IMPACT OF ARTIFICIAL INTELLIGENCE IN AUTOMOTIVE INDUSTRIES TRANSFORMATION

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International Journal of Information System and Engineering
www.ftms.edu.my/journals/index.php/journals/ijise
Vol. 9 (No 1.), April, 2021
ISSN: 2289-7615
DOI: 10.24924/ijise/2021.04/v9.iss2/01.35
This work is licensed under a
Creative Commons Attribution 4.0 International License.
Research Paper
IMPACT OF ARTIFICIAL INTELLIGENCE IN
AUTOMOTIVE INDUSTRIES TRANSFORMATION
Tai Yoon Chai
FTMS MBA Alumni,
FTMS Global Cyberjaya
Ismail Nizam
Head of Schoo (SOABM),
FTMS Global College
Lot No. 12159, Jalan Cyber Point 6, Cyber 8, 63000 Cyberjaya, Selangor
nizam@ftms.edu.my
ABSTRACT
The purpose for conducting this research is to investigate the impact of Artificial Intelligence
(AI) in automotive industry transformation. Artificial intelligence (AI) with many of its huge
variety of subfields are the key to a new future of value for the automotive industry. The
applications of artificial intelligence amplify its impact on automotive industry. As of now, no
research was being conducted on the impact of artificial intelligence on automotive industry,
it is necessary to study the impact towards automotive industry with the adoption of artificial
intelligence by automotive organisations in the context of their transformation leadership,
autonomous vehicle, smart factory and marketing & sales. Theories that being used and
reviewed include Technology Acceptance Model (TAM), Transformation Leadership, Hidden
Markov Model (HMM) and Marketing Mix 7Ps. The association amongst theories are being
validate by Explanatory research designed for this study by testing four hypotheses with
Confirmatory Factor Analysis through Multiple Regression. This research targeted 250
respondents but only 160 respondents were received. Data from sample population were
collected during the month of July through online questionnaire. SPSS was used to analyse and
interpret data which was obtained via the online questionnaire. After further careful
interpretation on data obtained, all dependent variables which include leadership change,
autonomous vehicle, smart factory and marketing & sales are perceived as significant positive
impact by artificial intelligence. Various valuable perspectives in this study will shed light on
the automotive industry transformation.
Key Terms: Artificial Intelligence, Automotive Industry, Leadership Change, Autonomous
Vehicle, Smart Factory, Marketing and Sales, Industry 4.0.

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1.0 INTRODUCTION
AI is the key to a new future of value for the automotive industry, AI applications in
automotive industry extend far beyond the development, engineering, logistics, production,
supply chain, customer experience, marketing, sales, after-sales and mobility services in
automotive industry (Hofmann, et al., 2017). The world of automotive industry with AI is
going towards a new transformation, in a big way. Oftentimes, when Artificial Intelligence
being mentioned in the context of automobiles, people tend to immediately relate it to self-
driving cars and overlooked the fact that AI is actually has much broader and much deeper
impact on the entire foundation of automotive industry (Zaki, 2019).
1.1 Research Rational
Artificial intelligence will enable autonomous vehicles to become mainstream while
transforming most aspects of the R&D, project management and business support
functions in auto-manufacturing. However, although the benefit of AI towards the
automotive industry is almost identical, the implementation and investment from
automotive industry towards AI are surprisingly slow. (Jacques, et al., 2017) reviewed that
implementation of AI promises boost profits and will transform the industries. But by
looking at the investment on AI by industries, investment by automotive industry is rather
low compare to other industries (PwC, 2017). Traditional OEMs will be facing severe
competition on Level 5 fully autonomous vehicle readiness by 2030 from ‘disruptors’
company such as Tesla and Faraday Future. Traditional OEMs need to maximize the
function and advantages of AI to streamline overall industry transformation.
1.2 Significant of the Research
Automotive industry is currently amidst a technological transformation which will change
the way we live and work fundamentally. These changes are an ushering of new era of
growth and opportunity. Research done by (McKinsey & Company, 2018) indicate that AI
adoption could widen gaps between countries, companies and work forces as it has large
potential to contribute to global economy activities and automotive industry plays a crucial
part in it. This research will create awareness of leadership change readiness when
adapting AI that could expedite the transformation of automotive industry and what are
the consumers’ expectation towards future mobility. Automotive organisations need to
allocate their investment in AI strategically across the organisation and equips their work
force with AI technology knowledge for better efficiency and flexibility in the process of
manufacturing
1.3 Research Aim and Objectives
Research Aim
The aim of this investigation is to explore the impact of Artificial Intelligence (AI) in
automotive industry transformation.

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Specific Objectives:
1. To examine the impact of Leadership Change on Artificial Intelligence (AI)
2. To examine the impact of Autonomous Vehicle on Artificial Intelligence (AI)
3. To examine the impact of Smart Factory on Artificial Intelligence (AI)
4. To examine the impact of Marketing & Sales on Artificial Intelligence (AI)
2.O LITERATURE REVIEW
2.1 Definition of Key Terms
2.1.1 Artificial Intelligence
Professor John McCarthy, father of Artificial Intelligence first coined Artificial Intelligence
at Dartmouth in 1956 when he held the first academic conference and defined artificial
Intelligence as “The science and engineering of making intelligent machines, especially
intelligent computer programs” (McCarthy, 2007). Artificial Intelligence is a way of making
a computer or a software to think intelligently in similar to the level of human intelligent
mostly based on scientific findings such as mathematics, statistics and biology algorithms
and models being designed. (Russell & Norvig, 2010) indicate that artificial intelligence
could be defined with eight definitions that could be categorised into four categories:
Thinking Humanly and Thinking Rationally which related with thought process and
reasoning, and Acting Humanly and Acting Rationally which related to behaviour. As
different goals and methods are being used to identify the definition of AI, these researches
are valid and fruitful and AI can have working definitions according to the ultimate
research goals as it could stress on the ‘big picture’ of an intelligent system (Wang, 2008).
2.1.2 Transformation
Transformation, according to (Daszko & Sheinberg, 2017) is to change and create a whole
new structure, function or form. The transformation will bring radical change to
organisation with a new direction and effectiveness. Studies by (Mahraz, et al., 2019)
indicate that adoption of transformation of management mode of work will allows
organisation to optimise operation and improve in performance, efficiency and
competitiveness. Transformation will also allow organisations to grasp new opportunities
to operate beyond their traditional business activities to accelerate organisation growth
and sustain competitive advantages. Refer to study conducted (Gilday, 2019), automobile
which was once the centre of the automotive industry will become a portion within wider
ecosystem as automotive industry has move from priori on product to customer
experience.
2.1.3 Leadership Change

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(Sun, 2018) defined leadership as ability to influence peoples’ values, beliefs, attitudes and
behaviours towards the accomplishment of goals. Leaders of an organisation are involved
and responsible for other people to move forward and achieve organisation goals.
According to (Yukl, 1999), functions of effective leadership is need to understand and
interpret the meaning of goals setting in order to create alignment and set objectives and
strategies with tasks commitment, hopefulness and confidence by building mutual trust
and cooperation with stakeholders. (Dumas & Beinecke, 2018) indicate that it is critical for
organisation to understand and practise leadership change effectively as organisations are
facing tremendous forces to change exert from the evolving trends surrounding the
organisations in the familiar ways, we working include influences from global
dissemination, educational levels and global mobility. Leaders are under more pressure to
identify the next major innovation that could lead organisations to become more
competitive in the industry as it has become more and more integrated and strategic
situations where the vision of the leader could see not only in organisation level but the
whole ecosystem of the industry (Caswell, et al., 2017).
2.1.4 Autonomous Vehicles
The fundamental of an autonomous vehicle defined as passenger vehicles that drives by
itself without any intervention by human. Autonomous vehicles also referred as autopilot
vehicle, driverless car, next-generation vehicle or automated guided vehicle where the
automated system could help to change transportation system by reduce road congestion
for fuel saving and lower emissions for environmental sustainability, providing mobility to
elderly and disabled and preventing deadly crashes by avoiding accidents. Under the SAE
J3016 standard defined by Society of Automotive Engineers (SAE International , 2019), 6
levels of autonomous vehicle are being defined where Level 0 is totally no automation and
human drivers responsible all aspects of driving. The main reason for having autonomous
vehicles is to accomplish several functionalities that human driver itself not able to achieve
such as maintain attention due to tiredness and sleepiness and capability of planning the
travel more accurately.
2.1.5 Smart Factory
7(Illa & Padhi, 2018) suggested that smart factory is manufacturing facility being
connected to optimise the single platform where it could accelerate new products
launching according to market dynamics by facilitating several business functions to work
together in order to meet overall organisation objective with real time analytics that
minimise product cost and increase profitability by improving efficiency with smart
machines, robots with sensors that could seamlessly create an ecosystem that collaborate
strongly. Deploying artificial intelligence technologies will improve the manufacturing
system performance especially with the Cloud-assisted Smart Factory (CaSF) (Wan, et al.,
2018). (Chen, et al., 2c018) suggested that hierarchical architecture of smart factory should

