Page 1 00-02-10Elements of Innovation Management in Computer Software andServicesMichaël Le Duc, Department of Business St...
Page 2 00-02-10adopter as being better than the idea it supersedes (Rogers, 1995, p. 212). (2) Compatibility “isthe degree...
Page 3 00-02-10Innovation in this model is basically fueled by supply side push – notably due to occasionallyfierce compet...
Page 4 00-02-10complementary products (e.g.suites) to installed base ofuserssoftware adhering to a standardlike Windows le...
Page 5 00-02-10When a graphical user interface was introduced in combination with a hypertext structure tonavigate anywher...
Page 6 00-02-10discounts to customers who switch from a competitor’s software are used partially to counteractdemand side ...
Page 7 00-02-10overtake the market (Abernathy and Utterback, 1978). In mature markets dominant designs haveemerged which l...
Page 8 00-02-10is the role of top management support, the role of innovative champions (Beath, 1991), availableslack resou...
Page 9 00-02-10Booch, G. (1994). Object-oriented Analysis and Design. Redwood City, California: TheBenjamin/Cummings Publi...
Page 10 00-02-10Rice, M. P., Colarelli OConnor, G., Peters, L. S., & Morone, J. G. (1998). Managingdiscontinuous innovatio...
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Elements of Innovation Management in Computer Software and Services


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Elements of Innovation Management in Computer Software and Services

  1. 1. Page 1 00-02-10Elements of Innovation Management in Computer Software andServicesMichaël Le Duc, Department of Business Studies and Informatics, Mälardalen University,Box 325, 631 05 Eskilstuna, Sweden. E-mail mlc@mdh.seAbstract:The paper reviews and analyses theoretically elements of innovation management inthe computer software market. Concepts related to the knowledge-based economy are discussed,mainly network effects, e.g. that de facto standards are highly demanded to enable users toconveniently share information. Everett M. Rogers’ seminal work on the diffusion of innovations isrelated to the software market, especially characteristics of innovations that lead to adoption orrejection. A model analyses the possible combinations of adoption criteria with three types ofnetwork effects. Furthermore, what Teece calls “complementary assets” to an innovation play acentral role in determining diffusion of a firm’s software technology.Keywords:packaged software, software services, innovation management, network effectsIntroductionThe following paper is a review and theoretical discussion on some innovation managementaspects of the software market. The main question explored is what makes an innovation insoftware adopted by some potential user or other decision-making unit and how softwareproducers manage these mechanisms. Software services play a central role in the diffusion ofsoftware packages, which makes them relevant for this discussion.A rich literature covers innovationprocesses in many fields from agriculture topharmaceuticals. However, the software and software services market needs much further study(Teece and Coleman, 1998), including from an innovation management perspective. Elements ofEverett M. Rogers’ influential work on innovation diffusion (Rogers, 1985) is here related to otherconcepts explaining salient features of the software market. Rogers’ work contributes withinnovation theory that is strongly supported by his comprehensive review of empirical research in awide range of disciplines. Not all elements necessarily apply to software markets however.Adoption criteria and network effects determine softwareinnovation diffusionAdoption is a key concept in the innovation literature. Rogers (1995) also terms it theinnovation-decision process, which “is the mental process through which an individual (or otherdecision-making unit) passes from first knowledge of an innovation to forming an attitude towardthe innovation, to a decision to adopt or reject, to implementation of the new idea, and toconfirmation of this decision.”Rogers (1995) has identified five pivotal characteristics that determine the adoption of aninnovation.(1) Relative advantage is the degree to which an innovation is perceived by a potential
  2. 2. Page 2 00-02-10adopter as being better than the idea it supersedes (Rogers, 1995, p. 212). (2) Compatibility “isthe degree to which an innovation is perceived as consistent with the existing values, pastexperiences, and needs of potential adopters” (Rogers, 1995, p. 224). (3) Complexity concernsthe degree to which an innovation is perceived as relatively difficult to understand and use (Rogers,1995, p. 242). (4) Trialability has to do with how much the potential adopter can experiment withthe innovation. (5) Observability is associated with the degree to which the results of an innovationare visible or communicated to others.Roger’s five key characteristics above should be complemented with some new aspects ofinnovation diffusion theory and economic theory that are highly visible in the software market.According to Carayannis (1998), the knowledge-based economy operates with new types ofdynamics where physical limits associated with most traditional capital are not at play. For instance,when a good software innovation has been produced, often at great effort, it can be easily spread,e.g. without having to build costly factories, if characteristics associated with adoption arecompetitive.Network effects (e.g. Katz and Shapiro, 