2020 14th International Symposium on Medical Information Communication Technology (ISMICT)

Components of Comfort in the Office and its
Individual Differences
Masashi Sugimoto
School of Science and Technology
Kwansei Gakuin University
Hyogo, Japansugimoto.masashi@kwansei.ac.jp

Fan Zhang
School of Science and Technology
Kwansei Gakuin University
Hyogo, Japanzhangfan@kwansei.ac.jp

Noriko Nagata
School of Science and Technology
Kwansei Gakuin University
Hyogo, Japannagata@kwansei.ac.jp

Kota Kurihara
Advanced Technology R&D Center
Mitsubishi Electric Corporation
Hyogo, Japan
Kurihara.Kota@ds.MitsubishiElectric.co.jp

Seiro Yuge
Advanced Technology R&D Center
Mitsubishi Electric Corporation
Hyogo, Japan
Yuge.Seiro@ay.MitsubishiElectric.co.jp

Makoto Takata
Advanced Technology R&D Center
Mitsubishi Electric Corporation
Hyogo, Japan
Takata.Makoto@ea.MitsubishiElectric.
co.jp

Koji Ota
Advanced Technology R&D Center
Mitsubishi Electric Corporation
Hyogo, Japan
Ota.Koji@cb.MitsubishiElectric.co.jp

Seiji Furukawa
Advanced Technology R&D Center
Mitsubishi Electric Corporation
Hyogo, Japan
Furukawa.Seiji@dw.MitsubishiElectric.co.jp

Abstract—People, especially office workers spend most of their days indoors. Designers and builders try to realize comfort in theoffice, which improves the productivity and wellness of workers. The present research investigated the components of subjective
comfort in an office environment and revealed its individual differences. We gathered data from office workers and university
students working in their offices using a combination of the experience sampling method and the evaluation grid method. The results
showed that comfort was composed of various factors such as thermal factor, inside factor, sound factor, humidity factor and others.
Although the thermal factor was the most dominant for subjective comfort, the relative frequency of these factors varied between
participants. To investigate these individual differences, we conducted a cluster analysis and found three clusters: the balanced,
thermal and inside clusters. The three clusters focused on different aspects of the environment. In addition, though subjective comfort
related strongly with subjective productivity, the objective index of the thermal environment (predicted mean vote; PMV) failed to
predict the participants’ subjective comfort and productivity. These results revealed that even in an environment where PMV
indicates relatively good thermal conditions, thermal factors can cause discomfort. That is, to realize comfort in an environment, weneed to be concerned with subjective indices and individual differences in addition to objective ones such as PMV.
Keywords—comfort, productivity, indoor environment, office, individual differences

I. INTRODUCTION
The office environment is important for the wellness ofoffice workers. With an increasing interest in wellness
within an office environment, the WELL certification was
introduced in 2014. The WELL certification is “the premier
standard for buildings, interior spaces and communities
seeking to implement, validate and measure features that
support and advance human health and wellness” [1].
Academic research has also focused on the factors relating
to wellness, such as comfort, productivity, job satisfaction
and wellness itself. These factors are affected by theoffice’s environment, which includes things such as indoor
air quality, office design and relationships between workers.
The office environment has been especially importantin the age of Information Communication Technology(ICT). ICT not only affects the way people work at theiroffices [2] but it enables unprecedented applications suchas real-time thermostats that incorporate software based on
a comfort equation [3]. These technologies allow people tohave proper environments that suit their preferences, needs
and wants.
Although many studies have pointed out factors that
affect indoor environments, an overall picture has not yet
been found. This is because most of these studies haveThis work was supported by JST COI Grant Number JPMJCE1314.

