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!

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

  1. 1. Sunday, February 12, 2012
  2. 2. Sunday, February 12, 2012
  3. 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. 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. 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. 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. 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. 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. 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. 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. 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. 12. Furnace developments Clearly, both Inco and Outokumpu achieved remarkable improvementsSunday, February 12, 2012
  13. 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. 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. 15. Constraint 1: energy Copper smelting furnaces in use, 1930-1990Sunday, February 12, 2012
  16. 16. Constraint 2: environment Copper smelting furnaces in use, 1930-1990Sunday, February 12, 2012
  17. 17. Constraint 3: 1973 oil crisis Copper smelting furnaces in use, 1930-1990Sunday, February 12, 2012
  18. 18. But! That’s just one case study - what does it prove?Sunday, February 12, 2012
  19. 19. That’s just one case study - what does it prove? Not very much.Sunday, February 12, 2012
  20. 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. 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. 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. 23. Example: one simulation 1 2 Performance 3 Period of stability; mature industry, 3 technologies in use in 100 firmsSunday, February 12, 2012
  24. 24. Example: one simulation 2 Performance 1 3 Constraint introduced; 2 out of 3 technologies affectedSunday, February 12, 2012
  25. 25. Example: one simulation 1 2 Performance Technology 3 dies off; former users copy Tech 2Sunday, February 12, 2012
  26. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 44. Representative? Copper smelting furnaces in use, 1930-1990Sunday, February 12, 2012
  45. 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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