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Industry 4.0 Entrepreneurship: Essential Characteristics and Necessary Skills



The manufacturing industry is facing a transformation with regard to Industry 4.0 (I4). A transformation towards full automation of production including a multitude of innovations is necessary. Startups and entrepreneurial processes can support such a transformation as has been shown in other industries. However, I4 has some specifics, so it is unclear how entrepreneurship can be adapted in I4. Understanding these specifics is important to develop suitable training programs for I4 startups and to accelerate the transformation. Objective: This study identifies and outlines the essential characteristics and constraints of entrepreneurial processes in I4. Method: 14 semi-structured interviews were conducted with experts in the field of I4 entrepreneurship. The interviews were analysed and categorized by qualitative analyses. Results: The interviews revealed several characteristics of I4 that have a significant impact on the various phases of the entrepreneurial process. Examples of such specifics include the difficult access to customers, the necessary deep understanding of the customer and the domain, the difficulty of testing risky assumptions, and the complex development and productization of solutions. The complexity of hardware and software components, cost structures, and necessary customer-specific customizations affect the scalability of I4 startups. These essential characteristics also require specialised skills and resources from I4 startups.
Industry 4.0 Entrepreneurship:
Essential Characteristics and Necessary Skills
Dario Wahl
Center for Entrepreneurship
Reutlingen University
Reutlingen, Germany
Jürgen Münch
Faculty of Informatics
Reutlingen University
Reutlingen, Germany
AbstractContext: The manufacturing industry is facing a
transformation with regard to Industry 4.0 (I4). A transformation
towards full automation of production including a multitude of
innovations is necessary. Startups and entrepreneurial processes can
support such a transformation as has been shown in other industries.
However, I4 has some specifics, so it is unclear how
entrepreneurship can be adapted in I4. Understanding these specifics
is important to develop suitable training programs for I4 startups and
to accelerate the transformation. Objective: This study identifies
and outlines the essential characteristics and constraints of
entrepreneurial processes in I4. Method: 14 semi-structured
interviews were conducted with experts in the field of I4
entrepreneurship. The interviews were analysed and categorized by
qualitative analyses. Results: The interviews revealed several
characteristics of I4 that have a significant impact on the various
phases of the entrepreneurial process. Examples of such specifics
include the difficult access to customers, the necessary deep
understanding of the customer and the domain, the difficulty of
testing risky assumptions, and the complex development and
productization of solutions. The complexity of hardware and
software components, cost structures, and necessary customer-
specific customizations affect the scalability of I4 startups. These
essential characteristics also require specialised skills and resources
from I4 startups.
KeywordsEntrepreneurship, startups, Industry 4.0, fourth
industrial revolution, entrepreneurship education, skill
development, startups, innovation, economic growth
Startups became a crucial part of modern economies [1].
In the field of I4, startups are not only a powerful driver for
innovation but also have a large impact on the innovation
activities of established companies [2]. However, I4 has
many specifics that could complicate the entrepreneurial
process. It can be assumed that I4 entrepreneurship would
have an even greater impact on the industrial landscape if
these specifics and, above all, their effects on the
entrepreneurial processes were better understood. Founders
and innovation projects in I4 could then be systematically
prepared for the specifics of the domain in order to increase
the chances of successful entrepreneurial activities in I4.
The vision of I4, also referred to as the fourth industrial
revolution, consists of the automation of all production
processes [3]. A key characteristic of I4 is the connection of
different systems and the independent exchange of
information between them [4]. This means, that I4 not only
includes the interaction of technical and physical components
but also the exchange of relevant information and the control
of components in the environment of production or
operations. The focus of I4 is on the core areas of Smart
Products, Smart Factory and Smart Services [5]. The term
Industry 4.0 is defined and discussed quite intensively in the
academic literature. Industry 4.0 is often used as a dictum
without an exact definition and there are no clear boundaries
between the terms Industry 4.0, cyber-physical systems
(CPS) or Industrial Internet of Things [4]. Sendler [6]
describes cyber-physical systems as "networks of interacting
elements with physical input and output". Essential in this
definition is the interaction of sensors, actuators and
corresponding software with mechanical components.
Müller and Härtig [3] distinguish I4 from this definition by
summarizing that Industry 4.0 consists of independent
components, so-called cyber-physical systems, which
cooperate in order to realize the vision of the fourth industrial
revolution of full automation of all production processes.
This includes intelligent machines, storage systems and
operating equipment that independently exchange
information, interact and control each other independently in
order to implement production processes. This definition
illustrates that Industry 4.0 can be seen as a subdomain of
cyber-physical systems, which not only include the
interaction of technical and physical components but also the
targeted exchange of relevant information and the control of
components against the background of a production process.
Some essential characteristics of I4 entrepreneurship have
already been identified in practice and mentioned in the
literature. For example, since I4 startups usually focus on
problem areas within industrial companies, tapping into such
fields can only be achieved through deep insights and a good
understanding of the customer and its processes [7]. It is
challenging to discover problems externally or through
experience, as is often possible with consumer goods or other
B2C products. In an I4 environment, the product often
consists of a complex construct of hardware, software and
data processing, leading to complexity and dependencies in
product development [8]. New solutions usually have to be
integrated into existing production processes, which cannot
simply be changed. Therefore, validation of risky
assumptions requires special test strategies and tactics. In
addition, I4 startups often have to work with large
corporations. The cultural mindset, processes and speed
usually differ between a startup and a corporation [9].
