Knowledge Requirements for Sustainable Smart Service Design
Jesus Mario Verdugo Cedeño
a
, Lea Hannola
b
and Ville Ojanen
c
School of Engineering Science, Department of Industrial Engineering and Management, LUT University,
Yliopistonkatu 34, Lappeenranta, Finland
Keywords: Service Ecosystem, Knowledge Requirements, Text Mining, Service Design, Service Lifecycle.
Abstract: Recent research have addressed the topic of smart services from distinct angles, covering both technical and
business aspects. However, a holistic approach in development processes of such services have yet to be fully
covered. Therefore, this paper proposes an elicitation of requirements process as the initial step of a smart
service design approach. The process takes information and knowledge needs as its core element for
development, also considering customer centricity, service lifecycle, and sustainability concerns. A text
mining tool was used to discover the unknown knowledge requirements from different text-data sources
presented in a case ecosystem. After a co-occurrence analysis performed by our text mining software, we
extracted the most relevant natural linguistic elements, which are expressed as knowledge requirements. The
proposed elicitation process aims to lay the foundations for further propositions with a holistic point of view.
Future research could aim the application of other technologies and methods for service design, as well as a
broader approach in business processes and interdisciplinary cooperation.
1 INTRODUCTION
The more connected world and accessibility of
resources have driven a seamless spread of ideas and
information in a globalized market. It is leading into
a relative easiness in imitation of product design,
resulting in a commoditization of tangible goods
(Stickdorn et al., 2018). This challenge creates a need
for innovation for organizations, guiding them to turn
their vision into services (Lüftenegger et al., 2017).
Value co-creation is hardly conceived without the
consideration of collaborative partnering interactions
between stakeholders from different organizations
(Lempinen and Rajala, 2014). Such collaborations are
known as service ecosystems, which are considered
as dynamic systems of connected actors interacting
each other through value co-creation, with the ability
of self-reconfiguring continuously (Vargo and Lusch,
2011, Lusch and Nambisan, 2015). Finally, a service
platform serves as the means to integrate actors and
resources of a service ecosystem for service exchange
(Hein et al., 2018).
a
https://orcid.org/0000-0001-5394-9568
b
https://orcid.org/0000-0002-9079-7839
c
https://orcid.org/0000-0001-8124-5082
Service design can be defined as a “human-
centered, collaborative, interdisciplinary and
iterative approach to create experiences that meets
stakeholders needs” (Stickdorn et al., 2018, p. 20),
usually through the approach of design thinking.
Design thinking is a collaborative approach of cross-
functional teams integrated by members from
different backgrounds (Lim et al., 2018). It
concentrates most of the attention on customer
centricity, utilizing tools such as customer journeys
and service blueprints (Bitner et al., 2008). Despite
being a proven approach for service design, it is not
clear how design thinking considers the design
process from a lifecycle point of view, where
“sustainability (environment, social, economic)
value” (Orellano et al., 2018) is approached, most
known as lifecycle thinking (Bauer et al., 2008). On
the other hand, lifecycle perspectives lack customer
centricity on their frameworks (Antikainen and
Valkokari, 2016, Orellano et al., 2018). Therefore, an
integrated process which considers both customer
centricity and sustainability matters should be
researched more in detail.
Cedeño, J., Hannola, L. and Ojanen, V.
Knowledge Requirements for Sustainable Smart Service Design.
DOI: 10.5220/0008069201950202
In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 195-202
ISBN: 978-989-758-382-7
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
195
The concept of “Smart Services” has gained
attention in recent years due to the digitalization of
industries. Several authors have defined the term to
refer data-driven services (Kim et al., 2018, Lim et
al., 2018, Neuhüttler et al., 2018). However, there is
not an official definition. Data is considered the raw
material or input needed to develop Smart Services.
Hence, it is fundamental to understand the methods,
technologies, and processes required to achieve the
collection and analysis required to transform raw data
to actual knowledge (Fayyad et al., 1996). There are
several approaches to convert data into knowledge,
for both qualitative and quantitative data. One of the
qualitative ones is text mining, which is a knowledge
discovery process to obtain unknown insights from
data contained in texts (Bird et al., 2009). According
to Lim and Maglio (2018), using text mining could
enhance the data collection and analysis of keywords
from different sources and related to the
characteristics (features, needs, pain points,
requirements, experiences) of a given service.
