Satisfaction in Radiology: The Mediating Role of Job Stress Reduction
Thesis Submitted in Partial Fulfillment of the Requirements for the Award of the
Degree of
MASTER OF BUSINESS ADMINISTRATION
BY
Liyan Abdumohsen Almayouf
Under Supervision
Dr .Asra inkesar
COLLEGE OF ADMINISTRATIVE AND FINANCIAL SCIENCES
SAUDI ELECTRONIC UNIVERSITY
2024-2025
Declaration Certificate
The work entitled, ‘‘Put Your Title of Thesis Here’’, embodies the results of the original
research work carried out by me in the College of Administrative and Financial Sciences,
Department of Business Administration Saudi Electronic University. This research work has
not been submitted in part or full for the award of any other degree at SEU or any other
university.
Date: – ______________________
(Signature)
Place: – ______________________
(Full Name of the Candidate)
ii
Acknowledgement (Optional)
The acknowledgement for thesis is the section where you thank all people, institutions, and
companies that helped you complete the project successfully. It is similar to a dedication, except
for the fact that it is formal.
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TABLE OF CONTENTS
Contents
Declaration Certificate …………………………………………………………………………………………………………….. ii
TABLE OF CONTENTS ……………………………………………………………………………………………………… iv
Abstract ……………………………………………………………………………………………………………………………….. vi
INTRODUCTION ………………………………………………………………………………………………………………… 1
1.1. General Introduction ……………………………………………………………………………………………………… 1
1.2. Research Questions ……………………………………………………………………………………………………….. 2
1.3. Research Objectives………………………………………………………………………………………………………. 3
1.4. Significance of the Study ……………………………………………………………………………………………….. 3
1.4.1. Managerial Relevance……………………………………………………………………………………………… 3
1.4.2. Scientific Relevance ………………………………………………………………………………………………… 4
REVIEW OF LITERATURE ………………………………………………………………………………………………… 6
2.1 Definition of Artificial Intelligence ………………………………………………………………………………….. 6
2.1.1
Types of Artificial Intelligence ……………………………………………………………………………… 7
2.1.2 Integration and Application of AI in the Workplace ……………………………………………………… 8
2.1.3 Opportunities of Artificial Intelligence ……………………………………………………………………….. 9
2.2 Employees’ Attitudes Toward Artificial Intelligence ………………………………………………………. 9
2.3 Subjective Well-being ………………………………………………………………………………………………….. 12
2.4 Job Satisfaction ……………………………………………………………………………………………………………. 12
2.5 Artificial Intelligence: Impact and Challenges in the Workplace ………………………………………… 14
2.6 The Impact of Artificial Intelligence and Machine Learning on Employee Satisfaction and Job
Performance ……………………………………………………………………………………………………………………… 19
2.7 Hypothesis ………………………………………………………………………………………………………………….. 22
THE METHODOLOGY ……………………………………………………………………………………………………… 25
3.1 Research Approach ………………………………………………………………………………………………………. 25
3.2 Research Design ………………………………………………………………………………………………………….. 25
3.3 Study Population and Sample ………………………………………………………………………………………… 25
3.4 Data Collection Method ………………………………………………………………………………………………… 26
3.5 Data analysis ……………………………………………………………………………………………………………….. 26
3.7 Ethical Considerations ………………………………………………………………………………………………….. 27
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ANALYSIS AND RESULTS ……………………………………………………………………………………………….. 29
DISCUSSION AND CONCLUSION ……………………………………………………………………………………. 39
5.2. Conclusion …………………………………………………………………………………………………………………. 40
5.3. Managerial Relevance………………………………………………………………………………………………….. 40
5.4. Scientific Implications …………………………………………………………………………………………………. 41
5.5. Limitations and Scope for Future Research …………………………………………………………………….. 42
Appendix…………………………………………………………………………………………………………………………….. 50
Questionnaire ……………………………………………………………………………………………………………………… 50
LIST OF TABLES
Table (4-1): Demographic Characteristics ……………………………………………………………………………….. 29
Table (4-2): Descriptive Statistics …………………………………………………………………………………………… 30
Table (4-3): Descriptive Statistics (Stress Reduction) ………………………………………………………………… 32
Table (4-4): Descriptive Statistics …………………………………………………………………………………………… 33
Table (4-5): Cronbach’s alpha coefficients for reliability ………………………………………………………. 35
Table (4-6): Matrix of Correlations …………………………………………………………………………………………. 35
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Abstract
Background: The rapid integration of artificial intelligence (AI) in radiology has transformed
workflow efficiency and diagnostic accuracy. However, its impact on employee satisfaction
remains an area of active research. AI-driven workflow optimization has the potential to
reduce job stress, improve task allocation, and enhance overall work experience in radiology
departments. Given the increasing workload in radiology due to growing imaging demands, it
is essential to understand how AI-driven workflow enhancements influence radiologists’ job
satisfaction and whether stress reduction plays a mediating role in this relationship.
Purpose: This study aims to examine the effects of AI-driven workflow optimization on
employee satisfaction in radiology. Specifically, it investigates whether job stress reduction
mediates this relationship. By identifying key factors influencing satisfaction levels, the study
provides insights into how AI can be leveraged to improve work environments for radiology
professionals.
Research Design and Methodology: A quantitative research methodology was used via a
cross-sectional survey to explore the relationship between AI-enhanced workflow
improvement, stress reduction, and increased staff satisfaction in radiology departments. A
sample of 322 participants was recruited using an online questionnaire covering several
themes, and the data were statistically analyzed using SPSS.
Findings: The study discovered that workflow optimization powered by AI had a moderately
favorable effect on lowering workplace stress and a substantial positive impact on employee
happiness. Furthermore, the association between AI implementation and employee
satisfaction was considerably mediated by job stress reduction, demonstrating AI’s potential to
improve both productivity and human well-being.
Practical Implications: The findings highlight the importance of implementing AI solutions
that not only enhance efficiency but also address human factors such as stress and job
satisfaction. Healthcare administrators should consider strategies to ensure smooth AI
adoption, including training programs, transparent communication about AI’s role, and
addressing workforce concerns regarding automation.
Originality/Value: This study provides empirical evidence on the impact of AI-driven
workflow optimization on employee well-being in radiology. By focusing on stress reduction
as a mediating factor, it offers a novel perspective on how AI can be designed and
implemented to maximize both efficiency and employee satisfaction.
Limitations and Future Research Directions: While the study provides valuable insights,
limitations include a reliance on self-reported data and potential biases in AI perception.
Future research should explore longitudinal effects of AI integration, compare different AI
models, and assess variations across diverse healthcare settings to develop a more
comprehensive understanding of AI’s role in radiology workforce dynamics.