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contain 4 layers which represent by Physical Resource Layer, consist of modular units and
software adapter.
2.1.6 Marketing & Sales
The definition of sales according to (Surbhi, 2014) indicate that the primary aim is all the
activities of an organisation are leading towards the increase of sales. With exceptional
communication and persuasive skills, the result of sales activities is to sell what company
offers to its customers by convincing customers that the products will beneficial to them
within the given period of time. (Rehme & Rennhak, 2011) suggested that although
marketing and sales departments are responsible for two different functions in an
organisation where marketing responsible for winning and retaining customers in long
term while sales is more transaction based, it is undeniable that both departments are
critical and essential in each organisation for market performance and business growth to
generate revenue. By aligning marketing and sales through frequent and disciplined cross-
function communication and team integration for joint projects so that performance and
rewards metrics could be share and embedding marketer in managing organisation’s key
account (Kotler, et al., 2006).
2.2 Empirical Studies
Table 1: Artificial intelligence and automotive industry empirical research summary
Author
(Year)
Findings
Variables
Methods
Limitation &
Strength
Context
(Müller &
Bostrom,
2016)
AI system will
reach human
ability within
50 years and
the risk of AI’s
superintelligenc
e should further
investigate.
HLMI,
Super-
intelligence,
Humanity
risk
Primary data,
Quantitative
survey,
Questionnaire
s to 550 AI
experts.
AI with
super-
intelligence
technology
will be bad
for humanity
but the
extend is
unclear.
Theory-
minded AI
expert, AI
expert with
technical
background,
Members of
the Greek
Association
for Artificial
Intelligence
and Top 100
AI authors
by citation.
(Rosenzwei
g & Bartl,
2015)
Continuous
increase on
researches
about
autonomous
vehicles
throughout the
Driverless
cars
adoption,
technology
development
, user
acceptance
Secondary
data, analysis
of
publications
over time,
journal
counts,
Most of the
research
focuses on
the
technology
development.
and the
Academic
journals
published
on Science
Direct,
EBSCO,
Emerald and

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last decades.
citation
analysis on
399 papers.
existing
research gap
on the user
acceptance of
the
technology
ISI Web of
knowledge
(Lin, et al.,
2018)
Strategic
response by
automotive
industry in
China towards
Industry 4.0
Smart
factory, IT
maturity,
Perceived
benefits,
government
policy
Primary data,
Quantitative
survey,
Questionnaire
s from 165
respondents
Respondents
are from
senior
management
and executive
with technical
knowledge
and
understandin
g on smart
factory and
Industry 4.0
FAW-
Volkswagen
(Chengdu
plant),
Geely-Volvo
Auto
(Chengdu
branch),
BYD Co. Ltd
(Changsha),
Bosch
(Chengdu
plant).
(Shin, et al.,
2015)
Consumer’s
sensitivity and
willingness to
pay for smart
vehicle and fuel
types options
Smart
Vehicle,
Consumer
preferences,
Multiple
discrete-
continuous
probit
(MDCP),
multinomial
probit (MNP)
Primary data,
Quantitative
survey,
Questionnaire
s from 633
respondents
Research on
consumer
preferences
on smart
vehicles and
fuel type
Six
metropolita
n cities in
South Korea
(Capgemini
Research
Institute,
2019)
Adoption of AI
in automotive
industry still
moderate
Artificial
intelligence
(AI),
Autonomous
Vehicle,
Automotive
industry
Primary data,
Quantitative
survey,
Questionnaire
s from 500
respondents
from
automotive
industry,
Interviews.
How
automotive
industry
organisation
focus on
impact of
Artificial
Intelligence
(AI) including
autonomous
vehicle.
China,
France,
Germany,
India, Italy,
Sweden,
United
Kingdom
and United
States.

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Table 2: Artificial intelligence and leadership change empirical research summary
Author (Year)
Findings
Variables
Methods
Limitation &
Strength
Context
(Piccinini, et
al., 2015)
Impact of
digital
transformation
intra-
organisational
challenges,
transformation
through digital
innovation and
emergence of
physical-
digital
paradoxes.
Digital
transformatio
n, digital
innovation,
automotive
organisations
Primary data,
Inductive
Delphi survey,
19
respondents
from
automotive
expert.
Study on
Digital
transformatio
n in
automotive
industry but
findings can
be applied to
other
industries
that must rely
on physical
core.
Six
different
automotiv
e firms in
Germany
(Grover, et
al., 2020)
Utilisation of
AI in operation
management
will increase
efficiency,
return on
investment,
quality and
employee
empowerment.
Artificial
Intelligence,
Technology
adoption,
Operation
management,
Job fit,
Perceived
consequences.
Primary data,
Academic
literature
review from
181 selected
research
article, Social
media
analytics.
Research
being conduct
on published
with limited
source.
Scopus
database,
Tweeter.
(Kolbjørnsru
d, et al.,
2017)
Successful of
AI
implementatio
n in
organisation
depend on
management
and all level
engagement is
important.
Artificial
intelligence,
Transformatio
n leadership,
Training
strategies,
Primary data,
Quantitative
survey,
Questionnaire
s from 1,770
managers,
Interview
with 37
senior
executive
Large
geographical
and cultural
being
surveyed.
India,
China,
Brazil,
United
States,
Spain,
France,
Germany,
Australia,
United
Kingdom,
Nordics
and
Ireland.

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Table 3: Artificial intelligence and autonomous vehicle empirical research summary
Author
(Year)
Findings
Variables
Methods
Limitation &
Strength
Context
(Panagiotopoulos
&
Dimitrakopoulos,
2018)
Autonomous
vehicles have
big potential to
enhance
mobility.
Consumers
expectation
should be
carefully
interpreted.
Autonomous
driving,
Perceived
usefulness,
Perceived easy to
use, Perceived
trust, Usage
intentions
Primary data,
Quantitative
survey,
Questionnaires
from 483
respondents.
Autonomous
vehicles are not
available
currently and
survey result rely
on people’s
imagination
regarding the
autonomous cars
in the future.
Adults in
Greece
(Manfreda, et al.,
2019)
Different
factors
considered by
millennials
towards
adoption of
autonomous
vehicles
Autonomous
vehicles, Smart
City, Perceived
technological,
Millennials
Primary data,
Quantitative
survey,
Questionnaires
from 382
respondents.
Research model is
based on
perceptions of
millennials and
the technology is
quite new and
perception of
respondents
might change and
adoption factors
have to be
further study.
Millennials in
Slovenia
(Nordhoff, et al.,
2018)
Acceptance of
autonomous
vehicles are
generally high
especially
respondents
from countries
with lower
GDP.
Autonomous
vehicles,
Perceived
usefulness,
Perceived easy to
use, Perceived
trust.
Primary data,
Quantitative
survey,
Questionnaires
from 7,755
respondents.
Respondents did
not get to see
autonomous
vehicles
physically, no
indication on
capability and
speed which
might bias in
results.
Respondents
from 116
countries.
(Molnar, et al.,
2018)
Trust in
autonomous
vehicle is a
complex and
multi-layered
concept and as
one of an
important
component of
acceptance of
the technology.
Autonomous
vehicles,
Perceived trust,
Age.
Primary data,
Quantitative
survey, Interview,
Questionnaires
from 72
respondents.
Respondents
were asked about
their perceptions
relative to driving
automated
vehicles without
actually driving a
real automated
vehicle.
Michigan,
United States.
(Knauss, et al.,
2017)
Ensure safety
of autonomous
vehicle and
usage of big
data is a
promising
strategy to
solve
complexity of
autonomous
vehicle testing.
Autonomous
vehicle, safety
reliability, virtual
testing, sensors.
Primary data,
Quantitative
survey, Focus
group, Interview
with 26
participants, third
degree 10
literature review.
Amount of testing
has to be expand
with increases
levels of
autonomous
vehicles making
right decisions to
guarantee safety
of passengers.
Sweden

