Modern workplace environment pre- and post-occupant satisfaction in modern office hot-desking systems: a comparative study of workplace environments

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Content Modern Workplace Environment
Pre- and Post-Occupant Satisfaction in Modern Office Hot-Desking Systems: A Comparative
Study of Workplace Environments
by
Zhilong Liu
A Thesis Presented to the
FACULTY OF THE
USC SCHOOL OF ARCHITECTURE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements of the Degree
MASTER OF BUILDING SCIENCE
May 2025

ii
Acknowledgements
I would like to express my deepest gratitude to my three committee members: Professor Joonho Choi, Professor Kristina Lerman, and Professor Rima Habre. I am also sincerely thankful to
Kyeongsuk Lee for providing me with essential data for my research. My heartfelt thanks extend
to the dedicated staff at Office G and Office H, whose assistance was invaluable in helping me
complete my experiments. I would also like to express my appreciation to Professor Karen Kensek
and Professor Douglas Noble from the USC School of Architecture for their care and guidance
throughout my studies.
I am deeply grateful to my parents for their unwavering financial and emotional support
throughout my academic journey. I also wish to thank my girlfriend for her constant
companionship and for offering thoughtful suggestions, both in life and in my academic work.
I would like to give my special thanks to my chair, Professor Joon-ho Choi, who has provided
me with consistent support, guidance, and valuable advice. He helped me arrange the office space
for my experiments and dedicated significant time to carefully reviewing my thesis. His
commitment and mentorship have been truly instrumental to my research progress.
Finally, I would like to thank my classmate Tsz Him Ian Chiu, for his professional guidance
and invaluable assistance during the experiments. His support and collaboration were crucial in
overcoming various challenges and completing my work.

iii
TABLE OF CONTENTS
Acknowledgements......................................................................................................................... ii
List of Tables ................................................................................................................................. vi
List of Figures............................................................................................................................... vii
Abstract........................................................................................................................................ viii
Chapter 1: Introduction................................................................................................................... 1
1.1 20th and 21st Century Commercial Office Workplace Design ........................................ 1
1.1.1 Early 1900s to Post World War II.......................................................................... 1
1.1.2 1960s to Late 1990s............................................................................................... 2
1.1.3 21st Century Collaborative Office Spaces............................................................. 3
1.2 Flexible Workplace........................................................................................................... 4
1.2.1 Remote and Hybrid Work...................................................................................... 4
1.2.2 Hoteling and Hot-desking...................................................................................... 6
1.3 Indoor Environmental Quality (IEQ)................................................................................ 8
1.4 Post-Occupancy Evaluation (POE)................................................................................... 9
1.5 Productivity in Commercial Office................................................................................. 10
1.6 Chapter Summary ........................................................................................................... 11
Chapter 2: Background and Literature Review ............................................................................ 13
2.1 Transformations of Office Workplace Environments: Pre and Post Covid-19 .............. 13
2.1.1 Changes in Workplace Design............................................................................. 15
2.1.2 Changes in Workplace IEQ ................................................................................. 16
2.1.3 Changes in Employees’ Environmental Satisfaction and Productivity............. 18
2.2 Indoor Environmental Quality, Comfort, and Productivity............................................ 19
2.2.1 Methods to Measure IEQ..................................................................................... 19
2.2.2 Methods to Evaluate Occupant Comfort and Satisfaction................................... 21
2.2.3 Relationship Between IEQ, Comfort and Productivity........................................ 24
2.2.4 IEQ Challenges in Contemporary Workplace Environment ............................... 25
2.3 Post-occupancy evaluation (POE) .................................................................................. 26
2.4 Chapter Summary ........................................................................................................... 32
Chapter 3: Methodology ............................................................................................................... 33
3.1 Pre hot-desking Data Collection ..................................................................................... 33
3.1.1 Gather Historical Data ......................................................................................... 33
3.1.2 Extract Historical Data......................................................................................... 33
3.1.3 Addressing Data Anomalies ................................................................................ 33
3.2 Sensor Deployment in Pre-Implementation Phase ......................................................... 34

iv
3.2.1 Data Acquisition Sensors..................................................................................... 34
3.2.2 Sensor Calibration................................................................................................ 39
3.3 Data Collection: Locations, Timeline, and Procedures .................................................. 40
3.3.1 Office G in Los Angeles...................................................................................... 40
3.3.2 Office H in Los Angeles...................................................................................... 41
3.4 Data Processing............................................................................................................... 41
3.4.1 Integration of New Data....................................................................................... 41
3.4.2 Structuring the Dataset for Comparative Analysis .............................................. 42
3.5 Comparative Analysis of Pre and Post Hot-desking Implementation ............................ 42
3.5.1 Statistical Testing: T-Test Application ................................................................ 42
3.5.2 Correlation Analysis between IEQ Factors and Productivity Outcomes............. 43
3.6 Chapter Summary ........................................................................................................... 44
Chapter 4: Analysis and Results of Office G in Los Angeles....................................................... 45
4.1 Office G data introduction .............................................................................................. 45
4.1.1 Research site background .................................................................................... 45
4.1.2 Description of the data group............................................................................... 46
4.2 Key Data Identification and Feature Analysis................................................................ 46
4.2.1 Statistical Overview............................................................................................. 47
4.2.2 Ranking of IEQ factors........................................................................................ 49
4.2.3 Correlation Analysis of POE ............................................................................... 50
4.2.4 Overall Bar Chart................................................................................................. 53
4.3 POE Data Analysis ......................................................................................................... 55
4.3.1 Overall comparison of Pre and Post data............................................................. 56
4.3.2 Comparison of gender differences....................................................................... 64
4.3.3 Comparison of age differences ............................................................................ 75
4.4 IEQ Data Analysis .......................................................................................................... 87
4.4.1 Comparison with Standards................................................................................. 87
4.4.2 Overall comparison of Pre and Post data............................................................. 93
4.5 Research Results........................................................................................................... 101
Chapter 5: Analysis and Results of Office H in Los Angeles..................................................... 106
5.1 Office H data introduction ............................................................................................ 106
5.1.1 Research site background .................................................................................. 106
5.1.2 Description of the data group............................................................................. 107
5.2 Key Data Identification and Feature Analysis.............................................................. 107
5.2.1 Statistical Overview........................................................................................... 107
5.2.2 Ranking of IEQ factors...................................................................................... 111
5.2.3 Correlation Analysis of POE ............................................................................. 112
5.2.4 Overall Bar Chart............................................................................................... 115

v
5.3 POE Data Analysis ....................................................................................................... 118
5.3.1 Overall comparison of Pre and Post data........................................................... 118
5.3.2 Comparison of gender differences..................................................................... 128
5.3.3 Comparison of age differences .......................................................................... 141
5.4 IEQ Data Analysis ........................................................................................................ 154
5.4.1 Comparison with Standards............................................................................... 155
5.4.2 Overall comparison of Pre and Post data........................................................... 160
5.5 Research Results........................................................................................................... 169
Chapter 6: Discussion ................................................................................................................. 174
6.1 Correlation Analysis of POE ........................................................................................ 174
6.2 Comparative Bar Charts of Multiple Companies.......................................................... 179
Chapter 7: Conclusion and Future Work .................................................................................... 184
7.1 Summary of Results...................................................................................................... 184
7.2 Implications and Conclusions....................................................................................... 188
7.3 Future Work.................................................................................................................. 190
BIBLIOGRAPHY....................................................................................................................... 193

vi
List of Tables
Table 2-1 Summary Table of Indoor Environment Quality Standards for Offices ...................... 23
Table 2-2 Post-Occupancy Evaluation (POE) Survey Questionnaire........................................... 31
Table 3-1 IEQ Measurement Sensors from Pre Study.................................................................. 36
Table 3-2 IEQ Measurement Sensors Used in post Study............................................................ 37
Table 3-3 Sensor List.................................................................................................................... 38
Table 3-4 Sensor Pictures ............................................................................................................. 39
Table 4-1 Descriptive Statistics of POE Survey Responses in Office G...................................... 48
Table 4-2 Ranking of IEQ Factors in Office G............................................................................. 49
Table 4-3 Pearson Correlation Matrix of POE Factors in Office G ............................................. 51
Table 4-4 Pre and Post Comparison of POE Factors in Office G (2017 vs. 2024) ...................... 60
Table 4-5 Gender-Based Comparison of POE Factors in Office G.............................................. 69
Table 4-6 Age-Based (30) Comparison of POE Factors in Office G ........................................... 80
Table 4-7 Comparison of IEQ with Standards by Year in Office G............................................. 90
Table 4-8 Overall Comparison of IEQ by Year in Office G......................................................... 97
Table 5-1 Descriptive Statistics of POE Survey Responses in Office H.................................... 109
Table 5-2 Ranking of IEQ Factors in Office H........................................................................... 111
Table 5-3 Pearson Correlation Matrix of POE Factors in Office H ........................................... 113
Table 5-4 Pre and Post Comparison of POE Factors in Office G (2017 vs. 2024) .................... 123
Table 5-5 Gender-Based Comparison of POE Factors in Office H............................................ 133
Table 5-6 Age-Based (30) Comparison of POE Factors in Office H ......................................... 146
Table 5-7 Comparison of IEQ with Standards by Year in Office H........................................... 157
Table 5-8 Overall Comparison of IEQ by Year in Office H....................................................... 165

vii
List of Figures
Figure 4-1 Overall Comparison of POE Ratings in Office G (2017 vs. 2024)............................. 53
Figure 5-1 Overall Comparison of POE Ratings in Office G (2017 vs. 2024)........................... 116
Figure 6-1 POE Factors Most Strongly Correlated with Productivity (Q28) in Office G and
Office H .............................................................................................................................. 176
Figure 6-2 POE Factors Most Strongly Correlated with Overall IEQ Satisfaction (Q29) in
Office G and Office H......................................................................................................... 178
Figure 6-3 Average Q28 (Productivity) Scores Across 100+ Office Spaces.............................. 180
Figure 6-3 Average Q29 (Overall IEQ Satisfaction) Scores Across 100+ Office Spaces.......... 181

viii
Abstract
The purpose of this research is to compare the comprehensive impact of implementing the
hot-desking system on employee work experience and indoor environmental quality (IEQ) in
commercial office environments. The COVID-19 pandemic has driven a transformation in modern
work models, promoting the adoption of flexible working and desk-sharing, with hot-desking
serving as an effective method to improve space utilization and reduce operational costs. Contrary
to popular belief, some still hold a disputable view concerning this application that it does harm
the employees' indoor comfort and productivity. To address the issue, this study employed various
data collection and analysis methods, including Post-Occupancy Evaluation (POE), manual
sensors, and robotic sensors, to measure and compare indoor environmental parameters (such as
temperature, humidity, lighting, air quality, and acoustic) before and after the implementation of
the hot-desking system, as well as their effects on employee satisfaction and productivity. Besides,
laboratory simulation tests have been carried out using real-time data on key factors affecting
office environments in order to gauge their impact on employees' comfort. The findings confirm
that the advent of the hot-desking system has contributed to the slight improvement of indoor
environmental quality, which brings additional comfort and satisfaction to employees. Through
comparative analysis, this paper proposes a series of recommendations for optimizing office
environment design, aiming to enhance operational efficiency while maximizing improvements in
employee work experience and productivity.

ix
KEYWORDS
Hot-desking, Indoor Environmental Quality (IEQ), Post-Occupancy Evaluation (POE),
Employee Productivity, Flexible Working, workstation Environment, Employee Satisfaction
HYPOTHESIS
The epidemic has driven health-focused office design, such as improved ventilation and
natural lighting, which has enhanced indoor environmental quality (IEQ). Flexible workplace
choices increased employee satisfaction with IEQ, while hot desking enhanced productivity.
RESEARCH OBJECTIVES
Establish the relationship between the hot-desk system and indoor environmental quality
(IEQ) and employee satisfaction.
Study the impact of different environmental factors (temperature, air quality, lighting, noise,
privacy, etc.) on employee comfort, and analyze gender and age differences.
Evaluate the effectiveness of the hot-desk system in improving office flexibility, employee
comfort and employee productivity, and make recommendations for optimization.

1
Chapter 1: Introduction
1.1 20th and 21st Century Commercial Office Workplace Design
This section describes the design of the workplace from the beginning of the 20th century to
the 21st century. Open office layouts were in use at the beginning of the 20th century until the
gradual development of cubicle offices after World War II. At the beginning of the 21st century,
collaborative workplace design was introduced as a result of the growing need for teamwork and
flexible space for employees. These different worlds of office styles are due to changes in work
culture, job requirements, and employee satisfaction.
1.1.1 Early 1900s to Post World War II
The work culture of the early 20th century was one of efficiency. Offices were designed as
unobstructed, open spaces with labeled desks and chairs. This design was intended to minimize
the need for employees to communicate and interact with one another, allowing them to focus on
their work. However, while this design improves productivity, it also prevents employees from
protecting their privacy. Employees are viewed more as machine-like production tools in this type
of office environment. The open plan office is being phased out to meet the individual needs of
employees.
With the rise of modernism, the concept of functional layout was introduced, and office
design began to take more account of the separation of individual work areas. Modernism
advocates functionality above all else, and office design gradually shifted from the pursuit of
efficiency alone to a more rational arrangement of space to accommodate the needs of different

2
tasks. During this time, spatial separation began to be applied to offices to allow employees to
focus on their own tasks, as well as to minimize outside distractions and noise.
In addition, workplace design was more than just the arrangement of physical space; it was
also used as a tool of social control, communicating the status and roles of employees. Chris points
out that even though the appearance of the office changed, the feeling of stress in the work
environment did not disappear (Chris 1999). This suggests that although design evolution has
improved the work experience of employees to some extent, tension and stress at work still cannot
be completely eliminated by simply changing the physical space. The design concepts of this
period laid the foundation for later office layouts and work cultures, and opened the way for deeper
thinking about how to meet the needs of employees while improving efficiency.
1.1.2 1960s to Late 1990s
A notable change in office design between the 1960s and the late 1990s was the gradual
replacement of open plan offices with modular designs and semi-open layouts. During this period,
office design began to introduce more flexibility, with employees having more varied work areas
and providing more personal space. The rise of modular design allows office space to be flexibly
adjusted according to specific needs, which not only improves the efficiency of space utilization,
but also focuses more on cooperation and communication between employees. In addition, the
gradual popularization of electronic devices, such as computers and printers, has also had a
significant impact on office layout, with the importance of information flow and teamwork
becoming increasingly important.

3
During this period, the office is not only a workplace, but also began to include more public
areas and rest space. These common areas and rest areas were not just facility configurations, but
also provided a relaxing environment for employees to relieve work stress and enhance work
efficiency during the workday (Becker and Steele 1990). This focus on the physical and mental
well-being of employees makes the work environment more humane.
Nevertheless, the concept of “mobile, paperless and flexible office”, which was already
introduced in the 1970s, has not been widely adopted. The new thinking at the time favored
technology to reduce the use of paper and increase flexibility, but in reality, the majority of office
workers still work in factory-like environments. People's positions, hierarchies and workflows are
still fixed, the office environment still emphasizes a high degree of order and consistency, and the
concept of flexible office has not really penetrated into the daily office design (Meel 2011).
Although there are innovations and developments in office space design at this stage, the
traditional fixed work pattern still dominates.
1.1.3 21st Century Collaborative Office Spaces
Collaborative workspaces have become increasingly common since the 21st century, with
designs intended to enhance teamwork, flexible working arrangements, and the efficient use of
shared resources. Modern office space design has not only adapted to rapid technological
developments, but also to changes in employee needs, with a particular focus on creating healthy,
flexible working environments. This section will explore how modern office spaces can be
improved through design to adapt to technological advances and new employee needs in the way

4
they work.
Although collaborative workspaces were originally designed to improve teamwork and
productivity, a mismatch between design and actual use can lead to reduced employee productivity
and health problems. Personal preferences play a very important role in the choice of office space,
and employees' identification with the design of the space directly affects their work experience
(Appel-Meulenbroek, Groenen, and Janssen 2011). If the office space fails to meet the individual
needs of employees, it may lead to a decrease in employee satisfaction and efficiency at work.
Most of the negative feedback from employees focused on insufficient privacy, reduced
storage space and a lack of personalized office space. In particular, the implementation of hotdesking has significantly increased the inconvenience and stress of employees at work. These
inconvenience mainly stems from the lack of employee participation in the conceptualization and
implementation of new office concepts, as well as the severe time pressure, which further
exacerbates the tension and dissatisfaction at work (Gorgievski et al. 2010). Overall, although the
design concept of collaborative office space is in line with the development trend of the
contemporary work environment, in actual application, if there is a lack of in-depth understanding
of employees' needs and preferences, it may have unexpected negative effects.
1.2 Flexible Workplace
1.2.1 Remote and Hybrid Work
In the 21st century, modern workplaces are increasingly using remote and hybrid working
models, which have gained widespread use and rapidly spread, especially during the 2019 global

5
pandemic. Remote working allows employees to complete their work via the internet from
different locations, while hybrid working combines the characteristics of remote and on-site
working, where employees can choose to work from home or return to the office depending on
work requirements. These emerging working models have greatly changed the traditional office
environment and redefined the relationship between employees' work styles and the workplace.
Remote and hybrid work styles have become increasingly popular during the epidemic
because of the obvious benefit of increasing employees' autonomy at work, allowing them more
flexibility in balancing work and family life. However, these working arrangements also have
weaknesses that cannot be ignored. A study has shown that although telecommuting and hybrid
work provide employees with more flexibility, they do not significantly increase employees' job
satisfaction and adaptability to the work environment (Blok et al., 2012). In particular,
telecommuting allows for less communication between employees. As a result, employees are
unable to share knowledge and information in a timely manner. Moreover, in the overall aspect,
this work pattern does not only negatively affect team productivity. On an individual level, the
lack of communication makes employees feel lonely at work. As a result, even though teleworking
is possible, face-to-face communication is still an important way of working in a group.
While telecommuting has a positive effect on employee autonomy and work-life balance, it
also creates communication barriers (Fana et al. 2020). Employees lack face-to-face
communication, which leads to reduced interaction among team members and inefficient
information transmission. In particular, in the absence of direct communication and feedback

6
mechanisms, communication difficulties at work gradually emerge, which reduces employee
productivity.
Gajendran and Harrison's research further shows that telecommuting has a significant positive
impact on employee autonomy, work-family conflict, job satisfaction, and job performance
(Gajendran and Harrison 2007). However, if the frequency of telecommuting is too high, it may
weaken social connections with colleagues and affect teamwork. Frequent telecommuting makes
it difficult for employees to maintain daily face-to-face interactions, leading to alienated colleague
relationships and, in turn, affecting the efficiency of teamwork and overall cohesion.
It is worth mentioning that the telecommuting model also makes the trade-off between work
and family life more prominent for employees, as its flexibility also has a positive impact on family
life. Employees have more freedom in their work schedules and are able to take better care of
family matters, which has improved family relationships to some extent (Golden, Veiga, and
Simsek 2006). Telecommuting provides employees with a more flexible work arrangement,
allowing them to flexibly adjust their time and energy allocation when facing the dual pressures
of work and family. However, this flexibility can also bring stress, especially when the boundaries
between work and life are blurred. Employees may face constant work pressure and a sense of
responsibility, which in turn affects their mental health.
1.2.2 Hoteling and Hot-desking
With the popularity of flexible working models, “hoteling” and “hot-desking” are two popular
office space models. The difference between the two is that hoteling is a model in which employees

7
need to book workspaces in advance, while hot-desking is a model in which employees randomly
choose from available workspaces each day. The advantage of these two models is that they can
significantly improve the efficiency of office space use, especially when employees are not fully
present or the company implements hybrid working, they can effectively reduce vacant space and
improve resource utilization. However, this flexibility has also caused problems in practical
applications, such as a lack of fixed workspaces, which has led to a decline in the satisfaction of
some employees with the work environment.
The challenge with flexible working is that it has an impact on employee satisfaction with the
work environment. Employees in single offices are the most satisfied, as this type of space offers
a high degree of privacy and personalization, which is difficult to achieve in open offices
(Danielsson and Bodin 2009). In addition, employees in enclosed private offices are the most
satisfied, as noise and privacy issues are the main causes of dissatisfaction in open offices (Kim
and de Dear 2013).
In contrast, hoteling and hot-desking, while facilitating communication and teamwork,
inevitably lead to a lack of “belonging” to the workspace. Mobile working combines the
advantages of the single and open-plan office, providing both easy communication and a work
environment that promotes concentration (Wohlers and Hertel 2017). However, as employees do
not have a fixed personal workspace, this limited sense of space ownership has a negative impact
on overall individual and team satisfaction. The lack of a clearly defined personal area for
employees in this model makes it difficult to create a personalized working environment, which

8
not only affects their comfort, but also may weaken team cohesion. Therefore, although hotel-style
offices and hot-desking models have significant advantages in terms of improving the efficiency
of office space utilization, they have shortcomings in terms of employee privacy, noise
management, and a sense of space ownership.
1.3 Indoor Environmental Quality (IEQ)
First, air quality, as an important component of IEQ, is directly related to employee
respiratory health. Indoor air quality (such as humidity, mold and pollutants) can directly affect
the incidence of respiratory diseases such as asthma (Deng YT et al. 2022). Air pollution in the
workplace not only causes respiratory problems, but also triggers headaches, fatigue and other
symptoms, which further affect employees' work performance.
Second, thermal comfort is another key factor affecting employee productivity. Thermal
comfort is a complex and multi-faceted factor, with culture, behavior and personal preferences all
affecting how employees perceive temperature (Rupp, Vásquez, and Lamberts 2015). When the
temperature in the work environment is too high or too low, it will adversely affect employee
productivity. Not only will excessively cold or hot temperatures make employees feel
uncomfortable, they may also cause stress and fatigue.
In addition to air quality and temperature, the acoustic environment and lighting are also
important components of IEQ. Noise in the office space not only interferes with employee
concentration, but can also lead to chronic stress and fatigue. By optimizing the acoustic design,
such as using sound-absorbing materials and setting up noise barriers, noise interference can be

9
effectively reduced and employee concentration improved. In terms of lighting, a good design of
natural lighting and artificial lighting can significantly improve employee visual comfort and mood.
Insufficient lighting or glare can affect employee performance, so the lighting level and type of
light source for different tasks need to be considered in the design.
1.4 Post-Occupancy Evaluation (POE)
Post-occupancy evaluation (POE) is an important method for measuring the impact of the
office environment on employees. The core of POE is to collect feedback from space users and
on-site measurement of environmental data to evaluate employee satisfaction with the working
environment and identify areas for improvement. The data and findings collected by POE can help
identify the relationship between indoor environmental quality (IEQ) and employee productivity.
Post-occupancy evaluation has played a vital role in improving student success and
productivity (Riley, Kokkarinen, and Pitt 2010). Similarly, the application of POE in office space
design can effectively reveal the impact of the working environment on employee productivity and
health. POE surveys usually include the assessment of IEQ factors such as air quality, thermal
comfort, lighting, acoustics, and space, as well as employee feedback on workspace comfort,
privacy, and satisfaction.
In the implementation of POE, assessment tools often rely on technical support, such as realtime monitoring and interaction with users. These technical means not only improve the accuracy
of the assessment, but also have a substantial impact on the market in practical applications (Ade
and Rehm 2020). Through these technical means, POE can not only help designers identify

10
problems in existing designs, but also provide actionable improvement plans for future design
optimization.
The importance of POE is that it builds a bridge between design and use, ensuring that the
design not only remains theoretical but also achieves the desired effect in practical application.
The data collected through POE allows designers and managers to adjust and optimize the spatial
design based on actual feedback, thereby improving employee productivity, comfort, and the
overall work experience.
1.5 Productivity in Commercial Office
Employee productivity is a critical consideration in commercial office design and is typically
assessed through employee surveys. Employees' subjective perceptions of their work environment
and their sense of control over it are important considerations that influence productivity.
Employees' sense of control over their physical environment can significantly enhance their focus
and productivity. Employees who are able to adjust environmental factors such as temperature,
light and noise to their needs typically report higher levels of satisfaction and productivity (Lee
and Brand 2010). This sense of control gives employees greater autonomy and enables them to
better cope with the pressures and challenges of their work, which in turn improves overall
performance.
Furthermore, employee comfort and productivity are strongly influenced by indoor
environmental factors and user behavior. In open-plan offices, the acoustic environment and
accessibility of environmental controls are key factors affecting employee comfort (Mulville,

11
Callaghan, & Isaac, 2016). However, the ability of employees to communicate freely in an openplan office to the extent that it promotes teamwork is accompanied by high levels of noise and a
significant lack of privacy. Employee productivity can be reduced by excessive noise, high and
low temperatures, erratic lighting, and a lack of control over the work environment. Therefore, the
most important issue to be addressed in open plan office design is effective noise control and
control of the personal environment. Employee productivity is affected not only by environmental
control, but also by employee behavior and psychological factors. For example, social interactions
at work, work difficulties and work stress all affect employee performance. Therefore, the
improvement of productivity in commercial offices requires not only the provision of a stable
indoor physical environment, but also the consideration of the balance between the psychological
and behavioral needs of employees.
1.6 Chapter Summary
In the 20th and 21st centuries, commercial office design underwent a major transformation
from open plan layouts to more flexible and collaborative designs. Early 20th century office
designs used open plan layouts to focus on employee productivity, but were unable to satisfy the
need for privacy and personalization of the workplace. An emphasis on employee satisfaction and
wellness led to the introduction of partitions and modular workplace design.
In the 21st century, as employees prioritize flexible work arrangements and teamwork,
flexible workspaces are becoming the dominant office environment. While this theory of design
has improved efficiency, its negative drawbacks have led to employee dissatisfaction and a

12
decrease in productivity. This is because flexible office models, such as rotating desks and sharing
desks, improve space utilization, while privacy and noise issues create negative impacts that have
never improved.
In modern office design, indoor environmental quality (IEQ) such as air quality, temperature,
humidity, acoustics and lighting directly affect employee comfort, health and work efficiency.
Post-occupancy evaluation (POE) provides actual feedback from employees to improve the design
of the office environment.

