STOCKS OF KNOWLEDGE AND ORGANIZATIONAL PERFORMANCE:
A DYNAMIC RELATIONSHIP
Fernando Arenas
Universidad ICESI
Calle 18 122-135, Cali, Colombia
tel: +57 2 5552334 x8858
faarenas @icesi.edu.co
Abstract
The relationship between the level of knowledge and organizational performance has been
studied by the academic community and is receiving growing attention from decision
makers in organizations. However it is not the case of the feedback relationship between
performance and the level of knowledge, although this relationship has a dynamic pattern
of behavior on both variables. This research poses a conceptual approach that involves a
causal model of this feedback relationship, theoretically founded on the resource based
view and the behavioral theory of the firm. The methodology involves the design and use of
a system dynamics simulation model based on a pharmaceutical company which relates
stocks of knowledge, innovation capability, financial performance and investments on
knowledge stocks. The concept of “managerial dynamic hypothesis” is defined and used to
explain, via the prospect theory, how much managers decide to invest over time on
knowledge stocks. Simulations, based on managerial dynamic hypotheses with two different
levels of complexity, were carried out. The results show that the more complex the
hypothesis is the more stable the investment flow is and a better performance is achieved.
Keywords: knowledge stocks, organizational performance, system dynamics,
organizational knowledge, prospect theory, simulation.
1. INTRODUCTION
The influence of variables related to knowledge (knowledge management, stocks of
knowledge, organizational learning) on organizational performance, has been the subject of
numerous studies. It has not occurred in the same way with the study of the influence of the
performance itself on these variables. It shouldthenbe asked, is organizational
performance just a result of these variables, or does it, in turn, influence them? If so,
how this interaction occurs and what its dynamic behavior is? There are few studies
exploring this relationship, although several authors suggest a relationship of mutual
interaction between learning and performance (Argyris and Schén, 1978, Lee et al., 1992,
Mintzberg et al., 1995) and that has raised the importance of considering the performance
as an endogenous variable (not just as a dependent variable) within the models formulated
in strategic management research in general and particularly in organizational learning
research (Bontis et al., 2002). Other authors, mainly from the perspective of organizational
behavior theory (Cyert and March, 1963; March, 1991) have studied the influence of
performance on organizational change processes as a one-way relationship, with the
exception, relevant for this research, of the work of Greve (2003) which establishes a
dynamic feedback between learning and organizational performance by means _ of the
"theory of learning from performance feedback" (Greve, 2003, 10). The scarcity of
research on the dynamic relationship between learning, knowledge and performance,
despite its obvious relevance (Bontis et al., 2002) , may be justified in part by the
methodological difficulties that longitudinal studies entail (von Krogh, Erat & Mackus,
2000), and partly by the high complexity involved in developing formal models capable of
describing this relationship. To cope with these difficulties, several authors have made use
of the development of simulation models as a methodological proposal that allows, usually
based onsecondary data, both development and verificationof theory (Davis,
Eisenhardt and Bingham, 2007), and makes possible the formulation of complex
models involving, as in thecase of this proposal, the integration of diverse but
complementary theoretical perspectives. Among the methodological approaches to the
development of simulation models, it is of particular interest for this proposal the system
dynamics approach. Developed by Forrester (1961), this methodology is based on
the presence of structures of feedback between constituent variables of complex systems
(e.g. organizations) and uses the concepts of stocks and flows to define the variables
that constitute the core of themodel. Simulation models based on system
dynamics provide the methodological basis of some recent studies that are of special
interest for thedevelopment of this proposal, like the one by
Rahmandad, Repenning and Sterman (2009) that explores the effects ofthe delays in
obtaining performance feedback on learning, and the one by Gary and Wood (2011) that
explores the mutual influence between the accuracy of mental models of decision makers
and organizational performance. These studies, although pose feedback relationships
between variables of interest for this proposal, do not study explicitly the performance
itself as a trigger factor of decisions that affect, a posteriori through knowledge generation,
the performance, in an iterative way.