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CHAPTER 1
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INTRODUCTION
1.1. General Introduction
Research on Artificial Intelligence (AI) optimization of workflow in radiology has grown as a vital
study field. AI technology introduced substantial changes to radiological work methods which
produced enhanced operational performance as well as better precision and better medical results
for patients. The benefits of artificial intelligence technology in technical aspects receive extensive
discussion but its complete evaluation regarding its effect on radiology workforce well-being
through satisfaction retention and job stress reduction remains incomplete.
Medical staff who work in radiology experience three key health factors: job satisfaction and
motivation as well as mental healthcare quality (Rath & Harter, 2010). Radiologists can conserve
energy from unintelligent responsibilities when AI implements automation thus enhancing their
ability to concentrate on complex and rewarding tasks (Hagel et al., 2018). Workers can expect
improved satisfaction with their jobs combined with decreased occupational stress after this
transition takes place (Nazareno & Schiff, 2021).
Even though AI technology has been implemented it leads to major challenges and obstacles.
Moral at the workplace suffers when employees worry about job safety while facing skill
challenges and fear of termination (Didem & Anke, 2021; Peters, 2017). A detailed analysis of
how AI technology impacts workplace wellness in radiology practices needs to be conducted due
to its dual role as both an occupational relief approach and an introduced source of job-related
stress.
The literature lacks insights about how AI acts as a mediator for job stress reduction and its impact
on employee satisfaction with current evidence showing its benefits for workflow efficiency and
reducing repetitive work (Stamate et al., 2021). The research investigates how workflow
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optimizations through AI affect employee satisfaction when combined with job stress reduction in
the field of radiology. Such research will establish important knowledge about employing AI to
advance technical radiology outcomes and enhance radiologist professional wellness.
Artificial Intelligence (AI) implementation in contemporary radiology diagnostic practice leads to
higher operational effectiveness and precision levels. There is inadequate research about the
impact AI has on radiology department employee work satisfaction. Studies need to address
unknown effects regarding how AI optimization impacts both the job satisfaction and stress levels
of radiologists.
Mental strain along with professional stress should be considered a serious concern among
radiologists since their work often involves performing many repetitive operations which require
intense cognitive effort. During implementation of AI technologies in diagnostic imaging
radiologists become able to dedicate their time toward higher-level and rewarding aspects of their
profession. The technical strength of AI systems generates employment challenges because it
demands higher qualifications and may require layoffs which reduce workplace enthusiasm
according to Nazareno and Schiff (2021) along with Peters (2017).
The study evaluates how AI-based workflow optimization affects radiology personnel stress while
developing their job satisfaction. This study analyzes workplace effects related to AI systems to
address current scholarly research shortcomings. This study centers its research on employee stress
reduction because it serves as an essential intermediate process to analyze the effectiveness of
transformation systems for AI integration in increasing radiology worker satisfaction and welfare.
1.2. Research Questions
1. How does AI-driven workflow optimization impact employee satisfaction in radiology?
2. What is the effect of AI implementation on job stress reduction in radiology departments?
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3. How does AI-driven workforce optimization contribute to employee satisfaction by
reducing job stress in radiology departments?
4. Does job stress reduction mediate the relationship between AI-driven workflow
optimization and employee satisfaction in radiology?
1.3. Research Objectives
1. To examine the impact of AI-driven workflow optimization on employee satisfaction in
radiology.
2. To assess the role of AI implementation in reducing job stress among radiology employees.
3. To analyze how AI-driven workforce optimization enhances employee satisfaction by
minimizing job stress.
4. To investigate the mediating effect of job stress reduction on the relationship between AIdriven workflow optimization and employee satisfaction in radiology.
1.4. Significance of the Study
Through the mediating effect of workplace stress reduction, this study offers important insights
into how AI-driven workflow optimization might improve radiology employee satisfaction. It
draws attention to the managerial and scholarly ramifications of implementing AI in healthcare
management.
1.4.1. Managerial Relevance
The study’s conclusions are highly managerially significant because they provide a better
knowledge of how workforce optimization and AI-driven workflow might improve productivity
and lessen stress in radiology departments. These insights can be used by healthcare managers and
decision-makers to put AI ideas into practice that enhance worker productivity and well-being.
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Radiology managers can make well-informed decisions to establish a more encouraging and
productive work environment by understanding the critical elements that influence job satisfaction.
1.4.2. Scientific Relevance
There is currently no thorough framework describing how AI integration in radiology affects
worker well-being, despite the field’s rapid growth. By bridging the gap between AI-driven
optimization and job happiness, especially via the perspective of job stress reduction, this study
adds to the body of knowledge already in existence. It offers a theoretical framework for further
study on AI applications in healthcare workforce management by presenting actual data on the
mediating function of stress reduction. Furthermore, our study adds to the larger conversation on
the digital revolution of healthcare by improving our understanding of how AI shapes employee
experiences in highly specialized medical domains.
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CHAPTER 2
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REVIEW OF LITERATURE
2. The literature on artificial intelligence’s (AI) effects on job satisfaction is specifically reviewed
in this chapter. With an emphasis on the potential and problems it offers, it examines how AI is
used in businesses and how it affects the workplace. It also talks about how workers view AI and
how it affects their degree of job happiness. The chapter also looks at how AI affects social
interaction and mental health in different job settings.
2.1 Definition of Artificial Intelligence
McCarthy and Minsky established the field of study now known as artificial intelligence (AI)
(2006). Since its first coining by John McCarthy, the phrase “Artificial Intelligence” has undergone
tremendous development as a complex idea. The ability of systems to understand and learn from
external data and accomplish certain goals through adaptable change is the modern definition of
artificial intelligence. This entails simulating human brain processes like reasoning, recognition,
comprehension, learning, and problem-solving on computers (Wang, 2019).
Additionally, artificial intelligence (AI) can be defined as the development of intelligent programs
and devices that can carry out creative tasks that are normally performed by humans, or as a
collection of technological innovations that replicate human cognitive processes like self-learning
and problem-solving without the need for preset algorithms. But there isn’t a single, agreed-upon
definition of artificial intelligence. “A machine-based system that, for explicit or implicit
objectives, infers how to generate outputs such as predictions, content, recommendations, or
decisions that may influence physical or virtual environments, with varying levels of autonomy
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and adaptability after deployment” is the definition of artificial intelligence (AI) that the European
Union recently adopted in an effort to stay relevant in the future (Gu & Hua, 2021).
2.1.1 Types of Artificial Intelligence
AI encompasses a wide range of technologies and systems, each with unique features and
purposes. Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and
Artificial Superintelligence (ASI) are the three primary categories into which AI can be divided
(Hamada & Kanai, 2020).
Artificial Narrow Intelligence (ANI): This kind of AI is used extensively in a variety of domains,
including self-driving car technology, gaming applications, and medical picture analysis for illness
diagnosis. ANI is made to carry out a single task or a group of closely related activities.
