Impact of Automation on Organisation in Retaining the Employees

In the era of global business, the automation system in the operational procedure is a prior factor that enables organisations to boost their performance. In the emerging competitive business automation system is an advanced technology that can evolve the overall system of industry in apex parameter.It has been seen that the automation system is driving the wheel of the operating system but it has an effect on employee turnover

Teesside University, Middlesbrough, United Kingdom
Afreen Sultana (W9115061)
MSc International Management (Applied)
Dissertation
Project: Impact of automation on organisation in retaining the employees.
Under:
Module Leader: Maryam Shadman-Pajouh
Supervisor: Sharmin Shobnom
School of Business Management

Table of Contents
Chapter 1: Introduction....................................................................................................................4
1.1 Research background.............................................................................................................4
1.2 Problem statement.................................................................................................................4
1.3 Research aims and objectives................................................................................................5
1.4 Research questions.................................................................................................................5
1.5 Research rational...................................................................................................................6
1.6 Research structure..................................................................................................................6
2. Literature Review........................................................................................................................7
2.1 Literature Overview...............................................................................................................7
2.2 Concept of Automation..........................................................................................................7
2.3 Impact of Automation on Employee Redundancy.................................................................9
2.4 Impact of Automation on Employee Retention...................................................................11
2.5 The Struggle of the Challenges the Employee retention is Facing Because Of Automation
...................................................................................................................................................13
2.6 The Steps Taken by Organizations to Avoid Making People Redundant...........................14
2.7 Analyzing How Automation Can Help the Retain Employees...........................................16
2.8 How Automation creates Difficulties for the Employees....................................................17
2.9 Applicable Training for Employees That Can Help to Adjust Themselves with the
Automation System...................................................................................................................18
2.10 Conceptual Model of Literature.........................................................................................20
3. Methodology..............................................................................................................................20
3.1 Introduction..........................................................................................................................20
3.2 Research Philosophy............................................................................................................21
3.3 Research Method.................................................................................................................22
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3.4 Research Design..................................................................................................................22
3.5 Research Approach..............................................................................................................23
3.6 Research Strategy................................................................................................................23
3.7 Data Type and Collection....................................................................................................24
3.7.1 Data Type......................................................................................................................24
3.7.2 Collection Method........................................................................................................25
3.8 Sampling Strategy................................................................................................................25
3.9 Data Analysis.......................................................................................................................26
3.10 Ethical Considerations.......................................................................................................26
3.11 Summary............................................................................................................................27
3.12 Limitation..........................................................................................................................27
4.1 Chapter Overview................................................................................................................27
4.2 Thematic Analysis...............................................................................................................28
4.3 Secondary Theoretical Analysis..........................................................................................30
4.3.1 Evidence-based benefits of automation and its influence behind rising trend of
automation integration...........................................................................................................30
4.3.2 Increasing instances of automation leading to employee workforce limitations and
recruitment reductions...........................................................................................................32
4.3.3 Strategic approaches integrated by businesses to retain employees with automation..33
References......................................................................................................................................34
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Chapter 1: Introduction
1.1 Research background
In the era of global business, the automation system in the operational procedure is a prior factor
that enables organisations to boost their performance. In the emerging competitive business
automation system is an advanced technology that can evolve the overall system of industry in
apex parameter. It has been seen that the automation system is driving the wheel of the operating
system but it has an effect on employee turnover (Bessen, 2019). In the developing countries, the
different industries have adapted the automation system for improving their placement at the
global level. Due to this fact, it has been seen that in the several industries automation system is
has developed the workplace of the employees. The incorporation of the IT system has enabled
the company to enlarge its periphery in the global world (Wollschlaeger, Sauter and Jasperneite,
2017). The adaptation of IT is enriched the capability of the employees that can potent the
platform of a corporate firm. In the context of a corporate world, there are various automation
systems for advanced technological system.
Advanced automation works as a valuable asset that can provoke the authority to ensure their
employees with proper training. The notion of the authority is to retain their employees in their
workplace that help to build their strength. Such kind matter, the authority and management of
the company have to stable and provided standard conceptual methods of training that make
them safe and secure in the workplace (Halawi and Haydar, 2018). A standard training among
them helps to encourage and adapt them with the automation services. IT companies use
automated systems to improve efficiency, as robots, software tools and artificial intelligence are
the most essential issue of operating. Over time, the machines have learned to use logic and
streamline the professional system.
1.2 Problem statement
The research could face such kind of considerable facts based on the various views about what
are the effective aspects that can evolve the process the retention. Regarding this matter, it is an
important factor to evolve the training system for the employees. The training module set by the
employees has to be through a proper measurement that can enable the employees to refine
themselves in a proper way. In the era of the modern business platform, the IT industry has
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adapted several advanced automation systems that can make them advanced in every area
(Pereira et al., 2018). Therefore, it can develop the parameter of the several areas that build a
potential image of the company.
This particular report has been identified as the developing areas of the IT sector as well as how
the employees adjust to this fact. On the other hand, in this thesis paper, the utilization of the
advanced technological automation system has been described. Besides all of these, the effective
paths about the retention of the employees are also critically evaluated in this research. The
appropriate and potent areas of the training have to stand as a prior focus on this research paper.
Additionally, this paper is found out the impact of the automation system on unemployment. The
drastic changes in the IT industry for the involvement of an automation system elevate the rate of
unemployment.
1.3 Research aims and objectives
This research aims to evaluate how an automation system in the organization can retain
employees in the workplace. On the other hand, this research paper tends to aim at effective
ways of training for the employees that can help them to adapt themselves to the advanced
automation system.
I. To understand the development of automation in the IT industry and how these
approaches help evolve social and economic growth.
II. To find out the causes that eliminate the role of human activity
III. To analyse how organisations provide the appropriate training to their employees that
make them comfortable with the automation system
IV. To grant applicable recommendation for future development to reduce unemployment
and social deprivation
1.4 Research questions
Q1. What are the development areas for automation system in the IT industry?
Q2. How much kind of development affect social and economic growth?
Q3. How automation system eliminates human activities in the workplace?
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Q4. What is the effective mode of training that can evolve the efficiency of the employees to
adopt the advanced automation system?
Q5. Which are the effective paths that can work as an effective recommendation for future
development?
1.5 Research rational
The advanced automation system has a broad context that can evolve the performance of the
organizations. Such kind of advanced system can build diverse performances of the company
through the standard path. In the era of globalization, the IT industry has adopted several
effective technological systems that can enlarge the business effectively. Due to this matter, the
automation system affects the employee segment (Ramaswamy, 2018). It has been seen that
several companies in the IT industry have adapted this advanced system of automation which
reduce human activities in the workplace. The advanced technological automation tools
sometimes are a big challenge to adapt for the employees.
Standard training is a driving factor that accelerates the wheels of the automation system easily.
In the competitive global market recently almost every IT companies are used as an automation
system to provide surplus performances. Now, this research is focused on the effectiveness of the
automation system on the employees. Rather than it, this research paper is tending to identify the
prompt paths for adequate training for the employees that can help to adjust themselves to the
automation system (Vermeulen et al., 2018). Another aspect is that for the automation system the
backdated employees are not able to hold their positions in their workplace. Due to this matter,
this particular thesis paper also concerns the probable appropriate requirements that will
beneficial for future improvisation of such kind issues.
1.6 Research structure
There is a great deal of confidence in the entire research framework. A research study can only
be fully appreciated if the process is successfully designed and structured. Current research has
been divided into five main sections; their importance is provided below:
I. The first part of the thesis is the introduction, a short discussion of the topic as a whole.
The fundamental concept and meaning of the analysis are analysed in this chapter. The
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research context and aim, goals and goals, research issues and reasons for research and
relevance are divided.
II. The second chapter of the paper discusses literature with the views of academic
newspapers, magazines and archive publications in mind.
III. The selected methods of analysis have been discussed in the third chapter. The approach
covers many aspects of the processes and methods of collection, sampling and
interpretation of the data.
IV. The fourth chapter of the study, the results, contains the conclusions explaining the
findings of the research. This chapter thus presents the actual results of the study.
V. The fifth chapter is a thorough analysis of the findings of the defined aims and goals
listed in the introductory section to check whether the research has been effective.
VI. The final chapter of the report provides an understanding of the complete work highlights
and includes workable recommendations to be made in the selected domain.
2. Literature Review
2.1 Literature Overview
In the following chapter, a comprehensive embassy on the concepts of automation is
contextualised. Exploration of particular themes such as the concept of automation, its ties to
redundancy, retention, challenges, their solutions and particular difficulties raised concerning the
technology is being carried out.
2.2 Concept of Automation
According to the statement of McClure (2018), when most people hear the word “automation,”
they portray gigantic robotic arms that assemble a vehicle on a mounting line. While this is not
the entire concept of automation, it represents industrial automation best. Industrial automation
means the use of control systems (such as machines or robots), which manage complex human
processes and machinery. In practice, industrial automation actually replaces human beings in
general, and that is why it is common with employees today.