1985; Arthur, 1996) should be added to Roger’sfive characteristics above since they strongly reinforce or downplay these criteria in the softwaremarket. Three major network effects are identifiable. Supply side increasing returns to scale, alsocalled the conversion effect (Majumdar and Venkataraman, 1998), emerge since the up-frontR&D costs of software are high relative to their unit production costs. In the case of Microsoft,“the first disk of Windows to go out the door cost Microsoft $50 million; the second andsubsequent disks cost $3. Unit costs fall as sales increase” (Arthur, 1996). Arthur’s estimate of thelow distribution cost probably concerns the Original Equipment Manufacturer (OEM) distributionchannel, where a computer manufacturer pre-installs software before shipment. Note thatmarketing and sales costs are high in the packaged software market, e.g. in comparison to softwareservices. Demand side increasing returns to scale, or the consumption effect (Majumdar andVenkataraman, 1998), adds to the former. The more people use a software package the morevaluable it becomes. For example, users can easily exchange files with others using the samesoftware and do not have to learn a new software if they switch employer. The third and finalnetwork effect discussed here is industry wide returns to scale, or the imitative effect (Majumdarand Venkataraman, 1998), which is visible when firms model their behavior after firms perceived tobe similar. The software industry is organized in a network of specialized firms that producemodules with agree-upon standard interfaces. These modules are combined into the many types ofinformation systems that are used at the workplace and at home. For example, a client-serverinformation system can be composed of a server with software from SAP, Oracle and IBM,personal computers running Microsoft Windows, office productivity tools from Lotus and somefirm-specific software developed by a local software consultancy firm. Each packaged softwarefirm can specialize where it has the best strategic position and let others supply complementarycomponents. Consultants specialize in services such as selecting, combining, adapting andcustomizing software packages as well as custom software development and maintenance. Theycontribute to the network effects for packaged software suppliers as well as on the demand side byrecommending and learning the most common software packages in the market for eachapplication type. On the other hand, consultants are qualified to recommend competing software towhat a customer uses if it is identified as significantly superior.
  3. 3. Page 3 00-02-10Innovation in this model is basically fueled by supply side push – notably due to occasionallyfierce competition, e.g. thrcough the complex interplay of rivaling product launches or the promiseof future innovation – demand side pull as software use increases in amount and sophistication aswell as industry wide organized innovation, e.g. in the current convergence of computing,telecommunications and other realms like the media and retail trade.The combination of Roger’s adoption criteria with the three types of network effects above,which reinforce or inhibit innovation, leads to a model that is presented in Table 1.NetworkeffectsAdoptioncriteriaInnovation enhancing andinhibiting forces associatedwith supply side networkeffectsInnovation enhancing andinhibiting forces associatedwith demand side networkeffectsInnovation enhancing andinhibiting forces associatedwith industry-wide networkeffectsRelativeadvantageSupply side increasing returnsto scale; high cost of R&D canbe spread among a largenumber of users. Productioncosts do not increasesignificantly with number ofusersDemand side increasing returnsto scale; the emergence of defacto standards enable users toreduce uncertainty and costssignificantlyIndustry wide increasing returns toscale; synergistic collaborationbetween firms withcomplementary products, e.g.operating systems suppliers,application suppliers, add-ondevelopers and consultantsOligopoly or monopoly canhamper innovation at the firmlevel. Comfortable butdangerous for the winnersUsers can get locked intosoftware that is not necessarilythe bestCompetition occurs withinestablished standards. Hard timefor alternative standards or non-standard productsCompatibility Developers can continuallydeliver incremental innovations(upgrades) as well asYou do not have to re-learn anew software or a new version ofan existing software. NewBy building oncompatible components,
  4. 4. Page 4 00-02-10complementary products (e.g.suites) to installed base ofuserssoftware adhering to a standardlike Windows leverage its lookand fellcomplex systems canquickly be diffused incomparison to a singlevendor approachInnovation process mustconsider establishedtechnology, the installed base.Backward compatibility maybe negative for innovationMissed opportunities frombetter but incompatible software.Usability is still a major problemarea, the WWW is not exemptedLock-in of the whole industry inplatforms of compatiblecomponents.Complexity Increasingly complex productscan be developed as existingcustomers become increasinglyproficient with software andtechnology maturesThe same software package canbe adapted to several usercategories, e.g. the average user,power user and consultantsThe whole structure of theindustry addresses theopportunities and challenges ofcomplex software. Standardisedmodularity is a key concept.The market may get saturatedwith too many new