Knowledge Transfer Factors for Internal Combustion Engine (ICE)

Industry to Electric Vehicle (EV) Industry by

Artificial Intelligent:

Machine Learning

Yinglak Dangjaroen

, Mongkolchai Wiriyapinit

and Sukree Sinthupinyo

Technopreneurship and Innovation Management Program, Graduate School,

Chulalongkorn University, Bangkok, Thailand

Department of Commerce, Chulalongkorn Business School, Chulalongkorn University, Bangkok, Thailand

Department of Computer Engineering, Faculty of Engineer, Chulalongkorn University, Bangkok, Thailand

Keywords: Knowledge Transfer, Knowledge Transfer Factors, EV Transition, Artificial Intelligent, Machine Learning.

Abstract: This study aims to identify the factors influencing knowledge transfer within companies transitioning from

the internal combustion engine (ICE) industry to the electric vehicle (EV) industry through an extensive

literature review. In addition to summarizing findings and proposing strategies for utilizing artificial

intelligence in knowledge transfer, our framework reveals the relevance of three key knowledge transfer

factors and three distinct forms of artificial intelligence, including machine learning, in facilitating knowledge

transfer. These insights can prove invaluable to entrepreneurs operating within the internal combustion engine

automotive sector, offering essential guidance for enhancing the knowledge transfer process and navigating

the transition to the electric vehicle industry. By implementing these strategies, businesses can maintain and

support their competitiveness in this evolving business.

1 INTRODUCTION

An overview of the automobile industry reveals a

consistent decline in sales since 2019, largely

attributed to the COVID-19 pandemic, which

significantly impacted overall economic growth.

Global car sales experienced a sharp decline,

resulting in a substantial 11% reduction in global

sales in 2021 compared to 2019 (Worldwide Car

Sales 2010–2022, Statista Research Department,

2022) (Richter, 2022). Another contributing factor is

the rise in fuel prices, stemming from reduced import

and export activities in major fuel-producing

countries during 2021–2022. This directly impacted

the sales of fuel-based vehicles. Conversely, an

examination of electric vehicle (EV) sales in 2021

revealed a remarkable growth trend, diverging

significantly from internal combustion engine

vehicles (ICE), which rely on gasoline as their

primary fuel source. This shift can be attributed to the

current surge in fuel costs, prompting consumers to

https://orcid.org/0009-0001-6055-6401

https://orcid.org/0009-0008-4959-9225

https://orcid.org/0009-0004-6079-6415

prioritize energy conservation and seek vehicles

frequently. Additionally, EVs have gained popularity

due to their energy-saving advantages (Egbue &

Long, 2012) , superior driving experience compared

to ICE vehicles, and diminishing concerns regarding

mileage per charge (Sovacool & Hirsh, 2009).

Furthermore, several governments in various

countries have shown support by reducing taxes on

electric vehicles. EVs also contribute significantly to

reducing carbon emissions (Hofmann et al., 2016)

and greenhouse gas emissions (Qiao et al., 2020) as

part of efforts to align with the Paris Agreement's

goals for addressing climate change (Horowitz,

2016). Consequently, electric vehicles are

increasingly appealing to consumers. The exponential

growth of various types of electric vehicle trends

worldwide has brought about a revolutionary and

disruptive transformation (Christensen et al., 2013)

within the automotive industry. The conventional

internal combustion engine (ICE) parts

manufacturing industry faces significant pressure to

142

Dangjaroen, Y., Wiriyapinit, M. and Sinthupinyo, S.

Knowledge Transfer Factors for Internal Combustion Engine (ICE) Industry to Electric Vehicle (EV) Industry by Artiﬁcial Intelligent: Machine Learning.

