Behavioural dynamics of marketing technology-based
products
M Govindara jan
N Ramaswamy
Dept. of Management Sciences,
College of Engineering, Anna University,
Madras-600 025, India
ABSTRACT
The technology-based products are marked by the severity of
learning requirements for the users. Marketing efforts
should, therefore, represent not only promotional but
detailing activities as well, to overcome the behavioural,
technological and related marketing constraints faced by
the products. Sales, though a significant factor, does
not, by itself, explain the intricacies of the dynamics of
marketing. The study tries to explain the nature of
interactions amongst behavioural variables that contribute
to the successful marketing of technology-based products.
INTRODUCTION
The innovation diffusion and adoption process is too
complex to allow realistic models to be evaluated
analytically and so these models must be studied by
simulation. System dynamics simulation methodology is
based on the idea that managerial problems should be
examined in terms of the total system and complex
interactions that take place over time in the system. The
simulation part aims to demonstrate the characteristic
behaviour of the system rather than to predict specific
events. It can be used as a kind of synthetic test market
to be investigated under alternative specifications of the
values of the instruments of the marketing mix.
System dynamics is preferred for the following reasons:
ability to incorporate time lags
ability to handle irregular functions conveniently
empirical data is not the foremost requirement
easy to model qualitative behavioural relationships.
OF
This report presents the details of the development of a
generic system dynamics model to study the innovation
diffusion and adoption process of an evolving
technology-based product in the Indian context namely, the
personal computer.
PRODUCT INNOVATION
Anderson and Ortinau (1988) have adopted an innovation
classification scheme based on behavioural interpretations.
The product innovation when perceived by consumers as
being a new product established by a major technological
advancement it is known as a discontinuous innovation.
This type of innovation represents a major change in the
benefits offered to consumers and in behaviours necessary
for them to own and use the product. Such product
innovations are marked by the severity of learning
requirements for the adopters. The product search
attributes can be highlighted by product display and
advertising. The experience attributes can only be
acquired by constant use of the said products. Using
customer needs as the foundation for marketing in
technology-based products is problematic, because potential
customers often cannot articulate what they need. Hence it
is difficult to forecast the diffusion rates since it may
be necessary to 'educate' potential customers about the new
technology before they can evaluate it and deduce a
judgement of desirability (Robertson and Gastignon, 1987).
Many products of discontinuous innovation type actually
establish entirely new product categories (Personal
Computer). There is no history or comparable products on
the market. Hence there is no experience curve on which to
make decisions concerning the appropriate marketing
programs.
A discontinuous innovation tends to be initially
manufacturer (or supply) driven and not market driven.
Hence perceptual differences to the innovativeness of the
product may exist between marketers and consumers.
Marketing programs and strategies must be designed to
enhance consumer's acceptance of the innovation
overcome consumer's resistance to change
develop primary demand for the innovation and
create new consumer consumption habits.
Oe OF
A more realistic perspective of new innovations within
consumer markets might be that innovativeness do have the
propensity to change from being discontinuous in nature to
continuous based on consumer's needs/wants, usage levels
and their degree of comfort or capability with the
innovation. .
OBJECTIVES
The objective of the model analysis are to
(1) develop an understanding of the relationship between
the structure of the model and the behaviour it
produced,
(2) verify the adequacy of the model as a representation
of the real system relative to the problem under
study.
METHODOLOGY
Details and data have been taken’ from various Indian
computer magazines and business newspapers. Wherever
needed, the data have been assumed based on judgment and
the model's ability to reproduce the historicity. The
values for table functions and constants are based on
informal discussions and interviews reported. Since the
model accommodates behavioural variables also, a number of
switches have been provided throughout the program,
written in DYNAMO language, to toggle between sectors. The
model depicts, inter alia, the corporate growth and
provides useful summary statistics.
Parameter variations were carried out to determine the
basic model. The subsequent model improvement was done in
two ways. Firstly by simulating the model for all
combinations of values of the parameters which provided
some improvement and the second by modifying the system
structure based on an understanding of the causes of the
problems and of the general principles of information
feedback system behaviour which had a greater potential for
system improvement. The interactions being complex, total
system behaviour could not be determined directly. A
personal computer with PROFESSIONAL DYNAMO* software was
used to produce the results over a series of small
intervals and updating the various states of the system
each time. The projections were made till the year 1995.
Though the model works well for periods beyond 2000, the
ever changing dynamics of marketing may nullify the
findings.
Sensitivity tests were conducted for the initial values,
table values and values for constants. Partial model
testing was also done to check the intended rationality of
the decisions.
BEHAVIOURAL DYNAMICS
A policy simulation model is goal oriented since, by
definition, it is designed as an aid to decision making.
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The extent to which a policy simulation is capable of
providing some solution to the stated problem is a
technical characteristic of the model; it is a precondition
to successful application. The fact that there are so many
cases of technically valid models that are unused suggest
that there are important behavioural factors that determine
successful application.