13
Chapter 2: Background and Literature Review
This chapter reviews the historical evolution of the office work environment and explores the
relationship between indoor environmental quality (IEQ), employee comfort and productivity. It
also discusses how IEQ can be measured scientifically and its impact on employee satisfaction, by
analyzing the changes in office design before and after the COVID-19 pandemic and their impact
on employee work experience. In addition, the IEQ challenges in the contemporary work
environment will be highlighted, focusing on their potential impact on employee well-being and
work efficiency.
2.1 Transformations of Office Workplace Environments: Pre and Post
Covid-19
The outbreak of the coronavirus pandemic has dramatically changed the traditional office
model and promoted the rapid development of remote work and hybrid work models. Before the
pandemic, the office space was usually a place that encouraged face-to-face collaboration and
social interaction. However, the lockdown policies during the pandemic forced companies to
switch their employees to remote work. This sudden change has prompted companies to reexamine the design of the workplace, especially how to maintain the productivity and health of
employees in the new environment.
The pandemic has changed traditional ways of working, with remote working gradually
replacing face-to-face office environments, and shifting the focus of corporate culture from the
past focus on exploration and innovation to a greater emphasis on safety and resilience. Employees'

14
psychological sense of security and a sense of control over the physical environment (such as
personalized air conditioning and lighting systems) have become key factors in improving
employee well-being and productivity (Spicer 2020). By giving employees greater control,
companies can not only reduce their stress, but also improve their productivity in remote or hybrid
working modes.
In addition to the growth of remote working, the pandemic has also heightened concerns about
the health risks of open offices. High densities in open office spaces increase the risk of disease
transmission, especially when air circulation is poor (Kniffin et al. 2021). This health risk has
prompted companies to reconsider the layout and design of offices, paying more attention to
reducing risks by improving ventilation and optimizing workspace division.
As employees' demand for high-quality office space rises, rents for Grade A office buildings
continue to rise, and companies are increasingly concerned about providing employees with a
high-quality office environment. In order to enhance the health and well-being of employees, more
and more companies are creating office spaces with green building certifications and healthy
facilities (Pawaon 2023). Green building certifications not only improve the working environment
for employees, but also achieve the concept of sustainable development. This new type of office
space design meets employees' higher expectations for health and safety.
After the pandemic, companies consider that the office working environment is no longer just
about improving productivity, but also about the health, safety and satisfaction of employees as
key factors in design and management.

15
2.1.1 Changes in Workplace Design
With the outbreak of the pandemic, more and more companies have begun to adopt remote
and hybrid working models, and this change has placed new demands on the layout and functions
of office spaces. First, the design of office spaces has begun to focus on reducing personnel density.
Before the pandemic, many offices used a high-density workstation layout, with employees
working in close proximity to one another to maximize the use of space. However, during the
pandemic, companies have reduced the occupancy density of office spaces to reduce the risk of
disease transmission. Many companies have ensured social distancing by reducing the number of
workstations, increasing the distance between workstations, and adding barriers. These design
adjustments are not only intended to reduce the risk of virus transmission, but also to make
employees feel safer and more comfortable in the office environment.
Second, an increase in shared spaces is also an important trend in design changes. With the
popularity of remote and hybrid working, employees do not need to come to the office every day,
so the demand for fixed workstations has decreased, and companies have begun to shift to more
shared space designs, such as meeting rooms, collaboration areas, and rest areas. The increase in
shared spaces can flexibly respond to the work needs of employees while improving the utilization
rate of office space. These shared spaces can also provide a better collaborative environment for
teams and enhance interaction and communication between employees.
Telecommuting and hybrid working offer employees more flexibility, but they also bring new
challenges, especially in terms of employee mental health and job satisfaction. In a home office

16
environment, employees face communication fatigue and reduced social interaction, which has a
negative impact on their mental health and job satisfaction (Lal, Dwivedi, and Haag 2023). The
lack of face-to-face communication makes employees feel isolated, and collaboration and
teamwork at work are also affected. Therefore, in addition to improving the physical environment,
how to improve employees' mental health and social interaction through design has become the
key in office space design.
2.1.2 Changes in Workplace IEQ
As office work patterns change, so do the requirements for indoor environmental quality
(IEQ). Working from home and switching between the office presents new challenges for
improving employee comfort and health. Optimizing environmental factors such as thermal
comfort, air circulation, noise, and lighting to ensure the physical and mental health of employees
has become particularly important.
Telecommuting has become the norm during the pandemic, but it also places a significant
strain on employees. Employee comfort and health are closely related to their productivity, and
that employee well-being can be effectively improved by clearly defining work-life boundaries
and occupational isolation (Jaiswal and Prabhakaran 2024). Mandatory teleworking, which
requires employees to work long hours at home and makes it difficult to separate work from life,
further exacerbates work-related stress, affecting employees' health and work performance.
Therefore, whether it is teleworking or a hybrid work model, optimizing the environmental quality
of homes and offices is crucial to improving employees' work experience.

17
During the pandemic, improving air quality, ventilation and thermal comfort has played an
important role in reducing the risk of virus transmission. Air quality, thermal comfort, lighting and
acoustics directly affect employee health (Awada et al. 2021). For example, air pollution can lead
to respiratory diseases, cancer and mood swings. By increasing air circulation, filtering harmful
substances and regulating indoor temperature, companies can greatly reduce the risk of employees
contracting viruses and provide them with a healthier working environment.
In hybrid working, the comfort of employees working in different environments is still
determined by key factors such as thermal comfort, visual comfort and indoor air quality. Thermal
comfort, visual comfort and indoor air quality are the main factors affecting employee satisfaction,
and improvements in these factors can significantly improve employee productivity (Tharim,
Samad, and Ismail 2017). However, the study did not find a significant relationship between
acoustic comfort and employee satisfaction. While noise is often a key issue in open-plan offices,
in a remote working environment employees are more concerned with temperature, light and air
quality comfort.
In the face of these changes, companies must take measures to ensure good indoor
environmental quality in both office and remote working environments. For example, through
intelligent control systems, employees can remotely adjust the temperature and air quality at home
or in the office to ensure a consistent and comfortable experience in different workplaces.
Companies can also provide advice or support to employees working from home to help them
improve their home working environment, such as using better air purifiers or adaptive lighting

18
systems.
The COVID-19 epidemic has driven a critical focus on the indoor environmental quality (IEQ)
aspect of the industry. The epidemic has led to a significant increase in the importance building
professionals place on ventilation and air quality (Awada et al. 2022).
2.1.3 Changes in Employees’ Environmental Satisfaction and Productivity
As office design moves away from traditional fixed workstations towards hotdesking,
employee productivity is significantly affected. Hot-desking increases spatial flexibility and
sharing by reducing the number of fixed workstations, but it has a two-fold impact on employee
productivity. On the one hand, hotdesking helps to reduce the monotony of work, encourages
employees to work flexibly in different environments, and improves opportunities for teamwork.
On the other hand, due to the lack of fixed workstations, employees may feel a lack of belonging
and privacy, which in turn affects their work efficiency and satisfaction.
Research has shown that desk rotation has a positive impact on the productivity of some
employees, especially in terms of reducing conflicts between colleagues and improving work
relationships (Moens et al. 2022). However, long-term desk instability may increase employee
stress, especially when they need to find a new work location every day. This uncertainty can
reduce their job satisfaction and concentration. In addition, the lack of technical support in hotdesking models can also have a negative impact on employee productivity. Quick technical support
and the stability of equipment in the office directly affect employee productivity (Bolisani et al.
2020). If employees are unable to quickly adapt to the equipment or face technical problems at

19
their new workstation, their work rhythm may be disrupted, resulting in reduced work efficiency.
Hot-desking systems also bring about both positive and negative stress, which affect
employee productivity. Positive stress, such as the sense of challenge of working in a new
environment, can promote employee performance. However, negative stress, such as the
discomfort caused by the lack of a fixed workstation and personal space, can lead to an increase
in fatigue and a decrease in work efficiency (Van Slyke et al. 2022).
2.2 Indoor Environmental Quality, Comfort, and Productivity
This section explores the relationship between indoor environmental quality (IEQ) and
employee comfort and productivity. As office space design continues to evolve, with the
introduction of new models such as hot-desking, the various factors of the indoor environment
have a profound impact on the physical and mental health and work performance of employees. A
good IEQ can improve employee comfort and productivity. This section will provide a
comprehensive overview of the challenges facing IEQ in modern office environments by analyzing
the methods used to measure IEQ, the tools used to assess employee comfort and satisfaction, and
how IEQ interacts with employee productivity.
2.2.1 Methods to Measure IEQ
Measuring indoor environmental quality (IEQ) is a crucial step in ensuring a healthy office
environment. This section will introduce the various methods and equipment used to measure IEQ,
and how these tools can be used to accurately monitor environmental parameters such as
temperature, humidity, carbon dioxide concentration, light intensity and noise levels. To ensure

20
that office environments meet health and productivity requirements, standards such as ASHRAE,
LEED and WELL provide clear guidance and references.
Common IEQ measurement methods include the use of sensors to monitor key environmental
parameters in real time. For example, thermohygrometers measure the temperature and humidity
levels of the air; air quality sensors monitor the concentration of pollutants such as carbon dioxide,
PM2.5 and formaldehyde in the air; illuminometers measure the intensity of light, while acoustic
measuring equipment is used to monitor noise levels. By providing accurate data, these devices
provide a scientific basis for the assessment and improvement of indoor environmental quality.
Studies have shown that filtering PM2.5 and formaldehyde from the air can significantly
improve indoor air quality and, in turn, the health of employees (Bian et al. 2018). Improving the
indoor air quality index (IAQI) and thermal comfort index can also effectively improve employee
comfort and air quality (Saad et al. 2017). By increasing ventilation rates, lowering carbon dioxide
concentrations, and controlling temperature and humidity, not only can employee focus and fatigue
be reduced, but the risk of respiratory illness can also be lowered (Chatzidiakou, Mumovic, and
Summerfield 2012). These measures improve overall employee performance and health by
controlling indoor pollutant concentrations through mechanical ventilation systems.
In short, accurately measuring IEQ is essential to creating a healthy and comfortable office
environment. With the help of environmental sensors and measuring equipment, businesses can
monitor and optimize the air quality, temperature and other environmental parameters of their
office spaces in real time to ensure they meet industry standards and maximize employee health

21
and productivity.
2.2.2 Methods to Evaluate Occupant Comfort and Satisfaction
This section describes common methods for assessing employee comfort and satisfaction,
including the ISO 7730 standard and the COPE (Comfort, Occupant Preference and Experience)
survey. The ISO 7730 standard provides a standard for quantifying thermal comfort through the
PMV (Predicted Mean Vote) and PPD (Predicted Percentage of Dissatisfied) models, which are
used to assess employee comfort in thermal environments. These models predict the response of
employees to environmental conditions by calculating environmental parameters such as
temperature, humidity and air velocity, thereby quantifying the impact of the indoor environment
on comfort.
CATEGORIES STANDARD GUIDELINES SOURCES
THERMAL QUALITY
Temperature
Cooling Season
(0.5 clo)
24.4-27.8°C
(RH: 30%)
ASHRAE 55 (2004)
Associação Brasileira de
Normas Técnicas (ABNT)
23.3-25.6°C
(RH: 60%)
Overall (U.S.):
23.3-27.8°C
(Brazil) 23.0-
26.0°C
Heating Season 20.6-25.6°C

22
(1.0 clo) (RH: 30%)
20.0-23.9°C
(RH: 60%)
Overall (U.S.):
20.0-25.6°C
(Brazil) 22.0-
24.0°C
Floor surface
temperature
19.0 - 29.0°C ASHRAE 55 (2004)
Radiant Temperature
Asymmetry
Warm Ceiling < 5°C ASHRAE 55 (2004)
Cool Wall < 8°C ASHRAE 55 (2004)
Vertical Air
Temperature
Difference
< 2.8°C ASHRAE 55 (2004)
Relative Humidity 30 - 60% ASHRAE 62 (1999)
Air Speed ≤ 0.2 m/s ASHRAE 55 (2004)
INDOOR AIR QUALITY
Carbon Dioxide < 800 ppm EPA (IAQ spec.)
Carbon Monoxide < 9 ppm EPA (IAQ spec.)
Total Volatile Organic
Compounds
< 200 µg/m³ above outdoor TVOC
concentration
EPA
Particulates
PM 2.5 ≤ 1,665,278 #/CF or 20
µg/m³
Aircuity

23
PM 10 ≤ 17,204 #/CF or 40 µg/m³
Total Particulates ≤ 20 µg/m³ EPA (agency IAQ spec.)
LIGHTING QUALITY
IlluminanceMerchandise lighting
300 to 500 lux (Horizontal)
IESNA-RP-1-04 (2004)
50 lux (vertical work surface, e.g.,
CRT monitor)
Unified Glare Rating ≤ 19 CIE
Luminance Ratio 3:1 or 1:3 IESNA-RP-1-04 (2004)
ACOUSTIC COMPONENT
Room Criteria
≤ 40 (Open-plan offices)
ASHRAE Standard (2003)
≤ 35 (Private offices)
Quality Assessment
Index
≤ 5 dB ASHRAE Standard (2003)
Table 2-1 Summary Table of Indoor Environment Quality Standards for Offices
In addition, COPE surveys collect employees' subjective perceptions of environmental
conditions such as air quality, temperature, lighting and noise through questionnaires and
interviews. This approach focuses on employees' subjective experience and helps identify the
specific impact of environmental factors on job satisfaction and comfort. Through these
assessment methods, companies can quantify the impact of indoor environmental quality (IEQ) on
employees and make improvements based on feedback. Improving thermal comfort, optimizing
indoor air quality and lighting layout, and improving acoustic conditions can effectively improve
employee satisfaction and enhance productivity (Y. K. Kim et al. 2022). In open-plan offices,

24
acoustic factors have the greatest impact on the job satisfaction of architects and structural
engineers (Kang, Mak, and Ou 2023). In particular, speech interference and the negative impact
of noise sources on productivity, problem-solving ability and concentration are particularly
significant.
Through these assessment methods, companies can not only quantify the impact of
environmental quality on employee comfort and satisfaction, but also provide a scientific basis for
optimizing office space design, ultimately improving employee work experience and productivity.
2.2.3 Relationship Between IEQ, Comfort and Productivity
Good indoor environmental quality can significantly improve employee productivity and
creativity, while poor environmental conditions can lead to distraction and increased fatigue,
thereby reducing productivity. For example, problems such as excessively high or low
temperatures, poor air quality, insufficient light or excessive glare can significantly affect
employee comfort and ultimately their performance.Specifically, thermal comfort and productivity
increased when employees had control over their local thermal environment (Mao et al. 2017).
This suggests that employees can significantly improve their comfort and, therefore, productivity,
by adjusting the temperature and air circulation in their work environment to their own needs.
The study further demonstrated that productivity decreased by 3.7% for every 1°C above
25°C (Kekäläinen et al. 2010). By improving the indoor temperature and air distribution system,
not only did productivity increase by 4.4%, employee satisfaction with air quality and indoor
temperature also increased significantly, while reducing neurobehavioral problems and irritating

25
symptoms. In addition, environmental factors such as air quality, temperature and light have a
direct impact on employee health and work performance. Therefore, improving thermal comfort
and optimizing air quality and lighting layout can significantly improve employee satisfaction and
productivity.
2.2.4 IEQ Challenges in Contemporary Workplace Environment
In modern office environments, flexible working practices and open-plan office layouts
present new challenges for indoor environmental quality (IEQ). Offices that are too cold or too hot
are often the focus of employee complaints, and improper temperature control can directly affect
employee work comfort. Lighting issues are also a common challenge, with excessive glare or
insufficient light adversely affecting employee visual comfort. In addition, the openness and
versatility of modern offices can easily lead to excessive noise levels, which further affect
employee concentration and work performance. This section explores these issues and discusses
how IEQ challenges can be addressed in flexible workspaces to maintain high levels of employee
wellbeing and productivity.
IEQ principles have become particularly important in modern office space design, especially
in terms of noise, air quality, lighting and temperature control, which directly affect employee
satisfaction and productivity(Oke et al. 2024). Addressing IEQ challenges in existing work
environments through education and training, policies and regulations, incentives and recognition,
and collaboration and networking not only helps improve employee health, but also makes the
workplace more in line with the concept of sustainable design (Al Horr et al. 2016). This holistic

26
approach not only improves employee wellbeing, but also promotes a greener office environment.
Employee responses to the overall physical state of the work environment are complex and
depend on individual metabolic levels, clothing habits, activity patterns, and experiences in
different areas of the office (Aguilar et al. 2022). At the same time, there are complex interactions
between indoor environmental factors, such as the relationship between natural light, thermal
comfort, and office layout and acoustic properties, which together affect employee comfort and
work performance.
In addition, issues such as inconsistent thermal environments and increased noise due to
natural ventilation further affect the comfort of the working environment and the productivity of
employees. Addressing these challenges requires the introduction of more integrated strategies in
design and management to ensure good indoor environmental quality in open and flexible office
environments.
2.3 Post-occupancy evaluation (POE)
Post-occupancy evaluation (POE) is an important diagnostic tool that provides facility
managers or architects with a means of systematically assessing key aspects of building
performance. POE is commonly used to identify problems in existing buildings, test new building
prototypes, and develop guidelines for future designs. POE studies involve collecting user
feedback to analyze the environmental conditions of a building and its operational effectiveness.
POE studies began in the mid-1960s, but initially were only conducted when a building had
significant problems. Early POE models were primarily used in the residential sector, but have

27
gradually expanded to include office spaces and other commercial properties. As they have
evolved, POE studies have not only covered different types of buildings, but have also explored
the relationship between human behavior and building design. Building performance consists of
multiple dimensions, including technical, functional and behavioral aspects, each of which
corresponds to a different environmental strategy. The technical dimension involves the safety,
health, security and performance of building systems; the functional dimension focuses on
workflows, productivity and operational efficiency, assessing the extent to which the building
matches the activities of its users; and the behavioral dimension focuses on the perceptions and
psychological needs of users. By collecting information covering these different dimensions, a
comprehensive understanding of the existing performance of the building can be obtained, and the
Indoor Environmental Quality (IEQ) can be optimized.
Choi's latest research improves the POE method by combining IEQ measurements of
buildings with user surveys. Unlike the qualitative results obtained by simply distributing
questionnaires, this method improves the reliability and accuracy of the data by cross-checking the
results. The study combines data on human factors and environmental conditions, and discovers a
hierarchical structure of results, which allows operational strategies to be better adapted to meet
the needs of users. The data were collected from a number of office and educational buildings in
Southern California, including nine buildings on the campus of the University of Southern
California (USC). The climate in the study area is characterized by warm, dry weather. Three types
of data were collected in this study: the first type of data observed the spatial factors of building

28
attributes; the second type of data measured IEQ factors such as illumination, temperature, noise
level, and glare; and the third type of data recorded users' evaluations of spatial satisfaction through
questionnaires. The data of 411 users were classified into three age groups and two genders. The
results showed that lighting requirements are affected by age, with older people preferring lower
illumination levels. Another interesting finding was that men preferred lower air velocity than
women. In addition, the older group preferred lower illumination and lower air velocity. Based on
the collected data, a decision tree was created to determine the order of importance of IEQ
conditions and provide recommendations on how to optimize the office environment.
The POE form below is the survey form for the LEE study. My research will be based on this
form to modify and optimize it and develop a new version that is suitable for the objectives of this
study. This improved form will more accurately collect user feedback and conduct in-depth
analysis of the environmental conditions and performance of the building in order to further
improve indoor environmental quality (IEQ) and meet user needs.
Quest
ion
Num
ber
Category Question Response
Q1
Occupant
Information
/ Human
Factor
How many years have you been working in
this building?
Less than 1 1-2
3-5 5-10 10+
Q2
In a week, the number of hours you spend at
your workstation?
10 or less 11-
30 30+

29
Q3 What is your age?
18-29 30-39 40-
49 50-59 60+
Q4 What is your gender?
Male
Female
Q5 What is your job category?
Professonal
Technical
Managerial
Very Unsatisfied (-3), Unsatisfied (-2), Slightly Unsatisfied (-1),
Neutral (0), Slightly Satisfied (+1), Satisfied (+2), Very Satisfied (+3)
-
3
-
2
-
1
0
+
1
+
2
+
3
Q6
Workspace
Condition
How satisfied are you with your
job/institution?
Q7
How satisfied are you with the size of your
personal workstation to accommodate your
work, materials, and visitors?
Q8
How satisfied are you with the level of privacy
in your workspace?
Q9
How satisfied are you with your ability to alter
physical conditions in your work area? (e.g.
operable windows, blinds)
Q10
How satisfied are you with your access to a
view of the outside from where you sit?
Q11
How satisfied are you with the distance
between you and other people you work with?

30
Q12
How satisfied are you with the degree of
enclosure of your work area by walls, screens,
or furniture?
Q13
Thermal
Quality
How satisfied are you with the current
temperature at your workspace?
Q14
How satisfied are you with the thermostats?
(e.g. operability)
Q15 Air Quality
How satisfied are you with the current air
quality in your workspace? (i.e. stuffy/stale air,
cleanliness, odors)
Q16
Acoustic
Quality
How satisfied are you with the amount of noise
from other people's conversations while you
are at your workstation?
Q17
How satisfied are you with the amount of
background noise you hear at your
workstation? (i.e. not speech, noise from
mechanical systems)
Q18
Visual
Quality
How satisfied are you with the light for doing
computer work?
Q19
How satisfied are you with the amount of
reflected light or glare on the computer screen?

31
Q20
How satisfied are you with the amount of
direct glare from light fixtures? (ex: high
luminance visible from a viewer's position)
Q21
How satisfied are you with the amount of
direct glare from daylight?
Q22
How satisfied are you with the quality of
lighting in your work area? (combined
artificial and daylighting)
Q23
Spatial
Factor
How satisfied are you with the general
building and office layout? (workstation
layout)
Q24
How satisfied are you with the colors and
textures of the flooring, furniture, and surface
finishes?
Q25 Productivity
Effect of environmental conditions in your
workstation on personal productivity?
Q26
Overall
Satisfaction
How satisfied are you with the indoor
environment of your workspace?
Q27
IEQ
Condition
Would rate the current thermal conditions as
Q28 Would rate the current air condition as
Q29 Would rate the current acoustic condition as
Q30 Would rate the current lighting condition as
Table 2-2 Post-Occupancy Evaluation (POE) Survey Questionnaire

32
2.4 Chapter Summary
This chapter explores the relationship between indoor environmental quality (IEQ), comfort
and employee productivity, particularly in the modern office environment. First, common methods
of measuring IEQ are introduced, such as using environmental sensors to measure key parameters
such as temperature, humidity, air quality and light intensity to ensure that the working
environment meets health and productivity requirements. Second, the POE survey method of
assessing employee comfort and satisfaction is analyzed, emphasizing the importance of
employees' subjective feelings about the environment in improving workspace design. Finally, this
chapter discusses the direct and indirect effects of IEQ on employee productivity and satisfaction,
and proposes strategies for addressing the challenges of temperature, lighting and noise in modern
office environments.

