Open collective innovation oui 2010 simplified hypo (slide 18)

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  • These are example of where the model might apply
  • Open innovation complements/substitute firm-based, but what affects its performance? Important for understanding design of open innovation systems and encouraging it.
  • The behavior of the remaining 14% is too inconsistent to be classified.
  • The behavior of the remaining 14% is too inconsistent to be classified.
  • Note that they interact freely, not in a pre-determined, structured way
  • 1a: Over a range of Reciprocator and free-rider levels, a higher ratio of Cooperators leads to better performance.
  • Changing population ratio can explain almost the entire variance in performance. This is low homogeneity (different needs), perfectly non-rival (no cost to contribute). Point in the back in previous slide.Note non-linear effect of changing percentage of cooperators – even a few make a big difference in performanceThe min-max points are the result of the characteristics of the other participants: reciprocators vs. free riders.Non-cooperators are a systematic mix of reciprocators and free-Riders
  • Again – non linear effect. In this case, adding reciprocators increases performance quickly, removing them deteriorates performance quickly. But the effect becomes weak as cooperators join in because cooperators substitute for reciprocators. Robust to free riders to a point, after which there’s a rapid deterioration.
  • There’s a systematic explanation for these points. 1% cooperators, many reciprocators=40% performance, but as free riders join in, performance deteriorates only when they are overwhelming majority. Injection of cooperators can raise performance quickly, but sensitive to the presence of free-riders.
  • 1a: Over a range of Reciprocator and free-rider levels, a higher ratio of Cooperators leads to better performance.
  • Performance = % of goals achieved through exchange
  • 1a: Over a range of Reciprocator and free-rider levels, a higher ratio of Cooperators leads to better performance.
  • High rivalry is detrimental to performance and we know that high similarity in needs is equally bad. Indeed, when we combine the two, the result is very low performance. An intuitive example is food: it’s a highly rival good, and if we all crave the exact same dish, few people will be satisfied which means that overall performance is low.As we move from similar to dissimilar needs, performance improves linearly. Note that rivalry is still high, but as we saw earlier, rivalry matters less when needs are dissimilar. Again, food is high rival, but if each one of us is after a different dish, we can swap, make more people happy, and overall performance increases.Now, some people assume that rivalry is bad for performance. If that’s true, then a decrease in rivalry should lead to increased performance. Let’s see what happens as we decrease rivalry. Well, not much. Performance inches higher, but the difference is negligible. Why is that? Because when needs are not similar, rivalry matters only little. So increasing or decreasing it (as we did here) has a negligible effect on performance.What is the effect of need similarity on performance? Is it critical? To examine that let’s move towards high need similarity. Well, surprise! As you can see, when rivalry is low, need similarity (or dissimilarity) doesn’t matter much by it self. Performance is still robust even with high need similarity, as long as the good is non-rival. The example here is an desired mp3 file – even if we all go for the same song, the good in non-rival, so all of us can end up happily.Finally, notice the shape of the edge that connect this final point to our starting point. It is non-linear, which means that the effect of rivalry drops quickly. these manipulations (need homogeneity and rivalry) were done with randomly sampling from Kurzban-Hauser space.
  • Even a sliver of cooperators can jumpstart a project.Performance is high even with a sliver of cooperators. Decreasing returns to increasing cooperators. Lerner, J. and J. Tirole2005 "Economic Perspectives on Open Source." In J. Feller, B. Fitzgerald, S. A. Hissam, and K. R. Lakhani (eds.), Perspectives on Free and Open Source Software: 47-78. Cambridge, Massachusetts: The MIT Press.Mockus, A., R. T. Fielding, and J. D. Herbsleb2005 "Two Case Studies of Open Source Software Development: Apache and Mozilla." In J. Feller, B. Fitzgerald, S. A. Hissam, and K. R. Lakhani (eds.), Perspectives on Free and Open Source Software: 163-209. Cambridge, Massachusetts: The MIT Press.
  • Even a sliver of cooperators can jumpstart a project.Performance is high even with a sliver of cooperators. Decreasing returns to increasing cooperators. Lerner, J. and J. Tirole2005 "Economic Perspectives on Open Source." In J. Feller, B. Fitzgerald, S. A. Hissam, and K. R. Lakhani (eds.), Perspectives on Free and Open Source Software: 47-78. Cambridge, Massachusetts: The MIT Press.Mockus, A., R. T. Fielding, and J. D. Herbsleb2005 "Two Case Studies of Open Source Software Development: Apache and Mozilla." In J. Feller, B. Fitzgerald, S. A. Hissam, and K. R. Lakhani (eds.), Perspectives on Free and Open Source Software: 163-209. Cambridge, Massachusetts: The MIT Press.
  • Open innovation complements/substitute firm-based, but what affects its performance? Important for understanding design of open innovation systems and encouraging it.
  • Performance = % of goals achieved through exchange
  • Open collective innovation oui 2010 simplified hypo (slide 18)

