Content uploaded by Uwe Wilkesmann
Author content
All content in this area was uploaded by Uwe Wilkesmann on Aug 17, 2018
Content may be subject to copyright.
VINE Journal of Information and Knowledge Management Systems
Industry 4.0 – organizing routines or innovations?
Maximiliane Wilkesmann, Uwe Wilkesmann,
Article information:
To cite this document:
Maximiliane Wilkesmann, Uwe Wilkesmann, (2018) "Industry 4.0 – organizing routines or
innovations?", VINE Journal of Information and Knowledge Management Systems, Vol. 48 Issue: 2,
pp.238-254, https://doi.org/10.1108/VJIKMS-04-2017-0019
Permanent link to this document:
https://doi.org/10.1108/VJIKMS-04-2017-0019
Downloaded on: 17 May 2018, At: 02:32 (PT)
References: this document contains references to 51 other documents.
To copy this document: permissions@emeraldinsight.com
The fulltext of this document has been downloaded 221 times since 2018*
Access to this document was granted through an Emerald subscription provided by
Token:Eprints:JVGGYNEPCDYEBCHZ5S6A:
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald
for Authors service information about how to choose which publication to write for and submission
guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as
well as providing an extensive range of online products and additional customer resources and
services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the
Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for
digital archive preservation.
*Related content and download information correct at time of download.
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
Industry 4.0 –organizing routines
or innovations?
Maximiliane Wilkesmann
Faculty of Business and Economics, TU Dortmund University,
Dortmund, Germany, and
Uwe Wilkesmann
Center for Higher Education, TU Dortmund University, Dortmund, Germany
Abstract
Purpose –The rise of new information and communication technologies forms the cornerstone for the
future development of work. The term Industry 4.0 refers to the vision of a fourth industrial revolution that is
based on a network of autonomous, self-controlling, self-configuring, knowledge-based, sensor-based and
spatially distributed production resources. All in all, different forms of the application of the Industry 4.0
concept can be observed, ranging from autonomous logistic transport systems drawn upon the idea of swarm
intelligence to smart knowledge management systems. This paper aims to develop a theoretical framework to
analyze different applications of Industry 4.0 on an organizing continuum. The general research questions
are: What forms of organizing digitalized work lead to the reproduction of routines, and what forms foster
innovation within Industry 4.0? The authors thus analyze the consequences of different forms of organizing
work on workers’perceptions and the results of the working process.
Design/methodology/approach –This paper provides case studies for different stages of the
organizing continuum in the context of Industry 4.0. The cases and a further analysis of all 295 funded
projects are based on the Platform Industry 4.0 Map, which is part of theIndustry 4.0 initiative of the German
Federal Ministry of Economic Affairs and Energy and the German Federal Ministry of Education and
Research. The consequences for people acting in such organizational and digitally supported structures are
discussed.
Findings –A variety of applications of Industry 4.0 can be found. These applications mainly vary in the
dimensions of the degree of formalization, the location of control authority, the location of knowledge and the
degree of professionalization. At the right side of the organizing continuum, the digitalization organizes a
work environment that supports highly qualified humans. They have broad leeway and a high degree of
autonomy to design and create innovative forms of digitalization for tomorrow. At the left side of the
organizing continuum, Industry 4.0 structures a work environment with narrow leeway, a low degree of
autonomy and a top-down structure of control authority predetermined by digital applications. In this case,
employees fill the gaps themachines cannot handle.
Research limitations/implications –As the paper focuses on Industry 4.0 developments in Germany,
the comparability with regard to other countries is limited. Moreover, the methodological approach is
explorative, and broader quantitative verification is required. Specifically, future research could include
quantitative methods to investigate the employees’perspective on Industry 4.0. A comparison of Industry 4.0
applications in different countries would be another interesting option for further research.
Practical implications –This paper shows that applications of Industry 4.0 are currently at a very early
stage of development and momentarily organize more routines than innovations. From a practical point of
view, professional vocational and academic training will be a key factor for the successful implementation of
digitalization in future. A joint venture of industry and educational institutions could be a suitable way to
meet the growing demand for qualified employees from the middle to the right-hand of the organizing
continuum in the context of Industry 4.0.
Social implications –Industry 4.0 is designed by men, and therefore, humans are responsible for whether
the future work situation will be perceived as supportive or as an alienated routine. Therefore, designers of
Industry 4.0, as well as politicians and scientists, absolutely must take the underlying outcomes of digitalized
work into account and must jointly find socially acceptable solutions.
VJIKMS
48,2
238
Received 7 April2017
Revised 4 June 2017
24 July 2017
27 September 2017
Accepted 8 October2017
VINE Journal of Information and
Knowledge Management Systems
Vol. 48 No. 2, 2018
pp. 238-254
©EmeraldPublishingLimited
2059-5891
DOI 10.1108/VJIKMS-04-2017-0019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2059-5891.htm
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
Originality/value –This paper provides a promising avenue for future research on Industry 4.0 by
analyzing the underlying organizational structures of digital systems and their consequences for employees.
Moreover, the paper shows how Industry 4.0 should be organized to simply reproduce routines or to support
innovation.
Keywords Digitization, Digitalization, Knowledge management, Industry 4.0, Digital Taylorism
Paper type Research paper
Introduction
In recent discussions about the development of new information and communication
technologies, we can observe a paradigm shift marking the transition of work from a real-
physical world to a virtual world. In this vein, real products are linked to Web-based
applications and are increasingly integrated into production processes. The rise of these new
information and communication technologies forms the cornerstone for the future
development of work. Since the beginning of industrialization, technological leaps have led
to paradigm shifts and have had a strong impact on the functional areas of individual work
systems; in retrospect, these changes can be defined as industrial revolutions. The term
Industry 4.0 refers to the vision of a fourth industrial revolution, which is currently
considered a future project (Lasi et al., 2014). The vision of Industry 4.0 can be understood as
a comprehensive digitalization and linkage of production processes, starting from the
customer’s order, through the creation of production processes, to downstream product
services. In this regard, predominantly self-organized value-creation networks are expected
to lead to profound changes in economic interactions. Therefore, considerations in the
context of Industry 4.0 go beyond the mere optimization of IT-supported processes.
According to Kagermann et al. (2013, p. 5):
Industry 4.0 enables continuous resource productivity and efficiency gains to be delivered across
the entire value network. It allows work to be organised in a way that takes demographic change
and social factors into account.
This phenomenon can be observed in various countries all over the world (for an overview
see Gausemeier and Klocke, 2016, p. 14f.), yet discussed under different terms, such as the
so-called “Second Machine Age”by Brynjolfsson and McAfee (2014) in the USA or “Made in
China 2025”(Zhang et al., 2014). Nevertheless, the term Industry 4.0 is increasingly used to
encompass these developments (Zhang et al., 2016).
