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The Innovation Engine for Team Building –The EU Aristotele ApproachFrom Open Innovation to the Innovation FactoryErnesto D...
 Innovation Open Innovation The ARISTOTELE Innovation Factory Recommendation in Collaborative Environments Lesson Lea...
 Innovation is the catalyst to economic growth. Joseph Schumpeter famously asserted that “creative destruction isthe ess...
 Open Innovation is the use of purposive inflows and outflows ofknowledge to accelerate internal innovation and expand th...
 ARISTOTELE research project is an IP funded underthe EC FP7. The aim is relating the learning process to theorganizatio...
 Supports addressing ill-defined, vague needs andtransforming them into requirements or virtual products Suggestions are...
Innovation Factory
Innovation Factory
Innovation Factory
 Methodology can draw upon three different types ofresources Results of a Collaborative Innovation Framework that descri...
 The information sources of innovation process are ofthree types: Contributions coming from innovation workers, defining...
 The results of the methodology can be represented by: Suggestions sets regarding new products or services Proposals of...
 The outputs of the first stage of Innovation Factory(Virtual Product Designer) can be used to generate VPsWorkflow (1)Vi...
 The VP definition, annotated with requirements andrequested competencies, is used as stimulus for theRecommender SystemW...
 Last stage of the workflow (Innovation Support System)gives suggestions to personal learning plans specificfor workers p...
 Stimulus: “A lot of complaints reach our help-desk” Crawler selects some components, most turn out to beabout lazy tech...
 SM: guidelines on tech assistance DM: entries from champion’s blog praising goodassistance Entries about latest read o...
 Seve teams. Each team was assigned with a task to beaccomplish in a limited timespan The members of the team was placed...
 H1: experimental groups will develop a communicationprocess more linear, with less objections and rejects onthe argument...
Results: global activities performedEXTeamsEXTeams
Results: global activities performedCONTeamsCONTeams
Results: spec. activities performedEXTeamsEXTeams
Results: spec. activities performedCONTeamsCONTeams
Results: conversation actionsEXTeamsEXTeams
Results: conversation actionsCONTeamsCONTeams
Results: conversation flowEX TeamsEX Teams
Results: conversation flowCON TeamsCON Teams
 Hypothesis are confirmed H1: experimental groups will develop a communication processmore linear, with less objections ...
 RSs have reached in the last years a good level of ac-curacy Our experiment show that RSs can have good impacton reduci...
 Modern RSs contaminate users experience withdissimilarity: dissimilarity can increase users’satisfaction and stimulate l...
Thank youAny questions?
ADDITIONAL SLIDES
 V. Bellandi, P. Ceravolo, E. Damiani, and F. Frati. CR2S: Competency Roadmap to Strategy. Proc.of 1st Int. Workshop on K...
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The Innovation Engine for Team Building – The EU Aristotele Approach From Open Innovation to the Innovation Factory

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ARISTOTELE approach has been presented at the Innovation Adoption Forum for Industry and Public Sector within the 6th IEEE International Conference on Digital Ecosystem Technologies (IEEE DEST - CEE 2012). The presentation about ARISTOTELE has been held by Paolo Ceravolo and Ernesto Damiani (University of Milan) during the keynote "The Innovation Engine for Team Building – The EU Aristotele Approach". Learn more on http://www.aristotele-ip.eu/

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The Innovation Engine for Team Building – The EU Aristotele Approach From Open Innovation to the Innovation Factory

  1. 1. The Innovation Engine for Team Building –The EU Aristotele ApproachFrom Open Innovation to the Innovation FactoryErnesto Damiani – Paolo CeravoloUniversità degli Studi di Milano
  2. 2.  Innovation Open Innovation The ARISTOTELE Innovation Factory Recommendation in Collaborative Environments Lesson Learned Future WorksOutline
