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Datatization as the Next Frontier of Servitization – Understanding the Challenges for Transforming Organizations

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Datatization as the Next Frontier of Servitization
Thirty Eighth International Conference on Information Systems, South Korea 2017 1
Datatization as the Next Frontier of
Servitization Understanding the Challenges
for Transforming Organizations
Completed Research Paper
Ronny Schüritz
Karlsruhe Institute of Technology
Kaiserstraße 89, 76133 Karlsruhe,
Germany
ronny.schueritz@kit.edu
Stefan Seebacher
Karlsruhe Institute of Technology
Kaiserstraße 89, 76133 Karlsruhe,
Germany
stefan.seebacher@kit.edu
Gerhard Satzger
Karlsruhe Institute of Technology
Kaiserstraße 89, 76133 Karlsruhe,
Germany
gerhard.satzger@kit.edu
Lucas Schwarz
Karlsruhe Institute of Technology
Kaiserstraße 89, 76133 Karlsruhe,
Germany
Abstract
Today, servitization has reached its saturation point as enterprises in almost every
business and continent pursued it as a differentiation strategy. Data analytics may offer
the next frontier of innovation and hold the potential for enterprises to create value for
their customers. Nevertheless, organizations face a series of barriers when utilizing the
technologies. We apply a rigorous qualitative analysis process based on grounded theory
and interview data of 15 business-to-business companies that already successfully utilize
data analytics to create value for their customers. We analyzed our results in the lights of
the barriers organization face in servitization and reveal that data analytics adds an
additional layer of complexity. Our work contributes to the fundamental understanding
of organizational transformation and should provide concrete guidance to business
leaders on how to address transformation regarding the utilization of data and analytics.
Keywords: Servitization, datatization, data analytics, big data, organizational transformation,
grounded theory
Introduction
For manufacturers in the US and Europe, servitization has been a key strategy to capture additional value
on top of their existing product portfolios and to differentiate themselves in commoditizing markets. By
now, servitization has become a broadly adopted concept (Neely 2008). Since services in product-centered
markets are moving towards commoditization, they are not a sufficient source for achieving competitive
advantage anymore (Opresnik and Taisch 2015).
Recent developments in the field of data analytics create new potentials for services and business models
(Davenport 2013; Hartmann et al. 2016; Porter and Heppelmann 2014; Schüritz and Satzger 2016; Wixom
and Ross 2017). Consequently data analytics stands on par with capital, technology, and people as core
assets of an organization (Porter and Heppelmann 2014). In fact, exploiting data analytics is expected to
drive the next wave of servitization (Opresnik and Taisch 2015)as a promising path to build competitive
advantage (Lavalle et al. 2011).
Datatization as the Next Frontier of Servitization
Thirty Eighth International Conference on Information Systems, South Korea 2017 2
Looking at the immense potential, the application of these technologies may not simply provide “some”
competitive advantage, but rather turns out to be a necessity for economic survival. Thus, venturing into
services that exploit data analytics is becoming an increasingly critical task for organizations. While already
the transition into a servitized organization bears major managerial challenges (Oliva and Kallenberg
2003), further progressing this path towards the exploitation of data analytics in new and more complex
service offerings poses additional difficulties (Coreynen et al. 2017). Solely relying on the available
knowledge from servitization literature may not be sufficient to effectively guide organizations towards
successfully offering these advanced services. Consequently, Ostrom et al. (2015) explicitly identify big data
as a top priority in service research - as this topic is assessed to show the widest gap between the importance
for the field and the available body of knowledge.
In this work, we aim to identify challenges that organizations face when utilizing data analytics to offer new
services or to enrich existing products and services in a business-to-business (B2B) context. We as well are
interested in the potential ways to overcome these barriers. For this purpose, we employ a rigorous
qualitative method (Fernández 2004) based on a series of cases and a systematic literature review. By
collecting data through in-depth interviews with service executives and product managers, we analyze 15
organizations across various industries that are already taking advantage of data analytics in their offerings
today. Via the use of coding mechanisms, we extract relevant information from these cases and establish an
understanding of the transformational challenges these organizations face. In addition, we compare the
challenges to the ones posed by servitization in general. Thus, we aim to contribute to the body of knowledge
in service and organizational transformation theory.
The paper is structured as follows: In section 2, we provide a brief description of servitization and outline
the potential impact of data analytics. Section 3 describes our research methodology - detailing the steps of
our method, including how we collected and analyzed our empirical data. Section 4 shows the result of the
literature review on organizational challenges in servitization. In Section 5, these challenges are further
elaborated and serve as a basis for analyzing potential changes that accompany the integration of data
analytics. Section 6 highlights the changes on servitization that are triggered by data analytic. Section 7
briefly summarizes our results, acknowledges limitations and provides managerial implications as well as
develops an agenda for future research.
Theoretical Foundations
This chapter puts our research in context to relevant extant literature. First, we summarize servitization
literature, illustrating the concept of servitization and the rationale behind it. Second, we address the
importance of data analytics for service innovation.
Servitization
The idea of providing services in addition to selling products or even entirely replacing them is not new.
While the term servitization itself was only coined in the late 1980s (Vandermerwe and Rada 1988),
Schmenner (2009) argues that organizations have in fact started to combine services with products since
the 1850s. Accordingly, servitization “the innovation of an organization’s capabilities and processes to
shift from selling products to selling integrated products and services that deliver value in use” (Baines,
Lightfoot, Benedettini, et al. 2009, p. 555) - has attracted fairly broad academic coverage. Closely related
topics include product-service-systems (PSS), service transition, and service transformation (Baines,
Lightfoot, Benedettini, et al. 2009).
Reasons for organizations to pursue servitization, also referred to as drivers, motivations or rationales, have
been discussed by many authors (e.g., Baines et al. 2009; Gebauer et al. 2006; Gebauer and Friedli 2005;
Lay 2014; Martinez et al. 2017; Mathieu 2001; Olivia and Kallenberg 2003; Rouse 2005; Vandermerwe and
Rada 1988; Wise and Baumgartner 1999). Many of them attempt to cluster these motives: Building on a
comprehensive literature review, Baines et al. (2009) differentiate between financial, strategic (competitive
advantage) and marketing-related factors. Similarly, Lay (2014) distinguishes three first-level rationales:
growth, profit and innovation. Martinez et al. (2017), building on a longitudinal analysis of service journeys,
separate competitive motivations, demand-based motivations and economic motivations.
Datatization as the Next Frontier of Servitization
Thirty Eighth International Conference on Information Systems, South Korea 2017 3
A question that repeatedly appears in servitization literature is how to actually servitize in a successful way.
Many papers attempt to provide more guidance on how to implement servitization (e.g. Alghisi and Saccani
2015; Baines et al. 2009b; Coreynen et al. 2017; Gebauer et al. 2005; Gebauer and Friedli 2005; Jovanovic
et al. 2016; Kucza and Gebauer 2011; Mathieu 2001; Olivia and Kallenberg 2003; Rabetino et al. 2016;
Shepherd and Ahmed 2000; Storbacka 2011). However, no standard reference on how to successfully
servitize has yet emerged.
Today, servitization has reached almost every business and continent (Neely 2008), thereby becoming a
necessity for companies to address in order to stay competitive. At the same time, when companies servitize
in a basic manner, offering common services, they have little potential for differentiation. Enterprises need
to identify other ways to offer unique value propositions and to stay competitive. In this context, the
integration of technology is becoming a crucial element for organizations to develop, integrate and deliver
novel services, and advancing the original limits of servitization (Dinges et al. 2015). Data analytics is
expected to drive the next wave of servitization (Opresnik and Taisch 2015) and, therefore, has the potential
to become a new source of competitive advantage (Lavalle et al. 2011).
