Developing a decision support system for business model design

GENERAL RESEARCH

Developing a decision support system for businessmodel design

Dave Daas & Toine Hurkmans & Sietse Overbeek & Harry Bouwman

Received: 16 September 2011 /Accepted: 3 December 2012# Institute of Information Management, University of St. Gallen 2012

Abstract Software vendors increasingly offer web-basedservices (SaaS) at a subscription fee. Because they need anetwork of service partners to generate customer value, SaaSproviders have to focus on business model (BM) design. Inthis study, a decision support system (DSS) is developed tohelp SaaS in this process, based on a design approach con-sisting of a design process that is guided by various designmethods. The outcome of the design process is a computer-based tool that takes important BM evaluation criteria, such ascritical design issues and success factors, into account, byintegrating market analysis based on conjoint analysis, pro-viding alternative designs for business models involving com-panies working together in a value network offering e-services, and including a multi-criteria decision analysis,based on AHP, that can be used to select the business modelof the core services and identify the implications for thebusiness model of all the actors involved in the network.The DSS is discussed in an illustrative case. The executionof the design process provides insight into how BM designprocesses can be improved and formalized. The BM decision-making tool illustrates the importance of modularity, i.e. theBM-DSS can be divided into several independent sub-components, while at the same time maintaining its usefulnesswithin an organizational network context.

Keywords Business model . Design science . Decisionsupport system . SaaS

JEL classificationD 81 . L 84 . L 86 .M 15 . M 31

Introduction1

Due to the proliferation of service-oriented architectures, moreand more IT services are being transformed into e-services(Gordijn et al. 2006) that are provided over the Internet andthat facilitate commercial transactions. In today’s cyberspace,a huge variety of e-services is provided by, for example,Internet access providers, online content providers, onlinebrokers, online retailers and, more recently, cloud computingservice providers, for instance Software-as-a-Service (SaaS)providers (Armbrust et al. 2009).

Depending on their level of complexity, e-services caninvolve one or more service providers. More advanced e-services often use value networkswith several service partnersexchanging value, the ultimate aim being to provide a serviceto customers (Allee 2000; Bouwman et al. 2008). Value net-works become crucial in the creation of value (Amit and Zott2001) and are central to the business models (BM) of firms. ABM can be described as “the way a company or network ofcompanies aims to make money and create customer value”(Haaker et al. 2006, p. 646), and it can be defined as ablueprint describing the service definition and the intendedvalue for the target group, the sources of revenue, and pro-viding an architecture for the service delivery, including adescription of the resources required, and the organizationaland financial arrangements between the business actors in-volved, including a description of their roles and the division

1 We would like to thank the editors and the five reviewers for theirdetailled comments.

Responsible Editor: Hans-Dieter Zimmermann

D. Daas : S. Overbeek :H. BouwmanDelft University of Technology, Delft, The Netherlands

T. HurkmansExact International Development N.V, Delft, The Netherlands

H. Bouwman (*)Information and Communication Technology,Faculty of Technology, Policy and Management, Delft Universityof Technology, PO Box 5015, 2600 GA Delft, The Netherlandse-mail: [email protected]

Electron MarketsDOI 10.1007/s12525-012-0115-1

of costs and revenues among the business actors ( Bouwmanet al. 2008, p. 33). The key element of e-services is thecreation of sufficient customer value and network value, byleveraging the value network and generating sufficient valuefor all the service partners involved (Bouwman et al. 2008).When adopting an SaaS approach, which is a strategic choicein itself, software vendors need to rethink, introduce andimplement new alternative BMs for selling software packagesto customers, if they want to be successful (Gold et al. 2004).

Various researchers who have contributed to the area ofbusiness model design (e.g. Bouwman et al. 2008; Osterwalder2004; and Gordijn and Akkermans 2003) have concluded thatadditional tools are needed to support the design and evaluationof BM, which has led to the development of computer-basedtools, for instance the e3value editor (Gordijn and Akkermans2003), a BM design tool box (Faber and De Vos 2008), and thebusiness model modeling language (BM2L) (Osterwalder2004). The e3value editor allows BM designers to captureand evaluate BMs from a financial point of view, while theBM design tool box offers a set of soft and hard methods fordesigning BMs, and the BM2L allows users to capture a BMusing a formal markup language.

While these tools are useful as far as their intended purposeis concerned, wewould argue that their effectiveness is limitedwhen informed management decisions have to be made onalternative business models. Key elements of a business plan,such as a market analysis and a prediction of revenues andrisks (Sahlman 1997), are neglected by existing BM designtools: the e3value methodology lacks a market analysis com-ponent (Weigand et al. 2007), the BM tool box offers tools andmethods, but none are automated, and the BM2L lacks ana-lytical and management support tools (Osterwalder 2004).Furthermore, e3value and BM2L ignore the fact that, in mostcases, multiple stakeholders with multiple objectives are in-volved in the design and operation of e-services and BMs. Thetool proposed in this paper makes it possible both to evaluatealternative business models for the core services and to assessthe implications for the business models of individual struc-tural partners within the value network. The tool is part of a setof tools that has been developed within the BM STOF pro-gram (Bouwman et al. 2012), which offers tooling that isfocused on strategy changes and road maps, on the relation-ship between BM and KPIs, and on more a operational eval-uation of what the implications of strategic choices are. In thispaper, we discuss one of the tools that is developed within thislatter scope, focusing on implementing strategic choices andmaking it possible for managers to assess the implications ofstrategic decisions for alternative BMs.

This paper contributes to BM research, by identifying keyBM components (Belton and Stewart 2002), as well as todesign science research. BM design has its roots in designscience (Owen 1998) and information systems (Hevner et al.2004). According to Bouwman and De Reuver (2010), design

science involves the creation of new artifacts, such as e-services, and the design of the associated BMs.. The design ofnew e-services and business models may involve specific de-sign steps, such as those used in new service design (Johnson etal. 2000) and business model design (Bouwman et al. 2008).This paper shows that generic design processes (Verschuren andHartog 2005) can be combined with design methods to developa DSS for BM design. The design methods guide the varioussteps in the design process, as discussed in the case study below.

