Chapter 16
Clarifying the Concept of Smart Service
System
Chiehyeon Lim and Paul P. Maglio
Abstract A new trend of smart service systems is emerging. Technology is
applied intensively to alleviate the cognitive and behavioral load of customers
and improve the operations of service systems, thereby enhancing value. Despite
the significance of smart service systems in this connected and data-rich world,
knowledge on this concept remains insufficient. This chapter builds a theoretical
background for smart service systems research. Motivated by recent cases of smart
service systems, this chapter reviews the definitions and characteristics of smart
service systems discussed in existing studies; integrates empirical insights from
studies on the design and development of smart service systems; and further
explores the nature of smart service systems by analyzing texts from the scientific
literature, news articles, and end-user opinions. By integrating all the information,
this chapter aims to clarify the concept of smart service system. Using the proposed
conceptual framework and related studies as basis, this chapter also categorizes
smart service systems into three types according to the portion of customer roles in
technology-based value co-creation: smart self-service, smart super service, and
smart interactive service systems. Finally, based on the proposed conceptual
framework, this chapter discusses future research topics related to the concept of
smart service system, such as autonomous service system as a new type of service
system that requires a minimum level of human–thing interaction but works
mainly based on thing–thing interaction. This chapter would serve as basis for
studying and developing smart service systems.
Keywords Smart service · Smart system · Smart service system · Definition ·
Categorization
C. Lim (*)
Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
e-mail: chlim@unist.ac.kr
P. P. Maglio
University of California, Merced, Merced, CA, USA
e-mail: pmaglio@ucmerced.edu
© Springer Nature Switzerland AG 2019
P. P. Maglio et al. (eds.), Handbook of Service Science, Volume II, Service Science:
Research and Innovations in the Service Economy,
https://doi.org/10.1007/978-3-319-98512-1_16
349
16.1 Introduction
Service systems in transportation, retail, healthcare, entertainment, hospitality, and
other areas are configurations of people, information, organizations, and technologies
that operate together for mutual benefit (Maglio et al. 2009). Service systems
have become “smarter” over time with the increasing use of technology in the
systems (Lim et al. 2016). Smart service systems can be found in homes (Alam
et al. 2012) as well as the energy (Strasser et al. 2015), healthcare (Raghupathi and
Raghupathi 2014), and transportation sectors (Pelletier et al. 2011), among many
others. As the concepts of service system and smartness intertwine, the academia,
industry, and government pay significant attention to the concept of smart service
system (e.g., Maglio et al. 2015; IBM Smarter Cities Challenge 2016; NSF 2016).
This concept is meaningful in developing and using technology, such as the Internet
of Things (IoT) and artificial intelligence (AI), because it represents the ultimate
application and integration of technology for value creation for people (Ng and
Vargo 2016).
Despite the widespread application and research on smart service systems,
knowledge on this concept for its development remains inadequate (Maglio et al.
2015; Larson 2016). This chapter aims to contribute to building a theoretical
background about the concept and stimulate debate about the sufficiency of existing
efforts at academic and practical levels. Establishing common ground for central
terms is essential for scientific progress (Boehm and Thomas 2013); thus, development
and innovation in smart service systems require a shared vocabulary across
multiple fields (Larson 2016). This chapter aims to establish a common ground
necessary for integrating perspectives and capabilities to encourage the research and
development of smart service systems and contribute to synergy among different
research fields and application areas.
Section 16.2 reviews emerging cases of smart service systems in practice from a
service science perspective to motivate the readers. Section 16.3 reviews the definitions
and characteristics of smart service systems discussed in existing studies to
help the readers develop a basic understanding of smart service systems.
Section 16.4 reviews studies on the design and development of smart service
systems to help the readers understand data-based value creation mechanisms of
smart service systems. Section 16.5 introduces the authors’ findings from analysis of
5378 scientific articles, 1234 news articles, and 444 user opinions related to smart
service systems (e.g., keywords, research topics, and technology factors of smart
service system) to help the readers understand the diverse features and core of smart
service systems at the same time. Section 16.6 aims to clarify the concept of smart
service systems by integrating findings from the previous sections. Section 16.7
integrates the proposed conceptual framework and related studies on service categorization
and value co-creation to categorize smart service systems according to the
portion of customer roles in technology-based value co-creation. Section 16.8 discusses
promising future research topics related to the concept of smart service
350 C. Lim and P. P. Maglio
system, such as the service systems analytics and engineering and autonomous
service system. Section 16.9 concludes this chapter.
16.2 Observation on Emerging Cases of Smart Service
System
Value may be co-created between customers and firms (Prahalad and Ramaswamy
2002) or between any two actors (Vargo and Lusch 2016). More generally, value is
constellated by a network of multiple actors and their interdependent relationships
(Normann and Ramirez 1993). Value co-creation and value constellation are powerful
concepts in analyzing and designing service systems (Payne et al. 2008;
Patrício et al. 2011) because these concepts enhance the degrees of freedom to
find fresh and innovative solutions for value creation, aside from existing product
or service concepts (Lusch and Nambisan 2015).
This chapter takes a service science—a value co-creation conscious—perspective
in viewing this connected and data-rich economy. From this perspective, the essence
of the IoT (Atzori et al. 2010) is the enhanced connectivity between stakeholders that
may increase encounters for value co-creation or constellation, whereas the term
“big data” (George et al. 2015) pertains to the increased types and amounts of traces
of the value co-creative activities and interactions within a service system that can be
used in understanding and improving the activities and interactions.
Increased connectivity and the proliferation of data produced various forms of
service in which stakeholders can monitor, be aware, and manage connected things
and other stakeholders better than before. People call such services “smart” may be
because of this intellectual property. For example, automobile manufacturers analyze
car conditions and driving data collected from connected cars, and they provide
various types of useful information to assist drivers on fuel efficiency, safety,
consumable, and navigation (Lim et al. 2018a). Smart band-based fitness tracking
services collect data from daily life, such as behavior, health, and food menu data, to
help people achieve specific fitness-related goals, such as walking 10,000 steps a day
(Takacs et al. 2014). Other examples include tire pressure monitoring (Velupillai and
Guvenc 2007), vehicle fleet management (Volvo 2009), screen golf training (Jung
et al. 2010), and precise farming solutions (Lim et al. 2012).
Apart from commercial examples, smart service systems are emerging in public
domains (Lim et al. 2018e). The Seoul government collected data from city buses,
identified patterns and demands of city bus usage at midnight, and subsequently
improved the midnight bus services for citizens (NIA 2013). Waste management in
Delhi collected data from trash bins using radio frequency identification tags and
scheduled trash collection locations and time (Purohit and Bothale 2011). Air
pollution monitoring in London analyzed data from pollution sources across the
city along with European weather forecasts to create a pollution map of London.
