The Descriptive Analysis

4.2.1 Prior Knowledge

An important characteristic explored by this survey is prior knowledge. Several items are focused on this specific construct, both in part A1 and in part F1. In detail, the questionnaire asks the interviewee if he/she has also founded other companies. Every entrepreneur answered this question. 55 interviewees answered “no” (71.4%), while 22 founders answered “yes” (28.6%).

Another question posed in the questionnaire regarding prior knowledge asked which was highest level of education they had obtained. The typical Italian

qualifications are a Ph.D., a Master’s Degree, a University Degree (Bachelor’s Degree), or a College certificate. Actually, all of the 77 entrepreneurs had a high level of education. They all have at least a University Degree (i.e. a Bachelor’s Degree). However, only 34 of them did not continue their education after taking that degree (44%), 6 reached a Master’s Degree (8%), whereas 34 entrepreneurs, representing the relative majority of the sample (48%) had obtained a Ph.D.. This data is presented in the table 7 below.

Table 7 – Highest Degree taken University

Degree Master’s Ph.D.

Frequency 34 6 37

Percentage 44% 8% 48% Source: author’s compilation

Moreover, another question has been asked to entrepreneurs in order to understand which kind of jobs they have already carried out. A list of 11 different possibilities was proposed; interviewees could give multiple answers.

Table 8 - Past Jobs

(n=77) Freq.

Production technician or engineer 6 Design technician or engineer 15 Administrative employee 4

Marketing 2

Selling and commercial 3 Finance 0 Logistic and Distribution 3 General Manager 8 Consultant / Self-employed 28 University Researcher 49 Public Research Center Researcher 17 None of the list 14

Source: author’s compilation

It can be observed that 49 entrepreneurs, out of the 68 of the sample, had been researchers in universities. We can also note that 17 entrepreneurs have answered that they had been researchers in public research centers. Nine people had been both university researchers and public research center researchers. Therefore, 57 entrepreneurs (77%) had been either a university researcher or a public research center researcher. Hence, we can argue that a strong academic background is present in the prior knowledge of this sample. An important datum is the one that 14 people did not selected any of the listed categories. They probably started working when they

established the spinoff, possibly immediately after having completed their final degree or doctorate. A large group of 28 entrepreneurs (36%) had work experience as consultant or had been self-employed. Fifteen individuals have been design technician or engineer and further six have been production technician or engineer. None had ever been employed in the field of finance, and very few had been employed in the field of marketing (two cases). A background in sales or commercial occupations was also rare (three cases) as was experience in logistics and distribution (three cases). Four entrepreneurs had been employed in the administrative field, and eight have been general mangers of another company previously.

In sum, it can be maintained that a business prior knowledge is limited to few cases, whereas, as it was expected, academic prior knowledge is much more widespread among the 77 entrepreneurs.

4.2.2 Patent & Inventors

One of the first questions proposed by the questionnaire in Section A.1 was if the founder had ever registered a patent, and if so, how many. This question is quite important because for two reasons. Firstly, this was an additional important indicator of the level of prior knowledge of the founder. Secondly it the question as to whether the interviewee founded the spinoff specifically in order to exploit one or more patents that they had personally registered was not explicitly asked. nonetheless it is clear that founders who had registered a patent are more likely to let their company take benefit of their their patents.

Table 9

How many patents have you registered? (N=77)

None 1 – 2 3 - 5 6 - 10 > 10 Total

Frequency 48 17 3 5 4 77

Percentage 62.34% 22.08% 3.90% 6.50% 5.19% 100% Source: author’s compilation

As may be noted from table 9 above, the majority (62%) of the interviewees is not the holder of any patent, i.e. 48 out of 77 of them. On the contrary, 29 entrepreneurs (38% of the sample) are the holder of at least one patent. Of these twenty-nine, seventeen entrepreneurs had registered one or two patents, three had registered from three to five patents, five of the interviewees responded that they had registered between six and ten patents, and, finally, in four cases they claimed to have registered more than ten patents.

4.2.3 Individual Skills

As for individual skills, these have been divided into two groups in the questionnaire: (1) organizational competences, and (2) technical and managerial competences. The results of the enquiry into the first category are presented in table 10 below.

