# Artificial Intelligence, VR, AR and Metaverse Technologies for Human
Resources Management

Ömer Aydın Department of Electrical and Electronics Engineering
Faculty of Engineering and Natural Sciences
Manisa Celal Bayar University
Manisa, Türkiye
<EMAIL_ADDRESS>
https://orcid.org/0000-0002-7137-4881  Enis Karaarslan Department of Computer
Engineering
Faculty of Engineering
Mugla Sitki Kocman University
Mugla, Türkiye
<EMAIL_ADDRESS>
https://orcid.org/0000-0002-3595-8783  Nida Gökçe Narin Department of
Statistics
Faculty of Science
Mugla Sitki Kocman University
Mugla, Türkiye
<EMAIL_ADDRESS>
https://orcid.org/0000-0002-4840-5408

###### Abstract

Human Resources (HR) technology solutions encompass software and hardware
tools designed to automate HR processes, gather, process, and analyze data,
utilize it for strategic decision-making, and execute HR professionals’ tasks
while prioritizing security and privacy considerations. As with numerous other
domains, Digital Transformation and emerging technologies have commenced
integration into HR processes. These technologies are utilized by HR
professionals and various stakeholders involved in HR operations. This study
evaluates the utilization of Artificial Intelligence (AI), Virtual Reality
(VR), Augmented Reality (VR), and the Metaverse within HR management, focusing
on current trends and potential opportunities. A survey was conducted to gauge
HR professionals’ perceptions and critiques regarding these technologies.
Participants were the HR department officers, academicians who specialized in
HR and staff who had courses at diverse levels about HR. The acquired results
were subjected to comparative analysis within this article.

Human Resources, Artificial Intelligence, Virtual Reality, Augmented Reality,
Metaverse, HR, VR, AI, AR

##  1 Introduction

The Metaverse can provide face-to-face interaction that is missing in a
dispersed work environment, which is critical to fostering employee
connections. Metaverse training can create a memorable, immersive, and
captivating learning environment that allows employees to learn and develop at
a pace that is conducive to the always-on, hybrid world we live in. The
Metaverse can also be used for virtual meetings, recruiting and hiring
interviews, onboarding, and learning and development, which can help employees
feel more connected to their colleagues and the organization. The benefits of
using Metaverse technology in HR are immense. The Metaverse has the potential
to improve the organizational climate, provide better accessibility, and make
more efficient use of resources. It can be used for virtual meetings,
recruiting and hiring interviews, onboarding, employee engagement, and
communication regardless of location. The Metaverse can also help HR improve
new employee inductions, employment information sharing, online aptitude
tests, and AI job capacity evaluation. The Metaverse can be used to enhance
employee engagement experiences by providing a space for employees to
collaborate, share ideas, and work on projects. It can also be used to train
employees and provide them with the opportunity to learn new skills, creating
a more engaging and motivating work environment. Other examples of Metaverse
technologies for employee engagement include linking company communications
with employee engagement, consistent messaging, regular daily/weekly updates,
increased executive visibility, and open, two-way communication. Virtual
reality training experiences can improve employee performance by 70%, and
employers can train and educate employees by helping them run through
scenarios in a virtual environment. However, HR professionals will need to
reassess how to educate and train employees in these new technologies and
software as they become more prevalent in the industry. Metaverse training can
create a memorable, immersive, and captivating learning environment that
allows employees to learn and retain information more effectively. The
Metaverse can also be used for virtual meetings, onboarding, and learning and
development, which can help employees feel more connected to their colleagues
and the organization. By leveraging established Metaverse technologies such as
avatars, gaming, VR, and hand-tracking controllers, HR leaders can ease
employees into the habit of using the Metaverse and make it a more engaging
experience. Overall, the Metaverse has the potential to enhance employee
engagement by providing a more immersive and interactive work environment. Our
study is conducted to seek answers to some research questions. The research
questions of this article are as follows:

  * •

How are Artificial Intelligence (AI) technologies currently used in Human
Resources (HR) management, and what are HR professionals’ opinions on the
advantages and disadvantages of their implementation?

  * •

What is the potential use of Virtual Reality (VR) and Augmented Reality (AR)
technologies in HR processes, and what are HR professionals’ opinions on the
advantages and disadvantages of using these technologies in HR processes?

  * •

What are the emerging trends in the use of the Metaverse in HR management, and
what are the possible advantages and disadvantages?

The structure of the article is as follows: In the second part, information
about HR operations will be shared. In the subsequent section, AI, VR, AR,
Metaverse, and HR-related publications in the literature will be examined.
Following that, AR, VR, AI, and Metaverse usage in HR operations will be
presented in detail under subheadings. This will be followed by a section
where information about the research method and the tools used will be shared.
Subsequently, the data will be introduced in the subsequent section. The
results obtained by processing the data will be shared under the seventh
section. In the last section, the results will be discussed, and then the
article will be concluded.

##  2 HR Operations

AI applications in HR management include resume scanning, task automation,
real-time talent management, reducing employee churn rates, data analysis,
recruitment, training, onboarding, performance analysis, and retention. AI can
streamline the recruitment process by designing more user-friendly job
application forms and identifying the best candidates for a job. AI can also
automate low-value HR tasks, such as employee records management and payroll,
freeing up HR professionals to focus on more strategic work. AI-powered
chatbots can assist with employee inquiries and provide real-time feedback to
employees. AI can analyze employee data to provide valuable insights into
employee performance, behavior, and engagement, which can help HR teams make
better decisions. Overall, AI technologies are transforming HR management by
providing valuable insights, automating low-value tasks, and improving the
efficiency and effectiveness of HR processes. Some examples of companies using
AI in HR workflows include SAP, which uses AI in HR recruitment to streamline
the hiring process and automate tasks like resume screening. ServiceNow is
another company that uses AI-powered processes to customize background checks,
ensure diversity in hiring, train employees, and improve employee engagement.
Other companies that are embracing AI in HR management include IBM, Google,
and Amazon. These companies use AI to automate low-value HR tasks, such as
employee records management and payroll, freeing up HR professionals to focus
on more strategic work. They are also using AI to analyze employee data to
provide valuable insights into employee performance, behavior, and engagement,
which can help HR teams make better decisions.

##  3 Literature Review

Some researchers investigate the impact of information technology on the human
resource (HR) function. The use of rapidly evolving information technology has
brought significant changes to HR. Haines et al. show that information
technology enables HR to be more involved in strategic roles such as being a
business partner and a change agent. Additionally, the study suggests that
information technology use is associated with increased technical and
strategic effectiveness in HR. This indicates the transformative potential of
information technology-supported HR management applications. Overall, the
study emphasizes the significant influence of information technology on
reshaping and enhancing HR function (Haines et al., 2008). Another research
discusses the potential impact of workforce management applications on
enterprise-wide applications market growth. However, it raises the question of
whether firms will genuinely benefit from these applications, considering
numerous horror stories about failed technology implementation efforts that
erode confidence. To address this issue, the text proposes treating the
installation of HR technology as a form of innovation. It introduces a model
that describes the technology implementation process and highlights various
issues that academics and practitioners should consider (Shrivastava & Shaw,
2003). It is one of the popular concepts of today’s digital transformation.
Some studies aim to examine the evolving role of human resource management in
the context of digital transformation. Digital transformation involves
leveraging new technology to transform business processes, operations and
structures for competitive advantage. Some researchers consider human capital,
intellectual capital and knowledge as critical components in achieving this
advantage. It shows that digitalization affects daily HR practices, especially
with the use of human resources information systems (Fenech et al., 2019).
Some articles discuss the impact of AI and robotics on various industries.
Gulliford and Dixon focused on organizational science and workforce data
analysis conducted by Qlearsite to discuss the impacts of these technologies.
The paper tracks the evolution of AI implementation, detailing how businesses
can leverage AI to understand their workforce better, identify performance
barriers, and enhance productivity. Despite initial challenges faced by senior
HR members, the article highlights the tangible benefits of AI implementation
in improving corporate success and productivity. It emphasizes AI’s ability to
solve complex issues, such as absenteeism, through data analysis, which proves
to be invaluable for HR leaders and senior decision-makers (Gulliford & Dixon,
2019). Michailidis discusses the impact of technological innovations, such as
blockchain and artificial intelligence (AI), on human resource (HR) practices
within business and non-profit organizations. It highlights the influence of
these innovations on employment patterns, hiring practices, and employee
management. The paper is structured into three main sections: the first
examines the effects of blockchain and AI on HR practices, the second
discusses hiring practices at firms, and the third analyzes employment
patterns in the age of high-tech super-automation. Additionally, there is a
concluding section that explores the implications of AI on employment and
income inequality in society (Michailidis, 2018). Pandey and Khaskel’s study
explores the challenges AI presents to HR functions and the technological
solutions adopted by companies like IBM and the UnitedHealth Group. With over
50% of the working population being Gen Y (under 25) and around 65% under 35,
their reaction to HR transformation is crucial. A qualitative study involving
interviews with Gen Y professionals identified HR themes suitable for AI
applications. Results show a clear majority supporting AI implementation in HR
systems, indicating readiness among the working population(Pandey & Khaskel,
2019). The paper, authored by Nyathani in 2023, establishes the foundational
principles underpinning effective HR analytics, accentuating the paramount
importance of meticulous employee data management encompassing collection,
storage, quality assurance, and governance across diverse employee data types.
Nyathani’s work highlights the central role of AI in transforming HR
analytics, demonstrating its capacity to augment the processing and analysis
of employee data. Ethical considerations, including bias mitigation, data
privacy, and responsible AI governance, are rigorously addressed by Nyathani,
alongside a forward-looking discussion on emerging trends and the evolving
role of HR professionals within an AI-driven HR landscape (Nyathani, 2023). AR
and VR technologies are poised to improve the onboarding process by providing
virtual office tours, eliminating the need for new hires to be physically
present. Likewise, the utilization of virtual and digital learning platforms
for reskilling and upskilling employees is set to become prevalent, negating
the necessity for organizations to invest in the infrastructure and logistics
needed for traditional hands-on training (Am et al., 2020). Yawson expressed
the notion that emerging technologies such as Artificial Intelligence (AI),
Virtual Reality (VR), Augmented Reality (AR), and Blockchain, along with data
science, are driving the automation and digitization of HR functions. These
advancements have the potential to facilitate decision-making processes with
reduced implicit bias, thereby establishing a foundation for a more equitable
decision-making process throughout the organization (Yawson, 2020). Vochozka
et al. (2022) emphasize the scarcity of research on immersive workspaces
within the fully connected Metaverse. Their article compiles previous research
indicating that augmented reality technologies, simulation modeling software
tools, and workplace tracking systems facilitate immersive work environments.
In their quantitative literature review conducted throughout May 2022, they
identified 157 articles meeting eligibility criteria from databases such as
Web of Science, Scopus, and ProQuest. Through meticulous scrutiny, they
refined their dataset to 31 generally empirical sources, ensuring the
reliability and relevance of their findings. Lim et al. (2023) conducted a
study examining the potential applications of the Metaverse in various aspects
of human resource development (HRD) and assessed its significance for both
employee and organizational growth. Utilizing a narrative review approach, the
authors meticulously examined 34 cases, uncovering several noteworthy findings
that contribute significantly to the HRD domain. Firstly, the study sheds
light on a critical emerging trend overlooked in the HRD field, urging
researchers and practitioners to pay closer attention. Secondly, it offers an
analytical perspective on the Metaverse in comparison to other training
technologies, evaluating its efficacy in fostering employee development.
Thirdly, Lim et al.’s research enriches the understanding of HRD professionals
regarding Metaverse-based interventions, providing practical and theoretical
insights into how such interventions can reshape organizational culture and
enhance employee performance. Finally, the study addresses pertinent
theoretical and practical inquiries concerning HRD challenges stemming from
the Metaverse.

