The Conceptualisation of GrowthAre Employment and Turnover Interchangeable Criteria?

http://joe.sagepub.comJournal of Entrepreneurship

DOI: 10.1177/097135570801800102 2009; 18; 21 Journal of Entrepreneurship

Frank Janssen Interchangeable Criteria?

The Conceptualisation of Growth: Are Employment and Turnover

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The Journal of Entrepreneurship, 18, 1 (2009): 21–45!"#$%&'()*+,-*./0 Los Angeles/London/New Delhi/Singapore/Washington DCDOI: 10.1177/097135570801800102

The Conceptualisation of Growth: Are Employment and Turnover

Interchangeable Criteria?

FRANK JANSSEN

Many different variables have been used to measure the rm’s growth. Employment and sales are the two most used criteria within the literature on growth determinants. Most of the time, researchers do not provide any theoretical justi cation for using one or the other criterion and appear to use them interchangeably, as if they constituted equivalent representations of the same observable reality, that is, the rm’s size. Using a sample of Belgian small and medium sized enterprises (SMEs), we have tested the interchangeability of these two criteria. Our results show that these two types of growth are determined by factors that are largely distinct. Organisational growth is thus shown to be a multifaceted phenomenon that cannot be reduced to a single dimension. This also means that growth cannot be measured through composite indicators, mixing different variables like sales or workforce, because they do not measure the same phenomenon. From a managerial point of view, this means that the resources that are mobilised and the strategies that are implemented by a rm should vary depending on whether it wishes to attain one or the other type of growth.

12,/3%4,/005/%*0%625752.75%89,*2%&2.:500.2%.:%$/-25;25/5'209*;%,-%<.'=,*/%!+9..)%.:%>,/,?5@5/-A%8,-9.)*+%B/*=520*-C%.:%<.'=,*/A%65)?*'@D

E'@52.'0%0-'7*50%9,=5 tried to identify the determinants of rms’ growth, mainly in order to isolate those factors which would allow us to distinguish the successful businesses of tomorrow from those which will fail to grow. Ideally, this would allow the implementation of better-targeted economic policies, since growing rms greatly contribute to the creation of jobs and of wealth. (Birley & Westhead, 1990; Gallagher & Miller, 1991; McMahon, 1999; Storey et al., 1987). However, the way in which growth has been conceptualised varies considerably from one study to the other. It has been measured by many different variables, such as pro t, assets, capital, added value, return on investment (ROI), em-ployment, sales or even subjective measures, generally based on the


22 / Frank Janssen

The Journal of Entrepreneurship, 18, 1 (2009): 21–45

objectives of the entrepreneur. Few studies have explained why they chose a given criterion rather than another (Birley & Westhead, 1990). The use of distinct measures, combined with an absence of justi cation for the choice made, can generate inconsistencies and constitutes an obstacle to the de-velopment of a coherent theoretical framework.

Several studies, however, tend to show that the different criteria used for measuring growth are correlated. This could mean that the criteria are interchangeable and would allow researchers not to have to justify their choice. Some studies have concluded that there is a correlation between employment growth and sales growth (Chrisman & McMullan, 2000; Delmar, 1997; Wiklund, 1999). Another study measuring growth in terms of assets, equity, added value, sales and employment has also observed im-portant correlations between these variables (Manigart, 1996). Similarly, Morrison and Roth (1992) found strong correlations between ROI, return on assets (ROA) and sales growth. McPherson (1992) also reached the conclusion that exports, pro t, added value, product and process devel-opment are signi cantly correlated with each other. Previously, Child (1973) had also observed signi cant correlations between the different measures of an organisation’s size, such as assets, workforce and sales.

Therefore, these measures could appear to encompass the same concept of rm size and could be interchangeable from an empirical point of view. This could also mean that the time lag between sales growth and employment growth is of little importance. However, a minority of re-searchers has taken the view that the factors in uencing some type’s growth are not the same as those affecting other types (Weinzimmer et al., 1998). Our purpose is to test the interchangeability of employment growth and sales growth.

In the rst part of this article, we will examine the main criteria used to conceptualise growth within the literature on growth determinants and show that employment and sales are the most frequently used variables. In the second part, on the basis of a literature review, we will try to iden-tify all potential determinants of growth previously used in empirical works. In the third part, we will investigate whether the use of different conceptualisations of growth has an effect, or not, on the results of causal tests on growth determinants. We will rst present our methodological framework and then test the impact of each of these determinants on em-ployment growth and on sales growth, in order to control whether these two types of growth are determined by the same factors.


The Conceptualisation of Growth / 23


Main Conceptualisations of Growth

According to Westhead and Birley (1995), the heterogeneity of research on growth determinants is partly due to an absence of consensus on how to de ne growth. Most studies contain no discussion of the appropriateness of the variable used to measure growth and most do not justify the use of one speci c criterion rather than another. Delmar (1997) stresses that this heterogeneity hinders the development of a coherent theoretical body.