The BM-DSS that is developed can be described as “asystem that supports a single manager or a relatively smallgroup of managers working as a problem-solving team in thesolution of a semi-structured problem by providing informa-tion or making suggestions concerning specific decisions”(McLeod and Schell 2001). DSS can improve the efficiencyof the problem-solving process (Power 2002) and is especiallysuitable for solving unstructured problems, where stakeholdersfind it hard to agree on the best course of action, for instancewith regard to selecting the optimum business model. Becausevarious service partners are involved in the design of e-servicesand their BMs, a DSS can offer valuable support in BM design,making potential outcomes of alternative BMs explicit andhelping managers to implement strategic choices in alternativeBMs. Building DSS remains a complex analytical task (Shim etal. 2002). Managers expect DSS to be easy to use and to beavailable from their office, home environment and customerpremises. Because the aim of this paper is to illustrate thedesign process of a decision support system (DSS) that helpsmanagers make and evaluate business model design decisionsand designs, the focus is not on DSS in general, but on a DSSthat helps managers make BM design decisions. The focus onBM design implies that the DSS should take the interests of thevarious service partners in the value network into account. TheDSS should offer functionalities that facilitate trade-offs be-tween various businessmodels design issues, in different phasesof the business model design process and for multiple networkpartners, as discussed extensively in Bouwman et al. (2008).

The DSS discussed in this paper is innovative in five ways.First of all, the DSS provides support in all phases of the BMdecision-making process. Secondly, it incorporates marketanalysis capabilities, which implies that BM design activitiescan be based on actual empirical research and market data.Thirdly, it incorporates critical design issues and successfactors of BMs, which makes it especially suitable for devel-oping alternative business models. Fourthly, it incorporatesdecision analysis capabilities, which allows for a holisticcomparison of BMs of the e-service in question, as well asof individual structural partners in the value network. Andfinally, because the DSS uses a flexible spreadsheet-basedtechnology, it can be adapted quickly.

The structure of this paper follows a general design ap-proach, which is elaborated in section 2. In section 3, the BMissues with regard to SaaS providers are discussed, after which

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an illustrative case involving the e-service business models of aSaaS provider that is used in the DSS design process is intro-duced. In section 4, we discuss our design framework (seeFig. 1) in greater detail, following the design framework. Thedifferent goals of the SaaS provider and its partners are dis-cussed in relation to operational BMs and, the BMdesign spaceis outlined, after which the design requirements for the DSS aredescribed. The design requirements are then used to design theBM-DSS, and we demonstrate how the DSS can be used in thedesign and selection of BMs. Next, tests that can be performedto validate and verify the DSS are discussed. Finally, wepresent the managerial implications, conclusions and limita-tions of this study, and outline future areas of research.

Design approach

Within design science, a design approach conceptualizes howthe design methods that are used in a design process lead to adesired design artifact (Bouwman and De Reuver 2010;Verschuren and Hartog 2005). By definition, a design ap-proach contains generic knowledge, codified in formal theo-ries, that can be applied to design a class of artifacts, either inthe form of a physical system or of more intangible objects,such as a BM, a DSS or a DDS that is specifically gearedtowards the design and evaluation of Business Models. Thedesign process describes the various design stages of theartifact, while the design methods guide the completion ofthe various stages within a design process and indicates whichdesign issues need to be considered at the various stages.

Design process

This paper builds on the generic conceptual design frameworkdeveloped by Herder and Stikkelman (2004). Although that

framework was originally created for the development ofphysical systems, it can also be applied to the developmentof DSS and BMs, for two reasons: (1) the design frameworkexplicitly includes and recognizes the identification of stake-holder goals as an important step, which is also critical in thedesign of viable BMs for networked enterprises, and (2) thedesign framework explicitly includes the development of a testas an important step in selecting the best artifact, in our casethe most viable and feasible BM. The design framework(Fig. 1) has been extended in two areas: (1) an explicit formu-lation of DSS requirements, and (2) an explicit verification &validation step. Because the BM-DSS is regarded as a maindeliverable of the design process, it has to meet specific designrequirements (Sprague 1980). Furthermore, to generate confi-dence among the DSS users, it has to be validated and verified(Robinson 1997).

In this paper, the generic design framework is used topresent the main results of the different process steps lead-ing to the final BM-DSS design. Our aim is not to focus onthe execution of the process steps, but on the results anddesign methods. We provide a brief summary of the processsteps and use these steps as an outline for the remainder ofthe paper.

The first step of the design process involves the specifica-tion of the goals of the stakeholders, i.e. all the organizationalunits and/or external partners involved in the development ofthe services and the enabling BM, which can be divided intogoals to be optimized and constraints to be met, i.e. goals to beimposed on the artifact. The second step involves outlining thedesign space, i.e. the core elements of the e-services and theBM. The business needs “are assessed and evaluated withinthe context of organizational strategies, structure, culture andexisting business processes” (Hevner et al. 2004, p. 79). Thescope of the design space is based on these business needs. Inthe third step, the functional and non-functional design

(6) Selection ofthe business

model

(1) Determinegoals

(2) Developdesign space

(4) Develop DSS

(5) Execution ofthe DSS test

(1a) Determineobjectives

(1b) Determineconstraints Performance

indicators

Performanceindicators

Design variables

Tests

Testresults

Constraints

Objectives

Algorithms & heuristics

Design variables

Externalfactors

VerifiedDSS

Modelrequirements

(3) Develop DSSrequirements

Viable businessmodel

(7) Validationand Verification

of the DSS

Fig. 1 Generic conceptualdesign framework

Developing a DSS for Business Model Design

requirements are determined, in this paper by reviewing exist-ing literature and interviewing end-users. In the fourth step,the computer-based DSS is developed on the basis of thefunctional and non-functional requirements. The fifth stepinvolves the design of several BM alternatives, which weredeveloped using the market analysis tool. In the market anal-ysis part of the BM-DDS, information about consumer pref-erences with regard to different services or service bundles isanalyzed based on a conjoint analysis. In the BM design, a setof consumer choice rules is implemented to simulate consum-er behavior in order to make BM design choices related topricing and multi-service bundles elements. Several DSSfunctionalities are used to optimize the BM design objectives.This step and the algorithms for reservation price estimation(Kohli and Mahajan 1991; Jedidi and Zhang 2002) merit adiscussion in a separate paper (Daas 2010; Daas et al underreview). In the sixth step, the most viable and feasible businessmodel is selected using the decision analysis part of the DSS.In this part of the DSS, a multi-criteria decision analysistechnique (AHP) (Saaty 1986) is used to evaluate and com-pare the different business model designs). The seventh stepinvolves testing the DSS for several types of validity. The testsare developed on the basis of existing literature concerning thevalidation and verification of simulation models.