On the one hand, particularly in the initial phase of a sociotechnical change, there are
great expectations and visions related to the arising possibilities. On the other hand, there is
a high degree of uncertainty about the social consequences of these new technologies. This
discrepancy seems explicable given that different scientific disciplines are involved in the
research and implementation of Industry 4.0. The understanding of Industry 4.0 is based on
the disciplines of computer science, engineering, political science, sociology and economics ,
which impedes a uniform definition. A definition upon which representatives from different
backgrounds (politics, business, science and trade unions) agree was created within the
framework of the “Industry Platform 4.0”, initiated by industrial associations, companies,
the Federal Ministry for Economic Affairs and Energy and the Federal Ministry of
Education and Research in Germany in 2015 and reads as follows:
The term Industrial 4.0 stands for the fourth industrial revolution, a new stage in the organization
and management of the entire value chain over the life cycle of products. This cycle addresses the
increasingly individualized customer requirements and extends from the idea of the development
and manufacturing, the delivery of a product to the customer up to the recycling, including
Industry 4.0
239
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
associated services. The basis is the availability of all relevant information in real time through
the networking of all entities involved in the value creation as well as the ability to derive from
the data the optimal value flow at any time. By linking people, objects and systems, dynamic,
real-time and self-organizing, cross-company value-added networks emerge, which can be
optimized according to different criteria such as cost, availability and resource consumption
(German Federal Ministry of Economics and Energy, 2015 translated by the authors).
Roblek et al. (2016) and Hermann et al. (2016) identified cyber-physical systems, the internet
of things (IoT), the internet of services (IoS), and the concept of smart factories as the four
key components of Industry 4.0. As Baheti and Gill (2011, p. 161) point out, cyber-physical
systems “refer to a new generation of systems with integrated computational and physical
capabilities that can interact with humans through many new modalities”. Big data analysis
plays one of the most critical roles in IoT frameworks. Vermesan et al. (2009, p. 9) define the
IoT “as a dynamic global network infrastructure with self-configuring capabilities based on
standard and interoperable communication protocols where physical and virtual ‘things’
have identities, physical attributes, and virtual personalities and use intelligent interfaces
and are seamlessly integrated into the information network”(for an overview of definitions,
see Li and Li, 2016, p. 75). In contrast to that, the IoS:
[...] presents a paradigm in which everything is available as a service on the Internet. It can also
be viewed as networked services or systems across the real and virtual worlds over the Internet.
In the IoS, services –as encapsulated functional entities containing interaction processes by
service providers and customers –are distributed, virtualized, and converged over the Internet to
meet the requirements of and create value for customers”(Xu et al., 2015, p. 81).
Another major goal is the implementation of an intelligent and self-organizing factory,
which is also referred to as a smart factory (Lucke et al., 2008). Within this intelligent
factory, smart logistics, intelligent objects and mobile communication devices are produced
in consideration of the efficient use of resources. The goal is to produce intelligent products
that can be further developed even after delivery; the products hence become active elements
of the production process themselves (Sendler, 2013). This vision describes the independent
management and organization of individualized customer orders (batch size = 1) across the
entire value-added chain by implementing a continuous horizontal and vertical network.
Industry 4.0 encompasses different forms of information exchange: information exchange
between humans, human–machine interaction (H2M) and, currently the most prominent,
information exchange between machines (M2M). M2M communication and smart products
build a single entity (Roblek et al., 2016). According to Dawid et al. (2017), smart products
can also be characterized and improved along the dimensions of input (e.g. sensors and
acquisition of relevant data), output (e.g. displays, including visual, auditory or haptic
signals), human–computer interfaces (e.g. control and interaction), interoperability (e.g.
exchange of information between artifacts), integration (e.g. meaningfulnessor usefulness of
a larger-scale system) and resource efficiency (e.g. obtaining, generating and saving energy
to deliver their service). From a technical view, the lack of standardized interfaces causes
most smart solutions to work in isolation, thus leading to a fragmentation of diverse
systems. Fragmentation occurs “when a larger number of products co-exist in an
environment without really cooperating”and “indicates missed opportunities for exploiting
synergies”(Dawid et al., 2017, p. 207). In summary, we conclude that Industry 4.0 currently
remains in a very early stage of implementation and can rather be considered a vision than a
reality. Apart from these technical issues, Industry 4.0 may be an interesting opportunity for
massive reorganization processes in companies by making production processes truly
smart.
VJIKMS
48,2
240
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
Industry 4.0, like all information technologies in general, inheres in its own types or
models of organizing things. In a nutshell, so-called smart systems can, on the one hand,
reproduce simple and monotonous routines in the form of digitized mass production. In this
case, the cyber-physical systems of Industry 4.0 can be considered a reproduction or an
analogy of real-world forms of organizing social behavior to reproduce routines. On the
other hand, cyber-physical systems can rebuild flexible, innovative and totally new H2M
interactions in a fluid network with very smart and innovative forms of cooperation and
collaboration.
What type of Industry 4.0 will ultimately emerge strongly depends on such underlying
concepts of organizing. Therefore, our research questions are as follows:
RQ1. What forms of organizing digitalized work lead to the reproduction of routines?
RQ2. What forms of organizing digitalized work allow the creation of innovations by
Industry 4.0?
To analyze different forms of organizing digital architectures, we will elucidate some
general assumptions regarding different forms of organizing from the perspective of
social sciences and organizational studies. Then, we will discuss how these types of
organizing are reproduced or rebuilt in digital structures and which underlying digital
architecture or form of organizing leads to imitating and reproducing routines or to
creating innovations. Finally, we will provide relevant empirical evidence by presenting
different case studies on Industry 4.0.
The continuum of organizing digitalized work
The digital architecture of Industry 4.0 is not an end in itself but could support people
working in and with those structures to make production or service processes smarter
and more efficient. Therefore, we focus on the relationship between the underlying
digital architecture and the work assumed by the underlying digital architecture
(Figure 1). From an organizational perspective, we describe the forms and types of
organizing as a continuum (Meyer and Rowan, 1977), with its two poles of routinized
and innovative work tasks. The origin of our theoretical underpinning is Burns and
Stalker’s(1961)differentiation between mechanistic and organic organizations. They
characterize the continuum with 11 features that are overlapping and not clearly
distinguished, yet according to Lam (2005, p. 118) can be summarized in four more
general attributes:
Figure 1.
Organizing
continuum and cases
in the context of
Industry 4.0
Industry 4.0
241
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
(1) degree of formalization;
(2) distinctness of control authority;
(3) location of knowledge; and
(4) degree of professionalization.
