How does the gestation process differ for technologically intensive & innovative start-ups compared...

8/13/2019 How does the gestation process differ for technologically intensive & innovative start-ups compared to less sophis

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How does the gestation process differ for technologically

intensive & innovative start-ups compared to less sophisticated

ventures?

An empirical study into gestation activities, decision-making strategies, growth

expectations and subsequent founding success.

Combined Master Thesis

Programs: Entrepreneurship & New Business Venturing / Business Information Management

Student: Paul DiekemaStudent #: 295033E-Mail: [email protected]: 27 September 2012

Coach: Rein Denekamp, Department of Organization and Personnel ManagementCo-Reader: Dr. Peter van Baalen, Department of Decision & Information Sciences


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PrefaceThis master thesis is written for the fulfilment of the master programs in Entrepreneurship & New

Business Venturing, and Business Information Management. My affinity with the dynamics of both

entrepreneurship and Information Technology triggered me to look into the processes at which

technology-based and innovative ventures come into existence. When starting with this research topic Ilargely had in mind what to study and how to do it, but along the process I learned that is difficult not to

diverge too much. In the end my efforts resulted in a very extensive piece of research with which I hope

to have contributed to existing theories, as well as inspired others to explore avenues for future research.

With the completion of this master thesis comes the end of an era as a student at the Erasmus

University/ the Rotterdam School of Management. Throughout these years I have collected quite the

intellectual baggage and developed a multitude of competencies. Both proved to be of great help during

the process of writing this research. The process was not easy and at times asked a lot of myself and the

people close to me. On a personal level I have had to overcome more obstacles than I care for, but I am

happy to have pulled it off.

Special thanks go out to my coach, Rein Denekamp, who was there to turn boring theory into relevant

practical examples. His business experience combined with academic know-how appeared to be a

welcome addition. Despite that the central topic of this research is aimed at entrepreneurship, I want to

thank my co-reader Peter van Baalen, who was nevertheless very interested in the research topic and

who was able to give relevant feedback on my writings.

For now, enjoy reading!

Paul Diekema

The copyright of the Master thesis rests with the author. The author is responsible forits contents. RSM is only responsible for the educational coaching and cannot beheld liable for the content.


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Executive SummaryThe importance of technology-based and innovative ventures for the development of economies is

rather undisputed. Schumpeter (1934) was among the first economists to acknowledge the critical role of

creative destruction that these types of ventures engage in. By making existing product offerings

obsolete or shape new markets altogether, technology-based ventures create value for themselves and

spur progress for incumbents. Research about technology ventures is far from new, neither are studies

on the entrepreneurs behind those ventures. Yet studies of how technology ventures come into

existence are scarce. In fact, not only research on the creation of technology ventures is scarce, research

on nascent entrepreneurship in general has been absent because of lacking research methodologies. With

the arrival of the Panel Study of Entrepreneurial Dynamics empirical research on nascent

entrepreneurship is made accessible. Besides juxtaposing nascent ventures based on their levels of

technological intensity and innovativeness, this thesis introduces Sarasvathys theory of effectuation.

Specifically this study focused on the likelihood of becoming a new firm, the number and type ofgestation activities used throughout the process, and the time that start-ups need to become a new firm.

Effectuation and causation constructs are introduced as possible moderating factors on the odds of

successful firm founding. Additionally, start-ups are studied on their aspiration and expectations towards

future performance.

The context in which technology-intensive and innovative ventures are created is more dynamic,

uncertain and complex than the environment of less sophisticated start-ups (Liao & Welsch, 2003b). The

intensity of embedded knowledge, the types of knowledge needed and the infrastructure to create

product offerings are different for high vs. low technology industries (Oakey, Rothwell and Cooper,

1988). The need to be adaptive following from changing external conditions is most prone for venturescharacterized by high levels of uncertainty and environmental dynamism. Liabilities of newness and

smallness (Stinchcombe, 1965) make legitimacy building an important issue faced by technology

entrepreneurs. TBVs also need more resources, both tangible and intangible, than their counterparts

(Liao & Welsch, 2003b). Aldrich & Martinez (2001) find that routines and competencies vary

significantly for TBVs and NTBVs: innovative organizations have to pay rather serious attention to

learn new roles, setting on operating procedures, creating a culture of learning the skills and efforts to

make relations with employees. Ventures in these kind of environments are confronted with complex

information-processing requirements that are such that it requires organizational designs (including

strategic processes, control systems, and communication patterns and structures) allowing real-time fast

information collection and interpretation (Atuahene-Gima & Li, 2004).

Technology-intensive and innovative ventures are hypothesized to reach the new firm status less often

than less sophisticated start-ups, they take longer to become a new firm and they need more and

different gestation activities. With greater uncertainty comes greater risk, so it is hypothesized that they

expect greater absolute levels of revenue and employees, as well as greater relative growth. Additionally

technology-intensive and innovative ventures are hypothesized to be more willing to grow their

companies as large as possible compared to less sophisticated start-ups. The boundary conditions in

which TBVs are founded suggest that entrepreneurs do well by following an effectuation approach,

introduced by Sarasvathy (1998). The emphasis that start-ups place on control over prediction, or vice

versa, can be regarded on a spectrum of which pure causation and effectuation processes are the

(conceptual) extremes.


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Sarasvathy (2001) provides a high-level definition: Causation processes take a particular effect as given and focus

on selecting between means to create that effect. Effectuation processes take a set of means as given and focus on selecting

between possible effects that can be created with that set of means.

The hypothesized relations are tested using the Panel Study of Entrepreneurial Dynamics, which found

its origin in the mid-90s in the United States and has evolved to the most extensive dataset on early-stageentrepreneurship. The dataset accurately reflects the start-up population in the US. Data-collection took

place between 2005 and 2011, and more than 1200 nascent entrepreneurs were interviewed on a yearly

basis. Statistical analyses were carried out for 998 observations. A series of bivariate methods and a

multinomial logistic regression yielded interesting results.

