Individual Differences in Ofﬁce Comfort:
What Affects Comfort Varies by Person
Masashi Sugimoto1 , Fan Zhang1, Noriko Nagata1(&) ,Kota Kurihara2, Seiro Yuge2, Makoto Takata2, Koji Ota2,
and Seiji Furukawa2
1

Kwansei Gakuin University, Sanda, Hyogo, Japan{sugimoto.masashi,nagata}@kwansei.ac.jp
2
Mitsubishi Electric Corporation, Amagasaki, Hyogo, Japan

Abstract. In the present research, we investigated the factors that affect comfort in the ofﬁce and individual differences therein. Aside from meta-analysis
research, factors that affect comfort were investigated individually (such as
thermal factors, lighting, air pollutants, and so on), and the relative importanceor relationship between them has not been investigated directly. We conducted a
two-week survey in a corporate ofﬁce and gathered 2075 responses fromoccupants. For data collection, we applied a method that combined experiencesampling method with the evaluation grid method, which allowed us to gather a
lot of data in daily situations. The results revealed that subjective comfort wasevoked by various factors such as thermal factors, light, sound, inside, and soon. Subjective comfort did not show a signiﬁcant correlation with the objective
thermal comfort index (predicted mean vote; PMV), and subjective productivity
was correlated with subjective comfort but not with objective comfort. These
results indicate the importance of subjective factors in addition to objective
factors. In addition, the 147 occupants were divided into three clusters (insidecluster, balanced cluster, and thermal cluster), each of which had different
characteristics indicating the individual differences in components of comfort. In
the present research, we succeeded in the reproduction of our previous research,which was conducted in a different season, emphasizing the validity of thepresent results.
Keywords: Ofﬁce environment  Comfort  Wellness  Productivity 
Individual differences

1 Introduction
In artiﬁcial environments, such as ofﬁces, houses, and classrooms, people interact withthe environment by various means to make it comfortable, such as by turning the air
conditioner up/down, wearing other clothes, or opening windows in the room. These
activities are induced by the environment and in turn change some aspects of theenvironment. For example, on a cold morning, ofﬁce workers will turn on the heater assoon as they arrive at the ofﬁce. Maybe in the afternoon, the heated up air will makethem open the window to ventilate.
© Springer Nature Switzerland AG 2020
V. G. Duffy (Ed.): HCII 2020, LNCS 12198, pp. 264–275, 2020.
https://doi.org/10.1007/978-3-030-49904-4_19

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Even when some people feel comfortable in an environment, others may feel
uncomfortable for some reason, which results in different reactions across people.
Some people might feel that a place is cold, whereas others feel it to be neutral. Somemay prefer environments with more noise than others. These individual differences
make it difﬁcult to realize a comfortable environment for all people and indicate thatdifferent components of comfort exist for each person.
Especially in the ofﬁce environment, where occupants spend a large part of the day,comfort is an important factor. The importance of ofﬁce comfort can be seen in WELL
certiﬁcate [1], which was founded to facilitate comfortable buildings. In addition,
comfort is closely related to the productivity of the occupants. Otherwise (or at least if
the companies do not recognize it), they will not spend a lot of money to satisfy theiremployees.
To realize a comfortable environment for each person, it is important (1) toinvestigate the components of the comfort and (2) to reveal the individual differences inthis. In the present study, we investigated the components of comfort in an ofﬁce andexamined individual differences by combining the experience sampling method [2]with the evaluation grid method [3]. We also discuss the relationship between comfort
and ofﬁce productivity. This study is a reproduction of our previous study conducted in
November 2018 with 23 participants [4]. The purposes of the reproduction were (1) totest the seasonal effects on comfort and make the results more valid and (2) toinvestigate the characteristics of individual differences with a larger sample size.

