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Purpose the title of a TIME magazine

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Purpose
of the research:

Automation can be defined as the
technology by which a process or procedure is performed without human
assistance.

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In other words, Automationor
automatic control, is the use of various control systems for operating
equipment such as machinery, processes in factories, boilers and heat treating
ovens, switching on telephone networks, steering and stabilization of ships,
aircraft and other applications and vehicles with minimal or reduced human
intervention, with some processes have been completely automated.

Automation has been achieved by
various means including mechanical, hydraulic, pneumatic, electrical,
electronic devices and computers, usually in combination. Complicated systems,
such as modern factories, airplanes and ships typically use all these combined
techniques. The benefit of automation include labor savings, savings in
electricity costs, savings in material costs, and improvements to quality,
accuracy and precision.

Activities most susceptible to
automation involve physical activities in highly structured and predictable
environments, as well as the collection and processing of data. In the United
States, these activities make up 51 percent of activities in the economy
accounting for almost $2.7 trillion in wages 1. They are most prevalent in
manufacturing, accommodation and food service, and retail trade, and include
some middle-skill jobs.

One survey shows that 40 per cent of
young people around the world are concerned about their jobs being automated in
the next decade 2.

There have been periodic warnings in
the last two centuries that automation and new technology were going to wipe
out large numbers of middle class jobs. The best-known early example is the
Luddite movement of the early 19th century, in which a group of English textile
artisans protested the automation of textile production by seeking to destroy
some of the machines. A lesser-known but more recent example is the concern
over “The Automation Jobless,” as they were called in the title of a TIME magazine
story of February 24, 1961: The number of jobs lost to more efficient machines
is only part of the problem. What worries many job experts more is that
automation may prevent the economy from creating enough new jobs. Throughout
industry, the trend has been to bigger production with a smaller work force.

Many of the losses in factory jobs
have been countered by an increase in the service industries or in office jobs.
But automation is beginning to move in and eliminate office jobs too.  In the past, new industries hired far more
people than those they put out of business. But this is not true of many of
today’s new industries. Today’s new industries have comparatively few jobs for
the unskilled or semiskilled, just the class of workers whose jobs are being
eliminated by automation.

How
Automation and Employment Interact

In 1988 average number of staff at a
bank was 20 and ATMs reduced this number to 13 per branch of a bank in the US,
in 1900, 41 percent of the US workforce was employed in agriculture; by 2000,
that share had fallen to 2 percent (Autor 2014), mostly due to a wide range of
technologies including automated machinery. The mass-produced automobile
drastically reduced demand for many equestrian occupations, including
blacksmiths and stable hands. Successive waves of earth-moving equipment and
powered tools displaced manual labor from construction. In more recent years,
when a computer processes a company’s payroll, alphabetizes a list of names, or
tabulates the age distribution of residents in each Census enumeration
district, it is replacing a task that a human would have done in a previous
era. Broadly speaking, many—perhaps most—workplace technologies are designed to
save labor. Whether the technology is tractors, assembly lines, or spreadsheets,
the first-order goal is to substitute mechanical power for human musculature,
machine-consistency for human handiwork, and digital calculation for slow and
error-prone “wetware.”