Precipitation monitoring in Rio de Janeiro used a flood prediction model based on
16 Clarifying the Concept of Smart Service System 351
land survey data, precipitation statistics, and radar data (Kitchin 2014). Transportation
management in Singapore collected data from roads and taxis to anticipate
future traffic and control traffic lights (Lee 2013).
A common aspect of these cases is the use of sensor data collected from
connected things and people. With recent advancements in sensing technologies,
various kinds and massive amounts of data are collected from individuals and
objects through sensor-equipped consumer electronics and industrial engineering
systems (Porter and Heppelmann 2014). Human-generated data involve a hypothesis
or bias of recorders, whereas sensor data are natural records that reflect real behaviors
of people and operations of objects. One difficulty in managing and improving a
service system, which is sometimes conceptual, was a lack of data required to track,
measure, model, modify, control, and manage individual behaviors and object
operations within the service system in question. The recent advancements in
sensing technologies unlock this limitation and provide numerous opportunities
for engineering service systems. Thus, the degree of freedom to develop smarter
service systems is increasing. From a service science perspective, using sensor data
contributes to operationalizing or even automating value co-creation among
connected people and things, and this is why smart service systems are meaningful
to people (for their value creation). The introduced cases of smart service systems
illustrate this advancement.
16.3 Definitions and Characteristics of Smart Service
System
Smart service systems are described with keywords such as learning, adaptation,
monitoring, decision making, sensing, actuation, coordination, communication,
control (NSF 2016), viability, open, optimization, intelligence (De Santo et al.
2011), compliance, sustainability, data analytics, cognitive system, service (Spohrer
and Demirkan 2015), self-reconfiguration, ICT, connection (Carrubbo et al. 2015),
wise, interacting (Oltean et al. 2013), people, AI, real-time, interconnected, interactivity,
context awareness, proactive, preventive, IT (Gavrilova and Kokoulina 2015),
self-detection, self-diagnostic, self-corrective, or self-controlled (Maglio and Lim
2016). Sectors that incorporate smart service systems include city, government, (e.g.,
smart city) (Lim et al. 2018e), health (e.g., smart healthcare) (Mukherjee et al. 2014),
energy (e.g., smart grid) (Strasser et al. 2015), transportation (e.g., smart car)
(Pelletier et al. 2011), and even manufacturing (e.g., smart factory) (Lee et al. 2014).
Table 16.1 lists the definitions or descriptions of smart service system. These
definitions and descriptions are consistent in that they specify capabilities or requirements
of smart service system (e.g., the capability of self-adaptation and requirement
of technology incorporation) but emphasize different capabilities or requirements. A
common requirement emphasized by most studies is intensive data use. Using this
observation as basis, we can understand smart service systems are data-based service
352 C. Lim and P. P. Maglio
Table 16.1 Definitions or descriptions of smart service system
Source Definition or description
NSF (2016) A “smart” service system is a system that amplifies or augments human
capabilities (Ng 2015) to identify, learn, adapt, monitor and make
decisions. The system utilizes data received, transmitted, or processed
in a timely manner, thus improving its response to future situations.
These capabilities are the result of the incorporation of technologies for
sensing, actuation, coordination, communication, control, etc.
Barile and Polese
(2010)
Smart service systems may be intended as service systems designed for
a wise and interacting management of their assets and goals, capable of
self-reconfiguration (or at least of easy inducted re-configuration) in
order to perform enduring behavior capable of satisfying all the
involved participants in time. . . . Because smart service systems inevitably
involve multiple actors, the organizational configurations need to
take account of network theory—especially the networking forces and
enablers required to keep the system tight and focused towards its goals
Medina-Borja (2015) A smart service system is a service system capable of learning,
dynamic adaptation, and decision making based upon data received,
transmitted, and/or processed to improve its response to a future
situation
De Santo et al. (2011) Smart service systems are open, according to the logic of viable system
approach, and capable of simultaneously optimizing the use of
resources and improving the quality of the services provided. ... In this
sense the intelligence of smart service systems derives not from intuition
or chance, but from systemic methods of learning, service thinking,
data collection, rational innovation, social responsibility and
networked governance
Spohrer and Hamid
(2015)
In the era of cognitive systems, smart service systems will increasingly
include cognitive or digital assistants (e.g., Watson and SIRI-like
systems) for all occupations and societal roles. It is foreseeable that
smart Service Science research focus on leveraging advances in AI, big
data-enabled intelligence and cognitive computing, and innovating to
enable the creation of intelligent technologies and societies that are
integrating well with human societies
Spohrer and Demirkan
(2015)
Smart service systems are ones that continuously improve (e.g., productivity,
quality, compliance, sustainability, etc.) and co-evolve with
all sectors (e.g., government, healthcare, education, finance, retail and
hospitality, communication, energy, utilities, transportation, etc.). ...
Because of analytics and cognitive systems, smart service systems
adapt to a constantly changing environment to benefit customers and
providers. Using big data analytics, service providers try to compete
for customers by (1) improving existing offerings to customers,
(2) innovating new types of offerings, (3) evolving their portfolio of
offerings and making better recommendations to customers,
(4) changing their relationships to suppliers and others in the ecosystem
in ways their customers perceive as more sustainable, fair, or
responsible
Spohrer (2013) Smart service systems are instrumented, interconnected, and intelligent.
Instrumented means sensors, sensors everywhere—more of the
information (real-time and historical, as well as monte carlo predictive
runs) that stakeholders, providers, customers, governing authorities,
(continued)
16 Clarifying the Concept of Smart Service System 353
Table 16.1 (continued)
Source Definition or description
etc.—need to make better win-win (value co-creation, capability
co-elevating) decisions is available. Interconnected means people have
easy access to information about a particular service system, as well as
others that interact with it via value propositions, perhaps displayed on
their smartphones. Intelligent means recommendations systems that
work to provide stakeholders useful choices—for example, Watsonstyle
recommendation systems, or Amazon-style recommendation
systems
Carrubbo et al. (2015) Smart service systems can be understood as service systems that are
specifically designed for the prudent management of their assets and
goals while being capable of self-reconfiguration to ensure that they
continue to have the capacity to satisfy all the relevant participants over
time. They are principally (but not only) based upon ICT as enabler of
reconfiguration and intelligent behavior in time with the aim of creating
a basis for systematic service innovation (IfM IBM 2008) in complex
environments (Basole and Rouse 2008; Demirkana et al. 2008). Smart
service systems are based upon interactions, ties and experiences
among the actors. Of course, among these actors, customers play a key
role, since they demand a personalized product/service, high-speed
reactions, and high levels of service quality; despite customer relevance,
indirectly affecting every participating actor, smart service
systems have to deal to every other actor’s behavior, who’s expectations,
needs and actions directly affect system’s development and
future configurations. The smarter approach applied to healthcare is
called “smarter healthcare”. As IBM highlights, a smarter healthcare
system is obtained through better connections for faster, more detailed
analysis of data
Oltean et al. (2013) Smart service systems may be intended as service systems designed for
a wise and interacting management of their assets and goals and
capable of self-reconfiguration in order to perform enduring behavior
capable of satisfying all the involved participants in time
Massink et al. (2010) Common recurring elements of smart service systems are: spaces;
displays; sensors; users. Users will interpret information on displays
and carry out actions as a result of what has been read
Lim et al. (2016) Smart service systems are those service systems in which connected
things and automation enable intensive data and information interactions
among people and organizations that improve their decision
making and operations. Thus, transforming a service system into a
smart service system means improving the decision making and operations
within the service system with connected things and automation.