##  4 AR, VR, AI and Metaverse Usage in HR Operations

VR and AR are two emerging technologies that have the potential to
revolutionize the way HR is conducted. VR creates a fully immersive experience
that allows users to interact with a virtual world, while AR overlays digital
information onto the real world. Both technologies hold promise for various HR
applications, including recruiting and onboarding, training and development,
employee engagement, performance management, wellness, and safety.
Additionally, AI enables talent acquisition through automated screening
processes that efficiently filter through large volumes of resumes to identify
top candidates. AI-powered chatbots further streamline employee queries and
support, providing instant assistance and enhancing the overall employee
experience. Moreover, predictive analytics driven by AI algorithms assist HR
professionals in forecasting workforce trends, facilitating proactive
strategies for talent retention and succession planning. Beyond VR, AR, and
AI, the integration of the Metaverse into Human Resources (HR) signifies a
profound shift in organizational workforce management. The Metaverse offers
immersive virtual environments where HR professionals can engage with
employees and candidates in novel ways. Now, let’s briefly explore the
relationship between these technologies and HR under the following
subheadings.

###  4.1 AI in HR

AI is being used in HR to automate tasks, improve decision-making, and provide
insights. This is freeing up HR professionals to focus on more strategic
tasks, such as talent development and employee engagement. AI is being used in
HR in recruiting and hiring, talent management, employee development and
employee engagement. AI can be used to screen resumes, conduct interviews, and
even make hiring decisions. AI can be used to analyze employee data to
identify trends and patterns that can help HR professionals make better
decisions about things like compensation, benefits, and training. AI can be
used to create personalized learning plans for employees, track their
progress, and provide feedback. AI can be used to track employee sentiment,
identify areas of improvement, and provide personalized feedback. The details
will be given in the following subsections.

####  4.1.1 Recruiting and hiring

AI can be used to screen resumes by identifying keywords and phrases that are
relevant to the job opening. This can help HR professionals quickly and easily
identify qualified candidates. AI can also conduct interviews by asking
questions and evaluating the candidate’s responses. This can help HR
professionals to get a better sense of the candidate’s skills and abilities.
Finally, AI can even be used to make hiring decisions by scoring candidates
and recommending the top candidates for the job (Li et al., 2021).

####  4.1.2 Talent management

AI can be used to analyze employee data to identify trends and patterns that
can help HR professionals make better decisions about things like
compensation, benefits, and training(Charlwood, 2021). For example, AI can be
used to analyze employee performance data to identify areas where employees
need improvement. This information can then be used to develop training
programs that can help employees improve their performance. AI can also be
used to analyze employee satisfaction data to identify areas where employees
are unhappy. This information can then be used to make changes to the
workplace that can improve employee satisfaction(Rath et al., 2023).

####  4.1.3 Employee development

AI can be used to create personalized learning plans for employees, track
their progress, and provide feedback. This can help employees develop the
skills they need to succeed in their careers (Eubanks, 2022). For example, AI
can be used to create learning plans that are tailored to each employee’s
individual needs and interests. AI can also be used to track employee progress
and provide feedback on their learning. This can help employees stay motivated
and on track to achieve their learning goals.

####  4.1.4 Employee engagement

AI can be used to track employee sentiment, identify areas of improvement, and
provide personalized feedback(Mer and Srivastava, 2023). This can help HR
professionals create a more positive and productive work environment for their
employees. For example, AI can track employee sentiment through surveys and
polls. This information can then be used to identify areas where employees are
unhappy. HR professionals can then work to address these issues and create a
more positive work environment for their employees.

###  4.2 VR/AR in HR

Potential benefits of using VR and AR in HR are reduced costs, improved
productivity and increased employee satisfaction. VR and AR can help to reduce
costs in several ways. For example, VR can be used to create virtual job
simulations that eliminate the need for travel and accommodation for
candidates. AR can be used to provide employees with training on new products
or services without the need for costly classroom training. VR and AR can help
to improve productivity in many ways. For example, VR can be used to create
immersive training simulations that allow employees to learn new skills more
quickly and effectively. AR can be used to provide employees with real-time
information and instructions that can help them to perform their jobs more
efficiently. VR and AR can help to increase employee satisfaction in several
ways. For example, VR can be used to create interactive and engaging
experiences that can help to improve employee morale and motivation. AR can be
used to provide employees with a more personalized and supportive work
environment.

###  4.3 Metaverse and HR

Beyond affecting a limited area (Bale et al., 2022; Henz, 2022), Metaverse is
preparing to significantly impact many professional business areas, such as
the human resources (HR) field. In the Metaverse environment, individuals can
interact with digital representations of themselves and others. This way, one
can participate in immersive experiences and create or consume content in
virtual environments. As organizations explore the potential of metadata, HR
professionals face new challenges and opportunities in talent acquisition,
training and employee engagement. One of the primary impacts of Metaverse for
HR is its potential to redefine traditional concepts of workplace and work
culture (Zvarikova et al., 2022). As virtual offices and remote working become
increasingly common, HR departments need to adapt their strategies to foster a
sense of belonging and community among geographically dispersed but digitally
connected employees (Bennett, 2022). Additionally, HR policies and practices
will need to address issues such as digital identity, privacy, and security in
the Metaverse environment and ensure that employees feel comfortable and safe
in their virtual interactions(Di Pietro & Cresci, 2021; Wang et al., 2022; Far
& Rad, 2022; Zhao et al, 2023). Moreover, the Metaverse also offers new
opportunities for talent acquisition and hiring. Companies can use virtual job
fairs, immersive experiences, and gamified assessments to attract and evaluate
candidates from diverse backgrounds and locations. Virtual reality (VR)
simulations can also be used for realistic job previews and skills
assessments, and have the potential to provide a more accurate representation
of a candidate’s abilities and suitability for the role. In terms of employee
training and development, Metaverse offers a rich environment for immersive
learning experiences. VR-based training programs simulate real-world
scenarios, allowing employees to practice skills and decision-making in a
risk-free environment(Hawkins, 2022). Additionally, virtual collaboration
tools and platforms enable employees to collaborate, learn from each other,
and share knowledge across geographic boundaries, fostering a culture of
continuous learning and innovation. However, as organizations embrace the
Metaverse, it is important to be mindful of potential challenges and ethical
considerations(Rozak et al, 2023). Issues such as digital equity,
accessibility and inclusion must be addressed to ensure all employees have
equal opportunities to participate and thrive in virtual environments. HR
professionals will play a pivotal role in promoting diversity, equity and
inclusion in the Metaverse, and they must take part in creating welcoming and
accessible virtual spaces for everyone. To summarize, Metaverse offers a
paradigm shift in the way HR works, opening up new possibilities for talent
management, employee development and organizational culture. By embracing
emerging technologies and reimagining traditional practices, HR departments
can leverage the full potential of the Metaverse to create engaging,
inclusive, and future-ready workplaces.

