The Industrial Internet of Things from a Management Perspective: A Systematic Review of Current Literature

Abstract

The Industrial Internet of Things (IIoT) refers to a novel manufacturing paradigm. In its core, it enables real-time, smart, horizontal, and vertical connection of machines, objects, and people resulting in a smart factory. To date, the IIoT has primarily been researched from a technical perspective, while economic research is still in its infancy. In order to promote scientific discussion from a management perspective, this paper aims at systematically analyzing and displaying the current state of economic IIoT research. Thus, research gaps can be identified and targeted future management research can be supported. A systematic literature review is chosen as research method since it is appropriate for the identification, evaluation, synthesis and discussion of existing academic works. A structured selection process with regard to high quality and subject relevance revealed 52 publications published between 2011 and 2016 to be further analyzed in detail. This examination identified four topics discussed in current management literature. Most of the identified articles address IIoT Ecosystem aspects. This includes IIoT-related strategic implications in terms of business partners and other stakeholders, e.g., non-governmental organizations. The topic IIoT Business Models deals with IIoT-triggered effects on established business models to ensure future viability as well as with novel, innovative business concepts. Literature focusing on IIoT Technology Adoption addresses strategic recommendations in terms of both manufacturing transition and adoption requirements. Lastly, IIoT Qualification articles dwell on implications of increasingly digitized work environments for appropriate job designs and qualification requirements. By providing a comprehensive and clearly displayed current state of research as well as showing respective research gaps, the findings are highly relevant for future economic IIoT research. Moreover, this article supports managerial practitioners in understanding the IIoT and its inevitable effects on industrial companies by presenting insights into strategic management in the era of digitized and connected industrial value creation and capture.

Full text

Emerging Trends in Marketing and Management– Vol II, No. 2/2017
http://www.etimm.ro/
The Industrial Internet of Things from a Management Perspective: A
Systematic Review of Current Literature
Abstract
The Industrial Internet of Things (IIoT) refers to a novel manufacturing paradigm. In its core, it enables real-time,
smart, horizontal, and vertical connection of machines, objects, and people resulting in a smart factory. To date,
the IIoT has primarily been researched from a technical perspective, while economic research is still in its infancy.
In order to promote scientific discussion from a management perspective, this paper aims at systematically
analyzing and displaying the current state of economic IIoT research. Thus, research gaps can be identified and
targeted future management research can be supported.
A systematic literature review is chosen as research method since it is appropriate for the identification, evaluation,
synthesis and discussion of existing academic works. A structured selection process with regard to high quality
and subject relevance revealed 52 publications published between 2011 and 2016 to be further analyzed in detail.
This examination identified four topics discussed in current management literature. Most of the identified articles
address IIoT Ecosystem aspects. This includes IIoT-related strategic implications in terms of business partners
and other stakeholders, e.g., non-governmental organizations. The topic IIoT Business Models deals with IIoT-
triggered effects on established business models to ensure future viability as well as with novel, innovative
business concepts. Literature focusing on IIoT Technology Adoption addresses strategic recommendations in
terms of both manufacturing transition and adoption requirements. Lastly, IIoT Qualification articles dwell on
implications of increasingly digitized work environments for appropriate job designs and qualification
requirements.
By providing a comprehensive and clearly displayed current state of research as well as showing respective
research gaps, the findings are highly relevant for future economic IIoT research. Moreover, this article supports
managerial practitioners in understanding the IIoT and its inevitable effects on industrial companies by presenting
insights into strategic management in the era of digitized and connected industrial value creation and capture.
Keywords: Industrial Internet of Things, Industry 4.0, Industrial Value Creation, Industrial Manufacturing,
Literature Review
JEL classification: L00, L60, M15, O32
Track number: 7. Entrepreneurship and Strategic Management
1. Introduction
The Industrial Internet of Things (IIoT) represents a novel paradigm of industrial value
creation. At its core, the IIoT, which refers to ‘Industry 4.0’ in the German-speaking world,
enables the real-time capable, intelligent, horizontal, and vertical connection of people,
machines, and objects by employing cyber-physical systems (CPS) and the internet (Bauer et
al., 2014). The equipment of machines and products with embedded systems like actuators,
sensors, and microcomputers provides them with intelligence, resulting in a so-called smart
factory. This autonomous factory enables a flexible and efficient execution of production and
results in increased resource efficiency (Rehage et al., 2013), higher degrees of customization
(Kagermann et al., 2013), highly profitable business models (Bauernhansl, 2014), and job
designs suitable for future employee requirements (Hirsch-Kreinsen and Weyer, 2014; Spath
et al., 2013). This new production approach aims at addressing arising challenges that
manufacturing enterprises have to deal with. Among others, those companies have to face
shortened technology and innovation cycles, the necessity of bringing highly customized
products in accordance with the cost of a large-scale production, and intensified competition
originating in Asia, in particular (Bauer et al., 2014; Bauernhansl, 2014; Dais, 2014).
The full exploitation of these IIoT-inherent benefits requires not only a targeted adjustment of
associated operative processes with regard to economic optimization. To a greater degree,
strategic implications related to the implementation of the IIoT have to be considered to ensure
a reasonable and targeted IIoT application (da Rosa Cardoso et al., 2012). Due to its technical