features inexisting software thus leadingto decreased salesComplexity increases as moreand more software modules canbe interconnected, e.g. throughthe Internet. Usability problems.Vulnerability due to networkorganisation if key componentsfail. A component can becomeorphan due to obsolescence or lossof marketTrialability See compatibility andcomplexitySee compatibility and complexity See compatibility and complexityObservability Network effects are strongly atplayNetwork effects are strongly atplayNetwork effects are strongly atplayTable 1. Possible combinations of Roger’s adoption criteria and network effects in thepackaged software market.The entries in Table 1’s cells are aimed at discussing key phenomena without claimingtheoretical or empirical completeness. Innovation in the software is so complex that a model shouldonly be used to clarify some aspects. The model and some other innovation issues of the softwaremarket are discussed in the following.The compatibility of a software package with the technical infrastructure of a user as well ashim or her as a person is an area of great effort in the industry. Usability management, or themanagement of human-computer interaction, is a central area associated with compatibility. Thisincludes visual clarity, consistency, compatibility, informative feedback, error prevention as well asuser guidance and support (Ravden and Johnson, 1989).Complexity needs special attention in software, since modern information systems areextremely complex. The art of software engineering is to create the illusion of simplicity in the usercommunity (Booch, 1994, p.6). The foremost instrument used to this end is software modularity,i.e. Object-Oriented Design (OOD) and Object-Oriented Programming (OOP). One key elementin OOD/OOP is the encapsulation of complex implementation details in separate modulescombined with publicizing essential properties and functions in interfaces between modules.Rogers’ adoption criteria as well as the three types of network effects discussed in this papercan explain the rapid diffusion of the Internet. The most striking example of software adoption inthe nineties is the diffusion of the Internet during the last half a dozen years or so. The basictechnology has been used since the 1980s, mainly by scientists and students. It was text-based andmainly used on the UNIX platform with arcane commands that you had to remember. As a user,you also had to know the exact address of each Internet site and other time consuming tasks.
  5. 5. Page 5 00-02-10When a graphical user interface was introduced in combination with a hypertext structure tonavigate anywhere in the world, complexity was managed by software such as NSCA Mosaic andNetscape, not the user. Thus, the tremendous potential of this new medium could be unleashed tothe ordinary software user, a case of relative advantage.The Internet has contributed to the diffusion of a number of software technologies thatpreviously had been used on a much lesser scale. For example, hypertext was limited to specificapplication types such as multimedia, e.g. the Macintosh HyperCard system in the 1980s anddigital encyclopedias. Online help systems also have traditionally used hypertext technology.However, with the advent of the World Wide Web a dramatic diffusion of hypertext andhypermedia has occurred. Even operating systems are now modified to take advantage of thisinnovation. Other technologies that the WWW has accelerated the diffusion process of include e-mail, home computing, digital network technologies, decision support systems (especially infinancial sites), groupware and electronic commerce.Innovation can be hampered on the supply side by hindering entrants with superiortechnology to challenge a firm dominating a segment of the market more strongly than in many otherindustries due to the mechanism of increasing returns to scale. On the demand side users can belocked-in (Katz and Shapiro, 1998) by their investment in a particular software, thus creatingswitching costs (Katz and Shapiro, 1998) when changing to a competing product or even buyingan upgrade. Established firms have even to compete with older versions of their own softwarewhen making a release. In the case of packaged software, rapid change equates a purchase to asunk cost. This is not necessarily the case of software developed in-house, as the Y2K problemindicates. The effect of lock-in is so powerful in the network economy that Arthur (1996) assertsthat a ”new product often has to be two or three times better in some dimensionprice, speed,convenience − to dislodge a locked-in rival”. The required relative advantages mentioned byArthur may not be universal. For example, Rice et al. (1998) define a “game changer”, or radicalinnovation, “to have the potential, (1) for a 5-10-times improvement in performance compared toexisting products; (2) to create the basis for a 30-50-percent reduction in cost; or (3) to have new-to-the-world performance features.” One reason for these negative aspects in an innovationperspective is that there is a problem of incompatibility and complexity in the software market.Thus, once an industry standard has been established incremental process innovation dominates.The power of lock-in is illustrated by the case of IBM that tried in the early 1990s toforcefully challenge Microsoft Windows with the operating system OS/2 but failed. Though partiallycompatible with Windows OS/2 did not become adopted on a wide scale, e.g. by the lack ofapplications specifically written for OS/2. IBM could not leverage enough commitment from thedeveloper community to start the imitative effect.Software producers have however learned that lock-in is not eternal. Once-dominantproducts whose market shares have plummeted include: WordStar, WordPerfect, Lotus 1-2-3,dBase, Paradox (Katz and Shapiro, 1998), all winners of the DOS era, and Netscape.Vendors furthermore endeavor to continue to lock-in users by releasing upgrades regularly(whether they are innovations for the customers or not); promising new features in future releases topreempt competitors’ releases, etc. The phenomenon of competitive upgrades offering substantial