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Table 4: Artificial intelligence and smart factory empirical research summary
Author
(Year)
Findings
Variables
Methods
Limitation &
Strength
Context
(Chauhan,
et al., 2020)
The complexity
of New product
development
(NPR) can be
reduce with AI
application to
gain competitive
advantages.
Smart
Factory, New
product
development
(NPD), NPD
risk factor.
Primary data,
Quantitative
survey,
questionnaire
from 348
respondents
across
automotive
organisations.
Analysis
performed
was based on
data obtained
from Indian
automotive
industry.
Wider
generalized of
the research is
applicable.
26
automotive
organisation
in India
(Jerman, et
al., 2019)
Smart factory
technologies
required high
level of technical
knowledge in
order to
transform from
traditional
factory to smart
factory.
Smart factory,
competencies,
industrial 4.0,
job profile
Primary data,
Quantitative
survey,
Interview
with 14
participants
Research does
not allow
statistical
generalization
to the
population.
Expert from
government,
education
and
members of
Slovenian
Automotive
Cluster,
Slovenia
(Li, et al.,
2019)
Big data is a key
component for
smart factory
implementation
but usage of big
data analytical
tools can be
fraught with
challenges and
problematic.
Smart factory,
Big data,
Information
systems,
Barriers.
Primary data,
Quantitative
survey, In-
depth
semi-
structured
interviews
with 10 highly
experienced
SAP
consultant
Research was
done based on
interview
with small
(but
experienced)
group of SAP
consultants
with context
of specific
manufacturin
g sector and
countries.
China

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Table 5: Artificial intelligence and marketing & sales empirical research summary
Author
(Year)
Findings
Variables
Methods
Limitation &
Strength
Context
(Jarek &
Mazurek,
2019)
AI application in
marketing is
currently
implemented at
operational level
but it is no doubt
that AI will impact
on functional of
marketing.
Artificial
intelligence,
Marketing, AI
applications, AI
implications
Secondary data,
validate of
selected
examples.
People with the
right knowledge
about AI could
design and
implementation
of new marketing
solutions
Internet
marketing
portal
(Shahid & Li,
2019)
Marketing
landscape has
been transformed
by AI as it keeps
updating and
employees need
to emergence
innovation.
Artificial
intelligence,
Marketing, AI
implications, AI
integration.
Primary data,
Quantitative
survey,
Interviews with
10 marketing
experts.
Secondary data
referring to
articles,
journals, books,
blogs and
website.
Research was
done based on
interview with
small group of
marketing
experts.
Marketing
firm in
Karachi,
Pakistan
(Khokhar &
Narang, 2019)
Edge over
competitors with
changes in
marketing
increased the
market
competition.
Artificial
intelligence,
Marketing,
Traditional
marketing.
Primary data,
Quantitative
survey,
questionnaire
from 200
respondents.
AI will be the
future of
marketing, but
there is some
traditional
marketing
method which
cannot be
replaced by AI.
India.
2.3 Conceptual Framework and Hypotheses

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2.3.1 Impact of Leadership Change on Artificial Intelligence (H1)
Empirical research by (Fleming, 2020) found that organisations are demanding their
leaders to adapt their workforces to relocate capital while delivering profit as the unique
capability of AI to increase employee productivity by rebalancing resources, workforce
reskill investment, advancing new models education and lifelong learning. AI is enabling
the world connect to digital network at broad with stunning speed and this required
business leaders to evolve their leadership skills rapidly and radically. Leader need to build
cooperative and cross-segment teams with continually expanding their knowledge base to
improve their leadership capability to lead across borders that could fit into culture
smoothly and with nonlinear mind-set that involve in certain level of discomfort (Caswell,
et al., 2017). (Fullerton, et al., 2018) research founds that ideal leaders working on artificial
intelligence should processes the ability to lead across border and build something from
nothing with their knowledge.
H1: There is a positive and significant impact of Leadership Change on Artificial
Intelligence (AI)
2.3.2 Impact of Autonomous Vehicle on Artificial Intelligence (H2)
(Sagar & Nanjundeswaraswamy, 2019) AI has become an absolute necessity to make
autonomous vehicles function properly and safely with the help of advanced sensors,
Fuzzy-Neutral Vehicles System Control and Cascaded Neural Network. Thousands of road
accident take place globally every year, AI as paves way to incorporating all active and
passive sensors could mitigate all these human errors and bring out the advantages such as
fuel efficiency, user’s comfortability and convenience. (Li, et al., 2018), artificial intelligence
which provide human-level intelligence for Vehicle (AIV) will be a boosting factor in the
automotive industry. There are mainly three major functionality categories of AI in
autonomous vehicles, (1) World models which serve as a map, (2) Planning and decision-
making which control on the path and (3) Motion planning and computing platform which
will help to detect pedestrians and identify lanes.
H2: There is a positive and significant impact of Autonomous Vehicle on Artificial
Intelligence (AI)
2.3.3 Impact of Smart Factory on Artificial Intelligence (H3)
Efficiency improvement from all aspects by smart factories have the potential to transform
the whole value chain of the automotive manufacturing. By achieving new levels of
efficiency and productivity, costs are being reduced and new revenue opportunities are
being revealed to boost the operating profit of automotive manufacturer (Gill, et al., 2018).
Although AI’s machine learning is being used in smart factory as strategic planning, data-
driven resources allocation and automation to increase production efficiency while
emphasizes on sustainability and eco-consciousness, AI for smart factory is not only
increasing manufacturing efficiency, AI is being used to achieve safety-oriented of robots
and human to avoid collisions and injuries at workplace (Nichols, 2018).

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H3: There is a positive and significant impact of Smart Factory on Artificial Intelligence
(AI)
2.3.4 Impact of Marketing & Sales on Artificial Intelligence (H4)
(Avinaash & Jayam, 2018) pointed out in their journal that marketer could make customer
more satisfied and loyal to the brand by accessing AI algorithms that improve personalizing
marketing techniques. Automated social media marketing and ad-targeting enhance
marketer’s marketing strategies, product planning and customer experience by gaining
competitive advantage and strengthen customer relationships based on customer’s
personalised preferences. AI as the heart of future-proofing technology should be deployed
in all area of marketing & sales including operations. It will significantly help automotive
OEMs not only to defend their market leadership, but also to win more customers by
offering personalised services to their customers. Sales forecasting, vehicle configuration
and distribution could be optimised with the help of AI (Grühn, et al., 2019)
H4: There is a positive and significant impact of Marketing & Sales on Artificial
Intelligence (AI)
3.0 RESEARCH DESIGN AND METHODOLOGY
3.1 Research Paradigm
Positivism Paradigm
This research will adopt positivism paradigm as it is the most appropriate method since
this research required to produce and collect big amount of quantitative data which will
then be analyse. With proper planning, structured design and guideline, it will be less space
for error and provide truthful facts where information is coming from empirical
methodology and unbiased from human influence (Crotty, 1998). With most suitable
framework for social world investigation, positivism offered scientific methods, techniques
and procedures in natural sciences to establish truth and reality as science is the only
foundation for true knowledge (Bryman & Bell, 2011).
3.2 Research Approach
Quantitative Approach
The primary research method for this research will be based on deductive approach and
hypothesis for impact of artificial intelligence to be support by current available theories. A
research can be completed speedily by implementing deductive approach within adequate

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time. As the research is depending on quantitative approach, numerical data and statistics
are important in order to produce diathesis conclusion.
3.3 Research Design and Data Collection Method
Primary Data
Due to research topic of artificial intelligence still a new domain, availability of secondary
information is rather limited. Thus, this research will be carry out using primary data
collection methods as it is the most suitable and appropriate way to perform analysis on
relationships between the variables of this research topic and awareness will be created
upon current literature gaps due to the continuous evolving development within the
artificial intelligence domain (Sekaran & Bougie, 2016). This research of impact of artificial
intelligence in automotive industry transformation will adopt quantitative research by
primary data collection by questionnaires as criterial to evaluate the correlation and
complexity being better explored from the huge data and response rate (Williams, 2007).
3.4 Data Collection Instruments
This research used self-developed questionnaire as data instrument to collect data related
to respondent’s opinion. There are two parts in the questionnaire where first part is
respondents’ demographic information of target sampling and second part include all
variables for the impact of artificial intelligence in automotive industry transformation. All
the self-developed questions were formulated in relation to conduct research and analyse
the hypothesis in 5-point Likert scale (Likert, 1932) option: Strongly Disagree, Disagree,
Neutral, Agree and Strongly Agree for respondents to response. Individual respondent
score is determined by adding the point values of all their statements.
3.5 Population and Sampling
Stratified random sampling is chosen for this research as the technique of this sampling
able to fulfil all parts of the population are represented for efficiency improvement as it
able to gain precision on the estimates of characteristics of the whole population (Brewer,
1999). To fulfil the request of this research, participants for this research is open to all
automotive and non-automotive related industry to measure the impact of artificial
intelligence on participant work-life and as the penetration of artificial intelligence is so
comprehensive and all-around. The self-designed questionnaire using simple yet easy to
understand sentence to ensure participants are able to fully understand the question to