Emerging Trends in Marketing and Management– Vol II, No. 2/2017
http://www.etimm.ro/
core, current scientific literature primarily concentrates on technological bases, challenges, and
opportunities. Contrary, management research, particular with regard to strategic implications,
is still in its infancy and has a lot of catching-up to do (Brettel et al., 2014; Emmrich et al.,
2015; Krückhans and Meier, 2013). Hence, the present article aims at fostering scientific
discussion regarding IIoT from a strategic management perspective by revealing the current
state of research as well as identifying areas to be addressed by future research efforts. By
doing so, the following research questions are pursued:
RQ1: Which research areas have been addressed by strategic management literature regarding
the IIoT so far?
RQ2: Which research areas can be proposed for future management research in the context of
the IIoT?
To answer these questions, a systematic review of relevant literature is applied. The next
section presents the theoretical foundations of the IIoT before the proceeding of the systematic
literature review is explained. The subsequent section elaborates on identified research areas
in the context of the IIoT from a strategic perspective. Next, the current body of literature is
discussed before, finally, future research areas to be approached are revealed.
2. Theoretical foundations: Industrial Internet of Things
Since the official introduction of the German equivalent of the term IIoT, i.e., ‘Industry 4.0’,
in 2011, an increasing amount of research has been published in the recent years. Nevertheless,
academic discussion still did not agree on one definition of the term ‘Industrial Internet of
Things’ (Hermann et al., 2015). This indeterminacy is reasoned by the fact that the IIoT
comprises various technical development steps, which are well-known. However, in
combination and connected via networks like the internet, allowing for interactions, they have
the potential to create significant innovations (Bienzeisler et al., 2014; Hermann et al., 2015).
This results in different definitions of IIoT, depending on the respective author’s focus and
perspective.
According to Spath et al. (2013), a smart and self-controlling production environment is the
key element of the IIoT. The upgrading of objects and systems to CPS is the crucial technology
that facilitates the autonomous steering of objects through the value chain. In the core, this
definition is congruent with those of the BMBF (2014) and Windelband (2014), who argue for
CPS as the key enabler of a smart, self-controlling production as well. Emmrich et al. (2015)
expand CPS as an elementary technology by embedded systems, cloud computing, and the
smart factory. Furthermore, they regard the development of software services as a key element
of the IIoT. Bienzeisler et al. (2014) are in line with these definitions by arguing that upgrading
machines and products to intelligent objects is the core of the IIoT. According to them,
manufacturing orders guide themselves through the production by considering real-time
information about employees’ capacities and competencies. Beyond that, they also see
potential for novel business models based on smart products, particularly with regard to new
service offerings. This is quite similar to Kaufmann’s (2015) definition, who emphasizes,
besides an autonomous and self-controlling production, the importance of real-time feedback
of information provided by downstream processes for real-time process improvements. Based
on the strong linkage of smart products and novel service offerings, service plays a crucial role
for the IIoT (Kaufmann, 2015). In line with the already stated definitions, Feld et al. (2013)
describe the IIoT as the opportunity for companies to create new technologies and services. In
addition, they separate the IIoT into the two key parts ‘smart products’ and ‘smart production’.
Contrary to the previous definitions, Sendler (2013) puts software in the center of his definition.
He argues that the application of software in the production, products, and services as well as
their interconnectedness is the core of the IIoT. In accordance with the above-mentioned
definitions, he also recognizes the high potential for new products and services. Bauer et al.

Emerging Trends in Marketing and Management– Vol II, No. 2/2017
http://www.etimm.ro/
(2014) have a broader view on the topic and identify horizontal and vertical value chain
connections, which are both characterized by real-time capability and intelligence. This is not
limited to just objects, but also includes human beings and entire information and
communication systems. This is supported by Bischoff et al. (2015), who view the IIoT as a
further development of production and value creation systems by linking the real world to the
digital world. Kempermann and Lichtblau (2014) are going even further and define the IIoT as
the opportunity for all participants in the value-adding process to communicate in real-time
using web technologies in order to achieve an autonomous and intelligent value chain.
In conclusion, these definitions comprise three essential topics: The first group of authors is
focused on causal technologies like CPS, smart factory, and cloud computing. The second
group of authors expands this approach by the creation of novel services. The third group of
authors includes the entire value chain and the potential of vertical and horizontal
interconnection and integration. For the further proceeding of this study, the various existing
definitions are combined in the following definition:
The IIoT refers to the progressing digitization and smart connection of industrial
manufacturing including all company functions, across all products and services, by
integrating the entire value chain, resulting in novel business models, and by means of new
digital technologies.
3. Methodology
A systematic and integrative literature review serves as an appropriate methodological
approach for the achievement of this article’s objectives for two reasons. Firstly, it is
systematic, scientific, transparent, elaborate, and replicable for the identification, evaluation,
synthesis, and discussion of existing works (Fink, 2013; Tranfield et al., 2003). Secondly, the
exemplary collocation of several studies serves not only the presentation of the state-of-
knowledge, but also the disclosure of critical and disregarded aspects or unsolved problems
enabling the derivation of needs for further research (Fink, 2013). The methodological
approach used in this article follows the works of Hohenstein et al. (2014), Rashman et al.
(2009), Soni and Kodali (2011), and Winter and Knemeyer (2013).
For the review and evaluation process, literature published between 2011, i.e., the first time the
German term ‘Industry 4.0’ emerged, and 2016 was considered. Keywords to be searched in
the databases Business Source Complete (EBSCO), ScienceDirect, ABI/Inform, and Google
Scholar were derived from existing literature and enriched by the results of discussions with
independent research colleagues. Eventually, the search queries comprised a combination of
several keywords describing the IIoT as well as topical related terms, e.g., connected, smart,
factory, manufacturing, industry, industry 4.0, and industrial internet of things. These were
extended by their respective German synonyms since the IIoT was first defined in Germany.
The single search terms were linked by “AND” and “OR”, which is recommended by Theisen
(2013).
Regarding journals to be considered in a literature review, there is wide consensus that the
integration of frequently cited papers results in an enhanced quality of a literature review (e.g.,
McKinnon, 2013). Nevertheless, Cooper (1989, p. 58) argues that relying on only journal
articles is appropriate “when the published research contains several dozen, or in some cases
several hundred, relevant works”. As this is not applicable to the term IIoT due to its degree of
novelty in research, reliable and relevant collected editions, book chapters, and other studies
extended journal articles.
The database research and scan of the results’ titles initially identified 186 articles, which
contained at least one of the keyword combinations in the title or abstract, were published
between 2011 and 2016, and were relevant for the purpose of this study. After removing
duplicates, their abstracts and conclusions were further assessed regarding their relevance to

