Knowledge, Dynamic Capabilities and Family Inertia in
Family Firms: A Computational Approach
Francesco Chirico
University of Lugano (USI)
Laboratory of System Modelling (LSM)
Institute of Management
Via G. Buffi 13, 6900 Lugano, Switzerland
Tel: +41586664548
Fax: +41586664647
franc: co@lu.u
ABSTRACT
The present research sets out through computer simulations in system dynamics the
positive dynamic interconnections between knowledge, capabilities, dynamic
capabilities, entrepreneurial performance and trans-generational value in family
business.
Interesting results and new insights emerge introducing family inertia in the model (as a
function of paternalism) which influences the creation of capabilities and dynamic
capabilities negatively, with some exceptions. We conclude that although a paternalistic
behaviour can be positive in guiding and training offspring at the beginning of the
activity, it may become less crucial if it persists over time preventing change even when
it is needed. Family firms should be able to understand the long-term effects and results
of actual events, decisions and behaviours, and, at the same time, prevent their negative
consequences.
Keywords: simulation modelling, system dynamics, family business, family inertia,
paternalism, knowledge, dynamic capabilities, trans-generational value.
Acknowledgments: My personal thanks to Prof Erik Larsen and Prof Gianluca Colombo for
helping me in this research. I gratefully acknowledge financial support from the Finnish
Ministry of Trade and Industry and from the European Union.
INTRODUCTION
Chirico (2006b) made a detailed examination of the complex dynamic process through
which dynamic capabilities are generated by knowledge and generate entrepreneurial
performance, allowing a family firm to compete in situations of rapid change and create
value over time. Part of this value may be reinvested in the family firm for the creation
of new knowledge (e.g. training courses).
For the sake of this argument, dynamic capabilities were seen as a double concept: basic
and advanced dynamic capabilities. In addition, family inertia (FI) was considered to be
a factor preventing the creation of dynamic capabilities. FI depends on the family-
business culture, where paternalism and entrepreneurial drive influence FI positively
and negatively, respectively (see figure 1). The research was based on the review of the
literature and four in-depth case studies analysed through interviews.
Figure 1: Dynamic model: from knowledge to trans-generational value creation*
KNOW. (DYNAMIC) CAPABILITIES ENTREPRE
LEDGE (Processes designedio acquire, exchange, transforma -NEURIAL
‘integrate and recombine’ and shed resources**) PERFOR-
Industy- MANCE
related, BASIC CAPABILITIES ADVANCED
Lae DYNAMIC DYNAMIC Entrepre-
and CAPABILITIES (High-level routines CAPABILITIES neural
ownership || | PAC RAC [—> that,together with [>| 1] sovatins
knowledge is upbaoting and strategic
aml ‘ adaptation to
(Processes of input flows, confers (Processes of ip
“acquisition, upon an acquisition, the market
assimilation’ and organization's exchange,
“transformation, management a set of transformation -Product-line
| | exploitation’ of decision options for and shedding of extension,
knowledge) producing capebilities) -Diversifice-
significant outputs tion,
ofa particular type) Fepey
x
Paternalism*** ' Entrepreneurial
Attitude not to Drive****
make changes Attitude to keep the
id FAMILY INERTIA (7 ariiees chanens
(no freedom to
express ideas and (e.g, procctiveness,
make autonomous innovativeness,
choices and willingness to take
changes) risks and enlarge
the business).
closed open and explicit
FAMILY CULTURE
TRANS-GENERATIONAL VALUE
Investments in Knowledge “Tangible assets;
(e.g, training, executive courses, -Intangble assets;
employingfusing extemal non-farnily-mercbers etc.) ~Total of the balance sheet.
Source: Adapted from Chirico (2006b, figure 2)
(*) ___: positive relation; ------: negative relation.
(**) Resources are here interpreted as knowledge and capabilities.
(***) Paternalism influences family inertia positively and family inertia influences (dynamic) capabilities
negatively. Consequently, paternalism influences (dynamic) capabilities negatively.
(****) Entrepreneurial drive influences family inertia negatively and family inertia influences (dynamic)
capabilities negatively. Consequently, entrepreneurial drive influences (dynamic) capabilities positively.
The theoretical framework (dynamic capabilities, from knowledge to trans-generational
value and the family inertia problem) behind the dynamic model in figure 1 has been
analysed by Chirico (2006b).
Table | provides a list of all the variables studied.
The present research aims at exploring, extending and testing the model above and its
dynamic implications through computer simulations in system dynamics, looking for
new insights.
First, we present the system dynamics methodology which explains what system
dynamics is and why it is useful to use it in this study. Then, we analyse the model
structure from the feedback loops to the dynamic models, followed by the results and
new insights of the research. In this section, we examine four different scenarios
describing the evolution of paternalism over three generations (90 years), and their
consequent effects on the model as a whole. Finally, we present the discussion and
conclusions.
Table 1: list of the variables
KNOWLEDGE. Chirico (2006a) refers to knowledge as “pure knowledge and skill which
family and non-family members working in the family firm have gained and developed
through education and experience (industry-related knowledge, business knowledge,
ownership knowledge)”.
DYNAMIC CAPABILITIES. Dynamic capabilities are defined as processes embedded in
firms designed to acquire, exchange and transform internal and external knowledge and
capabilities’ in new and distinctive ways and, at times, shed them to build and sustain
entrepreneurial performance in environments of rapid change (Teece et al., 1997; Eisenhardt
and Martin, 2000). Absorptive capacity is itself a dynamic capability (Zhara and George,
2002). Dynamic capabilities encompass:
- BASIC DYNAMIC CAPABILITIES (BDC) is absorptive capacity. The first
dimension of BDC is named Potential Absorptive Capacity (PAC) which is the
capacity of acquiring and assimilating external knowledge. Acquisition capability
means “the ability to identify and acquire externally generated knowledge”;
assimilation capability means “the ability to analyze, process, interpret and
understand knowledge acquired from external sources”. The second dimension of
BDC is named Realized Absorptive Capacity (RAC) which is the capacity to
transform and exploit the knowledge that has been absorbed. Transformation
capability means the ability to “develop and refine the routines that facilitate
combining existing knowledge and the newly acquired and assimilated knowledge”;
exploitation capability means “the ability to refine, extend, and leverage existing
competencies or to create new ones by incorporating acquired and transformed
knowledge into its operations” (Zahra and George, 2002);
- ADVANCED DYNAMIC CAPABILITIES defined as processes (e.g. product
development process) embedded in firms designed to acquire, exchange and
transform capabilities in new and distinctive ways and, at times, shed them to build
and sustain entrepreneurial performance in environments of rapid change.
ENTREPRENEURIAL PERFORMANCE. It is defined as a new and innovative form of
competitive advantage (Teece et al., 1997) given by entrepreneurial innovation and strategic
adaptation to the market (Barney, 1991; Zahra and George, 2002).
TRANS-GENERATIONAL VALUE CREATION IN FAMILY BUSINESS. It is
accumulated through continuous creation of business wealth and is measured by focusing on
the net asset value of the tangible assets, the estimated amount of intangible resources
(goodwill) and the total of the balance sheet of the family firm.
FAMILY INERTIA. It is defined as the tendency of family firms to resist change even when
it is needed to match the requirements of a changing environment. It is a function of
paternalism and entrepreneurial drive in family business where paternalism and
entrepreneurial drive influence family inertia positively and negatively, respectively. The
ideology of paternalism is protective and dominating in a fatherly way with a strong attitude
to preserve family firm’s traditions and not to make changes (Fotion, 1979; Johannisson et
al., 2000; Johannisson, 2002; Koiranen, 2003). Entrepreneurial drive is the attitude to keep
business changing through initiative and innovation (Johannisson, 2002; Habbershon and
Pistrui, 2002; Koiranen, 2003).
} According to Winter (2000: 991), “an organizational CAPABILITY is a high-level routine (or collection
of routines) that, together with its implementing input flows, confers upon an organization’s management
a set of decision options for producing significant outputs of a particular type”.
METHOD: A SYSTEM DYNAMICS APPROACH
System dynamics is an approach to modelling the dynamics of complex feedback
systems through formal computer simulations. Feedback is a core concept in system
dynamics. It refers to the situation of X affecting Y and Y, in turn, affecting X through a
chain of causes and effects. Causal loop diagrams are used to represent the feedbacks of
a system, that is, the way a system is connected by positive (self-reinforcing) and
negative (self-balancing or self-correcting) feedback loops (Forrester, 1961, 1968;
Sterman, 2000):
For more details, see Appendix D. Feedback loops are useful to capture and
communicate mental models but they have many limitations. For instance, they do not
take into consideration the stocks and flows of the system (Sterman, 2000).
System dynamics is based on the Principle of Accumulation. It states that all dynamic
behaviours in the world occur when flows accumulate in stocks (figure 2). Stocks and
flows are the basic building blocks of a system dynamics model which allow us to
analyse the feedback loops of the system” (Forrester, 1961, 1968; Morecroft, 1982,
1983; Morecroft and Sterman, 1992; Sterman, 2000; Mollona, 2000; Lomi and Larsen,
2001).
A stock is an entity which is accumulated over time by inflows and depleted by
outflows. It accumulates past events characterising the state of the system. A Stock
typically has a certain value at each moment of time (e.g. knowledge). Mathematically,
a stock (S) can be seen as an integration (accumulation) of the difference between
inflow and outflow (F) in the long term:
St= f [Inflow(t) — Outflow(t) [it + S(10)
A flow changes a stock over time by inflows (e.g. creation of knowledge) and outflows
(e.g. erosion of knowledge). It is typically measured over a certain interval of time.
Mathematically, a flow (F) can be seen as the derivative of the stock (S) with respect to
the time (t) that is its net rate of change:
dS
F = inflow — outflow; F = he
It
Figure 2: Stocks and Flows®
Variable (c.g, Rate of Knowledge Creation) Varlable (e.g. Rate of Knowledge Erosion)
st + —| sTocK }—, vA
a a >
INFLOW Knowledge)| OUTFLOW
(ex. (eg
Creation of Erosion of
Knowledge) Knowledge)
? A series of constant and auxiliary variables are also needed to simulate the model of the system.
* Sterman (2000:192) explains that “- Stocks are represented by rectangles; - Inflows by a pipe pointing
into (adding to) the stock; - Outflows by a pipe pointing out (subtracting from) the stock; - Valves (at the
center of flows) control the flows; - Clouds (at the extremities) represent the sources and sinks for the
flows (boundaries)”.
Stocks are the source of delays. A delay is the amount of time by which an event is
retarded. It is the time between the instant at which a given event occurs and the instant
at which a related aspect of that event occurs (e.g. time between the creation and
absorption of knowledge). Delays are responsible for generating effects which are very
often nonlinear and counter-intuitive in the real world (Sterman, 2000).
The system dynamics methodology follows three steps:
- taking into consideration a System’;
- Model’ the System;
- Simulate® the Model.
Simulation models are generally based on theories treated as mental models which must
be translated into computer models. System dynamics is a powerful method to build a
shared understanding and gain useful insights into situations of dynamic complexity
created by interdependencies, feedbacks, time delays and nonlinearities (Sterman, 1992;
Van Ackere et al., 1993; Sterman, 2000).
Conducting empirical researches and, then, formalising and testing them through
simulation in system dynamics can be useful to confirm or bring about radical changes
in basic assumptions. Propositions and hypothesis can be interlinked and new insights
may emerge for further research (Morecroft and Sterman, 1992; Senge and Sterman,
1992; Oreskes et. al, 1994; Sterman, 2000; Lomi and Larsen, 1999, 2001).
Larsen and Lomi (1999: 412) report what has been said by Sutton and Straw (1995):
“Hypothesis can be part of a well crafted theoretical argument...but hypothesis do not
(and should not) contain logical arguments about why empirical relationships are
expected to occur”.
For this reason, Larsen and Lomi (1999: 412) view “computer simulation as useful as
systematic empirical research for extending and testing organizational theories”.
Lomi and Larsen (2001: 11) posit that “computational and simulation models of
organizations differ from other kinds of models like empirical models, only in terms of
the constraints that define the specific language being used”. Masuch’s view (1995) is
that the validity of simulation models presents the same problems of any other kind of
empirical model. Oreskes et al., (1994) and Sterman (2000) agree that validation and
verification of numerical and simulation models are impossible but this is not limited to
computer models but to any theory and research which relies on simplifications of the
real world and assumptions.
This research relies on computer simulation models through system dynamics not as an
alternative to empirical research but as a way of exploring, extending and testing the
findings, thoughts and the dynamic implications of the complex dynamic feedback
* A system exists and operates in time and space.
* A model is a simplified representation of a system at some particular point in time or space intended to
promote understanding of the real system.
A simulation is the iteration of a model in such a way that it operates on time or space to compress it,
thus enabling one to perceive the interactions that would not otherwise be apparent because of their
separation in time or space.
structure of figure 1 (see Hanneman et al., 1995; Senge and Stermann, 1992; Larsen and
Lomi, 1999; Bothner and White, 2001). System dynamics allows us to explore and
understand the future effects and results of actual events and decisions taking into
account temporal delays. The existence of delays and feedback loops in the model in
figure 1 makes system dynamics once more useful to represent the interdependent
relations among variables studied over generations in family business.
Several simulations will be run and interesting results and new insights will emerge (see
figure 7, figure 10 and Appendix B).
Hanneman et al., (1995: 3) posit that “we do not really know what a theory is saying
about the world until we have experimented with it as a dynamic model” and Bothner
and White (2001: 206) point out that “simulation models are always formulated as
mechanisms for simplifying the moving parts of a social process down to it core
features...and yield surprising insights for further exploration”.
Experimental design
To make simulations, it was necessary to assign numerical values to all parameters,
initial values to stocks and proper shapes to graphic functions according to the literature
and case studies analysed by Chirico (2006b). Assumptions were also made when useful
information could not be taken from the research mentioned above. Numerical values
were not chosen randomly but have always been calibrated to be consistent across the
model (Larsen and Lomi, 1999, 2002; Lomi et al., 2005).
Sterman (1992: 10) points out that “the skilled modeller uses all available information
sources to specify the relationships in the model (numerical data, interviews, direct
observation and other techniques)”.
To make simulations simpler, graphic functions were built through linear relations.
This kind of representation through “graphic converters” (graphic functions), which
specify the functional relationship between two variables, makes very simple to test a
dynamic model and analyse possible implications derived by variations in the graphic
function(s) (Larsen and Lomi, 1999, 2002; Lomi et al., 2005).
More details in Appendices A, B and C.
The software used for the computer simulation is Vensim PLE for Windows, Version
5.4d. Table 2 indicates the settings of the software.
Table 2: Settings of the software Vensim PLE, 5.4d.
INITIAL TIME: 0;
FINAL TIME: 90 (the software Vensim makes the simulation over three
generations of a generic family firm in which each generation lasts 30 years);
TIME STEP: 0.125 (results of simulation are saved every 1.5 months. This way,
numerical integration errors are kept very small);
UNITS FOR TIME: | year (the software Vensim simulates the model every
year).
MODEL STRUCTURE: FROM FEEDBACK LOOPS TO DYNAMIC MODELS
The feedback loop representation of the dynamic model for the trans-generational value
creation in family business is plotted in figure 3. The reciprocal relations between key
variables emerge with four reinforcing loops (R). The first feedback loop (R1) specifies
the relation between knowledge and basic dynamic capabilities (PAC and RAC). The
second one (R2) identifies the relation between RAC and capabilities. The third
feedback loop (R3) shows the relation between capabilities and advanced dynamic
capabilities. It is also underlined the negative effect of family inertia on the creation of
new capabilities. Finally, the fourth feedback loop (R4) indicates the entire complex
dynamic process from knowledge to trans-generational value creation, and back to
knowledge through investments in knowledge (Chirico, 2006b).