No support was found that technology-based and high-newness ventures succeed less often in becoming

a new firm. Also the technology-based and high-newness start-ups that became a new firm did not

significantly take longer than less sophisticated counterparts. What is interesting though is that a

disproportionate number of technology-intensive start-ups were still trying as an active start-up at the

end of the research program. Combined with findings from other studies (point of diminishing founding

success) this either means that a disproportionate number of the active TBV start-ups will eventually fail,

or it means that the gestation period on average is indeed longer. Technology-based and high-newness

ventures furthermore are found to need more gestation activities in total, and more legitimacy-building

and planning activities. Findings are also highly significant for the growth willingness, expected revenues

and expected number of employees. Finally there was no support for a moderating effect of effectuation

or causation strategies. Rather interestingly, technology-based and high-newness ventures were found to

use a causation approach more often than effectuation and neutral approaches. Also, following a

causation approach is positively correlated with becoming a new firm, contrary to neutral and

effectuation strategies.

The most important conclusion of this study is that the extent to which start-ups are technologically

intensive and innovative (newness) has an effect on the way that new firms come into existence, and on

the expectations and aspirations towards future performance. Despite having a more complex founding

process, high-sophistication start-ups compensate for the difficulties they encounter. Combined with

findings of other studies into nascent entrepreneurship it can be concluded that these ventures

compensate with their levels of human and social capital, but also by using more planning and

legitimacy-building activities. By actively downplaying the uncertainties that these ventures encounter

they are able to relatively get a good founding success rate vis--vis less sophisticated start-ups.

Moreover this study provided solid confirmation for existing literature regarding expectations towardfuture performance and growth aspiration.

An important focal point, but yet explorative part of this thesis was the incorporation of the decision-

making strategy of nascent entrepreneurs. In conjunction with Hamel & Prahalads (1989) corporate

imagination theory this study concludes that prediction and control should be regarded as two

decoupled phenomena. Start-ups use both planning and learning elements depending on their quest for

resources, legitimacy and the right product-market combinations. This however does not undermine

Sarasvathys notion of effectuation. Rather on a variety of dimensions, start-ups should find a balance

that fits their needs at that time. The effectual or causational entrepreneur does not exist rather there is

an infinite number of combinations that changes as ventures go through different phases.


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List of Figures

Figure 1 - Effectuation & Causation pillars ......................................................................................................... 12

Figure 2 - Conceptual Model ................................................................................................................................. 15

Figure 4 - Topics covered in PSED ...................................................................................................................... 36

Figure 5 - Causation & Effectuation constructs ................................................................................................. 48

List of Tables

Table 1 - Overview Gestation Activities .............................................................................................................. 46

Table 2 - Startup Status * Technology Intensity ................................................................................................. 58

Table 3 - Startup Status (without Active) * Technology Intensity ................................................................... 59

Table 4 - Startup Status * Newness ....................................................................................................................... 60

Table 5 - Startup Status (without Active) * Newness ......................................................................................... 61

Table 6 - Startup Status (without Active) * Sophistication................................................................................ 62

Table 7 - Technology Intensity * Revenue .......................................................................................................... 63

Table 8 - Newness * Revenue ................................................................................................................................ 64

Table 9 - Sophistication * Revenue ....................................................................................................................... 65

Table 10 - Technology Intensity * Revenue Growth ......................................................................................... 65

Table 11 - Newness * Revenue Growth............................................................................................................... 66

Table 12 - Sophistication * Revenue Growth ..................................................................................................... 67

Table 13 - Technology Intensity * Employees .................................................................................................... 68

Table 14 - Newness * Employees ......................................................................................................................... 69

Table 15 - Sophistication * Employees ................................................................................................................ 69

Table 16 - Technology Intensity * Employee Growth ...................................................................................... 71

Table 17 - Newness * Employee Growth ............................................................................................................ 72

Table 18 - Sophistication * Employee Growth ................................................................................................... 73

Table 19 - Technology Intensity * Growth Willingness .................................................................................... 74

Table 20 - Newness * Growth Willingness .......................................................................................................... 75

Table 21 - Sophistication * Growth Willingness ................................................................................................. 76

Table 22 - Technology Intensity (new firms) * Gestation Length ................................................................... 77

Table 23 Technology Intensity (quitters) * Gestation Length....................................................................... 78

Table 24 - Technology Intensity (Active) * Gestation Length ......................................................................... 79

Table 25 - Newness (new firm) * Gestation Length .......................................................................................... 79

Table 26 - Sophistication (new firm) * Gestation Length ................................................................................. 81

Table 27 - Gestation Activities - Descriptives ..................................................................................................... 82

Table 28 - Technology Intensity * Gestation Activities..................................................................................... 83

Table 29 - Newness * Gestation Activities .......................................................................................................... 84

Table 30 - Sophistication * Gestation Activities ................................................................................................. 86

Table 31 - Technology Intensity * Gestation Activities (Anova) ..................................................................... 86

Table 32 - Sophistication * Gestation Activities (posthoc) ............................................................................... 88


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Table 33 - Multivariate Analysis (block 0) ............................................................................................................ 90

Table 34 - Multivariate Analysis (block 1) ............................................................................................................ 91

Table 35 - Business Status * Decision-Making Strategy .................................................................................... 92

Table 36 - Multivariate Analysis with Control Variables ................................................................................... 94

Table 37 - Technology Intensity * Decision-Making Strategy .......................................................................... 94

Table 38 - Overview hypotheses & Support ....................................................................................................... 97

Table 39 - Kruskal-Wallis Technology Intensity * Revenue ...........................................................................124

