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Designing Analytics-Based Services - Exploring Design Requirements for Methodological Tool Assistance in Service Design Teams

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Analytics-based services (ABS) apply analytical methods to data in order to enable customers to make better decisions and solve more complex problems. While it is widely acknowledged that ABS pave the way for new value creation opportunities, surprisingly little is known about their systematic design. Service design teams still struggle to create ABS solutions systematically, i.e. to define what is to be done, how this is going to be achieved and how decisions are taken during ABS design projects. In this research, we report on the first iteration of our design science research project which aims to build design knowledge on methodological tools that can support service design teams in this particular context. We derive and evaluate four meta-requirements and four design principles-thus contributing to a more profound design knowledge base that can support researchers in developing new methodological tools in the field of ABS in the future.
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15th International Conference on Wirtschaftsinformatik,
March 08-11, 2020, Potsdam, Germany
Designing Analytics-Based ServicesExploring Design
Requirements for Methodological Tool Assistance in
Service Design Teams
Fabian Hunke1 and Florian Kiefer2
1 Karlsruhe Institute of Technology, Institute of Information Systems and Marketing,
Karlsruhe Service Research Institute, Karlsruhe, Germany
fabian.hunke@kit.edu
2 EnBW Energie Baden-Württemberg AG, Karlsruhe, Germany
f.kiefer@enbw.com
Abstract. Analytics-based services (ABS) apply analytical methods to data in
order to enable customers to make better decisions and solve more complex
problems. While it is widely acknowledged that ABS pave the way for new value
creation opportunities, surprisingly little is known about their systematic design.
Service design teams still struggle to create ABS solutions systematically, i.e. to
define what is to be done, how this is going to be achieved and how decisions are
taken during ABS design projects. In this research, we report on the first iteration
of our design science research project which aims to build design knowledge on
methodological tools that can support service design teams in this particular
context. We derive and evaluate four meta-requirements and four design
principlesthus contributing to a more profound design knowledge base that can
support researchers in developing new methodological tools in the field of ABS
in the future.
Keywords: Analytics-Based Services, Service Design, Methodological Tool
Support, Design Science Research.
1 Introduction
In the wake of business changes enabled by the digital transformation [1], large
amounts of data accompanied with advances in analytics technology provide companies
with novel opportunities to create meaningful value for their customers [24].
Analytics-based services (ABS), a new type of services, are introduced to the market
that leverage analytical methods (‘analytics’) applied to data in order to help their
customers make better decisions and solve more complex problems to ultimately reach
their goals more effectively [5, 6]. Researchers and industry experts agree that ABS
provide a promising path for companies to gain new competitive advantages [7, 8] by
enabling a much deeper customer connection [9], by allowing a broader role in
supporting the customer's value creation [10], or even by opening up entirely new
markets [11]. Thus, systematically realizing new ABS opportunities has received
research priority [7] and is being actively explored by companies [12].
Service design is a formalized, multi-disciplinary approach that helps innovate
service offerings. Thus it could potentially serve as a means to guide and stimulate the
development of new ABS [13]. Surprisingly, little research has been reported to
advance service design literature in this regard and actionable insights to better manage
ABS design are still lacking [14]. As a result, service designers in practice still struggle
to develop services that appropriate the expected value from data and analytics in novel
service offerings [14, 15]. In particular, creating and refining the service concept
remains a challenge in ABS design. At the core of service design processes, the service
concept serves as a means of specifying the nature of the service to be provided [16].
As such, it provides a detailed description of a service idea with regard to its value
creation and delivery processes [17]. Thus, a clear and shared understanding of the
service concept within service design teams, which we refer to as service concept
comprehension, is crucial in order to ensure consistent decision making during the
course of a service design project yet, actionable insights on ABS-related service
concept comprehension remain scare.
Methodological tools assisting service design have the potential to solve this issue.
Prior research has found that creative and interdisciplinary tasks can particularly benefit
from tool-support by promoting a better comprehension through visualization,
communication, and documentation [18]. This could positively influence the
performance of service design teams during ABS-concept development in practice.