##  5 Method

A survey was conducted involving 158 participants comprising academicians
specializing in HR-related topics, HR professionals, HR officers, and
individuals who have undergone HR-related courses. The survey aimed to gather
insights into the utilization of emerging technologies, namely Virtual Reality
(VR), Augmented Reality (AR), Artificial Intelligence (AI), and the
integration of the Metaverse, within the field of HR. To execute the survey,
Google Forms was utilized to create the questionnaire, and invitations were
distributed via email to the specified participant groups. Participants were
asked to respond to various questions regarding their perceptions and
critiques of these technologies in HR management. Upon completion of the
survey, the responses were collected and compiled into an Excel file for data
management. Preprocessing of the data was conducted, which involved grouping
certain responses and analyzing and processing responses provided under the
”Other” option. Subsequently, the processed data were subjected to statistical
analysis using the Jamovi software, a powerful R-based tool for statistical
analyses. Jamovi was selected for its user-friendly interface and robust
analytical capabilities. Various statistical tests and analyses were performed
using Jamovi (Edelsbrunner, 2017; Jamovi, 2024) to derive insights and draw
conclusions from the survey data. The utilization of Jamovi facilitated the
examination of relationships, trends, and patterns within the dataset,
allowing for a comprehensive evaluation of the survey results (Şahin & Aybek
2019; Bartlett & Charles, 2022). The statistical findings obtained through
Jamovi were instrumental in informing the discussion and conclusions of this
study regarding the application and implications of emerging technologies in
HR management.

##  6 Data

The data file includes participants’ demographic characteristics (age, gender,
educational level), professional experience (time working in the field of
human resources), and their opinions on the use of these new technologies.
Participants’ responses reflect their perceptions and expectations about the
advantages and disadvantages of artificial intelligence, virtual reality,
augmented reality and Metaverse technologies in human resources processes. The
questions in the survey and the answer options that can be given to these
questions are as follows:

Demographic Information:

  * •

Country

  * •

Age

  * •

less than 20, 20-30, 31-40, 41-50, 51-60, over 60

  * •

Gender

  * •

(Male, Female)

  * •

Educational Status

  * •

Secondary School or lower education level, High School, University (Associate
Degree), University (Bachelor’s Degree), University (Master’s Degree),
University (PhD)

Experience in Human Resources:

  * •

What is the basis of human resources expertise?

(Working in the HR unit, undergraduate education related to HR, received
training/certificate related to HR, other)

  * •

Professional Experience

(0-1 Years, 1-3 Years, 3-5 Years, 5-10 Years, 10-20 Years, More than 20 Years)

Evaluating the Use of Artificial Intelligence (AI):

  * •

Advantages of using artificial intelligence (AI) technology in Human Resources
processes?

(Increasing productivity, improving the recruitment process, supporting
training and development, facilitating performance management, no advantage,
Other)

  * •

Disadvantages of using artificial intelligence (AI) technology in Human
Resources processes?

(Reduced human interaction, risk of producing erroneous results, ethical
issues, data security risks, will cause employees to lose their jobs, makes
employees accustomed to laziness, no disadvantage, other)

Evaluation of Virtual Reality (VR) Usage:

  * •

Benefits of using virtual reality (VR) technology in Human Resources
management?

(Improving training and simulation experiences, facilitating remote
collaboration and communication, improving recruitment and interview
processes, increasing employee loyalty, no benefit, other)

  * •

Benefits of using virtual reality (VR) technology in Human Resources
management?

(High costs, risk of physical discomfort, difficulty in the acceptance
process, data security risks, no disadvantages, other)

Evaluation of Augmented Reality (AR) Usage:

  * •

Benefits of using augmented reality (AR) technology in Human Resources
management?

(Better candidate evaluation during the recruitment process, enriching
training and development programs, increasing the work efficiency of
employees, improving workplace safety and risk management, no advantage,
other)

  * •

Disadvantages of using Augmented Reality (AR) technology in Human Resources
processes?

(Ensuring adequate infrastructure and technical requirements, high operating
costs, data security risks, employee resistance or difficulty in the
acceptance process, no disadvantages, other)

Evaluation of Metaverse Usage:

  * •

Benefits of using Metaverse technology in Human Resources Management?

(Global collaboration and remote working opportunities, creative and
interactive training experiences, increasing employee participation and
motivation, improving recruitment and interview processes, none, other)

  * •

Disadvantages of using Metaverse technology in Human Resources processes?

(Complexity of technological infrastructure, decrease in human interaction,
data privacy and security concerns, risk of people disconnecting from reality,
possibility of people experiencing psychological problems, no weaknesses,
other)

A total of 158 participants participated in the survey. When we examine the
data obtained as a result of the survey, the frequencies are as follows.

Table 1: Frequency of Survey Language

Survey Language | Counts | % of Total | Cumulative %
---|---|---|---
English | 3 | 1.9% | 1.9%
Turkish | 155 | 98.1% | 100.0%

The survey was applied with forms created in two different languages. In the
survey form prepared with Google Forms, the participant was first asked about
his/her language preference and then the questions in the relevant language
were shown. The questions asked and the answer options offered are the same in
both languages. In Table I, we can see in which language the survey
participants answered the survey questions. Only 3 of the participants
answered the survey by choosing the English language. 155 participants
preferred to answer the questions in Turkish.

Table 2: Frequency of Country

Country | Counts | % of Total | Cumulative %
---|---|---|---
India | 3 | 1.9% | 1.9%
Kyrgyzstan | 1 | 0.6% | 2.5%
Türkiye | 154 | 97.5% | 100.0%

The survey was shared in different environments so that the participants were
from different geographies and countries. In addition to inviting participants
via email, posts were also shared on Linkedin and other social media
platforms, and invitations were sent to people. Table II shows which countries
participated in the survey. 1 of the participants is from Kyrgyzstan, 3 are
from India and 154 are from Türkiye.

Table 3: Frequency of Age

Age | Counts | % of Total | Cumulative %
---|---|---|---
less than 20 | 4 | 2.5% | 2.5%
20-30 | 34 | 21.5% | 24.1%
31-40 | 44 | 27.8% | 51.9%
41-50 | 54 | 34.2% | 86.1%
51-60 | 17 | 10.8% | 96.8%
over 60 | 5 | 3.2% | 100.0%

The ages of the participants in our survey is an issue that we care about in
our evaluations. In particular, determining how participants in different age
groups will answer the relevant survey questions will enable us to make
important inferences. Table III shows the age groups of the participants.

Table 4: Frequency of Gender

Gender | Counts | % of Total | Cumulative %
---|---|---|---
Female | 81 | 51.3% | 51.3%
Male | 77 | 48.7% | 100.0%

Table IV shows the distribution of participants by gender. It can be said that
the distribution is balanced. 51.3% of the participants are women and 48.7%
are men.

Table 5: Frequency of Education Level

Education Level | Counts | % of Total | Cumulative %
---|---|---|---
Secondary School or lower education level | 0 | 0.0% | 0.0%
High School | 3 | 1.9% | 1.9%
University (PhD) | 91 | 57.6% | 59.5%
University (Master’s Degree) | 28 | 17.7% | 77.2%
University (Bachelor’s Degree) | 30 | 19.0% | 96.2%
University (Associate Degree) | 6 | 3.8% | 100.0%

Another question asked to the participants in the survey is the education
level of the participants. Table V shows the distribution of participants
according to their education levels. The highest option was ”University
(PhD)”, chosen by 97 participants. This is followed by ”University (Bachelor’s
Degree)” with 30 participants and ”University (Master’s Degree)” with 28
participants.

Table 6: Frequencies of “I work/have worked in a unit related to human
resources”

I work/have worked in a unit related to human resources | Counts | % of Total | Cumulative %
---|---|---|---
Yes | 53 | 33.5% | 33.5%
No | 105 | 66.5% | 100.0%

The working status of the participants in HR-related units is given in Table
VI. When the table is examined, it is seen that 33.5% of the participants work
in HR-related units, while 66.5% do not work in relevant units. The results
regarding the participants’ graduation from an undergraduate degree related to
HR are shared in Table VII. While 21.5% of the participants declared that they
graduated from HR-related undergraduate departments, 78.5% did not graduate
from HR-related departments.

Table 7: Frequencies of “I received my undergraduate education in a department
related to HR”

I received my undergraduate education in a department related to HR | Counts | % of Total | Cumulative %
---|---|---|---
Yes | 34 | 21.5% | 21.5%
No | 124 | 78.5% | 100.0%

In addition, the survey also asked whether the participants had received a
certificate or training related to HR. Table VIII contains information
regarding this. While approximately 36% of the participants declared that they
had received HR-related training/certificate, approximately 64% declared that
they did not have HR-related training or certification.