Table 40 - Anova Technology Intensity * Revenue .........................................................................................124

Table 41 - Posthoc Results Technology Intensity * Revenue .........................................................................125

Table 42 - Kruskal-Wallis Technology Intensity * Revenue Growth ............................................................126

Table 43 - Anova Technology Intensity * Revenue Growth ..........................................................................126

Table 44 - Posthoc Results Technology Intensity * Revenue Growth..........................................................127

Table 45 - Kruskal-Wallis Newness * Revenue .................................................................................................128

Table 46 - Anova Sophistication * Revenue ......................................................................................................128

Table 47 - Anova (multiple comparisons) Sophistication * Revenue ............................................................129

Table 48 - Kruskal-Wallis Newness * Revenue Growth .................................................................................129

Table 49 - Anova Newness * Revenue Growth ................................................................................................130

Table 50 - Anova Sophistication * Revenue ......................................................................................................130

Table 51 - Posthoc results Sophistication * Revenue Growth .......................................................................131

Table 52 - Anova Sophistication * Revenue Growth .......................................................................................131

Table 53 - Posthoc results Sophistication * Revenue Growth .......................................................................132

Table 54 - Kruskal-Wallis Newness * Employees ............................................................................................132

Table 55 - Anova Sophistication * Employees .................................................................................................133

Table 56 - Posthoc results Sophistication * Revenue .......................................................................................133

Table 57 - Kruskal-Wallis Technology Intensity * Employee Growth .........................................................134

Table 58 - Anova Technology Intensity * Employee Growth .......................................................................134

Table 59 - Posthoc results Technology Intensity * Employee Growth ........................................................135

Table 60 - Kruskal-Wallis Newness * Employee Growth ...............................................................................136

Table 61 - Anova Newness * Employee Growth .............................................................................................136

Table 62 - Posthoc results Newness * Employee Growth ..............................................................................137

Table 63 - Anova Sophistication * Employee Growth ....................................................................................138

Table 64 - Posthoc results Sophistication * Employee Growth .....................................................................138

Table 65 - Mann-Whitney Technology Intensity * Gestation Length ...........................................................139

Table 66 - Anova Technology Intensity * Gestation Length ..........................................................................139

Table 67 - Mann-Whitney Technology Intensity * Gestation Length ...........................................................140

Table 68 - Mann-Whitney Newnes * Gestation Length ..................................................................................140

Table 69 - Anova Newness * Gestation Length ...............................................................................................141

Table 70 - Mann-Whitney Newness * Gestation Length ................................................................................141

Table 71 - Kruskal-Wallis Sophistication * Gestation Length ........................................................................142


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Introduction

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propensity to fail exists because young firms have not established effective work roles and relationships

and because they lack a track record with outside buyers and suppliers; he calls it the liability of

newness. Also start-ups vary considerably in their access to resources and stable relationships, and these

variations may lead to differences in their early fates (Baum, 1996).

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traditional view of causation. The emphasis that start-ups place on control over prediction, or vice versa,

can be regarded on a spectrum of which pure causation and effectuation processes are the (conceptual)

extremes. Sarasvathy (2001) provides a high-level definition of both causation and effectuation:

Causation processes take a particular effect as given and focus on selecting between means to create that effect.

Effectuation processes take a set of means as given and focus on selecting between possible effects that can be created

with that set of means.

To make a clear distinction between causation and effectuation processes, Sarasvathy (2001) uses the

analogy of a chef cooking dinner for a guest. In the traditional causation view of entrepreneurship, the

guest would choose a dish featured on the menu, after which the chef shops for groceries to prepare the

meal using a pre-defined recipe. In this situation, the end is given and the chefs focus lies on selecting

and acquiring the means, or resources, to reach favourable outcomes. Using effectual reasoning, the chef

would prepare a meal based on the ingredients and kitchen utensils that are at his disposal. This is a

means-driven approach and it might involve changing course based on unexpected events during the

process.

Sarasvathy (2008) exemplifies effectuation processes in her latest book along five pillars, or principles:

the pilot-in-the-plane principle, the bird-in-hand principle, the affordable loss principle, the crazy

(or patchwork) quilt principle, and the lemonade principle. The first principle, that of the pilot-in-the-

plane, plays a pivotal role in the underlying logic of effectuation processes. It expresses the extent to

which individuals believe they can actively shape the future rather than passively predicting it

(Kraaijenbrink & Ratinho, 2010). The other pivotal pillar, the bird-in-hand principle, reflects the

starting point of entrepreneurial actions; whether entrepreneurs emphasize means or ends. As

mentioned earlier, the effectual process is clearly distinguished by taking means as a starting point,

whereas causation is characterized by goal setting, hence working towards a pre-defined end. The crazy

quilt principle is about engaging in actively building a network of self-selected stakeholders instead of

using competitive analysis. Creating stakeholder commitment is one of the important tools of the

effectuation process. The effectual entrepreneur is a socially embedded entrepreneur who co-creates his

venture with partners, thereby developing new means and ends. Another important theme is the

affordable loss principle: effectual entrepreneurs show a pre-disposition towards investing based on the

amount one can afford to lose rather than investing based on expected return. This clearly demonstrates

an investment strategy that is focused on control over prediction. The final theme, the lemonade

principle, reflects the notion that effectual entrepreneurs acknowledge and appropriate contingencies

by leveraging surprises rather than trying to avoid them, overcome them, or adapt to them (Sarasvathy,2008). This notion is somewhat similar to the bird-in-hand principle, since it emphasizes that the course

of action might change because of contingencies: hence, means over ends.