Figure 3: Feedback loops*
Paternalism Entrepreneurial Drive
PAC (acquisition,
assimilation) * :
FAMILY INERTIA,
An)
RAC (rathtoraim, AR)
KNOWLEDGE exploitation)
ie
a a ; .
a ADVANCED
DYNAMIC
— Ans) ene
Knowledge
ENTREPRENEURIAL,
TRANS-GENERATIONAL AEREGRN ANCE
VALUE <—_
PAC = Potential Absorptive Capacity
RAC-R Absorptive Capacity
PAC + RAC = ABSORPTIVE CAPACTTY = BASIC DYNAMIC CAPABILITIES (BDC)
Erik Larsen, 2006; Personal Communication
Source: Adapted from Chirico (2006b, figure 2)
(*) The “+” means that the two variables move in the same direction, all other things being equal.
The “-” means that the two variables move in opposite directions, all other things being equal.
To understand all dynamic implications of the model, feedback loops will be translated
into a system of equations rather than a series of different propositions. Variables will
be represented as stocks/flows and measured in dimensionless units (see Sastry, 1997;
Larsen and Lomi, 1999) (see figure 4).
Computer simulations will give evidence of the positive dynamic interconnections
between variables studied and the negative effect of family inertia on the model as a
whole. After this general overview, it is now possible to represent the micro-structure of
the model, trying to develop the same strategy used by Larsen and Lomi (2002: 279):
“Our strategy is to keep notation as much as possible intuitive with the development of
a minimal amount of formalism”.
Graphic details of the micro-structure of the dynamic model are plotted in figure 4.
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(*) To make the model simpler, entrepreneurial drive is not included in the simulation model.
” means that the two variables move in the same direction, all other things being equal.
“...” means that the two variables move in opposite directions, all other things being equal.
(#)
Representing knowledge (K). Knowledge is represented as a stock (accumulator’) that
integrates the difference between increase (new knowledge “NK”) and decrease
(erosion of knowledge “EK’’) in knowledge.
Kt= f [NK (s) — EK(s)}/s + K(t0)
New knowledge (NK) is positively affected by a fixed annual amount of possible
overall knowledge which may be generated (OKG). OKG positively depends on the
effect of absorptive capacity (EAC) and the effect of investments in knowledge (EIK)
on the creation of new knowledge.
NKt = OKG * (EACt + EIKt)
To represent EAC, is specified a linear functional relation (graphic function as
described in Appendix A.a) between knowledge acquisition through realised absorptive
capacity (KA) and realised absorptive capacity (RAC) in which:
Min(KA) if RACt=0 (there is no RAC but the firm is still able to acquire some
knowledge from outside “Min(KA)")
Max(KA) if RACt=1 (RAC is very high and consequently KA is very high, as well)
EACt= KA (time)
where KA is a function that specifies the effect of absorptive capacity on the increase in
new knowledge over time.
EIK is equal to investments in knowledge (IK) multiplied by the evolution of the rate of
knowledge creation through investments in knowledge over time (ERKC).
EIKt = IKt * ERKCt
To represent ERKC, is specified a linear functional relation (graphic function as
described in Appendix A.b) between rate of knowledge creation through investments in
knowledge (RKC) and time, in which RKC decreases as time passes according to
Argote’s view (1999).
Max(RKC) when time=0 years (RKC is very high at the beginning of the first generation)
RKC =...
Min(RKC) when time=90 years (RKC is very low at the end of the third generation
“Min(RKC)”)
ERKCt = RKC (time)
7 Accumulator means that the variable (in this case, knowledge) is accumulated over time.
10
where RKC is a function that specifies the evolution of the rate of knowledge creation
through investments in knowledge over time.
Erosion of knowledge (EK) is affected by a fixed annual rate of knowledge erosion
(a).
EKt = Kt*a
Representing outside industry knowledge to discover (INKD). Outside industry
knowledge to discover is the total outside industry knowledge which has not been
discovered yet. It is represented as a stock (accumulator) that integrates the difference
between increase (outside industry inventions “INI”) and decrease (outside new industry
knowledge “NINK”) in outside industry knowledge to discover.
INKDt = [' [INI(s) — NINK(s)}is + INKD(10)
fl H
Outside industry inventions (INI) refer to outside fundamental base inventions which
are, at this time, not well explained yet. They still have to be discovered in order to be
exploited. INI are modelled through a random function which generates inventions
randomly because they cannot be predicted.
INIt = IF THEN ELSE (RANDOM UNIFORM (0, 1, 99632)>0.98,4,0)
Outside new industry knowledge (NINK) refers to the outside industry knowledge
which has been invented, explained and can be exploited. It is affected by a fixed annual
discovery rate (B).
NINKt = INKDt/B
Representing outside industry knowledge (INK). Outside industry knowledge is the
total industry knowledge which exists outside the family firm. It is represented as a
stock (accumulator) that accumulates over time from outside new industry knowledge
(NINK).
INKt = f [NINK (s) [ls + INK (t0)
Therefore, NINK is the outflow of INKD and the inflow of INK (see figure 4).
Representing potential absorptive capacity (PAC). PAC is represented as a stock
(accumulator) that integrates the change in PAC (Ch PAC).
PACt = f [ChPAC(s)}s + PAC(t0)
!
0
11
Change in PAC (Ch PAC) depends on the relative knowledge (RK) of the family firm,
that is, the percentage of the outside industry knowledge (INK) possessed by the family
firm. RK is equal to the knowledge of the family firm (K) divided by the total industry
knowledge outside the family firm (INK).
RKt = Kt/INKt
PAC is calculated with a first order exponential smoothing (information delay* from RK
to PAC) of the observed value of RK, whose formulation is similar to an adaptive
expectations mechanism (Forrester, 1961; Larsen and Lomi, 1999; Sterman, 2000;
Mollona, 2000; Lomi, Larsen and Freeman, 2005).
Forrester (1961: 407,408) posits that “smoothing is a process of taking a series of past
information values and attempting to form and estimate of the present value of the
underlying significant content of the data. In particular, the exponential smoothing gives
the greatest weight to the most recent value and attaches progressively less significance
to older information”.
Sterman (2000) explains exponential smoothing with the concept of adaptive
expectations. Sterman (2000: 428,429) argues that “adaptive expectations mean the
perceived or expected value (belief) gradually adjusts to the actual value of the variable.
The expected value changes when it is in error, that is, when the actual value differs
from the expected value of the variable. The state of the system adjusts (with an
adjustment time, i.e. delay) in response to the gap between expected value and actual
value”.
According to the concepts mentioned above, change in PAC is given by:
Ch PACt = (RKt — PACt)/ Delay PAC
where PAC is the expected value (or average); RK is the actual value; delay PAC is the
information delay (time to average).
In other words, this process assumes that the gap between RK (actual value) and PAC
(expected value) closes only gradually causing delays.
Representing realised absorptive capacity (RAC). RAC is represented as a stock
(accumulator) that integrates the change in RAC (Ch RAC).
!
RACt = f j[ChRAC(s) fs + RAC(t0)
Change in RAC (Ch PAC) depends on the PAC of the family firm.
As before, RAC is calculated with a first order exponential smoothing (information
delay from PAC to RAC) of the observed value of PAC, as follows:
Ch RACt = (PACt— RACt)/ Delay RAC
5A temporal delay is defined as the amount of time by which an event is retarded.
12
where RAC is the expected value; PAC is the actual value; delay RAC is the
information delay.
In addition, Ch RAC is positively influenced by the capabilities (C) of firm members
(see figure 4).
Ch RACt = [(PACt — PACt)/ Delay RAC] *Ct
Representing capabilities (C). Capabilities are represented as a stock (accumulator) that
integrates the difference between increase (new capabilities “NC”) and decrease
(erosion of capabilities “EC”) in capabilities.
Ct= f [NC(s) — EC(s) [ls + C(20)
New capabilities (NC) are positively affected by RAC and advanced dynamic
capabilities (ADC); and negatively affected by family inertia (FI).
In addition, C is calculated with a first order exponential smoothing (information delay
from RAC to C) of the observed value of RAC, as follows:
NCt = {{(RACt— Ct)/Delay C] * ADC1}/FIt
where C is the expected value; RAC is the actual value; delay C is the information
delay.
Erosion of capabilities (EC) is affected by a fixed annual rate of capabilities erosion
(y).
ECt = Ct *y
Representing advanced dynamic capabilities (ADC). Advanced dynamic capabilities
are represented as a stock (accumulator) that integrates the difference between increase
(new ADC “NADC’”) and decrease (erosion of ADC “EADC”) in advanced dynamic
capabilities.
ADCt = j [NADC(s) — EADC(s) ls + ADC(10)
7
0
New ADC (NADC) are positively affected by new capabilities which depend on ‘the
effect of change of capabilities on the creation of new ADC’(5):
NADCt= NCt * 8
Erosion of ADC (EADC) is affected by a fixed annual rate of ADC erosion ().
EADCt = ADCt * €
13
As shown above, a strong positive correlation exists between C and ADC (see figure 3).
Indeed, NC influence the creation of NADC and the stock ADC influences the creation
of NC (see figure 4).
Representing entrepreneurial performance (EP). Entrepreneurial performance is
represented as a stock (accumulator) that integrates the difference between increase
(creation of EP “CEP”) and decrease (erosion of EP “EEP”) in entrepreneurial
performance.
EPt = [' [CEP(s)- EEP(s) ds + EP(10)
fil fi
Creation of EP (CEP) is positively affected by ADC at a fixed annual rate of
entrepreneurial performance creation (¢). A delay between the creation of ADC and the
creation of EP (delay EP) is taken into consideration in the simulation model.
CEPt = (ADC * ©)/Delay EP
Erosion of EP (EEP) is affected by a fixed annual rate of EP erosion ().
EEPt = EPt *
Representing trans-generational value (TGV). Trans-generational value is represented
as a stock (accumulator) that integrates the difference between increase (creation of
TGV “CTGV”) and decrease (family wealth “FW” and investments in knowledge “IK’’)
in trans-generational value.
TGVt = f [CTGV(s)— FW(s)— IK(s) ls + TGV (10)
Creation of TGV (CTGV) is positively affected by EP at a fixed annual rate of TGV
creation (). A delay between the creation of EP and the creation of TGV (delay TGV)
is taken into consideration in the simulation model.
CTGVt = (EPt * 6)/Delay TGV
Family wealth’ (FW) is affected by a fixed annual rate of withdrawals (1).
FWt=TGVt *1
Investments in knowledge (IK) are affected by a fixed annual rate of investments in
Knowledge (k).
IKt = TGVt*«
2 Family wealth is represented by the family’s patrimony outside the family business, invested in fixed
assets (such as land, buildings, etc.) and held in current assets (deposits, outside shares etc.).
14
Representing family inertia (FI). Family inertia is represented as a stock (accumulator)
that integrates the change in family inertia (Ch FI).
Fit = f | [ChEr(sy}s + FI(t0)
Ch FI depends on the effect of paternalism on family inertia (EPFI).
FI is calculated with a first order exponential smoothing (information delay from EPFI
to FI) of the observed value of EPFI, as follows:
Ch Fit = (EPFlt — Flt)/Delay Flt
where FI is the expected value; EPFI is the actual value; delay FI is the information
delay.
To represent EPFI, it is specified a functional relation (graphic functions, as described
in Appendix B) between paternalism (P) and time, considering four scenarios. The first
scenario does not consider paternalism in the simulation, whereas the other three
scenarios take into account three different evolutions of paternalism over three
generations.
- Scenario 1: Basic case without paternalism.
- Scenario 2: Paternalism decreases over time (Appendix B.a).
EPFiIt2 = P2(time)
where P2 is a function that specifies the effect of paternalism on family inertia over time
in scenario 2 (P decreases > FI decreases).
- Scenario 3: Paternalism increases over time (Appendix B.b).
EPFIts = P3 (time)
where P3 is a function that specifies the effect of paternalism on family inertia over time
in scenario 3 (P increases > FI increases).
- Scenario 4: Paternalism fluctuates over time (Appendix B.cd.e).
Details in the next paragraph.
15
RESULTS AND NEW INSIGHTS
As mentioned before, we examine four different scenarios describing the evolution of
paternalism over three generations in family business and the consequent effects on
family inertia and capabilities, and then on the model as a whole.
Figure 7 shows the simulation results of the model over three generations (90 years) of a
generic family firm considering scenario 1, scenario 2 and scenario 3.
Scenario 1: Basic case without paternalism. As it was expected, if knowledge (K)
increases over three generations, basic dynamic capabilities (BDC: PAC and RAC),
capabilities (C), advanced dynamic capabilities (ADC), entrepreneurial performance
(EP) and trans-generational value (TGV) also increase through dynamic reinforcing
loops (figure 7).
In particular, capabilities and dynamic capabilities (PAC, RAC and ADC) increase at a
slower rate in the third generation (about from the 60th to 90th year) probably because
psychological ownership’? (including motivation and commitment) of family members
usually decreases after the second generation, as pointed out by Astrachan et al., (2002).
Interesting results emerge when family inertia (as a function of paternalism) is included
in the model.
Scenario 2, scenario 3 (figure 7) and Appendix B show the simulation results
concerning the effect of paternalism on family inertia (EPFI) and the consequent effects
on C, ADC, EP, TGV, K and BDC.
Scenario 2: Paternalism decreases over time. As paternalism decreases, family inertia
decreases, as well. Consequently, the creation of capabilities increases (see figure 5) and
a positive effect drives all the variables of the model (figure 7, Appendix B.a).
Results are similar to the first simulation (scenario 1) but values become higher due to
the decrease in family inertia and diverge during the second and third generation.
For instance, the maximum value of knowledge at the end of the third generation is
equal to 36.93 in scenario | and it is equal to 39.48 in scenario 2. The maximum value
of trans-generational value at the end of the third generation is equal to 2.07 in scenario
1 and it is equal to 2.67 in scenario 2 (figure 7).
Figure 5: Effect of family inertia on capabilities in scenario 2
FAMILY INERTIA Capabilities
=I 2
|
of bi
._ U4
os AR 1
al
° ober
0 2% w 4 5 60 70 80 9% ot 2% 30 4 5 o 7 8 9
‘Tare (Year) ‘Tame (Year)
FAMILY INERTIA: Scenario 2, Patera decreases Capable : Scenario 2, Patemasm decreases
a Psychological ownership is the psychologically experienced-phenomenon where owners, managers and
employees develop possessive feelings that the family firm is “mine” or “ours”. For instance, strength of
identifying oneself with the family business, sense of belonging to the family business, strong feeling of
responsibility towards the family business and so on (Koiranen, 2006: adopted from Pierce et al., 2003).
16
Scenario 3: Paternalism increases over time. As paternalism increases, family inertia
increases, as well. Consequently, the creation of capabilities decreases (see figure 6) and
a negative effect drives all the variables of the model (figure 7, Appendix B.d).
The negative effect can be clearly observed for C, ADC, EP and TGV; whereas, K,
PAC and RAC increase but their values become lower compared to the simulations
made before and completely diverge during the third generation when usually problems
rise (see Astrachan et al., 2002).
Results are consistent with the literature that stresses how critical is the third generation
in family firms and how issues may be amplified during this generation (see Aronoff
and Ward, 2001, Astrachan et al., 2002).
For example, the maximum value of knowledge at the end of the third generation is
equal to 36.93 in scenario 1; it is equal to 39.48 in scenario 2; and it is equal to 24.99 in
scenario 3 (figure 7).