Emerging Trends in Marketing and Management– Vol II, No. 2/2017
http://www.etimm.ro/
the research questions. Additionally, the relevant publications’ bibliographies were scanned
(snowball method) to avoid leaving out potential relevant articles not registered in the searched
databases. This procedure is widely accepted and has been employed in existing literature
reviews (e.g., David and Han, 2004; Franke and zu Knyphausen-Aufseß, 2014; Soni and
Kodali, 2011; Webster and Watson, 2002; Winter and Knemeyer, 2013). This resulted in a
sample of 79 articles to be subsequently read in their entirety. Finally, the articles’ relevance
and quality was evaluated, particularly with regard to research goals, definitions of key terms,
methodological rigor, and results. Non-relevant documents were consequently extracted
resulting in a final list of 52 articles, which represented the data corpus of the subsequent
integrative literature review. Thereby, the high quality and comprehensiveness of the article at
hand is ensured. Figure 1 gives an overview of the article selection process in order to ensure
a systematic, transparent, and replicable literature review.
Figure 1. Article selection process
The final sample of 52 documents was analyzed in depth and classified according to the
inductively developed categories IIoT Business Models, IIoT Technology Adoption, IIoT
Ecosystems, and IIoT Qualification. In the final step, the classified articles were compared,
critically reflected, and discussed to work out the current state of IIoT-related strategic
management research. This facilitates the revealing of research areas, which are still
underrepresented and in need of further examination.
4. Findings
In the following, the results of the present literature review are broken down by explicitly
describing the four developed research areas.
4.1. Research area 1: IIoT Business Models
Proceeding digitization and interconnection of industrial manufacturing has not only the
potential to adjust and innovate manufacturers’ business models, but to a greater degree even
premises novel business models. Accordingly, several authors argue that a company’s survival
in an industrial environment characterized by highly digitized and interconnected factories,
products, machines, and humans can only be ensured by means of new business models
(Bauernhansl, 2014; Buhr, 2015; Rudtsch et al., 2014). This is reasoned by a radically changing
behavior of customers in terms of increasing orientation towards using instead of possessing a
product, intensified competition emanating from new players mastering the new digital
environment, and new disruptive technologies like virtual reality, blockchain, and mobile
computing that represent the impetus of the present and future digitization (Gassmann and
Sauer, 2016). Similarly, Hartmann and Halecker (2015) emphasize the danger of new players,
which will innovate and dominate the customer interface, using the example of the automotive
industry. Hence, automobile manufacturers are forced to adjust the customer perspective of
their business models. Otherwise, the IT industry will make advantage of the transition from
traditional to electric engines and occupy the customer interface while degrading traditional
manufacturers to mere suppliers. These changes do not necessarily result in radically, i.e.,
Search in databases:
EBSCO
Science Direct
ABI/Inform
Google Scholar
Required criteria:
time period,
keywords,
full text access
Reading of
abstracts and
conclusions, adding
of references
Required criteria:
relevance, quality,
no duplicates
Reading articles in
their entirety
Required criteria:
high quality,
relevance
Final
sample:
52 articles
79
articles
186
articles

Emerging Trends in Marketing and Management– Vol II, No. 2/2017
http://www.etimm.ro/
disruptively new products or services, but can also be an evolutionary further development of
respective offers (Emmrich et al., 2015).
Academic literature seems to agree on the importance of an increasing service-orientation as a
fundamental characteristic of novel, future-oriented, and viable business models since the
previously dominant separation of product and service businesses will increasingly disappear
(Fleisch et al., 2014; Kagermann, 2014; Lasi et al., 2014; Rennung et al., 2016; Wolter et al.,
2015; Xu, 2012). Consequently, manufacturing companies being focused on their products
have to adjust their value offers with reference to a combination of products and services, so-
called hybrid solutions. This enables a greater range of offers, which represents a crucial
element in terms of facing increasing competition (Bollhöfer et al., 2015; Kans and Ingwald,
2016). Such new services are predominantly based on the utilization of data, which represent
an important part of new, IIoT-related business models (Kaufmann, 2015). Moreover, the
application of data enables novel billing models based on real usages and performance demands
(Kaufmann, 2015; Xu, 2012).
Besides increasing service-orientation, ecosystem integration plays another important part in
new business models for the IIoT. According to Leminen et al. (2012), new business
relationships based on adjusted business models will result in a more and more collaborative
business ecosystem. This is supported by Iivari et al. (2016), who argue that the IIoT
necessitates novel ecosystem-capable business models, particularly with regard to increasing
value co-creation and co-capturing.
Regarding precise implications of the IIoT on established business models respectively for
novel business models, Obermaier (2016) points out the importance of distinguishing between
users, who apply IIoT technologies to increase process efficiency, and providers, who aim at
establishing innovative, intelligent, and connected products. Independent of this
differentiation, IIoT-specific business models have the potential to transform entire industries
(Bauernhansl, 2014). Thus, Emmrich et al. (2015) emphasize the importance of regarding
business models due to their complexity always in their entirety. Finally, Burmeister et al.
(2016) as well as Rudtsch et al. (2014) provide concrete assistance for managers to innovate
their companies’ business models.
4.2. Research area 2: IIoT Technology Adoption
The review of relevant publications revealed that a successful implementation and utilization
of the IIoT is in need of strategic support by companies’ top management. Nevertheless,
companies still feel uneasy with regard to the adoption of advanced manufacturing
technologies like the IIoT due to high investments, lack of expertise, and unclear benefits
(Khan and Nasser, 2016). Similarly, Hartmann and Halecker (2015) argue that industrial
manufacturers have no clear understanding about successful implementation of CPS, i.e., a
core technology of the IIoT. However, as Saberi and Yusuff (2011) ascertain, the adoption of
advanced manufacturing technologies results in enhanced performance, e.g., in terms of
increased flexibility and reaction time, making IIoT adoption of strategic importance. Hence,
Ganzarain and Errasti (2016) emphasize the need of a strategic process for a successful
implementation of the IIoT. Therefore, it is important to consider a company’s production in
its entirety and not focusing on only single parts of the production (Becker, 2015; Hirsch-
Kreinsen, 2014). Otherwise, it might come to problems regarding the coordination and
communication within the production, which outweigh potential benefits and advantages. This
does not mean that it is always reasonable to implement the IIoT in the whole production. A
company’s management always has to identify suitable areas to be upgraded by the IIoT against
the background of the company’s specific situation, strategic orientation, and aims (Krückhans
and Meier, 2013).