However, design knowledge which helps researchers to build tools specifically
assisting service concept comprehension is scarce. Researchers lack theoretically sound
guidance through clear principles for designing such tools in general terms. To this end,
we propose a design science research (DSR) project seeking to address this literature
gap by tackling the following research question: How to design methodological tools
assisting ABS design in order to increase service concept comprehension?
In this research, we report on the first iteration of our project and present initial
design requirements which are meant to serve researchers as a more profound design
knowledge base when building new methodological tools in the specific field of ABS
design. In the remainder of the paper, we first provide necessary foundations and related
work relevant to our research. This is followed by our research methodology in section
3. The findings of our research are reported in section 4 and 5, structured
chronologically reaching from the design requirements derived to their instantiation
and evaluation. We then discuss our findings in section 6 before we briefly summarize
our results, acknowledge limitations, and provide an outlook on future research in
Section 7.
2 Foundations and Related Work
This paper aims to discover design knowledge for methodological tools supporting the
systematic development of ABS concepts during service design. For that purpose, we
first review prior research that contributes to conceptualizing the use of data and
analytics in services. Second, we provide an overview of the service design discipline
and the role of methodological tools within it.
2.1 Data and Analytics as Drivers for Customer-Oriented Value Creation
Researchers increasingly strive to understand how data and analytics contribute to new
customer value creation. The application of statistical methods (‘analytics’) to data has
been investigated quite intensively as a means to streamline organizations’ internal
business processes and to derive insights for managerial decision making (e.g. [19
21]). Yet, we increasingly observe how companies offer novel, customer-facing
services that build upon data and analytics to create new, meaningful value to its
customers [14]. Such ABS enable customers to make better decisions and to solve more
complex problems to ultimately achieve their goals [5, 6].
The proliferation of data provides companies with numerous opportunities to infuse
their service offerings with data [22]. Huang and Rust stress that customer data hold
the potential to “figure out […] why customers make decisions they make and why they
behave in a certain way” [[23], p. 255]; thereby facilitating a much deeper access to the
customer. Further, tangible products are increasingly equipped with sensor technology
which enables them to sense their own condition and their external environments and
thus allows for real-time data collection [24]. Based on these increasingly ‘smart’
objects, organizations can use information from this collected data to offer contextual
and preemptive services, predominantly referred to as smart services giving them the
opportunity to strategically differentiate themselves in the market [25]. These services
allow for a value-add based on data and analytics by ‘wrapping’ information derived
from collected data around the core product [26]. Yet, smart services require an
intelligent object and some ABS service do not (e.g., using medical insurance data
[14]).
However, extant research mainly describes the utilization of data and analytics in
customer-facing services from a phenomenological perspective [14]. Despite the
priority it has received in service research [7], we still lack sufficient knowledge from
a service design perspective that would systematically guide the conceptualization and
design of new analytics-based services in practice [14].
2.2 Service Design and the Role of Methodological Tools
As described above, a number of studies have investigated the use of data and analytics
to create new customer value in services. Service design, a formalized approach that
helps innovate service offerings, could serve as a means to guide and stimulate the
development of new ABS [13]. Various researchers have developed process models in
which they define the necessary steps for the development of new services (e.g. [17,
27]). In essence, these processes consist of five elementary activities [28]: 1)
opportunity identification, 2) customer understanding, 3) concept development, 4)
process design, and 5) refinement and implementation. Particularly, the literature
stresses the importance of the service concept, whose refinement during the service
design process ultimately leads to the desired service innovation [29]. A service concept
describes and tangiblizes the specific features of a service idea [28]. It identifies the
benefits the service is intended to provide to customers, indicates how to offer the
service [17], and mediates between customer needs and the strategic intent of the
company [16].