Yet this thought according to Peper (2017), particularly as prominent public figures generally
refer to automation as a “new wave” of technology, may be a little misleading. The first
automated production line to rationalize the production of motor cars, Henry Ford launched in
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1913 (over 100 years ago). Naturally, this production line was not as advanced as today. But the
truth remains that factory automation is definitely nothing new in particular. In reality as per
Gutiérrez et al. (n.d) (even at the beginning of the 1900s), the advent of industrial automation
opened up a new age of value of employee intellectual property. In comparison, mitigated human
error not only considerably optimized productivity through industrial robotics, but also preserved
workers’ jobs for a long time. This transition has the advantage of eliminating a large amount of
manual labour, resulting in increased efficiency in terms of energy and material savings, as well
as improved consistency, performance, and precision. But what automation has actually done is a
change in the operator’s position from manual to supervisory supervision (Bughinet al., 2018).
As a consequence, they execute analytical or emotional activities such as diagnosis, preparation,
and problem-solving instead of executing tasks such as triggering manual switches and
implementing operating procedures according to Chui, George and Miremadi (2017).
Automation technologies should be built and applied in a manner that provides the best possible
match for both human and computer capacities, advantages, and drawbacks. Belloc et al. (2020)
offer a short and simplified description of automation, claiming that automation is described as
the use of electronics and computer-controlled equipment to take care of processes. According to
Lander, Nielsen and Lauritsen (2016), the purpose of automation is to improve productivity and
accuracy. Automation, on the other hand, eliminates labour in the vast majority of situations.
Indeed, economists, today fear that emerging technologies could drive up unemployment rates
dramatically in the future. Robotic production lines are increasingly taking over roles
traditionally done by humans in several production plants today. Manufacturing is the process of
transforming raw materials and parts into completed products on a wide scale, typically in a
warehouse. Automation includes several main components, processes, and work functions in
nearly all sectors, according to Sorells (2018). Manufacturing, transportation, plant maintenance,
and services are also impacted. Furthermore, national defence programs are getting more and
more integrated. Integration, implementation, sourcing, servicing, and even promotion and
distribution are all areas where automation is used today in manufacturing. As a result,
automation, in its most basic form, corresponds to the idea of replacing manual activities with
technology and computer-controlled equipment. Software applications, for example, have
replaced manual typewriters, file cabinets, and paper appointment books (Manyika et al., 2017).
Since robotic arms pick cartridges from a stacker and transfer them to the drives, tape and disk
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libraries have been dubbed “automation systems”. Finally, artificial intelligence is slowly but
steadily infiltrating every part of our lives. It is getting more popular not only in the workplace
but even at home and also outdoors.
2.3 Impact of Automation on Employee Redundancy
The improvements caused by the technological revolutions had a significant effect on industry,
jobs and culture. Chui, George and Miremadi (2017) assessed and outlined the effect they had on
industry on both first and second industrial revolutions. After these developments, technology
has started to evolve rapidly and many futurists expect that this progress will be exponentially
realized eventually (Gruchmannet al. 2020; Belloc et al. 2020). Historically, technology has
affected both primary and secondary occupations. But all careers, especially the services industry
implied by Brougham and Haar may be affected by this new revolution (2017). Labor division
and expertise became a major feature of the second industrial revolution. McClure (2018), a
leading theorist who wrote regarding the separation of labor, indicated that under Taylorism
work has been reduced to the simplest components. The division of duties and simplification of
roles make it easy for employees to be prepared and thereby substituted for their new profession.
Job specialisation ensures that after the work phase is completed decision making is removed
from the workforce and thus little discretion is given about how they decide on their job (Belloc
et al. 2020). This work-related expertise was spread to several employers and occupations
(Cohen 2015). In this next industrial revolution, the observations by Braverman about deskilling
and labor theory are both important and a critical factor, as workers that are easily modifiable in
their basic tasks can be easily replaced. The technology is becoming more advanced; computer
education and artificial intelligence will, for example, be able to see how workers do their jobs
and then codify it in the future (Gutiérrez et al. n.d). However, companies also have to analyze
and codify what an individual does for people and programmers now. Braverman focused on the
usage of technologies to shift ownership of the working process from employees to management
(Manyika et al., 2017). Their crucial aspect is that “the aim of machinery is not to increase, it's
also to reduce the number of workers attached to it.” as Harris, Kimson, and Schwedel (2018)
mention the technologies in several ways. They also concluded that the less research employed
by the worker is used in technology, the more machines produced as a labor aid, and that the
employees then became machinery workers.
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It is the key catalyst that is leading to the emergence of job complexities in wider workplace
environments. According to Gruchmannet al. (2020), technology has radically reformed society
and will add doing it. Yet the biggest step is not more humans that create more technology, but
computers that create technology and provide services. The labour market is declining in this
way. In March 2017, World Bank Chairman Jim Kim said that technology in many societies
would radically change the patterns of the conventional economy and endanger 69% of
employment in India and 77% in China as implied by (Ivanov, 2021). However, the relative pay
dictates the relative automation levels and the Indian IT sector is the most fragile since the cost
structure is directly aligned with the customer’s capacity to bear the cost in industrialized
economies.
Thus, as stated in the article of Kraft (2021), today, many workers are worried that fast technical
development could replace employment with machinery, in particular older employees who are
now working or are increasingly automated. According to an estimate by the Office of National
Statistics, over two thirds (68 per cent) of British employees with employment at the highest
automation risk claim that this won’t happen in the following decade. In order to address those
workers that have been deemed most sensitized to automation, such as employees at the table,
cooks, retailers, Landscapers, bus and coach drivers, Bar staff and scaffowers, the YouGov
report, which commissioned Neste’s global innovation base as depicted in the article of
McClure(2018). According to Nesta, a total of six million workers will work in occupations that
will drastically change or go missing by 2030. The failure to become mindful of the danger of
automating their work leads to self-sufficiency as depicted in the research of Belloc et al. (2020).
Almost half (47%) of respondents did not know the career form they would be applying for, and
34% said they did not receive any training in the last five years, which resulted in a lack of skills
growth. According to the research of Lander, Nielsen and Lauritsen (2016), among the survey of
about 100 employees, some halves (46 percent) are found to either lost their jobs or put into low-
paid positions which the research considers to be a significant indication towards mass employee
redundancy. The study of Kraft (2021) reported that since the financial costs of training are a
deterrent to new skills, organisations have to spend large sums on training their employees,
however, with the emergence of automation, a vast majority of organisations shifted to investing
more on automation technologies and perfecting them and ended up ignoring the skill
development needs. Because of that large majorities of employees around the sectors of UK and
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larger MNCs such as Arla Foods UK faced substantive redundancy in their human resource,
primarily due to the lack of proper training, investments etc. Nevertheless, Nesta calls on
government and businesses to support prospective employees by increasing the accessibility of
career data and trends in skills and facilitating participation in training. Thus, Bughinet al. (2018)
in their study stated that “People need to realize their future in order to plan for it. When some
two-thirds of the most vulnerable employees don’t know the facts, something is wrong.”
2.4 Impact of Automation on Employee Retention
According to the article of Chui, George and Miremadi (2017), the retention of staff remains a
challenge even today for companies. This article suggests that the largest first-year attrition rate
in eight years was approximately 40 per cent of employees who remained within 12 months of
being employed. Although one may have learned that worker’s ‘job loss is due to a lack of
evaluation and unquestioned work, there is a second explanation why focus and intervention are
needed immediately (Chui, Manyika and Miremadi, 2015). The demand for mobile and portable
workplaces now more than ever is quickly shifting from conventional to bureau hotels, remote
employment and office sharing. Also, this movement would not slow in the near future as well.
In reality, 42.5% of worldwide workers by 2022 will be mobile workers, up from 38.8% in 2016
(Ivanov, 2021).
According to Harris, Kimson and Schwedel (2018), around 2009 majorities of Human Resources
Departments were used to be strongly reliant on document-led methods which were used to be
time-consuming and demanded greater efforts from the employees and fostered stress. However,
with the implementation of automated computer-based systems the departments gradually are
able to complete tasks in much more proficient manner which eventually have helped them to
save time by automating administrative activities and allowed HR staffs to concentrate on
essential tasks such as interpreting and making decisions. This in consequence made the jobs of
employees much easier, accessible and efficient and eventually aided HR managements to retain
their staffs more extensively (Manyika et al., 2017). Moreover, research of Kraft (2021) says that
96 per cent of senior human resources executives agree that AI has the ability to increase and
retain talent tremendously. Artificial intelligence and algorithms allow HR to expend less time
on administrative activities and minimize the screening of applicants. While researchers such as
Lauritsen (2016) and Sorells (2018) argued that AI could replace people with human capital that
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is not the case. David(2015) also asserts that automation aims to save a third of the week (13
hours) of work for talent management managers or more than they expend on the acquisition on
one source. With machine learning algorithms, time-consuming tasks such as drawing up job
requirements can be automated. This algorithm can determine how the definition can be efficient
and how to work candidates can be maximised. Once applications have been received,
automation tools may choose the most suitable applicants. In addition, new-age robotics systems
can also monitor applicant voices in order to determine who the right fit for the work is.
In his study, Ivanov (2021) proclaimed that the Elixir of working life is the combination of work
and life. When businesses and start-ups expand more, workers begin to spend hours working.