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Figure 1 - Effectuation & Causation pillars

In her collection of seminal and conceptual work on effectuation, Sarasvathy has portrayed the two setsof processes as opposing phenomena, or as two dichotomous ends of a continuum (Kraaijenbrink &

Ratinho, 2010). In her book it is put that effectuation is the inverse of causation, however adding that

empirically both causation and effectuation processes can go together, but for conceptual reasoning the

two should be considered inverses. Throughout this thesis, causation and effectuation will be treated as

opposing phenomena for explanatory reasons. In the measurement section of the concepts however I

will go into further detail about the underlying constructs of causation and effectuation processes.


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elaborated on into more detail later, is a unique dataset that provides countless research opportunities. It

is highly valuable because it gives researchers the opportunity to get insight into the decisions nascent

entrepreneurs make throughout the start-up process. Hence, there are two unique selling points:

longitudinal data, which is relatively scarce in entrepreneurship research; and data on nascent

entrepreneurs, those that took the plunge and are in the process of starting a company.

The research goal is twofold:

" To empirically investigate the differences in gestation processes between technology-basedventures and non-technology-based ventures and the effect of these differences on gestation

outcomes, and

" To empirically investigate the moderating effect of nascent entrepreneurs decision-makingstrategy on the relation between the type of venture and gestation outcomes.

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Theoretical frameworkIn the next section of this thesis the most relevant literature will be reviewed in order to establish a solid

theoretical framework that will contribute to answering the research question. Key concepts and their

linkages will be explained, resulting in a series of testable hypotheses and a graphical representation in

the conceptual model.

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The definition by Wennekers & Thurik (1999) fits, more or less, all the previously mentioned views and

also takes into account the role of the environment, considering it as uncertain and hence, unpredictable.

The entrepreneurial process is characterized by events that are unaccounted for, and that require

adaptive skills and creativity to be successful. This definition of entrepreneurship by Wennekers &

Thurik (1999) will function as an anchor point throughout this thesis.

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industries, according to Oakey, Rothwell and Cooper (1988). An important issue for nascent

entrepreneurs is the need to be adaptive following from changing external conditions. This need is most

prone in industries characterized by high levels of uncertainty and environmental dynamism: technology

intensive industries. The pace at which offerings are renewed and products become obsolete make

technology entrepreneurs need to focus on the continuous assessment of technological advantage and

new market opportunities. The aforementioned liabilities of newness and smallness make legitimacy

building another important issue faced by technology entrepreneurs; establishing a legitimate market

position is important for securing vital resources. Research has shown that technology based ventures

need more resources, both tangible and intangible, than their counterparts (Liao & Welsch, 2003b). Also

since the product offerings by TBVs are more sophisticated the marketing and sales efforts are likely to

be different. Moreover, Aldrich & Martinez (2001) find that routines and competencies vary significantly

for TBVs and NTBVs: innovative organizations have to pay rather serious attention to learn new roles,

setting on operating procedures, creating a culture of learning the skills and efforts to make relations

with employees (Semasinghe, 2011).

In 2004, the Academy of Management Journal published an article by Atuahene-Gima and Li, featuring

their empirical research into strategic decision comprehensiveness and its effect on performance in new

technology ventures. The authors note that ventures in highly turbulent environments are confronted

with complex information-processing requirements that are such that it requires organizational designs

(including strategic processes, control systems, and communication patterns and structures) allowing

real-time fast information collection and interpretation. Furthermore, the uncertainty that technology

ventures have to deal with imposes challenges on firms capabilities. In line with Aldrich & Martinez

(2001), Tushman & Nelson (1990) found that technology ventures experience a disrupted balance

between the resources they need and those that are available. Combining all the above evidence leads us

to believe that technology-based ventures have a harder time establishing a company than NTBVs.With greater uncertainty comes greater risk, so it can be hypothesized that higher technological intensity

decreases the chances of successfully establishing a company:

Hypothesis 1a:The higher the technological intensity associated with a venture, the less likely it is that this venture

becomes a new firm.

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capabilities entrepreneurs cannot do all activities at the same time; 3) activities may be dependent on

each other, influencing the order in which they are performed.

Gestation activities can be subdivided in several categories. Katz & Gartner (1988) identified four

categories: intentionality, resources, boundary, and exchange. These are much alike the ones put forward

by Delmar & Shane (2002): operational and planning activities. Operational activities consist oflegitimacy building, resource transformation and market & promotion. Planning activities entail events

that enable coordination, and are similar to intentionality. Exemplar activities are writing a business

plan, making financial forecasts and doing market research. Legitimacy building activities (boundary)

involve events that give the new firm some sort of right of existence, of which exemplars are the actual

registration of the firm, getting a tax number and joining an industry association. Resource

transformation activities speak for themselves: they involve combining and transforming physical,

financial, human and technological resources into a sellable product or service.

In his study on market dynamism and new firm formation, Newbert (2005) reasons that the gestation

process should become simpler (fewer activities) the more dynamic the market is. Since there are few

situations in which existing knowledge and experience will guide the entrepreneurs actions in a dynamic

market, there are also fewer activities needed to prepare for those situations. Paradoxically, he reasons

that because companies in dynamic markets have greater resource needs they are likely to engage in

more planning activities. The activities mentioned also include legitimacy building. Newbert (2005) thus

expects that in dynamic markets start-ups need more planning and legitimacy building activities, but not

more resource combination or sales & promotion activities. The author finds significant results to accept

the hypothesis that the new firm creation process consists of fewer activities when the velocity of the

market increases. In addition he found that more planning activities does not contribute to the odds of

becoming a successful new firm.

Elfring & Hulsink (2003) have studied the dynamics behind networking efforts in both Schumpetarian

(radical innovation) and Kirznerian (incremental innovation) technology ventures. They conclude that

building legitimacy is one of the single most critical aspects of launching a technology venture, especially

for radical innovations (high newness). Radical innovations require new ways to combine resources or

enter new markets and create conditions of high uncertainty. These situations call for an increased

network of weak ties. Low technology and low newness start-ups however may rely on exploiting their

strong ties. Expanding a network of weak ties takes time and effort, thereby adding to the expected

number and length of gestation activities.