Service design is described as a multi-disciplinary approach in which service design
teams are composed of members who contribute to problem solving with their different
backgrounds, areas of knowledge, and competencies [13]. To manage this diversity and
channel it for problem solving, team collaboration is increasingly supported by
methodological tools. These tools support collaboration in different ways, such as
aligning distributed information, improving idea generation, or increasing the
understanding of the problem [30]. Depending on the stage of the project, service
design teams use a variety of tools to augment their capabilities. For instance, the Team
Alignment Map helps teams to better plan and more effectively coordinate their joint
efforts during projects [31]. The Business Model Canvas helps teams to define
appropriate business models for the service to be provided [32].
Methodological tools assisting service design teams determine how team members
(visually) frame a problem. Thereby, they contribute to problem solving as all
participants can refer to the same structure of the problem [30]. Thus, they play a critical
role in service design as they use a common visualization as the problem space to create
a basis for collaboration in which team members can apply their diverse knowledge,
experience, and insights to create innovative services [33]. Despite the emergence of
various tools in the past, formalized design knowledge is very limited. So far, there is
a lack of clear principles for the design of such tools in general terms documenting how
methodological tools have to be built to achieve the desired outcomes. Further research
is needed to drive the rigorous development of such tools in the futureparticularly in
the field of ABS design.
3 Research Methodology
This paper aims to generate design knowledge for methodological tools which support
service design teams by increasing service concept comprehension of ABS among its
team members. For that purpose, we conduct a design science approach. DSR aims to
systematically design and develop artifacts to solve real-world problems following a
“build-and-evaluate loop” in order to iteratively arrive at an optimized artifact
instantiation [34]. Following the approach suggested by Kuechler and Vaishnavi [35],
this research project is conducted in five steps consisting of a problem awareness,
suggestion, development, and an evaluation phase.
For the first cycle which we focus on in this paper, we started by conducting a
systematic literature review on existing research addressing the use of data and
analytics to advance service to gain a deeper understanding of the academic discourse
(reported in [36]). Unveiling that existing literature falls short in guiding a systematic
design of ABS, we decided to build a taxonomy which helped us to conceptualize the
nature of ABS (reported in [6]). To also gain insights from the real-world environment,
i.e. actual service design teams that struggle to develop ABS in practice, we also
conducted a series of interviews with practitioners that are currently involved in ABS
development projects. With service design being a multi-disciplinary approach that
incorporates contributions from several disciplines, such as 1) service marketing and
operations, 2) information technology, and, in the context of analytics-based services,
3) data science [13, 37], we purposively sampled our interview partners along these
three expert domains [38]. Our main focus was to understand how service design teams
currently tackle ABS development and what problems they perceive during their
projects. To this end, we asked our interview partners about their history of ABS-related
projects, their role within service design teams, and about working practices they had
established over the course of their ABS-related projects. The interviews were recorded
and transcribed. In total, we collected eleven interviews which lasted 50 minutes on
average. We applied a qualitative content analysis to analyze the interviews [39] and
used an open coding approach to ensure openness towards any aspects unveiling
problems in the context of ABS design and development [40].
The results of these research activities a literature review, a taxonomy
development, and an interview series served as a basis to generate initial design
knowledge for methodological tools increasing service concept comprehension of ABS
among service design teams. For that purpose, we first identified meta-requirements
which comprise generic requirements that must be fulfilled by an artifact [41]. In a
second step, we derived design principles that encompass generic capabilities of
designed artifacts to comply with the identified meta-requirements [42].
4 Meta-Requirements and Design Principles
This paper derives four initial meta-requirements (MR) and four associated, initial
design principles (DP) to contribute to design knowledge for methodological tools
increasing service concept comprehension during ABS design. In the following, we
provide a more detailed description of the MR and DP.
4.1 Meta-Requirements
For service design teams, the service concept is a key driver for their decision-making
and thus an essential instrument for their work during the service design and
development process. The service concept defines the nature of a service by providing
a “detailed description of what is to be done […] and how this is to be achieved” [16,
p. 149]. It helps teams to mediate between the organization’s strategic intent and
customer needs. Service concepts serve service design teams to specify a service idea
by deconstructing it into its relevant components that need further attention and require
design decisions. In the context of ABS, practitioners stressed during our interviews
that they are still inexperienced with regard to the role of data and analytics in services
and that they still suffer from a lack of data-centricity in their work. Thus, MR1 imposes
the following requirement on methodological tools assisting their design process: The
methodological tool should conceptualize analytics-based services from a data-driven
perspective (MR1).