Increased workload causes stress and anxiety to rise. A study by of Kraft (2021), indicates
employees who perceive a workplace perspective that rank the work-life balance higher than the
wage. He also suggests that a flexible plan ensures the staff follow the needs of the corporation
in a consistent and equal schedule. Employers have traditionally often been responsible for
selecting and preparing staff schedules, but they do not always perform, yet with automation
effective scheduling and job, orientation is possible which gives the way for better retentions.
The timeframe is versatile for AI according to Bughin et al. (2018). Employees will also choose
the shift that fits well for them on their own timeline. However, in non-shift places, automation
will also function by streamlining and handling time out applications. It allows HR to examine
any proposal for time off and allows AI tools to return to these workers which also provides
greater freedom and leverage over their leisure time for workers. The Corporate Leadership
Council in 2014 research notes that highly committed workers are 87% less likely to abandon
their business (Lauritsen, 2016). Although as suggested by the report of Chui, Manyika and
Miremadi(2015), automation cannot magically include the workforce more explicitly, it may
figure out why employees are uncompromising or inspired. AI-based data mining can forecast
the motives and behaviours of workers, and sophisticated sentimental analytical techniques can
illustrate. The management will then try to rectify those issues after realizing the challenges that
cause workers to disengage. Otherwise, specialized AI solutions can still be installed, to offer
data-based guidance, to increase employee engagement and overcome possible attrition issues.
People sleep, get up for work or go to work about a third of their lives. It is only logical for them
to appreciate this moment. So, inevitably they tend to look for jobs elsewhere whether their job
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seems unchallenging or uninterested (David, 2015). With Automation, the management can
evaluate the success improvement of an employee with the help of technology and propose the
next phase in progress. They also have the function to warn managers if an employee is willing
to progress his career. They will also help individual workers develop tailored learning and
preparation tools.
In a closing note Chui, Manyika and Miremadi (2015) imply the time an employee spends in an
organization and their whole life span in connection with their respective company, which
defines an employee’s experience. A full experience starts as soon as they apply for a job in an
organisation and which lasts until the day, they get a final interview with the management. This
is further exemplified by the researchers by showcasing the recruitment patterns of businesses
such as GlaxoSmithKline and ASDA (Chui, Manyika and Miremadi, 2015).
Against this backdrop, automation saved time and rendered the whole process easily accessible.
It can be used to rationalize and pick the right career applicant. It will forecast behaviour that can
be recognized if the workers are unmotivated or are less involvedHarris, Kimson and
Schwedel(2018). With automation, the company can quickly recognize and create hack tags of
appreciation for its workers in the cloud.
AI and machine learning algorithms can help workers learn to make the process exciting and
enjoyable, in real-time. All these aspects contribute to a satisfied and enhanced experience for
employees, which in essence ensures that all the wishes and desires of an employee are met and
extensive employee retention is being made.
2.5 The Struggle of the Challenges the Employee retention is Facing Because Of
Automation
We often see individuals quitting and entering (more or less often) in businesses, and no business
is an exception. The turnover in employees’ costs businesses thousands of millions in salaries
and efficiency. The reality is that LinkedIn surveyed big corporations and sew workers’ retention
as their highest concern in global recruiting patterns (32 percent). Employee satisfaction is an
important challenge for all businesses and is closely linked to their long-term sustainability. Any
business is faced with its own particular industrial-dependent problems or several other
considerations. According to Lauritsen (2016), a significant issue behind the diminishment of
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turn-over for employees is increasing labour and not being able to sustain them because of IT
automation. In this aspect the authors assert that following the implication of IT automation, the
businesses are followed by a substantive range of investment requirements which generally
involves maintenance, re-adjustments etc. Due to such concerns, business is readily being
stressed with financial burden and become limited to financial assistance to the employees which
eventually leads to the issues such inadequate boarding process, training etc. According to
Bughin et al. (2018), another significant prospective issue is salary dissatisfaction which also
gets triggered due to the financial limitations. These phenomena are gradually prone towards
small to medium-size businesses. Chui, Manyika and Miremadi (2015) also notes, the emergence
of automation has also raised uncertainty towards a clear career path or progress that negatively
affected their retention as well, since workers are not being able to ascertain whether their future
job growth or positional relocation is possible, since majorities of labours are increasing being
taken over by both software and hardware automation technologies.
Bughin et al. (2018) implies that these issues may also leads to relationship deterioration,
increase of distrust and loyalty among the worker groups as well, hampering the positive
workplace culture and diminishing the chances of worker retentions for extensively. Thus,
Rodrik (2018) have stated in their research, that automation is like a double aged sword for the
new small or medium size enterprises, since as it can streamline the operational processes of the
business but also can lead to job insecurity, dissatisfaction and inadequate training, sustenance
and retention of employees.
2.6 The Steps Taken by Organizations to Avoid Making People Redundant
In the era of global business, IT organizations are already installed automation system for their
faster process of business expansion. Regarding this matter, IT departments tend to reject the
placement of the employees from the workplace. As per the statement of Al-Hila et al. (2017), it
has seen that there are several ways that can evolve the process and help to hold the redundancy
of the employees. Due to this matter from the several articles, it has been concluded that there
are several paths that can determine aspects to drive the solution wheels of the employee
redundancy from the workplace. As per the statement of, organizations have to think the human
mind strategies are always better than robotics performances. It is true that the automation
system is playing the lead role that can accelerate the chamber of progress exquisitely. But rather
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than it gives value to human work is important. It has been a consequence that, famous IT
companies Tata consultancy services equally concern for its employees and they are not easily
dejected their employees from the workplace (Rodrik, 2018). They are focus on their abilities to
learn and provide the appropriate training to adapt the advanced automation system that can help
them to adjust themselves according to the automated requirements of the firm A standard and
training process is working as an essential key that holds the performances of the employees to
sustain in the automation platform.
According to the statement of Iatsyshynet al. (2020), the automation system is adapted by every
IT companies to foster the apex performance to the competitive market. But it is a sometimes-big
challenge for the authority to hold both human activities and technological system equally.
Therefore, it is necessary to give them the conceptualized advantages of the automation system.
If the authority of the company provides the necessary components for training, then they are
prepared themselves with the automation system (Iatsyshynet al., 2020). Regarding this fact, the
employees are responses with the automation system that can also impress the authority.
Therefore, the mutual understanding among them facilitates to hold the employees that reduce
redundantly. As per the statement of Misra and Khurana (2017), the human brain builds with
some potential components that is more effective rather than an automation system. To decide
this factor, rather than thinking that automation systems are being launched to dejection, the IT
organizations explore this view that it is helpful to boost the performance. This kind of mentality
ensures the authority to avoid employee redundancy. In that case, among the employees, it is
important to clear them that, the implementation of the robotics system can scatter the unity
among the employees. In the training section of the company, the management has to explain
and shares ideas among them.
The demand for human abilities is not in decline. There is a net positive outlook for employment
despite significant job disruption, and human skills, as well as jobs with distinctly human traits,
are still in demand. According to the Future of Jobs, the shift between people, machines and
algorithms in workplaces would displace 75 million current jobs, but there would also be 133
million new jobs (Al-Hila et al., 2017). This particular fact said that the automation system has
both types of ramification. However, the prevention of the dejection of the employees in the
organizations the authority has to provide efficiency that helps to transform the basement of the
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employees which is a prolific matter. According to the statement of Misra and Khurana (2017),
said that after recruitment motivation and training is not always helpful. The authority has to
select the employees based on their proper intelligence for automation. If they are able to
analyze the capabilities to tackle and adjustment with the automation system then it will help
them in future paths to avoid the redundant (Al-Hila et al., 2017). Therefore, it is an effective
method that is beneficial for the companies to neglect to redundant of the workers.
2.7 Analyzing How Automation Can Help the Retain Employees
In the era of globalization, automation system has a profound implication for enhancing the
performance of the IT Industry. In the context of the IT industry, there are several renowned
firms that enhance the performances (Anagnoste, 2018). It has seen that the automation system
has dejected a wide range of employees from the workplace. According to Ivanov (2017), despite
the common belief, AI and automation will not replace human work. Instead, what automation
will do is help reduce the overall workload of workers and allow them more flexibility to focus
on complex and creative activities. Covering through automation, HR managers can focus more
on tasks such as establishing relationships with prospective employers, managing the concerns of
employees, setting up training and training programs and strengthening employee participation
(Sain, Singh and Kaur, 2020). Including increased productivity for employees, better consumers’
satisfaction, and faster time to market can be some of the immediate benefits of automation. In
such areas, the overall productivity of the organization is fetching to the enriched level that is
why the employees are satisfied and stayed with the company.
As per the statement of Estlund (2018), the automation system of the IT organization is helping
to evolve the overall working process with progression. With the help of an automation system,
the IT industry is enabling to improve its overall production rate. The automation system helps to
reduce the workload of the employees in their workplace (Ivanov, 2017). The automation system
in the workplace has given various advantages to the employees in their way of working. The
automatic machinery system of the organization has given free time to the employees and it is
easy to access (Wijayantiet al., 2020). The installation of the automation system is cluster the
productivity rate which is satisfied the employees. For instance, Automation is an excellent way
to improve efficiency for corporations. The US economy costs 13,000 dollars per employee for
repetitive automated tasks.