Design methods In each design step, several design methodsare used. These methods are based on existing studies onBM design (Bouwman et al. 2008; Osterwalder and Pigneur2010), and on DSSs (Sprague 1980; Alter 1976; Simon1977). For each design step, the design method is discussed,after which the findings from the case studies are presented.

For steps 1, 2, 5 and 6, several service-related and financialcritical success factors (CSFs), including the value proposi-tion, the quality of the service delivery and acceptable risksand profitability, and critical design issues (CDIs) are used,including pricing, customer retention, security and user profilemanagement, derived from Bouwman et al. (2008). CSFsrefer to “the limited number of areas in which satisfactoryresults will ensure that the businessmodel creates value for thecustomer and for the business network” (Bouwman et al.2008, p. 83; Bullen and Rockart 1981), while CDIs refer todesign variables that are “perceived to be (by practitioner and/or researcher) of eminent importance to the viability andsustainability of the business model under study” (Bouwmanet al. 2008, p. 72). For steps 3 and 4, managerial performanceobjectives are defined, as suggested by Sprague (1980), which“represent a set of capabilities which characterize the fullvalue of the DSS concept” (Sprague 1980, p. 12). The rele-vance of the performance criteria depends entirely on the task,the organizational environment and the decision-maker(s)involved (Sprague 1980). For step 7, the taxonomy developedby Sargent (2005) is used to distinguish different types ofvalidation and verification.

Empiricalvalidation Bouwman and De Reuver (2010) sug-gests that empirical research can be used in a design ap-proach. Every phase requires a specific evaluation approach.In this paper, the main approach to evaluating the designprocess involves the execution a real-life design case (seealso Menor et al. (2002)). As outlined in the next section, thecase involves a software company that required assistance indesigning its e-service (SaaS) offerings. Each step of thedesign process is evaluated.

Firstly, the CSFs-based goals are validated through qual-itative research involving existing BM frameworks. Variousauthors have proposed frameworks to measure BM successfactors (for an overview, see Bouwman and van den Ham2004). Furthermore, interviews with employees from thesoftware company were used to determine whether or notthe BM goals are relevant and whether goals have beenomitted inadvertently. Secondly, the CDIs-based designspace is also evaluated via interviews with employees.Thirdly, the DSS artifact designed to support certain perfor-mance objectives is assessed, based on participant observa-tion. The design requirements were based on the workingpractices of the employees, who were actively involved inthe design process.

Case study: a BM-DSS for a SaaS provider

The trend of providing SaaS will very likely continue in yearsto come, reinforced by Cloud concepts (IDC 2009). Vendorshost applications that can be accessed by customers via theWeb or the Cloud, in exchange for a recurring subscription fee(Dubey and Wagle 2007). These pricing models are generallyviewed as new ways of appropriating customer value into therevenue streams of service providers (Cusumano 2008). Assuch, the SaaS model is a revenue model that “measures theability of a firm to translate the value it offers to its customersinto money and therefore generate incoming revenue streams”(Osterwalder and Pigneur 2002, p. 7).

In accordance with the description of e-services, moreadvanced SaaS solutions involve a value network of busi-ness partners that is characterized by the outsourcing ofbusiness functions to external service providers under ser-vice level agreements, by application service provisioning(ASP) of complete software applications that can be com-posed of fine-grained to larger-grained standardized appli-cations and by the involvement of a large supplier networkto enable service delivery (Gold, et al 2004).

Following this line of reasoning, SaaS is more than a mererevenue model focused on the appropriation of customer valueby networked enterprises. Instead, SaaS can also be seen as abusiness model that focuses more on the creation of customervalue of specific services and service bundles, enabledthrough the exchange of value, information, resources and

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capabilities between different networked service partners. TheDSS is aimed at supporting the business model design deci-sions of an SaaS provider working together with multipleservice partners. In this paper, the SaaS provider is a multina-tional software company established in the 1980’s, operatingin 40 countries and servicing well over 100,000 customers.The software company in question offers integrated applica-tions like ERP, CRM, HRM, Corporate PerformanceManage-ment, project management and electronic work flow tools toits customers as a software package. Currently, the company’srevenues are based on maintenance, licenses and services. Ifthe company adopts SaaS as a distribution and pricing model,offering software online via a common platform, it has toreconsider its existing business model. SaaS offerings aregenerally suitable for SMEs with a low complexity and size.The company’s online SaaS platform is expected to createvalue for the low-end SME market. In this context, valuecreation lies in the fact that previously expensive functionsare brought within reach of low-end customers. The SaaSprovider is looking into the possibilities to combine corefunctionalities, for instance based on routine and process-based HRM solutions with services provided by HRM lessroutinized agencies. Alternative examples are bundling ERPsolutions with applications offered by the tax office, or comb-ing process systems with tools for project collaboration withina network environment. For instance, the introduction ofbookkeeping, accounting, invoicing, time registration, projectcollaboration, customer matching software over the platformis a first step in that direction. The stakeholders or rolesinvolved range from customers, i.e. SMEs and freelancers,via the SaaS provider that is core in the value network, toapplication providers, e.g. Microsoft, hosting providers, e.g.Rackspace, security providers, e.g. Verisign, and data networkproviders, but also banks, payment providers, accountants andbookkeepers, recruitment agencies, HRM support agencies,tax office, debt collectors, matching sites, etc. The involve-ment of actors will vary according to the specific e-servicebeing offered. Therefore, in the BM-DSS development, theSaaS provider initially plays a key role.