Emerging Trends in Marketing and Management– Vol II, No. 2/2017
http://www.etimm.ro/
When a manufacturer’s top management has decided to adopt the IIoT, Binner (2014) argues
that it is crucial to not concentrating exclusively on a technological implementation. It is
decisive for the success to consider the three dimensions human, organization, and technology
in a balanced manner. Employees have to be motivated to implement and apply the new
production approach, required IIoT technologies have to be developed or acquired and
integrated, and the entire organization and its structures have to be adjusted to support the first
two dimensions. Erol et al. (2016) and Hartmann and Halecker (2015) developed a process to
support companies’ top management in adopting the IIoT. They consist of three respectively
five steps, both starting with the generation of an understanding of the IIoT and novel business
dynamics. This is followed by the development of new business models and the adjustment of
business strategies before, finally, these new strategies and business models are translated into
concrete projects.
4.3. Research area 3: IIoT Ecosystems
According to several publications, the IIoT is closely linked to a change in the business
ecosystems of affected companies (Iivari et al., 2016; Lasi et al., 2014). The IIoT enables
increased transparency of production and data as well as data processing and transfer, which
are a prerequisite for the intensified integration of a company’s ecosystem (Schließmann, 2014;
Schuh et al., 2015). Kleinemeier (2014) describes that the IIoT in general will supersede the
rigid structures of the traditional atomization pyramid by interconnected, decentralized, and
self-organizing services. Consequently, future networks and business ecosystems are
characterized by their ability to quickly establish and break up (Lasi et al., 2014; Pau, 2012).
In their study, Hartmann and Halecker (2015) conclude that in many cases, managers are not
yet aware of potential benefits associated with an intensified integration of their business
ecosystems, although they are manifold. The IIoT does not only facilitate cross-company
connection and information exchange, but also necessitates an increasing cooperation and
collaboration between different ecosystem participants. Manufacturing companies have to
draw on partners supplying them with unavailable resources and complementing products and
services to be able to offer novel hybrid solutions that are enabled by the IIoT (Kagermann et
al., 2013; Spring and Araujo, 2013; Weill and Woerner, 2015). Hence, such cooperations are
crucial for the long-term success of manufacturers (Shermann and Chauhan, 2016). As a result,
the IIoT leads to novel collaborations between different companies, which did not exist before
(Diemer, 2014; Wischmann et al., 2015). Participants in these new ecosystems can be, among
others, companies from other industries (Bermann and Korsten, 2014, Geisberger and Broy,
2012), governments as well as non-governmental organizations (Bermann and Korsten, 2014),
industrial associations (Rong et al., 2015), and providers of new platforms, applications,
services, and cloud technologies (Sendler, 2016). In this context, Rong et al. (2015) emphasize
the particular strategic importance of partners possessing software-related competencies and
technologies for manufacturing companies. Of course, the integration of a company’s
ecosystem and associated increasing cooperation with novel partners is not without challenges,
which have to be addressed on a strategic level. In this regard, literature instances lack of trust
(Brettel et al., 2014), missing standardization (Köhler et al., 2015), and uncertainty regarding
IT responsibilities (Hornung, 2016).
4.4. Research area 4: IIoT Qualification
Finally, various authors deal with strategic IIoT-related issues regarding employee
qualifications. In general, the analyzed literature agrees on a higher level of qualification as a
prerequisite of IIoT application (Ahrens and Spöttl, 2015; Dombrowski et al., 2014). According
to Hirsch-Kreinsen (2016), this development can be traced back to two reasons. On the one
side, easy tasks that can be performed autonomously by computers based on algorithms and

Emerging Trends in Marketing and Management– Vol II, No. 2/2017
http://www.etimm.ro/
rules will disappear. Consequently, only jobs, which require better skilled employees, will
remain. On the other side, associated with the IIoT, IT increasingly finds its way into all jobs.
This necessitates a general adjustment of workforce qualifications. In particular, the IIoT
results in increasing knowledge-based work (Arnold et al., 2016; Ecker and Weyerstraß, 2016)
and the need to control more complex machinery (Khan and Nasser, 2016). In particular, IIoT-
ready jobs are in need of employees, which master complexity, uncertainty, and a flexible
handling of changing conditions (Bonekamp and Sure, 2015; Pfeiffer and Suphan, 2015). In
addition, Bochum (2015) emphasizes the more than ever importance of life-long learning.
5. Concluding discussion
The following section briefly and concisely addresses the research goals defined in the
introduction, i.e., the identification of IIoT-related research areas in management literature and
research gaps, which put themselves forward for further research activities.
Regarding the revealing of recent research topics covered by IIoT literature from a strategic
perspective, i.e., research question 1, the review at hand identifies four areas. Most of the
examined articles are concerned with IIoT Ecosystems (20 out of 52 articles) closely followed
by IIoT Business Models (19 out of 52 articles). With reference to the first, literature shows
that the IIoT enables the intensified integration of business ecosystems that are more dynamic
and flexible than ever before. Associated with this dynamization and increasing integration of
the entire ecosystem into a company’s own production processes, manufacturers have the
potential to take advantage of several benefits. Therefore, companies have to collaborate with
completely new ecosystem participants. This allows the access to required but not internally
available resources to offer novel hybrid IIoT solutions. With regard to the latter, the IIoT not
only fosters new, innovative business models, but also requires established companies to
innovate their current business models. Literature reasons this by changing business
environments as well as the danger of novel players new to the industry. What these new
business models all have in common is their consequent service orientation, which is
predominantly based on the utilization of data. Furthermore, as already outlined above, the
integration of a company’s business ecosystem into its business model is ever more crucial for
its future success and viability.
Literature dealing with IIoT Technology Adoption and IIoT Qualification follows, both
addressed by 9 out of 52 articles. Referring to the first, literature agrees that the adoption of the
IIoT is associated with increased firm performance. Nevertheless, many companies still feel
uneasy regarding the adoption of IIoT-related technologies. As emphasized by some authors,
companies have to consider their entire production process, but identify and focus on suitable
areas, where the implementation of this new production approach is promising. Moreover,
literature argues that it is important to concentrate not only on technology when it comes to
IIoT adoption, but also to consider human and organizational aspects. Regarding the latter,
contrary to the fear of many people, the IIoT will not result in completely autonomous factories
without any human being involved. Employees are still necessary for future production, but
require a higher level of qualification since easy tasks will be performed autonomously. In
particular, literature indicates that future employees have to be skilled in terms of IT know-
how and problem-solving.
The following table 1 gives an overview of the identified literature in the context of strategic
IIoT research, their assessment regarding the allocation to the four developed research areas,
as well as the frequencies of each of the research areas.