Following the notion on the service concept in the literature, it is key to a successful
service design process. Yet, identifying relevant components that require further
decisions is by no means sufficient. Only if decisions are made consistently among
team members and in line with the service idea throughout the design process, service
design teams are able to take a new service from the idea stage through the evolving
design phases to a deliverable service [16, 28]. Thus, a shared and clear understanding
of the service idea is a necessary prerequisite of successful service design and the basis
for targeted communication among team members. In line with that, our interviews also
agreed that an interdisciplinary service design team is perceived as a key to success in
ABS design. Yet, the experts also stressed that communication between the various
actors is often inadequate, e.g. because they are not informed about challenges outside
their specific field of expertise. Therefore, MR2 imposes the following requirement on
tools assisting the design of ABS: The methodological tool should serve as a
communication construct and enable ABS design teams to establish a shared
understanding (MR2).
While the literature on service design gives practitioners a general overview of the
key activities they should focus on in their projects, we identified in our interviews a
fundamental uncertainty regarding the approach to design ABS. Particularly,
practitioners stressed that they still lack the maturity and experience to clearly decide
which aspects to focus on. Accordingly, we MR3 addresses this issue: The
methodological tool should provide guidance in the ABS design process (MR3).
Service design also is a creative and iterative approach [13] and our interview
partners highlighted the multitude of service concept variants that are created in the
course of a service design project. In line with a "fail often and fail early" mentality
[43], service concepts are quickly ideated, tested and refined or even discarded. A tool
supporting service concept comprehension within service design is required to reflect
this mutability. Thus, MR4 becomes: The methodological tool should support agile
routines of service design practices (MR4).
4.2 Design Principles
These meta-requirements are translated into initial design principles which are meant
to serve as a blueprint conceptualization of methodological tools assisting in ABS
design. To this end, we heavily build on research previously conducted to conceptualize
the nature of ABS which introduced a taxonomy identifying commonly shared
characteristics of this service type [6]. Taxonomies are a well-established instrument to
describe and analyze new phenomena using a unified classification schema [44]. The
ABS taxonomy consists of six dimensions data generator, data origin, data target,
analytics type, portfolio integration, and customer role each represented by a distinct
set of generic characteristics. In the following, these six dimensions and their respective
characteristics, key to conceptually describing ABS, are briefly explained.
Data generator specifies the entities that generate relevant data for an ABS. Data
may be generated by customers consuming the service or by non-customers who
generate relevant data but have no direct links to the service provider (e.g. data from
social media). Apart from that, physical objects increasingly equipped with sensor
technology and networking capabilities allow organizations to draw on different kinds
of data that result from objects’ operations. Further, with digitalization actively
transforming organizations, this accounts for business processes (e.g. production
processes within manufacturing) indicating key process indicators as well.
Data origin specifies where the generated data comes from. An internal origin refers
to data the service provider has direct access to ranging from its machine data to event-
based data. Opposingly, external data refers to data that comes from outside the
company, i.e. publicly available data, private data provided by the customer, or
purchasable data (e.g. weather data) which requires to have a third party to be involved
in the service.
Data target specifies about whom or what the generated data contains information.
The most obvious perception would be that data generating entities produce data about
themselves respectively about their state or operations. Yet, data might also contain
information related to other ‘targets’ of interest. For example, objects might also
generate data containing information about their environment (e.g. surroundings’
weather conditions). Thus, in addition to the data generator characteristics, environment
is also accounted as a possible data target.
Analytics type specifies the analytical methods applied to deliver ABS. Descriptive
analytics process past data resulting in aggregated reports or accumulated
visualizations. Diagnostic analytics also work on past data aiming to deductively derive
from data why certain events occurred. In turn, predictive analytics follow an inductive
approach focusing on predicting what will happen according to past and current data.