This research proposal formulates a model that dynamically links organizational learning,
knowledge and performance, and intends, based on the related academic literature, the
development of a simulation model that allows the application of a longitudinal study.
Finally, we intend to use the simulation model for conducting an empirical study to validate
the propositions that may arise as a result of the research.
The object of study of this research is the firm, viewed from the perspective of those
making investment decisions (budget) within it, and is framed within the strategic issue,
that is, the achievement of the long-term objectives of the organization.
2. CONCEPTUAL MODEL PRECEDENTS
2.1. Stocks and flows of knowledge
Within the vast literature related to the topic of knowledge management is particularly
interesting the proposal from Bontis, Crossan & Hulland (2002), regarding the framework
that it provides for defining and linking the issues of organizational learning, intellectual
capital and knowledge management , doing so through the analogy of stocks and flows of
knowledge. The Bontis, Crossan and Hulland proposal identifies the intellectual capital
with the stocks of knowledge and states that intellectual capital, so defined, should be
considered at the level of individuals, groups and organization. Organizational learning will
then represent the flow of knowledge that feeds these stocks, and proper knowledge
management should have as one of its objectives the alignment of these flows and stocks at
all levels, in order to improve performance of the organization. The dynamic relationship
between the stocks of each level with each other and the organization's performance turns
out to be another important and interesting aspect of this framework, since it suggests to
consider both relationships, the forward one (feed-forward) between knowledge stocks
(individual / group / organization) and its feedback one (organization / group /
individual). In fact, one of the suggestions therein made for future research is to expand the
framework of the dynamic relations between stocks, to go beyond the influence of the stock
of knowledge on organizational performance and include the feedback relationship between
organizational performance and the stock of knowledge. Does this relationship exist?
Mediated by which variables? These are some of the questions that gave rise and
justification to this research.
2.2. Stocks, flows and sustainable competitive advantage
The relationship between organizational performance and stocks of knowledge implies a
strategic pint of view. The theoretical proposition of Dierrickx & Cool (1989) is not only
suitable but relevant for a strategic approach. This research considers that in order to
sustain a competitive advantage, a stock of strategic assets must be accumulated and that
this accumulation is achieved by choosing time paths of flows that properly feed these
stocks. The difference between stocks and flows is illustrated by the metaphor of the
bathtub, where the stock of water is determined by the level of water in the bathtub, which
is in turn the result of flows feeding and draining the bath, accumulated over time. For
example, according to Dierickx and Cool, the amount of water in the tub could represent
the stock of know-how at a given moment in time, while investment in R & D would be
represented by the inflow and the depreciation of know-how by the outflow.
An important point, illustrated by the bathtub metaphor, is the fact that stocks do not
change instantaneously, since management wants a change in stocks it should make
decisions affecting the flows that, in turn, modify these stocks, and since these changes
only occur after a period of time, these decisions should take the form of policies that make
these flows consistent over time. A competitive position, associated with a potential payoff,
will come from selecting time paths that are appropriate to accumulate stocks of strategic
assets, defined as assets that are non-tradable, non-imitable and not substitutable. Suffice it
to say here that the stocks of knowledge, those that are of particularly interest for this
research, fit, in general, within the latter definition. It should be mentioned also that the
accumulation of strategic stocks depends on the level of other interrelated stocks.
From what has been described here it can be concluded that the proposal of Dierrickx and
Cool, allows us, considering the stock of knowledge as a strategic asset stock, to close the
dynamic cycle that concerns us (stocks of knowledge > performance > stocks of
knowledge) through considering the investments in strategic assets as the variable that
mediates between performance and the strategic assets themselves. We must remember that
what may change are the flows in and out of stocks and not the stocks themselves, which
means that investments will be the flows that affect the stocks. For our case then,
investments are the flows that affect the stock of organizational knowledge and the
dynamic causal structure of — the proposed model(Model 1), willinclude the
relationship between performance feedback and stocks of knowledge, mediated by
investment flows in organizational knowledge (see Figure 1).