Emerging Trends in Marketing and Management– Vol II, No. 2/2017
http://www.etimm.ro/
Research areas
Author(s)
IIoT Business
Models
IIoT Technology
Adoption
IIoT
Ecosystems
IIoT
Qualification
Overall results
19
9
20
9
Ahrens & Spöttl (2015)
X
Arnold et al. (2016)
X
Bauernhansl (2014)
X
Becker (2015)
X
Berman & Korsten (2014)
X
Binner (2014)
X
Bochum (2015)
X
Bollhöfer et al. (2015)
X
Bonekamp & Sure (2015)
X
Brettel et al. (2014)
X
Buhr (2015)
X
Burmeister et al. (2016)
X
Diemer (2014)
X
Dombrowski et al. (2014)
X
Ecker & Weyerstraß (2016)
X
Emmrich et al. (2015)
X
Erol et al. (2016)
X
Fleisch et al. (2014)
X
Ganzarain & Errasti (2016)
X
Gassmann & Sauer (2016)
X
Geisberger & Broy (2012)
X
Hartmann & Halecker (2015)
X
X
X
Hirsch-Kreinsen (2016)
X
Hirsch-Kreinsen (2014)
X
Hornung (2016)
X
Iivari et al. (2016)
X
X
Kagermann (2014)
X
Kagermann et al. (2013)
X
Kans & Ingwald (2016)
X
Kaufmann (2015)
X
Khan & Nasser (2016)
X
X
Kleinemeier (2014)
X
Köhler et al. (2015)
X
Krückhans & Meier (2015)
X
Lasi et al. (2014)
X
X
Leminen et al. (2012)
X
Obermaier (2016)
X
Pau (2012)
X
Pfeiffer & Suphan (2015)
X
Rennung et al. (2016)
X
Rong et al. (2015)
X
Rudtsch et al. (2014)
X
Saberi a et al. (2011)
X
Schließmann (2014)
X
Schuh et al. (2015)
X
Sendler (2016)
X
Shermann & Chauhan (2016)
X
Spring & Araujo (2013)
X
Weill & Woerner (2015)
X
Wischmann et al. (2015)
X
Wolter et al. (2015)
X
Xu (2012)
X
Table 1. Overview of the 52 analyzed articles

Emerging Trends in Marketing and Management– Vol II, No. 2/2017
http://www.etimm.ro/
The focus of strategic management literature on these four research areas in combination with
the fact that the IIoT still represents a rather young research field emerged not before 2011
emphasizes the need for further research. Since IIoT research from a management perspective
is still in its early stages, it is one aim of the present article to identify possible starting points
for future studies. Hence and with regard to the second research question, the following
directions of further research are derived from the findings elaborated on above:
1. Current literature agrees that established business models have to change due to the IIoT
resulting in novel business models characterized by an increasing service-orientation. Over
and above this, the body of literature lacks details about concrete business model changes
and the characterization of IIoT-adapted business models. Hence, future research should
address this gap by conducting qualitative, exploratory studies, which analyze in general
how single business model components are changing due to the IIoT and how these changes
are linked. Here, it is important to examine the business models in their entirety and not only
single parts of them. Additionally, future studies should also consider industry-specific
differences in terms of business model changes. By identifying those changes that are
particularly true for single industry sectors, this would serve as valuable and helpful support
for companies from respective industry sectors.
In addition, future research efforts should address not only potentials and possibilities of the
IIoT for the change of established business models, but also for completely new, innovative
business models. In this context, it is very interesting to examine, which players will operate
these innovative business models: established companies or new companies, e.g., from other
industries.
2. The review of current literature dealing with strategic aspects of the IIoT clearly shows that
affected companies still feel very uneasy regarding the decision as to whether adopt the IIoT
and if yes, in which areas of the company to adopt it to what extent. In order to shed some
light on this aspect, researchers are recommended to examine different potential IIoT
adoption factors and consequently identify those factors, which either are an obstacle to IIoT
adoption or facilitate IIoT adoption in a first step. Based on this and against the background
that literature appreciates the necessity of a strategic adoption process, further research can
subsequently develop a detailed process that considers relevant factors determining the
adoption of the IIoT. In this context, future studies should additionally consider different
roles of respective companies, e.g., IIoT providers and users, firm sizes, and business
models.
3. Current research emphasizes the strategic importance of business ecosystems for companies
in the context of the IIoT and the associated necessity of intensified ecosystem integration.
Although some publications identify single players that are new in IIoT ecosystems,
literature lacks a comprehensive examination of ecosystem changes. Consequently,
academics should investigate how business ecosystems of established companies are
changing. This involves the examination of the emergence of entirely new participants,
which so far did not play any role, as well as the changing importance of different,
established roles. Moreover, it is interesting to analyze how the relationships between single
ecosystem players are set up.
4. Regarding employees, literature agrees that they are in need of another profile of
qualifications in the future, but respective authors stay on a rather general level. Thus, future
research efforts are recommended to analyze the concrete roles of future employees and
relating thereto which specific skills and know-how are required. Based on these
qualification profiles, recommendations for strategic actions can be derived to both adjust
and improve the skills of existing employees, whose qualifications are no longer adequate,