Application of the design approach

Business model design goals

Following the design process, the goals of stakeholders shouldbe specified, on the basis of which design options from thedesign space can be evaluated. In designing BMs, firmsshould aim for BM viability and sustainability (Bouwmanand van den Ham 2004). Generally speaking, there are twoviews on BM viability: a firm-centric view (e.g. Hamel 2000;Osterwalder and Pigneur 2010) and a network-centric view(Bouwman et al. 2008). In firm-centric BMs, the main aim is

to generate sufficient value for the customer and for the firm tomake the BM viable and sustainable, while network-centricBMs also recognize that network value, i.e. value created forall partners involved in the development, is a critical determi-nant of BM viability. Within network-centric BMs, the impor-tance of the structural service partners depends on (1) theextent to which they provide critical value elements, i.e.resources and capabilities, to the overall value propositionand whether these resources and capabilities are specific orgeneric to the value network (Bouwman et al. 2008), as wellas (2) the extent to which they are able to leverage their ownbusiness model. A network-centric view is increasingly im-portant when the number of structural partners in the networkincreases. Structural partners (Hawkins 2002), who providespecific and non-substitutable services, are core to the valuenetwork.

In BMs, service partners can have different goals. For thesake of brevity, only financial goals are summarized here,while objectives with regard to services, service bundling,customers, the value network, network governance etc. areleft out of the equation. On the basis of BM research, thefollowing (financial) goals/CSFs can be identified: profitmaximization, acceptable risks, and maximization of marketpenetration and revenue (Bouwman et al. 2008; Pateli andGiaglis 2003; Osterwalder 2004; Gordijn and Akkermans2003; Hamel 2000; Afuah and Tucci 2003; Baumol 1958;Baumol 1962, Amihud & Kamin, 1979).

The case study confirms the importance of these financialCSFs. From interviews with several of the software com-pany's managers, we were able to conclude that SaaS pro-viders in general focus on market penetration in order torealize economies of scale in the early stages of the marketlife cycle, after which the focus shifts towards revenuegeneration and profitability. In addition, we found that thefocus of the SaaS provider is shifting from a firm-centricview towards a network-centric view (see also Kaplan andSawhney 2000), which implies that the objectives of allstructural service partners need to be taken into account tosustain the BM. The objectives are used to evaluate thedesign options identified in the next section.

Business model design space

Following the design process, the business model designspace has to be specified. The business model design space,which consists of relevant design variables and options, wasexplored on the basis of a design method developed byBouwman et al. (2008) and then operationalized on the basisof employee interviews. For a BM design, several designvariables need to be specified, each with several options.The various combinations of design options for all thedesign variables represent a possible BM design. There arefour design variables: creating value elements, pricing (and


bundling), targeting and division of investments, costs andrevenues. Although other CDIs identified by Bouwman etal. (2008) were also evaluated, for the sake of brevity theyare not discussed here.

Creating value elements is closely related to the valueproposition of a service. Value elements can include thingslike speed of service delivery, personalization and trust. Aspecific combination of value elements determines the valueproposition (e.g. an e-service).

The pricing of the services influences their adoptionlevels. For a service to be adopted by potential customers,its perceived monetary value should at least be equal to itsprice. The chosen price level depends on several factors (seee.g. Tellis 1986). In this study, it is assumed that the pricelevel depends on whether a firm aims at maximizing marketpenetration, revenue or profitability under acceptable risks(see e.g. Stremersch and Tellis 2002). Another importantaspect of pricing is the billing method, which specifies thecharging unit (see e.g. Docters et al. 2003). There are manyoptions, including usage-based fees per invoice and flat-ratemonthly fees.

Bundling is often considered part of the pricing strategy.Guiltinan (1987, p. 74) defines bundling as “the practice ofmarketing two or more products and/or services in a single‘package’ for a special price”. Generally speaking, threebundling strategies can be distinguished (Penttinen 2004):(1) pure bundling, (2) mixed bundling, and (3) unbundling.Bundles can take on two forms (Stremersch and Tellis2002): (1) price bundles, whereby only a discount on acombined package is offered, and (2) product bundles,whereby integration between services is achieved. The bun-dle composition can consist of services that to some extentare competing (or reinforcing (Guiltinan 1987). Further-more, services can complement and supplement a core ser-vice (Telser 1979).

Targeting has to do with choosing profitable customersegments for a given service (Bouwman et al. 2008). In thisstudy, three target segment types are distinguished (Kotler2000): (1) homogenous (i.e. a low variance in willingness topay), (2) heterogeneous (i.e. a high variance in willingnessto pay), and (3) clustered preference target segments (i.e.natural groupings of willingness to pay). A service providercan apply price discrimination (e.g. bundling) to attractdifferent types of customers (Shapiro and Varian 1999).

The division of investments, costs and revenues, which isachieved by designing financial arrangements between theservice partners and the SaaS provider, directly relates to thedivision of risks among the partners involved. Althoughthere are many options, in this study, three types of financialarrangements are distinguished (Faber and De Vos 2008):(1) cost-based (i.e. partners are rewarded for the time andresources they contribute), (2) performance-based (i.e. part-ners are rewarded for meeting predefined goals) and (3)

revenue-based (i.e. partners are rewarded according toinvestments and risks).

For the case study, several interviews were conducted,resulting in the above-mentioned design variables and designoptions. While the design methods that were used imply thatother design variables and design options are also important,they were considered less critical by the representatives of theSaaS provider. From the interviews, it became clear that flat-rate billing methods is the most relevant one for the SaaSprovider. Furthermore, the interviews confirmed that the DSShad to help determine whether a (new) service should be soldseparately, as a bundle with services or both. The SaaS pro-vider considered 10 services to be relevant. For the sake ofbrevity, only three of them are discussed in the remainder ofthis paper. The assumption is that the SaaS provider is design-ing a BM for an invoicing service offering digital and postalinvoicing functionalities to freelancers. There are two existingservice offerings: a time registration service, which free-lancers can use to log their billable hours, and an expenseregistration service, which freelancers can use to log theirbusiness expenses.

The design variables for the BM provide the parametersthat need to be implemented in the DSS. In the next section,the specific design requirements for the DSS are identified.

DSS requirements

Next, the functional and non-functional requirements for theDSS were specified on the basis of DSS-design methods.Subsequently, the generic operations of a DSS, the types ofDSS and the performance objectives of a DSS were ex-plored, on the basis of which the design requirements werespecified.

