Synchronicity and alignment of productivity: the real value from Service Science

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Synchronicity and alignment of productivity: the realvalue from Service Science?Stuart Maguire a , Udechukwu Ojiako b , Thanos Papadopoulos c , Farhad Shafti d , Lenny Koha & Panagiotis Kanellis ea The Management School, University of Sheffield, Sheffield, UKb Faculty of Engineering and the Built Environment, University of the Witwatersrand, 1 JanSmuts Avenue, Braamfontein 2000, Johannesburg, South Africac The Business School, University of Hull, Yorkshire, UKd The Business School, University of Strathclyde, Glasgow, UKe Management Division, Ernst & Young, Athens, Greece

Available online: 04 Jan 2012

To cite this article: Stuart Maguire, Udechukwu Ojiako, Thanos Papadopoulos, Farhad Shafti, Lenny Koh & Panagiotis Kanellis(2012): Synchronicity and alignment of productivity: the real value from Service Science?, Production Planning & Control: TheManagement of Operations, 23:7, 498-512

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Production Planning & ControlVol. 23, No. 7, July 2012, 498–512

Synchronicity and alignment of productivity: the real value from Service Science?

Stuart Maguirea, Udechukwu Ojiakob*, Thanos Papadopoulosc, Farhad Shaftid, Lenny Koha andPanagiotis Kanellise

aThe Management School, University of Sheffield, Sheffield, UK; bFaculty of Engineering and the Built Environment,University of the Witwatersrand, 1 Jan Smuts Avenue, Braamfontein 2000, Johannesburg, South Africa; cThe Business

School, University of Hull, Yorkshire, UK; dThe Business School, University of Strathclyde, Glasgow, UK;eManagement Division, Ernst & Young, Athens, Greece

(Received 8 November 2011; final version received 8 November 2011)

The ability of services to pervade all aspects of productivity creates the need for an interdisciplinary framework ofservice to be developed. It is, however, critical that any proposed service framework is jointly developed betweenthe service purveyor and the stakeholders involved. An expected outcome from a focus on productivity in theService Science arena is that a much closer relationship between the purveyor of a service and the customer isinitiated and fostered. This requires a clear focus on the requirements of the customer and the various ways inwhich the service can be conveyed. This is not too far removed from what is required in other areas such asproduct specification that should also be carefully crafted from the needs of the customer. The research utilisestwo case studies to highlight the impact of Service Science as a co-producer of service productivity. We find fromthe case studies that human factors play an extremely important role in improving service productivity.

Keywords: service delivery; productivity; customers; synchronicity

1. Introduction

The service sector dominates the economies of thewestern world (Nissan et al. 2011). Hence, organisa-tions and governments are striving to understand andrespond to the constantly changing expectations andrealities of customers, citizens and markets andinstabilities in economies and societies. They areheavily investing in associated infrastructures, aimingto build their futures on technology and innovation(Bitner et al. 2008), enhancing the services theyprovide. This fact highlights the importance of ser-vice in the post-industrial economy (Chase andApte 2007).

The pervasiveness of service creates the need for aninterdisciplinary support service framework (Batistaet al. 2008). These links come together to improveproduction–consumption relationships and improveservice productivity. This service framework has beenreferred to as Service Science. The aim of ServiceScience is to explore the dynamics created by theshifting value of knowledge between stakeholders,organisations, technology and culture. These con-structs which are embedded in interdependency net-works create efficiency, co-produce value, enhanceproductivity, and determine the way the service systemevolves (Spohrer and Maglio 2008).

This article is structured as follows: after a briefdiscussion of the service dominance and emergence ofService Science, the issue of service productivity isexplored. Two short qualitative case studies follow thatexplore service productivity from the angle ofco-production. The last two sections discuss theimplications of the case studies and conclude thearticle.

2. The dominance of service

The competitiveness (and survival) of any companywill be determined by the nature of its interaction withcustomers (Lusch et al. 2007). Usually, such interac-tions can be expressed through its service framework(Roth and Menor 2003, Bitner and Brown 2008a) andthe impact of the experience on the customer(Schembri 2006). Initially seen as a poor relation tothe four pillars of marketing (see Booms and Bitner1981, Christopher et al. 1991), the question of serviceframeworks is particularly critical based on its currentrole in the global economy.

Indeed, service, according to Regan (1963), relatesto a selection of activities which may be on offer forsale, or which on other occasions are provided whengoods are offered for sale. They represent intangibles

*Corresponding author. Email: [email protected]

ISSN 0953–7287 print/ISSN 1366–5871 online

� 2012 Taylor & Francis

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(insurance) or tangibles (transportation) which providedirect satisfaction, or intangibles which yield satisfac-tion when they are jointly purchased with eithercommodities or other services (credit, delivery). Otherdefinitions include Maglio and Spohrer (2008) whosuggest that service can be defined in many differentways in the literature, depending on the scope of thediscipline within which it is located. For instance, it canbe defined as any activity or series of activities that aimto provide solutions to customers’ problems (Gronroos1990); intangibles and perishable goods which aresimultaneously created and used (Sasser et al. 1978);and time-perishable, intangible experiences which areproduced and consumed by the customer themselves(Fitzsimmons and Fitzsimmons 2001).