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ensure data consistency. For this research questionnaires will be sent to 250 respondents
to ensure data consistency via online questionnaire and received 160 completed responses.
3.6 Data Analysis Plan
Statistical Package for the Social Science (SPSS) software will be used after completion of
data collection. Data collected from questionnaires were coded using SPSS to establish
relationship among hypothesises and variables in this research, performing descriptive and
normality analysis, conducting reliability analysis and regression test (Arkkelin, 2014).
4.0 RESULT, ANALYSIS AND DISCUSSION
4.1 Demographics Analysis
Demographic data are collected by researchers routinely to describe the sample of people
or organisations in their research and the demographic data should demonstrate the
respondent’s appropriateness for the research statistically (Connelly, 2013).
Table 6: Demographic Attributes of Respondents
Variable
Frequency
Percentage
Cumulative
Percentage
Gender
Female
78
48.8%
48.8%
Male
82
51.3%
100.0%
Age Group
25 - 35 years
63
39.4%
39.4%
36 - 40 years
41
25.6%
65.0%
41 - 50 years
42
26.3%
91.3%
51 years and above
7
4.4%
95.6%
Below 25 years
7
4.4%
100.0%
Nationality
Malaysian
152
95.0%
95.0%
Non-Malaysian
8
5.0%
100.0%
Education Level
Bachelor Degree
91
56.9%
56.9%
College (Certificate / Diploma)
35
21.9%
78.8%
High School (SPM / STPM / O Level / A Level)
14
8.8%
87.5%
Postgraduate (Masters / PhD)
20
12.5%
100.0%
Designation
Assistant Manager / Manager / Senior Manager
61
38.1%
38.1%
Executive / Senior Executive / Supervisor / Team Leader
55
34.4%
72.5%
General Manager and above
17
10.6%
83.1%
Non-Executive / Operator / Administrator
27
16.9%
100.0%
Business Nature
Automotive related (including motorcycle)
77
48.1%
48.1%
Non-Automotive related
83
51.9%
100.0%
Annual Income
RM100,001 and above
52
32.5%
32.5%

Page 15
RM18,000 and below
14
8.8%
41.3%
RM18,001 - RM48,000
24
15.0%
56.3%
RM48,001 - RM84,000
40
25.0%
81.3%
RM84,001 - RM100,000
30
18.8%
100.0%
Service Year with
Current Organisation
11 - 15 years
22
13.8%
13.8%
15 years and above
19
11.9%
25.6%
2 - 5 years
50
31.3%
56.9%
6 - 10 years
47
29.4%
86.3%
Less than 2 years
22
13.8%
100.0%
4.2 Descriptive and Normality Analysis
The table below indicates the distribution with respect to impact of artificial intelligence in
automotive industry. The mean data is range between 2.662 to 4.506 and standard
deviation range between 0.7534 to 1.2595. As the result, it can be concluded that most
respondents consent with the association amongst the concepts and the distribution is
fairly normal with acceptable deviation range. To test the data normality of this research,
skewness and kurtosis statistics are selected to determine the normality. Skewness is a
measure of the asymmetry of the distribution of a variable and (West, et al., 1995) suggest
that if skew value > 2, the reference of substantial is departure from normality. Kurtosis is a
measure of the flat-toppedness of a distribution and if the value is >7, it is suggested that
reference of substantial departure from normality (Kim, 2013). Based on the 160
responses collected as shown at table below, Skewness statistical numbers ranging from -
1.324 to 0.083 and Kurtosis statistical numbers ranging from -1.038 to 0.964. It is
justifiable to conclude that the data are normally distributed as Skewness values ranging
between -2 and 2 and Kurtosis values ranging between -7 and 7 are widely acceptable
(Hair, et al., 2010).
Table 7: Descriptive Statistics and Normality Analysis
Descriptive Statistics
N
Minimum
Maximum
Mean
Std.
Deviation
Skewness
Kurtosis
Statistic
Statistic
Statistic
Statistic
Statistic
Statistic
Std.
Error
Statistic
Std.
Error
AI1
160
1.0
5.0
3.463
1.0867
-.439
.192
-.336
.381
AI2
160
1.0
5.0
3.744
.9921
-.874
.192
.444
.381
AI3
160
1.0
5.0
3.869
.9392
-.841
.192
.964
.381
AI4
160
1.0
5.0
3.262
1.0186
-.113
.192
-.395
.381
AI5
160
2.0
5.0
3.669
.8144
-.028
.192
-.556
.381

Page 16
LC1
160
1.0
5.0
2.662
1.0925
.529
.192
-.277
.381
LC2
160
1.0
5.0
3.338
1.1486
-.263
.192
-.675
.381
LC3
160
1.0
5.0
3.663
1.1153
-.512
.192
-.423
.381
LC4
160
1.0
5.0
3.650
1.0885
-.362
.192
-.599
.381
LC5
160
1.0
5.0
3.069
1.2595
-.188
.192
-1.038
.381
AV1
160
1.0
5.0
3.569
.9425
-.315
.192
-.604
.381
AV2
160
1.0
5.0
3.256
1.1341
-.179
.192
-.706
.381
AV3
160
1.0
5.0
3.200
1.0389
.067
.192
-.460
.381
AV4
160
1.0
5.0
3.400
1.0943
-.266
.192
-.449
.381
AV5
160
1.0
5.0
3.356
1.1674
-.272
.192
-.659
.381
SF1
160
2.0
5.0
4.088
.7721
-.484
.192
-.266
.381
SF2
160
2.0
5.0
4.069
.7534
-.382
.192
-.391
.381
SF3
160
2.0
5.0
4.175
.8434
-.724
.192
-.259
.381
SF4
160
1.0
5.0
3.988
.8008
-.424
.192
.058
.381
SF5
160
1.0
5.0
4.188
.9261
-1.105
.192
.888
.381
MS1
160
1.0
5.0
3.262
1.2413
-.252
.192
-.995
.381
MS2
160
2.0
5.0
4.113
.8467
-.595
.192
-.454
.381
MS3
160
2.0
5.0
3.794
.8549
-.262
.192
-.564
.381
MS4
160
1.0
5.0
4.131
.8840
-.869
.192
.618
.381
MS5
160
2.0
5.0
4.506
.7770
-1.324
.192
.534
.381
AI
160
1.4
5.0
3.601
.6897
-.109
.192
-.214
.381
LC
160
1.4
5.0
3.276
.8192
-.096
.192
-.293
.381
AV
160
1.2
5.0
3.356
.7769
.083
.192
-.122
.381
SF
160
2.6
5.0
4.101
.5789
-.410
.192
-.284
.381
MS
160
2.6
5.0
3.961
.5478
-.235
.192
-.285
.381
Valid N
(listwise)
160
4.3 Reliability Analysis
The reliability analysis of this study measured using Cronbach’s Alpha values which
provide the internal consistency of a test or scale measurement expressed as a number
between 0 and 1 (Tavakol & Dennick, 2011). According to Cronbach’s Alpha, the nearer the
value to 1, the higher the reliability as it equivalents to high internal consistency. Table
below indicates the Cronbach’s Alpha value for all variables as 0.874, artificial intelligence
as 0.751, leadership change as 0.764, autonomous vehicles as 0.768, smart factory as 0.747
while marketing and sales as 0.521. The coefficients for all variables where the strength of
association is mostly good and only marketing and sales is neutral.

Page 17
Table 8: Reliability Analysis
Reliability measurement
Research of 160 respondents
Strength of
association
Number of items
Cronbach’s Alpha
All variables
25
0.874
Good
Artificial Intelligence
5
0.751
Good
Leadership Change
5
0.764
Good
Autonomous Vehicles
5
0.768
Good
Smart Factory
5
0.747
Good
Marketing and Sales
5
0.521
Neutral
4.4 Assumption of Regression
4.4.1 Analysis of Autocorrelation
The autocorrelation of this study is tested using Durbin-Watson to identify the values
during a certain time period and forecast. Durbin-Watson statistic value range from 0 to 4.
It is a positive autocorrelation if the value towards zero and conversely, it is a negative
autocorrelation if the value towards 4 (Durbin & Watson, 1950). The values in between 1.5
to 2.5 is categorised as normal but researchers need to be alert if the value become lower
than 1 or higher than 3 as it might lead to the result of regression analysis are less likely to
be reliable (Saunders, et al., 2016). Tables below indicates Durbin-Watson statistic for all
dependent variables are ranged between 1.77 to 2.347 which indicating there is no
autocorrelation among residuals.
Table 9: Artificial intelligence and leadership change autocorrelation analysis
Model Summaryb
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
Durbin-Watson
1
.508a
.258
.254
.7077
1.836
a. Predictors: (Constant), AI
b. Dependent Variable: LC

Page 18
Table 10: Artificial intelligence and autonomous vehicle autocorrelation analysis
Model Summaryb
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
Durbin-Watson
1
.476a
.226
.221
.6856
1.893
a. Predictors: (Constant), AI
b. Dependent Variable: AV
Table 11: Artificial intelligence and smart factory autocorrelation analysis
Model Summaryb
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
Durbin-Watson
1
.460a
.211
.206
.5157
2.347
a. Predictors: (Constant), AI
b. Dependent Variable: SF
Table 12: Artificial intelligence and marketing & sales autocorrelation analysis
Model Summaryb
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
Durbin-Watson
1
.483a
.234
.229
.4811
1.770
a. Predictors: (Constant), AI
b. Dependent Variable: MS
4.4.2 Analysis of Multicollinearity
The multicollinearity of this study is tested by reviewing the collinearity statistics tolerance
and Variance Inflation Factor (VIF). The rule of thumb is that high correlations which
generally 0.90 and above shows substantial collinearity (Hair, et al., 2010). Another
methodology to check and measure the presence of multicollinearity is the Variance
Inflation Factor (VIF) where VIF value with 10 or above indicates high collinearity
(Saunders, et al., 2016). The coefficients tables below indicate that all tolerance and VIF
value of 1.000. It indicates no multicollinearity and it is therefore concluded the
assumption of no correlation between independent variables.