DSS type DSSs can be classified based on their degree ofproblem-solving support, which refers to “the degree towhich the system’s output could directly determine thedecision” (Alter 1976, p. 3). Alter (1976) distinguishes sixgeneric operations, i.e. retrieving a single item of informa-tion; providing a mechanism for ad hoc data analysis; pro-viding pre-specified aggregations of data in the form ofreports; estimating the consequences of proposed decisions;proposing decisions; and making decisions. The genericoperations extend along a single spectrum, from purelydata-oriented DSSs to purely model-oriented DSSs.

Based on interviews with end-users, generic operations tobe provided by the DSS were identified, as was the level ofmanagement support. The interviews made it clear that mostmanagers wanted to estimate the consequences of specificalternative BM design choices. The possibility to run alter-native simulations based on marketing intelligence was andstill is highly relevant to managers in the case organization.The DSS should support, rather than replace, the decisions

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made during the BM design process. A model-driven DSSshould be developed (Holsapple and Whinston 1996). Amodel-driven DSS offers functionalities for statistical, opti-mization and simulation modelling, using data and parame-ters provided by the users to support the decision–makingprocess. Spreadsheet-based technology (e.g. Microsoft Ex-cel) is a major and popular enabling technology for model-driven DSS. In this case, there are two reasons for using thistechnology (Power and Sharda 2007). Firstly, spreadsheetsare especially suitable for building a DSS consisting ofsmaller sub-models, for instance for each CDI. Secondly,spreadsheets are especially suitable in situations where end-users help develop and extend the DSS with new or im-proved functionalities.

Performance objectives DSSs can have several perfor-mance objectives, which are achieved by specifying andimplementing functional and non-functional design require-ments. Sprague (1980, pp. 12–14) outlines six performanceobjectives to evaluate the effectiveness of DSSs, which areoutlined in the first column of Table 1. One of the perfor-mance objectives is that the DSS should support all thephases of the BM design decision-making process. Probablythe most widely used management decision-making processcategorization was developed by Simon (1977), who distin-guishes three phases:

Intelligence the problem or opportunity is identified, i.e.identification of profitable market segments; Design: sever-al solutions are designed to benefit from the opportunity;and Choice: the ‘best’ solution is selected.

For the case study, it was determined which of the perfor-mance objectives were relevant. Interviews with end-usersindicated that the DSS should provide the functionalitiesmanagers or users can use in the various BM decision-making steps. Furthermore, the functionalities should providesupport in various types of situations, at different levels withinthe organization involved in decision-making on BMs andtheir components in a transparent and legitimate way to sup-port the efficiency of the decision-making process. Finally,end-users emphasized that the functionalities should beeasy to use, adaptable and extendable. The second column ofTable 1 provides an overview of the importance of thevarious performance objectives in the area of business modeldesign.

Design requirements To develop the spreadsheet-basedDSS, the functional and non-functional requirements shouldbe determined. The former requirements relate to whichfunctionalities are offered, while the latter relate to howthe functionalities are offered. The design requirementsshould directly relate to and hence support the performanceobjectives. The functional requirements relate directly to the

performance objectives PR1-PR5 (see Table 1), while thenon-functional requirements relate to performance objectivePR6.

For the case study, the techniques and methodologies thatshould be implemented in the DSS (functional requirements)were specified on the basis of interviews with end-users and aliterature review. The first column of Table 2 provides anoverview of the design requirements. Specifically, the factthat empirical data (intelligence) could be incorporated andthat simulations (what-if analysis) could be executed inassessing alternative BMs was important.

Once the design requirements were specified, the next stepwas to determine how the functionalities should be imple-mented in the DSS. This step was carried out via a literaturereview and expert interviews, resulting in a set of DSSfunctionalities.

The second column of Table 2 provides an overview anddescription of the functionalities. Conjoint analysis was se-lected for its ability to estimate people's willingness to pay fordifferent bundle compositions. What-if analysis, sensitivityanalysis and optimization functionalities were selected fortheir ability to support in making design choices under uncer-tain and risky circumstances. Analytical hierarchy process(AHP) was selected to structure the decision-making problemand identify the optimal BM design. This multi-criteria deci-sion analysis (MCDA) technique provides a high level ofprocedural efficiency in guiding the BM selection process(Simons and Wiegel 2008).

The decision support system for BM design

Conceptual design To achieve modularity of the DSS, thefunctionalities were divided into three parts: (1) the marketanalysis, (2) the business model design and (3) the decisionanalysis. An extra configuration part was added, which canbe used by the DSS builder to configure and prepare theDSS for use. The models for each of these parts can bedivided into several sub-models, each dealing with specificcritical design issues (CDIs), that go beyond the financialCDIs discussed in this paper. Figure 2 shows the conceptualdesign of the DSS. Each worksheet should provide a dash-board, including design parameters and evaluation indica-tors tailored to a particular decision-maker within businessunits of the Saas providers or within the organization of astructural partner.

DSS implementation For the sake of brevity, a limited num-ber of worksheets is discussed. For instance, Fig. 3 showsthe service bundling dashboard, which has been dividedinto three sections: (1) the design parameters, (2) the deci-sion-making criteria and (3) the evaluation criteria section.The design parameters are used to specify different design


options for the CDIs. In this example, the design parametersare used to specify bundles consisting of a maximum of threeservices, i.e. invoicing, time-sheets and expense accounts.Also, in case a subscription is conditional on another subscrip-tion, the user is able to specify conditional offerings. Further-more, the user is able to specify segments at which the offeringis targeted. Finally, the user can specify possible bundle dis-counts, as compared to the individual component prices. Thedecision-making criteria assist users in their design decisions(e.g. the correlations between the reservation prices of thebundled offerings and additional services provide some guid-ance in specifying bundles). The evaluation criteria show themost important figures for the firm’s objectives, i.e. marketpenetration, revenues and profitability.

The service pricing dashboard (Fig. 4) follows the samestructure of design parameters, decision-making and evalu-ation criteria. For the decision-making criteria, sensitivityanalysis tables are used. The tables can guide the decision-maker in reducing or increasing some of the service prices,or to see if consumers within certain segments are sensitiveto small changes in price, based on data as collected in themarket analysis part of the DSS.