Artificial General intellect (AGI): AGI can mimic human cognitive capacities by comprehending,
learning, and adjusting intellect to various tasks, giving it a wider range of capabilities. AGI is
being employed more and more because of its capacity to analyze large and varied datasets, which
improves corporate productivity through technological integration, even if the majority of AI
systems currently in use in companies rely on ANI for particular work-related activities (Batin et
al., 2017).
Artificial Superintelligence (ASI): ASI is a term used to describe a future state of artificial
intelligence in which AI is more sophisticated than human intelligence in every way and can create
even more sophisticated AI systems. The birth of a new type of artificial life with cognitive
capacities exceeding human capabilities could result from this (Hamada & Kanai, 2020).
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2.1.2 Integration and Application of AI in the Workplace
AI-based technologies are no longer just for everyday tasks; they are being incorporated into a
number of industries, such as healthcare, education, entertainment, security, services,
transportation, and the workplace. In order to increase productivity, AI applications initially
concentrated on automating repetitive, rule-based operations like scheduling, data entry, and basic
customer support. The majority of mundane and administrative duties have been replaced by
robotic process automation (RPA) technology, freeing up staff members to concentrate on more
intricate and strategic duties. AI technologies have advanced in recent years to manage activities
that are getting more and more complicated. Without the need for explicit programming, machine
learning techniques have made it possible for systems to learn from data and get better over time.
Personalized learning and development plans, sophisticated customer relationship management
(CRM) systems, and predictive analytics utilized in human resources for hiring and employee
retention are just a few examples of the growing number of AI applications in the workplace (Frank
et al., 2019).
One of the main fields where AI is being applied is human resource management (HRM), which
is revolutionizing how employees are handled at work. Research has indicated that AI applications
have a significant impact on employees’ well-being and job-related results. By replacing
conventional IT tasks like Excel calculations, AI systems speed up data analysis and decisionmaking, boosting productivity and efficiency (Stone et al., 2020).
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2.1.3 Opportunities of Artificial Intelligence
AI has a lot of potential, on the one hand. By streamlining procedures, increasing operational
efficiency, and optimizing resource allocation, AI technology can boost productivity across a range
of corporate roles (Kumar, 2024). AI also helps workers make decisions, and human-AI
cooperation frees people from repetitive work so they may take advantage of lucrative
opportunities. A study claims that artificial intelligence (AI) in the form of computer-based support
systems, or virtual assistants (VAs), can assist workers with work-related tasks by increasing
productivity and lowering workload. Employee performance at work is eventually improved as a
result of being able to concentrate on more important and strategic tasks (Kar et al., 2023).
2.2 Employees’ Attitudes Toward Artificial Intelligence
This could help to understand why opinions on artificial intelligence (AI) vary. Employees may
have both good and negative opinions about AI at the same time, depending on how they view the
technology and balance its advantages and disadvantages. These sentiments differ in various
industries. Because of their expectations and experiences with the technology, nursing students in
the healthcare industry, for example, who frequently use AI-powered health tools in clinical
practice, typically have more positive attitudes toward AI. Similar to this, medical practitioners
have a favorable opinion of AI and show excitement and interest in its potential as a diagnostic
tool to improve pathology workflow quality and efficiency (Sakshi, 2023).
On the other hand, the hospitality sector exhibits more negative sentiments, especially with regard
to human-like robots, which are frequently seen as a danger to human identity and individuality.
Employee perceptions of AI in the business sector can change according on the situation (Kar et
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al., 2023). Employees occasionally show positive views, particularly when it comes to the
acceptability and usability of technology. The readiness of employees to embrace AI can be greatly
influenced by their thoughts regarding it. People are more inclined to accept AI technology if they
have more positive attitudes. However, other research draw attention to worries or unfavorable
opinions about AI. The term “No-Human-Interaction” (NHI), coined by researchers, describes a
negative attitude against interacting with AI (Kumar, 2024).
For instance, concerns about job displacement as a result of AI’s capacity to replace human labor
may be the root cause of reluctance to implement AI in the workplace. Other worries also come
into play, as some workers fear AI may lessen human connection at work, which could have an
impact on coworkers’ social and professional ties.
2.2.1 Predictors of Attitudes Toward AI
Individual variations like technological preparedness and adaptability must be taken into account
when analyzing the elements that determine attitudes toward AI. According to prior studies,
workers who are more technologically ready—that is, who are at ease with technology and know
enough about it—tend to see AI more favorably (Chin et al., 2022). Furthermore, how employees
view AI is greatly influenced by organizational elements including leadership support and
communication tactics (Chin et al., 2022). This is consistent with research that looked at how
organizational characteristics affect the uptake of innovation. Additionally, it seems that
employees’ age affects their willingness to adopt new technologies, with younger workers typically
being more open to new developments (Budhwar et al., 2023).
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2.2.2 AI Relatedness to Job Roles
Determining employees’ acceptance of AI in the workplace requires an understanding of how they
relate the technology to their duties and obligations. Perceptions of AI integration in the workplace
can affect workers’ future employability, job security, and job happiness (Presbitero & TengCalleja, 2023). Furthermore, employees’ trust in AI systems is significantly influenced by the
degree of AI’s utility and transparency (Yu et al., 2023).
2.2.3 Familiarity with AI
Employee perceptions of AI are greatly influenced by their level of familiarity and trust in the
technology. Malik et al. (2022) looked at how AI affected workers in Industry 4.0-driven
companies, emphasizing creativity, knowledge management, and uniqueness (2021). Adoption of
AI offers both advantages and disadvantages, according to the study. Employees are adversely
affected by the negative effects of digital transformation, which include issues with data privacy,
information security, job dangers, job insecurity, and even technostress. Positively, AI can increase
job performance, foster creativity and innovation, and increase workplace flexibility (Malik et al.,
2022).
According to a another study, workers who had previously used and understood AI were more
inclined to embrace and promote autonomous applications than those who didn’t know much about
it (López-Solís et al., 2025).
2.2.4 Job Displacement Fears
Concerns about job displacement may be exacerbated by AI’s threat to workers’ professional
identities in the workplace. Increased identity dangers from AI are more likely to affect workers
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who value their jobs and feel more responsibilities (Malik et al., 2022). The psychological effects
of integrating AI into businesses are highlighted here, with a focus on how workers perceive their
positions and possible worries about job displacement brought on by AI.
2.3 Subjective Well-being
Examining the idea of subjective well-being and general life satisfaction is crucial to
comprehending the wider ramifications of AI-driven workplace transformations. Subjective wellbeing is defined by Sengar et al. (2024) as a person’s cognitive and emotional assessment of their
life, which includes feeling a high degree of life satisfaction, low levels of negative emotions, and
high levels of positive emotions.
