A taxonomy of business start-up reasons and their impact on firm growth and size

A TAXONOMY OF BUSINESS START-UP REASONS AND THEIR IMPACT ON FIRM GROWTH AND SIZE SUE BIRLEY Imperial College of Science, Technology and Medicine

PAUL WESTHEAD Warwick Business School

Based on a survey of 405 principal owner-managers of new independent businesses in Great Britain this paper explores two research questionsEXECUTIVE are there any differences in the reasons that owner-managers articulate for SUMMARY starting their businesses, and, if there are, do they appear to affect the subsequent growth and size of the businesses? The results of the study indicate an affirmative answer to the first question. From the 23 diverse reasons leading to start-up that were identified in the literature, an underlying pattern emerged via the Principal Components Analysis. Moreover, these were similar to those found in earlier studies. Thus, five of the seven components identified by the model correspond to those identified by Scheinberg and MacMillan (1988) in their eleven-country study of motivations to start a business: “Need for Approval, ” “Need for Independence, ” “Need for Personal Development,” “Welfare Considerations,” and “Perceived Instrumentality of Wealth.” Two further components were identified by this current study. The first vindicates the decision to add a question not included in the previous study that related to “Tax Reduction and Indirect Benefits,” and the second, the desire to “Follow Role Models” was identified by Dubini (1988) in her study in Italy. In order to take account of possible multiple motivations in the start-up period, cluster analysis was used to provide a classijication of founder “types.” The seven generalized “types” of owner-managers were named as follows--the insecure (104 founders), the followers (49 founders), the status avoiders (169 founders), the confused (1.5 founders), the tax avoiders (18 founders), the community (49 founders), and the unfocused (1 founder). Further, evidence ffom the final discriminant analysis model suggested that the seven-cluster classi$cation of owner-managers was appropriate and optimal. However, despite these clear differences between clusters, this was not found to be an indicator of subsequent size or growth, as measured by sales and employment levels. The answer to the second research question would be in the negative. Therefore, we conclude that, whereas new businesses are founded by individuals with significantly different reasons leading to start-up, once the

Address correspondence to Professor Sue Birley, The Management School, Imperial College of Science, Technology and Medicine, 53 Princes Gate, Exhibition Road, London SW7 2F’G. The authors would like to acknowledge the very helpful comments received from one of the reviewers of this paper. Journal of Business Venturing 9,7-31

0883-9026/94/$6.00

0 1994 Elsevier Science Inc., 655 Avenue of the Americas, New York, NY 10010

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S. BIRLEY

AND P. WESTHEAD

new ventures are established these reasons have a minimal influence on the growth of new ventures and upon the subsequent wealth creation and job generation potential. This result is important for investors and policy-makers. It suggests that strategies for “picking winners” solely based upon the characteristics of owner-managers and their stated reasons for wanting to go into business are not supported. Thus, for example, targeting scarce resources to those with high opportunistic and materialistic reasons for venture initiation would miss those with a wider sense of community or those with personal needs for independence who establish similarly sized businesses with comparable levels of wealth creation.

INTRODUCTION “Personal characteristics of entrepreneurs, and especially their motivations and work experiences, arc therefore relevant factors in the study of entrepreneurship, since they will lie behind the supply side of entrepreneurial activities and will have to be closely identified in any public policies orientated to promote such activities”

{Lujitente and Salas 1989, p. 18).

Since 1979 the focus of monetarist-inspired free-market government policy in Great Britain has shifted towards the development of an active and vibrant “enterprise” culture. Associated with increasing interest in creating greater competitiveness, a desire to privatize public sector production, moves to switch resources away from traditional industries towards high-tech small firms, and an ideological objective of reducing reliance on the state by fostering the principles of “individualism,” choice, and “self-help” (Martin 1985, p. 385), government has introduced a torrent of measures that have actively encouraged individuals to become self-employed or to start their own businesses (Beesley and Wilson 1982). All of these initiatives have been predicated on the assumption that it is possible to influence individuals to the extent that a larger percentage of the population than previously can be encouraged successfully to create their own businesses. Unfortunately, this was based primarily upon casual assumption rather than upon an empirical evidence. However, empirical evidence has shown that only a small proportion of businesses has the potential for significant wealth creation and job generation (Storey et al. 1987; Reynolds 1987; Storey and Johnson 1987a). As a result, a small firm’s policy of “picking winners” has been suggested as a way of using scarce resources more efficiently (Storey and Johnson 1987b, Storey et al., 1987), a view that Hakim (1989) believes is unlikely to be a viable and practical strategy in a complex real world. Nevertheless, Gibb and Davies (1990, p. 16) argue that, “it is perhaps an unrealistic expectation that it will be possible definitively to pick winners or indeed to produce a comprehensive theory that leads to this. But arguably it is possible to make further strides towards better understanding of the factors that influence the growth process and therefore to better assist those who are appraising companies to make better decisions as well as assist those who are seeking to improve support for company development.” Supporting this latter theme a number of recent studies have explored the influence of variety of founder- as well as business-related factors surrounding the survival and growth of new and small businesses (Davidsson 1988; Woo et al. 1988, 1991; Lafuente and Salas 1989; Birley and Westhead 1990; Westhead 1990). Reasons and motivations leading to start-up have traditionally been regarded as an important element influencing not only the start-up of the new business but also its characteristics, survival, and performance (McClelland 1961; Brockhaus 1980, Atkinson and

TAXONOMY OF BUSINESS START-UP REASONS

9

Hilgard 1983; Hofer and Sandberg 1987; Begley and Boyd 1987; Jenssen and Kolvereid 1991). Results of empirical studies (summarized in Westhead 1988, p. 655) suggest that the overwhelming motivations for entrepreneurship have been an amalgam of a desire for independence and financial betterment, with frustration in previous employment playing a secondary role. However, a number of studies in the United Kingdom have also indicated that redundancy or firm closure has provided a particular trigger “pushing” founders to leave their previous jobs (Storey 1982; Keeble and Gould 1984, p. 10; Binks and Jennings 1986; Westhead 1988, p. 375; Turok and Richardson 1989, p. 29). In their “social development” model of venture initiation Gibb and Ritchie (1982, p. 27) claim that class structure, family origin, education, occupational choice and development, career and organizational history and experience, present lifestyles and attachments have marked impacts on why certain individuals are most likely to establish new businesses (Cooper and Dunkelberg 1986; O’Farrell and Pickles 1989). This approach, like the “trait approach” where entrepreneurs are born not made (McClelland 1961; Brockhaus 1980, 1982), the “psychodynamic” models (Kets de Vries 1977) associated with social marginality (Stanworth and Curran 1973, 1976; Scase and Goffee 1980, 1982) and the “person variable approach” (Chell 1985, pp. 48-51) that uses Mischel’s (1973) “cognitive social learning variables,” is not without its limitations (for a full discussion of these models and their limitations see Chell 1985). The “social development” model is preferred because it leaves open the possibility of the exploration of a variety of influences not only on the formation but also on the subsequent survival and growth of the new enterprises established. Clearly, in the early days of a business, growth objectives are synonymous with those of the owner-manager (Simon 1964; O’Farrell and Hitchens 1988a, p. 1373). “And, because of the organic nature of the development process, the resource constraints and owner’s limited horizons, assumptions of conventional rational economic behaviour in response to assistance stimuli cannot be made” (Gibb and Scott 1986, pp. 99-100). Consequently, it is assumed that the choice of a “growth” rather than purely a “survival” policy principally rests with the objectives of the owner-manager which are, in part, influenced by the initial reasons leading to venture initiation. Thus, future business goals are influenced not only by commercial considerations but also by personal lifestyle. However, Milne and Thompson (1982) claim that the growth of a small business is not solely related to the personal traits and characteristics of small firm founders but also to how quickly they can adapt and learn from the experience of dealing with the environment within which the new venture does its business (Hjem et al. 1980). The implication of this is twofold-original motivations may change and/or may not be related to the subsequent size and performance of the firm. Therefore, this research asks two basic questions: 1. Are there differences in the reasons that owner-managers articulate for starting their businesses? 2. Assuming that there are differences, do they appear to affect the subsequent growth and size of the businesses?