Meanwhile, there is ample evidence
that automation does not lead to job substitution, but rather to a
re-allocation of both jobs and tasks in which robots complement and augment
human labour by performing routine or dangerous tasks. This in turn places a
premium on higher-skilled labour in the sectors in which automation has
substituted for labour, but also may create new lower-skilled jobs in other
sectors due to spillover effects. As economist James Bessen comments, ‘Although
computer automation is not causing a net loss of jobs, it does imply a
substantial displacement of jobs from some occupations to others.’ (Bessen
2016). Various studies show a positive correlation between automation and jobs.
For example a 2016 discussion paper for the Centre for European Economic
Research found that, ‘Overall, labor demand increased by 11.6 million jobs due
to computerization between 1999 and 2010 in the EU 27, thus suggesting that the
job-creating effect of RRTC6 overcompensated the job-destructing effect.’7
(Zierahn, Gregory and Arntz 2016). A review of the economic impact of industrial
robots across 17 countries found that robots increased wages whilst having no
significant effect on total hours worked (Graetz and Michaels 2015). And
although manufacturing jobs have been declining over a number of years
Brookings Institution analysts report that countries that invested more in
robots lost fewer manufacturing jobs than those that did not (Muro and Andes
2015). Indeed a study by Barclays in the UK argues that an investment in
automation of £1.24 billion over the next decade could safeguard 73,500
manufacturing jobs and create over 30,000 jobs in other sectors. (Barclays
2015). According to analysis by PwC of data from the U.S. Bureau of Labor
Statistics, the most robotics-intensive manufacturing sectors in the US as a
proportion of the total workforce – i.e., automotive, electronics and metals –
employ about 20% more mechanical and industrial engineers and nearly twice the
number of installation maintenance and repair workers than do less
robotics-intensive manufacturing sectors and pay higher wages than other
manufacturing sectors. These sectors also tend to have a higher proportion of
production-line workers – and these workers earn higher wages than sectors that
are less robotics-intensive. (PwC 2014). Consultants Deloitte argue that, ‘While
technology has potentially contributed to the loss of over 800,000
lower-skilled jobs (in the UK) there is equally strong evidence to suggest that
it has helped to create nearly 3.5 million new higher-skilled ones in their
place.’ (Deloitte LLP 2015). And countries with the highest robot density,
notably Germany and Korea, have among the lowest unemployment rates. Economist
David Autor sums it up with the statement that ‘Automation does indeed
substitute for labor – as it is typically intended to do. However, automation
also complements labor, raises output in ways that lead to a higher demand for
labor, and interacts with adjustments in labor supply. Even expert commentators
tend to overstate the machine substitution for human labor and ignore the strong
complementarities between automation and labor that increase productivity,
raise earnings and augment demand for labor.

Now the purpose
of this research is figure out how automation impacts individual’s jobs, the
reasons that people fear about their jobs and how to eliminate their fear.

Research
design

Literature Review

The research is about how employees
perceive automation and do they view it as a boon or a creeping but a sure
factor that will put them out of a job? These are the questions that we are
trying to find out in our research and data collection and to finally find out
whether that automation does really put fear in the hearts of current employees
and in what ways does it affect them.

In the research we are trying to
find out do employees really find out if they really do fear automation
replacing their jobs, or do they find it a convenience because as humans
automation is an inevitable consequence, as it is in out nature that we find
the path of least resistance.

Keep in mind that some countries are
more open to automation than other countries such as Sweden as cited here
“recent survey by the European Commission found that 80 percent of Swedes have
a positive view of robots and AI. Why such enthusiasm? Swedish citizens tend to
trust that their government and the companies they work for will take care of
them, and they see automation as a way to improve business efficiency. Since
Swedish employees actually do benefit from increased profits by getting higher
wages, a win for companies is a win for workers.”

“Communication will be key to the
successful implementation of automation, according to Matkin. “It is vital that
organizations clearly communicate what their automated business would look like
with their employees,” she said. “This should include how it will affect their
role, the workplace and the possible benefits it presents. This could be in
terms of, for example, upskilling, further training and freeing up employee
time to focus on more creative, less menial activities”

Theory and Hypotheses

Existing studies have shown that
automation although slow is an inevitable process of any company, because
anyway to increase productivity any company, given time, will adopt those
technologies.

But this a slow and expensive
process for any company to adopt and use it efficiently and effectively and to
its fullest potential, you might think that if a company adopts the latest
technology then they immediate increase productivity but no, it’s a time
consuming process as the each of the company employees has to trained to use of
the technology (such as Manipal Dubai and its recent implementation of LMS as a
current example).

People in theory would be averse to
change especially when it concerns with their job role but in fact may add
value or even make it more convenient and ergonomic, people were first cautions
of computers and it’s potential to make the world a smaller place and increase
to potential for global commerce, but now we cannot imagine a world without
computers and its impact on our daily lives be it small or large.

