SERVICE SCIENCE

Introducing Service Networks Performance Analytics

Noel Carroll, Ita Richardson

Lero - The Irish Software Engineering Research Centre, Department of Computer Science & Information Systems

University of Limerick, Limerick, Ireland

Eoin Whelan

Department of Management and Marketing, Kemmy Business School, University of Limerick, Limerick, Ireland

Keywords: Service science, Service network, Actor network theory, Performance analytics.

Abstract: Although services are delivered across dispersed complex service eco-systems, monitoring performance

becomes a difficult task. This paper explores a number of areas to support the development of service

performance analytics within the discipline of service science. The paper provides a comprehensive account

for the need to introduce modelling techniques to address the significant research void and explains how

actor network theory (ANT) can be introduced as one of the core theories to examine service operations and

performance. ANT sets out to develop an understanding on both how and why networks exist and to

understand processes co-creation between human and non-human actors. By examining performance, this

paper draws our attention towards the need to formulate methods to examine service network key

performance indicators and the need to model service interaction, structure, and behaviour which impact on

performance and consequently on service evolution.

1 INTRODUCTION

Nowadays, organisations are becoming increasingly

interested in understanding the operations of service

networks as a means to adapt to the ever-changing

business environment. However, as services are

delivered across dispersed complex service eco-

systems, monitoring performance becomes a

difficult task. Management must attempt to develop

a greater understanding of service processes to

identify where improvements may be made by

employing business process management (BPM).

We are often led to believe that we live in a ‘global

service network’, surrounded by networks of power,

influence, and relationships (for example, Law,

1999). Therefore, we can view a network as a

specific set of linkages among a defined set of

actors, whose properties can characterise the

linkages which influence service behaviour. The

critical problem here is the lack of research to bridge

service computing and service management

developments, for example, modelling service

operations and analytics to enhance service

requirements. The interaction patterns exhibited

within service environments (physical and virtual)

are of critical importance to performance analytics.

We adopt actor network theory (ANT) as one of core

theories upon which we can examine service

relations and their effects on service performance

between service actors (for example, people,

organisations, and IS). ANT was originally created

to understand processes of technological innovation

and scientific knowledge-creation between human

and non-human actors (Latour, 2005; Callon, 1986).

ANT is not typically concerned with why a

particular network exists but rather the infrastructure

which supports the network and understanding its

evolution, i.e. how the network exists. We discuss

how ANT offers us a scientific lens to view service

performance and supports our quest to develop

service performance analytics.

2 THE SERVICE ENVIRONMENT

The service environment is comprised of complex

253

Carroll N., Richardson I. and Whelan E..

SERVICE SCIENCE - Introducing Service Networks Performance Analytics.

DOI: 10.5220/0003357002530259

In Proceedings of the 1st International Conference on Cloud Computing and Services Science (CLOSER-2011), pages 253-259

ISBN: 978-989-8425-52-2

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

business interactions often influenced by the

affordance of technology. The growth in ‘service

science’ as a discipline has underscored the need to

investigate the contributory value of business

processes and its influence on service performance.

Within organisational and technological

management theory, understanding and measuring

value (i.e. application of competences) of service

networks is considered one of the key problems

which prevent the sustainability of service growth.

Service science explores the value co-creation of

interactions between service systems (Vargo et al.,

2008). Modern service systems have become very

complex. Technological advances continue to act as

a driving force for ‘making new patterns and a new

elevated level of value creation possible’ (Normann,

2001; p. 8), which places greater emphasis on the

need to understand how process patterns influence

service performance.

3 DEFINING SERVICE SCIENCE

Service science is an attempt to “study the

application of the resources of one or more systems

for the benefit of another system in economic

exchange” (Spohrer et al., 2007, p. 2). One of the

fundamental objectives of service science is to

understand the mechanics of service networks and

define how and why they generate value. As service

science undergoes numerous theoretical

developments it may be premature to expect that we

can define service science. However, Spohrer et al.,

(2007) identifies four key observations about these

disciplines:

1. Heavily resource dependent.

2. Tend to integrate or coordinate resources.

3. Measuring performance is very important.

4. Disciplines incorporate the word “service”, e.g.

service engineering.

