Table 2 examines 24 attributes of each of the sample groups. The attributes are selected from a much longer list that researchers have proposed, and have empirically validated, as having relevance to start-ups. The principal selection criterion is that the attribute should not involve judgment by the researcher. Thus the "tenacity" of the founders would be excluded from the list, because it cannot be captured with reasonable objectivity in an interview format. The 24 attributes span six of Baum's (1995) seven determinants of growth although the specific measures are different, and cover all four of Gartner's (1985) categories for describing entrepreneurship.
Large & Small Start-ups: A Useful Distinction?
A cursory examination of Table 1 shows that the four types of companies exhibit substantial commonality within a type. The number of attributes with common behavior varies from 9 of 24 for the small start-ups that grew rapidly to 19 of 24 for the high-tech venture capital-backed start-ups. The fact that large start-ups have a higher number of common attributes than smaller start-ups suggests that smaller start-ups are less likely to fit a specific pattern.
Table 1 also suggests that the two groups -- large and small -- exhibit internal similarities as well as differences between the groups. That is, the two types of large start-ups have a substantial number of attributes with the same rating, and the same is true for the small start-ups. In contrast, a comparison of high-tech start-ups with small start-ups shows few attributes with common ratings. This impressionistic interpretation is formalized in Table 3, which uses an index of similarity to quantify the number of agreements and disagreements. The index of similarity can vary from -1 in the case of 100 percent disagreement to +1 in the case of 100 percent agreement. Table 3 shows that high-tech ventures have little similarity to small start-ups and a moderate similarity to VC-backed non-tech start-ups. Both members of the small start-up group have high similarity to each other with a score of 0.67. They also behave similarly in the categories where they do not show commonality. There are 12 possible categories where they could both show variable behavior (i.e., the twelve categories that do not show commonalties for the small sub-group; in principle, the high growth sub-group could also be variable in all 12, and at a minimum it will be variable in 3.). Of the twelve they are both variable in eight. Thus the two sub-groups of the "small" category exhibit similar areas of commonality and of variation.
The small, high growth companies also have some similarity to the non-tech VC-backed group, particularly as to strategy and the fact that they found their business idea without any active search. The main differences are that high growth small start-ups did not have any outside review of their plans nor did they respond quickly when a crisis developed. A qualitative
Identifying Common Attributes for Each of the Sample Groups
Description of the Sample
|Experience in same industry?||-||-||yes||yes|
|Was this motive stated?|
|Make more money||no||-||yes||no|
|Alternative to job||yes||-||no||no|
|If trigger: negative event?||-||-||-||yes|
|Review by outsider?||no||no||yes||yes|
|Vesper: New prod vs parallel||-||-||-||-|
|Porter: Low cost vs. diff.||diff||diff||diff||diff|
|High growth a goal?||no||no||yes||yes|
|Occur in early years?||-||-||-||yes|
|If crisis, cause external?||yes||-||yes||-|
|Constraints on growth|
|Early learning important?||yes||-||-||no|
|# with 75%
(out of 24 attributes)
Notes: 1. "Yes" means at least 75 percent of the firms in a category fit the description in the left hand column, and "no" means at least 75 percent do not. A dash ("-") indicates less than 75 percent agreement.
2. The probability of 75 percent agreement if no pattern exists (i.e., if individual cases had a 50-50 chance of being rated "yes" or "no") is 0.04 when N=15 and 0.28 when N = 8. With 24 attributes the expected number of false patterns, if the odds of "yes" and "no" are 50-50, is 0.04 x 24=0.96 when N=15. When N=8, the expected number of false patterns is 7
review of interviews from the two groups suggests their differences stem from the businesses they chose to enter. Both groups started on a relatively small scale, but the VC-backed, non-tech group quickly moved to secure outside capital for expansion. In all eight cases the non-tech firms could only expand by opening additional outlets. Only one of fourteen among the small, high growth group needed to do so; the rest could expand incrementally, and were not forced to seek outside funds. The one high growth company that expanded via new outlets was one of only two from that group that viewed financing as a constraint on its growth. Expansion via new stores requires up-front investment to open each store as well as an investment in a management infrastructure capable of managing a dispersed operation.
Index of Similarity
|Small Start-ups||Large Start-ups|
|Small||High Growth||Non-Tech||High Tech|
Note: The index of similarity is derived from Table 2. In pairwise comparisons between groups it counts the number of categories that are both "yes" or both "no" ("agreements"), as well as the number that have one "yes" and one "no" ("disagreements").