Firm growth is, in essence, the result of an increased demand for the products or services of the rm. First, it will result in an increase in sales. Second, it will allow the rm to invest in additional factors of production in order to adapt itself to the new level of demand. Theoretically, sales growth is chronologically the rst form of growth and precedes employment growth. It is also possible for a rm to increase its sales without hiring add-itional personnel and without acquiring other resources by, for example, sub-contracting or outsourcing (Delmar 1999). Increasing productivity could also allow an increase in the level of production without additional hiring. It would also be possible to replace workers by capital. Hence there should be an inverse relation between capital investment and employment growth (Wiklund, 1999). In some European countries, the high wage costs could also be an incentive to choose capital, rather than labour.

For Kirchoff, the creation of jobs is of primary importance because it measures economic growth (1991). For the entrepreneur, it can be an in-dicator of his or her success and, for society as a whole; it is a measure of the economic contribution of the rm to the common good (Dunkelberg & Cooper, 1982). This is why many economists and sociologists have used the criterion of employment growth to measure performance. According to Child (1973), employment is the most appropriate criterion to meas-ure the size of an organisation because it is, above all, human beings who are ‘organised’. Further, to the extent that the manager will, in principle, wait for demand to stabilise before recruiting more personnel, employment is a less volatile measure of growth than the sales criterion (Delmar, 1997). In certain European countries, the stability of this criterion is increased by labour-market rigidities linked to restrictive social legislation.

Within rms themselves, however, it would appear that success is mainly measured in terms of sales growth (Donckels, 1990). A study conducted in the UK on the barriers to growth faced by small and medium-sized enterprises (SMEs) found that, although the rms composing their survey


24 / Frank Janssen


sample had been selected on the basis of employment growth, the managers themselves generally expressed their growth objectives in terms of sales growth (Hughes, 1998).

The use of employment or sales criteria could also depend on the sector under study. For highly capitalised rms, such as those belonging to certain manufacturing sub-sectors, variation in assets would be a more accurate measure of growth than variation in the workforce. This criterion cannot be used in the service sector, which is more labour-intensive, and for which the employment criterion is a better t. These observations cannot, however, be generalised across all sectors. In fact, certain service sub-sectors, such as the transport sector, for instance, are highly capitalised, while certain manufacturing sub-sectors, such as the garment industry, are highly labour-intensive (Weinzimmer et al., 1998). In addition, the growth of a rm which is capital-intensive will be re ected in its sales before impacting on its assets.

On the whole, the employment criterion seems to be more relevant from a societal point of view, while the sales criterion is more relevant from the manager’s point of view. It is therefore logical that these two cri-teria predominate within the empirical literature on growth determinants (Delmar, 1997).

Table 1 presents the studies on growth determinants that we consider to be the most representative. We have selected these for ve reasons. First, we have mainly chosen studies that integrate the ndings of earlier work and study the simultaneous impact of several variables. Certain studies are not mentioned because they have been either partially or entirely replicated by other authors. Further, we have restricted this overview to studies of causality. We exclude studies that do not establish a clear causal link between the independent variables and growth. The studies that supply all the data necessary for their comprehension have been selected. Some studies have not been selected because the resulting publications are not suf ciently precise about the elements necessary to replicate the results. We have also sought to list studies that can be linked to different scienti c disciplines, mainly economics and management. Finally, even though the literature on this topic is mainly American, we have sought to present studies from other countries as well.

The table identi es the country where the research was carried out, the theme of the research, the conceptualisation of growth that each study used (employment, sales, and so on) and nally, the type of independent variables used.


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Con

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ualis

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Gro

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Type

s of I

ndep

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aria

bles

Hym

er a

nd P

ashi

gian

(196

2)U

.S.A

.Im

pact

of

rm si

ze

Ass

ets

Firm

Sing

h an

d W

hitti

ngto

n (1

975)

U.K

.Im

pact

of

rm si

ze

Net

ass

ets

Cap

ital

Firm

Dun

kelb

erg

and

Coo

per

(198

2)U

.S.A

.Im

pact

of t

he c

hara

cter

istic

s and

ob

ject

ives

of t

he e

ntre

pren

eur,

as

wel

l as c

hara

cter

istic

s of t

he

rm

Empl

oym

ent

Man

ager

Firm

Envi

ronm

ent

Kum

ar (1

985)

U.K

.Im

pact

of

rm si

ze

Net

ass

ets

Phys

ical

ass

ets

Ow

ner’s

equ

ityEm

ploy

men

tSa

les

Firm

Mill

er a

nd T

oulo

use

(198

6)C

anad

a Im

pact

of t

he p

sych

olog

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ch

arac

teris

tics o

f the

ent

repr

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ce re

lativ

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the

indu

stry

ave

rage

Sale

s N

et re

venu

es

RO

I

Man

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Envi

ronm

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Fit

Evan

s (19

87)

U.S

.A.

Impa

ct o

f rm

size

and

age

Em

ploy

men

tFi

rmEi

senh

ardt

and

Sc

hoon

hove

n (1

990)

U.S

.A.

