

|  Psychological safety | Productivity | Pleasantness
---|---|---|---
1\. I received the appropriate attention when sharing my thoughts. | .835 |  |
2\. I felt comfortable sharing my thoughts and making contributions. | .814 |  |
3\. I felt that my peers were comfortable sharing their thoughts and making contributions. | .804 |  |
4\. I found the meeting to be useful for all attendees. | .774 |  |
5\. I found the meeting to be useful for me. | .747 |  |
6\. I felt overall satisfied during the meeting. | .528 |  |
7\. Summary points were sent out before the meeting ended. |  | .773 |
8\. Summary points were sent out after the meeting has ended. |  | .647 |
9\. The meeting had a clear purpose. |  | .611 |
10\. Amount of space in the meeting room. |  |  | .865
11\. Air quality in the meeting room. |  |  | .790
_component loadings below .5 are suppressed._ |  |  |

###  3.2. Principal Component Analysis

We performed a Principal Component Analysis (PCA) on the survey responses to
identify the main orthogonal dimensions that explain the responses. PCA
“determine[s] the linear combinations of the measured variables that retain as
much information from the original measured variables as possible.” (Fabrigar
et al., 1999). The filtered dataset contained 288 responses and a ratio of
10.2 cases per variable, well above the recommended ratio of 1:10 for PCA
analysis (Nunally and Bernstein, 1978).

To ascertain the validity of our PCA results, we conducted three tests. First,
we tested the correlation matrix between the 28 questions of the survey
(excluding demographics). It showed significant correlations of above .3 with
at least one other item for the 20 out of 28, suggesting reasonable
factorability. Secondly, we used Bartlett’s Test of Sphericity which was found
to be significant (${\chi}^{2}$(378) = 3092.43, p = .000). Thirdly, the
Kaiser-Meyer-Olkin measure of sampling adequacy (MSA) was .716, which was
above the recommended threshold of .7 (Lloret et al., 2017). Subsequently,
fifteen items with MSA below .7 in the anti-image correlation matrix were
excluded from the analysis (Norman and Streiner, 2008). These items included
questions related to a respondent’s current position (e.g., years of
employment, employer); and the meeting’s diversity (e.g., number of attendees,
gender), interactions/conversation style, and time of day. This exclusion
resulted in improved values of (${\chi}^{2}$(78) = 1302.54, p = .000) in
Barlett’s Test of Sphericity, and in an overall MSA score of .859. This made
the PCA more robust with the remaining 13 items.

To determine the number of components that explain the most variance in our
survey, we inspected the eigenvalues of the principal components (Figure 1).
The eigenvalue of a principal component reflects the variance in all the
variables explained by this component, and a higher value indicates higher
variance in the variables loaded together in this component. The Kaiser’s rule
(Kaiser, 1960) suggests eigenvalues above 1.0 to be retained (resulting in 4
principal components), while the Cattell (Cattell, 1966) proposed a visual
inspection of the Scree Plot (Figure 1) to determine the ‘elbow’ point in
which a leveling effect is observed. This resulted in 3 principal components
that explained 56.12% of the total variance in the data. We considered the
loadings of the 13 questionnaire items of the 3 principal components. The
loadings describe the correlations between a single item and the principal
component. We removed 2 items from further analysis due to their low component
loading below .5. This resulted in 11 remaining items, which accounted for
62.31% of the total variance; a solution of 60% variance explainability is
often considered as satisfactory in social sciences ((Hair et al., 1998)
p.107). The additional two items left out concerned the respondent’s meeting
role (e.g., organizer, attendee), and the level of excitement during their
last meeting, either of their loading components was below .5.

Table 2 shows the resulting components and their items. The first component is
about _psychological safety_ , which refers to personal and group’s safety of
contributing to the meeting, personal and groups’ views on usefulness,
attention received, and personal overall satisfaction. The second component is
about _productivity_ , which refers to whether the meeting had clear purpose
and structure, and resulted in a list of actionable items (i.e., whether a
meeting has been productive). The third component is about _pleasantness_ ,
which refers to the meeting’s room overall pleasantness (e.g., good indoor air
quality and physical space utilization).

###  3.3. Three Factors of Meetings’ Psychological Experience

To make use of these three PCA components in our user study (§5), we
paraphrased them into three questions our participants are likely to
understand without considerable effort:

  * $Q_{psychological}$: Did you feel _listened to_ during the meeting or _motivated to be involved in_ it?

  * $Q_{productive}$: Did the meeting have a _clear purpose and structure_ , and did it result in a list of _actionable points_?

  * $Q_{pleasantness}$: Did the _room feel pleasant_ (in regards to air quality and crowdedness)?

##  4\. ComFeel Framework

As our study aims to investigate the short-term effects of sensory layers on
people’s productivity in the context of meetings, we resorted to sensors to
obtain objective measurements about the indoor environmental conditions. In
what follows, we describe our data collection framework, namely ComFeel. It is
a three-tier framework (Figure 2) composed by an application layer (a Gecko
device and a Raspberry Pi), a web service layer, and a data access layer.

Figure 2. ComFeel sensing infrastructure. Our Gecko device continuously
monitors indoor environmental properties (i.e., light, temperature, gas
resistance), which are transmitted to an intermediate agent implemented using
a Raspberry Pi. Upon data acquisition, the Raspberry Pi transmits the data to
our backend infrastructure for further processing.