DOI: 10.5220/0012162800003598

In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) - Volume 3: KMIS, pages 142-149

ISBN: 978-989-758-671-2; ISSN: 2184-3228

adapt swiftly in response to rapidly advancing

technologies. Every entrepreneur within this sector

must act promptly to effect necessary modifications

and prevent falling behind. It is noteworthy that all

entrepreneurs in the ICE industry possess substantial

potential, primarily due to their ability to manufacture

automotive components and adhere to highly

systematic and stringent quality, safety, and cost

control procedures. Entrepreneurs in the auto parts

sector require both explicit and tacit knowledge.

Explicit knowledge, which can be documented in

forms such as operation manuals and work standards,

including a knowledge database system stored

electronically, is essential. Additionally, tacit

knowledge, unique to individuals within the

organization, plays a crucial role. This includes

accumulated past working expertise, encompassing

problem-solving techniques developed through

practical experience (Nonaka, 1994). Such invaluable

knowledge assets are integral to assisting ICE

entrepreneurs in adapting to the evolving global

electric vehicle (EV) industry and maintaining their

competitiveness. This paper explores knowledge

transfer factors that facilitate the transition from the

Internal Combustion Engine (ICE) industry to the

Electric Vehicle (EV) industry, leveraging Artificial

Intelligence and Machine Learning.

Knowledge management stands as a crucial

strategy employed to bolster an organization's

adaptability. Emerging from the framework of a

learning organization (Senge, 1990), the concept of

the 'fifth discipline' offers a blueprint for cultivating

organizational learning. This discipline comprises

five fundamental principles that play a pivotal role in

propelling, promoting, and for nurturing the

development of a learning organization: personal

mastery, mental models, shared vision, team learning,

and systematic thinking. This paper explores the

significance of knowledge management in the context

of transitioning from the Internal Combustion Engine

(ICE) industry to the Electric Vehicle (EV) industry,

leveraging Artificial Intelligence and Machine

Learning.

Knowledge transfer within a global organization,

particularly when aligned with the overarching

vision, necessitates a profound comprehension of the

firm, encompassing its sources of knowledge and

internal information storages. This comprehensive

understanding not only expedites but also fosters the

efficient dissemination of knowledge throughout the

organization. Such knowledge dissemination, in turn,

serves as a catalyst for innovation, enhancing the

competitiveness of the organization's products and

corporate services (Rios-Ballesteros & Fuerst, 2022).

Artificial intelligence (AI) technology finds extensive

applications across diverse industries. AI,

characterized by a machine's capacity to learn and

comprehend knowledge across various domains,

including reasoning abilities (Miller, 2019), has

significantly impacted and benefited the commercial

sector. Its ability to enhance efficiency is achieved by

streamlining processes, automating repetitive tasks,

and simplifying complex procedures, originally

designed to enhance precision and overall workforce

productivity (Agrawal et al., 2017). In the realm of

knowledge management, researchers have long

explored knowledge transfer in various contexts.

However, scant attention has been devoted to the

transfer of knowledge from the internal combustion

engine (ICE) industry to the electric vehicle (EV)

industry. More specifically, the integration of AI into

the knowledge transfer process remains an

underexplored area, lacking comprehensive training

and alignment with knowledge management best

practices. Consequently, our study seeks to address a

fundamental question: What factors influence

knowledge transfer from the ICE industry to the EV

industry through the utilization of Artificial

Intelligence?

2 KNOWLEDGE TRANSFER

The authors have conducted a literature review,

which serves as a methodology for synthesizing

knowledge and evaluating the applicability of

findings from significant studies to practical contexts

(Souza et al., 2010).

2.1 Knowledge Transfer Definition

Knowledge transfer refers to the conveyance of

information or knowledge from individual, group, or

organization to another, addressing the knowledge

needs of the recipients. It constitutes a pivotal step

within the realm of knowledge management.

Szulanski (1996) defines knowledge transfer as a

form of communication involving the transmission of

messages from the sender to the recipient. Davenport

and Prusak (1998) offer an intriguing perspective,

considering knowledge transfer as an integral part of

daily work within an organization. Knowledge-

seeking practitioners actively seek sources of

knowledge and information to apply in their work.