The present study aims to utilize text mining as
the means to determine the unknown knowledge
requirements from the data generated by
stakeholders’ service interactions. The purpose of this
paper is to present a requirements elicitation process
for the initial phases of a smart service design
method, considering the customer centricity and
service lifecycle. The organization of this paper is
presented as follows: Section 2 presents a literature
review of the relevant theory related to smart services
design and applicability of text mining tools. As well
as a brief description of text data mining and its role
on determining service keywords; Section 3
illustrates in detail the requirements elicitation
process proposition, the justification of the chosen
approach and the gaps filled through its application;
Section 4 describes the utilized research method of
case study to demonstrate and justify the applicability
of text mining for requirements discovery; Section 5
discusses the results of the case study and the
requirements elicitation process; and Section 6
concludes with the limitations, practical and
theoretical contributions, as well as its future research
approaches of the study.
2 LITERATURE REVIEW
2.1 Smart Service Design
The most novel approaches for service design
surged to the field with a strong focus on data,
human centricity, collaboration, user experience,
sustainability, lifecycle, and ecosystems. For this
paper, the main focus of study are the service
offerings which take data as their raw material and
outcome, best known as smart services. Such
services are defines by Neuhüttler et al. (2018) as a
self-configurable bundle of digital and physical
services based on data collected from different
sources.
Recently, authors have proposed tools, method
and frameworks covering the novel approaches
mentioned above. First, there is the method suggested
by Kim et al. (2018), which address the data
collection gap of interviews, surveys, and
experiments by collecting customer and business data
from electronic sources. Second, Stickdorn et al.
(2018) aim to deliver a service design based on
customer experiences, with roots in collaborative
design thinking and an ecosystem point of view.
Third, Orellano et al. (2018) propose a framework for
PSS offerings with a lifecycle and sustainable
perspective. It contemplates all the phases of PSS
lifecycle: Beginning of Life (BOL), Middle of Life
(MOL) and End of Life (BOL) (Orellano et al., 2018).
BOL phases consider PSS design, production and
distribution; MOL phases the PSS use, repair and
maintenance; and finally EOL phases involve
disposal and backtracking processes. Such
framework utilizes the lifecycle thinking method,
which is defined by the “ability to decouple the
lifecycle of any offer into sub-processes” (Orellano et
al., 2018, p. 293), translating them into environmental
and societal requirements.
All the approaches follow the current trends of
service design separately. However, there is a
significant research gap of proposing a framework,
method, approach or a process for smart service
design, which considers and integrates the aspects
covered by the previous authors.
2.2 Text Mining as a Tool to Obtain
Knowledge Requirements
As suggested by Kim et al. (2018) the service design
process consists of the following five steps: (1)
Definition of service objectives. A multidisciplinary
agreement is required in order to define the market
trends and constraints, as well as a precise definition
of stakeholders’ objectives. (2) Data collection of
customers’ data sources. The common methods
usually utilize traditional tools for data collection,
such as surveys, or experiments. However, recent
studies have been using data collection from
electronic sources to overcome the bias and trends
from previous sources, e.g., text documents, social
KMIS 2019 - 11th International Conference on Knowledge Management and Information Systems
196
media, data from sensors, emails, and so on
(Ordenes et al., 2014). (3) Analysis of data
collection using both quantitative and qualitative
methods, as well as IT related tools such as big data
analytics and machine learning for requirements
elicitation. For example, the study presented by
Mankad et al. (2016), aims the utilization of
automated text analysis tools for understanding
customers’ reviews. (4) Idea generation for service
propositions. This step also considers the
multidisciplinary approach to reach an agreement of
what are the options that best meet the requirements
of customers. (5) Concept generation. In this final
step, the ideas that best suit the customers’ demands
from the previous step are selected for prototyping
and market test.
For Stickdorn et al. (2018) data collection task
should include different types of data outputs in
order to get a more plentiful and comprehensive
researcher’s findings, known as Data Triangulation.
The terms refers to the principle of combining
distinct data sources, such as text in the form of
documents, transcripts, interviews, audio
translations, notes; images, photos and pictures;
audios, videos; artifacts in the form of brochures,
flyers; tickets and finally quantitative data such as
statistics, tables, numbers, and so on. One of the
most common processes for working with
qualitative data is the Qualitative Content Analysis
(Hsieh, 2005), which interprets and codifies data
from text sources to find patterns and meanings from
them. A regular challenge of text data is its lack of
structure, given its degree of difficulty of analysis
and interpretation (Orellano et al., 2018, Ordenes et
al., 2014). Meanwhile, structured data in the form of
numbers have been a convenient path of
organizations to evaluate a product or service by
giving numerical ponderations to their attributes,
being questionnaires the most traditional method for
customer feedback (Macdonald, 2011).