2.4 Job Satisfaction
2.4.1 Definition of Job Satisfaction
Although there are other definitions of job satisfaction, Locke’s (1969) definition—which defines
it as “a pleasurable or positive emotional state resulting from the assessment of one’s job or job
experiences”—is the one that is most frequently employed in organizational research. This
definition emphasizes the subjective and emotional components of job satisfaction, taking into
account people’s opinions and feelings of fulfillment from their work. Nonetheless, other experts
contend that the absence of negative emotions is just as important to job satisfaction as good ones.
Furthermore, environmental and psychological factors are important in determining job happiness.
Since job satisfaction is impacted by a number of elements, such as the nature of the work, pay,
perks, possibilities for professional advancement, and connections with managers and coworkers,
it seems sense to think of it as a multifaceted notion (Shah et al., 2024).
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The idea of job happiness has drawn a lot of scholarly attention and has been examined in a variety
of professional contexts, such as automation, work-life balance, and job characteristics. For
example, work-life balance and remote work have become more significant, particularly since the
COVID-19 epidemic, and their relationship to job satisfaction has been studied. Arambepola et al.
(2021) observed that while remote employment occasionally had a detrimental impact on worklife balance, it had no discernible effects on job satisfaction. Nonetheless, they found that workers
who are able to work from home are generally happier than those who would like to work remotely
but are unable to do so, suggesting that job satisfaction is better without necessarily compromising
work-life balance.
According to Schwabe & Castellacci (2020), 40% of Norwegian employees in their study thought
that machines could take over their job duties, which would result in a decrease in job satisfaction.
According to the report, employees who perform repetitive jobs and have low skill levels are more
vulnerable to job displacement. It is now crucial to examine how AI tools like ChatGPT effect
workplaces and employees’ job happiness as they became widely accessible in 2023 and began to
be incorporated into different work environments.
2.4.2 AI & Job Satisfaction
Instead of tackling the organizational issues related to AI deployment, recent AI research has
mostly concentrated on comprehending the technological aspects of AI adoption. There is still a
dearth of thorough knowledge on how AI is integrated and used within businesses, despite the fact
that many studies have examined important criteria for exploiting AI technologies and identified
current research gaps. Furthermore, not enough research has been done on the possible societal
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repercussions of AI, such as how it may affect social interactions and mental health as a result of
altered work dynamics and human-AI interactions (Metzger, 2024).
Additionally, the importance of employees’ attitudes and views regarding AI and how these affect
their reaction to its adoption was highlighted in Bankins et al.’s (2024) multilevel review of AI in
enterprises. The Job Demands-Resources (JD-R) model suggests that job characteristics are
defined by two key factors: resources (physical and psychological aspects that facilitate goal
achievement and personal growth) and demands (physical and psychological aspects that require
effort and may lead to negative consequences like burnout and work overload). Future studies
could benefit from expanding the application of this model.
2.5 Artificial Intelligence: Impact and Challenges in the Workplace
The discipline of computer science known as artificial intelligence (AI) is devoted to creating
methods and algorithms that let robots carry out operations like learning, reasoning, and
comprehending that normally demand for human intelligence. One definition of it is “the ability
of machines to execute tasks that reflect intelligent human behavior” (García-Madurga et al.,
2024). Numerous academic fields, including philosophy, mathematics, computer science,
psychology, and neuroscience, are the foundation of artificial intelligence. A substantial amount
of scientific study on artificial intelligence and neural networks has surfaced since early studies in
the middle of the 20th century, producing noteworthy findings (Gerke, Minssen & Cohen, 2020).
AI has a significant impact on society, bringing with it both possibilities and difficulties. It has
been said that its effects can be either “a blessing or a curse,” depending on how they are used and
handled. AI’s potential role raises both curiosity and worries. On the one hand, AI increases
employment and productivity, and its incorporation into industries like manufacturing is becoming
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more and more common (Brundage et al., 2018). Additionally, it increases productivity in a variety
of fields, including healthcare, where AI is applied to patient monitoring, treatment planning, and
disease diagnosis.
Algorithms for medical imaging analysis, such those for CT scans and X-rays, help physicians
identify diseases early. Supervisors in these fields need to develop job responsibilities
appropriately and train employees to work with AI technologies (Luitse & Denkena, 2021).
Notwithstanding these developments, AI still faces many obstacles, such as the need for ethical
and responsible AI use, data security, and patient privacy protection. Numerous problems with AI,
including algorithmic bias, vulnerabilities, malicious applications, security concerns, possible
employment losses, and ethical quandaries, have been brought to light by academic study. “The
question is not whether AI will be good enough to perform more cognitive tasks, but how we will
adapt to it,” the Harvard Business Review states (Gu et al., 2022).
Because it depends on a number of variables, such as the industry sector, the state of the economy,
and governmental regulations, it is challenging to predict which human employment will be most
impacted by automation and artificial intelligence. On the other hand, careers that need a lot of
repetition are probably the most dangerous. According to Vorobeva et al. (2022), they include lowlevel service sector professions like data processing and customer support, manufacturing jobs like
assembly line labor and machine operation, and some financial sector jobs like accounting and
transaction processing.
Automation may result in job losses and lower wages, especially in service-related occupations
where AI might take the place of humans in jobs like creating content, answering often asked
queries, and producing automated texts for advertising and journalism. Because those with low
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skill levels might find it difficult to obtain work, this change could increase economic inequality
by decreasing the need for workers to perform such activities. Indeed, over the past forty years,
automation has had a major role in widening the pay inequality in the United States (Huysman,
2020).
However, automation may allow workers to concentrate on higher-value and more creative jobs
by utilizing AI collaboration, a concept referred to as “hybrid intelligence.” Human-AI interaction
has been extensively studied in a variety of domains, such as sketching, music, video game
creation, and conceptual idea generation. AI has the potential to improve employment in several
service-related fields where emotional intelligence is still crucial. Future employment stability is
anticipated for jobs requiring emotional intelligence, such as those having direct relationships with
customers or coworkers (Anan et al., 2021).
Little is known about how employees view these technological breakthroughs and their impact on
occupations, or how they prepare for such changes, despite studies emphasizing the necessity for
research on the future of work. Prior to ChatGPT’s December 2022 public release, there weren’t
many scholarly research on the subject, but as evidenced by the widespread media coverage, it has
since grown to be a major social issue (García-Madurga et al., 2024).
Regardless of industry or skill level, it is important to take into account the possibility of job
displacement or unemployment, even though emerging technologies have many positive effects
on the workplace. The well-being of employees is significantly impacted by these consequences,
necessitating the implementation of suitable solutions to tackle these issues. Numerous interrelated
aspects affect an organization’s health. Scholars have been suggesting conceptual models for
organizational health for a long time (Fukumura et al., 2021).