A systematic analysis of the essential characteristics of
entrepreneurship in I4 is largely lacking. Therefore, I4
entrepreneurship needs appropriate attention in research,
education and practice. While startup centres at universities
and accelerator programs for specific fields such as social
entrepreneurship or healthcare have received a lot of attention
in research, I4 entrepreneurship has lacked similar focus so
far and remains comparatively little researched. There is also
dearth of research at the specific interface between processes
and theories of entrepreneurship in general and in the field of
I4 resulting in the absence of scientific basis especially for
the support of I4 entrepreneurship and for the education of I4
founders. A requirement for a better understanding of I4 is an
analysis of the specifics of this field and a derivation of the
necessary skills and resources for entrepreneurial actions in
To address this research gap, this article presents a study
that identifies and outlines the essential characteristics of I4
with respect to entrepreneurship. Chapter 2 provides an
overview of selected related work. Subsequently, Chapter 3
presents the research approach used. Chapter 4 describes the
findings of the study. Finally, Chapter 6 discusses the results,
outlines open research questions, and provides an outlook for
future work.
There are several studies on skillsets or abilities of
employees working in I4 teams. Marnewick and Marnewick
[10] conducted a systematic literature review to identify the
current state of empirical research in respect to I4s impact on
project teams. Thus, they extracted and ranked the most
important factors for I4 product teams within companies and
clustered them into “personal”, “process” and “social
depending” categories.
Grzybowska and Łupicka [11] conducted a questionnaire
survey amongst 20 selected experts in the automotive and
pharmaceutical industry. They used the findings to cluster the
key managerial competencies into the three dimensions
“technical competencies”, “managerial competencies” and
“social competencies”. Based on this classification, they
worked out the most important “managerial competencies”
such as Creativity, Entrepreneurial Thinking, Problem
Solving, Conflict Solving, Decision Making, Analytical
Skills, Research Skills and Efficiency Orientation.
Prifti, Knigge, Kienegger, and Krcmar [12] used a
systematic literature review and focus group interviews to
work out a competency model for I4 employees. They used a
generic framework developed by Batram [13] to outline 8
dimensions of skills. Next, they assigned competency
dimensions and competencies to these dimensions. They
differentiated between competencies regarding Information
Systems, Computer Science and Engineering.
These three studies mainly refer to the skills needed to work
successfully in I4 environments. However, they do not refer
to the specific skills needed to develop innovations for I4 or
to operate entrepreneurially in I4.
On the entrepreneurship side, Mitchelmore and Rowley
[14] conducted a literature review and developed a summary
of key entrepreneurial competencies. They used the findings
of existing frameworks to develop their own overview of
entrepreneurial competencies. They divided their model into
entrepreneurial competencies, business and management
competencies, human relations competencies, and conceptual
and relationship competencies. The authors concluded that the
concept of entrepreneurial competencies is used at present by
different parties. However, wide parts of the topic needs to be
further investigated and implemented in practice.
Kruger and Steyn [15] developed “a conceptual model of
entrepreneurial competencies needed to utilise technologies of
Industry 4.0” with a focus on South-African startups. Their
understanding of I4 is consistent with that of Müller and
Härtig [3] assenting to I4 applications necessarily consisting
of software, hardware and digital components. Kruger &
Steyn call this symbiosis the “three layers” of I4 products
(physical layer, connectivity layer and digital layer). Kruger
and Steyn reviewed I4 technologies that were supported by
qualitative data collection. The methodology aimed to gain
insights in the field of entrepreneurial processes linked to I4.
The authors conducted 17 in-depth interviews with founders.
The main finding of Kruger and Steyn is a conceptual model
of skills needed to utilize I4 technologies. The model is
divided into five dimensions: Innovation; Creativity; business
integration and technology skills; Leadership and
communication and Networking and Sales. Each dimension is
further distributed into 8 to 25 sub dimensions or skills. In
their findings, they describe I4 as “a complex concept, where
technology and business unite. Kruger and Steyn focus
primarily on the technical skills required and the appropriate
technical understanding of I4. Compared to Kruger & Steyn,
the study presented here focuses on identifying the underlying
characteristics of I4 entrepreneurship.
In summary, related work has focused on either essential
characteristic of I4 or essential characteristics of
entrepreneurial processes, but not on the combination of both,
i.e. essential characteristics of I4 entrepreneurship.
A. Research Question and Hypothesis
Based on the findings of the analysis of related work, the
following research question was developed on a macro level:
RQ: What are the essential characteristics and constraints
of entrepreneurial processes in I4?
B. Applied Research Method
According to Seaman [16] interviews are suitable to collect
opinions and impressions. In unstructured qualitative
interviews, the goal is to elicit as much information as possible
on a broad topic. Interviews are likely to obtain information
that cannot be expected from previous experience or research.
Therefore, questions must be as open-ended as possible. Since
this study aimed at identifying essential characteristics for an
area that is still largely unknown, the research method must
take the generation of unpredictable results into account.
Additionally, it was necessary to include structured questions
to put the information in a framework of certain topics.
Therefore, a semi-structured qualitative interview study was
chosen as research method for the interviews to educe not only
the information foreseen, but also unexpected types of
C. Data Collection and Analysis
Following this research method, each interview was started
with a brief explanation of the objective of the study and a
definition of the term "Industry 4.0" to ensure that participants
were aware of the exact meaning and could adjust their
answers accordingly. During this introduction, careful
consideration was given to what information was shared with
the interviewees so as not to influence the participants.
It was often a challenge to steer participants back to the core
of the topic if they digressed too far in other directions without
upsetting or alienating them. To facilitate the collection of
specific information, an interview guide was used by the
interviewer (we uploaded the specific guide to Figshare [17]).
It contains a list of questions that helped the interviewer start
the conversation and steer the interview in the right direction.