According to Lovelock (1983), service is charac-terised by its constituent elements, interaction withcustomers and delivery mode (Spohrer and Maglio2005, Posignon et al. 2010, Wang 2011). However, inthe literature (Regan 1963, Rathmell 1966, Shostack1977, Zeithaml et al. 1988, Roth and Menon 2003),service characteristics are defined as intangibility,inseparability, heterogeneity and perishability (Regan1963, Rathmell 1966, Shostack 1977, Zeithaml et al.1988, Wang 2011). Chase (1978) studied the role ofcustomer contact in service provision (van Beuningenet al. 2011), suggesting that when there is less customercontact in the service system, then it is more likely tooperate at maximum efficiency, and vice versa. Finally,a service matrix was suggested by Schmenner (1986), inwhich the customer interaction and customisation wasassociated with the degree of labour intensity, givingrise to four classes of services: service factory, serviceshop, mass service and professional service. Otherdimensions were added by Huete and Roth (1988),who classified them into professional service, serviceshop and mass services, based on higher low peoplecontent and higher low numbers of customers pro-cessed. Finally, Johns (1999) was able to project fourdifferent conceptualisations of ‘service’ encompassingdenotation, connotation, metaphor and metonymy.Overall, service implies being in receipt of a set ofactions (Johns 1999) and processes (Gronroos 1988),for which one would otherwise have expended avoid-able effort to achieve.

Literature on service so far has shown its multi-dimensional nature (Gummesson 1993, Roth andMenor 2003, Posignon et al. 2010) which has beenused extensively, thus indicating its elusiveness (Johns1999). Thus, to understand service, one has to explorethe question of its tangibility (Gummesson 1993).Earlier conceptualisations of service (Regan 1963,Rathmell 1966) were made based on distinctionsbetween goods (tangible and measurable offering)

and service (intangible and un-measurable offering).However, over the years, perceptions of service havecontinued to change (Reichheld and Sasser 1990),leading to the blurring of distinctions between goodsand services. According to Gummesson (1993), thisblurring had occurred because it had been recognisedthat both goods and services have their own tangibleand intangible constituent elements.

The aforementioned literature highlights the impor-tance of customer involvement in service. Johnston andClark (2001) postulate that this involvement makesservice operations overall more complex thanmanufacturing operations, and they define three rolesfor the customer: service specifier, quality inspectorand co-producer. This idea was also complemented byParasuraman (2002) and Johnston and Jones (2004).The main message of this research is that customerinput has a direct effect on the provision of servicesand efficiency (van Beuningen et al. 2011). It has to benoted, however, that Information Systems andInformation Technology (IS/IT) is also essential tothe delivery of service based on the experience of thecustomers (Ojiako and Maguire 2008, Posignon et al.2010). IS/IT represents a dominant source of compet-itive advantage and a strategic tool (Ostrom et al.2010).1 For example, it plays a vital role in thefunctions of promotion, distribution, integration,management control and delivery of telecommunica-tion products and services (Hickey and Siegel 2008). Inthis specific sector, the use of IS/IT is increasinglyrequired as the main prerequisite to forming strategicalliances within the telecommunications industry(Millar and Audisio 2006), particularly in its supplychain. IS/IT also facilitates the co-creation of experi-ences with telecommunications customers (Varki andWong 2003).

Overall, it is important to highlight our recognitionthat a general theory on emerging conceptualisationsof service has been developed with, in some cases, well-defined questions, tools, methods and practical impli-cations for society (Spohrer et al. 2007). However,recognition of challenges associated with integratingthese varying aspects of service into a single andcoherent concept has led to some, such as Johns (1999),pointing out that because of conceptual ambiguity, thecontext within which ‘service’ is being used must bearticulated prior to, or at least, during use. Others(Batista et al. 2008, Lavikka et al. 2009) suggest thatactually what is required is a new conceptualisation ofservice which merges technology with an understand-ing of business processes, the company (Horn 2005)and human resources (Grandison and Thomas 2008).Under such intellection, IS/IT professionals(mainly systems analysts and designers rather

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than programmers) will take a lead in the articulationof the service experience (Fynes and Lally 2008). Thereality is that service is actually being conceptualisedin a much broader form (Spohrer and Maglio 2008)with a requirement to address interrelationships withdiverse and established fields (Chesbrough andSpohrer 2006, Patricio et al. 2011).

It is interesting to note that certain scholars(Hidaka 2006) have emphasised an additional focusfor the management of services by citing the applica-tion of scientific methods to problem-solving as its keycomponent. Either way, the conceptualisation of ser-vice remains a constantly evolving field of researchwith its scope expected to continue expanding throughindustry, academia and government collaboration(Maglio et al. 2006, Bitner and Brown 2008b).

