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OSWC 2012: Modeling non-financial constraints in the development and adoption of new technologies

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A presentation of a paper by me and Mikito Takada, "Modeling the role of non-financial constraints in the development and adoption of new technologies." See also the accompanying paper!

A presentation of a paper by me and Mikito Takada, "Modeling the role of non-financial constraints in the development and adoption of new technologies." See also the accompanying paper!

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  • 1. Sunday, February 12, 2012
  • 2. Sunday, February 12, 2012
  • 3. Modeling the role of non-financial constraints in the development and adoption of new technologies Organization Science Winter Conference 2012 Pre-Conference Session I Janne M. Korhonen & Mikito Takada Aalto University School of Economics janne.m.korhonen@aalto.fiSunday, February 12, 2012
  • 4. How... ...resource constraints affect innovation? ...some studies say constraints are good for creativity and innovation - others say that they’re bad?Sunday, February 12, 2012
  • 5. What the science says • constraints can spur innovation and/or technological change (e.g. “bottlenecks”) • discontinuities may trigger an “era of ferment” • but constraints are lumped with other exogenous environmental events • in particular, the role of “limiting” discontinuities is poorly understood • and resource slack is seen as desirable for innovationSunday, February 12, 2012
  • 6. Research questions What are the odds of a constraint inducing innovation, i.e. improvement in technology’s performance that stays in use after the constraint is lifted? What is the mechanism that improves performance? Is it R&D or imitation of already existing technologies? What effect does a constraint have on technological variety?Sunday, February 12, 2012
  • 7. Defining constraints: Constraints are restrictions to some resource that force an organization to change, possibly against its will, its accustomed working practices , e.g. invest in new equipment outside normal investment cycle.Sunday, February 12, 2012
  • 8. Defining technologies: Technologies are composed of components or elements that work together as a whole. The components’ functionality may be dependent on other components. Each component may be a technology in its own right and consist of sub-components. See e.g. Murmann & Frenken 2006Sunday, February 12, 2012
  • 9. Case: Copper smelting After the WW2, copper smelting technology achieved a breakthrough in efficiency. Two companies developed new technologies:Sunday, February 12, 2012
  • 10. Case: Copper smelting After the WW2, copper smelting technology achieved a breakthrough in efficiency. Two companies developed new technologies: • one had essentially inexhaustible resources Inco, CanadaSunday, February 12, 2012
  • 11. Case: Copper smelting After the WW2, copper smelting technology achieved a breakthrough in efficiency. Two companies developed new technologies: • another was forced to invent something, or else Outokumpu, FinlandSunday, February 12, 2012
  • 12. Furnace developments Clearly, both Inco and Outokumpu achieved remarkable improvementsSunday, February 12, 2012
  • 13. Case: Copper smelting However, the technologies developed were not “new” in a real sense: • Basic principles well-known • Technology already patented 50 years before • R&D time ≈ 3 months - no time for research! • Components scrounged from existing plantsSunday, February 12, 2012
  • 14. ces na r fu sh fla p u u m o k u t O Copper smelting furnaces in use, 1930-1990Sunday, February 12, 2012
  • 15. Constraint 1: energy Copper smelting furnaces in use, 1930-1990Sunday, February 12, 2012
  • 16. Constraint 2: environment Copper smelting furnaces in use, 1930-1990Sunday, February 12, 2012
  • 17. Constraint 3: 1973 oil crisis Copper smelting furnaces in use, 1930-1990Sunday, February 12, 2012
  • 18. But! That’s just one case study - what does it prove?Sunday, February 12, 2012
  • 19. That’s just one case study - what does it prove? Not very much.Sunday, February 12, 2012
  • 20. That’s just one case study - what does it prove? Not very much. And case studies of constrained innovation are rare.Sunday, February 12, 2012
  • 21. That’s just one case study - what does it prove? Not very much. And case studies of constrained innovation are rare. So: instead of case studies, let us compute. cf. e.g. Davis et al. (2007)Sunday, February 12, 2012
  • 22. Example: one simulation 1 2 Performance 3 Initial innovation: Very rapid convergence to three technologies 95 % copying = firms will copy if their performance is less than 95 % of averageSunday, February 12, 2012