2 Related Studies
2.1

The Effect of Environment on Its Occupants

Many studies about the relationship between an environment and its occupants wereconducted according to models in which the physical factors of the environment (independent variable) affected the mental states of the people within it (dependent
variable). Among the various environmental factors that affect indoor comfort, the mostsigniﬁcant one is the thermal factor (e.g., [5]). Thermal factors affect not only comfort
but also motivation and productivity [6]. Another study focused on the physiologicalaspects of thermal comfort. Low- and high-frequency ratios of heart rate variability
may be used to predict thermal comfort [7]. This kind of approach is especially usefulwith wearable devices [8].
An objective index that measures thermal comfort is predicted mean vote(PMV) [9]. PMV is a scale that predicts the psychological evaluation of the thermal
state in an environment, and it is used worldwide [10]. The PMV is calculated using
thermal parameters (air temperature, mean radiant temperature, relative air velocity,and vapor pressure in the ambient air) and human parameters (activity level andthermal resistance of clothing), and it is represented on a scale from −3 (cold) to 3(hot) via 0 (neutral). The PMV corresponds to the predicted percentage of dissatisﬁed(PPD) [11]. When the PMV is 0, 5% of the people in the environment are dissatisﬁedwith it. When the PMV is 0.5 or −0.5, and 3 or −3, 10% and 80% people are
dissatisﬁed with the environment, respectively.

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As well as the thermal factors, various factors affect comfort. These factors include
visual factors, sound factors, and indoor air quality. Visual factors usually refer to light.
Lighting affects not only psychological health and productivity [12] but also otherenvironmental factors such as perceived temperature and air quality [13]. In addition to
the lighting, environmental features perceived through vision affect occupants. For
example, ceiling height and wall color change occupants’ way of thinking [14] andmood [15].
The environment’s effect on occupants varies among people. Sex is one factor thatcauses individual differences [16, 17]. The “neutral” temperature for a Japanese male is24.3 °C, whereas the neutral temperature for a Japanese female is 25.2 °C [17]. Thisdifference between males and females in thermal preference was also found in anotherstudy [18]. Race can also cause individual differences. A previous study found that theneutral temperature for a non-Japanese male was 22.1 °C. Psychological characteristics
can cause individual differences in task performance. A positive mood induced by wallcolor improved task performance more in participants whose performances were better
than average [15].
Even if an environment is consistent, the environmental effects on the people there
may change chronically. Being in the same environment for a certain period of time canchange how one perceives the environment. One study revealed that taking a rest after
bathing decreased the arousal score of participants over time [19]. Another study
revealed that although indoor air quality affected the people inside, those who stayed in
the environment longer became insensitive to it [20].

3 Components in the Ofﬁce and Individual Differencesin Them: Examination Using the Combinationof Experience Sampling Method and Evaluation Grid
Method3.1

MethodParticipants. We asked 208 ofﬁce workers to participate in the study. Among them,178 (155 males and 23 females) agreed to participate. Their average age was 40.9
(ranging from 24 to 66). All ofﬁce workers had been working in a common room
(2,704 m2) and participated in the study there. They each had their own desks in theroom.
Tasks. In this study, we asked the participants to provide their sequential staying timeat their desk, their subjective comfort and the factors that affected it, and their subjective productivity.
The participants reported their sequential staying time by choosing one of the following options: 1: shorter than 5 min, 2: from 5 to 10 min, 3: from 10 to 30 min, or 4:
longer than 30 min.
They then responded about their subjective comfort. First, they rated how comfortable their indoor environment was on a scale from 1 (very uncomfortable) to 7 (veryIndividual Differences in Ofﬁce Comfort

267

comfortable) (subjective comfort). Afterward, they reported up to three factors that
affected their subjective comfort (comfort-evoking factors). They also rated how
pleasant/unpleasant and how activated/deactivated the factors were in general; thisquestion was asked based on Russell’s core affect model [21]. In addition, they rated
what kind of factors affected their subjective comfort. These questions were asked with
the idea of the evaluation grid method [3]. In the original evaluation grid method, thedata are collected through interviews. First, the interviewer asks the interviewee (theparticipant) to compare items and select which one is better. Then, the interviewer 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 to be xxx?” andextracts objective understandings for the items. These responses are summarized andrepresented as a construct system. In the present study, we simpliﬁed these procedures
to implement it on the Web and gather multiple responses.
The participants also rated their subjective productivity. They were asked to respondto the question, “How would you rate your present work efﬁciency if your maximum
work efﬁciency in the most proper environment corresponds to 100?”
Procedure. The experiment was conducted during the ten weekdays from April 10,
2019, to April 23, 2019. Over these days, we sent e-mails to the participants ﬁve times
a day (at 10:00, 11:45, 13:30, 15:15, and 17:00) and asked for a response to thequestionnaire in Google Forms. Speciﬁcally, we asked participants to respond to thequestionnaire at least three times each time. All e-mails were received in the ofﬁce by
the participants, and participants responded to the questionnaire there. During thestudy, we measured the PMV in the ofﬁce using HD32.3 (Delta OHM). The present
study was conducted according to Kwansei Gakuin University regulations for behavioral research with human participants.
3.2