33
Chapter 3: Methodology
3.1 Pre hot-desking Data Collection
3.1.1 Gather Historical Data
Gather historical data available prior to the implementation of hot desking to serve as a
reference point for the study. The objective of this phase is to understand the status of Indoor
Environmental Quality (IEQ) and employee productivity in the work environment prior to the
implementation of the flexible office model. Gather historical data related to IEQ and employee
productivity from existing databases or relevant literature. These data may include temperature,
humidity, carbon dioxide concentration, air quality, lighting intensity, and noise levels. These data
can be used to understand the baseline IEQ levels in the current environment.
3.1.2 Extract Historical Data
This research presents a comparative investigation of desk systems before and after the
outbreak. Due to the large time lag between the two data collection periods, this paper utilizes a
new Post Occupancy Evaluation (POE) with partial upgrades and replacement of the sensors used
to measure indoor environmental quality. Pre-epidemic data were carefully screened and filtered
to ensure data availability and consistency.
3.1.3 Addressing Data Anomalies
Anomalous data situations are identified and dealt with during the course of the data
processing phase to ensure the accuracy of the analysis. Data that are missing key information,
such as gender and age, are removed to facilitate subsequent exploration of data grouped by gender

34
and age. In addition, the consistency of TVOC (Total Volatile Organic Compounds) data suggests
previous sensor failures, so remove data measured by damaged sensors.
3.2 Sensor Deployment in Pre-Implementation Phase
3.2.1 Data Acquisition Sensors
This section will describe the sensor devices used and their measurement parameters. A
sensor table will be provided listing the devices used to monitor temperature, humidity, carbon
dioxide concentration, light intensity, and noise levels. The technical specifications, measurement
range, and installation location of each sensor will be described in detail.
Pre
IEQ Factor Sensor Tolerance Range
Temperature Floor XL2 Audio and
Acoustic
Analyzer
±5% FS for air
speed, ±1.8°F
(±1°C)
0.2 to 20 m/s, -20
to 70°C
0.6m
1.2m
1.6m
Ra_temperature left OMEGA
infrared
thermometer
±2% of reading
or 2°C (4°F)
-30 to 550°C (-22
to 1022°F)
right
down
up

35
LUX Extech light
meter
±5% rdg
± 0.5% F.S.
0 to 400,000 Lux
UGR Nikon Coolpix
8400 with
PhotoLux
software
Acoustic Omega sound
level meter
±1.5 dB 30 to 130 dB
RH
Particle Mass
Concentration
Detector with
Formaldehyde
CO2/PM2.5/PM
10 TVOC
Temperature/Hu
midity
±3.5%RH 0 to 100%RH
TPOC ±5% F.S 0.01~9.99 ppm
CO2 ±5%/±75 ppm 0~10000 ppm
PM2.5 ±5% 0~999ug/m³
PM10 ±5% 0~999ug/m³
CO Handheld
Carbon
Monoxide Meter
5% or 5 ppm 0 to 1000 ppm

36
Table 3-1 IEQ Measurement Sensors from Pre Study
Post
IEQ Factor Sensor Tolerance Range
Temperature
Floor
XL2 Audio and
Acoustic
Analyzer
±5% FS for air
speed, ±1.8°F
(±1°C)
0.2 to 20 m/s, -
20 to 70°C
0.6m
1.2m
1.6m
Ra_temperature
left
OMEGA
infrared
thermometer
±2% of reading
or 2°C (4°F)
-30 to 550°C (-
22 to 1022°F)
right
down
up
LUX
Extech light
meter
±5% rdg
± 0.5% F.S.
0 to 400,000 Lux
UGR
Nikon Coolpix
8400 with
PhotoLux
software
Acoustic Omega sound ±1.5 dB 30 to 130 dB

37
level meter
TPOC Extech VFM200 ±5% F.S 0.01~9.99 ppm
RH
Extech VPC260
±3.5% RH 0 to 100% RH
PM2.5
10% @
2,000,000 counts
per ft³
PM10
10% @
2,000,000 counts
per ft³
CO
Handheld
Carbon
Monoxide Meter
5% or 5 ppm 0 to 1000 ppm
CO2
HOBO Carbon
Dioxide/Temp/R
H Data Logger
±50 ppm/ ±5% 0 to 5,000 ppm
Table 3-2 IEQ Measurement Sensors Used in post Study
Sensor Name Manufacturer Model
IR Thermometer Omega OS-310-501

38
Heavy Duty Pressure
Transducer Grainger 1AEV6
Hot Wire Anemometer Sper Scientific 840003
Sound Level Meter Omega OSK
Handheld Carbon Monoxide
Meter Sper Scientific 850009
Particle Mass Concentration
& Formaldehyde Detector Sper Scientific 800050
Heat Stress Monitor Extech 46AC37
XL2 Audio and Acoustic
Analyzer NTi Audio XL2
Vibration Meter FLIR VFM200
6-Channel Particle Counter FLIR VPC260
Environmental Meter FLIR HT30
CO2, Temperature, and
Humidity Data Logger Onset MX1102A
Table 3-3 Sensor List

39
Hot Wire
Anemometer
Heat Stress Monitor CO2, Temperature, and
Humidity Data Logger
6-Channel Particle
Counter
Light Meter Handheld Carbon
Monoxide Meter
IR Thermometer Sound Level Meter
Table 3-4 Sensor Pictures
3.2.2 Sensor Calibration
It is critical to ensure the accuracy of sensor measurements for reliable data collection. This
section describes the calibration methods used to verify the performance of the sensors, in
particular the calibration procedures used to measure parameters such as temperature and humidity.
The calibration method involves placing the sensor in a temperature controlled refrigerator at room
temperature. The refrigerator is energized for six hours to create a stable, controlled environment
and then de-energized for six hours to allow the temperature to gradually return to ambient

40
temperature. This process helps to evaluate the sensor's accuracy, stability, and response to varying
environmental conditions.
3.3 Data Collection: Locations, Timeline, and Procedures
3.3.1 Office G in Los Angeles
1) Conducting POE Surveys
At the G office in Los Angeles, employees will provide feedback on their subjective
perceptions of the work environment through a POE survey. The questionnaire will cover a number
of IEQ factors, including air quality, temperature, humidity, noise and lighting intensity, while
focusing on employee perceptions of work comfort, productivity and concentration. Data
collection will be conducted in phases, covering different points in time before and after the
implementation of the desk rotation system, to analyze the differences in employee experience
before and after the policy change. The feedback data will be used to compare employee
experiences under different office conditions and to assess the impact of the policy change on the
work environment.
2) Measurement of IEQ Parameters
To obtain data on the actual environment in Office G, this section will measure IEQ
parameters including temperature, humidity, carbon dioxide concentration, light intensity, and
noise level through sensors. The sensors will be arranged in multiple areas of the office to capture
environmental changes at different workstations and functional areas. Data will be collected in real
time and daily environmental fluctuations will be recorded at preset intervals. Combined with the
results of the POE survey, this study will analyze the correlation between environmental

41
parameters and employees' subjective perceptions to further understand the impact of IEQ on
employees' work experience.
3.3.2 Office H in Los Angeles
1) Conducting POE Surveys
At Office H, POE surveys will be used to gather employees' subjective perceptions of the
work environment. The survey questionnaire will be designed to be consistent with that of Office
H, covering IEQ factors such as air quality, temperature, humidity, noise and lighting, while
assessing employee feedback on job satisfaction, comfort and productivity. To ensure
comparability of data, the survey will be conducted before and after the implementation of the
desk rotation system to analyze the actual impact of the policy on employees' work experience.
2) Measurement of IEQ Parameters
Office H's IEQ parameters will be measured in a manner consistent with the Los Angeles H
office, using the same type of sensors for environmental monitoring. The sensors will be installed
in key locations in the office area and will continuously record temperature, humidity, carbon
dioxide concentration, light intensity and noise levels. Through continuous monitoring, this data
will provide a high-quality basis for analysis and help provide insight into the impact of
environmental changes on employee comfort and performance.
3.4 Data Processing
3.4.1 Integration of New Data
Data integration is a key component to ensure consistency and analytical reliability. This
section describes how subjective feedback from POE (Post Occupancy Evaluation) surveys was

42
combined with IEQ (Indoor Environmental Quality) parameters collected by sensors to construct
a unified dataset. The integration process involves normalizing data from different locations and
time points to maintain consistency in data format and to remove duplicate or invalid data. In
addition, the data will be categorized according to the study design to allow for in-depth analyses
for different study objectives, thus improving the accuracy and comparability of the studies.
3.4.2 Structuring the Dataset for Comparative Analysis
For effective comparative analysis, the dataset needs to be structured. IEQ parameters (e.g.,
temperature, humidity, carbon dioxide concentration, light intensity, and noise level) will be
matched with employee feedback (e.g., comfort, productivity, and satisfaction, etc.) from the POE
surveys to construct a complete dataset that can be used for statistical analysis. This process also
involves transforming the data to conform to t-tests and correlation analyses to ensure the accuracy
and rigor of the analysis results. At the same time, data consistency and comparability are
improved by standardizing the data format to more accurately assess the impact of environmental
factors on employee experience.
3.5 Comparative Analysis of Pre and Post Hot-desking Implementation
3.5.1 Statistical Testing: T-Test Application
The t-tests are a common statistical method used to compare whether there is a significant
difference between the means of two sets of data. It assesses the difference between samples
through hypothesis testing and uses the t-distribution to calculate the confidence interval and the
difference in means. In this study, we will use a two-sample t-test to analyze the changes in POE

43
survey results and IEQ parameters before and after the implementation of the hot-desking system,
focusing on significant differences in environmental conditions and employee experience.
Specifically, we will conduct t-test analyses on IEQ variables (e.g., temperature, humidity,
air quality, light, and noise) as well as indicators of employee productivity, comfort, and
satisfaction. The null hypothesis (no significant difference in IEQ factors before and after
implementation) and the alternative hypothesis (significant difference before and after
implementation) are the basic assumptions of the two-sample t-test. The statistical significance of
the results will be assessed based on a set significance level (e.g. p < 0.05), and the robustness of
the test and reliability of the results will be improved by increasing the sample size.
This analysis not only helps quantify the impact of the desk rotation system on IEQ factors,
but also assesses its actual effectiveness in improving the working environment and experience of
employees. Through this statistical test, we can provide a scientific basis for the effectiveness of
the desk rotation system and provide data support for optimizing work environment management
strategies.
3.5.2 Correlation Analysis between IEQ Factors and Productivity Outcomes
This section will explore the relationship between IEQ factors and employee productivity
through a correlation analysis. The study will analyze the correlation between IEQ parameters such
as temperature, humidity, air quality, light intensity, and noise levels, and employee productivity,
comfort, and satisfaction, in order to identify the environmental factors that have the most
significant impact on productivity. The correlation analysis will not only reveal the strength of the

44
association between IEQ variables and employee experience, but also provide a basis for further
research into potential causal relationships. Through this analysis, the study will provide data
support for the optimization of office environment design and a scientific basis for adjusting
strategies to improve employee productivity and comfort.
3.6 Chapter Summary
This chapter describes the measurement of indoor environmental quality (IEQ) for hot
desking desks and the use of POE to consider employee well-being and productivity. This chapter
describes the collection and processing of pre-implementation data, including the screening and
filtering of historical data to ensure consistency between two data comparisons. The chapter also
discusses sensor deployment and calibration to ensure accurate measurement of key IEQ
parameters. In addition, this chapter outlines the data collection process at selected office locations,
integrating objective sensor data and subjective feedback from post-occupancy evaluation (POE)
surveys. Finally, the chapter describes the methods used for data processing and statistical analysis
to ensure a comprehensive comparison of conditions before and after hot-desking.

45
Chapter 4: Analysis and Results of Office G in Los Angeles
4.1 Office G data introduction
This section describes Office G, including its spatial layout, functional areas, and
environmental conditions. In addition, the occupational category, educational background, age
distribution, and gender composition of the survey participants are described. This information
served as the basis for subsequent analysis of the Indoor Environmental Quality (IEQ) and Post
Occupancy Evaluation (POE) data.
4.1.1 Research site background
Office G is located on a high floor of a modern commercial building in downtown Los
Angeles. The office features large floor-to-ceiling windows that provide ample natural light to the
interior and expansive city views. The office space was designed with different work requirements
in mind, incorporating open desks, collaborative meeting spaces and separate small conference
rooms.
The offices are divided into functional areas, including designated workstations, meeting
rooms, breakout areas and informal collaboration spaces. The different orientations of the office
areas are influenced by the light conditions. North-facing areas receive mainly indirect light, which
is more consistent; south-facing areas are exposed to stronger direct sunlight, which affects the
indoor temperature and lighting comfort; and east-west oriented workstations are more affected by
changes in sunlight, and may experience varying degrees of glare and temperature fluctuations
throughout the day.

46
4.1.2 Description of the data group
This study surveyed 24 employees at Office G across multiple occupational categories,
including professional and technical, administrative, and managerial positions. The educational
background of the respondents was primarily undergraduate, with 18 having completed a
bachelor's degree, 4 having a graduate degree, and 1 still enrolled. The age distribution of the
respondents is relatively balanced, with 7 employees aged 18-29, 9 employees aged 30-39, 3
employees aged 40-49, and 4 employees aged 50-59. In terms of gender composition, there were
14 males and 10 females among the respondents. Overall, the data set covers employees of
different age groups and genders, providing a reliable basis for subsequent analysis of perceived
differences in the environment and workplace comfort, and will be further used for IEQ and POE
data analysis.
4.2 Key Data Identification and Feature Analysis
This section identifies and analyzes key data for Office G to gain a deeper understanding of
the impact of indoor environmental quality (IEQ) on employee comfort, productivity, and
satisfaction. The analysis methodology includes summarizing the basic statistical characteristics
of the data, assessing the relative importance of each IEQ factor, exploring the correlation between
POE scores, and using visualizations for comparative analysis of the overall data. Through these
analyses, this study aims to reveal the key influencing factors in the office environment and provide
a scientific basis for improving the workplace environment.

47
4.2.1 Statistical Overview
In this study, IEQ-related factors were statistically analyzed to assess employees' overall
perception of the office environment. By calculating the Mean, Median, Minimum, Maximum and
Standard Deviation, it is possible to gain a preliminary understanding of the distributional
characteristics of the different environmental factors, and then analyze their impact on employees'
comfort and work efficiency.
Question Mean Median Min Max SD
Q3 (T) 5.30 5 4 7 0.979
Q6 (L) 5.85 6 4 7 1.182
Q10 (L) 5.90 6 4 7 0.912
Q11 (L) 5.40 5 4 7 0.995
Q12 (L) 5.80 6 4 7 0.951
Q13 (L) 5.50 6 4 7 0.946
Q2 (AQ) 5.90 6 4 7 1.021
Q7 (A) 5.05 5 4 7 1.146
Q9 (A) 5.45 5 4 7 1.050
Q4 (A) 5.50 6 4 7 0.889

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Q8 (A) 5.85 6 4 7 0.813
Q15 (S) 4.95 5 4 7 1.099
Q16 (S) 6.55 7 5 7 0.759
Q17 (S) 5.35 5 4 7 0.813
Q18 (S) 5.75 6 4 7 0.910
Q20 (S) 5.60 5.5 4 7 1.142
Table 4-1 Descriptive Statistics of POE Survey Responses in Office G
In terms of mean values, air quality (Q2, mean 5.90), spatial factors (Q16, mean 6.55), and
overall lighting quality (Q10, mean 5.90) received high ratings, suggesting that these factors are
more satisfactory in the office environment. Thermal comfort (Q3, mean 5.30) and some air quality
factors (Q7, mean 5.05), on the other hand, were relatively low, possibly reflecting some
dissatisfaction among some employees with temperature fluctuations or air circulation.
In terms of median, most of the IEQ variables have medians close to the mean, indicating a
relatively even distribution of data. For example, air quality (Q2, median 6.0) and overall lighting
quality (Q10, median 6.0) continue to have high ratings, indicating that most employees are more
consistent in their evaluations of these factors. However, some variables such as Q20 (median 5.5)
and Q15 (median 5.0) are slightly below the mean, indicating some individual variation in
employee experience in these areas.
The maximum and minimum values reflect the extremes of the data distribution. The

49
maximum value of 7 for all IEQ variables suggests that at least some employees find the office
environment ideal in all areas. However, there are more factors with a minimum value of 4, such
as Thermal Comfort (Q3, min. 4) and Space Factors (Q15, min. 4), which suggests that some
workstations may be uncomfortably warm or spatially inappropriate.
Taken together, although the overall IEQ scores for Office G are high, there are still individual
variations, particularly in thermal comfort, air quality and some space factors. Subsequent sections
will further analyze the impact of environmental characteristics on employee comfort in
conjunction with the IEQ sensor data to provide more targeted recommendations for improvement.
4.2.2 Ranking of IEQ factors
This section analyzes the level of employee concern for different IEQ factors based on data
from the POE questionnaire Q21 (Ranking of Importance). Lower mean scores indicate that the
factor is more important to employees, while higher scores indicate that it is relatively less
important.
Noise
Temperature
Privacy
Air
quality
Size of
workspace
Window
access Lighting
Ave 4.95 3.75 5.25 5.15 6.2 5.35 5.35
Table 4-2 Ranking of IEQ Factors in Office G
Of all the IEQ variables, temperature (Mean 3.75) is the indoor environmental quality factor
that employees are most concerned about. Temperature comfort had the lowest score, indicating

50
that it has a significant impact on employee comfort and productivity. Temperature may be uneven
across workstations, and some employees may feel too cold or too hot, which is particularly
noticeable in open office environments or shared workstation (hot-desking) systems, and may
affect employees' concentration and long-term job satisfaction.
Privacy (Mean 5.25) and Air Quality (Mean 5.15) are of moderate concern. Privacy scores
indicate that employees still value personal space in open office environments, especially when
conducting online meetings or private conversations that may be disruptive. In addition, Air
Quality scores reflect the discomfort some employees may feel about ventilation conditions, high
CO₂ concentrations, or other air pollutants such as PM2.5.
In contrast, workspace size (Mean 6.2), window accessibility (Mean 5.35) and lighting (Mean
5.35) were considered relatively minor factors. This suggests that although workspace and lighting
conditions affect the work experience, these factors have less impact on employees compared to
temperature, noise and air quality. This may be due to the fact that current office layouts and
lighting designs largely meet the daily needs of employees.
Taken together, temperature, noise and air quality are the three key IEQ factors that
employees are most concerned about. In subsequent chapters, how these factors specifically affect
employee comfort and job satisfaction will be further analyzed in the context of POE feedback and
IEQ sensor data.
4.2.3 Correlation Analysis of POE
In this section, Pearson Correlation Analysis was used to explore the relationship between

51
different factors in the POE questionnaire. To ensure the validity of the analysis, only
combinations of variables with correlation coefficients |r| > 0.4 are discussed, which have a
significant relationship with each other and help to reveal the key factors affecting employee
comfort and job satisfaction.
Table 4-3 Pearson Correlation Matrix of POE Factors in Office G
Among the thermal comfort correlates, Q2 (air quality) was moderately positively correlated
(r = 0.42) with Q6 (visual privacy), suggesting that employees' perceptions of air quality may be
related to the degree of openness of the office environment. In more enclosed work areas, air
circulation may be poorer, thus affecting subjective ratings of air quality. Additionally, Q2 (Air
Quality) was highly correlated (r = 0.44) with Q10 (Overall Lighting Quality), suggesting that a
better lighting environment may enhance employees' overall perception of air quality.
Among the lighting-related variables, Q12 (direct glare from lamps) showed a strong positive
correlation (r = 0.47) with Q13 (natural light glare), suggesting that the angle and brightness of
lights may have a significant impact on visual comfort. For example, direct light may enhance
screen reflections and lead to visual discomfort. In addition, the high correlation (r = 0.54) between

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Q7 (Conversation Noise) and Q12 (Direct Light from Luminaires) suggests that noise and glare
may work together to affect employee concentration and comfort, and that higher noise levels in
open office environments may make employees more sensitive to light disturbances.
In terms of spatial factors, Q15 (space privacy) is negatively correlated with Q8 (air quality)
(r = -0.54), which suggests that the sense of enclosure of a space may affect air circulation, and
that enclosed spaces may lead to a lack of air circulation, which may worsen employees'
perceptions of air quality. In addition, Q16 (workstation spacing) was moderately positively
correlated (r = 0.41) with Q17 (sense of spatial enclosure), suggesting that the distance between
workstations may affect employees' sense of privacy and spatial satisfaction.
In terms of overall office environment experience, Q15 (Space Privacy) was positively
correlated (r = 0.41) with Q4 (Aesthetics), implying that higher space privacy may be associated
with a better office environment aesthetic experience. In contrast, Q20 (Overall Quality of
Workstations) was negatively correlated (r = -0.40) with Q17 (Sense of Space Enclosure),
suggesting that more enclosed spaces may reduce employees' overall satisfaction with the office
environment. In addition, Q18 (Overall Lighting Quality) had a low positive correlation (r = 0.41)
with Q20 (Overall Quality of Workstations), which may indicate that better lighting conditions
can enhance employees' overall office experience.
Taken together, this correlation analysis shows that air quality, lighting, and spatial factors
are important variables affecting POE scores. Among them, spatial privacy has a greater impact
on the perception of air quality, while the interaction of noise and lighting may affect employees'

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visual comfort and work efficiency. In addition, the sense of workstation enclosure and
workstation spacing also have an impact on employees' spatial satisfaction. Subsequent chapters
will further explore how these environmental factors affect employees' work experience and
comfort in conjunction with IEQ sensor data.
4.2.4 Overall Bar Chart
This section uses bar charts to provide an overall comparison of POE data from 2017 and
2024 to assess trends in office environment satisfaction before and after the implementation of the
Hot Desking system. By visualizing the average ratings of different IEQ variables, it is possible to
intuitively analyze how employee satisfaction with the office environment has changed over time
and provide a basis for subsequent detailed data analysis.
Figure 4-1 Overall Comparison of POE Ratings in Office G (2017 vs. 2024)

54
From the overall trend, compared to 2017, the POE scores in 2024 show an upward trend,
indicating that the implementation of the hot workstation system has improved employee
satisfaction with the office environment to a certain extent. As can be seen from the chart, most of
the IEQ variables have higher ratings in 2024 than in 2017, which may reflect the optimization of
the quality of the office environment, the increase in the efficiency of space utilization, and the
improvement of employees' work experience. This trend may be related to new office layouts,
adjustments in environmental control strategies, and increased employee adaptability to flexible
office models.
Within the overall trend, the factors with the most significant improvement in ratings in 2024
are mainly comfort, space adaptability and adjustability of the office environment. Some of the
IEQ variables show large increases in ratings, indicating that employees recognize the new office
model in these areas. In addition, questions dealing with Environmental Control and Individual
Adjustability showed a significant increase in 2024 compared to 2017, suggesting that the hot
workstation system may provide employees with a better experience of environmental control,
enabling them to adjust their comfort levels more effectively. This means that under the new office
model, employees are more accepting of the flexibility and adjustability of the space, which
improves overall job satisfaction.
However, despite the upward trend in overall ratings, there are still some IEQ variables that
have lower ratings in 2024 than in 2017, suggesting that the implementation of hot workspace

55
systems may have had some impact on certain environmental factors. For example, ratings related
to workspace privacy, noise environment, or individual temperature perception have declined,
possibly reflecting that some employees are uncomfortable in these areas under the new system.
This decline may be related to the increased openness of office space, increased mobility of
personnel, or standardization of environmental control methods, resulting in some employees
being inconvenienced by specific environmental factors.
Taken together, POE scores in 2024 show a more polarized trend compared to 2017. Most of
the IEQ variables showed a significant increase in ratings, indicating better acceptance of the hot
workstation system overall and an increase in employee adaptation and satisfaction with the new
environment. However, a few variables showed decreasing ratings, indicating that there is still
room for improvement in optimizing the new system for certain environmental factors. This trend
reflects that although the flexible office model can optimize the overall office environment, there
is still a need to focus on adapting to the individual needs of employees to further enhance the
overall office experience.
4.3 POE Data Analysis
This section analyzes Post-Occupancy Evaluation (POE) data collected before and after the
implementation of the hot-desking system. It examines overall changes in user satisfaction and
comfort, as well as differences based on demographic factors. Differences in workplace
experiences and perceptions of the environment are explored by comparing pre and post hotdesking system data, gender groups, and age groups.

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4.3.1 Overall comparison of Pre and Post data
This subsection compares the POE feedback before and after desk adjustments, focusing on
employee satisfaction, comfort and changes in their perception of environmental quality. The
comparison reveals the trends of the new working model and identifies possible optimization
directions or potential problems.
2017 vs 2024 POE interval plot on Thermal satisfaction
Q3 (Workspace Temperature);
P-Value=0.018
2017 vs 2024 POE interval plot on Lighting satisfaction

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Q6 (Visual Privacy);
P-Value=0.573
Q10 (Computer Task Lighting);
P-Value=0.848
Q11 (Glare on Screen);
P-Value=0.346
Q12 (Glare from Light Fixtures);
P-Value=0.542

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Q13 (Glare from Daylight);
P-Value=0.721
Q18 (Overall Lighting Quality);
P-Value=0.595
2017 vs 2024 POE interval plot on Air Quality satisfaction
Q2 (Overall Air Quality);
P-Value=0.521
2017 vs 2024 POE interval plot on Acoustic satisfaction

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Q7 (Speech Noise at Workstation);
P-Value=0.133
Q9 (Background Noise Level);
P-Value=0.302
2017 vs 2024 POE interval plot on Spatial Quality satisfaction
Q4 (Office Aesthetic);
P-Value=0.383
Q8 (Personal Workspace Size);
P-Value=0.879

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Q15 (Control Over Physical Conditions);
P-Value=0.349
Q16 (Access to Outdoor View);
P-Value=0.301
Q17 (Distance to Colleagues);
P-Value=0.580
Q20 (Workstation Enclosure);
P-Value=0.402
Table 4-4 Pre and Post Comparison of POE Factors in Office G (2017 vs. 2024)
Q3 (Temperature perception) In 2017, the mean value of the ratings was 6.10 and decreased
to 5.30 in 2024 with a p-value of 0.018, which is a statistically significant level, indicating that the
change is statistically significant. The confidence intervals overlap less and show that the ratings
in 2024 are significantly lower than those in 2017, indicating a decrease in employee satisfaction

61
with this factor.
Q6 (Privacy perception) In 2017, the mean value of the ratings was 5.63 and increased to 5.85
in 2024 with a p-value of 0.573, which does not reach the level of statistical significance, indicating
that the change is not significant. The confidence intervals are highly overlapping, indicating that
employee satisfaction with this factor remained stable and did not change significantly between
the two years.
Q10 (Lighting satisfaction) In 2017, the mean value of the score was 5.84 and increased to
5.90 in 2024 with a p-value of 0.848, which does not reach the level of statistical significance,
indicating that the change is not significant. The confidence intervals overlap completely,
indicating that the factor remains essentially unchanged between the two years and has a small
impact on employees.
Q11 (Glare perception) In 2017, the mean of the scores was 5.74 and decreased to 5.40 in
2024, with a p-value of 0.346, which does not reach the level of statistical significance, indicating
that the change is not significant. The confidence intervals partially overlap, indicating that the
ratings in 2024 are slightly lower than in 2017, but the change is small and has limited impact on
overall satisfaction.
Q12 (Glare from artificial lighting) In 2017, the mean of the ratings was 5.58, rising to 5.80
in 2024, with a p-value of 0.542, which did not reach the level of statistical significance, indicating
that the change was not significant. The confidence intervals are highly overlapping, indicating
that the factor remains largely stable between the two years and has a small impact on employee

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experience.
Q13 (Glare from daylight) In 2017, the mean value of the score was 5.37, and it increased to
5.50 in 2024, with a p-value of 0.721, which does not reach the level of statistical significance,
indicating that the change is not significant. The small magnitude of the change in ratings between
the two years and the almost complete overlap of the confidence intervals indicate that employees'
perceptions of this factor have remained largely unchanged in a significant way.
Q2 (Overall air quality) In 2017, the mean value of the ratings was 5.68 and increased to 5.90
in 2024, with a p-value of 0.521, which does not reach the level of statistical significance,
indicating that the change is not significant. The confidence intervals are highly overlapping,
indicating that although the ratings in 2024 are slightly higher than those in 2017, the overall trend
is more stable, and there is no significant change in employees' perceptions of air quality.
Q7 (Noise from conversations) In 2017, the mean value of the ratings was 5.63 and decreased
to 5.05 in 2024 with a p-value of 0.133, which does not reach the level of statistical significance,
indicating that the change is not significant. The confidence intervals partially overlap, showing
that the ratings in 2024 are slightly lower than in 2017, possibly indicating that the noise
environment has changed under the new office model, but the overall impact is small.
Q9 (Background noise level) In 2017, the mean of the ratings was 5.79, decreasing to 5.45 in
2024, with a p-value of 0.302, which does not reach the level of statistical significance, indicating
that the change is not significant. The confidence intervals partially overlap, showing that the
ratings in 2024 are slightly lower than in 2017, possibly indicating that background noise has

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increased under the new office model, but the overall impact is small.
Q4 (Aesthetic appearance of the office) In 2017, the mean rating was 5.21 and increased to
5.50 in 2024 with a p-value of 0.383, which does not reach a statistically significant level,
indicating that the change is not significant. The confidence intervals are highly overlapping,
indicating that employees' ratings of the aesthetics of the office environment basically remained
the same between the two years without significant changes.
Q8 (Workspace size sufficiency) In 2017, the mean value of the ratings is 5.89, and in 2024
it decreases to 5.85 with a p-value of 0.879, which is not statistically significant, indicating that
the change is not significant. The confidence intervals overlap completely, indicating that
employee satisfaction with the size of their personal workspace is essentially stable between the
two years.
Q15 (Ability to adjust physical conditions) has a mean score of 5.32 in 2017 and decreases to
4.95 in 2024, with a p-value of 0.349, which is not statistically significant, indicating that the
change is not significant. The confidence intervals partially overlap, indicating that the ratings in
2024 are slightly lower than those in 2017, possibly indicating a decrease in employees' perceived
ability to adjust to the physical environment under the new office model, but with limited overall
impact.
Q16 (Access to outdoor view) In 2017, the mean of the ratings was 6.26, which increased to
6.55 in 2024, with a p-value of 0.301, which did not reach the level of statistical significance,
indicating that the change was not significant. The confidence intervals partially overlap, showing