“The reality is, people fear what
they don’t understand. The mistake many organizations make when implementing
automation is to leave employees out of the loop. Not only does this lack of
communication breed fear of the unknown, but it also allows legitimate
questions to go unanswered and irrational concerns to go unaddressed”

“Whether they’re just uncomfortable
with change or they’re worried that the technology will make their jobs
redundant, without buy-in across the board, your automation implementation
project will be much more difficult than it has to be. Thankfully with the
right approach and some strategic planning, this fear of automation can be
overcome”

The Hypotheses

Do employees fear their jobs being
replaced because of automation? Or are those fears are based on unfounded
statements or news stories spreading unwanted fear and caution amongst
employees of the companies that push for automation.

Model

Unit of analysis- Employees of any
given company at any time or employees that are available at that time.

Sample size-50

Dependent variable- Employees Fear they
might lose their job because of automation

Independent variables: Occupation- Education-
Skill Level –Age.

 

Data collection

The data collection method that is
taken will be a survey method and this survey would be given to all employees
regardless of position, age and gender.The data in the above model will then be
analyzed using the statistical technique of linear regression.

Sample
Design

 

We can conduct an survey for
ascertaining the level of fear in individuals for automation in our college
(professors, students, employees), our office colleagues our bosses and our
friends and family.

Considering the fact of job security
for employees we can provide some set of questions to around 40-50 people as a
sample so that we could get rough idea about the mindset of people at large
scale.

Sample can include individuals from
different backgrounds such as businessmen, salaried person, professionals,
skilled and unskilled workers, casual labors and so on.

 

The degree of automation will be
different on individual to individual depending upon the nature of the work
they are involved in.

For instance-

a)       Professionals-
professionals such as legal advisors and Chartered accountants and consultants,
teachers and professors  will not be
affected much by the automation hit.

b)       Businessmen-
mostly big businessesby nowhave already inculcated big plants and machinery  for getting things done at a faster and
better way avoiding the labor cost. However, small businesses are at greater
risk since they have to cope up with the technology or else they will collapse
and hence the fear of automation is more in these small businesses.

c)       Salaried
person- this is most vulnerable category since they are in a job which can
anytime be replaced with skilled Robots and to consider the fact that
automation might keep their jobs on stake. Hence hey need to acquire that
qualities or train themselves in which the machines can’t perform.

d)       Skilled
and unskilled workers- below figure depicts overall categories of jobs which
will affect the jobs in future.

Sample
selection-

Sample represents the entire characteristics of population,
therefore the selection of sample should be done very carefully so as to get
the reliable figures/results.

There are many methods of selecting samples, few of which are shown
below-

1.       Simple Random
sampling-  this is the simplest form of
sampling. In this to select the sample you just need to make a number of all
the units in the population from which you want to take the sample, decide the
sample size, and just collect the response the then analyze it.

2.       Systematic sampling-
in systematic sampling, individuals/employees are chosen are chosen at regular
intervals from the sampling frame. For this method we randomly select a number
to tell us where to start selecting individuals from the list.

Systematic sampling is usually less time-consuming and easier to
perform than simple random sampling. However, there is a risk of biasness.

For example- we could take 25 out of 50 staff members from same
department. The sampling interval would be 2 (50/25). And hence we could ask
randomly a member from every 2 member of what he/she thinks of artificial
intelligence would affect their respective jobs.

3.       Stratified
sampling-sampling frame must be divided into groups, or strata, according to
certain common characteristics. Random or systematic samples of a predetermined
size will then have to be obtained from each group (stratum). This is called
stratified sampling.

Stratified sampling makes it possible to get a sample that is big
enough to enable researchers to draw valid conclusions about a relatively small
group without having to collect an unnecessarily large (and hence expensive)
sample of the other, larger groups.

 

Data Analysis

Objective

After the research question is formed, and after results are
collected; the next step would be to extract meaning out of them. This is done
through result analysis, where the research team examines the results from
different perspectives and in different ways, in order to confirm or deny any
underlining assumptions relating to the research topic. In other words, data
analysis is used to interrupt the data obtained throughout the research, and
use it to gain insight about the research topic.