As the definition indicates, a smart service system consists of four
components: (1) connected things, (2) automation, (3) people and
organizations, and (4) data and information interactions
Gavrilova and
Kokoulina (2015)
The term “smart” implies two main properties. First, it highlights
anthropomorphic features of the smart service. For example, technology
research company Gartner, Inc. claims that smart technologies are
“. . . technologies that do what we thought only people could do. Do
what we thought machines couldn’t do” (Austin 2009). Second, term
“smart” is usually related to artificial intelligence (i.e. intelligence of
(continued)
354 C. Lim and P. P. Maglio
systems, in which data use contributes significantly to value creation. “Smart”
modifies behaviors and operations of people, operations and condition management
of organizations and things, and interactions within the service system. Sensing from
things and people within the service system produces data that indicate these
behaviors, operations, conditions, and interactions. Data analytics contributes to
the effectiveness and efficiency of these processes. Data analytics is core to smart
service systems given the capability for continuous monitoring and learning with
data. For instance, customer data may be converted into information that is useful in
customer value creation processes in service systems and also can be used to adjust
service operations (e.g., use of energy usage data for energy management). Imagining
a smart service system without intensive use of data is difficult. Thus, a key to
transform service systems into smart ones lies in exploiting data.
16.4 Design and Development of Smart Service System
The findings from the previous sections show that a smart service system features a
data-based value creation mechanism. Previous studies on the design and development
of smart service systems provide further insights into data-based value creation
mechanisms of smart service systems. This section briefly introduces findings from
the studies in Table 16.2.
Table 16.1 (continued)
Source Definition or description
machine) “[. . .] because it is impractical to deploy humans to gather
and analyze the real-time field data required, smart services depend on
“machine intelligence” (Allmendinger and Lombreglia 2005). ... Smart
service systems often have the following characteristics of the intelligent
system: Self-configuration (or at least easy-triggered
reconfiguration) (Barile and Polese 2010), Proactive behavior (capability
for prognosis or preventive actions, as opposed to the reactive
behavior) (Allmendinger and Lombreglia 2005), Interconnectedness
and continuous interactivity with internal and external system elements
(Gershenfeld et al. 2004). ... Smart service attributes include dynamic
properties (without modelling of the changing environment; past-based
modelling; stochastic modelling), intelligence (knowledge-based; databased;
content-based), Knowledge awareness (context-oriented;
explicit knowledge; business intelligence), IT platform (mobile; SaaS;
hybrid cloud; corporate servers), and elements (IT; people; hybrid)
POSTECH IME (2016) A smart service system refers to a system that delivers various services
effectively and efficiently by considering the needs and context of
stakeholders through smart technologies. Smart service systems providing
smart service offerings such as adaptive control, prognostic
monitoring, personalized guidance, and user-friendly interfaces are
flourishing in business and society
16 Clarifying the Concept of Smart Service System 355
Table 16.2 Studies on the design and development of smart service system
Study Brief description
Related service
system
Lim et al.
(2018a)
Designed car infotainment services for individual drivers that
use vehicle operations and condition data, based on analyses
of 7.6 million trip data of 18,943 vehicles (vehicle operations
data) and 3662 cases of warning code occurrences (vehicle
condition data)
Smart
transportation
Lim et al.
(2018d)
Designed driving safety enhancement services for commercial
drivers that use vehicle operations data, based on analyses
of operations data of commercial vehicles (278 buses,
46 taxis, and 931 trucks) and accident data of commercial
vehicle drivers (4289 bus, 1550 taxi, and 490 truck drivers)
Kim et al.
(2018)
Designed an eco-driving support service for bus drivers that
use vehicle operations data, based on analyses of bus operations
and fuel consumption data of 33 bus drivers
Winkler et al.
(2016)
Designed and implementing a thermal comfort enhancement
service for building occupants that uses building energy
operations data and occupant feedback data
Smart building
Kim et al.
(2014a)
Designed hypertension patient management services that use
a national health insurance database, based on analyses of a
sample data from the database for one million people for
9 years (2002–2010)
Smart health
Kim et al.
(2016)
Designed a smart wellness service for college students that
use daily behavior data of students, with an IT company and
a student counseling center at a university based on data
from 47 students
Kim et al.
(2014b)
Designed health-related data-based services for healthrelated
stakeholders with a government organization, based
on interviews with 34 experts such as doctors, public health
scientists, managers and executives in the industry, and
government employees
Chung and
Park (2016)
Proposed a personal health record (PHR) open platform
based smart health services using the distributed object
group framework for managing chronic diseases
Yu et al.
(2011)
Designed a home portal service with a smart door interface
which combines virtual and physical door control and provides
the place for home members to communicate each other
no matter he is at home or not
Smart home
Cai and Li
(2014)
Designed a micro grid renewable energy service system for an
island that uses wind, photovoltaic and biomass energy
Smart energy
Perera et al.
(2014)
Proposed a “Sensing as a Service” model based on IoT
infrastructure for smart cities, such as waste management and
smart agriculture services
Smart city
Maglio and
Lim (2016)
Categorized design models of smart service system into four
groups according to the source and usage of data: smart
operations management, smart customization and prevention,
smart coaching, and smart adaptation and risk management
services
Any smart
service system
356 C. Lim and P. P. Maglio
From these studies, we can derive a generic mechanism of data-based value
creation in smart service systems. Each study focused on a specific part of the
spectrum from data to value creation in smart service systems. Considering these
studies, smart service systems, regardless of cases, entail the collection of data from
certain sources, creation of useful information on the data sources through data
analysis, and delivery of information to users to help them create value. More
specifically, data-based value creation in smart service systems involves at least
nine factors: (1) data source, (2) data collection, (3) data, (4) data analysis, (5) information
on the data source, (6) information delivery, (7) information user, (8) value in
information use, and (9) provider network (Lim et al. 2018b).
Data source (factor 1) includes specific objects such as vehicles, facilities such as
city infrastructure, management activities such as city administration, and customers
such as drivers and citizens. The methods of data collection (factor 2) include using
sensors, recording logs of IT system users, and crowdsourcing of opinion data. Data
(factor 3) include condition traces of engineering systems, event logs of business
systems, health and behavioral records of people, and bio-signals of animals. The
methods of data analysis (factor 4) include using specific algorithms pre-installed on
servers and expert knowledge, which entail time for decision making.