###  4.1. Application Layer

####  4.1.1. Gecko Device

We assembled Gecko, a small (55x25x15), wearable environmental device, using
off-the-shelf commercial sensors. By building our own device with low-
manufacturing costs, we had full control of the firmware and we owned the raw
data, which is often not the case in commercial devices. Additionally, its
flexible design and plug&play functionality make it easy to deploy, and
attractive for large-scale deployments. Its total costs is 88 USD, which
includes a solar cell (2 USD), a battery (2 USD), a plastic case (8 USD), and
a PCB board with all the electronic components (76 USD). This estimation is
based on production of 50 units, whereas, in case of mass production (~1000
units), the costs could further be reduced to 50 USD. Assembly costs are not
included in the aforementioned costs, as they are deemed negligible due to a
Gecko’s design being assemblable in less than 10 minutes.

We used Bosch’s environmental sensor BME680777https://www.bosch-
sensortec.com/products/environmental-sensors/gas-sensors-bme680/ for
monitoring indoor temperature levels (C°) and gas resistance (ohm), and a
BH1745NUC digital 16-bit serial output type color sensor IC for obtaining
light luminosity888Color sensor:
https://www.mouser.co.uk/datasheet/2/348/bh1745nuc-e-519994.pdf (fc: foot-
candle). We configured both sensors to sample every 60 seconds, and sensors
readings were obtained through a micro-controller using an I2C interface on
the predefined interval. Upon reading, Gecko packs up each sensor’s raw
readings and sends it over using a Bluetooth Low Energy (BLE) protocol. Given
that the payload of each BLE packet cannot exceed 244 bytes, as specified in
the Bluetooth standard 4.2 onwards, each sensor reading is wrapped up in a
packet. Therefore, a packet contains up to 244 bytes of information, and has
the following signature: [packet identifier, timestamp, sensor id, sensor data
length], where _packet identifier_ indicates that a packet carries sensor data
payload, the _timestamp_ contains the time in which the reading occurred, the
_sensor id_ is a unique identifier for each sensor, and the _sensor data
length_ is the actual reading in bytes. When the packet is ready, we used a
Nordic UART
service999https://developer.nordicsemi.com/nRF_Connect_SDK/doc/latest/nrf/include/bluetooth/services/nus.html
to transmit it to an intermediate agent (i.e., Raspberry Pi), which we
describe next. The choice of the Nordic UART service was primarily reinforced
by the fact that it is an easy to implement and adapt it to our streaming
requirements, which are independent of the type of reading and the sampling
rate. Additionally, the BLE protocol itself ensures that packet loss or data
errors during transmission would not happen. At the end of every link layer
packet there is a 24-bit CRC; a mechanism that BLE uses, which contains a
sequence number and the next expected one. Every packet is then acknowledged
at the link layer, thus if no acknowledgment is received the packet will be
retransmitted. To further validate any potential errors due to packet drops,
in a subsequent section (§4.1.3), we describe the reliability of the Raspberry
Pi gateway.

(a)

(b)

(c)

Figure 3. (a) Geckos’ vs. environmental chamber temperature measurements at 15
and 45, (b) zoomed-in temperature measurements at 45 for which the accuracy
was estimated, and (c) deviations of the two Gecko devices against the
environmental chamber’s measurements.

####  4.1.2. Gecko Sensors Reliability

To ascertain our device’s sensors reliability, we carried out experiments that
compared our devices’ readings against the measurements operated in an
environmental chamber; a typical instrument for reliability testing of
different devices at different temperatures (Lee et al., 2001). The accuracy
of the temperature sensor inside the chamber is of 0.1.

According to the manufacturer101010https://www.bosch-
sensortec.com/products/environmental-sensors/gas-sensors-bme680/, the
temperature sensor of our Gecko has an operating range from -40 to 85 with an
accuracy of 1; the gas sensor has a response time of 1s, and an accuracy of
0.08%; and the light sensor is capable to measure a wide dynamic lux range up
to 40K lux with an accuracy of
0.005111111https://www.mouser.co.uk/datasheet/2/348/bh1745nuc-e-519994.pdf.

To test the temperature sensor readings, we placed two Gecko devices in the
environmental chamber. We set the chamber’s temperature to 15 and waited until
it stabilized in order to place the devices inside. After 10 minutes of
stabilization, we increased the temperature to 45 and, in turn, after roughly
50 minutes, we decreased it again to 15 for another 10 minutes (Figure 3a).
Using the values obtained at 15, we calibrated our sensors, and computed the
accuracy of our temperature sensor from the measurements at 45 (Figure 3b). We
then estimated the accuracy as the mean value of all deviations between the
Gecko measurements and the chamber temperature values at 45 (Figure 3c). We
observed worst-case precision of 0.5, which is in the range of the accuracy
stated by the manufacturer121212https://www.bosch-
sensortec.com/products/environmental-sensors/gas-sensors-bme680/.
Additionally, we observed that the two Gecko devices showed very similar
response, which suggests low sensor-to-sensor variability.