Index of similarity = (# of agreements - # of disagreements)/(# of agreements + # of disagreements)
Within the large start-up group, the high tech and non-tech sub-groups show strong similarities in start-up processes, strategies, and early history. Their differences, among the attributes listed in Table 2, all deal with founder characteristics and motives. Founders of technical companies rarely had business educations; they had studied technical fields and had moved into business later in their careers. Technical founders focused on the challenge of starting a company, seldom mentioning money or "being one's own boss." Founders of non-technical companies placed much more emphasis on money and independence. The difference may simply reflect a cultural difference between high-tech and non-tech companies with each founder making a "politically correct" statement for his or her industry. It may also stem from real differences in the industries. Most of the high-tech founders had been successful in established high-tech companies before starting their own companies. They were accustomed to working in a team-oriented environment. The non-tech companies featured a wider range of backgrounds--three of eight companies had founders with no experience in large companies--and even the large companies gave considerable autonomy to department managers to run a "store-within-a-store." Thus the non-tech founders had operated with fewer constraints prior to starting a company, and apparently placed a high value on independence
We conclude that the large vs. small start-up dichotomy produces meaningful groupings of other attributes. Within the large start-up group, the high-tech subsample has little similarity to the small start-up group, but the non-tech subsample shares some qualities with the high growth part of the small start-ups. The two subsamples among the small start-ups have substantial similarity as do the two subsamples of large start-ups.
What Causes the Similarity in Companies with Venture Capital Backing?
The large start-ups exhibit a greater degree of uniformity than do the smaller ones, raising the question, "Why?". Perhaps their business circumstances are less diverse than those of small start-ups and hence require uniformity in solutions. Alternatively, large start-ups might be more aware than small ones of a broadly applicable set of "best practices" and adopt such practices. Finally, the uniformity could be imposed by their outside investors.
The three causes of similarity can all exert influence simultaneously. Evidence from the interviews suggests the large companies adopted widely accepted models of how to grow a business within their respective industries. For the non-technological companies, growth came from opening additional, but identical, locations. Monitoring performance was a matter of tracking sales, expenses, and turnover for comparable locations from year-to-year. The organization covered the same functions: merchandising, real estate, store operations, control. Companies monitored the tastes of their customers and changed their offerings in small increments with the intention of staying abreast of consumer tastes and not giving competitors an opening. The technology based companies relied on proprietary products and spent heavily to maintain their technological edge as they grew. Most had team-oriented, egalitarian organizations and placed great emphasis on speed--getting their new products to market in the minimum time. Both groups needed sophisticated, motivated managers and outside funds in order to take advantage of their opportunities.
Venture capitalists did not impose requirements, although their screening processes could very well eliminate any potential investees that did not conform to the widely accepted models. The venture capitalists, as well as company founders, undoubtedly knew the general wisdom about building a high growth company. Indeed, through seminars and articles the venture capitalists had helped to disseminate that wisdom.
The high-growth segment of the small start-ups included a few companies that were similar to the venture capital-backed group. That is, they had highly experienced founders who saw an opportunity that offered very high growth. But in most cases companies in the high growth group far exceeded their founders' initial expectations. Thus the founders adapted to the growth as it occurred, rather than building the infrastructure for a large company at the start. The VC-backed companies had planned on extremely high growth prior to raising outside funds, and used the funds to build the structure of a large company. Most fell below plan, but given the aggressiveness of their plans they would argue that they needed to be prepared.
On the whole our evidence suggests that the expectation of explosive growth caused the VC-backed companies to follow accepted practices on how to build a high growth company. The smaller start-ups had more diverse expectations about the future, as well as less experience in high growth companies. The two forces--uncertainty and less knowledge--led them to greater diversity in their practices.
What qualities that predispose large and small companies toward profitable growth?
The third research question, whether certain qualities in a start-up predispose it to grow rapidly and profitably, involves finding the reasons that some companies grew and others did not. From the sample of 52 companies we must eliminate eight small start-ups that selected small markets, knowing that they would never have an opportunity to become large. The other 44 companies, that hoped for significant growth, include 23 with venture capital backing and 21 without venture capital backing. All described their markets as offering the potential for rapid growth. Of the 44 companies, 28 achieved rapid, profitable growth and 16 did not.
Among the small start-ups the background of the founder appears to influence performance. Three types of founders may be defined: (1) inexperienced as managers, (2) experienced managers in the industry being entered, and (3) experienced managers entering a different industry. The three types have different odds of success, as Table 4 indicates.
Success Rates of Small Start-ups vs. Founder Background
|High Growth & profitable||10||1||4||15|
|Low Growth or unprofitable||0||4||2||6|
Notes: 1. Totals for high and low growth do not represent
these outcomes. The sample is not random with respect to growth.
It is random with respect to founder background.