Table 10 - Level of Competences

Field of Compentence Mean Var

Product design 4.92 3.41 Process design 4.50 4.09 Production 3.70 3.20 Accounting and administration 3.17 2.84

Marketing 3.79 2.30

Commercialization and sellings 3.39 2.78 Distribtion and logistics 3.00 2.75

Finance 2.92 2.53

Source: author’s compilation

It may be noted that the higher levels of competence were registered in the item “product design” with a mean of 4.9 (1-7 Likert scale, 7 beign the maximum), followed by process design with a mean of 4.5. These two are the only field of business competence where a score higher than four is registered. Marketing (3.8), production (3.7), sales (3.4), accounting and administration (3.2) and distribution and logistics (3) follow in a descreasing order, but obtain results between 3 and 4. The field of compentence with the lowest mean is finance (2.9), which also registers second lowest variance showing that results are quite similar among the interviewees.

The second construct concerning individual skills, presented in this topic is

Organizational Competences. These competences are valued through five items on 1-

7 Likert scale. The average result of the interviewees for this construct was 5.7 which is very high score, and variance has been found to be very low at 1.3. This result tells us that generally all the entrepreneurs of public research spinoffs of Bologna believe to have, and most probably do have, very high levels of organizational competence.

Table 11

Mean Var

Organizational skills 5.69 1.29 Source: author’s compilation

As introduced in chapter 2, an important feature for determining the probable success of the new ventures is the personal traits of founders. In particular two personal traits have been highlighted in entrepreneurial literature: tenacity and

passion. Both have been measured trough five 1-7 Likert items. Results are presented

in table 12 below.

Table 12 – Tenacity and Passion

Mean Var

Tenacity 5.53 1.60 Passion 3.80 3.43

Source: author’s compilation

It can be immediately observed that statistical mean of tenacity is almost as high as the one observed for the organizational skills (5,53 vs 5,69) and variance is also quite low. On the other hand, the way in which reports about passion are made is very different. In fact, in this case the result does not show a particularly high level of passion towards working for a spinoff, but only an average level. This can be partly explained through an observation that must now be introduced at this point of the analysis.

Actually, the items proposed by Smilor (1997), and here used, were not intended for evaluating passion simply as a personal trait, which would have had very little importance for this piece of research. Thes items were instead focused on passion towards the entrepreneur’s involvement in the spinoff11. But, not all of the entrepreneurs contacted work full time for the spinoff they are involved with. Some are only shareholder founders, their involvement being limited to their investment in equity, others work part time for the company while continuing their academic careers at the university or PRC. In order to understand these differences among the 77 founders in more detail, they were asked if they considered their involvement in their spinoff were “full time” or only “part time”. To this question, 55 interviewees answers were part time (71.4%), whereas only 22 founders considered their involvement full time (28.6%). In fact, if we take into consideration only the answers to these items given by full time entrepreneurs, the score increases to 4.49, while for part time entrepreneurs this score is only 3.52. Thus, the difference between actual scores and expected scores are explained.

4.2.5 Risk taking

11

An important characteristic of entrepreneurs is their attitude to risk taking. In chapter 2, entrepreneurial and psychological literature about risk taking was thoroughly reviewed. The questionnaire proposed three main constructs pertaining to risk taking which were included: generic taking, financial risk, gambling. Let us explore each of these features.

Table 13 – Propensity for Risk Taking

Mean Var

Generic risk taking 5.51 2.47 Financial risk taking 3.52 3.60 Gambling 1.48 1.51

Source: Author’s compilation

Statistical results show that general propensity of entrepreneurs for risk taking is quite high (5.51/7). Variance shows that difference within the sample exist and are still within the average values. On the other side, their propensity for financial risk taking is lower than average, and its variance is quite high. Items proposed in the questionnaire for this construct, were concerned with the attitude towards investing a part of their revenue in speculative stocks, blue chip stocks, government bonds and investment funds. Results show that on average there is not a particularly high inclination towards this kind of invenstments, but this inclination varies greatly among the individuals surveyed. The propensity for gambling is extremely low. Indeed this construct registers the lowest scores in the whole questionnaire and also presents one of the lowest variances.

4.2.6 Attitude Towards Innovation and Business Growth

Attitudes towards innovation and business growth were measured through a validated scale of ten 7-point uniform fashion semantic differential items proposed by Caprara (2000). In this case the results are unanimous in asserting that entrepreneurs have an extremely positive attitude toward innovation and business growth. The answers of the interviewees to the list of eleven adjectives is reported in table 14 in the following page.