Organizational Performance
Investments in knowledge stocks
Knowledge Stocks
Figure 1. Model 1.
2.3. Intellectual Capital
Among the approaches to the subject of knowledge, are of particular relevance to this
research those that Oltra (2002) calls "measurement focused approaches". Within — this
category, the one proposedby Roos, Roos, Dragonetti & Edvinsson (1997)
that defines intellectual capital asthe sum ofhuman capital and structural capital,
agrees with our proposal in the formulation of a dynamic feedback between knowledge
(implicit inhuman capital and structural capital) and financial performance (financial
capital). Figure 2 illustrates this relationship. There, human capital is defined as the sum of
skills, attitudes and intellectual agility of employees, and structural capital as everything
that is owned by the company and whose value for the company is greater than its material
value.
intellectual
Capital
Financial ital Structural
Capital Bomar Capital Capital
ieee
Figure 2. Relationships between intellectual capital components. Adapted from Roos et al
(1997).
Furthermore, Sveiby (1998-2001) mentions the tendency toconsider only the _ static
aspects of knowledge and emphasizes the desirability of approaching knowledge asa
dynamic process.
Although useful, the concept of intellectual capital brings its own difficulties. Roos et al
(1997) highlight three of them:
1. Delays: an increase in intellectual capital does not produce an immediate increase in
total value
2. Non-linearity: big investments in intellectual capital do not necessarily produce big
results (and vice versa).
3. Units of measurement: each company has its own definition of intellectual capital.
Units of measurement of the intellectual capital components are heterogeneous.
2.4. Resources, Capabilities and Strategy Maps
Grant (2010) approaches the strategic topic from the resource-based theory or resource
based view (Barney, 1991) and define resources as the productive assets owned by the firm,
and capabilities as what the firm can make, and states that resources alone do not confer
competitive advantages but must be combined to generate organizational skills, being these
capabilities those that enable superior performance. Thus, resources - classified by Grant as
tangible resources, intangible resources and human resources - generate organizational
capabilities and these, in turn, performance.
Grant suggests that one way to identify organizational capabilities is relating them to major
functional areas of the firm and proposes a hierarchy of these capabilities, where the most
important from the strategic performance point of view, are those that involve the
participation of several functional areas (e.g., new product development, customer service,
quality management). Finally, we would like to mention two concepts from Grant (2010)
that relate significantly to this research: 1) "in terms of resources, knowledge is recognized
as an extremely important productive resource" and 2) "capabilities can be seen as the
manifestation of the knowledge of the organization" (Grant, 2010,159).
Using a similar approach, Kaplan and Norton (2004) present in what they call a strategic
map, a sequence of causal relationships between elements of the four perspectives
previously outlined in the concept of balanced scorecard (Kaplan and Norton, 1992 ) and
propose a number of elements constituent of these four perspectives (financial, customer,
internal processes, and learning and growth).
Some similarities with the proposal of Grant (2010) can be noticed. The learning and
growth perspective includes what Grant called human resources and intangible resources.
The internal process perspective is similar to the functional classification of capabilities by
Grant. Both proposals consider the effect on final performance, but Kaplan and Norton
(2004) include the value proposition to the customer (customer perspective) as a variable
that mediates between the organizational skills and performance and emphasize financial
performance (financial perspective). Finally, neither proposal establishes a feedback
(dynamic) relationship between performance and the generation of intangible resources.
At this point, having reviewed the relationship between knowledge and performance, then
we can wondered what does make the performance lead to investments in organizational
learning, or, put in other words, if investment is a decision managers make, what variable
or variables influence this investment decision and the amount of such investment?