Taking into account typical illumination levels of office spaces (i.e., in the
range of 500 lux (Mui and Wong, 2006)), and given the capability of our light
sensor to measure a wide dynamic lux range from 0.005 to 40K lux with an
accuracy of 0.005
lux131313https://www.mouser.co.uk/datasheet/2/348/bh1745nuc-e-519994.pdf, even
an error of one order of magnitude would still provide accurate measurements.
The same applies to the high accuracy of the gas resistance measurement as
stated by the manufacturer (i.e., 0.08%141414https://www.bosch-
sensortec.com/products/environmental-sensors/gas-sensors-bme680/).

####  4.1.3. Raspberry Pi

As Gecko is designed to be a cheap and flexible indoor environmental device,
it has no capabilities of streaming its data to a web service. To that end, we
implemented an intermediate agent using Raspberry Pi which acts as a gateway
between a Gecko device and our backend server. The Raspberry Pi collects data
from one or more Gecko devices and forwards it to a web server. The gateway is
implemented in such a way to actively listen for connections over BLE. Once a
Gecko device establishes a BLE connection with the Raspberry Pi, the gateway
decodes the bytes array sent from Gecko, formats it in as a JSON, and
transmits it to our backend for further processing and analysis. To further
validate whether any potential errors occurred at the Rasperry Pi side, we
transmitted data to the gateway from two Gecko devices. (Figure 2). In total,
both devices transmitted 1178 packets, and we observed 5 misses out of 587
packets sent from the first device, and 8 misses out of 591 packets sent from
the second device. On average, this accounts for no more than 1.1% error rate
at the gateway level.

###  4.2. Web Services Layer

Using the Python Tornado framework, we developed a RESTful API that runs
continuously on a server. It exposes an endpoint that handles the
communication with the Raspberry Pi module and the data access layer. The
endpoint is responsible for receiving the data from the gateway and flushing
it to the database.

###  4.3. Data Access Layer

The data acceess layer is implemented by a MongoDB instance, and the raw data
is stored in a database collection with the following generic schema:
[deviceID, sensor_type, sensor_value, ts], where _deviceID_ is a unique
identifier that distinguishes Gecko devices, _sensor_type_ is a key identifier
that corresponds to the type of environmental data (i.e., temperature, gas
resistance, or light luminosity), _sensor_value_ is the sensor reading, and
_ts_ is the time that the sensor reading was sampled.

(a)

(b)

Figure 4. The two meetings rooms utilized in our user studies. (a) the _small_
room has a maximum capacity of six people
($16.25\text{\,}\frac{{\mathrm{m}}^{2}}{}$), while (b) the _large_ room has a
maximum capacity of ten people ($24.70\text{\,}\frac{{\mathrm{m}}^{2}}{}$).

##  5\. User study

Having developed a sensing infrastructure to obtain objective measurements of
indoor environmental quality, we conducted a longitudinal study, spanned over
a period of one month, to understand the sensorial pleasantness of a meeting’s
room and the meeting’s productivity.

###  5.1. Study Setup

The study was conducted at the facilities of a research laboratory in a
corporate setting during February-March. Two meeting rooms with varying
capacities and dynamics were utilized (Figure 4). We refer to them as the
_small_ room and the _large_ room. The small room’s is
$16.25\text{\,}\frac{{\mathrm{m}}^{2}}{}$, which is ideal for small-size
meetings (six seats), while the large room’s ones is
$24.70\text{\,}\frac{{\mathrm{m}}^{2}}{}$ (ten seats), which is more
preferable for hosting larger-size meetings. The choice of these two
particular rooms sizes was reinforced by their popularity. Therefore, we did
not set up any ‘experimental’ rooms, rather we deployed our framework in rooms
people regularly use. This also allowed us to understand people’s behavior and
perception under real-world corporate settings as opposed to controlled lab
settings. Additionally, these two rooms, when empty, are similar in terms of
light (same view), temperature, and gas resistance. We also kept both rooms’
blinds open during the trial, thus ensuring similar exposure to outside views
in both rooms. We deployed two Gecko and Raspberry Pi devices, one in each
room, which were continuously monitoring and storing the indoor environmental
information on our backend server (Figure 2).

Table 3. The four-item survey which our participants completed at the end of each meeting. Name | Question | Responses
---|---|---
$Q_{type}$ | What was the type of the meeting you just had? |  (a) status update (b) information sharing /presentation (c) decision making/problem solving
$Q_{productive}$ |  Did the meeting have a clear purpose and structure, and did it result in a list of actionable points? |  Likert Scale (1: Strongly disagree; 7: Strongly agree)
$Q_{psychological}$ |  Did you feel _listened to_ during the meeting, or _motivated to be involved in_ it? |  Likert Scale (1: Strongly disagree; 7: Strongly agree)
$Q_{pleasantness}$ |  Did the room feel pleasant? (in regard to air quality and crowdedness) |  Likert Scale (1: Strongly disagree; 7: Strongly agree)

###  5.2. Materials and Apparatus

To capture our participants’ perceptions during a meeting, we asked them the
three questions we identified in the online survey (§3), at the end of their
meeting. We included an additional question to report the type of the meeting.
All questions along with their response options are listed in Table 3.
Finally, participants noted in the survey the start and end time of their
meeting, which we made use in our analysis (§6). Participation in the
questionnaires was anonymous. We opted-in for completely anonymized responses
to ensure privacy compliance according to the EU General Data Protection
Regulation (GDPR). As we did not have any particular requirements for
participation, we neither adopted a recruitment protocol nor specified any
eligibility criteria; instead, we simply installed an information sheet in
both rooms, detailing the study’s protocol.