Data Analytics for Service Innovation
Today, both academia and industry attribute great opportunities to the emergence of “big data”, a term that
not just addresses the volume of information, but also refers to its variability, variety, velocity, veracity, and
value (Chen and Zhang 2014). By 2020, the amount of data is supposed to reach 44 zetabytes of valuable
and “target rich” data, supposedly doubling compared to 2013 (Turner et al. 2014). To unlock value from
data, the application of some form of analytics becomes a prerequisite (Ackoff 1989).
Organizations find a wide range of possible scenarios to benefit from data analytics for innovating their
existing business. Various studies reveal, though, that in doing so organizations still have a strong internal
focus (eg. Capgemini and Informatica 2016; IBM 2016; Manyika et al. 2011). In a broad multiple case study,
more than 70% out of 115 data-based innovations analyzed were internally focused (Schüritz and Satzger
2016)organizations optimize process efficiency, increase productivity, support strategic decision making
and create additional insight into their customer base (Lavalle et al. 2011; Manyika et al. 2011; Philip Chen
and Zhang 2014). As depicted in Figure 1, we refer to these internally focused endeavors as data-enabled
improvements.
Figure 1. Potential Manifestations of Data Analytics
In contrast, only a small portion of organizations actually augment their (external) value propositions by
applying data analytics (Schüritz and Satzger 2016; Zolnowski et al. 2016)and thus in fact ride the “third
wave” of analytics (“Analytics 3.0” in Davenport (2013)). This may either be achieved by enriching products
or services with data, i.e. “wrapping” data services around them (Wixom and Ross 2017), or by creating
completely new stand-alone data-driven services (Hartmann et al. 2016; Manyika et al. 2011).
Stand-alone
service
Value Proposition
changed
Value Proposition
not aff ected Data-Enabled Improvements
Data-Driven
Services
Integration
with product or service
Data-Enriched
Products &
Services
datatization
Datatization as the Next Frontier of Servitization
Thirty Eighth International Conference on Information Systems, South Korea 2017 4
Both, the design of new data-driven services or data-enriched products & services are by no means trivial.
Any organization will have to develop capabilities to be able to reap the benefits of data-driven innovation,
in particular when changing the value proposition (Wixom et al. 2017). As an advanced step of servitization,
we refer to this transformation as datatization and define it as the innovation of an organization's
capabilities and processes to change its value proposition by utilizing data analytics.
Methodology
Our work has the objective to extend the body of knowledge in service science and organizational theory by
understanding the barriers organizations face in datatization. We apply a rigorous research process as
suggested by Fernandez (2004) that is based on grounded theory (Glaser and Strauss 1967) and the
collection of data case data via interviews. Figure 2 illustrates the pursued research process.
Following the process, the research field is entered with a general understanding of the phenomenon that
motivates the research inquiry, i.e. in our case the challenges arising from datatization. Therefore, we collect
data (step 1) from organizations that already offer data-driven or data-enriched services, as we assume that
they have accumulated extensive experience and expert knowledge in the field. The corresponding in-depth
interviews are analyzed applying an open coding approach (step 2). At this point, the research process by
Fernandez (Fernández 2004) requires the enrichment of the intermediary results by extant literature, in
line with the grounded theory methodology (Glaser and Strauss 1967). As the utilization of data analytics
leads to new value propositions that are essentially services (Davenport 2013; Schüritz and Satzger 2016;
Wixom and Ross 2017), we conducted a structured literature review (step 3) on servitization barriers in
order to evaluate if the challenges organization face differ when pursuing this advanced step of servitization.
The reviewed literature is then used to develop theoretical codes, which serve as orientation for categories
in a second coding cycle (step 4). Following an iterative approach, open codes are created and assigned to
categories.
The process is continued until additional cases do not yield new insights on datatization challenges, i.e.
theoretical saturation is reached. We are adapting the approach of Fernandez (2004), which originally
focuses on assessing human behavior phenomena, to explore organizational implications of datatization.
The research process ends with a stable set of categories that explain the phenomenon and represent a
Figure 2. Research Process (based on Fernández 2004)
Extant Literature
Entering
the Field
Theoretic al
Saturation?
Case 2
Case 2
Case 2
Case 1 .. n
Theor etical Sampling
No
Yes
Coding
Open Coding
Theor etical Coding
Literature Rev iew
4
3
1
Category
Category
Category
(Properties)
Stable Categories
Category
Category
Category
(Properties)
2
Datatization as the Next Frontier of Servitization
Thirty Eighth International Conference on Information Systems, South Korea 2017 5
contribution to the general understanding instead of a substantive theory. (Muller 2014). A more detailed
description of the research process and the applied methodology is given in the following.
Data collection (Step 1)
The novelty of the subject at hand - datatization - supports the approach of using an inductive approach, as
there is a lack of theoretical foundations in this specific area (Eisenhardt and Graebner 2007). The cases
draw on in-depth field expert interviews to gather insights from the companies’ current endeavors to utilize
data analytics for new service offerings.
The case selection is driven by theoretical sampling, leading to a sample of 15 companies (cf. Table 1). All of
them operate in a B2B context and apply data analytics to offer data-enriched or data-driven products and
services, thus have datatized over the last years. Available extant research on servitization has almost
exclusively focused on the manufacturing sector (e.g. Gebauer and Friedli 2005; Olivia and Kallenberg
2003). To avoid contextual bias, though, we addressed other areas than manufacturing as well (Eloranta
and Turunen 2015).
Initially, web research is used to obtain a broad overview of publicly available information on a specific
service offering (e.g. organization’s website, newspaper articles, etc.), thereby generating a basic
understanding of the respective service and enabling the researcher to identify critical aspects beforehand
and to elaborate on more sophisticated questions for subsequent interviews. Afterwards, for each case an
interview is conducted with a company representative, who is either responsible for the operation or the
development of the service, and, therefore, has a comprehensive knowledge of service offering.
The interviewees are confronted with the three open questions: “What were the biggest challenges when
developing and implementing the service?”, “What is critical for the success of the service today?” and
“What are your plans for the future and what are the barriers moving forward?”. Further probing to deepen
the understanding of the challenges is performed with respect to the situation, the topic and the case. This
semi-structured interview approach combines structure with flexibility (King 2004). Almost all interviews
are conducted over the phone, are run in either English or German and recorded in case permission is
granted. The interviews have been conducted between August 2016 and March 2017. Each audio recording
is transcribed and serves as a basis for analysis by means of coding.
Number
Case
Industry
Revenue
Employees
1
Alpha
Manufacturing
70B 150B
>300.000
2
Beta
Manufacturing
3
Gamma
Manufacturing
4
Delta
Telco Provider
20B - 50B
20,000-300,000
5
Epsilon
Medical equipment
6
Zeta
Energy provider
7
Eta
Manufacturing
900M - 7B
6,000 20,000
8
Theta
Manufacturing
9
Iota
Manufacturing
10
Kappa
Manufacturing
11
Lambda
Insurance
12
Ny
Financial services
13
Xi
Technology provider
<100M
<150
14
Omikron
Technology provider
15
Pi
Technology provider
Datatization as the Next Frontier of Servitization
Thirty Eighth International Conference on Information Systems, South Korea 2017 6
Open Coding (Step 2)
The gathered data is analyzed via a coding mechanism based on grounded theory (Glaser and Strauss 1967).