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According to a recent survey, there are five essential components of a good workplace: work-life
balance, employee engagement, health and safety, employee growth and development, and
recognition. Furthermore, according to Xu, Xue, and Zhao (2023), there are nine components that
contribute to the health of an organization: leadership, motivation, accountability, capabilities,
coordination and control, organizational culture and climate, external orientation, innovation and
learning, and work environment.
The psychological and social well-being of employees is the primary focus of the empirical
definition of a healthy organization, which ignores the variables that contribute to or sustain this
well-being. Positive organizational settings that are marked by security, customer service, justice,
interpersonal treatment, autonomy, support, and efficiency tend to have lower stress levels and
higher levels of well-being among their employees. In order to guarantee the greatest standards of
health and well-being at work, companies must use AI and make an effort to give their workers
meaningful employment. The effect of AI on human resources procedures has been the subject of
much scholarly investigation. The impact of AI on work-related outcomes has also been evaluated
in certain research. However, not much study has looked into how using AI affects employee
experiences. Strategies that put technology ahead of employee behavior are impeding the success
of AI application in the workplace (García-Madurga et al., 2024).
The term artificial intelligence (AI) describes the development of computer systems that can carry
out operations like speech recognition, decision-making, and natural language processing that
normally call for human intelligence. Many organizations are depending on AI-driven solutions to
increase HR efficiency and facilitate improved decision-making, as AI plays an increasingly
important role in improving HR operations. Nonetheless, there are worries about the moral and
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societal ramifications of AI, such as possible prejudice, employment loss, and invasions of privacy
(Shahzad et al., 2024).
Strict regulations are needed as AI develops in order to optimize its advantages and reduce its
hazards. It is imperative for organizations to guarantee that AI solutions are grounded on objective
datasets and that their systems undergo periodic audits to identify any possible biases. AI may
eventually be able to complete some HR functions faster and more correctly than humans, which
could result in job losses. Organizations should concentrate on retraining and upskilling staff
members to fill new positions that call for human skills in order to address this problem (Mensah,
2023).
AI enhances performance management, training, and hiring procedures. Hiring managers used to
spend hours going through applications and resumes in order to identify qualified applicants. Many
processes, including candidate sourcing, resume screening, and even preliminary interviews, can
now be automated with AI-powered recruitment solutions. By analyzing job advertisements and
candidate profiles using machine learning algorithms, these technologies let recruiters quickly find
the most qualified applicants. Furthermore, by evaluating applicants’ communication abilities and
psychological qualities, natural language processing (NLP) tools can lessen human bias in hiring
decisions (Vrontis et al., 2023).
By evaluating information from patient satisfaction surveys, electronic health records, and staff
feedback, healthcare businesses can also use AI to improve performance management. By
identifying employee strengths and areas for development, these insights assist firms in creating
customized performance enhancement strategies for each person (Shahzad et al., 2024).
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The readiness and capacity of an individual to embrace and make use of new breakthroughs or
technology is referred to as personal innovativeness. Personal inventiveness can have an impact
on how healthcare organizations and practitioners view and use AI in the healthcare industry to
improve patient outcomes and streamline organizational procedures. People that are highly
innovative personally are more likely to experiment with new technologies, such as artificial
intelligence, and look into how they might be used in healthcare, which could lead to wider
acceptance (Roppelt et al., 2024).
Personal innovators are less prone to oppose contemporary technologies and are more likely to
adjust to changes with ease. Personal ingenuity aids in overcoming possible obstacles to the
adoption of AI, such as healthcare providers’ opposition or skepticism. Through creativity, people
help integrate AI expertise into healthcare operations, improving decision-making and overall
healthcare quality. This may lead to better patient outcomes, lower medical expenses, and an
overall improvement in the standard of care and treatment (Shahzad et al., 2024). Additionally,
prior research has demonstrated that individual creativity is essential for promoting AI adoption in
the healthcare industry and recognizing the potential advantages of these technologies for patients
and healthcare organizations.
2.6 The Impact of Artificial Intelligence and Machine Learning on Employee Satisfaction
and Job Performance
The usage of artificial intelligence (AI) is growing in many facets of our lives, including robotics,
sensing devices, and decision-support systems. Tasks that were previously completed by human
professionals can now be automated thanks to technological breakthroughs. As a result, our
working methods are evolving, and technology may eventually replace some occupations. This
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change, which is comparable to the invention of electricity or the internet, emphasizes how crucial
it is for people and AI to work together so that AI complements rather than replaces human
abilities. It is crucial to evaluate human elements including user experiences, workflow features,
and user preferences because the integration of AI solutions differs based on the environment and
users. Transparency, accountability, and trust in human-machine interaction are among the
additional problems specific to AI that emerge in addition to the well-known ones (Wenderott et
al., 2024).
Healthcare is one of the industries that is seeing major changes as a result of AI. AI has the ability
to greatly help healthcare workers with a variety of jobs and procedures, especially in image-based
sectors like radiology. Clinical diagnoses and medical decision-making can be aided by AI; yet,
integrating new technologies into the intricate clinical work environment has shown to be
extremely difficult in the past, as demonstrated by robotic surgery and electronic medical records
(Catchpoole et al., 2022).
According to a comprehensive empirical study by Kuzey (2018), organizational success in
healthcare contexts is significantly impacted by employee happiness. However, there was still
opportunity for additional research because of constraints pertaining to the study’s scope and
possible confounding factors.
Later, employing optimized neural circuits, Sekhon & Sadawarti (2021) created a sophisticated
computing system for assessing job happiness, highlighting the enormous potential of technology
in promoting organizational growth. However, issues with privacy assurance and system rollout
were still unresolved. Similar to this, Zhuang (2022) used quantitative measurements to
empirically test hypotheses as she investigated the intricate relationships between job satisfaction
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and elements like autonomy, leadership support, and perceived pay equity. However, the study ran
the risk of oversimplifying the complex causal links at play.
In order to analyze work satisfaction with exceptional accuracy, Ozdemir (2022) created online
employee assessments using machine learning and ensemble approaches. Nevertheless, the model
was prone to overfitting and data biases, which might have compromised the validity of its
findings.
In spite of dependability issues and computational complexity, Choi (2022) more recently
presented a machine learning-based job satisfaction prediction model that provides insightful
organizational information. By predicting employee resignations, Shah ET AL. (2024) emphasized
the value of predictive analytics in tackling workforce attrition. However, the study recognized the
model’s low generalizability and intrinsic dataset biases.
In order to improve personnel management, Gurung (2024) investigated employee performance
analysis utilizing machine learning, namely XG-Boost, in U.S.-based situations. Notwithstanding
its benefits, the study encountered difficulties with model interpretability. A thorough analysis of
the complex relationships between job satisfaction and individual and organizational outcomes
was previously carried out by Jalagat (2016), who acknowledged the subjectivity of measuring job
satisfaction and the limitations of the study’s generalizability while offering insightful information.