###  5.3. Protocol, Procedure, and Participants

The protocol stated the aims of the study, the data collection, and made
explicit to participants that they agree that their responses will be analyzed
for research purposes. In accordance to GDPR, no researcher involved in the
study could have tracked their identity by any means, and all their anonymous
responses were analyzed at an aggregated level. In addition to the data
handling and data analysis, the protocol stated the experimental procedure.
Participants were informed that, for a period of one month, two Gecko devices
were installed in the two meeting rooms, and these devices were monitoring
both rooms’ environmental conditions. In total, we received 364 self-reported
answers to the four questions (Table 3) from 91 participants who attended
those meetings (Table 4).

###  5.4. Dataset

In total, we collected 1373 minutes of environmental data across 29 unique
meetings that were held in the two meeting rooms (Table 4). As there is no
unified classification of meeting types, we grouped our meetings into three
common meeting types found in previous literature (MeetingSift, 2020; Kim,
2019; Goff-Dupont, 2018; Minute, 2020). These are: (a) _status update_ , (b)
_information sharing/presentation_ , and (c) _decision making/problem
solving_. In total, our participants attended 58.2% status update (_type 0_),
28.6% information sharing/presentation (_type 1_), and 13.2% decision
making/problem solving (_type 2_) meetings (Figure 6a), with varying duration
(Figure 6e). To assess the quality of the responses to our three questions, we
looked at the inter-rater agreement (IRA) among participants of the same
meeting. We computed an IRA statistic called $r_{wg}$ score (James et al.,
1984) and the standard deviation across participants’ answers, which are
commonly applied IRA measures (O’Neill, 2017) for Likert-scale questionnaires.
The $r_{wg}$ statistic (1) is a function of the observed variance in
participants’ answers ($S_{x}^{2}$), and the variance in the ratings, if they
were random, i.e., null distribution ($\sigma_{eu}^{2}$):

(1) $\displaystyle r_{wg}=1-(S_{x}^{2}/\sigma_{eu}^{2}),$

where $x$ is the set of the participants’ answers, $S_{x}^{2}=var(x)$, and
$\sigma_{eu}^{2}$ is set by convention to a constant value of $(A^{2}-1)/12$,
and $A$ is the number of Likert-scale options (in our case, $A=7$). The values
of $r_{wg}$ range from 0 to 1, with those closer to 1 representing a better
agreement. The distributions of scores across meetings shown in Figure 5b
reveal a relatively high level of agreement among our participants for all the
three questions (average $r_{wg}$ for $Q_{psychological}$ is .78, for
$Q_{productive}$ is .87, and for $Q_{pleasantness}$ is .75). Unsurprisingly,
our participants agreed more on the meeting’s productivity than on the other
two more subjective aspects.

Table 4. Statistics of the twenty nine meetings held in the two meeting rooms. | small room | large room
---|---|---
#Meetings | 18 | 11
#Minutes of sensing data | 843 | 530
#responses | 220 | 144
#participants responded | 55 | 36

##  6\. Analysis

(a)

(b)

Figure 5. Distributions of inter-rater agreement among meeting participants
for the three questions in terms of: (a) standard deviation $\sigma$ of their
answer scores, and (b) the Likert-scale inter-rater agreement measure $r_{wg}$
(James et al., 1984).

Having collected objective environmental measurements and employees’ self-
reported experiences of their meetings in terms of their perceived
psychological safety, productivity, and room pleasantness, we set out to
investigate the interplay of these factors with productivity. Based on prior
work, we formulated three hypotheses, and defined a set of metrics to test
them.

###  6.1. Hypotheses and Metrics

As one expects, the outcome of a meeting (i.e., productive or not) is
dependent on its type. In the fields of the Management and Organizational
Science, the most common types include _status update_ , _information
sharing/presentation_ , and _decision making/problem solving_ (MeetingSift,
2020; Kim, 2019; Goff-Dupont, 2018; Minute, 2020). It is also known that the
length of a meeting might influence its productivity. Often, meetings that do
not start or end on time tend to leave attendees’ unsatisfied (Cohen, 2016;
Schwarz, 2016). Therefore, we formulated our first hypothesis as:

H1: Whether a meeting is productive depends on the meeting’s _type_ and
_duration_.

To test it, we used a meeting’s _type_ by asking our participants the
$Q_{type}$ in Table 3, and _duration_ computed from their timestamped
responses for each meeting (Figure 6e). Since the _meeting type_ question
(Figure 6a) is a categorical variable, we converted it into three dummy
variables to then make use of them in a logistic regression: _type 0_ , _type
1_ , and _type 2_.