Table 8: Frequencies of ”I received training/certificate on this subject”

I received training/certificate on this subject | Counts | % of Total | Cumulative %
---|---|---|---
Yes | 57 | 36.1% | 36.1%
No | 101 | 63.9% | 100.0%

Tables VI, VII and VIII attempt to understand how the participants obtained
their information about HR. Apart from the options in these tables, some
participants declared where they got their information about HR by choosing
the ”Other” option. This information is shared in Table IX. Among those who
chose the ”Other” option, the options ”I teach or am an academic in this
field” and ”I have no expertise” stand out with the highest rates.

Table 9: Frequencies of ”Other” option

Other | Counts | % of Total | Cumulative %
---|---|---|---
I teach or am an academic in this field | 16 | 10.1% | 10.1%
I Have General Knowledge | 1 | 0.6% | 10.8%
Not selected this option | 127 | 80.4% | 91.1%
I am working in the field of HR. | 1 | 0.6% | 91.8%
Insurance and Actuarial | 1 | 0.6% | 92.4%
I have no expertise | 11 | 7.0% | 99.4%
I will work | 1 | 0.6% | 100.0%

The participants’ HR-related experiences in terms of time were also questioned
in the survey. In this regard, the participants were asked about the duration
of their HR experience. The results are shared in Table X. While 30.4% of the
participants had 10-20 years of experience, the rate of participants with more
than 20 years of experience was 24.1%, and the rate of participants with 5-10
years of experience was 19.6%. 13.9% of the participants declared that they
had 0-1 year of experience.

Table 10: Frequencies of Professional Experience?

Professional Experience (Time) | Counts | % of Total | Cumulative %
---|---|---|---
0-1 Year | 22 | 13.9% | 13.9%
1-3 Years | 11 | 7.0% | 20.9%
3-5 Years | 8 | 5.1% | 26.0%
5-10 Years | 31 | 19.6% | 45.6%
10-20 Years | 48 | 30.4% | 76.0%
More than 20 years | 38 | 24.1% | 100.0%

##  7 Results

The survey results on the utilization of AI, VR, AR, and Metaverse
technologies in HR methods are outlined in this section. As a result of the
survey, the rate of those who think that using AI technologies in HR processes
will be beneficial was 84.8% (A111A: I agree: 50.6%; TA222TA: I totally agree:
34.2%). The rate of people who think that there will be no advantage is 11.4%
(D333D: I disagree:5.7%; SD444SD: Strongly disagree:5.7%). Similarly, when the
respondents were asked about their opinions on the disadvantages of using
Artificial Intelligence technologies in HR processes, 14.6% stated that they
had no opinion, while 51.9% stated that they had a disadvantage (A:44.3%; TA:
7.6%). The rate of those who stated that there was no disadvantage was 33.5%
(D: 25.3%; SD: 8.2%). These results show that the participants were able to
consider together the advantages and disadvantages of the use of AI
technologies in HR management. The rate of those who think that using AI
technologies in HR management will increase efficiency, improve the
recruitment process, support training and development, and facilitate
performance management is over 70%. Ethical problems were determined as the
most crucial disadvantage, with 58.9%. It was observed that the risk of data
security came next at 55.1%, and the risk of producing erroneous results came
at 49.4%. 65.8% of the participants believe that using VR technology in HR
processes would be advantageous (A: 39.2%; TA: 26.6%), while 13.3% (D:7.6%:
SD:5.7%) thought that it would not have any advantage. Similarly, the rate of
those who participated in the survey stating that there would be disadvantages
in using virtual reality technology in HR processes was 39.2% (A: 33.5%; TA:
5.7%). 27.8% of the participants did not express a clear opinion or were
undecided. It is seen that the most important benefit of using VR technology
in HR management is to improve training and simulation experiences, with
77.8%. While the rate of those who think it will facilitate remote
collaboration and communication is 69.6%, the rate of those who believe it
will improve the recruitment and interview processes is 57.6%. 84.8% of the
participants think that the use of virtual reality will not increase employee
addiction. As with AI technologies, the most significant disadvantage of using
VR was the data security risk (50.6%). Other concerns include high costs
(48.7%), difficulty in the acceptance process (32.9%), and risk of physical
discomfort (24.7%). While the rate of those who think that using AR technology
in HR processes would be advantageous is 65.8% (A: 38.6%; TA:27.2%), the rate
of those who think that the use of AR will not be beneficial is 14,6% (D:8.9%;
SD:5.7%). Similarly, when the respondents were asked whether there would be
disadvantages of using Augmented Reality technology in HR processes, 30.4%
remained undecided, while the rate of those who stated that it would have
disadvantages was 38.9% (A: 32.3%; TA: 6.3%). Enriching training and
development programs with AR was determined as the most important benefit,
with 70.9%. While the rate of those who think it will improve candidate
evaluation during the recruitment process is 57.0%, the rate of those who
believe it will improve workplace safety and risk management is 42.4%.
However, 58.9% of the respondents stated that AR would not increase employees’
internal efficiency. The most significant disadvantage of using AR
technologies in HR management is high operating costs (53.2%). This is
followed by concerns regarding data security risk (49.4%), provision of
adequate infrastructure and technical requirements (44.9%), employee
resistance, and difficulty of the acceptance process (43.7%). While the rate
of those who stated that it would be beneficial to use Metaverse technology in
HR processes was 53.8% (A: 32.9%; TA: 20.9%), the rate of those who said that
it would have disadvantages was 36.5% (A: 30.4%; TA:5.1%). The most
significant benefits of using Metaverse technologies in HR management include
global collaboration and remote working opportunities (67.7%) and creative and
interactive educational experiences (61.4%). The rate of those who think it
will increase employee participation and motivation is 43.0%. 60.8% of the
participants believe it will not improve the recruitment and interview
processes. The most significant disadvantage of using Metaverse technology in
HR management was determined as the risk of disconnecting people from reality
with 54.4%. While half of the participants saw Metaverse technology’s
reduction in human interaction and data privacy and security concerns as a
disadvantage, the other half did not consider them as such. The rate of those
who indicated that it could cause people to experience psychological problems
was 41.8%. Chi-square analysis (Howell, 2011), which is frequently used in the
analysis of categorical data, investigated whether there was a significant
difference between the participants’ characteristics such as age, gender,
professional experience, and education level and their opinions about the use
of AI, AR, VR and Metaverse technologies in HR processes. The following
results were obtained, with a p-value ¡ 0.05 indicating a statistically
significant difference;

  * •

There is a significant difference of opinion according to gender that AI
technology will contribute to improving training and simulation experiences in
HR processes (p = 0.023).

  * •

There is a significant difference of opinion according to gender regarding
whether Metaverse technology is a strength in its use in HR management
(p=0.004). While 94% of women think they have strengths, 78% of men say they
have strengths.

  * •

There is a significant difference in opinion regarding the possibility of
producing erroneous results due to the use of AI technologies in HR processes,
depending on the level of education (p = 0.0024). 41% of PhD graduates, 53% of
undergraduate graduates, and 75% of master’s graduates think it can produce
erroneous results.

  * •

It was observed that there was a significant difference in the advantages of
using AI technologies in HR processes according to the educational level of
the participants (p = 0.02). 91% of postgraduate graduates stated that they
thought it was advantageous.

  * •

It was observed that there was a significant difference in the disadvantages
of using artificial intelligence technologies in HR processes according to
educational level (p = 0.024). While 55% of postgraduate graduates stated that
using artificial intelligence technologies in HR processes has disadvantages,
33% disagreed with this opinion.

  * •

While 77% of graduate graduates think that the use of AI technologies in HR
processes will support training and development, 77% of high school and
associate degree graduates expressed an utterly negative opinion, which was
found to be statistically significant (p = 0.003).

  * •

There is a statistically significant difference of opinion (0.026) that using
Metaverse technologies in HR processes will reduce human interaction. While
55% of doctoral graduates and all high school graduates expressed the opinion
that it would not reduce human interaction, 60% of undergraduate graduates,
50% of master’s graduates, and all associate degree graduates stated that they
thought it would.

  * •

Opinions that the use of VR technologies in HR processes will improve the
training and simulation experience show a significant difference according to
gender (p = 0.023). Seventy percent of men and 85 percent of women stated that
they would make a positive contribution. In addition, all of the women
participating in the survey think that the use of virtual reality technologies
will benefit them during their HR hours.

  * •

40% of men and 60% of women think that Virtual reality technologies pose a
data security risk, and the differences in opinion according to gender are
statistically significant (p = 0.011).

  * •

22% of men and only 6% of women stated that using Metaverse technologies in HR
processes is not a strong aspect (p=0.004).

  * •

There is a significant difference between opinions on whether using AR
technologies in HR processes will cause physical discomfort depending on
whether the participant works in an HR-related unit (p = 0.017). While 87% of
those who work in an HR-related unit state that there is no risk of physical
discomfort, 70% of those who do not work in an HR-related unit think there is
no risk.