Martinkenaite (2011), emphasizes that knowledge

transfer is a vital process aimed at equipping

individuals within an organization with the necessary

knowledge to enhance job practices, foster efficiency,

Knowledge Transfer Factors for Internal Combustion Engine (ICE) Industry to Electric Vehicle (EV) Industry by Artiﬁcial Intelligent:

Machine Learning

143

and bolster the organization's competitiveness.

Moreover, the concept of knowledge transfer extends

to open innovation practices. Olaisen and Revang

(2017), stress the importance of knowledge becoming

valuable when transferred to those who seek to apply

it within the organization. This enhances

organizational learning and overall effectiveness (Al-

Emran et al., 2018). Efficiency in organizational

knowledge transfer hinges on the relevance of the

knowledge, measurable outcomes, and the

organization's ability to learn and absorb information.

Additionally, it must satisfy the recipients' needs

(Bacon et al., 2020). Rios-Ballesteros and Fuerst

(2022), examine knowledge transfer in the context of

international organizations, where it plays a pivotal

role in facilitating and encouraging the efficient

transfer and utilization of innovative developments to

enhance products and corporate services, ultimately

elevating the organization's competitiveness.

Based on the results of the literature review, the

authors have defined knowledge transfer as the

process of transmitting experience and expertise from

a sender to a recipient within a specific context,

empowering the recipient to become an expert within

their own domain.

2.2 Knowledge Transfer Effectiveness

Research studies on knowledge transfer effectiveness

often investigate the indicators of success within the

knowledge transfer context. The authors aim to gather

pertinent research ideas as follows:

Davenport and Prusak (1998) delineated success

indicators for the knowledge transfer process as the

recipient's ability to efficiently apply the transferred

knowledge for the benefit of their organizations,

thereby enhancing their effectiveness in tasks

requiring that knowledge. This efficacy is a direct

outcome of our capacity to communicate and

effectively transfer knowledge. In cases where the

knowledge is less complex, the likelihood of

successful transfer is higher, resulting in improved

organizational efficiency and keep competitiveness

(Argote et al., 2000). Gupta and Govindarajan (2000)

expounded on success indicators for knowledge

transfer, considering them from the perspective of

knowledge transfer recipients. These indicators

encompass the willingness of recipients to embrace

knowledge transfer, their motivational disposition,

their aptitude for absorbing knowledge, absorptive

capacity, and the choice of transmission channels.

Easterby ‐ Smith et al. (2008) described success

indicators in the knowledge transfer process as

contingent on the recipient's ability to effectively

utilize the knowledge in tasks, adapt it to their

working context, and apply it to generate new

knowledge, all aligned with the organization's

objectives. Pérez‐Nordtvedt et al. (2008) identified

four pivotal indicators for the success of knowledge

transfer: comprehension, usefulness, speed, and

economy. Furthermore, success is measured by the

recipient's ability to employ the transferred

knowledge effectively to ensure the success of

assigned projects. Zhu and Xu (2019) emphasized

that the success of knowledge transfer hinges on

organizations' capacity to discover new knowledge

through the application of knowledge by recipients,

ultimately enhancing innovation capabilities and

sustaining competitiveness in the business area. Li

and Zhu (2021) conducted comprehensive research

on the success of knowledge transfer across five

dimensions:

1. The speed of knowledge transfer.

2. The appropriate cost for utilizing knowledge in

the transfer processes.

3. The effectiveness of leveraging knowledge.

4. The validity of the information being transferred.

5. The satisfaction of knowledge recipients.

2.3 Knowledge Transfer for EV Shift

In the current electric vehicle industry landscape,

knowledge and technology have significantly

evolved across various domains critical for electric

car production. Notably, some of this knowledge is

transferable from the internal combustion automobile

industry. The authors have diligently conducted

research, scrutinizing literature across diverse fields

essential for the development and manufacturing of

electric vehicles.