Page 19
Table 13: Artificial intelligence and leadership change multi-collinearity analysis
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
Collinearity
Statistics
B
Std. Error
Beta
Toleranc
e
VIF
1
(Constant)
1.102
.298
3.694
.000
AI
.604
.081
.508
7.419
.000
1.000
1.000
a. Dependent Variable: Leadership Change
Table 14: Artificial intelligence and autonomous vehicle multi-collinearity analysis
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
Collinearity
Statistics
B
Std. Error
Beta
Toleranc
e
VIF
1
(Constant)
1.427
.289
4.937
.000
AI
.536
.079
.476
6.797
.000
1.000
1.000
a. Dependent Variable: Autonomous Vehicle
Table 15: Artificial intelligence and smart factory multi-collinearity analysis
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
Collinearity
Statistics
B
Std. Error
Beta
Toleranc
e
VIF
1
(Constant)
2.712
.217
12.474
.000
AI
.386
.059
.460
6.506
.000
1.000
1.000
a. Dependent Variable: Smart Factory

Page 20
Table 16: Artificial intelligence and marketing & sales multi-collinearity analysis
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
Collinearity Statistics
B
Std. Error
Beta
Tolerance
VIF
1
(Constant)
2.578
.203
12.714
.000
AI
.384
.055
.483
6.943
.000
1.000
1.000
a. Dependent Variable: Marketing & Sales
4.4.3 Analysis of Normality of the Dependent Variable
(Saunders, et al., 2016) mentioned that when data value for each quantitative variable
being normally distributed, variables in a symmetrical pattern will form a bell-shaped
frequency distribution. Refer to this research, it shows that all dependent variables
distribution pattern histogram is normal as below.
Figure 1: Leadership change histogram for normality of standardized residuals

Page 21
Figure 2: autonomous vehicle histogram for normality of standardized residuals
Figure 3: Smart factory histogram for normality of standardized residuals

Page 22
Figure 4: Marketing & sales histogram for normality of standardized residuals
4.4.4 Analysis of Normality of the Residuals
As refer to graph below, this study shows a strong linear relationship between the
dependent variable and independents variables.

Page 23
Figure 5: Leadership change linear relationship Figure 6: Autonomous vehicle linear relationship
Figure 7: Smart factory linear relationship Figure 8: Marketing & sales linear relationship
4.4.5 Analysis of Homoscedasticity
Referring to figures for this research as below, the scatter plots for dependent variable are
equally distributed without any specific or fix distribute pattern. It shows artificial
intelligence has a positive correlation with dependent variables. It also demonstrates the
normality where standard residuals are distributed equally

Page 24
Figure 9: Leadership change Scatter plot for Homoscedasticity Analysis
Figure 10: Autonomous vehicle Scatter plot for Homoscedasticity Analysis

Page 25
Figure 11: Smart factory Scatter plot for Homoscedasticity Analysis
Figure 12: Marketing & sales Scatter plot for Homoscedasticity Analysis

Page 26
4.5 Regression Analysis
4.5.1 Model Fitness
Based on below table where leadership change as dependent variable, the R square is
valued at 0.258. It can be concluded this is not a good fit model as the R Square is weak
where the data is 25.8% towards the coefficient.
Table 17: Artificial intelligence and leadership change model summary
Model Summaryb
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
Durbin-Watson
1
.508a
.258
.254
.7077
1.836
a. Predictors: (Constant), AI
b. Dependent Variable: LC
For autonomous vehicle as dependent variable, below table indicated that the R square is
valued at 0.226. It can be concluded this is not a good fit model as the R Square is weak
where the data is 22.6% towards the coefficient.
Table 18: Artificial intelligence and autonomous vehicle model summary
Model Summaryb
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
Durbin-Watson
1
.476a
.226
.221
.6856
1.893
a. Predictors: (Constant), AI
b. Dependent Variable: AV
Based on below table where smart factory as dependent variable, the R square is valued at
0.211. It can be concluded this is not a good fit model as the R Square is weak where the
data is 21.1% towards the coefficient.
Table 19: Artificial intelligence and smart factory model summary
Model Summaryb
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
Durbin-Watson
1
.460a
.211
.206
.5157
2.347
a. Predictors: (Constant), AI
b. Dependent Variable: SF
For marketing and sales as dependent variable, below table indicated that the R square is
valued at 0.234. It can be concluded this is not a good fit model as the R Square is weak
where the data is 23.4% towards the coefficient.

Page 27
Table 20: Artificial intelligence and marketing & sales model summary
Model Summaryb
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
Durbin-Watson
1
.483a
.234
.229
.4811
1.770
a. Predictors: (Constant), AI
b. Dependent Variable: MS
4.5.2 Model Significance
Table 21: Artificial intelligence and leadership change Model Significance- ANOVA
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
27.568
1
27.568
55.038
.000b
Residual
79.141
158
.501
Total
106.710
159
a. Dependent Variable: LC
b. Predictors: (Constant), AI
The ANOVA table above explained the artificial intelligence as independent variables impacted the
leadership change as dependent variable. The significant value indicates 0.000 and the F- Statistic is
55.038 indicated 55.04% of probability results would be similar if this study conducted by other
researchers. Therefore, it is concluded that this regression model is significant and relevance of this
model.
Table 22: Artificial intelligence and autonomous vehicle Model Significance- ANOVA
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
21.716
1
21.716
46.205
.000b
Residual
74.258
158
.470
Total
95.974
159
a. Dependent Variable: AV
b. Predictors: (Constant), AI
Table 23: Artificial intelligence and smart factory Model Significance- ANOVA
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
11.257
1
11.257
42.324
.000b

Page 28
Residual
42.023
158
.266
Total
53.280
159
a. Dependent Variable: SF
b. Predictors: (Constant), AI
Table 24: Artificial intelligence and marketing & sales Model Significance- ANOVA
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
11.155
1
11.155
48.203
.000b
Residual
36.565
158
.231
Total
47.720
159
a. Dependent Variable: MS
b. Predictors: (Constant), AI
4.5.3 Hypothesis Testing
Artificial intelligence has significant impact on leadership change as Beta Coefficients is
0.508 with significant value of 0.000 as shown in below table.
Table 25: Leadership Change Hypothesis Testing Beta Coefficients
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t
Sig.
Collinearity Statistics
B
Std. Error
Beta
Tolerance
VIF
1
(Constant)
1.102
.298
3.694
.000
AI
.604
.081
.508
7.419
.000
1.000
1.000
a. Dependent Variable: LC
Table below indicate that artificial intelligence has significant impact on autonomous
vehicle as Beta Coefficients is 0.476 with significant value of 0.000.
Table 26: Autonomous vehicle Hypothesis Testing Beta Coefficients
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t
Sig.
Collinearity Statistics
B
Std. Error
Beta
Tolerance
VIF
1
(Constant)
1.427
.289
4.937
.000
AI
.536
.079
.476
6.797
.000
1.000
1.000
a. Dependent Variable: AV

Page 29
Table below indicate that artificial intelligence has significant impact on smart factory
where Beta Coefficients indicate value of 0.460 with significant value of 0.000.
Table 27: Smart Factory Hypothesis Testing Beta Coefficients
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
Collinearity
Statistics
B
Std. Error
Beta
Tolerance
VIF
1
(Constant)
2.712
.217
12.474
.000
AI
.386
.059
.460
6.506
.000
1.000
1.000
a. Dependent Variable: SF
Artificial intelligence has significant impact on marketing and sales as Beta Coefficients
value indicate 0.483 with significant value of 0.000 as shown in below table.
Table 28: Marketing & Sales Hypothesis Testing Beta Coefficients
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
Collinearity
Statistics
B
Std. Error
Beta
Tolerance
VIF
1
(Constant)
2.578
.203
12.714
.000
AI
.384
.055
.483
6.943
.000
1.000
1.000
a. Dependent Variable: MS
4.5.4 Summary of Findings
Table 29: Hypotheses Acceptance and Rejection Summary
Hypotheses
Sig. value
(P ≤0.05)
Beta
Coefficient
Result
Interpretation
H1: AI has
significant
impact on
leadership
change
0.000
0.508
Accepted
H1 is accepted as artificial
intelligence has 50.8% positive
impact on leadership change with
0.000 p-value. This shows that AI
impact on leadership change is
significant.
H1: AI has
significant
impact on
autonomous
vehicle
0.000
0.476
Accepted
H2 is accepted as artificial
intelligence has 47.6% positive
impact on autonomous vehicle with
0.000 p-value. Therefore, AI impact
on autonomous vehicle is vital.
H1: AI has
significant
impact on
0.000
0.460
Accepted
H3 is accepted as artificial
intelligence has 46.0% positive
impact on smart factory with 0.000 p-