Impa

ct o

f cha

ract

eris

tics o

f the

fo

undi

ng g

roup

, of t

he st

rate

gy a

nd

the

envi

ronm

ent

Sale

sM

anag

erSt

rate

gyEn

viro

nmen

tFi

tA

cs a

nd A

udre

tsch

(199

0)U

.S.A

.Im

pact

of s

ecto

r var

iabl

es o

n gr

owth

Sale

sEn

viro

nmen

t (Tab

le 1

con

tinue

d)


(Tab

le 1

con

tinue

d)

Refe

renc

eC

ount

ryTh

eme

Con

cept

ualis

atio

n of

Gro

wth

Type

s of I

ndep

ende

nt V

aria

bles

Kal

lebe

rg a

nd L

eich

t (1

991)

U.S

.A.

Impa

ct o

f the

cha

ract

eris

tics o

f th

e en

trepr

eneu

r, of

org

anis

atio

nal

stru

ctur

es a

nd o

f the

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stry

Gro

ss re

venu

esM

anag

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rmSt

rate

gyEn

viro

nmen

tM

cCar

thy

et a

l. (1

993)

U.S

.A.

Impa

ct o

f esc

alat

ion

of c

omm

itmen

t A

sset

sM

anag

erW

einz

imm

er (1

993)

U.S

.A.

Impa

ct o

f the

cha

ract

eris

tics o

f the

m

anag

ing

team

, of t

he st

rate

gy a

nd

the

envi

ronm

ent

Sale

s A

sset

s Em

ploy

men

t

Man

ager

St

rate

gy

Envi

ronm

ent

Fit

Sieg

el e

t al.

(199

3)U

.S.A

.D

istin

guis

hing

cha

ract

eris

tics o

f r

ms s

how

ing

stro

ng g

row

thR

even

ueM

anag

er

Stra

tegy

En

viro

nmen

tW

esth

ead

and

Birl

ey

(199

5)U

.K.

Impa

ct o

f cha

ract

eris

tics o

f the

m

anag

er, o

f the

rm

and

of t

he

envi

ronm

ent

Empl

oym

ent

Man

ager

Fi

rm

Envi

ronm

ent

Wes

thea

d (1

995)

U.K

.Im

pact

of c

hara

cter

istic

s of t

he

man

ager

, of t

he

rm a

nd o

f the

en

viro

nmen

t

Empl

oym

ent

Man

ager

Firm

St

rate

gyB

rush

and

Cha

gant

i (1

999)

U.S

.A.

In u

ence

of

rm’s

reso

urce

s Em

ploy

men

t N

et c

ash o

wM

anag

er

Stra

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En

viro

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tD

elm

ar (1

999)

Swed

enR

elat

ion

betw

een

grow

th

mot

ivat

ion

and

real

gro

wth

Empl

oym

ent

Sale

sM

anag

er

Firm

St

rate

gy

Envi

ronm

ent


Wije

war

dena

and

Ti

bbits

(199

9)A

ustra

liaIm

pact

of c

hara

cter

istic

s of t

he

rm,

of st

rate

gy a

nd o

f the

env

ironm

ent

Sale

sFi

rm

Stra

tegy

En

viro

nmen

tW

iklu

nd (1

999)

Swed

enIm

pact

of t

he e

ntre

pren

euria

l or

ient

atio

n of

the r

m

Empl

oym

ent

Sale

sEm

ploy

men

t gro

wth

rela

tive

to th

at a

mon

g co

mpe

ting r

ms

Sale

s gro

wth

rela

tive

to th

at a

mon

g co

mpe

ting r

ms

Firm

St

rate

gy

Envi

ronm

ent

Alm

us (2

002)

Ger

man

yC

hara

cter

istic

s of f

ast g

row

ing r

ms

Empl

oym

ent

Man

ager

Fi

rmSt

rate

gy

Envi

ronm

ent

X (2

006)

Bel

gium

Impa

ct o

f man

ager

’s c

hara

cter

istic

sEm

ploy

men

tM

anag

er


28 / Frank Janssen


As the table shows, a multiplicity of criteria has been used to con-ceptualise growth. The authors mentioned within this table have, for ex-ample, used nancial measures (assets, ROI, pro ts, owner’s equity, added value and cash ow), employment and sales criteria as well as sub-jective measures such as the entrepreneur’s expectations for relative growth. In most cases, the choice of a speci c criterion was determined by the available data, and did not depend on any theoretical justi cation.

As we had expected, the two most frequently used criteria are employ-ment (58 per cent) and sales (53 per cent). This observation con rms the results of a study carried out by Delmar (1997). It also raises two new questions: Do these variables indeed constitute interchangeable measures within a single conception of organisational growth, which seems to be the assertion made by many authors? Conversely, does the use of different conceptualisations of growth in uence the results of studies on growth determinants?

In order to answer these questions, we will test the interchangeability of the two principal conceptualisations of growth by verifying whether the two types of growth are determined by the same factors. Before that we will brie y present the determinants of growth that we have identi ed in literature and that we will use in our empirical tests.