Accordingly, we draw on these four broad categories and demonstrate their applicability
with regard to the topic of Industry 4.0. In addition, we refer to other approaches that
support the distinction between mechanistic andorganic organizations.
As Figure 1 shows, mechanistic and organic types of organizations are opposite ends of
an organizing continuum. Between the two poles, mixtures and coexisting forms in one
organization (e.g. within different units) may occur (Lam, 2005,Lawrence and Lorsch, 1967).
In a meta-analysis, Damanpour (1991) found empirical evidence for the mechanistic-organic
model, especially regarding four characteristics we will discuss in more depth in the
following sections.
Degree of formalization
On the one hand, mechanistic organizations follow a strict and rigid structure whilst the
environment is assumed to be relatively stable and predictable (Burns and Stalker, 1961).
Hence, the employees’working tasks are characteristically highly standardized and can
easily be broken down into functionally differentiated duties, making them suitable for a
support by digitalized structure (Lam, 2005). Task requirements in a mechanistic
organization are Taylor-made (Taylor, 2006/1911), e.g. the software or digital architecture
produces reiterative, specialized, easy to handle and formalized duties, what we call a high
degree of formalization. A high degree of formalization in a mechanistic-oriented
organization increases the probability for the workforce to be replaced by other employees
or by intelligent cyber-physical systems. This is true even for intelligent cyber-physical
systems, like the cellular transport system discussed below, since such systems exclusively
work in a predefined and programmed logistic environment and cannot develop a new
environment or new strategic goals by themselves. The underlying process can be called
digitization, meaning that a pure reproduction of analog signals into a digital form (Tilson
et al., 2010). As of yet, there is no change in the former working tasks. In contrast to the
technical process of digitization,digitalization includes a “sociotechnical process of applying
digitizing techniques to broader social and institutional contexts that render digital
technologies infrastructural”(Tilson et al., 2010, p. 749), which is especially relevant for
organic organizations.
On the other hand, organic organizations are based on a more fluid set of work
arrangements, which result from changing environmental conditions (Lam, 2005).
Therefore, organic organizations show a low emphasis on setting strict working rules,
which in turn facilitates innovations. Here, digitalization occurs as the underlying learning
algorithms develop a self-governing and self-changing digital structure of human working
tasks. A low degree of formalization (few formalized duties and broader working rules)
permits openness, which encourages the growth of new ideas and behaviors, as many
authors have already shown (Burns and Stalker, 1961;Damanpour, 1991;Pierce and
Delbecq, 1977). The organic structure can be seen as a learning work environment, which
creates the opportunity to constantly change working tasks. The low degree of formalization
assumes that the workforce is more specialized and skilled to fill the missing knowledge
gaps.
VJIKMS
48,2
242
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
In this sense, we can differentiate a degree of formalization that can be reflected in the
continuum from strong to fuzzy boundaries (e.g. communities of practice). The
characteristics of the underlying digital architecture can differ in various ways, i.e.
the architecture can range from strictly determining every employee’s single step to
complete a working task to facilitating the exchange of innovative ideas in the form of a
knowledge management system or e-learning system (Tsui, 2005;Lau and Tsui, 2009).
Thus, we must take into account whether a digital device can lead a fixed, defined group of
employees to fulfill a clearly structured and highly formalized work task (mechanistic) or
whether in an organic organization caused by a low degree of formalization, it can facilitate
an open and flexible exchange for different networks and people who can use it in many
different ways (organic).
Distinctness of control authority
In mechanistic organizations, rights and obligations are attached to roles, and these are
translated to functional positions and hierarchical structure (Lam, 2005;Burns and Stalker,
1961). The overall direction for information processing and decision implementation is top-
down. A senior monitors and aligns the work of the subordinates top-down so that they co-
produce the required good. Here, digitalization is rebuilding given organizational structures
without any leeway for employees. They must follow the given digital structure alongside
strict rules and constraints without any divergence and can be thus characterized as
Taylor’s agents (Taylor, 2006/1911) in a digital environment. In mechanistic organizations,
no means or ends are flexible, everything is default and no leeway for behavior or learning is
possible. All employees must follow the predefined steps of the digital system.
In organic organizations, control authority is horizontal or organized in networks. Hence,
peers must discuss how they coordinate themselves requiring digital devices to support
interaction in a flexible and open way. A spread of communication beyond any technical
definition exists (Lam, 2005). Means and ends of the working process are part of the
decision-making process within the structure (Offe and Wiesenthal, 1980). Participation is
incorporated into the analog and the digital structures of organizing, and employees are
highly valued experts (Wilkesmann and Wilkesmann, 2011). They may select their own
means to a prescribed end and jointly discuss the common goals in such network structures.
The digital architecture enables and supports this type of autonomy. The perception of self-
determination establishes fundamental differences in the perception of work, whether in
digitalized or organizational structures (Ryan and Deci, 2000;Ryan and Deci, 2006;
Wilkesmann and Schmid, 2014).
Location of knowledge
In a mechanistic organization, knowledge is located exclusively at the top (Lam, 2005;Burns
and Stalker, 1961). Therefore, a strict hierarchy communicates information only top-down.
In terms of digitalization, the software is the host of the knowledge that processes
information in a strict hierarchical way to the work activities.
In an organic organization, relevant knowledge to solve organizational problems can be
located anywhere, i.e. within the organization or in a broader network (Lam, 2005). As a
result, specialization is required in ensuring a broader knowledge base and increasing the
cross-fertilization of ideas (Damanpour, 1991). Moreover, expertise and personal knowledge
are important to enforce boundary-spanning activities to acquire new knowledge and new
ideas outside the organization (Pierce and Delbecq, 1977;O’Mahony and Bechky, 2008;
Kinnie and Swart, 2012;Swart et al., 2014). Digitalization supports the knowledge transfer in
Industry 4.0
243
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
networks with different experts within or outside organizational boundaries (Wilkesmann
and Wilkesmann, 2011).
Degree of professionalization
In a mechanistic organizations, vertical communication between the superior and
subordinates prevails (Burns and Stalker, 1961). The employees need no expertise to fulfill
their work tasks: therefore, no professionalization is required. With regard to the
implementation of structures of Industry 4.0, either low-qualified workers will fill the gaps,
that the digital device cannot fully automate, or the digitalization will establish a fully
standardized and automated routine. In organic organizations, importance and prestige are
attached to affiliations, and expertise can also be also external to the firm (Lam, 2005).
Employees are highly qualified and mostly academics, which frequently entails a high
commitment to their profession rather than to their organization (Kinnie and Swart, 2012;
Wilkesmann et al., 2009;Wilkesmann 2015). On the one hand, digitalization should both be
based on and must foster professional networks to spread new ideas. On the other hand,
those new ideas require protection against data loss and leakage.