We treated this problem as a classification task, and we used the binarized
self-reported $Q_{productive}$ as the dependent variable. To binarize it into
a positive and negative class, we defined a set $E$ as the set of all
participants’ responses to $Q_{productive}$, and we partitioned it into two
groups such that $E=E^{>}\cup E^{<}$, where $E^{>}$ is the set of participants
who reported higher levels of meetings’ productivity (above the median)
defined as $E^{>}=\\{{e\mid>\bar{e}\\}}$, where $e$ is a participant’s
$Q_{productive}$ response, and $E^{<}$ is the set of participants who reported
lower levels of of meetings’ productivity (below the median) defined as
$E^{<}=\\{{e\mid>\bar{e}\\}}$, where $e$ is a participant’s $Q_{productive}$
response. Then, the $E^{>}$ responses were assigned to the positive class,
while the $E^{<}$ ones to the negative (Figure 6c).

We built the $M_{1}$ logistic regression model (3) to test $H_{1}$ by
including meetings’ _duration_ and the three dummy variables (i.e., _type 0_ ,
_type 1_ , and _type 2_), which reflect the _type_ of meeting:

$\operatorname{Pr}(\text{Q\textsubscript{productive)}}=1\mid\text{type 0, type
1, type 2, duration})=$ (3)
$\displaystyle\frac{1}{1+exp^{-}\textsuperscript{($\alpha$ \text{ + }
$\beta_{1}$ $type0$ \text{ + } $\beta_{2}$ $type1$ \text{ + } $\beta_{3}$
$type2$ \text{ + } $\beta_{4}$ $duration$ )}},$

where Pr is the posterior probability of an instance belonging to the positive
class of $Q_{productive}$, estimated through a linear combination of the input
features (i.e., type 0, type 1, type 2, and duration) passed through a
sigmoidal function, and $\alpha,\beta_{1},\beta_{2},\beta_{3},and\beta_{4}$
are the parameters to be learned by model $M_{1}$.

Furthermore, a productive meeting might also depend on the psychological
safety experienced by participants (i.e., whether they felt listened to). As
we previously illustrated (§3), psychological factors such as inclusiveness
(Axtell, 2016, 2017), dominance (Romano and Nunamaker, 2001), and comfort in
sharing and contributing (Grenny, 2017; Axtell, 2018) are linked with people’s
overall experience of a meeting. To understand whether these factors also
impact meetings’ productivity, we formulated our second hypothesis as:

H2: Whether a meeting is productive depends on the meeting’s _type_ ,
_duration_ , and on the _psychological safety_ experienced by participants.

To investigate $H_{2}$, we used the z-score transformed
($\frac{x-\mu_{x}}{\sigma_{x}}$) of $Q_{psychological}$ reported by our
participants (Figure 6b) along with meetings’ _duration_ , and the three dummy
variables, which reflect the meeting _type_.

We built the $M_{2}$ logistic regression model (5) to investigate $H_{2}$ by
adding _psychological safety_ to the $M_{1}$ model. We used the binarized
self-reported $Q_{productive}$ as the dependent variable:

$\operatorname{Pr}(\text{Q\textsubscript{productive}}=1\mid\text{type 0, type
1, type 2, duration, Q\textsubscript{psychological}})=$ (5)
$\displaystyle\frac{1}{1+exp^{-}\textsuperscript{($\alpha$ \text{ + }
$\beta_{1}$ $type0$ \text{ + } $\beta_{2}$ $type1$ \text{ + } $\beta_{3}$
$type2$ \text{ + } $\beta_{4}$ $duration$ \text{ + } $\beta_{5}$
$Q_{psychological}$ )}},$

where Pr is the posterior probability of an instance belonging to the positive
class of $Q_{productive}$, estimated through a linear combination of the input
features (i.e., type 0, type 1, type 2, duration, and $Q_{psychological}$)
passed through a sigmoidal function, and
$\alpha,\beta_{1},\beta_{2},\beta_{3},\beta_{4},and\beta_{5}$ are the
parameters to be learned by model $M_{2}$.

Finally, the role of indoor environmental conditions into workplace’s
productivity has been the subject of several reports. For example, the
lighting conditions were among the most popular
perks151515https://futureworkplace.com/ employees crave for at the workplace
(Meister, 2018). Additionally, studies have shown that stuffy and stale air
offices reduce productivity (Allen et al., 2016; Tom Y. Chang and Neidell,
2016; Allen, 2017). Other studies have reported that the actual environmental
conditions might not always match the subjective perception of the physical
space though (De Giuli et al., 2014; Kang et al., 2017). Therefore, we
formulated our third hypothesis as:

H3: Whether a meeting is productive depends on the meeting’s _environmental
conditions_ , both actual and perceived conditions.

To investigate $H_{3}$, we used the z-score transformed self-reported
$Q_{psychological}$ along with two metrics that capture the _sensed
pleasantness_ and the _not-sensed_ one. The sensed pleasantness is a metric
that captures the environmental conditions our ComFeel framework is able to
capture. To built this metric, we resorted to the sensor readings obtained
using our Gecko devices, and we did so in two steps. First, we used the indoor
_temperature_ level (C°), _gas resistance_ (ohm), and _light luminosity_ (fc:
foot-candle) (Figure 6f-h). For the sake of brevity, we refer to them as
_temp_ , _gas_ , and _light_ respectively for the rest of our analysis. As the
device was set to sample sensor readings every 60 seconds (§4.1.1), we
aggregated the minutely obtained values across each meeting’s duration, took
their median value, and z-score ($\frac{x-\mu_{x}}{\sigma_{x}}$) transformed
them to then make use of them in a logistic regression. We chose the median
statistic as a more representative value due to its robustness against
outliers, while the choice of using z-score transformations as opposed to raw
values allowed us to incorporate the variance of the sensor-derived data. We
then computed a logistic regression as:

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Figure 6. Frequency distribution of variables used in the analysis. (a)
$Q_{type}$; (b) $Q_{psychological}$; (c) $Q_{productive}$; (d)
$Q_{pleasantness}$; (e) duration; (f) temperature; (g) gas resistance; (h)
light.