Determinants of Growth

To test this interchangeability, on the basis of a literature review, we have attempted to identify all potential determinants of growth used in empirical studies. In order to create homogeneous groups, we have linked each of these determinants to one of ve groups of variables that might in uence the growth of a rm1: determinants linked to the man-ager(s), determinants linked to the characteristics of the rm, strategic determinants, environmental determinants and interactions (fit) be-tween different variables. Within the rst group, that is, managerial char-acteristics, we have listed 28 previously studied independent variables. For the second group, composed of determinants relative to the rm, our literature review leads us to retain 11 potential determinants. Among studies devoted to the impact of strategy on growth, we have identi ed 15 variables potentially in uencing growth. For environmental determinants, 15 potential determinants emerge from earlier studies. Finally, we have also identi ed 15 interactions between variables. Table 2 classi es the




F"6<$%HList of Independent Variables Potentially In uencing Growth

Type of Independent Variable Independent Variable

Expected Relationship

1. Internal locus of control + 2. Management experience + 3. Marketing, sales or R&D experience + 4. Consulting experience + 5. Experience within higher education institutions – 6. Experience within a large rm + 7. Former start-up experience + 8. Independent work experience + 9. Number of years of professional experience +10. Experience within the sector +11. Owner of several rms –12. Other activities during start-up phase +13. Previous entrepreneurial failure –

Characteristics 14. Education level +of manager 15. Education related to activity of the rm +

16. Management training +17. Entrepreneurship training +18. Belongs to entrepreneurial family +19. External manager +20. Second generation manager –21. ‘Pull’-type motivation at time of start-up +22. ‘Push’-type motivation at time of start-up –23. Age of manager –24. Gender (female) –25. Immigrant –26. Team of managers +27. Heterogeneous team +28. Previous common work experience +29. Size of rm –30. Age of rm –31. Bureaucratic and/or centralised structure –32. Limited liability +

Characteristics 33. Corporate governance +of the rm 34. Autonomy of the rm –

35. Local branch of foreign rm +36. Amount of nancial resources at start-up +37. Openness of capital +38. Amount of shares owned by employees +39. Rented premises +40. Exports +

(Table 2 continued)


30 / Frank Janssen




41. Aggressive strategy + 42. Product innovation + 43. New technology + 44. External communication policy + 45. External advisers +

Strategy 46. Network development + 47. National or international network + 48. Partnership with universities or research centres + 49. Formalised partnership + 50. Partnership with large rms + 51. Planning + 52. Personnel training programmes + 53. Human resource planning policy + 53a. Internal communication policy + 54. Sector growth rate + 55. Market concentration rate + 56. Barriers to entry + 57. Public aid + 58. Social and scal pressure – 59. Level of unionisation – 60. Level of local crime –

Environment 61. Appearance of neighbourhood + 62. Proximity of university institutions or research

parks+

63. Situation within an industrial park + 64. Situation within an urban zone + 65. Well-developed road, transport and communication infrastructure

+

66. Economically dynamic region + 67. Dynamism of the environment + 68. Complexity of the environment – 69. Fit between external manager and passive board of directors

+

70. Fit between founder or family manager and external managers

+

71. Fit between young manager and aggressive strategy + 72. Fit between divisional structure and diversi cation + 73. Fit between functional structure and single-product strategy

+

74. Fit between size of rm and choice and width of market

+

(Table 2 continued)

(Table 2 continued)




variables according to the group to which they belong, together with the expected sign of the relationship between each variable and growth ac-cording to the results of earlier studies.

We then tested the impact of these variables on employment growth, on the one hand, and on sales, on the other.

Method

Population, Sample and Representativeness

In order to determine the population of SMEs to be analysed, a database compiled by ING Bank (‘Internationale Nederlandse Groep’) that included all rms established in Belgium that have delivered their annual accounts to the Accounts Central of the Belgian National Bank, was used. We have retained all rms that were active over the period studied (from 1994 to 2000), for which we have data on employment for 1994 and 2000 and which corresponded in 1994 to the de nition of an SME given by the European Commission.2 In so far as numerous rms in Belgium have been created purely for scal reasons3 and do not really undertake activities,



Interactions ( t) 75. Fit between age of the rm and type of growth +76. Fit between rm size and type of growth +77. Fit between rm size and competitive strategy +78. Fit between heterogeneous team and dynamic environment

+

79. Fit between age of manager and dynamic environment

+

80. Fit between size of team and dynamic environment +81. Presence of a bureaucratic structure in a dynamic environment

–

82. Fit between aggressive strategy and a generous environment

+

83. Formalised or rigid planning within a dynamic environment

–

E.-50I + = positive relationship between determinant and growth. – = negative relationship between determinant and growth.

(Table 2 continued)


32 / Frank Janssen


we have eliminated rms that were already active in 1994, but that still employed less than ve people in 2000.

On the basis of these criteria, the population was composed of 11,481 rms. We randomly selected 788 rms, while at the same time ensuring proportions of micro (less than 10 people), small (between 10 and 49 people) and medium-sized (between 50 and 249 people) rms identical to those of the total population. In order to allow a dynamic analysis, this size criterion was checked at the beginning of the period studied, that is, 1994. We also kept the proportions of rms from the three regions of the country (Flanders, Wallonia and Brussels) identical to those of the population.