  6. 6. Page 6 00-02-10discounts to customers who switch from a competitor’s software are used partially to counteractdemand side network effects.Another example of obstacles to diffusion due to network effects and critical adoptioncriteria is the initiative of the United States Department of Defense (DoD) that stemmed from theproblem of the multitude of software programming languages used in its procured systems,especially in embedded software such as in fighter planes, from the 1950s and onward. In theeighties, the DoD imposed a standard programming language, Ada. However, few non-defenseapplications have been developed in the Ada programming language since it is not object-oriented,which is now mainstream in the civilian software sector, and many functional features are specific todefense applications (Mowery and Langlois, 1996). Ada was not compatible with the mainstreamprogramming community and thus not adopted outside defense applications.One concept that ought to be considered in this context is what Teece (1986) callscomplementary assets. Complementary assets of companies encompass assets that support theinnovation process. Such assets for a software firm include critical human resources, marketing andsales power, accumulated capital, service network effectiveness, accumulated goodwill and theexistence of complementary technologies in the involved industry. Another key complementaryasset in software is installed base, which can be leveraged if a new software or software version issufficiently compatible. All these complementary assets can block competitors’ entry, despite theirinnovations’ potential technological superiority. The perception of a firm’s complementary assetsfurthermore influence adoption strongly in the software sector, in part due to switching costs ofusers associated with changing an existing software with an incompatible one, e.g. through alearning effort. The media image of a company and its leaders is another complementary asset thatcan turn against the company, e.g., in the case of negative articles in the press that can lead to keypeople leaving their jobs, which leads to further negative signals, etc. in a vicious cycle.A central concept here is software platform. When two alternative platforms or standardscompete, the one that gets ahead early in the establishment of the market tends to get even furtherahead in a positive feedback loop (Arthur, 1994) since the value of a platform increases rapidlywith the number of users. The winning standard takes it all or almost all as in the case of theWindows and Intel (“WINTEL”) platform (over 90% of the personal computer market). In thecomputing market, different platforms compete in networks of firms (Bresnahan & Greenstein,1999).One well-known example, is how the personal computer platform Intel/MS-DOS in the mid-eighties overtook the early market dominated by the CP/M platform as well as lesser platforms likeApple and Atari. One of the factors that made MS-DOS a winner was that Microsoft activelystimulated application software firms thus gaining advantage through imitation network effects (seenext section), i.e. by creating a platform of hardware, compatible operating systems and thousandsof applications for an unprecedented number of computing segments in comparison to mainframesand minicomputers.There is a significant difference in the market mechanisms, especially pertaining to networkeffects, in early and rapidly expanding markets, when product innovations from different firmscompete, and mature markets, where process innovations of the winning designs and firms
  7. 7. Page 7 00-02-10overtake the market (Abernathy and Utterback, 1978). In mature markets dominant designs haveemerged which leads to a shakeout of firms to a few surviving suppliers. Utterback (1994)substantiates this in a number of industries. The software industry is not an exceptionIn the computer industry, Borrus and Zysman (1997) even write about the "Wintelist" erathat has emerged, which “is a struggle over setting and evolving de facto product standards in themarket, with market power lodged anywhere in the value-chain, including product architectures,components, and software. Those constituent system elements--from components and subsystemsthrough operating and applications software--become separate and critical competitive markets.”Network effects and other factors lead to a number of expert Wintelists that in their platformnetwork control “open-but-owned” systems built to “restricted” standards (Borrus and Zysman,1997). Completely open and successfully adopted standards can be imitated by competitors thatdo not have to amortize R&D costs as much as if they entered early in the R&D process, thusmitigating first-mover advantages. Candidate “Wintelist” companies include Microsoft (operatingsystems and office automation software for