Since it was difficult to take notes and conduct the interview
at the same time, the interviews were recorded with the
consent of each interviewee. The recordings were later used to
create field notes.
The interviewees were recruited among experts in I4
entrepreneurship. Experts were defined as people who either
are founders themselves or have several years of professional
experience in supporting I4 startups. Specifically, this means
investors, coaches, mentors and executives from accelerators
and incubators.
Based on these selection criteria, 14 interviews were
conducted, scheduled for 30 minutes. In some cases, the
interviews exceeded this period.
Interviewee 1
Founder of accelerator and company
Interviewee 2
Founder of I4 startup
Interviewee 3
Mentor of I4 startups
Interviewee 4
Founder of I4 startup
Interviewee 5
Founder of I4 startup
Interviewee 6
Founder of I4 startup
Interviewee 7
Founder of I4 startup
Interviewee 8
Investor in I4 startups
Interviewee 9
Founder of I4 consultancy and I4
innovation scout for SMEs
Interviewee 10
Angel investor and mentor (prior to
that: executive employee responsible
for I4 startup activities for a DAX 30
Interviewee 11
Founder of I4 startup
Interviewee 12
Mentor &
and coordinator of an incubation
program for B2B startups with a
focus on I4
Interviewee 13
Investor in I4 startups
Interviewee 14
Head of startup scouting at an open
innovation platform focusing on I4
and investor
Because the timing of the interviews (November 2020 to
February 2021) fell during one of the peak periods of the
COVID-19 pandemic, interviews were conducted digitally via
video conferencing tools such as Zoom or Microsoft Teams.
According to the research method, statements or
propositions were extracted from a series of field notes that
were supported by the data in a variety of ways. The
statement or proposition was first constructed from a passage
recorded in the notes and then refined, modified, and further
elaborated as other related passages. The result is statements
or propositions that describe events in an insightful and
powerful way [16].
Following the method of qualitative analysis [18], post
formed categories were designed during the coding process.
The statements of the interview partners were assigned to
these categories. In order to put the statements into context,
each interview was analysed again using the categories.
Overall, according to most respondents, the
entrepreneurial processes differ significantly between startups
in I4 and other fields. We present the results grouped along the
phases of an entrepreneurial process (based on a Maurya [19])
below. In addition, we describe the results with respect to
necessary skills and resources that the interviewees
considered particularly relevant for I4.
A. Customer/Problem Fit
In the first phase of a typical entrepreneurial process, the
question is whether the founder has identified a group of
potential customers who have a problem worth solving. The
following section will show the findings of the study regarding
the Customer/Problem Fit.
The participants of the study mentioned that there are
typically only few customers that are relevant for a startup in
the field of I4, especially when compared to startups in other
industry sectors. Moreover, in the customer organizations, few
people know and understand the real problems. This
complicates finding and understanding appropriate and
important problems. One usually has to find suitable
customers as well as the few suitable people who can be
interviewed for customer development.
Participants broadly agreed that the most important factor
in achieving a successful Customer/Problem Fit is a deep and
comprehensive understanding of customers, their
environments, their constraints, and the technical domains at
hand. The interviewees concluded that it is crucial to
understand exactly the problems of the customers.
The understanding of the customers should include their
whole value chain. One interviewee stated, “The whole value
chain has to be considered from the integration of the supplier
to the user experience at the end customer”. Understanding
the value chain helps to assess the importance of existing
problems. Finally, a startup has to solve a business case for the
customers. One interviewee said, You have to know how
much money you save the customer. The interviewees
stressed intensively, how important it is for a startup to get
insights into the customer and understand their business cases.
A founder who had to master this challenge himself stated that
it is important that “your business model really solves a
The interviewees mentioned that typical startup founders
in I4 are not aware that a profound problem exploration is
necessary in order to create the products wanted by the
customers. They often do not understand that desirability is
required and highly important. The interviewees reported that
many startup founders focus on the technology and the
solution instead of first focusing on the problem and the use
case. Consequently, startup founders in I4 often develop
products that have no monetary value for the customers or a
monetary value that is not able to fulfil the financial needs of
the startup. This is one of the key reasons why many I4
startups fail according to one of the interviewees. Another
interviewee summarizes the importance of understanding the
problem as follows “If you don’t understand the problem
precisely, or experienced the problem yourself, it will be
difficult for you to go into the ideation phase.
Another difficulty associated with this I4 domain is the
cultural mindset on the customer side, mostly by established
companies. This seems to be a key issue since many
interviewees mentioned this point. One founder summarized
this difficulty as “You come upon an industry that grew over
decades. Disruptive business models are linked to a
behavioural change. What you encounter is rigidity and
perfectionism. How to crack the nut of the status-quo? Our
competitors are not other startups but the status-quo.
Another essential characteristic of I4 is that it is difficult
to test risky assumptions about the business model early on.
Testing critical assumptions is one of the essential activities of
the entrepreneurial process. Difficult access to customers, few
contacts within customer organizations, necessary domain
knowledge, cultural mindset and difficulty in getting feedback
are some of the reasons why early testing of critical
assumptions is challenging. This is true not only in the early
stages of a startup, but also in the later stages.