Results

Basic Data
Response Rate. The number of valid responses gathered in the present study was 2075.
The number of responses on each day and at each time is provided in Tables 1 and 2.
Many of the responses were given in the earlier part of the study. Regarding response
time, many of the responses were given just after the e-mail that asked for the response.
PMV, Comfort and Productivity. Tables 1 and 2 show the participants’ PMV, comfort,
and productivity changes in this study. PMV was relatively stable, between 0 and 0.5,
indicating a stable and comfortable temperature in the ofﬁce. Comfort and productivity
were also stable throughout the study. Although there is a V-shaped decrease andincrease from 8:00 to 10:00, the responses were quite few (less than 0.5% of all
responses).

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Table 1. Daily changes in the indices (numbers in the round bracket indicate SEs)

Day
Number of responses Average comfort Average productivity Average PMV
April 10th 400
4.25 (0.06)
71.46 (0.79)
0.40 (0.01)
April 11th 370
4.44 (0.07)
71.39 (0.87)
0.40 (0.01)
April 12th 290
4.35 (0.07)
72.75 (0.93)
0.41 (0.01)
April 15th 269
4.22 (0.07)
68.38 (0.99)
0.45 (0.01)
April 16th 221
4.50 (0.08)
70.71 (1.02)
0.29 (0.02)
April 17th 150
4.45 (0.10)
71.89 (1.28)
0.38 (0.02)
April 18th 127
4.49 (0.10)
71.78 (1.44)
0.38 (0.02)
April 19th 59
4.61 (0.18)
71.07 (2.39)
0.43 (0.03)
April 22nd 114
4.36 (0.13)
70.79 (1.66)
0.33 (0.02)
April 23rd 75
4.44 (0.13)
70.33 (1.84)
0.39 (0.02)

Table 2. Hourly changes in the indices (numbers in the round bracket indicate SEs)
Time Number of responses Average comfort Average productivity Average PMV
08:00
1
5.00 (0.00)
80.00 (0.00)
0.49 (0.00)
09:00
93.56 (0.59)
54.22 (11.52)
0.46 (0.05)
10:00 390
4.43 (0.06)
72.03 (0.84)
0.39 (0.01)
11:00 302
4.50 (0.07)
73.91 (0.87)
0.41 (0.01)
12:00 224
4.54 (0.08)
71.42 (1.05)
0.42 (0.01)
13:00 254
4.29 (0.08)
69.11 (1.16)
0.43 (0.01)
14:00 144
4.24 (0.10)
69.80 (1.29)
0.38 (0.02)
15:00 320
4.15 (0.07)
70.28 (0.84)
0.37 (0.01)
16:00 74
4.41 (0.15)
70.78 (1.82)
0.33 (0.03)
17:00 357
4.41 (0.06)
70.72 (0.81)
0.35 (0.01)

Subjective Comfort and Factors That Affect it. Figure 1 represents the evaluation
structure of the 11 participants who were selected from the beginning of participants’
ID numbers. We used only part of the data because all data are beyond the limitationsof the processing capability of the software that we used in the analysis [22]. As in theprevious study that we conducted in November 2018, factors that relate to thermal
aspects, sound, light, and indoor environment were extracted. Factors that were notextracted in the previous study include wet clothing, work, and pollen. These factors
might have been extracted as a result of weather and seasonal changes.
Figure 2 represents the characteristics of the factors that affect comfort. The factors
in Fig. 2 are the ones that were listed by the participants more than 20 times (words thatseem to represent close meaning were not categorized into one item, and similar words
appear in Fig. 2, e.g., silent, tranquility, and silent around). The most frequent factor
that affects comfort is “moderate temperature,” followed by “silent,” “brightness,”“noise,” “fatigue,” and “sleepiness.” These factors include not only physical factors(thermal, sound, and light) but also mental factors of the occupants. In addition, theIndividual Differences in Ofﬁce Comfort

