More “Interruptions” for NTBFs’ Developments
4.3.4 Cybernetic Principles and Concepts for Technology Entrepreneurship Technology Entrepreneurship
4.3.4 Cybernetic Principles and Concepts for
tor. Within GST we concentrate on a system’s states: A state of a system ψ is any well-defined representation of the conditions of its existence and an associated pro- perty that can be recognized if it occurs again.
Every system will naturally have many possible states. That which is acted on will be called the operand; the factor inducing a change will be called the operator (given in Script font similar to the Hamilton operator in Equation I.6), and what the operand is changed to will be called the transform.
The change that occurs, which one can represent by a relation in terms of a mono- directional graph, A → B, is the transition. A set of transitions, on a set of operands, is a transformation. The series of positions taken by the system in time defines a trajectory or line of behavior [Ashby 1957]. The transition is specified by the two states ψ1 and ψ2 and the indication of which changed to which.
∆ψ = ψ
i→ ψ
i+1A priori, the transformation is defined in the sense of cybernetics, “not by any refe- rence to what it ‘really’ is, nor by reference to any physical cause of the change, but by the giving of a set of operands and a statement of what each is changed to. The transformation is concerned with what happens, not with why it happens.” [Ashby 1957].
Cybernetics does not treat things but ways of behaving. It does not ask “what is this thing?” but “what does it do?” and “what can it do?” (for which purpose) [Ashby 1957].
However, with GST as the overall framework, to make these concepts appli- cable to the field of entrepreneurship, one must consider the related fields (psychology, sociology, economics, business administration, etc.; Figure I.1) and any kinds of their relevant observations and their ways of measurements, principles and concepts. This will provide an abstract system of combined theories, empirical basements, principles, concepts, etc. from the various (sci- entific) disciplines as the basis.
The resulting abstract theoretical system will rely on “borrowed” knowledge, approaches and methods from the various involved fields. This means, a particular approach provided in one abstract system may be “switched” to another (usually higher) system to find an “appropriate” description or expla- nation or causal interrelationship for presenting expectations (or probably forecasts) for a company system under consideration as characterized in Figure I.128.
{A B F D}
↓↓↓
↓ A transformation will be called closed if all
the transforms involve only the elements of the original basis set (all transforms restricted to A, B, F, D) [Ashby 1957]. There is no “inflow” and no “outflow as in open systems.
{D B F A}
This set of transforms obtained contains no element that is not already present in the set of operands. A closed transformation creates no new element, the “domain” and the “range” being identical. And for non-interacting elements the closed transformation corresponds to a permutation. A large capital influx into a firm by investors would represent a typical “open transformation” which is common for open systems.
A test for closure is made by reference to the details of the transformation itself. It can therefore be applied even when one knows nothing of the cause responsible for the changes.
Furthermore, a transformation increasing the number of entities it acts on is an “exact transformation” in the sense of self-replication if the original is retained (copy of A B F D as above), otherwise it is a “similarity transformation” like (D B F A). Hence, in reality forming firm culture (Figure I.120) in this sense should be considered a simi- larity transformation rather than a copy.
A special transformation is the identity transformation, in which no change occurs, in which each transform the same as its operand is.
Which effect of a transformation we observe (or disregard or do not detect) enters essentially into our “reasons for thinking that.” For instance, the change from a square with four corners to a four-pointed star, one with a fourfold and the other with a twofold rotation axis with regard to the plane in Figure I.2 can be achieved by similarity transformations such that the ratio of the two diagonals in the square and the stars is kept, leaving them invariant.
Hence, investigating or observing just ratios of diagonals and not additionally the shapes of the objects and the lengths of the individual diagonals “make both objects identical,” the result of an identity transformation. Furthermore, there may be more attributes associated with change. Including also colors (“colored symmetries”
[Shubnikov and Koptsik 1974]) for representing objects the right hand side of Figure I.2 exhibits three different objects (or systems).
A transformation is single-valued if it converts each operand to only one transform. If it is not single-valued and not one-to-one it will be open and correspond to a “one-to- many” situation.
A transformation of the kind
A B C D
B or D A B or C D
is not single-valued.
We have just seen that after a transformation T has been applied to an operand α, the transform T(α) can be treated as an operand for T again, getting T(T(α)), which is written T2(α). In exactly the same way T(α) may perhaps become an operand to a transformation P, which will give a transform P(T(α)). Generally, operators are not commutative: P(T(α)) ≠ T(P(α)).
In the current context of time developments a transformation of a firm’s state empha- sizing a change by an event (“interruption”) will always refer to a current state. This means, for instance, an event that a venture capital firm (VC) participates in an NTBF (Figure I.126). Empirically founded, the transformation will induce a change by the combined or interrelated, respectively, effects of several factors which are sufficiently strong to be observable in totality after a certain period of time and described by the transformation Equation I.11. Here V C is the corresponding operator changing, for instance, equity, ownership, control, management and number of employees and organization of the firm (Figure I.126).