Customer/Problem Fit
Difficult access to customers: only few customers, very
few persons in the customer organization familiar with the
Significant I4 domain knowledge and familiarity with the
customer’s environments and constraints required
Deep understanding of the value chains of individual
customers necessary
Lack of problem orientation: I4 founders often focus on
tech-savvy solutions instead of important and unmet
customer needs
Challenging culture and mindset: often traditional
mindset, often rigor and perfection valued, difficult to
change the status quo
Difficult to test risky assumptions and get feedback
B. Problem/Solution Fit
In the second phase of a typical entrepreneurial process, the
question is whether the start-up manages to solve the problem
in an appropriate way. The Problem/Solution Fit phase
evaluates to what extent the solution developed by a startup is
suitable for solving a relevant problem of the customer. This
involves checking if the solution is better than the status-quo
or alternatives and if it creates real value for the customer [19].
The following section shows the findings of the study
regarding the Problem/Solution Fit.
One aspect that was mentioned by nine interviewees
without being specifically asked about it is the development
of Minimum Viable Products (MVPs) for I4, which is seen as
crucial for product development. The development of an MVP
in the field of I4 is described as way more complex than in
other domains. The first difficulty is the limited possibility to
get feedback. This was often compared to B2C-products. The
interviewees stated that in B2C business models, it is
comparably easy to get feedback with a reasonable amount of
time and resources, e.g. via Ad-Word campaigns, customer
feedback, landing pages, or other approaches. In I4, MVPs
and then at a later stage the solutions, often have to be
integrated into ongoing production processes. The
interviewees mentioned that there is always the risk of an
MVP leading to production stoppage and therefore leading to
high cost for the customer. Thus, the customers seem to be
hesitant to test new solutions. One of the interviewees, who
experienced this issue in a corporate environment and
compared it to a B2C use-case, stated, The day-to-day
operations have to run smoothly. It is not acceptable to lose a
single unit, decrease quality or increase cost. It is a whole
different thing of integrating an MVP into a large or medium-
sized company than handing out a prototype to a random
customer. A founder who experienced the issue himself
backs this opinion “You cannot just put an MVP in place. If
you integrate something that doesn’t work already, you will
be put into the Pilot Purgatory”.
According to the interviewees, long development and long
testing cycles characterize product development of I4
solutions. This is due to the complexity caused by hardware
and software components and the fact that I4 solutions are
often critical for the customer's production processes.
Industry-specific requirements and constraints such as safety
issues or standardization (e.g. ISO 200000; 10303-1486) must
also be considered during product development. Since the
product development process is described as difficult and
time-intensive, the interviewees mentioned that entrepreneurs
need to have sophisticated and efficient development
processes in place.
It is typical for I4 that a startup has only a few customers at
the beginning, on whom it depends heavily. These strong
customers often have individual wishes such as feature
requests, which are then difficult to turn down if they do not
fit the product strategy. As a result, startups in I4 have a hard
time developing a clear product strategy from which products
for a larger group of customers can evolve and grow. One
interviewee summarised “There is a difficulty in
distinguishing between customer requirements and your own
product it is a fine line between specialization and becoming
a dependent service provider.
Problem/Solution Fit
Difficult development of MVPs and solutions: integration
into ongoing production processes often required, which
entails high risks
Complex and difficult development process: time-
consuming, long development and testing cycles, I4-
specific requirements
Difficult to productize solutions
C. Product/Market Fit
In the third phase of a typical entrepreneurial process, the
question is whether the startup builds something that people
want. In the Product/Market Fit phase, it is evaluated whether
there is a market for the product or service that is sufficiently
large to build a profitable business model and that the
customers are willing to pay for the solution [19]. The
following section will show the findings of the study regarding
the Product/Market Fit:
An important finding that makes reaching Product/Market
Fit difficult is that a customer organisation typically consists
of different customer types. The organisational structure on
the customer side is typically high. Different types of persons
are usually involved in identifying, selecting, buying and
using a product. In most companies that are relevant for I4
solutions, the organisation is so big that the users of the
application are not the decision makers or budget owners.
These different types of customers (i.e. actors involved in
the decision-making and purchasing process) lead to
complications in communicating with the customer. One
interviewee summarised this as follows “There are a lot of
stakeholders. The one buying is not the one using.
Additionally, there are a lot of decision makers in the
background”. Another interviewee said, “It depends on how
good [startups] do not only understand the user but also the
buyer. They have to persuade both, and this can be really
The interviewees mentioned that a high development
speed is often necessary to adjust products or services quickly
to changing customer requirements. This needs to be ensured
by appropriate internal development processes and
organisational structures in a startup.
The rollout of products or services after successful
development is also typically difficult in I4. Integrating a new
technology in the production processes of a company usually
includes the risk of a malfunction and production stop. The
solutions often need to be integrated into individual
environments and adhered to specific standards. One
interviewee summarised the difficulties with the rollout as
follows “It just takes a lot more time, and a lot more steps.
The step from pilot to serial production is unbelievably long.
Product/Market Fit
Different customer types: many stakeholders, difficult
communication with customers
Changing requirements: quick reactions to changes and
high development speed necessary
Long and difficult rollout: very long way from pilot to
series production
D. Scale
In the fourth and last phase of a typical entrepreneurial
process (often also referred to as Business Model Fit) the
question is how to scale up. After evaluating the existence of
a market and proving the viability of the business model, the
scaling phase verifies that the company can grow sufficiently
efficient and fast [19]. The following section shows the
findings of the study regarding scaling.
Business models in I4 were seen by the interviewees as
often not as scalable as other business models. One
interviewee mentioned that an essential reason is the hardware
component of the business model that leads to increasing
variable cost with every produced unit. In addition, the high
dependency on a few customers leads to the difficulty of
handling the gap between an independent solution and the
satisfaction of individual customer needs. One interviewee
stated, “The most difficult part is the scaling. Many startups
get stuck in the project business”.
