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Service Science: Exploring the Implications of IT-enabled Relational
Structures on Service Performance
Noel Carroll
Lero - the Irish Software Engineering Research Centre, Dept. of CSIS, University of Limerick
noel.carroll@lero.ie
Abstract
Although services are delivered across dispersed
complex service eco-systems, monitoring performance
becomes a difficult task. This research explores the
development of service performance analytics within the
discipline of service science and explains how actor
network theory (ANT) and social network analysis (SNA)
can be introduced as the core theories to examine
service operations and performance.
1. Introduction
The emerging discipline of ‘service science’ sets out
to explore the socio-technical and socio-economic
factors which contribute towards service delivery [1].
While the literature often investigates services from
aspects of people, computer hardware and software,
business, and information systems [2], few efforts
encapsulate all of these factors to understand the
intertwining trajectories of service networks. Services are
complex socio-technical entities which result in
intertwining human and non-human factors. Thus,
viewing services through an actor network theory (ANT)
lens allows us to develop an understanding of the
complexity of service environments as they operate
across heterogeneous networks.
2. Research Approach
This research adopts ANT to explore the implications
of technology on relational structures (links within a
network) and their affects on service performance across
a service network. In addition, we examine how service
actors impact on change and we visualise this through a
technique called social network analysis (SNA). SNA is
an approach and set of techniques which studies the
exchange of resources (for example, information) among
actors.
Figure 1 Example of a SNA Map
SNA focuses on patterns of relations among nodes
(figure 1) and demonstrates the value of ties and
relationships between each node by providing a visual
and mathematical representation of interaction and
exchanges which influence behaviour.
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3. Service Operations and Performance
There have been minimal research efforts to explore
service networks from both a service management and a
service computing perspective. This has significant
consequences on manager’s ability to understand the
implications of implementing and managing technology
to support service operations. Service network
performance analytics (SNPA) is an area which we
develop as it is becoming a crucial topic within service
environments to allow managers gain greater insights on
service operations, i.e. what works well, what needs
improvement, and what needs to be removed.
Performance is often influence by external entities
causing structural variability across a service eco-system.
We explore this from a service management and service
computing perspective which attempts to unite the two
disciplines for two main reasons:
1. To enhance service management decision-making
tasks (service management),
2. To feed performance information into service
requirements engineering (service computing).
Figure 2 illustrates the service anatomy in which we
focus on service process and key performance indicators
Figure 2 Service Network Anatomy [2]
(KPI) to model service performance – SNPA. The focus
on service network relational structures acknowledges
the fundamental role that relational structures play on
the generation of value through the sustainability of
service network relationships, interaction, exchange of
resources and competencies to enhance service
performance.
4. References
[1] Spohrer, J., Maglio, P. P., Bailey, J., and Gruhl, D. ‘‘Steps
Toward a Science of Service Systems,’’ IEEE Computer,
Volume 40, Number 1, 2007, pp. 71-77.
[2] S-Cube. Documents available from Website: http://www.scube-network.eu/
2009