Liao & Welsch (2003b) show that TBVs are more engaged in scanning the external environment,

assessing business opportunities, defining organizational boundaries, need more resources, and are more

active in exchanging with internal and external stakeholders. From Penroses theory of growth of the

firm (1959) it is known that new ventures start by assessing the initial set of resources and set to acquire

those tangible and intangible assets currently not in possession. For TBVs specifically the acquisition and

refinement of intangible assets like technologies and know-how takes time, which suggests that it is likely

that the gestation process of TBVs is probably lengthier. On top of that the uncertainty involved in

creating technology ventures implies more trial-and-error processes compared to NTBVs, also adding to

the duration and number of gestation activities, as well as the number of iterations of those gestation

activities. Moreover the liability of newness is bigger for technologically intensive and/ or high-newnessstart-ups. One of the most important aspects on which TBVs (are expected to) differ is the need for


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legitimacy. Because these firms often need funding, gaining legitimacy with possible investors and other

parties is important. It is thus expected that technologically intensive firms (and also high-newness firms)

need more legitimacy building activities than other start-ups. Because of the order of magnitude of the

resource base that is needed for these types of firms it is also expected that the amount of planning

activities is more extensive. Outside funding requires solid business cases and financial forecasts since

investors are looking for a return on the long term. They expect from the entrepreneur that he engages

in long-term planning. Resource transformation activities, like developing a product, filing for a patent

or making pre-agreements with suppliers are also likely to be more extensive for technology intensive

and high-newness firms. Low-tech and low-newness firms often offer relatively simple products or

services that require fewer activities to reach the commercial (sellable) stage. Finally, it is also

hypothesized that high-tech and high-newness firms need to engage in more sales & promotion activities.

These types of firms offer more sophisticated products and services and therefore need to go at greater

lengths to sell and promote. In addition, these firms are expected to have higher expectations for growth,

possibly in a geographic manner also. This would add to the amount of sales effort that is needed. In

sum it leads to believe that technology intensity and newness of start-ups are both positively correlatedto the total number of gestation activities that is needed to successfully start a new firm. Second, it is

also believed that the positive correlation mentioned above goes for all four subcategories of gestation

activities.

Hypothesis 1b: There is a positive relationship between the technological intensity associated with a venture and the

number of gestation activities.

Hypothesis 1c: There is a positive relationship between the technological intensity associated with a venture and the

length of the gestation process.

The term technology ventures has been coined throughout this section, yet it has not been formally

defined. Various studies mention high-tech as a term but it appears to be used rather loosely. Allen &

Stearns (2003) mention some examples of definitions of high-tech in their study on TBVs: dependent

upon innovation in science and technology, firms engaging in activities that have high rates of change,

R&D expenditures, and innovative products, and technology that obsoletes previous technology, as in

breakthrough or radical innovation, to name a few. When referring to technology or high-tech ventures,

studies roughly describe three types: those inventing and developing truly radical innovations, those

improving existing technologies, and those using technology to facilitate business processes. Allen &

Stearns (2003) have identified three types of technology ventures: first movers, practitioners, and

innovators. The first mover usually moves disruptive technology from the development stage to thecommercial stage. Clearly it is associated with the highest risk of the three categories and is thus most

prone to failure, but also offers the most promising returns. Commercialization of such novel

technology is difficult because user acceptance is hard to predict; the market has yet to be convinced of

the value of the technology. The second category are practitioners; ventures that take on existing

technologies and improve them by combining products or services into new unique offerings.

Practitioners are driven by the changing needs of customers and thus impose less risk than the first

mover; it actually sails on the R&D efforts incurred by first movers and innovators. Innovators are much

alike: they use technological know-how to incrementally improve existing technologies. Allen & Stearns

(2003) show that ventures can deploy a single strategy, or combine two or even all three. Their findings

show that technology ventures clearly differ in their technological intensity, whereas previous studieswould just distinguish between high-tech and low/ no-tech.


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The research of Allen & Stearns (2003) uncovers some interesting results with regard to the differences

between TBVs and NTBVs. Technology entrepreneurs are most likely to be men, and are younger then

non-technology entrepreneurs. Moreover TBVs are significantly more often established by teams.

Surprisingly, NTBVs develop prototypes of their product or service more often during the start-up

process yet this might be explained by the time and resources needed for technology-based prototypes

to be developed. NTBVs also show to hire employees quicker than technology ventures (yet not

necessarily more employees), whereas the latter are more likely to seek advice through assistance

programs. With regard to strategic intentions, technology ventures show a higher desire to grow and to

satisfy unmet needs by customers. Overall, TBVs appear to be more strategically oriented than NTBVs.

Following from Chan & Heide (1993), technology entrepreneurs more often engage in strategic alliance

making to fill the gaps in knowledge and capability. Technology licensing, access to marketing and

distribution channels, and equity investments are but a few reasons for technology ventures to partner

with incumbent firms (Allen & Stearns, 2003).