The market segmentation dashboard (Fig. 5) shows howwhat-if analysis can be applied to test the robustness of aBM under different economic conditions. The decision-maker performs simulations for different BM-scenariosbased on different growth levels.

The decision analysis dashboard (Fig. 6) shows how aBM selection decision can be structured on the basis of ahierarchy of stakeholder objectives. The figure provides anexample involving the service provider and one service

partner. For each of the stakeholders, four objectives arerelevant. However, the importance of these objectives variesfor each stakeholder. In this case, three BM designs arecompared from the SaaS provider's point of view. The toolpresented here can be used by every individual structuralpartner to assess the common as well as the individualbusiness model.

Design 3 receives the highest share-of-preference seenfrom the perspective of the SaaS provider. To use the decisionanalysis method, the decision-maker should first determinethe importance of each actor to the overall BM viability.Secondly, the decision-maker, in collaboration with the ser-vice partners in question, should determine the objectives foreach stakeholder, which can be different from the core serviceprovider objectives Next, the results of the BM design can becompared directly with alternative BM designs. In Fig. 6, theexample is provided from the perspective of the SaaS providerand one partner only for the common BM, but both theperspective as well as the number and importance of partnerscan be changed

Business model design and selection

In this section, we describe the use of the BM-DSS withinan organizational and networked context and reflect on howthe DSS supports the performance objectives. Firstly, it isexplained how organizational departments and structuralpartners are involved in the BM design process. Secondly,the results that can be obtained from the design and selectionsteps are demonstrated.

Table 1 Performance objectives for DSSs

Performance objectives Importance for business model design

PR1. A DSS should provide decision-makingsupport, with an emphasis on semi-structuredand unstructured decisions.

Managers from different departments should be able to testdifferent business model design choices. The output of theDSS should help in reaching an agreement on the bestcourses of action.

PR1. A DSS should provide decision-making supportfor managers at all levels, assisting in the integrationbetween the levels whenever appropriate.

The design and selection of business model components take placein different departments within the organization. The functionalitiesof the DSS should support different kinds of decisions.

PR3. A DSS should support decisions that areinterdependent as well as those that are independent.

In business model design, different decision-makers from differentdepartments are involved. Business model design decisionsare generally negotiated. However, the ultimate authority with regardto business model selection lies with the higher level managers.

PR4. A DSS should support all phases of thedecision-making process.

Business model design involves activities regarding market andopportunity analysis (intelligence phase), the actual business modeldesign (design phase) and business model selection (choice phase).

PR5. A DSS should support a variety of decision-making processes, but not be dependent on any one.

Business model design is hardly a linear process. The DSS should providethe functionalities that can be deployed in any sequence of design steps.

PR6. A DSS should be easy to use. Managers are not necessarily experienced users of computer systems.To support the organizational adoption of the DSS, it should be user-friendly.Furthermore, it should be flexible enough to include different business modeldesign options and be able to include other business model design issues.

D. Daas et al.

Tab

le2

Designrequ

irem

entsfortheDDS

Phase

Designrequ

irem

ents

Designissue

Descriptio

nof

implem

entedfunctio

nality

Fun

ctionalrequ

irem

ents

Intelligence

-Service

andbu

ndle

preference

estim

ation:

Con

jointan

alysis(JedidiandZhang

2002

)Creatingvalueelem

entsan

dtargeting

Conjointanalysisisatrade-offmethodwhich

canindirectly

measure

consum

erpreference

forservices

-Calculatio

nof

peop

le'swillingn

essto

pay:

Reservatio

npricecalculation(K

otler20

00)

The

reservationpriceisameasure

forthewillingn

essto

payfor

aservice.Itisthepriceforwhich

theconsum

erisindifferentin

subscribingto

aserviceor

not.

Design

-Sim

ulationsupp

ort:Wha

t-ifan

dsensitivity

analysis(Saltelli

etal.,20

04)

Servicepricing,

bund

lecompo

sitio

n,bu

ndle

pricingan

dfin

ancial

arrang

ements

(coststructure)

What-ifanalysisisused

totestseveralenvironm

entalscenarios

with

differentsetsof

variables.Sensitiv

ityanalysisisused

tostud

yho

wun

certaintyin

theou

tput

(aroun

dthemarketpenetration,

revenu

eandprofitability)

ofamod

elcanbe

appo

rtionedto

uncertaintysources

inthemod

elinpu

t.

-Optim

izationsupp

ort:So

lver

functio

nality

(Bou

wman

&DeReuver,20

10;Strem

ersch

andTellis

2002

)

The

MicrosoftExcel

solver

isused

toop

timize(m

axim

izeor

minim

ize)

certainindicators,basedon

varyingasetof

parameters.

Cho

ice

-Decisionanalysissupp

ort:Ana

lytic

Hierarchy

Process

(Saaty

1986)

Businessmod

elselection

AHPisamulti-criteriadecision

analysismetho

dthat

structures

adecision

prob

lem

into

ahierarchyof

parent

andchild

objectives.Based

onpair-w

isecomparisons

oftheevaluatio

ncriteriaregardingtheirim

portance

andof

thescores

ofeach

design

onthecriteriathepreference

shares

arecalculated.

Non

-fun

ctionalrequ

irem

ents

-Mod

ularity

(Nuteet

al.,20

05)

Mod

ularity

refers

tothedecompo

sitio

nof

thebu

siness

mod

elandDSS

into

distinct

business

mod

elcompo

nentsanduser

interfaces.Several

sub-mod

elswith

intheDSSshou

ldcorrespo

ndto

thedesign

issues

ofthebu

siness

mod

el.