Roger's (1976) five dimensions of innovation-relative
advantage, compatibility, complexity, divisibility and
communicability were expanded to include characteristics
such as financial cost, social cost, return to the
investment, risk associated with the product and efficiency
of the product in terms of time saving and avoidance of
discomfort (Dickerson, 1983).
The most effective means of conceptualizing adoption and
diffusion behaviour is to view this behaviour in its most
basic and elementary form.
(1) Behaviour is oriented towards attaining ends or goals.
(2) It takes place in situations
(3) It is normatively regulated
(4) It involves an expenditure of effort or motivation.
In order for adoption to occur the individual must perceive
that the potential rewards for adoption outweigh the
expected efforts required for adoption.
Following Bernhardt and Mackenzie (1972), the diffusion
process for economic goods can be defined as the adoption
over time of a specific product, by customers who are
linked by channels of communication to a given social
structure and by a given system of values or culture. The
adoption process is. influenced by the product, the
potential adopter's characteristics, his linkage to a
social structure, change agents and the adopter's culture.
Different assumptions regarding these may lead to different
diffusion models.
If adoption of an innovation is plotted against time from
introduction to complete diffusion they will assume the
characteristics of a normal distribution, or if plotted
cumulatively assume the familiar S-shaped curve which
characterises the product life cycle curve.
The diffusion process can be characterized in terms of
three dimensions: the rate of diffusion, the pattern of
diffusion and the potential penetration level. The rate of
diffusion reflects the speed at which sales occur over
time. The diffusion pattern concerns the shape of the
diffusion curve. The potential’ penetration level is a
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separate dimension indicating the size of a _ potential
market. That is, the maximum cumulative sales (or
adoption) over time (Gastignon, 1985).
A potential adopter may be viewed as engaging in
activities that convert inputs, including the potential
adopter's time into benefits. The inputs and benefits are
evaluated in terms of the potential adopter's objectives.
Two aspects of changes are induced by adoption.
(1) The nature of the changes consisting in the difference
between the pre-adoption set of activities and the
post-adoption set.
(2) The consequences of the change consisting in the
difference in value.
The potential adopter will be uncertain regarding both the
nature and the consequences of the changes due to adoption.
It is reasonable to assume that people avoid risk if
possible. One way to model such conservative behaviour is
to assume that potential adopters apply safety margins to
their estimates of the nature and the consequences of
changes due to adopting an innovation. Only if the
perceived value of the post-adoption activities, and their
value modified by safety margins, exceeds the value of the
pre-adoption activities will an innovation be adopted. The
size of the safety margin depends on the degree of
uncertainty, regarding the innovation and the importance of
the affected activities in terms of the potential adopter's
objectives. The stranger the innovation and the more
important, the activity, the larger the safety margin. We
may define a potential adopter of an innovation as an
entity that would adopt an innovation if its uncertainty
were reduced sufficiently for its safety margin to be zero.
For potential adopters, adoption usually requires a
reduction in initial safety margin. This reduction takes
time. The adoption process can be conceptualized in terms
of stages;
AWARENESS INTEREST EVALUATION TRIAL ADOPTION
The time interval between awareness and adoption may range
from minutes to decades. Movement from one stage to next
requires an information search and a decision. Different
information sources are more potent at different stages
although this is not uniform for all innovations.
INDIAN MICRO MARKET
The personal computer was introduced to the Indian market
at the end of 1985. In terms of volume, the number of PCs
sold in India during 1985-86 is 15000; 1986-87 is about
20000; 1987-88 is 22000; 1988-89 is 50000; 1989-90 is 75786
and 1990-91 is 91308.
The gross industry turn over for 1990-91 is Rs.2214/-
crores, a 32 percent growth rate from 1989-90. The micros
alone accounted for Rs.495/- crores, a 16 percent increase
over 1989-90. The average price of micros (including
PC/XT/AT/386/386SX) came down from Rs.0.62 lakh in 1988-89
to Rs.0.54 lakh in 1990-91.
The 1987-88 import-export policy provided a number of sops
for the hardware industry. While the imported computers
attracted only 90 percent duty, the duly level on imports
of inputs to Indian-made systems went up to 98 percent.
The year 1988-89 saw a transition in product cycles,
shifting standards, take overs and strategic realignment of
products and markets. The year 1989-90 was a bleak period.
The value of goods and services on IT-related goods which
grew by 44 percent in 1988-89 fell down to 32 percent in
1989-90 due to socio-political environment. However, the
overall picture is bright with 32 percent growth rate.
Selling PCs to Indian customers can be exceedingly complex.
The pattern of buying is significantly different across
regions. The customer support function has its own
woes-changing products, mobile manpower, hostile
environment (dust, frequent interruption in power supply
and not-—so-confident after-sales service. on the
manufacturing side, obsolescence is the main problem. It is
also not possible to fix any stable kind of a price/volume
relationship due to varying governmental regulations.
Then, of course, the R & D problems.
The market is dominated by a few companies as indicated in
the Tables. The Tables provide a snap shot of the present
Indian Micro Market.