  3. 3.  Innovation is the catalyst to economic growth. Joseph Schumpeter famously asserted that “creative destruction isthe essential fact about capitalism.” Entrepreneurs continuouslylook for better ways to satisfy their consumer base with improvedquality, durability, service, and price which come to fruition ininnovation with advanced technologies and organizationalstrategies. There are several sources of innovation. According to the Peter F.Drucker the general sources of innovations are different changesin industry structure, in market structure, in local and globaldemographics, in human perception, mood and meaning, in theamount of already available scientific knowledge, etc.Innovation
  4. 4.  Open Innovation is the use of purposive inflows and outflows ofknowledge to accelerate internal innovation and expand themarkets (Chesbrough 2003). Innovation is seen as an outcome of a collision betweentechnological opportunities and user needs. The focus is upon theinteraction between producers and users. One outcome of this approach is a more realistic understanding ofmarkets and vertical integration than the ones offered byneoclassical economics and transaction economics. Another outcome is treating research and development ascollaborative and open systems.Open Innovation
  5. 5.  ARISTOTELE research project is an IP funded underthe EC FP7. The aim is relating the learning process to theorganizational one (including innovation processmanagement). In particular: Organizational processes (marketing&communication, humanresources management, business) Learning processes (group training sessions) Social collaboration processes (spontaneous formation ofgroups within the organization)ARISTOTELE Project
  6. 6.  Supports addressing ill-defined, vague needs andtransforming them into requirements or virtual products Suggestions are derived based on open-innovation-sources like help desk messages Reactive mode only (for now)Innovation Factory
  7. 7. Innovation Factory
  8. 8. Innovation Factory
  9. 9. Innovation Factory
  10. 10.  Methodology can draw upon three different types ofresources Results of a Collaborative Innovation Framework that describesneeds and general requirements for new products/services External Stimuli, posing challenges related to innovation andcompetence improvement, ordinarily, not specified in terms ofresources Explicit enterprise knowledge formalized in instances of theARISTOTELE models, mainly in the Knowledge, Competenceand Worker modelsMethodologies to Foster the InnovationFactories (1)
  11. 11.  The information sources of innovation process are ofthree types: Contributions coming from innovation workers, defining orbrainstorming requirements for a new product Contributions coming from partners (i.e. employees, suppliers,customers) who send comments and ideas that can becollected and transformed in requirements to be analyzed Contribution from external sources, e.g. using a softwarecrawler to analyze electronic resources and extract information(e.g. web site competitors, forums, blogs)Methodologies to Foster the InnovationFactories (2)
  12. 12.  The results of the methodology can be represented by: Suggestions sets regarding new products or services Proposals of innovative activities and their impact on theorganization Suggested interactions with experts and peers that mayimprove creativity in the organizationMethodologies to Foster the InnovationFactories (3)
  13. 13.  The outputs of the first stage of Innovation Factory(Virtual Product Designer) can be used to generate VPsWorkflow (1)Virtual ProductDesignerRecommenderSystemInnovationSupport SystemVirtualProductSuggestionsTarget: WorkingTeamConfigurationSettingsExplicit OrganizationKnowledgeExternal Stimuli
  14. 14.  The VP definition, annotated with requirements andrequested competencies, is used as stimulus for theRecommender SystemWorkflow (2)Virtual ProductDesignerRecommenderSystemInnovationSupport SystemVirtualProductSuggestionsTarget: WorkingTeamConfigurationSettingsExplicit OrganizationKnowledgeExternal Stimuli
  15. 15.  Last stage of the workflow (Innovation Support System)gives suggestions to personal learning plans specificfor workers profiles and organization needsWorkflow (3)Virtual ProductDesignerRecommenderSystemInnovationSupport SystemVirtualProductSuggestionsTarget: WorkingTeamConfigurationSettingsExplicit OrganizationKnowledgeExternal Stimuli
  16. 16.  Stimulus: “A lot of complaints reach our help-desk” Crawler selects some components, most turn out to beabout lazy tech assistance Brainstorming in VP points at shorter response time,but highlight high marginal cost of achieving itExample (1)
  17. 17.  SM: guidelines on tech assistance DM: entries from champion’s blog praising goodassistance Entries about latest read of champion,the book “Neuromancer”, is aboutsmall communities taking overExample (2)SERENDIPITY!!
