1. Identifying Feedbacks in Technological Policy
Implementation
Jose Edgar S. Mutuc
Industrial Engineering Department, De La Salle University
2401 Taft Avenue, Manila, Philippines
jose.edgar.mutuc@dlsu.edu.ph
Abstract – Policies that support and encourage technological
innovations and growth have been a common intervention
within and outside industry. Unfortunately, some of these
interventions fail to achieve their goals and sometimes create
more problems than improvement. This paper aims to study
implementation of these policies. The System Dynamics
approach suggests that hidden feedback structures inherent in
these systems are neglect and are not considered in the policy
design. These unaccounted for feedbacks complicate
implementation processes leading to failure to deliver on the
intended benefits. Some mathematical simulations are
presented to study and test specific policy implementation.
Keywords – technology policy implementation, feedback,
system dynamics, simulation
I. INTRODUCTION
The rapid technological developments in industry, both
in terms of products and processes, in the recent years have
been astounding. This is coupled with growing and
demanding needs and wants of consumers. Industry does
seem to have much choice but to account for increased
involvement with more technical processes and products.
At the higher level, governments are similarly faced with
country competitiveness issues to attract foreign direct
investments as well as industry performance that generate
national revenues.
Lall [1] notes that “The main reasons for the growing
importance of international competitiveness are
technological. … Since new technologies benefit all
activities, traded and non-traded, rapid access to such
technologies in the form of new products, equipment and
knowledge becomes vital for national welfare.” Further, the
process of improving competitiveness is related to
“something that has to be built”. Moreover, the process is
generally complex, demanding and costly [2]. The process
of adopting technological change seems to be simple yet
evidence indicates varying successes [1].
In addition, policy models are often normative
statements rather than operational policy instruments [3],
thereby limiting their usefulness in implementation efforts.
What may be needed are models that explore points of
intervention to improve the system.
This paper aims to explore the seemingly simple process
of technology adoption at the national level in an attempt to
improve industry performance. It uses the System
Dynamics approach where feedback loop structures are
proposed and simulation models are built.
II. BASIC INTERVENTION MODEL
The basic intervention model is a negative feedback
loop that attempts to correct the perceived performance
gap. There is a gap between average industry performance
with a standard that could be other national standards or
simply expected performance. The gap elicits a need to
intervene and improve the current industry performance to
close the gap. The relationships are presented in Fig. 1.
average
industry
performance
performance
gap
standard
need to
intervene
interventions
intervention index
Fig. 1. The basic intervention model
The simple negative feedback loop above hides the
complexity and difficulty of improving national industry
performance. The marks on the arrows indicate the delays
while the boxes indicate states of the system. The delays
suggest that transfer of information between states is
governed by time and reactions are not instantaneous. The
states, on the other hand, indicate that information can be
transmitted only when the state variables achieves certain
level.
The delays and states combine to postpone effects and
improvements. As a result, the conditions of the system
have changed when reaction (or improvement) arrives at
that time period. This new imbalance between the new

2. performance and the standard can begin a cycle of
adjustments. The results of the cycles are shown in Fig. 2.
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Fig. 2. Simulation results of the basic intervention model.
The simple intervention model resulted in oscillations of
the average industry performance variable as well as other
major variables, with a period of about 20 time periods.
The delays occurring at the reactions caused over and under
shoots as conditions changed when the appropriate
response was delivered.
The simulation in Fig. 2 confirms the simplicity and
difficulty of implementing technology interventions [1].
Because of the time delays and state conditions, the
resulting intervention is a late reaction being a function of
past conditions rather than the present conditions.
III. IDENTIFYING FEEDBACK LOOPS
As suggested by Saeed [3], the generic manners in
which governments encourage and/or develop interventions
to contribute to industry do not simply cause improvement
to occur. Instead, the intervention is connected to the
causal structure of the system creating new feedback loops
and/or impacts other variables. This section identifies three
possible feedback loops.
A. Technology as an intervention
Technology as a specific solution is integrated into the
basic intervention solution and creates a new feedback loop
(Fig. 3). The technology state variable is influenced by the
need to intervene with the system and specified by the
performance gap. The performance gap indicates the
amount to be addressed to by the new technology while the
need to intervene variable indicates the necessity and the
actual amount of technology that would be acquired.
The response (i.e. technology to be acquired) is governed
by cost factors and acquisition time, in addition to need to
intervene and the performance gap. These factors put
together resulted in an additional delay that significantly
increased periodicity of the simple model to about 50 time
periods.
average
industry
performance
performance
gap
standard
need to
intervene
interventions
intervention index
technology
Fig 3. Basic intervention model with technology loop
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Graph 2: p1 (Untitled)
Fig. 4. Simulation results of additional technology loop
Sensitivity tests were implemented on the model to
explore the effects of varying the time delays. Fig 5 shows
that shortening the delays can lead to more oscillations in
the same period. Shorter delays result to faster reactions
leading to reaching standards more quickly. This, in turn,
begins cycles earlier.
However, it may be pointed out that the time delays are
more likely to longer rather than shorter leading to loner
cycles. The adoption of new technology is a big
organizational decision as it is expensive and risky. As
such it tends to take more time involving awareness and
understanding of the technology, search for suppliers,
demonstration and training, as well as resistance and
perceived risk to the new technology acquisition.
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Graph 3 (Untitled)
Fig. 5. Comparison of time delay improvements