  * •

76% of the participants who received their undergraduate education from a
department related to HR think that the use of AI technologies in the HR
process will cause ethical problems (p = 0.019).

  * •

50% of the participants who received their undergraduate education from a
department related to HR think these technologies may cause employees to lose
their jobs. However, 73% of the other participants stated that there was no
such risk (p = 0.013).

  * •

While 41% of the participants who received their undergraduate education from
a department related to HR thought that using VR technologies in HR processes
involved the risk of physical discomfort, 80% of the remaining participants
stated that there was no such risk (p = 0.012).

  * •

While 53% of the participants who received their undergraduate education from
a department related to HR thought that the use of AR technologies in HR
processes would contribute to enriching training and development programs, 24%
of the remaining participants stated that it did not have such a contribution
(p = 0.009).

##  8 Discussion and Conclusion

When the participants in the study are evaluated, it is seen that the majority
of them have more than 5 years of professional experience. This indicates that
the participants consist of experienced individuals in the field. When the
education levels of the participants are examined, it is seen that the
majority of them hold undergraduate, graduate, and doctoral degrees. There is
an almost equal distribution in terms of gender. Regarding age groups, the
majority of the participants fall between the ages of 20 and 50. When the
study results were analyzed, it was observed that the rate of those who
believed that the use of artificial intelligence technologies in HR processes
would be beneficial was very high. Similarly, when the participants were asked
about their opinions on the disadvantages of using Artificial Intelligence
technologies in HR processes, it was observed that half of them thought there
would be disadvantages. Approximately one-third of the total participants
stated that artificial intelligence has no disadvantages. These results
indicate that participants were able to evaluate the advantages and
disadvantages of using artificial intelligence technologies in HR management
collectively. Those who believe that the use of artificial intelligence
technologies in HR management will increase efficiency, improve the
recruitment process, support training and development, and facilitate
performance management are predominant. Ethical problems were seen as the most
significant disadvantage, followed by data security risks and the risk of
producing incorrect results. Concerning virtual reality, it was observed that
more than half of the participants expressed the opinion that its use would be
advantageous. Approximately one in ten participants thought it would not
provide any advantage. Around 40% of the participants believed that the use of
VR in human resources processes would have disadvantages, while one-third of
the participants were undecided on this question. It was stated that the most
important benefit of this technology is to improve the training and simulation
experience. Additionally, many people believe that remote collaboration and
communication opportunities will become easier. More than half of the
participants stated that the use of VR would benefit the recruitment and
interview processes. Security risks, high costs, difficulty in the acceptance
process, and risks of physical discomfort were cited as the disadvantages of
VR technology. The number of people who think that the use of augmented
reality technology in HR processes will be advantageous is approximately 65%.
The rate of those who think it will not be beneficial remains low. It has been
determined that the most important benefit of AR is the enrichment of training
and development programs with AR. More than half of the participants think
that it will improve candidate evaluation during the recruitment process.
Those who believe that it will improve occupational safety and risk management
comprise nearly half of the participants. More than half of the participants
stated that AR would not increase employees’ internal productivity. The most
important disadvantage of using AR technologies in HR management was chosen as
high operating costs, followed by data security risks, provision of adequate
infrastructure and technical requirements, employee resistance, and difficulty
in the acceptance process. More than half of the participants stated that
using Metaverse technology in HR processes would be beneficial. One in every
three participants stated that using Metaverse in HR processes would have
disadvantages. Global collaboration and remote work opportunities, as well as
creative and interactive training experiences, were singled out as the most
important benefits of using Metaverse technologies in HR management. The rate
of those who think that it will increase employee participation and motivation
is around 40%. 60.8% of respondents think this will not improve recruitment
and interview processes. The most important disadvantage of using Metaverse
technology in HR management was determined to be the risk of disconnecting
people from reality, with one in every two participants choosing this. Half of
the participants see Metaverse technology’s reduction of human interaction and
data privacy and security concerns as a disadvantage. The rate of those who
say that it may cause people to have psychological problems is approximately
40%. In conclusion, this study has provided valuable insights into the
perceptions of participants regarding the integration of emerging
technologies, such as artificial intelligence, virtual reality, augmented
reality, and Metaverse, into human resources management processes. The
findings suggest that there is a significant interest among participants in
leveraging these technologies to enhance various aspects of HR management,
including recruitment, training, and performance evaluation. While there is a
general optimism about the benefits of these technologies, such as increased
efficiency and improved candidate evaluation, participants also acknowledge
potential challenges and drawbacks, such as ethical concerns, data security
risks, and the risk of disconnecting from reality. Overall, this study
highlights the importance of carefully considering both the opportunities and
challenges associated with the adoption of emerging technologies in HR
management. Future research and implementation efforts should focus on
addressing these concerns while maximizing the potential benefits to create
more effective and sustainable HR practices in the digital age.

## Acknowledgment

The study is carried out under the decision of the Manisa Celal Bayar
University Science and Engineering Sciences Scientific Research and
Publication Ethics Board, designated as number 1, made at Meeting Number
2023/04 dated 15.09.2023. We thank Manisa Celal Bayar University for their
support.

## Authors’ Contribution

Ö.A. participated in writing the article, as well as in the creation and
implementation of the survey, and the processes of data acquisition and
analysis. E.K. contributed to the writing of the article, as well as to the
survey processes and evaluations. N.G.N. contributed to the evaluation of the
survey results in writing the article. All authors read and approved the final
version of the manuscript.

## Conflict of Interest

The author(s) declare that they have no conflicting interests.

## Funding

This research did not receive any outside funding or support. The authors
report no involvement in the research by the sponsor that could have
influenced the outcome of this work.

## Data Availability

The data used in the article can be used for scientific purposes and shared
with anyone interested in compliance with ethical principles. To share the
data, it is necessary to contact the corresponding author of the article.

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# New Insight into the Formation Mechanism of the Energetic Particle
Reservoirs in the Heliosphere

H.-Q. He1,2,3
1 Key Laboratory of Earth and Planetary Physics, Institute of Geology and
Geophysics, Chinese Academy of Sciences, Beijing 100029, China
2 Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing
100029, China
3 Beijing National Observatory of Space Environment, Institute of Geology and
Geophysics, Chinese Academy of Sciences, Beijing 100029, China E-mail:
<EMAIL_ADDRESS>(H.-Q. He)

(Accepted 2021 August 4. Received 2021 August 4; in original form 2021 May 2)

###### Abstract

The concept of energetic particle reservoirs, essentially based on the
assumption of the presence of outer reflecting boundaries/magnetic mirrors or
diffusion barriers (deterministic) rather than on the effect of particle
diffusive propagation (stochastic) in magnetic turbulence, has been used for
decades to describe the space-extended decay phases of energetic particle
events within the fields of space physics, solar physics, and plasma physics.
Using five-dimensional time-dependent Fokker-Planck transport equation
simulations, in this work we demonstrate that the so-called particle
reservoirs are naturally explained and quantitatively reproduced by diffusion
processes in turbulent magnetic fields, without invoking the hypothesis of
reflecting boundaries. Our results strongly suggest that the so-called
“reservoir” (based on deterministic structure) should be renamed “flood”
(based on stochastic diffusion), which symbolizes an authentic shift in
thinking and in pragmatic rationale for the studies of energetic particles and
relevant plasma phenomena in heliophysics and in astrophysics.

###### keywords:

Sun: particle emission – diffusion – scattering – turbulence – interplanetary
medium – Sun: heliosphere

††pubyear: 2021††pagerange: New Insight into the Formation Mechanism of the
Energetic Particle Reservoirs in the Heliosphere–B

##  1 Introduction

Solar energetic particles (SEPs), emitted by powerful solar bursts, can serve
as sample particles for cosmic ray studies by providing fundamental
information regarding particle acceleration and propagation in the turbulent
magnetic fields. Moreover, SEPs risk astronauts and damage satellites in
space, and significantly influence the solar-terrestrial space environment,
space weather, and space climate. Hence, the subject of energetic particles
has become one of the most important foci in space physics, solar physics,
plasma physics, and astrophysics. Basically, the propagation of energetic
particles in the turbulent magnetic fields consists of a handful of
fundamental mechanisms, such as field-aligned particle streaming, convection
with the plasma medium, adiabatic focusing, adiabatic deceleration, pitch-
angle diffusion, and perpendicular diffusion. The concept of particle
diffusion process at microscale is essentially based on the philosophy and
thought of stochasticism (probabilism) in a mathematically tractable style
(e.g. Brown, 1828; Kubo, 1957; Parker, 1965; Jokipii, 1966). The energetic
charged particles in the turbulent magnetic fields undergo significant
diffusion processes due to the effects of stochastic magnetic fluctuations.
The parallel diffusion along the guide magnetic field has been intensely and
extensively studied; however, the perpendicular diffusion across the guide
magnetic field has remained a puzzle for quite a long time. Recently, the
important effect of the perpendicular diffusion mechanism in the transport
processes has been found (e.g. Zhang et al., 2003; Matthaeus et al., 2003;
Shalchi et al., 2004; He, 2015). The effects of nonlinear terms have been
introduced and discussed so as to explain perpendicular diffusion and
scattering of cosmic rays near $90^{\circ}$ pitch-angle.