RESEARCH The empirical evidence presented in this paper is derived from a wider international study of new venture creation and growth (refer to Shane et al. (1991) for a comparison of the reasons leading to start-up of new businesses in Great Britain, New Zealand, and Norway). Thirty-eight statements were identified from the literature as factors that entrepreneurs have

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S. BIRLEY AND P. WESTHEAD

described as reasons for starting their businesses, and were piloted in a survey of over 1,000 entrepreneurs in 11 countries. These data were analyzed and, using factor analysis, the 38 statements were reduced to 22 (Scheinberg and MacMillan 1988; Alange and Scheinberg 1988; Dubini 1988; Blais and Toulouse 1990). After discussion among the international collaborators, an additional tax-related statement was introduced for this current study. These 23 statements are reproduced verbatim in Table 1, but in a different order from that in the questionnaire. Reflecting the increased recognition of the importance of both locational considerations in the functioning of the economy (Cooke 1986; Allen and Massey 1988) and of spatial variations in both business formation and small firm survival and growth (Blackbum et al. 1990; Mason 1991; Moyes and Westhead 1990), 12 contrasting locations were selected for the Great Britain database. The environments were subjectively selected by these researchers to include government designated assisted areas, rural as well as urban environments, areas with persistently high levels of unemployment, areas associated with specialized declining traditional heavy industries and high concentrations of external ownership, particularly in the manufacturing industry, as well as localities with high personal disposable income, high service new firm formation, centers covering diverse ethnic communities, and “munificent” areas with strong and varied enterprise promotion through the activities of enterprise agencies and development agencies. Because there is no comprehensive list of new business start-ups in Great Britain, a pragmatic approach was taken in the building up of the survey frame. It was assumed that county, regional, and borough business directories contained the vast majority of all businesses in each area. The most recent available directories were cross-referenced with county and regional directories, Dun and Bradstreet, and Industrial Market Location Directories produced prior to 1986 in order to identify “potential” privately owned new businesses. The list of businesses was further cleaned and checked by cross referencing with pre-1986 Yellow Pages telephone directories, and reference was also made to the Companies House Register of Limited Companies in the United Kingdom. Community businesses and the subsidiaries and branches of companies were excluded. On the basis of this data cleaning, a list of 4,914 names and addresses of “potential” independent new businesses was identified. Questionnaires were sent by post to the principal owner-managers of these businesses. In total, 744 questionnaires were returned of which 408 were from owner-managers of independent businesses receiving their first order between 1 January 1986 and 31 December 1990. This paper draws upon data from 40.5 new businesses in the database. Due to the statistical requirement to provide complete responses to the 23 reasons leading to start-up statements, responses from three owner-managers were removed from the database for this analysis. For a discussion of the limitations of this methodology see Birley and Westhead (1991). Businesses contacted during the survey are drawn from a wide range of locations and industries. Their characteristics are:

Business Characteristics l

ranged from 1 to 290 total employees. Total number of employees = 3,732 people. Mean employment per firm = 9.22 (median = 4).

Employment:

. Sales: ranged from less than E100,000 (50.8% of firms) to more than sales turnover (0.5% of firms).

5 million

*Statistically significant difference at the 0.01 level

ofsignificance

To have considerable freedom to adapt my own approach to my work To take advantage of an opportunity that appeared To control my own time It made sense at that time in my life To give myself, my spouse, and children security To have greater flexibility for my personal and family life Desire to have high earnings To be challenged by the problems and opportunities of starting and growing a new business To achieve something and to get recognition for it To continue learning To contribute to the welfare of my relatives To achieve a higher position for myself in society To be innovative and to be in the forefront of technological development To develop an idea for a product To have access to indirect benefits such as tax exemptions To increase the Status and prestige of my family To contribute to the welfare of the community that I live in To be respected by friends To contribute to the welfare of people with the same background as me As a vehicle to reduce the burden of taxes I face To have more influence in my community To follow the example of the person that I admire To continue a family tradition

Reasons Leading to Start-Up

6.9 7.4 6.2 5.9 7.2 11.4 14.8 15.1 15.6 15.1 17.0 14.8 8.9 21.0 19.3 18.8 17.3 14.1 12.8 19.0 8.9 5.7

8.6 11.4 13.1 17.3 15.3 17.8 18.0 20.2 19.5 37.3 38.5 44.0 49.6 50.4 54.3 57.3 62.5 72.6 75.6 70.9 80.0 85.7 (two-tailed tests).

6.7

To little extent (2)

8.1

To no extent (1)

TABLE 1 Reasons Leading to Start-Up of the Current Business

5.4 5.9 6.9 4.2

18.5 20.5 15.6 17.3 13.3 9.4

30.1 33.1 24.7 28.6 20.7

21.7 21.0 25.7 22.7 26.4 31.9 24.2

22.2

To some extent (3)

Percentages

3.2 2.7 2.2 3.2

13.8 4.0 7.2 5.2 4.4 2.0

20.5 20.2 12.6 10.1 12.6

34.1 30.6 25.7 23.2 27.4 19.3 24.2

29.1

To a great extent (41

3.0 1.5 2.0 1.2

9.1 4.2 3.7 1.5 2.5 2.0

14.1 11.6 10.4 5.7 7.9

28.6 29.6 29.4 30.9 23.7 19.8 18.8

33.8

To a very great extent (5)

1.37* 1.29*

1.45*

1.45*

2.24* 1.91’ 1.s7* 1.75* 1.67* 1.47’

2.93 2.89 2.44* 2.27* 2.26*

3.67’ 3.60’ 3.52* 3.44* 3.37* 3.12 3.11

3.14’

Mean Score

0.95 0.84 0.86 0.79

1.42 1.12 1.14 1.01 1.03 0.89

1.31 1.26 1.37 1.23 1.34

1.36

-

1.34

1.21 1.29 1.33 1.42 1.33

1.22

Standard Deviation

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S. BIRLEY

AND P. WBSTHEAD

Profits: During the past financial year the majority of businesses (58.6%) had operated

at a profit, 23.5% had recorded losses, and the rest had broken even. Industry: Manufacturing-l tion-21 firms (5.2%).