The impact of this reality is that there is a need foran immediate re-evaluation by companies of theirinnovation agenda. This innovation agenda, necessaryfor the achievement of competitive advantage, mustfocus on enhancing service experiences of customers(Patricio et al. 2011), supported by an exploitation ofIS/IT. Moreover, one of the outcomes that haveresulted from scholarly work seeking to advance there-evaluation of the innovation agenda has been thepropagation of the service-dominant logic (SDL),which states the need for service to dominate theaforementioned areas of study (Paulson 2006,Ordanini and Parasuraman 2011). SDL (Vargo andLusch 2004) focuses on service exchange (as againstdelivery) for the purpose of the company, the market(customers) and the wider society. In effect, SDL isbased on the premise that markets are solely createdfor service exchange (Lusch et al. 2007, Ordanini andParasuraman 2011), and that the markets no longerfocus on the exchange of goods (the so-called goods-dominant logic). Instead, markets (solely created forthe exchange of service) are best served when servicedefinitions are jointly articulated by constituent partiesthat operate within the market (that is the concept ofco-creation); the major departure from earlier percep-tions being that SDL reinforced the notion thatmarkets would no longer see customers as one aspectof service (Ordanini and Parasuraman 2011). Instead,the customers became, in the words of Lusch et al.(2007), an operant resource that had the ability tocollaborate in the mutual creation of value with othermarket partners. To assess whether service has beensuccessfully created, one must assess whether theservice created value for all interested parties (Vargoand Lusch 2008, Lusch et al. 2010, Ordanini andParasuraman 2011). To support SDL, an organisationmust be able not only to redesign its entire structure(Lusch et al. 2007) but also successfully adopt

a radically new philosophical outlook to service.To support this process, it is not unreasonable toexpect that the organisation will undergo a completetransformation (strategy, identity, branding). Such atransformation is required because to effectively com-pete based on service will require changes within thecore of the organisation (staff, processes, IS/IT infra-structure). SDL includes nine foundations (Vargo andLusch 2004, Vargo and Lusch 2006, Ordanini andParasuraman 2011), of which the eighth (FP8),‘A service-centred view is customer oriented andrelational’, which refers to the centrality of thecustomer, forms the major foundation of this article.

It needs to be highlighted that SDL does have itscritics. For example, scholars such as Achrol andKotler (2006) point out that SDL has been unable topropose any new theory that is actionable. In fact, insome circles, it is being pointed out that within thetelecommunications industry it had long been recog-nised that service and not necessarily technologyrepresented the competitive driver.

This brief review of the literature has shown thelongstanding dominance of service in the economy andthe importance of research in the services sector.However, the contribution of services in the profits ofcompanies is low – in IBM, for instance, it is aboutone-third (Chase and Apte 2007). This discrepancy,despite the leverage of IS/IT as valuable enablers ofservice innovation and delivery (Ordanini andParasuraman 2011), can be justified by the fact thatservices generally tend not only to be more labour-intensive, but at the same time are less amenable toeconomies of scale (Chase and Apte 2007). Therelationship between service and productivity is dis-cussed in the following section.

3. Service productivity

Academic literature (for example, Sheehy and Schone2003, Verma et al. 2005) has discussed in detail thecommonly held belief that productivity in the servicesector is lower than in manufacturing. In many of thesediscussions, the researchers place a serious questionmark against this commonly held opinion; for instance,Nachum (1999) argues that at least part of thedisparity in productivity between services andmanufacturing is a statistical illusion resulting frominadequacy of existing data and techniques of mea-surement, whereas Blois (1984) argues that productiv-ity measurement itself has difficulties; and when itcomes to service operations, the difficulties are evenworse. Gummesson (1993) blames traditional measuresof productivity for being ‘provider productivity’ and

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‘internally oriented’ and thus not adequate for servicesthat are very much customer oriented.

In relation to the concerns about productivity inservices, the use of another term – that is servicity – wassuggested to denote the similar concept in services(Jones and Hall 1996). This was in fact a reaction tothe concept of industrialisation of service (product-lineapproach) as a solution to service productivity,suggested by Levitt (1972). It is, thus, not surprisingthat the field of service productivity has beensummarised as being in a mess (Adam and Gravesen1996). Johnston and Jones (2004) identify the uniquecharacteristics of service to be the main reasons behinddiscontinuance in service productivity. It has beennoted that the common denominator in the academicresearch into service productivity is the word ‘diffi-culty’ (Adam and Gravesen 1996).

When concerns of service productivity are dis-cussed amongst researchers, the issue of relationshipbetween productivity and quality comes to the fore.Debates range from studying the relationship betweenthe two as separate concepts to those who include theconcept of quality (or some of the aspects of quality) intheir definition of productivity. For example,Gummesson (1998) argues that productivity, qualityand profitability are a triplet and separating them willmake an unhappy family. He later introduces threeperspectives in organisations that determine the rela-tion between these three concepts, and proposes amodel in which both the customer’s and the provider’scontributions to productivity and quality are recog-nised as two sides of one concept, between which thereis interactive quality and productivity. In the samevein, further literature (e.g. Gronroos and Ojasalo2002) asserts that productivity and perceived qualityare inseparable phenomena. These studies furtherpostulate that the inputs to the service process consistof service provider and customer, and that the outputsconsist of quantity and quality. This points out that inservice, the client has the roles of both co-producer andcustomer. In a similar vein to Martin et al. (2001),Parasuraman (2002) develops a model in which acompany’s perspective of productivity is linked to thecustomer’s perspective of productivity. Service qualityis shown to be affected by inputs from both thecompany’s and the customer’s perspectives; and con-sequently to affect the output of both (Eisingerich andBell 2008).

However, despite the interest in service productivityand the apparent need to improve productivity inservices, very little work has been conducted in thisarea. The specific area may have enjoyed a number ofinteresting and thought-provoking contributions, but,as the literature (see Johnston and Jones 2004) asserts,

the current debate on service productivity is in itsinfancy and it is therefore essential to start from basics.These authors suggest that more research needs to beconducted in terms of the customer’s role in serviceproductivity. Johnston and Jones (2004) introduce thearea as one that has much potential for developmentand assert that one of their motivations in writing theirpaper is to encourage more research in the field.

