Présentation MP-RP

Castiaux & Timsit AIMS 2011 1

Market or resources,What impact on performance?

An approach through multi-agent simulations

Annick Castiaux1 and Jean-Philippe Timsit2

1University of Namur, Belgium2Centre de Recherche Public Henri Tudor, Luxembourg

[email protected]@tudor.lu

mailto:[email protected]


Context

The larger objective:

– Using multi-agent simulations to offer a bridge the gap between theoretical works and empirical results

The original question:

– Which strategic model - RBV or IO - does lead to the highest performance?

The final question(s):

– Resource-push or Market-pull?

– de alio or de novo?


Research questions

This study compares the performances obtained by firms following both their entry mode and their entry strategy:

– The entry mode – entry status – differentiates de novo and de alio firms;

– The entry strategy can be either pulled by the perceived market performance (Market-Pull, MP) or pushed by the firm's pool of resources (Resource-Push, RP)


Entry Modes: de Novo and de Alio Firms

The entry on a new market plays a very important in industrial economics and in strategic management

-Klepper & Simons (2000)

de novo and de alio differ mainly by their market entry conditions, their behavior on this market and thus their fate

-Khessina et Carroll (2008)

However, the precise mechanisms though which entry mode can lead to over-performances remain unclear

-Fan (2010)

Differences in resource pools are intrinsic to the nature of de novo and de alio firms. As a matter of fact, de alio firms emerge from the diversification of a parent firm. The parent firm decides to enter a market not by itself but by creating another organization.

-Carroll et al. (1996)


Entry Strategies: Resources and Markets

"For the firm, resources and products are two sides of the same coin“

-Wernerfelt (1984)

The foundations of the IO model are based on the paradigm Structure-Conduct-Performance.

-Mason (1939; 1957) and Bain (1951; 1968)

The resource-based theory takes its origins in the work of Penrose. (1955, 1959)

The firm is composed of resources that constitute its substance.

-Penrose (1959) and Wernerfelt (1984)


Method

Simulating through a multi-agent system the interaction between : Firms characterized by

� An entry mode (DN or DA) simulated by different initial pools of resources

� A strategic orientation (RP or MP) leading to different conditions of market choice

� Pools of resources varying following sales and purchases

� Financial assets

Markets characterized by

� Entry barriers (assets required to enter the market)

� A size (number of market shares)

� A market share value depending on the competition intensity

A strategic factors market


Model


Computing the performance of a given firm i :

et

Choosing market j following strategic orientation :

MP :

RP :


Algorithm


Parameters

200 firms interacting with 20 markets

1046 simulations of 200 cycles each

Recording every 10 cycles of Number of firms of each type (MP, RP ; MPDN, MPDA, RPDN, RPDA) among the

10 best performing firms

Number of surviving firms of each type

Total performance of the best firm of each type

Average performance of the 5 best performing firms from each type

Average performance of the 10 best performing firms from each type

Average performance of all surviving firms of each type


10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 2000

1

2

3

4

5

6

7

8

9

10

MP RP

Simulation cycles

Nu

mb

er

in b

est

10

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 2000

1

2

3

4

5

6

7

8

9

10

MPDN MPDA RPDN RPDA

Simulation cycles

Nu

mb

er

in b

est

10

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 2000%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

MPDN MPDA RPDN RPDA

Simulation cycles

Le

ad

ers

hip

in a

ll si

mu

latio

ns

(a.2)

(b.2)

(a.1)

(a) Number of firms of each type present in the 10 best performing firms

(b) Percentage of simulations where a given type is predominant in 10 best performing firms

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 2000%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

MP RP

Simulation cycles

Le

ad

ers

hip

in a

ll si

mu

latio

ns

(b.1)

Which types of firms in the 10 best ones?