(6) $\operatorname{Pr}(\text{$Q_{pleasantness}$}=1\mid\text{temp, gas,
light})=\frac{1}{1+exp^{-}\textsuperscript{($\alpha$ \text{ + } $\beta_{1}$
$temp$ \text{ + } $\beta_{2}$ $gas$ \text{ + } $\beta_{3}$ $light$)}},$

where Pr is the posterior probability of being in the positive class of
$Q_{pleasantness}$, and $\alpha,\beta_{1},\beta_{2},and\beta_{3}$ are the
parameters to be learned. To binarize $Q_{pleasantness}$ (i.e., positive and
negative class), we defined a set $P$ as the set of all participants’
responses to $Q_{pleasantness}$, and we partitioned it into two groups such
that $P=P^{>}\cup P^{<}$, where $P^{>}$ is the set of participants who
reported higher levels of room pleasantness (above the median) defined as
$P^{>}=\\{{p\mid>\bar{p}\\}}$, where $p$ is a participant’s $Q_{pleasantness}$
response, and $P^{<}$ is the set of participants who reported lower levels of
room pleasantness (below the median) defined as $P^{<}=\\{{p\mid>\bar{p}\\}}$,
where $p$ is a participant’s $Q_{pleasantness}$ response. Then, the $P^{>}$
responses were assigned to the positive class, while the $P^{<}$ ones to the
negative (Figure 6d).

Secondly, using the learned parameters (i.e.,
$\alpha,\beta_{1},\beta_{2},and\beta_{3}$) from (6), we linearly combined them
and computed _sensed_ as:

(7) $sensed=(\alpha+\beta_{1}temp+\beta_{2}gas+\beta_{3}light),$

where $temp$, $gas$, and $light$ are the sensor readings obtained using our
Gecko devices. We then computed _z-sensed_ as the z-score
($\frac{x-\mu_{x}}{\sigma_{x}}$) of _sensed_.

The _not-sensed_ pleasantness metric captures the difference between the
sensed and the self-reported pleasantness, and we computed it as:

(8) $\text{z-not-
sensed}=(\text{\emph{z-sensed}}-\text{\emph{z-$Q_{pleasantness}$})}$

The higher its value, the more our sensors overestimated what people felt,
while the lower its value, the more our sensors underestimated what people
felt.

We built the $M_{3}$ logistic regression model (10) to address $H_{3}$ using
the _$Q_{psychological}$_ , _z-sensed_ , and _z-not-sensed_.

$\operatorname{Pr}(\text{Q\textsubscript{productive}}=1\mid\text{Q\textsubscript{psychological},
z-sensed, z-not-sensed})=$ (10)
$\displaystyle\frac{1}{1+exp^{-}\textsuperscript{($\alpha$ \text{ + }
$\beta_{1}$ $Q_{psychological}$ \text{ + } $\beta_{2}$ z-sensed \text{ + }
$\beta_{3}$ z-not-sensed )}},$

where Pr is the posterior probability of being in the positive class of
$Q_{productive}$, estimated through a linear combination of the input features
(i.e., $Q_{psychological}$, z-sensed, and z-not-sensed) passed through a
sigmoidal function, and $\alpha,\beta_{1},\beta_{2},and\beta_{3}$ are the
parameters to be learned by model $M_{3}$.

(a)  Figure 7. Cross-correlation matrix of the variables in our analyses.
Overall, the independent variables are fairly orthogonal, while some pairs of
variables such as light and gas resistance show weak to moderate correlation.

##  7\. Results

To evaluate the performance of our models, we used the standard classification
performance metrics. Specifically, we calculated the _Area Under the Receiver
Operating Characteristics (ROC) curve (AUC)_ for our predictions. The ROC is a
function of the true positive rate (TPR) against the false positive rate (FPR)
of a classifier. Hence AUC will be larger for models that are good at
predicting the positive class while not, at the same time, misclassifying the
negative class.

All three models’ AUC scores and their significant predictors are shown in
Table 5. We observed that $M_{1}$ predicts meetings’ productivity slightly
better than tossing a coin (AUC = 56%), which led us to _reject_ H1 based on
the finding that whether a meeting was productive or not did not depend on the
meeting’s type or duration only. When adding the _$Q_{psychological}$_ into
$M_{2}$ model, however, the performance drastically improved, reaching an AUC
of 82%. This suggests that participants’ perception on whether a meeting was
productive is mediated by the psychological safety that they experienced, in
line with previous findings on the importance of psychological factors that
allows for effective coordination and cooperation in a meeting, leading us to
_accept_ H2. Finally, we found that the performance of our $M_{3}$ model
further improves reaching up to 86%. This translates into saying that the
environmental conditions, both actual and perceived ones, further mediate
people’s perception on whether a meeting was productive. Therefore, we
_accept_ H3, and we attempt to explain these observations by interpreting
$M_{3}$’s model coefficients.