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As per the statement of Anagnoste (2018), good and potential communication is an essential
aspect that can improve the performance of the employees which hold them in the workplace.
Therefore, the automation system is helpful to build better communication among the employees.
The automation system in the company automatically notifies the appropriate employee when the
task is done. Another crucial factor is less error in the workplace (Burton et al., 2019).
Automation exactly makes zero input and administrative mistakes, thus removing hours spent by
the employee to correct them. The advanced technological system has improved the working
quality and removes mistakes (Acemoglu and Restrepo, 2018). Hence, this has helped the
employees to boost their confidence in work that can evolve their performance. One thing which
hits employees is to spend hours and hours on unproductive tasks, and replacing unproductive
tasks with automation is an easy and inexpensive retention strategy. It has seen that the
automation system has reduced the working time in the workplace (Pham et al., 2018).
Therefore, flexible working time for the automation system is a beneficial factor for the workers.
Hence, for this particular fact, the employees are feeling healthy in their workplace that can
enable them to retain in organizations.
2.8 How Automation creates Difficulties for the Employees
The modern concept of global automation facilities is a common aspect that caters for the
improvisation of the business. In that case, the automation system is a big challenge for the
employees. Because besides the positive sides of the automation system there are virtuous issues
that are faced by the employees in the workplace. As per the statement of Davlyatova (2020), the
automation system is an advanced process that is unable for older employees to adapt. The older
employees are not always adjusting to the automation system. In the current era, the huge
establishment of the automation system is a big challenge for them (Davlyatova, 2020). Now, in
that case, the older employers are encompassing with the fear and nervousness that harm their
healthy mental state. There is always consisting of the initial stage of fear of failure and
incompetence. Thus, the automation system in the Information technological organization has
contributed through the vast path as well as it is creating challenges for the workers.
According to the statement of Lu et al., (2020), it has seen that the automation system is
gradually growing the rate of employee turnover. These patterns, which predict employee
retention in the workplace, have been steadily growing over the last few years. The advanced
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automation system drives the wheels of the operating system faster than the previous. In several
areas like cost, time and productivity the companies are seen as the face of success (Embregts,
Tournier and Frielink, 2021). Therefore, they are found to install a new and advanced automation
system that can expand their business easily (Säther, 2021). The massive use of automation
provides a valueless image towards the manual works, which affect the employees. The authority
does not want to bear the labor expenses with the automation system which provoke them to cut
off the rate of employees from the workplace. Hence, increasing the rate of the automation
system in the workplace is a big fear among the employees which considered a challenge.
As per the statement of Säther (2021), the automation system is a big challenge for the
employees. When the organization start to launch the automation system in the workplace, the
system is constantly evolving. The evolving factors of the automation system have created a big
issue among the employees. It is very difficult for the employees to adjust constantly to the
changing procedure of the automation system. The employees are not able to cop up with the
automation system that brings out a great issue among them (Säther, 2021). It has seen from
2019, that the level of employee dejection is high. In the pandemic situation of the Covid-19, a
massive rate of employee reduction has seen in every IT companies (Embregts, Tournier and
Frielink, 2021). Save the financial loss the authority has taken this step against the employees
and the automation system is only giving them assurance. Hence, it has seen that the automation
system has taken the place of human workability in the workplace. The automation system is
obviously needed the assistance of human but not in a vast range. As a result, it is big challenges
for the employees to survive in the workplace.
According to the statement of Oldinget al.(2021), the automation system increases the cost of the
IT companies that affected the wages of employees sometimes. The excessive cost of the
automation system compels the reduction of the wages of the employees. Nowadays, it has seen
that IT companies tend to focus more on the automation system which encourages them to invest
a wide range of expenses that hampers to pay the proper wages of the workers.
2.9 Applicable Training for Employees That Can Help to Adjust Themselves with the
Automation System
As per the statement of Bannikov and Abzeldinova (2021), it has seen that the automation
system has been generated by different problems that have built challenges among employees.
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For this matter, there is several appropriate training that helps to improve them with the
automation system. Therefore, in that case, the authority has to provide proper training to the
employees that help them to understand the advanced automation system (Winasis, Riyanto and
Ariyanto, 2020). In the training period, the management of the employees has to provide the
appropriate knowledge and skills to the employees. If the employees are able to understand the
process of the automation system then it is implied as a prolific factor for them. Therefore, the
appropriate training for the employees is to foster skill development among the older employees
that can help them to adjust to the automation system.
According to the statement of Baek (2021), the conceptualization of positive thoughts is an
important fact for mental stability. In that case, in the workplace, the concept of positive notion
helps to accelerate to evolve of the capability of the employees. Therefore, the positive
encouragement in the training section is the inescapable part that encourages them to adjust to
the automation system (Ivanov, Kuyumdzhiev and Webster, 2020). In the automation system, the
biggest fear is that new and advanced technology is had to use. Positive encouragement is the
solution that engages them more with automation technology.
As per the statement of Kurth (2020), effective thoughts about the benefits of the automation
system among the employees is important in that case. If the employees understand the
importance of the automation system, then it is easy for them to tackle this operating system. In
the training period, the effective expression of the knowledge about automation system is
enabling to provide a clear perception of it (Baek, 2021). This is improving the foundation for
employees who would be able to embrace automation. Many well-known IT firms, for example,
offer specialized training and inspire their staff to stick with this cutting-edge method. Therefore,
the good and healthy communication is important factor that helpful to lead this particular matter
exquisitely.
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2.10 Conceptual Model of Literature
Figure: Literature Review Conceptual Model
(Source: As Created by Author)
3. Methodology
3.1 Introduction
This chapter has detailed the methodology and its components for research on the “implications
of automation in the IT industry”. The qualitative thematic and theoretical content analysis of
methods by which the anticipated information for this research has been acquired is also
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described in this chapter. It aims to provide the research with a work plan following
interpretivism in research paradigm and epistemological approach, as well as subjective
ontological position. The researcher has developed a thorough methodology for the research
problem defined. An exploratory design and deductive reasoning approach have been taken to
theoretically and thematically analyse the collected secondary data from articles and journals.
The ethical challenges and their mitigation approaches have also been discussed in brief.
3.2 Research Philosophy
The philosophy of research addresses the origins, existence and creation of knowledge and
understanding (Almheiri, n.d.). While the concept of creating knowledge may seem profound, as
part of a thesis, a researcher works on knowledge formation. Essentially, as per Edson, Henning
and Sankaran (2016), it means understanding and formulating the views and conclusions to
answer research theory in the research. Being positioned in the first layer of the research onion
depicted by Saunders, Lewis and Thornhill (2009), it is the first topic of concern for clarification
during the discussion of a research methodology.
The current research has chosen interpretivism philosophy over pragmatism, positivism and
realism, as none of the others can fulfil the needs of the research. Being deductive qualitative
research with an exploratory approach, the research contains a small sample size for in-depth
investigations in an unstructured way on the “implications of automation in IT industry”, and all
these criteria are fulfilled together in interpretivism approach only (Alharahsheh and Pius, 2020).
Besides, the need for evaluating the concept regarding the development areas of automation, the
way it affects social and economic growth and eliminates human activities, an effectual training
mode for employees to be adoptive towards automation system and effective recommendations
for future developments in this field, the researcher needs to interpret necessary information from
the literary articles and journals.
Ontology assesses the reality of the topic concerned based on the perspectives of subjectivism or
objectivism. As automation is a threat to employment in any industry, and the redundancy of
individual lives is a fragile phenomenon that shape and reshape according to various culture and
economic and social forces, this research has a subjective position as per ontology. On the other
hand, epistemology is focused on assuming the knowledge, the factors that constitute legitimate,
valid and acceptable knowledge and the approaches of communicating that knowledge to others.
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Among the three types of epistemological position such as positivism, interpretivism and critical
realism, this research falls under interpretive epistemology, since it has broadly studied the life of
employees with redundancy, with an aim of comprehending the tragedies, opportunities and
challenges etc, followed by interpretation from their perspectives.
3.3 Research Method
The different techniques, schemes and algorithms used in researches are the research methods. It
is mainly the strategies, procedures and techniques applied and implicated to collect evidence or
data that is analysed for uncovering new information of developing superior comprehension of a
topic, according to Basias and Pollalis (2018). Methods of research allow researchers to gather
samples, data and solve certain issues. Research is widely divided into two major groups, namely
Basic and Applied Researches (Kaldewey and Schauz, 2018). Basic research is a study of
fundamental concepts and explanations for a certain event or mechanism or phenomenon. This is
often called theoretical analysis as well. Applied research resolves these issues by using ideas
and concepts well established and accepted. Essentially applied research is the bulk of scientific
research, case studies and interdisciplinary research.
Both basic and applied research can be quantitative, qualitative, or both. The calculation of the
volume or quantity is focused on quantitative analysis. As per Basias and Pollalis (2018), process
in one or more quantities is expressed or defined here. In theory, the findings of such research
are a number or a number-set. As per Easterby-Smith et al. (2021), qualitative research focuses
on quality-related phenomena. This is non-numerical, descriptive, implicates reasoning, and uses
terms and exploratory concepts. Hence, qualitative data has been selected for the fulfilment of
the aim of the present basic research, as the research questions demand answers that explore
concepts with deductive reasoning through non-numerical solutions through qualitative content
and thematic analysis.