Codes can be interpreted as data points, which can be aggregated to form categories, which then may serve
as a basis for theory development. In our case, we intend to derive a sound understanding of the industry
reality and capture datatization challenges and management practices. So far, extant research neither
provides comprehensive guidance on specific challenges nor on how they are tackled by practitioners, we
apply open coding, i.e. we do not rely on predefined categories. The transcripts are independently coded by
two researchers, who discuss their results after each case, thereby comparing their results and reaching an
inter-coder agreement. If necessary, conflicting codes are resolved by including a third researcher familiar
with the topic (Fastoso and Whitelock 2010). Within an iterative approach, the resulting coding system of
all analyzed cases then forms the starting point for analyzing the next case until theoretical saturation is
reached. The actual coding is supported by the coding software MAXQDA.
Literature Review (Step 3)
A systematic literature review is performed to gain a comprehensive understanding of the servitization
concept and, especially, its associated challenges. Löfberg et al. (2015) point out that the term challenge is
regularly used in servitization literature without clarifying its notion. We will view challenges as general
hurdles and difficulties in achieving the objective of becoming more service-oriented and having an
extended service offerings. The review is conducted in a structured two-step approach. First, we search four
established scientific databases that we believe possess a comprehensive coverage of scientific publications
(EBSCOhost, AISeL, SCOPUS, ScienceDirect) using strings of word combinations (cf. Table 2) to identify
relevant article titles and abstracts. The chosen keywords focus only servitization, because we wanted to
compare datatization challenges to servitization challenges. Second, we additionally use backward- and
forward search as proposed by Webster and Watson (2002). Thereby, we can include several relevant
contributions we did not detect as part of the initial keyword search. The combination of both approaches
ensures that we are broadly covering relevant contributions. The identified servitization literature is then
analyzed using a concept matrix and, subsequently, is synthesized based on a workshop among fellow
researchers.
Table 2. Literature Research
Search String 1
Search String 2
Databases
Sum
(w/o
duplicates)
Relevant
AISeL
EBSCO
Scopus
ScienceDirect
Servitization/
servitisation/
servitizing/ servitising
AND
Challenges OR
barriers OR
obstacles OR
difficulties
13
40
111
40
136
43
Service Transition
2
3
29
8
35
4
Service
Transformation
2
0
40
6
42
2
Sum (w/o duplicates)
45
Added through
forward/backward search
13
Total Sum
58
Theoretical Coding (Step 4)
The general servitization barriers in step 3 serve as a rich basis for theoretical codes, which are used to
structure our existing categories and codes from the open coding in step 2. Starting with the set of
theoretical codes, we extend and refine this list through the inclusion of new codes or the adjustment of
existing ones (Saldaña 2009).
Datatization as the Next Frontier of Servitization
Thirty Eighth International Conference on Information Systems, South Korea 2017 7
The next sections present the results of the study, starting with the outcome of the literature review in the
form of a concept matrix, which is followed by a section looking at the barriers of servitization and
additional challenges when pursuing datatization.
Barriers of Servitization
Although a number of publications attempt to give an overview over servitization challenges, these papers
categorize the barriers very broadly by only providing high-level conceptual categorizations (e.g. Annarelli
et al. 2016; Hou and Neely 2013; Martinez et al. 2010; Neely 2008), or do not have a transparent process
of deriving the categories (e.g., Alghisi and Saccani 2015) and are therefore not helpful in our endeavor. Our
analysis of the papers yields a total of 45 different challenges that can be grouped into ten categories. Table
3 shows the concept matrix, structured according to the ten synthesized categories as well as the papers in
which they are mentioned. This gives us a comprehensive overview on servitization challenges that
synthesizes findings from existing work and builds the basis for our analysis in the next section.
Table 3. Concept Matrix of Servitization Challenges
Processes
Org.
Structure &
Governance
Strategy
Skills &
Capabilities
Culture
Market
Design of
offering
Design of
revenue model
Transformation
Co-creation
Adrodegari et al. 2015
x
x
x
x
Ahamed et al. 2013
x
x
x
x
Akram et al. 2014
x
x
x
x
x
Alghisi and Saccani 2015
x
x
x
x
x
x
x
x
x
Annarelli et al. 2016
x
x
x
x
x
Ayala et al. 2016
x
x
Baines and Lightfoot 2013
x
x
x
x
x
x
x
Baines, Lightfoot, and Kay 2009
x
x
x
x
x
x
x
Baines, Lightfoot, Benedettini, et al. 2009
x
x
x
Baines et al. 2011
x
x
x
Baines et al. 2017
x
Bao and Toivonen 2015
x
x
Brax 2005
x
x
x
x
x
x
x
Burton et al. 2017
x
x
x
x
x
Buschmeyer et al. 2016
x
Cenamor et al. 2017
x
x
x
x
x
Chakkol et al. 2014
x
x
x
Chen 2015
x
Confente et al. 2015
x
x
x
x
x
x
Conger 2009
x
Coreynen et al. 2017
x
x
x
x
x
Elfving et al. 2014
x
x
x
Eloranta and Turunen 2016
x
x
x
x
x
x
Fang et al. 2008
x
x
x
x
x
x
Gebauer and Friedli 2005
x
x
x
x
x
x
x
x
x
Datatization as the Next Frontier of Servitization
Thirty Eighth International Conference on Information Systems, South Korea 2017 8
Gebauer et al. 2006
x
x
x
x
x
Grubic 2014
x
x
x
x
x
Helms 2016
x
x
x
x
x
x
x
x
Hou and Neely 2013
x
x
x
x
x
x
x
x
x
x
Jovanovic et al. 2016
x
x
x
x
x
x
x
Kindstrom and Kowalkowski 2009
x
x
x
x
x
x
Kowalkowski et al. 2015
x
x
x
x
x
x
x
Laine et al. 2012
x
x
x
x
x
x
x
Lay 2014
x
Lienert 2015
x
Löfberg et al. 2015
x
x
x
x
x
x
x
x
Martinez et al. 2010
x
x
x
x
x
x
x
x
x
Martinez et al. 2017
x
x
x
x
x
x
Mathieu 2001
x
x
x
x
x
x
x
Mont 2002
x
x
x
x
x
x
x
Novales et al. 2016
x
x
x
Nudurupati et al. 2016
x
x
x
x
x
x
x
x
Oliva and Kallenberg 2003
x
x
x
x
x
x
x
x
x
Opresnik and Taisch 2015
x
x
x
x
Ostrom et al. 2015
x
Parida et al. 2014
x
x
x
x
x
Parida et al. 2015
x
x
x
x
Paslauski et al. 2016
x
x
Porter and Heppelmann 2014, 2015
x
x
x
x
x
x
Raja et al. 2010
x
x
x
x
x
x
Robinson et al. 2016
x
x
x
x
Roos 2015
x
x
x
Turunen and Finne 2014
x
x
x
Ulaga and Loveland 2014
x
Ulaga and Reinartz 2011
x
x
x
x
x
Vandermerwe and Rada 1988
x
x
x
x
x
x
x
Wise and Baumgartner 1999
x
x
x
x
x
x
x
Challenged mentioned in total
43
33
19
23
28
27
32
27
15
35
Impact of Datatization
Datatization represents an advanced step of servitization enabling organizations to offer services that
exploit data analytics and potentially yield a competitive advantage for organizations (Davenport 2013;
Opresnik and Taisch 2015; Schüritz and Satzger 2016; Wixom and Ross 2017). Datatization expands the
current understanding of servitization, moving the frontier of knowledge. Pioneers exploring new areas still
have to cope with existing, general servitization challenges, but are also confronted with new, specific
datatization challenges.