BEHAVIOURAL PARAMETERS
The behavioural parameters are too many. However, an
operational classification could be as follows:
(1) In user's perception
Overall image of the company, After-sales service,
Dynamic management, Best talent available and the size
of the company (large or small).
ee
(2) In Professional's perception
Overall image of the company, Challenging work
environment, Professionally managed, Dynamic chief
executive, Remuneration and Career growth.
Sales, though a significant factor is seldom enough of a
determinant about a company's intrinsic value and standing
in the market. The inclusion of qualitative factors
becomes necessary in an industry which is in the process of
consolidation on the one hand, while witnessing rapid
changes on the other, as new markets open and new
technology appears with increasing regularity: The image
and credibility of the company, its marketing clout,
reputation have all: been included in the model.
BEHAVIOURAL MODULES
The various behavioural modules in the model are user
perception of product improvement, usage rate, functional
capability, post-adoption behaviour, market acceptance and
new product sector. For the sake of brevity, only the new
product sector is discussed (Govindarajan and Ramaswamy,
1990, 1991).
>
PINNEFF . K=TABHL (PINNEF , RATINP.KL,O,1,1)
RATINP.KL=DELAY3(DEINP.KL, DELINP)*PPC
DEINP .K=DEINP.. JK+(DT) (1/TRESSP) (NEDVNP. JK-DEINP. JK)
NEDVNP . KL=TABHL (NEDVN, EFFSPER.K,-1,0,1)*SWNEED
AVPAGE.K=AVPAGE. J+(DT) (1-(RATINP. JK*AVPAGE. J) /NUM)
AVPAGE=1AVPAGE
BENDIST . K=NOR* ( PRELIAB.K+PEXPAND .K+PUSERF . K+PQUALITY.K
+PAPPEAL.K)
PRELIAB. K=TABHL (PRELIA ,AVPAGE.K,0,10,1)
PEXPAND.K=TABHL, (PEXPAN, FUNCAP.K,0,1,1)
PUSERF .K=TABHL ( PUSER , EASEOP.K,0,1,-2)
EASEOP . K=(IURATE.KL/URATE.K) *EEXPFC.K
PQUALITY . K=TABHL (PQUALT , ESTREP.K,0,1,.5)
ESTREP .K=ESTREP . J+(DT) (1/TEREP) (IREPUT . J-ESTREP. J)
ESTREP=IREPUT
IREPUT . K= (1—FMKTLNC.K) *SWREP+ ( 1-SWREP ) *4/5
P2Z2rm rig
>Zer pp DD Db
The new product sector deals with the need to develop new
products and the decision to introduce new products. The
product innovation effect (PINNEFF) is a non-linear TABHL
function of rate of introducing new products (RATINP). The
RATINP is a function of decision to introduce new product
(DEINP) delayed over a period of introduction (DELINP).
This is multiplied by the probability of product
development project completion (PPC) to give the RATINP.
The DEINP is assumed to follow the need to develop new
products (NEDVNP). The need to develop new products, in
turn, depends on the effectiveness of sales performance.
The perceived product development is thought of as a TABHL
function of benefit distribution (BENDIST). The user's
perception of an improved product in the personal computer
class is probably a function of reliability, quality,
expandability, user friendliness and aesthetics or all of
these put together. This is divided by the rate of
incorporation of product development into the existing
product to give the value for BENDIST. Reliability (PRELIAB)
in turn, is related to the average product age (AVPAGE).
More the longevity of the product, the more is the
reliability. Expandability (or flexibility) is linked to
the functionality may be effected by both the
characteristics of the product and the relative skill of
user. Similarly the user friendliness (PUSERF), ease of
operation (EASEOP), quality (PQUALTY) and appeal (PAPPEAL)
have been defined. The average age of product is defined
The product's (personal computer) useful age period is
assumed to be 5 years. The number of new products (NUM)
introduced by the company is also known. Executives in
mature companies believe a major factor in determining
reputation is the average experience of firms in the
industry. PC systems are so technically sophisticated that
customers are rarely in a good position to judge the merits
of systems offered by alternative vendors. The established
reputation of a company (ESTREP), which is built over a
period of years (TEREP).as a level variable. The indicated
reputation (IREPUT) can be calculated in terms of fraction
of market lost to new competition.
CONCLUSION
The evolution process of a technology-based product is
subject to the dynamics of human behaviour and competition.
A system dynamics framework has been suggested. Sales
alone does not explain the intricacies of the dynamics of
marketing. Hence the behavioural aspects are emphasized.
TABLE 2
INDIAN MICRO MARKET
NUMBER OF MICROS SOLD
COMPANY 1986-87 1987-88 1988-89 1989-90
1990-91
A 3920 5900 8284 12205 9764
B 4112 4885 7899 14562 18582
Cc 3344 5210 6834 8590 9662
D 1184 3053 3750 2640 1815
E 950 1800 2700 1448 2631
F 815 = 973 - -
G = 2100 4244 7800 14117
INDUSTRY 40000 52000 75786 91308
VALUE 200 340.13 424 495
(in crores
of Rupees)
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