4. Two case studies from the service sector

In this section, two case studies from the service sectorare presented. Both follow the interpretive paradigmand are based on qualitative data derived from in-depth interviews (Silverman 2001). Research (Felleret al. 2005, Cigolini and Rossi 2008, Wagner andLindemann 2008) has already demonstrated the wideuse of case interviews of selected organisations in theconduct of exploratory research. Data are collectedutilising case interviews (twelve) from which processmodelling is then undertaken. The cases were chosen toreflect service clusters as described by Schmenner(1986). In this vein, theoretical sampling, hence, wasused (Eisenhardt 1989, Eisenhardt and Graebner 2007)due to our aim for extension of theory. Two expertswere contacted from each sector; ‘experts’ here refersto high-level managers in a service sector with extensiveexperience of working in more than one organisationwithin that sector. The aspects discussed includedproductivity and quality trade-off (Gronroos 2000);productivity factors (Prokopenko 1996) and relatedproblems, approaches and measurement (e.g.Schmenner 1995). The interviews lasted for about2 hrs and data analysis followed data reduction, datadrawing conclusions and verification (Miles andHuberman 1994). In particular, the interviews weretranscribed ad verbatim and initial descriptive codeswere assigned to the transcripts; these were refined asthe analysis proceeded through reviewing again theinterview data, and interpretive codes were assigned,which were later refined into pattern codes – whichcomposed clusters – as the analysis proceeded.

4.1. Case study 1: understanding service productivityin the service sector

The interview results show that in department storesand fast-food outlets/chains, poor service was seen as afactor that reduces productivity. In both cases, wherethe customer serves themselves by browsing throughthe products, they are contributing to quality withoutan aspect of productivity being significantly affected.It can be the case that self-service in the fast-food

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market/industry actually increases productivity(Lovelock and Young 1979). Moreover, the resultsfrom the telecommunications (quality in the field andproductivity in the back office are separate butinterdependent), department stores (in which produc-tivity is more of a concern in the back office whilequality is very much related to delivery) and univer-sities (where – according to the two experts in thissector – quality is more important for researchactivities, while productivity is more important forteaching activities) suggest that productivity is inextri-cably related to quality.

The service organisations shift their focus betweenproductivity and quality. The factors behind this shiftcan be classified into competition, regulation andeconomic pressure. Under these pressures, Vuorinenet al. (1998) have highlighted the need to makeproductivity improvement choices between quality,volume and cost. The first condition to be consideredis whether the customer is seeking more quality, thuslocating the customer at the centre of service provision.In cases where more quality is not required, there is noneed to prioritise quality improvement. Another con-dition is whether the improvement is costly, since thefirst thing needed to be considered is whether it ispractically possible to decrease the cost without neg-atively affecting other areas of the business. Economiesof scale and market demands need to be met before apolicy of increasing the service provision (volume) isimplemented (van Beuningen et al. 2011).

The results of the interviews show that the studiedservices could be put into different clusters: factoryservices (fast-food), professional services (legal andconsultancy services) and changing services (telecom-munications, power utilities, banks, insurance services).

The rest of the services examined seemed to have amixture of features associated with two or all the above– University: Professional�Changing Services;Department Stores: Factory�Changing Services;Auto-Repair and Hotels: Factory�ProfessionalServices; Airlines: Factory�Professional�ChangingServices. Table 1 presents a summary of the overallfeatures of each of the clusters which followed theprocess by Miles and Huberman (1994) described in theprevious section.

Part of the aforementioned interview results wereincorporated into a framework to account for the roleof customer as co-producer in influencing customerproductivity (Gronroos and Ojasalo 2002,Parasuraman 2002). Such a framework can assist inmeasuring and studying service productivity in whichthe customer as co-producer is taken into account.

The framework (Figure 1) is a flow chart oftransitions and aims at depicting a simple bankingprocess of a customer paying money into an account.The customer arrives and waits to receive service. Theservice encounter begins by the customer specifyingthe service needed. The member of staff then swipes thecustomer’s card, the customer enters the pin numberand the worker then processes the transaction andgives the customer documented evidence of the trans-action. The customer collects the payment card anddocuments and leaves. This completes the encounter.

This framework depicts this process in three stagescompleted by the customer (icon of a standing man)and two completed by the member of staff. In three ofthe work centres, the source of input is the customer,and in two of them it is the member of staff whoprovides input to the task. By separating customer andservice provider, input analysis can be conducted on

Table 1. Identified clusters of services amongst the case studies.

Cluster Advantages Challenges

Factory environment StandardisationStandard customer expectationsEasy performance measurementLow appraisal and external costs

Human conflictsHigh prevention costLess customer focus in performancemeasurement

OverspecialisationLoyalty and motivationproblems

Professional environment Low prevention and appraisal costsEffective team workingGood human relations between back and

front officeHigh motivation of front line staff

Not defined customer expectationsDifficulties in measuring intangibilityInflexibility and scarcity of expertsLow motivation of supporting staffBalancing back and front office

Changing environment Technological advancesGrowthEasy to compete, for the newcomers

Marketing gapStaff difficulty (morale, loyalty)High prevention costRapid change of customer expectation

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the contribution of each of the sources of input(customer and staff) to the overall service productivity.A simplified framework of input and output is depictedin Figure 2.