In this section, we will briefly describe each servitization barrier an organization faces - based upon our
literature review, and then for each illustrate any additional datatization challenges and observed ways to
Datatization as the Next Frontier of Servitization
Thirty Eighth International Conference on Information Systems, South Korea 2017 9
overcome them - based on the analysis of our cases. Figure 3 shows a simplified category structure to guide
the following sections.
Figure 3. Overview of Servitization and Datatization (Sub-)Categories
Strategy
When crafting a service strategy, organizations are challenged to achieve a fit with their overall strategy and
vision. Pursuing both a product and a service strategy potentially diverts attention due to resource
constraints and, therefore, poses a difficult managerial challenge (Fang et al. 2008). Turunen and Finne
(2014) stress the impact of the business environment on selecting a service strategy, while Gebauer et al.
(2006) emphasize the importance of involving all relevant parts of the organization in formulating it.
Organizations that want to take advantage of datatization are challenged to make a clear decision about the
role of data analytics in their service strategy as well as to develop a clear data strategy. Our interviews
revealed that such a strategy involves series of decisions regarding the access and usage of data, should
exhibit a fit with the overall strategy of an organization and follow local regulations and ethical boundaries.
Formulating how to obtain and maintain data access is a crucial element when devising a data strategy.
This can be particularly difficult if necessary data is not in possession of an organization, but is generated
and stored by its customers or another partner and needs to be transferred to the organization. Especially
some manufacturing companies in our sample (Eta, Iota, Kappa) pointed out that they first need to establish
a continuous transfer of customer data. Further, even if the service provider has already obtained customer
data within a particular service or through product usage, the customer might still need to agree on
accessing the data in a different context or for a different purpose. Alpha and Ny point out that receiving
customer approval can be challenging.
As data builds the foundation of datatized services, organizations need to ensure a continuous data
provision within their strategic orientation. Further research is needed to understand how data access can
be facilitated. In our sample, Eta and Ny ensure continuous data access by including it as part of the initial
service contract. Iota and Gamma provide a free online portal that gives its customers access to a dashboard
with prepared data of the manufacturing equipment in use. Customers that sign-up for the service thereby
Datatization as the Next Frontier of Servitization
Thirty Eighth International Conference on Information Systems, South Korea 2017 10
grant access to data of the products in use. Additional, more sophisticated services (e.g. predictions) are
charged.
Apart from ensuring data access, organizations need to make decisions about the usage of data and align
these decisions with their strategy and values. Ny describes, that there is a no organization wide
understanding of how data can be used. This uncertainty is driven by a fear of “ruining [its] image and being
perceived as a data leech, that is absolutely worst case” (Product Manager Ny). Therefore, setting a data
strategy ensures that data is handled correctly across the organization and new services can be developed
within its boundaries. Current research started to investigate how company norms may help to develop
acceptable data use principles (Wixom and Markus 2017)
In our sample, we can identify three approaches for handling data from client interactions: One group of
organizations uses data resulting from such interactions solely in the service provision for the particular
client, e.g. Beta produces manufacturing machinery. Based on the collected data, Beta offers a service on
the energy consumption of the production line, but does not use the collected data otherwise. A second
group uses data internally to enable improvements of products and services, e.g. Gamma, Zeta and Lambda
use the collected data of every client to optimize the prediction models for their services. A third group uses
the client data across customer boundaries, offering new services to a potentially new group of customers,
e.g. Delta aggregates customer location data to create new services to a different set of customers.
Organizational Structure & Governance
Introducing services into a product-focused portfolio entails changes in the organizational structure and
governance. It is often recommended to set up a decentralized structure in the form of a profit center or
even a separate business unit with profit and loss responsibility (e.g. Gebauer and Friedli 2005; Olivia and
Kallenberg 2003). This separated structure is helpful in fostering service thinking without having to
sacrifice product-related manufacturing values (Baines, Lightfoot, and Kay 2009). However, other authors
point out the importance of integrating the entire organization (Oliva and Kallenberg 2003) and, therefore,
suggest a segmented structure, in order to spread service culture throughout the organization (Mathieu
2001). Thus, it is challenging for an organization to identify the appropriate structure for their individual
servitization endeavor.
In addition to structural changes, services require new performance measurements to manage services
successfully (Ulaga and Reinartz 2011). Furthermore, the coordination between formerly siloed
departments in an organization needs to increase significantly with the adoption of service business models
(Oliva and Kallenberg 2003).
Analogous to service transformation, organizations initiating datatization need to decide if involved teams
are placed in a centralized, new unit that works more independently from the rest of the organization (e.g.
Iota, Alpha, Beta, Delta, Zeta, Iota) or if the teams are segmented across the organization in line with their
product or service association (e.g. Gamma, Epsilon, Lamda, Ny). The results of our analysis indicate, that
the degree of autonomy of the datatized service and the connection to the core offering currently differ
across organizations. Similar to servitization, we can see organizations building separate organizational
units that differ across the sample as to their degree of autonomy. These separate units are enabled to
explore data-driven and data-enriched services without the limitations of existing structures often
working in a more agile and explorative manner: “[...] we decided it is much quicker and much nimbler for
us to create our own little universe, [...], because as you can imagine there are steering groups and lots of
procedures and hearing rounds and things like that and we have to be quicker in our maneuvering” (Head
of Service Iota). However, this separation might pose severe challenges in coordinating, aligning and
collaborating with the core business.
Depending on the nature of the datatized service and the degree of embeddedness into its core product and
service portfolio, we can also identify a high necessity for collaboration and coordination with other units
such as R&D, production, sales, etc. - as suggested by Porter and Heppelmann (2015).
Processes
Servitization requires a new set of processes for developing, implementing, enabling and delivering services
that differ from those of product-centered organizations (Oliva and Kallenberg 2003). In new service
Datatization as the Next Frontier of Servitization
Thirty Eighth International Conference on Information Systems, South Korea 2017 11
development (Burton et al. 2017; Chen 2015), there is a need for an integrated development process
considering product development and available technologies (Parida et al. 2014).
While most organizations have gained experiences on service development and service delivery through
their servitization efforts, data analytics imposes a set of new challenges. While software may have been one
of many elements in the service development before, now software becomes a cornerstone of the service.
Furthermore, the service development also needs to define processes for data acquisition, generation and
processing and infrastructure set-up to cope with the new requirements. A close integration with the
development of the core product might be necessary: In our sample for example, one firm needed to
integrate a set of sensors in heavy machinery equipment that can be used to capture the required data for
the enablement of a predictive maintenance service.
While the provision of services might already be very different from the delivery of products, data analytics
brings additional changes to the service delivery. As Ostrom et al. (2015) point out, a “dehumanization of
service” can be noticed in some areas - leading to a transition from human-centered to technology-centered
service delivery. This holds especially true in the cases of Gamma, Xi, Omikron, and Pi where ”e-services”
are delivered via web-interface or API and, thus, human interaction is not needed to the same extent as in
traditional services (Fromm and Cardoso 2015). However, we can also see organizations challenged in areas
where a deeper integration of human interaction and technology is needed: In the case of Beta, the company
collects and analyzes the live data of a machinery used by the customer. In addition to providing aggregated
information via an online portal, a consultant is in close interaction with the customer to make sense of the
data and make suggestions to improve the efficiency of the product.