46 firms (36.0%). Service-238

firms (58.8%). Construc-

Location: Urban locations 75.1% of firms. “Assisted” area locations 59.0% of firms. Business Age: Mean age = 3.35 years (median = 3). Range from less than 1 year to 5

years.

Owner-Manager

Characteristics

.

Gender: 90% of owner-managers in the sample were male.

l

Partners: 66.4% of the new businesses had two or more partners or shareholders.

l

Founders’ Age: Mean age at the start of the current business = 37.3 years (median =

37.0). Age range from 18 to 75 years. Only ten respondents (2.5%) had been unemployed prior to start-up, and so it is unlikely that many of these founders had established their new ventures with the prime intention of adhering to the “livelihood principle” by maintaining the entrepreneur in work (Oxenfeldt 1943; Wendervang 1965; Dahmen 1970). Interestingly, this proportion is markedly lower than those reported in new firm studies covering acute recessionary periods in the north-east of England (26%) (Storey 1982, p. 117), Wales’ (29%) (Westhead 1988, p. 392), and West Lotbian in Scotland (38%) (Turok and Richardson 1989, p. 28).

Data Analysis Although the pilot study had reduced the list of reasons for start-up from 38 to 22, an underlying assumption in the design of the research was, clearly, that there would be significant inter-correlations. It is only by combining these dimensions to produce “gestalts” (Gartner et al. 1989) which “ . . . capture the interdependencies among attributes . . .” (Woo et al. 1991, p. 96) that a more holistic and realistic perspective of the entrepreneurial triggers can be obtained. Moreover, this approach is not new in the entrepreneurial field. The use of descriptive typologies to classify entrepreneurs into heterogeneous groupings had been used by, for example, Smith (1967), Stanworth and Curran (1976), and Dunkelberg and Cooper (1982a). However, a primary issue in such a process is the decision as to the choice of classification variables (or attributes). In an attempt to test the dichotomy of “craftsmen” and “opportunist” entrepreneurs, Woo et al. (1991, p. 101) suggest a hierarchical structure of dimensions as: Classification A: Goals. Classification

B:

Goals, background (education/experience).

Classification C: Goals, background (education/experience), management style. Woo et al. (1991, p. 102) claim that, “These three schemes progress from a narrow set (special classification) to a broad scheme with all three classes of characteristics included (general classification).” In analyzing data from a group of entrepreneurs across each of these 1This study was concerned solely with manufacturing firms.

TAXONOMY OF BUSINESS START-UP REASONS

Categorising

Reasons

13

for Start-up

* Data Aggregation: Table 1 * Principal Components Analysis:

Table 2

4

Grouping

Analysis

f

Cluster Analysis: Tables 3, 4 & 5 * Discriminant Analysis: Table 6 v

Comparing

Groups with Subsequent Growth and Size

* Cross-sectional

FIGURE 1

Analytical

Analysis:

Table 7

stages

dimensions, they found that the different classification criteria produced different groupings. They conclude that this particular derivation of entrepreneurial types does not appear to be robust. However, as the authors note, respondents were asked to rank individual goals but they were not able to place equal importance on more than one goal, and so the subsequent analysis was not able to detect individual “trade offs.” Woo et al. (1991, p. 107) deduce from this that motivations are likely to be simultaneous rather than mutually exclusive. This is intuitively obvious to these researchers and, indeed, it is for this reason that surveyed owner-managers were asked to rate the importance of each reason leading to a start-up on a scale of 1 (to no extent) to 5 (to a very great extent). Following from this, the most important theoretical issue is the decision as to which variables to include in the initial classification and which to use for subsequent explanatory analysis. For example, Gartner et al. (1989) followed the “Classification C” procedure and included the characteristics of the individual, the organization, the environment, and the start-up process in producing their taxonomy of new business ventures. However, on a cautionary note, Woo et al. (1991) argue in their conclusion that “Classification C” procedure is, as yet, not widely supported in the literature. In this analysis, the focus of the “gestalt” is the combination of factors that projected the individual into starting a new business. The subsequent analysis follows the implicit research question in the papers of Gartner et al. (1989) and Woo et al. (1991~in what ways, if any, are the groups of entrepreneurs difherent in their personal background and in the particular characteristics of the businesses they created? The analytical procedure adopted in this study

is shown in Figure 1. Not surprisingly, there was considerable inter-correlation between the 23 reasons leading to start-up variables from 405 owner-managers. Therefore, in order to develop an “objective” classification of founders based on these variables a “minimum variance” clustering procedure was used. The 23 variables were first “ortho-normalized” using Principal Components Analysis (PCA) into a smaller number of new independent orthogonal reference axes of stated reasons. This technique removed the distorting effect that strong intercorrelations among the 23 variables (or attributes) would have on the calculation of the various “distance” and “variance” measures used in the QUICK CLUSTER analysis. On the basis of the new independent and orthogonal component scores from the factor analytic model a typology of owner-managers was produced using cluster analysis. Clearly, by allocating individual founders (and businesses) to a small number of groups (in this following example, seven) there will inevitably be a loss of detail. In compensation, however, there is a

14

S. BIRLEY AND P. WESTHEAD

considerable gain in the resultant level of generalization. Cluster analysis serves a useful purpose in helping to distinguish broad patterns of founder reasons leading to start-up “types” using multivariate sets of information (Carter and Aitchison 1986, pp. 19-20). It allows the analyst to develop an integrated classification of founder “types” while the accuracy of the typology can be assessed by discriminant analysis.

RESULTS For the 23 statements Table 1 shows the resultant total scores ranked in order of the mean score. The first six statements show a mean score that is significantly greater than the mid-point score of 3 at a 1% level of significance. All reflect a positive economic environment combined with a personal need to change direction: l

To have considerable freedom to adapt my own approach to my work (mean score of 3.74).

l

To take advantage of an opportunity that appeared (3.67).

l

To control my own time (3.60).

l

It made sense at that time in my life (3.52).

l

To give myself, my spouse and children security (3.44).

. To have greater flexibility for my personal and family life (3.37). The second four statements which include a “desire for high earnings” and a “need to achieve something and get recognition for it,” the popular conceptions of entrepreneurial motivation, are stimuli that evenly divide the group with approximately one-third showing indifference (at a 1% significance level). The remaining 13 statements have a mean score that is significantly lower than 3 (at a 1% level of significance) and reflect reasons for start-up drawn from the literature but to which these owner-managers do not appear to subscribe strongly. So, for example, “having more influence in the community” (1.45), “following an admired person” (1.37), and “continuing a family tradition” (1.29) were hardly rated at all. Overall, this preliminary analysis would appear to support the results found in the earlier study of new firm founders in England, Australia, Sweden, Finland, Norway, Denmark, and the United States (Scheinberg and MacMillan 1988, p. 687) that the drive to be independent was the dominant reason leading to venture initiation. However, by asking the owner-managers to rate all the 23 reasons, the researchers recognized that starting a business is a complex process which involves a variety of motivations and stimuli. Therefore, the second part of the analysis is concerned with identifying the underlying patterns in the responses. PRINCIPAL