There is a well-known phenomenon or conception in the field of energetic
particles, especially in the SEP community, i.e., the “reservoir” (e.g.
Simnett, 1995; Poletto & Suess, 2004; Klecker et al., 2006; Reames, 2013;
Desai & Giacalone, 2016; Reames, 2017; Simnett, 2017; Reames, 2018;
Anastasiadis et al., 2019). The reservoir effect refers to the interesting
phenomenon where the flux-time profiles of particles evolve similarly in time
with almost the same declining rate in the decay stage of the particle events.
This intriguing feature was first reported from the wide-heliolongitude ($\sim
180^{\circ}$) concurrent observations of $\sim 20$ MeV protons measured by the
spacecraft Interplanetary Monitoring Platform (IMP) 4 and Pioneer 6 and 7
(McKibben, 1972). Twenty years later, uniform intensities were observed
spanning $\gtrsim 2.5$ AU in the radial direction between spacecraft IMP 8 and
Ulysses, and thus were named “reservoirs” to illustrate the uniformity of
particle distribution (Roelof et al., 1992). The particle (protons, electrons,
and heavy-ions) reservoirs are often seen by widely separated spacecraft at
very different heliolongitudes, heliolatitudes, and radial distances (e.g.
Reames et al., 1996; Maclennan et al., 2001; Dalla et al., 2003; Sanderson et
al., 2003; Daibog et al., 2003; McKibben et al., 2003; Lario et al., 2003;
Lario, 2010). Basically, the notion of reservoir is based solely on the
hypothetical presence of deterministic magnetic structures at macroscale, such
as diffusion barriers, outer reflecting boundaries or magnetic mirrors
produced by plasma disturbances in the solar wind. This idea invokes the
presence of such diffusion barriers or reflecting boundaries to contain the
particles long enough to gradually and uniformly redistribute them in
heliographic azimuth and latitude. In this picture, the so-called reflecting
boundaries or diffusion barriers are assumed to exist in the space to play the
role of “reservoir dam” to impede, reflect, and redistribute energetic
particles within the reservoirs. Ultimately, the concept of “reservoir”
originates from conventional thought of scatter-free (deterministic)
propagation, meanwhile ignoring the important effects of diffusive
(stochastic) transport (e.g. Roelof, 1975; Nolte & Roelof, 1975).

Recently, multi-spacecraft SEP observations, achieved with the spacecraft
STEREO-A/B, ACE, SOHO and Wind, have shown that SEPs originating from a
limited solar source can transport to very distant heliographic longitudes as
wide as $136^{\circ}-360^{\circ}$ both in the so-called gradual and impulsive
(3He-rich) events (e.g. Dresing et al., 2012; Wiedenbeck et al., 2013;
Richardson et al., 2014; Cohen et al., 2014; Gómez-Herrero et al., 2015).
Essentially, these wide longitudinal spreads of SEP events are identical to
the SEP “reservoir” phenomenon, since both of these two SEP effects indicate
the same manifestation: the broad and gradually uniform particle distribution
in the heliosphere. Therefore, the key physical mechanism for fundamentally
understanding these novel observations still remains a debatable conventional
issue regarding scatter-free propagation (determinism) and diffusive transport
(randomness). To quantitatively explain and reproduce these intriguing
particle phenomena in a compelling fashion eagerly requires a thorough and
detailed investigation by means of physics-based numerical simulations and
computationally tractable descriptions of particle transport in three-
dimensional turbulent interplanetary magnetic fields (IMF).

##  2 Fokker-Planck Model

We present the physics-based multi-dimensional Fokker-Planck transport model
and relevant numerical calculations to shed new light on the formation
mechanism of reservoirs of energetic particles in the heliosphere. The Fokker-
Planck model is based upon dynamical stochastic diffusion motions of charged
energetic particles in magnetic fields with turbulent effects, and can be
formatted as (e.g. Schlickeiser, 2002; Parker, 1965; Zhang et al., 2009; He et
al., 2011; Dröge et al., 2016)

$\displaystyle\frac{\partial f}{\partial t}+\mu v\frac{\partial f}{\partial
z}+{\bf V}^{sw}\cdot\nabla f+\frac{dp}{dt}\frac{\partial f}{\partial
p}+\frac{d\mu}{dt}\frac{\partial f}{\partial\mu}$
$\displaystyle-\frac{\partial}{\partial\mu}\left(D_{\mu\mu}\frac{\partial
f}{\partial\mu}\right)-\frac{\partial}{\partial
x}\bigg{(}\kappa_{xx}\frac{\partial f}{\partial x}\bigg{)}$
$\displaystyle-\frac{\partial}{\partial y}\left(\kappa_{yy}\frac{\partial
f}{\partial y}\right)=Q({\bf x},p,t),$ (1)

where $f(\textbf{x},\mu,p,t)$ denotes the gyrophase-averaged distribution
function of particles, x is spatial position of particles, $z$ is coordinate
along magnetic field line, $p$ is particle momentum, $\mu$ is particle pitch-
angle cosine, $t$ is time, $v$ is particle velocity, $\textbf{V}^{sw}$ is
solar wind velocity, $\kappa_{xx}$ and $\kappa_{yy}$ are perpendicular
diffusion coefficients of particles, $dp/dt$ represents adiabatic deceleration
effect, $d\mu/dt$ represents magnetic focusing effect and solar wind flow
divergence, and $Q(\textbf{x},p,t)$ is source term of particles. We use the
technique of time-backward Markov stochastic processes to solve the above
five-dimensional Fokker-Planck model (Zhang et al., 2009; He et al., 2011). A
constant solar wind speed of $400~{}km~{}s^{-1}$, a Parker-type magnetic field
with magnitude $B=5$ nT at $1$ AU, and a particle source with limited
longitudinal and latitudinal coverage centered at $0^{\circ}$ heliolatitude
(Equator) are typically used in the simulations. We simulate $6\times
10^{7}-1.2\times 10^{8}$ particles on a supercomputer cluster. We analyze
statistical data from numerical simulations and obtain intensity-time profiles
of SEP events with different features. An arbitrary unit for particle flux is
conveniently used in plotting figures. It is important to note that the
simulation results and conclusions shown here essentially hold for varying
values of the model parameters.

##  3 Results

The reservoirs were first noted in the wide-heliolongitude observations of
$\sim 20$ MeV protons (McKibben, 1972). Fig. 1 presents our numerical
simulations of flux-time profiles of $20$ MeV protons transporting in the
interplanetary space. The diffusion coefficients of energetic particles are
set as follows: the radial mean free path $\lambda_{r}=0.32$ AU (corresponding
to the parallel mean free path $\lambda_{\parallel}=0.64$ AU at 1 AU), and the
perpendicular mean free paths $\lambda_{x}=\lambda_{y}=0.008$ AU. We note that
the values of the mean free paths chosen for numerical simulations in this
work are consistent with the observational and theoretical results within the
SEP community (e.g. Bieber et al., 1994; Dröge, 2000; Matthaeus et al., 2003;
Bieber et al., 2004; He & Wan, 2012a, b, 2013). The observers (spacecraft) are
located at 1 AU Equator ($0^{\circ}$ heliolatitude). The different
longitudinal separations between the particle source and the magnetic field
line footpoints of different observers are: $0^{\circ}$, $30^{\circ}$,
$60^{\circ}$, $90^{\circ}$, $120^{\circ}$, $150^{\circ}$, and $180^{\circ}$.
In Fig. 1, the solid and dashed lines denote the flux-time observations made
by spacecraft located at west and east, respectively, relative to magnetic
field line connecting the center of the source. As one can see, in the
beginning, the farther the magnetic footpoint of the observer is away from the
particle source, the smaller is the particle flux observed and also the later
the onset and the peak flux of the event appear (He et al., 2011). In
addition, with the same heliolongitude separation between particle source and
magnetic footpoints of observers, the particle fluxes observed by spacecraft
located at western side of source are systematically larger than the fluxes
observed by spacecraft located at eastern side, and also the SEP event (onset
and peak) arrives at the western spacecraft earlier than at the eastern
spacecraft (He et al., 2011; He & Wan, 2017). Most interestingly, however, in
the late phase of SEP events, the particle fluxes observed by these widely
longitudinally separated spacecraft show nearly the equal intensities and
decay at the same rate. Therefore, the typical features of a circumsolar
particle reservoir which spans $360^{\circ}$ in heliolongitude at $1$ AU have
been successfully reproduced in the numerical simulations based upon five-
dimensional Fokker-Planck focused transport equation incorporating parallel
diffusion and perpendicular diffusion. It is noteworthy that in the
simulations, no diffusion barriers (reflecting boundaries/magnetic mirrors)
have been assumed and employed to play the role of “reservoir dam” to
reflect/redistribute energetic particles. That is to say, the traditional
hypothesis of reflecting boundaries needs not to be necessarily invoked in the
numerical reproduction of energetic particle reservoirs. Actually, contrary to
the expectations of the traditional thoughts, the presence of outer reflecting
boundaries, diffusion barriers, or magnetic mirrors (magnetic bottles) will
prevent the uniform distribution and reservoir formation of energetic
particles to occur for the observers at different locations in the
interplanetary medium, which has recently been confirmed by Wijsen et al.
(2020).