The interviewees also emphasised the special
characteristics of sales and marketing for startups in I4. Here,
they saw major differences compared to other industry
domains. Some interviewees mentioned that the marketing
channels needed to be chosen differently. Social media, for
example, does not seem to be very relevant for I4 business
models now. Fairs and personal interaction between startups
and customers seem to be relevant. Besides that, enterprise
sales was described as difficult and slow. However, I4 is also
taking on an even more challenging role in enterprise sales.
One interviewee mentioned, Feedback loops of 6-8 months
until you know if your customer wants to buy or not”. Other
participants describe the sales process as “way more
complicated” or “way longer”.
Despite the challenge of sales, the study also shows some
evidence on how to face this challenge. One of the things that
can influence the sales process in I4 is the credibility that the
startup is able to gain in the eye of the customer. One
interviewee describes it as follows “you have to be a
networker. You have to build up relationships throughout
years, dig deeper very often and build up trust”. Two other
aspects that seem to be important in the sales process are to
work out and communicate a clear USP and to focus on more
things than technical aspects. Especially, the last aspect seems
to be important since many founders tend to not show the
value for the customer but rather explain every technical
feature of the product.
The difficulties of building a scalable business model have
implications for the financial aspect of an I4 startup. A founder
of an I4 startup interpreted the behaviour of investors quite
negatively I can understand investors pulling out of this
market because it's not a classic venture case”. Another
investor agreed to a certain degree but also showed another
side by saying “The venture capital model is not that suitable
for I4 startups. Not many startups make it from a seed
investment to Series A. On the other side however, there are
investors that exclusively invest in [I4 startups]”. Another
essential characteristic regarding the financial aspects of the
business model is the cost intensity due to the hardware- and
software parts and the long development cycles.
Difficulty to scale: high variable cost, dependence on few
Specific marketing and sales characteristics: special
channels, difficult and slow enterprise sales, long feedback
loops, importance of credibility
Difficulty in obtaining financing: only few specialised
investors for I4, cost-intensive business models
E. Entrepreneurial skills for I4
Skills or competencies allow individuals to act in a self-
organised manner in practical and organisational contexts
[20]. While there are skills that are beneficial to a founder
regardless of a specific domain, the study showed that there
are skills that are more relevant in an I4 startup than in other
domains. The following section will show the findings of the
study regarding the specific entrepreneurial skills for I4.
Based on the above-mentioned specifics in the various
phases of I4 startups, a profound domain knowledge, the
ability to understand the customers’ environments and
processes, and technological skills quickly to a certain extent
are highly relevant. Since the target group of I4 startups are
usually established companies, it is necessary to understand
their problems in depth. This was considered the most
important skill. According to the interviewees, the difficulty
of understanding the customers problem exceeds the difficulty
of problem understanding in many other domains by far.
Regarding the psychological characteristics and soft skills,
the interviewees mentioned some important aspects. One
characteristic especially worth mentioning is persistence.
Interviewees consistently felt that I4 requires a high level of
persistence and continuity. One interviewee said, “You should
realize how hard it really is. Many [founders] are naive, but
it's incredibly hard”. A founder who experienced it himself
stated, “It is extremely hard. It takes its toll”. One of the
mentors added, “The founder has to be willing to torment
himself”. Another highly relevant skill is communication. One
founder said, "You have to clearly know and be able to
communicate the benefit to the customer. You have to prove it
with traction. You have to show demonstrable customer
value". It was seen as a critical success factor for a startup to
be able to communicate with customers at eye level and
leverage customer insights. There are several tactics to
achieve this. One of these tactics is to leverage existing
experience from previous work in a related field. Another
tactic is to generate insights through a dedicated customer
exploration process. Yet another tactic is to start with more
low-cost industry projects that have the goal of learning and
developing insights.
Since I4 comprises hardware and software components, a
technological understanding of the own product is necessary.
However, this was not as important as having a deep
understanding of the business side. The interviewees
consistently felt that it was more important to have an
overview of the business side than the technology side. One
interviewee noted, "the founders are often very capable
technically, but they lack experience on the business side”.
One of the founders mentioned that it is important to
understand that a founder cannot know everything about a
problem or technology. Rather, a founder should focus on
solving problems to some degree. This thought also aligns
with a coach who said that most founders who are brilliant at
product development often have no idea about the business
side. It seems to be difficult to serve both sides. In the words
of one interviewee, "It's not only important to build the
product, but also the business model around it". Overall, a
good balance of technological and business skills is required
for I4 startups.
As previously described, the financial aspects of an I4
startup are quite complex. Therefore, interviewees
emphasised financial understanding as an additional skill that
founders in I4 should have.
Domain knowledge, process knowledge and technological
Persistence, networking and communication skills
Balance between technological and business skills needed
Understanding of finance and investments needed
F. Resources
A resource is a tangible or intangible good that can be used
for pursuing goals or meeting requirements. In the following
section, we summarise the results with regard to the resources
(in particular human resources, investment capital, and
networks) that the respondents consider necessary for
entrepreneurship in I4.
Since a startup team consists of only a few people,
especially at an early stage, there is a need to implement an
interdisciplinary team from the beginning. For the investors
interviewed, the team was the most relevant factor when
analysing a potential investment case, especially considering
that not only the technological component but also the
business aspect is relevant for success. As a result, the initiator
of a startup should always make sure to assemble a
heterogeneous team. One investor noted, "I would first focus
on the team and check if it is complementary and if there is
someone in the team who always focuses on the business
model and sales". One aspect that was discussed intensively
by the interview partners was the necessary work experience.