Technology ventures obviously act in a different arena than most other startups. The pace at whichtechnology changes and new products become obsolete implies greater uncertainty. New ventures are

only willing to face this uncertainty with greater potential rewards at stake. The research by Allen &

Stearns (2003) provides initial empirical evidence for the positive relation between technology intensity

and new venture (revenue) growth expectancy. Medcof (1999) has shown that technology-based

ventures have greater potential to reach above-average growth rates, and this is reflected by overall

revenue growth. Using technology-intensive and/ or new, disruptive products and services, ventures can

explore new markets or expand existing ones. Disrupting the status quo means making existing offerings

obsolete and clearing the way for accelerated sales. It seems safe to assume that high-technology and

high-newness firms have greater growth expectations with regard to revenues, but literature is less

conclusive on employee growth figures. In fact, there is very little to find about employee growth(expectations). Allen & Stearns (2003) only show that TBVs hire employees later on in the process, but

not necessarily less than NTBVs. This might be due to the fact that TBVs spend more time in the

beginning developing the product before going live, and that it is only when going operational and

realizing cash flow the firm has (financial) room for extra human resources. However it is believed that

there is a positive correlation between revenue growth and employee growth; there are only very few

products or services where growth in sales is combined with a stagnating employee count. Eventually all

firms reach a state where the product cannot further evolve without adding human resources.

It can thus be summarized that technology-based ventures have higher expectations for future growth

and also have a higher intention to grow than NTBVs. The following can be hypothesized:

Hypothesis 1d: There is a positive relationship between the technological intensity associated with a venture and the

revenue expectancy of the nascent entrepreneur.

Hypothesis 1e: There is a positive relationship between the technological intensity associated with a venture and the

employee count expectancy of the nascent entrepreneur.

Hypothesis 1f: There is a positive relationship between the technological intensity associated with a venture and the

growth willingness of the nascent entrepreneur.


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3


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3


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flexible and adaptive so they are able to respond quickly to unexpected events and grasp new

opportunities (Mosakowski, 1997; in: Garonne et al., 2010). Combining means and ends is no longer a

sequential process, but rather can be done simultaneously; this is the definition of incremental planning

(Garonne et al., 2010). Earlier on I mentioned that new firms have difficulties organising and aligning

their resources with objectives, especially in conjunction with changing market conditions. Instead of

working with a set of means towards predefined ends, new firms may engage in incremental, or

emergent strategies to be successful. In uncertain situations, the planning process is regarded a waste of

precious (management) time, slowing down the adaptation process and flexibility of the firm, while at

the same time blinding the firm for environmental changes (Mintzberg, 1990). Wiltbank et al. (2006)

show that there is ample empirical support for the learning approach.

Where Mintzberg was a clear advocate of the learning school of strategy, Ansoff and Porter vow for a

more planning oriented approach. In both dynamic and stable environments, a rational process towards

formalized and detailed planning would result in superior performance (Ansoff, 1979). Planning results

in more efficient use of resources, increased decision-making speed, and the support of flexible actuation(Delmar & Shane, 2003). Wiltbank et al. (2006) carried out an extensive research into the planning

performance literature and conclude that a decent amount of work has provided empirical support in

favour of the planning school. Short cuts like heuristics and intuition are heavily biased and systematic

planning helps to eliminate these inconsistencies.

The key thought behind Porters strategy is about being different it means deliberately choosing a

different set of activities to deliver a unique mix of value (Porter, 1980). Understanding the driving

forces behind markets and positioning the firm accordingly results in superior competitive performance.

Porter argues that strategy is about competitive position and planning on the combination of ends

(goals) and ends (policies). In Ansoff and Porters definition, more attention to situational detail, morefrequent analysis, more scanning for trends, and evaluation of more alternatives guide the firm to their

best possible strategy going forward (Wiltbank et al., 2006).

A third view on strategy, possibly to be considered as a third school of thought, is given by Hamel &

Prahalad (1989). The planning versus learning debate is based on the prediction control trade-off,

where the planning school takes a rational prediction based approach towards strategy making and the

learning school advocates flexibility and adaptability to stay in control. Hamel & Prahalad (1989)

however pose that firms can both emphasize their need for control and prediction. It is about

understanding an organizations own strengths and building on internal capabilities to be successful: the

strategists goal is not to find a niche within the existing industry space, but to create a new spaceuniquely suited to the companys own strengths, space that is off the map (Hamel & Prahalad, 1989:

p74). In their 2006 seminal paper, Wiltbank et al. (2006) make a case for non-predictive strategy and

propose a framework where control and prediction can be seen as independent dimensions, rather than

mutually exclusive phenomena. Whichever type of strategy a firm decides to use is dependent on the

confidence it has in its abilities to predict the environment. Key to both schools of thought is the

acknowledgement of an exogenously given environment in which the firm has to position itself. The

primary difference between the two is how they handle that given uncertainty (Wiltbank et al., 2006). It

is argued that the practical usefulness of prediction as a means of control depends crucially on certain

features of the environment (Mintzberg, 1994). Critical to understanding the trade-off between

prediction and control and eventually disbanding prediction from control is the framework byKnight (1921). He identified three types of uncertainty: 1) uncertainty arising out of known distributions


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and unknown draws; 2) uncertainty from unknown distributions and unknown draws; and 3) non-

existent distributions where the instances are unclassifiable (Wiltbank et al., 2006). The third type is also

dubbed Knightian uncertainty, and in such environments, prediction and control are conceptually at

odds. It occurs when economic artefacts such as markets have not yet been created, and hence cannot

be predicted. This opens the case for decoupling prediction and control: influencing an endogenously

created environment may result in favourable results. Whereas planning and learning strategies are about

positioning the firm, another stream of strategies can be regarded as construction approaches. The figure

below (source) displays the framework proposed by Wiltbank et al. (2006).

Constructive approaches are characterized by a high emphasis on control, and are based on means ends relationships instead of firm environment positioning. Constructive approaches assume either

the non-existence of key elements of the environment (presenting opportunities for constructing them),

or the organizations ability to affect the evolution of those elements in significant ways (Wiltbank et al.,

2006: 989). The authors distinguish between visionary and transformative approaches. Earlier on, one

example of a visionary approach has been given: the framework by Hamel & Prahalad. The focus for the

remainder of this research however will lie on the bottom-right quadrant of the matrix: transformative

strategy, and more specifically on the effectuation theory by Sarasvathy (2001).