Figure 1: Numerical simulations of $360^{\circ}$-longitude distribution and
reservoir (flood) effect of $20$ MeV protons. The flux-time profiles are
obtained by numerically solving five-dimensional Fokker-Planck focused
transport equation incorporating diffusion processes. A typical circumsolar
particle reservoir spanning $360^{\circ}$ in longitude is reproduced without
invoking a hypothetical outer reflecting boundary.

The particle reservoirs are observed radially spreading out over several AU
between IMP 8 near Earth and Ulysses in the outer heliosphere (e.g. Roelof et
al., 1992; Lario, 2010). Fig. 2 presents the numerical simulation results of
radial distribution of $20$ MeV protons in the inner and outer heliosphere.
The diffusion coefficients of particles are the same as those used in Fig. 1.
The observers here are located in the Sun’s equatorial plane and are
magnetically connected with the center of the particle source through a
nominal Parker spiral. The different heliocentric radial distances of
different observers are: $0.5$, $1.0$, $1.5$, $2.0$, $2.5$, $3.0$, $3.5$,
$4.0$, $4.5$ and $5.0$ AU. As one can see, during the early phases of particle
events, the farther the observer is radially away from the solar source, the
smaller is the particle intensity detected and also the later the onset and
the peak intensity of the event occur. However, during the late phases of
particle events, all the intensity profiles present nearly the same
intensities and decay rates within the range of statistical errors. Hence the
radial particle reservoir has been reproduced in the Fokker-Planck equation
simulations without invoking the hypothetical reflecting boundaries or
diffusion barriers.

Figure 2: Numerical simulations of radial-spread distribution and reservoir
(flood) effect of $20$ MeV protons in inner and outer heliosphere. A typical
radial particle reservoir spanning inner and outer heliosphere is reproduced
without invoking the artificial hypothesis of outer reflecting boundaries or
diffusion barriers.

The SEP reservoirs are also found extending to quite high heliolatitudes,
e.g., up to $>70^{\circ}$, both North and South (e.g. Dalla et al., 2003;
Sanderson et al., 2003; McKibben et al., 2003; Lario et al., 2003). Fig. 3
displays the simulation results of latitudinal distribution of $20$ MeV
protons. The parallel and perpendicular diffusion coefficients of particles
are the same as those used in Fig. 1. The observers are located at 1 AU. The
heliolongitudes of magnetic footpoints of observers are the same as the
heliolongitude of the center of particle source. The different heliolatitudes
of different observers are: $0^{\circ}$ (Equator), $30^{\circ}$, $60^{\circ}$,
and $90^{\circ}$ (Poles). The solid and dashed curves in Fig. 3 denote the
flux-time profiles of SEPs observed in the northern and southern hemispheres,
respectively. We can see that during the beginning stage of SEP events, the
farther the magnetic footpoint of observer is away from the particle source in
latitude, the smaller is the SEP flux observed and also the later the SEP
event arrives at the observer’s position. Due to the important effect of
perpendicular diffusion, even the observers at the highest heliolatitudes of
$90^{\circ}$ (magnetic poles), both North and South, can detect considerable
particle fluxes. Different from the East-West longitudinal asymmetry of
particle distribution shown in Fig. 1, there exists no North-South latitudinal
asymmetry phenomenon of particle intensity within the range of statistical
errors. The reason is that the geometry of the Parker IMF is North-South
latitudinally symmetric. During the late stage of the particle events, all the
particle fluxes observed by these very widely latitudinally separated
spacecraft show nearly equal fluxes (to within a small factor of $\sim 2-3$)
that decline similarly with time. Therefore, the simulations based on five-
dimensional Fokker-Planck focused transport equation have quantitatively
reproduced the latitudinal particle reservoirs in the heliosphere. See
Appendix A for additional simulation results and some comparisons with
multispacecraft observations, and Appendix B for some implicating remarks.
Note that both Appendices A and B are in the Supplementary Material
(Supporting Information).

Figure 3: Numerical simulations of $180^{\circ}$-latitude distribution and
reservoir (flood) effect of $20$ MeV protons. A typical latitudinal particle
reservoir extending to the highest latitudes of $90^{\circ}$ (magnetic poles),
both North and South, is quantitatively reproduced without invoking the
hypothetical outer reflecting boundaries.

The five-dimensional Fokker-Planck focused transport model incorporating both
parallel and perpendicular diffusion has provided us an essential basis and a
very useful tool to better understand the three-dimensional propagation of
energetic particles in the heliosphere filled with turbulent IMF. By
numerically solving the Fokker-Planck model on a supercomputer cluster, we
have simulated and reproduced a number of energetic particle reservoirs under
various physical conditions and in various physical scenarios. We point out
that altering the values of the model parameters does not qualitatively
influence the reproduction of particle reservoirs. The simulations based on
five-dimensional Fokker-Planck focused transport equation help us obtain
quantitative and physical insights into the realistic formation mechanism of
the so-called energetic particle reservoirs.

##  4 Discussion and Summary

The notion of particle reservoirs and the relevant concepts (e.g., collimated
convection) have been employed for several decades in the data analyses and
interpretations within the observational community. Essentially, these notions
stem from the extreme assumption of scatter-free transport of particles and
invoke an intuitive hypothesis of deterministic plasma structures at
macroscale, e.g., reflecting boundaries or diffusion barriers (e.g. Roelof,
1975; Nolte & Roelof, 1975; Roelof et al., 1992). This reservoir paradigm can
be illustrated on the left-hand side of Fig. 4. As shown, this popular
perception invokes the existence of hypothetical outer reflecting boundaries
or diffusion barriers in the heliosphere, so as to contain the particles long
enough to uniformly distribute them in heliolongitude, heliolatitude, and
radial distance. In this paradigm, the conceived outer reflecting boundaries
or diffusion barriers play the central role of “reservoir dam” to inhibit,
reflect, and redistribute energetic particles in the so-called reservoirs.
Besides, the reservoir concept completely ignores the effects of stochastic
diffusion and scattering processes, especially for the cross-field diffusion
process. This reflecting and redistribution process must be quite efficient to
quickly dissipate all the particle intensity gradients (longitudinal,
latitudinal, and radial) in the reservoir. To our knowledge, however, so far
no explicit evidence or quantitative mechanism of such reflecting boundaries
or diffusion barriers has been provided within the community (He, 2015).

Figure 4: Fundamental shift in thinking and paradigm for understanding
energetic particles and magnetized plasmas in magnetic turbulence.
Conventional paradigm of “reservoir” concept on the left: the hypothetical
outer reflecting boundaries or diffusion barriers (i.e., deterministic
structures at macroscale) play the role of “reservoir dam” to inhibit,
reflect, and redistribute energetic particles to form the uniform
distribution. Novel paradigm of “flood” concept on the right: the parallel and
perpendicular diffusion processes (i.e., stochastic transport at microscale)
caused by the ubiquitous magnetic turbulence effectively and uniformly
distribute the energetic particles in radial direction and in longitude and
latitude to form the wide-spread flood-like distribution in the heliosphere.
The authentic shift from “reservoir” (left) to “flood” (right) offers new
insights into energetic particles and space plasmas in the turbulent magnetic
fields.

According to the traditional pragmatic rationale of SEP event classification
(gradual and impulsive events), the energetic particles in the so-called
impulsive events can only form very narrow longitudinal and latitudinal
distributions in the heliosphere. However, recent multi-spacecraft
observations have revealed that SEPs released from a very small-sized source
(e.g., solar flare) can also form the wide-range distribution phenomenon and
the reservoir effect, even in the impulsive or 3He-rich SEP events (e.g.
Dresing et al., 2012; Wiedenbeck et al., 2013; Richardson et al., 2014).
Therefore, these new observations question the validity of the conventional
classification paradigm of SEP events. Furthermore, the observations indicate
that the plasma structures at macroscale must not be the dominant reason
causing the formation of the reservoir effect. Actually, the up-to-date multi-
spacecraft observations and numerical simulations of SEP three-dimensional
transport have proven the violation of the notion of particle reservoirs in
the heliosphere. Instead, we introduce a novel concept and paradigm, called
“flood”, to much better describe the realistic transport processes of
energetic particles. We point out that the notion of particle “flood” is based
solidly on the physically meaningful and mathematically tractable five-
dimensional Fokker-Planck focused transport model and the relevant numerical
simulations. The physical scenario of the particle “flood” is illustrated on
the right-hand side of Fig. 4. As we can see, the energetic particles, after
being released near the Sun, will diffusively transport in IMF with turbulence
and fluctuations. Due to the effects of parallel diffusion along and
perpendicular diffusion across the large-scale guide magnetic field, the
energetic particles can transport to distant radial locations and meanwhile to
distant longitudes and latitudes in the heliosphere. The mechanism of
perpendicular diffusion is natural and successful to explain and reproduce the
extended decay phases of SEPs (“reservoirs”) observed in both gradual and
impulsive events. Due to the effect of perpendicular diffusion, the particles
can cross the IMF lines and transport to distant heliospheric locations, and
consequently, the gradually space-filling phenomenon (“reservoir”) can be
naturally formed. In the scenario of perpendicular diffusion, no artificial
outer reflecting boundaries (diffusion barriers/magnetic mirrors) need to be
invoked to play the role of “reservoir dam” to reflect/redistribute the
particles to form the so-called “reservoir” phenomenon, especially for
explaining the wide-spread particle distribution of 3He-rich SEP events often
detected in recent multi-spacecraft observations. In this “flood” paradigm,
the stochastic diffusion processes caused by ubiquitous magnetic turbulence
enable the energetic particles to cross the IMF lines and gradually transport
to distant heliospheric locations, analogous to that the surging dynamical
river overflows its banks.