Prescriptive analytics take it a step further and investigate what should be done based
on available data and the resulting predictions; thereby, prescriptive analytics heavily
build on simulation - and optimization techniques.
Portfolio integration specifies how the ABS relates to the service provider’s
business. The literature distinguishes between services that accompany another product
or service (value-added service) and those that are offered independently from other
products or services (stand-alone service). Stand-alone services predominantly create a
self-sufficient value for the customer such that a coupling with other products or
services is possible, but not mandatory. Value-added services, on the other hand, serve
to increase the customer's benefit from a product or service by ‘wrapping’ additional
value created from data and analytics around them. Accordingly, value-added services
are further divided into wrapped around product and wrapped around service.
Service User Role refers to the customer’s role within the ABS concept. The
recipient role describes customers simply consuming the service. The customer is not
involved during value creation. Using a weather forecast is such an example, where the
customer is neither required to provide data nor to participate in the analysis process.
The provider of data role requires the customer to actively provide data for the ABS.
This allows the service provider to gain knowledge about the customer to deriver
insights from that data (e.g. predictive maintenance services). The interactor role
requires the customer to integrate the service provider into his processes. The service
provider is empowered to make decisions and changes in customer processes and
practices.
As pointed out earlier, conceptual work on the nature of ABS remains limited in
academic literature making service design teams struggle to systematically develop a
service concept. Thus, building on our research results unveiling six key dimensions to
conceptually describe ABS, this knowledge may function as affordances in the sense
of generic design options for service concepts and may highlight the ABS-specific
aspects service designers should focus on. Therefore, we specify the aforementioned
MRs and formulate the following DPs for methodological tools: Conceptualize the
purpose and core design decisions of the ABS from a data-driven perspective (DP1);
Provide affordances for designing an ABS concept (DP2).
Service design requires many domain-specific experts in service design teams
ranging from business strategists to user-experience designers or IT specialists [13].
Consequently, the team is characterized by heterogeneously distributed knowledge that
leads to many different opinions and perceptions within the team. In order to establish
a commonly shared understanding of a service idea in a service concept, it requires
support to facilitate communication among the team members. In similar contexts,
frameworks such as the Business Model Canvas [32] have already demonstrated that
visual tools are effective and result enhancing in this respect. While the visual stimulus
is only one aspect of these frameworks, it is considered essential to the success of
developing a commonly shared understanding by structuring the most important
conceptual elements logically [30]. Further specifying MR1 in this regard, the third DP
becomes: Visualize ABS in a pre-defined template and provide a common language to
address ABS (DP3).
In addition, the documentation of these agile iterations, their key learnings and ideas
are crucial to provide traceability and ensure service concept comprehension at later
stages. From our interviews we had learned, that the success of service design projects
often depends on tacit knowledge from experienced team members. In order to solve
this problem and make lessons learned from ABS design projects available in the long
term, we formulate the fourth DP as follows: Enable the documentation and
communication of the developed individual service concept (DP4).
5 Instantiation and Evaluation
In order to evaluate the elaborated DP, we instantiated them in an initial prototype (cf.
Figure 1). As we had learned, service design being a creative and interdisciplinary
approach often takes place in workshop settings. Thus, we decided to use a canvas
format. In similar contexts, tools like the Business Model Canvas [32] have already
shown that such frameworks are effective and result enhancing in this respect. To
implement a pre-defined template (DP3), we built on the previously developed
taxonomy of ABS and used the identified general dimensions to visually materialize
ABS along predefined key factors. This also allowed us to provide possible users with
the core design dimensions of an ABS concept (DP1). In addition, we used the
dimensions’ underlying characteristics to provide possible affordances for designing
ABS for users (DP2). A service concept is now developed by formulating concrete
specifications for each design dimension (cf. Figure 2). The characteristics from the
taxonomy of the ABS are used as generic guidelines within the framework.
Figure 1. Instantiations of the DP in an initial prototype.