Table 5. Logistic regression coefficients. $M_{0}$ model’s coefficients were
used to predict _sensed_ pleasantness; $M_{1},M_{2}\text{ and }M_{3}$ tested
hypotheses $H_{1},H_{2}\text{, and }H_{3}$ respectively. Statistically
significant predictors at $<.05$ are marked in bold. (-) indicates that a
model was fit without that predictor.

| M0
---|---
Predictors |
Intercept ($\alpha$) | -0.576
temp | -0.630
gas | 1.121
light | 0.752
AUC score | 77.08%

| M1 | M2 | M3
---|---|---|---
Predictors |  |  |
Intercept ($\alpha$) | -0.089 | -0.613 | -0.646
type 0 | -0.165 | 0.051 | -
type 1 | -0.089 | -0.635 | -
type 2 | 0.064 | -0.028 | -
duration | -0.009 | -0.001 | -
$Q_{psychological}$ | - | 1.526 | 1.389
z-sensed | - | - | 1.005
z-not-sensed | - | - | -0.256
AUC score | 55.72% | 82.56% | 85.84%

Additionally, we run a fourth model by including all the seven predictors and
observed an AUC of 86.6%. This yielded very little gain in AUC compared to
$M_{3}$. Further, to explore whether there are any interactions between the
variables in our analyses, we measured whether pairs of the independent
variables correlated with each other. To do so, we computed the Spearman’s
rank cross-correlation matrix among all pairs (Figure 7). Some pairs such as
light and gas resistance, and light and meeting type (as certain types of
meeting, such as status updates, tended to happen more often in the morning)
show weak to moderate correlations, but overall our independent variables are
fairly orthogonal.

To get an upper bound of the predictive difference corresponding to a unit
difference in each predictor, we use the ‘divide by 4’ rule (Gelman and Hill,
2007) (p. 82). This says that, in a logistic regression, by dividing each
predictor’s coefficient by 4 one can draw conclusions for 1 unit increase in
each predictor. Simply put, we can quantify the percentage of increase or
decrease in the probability of the dependent variable to be in the positive
class.

###  7.1. The Known Unknowns of Productivity

By applying this rule, we inspected $M_{3}$’s model predictors’ coefficients
and found that the probability of a meeting perceived as a productive one is
increased by 35% every one standard deviation increase in the
_$Q_{psychological}$_. This corroborates previous findings, which highlighted
the importance of inclusiveness (Axtell, 2016, 2017), dominance (Romano and
Nunamaker, 2001), and comfort in sharing and contributing (Grenny, 2017;
Axtell, 2018) in meetings. Furthermore, we found that the probability of a
meeting perceived as a productive one is increased by 25% every one standard
deviation increase in the _z-sensed_ pleasantness. This suggests that
environmental conditions also impact meetings’ productivity, supporting this
work’s overall hypothesis that environmental conditions impact not only long-
term productivity (as suggested by previous work (Meister, 2018; Allen et al.,
2016; Tom Y. Chang and Neidell, 2016; Allen, 2017)) but also short-term and
immediate one. We also found that the probability of perceiving meetings to be
productive is increased by 6% every one standard deviation decrease in the
part of people’s self-reported _$Q_{pleasantness}$_ that our sensors could not
capture (encoded into _z-not-sensed_), confirming that ComFeel captured most
of the variability in the environmental conditions people experienced (adding
external validity to our framework’s evaluation) and, at the same time,
suggesting the orthogonal importance of asking a simple question even in the
presence of an effective sensing infrastructure.

##  8\. Discussion

Our operationalization of the concept of sensorial pleasantness and
productivity allowed us to study, for the first time, the short term impact of
environmental conditions on meetings’ productivity. By analyzing an online
survey, which we administered to 363 participants, we unveiled three
orthogonal dimensions that capture meetings’ psychological experience: (a)
_psychological comfort_ , (b) _productivity_ , and (c) _room pleasantness_.

Using the collected data obtained from our ComFeel framework and employee’s
self-reported experiences during their meetings, we investigated the interplay
of environmental conditions with productivity. We first captured the sensed
pleasantness by employing a machine learning method on three environmental
conditions (i.e., temperature, light, and gas resistance), moving beyond
thermal comfort previous work focused on (Song et al., 2019). We built a model
that learned the linear combination of these factors, and we found that gas
resistance was the most predictive one, followed by light and temperature. We
then built three models to test our three hypotheses by incrementally
introducing variables. In our final $M_{3}$ model, we found that, on average,
the probability of a meeting perceived as a productive one increased by 35%
for each standard deviation increase in the psychological safety participants
experience. This result confirms previous findings reported in literature
(Axtell, 2016, 2017; Romano and Nunamaker, 2001; Grenny, 2017; Axtell, 2018),
and suggests that meetings’ productivity is mediated by whether participants
felt listened to and were able to freely contribute. Additionally, we found
that the very same probability increased by as much as 25% for each standard
deviation increase in room pleasantness, suggesting the short-term impact of
environmental conditions in meetings’ productivity.