3.4 Research Design
A research design must be prepared for any research study. The different methods used to deal
with the research question, the sources and the information relating to the problem, the timing
and the cost budget should be established. Akhtar (2016) has stated that the actual work can be
started after the research design is formed. In particular, for the current research, it is important
to collect and learn theoretical methods, non-numerical techniques, analytical technologies and
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other related data and resources. A researcher must recognise and choose materials useful for the
work at hand. Besides, before using the information obtained, the validity and usefulness should
be verified. Before applying these sources to the issue of interest, they must be properly
examined and evaluated.
The exploratory approach has been selected to conduct this research, as the study aims to explore
the “implications of automation in the IT industry”. Such an exploration needs analysis of non-
numerical data which can provide a theoretical conceptualisation of the research questions,
which are based on the exploration of concepts researched already (Meyers, Gamst and Guarino,
2016). The research, therefore, followed the qualitative method to gain such data and reach the
anticipated research outcome. On the other hand, the explanatory or descriptive design would not
have been suitable here, as the former one concentrates on explaining the research perspectives
in detail which were not well-researched before, while the second one focuses on recognising
various aspects of a research topic when it is not known.
3.5 Research Approach
The research approach refers to the research reasoning. The reasoning is the capacity to consider
fair decisions critically and justify positions (Armat et al., 2018). There are a core argument,
sometimes defined as inductive or deductive, and at least one explanation on why the claim is
valid, or false in objection-related cases. Because of their logical nature, deductive reasoning
consists of true arguments, implying that the inference must be true if the premises are correct, as
Armat et al. (2018) further stated. It guarantees the validity of the inference if the premises are
right; hence an argument with deductive reasoning is convincing. Inductive statements have a
probability that the conclusion will be accurate if the premises are real, as per Woiceshyn and
Daellenbach (2018). As mentioned earlier, the reasoning for the current research is based on the
deductive approach as the study needs a generalised conclusion based on already established
theories on the topic of automation influencing employee retention in various sectors.
3.6 Research Strategy
Research Strategy is a stepwise action plan to help plan the thoughts and actions of a
researcher consistently and scheduling to deliver quality results and reporting in detail. Its main
objective is to present the major elements of the study, like the subject, areas, designs and, lastly,
methods of research. The research paradigm, research design, research methods and sampling
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techniques are the key components of the research strategy. According to Rahi (2017), a research
model must be in a way that will direct the research concept which, together with the proper
sampling strategy, should lead to the proper choice of the research methods. The current research
is strategized with the interpretivism philosophy, while follows the deductive reasoning
approaches and qualitative method for the study. Besides, the researcher has selected theoretical
content and thematic analysis of secondary data to interpret the required data for the study.
3.7 Data Type and Collection
3.7.1 Data Type
For research, there are two types of data based on its source type, such as primary and secondary.
The primary data are original and exclusive data, obtained directly from sources in compliance
with their specifications by the researcher, as per Lowry (2015). However, primary data might
not contain clear and strong perception and opinion of experts or academic scholars in the
respective field. Secondary data applies, on the other hand, to data gathered for a specific
purpose and documented elsewhere. The limitation of this type of data is that it is not the first-
hand information on the perception of the individuals linked to the research subject concerned.
The anticipated data-type for the research may influence the collection process of the data.
On the other hand, by nature, research data can be of two types as well, such as qualitative and
quantitative data (Rutberg and Bouikidis, 2018). Quantitative data is the one that owns a value
that is measured in the form of counts or numbers, along with a unique numerical value linked
with every set of data. Such data is collected from surveys, interviews, observations and certain
secondary data like accounts. On the contrary, qualitative data provides characterisation and
approximation, and it can be recorded and observed. These kinds of data are collected from
various methods like interview, focus groups, observations of already researched data etc. The
chosen data type for this research is the secondary qualitative data, which aligns with the
philosophy, design and approach of the research.
The current research has collected and analysed the secondary qualitative data according to the
requirement for the type of outcome anticipated in addressing the research questions. Secondary
qualitative data collected from already researched conceptual data from experts and scholars
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have provided in-depth theoretical idea from reliable sources like expert authors, who have
profound knowledge in their prospective fields.
3.7.2 Collection Method
Methods for data collection are the techniques for searching and collecting the required data for a
study. Several methods of data collection, like observations, textual or visual analysis and
interviews, can be used in qualitative research (Paradis et al., 2016). The present research
required an understanding of researched concepts through further exploration. In this process, the
collection methods that have been selected for gaining the required type of secondary data are
literary articles and journals. The conceptual data can be gathered through brief insight on the
factors of research objectives from literary articles, which will provide the researcher with
conceptual knowledge on the subject. Nevertheless, as per Hox and Boeije (2005), collecting
data from secondary sources might not go completely addressing the potential problem, research
purpose and aim and objectives set for the research, as these are uniquely set for every research.
3.8 Sampling Strategy
A sample is a collection of individuals, items or articles taken for measurement from a wider
population. For example, to ensure that the outcomes of the study survey are available for the
whole population, the sample must be representative of the entire population (Taherdoost, 2016).
The sampling error includes the variations that are purely attributed to the individual participants
chosen between the samples and the population. Two types of sampling methods exist
essentially, including probability sampling which involves random selecting and makes strong
statistical conclusions about the entire group, and non-probability sampling that includes the
non-random collection based on convenience or other criteria and enables data to be collected
effortlessly, as per Sharma (2017). The most popular methods of sampling are convenience,
consecutive, quota, judgemental or purposive and snowball samplings. The purposive non-
probability sampling has been used to gather the required data for this research, based on the
opinion of Etikan, Musa and Alkassim, (2016). Certain literary article have been selected for a
better grasp with purpose of the conceptualisation of the subject matter, and better establish the
answers to the research questions by linking all the samples during qualitative analysis.
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3.9 Data Analysis
One of the most important aspects after data collection process of any study is data analysis
(Castleberry and Nolen, 2018). The authors state that it consists of the analysis by empirical and
logical reasons of collected data to establish trends, relations or tendencies. The researchers also
use the data in the analysis of qualitative data to build pieces of reflection, thoughts, hypotheses
and concepts. For this study, the researcher uses the theoretical content analysis and thematic
analysis approach. Such analysis is a very commonly adopted form of qualitative data analysis,
very versatile and highly common. Roberts, Dowell and Nie (2019) have stated that both of these
techniques are typically used in a variety of documents, for example, transcribed interviews or
scholarly articles etc. Such techniques analysis is straightforward approaches of carrying out
analysis of contents that emerges from a bunch of analysis, designed for data that are non-
numerical (Mayring, 2014). Researcher has gathered data from a bunch of articles and journals
and then evaluated them to answer the research questions with the help of thematic and
theoretical content analysis. However, as per Vaismoradi et al. (2016), navigation of the area of
qualitative analysis can be challenging, as the detailed descriptions of methods are sometimes
removed from such discussion, and often, there remains a gap in sheer detail throughout the
discussion. But, qualitative analysis benefits to view the data with a great deal of versatility and
enables to more effectively approach vast data sets by sorting them into broad subjects.
3.10 Ethical Considerations
In any research study, the security of human subjects is critical by applying suitable ethical
principles. In a qualitative analysis, the profound essence of the research method gives ethical
concerns special resonance. Because of the nature of qualitative research, it can be ethically
challenging for the researchers to identify and adopt the secondary data, since they are involved
in various research-stages. For this purpose, it seems important to formulate clear ethical
guidelines. This includes anonymity, credibility, confidentiality, informed consent and voluntary
engagement, the possible impacts of researchers on samples and the reverse (Arifin, 2018). As
the research follows data collection process from secondary sources, the sources, like articles,
needed proper referencing and credibility in the paper, while the source-papers also needed
assurance of its viability in the context of the present research. Confidentiality and privacy of
their personal data, as well as some professional data, have been maintained from ethical
viewpoint. However, as the research follows purposive non-probability sampling, the ethical
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issue of the sources to be viable and acceptable for this research has been solved, since the
researcher has focused on the purpose of collecting the data on the “impact of automation on
employee retention” from the aspect of IT industry.
3.11 Summary
After the discussion from the entire chapter, it can be summarised that the objectives or the
questions set for the current research needs the researcher to dig deep into the factors of
automation and its implication in IT industry, as well as the adaptability of the IT employees in
alignment with automation trends. In this process, the exploratory research design has been
assessed to be suitable to explore more insights on these concerns. Deductive reasoning will help
to reach a generalised conclusion, while interpretivism philosophy will help to interpret the data
collected from the secondary literature articles and journals. For in-depth conceptual analysis,
theoretical or content and thematic data analysis has been selected under the qualitative research
method.