5.1 Evaluation Approach
In order to evaluate our prototype respectively its underlying DP, we conducted
exploratory focus group interviews [45, 46]. In sum, twelve potential users participated
in three workshops. The participants’ age ranged from 23 to 26, five participants were
female and seven were male. They were recruited in their capacity as students attending
a master level university course “Service Design Thinking”. This course follows a
unique ten-month teaching concept, in which students are grouped in teams, each
receiving the challenge to design a new service for a real-world challenge from a
business partner. At the time of the evaluation, these teams were involved in ongoing
service design projects developing digital services. Hence, they were experienced in
service design within our application domain, familiar with its routines, and thus were
perceived appropriate as focus group participants.
For each focus group, we asked the participants to independently read a “typical”
case of an ABS we had purposefully selected [47], i.e. the description of an ABS that
had been successfully established in the market. Table 1 provides the respective case
vignette highlighting the fundamental aspects of the ABS. Afterwards, we asked the
team to establish a commonly shared understanding of the ABS concept using our
prototype. Through the independent reading assignment, we achieved that each
participant gained an individual understanding of the underlying service concept
described in the case. The subsequent team assignment required the team to build a
INTERNAL EXTERNAL
DIAGNOSTICDESCRIPTIVE PRED I CT I VE PRESC R I PTI V E
NON-CUSTOMERCUSTOMER PROCESS OBJ ECT
STAND -AL ONE
SOLU T I O N
WRAPPED -AR OUN D
PROD U C T
WRAPPED -AR OUN D
SERV I C E RECIPIENT DATA PROVIDER INTERACTOR
NON-CUSTOMERCUSTOMER PROCESS ENVIR ONMENTOBJ ECT
Purpos e Statement
Data Generator
Data Origin
Data Target
Analytics Type
Portfolio Integration Serv ice User Role
shared service concept comprehension. After that, we conducted the focus group
interviews and did a Strength-Weakness-Opportunities-Threats (SWOT) analysis
which led the participants to discuss with each other.
Table 1. Case vignette of the ABS used during the focus group interviews.
Case vignette: Cochlear’s ABS to create customer value around hearing aid solutions
Cochlear is an Australian-based, market-leading company that develops, manufactures and
sells hearing aid solutions. The products combine a surgically inserted implant and an external
sound processor worn behind the ear. The latest generation of the "Nucleus 7" product line
features a scene classifying service which analyses the user's environment (e.g. quiet living
area vs. busy road) and automatically adjusts the settings of the processor to ensure the best
performance for the environment. In previous generations of products, such adjustments to
the hearing aids had to be made manually by the client. However, Cochlear realized that
patients did not always select the optimal settings for the situation, which meant that the
product potential was not fully exploited. The current product now uses an embedded ABS to
identify different situational contexts and automatically makes customer-specific adjustments
in real-time. [48, 49]
Figure 2. Illustrative application of the prototype to the evaluation case from a focus group.
5.2 Evaluation Results
In sum, we received promising feedback on our approach to assisting service design
teams with methodological tools for ABS design. Regarding the proposed DP, the
INTERNAL EXTERNAL
DIAGNOSTICDESCRIPTIVE PRED I CT I VE PRESC R I PTI V E
NON-CUSTOMERCUSTOMER PROCESS OBJ ECT
STAND -AL ONE
SOLU T I O N
WRAPPED -AR OUN D
PROD U C T
WRAPPED -AR OUN D
SERV I C E RECIPI ENT DATA PROVIDER INTERACTOR
NON-CUSTOMERCUSTOMER PROCESS ENVIR ONMENTOBJ ECT
Purpos e Statement
Data Generator
Data Origin
Data Target
Analytics Type
Portfolio Integration Serv ice User Role
Product
sensors
Customer -
specific data
(e.g . age )
Data is
internally
available
Product’s
curr ent
environment
Supervised
Machine
Lear nin g
Wrapped a round
the core product
Customer
provi des dat a
(vi a App)
Automatically adjust
the hearing solution
to the scene
Product’s
optima l set tings
for th e s it uat ion
participants acknowledged the importance of all four. Table 2 summarizes the
aggregated feedback we received during the SWOT analysis of the DP.