2.5. Dominant logic and investment decisions
A concept related to the process of making investment decisions , appropriate for its
inclusion in our model, is that of "general management dominant logic" or simply
"dominant logic" (Prahalad, CK & Bettis, RA, 1986) , that considers the managers as
groups of individuals who significantly influence the allocation of key resources
(Donaldson and Lorsch, 1983), and is defined as "the way in which managers conceptualize
the business and make critical decisions for allocating resources either in technology,
product development, distribution, advertising or human resource management. "(Prahalad,
CK & Bettis, RA, 1986, 490). It can be concluded then, that one of the decisions on
allocation of key resources, influenced by the dominant logic is the investment in
organizational knowledge.
A relevant source within the scarce literature found related to empirical application of the
concept of dominant logic is the work of von Krogh, Erat and Mackus (2000), who
approach the dominant logic through a dynamic analysis and describe it by means of a
construct based on two conceptual domains (internal and external conceptualization) and
six categories (people, culture, product and brand, competitors, customers, and technology),
and develop a numerical value for the "bandwidth" (Bettis and Prahalad, 1995) of the
dominant logic. The causal model proposed by von Krogh, Erat and Mackus, is shown in
Figure 3. As can be seen, feedback influences are established between the strategic actions,
including investments in organizational learning, performance, and the dominant logic,
generating a dynamic cycle that is similar to the one previously defined as our Model | (see
Figure 1). For their empirical study, von Krogh, Erat and Mackus decided to take into
account only the relationship between dominant logic and performance. This longitudinal
work was based on secondary data collected over a period of three years in two companies
in the telecommunications sector (Nokia and Ericsson).
Dominant Logic
Strategic Actions
Figure 3. von Krogh, Erat & Mackus causal model. Adapted from von Krogh, Erat &
Mackus (2000)
2.6. Dynamic hypothesis and prospect theory
One elementthat we poseas relevant within the dominant logicis the dynamic
hypothesis. The idea of incorporating this variable in the model comes from our experience
in consulting andin teaching in graduate courses, through which we have noticed the
existing gap between the actual behavior over time of the results of a given action, and
the expected dynamic behavior of that action by those who have taken the decision to
initiate such action. Then, we define the dynamic hypothesis as the dynamic behavior of the
outcomes of a given action, expected by those who have taken the decision to initiate that
action. The dynamic hypothesis thus defined, can be considered as a component of the
dominant logic and then will determine what is the expected outcome of an investment
in intellectual capital in terms of financial results, after a period of time. In turn, the gap
between the expected and the actual outcome will determine the new flow of investment in
intellectual capital. Buthow will this new flow of investment be determined? This
decision, in the model, will be a function of the value curve proposed
by the prospect theory (Kahneman andTversky,2000b ;Tversky and Kahneman,
2000). A typical value function curve based on this theory is shown in Figure 4.
VALUE
LOSSES GAINS
Figure 4. A hypothetical value curve. Source: Kahneman andTversky (2000b).
According to the expected utility theory (von Neumann and Morgenstern, 1947), commonly
used in decision making studies, decision makers assign values of utility and probability of
occurrence to the possible outcomes of their decisions and choose that decision with the
greatest weighted value of utility and probability. However, despite its widespread use,
numerous studies have shown that, in practice, decision makers do not follow the theory
of expected utility. To fill the need for a theory that is closer to the criteria used in reality,
Kahneman and Tversky (2000b) formulated the theory of perspective, which, in contrast
to the theory of expected utility, states that decision makers do not consider the expected
monetary value resulting fromthe decision, but assigna subjective value to the
result depending on whether itis above (gain) or below (loss) a value or reference
point. The magnitude of the subjective value assigned to each gain or loss will be given by
a curve similar to that shown in Figure 3. As can be seen, this figure is concave in the
area of gains and convex in the area of losses, implying loss aversion, that is to say, the
decision maker will assign a greater magnitude (loss) to a value lower than the reference
point, than to a value that is at equal distance greater than the reference point (gain) and,
moreover, in decisions involving only losses, the tendency will be to take risks (risk
seeking behavior) and in decisions that involve only gains the trend will be
avoid risk (risk aversion behavior). The shape of the figureimplies a reduction
in sensitivity, that is,to the extentthat the values go far from __ the point of
reference, either way,an increase or decrease inthe result will represent an
increasingly smaller increase or decrease in subjective value. Figure 5 shows an
example of loss aversion. There is a difference of magnitude between points LI and L2, to
the left of the reference point PR (loss), equal to that between points G1 and G2, to the
right reference point (profit), but, as can be seen, the increase in the subjective value of loss
(RL2 - RL1) is greater than the increase in the subjective value of gain (RG2 - RG1).