focused on sole factors (such as thermal factors, air quality
and so on) and investigated their effects. This has made itdifficult to compare the effect sizes of these factors.
The absence of an overall picture leads to anotherproblem. It is unclear how environmental factors affectevaluations of the environment. That is, though previous
studies have supposed the effect of independent variableson the dependent variables, the detailed mechanisms
underlying these effects have not been determined. For
example, a warm temperature may make a room more
comfortable, but this warmth may also make people sleepy
and decrease productivity, which results in a lesscomfortable environment. Therefore, although the use of
ICT in a building environment can create a personalized
environment that increases workers’ wellness, it is
impossible to decide what kind of environment should bedelivered to individual workers.
In this paper, we focused on comfort in the office asconsisting of wellness within it, and we propose a method
to reveal the indoor environmental factors that affectcomfort. Our model also concerns individual differences inindoor environments by classifying office workers into
different types based on the factors that affect theirsubjective comfort.
In chapter II, we introduce studies that relate to thepresent study. In chapter III, we test our approach to
extracting comfort factors and classifying office workers

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2020 14th International Symposium on Medical Information Communication Technology (ISMICT)

based on the factors that affect their comfort, and we also
test the validity of the results. In chapter IV, we makeconclusions about our approach and mention some future
directions.
II. RELATED STUDIES
A. Indoor Environments and Wellness
Many previous studies have revealed the factors that
affect evaluations of indoor environments. These studies
were conducted according to models in which the physicalfactors of the environment (independent variable) affected
the mental states of the people within it (dependent
variable). The independent variables usually included
various physical factors such as indoor air quality [4],actual air temperature [5–7], indoor color [8] and ceilingheight [9]. Sometimes the independent variables also
included mental factors such as satisfaction in theenvironment [10] and levels of control (e.g., whether thebuilding user could control the temperature of the room)[11]. In addition to these studies, a meta-analysis revealed
the factors that consistently affect evaluations of indoor
environments [12]. They showed that education level, typeof work, psychosocial atmosphere at work and timepressure all consistently affect evaluations of indoor
environments. Among these, the thermal factor was themost important factor in improving satisfaction with regard
to the quality of the indoor environment.
The dependent variables included various indices suchas subjective symptoms (e.g., headaches and concentration)[4] and the performance of a specific task [9]. More abstract
dependent variables included satisfaction toward thethermal environment, sound, and air quality [10] as well as
self-reported productivity [11].
B. Comfort and Individual Differences
Previous research has also shown individual differencesin the effect of the independent variables. Sex is one
example of these individual differences. The “neutral”
temperature for Japanese male is 24.3°C, whereas theneutral temperature for Japanese female is 25.2°C [6]. Racecan also cause individual differences. The same study
found that the neutral temperature for non-Japanese malewas 22.1°C [6]. Another factor that causes individual
differences is task performance. Positive mood induced bywall color improved task performance more in participants
whose performance was better than average [13].
Although an environment might be consistent, theenvironmental effects on the people inside it sometimes
differ. One study revealed that taking a rest after bathing
decreased the arousal score of participants over time [14].
Another study revealed that although indoor air quality
affects the people inside, those who stayed in theenvironment longer became insensitive to it [4].
III. EXTRACTION OF COMFORT COMPONENTS AND USER
CLASSIFICATION BASED ON THE COMFORT EVOKING
FACTORSA. Method
1) Participants: Twenty-three adults (14 office
workers [10 males and 4 females] and 9 university students[5 males and 4 females]) participated in the study. The
average age of the office workers was 38.1 (with a range
from 24 to 56) and that of the university students was 22.7
(with a range from 21 to 24). All office workers had beenworking in a common room and participated in the study
there. They each had their own desks in the room. The
university students did the same.
2) Tasks: In this study, we asked the participants toreport (a) their sequential staying time at their desk, (b)their subjective comfort and the factors that affected it and(c) their subjective productivity.
The participants reported their sequential staying timeby choosing one of the following options: 1: shorter than 5
minutes, 2: from 5 to 10 minutes, 3: from 10 to 30 minutes
or 4: longer than 30 minutes.
They then responded about their subjective comfort.
First, they rated how comfortable their office was on a scalefrom 1: very uncomfortable to 7: very comfortable.
Afterward, they reported up to three factors that affected
their subjective comfort. They also rated how
pleasant/unpleasant and how activated/deactivated thefactors were in general; this was conducted based on
Russell’s core affect model [15]. They also rated what kindof factors affected the factors that affected subjective
comfort. These questions were conducted with the idea ofthe evaluation grid method [16]. In the original evaluation
grid method, the data are collected by interview. First, theinterviewer asks the interviewee (the participant) tocompare items and select which one is better. Then theinterviewer asks the interviewee, “Why is this one better?”and extracts an abstract value judgement. The interviewer
also asks the interviewee, “What is needed for the item tobe xxx?” and extracts objective understandings for theitems. These responses are summarized and represented asa construct system. In the present study, we simplified theseprocedures to implement it on the Web and gather multiple
responses.
The participants also rated their subjective productivity.
They were asked to respond to the question “How would
you rate your present work efficiency if your maximum
work efficiency in the most proper environment
corresponds to 100?”
3) Procedure: We conducted this study from
November 26th, 2018, to November 28th, 2018. Over thesethree days, we sent e-mails to the participants five times aday (at 10:00, 11:45, 13:30, 15:15 and 17:00) and asked fora response to the questionnaire in the Google Forms.
This procedure, which asks participants for immediate
responses several times a day in their daily lives, is called
the experience sampling method [17]. This method isadvantageous in that it can avoid response biases in recall
and it can allow for analysis with a high time resolution.
During the study, we measured the predicted mean vote(PMV) [18] in the office using HD32.3 (Delta OHM). PMV
is a scale that measures the psychological evaluation of thethermal state in an environment. The PMV is calculatedusing thermal parameters (air temperature, mean radianttemperature, relative air velocity and vapor pressure in theambient air) and human parameters (activity level andthermal resistance of clothing), and it is represented on ascale from -3 (cold) to 3 (hot) via 0 (neutral) [19]. The PMV