Drawing on these four criteria, we will now characterize and distinguish the two poles of
the organizing continuum within Industry 4.0, i.e. organizing routines in mechanistic
organizations vs organizing innovations in organic structures.
Organizing routines by Industry 4.0
At the mechanistic end of the continuum, we find types of organizations or organizational
units that mostly operate under strong behavior controls (Ouchi and Maguire, 1975), as in
traditional production organizations, which Mintzberg (1979) describes as “machine
bureaucracies”. Most theories of organizational studies more or less describe this type of
organization (Eisenhardt, 1989). The core of these organizations or organizational units
consists of blue-collar workers. The classical form of organizing work or information
processes in a mechanistic organization is hierarchical, i.e. interests and information are
enforced top-down via domination. A digital system, which is implementedin a mechanistic
organization, constrains, monitors and steers people and coerces obedience. People cannot
truly interact with the digital system and much less define the goals that must be fulfilled.
The perceived self-determination is low, and the leeway for action is constrained. For people
working in such an environment, individual and organizational goals are not congruent.
Moreover, the scope of influence of the workers is very narrow. As Coleman (1990)
emphasizes, this perception could lead to a perception of alienation among the people
involved as the behavior of members and the actions of the organization or the digital
structure are entirely unrelated. Recurring activities can be routinized very easily, which is
the reason why such a Tayloristic organization produces, reproduces and improves routines
that are indispensable in a mass production system. A stricterversion of this understanding
can be realized by taking humans completely out of the organizing process, permitting
cyber-physical systems (M2M) to autonomously undertake classic routine operations. Our
first two case studies (see below) will give examples that are located at the left-hand of the
organizing continuum.
Organizing innovations by Industry 4.0
At the organic end of the continuum, we find more knowledge- and service-oriented
organizations or organizational units, so-called “professional bureaucracies”or
“adhocracies”(Mintzberg, 1979), where the expertise of white-collar or gold-collar workers
(Kelley, 1985) is demanded. Burns and Stalker (1961) were the first to compare the
VJIKMS
48,2
244
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
characteristics of organizations that positively influence innovation. In their study, they
found that the adoption of innovation is more likely when organizations have an organic
rather than a mechanistic structure to respond to the environmental requirements. In this
sense, decentralized, flat (organic) structures support the generation and the implementation
of innovative ideas effectively throughout the organization, whereas this is not found to be
the case for centralized, tall (mechanistic) organizations (Damanpour, 1991;Lam, 2005;
Burns and Stalker, 1961).
Knowledge and new ideas can flow very easily in an organic organization, because
all expertise is integrated in a horizontal decision-making or information process
(Wilkesmann and Wilkesmann, 2011). All the power of this type of organizing
originates from the network. The people involved determine the decision-making or
information process by horizontal communication because they possess the relevant
knowledge and need support from intelligent knowledge management systems or
digital devices to enhance their skills and competencies. These forms are located from
the middle to the right-hand side of the organizing continuum. The individual and
organizational goals are congruent. People act in a structure of that kind because their
goals and needs are congruent with the goals and needs of the organizational structure.
Therefore, the members of the organization receive no selective incentives but rather
invest their own resources in the form of time and engagement. Membership is clearly
defined but overall weaker than at the other end of the organizing continuum; owing to
their personal expertise, it is usually comparatively easy for the members to exit the
organic organization (Hirschman, 1970). At the right-hand side of the organizing
continuum (Figure 1), we find organizing forms with fuzzier boundaries, which have in
recent years been called “collaborate communities”(Adler and Heckscher, 2006)or
“communities of practice”(Wenger, 1998). This form enables highly self-determined
actions, leading to a high degree of intrinsic motivation (Ryan and Deci,2000, 2006).
Members are engaged in those communities because they jointly pursue a common
goal. A digital system is open to every potential user for different purposes. The
employees’scope of influence is very broad. High social homogeneity and a high degree
of specialization are necessary because of the missing formal structure. Technical
structures are flexible and open; they permit autonomy to the employees and help
organize a structure with fuzzy boundaries. The likelihood that innovations will emerge
in such an open and flexible organizing structure is high. Our last example will give
some empirical illustration of this extreme end of the continuum. In the following, we
will provide examples of all the different stages of the organizing continuum in the
context of Industry 4.0. Moreover, we will discuss consequences for people acting in
such organizational and digitally supported structures.
Case studies
Except for the last case,which remains only a vision, thefirst four cases presented below are
part of the so-called Platform Industry 4.0 Map[1], which belongs to the Industry 4.0
initiative of the German Federal Ministry of Economic Affairs and Energy and the German
Federal Ministry of Education and Research. In Summer 2017, the Platform Industry 4.0
Map contained a total of 295 cases. The selected cases reflect the characteristics and
problems that we have identified in the underlying theoretical framework. In this sense, our
methodological approach refers to multiple-case studies design described by Yin (2014,
p. 47): “Each case must be carefully selected so that it either predicts similar results (a literal
replication) or produces contrasting results, but for predictable reasons (a theoretical
replication)”. Therefore, five cases were selected to provide theoretical and practical
Industry 4.0
245
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
evidence for different stages of the organizing continuum described above. After the
presentation of the selected cases, we further analyze all 295 listed cases in terms of their
distribution within the organizing continuum. The selected cases are in their own way quite
innovative along the continuum; however, most of them are not creating innovations per se.
In the following, we start at the mechanistic end and move on step by step toward the right-
hand end of the organizing continuum.
Intelligent automation
The first case study is located in the field of logistics. In 2011, the research center for
“Cellular Conveyor Technology”[2] of the Fraunhofer Institute for Material Flow and
Logistics (IML) was opened. The practical aim of this project is to make logistics supply
chains more energy-efficient and to react more flexibly to unforeseen events. The starting
point of the center is to transfer the concept of swarm intelligence to the subject of logistics.
Based on the organizing of fish, bees or birds, which are more successful in swarms than by
themselves, the Fraunhofer IML simulates a complete storage center in a 1,000-m
2
research
hall using this nature-related organizing principle.
Agent-based software enables the vehicles to work together autonomously with other
components (e.g. lift) and coordinate with each other. Approximately 50 unmanned
transport vehicles autonomously seek out their tasks and autonomously decide on the best
method of transportation. The vehicles range freely within the research hall and move
practically everywhere, i.e. from goods arrivals to picking and shipping. Hence,
implemented into practice, this entire system would be able to adjust its capacities to
seasonal or daily fluctuations, as well as to changed orders or customer demands. Moreover,
it can randomly shift output between the storage and transport processes and the single sub-
areas (Kamagaew et al., 2011).