Similar to Equation I.6 (ch. 2.1.2.9) the following notation will be used to describe the intrinsic relation between the two components, operator and operand. The result will be a changed state of the entity associated with an observable and measurable value VC induced by several presumed effects given in braces.
Equation I.11:
V C |ψi > → VC {add equity – (other, more) owners – other firm control – install management – add employees – re-organize firm}|ψj >
or, in short, V C → VC {add equity – (other, more) owners – other firm control – install management – add employees – re-organize firm}
The above notion separating contribution by dashes represents a “systemic transfor- mation” were the various changes represent an overall change by the combined ac- tion of the given transforms. The dashes bare the relation to a representation by an
“array.” Viewed in this sense for an array to be unchanged, each component must be unchanged.
The effects of the individual transforms can rarely be observed in isolation. Moreover, a selected measurement by a selected indicator may not be meaningful due to the
“impact time” periods of the individual “(non-systemic”) transforms. These may be
rather different. Compare adding equity being observable for a particular day or week versus number of employees observed at the end of the year or as a yearly average.
The particular types of measurement have different “time resolutions.” In soft sciences the minimum or even common time span between two subsequent measurements that can be meaningfully interpreted (as a change) is the resolution of the measure- ment. For instance, tracking numbers of employees year after year will make most associated transforms with shorter impact time unobservable and will induce the transformation to appear as an overall systemic effect.
On the other hand, if there is a possibility to (largely) separate the individual trans- forms of a transformation (“weak coupling”) we will continue to use the common set notation (“listing”), separating elements by commas {A, B, C, …}.
In our context, a transform with an observable effect would be, for instance, if an NTBF catches a very huge order of the NTBF’s product from a major customer. On the other hand, changing the legal form of a limited liability company (LLC, GmbH) to a non-public stock company (AG in Germany) may represent a transform retaining largely everything else of the firm and may be a transform without any measurable effect on the other sub-states of the firm.
If we associate a transform with a measurable quantity of interest, the transform may turn out to be a “positive” (“growth-inducing”) or a negative (“decline-inducing”) change of the related particular measurable quantity. Consequently, existing transformations are not necessarily observable, positive and negative effects may level off, if they occur (almost) simultaneously.
In terms of cybernetics control was previously defined as the purposive influence toward a predetermined goal involving continuous comparison of current states to future goals (“is” versus “shall” assessment). The above transformation “V C → “ with attributed transforms has an inherent shortcoming. It does not take into account any possible changes that concern the pace (time) to reach the goal or even modifying the original goal (Figure I.122, Figure I.126).
A teleological relation (start to end; Figure I.78) which may be associated with signifi- cant changes (observable through appropriate variables, parameters and indicators) can be viewed as a set of transformations. Specifically, according to GST, a firm’s foundation (the “birth”) is the first transform which causes the founder or founding team with their ideas and perceived business opportunities, motivation, aspirations and expectations to strive for their particular goal(s) by means of a firm. And this changes, for instance, their states of personalities (Figure I.16, Figure I.122).
Connected to the goal expectation can be viewed as a transformation of the personal- ity. The entrepreneur as a firm founder, owner and leader with control over the firm has explicit qualitative and quantitative goals which induce expectations. Basically, there are founders who tend to emphasize keeping control over the firm (autonomy
and vision) or making money (wealth) accepting venture capital, losing control as seen in Table I.39, Table I.40, Figure I.65 and Figure I.66. The corresponding “entrepreneu- rial expectations” E E (type) can be represented, for instance, by Equation I.12. The second example is not single-valued.
Equation I.12:
E E (aspiration, motivation) → (autonomy, vision) {confidence in own business idea – revealed opportunity – internal locus of control – perseverance – risk taking - tolerance for ambiguity – self efficacy – ownership/control – organic growth}
E E (aspiration, motivation)→ (wealth) {confidence in own business idea – revealed opportunity – internal locus of control – perseverance – risk taking – tolerance for ambiguity – self efficacy – accept venture capital – lose OR keep control – organic growth OR non-organic growth – selling firm is an option}
Following Ashby [1957], although the system may be passing through a series of changes, there is (often) some aspect that is unchanging. Hence, some statement can be made that, in spite of the incessant changing, is true unchangingly. The simplest case occurs when a state α and a transformation are so related that the transforma- tion does not cause the state to change. Algebraically it occurs when T(α) = α. That means, the state α is a state of equilibrium under T.
The same phenomenon may occur with a set of states. Take S(↓) to be a non-sys- temic transformation or one in which the operands of a state are only slightly coupled, that is open (“unclosed”) and has no state of equilibrium, but exhibits a domain that generates no new state. Such a state is stable with respect to S.
a b c d e f g h b g
S(↓):=
p g b f a a b m
S(↓):=
g b
Using transformations and the stability concept are associated with an issue of reduci- bility of a complex situation that avoids dealing with a situation that every factor (vari- able or parameter) had an effect, immediate or delayed, on every other factor. When a dynamic system can vary continuously, disturbances are, in practice, usually acting on it incessantly. For this reason the only states of equilibrium that can, in practice, per- sist are those that are “stable” in the above sense.