Broadly speaking, services science may be described

as a discipline which sets out to develop methods to

extend the availability and accessibility of business

processes. It is also concerned with improving

manager’s ability to predict risk, estimate their

effects, and reduce uncertainty through modelling

the value-exchange which results from provider and

client interaction during intellectual, behavioural,

economic, and/or social activities.

3.1 Complexity of Service Networks

Technology is often referred to as the backbone to

many of the service providers. In addition, the

Internet has fuelled the expansion of a plethora of

services and service networks, for example, service

clouds. As the number of services and variety of

services continue to increase, so too will their

complex environments. However, the problem here

is understanding the dynamics and complexity of

service science: “powerful dynamics are in play

around the world when it comes to applying

resources effectively to solve problems and create

value” (Spohrer et al., 2007; p. 10). Therefore,

understanding the complexity of network structures,

process patterns, and methods to improve network

performance is critical to the success of service eco-

systems, for both the service provider and client.

Spohrer et al., (2007) identify five main criteria

within a service (summarised in table 1 below):

Table 1: Main Criteria within a Service.

Criteria Explanation

Resource Value of resources and how service interaction

behaviour influences value.

Entity The service system (or an actor; person,

organisation, information and technology or the

configuration of all four). It must dynamically adapt

the value proposition and evolve over time.

Service One or more entities must perform the application of

competencies and one or more entities must receive

the benefit and co-create value.

Interaction Interactions generate an outcome. Value is

determined whether it has been added or destroyed

through unique frames of reference. Four main

outcomes from interaction:

Win-win value co-creation

Lose-lose value co-destruction

One entity judges that value is created

One entity judges that value is destroyed

Assessment of value depends on the frame of

reference of the service system which may judge on

historical performance as well as expectations

(goals), quality, satisfaction of customer experience,

improved value, and agility.

Success

criteria

What constitutes success? Calls for a rigorous

theory of

ervice systems to explore how entities

interact, how they persist, what value they co-create.

As identified above, service science plays a

central role in supporting our quest to learn how

service network and service exchange influence

service performance. We suggest that the application

of actor network theory (ANT) as a suitable theory

to understand the dynamics of service networks and

consequently, service network performance

analytics.

4 ADOPTING ANT

Modern organisational structures promote flat hie-

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rarchies and more flexible structures, which are

fundamental characteristics of the modern

organisational architecture. To explore such network

architectures, there is a growing body of evidence

which suggests that actor network theory (ANT)

may assist us to gain a greater understanding of

networks within the IS discipline. ANT can provide

a deeper understanding about how and why

processes of ‘technological innovation and scientific

knowledge-creation’ and is not concerned with the

network per se, but rather the infrastructure which

supports the network’s evolution (Monteiro, 2000).

It examines the performance of network relations

and explores the influence of objects towards those

relations (Law, 1999; p. 7). ANT research examines

socio-technical influences and relational effects of

actor (i.e. human and non-human) interaction within

networks which support, for example, people,

organisations, and technology. ANT is based upon

three main principles. These are; agnosticism,

generalised symmetry, and free association (Callon,

1986):

1. Agnosticism: analytically impartiality is

demanded towards all the actors involved in the

network.

2. Generalised symmetry: explains the conflicting

viewpoints of different actors in same terms by use

of an abstract and neutral vocabulary.

3. Free association: requires the elimination and

abandonment of all priori distinctions between the

technological, natural, and social factors.

ANT directs out attention towards networks, links,

interactions, assemblages, and associations and

presents questions such as, are the associations weak

or strong?; Is the network stable or unstable?; What

elements, if changed, would create new entities, and

both how and why are these created and supported?