3.12 Limitation
The entire research methodology has followed thematic and theoretical analysis of secondary
data collected by following purposive non-probability sampling. However, one significant
limitation remains in this context, which is the absence of a survey in a large population or an
interview of the individuals working or associated with the IT industry. A regression analysis of
the data collected from the survey would have given more numerical validity to the explored
data. Besides, the major limitation of the adopted sampling strategy, which has been discussed
already, is the possible lack of credible and acceptable data that had to give special attention for
mitigation.4. Findings
4.1 Chapter Overview
The following chapter comprehensively explores secondary data analyses for the research
objectives. The data analysis is being put forwarded by means of thematic analysis involving
relevant to the research objectives. Along with the conduction of article based theoretical
analysis stressing significant non-empirical data for meeting research questions. The insights
gained from both these analytics are thereafter being rationalised by discussion in the next
chapter of the thesis.
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4.2 Thematic Analysis
The following chapter encompasses rationalised secondary theories putting emphasis on the
immediate outcome of implicating automation, knowledge regarding its detrimental effects on
employment and human activities and lastly the key theories on the best approaches for training
employees to adapt to automation. These key areas are thematised and evaluated by means of
thematic analysis in the following:
Theme 1: implication to automation ensures growth to IT industry?
Technology and automation can help businesses to increase efficiency through functions – from
production to promotion, and can guarantee that expensive mistakes are avoided. Big data
analytics can play a crucial role in decision making and ensure corporate development.
According to the article of Acemoglu and Restrepo (2018), automation enables to collect and
analyse business information about total success and any business transaction efficiently and
reliably and thus lets the businesses to make more informed business decisions. Consequently,
numerous companies have been able to deliver products at reduced cost for the corporate
incorporation in automation. Automation often contributes to large economies of scale – critical
in high-investment industries. Automation allows companies to reduce the number of employees,
thereby limiting labour union control and perhaps disruptive strikes. As has been the case in
history, automation often leads to efficiency (Acemoglu and Restrepo, 2018). This would
provide a necessary boost to economic development and competitiveness in a period of lacking
productivity growth and would compensate for the effects on many business units of a
diminishing share of workers, contributing to the organization’s overall competence, improving
its sales position, and also profitability. With the extensive inclusion of automation technologies
there are range of benefits an IT business gradually experiences these benefits are largely in line
with the increase of effectiveness and performance boosting which generally includes boosting
of precision and data accuracy, improvement in flexibility, faster data processing, enhance data
synchronisation and collaboration, better customer support, enhance transparency (Jämsä-
Jounela, 2007). The whole automation purpose is to increase performance, competitiveness and
eliminate the slightest chance of a malfunction. It streamlines workflows and procedures to make
everything more visible. It will allow companies to improve optimization and centralize data,
which ultimately promises corporate organizational and economic development.
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Theme 2: Automation eliminate human activity roles
While detrimental effects of automation are not substantial in other industries, the IT industry
however faces significant danger since instance if technological unemployment is considerably
higher in due to automation. The primary cause behind this situation in rapid inclusion of
technologies that is gradually replacing the human role activities. IT agencies are more prone to
reassigning staffs with more specialized roles and utilize robotics to fill holes, as IT is constantly
required to do more every year without significant expenditure increases. It’s less about having
employees go, and all about reduced recruiting (Dwivedi et al., 2019). While technology will
remove relatively few employments completely in the next decade, it will have a major or minor
impact on portions of almost all jobs, based on the job category involved. The promise of
automation, which now goes beyond regular production operations, is to turn industries such as
healthcare and IT with considerable information practice, at least in terms of its technological
viability. This considerably makes extensive impact on the IT industries as well. Since, software
automation increases the feasibility of operational processes and ensures task execution in much
reduced timeframe with utmost proficiency, the IT businesses do not see any necessity to use
human activities which is certainly more time-consuming, requires training and investments and
costs more finance in meeting their salaries than the maintenance of IT systems (Araujo et al,
2020). Thereby, IT automation not only replaces major human activities within the organisation
and limits the future employability but also assures better efficiency, accuracy and quality
improvements within very small amount of time.
Theme 3: Best approaches for training employees and ensure future development with the
inclusion of automation
The administration and implementation of training must be effective and flexible as a growing
enterprise. It takes days or weeks to set up and complete and it doesn’t take you to provide
instruction for any learner. One of the most powerful things about training employees to be
automated is that repeated behavior can be standardized. The management can build workflows
for hundreds of learners quickly (Oesterreich and Teuteberg, 2016). This would offer them the
preparation they require, the information they need, and much more.
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Since, future integration of automation is likely inevitable it is best for the existing employees to
cope with it and adapt with it, to access new set of opportunities that only automation ensures. In
this regard, IT businesses could implement some significant steps to encourage and train
employees to cope with new automation integrations. The first way is to reassure the employees
that robotics takes care of the type of job below them, enabling them to free up their resources to
deal with more demanding matters. By letting the employees worry deeply about the wider
prospects of their careers and the general image, they are more rewarding and motivating.
However, certain employees simply would not be able to improve because of the nature of their
careers. The management then have to incite them to stand back for themselves (Rosenberg,
2005). This does not imply leaving the door for a different career in the business, but really
getting them to see opportunities to contribute abilities – like interpersonal skills, innate
capabilities and the like. Automation will help them get automation and AI work completed that
they really cannot. The businesses have to encourage workers to find and then provide them tools
to enhance their unmodifiable capabilities.
4.3 Secondary Theoretical Analysis
4.3.1 Evidence-based benefits of automation and its influence behind rising trend of
automation integration
The primary motivation for automation is to have opportunities for improved effectiveness and
competitiveness based on the findings of the studies in relation to previous experiments by
Merchant (2000) and Wei et al. (1998). Automation would also enable cost savings to improve
competition on a more dynamic sector, along with improved production and productivity. Many
enterprises recognize that technology will improve the workplace by eliminating the monotonous
and physically exhausting working conditions.
Google is, for example, introducing a new free marketing app, which will help companies and
advertisers more efficiently manage data by automation. This platform will help advertisers
effectively reach audience audiences with material that relates to them, called the Google
Attribution. The marketers can use Google Attribution to collect data from those tools instead of
examining data from Google Analytic and advertisement tools AdWords and DoubleClick
separately (Yarlagadda, 2017). Apart from that a case of significant IT automation is emergence
of Automation Mobile Robots (AMRs), The new breakthrough in conventional robotic tasks is
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autonomous mobile robots (AMRs), through improved stability and diverse applications, which
include their uniqueness to navigate in a more understandable, unregulated setting (Micheler,
Goh and Lohse, 2016). Companies in all industries are now investigating ways AMRs can
be facilitated, as demonstrated by our 2019 sponsored trade association’s out-of-sale
Autonomous Mobile Robot conference.
Concerning the benefits and substantial benefits and promises made by automation business
around the world are now coming in more numbers and investing in new automation
technologies such as intelligent process automation and artificial intelligences throughout the IT
industry (Australia, 2019).
Figure: 1. Industry Based Investment graph on automation operations and technologies
(Source: Manyika, Chui and Miremadi, 2017)
Since, more industries are readily acknowledging the potentials of automation technologies the
IT businesses are also getting more opportunities to develop and indulge in B2B business
regarding the provision of automation technologies.
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4.3.2 Increasing instances of automation leading to employee workforce limitations and
recruitment reductions
Increased artificial intelligence (AI) and other aspects of automation technologies seem to be
unpredictable in the longer running labour market implications. The result of this ambiguity is
that although many workers are certainly impacted, it is hard to foresee growing jobs are in
jeopardy and in which industries new jobs will be generated. There is no unwanted problem here,
since every six Americans assume robots and machines taking over a large number of
workplaces operated by humans. Nevertheless, the figures remain very complex for workers at
risk.
In a 2017 McKinsey Global Institute report for example, about 50% of work is automated
utilizing modern technology. Another Herjanto et al (2014) argues that 47 percent of U.S. jobs
are at high automation risk. By comparison, a survey carried out by Sarter, Woods and Billings
(1997) shows that just 9% of people have high-risk careers. Of course, other obstacles, such as
acceptance or relocation costs for companies, will, as previously stated, restrict the scale and
magnitude of transportations, although the share of employment at risk is very large. Employers
and policy makers require reliable details, particularly in the event of employees’ displacements,
for the taking of good decisions. Indeed, Anestis and Kleopatra (2017) research the probability of
an automated job and forecast that shipping, logistics, operation and bureau employees, many
middle-class jobs, are among the workers at greatest risk of technology replacement. Overall,
they conclude that in the United States 47 per cent of overall work is at “high risk,” i.e., in an
occupation with an automation probability of at least 70 per cent in the coming decade or two.
While the forecasts of work risk differ, economists agree that the essence of work would be
further transformed by automation and artificial intelligence technology. Some employees are
losing their employment in robotics, others are getting new jobs and all may need to gain new
qualifications to move from one career to another (Harahap and Rafika, 2020). Given these shifts
to societal structures, policymakers, private sector organisations and business community
stakeholders would have to work together to reach agreement on the right direction. AI and
robotics are also a major threat for medium-sized jobs and well-being.
According to the study of Harahap and Rafika (2020), rapid integration of automation has
significant potential to make substantive changes in the employment by the occupational skill
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percentile which not only extensively change the recruitment patterns in the coming ages but
would also change the employment positions as well, since the changes are already evident
during the time span of 1900 to 2010 which more staggering to IT industry.