Skills and Capabilities
For product-focused companies seeking to add services to their portfolio it is inherently challenging to
acquire a variety of necessary skills and capabilities. Creating integrated service offerings leads to a greater
number of customer encounters, resulting in a broader range of employees being exposed to the customer
than was the case before (Martinez et al. 2010). Organizations that want to cultivate services, therefore,
need to build customer interfacing skills (Coreynen et al. 2017). Further, Ulaga and Loveland (2014)
underline the importance of building new sales capabilities that are able to sense market opportunities. This
is quite challenging because existing sales personnel is typically biased towards selling products and tends
to lack both intention and skill to sell service offerings (Kindstrom and Kowalkowski 2009; Ulaga and
Loveland 2014). Similarly, product-focused companies now face the challenges to hire and train personnel
with customer orientation and the necessary domain know-how (Brax 2005; Helms 2016), to deploy people
and services in an effective and efficient manner (Burton et al. 2017), and to appropriately manage these
service employees (Gebauer and Friedli 2005; Raja et al. 2010).
When it comes to data analytics, it is claimed that data scientists hold “the sexiest job of the 21st century”
(Davenport and Patil 2012). They are broadly demanded by the market and seem to be in tremendous
shortage right now. Interviewees point out, though, that data scientist is rather an umbrella term, while
needed profiles include “[…] a whole bandwidth of skills and the differences are huge, really huge
(Product Manager Ny). In fact, the collected codes regarding required data science skills show a much
broader variety and sophistication compared to required skills in servitization.
Due to the strong technological focus of these new offerings, IT capabilities become an essential cornerstone
for datatization as well. One interviewee states: “Along the way we had many challenges in going from
being a machine manufacturer, which we are, to become a software player. […] It is two very different
processes required and two very different skill sets required to do these things” (Head of Service – Iota).
Organizations are challenged to build these skills and capabilities that enable the infrastructure, develop
the software, ensure the required level of security, as the integration of new services into a “[…] grown IT
landscape can be real drama (Product Manager Zeta).
Design of offering
A foundation for successful servitization efforts is the careful design of service offerings (Ahamed et al.
2013). Designing service-based offerings is significantly different to the design of products (Baines,
Lightfoot, Benedettini, et al. 2009; Burton et al. 2017). Jovanovic et al. (2016) stress the importance of
matching service offerings to product operations as a key success factor. In designing the service offering,
Datatization as the Next Frontier of Servitization
Thirty Eighth International Conference on Information Systems, South Korea 2017 12
servitizing organizations need to take into account that risk formerly borne by the customer might now be
transferred to the provider (Baines, Lightfoot, and Kay 2009). The design of the offering needs to allow for
a service delivery that meets customer expectations and ensures ethical compliance (Grubic 2014; Martinez
et al. 2010). As a consequence of their product-focused background, companies may also need to design
new infrastructure or networks as part of the service offering and to enable service delivery in the first place
(Mont 2002). Another important aspect in service offer design consists in identifying a communication
strategy that clearly describes the value proposition to the customer (e.g. Mathieu 2001; Ulaga and Reinartz
2011).
Designing new data-driven services or enriching existing products or services brings to bear new challenges
for organizations. First, it is still particular challenging for some organizations to identify business value in
the gathered data, develop innovative offerings on top of them and “[…] identifying the right functions that
make sense in practice” (Product Manager Gamma). Once developed the added value needs to be
presented to the customer requiring “[…] new websites [and] new communications” (Product Manager
Lambda).
Further, at this point another major challenge is to ensure scalability through standardization: For Beta
every analytics solution is individualized, fulfilling a diverse base of customer requirements as well as
integrating heterogeneous input data. But in this context, Beta’s product manager “believe[s] that industry
specific approaches must be rendered possible, so that not every solution needs to be developed from
scratch. For our teams that consult the customer we are starting to deploy more and more such templates“
(Product Manager Beta). Smaller specialized providers such as Xi or Zeta seem to aim for standardized
solutions from the start: “[We] don’t want to develop an individual solution for every client, but instead
offer a standardized solution that we can improve over time” (Product Manager - Xi). That is why “[…] the
biggest mistake is, if you identify a valuable use case with a client to believe this is the standard. You
immediately need to evaluate and prioritize with other market participants. Otherwise you develop an
individual solution.”
Design of revenue model
Designing a revenue mechanism for service-based offerings becomes a challenge (Eloranta and Turunen
2016; Novales et al. 2016; Wise and Baumgartner 1999), because transitioning from product-oriented to
service-oriented offerings creates a need to change the earning logic (Storbacka et al. 2013; Ulaga and
Reinartz 2011): Discrete payments change to continuous cash flows, giving customers full access to the
offering without transferring the ownership (Chesbrough 2010; Porter and Heppelmann 2014), and thereby
taking the risk from the customer and shifting it to the provider (Baines, Lightfoot, Benedettini, et al. 2009).
In addition, customers are often reluctant to pay extra for services (Löfberg et al. 2015) and exhibit what
can be called a service-for-free attitude (Coreynen et al. 2017). This makes service pricing highly difficult
and poses an issue that has not been solved as of today (Ulaga and Reinartz 2011).
Organizations moving into services may on top be challenged by something referred to as the ‘service
paradox’; a situation in which adding services to the offering portfolio of a product focused company leads
to increased revenues, but decreasing profits (Baines and Lightfoot 2013). This phenomenon has been
discussed by multiple authors (Gebauer et al. 2006; Neely 2008) and further emphasizes the challenge of
designing a profitable revenue model. To avoid running into the ‘service paradox’, a servitizing product-
focused company must solve the challenge of maintaining cost levels (Cenamor et al. 2017; Confente et al.
2015), achieving critical mass (Roos 2015) and scalability (Mathieu 2001).
The design of the revenue model for datatized offerings can be challenging, but shows similarities to the
servitization endeavor. In our sample, we could see a variety of approaches organizations have taken:
In the case of Lambda or Gamma, data analytics is bundled with and, thus, is an integral part of the core
offering. In these cases, the organization receives an indirect pay-off through an uplift of the product or
service via a higher price or additional sales.
Organizations that decides to uncouple it from the core product have the potential to open up a new revenue
stream and take advantage of new revenue models (e.g. subscription, usage fee, gain-sharing (Schüritz,
Seebacher, et al. 2017)). Ny, Aplha, Epsilon, Theta and Iota offer data analytics as an add-on that can be
optionally ordered and utilize a different revenue mechanism compared to the core offering. For Beta, Delta,
Zeta, Xi and Omikron, the offering is a stand-alone service that works without the core offering, thus is a
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Thirty Eighth International Conference on Information Systems, South Korea 2017 13
new data-driven service and utilizes a new revenue model as well. However, organization are challenged by
the configuration of the revenue mechanism such as the definition of suitable tiers in subscription models
that may differ in features or in volume.
Further, again finding the right price is challenging as well: “Pricing is extremely hard and you never quite
know how to do that” (CEO Omikron). Similar to services, when the data analytics is utilized to enrich a
current product, the service is expected to be free: “We come from a world where all customers are
accustomed to receive free software” (Head of Service Iota). Defining the added value of the enrichment
was a challenge among all cases in our sample.