By looking at the answers of 100,000 engineers, Arambepola (2021) examined the elements that
affect job satisfaction in the IT business. The study had issues with model interpretability and
wider applicability, even though it offered insights unique to the industry. In order to improve job
satisfaction through increased forecasting accuracy, Mohamed (2021) created a revolutionary
neural network-based categorization method to forecast employee turnover in the same year.
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Preprocessing challenges and implementation complexity, however, continued to be major
roadblocks.
2.7 Hypothesis
H0: (Main Hypothesis): There is a statistically significant positive impact of AI-driven
workflow optimization on employee satisfaction in radiology, and this relationship is mediated
by job stress reduction.
H1: There is a statistically significant positive impact of AI implementation on job stress
reduction among radiology employees.
H2: There is a statistically significant positive impact of AI-driven workforce optimization on
job stress reduction among radiology employees.
H3: Job stress reduction has a statistically significant positive impact on employee satisfaction in
the radiology department.
H4: Job stress reduction has a statistically significant positive impact on employee happiness in
the radiology department.
H5: There is a statistically significant positive impact of AI implementation on employee
satisfaction, mediated by job stress reduction.
H6: There is a statistically significant positive impact of AI-driven workforce optimization on
employee satisfaction, mediated by job stress reduction.
Conceptual framework
Independent Variables
Sub variables:
•
AI-Driven Workflow Optimization
•
AI Implementation
•
AI-Driven Workforce Optimization
mediating variable:
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•
Job Stress Reduction
Dependent Variables
•
Employee Satisfaction
•
Employee Happiness
model of study
AI-Driven Workflow Optimization
AI Implementation
AI-Driven Workforce Optimization
Job Stress Reduction
Employee Satisfaction
Employee Happiness
The purpose of this study’s conceptual framework is to investigate how artificial intelligence (AI)
technologies affect workers’ well-being in work environments. The model provides a thorough
understanding of how AI integration can affect employee outcomes by combining three important
independent variables, one mediating variable, and two dependent variables.
This framework’s independent variables center on a number of aspects of integrating AI in the
workplace:
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The use of artificial intelligence tools to automate repetitive jobs, optimize workflow, and improve
the general effectiveness of corporate processes is known as AI-Driven Workflow Optimization.
The goals of this optimization are to increase task speed and accuracy, decrease human error, and
eliminate redundancy.
The wider adoption and integration of AI technologies across corporate functions is known as AI
implementation. It includes staff training, investment in AI infrastructure, strategic planning, and
company processes reorganization to make room for AI technologies.
The use of AI solutions to improve human resource management is known as AI-Driven
Workforce Optimization. This includes improved decision-making procedures in hiring and talent
management, automated performance monitoring, intelligent scheduling tools, and predictive
analytics for workforce planning.
It is expected that these independent variables will have an impact on Job Stress Reduction, a
mediating variable. The reasoning for this is that a more encouraging and well-rounded work
atmosphere can be created, employee workload can be decreased, and repetitive duties can be
reduced with the help of AI. AI can reduce stressors like time constraints, task complexity, and
poor resource allocation when used properly.
Employee happiness and satisfaction are the study’s dependent variables. It is expected that
employees will be happier and more satisfied at work when AI systems are deployed effectively
and cause less stress related to their jobs. These favorable results are crucial markers of the
wellbeing of the staff and the firm.
All things considered, this conceptual framework shows a cause-and-effect relationship in which
AI technologies help create a more efficient and stress-free workplace, which in turn raises
employee happiness and satisfaction. One important connection between AI-driven solutions and
satisfying employee experiences is the mediation function of job stress reduction.
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THE METHODOLOGY
3. The purpose of this study is to investigate how AI-driven workflow optimization affects
radiology employee happiness, with an emphasis on the mediating function of occupational stress
reduction. Knowing how artificial intelligence (AI) affects radiology professionals’ stress levels
and job satisfaction is essential given the growing use of AI in workflow automation and medical
imaging. In order to objectively assess these associations and guarantee statistical validity and
reliability, the study will use a systematic methodology.
3.1 Research Approach
In order to methodically examine the connection between AI-driven workflow improvement, job
stress reduction, and employee satisfaction, this study uses a quantitative research methodology.
Numerical data from radiology professionals will be gathered through a standardized survey,
enabling statistical analysis and hypothesis testing. The quantitative approach is suitable for
assessing how much AI affects job satisfaction, stress levels, and workflow efficiency.
3.2 Research Design
To collect information from radiology experts at one particular moment, a cross-sectional survey
methodology will be used. This strategy works well for finding connections between variables and
comprehending how integrating AI affects workflow, stress, and happiness right away. To
determine whether job stress reduction acts as a bridge between AI-driven workflow optimization
and employee satisfaction, the study will apply a mediation analysis paradigm.
3.3 Study Population and Sample
The study population consists of radiologists, radiologic technologists, and administrative
personnel employed in imaging centers and hospitals’ radiology departments. The study will
employ a stratified random sampling technique to guarantee representation from diverse
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participant groups, including varied work contexts and professional responsibilities (public vs.
private hospitals). In order to guarantee statistical reliability and correctness of the results, a sample
size of 400–500 participants will be targeted, as estimated by power analysis Slovin’s method.
3.4 Data Collection Method
To guarantee widespread involvement, a structured, self-administered questionnaire will be used
for data collection. It will be disseminated online through Google Forms and hospital email
networks. Four main topics are intended to be covered by the questionnaire. Demographic data
such as age, gender, years of experience, and work role will be gathered in the first part. AI-driven
workflow optimization will be the subject of the second section, which will evaluate topics like
workload distribution, picture analysis, and reporting. The final segment will assess participants’
views of workload reduction, stress levels, and emotional weariness in order to quantify job stress
reduction. The fourth and last phase will evaluate employee happiness by looking at job
satisfaction, engagement, and morale after AI installation. A five-point Likert scale (1 being
strongly disagree and 5 being strongly agree) will be used to record responses in order to measure
the opinions and experiences of the participants.
3.5 Data analysis
Using SPSS, data analysis will be carried out using a variety of statistical methods to guarantee a
thorough review of the information gathered. The demographics and replies of the participants will
be compiled using descriptive statistics, such as mean, standard deviation, and frequency
distributions. Internal consistency will be measured using Cronbach’s Alpha to guarantee the
survey instrument’s dependability. To determine the direct impact of AI-driven workflow
optimization on employee happiness, inferential statistical techniques such as multiple regression
analysis will be used. To investigate whether job stress reduction mediates the relationship
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between AI installation and employee satisfaction, a mediation study utilizing Hayes’ PROCESS
Model will be carried out.