Figure 3 - Overview Strategy Schools


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vision together (Wiltbank et al., 2006). Eventually the process of expanding means comes to a stop

because an increasing set of constraints converges around the network of stakeholders. Subsequently,

path dependence takes over and goals will become less ambiguous.

Stakeholder commitment forms the second important principle of the effectuation model; it was

mentioned in the introduction as the crazy quilt principle, after a set of stakeholders graduallyexpanding a quilt by adding pieces of patchwork. This principle demonstrates a cooperative view on

outsiders, whereas the causation model takes competitive analysis as a starting point (Porter, 1980).

Looking at Porters five forces model, effectuation would involve engaging in strategic alliances and pre-

commitments from stakeholders in order to downplay uncertainty and to erect entry barriers.

The third principle is that of using affordable loss instead of expected return as a basic assumption for

investment decisions. Effectual stakeholders only invest what they can afford to lose. In causal reasoning,

selecting between investment alternatives is a rational process of choosing the optimal strategy, since

accurate predictions of the expected return can be made. In the effectual process it becomes gradually

apparent what the size of the pie will be, so at first no accurate predictions can be made of the future

(and current) value of the pie, not to mention the share that would belong to every stakeholder.

Therefore expected return is not an effective tool to make investment decisions. Instead, stakeholders

each decide what they are affording to invest, and possibly lose, thereby keeping them in control.

Leveraging contingencies rather than exploiting pre-existing knowledge reflects the fourth principle (the

lemonade principle). Unexpected events will keep on happening, be it a technological breakthrough or

unfavourable legislation, and instead of bracing the company for impact through hedging practices it

should focus on being flexible so it can capitalize on such occurrences. Unexpected events may

undermine the value of current means ends relationships, but they can also create new (more) valuable

relationships.

The final principle, which reflects the central tenet of effectuation theory, is the pilot-in-the-plane

principle: controlling an unpredictable future rather than predicting an uncertain one (Sarasvathy,

2001). This principle overlaps most of the other principles and provides merely a reflection of the role of

effectuation in the control paradigm.

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partnerships than MBA students (Dew et al., 2008 in: Perry, 2011). This again shows that expert

entrepreneurs have a preference for effectual decision-making. Earlier, in 2005, Sarasvathy and Dew had

conducted a study of expert entrepreneurs and the way in which they predict uncertain future

preferences. They preferred a logic of identity (who are you) over a logic of preferences; a logic of action

(what you know) over a logic of belief; and a logic of commitment (whom you know) as opposed to a

logic of transaction thereby providing empirical evidence for the three categories of means that

effectual entrepreneurs use.

Using the same data on MBA students and expert entrepreneurs, Read et al. (2009) prove that expert

entrepreneurs use effectual decision-making logic when framing marketing decisions. They were less

likely to believe market data, used analogical reasoning, were more likely to think holistically about

business, were focused on affordable loss, thought about unmentioned markets, used skim pricing and

were more likely to make initial sales themselves.

The studies mentioned above all use verbal protocol (think aloud) analysis of entrepreneurial decision-

making. An important field study carried out by Wiltbank et al. (2009) shows that in situations of

uncertainty, investors do better when emphasizing control strategies than when they would use

prediction strategies. In managing an investment portfolio, angel investors using control-based logic

(effectuation) experienced fewer failures while maintaining homeruns, or successful investments.

Investors using prediction-based strategies made significantly larger investments but did not experience

superior returns. In highly uncertain contexts (those of early stage ventures/ opportunities) this proves

that control-based strategies result in fewer failures while maintaining success rates.

Moreover, when interviewing a network of entrepreneurs in the streaming video industry, Sarasvathy

and Kotha (2001) found that when entrepreneurs are faced with Knightian uncertainty they rely more on

effectual logic than on causal logic. Also the stage of the entrepreneurial venture seems to be of

importance. Harting (2004) shows that entrepreneurs rely on effectual decision-making in the earlier

phases of the new ventures development, whereas causal reasoning is used more in later stages.

Harmeling et al. (2004) show similar results: goal flexibility and the incidence of contingency exploitation

decrease over time. Both notions confirm the conceptualization of goal ambiguity becoming less

apparent gradually throughout the development of the venture. Thus, in cases of uncertainty and goal

ambiguity, effectual logic is used more.

Honig and Samuelsson (2009) studied 419 nascent entrepreneurs over a six-year timespan to examine

whether nascent entrepreneurs use effectual or causal action, and to what extent the planning decisions

impact venture performance, both in high and low growth firms. Causal strategies (planning) in high

growth firms (over 20% annual employee + revenue growth) had little relation to gestation activity,

survival or performance, whereas effectuation did thereby supporting the use of effectuation strategies.

For high growth ventures the study clearly supports effectual strategies rather than causal, planning-

based strategies. Findings for the low growth firms are more mixed: using causation strategies and

formal planning is negatively related to gestation activities and survival. Nascent, low growth firms are

more likely to disband when planning more formally. However in terms of performance, more planning

in the early stages of low growth firms and less planning later on seems to be beneficial. The authors

argue that this might be because of the type of organizations; since the bulk of start-ups are replication-

based businesses like nail-salons and coffee shops. Less change, thus less uncertainty, calls for morecausation based strategies. These results are fairly contradicting, yet they do support that effectuation is


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better suited for uncertain environments that are characterized by innovative, technology based

organizations.