64
that the ratings in 2024 are slightly higher than in 2017, but the overall trend is relatively stable,
and there is no significant change in the accessibility of outdoor views to employees.
Q17 (Distance between colleagues) In 2017, the mean value of the ratings was 5.53, which
decreased to 5.35 in 2024, with a p-value of 0.580, which does not reach the level of statistical
significance, indicating that the change is not significant. The confidence intervals are highly
overlapping, indicating that the change in this factor between the two years is small, and
employees' perception of distance between coworkers is basically stable.
Q18 (Overall lighting quality) In 2017, the mean value of the score was 5.58, and it increased
to 5.75 in 2024, with a p-value of 0.595, which does not reach the level of statistical significance,
indicating that the change is not significant. The confidence intervals are highly overlapping,
indicating that employee ratings of lighting quality remained stable between the two years, with
no significant improvement or decline occurring.
Q20 (Enclosure of workstation) In 2017, the mean value of the ratings was 5.26 and increased
to 5.60 in 2024, with a p-value of 0.402, which does not reach the level of statistical significance,
indicating that the change is not significant. The confidence intervals partially overlap, showing
that the ratings in 2024 are slightly higher than those in 2017, which may indicate that the new
office model has improved employee perceptions of workstation enclosure to some extent, but the
overall change is limited.
4.3.2 Comparison of gender differences
This subsection explores the differences in POE feedback between male and female

65
employees, focusing on their different performance in terms of perceived comfort, environmental
satisfaction, and workplace preferences. This analysis provides a deeper perspective for
understanding gender differences in adapting to hot-desking mechanisms.
2017 vs 2024 POE interval plot on Thermal satisfaction by Gender
Q3 (Workspace Temperature);
P-Value 2017=0.828, P-Value 2024= 0.193
2017 vs 2024 POE interval plot on Lighting satisfaction by Gender
Q6 (Visual Privacy); Q10 (Computer Task Lighting);

66
P-Value 2017=0.287, P-Value 2024= 0.691 P-Value 2017=0.409, P-Value 2024= 0.086
Q11 (Glare on Screen);
P-Value 2017=0.226, P-Value 2024= 0.755
Q12 (Glare from Light Fixtures);
P-Value 2017=0.149, P-Value 2024= 0.590
Q13 (Glare from Daylight);
P-Value 2017=0.081, P-Value 2024= 0.267
Q18 (Overall Lighting Quality);
P-Value 2017=0.021, P-Value 2024= 0.069
2017 vs 2024 POE interval plot on Air Quality satisfaction by Gender

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Q2 (Overall Air Quality);
P-Value 2017=0.079, P-Value 2024= 0.866
2017 vs 2024 POE interval plot on Acoustic satisfaction by Gender
Q7 (Speech Noise at Workstation);
P-Value 2017=0.744, P-Value 2024= 0.600
Q9 (Background Noise Level);
P-Value 2017=0.123, P-Value 2024= 0.056
2017 vs 2024 POE interval plot on Spatial Quality satisfaction by Gender

68
Q4 (Office Aesthetic);
P-Value 2017=0.740, P-Value 2024= 0.253
Q8 (Personal Workspace Size);
P-Value 2017=0.002, P-Value 2024= 0.113
Q15 (Control Over Physical Conditions);
P-Value 2017=0.239, P-Value 2024= 0.125
Q16 (Access to Outdoor View);
P-Value 2017=0.782, P-Value 2024= 0.600

69
Q17 (Distance to Colleagues);
P-Value 2017=0.002, P-Value 2024= 0.611
Q20 (Workstation Enclosure);
P-Value 2017=0.007, P-Value 2024= 0.845
Table 4-5 Gender-Based Comparison of POE Factors in Office G
Q3 (Temperature perception) In 2017, the mean score for females was 6.2, which was higher
than that of males at 6.07, with a p-value of 0.828, which did not reach the level of statistical
significance, suggesting that the gender difference was not significant. 2024 saw a decrease in the
scores for females to 4.83, while the scores for males dropped to 5.5, with a p-value of 0.193,
which still did not reach the level of statistical significance. significance level. Compared to 2017,
the decline in ratings was more pronounced for females, while the change for males was relatively
small, with less overlap in the confidence intervals, which may indicate that females are less
acclimatized to the perception of temperature in the new office model.
Q6 (Air movement) In 2017, the mean value of women's ratings was 6.2, higher than men's
5.43, with a p-value of 0.287, which did not reach the level of statistical significance, indicating
that the gender difference was not significant.2024, women's ratings dropped slightly to 5.67 while

70
men's ratings increased to 5.93, with a p-value of 0.691, which still did not reach the level of
statistical significance. Overall, the magnitude of change in the gender scores is small and the
confidence intervals still overlap considerably, indicating that the perception of air movement is
not significantly differentiated between genders.
Q10 (Reflected glare on screen) In 2017, the mean rating for females was 6.2 and for males
was 5.71, with a p-value of 0.409, which did not reach a statistically significant level. In 2024,
females' ratings increased to 6.83, while males' ratings remained at 5.71, with a p-value of 0.086,
which is close to, but still does not reach, a statistically significant level. Satisfaction with reflected
screen glare increased for women in 2024, while it remained the same for men, possibly indicating
that adjustments to the light environment in the new office model had a more pronounced effect
on women.
Q11 (Amount of reflected light or glare in the workspace) In 2017, the mean ratings were 6.4
for females and 5.5 for males, with a p-value of 0.226, which did not reach the level of statistical
significance. In 2024, the ratings dropped to 5.5 for females and 5.36 for males, with a p-value of
0.755, which The p-value is 0.755, which still does not reach the level of statistical significance.
The confidence intervals are highly overlapping, suggesting that the perceptions of both genders
remain largely the same and that there is no significant difference in adaptation to the new office
model.
Q12 (Artificial lighting quality) In 2017, the mean score for females was 6.4, which was
higher than males' 5.29, with a p-value of 0.149, which did not reach the level of statistical

71
significance, indicating that the gender difference was not significant. In 2024, females' scores
decreased to 6.0, while males' scores increased to 5.71, with a p-value of 0.590, and still not
reaching the level of statistical significance. Overall, women's satisfaction with the quality of
artificial lighting decreased slightly, while men's ratings increased, with a large overlap in the
confidence intervals, suggesting that both genders' perceptions of the quality of artificial lighting
do not change much under the hot-desking system, and that the adaptations are relatively consistent.
Q13 (Glare from artificial lighting) In 2017, the mean value of women's ratings was 6.4,
higher than that of men's 5.0, with a p-value of 0.081, close to, but not reaching, the level of
statistical significance. In 2024, women's ratings decreased to 5.83, while men's ratings increased
to 5.36, with a p-value of 0.267, still not reaching the level of statistical significance. statistical
significance level. The larger decrease in female ratings and the slight increase in male ratings,
with less overlap in the confidence intervals, may indicate that the hot-desking system has a more
pronounced effect on women's experience of artificial lighting glare, while men's perceptions of
this factor have changed less.
Q2 (Satisfaction with overall lighting conditions) was rated at a mean of 6.4 for females and
5.43 for males in 2017, with a p-value of 0.079, which is close to, but does not reach, the level of
statistical significance. In 2024, the ratings for females declined to 5.83, and the ratings for males
rose to 5.93, with p value of 0.866, still not reaching statistical significance. Overall, women's
satisfaction with overall lighting conditions declined, while men's ratings increased slightly, with
a large overlap in confidence intervals, suggesting that both genders are not experiencing

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significant changes in their lighting experiences in the new office model.
Q7 (Control over lighting conditions) had a mean rating of 5.8 for females and 5.57 for males
in 2017, with a p-value of 0.744, which did not reach a statistically significant level. In 2024,
females' ratings declined to 4.83 while males' declined to 5.14, with a p-value of 0.600, which still
did not reach a statistically significant level. The larger decrease in the ratings for females and the
relatively smaller change in the ratings for males, with less overlap in the confidence intervals,
may indicate that females are less satisfied with the lighting controls under the hot-desking system,
while males are relatively more adaptable.
Q9 (Glare on computer screen) In 2017, the mean score for females was 6.4 and for males
was 5.57, with a p-value of 0.123, which did not reach the level of statistical significance,
indicating that the gender difference was not significant.In 2024, the score for females decreased
to 4.83, while the score for males increased slightly to 5.71, with a p-value of 0.056, which was
still not reaching the level of statistical significance. Compared to 2017, women's satisfaction with
screen glare decreased more significantly, while men's change was smaller, and the overlap of
confidence intervals was reduced, which may indicate that there are some differences in the impact
of the hot-desking system on the perception of screen glare by employees of different genders, and
that women may be less adaptable to the new light environment.
Q4 (Lighting satisfaction) In 2017, the mean rating for females was 5.4 and that for males
was 5.14, with a p-value of 0.740, which did not reach a statistically significant level. In 2024,
females' ratings increased slightly to 5.17, while males' ratings increased to 5.64, with a p-value of

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0.253, which still did not reach a statistically significant level. Overall, women's ratings change
less, while men's ratings increase, with more overlap in the confidence intervals, suggesting that
gender differences in overall lighting satisfaction are still not significant under the hot-desking
system.
Q8 (Illuminance level) In 2017, the mean value of women's ratings was 6.8, higher than that
of men's 5.57, with a p-value of 0.002, which reached the level of statistical significance, indicating
that the difference between genders in the perception of illumination level is significant. In 2024,
women's ratings decreased to 6.33, while men's ratings increased slightly to 5.64, with a p-value
of 0.113, which did not reach the level of statistical significance. The p-value was 0.113, which
did not reach the level of statistical significance. Although women's ratings were still higher than
men's, the narrowing of the gap and the increased overlap of the confidence intervals may indicate
that the hot-desking system's adjustment of illumination levels has reduced the perceived
difference between genders to some extent.
Q15 (Color rendering) In 2017, the mean value of women's ratings was 6.0 and men's ratings
were 5.07, with a p-value of 0.239, which did not reach the level of statistical significance. In 2024,
women's ratings declined to 4.5 while men's ratings increased to 5.14, with a p-value of 0.125,
which still did not reach the level of statistical significance. Overall, women's satisfaction with
color reproduction decreased after the adoption of the hot-desking system, while men's ratings
increased slightly, but the overall change was still within the confidence interval, indicating that
the difference in perception between genders is still not significant.

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Q16 (Workspace Comfort) In 2017, the mean score for females was 6.4 and for males was
6.21, with a p-value of 0.782, which did not reach the level of statistical significance, indicating
that the gender difference was not significant. 2024 saw an increase in the scores for females to
6.67 and for males to a slightly higher 6.5, with a p-value of 0.600, which still did not reach the
level of statistical significance. Overall, both genders' ratings increased in 2024, with a large
overlap between the confidence intervals, suggesting that there is no significant gender difference
in perceived comfort in the workspace under the hot-desking system, and that the overall trend is
positive.
Q17 (Workspace Privacy) In 2017, the mean rating for females was 6.6 and for males was
5.14, with a p-value of 0.002, indicating that females were significantly more satisfied with their
workspace privacy than males.2024, the ratings for females declined to 5.5 and for males increased
slightly to 5.29, with a p-value of 0.611, which did not reach statistical significance. Compared to
2017, women's ratings decreased more, while men's changed insignificantly, and the overlap of
the confidence intervals increased, indicating that the gender difference in privacy perceptions
narrowed under the hot-desking system, but women's satisfaction with privacy decreased more
significantly.
Q18 (Workspace Layout Suitability) In 2017, the mean rating for females was 6.6 and for
males was 5.21, with a p-value of 0.021, indicating that females were significantly more
comfortable with workspace layout than males.2024, females' ratings increased to 6.83, while
males' ratings decreased to 5.5, with a p-value of 0.069, which is close to, but does not reach, the

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statistically significant level.2023, females' ratings increased to 6.83 and males' ratings decreased
to 5.5, with a p-value of 0.069. Compared to 2017, women's adaptation to the new office
environment increased, while men's satisfaction decreased, with less overlap in the confidence
intervals, possibly indicating that the hot-desking system affects different genders differently, with
women being more adaptable to the new layout.
Q20 (Workspace Cleanliness) In 2017, the mean rating for females was 6.6 compared to 4.78
for males, with a p-value of 0.007, indicating that females were significantly more satisfied with
the cleanliness of their workspace than males.2024, the ratings for females declined to 5.67
compared to males, increasing to 5.57, with a p-value of 0.845, which did not reach a statistically
significant level.2023, the ratings for females increased to 5.67 compared to males. Compared to
2017, women's ratings decreased significantly while men's ratings increased, and the overlap of
the confidence intervals increased, suggesting that under the hot-desking system, the gender
difference in cleanliness decreased but the overall ratings tended to converge, possibly reflecting
changes in cleanliness management in the new office paradigm.
4.3.3 Comparison of age differences
This subsection explores the responses of employees of different age groups to workplace
change. The impact of age factors on this transition is identified by comparing POE results across
age groups and analyzing whether there is a significant change in comfort, satisfaction, and
productivity between younger and older employees after adapting to hot-desking.

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2017 vs 2024 POE interval plot on Thermal satisfaction by Age
Q3 (Workspace Temperature);
P-Value 2017=0.655, P-Value 2024= 0.179
2017 vs 2024 POE interval plot on Lighting satisfaction by Age
Q6 (Visual Privacy);
P-Value 2017=0.802, P-Value 2024= 0.090
Q10 (Computer Task Lighting);
P-Value 2017=0.086, P-Value 2024= 0.344

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Q11 (Glare on Screen);
P-Value 2017=0.172, P-Value 2024= 0.004
Q12 (Glare from Light Fixtures);
P-Value 2017=0.253, P-Value 2024= 0.651
Q13 (Glare from Daylight);
P-Value 2017=0.020, P-Value 2024= 0.018
Q18 (Overall Lighting Quality);
P-Value 2017=0.360, P-Value 2024= 0.230
2017 vs 2024 POE interval plot on Air Quality satisfaction by Age

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Q2 (Overall Air Quality);
P-Value 2017=0.627, P-Value 2024= 0.025
2017 vs 2024 POE interval plot on Acoustic satisfaction by Age
Q7 (Speech Noise at Workstation);
P-Value 2017=0.634, P-Value 2024= 0.344
Q9 (Background Noise Level);
P-Value 2017=0.389, P-Value 2024= 0.538
2017 vs 2024 POE interval plot on Spatial Quality satisfaction by Age

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Q4 (Office Aesthetic);
P-Value 2017=0.458, P-Value 2024= 0.136
Q8 (Personal Workspace Size);
P-Value 2017=0.667, P-Value 2024= 0.425
Q15 (Control Over Physical Conditions);
P-Value 2017=0.774, P-Value 2024= 0.323
Q16 (Access to Outdoor View);
P-Value 2017=0.195, P-Value 2024= 0.392

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Q17 (Distance to Colleagues);
P-Value 2017=0.275, P-Value 2024= 0.792
Q20 (Workstation Enclosure);
P-Value 2017=0.647, P-Value 2024= 0.449
Table 4-6 Age-Based (30) Comparison of POE Factors in Office G
Q3 (Temperature perception) In 2017, the mean score for the under 30 group was 6.22, higher
than the 6.0 for the over 30 group, with a p-value of 0.655, which did not reach the level of
statistical significance, suggesting that the age difference was not significant. In 2024, the score
for the under 30 group decreased to 5.0, while the score for the over 30 group decreased by a
smaller amount, 5.6, with a p-value of 0.179, which still did not reach the level of statistical
significance. was smaller at 5.6, with a p-value of 0.179, still not reaching the level of statistical
significance. The more significant decrease in satisfaction with perceived temperature and the
reduced overlap of confidence intervals in the under-30 group compared to 2017 may indicate that
this group is less adaptable to temperature under the hot-desking system.
Q6 (Air movement) In 2017, the mean rating for the under-30 group was 5.56, slightly lower
than the 5.7 for the over-30 group, with a p-value of 0.802, which did not reach the level of

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statistical significance. In 2024, the rating for the under-30 group increased to 6.3, while the rating
for the over-30 group decreased to 5.4, with a p-value of 0.090, which still did not reach the level
of The p-value is 0.090, still not at the level of statistical significance. Overall, the confidence
intervals still overlap considerably, indicating that the perceptions of air flow among different age
groups did not differ significantly after the change in the hot-desking system.
Q10 (Reflected glare on screen) In 2017, the mean score of the under-30 group was 5.44,
lower than that of the over-30 group (6.2), with a p-value of 0.086, which is close to but not yet at
the statistically significant level. In 2024, the score of the under-30 group increased slightly to 6.1,
and that of the over-30 group dropped to 5.7, with a p-value of 0.086. 5.7 with a p-value of 0.344,
which is still not statistically significant. The change in confidence intervals suggests that the
changes in the perception of reflected screen glare in the new office environment are small and the
adaptation is relatively similar among different age groups.
Q11 (Amount of reflected light or glare in the workspace) In 2017, the mean value of the
ratings for the under-30 group was 5.33, which was lower than that of the over-30 group, which
was 6.1, with a p-value of 0.172, which did not reach the level of statistical significance. In 2024,
the ratings for the under-30 group decreased to 4.8, and the ratings for the under-30 group
decreased to 4.8, while the ratings for the over-30 group decreased to 4.8. In 2024, the ratings for
the under 30 group decreased to 4.8 and the ratings for the over 30 group decreased to 6.0 with a
p-value of 0.004, which is a level of statistical significance. The decrease in confidence intervals
suggests that the under-30s group had more pronounced changes in their perception of reflected

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light in the workspace, which may imply that they are less adapted to the light environment in the
hot-desking system.
Q12 (Amount of daylight) In 2017, the mean value of the scores for the under 30 group was
5.22, and the score for the 30 and over group was 5.9, with a p-value of 0.253, which did not reach
the level of statistical significance, indicating that the age difference did not have a significant
impact on this indicator. In 2024, the score for the under 30 group increased to 5.9, while the score
for the 30 and over group decreased slightly to 5.7 with a p-value of 0.651, still not reaching the
level of significance. Overall, the ratings of different age groups on the amount of natural light did
not change much between the two years, and there was a large overlap in the confidence intervals,
suggesting that the hot-desking system has a small effect on the perception of natural light in
different age groups.
Q13 (Satisfaction with amount of daylight) had a mean rating of 4.67 for the under 30 group
and 6.0 for the 30 and over group in 2017, with a p-value of 0.020, which is close to the level of
significance, suggesting that there may be some effect of age on this satisfaction item. In 2024, the
rating for the under 30 group decreased to 5.0, while the 30 and over group stayed at 6.0 with a pvalue of 0.018, reaching the significance level. Compared to 2017, the satisfaction of the under 30
group increased but remained lower than the 30 and above group with a decrease in the overlap of
confidence intervals, suggesting that the HOT-DESKING system may have affected the natural
light satisfaction of different age groups to different degrees, and that the satisfaction of the older
employees remained high.

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Q2 (Satisfaction with artificial lighting) had a mean rating of 5.56 for the under 30 group and
5.8 for the 30 and over group in 2017, with a p-value of 0.627, which did not reach the level of
statistical significance, suggesting that the age difference was not significant.In 2024, the rating
for the under 30 group increased to 6.4, while the ratings for the 30 and over group decreased to
5.4 with a p-value of 0.025, reaching the level of significance. Compared to 2017, the satisfaction
of younger employees with artificial lighting has increased while the ratings of older employees
have decreased, the change in confidence interval indicates that there is a difference in the level of
adaptation of different age groups to the artificial lighting environment under the HOT-DESKING
SYSTEM.
Q7 (Glare from windows) In 2017, the mean value of the ratings for the under 30 group was
5.78 and the ratings for the 30 and above group was 5.5, with a p-value of 0.634, which did not
reach the level of statistical significance, indicating that there was little effect of the age
difference.In 2024, the ratings for the under 30 group decreased to 5.3, and the ratings for the 30
and above group further decreased to 4.8, with a p-value of 0.344, still not reaching the level of
significance. Overall, the ratings of both groups decreased and there was a large overlap in the
confidence intervals, suggesting that the hot-desking system may not be effective in improving the
perception of window glare among employees of different ages and that the adaptation of
employees in the two groups was more similar.
Q9 (Speech Privacy) In 2017, the mean score for the under 30 group was 6.0, higher than the
5.6 for the over 30 group, with a p-value of 0.389, which did not reach the level of statistical

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significance, suggesting that the difference in age was not significant. In 2024, the score for the
under 30 group dropped to 5.3, while the score for the over 30 group remained at 5.6, with a pvalue of 0.538, still not reaching the level of statistical significance. Compared with 2017, the
ratings of the group under 30 years old decreased, and the overlap of the confidence intervals is
large, indicating that age has a smaller impact on the perception of voice privacy, and the change
in the adaptability of different age groups under the new office model is not significant.
Q4 (Lighting Satisfaction) In 2017, the mean value of the ratings of the group under 30 years
old was 5.0, slightly lower than that of the group over 30 years old, which was 5.4, with a p-value
of 0.458, which did not reach the level of statistical significance. In 2024, the ratings of the group
under 30 years old slightly decreased to 5.2, while the ratings of the group over 30 years old
increased to 5.8, with a p-value of 0.136, still not reaching statistical significance. Compared to
2017, there is an increase in lighting satisfaction in the group over 30 years old, while there is little
change in the group under 30 years old, and the confidence intervals still overlap considerably,
indicating that the perceived difference in lighting satisfaction between the two age groups is not
significant.
Q8 (Visual Comfort) In 2017, the mean rating of the under-30 group was 6.0, slightly higher
than that of the over-30 group, which was 5.8, with a p-value of 0.667, which did not reach the
level of statistical significance. In 2024, the rating of the under-30 group declined to 6.0, while
that of the over-30 group declined to 5.7, with a p-value of 0.425, which still did not reach the
level of statistical significance. In 2024, scores for the under 30 group drop to 6.0, while those for

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the over 30 group drop to 5.7 with a p-value of 0.425, still not reaching statistical significance.
Overall, there is less variation in the visual comfort scores of the different age groups, and the
overlap of the confidence intervals is larger, indicating that the impact of the new office model on
visual comfort is more consistent across age groups.
Q15 (Desk Cleanliness) In 2017, the mean score for the under-30 group was 5.2, slightly
lower than the 5.4 for the over-30 group, with a p-value of 0.774, which did not reach the level of
statistical significance. In 2024, the score for the under-30 group dropped to 5.2, while the score
for the over-30 group dropped to 4.7, with a p-value of 0.323, still not reaching the level of
statistical significance. reach the level of statistical significance. Compared to 2017, the ratings for
both age groups decreased, with a large overlap in the confidence intervals, indicating that the
impact of the new office model on perceived desktop cleanliness is more consistent across age
groups.
Q16 (Air freshener) In 2017, the mean score for the under 30 group was 6.56, slightly higher
than the 6.0 for the over 30 group, with a p-value of 0.195, which did not reach a statistically
significant level, suggesting that the difference in age was not significant. In 2024, the score for
the under 30 group dropped to 6.4, while the score for the over 30 group increased to 6.7, with a
p-value of 0.392, still not reaching the level of statistical significance. Overall, the small magnitude
of change in the ratings and the high overlap of confidence intervals indicate that the perceptions
of air freshness of different age groups did not diverge significantly after the change in the hotdesking system.

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Q17 (Odor in the workspace) In 2017, the mean value of the ratings of the group under 30
years old was 5.22, which was lower than that of the group over 30 years old of 5.8, with a p-value
of 0.275, and did not reach the level of statistical significance. In 2024, the ratings of the group
under 30 years old decreased slightly to 5.3, while the group over 30 years old decreased to 5.4,
with a p-value of 0.792, and still did not reach the level of statistical significance. The p-value is
0.792, which is still not at the level of statistical significance. On the whole, the scores of both
groups decreased, but the magnitude of change was small, and the confidence intervals still
overlapped considerably, indicating that the perception of odor did not change significantly in
different age groups under the new office model.
Q18 (Overall noise level) In 2017, the mean value of the ratings of the group under 30 years
old was 5.33, which was lower than that of the group over 30 years old of 5.8, with a p-value of
0.360, which did not reach the level of statistical significance. In 2024, the ratings of the group
under 30 years old increased to 6.0, while the group over 30 years old decreased to 5.5, with a pvalue of 0.230, which still did not reach the level of statistical significance. In 2024, scores for the
under 30 group increased to 6.0, while scores for the over 30 group decreased to 5.5 with a p-value
of 0.230, which is still not statistically significant. Although the ratings of the two groups changed
somewhat in 2024, the confidence intervals still overlapped considerably, indicating that the age
factor had less of an impact on the overall perceived noise level.
Q20 (Visual privacy) In 2017, the mean value of the ratings for the under-30 group was 5.11,
slightly lower than the 5.4 for the over-30 group, with a p-value of 0.647, which did not reach a

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statistically significant level. In 2024, the ratings for the under-30 group increased to 5.8, while
the over-30 group remained at 5.4, with a p-value of 0.449, which still did not reach a statistically
significant level. The p-value is 0.449, which still does not reach the level of statistical significance.
Despite the fluctuation of the scores, the confidence intervals still overlap a lot, indicating that
there is little change in the visual privacy perception of different age groups under the hot-desking
system.
4.4 IEQ Data Analysis
This section focuses on analyzing the Indoor Environmental Quality (IEQ) data collected
from Office H and evaluating the environmental parameters based on established comfort criteria.
By comparing changes before and after desk rotation, the aim is to identify factors that meet
comfort thresholds, as well as to identify environmental conditions that may trigger employee
discomfort.
4.4.1 Comparison with Standards
This subsection compares recorded IEQ data to industry-standard benchmarks for
temperature, humidity, lighting, acoustics, and air quality to assess whether environmental
conditions meet, exceed, or fall below recommended levels. This analysis helps identify key
factors that may affect employee comfort and locate potential problem areas.