Table 14 - Attitude Towards Innovation and Business Growth Mean Var Pleasant 6.51 0.57 Useful 6.55 0.60 Desireble 6.41 1.15 Positive 6.57 0.70 Commendable 6.22 1.00 Likeable 6.24 1.14 Beneficial 6.36 0.66 Good 6.38 0.88 Wise 6.14 0.79 Stimulating 6.80 0.53 Secure 4.47 1.92

Source: author’s compilation

It can be clearly observed that the first ten adjectives all indicate positive qualities and most of them are synonimous. The results of these ten observations are very similar in figures. The highest score is obtained for the adjective stimulating (6.80) while the lowest is reported for the adjective wise (6.14) but it is still a very high score. One adjective has been added to the list proposed by Caprara, which is

risky. As for this adjective responses were quite different in terms both of mean and

variance.

This observation tells us that by far public research spinoffs entrepreneurs consider entrepreneurial behavior to be an extremely stimulating activity and associate to it every positive adjective. On the other hand, they do not tend to consider entrepreneurial behavior to be a particularly secure activity, in fact this adjective receives the score of 4.47, which is only slightly higher than the average. Responses to the item “Secure” also had the highest variance of all the terms on this list (1,92). However, these responses show that entrepreneurs perceive that entrepreneurial activity is something risky that should be handled and undertaken with care. A very high score even for this adjective would have signified that interviwees would probably underestimate the risks involved in their activities and, thankfully, this was not the case.

4.2.7 Subjective Norms and Perceived Behavioral Control

According to Ajzen Theory of Planned Behavior, “subjective norms” and “perceived behavioral control”, together with the “attitude toward the behavior” are the three fundamental constructs in the definition of intention, although the specific weight of each of these constructs can vary from behavior to behavior.

Subjective norms have been evaluated through two items, while perceived behavioral control was evaluated through three items. The result of the survey in this respect are present below.

Table 15 – Subjective Norms and Perceived Behavioral Control

Mean Var

Subjective norms 5.48 1.74 Perceived behavioral control 3.65 2.44

We notice here that subjective norms take a high score, associated with a not particularly relevant variance. Perceived behavioral control gets a lower mean and variance is higher. This data shows that in general the perceived social pressure to carry out that entrepreneurial behavior is quite high, whereas the environmental perception of ease of fulfillment of entrepreneurial behavior is lower than average.

4.2.8 Entrepreneurial Orientation

As described in paragraph 2.5, entrepreneurial orientation is considered as composed of three main conctructs, ie entrepreneurial innovativeness (EI),

entrepreneurial proactiveness (EP) and entrepreneurial risk taking (ERT) and they

have been measured through a well validated tool know as the “strategic posture scale” (Covin & Slevin, 1989). A clarification is here needed. Entrpreneurial risk raking is different from the categories of risk taking previously analyzed. Actually, while in sub-paragraph 4.2.5 items were focusing on a personal attitude towards risk- taking, now interviewees are asked three questions in order to understand what according to them was the level of risk exposure would be the most suitable for their company. We should expect to find a high correlation between the generic risk taking construct and the one who was defined as entrepreneurial risk taking. Here are the results of the three components of entrepreneurial orientation as it is distilled from the questionnaires.

Table 16 – Entrepreneurial Orientation

EO Mean Var

Entrepreneurial innovativeness 5.74 1.90 Entrepreneurial proactiveness 5.66 1.88 Entrepreneurial risk taking 4.49 2.00

Source: author’s compilation

It can be noticed here that all the three constructs get a very similar variance, while means vary slightly more variable. EI gets the highest scores of the three (5.74),

immediately followed by EP (5.66). ERT receives the lowest scores, even if they are still higher than the average (4.49). Comparing these results with generic risk taking (GRT) and financial risk taking (FRT) it can be observed that ERT results are exactly the average of GRT and FRT. In fact, GRT scores are 5.51, FRT 3.52 and ERT are found to be 4.49. A more profound close grained analysis could discover if this hypothesized correlation between GRT, FRT and ERT is consistent. In conclusion, it can be assumed that entrepreneurs generally have very good entrepreneurial orientation, and are more focused on proactiveness and innovativeness than risk taking.

4.2.9 Forecasts of Sales Revenue and Number of Employees

Section C2 of the questionnaire was drawn up in order to gather information on entrepreneurs forecasts for the following two years concerning sales revenues and number of employees they have expected to have. Interviwees could provide an evaluation of their level of confidence on a scale from 1 to 7 (7 being absolutely certain) to a list of 9 different intervals ranging from an increase of more than 100% to decrease of 25%. Figures below provide the distribution of results for each of the four questions proposed.