Modelling a system which consists of multi-serverseach with a number of job descriptions by separatingthe tasks based on the source of input can be achallenging task. Ideally, when modelling servicedelivery, a user would only want one workstation todenote the encounter. The user may define whatpercentage of input is being provided by customersand staff (interviews). The user is able to alter theproportion of input provided and observe the results interms of overall productivity. The efficiency of cus-tomers and members of staff can also be adjusted(from a default 100%) to distinguish between lessefficient customers (normally the less experienced ones)and the efficient customers (normally the repeatcustomers).

The following section presents a prediction modelwhich adds to the debate on the customer as co-producer of service – and thus to the synchronicity ofservice processes.

4.2. Case study 2: prediction model and the notion ofcustomer as co-producer

Taking a customer-centred perspective to service pro-ductivity, a model that predicts the outcome of aspecific service and its contribution to productivitycannot be based solely on cost/benefit analyses ormethods that focus on technical/financial terms, but on

the customer as the fundamental stakeholder of the

service. To support our position, an interpretive stance

was adopted, in which the different views of customers

as stakeholders have to be incorporated into a single

analytical approach. Such a perspective transforms the

modeller from a passive and objective observer to an

organiser and facilitator of the prediction process.The complex nature of service (Maglio et al. 2006,

Spohrer et al. 2007) and service productivity requires

multiple criteria that need to be considered by using a

multi-objective multi-criteria (MOMC) approach

(Sarkis and Sundarraj 2003). The authors do not

suggest that the complex nature of Service must be

analysed using MOMC; on the contrary, they suggest

that MOMC can be used as a sense-making device in

understanding the complexities of service.The results from the final calculation should be

interpreted in the context of the service organisation in

which the particular service is to be adopted. The

model (Figure 2) suggests that service productivity is

dependent on the customer acting as co-producer of

the service (Parasuraman 2002). The ‘verdict’ of the

model depends on the estimation of the customer

regarding the ‘Value’ and ‘Risk’. ‘Value’ is equal to all

expected benefits, subtracting the cost of the service;

the ‘risk’ of the service is defined as the product of

probability of the event and the associated severity

(loss) (Stewart and Mohamed 2002). The assessments

of the model rely on the subjective assessment of value

and risk (Stewart and Mohamed 2002). The ‘Value’ of

a service, however, depends on the type of service

systems; for instance, the difference in context between

Figure 1. Customer co-production of service in the banking sector.

Figure 2. Simplified framework input and output.

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businesses and governmental agencies. These different

types of systems may interact to co-create value

ranging from monetary to reputation value (Spohrer

and Maglio 2008) and this is where Service Science

comes to the forefront; to enable the design and

implementation of services that deliver and co-create

value. In the proposed model, the benefits follow the

structure suggested by Anthony (1965) and are classi-

fied as strategic, tactical and operational, whereas the

costs are direct and indirect and classified as low,

medium or high.Figure 4 illustrates the hierarchical structure of our

model, consisting of five levels. At the last level (Level

5), a decision is made about the investment’s outcome,

which is analysed in the following levels in the axes of

the model; namely ‘Stakeholders’, ‘Value’ of the

investment and ‘Risk’. This separation between Value

and Risk follows the one that has been used in the

creation of similar models for IT/IS investments’

selection and decision making in the past (Stewart

and Mohamed 2002).At the fourth level (Level 4), different customer

perceptions of ‘Value’ and ‘Risk’ (Level 3) of the

service are presented, whereas in the second level

(Level 2), ‘Value’ and ‘Risk’ are decomposed.

Consequently, ‘Value’ is equal to the service benefits

(‘strategic’, ‘tactical’, ‘organisational’) against cost

(‘high’, ‘medium’ and ‘low’). ‘Risk’ is further brokendown into four categories of/for analysis (‘organisa-tional risk’, ‘definitional uncertainty risk’, ‘technicaluncertainty risk’ and ‘technology infrastructure risk’).The first level (Level 1) sets the criteria for thecategories of benefits and risks, which are scored bythe customer who acts as co-producer in the centre ofthe decision for service productivity. The modelconsiders the knowledge of the customer and theculture and technology existing within the service provi-sion (Spohrer and Maglio 2005, van Beuningen et al.2011).

The views of Mitchell et al. (1997) are adopted foridentifying and determining stakeholders, their salienceand power in decision making. Stakeholder theory hasbeen used in the past to support the process ofdetermining stakeholder requirements and managingstakeholder relations (Pardo and Scholl 2002, Chanet al. 2003). According to Mitchell et al. (1997), thesalience and therefore power of a stakeholder is basedon their possession of three characteristics: ‘Power’,‘Legitimacy’ and ‘Urgency’. We judge these particu-larly relevant to the contextual situation of the model,and based on the work of Mitchell et al. (1997),we argue that:

(1) ‘Power’ is the capability of a stakeholder to‘force’ the organisation to take a decision that

Figure 3. The application of the model.

