oswc 2012: modeling non-financial constraints in the development and adoption of new technologies

Sunday, February 12, 2012

Janne M. Korhonen & Mikito TakadaAalto University School of [email protected]

Modeling the role of non-financial constraints in the development and adoption of new technologiesOrganization Science Winter Conference 2012 Pre-Conference Session I


How......resource constraints affect innovation? ...some studies say constraints are good for creativity and innovation - others say that they’re bad?


• 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

innovation

What the science says


Research questionsWhat 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?


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.


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 2006

Defining technologies:


Case: Copper smeltingAfter the WW2, copper smelting technology achieved a breakthrough in efficiency.

Two companies developed new technologies:



Two companies developed new technologies:• one had essentially inexhaustible resources

Inco, Canada



Two companies developed new technologies:• another was forced to invent something, or else

Outokumpu, Finland


Clearly, both Inco and Outokumpu achieved remarkable improvements

Furnace developments


Case: Copper smeltingHowever, 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 plants


Copper smelting furnaces in use, 1930-1990

Outokumpu flash

furn

aces



Constraint 1: energy



Constraint 2: environment



Constraint 3: 1973 oil crisis


But!That’s just one case study - what does it prove?


That’s just one case study - what does it prove?Not very much.


That’s just one case study - what does it prove?Not very much. And case studies of constrained innovation are rare.


cf. e.g. Davis et al. (2007)

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.


95 % copying = firms will copy if their performance is less than 95 % of average

Example: one simulation

Initial innovation:Very rapid convergence

to three technologies

Per

form

ance

123



Period of stability;mature industry,

3 technologies in usein 100 firms

123

Per

form

ance



Constraint introduced; 2 out of 3 technologies affected

Per

form

ance 1

2

3



Technology 3 dies off;former users copy Tech 2

Per

form

ance

12



Technology 3 dies off;former users copy Tech 2

Technology 1 is hit, butfinds a new path

Per

form

ance

12



Constraint lifted;Technology 1 improves

even further;new stable states

found & performance is increased, but

variety is reduced

Per

form

ance


No statistically significant change; variance high, however!

Results: improvement-4-2

02

4

a) No imitation allowed

K value

Cha

nge

(per

cent

)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

-4-2

02

4

b) Imitation threshold at 75%

K value

Cha

nge

(per

cent

)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

-4-2

02

4

c) Always imitate

K value

Cha

nge

(per

cent

)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

-4-2

02

4

d) Radical innovation, short search

K value

Cha

nge

(per

cent

)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

-4-2

02

4

e) Radical innovation, medium search

K valueC

hang

e (p

erce

nt)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

-4-2

02

4f) Radical innovation, distant search

K value

Cha

nge

(per

cent

)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

-4-2

02

4


K value

Cha

nge

(per

cent

)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

-4-2

02

4


K value

Cha

nge

(per

cent

)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

-4-2

02

4

c) Always imitate

K value

Cha

nge

(per

cent

)0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

-4-2

02

4


K value

Cha

nge

(per

cent

)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

-4-2

02

4


K value

Cha

nge

(per

cent

)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

-4-2

02

4

f) Radical innovation, distant search

K value

Cha

nge

(per

cent

)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

variance

standard error

Imitation/competition intensity 75% Imitation/competition intensity 100%

zero change line

K value (complexity) K value (complexity)


Stable results, only real variance due to high imitation/competition intensities.

Results: Odds of changeLikelihood of change

at varying intensity levelsLikelihood of change

at varying long jump lengths

K value (complexity) K value (complexity)

% o

f ev

ents

% o

f ev

ents


Average share of pre-existing technologies over 50% in all cases; however, high variance!

Results: Old/new tech?

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100

c) Always imitate

K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100

c) Always imitate

K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100

c) Always imitate

K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

No imitation Imitation at 75% Always imitate

K value (complexity) K value (complexity)K value (complexity)

% o

f ol

d t

ech

50/50 old & newtechnologies


High variance!

Results: Old/new tech?

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100

c) Always imitate

K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100

c) Always imitate

K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100

c) Always imitate

K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

020

4060

80100


K value

% p

re-e

xist

ing

tech

nolo

gies

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15


K value (complexity) K value (complexity)K value (complexity)

% o

f ol

d t

ech


Entropy (variety) drops with most constraints, except when imitation is not allowed.

Results: Variety


Time (turns)


Entropy (variety) drops with most constraints, except when imitation is not allowed.

Results: Variety


Period of constraints


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 changes


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)



paper for full description)• Additions: constraints and imitation

Alternative 1 forced for component 1 (for 5 turns) If performance < X % of the

average, then imitate



paper for full description)• Additions: constraints and imitation• Assumptions: • product development as myopic process• absolute constraints• imitation of successful technologies (at X %)



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 case


• 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”

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 back

Under the hood (2)


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!

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)

Parameters (nuts and bolts)


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 changes



Representative?


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

[email protected], February 12, 2012