While some interviewees stated that work experience is
essential for a successful I4 company, other participants
contradicted this statement. However, regardless of the actual
necessity of professional experience, it can be stated that
professional experience or internships in the relevant domain
are undoubtedly helpful for founding an I4 startup. One
interviewee put it as follows “The best program you can go
through is an internship in an industrial company. One
founder underpinned this statement by saying that one of their
most important success factors was the knowledge they had
about the domain that they gained by their work experience of
many years.
The capital-intensive development of an I4 solution must
be considered when founding a startup. One founder stated,
"The funding [venture capital] of an I4 startup must be at least
twice as high as in other areas". The difficulties in raising
capital have already been described above.
As for other startups, the ecosystem and exchange with
other startups is important. However, according to the opinion
of the interviewees, startups in the field of I4 should focus on
experts with a proven record of accomplishment in this
specific field “Other startups that have done it themselves are
always the most helpful”. One of the interviewed founders
agreed and stated “Knowledge and experience sharing with
other founders is very important. With the ones who did it
themselves”. One of the interviewed investors added, “The
network is really important because it inspires itself.
Experienced founders that invest in new companies that’s
really valuable”. Another important resource for startups is
the experts. The interviewees mainly mentioned investors and
mentors whose integration can be helpful. One founder cited
early incorporation of investor feedback as "one of the success
factors in not going in the wrong direction". Another founder
extends this statement by the integration of mentors
“Renowned experts who accompany us and support us with
short-term feedback, that's a great added value for us.
Especially at an early stage, these experts can make the
difference between the successes or failure of a startup.”
Another interviewee pointed out, "At the very beginning, you
don't have a track record yet. People are more willing to try
something if they get a recommendation".
Heterogeneous, multidisciplinary startup teams
High demand for investment capital
Network and ecosystem support (experts, mentors,
We used the framework proposed by Yin [21] as the basis
for the discussion of the validity and to show the threats posed
to the validity in the presented study.
Construct validity: The construct validity was supported
by explaining the goal and purpose of the interviews to the
interviewees in advance. Additionally, the terminology of I4
was explained to the interviewees. This included the definition
of the term.
External validity: The external validity of the presented
study is limited due to the limited number of interviews. In
addition, the interviewees were all from Germany. It can be
assumed that there are additional specifics or constraints for
other markets or for international businesses.
Reliability: The analysis of the interviews was done in a
systematic and repeatable manner. Therefore, a replication of
the study is possible.
We conducted an interview-based study to identify the
specifics and characteristics as well as the special skills for I4
entrepreneurship. The results show that I4 startups have
specific requirements in all startup phases and at the same time
need special skills and resources.
These essential characteristics are mainly found in the
customer perspective. It is difficult for startups to develop an
adequate understanding of the customer's problems. This
often leads to the problem of not developing a relevant product
or solution. It is also difficult for startups to test critical
assumptions and integrate prototypes or MVPs into existing
production environments due to the risk of production
stoppage and long feedback cycles. It is a critical success
factor for startups to be able to capture the customer's
knowledge necessary for understanding the customer’s
problems and developing adequate solutions. Another aspect
that influences startup processes is the mindset within the
customer organisation, which has been described as
conservative and rigid. The complexity of the hardware and
software components affects the scalability of startups. This
has an impact on the financial structure of the company.
The aim of the study presented was to gain initial insights
into the special features and characteristics of I4
entrepreneurship. The findings can be expanded and further
validated in a subsequent study. It is planned to quantify and
weight the identified special features (e.g. capabilities) in a
succeeding study so that they can be considered and evaluated
according to their priority.
Based on the results of the study, the following areas for future
research emerge: To a small extent, tactics for overcoming the
challenges already emerged from the answers or insights of
the interviews. A supplementary qualitative study could be
conducted with the goal of gaining detailed insights into
promising entrepreneurial tactics for I4. The interviewees said
little about what specifics I4 entrepreneurship holds for those
customers who are themselves startups rather than established
companies. This could also be an interesting research
direction. Another question that has not been answered
sufficiently in this study is whether it is possible to develop
products for I4 that can be integrated quite easily into existing
production processes (for example, the processing and
monetisation of production data).
Since the missing link between I4 and entrepreneurship
has also been identified in the educational landscape, future
work of the authors focuses on entrepreneurship education for
I4. The creation of an educational program for I4 is planned.
For this purpose, the findings of the present study will be used
as basis. The accuracy of the curriculum and teaching methods
of this program will be scientifically evaluated.
We thank the participants of the study for their time and
contributions. All feedback collected gave us great insights
and motivates us to continue our research.
D. F. Kuratko, M. H. Morris und M. Schindehutte,
„Understanding the dynamics of entrepreneurship through
framework approaches,“ Small Business Economics, pp. 1-13,
C. Rammer und B. Peters, „Innovation as a Success Factor for
German Industry? The Contribution of Product and Process
Innovations to Employment and Exports (in German),“
Vierteljahrshefte zur Wirtschaftsforschung, pp. 13-36, 2015.
F. Härtig und B. Müller, „Challenges and solutions for consistent
communication of measurement data for Industry 4.0 and the
Internet of Things (in German),“ in Industrie 4.0:
Herausforderungen, Konzepte und Praxisbeispiele, Wiesbaden,
Springer Vieweg, 2017, pp. 49-58.
T. Hänisch, „Basics of Industry 4.0 (in German),“ in Industrie 4.0
- Wie cyber-physische Systeme die Arbeitswelt verändern,
Wiesbaden, Springer Gabler, 2017, pp. 9-32.