  • 23. Example: one simulation 1 2 Performance 3 Period of stability; mature industry, 3 technologies in use in 100 firmsSunday, February 12, 2012
  • 24. Example: one simulation 2 Performance 1 3 Constraint introduced; 2 out of 3 technologies affectedSunday, February 12, 2012
  • 25. Example: one simulation 1 2 Performance Technology 3 dies off; former users copy Tech 2Sunday, February 12, 2012
  • 26. Example: one simulation 1 2 Performance Technology 1 is hit, but finds a new path Technology 3 dies off; former users copy Tech 2Sunday, February 12, 2012
  • 27. Example: one simulation Performance Constraint lifted; Technology 1 improves even further; new stable states found & performance is increased, but variety is reducedSunday, February 12, 2012
  • 28. -2 -2 -2 Results: improvement -4 -4 -4 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 10 21 32 43 54 65 76 87 98 9 10 10 11 12 13 14 11 12 13 14 15 15 K value K value value K Imitation/competition intensity 75% b) Imitation threshold at 75% Imitation/competition intensity 100% e) Radical innovation, medium search c) Always imitate 4 4 ce 4 a n a ri standard error 2 2 (percent)2 v Change (percent) Change (percent) 0 0 -2 Change0 -2 -2 zero change line -4 -4 -4 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 10 21 32 43 54 65 76 87 98 9 10 10 11 12 13 14 11 12 13 14 15 15 K value K(complexity) value K value (complexity) K value value K c) Always imitate f) Radical innovation, distant search 4 4 No statistically significant change; variance high, however! 2 2 nge (percent) nge (percent) 0 0Sunday, February 12, 2012
  • 29. Results: Odds of change Likelihood of change Likelihood of change at varying intensity levels at varying long jump lengths % of events % of events K value (complexity) K value (complexity) Stable results, only real variance due to high imitation/competition intensities.Sunday, February 12, 2012
  • 30. Results: Old/new tech? 80 80 80 % pre-existing technologies % pre-existing technologies % pre-existing technologies 60 60 60 40 40 40 20 20 20 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 03 14 25 36 47 58 69 710 811 9 10 11 12 13 12 13 14 15 K value K value K value NoNoimitation a) imitation allowed Imitation at at 75% short search 75% d) Radical innovation, b) Imitation threshold Always imitate medium search c) Always imitate e) Radical innovation, 100 100 100 100 100% of old tech 80 80 80 80 80 % pre-existing technologies % pre-existing technologies % pre-existing technologies % pre-existing technologies % pre-existing technologies 60 60 60 60 60 40 40 40 40 40 50/50 old & new 20 20 20 20 20 technologies 0 0 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 03 14 25 36 47 58 69 7 10 8 11 9 10 11 12 13 12 13 14 15 14 0 15 1 2 03 14 25 36 47 58 69 710 811 9 10 11 12 13 12 13 14 15 K value (complexity)K value K value (complexity) K value K value K value (complexity)K value K value b) Imitation threshold at 75% e) Radical innovation, medium search c) Always imitate f) Radical innovation, distant search 100 100 100 100 80 80 80 80 % pre-existing technologies % pre-existing technologies % pre-existing technologies % pre-existing technologies 60 60 60 60 Average share of pre-existing technologies over 40 40 40 40 50% in all cases; however, high variance! 20 20 20 20 0 0 0 0 0 1 2 3 4 Sunday, February 12, 2012 5 6 7 8 9 10 11 12 13 14 15 0 1 2 03 14 25 36 47 58 69 7 10 8 11 9 10 11 12 13 12 13 14 15 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13
  • 31. Results: Old/new tech? 80 80 80 % pre-existing technologies % pre-existing technologies % pre-existing technologies 60 60 60 40 40 40 20 20 20 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 03 14 25 36 47 58 69 710 811 9 10 11 12 13 12 13 14 15 K value K value K value NoNoimitation a) imitation allowed Imitation at at 75% short search 75% d) Radical innovation, b) Imitation threshold Always imitate medium search c) Always imitate e) Radical innovation, 100 100 100 100 100% of old tech 80 80 80 80 80 % pre-existing technologies % pre-existing technologies % pre-existing technologies % pre-existing technologies % pre-existing technologies 60 60 60 60 60 40 40 40 40 40 20 20 20 20 20 0 0 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 03 14 25 36 47 58 69 7 10 8 11 9 10 11 12 13 12 13 14 15 14 0 15 1 2 03 14 25 36 47 58 69 710 811 9 10 11 12 13 12 13 14 15 K value (complexity)K value K value (complexity) K value K value K value (complexity)K value K value b) Imitation threshold at 75% e) Radical innovation, medium search c) Always imitate f) Radical innovation, distant search 100 100 100 100 80 80 80 80 % pre-existing technologies % pre-existing technologies % pre-existing technologies % pre-existing technologies 60 60 60 60 High variance! 40 40 40 40 20 20 20 20 0 0 0 0 0 1 2 3 4 Sunday, February 12, 2012 5 6 7 8 9 10 11 12 13 14 15 0 1 2 03 14 25 36 47 58 69 7 10 8 11 9 10 11 12 13 12 13 14 15 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13