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Air Temperature (TA) Temperature (Floor)
Temperature (1.2m) Radiant Temperature Asymmetry (Wall)
Radiant Temperature Asymmetry (Ceiling) Illuminance (Lux) at the Center

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Air Velocity (Top) Air Velocity (Bottom)
Acoustic Relative Humidity (RH)
CO₂

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Table 4-7 Comparison of IEQ with Standards by Year in Office G
The overall performance of air temperature (TA) in 2024 is better than that of 2017 and closer
to the standard comfort zone. The data show that the average temperature in 2024 was 23.13°C,
slightly below the lower limit of the comfort interval of 23.30°C, but with a more centralized
distribution of temperatures compared to 2017, with the standard deviation decreasing from 0.77°C
to 0.63°C, suggesting fewer fluctuations in temperatures and more stable environmental control.
This improvement may be related to the fact that the hot-desking mode optimizes space use and
air conditioning regulation strategies, resulting in a more homogeneous overall thermal
environment and improved office comfort.
The overall performance of the ground temperature (Floor Temperature) in 2024 is better than
in 2017 and closer to the standard comfort range. The data show that the average floor temperature
in 2024 was 23.38°C, which is closer to the median comfort range than the 24.24°C in 2017, while
the temperature distribution was more centralized, with the standard deviation decreasing from
0.78°C to 0.73°C, suggesting fewer fluctuations in the floor temperatures and more stable
environmental control. This improvement may be related to the fact that the HOT-DESKING
mode optimized space use and indoor thermoregulation strategies, resulting in more balanced
ground temperatures and improved overall thermal comfort.
The 2024 air temperature at 1.2m altitude is overall low compared to 2017 and some of the
data fails to fall into the comfort zone. The data shows that the average temperature in 2024 was
23.41°C, lower than the 24.50°C in 2017, and that the distribution of the 2024 data was more

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spread out, with the standard deviation increasing from 0.56°C to 0.69°C, indicating increased
temperature fluctuations. The histogram shows that all data points for 2017 are within the comfort
range, while some of the 2024 data have fallen outside the comfort range, which may lead to
localized thermal discomfort.
Radiant Temperature Asymmetry - Wall improved in 2024, with the mean decreasing from
1.05°C to 0.96°C and the standard deviation decreasing from 0.84°C to 0.71°C, indicating that the
magnitude of variation in wall temperatures has decreased and the temperature distribution has
become more uniform. Overall, the wall radiant temperature asymmetry in 2024 remains within
the standard range.Ceiling radiant temperature asymmetry in 2024 has improved compared to 2017,
with the mean decreasing from 1.05°C to 0.76°C and the standard deviation decreasing from
0.67°C to 0.51°C, indicating fewer temperature fluctuations and a more stable overall environment.
Indoor air velocities (Air Velocity) in 2024 have not changed much from 2017, remaining
low overall, and most of the data fall within the comfort range, suggesting that airflow has less of
an impact on thermal comfort.
Noise levels (Acoustic) in 2024 are significantly higher than in 2017, with most data falling
outside the comfort range. The data shows that the average noise level in 2024 reached 60.83 dB,
which is a significant increase compared to 49.48 dB in 2017, and the standard deviation increased,
implying greater noise fluctuations. The distribution of the data shows that the noise in 2017 was
more concentrated and mostly in the comfort range, while the noise level in 2024 is more dispersed
with more high-noise areas. hot-desking mode reduces the physical partitions, which widens the

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range of sound propagation, and may lead to an increase in the overall noise level, which may
affect the concentration and communication efficiency of the employees. Therefore, improving the
acoustic environment, such as adding sound-absorbing materials or optimizing the layout of the
space, may help to reduce noise impacts.
Relative humidity (RH) in 2024 is significantly lower than in 2017 and some of the data is
well below the comfort zone. The data shows that the average humidity in 2024 was 25.51%, a
significant decrease from 45.43% in 2017, and the data fluctuated more, with the standard
deviation increasing from 2.68% to 17.32%. While most of the humidity data in 2017 was
concentrated in the comfort zone, the data in 2024 was more spread out, with some of the values
unusually low. Low humidity may lead to dryness and discomfort, such as dry eyes and dry skin,
indicating that humidity control in HOT-DESKING mode still needs to be optimized, and
humidification systems may need to be adjusted to improve air humidity and enhance comfort.
Carbon dioxide concentration (CO₂) in 2024 decreased compared to 2017, improving air
quality. The data shows that the average CO₂ concentration in 2024 was 428.4 ppm, significantly
lower than the 592.1 ppm in 2017, and with a lower standard deviation, indicating a more even
distribution of data. From the data distribution, the overall CO₂ concentration in 2024 remained
within the comfort zone, while some of the data in 2017 were close to the upper limit. hot-desking
mode may have optimized the ventilation strategy and improved the efficiency of air circulation,
which in turn reduced CO₂ accumulation and contributed to the improvement of indoor air quality.

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4.4.2 Overall comparison of Pre and Post data
This subsection assesses the changes in IEQ conditions before and after the implementation
of desk rotation. By comparing the differences in environmental parameters, it analyzes whether
the new office layout led to significant improvements or whether it triggered new challenges in
terms of workplace comfort.
Air Temperature (TA);
P-Value=0.982
Global Temperature (TG);
P-Value=0.000
Temperature (Floor);
P-Value=0.001
Temperature (0.6m);
P-Value=0.000

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Temperature (1.2m);
P-Value=0.000
Temperature (1.6m);
P-Value=0.000
Radiant Temperature (Left);
P-Value=0.017
Radiant Temperature (Right);
P-Value=0.003

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Radiant Temperature (Down);
P-Value=0.076
Radiant Temperature (Up);
P-Value=0.000
Illuminance (Lux) at the Center;
P-Value=0.428
Illuminance (Lux) on the Monitor;
P-Value=0.106

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Illuminance (Lux) on the keyboard;
P-Value=0.086
Air Velocity (Top);
P-Value=0.223
Air Velocity (Bottom);
P-Value=0.110
Acoustic;
P-Value=0.000

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Relative Humidity (RH);
P-Value=0.000
CO₂;
P-Value=0.000
Table 4-8 Overall Comparison of IEQ by Year in Office G
Air Temperature (TA) has a mean value of 23.13°C in 2017 and a mean value of 23.13°C in
2024 with a p-value of 0.982 indicating that there is almost no change in temperature between the
two years and the confidence intervals are highly overlapping implying that the impact of this
change on the indoor environment is negligible.
Global Temperature (TG) averages 25.03°C in 2017 and decreases to 23.07°C in 2024 with
a p-value of 0.000, indicating that this change is statistically significant. The decrease in
temperature is about 1.96°C with less overlap between confidence intervals, indicating that the
overall temperature in 2024 is significantly lower than in 2017. Despite the decrease, the overall
temperature remains within the comfort range and has limited impact on employee thermal comfort.
In 2017, the mean ground temperature was 24.24°C, while in 2024 it decreased to 23.38°C
with a p-value of 0.001, indicating that this change is statistically significant. The decrease in

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temperature of about 0.86°C, despite the decrease, is still within the comfort range and has a minor
impact on overall comfort. There is less overlap in the confidence intervals, further suggesting that
ground temperatures in 2024 are significantly lower than in 2017, which may result in some
employees feeling a slight change in temperature. Temperatures at the 0.6 meter height decreased
from 24.67°C in 2017 to 23.46°C in 2024 with a p-value of 0.000, indicating that this change is
statistically highly significant. The decrease in temperature of approximately 1.21°C is relatively
large, but is still within the comfort range and has limited overall impact on the office environment.
There is less overlap in the confidence intervals, further confirming the decrease in temperature in
2024 compared to 2017. In 2017, the average temperature at a height of 1.2 meters was 24.50°C,
while in 2024 it decreased to 23.41°C, with a p-value of 0.000, showing a statistically significant
change. The decrease in temperature of about 1.09°C is more significant, but is still within the
acceptable comfort range and has a relatively limited impact on employee perception. There is less
overlap in the confidence intervals, further indicating a decrease in temperature in 2024 compared
to 2017. The average temperature at the 1.6 meter height was 24.44°C in 2017 and decreased to
23.32°C in 2024 with a p-value of 0.000, again showing a statistically significant change. The
decrease in temperature of about 1.12°C is consistent with the trend at the other measured heights,
indicating an overall decreasing trend in temperature, but still within the comfort range. There is
less overlap in the confidence intervals, further validating that temperatures in 2024 show a
significant decrease compared to 2017.
Radiant Temperature shows a decreasing trend at all four measurement points (left, right,

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below and above), indicating a decrease in the overall thermal radiation environment in 2024.
Specifically, the mean value at the left measurement point decreases from 24.64°C in 2017 to
23.59°C in 2024 with a p-value of 0.017, showing a statistically significant decrease. The mean
value of the right-hand side point decreased from 24.49°C to 23.68°C with a p-value of 0.003,
showing a more significant change. The mean value of the lower measurement point decreased
from 24.54°C to 23.75°C with a p-value of 0.076, which did not reach the level of significance,
while the mean value of the upper measurement point decreased from 24.21°C to 23.50°C with a
p-value of 0.000, which indicates a significant decreasing trend. Overall, radiant temperatures in
2024 were lower than in 2017 at all measurement points, and p-values at several points reached
the level of statistical significance, suggesting that this change may be of practical significance.
However, despite the decrease in temperature, the mean values are still within the comfort range,
so the impact on overall thermal comfort may be limited. The partial overlap of the confidence
intervals suggests that, despite the downward trend, the magnitude of temperature change between
years is relatively stable and does not significantly affect environmental comfort.
Illuminance measurements in 2017 and 2024 show trends in brightness at different locations.
At the center, the average illuminance is 227.85 lux in 2017 and increases to 245.12 lux in 2024,
with a p-value of 0.428, indicating that this change is statistically insignificant, with a large overlap
in the confidence intervals, which suggests that changes in illuminance have a small impact on the
overall visual environment. At the keyboard location , the average illuminance is 253.35 lux in
2017, while it decreases to 225.3 lux in 2024, with a p-value of 0.086, which does not reach the

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level of statistical significance, but the decreasing trend is obvious. The confidence intervals
overlap slightly, suggesting that the illumination in some work areas may have changed, but the
overall effect is still small. At the monitor location, the average illuminance is 191.75 lux in 2017
and decreases to 160.15 lux in 2024, with a p-value of 0.106, which is a significant decrease but
does not reach a statistically significant level. The overlap of the confidence intervals suggests that
this change may not have a significant impact on the overall work environment. However, the
decrease in monitor illuminance may have some impact on screen readability, and the specific
impact on visual comfort needs to be further evaluated in conjunction with user feedback.
Air Velocity has a mean value of 0.0065 m/s at the top measurement point in 2017 while it
increases to 0.07 m/s in 2024 with a p-value of 0.223 and the change does not reach the level of
statistical significance. Similarly, at the bottom measurement point, the mean value was 0.005 m/s
in 2017, while it increased to 0.0505 m/s in 2024 with a p-value of 0.110, which also did not reach
the level of statistical significance. Overall, the air flow velocity has increased but is still at a very
low level, which is almost negligible in indoor environments and does not have a significant impact
on comfort.
In 2017, the mean value of the acoustic environment is 49.48 dB, while in 2024 it significantly
increases to 60.84 dB with a p-value of 0.000, indicating that the change is statistically significant.
There is less overlap in the confidence intervals, indicating that the change in the acoustic
environment is significant. Overall, the higher level of sound environment in 2024 could mean an
increase in office noise levels, which could affect employee concentration and comfort.

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Relative Humidity (RH) In 2017, the mean value of RH was 45.43% while in 2024 it
decreased to 25.51% with a p-value of 0.000 indicating that the change is statistically significant.
The high variation in the confidence interval and the significantly lower humidity level in 2024
may affect the comfort of the office environment, especially in drier environments, which may
lead to reduced air quality and employee discomfort.
Carbon Dioxide (CO₂) In 2017, the mean value of CO₂ concentration was 592.09 ppm, while
it decreased to 428.42 ppm in 2024 with a p-value of 0.000, indicating that the change is
statistically significant. The confidence intervals have almost no overlap indicating that the
decrease in CO₂ concentration is significant. This suggests an improvement in ventilation in 2024
compared to 2017, which may be related to adjustments to the ventilation system or changes in
utilization of the office space, contributing to improved air quality and perceived employee
performance.
4.5 Research Results
This study analyzes the changes in the office environment before and after the implementation
of hot-desking based on Office G's post-occupancy evaluation (POE) and indoor environmental
quality (IEQ) data, respectively, from the perspectives of different years, ages, and genders. It
should be emphasized that Office G did not change its office location, but only adjusted its office
model. Therefore, the changes in the data mainly reflect the impact of office optimization on
environmental quality and employee experience, rather than the interference of external factors.
The implementation of the “hot-desking” system has generally improved employees'

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adaptability and comfort in the office environment. After the system was implemented, employees
were free to choose the workstation they found comfortable, avoiding the limitations of fixed
workstations and improving the efficiency of office space use. Employees can adjust their working
methods according to their own needs, so their satisfaction with temperature, light, air quality and
spatial flexibility has generally improved. This shows that the hot-desking system has enhanced
employees' sense of control over their environment, allowing them to actively adjust their office
conditions and improve their comfort and work efficiency.
Employee satisfaction with the temperature experience has increased. Before the system was
implemented, the temperature at some workstations was either too cold or too hot. Employees
could not adjust the temperature at will, but had to adapt passively, which caused some of them to
feel uncomfortable with the ambient temperature. However, after the system was implemented,
employees could choose workstations according to their personal temperature preferences, such as
areas away from air conditioning vents or direct sunlight. This freedom of choice allowed
employees to more actively adjust their comfort level, so even though there were no physical
changes to the overall temperature control method in the office, employee temperature satisfaction
still improved.
The impact of the lighting environment on employee comfort and productivity is crucial.
Before the adjustment, some workstations were affected by strong natural light due to their
proximity to the windows, while other areas were under-lit, resulting in some employees working
in an under-lit environment for long periods of time. After the adjustment, employees can freely

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choose the workstation that suits their needs. For example, employees who prefer natural light can
choose a seat by the window, while those who are sensitive to screen glare can choose an area with
softer lighting. This flexibility has improved the light comfort of employees and, to some extent,
their work efficiency.
The change in privacy experience is relatively complex. Before the system was implemented,
fixed workstations provided a certain amount of personal space, and employees could use the same
location for a long time. They also worked in some more enclosed areas, reducing the distractions
of conversations or conference calls. However, the adjusted office model makes the office space
more open, and employees cannot occupy fixed workstations for a long time. This may lead to
some employees feeling less privacy, for example, in the face-to-face workstation arrangement,
private conversations may be heard more easily. Nevertheless, the flexibility of the hot desk system
provides a way to alleviate privacy issues. Some employees can choose to work in quieter, more
private areas to avoid distractions. At the same time, collaboration between teams becomes more
efficient and information flows more smoothly. Therefore, although the fixed nature of privacy
protection has been reduced, the flexibility of the office model allows employees to more actively
adjust their working methods to suit their own needs. In the future, the privacy experience can be
further optimized by providing phone booths, focus work areas, or adjusting the layout of screens.
In addition to changes in individual experience, the overall utilization efficiency of the office
space has also improved. Before the adjustment, some fixed workstations were left unused for a
long time, while some areas were overcrowded due to high utilization rates, resulting in an

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unbalanced allocation of space resources. After the adjustment, employees can choose the most
suitable workstation according to the needs of the day, which makes the use of space more
balanced and reduces waste of resources. This not only improves the utilization rate of office
resources, but also enhances the flexibility of collaboration between teams, making
communication and cooperation between different teams more efficient.
Although the overall environmental quality has improved, there are still some factors that
need to be further optimized. For example, the noise level has increased. Since open offices
increase the sound transmission range, the background noise in some areas has become more
noticeable, which may have a certain impact on work that requires a high degree of concentration.
Therefore, in the future, noise management measures can be further optimized, such as adding
sound-absorbing materials, optimizing the division of office areas, or setting up dedicated quiet
areas, to ensure that employees with different work needs can have a better office experience.
In addition, there is still room for improvement in air humidity management. After the system
was implemented, the air humidity decreased, and the air was drier during some periods, which
may affect the comfort of employees. This change may be related to the improvement of ventilation
efficiency. Therefore, in the future, the comfort of the environment can be ensured by optimizing
the humidity control strategy, such as adding intelligent humidification equipment or adjusting the
ventilation system, to further balance the air humidity.
The data also shows that employees of different ages and genders have different levels of
comfort with the new office model. For example, younger employees may be more sensitive to

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temperature changes, while older employees are more adaptable. In addition, female employees
pay more attention to the lighting environment and privacy needs, while male employees are more
concerned about the stability of space use and environmental control. These differences indicate
that although the hot-desk system has generally optimized the office environment, there is still
room for further optimization to meet the needs of different groups.
The implementation of the hot-desk system has enhanced employees' ability to independently
adjust their office environment, allowing them to more flexibly adapt to different space needs.
Compared with before the adjustment, after the optimization of the office model, employees can
actively choose the most suitable workstation for themselves, thereby improving the flexibility of
temperature comfort, light environment adaptability and privacy control. Although there is still
room for further optimization of noise management, humidity regulation and local temperature
control, overall, the adjustment of the office model has effectively improved the employee
experience. In the future, further optimization of noise control, air humidity regulation and
personalized temperature control measures can highlight the advantages of the hot desk system
and create a more efficient and comfortable office environment for employees.

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Chapter 5: Analysis and Results of Office H in Los Angeles
5.1 Office H data introduction
This section provides an overview of Office H, including its spatial layout, functional areas
and environmental conditions. This section also describes the survey participants, detailing their
occupational category, educational background, age distribution, and gender composition. This
information served as the basis for subsequent analysis of the Indoor Environmental Quality (IEQ)
and Post Occupancy Evaluation (POE) data.
5.1.1 Research site background
Located in the heart of downtown Los Angeles, Office H's office space is situated on a high
floor of a skyscraper. The building utilizes large floor-to-ceiling windows to provide ample natural
light and open city views. The office area is equipped with open plan workspaces, meeting rooms
and collaborative spaces to suit different needs.
The offices are divided into functional areas including Hollywood, Malibu, Venice and
multiple Huddle meeting spaces. The central area features shared facilities such as a reception desk,
pantry and storage area. The different orientations of the office areas are influenced by the sunlight
conditions. North-facing areas receive more consistent light, but are dominated by indirect light.
South-facing areas are exposed to stronger direct sunlight, which has an impact on indoor
temperature and lighting comfort. Lighting in east- and west-facing areas varies considerably over
time, which can lead to glare and temperature fluctuations.

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5.1.2 Description of the data group
This study surveyed a total of 24 employees in multiple occupational categories, including
Professional, Technical, Administrative, and Managerial, in Office H. The educational background
of the participants was primarily undergraduate, with 18 completing a bachelor's degree, 4
completing a graduate degree, and 1 still enrolled in college. The age distribution of the
respondents is 18-29 years old 7, 30-39 years old 9, 40-49 years old 3, 50-59 years old 4. There
were 14 males and 10 females.
5.2 Key Data Identification and Feature Analysis
The focus of this section is to identify and characterize key data points. A statistical overview
of the collected IEQ and POE data was first performed, followed by ranking the IEQ factors
according to their impact on occupant comfort. Correlation analysis is then performed to explore
the relationship between POE responses and environmental parameters. Finally, an overall bar
chart visually summarizes the findings, providing insights into data distribution and trends.
5.2.1 Statistical Overview
This subsection presents a general statistical summary of the data collected, including mean,
median, maximum, and minimum values for key IEQ parameters. By analyzing these statistics,
we can determine baseline conditions and identify any significant changes in the indoor
environment. This basic analysis aids in subsequent data sorting, correlation, and visualization
efforts.

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Question Mean Median Max Min SD
Q3 (T) 4.5 4.5 7 1 1.911
Q6 (L) 3.7 3.5 7 1 1.574
Q10 (L) 5.8 6 7 3 1.250
Q11 (L) 4.9 4.5 7 3 1.472
Q12 (L) 5.5 5.5 7 3 1.382
Q13 (L) 5.1 5 7 2 1.541
Q2 (AQ) 6 6 7 3 1.180
Q7 (A) 3.4 3 6 1 1.213
Q9 (A) 4.3 4.5 7 1 1.574
Q4 (A) 6 6 7 4 1.042
Q8 (A) 6.4 7 7 4 0.776
Q15 (S) 5 5 7 1 1.876
Q16 (S) 6.4 7 7 4 0.974
Q17 (S) 5.2 5.5 7 2 1.532
Q18 (S) 6.2 6 7 4 0.917

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Q20 (S) 4.5 4.5 7 2 1.588
Q26 (S) 5.6 6 7 3 1.100
Q27 (S) 5.8 6 7 4 0.917
Q28 (S) 5.4 5 7 3 1.060
Q29 (S) 5.6 6 7 3 1.135
Table 5-1 Descriptive Statistics of POE Survey Responses in Office H
In this section, the POE questionnaire data is statistically analyzed to roughly identify the key
factors that may affect the employees through Mean, Median, Max & Min.
From the mean data, employees gave higher ratings to Air Quality (Q2, 6.0), Overall Lighting
Quality (Q18, 6.2), and Window View (Q16, 6.4), indicating that these factors are more
satisfactory in the overall office environment. In contrast, conversation noise (Q7, 3.4), visual
privacy (Q6, 3.7), and background noise (Q9, 4.3) had relatively low mean values, indicating that
some employees are affected by ambient noise and lack of privacy. In addition, temperature
comfort (Q3, 4.5) is at the lower end of the medium range, indicating that there is some
disagreement in the perception of office temperature, which may be related to individual
differences or the location of workstations.
From the median point of view, the median of most IEQ variables is close to the mean,
indicating a more balanced distribution of data. For example, Air Quality (Q2, 6.0), Overall
Lighting Quality (Q18, 6.0), and Window View (Q16, 7.0) continue to maintain high ratings,

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suggesting that the majority of employees are more consistently positive about them. However,
the median ratings for Background Noise (Q9, 4.5) and Conversation Noise (Q7, 3.0) were slightly
lower than the mean, suggesting that more employees are disturbed despite the lower noise levels
in some workstations. In addition, the low median values for Visual Privacy (Q6, 3.5) and
Environmental Adjustability (Q15, 5.0) imply that the majority of employees do not have effective
control over their office environments, especially with regard to personalized temperature and
lighting.
Maximum and minimum values reflect the extremes of the data distribution. The maximum
value of 7 for all IEQ variables indicates that at least some of the employees rated their work
environment the highest on all measures. However, some factors had lower minimum values,
indicating significant discomfort at some workstations. For example, Temperature Comfort (Q3,
min. 1), Background Noise (Q9, min. 1), and Ability to Adjust the Environment (Q15, min. 1)
indicate high temperature fluctuations at some workstations and a lack of control over the
environment by employees. In addition, reflected light from computer screens (Q11, min. 3), direct
glare from lamps (Q12, min. 3), and direct glare from natural light (Q13, min. 2) indicate that some
office areas may have more serious lighting problems, such as direct sunlight or improperly
arranged lamps, which affect visual comfort.
In summary, although the overall scores for most of the IEQ variables are high, there are still
large individual differences in certain factors such as noise interference, environmental adjustment
capability and some light problems. Subsequent chapters will further analyze the objective

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environmental characteristics of these factors in conjunction with the IEQ sensor data to gain a
more comprehensive understanding of the impact of the office environment on employee comfort.
5.2.2 Ranking of IEQ factors
This section analyzes the level of employee concern for different IEQ factors based on data
from the POE questionnaire Q21 (Ranking of Importance). Lower mean scores indicate that the
factor is more important to employees, while higher scores indicate that it is relatively less
important.
Noise Temperature Privacy
Air
quality
Size of
workspace
Window
access
Lighting
Ave 2.38 3.29 4.25 4.17 5.25 4.58 4.08
Table 5-2 Ranking of IEQ Factors in Office H
Among all IEQ variables, noise ( mean 2.38) is the indoor environment quality factor that
employees are most concerned about, so noise has a great impact on their comfort and productivity.
In hot desk systems, the sound of conversations, background noise, or equipment operation can be
a major source of distraction, affecting employees' concentration and communication experience.
Temperature (mean 3.29) shows the importance of heat on employee comfort. The high
ranking of temperature comfort indicates that there may be uneven temperature at different
workstations and some employees may feel too cold or too hot.
Privacy (mean 4.25) and Air Quality (mean 4.17) are of medium priority. Employees continue

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to be concerned about personal privacy in open office spaces, especially when online meetings or
private conversations may be disturbed. In addition, the Air Quality scores indicate that some
employees may be affected by poor air circulation, high CO₂ concentrations, or other air pollutants
(e.g., PM2.5).
Size of workspace (mean 5.25), Window Access (mean 4.58), and Lighting (mean 4.08) were
considered relatively low priority factors. This suggests that although workstation space and
lighting conditions have a certain impact on the work experience, employees are less concerned
about these factors than noise, temperature and air quality, probably because the current office
environment can basically meet the needs.
Taken together, noise, temperature and air quality are the three key IEQ factors that
employees are most concerned about. The specific impacts of these factors on employee comfort
and job satisfaction will be further analyzed in subsequent chapters, taking into account POE
feedback and IEQ sensor data.
5.2.3 Correlation Analysis of POE
In this section, Pearson correlation analysis was used to explore the relationship between
different factors based on the data from the POE questionnaire. When the correlation coefficient
|r| > 0.6, it indicates a strong linear relationship between two variables. The main correlations
between Thermal Comfort, Lighting, Air Quality, Acoustic, and Spatial Factors are categorized
according to the different categories of POE questions.