W. Huber, Industry 4.0 compact - How technologies are changing
our economy and our companies: Transformation and change of
the entire company (in German), Wiesbaden: SpringerVieweg,
U. Sendler, „Industry 4.0- Mastering Industrial Complexity with
SysLM (in German),“ in Industrie 4.0 Beherrschung der
industriellen Komplexität mit SysLM, Berlin, Springer-Vieweg,
2013, pp. 1-20.
R. Heinze, Industry 4.0 in an international context: core concepts,
results, trends (in German), Berlin: Beuth, 2017.
T. Hermann, S. Hirschle, D. Kowol, J. Rapp, U. Resch und J.
Rothmann, „Effects of Industry 4.0 on the requirements profile of
employees and the consequences in the context of education and
training (in German),“ in Industrie 4.0 - wie cyber-phsysische
Systeme die Arbeitswelt verändern, Wiesbaden, Springer Gabler,
2017, pp. 239-254.
R. Eckert, Lean Startup in corporations and medium-sized
companies (in German), Wiesbaden: Springer Gabler, 2016.
C. Marnewick und A. L. Marnewick, „The Demands of Industry
4.0 on Project Teams,“ IEEE TRANSACTIONS ON
ENGINEERING MANAGEMENT, pp. 941-949, 3 August 2020.
K. Grzybowska und A. Łupicka, „Key competencies for Industry
4.0,“ Economics & Management Innovations(ICEMI), pp. 250-
253, 11 October 2017.
L. Prifti, M. Knigge, H. Kienegger und H. Krcmar, „A
Competency Model for “Industrie 4.0” Employees,“ in
Proceedings der 13. Internationalen Tagung
Wirtschaftsinformatik, St. Gallen, 2017.
D. Batram, „The great eight competencies: a criterion-centric
approach to validation,“ Journal of Applied Psychology, pp. 1185-
1203, 2005.
S. Mitchelmore und J. Rowley, „Entrepreneurial competencies: a
literature review and development agenda,“ International Journal
of Entrepreneurial Behaviour & Research, pp. 92-112, 2010.
S. Kruger und A. A. Steyn, „A conceptual model of
entrepreneurial competencies needed to utilise technologies of
Industry 4.0,“ The International Journal of Entrepreneurship and
Innovation, pp. 1-12, 2020.
C. B. Seaman, „Qualitative Methods,“ in Guide to Advanced
Empirical Software Engineering, Heidelberg, Springer, 2008, pp.
D. Wahl, “Figshare,” 27 February 2021. [Online]. Available:].. [Accessed 27
February 2021].
P. Mayring, „Qualitative Content Analysis,“ FORUM:
A. Maurya, Running Lean: Iterate from Plan A to a Plan That
Works, Sebastopol, CA: O'Reilly Media, 2012.
J. Zeelie und C. Nieuwenhuizen, Entrepreneurial Skills, Kenwyn:
Juta and Company Ltd, 2007.
R. K. Yin, Case Study Research - Design and Methods, London:
SAGE Publications Inc., 2014.
... Previous studies have shown that startups in the field of I4 have specifics that potentially have an impact on a broad spectrum of the entrepreneurial process and accordingly, the education and support of entrepreneurs in this field must be adapted. However, entrepreneurship education is not addressing this topic sufficiently yet (Wahl & Münch, 2021). While there is basic research as well as dedicated teaching and training opportunities for other sub-disciplines (see Ansari et al., 2020;Nambisan, 2017or Kollmann et al., 2022, the field of I4 entrepreneurship remains largely untouched. ...
... As part of the interview study, 14 experts from the field of I4 entrepreneurship were interviewed. The results were analyzed qualitatively and discussed on a scientific conference (Wahl & Münch, 2021). ...
... This is a prerequisite for developing a tailored study program. Since there was little evidence on entrepreneurship in the field of I4, we conducted a study to develop a better understanding of the topic (Wahl & Münch, 2021). The study showed that there are essential characteristics in entrepreneurship in I4 that are fundamentally different from other domains. ...
Full-text available
Startups in the field of Industry 4.0 could be a huge driver of innovation for many industry sectors such as manufacturing. However, there is a lack of education programs to ensure a sufficient number of well-trained founders and thus a supply of such startups. Therefore, this study presents the design, implementation, and evaluation of a university course tailored to the characteristics of Industry 4.0 entrepreneurship. Educational design-based research was applied with a focus on content and teaching concept. The study program was first implemented in 2021 at a German university of applied sciences with 25 students, of which 22 participated in the evaluation. The evaluation of the study program was conducted with a pretest–posttest-design targeting three areas: (1) knowledge about the application domain, (2) entrepreneurial intention and (3) psychological characteristics. The entrepreneurial intention was measured based on the theory of planned behavior. For measuring psychological characteristics, personality traits associated with entrepreneurship were used. Considering the study context and the limited external validity of the study, the following can be identified in particular: The results show that a university course can improve participants' knowledge of this particular area. In addition, perceived behavioral control of starting an Industry 4.0 startup was enhanced. However, the results showed no significant effects on psychological characteristics.
... Endüstri 4.0 teknolojisi ile müşteri ilişkileri ve müşterileri daha iyi anlama, maliyet kontrolü, ürün tanıtımı, üretimde akıllı sistemler vb. kompleks çalışmalar için de daha başarılı bir portal oluşturmak mümkün olmuştur (Wahl & Münch, 2021). ...