Information (factor 5) created from data analysis indicates interested facts about
the original data source. In many cases, the terms “data” and “information” are used
interchangeably. For smart service systems, this chapter distinguishes data from
information based on the data–information–knowledge–wisdom (DIKW) hierarchy
(Braganza 2004): Data are raw materials and ingredients of information, and information
is the outcome of the data analysis used for a specific purpose (Lim and Kim
2015). The methods of information delivery (factor 6) include e-mail, phone calls,
smartphone applications, or onboard displays in vehicles (Lim and Kim 2014).
Information user (factor 7) includes drivers who use car infotainment services,
parents who utilize baby-monitoring services, and citizens and local organizations
that use services in smart cities. A common aspect of these studies in Table 16.2 is
that each study focused on specific stakeholders (i.e., information users) of a service
system as main targets for data-based value creation, such as passenger car drivers
(Lim et al. 2018a), riders and drivers of commercial vehicles (Kim et al. 2018),
building occupants (Winkler et al. 2016), and people and government (Kim et al.
2014a, b).
Examples of value (factor 8) include evidence-based health management,
improvement of operational processes of certain service systems, and prevention
of potential user problems. Note that value is not created until users actually use the
received information for a specific purpose. In other words, value is created in
information use (Vargo and Lusch 2004; Lusch and Nambisan 2015). For example,
the safety of driving can be improved when information for safe driving is used by
drivers and health can be improved when health-related advice information is used.
The provider network (factor 9) consists of the main service provider (which
interacts with customers) and its outsourcing partners, such as sensor manufacturing,
data management, and analytics companies. Data and information can be digitized
16 Clarifying the Concept of Smart Service System 357
into bits unlike other types of deliverables in business; thus, outsourcing is common
in smart service systems.
A service concept is a description of what needs to be done for customers and
how this can be done (Edvardsson and Olsson 1996; Goldstein et al. 2002; Kim et al.
2012). Thus, designing services requires understanding the things that constitute the
what and how of service (Lim et al. 2012; Kim et al. 2013). Service design is difficult
because a design space is wide and complex, which is attributed to the variety of the
what and the how of the candidates (Lim et al. 2018c). The nine factors embrace key
areas of smart service system analysis and design: (5) what to deliver, (8) why, (7) to
whom, (1–4) how to produce it, (6) how to deliver it when and where, and (9) who
creates and delivers it. The use and management of data in smart service systems
should consider these areas to facilitate value creation with.
16.5 Understanding Smart Service System Through Text
Mining
Including the intensive use of data discussed in the previous sections, smart service
systems involve diverse features. The functions and operations of smart service
systems depend on sensing (Sim et al. 2011), big data (Maglio and Lim 2016),
computation (Lee et al. 2012), and automation (Jacobsen and Mikkelsen 2014).
Customer (Wuenderlich et al. 2015) and business aspects (San Roman et al. 2011)
must be considered as well. A search for “smart service system” in the Web of
Science generates more than 5000 results across engineering, computer science,
information systems, control, transportation, healthcare, and other fields. Given the
wide range of various research related to smart service systems, a unified understanding
of the concept across different fields may facilitate development and
innovation; such unification would promote the use, integration, and improvement
of technologies from a broad and application-oriented perspective. However, such
integrative work is difficult to achieve because of the variety and number of studies
and applications related to smart service systems. Text mining is an appropriate
method to address this challenge given its ability to automatically explore aspects
and areas of smart service systems in a comprehensive manner (Lim et al. 2017).
Lim and Maglio (2018) developed a unified view of smart service systems by
mining text related to these sorts of systems. The text they analyzed includes
scientific literature, news articles, and user opinions. Their analytics method
uniquely incorporates metrics to statistically measure the importance of wordfeatures
of data and unsupervised machine learning algorithms, such as spectral
clustering (Von Luxburg 2007) and topic modeling (Blei et al. 2003), to capture the
essence of the data. Their analysis of 5378 scientific studies, 1234 news articles, and
444 opinion surveys identifies statistically significant keywords, research topics,
technology factors (sensing, connected network, context-aware computing, and
wireless communications), a definition, application areas, and end-user-perceived
358 C. Lim and P. P. Maglio
values. This section briefly reviews their findings to advance the readers’ understanding
about smart service systems after the previous sections.
Findings from the analysis of 5378 literature data indicate that a smart service
system requires technologies for networking, data and information processing,
control, communications, devices, and applications to provide specific functions to
system users. Related topics in the literature include “design of smart service
systems,” “sensing,” “Internet/Web of Things,” “wireless networks/communications,”
“mobile devices,” “cloud computing/environment,” “security,” “smart
home,” “smart health,” “smart energy management,” and “smart city.” Lim and
Maglio (2018) also identified a set of 53 generic word-features that may represent the
generic structure of smart service systems, including “thing,” “internet,” “contextaware,”
“control,” “sensing,” “wireless,” “location,” “access,” “communication,”
“computing,” and “data.” An exploratory factor analysis of the 53 generic wordfeatures
of 5378 articles from the literature (i.e., those extracted from the 5378 by
53 matrix dataset that may represent the generic structure of smart service systems)
revealed four key technology factors of (any) smart service systems; based on factor
loading of the 53 word-features (i.e., variables), these four factors can be called
“connected network,” “sensing,” “context-aware computing,” and “wireless
communications.”
Using the four factors and the list of 53 generic words as basis, Lim and Maglio
(2018) propose a statistically significant definition of smart service system: “A smart
service system is a service system that controls things based on the resources for
connected network, sensing, context-aware computing, and wireless communications”.
Examples of the resources include specific environments, infrastructure, devices, and
applications (software). Examples of the things to be controlled include specific
objects, processes, and users. These definitions and examples are meaningful in that
they consist of the important words quantitatively identified from the literature data.
Findings from the analysis of 1234 news data indicate that application areas of
smart service systems can be categorized into “smart device,” “smart environment,”
“smart home,” “smart energy,” “smart building,” “smart transportation,” “smart
logistics,” “smart farming and gardening,” “smart security,” “smart health,” “smart
hospitality,” “smart education,” and “smart city and government.” These 13 areas
can be distinguished according to the type of application: Smart device and environment
are resource-type areas, which are required in any kind of smart service
system. Smart home, energy, building transportation, logistics, farming and gardening,
security, health care and management, hospitality, and education are business
system-type areas. Smart city and government systems are a public administrationtype
area. Common keywords found from the news data include “device,” “product,”
“app,” “data,” and “information.” This list implies that the essence of smart
service systems, which have been discussed in various news articles, is the use of a
device or product with a smartphone application to collect data from people and to
deliver information to them. A network analysis of these areas indicate that a smart
service system is related to other systems across different contexts of the users (e.g.,
smart health and smart home are highly relevant, while smart transportation is one of
the key businesses in smart cities) and resources (e.g., smart device and
16 Clarifying the Concept of Smart Service System 359
environment). Thus, achieving synergy between different smart service systems will
effectively streamline the development and operations of a system.