COMPONENTS

ANALYSIS

An R-Mode Principal Components Analysis (PCA) was used to transform and “orthonormalize” the original data for the 23 selected reasons leading to business start-up. Norusis (1988, B-46) has suggested that PCA “ . . . can be used whenever uncorrelated linear combinations of the observed variables are desired. All it does is transform a set of correlated variables to a set of uncorrelated variables (principal components).” Further, “its biggest advantage is that no assumptions are made about the size of the communality, as 1.Ois entered into all the diagonal cells of the similarity matrix. This means that each variable is given equal

TAXONOMY

OF BUSINESS START-UP

REASONS

15

weight in the analysis. As such the model is best utilized either for re-writing data in a concise, parsimonious form as a prelude to some other analysis, or for simply exploring or describing the relationships in a data set” (Davies 1984, p. 131). In this study the resulting independent and orthogonal reference axes, as reflected in the component scores for each founder (or case), form the data for a general classification (Woo et al. 1991, pp. 98-99) of owner-manager reasons leading to start-up “types” using cluster analysis. The initial extraction of orthogonal reference axes by PCA produced seven components with an eigenvalue above one but they could not be easily labeled. Here, it is appreciated that, “Rotated components do allow better group identification, which may be desirable, if, for example, the scores are to be used as independent variables . . .” (Johnston 1980, p. 172). Therefore, in order to achieve a solution in which each of the original variables was highly correlated with only one component, a varimax rotation was undertaken (see Table 2). The first seven components, all of which had sums of squares of the component loadings greater than 1, accounted for 60.6% of the total variance. Moreover, the choice of the 23 variables as representing the potential primary reasons leading to start-up of the owner-manager are justified because all load on to at least one of the components identified. This rotated component model was found to be an appropriate factor-analytic model (Norusis 1985) as indicated by Bartlett’s test of sphericity, the Kaiser-Meyer-Olkin measure of sampling adequacy (O-79), the anti-image correlation matrix, the test for sampling adequacy, and the test for communality. The following description of the components uses those variables with factor loadings greater than 0.3 (Hair et al. 1979). Description of the components here enables the data collected for new firm founders in Great Britain to be compared with the results of earlier studies using “factor analytic” methods to identify the leading or “primary” groups of reasons leading to new business start-up. Five of the components correspond to those identified by Scheinberg and MacMillan (1988) and so, for reasons of consistency, the labels that they ascribed are used in the following discussion.

Component

1: Need for Approval

All except one of the variables in this component load greater than 0.5 and so are considered to be very meaningful (Hair et al. 1979). Moreover, the component closely corresponds to Scheinberg and MacMillan’s Factor 1 (1988, pp. 680-681) in the previously presented eleven-country study of reasons leading to start-up. As the earlier research suggested, this indicates a founder primarily concerned with external approval and input, a strong “Need for Approval.” Linked with McClelland’s theories on need for achievement (McClelland et al. 1953; McClelland 1961), the component scores highly on position and recognition and so is strongly associated with Maslow’s (1943) fourth level need, “esteem” or self-actualization (Maslow 1954). Thus, venture initiation is seen as part of personal development, a means by which the founder can progress ideas and, consequently, corresponds to the traits of individualistic behavior discussed by Hofstede (1980).

Component

2: Need for Independence

This “Need for Independence” component corresponds to Scheinberg and MacMillan’s (1988) Factor 5. Again, this empirical evidence provides support for Hofstede’s (1980) scale of “Individualism” where personal control and freedom of choice are paramount to the new business founder.

Loading

to Start-Up:

Varimax

Sums’of squares of the component Percent of variance Cumulative percent of variance

loadings

To be challenged by the problems and opportunities of starting and growing a new business To continue learning To be innovative and be in the forefront of technological development To develop an idea for a product To follow the example of the person that I admire To have considerable freedom to adapt my own approach to my work To control my own time It made sense at that time in my life To take advantage of an opportunity that appeared To give myself, my spouse, and children security Desire to have high earnings To have access to indirect benefits such as tax exemptions As a vehicle to reduce the burden of taxes I face To have greater flexibility for my personal and family life To achieve something and get recognition for it To achieve a higher position for myself in society To increase the status and prestige of my family To be respected by friends To have more influence in my community To continue a family tradition To contribute to the welfare of my relatives To contribute to the welfare of the community that I live in To contribute to the welfare of people with the same background as me

Variables

TABLE 2 Reasons

0.005 0.792 0.283 0.002 0.025 0.012 0.058 -0.052 0.072 0.094 0.067

0.029 0.056 0.654 0.795 0.668 0.685 0.443 0.035 0.056 0.094 0.115

2.522 10.97 22.82

0.264 0.055 -0.022 -0.034 0.084 0.041 0.037 0.188

0.779 0.376 -0.080 0.125 0.134 0.184

0.006 0.067 0.085 0.201 0.543 0.144

2.725 11.83 11.85

-0.015 0.038

-0.029 0.122 0.809

0.003 0.180 0.096

1.906 8.29 31.11

0.153

0.008 0.065 0.382 -0.034 0.060 0.026

0.755 0.167 0.099

0.566 0.795

0.353 0.125

0.214

3

1.857 8.07 39.18

0.696

0.03 1 0.085 0.166 0.209 0.498 0.228 0.307 0.801

0.164 0.091

0.113 -0.060 -0.254 0.021 -0.253 0.066

0.123 -0.09 1 -0.021

0.132 0.164

0.068

4 5

1.691 7.35 46.53

0.144

-0.120 0.138 0.278 -0.075 -0.074 0.101 0.696 0.106

0.020 0.182

0.073 -0.077 0.310 0.793 0.349 0.112

-0.008 0.039 0.016

-0.092 0.011

-0.358

Varimax Rotated Components

0.152 0.076

Matrix

0.396

2

Component

0.292

1

Rotated

1.678 7.30 53.83

0.110

-0.118 0.019 0.131 0.156 0.203 0.113 0.062 0.042

0.835 0.102

0.038 0.374 0.223 -0.003 0.202 0.767

-0.055 0.087 0.075

0.071 0.039

0.016

6

1.558 6.77 60.60

0.206

-0.026 -0.058 0.181 0.321 0.299 0.728 0.131 -0.097

0.131 0.020

0.007 0.046 -0.246 0.037 -0.008 0.062

0.016 0.768 0.017

0.181 0.136

0.048

7

13.937

0.601

0.607 0.665 0.602 0.647 0.591 0.611 0.609 0.717

0.743 0.684

0.627 0.302 0.430 0.688 0.543 0.664

0.589 0.682 0.680

0.532 0.700

0.423

012)

Communality

TAXONOMY OF BUSINESS START-UP REASONS

Component

17

3: Need for Personal Development

This “Need for Personal Development” component (Factor 4: Scheinberg and MacMillan 1988) stresses reasons linked to individual personal development and to learning, traits that Scheinberg and MacMillan (1988, p. 682) indicated correspond to a “non-masculine” approach and “It is here where the personal effect of the entrepreneur is seen as directly affecting the performance of the business; while simultaneously the business is seen as the means to keep developing the entrepreneur.”