VALUE
LOSSES GAINS
Figure 5. An illustrative example of loss aversion.
Within this proposal, the reference point of the value function is the expected outcome in a
given moment in time, in terms of performance, of an investment decision, and will be
determined by the dynamics of the decision maker hypothesis. Figure 6 shows an
illustrative example of how the dynamic hypothesis relates to the theory of perspective. The
graph shows the expected result (performance) and the actual outcome, for two different
dynamic hypothesis, from an investment made in month |. The blue line represents a
simple dynamic hypothesis (hypothesis 1) where it is simply expected to have a result of
100 in month 12. The red line represents a more elaborated dynamic hypothesis (hypothesis
2) in which the result of 100 is achieved around month 48. The green line represents a
hypothetical actual performance behavior. The graph shows what would happen if the
performance is reviewed after 18 months from the time investment was made (brown
dotted line). The intersection points PR1 and PR2 will correspond to the reference point in
the value function curve (PR in Figure 5) for scenarios | and 2 respectively, and the RR
point will correspond to actual performance (points L1, L2, Gl, G2,Figure 5). Since the
values for PRI, PR2, and RR are 100, 80 and 88 respectively, the decision maker that uses
the hypothesis | (decision maker 1) will interpret the result as a loss because RR is lower
than PRI and will assign a subjective value to that loss (RL1, RL2 in Figure 5), while the
decision maker that uses the hypothesis 2 (decision maker 2) will interpret the result as a
gain because RR is greater than PR2 and will gn a subjective value to that gain (RGI,
RG? in Figure 5). The value function that defines the curve on which subjective values are
assigned, follows the equation (Kahneman and Tversky, 2000)
V(xj) = (x))* si xj>0, y v(x) = -M-xj)? si x) <0 (Equation 1)
Where v(x;) is the subjective value assigned to x;, and a, f, y 2, are parameters that depend
on decision maker.
Thus, what kind of decisions might be expected from the subjective values assigned in each
case? Prospective theory suggests that,in the case ofdecision maker 1 arisk-
seeking behavior could take place, that would increase the amount of investment or, if the
resultis far from the reference point, decision maker | might decide to keep the
same amount of investment, reduce it or go out from investment (decreased sensitivity); in
the case of decision maker 2, according to the prospect theory a risk-averse behavior could
take place, that would led the decision maker to maintain or reduce the amount
of investment.
MANAGERIAL DYNAMIC HYPOTHESIS
a1 7K
RR
0 6 12 BW 224 30 3% 42 4 54 60
Time (Month)
Expected Performance 1 : Sim0
Expected Performance 2 : Sim0
Actual Performance : Sim0
Figure 6. An illustrative example of the relationship between the dynamic hypothesis and
prospect theory.
3. METHODOLOGY
3.1. Simulation
Because we are going to study a series of dynamic relationships between variables, the
study should be longitudinal. In fact, von Krogh, Erat and Mackus (2000), state that any
study involving the dominant logic as a variable, must be longitudinal (von Krogh, Erat and
Mackus, 2000, 85). The object of study should be organizations located in highly dynamic
sectors, where intellectual capital and innovation capability are considered as strategic
resources.