Page 30
smart
factory
value. This shows that AI impact on
smart factory is significant.
H1: AI has
significant
impact on
marketing
and sales
0.000
0.483
Accepted
H4 is accepted as artificial
intelligence has 48.3% positive
impact on marketing and sales with
0.000 p-value. The impact of AI on
marketing and sales is significant.
4.6 Discussion
Table 27 indicate all hypotheses in this research were accepted with beta coefficient values
ranging from 0.460 to 0.508 with significant value of 0.000 and VIF of 1.000 for which
denotes accuracy for all models. The purpose of this research is to examine the impact of
artificial intelligence on leadership change, autonomous vehicle, smart factory and
marketing & sales for automotive industry. Leadership change obtained good reliability
score of 0.764 with mean value of 3.276 which indicates most of the respondents have
acknowledge the impact of artificial intelligence on leadership change. Autonomous vehicle
obtained good reliability score of 0.768 with mean value of 3.356 which translates that
most of the respondents have a positive impression on autonomous vehicle.
Multicollinearity analysis was done to ensure consistency with VIF score 1.000 which
indicate accuracy of the model. The reliability score for smart factory with score 0.747 and
mean value of 4.101 indicate the positive impact of artificial intelligence on smart factory
by most of the respondents. The accuracy of the model of being acknowledge with VIF
score of 1.000. Marketing and sales obtained a good reliability score of 0.521 with a mean
of 3.961 which translates to most of the participants have a positive impression on the
impact of artificial intelligence on marketing and sales where the VIF score was 1.000
which denotes accuracy of the model. The empirical validate model is summarised as
below with significant effects results together with the beta coefficients values.
Artificial Intelligence
(AI)
Leadership Change
Autonomous Vehicles
Smart Factory
Marketing & Sales
H1 0.508 (7.419)
H2 0.476 (6.797)
H3 0.460 (6.506)
H4 0.483 (6.943)

Page 31
Figure 13: Empirically validated model- coefficients
5.0 CONCLUSION
This study has proven that artificial intelligence in the context of automobiles is not only
related to self-driving cars. Artificial intelligence has much significant impact on the entire
foundation and supply chain flow of automotive industry (Zaki, 2019). Artificial
intelligence is being optimised and used in automotive industry (Lin, et al., 2018). Although
AI implementation would eliminate some of the existing job roles, but it is creating new job
roles as well. AI application such as Internet of Things (IoT), cloud computing and big data
will improve efficiency of manufacturing process by reducing waste, producing parts with
better quality and offer order-to-made to fulfil increasing demanding consumers. Workers
in manufacturing need to enhance their knowledge in order to become skilful workers
while automotive organisation as industry pioneer for technology adoption will justify the
return on investment towards smart factory. As artificial intelligence is being pursued and
developed by multinational and governmental organisations and its potentials are yet to be
fully discover especially in automotive industry, it is crucial that artificial intelligence
applications are being develop with appropriate ethic and legal framework which
compliance with Personal Data Protection Act and respective laws.
REFERENCES
Adamashvili, N. & Fiore, M., 2017. Investigating the role of business marketing techniques in sales process. European Journal of
Management Issues, 25(3-4), pp. 135-143.
Adeboye, N., Fagoyinbo, I. & Olatayo, T., 2014. Estimation of the Effect of Multicollinearity on the Standard Error for Regression
Coefficients. IOSR Journal of Mathematics, 10(4), pp. 16-20.
Ajzen, I. & Fishbein, M., 1969. The Prediction of Behavioral Intentions in a Choice Situtation. Journal of Experimental Social
Psychology, 5(4), pp. 400-416.
Ajzen, I. & Fishbein, M., 1973. Attitudinal and Normative Variables As Predictors of Specific Behaviors. Journal of Personality
and Social Psychology, 27(1), pp. 41-57.
Akhtar, M. I., 2016. Research Design. Research in Social Science: Interdisciplinary Perspectives, pp. 68-84.
AMA, 2017. American Marketing Association. [Online]
Available at: https://www.ama.org/the-definition-of-marketing-what-is-marketing/
[Accessed 21 June 2020].
Arkkelin, D., 2014. Using SPSS to Understand Research and Data Analysis. Indiana: Psychology Curricular Materials.
Aulakh, P. S. & Kotabe, M., 1993. An Assessment of Theoretical and Methodological Development in International Marketing:
1980-1990. Journal of International Marketing, 1(2), pp. 5-28.
Avinaash, M. & Jayam, R., 2018. Artificial Intelligence - The Marketing Game Changer. International Journal of Pure and Applied
Mathematics, 119(17), pp. 1881-1890.
Bass, B. M. & Avolio, B. J., 1993. Transformational leadership: A response to critiques. In: Leadership theory and research:
Perspectives and directions. s.l.:Academic Press, pp. 49-80.
Bass, B. M. & Riggio, R. E., 2006. Transformational Leadership. 2nd ed. New Jersey: Lawrence Erlbaum Associates, Publishers.
Bell, E. & Bryman, A., 2007. The Ethics of Management Research: An Exploratory Content Analysis. British Journal of
Management, Volume 18, pp. 63-77.
Bera, A. K., Jarque, C. M. & Lee, L.-F., 1984. Testing the Normality Assumption in Limited Dependent Variable Models.
International Economic Review, 25(3), pp. 563-578.

Page 32
Berkovich & Izhak, 2016. School Leaders and Transformational Leadership Theory: Time to Part Ways?. Journal of Educational
Administration, 54(5), pp. 609-622.
Bitner, M. J., Booms, B. H. & Tetreault, M. S., 1990. The Service Encounter: Diagnosing Favorable and Unfavorable Incidents.
Journal of Marketing, 54(1), pp. 71-84.
Blee, L. & O'Brien, J. M., 2019. Marketing. In: Monumental Mobility: The Memory Work of Massasoit. s.l.:University of North
Carolina Press, pp. 161-201.
Booms, B. H. & Bitner, M. J., 1982. Marketing Services by Managing the Environment. Cornell Hotel and Restaurant
Administration Quarterly, 23(1), pp. 35-40.
Brewer, K., 1999. Design-Based or Prediction-Based Inference? Stratified Random vs Stratified Balanced Sampling. International
Statistical Review, 67(1), pp. 35-47.
Brock, J. K.-U. & Wangenheim, F. v., 2019. Demystifying AI: What Digital Transformation Leaders Can Teach You about Realistic
Artificial Intelligence. California Management Review, 61(4), pp. 110-134.
Bryman, A. & Bell, E., 2011. Business Research Methods. 3rd ed. New York: Oxford University Press.
Burgess, A., 2018. The Executive Guide to Artificial Intelligence: How to identify and implement applications for AI in your
organization. 1st ed. London: Springer Nature.
Capgemini Research Institute, 2019. Accelerating automotive's AI transformation: How driving AI enterprise-wide can turbo-
charge organizational value, Paris: Capgemini.
Carroll, L. S. L., 2017. A Comprehensive Definition of Technology from an Ethological Perspective, New York: MDPI.
Caswell, L. J., Coplen, C. E. & Visbal, J. R., 2017. Who's at the wheel? Changing culture and leadership to support innovation in
autonomous vehicles. Voice, pp. 1-4.
Chauhan, A. S., Nepal, B., Soni, G. & Rathore, A. P. S., 2020. Taxonomy of New Product Development Process Risks: An Empirical
Study of Indian Automotive Industry. IEEE Transactions on Engineering Management , pp. 1-12.
Connelly, L. M., 2013. Demographic Data in Research Studies. MedSurg Nursing, 22(4), pp. 269-275.
Coppin, B., 2004. Artificial Intelligence Illuminated. 1st ed. Sudbury: Jones and Bartlett Publishers.
Crotty, M., 1998. The Foundations of Social Research. 1st ed. London: SAGE Publications.
Daoud, J. I., 2017. Multicollinearity and Regression Analysis. Journal of Physics, pp. 1-6.
Daszko, M. & Sheinberg, S., 2017. Survival is Optional: Only Leaders With New Knowledge Can Lead the Transformation. Theory
of transformation FINAL to SHORT, pp. 1-12.
Davenport, T., Guha, A., Grewal, C. & Bressgott, T., 2020. How artificial intelligence will change the future of marketing. Journal
of the Academy of Marketing Science, Volume 48, pp. 24-42.
Davis, F. D., 1985. A Technology Acceptance Model For Empirically Testing New End-User Information System: Theory and
Results, s.l.: s.n.
Davis, F. D., 1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly,
13(3), pp. 319-340.
Davis, F. D., Bagozzi, R. P. & Warshaw, P. R., 1989. User Acceptance of Computer Technology: A Comparison of Two Theoretical
Models. Management Science , 35(8), pp. 982-1003.
Dumas, C. & Beinecke, R. H., 2018. Change Leadership in the 21st Century. Journal of Organizational Change Management,
31(4), pp. 867-876.
Durbin, J. & Watson, G., 1950. Testing for Serial Correlation in Least Squares Regression. I. Biometrika, 37(3), pp. 409-428.
Faber, J. & Fonseca, L. M., 2014. How Sample Size Influences Research Outcome. Dental Press J Orthod, 19(4), pp. 27-29.
Fleming, M., 2020. Harvard Business Review. [Online]
Available at: https://hbr.org/2020/03/ai-is-changing-work-and-leaders-need-to-adapt
[Accessed 31 July 2020].
Friedman, M., 1937. The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. Journal of the
American Statistical Association, 32(200), pp. 675-701.
Fullerton, K., Scanlan, T. & Fayyad, U., 2018. SpencerStuart. [Online]
Available at: https://www.spencerstuart.com/leadership-matters/2018/february/what-ai-means-for-the-human-side-of-hr
[Accessed 31 July 2020]..
Ghahramani, Z. & Jordan, M. I., 1997. Foctorial Hidden Markov Models. Machine Learning, Volume 29, pp. 245-273.
Gilday, P., 2019. Medium. [Online]
Available at: https://medium.com/speedinvest/automotive-transformative-more-than-just-electric-9118078e1bc1
[Accessed 3 July 2020].
Gill, N. et al., 2018. Automotive Smart Factory, Paris: Capgemini Digital Transformation Institute.
Gliem, J. A. & Gliem, R. R., 2003. Calculating, Interpreting, and Reporting Cronbach’s Alpha Reliability Coefficient for Likert-Type
Scales. Ohio, Midwest Research to Practice Conference.
Goi, C. L., 2009. A Review of Marketing Mix: 4Ps or More?. International Journal of Marketing Studies, 1(1), pp. 2-15.
Grewal, D. S., 2014. A Critical Conceptual Analysis of Definitions of Artificial Intelligence as Applicable to Computer Engineering.
IOSR Journal of Computer Engineering, 16(2), pp. 9-13.
Grover, P., Kar, A. K. & Dwivedi, Y. K., 2020. Understanding artificial intelligence adoption in operations management: insights
from the review of academic literature and social media discussions. Annals of Operations Research, pp. 1-37.