Business model design within an organizational and/ornetworked context The case study shows that a variety oforganizational departments of the core services provider andits structural partners are involved in designing BMs. Thesedepartments may or may not work closely together withregard to the CDIs. The interviews indicate that, in practice,the BM design process is rarely linear in nature. Often,several iterations take place (Faber and De Vos 2008) andthe type of service may determine which departments orstructural partners are involved in designing a BM. However,some general remarks can be made with regard to what kindsof departments generally deal with which issues. In the case ofthe SaaS provider, the following departments were involved.Marketing intelligence department: this department analyzesmarket opportunities, segment growth levels, etc., and con-ducts customer preference studies.Marketing department: thisdepartment studies customer needs and how they can be met.Hence, this department is concerned with creating value ele-ments and service bundling Sales department: this departmentis involved in the pricing of different offerings. Product man-agement/development department: this department is ofteninvolved in the development and maintenance of services

together with structural partners. Hence, this department gen-erally possesses information regarding the cost structure andcan specify the financial arrangements. And finally, the man-agers who ultimately decide whether or not to implement aBM, whether changes should be made or whether the projectshould be aborted. The management board will ultimatelymake decisions regarding multiple BM designs. The market-ing department and the product management department workclosely together with the relevant departments of structuralpartners. Depending on the type of relations between structur-al partners, representatives of the board of the partners canalso be involved and use their own version of the BM designtool, being aware that, in principal, the joint BM should beviable and feasible for the SaaS and the structural partners, aswell as for the individual partners within the networked envi-ronment. In this sense, we can say that there is a multi-levelproblem involved, i.e. overarching BM and business modelsof structural partners that all have to be feasible and viable.

The division of the BM-DSS into different components andworksheets supports four of the six performance objectives.Separating intelligence, design and choice allows the DSS tobe used at different levels in the organization and in the

BM DSS

Configurationmodule

Market Analysismodule

BM design module

Decision Analysis module (figure 6)

DssConfigurationworksheet

Conjoint Analysis

worksheet

ReservationPrice

worksheet

Bundling worksheet(figure 3)

Pricingworksheet(figure 4)

MarketSegmentation

Worksheet(figure 5)

Division of costs

AHPworksheet

Overview worksheet

Fig. 2 Conceptual designof DSS

Fig. 3 Service bundling dashboard

D. Daas et al.

networked environment. The decision analysis can be used bythe board, while other departments can be involved in intelli-gence and design. The board most probably consists of thecore provider and its structural partners. For each CDI, a rangeof decision-making criteria is provided, allowing designers towork independently on a specific CDI. In this paper, the corefocus is on market segmentation (bundling) and financials(price, costs, and revenues).. However, some common evalu-ation criteria are also provided, which in turn allow designersto collaborate. The division of the DSS into several compo-nents supports decision-makers in all phases of the decision-making process. The division allows users to deal with CDIsin different sequences, which means that a variety of decision-making processes is supported.

Design and selection For the case study, several BM designswere created using the BM-decision tool. The outcomes arepresented in Table 3 and Fig. 6. The case study shows that theDSS is effective in optimizing business model design fordifferent objectives (i.e. profit, revenue and market penetra-tion), while dealing with different objectives with differentlevels of importance. The objective importance scores for theservice provider may, for instance, be determined by theboard, while the objective’s importance, as far as the servicepartners are concerned, may be determined on the basis ofnegotiations between the service provider and service partner(s). Dealing with risk and uncertainty becomes easier. Forinstance, in the illustrative case, an additional risk criterionwas added to take the differences in uncertainty and risks

Fig. 4 Service pricing dashboard

Fig. 5 Market segmentation dashboard


resulting from different financial arrangements into account.The scores regarding the risk criterion were determined on thebasis of a qualitative assessment of the financial arrangements.For the lowest level criteria, the design evaluation scores arecalculated, after which the different designs are ranked, help-ing decision-makers to choose the best available BM.

Validation and verification

For the case study, the DSS was tested on four types ofvalidity (Sargent 2005). Firstly, data validity was tested viaconjoint analysis, which was used to gather the data based onwhich decisions are made with regard to bundling and pricing,and combined with predictive validity tests (see e.g. Greenand Helsen 1989; Huber et al. 1993; Orme et al. 1997). Thesetests helped determine the extent to which the conjoint anal-ysis data can correctly predict consumer choice.

Next, conceptual validity was tested on the basis of facevalidity (Sargent 2005). Each sub-model of the DSS wasexamined by employees and structural partners to determinewhether the implementation is suitable for its intended pur-pose. It became clear that usability depends on the view of thevarious stakeholders, which underlines the need for adaptabil-ity when implementing the DSS within an organizational andnetworked context. Furthermore, the goals and design spaceformulation steps turned out to be critical in terms of guaran-teeing the conceptual validity of the DSS. These steps deter-mine the decision makers' objectives in using the BM-DSS.

To test the DSS for its operational validity, several tests canbe used (Sargent 2005), including graphical comparison ofdata simulated and real data, confidence interval tests andhypothesis tests. The first and second types of tests requirereal world data (i.e. sales figures) of the services involved,

which were not available at the time of this study, becauseboth the service and BM were newly designed. To buildconfidence, it is possible to compare simulated results withactual sales figures. However, interviews with stakeholdersindicated that decision-makers are often interested in simulat-ing the direction of change, which implies that hypothesistesting can be used to test the operational validity of a BM.Based on existing literature on segmentation, bundling andpricing (e.g. Stremersch and Tellis 2002), several hypotheseswere formulated, which were used to determine whether ornot the BM-DSS resulted in the anticipated change and hencewhether or not the DSS was valid with regard to specificaspects. It turned out that the DSS showed the expectedbehavior for different hypotheses (Daas 2010).

Finally, the model was verified. The DSS was tested formechanical errors by documenting important simulation func-tions regarding the estimation of the reservation price, simula-tion of the consumer choice and implementation of thedecision analysis technique. The conceptual formulas, whichwere based on existing literature, were compared to actualworksheet formulas to verify the correct implementation.To test the DSS for logical errors, two methods were used(Ayalew 2001): (1) symbolic testing and (2) interval testing. Inthe case of symbolic testing, symbolic values instead of actualvalues are used as input. If the formulas yield correct outputsfor the symbolic values, it is assumed that they will also bevalid for other input variables. In the case of interval testing,intermediate variables are used to narrow down computedintervals. Several tests were developed to verify different partsof the model, including: comparison of the results from theconjoint analysis conducted by the DSS and an alternativestatistical program; comparison of calculated and expectedconsumer choice figures for different sales collaboration fac-tors; comparison of manually calculated market size and

Fig. 6 Decision analysis dashboard

D. Daas et al.

number of customers with the output generated by simulationsmaking use of the BM- DSS; performing extreme value anal-ysis using symbolic pricing values by comparing differentestimated choice distributions, as calculated using the BM-DSS with their expected choice distributions; and comparisonof estimated ‘eigenvalues’ for AHP method with expectedfigures based on a known case study.