The participants perceived the given dimensions as valuable for structuring their
approach in the service design process in general (DP1). The selection options in each
dimension also helped them to provide a "shared vocabulary" in the comprehending
process (DP2). It was emphasized that the visualization in the shape of a predefined
template helps to capture the "big picture", i.e. a holistic service concept (DP3). To this
end, visualizing ideas in a common structure was perceived as an asset during service
design projects. The participants also stressed the possibility in using the tool as a
communication channel to mediate ideas during creative ideation sessions and they
agreed that such a tool helps to document developed service concepts and to make them
available to the team at later stages in the project (DP4).
Table 2. Summary of SWOT-analysis results.
Strength
Weakness
DP1:
Enables a shared understanding of the ABS
concept within the team
Provides a good entry point to the concept
development phase
DP2:
Forces to actively think about
specific/alternative ABS features
Helps to explore initial ideas
Allows to further enrich initial ideas
DP3:
Enables to create a high-level overview of the
ABS concept without going into technical details
Enables a deeper communication between team
members
Allows to easier articulate ABS concepts to
higher management
Helps to structure the service concept
development phase
DP4:
Provides the basis for a common terminology
which fosters and improves communication
Opportunities
Threats
DP1:
DP2:
Support intelligently by guiding service design
teams in the service creation process through
“success stories”
DP3:
Ability to portray existing ABS for inspiration
and analysis
Foster further discussions on more detailed
topics
DP4:
Visualize iterations of service concept
evolvement
Allows to receive feedback from relevant
stakeholders
6 Discussion
Using data and analytics in service offerings as a means to create new customer value
has recently become a much-regarded strategy by companies [8] and is now being
actively explored by academics [7]. Despite the growing interest in ABS from research
and practice, their systematic design is still in its infancy. As a result, service design
teams struggle to systematically create service concepts that would enhance a more
effective design process of ABS in practice. Tackling this issue, we are able to capture
initial design knowledge for methodological tools generally supporting during ABS
design. It consists of four meta-requirements and four generic principles for the design
of such tools in general terms. This design knowledge may serve future research for the
development of new methodological tools assisting service design teams in the specific
field of ABS as it provides prescriptive knowledge for designing such tools [34, 50].
The evaluation of the proposed DP through a first prototype implementing them
suggests that they facilitate to empower ABS concept development during service
design. Thus, we provide a promising base for IS researchers to further investigate
methodological tools supporting the systematic design of ABS.
Nevertheless, the evaluation also unveiled several opportunities for further
refinement of our DP. Participants from all focus groups highlighted the manual effort
required for post-processing the application of a tool to digitize the results. As we learnt,
the documentation and communication of service concepts is preferably done digitally
within service design teams; hence we modify the existing DP4 to: Enable the digital
documentation and communication of the developed individual service concept (DP4).
In addition, the focus group participants identified the benefits of a systematic, pre-
defined visualization of the ABS concept in order to request feedback from key
decision-makers at an early stage. Thus, we formulate a new design principle as
follows: Enable service design teams to obtain early feedback to quickly evaluate
different ABS concepts and investigate improvement activities (DP5).
7 Conclusion
ABS are a novel type of service in which the application of analytics to data provides
ground for new customer value by delivering context-relevant insights or making
valuable decisions for the customer thus, enabling customers to reach their goals more
effectively or efficiently. They provide companies with new opportunities to create
customer value and to achieve competitive advantages in the market. Despite the
growing interest among researchers and practitioners on ABS, academic literature
surprisingly lacks actionable insights to assist their systematic design. This research
presents the results of the first iteration of an ongoing DSR project that aims to build
generalizable design knowledge about methodological tools that support service design
teams during their ABS design project. To this end, four MRs and four DPs are derived
in this research that provide an initial design knowledge base. The instantiation of the
design principles in a prototype enables us to evaluate them with domain experts. The
results of the exploratory focus group analysis are promising and provide substantial
feedback for further improvements in subsequent design cycles. Thus, this research
contributes to the community by advancing design knowledge that can guide
researchers in developing new methodological tools in the field of ABS in the future
and we encourage researchers to build future work on our design requirements.