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2020 14th International Symposium on Medical Information Communication Technology (ISMICT)

Table 1 The correlation between absolute PMV andsubjective comfort and subjective productivity

1.2
1Subjective
comfortSubjective
productivity

All

-0.06

-0.19

Balance

-0.24

-0.05

Thermal-0.07-0.30*

Inside

0.15

0.22

0.8

0.4
0.2

Note: * p < .05

0-0.2

09:00:00
09:35:00
10:10:00
10:45:00
11:20:00
11:55:00
12:30:00
13:05:00
13:40:00
14:15:00
14:50:00
15:25:00
16:00:00
16:35:00
17:10:00
17:45:00

Average PMV

0.6

-0.4

Time

-0.6
-0.8

University day 1

University day 2

University day 3

Office day 1

Office day 2

Office day 3

Fig. 1 Average PMV in each condition.
corresponds to the predicted percentage of dissatisfied(PPD). When the PMV is 0, 5% of the people in theenvironment are dissatisfied with it. When the PMV is 0.5
or -0.5, and 3 or -3, 10% and 80% people are dissatisfied
with the environment, respectively [19].
The present study was conducted according to Kwansei
Gakuin University regulations for behavioral research withhuman participants.
B. Results
1) Basic Data: The average response rate of theparticipants was 78.6% (with a range from 33% to 100%).
We compared all responses with those from participants
who stayed at their desks for more than 30 minutes insuccession and were used to the environment. We foundthat the general tendency was almost the same. Therefore,
we analyzed the data of all the participants.
The average PMV is shown in Fig. 1. The PMV during
the study time (from 10:00 to 17:00) was from 0 to 1, whichindicates a neutral and slightly warm thermal condition.
There was neither an acute increase nor an acute decreasein the PMV. These results indicated that the office wasrelatively comfortable considering from PMV.
a) Subjective Comfort and Factors That Affect it:
The responses of the participants were summarized using
E-grid [20]. First, we sorted the participants’ responses that
represented similar meanings into common categories. For
example, the response “cold” and “slightly cold”
represented almost the same meaning; thus, they were bothput into the category “cold.” We then applied a 0.15
threshold so that the whole construct system could be easily
understood. The participants’ construct system is shown in
Fig. 2. The left side of Fig. 2 represents the higher items(abstract judgement of value), and the right side represent
the lower items (objective understanding). The items
connected with lines indicate that the right item evoked theleft item. In the present study, we focused on comfort and
set it at the top of the hierarchical structure.
Like the studies that have pointed out the importance ofthermal factors [12], more than half of the factors in Fig. 2
related to thermo (temperature itself and things related totemperature). This result indicates that in an environment
where the thermal condition is controlled, which isindicated by the PMV (Fig. 1), thermal comfort is not
perfectly realized. This is also supported by the weakcorrelation between the absolute PMV and subjective
comfort (Table 1).
Other factors relate to inside (mental state andpersonality of the participants), sound (noise and silence inthe environment), humidity (moisture in the environment)
and others. One interesting finding derived from Fig. 2 isFig. 2 Components of the subjective comfort and its relationships.