269

Fig. 1. Evaluation structure on ofﬁce comfort

valence (pleasant/unpleasant) and arousal (activated/deactivated) varied across factors,
indicating the complex effects of the occupants’ comfort.
Classiﬁcation of the Participants. Then, to consider the individual differences amongoccupants, we conducted cluster analysis based on their responses.
Classiﬁcation Method. First, we classiﬁed the participants’ comfort-evoking factors
into six categories: thermal factors (52.4% of all responses, e.g., hot, moderate temperature…), sound factors (15.8%, e.g., noisy, silent…), light factors (4.0%, dazzling,dim), inside factors (19.6%, e.g., hungry, sleepy, concentration, body condition,
emotion…), work factors (1.8%, e.g., deadline, progress of work…), and other factors(6.4%, e.g., few people around, someone is coughing…). These categories were
derived from our previous data and the present data. The classiﬁcation was conductedby two dependent raters, who were unaware of the purpose of the study. The kappa
coefﬁcient was 0.85, indicating almost perfect agreement [23]. When the evaluations by
the two raters were different, a third rater evaluated the response.
We conducted clustering analysis based on the relative frequency of the comfortevoking factors with Ward method. We analyzed data from 147 participants, who had
responded to the questionnaire more than four times. We applied three factors, based on
the number of each cluster and cluster stability. The demographic features of each

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Noise
Hot

Silence

Hotness

Being concentrated

Tanquility

Brightness

HungryNone
Fatigue
Fatigue

SilentModerate temperature
Silent around
SleepySleepiness
Cool

Fig. 2. Circumplex of the factors that affect ofﬁce comfort (The size of the circle indicates thetimes of the response of the item)

cluster are represented in Table 3, and the average relative frequency is represented inFig. 3. A one-way ANOVA of occupants’ comfort, productivity, and PMV was conducted. Cluster 1 showed lower comfort and productivity than clusters 2 and 3 (Fs(2,144) = 9.53, 8.07, η2ps = .12, .10, ts(144) = 3.67, 3.90, 3.26, 3.67), whereas there
was no signiﬁcant difference in PMV between clusters.

Average ralative frequency

1

Thermal

Sound

Light

Inside

Work

Other

0.8
0.6
0.4
0.2
0

1

2

ClusterFig. 3. Average relative frequency of each factor3

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Table 3. Correlations between comfort, productivity, and PMV in each cluster
Cluster nCorrelation
Comfort and productivity Productivity and PMV PMV and comfort
0.05
0.04
Overall 2075 0.54***1
626 0.59***−0.10*−0.05
2
824 0.41***0.17**
0.05***
3
564 0.52
0.06
0.07
Note: * p < .05, ** p < .01, *** p < .001

Table 4. Demographic data of each cluster
Cluster Age n

Sex
Male Female Unknown
37.3 47 405242.6 61 49
111
42.9 39 34
3
2

1
2
3

Characteristics of the Clusters. Cluster 1 is distinguished from other clusters by thehigh relative frequency of the inside factor. Therefore, we named cluster 1 the “insidecluster.” Cluster 2 was distinguished by the fact that those in cluster 2 provided
relatively different comfort-evoking factors. Therefore, we named cluster 2 the “balanced cluster.” Cluster 3 was distinguished by the high relative frequency of thermal
factors (nearly 90%). Therefore, we named cluster 3 the “thermal cluster.”

Average productivity

Average comfort

7
6
5
4
3
2
1
1

2

3

ClusterFig. 4. Average comfort in eachcluster (bars indicate SEs)

100
90
80
70
60
50
40
30
20
10
0
1

2

3

ClusterFig. 5. Average productivity in eachcluster (bars indicate SEs)

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1

Average PMV

0.8
0.6
0.4
0.2
0
1

2

3

ClusterFig. 6. Average PMV in each cluster (bars indicate SEs)

The clusters also differed in the evaluation of
the environment. The inside cluster showed lower
subjective comfort and subjective productivity,
whereas the average PMV did not show signiﬁcant differences between clusters (Fig. 4, 5 and 6).
To investigate the relationships between
objective thermal comfort (PMV), subjective
comfort, and subjective productivity, we conducted correlation analysis between these variables. In all clusters, PMV showed no signiﬁcantcorrelation with subjective comfort and subjective
productivity, whereas subjective comfort showeda strong positive correlation with subjective productivity (Table 4).