###  8.1. Implications

The theoretical implications of our work concern the fields of Management and
Organizational Science. By analyzing our online questionnaire, we found that
productivity, psychological safety, and room pleasantness capture, to a great
extent, people’s experience of meetings. This finding corroborates previous
ones, which highlighted the importance of inclusiveness (Axtell, 2016, 2017),
dominance (Romano and Nunamaker, 2001), and comfort in sharing and
contributing in meetings (Grenny, 2017; Axtell, 2018). All these aspects could
be potentially captured by simply administering only three questions, making
it possible for a large organization to understand their employees’ overall
experience during meetings.

Practically, our work speaks to the broader Ubicomp community. We found that
particular environmental properties were more significant than others to
predict perceived environmental conditions, and this could drive the focus of
the research on sensing. Specifically, among the three environmental
properties used to capture room pleasantness, we found that gas resistance was
the most significant one, while lighting conditions and temperature had a
second-order, yet significant and considerable effect. A finding also aligned
with previous work that showed that high exposures to volatile compounds
(which gas resistance captures) were associated with a decline in cognitive
functions (Allen et al., 2016). As of immediate practical use, our ComFeel
framework is an easy-to-deploy infrastructure that could be used across any
meeting room within an organization. Its flexible design, data reliability
(off-the-shelf environmental sensors), and low manufacturing costs make it an
appealing device for future deployments across any organization, independently
to its size. More broadly, our ComFeel framework could further facilitate
research on Human-Building Interaction (HBI); an emerging area aiming at
unifying HCI research in the built environment (Alavi et al., 2019). Building
on HBI’s mission, we foresee to be able to answer questions related to human
comfort (consider situations in shared offices where comfort needs to be
agreed upon, even with basic interventions such as opening a window to more
automated ones like adjusting automatically the thermostat setting) and the
broader social dimensions of the workplace (Alavi et al., 2017; Alavi et al.,
2016). Beyond the workplace, ComFeel could also be utilized in smart
factories, which are considered an integral part of the fourth industrial
revolution (Wang et al., 2016). The very same technology used here might well
be used in an industrial setting to allow companies to optimize for their
workers’ productivity and well-being in real time.

###  8.2. Limitations and Future Work

The current work is subject to three main limitations that call for further
work. The first limitation concerns the online survey. While we adopted a
rigorous statistical approach to derive the three main principal components,
we eliminated seventeen questions from the initial set that account for 37.69%
of the variance. As previously stated though (§3), a set of three questions
that accounts for 60 percent of the total variance can be deemed as
satisfactory (Hair et al., 1998). Additionally, the choice of the main three
principal components was also reinforced by the practicalities of asking three
questions only, making it attractive for deployments in a real application.
Although a questionnaire’s length and its response rate is often debatable
(Rolstad et al., 2011), a shorter set of questions makes our application
practical to use. The second limitation is about the sample size used in the
analyses. Despite the fact that we obtained a fairly large amount of raw
environmental readings, and hundreds of self-reported answers, a larger scale
deployment would further benefit our analyses beyond data obtained at the
facilities of one building. Immediate future work includes the deployment of
our ComFeel framework in a broader set of meetings within our office’s
environment and then across the entire organization, thus enabling us to
investigate other factors such as people’s role within the company (e.g.,
individual contributors, managers, executives). The third limitation concerns
the data collection and, particularly, the off-the-shelf environmental sensors
used to obtain the readings. Despite these sensors are commercial ones, and
were validated separately, future directions include the integration of
additional and alternative vendors into our framework. Additionally, in
future, we plan to extend our devices by incorporating additional sensors that
capture a wider set of environmental factors such as noise or humidity. Along
these lines, our models make use of the perceived productivity levels that our
participants reported, whereas alternative ways of obtaining objective
measurements of meetings’ productivity (e.g., number of completed agenda
items) could also be investigated in future work.

##  9\. Conclusion

We studied the short term impact of indoor environmental quality on
productivity at one of the most common daily activity at work: _meetings_. We
operationalized the concept of sensorial pleasantness and productivity in
meetings by analyzing an online questionnaire, and identified three factors
that capture people’s experience of meetings, namely psychological comfort,
productivity, and room pleasantness. Building on these insights, we measured
room pleasantness using ComFeel, an indoor environmental sensing
infrastructure we developed that captures light, temperature, and gas
resistance readings through our Gecko devices. We deployed our ComFeel across
29 real-world meetings, which spanned over a period of one month, and
collected 1373 minutes of environmental sensor readings. For each of these
meetings, we also collected whether each participant felt the meeting to have
been productive, the setting to be psychologically safe, and the meeting room
to be pleasant. As one expects, we found that, on average, the probability of
a meeting being productive increased by 35% for each standard deviation
increase in the psychological safety participants experienced. Importantly,
the very same probability increased by as much as 25% for each standard
deviation increase in room pleasantness. These results suggest that
environmental conditions do matter not only in the long term but also in the
short term so much so that significant differences in productivity were
observed even within the constrained space of individual meetings.

## Acknowledgments

We thank Philip Derrick for his contributions in the development of the Gecko
devices. Thanks also to our participants and anonymous reviewers for their
comments and feedback on this manuscript.

## References

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