Component

4: Welfare Considerations

This component is clearly strongly related to Hofstede’s “Collectivism” index and Lodge’s (1976) concept of “Communitarianism” (Factor 3: Scheinberg and MacMillan 1988) and may be linked to Friberg’s (1976) moral and ideological “internalized incentives” group. Thus, starting a business due to “Philanthropy” is a way for the founder to contribute to the wider welfare of the group of which he is part, whether it be his community, people with the same background, or his immediate family (Dubini 1988, p. 15-16).

Component

5: Perceived Instrumentality

of Wealth

This component corresponds to Factor 2 in the Scheinberg and MacMillan (1988, p. 680) study and to the “Materialism” component in Dubini’s (1988, pp. 15-16) analysis of owner-managers in Italy, a component that closely relates to characteristics of ideal compensation methods reported by Peterson and Stevenson (1987).

Component

6: Tax Reduction and Indirect Benefits

This group of reasons leading to start-up were not directly identified in the earlier study. However, it must be recalled that during this current study an additional tax reduction question (“as a vehicle to reduce the burden of taxes I face”) was incorporated into the revised questionnaire to take into account the importance of this reason in Scandinavian countries. The component reflects, in an indirect way, the objective of founders to increase personal wealth by retaining previously earned money, a reason that Dubini (1988, p. 17) suggests is a means of allowing for independence and freedom.

Component

7: Follow Role Models

This component more closely relates to a wider social incentive, identification with other individuals, rather than with personal initiative (Friberg 1976). Dubini (1988, p. 17) identified a similar “Role Model” component and concluded that members of this group “ . . . confirm the importance of role models and family attitudes (Shaper0 and Sokol 1982) in influencing the desirability of starting a new business.

GROUPING ANALYSIS Reassuringly, it is clear from the presented evidence that the linked trends isolated in the rotated component structure do have meaningful expression in relation to the literature. However, whereas the above analysis enables a description of the pattern for each single component, nothing other than intuitive classification can be attempted. Therefore, the responses from the 405 owner-managers were re-classified using the varimax rotated

18

S. BIRLEY AND P. WESTHJZAD

component scores-these “ortho-normalized” on the seven basic patterns of the original

scores evaluate the owner-manager’s ratings data identified by the Principal Components

Analysis. The 405 by 7 matrix of component CLUSTER analysis (Norusis 1988). Determination in cluster analysis

of the appropriate

scores formed

the basis for the QUICK

number of groups or types is a key arbitrary decision

that must be made with strong prior theoretical

assessment

(Woo et al.

1991, p. 103). In this study seven clusters were specified because the PCA had identified seven themes within the database. Scanning the data in Table 3, cluster 7 is immediately distinguishable in two ways. First, it has only one member and second, the number of variables that are significant suggests a varied and unfocused set of reasons leading to venture initiation. Therefore, this cluster was eliminated from any further analysis. For the six remaining clusters, one-way analysis of variance was conducted for each of the seven components. The results, presented in Table 4, show that six clusters are well separated based on the Euclidean distances from their centers. However, because this analysis had only accounted for 60.6% of the variance in the data, chi-square statistics were calculated between each of the six clusters with respect to the original “raw” data for the 23 reasons leading to start-up statements on the five-point Likert scale (Table 3). Statistically significant contrasts were recorded among the six remaining cluster groups with respect to 22 variables. Interestingly, no statistically significant difference was recorded among the clusters with regard to the reason “to take advantage of an opportunity” on which all groups scored greater than the indifference score of 3. In order to allocate a descriptive label to each of the seven clusters, the cluster mean for each of the 23 variables from the original “raw” data was compared to the respective global mean for that variable (Table 3). The combination of variables that contributed to each of the components (discussed above and detailed in Table 2) were then scanned for each cluster. Further, the cluster mean for each of the earlier identified principal components was calculated (Table 5). Overall, the results from the two analyses are reasonably consistent. Therefore, component descriptions are used in the description of the clusters. Cases where cluster means for a variable deviate by more than a full or half a standard deviation from the respective global mean are used in the commentary below to highlight the distinguishing characteristics of each of the clusters (Openshaw 1983). Two perspectives are used-those components where the majority of the variables score significantly different from the global mean, and those components where the majority of variables score greater than the indifference score of 3. The resulting generalized description of the clusters is shown below.

Cluster 1: N = 104: The Insecure Founders in this cluster show a significantly higher score than those in the other clusters on only one component-in their need for approval.

Cluster 2: N = 49: The Followers Owner-managers in this cluster show a significant out of three variables that contribute to component the scores are below the indifference point. There concerned with a need for personal development

difference from the rest of the group in two 7, the need to folZow role models, although is also some indication that this group was and a need for independen.ce.

Notes:

4.00 3.73 3.86 3.96 3.41 2.04 1.24 3.90 3.12 1.92 1.86 1.68 1.29 1.90 2.78 1.43 1.45

3.60 3.52 3.61 3.55 3.76 1.85 1.22 3.38 3.15’ 3.20* 2.53* 2.29* 1.59 1.11 2.22 1.45 1.19

2 6,2 5, 3 5 5, 1 6 6 2 1 1 1 7, 1 4, 1 7 5, 4 4 4

169

3.24 3.30 3.51 2.92 2.44* 1.54 1.24 2.93 2.21 1.53* 1.22* 1.14* 1.17 1.08 1.98 1.60 1.24

2.90 2.51** 4.35

1.69 1.29 3.90

3 7 2

49

2.18 1.04 3.27

3.63 3.20*

2.86 1.81

3,2 3

104

2.5 1 1.95

3.41

2.69

3

3.48

2

592

1

4.00 4.17 4.39* 3.56 3.50 3.72” 4.17” 3.94 2.33 2.17 1.50

4.00 4.13 3.60 3.93 3.60 3.27*’ 3.13** 3.60 3.60’ 3.47* 3.40** 3.53** 3.40” 3.21** 3.47* 2.73* 2.93**

15

1.61 1.39 4.17

3.07* 2.47*’ 3.53

18

1.39 1.28 2.56 1.28 1.17

1.50

2.67 2.22

3.00

5

3.60 2.87

3.53

4

Clusters

49

4.20 3.63 4.00 4.39* 3.51 2.00 1.31 4.12* 3.53 2.92* 2.29 1.61 1.65 1.08 3.13% 3.00” 2.45**

2.94 1.08 4.35

3.34 3.10*

3.43

6

** Cluster mean which deviates by more than a standard deviation from the respective global mean. * Cluster mean which deviates by more than half a standard deviation from the respective global mean. (ti) Chi-square coefficient statistically different at the 0.001 level of significance for the six clusters (excluding the one founder in cluster 7). (i) Chi-square coefficient statistically different at the 0.05 level of significance for the six clusters. (a.~.) No statistically significant difference between the six cluster “types.”