A new insight emerges from the simulation of scenario 3. In fact, the creation of
capabilities is not negatively influenced by the increase in family inertia at the
beginning of the activity for about 10 years (see figure 6). Consequently, a positive
effect drives advanced dynamic capabilities for about 10 years. The effect is even bigger
for entrepreneurial performance and trans-generational value which keep on increasing
for about 20-25 years (see figure 7).
Figure 6: Effect of family inertia on capabilities in scenario 3
FAMILY INERTIA Capabilities
10 2 02
oo 2% 3% 4% 30 60 70 80 90 oo 2% 0 4% 50 «0 70 80 9%
Time (Year) ‘Tine (Year)
FAMILY INERTIA : Scenario 3, Patera neeases Capabilities : Scenario 3, Pateralsm increases
This behaviour / phenomenon can be explained going back to the definition of family
inertia and the meaning of paternalism:
Family inertia is defined as the tendency of family firms to resist change even when it is
needed to match the requirements of a changing environment. It is a function of
paternalism which affects family inertia negatively. The ideology of paternalism is
protective and dominating in a fatherly way with a strong attitude to preserve family
firm’s traditions and not to make changes.
But at the beginning of the activity a paternalistic behaviour may be seen to some extent
positive in order to guide and train the new generation. Problems (caused by FI) rise if
such paternalistic behaviour persists over time especially when the two generations
work actively together as shown in figure 7 (from the 10th year). To explain better such
an effect, we refer to the article of Giddings (2003). He posits that the founder of a
family firm often has much energy and personality to run things his way. He is a
17
paternalistic person but this is good at the beginning of the family firm when a
mentor/leader is needed. Indeed, offspring must be guided and trained. But as time
passes, a dominating and autocratic climate might predominate and make working
conditions difficult for offspring, to express their ideas, to make autonomous choices
and to make changes in the business for the good of the business (Giddings, 2003;
Koiranen 2003; Chirico, 2006b). In other words, the paternalistic behaviour which was
essential at the beginning of the activity may become less crucial if it persists over time
preventing change even when it is needed. As a consequence, the effect of paternalism
on family inertia increases more compared to before with negative effects on the
creation of capabilities and dynamic capabilities (Larsen and Lomi, 2002; Collis, 1994;
Sharma et al., 2005; Chirico, 2006b). A negative effect drives all the variables of the
model.
18
Figure 7: Simulations in Scenario 1, 2 and 3
KNOWLEDGE
40
20
0
0 10 2 30 40 50 60 670 80) (0
‘Time (Year)
KNOWLEDGE: Scenario 1, No Paternalism —-—+—+—+—+—+—
KNOWLEDGE : Scenario 2, Pateralismdecreases. —2—2——2 2a
KNOWLEDGE: See \emalism increases. —3——3—3 33
POTENTIAL ABSORPTIVE CAPACITY,
REALIZED ABSORPTIVE CAPACITY
2 2
La
1 SE ees | 1
0 0
0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90
‘Time (Year)
POTENTIAL ABSORPTIVE CAPACITY: Scenario 1, No Paternalism 4—+—
POTENTIAL ABSORPTIVE CAPACITY: Scenatio 2,
POTENTIAL ABSORPTIVE CAPACITY’
aemalismdecreases, —2—
enario 3, Paternalism inemeases. —2
‘Time (Year)
REALIZED ABSORPTIVE CAPACITY : Scenario 1, No Paternalism —+—+—
REALIZED ABSORPTIVE CAPACITY Scenario 2 Patemalismdecreases. —2—
REALIZED ABSORPTIVE CAPACITY: Scenario 3, Paternalismincreases. —
Capabilities ADVANCED DYNAMIC CAPABILITIES
2 2
|
ce
i —pieey ||, aE
Ta eee |
ae
0 : 0 onal
0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90
‘Time (Year)
‘Capabilies : Scenario, No Patemalism ——¢——+——+ +34
Capabilities: Scenario 2, Patemalism decreases. ——2—2—2——2 2
Capabilities: Scenario 3, Patemalismincreases. 23333
‘Time (Year)
ADVANCED DYNAMIC CAPABILITIES : Scenario, No Patemalisen. ——+—
‘ADVANCED DYNAMIC CAPABILITIES : Scenario ,Paternalismdecreases -2—
ADVANCED DYNAMIC CAPABILITIES : Scenario 3, aternalsminereases. —3
ENTREPRENEURIAL PERFORMANCE : Scenario |, No Patemalism —+—4+—
ENTREPRENEURIAL PERFORMANCE: Scenario 2, Patemalism decreases. —2—
ENTREPRENEURIAL PERFORMANCE: Scenario 3, Patemalismincreases. —3
ENTREPRENEURIAL PERFORMANCE,
TRANS-GENERATIONAL VALUE
:
tH ||,
a Toler TT.
PE eGIe) | | ee ee la|al|
0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90
Time (Year)
‘Time (Year)
"“TRANS-GENERATIONAL VALU
"TRANS-GENERATIONAL VALU
"“TRANS-GENERATIONAL VALL
scenario 2, Paternalism decreases. —2—
Scenario 3, Paternalism increases, —3—3
19
The results of scenario 3 allow us to consider a new scenario where paternalism
fluctuates over time (scenario 4).
Scenario 4: Paternalism fluctuates over time. Paternalism does not affect much family
inertia at the start-up and at the beginning of each generation for a certain period (t).
Then, it rises up to a peak but falls down again with the following generation. It is a
kind of resetting clock process in which the behaviour described above recurs every
generation.
As before, to represent the effect of paternalism on family inertia (EPFI), it is specified
a functional relation (graphic function, as described in Appendix B.c) between
paternalism (P) and time. It is assumed that t is equal to 10 years (Scenario 4.1):
(t) = 10 years
EPFIs.1 = P41 (time)
where Ps. is a function that specifies the effect of paternalism on family inertia over
time in scenario 4.1 with t= 10 (P fluctuates FI fluctuates).
The simulation depicted in figure 10 and Appendix B.c show that when paternalism
does not have much affect on family inertia at the beginning of each generation for
about 10 years, C (see figure 8), ADC, EP and TGV keep on increasing. After that when
the effect of paternalism on family inertia starts rising up to a peak while the two
generations work actively together, C (see figure 8), ADC, EP and TGV are negatively
influenced.
Figure 8: Effect of family inertia on capabilities in scenario 4.
FAMILY INERTIA Capabilities
0 02
\ AN “
f\ TN /
5 f+ \ 04 i
\ \ / /| [
XN v
° os
. 0 2 30 450 RO oo 30-30 4307080
Time (Yen) Tae (Yen)
FAMILY INERTIA Scenario 4.1, P. acts, 10 ——— CCapbition “Scenario 4.1, P, Heute, 10
As before, this behaviour / phenomenon can be explained through the article of
Giddings (2003): a paternalistic behaviour is essential at the beginning of the activity to
guide and train offspring (EPFI very low) but may become less crucial if it persists over
time preventing change even when it is needed (EPFI increases). With the following
generation, the effect of paternalism on family inertia (EPFI) declines as a kind of
resetting clock. In fact, the beginning of the new generation and the complete end of the
previous one (which might represent a dramatic change in the business), can be thought
20
of as “resetting the clock” similarly to the resetting clock used by Amburgey et al.,
(1993) in describing the dynamics of organisational change and failure.
The behaviour above-described recurs every generation.
The increase in entrepreneurial performance and trans-generational value is very high
during the first generation for about 25-30 years and then stabilises in the second and
third generation, even though capabilities and advanced dynamic capabilities go up and
down because of the effect of family inertia (figure 10).
Results become more significant with the assumption that paternalism does not affect
much family inertia at the beginning of each generation for a longer period (i.e. t =15 in
scenario 4.2 and t = 20 in scenario 4.3; see figure 10 and Appendix B.d,e):
(t) = 15 years
EPFIs2 = P42 (time)
where Ps2 is a function that specifies the effect of paternalism on family inertia over
time in scenario 42 with t = 15 (P fluctuates > FI fluctuates).
and
) = 20 years
EPFIs3 = P43 (time)
where P43 is a function that specifies the effect of paternalism on family inertia over
time in scenario 43 with t = 20 (P fluctuates > FI fluctuates).
As plotted in figure 10 when paternalism does not affect much family inertia at the
beginning of each generation for a longer period, the family firm is able to better react
to the period in which the effect of paternalism on family inertia increases. Indeed, C
(see figure 9), ADC, EP and TGV increase over time but with some fluctuations in
scenario 42 and 43.
Figure 9: Effect of family inertia on capabilities in scenario 42 and 43
FAMILY INERTIA Capabilities
10 06
A A
s - [ \ 03 YA
LA LEE
o 0 2% 3 4 50 «0 7 80 0 o 1 2 30 40 50 0 7 80
Time (Year) ‘Tine (Year)
FAMILY INERTIA : Seeanrio 4.2, P. ductustes, 15
Capabilities: Sceanrio 4.2, P.fuctuates, 15
21
FAMILY INERTIA Capabilities
8
/\ \
f |
NPE a
/ os
ANTAN TO DRS
002030400 0102030405000
Time (Year) ‘Tine (Year)
FAMILY INERTIA :Scenario 4.3, P. fctuates, = 20,
Capabilities: Scenario 4.3, P.fctates, = 20
K, PAC and RAC keep on increasing following the same path during the first and the
second generation in scenario 4.1, 42 and 43 and diverge during the third generation. For
instance, at the end of the third generation the maximum value of knowledge is equal to
28.06 in scenario 4.1; it is equal to 31.05 in scenario 42; and it is equal to 33.02 in
scenario 4.3 (figure 10).
In other words, the longer is “t”, the higher and faster is the increase of all the variables
represented in the model (see figure 10). For example, at the end of the third generation
the maximum value of TGV is 0.34 when “t” is equal to 10 (scenario 4.1); it is 0.72
when “t” is equal to 15 (scenario 42); and it is 1.18 when “t’” is equal to 20 (scenario
43).
22
Figure 10: Simulations in scenario 4.:, 42 and 43
KNOWLEDGE
40
see
gE
20
0
0 10 20 30 40 50 60. 70 80 90
‘Time (Year)
KNOWLEDGE Scena, tutes, t-10 —¢——+—§—+—
KNOWLEDGE: Sceanno 42,P fetutes,18. —-2—@—e ae
KNOWLEDGE Scena 42,0. Muctute, (020 3—3—8—8 39
POTENTIAL ABSORPTIVE CAPACITY REALIZED ABSORPTIVE CAPACITY
2 2
—| Pen|
1 1 ae
CE arr aera
0 0
0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80. 90
‘Time (Year) ‘Time (Year)
POTENTIAL ABSORPTIVE CAPACITY: Scenario 41,P eta REALIZED ABSORPTIVECAPACITY: Scenario 4.1. tates, (10 —+—
POTENTIAL ABSORPTIVE CAPACITY : Sceanrio 42, fluctuate
POTENTIAL ABSORPTIVE CAPACITY : Scenario 43, P, fuctuate
REALIZED ABSORPTIVE CAPACITY: Seeanrio 42, P. fuctuates, 1S. ——2—
REALIZED ABSORPTIVE CAPACITY : Scenario 43, P. fuctuates, = 20 —3—3
Capabilities ADVANCED DYNAMIC CAPABILITIES,
i 0.6
OS 03
detest ipean.
z Sasser
Lp ae pa Sa Ae lara
0 0
a
‘Time (Year) ‘Time (Year)
ENTREPRENEURIAL PERFORMANCE,
TRANS-GENERATIONAL VALUE
2 2
1
0
o 0 2 30 4 50 6 70 0 o 0 20 30 4 30 60 7 80 9%
Time (Year) ‘Time (Year)
ENTREPRENEURIAL PERFORMANCE: Scenario 41, P-uetuates,t-10. —+t— "TRANS. GENERATIONAL VALUE" : Scenario 41, P. fuetuates,
ENTREPRENEURIAL PERFORMANCE: Sceanrio 42, P. fluctuates, 15. —2—
ENTREPRENEURIAL PERFORMANCE: Scenario 43, P. Muetuates,(=20 3—3
"TRANS-GENERATIONAL VALU
"“TRANS-GENERATIONAL VALL
sceanrio 42,
Scenario 43,
Auetuates, t=
fuetuates,
23
DISCUSSION AND CONCLUSIONS
Bothner and White (2002) posit that simulation models are useful when in reducing the
real world complexity, they almost inviolate the established facts and yield surprising
insights for further exploration.
By using simulations, it was possible to explore further the dynamic feedback loops of
figure 3 and test its internal consistency, representing variables as dynamic
accumulation processes (stocks).
A simulation model (figure 4) was built using the system dynamics methodology and
interesting results and new insights emerged as reported in this research. The dynamic
model allowed us to test different sets of assumptions at the same time.
Simulations gave evidence of the positive dynamic relations between knowledge, basic
dynamic capabilities, capabilities, advanced dynamic capabilities, entrepreneurial
performance and trans-generational value (Scenario 1, 2, 3 and 4); the positive relation
between paternalism and family inertia (Scenario 2, 3 and 4); and the negative relation
between family inertia and capabilities, with some exceptions (Scenario 2, 3 and 4).
In particular:
- Scenario 2: if paternalism decreases, family inertia decreases, as well.
Consequently, the creation of capabilities increases and a positive effect drives
all the variables of the model (figure 7, Appendix B.a).
- Scenario 3: If paternalism increases, family inertia increases, as well.
Consequently, the creation of capabilities decreases and a negative effect drives
all the variables of the model (figure 7, Appendix B.b). However, the creation of
capabilities is not negatively influenced by the increase in family inertia at the
beginning of the activity with positive effects on the model.
- Scenario 4: Paternalism fluctuates over time. Paternalism does not affect much
family inertia at the start-up and at the beginning of each generation for a certain
period (t). Then, it rises up to a peak but falls down again with the following
generation. Problems (caused by family inertia) rise after “t” years from the
beginning of each generation when the effect of paternalism on family inertia
increases. The longer is “t”, the better the family firm is able to react to the
period in which the effect of paternalism on family inertia increases (figure 10,
Appendix B.c,d,e).
Results become more evident and visible during the third generation when usually
problems rise in family business. Indeed, according to the literature, the most critical
period faced by family firms is the third generation, and the positive and negative
effects of past events, decisions and behaviours may be amplified during this generation
(see Aronoff and Ward, 2001, Astrachan et al., 2002; Bridge et al., 2003). Family firms
should be able to understand the long-term effects and results of actual events, decisions
and behaviours, and, at the same time, prevent their negative consequences. System
dynamics may be a useful tool to achieve such a result.
24
John Sterman'', who is a leading expert on system dynamics, explains in one of his
business courses for managers, planners, and strategists (Sterman, 2006) that “in a
world of growing complexity, many of the most vexing problems facing managers arise
from the unanticipated side-effects of their own past actions. In response, organizations
struggle to speed learning and adopt a more systemic approach. The challenge is to
move past slogans about accelerating learning and ‘thinking systemically’ to practical
tools that help managers understand complexity, design better operating policies, and
guide effective change. System dynamics is a powerful framework for identifying,
designing, and implementing high-leverage interventions for sustained success in
complex systems”.
For this reason, the study conducted here encourages family-business researchers to
make use of this powerful methodology of system dynamics. Interesting results and
insights may emerge and help researchers to better understand the complexity of
dynamism in a family business context and assist family firms to better manage their
business within the firm and the family.
Limitations of this paper are related to the assumptions made and the impossibility of
representing the real trend/degree of paternalism of a generic family firm. The use of
graphic converters (graphic functions) solved some of those problems for the simulation
in system dynamics.