4 Conclusions
4.1

Summary of the Results

In the present study, we succeeded in reproducing the previous study we conducted in
November 2018 [4].
First, we extracted almost the same comfort-evoking factors. In the present study,
we extracted thermal factors, sound factors, light factors, inside factors, work factors,
and other factors. In the previous study, we extracted thermal factors (71.4% of all
responses), humidity factors (4.5%), light factors (0.9%), sound factors (7.6%), smell
factors (0.6%), inside factors (14.4%), and other factors (2.0%) (in the previous study,
responses were categorized, allowing duplex classiﬁcation, and the sum of all factors
exceeded 100%). The majority of the responses were classiﬁed as common factors(thermal, light, sound, and inside), indicating strong and persistent effects of thesefactors. This also supports the validity of the method of the present study, a combination of the evaluation grid method and the experience sampling method.
Second, we extracted the same clusters of occupants (inside, balanced, and thermal)
as we did in the previous research. This emphasizes the existence of individual differences in ofﬁce comfort and the validity of our classiﬁcation. It also suggests that theclusters are independent from the seasonal effect, indicating the robustness of theclusters. All participants in the present study were ofﬁce workers, and those of theprevious one were university students (n = 9) and ofﬁce workers (n = 14). This also
indicates the relative independence of the clusters from their jobs.
In addition to that, participants in each cluster differed in their levels of subjective
comfort and subjective productivity in spite of the common thermal environment suggested by the similar level of PMV (Fig. 4, 5, 6). People in the inside cluster showed
lower subjective comfort and subjective productivity under similar physical conditions.
This might be due to the differences in the characteristics of the people in each cluster.

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273

Throughout the present research, we relied on the subjective responses of theoccupants. As a previous study revealed [24], at least in some aspects, subjective
responses are better indicators than objective ones. This was true in the present
research. The subjective comfort correlated with the subjective productivity, whereas
there was no signiﬁcant correlation between objective index (PMV) and subjective
comfort and subjective productivity. This might be due to the fact that the present
research was conducted in a daily setting, in which various kinds of factors affectcomfort. This was advantageous in the present research, the aim of which was to reveal
the components of ofﬁce comfort.
4.2

Novelty of the Present Research

The novelty of the present research is revealing the inside factors and their effect, as
well as the development of the indoor comfort investigation method, which combines
the evaluation grid method and experience sampling method.
The existence and effect of inside factors has been overlooked in previous research.
For example, a literature review divided comfort into four subtypes: thermal, visual,
acoustic, and respiratory [25], all of which focused on the physical aspects of theenvironment. In addition to these factors, inside factors were extracted. The inside
factors were the second largest type that affect comfort and have a signiﬁcant effect onofﬁce comfort, and they are important for inside and balanced clusters.
One might think that inside factors are not environment ones but to occupants’
ones. However, as we stated in the introduction, indoor comfort is realized through theinteraction between the outer environment and occupants. Therefore, inside factors
should be focused to realize a comfortable environment. The more important thing isthat occupants recognize that inside factors affect ofﬁce comfort, indicating that variation of the levels of inside factors would change the levels of comfort in theenvironment.
The other novelty of the present research is the development of the comfort
measurement method combining the evaluation grid method with the experiencesampling method. This method allowed us to conduct a detailed investigation of the
comfort in the ofﬁce, not in the experiment room. The experiment room, which is quite
different from daily life environments in multiple aspects, is where a detailed investigation of comfort was conducted. Although this let us examine the causal relationshipbetween an environment and people there, the experiment room and manipulation
themselves could have changed how people experienced comfort there, thus hindering
expanding the ﬁndings to daily situations. This method also allowed us to extract
factors that vary in category (Fig. 3) and characteristics (Fig. 2). Using the present
method, we could infer what and how environmental factors affect comfort. This was
also due to the feature of the present method that let us gather multiple data in a daily
environment.

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4.3

Future Direction

Future researches have to tackle two things: applying objective indices and providing
various occupants with the proper environment.
The present methods rely on subjective responses: how much they feel comfort andproductivity, and what kind of factors they attribute to the comfort. In a future study,
more objective indices are needed (e.g., physiological variables). These indices would
let us shed light on aspects that subjective responses cannot reveal (e.g., minimal
comfort changes under sensory threshold and high-resolution sequential changes).
Providing a proper environment for each occupant is another task to tackle. As
previous studies point out, the proper thermal conditions differ across people. In
addition to the quantitative individual differences, qualitative differences, which thepresent research revealed, need to be focused on. To realize a proper environment forevery person, it will be necessary to manipulate proper variables with personalized
environmental controls.

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