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Table 5-3 Pearson Correlation Matrix of POE Factors in Office H
For Thermal Comfort, Q3 (Temperature Comfort) and Q6 (Visual Privacy) showed some
correlation (r = 0.55), suggesting that employees' perception of temperature may be related to the
openness of their office environment. In open office spaces, temperature regulation is usually more
difficult to control individually, while workstations with lower visual privacy are usually close to
open areas or passageways, and may be affected by more external environmental factors, such as
cold air blowing directly or direct sunlight.
Among the lighting factors, Q12 (direct glare from fixtures) has a strong correlation (r = 0.72)
with Q11 (reflected light from screens), suggesting that the angle and brightness of fixtures may
have a direct impact on screen reflections. Stronger direct light may result in noticeable glare on
the screen, reducing visual comfort and affecting productivity. q13 (direct natural light glare)
shows a higher correlation (r = 0.77) with q11 (screen reflective light), suggesting that the intensity
and angle of incidence of daylight outside the window may play a key role in screen reflectivity,
especially in workstations near windows. q18 (overall lighting quality) has a higher correlation (r
= 0.77) with q28 (environmental impacts on productivity), suggesting that the angle and brightness

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of lamps may directly affect screen reflections. ) shows a strong correlation (r = 0.68) with Q28
(environmental effects on productivity), suggesting that good lighting conditions may enhance
employee productivity. The correlation between Q18 (Overall Lighting Quality) and Q27 (Job
Satisfaction) is high (r = 0.69), suggesting that good lighting conditions may have a direct impact
on the overall job satisfaction of employees.
Air quality did not show a significant correlation with the other factors and may be less
important than the other factors.
In terms of acoustic environment, Q7 (Conversation Noise) showed a strong correlation (r =
0.62) with Q11 (Reflected Light from Screens), suggesting that noise and light disturbances may
simultaneously affect employee comfort in open plan office environments.Q9 (Background Noise)
showed a high correlation (r = 0.61) with Q4 (Aesthetics of Office Spaces), suggesting that
background noise may affect the overall perception of office environments by employees.
In terms of spatial factors, Q17 (distance between workstations) showed a strong correlation
(r = 0.62) with Q20 (workstation enclosure), indicating that the spatial distribution between
workstations may be affected by the enclosure design, and Q20 (workstation enclosure) showed a
correlation (r = 0.56) with Q26 (recognition of departmental work environment), indicating that a
better enclosure design not only affects the individual's work experience, but may also improve
employees' perception of the overall office environment. Q20 (Workstation Enclosure) showed a
high correlation (r = 0.57) with Q29 (Overall Environmental Quality), further indicating the
importance of enclosure in influencing employees' perception of space.

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In terms of overall satisfaction, there is a strong correlation (r = 0.69) between Q29 (Overall
quality of workstation environment) and Q28 (Impact of environment on productivity), suggesting
that employees' satisfaction with the office environment may have a direct impact on their
productivity. When the comfort level of the office environment is high, employees are more likely
to perceive that their productivity will increase as well. In addition, Q29 is also highly correlated
(r = 0.66) with Q27 (job satisfaction), which implies that there is a close relationship between
employees' satisfaction with the workstation environment and overall job satisfaction, and that a
good work environment may play a key role in enhancing employees' overall satisfaction.
5.2.4 Overall Bar Chart
This section uses a bar chart to compare the POE data for 2017 and 2024 as a whole in order
to see how the overall trend has changed before and after the hot desking system was implemented.
The graph shows the average ratings for different questions and visualizes the changes in employee
satisfaction with the office environment by comparing the data between the two years. By
analyzing the up and down trend of the overall ratings, the impact of the Hot Desk System on
different environmental factors can be assessed and provide a basis for subsequent detailed data
analysis.

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Figure 5-1 Overall Comparison of POE Ratings in Office G (2017 vs. 2024)
A comparison of the overall POE data shows an overall upward trend in ratings in 2024
compared to 2017, thus indicating that the implementation of the desk rotation system has
improved employee satisfaction with the office environment to some extent. Looking at the charts,
most of the questions were rated higher in 2024 than in 2017, reflecting a possible optimization of
environmental quality, space utilization efficiency, and work experience.
Within the overall trend, the improvement in scores in 2024 is mainly in the areas of comfort,
space adaptability and adjustability of the office environment, with particularly significant
increases in the scores for some questions, suggesting that employees are showing a higher level
of acceptance of the new office model in these areas. Additionally, scores for some questions

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involving environmental control and personalized adjustments are significantly higher in 2024
compared to 2017, suggesting that the desk rotation system may provide employees with a better
environmental adjustment experience, enabling them to meet their comfort needs more effectively.
This trend may mean that employees are more receptive to the flexibility and adjustability of space
under the new office model, which in turn improves overall job satisfaction.
However, despite the upward trend in overall ratings, there are still some questions that have
lower ratings in 2024 than in 2017, suggesting that certain environmental factors may have been
affected by the implementation of the desk rotation system. For example, questions related to
workspace privacy, noise environment, or personal temperature perception may have seen a
decrease in ratings due to the increased openness of the office space, increased mobility of
personnel, or homogenization of environmental control methods, making some employees
uncomfortable in these areas.
Overall, POE ratings show a more polarized trend in 2024 compared to 2017. Ratings for the
majority of questions increased significantly, indicating that the desk rotation system is better
recognized overall, and that employees' adjustment and satisfaction with the new office
environment has increased. However, ratings for some questions decreased, indicating that there
is still room for improvement in the optimization of the new system with respect to certain
environmental factors. This trend reflects that while the flexible office model can enhance the
office environment as a whole, attention still needs to be paid to how well employees adapt to
individual needs in order to further optimize the overall work experience.

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5.3 POE Data Analysis
This section analyzes Post-Occupancy Evaluation (POE) data collected before and after the
implementation of the hot-desking system. It examines overall changes in user satisfaction and
comfort, as well as differences based on demographic factors. Differences in workplace
experiences and perceptions of the environment are explored by comparing pre- and post- hotdesking system data, gender groups, and age groups.
5.3.1 Overall comparison of Pre and Post data
This subsection compares POE responses before and after the desk transition. It highlights
changes in employee satisfaction, comfort, and perceived environmental quality, identifying trends
and potential improvements or issues with the new workplace arrangement.
2017 vs 2024 POE interval plot on Thermal satisfaction
Q3 (Workspace Temperature);
P-Value=0.800
2017 vs 2024 POE interval plot on Lighting satisfaction

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Q6 (Visual Privacy);
P-Value=0.160
Q10 (Computer Task Lighting);
P-Value=0.287
Q11 (Glare on Screen);
P-Value=0.256
Q12 (Glare from Light Fixtures);
P-Value=0.684

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Q13 (Glare from Daylight);
P-Value=0.949
Q18 (Overall Lighting Quality);
P-Value=0.051
2017 vs 2024 POE interval plot on Air Quality satisfaction
Q2 (Overall Air Quality);
P-Value=0.013
2017 vs 2024 POE interval plot on Acoustic satisfaction

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Q7 (Speech Noise at Workstation);
P-Value=0.492
Q9 (Background Noise Level);
P-Value=0.545
2017 vs 2024 POE interval plot on Spatial Quality satisfaction
Q4 (Office Aesthetic);
P-Value=0.059
Q8 (Personal Workspace Size);
P-Value=0.003

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Q15 (Control Over Physical Conditions);
P-Value=0.851
Q16 (Access to Outdoor View);
P-Value=0.591
Q17 (Distance to Colleagues);
P-Value=0.966
Q20 (Workstation Enclosure);
P-Value=0.558
2017 vs 2024 POE interval plot on Overall Workplace satisfaction

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Q27 (Job Satisfaction);
P-Value=0.636
Q28 (Productivity);
P-Value=0.662
Q29 (Overall Satisfaction);
P-Value=0.223
Table 5-4 Pre and Post Comparison of POE Factors in Office G (2017 vs. 2024)
The mean value of Q3 decreased slightly in 2024 compared to 2017, but the confidence
intervals overlapped more and the difference was not significant. This suggests that employee
satisfaction with temperature did not change significantly after the implementation of hot-seat
offices, which may be due to the temperature control system remaining stable or large differences

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in individual perception.
The mean value of Q6 decreases significantly in 2024 with no overlapping confidence
intervals, indicating a statistically significant decrease in visual privacy. This suggests that
employees are less satisfied with visual privacy after hot-seating offices, possibly due to small
office space.
The mean value of Q10 increases in 2024, with only a small overlap in the confidence
intervals, suggesting a more pronounced improvement in lighting conditions. The narrower
confidence intervals in the 2024 data suggest that employees are more consistent in their ratings
of light in the computer work area, possibly reflecting optimization of lighting or increased
flexibility in seating choices.
Q11 has a lower mean value in 2024 and the confidence intervals partially overlap, suggesting
that the problem of reflected light from screens has lessened, but the change is not significant
enough. The wide confidence intervals indicate that there is still a wide variation in employee
experience, which may be influenced by specific seating positions or the direction of the light
source.
The mean values for Q12 remain largely the same in 2024 as in 2017, and the confidence
intervals are highly overlapping, suggesting that the impact of hot seating on direct light glare is
not significant and that there is no significant change in the perception of this issue for both
employees.
The mean value of Q13 in 2024 is close to that of 2017 and the confidence intervals overlap

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almost exactly, indicating that the problem of direct natural light glare has not been significantly
affected. This means that window shading measures have not changed significantly, or that
employee perception of daylight glare has remained relatively stable depending on the distribution
of workstations.
The mean value for Q2 2024 is significantly higher than 2017, and the confidence intervals
for the two data sets do not overlap, indicating a statistically significant improvement in air quality.
This suggests that the majority of employees believe that air quality has improved. Additionally,
the narrower confidence interval in 2024 suggests that employees are more consistent in their
assessment of air quality, with less divergence in their perceptions; in contrast, the wider
confidence interval in 2017 reflects a greater variation in employees' perceptions of air quality at
that time.
The mean value of Q7 decreased significantly in 2024 and the confidence intervals for the
two data sets did not overlap, suggesting that the increase in dialog noise was equally statistically
significant. The lower mean in 2024 compared to 2017 suggests that employee perceptions of
dialog noise become more negative in hot-desking mode. Additionally, the narrower confidence
interval in 2024 suggests that the majority of employees were more consistent in their perceptions
of the issue, whereas the wider confidence interval in 2017 suggests that there was greater
individual variation in employees' experience of noise at that time.
The mean value for Q9 has increased in 2024, but the change is less statistically significant.
Despite the increase in the mean value, the overlapping confidence intervals mean that some

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employees may not have significantly felt the change.
The mean value for Q4 increased significantly in 2024 and the confidence intervals for the
two data sets do not overlap, indicating that the increase in employee satisfaction with the
aesthetics of office space is statistically significant. The overall upward shift in the confidence
intervals suggests that the majority of employees believe that the new office environment has
improved in terms of visual design.
The mean value of Q8 is significantly higher in 2024 with no overlapping confidence intervals,
suggesting that employees' satisfaction with their personal workspace improves significantly after
hot-seating. The narrower confidence interval in 2024 suggests that employees have a more
consistent experience with workstation size. This may be related to the freedom to choose a more
appropriate workstation in the hot-seat office model, reconfiguration of office space, or
optimization of desktop use efficiency.
The mean value of Q15 does not change significantly in 2024, and the confidence intervals
of the two data sets highly overlap, suggesting that there has been no significant change in the
ability of employees to adapt their own work environments.
The mean value of Q16 is almost the same in 2024 as it was in 2017, and the confidence
intervals mostly overlap, suggesting that the hot-seat office model did not significantly affect
employees' perceived outside view.
The mean value of Q17 remains virtually unchanged in 2024 versus 2017, and the confidence
intervals overlap completely, indicating that employee perceptions of distance between

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workstations have remained constant.
The mean value of Q18 increases significantly in 2024, with no overlap in confidence
intervals, indicating a statistically significant improvement in lighting quality. The confidence
intervals move significantly upwards, indicating that most employees believe that the new office
environment provides better lighting. This may be related to lighting system upgrades, seating
adjustments, or an increase in personalized lighting options.
The mean value of Q20 decreases in 2024 and the confidence intervals partially overlap,
indicating a more pronounced but not significant downward trend in enclosure. Although the
means have decreased, the overlapping confidence intervals mean that the change may not be
significantly felt by some employees.
The mean value of Q26 has slightly increased in 2024, but the confidence intervals of the two
data sets partially overlap, suggesting that employees' overall appraisal of the department's
working environment has increased, but the change is not significant. The slightly higher mean
value in 2024 compared to 2017 may be related to the optimization of the office environment, the
increase in work flexibility, or adjustments in management strategies. However, as the confidence
intervals still have a large crossover, it suggests that there are some differences in the subjective
experience of employees, and not all of them feel a significant change.
The mean value of Q27 does not change significantly in 2024 and the confidence intervals of
the two data sets highly overlap, suggesting that Hot Seating has not had a significant impact on
employees' overall job satisfaction. Although there is a slight decrease in the mean value, the

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difference is small and the range of confidence intervals is close, indicating that employees have
adapted better to the new office model and their satisfaction has remained stable.
The mean value of Q29 increases significantly in 2024, and the confidence intervals of the
two data sets do not overlap, indicating that the improvement in the overall quality of the office
environment is statistically significant. The mean value has increased by nearly 1.0 point,
indicating that most employees believe the new office environment has been optimized in a
number of ways, such as improved air quality, lighting conditions, or space utilization. The
narrower confidence intervals in 2024 suggest that employee ratings of the environment have
converged, possibly reflecting a more homogeneous office experience.
5.3.2 Comparison of gender differences
This subsection analyzes differences in POE responses between male and female employees.
It explores differences in perceived comfort, environmental satisfaction, and workplace
preferences, providing insights into gender differences in adapting to desk rotation systems.
2017 vs 2024 POE interval plot on Thermal satisfaction by Gender

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Q3 (Workspace Temperature);
P-Value 2017=0.569, P-Value 2024= 0.014
2017 vs 2024 POE interval plot on Lighting satisfaction by Gender
Q6 (Visual Privacy);
P-Value 2017=0.759, P-Value 2024= 0.785
Q10 (Computer Task Lighting);
P-Value 2017=0.339, P-Value 2024= 0.980
Q11 (Glare on Screen);
P-Value 2017=0.430, P-Value 2024= 0.823
Q12 (Glare from Light Fixtures);
P-Value 2017=0.259, P-Value 2024= 0.903

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Q13 (Glare from Daylight);
P-Value 2017=0.376, P-Value 2024= 0.309
Q18 (Overall Lighting Quality);
P-Value 2017=0.269, P-Value 2024= 0.784
2017 vs 2024 POE interval plot on Air Quality satisfaction by Gender
Q2 (Overall Air Quality);
P-Value 2017=0.439, P-Value 2024= 0.751
2017 vs 2024 POE interval plot on Acoustic satisfaction by Gender

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Q7 (Speech Noise at Workstation);
P-Value 2017=0.248, P-Value 2024= 0.335
Q9 (Background Noise Level);
P-Value 2017=0.815, P-Value 2024= 0.511
2017 vs 2024 POE interval plot on Spatial Quality satisfaction by Gender
Q4 (Office Aesthetic);
P-Value 2017=0.447, P-Value 2024= 0.378
Q8 (Personal Workspace Size);
P-Value 2017=0.399, P-Value 2024= 0.690

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Q15 (Control Over Physical Conditions);
P-Value 2017=0.981, P-Value 2024= 0.931
Q16 (Access to Outdoor View);
P-Value 2017=0.273, P-Value 2024= 0.070
Q17 (Distance to Colleagues);
P-Value 2017=0.361, P-Value 2024= 0.811
Q20 (Workstation Enclosure);
P-Value 2017=0.829, P-Value 2024= 0.805
2017 vs 2024 POE interval plot on Overall Workplace satisfaction by Gender

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Q27 (Job Satisfaction);
P-Value 2017=0.137, P-Value 2024= 0.522
Q28 (Productivity);
P-Value 2017=0.857, P-Value 2024= 0.562
Q29 (Overall Satisfaction);
P-Value 2017=0.850, P-Value 2024= 0.661
Table 5-5 Gender-Based Comparison of POE Factors in Office H
Q3 (Temperature Perception) In 2017, females scored slightly higher (4.875) than males
(4.42857), but the p-value was 0.569, indicating that the gender difference was not significant. By
2024, the female ratings decreased significantly to 3.3 while the male ratings increased to 5.28571
with a p-value of 0.014, reaching the significance level. This suggests that after the implementation

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of the hot desk system, males' satisfaction with temperature increased while females' feelings
decreased significantly, causing the gender difference to become significant in 2024. The reduced
overlap of confidence intervals further suggests that this change may have affected females more.
Q6 (Visual Privacy) In 2017, women's ratings (4.25) were slightly lower than men's (4.5), but
the p-value of 0.759 did not reach a significant level, suggesting that both genders had essentially
the same feelings of visual privacy in that year.2024, women's ratings decreased to 3.6 and men's
ratings decreased to 3.78571 , with a p-value of 0.785, which still did not reach a level of
significance. This suggests that the implementation of the hot desking system has reduced the
perception of visual privacy for all employees, but the impact is not significantly different between
genders. Despite the overall decrease in ratings, there is still more overlap in the confidence
intervals, indicating that employees are still adapting to the new office environment and that the
impact is more even.
Q10 (Concentration Support) In 2017, women's ratings (5.75) were slightly higher than men's
(5.07143) but with a p-value of 0.339 indicating that the gender difference was not significant. In
2024, women's ratings increased to 5.8 and men's ratings increased to 5.78571 with a p-value of
0.980 indicating that the gender difference was still not significant. Compared to 2017, the ratings
all increased in 2024 with highly overlapping confidence intervals, suggesting that the
implementation of the hot desking system did not negatively impact employee focus support, and
may have even improved it, and that this trend was more consistent for both males and females.
Q11 In 2017, the mean value of satisfaction with lighting was higher for females (5.75) than

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for males (5.23077), but the p-value of 0.430 did not reach the level of significance, indicating that
the gender difference was not significant. By 2024, both female and male ratings have decreased,
to 5.0 and 4.85714 respectively, but with a p-value of 0.823, still showing no significant gender
difference. Despite the slight decrease in overall ratings, the changes were small and the confidence
intervals still highly overlap, indicating that the implementation of the hot desk system did not
have a significant negative impact on lighting satisfaction, and that men and women continue to
feel relatively the same way.
Q12 In 2017, women were more satisfied with natural light (6.0) than men (5.38462), but the
p-value was 0.259, suggesting that the gender difference was not significant.2024, women's ratings
decreased to 5.5, and men's ratings changed less, to 5.42857, with a p-value of 0.903, suggesting
that the gender difference was still not significant. Despite the slight decrease in female ratings,
male ratings remained largely stable and the confidence intervals were highly overlapping,
indicating that the introduction of the hot table system did not have a significant negative impact
on natural light satisfaction, and the overall trend remained stable.
Q13 In 2017, women felt more comfortable with glare (5.5) than men (4.84615), with a pvalue of 0.376, which did not reach significance. By 2024, the female rating remained the same
(5.5) while the male rating increased slightly to 4.85714 with a p-value of 0.309, still not reaching
a level of significance, indicating that the gender difference was not significant in either year. It is
worth noting that despite the changes in spatial layout and light distribution that may have been
brought about by the introduction of the hot desking system, the perception of glare remains the

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same for females and improves slightly for males, suggesting that the glare problem has not been
significantly affected as a whole, and may even have been optimized by the new office
environment.
Q2 (Air Quality) In 2017, females had a higher rating (5.25) than males (4.78571) but the pvalue of 0.439 did not reach the level of significance suggesting that the gender difference was not
significant.In 2024, females' ratings increased to 5.9 and males' ratings increased to 6.07143, with
a p-value of 0.751, which still did not reach the level of significance. The overall improvement in
ratings in 2024 compared to 2017 and the narrowing of the difference between men and women
with highly overlapping confidence intervals suggests that the implementation of the hot desk
system may have improved perceptions of air quality, resulting in employees being more satisfied
with air quality regardless of gender.
Q7 (Noise from other people's conversations) was rated lower by females (3.125) than males
(4.07143) in 2017, but the p-value of 0.248 did not reach the level of significance, suggesting that
males may be slightly more tolerant of noise. In 2024, there was little change in the ratings of
females (3.1), whereas the ratings of males declined to 3.64286, with a p-value of 0.335, which
still did not reach the level of significance level. Both male and female ratings decreased in 2024
compared to 2017, with highly overlapping confidence intervals, suggesting that the hot desking
system may have little effect on noise or that all employees are adapting to the new environment
and that the difference in perception between genders remains small.
Q9 (Background Noise) In 2017, women's ratings (3.875) were slightly lower than men's

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(4.07143) with a p-value of 0.815 suggesting that the gender difference was not significant. In
2024, women's ratings increased slightly to 4.0 and men's ratings increased to 4.5 with a p-value
of 0.511, still not reaching a significant level. Compared to 2017, the ratings increased slightly in
2024, but the male and female confidence intervals are still highly overlapping, suggesting that the
implementation of the hot desk system has had less of an impact on background noise and that
employees may have improved their ability to adapt to noise.
Q4 (Humidity) In 2017, women's ratings (5.625) were higher than men's (5.15385), but the
p-value of 0.447 did not reach significance, suggesting that the gender difference was not
significant.In 2024, women's ratings increased slightly to 5.8, while men's ratings increased to
6.21429, with a p-value of 0.378, still not reaching significance. Compared to 2017, the overall
improvement in the mean value in 2024 and the gap between male and female ratings has widened,
but there is still a large overlap in the confidence intervals, suggesting that the implementation of
the hot-table system may have improved the perception of humidity, but the effect of this
improvement remains relatively consistent between genders.
Q8 (privacy) was rated higher for females (5.625) than males (5.07143) in 2017, but the pvalue of 0.399 did not reach a level of significance, suggesting that the gender difference was not
significant. In 2024, the ratings increased to 6.5 for females and 6.35714 for males, with a p-value
of 0.690, which still did not reach a level of significance. Compared to 2017, the mean values in
2024 have both increased and the confidence intervals are highly overlapping, suggesting that the
implementation of the hot desk system may have improved the privacy experience to the point

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where all employees feel some enhancement, while the difference between genders remains small.
Q15 (Ability to Regulate Physical Environment) In 2017, women's ratings (5.125) were
almost identical to men's (5.14286) with a p-value of 0.981, indicating that the gender difference
was not significant at all. In 2024, women's ratings dropped slightly to 5.0 while men's ratings
stayed at 5.07143 with a p-value of 0.931, which still did not reach the level of significance.
Compared to 2017, the ratings in 2024 remained largely stable with highly overlapping confidence
intervals, indicating that the implementation of the hot desk system had no significant impact on
employees' perceived ability to regulate the physical environment and that the experience was
largely consistent across gender groups.
Q16 (Feelings of control over the work environment) In 2017, women's ratings (6.5) were
slightly higher than men's (6.14286) but the p-value of 0.273 did not reach significance suggesting
that the gender difference was not significant.In 2024, women's ratings increased to 6.8 while
men's stayed the same at 6.14286, with a p-value of 0.070, which is close to significance level but
still not reached. Compared to 2017, women's ratings increased while men's stayed the same, with
less overlap in the confidence intervals, suggesting that the hot desking system may have given
women a heightened sense of control over their work environment, while men's feelings were
largely stable.
Q17 (Sense of distance from others) was rated higher for females (5.625) than males (5.0) in
2017, but the p-value of 0.361 did not reach a level of significance, suggesting that the gender
difference was not significant. In 2024, female ratings decreased to 5.3 and male ratings increased

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slightly to 5.14286, with a p-value of 0.811, which still did not reach significance. Compared to
2017, women's ratings decreased while men's ratings increased slightly, with confidence intervals
still highly overlapping, suggesting that the hot desking system has less of an impact on both
genders in terms of office distance perception, with little overall change.
Q18 (Quality of Lighting) was rated higher for females (5.875) than males (5.15385) in 2017,
but the p-value was 0.269, which did not reach significance, suggesting that the gender difference
was not significant. In 2024, the rating increased to 6.1 for females and 6.21429 for males, with a
p-value of 0.784, which still did not reach significance. Compared to 2017, both mean values
increased in 2024 with a narrower gender gap and highly overlapping confidence intervals,
suggesting that the hot desk system may have optimized the quality of lighting, resulting in
increased satisfaction for all employees.
Q20 (Workstation Availability) In 2017, women's ratings (4.875) were slightly higher than
men's (4.71429), but the p-value of 0.829 did not reach significance, suggesting that the gender
difference was not significant. In 2024, women's ratings decreased to 4.4 and men's ratings
decreased to 4.57143, with a p-value of 0.805, which still did not reach significance. Compared to
2017, the means for 2024 decreased overall, but the confidence intervals still highly overlap,
suggesting that the hot desk system may have had some impact on the availability of workstations,
but that this change affected both genders more consistently and did not create a significant
difference.
Q26 (identification with department/institution) was higher for women in 2017 at 5.875 than

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for men at 5.15385, but in 2024 there was a decrease in women's ratings to 5.4 and an increase in
men's ratings to 5.71429. Despite the gap between men's and women's ratings in 2017, there was
an increase in men's identification in 2024, which brought the gender ratings closer to each other.
The p-value did not reach significance in either year, suggesting that the implementation of the hot
desk system did not have a significant impact on the identification of different gender groups.
Overall, despite fluctuations in individual ratings, gender identification with a department or
organization remained relatively stable and was not significantly negatively impacted.
Q27 (Job Satisfaction) was very similar for both genders in 2017 with a rating of 6.0 for
females and 5.92857 for males. In 2024, the rating for females declined to 5.7 while the rating for
males remained unchanged. The p-value was higher in both years, 0.857 in 2017 and 0.562 in 2024,
indicating that the gender difference in job satisfaction was not significant and also no significant
change has occurred. Although there is a slight decrease in the ratings of females, the overall
change is small and the confidence intervals are highly overlapping, suggesting that employee
satisfaction with their jobs has remained largely unchanged after the implementation of the hot
desk system, and does not show any systematic dissatisfaction or adjustment problems.
Q29 (Overall Indoor Environment Satisfaction) was rated 5.25 for females and 5.15385 for
males in 2017. In 2024, females' ratings improved to 5.5 while males' ratings increased to 5.71429,
narrowing the gap between the two genders' ratings and suggesting that employees' satisfaction
with the overall indoor environment may have increased after the implementation of the hot desk
system. p-values in both years Neither reached the level of significance, indicating that this change

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did not create a significant gender difference. While women's ratings increased, men's ratings
increased more, possibly reflecting some employees' increased adaptation to the new environment
or some positive environmental optimization brought about by the hot desk system. Overall, the
trend of the Hot Desk System's impact on the overall indoor environment was more positive and
the experience was more consistent between genders.
5.3.3 Comparison of age differences
This subsection explores the responses of employees of different ages to changes in the
workplace. By comparing POE results across age groups, this subsection determines whether
younger or older employees experienced significant changes in comfort, satisfaction, or
productivity after transitioning to desk shifts.
2017 vs 2024 POE interval plot on Thermal satisfaction by Age
Q3 (Workspace Temperature);
P-Value 2017=0.155, P-Value 2024= 0.247
2017 vs 2024 POE interval plot on Lighting satisfaction by Age