Regarding the organizing type of the cellular transport system, we observe that humans
have completely been replaced by the system. The system directs its goals autonomously
and is no longer bound to rigid line management. Ultimately, the system seems to realize the
old dream, conceived in the 1980s, of an automated factory running without humans. In
contrast to that former vision, the developed cellular transport system now functions
without digital hierarchies by following the swarm approach.
Wearables and augmented reality
The next case is also located in the field of logistics. In 2016, a German IT service
provider began using augmented reality (AR) glasses at its headquarters’logistics
hub[3]. The practical aim of this implementation is to allow hands-free picking in the
warehouse with the help of smart glasses. The smart glasses communicate with the
digital warehouse management system and the mobile AR warehouse picker app. A
small loudspeaker is installed in the earpiece of the frame. At the right edge of the glass
the word “Welcome”appears, while a voice greets the picker and indicates what time it
is. As soon as the picker sits on the pallet truck, the glasses report, “There is an order
for the customer 351, a delivery of 22 packages is to be put together”.“Confirmed”,says
the picker, and the glasses navigate the person with colored arrows to the right shelf,
until a colored rectangle in the field of view signals hit to the picker. The picker takes
the marked package, fixes the glasses on it and the glasses scan the barcode.
Immediately, the confirmation done appears in the visual field. Then, a new order pops
up, and the picker navigates to the next order, following the commands of the smart
glasses. The entire system also offers an integrated low stock check to ensure the
adequate inventory of the product just picked. The possibility of having both hands
VJIKMS
48,2
246
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
free at work and no longer having to handle large-hand scanners and delivery notes can
significantly accelerate the work. In particular, AR can optimize standardized working
processes based on the decrease in mistakes and the reduction in paths and processing
times.
This case shows that the formalization of the tasks to be fulfilled is quite high, and
the control authority is vertically organized as the entire working process is managed
top down by the applied digital device. Accordingly, the picker has no leeway to self-
organize the working procedure because the knowledge is located at the top of the
organization. As the pickers are not self-determined –the control authority of
the digital system externally monitors them –their motivation is extrinsic because even
the path the picker must follow is literally dictated by the AR system. The picker must
stick to the digital rules and follow the commands, which are stored within the software
system. In this sense, the employees’degree of professionalization is low as the digital
device completely determines their behavior.
Collaborative robots In 2015, a large supplier of industrial motors introduced a
collaborative robot (Co-Bot) called YuMi
®
[4]. The company developed a collaborative, dual-
arm, small-parts-assembly robot solution that includes flexible hands, parts-feeding
systems, camera-based part location and robot control. YuMi
®
comes from the words you
and me. In this sense, the main field of application is smart-parts assembly, where humans
and robots work side-by-side on the same tasks. The important aspect of safety is built into
the functionality of the robot itself. For that reason, barriers to human–machine
collaboration by erecting fencing and cages, as we know from automotive production, are no
longer necessary. The acceptance by the production staff is most important. The use of Co-
Bots in production must therefore be designed in such a way that the employees experience
the new technology as supportive rather than as a rival. This implies that during the
introductory phase, employees should be familiarized with the functions. The
conceptualization of the Co-Bot allows to easily teach the Co-Bot new moves. In this sense,
the workers obtain leeway to (re-)program the Co-Bot in a way that is supportive to their
own tasks. In terms of hierarchy, it is the workers who apply their knowledge to the Co-Bot;
hence, humans direct the digital system and not the other way around. The field of
application is more flexible than in the case described above, because the Co-Bot can be used
in different parts and for different purposes of small-parts assembly. In this sense, the
boundaries of the underlying digital structure are fuzzier.
Intelligent-adaptive assistance systems
Intelligent-adaptive assistance systems form a new generation of mobile, context-sensitive
systems for knowledge and management support in smart manufacturing. One of the main
aims is to broaden the qualification of the staff by compensating for skills that may be
lacking. APPsist[5] is an app developed by researchers, which automatically adjusts the
support requirements of employees working on the shop floor. The app takes into account
the specific, existing skills of the workers. If a machine breaks down, for example, the
worker usually contacts a colleague from maintenance. The app, installed on a device like a
tablet, allows the worker to fix the problem on site. The worker is able to repair the machine
by him- or herself because the app provides the relevant knowledge to address the specific
problem. Thus, learning processes are built for a wide variety of requirements, e.g.
commissioning, operation, troubleshooting, maintenance, repair and preventive
maintenance of plants. In this Industry 4.0 case, the workers at the shop-floor level are
supported by the system to expand their own knowledge. As a result, the former lower-
qualified workers gain more competencies and, in return, more leeway within their
Industry 4.0
247
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
workplace. Ultimately, the digital system facilitates work tasks and can be seen as a further
development of knowledge management (KM) practices within an industry-related context.
Therefore, typical problems of storing expert knowledge into the system can be anticipated.
In particular, employees at the next higher level are expected to lose their professional
expertise, as by storing their knowledge in the app, they make themselves superfluous. In
turn, the location of their knowledge can be situated anywhere within the organization.
Nevertheless, the degree of formalization can be considered medium because the tasks are
neither reiterative nor highly complex. Additionally, this is also true for the distinctness of
control authority, because the app gives no strict instructions but proposed solutions. This
case can be interpreted as a practical application of a KM system. The only restriction is that
workers on the shop floor solely apply existing and already-stored knowledge; they do not
store their expertise in the digital system.
Knowledge management in the internet of things
Our last description is not an empirical case but rather a vision of the future of Industry 4.0.
It can be considered a further development of KM in the direction of the so-called IoT. KM
IoT refers to building a broader network among people, objects and systems (Roblek et al.,
2016). In comparison to classical knowledge management systems, automatically collected
data and information by Industry 4.0 applications (e.g. data collected by RFID-chips) allow
access to previously non-digital centralized data. A KM IoT system like that continuously
collects data (e.g. tracking transport goods, filling tanks and silos, measuring temperature of
compressors and monitoring machines and systems) and combines the data with smart
algorithms. In this way, organizations gain access to new sources of data and information
that can be used for decision-making processes in a more comprehensive view. Such data
systems may contribute to the detection of new patterns of behavior or machine interaction,
in this vein promoting think tanks of new services, products or ideas. In other words, the
access to those data enables managers at the strategic level or researchers in R&D units to
create new innovations. The analysis of the acquired data fosters the development of new
Industry 4.0 applications and novel business models, e.g. a change from pure production to
customer-oriented and personalized services for special solutions is possible. The roll-out of
those innovations is still linked to humans’expertise; however, KM IoT may strongly
support the underlying decisions.
