innovation, strategic alliances and...

Innovation, Strategic Alliances and Networks

Nicolas Jonard

DIMETIC Strasbourg, March 30, 2009

2

BackgroundWe have seen, over the past 2 decades, a changing perspective on firms:

From firms as atomistic, autonomous agents competing in the anonymous marketplace To firms as embedded in a rich network of horizontal and vertical relationships with other organizational actors

Lasting, strategic relations tie firms to suppliers, customers, competitors and other entities, across and within industries and countriesIs the conduct and performance of firms affected by the network of relationships in which they are embedded?

3

Strategic alliances and networksAlliance: Voluntarily initiated cooperative agreement between firms that involves exchange, sharing or co-development and can include contributions by partners of capital, technology or firm-specific assets

Strategic network as a set of alliances

Firms engaging in alliances are relationally and structurally embedded

4

Neither markets nor hierarchiesMake or buy depending on contracting hazard and transaction costs

Market exchange is better when contracts are readily written and enforced, and transaction costs are low

Hierarchies are better when opportunism is likely and transaction costs are high

Alliances (and networks) make sense in between: when transaction costs are not so high that they require hierarchical control but not so low that market exchange is simple

5

The resource-based viewEnduring competitive advantage originates in the firm holding inimitable and non-substitutable resources (INSRs)

Networking is a strategy for reaching beyond the boundaries of the firm for complementary INSRs: knowledge and information, labor, capital, goods and services, access to further resources,…

A firm's network attributes themselves constitute INSRs (and constraints...), providing a possible answer to the question of the origin of resources

6

Social capitalTransactions/ties are embedded in a history of interaction/ties

Alliances do not exist in a vacuum, but in a larger network of alliances

Social capital theories tell us that there is value from social network position

Do firms differ in their conduct and profitability because they hold different network positions?

7

Relational embeddednessEvidence that firms go back over and over to their past partners

Value of strong/repeated ties: Exchange of high-quality information and tacit knowledge: deeper understanding of the partner, shared goals and representations,…Part of a social control mechanism: trust facilitates interaction, mitigates appropriation concerns (thus perhaps implies simpler and less costly contracts): knowledge-based trust

8

Structural embeddednessLocal structure beyond neighbours: indirect tiesValue from closure (Coleman):

Clustered neighbourhoods imply shared information about others' trustworthiness, capabilities, objectives (ex ante) and induce good behaviourDeterrence-based trust from reputational concerns

Value from holes (Burt):Optimizing knowledge flows implies maintaining structural holes to avoid redundanciesConnect distant parts of the network for rapid access to resources and informationInsurance against technological surprises unforeseeable from local cluster

9

ImplicationsA network perspective can help understanding the nature of competition/degree of profitability/barriers to entry across industriesA network perspective can help understanding intra-industry differences, groups (cliques) and barriers to mobility across groupsNetwork characteristics (density, holes, structural equivalence, core vs. periphery,…) matter:

Dense networks can be conducive to tacit or explicit oligopoly coordination, implying increased profitabilityStructural holes can confer power through control, implying increased profitability

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V={1,2,3,4,5,6,7,8}

NetworksVertices

Edges

Neighbourhood

Distance

Clustering

Betweenness

4 2

5 6

1

7 8

3

11

g={12,13,24,25,26,37,38,

45,46,56,78}

NetworksVertices

Edges

Neighbourhood

Distance

Clustering

Betweenness

4 2

5 6

1

7 8

3

12

N2(g)={1,4,5,6}

NetworksVertices

Edges

Neighbourhood

Distance

Clustering

Betweenness

4 2

5 6

1

7 8

3

13

d(2,i;g)=1 ∀ i∈N2(g)

d(2,7;g)=3

NetworksVertices

Edges

Neighbourhood

Distance

Clustering

Betweenness

4 2

5 6

1

7 8

3

14

NetworksVertices

Edges

Neighbourhood

Distance

Clustering I

Betweenness

4 2

5 6

1

7 8

3

3

3x2/2c4= =1

15

NetworksVertices

Edges

Neighbourhood

Distance

Clustering II

Betweenness

4 2

5 6

1

7 8

3

4x3/2

3c2= =1/2

16

NetworksVertices

Edges

Neighbourhood

Distance

Clustering III

Betweenness

4 2

5 6

1

7 8

3

2x1/2

0c1= =0

17

NetworksVertices

Edges

Neighbourhood

Distance

Clustering

Betweenness I

4 2

5 6

1

7 8

3

b4= 0

18

NetworksVertices

Edges

Neighbourhood

Distance

Clustering

Betweenness II

4 2

5 6

1

7 8

3

b1= 4x3 = 12

19

Innovation networksInnovation networks: networks emerging from firms' decisions to form strategic alliances aimed at learning and producing new knowledge