Number of founders in the cluster

To be challenged by the problems and opportunities of starting and growing a new business (aa) To continue learning (aa) To be innovative and be in the forefront of technological development (aa) To develop an idea for a product (aa) To follow the example of the person that I admire (aa) To have considerable freedom to adapt my own approach to my work (aa) To control my own time (iii) It made sense at that time in my life (a) To take advantage of an opportnnity that appeared (n.s.) To give myself, my spouse, and children security (aa) Desire to have high earnings (aa) To have access to indirect benefits such as tax exemptions (aa) As a vehicle to reduce the burden of taxes I face (aa) To have greater flexibility for my personal and family life (aa) To achieve something and get recognition for it (aa) To achieve a higher position for myself in society (aa) To increase the status and prestige of my family (aa) To be respected by friends (aa) To have more influence in my community (aa) To continue a family tradition (aa) To contribute to the welfare of my relatives (aa) To contribute to the welfare of the community that I live in (aa) To contribute to the welfare of people with the same background as me (aa)

Variables

Variables Related to Principal Comwnents

TABLE 3 Cluster Characteristics of Owner-Managers Reasons Leading to Start-Up

1

1.Ol 0.89

1.37

0.84 0.79

1.03

1.29 1.32 1.21 1.42 1.34 1.11 0.95 1.33 1.31 1.23 1.14

1.42 0.86 1.22 2.24 1.37 3.74 1.oo* 5.00** 1.OO**

3.60 3.52 3.67 3.44 3.12 1.91 1.45 3.37 2.93 2.27 1.87 1.67 1.45 1.29 2.44 1.75 1.47

1.26 1.34

2.89 2.26

5.00** 5.00**

1.OO** 5.00** 1.OO** 1.OO** 1.OO** 5.00** 5.00** 1.oo** 1.OO** 1.OO** 5.00** 5.00”” 5.00** 5.00** 5.00** 5.00** 5.00**

1.36

Standard Deviation

3.11

Global Mean

1.OO**

I

20

S. BIRLEY AND P. WESTHEAD

TABLE 4 Between- and Within-Cluster (Excluding the Sole Founder in Cluster 7) Between-Cluster Mean Square (Cluster MS)

Variable Component Component Component Component Component Component Component

1 2 3 4 5 6 7

Mean Square Variability Within-Cluster Mean Square (Error MS)

d.f.

0.48

42.76 9.07 13.29 27.01 11.50 35.11 36.78

0.88 0.85 0.62 0.86 0.55 0.47

for the Six Cluster

Solution

d.f.

F

Probability

398 398 398 398 398 398 398

89.18 10.30 15.68 43.30 13.30 64.38 78.56

0.000 0.000 0.000 0.000 0.000 0.000 0.000

Cluster 3: IV= 169: The Status Avoiders All the four variables that score significantly lower than the global means load inversely onto the component needfor approval; the remaining three variables show the same pattern, also being below. The only other component that scores consistently above 3, although not significantly greater than the global mean, is component 2-need for independence.

Cluster 4: N = 15: The Confused small group shows a significant difference from the rest for the majority of variables in four of the seven components-need for approval, welfare considerations, for reasons of tax reduction and other indirect benefits and to follow role models. Overall, this group scored highly on almost all the variables measured.

This

Cluster 5: N = 18: The Tax Avoiders This group was driven significantly more than the rest by a need for tax reduction and other indirect benefits. They also consistently scored highly on a need for independence.

Cluster 6: N = 49: The Community The results for this group are less consistent with the original principal component analysis than the previous five in that four of the variables that score significantly greater than the global mean load on different components. However, the remaining three show that this group

TABLE 5 Final Cluster Centers Based on the Average Values of the Component

Scores

Clusters Component 1. 2. 3. 4. 5. 6. 7.

Need for approval Need for independence Need for personal development Welfare considerations Perceived instmmentality of wealth Tax reduction and indirect benefits Follow role models

1

2

3

4

5

6

7

1.04 0.09 -0.42 -0.34 -0.18 -0.22 -0.20

-0.27 0.51 0.76 -0.69 0.41 -0.24 1.34

-0.66 -0.32 -0.11 -0.02 -0.27 -0.15 -0.23

1.01 -0.36 0.14 1.49 0.08 1.39 1.94

-0.49 0.18 -0.22 -0.53 0.08 2.70 -0.24

0.24 0.50 0.57 1.12 0.83 -0.26 -0.72

-0.44 -2.93 -0.19 4.53 -1.01 3.5 1 5.76

TAXONOMY OF BUSINESS START-UP REASONS

21

is significantly different from the rest in their concern for we&are considerations. For the rest need for independence, need for personal development, and the perceived instrumentality of wealth also score consistently above the indifference point.

DISCRIMINANT ANALYSIS: EVALUATION OF THE ACCURACY OF THE CLASSIFICATION The appropriateness of the seven-cluster classification of owner-managers’ reasons leading to start-up was tested using discriminant analysis (Johnston 1980, p. 239; Norusis 1985, p. 73) based on the original “raw” reasons leading to start-up data for the 405 founders (or cases). The final discriminant analysis model that minimized the Wilks’ lambda included 20 out of the original 23 variables (the three reasons not included in the final model were “to control my own time, ” “it made sense at that time in my life,” and “to have greater flexibility for my personal and family life”). Classification results from the final discriminant model presented in Table 6 show that the seven-cluster solution of reasons leading to start-up founder “types” is optimal. The vast majority of owner-managers were allocated by the model to the group specified by the cluster analysis at a level that is significantly higher than that which could be achieved by chance alone. Approximately 90% of owner-managers were correctly classified with only 41 individuals allocated to a group other than that defined by the cluster analysis.

CROSS-SECTIONAL ANALYSIS: CHARACTERISTICS OF OWNERMANAGERS AND NEW BUSINESSES The aim of this final section was to test whether or not the clusters fall into a logical sequence when the characteristics of the owner-managers and those of their firms are taken into account. Therefore, analysis of variance and chi-square analyses were conducted on a total of 48 variables listed in Table 7. Surprisingly, significant differences between clusters were observed in only 12.5% of the analyzed dimensions. Thus . A significantly larger proportion of the followers and the status avoiders founders in clusters 2 and 3 were engaged in manufacturing activities, whereas over 65% of founders in the remaining clusters had established construction or service ventures. l

l

The mean age of owner-managers when their businesses received their first order was 37.3 years. However, the status avoiding founders of businesses located in cluster 3 were significantly older than their counterparts (mean = 40.1 years), particularly those in the confused cluster 4 (mean = 28.1 years). Although this is an unsought result, it does suggest a possible relationship between age and clarity of direction when starting a new firm. A significantly larger proportion of the foZZowers, running primarily manufacturing firms, had parents who had been business owners (45.8%), whereas those in clusters 1, 5, and 6-the insecure, the tax avoiders, and the community groups-were more likely to have parents from professional or managerial backgrounds (Criteria 13). Gibb and Ritchie (198 1, p. 36) suggest that a high level of formal educational attainment is not inconsistent with entrepreneurial intention (for a dissenting view see Pickles and O’Farrelll987) and small business growth (Dunkelberg and Cooper 1982b). In terms of this study the majority of owner-managers in each of the cluster “types” had

42 85.7%

0 0.0% 0 0.0% 1 2.0% 0 0.0% 96 92.3%

15

18

49

1

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Total “grouped” cases correctly classified