Conclusions

This paper illustrates how a design approach can be used todevelop a BM decision tool to be implemented within asetting in which multiple structural partners are involved.The number of available Business Model tools is limited.This paper contributes to BM research by moving awayfrom brainstorm tools to more formalized tools, enablingdecision-makers in networked environments to assess, basedon market intelligence and the use of critical success factorsand design issues to look into alternative business models.The tool helps analyze alternative BM solutions. We alsocontribute to design science literature. In the design ap-proach, business model design methods are used to designa specific artifact. We used theories related to service bun-dling and price optimization. The use of design methodsmaking use of relevant theories was found to be important inguiding the design process. The specification of the goals onthe basis of Business Model critical success factors, theoutlining of the design space on the basis of Business Modelcritical design issues and the requirements engineering ofthe BM-DSS on the basis of performance objectives all havea direct impact on the actual design of the business modeldecision tool. The performance objectives were all found tobe critical with regard to supporting BM design within anorganizational context, with multiple users at different levelswithin the organization and with multiple structural partners.Furthermore, different departments and structural partnersmay use different design processes, which underlines theimportance of DSS modularity.

The DSS has several advantages and disadvantages. Firstly,it offers support tools in all the phases of the decision-makingprocess, while other tools focus only on capturing or design-ing generic and holistic business models. However, this alsomeans that more stringent information requirements apply t(e.g. the need to gather market intelligence for instance aboutreservation prices). Secondly, the DSS incorporates marketanalysis capabilities, which means BM design activities canbe based on real data. However, this also means that a marketanalysis has to be carried out prior to designing a businessmodel. Thirdly, the DSS divides BM design into particulardesign issues, which makes it especially suitable to be imple-mented within organizational contexts. However, this requiresclose coordination between multiple users and decision-T

able

3DSSou

tcom

esforthreebu

siness

mod

eldesign

s

Bundling

Pricing

Financial

arrangem

ents

Subscriptions

for3services

Revenue

for3

services

Revenue

partner

forinvoicing

Profit/m

arginfor

3services

Profitspartner/margin

forInvoicing

Unbundling*

Service

prices:10,4and4.

Revenue-based:sharingof

10%

oftherevenues

with

the

Invoicingpartner

10,843

1,583,221

331,003

325,445/9.8%

133,865/40.4

%12,780

614,761

13,167

1,112,050

36,790

3,310,032

Purebundlin

g*Bundleprice:

18Perform

ance-based:Monthly

compensationof

5,000

12,005

1,975,130

300,000

698,646/19.7

%81,740/27.2%

12,005

790,051

12,005

790,051

36,015

3,555,232

Mixed

bundlin

g*Service

prices:11,4.50,6.50

Cost-based:

Com

pensationof

0.90

persubscriptio

npermonth

10,844

1,845,088

253,463

570,337/14.7

%56,325/22.2%

Bundleprice:

18.70

14,716

955,412

10,456

1,069,921

36,016

3,870,421


makers. Fourthly, the DSS incorporates decision analysis ca-pabilities, which makes it possible to compare business mod-els from different stakeholder perspectives. However, thisassumes that the decision-making problem can be structuredhierarchically and that the importance of each objective ofeach structural partner can be determined. Finally, the DSSuses a flexible spreadsheet-based technology, which can alsobe made available as a web-tool and which makes it possibleto adapt the DSS quickly to specific situations. The designprocess includes an explicit validation and verification step.While a complete validation of the DSS can only take placeover time, as actual services are designed with the use of theDSS, we validated and verified the DSS in several tests, whichare critical in building confidence in the DSS’s outcomes. Inpractice, the tool was used a number of times within the caseorganization. However, due to the economic crisis and therestructuring of the company, priorities were redefined. Re-cently, however, SaaS business models are being reassessedand it is expected that they will play a role in recapturingdeveloping regional SaaS markets.

There are several managerial implications regarding thedevelopment and use of a DSS for BM design. Firstly, thedevelopment of support tools for BM design is an on-goingprocess. It is a tool that helps managers to go beyond creativebrainstorm sessions and make genuinely rational choices be-tween business models, based on clear trade-offs betweencritical design issues and their outcomes in terms of alternativebusiness models. Secondly, developers should constantly askthemselves whether a BM-DSS has the potential to improvedecision-making regarding specific design issues. This willgenerally be done in an adaptive manner (Keen 1980). Thisinvolves finding a trade-off between the quality of the resultsand the effort required to complete a design step. In a com-mercial setting, extensive validation may not be feasible.Thirdly, managers using the BM-DSS should also focus onachieving procedural efficiency. They should use a BMDSS tosupport the decision process and increasing the proceduralefficiency, not to replace it. Our case study provides anexample of how managers can challenge their ‘gut feeling’and help improve the quality and efficiency of decision-making processes.

Several limitations apply to this study. Firstly, the designproject was carried out in collaboration with a software vendorwanting to offer more SaaS solutions. Hence, the DSS islimited to design issues that were relevant to this particularsoftware vendor. Furthermore, it must be recognized that notall design issues can be supported via a DSS, because the useof a spreadsheet-based DSS assumes that the issues involvedare quantifiable. Other important intangible aspects, for in-stance trust and network governance, are not included. Sec-ondly, the research methods that were used to evaluate theoutcomes of the various design steps were all qualitative innature and applied retrospectively. More quantitative and

empirical methods can improve the validation and formaliza-tion of the design approach, as can increasing the number ofcase studies (Menor et al. 2002). Thirdly, the design processdiscussed in this paper adopts a linear approach, while, inpractice, the process involved tends to be iterative in nature,which plays a central role in the evolutionary and adaptivedesign approaches to DSS. Future studies may benefit fromincluding empirical research methods (Bouwman and DeReuver 2010). Furthermore, research may be directed at imple-menting BM design DSSs in organizational contexts, usinginsights from evolutionary and adaptive design approaches toimprove the design processes involved.

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Developing a decision support system for business model design