4.1 Applying ANT to Service Networks

Service actors (organisations, people, IS) may be

viewed as representations of a networked effort to

deliver a service, while unfolding the meaning (or

value) of influential service actors. ANT may be

adopted as a research method for a soft case study

approach to examine the trajectories of service

networks and service actor interaction. The effects

of such interactions are of significant interest when

we examine service network interaction performance

or outcomes. Law (1999) refers to these interactions

as relational materiality and performativity which

examines the “consequence of the relations in which

they are located” (p. 4). Thus, ANT provides and

alternative view from management literature of

service management with a view to understand how

service systems and business strategies align. ANT

also presents a lens or a framework which provides a

detailed description of the underlying mechanics and

its infrastructure which support dynamic networks

and the unbiased viewpoint of the network actors

(Monteiro, 2000).

4.2 ANT and Service Analytics

ANT is essentially concerned with a bottom-up

concept of alignment and strategy formation, while

alignment is traditionally more concerned with a

top-down view on planning and decision-making

processes. Therefore ANT provides a theoretical

platform upon which we can begin to analyse the

implications of service relational structures on

performance analytics. This allows managers to

establish clearer facts, effects, beliefs or

technological solutions within service networks and

learn how IT enables and inhibits service

performance. Networks are considered to be

“processual, built activities, performed by the

actants out of which they are composed” (Crawford,

2005; p. 1). To summarise, the following list

summarises some of the key concepts within ANT

(Monteiro, 2000):

 Actor/Actant: any element (human or non-human

– ‘black box’) that performs actions and influences

other elements it interacts with and whose patterns

are known as inscriptions.

 Inscription: the behavioural pattern between the

actant and another element in the network, i.e.

interests are inscribed in written material (e.g.

service level agreements or SLA).

 Translation: the process of aligning actors across

a specific network through the adaptation of the

inscriptions when a new actor is created.

 Enrolment: process of becoming a member of a

stable network.

 Alignment: result of the enrolment process when a

network becomes stable and unified through the

process of translation. Alignment must also ensure

the all inscriptions are agreed upon during the

process of enrolment.

 Irreversibility: measures how difficult it is to undo

a decision and how to determine the subsequent

measures.

 Black Boxing: an approach to analyse an ANT

network through the simplification of a network by

removing identities from actors. Black boxes may

always be reopened as networks demand continual

maintenance to order.

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As the list above suggests, actors are therefore

responsible for an action which supports the

evolution of a network.

Therefore ANT provides a

theoretical platform upon which we can begin to

analyse the implications of service relational

structures on performance analytics.

5 VALUE OF SERVICE

NETWORKS

Reporting on the value of service network

relationships, especially from a business perspective

can prove to be extremely beneficial (Carroll et al.,

2010). In this sense, value may be referred to as “the

adaptability and survivability of the beneficiary

system” (Vargo et al., 2008; p.148). Service value

also refers to the relational exchanges and examines

how network interaction generates a value to satisfy

a service client’s need (i.e. value exchange). Thus,

the value of a service network is “a spontaneously

sensing and responding spatial and temporal

structure of largely loosely coupled value proposing

social and economic actors interacting through

institutions and technology, to: (1) co-produce

service offerings, (2) exchange service offerings, and

(3) co-create value” (Lusch et al., 2010). Within

service systems there is a large element of barter

(method of exchange) involved in the transactions

and it is often difficult to examine the

‘complementary resources’ which are exchanged

within a service system, for example, information

resources.

5.1 Our Approach towards

Service Analytics

We propose that one solution towards modelling

service performance analytics is to examine the

relational structures to support service networks.

Despite the volume of research which concentrates

on complex business applications and modelling

processes there are no research efforts to explore the

implications of relational structures on service

network performance. Thus the relational structure

of service networks shared amongst organisations to

support business operations may prove to be the key

to modelling service networks and their

performance. We identify the need to visualise and

understand the relational contributions of service

structures to further enhance decision making tasks

while restructuring service network business

processes (Carroll et al., 2010). We posit that the

implications of relational structures and service

behaviour allow us to develop service network

performance analytics.