Page 33
Grühn, B., Kässer, M. & Köstring, J.-C., 2019. How to Win Tomorrow's Car Buyer- Artificial Intelligence in Marketing & Sales, New
York: McKinsey & Company.
Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E., 2010. Multivariate Data Analysis: A Global Perspective. 7th ed.
s.l.:Pearson.
Hair, J. F. J., Black, W. C., Babin, B. J. & Anderson, R. E., 2010. Multivariate Data Analysis. 7th ed. s.l.:Pearson.
Hair, J. F., Matthews, L. M., Matthews, R. L. & Sarstedt, M., 2017. PLS-SEM or CB-SEM: updated guidelines on which method to
use. International Journal Multivariate Data Analysis, 1(2), pp. 107-123.
Han, L., Wang, S., Zhao, D. & Li, J., 2017. The intention to adopt electric vehicles: Driven by functional and non-functional
values. Transportation Research Part A, 103(1), pp. 185-197.
Harb, Y. & Alhayajneh, S., 2019. Intention to use BI tools: Integrating technology acceptance model (TAM) and personality trait
model. s.l., IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), pp. 494-
497.
Hengstler, M., Enkel, E. & Duelli, S., 2015. Applied artificial intelligence and trust—The case of autonomous vehicles and medical
assistance devices. Technological Forecasting & Social Change, Volume 105, pp. 105-120.
Hermann, D. S., 2019. The CASE is Building for Automotive Display. Kyoto, 2019 26th International Workshop on Active-Matrix
Flatpanel Displays and Devices (AM-FPD).
Hofmann, M., Neukart, F. & Back, T., 2017. Artificial Intelligence and Data Science in the Automotive Industry, New York: arXiv,
Cornell University.
Hofmann, M., Neukart, F. & Back, T., 2017. Data Science Blog. [Online]
Available at: https://data-science-blog.com/blog/2017/05/06/artificial-intelligence-and-data-science-in-the-automotive-
industry/
[Accessed 14 June 2020].
Homburg, C., Jensen, O. & Krohmer, H., 2008. Configurations of Marketing and Sales: A Taxonomy. Journal of Marketing,
Volume 72, pp. 133-154.
Hox, J. J. & Boeije, H. R., 2005. Data Collection, Primary vs. Secondary. Encyclopedia of Social Measurement, Volume 1, pp. 593-
599.
Huang, M.-H., Rust, R. & Maksimovic, V., 2019. The Feeling Economy: Managing in the Next Generation of Artificial Intelligence
(AI). California Management Review, pp. 43-65.
Huitema, B. E. & Laraway, S., 2006. Autocorrelation. In: Encyclopedia of Measurement and Statistics. s.l.:SAGE Publications.
Illa, P. K. & Padhi, N., 2018. Practical Guide to Smart Factory Transition Using IoT, Big Data and Edge Analytics. IEEE Access,
Volume 6, pp. 55162- 55170.
Infosys, 2018. Leadership in the Age of AI: Adapting, Investing and Reskilling to Work Alongside AI, Bengaluru: Infosys Limited.
Jain, M. K., 2013. AN Analysis of Marketing Mix: 7Ps or More. Asian Journal of Multidisciplinary Studies, 1(4), pp. 23-28.
Jarek, K. & Mazurek, G., 2019. Marketing and Artificial Intelligence. Central European Business Review, 8(2), pp. 46-55.
Jarque, C. M. & Bera, A. K., 1987. A Test for Normality of Observations and Regression Residuals. International Statistical
Review, 55(2), pp. 163-172.
Jerman, A., Bach, M. P. & Aleksic, A., 2019. Transformation towards smart factory system: Examining new job profiles and
competencies, s.l.: John Wiley & Sons, Ltd..
Jurafshy, D. & Marin, J. H., 2019. Hidden Markov Models. In: Speech and Language Processing. s.l.:s.n., pp. 1-17.
Kang, E., 2017. Medium. [Online]
Available at: https://medium.com/@kangeugine/hidden-markov-model-7681c22f5b9
[Accessed 27 June 2020].
Kao & Rui-Hsin, 2017. The relationship between work characteristics and change-oriented organizational citizenship behavior: A
multi-level study on transformational leadership and organizational climate in immigration workers. Personnel Review, 46(8),
pp. 1890-1914.Khokhar, P. M. & Narang, C., 2019. Evolution Of Artificial Intelligence In Marketing, Comparison With Traditional
Marketing. Our Heritage, 67(5), pp. 375-389.
Kim, H.-Y., 2013. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. The
Korean Academy of Conservative Dentistry, pp. 52-54.
Kivunja, C. & Kuyini, A. B., 2017. Understanding and Applying Research Paradigms in Educational Contexts. International Journal
of Higher Education, 6(5), pp. 26-41.
Knauss, A., Schröder, J., Berger, C. & Eriksson, H., 2017. Paving the Roadway for Safety of Automated Vehicles: An Empirical
Study on Testing Challenges. California, IEEE Intelligent Vehicles Symposium (IV).
Kolbjørnsrud, V., Amico, R. & Thomas, R. J., 2017. Partnering with AI: how organizations can win over skeptical managers.
Strategy & Leadership, 45(1), pp. 37-43.
Kotler, P., Rackham, N. & Krishnaswamy, S., 2006. Ending the War Between Sales and Marketing, Brighton: Harvard Business
Review.
Kouemou, G. L., 2011. History and Theoretical Basic of Hidden Markov Models. In: P. Dymarski, ed. Hidden Markov Models:
Theory and Applications. Rijeka: InTech, pp. 3-26.
Lee, M., 2014. Transformational Leadership: Is It Time For A Recall?. International Journal of Management and Applied
Research, 1(1), pp. 18-29.