Out of the 788 rms, 331 refused to participate in the survey and 186 were not available during the interview period. For 121 other rms, the telephone numbers in the database were incorrect or corresponded to fax numbers. Our study therefore focused on a sample of 150 rms. Ac-cording to Harris (1985), the size of the sample must exceed the number of predictors by at least 50. Our sample of 150 observations respects this rule.4

In order to determine the representativeness of our sample in relation to the original population, we compared the average growth5 of the sample rms to the one of the population6 using a bilateral t test. One of the ap-plication conditions underlying this test on two independent samples is the homogeneity of variances (Howell, 1997). We rst used Levene’s test to check that there is no signi cant difference in the variances (F = 1.476 and p = 0.224), and then tested the difference between the averages of employment growth for the two groups. The results of the bilateral t test (t = –0.823; df = 11.479; p = 0.411) indicate that the average employ-ment growth of the rms in our sample is not signi cantly different from that of the rms of the overall population.

As our sample was composed on the basis of size and regional location constraints characteristic of the population, it is no longer necessary to examine the representativeness of the sample in relation to the population with regard to these two criteria. Finally, we also examined the percentages of independent rms within the population and the sample. These are also identical (66.7 per cent). These particular elements of comparison were chosen because they appear in the initial database.




Data Collection Method and Measurement of the Dependent Variable

The data published by Belgian rms do not make it possible to test the vast majority of the hypotheses developed in our research. Hence, we opted for a telephone survey.7 We rst established a questionnaire consisting of closed questions that we had pre-tested on several SME managers. The managers of 150 SMEs were interviewed by phone in November 2001.

The value of the dependent variable was calculated using the initial database. The choice of an appropriate growth index has given rise to a number of theoretical discussions (Birch, 1986; OECD, 1998; Wooden and Hawke, 2000). As none of the proposed measures is neutral, we decided to use a simple measure, namely the relative variation ‘(Et – Et–1)/Et–1)’, as this is the most frequently used index in studies on growth determinants (Delmar, 1997). In our case, this measure reads (E2000 – E1994/E1994).

In order to carry out a logistical regression (see infra), these dependent variables were split into ‘high growth’ (code one) and ’low growth’ stagnation or ‘regression’ (code zero). We de ned ‘high growth’ as being growth above or equal to 50 per cent over the period studied. 34.3 per cent of the rms in our sample can be considered as having undergone high growth.8

Previous studies differ enormously in terms of the time period studied. In order to identify irregular short-term tendencies and to allow for a reliable estimation of organisational performances, the time period studied should be at least ve years (Weinzimmer et al., 1998). On the basis of the constraints of our database, we have measured growth over a period of seven years, stretching from 1994 to 2000.9

So as to avoid static measures, when growth is essentially a dynamic phenomenon, we have excluded rms that were established during the period studied.

Data Processing

In order to test our hypotheses, we have rst carried out ve binomial logistic regressions of employment growth and ve binomial logistic regressions of sales growth, which means one for each group of inde-pendent variables. This method presents certain advantages in comparison to the standard multiple regressions that are subject to more restrictive


34 / Frank Janssen


application conditions (Garson, 2001; Howell, 1997).10 Among these advantages, we could draw particular attention to the fact that, contrary to standard regression, logistic regression does not presuppose a linear re-lationship between the dependent variable and the independent variables, and does not require a normal distribution of the variables. We had ob-served that our dependent variable did not present a normal distribution. The logistic regression also made it possible to integrate dichotomous or polytomous and metrical predictors into one single model. Each modality of an original variable gave rise to a dummy variable coded one if the characteristic was realised and zero in the opposite case. In order to avoid a linear relation between the independent variables, for each original vari-able, one of the binary variables created was excluded from the model. In the case of ‘ ltering’, in other words when part of the sample is not concerned by a question, we created a dummy variable composed of the rms not concerned.

Having tested the variables by groups of determinants, we next sought to set up two ‘optimal’ regression equations by means of a selection pro-cedure, in order to identify the most important predictors for employment growth, on the one hand, and for sales, on the other. To this end, we carried out a forward stepwise regression11 which, according to Howell (1997), is the best selection procedure. A selection procedure aims to identify those variables which, taking all else into consideration, allows us to explain a signi cant proportion of the total variation. The independent variables or interactions between variables that are generated by the hypotheses are tested one by one on the basis of the degree of signi cance of the score test. The score test is a statistic based on the comparison between two models that allows us to make a judgment on the statistical signi cance of the model coef cients (Bernard, 1999). This variable selection procedure results in the model with the highest !2 score. We set the entry threshold at 0.05. The variable with the lowest probability below 0.05 enters the model. The variable with the next lowest probability enters next, and so on. After each variable enters the equation, the previously entered vari-ables are tested by means of a Wald test to determine whether they should be eliminated from the model. We xed the exit threshold at 0.1.12 At each step of the procedure, the variables introduced in previous steps are re-examined. The variable with the highest probability greater than 0.1 is removed from the model, which is then re-estimated. The other variables are then re-evaluated in order to determine whether they




should be eliminated in turn. A previously signi cant variable can lose its signi cance following the joint introduction of other variables and be eventually eliminated from the model. A variable can also be eliminated on the basis of a likelihood ratio test. The construction of the model is completed when no variable satis es the criteria for entry or exit or when a model identical to the preceding one is selected. When we study the different growth predictor groups concomitantly within the framework of a stepwise regression, only those variables, which are individually the most signi cant, all else being equal, and which make the most signi cant contribution to the predictive power of the model as a whole are selected. The two nal optimal equations with which we end up therefore comprise variables, which do not necessarily appear to be statistically signi cant in the group regressions or exclude variables which, in these regressions, constitute important predictors.