4.3.3 Strategic approaches integrated by businesses to retain employees with automation
Since, automation is becoming a well-recognized strategic approach in the modern competitive
corporate world businesses are looking for new ways to retain their employees to cope and adopt
new automation technologies. For instance, in Microsoft the management uses empowered
technicians to connect to their staff with distributed assets. They use software for teamwork to
remotely fix problems, and send technicians only if necessary (Harahap and Rafika, 2020). The
company also enables technicians to obtain first-time intelligence, technology and support from
remote experts. Automate and improve schedules so that the correct technician will take use of
the company properties. It lets the company redefine training and transition of expertise to
address deficiencies in skills and boosts employee satisfaction and competitiveness across the
value chain by increasing people’s absence from increased organizational, maintainable and
process sophistication through team communication instruments, intelligent Mixed reality
systems, IoT-enabled machinery and enhanced AI applications (Willcocks, Lacity and Craig,
2017). The strategy of Google is 20% time for that. Each employee consumes up to 20% of his
working time on businesses which inspire the management. This idea encourages staff as it helps
them to focus on what they love or love. It will deter burnout, reduce attrition, and improve
participation and during this process Google supports the employees with encouragement to
adopt new automation technologies and find creative ways to foster innovation which
consequently inspires employees as well as make successful integration and acceptance of
automation on workplace operations and processes.
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References
Acemoglu, D. and Restrepo, P., 2018. Automation and new tasks: The implications of the task
content of production for labor demand. Journal of Economic Perspectives, 33(2), pp.3-30.
Akhtar, D.M.I., 2016. Research design. Research Design (February 1, 2016).
Alharahsheh, H. and Pius, A., 2020. A review of key paradigms: Positivism VS
interpretivism. Global Academic Journal of Humanities and Social Sciences, 2(3), pp.39-43.
Al-Hila, A.A., Alhelou, E., Al Shobaki, M.J. and Abu Naser, S.S., 2017. The Impact of Applying
the Dimensions of IT Governance in Improving e-training-Case Study of the Ministry of
Telecommunications and Information Technology in Gaza Governorates. International Journal
of Engineering and Information Systems (IJEAIS), 1(7), pp.194-219.
Almheiri, S. n.d. Research Philosophy and Strategy.
Anagnoste, S., 2018, May. Robotic Automation Process–The operating system for the digital
enterprise. In Proceedings of the International Conference on Business Excellence (Vol. 12, No.
1, pp. 54-69). Sciendo.
Araujo, T., Helberger, N., Kruikemeier, S. and De Vreese, C.H., 2020. In AI we trust?
Perceptions about automated decision-making by artificial intelligence. AI & SOCIETY, 35(3),
pp.611-623.
Arifin, S.R.M., 2018. Ethical considerations in qualitative study. International Journal of Care
Scholars, 1(2), pp.30-33.
Armat, M.R., Assarroudi, A., Rad, M., Sharifi, H. and Heydari, A., (2018). Inductive and
deductive: Ambiguous labels in qualitative content analysis. The Qualitative Report, 23(1),
pp.219-221.
Australia, P., 2019. Transforming Australian manufacturing: preparing businesses and
workplaces for Industry 4.0.
Page | 34

Baek, S., 2021. System integration for predictive process adjustment and cloud computing-based
real-time condition monitoring of vibration sensor signals in automated storage and retrieval
systems. The International Journal of Advanced Manufacturing Technology, 113(3), pp.955-966.
Bannikov, S.A. and Abzeldinova, K.T., 2021, February. Digital Transformation of HR
Management System. In International Scientific and Practical Conference “Russia 2020-a new
reality: economy and society”(ISPCR 2020) (pp. 54-58). Atlantis Press.
Basias, N. and Pollalis, Y., 2018. Quantitative and qualitative research in business & technology:
Justifying a suitable research methodology. Review of Integrative Business and Economics
Research, 7, pp.91-105.
Belloc, F., Burdin, G., Cattani, L., Ellis, W. and Landini, F., 2020. Coevolution of job
automation risk and workplace governance. DEPS WP–University of Siena, (841).
Bessen, J., 2019. Automation and jobs: When technology boosts employment. Economic
Policy, 34(100), pp.589-626.
Bhargava, A., Bester, M. and Bolton, L., 2021. Employees’ perceptions of the implementation of
robotics, artificial intelligence, and automation (RAIA) on job satisfaction, job security, and
employability. Journal of Technology in Behavioral Science, 6(1), pp.106-113.
Biernacki, P. and Waldorf, D., 1981. Snowball sampling: Problems and techniques of chain
referral sampling. Sociological methods & research, 10(2), pp.141-163.
Bughin, J., Hazan, E., Lund, S., Dahlström, P., Wiesinger, A. and Subramaniam, A., 2018. Skill
shift: Automation and the future of the workforce. McKinsey Global Institute, 1, pp.3-84.
Burton, R.J., Albur, M., Eberl, M. and Cuff, S.M., 2019. Using artificial intelligence to reduce
diagnostic workload without compromising detection of urinary tract infections. BMC medical
informatics and decision making, 19(1), pp.1-11.
Castleberry, A. and Nolen, A., (2018). Thematic analysis of qualitative research data: Is it as
easy as it sounds?. Currents in Pharmacy Teaching and Learning, 10(6), pp.807-815.
Page | 35

Chui, M., George, K. and Miremadi, M., 2017. A CEO action plan for workplace
automation. McKinsey Quarterly, 3, pp.88-94.
Chyan, P., 2021, February. Automatic monitoring system for the elderly based on internet of
things. In IOP Conference Series: Materials Science and Engineering (Vol. 1088, No. 1, p.
012041). IOP Publishing.
David, H.J.J.O.E.P., 2015. Why are there still so many jobs? The history and future of workplace
automation. Journal of economic perspectives, 29(3), pp.3-30.
Davlyatova, G.M., 2020. Issues of effective use of employees of industrial enterprises. EPRA
International Journal of Multidisciplinary Re-search (IJMR)-Peer Reviewed Journal, (6).
Dwivedi, Y.K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi,
R., Edwards, J., Eirug, A. and Galanos, V., 2019. Artificial Intelligence (AI): Multidisciplinary
perspectives on emerging challenges, opportunities, and agenda for research, practice and
policy. International Journal of Information Management, p.101994.
Easterby-Smith, M., Jaspersen, L.J., Thorpe, R. and Valizade, D., 2021. Management and
business research. Sage.
Edson, M.C., Henning, P.B. and Sankaran, S. eds., (2016). A guide to systems research:
Philosophy, processes and practice (Vol. 10). Springer.
Embregts, P.J., Tournier, T. and Frielink, N., 2021. Experiences and needs of direct support staff
working with people with intellectual disabilities during the COVID‐19 pandemic: A thematic
analysis. Journal of Applied Research in Intellectual Disabilities, 34(2), pp.480-490.
Estlund, C., 2018. What Should We Do after Work: Automation and Employment. Yale LJ, 128,
p.254.
Etikan, I., Musa, S.A. and Alkassim, R.S., 2016. Comparison of convenience sampling and
purposive sampling. American journal of theoretical and applied statistics, 5(1), pp.1-4.
Georgieff, A. and Milanez, A., 2021. What happened to jobs at high risk of automation?.
Page | 36

Gruchmann, T., Mies, A., Neukirchen, T. and Gold, S., 2020. Tensions in sustainable
warehousing: including the blue-collar perspective on automation and ergonomic workplace
design. Journal of Business Economics, pp.1-28.
Gutiérrez, I., Ponce, P., Balderas, D., Khakifirooz, M. and Molina, A., Automation Taxonomy
for Industry 4.0 based on human features.
Halawi, A. and Haydar, N., 2018. Effects of Training on Employee Performance: A Case Study
of Bonjus and Khatib & Alami Companies. International Humanities Studies, 5(2).
Harahap, N.J. and Rafika, M., 2020. INDUSTRIAL REVOLUTION 4.0: AND THE IMPACT
ON HUMAN RESOURCES. ECOBISMA (JURNAL EKONOMI, BISNIS DAN
MANAJEMEN), 7(1), pp.89-96.
Harris, K., Kimson, A. and Schwedel, A., 2018. Labor 2030: The collision of demographics,
automation and inequality. Bain & Company, 7.
Herjanto, H., Gaur, S.S., Saransomrurtai, C. and Quik, W.H., 2014. Allowing digital piracy for
strategic benefits to businesses. Journal of Information, Communication and Ethics in Society.
Hmoud, B. and Laszlo, V., 2019. Will Artificial Intelligence Take Over Humanresources
Recruitment And Selection?. Network Intelligence Studies, 7(13), pp.21-30.
Hox, J.J. and Boeije, H.R., 2005. Data collection, primary versus secondary.
Iatsyshyn, A., Kovach, V., Romanenko, Y., Deinega, I., Iatsyshyn, A., Popov, O., Kutsan, Y.,
Artemchuk, V., Burov, O. and Lytvynova, S., 2020. Application of augmented reality
technologies for preparation of specialists of new technological era.