Kumar and Revankar (2017) assert that the

knowledge and technology employed in electric

vehicle production encompass:

1. Energy storage systems, notably batteries.

2. Electric propulsion systems, involving the

conversion of electrical energy into mechanical

energy through a driving system.

3. Microelectronics controllers for electric

propulsion systems, which are crucial for overall

efficiency.

Furthermore, electric vehicle manufacturers,

particularly those focused on electric propulsion,

should possess fundamental knowledge of electrical

engineering. This knowledge is essential for

designing components, such as strategically

positioning permanent magnets in electric motor

drives to meet the desired characteristics of electric

vehicles. (Feng & Magee, 2020) conducted extensive

KMIS 2023 - 15th International Conference on Knowledge Management and Information Systems

144

research on the requisite knowledge and technology

for electric vehicles. They assert that electric vehicle

production necessitates expertise in four key areas:

Energy storage knowledge, encompassing

equipment used for battery energy storage.

2. Charge and discharge expertise, including the

design of infrastructure for charging.

3. Knowledge of drive systems that convert electrical

energy into mechanical energy, such as motors.

4. Proficiency in electric power control devices for

electric propulsion systems, with particular emphasis

on battery management systems.

Bhatti et al. (2021)

conducted noteworthy

research on program development knowledge and

technology, highlighting the importance of collecting

data from various sources, including wireless

connection technology systems (wireless), the

Internet of Things (IoT) system, and artificial

intelligence. These technologies are vital for driving

models, such as autonomous driving systems and

various driver assistance systems, as well as for

monitoring battery and vehicle health. Adu-Gyamfi et

al. (2022) assert that electric vehicles play a pivotal

role in reducing carbon emissions and mitigating

climate change through the use of renewable energy.

Therefore, knowledge of renewable energy is

indispensable in the electric vehicle industry.

Manufacturers need expertise in various fields related

to renewable energy to design equipment used in

electric vehicles and effectively engage in renewable

energy initiatives. Costa et al. (2022) conducted a

study affirming that knowledge of renewable energy

is essential for businesses operating in the electric

vehicle industry. Such knowledge is crucial for the

deployment of devices like meters or smart sensors,

facilitating activities related to renewable energy

systems. This expertise is invaluable for

organizations seeking to enter the electric vehicle

industry.

To sustain their business

from the disruptive

transition to electric vehicles current internal

combustion engine manufacturers must focus on

acquiring knowledge related to automotive design

and manufacturing, which can facilitate a successful

transition to EVs. This transformation includes

considerations such as material selection knowledge,

as discussed by (Czerwinski, 2021) . Additionally,

factors like quality control, lean manufacturing

concepts, automation, and robotics play a crucial role

in this transition. Casper and Sundin (2021) delve into

the challenges and opportunities faced by the

remanufacturing industry as electric vehicles gain a

larger market share compared to internal combustion

engine vehicles. They emphasize the necessity of

investing in knowledge, equipment, and sustainable

solutions. In terms of manufacturer knowledge,

supply chain management is an importance. Beltagui

et al. (2022) explore the convergence of digital

transformation and sustainability in supply chains,

emphasizing the need to carefully assess the impact

of digital technologies on sustainability initiatives.

Through case studies of electric vehicles, they

highlight the importance of this assessment. Creating

an electric vehicle ecosystem also involves

considerations related to environmental and safety

standards in public regulations. explored the

convergence of digital transformation and

sustainability in supply chains, examining their

interplay and potential conflicts. Through case studies

of electric vehicle emphasizes the importance of

carefully assessing the impact of digital technologies

on sustainability initiatives. For making electric

vehicle eco system, also relate to environment and

safety standard in public regulation. Meckling and

Nahm (2019) argue that recent announcements to

phase out internal combustion engine vehicles

primarily serve as a form of political signalling in a

competitive race among countries for leadership in

green industrial advancements in electric vehicles.