    1. 1. Where & When Can Open Collaborative Innovation Thrive? A Theory of Performance<br />Eighth Annual International Open and User Innovation Workshop<br />
    2. 2. Characteristics of Phenomenon<br />Creates products <br />Interaction <br />Work <br />Open access <br />of economic <br />and exchange <br />yet <br />purposeful<br />to contribute <br />value, has <br />activities are <br />loosely <br />and consume <br />1,2,3<br />measureable <br />central <br />5<br />coordinated <br />performance <br />1,3<br />2,4<br />1von Krogh & von Hippel 2003 3 Shah 2005 5 von Krogh, Spaeth & Lakhani 20032 Lee & Cole 2003 4Mockus, Fielding & Herbsleb 2005 6 Lakhani & von Hippel 2003<br />
    3. 3. Questions about Performance<br />Open collaborative innovation differs from firm-based innovation<br />(Lee & Cole 2003; von Hippel & von Krogh 2003)<br />How it survives despite massive free-riding/non-contributing users?<br />When expands beyond software? In which environments can it thrive? <br />How to design open innovation systems?<br />What affects performance?<br />
    4. 4. A General Model of Performance<br />Propose a general modelof performance based <br />on:<br />Inductivefieldworkin a non-software setting<br />Deductive agent-based simulationto generate propositions<br />Complements literature on <br />Motivation<br />(Roberts, Hann & Slaughter; Shah 2006; von Hippel & von Krogh 2003)<br />Organization<br />(O'Mahony & Ferraro 2007; von Krogh, Spaeth & Lakhani 2003) <br />
    5. 5. The ModelGoods, Behavior, Needs<br />
    6. 6. GoodsHow rival?<br />NeedsHow similar or dissimilar?<br />BehaviorHow cooperative are participants? <br />
    7. 7. How Rival are the Goods?<br />To what extent does one’s consumption of the good interfere with another’s consumption of the same good<br />
    8. 8. How Cooperative are Participants?<br />Empirically, human population composed of individuals with different inclinations (or strategies) for cooperation<br />(Kurzban& Hauser, 2005)<br />Cooperators<br />Reciprocators<br />Free riders<br />Contribute to <br />Contribute if <br />Do not <br />others <br />others <br />contribute <br />unconditionally<br />contribute too<br />13%<br />53%<br />20%<br />Remaining 14% are inconsistent<br />
    9. 9. How Cooperative are Participants? MASTER<br />Empirically, human population composed of individuals with different inclinations (or strategies) for cooperation<br />(Kurzban& Hauser, 2005)<br />Remaining 14% are inconsistent<br />
    10. 10. How Similar are the Needs?<br />Participants may have a variety of needs or very similar needs. Their needs can differ or resemble each other.<br />
    11. 11. The MethodAgents interact & exchange<br />
    12. 12. Agent-based Model<br />Each agent has skills and needs, which rarely overlap<br />Searches the network to fulfill needs, subject to cooperation and rivalry characteristics<br />If search fails, develops or finds outside network<br />PerformanceTo what extent goals are accomplished through collaboration?<br />
    13. 13. Single Agent’s Algorithm<br />
    14. 14. Agents Interact Throughout<br />
    15. 15. Network of Exchange Interactions<br />
    16. 16. What Affects Performance?The Impact of Cooperation<br />
    17. 17. Cooperators Matters<br />
    18. 18. How Cooperation Matters?<br />Cooperators improve performance<br />Decreasing marginal returns from cooperators<br />
    19. 19. Reciprocators Matter Greatly<br />Max<br />Min<br />What causes the variance?<br />
    20. 20. Performance Robust with Few Cooperators, Many Reciprocators<br />Max<br />Many Reciprocators<br />Few Free-Riders<br />Min<br />Many Free-Riders<br />Few Reciprocators<br />
    21. 21. Kurzban-Hauser Ratio<br />Many Reciprocators<br />Few Free-Riders<br />Many Free-Riders<br />Few Reciprocators<br />
    22. 22. How Cooperation Matters?<br />Cooperators improve performance<br />Decreasing marginal returns from cooperators<br />Reciprocators substitute cooperators<br />Free riders matter little<br />
    23. 23. What Affects Performance?The Impact of Rivalry and Needs<br />
    24. 24. Need Similarity Matters<br />Kurzban-Houser<br />
    25. 25. How Rivalry & Needs Matter?<br />Rivalry decreases performance<br />Rivalry interacts with need similarity<br />
    26. 26. What does it Mean?Implications<br />
    27. 27. Rivalry & Needs Interact to Affect Performance<br />Performance<br />Similarity in Needs<br />Rivalry<br />
    28. 28. Rivalry has non-linear effect on performance<br />Rivalry–Needs compensatory effect<br />28<br />
    29. 29. Implications to Practice<br />Goods<br />Low rivalry produces higher performance, but...<br />When needs are dissimilar, high performance possible even with high rivalry<br />Cooperation<br />Cooperators are important, not very important; Good performance even with tiny core<br />Reciprocators are underappreciated majority<br />Free-riders matter little in realistic settings<br />
    30. 30. Implications<br />Needs<br />Dissimilar needs are an advantage; diversity is valuable<br />Yet, even similar needs can be satisfied in most cases<br />
    31. 31. Propositions about Performance<br />How it survives despite massive free-riding/non-contributing users?<br /> Free riders matter only in the extreme<br />In which environments can it succeed? How to design open innovation systems? <br /> Near non-rival goods, diversity of participant needs, many cooperators or reciprocators<br />If conditions are less than ideal... <br /> Some elements can compensate for others!<br />
    32. 32. Tack<br />Tak<br />
    33. 33. Cooperation & Needs Interact<br />

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