Figure 9 - Expected % variation in sales revenues in 2007 with respect to 2006 (n=68) 0 5 10 15 20 25 -15 -5 5 15 25 35 45 55 65 75 85 95 105 115

Figure 10 - Expected % variation in sales revenues in 2008 with respect to 2007 (n=67) 0 5 10 15 20 25 30 -15 -5 5 15 25 35 45 55 65 75 85 95 105 115

Figure 11 - Expected % variation in number of employees in 2007 with respect to 2006 (n=64) 0 5 10 15 20 -25 -15 -5 5 15 25 35 45 55 65 75 85 95 105 115

Figure 12 - Expected % variation in number of employees in 2008 with respect to 2007 (n=65) 0 5 10 15 20 25 -15 -5 5 15 25 35 45 55 65 75 85 95 105 115

source: author’s compilation

in number of their employees. As for the expected percentage variation in the number of employees only two entrepreneurs thought that they would have a decrease in sales revenue in 2007, and only one in 2008. This fuction looks like a gaussian distribution but present some analogies with a log-normal function. The peak in both cases is around +15% of sellings growth. As for the function “number of employees” it is possible to notice that it has a lower peak at around 5 or 10%, but it loooks like a log- normal function than the previous one. Actually seventeen entrepreneurs replied that their company did not have any plans to engage anybody in the year 2007 and about 12 confirmed these plans also for 2008.

Nevertheless, all these four fuctions present a particularity, which is the aparently a-normal little peak in around +100%. This is due to four five entrepreneurs working for very young companies which in 2006 had almost no selling activity and had one employee or no employees at all. In these cases even a limited increase in absolute terms involves a high rise in percentual terms.

Table 17 - Forecasts of Sales Revenue and Number of Employees

Mean Var

Sales revenues (2007) + 23.59% 514.57 Sales revenues (2008) + 20.24% 372.96 Number of employees (2007) + 20.60% 723.98 Number of employees (2008) + 20.60% 658.37

Source: author’s compilation

As for number of employees, the estimates are only slightly worse than those for sales revenues and only for 2007. In fact, table 16 shows that the statistical means are quite similar for the four categories analyzed. Sales revenues for 2007 are expected to increase of averagely 23%, and 20% in 2008. Whereas, the number of employees this percentage in 20% both for 2007 and 2008. Variances are extremely high, showing that there are great differences between companies in what they consider their future growth trend. In particular it can be argued that expectations for 2007 are slightly superior to those for 2008. Some explanations can be proposed for this result. Firstly, entrepreneurs could tend to be more pessimistic than optimisitc. As a result, while for 2007 they could already have purchases on their order books, in 2008 they might prefer to reduce these in their forecasts. Secondly, for seed and start-up companies it is likely that they will have high growth rate in their first years and than it will decreases year on year, when they reach a larger dimension. In fact,

while it is relatively easy to double the first year sales revenues in the second year, it is more and more difficult to keep on doubling them when the company has already reached already a larger dimension.

4.2.10 The Business Environment

A fundamental factor that this analysis has focused on is represented by the analysis of the business environment surrounding the spinoff. In this field, four constructs were investigated by the questionnaire: obstacles, supports, heterogeneity and dynamism. First, let us concentrate the analysis on obstacles. Seven categories have been elected as being more representative of the problems that a new venture may run into. Among these seven, in Table 18 below it is to be observed that difficulty in access to capital is perceived by public research entrepreneurs as the most relevant obstacle. “Lack of laws and policies in fostering entrepreneurship” follows with a score of 4.01. On the other hand, a “lack of intellectual property protection systems” is perceived as being a lesser obstacle with a score of 2.66 followed by “difficulty in access to suppliers and production firms” which gets a mean of 2.74. This item is the one which get also the lowest variance score (2.86) among those in the list.

Table 18 - Obstacles

Obstacles mean var

Difficulty in accessing to capital 4.29 4.53 Difficulty in accessing to distribution channels 3.59 4.24 Difficulty in accessing to suppliers and production firms 2.74 2.86 Difficulty in accessing to qualificated technical personnel 3.11 3.67 Difficulty in accessing to qualificated managerial staff 3.33 3.48 Difficulty in accessing to commercial staff 3.78 4.31