2. Chi square = 29.72, df=5. p<0.001
The chi square statistic for Table 4 indicates that the outcomes for the small start-ups are strongly related to the founder's background, with experienced-same industry founders having much better odds of success. This result is consistent with Baum's (1995) model shown in Figure 1. Industry experience is one of his of "specific competencies." Experienced founders entering a different industry have lower than average odds of success. Inexperienced founders appear to have average odds of success.
The pattern of Table 4 does not hold for venture capital-backed start-ups. As shown in Table 5, the odds of success are the same for all three types of founders. Experience
Success Rates of Large Start-ups vs. Founder Background
|High Growth & profitable||9||2||1||12|
|Low Growth or unprofitable||7||2||2||11|
Notes: 1. Sample outcomes are representative of the venture
to which they belonged.
2. Chi square = 1.51, df=5. p<0.90--no significant differences.
in the same industry helps the founder of a small start-up succeed, but not the founder of a venture capital-backed start-up. Part of the explanation may come from Keeley and Roure's (1993) analysis of the high-tech subsample. They concluded that no obvious difference existed among the 15 companies as to the effectiveness of their management groups. In that subsample, the key to profitable growth was to avoid a market-related crisis, e.g., a lack of acceptance of the product or direct competition from an established firm. The only predictor of a company's ability to avoid a market-related crisis was the functional completeness of the founding team. Keeley & Roure speculated that a more complete team examines the product idea from multiple points of view prior to starting the company. A narrower founding team may become enamored with only one aspect of the idea--e.g., its technical elegance.
Unfortunately the analysis does not extend even to the non-tech group of large start-ups. Such companies usually had a single founder, and never more than two. Unlike the high-tech group, the non-tech companies all had a pilot operation before raising venture capital. A venture capitalist could tell, based on the pilot, whether the idea had consumer acceptance. Indeed, many non-tech start-ups may have had a "crisis" when they opened their pilot operation with only the survivors being in a position to seek venture capital, and thus become a part of our sample. The remaining hurdles were to show the idea could succeed in other locations, to demonstrate that management team would be effective, and to learn whether stiff competition could be avoided in the future. In the non-tech group, the appearance of competition appears to be a prime cause of unprofitability and diminished growth.
For the two groups of large start-ups, management experience may fail to predict outcomes for several reasons:
Venture capitalists may have far better methods for assessing managerial ability than simply looking at an indicator such as industry experience.
In a fast-changing, high-tech industry, a highly experienced founder may have learned out-of-date lessons. Keeley, Roure, Goto and Yoshimura (1990) find that average experience of more than 10 years by the founders of a high-tech company has a significant negative association with performance.
In non-tech companies the venture capitalists have a pilot operation from which to judge market acceptance. If experience weakly predicted the success of a pilot operation, the effect would be further obscured if venture capitalists apply many additional criteria in deciding which pilot operations to back.
Industry experience may have more effect on a manager's ability to operate a company than on that manager's ability to discover and recognize a business idea with very high value. The venture capital-backed companies depend heavily on a truly novel business idea where experience may not count for much, whereas high performers from the small start-up group are more likely to be a very well executed extension of an existing business idea.
In a small start-up the founder provides most of the managerial know-how in the early years. In large start-ups a management team is assembled early (in high-tech start-ups most of the team is present at the time of formation). The total knowledge of the team may be the key indicator of managerial ability in such companies. To the extent that the company is defining new directions in an industry, that team may intentionally include experience from other industries. Entrepreneurship rests with one person (occasionally two) in a small start-up, and in a management team for a large start-up.
To complete the discussion of experience, we should consider the six inexperienced founders among the small start-ups. Four succeeded and three of those were knowledgeable consumers in the field they entered. The fourth appears simply to have been energetic, adaptable and talented. The companies of two other inexperienced founders failed to grow. In both cases, inexperience contributed to eventual failure. One succeeded for a few years, but failed to recognize a market downturn. The second designed an interesting product, but could not produce it cheaply enough. Even so, both of these managers appeared roughly as resourceful as a number of others who succeeded. On the whole, managerial ineptitude did not seem to be a heavy constraint on the small start-ups or the large ones.
The prime determinant of success, based on our sample of 45 companies that pursued growth, is broadly the same for large and small start-ups: developing a product/service that has a ready market and is not subject to stiff competition. Finding such a product/service is apparently not easy, and judging whether it truly meets the criteria for success is also an uncertain process. The large start-ups underwent scrutiny by sophisticated investors who specialize in new companies. Only half succeeded. Our highly successful start-ups probably represent the top two or three percent of new companies, based on Reynolds' (1993) study of a random sample of new firms. A founder with experience in the industry being entered appears to have better odds than founders entering new industries, but even so the chances for high growth cannot be high.
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