This research can be viewed as a contribution to the literature on the simulation of first-
order models of theory-testing, and, partially, on the simulation of second-order models
of theory building. More work and simulations are needed before generalising and
building a new theory based on the results and insights of this study. Furthermore, in the
future, specific components of the model may be disaggregated to focus on particular
issues related to family businesses.
"' Professor of Management at the Sloan School of Management, Massachusetts Institute of Technology
and Director of MIT’s System Dynamics Group.
25
REFERENCES
Aronoff, C., and Ward, J.L., (2001). Family business values: How to assure a legacy of
continuity and success. Family Business Leadership Series, No. 12. Marietta, GA,
Family Enterprise Publishers.
Amburgey, T.L., Kelly D., and Barnett, WP., (1993). Resetting the clock: The dynamics
of organizational change and failure. Administrative Science Quarterly, Vol. 38,
No. 1, pp. 51-73.
Argote, L., (1999). Organizational learning: Creating, retaining, and transferring
knowledge. Boston, Kluwer Academic Publishers, Norwell.
Astrachan, J.H., Klein, S.B., and Smyrnios, K.X., (2002). The F-PEC scale of family
influence: A proposal for solving the family definition problem. Family Business
Review, Vol. 15 No. 1, pp. 45 58.
Barney, J., (1991). Firm resources and sustained competitive advantage. Journal of
Management, Vol. 17, No. 1, pp 99-120.
Bothner, M., and White, H., (2001). Market orientation and monopoly power. Jn Lomi
and Larsen, (eds). Dynamics of Organizations, AAAI/MIT- Press, Menlo Park,
CA, pp. 182-209.
Bridge, S., O’Neil, K., and Cromie, S., (2003). Understanding enterprise,
entrepreneurship and small business, Palgrave Macmillan.
Chirico, F., (2006a). Knowledge models in family business: Evidence from Switzerland.
Compendium on the Family Business Models Around the World (ten volumes).
Hyderabad: ICFAI. Gupta, Vipin; Levenburg, Nancy; Moore, Lynda; Motwani,
Jaideep; and Schwarz, Thomas (eds.). University Press.
Chirico, F., (2006b). From knowledge to trans-generational value creation in family
firms. A dynamic capabilities and cultural approach. Family firms as arenas for
trans-generational value creation: a qualitative and computational approach.
University of Jyvaskyla, Press.
Collis, D.J., (1994). Research note: How valuable are organizational capabilities?.
Strategic Management Journal, Vol. 15, Special issue, pp. 143-152.
Eisenhardt, K.M., and Martin, J.A., (2000). Dynamic capabilities: What are they?
Strategic Management Journal. Vol. 21, No. 10-11, pp. 1105-1121.
Forrester, J.W., (1961). Industrial dynamics, Cambridge, Productivity Press.
Forrester, J.W., (1968). Market growth as Influenced by capital investment. Industrial
Management Review, Vol. 9, No. 2, pp. 83-105.
Fotion, N., (1979). Paternalism. Ethics, vol 89, No 2, pp. 191-198.
Giddings, T., (2003). Avoid these family business obstacles. San Fernando Valley
Business Journal, March 17; Los Angeles Business Journal. October 13.
Habbershon, T.G., and Pistrui, J., (2002). Enterprising families domain: Family-
influenced ownership groups in pursuit of transgenerational wealth. Family
Business Review, Vol. 15, No. 3, pp. 223-238.
Hanneman, R., Collins R., and Mordt, G., (1995). Discovering theory dynamics by
computer simulation: Experiments on state legitimacy and imperialist capitalism.
Sociological Methodology, ed. P. Marsden, pp. 1-46. Oxford: Blackwell.
Koiranen, M., (2004): Entrepreneurialism, managerialism and paternalism as clashing
ideologies in family business systems. Jn Watkins, D. (ed.): ARPENT, Annual
Review of progress in Entrepreneurship Research: 2, 2002-2003, Brussels, pp.
299-310.
26
Koiranen, M., (2006). Psychological ownership and entrepreneurial drive as value
creators. Family firms as arenas for trans-generational value creation: a
qualitative and computational approach. University of Jyvaskyla, Press.
Johannisson, B., and Huse, M., (2000). Recruiting outside board members in the small
family business: an ideological challenge. Entrepreneurship and Regional
Development, Vol. 12, No. 4, pp. 353-378.
Johannisson, B., (2002). Energising entrepreneurship. Ideological tensions in the
medium-sized family business. In Fletcher, D (ed.) Understanding the Small
Family Business. London: Routledge, pp. 46-57.
Larsen, E., and Lomi A., (1999). Resetting the clock: A feedback approach to the
dynamics of organizational inertia, survival and change. Journal of the
Operational Research Society, Vol. 50, No 4, pp. 406-421.
Larsen, E., and Lomi, A., (2002). Representing change: A systems model of
organizational inertia and capabilities as dynamic accumulation processes.
Simulation Modelling Practice and Theory, Vol 10, Special Issue, pp. 271-296.
Lomi, A., and Larsen, E., (2001). Dynamics of organizations: Computational modeling
and organization theories, AAAI/MIT- Press, Menlo Park.
Lomi, A., Larsen, E., and Freeman J., (2005). Things change: Dynamic resource
constraints and system-dependent selection in the evolution of organizational
populations. Management Science, Vol. 51, No 6, pp. 882-903.
Masuch M., (1995). Computer models. Encyclopedic Dictionary of Organizational
Behaviour, ed. N. Nicholson, 91-92. London: Blackwell.
Mollona, E., (2000). Analisi dinamica dei sistemi aziendali; EGEA.
Morecroft, J.D.W., (1982). A critical review of diagraming tools for conceptualizing
feedback system models. Dynamica, Vol. 8, No. 1, pp. 20-29.
Morecroft, J.D.W., (1983). System dynamics: Portraying bounded rationality. Omega,
Vol. 11, No. 2, pp. 131-142.
Morecroft, J., and Sterman, J., (1992). Modeling for learning organizations. European
Journal of Operational Research. Vol 59, No.1.
Oreskes, N., Shrader-Frechette, K., and Berlitz, K., (1994). Verification, validation, and
confirmation of numerical models in the earth sciences. Science, Vol. 263, pp.
641-646.
Pierce, J., Kostova, T., and Dirks, K., (2003). The status of psychological ownership:
Integrating and extending a century of research. A Review of General Psychology,
Vol. 7, No. 1, pp. 84-107.
Sastry, M.A., (1997). Problems and paradoxes in a model of punctuated organizational
change. Administrative Science Quarterly, Vol. 42, No. 2, pp. 237-275
Senge, P., and Sterman, J., (1992). Systems thinking and organizational learning:
Acting locally and thinking globally in the organization of the future. European
Journal of Operations Research, Vol. 59, pp. 137-150.
Sharma, P., and Manikutty, S., (2005). Strategic divestments in family firms: Role of
family structure and community culture . Entrepreneurship Theory and Practice,
Vol. 29, No. 3, pp. 293-312.
Sterman, J., (1992). System dynamics modeling for project management. MIT
Cambridge MA 02139.
Sterman, J., (2000). Business dynamics, systems thinking and modeling for a complex
world. New York, McGraw-Hill.
27
Sterman, J., (2006). Business Dynamics. MIT’s approach to diagnosing and solving
complex business problems. MIT Sloan, Executive —_ Education.
http://bmitsloan.mit.edu/execed/epp/courses/busdynamics06.pdf
Sutton, R, and Straw, B., (1995). What theory is not. Administrative Science Quarterly,
Vol. 40, No. 3, pp. 371-785.
Teece, D.J., Pisano, G., and Shuen, A., (1997). Dynamic capabilities and strategic
management. Strategic Management Journal, Vol 18, No. 7, pp 509-533
Van Ackere, A., Larsen E.R., and Morecroft J.D.W, (1993). Systems thinking and
business process redesign: an application to the Beer Game. European
Management Journal, 11:412-423.
Winter, S., (2003). Understanding dynamic capabilities. Strategic Management Journal,
Vol. 24, No. 10, pp. 991-995.
Zahra, S.A., and George, G., (2002). Absorptive capacity: A review,
reconceptualization, and extension. Academy of Management Review, Vol. 27, No.
2, pp. 185-203.
Appendix A
a- Relation between KA (Y-axis) and RAC (X-axis)
Graph Lookup - "Knowledge Acquisition RAC”
Print
put Output ania!
9 1 : : : —
1
|
[—
=
_
|/-—
=
[—
— pars
Ne op
i
Mminj0 wf x=0.01895 0.03847 Xmax|1 >| Reset Seating
OK | CearPoints | CkaraliPoits | CursRet| ClearReerence | Ref>Cur| Cancel |
b- ERKC: relation between RKC (Y-axis) and time (X-axis)
Graph Lookup - “Rate of Knowledge Creation through IK /
put Output Bins
ro [io =I ae
a
> hz
= a
= a
_—
= a
el ‘ain
I
I
xin _+|¥9952— ye0316 —«Xmax|90 >|) Reset Scaing
OK | CearPoinis | _CearAlPonis | Curfiet| _CearRefernce | RetvCu| Cancel |
28
Appendix B: ‘Effect of paternalism on family inertia’ and ‘family inertia’
- Scenario 2 (Paternalism “P2” decreases over time)
Effect of Paternalism on Family Inertia FAMILY INERTIA
1
ae
mS
os os ™
o o
oo 2% 3 4% 5 « 7 a0 ° 0 2 3 4 3 «0 7 30 %
“Te (Year) Ta (Year)
‘et of Paternalism on Family Ini Scenario 2, Pateralamdeercases || FAMILY INERTIA : Scenario 2, Patermalam decreases
b- Scenario 3 (Paternalism “P3” increases over time)
Effect of Paternalism on Family Inertia FAMILY INERTIA
0 o
ann ae
a Lr
5 oer 5 CLEP
mez ca
Jar]
° o
0 0 0 4 3% «0 10 a0 00 ow 2 30 «0 50 4 708090
Tave (Year) Tine (Year)
et of Patera on Famiy Inia: Scenario 3, Patera nereases || FAMILY INERTIA : Scena 3, Patera increases
c-__ Scenario 4.1 (Paternalism “P4.1” fluctuates over time; “t” = 10)
Effect of Paternalism on Family Inertia FAMILY INERTIA
10 7] .
A | ViTI : \
7 7 tot |
d- Scenario 4.2 (Paternalism “P4.2” fluctuates over time; “1” = 15)
Effect of Paternalism on Family Inertia FAMILY INERTIA
o
/\ /\
. A
5
e- Scenario 4.3 (Paternalism “P4.3” fluctuates over time; “t” = 20)
Effect of Paternalism on Family Inertia FAMILY INERTIA
0 5 r "
| j
a ‘alt |
/\
AC
CTT
NEC
J
6 °
|)
Tine (Yeu) Time (Yes)
[et of Patel on Famer: Scemrib43,. heats, =20 |] FAMILY INERTIA: Scenario 4.3, P cts, 20
29
Min and Max of paternalism in scenario 2, 3 and 4
Max(P2) when time = 0 years (P2 is at its maximum value at the beginning of the
first generation)
P2= ..
Min(P2) when time = 90 years (P2 is at its minimum value at end of the third
generation)
pe Max(P3) when time = 90 years (P3 is at its maximum value at the end of the third
generation)
~ Min(P.)) when O<time<I0years
Max(P: 1) when time = 30 years
Min(Ps.1) when 31<time<40 year
Par=
Max(P:1) when time = 60 years
Min(Ps.1) when 61<time< 70 years
Max(P1) when time = 90 years
Min(Ps2) when 0<time<15 years
Max(P2) when time = 30 years
Min(Ps2) when 31<time<45 year
Paz =...
Max(P2) when time = 60 years
Min(Ps2) when 61<time< 75 years
Max(P+2) when time = 90 years
Min(Ps.3) when 0<time<20 years
Max(P:3) when time = 30 years
Min(Ps3) when 31<time<50 year
Pas =...
Max(P+3) when time = 60 years
Min(P:3) when 61<time< 80 years
Max(P:3) when time = 90 years
30
Appendix C
Variables
Sector Variable Type Value
K K Initial Value 2
K OKG Constant 1
K Min(KA) Constant 0.1
K Max(KA) Constant 1
K Min(RKC) Constant 3
K Max(RKC) Constant 10
K a. Constant 0.03
INKD INKD Initial Value 12
IK IK Initial Value 4
INKD and IK B Constant 8
PAC PAC Initial Value 0.5
PAC Delay PAC Constant 3
RAC RAC Initial Value 0.5
RAC Delay RAC Constant 3
Cc Cc Initial Value 0.1
Cc Delay C Constant 3
Cc Y Constant 0.05
ADC ADC Initial Value 0.1
ADC 5 Constant 0.7
ADC & Constant 0.05
EP EP Initial Value 0.1
EP C Constant 0.6
EP Delay EP Constant 3
EP y Constant 0.05
TGV TGV Initial Value 0.1
TGV 9 Constant 0.4
TGV Delay TGV Constant 3
TGV L Constant 0.1
TGV kK Constant 0.05
FI FI Initial Value 1
FI Delay FI Constant 3
FI Min(P2) Constant 0.1
FI Max(P2) Constant 1
FI Min(P3, P41, Constant 1
P42, Pas)
FI Max(P3, P41, Constant 10
P42, Pas)
FI Tt Constant 10; 15; 20
31
Appendix D
Positive and negative feedback loops
a. Reinforcing Loop (R) is a structure which feeds on itself to produce growth or
decline: ‘State 1’ (the cause) increases or decreases ‘State 2’ (the effect) which,
in turn, increases or decreases ‘State 1’, respectively. In other words, R tends to
reinforce or amplify whatever is happening in the system. Of course, nothing
grows forever. There must be some limits to growth which are created by
negative feedbacks (Sterman, 2000):
b. Balancing Loop (B) counteracts and opposes change. It attempts to move some
“Current State’ to a ‘Desired State’ (it is assumed that ‘Current State’ is lower
than ‘Desired State’) through some ‘Action’: the ‘Desired State’ interacts with
the ‘Current State’ to produce a ‘Gap’. The larger the ‘Gap’, the stronger the
influence to produce ‘Action’. The ‘Action’ taken then moves the ‘Current
State’ toward the ‘Desired State’ reducing the ‘Gap’ to zero:
Desired State Gap B Action
‘Current State’ *
An example will be helpful to clarify the above concepts. Figure 2 shows a simplified
representation of the causal loop diagrams of knowledge in which the two feedback
loops described above emerge (R and B):
Creation of Erosion of
KNOWLEDGE BB
Knowkdge * SE 5 Knowiedees
+ SS Se <a
.
Rate of Knowledge Rate of Knowledge
Creation Erosion
‘Knowledge’ is positively influenced by the ‘creation of knowledge’ and negatively
influenced by the ‘erosion of knowledge’. ‘Creation of knowledge’ is given by
‘knowledge’ multiplied by the ‘rate of knowledge creation’, whereas ‘erosion of
knowledge’ is given by ‘knowledge’ multiplied by the ‘rate of knowledge erosion’.
32
Two balancing loops can be identified:
1.
Reinforcing loop (R):
Positive relation between ‘creation of knowledge’ and ‘knowledge’:
knowledge is created, the more knowledge is accumulated;
Positive relation between ‘knowledge’ and ‘creation of knowledge’:
knowledge is accumulated, the more knowledge is created.
Balancing loop (B):
Negative relation between ‘erosion of knowledge’ and ‘knowledge’:
knowledge is eroded, the less knowledge is accumulated;
Positive relation between ‘knowledge’ and ‘erosion of knowledge’:
knowledge is accumulated, the more knowledge is eroded.
the more
the more
the more
the more
Balancing and reinforcing loops can be identified by counting the number of “-” and
“+ in the feedback loop. A feedback loop is a balancing loop if the number of “-” is
odd; it is a reinforcing loop if the number of “-” is even or zero.