0 0.0%

3 1.8%

169

Cluster 3

0 0.0%

0 0.0%

0 0.0%

1 0.6%

42 85.7%

1 2.0%

49

Cluster 2

2 1.9%

2

96 92.3%

1

148 87.6%

0 0.0%

3 6.1%

0 0.0%

0 0.0%

148 87.6%

2 4.1%

4.8%

5

3

15 100.0%

0 0.0%

0 0.0%

0 0.0%

15 100.0%

0.6%

1

1 2.0%

1 1.0%

4

18 100.0%

0 0.0%

1 2.0%

18 100.0%

0 0.0%

4 2.4%

44 89.8%

0 0.0%

44 89.8%

0 0.0%

0 0.0%

12 7.1%

1 100.0%

364 89.9%

1 100.0%

44 89.8%

0 0.0%

1 100.0%

18 100.0%

15 100.0%

148 87.6%

0.0%

0

0.0%

0

0.0%

0

42 85.7%

0 0.0%

3 6.1%

0.0%

0

96 92.3%

0 0.0%

0

0.0%

0

0.0%

7

6

Percent of “Grouped” Cases Correctly Classified

5

predicted Group Membership

Analysis Model Evaluating the Accuracy of the Typology Produced by Cluster Analysis

104

Number of Cases

Results from a Discriminant

Cluster 1

Actual Cluster/Group

TABLE 6 Classification

(c) 15. 16. 17. 18. 19. 20. 21. 22.

(b) 10. 11. 12. 13. 14.

9.

6. 7. 8.

2. 3. 4. 5.

Job title of last employer Business started in the same industry as last employer “Type” of last employer Employment size of last employer Current business relationship with last employer Number of organizations worked for by the founder Number of businesses established prior to this current one Business started on a part-time basis

Work Experience of the Founder

Age of founder when business received first order Gender of founder Parents immigrants Occupational status of parents Founders’ highest education level

Personal Background of the Founder

Number of shareholders or partners Industrial activity of the new business Primary operational premises located in a rural area Primary operational premises located in an “assisted” area main operational premises Absolute present employment size Standardized present employment size Change in absolute number of employees since received first order Change in standardized number of employees since received first order

1. Age of the business

(a) Basiness Data

Criteria

5 5 5 5 5

5

6.16

10 10

5 5 5

Degrees of Freedom (d.f.)

2.47 4.55 11.04 2.93 4.84

34.49 15.18

11.33 4.5 1 3.75

Statistic (X2)

Chi-square

0.291

0.78 1 0.474 0.051 0.710 0.436

0.000 0.126

0.045 0.479 0.580

Significance Level

1.29 0.51

7.12

5 5

386 389

396

(continued)

0.267 0.772

0.000

0.919 398

0.29

0.303 0.190

Significance Level

0.877 0.847 0.934

398 396

V2

398 398 398

5

5 5

V’

0.36 0.40 0.26

1.21 1.50

“F” Statistic

Degrees of Freedom

TABLE 7 Statistically Significant Differences Between New Firms by Owner-Manager Reasons Leading to Start-Up Cluster “Types”

g

34. 35. 36. 3-l.

(e) 31. 32. 33.

28. 29. 30.

23. 24. 25. 26. 27.

Significant

Differences

Customer and Supplier Base Number of customers Location of majority of customers Four customers or fewer accounting for 75% of sales revenue Number of suppliers Location of majority of suppliers Four suppliers or fewer accounting for 75% of purchases Percentage of sales exported 8.22 15.06

19.49 18.90 9.75

6.97 5.02 1.62

8.42 7.78

(X2)

Statistic

10.29

Chi-square

10 10 5

5

10

0.608 0.130 0.068

0.035 0.042 0.083

0.223 0.413 0.899

5 5 5 10

0.135 0.169

Significance Level

0.52

2.92

0.53 1.14

“F” Statistic

5

5

5 5

V’

395

309

398 398

V2

Degrees of Freedom

(continued)

0.763

0.014

0.75 1 0.338

Significance Level

Reasons Leading to Start-Up Cluster “Types” (continued)

5 5

Degrees of Freedom (d.f.)

Between New Firms by Owner-Manager

Base Number of sources of start-up capital Number of current sources of capital Level of sales for the last financial year ( f ‘s) Percentage change in sales in the past year Percentage of sales revenue accounted for by major product line or service group Level of profitability Change in the level of profitability in the last year Current profit performance relative to competition

Cd) Financial

Criteria

TABLE 7 Statistically

2

g

.”

3

9

Significant Differences

Future of the Business Standard of living today Future of the business Desire for growth Intend to increase number of employees years

in the next two

0.099

5

5

0.479 0.547

0.55 1 0.166

0.553

10

5 5

3.99 7.83 9.58 4.01 (a) 9.26

10

8.78

0.83 1

10

5.81

Nore: (a) Due to the assumptions of the chi-square test it was not possible to calculate a coefficient.

(g) 45. 46. 47. 48.

43. 44.

42.

direct competitors Design quality of major product or service relative to direct competitors Quality of labor force Quality of local material input supplies

0.947 0.755 0.558

Significance Level

5 5 5

Degrees of Freedom (d.f.) “F” Statistic V’

V*

Degrees of Freedom

Reasons Leading to Start-Up Cluster “Types”

1.18 3.94 2.64

Chi-square Statistic (X2)

Between New Firms by Owner-Manager

(0 Competitive Structure 38. Number of competitors 39. Employment size of major competitor 40. Price of major product or service relative to direct competitors 41. Quality and finish of major product or service relative to

Criteria

TABLE 7 Statistically

Level

Significance

(continued)

26

S. BIRLEY AND P. WESTHEAD progressed beyond compulsory school education. Whereas over 50% of founders in each of the clusters had obtained technical or professional qualifications, the larger proportion of owner-managers (although not statistically significant) in clusters 2 and 3, the foZZowers and the suuus avoiders, who were more likely to have established manufacturing ventures, had also obtained some form of university degree. l

.

More than 60% of businesses in clusters 1, 4, and 6-the insecure, the confused, and the community clusters-had more than 50 customers, whereas 41.2% of businesses in the fax avoiders cluster 5 had between 11 and 50 customers (Criteria 3 1). In marked contrast, those businesses located in clusters 2 and 3 with a significantly greater propensity to be engaged in manufacturing activities (Criteria 3) also had a markedly greater tendency to have fewer than 11 customers. However, in terms of the location of the majority of customers (i.e., more than 50%) founders in clusters 2 and 3 were also more likely to serve customers located outside in the county region of the businesses’ primary operational premises whereas the generally more non-manufacturing positioned businesses in clusters 4, 5, and 6 had the majority of their customers within the same county. Only a small minority of the surveyed businesses appear to have diversified their product/market base. With the exception of the confused cluster 4, more than 40% of businesses in each cluster had more than 75% of their sales revenue dependent on the performance of the single major product line or service group. However, significantly more founders in this clusters were less dependent on the fortunes of their major product line or service group than their counterparts, particularly in the tau avoiders cluster 5.