The
Process

Data analysis is to done to prove through mathematical and
empirical means; the existence of a relationship between the dependent variable
and independent variable(s), and if such relation can’t be supported after the
statistical models are applied; then this affects the conclusion of the
research and it ultimately means that the research question and any assumptions
previously made, must be reexamined. This analysis would utilize several
methods and models of empirical and statistical nature; this is discussed in more
detail below.

Relationships

The research topic is concerned with the attitude of the workforce
towards automation, and whether they fear losing their jobs to it or not. Thus
the analysis would be done to establish a relation between the work force (as
function of the independent variables) and the fear of losing job to automation
(the dependent variable).  If the
underlining assumptions are true; then one would expect to see correlation
between (for example) skill level and fear of job loss to automation.

Methods
of Analysis

The research team means to employ several statistical methods to
inspect the existence of a relation between the variables. These methods
include the following:

Descriptive
statistics

Descriptive statistics are basic statistical tools and concept that
give a general and over the top view of the main characteristics of the sample and
what are the most distinct traits and possible causes of error. These tools
include but not limited to:

·       
Frequency
Distribution & Visual Representation (ie: Histograms)

Frequency
distribution is simply a way to arrange the data in and orderly fashion and
classify it according to the number of observations. Frequency distribution is
obtained through grouping data in an classes of adequate size and listing the
frequency of observation included in each class, and this method can summarize
data in an effective manner and allow for visual graphs to be used such as
Histogram. Lastly, it is worth noting that frequency distributions can be
applied to qualitative data sets and quantitative data as well

 Mean

The
expected value of a population is known as the Mean, it also refers to the
central tendency of a given data set. This value is obtained by dividing the
value sum over number the of observations, and it can be sometimes used as an
expression of the average of a data set. The mean is useful when dealing with
data set; for it can reveal what is the overall tendency in one value (relative
to the set), which can lead to finding any skewness present in the data.

·       
Standard
Deviation

The
term Standard Deviation is an expression used to indicate (in quantifiable
amounts) the amount of dispersion present in any given data set. This value
gives a measure of how close the points in the data set are to the expected
value or the mean.

·       
Contingency
Table

Contingency table
is sets of values presented in a tabular matrix that show the frequency
distribution for multiple variables. They are one common way that is used for
survey analysis. They give a big picture of all of the variables which allows
for comparison between the variables and reveals initial relation between them

 

Factor
Analysis

In the field of statistics, one method which can be used to examine
the interdependence of variables is Factor Analysis. This method is used to
express the changeability of observed variables in terms of possibly fewer
hidden or latent variables. An example of that may be lowering the variables from
eight to four due to the fact the only four are essential and the rest change
as these essential variables change. This method is useful to examine the
degree of dependency of each variable on one another; which in turn allows to
researchers to focus on the most relevant variables to the topic.

Correlation
Coefficient

Correlation Coefficient is a value used in statistics that indicate
how strong is the relation between two variables. This method can be a very
useful way to find out if there is any relation between the two variables and
if so how significant it is. This means if a change in variable happens; the
other variable would exhibits some sort of change as well tough that doesn’t
necessarily means that one causes the other. If the change is proportional;
than this means that it is positive correlation, and if it is disproportional;
then it is a negative correlation.

Bivariate
analysis (Trend Analysis) & Multiple Regressions

Trend Analysis or Bivariate analysis is a way that is used in
research, engineering and since; to analyze the relation between two variables
(one of which is dependent on the other). it is a useful way to examine how
strong of a relation the dependent variable has to the independent one. This is
expressed by the degree of accuracy of a prediction of the dependent variable
based of the independent variable within a specific range of the data set. The
relation is usually expressed in terms of a linear function Y=mX+b.

Bivariate analysis applies only for two variables. For more than
two variables; multiple regressions is used. Multiple regression is similar to
linear or Bivariate regression in the sense that it is used to examine the
relation between variables, but it can be used for multiple variables. Same
concept applies in term of how good is the relation, obtained from multiple regression,
in anticipating the value of dependent variable. It also can be used to see
which one of the independent variables can best predict the value of
independent variables, and this may lead to finding out which variable is the
most dominant. 

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