33
Knowledge, Dynamic Capabilities and Family Inertia in
Family Firms: A Computational Approach
Francesco Chirico
University of Lugano (USI)
Laboratory of System Modelling (LSM)
Institute of Management
Via G. Buffi 13, 6900 Lugano, Switzerland
Tel: +41586664548
Fax: +41586664647
franc: co@lu.u
ABSTRACT
The present research sets out through computer simulations in system dynamics the
positive dynamic interconnections between knowledge, capabilities, dynamic
capabilities, entrepreneurial performance and trans-generational value in family
business.
Interesting results and new insights emerge introducing family inertia in the model (as a
function of paternalism) which influences the creation of capabilities and dynamic
capabilities negatively, with some exceptions. We conclude that although a paternalistic
behaviour can be positive in guiding and training offspring at the beginning of the
activity, it may become less crucial if it persists over time preventing change even when
it is needed.
Keywords: simulation modelling, system dynamics, family business, family inertia,
paternalism, knowledge, dynamic capabilities, trans-generational value.
Acknowledgments: My personal thanks to Prof Erik Larsen and Prof Gianluca Colombo for
helping me in this research. I gratefully acknowledge financial support from the Finnish
Ministry of Trade and Industry and from the European Union.
INTRODUCTION
Chirico (2006b) made a detailed examination of the complex dynamic process through
which dynamic capabilities are generated by knowledge and generate entrepreneurial
performance, allowing a family firm to compete in situations of rapid change and create
value over time. Part of this value may be reinvested in the family firm for the creation
of new knowledge (e.g. training courses).
For the sake of this argument, dynamic capabilities were seen as a double concept: basic
and advanced dynamic capabilities. In addition, family inertia (FI) was considered to be
a factor preventing the creation of dynamic capabilities. FI depends on the family-
business culture, where paternalism and entrepreneurial drive influence FI positively
and negatively, respectively (see figure 1). The research was based on the review of the
literature and four in-depth case studies analysed through interviews.
Figure 1: Dynamic model: from knowledge to trans-generational value creation*
KNOW. (DYNAMIC) CAPABILITIES ENTREPRE
LEDGE (Processes designedio acquire, exchange, transforma -NEURIAL
‘integrate and recombine’ and shed resources**) PERFOR-
Industy- MANCE
related, BASIC CAPABILITIES ADVANCED
Lae DYNAMIC DYNAMIC Entrepre-
and CAPABILITIES (High-level routines CAPABILITIES neural
ownership || | PAC RAC [—> that,together with [>| 1] sovatins
knowledge is upbaoting and strategic
aml ‘ adaptation to
(Processes of input flows, confers (Processes of ip
“acquisition, upon an acquisition, the market
assimilation’ and organization's exchange,
“transformation, management a set of transformation -Product-line
| | exploitation’ of decision options for and shedding of extension,
knowledge) producing capebilities) -Diversifice-
significant outputs tion,
ofa particular type) Fepey
x
Paternalism*** ' Entrepreneurial
Attitude not to Drive****
make changes Attitude to keep the
id FAMILY INERTIA (7 ariiees chanens
(no freedom to
express ideas and (e.g, procctiveness,
make autonomous innovativeness,
choices and willingness to take
changes) risks and enlarge
the business).
closed open and explicit
FAMILY CULTURE
TRANS-GENERATIONAL VALUE
Investments in Knowledge “Tangible assets;
(e.g, training, executive courses, -Intangble assets;
employingfusing extemal non-farnily-mercbers etc.) ~Total of the balance sheet.
Source: Adapted from Chirico (2006b, figure 2)
(*) ___: positive relation; ------: negative relation.
(**) Resources are here interpreted as knowledge and capabilities.
(***) Paternalism influences family inertia positively and family inertia influences (dynamic) capabilities
negatively. Consequently, paternalism influences (dynamic) capabilities negatively.
(****) Entrepreneurial drive influences family inertia negatively and family inertia influences (dynamic)
capabilities negatively. Consequently, entrepreneurial drive influences (dynamic) capabilities positively.
The theoretical framework (dynamic capabilities, from knowledge to trans-generational
value and the family inertia problem) behind the dynamic model in figure 1 has been
analysed by Chirico (2006b).
Table | provides a list of all the variables studied.
The present research aims at exploring, extending and testing the model above and its
dynamic implications through computer simulations in system dynamics, looking for
new insights.
First, we present the system dynamics methodology which explains what system
dynamics is and why it is useful to use it in this study. Then, we analyse the model
structure from the feedback loops to the dynamic models, followed by the results and
new insights of the research. In this section, we examine four different scenarios
describing the evolution of paternalism over three generations (90 years), and their
consequent effects on the model as a whole. Finally, we present the conclusions.
Table 1: list of the variables
KNOWLEDGE. Chirico (2006a) refers to knowledge as “pure knowledge and skill which
family and non-family members working in the family firm have gained and developed
through education and experience (industry-related knowledge, business knowledge,
ownership knowledge)”.
DYNAMIC CAPABILITIES. Dynamic capabilities are defined as processes embedded in
firms designed to acquire, exchange and transform internal and external knowledge and
capabilities’ in new and distinctive ways and, at times, shed them to build and sustain
entrepreneurial performance in environments of rapid change (Teece et al., 1997; Eisenhardt
and Martin, 2000). Absorptive capacity is itself a dynamic capability (Zhara and George,
2002). Dynamic capabilities encompass:
- BASIC DYNAMIC CAPABILITIES (BDC) is absorptive capacity. The first
dimension of BDC is named Potential Absorptive Capacity (PAC) which is the
capacity of acquiring and assimilating external knowledge. Acquisition capability
means “the ability to identify and acquire externally generated knowledge”;
assimilation capability means “the ability to analyze, process, interpret and
understand knowledge acquired from external sources”. The second dimension of
BDC is named Realized Absorptive Capacity (RAC) which is the capacity to
transform and exploit the knowledge that has been absorbed. Transformation
capability means the ability to “develop and refine the routines that facilitate
combining existing knowledge and the newly acquired and assimilated knowledge”;
exploitation capability means “the ability to refine, extend, and leverage existing
competencies or to create new ones by incorporating acquired and transformed
knowledge into its operations” (Zahra and George, 2002);
- ADVANCED DYNAMIC CAPABILITIES defined as processes (e.g. product
development process) embedded in firms designed to acquire, exchange and
transform capabilities in new and distinctive ways and, at times, shed them to build
and sustain entrepreneurial performance in environments of rapid change.
ENTREPRENEURIAL PERFORMANCE. It is defined as a new and innovative form of
competitive advantage (Teece et al., 1997) given by entrepreneurial innovation and strategic
adaptation to the market (Barney, 1991; Zahra and George, 2002).
TRANS-GENERATIONAL VALUE CREATION IN FAMILY BUSINESS. It is
accumulated through continuous creation of business wealth and is measured by focusing on
the net asset value of the tangible assets, the estimated amount of intangible resources
(goodwill) and the total of the balance sheet of the family firm.
FAMILY INERTIA. It is defined as the tendency of family firms to resist change even when
it is needed to match the requirements of a changing environment. It is a function of
paternalism and entrepreneurial drive in family business where paternalism and
entrepreneurial drive influence family inertia positively and negatively, respectively. The
ideology of paternalism is protective and dominating in a fatherly way with a strong attitude
to preserve family firm’s traditions and not to make changes (Fotion, 1979; Johannisson et
al., 2000; Johannisson, 2002; Koiranen, 2003). Entrepreneurial drive is the attitude to keep
business changing through initiative and innovation (Johannisson, 2002; Habbershon and
Pistrui, 2002; Koiranen, 2003).
} According to Winter (2000: 991), “an organizational CAPABILITY is a high-level routine (or collection
of routines) that, together with its implementing input flows, confers upon an organization’s management
a set of decision options for producing significant outputs of a particular type”.
METHOD: A SYSTEM DYNAMICS APPROACH
System dynamics is an approach to modelling the dynamics of complex feedback
systems through formal computer simulations. Feedback is a core concept in system
dynamics. It refers to the situation of X affecting Y and Y, in turn, affecting X through a
chain of causes and effects. Causal loop diagrams are used to represent the feedbacks of
a system, that is, the way a system is connected by positive (self-reinforcing) and
negative (self-balancing or self-correcting) feedback loops (Forrester, 1961, 1968;
Sterman, 2000):
For more details, see Appendix D. Feedback loops are useful to capture and
communicate mental models but they have many limitations. For instance, they do not
take into consideration the stocks and flows of the system (Sterman, 2000).
System dynamics is based on the Principle of Accumulation. It states that all dynamic
behaviours in the world occur when flows accumulate in stocks (figure 2). Stocks and
flows are the basic building blocks of a system dynamics model which allow us to
analyse the feedback loops of the system” (Forrester, 1961, 1968; Morecroft, 1982,
1983; Morecroft and Sterman, 1992; Sterman, 2000; Mollona, 2000; Lomi and Larsen,
2001).
A stock is an entity which is accumulated over time by inflows and depleted by
outflows. It accumulates past events characterising the state of the system. A Stock
typically has a certain value at each moment of time (e.g. knowledge). Mathematically,
a stock (S) can be seen as an integration (accumulation) of the difference between
inflow and outflow (F) in the long term:
St= f [Inflow(t) — Outflow(t) [it + S(10)
A flow changes a stock over time by inflows (e.g. creation of knowledge) and outflows
(e.g. erosion of knowledge). It is typically measured over a certain interval of time.
Mathematically, a flow (F) can be seen as the derivative of the stock (S) with respect to
the time (t) that is its net rate of change:
dS
F = inflow — outflow; F = he
It
Figure 2: Stocks and Flows®
Variable (c.g, Rate of Knowledge Creation) Varlable (e.g. Rate of Knowledge Erosion)
st + —| sTocK }—, vA
a a >
INFLOW Knowledge)| OUTFLOW
(ex. (eg
Creation of Erosion of
Knowledge) Knowledge)
? A series of constant and auxiliary variables are also needed to simulate the model of the system.
* Sterman (2000:192) explains that “- Stocks are represented by rectangles; - Inflows by a pipe pointing
into (adding to) the stock; - Outflows by a pipe pointing out (subtracting from) the stock; - Valves (at the
center of flows) control the flows; - Clouds (at the extremities) represent the sources and sinks for the
flows (boundaries)”.
Stocks are the source of delays. A delay is the amount of time by which an event is
retarded. It is the time between the instant at which a given event occurs and the instant
at which a related aspect of that event occurs (e.g. time between the creation and
absorption of knowledge). Delays are responsible for generating effects which are very
often nonlinear and counter-intuitive in the real world (Sterman, 2000).
The system dynamics methodology follows three steps:
- taking into consideration a System’;
- Model’ the System;
- Simulate® the Model.
Simulation models are generally based on theories treated as mental models which must
be translated into computer models. System dynamics is a powerful method to build a
shared understanding and gain useful insights into situations of dynamic complexity
created by interdependencies, feedbacks, time delays and nonlinearities (Sterman, 1992;
Van Ackere et al., 1993; Sterman, 2000).
Conducting empirical researches and, then, formalising and testing them through
simulation in system dynamics can be useful to confirm or bring about radical changes
in basic assumptions. Propositions and hypothesis can be interlinked and new insights
may emerge for further research (Morecroft and Sterman, 1992; Senge and Sterman,
1992; Oreskes et. al, 1994; Sterman, 2000; Lomi and Larsen, 1999, 2001).
Larsen and Lomi (1999: 412) report what has been said by Sutton and Straw (1995):
“Hypothesis can be part of a well crafted theoretical argument...but hypothesis do not
(and should not) contain logical arguments about why empirical relationships are
expected to occur”.
For this reason, Larsen and Lomi (1999: 412) view “computer simulation as useful as
systematic empirical research for extending and testing organizational theories”.
Lomi and Larsen (2001: 11) posit that “computational and simulation models of
organizations differ from other kinds of models like empirical models, only in terms of
the constraints that define the specific language being used”. Masuch’s view (1995) is
that the validity of simulation models presents the same problems of any other kind of
empirical model. Oreskes et al., (1994) and Sterman (2000) agree that validation and
verification of numerical and simulation models are impossible but this is not limited to
computer models but to any theory and research which relies on simplifications of the
real world and assumptions.
This research relies on computer simulation models through system dynamics not as an
alternative to empirical research but as a way of exploring, extending and testing the
findings, thoughts and the dynamic implications of the complex dynamic feedback
* A system exists and operates in time and space.
* A model is a simplified representation of a system at some particular point in time or space intended to
promote understanding of the real system.
A simulation is the iteration of a model in such a way that it operates on time or space to compress it,
thus enabling one to perceive the interactions that would not otherwise be apparent because of their
separation in time or space.
structure of figure 1 (see Hanneman et al., 1995; Senge and Stermann, 1992; Larsen and
Lomi, 1999; Bothner and White, 2001). System dynamics allows us to explore and
understand the future effects and results of actual events and decisions taking into
account temporal delays. The existence of delays and feedback loops in the model in
figure 1 makes system dynamics once more useful to represent the interdependent
relations among variables studied over generations in family business.
Several simulations will be run and interesting results and new insights will emerge (see
figure 7, figure 10 and Appendix B).
Hanneman et al., (1995: 3) posit that “we do not really know what a theory is saying
about the world until we have experimented with it as a dynamic model” and Bothner
and White (2001: 206) point out that “simulation models are always formulated as
mechanisms for simplifying the moving parts of a social process down to it core
features...and yield surprising insights for further exploration”.
Experimental design
To make simulations, it was necessary to assign numerical values to all parameters,
initial values to stocks and proper shapes to graphic functions according to the literature
and case studies analysed by Chirico (2006b). Assumptions were also made when useful
information could not be taken from the research mentioned above. Numerical values
were not chosen randomly but have always been calibrated to be consistent across the
model (Larsen and Lomi, 1999, 2002; Lomi et al., 2005).
Sterman (1992: 10) points out that “the skilled modeller uses all available information
sources to specify the relationships in the model (numerical data, interviews, direct
observation and other techniques)”.
To make simulations simpler, graphic functions were built through linear relations.
This kind of representation through “graphic converters” (graphic functions), which
specify the functional relationship between two variables, makes very simple to test a
dynamic model and analyse possible implications derived by variations in the graphic
function(s) (Larsen and Lomi, 1999, 2002; Lomi et al., 2005).
More details in Appendices A, B and C.
The software used for the computer simulation is Vensim PLE for Windows, Version
5.4d. Table 2 indicates the settings of the software.
Table 2: Settings of the software Vensim PLE, 5.4d.
INITIAL TIME: 0;
FINAL TIME: 90 (the software Vensim makes the simulation over three
generations of a generic family firm in which each generation lasts 30 years);
TIME STEP: 0.125 (results of simulation are saved every 1.5 months. This way,
numerical integration errors are kept very small);
UNITS FOR TIME: | year (the software Vensim simulates the model every
year).
MODEL STRUCTURE: FROM FEEDBACK LOOPS TO DYNAMIC MODELS
The feedback loop representation of the dynamic model for the trans-generational value
creation in family business is plotted in figure 3. The reciprocal relations between key
variables emerge with four reinforcing loops (R). The first feedback loop (R1) specifies
the relation between knowledge and basic dynamic capabilities (PAC and RAC). The
second one (R2) identifies the relation between RAC and capabilities. The third
feedback loop (R3) shows the relation between capabilities and advanced dynamic
capabilities. It is also underlined the negative effect of family inertia on the creation of
new capabilities. Finally, the fourth feedback loop (R4) indicates the entire complex
dynamic process from knowledge to trans-generational value creation, and back to
knowledge through investments in knowledge (Chirico, 2006b).