R&D collaborative agreement, research joint ventures,…

Purpose: reaching beyond the boundaries of the firm for complementary knowledge resources in order to gain competitive advantage

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Innovation in alliancesInnovation as knowledge recombination

Firms heterogeneous in their knowledge endowments

Properties of the innovation process:Inverted-U relationship between “distance” and the likelihood of successIncreased post-alliance overlap: learning from partners makes partners less attractive

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Properties of innovation networks

Sparse

Clustered

Low diameter

Asymmetric degree distribution

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Small worldsAre innovation networks small-world networks?

Small worlds are ubiquitous:6 degrees of separation

Board membership and ownership networks

Power grid US

Neural network of worm

Kevin Bacon game

Scientific co-authorship

Biotech alliances

...

23

Standard explanationsSparse: costs of link formation

Skewed link distribution:Heterogeneity in attributes and goalsPreferential attachment

Clustered:Relational and structural embeddedness, social capital, trust and controlAgglomeration effects (innovation in the air, industrial districts, labor, face-to-face interactions, tacit knowledge,…)

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But…Empirical studies emphasize the causal role of network-oriented structural and strategic motives in partner selection

Very little (static), if any consideration at all of partner complementarity in alliance formation

Is partner complementarity or embeddedness causal (spurious…)?

What about the relationship between firm position and firm performance?

25

A simple modelFirms located in a knowledge space, holding distinct endowments

Strategic alliances form with partners neither too similar nor too dissimilar

Alliances permit learning: similarity ↑

Alliances permit innovation: similarity ↓

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Alliance decisionKnowledge space: K=[0,1]×[0,1]

Address of firm i: (xi,yi), with 0≤xi,yi≤1

Distance between i and j in knowledge space is standard Euclidean distance

Alliance partners must be both similar and complementary: δ1≤di,j(k)≤δ

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Alliance decision

Consider the red vertex:

Vertices in the white area satisfy the similarity constraint

Vertices in the white area satisfy the complementarity constraint

The red vertex forms only 2 links 1

y

0

δ

δ1

x

1

28

Alliance decision

All firms behave in a symmetric manner

A network of strategic alliances forms

This one has 2 singletons and 2 connected components

1

y

0 x

1

29

EquilibriumThe strategic alliance game is a simultaneous link formation game

Firms’ incentives to form (or not to form) a partnership are symmetric

There is a unique equilibrium network g*={ij: δ1≤dij(k)≤δ}

30

Knowledge dynamicsTwofold purpose of joint R&D activities:

Absorb existing knowledge (learning)

Produce new knowledge (innovation)

Learning increases the overlap of the technological portfolios of the partnering firms

Firms move closer to each other in K (partial linear adjustment): xi(t+1)=αxj(t)+ (1−α)xi(t)

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Knowledge dynamicsInnovation causes a random reorganization of the knowledge space and partnering possibilities

Dislocation for any firm is determined by:Where in knowledge space the innovation takes place

How disruptive it is

Formally, the (expected) shock on any firm is:Decaying with the distance to the innovating partnership

Scaled by an industry-wide disruptiveness parameter θ

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Knowledge dynamics

The relationship between the range of dislocation and the distance d from the firm to the innovators:

Disruptive: black

Incremental: red

y

0

d

1/2

0

-1/2

33

Numerical experimentAt each time step firms form all possible alliances

Firms learn from and move towards partners

With small probability one innovation occurs, imposing a relocation on all firms in the industry

Settings:Industry size n=75 firms

Similarity and complementary constraints: δ=0.2, δ1=0.06

Absorptive capacity α=0.01 (speed of partial adjustment)

History length: 1,500 periods

25 replications

θ varies from 1/20 to 1

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Snapshot results I (t=500)

Industry network

Incremental innovation

Component sizes 32, 42

Average degree 12.7

Density 0.17

Clustering0.56

Rescaled clustering 3.2

Average distance 2.01

Rescaled distance 1.02

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Snapshot results II (t=500)

Industry network

Disruptive innovationComponent size 75

Average degree 7.01

Density 0.095

Clustering0.52




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Snapshot results III (t=500)

Industry network

Radical innovationComponent size 75

Average degree 5.01

Density 0.067

Clustering0.40




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Time series results IAverage number of partners per firm (moving average)