Findings from the analysis of 444 user opinion data indicate that people used the
following phrases to describe values of the smart service system they like: “ask Siri
thing,” “easy order,” “people make money,” “behavior make decision,” “computer
make choice,” “people living assisted,” “advance contextual awareness,” “better
sleep tracking,” “monitor many different,” “ready go get,” “people want know,”
“house living remotely,” “allowing happen without,” “accomplished less time,”
“ability reduce load,” “make job,” “manage human resource,” and “surroundings
make decision.” Based on this result and a detailed reading of the full list of
444 opinion data, we found that the end-user-perceived values of smart service
systems may include (1) save time, cost, or other resource; (2) reduce undesired
outcomes; (3) increase desired outcomes; (4) allow things to happen without something;
(5) monitoring or tracking ability; (6) know the user or the contexts; (7) easy
or autonomous decision making; and (8) easy order or remote control.
Combining all the findings, Lim and Maglio (2018) describe smart service
systems, such as smart homes and health, energy, transportation, and hospitality
systems, as follows: Smart service systems automate or facilitate the value-creating
activities of users and providers based on technological resources for connected
network, sensing, context-aware computing, and wireless communications. These
systems enable users to get their jobs done efficiently and effectively. Sensing of
data obtained from connected networks of people and things is the key mechanism of
smart service systems. Context-aware data analysis creates information that can be
used by users to manage and improve their things (e.g., specific objects, processes,
and resources) and people concerned. User-friendly smart devices enhance the
delivery of benefits of smart service systems to users through wireless communications.
The design of a smart service system can be improved by considering the
factors and values of the system in question and seeking a synergy with other
application areas.
16.6 Clarification of the Concept of Smart Service System
Each of the previous sections emphasized specific characteristics of smart service
systems. Section 16.2 emphasized the importance of sensor data collected from
connected things and people. Section 16.3 emphasized that capabilities of smart
service systems require a data-based mechanism. Section 16.4 emphasized the
importance of managing the spectrum from data to value creation in smart service
systems. Section 16.5 emphasized the four technology factors required to create
value, namely, connected network, sensing, context-aware computing, and wireless
communications.
360 C. Lim and P. P. Maglio
By integrating findings from the previous sections, this section aims to clarify the
concept of smart service system. Smart service systems are service systems in which
value co-creation between customers, providers, and other stakeholders are automated
or facilitated based on a connected network, data collection (sensing),
context-aware computation, and wireless communications. These systems enable
customers to accomplish their tasks efficiently and effectively. Using data from
people and things (e.g., specific objects, processes, and resources) is the key in
smart service systems to manage and improve the value co-creation and system
operations. Given this definition, a smart service system exhibits five essential
attributes, namely, the 5Cs: (1) Connection between things and people, (2) Collection
of data for context awareness, (3) Computation in the cloud, (4) Communications by
wireless, and (5) Co-creation of value.
Figure 16.1 illustrates a conceptual framework of a smart service system based on
the 5Cs. Customers and providers who are connected to each other co-create value
through data and information communications. Data are collected from connected
things (e.g., specific objects, processes, and resources), customers, and providers and
then transformed into information through computational processes. Information
generated from computational processes is used by the customers to use, manage,
and improve their concerned things and people.
Connection between things and people is the first attribute that a smart service
system should manage. Connected things include tangible goods directly used by
customers, as well as dedicated infrastructures generally required by customers and
providers; these goods and infrastructures can be connected to other things. We live
in a connected world; the buzzwords “IoT,” “Connected Car,” or “Connected
Fig. 16.1 Conceptual framework of smart service system
16 Clarifying the Concept of Smart Service System 361
Home” reflects our ability and desire to effectively control things around us. The
development of a connected network of people and things, which is the base
infrastructure for a system, is the groundwork for collection and communications
in a smart service system. In fact, a connected network represents the network of
“data sources” for smart service systems. Where to collect data is directly relevant to
data use (i.e., purpose of service system) and to the scope and potential of a service
system. IoT is crucial because IoT is about creating a cyber-physical infrastructure
for connection. Technologies for data analytics, cloud computing, and mobile
communications, among others, can effectively work together only with a connection
infrastructure.
Collection of data from connected people and things is the second attribute of a
smart service system. Data include condition traces of engineering systems, event
logs of business processes, health and behavioral records of people, and bio-signals
of animals. Given our capability for continuous monitoring and learning from data,
data are the core resources for context awareness. The term “smart” pertains to
information actions rather than to physical or interpersonal actions; hence, this term
is inevitably related to the use of data. A major distinction between traditional and
recent data collection is the data source (i.e., engineering systems versus human
systems). Current sensing methods include physical plus social sensing. Physical
sensing refers to a process conducted using physical sensors, whereas social sensing
includes any type of sensing enabled or conducted by people without using physical
sensors. Examples of social sensing include data collection from social network
services, surveys, interviews, queries, and documents. Physical and social sensing
from things and people within a service system produce data that indicate behaviors
and operations of people, operations and condition management of organizations
and things, and interactions within a service system. Data use contributes in the
effectiveness and efficiency of these processes.
Computation is the third key attribute of a smart service system. Computational
processes involve the use of specific algorithms and expert knowledge for decision
making. Computation is the prerequisite for data and information communications in
a connected network because these processes transform raw data into standardized
data or information that enable machine-understandable data or humanunderstandable
information. The key functions of smart service systems (e.g.,
context awareness, predictive and proactive operations, adaptation, real-time and
interactive decision making, self-diagnosis, and self-control) can be created only
through computation of specific data. This often requires several pre-tasks for data
analytics, such as analysis planning, data cleaning, anonymization, aggregation,
integration, and storage. Two of the key requirements of computation in smart
service systems are cloud computing availability and security because of the distributed
nature of connections in the service system.
Communications by wireless between people and things is the fourth attribute of
a smart service system. The contexts of communications include machine-tomachine
actuation and machine-to-human guidance. Thus, the issues of this attribute
encompass the issues in communicating machine-understandable data and humanunderstandable
information, such as visualization methods and other information
362 C. Lim and P. P. Maglio
delivery methods through auditory, olfactory, palate, and tactile stimulation in
physical, virtual, and augmented reality. Although the same goods, infrastructures,
and stakeholders can be involved in multiple service systems, interactions are
relatively unique in each service system. Although technologies for connection,
collection, and computing are fulfilled in a specific service system, the key to
transforming such system into a smarter service system or to creating a new smart
service system lies in improving the unique interactions within the system in
question. As such, communications technology that facilitates interactions is crucial
in any smart service system and is considered the circulating blood of the system.