As discussed above, the wide-range flooding (space-filling) transport of SEPs
in the heliosphere is an intrinsic property caused by stochastic diffusion at
microscale rather than deterministic structure at macroscale. To accurately
illustrate the realistic transport processes of energetic particles, we
strongly suggest that the so-called “reservoir” should be renamed “flood”.
Accordingly, the textbooks/books regarding space physics or solar physics that
contain the term “reservoir” must be revised and updated. The traditional
ideas of static structures (including “reservoirs”) and linear instabilities
have been a cornerstone for many theoretical and observational investigations
of space plasmas (e.g. Matthaeus & Velli, 2011). Therefore, the renaming and
redefinition (based on stochasticity and nonlinearity) of “reservoirs”
signifies a fundamental shift in thinking and in paradigms for studying
energetic particles and magnetized plasmas in turbulent magnetic
circumstances. In light of this paradigm shift, the relevant theories and
ideas based on determinism and linearity in space plasma physics need to be
rethought and revised. The new paradigm of interpretation removes determinism
from its central position in understanding the physical nature of the space
plasma behaviors, phenomena, and effects. Instead, this central role should be
given to stochasticism, especially at microscale in plasmas and in turbulence.
More generally, the philosophical debate between determinism and stochasticism
for understanding the essence of physics has remained a fundamental and long-
standing topic in the literature. The results in this work, obtained in a
quantitative fashion, favor the explanation of stochasticism (randomness), as
the fascinating nature always reveals to us eventually.

## Acknowledgements

We were supported partly by the B-type Strategic Priority Program of the
Chinese Academy of Sciences under grant XDB41000000, the National Natural
Science Foundation of China under grants 41874207, 41621063, 41474154, and
41204130, and the Chinese Academy of Sciences under grant KZZD-EW-01-2.
H.-Q.H. benefited from the partial support of the Youth Innovation Promotion
Association of the Chinese Academy of Sciences (No. 2017091). We acknowledge
SOHO/COSTEP and STEREO/HET for providing the energetic particle data analyzed
in this work.

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##  Appendix A Additional Figures of Simulation Results and Comparisons with
Multispacecraft Observations

The particle reservoir is a universal phenomenon independent of particle
energy. We also investigate the reservoir effect formed by energetic particles
with other energies. Fig. 5 presents the simulation results of longitudinal
transport of $5$ MeV protons. The diffusion coefficients of $5$ MeV protons
are set as follows: $\lambda_{r}=0.28$ AU (corresponding to
$\lambda_{\parallel}=0.56$ AU at 1 AU), and $\lambda_{x}=\lambda_{y}=0.007$
AU. Other physical parameters and conditions are the same as in Fig. 1.
Similar to Fig. 1, the east-west longitudinal asymmetry of particle
distribution is also obviously seen in Fig. 5. During the late phases of the
energetic particle events, the particle intensities detected by the widely
separated observers display equal intensities and evolve similarly in time
with the same decay rate. Therefore, a typical particle reservoir spanning
$360^{\circ}$ in longitude has been reproduced in the numerical simulations
based on multidimensional Fokker-Planck transport equation including diffusion
mechanism.

Figure 5: Same as Fig. 1, but for $5$ MeV protons with $\lambda_{r}=0.28$ AU
and $\lambda_{x}=\lambda_{y}=0.007$ AU.

The heliospheric energetic particle reservoirs are also observed in electron
and heavy-ion data. We also numerically investigate the reservoirs formed by
these particle species. Fig. 6 shows the numerical calculation results of
azimuthal propagation of $100$ keV electrons with diffusion coefficients as
follows: $\lambda_{r}=0.3$ AU (corresponding to $\lambda_{\parallel}=0.6$ AU
at 1 AU), and $\lambda_{x}=\lambda_{y}=0.006$ AU. Other physical parameters
and conditions are the same as those in Fig. 1. As we can see in Fig. 6, the
distribution of energetic electrons clearly presents the feature of east-west
azimuthal asymmetry. During the decay phase of the energetic electron events,
the electron fluxes observed by the very widely azimuthally separated
spacecraft present equal fluxes and decrease similarly in time within the
range of statistical errors. Hence, a typical energetic electron reservoir
spreading across $360^{\circ}$ in heliolongitude has been reproduced in the
large-scale simulations based on Fokker-Planck transport equation
incorporating particle diffusion processes.

Figure 6: Same as Fig. 1, but for $100$ keV electrons with $\lambda_{r}=0.3$
AU and $\lambda_{x}=\lambda_{y}=0.006$ AU.

The longitudinal flood/reservoir effect has been widely investigated with
multispacecraft measurements (e.g., SOHO, STEREO-A, STEREO-B, ACE, Wind, etc.)
in the observational community. Undoubtedly, the multispacecraft observations
have provided important information on the transport and distribution of SEPs
in the heliosphere. Fig. 7 displays the multispacecraft detections of the
flood/reservoir phenomenon observed by SOHO and the twin spacecraft STEREO-A
and STEREO-B during the 2013 October 11 (DOY 284) SEP event (adapted from
Anastasiadis et al., 2019). At the onset of this SEP event, the longitudes of
spacecraft SOHO, STEREO-A, and STEREO-B in the Heliographic Inertial (HGI)
Coordinate System are $302^{\circ}$, $89^{\circ}$, and $162^{\circ}$,
respectively. The solar eruptive activity associated with this SEP event
occurred at E104 in longitude as seen from Earth. In Fig. 7, the black, red,
and blue curves denote the time-flux profiles of 25-53 MeV protons in this SEP
event measured by SOHO, STEREO-A, and STEREO-B, respectively. As we can see,
the time-flux profiles observed at the three widely separated spacecraft
display quite different shapes during the onset and early phases of the SEP
event. However, after 2013 October 15 (DOY 288), all the SEP fluxes detected
at the three locations present approximately the same intensities and
gradually decline to lower levels with nearly the same decay rate, which is
the typical feature of the SEP flood/reservoir effect. The flood/reservoir
phenomenon in this SEP event lasted for nearly 8 days (denoted by the gray
shaded area). Through qualitative comparisons between observation results in
Fig. 7 and simulation results in Figs. 5 and 6 and in Fig. 1 in the main text,
we can readily see that all the essential features of flood/reservoir effect
in SEP events have been numerically reproduced in the three-dimensional
transport simulations without invoking the hypothesis of outer reflecting
boundaries or diffusion barriers. Therefore, the conventional hypothesis of
reflecting boundaries or diffusion barriers needs not to be necessarily
invoked in the numerical reproduction of SEP flood/reservoir effect. In
addition, note that the numerical reproduction results of SEP
floods/reservoirs will remain qualitatively unaffected by the varying values
of the model parameters in the three-dimensional transport simulations. We can
further conclude that the stochastic diffusion processes (both parallel and
perpendicular) at microscale in magnetic turbulence play a crucial role in the
formation of particle floods/reservoirs and other relevant phenomena in space
and astrophysical plasmas.

Figure 7: Multispacecraft observations (SOHO, black; STEREO-A, red; STEREO-B,
blue) of flood/reservoir effect (denoted by the gray shaded area) in the 2013
October 11 (DOY 284) SEP event.

##  Appendix B Remarks

The gap of understanding between the observational and theoretical communities
persists in space science for quite a long time. For simplicity in analyzing
and interpreting data, the authors in the observational community usually
employ some specific assumptions, such as “collimated convection” or scatter-
free propagation (deterministic). Undoubtedly, these simplified scenarios can
be tackled more easily. However, the analysis results obtained may
significantly deviate from the realistic physical mechanisms behind the
observations and the phenomena. The solar wind and the particles, fields,
waves, and structures immersed in it naturally constitute a highly turbulent
complex system. In principle, statistical descriptions based on nonlinearity
and stochasticity are essential for the studies of space plasma physics.
Therefore, it is quite pressing to bridge the wide gap between the theoretical
and observational communities, within which there exist somewhat different
terminologies (some misnomers) for the same phenomena or effects. In this
work, we have demonstrated this important issue with a typical example, i.e.,
the so-called particle reservoir (flood) effect or phenomenon. In the future,
we shall pay attention to and investigate other space plasma behaviors,
phenomena, and effects.