Annick Castiaux

(a.1) Number of MP or RP firms in 10 best following time(a.2) Segmentation of preceding diagram differentiating MPDN, MPDA, RPDN and RPDA(b.1) Every 10 cycles, percentage of simulations where MPF or RPF are majoritary in 10 best(b.2) Every 10 cycles, percentage of simulations where MPDN, MPDA, RPDN or RPDA are majoritary in 10 bestNote: In b diagrams, the cases where equal leading numbers of firms from different types where found have been distributed equally on the corresponding populations


0 20 40 60 80 100 120 140 160 180 2000

20

40

60

80

100

120

Simulation cycles

Nu

mb

er

of s

urv

ivin

g fi

rms

MP firms

RP firms

0 20 40 60 80 100 120 140 160 180 2000

10

20

30

40

50

60

Simulation cycles

Nu

mb

er

of s

urv

ivin

g fi

rms MPDA firms

MPDN firms

RPDN firms

RPDA firms

Survival of firms following in each population(a) (b)

Survival rate of firms following their type


0 20 40 60 80 100 120 140 160 180 2000

1000

2000

3000

4000

5000

6000

7000

Simulation cycles

Ave

rag

e p

erf

orm

an

ce (

a.u

.)

0 20 40 60 80 100 120 140 160 180 2000

1000

2000

3000

4000

5000

6000

7000

Simulation cycles

Ave

rag

e p

erf

orm

an

ce (

a.u

.)

0 20 40 60 80 100 120 140 160 180 2000

1000

2000

3000

4000

5000

6000

7000

Simulation cycles

Ave

rag

e p

erf

orm

an

ce (

a.u

.)

0 20 40 60 80 100 120 140 160 180 2000

1000

2000

3000

4000

5000

6000

7000

Simulation cycles

Ave

rag

e p

erf

orm

an

ce (

a.u

.)

MP firm

RP firm

MP firms

RP firms

(a) Average performance of best firm in each type

(b) Average performance of the 5 best firms in each type

(a.2)(a.1)

(b.1) (b.2)

RPDN firm

MPDA firm

MPDN firm

RPDA firm

RPDN firms

MPDN firms

MPDA firms

RPDA firms

Performance evolution following firm type


0 20 40 60 80 100 120 140 160 180 200

-2000

-1000

0

1000

2000

3000

4000

5000

Simulation cycles

Ave

rag

e p

erf

orm

an

ce (

a.u

.)

(c) Average performance of the 10 best firms in each type

0 20 40 60 80 100 120 140 160 180 2000

1000

2000

3000

4000

5000

6000

Simulation cycles

Ave

rag

e p

erf

orm

an

ce (

a.u

.)

0 20 40 60 80 100 120 140 160 180 2000

1000

2000

3000

4000

5000

6000

Simulation cycles

Ave

rag

e p

erf

orm

an

ce (

a.u

.)

(c.2)(c.1)

MP firms

RP firms RPDN firms

MPDN firms

RPDA firms

MPDA firms

0 20 40 60 80 100 120 140 160 180 200

-1000

0

1000

2000

3000

4000

5000

Simulation cycles

Ave

rag

e p

erf

orm

an

ce (

a.u

.)

(d) Average performance of all firms in each type

MP firms

RP firms

(d.1) (d.2)

RPDN firms

MPDN firmsRPDA firms

MPDA firms


Discussion

Higher performance of RP firms Performance accumulation thanks to a higher stability

Luck factor: Initial compliance with a high-profit market

The majority of highly performing RP firms are de novo Higher flexibility in their initial choice

More opportunities to find an attractive market

Low survival rate of RP firms, especially de alio RP firms Difficulty to move to another market if no profit

For some of them, no market corresponding to their assets is found

Exceptional survival rate of MP firms, especially de alio MP firms Flexibility to choose any market, even more if they have a lot of resources


Conclusions

Simulation method

Opportunities Limits

Future projects

Change of strategy Resources variability Innovation ...


Thank you for your attention

Questions and comments ?

[email protected]@fundp.ac.be



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