Figure 3: Feedback loops*
Paternalism Entrepreneurial Drive
PAC (acquisition,
assimilation) * :
FAMILY INERTIA,
An)
RAC (rathtoraim, AR)
KNOWLEDGE exploitation)
ie
a a ; .
a ADVANCED
DYNAMIC
— Ans) ene
Knowledge
ENTREPRENEURIAL,
TRANS-GENERATIONAL AEREGRN ANCE
VALUE <—_
PAC = Potential Absorptive Capacity
RAC-R Absorptive Capacity
PAC + RAC = ABSORPTIVE CAPACTTY = BASIC DYNAMIC CAPABILITIES (BDC)
Erik Larsen, 2006; Personal Communication
Source: Adapted from Chirico (2006b, figure 2)
(*) The “+” means that the two variables move in the same direction, all other things being equal.
The “-” means that the two variables move in opposite directions, all other things being equal.
To understand all dynamic implications of the model, feedback loops will be translated
into a system of equations rather than a series of different propositions. Variables will
be represented as stocks/flows and measured in dimensionless units (see Sastry, 1997;
Larsen and Lomi, 1999) (see figure 4).
Computer simulations will give evidence of the positive dynamic interconnections
between variables studied and the negative effect of family inertia on the model as a
whole. After this general overview, it is now possible to represent the micro-structure of
the model, trying to develop the same strategy used by Larsen and Lomi (2002: 279):
“Our strategy is to keep notation as much as possible intuitive with the development of
a minimal amount of formalism”.
Graphic details of the micro-structure of the dynamic model are plotted in figure 4.
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(*) To make the model simpler, entrepreneurial drive is not included in the simulation model.
” means that the two variables move in the same direction, all other things being equal.
“...” means that the two variables move in opposite directions, all other things being equal.
(#)
Representing knowledge (K). Knowledge is represented as a stock (accumulator’) that
integrates the difference between increase (new knowledge “NK”) and decrease
(erosion of knowledge “EK’’) in knowledge.
Kt= f [NK (s) — EK(s)}/s + K(t0)
New knowledge (NK) is positively affected by a fixed annual amount of possible
overall knowledge which may be generated (OKG). OKG positively depends on the
effect of absorptive capacity (EAC) and the effect of investments in knowledge (EIK)
on the creation of new knowledge.
NKt = OKG * (EACt + EIKt)
To represent EAC, is specified a linear functional relation (graphic function as
described in Appendix A.a) between knowledge acquisition through realised absorptive
capacity (KA) and realised absorptive capacity (RAC) in which:
Min(KA) if RACt=0 (there is no RAC but the firm is still able to acquire some
knowledge from outside “Min(KA)")
Max(KA) if RACt=1 (RAC is very high and consequently KA is very high, as well)
EACt= KA (time)
where KA is a function that specifies the effect of absorptive capacity on the increase in
new knowledge over time.
EIK is equal to investments in knowledge (IK) multiplied by the evolution of the rate of
knowledge creation through investments in knowledge over time (ERKC).
EIKt = IKt * ERKCt
To represent ERKC, is specified a linear functional relation (graphic function as
described in Appendix A.b) between rate of knowledge creation through investments in
knowledge (RKC) and time, in which RKC decreases as time passes according to
Argote’s view (1999).
Max(RKC) when time=0 years (RKC is very high at the beginning of the first generation)
RKC =...
Min(RKC) when time=90 years (RKC is very low at the end of the third generation
“Min(RKC)”)
ERKCt = RKC (time)
7 Accumulator means that the variable (in this case, knowledge) is accumulated over time.
10
where RKC is a function that specifies the evolution of the rate of knowledge creation
through investments in knowledge over time.
Erosion of knowledge (EK) is affected by a fixed annual rate of knowledge erosion
(a).
EKt = Kt*a
Representing outside industry knowledge to discover (INKD). Outside industry
knowledge to discover is the total outside industry knowledge which has not been
discovered yet. It is represented as a stock (accumulator) that integrates the difference
between increase (outside industry inventions “INI”) and decrease (outside new industry
knowledge “NINK”) in outside industry knowledge to discover.
INKDt = [' [INI(s) — NINK(s)}is + INKD(10)
fl H
Outside industry inventions (INI) refer to outside fundamental base inventions which
are, at this time, not well explained yet. They still have to be discovered in order to be
exploited. INI are modelled through a random function which generates inventions
randomly because they cannot be predicted.
INIt = IF THEN ELSE (RANDOM UNIFORM (0, 1, 99632)>0.98,4,0)
Outside new industry knowledge (NINK) refers to the outside industry knowledge
which has been invented, explained and can be exploited. It is affected by a fixed annual
discovery rate (B).
NINKt = INKDt/B
Representing outside industry knowledge (INK). Outside industry knowledge is the
total industry knowledge which exists outside the family firm. It is represented as a
stock (accumulator) that accumulates over time from outside new industry knowledge
(NINK).
INKt = f [NINK (s) [ls + INK (t0)
Therefore, NINK is the outflow of INKD and the inflow of INK (see figure 4).
Representing potential absorptive capacity (PAC). PAC is represented as a stock
(accumulator) that integrates the change in PAC (Ch PAC).
PACt = f [ChPAC(s)}s + PAC(t0)
!
0
11
Change in PAC (Ch PAC) depends on the relative knowledge (RK) of the family firm,
that is, the percentage of the outside industry knowledge (INK) possessed by the family
firm. RK is equal to the knowledge of the family firm (K) divided by the total industry
knowledge outside the family firm (INK).
RKt = Kt/INKt
PAC is calculated with a first order exponential smoothing (information delay* from RK
to PAC) of the observed value of RK, whose formulation is similar to an adaptive
expectations mechanism (Forrester, 1961; Larsen and Lomi, 1999; Sterman, 2000;
Mollona, 2000; Lomi, Larsen and Freeman, 2005).
Forrester (1961: 407,408) posits that “smoothing is a process of taking a series of past
information values and attempting to form and estimate of the present value of the
underlying significant content of the data. In particular, the exponential smoothing gives
the greatest weight to the most recent value and attaches progressively less significance
to older information”.
Sterman (2000) explains exponential smoothing with the concept of adaptive
expectations. Sterman (2000: 428,429) argues that “adaptive expectations mean the
perceived or expected value (belief) gradually adjusts to the actual value of the variable.
The expected value changes when it is in error, that is, when the actual value differs
from the expected value of the variable. The state of the system adjusts (with an
adjustment time, i.e. delay) in response to the gap between expected value and actual
value”.
According to the concepts mentioned above, change in PAC is given by:
Ch PACt = (RKt — PACt)/ Delay PAC
where PAC is the expected value (or average); RK is the actual value; delay PAC is the
information delay (time to average).
In other words, this process assumes that the gap between RK (actual value) and PAC
(expected value) closes only gradually causing delays.
Representing realised absorptive capacity (RAC). RAC is represented as a stock
(accumulator) that integrates the change in RAC (Ch RAC).
!
RACt = f j[ChRAC(s) fs + RAC(t0)
Change in RAC (Ch PAC) depends on the PAC of the family firm.
As before, RAC is calculated with a first order exponential smoothing (information
delay from PAC to RAC) of the observed value of PAC, as follows:
Ch RACt = (PACt— RACt)/ Delay RAC
5A temporal delay is defined as the amount of time by which an event is retarded.
12
where RAC is the expected value; PAC is the actual value; delay RAC is the
information delay.
In addition, Ch RAC is positively influenced by the capabilities (C) of firm members
(see figure 4).
Ch RACt = [(PACt — PACt)/ Delay RAC] *Ct
Representing capabilities (C). Capabilities are represented as a stock (accumulator) that
integrates the difference between increase (new capabilities “NC”) and decrease
(erosion of capabilities “EC”) in capabilities.
Ct= f [NC(s) — EC(s) [ls + C(20)
New capabilities (NC) are positively affected by RAC and advanced dynamic
capabilities (ADC); and negatively affected by family inertia (FI).
In addition, C is calculated with a first order exponential smoothing (information delay
from RAC to C) of the observed value of RAC, as follows:
NCt = {{(RACt— Ct)/Delay C] * ADC1}/FIt
where C is the expected value; RAC is the actual value; delay C is the information
delay.
Erosion of capabilities (EC) is affected by a fixed annual rate of capabilities erosion
(y).
ECt = Ct *y
Representing advanced dynamic capabilities (ADC). Advanced dynamic capabilities
are represented as a stock (accumulator) that integrates the difference between increase
(new ADC “NADC’”) and decrease (erosion of ADC “EADC”) in advanced dynamic
capabilities.
ADCt = j [NADC(s) — EADC(s) ls + ADC(10)
7
0
New ADC (NADC) are positively affected by new capabilities which depend on ‘the
effect of change of capabilities on the creation of new ADC’(5):
NADCt= NCt * 8
Erosion of ADC (EADC) is affected by a fixed annual rate of ADC erosion ().
EADCt = ADCt * €
13
As shown above, a strong positive correlation exists between C and ADC (see figure 3).
Indeed, NC influence the creation of NADC and the stock ADC influences the creation
of NC (see figure 4).
Representing entrepreneurial performance (EP). Entrepreneurial performance is
represented as a stock (accumulator) that integrates the difference between increase
(creation of EP “CEP”) and decrease (erosion of EP “EEP”) in entrepreneurial
performance.
EPt = [' [CEP(s)- EEP(s) ds + EP(10)
fil fi
Creation of EP (CEP) is positively affected by ADC at a fixed annual rate of
entrepreneurial performance creation (¢). A delay between the creation of ADC and the
creation of EP (delay EP) is taken into consideration in the simulation model.
CEPt = (ADC * ©)/Delay EP
Erosion of EP (EEP) is affected by a fixed annual rate of EP erosion ().
EEPt = EPt *
Representing trans-generational value (TGV). Trans-generational value is represented
as a stock (accumulator) that integrates the difference between increase (creation of
TGV “CTGV”) and decrease (family wealth “FW” and investments in knowledge “IK’’)
in trans-generational value.
TGVt = f [CTGV(s)— FW(s)— IK(s) ls + TGV (10)
Creation of TGV (CTGV) is positively affected by EP at a fixed annual rate of TGV
creation (). A delay between the creation of EP and the creation of TGV (delay TGV)
is taken into consideration in the simulation model.
CTGVt = (EPt * 6)/Delay TGV
Family wealth’ (FW) is affected by a fixed annual rate of withdrawals (1).
FWt=TGVt *1
Investments in knowledge (IK) are affected by a fixed annual rate of investments in
Knowledge (k).
IKt = TGVt*«
2 Family wealth is represented by the family’s patrimony outside the family business, invested in fixed
assets (such as land, buildings, etc.) and held in current assets (deposits, outside shares etc.).
14
Representing family inertia (FI). Family inertia is represented as a stock (accumulator)
that integrates the change in family inertia (Ch FI).
Fit = f | [ChEr(sy}s + FI(t0)
Ch FI depends on the effect of paternalism on family inertia (EPFI).
FI is calculated with a first order exponential smoothing (information delay from EPFI
to FI) of the observed value of EPFI, as follows:
Ch Fit = (EPFlt — Flt)/Delay Flt
where FI is the expected value; EPFI is the actual value; delay FI is the information
delay.
To represent EPFI, it is specified a functional relation (graphic functions, as described
in Appendix B) between paternalism (P) and time, considering four scenarios. The first
scenario does not consider paternalism in the simulation, whereas the other three
scenarios take into account three different evolutions of paternalism over three
generations.
- Scenario 1: Basic case without paternalism.
- Scenario 2: Paternalism decreases over time (Appendix B.a).
EPFiIt2 = P2(time)
where P2 is a function that specifies the effect of paternalism on family inertia over time
in scenario 2 (P decreases > FI decreases).
- Scenario 3: Paternalism increases over time (Appendix B.b).
EPFIts = P3 (time)
where P3 is a function that specifies the effect of paternalism on family inertia over time
in scenario 3 (P increases > FI increases).
- Scenario 4: Paternalism fluctuates over time (Appendix B.cd.e).
Details in the next paragraph.
15
RESULTS AND NEW INSIGHTS
As mentioned before, we examine four different scenarios describing the evolution of
paternalism over three generations in family business and the consequent effects on
family inertia and capabilities, and then on the model as a whole.
Figure 7 shows the simulation results of the model over three generations (90 years) of a
generic family firm considering scenario 1, scenario 2 and scenario 3.
Scenario 1: Basic case without paternalism. As it was expected, if knowledge (K)
increases over three generations, basic dynamic capabilities (BDC: PAC and RAC),
capabilities (C), advanced dynamic capabilities (ADC), entrepreneurial performance
(EP) and trans-generational value (TGV) also increase through dynamic reinforcing
loops (figure 7).
In particular, capabilities and dynamic capabilities (PAC, RAC and ADC) increase at a
slower rate in the third generation (about from the 60th to 90th year) probably because
psychological ownership’? (including motivation and commitment) of family members
usually decreases after the second generation, as pointed out by Astrachan et al., (2002).
Interesting results emerge when family inertia (as a function of paternalism) is included
in the model.
Scenario 2, scenario 3 (figure 7) and Appendix B show the simulation results
concerning the effect of paternalism on family inertia (EPFI) and the consequent effects
on C, ADC, EP, TGV, K and BDC.
Scenario 2: Paternalism decreases over time. As paternalism decreases, family inertia
decreases, as well. Consequently, the creation of capabilities increases (see figure 5) and
a positive effect drives all the variables of the model (figure 7, Appendix B.a).
Results are similar to the first simulation (scenario 1) but values become higher due to
the decrease in family inertia and diverge during the second and third generation.
For instance, the maximum value of knowledge at the end of the third generation is
equal to 36.93 in scenario | and it is equal to 39.48 in scenario 2. The maximum value
of trans-generational value at the end of the third generation is equal to 2.07 in scenario
1 and it is equal to 2.67 in scenario 2 (figure 7).
Figure 5: Effect of family inertia on capabilities in scenario 2
FAMILY INERTIA Capabilities
=I 2
|
of bi
._ U4
os AR 1
al
° ober
0 2% w 4 5 60 70 80 9% ot 2% 30 4 5 o 7 8 9
‘Tare (Year) ‘Tame (Year)
FAMILY INERTIA: Scenario 2, Patera decreases Capable : Scenario 2, Patemasm decreases
a Psychological ownership is the psychologically experienced-phenomenon where owners, managers and
employees develop possessive feelings that the family firm is “mine” or “ours”. For instance, strength of
identifying oneself with the family business, sense of belonging to the family business, strong feeling of
responsibility towards the family business and so on (Koiranen, 2006: adopted from Pierce et al., 2003).
16
Scenario 3: Paternalism increases over time. As paternalism increases, family inertia
increases, as well. Consequently, the creation of capabilities decreases (see figure 6) and
a negative effect drives all the variables of the model (figure 7, Appendix B.d).
The negative effect can be clearly observed for C, ADC, EP and TGV; whereas, K,
PAC and RAC increase but their values become lower compared to the simulations
made before and completely diverge during the third generation when usually problems
rise (see Astrachan et al., 2002).
Results are consistent with the literature that stresses how critical is the third generation
in family firms and how issues may be amplified during this generation (see Aronoff
and Ward, 2001, Astrachan et al., 2002).
For example, the maximum value of knowledge at the end of the third generation is
equal to 36.93 in scenario 1; it is equal to 39.48 in scenario 2; and it is equal to 24.99 in
scenario 3 (figure 7).