personal computers), IBM (mainframe computingtechnology and even services in very large information systems), Oracle (non-PC relationaldatabases), and SUN Microsystems (workstations and UNIX servers). The judicious point here isfor a company to control the standard of important components of information systems. Customersalso require de facto standards in the different components of their information systems.Consultants have to comply with this.On the open-but-owned principle in the software market, commercial software is usuallysold in a compiled form, which means that users can execute it but not analyze how it is designedand implemented in all its details. The source code in which the software is written is a highlyprotected asset, which is not publicized to guard against imitation from competitors. The softwarevendor can choose to publicize interfaces, so called "Application Programming Interfaces” (APIs),to the compiled software to allow developers in the market to hook onto the software, e.g. todevelop complementary software. For example, in Microsoft Windows compiled Dynamic LinkedLibraries (DLLs) are available for many routine tasks such as setting the properties of a printerdriver and playing a multimedia file. A developer does not have to write the printer driver or themultimedia library anew. A simple call to the library suffices. Furthermore, Microsoft and otherssupply powerful development tools that allow the average developer to produce software quickly.In conclusion, as long as users have a license for the operating system, developers can use manyready-made functions in the operating system, database software and development tools. This is aninstance of how operating systems suppliers take advantage of imitative network effects.Organizational implementation issues, which are essential for organizational adoption, havebeen studied extensively in the information systems literature since they are often challenging.Rogers (1995) conceptualizes five steps in the adoption process: (1) knowledge, (2) persuasion,(3) decision, (4) implementation, and (5) confirmation. The stages of the adoption process ofsoftware and information systems have been studied extensively and Roger’s model is theoreticallycompatible with those studies, even if iterations are frequent. See for example Cooper and Zmud(1990) for an influential article, Lai and Malapert (1997) for a meta-analysis of relevant scientificarticles, Wildemuth (1992) on the adoption of intellectual technologies such as software, Barnettand Siegel (1988) and, on client/server technology adoption, Chengalur-Smith and Duchessi(1999). Other dimensions in the software adoption process within an organization often mentioned
  8. 8. Page 8 00-02-10is the role of top management support, the role of innovative champions (Beath, 1991), availableslack resources, etc.Software adoption within organizations has however some distinctive characteristics.Fichman and Kemerer (1995) discuss for example the “assimilation gaps” that portray thedifference over time between acquisition of software and deployment. Jurison (1993) has found ina study on office information systems that software adoption varies over time and by the type ofprofession involved (managers, project engineers, professionals and secretaries). E-mail wasshown in the study to be highly adopted over time (3 years) and among all studied professions. Incontrast, project management software had a low and decreasing average adoption level in thestudied population. Adoption was limited to project engineers. Anyone using an office suite such asMicrosoft Office or Lotus SmartSuite knows that only a small part is used by each user. Thepackage or bundle targets a number of user categories. The problem in software package designthat significantly differentiates it from software consulting services is that the design must be set forthe total market when the package is released. Microsoft and other major packaged software firmsconduct extensive and continual market intelligence on a global scale to match softwarefunctionality with needs for each release thus managing the innovation concepts of assimilation gaps,compatibility, complexity, etc. OOD/OOP is an important instrument in this R&D process.Conclusions associated with winning the adoption and networkeffects gameIn the early market of an information technology, the positive feedback mechanismsultimately leading to dominant designs force companies to struggle to control their organization andmarket like if they would be on a soaped slope resembling an inverted S-curve with haphazardand sometimes fatal obstacles. Companies have to frequently measure the trajectories and stopsin order to adjust their path. This uncertainty explains in part why companies concentrate onone or a few selected segments of the IT-market.Innovation theory with concepts such as adoption and network effects can explain salientmechanisms of the software market. Further theoretical and empirical research on network effectsand innovation are needed to increase our understanding of the complex software market.ReferencesAbernathy, W. J., & Utterback, J. M. (1978). Patterns of innovation in technology. TechnologyReview, 80, 40-47.Arthur, W. B. (1996). Increasing returns and the new world of business. Harvard BusinessReview, 74, 100-109.Arthur, W. B. (1994). Positive feedback in the economy. The McKinsey Quarterly, 81-95.Beath, C. M. (1991). “Supporting the information technology champion.” MIS Quarterly 15(3):355-372.