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2020 14th International Symposium on Medical Information Communication Technology (ISMICT)

5

Concentration
100

Noise
Subjective productivity

Deactivation - Activation

Thermalunbalance
between body
parts

Cool
4

Fatigue

Moderate
temp.

Silence

Hot
3
2

1

3
Cold

Dry air2
Sleepiness

4

5
Warm

Thermal balance
between body
parts

80
60
40
20
0
0

1

2

3

4

5

6

7

Subjective comfort

1
Displeasure - Pleasure

Fig.4 The correlation between subjective comfort andsubjective productivity.

Fig. 3 Circumplex model in office comfot factors.
that the participants reported the factors that related to
productivity (sleepiness and concentration). This might bea specific tendency of the present study, which was
conducted in office (and office-like) environments.
Figure 2 not only shows the factors that affectedsubjective comfort; it also reveals the relationships between
them, which sometimes go beyond their categories. For
example, “sleep” is an item in inside category, but it isaffected by the “warm” factor, an item in the thermal
category. These cross-category relationships indicate theimportance of considering various factors to reveal overall
comfort in an environment.
We chose items that the participants used more than
five times and plotted them on the displeasure – pleasureand degree of arousal axes (Fig. 3). The factors on the rightside would improve comfort, and those on the left side
would deteriorate comfort.
b) Comfort and Productivity: Figure 4 shows therelationship between the participants’ subjective comfort
and their subjective productivity. As shown in Fig. 4,

subjective comfort and subjective productivity showed astrong positive correlation (r = .54).
Table 1 shows that the PMV failed to predict subjective
comfort and subjective productivity. This might be due tothe fact that the present study was conducted in anenvironment whose thermal condition was well controlled(with PMV ranges from 0 to 1). The other reason behind
this weak correlation might be individual differences.
Although the PMV reflects part of the individual
differences by considering metabolic equivalent of task andthe clothing amount of individuals, other individual
differences such as thermal sensation and thermal
unbalance between body parts were not considered.
2) Classification of the Participants: We classified
every response of the participants (the factors that directly
affected comfort) into seven categories: thermal, humidity,
light, sound, smell, inside and others.
As shown in Table 2, although the thermal factor was
the most frequent, the factors that affected comfort differedacross participants. Additionally, the frequency of each

1

Average relative frequency

0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1

2

3

Cluster
ThermalHumidity

Light

Sound

Smell

InsideOthersFig. 5 Average relative frequency rate of factors which directly affects subjective comfort.

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2020 14th International Symposium on Medical Information Communication Technology (ISMICT)