A new insight emerges from the simulation of scenario 3. In fact, the creation of
capabilities is not negatively influenced by the increase in family inertia at the
beginning of the activity for about 10 years (see figure 6). Consequently, a positive
effect drives advanced dynamic capabilities for about 10 years. The effect is even bigger
for entrepreneurial performance and trans-generational value which keep on increasing
for about 20-25 years (see figure 7).
Figure 6: Effect of family inertia on capabilities in scenario 3
FAMILY INERTIA Capabilities
10 2 02
oo 2% 3% 4% 30 60 70 80 90 oo 2% 0 4% 50 «0 70 80 9%
Time (Year) ‘Tine (Year)
FAMILY INERTIA : Scenario 3, Patera neeases Capabilities : Scenario 3, Pateralsm increases
This behaviour / phenomenon can be explained going back to the definition of family
inertia and the meaning of paternalism:
Family inertia is defined as the tendency of family firms to resist change even when it is
needed to match the requirements of a changing environment. It is a function of
paternalism which affects family inertia negatively. The ideology of paternalism is
protective and dominating in a fatherly way with a strong attitude to preserve family
firm’s traditions and not to make changes.
But at the beginning of the activity a paternalistic behaviour may be seen to some extent
positive in order to guide and train the new generation. Problems (caused by FI) rise if
such paternalistic behaviour persists over time especially when the two generations
work actively together as shown in figure 7 (from the 10th year). To explain better such
an effect, we refer to the article of Giddings (2003). He posits that the founder of a
family firm often has much energy and personality to run things his way. He is a
17
paternalistic person but this is good at the beginning of the family firm when a
mentor/leader is needed. Indeed, offspring must be guided and trained. But as time
passes, a dominating and autocratic climate might predominate and make working
conditions difficult for offspring, to express their ideas, to make autonomous choices
and to make changes in the business for the good of the business (Giddings, 2003;
Koiranen 2003; Chirico, 2006b). In other words, the paternalistic behaviour which was
essential at the beginning of the activity may become less crucial if it persists over time
preventing change even when it is needed. As a consequence, the effect of paternalism
on family inertia increases more compared to before with negative effects on the
creation of capabilities and dynamic capabilities (Larsen and Lomi, 2002; Collis, 1994;
Sharma et al., 2005; Chirico, 2006b). A negative effect drives all the variables of the
model.
18
Figure 7: Simulations in Scenario 1, 2 and 3
KNOWLEDGE
40
20
0
0 10 2 30 40 50 60 670 80) (0
‘Time (Year)
KNOWLEDGE: Scenario 1, No Paternalism —-—+—+—+—+—+—
KNOWLEDGE : Scenario 2, Pateralismdecreases. —2—2——2 2a
KNOWLEDGE: See \emalism increases. —3——3—3 33
POTENTIAL ABSORPTIVE CAPACITY,
REALIZED ABSORPTIVE CAPACITY
2 2
La
1 SE ees | 1
0 0
0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90
‘Time (Year)
POTENTIAL ABSORPTIVE CAPACITY: Scenario 1, No Paternalism 4—+—
POTENTIAL ABSORPTIVE CAPACITY: Scenatio 2,
POTENTIAL ABSORPTIVE CAPACITY’
aemalismdecreases, —2—
enario 3, Paternalism inemeases. —2
‘Time (Year)
REALIZED ABSORPTIVE CAPACITY : Scenario 1, No Paternalism —+—+—
REALIZED ABSORPTIVE CAPACITY Scenario 2 Patemalismdecreases. —2—
REALIZED ABSORPTIVE CAPACITY: Scenario 3, Paternalismincreases. —
Capabilities ADVANCED DYNAMIC CAPABILITIES
2 2
|
ce
i —pieey ||, aE
Ta eee |
ae
0 : 0 onal
0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90
‘Time (Year)
‘Capabilies : Scenario, No Patemalism ——¢——+——+ +34
Capabilities: Scenario 2, Patemalism decreases. ——2—2—2——2 2
Capabilities: Scenario 3, Patemalismincreases. 23333
‘Time (Year)
ADVANCED DYNAMIC CAPABILITIES : Scenario, No Patemalisen. ——+—
‘ADVANCED DYNAMIC CAPABILITIES : Scenario ,Paternalismdecreases -2—
ADVANCED DYNAMIC CAPABILITIES : Scenario 3, aternalsminereases. —3
ENTREPRENEURIAL PERFORMANCE : Scenario |, No Patemalism —+—4+—
ENTREPRENEURIAL PERFORMANCE: Scenario 2, Patemalism decreases. —2—
ENTREPRENEURIAL PERFORMANCE: Scenario 3, Patemalismincreases. —3
ENTREPRENEURIAL PERFORMANCE,
TRANS-GENERATIONAL VALUE
:
tH ||,
a Toler TT.
PE eGIe) | | ee ee la|al|
0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90
Time (Year)
‘Time (Year)
"“TRANS-GENERATIONAL VALU
"TRANS-GENERATIONAL VALU
"“TRANS-GENERATIONAL VALL
scenario 2, Paternalism decreases. —2—
Scenario 3, Paternalism increases, —3—3
19
The results of scenario 3 allow us to consider a new scenario where paternalism
fluctuates over time (scenario 4).
Scenario 4: Paternalism fluctuates over time. Paternalism does not affect much family
inertia at the start-up and at the beginning of each generation for a certain period (t).
Then, it rises up to a peak but falls down again with the following generation. It is a
kind of resetting clock process in which the behaviour described above recurs every
generation.
As before, to represent the effect of paternalism on family inertia (EPFI), it is specified
a functional relation (graphic function, as described in Appendix B.c) between
paternalism (P) and time. It is assumed that t is equal to 10 years (Scenario 4.1):
(t) = 10 years
EPFIs.1 = P41 (time)
where Ps. is a function that specifies the effect of paternalism on family inertia over
time in scenario 4.1 with t= 10 (P fluctuates FI fluctuates).
The simulation depicted in figure 10 and Appendix B.c show that when paternalism
does not have much affect on family inertia at the beginning of each generation for
about 10 years, C (see figure 8), ADC, EP and TGV keep on increasing. After that when
the effect of paternalism on family inertia starts rising up to a peak while the two
generations work actively together, C (see figure 8), ADC, EP and TGV are negatively
influenced.
Figure 8: Effect of family inertia on capabilities in scenario 4.
FAMILY INERTIA Capabilities
0 02
\ AN “
f\ TN /
5 f+ \ 04 i
\ \ / /| [
XN v
° os
. 0 2 30 450 RO oo 30-30 4307080
Time (Yen) Tae (Yen)
FAMILY INERTIA Scenario 4.1, P. acts, 10 ——— CCapbition “Scenario 4.1, P, Heute, 10
As before, this behaviour / phenomenon can be explained through the article of
Giddings (2003): a paternalistic behaviour is essential at the beginning of the activity to
guide and train offspring (EPFI very low) but may become less crucial if it persists over
time preventing change even when it is needed (EPFI increases). With the following
generation, the effect of paternalism on family inertia (EPFI) declines as a kind of
resetting clock. In fact, the beginning of the new generation and the complete end of the
previous one (which might represent a dramatic change in the business), can be thought
20
of as “resetting the clock” similarly to the resetting clock used by Amburgey et al.,
(1993) in describing the dynamics of organisational change and failure.
The behaviour above-described recurs every generation.
The increase in entrepreneurial performance and trans-generational value is very high
during the first generation for about 25-30 years and then stabilises in the second and
third generation, even though capabilities and advanced dynamic capabilities go up and
down because of the effect of family inertia (figure 10).
Results become more significant with the assumption that paternalism does not affect
much family inertia at the beginning of each generation for a longer period (i.e. t =15 in
scenario 4.2 and t = 20 in scenario 4.3; see figure 10 and Appendix B.d,e):
(t) = 15 years
EPFIs2 = P42 (time)
where Ps2 is a function that specifies the effect of paternalism on family inertia over
time in scenario 42 with t = 15 (P fluctuates > FI fluctuates).
and
) = 20 years
EPFIs3 = P43 (time)
where P43 is a function that specifies the effect of paternalism on family inertia over
time in scenario 43 with t = 20 (P fluctuates > FI fluctuates).
As plotted in figure 10 when paternalism does not affect much family inertia at the
beginning of each generation for a longer period, the family firm is able to better react
to the period in which the effect of paternalism on family inertia increases. Indeed, C
(see figure 9), ADC, EP and TGV increase over time but with some fluctuations in
scenario 42 and 43.
Figure 9: Effect of family inertia on capabilities in scenario 42 and 43
FAMILY INERTIA Capabilities
10 06
A A
s - [ \ 03 YA
LA LEE
o 0 2% 3 4 50 «0 7 80 0 o 1 2 30 40 50 0 7 80
Time (Year) ‘Tine (Year)
FAMILY INERTIA : Seeanrio 4.2, P. ductustes, 15
Capabilities: Sceanrio 4.2, P.fuctuates, 15
21
FAMILY INERTIA Capabilities
8
/\ \
f |
NPE a
/ os
ANTAN TO DRS
002030400 0102030405000
Time (Year) ‘Tine (Year)
FAMILY INERTIA :Scenario 4.3, P. fctuates, = 20,
Capabilities: Scenario 4.3, P.fctates, = 20
K, PAC and RAC keep on increasing following the same path during the first and the
second generation in scenario 4.1, 42 and 43 and diverge during the third generation. For
instance, at the end of the third generation the maximum value of knowledge is equal to
28.06 in scenario 4.1; it is equal to 31.05 in scenario 42; and it is equal to 33.02 in
scenario 4.3 (figure 10).
In other words, the longer is “t”, the higher and faster is the increase of all the variables
represented in the model (see figure 10). For example, at the end of the third generation
the maximum value of TGV is 0.34 when “t” is equal to 10 (scenario 4.1); it is 0.72
when “t” is equal to 15 (scenario 42); and it is 1.18 when “t’” is equal to 20 (scenario
43).
22
Figure 10: Simulations in scenario 4.:, 42 and 43
KNOWLEDGE
40
see
gE
20
0
0 10 20 30 40 50 60. 70 80 90
‘Time (Year)
KNOWLEDGE Scena, tutes, t-10 —¢——+—§—+—
KNOWLEDGE: Sceanno 42,P fetutes,18. —-2—@—e ae
KNOWLEDGE Scena 42,0. Muctute, (020 3—3—8—8 39
POTENTIAL ABSORPTIVE CAPACITY REALIZED ABSORPTIVE CAPACITY
2 2
—| Pen|
1 1 ae
CE arr aera
0 0
0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80. 90
‘Time (Year) ‘Time (Year)
POTENTIAL ABSORPTIVE CAPACITY: Scenario 41,P eta REALIZED ABSORPTIVECAPACITY: Scenario 4.1. tates, (10 —+—
POTENTIAL ABSORPTIVE CAPACITY : Sceanrio 42, fluctuate
POTENTIAL ABSORPTIVE CAPACITY : Scenario 43, P, fuctuate
REALIZED ABSORPTIVE CAPACITY: Seeanrio 42, P. fuctuates, 1S. ——2—
REALIZED ABSORPTIVE CAPACITY : Scenario 43, P. fuctuates, = 20 —3—3
Capabilities ADVANCED DYNAMIC CAPABILITIES,
i 0.6
OS 03
detest ipean.
z Sasser
Lp ae pa Sa Ae lara
0 0
a
‘Time (Year) ‘Time (Year)
ENTREPRENEURIAL PERFORMANCE,
TRANS-GENERATIONAL VALUE
2 2
1
0
o 0 2 30 4 50 6 70 0 o 0 20 30 4 30 60 7 80 9%
Time (Year) ‘Time (Year)
ENTREPRENEURIAL PERFORMANCE: Scenario 41, P-uetuates,t-10. —+t— "TRANS. GENERATIONAL VALUE" : Scenario 41, P. fuetuates,
ENTREPRENEURIAL PERFORMANCE: Sceanrio 42, P. fluctuates, 15. —2—
ENTREPRENEURIAL PERFORMANCE: Scenario 43, P. Muetuates,(=20 3—3
"TRANS-GENERATIONAL VALU
"“TRANS-GENERATIONAL VALL
sceanrio 42,
Scenario 43,
Auetuates, t=
fuetuates,
23
DISCUSSION AND CONCLUSIONS
Bothner and White (2002) posit that simulation models are useful when in reducing the
real world complexity, they almost inviolate the established facts and yield surprising
insights for further exploration.
By using simulations, it was possible to explore further the dynamic feedback loops of
figure 3 and test its internal consistency, representing variables as dynamic
accumulation processes (stocks).
A simulation model (figure 4) was built using the system dynamics methodology and
interesting results and new insights emerged as reported in this research. The dynamic
model allowed us to test different sets of assumptions at the same time.
Simulations gave evidence of the positive dynamic relations between knowledge, basic
dynamic capabilities, capabilities, advanced dynamic capabilities, entrepreneurial
performance and trans-generational value (Scenario 1, 2, 3 and 4); the positive relation
between paternalism and family inertia (Scenario 2, 3 and 4); and the negative relation
between family inertia and capabilities, with some exceptions (Scenario 2, 3 and 4).
In particular:
- Scenario 2: if paternalism decreases, family inertia decreases, as well.
Consequently, the creation of capabilities increases and a positive effect drives
all the variables of the model (figure 7, Appendix B.a).
- Scenario 3: If paternalism increases, family inertia increases, as well.
Consequently, the creation of capabilities decreases and a negative effect drives
all the variables of the model (figure 7, Appendix B.b). However, the creation of
capabilities is not negatively influenced by the increase in family inertia at the
beginning of the activity with positive effects on the model.
- Scenario 4: Paternalism fluctuates over time. Paternalism does not affect much
family inertia at the start-up and at the beginning of each generation for a certain
period (t). Then, it rises up to a peak but falls down again with the following
generation. Problems (caused by family inertia) rise after “t” years from the
beginning of each generation when the effect of paternalism on family inertia
increases. The longer is “t”, the better the family firm is able to react to the
period in which the effect of paternalism on family inertia increases (figure 10,
Appendix B.c,d,e).
Results become more evident and visible during the third generation when usually
problems rise in family business. Indeed, according to the literature, the most critical
period faced by family firms is the third generation, and the positive and negative
effects of past events, decisions and behaviours may be amplified during this generation
(see Aronoff and Ward, 2001, Astrachan et al., 2002; Bridge et al., 2003). Family firms
should be able to understand the long-term effects and results of actual events, decisions
and behaviours, and, at the same time, prevent their negative consequences. System
dynamics may be a useful tool to achieve such a result.
24
John Sterman'', who is a leading expert on system dynamics, explains in one of his
business courses for managers, planners, and strategists (Sterman, 2006) that “in a
world of growing complexity, many of the most vexing problems facing managers arise
from the unanticipated side-effects of their own past actions. In response, organizations
struggle to speed learning and adopt a more systemic approach. The challenge is to
move past slogans about accelerating learning and ‘thinking systemically’ to practical
tools that help managers understand complexity, design better operating policies, and
guide effective change. System dynamics is a powerful framework for identifying,
designing, and implementing high-leverage interventions for sustained success in
complex systems”.
For this reason, the study conducted here encourages family-business researchers to
make use of this powerful methodology of system dynamics. Interesting results and
insights may emerge and help researchers to better understand the complexity of
dynamism in a family business context and assist family firms to better manage their
business within the firm and the family.
Limitations of this paper are related to the assumptions made and the impossibility of
representing the real trend/degree of paternalism of a generic family firm. The use of
graphic converters (graphic functions) solved some of those problems for the simulation
in system dynamics.
This research can be viewed as a contribution to the literature on the simulation of first-
order models of theory-testing, and, partially, on the simulation of second-order models
of theory building. More work and simulations are needed before generalising and
building a new theory based on the results and insights of this study. Furthermore, in the
future, specific components of the model may be disaggregated to focus on particular
issues related to family business.