A second longitudinal study of nascent entrepreneurs and their decision-making strategies was done by

Garonne et al. (2010) in Australia. From a random sample of 625 nascent entrepreneurs, the authors

drew the conclusion that in uncertain environments those entrepreneurs using effectual strategies weremore likely to reach the operational stage than their causal counterparts. Overall, causal logic shows

positive results for reaching the operational stage, however effectuation is not negatively related. Though

the two types of strategies are conceptually at odds, empirically they are not. When uncertainty and the

degree of newness of the venture idea are incorporated, effectuation appears to be significantly more

suited in getting operational; that is when uncertainty and newness are high. Causal strategies are better

in situations of low newness, and hence low Knightian uncertainty.

The empirical evidence that has been published to date is largely delivered by a select group of scholars.

Results show that, although evidence is not crystal clear yet, effectual reasoning is beneficial in certain

situations. The studies by Garonne et al. (2010) and Honig & Samuelsson (2009) are the first to relate

the effectuation model to performance in nascent firms. This thesis will contribute to this empirical

stream of research by attempting to measure effectual and causal constructs in an early phase of the

entrepreneurial process.

From Sandberg and Hofer (1987) we know that start-up performance is a product of the entrepreneurs

attributes, the strategy that is used and the industry characteristics. The liabilities of smallness and

newness (Stinchcombe, 1965; Baum et al., 1995) make start-ups face specific issues. Not having

established working routines, stable relationships and market legitimacy makes that start-ups need to

develop different strategies in terms of planning, goal definition, market entry, alliances, investment and

the product portfolio, to name a few (Garonne et al., 2010). Moreover, we have seen that those liabilities

are different for TBVs and NTBVs. In the next section I will work towards a set of testable hypotheses

where venture sophistication (newness and technological intensity), the decision-making strategy and the

outcomes of the venture creation process come together.

3


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probabilities of success or failure can be assigned ex ante. Previous research has reported that

sophistication of the firm such as the level of technology being developed influences the venture

process by delaying it (Liao and Welsh, 2008). Nascent firms engaging in newness development provide

more sophisticated products or services, and as a result they may experience a longer time to realize

those products or services and launch them into the market. They also have to interact with a large range

of contacts to diffuse and increase the awareness of their newness while decreasing their liability of

newness.

High degrees of Knightian uncertainty are thus related to more radical innovations and more

sophisticated products or services, mostly based on technological advancements. What does this mean

for nascent entrepreneurship and the strategies deployed? Since radical innovation does not occur as

often as incremental the majority of new firms are replication based most nascent ventures will be

characterized by low levels of newness. In this situation a causation strategy, by using planning and

prediction tools, is preferred over an effectuation strategy.

Based on the discussion above the following can be hypothesized:

Hypothesis 4a:Effectuation positively affects the chance of becoming a new firm for ventures with a higher degree

of technological intensity.

Hypothesis 4b:Effectuation positively affects the chance of becoming a new firm for ventures with a higher degree

of newness.

Hypothesis 5a: Causation positively affects the chance of becoming a new firm for ventures with a lower degree of

technological intensity.

Hypothesis 5b: Causation positively affects the chance of becoming a new firm for ventures with a lower degree of

newness.


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Research MethodologyThis chapter describes the research methodology that will serve as a conduit for the empirical analysis of

the proposed conceptual framework. In the first section the appropriate research strategy will be

discussed. Next, the Panel Study of Entrepreneurial Dynamics will be introduced and the method for

data collection and sampling will be described in detail. Section X describes the variables and the way inwhich they are measured. The last section contains the data analysis methodology.

(


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%FRMA


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background that technical students have. Entrepreneurial activity should be promoted amongst these

students, but even so important is creating awareness under business students of how to look for and

exploit technological/ innovative opportunities. Higher-educated students have the human capital, are

used to working in teams and create dense social networks that are prerequisites to successful TBVs,

however these characteristics also give them safe career options which act as an impediment to

entrepreneurial activity. In economic times like these where students are having a harder time finding a

job that fits their capabilities, founding innovative ventures is not such a bad idea.

A concluding remark of the author would be that scholarly research on for instance strategy, and more

specifically on effectuation is fond of putting definite labels on certain phenomena. Effectuation and

causation are new, fresh labels to combine factors on strategy, decision-making, individual traits and

environmental dynamics that might hinder the explanatory value of those underlying factors. These two

labels should thus be applied carefully.

.NRNIFIN@?G L $S


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extent effectuation and causation are deliberately chosen approaches, or that it is an intuitive process.

Other studies have shown that the sequencing of gestation activities differs for technology ventures.

Due to the high coupling between strategy and gestation activities it is interesting to look into the

sequencing of effectuation and causation constructs. Maybe start-ups use causation more in the early

phases and effectuation more in the phase after becoming a new firm.

This study hinted at the different circumstances under which ventures come into existence. Dedicated

research is needed in which the specific issues that TBVs and NTBVs encounter are set off against for

instance the strategy used in terms of causation and effectuation. What is also argued to be of

importance for successfully completing the gestation process is the motivation that entrepreneurs have

for starting a company; this study has not looked into that, but for instance the level of perseverance and

the push/pull motivation to becoming an entrepreneur are highly interesting phenomena. Also this

study deliberately juxtaposed TBVs and NTBVs, but within those two categories are a lot of differences.

Future research should not only use these categories, but should also look within. So for example do

paradigm innovators differ from TBVs that are merely launching an existing technological product in anew market?

A last limitation and suggestion for future research is the nature of the database; the PSED provides an

accurate and generalizable sample of the US population of business start-ups. It is thus safe to

extrapolate findings in the US. Entrepreneurship in the US however has different norms and values than

in Europe, or in The Netherlands. Failure for instance is much less stigmatized, in contrast to failure in

Europe. Extending the findings to for instance Dutch start-ups should thus be done carefully. Preferably

a European database on nascent entrepreneurship should be built; currently the CAUSEE effort in

Australia has mirrored the setup of PSED, AGSE has done so in Sweden and China also has a variant.


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