Results and Discussion

Prior to the regression, we compared the growth averages of rms that had responded to our survey with those of the rms who had refused to respond by using a bilateral t test. The growth averages for the rms that had responded to the survey were not signi cantly different from those of the rms that had refused to respond. We then compared the size, independence and regional location of the rms of the two groups using Pearson’s !2 test. Whether the rm had responded or not to the survey is independent of its size at the start of the period, its independence or dependence and also of its regional situation.

The statistically signi cant results at the ve per cent threshold (p < 0.05) of the logistic regression analysis of employment and sales growth on the different groups of variables are given in Table 3.

Tables 3 and 4 show that six variables linked to the manager have a signi cant in uence on employment growth and three on sales growth. A single variable is common to both models, that is, whether the entre-preneur is an immigrant. Further, the concordance degree between the model predictions and the observed values is higher for the regression on employment growth.

Tables 5 and 6 show that, among the rm’s characteristics, only its size has a signi cant effect on both the probability of employment growth and


36 / Frank Janssen


F"6<$%JStatistically Signi cant Predictors of the Binomial Logistic

Regression of Sales Growth on Variables Linked to the Entrepreneur

Independent Variables Coeff. S.E. Wald D.f. Sig. Exp. (b)

Variable 25: immigrant manager 2.722 1.212 5.046 1 0.025 15.212Variable 26: team of managers –3.23 0.160 4.074 1 0.044 0.724Variable 28: experience of working together among team of managers

–2.076 0.957 4.703 1 0.030 0.125

Model !2: 37.873 Sign.: 0.219Concordance degree between predicted values and observed values : 73.6 per cent.

F"6<$%K%Statistically Signi cant Predictors of the Binomial Logistic Regression

of Employment Growth on Variables Linked to the Entrepreneur


Variable 3: experience of marketing, sales or of R&D

–4.453 1.569 8.051 1 0.005 0.012

Variable 4: consultancy experience 3.092 1.425 4.706 1 0.030 22.023Variable 12: other activities during start-up 4.574 2.221 4.242 1 0.039 96.969Variable 15: studies connected to activity of the rm

3.237 1.283 6.361 1 0.012 25.446

Variable 24: gender (female) –4.948 2.178 5.161 1 0.023 0.007Variable 25: immigrant entrepreneur 3.626 1.408 6.630 1 0.010 35.571

Model !2: 51.890 Sign.: 0.015Concordance degree between predicted values and observed values: 85.9 per cent.

F"6<$%LStatistically Signi cant Predictors of the Binomial Logistic

Regression of Sales Growth on Variables Linked to the Firm

Independent variables Coeff. S.E. Wald D.f. Sig. Exp. (b)

Variable 29 : rm size –0.028 0.014 3.990 1 0.046 0.972


of sales growth. Centralised organisational structure affects employment only. Once again, the concordance degree between predicted and observed values is better for the regression on employment growth.

As shown in Tables 7 and 8, only a few of the strategic variables have a signi cant in uence on the probability of growth. Three of them have




F"6<$%MStatistically Signi cant Predictors of the Binomial Logistic

Regression of Employment Growth on Variables Linked to the Firm


Variable 29: rm size –0.045 0.018 5.929 1 0.015 0.956Variable 31: centralised structure 1.765 0.609 8.409 1 0.004 5.839


F"6<$%NStatistically Signi cant Predictors of the Binomial

Logistic Regression of Sales Growth on Strategic Variables


Variable 43: new technologies 1.008 0.510 3.907 1 0.048 2.739Variable 47: national or international networks

1.881 0.926 4.125 1 0.042 6.561

Variable 50: partnership with big rms –1.367 0.665 4.227 1 0.040 0.255


F"6<$%O%Statistically Signi cant Predictors of the Binomial Logistic Regression of Employment Growth on Strategic Variables


Variable 53A: internal communication 1.072 0.467 5.263 1 0.022 2.921


a signi cant impact on sales growth and only one affects employment growth. This last variable is absent from the rst model. Contrary to pre-vious regressions, the concordance degree between predicted and observed values is higher for the regression on sales growth.

None of the coef cients of the independent variables of the binomial logistic regression of sales growth on environmental variables was sig-ni cant. In the regression on employment, two signi cant factors appeared, as shown in Table 9. None of the coef cients of the binomial logistic re-gression of sales growth on the t between variables is statistically signi cant. On the other hand, Table 10 shows that for employment growth, the coef cients of four interactions are statistically signi cant.