Taking the other cases into account, the underlying knowledge work of those systems
(e.g. APPsist, Wearables/AR and Co-bots) would be arranged at the right-hand side of the
organizing continuum. As in this case, the leeway for creating innovations in general is
broad, employees are assumed to be highly intrinsically motivated. In addition, involved
persons, e.g. in communities of practice, can be part of networks (also beyond
organizational boundaries). The formalization of the underlying digital system of a KM
IoT is characteristically rather low, while the organization of activities proceeds in a
bottom-up way. Ultimately, such a system fosters the growth of innovative ideas, in
contrast to the strict replication of routines.
Distribution of Industry 4.0 cases
An interesting aspect is to further analyze how and to what extent the currently existing
cases of Industry 4.0 are located and distributed within the organizing continuum. To this
end, we conducted a document analysis of all 295 cases that are currently funded by the
German Federal Ministry of Economic Affairs and Energy and the German Federal
Ministry of Education and Research. The document analysis was based on their
descriptions within the German Platform Industry 4.0 Map[6]. The project descriptions were
VJIKMS
48,2
248
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
collected, jointly categorized and counted. The analysis of all the cases discloses some
interesting and, to some extent, unexpected results: Almost one-third of the funded projects
(n= 186) develop applications where the term Industry 4.0 seems barely justified. Most of
them (n= 146) can be interpreted as digitization efforts, i.e. merely converting formerly
analog processes into digital ones. The applications range from making IT infrastructures
available to simple information management systems to connecting data exchange
processes from one corporate division to another. The lack of innovation even applies to
applications in the context of (intelligent) automation. Only two cases contain self-adaptive
components, as we presented above, whereas the remaining 60 cases aim at simple digitized
automation processes (Figure 2). Actually, efforts of digitalization play only a secondary
role: ultimately, 15 cases focus on the implementation of wearables and/or AR within
industry-related working processes. With a total number of nine, Co-Bots also play a
secondary role. Cases that can be categorized under the term intelligent-adaptive assistant
systems and/or classical KM applications constitute a number of 23 projects. What remain
are 20 miscellaneous cases that range from virtual simulation environments or learning
factories (which are primarily run by universities not companies) to special sensor
applications and solely consulting activities to projects that focus primarily on data
protection (n= 4). The latter are remarkable, as a lively public debate on lacking data
protection is taking place in the context of Industry 4.0. Apart from that, only 41 of 295 cases
take enterprise resource planning systems as a future common facilitator into account.
Another result of our analysis reveals that in total, only nine projects focus on batch size of
one, of which the majority (n= 6) focus on three-dimensional printing. To date, the original
promise of Industry 4.0 has not been fulfilled.
In summary, the figure shows that most of the currently funded projects are located at
the left-hand side of the organizing continuum, whereas only few cases can be found in the
middle or at the right-hand end.
Discussion and conclusion
Starting from the definition of Industry 4.0, we must clarify that most fields of application
are still in the development phase. Moreover, currently, most applications do not fit the
initial definition of Industry 4.0 because they are single solutions and do not encompass all
aspects in terms of self-organized value-creation networks. Nevertheless, we identify
Figure 2.
Distribution of
Industry 4.0 cases
Industry 4.0
249
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
interesting practical solutions for digitalizing working processes. In this paper, we describe
an organizing continuum for analyzing digitalized work in the context of Industry 4.0
(Figure 1), i.e. from reproducingand improving routines (exploitation) at one end to enabling
inventions and innovations (exploration) at the other end –in the sense of March (1991). This
framework constitutes a useful instrument to analyze the working conditions structured by
digitalization.
At the right-hand side, the digitalization of work processes organizes a work
environment that supports highly qualified humans with tools to enhance the flow of new
ideas. Employees working at the right-hand side of the continuum are highly qualified
people who probably program Industry 4.0 tools for the middle and left-hand side of the
continuum. They have broad leeway and a high degree of autonomy to design digital
applications for tomorrow. For that reason, these employees obtain rare skills which in turn
leads to a high degree of bargaining power within the organization. Employees with high
self-esteem cannot be effectively led by top-down decision structures. Highly qualified
persons solve complex problems in an egalitarian structure with vertical or bottom-up
participation. Often, commitment is given to a community or an innovative idea, not to a
formal organization. Therefore, they prefer working in organic structures that have fuzzy
boundaries given that boundary spanning is more probable. Most important, the form of
digitalized work at the right-hand side of the continuum organizes a structure that meets
their needs, as shown in the case of KM IoT.
Digitalization also organizes structures located at the left-hand side of the continuum,
which can be considered as the applied knowledge from the right-hand side of the
continuum. Knowledge workers from the right side program their vision of tomorrow’s
production process. As our cases show, Industry 4.0 also structures a work environment
with narrow leeway caused by a strict top-down digital control structure and a low degree of
autonomy. The high degree of formalization in a rather mechanistic-oriented organization
leads to a comparatively low skill level of the workforce, i.e. low-professionalized employees
are easily replaceable by other employees or intelligent cyber physical systems. The fear of
being replaced by machines or artificial intelligence is a predominant motif in the discussion
of the societal consequences of Industry 4.0 and the future of employment against the
background of computerization, as Frey and Osborne (2013) empirically but more or less
hypothetically analyzed. If employees remain in the production processes, they must fill in
the gaps the machines cannot handle. At the left side of the continuum, workers do not have
to think; the knowledge is incorporated in the digital device, they thus have to perform in a
given structure. Even if digital systems follow the idea of swarm intelligence as shown in
the case of intelligent automation, the application itself momentarily remains at the stage of
reproducing and automating routine work, as the applications are not able to create
innovations themselves.
The application of Industry 4.0 at the left-hand side of the continuum strongly
reminds us of the debate and visions of the so-called computer integrated manufacturing
(CIM) of the 1980s (AWF, 1985) and basic principles of Taylorism (Taylor, 2006/1911).
Industry 4.0 and CIM have in common that all production processes are computerized,
leading to a deserted shop floor as humans have become superfluous. The difference
between the two concepts is that Industry 4.0 allows for the fully automated production
of a batch size of one, thus meeting the individualized needs of the customers. Industry
4.0 can also be considered comparable with Taylorism because the right-hand side of the
continuum describes brainwork, whereas the left side refers to highly standardized
manual labor. The only difference is that in Industry 4.0, the work environment is
organized by digital structures and not by human hierarchies. In that sense, workers
VJIKMS
48,2
250
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
using wearables or AR systems in warehouses become a variation of Taylor’s vicarious
agents, as the digital system specifies every movement of warehousemen. Moreover, the
rise of digital Taylorism leads to an aggravated form of Tayloristic principles, as the
introduced systems do not allow any conversation with co-workers, which in turn
increases isolation and alienation in Marx’s sense among workers (Marx, 1981/1844).