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is in accordance with their demands, i.e. neg-

atively affecting the organisation’s public pro-

file. It is not only hierarchical – dependent on

the level of the organisation to which the

stakeholder belongs – but also influenced by

the organisation’s culture, i.e. the public ser-

vants’ participation in decision making.(2) ‘Legitimacy’ refers to the capacity of the

stakeholders’ demands to be regarded as

proper, according to the role they play in the

organisation.(3) ‘Urgency’ is the importance or criticality the

stakeholders themselves ascribe to their

demands and how quickly they wish to be

satisfied. It is usually correlated with the direct

stake they have in the investment, the level of

the organisation to which they belong and the

existing organisational culture.

This framework has been used in the past as a ‘tool’

for allocating stakeholder weights (Gago and Antolin

2004). We follow Page (2002) and assign the stake-

holders’ weights on a scale of 1 to 3 – a low to a high

salience stakeholder. Thus, a stakeholder can have a

weight of ‘1’ (low importance – possession of only one

attribute) to 3 (high importance – possession of three

attributes).To assign weights to the criteria of the model, they

are ranked in order of importance on a scale 0–1 and

then the assignment is carried out, so that the sum of all

weights that belong to the same level of the model is ‘1’

(Keeney and Raifa 1993). The assignment of weights to

benefits, costs and risks is determined by the customers

themselves. The business plan provides the justification

for the business value of the service. The benefits are

scored on an 8-point scale from ‘1’ (benefit of no

importance) to ‘8’ (very important benefit). For the

costs, the model uses a 3-point scale, from ‘1’ to ‘3’

(Stewart andMohammed 2002). Risk is measured on an

8-point scale, ranging from ‘1’ to ‘2’ for high risk, to ‘7’

to ‘8’ for no risk (Stewart and Mohammed 2002; see

Table A.1). After assigning weights to criteria and

receiving customer scores for each one, the elements

needed for defining the scores for the value and the risk

of the service are given as follows

SValue=Risk,Stakeholder ¼Xi

wCi� SCi

!� wstakeholder

ð1Þ

SValue=Risk,Stakeholder is the score for the value/risk as

given by each stakeholder, wCithe weight of each of the

i criteria, sCithe scores that each stakeholder gives for

each of the i criteria and wstakeholder the weight assigned

to each stakeholder.The final scores for the ‘Value’ and the ‘Risk’ of the

investment are calculated using the median function:

SValue, final ¼ medianðSValue,StakeholderÞ and

SValue, final ¼ medianðSValue,StakeholderÞð2Þ

Figure 4. The model as applied in the Greek context.

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The final scores of ‘Value’ (SValue, final) and ‘Risk’(SRisk, final) are characterised as ‘High’ or ‘Low’ and arein the organisational context, where the service issituated.

The specific model was tested in the context of theGreek public administration (Papadopoulos andKanellis 2007). The proposed service was anIS/IT-based application that enabled the system inte-gration of all 54 prefectures belonging to the HellenicMinistry of Transport and Communications(HMT&C). The study participants were selected fromthe prefectures (customers), and each was interviewedfor 25min on average. They were chosen depending ontheir direct/indirect involvement with the project andtheir seniority. The interview questions included firstthe validation of the model and then the intervieweesperceptions of the benefits, costs and risks associatedwith it. They were also asked to assess each of thebenefits, costs and risks. Data were analysed followingdata reduction, data display and conclusion-drawingand verification (Miles and Huberman 1994).

The detailed application of the model(Papadopoulos and Kanellis 2007) was a result ofcollaborative team reasoning and the interviewingof appropriate customers (Figure 4). The applicationof the model was done by describing the nature andextent of the initiative of various stakeholders acrossthe Prefectures and HMT&C (see Tables A.1–A.3),and clarifying the goals and benefits of the futureservice. These factors were classified using the modeland verified by the implementation team which hadsignificant experience acquired over the last 4 yearsthrough engagement in large service projects.Figures 5–7 explicate the strategic, tactical and oper-ational level benefits of the service as they emergedfrom the interviews with the aforementionedstakeholders.

The allocation of the weights reflected theinterviewees’ opinion that every benefit that stemsfrom the specific service is of strategic importance,with the cost as insignificant compared with theexpected benefits. The costs were overlooked whenstakeholders use the service as a panacea foralleviating the pains caused by bureaucracy in theHellenic Prefectures. In particular, in assigningweights to each of the ‘benefit’ categories (strategic –wSB, tactical – wTB, operational – wOB), the strategicweights (Figure 5) were the most important of all,providing better service to citizens through a re-engineering of processes and the shaping of policies.The tactical benefits (Figure 6) seem to be impor-tant, albeit clearly playing a supporting role to thestrategic ones, while operational benefits (Figure 7)are deemed of lower importance than both strategic

Figure 7. The operational benefits of the investment.

Figure 5. The strategic benefits of the investment.

Figure 6. The tactical benefits of the investment.