Co-creation of value between customers and the provider of a service system is
the fifth attribute of a smart service system. Value creation is the core purpose and
central process in economic exchange. Any type of socio-technical service system
involves value co-creation that brings different stakeholders together to jointly
produce a mutually valued outcome. In this respect, the development and use of
technologies ultimately aim for enhanced or new value creation. Examples of value
co-creation stakeholders include customers of IT goods, manufacturers, government
agencies of infrastructure, and application developers. The first four attributes
represent the technological resources for smart service systems, whereas the fifth
attribute represents the application objective of various resources. In fact, the first
four attributes of smart service systems contribute to increasing the opportunities for
active value co-creation. As we become more connected, encounters for value
co-creation increase; as we collect and compute more quality data (quality in terms
of variety and volume), the informational or intellectual resources for value
co-creation increase; and as we communicate more efficiently and effectively, the
frequency and intensity of value co-creation increase.
Figure 16.1 indicates that smart service systems have a closed-loop mechanism.
Data and information interactions within a service system are iterative, and stakeholders
can develop their relationships and improve value co-creation continuously
through a cycle of monitoring and learning. This feature shows the true importance
of service system thinking for the use of various technologies. The direction of the
evolution of smart service systems is clear, that is, continuous development of the
loop of value co-creation by integrating technologies for connection, collection,
computation, and communications. The real advantage of service system thinking is
that it deviates from the existing technology concepts and focuses on the final
function (i.e., application) that must be fulfilled.
The 5Cs are useful in describing and analyzing a smart service system. For
example, a smart home can be defined as a service system that automates or
facilitates value co-creation activities (e.g., lighting, cooking, temperature control,
garage opening, and exercising) between residents and related stakeholders through
in-home or home-around connectivity, collection of living-related data, computation
for context awareness, and wireless communications achieved within or through a
technology-equipped house. Similarly, a smart health service system automates or
facilitates value co-creation activities among patients, healthy people, healthcare
providers, and other stakeholders through connectivity among people, devices, and
healthcare environment; collection of health-related data; computation for diagnosis
16 Clarifying the Concept of Smart Service System 363
and prognosis; and communications within or through technology-equipped people,
living, and care environment.
Another significance of describing smart service systems in this manner is that the
5Cs provide a basis for interconnecting different fields, with emphasis on applications.
Recent concepts, such as IoT, big data management, AI, cloud computing, and
wearable devices, are related to smart service systems, wherein each concept corresponds
to one or more system attributes. For example, wearable devices (e.g.,
smartphones, wristbands, and watches) are related to collection and communications
and serve mainly as data collection and information delivery channels. AI is related
to computation, whereas IoT is about connection. Moreover, each of the research
fields related to smart service systems, for instance, electronic engineering on
collection and communications, computer science on computing, and marketing
and business on co-creation, may focus on one or some of the system attributes
and seek synergy with different fields related to other attributes. A challenge for
R&D projects is the integration of the expertise of different professionals into
knowledge for value creation. The proposed concept of smart service system with
its five attributes can guide the entire project team to work effectively in managing
and improving a technology-based service system.
16.7 Categorization of Smart Service System
The conceptual framework of smart service system illustrated in Fig. 16.1 shows
how technologies contribute to the interactions and value co-creation within a
network of customers, providers, and things. By integrating the framework and
related studies on service categorization (Frei 2006; Bolton and Saxena-Iyer 2009;
Campbell et al. 2011; Wünderlich et al. 2013) and value co-creation (Payne et al.
2008), we can categorize different smart service systems into three types, namely,
smart self-service, smart super service, and smart interactive service systems (see
Fig. 16.2).
Payne et al. (2008) developed a process-based conceptual framework for understanding
and managing a detailed mechanism of value co-creation. Their framework
shows that customer value is co-created based on continuous interactions between
customer and provider processes through encounter processes. From this perspective,
services can be differentiated according to the portion of customer roles in value
co-creation processes. In traditional self-services or classic reduction of customer
variability (Frei 2006), such as the use of ATM and self-check-in hotels, the
customer performs many of the tasks previously done by the provider. By contrast,
the boundary can shift in the opposite direction. In traditional super services (Campbell
et al. 2011) or classic accommodation of customer variability (Frei 2006), the
provider performs many tasks previously done by the customer, such as a car rental
service that delivers cars to customers or luxury hotel service. Meanwhile, in
traditional interactive services (Bolton and Saxena-Iyer 2009), such as fitness
364 C. Lim and P. P. Maglio
training and counselling services, interpersonal interactions between customer and
employee are essential for value co-creation.
The four technology factors of smart service system (i.e., connection, collection,
computation, and communications) have advanced all three types of service. For
example, smart band-based fitness tracking services (Takacs et al. 2014) take the
traditional role of fitness trainers and enable a smart self-service system for health
management powered by the four technology factors (e.g., coaching capability for
people). Such smart self-service systems require significant customer–thing interactions
for value creation. By contrast, many equipment manufacturers take the
traditional role of customers in maintaining and using equipment and enable a
smart super service system powered by the four technology factors (e.g., optimization
capability for equipment). Such smart super service systems significant
provider–thing interactions for value creation. Meanwhile, a smart interactive service
system “comprises not only an embedded technology within the product that
communicates object-to-object but also personal interactions between the user and
the service provider employee as part of the smart service delivery process”
(Wünderlich et al. 2013). Examples include industrial remote interactive repair and
online transportation network (e.g., Uber) services. Smart interactive service systems
are somewhere in between the other two types and require significant customer-toprovider
interactions for value creation.
Service systems depend not only on people, information, organizations, and
technologies but also on their interactions, which have emergent consequences
(Maglio and Lim 2016). Thus, managing the interactions among customers, providers,
and things involved in the service system is key to improve value co-creation.
Figure 16.2 shows how different types of smart service system improve the interactions
with technology. Lusch and Nambisan (2015) provide a broadened view of
service innovation based on the service-dominant (S-D) logic (Vargo and Lusch
Fig. 16.2 Three types of smart service system
16 Clarifying the Concept of Smart Service System 365
2004) and describe the integration of the resources of a service system as the
fundamental method to innovate the system. In the proposed conceptual framework
of smart service system, the connection and communications factors mainly contribute
to a tighter integration of resources (e.g., people and their concerned things),
while the collection and computation factors are critical in creating new useful
resources (e.g., data on the things, information for people, and enhanced knowledge
of people). From this perspective, Fig. 16.2 further illustrates the three areas of
technology-based service innovation: The integrative use of the four technology
factors (i.e., connection, collection, computation, and communications) contribute to
the smart self-service innovation by enhancing the controllability of customers over
their concerned things; to the smart super service innovation by allowing providers
to infiltrate into the value creation process of customers, embed themselves in the
process, and take the roles previously done by the customer; and to the smart
interactive service innovation by expanding and intensifying the interactions
between customers and providers.
16.8 Future Research Topics
By integrating existing studies on smart service system in this chapter, a number of
research issues have been observed. This section discusses the four following
priorities: (1) service systems analytics and engineering, (2) operationalizing value
co-creation in smart service systems, (3) autonomous service systems, and
(4) extending the smart service system concept.