However, and there is always a however, digitalization also generates great
opportunities: knowledge workers at the right-hand side of the continuum can program,
organize and produce work environments with broad leeway and autonomy for
employees at the middle of the continuum, which means that workers gain more
autonomy and competencies by using the developed applications (e.g. APPsist). At the
far left-hand side, humans are released from performing rather boring and meaningless
human work. Apart from that, Industry 4.0 in this early stage of development causes new
forms of uncertainty and threats (e.g. societal changes, data protection and data security)
that must be taken more seriously into account (Magruk, 2016).
In summary, Industry 4.0 is designed by men, and therefore, humans are responsible for
whether the future work situation will be perceived as supportive or as an alienated routine.
Therefore, designers of Industry 4.0 applications, as well as politicians and scientists,
absolutely must take the underlying outcomes of digitalized work into account and must
jointly find socially acceptable solutions (e.g. unconditional basic income to absorb negative
societal effects of unemployment caused by digitalization). Finally, we can conclude that
applications of Industry 4.0 are currently at a very early stage of development, and they
mostly focus on the left side of the organizing continuum, i.e. Industry 4.0 momentarily
organizes more routines than innovations.
From a practical point of view, professional vocational and academic training will be a
key factor for the successful implementation of digitalization in future. Training and further
training measures have to be created which take up the theoretical aspects of Industry 4.0
and link them to the practical aspects of the increasing complexity of machines. A joint
venture of industry and educational institutions could be a suitable way to meet the growing
demand for qualified employees from the middle to the right-hand of the organizing
continuum in the context of Industry 4.0.
The limitations of our paper are twofold: as we focus on the developments of Industry 4.0
in Germany, we cannot compare our results with the developments in different countries.
Moreover, our methodological approach is explorative, and quantitative verification is
necessary. Future research should include quantitative methods to investigate the employee
perspective on Industry 4.0, for example. A comparison of Industry 4.0 applications in
different countrieswould be another interesting option for further research.
Notes
1. Source: www.plattform-i40.de/I40/Navigation/EN/InPractice/Map/map.html
2. Source: www.iml.fraunhofer.de/content/dam/iml/en/documents/OE140/Themenbroschuere_ZFT_EN_
2012.PDF
3. Source: www.bechtle.com/press/press-releases/archive/2016-2/bechtle-launches-use-of-smart-glasses?
sl=en_GB
4. Source: http://new.abb.com/products/robotics/industrial-robots/yumi
5. Source: www.appsist.de/en/
6. Source: www.plattform-i40.de/I40/Navigation/EN/InPractice/Map/map.html
Industry 4.0
251
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
References
Adler, P.S. and Heckscher, C. (2006), “Towards collaborative community”,The Firm as a Collaborative
Community: Reconstructing Trust in the Knowledge Economy, Oxford University Press, Oxford,
p. 94.
AWF (Ausschuss Für Wirtschaftliche Fertigung) (1985), CIM –Integrierter EDV-Einsatz in Der
Produktion: Beispiele, Definitionen, Funktionszuordnungen, Ausschuß für wirtschaftliche
Fertigung AWF, Eschborn.
Baheti, R. and Gill, H. (2011), “Cyber-physical systems”, in Samad, T. and Annaswamy, A. (Eds), The
Impact of Control Technology, Overview, Success Stories, and Research Challenges, IEEE Control
Systems Society, pp. 161-166.
Brynjolfsson, E. and Mcafee, A. (2014), The Second Machine Age: Work, Progress, and Prosperity in a
Time of Brilliant Technologies, W.W. Morton, New York, NY.
Burns, T. and Stalker, G.M. (1961), The Management of Innovation, Tavistock, London.
Coleman, J.S. (1990), Foundations of Social Theory, Belknap Press of Havard University Press,
Cambridge, MA.
Damanpour, F. (1991), “Organizational innovation: a meta-analysis of effects of determinants and
moderators”,The Academy of Management Journal, Vol. 34 No. 3, pp. 555-590.
Dawid, H., Decker, R., Hermann, T., Jahnke, H., Klat, W., König, R. and Stummer, C. (2017),
“Management science in the era of smart consumer products: challenges and research
perspectives”,Central European Journal of Operations Research, Vol. 25 No. 1, pp. 203-230.
Eisenhardt, K.M. (1989), “Agency theory: an assessment and review”,Academy of Management Review,
Vol. 14 No. 1, pp. 57-74.
Frey, C.B. and Osborne, M.A. (2013), “The future of employment: how susceptible are jobs to
computerization”, OMS Working Paper, Oxford Martin School, Oxford.
Gausemeier, J. and Klocke, F. (Eds) (2016), Industrie 4.0. International Benchmark, Options for the
Future and Recommendations for Manufacturing Research, Paderborn, Aachen: Heinz Nixdorf
Institute, Paderborn University.
German Federal Ministry of Economics and Energy (Bundesministerium Für Wirtschaft Und Energie)
(2015), Memorandum der Plattform Industrie 4.0, Berlin.
Hermann, M., Pentek, T. and Otto, B. (2016), “Designprinciples for industrie 4.0 scenarios”,49th Hawaii
International Conference on System Sciences, Computer Society IEEE.
Hirschman, A.O. (1970), Exit, Voice and Loyalty, Responses to Decline in Firms, Organizations and
States, Harvard University Press, Cambridge.
Kagermann, H., Wahlster, W. and Helbig, J. (Eds) (2013), “Securing the future of German manufacturing
industry”,Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0. Final
Report of the Industrie 4.0 Working Group, Acatech-National Academy of Science and
Engineering, Frankfurt A.M.
Kamagaew, A., Stenzel, J., Nettsträter, A. and Ten Hompel, M. (2011), “Concept of cellular transport
systems in facility logistics”,Proceedings of the 5th International Conference on Automation,
Robotics and Applications,Wellington.
Kelley, R.E. (1985), The Gold-Collar Worker: Harnessing the Brainpower of the New Work Force,
Addison-Wesley, Reading.
Kinnie, N. and Swart, J. (2012), “Committed to whom? Professional knowledge worker commitment in
cross-boundary organisations”,Human Resource Management Journal, Vol. 22 No. 1, pp. 21-38.
Lam, A. (2005), “Organizational innovation”, in Fagerberg, J., Mowery, D.C. and Nelson, R.R. (Eds), The
Oxford Handbook of Innovation, Oxford University Press, Oxford, pp. 115-147.
Lasi, H., Fettke, P., Kemper, H.-G., Feld, T. and Hoffmann, M. (2014), “Industrie 4.0”,
Wirtschaftsinformatik, Vol. 56 No. 4, pp. 261-264.