Time runs from 0 to 1,500

3 levels of disruptiveness

Outbursts and collapses in network activity

1 201 401 601 801 1001 1201Time

4

6

8

10

12

14

16

18

20

Ave

rage

deg

ree

θ = 0.05 θ = 0.1 θ = 1

38

Time series results IIAverage clustering coefficient (moving average)



Persistent fluctuations in network organization

1 201 401 601 801 1001 1201Time

0.3

0.4

0.5

0.6

0.7

Clu

ster

ing

θ = 0.05 θ = 0.1 θ = 1

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Time series results IIIDistance among reachable pairs (moving average)



Persistent fluctuations in network organization

1 201 401 601 801 1001 1201Time

1

2

3

4

5

6

7

Ave

rage

dis

tanc

e

θ = 0.05 θ = 0.1 θ = 1

40

Aggregate results IRelationship between average degree and the disruptiveness of the innovation regime

Display the range and central tendency over the set of replications

Lowest for intermediate disruptiveness

0.05 0.08 0.14 0.22 0.37 0.61 1.00θ

10

15

20

25

30

35

40

45

50

Deg

ree

Median 25%-75%

41

Aggregate results IIRelationship between rescaled weighted clustering and disruptiveness of innovation regime

Rescaling with respect to random benchmark

Always > 1, strongest for intermediate disruptiveness

0.05 0.08 0.14 0.22 0.37 0.61 1.00θ

1

2

3

4

5

Res

cale

d w

eigh

ted

clus

terin

g

Median 25%-75%

42

Aggregate results IIIRelationship between rescaled weighted distance and disruptiveness of innovation regime

Rescaling with respect to random benchmark

Always > 1, strongest for intermediate disruptiveness

0.05 0.08 0.14 0.22 0.37 0.61 1.00θ

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

Res

cale

d w

eigh

ted

dist

ance

Median 25%-75%

43

ImplicationsHigh clustering and low characteristic path length as produced by the model are the defining features of small worlds

Results so far suggest that small worlds arise from the conjunction of randomness in innovation and the short-term quest for suitable partners

No sophisticated attempts from firms to strategically manipulate their network, no social capital value to specific positions

44

Position and performance IPerformance: total disruption imposed on other firms

Relation between degree (number of partners) and performance?

Always positive, strongest for intermediate disruptiveness

0.05 0.08 0.14 0.22 0.37 0.61 1.00θ

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Cor

rela

tion

of d

egre

e an

d pe

rform

ance

Median 25%-75%

45

Position and performance IIRelation between weighted clustering (density of ego-network) and performance?

Positive for incremental regimes: constraint is good

Negative for disruptive regimes: holes are good

0.05 0.08 0.14 0.22 0.37 0.61 1.00θ

-0.2

-0.1

0.0

0.1

0.2

Cor

rela

tion

of w

eigh

ted

clus

terin

g an

dpe

rform

ance

Median 25%-75%

46

Position and performance IIIRelation between betweenness (centrality) and performance?

Negative for incremental regimes: holes are bad

Positive for disruptive regimes: holes are good 0.05 0.08 0.14 0.22 0.37 0.61 1.00

θ

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

Cor

rela

tion

of b

etw

eenn

ess

and

perfo

rman

ce

Median 25%-75%

47

ImplicationsThe relative benefits of structural holes and cliques are contingent on industry life-cycles and the extent to which innovation is disruptive

Similar findings in the literature: cf. steel (incremental) vs. semi-conductor (more disruptive) industries

Firms perform no sophisticated calculation in order to optimize their network position:

No attempt to span holes in order to be insured against “distant” innovationsNo considerations of social capital such as partner referrals or returning to known partners

48

ConclusionsA network approach can help understand persistent differences in the conduct and performance of firms

Allegedly, learning, social capital and network-oriented strategic motives materialize in partner selection

A simple model has replicated all the conduct (repeated ties and transitivity) and properties (clustering and short distances) characteristic of observed alliance networks

49

ConclusionsSpecifically, cliques enhance performance when innovation is incremental; structural holes enhance performance when innovation is disruptive

Moderately disruptive innovation yields pronounced small world features and no impact of position on performance

Consistent with the small world view of cliques and structural holes as complementary factors jointly enhancing network efficiency in moving resources

50

ConclusionsResults stems from:

Complementarity in the knowledge space, combined with learning, generates inertia and transitivity in firms’partnering decisions

Discontinuities in knowledge endowments resulting from innovations generate ties spanning cliques in disconnected regions of the network

Incorporating time-varying measures of partner complementarity could allow to identify the real effects of network position

innovation, strategic alliances and...

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