First, this chapter calls for research on service systems analytics and engineering.
A system is “a combination of interacting elements organized to achieve one or more
stated purposes” (ISO/IEC 2008). Human-designed systems, such as vehicles and
transportation systems, have well-defined architectures and understood mechanisms
of operation. For other systems, parts may be designed but the systems themselves
may evolve, such as cities or universities, with system architectures and mechanisms
emerging over time. Service systems tend to fall between fully designed and fully
emergent systems, and their architectures or mechanisms are not yet clearly understood,
although such an understanding is the key to engineering and improvement.
One difficulty in engineering and managing complex service systems is the lack of
data required to monitor and improve the system elements. However, with recent
advances in sensing technologies, various kinds and massive amounts of data can be
collected from the elements of the service system, such as people and physical
goods.
This advancement contributes to unlocking the limitations of engineering and
managing service systems, that is, a way of transforming specific service systems
into smarter service systems. At least, the proliferation of (big) data alleviates two
challenges in service systems engineering and management. First, articulating the
concept, architecture, and mechanism of a service system was difficult because
service systems are complex (Maglio et al. 2009) and fuzzy (Glushko 2013), in
366 C. Lim and P. P. Maglio
contrast to physical systems, such as vehicles and factories. Section 16.5 illustrates
how newly available text data from social sensing can be used to understand the
mechanism and define the concept of a specific type of service system, that is, the
smart service system. Second, designing a service system was difficult because
service systems involve numerous variabilities originating from customers (Frei
2006), contexts (Glushko 2010), and operations (Roels 2014). Section 16.4 illustrates
how newly available physically sensed data from customer processes or
operations within the service system in question can be used to design new services
or improve existing services. Transforming specific service systems into smarter
service systems requires understanding of the service system in question and modification
of the system operations. Under the proposed framework of smart service
system, it is apparent that data analytics can contribute to such understanding and
modification. Research on using data through descriptive, predictive, and prescriptive
analytics will contribute to understanding and improving service systems in
multiple application areas.
Second, this chapter calls for research on operationalizing value co-creation in
smart service systems. Many notable studies have discussed concepts, utilities, and
mechanisms of value co-creation theoretically and empirically in marketing, information
systems, innovation, design, and multidisciplinary contexts (e.g., Vargo and
Lusch 2004; Maglio and Spohrer 2008; Payne et al. 2008; Vargo and Lusch 2016).
Despite the emergence of a broad range of research on value co-creation over the last
decade, a review of the literature revealed a surprising lack of work directed at
providing frameworks to help organizations operate value co-creation with customers
effectively and systematically, except for few related studies (Payne et al.
2008; Ulaga and Reinartz 2011; Smith et al. 2014). The fifth axiom of S-D logic
states that “value co-creation is coordinated through actor-generated institutions and
institutional arrangements” (Vargo and Lusch 2016). Thus, S-D logic indicates the
research necessity of operationalizing value co-creation in service settings to account
for all agents, organizations, and resources involved.
One difficulty in operationalizing value co-creation is the lack of data required to
track, measure, model, modify, control, and manage individual customer behavior
and perceptions (Lim et al. 2018f). Considering the field of industrial operations
management before the emergence of “big data,” data from production and business
operations were routinely used to better operate specific processes (e.g., Linderman
et al. 2003; van der Aalst and Weijters 2004). With recent advances in sensing
technologies, various kinds and massive amounts of data are collected from individuals
through sensor-equipped consumer electronics (Porter and Heppelmann
2014). Traditional survey or observation data on customers are essentially research
data involving a hypothesis before data collection, whereas sensor data on customers
are natural records being collected and archived independent of a specific research
project, which reflect the real behavior and contexts of customers. From a marketing
perspective, the IoT enables the Internet of Customers that creates many new types
of customer touchpoints and provides opportunities for managing the customer
relationship (Johnson 2016). In this context, it is the time to operationalize value
co-creation with newly available data on customers.
16 Clarifying the Concept of Smart Service System 367
As discussed, various kinds and massive amounts of data are collected in smart
service systems from sensors located around people. Such newly available data can
be used for operationalizing value co-creation on the connected network. The
previous section illustrates that, for effective value co-creation, smart self-services
operationalize customer–thing interactions using data from customers and things,
smart super services operationalize provider–thing interactions using data from
providers and things, and smart interactive services operationalize customer–provider
interactions using data from customers and providers. However, only few
studies addressed this topic though it is key to the application of the value
co-creation concept and S-D logic in practice. Although the studies reviewed in
Sect. 16.4 provide some insights into smart service system operations, most of the
studies do not focus on the context of value co-creation. Research on
operationalizing value co-creation in smart service systems will contribute to managing
and improving service systems in this connected and data-rich world from a
service science perspective.
Third, this chapter calls for research on autonomous service systems. The proposed
framework of smart service system can be used to describe any service system
as the network of customers, their concerned things, and providers is generic. From
this perspective, Fig. 16.3 illustrates the evolvement of service systems according to
Fig. 16.3 Evolvement of service systems and emergence of the autonomous service system
368 C. Lim and P. P. Maglio
the intensity of technology use in the service system network. Value is co-created
through “the application of competences (knowledge and skills) by one entity for the
benefit of another” (Vargo and Lusch 2004). In traditional service systems
(Fig. 16.3a), customers employ the competences of providers to achieve specific
goals and interact with concerned things. In the three types of smart service system
(Fig. 16.3b), the interactions among customers, their concerned things, and providers
are facilitated or automated based on the four technology factors (i.e., connection,
collection, computation, and communications). In this continuum, a new type of
service system that is rapidly emerging is the autonomous service system
(Fig. 16.3c), such as service systems based on a self-driving car (e.g., Google) and
a fully automated building (e.g., Edge in Amsterdam).
As shown in Fig. 16.3, interactions exist within customers, things, and providers.
As recent AI can recognize and deliberate various things, we must pay specific
attention to thing–thing interactions. In autonomous service systems, thing–thing
interactions are fully automated and the things behave autonomously. Following Frei
(2006), traditional “automation” technologies and modern “autonomous” technologies
in service systems can be distinguished in terms of variability, with traditional
“automation” technologies “reducing” and controlling variability (e.g., trains use
specified routes and ATMs cover limited options) and modern “autonomous”
technologies “accommodating” variability (e.g., self-driving cars use any route and
AI-based financial services invest by themselves). Autonomy must incorporate
automation technologies for the four technology factors of smart service system to
be connected with, use data on, deliberate about, and communicate with humans and
things. We are at a tipping point of the true integration of these factors for autonomy
for monitoring, communication, context awareness, learning, predictive and proactive
behavior, adaptation, optimization, real-time and interactive decision making,
control, self-detection, self-diagnosis, self-reconfiguration, and self-control. As
shown in Table 16.1 in Sect. 16.3, these terms have been used to describe smart
service systems. Thus, the emergence of autonomous service systems means the
emergence of truly smart service systems as indicated in the continuum shown in
Fig. 16.3.