"' Professor of Management at the Sloan School of Management, Massachusetts Institute of Technology
and Director of MIT’s System Dynamics Group.
25
REFERENCES
Aronoff, C., and Ward, J.L., (2001). Family business values: How to assure a legacy of
continuity and success. Family Business Leadership Series, No. 12. Marietta, GA,
Family Enterprise Publishers.
Amburgey, T.L., Kelly D., and Barnett, WP., (1993). Resetting the clock: The dynamics
of organizational change and failure. Administrative Science Quarterly, Vol. 38,
No. 1, pp. 51-73.
Argote, L., (1999). Organizational learning: Creating, retaining, and transferring
knowledge. Boston, Kluwer Academic Publishers, Norwell.
Astrachan, J.H., Klein, S.B., and Smyrnios, K.X., (2002). The F-PEC scale of family
influence: A proposal for solving the family definition problem. Family Business
Review, Vol. 15 No. 1, pp. 45 58.
Barney, J., (1991). Firm resources and sustained competitive advantage. Journal of
Management, Vol. 17, No. 1, pp 99-120.
Bothner, M., and White, H., (2001). Market orientation and monopoly power. Jn Lomi
and Larsen, (eds). Dynamics of Organizations, AAAI/MIT- Press, Menlo Park,
CA, pp. 182-209.
Bridge, S., O’Neil, K., and Cromie, S., (2003). Understanding enterprise,
entrepreneurship and small business, Palgrave Macmillan.
Chirico, F., (2006a). Knowledge models in family business: Evidence from Switzerland.
Compendium on the Family Business Models Around the World (ten volumes).
Hyderabad: ICFAI. Gupta, Vipin; Levenburg, Nancy; Moore, Lynda; Motwani,
Jaideep; and Schwarz, Thomas (eds.). University Press.
Chirico, F., (2006b). From knowledge to trans-generational value creation in family
firms. A dynamic capabilities and cultural approach. Family firms as arenas for
trans-generational value creation: a qualitative and computational approach.
University of Jyvaskyla, Press.
Collis, D.J., (1994). Research note: How valuable are organizational capabilities?.
Strategic Management Journal, Vol. 15, Special issue, pp. 143-152.
Eisenhardt, K.M., and Martin, J.A., (2000). Dynamic capabilities: What are they?
Strategic Management Journal. Vol. 21, No. 10-11, pp. 1105-1121.
Forrester, J.W., (1961). Industrial dynamics, Cambridge, Productivity Press.
Forrester, J.W., (1968). Market growth as Influenced by capital investment. Industrial
Management Review, Vol. 9, No. 2, pp. 83-105.
Fotion, N., (1979). Paternalism. Ethics, vol 89, No 2, pp. 191-198.
Giddings, T., (2003). Avoid these family business obstacles. San Fernando Valley
Business Journal, March 17; Los Angeles Business Journal. October 13.
Habbershon, T.G., and Pistrui, J., (2002). Enterprising families domain: Family-
influenced ownership groups in pursuit of transgenerational wealth. Family
Business Review, Vol. 15, No. 3, pp. 223-238.
Hanneman, R., Collins R., and Mordt, G., (1995). Discovering theory dynamics by
computer simulation: Experiments on state legitimacy and imperialist capitalism.
Sociological Methodology, ed. P. Marsden, pp. 1-46. Oxford: Blackwell.
Koiranen, M., (2004): Entrepreneurialism, managerialism and paternalism as clashing
ideologies in family business systems. Jn Watkins, D. (ed.): ARPENT, Annual
Review of progress in Entrepreneurship Research: 2, 2002-2003, Brussels, pp.
299-310.
26
Koiranen, M., (2006). Psychological ownership and entrepreneurial drive as value
creators. Family firms as arenas for trans-generational value creation: a
qualitative and computational approach. University of Jyvaskyla, Press.
Johannisson, B., and Huse, M., (2000). Recruiting outside board members in the small
family business: an ideological challenge. Entrepreneurship and Regional
Development, Vol. 12, No. 4, pp. 353-378.
Johannisson, B., (2002). Energising entrepreneurship. Ideological tensions in the
medium-sized family business. In Fletcher, D (ed.) Understanding the Small
Family Business. London: Routledge, pp. 46-57.
Larsen, E., and Lomi A., (1999). Resetting the clock: A feedback approach to the
dynamics of organizational inertia, survival and change. Journal of the
Operational Research Society, Vol. 50, No 4, pp. 406-421.
Larsen, E., and Lomi, A., (2002). Representing change: A systems model of
organizational inertia and capabilities as dynamic accumulation processes.
Simulation Modelling Practice and Theory, Vol 10, Special Issue, pp. 271-296.
Lomi, A., and Larsen, E., (2001). Dynamics of organizations: Computational modeling
and organization theories, AAAI/MIT- Press, Menlo Park.
Lomi, A., Larsen, E., and Freeman J., (2005). Things change: Dynamic resource
constraints and system-dependent selection in the evolution of organizational
populations. Management Science, Vol. 51, No 6, pp. 882-903.
Masuch M., (1995). Computer models. Encyclopedic Dictionary of Organizational
Behaviour, ed. N. Nicholson, 91-92. London: Blackwell.
Mollona, E., (2000). Analisi dinamica dei sistemi aziendali; EGEA.
Morecroft, J.D.W., (1982). A critical review of diagraming tools for conceptualizing
feedback system models. Dynamica, Vol. 8, No. 1, pp. 20-29.
Morecroft, J.D.W., (1983). System dynamics: Portraying bounded rationality. Omega,
Vol. 11, No. 2, pp. 131-142.
Morecroft, J., and Sterman, J., (1992). Modeling for learning organizations. European
Journal of Operational Research. Vol 59, No.1.
Oreskes, N., Shrader-Frechette, K., and Berlitz, K., (1994). Verification, validation, and
confirmation of numerical models in the earth sciences. Science, Vol. 263, pp.
641-646.
Pierce, J., Kostova, T., and Dirks, K., (2003). The status of psychological ownership:
Integrating and extending a century of research. A Review of General Psychology,
Vol. 7, No. 1, pp. 84-107.
Sastry, M.A., (1997). Problems and paradoxes in a model of punctuated organizational
change. Administrative Science Quarterly, Vol. 42, No. 2, pp. 237-275
Senge, P., and Sterman, J., (1992). Systems thinking and organizational learning:
Acting locally and thinking globally in the organization of the future. European
Journal of Operations Research, Vol. 59, pp. 137-150.
Sharma, P., and Manikutty, S., (2005). Strategic divestments in family firms: Role of
family structure and community culture . Entrepreneurship Theory and Practice,
Vol. 29, No. 3, pp. 293-312.
Sterman, J., (1992). System dynamics modeling for project management. MIT
Cambridge MA 02139.
Sterman, J., (2000). Business dynamics, systems thinking and modeling for a complex
world. New York, McGraw-Hill.
27
Sterman, J., (2006). Business Dynamics. MIT’s approach to diagnosing and solving
complex business problems. MIT Sloan, Executive —_ Education.
http://bmitsloan.mit.edu/execed/epp/courses/busdynamics06.pdf
Sutton, R, and Straw, B., (1995). What theory is not. Administrative Science Quarterly,
Vol. 40, No. 3, pp. 371-785.
Teece, D.J., Pisano, G., and Shuen, A., (1997). Dynamic capabilities and strategic
management. Strategic Management Journal, Vol 18, No. 7, pp 509-533
Van Ackere, A., Larsen E.R., and Morecroft J.D.W, (1993). Systems thinking and
business process redesign: an application to the Beer Game. European
Management Journal, 11:412-423.
Winter, S., (2003). Understanding dynamic capabilities. Strategic Management Journal,
Vol. 24, No. 10, pp. 991-995.
Zahra, S.A., and George, G., (2002). Absorptive capacity: A review,
reconceptualization, and extension. Academy of Management Review, Vol. 27, No.
2, pp. 185-203.
Appendix A
a- Relation between KA (Y-axis) and RAC (X-axis)
Graph Lookup - "Knowledge Acquisition RAC”
Print
put Output ania!
9 1 : : : —
1
|
[—
=
_
|/-—
=
[—
— pars
Ne op
i
Mminj0 wf x=0.01895 0.03847 Xmax|1 >| Reset Seating
OK | CearPoints | CkaraliPoits | CursRet| ClearReerence | Ref>Cur| Cancel |
b- ERKC: relation between RKC (Y-axis) and time (X-axis)
Graph Lookup - “Rate of Knowledge Creation through IK /
put Output Bins
ro [io =I ae
a
> hz
= a
= a
_—
= a
el ‘ain
I
I
xin _+|¥9952— ye0316 —«Xmax|90 >|) Reset Scaing
OK | CearPoinis | _CearAlPonis | Curfiet| _CearRefernce | RetvCu| Cancel |
28
Appendix B: ‘Effect of paternalism on family inertia’ and ‘family inertia’
- Scenario 2 (Paternalism “P2” decreases over time)
Effect of Paternalism on Family Inertia FAMILY INERTIA
1
ae
mS
os os ™
o o
oo 2% 3 4% 5 « 7 a0 ° 0 2 3 4 3 «0 7 30 %
“Te (Year) Ta (Year)
‘et of Paternalism on Family Ini Scenario 2, Pateralamdeercases || FAMILY INERTIA : Scenario 2, Patermalam decreases
b- Scenario 3 (Paternalism “P3” increases over time)
Effect of Paternalism on Family Inertia FAMILY INERTIA
0 o
ann ae
a Lr
5 oer 5 CLEP
mez ca
Jar]
° o
0 0 0 4 3% «0 10 a0 00 ow 2 30 «0 50 4 708090
Tave (Year) Tine (Year)
et of Patera on Famiy Inia: Scenario 3, Patera nereases || FAMILY INERTIA : Scena 3, Patera increases
c-__ Scenario 4.1 (Paternalism “P4.1” fluctuates over time; “t” = 10)
Effect of Paternalism on Family Inertia FAMILY INERTIA
10 7] .
A | ViTI : \
7 7 tot |
d- Scenario 4.2 (Paternalism “P4.2” fluctuates over time; “1” = 15)
Effect of Paternalism on Family Inertia FAMILY INERTIA
o
/\ /\
. A
5
e- Scenario 4.3 (Paternalism “P4.3” fluctuates over time; “t” = 20)
Effect of Paternalism on Family Inertia FAMILY INERTIA
0 5 r "
| j
a ‘alt |
/\
AC
CTT
NEC
J
6 °
|)
Tine (Yeu) Time (Yes)
[et of Patel on Famer: Scemrib43,. heats, =20 |] FAMILY INERTIA: Scenario 4.3, P cts, 20
29
Min and Max of paternalism in scenario 2, 3 and 4
Max(P2) when time = 0 years (P2 is at its maximum value at the beginning of the
first generation)
P2= ..
Min(P2) when time = 90 years (P2 is at its minimum value at end of the third
generation)
pe Max(P3) when time = 90 years (P3 is at its maximum value at the end of the third
generation)
~ Min(P.)) when O<time<I0years
Max(P: 1) when time = 30 years
Min(Ps.1) when 31<time<40 year
Par=
Max(P:1) when time = 60 years
Min(Ps.1) when 61<time< 70 years
Max(P1) when time = 90 years
Min(Ps2) when 0<time<15 years
Max(P2) when time = 30 years
Min(Ps2) when 31<time<45 year
Paz =...
Max(P2) when time = 60 years
Min(Ps2) when 61<time< 75 years
Max(P+2) when time = 90 years
Min(Ps.3) when 0<time<20 years
Max(P:3) when time = 30 years
Min(Ps3) when 31<time<50 year
Pas =...
Max(P+3) when time = 60 years
Min(P:3) when 61<time< 80 years
Max(P:3) when time = 90 years
30
Appendix C
Variables
Sector Variable Type Value
K K Initial Value 2
K OKG Constant 1
K Min(KA) Constant 0.1
K Max(KA) Constant 1
K Min(RKC) Constant 3
K Max(RKC) Constant 10
K a. Constant 0.03
INKD INKD Initial Value 12
IK IK Initial Value 4
INKD and IK B Constant 8
PAC PAC Initial Value 0.5
PAC Delay PAC Constant 3
RAC RAC Initial Value 0.5
RAC Delay RAC Constant 3
Cc Cc Initial Value 0.1
Cc Delay C Constant 3
Cc Y Constant 0.05
ADC ADC Initial Value 0.1
ADC 5 Constant 0.7
ADC & Constant 0.05
EP EP Initial Value 0.1
EP C Constant 0.6
EP Delay EP Constant 3
EP y Constant 0.05
TGV TGV Initial Value 0.1
TGV 9 Constant 0.4
TGV Delay TGV Constant 3
TGV L Constant 0.1
TGV kK Constant 0.05
FI FI Initial Value 1
FI Delay FI Constant 3
FI Min(P2) Constant 0.1
FI Max(P2) Constant 1
FI Min(P3, P41, Constant 1
P42, Pas)
FI Max(P3, P41, Constant 10
P42, Pas)
FI Tt Constant 10; 15; 20
31
Appendix D
Positive and negative feedback loops
a. Reinforcing Loop (R) is a structure which feeds on itself to produce growth or
decline: ‘State 1’ (the cause) increases or decreases ‘State 2’ (the effect) which,
in turn, increases or decreases ‘State 1’, respectively. In other words, R tends to
reinforce or amplify whatever is happening in the system. Of course, nothing
grows forever. There must be some limits to growth which are created by
negative feedbacks (Sterman, 2000):
b. Balancing Loop (B) counteracts and opposes change. It attempts to move some
“Current State’ to a ‘Desired State’ (it is assumed that ‘Current State’ is lower
than ‘Desired State’) through some ‘Action’: the ‘Desired State’ interacts with
the ‘Current State’ to produce a ‘Gap’. The larger the ‘Gap’, the stronger the
influence to produce ‘Action’. The ‘Action’ taken then moves the ‘Current
State’ toward the ‘Desired State’ reducing the ‘Gap’ to zero:
Desired State Gap B Action
‘Current State’ *
An example will be helpful to clarify the above concepts. Figure 2 shows a simplified
representation of the causal loop diagrams of knowledge in which the two feedback
loops described above emerge (R and B):
Creation of Erosion of
KNOWLEDGE BB
Knowkdge * SE 5 Knowiedees
+ SS Se <a
.
Rate of Knowledge Rate of Knowledge
Creation Erosion
‘Knowledge’ is positively influenced by the ‘creation of knowledge’ and negatively
influenced by the ‘erosion of knowledge’. ‘Creation of knowledge’ is given by
‘knowledge’ multiplied by the ‘rate of knowledge creation’, whereas ‘erosion of
knowledge’ is given by ‘knowledge’ multiplied by the ‘rate of knowledge erosion’.
32
Two balancing loops can be identified:
1.
Reinforcing loop (R):
Positive relation between ‘creation of knowledge’ and ‘knowledge’:
knowledge is created, the more knowledge is accumulated;
Positive relation between ‘knowledge’ and ‘creation of knowledge’:
knowledge is accumulated, the more knowledge is created.
Balancing loop (B):
Negative relation between ‘erosion of knowledge’ and ‘knowledge’:
knowledge is eroded, the less knowledge is accumulated;
Positive relation between ‘knowledge’ and ‘erosion of knowledge’:
knowledge is accumulated, the more knowledge is eroded.
the more
the more
the more
the more
Balancing and reinforcing loops can be identified by counting the number of “-” and
“+ in the feedback loop. A feedback loop is a balancing loop if the number of “-” is
odd; it is a reinforcing loop if the number of “-” is even or zero.
33