506 S. Maguire et al.

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and tactical, for the reason that the users themselvesshow no interest in any potential improvement, astenure results in a passive (at best) attitude towardsthe realisation of this kind of benefit. Thus,

wSB 4wTB 4wOB: ð3Þ

Moreover, ‘Organisational Risk’ was of low impor-tance, as it is the sole responsibility of the implementerwho will build the service – since the service would beoutsourced. ‘Definitional Uncertainty Risk’ was highenough, as during the service’s conceptualisation anddesign user requirements were not considered. Alldecisions were taken by HMT&C officials and high-level public sector managers who nonetheless weredetached from the context. ‘Technical UncertaintyRisk’ and ‘Technology Infrastructure Risk’ were ofequal and high importance reflecting concerns over theneed to ensure that the particular service must beintegrated with a large number of heterogeneous publicservices to deliver the required functionality. Thus,

wTUR ¼ wTIR 4wDUR 4wOR: ð4Þ

Weights were assigned to the stakeholders, depend-ing on their power, legitimacy and urgency (Mitchellet al. 1997). These weights reflected the context of theHellenic Public Sector. Low-level users possessed onlyone characteristic: that was power, which could beexpressed by an opposition to the service that percep-tibly does not meet their expectations. Their demandsmay have been legitimate but not justifiable whencompared to those of high-level public servants andpolitical leaders. For reasons of political exposure, thelatter were anxious about showing improvements in allaspects of the citizens’ transactions with governmentalorganisations and achievements for voting purposes.

High-level stakeholders possessed not only power,but urgency and legitimacy as well, as they could takeadvantage of their position in order to simply imposetheir decisions on subordinates. They might use thenew service as a means for political promotion andadvancement, trying at the same time to assure thepublic of their capability to think of and apply newtechnologies for public service. These stakeholders hadlegitimate demands – they were constantly underpressure from the political echelon to prepare thefieldwork so that the latter could support their ‘vote-hunting’ activities.

Moreover, the cost weightings reflected the periodin which the tendency was to care more aboutthe allocation of the European Union budget to thespecific projects, rather than the total cost of thesuggested service. However, the budget for the serviceis finite and in any case not unlimited. By applying the

weights and estimates (Table A.4), the final score of‘Value’ (SValue, final) was significantly higher than thatof ‘Risk’ (SRisk, final):

SValue, final 4SRisk, final ð5Þ

Therefore, the model uses the perceptions ofcustomers to estimate the impact of the proposedservice on productivity. Considering the role of thecustomer as service specifier, quality inspector andco-producer (Lovelock and Young 1979, Johnston andClark 2001) as a factor for predicting productivity,the model can be used as a sense-making tool from themanager’s side to study Value and Risk as enablers orinhibitors of that improved productivity. The mainmessage of the model is that the customer input has adirect effect on synchronising the service processes; thissynchronisation, we believe, is extremely important tothe reform of service processes and to their efficacy.This processes, however, cannot co-exist without beingaligned with each other, contributing thereby to theoverall service productivity (Parasuraman 2002,Johnston and Jones 2004).

5. Visioning Service Science from a productivity

perspective

It is generally accepted that the services sector tends tomake up over 75% of the value of economies of westernindustrialised nations. There is a growing realisationthat the emergent area of Service Science will have animportant role to play in the effectiveness and efficiencyof organisations around the world in the twenty-firstcentury. The authors believe that a major aspect ofService Science is the requirement to investigate whetherwe can use all the tools in our management armoury tomake this sector as efficient and effective as possible. Itis also believed that there is much that could be learnedfrom previous research and practice in other areas suchas production and manufacturing. From the cases, itcan be induced that the customer, as co-producer of aservice, has a two-fold contribution: (s)he acts as thelink between service processes and contributes towardsthe synchronicity of the service provided; but, also, (s)heacts as the link which aligns the processes to a commongoal, that is to the efficacy of the service provided.Synchronicity, alignment and efficacy of processesdefine the co-production of service and make explicitthe nature of interaction between customers and thecompanies but, more importantly, customers with theservice provided.

Nowhere is this potential more marked than in thearea of productivity. For centuries, ways of working inmanufacturing have been honed to ensure that the

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production process is efficient. The authors believe that

a focus on Service Science may help the service sector

to improve its own efficiency and effectiveness. This

will translate into improved customer service that

should also provide intangible benefits such as

improved well-being that lead to longer term benefits

for the service provider. It is argued that there are more

opportunities for real-time relationships to develop

that lead to increased productivity for organisations in

the service sector. This is only one aspect of an

interdisciplinary approach to the study and design of

service sector systems. Whatever business improve-

ments can be achieved, there should be a consequential

increase in the firms’ bottom-line profitability.

6. Conclusion

This research utilised two case studies to identify the

customer co-production of a service as a solution to the

difficulties in defining service productivity (Adam and

Gravesen 1996). The exploratory case/framework and

the subsequent presentation and application of a

model for service productivity prediction served to

link productivity to the customer’s perspective, offer-

ing insights to the organisational and customer aspects

of service, and service productivity and quality. Both

cases postulated that service represents dynamic

resource configurations which are co-created (Maglio

and Spohrer 2008), and hence asserted the view that

Service Science is capable of advancing the ability of

the researcher to grasp the underlying dynamics of

service creation and productivity.Future studies may focus on the role of various

service systems in creating value, as well as on the

application of engineering and scientific theories – for

instance, computational systems theories – to service

productivity. Future studies should also focus on ways

of integrating different disciplines and creating a

common language and a culture of multidisciplinary

collaboration. The authors believe it is the synchro-

nicity, alignment and efficacy of the overall process(es)

that will differentiate the winners and losers in the

twenty-first century.