Emerging Trends in Marketing and Management– Vol II, No. 2/2017
http://www.etimm.ro/
as well as regularly adapt present skills and know-how to changing requirements in the
context of the IIoT.
5. Finally, the present literature review shows that there exists rather little literature on strategic
aspects of the IIoT in general and high-quality academic literature in particular, compared
to other well-researched topics like business models, technology management, or human
resource management. Therefore, future high-quality research is indispensable, not only
with regard to the four highlighted research areas, but also above, to take a further step
towards sufficiently examining the young research field of the IIoT.
In conclusion, the present systematic review and analysis of 52 publications from 2011 until
2016 contributes to a better comprehension of the state of research in the context of strategic
aspects of the IIoT. Hence, the review provides scholars with a guidance for necessary future
research approaches to gain a more comprehensive understanding of the IIoT from a strategic
perspective. Furthermore, companies operating in the field of the IIoT obtain a thorough
workup of existing literature dealing with strategic aspects of the digitized and interconnected
industrial value creation. This fosters their understanding of possible implications for their own
businesses and future viability.
References
AHRENS, D. & SPÖTTL, G. 2015. Industrie 4.0 und Herausforderungen für die
Qualifizierung von Fachkräften. In: H. Hirsch-Kreinsen, P. Ittermann & J. Niehaus, eds.
Digitalisierung industrieller Arbeit, Nomos. pp.184-205.
ARNOLD, D., ARNTZ, M., GREGORY, T., STEFFES, S. & ZIERAHN, U. 2016.
Herausforderungen der Digitalisierung für die Zukunft der Arbeitswelt. ZEW policy brief,
2016, 1–5.
BAUER, W., SCHLUND, S., MARENBACH, D. & GANSCHAR, O. 2014. Industrie 4.0 –
Volkswirtschaftliches Potenzial für Deutschland. Berlin & Stuttgart, Germany: BITKOM
& Fraunhofer IAO.
BAUERNHANSL, T. 2014. Die Vierte Industrielle Revolution – Der Weg in ein
wertschaffendes Produktionsparadigma. In: T. Bauernhansl, M. Hompel & B. Vogel-
Heuser, eds. Industrie 4.0 in Produktion, Automatisierung und Logistik, Springer
Fachmedien. pp.5-35.
BECKER, K.-D. 2015. Arbeit in der Industrie 4.0 – Erwartungen des Instituts für angewandte
Arbeitswissenschaft e.V. In: A. Botthof & E.A. Hartmann, eds. Zukunft der Arbeit in
Industrie 4.0, Springer. pp.23-29.
BERMAN, S. & KORSTEN, P. 2014. Leading in the connected era. Strategy & Leadership,
42, 37–46.
BINNER, H.F. 2014. Industrie 4.0 bestimmt die Arbeitswelt der Zukunft. e & i Elektrotechnik
und Informationstechnik, 131, 230-236.
BIENZEISLER, B., SCHLETZ, A. & GAHLE, A.-K. 2014. Industrie 4.0 Ready Services.
Technologietrends 2020. Stuttgart, Germany: Fraunhofer IAO.
BISCHOFF, J., TAPHORN, C., WOLTER, D., BRAUN, N. FELLBAUM, M. et al. 2015.
Erschließen der Potenziale der Anwendung von ‚Industrie 4.0‘ im Mittelstand. Berlin,
Germany: BMWi.
BMBF. 2014. Industrie 4.0: Innovationen für die Produktion für morgen. Bonn, Germany:
BMBF.
BOCHUM, U. 2015. Gewerkschaftliche Positionen in Bezug auf "Industrie 4.0". In: A. Botthof
& E.A. Hartmann, eds. Zukunft der Arbeit in Industrie 4.0, Springer. pp.31-44.

Emerging Trends in Marketing and Management– Vol II, No. 2/2017
http://www.etimm.ro/
BOLLHÖFER, E., BUSCHAK, D., LERCH, C. & GOTSCH, M. 2015. B2B-Dienstleistungen
im Kontext von Industrie 4.0 – Neue Formen der Interaktion im Maschinen- und
Anlagenbau. In: M. Bruhn & K. Hadwich, eds. Interaktive Wertschöpfung durch
Dienstleistungen, Springer Fachmedien. pp.517-539.
BONEKAMP, L. & SURE, M. 2015. Consequences of Industry 4.0 on Human Labour and
Work Organisation. Journal of Business and Media Psychology, 6, 33-40.
BRETTL, M., FRIEDERICHSEN, N., KELLER, M. & ROSENBERG, M. 2014. How
Virtualization, Decentralization and Network Building Change the Manufacturing
Landscape: An Industry 4.0 Perspective. International Journal of Mechanical, Aerospace,
Manufacturing, Industrial Science and Engineering, 8, 37-44.
BUHR, D. 2015. Weit mehr als Technik: Industrie 4.0. Ifo Schnelldienst, 68, 10-12.
BURMEISTER, C., LÜTTGENS, D. & PILLER, F.T. 2016. Business Model Innovation for
Industrie 4.0. Why the "Industrial Internet" Mandates a New Perspective on Innovation.
Die Unternehmung, 70, 124-152.
COOPER, H. 1989. Integrating Research. A Guide for Literature Reviews (= Applied Social
Research Methods Series, Vol. 2) (2nd ed.), Sage.
DAIS, S. 2014. Industrie 4.0 – Anstoß, Vision, Vorgehen. In: T. Bauernhansl, M. ten Hompel
& B. Vogel-Heuser, eds. Industrie 4.0 in Produktion, Automatisierung und Logistik –
Anwendung, Technologien, Migration, Springer. pp.625-634.
DA ROSA CARDOSO, R., DE LIMA, E.P. & DA COSTA, S.E.G. 2012. Identifying
organizational requirements for the implementation of Advanced Manufacturing
Technologies (AMT), Journal of Manufacturing Systems, 31, 367-378.
DAVID, R.J. & HAN, S.-K. 2004. A systematic assessment of the empirical support for
transaction cost economics. Strategic Management Journal, 25, 39-58.
DIEMER, J. 2014. Sichere Industrie 4.0-Plattformen auf Basis von Community-Clouds. In: T.
Bauernhansl, M. ten Hompel & B. Vogel-Heuser, eds. Industrie 4.0 in Produktion,
Automatisierung und Logistik: Anwendung Technologien Migration, Springer
Fachmedien. pp.369-396.
DOMBROWSKI, U., RIECHEL, C. & EVERS, M. 2014. Industrie 4.0 - Die Rolle des
Menschen in der vierten industriellen Revolution. In: W. Kersten, H. Koller & H. Lödding,
eds. Industrie 4.0. Wie intelligente Vernetzung und kognitive Systeme unsere Arbeit
verändern, Gito. pp.129-153.
ECKER, B. & WEYERSTRAß, K. 2016. Industrie 4.0 als Chance für eine stärkere Industrie,
als Schlüssel für mehr Wettbewerbsfähigkeit. Wirtschaftspolitische Blätter, 63, 321–334.
EMMRICH, V., DÖBELE, M., BAUERNHANSL, T., PAULUS-ROHMER, D., SCHATZ, A.
& WESKAMP, M. 2015. Geschäftsmodell-Innovation durch Industrie 4.0. Chancen und
Risiken für den Maschinen- und Anlagenbau. Munich, Germany: Dr. Wieselhuber &
Partner GmbH, Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA.
EROL, S., SCHUMACHER, A. & SIHN, W. 2016. Strategic guidance towards Industry 4.0 –
a three-stage process model. 6th International Conference on Competitive Manufacturing.
Stellenbosch, South Africa, 27-29 January 2016.
FELD, T., HOFFMANN, M. & SCHMIDT, R. 2013. Industrie 4.0. Vom intelligenten Produkt
zur intelligenten Produktion. In: A.-W. Schee,r ed. Industrie 4.0 - Wie sehen
Produktionsprozesse im Jahr 2020 aus?, imc information multimedia communication. pp.
36-43.
FINK, A. 2013. Conducting research literature reviews: from the Internet to paper (4th ed.),
Sage.
FLEISCH, E., WEINBERGER, M. & WORTMANN, F. 2014. Geschäftsmodelle im Internet
der Dinge. HMD Praxis der Wirtschaftsinformatik, 51, 812-826.