6 PERFORMANCE ANALYTICS

A service network is a complex system which relies

on the harmonisation of numerous actors. Service

performance is often influence by external entities

causing structural variability across a service eco-

system which impacts of the networks

characteristics and ultimately, its performance.

Therefore, it is critical that we gain a thorough

understanding of what influence service

performance for two main reasons; firstly to enhance

service management decision-making tasks (service

management), and secondly, to feed this information

into service requirements engineering (service

computing). This is appropriate as the relationship

between service computing and service management

relies on the exchange of service resources to

support several key factors of service orientation:

organisation, people, and software. This view unites

two main disciplines of service computing and

service management. Figure 1 below illustrates six

main types of service relationships (Zhao et al.,

2008) where service computing is largely concerned

with software components, while service

management is mainly concerned with the people

although both service computing and management

are required to successfully deliver a service.

Figure 1 also illustrates the unification of these

broad concepts which makes communication

between engineers and managers more effective.

Figure 1: Service Orientation (Zhao et al., 2008).

Across business and IS research, there is a

significant gap in our ability to bridge and advance

our understanding of technology and management in

this so called ‘service-dominant’ business

environment (Normann, 2001).

Figure 2 above illustrates the five tiers which

form the service network anatomy; the human and

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software infrastructure and the software and human

services governed by SLA and Quality of Service

(QoS); the atomic services monitored controlled by

process metrics; the service processes managed by

participant metrics; and the business transactions

managed by network key performance indicators

(KPIs).

Figure 2: Service Network Anatomy (S-Cube, 2009).

Before we attempt to measure service network

though performance analytics, we are reminded of

Hubbard’s (2007) advice to first question how and

what gets measured as it has some conceivable

effect on decisions and behaviour (p. 43):

1. What decision is this supporting?

2. What really is the thing being measured?

3. Why does this thing matter to the decision being

asked?

4. What do you know about it now?

5. What is the value to measuring it further?

Managers must rethink (design, innovate, deliver,

and support) new strategies and possible structures

to transcend their competencies across service

networks. This includes technology, network

topology, human behaviour, business strategy,

service design, and economics. More specifically,

managers must pay close attention to how service

management is conceptualised (capabilities,

structures, and processes) and how behaviour is

orchestrated to interact and innovate service

development.

Applying this business logic the service actors

and service competencies draws our attention

towards the relationship or tie which determines the

exchange patterns within a service network.

Therefore, service (actor) interaction patterns should

be possible to model and provide insight on how

specific actor relations enable or inhibit service

business processes. We can also categorise the type

of relationship within performance analytics and

KPIs. It can also provide greater insight within the

service exchange process and the ‘value’ of the

exchange, for example, information and financial

data. For example, there are three main types of

performance measures (table 2):

Table 2: Main Types of Performance Measures.

Performance

Measure

Explanation

Key Result

Indicators (KRIs)

Determine how service has

performed in the past, for

example, sales last month.

Performance

indicators (PIs)

Inform what you ought to do.

Key Performance

Indicators (KPIs)

Prescribes what you ought to do

to increase performance.

As summarised above, performance measures

(KRIs, PIs, and KPIs) analyse how key activities

influence service performance e. Service delivery

systems also distinguish five main factors which are

invariably influenced by the physical setting of

technical tools to deliver a service: cost

rationalisation, quality enhancement, beneficial

customer linkages, behavioural implications, and

technology adaption.

The first question is where do you want to be

which suggests that organisation must be committed

to service transformation and cooperated to meet the

business objectives, mission, and vision. The

second question, “where are we now?” may be a

difficult question to answer but managers must

identify where changes are needed, for example,

people, process, practice, technology/technical

infrastructure, and data (i.e. metrics) to steer the

service towards the service vision. The third

question asks, “how do we get to where we want to

be?” which requires a more detailed plan including

a top-down (process-orientated technical

infrastructure) and bottom-up (influence the

development of processes) of a service system The

fourth and final question is “how do we know when

we have arrived?” This is a critical question as it

determines the success criteria (which are a major

factor within service science). One of the greatest

concerns within today’s service network landscape

is the inability of business models to cater for the

pace and dynamics of business. Failing to examine

the service network value increases the chances of

ignoring the spatial and temporal structure of largely

loosely coupled value proposing actors which

dynamically interact through ‘institutions and

technology’ offers little insights on service

performance (Lusch et al., 2010).