Note

1. This position is taken for pure simplicity. It is imperativethat readers recognise that there is substantial researchwhich argues that the link between the IS/IT constructand firm performance is not direct but indirect; such thatIS/IT helps to improve the business process and todevelop capabilities but does not directly increaseperformance.

Notes on contributors

Stuart Maguire is the course directorof the MSc in Management at theManagement School, University ofSheffield, UK. He obtained his PhDin Systems from Lancaster University.He is a member of the BritishAcademy of Management and theUnited Kingdom Systems Society.His articles have been accepted and

published in journals such as International Journal of Servicesand Operations Management, Strategic Change, IMDS andProduction Planning and Control (Vol. 16, 2005).

Udechukwu (Udi) Ojiako is an asso-ciate professor of ProjectManagement at the University of theWitwatersrand, South Africa. Udiobtained his PhD in ProjectManagement from the University ofNorthumbria, Newcastle in 2005 as aBT-Sponsored Research Student. Hisarticles have been accepted and

published in journals such as Project Management Journal,International Journal of Operations and ProductionManagement, International Journal of Project Managementand the International Journal of Logistics Management.

Dr Thanos Papadopoulos is a seniorlecturer in Information Systems. Hejoined Hull University BusinessSchool in September 2011. Beforejoining Hull University, he was alecturer in Knowledge andInformation Systems Management atthe School of Management,University of Southampton, UK.

He holds a PhD from Warwick Business School, UK.He also holds a Diploma (Dipl-Eng, equivalent to MEng) inComputer Engineering and Informatics from the School ofEngineering of Patras University, Greece, and an MScin Information Systems from the Department ofInformatics of the Athens University of Economics andBusiness, Greece. He has published more than 30 papers ininternational journals and conferences, and has beenawarded the Best Paper Award in the 2007 InternationalConference on the Management of Healthcare and MedicalTechnology.

Farhad Shafti has a BSc and an MScin Industrial Engineering from(respectively) University of Scienceand Technology and University ofTarbiat Modarres in Iran and a PhDin Management Science fromStrathclyde. Before joiningStrathclyde, he was working in theNational Iranian ProductivityOrganisation, providing training and

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consultancy projects for the Iranian public and private sectorindustry. His work is focused on Service OperationsManagement and (conceptual) Modelling of ServiceSystems. His particular research interests within these areasare Typology of Services, Performance Measurement andEffects of Cultural Factors on Service Organisations.

Professor SC Lenny Koh, BEng(Hons), PhD, is an associate dean,chair professor in OperationsManagement, founder and directorof the Logistics and Supply ChainManagement (LSCM) ResearchCentre, and director of Faculty’sCentre for Energy, Environment andSustainability (CEES) at The

University of Sheffield Management School, UK. She is alsothe co-founder of Supply Chain Management andInformation Systems (SCMIS) Consortium, a global networkof leading academic and practitioners driving research andknowledge exchange on supply chain and informationsystems.

Dr Panagiotis Kanellis is currently anexecutive director with Ernst & Youngin Athens, Greece. Previous to that heheld senior positions both in theprivate and public sectors where heled large transformational pro-grammes. He was educated atWestern International University inBusiness Administration (BSc), at the

University of Ulster in Computing and Information Systems(Post-Graduate Diploma) and at Brunel University in DataCommunication Systems (MSc) and Information Systems(PhD). He has taught at the Athens University of Economicsand Business and is a research associate in the Department ofInformatics and Telecommunications at the University ofAthens. He has published more than 60 papers in peer-reviewed journals and international conferences. He is fellowof the British Computer Society and a chartered informationtechnology professional (FBCS CITP), a chartered engineer(CEng), a chartered scientist (CSci) and a certified informa-tion systems auditor (CISA).

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Table A.4. The application of the model in the Hellenic Public Sector.

Stakeholder type Value (score) Risk (score)

Users (prefectures) 12.56 –Directors (prefectures) 11.69 –Heads of prefectures 35.595 –Prefect 40.215 –Users (HMT&C) 14.28 –Directors (HMT&C) 14.035 –Head of HMT&C 39.585 –General secretary of HMT&C 42.21 –Project manager 44.265 28.5Technical project manager – 28.5

Notes: Using the median function, the final scores for ‘Value’ and ‘Risk’ of the investment are as follows:

SValue, final ¼ 35:595

and

SRisk, final ¼ 28:5

which means that : SValue, final 4SRisk, final

Appendix

Table A.1. Estimations for the benefits of the investment (scale 1–8).

Benefit (importance) Definition Score (1–8)

No importance The benefit has no importance for the stakeholder 1–2Low importance The benefit has low importance for the stakeholder 3–4Moderate importance The benefit has medium importance for the stakeholder 5–6High importance The benefit has high importance for the stakeholder 7–8

Table A.3. Estimations for the risks of the investment (scale 1–8).

Risk Definition Score (1–8)

No risk Pr (lowest possibility of failure, lowest severity of failure) 7–8Low risk Pr (low possibility of failure, low severity of failure) 5–6Moderate risk Pr (moderate possibility of failure, moderate severity of failure) 3–4High risk Pr (high possibility of failure, high severity of failure) 1–2

Table A.2. Estimations for the cost of the investment (scale 1–3).

Cost Definition Score (1–3)

Low cost Depending on the organisation and IT budget 3Medium cost Depending on the organisation and IT budget 2High cost Depending on the organisation and IT budget 1

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