The (last b g) transformation is closed, so something persists, and the observer who looks only at this level of discrimination can say of the sub-system: “it persists,” and can say no more [Ashby 1957]. Classification and taxonomy according to given criteria is related to such a persistence (Figure I.128).
In a new firm’s development there is no invariant overall state because dif- ferent problems arise, affect particular sub-states and are addressed corre- spondingly in different ways leaving the “rest” stable. Without observations at the firm level and identifying the prototypical problems, the mechanisms and processes of growth as linked to expectation or goals remain obscure.
“That something is ‘predictable’ implies that there exists a constraint.” If an aircraft, for instance, were able to move, second by second, from any one point in the sky to any other point, then the best anti-aircraft prediction would be helpless and useless. The latter can give useful information only because an aircraft cannot so move, but must move subject to several constraints. There is that due to continuity – an aircraft cannot suddenly jump, either in position or speed or direction. There is the constraint due to the aircraft’s individuality of design” [Ashby 1957:132] and its “resources” (engine and fuel) determine the distance it can cross.
This reference to a process is essentially that of using a mapping – using a convenient (for instance, mathematical or graphical) representation rather than the inconvenient reality.
Figure I.128 summarizes the landscape of elaborated constraints for technology entre- preneurship which provides a “navigator” for the entrepreneur where he/she wants to be or be active in and the advisor or consultant to properly advise, propose and guide the entrepreneur and the entrepreneurship researcher to properly select criteria to create samples and interpret measurements and findings.
Leaving out a constraint reduces the strength of “prediction” and, in case of complex- ity, requires being very conscious about the limits of the domain of interpretations and even more “predictions.”
The many seemingly different, controversial and even contradictional results and find- ings concerning growth of young firms [Garnsey et al. 2006] means that the people selected different constraints for inquiry and often are talking about different systems of investigation. The related issues of statistics are often associated with selecting a sample which is assumed to provide class properties and, furthermore, how response rates of questionnaires distort the originally selected sample structure.
For the taxonomies of industries related to characteristics of technologies (Table I.1;
TVT, HVT) in terms of “research intensity” (RI = R&D expenses / total revenues) the differentiation is based on the proportion of financial quantification of research and development expenditures which cuts across industry taxonomies according to busi- ness or offerings.
Figure I.128 illustrates a fundamental dilemma of technology entrepreneurship, the complexity of constellations and the question whether and how results of macro-ap- proaches have relevance for practice and, in particular, for individual entrepreneurs and those providing advice and consulting to them.
Industry taxonomy as given in Figure I.128 combined with firm type (RBSU versus other academic NTBFs) and ownership/control and financing (VC-based versus non VC-based) seems to be the bare minimum. How just one industry – biofuels – for understanding technology entrepreneurship has to be boiled down is illustrated in Figure I.183, Figure I.184, Figure I.185 (A.1.1.5) and Table I.17.
Figure I.128: Constraints as a basis of taxonomies for technology entrepreneurship to characterize configurations of NTBFs (read sub-tables from left to right).
And there seems to be even notable differences in industry segments in attracting entrepreneurial personalities. Entrepreneurs may take big risks to bring the latest scientific tools to market. For instance, the people who take personal risks to bring new scientific instruments to market are a special breed. Many of these entrepreneurs are well-educated scientists who could make a fine living working as consultants or as employees in high-technology companies. Yet they risk their livelihoods and their own money for the chance to start up their own firms [Reisch 2011a].
Stability is commonly thought of as desirable, for its presence enables the system to combine of flexibility and activity in performance with something of permanence, something “generic” which, for instance, is the focus when dealing with entrepreneur- ship over time (history) and space (regional culture; comparing Germany and the US).
Goal-seeking behavior is an example that stability around a state of equilibrium is ad- vantageous. Nevertheless, stability is not always good, for a system may persist in re- turning to some state that, for other reasons, is considered undesirable or proceeding to some new state that is highly necessary, due to a changed environment.
In this way, these concepts may be used to explain and illustrate the transition from core competencies to core rigidities (ch. 2.2.1, Box I.8) as a combined effect of per- sistence and self-reinforcement (“success breeds success”) resulting in a firm’s “rou- tines” and “routinized decisions” – how things are done here or how things are de- cided here (Figure I.129). This kind of persistence is, of course, a special property of the whole system focusing on just one aspect.
Self-reinforcement is essentially determined by decision-making self-reinforcement (that is, past acceptances make future acceptances more likely) and adaptive expec- tations (further belief in prevalence; Box I.17) (ch. 2.1.2.5).
Figure I.129: The progression of core competencies toward core rigidities.
The descriptive path to “core rigidities” can help understand the well-known fact that the founding configuration (including firm culture) and the early development of a start- up influences further development of the firm. This means it can account for path- dependency of NTBF development, if observed or searched for.
If the founder has industrial experience (or is a serial entrepreneur) corresponding path-dependency (decision-making, behavior) may already enter the starting configu-
ration of a new firm. The same is true if the founder(s) has hired early on an “experi- enced manager.”