Emerging Trends in Marketing and Management– Vol II, No. 2/2017
http://www.etimm.ro/
FRANKE, T. & ZU KNYPHAUSEN-AUFSESS, D. 2014. On dominant logic: review and
synthesis. Journal of Business Economics, 84, 27-70.
GANZARAIN, J. & ERRASTI, N. 2016. Three stage maturity model in SME’s toward industry
4.0. Journal of Industrial Engineering and Management, 9, 1119-1128.
GASSMANN, O. & SAUER, R. 2016. Kreative Zerstörung 4.0: Trends und neue
Geschäftslogik. Wirtschaftspolitische Blätter, 63, 375–386.
GEISBERGER, E. & BROY, M. 2012. AgendaCPS. Integrierte Forschungsagenda Cyber-
Physical Systems. Munich, Germany: acatech – Deutsche Akademie der
Technikwissenschaften.
HARTMANN, M. & HALECKER, B. 2015. Management of Innovation in the Industrial
Internet of Things. XXVI International Society for Professional Innovation Management
(ISPIM) Conference. Budapest, Hungary, 14-17 June 2015.
HERMANN, M., PENTEK, T. & OTTO, B. 2015. Design Principles for Industrie 4.0
Scenarios. A Literature Review. Dortung, Germany: TU Dortmund.
HIRSCH-KREINSEN, H. 2016. Digitization of industrial work: Development paths and
prospects. Journal for Labour Market Research, 49, 1–14.
HIRSCH-KREINSEN, H. 2014. Smart production systems. A new type of industrial process
innovation. DRUID Society Conference. Copenhagen, Denmark, 16-18 June 2014.
HIRSCH-KREINSEN, H. & WEYER, J. 2014. Wandel von Produktionsarbeit – ‚Industrie
4.0‘. Dortumund, Germany: Technische Universität Dortmund.
HOHENSTEIN, N.-O., FEISEL, E. & HARTMANN, E. 2014. Human resource management
issues in supply chain management research: A systematic literature review from 1998 to
2014. International Journal of Physical Distribution & Logistics Management, 44, 434-
463.
HORNUNG, G. 2016. Rechtliche Herausforderungen der Industrie 4.0. In: R. Obermaier, ed.
Industrie 4.0 als unternehmerische Gestaltungsaufgabe. Betriebswirtschaftliche,
technische und rechtliche Herausforderungen, Springer Gabler. pp.69-81.
IIVARI, M.M., AHOKANGAS, P., KOMI, M., TIHINEN, M. & VALTANEN, K. 2016.
Towards Ecosystemic Business Models in the Context of Industrial Internet. Journal of
Business Models, 4, 42–59.
KAGERMANN, H. 2014. Industrie 4.0 und Smart Services. In: W. Brenner & T. Hess, eds.
Wirtschaftsinformatik in Wissenschaft und Praxis, Springer. pp.243-248.
KAGERMANN, H., WAHLSTER, W. & HELBIG, J. 2013. Recommendations for
implementing the strategic initiative INDUSTRIE 4.0. Final report of the Industrie 4.0
Working Group. Frankfurt, Germany: Communication Promoters Group of the Industry-
Science Research Alliance.
KANS, M. & INGWALD, A. 2016. Business Model Development towards Service
Management 4.0. Procedia CIRP, 47, 489-494.
KAUFMANN, T. 2015. Geschäftsmodelle in Industrie 4.0 und dem Internet der Dinge.
Springer Fachmedien.
KEMPERMANN, H. & LICHBLAU, K. 2014. Dienstleistungspotentiale im Rahmen von
Industrie 4.0. Munich, Germany: vbw.
KHAN, A. & NASSER, K. 2016. Advanced Manufacturing Technologies for Smart and
Competitive Businesses. The IUP Journal of Operations Management, 15, 7–17.
KLEINEMEIER, M. 2014. Von der Automatisierungspyramide zu
Unternehmenssteuerungsnetzwerken. In: T. Bauernhansl, M. ten Hompel & B. Vogel-
Heuser, eds. Industrie 4.0 in Produktion, Automatisierung und Logistik: Anwendung
Technologien Migration, Springer Fachmedien. pp.571-579.