VJIKMS
48,2
252
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
Lau, A. and Tsui, E. (2009), “Knowledge management perspective on e-learning effectiveness”,
Knowledge-Based Systems, Vol. 22 No. 4, pp. 324-325.
Lawrence, P.R. and Lorsch, J.W. (1967), “Differentiation and integration in complex organizations”,
Administrative Science Quarterly, Vol. 12 No. 1, pp. 1-47.
Li, B. and Li, Y. (2016), “Internet of things drives supply chain innovation: a research framework”,The
International Journal of Organizational Innovation, Vol. 9 No. 3, pp. 71-92.
Lucke, D., Constantinescu, C. and Westkämper, E. (2008), “Smart factory –a step towards the next
generation of manufacturing”, in Mitsuishi, M., Ueda, K. and Kimura, F. (Eds), Manufacturing
Systems and Technologies for the New Frontier, Springer, London, pp. 115-118.
Magruk, A. (2016), “Uncertainty in the sphere of the industry 4.0 –potential areas to research”,
Business, Management and Education, Vol. 14 No. 2, pp. 275-291.
March, J.G. (1991), “Exploration and exploitation in organizational learning”,Organization Science,
Vol. 2 No. 1, pp. 71-87.
Marx, K. (1981/1844), “Ökonomisch-philosophische manuskripte (1844)”, in Marx, K. and Engels, F.
(Eds), Ergänzungsband, Schriften bis 1844, Dietz, Berlin, pp. 467-588.
Meyer, J.W. and Rowan, B. (1977), “Institutionalized organizations: formal structure as myth and
ceremony”,American Journal of Sociology, Vol. 83 No. 2, pp. 340-363.
Mintzberg, H. (1979), The Structuring of Organizations: A Synthesis of the Research, Prentice Hall,
Englewood Cliffs.
O’mahony, S. and Bechky, B.A. (2008), “Boundary organizations: enabling collaboration among
unexpected allies”,Administrative Science Quarterly, Vol. 53 No. 3, pp. 422-459.
Offe, C. and Wiesenthal, H. (1980), “Two logics of collective action”,Political Power and Social Theory,
Vol. 1, pp. 67-115.
Ouchi, W.G. and Maguire, M.A. (1975), “Organizational control: two functions”,Administrative Science
Quarterly, Vol. 20 No. 4, pp.559-569.
Pierce, J.L. and Delbecq, A.L. (1977), “Organizational structure, individual attitudes, and innovation”,
Academy of Management Review, Vol. 2 No. 1, pp. 27-37.
Roblek, V., MešKo, M. and Krapež, A. (2016), “A complex view of industry 4.0”,Sage Open, Vol. 6 No. 2,
pp. 1-11.
Ryan, R.M. and Deci, E.L. (2000), “Intrinsic and extrinsic motivations: classic definitions and new
directions”,Contemporary Educational Psychology, Vol. 25 No. 1, pp. 54-67.
Ryan, R.M. and Deci, E.L. (2006), “Self-regulation and the problem of human autonomy: does
psychology need choice, self-determination, and will?”,Journal of Personality, Vol. 74 No. 6,
pp. 1557-1585.
Sendler, U. (Ed.) (2013), Industrie 4.0. Beherrschung Der Industriellen Komplexität Mit SysLM, Springer,
Heidelberg.
Swart, J., Kinnie, N.,Van Rossenberg, Y. and Yalabik, Z.Y. (2014), “Why should I share my knowledge?
A multiple foci of commitment perspective”,Human Resource Management Journal, Vol. 24
No. 3, pp. 269-289.
Taylor, F.W. (2006/1911), The Principles of Scientific Management, Cosimo, New York, NY.
Tilson, D., Lyytinen, K. and Sørensen, C. (2010), “Research commentary –digital infrastructures: the
missing IS research agenda”,Information Systems Research, Vol. 21 No. 4, pp. 748-759.
Tsui, E. (2005), “The role of IT in KM: where are we now and where are we heading?”,Journal of
Knowledge Management, Vol. 9 No. 1, pp. 3-6.
Vermesan, O., Friess, P., Guillemin, P., Gusmeroli, S., Sundmaeker, H., Bassi, A., Soler Jubert, I., Mazura,
M., Harrison, M., Eisenhauer, M. and Doody, P. (2009), Internet of Things Strategic Research
Roadmap, European Commission –Information Society and Media DG, Bruessels.
Industry 4.0
253
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
Wenger, E. (1998), Communities of Practice, Learning, Meaning, and Identity, Cambridge University
Press, Cambridge.
Wilkesmann, M. and Wilkesmann, U. (2011), “Knowledge transfer as interaction between experts and
novices supported by technology”,VINE –Journal of Information and Knowledge Management
Systems, Vol. 41 No. 2, pp. 96-112.
Wilkesmann, U. (2015), “Imaginary contradictions of university governance”, in Welpe, I.M.,
Wollersheim, J., Ringelhan, S. and Osterloh, M. (Eds) Incentives and Performance: Governance of
Research Organizations, Springer International Publishing, Cham, pp. 189-205.
Wilkesmann, U. and Schmid, C.J. (2014), “Intrinsic and internalized modes of teaching motivation”,
Evidence-Based HRM: A Global Forum for Empirical Scholarship, Vol. 2 No. 1, pp. 6-27.
Wilkesmann, U., Wilkesmann, M. and Virgillito, A. (2009), “The absence of cooperation is not
necessarily defection: structural and motivational constraints of knowledge transfer in a social
dilemma situation”,Organization Studies, Vol. 30 No. 10, pp. 1114-1164.
Xu, X., Sheng, Q.Z., Zhang, L.-J., Fan, Y. and Dustdar, S. (2015), “From big data to big service”,
Computer, Vol. 48 No. 7, pp.80-83.
Yin, R.K. (2014), Case Study Research. Design and Methods, Sage, Thousand Oaks, CA.
Zhang, X., Peek, W.H., Pikas, B. and Lee, T. (2016), “The transformation and upgrading of the Chinese
manufacturing industry: based on “German industry 4.0”,Journal of Applied Business and
Economics, Vol. 18 No. 5, pp. 97-105.
Zhang, Z., Liu, S. and Tang, M. (2014), “Industry 4.0: challenges and opportunities for Chinese
manufacturing industry”,Tehnicki Vjesnik/Technical Gazette, Vol. 21 No. 6, pp. 3-4.
Corresponding author
Maximiliane Wilkesmann can be contacted at: maximiliane.wilkesmann@tu-dortmund.de
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
VJIKMS
48,2
254
Downloaded by 84.141.151.100 At 02:32 17 May 2018 (PT)