  18. 18.  Seve teams. Each team was assigned with a task to beaccomplish in a limited timespan The members of the team was placed in differentrooms and was provided with IF (mikiwiki based) The IF was the only tool allowed for cooperating andcommunicating in the team, all other channels toaccess the web was disabled Four teams was set as experimental groups and wasprovided with the ARISTOTELE RS Three teams was set as control groups and wasprovided with the standard IF servicesExperiment
  19. 19.  H1: experimental groups will develop a communicationprocess more linear, with less objections and rejects onthe arguments proposed during the discussion H2: experimental groups will develop the task in a morelinear process, executing activities in a more orderedflow H3: experimental groups will develop the task withbetter result in time management, distributing theactivities on the whole timespanExperiment
  20. 20. Results: global activities performedEXTeamsEXTeams
  21. 21. Results: global activities performedCONTeamsCONTeams
  22. 22. Results: spec. activities performedEXTeamsEXTeams
  23. 23. Results: spec. activities performedCONTeamsCONTeams
  24. 24. Results: conversation actionsEXTeamsEXTeams
  25. 25. Results: conversation actionsCONTeamsCONTeams
  26. 26. Results: conversation flowEX TeamsEX Teams
  27. 27. Results: conversation flowCON TeamsCON Teams
  28. 28.  Hypothesis are confirmed H1: experimental groups will develop a communication processmore linear, with less objections and rejects on the argumentsproposed during the discussion H2: experimental groups will develop the task in a more linearprocess, exe- cuting activities in a more ordered follow H3: experimental groups will develop the task with better resultin time management, distributing the activities on the wholetimespan What does it means?Experimental Results
  29. 29.  RSs have reached in the last years a good level of ac-curacy Our experiment show that RSs can have good impacton reducing the overhead required to a tem forcollaborating RSs however can create a close community RSs still fail in discovering users latent interests: theyoften suggest items that, although accurately tailoredon the users’ past behavior, and create communitiesthat are overspecifiedOverspecialisation Problem
  30. 30.  Modern RSs contaminate users experience withdissimilarity: dissimilarity can increase users’satisfaction and stimulate latent interests Mentor Approach: instead of choosing a randommusical world, to exploit the knowledge of the bestreputed users Instead of taking into consideration the set of all theitems to select suggestions, we prefer items exploitedby mentors this means that this approach could for example prefer, asneighbour for a user Ui, user Uj respect to user Uz even ifsimilarity(Ui, Uj) < similarity(Ui, Uz) if Uj is an eclectic user andUj is notThe mentor approach
  31. 31. Thank youAny questions?
  32. 32. ADDITIONAL SLIDES
  33. 33.  V. Bellandi, P. Ceravolo, E. Damiani, and F. Frati. CR2S: Competency Roadmap to Strategy. Proc.of 1st Int. Workshop on Knowledge Management and e-Human Resources Practices for Innovation(eHR-KM ‘11), 2011 R. Phaal, C.J.P. Farrukh, D.R. Probert. Technology roadmapping - A planning framework forevolution and revolution. In Technological Forecasting and Social Change 71:1, January 2004 F. Ricci, L. Rokach, B. Shapira, P.B. Kantor (Eds.). Recommender Systems Handbook, Springer,2011 R. Maier. Knowledge Management Systems: Information and Communication Technologies forKnowledge Management. Knowledge Management. Springer, 2007 L. Iaquinta, M. de Gemmis, P. Lops, G. Semeraro. Recommendations toward SerendipitousDiversions. In Proc. of Ninth International Conference on Intelligent Systems Design andApplications, 2009 P. Ceravolo, E. Damiani, M. Viviani. Bottom-up Extraction and Trust-based Refinement ofOntology Metadata. IEEE Transactions on Knowledge and Data Engineering 19 (2), 2007 G. Adomavicius, A. Tuzhilin. Toward the next generation of recommender systems: a survey of thestate-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering,17(6), 2005 P.S. Adler, C. Heckscher. Towards Collaborative Community. In The Firm as a CollaborativeCommunity: Reconstructing Trust in the Knowledge Economy. Oxford University Press, 2006 A. Hargadon, R.I. Sutton. Innovation Factory. Harvard Business Review, 2000 C. Edquist. Systems Of Innovation: Perspectives and Challenges. In J. Fagerberg, D. C. Mowery,& R. R. Nelson (Eds.), The Oxford Handbook of Innovation. New York., 2005References

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