3. B. Technology adoption in industry
The success of interventions is also dependent on the
adoption by member of the industry. The more members of
the industry adopt the change in technology the higher will
overall performance be. The loop for adopters is completed
by the impact of industry performance. As members need
justification for adopting new technology, the industry
performance becomes the evidence of the success of new
technology and the rationale for its adoption. Moreover, as
more adopters adopt the technology, pressure is exerted on
the non adopters to accept the new technology. These are
summarized in Fig. 6.
The new loop is a positive feedback loop. This will
reinforce the adoption process and hasten the improvement
in performance. However, the impact of this positive
feedback loop is somewhat delayed as industry takes a
“wait and see” stance. There is a minimum level before
new members adopt the technology.
average
industry
performance
performance
gap
standard
need to
intervene
interventions
intervention index
technology
adopters
Fig. 6. The basic intervention model with technology and adopters
The additional loop representing the industry adoption
of technology involves an imitation process that is based on
current adopters and the improvement in industry
performance that initially is influenced by the percentage of
pioneer adopters. The initial adopters can influence new
adopters, thereby increasing more pressure on non-adopters
as industry performance improves.
The simulation results in Fig. 7 indicate behaviour
sensitivity due to changing the initial number of adopters.
They showed delayed improvement in performance. When
the pioneer adopters were few, new adopters were
insignificant so that industry performance did not
considerably improve. With a bigger percentage of
adopters, the industry performance declined but after some
time delay went up to reach a peak and start an oscillation.
The performance improvement occurred earlier as a larger
proportion of the industry were technology pioneers at the
beginning.
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Fig. 7. Simulation results from varying initial adopters
C. Skills using technology
The third loop explored in this paper involves skills that
are developed due to new technology. These are learned
skills that follow the adoption of new technology. On the
other hand, skills will involve natural decay of accumulated
skills.
average
industry
performance
performance
gap
standard
need to
intervene
interventions
intervention index
technology
adopters
skills
Fig. 8. Basic intervention model with technology, adopters and skills
Developed skill is the third factor that determines the
success of the intervention. It also involves another delay
as skills take time to learn and can have impact only as they
accumulate in stocks. The effect of the additional feedback
loop varies with the assumptions made on the depreciation
of skills. A constant loss of skills results in the graphs in
Fig.9.
The graphs were derived from varying the initial skill
levels in the industry. At lower levels of initial skill,
performance never quite improves as low skills cancel off
the effect of technology. As the initial skills increase,
amplified effects on performance lead to damped
oscillations and at even higher values, sustained
oscillations. Higher skill values provide some initial kick
into the system that reinforces skills learning in the next
cycle.

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Fig. 9. Simulation results from varying the initial skills level with the
constagnt decay assumption
Interestingly, the skills loops create more oscillations
but at lower amplitude and peaks than earlier runs. These
result from increased impact on performance in the short
term. Such impact, though, is small, leading to similarly
small improvements in performance and lower needs for
new technology.
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Fig. 11. Simulation results from varying initial skills with the constant
proportion decay assumption
The second case involves a constant proportion of skill
that degrades. In this case, the oscillations were eliminated
by a skill loop. The skill learning triggered by new acquired
technology is less than the normal depreciation rate. As a
result, skills do not improve, thus, constraining actual
improvement.
As the initial skill level supports the intervention
variable, performance levels improve. The performance
gaps are closed, no additional technology is needed and no
new skills are learned. On the next cycle, skills limit the
impact of technology and the adoption process on
performance, causing the decline of performance. The
system never quite recovers because there is a high level of
technology that cannot be supported by appropriate skills.
The graph shows varying levels of initial skills.
Apparently, more skills can lead to higher peaks but
similarly lead to a decline that new skills cannot support.
IV. CONCLUSION
This paper outlined the basic model in the
implementation of technology policy. It confirmed the
observations that the simple recommendation for adoption
of new technology by countries to improve national
competitiveness is a rather complex process. The search for
feedback loops was intended to draw out the complexity of
the system.
However, the study highlighted the fact that the
complexity does not result from complex variables but is
largely due to delays inherent in the system and secondly
from sensitivity of behaviour to initial conditions of the
system. First, the delays prevented the system from
immediately adjusting to the standard performance and
closing the gap, thus creating unwanted oscillations.
Secondly, the starting points of the system determines the
initial reactions that later cascade in later periods. This
resulted into different behaviour patterns with different
success patterns.
These two observations have wider implications on
actual implementation of technology adoption. Natural
oscillations involve downswings that in practice will be
interpreted as failures of the system. The managerial issue
suggests that the normal reaction to underachievement of
objectives is the withdrawal of support and resources from
the initiative. Such thinking discounts the effect of delays
to achievement of the goals.
The sensitivity to initial conditions, on the other hand,
leading to apparently different behaviour patterns suggests
that better initial conditions lead to more favourable
outcomes. However, better conditions will involve large
investments, in addition to technology acquisition costs.
Specifically, more pioneer adopters at the beginning will
need to be funded by government to significantly trigger
the positive adopters loop. Similarly, considerable training
costs will be incurred for the skills loop prevent skills
decay loop from dominating the system.
This study represents some initial efforts to understand
failures in implementing technological policies. The
present effort will need to be extended to involve other
factors, other mechanisms to acquire technology, promote
the use of technology in industry and development skills.
Further study will also need to focus on the study of delays
and processes to optimize their impact on the entire system.
Finally, solutions to control the observed oscillations can
be explored with a more or less complete model.
REFERENCES
[1] S. Lall, Investment and Technology Policies for Competitiveness:
Review of Successful Country Experiences, Geneva,
UNCTAD/ITE/IPC, 2003.
[2] UNIDO, Industrial Development Report 2002/2003: Competing
through Innovation and Learning, Vienna, 2002.
[3] K. Saeed, Policy Space Considerations for System Dynamics
Modeling of Environmental Agenda: An Illustration Revisiting the
“Limits to Growth Study”, presented at Symposium on
Environment, Energy, Economy, Rome, 1998.