Autonomous service systems may be viewed as an ideal form of smart selfservice
systems in that customers only need to consider the outcome of the service
system or the same of smart super service systems in that providers encapsulate their
capability in the service system. However, autonomous service systems are clearly
different for the adjacent types shown in Fig. 16.3 in that no or a minimum level of
human–thing interactions is required and things take deliberate action on their own.
Given a full automation of the interactions within things and consequent autonomous
behavior and operations of the things, customers and providers (nearly) do not
have to interact with each other and with the things.
In autonomous service systems, value co-creation processes are nearly automated
by the things as humans only need to take (either consciously or unconsciously) the
value in use produced and delivered by the things automatically. Many areas of
smart service system, such as autonomous farming, building, energy, logistics,
transportation, and security, can be evolved into autonomous service systems,
16 Clarifying the Concept of Smart Service System 369
whereas smart home, health, hospitality smart education, smart city, and government
service systems require human interventions. In any case, what should be automated
or whether and when automation will improve the overall value co-creation (or it
should only be value creation) is unclear. Research on autonomous service systems
from a service science perspective can extend the literature on value co-creation to
the world of interactions within things and improve the partnership between humans
and computers for value co-creation.
Finally, the smart service system concept proposed in this chapter should be
further extended and studied in depth. This chapter has mainly focused on a digital
data-centric perspective, such as that on the connection of different data collection
sources to develop a smart service system with high-quality computation and
communications functions for value co-creation. However, this perspective may be
incomplete due to the limited scope of the reviewed studies on smart service systems
and the authors’ limited background in industrial engineering, computer science, and
management. For example, this chapter has not fully covered the engineering
perspectives on physical human–machine interaction (HMI) as well as those on
physical control and actuation mechanisms. Within most of the existing smart
service systems, humans interact with machines. Using advances in haptics and
actuators enhances the interactions between them and improves function execution
accuracy. For example, the ability to physically retrieve cash from an ATM or
deposit a check may co-create more value than the action of checking the balance
only. Thus, advancements in the HMI and control aspects of ATMs are important.
The future AI-based smart banking service systems are not different and are required
to be equipped with high-quality functions for HMI and controls. This requirement
applies to other types of smart service system as well, such as physical driving with
received information in smart transportation systems and physical intervention in
smart health systems, which are adaptive to the patients’ behavioral and bio-signal
data. These perspectives are also important in autonomous service systems because
humans are always involved and receive physical outcomes in such systems.
Our intention was not to propose an exhaustive concept of smart service system in
one chapter but to clarify some important perspectives and elements of the concept.
Value co-creation in smart service systems can be enhanced by advances in data
collection and use, but also through the spectrum of engineering advances in
actuation, such as a robotic hand exoskeleton that can provide an assistant service
to a customer by reacting to the input provided through the electromagnetic waves
from the customer (e.g., electromyography; Yun et al. 2017a, b). In general, the
physical interaction of a machine with a human to co-create value is only possible
because of the existence of both elements.
Although we believe the aforementioned perspectives are important, we could not
state these perspectives in an earlier section because we could not find these from our
data analytics and specific references focused on the smart service system concept.
For example, in the factor analysis discussed in Sect. 16.5, we could not derive a
control-related factor when we set up the number of factors as five or six, though
“control” was one of the 53 generic word-features that may represent the generic
structure of smart service systems. We believe future studies on the HMI and control
370 C. Lim and P. P. Maglio
aspects of smart service systems can extend our findings significantly. For example,
factors about the physical actuators and physical interaction with technology can be
added to the current nine factors of value creation mechanisms of smart service
systems. The 5Cs can also be further extended to the 6Cs, including the following:
(1) Connection between things and people, (2) Collection of data for context
awareness, (3) Computation in the cloud, (4) Communications by wireless, (5) Control
and actuation of physical elements, and (6) Co-creation of value. Smart service
systems with a strong physical component, such as smart homes, transportation, and
farming, may be described and developed with the extended 6Cs concept.
Likewise, other perspectives on smart service systems can be further investigated
and added to the findings of this chapter. The level of service system smartness
should also acknowledge the partnerships between machines and humans that
interact to co-create value. For example, a team consisting of robotic assistant nurses,
human nurses, human doctors, and surgery robots can co-create value with patients
as they deliver smart health services to such patients. Strategies for the symbiosis,
where the machine and the human interact in a perfect and harmonic way, should be
studied and included in an extended smart service system concept. Indeed, no
machine can complete the value co-creation process without the human, and no
human can do it without the machine. Finally, the augmentation of human capabilities,
such as vision, sensing, touching, hearing, and movement in smart service
systems, can be investigated and integrated with all the aforementioned points.
16.9 Conclusion
This chapter takes a service science perspective in viewing service systems in this
connected and data-rich economy, following the call for proposals on smart service
systems by the NSF (2016). The concept of smart service system discussed in this
chapter recognizes the smartness in modern service systems as multi-dimensional
(i.e., the 5Cs). The concept can facilitate the application of a service science
perspective to develop and use technologies for people for their value co-creation.
Our work is significant in that the findings were derived from a literature review
(Sect. 16.3), real projects with industry and government on the design and development
of smart service systems (Sect. 16.4), and an analytics of 5378 scientific studies
and 1234 news articles (Sect. 16.5). The findings in Sects. 16.6 and 16.7 aggregate
the key concepts and areas of broad studies and applications of smart service system.
Our work will bring significant clarification and elaboration to the definition of
smartness in modern service systems. Smart people co-create value by creating
connections between relevant things and the concerns of people, by collecting and
computing data, and by communicating with things and people to address the
concerns. This mechanism can be applied to describe and develop any type of
smart service system. Following Larson (2016), the authors believe that this chapter
would provide an effective framework for the design and development of smarter
service systems across multiple disciplines.
16 Clarifying the Concept of Smart Service System 371
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Chiehyeon Lim is an assistant professor in the School of Management Engineering at UNIST
(Ulsan National Institute of Science and Technology). He obtained his B.S. (2009) and Ph.D.
(2014) from the Department of Industrial and Management Engineering at POSTECH (Pohang
University of Science and Technology). As part of his postdoctoral experience, he served as an
assistant project scientist and lecturer in the School of Engineering at University of California,
Merced. His research interests include smart service systems and service systems engineering and
management.
Paul P. Maglio is a professor of technology management in the School of Engineering at the
University of California, Merced. He holds an SB in computer science and engineering from MIT
and a Ph.D. in cognitive science from UCSD. One of the founders of the field of service science,
Dr. Maglio is currently Editor-in-Chief of INFORMS Service Science, and was lead editor of the
Handbook of Service Science (Springer).
376 C. Lim and P. P. Maglio