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F"6<$%P%Statistically Signi cant Predictors of the Binomial Logistic

Regression of Employment Growth on Environmental Variables


Variable 63: situation within an industrial park

0.799 0.451 3.143 1 0.076 2.224

Variable 66: economically dynamic region

–1.022 0.503 4.126 1 0.042 0.360


F"6<$%GQStatistically Signi cant Predictors of the Binomial Logistic

Regression of Employment Growth on the Fit Between Variables


Variable 73: t between functional structure and a single product strategy

1.379 0.658 4.395 1 0.036 3.970

Variable 76: t between rm size and type of growth

2.088 0.771 7 .332 1 0.007 8.072

Variable 80: t between the size of the management team and a dynamic environment

1.064 0.496 4.598 1 0.032 2.899

Variable 81: centralised structure in a dynamic environment

1.222 0.458 7.134 1 0.008 3.394


From these 10 regressions carried out on the groups of independent vari-ables, it clearly appears that the variables affecting employment growth are mostly different from those that in uence sales growth. With just two exceptions, that of whether the entrepreneur is of immigrant origin and the rm size, the predictors of growth are distinct. In addition, the last two regressions concerning the impact of environmental variables and the ts between variables did not produce any signi cant results for sales growth.

These results con rm the study by Weinzimmer et al. (1998) which also tested various models using the same independent variables, but different dependent variables. The signi cant determinants of growth are speci c to each particular model. This nding is con rmed by the stepwise regressions. The stepwise regression procedure on sales comprised nine steps.




The procedure ended at the ninth step because the addition of the most signi cant variable led to the duplication of the model developed at the previous step. The nal equation contained ve statistically signi cant predictors (score statistic at ve per cent level and Wald !2 at 10 per cent level). The model’s !2 increases from 7.066 with a statistical signi cance of 0.008 (d.f. = one) at the rst step, to 27.616 with a statistical signi cance of 0.000 (d.f. = ve) at the ninth step. The likelihood ratio test (« -two Log likelihood ») changes from 63.616 to 43.066. The R2 of Cox and Snell and of Nagelkerke improve respectively from 0.129 to 0.418 and from 0.173 to 0.558. The concordance degree between observed and predicted model values is 64.7 per cent at the rst step. At the ninth and last step, it is 78.4 per cent.

F"6<$%GG%Results of the Final Step of the Stepwise Binomial Logistic

Regression of Sales Growth on the Full Set of Independent Variables%


Constant –3.329 1.373 5.880 1 0.015 0.036Variable 34: autonomy of the rm 2.747 1.064 6.661 1 0.010 15.600Variable 39: rented premises –2.076 0.908 5.219 1 0.022 0 .125Variable 65: suf ciently well developed road, transport and communication infrastructure

2.446 1.045 5.473 1 0.019 11.539


2.625 1.095 5.743 1 0.017 13.803

Variable 82: consistency between aggressive strategy and a liberal environment

–3.637 1.343 7.337 1 0.007 0.026

The stepwise procedure relating to the predictors of employment

growth gave rise to nine steps. The nal equation contains ve statistically signi cant predictors (Score statistic at ve per cent level and Wald !2 at 10 per cent level). The model !2 changes from 8.359 with a statistical signi cance of 0.004 (d.f. = one) to 32.256 with a statistical signi -cance of 0.000 (d.f. = ve). The likelihood ratio test (« -two Log like-lihood ») changes from 62.492 to 38.596. The R2 of Cox and Snell and of Nagelkerke improve respectively from 0.148 to 0.462 and from 0.200 to 0.621. The concordance degree between observed and predicted model values is 69.2 per cent at the rst step. At the ninth and last step, it is 82.7 per cent.


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When we study all the groups of variables concomitantly, the optimal prediction equation of sales growth retains two determinants relating to the rm, namely its independence and whether its premises are related, one environmental variable, namely the level of development of the road, transport and communication infrastructure, and two external ts, one between the rm and its environment and the other between its strategy and its environment. This equation does not contain any isolated variable relating to the characteristics of the entrepreneur or any strategic variable.

On the other hand, the optimal equation for employment growth con-tains two entrepreneurial characteristics, namely their gender and their previous start-up experience. It also includes a variable relating to the char-acteristics of the rm, that is, its size and two interaction terms, one relating to the t between organisational structure and strategy, and the other, to the t between organisational structure and the environment. As in the case of sales growth, no isolated strategic variable appears in the nal equation. In addition, isolated environmental variables are also absent.

A single variable, concerning the t between the rm’s characteristics and its environment, appears in both optimal equations. The t between the model’s predicted values and the observed values is higher for the regression on employment growth.

Conclusions

Numerous studies show that different conceptualisations of growth are correlated with each other and suggest that these are interchangeable, which allows researchers interested in growth determinants to avoid

F"6<$%GH%Results of the Final Step of the Stepwise Binomial Logistic

Regression of Employment Growth on the Full Set of Independent Variables


Constant –1.320 0.686 3.704 1 0.054 0.267Variable 7: experience of rm start-up 1.592 0.923 2.974 1 0.085 4.913Variable 24: gender (feminine) –3.696 1.815 4.146 1 0.042 0.025Variable 29: rm size – 0.047 0.016 8.309 1 0.004 0.954Variable 73: consistency between functional structure and a single-product strategy

4.315 1.665 6.713 1 0.010 74.826


3.304 1.081 9.351 1 0.002 27.233




all discussion on the theoretical justi cation of the chosen variable. We have asked ourselves if the use of different conceptualisations of growth does or does not in uence the results of studies on growth determinants. If it does not, the different concepts could indeed be considered as interchangeable.