Text mining or text data mining is the process to
discover unknown knowledge by the analysis of
textual information, finding its structure and hidden
meaning (Mikroyannidis, 2006, Bird et al., 2009). It
is a continually evolving technology which
integrates machine learning algorithms, data
mining, natural language processing and knowledge
management (Yu, 2011, Lim and Maglio, 2018). For
structuring text data, data mining utilizes a
processing method to extract the so-called concepts,
which are words or complete sentences explaining a
general interpretation of a text (Mikroyannidis,
2006).
Several studies have used the text mining methods
to identify customer feedback, reviews, opinions and
informative documents (Jansen, 2009, Ordenes et al.,
2014, Mankad et al., 2016). However, no study
presents the integration of diverse interactions among
several members of an ecosystem in a systematic
approach, either analyse the whole lifecycle
perspective of such interactions.
3 PROPOSED KNOWLEDGE
REQUIREMENTS
ELICITATION PROCESS
The elicitation process suggested in this paper
considers business related aspects, such as system
mapping, customer centricity and service lifecycle; as
well as technical related aspects, such as text data
mining analysis. The elicitation proposition adheres
to the service design process explained above,
focusing on the three first steps of it. The reason why
the elicitation process is developed in such way lies
in two arguments: (1) the need of practical studies that
consider a holistic approach of identifying, collecting
and analysing different data sources presented in a
service ecosystem and in each phase of service
lifecycle, and (2) the applicability of text data mining
tools to discover knowledge insights from non-
structured natural language sources. In industry, there
is a saturation of quantitative structured data with a
wide offer of technological and managerial
developments able to reach a high level of automation
in decision making.
The first step of the process consist in the use of
the system map tool proposed by Stickdorn et al.
(2018) in their collaborative service design approach
as a reference to present the relationships between
members of a product-service ecosystem. It
represents the value exchanges (money, goods,
services, and information) among such members. The
adaptations of the Stickdorn et al. (2018) system map
for our process consisted in solely two aspects. The
first one is the simplification of the value
representation. While the original map considers four
forms of value exchange, this proposed process only
contemplates the information exchanges and
interactions, since knowledge (information) is the
primary resource for creating smart services. The
second one is the integration of a lifecycle approach
into the system map. Namely, the process also maps
information exchanges and interactions between
ecosystem members through the whole service
lifecycle as shown in Figure 1.
Knowledge Requirements for Sustainable Smart Service Design
197
Figure 1: Service ecosystem value exchange (information)
mapping from a customer-centric and lifecycle approach
(adapted from (Stickdorn et al. 2018).
The purpose of the lifecycle approach is to give
deserving importance to the middle and last phases of
the lifecycle. In such a way, we cannot ignore the
environmental and social aspects, i.e., sustainability
issues. The system mapping is necessary at the
beginning to understand all the information
interactions between value partners in the ecosystem,
for later identify the data sources of such value
exchanges. In fact, the initial step of the process is to
identify and describe the sources of data collection
presented on every phase of the service lifecycle,
which can be presented in the form of text content,
reviews, interviews, brochures, flyers, reports and so
on. The further step consists of the data collection,
text data mining analysis and report of the analysis
results by presenting the most common and usual
Figure 2: Framework for determining Knowledge
Requirements for Sustainable Smart Service Design.
keywords obtained from such data sources. In the
particular case of our study, we decided to utilize the
data elements (sentiments, keywords, concepts,
categories, entities and text fragments) offered by
our text mining software enrichments. Data
enrichment is defined as an algorithm that process
unstructured text data from different sources to find
insights that were not referenced explicitly in the
text, interpreting the data into the context of other
information sources and simplifying the definitions
of specific topics (Hasan et al., 2011, IBM). A data
enrichment utilizes a linguistic approach which
considers the natural language contained in the data
sources, identifying the domain (topic) predominant
for effective analysis (Ordenes et al., 2014). The
data enrichment algorithm used by the software
employed in this study removes the irrelevant
elements and performs interdependency analysis to
bring the key or most relevant element set on the
members’ interaction. The elements that present a
higher co-occurrence are considered as knowledge
requirements. The whole requirement elicitation
process is illustrated in Figure 2.
4 CASE STUDY: OIL LUBRICANT
AND ADDITIVE
MANUFACTURER
ECOSYSTEM
An empirical study was used to apply and test the
elicitation process previously presented. The
members involved in the study were a car lubricant
and additive manufacturer and one of its main
customers, a fuel stations group providing full-
service offerings to its customers. The manufacturer
supplies several customers segments in its value
chain. Their customer segments are car workshops,
spare parts stores, retail stores, and its main
customer fuel stations. The fuel stations group
provide full-service offerings to its customers,
including gasoline and diesel filling, express car
washing and car liquid level checking, serving both
consumers and corporate clients. The optimal scope
of this study would be the full integration of all the
members involved in the manufacturer’s ecosystem.