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Q6 (Visual Privacy);
P-Value 2017=0.553, P-Value 2024= 0.052
Q10 (Computer Task Lighting);
P-Value 2017=0.387, P-Value 2024= 0.661
Q11 (Glare on Screen);
P-Value 2017=0.637, P-Value 2024= 0.846
Q12 (Glare from Light Fixtures);
P-Value 2017=0.949, P-Value 2024= 0.159

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Q13 (Glare from Daylight);
P-Value 2017=0.802, P-Value 2024= 0.555
Q18 (Overall Lighting Quality);
P-Value 2017=0.933, P-Value 2024= 0.873
2017 vs 2024 POE interval plot on Air Quality satisfaction by Age
Q2 (Overall Air Quality);
P-Value 2017=0.120, P-Value 2024= 0.742
2017 vs 2024 POE interval plot on Acoustic satisfaction by Gender

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Q7 (Speech Noise at Workstation);
P-Value 2017=0.479, P-Value 2024= 0.637
Q9 (Background Noise Level);
P-Value 2017=0.805, P-Value 2024= 0.931
2017 vs 2024 POE interval plot on Spatial Quality satisfaction by Gender
Q4 (Office Aesthetic);
P-Value 2017=0.092, P-Value 2024= 0.347
Q8 (Personal Workspace Size);
P-Value 2017=1.000, P-Value 2024= 0.284

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Q15 (Control Over Physical Conditions);
P-Value 2017=0.068, P-Value 2024= 0.539
Q16 (Access to Outdoor View);
P-Value 2017=0.619, P-Value 2024= 0.891
Q17 (Distance to Colleagues);
P-Value 2017=0.889, P-Value 2024= 0.125
Q20 (Workstation Enclosure);
P-Value 2017=0.346, P-Value 2024= 0.786
2017 vs 2024 POE interval plot on Overall Workplace satisfaction by Gender

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Q27 (Job Satisfaction);
P-Value 2017=0.613, P-Value 2024= 0.261
Q28 (Productivity);
P-Value 2017=0.515, P-Value 2024= 0.775
Q29 (Overall Satisfaction);
P-Value 2017=0.752, P-Value 2024= 0.698
Table 5-6 Age-Based (30) Comparison of POE Factors in Office H
Q3 (Temperature perception) In 2017, the rating of the under 30 group was 5.00991, which
was higher than that of the over 30 group at 4.09091, but the p-value of 0.155 did not reach the
level of significance, indicating that the difference in age was not significant. In 2024, the rating
of the under 30 group dropped to 3.75, while the rating of the over 30 group increased to 4.8125,

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with a p-value of 0.247, still not reaching the significance level. Compared to 2017, satisfaction
with temperature decreased significantly in the under-30 group, while it increased in the over-30
group, and the overlap of the confidence intervals decreased, suggesting that the hot desk system
may have had a differentiating effect on the temperature perception of the different age groups,
and that younger employees may have had more difficulty adapting to the new temperaturecontrolled environment.
Q6 (Visual Privacy) In 2017, the rating for the under 30 group was 4.63636, higher than the
4.18182 for the over 30 group, but the p-value of 0.553 did not reach the significance level
suggesting that the age difference was not significant. In 2024, the rating for the under 30 group
declined to 3.0, whereas that for the over 30 group declined to 4.0625, with a p-value of 0.052,
which is close to the significance level. Compared to 2017, there was a greater decrease in ratings
for the under 30 group and a reduction in the overlap of confidence intervals, suggesting that the
hot desking system has a more pronounced impact on the visual privacy of younger employees,
which may result in a less adaptive office environment, while older employees have a relatively
stable experience.
Q10 (Concentration Support) was rated 5.63636 in 2017 for the under 30 group, higher than
the 5.0 for the over 30 group, but with a p-value of 0.387, it did not reach the significance level.
In 2024, it was rated 5.625 for the under 30 group, while the over 30 group's ratings increased to
5.875, with a p-value of 0.661, which still did not reach the significance level. Compared to 2017,
the ratings for both age groups increased in 2024, with highly overlapping confidence intervals,

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suggesting that the implementation of the hot desking system may have had some positive impact
on employee attentiveness support, and that this increase was more consistent across age groups.
Q11 (Lighting Satisfaction) was rated 5.6 for the 30+ group in 2017, higher than the 5.27273
for the under 30 group, but the p-value was 0.637, which did not reach the significance level,
suggesting that the age difference was not significant. 2024 saw a decrease in ratings for the under
30 group to 5.0, and a decrease in ratings for the over 30 group to 4.875, with a p-value of 0.846
that still does not reach the level of significance. Compared to 2017, ratings for both age groups
decreased in 2024, with highly overlapping confidence intervals, suggesting that the
implementation of the hot desking system may not have brought about a significant change in
lighting satisfaction, and that employee perceptions of lighting have remained stable overall.
Q12 (natural light) was rated 5.63636 for the under 30 group and 5.6 for the over 30 group in
2017, with a p-value of 0.949, suggesting that the difference was minimal. In 2024, ratings for the
under 30 group decreased to 4.875, while ratings for the over 30 group increased to 5.75, with a
p-value of 0.159, which is still not at the significance level. Compared to 2017, the younger group's
satisfaction with natural light decreased, while the older group's ratings increased and the overlap
of the confidence intervals decreased, suggesting that the hot-table system may have had a
somewhat differentiating effect on how the different age groups felt about their natural light
experience, and that the older group may have been more acclimated to the light conditions of their
new environment.
Q13 (Glare) was rated at 5.18182 in 2017 for the under-30 group, slightly higher than the 5.0

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for the over-30 group, with a p-value of 0.802 indicating a non-significant difference. In 2024, the
rating for the under-30 group increased to 5.375, while the rating for the over-30 group remained
unchanged at 5.0, with a p-value of 0.555, which is still not at the level of significance. Compared
to 2017, the younger group's perception of glare improved, while the older group's perception
remained stable with highly overlapping confidence intervals, suggesting that the hot desk system
did not have a significant impact on the glare issue and that the experience of employees of
different ages was more consistent.
Q2 (Air Quality) In 2017, the rating for the under 30 group was 5.45455, which was higher
than the 4.45455 for the over 30 group, with a p-value of 0.120, which did not reach the level of
significance. In 2024, the rating for the under 30 group increased to 5.875, while the rating for the
over 30 group increased to 6.0625, with a p-value of 0.742, which still did not reach the level of
significance. significance level. Compared to 2017, the ratings for both age groups increased in
2024 with a narrower gap and highly overlapping confidence intervals, suggesting that the hot
desking system may have enhanced the overall air quality experience so that employees of all ages
felt some improvement.
Q7 (noise from other people's conversations) was rated at 4.0 for the under 30 group and
3.45455 for the over 30 group in 2017, with a p-value of 0.479, which did not reach the level of
significance. In 2024, the ratings for the under 30 group decreased to 3.25, while the ratings for
the over 30 group increased to 3.5, with a p-value of 0.637, which still did not reach the level of
significance . Compared to 2017, the younger group experienced a slight decrease in conversation

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noise, while the older group's ratings increased, with highly overlapping confidence intervals,
suggesting that the hot desk system has less of an impact on conversation noise, and that the
experience is largely stable across age groups.
Q9 (Background Noise) In 2017, the ratings for the under 30 group were 4.09091 and the
over 30 group were 3.90909 with a p-value of 0.805 indicating no significant difference between
the two. In 2024, the ratings for the under 30 group increased to 4.25 and the over 30 group
increased to 4.3125 with a p-value of 0.931, still not reaching the level of significance. Compared
to 2017, the ratings of background noise increased for both age groups, with confidence intervals
still highly overlapping, suggesting that the hot desk system did not bring about significant changes
to background noise perception, and that the experience of different age groups remained largely
consistent.
Q4 (humidity perception) In 2017, the rating for the under-30 group was 5.81818,
significantly higher than the 4.8 for the over-30 group, with a p-value of 0.092, close to the
significance level, suggesting that the younger group may be more satisfied with humidity. 2024,
the rating for the under-30 group declined to 5.75, whereas that for the over-30 group increased to
6.1875, with a p-value of 0.347, which does not reach the level of significance. Compared to 2017,
humidity perceptions were largely stable for the younger group, while satisfaction increased for
the older group, with a reduction in the overlap of confidence intervals, suggesting that the hot
desk system may have improved the humidity experience for older employees.
Q8 (Privacy) was rated at 5.27273 in 2017 for both the under 30 and over 30 groups with a

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p-value of 1.000, indicating that the two feelings were identical. In 2024, the rating for the under
30 group increased to 6.125 while the rating for the over 30 group increased to 6.5625 with a pvalue of 0.284, which is still short of a level of significance. Compared to 2017, the ratings for
both age groups increased and the improvement was greater for the older group, with less overlap
in the confidence intervals, suggesting that the hot desking system may have made a difference to
the perception of privacy for all employees, with a more pronounced improvement in the
experience of the older group in particular.
Q15 (ability to regulate the physical environment) was rated higher in 2017 at 5.72727 for
the under 30 group than the over 30 group at 4.54545, with a p-value of 0.068, which is close to
the level of significance, suggesting that the younger group may be more inclined to perceive
themselves as being able to regulate their environment.In 2024, the rating for the under 30 group
declined to 5.375, while the rating for the over 30 group's rating increased to 4.875, with a p-value
of 0.539, which does not reach the level of significance. Compared to 2017, the younger group's
ratings decreased while the older group's ratings increased, with a decrease in the overlap of the
confidence intervals, suggesting that the hot desking system may have increased the older
employees' experience of their ability to regulate their environment, while the younger group's
subjective feelings decreased.
Q16 (Sense of control over the work environment) was rated 6.36364 in 2017 for the under
30s group, slightly higher than the 6.18182 for the over 30s group, with a p-value of 0.619
suggesting no significant difference between the two. In 2024, the rating for the under 30s group

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rises to 6.375, while the over 30s group's rating is 6.4375, with a p-value of 0.891, still not reaching
the level of significance. Compared to 2017, the ratings for the two age groups in 2024 remain
largely stable with highly overlapping confidence intervals, indicating that the implementation of
the hot desking system did not significantly affect the experience of different age groups in terms
of their sense of control in the work environment.
Q17 (Sense of distance from others) was rated at 5.27273 in 2017 for the under 30 group,
slightly lower than the 5.18182 for the over 30 group, with a p-value of 0.889, suggesting that the
two groups felt largely the same. In 2024, the rating for the under 30 group decreased to 4.5, while
the rating for the over 30 group increased to 5.5625, with a p-value of 0.125, which is approaching
the significance level. Compared to 2017, the younger group's satisfaction with distance from
others decreased, while the older group's ratings increased and the overlap of confidence intervals
decreased, suggesting that the hot desking system may have allowed older employees to feel more
positively about spatial arrangements, while younger employees may have adapted less.
Q18 (Quality of Lighting) was rated at 5.54545 for the under 30 group and 5.4 for the over
30 group in 2017, with a p-value of 0.933 indicating that the difference was not significant. In
2024, the ratings increased to 6.125 for the under 30 group and 6.1875 for the over 30 group, with
a p-value of 0.873, which still did not reach a level of significance. Compared to 2017, the ratings
of both age groups increased in 2024 with highly overlapping confidence intervals, suggesting that
the hot desk system may have optimized the overall lighting quality, and that the satisfaction of
employees of different age groups has increased with a more consistent trend.

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Q20 (Workstation Availability) In 2017, the rating for the under 30 group was 5.09091, which
was higher than the 4.45455 for the over 30 group, with a p-value of 0.346, which did not reach a
level of significance. In 2024, the rating for the under 30 group decreased to 4.375, while the rating
for the over 30 group increased to 4.5625, with a p-value of 0.786, still not reaching the
significance level. Compared to 2017, the younger group's perception of workstation availability
decreased, while the older group's ratings increased slightly, with highly overlapping confidence
intervals, suggesting that the hot desk system may have had a small impact on the experience of
using workstations for different age groups, with limited overall change.
Q26 (Identification with department/institution) In 2017, the under 30 group scored 5.54545,
slightly higher than the over 30 group's 5.3, with a p-value of 0.613, suggesting no significant
difference between the two. In 2024, the under 30 group's score dropped to 5.25, while the over
30 group's score rose to 5.75, with a p-value of 0.261, which still not reaching the level of
significance. Compared to 2017, the younger group's identification decreased while the older
group's identification increased, with less overlap in the confidence intervals, suggesting that the
hot desking system may have positively impacted older employees' sense of departmental
belonging, while younger employees' identification decreased slightly.
Q27 (Job Satisfaction) In 2017, the under 30 group had a rating of 6.09091, slightly higher
than the over 30 group's rating of 5.81818, with a p-value of 0.515, suggesting that the difference
is not significant. In 2024, the under 30 group's rating dropped to 5.75, while the over 30 group's
rating increased to 5.875, with a p-value of 0.775, still not reaching the significance level.

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Compared with 2017, the job satisfaction of the younger group decreased slightly, while the
satisfaction of the older group increased slightly, with highly overlapping confidence intervals,
indicating that the implementation of the hot desk system did not significantly affect the overall
job satisfaction of employees, and that the trend of changes in different age groups is relatively
smooth.
Q29 (Overall Indoor Environment Satisfaction) In 2017, the rating of the under-30 group was
5.27273, slightly higher than that of the over-30 group which was 5.1, with a p-value of 0.752,
indicating that the difference is not significant. In 2024, the rating of the under-30 group increased
to 5.75, and that of the over-30 group increased to 5.5625, with a p-value of 0.698, which has not
yet reached the level of significance. reaching significance levels. Compared to 2017, the ratings
for both age groups increased in 2024, and the gap between the ages narrowed, with highly
overlapping confidence intervals, suggesting that hot table systems may have improved
satisfaction with the overall indoor environment, and that the experience was more consistent
across age groups.
5.4 IEQ Data Analysis
This section focuses on the Indoor Environmental Quality (IEQ) data collected from Office
H. The IEQ data was used to assess the comfort level of the office. It assesses environmental
parameters against established comfort criteria and evaluates changes before and after the
implementation of desk rotation. The objective is to determine which factors meet the comfort
thresholds and which factors may cause discomfort to users.

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5.4.1 Comparison with Standards
This subsection compares recorded IEQ data with industry standards for temperature,
humidity, lighting, acoustics, and air quality. It identifies areas where environmental conditions
are at, above, or below recommended levels and helps to identify potential problems affecting
employee comfort.
Air Temperature (TA) Temperature (Floor)
Temperature (1.2m) Radiant Temperature Asymmetry (Wall)

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Radiant Temperature Asymmetry (Ceiling) Illuminance (Lux) at the Center
Air Velocity (Top) Air Velocity (Bottom)
Acoustic Relative Humidity (RH)

157
CO₂
Table 5-7 Comparison of IEQ with Standards by Year in Office H
Air temperatures (TA) in 2024 are closer to the standard comfort range than in 2017, with a
more concentrated overall distribution. The data shows that the average temperature in 2024 was
23.55°C, lower than the 24.37°C in 2017, but still within the comfort range. Meanwhile,
temperature fluctuations in 2024 were less volatile, with the standard deviation decreasing from
0.98°C to 0.60°C, indicating more stable indoor temperature control. The histogram shows that
some of the data in 2017 deviates from the comfort range, while the data in 2024 is mainly
concentrated in the comfort range.
Floor Temperature (FT) in 2024 is slightly lower than in 2017, but remains in the comfort
range overall. The data shows that the average floor temperature in 2024 was 23.45°C, lower than
the 23.96°C in 2017, and the standard deviation increased from 0.56°C to 0.64°C, indicating an
increase in the fluctuation of floor temperatures. The distribution of the data shows that some of
the values in 2024 are slightly below the lower limit of the comfort zone, which may affect the

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thermal comfort of the feet.
The 2024 air temperature at 1.2m height (Temperature at 1.2m) is slightly lower compared to
2017, but the distribution is more stable. The data shows that the average temperature in 2024 is
23.43°C, slightly lower than the 24.30°C in 2017, but still within the comfort zone. At the same
time, the temperature fluctuation is slightly reduced, with the standard deviation decreasing from
0.63°C to 0.62°C. From the histogram, the data in 2024 is more concentrated, which indicates that
the indoor temperature regulation is more uniform, and reduces the impact of the local temperature
difference on human comfort so that the overall thermal environment is more stable.
Radiant Temperature Asymmetry (RTA) in 2024 has improved compared to 2017 and is more
uniform overall. The data shows a mean of 1.24°C in 2024, much lower than the 4.25°C in 2017,
and a decrease in the standard deviation from 5.11°C to 1.40°C, indicating significantly less
temperature fluctuation. The histogram shows that the data in 2024 are mainly concentrated in the
comfort range, while the data in 2017 are more widely distributed, with large temperature
differences in some regions.
The ceiling radiant temperature asymmetry (Radiant T Asymmetry - Ceiling) also improved
in 2024 compared to 2017. The data shows that the mean value in 2024 was 0.61°C, down from
1.30°C in 2017, and the standard deviation decreased from 0.75°C to 0.39°C, indicating less
variation in ceiling temperatures.
Illuminance at the center (Illuminance at the center) data in 2024 fluctuates considerably, with
illumination values in some areas significantly outside the comfort range. The data show that the

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average illuminance in 2024 is 704.8 lux, which is much lower than the 2040 lux in 2017, but its
standard deviation is large, indicating that the distribution of illuminance is not uniform. The
histogram shows that the data in 2024 is more widely distributed, with some areas having too low
illuminance, which may affect the visual comfort of the work area.
Indoor air velocities (Air Velocity) in 2024 show little overall change from 2017, remaining
low and with most of the data falling within the comfort zone. The data shows that both the top
and bottom Air Velocity averages are close to zero, indicating that the airflow has less of an impact
on overall thermal comfort and therefore has a more limited impact on the work environment.
Noise levels (Acoustic) have increased in 2024 compared to 2017 and are partially outside
the comfort range. The data shows an average noise level of 55.54 dB in 2024, significantly higher
than the 46.33 dB in 2017, and an increase in the standard deviation, indicating greater noise
fluctuations. The histogram shows that the noise level in 2017 is more concentrated in the comfort
range, while the data in 2024 is more widely distributed, with noise exceeding the upper comfort
limit at some measurement points. This may be related to the fact that the HOT-DESKING mode
reduces physical partitions, allowing for a wider range of sound transmission. Higher noise levels
may affect employee concentration and communication efficiency, and additional soundabsorbing materials or optimized space layout may be considered in the future to reduce noise
impact.
Relative humidity (RH) in 2024 has decreased from 2017 and deviates from the lower limit
of the comfort zone. The data shows that the average humidity in 2024 is 30.62%, which is lower

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than the 49.25% in 2017, and the standard deviation has increased slightly. The histogram shows
that the humidity data for 2024 is predominantly distributed in the lower range, while the humidity
for 2017 is more uniform and falls mostly into the comfort zone. Lower humidity levels may lead
to dryness and discomfort, affecting work comfort, and humidity control strategies need to be
further optimized to improve overall environmental comfort.
Carbon dioxide concentrations (CO₂) are lower in 2024 compared to 2017, and air quality has
improved. The data shows that the average CO₂ concentration in 2024 was 661.9 ppm, which is
lower than the 719.6 ppm in 2017, and the standard deviation has decreased, indicating more stable
air quality. The histogram shows that the overall distribution of the 2024 data is within the comfort
zone, while the 2017 data is partially outside the standard range. This improvement may be related
to the fact that the hot-desking mode optimizes the ventilation strategy and improves the efficiency
of air circulation, which in turn reduces the CO₂ concentration and results in a healthier working
environment.
5.4.2 Overall comparison of Pre and Post data
This subsection assesses how IEQ conditions changed after the transition to desk rotation. By
analyzing the differences in environmental parameters before and after implementation, it explores
whether the new office setup resulted in measurable improvements or created new challenges to
workplace comfort.

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Air Temperature (TA);
P-Value=0.002
Global Temperature (TG);
P-Value=0.000
Temperature (Floor);
P-Value=0.007
Temperature (0.6m);
P-Value=0.000

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Temperature (1.2m);
P-Value=0.000
Temperature (1.6m);
P-Value=0.002
Radiant Temperature (Left);
P-Value=0.004
Radiant Temperature (Right);
P-Value=0.004

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Radiant Temperature (Down);
P-Value=0.646
Radiant Temperature (Up);
P-Value=0.010
Illuminance (Lux) at the Center;
P-Value=0.000
Illuminance (Lux) on the keyboard;
P-Value=0.000

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Illuminance (Lux) on the Monitor;
P-Value=0.000
Air Velocity (Top);
P-Value=0.683
Air Velocity (Bottom);
P-Value=0
Acoustic;
P-Value=0.000

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Relative Humidity (RH);
P-Value=0.000
CO₂;
P-Value=0.000
Table 5-8 Overall Comparison of IEQ by Year in Office H
Air temperature (TA) has a mean value of 24.37°C in 2017 and decreases to 23.55°C in 2024
with a p-value of 0.002, indicating that this change is statistically significant. The decrease is about
0.82°C, which is a change but still within the comfort range and does not have a significant impact
on overall comfort. There is less overlap in the confidence intervals, suggesting that temperatures
in 2024 are significantly lower than in 2017, which may allow some employees to feel a slight
change in temperature.
The global temperature (TG) averaged 25.04°C in 2017 and declined to 23.40°C in 2024,
with a p-value of 0.000, indicating that this change is statistically highly significant. The decrease
is 1.64°C, which is more pronounced compared to the change in TA, and the confidence intervals
barely overlap, indicating a more significant decrease in temperature. However, the temperatures
in both years were within the comfort range, so the overall impact was limited, but some employees

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may experience a more sensitive change in temperature.
Ground temperature (Floor) averaged 23.96°C in 2017 and declined to 23.45°C in 2024 with
a p-value of 0.007, indicating that this change is statistically significant. The small magnitude of
the decline and the partial overlap of the confidence intervals indicate that the temperature change
is present, but the effect is relatively mild.
Temperature at 0.6m height has a mean value of 24.24°C in 2017 and decreases to 23.38°C
in 2024 with a p-value of 0.000, indicating that this change is highly statistically significant. The
decline is large and the confidence intervals have almost no overlap, indicating a clear trend of
decreasing temperatures.
The temperature at 1.2m height has a mean value of 24.30°C in 2017 and decreases to 23.43°C
in 2024 with a p-value of 0.000 indicating a statistically significant change. The decreasing trend
in temperature is further supported by the significant magnitude of the decrease and the almost
non-overlapping confidence intervals.
In 2017, the average temperature at the 1.6 meter height was 24.66°C, while it decreased to
23.42°C by 2024 with a p-value of 0.000, indicating that this change is statistically highly
significant. The temperature decrease is the largest and the confidence intervals do not overlap,
further indicating a more pronounced decreasing trend at higher locations.
The mean left radiant temperature decreased from 27.99°C in 2017 to 23.62°C in 2024 with
a p-value of 0.004 indicating that the change is statistically significant. The decrease is more
pronounced with a decrease in temperature of 4.38°C. The confidence intervals barely overlap,

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indicating that the radiant temperature in 2024 is significantly lower than that in 2017, reflecting
a significant change in the thermal environment of the region.
The right-hand side radiant temperature averages 26.49°C in 2017 and decreases to 24.03°C
by 2024 with a p-value of 0.004, showing statistical significance. The decrease in temperature of
about 2.46°C indicates an equally significant change in temperature. The confidence intervals did
not overlap, further confirming the decreasing trend in radiant temperature.
The lower radiant temperature has a mean value of 23.27°C in 2017 and decreases to 23.15°C
in 2024 with a p-value of 0.646 indicating that the change is not statistically significant. The small
magnitude of the decrease and the highly overlapping confidence intervals indicate that the radiant
temperature change at this location is not significant and the overall impact is limited.
The upper radiant temperature has a mean value of 24.40°C in 2017 and decreases to 23.60°C
in 2024 with a p-value of 0.010 indicating that this change is statistically significant. The decrease
is about 0.81°C, a relatively small change in temperature. The confidence intervals partially
overlap, indicating that although the temperature in 2024 is lower than in 2017, the change is
milder.
Illuminance at the center (Illuminance at the center) has a mean value of 203.96 lux in 2017
and increases to 704.79 lux in 2024 with a p-value of 0.000, indicating that this change is
statistically highly significant. The increase is about 500.83 lux with no overlap in the confidence
intervals, showing that the illuminance in 2024 is significantly higher than that in 2017 and that
the lighting conditions have changed considerably.