Among the different measures of growth, employment can be considered as the criterion that is the most relevant from the societal point of view, and sales, from the manager’s point of view. Our study shows that these are also the two criteria that are the most frequently used within research on growth determinants, as much in economics as in management. In order to test the interchangeability of these criteria, on the basis of a literature review, we tried to identify all the potential determinants of growth that had been tested in former studies. We classi ed these into ve groups and then carried out ve binomial logistic regressions on employment growth and ve binomial logistic regressions on sales growth, that is, one for each group of independent variables.

The results of these 10 regressions clearly show that the variables af-fecting employment growth are largely different from those that in uence sales growth. With just two exceptions, the predictors of growth are distinct. Furthermore, the last two regressions concerning the impact of environmental variables and of ts between variables on sales growth do not contain any signi cant predictor. We also constructed two ‘optimal’ regression equations by means of a selection process. These con rmed our ndings. The two optimal equations only contained a single common variable.

As a result, employment and sales cannot be considered as inter-changeable conceptualisations of the same phenomenon. The two types of growth are determined by quite distinct factors. The use of different conceptualisations of growth does actually in uence the results of studies on growth determinants. This explains, at least partly, the inconsistencies found within the literature on growth. The choice of a particular concept of growth must therefore be justi ed theoretically. This also means that research on organisational growth has to recognise this heterogeneity and cannot approach the different forms of growth in an undifferentiated way. The results of research on employment growth, for example, cannot be generalised to all types of growth and each piece of research has to de ne precisely the type of growth being measured, while also providing a theoretical foundation for its choice.


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In addition, our results show that organisational growth is a multifaceted phenomenon, which cannot be reduced to a unique dimension. This also means that growth cannot be measured through composite indicators, mixing different variables like sales or workforce, because they do not measure the same phenomenon.

From a managerial point of view, this means that the resources that are mobilised and the strategies that are implemented by a rm whose objective is to grow should vary depending on whether it wishes to attain one or the other type of growth. In the same way, economic policies designed to stimulate growth must also take this heterogeneity into account.

The results of our research are subject to certain limitations. First of all, this study is solely concerned with individual companies. However, some organisations are liable to grow thanks to creation of franchises. We did not use groups as our analysis unit, so this type of growth was inevitably ignored. Furthermore, we measure growth on the basis of data relating to the start and end of the period. However, growth does not necessarily follow a regular pattern. The development process may in fact be full of ups and downs. Nevertheless, our study does not take this phenomenon into consideration, because it does not take account of intermediate data. Moreover, the type of survey conducted and the questions asked prevented us from obtaining longitudinal data for a number of variables.

Notes

1. The nal column of Table 1 classi es the different types of independent variables used in the context of these studies within each of these groups.

2. According to the European Commission’s Recommendation of 3 April 1996, the following rms must be considered as SMEs:

— those employing less than 250 people; the number of people employed corres-ponds to the number of annual work units.

— those whose either sales does not exceed 40 million EUR, or the annual balance sheet total does not exceed 27 million EUR.

— those that respect an independence criterion. Independent rms are those which are not owned as to 25 per cent or more of the capital or the voting rights by one or several large rms. We have not used this criterion, given that one of our hypotheses presupposed that the fact that a rm is dependent on another rm would have a positive in uence on the growth of the former.

The Commission also establishes a distinction between medium-sized, small and micro-sized enterprises. The small rm is that which employs less than 50 people, who




respects the independence criterion de ned above and for which either the sales does not exceed 7 million EUR, or the annual balance-sheet total does not exceed 5 million EUR. A rm is considered to be micro-sized if it has less than 10 workers.

3. The scal regime for companies is in fact more advantageous than the regime for physical persons. As a result many people create ‘empty’ rms only in order to deduce expenses. These empty shelves do, of course, not grow.

4. According to other authors (Bernard, 1999), a minimum of 10 observations per predictor is necessary. Harris (1985) underlines that this principle is not based on any empirical proof. Others suggest more liberal rules than Harris and consider that the number of observations must only exceed the number of variables by 40 (see Howell, 1997).

5. For the measure of this variable, see next section. 6. From which we withdrew rms that belonged to the examined sample. 7. The major advantage of this method in relation to personal surveys or by post is

its rapidity. In comparison with the personal survey, it also presents a lower risk of bias linked to the person of the interviewer (Lambin, 1990). Finally, it allows for the immediate codi cation of the responses, thus reducing risks of error.

8. The cut-off point of 50 per cent has been chosen in order to generate a proportion of high-growth rms (34.3 per cent) important enough to generate signi cant results.

9. During this period, the average annual growth rate of the Belgian GDP in constant prices has been of 2.68 per cent (National Bank of Belgium).

10. This also represents certain advantages in comparison to the discriminant analysis that can also be used when the dependent variable is dichotomised. Apart from the fact that the discriminant analysis involves a normal distribution of the variables, it can give rise to ‘impossible’ probabilities of success situated outside the 0–1 range (Howell, 1997).

11. This procedure is a combination of forward and backward procedures.12. It is necessary to use an exit threshold that is higher than the entry threshold in order

to avoid having the same variable selected and then discarded at consecutive steps of the procedure.

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The Conceptualisation of GrowthAre Employment and Turnover Interchangeable Criteria?

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