Due to the difficulties inherent to conducting a longitudinal empirical study, we decided to
use simulation as a research method. This method is gaining increasing acceptance within
the academic community for studies like the one we are proposing, that involves dynamic
and longitudinal phenomena (Repenning, 2002; Zott, 2003), and even for the study of
complex theoretical relationships between constructs, where there are serious limitations
in the availability of empirical data (Zott, 2003). When is simulation justified, what type of
simulation to use and what should be the methodology for research that uses simulation, are
questions thatare answered in apaper writtenby Davis, Eisenhardt,
and Bingham (2007), who argue that "simulation is particularly useful when the theoretical
approach is longitudinal, nonlinear, or involves processes, or when empirical data are
difficult to obtain "(Davis, Eisenhardt and Bingham, 2007, 481).
3.2. System dynamics
Within the existing simulation methods we have chosen the system dynamics approach in
part because the use it makes of the hydraulic analogy posed by Dierickx and Cool (1989)
for describing the evolution of strategic assets, as knowledge, and partly because it is the
most suitable for studying the behavior of systems with high dynamic and causal
complexity (Davis, Eisenhardt and Bingham, 2007). Besides, simulation models based
on system dynamics provide the methodological basis of some recent studies that are
of special interest for thedevelopment of this research, like the one by
Rahmandad, Repenning and Sterman (2009) that explores the effects ofthe delays in
obtaining performance feedback on learning, and the one by Gary and Wood (2011) that
explores the mutual influence between the accuracy of mental models of decision makers
and organizational performance. Studies using system dynamics to build models that
include prospect theory are scarce, the only one study found in this preliminary stage of the
research was that of Gonzalez and Sawicka (2003) that seeks to explain why people
choose not to follow prescribed safety measures in high risk environments.
The need for further empirical verification and the method used for this verification will
depend on the results obtained through experimentation with the model.
3.3. Conceptual model
As has been mentioned previously, the study ofa dynamic relationship
that involves feedback and delays between cause and effect, necessarily entails the
application of a longitudinal study. Given the time constraints for conducting this research,
we will use secondary data, so that the operationalization of the model should be based
onthe availability of data from empirical studies or databases. This limitation has
been considered for the definition and subsequent measurement of the variables involved
in the final model of this research.
3.3.1. Relationship between investments in knowledge, stocks of knowledge and
performance
From the point of view of data availability, which appears as a major difficulty in
conducting longitudinal studies, it is convenient to use the VAIC model (Pulic, 2000, 2004)
to measure intellectual capital, not only because it is based on data gathered from
the financial statement of companies, which are audited and publicly available, but because
it establishes, through the extended model of Nazari et al (2007) a relationship between
investment and intellectual capital. Although VAIC has a number of limitations,
highlighted by Matidinos et al (2011), it is increasingly used to explore the relationship
between intellectual capital and financial performance. Among the empirical studies
examining this relationship using the VAIC, are of particular interest to this proposal those
from Riahi-Belkaoui (2003), Chen et al (2005) and Tan et al (2007), since they establish
meaningful relationships between intellectual capital measured by VAIC and financial
performance, and also meaningful inter-temporal relationships among these variables, that
is, how intellectual capital affect future performance, a relationship that is not considered in
most studies using VAIC and that has been already mentioned as critical earlier in this
document. The three works mentioned use some kind of return on assets as a measure of
financial performance. Chen et al (2005) use return on investment (ROI) as a proxy
measure of financial performance, coinciding with other authors who have studied the
relationship between intellectual capital and financial performance like Firer and Williams
(2003), Shiu (2006), and Zégal and Maaloula (2010). For the model we will use ROI as the
final measure of organization’s financial performance, and income from new products will
be used as the measure of financial performance that determines the flows on investments
that feed knowledge stocks . Regarding investments in intellectual capital (knowledge
stocks), and following the definitions of the extended model of VAIC proposed by Nazari
et al (2007) that directly relate investments in R & D to structural capital (innovation
capital) and investment in salaries and benefits to human capital, we will focus in these two
types of investments within the model making clear that we are referring in the model to
people involved in R&D when we refer to human capital . Innovation capability (defined as
a function of the level of stocks of knowledge) will mediate the relationship between
intellectual capital and income from new products via new product development.