We are aware that our research comes with some limitations. In particular, the results
from the explorative focus group interviews are limited in their generalizability at this
point as we only evaluated the users’ feedback using an ex-post concept development.
Still, we believe that the evaluation’s results indicate the MR’ and DP’ usefulness when
building methodological tools to assist during ABS design and provide a promising
base for future research. As discussed in the previous section, we identified the need to
adjust DP4 and to formulate a new DP. Thus, we intend to refine our prototype in a
second design cycle.
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... Beverungen et al. [73] derived design principles from the literature to design a method for service systems engineering but do not conduct empirical research. Another canvas was developed by Hunke and Kiefer [74] that instantiates the created design principles within a DSR project. The methodological tool assists practitioners in designing analytics-based services, but there is no domain focus set. ...
... While nearly all the works examined have a digital component in the design principles, the methodology by which the artifact development has been undertaken varies widely. For example, Rose et al. [72], Möller et al. [76], and Hunke and Kiefer [74] used DSR first to gather MR and then formulate design principles. At the same time, other authors do not develop design principles using this methodology. ...
... At the same time, other authors do not develop design principles using this methodology. About empirical investigation, it appears that some authors utilize case study research and expert interviews for data collection (cf., [12], [74]). In contrast, others rely on purely literature-based data collection (cf., [73]). ...
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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.
... For this purpose, initial exploratory interviews serve as a starting point and provide first empirical insights. These interviews with IT architects, data strategy consultants, and service operators were conducted within the context of a previous research project [59]. The interviews are used to develop a first understanding of the practitioners' general approach to innovating services based on data and analytics and the problems they encounter. ...
... The interviews are used to develop a first understanding of the practitioners' general approach to innovating services based on data and analytics and the problems they encounter. In total, eleven interviews which last 50 minutes on average provide a profound basis to this end [59]. In the second research cycle, we review existing literature on service design, data-/analytics-based services, and business modeling, which we identified as fruitful streams we could consider deducting relevant conceptual knowledge for our research. ...
... Service design should embed the language of data [6] and integrate data as the key working principle during the design process. Similarly, the insights from our interviews highlight that data centricity is essential and not yet central enough in the work of service design teams [59]. However, service design represents a human-centered discipline [22] so that tools must integrate both the customer-centric and the data perspective [6,13]. ...
... Their underlying flexibility and utility have led to VITs being used in a variety of diverse application domains. For example, scholars propose VITs in design science research (e.g., see [3,7,10,11]), data innovation (e.g., see [6,12]), literature reviews (e.g., see [13]), or service innovation (e.g., see [14][15][16]). Given the plethora of application domains and scenarios, we see an opportunity to structure the field of VIT design and strengthen the rigor and effectiveness of the artifact through a taxonomic approach [17]. ...
... Each 'building block' is usually specified and 'filled out' using sticky notes. That allows information and ideas to be easily added, 1 [16] refers to [31 p. 10]. modified, and replaced in live settings and crystalize ideas and solutions that stick [4,8]. ...
... Given the importance of data for business model innovation [38,39] and, in general, digital transformation, generating VITs on that is not surprising. For example, these include VITs for data products [6], data-driven business models [12], or data-based (analytics) services [16]. The second-largest segment develops VITs to represent research processes. ...
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... Therefore, the presented paper aims to discover design knowledge for developing data-driven services within the industrial environment as part of service innovation. The process of new service development can be conducted in a service design approach, which is a formalized, multi-disciplinary concept that aids to innovate service offerings [13]. In that context, design principles are meant to codify design knowledge and, considering the respective boundaries, to enable its reuse [14]. ...
... On the contrary, service design adopts a design thinking approach that contains several stages to create new innovative services [38,39]. It is described as a multi-disciplinary approach in which service design teams benefit from their interdisciplinary members having different backgrounds, areas of knowledge, and competencies [13]. Therefore, service design poses a suitable way of guiding and advancing the development of data-driven services [40]. ...
... Similar research carried out by [13] provides design principles for analytics-based services. ...
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