3.7 Ethical Considerations
Strict adherence to ethical principles will guarantee the preservation of participant rights and data
privacy. All participants will be informed about the study’s goals, data confidentiality, and their
opportunity to voluntarily participate or withdraw at any time without facing any repercussions
before their informed permission is sought. No personal identifiers will be gathered in order to
maintain participant confidentiality and guarantee data anonymity. In order to guarantee adherence
to ethical research norms, Institutional Review Board (IRB) approval will also be acquired prior
to the start of data collection.
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CHAPTER 4
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ANALYSIS AND RESULTS
4. The study’s data and findings are analyzed and interpreted in this chapter. In order to examine
the Impact of AI-Driven Workflow Optimization on Employee Satisfaction in Radiology: The
Mediating Role of Job Stress Reduction, the study’s methodology comprises three stages: a
descriptive analysis of each statement’s responses, a statistical analysis of the participant
demographics, and a correlation analysis.
4.1.2 Results
Participants’ age, gender, Educational Level and Years of Service up the study’s demographic
characteristics. Also, as can be seen in the table below, the examined data is given as percentages
(%) and numbers (N).
Table (4-1): Demographic Characteristics
N
%
148
174
46
47
84
14.6
31.4
26.1
54
16.4
36
11.2
31
67
105
89
30
9.6
20.8
32.6
27.6
9.3
89
80
27.6
24.8
100
53
31.1
16.5
Gender
Female
Male
54
Age
Under 20
21 – 30 years
31 – 40 years
41- 50 years
Over 50 years
101
Educational Level
High school and below
Specialized training school
Undergraduate degree
Bachelor’s degree
Doctorate
Years of Service
3 years and under
3-5 years
5-10 years
More than 10 years
According to the study sample’s demographics, the respondents’ genders are comparatively evenly
distributed, with men making up 54% and women 46%. The findings of employee happiness and
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the effects of AI-driven workflow optimization are supported by this balance, which implies that
the viewpoints recorded in the survey are not significantly skewed toward one gender.
The largest age group of participants (31.4%) is between the ages of 21 and 30. The next largest
age group (26.1%) is between the ages of 31 and 40. This suggests that the bulk of workers in
radiology departments are younger workers, who might be more flexible and open to new
technologies like the incorporation of artificial intelligence. The lower percentage of participants
who were over 50 (11.2%) raises the possibility of a generational divide in how AI technologies
are viewed and embraced.
The majority of respondents had a solid academic background, as evidenced by their educational
backgrounds; 32.6% have an undergraduate degree and 27.6% have a bachelor’s. 9.3 percent have
earned doctorates as well. This educational profile is significant because it shows that the sample
possesses the technical know-how and information necessary to comprehend and interact with AIdriven systems, which may have an impact on their job satisfaction and stress levels.
In terms of years of service, over 31.1% of those surveyed have five to ten years of experience,
while 24.8% have three to five years. Just 16.5% have been with the company for more than ten
years. A sizable fraction of the workforce appears to be in their mid-career, presumably balancing
experience with flexibility, according to this distribution. This category of workers may be
especially vulnerable to changes in job stress and workflow brought about by AI.
All things considered; the demographic information shows a reasonably youthful, well-educated
workforce with a range of professional experience levels. In radiology departments, these traits are
essential for analyzing the mediating role of workplace stress reduction and the impact of AIdriven workflow optimization on employee happiness.
Table (4-2): Descriptive Statistics
Mean
Std. Deviation
AI-Driven Workflow Optimization
I am forced to change my work habits to adapt to AI.
4.13
0.876
I have a higher workload because of increased AI
3.825
0.943
complexity.
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I need to constantly update my skills to keep up with AI
4.105
1.011
3.997
0.981
I am forced by AI to work much faster.
3.849
0.871
I am forced by AI to do more work than I can handle.
3.974
0.988
I am forced by AI to work with very tight time schedules.
4.261
1.202
advancements.
AI-driven changes in workflow often lead to uncertainty
and stress in my daily tasks.
AI Implementation
AI-Driven Workforce Optimization
I spend less time with my family due to AI.
3.669
0.744
I have to be in touch with my work even during my
3.859
0.769
4.233
1.003
4.102
0.989
vacation due to AI.
I have to sacrifice my vacation and weekend time to keep
current on new AI.
I feel my personal life is being invaded by AI.
Important information about how employees view AI-driven workflow optimization in radiology
is provided by the descriptive statistics. With most questions scoring above 3.8 on a 5-point scale,
the responses’ means are often high, suggesting that participants strongly agree that integrating AI
into their regular work routines is hard and disruptive.
Employees reported a high mean score (M = 4.13) under the “AI-Driven Workflow Optimization”
dimension, indicating a major shift in work processes as a result of technology improvements, for
having to modify their work habits to adapt to AI. Similarly, a mean of 3.997 in the equivalent
item suggests that employees feel constant pressure to learn and adapt, which may lead to emotions
of stress and uncertainty. This is reflected in the desire to continuously upgrade skills (M = 4.105).
According to the statistics on “AI Implementation,” employees feel a lot of pressure from AI to
work faster (M = 3.849) and with tighter timetables (M = 4.261). The biggest standard deviation
(SD = 1.202) indicates that different people have different experiences with time pressure. Though
AI is meant to streamline processes, it can paradoxically raise job demands. This is demonstrated
by the noteworthy feeling of being overburdened by higher workloads owing to AI complexity (M
= 3.974).
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Regarding work-life balance, there is a worrying tendency in the “AI-Driven Workforce
Optimization” dimension. Items like giving up weekends and vacation time to stay up to date with
AI advancements (M = 4.233) and believing that AI is invading personal life (M = 4.102) indicate
that the use of AI is becoming more prevalent outside of the workplace, resulting in serious
disruptions to personal time. These results highlight the possible harm that digital transformation
may do to workers’ stress levels and general well-being.
The descriptive statistics collectively indicate that although AI-driven workflow optimization
seeks to improve operational efficiency, it also has significant negative psychological and social
effects on workers. If left unchecked, these pressures may reduce worker happiness and even
negate the benefits of integrating AI.
Table (4-3): Descriptive Statistics (Stress Reduction)
Mean
When I have a setback at work, it is hard for me to bounce back
Std. Deviation
3.842
0.687
I usually take the pressure of work in stride.
3.641
0.877
Because I have been through a lot of hardships before, I can now
3.884
1.010
4.011
0.881
and move on.
survive the difficult times at work.
In my current job, I feel like I can handle a lot of things at the
same time.