  9. 9. Page 9 00-02-10Booch, G. (1994). Object-oriented Analysis and Design. Redwood City, California: TheBenjamin/Cummings Publishing Company, Inc.Borrus, M. and J. Zysman (1997). Wintelism and the Changing Terms of Global Competition:Prototype of the Future? Working Paper 96B. Berkeley Roundtable on the International Economy(BRIE), University of California at Berkeley.Bresnahan, T. F., & Greenstein, S. (1999). Technological competition and the structure of thecomputer industry. The Journal of Industrial Economics, 47, 1-40.Carayannis, E. G. (1998). The strategic management of technological learning in project/programmanagement: the role of extranets, intranets and intelligent agents in knowledge generation,diffusion, and leveraging. Technovation, 18, 697-703.Chengalur-Smith, I., & Duchessi, P. (1999). The initiation and adoption of client-server technologyin organizations. Information & Management, 35, 77-88.Cooper, R. B., & Zmud, R. W. (1990). Information technology implementation research: Atechnological diffusion approach. Management Science, 36, 123-139.Fichman, R. G. and C. Kemerer (1995). The Illusory Diffusion of Innovation: An Examination ofAssimilation Gaps. Pittsburgh, University of Pittsburgh, Katz School working paper no. 746.Jurison, J. (1993). Adoption of OIS by four groups of office workers: An analysis from theperspective of innovation diffusion theory, SIGCPR 93. Proceedings of the 1993 conference onComputer personnel research (pp. 178-187).Katz, M. L., & Shapiro, C. (1985). Network externalities, competition and compatibility.American Economic Review, 75, 424-440.Katz, M. L., & Shapiro, C. (1998). Antitrust in Software Markets, unpublished Working Paper.Berkeley: University of California at Berkeley, 22 September 1998.Lai, V. S., & Mahapatra, R. K. (1997). Exploring the research in information technologyimplementation. Information & Management, 32, 187-201.Majumdar, S. K., & Venkataraman, S. (1998). Network effects and the adoption of newtechnology: evidence from the U.S. telecommunications industry. Strategic Management Journal,19, 1045-1062.Mowery, D. C., & Langlois, R. N. (1996). Spinning off and spinning on(?): the federal governmentrole in the development of the US computer software industry. Research Policy, 25, 947-966.Ravden, S., & G. Johnson, (1989). Evaluating Usability of Human-Computer Interfaces: aPractical Method. Ellis Horwood, Chichester.
  10. 10. Page 10 00-02-10Rice, M. P., Colarelli OConnor, G., Peters, L. S., & Morone, J. G. (1998). Managingdiscontinuous innovation. Research Technology Management, 41, 52-58.Rogers, E. M. (1995). Diffusion of Innovations. New York: The Free Press.Teece, D. J. (1986). Profiting from technological innovation: Implications for integration,collaboration, licensing and public policy. Research Policy, 15, 285–305.Teece, D. J., & Coleman, M. (1998). The meaning of monopoly: antitrust analysis in high-technology industries. The Antitrust Bulletin, Fall-Winter 1998, 801-857.Utterback, J. M. (1994). Mastering the Dynamics of Innovation: How Companies Can SeizeOpportunities in the Face of Technological Change. Boston, Mass.: Harvard Business SchoolPress.Wildemuth, B. M. (1992). An Empirically Grounded Model of the Adoption of IntellectualTechnologies. Journal of the American Society for Information Science, 43, 210-224.Acknowledgements: I would like to thank Dr. Frédéric Delmar, Entrepreneurship andSmall Business Research Institute, Stockholm, Sweden and Professor Dilek CetindamarKaraomerlioglu, reviewer of the IAMOT 2000 conference, for precious feedback. The SwedishCouncil for Planning and Coordination of Research (FRN) as well as Mälardalen University havefunded this work.Bio-Sketch of Author: Michaël Le Duc is an Assistant Professor. He received his Ph. D. ininformatics from the Stockholm University in 1996. His research interests include innovationmanagement in software and information systems services, Decision Support Systems, SoftwareEngineering and Geographical Information Systems.