... Girişimler modern ekonomilerin önemli birer parçaları olduğu gibi (Wahl & Münch, 2021) Dördüncü endüstri devriminin kısaltılmış ifadesi olan Endüstri 4.0, 2010'larda Almanya'da başlamış olan endüstriyel dönüşümdür (Alexopoulos vd., 2016: 840;Qin vd., 2016: 173;Li, 2017: 67;Xu vd., 2018Xu vd., : 2941. Endüstri 4.0, "müşteri ihtiyaçlarını karşılamak amacıyla üretimi temsil eden, verimlilik, kalite ve karlılık gibi amaçlar ile örgütlerde bütün fonksiyonların döngüsünü etkileyen örgütün üretim süreçlerinin dijitalleşmesi" olarak tanımlanmıştır (Neugebauer, Sophie, Leis, & Landherr, 2016: 7;Gaub, 2015: 401;Lu, 2017: 7;Altıntaş & Özata, 2021: 62). ...
Conference Paper
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Today, intensively digital, and robotic technologies are used in organizations in every field and the importance of Industry 4.0 is increasing and attracting more attention. With the technology at Industry 4.0 level, organizations provide more efficiency in time and cost in their activities and improve the quality of their products or services. Cyber physical systems, internet of things, artificial intelligence, robotics, big data, cloud computing and augmented reality are the technological developments that used within the scope of Industry 4.0. The aim of this study is to investigate whether there is a significant difference between the education levels and entrepreneurial tendencies of individuals and their attitudes towards Industry 4.0. In other words, it is to determine whether education level and entrepreneurial tendency have an effect on the attitude towards Industry 4.0. It is also to make recommendations based on the resulting results. The data of the study were collected through a questionnaire from 257 students studying at the associate, undergraduate and graduate levels at Artvin Coruh University. The obtained data were analysed with correlation and MANOVA using SPSS 25.0 program. The results of the analysis revealed that the adaptation, readiness, and pessimism dimensions of the attitude towards Industry 4.0 differed significantly with regard to the education level, while the readiness and pessimism dimensions differed significantly with regard to the entrepreneurial tendency. However, it has been observed that the adaptation and relevance dimensions of the attitude towards Industry 4.0 do not differ significantly with regard to the education level, and the interested in Industry 4.0 dimension does not differ significantly with regard to the entrepreneurial tendency. Based on these results, it has been suggested to be aware of the fact that the level of education is effective in the development of adaptation and readiness attitudes towards Industry 4.0, the entrepreneurial tendency is effective in the development of the attitude of readiness, and on the other hand, both the education level and the entrepreneurial tendency are effective in reducing the pessimistic attitude. In addition, it has been suggested that organizational policies regarding human resources management and business processes should be developed in this direction.
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The Fourth Industrial Revolution, also known as Industry 4.0 (I4), has provided an unprecedented platform for innovation in various spheres. For entrepreneurs then, who are known to drive innovation and progress in various fields, I4 offers a wide scope of opportunities. The purpose of this article is to provide a conceptual model of needed entrepreneurial competencies to effectively utilise novel technologies I4 has to offer. To develop the model, this exploratory study adopts an action research approach. Using predefined questions developed from previous literature, a representative sample was obtained from 17 in-depth interviews with entrepreneurs. From the data analyses, where machine learning technologies were used, it was found that to respond and navigate the layers of I4 technologies and enable new possibilities, entrepreneurs require certain competencies in this globally connected and technology-fuelled world. This article advances entrepreneurial research as it provides a platform to guide and support their development, which has been a key focus area both internationally and in South Africa, to address one of the key sustainable development goals, economic growth. Despite this contribution, further investigation is required on how to develop these competencies through supportive mechanisms and corresponding education relating to I4.
Conference Paper
This paper analyzes employee competencies for employees with higher education in Industry 4.0. An Industry 4.0 competency model based on a behavioral oriented approach concerning three variants, namely Information Systems, Information Technology and Engineering is developed by extending the SHL Universal Competency Framework through a structured literature review and focus groups with academic staff. The presented study contributes to research by providing a starting-point for further research regarding employee competencies for Industry 4.0. It contributes to practice as the provided competency model can be applied to Industry 4.0 job descriptions.
Purpose – Entrepreneurial competencies are seen as important to business growth and success. The purpose of this paper is therefore to undertake a literature review of research on entrepreneurial competence in order to: provide an integrated account of contributions relating to entrepreneurial competencies by different authors working in different countries and different industry sectors and at different points in time; and, develop an agenda for future research, and practice in relation to entrepreneurial competencies. Design/methodology/approach – The article starts with a review of the development of the concept of competence, with particular reference to its use in the context of management competencies. It then draws together views on the notion of entrepreneurial competence before exploring and summarising research on the link between entrepreneurial competencies and business performance and growth. A core section then compares the models of entrepreneurial competencies cited in the literature, and on this basis proposes a set of entrepreneurial competencies which can be used as the basis for further research and practice. Finally, the different perspectives adopted by researchers to the measurement of entrepreneurial competencies are reviewed. Findings – Conclusions suggest that although the concept of entrepreneurial competencies is used widely by government agencies and others in their drive for economic development and business success, the core concept of entrepreneurial competencies, its measurement and its relationship to entrepreneurial performance and business success is in need of further rigorous research and development in practice. Originality/value – This article integrates previous models of entrepreneurial competencies towards the development of an entrepreneurial competency framework.
Software engineering involves a blend of non-technical as well as technical issues that often have to be taken into account in the design of empirical studies. In particular, the behavior of people is an integral part of software development and maintenance. This aspect of our subject presents complexities and challenges for the empirical researcher. In many other disciplines, qualitative research methods have been developed and are commonly used to handle the complexity of issues involving people performing tasks in their workplace. This chapter presents several qualitative methods for data collection and analysis and describes them in terms of how they might be incorporated into empirical studies of software engineering, in particular how they might be combined with quantitative methods. To illustrate this use of qualitative methods, examples from real software engineering studies are used throughout.