Market
Servitizing an existing business eventually leads to offer services, which face the organization with entering
a new market. This is connected to challenges such as insufficient market knowledge due to the former
product-focused orientation (Parida et al. 2015), potentially low market maturity as services in that field
may have just emerged (Coreynen et al. 2017; Wise and Baumgartner 1999) and/or new competition
through other service providers (Mathieu 2001).
Gaining a comprehensive understanding of the market and the industry structure is necessary for the
successful development of a service strategy (Gebauer et al. 2006; Turunen and Finne 2014). Apart from
knowing the market and its structure, an immature market poses an extensive challenge to servitizing
companies as customers may not be willing to engage in the offered services (Wise and Baumgartner 1999).
This inertia can be explained by both dominant industry logics and mindsets as well as the manufacturing
company’s rather product-oriented marketing focus (Roos 2015). Furthermore, product-focused
companies looking to expand into the field of services have to be aware of new competitors outside their
familiar domain from unexpected rivals, who may include their own suppliers, distributors, and customers
(Mathieu 2001; Oliva and Kallenberg 2003; Robinson et al. 2016; Vandermerwe and Rada 1988).
Organization in our sample striving towards datatization describe the same challenges as organizations that
pursue a servitization strategy. Analogously, it is crucial to gain a sound understanding of the respective
market in order to develop a data strategy. The market for data analytics offerings is still immature. Some
customers are reluctant to take advantage of such services. A manufacturing company stated, that in some
cases they “[…] cannot identify the correct contact person” (Head of Service Iota) and some customers
have trouble to gain approval for purchasing such services. Unexpected competition is also entering the
market: Theta is now in competition with small specialized software companies that offer services based on
the data that is generated by their machinery.
Culture
In order to facilitate servitization efforts, organizational culture often needs to be adapted (Ostrom et al.
2015). Establishing a service-oriented culture in product-focused companies has been stated as the one key
factor of servitization success and the biggest challenge since the inception of this research field
(Vandermerwe and Rada 1988; Mathieu 2001), as servitization requires a shift of corporate mindset from
product centricity to service-orientation and customer centricity (Oliva and Kallenberg 2003). This entails
managing customer relationships as long term engagements instead of transaction-based encounters
(Alghisi and Saccani 2015). Further, firms with a manufacturing background tend to focus on technical
product features and need to integrate and emphasize services at an earlier point in time in their innovation
efforts (Ulaga and Reinartz 2011). Additionally, services often demand a 24/7-mindset and customer-
oriented employees with great communication capabilities to embody the service-oriented culture (Baines
and Lightfoot 2013; Brax 2005). For that matter, it is necessary for the entire organization to internalize
this new culture, reaching from a service employee to a management level (Gebauer and Friedli 2005). Oliva
and Kallenberg (2003, p. 166) state that “at the core of this cultural transformation, then, the manufacturing
firm must learn to value services.”
Organizations that want to take advantage of data analytics face the challenge of incorporating data
analytics specific aspects in their culture. As addressed in organizational structure and governance, a high
degree of collaboration across departments is necessary. While not pointed out by our interviews as a
challenge, other research suggest that organizations or at least their analytics departments need to adapt a
culture that accepts failure at an early stage as it is sometimes tedious to find value in data sets (Schüritz,
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Thirty Eighth International Conference on Information Systems, South Korea 2017 14
Brand et al. 2017). This change of mindset can be particularly difficult for organizations with a strong
product-focus as they entail a culture, which is used to long and extensive development cycles.
Co-creation
The nature of services inherently implies a co-creation of value. That means that value is always co-created
“jointly and reciprocally, in interactions among providers and beneficiaries” (Vargo et al. 2008, p. 146).
Therefore, product-focused companies pursuing a service strategy need to learn how to collaborate with
customers and partners more intensively both in developing meaningful services as well as in delivering
them in a fashion that meets customer expectations (Brax 2005; Coreynen et al. 2017; Oliva and Kallenberg
2003). In order to perform a service in a mutually beneficial way, it is necessary to re-think customer
interaction from being transaction-based to being relationship-based (Baines, Lightfoot, and Kay 2009;
Martinez et al. 2010). Apart from collaborating with customers, there is also an increased need to integrate
and coordinate with third parties (Kowalkowski et al. 2015). Managing these partner and supplier network
configurations becomes an increasingly complex task, when advancing in servitization (Chakkol et al. 2014).
Facilitating the transfer of knowledge among different functional areas within a servitizing organization as
well as network partners is an essential task and a complex challenge (Paslauski et al. 2016).
When utilizing data analytics to deliver new innovative services, co
-creation even seems to be of higher importance. Data becomes a resource to which an organization needs
reliable access and which may not exclusively be generated in the spheres of an organization but very well
be created within the boundaries of the customer. Alpha, Iota and Ny describe that they need to work closely
together with their customers and establish a high level of trust, which builds upon the long-term
relationship that evolved during servitization and is further deepened.
However, co-creation does not solely include customers. In all our cases, companies have to collaborate
with existing partners or engage with completely new partners - the CEO of Omikron describes this in the
following way: “That´s the hardest part, to find the right people and the right partners”. However, it is
necessary to engage with partners in order to gain access to data and capabilities that are needed to offer
the desired service - “We would like to do the core things ourselves but if someone is better in doing them,
we engage with a third party. You can’t do everything yourself” (Head of Services - Eta).
Transformation
Servitization requires the transformation of the entire organization, which is often underestimated
regarding its complexity and management of behavioral change (Buschmeyer et al. 2016). Transforming an
organization from solely selling products to marketing integrated products and services is likely to lead to
severe internal resistance, because the inherently characteristic of both people and organizations to be
reluctant towards change (Mathieu 2001). This resistance may be caused by having little insights on the
functioning and the content of the service strategy (Gebauer and Friedli 2005) or be due to the fear of
change (Baines, Lightfoot, and Kay 2009). Not only the belief system of the organization must be
transformed, but also its business models (including its value propositions, customer relationships,
channels, cost structure, and revenue streams). Since servitization progresses gradually (Oliva and
Kallenberg 2003), both the old product-oriented and the new service-oriented business model need to be
managed simultaneously (Martinez et al. 2017).
When pursuing datatization, organizations face change in many aspects of their business, posing a variety
of obstacles as described above. Our interviews have shown that organizations, just as in servitization, are
challenged by managing this transformation coping with resistance to change.
From servitization to datatization
Datatization represents the next frontier for many servitized organizations to explore. Our analysis shows
that, while pursing this journey, companies experience an extract of servitization challenges, which are
further extended by distinct data-specific hurdles. Thereby, data analytics leads to an evolution of certain
transformation characteristics, which organizations encounter during their endeavor. Building upon the
analysis of last section’s servitization and datatization challenges, we now summarize (cf. Table 4) how
datatization has progressed the characteristics of servitization.
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Thirty Eighth International Conference on Information Systems, South Korea 2017 15
Table 4. Transformation characteristics
Product-focused
organization
Servitized
organization
Datatized
organization
Strategy
Product-focused strategy
Integrated Product-Service-
Strategy
Additional Data Strategy
Network
Supplier Network
Partner Network
Partner Information Ecosystem
Customer
Relationship
Short-term transaction-based
relationship
§ Long-term Relationships
§ New customer facing roles
§ Deep-Relationships (if data
access is required)
§ New interfaces (API, Portal,
Apps, etc.)