3.3.2. Relationship between financial performance, dynamic hypothesis and
investment in intellectual capital
Earlier in this document, it was explained by an illustrative example, how the dynamic
hypothesis will be related to investment decisions through the theory of perspective, and the
equation of the curve through which a value is assigned subjective to a given outcome was
described. There was mentioned that the curve is determined by the parameters a, B, and A,
that depend on the decision-maker. To define these parameters within our model, we will
refer to the work of Bromiley (2009), who develops a model of resource allocation to R
& D, previously defined as one of the investments that will be included in the model, based
on the perspective theory. Therefore, we will adopt the range of values assigned
by Bromiley (2009) for these parameters in our proposed model.
Based then, on what has been previously exposed, the model of our proposal adds to
the variables involved in the Model | (see Figure 1) the variable managerial dynamic
hypothesis asa mediator between the financial performance measured as return on net
assets, andthe decision to invest in intellectual capital, specifically R & D, salaries
and benefits. The level of organizational knowledge, defined as intellectual capital is
measured through the level of knowledge stocks. The final conceptual model (Model 2),
including these modifications is shown in Figure 7.
Financial Performance
Income from New
Products
Managerial dynamic hypothesis
Human
Capital
Innovation
Capital
Knowledge Stocks
Investments in Knowledge Stocks
Figure 7. Proposed model.
3.4. Simulation model
3.4.1. Innovation capability, new products and financial performance
In the simulation model, intellectual capital, measured as Intellectual Capital Index, is a
function of the level of knowledge stocks (Human Capital and Innovation Capital). Human
Capital stock is increased by the flow of Investments in Human Capital and reduced by
Human Capital Erosion (personnel rotation, knowledge obsolescence) whilst Jnnovation
Capital stock is increased by the flow of Investments in R&D, and reduced by Innovation
Capital Obsolescence. The level of knowledge stocks determines Innovation Capability
that, in turn, influences how many new product projects start and how much time the
development process takes, that is, how many new products are developed in a period of
time. These New Products increase New Products Portfolio which is depleted by those
products that have gotten a certain lifetime period (New Products Maturity Time) and must
be classified as mature products.
3.4.2. Financial performance
Three indicators are used to measure financial performance: Income from New Products,
determined by New Products Portfolio size, Return on Investment (ROI) defined as the
ratio between Income from New Products and Investments in Intellectual Capital, and
Margin, defined as the difference between Income from New Products and Investments in
Intellectual Capital.
3.4.3. Managerial dynamic hypothesis and investments in intellectual capital
The decision on how much, as a fraction of Income from New Products, will be invested in
intellectual capital in each period, is determined by the Relative Value that management
assigns to that income. This relative value is calculated through the prospect theory
function (Equation 1) in which the reference point, used to calculate “x” is set according to
the managerial dynamic hypothesis, and a, B, and A, are the median of the values used by
Bromiley (2009), that is a=B=0.88, and A=2.25.
Two different managerial dynamic hypothesis are used, an elaborated one (Hypothesis 1),
that takes into account the delay between investments and their impact on financial
performance, and a simple linear one (Hypothesis 2). Both hypotheses are shown in Figure
Expected Income from New Products Expected Income from New Products
200 200
170 170
140 140
110 110
C=
so 0
o + 6 8 © 2 MW I 18 20 0 + 6 8 0 2 WW is 2
Time (Quarter ‘Tene (Quaner)
pected
Figure 8. Managerial dynamic hypothesis. Hypothesis | (left) and Hypothesis 2 (right).