Table 2 Variation of the relative frequency of each factor
ThermalHumidity

Light

Sound

Smell

InsideOthersMinimum

0.33

0.00

0.00

0.00

0.00

0.00

0.00

Maximum

1.00

0.25

0.06

0.35

0.12

0.44

0.11

M

0.75

0.04

0.01

0.07

0.01

0.13

0.02

SD

0.20

0.07

0.02

0.10

0.02

0.14

0.04

factor varied between participants, which indicates that theindividual differences in these factors compose comfort.
We conducted a cluster analysis based on theparticipants’ response categorization data (Table 2). We
applied three clusters with regard to the number ofparticipants in every cluster. Figure 4 shows thecharacteristics of the clusters. We named cluster 1 (n = 5)
the balanced cluster because the participants in this cluster
reported that factors in various categories affected their
comfort. Cluster 2 (n = 15) was named the thermal cluster
due to the fact that the participants in this cluster reported
that their subjective comfort depended mainly on thermal
factors. Cluster 3 (n = 3) was named the inside cluster
because the participants in this cluster reported that thefactors that affected their comfort contained many inside
factors.
We conducted a correlation analysis between subjective
comfort and subjective productivity within each cluster.
Though the balanced cluster and thermal cluster showedstrong positive correlations (rs = .69, .71), the inside cluster
showed a weaker correlation (r = .46).
The absolute PMV correlated with subjective
productivity in the thermal cluster (r = -.30) but not in thebalanced or inside clusters (rs = -.19, -.05). This indicatesthat thermo was only an important factor for the comfort ofthose in the thermal cluster. Previous studies have also
shown that actual air temperature poorly correlated withsubjective productivity [7]. The present study revealed thatthe classification of people allows us to shed light on thisweak correlation and investigate its details.
IV. CONCLUSIONS
A. Summary of the Results
In the present study, we focused on comfort in the officeenvironment and investigated its components andindividual differences. We conducted the study using theexperience sampling method and the evaluation grid
method. The results revealed that the components of
comfort in the office were mainly composed of thermal
factors, though they also included inside factors, soundfactors, humidity factors and other factors. There wereindividual differences in the relative frequency, which isrelated to subjective comfort. We conducted a cluster
analysis and found three clusters: the balanced, thermal andinside clusters. These clusters differed in theircharacteristics and in the relationships between the PMV
and subjective productivity. Only the thermal factors had asignificant correlation between the PMV and subjective
productivity.

B. Novelty of the Present Research
The present study achieved three new findings.
The first finding is that some people complain about
thermal factors in an environment where physical thermal
conditions are properly controlled. This shows that the
PMV is not a perfect indicator of thermal comfort,
especially in environments with relatively good thermal
conditions (PMV ranges from 0 to 1). This may be because
of individual differences in psychological thermal
sensations or other factors that PMV cannot consider. This
indicates the importance of measuring environmental
comfort with more subjective factors. This is also indicatedby the result that there was no significant correlation
between subjective comfort and PMV (Table 1).
The second finding is that there were individual
differences in comfort. Some people focused on only
thermal factors (thermal cluster), though others focused onthermal and inside factors (inside cluster) or various factors(balanced cluster). These individual differences indicatethat although the thermal factor is the most important for
comfort in the office, other factors also affect people’s
comfort, and the importance of these factors differs
between people. The importance of focusing on theindividual differences themselves was pointed out by aprevious study that used an individual’s characteristics [21].
The present study focused more directly on the factors that
affect comfort and paved the way for the realization of apersonalized comfort environment.
The third finding is that subjective comfort andsubjective performance were strongly related. This
indicates that comfort may be a good facilitator in realizing
a productive office (of course, an opposite hypothesis isthat a productive office brings comfort to workers).
Previous studies have listed various factors asintermediaries between environment and productivity, suchas motivation [5], activation of the idea that relates to theenvironment [9] and the improvement of informationexchange [22]. The present results showed that there is apossibility that comfort relates to productivity.
In addition, we took a novel approach that combined theexperience sampling method and the evaluation grid
method. This approach allowed us to do two things. It
allowed us to extract the factors that affected subjective
comfort precisely (i.e., without recall bias), and it allowed
us to reveal the relationships between factors that relate to
subjective comfort, which helps us reveal the mechanismsthat define how environmental factors affect people.

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2020 14th International Symposium on Medical Information Communication Technology (ISMICT)C. Future Directions
Future research needs to consider two things: making
the clusters more valid and realizing an actual application.
The number of participants was 23, and this might nothave been enough to define the characteristics of theclusters. Future research needs to replicate the present
results with more participants, which would allow us tomake more reliable inferences about the individual
differences.
Although each cluster had its own characteristics, it isstill difficult for us to improve the participants’ comfort ina systematic way. For example, try to think of a way to
improve comfort for the thermal cluster. Those in thethermal cluster have various characteristics. One
participant might tend to feel colder than the others andreport “It is cold” as the factor that affects his or her
comfort. Others might be insensitive to thermal changes,
which means they always feel comfortable, and they mightreport that “The temperature is neutral.” A larger sample
and a more detailed analysis of the participants’ responses
is needed to solve these problems.

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