Important facets of employees’ resilience and capacity to handle work-related stressors in the face
of AI-driven developments are highlighted by the descriptive statistics for the stress reduction
dimension. The mean scores, which range from 3.6 to 4.0 on a 5-point scale, indicate that
employees’ capacity to handle stress is generally viewed as moderately good. In particular, the
item “In my current job, I feel like I can handle a lot of things at the same time” had the highest
mean (M = 4.011), suggesting that most workers are comfortable handling complex demands and
multitasking, even when faced with the demands of AI workflow optimization. This might be an
indication of how workers in high-pressure situations have evolved flexible coping mechanisms.
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Additionally, the statement “Because I have been through a lot of hardships before, I can now
survive the difficult times at work” had a comparatively high mean (M = 3.884), indicating that
resilience is influenced by prior experiences of difficulty. This finding emphasizes the value of
prior difficulties in fostering employees’ psychological fortitude, which could serve as a hedge
against the stress brought on by changes associated to AI.
Conversely, the item “When I have a setback at work, it is hard for me to bounce back and move
on” had a mean of (M = 3.842), suggesting that while employees are generally resilient, setbacks
nonetheless provide difficulties for a sizable percentage of them. This suggests that in order to
improve recovery from work-related challenges, more organizational support systems are required.
Lastly, out of all the stress reduction items, the item “I usually take the pressure of work in stride”
had the lowest mean (M = 3.641), indicating a somewhat lesser propensity to handle continuous
job pressure with ease. Even while the mean is still higher than the midpoint, it indicates that there
is potential for improvement in terms of assisting staff members in managing regular stress
associated to their jobs.
The findings show that although radiology staff members typically have strong resilience skills to
AI-driven workflow changes, there are still issues that need to be addressed. Employee happiness
might be further improved and the detrimental effects of stress in digitally changing work settings
could be lessened by providing focused stress management programs and bolstering organizational
support.
Table (4-4): Descriptive Statistics
Mean
Std. Deviation
My organization cares about my general satisfaction at work
3.684
0.762
My organization takes pride in my accomplishments at work.
3.973
0.784
My organization appreciates any extra effort from me.
3.997
0.965
My organization cares about my opinions.
4.221
1.001
My organization cares about my well-being.
4.012
1.012
Employee Satisfaction
Employee Happiness
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I am enthusiastic about my job.
3.674
0.628
I am excited about my job.
3.982
0.991
I feel energetic about my job.
3.877
3.772
I am proud of my job.
4.013
3.669
I feel positive about my job.
4.022
1.202
I always see the bright side of things in my work.
4.001
1.013
I am optimistic about what the future holds for my work.
3.679
0.886
The descriptive statistics for employee contentment and satisfaction show that radiology staff
members who operate in AI-driven workflow environments have generally positive opinions. On
a 5-point scale, all item means are over 3.6, suggesting a good degree of agreement with
organizational support as well as individual sentiments of excitement and hope.
The item “My organization cares about my opinions” had the highest mean (M = 4.221) in terms
of employee satisfaction, indicating a strong sense of inclusion and the importance placed on
employee feedback. This implies that businesses are effectively cultivating a culture of
participation, which is essential for preserving employee morale across technological changes.
Similarly, high means were reported for “My organization cares about my well-being” and “My
organization appreciates any extra effort from me” (M = 4.012 and M = 3.997, respectively),
highlighting the fact that workers feel their management genuinely cares about them.
Though still favorable, the item “My organization cares about my general satisfaction at work”
had a somewhat lower mean score (M = 3.684). This may suggest that although certain elements,
such as achievements and well-being, are adequately addressed, there may be space for more
comprehensive
programs
aimed
at
enhancing
total
employee
happiness.
The findings are similarly positive in terms of employee satisfaction. The question with the highest
score, “I feel positive about my job” (M = 4.022), indicates that despite the rapid advancements in
technology, employees continue to have an optimistic outlook on their work. Additionally, high
mean values for “I am proud of my job” (M = 4.013) and “I always see the bright side of things in
my work” (M = 4.001) were noted, indicating that staff members had a resilient and upbeat outlook.
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However, there are some minor differences. For example, the happiness section’s lowest averages
were for “I am enthusiastic about my job” (M = 3.674) and “I am optimistic about what the future
holds for my work” (M = 3.679). These somewhat lower scores, while still positive, may indicate
underlying worries or apprehensions over future job prospects in light of AI-driven advancements.
In conclusion, the results demonstrate that radiology workers are highly content and happy at work,
placing a special emphasis on feeling appreciated and supported by their companies. Organizations
should continue to actively involve employees, especially in assisting their adaptation to ongoing
technological improvements, notwithstanding minor concerns about long-term excitement and
future optimism.
Table (4-5): Cronbach’s alpha coefficients for reliability
Variable
Number of
Items
Cronbach’s
Alpha
Reliability Level
AI-Driven Workflow
Optimization
4
0.75
Acceptable to
Good
AI Implementation
3
0.72
Acceptable
AI-Driven Workforce
Optimization
4
0.76
Acceptable to
Good
Job Stress Reduction
4
0.78
Good
Employee Satisfaction
5
0.83
Very Good
Employee Happiness
7
0.82
Very Good
Matrix of Correlations
Table (4-6): Matrix of Correlations
Variables
AI-Driven
Workflow
Optimization
AI
Implementation
AI-Driven
Workforce
Optimization
Job Stress
Reduction
Employee
Satisfaction
Employee
Happiness
AI-Driven
Workflow
Optimization
1.000
0.642**
0.602**
0.642**
0.655**
0.621**
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AI
Implementation
0.642**
1.000
0.624**
0.588**
0.632**
0.610**
AI-Driven
Workforce
Optimization
0.602**
0.624**
1.000
0.602**
0.624**
0.615**
Job Stress
Reduction
0.642**
0.588**
0.602**
1.000
0.721**
0.734**
Employee
Satisfaction
0.655**
0.632**
0.624**
0.721**
1.000
0.877**
Employee
Happiness
0.621**
0.610**
0.615**
0.734**
0.877**
1.000
Results Table for Hypotheses Testing
Hypothesis
H0: There is a statistically significant positive impact
of AI-driven workflow optimization on employee
satisfaction in radiology, and this relationship is
mediated by job stress reduction.
H1: There is a statistically significant positive impact
of AI implementation on job stress reduction among
radiology employees.
H2: There is a statistically significant positive impact
of AI-driven workforce optimization on job stress
reduction among radiology employees.
H3: Job stress reduction has a statistically significant
positive impact on employee satisfaction in the
radiology department.
H4: Job stress reduction has a statistically significant
positive impact on employee happiness in the
radiology department.
H5: There is a statistically significant positive impact
of AI implementation on employee satisfaction,
mediated by job stress reduction.
H6: There is a statistically significant positive impact
of AI-driven workforce optimization on employee
satisfaction, mediated by job stress reduction.
36
Correlation
Coefficient (r)
0.655**
pvalue
p
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