However, due to the extension limitations of our
work, we will consider the system integrated by the
manufacturer and the service station group,
including its members and regulators, defined as the
micro-ecosystem.
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198
Figure 3: Service ecosystem value exchange (information)
mapping among members of case study.
The study began with two interviews made to the
Operations Managers from both companies. The
purpose of such meetings was to identify the data
sources presented in each interaction, considering the
whole service lifecycle. The members involved
within the manufacturers ‘side were sales
representatives, training leaders, technical
representatives and operations managers. From the
fuel stations group side, the members involved were
service operators, sales and operations managers.
Finally, the regulator´s side was represented by
governmental institutions and private quality
assurance entities. The value (information) exchanges
between members are graphically represented in the
system map shown in Figure 3.
The managers from both companies provided the
data sources resulting from the previously mentioned
interactions. As shown in figure 4, several data
sources were identified for the different members
interactions across the whole service lifecycle,
representing the first part of our elicitation process.
Diverse areas involving the performance, customer
feedbacks, safety issues and product handling were
found. According to our interviews and employees´
recommendations, we selectively categorized four
areas or fields for our analysis: Safety, Regulations,
Performance and Disposal. The next step was to
perform the text mining analysis. The software used
for this study was IBM Watson Discovery, a cloud-
based solution which uses algorithms to process text
data from diverse sources, transforming raw
unstructured data into explicit and implicit findings.
We proceed to upload all the text documents
previously into the software cloud in order to carry
out the text mining process and knowledge discovery.
After the text mining process was finished and data
enrichment analysis was performed, the software
brought us outputs composed of multiple key
elements. The chosen elements were selected based
on their co-occurrence, which are the elements with
Figure 4: Sources of data collection through the phases of service lifecycle.
Knowledge Requirements for Sustainable Smart Service Design
199
more interdependency with each other once the
software eliminates irrelevant elements and performs
the statistical analysis for term frequency. The last
one is an automate process that brings the most
common and used keywords presented in the
documents. As shown in Table 1, we organized the
key elements (outputs) presented according to the
areas previously described and the three phases of
service lifecycle as illustrated in Figure 4. Finally,
once the key element set is obtained and organized, it
is utilized as the main input to construct the whole
knowledge requirements elicitation process,
necessary for further smart service design process
steps, which are object of study for future research.
5 RESULTS AND DISCUSSION
In order to describe the results obtained and
illustrated in Table 1, the following lines will explain
the origin and application of the key elements
discovered, henceforth expressed as knowledge
requirements. We begin presenting the keywords
output brought by the statistical analysis for term
frequency, which is one of the previous analysis
before obtaining the key elements by co-occurrence
or interdependency. The most frequent terms by study
area are: (1) Safety keywords related to health
hazards and risks that the handling of lubricant and
additives can cause to human health, such as
Inhalation, Skin, Toxicity, Breathing and Irritation;
(2) Regulations keywords with strong emphasis on
the compliance and environmental preservation
fields, such as Pollution, Carbon Monoxide, Hazards,
and ISO and OSHA standards; (3) Performance
keywords aiming the knowledge of the recommended
product to use in each car and situation, such as
Application, Benefits, Applications, 100000 Km,
Transmission, Corrosion, Viscosity Index, Synthetic;
(4) Disposal keywords related to waste management
and handling of liquid and solid hazardous waste,
such as Residues, Collectors, Spills, Environment,
Containers, Lifecycle and Transportation.
As we can observe, the keywords brought by the
term frequency statistical analysis only provide single
words without any natural linguistic-based outcomes
involving the syntax, grammar and interdependency
of elements. The resulted keywords are important to
understand the repeatability in words an identify
certain parameters. However, the analysis concerned
to this study involves the more complex process
involved after the software eliminates irrelevant
elements and brings term frequency, the linguistics-
based co-occurrence analysis, which bring us key
data elements in form of composed keywords.
As shown in Table 1, we can identify several key
elements that illustrate the knowledge requirements
in a structured way after the co-occurrence analysis.
It reveals us the exact knowledge need presented in
each phase of the service lifecycle. Firstly, at the
Beginning of Life phase we can find elements related
to the information of the properties, characteristics
and classification and disposal of the product. It
interesting in this case that the main requirements are
not service concerned, rather product orientated.