As it can be seen from Figure 8, the time unit used is quarter and the simulation time
horizon 20 quarters (five years). The Expected Income from New Products (goal) is 200 in
the 20" quarter for both of the hypothesis. An investment policy of 25%, as a maximum, of
Income from New Products to be reinvested in intellectual capital is assumed.
A simplified version of the simulation model structure is shown in Figure 9.
Relative Value
Intolctual Capital Income from New Products
Expected income
from New Preducts
New Products Portfolio
Investments in Managerial Dy namic Hy pothesis
Human Capital © ©. 3
New Producte New Products that
Human Capital Mature
Human Capital Erosion
‘ Naw Products Maturity Time
Human Capital New Praducte
Increment Innovation Capability Development
Innovation Capit Now Products that ‘ew Products
5 Star Dey opment Developed
Innovation Capital
Obsolescence
Investments in R&D
Innovation Capa
Development Time
Figure 9. Simulation model structure.
4. RESULTS
4.1, Financial performance
Figure 10 shows simulation results for Income from New Products, comparing investment
decisions made with Hypothesis 1 (blue line 1) and Hypothesis 2 (red dotted line 2).
Income from New Products shows a growth behavior after the 10" quarter as a result of
investments delayed impact. This growth is higher in the case of Hypothesis 1, with a final
result of 173.1, relatively close to the goal of 200. For Hypothesis 2, growth in income is
lower with a final result of 156.9.
Income from New Products
200
170
140
110
80
o 2 4 6 8&8 0 2 @ 1 I 20
Time (Quarter)
Income from New Products : HYP-1
Income from New Products : HYP-20 2-2-2222 BB
Figure 10. Simulation results for Income from New Products.
4.2. Investments in knowledge stocks and fraction invested in innovation capital
Behavior over time of Investments in Knowledge Stocks and Fraction Invested in
Innovation Capital is shown in Figure 11. Investments in Knowledge Stocks shows a
growing behavior from the 6th quarter on with a final value of 43.3 in the case of
Hypothesis | (blue line 1), and a decreasing behavior from the 6" quarter on with a final
value of 22.2 that tends to be stable, in the case of Hypothesis 2 (red dotted line 2). This
behavior follows what should be expected as an effect of the aversion to losses (section 2.6)
affecting the Innovation Capability and explaining the results obtained for Income from
New Products (Figure 10). On the other hand, the Fraction Invested in Innovation Capital
shows a rather stable oscillatory behavior (around a value of 0.55) in the case of Hypothesis
1 (blue line 1) that follows the risk averse behavior predicted by the prospect theory, whilst
in the case of Hypothesis 2 (red dotted line 2) the results show a higher amplitude
oscillation curve with a peak of 0.8 by the 10th quarter and a further stabilization around a
value of 0.71, following the risk seeking behavior predicted by the prospect theory.
Investments in Knowledge Stocks
Fraction Invested in Innovation Capital
0 1
& os
be}odae
et hr
5 - 4
30 eer on |
| pt I
+1 |
8 si ie oo oss | 4 seb retrt
‘or
° os
o 2 @ 6 & 0 2 WW I 18 20 - > 5
Time (Quarter) o 2 4 6 8 © 2 W 6 i 2
Investments in Knowkdge Stocks : HYP-1
Figure 11. Simulation results for Investments in Knowledge Stocks (left) and Fraction
Invested in Innovation Capital (right).
5. CONCLUSIONS
A managerial dynamic hypothesis that does not take into account the delay between
investments in intellectual capital and its impact on financial performance, may lead to
decisions, based on short term results, that reduce or cut investments in a premature way,
resulting in poor financial performance.
The results obtained from the simulation are consistent with those which should be
expected from the application of the prospect theory.
An empirical validation of the results should be made through experimentation with
decision makers, using a management flight simulator based on this simulation model.
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