They stress the significance of safety standards,

particularly in lithium-ion batteries for automotive

applications, and provide an overview of international

standards and regulations governing safety testing

under various and abusive conditions. This

comprehensive review aims to identify areas for

improvement and boost confidence in the adoption of

electromobility. (Ruiz et al., 2018).

2.4 Artificial Intelligent: Machine

Learning

Computer engineering within the field of information

technology has seen significant development and

widespread adoption across various industries,

including the automotive sector. Artificial

intelligence (AI), which has the capacity to emulate

human cognitive processes, finds extensive

application in the automotive industry. The authors

have conducted a thorough review of the literature

concerning artificial intelligence and the different

types of machine learning. This review forms the

basis for establishing a research framework aimed at

facilitating the transfer of essential knowledge

required for transitioning to the electric vehicle

industry.

The inception of artificial intelligence can be

attributed to a computer scientist who devised a test

to evaluate a computer's ability to answer questions

Knowledge Transfer Factors for Internal Combustion Engine (ICE) Industry to Electric Vehicle (EV) Industry by Artiﬁcial Intelligent:

Machine Learning

145

and engage in conversations under specific

conditions. If humans cannot distinguish whether the

responses come from a person or a computer, it is

deemed that the computer possesses the intelligence

to answer questions autonomously. This test,

famously known as the Turing Test (Turing, 1950),

marked the beginning of artificial intelligence.

Modern interpretations and applications of artificial

intelligence have expanded significantly. Rajkomar et

al. (2018) highlighted the potential of proper artificial

intelligence utilization, enabling rapid development

and providing organizations across various industries

with a competitive edge. Today, nations worldwide

prioritize the use of artificial intelligence as a pivotal

strategy for national development. Lu (2019)

described artificial intelligence as a technology

capable of synthesizing knowledge and information,

encompassing diverse scientific disciplines to

replicate human cognitive processes, including

learning, memory, decision-making, differentiation,

and response to stimuli. In the current landscape,

artificial intelligence systems have access to vast

amounts of network data, leading many industries to

harness this technology to reduce costs and enhance

competitiveness (Cai-Ming & Hao-Nan, 2020).

When applied in educational systems, artificial

intelligence has demonstrated a profound impact on

teaching methodologies and knowledge management

practices (Chen et al., 2020). Individualized

instruction has proven highly effective for learners,

enabling them to take charge of their learning and

decision-making processes (Nilsson, 1982). Machine

learning, categorized into four types - supervised

learning, unsupervised learning, semi-supervised

learning, and reinforcement learning, plays a pivotal

role in this context Janiesch et al. (2021). Machine

learning involves training artificial intelligence

systems to learn from data or algorithms, enabling

them to make decisions based on virtual models and

analyze data to devise problem-solving strategies. It

is important to note that the aforementioned studies

do not delve into the factors influencing knowledge

transfer from the internal combustion engine industry

to the electric vehicle industry through artificial

intelligent machine learning.

The paper, however, does not explore the factors

influencing knowledge transfer from the internal

combustion engine industry to the electric vehicle

industry through artificial intelligent machine learning.

2.5 Knowledge Transfer by Artificial

Intelligent: Machine Learning

Artificial intelligence has significantly contributed to

the business sector, resulting in increased overall

work efficiency, and allowing organizational

members to dedicate more time to the development of

essential and value-added tasks. It has also brought

about notable improvements in knowledge transfer.

Lombardi (2019) suggests that the future of

knowledge transfer research related to organizational

performance and business processes may focus on

exploring emerging and captivating areas, such as the

Internet of Things (IoT), artificial intelligence, big

data, analytics, cyber-security, simulations, digital

integrations, and the broader context of Industry 4.0

environments.