Emerging Trends in Marketing and Management– Vol II, No. 2/2017
http://www.etimm.ro/
KÖHLER, P., SIX, B. & MICHELS, J.S. 2015. Industrie 4.0: Ein Überblick. In: C. Köhler-
Schute & J. Amberg, eds. Industrie 4.0. Ein praxisorientierter Ansatz, KS-Energy-Verlag.
pp.17-43.
KRÜCKHANS, B. & MEIER, H. 2013. Industrie 4.0 - Handlungsfelder der digitalen Fabrik
zur Optimierung der Ressourceneffizienz in der Produktion. In: W. Dangelmaier, C. Laroque
& A. Klaas, eds. ASIM-Fachtagung „Simulation in Produktion und Logistik 2013, Heinz
Nixdorf Institut. pp.31-40.
LASI, H., KEMPER, H.-G., FETTKE, P., FELD, T. & HOFFMANN, M. 2014. Industry 4.0.
Business & Information Systems Engineering, 6, 239–242.
LEMINEN, S., WESTERLUND, M., RAJAHONKA, M. & SIURUAINEN, R. 2012. Towards
IOT Ecosystems and Business Models. In: S. Andreev, S. Balandin & Y. Koucheryavy,
eds. Internet of Things, Smart Spaces, and Next Generation Networking, Springer. pp.15-
26.
MCKINNON, A.C. 2013. Starry-eyed: journal rankings and the future of logistics research.
International Journal of Physical Distribution & Logistics Management, 43, 6-17.
OBERMAIER, R. 2016. Industrie 4.0 als unternehmerische Gestaltungsaufgabe: Strategische
und operative Handlungsfelder für Industriebetriebe. In: R. Obermaier, ed. Industrie 4.0
als unternehmerische Gestaltungsaufgabe, Springer Fachmedien. pp.3-34.
PAU, L. 2012. Smart business networks. Interaction‐coordination aspects and risks. Business
Process Management Journal, 18, 829–843.
PFEIFFER, S. & SUPHAN, A. 2015. Industrie 4.0 und Erfahrung – das Gestaltungspotenzial
der Beschäftigten anerkennen und nutzen. In: H. Hirsch-Kreinsen, P. Ittermann & J.
Niehaus, eds. Digitalisierung industrieller Arbeit, Nomos. pp.206-231.
RASHMAN, L., WITHERS, E. & HARTLEY, J. 2009. Organizational learning and knowledge
in public service organizations: a systematic review of the literature. International Journal
of Management Reviews, 11, 463-494.
REHAGE, G., BAUER, F., GAUSEMEIER, J., JURKE, B. & PRUSCHEK, P. 2013.
Intelligent Manufacturing Operations Planning, Scheduling and Dispatching on the Basis
of Virtual Machine Tools. In: G.L. Kovács & D. Kochan, eds. Digital Product and Process
Development, Springer. pp.391-400.
RENNUNG, F., LUMINOSU, C.T. & DRAGHICI, A. 2016. Service Provision in the
Framework of Industry 4.0. Procedia - Social and Behavioral Sciences, 221, 372-377.
RONG, K., HU, G., LIN, Y., SHI, Y. & GUO, L. 2015. Understanding business ecosystem
using a 6C framework in Internet-of-Things-based sectors. International Journal of
Production Economics, 159, 41–55.
RUDTSCH, V., GAUSEMEIER, J., GESING, J., MITTAG, T. & PETER, S. 2014. Pattern-
based Business Model Development for Cyber-Physical Production Systems. Procedia
CIRP, 25, 313-319.
SABERI, S. & YUSUFF, R.M. 2011. Advanced manufacturing technology implementation
performance. Towards a strategic framework. 2nd International Conference on Industrial
Engineering and Operations Management, Kuala Lumpur, Malaysia, January 22-24 2011.
SCHLIEßMANN, A. 2014. iProduction, die Mensch-Maschine-Kommunikation in der Smart
Factory. In: T. Bauernhansl, M. ten Hompel & B. Vogel-Heuser, eds. Industrie 4.0 in
Produktion, Automatisierung und Logistik, Springer Fachmedien. pp.451-480.
SCHUH, G., GARTZEN, T., RODENHAUSER, T. & MARKS, A. 2015. Promoting Work-
based Learning through INDUSTRY 4.0. Procedia CIRP, 32, 82-87.
SENDLER, U. 2016. Die Grundlagen. In: U. Sendler, ed. Industrie 4.0 grenzenlos, Springer.
pp.17-40.

Emerging Trends in Marketing and Management– Vol II, No. 2/2017
http://www.etimm.ro/
SENDLER, U. 2013. Industrie 4.0– Beherrschung der industriellen Komplexität mit SysLM
(Systems Lifecycle Management). In: U. Sendler, ed. Industrie 4.0. Beherrschung der
industriellen Komplexität mit SysLM, Springer Vieweg. pp.1-19.
SHERMAN, R. & CHAUHAN, V. 2016. Just my (re-) imagination. Supply Chain Management
Review, 20, 28-35.
SONI, G. & KODALI, R. 2011. A critical analysis of supply chain management content in
empirical research. Business Process Management Journal, 17, 238-266.
SPATH, D., GANSCHAR, O., GERLACH, S., HÄMMERLE, M., KRAUSE, T. &
SCHLUND, S. 2013. Produktionsarbeit der Zukunft – Industrie 4.0. Stuttgart, Germany:
Fraunhofer Verlag.
SPRING, M. & ARAUJO, L. 2013. Beyond the service factory: Service innovation in
manufacturing supply networks. Industrial Marketing Management, 42, 59–70.
THEISEN, M.R. 2013. Wissenschaftliches Arbeiten: Erfolgreich bei Bachelor- und
Masterarbeit (16th ed.), Vahlen.
TRANFIELD, D., DENYER, D. & SMART, P. 2003. Towards a methodology for developing
evidence informed management knowledge by means of systematic review. British
Journal of Management, 14, 207–222.
WEBSTER, J. & WATSON, R.T. 2002. Analyzing the past to prepare for the future: Writing
a literature review. MIS Quarterly, 26, xiii-xxiii.
WEILL, P. & WOERNER, S.L. 2015. Thriving in an Increasingly Digital Ecosystem.
MITSloan Management Review, 56, 27-34.
WINDELBAND, L. 2014. Zukunft der Facharbeit im Zeitalter "Industrie 4.0". Journal of
Technical Education, 2, 138-160.
WINTER, M. & KNEMEYER, A.M. 2013. Exploring the integration of sustainability and
supply chain management: current state and opportunities for future inquiry. International
Journal of Physical Distribution & Logistics Management, 43, 18-38.
WISCHMANN, S., WANGLER, L. & BOTTHOF, A. 2015. Industrie 4.0. Volks- und
betriebswirtschaftliche Faktoren für den Standort Deutschland. Berlin, Germany: BMWi.
WOLTER, M.I., MÖNNIG, A., HUMMEL, M., SCHNEEMANN, C., WEBER, E., ZIKA, G.,
HELMRICH, R., MAIER, T. & NEUBER-POHL, C. 2015. Industrie 4.0 und die Folgen
für Arbeitsmarkt und Wirtschaft. Szenariorechnungen im Rahmen der BIBB-IAB-
Qualifikations- und Berufsfeldprojektionen. Nürnberg, Germany: Institut für
Arbeitsmarkt- und Berufsforschung der Bundesagentur für Arbeit.
XU, X. 2012. From cloud computing to cloud manufacturing. Robotics & Computer-Integrated
Manufacturing, 28, 75-86.