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6.1 Key Performance Indicators

Key performance indicators (KPIs) are quantifiable

measures of organisations’ progress to meet specific

goals. KPIs also assist managers in decision making

to determine the right course of action. The level of

dimensional support across the process structures is

expressed in several forms including, structural,

functional, compositional, and behavioural. Often

these dimensions are taken for granted and

overlooked although this information provides both

tangible and intangible metrics on service network

performance. There are several reasons why service

metrics often fail, for example, service networks

may use incorrect metrics which do not measure the

business value of the network. Incorrect metrics may

also mean that the performance findings are not

actionable as probing for a complete analysis of the

network is more difficult to collect data. In other

cases, managers may set poor performance targets

and fail to implement incentives or penalties to

enhance the service performance. Another reason

may include the over emphasis on service cost over

business benefits.

Many services are exceedingly complex

phenomena which can be conceptualised in several

different ways. Taking a qualitative perspective and

trying to really understand primarily what relational

structures mean in service network, how they

evolve, and then try and address and look at how

they change with the impact of IT and service

performance. The relationships which exist between

these services can determine the service innovation

and operations efficiencies across networks. This

will also allow us to identify the critical success

factors (CSFs) which enable (KPI) or inhibit

business processes. Freeing up resources to develop

value-added information is critical to managerial

activities (e.g. rapid decision making and execution).

To address these issues we must uniquely define the

business KPIs. KPIs allow us to measure the

success of goal achievement and to generate insight

to discover how service performance and value may

be enhanced. Characteristically, service network

KPIs should be simple for decision making, relevant

to unique (service-dominant) business models,

present timely results, useful, and instant for

actionable insights. Here, one is reminded of

services seeking the right balance or requisite variety

between use, usage, and usability of their resources

and processes through service-oriented approaches.

We also encapsulate this when we refer to the notion

of ‘performance analytics’ within a service

environment (figure 3) as follows:

Figure 3: Service Network Performance Analytics.

Within a service environment, it is paramount to

begin the process of establishing performance

measures using the service mission, vision, and

values. Considering services are typically unique in

many ways, each service must determine their

mission, vision, and values. In addition, managers

must develop a vision (often an intangible or

philosophical view) on what they must achieve in

order to successfully meet their goals. Services must

also devise strategies to achieve their visions.

Within the service environment, managers need to

identify areas to introduce service innovation,

service initiatives, and identify issues which may

present opportunities or threaten service

sustainability. This may be achieved through a

SWOT-like analysis (strength, weaknesses,

opportunities, and threats) of the service

environment while adopting the balanced scorecard

critical success factors; financial results, customer

satisfaction, learning and growth, internal processes,

staff satisfaction, and community and environment.

These may be adapted to suit a service environment

and identify KPIs to examine service competencies,

relations, and resource exchange. Freeing up

resources to develop value-added information is

critical to managerial activities (e.g. rapid decision

making and execution).

7 CONCLUSIONS

This paper presents a platform which introduces the

need to explore service network performance

analytics though the application of ANT. The focus

on service network relational structures

acknowledges the fundamental role on the

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generation of value through the sustainability of

service network relationships and performance. As

part of other research work, we have incorporated

the use of social network analysis (SNA) to model

service performance and borrow SNA metrics

(Carroll et al., 2010) to examine service performance

analytics.

ACKNOWLEDGEMENTS

The research leading to these results has received

funding from the European Communities Seventh

Framework Programme FP7/2007-2013 under grant

agreement 215483 (S-Cube). For further information

please visit: http://www.s-cube-network.eu/. This

work was supported, in part, by Science Foundation

Ireland grant 03/CE2/I303_1 to Lero - the Irish

Software Engineering Research Centre

(www.lero.ie).

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