GROWTH AND SIZE The above analysis has indicated

clear differences in the reasons that owner-managers articulate for starting their business and has shown some differences in the characteristics of their firms. However, the primary aim of the study was to determine whether or not there were any observable differences in the company size, performance, and the owner-managers’ expressed desire for growth. For example, did the owner-managers in cluster 5, the tax avoiders, who were primarily concerned with the need to control their personal finances, grow larger, more profitable businesses than either the confused cluster, apparently unsure of their original goals, or the community cluster?

Size: Surprisingly, there were no statistically significant differences in size, as measured by sales revenue or employment levels, among businesses located in the six clusters. However, there is some indication that owner-managers of businesses in clusters 2, 3, and 5 (the foZZowers, the staatus avoiders and the tax avoiders) had established larger employment-sized businesses (mean values of 10.6,10.0, and 10.0 people, respectively) than their counterparts, particularly those located in clusters 4 and 6, the confused and community groups (4.7 and 7.8 people, respectively). Similarly, the foZZowers, status avoiders, and tax avoiders recorded larger mean absolute increases in total employment since start-up than did their colleagues. The lowest level of job generation was recorded by the confused cluster 4. Overall, these results were sustained when part-time and casual employees were taken into account by scoring full-time,

TAXONOMY OF BUSINESS START-UP REASONS

part-time, 323-324).

and casual employees

1, 0.5, and 0.25, respectively

(Cooper

27

et al. 1989, pp.

Growth: As with measures of size, no significant differences emerged with regard to growth. Indeed, over 58% of all founders indicated that their sales had increased during the past year, although more founders in thefiZZower cluster 2 stated their sales had declined or were about the same. Moreover, during this recessionary time period over 46% of founders in each of the cluster groups stated their businesses had made a profit during the previous financial year, with the largest proportions being recorded in the tax avoiders and followers clusters (77.8% and 65.3%, respectively). No significant differences were recorded among clusters with regard to current profit performance relative to competition.

Future of the Business Past performances may not necessarily be an indication of future intentions. Therefore, the owner-managers were also asked to indicate their expectations for the future. No significant differences emerged. The majority of aZZfounders indicated that their standard of living had been sustained since they had started the business, and over 39% indicated they were “better off.” Also, the majority of founders believed that their businesses would be growing/ expanding during the next two years, with over 88% stating that they wished to grow their business in the future. Within this, a slightly larger proportion of founders in cluster 5, the tax avoiders group, indicated they had no desire for further growth! Similarly, the majority of founders (over 55%) in all except cluster 4, the confused cluster, stated they intended to increase the employment size of their business in the next two years.

SUMMARY AND CONCLUSIONS The first aim of this study was to explore the ways inwhich owner-managers

in Great Britain articulated their reasons for starting their business and to determine if these differed from those expressed by their colleagues in other countries. As expected, these surveyed “surviving” founders of new businesses are not a homogeneous group and so it would be unwise to treat them as such (Birley and Westhead 1990). However, within the diverse mix of reasons leading to start-up listed in Table 1 an underlying pattern emerged from the Principal Components Analysis. Moreover and reassuringly, linked trends isolated in the rotated component structure had meaningful expression in relation to the literature. Indeed, five of the seven components identified by the factor analytic model correspond to those identified by Scheinberg and MacMillan (1988) in their eleven-country study of motivations to start a business: “Need for ApprovaZ,” “Need for Independence,” “Need for Personal Development,” “Welfare Considerations,” and “Perceived ZnstrumentaZity of Wealth.” Two further components were identified by this current study. The first clearly vindicates the addition of the questions related to “Tax Reduction and Zndirect Benefits”; the second, a desire to “Follow Role Models,” was identified by Dubini (1988) in her study of owner-managers in Italy. Therefore, the first important conclusion from this study is that these owner-managers would appear to be driven by similar types of start-up triggers to those of their colleagues in other countries. However, this result does not imply mutual exclusivity, that, for example, an owner-manager driven by a need for personal development may not also be concerned to reduce his tax burden. Therefore, the second stage of the analysis was

28

S. BIRLEY AND P. WESTHEAD

concerned to determine if there was any overriding pattern in the combination of start-up reasons articulated by the owner-managers. This was found to be the case. The cluster analysis identified several generalized “types” of owner-managers that were named as follows-the insecure, the followers, the status avoiders, the confused, the tax avoiders, the community, and the unfocused. Moreover, results from the final discriminant analysis model suggested that this seven-cluster classification of owner-managers was appropriate and optimal. The above result is important because it reinforces the view of the recent researchers that it is dangerous to dichotomize potential entrepreneurs into simple bivariate categories. However, the more important question for policy-makers and investors is whether or not these identifiable start-up triggers are predictors of the nature of the subsequent business. In other words, are they likely to be helpful in the process of directing policy and “picking winners”? The results from the final stages of analysis would suggest not. No significant differences were recorded between the defined clusters with regard to business size or business growth or, indeed, desire for further increases in growth and size. For example, the insecure founders, who might be expected to have a strong drive to succeed, did not consistently outperform their community counterparts who went into business “to give myself, my spouse, and children security” as well as wider “welfare considerations” not only to their family and relatives but to their community. Whereas the insecure owner-managers had a greater tendency to have established profitable business that were slightly larger in terms of present employment size, the community founders initiated ventures that had a greater propensity to have sales more 100,000 and to be engaged in exporting sales abroad. This evidence supports the view than of Milne and Thompson (1982) and Hjem et al. (1980) that, although new businesses are founded by individuals with significantly different reasons leading to start-up, once the new ventures are established these reasons have a minimal influence on the growth of new ventures and upon the subsequent wealth creation and job generation potential. Indeed, they would appear more likely to be influenced by market factors of industry, the diversity of the major product line or the service group, and the ability to sell to a large number of customers, many of which are outside the “local” area of the business. This is an exploratory study and, relatively speaking, the sample size is small. Nevertheless, the findings are consistent with those of researchers elsewhere. Moreover, the most important finding is not the particular labeling of the typologies of owner-managers because these may well vary over time, but that there is no apparent relationship between the clusters and the subsequent growth of the business. This has clear implications for government and for policy-makers. There is no evidence to support strategies and policies to assist new small business start-ups based solely on the characteristics of owner-managers and their stated initial reasons for wanting to go into business. Targeting scarce resources to potential “winners” with high opportunistic and materialistic reasons for venture initiation would miss those individuals either with a wider sense of community or even personal need for independence who establish similar sized businesses with comparable levels of wealth creation.

REFERENCES Alange, S., and Scheinberg, S. 1988. Swedish entrepreneurship

in a cross-cultural perspective. In B.A. Kirchhoff, W.A. Long, WE. McMullan, K.H. Vesper, and WE. Wetzel, Jr., eds., Frontiers of Entrepreneurship Research. Wellesley, MA: Babson College, pp. l-15.

Allen, J., and Massey, D., eds., 1988. Restructuring Britain, The Economy in Question. London: Sage.

29

TAXONOMY OF BUSINESS START-UP REASONS Atkinson, R.L., and Hilgard, E.R. 1983. Introduction Jovanovich Inc.

to Psychology.

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