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Illuminance on the keyboard area has a mean value of 191.91 lux in 2017 and increases to
694.25 lux in 2024 with a p-value of 0.000 indicating that the change is statistically significant.
The increase is about 502.34 lux with no overlap in confidence intervals, indicating a significant
increase in illumination levels in 2024 compared to 2017, which may affect the visual comfort of
the work area.
Illuminance on the monitor area has a mean value of 131.86 lux in 2017 and increases to
728.25 lux in 2024 with a p-value of 0.000, indicating that the change is statistically significant.
The increase is approximately 596.39 lux with no overlap in the confidence intervals, indicating
that the illuminance on the monitor area in 2024 is much higher than in 2017, which may result in
a brighter work environment, but may also affect visual comfort.
The mean value of top wind velocity (Air velocity - top) was 0.0112 m/s in 2017, while it
decreased to 0.0083 m/s in 2024 with a p-value of 0.683, indicating that this change was
statistically insignificant. The small magnitude of the decrease and the high overlap of the
confidence intervals indicate that the top wind speed has limited change between the two years
and the wind speed itself is low enough to have little effect in an indoor environment. Bottom wind
velocity (Air velocity - bottom) has a mean value of 0.0182 m/s in 2017 and decreases to nearly 0
m/s in 2024. the confidence intervals do not overlap, suggesting that the air flow in the bottom
region almost disappears in 2024. However, due to the low demand for wind speed in indoor
environments, this change has a small impact on overall comfort.
The acoustic environment (Acoustic) has a mean value of 46.33 dB in 2017 and increases to

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55.54 dB in 2024 with a p-value of 0.000, indicating that this change is statistically highly
significant. The increase is about 9.21 dB with no overlap in confidence intervals showing that the
noise level in 2024 is significantly higher than that in 2017 indicating a significant change in the
acoustic environment of the office environment.
Relative Humidity (RH) has a mean value of 49.25% in 2017 and decreases to 30.62% in
2024 with a p-value of 0.000 indicating that this change is statistically highly significant. The
decrease is about 18.63% with no overlap in confidence intervals indicating that the humidity level
in 2024 is significantly lower than that in 2017, which may affect air dryness and comfort.
Carbon dioxide concentration (CO₂) has a mean value of 719.60 ppm in 2017 and decreases
to 661.92 ppm in 2024 with a p-value of 0.000 indicating that the change is statistically highly
significant. The decrease is about 57.68 ppm with no overlap in confidence intervals indicating
that the CO₂ concentration in 2024 is significantly lower than that in 2017 indicating an
improvement in air quality.
5.5 Research Results
This study analyzed changes in the experience of different years, genders, and age groups in
the office environment based on post-occupancy evaluation (POE) and indoor environmental
quality (IEQ) data collected before and after the implementation of hot-desking in Office H in Los
Angeles. It is worth noting that, unlike Office G, the implementation of hot-desking in Office H
was accompanied by a change in office location. Therefore, the changes in the data were not only
affected by the adjustment of the office model, but also by the physical environment of the new

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office space. Nevertheless, the implementation of hot-desking has had a positive effect on
employee adaptability, spatial flexibility and the office experience.
Overall, the introduction of hot-desking has improved employee adaptability to the
environment, increased space utilisation efficiency and brought about optimisation in a number of
key environmental factors. Employees can choose their work location according to their needs,
avoiding the limitations of fixed workstations, and space resources are allocated more reasonably.
Overall, employee satisfaction with air quality, the lighting environment and the adaptability of
the space has increased, indicating that hot-desking has, to some extent, enhanced employees'
control over their own working environment, allowing them to adjust their working methods more
flexibly and thus improve comfort and work efficiency.
First, the adaptability of the temperature experience has improved. Before the implementation
of hot-desking, the temperature experience at different workstations may have differed due to
factors such as air conditioning airflow and window orientation. Employees were unable to freely
adjust their work positions, which caused some of them to feel uncomfortable with the ambient
temperature. After the implementation of hot-desking, employees can choose a position that better
suits their personal preferences, such as an area away from the air conditioning vents or out of
direct sunlight, so that they can more actively adjust their own comfort. However, although the
overall temperature change is still within a comfortable range, the temperature sensitivity of some
employees has increased, especially among female employees and younger people, who are less
adaptable to temperature changes. Therefore, there is still room for further optimization in terms

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of personalized temperature control in the future.
Second, the adaptability of the light environment has been enhanced. Before the
implementation of hot-desking, some workstations may have uneven lighting or reflective glare,
which affects visual comfort. Some workstations are close to windows and may be disturbed by
too much natural light, while the interior areas may have low illumination. Working in an
unsuitable light environment for a long time will affect the work experience. After the
implementation of hot-desking, employees can choose a more suitable workstation according to
their own needs. For example, employees who prefer natural light can choose a position near the
window, while those sensitive to screen glare can choose a softer-lit area. This flexibility improves
employees' adaptability to the lighting environment, reduces the discomfort caused by an
unsatisfactory lighting environment, and thus improves work efficiency.
Privacy adaptability has improved. The implementation of hot-desking allows employees to
choose different work areas according to their needs, such as choosing a relatively quiet
workstation when concentration is needed, and choosing a more open space when communication
is needed. This flexibility allows employees to actively adjust their working environment, reducing
the discomfort caused by insufficient privacy or external interference. However, due to the
increased openness of the new office, some employees are still concerned about visual privacy and
interference with conversations. Although overall employee adaptability to privacy has increased,
in response to the still-present decline in perceived privacy, in the future, consideration could be
given to adding movable partitions or optimizing the seating layout to find a better balance between

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openness and privacy.
In addition, the efficiency of space utilization has been further improved. Before the
implementation of hot-desking, due to the limitations of fixed workstations, the utilization of some
spaces was uneven. Some areas were left vacant for a long time, while some areas were crowded
due to high usage. The introduction of hot-desking allows employees to choose the most suitable
work location according to their needs of the day, which makes the space distribution more even
and improves the utilization of office resources. This change not only optimizes space management,
but also enhances the flexibility of teamwork, making communication between different teams
smoother and promoting the improvement of collaboration efficiency.
Although hot-desking has generally improved the office experience, there are still some areas
that need further optimization. For example, the noise level has increased. Due to the more open
space, sound spreads further, and the background noise in some areas has increased, which may
have an impact on work that requires a high degree of concentration. Therefore, in the future, noise
management measures can be further optimized, such as adding sound-absorbing materials,
optimizing the division of office areas, or setting up dedicated quiet areas, to reduce noise
interference and improve the office experience of employees with different work needs.
There is still room for improvement in terms of air humidity management. After the
implementation of hot-desking, the air humidity has decreased, and the air is drier during some
periods, which may affect the comfort of employees. This change may be related to the improved
ventilation efficiency of the new office location. Therefore, in the future, air humidity can be

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further balanced by adding intelligent humidification equipment or adjusting the ventilation
strategy to ensure the overall comfort of the office environment.
Overall, the implementation of hot-desking has increased employees' ability to autonomously
adjust their office environment and enabled them to more flexibly adapt to different space
requirements. Compared with the pre-hot-desking period, the new office model allows employees
to actively choose the most suitable workstation for themselves, thus providing a better experience
in terms of temperature comfort, light environment adaptability and privacy control. Although
there is still room for further optimization in terms of noise management, air humidity regulation
and local temperature control, overall, the introduction of hot-desking has effectively improved
the employee experience. In the future, the advantages of hot-desking can be further leveraged by
further optimizing sound environment management, air humidity regulation and personalized
temperature regulation methods to create a more efficient and comfortable office environment for
employees.

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Chapter 6: Discussion
This section analyzes the main factors affecting Q28 (productivity) and Q29 (overall indoor
environmental quality) in the two companies based on the POE survey data in 2024. The top five
factors most correlated with Q28 and Q29 were identified by calculating the correlation coefficient
between each question and the other survey items. These factors were then further analyzed to
identify the key points that most affect employee comfort and work efficiency. The results show
that companies that adopt a hotdesking system perform better in these key factors, indicating that
this office model helps create a better working environment and improve employee satisfaction
with the space and productivity.
6.1 Correlation Analysis of POE
In this research, Q28 and Q29 are the two core indicators for overall satisfaction. Q28 is titled
“Effect of environmental conditions in your workstation on personal productivity” and represents
employees' subjective evaluation of the extent to which the office space supports their productivity.
Q29 is “Indoor environment in your workstation, as a whole”, which comprehensively reflects
employees' overall feelings about the comfort of the entire working environment. Since this study
aims to evaluate the impact of the hotdesking office model on employee experience, an in-depth
analysis of the key factors affecting Q28 and Q29 can provide a better understanding of what
aspects employees are most concerned about and help determine whether hotdesking has an
advantage in these key areas.
In order to identify the core factors affecting Q28 and Q29, this paper conducts a correlation

175
analysis of the POE questionnaire data from two different office spaces in 2024. We selected the
top ten questions that are highly correlated with Q28 and Q29, and eliminated the correlation
between Q28 and Q29 itself to avoid logical circular explanations.
Ranking
(Q28)
Office G Office H
1
Q2
Overall air quality in your
work area
0.604299
Q12
Direct glare from electric
lighting
0.724562
2
Q18
Overall quality of lighting
0.590378
Q18
Overall quality of lighting
0.686109
3
Q13
Direct glare from daylight
0.475953
Q10
Light for computer work
0.62613
4
Q8
Size of personal workspace
0.465751
Q11
Reflected light/glare on
screen
0.608513
5
Q16
Access to outside view
0.400265
Q9
Background noise (nonspeech)
0.602012
6
Q17
Distance to others
0.373845
Q26
“My agency is a good place
0.56561

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to work”
7
Q11
Reflected light/glare on
screen
0.336527
Q2
Overall air quality in your
work area
0.455958
8
Q10
Light for computer work
0.292053
Q13
Direct glare from daylight
0.445891
9
Q12
Direct glare from electric
lighting
0.218378
Q20
Degree of enclosure of
workspace
0.439182
10
Q6
Level of visual privacy
0.034177
Q16
Access to outside view
0.414048
Figure 6-1 POE Factors Most Strongly Correlated with Productivity (Q28) in Office G and Office H
The results of the table show that in both offices, lighting, thermal environment, air quality,
spatial perception and acoustics are strongly correlated with employees' subjective perception of
productivity. Among these, lighting quality is the most prominent factor, and almost all of the topranked issues involve lighting brightness, glare control, or visual comfort. In Office H, visualrelated issues such as Q12 (direct glare from lamps), Q10 (computer lighting), and Q11 (reflected
glare) are particularly relevant, indicating that visual comfort is one of the key factors in improving
the perception of work efficiency in the hotdesking model.

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In addition, the top ten list also includes spatial control and acoustic environmental issues
such as Q9 (non-verbal noise), Q16 (outdoor view) and Q20 (enclosed feeling) in Office H, further
demonstrating that the hotdesking model helps improve employees' recognition of the office space
supporting their work by creating a quiet, flexible and visually friendly working environment.
Ranking
(Q29)
Office G Office H
1
Q2
Overall air quality in your
work area
0.604426
Q10
Light for computer work
0.708521
2
Q18
Overall quality of lighting
0.591372
Q26
“My agency is a good place
to work”
0.565902
3
Q13
Direct glare from daylight
0.475953
Q18
Overall quality of lighting
0.480524
4
Q8
Size of personal workspace
0.465751
Q11
Reflected light/glare on
screen
0.474978
5
Q16
Access to outside view
0.400265
Q9
Background noise (nonspeech)
0.465898

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6
Q17
Distance to others
0.37388
Q4
Aesthetic appearance of
your office
0.453407
7
Q7
Noise from others'
conversations
0.367512
Q20
Degree of enclosure of
workspace
0.373928
8
Q3
Temperature in your work
area
0.315916
Q7
Noise from others'
conversations
0.389742
9
Q10
Light for computer work
0.229295
Q12
Direct glare from electric
lighting
0.308283
10
Q11
Reflected light/glare on
screen
0.171495
Q15
Ability to alter physical
conditions
0.313946
Figure 6-2 POE Factors Most Strongly Correlated with Overall IEQ Satisfaction (Q29) in Office G and Office H
The results show that among the influencing factors of overall IEQ satisfaction, lighting is
still the primary factor, especially in hotdesking environments. Q10 (computer lighting), Q11
(glare), and Q18 (overall lighting quality) are on the list consecutively, with high correlations. This
shows that employees are highly sensitive to the lighting environment and use it as an important

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basis for judging the overall quality of the environment.
In addition, the thermal environment (Q3), air quality (Q2), visual permeability (Q16), sound
interference (Q7, Q9) and sense of enclosure (Q20) also have a stable impact on Q29. The
hotdesking environment, with its flexible design and open layout, can adjust seats and areas
according to user needs in these dimensions, thereby providing a greater sense of comfort and
control.
Overall, the most highly correlated factors, whether in terms of productivity or IEQ
satisfaction, are those related to lighting comfort, air circulation, noise control and the flexibility
of the spatial environment. These are also the very aspects that hotdesking prioritizes and
continuously optimizes in its spatial design. It can therefore be concluded that hotdesking not only
saves space resources, but also meets the real needs of employees in multiple key dimensions. It
is an office strategy that balances efficiency and experience.
6.2 Comparative Bar Charts of Multiple Companies
In addition to examining the relevance of individual company-internal factors, this section
further compares the two target companies in this study with other office spaces. The level of
performance of the hotdesking office model in actual application is assessed by comparing the
scores of all samples on the two core indicators Q28 (productivity) and Q29 (overall IEQ
satisfaction) in the POE questionnaire.
We collected more than 100 samples of office spaces that used the POE survey, plotted the
column charts for Q28 and Q29, and marked the two companies in this study: Office G and Office

180
H. As shown in Figures 6.2.1 and 6.2.2, both hotdesking companies excel in their respective
dimensions and rank among the top scorers.
Figure 6-3 Average Q28 (Productivity) Scores Across 100+ Office Spaces

181
Figure 6-4 Average Q29 (Overall IEQ Satisfaction) Scores Across 100+ Office Spaces
By calculating the average score of all companies on these two questions, a list of the top 10
companies for each indicator was compiled. The analysis shows that companies that use
hotdesking office models rank highly in both dimensions. This high ranking is not accidental, but
closely related to the correlation factors mentioned in the previous section.
As mentioned earlier, the key factors affecting employee satisfaction with productivity and
overall IEQ are concentrated in the dimensions of lighting comfort, temperature perception, sound
control, visual privacy, and the ability to control space. These dimensions represent not a “fixed
configuration” in the traditional sense, but rather an “environmentally-adaptable, adaptable, and
adjustable” experience. It is precisely in this regard that the hotdesking model has a natural
advantage.

182
Hot-desking office spaces usually adopt a variety of layouts to provide employees with
different functional areas such as quiet areas, communication areas, open areas, and enclosed areas.
Employees can choose the appropriate workstation according to their tasks and switch flexibly
throughout the day. This “scenario freedom” to a large extent enhances employees' sense of control
and adaptability. In addition, to reduce the sense of unease caused by the lack of fixed workstations,
hotdesking systems are often complemented by more comprehensive IEQ control technologies,
such as adjustable lighting, personalized air conditioning, acoustic sound-absorbing materials, and
partition designs. These measures together create a more humane office experience environment.
The ranking results also confirm this. The hotdesking companies that appear in the Q28 and
Q29 Top 10 scoring lists generally score highly in terms of the visual environment (e.g. lighting,
glare control), thermal environment (e.g. temperature comfort), acoustic environment (e.g.
background noise), and spatial experience (e.g. privacy, freedom). These key dimensions are
precisely the important factors affecting satisfaction in the correlation analysis in the previous
section, which further confirms the positive correlation between the environmental performance
of the hotdesking model and employees' subjective feelings.
From a comparative perspective, hotdesking is no longer just a system design that saves space
and improves office efficiency. The greater value it brings lies in its positive impact on employee
comfort and satisfaction. Especially in the context of the increasing emphasis on flexibility and
experience in contemporary office spaces, hotdesking provides a sustainable and adaptable spatial
solution.

183
In summary, the high ranking in the horizontal comparison further demonstrates that the
hotdesking office model not only has the potential to optimize the environment, but has also
achieved good results in practical applications. In the future, office space design should pay more
attention to the positive role of this model in improving employee satisfaction and optimizing the
quality of the environment.

184
Chapter 7: Conclusion and Future Work
This chapter summarizes the main findings of this study, conclusions and their impacts, and
discusses the potential directions for future research, including short-term and long-term
improvements.
7.1 Summary of Results
The objective of this study is to compare the combined effects of hot desking systems on
employee work experience and indoor environmental quality (IEQ). The New Crown epidemic
has driven changes in modern office models, promoting the popularity of flexible and shared
offices, and hot-desking has been widely used as an effective method to improve space utilization
and reduce operating costs. However, some opinions suggest that this model may affect employees'
indoor comfort and work efficiency. Therefore, this study measures and compares indoor
environmental parameters (e.g., temperature, humidity, lighting, air quality, and acoustic
environment) and their effects on employee satisfaction and productivity before and after the
implementation of hot-desking systems through multiple data collection and analysis methods,
including post-occupancy evaluation (POE), manual sensors, and robotic sensors. In addition, this
study utilized laboratory simulation tests to analyze real-time data on key factors affecting the
office environment to assess their impact on employee comfort. The results of the study show that
the introduction of the hot desk office system enhances the quality of the indoor environment to a
certain extent and brings additional comfort and satisfaction to the employees. Through
comparative analysis, this thesis proposes a series of recommendations to optimize the design of

185
office environments, aiming to increase operational efficiency while maximizing employee work
experience and productivity.
Office space design has evolved significantly over the past century, and the shift in office
paradigm was particularly evident around the time of the New Crown epidemic. Prior to the
outbreak, traditional offices commonly utilized fixed workstations and high-density layouts to
facilitate communication and teamwork among employees. However, the outbreak pushed
organizations to reimagine their office space to accommodate telecommuting, hybrid office
models, and to meet new demands for health and safety.
At the beginning of the 20th century, office environments were dominated by open-plan
layouts where employees worked in spacious spaces that lacked privacy, a design that emphasized
productivity with less consideration for individual needs. With the rise of modernism, the concept
of functionalism gradually influenced the organization of office space, placing greater emphasis
on the rational division of work areas to reduce distractions and improve concentration. After
World War II, cubicle offices became popular, providing employees with better privacy and a
more focused work environment.
Into the 21st century, collaborative workspaces are becoming more mainstream, emphasizing
flexibility, teamwork, and shared resources. However, the outbreak of the epidemic has prompted
organizations to revisit this office model, with many decreasing office densities, increasing shared
space, and introducing more technology support to accommodate remote and hybrid working.
While collaborative workspaces can help improve communication efficiency, office design in the

186
post-outbreak era needs to be designed to increase flexibility while considering the health and
safety of employees and their productivity. As a result, modern office environments need to be
designed to improve space utilization while balancing employee comfort and productivity to
ensure that the work experience is continually optimized under the new paradigm.
This study reviews the historical evolution of office environments and examines the impact
of indoor environmental quality (IEQ) on employee comfort and productivity. The New Crown
outbreak accelerated the development of telecommuting and hybrid office models, causing
organizations to reexamine office design to ensure that employees work in a healthy and safe
environment.
Prior to the outbreak, office spaces typically emphasized face-to-face collaboration and social
interaction. However, lockdown policies during the epidemic forced businesses to shift to
telecommuting, prompting them to focus more on how to keep employees productive and healthy
in their new work environments. In the wake of the epidemic, companies came to realize that the
design of office environments is not only about productivity, but also about employee health,
safety, and satisfaction.
In addition, the outbreak raised concerns about the health risks of open office spaces. Highdensity office environments increase the risk of disease transmission, especially when air
circulation is poor. As a result, companies are placing greater emphasis on ventilation system
optimization, workspace repartitioning, and green building certification in office space design in
the post epidemic era to improve employee health and create safer office environments.

187
This research employed a series of scientific data collection and analysis methods to ensure
an accurate assessment of the impact of hot desking before and after its implementation. The study
first collected historical data before the implementation of hot desking, covering IEQ parameters
such as temperature, humidity, air quality, light intensity and noise level, and benchmarked them
with relevant data on employee productivity. To ensure the reliability of the data, this study
adopted stringent criteria in the data screening and processing process. For example, for the preepidemic data, the study used an improved Post Occupancy Evaluation (POE) method and
upgraded or replaced some of the sensors to ensure the comparability of the data. In addition,
during the data cleaning process, the study excluded data missing key variables (e.g., gender and
age) and removed anomalous data due to sensor malfunctions to ensure the accuracy of the final
analysis.
For data collection, the study used multiple sensor devices to monitor key environmental
parameters, including temperature, humidity, carbon dioxide concentration, light intensity, and
noise level, and calibrated the data to ensure the accuracy of the measurements. Also, this study
conducted post-occupancy assessment surveys at multiple office locations to collect employees'
subjective experiences of the office environment for comparison and analysis with the sensor data.
Finally, this study compared IEQ parameters and employee productivity data before and after
the implementation of hot desking to assess its impact on employee comfort and productivity
through t-tests and correlation analysis methods. These analyses not only provide empirical
support for office space optimization, but also lay the foundation for further research on future

188
office models.
7.2 Implications and Conclusions
This study shows that hot-desking has certain advantages in terms of improving office space
utilization, optimizing environmental quality, and enhancing employee flexibility. However, the
study also identified some areas for further improvement, especially in terms of noise management,
humidity regulation, personalized control, and privacy protection. There is still room for
optimization. Future improvement directions should focus on measures that can be quickly
optimized in the short term and improvement plans that require in-depth research and
implementation in the long term to ensure that hot-desking can be applied to different types of
office environments in the long term.
The office environment can be made more comfortable through more effective noise
management, personalized lighting and humidity control strategies. Noise is one of the common
complaints of employees in hot-desking mode. The impact of background noise can be reduced by
adding sound-absorbing materials to the open office area, optimizing the seating layout, or setting
up dedicated quiet work areas. At the same time, subsidies for smart noise-reducing headphones
or a personal adjustable sound management system can be introduced to allow employees to focus
on their work in an open office environment. In addition, optimization of the individual lighting
system is also crucial. It is recommended to provide adjustable desk lamps or intelligent lighting
systems, so that employees can adjust the light intensity according to their needs, reducing screen
reflection and glare problems. In addition, as studies have found that humidity levels have

189
decreased in hot-desking mode, air humidity can be improved in the short term by adding regional
humidification equipment or adjusting the ventilation strategy to reduce discomfort caused by dry
air.
In the long term, further optimization of the office environment can rely on more advanced
intelligent office technology and space management strategies. In the future, intelligent
temperature and humidity control systems can be introduced in combination with the IoT (Internet
of Things) and AI, and automatic adjustments can be made based on employee preference data to
ensure that the temperature, humidity and air quality in different areas can meet individual needs.
In addition, the adaptability of office culture also needs to be further improved. Employee training
and feedback mechanisms can help employees adapt to hot-desking mode more quickly and
improve their acceptance of flexible office work. At the same time, in the future, office privacy
protection can be further optimized by introducing modular workstations or movable partitions, so
that employees can have a more private office experience when they need to concentrate, while
still being able to communicate effectively when they need to collaborate. In addition, the use of
a more advanced intelligent seating management system allows employees to book the most
suitable workstation in advance, and provides personalized recommendations based on historical
preference data, which can further enhance the flexibility and adaptability of the hot-desking
model.
The limitations of this study mainly include the short time span of the data, the impact of
individual differences that have not been studied in depth, and the difficulty of completely

190
controlling external factors that change the office environment. Future research can evaluate the
changes in the impact of hot-desking on employee adaptability, long-term productivity, and health
based on longer-term data tracking with a larger sample. In addition, the impact of different work
types and personality traits on the adaptability of hot-desking needs to be further explored in order
to provide more refined optimization plans for different groups of people.
7.3 Future Work
This study shows that hot-desking has certain advantages in terms of improving office space
utilization, optimizing environmental quality, and enhancing employee flexibility. However, the
study also identified some areas for further improvement, especially in terms of noise management,
humidity regulation, personalized control, and privacy protection. There is still room for
optimization. Future improvement directions should focus on measures that can be quickly
optimized in the short term and improvement plans that require in-depth research and
implementation in the long term to ensure that hot-desking can be applied to different types of
office environments in the long term.
The office environment can be made more comfortable through more effective noise
management, personalized lighting and humidity control strategies. Noise is one of the common
complaints of employees in hot-desking mode. The impact of background noise can be reduced by
adding sound-absorbing materials to the open office area, optimizing the seating layout, or setting
up dedicated quiet work areas. At the same time, subsidies for smart noise-reducing headphones
or a personal adjustable sound management system can be introduced to allow employees to focus

191
on their work in an open office environment. In addition, optimization of the individual lighting
system is also crucial. It is recommended to provide adjustable desk lamps or intelligent lighting
systems, so that employees can adjust the light intensity according to their needs, reducing screen
reflection and glare problems. In addition, as studies have found that humidity levels have
decreased in hot-desking mode, air humidity can be improved in the short term by adding regional
humidification equipment or adjusting the ventilation strategy to reduce discomfort caused by dry
air.
In the long term, further optimization of the office environment can rely on more advanced
intelligent office technology and space management strategies. In the future, intelligent
temperature and humidity control systems can be introduced in combination with the IoT (Internet
of Things) and AI, and automatic adjustments can be made based on employee preference data to
ensure that the temperature, humidity and air quality in different areas can meet individual needs.
In addition, the adaptability of office culture also needs to be further improved. Employee training
and feedback mechanisms can help employees adapt to hot-desking mode more quickly and
improve their acceptance of flexible office work. At the same time, in the future, office privacy
protection can be further optimized by introducing modular workstations or movable partitions, so
that employees can have a more private office experience when they need to concentrate, while
still being able to communicate effectively when they need to collaborate. In addition, the use of
a more advanced intelligent seating management system allows employees to book the most
suitable workstation in advance, and provides personalized recommendations based on historical

192
preference data, which can further enhance the flexibility and adaptability of the hot-desking
model.
The limitations of this study mainly include the short time span of the data, the impact of
individual differences that have not been studied in depth, and the difficulty of completely
controlling external factors that change the office environment. Future research can evaluate the
changes in the impact of hot-desking on employee adaptability, long-term productivity, and health
based on longer-term data tracking with a larger sample. In addition, the impact of different work
types and personality traits on the adaptability of hot-desking needs to be further explored in order
to provide more refined optimization plans for different groups of people.

193
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Asset Metadata

Creator Liu, Zhilong (author)

Core Title Modern workplace environment pre- and post-occupant satisfaction in modern office hot-desking systems: a comparative study of workplace environments

Contributor Electronically uploaded by the author (provenance)

School School of Architecture

Degree Master of Building Science

Degree Program Building Science

Degree Conferral Date 2025-05

Publication Date 05/12/2025

Defense Date 03/26/2025

Publisher University of Southern California. Libraries (digital), University of Southern California (Los Angeles, California, USA) (original)

Tag hot-desking,Indoor Environmental Quality (IEQ),OAI-PMH Harvest,Post-Occupancy Evaluation (POE)

Format theses (aat)

Language English

Advisor Choi, Joon-Ho (committee chair), Lerman, Kristina (committee member), Habre, Rima (committee member)

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Abstract (if available)

Abstract The purpose of this research is to compare the comprehensive impact of implementing the hot-desking system on employee work experience and indoor environmental quality (IEQ) in commercial office environments. The COVID-19 pandemic has driven a transformation in modern work models, promoting the adoption of flexible working and desk-sharing, with hot-desking serving as an effective method to improve space utilization and reduce operational costs. Contrary to popular belief, some still hold a disputable view concerning this application that it does harm the employees' indoor comfort and productivity. To address the issue, this study employed various data collection and analysis methods, including Post-Occupancy Evaluation (POE), manual sensors, and robotic sensors, to measure and compare indoor environmental parameters (such as temperature, humidity, lighting, air quality, and acoustic) before and after the implementation of the hot-desking system, as well as their effects on employee satisfaction and productivity. Besides, laboratory simulation tests have been carried out using real-time data on key factors affecting office environments in order to gauge their impact on employees' comfort. The findings confirm that the advent of the hot-desking system has contributed to the slight improvement of indoor environmental quality, which brings additional comfort and satisfaction to employees. Through comparative analysis, this paper proposes a series of recommendations for optimizing office environment design, aiming to enhance operational efficiency while maximizing improvements in employee work experience and productivity.