Ivanov, S., 2021. Robonomics: The rise of the automated economy. ROBONOMICS: The
Journal of the Automated Economy, 1, pp.11-11.
Ivanov, S.H. and Webster, C., 2017. Adoption of robots, artificial intelligence and service
automation by travel, tourism and hospitality companies–a cost-benefit analysis. Artificial
Intelligence and Service Automation by Travel, Tourism and Hospitality Companies–A Cost-
Benefit Analysis.
Page | 37

Ivanov, S.H., 2017. Robonomics-principles, benefits, challenges, solutions.
Jämsä-Jounela, S.L., 2007. Future trends in process automation. IFAC Proceedings
Volumes, 40(1), pp.1-10.
Kaldewey, D. and Schauz, D. eds., 2018. Basic and Applied Research: The Language of Science
Policy in the Twentieth Century (Vol. 4). Berghahn Books.
Kraft, A., 2021. FUTURE SKILL TRENDS: HOW IS JOB AUTOMATION CHANGING THE
SKILL REQUIREMENTS OF JOBS? (Doctoral dissertation, University Innsbruck).
Kurth, J.S., 2020. Factors Encouraging Upward Employee Voice in an Online Workplace
Portal: A Case Study of an Academic Research Institute (Doctoral dissertation, Arizona State
University).
Lander, F., Nielsen, K.J. and Lauritsen, J., 2016. Work injury trends during the last three decades
in the construction industry. Safety science, 85, pp.60-66.
Lowry, L.D., 2015. Bridging the business data divide: insights into primary and secondary data
use by business researchers.
Lu, V.N., Wirtz, J., Kunz, W.H., Paluch, S., Gruber, T., Martins, A. and Patterson, P.G., 2020.
Service robots, customers and service employees: what can we learn from the academic literature
and where are the gaps?. Journal of Service Theory and Practice.
Manyika, J., Chui, M. and Miremadi, M., 2017. A future that works: AI, automation,
employment, and productivity. McKinsey Global Institute Research, Tech. Rep, 60.
Mayring, P., 2014. Qualitative content analysis: theoretical foundation, basic procedures and
software solution.
McClure, P.K., 2018. “You’re fired,” says the robot: The rise of automation in the workplace,
technophobes, and fears of unemployment. Social Science Computer Review, 36(2), pp.139-156.
Merchant, M. E. 2000. The Future of Manufacturing. In: Handbook of Industrial Automation
(Shell, Richard L. and Hall, Ernest L. (Ed)), Marcel Dekker, Inc., Cincinnati.
Page | 38

Meyers, L.S., Gamst, G. and Guarino, A.J., 2016. Applied multivariate research: Design and
interpretation. Sage publications.
Micheler, S., Goh, Y. and Lohse, N., 2016, September. Influencing factors for implementing
automation in manufacturing businesses–a literature review. In IN: Proceedings of the 14th
International Conference on Manufacturing Research (ICMR), Loughborough University,
Loughborough, UK (pp. 6-8). Loughborough University.
Misra, R.K. and Khurana, K., 2017. Employability skills among information technology
professionals: A literature review. Procedia computer science, 122, pp.63-70.
Oesterreich, T.D. and Teuteberg, F., 2016. Understanding the implications of digitisation and
automation in the context of Industry 4.0: A triangulation approach and elements of a research
agenda for the construction industry. Computers in industry, 83, pp.121-139.
Olding, M., Barker, A., McNeil, R. and Boyd, J., 2021. Essential work, precarious labour: The
need for safer and equitable harm reduction work in the era of COVID-19. International Journal
of Drug Policy, 90, p.103076.
Paradis, E., O’Brien, B., Nimmon, L., Bandiera, G. and Martimianakis, M.A.T., 2016. Design:
Selection of data collection methods. Journal of graduate medical education, 8(2), p.263.
Peper, N.A., 2017. Systems thinking applied to automation and workplace safety (Doctoral
dissertation, Massachusetts Institute of Technology).
Pereira, M., Oliveira, M., Vieira, A., Lima, R.M. and Paes, L., 2018. The gamification as a tool
to increase employee skills through interactives work instructions training. Procedia computer
science, 138, pp.630-637.
Pham, Q.C., Madhavan, R., Righetti, L., Smart, W. and Chatila, R., 2018. The impact of robotics
and automation on working conditions and employment. IEEE Robotics & Automation
Magazine, 25(2), pp.126-128.
Pritchett, A.R., Kim, S.Y. and Feigh, K.M., 2014. Measuring human-automation function
allocation. Journal of Cognitive Engineering and Decision Making, 8(1), pp.52-77.
Page | 39

Rahi, S., 2017. Research design and methods: A systematic review of research paradigms,
sampling issues and instruments development. International Journal of Economics &
Management Sciences, 6(2), pp.1-5.
Ramaswamy, K.V., 2018. Technological change, automation and employment: A short review of
theory and evidence. Indira Gandhi Institute of Development Research, January 2018.
Reid, A.M., Brown, J.M., Smith, J.M., Cope, A.C. and Jamieson, S., 2018. Ethical dilemmas and
reflexivity in qualitative research. Perspectives on medical education, 7(2), pp.69-75.
Roberts, K., Dowell, A. and Nie, J.B., 2019. Attempting rigour and replicability in thematic
analysis of qualitative research data; a case study of codebook development. BMC medical
research methodology, 19(1), pp.1-8.
Robinson, O.C., 2014. Sampling in interview-based qualitative research: A theoretical and
practical guide. Qualitative research in psychology, 11(1), pp.25-41.
Rodrik, D., 2018. New technologies, global value chains, and developing economies (No.
w25164). National Bureau of Economic Research.
Rosenberg, M.J., 2005. Beyond e-learning: Approaches and technologies to enhance
organizational knowledge, learning, and performance. John Wiley & Sons.
Rutberg, S. and Bouikidis, C.D., 2018. Focusing on the fundamentals: A simplistic
differentiation between qualitative and quantitative research. Nephrology Nursing Journal, 45(2),
pp.209-213.
Sain, M., Singh, R. and Kaur, A., 2020. Robotic Automation in Dairy and Meat Processing
Sector for Hygienic Processing and Enhanced Production. Journal of Community Mobilization
and Sustainable Development, 15(3), pp.543-550.
Sarter, N.B., Woods, D.D. and Billings, C.E., 1997. Automation surprises. Handbook of human
factors and ergonomics, 2, pp.1926-1943.
Säther, J.K., 2021. Automated vehicles on airports: A case study of process challenges and
opportunities in developing employee acceptance.
Page | 40

Saunders, M., Lewis, P. and Thornhill, A., 2009. Research onion. Research methods for business
students, pp.136-162.
Sharma, G., 2017. Pros and cons of different sampling techniques. International journal of
applied research, 3(7), pp.749-752.
Sorells, B., 2018. Will Robotization Really Cause Technological Unemployment? The Rate and
Extent of Potential Job Displacement Caused by Workplace Automation. Psychosociological
Issues in Human Resource Management, 6(2), pp.68-73.
Taherdoost, H., 2016. Sampling methods in research methodology; how to choose a sampling
technique for research. How to Choose a Sampling Technique for Research (April 10, 2016).
Tilley, J., 2017. Automation, robotics, and the factory of the future. McKinsey. https://www.
mckinsey. com/business-functions/operations/our-insights/automation-robotics-and-the-factory-
of-the-future.
Vaismoradi, M., Jones, J., Turunen, H. and Snelgrove, S., 2016. Theme development in
qualitative content analysis and thematic analysis.
Vermeulen, B., Kesselhut, J., Pyka, A. and Saviotti, P.P., 2018. The impact of automation on
employment: just the usual structural change?. Sustainability, 10(5), p.1661.
Wei, Z.-G., Macwan, A. P. and Wieringa, P. A. 1998. A Quantitative Measure for Degree of
Automation and Its Relation to System Performance and Mental Load. Human Factors, 40, 277-
295.
Wijayanti, T.C., Setini, M., Darma, D.C., Purwadi, P. and Alka, P.R., 2020. Connectivity
Continuous Improvement Program and Employee Performance. International Review of
Management and Marketing, 10(1), pp.54-62.
Willcocks, L., Lacity, M. and Craig, A., 2017. Robotic process automation: strategic
transformation lever for global business services?. Journal of Information Technology Teaching
Cases, 7(1), pp.17-28.
Page | 41

Winasis, S., Riyanto, S. and Ariyanto, E., 2020. Digital Transformation in Indonesian Banking
Industry: Impact on Employee Engagement. International Journal of Innovation, Creativity and
Change, pending for published March, 2020.
Woiceshyn, J. and Daellenbach, U., 2018. Evaluating inductive vs deductive research in
management studies: Implications for authors, editors, and reviewers. Qualitative Research in
Organizations and Management: An International Journal.
Wollschlaeger, M., Sauter, T. and Jasperneite, J., 2017. The future of industrial communication:
Automation networks in the era of the internet of things and industry 4.0. IEEE industrial
electronics magazine, 11(1), pp.17-27.
Yarlagadda, R.T., 2017. AI Automation and it’s Future in the UnitedStates. International
Journal of Creative Research Thoughts (IJCRT), ISSN, pp.2320-2882.
Page | 42