Development
Practice
§ Product-oriented
§ Separate and function based
§ Service-oriented
§ Partially separated and
function based
§ Analytics and software oriented
§ Integrative and cross-
functional
Revenue
stream
One revenue stream
Additional revenue stream or
replacing of existing revenue
stream
§ Additional revenue stream
§ Indirect pay-off through
product or service uplift
Culture
Product-oriented culture
Service-oriented culture
Data-driven culture
Skills &
Capabilities
Manufacturing capabilities
Customer facing skills
Data Science, IT infrastructure
capabilities and software
development skills
Organizations that pursue servitization needed to realize the potential of services and turn their strategy
from a product-focus into an integrated product-service-strategy (Gebauer et al. 2005). Additionally,
datatization requires a data-strategy that defines how data is accessed and used.
In product-focused organizations, effectively managing the supplier network is a critical success factor,
whereas in servitized organizations a partner network becomes a necessity for delivering integrated
product-services (Alghisi and Saccani 2015). In addition to the partner network, organizations that want to
datatize need to establish and maintain a partner information ecosystem, in which information is shared
between relevant parties for developing, delivering and consuming data-driven and data-enriched services.
In product-focused organizations, customer relationships usually have a short-term and transaction-based
nature. Through servitization, this relationship is transformed into long-term engagements with a much
broader range of potential customer encounters (Nuutinen and Lappalainen 2012). This requires new
customer facing roles and, thus, service personnel becoming an important asset in a servitized organization.
Datatization requires a deepening of this relationship, if the provider needs to gain access to the data of the
customer. A deep relationship in data-driven or data-enriched products and services is characterized by a
high level of trust and a close integration of the operating infrastructure between a provider and its
customer. This technical integration also changes the interaction and new touch points are creating new
interfaces (API, portal, etc.) that contribute to some extent to a “dehumanization” of services (Ostrom et al.
2015).
Development practices change from being product-oriented to being service-oriented as services gain
importance (Oliva and Kallenberg 2003), while development practices are still rather separated and
function-based in servitized organizations (Nuutinen and Lappalainen 2012). Porter and Heppelmann
(2015) already emphasize that in order to leverage the potential presented by data analytics, organizations
must develop new services in an integrative and cross-functional manner. Further, rooted in the digital
nature of data-driven and data-enriched products and services the respective development practices need
to become highly software-oriented.
Servitization, and subsequently datatization, enable additional revenue streams for their business. Adding
services creates revenue streams that tends to be more stable and of a higher margin than the initial
product-based revenue stream (Opresnik and Taisch 2015). In datatization, an additional revenue might be
realized by adding an additional stream or by an indirect pay-off through product and service uplift.
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Thirty Eighth International Conference on Information Systems, South Korea 2017 16
Converting the culture of a product-focused organization into an organization that embraces and values
services, is an essential step in the transformation path (Oliva and Kallenberg 2003; Vandermerwe and
Rada 1988). Servitized organizations pursuing datatization need to cultivate a data-driven culture, that
allows the discovery of value in data.
Transformation towards a datatized organization requires new skills. As part of servitization, organizations
need to attain customer facing skills since offering services exposes the organization to unfamiliar but
critical customer encounters (Baines and Lightfoot 2013). In order to utilize data analytics for new or
enriched service offerings the organization needs to build up data science and software development skills.
Conclusion
Our qualitative analysis of 15 datatized organizations provides a comprehensive overview of the barriers
organization face when pursuing this advanced step of servitization. An exhaustive literature review
regarding serivitization barriers serves as foundation and is compared and extended with datatization
challenges. Our analysis has revealed that datatization builds upon similar challenges. However, data
analytics add an additional level of complexity to some aspects of the transformation such as the necessity
of an additional data strategy, a closer integration of stakeholders, an increased importance and impact of
the customer relationship, various changes in the service development practice, the management of new
revenue streams, a data culture, and skills & capabilities, enabling a new source of competitive advantage.
This paper, therefore, contributes to the general understanding of service transformation by providing a
comprehensive overview of challenges and, furthermore, unveiling the additional barriers organization face
when utilizing data analytics to enrich their offerings. Based on these findings, this paper lays the
foundation for future research and opens a research agenda, addressing the described challenges.
Managerial Implications
The analysis of barriers organization face when pursuing datatization have direct implications for
managers. Organizations that plan to start this endeavor can benefit from this study and can manage the
transformation more effectively and efficiently.
First, the paper at hand gives insights on the crucial challenges an organization faces when pursuing
datatization. Besides the more obvious necessities around skills & capabilities, the study reveals additional
challenges in the areas of strategy, culture and the customer relationship.
Second, the paper uncovers some of the strategies organizations chose to overcome their transformation
hurdles. It, therefore, offers guidance to datatizing organizations to overcome the barriers in their own
transformation.
Limitations and Outlook
The research strategy, the ways of gathering data and the selection of the sample certainly do pose a number
of limitations that will be further addressed in future research.
First, the paper focuses on the use of data analytics for new value propositions. However, organization may
also use data analytics internally to improve decision making. Our research does not cover the challenges
connected with this strictly internal focus.
Second, in our cases we focused on the business perspective by choosing interview partners such as service
executives and product managers. Thereby, we were able to distill transformational challenges and did not
focus on the non-trivial IT challenges.
Third, in our research we have identified a series of best practices to overcome the mentioned challenges.
Both the identified practices and challenges have a high need and potential for future research, e.g.: (1) new
value propositions that can be created by data analytics, (2) revenue mechanisms that apply, (3) new
networks that form and (4) integration of data analytics in the development process of the organization.
Fourth, as we solely address challenges, which were mentioned in multiple interviews, thereby seeking
qualitative saturation, we do not pursue a quantitative verification of the results. Future research should,
therefore, be used to quantify and verify our results.
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Thirty Eighth International Conference on Information Systems, South Korea 2017 17
Still, there is more work to be done to systematically reap the benefits of datatization. Data analytics has
added an additional layer of complexity and change: collaboration with a broader range of internal &
external stakeholders, evolution of information systems from enablers to essential drivers of service
offerings, higher requirements to information systems, uncertainty regarding security and legal issues,
cultural change and new skills, capabilities and processes. Managing these difficulties of the transformation
opens up a whole research agenda on its own. However, creating awareness for a well-founded systematic
set of challenges should enable practitioners to professionally manage transformation as well as academics
to structure their research agenda.
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... Apart from the practical implications, initial contributions can also be found in the literature on both barriers and challenges in the area of service innovations in manufacturing (Kowalkowski et al., 2015;Kindström & Kowalkowski, 2014) and on the development of DDS, the so-called smart services in general (Schüritz et al., 2017;Klein et al., 2018), as well as in the Industry 4.0 SME context (Orzes et al., 2020). However, both in practice and in academia, there is a lack of up-to-date methodological analysis of the correlation of the main barriers to the development of DDS for SMEs. ...
... The barriers are discussed in detail in the following section. Coreynen et al., 2017;Enders et al., 2019;Fritsch & Krotova, 2020;Schüritz et al., 2017 Strategy: First, designing a service plan that is compatible with their overall strategy is a difficulty for product-oriented companies (Schüritz et al., 2017). Due to an imprecise service strategy, some firms are unsure of what they aim to achieve with their datadriven businesses (Klein et al., 2018). ...
... The barriers are discussed in detail in the following section. Coreynen et al., 2017;Enders et al., 2019;Fritsch & Krotova, 2020;Schüritz et al., 2017 Strategy: First, designing a service plan that is compatible with their overall strategy is a difficulty for product-oriented companies (Schüritz et al., 2017). Due to an imprecise service strategy, some firms are unsure of what they aim to achieve with their datadriven businesses (Klein et al., 2018). ...