Makes sense in the way fuel stations operators and
managers demand all the crucial knowledge needed
to perform an optimal service, keeping the customers
and employees in the safest condition before their
activities begin. Elements such as Toxicological
Information, Component Information, Oils and
Additives Classification and Uses, and Confined
Spaces confirm us the concerns. Currently,
employees do not have the technological tools to
extract the most important information from data
sources, given the fact that such information is
contained in hundreds of electronic or paper
documents in form of technical data sheets, safety
sheets, training presentations, and so on. Making the
request of single query a very challenging and time-
consuming task to be done. Therefore, even when
employees are constantly trained, some valuable
knowledge is still hidden within the document pages.
Secondly, at the Middle of Life phase we observe a
transition towards the performance of the service per
se. In the performance area, the elements of
Recommended Applications and Car Condition
Determination explain us the need of recognizing the
diverse conditions a car could face. As we previously
observed in the frequent keywords, information
regarding the condition of the transmission, high-
kilometrage engines, corrosion and many more car
states are concerns to determine the correct product
and service offering. However, nowadays service
providers are guided on their previous experience,
traditional training and colleague advise, guiding
them to offer a service susceptible to errors. In the
Safety and Regulations areas, we notice the interest to
preserve human integrity while providing the service.
The elements Security Practices, Risk Control
Procedures and Chemical Substance Practice are
related to the protocols the employees must follow in
order to mitigate risks and the way they respond to
possible emergencies when handling chemical
products in the service activities. In the Disposal area,
the elements of
Product Spills and Cleaning Methods
clearly describe the need to know the procedures in
KMIS 2019 - 11th International Conference on Knowledge Management and Information Systems
200
Table 1: Knowledge discovery of key data elements for requirements elicitation.
case the product leaks in certain surfaces when giving
the service, causing an environmental damage and
potential hazards. Finally, at the End of Life phase,
naturally the areas of Disposal and Regulations are
presented. The elements of Appropriate Containers,
Dangerous Residues and Waste Collection provide us
a convinced idea that the main knowledge
requirements are related to processes and regulations
in storage, transportation and confinement of residues
for sustainable and safety purposes.
6 CONCLUSIONS
The practical contribution of this paper is to bring us
an understanding of the main knowledge needs from
the people involved in the operational and customer
service processes every day. Which is one of the pillar
steps in the development of the smart worker or the
so-called Operator 4.0, also presented at service and
retail industries. Previous research mainly focuses on
the development of the smart worker within purely
manufacturing environments, without mentioning the
opportunities on their peripheric ecosystem members.
Two main novel tools contributed to the development
of the proposed process. First, the use of the system
mapping tool to understand the information value
flows and data sources needed, considering all the
phases of the service proposition and all the members
involved within the ecosystem. Second, the
application of text data mining from the data sources
allows us to comprehend the key elements presented
in different ecosystem process, identifying the
information or knowledge needs for the further smart
service design phases.
We identified three main limitations to our study
that can be tackled for future research. First, our
results depend on the analysis of the given data
sources documents from case companies. We infer
that the quality and amount of information contained
on the documents provided by the managers from the
two companies were adequate. However, the
information contained on the documents can change
overtime and the amount naturally will raise. That
could be an opportunity to address a more complex
analysis and better algorithm training. Second, the
study did not describe in detail each business process
involved within the ecosystem members. For general
purposes, this the scope of this study was appropriate.
However, future research may address the analysis of
business processes in a systematic way to
comprehend the pain points and needs involved.
Third, this study employed tool IBM Knowledge
Discovery for text data mining. Which is a software
with a high degree of process automation, making
easier to non-field experts in the data science area to
perform high-quality analyses. However, future
research could focus on providing more specialized
Fields BOL MOL EOL
Safety Hazard
Communication
Security Practices
Toxicological
Information
Risk Control
Procedures
Regulations Component
Information
Chemical Substance
Handling
Appropriate
Containers
Performance Product Classification
Recommended
Applications
Disposal
Product Uses Car Condition
Determination
Confined Spaces
Product Spills Dangerous Residues
Contractor
Competence
Cleaning Methods Waste Collection
Knowledge Requirements for Sustainable Smart Service Design
201
data analysis performed by experts as collaborators of
further works, who could exploit state-of-the-art
methods. Since the study involves both managerial,
business and technical work, it would be necessary to
achieve a collaborative and multidisciplinary
environment, integrated by field experts, operators,
managers, regulators, and other members presented in
the ecosystem.
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