  • 32. Results: Variety No imitation Imitation at 75% Always imitate Time (turns) Entropy (variety) drops with most constraints, except when imitation is not allowed.Sunday, February 12, 2012
  • 33. Results: Variety No imitation Imitation at 75% Always imitate Period of constraints Entropy (variety) drops with most constraints, except when imitation is not allowed.Sunday, February 12, 2012
  • 34. Results: summary • No statistically significant change in performance - but it’s a possibility • Odds of negative and positive change roughly equal • Constraints are more likely to accelerate adoption of existing technologies, instead of development of new technologies • Constraints decrease variety if imitation is allowed • Results are robust to parameter changesSunday, February 12, 2012
  • 35. Under the hood • NK model of problem solving as search (see paper for full description) • Additions: constraints and imitation Alternative 1 forced for component 1 (for 5 turns)Sunday, February 12, 2012
  • 36. Under the hood • NK model of problem solving as search (see paper for full description) • Additions: constraints and imitation Alternative 1 forced for component 1 (for 5 turns) If performance < X % of the average, then imitateSunday, February 12, 2012
  • 37. Under the hood • NK model of problem solving as search (see paper for full description) • Additions: constraints and imitation • Assumptions: • product development as myopic process • absolute constraints • imitation of successful technologies (at X %)Sunday, February 12, 2012
  • 38. Under the hood • NK model of problem solving as search (see paper for full description) • Additions: constraints and imitation • Assumptions: • product development as myopic process • absolute constraints • imitation of successful technologies • relatively stable industry - all in all, adequate fit for copper caseSunday, February 12, 2012
  • 39. Under the hood (2) • NK model of problem solving as search • “Firms” develop “technologies” composed of 16 “components” (think “production recipe” etc.) • Each component has two options: 0 or 1 • The firm knows the “performance” of the 16-bit string it uses • It tries to change one component at a time to improve the performance Think as a “game”Sunday, February 12, 2012
  • 40. Under the hood (2) • NK model of problem solving as search • “Firms” develop “technologies” composed of 16 “components” (think “production recipe” etc.) • Each component has two options: 0 or 1 • The firm knows the “performance” of the 16-bit string it uses • It tries to change one component at a time to improve the performance • Path dependency: no going backSunday, February 12, 2012
  • 41. Parameters (nuts and bolts) N = 16, K = 0...15, 1 component constrained. 10-200 firms, 50 runs at each K value. • Intensity of imitation = intensity of competition • Radical innovation (long jumps) with variable search lengths (1...16) Long jumps had little effect, however!Sunday, February 12, 2012
  • 42. Parameters (nuts and bolts) N = 16, K = 0...15, 1 component constrained. 10-200 firms, 50 runs at each K value. • Intensity of imitation = intensity of competition • Radical innovation (long jumps) with variable search lengths (1...16) • (Choice of N and other simulation details “usual assumptions” in management simulations)Sunday, February 12, 2012
  • 43. Results: summary • No statistically significant change in performance - but it’s a possibility • Odds of negative and positive change roughly equal • Constraints are more likely to accelerate adoption of existing technologies, instead of development of new technologies • Constraints decrease variety if imitation is allowed • Results are robust to parameter changesSunday, February 12, 2012
  • 44. Representative? Copper smelting furnaces in use, 1930-1990Sunday, February 12, 2012
  • 45. Discussion &c. Validity and generalizability: OK, for relatively stable industries? If so, • Constraints can both constrain and facilitate • Clear success stories will be rare • Normative demand-pull model inadequate? • Competition is not good for resilience • The future is already here - it’s just not widely distributed janne.m.korhonen@aalto.fiSunday, February 12, 2012

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