The characteristics of knowledge are closely

related with knowledge transfer and the imperative of

AI machine learning. Taherdoost and Madanchian

(2023) delved into the evolving landscape of

knowledge management within the context of remote

and hybrid work arrangements, underscoring the

pivotal role of artificial intelligence (AI) in

addressing KM challenges. Some studies have

introduced a framework for the application of AI in

tackling training and managerial challenges. They

elucidate how AI can enhance various facets of the

training process, encompassing knowledge

management, needs analysis, training organization,

and feedback. This transformation ultimately

empowers organizations to become knowledge-

driven entities capable of delivering personalized

training and elevating learning quality (Chen, 2022).

Despite the existing research on AI, machine

learning, and knowledge transfer, there remains a

dearth of information pertaining to the application of

knowledge transfer in facilitating the transition from

the internal combustion engine industry to electric

vehicles. This gap in knowledge has made the authors'

interest in investigating the factors that influence this

transition to electric vehicles, leveraging AI and

machine learning to facilitate an efficient.

3 FINDING AND DISCUSSION

Drawing from a comprehensive literature review

encompassing various viewpoints, the authors have

synthesized and consolidated multiple fields, as

outlined in Figure 1. These characteristics,

accompanied by relevant authors, have been

organized based on knowledge transfer components.

The conceptual framework depicted in Figure 1 is

constructed upon these variables. The resultant study

framework will yield knowledge transfer guidelines

applicable to the internal combustion engine industry

across all organizational levels. This framework not

KMIS 2023 - 15th International Conference on Knowledge Management and Information Systems

146

only promotes knowledge development but also

showcases skills and abilities, facilitating the pursuit

of business opportunities and enabling entrepreneurs

to attain a competitive advantage over their

counterparts.

Figure 1: A Conceptual Framework

Literature Review

The comparison between the characteristics of

automotive design and manufacturing knowledge and

the necessity of knowledge transfer through AI and

machine learning is shown in Figure 2.

Figure 2: Characteristics of Knowledge: Literature Review.

4 CONCLUSIONS

This study aims to identify the factors influencing the

transition from internal combustion engine (ICE)

parts production to electric vehicle (EV) component

manufacturing, which presents a significant challenge

for suppliers in terms of knowledge transfer. This

challenge is well-acknowledged in the industry due to

the fundamental differences in technology, materials,

and manufacturing processes required for EVs. Based

on a thorough literature review, the results provide six

criteria outlined in the conceptual framework.

Additionally, the study reveals that these six

characteristics positively impact knowledge transfer

between the ICE and EV industries. The conclusions

drawn from this research have both academic and

practical implications. It contributes to the field of

academic knowledge management and highlights the

factors essential for effective knowledge transfer in

the context of industry transformation. While

knowledge transfer is crucial, specific strategies,

particularly focused on the nature of knowledge

transfer from ICE to EV, are essential. A more in-

depth exploration of this specific knowledge transfer

could offer practical guidance to suppliers.

Identifying key knowledge domains and aligning

them with EV manufacturing processes can provide

valuable insights. Developing strategies tailored to

address specific learning challenges is a valuable

approach, and integrating AI technologies into these

strategies can enhance knowledge transfer efficiency,

especially in complex domains. The expected

practical contributions include enhanced knowledge

transfer support and increased competitiveness for

enterprises in the internal combustion engine vehicle

industry. This can be achieved by harnessing

knowledge transfer and knowledge management as

innovative tools facilitated by machine learning-

based artificial intelligence to improve the efficiency

of the transition to electric vehicle production.

Entrepreneurs involved in the automotive industry

and related businesses can use these factors as

guidance for engineers seeking a competitive

advantage through practice, improvement, and

development. However, it's important to note that the

study is based on a literature review, and further

empirical testing of the conceptual framework is

recommended before practical application.

Knowledge Transfer Factors for Internal Combustion Engine (ICE) Industry to Electric Vehicle (EV) Industry by Artiﬁcial Intelligent:

Machine Learning

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