Page 34
Lee, Y., Kozar, K. A. & Larsen, K. R., 2003. The Technology Acceptance Model: Past, Present, and Future. Communications of the
Association for Information Systems , Volume 12, pp. 752-780.
Li, D. & Du, Y., 2017. Artificial Intelligence with Uncertainty. 2nd ed. Boca Raton: CRC Press.
Li, J. C. H., Hongliang, G. & Qiu, S., 2018. Survey on Artificial Intelligence for Vehicles. Automotive Innovation, pp. 1-13.
Likert, R., 1932. A Technique for the Measurement of Attitudes. Archives of Psychology, Volume 22, pp. 5-55.
Lin, D., Lee, C., Lau, H. & Yang, Y., 2018. Strategic response to Industry 4.0: an empirical investigation on the Chinese
automotive industry. Industrial Management & Data System, 118(3), pp. 589-605.
Li, S., Peng, G. C. & Xing, F., 2019. Barriers of embedding big data solutions in smart factories: insights from SAP consultants.
Industrial Management & Data System, 119(5), pp. 1147-1164.
Liu, T. & Lemeire, J., 2017. Efficient and Effective Learning of HMMs Based on Identification of Hidden States. Mathematical
Problems in Engineering, Volume 2017, pp. 1-27.
Li, X., Wong, W., Lamoureux, E. L. & Wong, T. Y., 2012. Are Linear Regression Techniques Appropriate for Analysis When the
Dependent (Outcome) Variable Is Not Normally Distributed?. Investigative Ophthalmology & Visual Science, Volume 53, pp.
3082-3083.
Luckow, A. et al., 2016. Deep learning in the automotive industry: Applications and tools. Washington DC, IEEE Xplore.
Mackenzie, N. & Knipe, S., 2006. Research Dilemmas: Paradigms, Methods and Methodology. Issues in Educational Research,
16(2), pp. 1-13.
Mahraz, M.-I., Benabbou, L. & Berrado, A., 2019. A Systematic literature review of Digital Transformation. Toronto, Proceedings
of the International Conference on Industrial Engineering and Operations Management.
Manfreda, A., Ljubi, K. & Groznik, A., 2019. Autonomous vehicles in the smart city era: An empirical study of adoption factors
important for millennials. International Journal of Information Management, pp. 1-12.
Martin, J., Ruiz de Adana, D. D. & Asuero, A. G., 2017. Fitting Models to Data: Residual Analysis, a Primer. In: Uncertainty
Quantification and Model Calibration. s.l.:Intech Open Science, pp. 134-173.
Mason, C. H. & Perreault, W. D. J., 1991. Collinearity, Power, and Interpretation of Multiple Regression Analysis. Journal of
Marketing Research, 28(3), pp. 268-280.
McCarthy, J., 2007. What is Artificial Intelligence?. pp. 2-15.
McCarthy, J. E., 1960. Basic Marketing: A Managerial Approach. 1st ed. s.l.:Richard D. Irwin, INC..
McKinsey & Company, 2018. Notes from the AI Frontier: Modeling the impact of AI on the World Economy, Brussels: McKinsey
Global Institute.
Mishra, P. et al., 2019. Descriptive Statistics and Normality Tests for Statistical Data. Annals of Cardiac Anaesthesia, 22(1), pp.
67-72.
Molnar, L. J. et al., 2018. Understanding trust and acceptance of automated vehicles: An exploratory simulator study of transfer
of control between automated and manual driving. Transportation Research Part F, Volume 58, pp. 319-328.
Mor, B., Garhwal, S. & Kumar, A., 2020. A Systematic Review of Hidden Markov Models and Their Applications. Archives of
Computational Methods in Engineering, pp. 1-20.
Müller, V. C. & Bostrom, N., 2016. Future Progress in Artificial Intelligence: A Survey of Expert Opinion. In: O. Bueno, ed.
Fundamental Issues of Artificial. Berlin: Springer, pp. 553-571.
Nichols, M. R., 2018. Artificial Intelligence Will Power Smart Factories of The Future, Rockaway: Advantage Business Media.
Nordhoff, S. et al., 2018. Acceptance of Driverless Vehicles: Results from a Large Cross-National Questionnaire Study. Journal of
Advanced Transportation, pp. 1-23.
Panagiotopoulos, I. & Dimitrakopoulos, G., 2018. An empirical investigation on consumers’ intentions towards autonomous
driving. Transportation Research Part C 95, pp. 773-784.
Piccinini, E., Hanelt, A., Gregory, R. W. & Kolbe, L. M., 2015. Transforming Industrial Business: The Impact of Digital
Transformation on Automotive Organizations. Fort Worth, Thirty Sixth International Conference on Information Systems.
Rabiner, L. & Juang, B., 1986. An Introduction to Hidden Markov Models. iEEE ASSP Magazine, pp. 1-13.
Rehme, S. & Rennhak, C., 2011. Marketing and Sales: Successful Peacekeeping. Reutlinger Diskussionsbeiträge zu Marketing &
Management, Volume 11, pp. 1-37.
Rosenzweig, J. & Bartl, M., 2015. A Review and Analysis of Literature on Autonomous Driving. THE MAKING-OF INNOVATION, E-
JOURNAL, pp. 1-57.
Russell, S. j. & Norvig, P., 2010. Artificial Intelligence: A Modern Approach. 3rd ed. New Jersey: Prentice Hall.
Russell, S. J. & Norvig, P., 2010. Artificial Intelligence: A Modern Approach. 3rd ed. New Jersey: Pearson.Sagar, V. D. &
Nanjundeswaraswamy, T., 2019. Artificial Intelligence in Autonomous Vehicles- A Literature Review. i-manager’s Journal on
Future Engineering & Technology, 14(3), pp. 56-62.
Saunders, M., Lewis, P. & Thornhill, A., 2016. Research Methods for Business Students. 7th ed. Essex: Pearson Education
Limited.
Sekaran, U. & Bougie, R., 2016. Research Methods for Business: A Skill-Building Approach. 7th ed. Sussex: John Wiley & Sons
Ltd..
Shahid, M. Z. & Li, G., 2019. Impact of Artificial Intelligence in Marketing: A Perspective of Marketing Professionals of Pakistan.
Global Journal of Management and Business Research: E Marketing, 19(2), pp. 1-8.
Shin, J. et al., 2015. Consumer preferences and willingness to pay for advanced vehicle technology options and fuel types
Transportation Research Part C, Volume 60, pp. 511-524.

Page 35
Slater, L. J., 1962. Regression Analysis. The Computer Journal, 4(4), pp. 287-291.
Soto, T., 2013. Regression Analysis. Encyclopedia of Autism Spectrum Disorders.
Stone, M. et al., 2020. Artificial intelligence (AI) in strategic marketing decision-making: a research agenda. The Bottom Line,
33(2), pp. 183-200.
Sun, Z., 2018. Artificial Intelligence Driven Leadership. PNG UoT BAIS, 3(9), pp. 1-6.
Surbhi, S., 2014. Key Differences. [Online]
Available at: https://keydifferences.com/difference-between-sales-and-marketing.html
[Accessed 17 June 2020].
Tan, L., Song, H. & Fu, P., 2011. The impact of leaders' passion at work on leader effectiveness: The mediating role of
transformational leadership. 2nd International Conference on Artificial Intelligence, Management Science and Electronic
Commerce (AIMSEC), s.n.
Tan, L., Song, H. & Fu, P., 2011. The impact of leaders' passion at work on leader effectiveness: The mediating role of
transformational leadership. Dengleng, 2011 2nd International Conference on Artificial Intelligence, Management Science and
Electronic Commerce (AIMSEC).
Tonidandel, S. & LeBreton, J. M., 2011. Relative Importance Analysis: A Useful Supplement to Regression Analysis. J Bus Psychol,
Volume 26, pp. 1-9
Uyanik, G. K. & Guler, N., 2013. A Study on Multiple Linear Regression Analysis. s.l., Procedia- Social and Behavioral Sciences
106, pp. 234-240.
Venkatesh, V. & David, F. D., 1996. A Model of the Antecedents of Perceived Ease of Use: Development and Test. Decision
Science, 27(3), pp. 451-481.
Wang, P., 2008. What Do You Mean by "AI"?. s.l., First AGI Conference.
Wan, J., Yang, J., Wang, Z. & Hua, Q., 2018. Artificial Intelligence for Cloud-Assisted Smart Factory. IEEE Access, Volume 6, pp.
55419-55430.
West, S. G., Finch, J. F. & Curran, P. J., 1995. Structural equation models with nonnormal variables: Problems and remedies. In:
Structural equation modeling: Concepts, issues, and applications. s.l.:Sage Publications, Inc., pp. 56-75.
Winkelhake, U., 2018. Vision Digitised Automotive Industry 2030. In: The Digital Transformation of the Automotive Industry.
Cham: Springer, pp. 77-126.
Yukl, G., 1999. An Evaluation of Conceptual Weakness in Transformational and Charismatic Leadership Theories. Leadership
Quarterly, 10(2), pp. 285-305.Zaki, M., 2019. Qualcomm. [Online]
Available at: https://www.qualcomm.com/news/onq/2019/02/11/artificial-intelligence-transforming-automotive-industry
[Accessed 13 June 2020].