Conference Paper
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Data is nowadays considered as a key resource and represents the most valuable asset of our technology-driven world. However, the ability to use this resource in a value-adding way requires a holistic perspective. Small-and medium-sized enterprises in particular face major challenges in the innovation and development process. Despite preliminary research in the area of data-driven services (DDS), there is a lack of methodological analysis of the key barriers for SMEs in the context of DDS development. To address this shortcoming, we have developed an interpretive structural model based on a two-stage mixed-method approach by combining a structured literature review with practice-oriented focus group interviews to identify key barriers and their interdependencies and interactions. Our paper strengthens the knowledge of DDS development through a methodological barrier analysis and provides a guide for practitioners to eliminate the most relevant barriers to DDS development.
... In 2021, services represented more than 73% of the Gross Domestic Product (GDP) in the United States and the Euro area [1], and in 2019 they were responsible for 71% of the European Union's total jobs [2]. Since products became a commodity, providing services is a new way for enterprises to generate value, in a move called servitization or product-service system (PSS) [3]. Within this context, research in Service Science Management and Engineering (SSME) is growing in relevance [4]. ...
... Servitization is the process that supports the organizational operating changes to create value disruptively [5]. It is defined as the shift towards the PSS process and the organizational transformation it causes [3,6,7]. PSS can be classified as product-oriented, use-oriented, or result-oriented [8]. ...
... Figure 1 represents the search process. We identified, in the SLR, three other papers that are also literature reviews [3,9,10]. The first focused on comparing organizational transformation focused on data and analytics. ...
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We propose a framework based on ADKAR (awareness, desire, knowledge, ability, and reinforcement) with guidelines to manage the organizational culture change required for servitization—the transition of a company towards a product–service system (PSS) business model that provides cohesive delivery of products and services, increasingly supported on digital technologies. We departed from a systematic literature review across five academic databases, covering human and technological aspects, that confirmed corporate culture as one of the pillars of a successful transformation, along with relevant factors to account for. The results of this work have both theoretical and managerial implications. Companies can apply the framework to support planning implementation strategies that require a corporate mind shift. Finally, we identified directions for future servitization research.
... However, developing data-driven services is still a significant challenge for most traditional companies. They struggle to leverage data-driven service innovation and exploit the full potential of data [10]- [13]. Especially, product-centric companies face significant challenges in developing data-driven services [14]- [16]. ...
... Data and analytics support the customer's decision-making process giving sound data-based insights and creating new customer value [39], [43]. These novel value propositions use data as the key resource and are called data-driven services [10], [13]. ...
... A detailed overview of a machine or system's lifecycle gives the service developer a better orientation as the customer's activities can be investigated [104]. Furthermore, data-driven services should be an integrated part of future product developments to foster data-driven innovation [13]. ...
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The continuously growing availability and volume of data pressure companies to leverage them economically. Subsequently, companies must find strategies to incorporate data sensibly for internal optimization and find new business opportunities in data-driven business models. In this article, we focus on using data and data analytics in product-oriented industrial companies. Although data-driven services are becoming increasingly important, little is known about their systematic design and development in research. Surprisingly, many companies face significant challenges and fail to create these services successfully. Against this background, this article presents findings from a multicase based on qualitative interviews and workshops with experts from different industrial sectors. We propose ten design principles and corresponding design features to successfully design industrial data-driven services in this context. These design principles help practitioners and researchers to understand the peculiarities of creating data-driven services more in-depth on a conceptual, technical, and organizational level.
... Although the number of research has increased significantly in recent years, there is a lack of a holistic view and a common understanding about the roles of digital technologies and especially the use of data and analytics to innovate digital services (Hunke et al. 2019). Overall more research is needed on the use of advantages brought by digital technologies in the context of service innovation (Schüritz et al. 2017). Against this, we seek to investigate the following research question: How do digital technologies influence service innovations in the manufacturing industry, and what specific roles do they play in the service system? ...
... Digital services derived from these are at the core of smart PSS business strategy. Schüritz et al. (2017), in turn, examine digital servitization specifically from the perspective of data analytics. In doing so, they describe data analytics capability as an advanced development of servitization and introduce the term datatization, which can be defined as the "innovation of an organization's capabilities and processes to change its value proposition by utilizing data analytics" (Schüritz et al. 2019, p. 4). ...
... Customer relation (Berger et al. 2020;Coreynen et al. 2017;Culot et al. 2020;Kamalaldin et al. 2020;Paiola and Gebauer 2020;Schüritz et al. 2017;Vargo and Lusch 2004;Zhang et al. 2019) Co-creation (Bu et al. 2021;Herterich et al. 2016a;Kohtamäki et al. 2019;Rabetino et al. 2017;Schüritz et al. 2017;Vargo and Lusch 2004) Continuous improvement (Beverungen et al. 2019;Frank et al. 2019b;Kamalaldin et al. 2020;Porter and Heppelmann 2014;Schüritz et al. 2019) Process ownership (Berger et al. 2020;Raddats et al. 2019;Riasanow et al. 2017;Schüritz et al. 2017) IT-organization type (Bilgeri et al. 2019;Bilgeri et al. 2017) Customization ( Pricing (Cenamor et al. 2017;Paiola and Gebauer 2020;Rabetino et al. 2017;Schüritz et al. 2017) Value creation in networks (Bürger et al. 2019;Lusch and Nambisan 2015;Riasanow et al. 2017;Vargo and Lusch 2004) Continuous innovation (Frank et al. 2019b;Kamalaldin et al. 2020;Porter and Heppelmann 2014) Skillset (Culot et al. 2020;Kohtamäki et al. 2019;Porter and Heppelmann 2014;Rabetino et al. 2017) Interdisciplinary teams (Dremel et al. 2017;Herterich et al. 2016b) Openness & agility (Berger et al. 2020;Bueno et al. 2020;Gimpel et al. 2018;Tronvoll et al. 2020) Costs (Gebauer et al. 2005;Kohtamäki et al. 2019;Rabetino et al. 2017; Data-driven culture (Baines and Lightfoot 2014;Dremel et al. 2017;Schüritz et al. 2017) Integration and interfaces (Birch-Jensen et al. 2020;Coreynen et al. 2017;Porter and Heppelmann 2015) Strategic Partnerships (Herterich et al. 2016b;Kamalaldin et al. 2020;Rabetino et al. 2017;Sjödin et al. 2020) Simultaneously, this means that service system participants need new skills and must work in new organizational structures. On the one hand, interdisciplinarity and, on the other, the collaboration between IT and the business are emphasized (IT-organization type). ...
... On the one hand, new potential sources of revenue arise for companies due to changes within their business models, such as the introduction of new sales channels or access to new markets [34][35][36]. Further potential lies in the collection and analysis of data, which can be used to either make internal value chains more efficient, wrap new functions around existing value propositions, or even innovate completely novel business models [37][38][39]. Besides that, data and analytics can increase the probability of success of marketing measures through targeted customer targeting [40,41]. ...
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... This changes the division of labor and gives rise to manufacturing ecosystems with a multitude of players. Data facilitates planning, connecting, monitoring, steering, allocating, and optimizing the involved resources and processes (Martín-Peñ et al. 2018;Schüritz et al. 2017). All these managerial processes can benefit from analytics in general and advanced and predictive analytics in particular, enabling competitive advantages (LaValle et al. 2011;Rymaszewska et al. 2017). ...
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