Patterns of Involuntary Technology Adoption
Ozge Dilaver Kalkan - Lancaster University
INTRODUCTION
This paper models two types of consumer interdependencies that are not
adequately addressed in the diffusion of innovations literature. These are:
early adoption advantages (EAA hereafter) and institutional change (IC
hereafter). The paper studies the societal implications of these
interdependencies with agent-based computer simulations. While
discussing these implications, the paper also questions the level of control
consumers have on technology.
EAA corresponds to the use-value that agents create with the innovation if
they can adopt earlier than others. When most of the EAA acquired at the
individual level corresponds to a re-distribution of existing values at the
society level, EAA initiates a constant-sum game.
The institutions modelled in this study shared thoughts and routines in
society following the definition of Veblen (1919). These can be very formal,
such as markets or organisations, or informal such as values and roles. In
this context, IC refers to changes in institutions induced by the increasing
levels of diffusion.
This study examines the diffusion outcomes these interdependencies yield
under different scenarios. One of the interesting characteristics of these
two forms of interdependencies is that they can create involuntary
technology adoptions. That is; for some society members, adoption is a
worse state than their initial state before the launch of an innovation. Once
the innovation is launched and some adoptions occur, however, nonadoption becomes an even worse state, hence the agents adopt, the
innovation albeit not happily.
Depending upon their power and their conformity level the institutions
influence the adoption decisions of agents. In return, the adoption decisions
of agents affect the power of institutions.
In the Scenarios 3 and 4, IC is also added into the experiments. Hence, the
decision rule is that the agents adopt if:
E(VCPikt) + [E(VCPikt) * E(Nit)] / E(Nit)` + ICit > P
(3)
A total of 6,000 simulation experiments are run (for 10 pseudo-populations,
3 institution systems, 50 values of βk and 4 scenarios).
RESULTS
Scenario 1: In Scenario 1, the variable-sum EAA are studied. Variable-sum
EAA means that the use-value that early adopters create with the
innovation does not correspond to re-distribution of existing value in the
society. Instead, all of the use-value offered by the innovation at the
individual level corresponds to new and additional value at the societal
level.
In the first period, all agents expect that the diffusion will be low and so
their returns from adoptions will be high enough. In the next period,
however, they see that the actual diffusion level is very high; all agents are
adopters. This means they are not creating any use-value with the
innovation at all and they cease adoption.
MODEL
It is assumed that the value-creating potential (VCP hereafter) of the
innovation k is a function of an intrinsic capacity of the innovation (βk) and
the number of adopters in the society at time t. Innovations provide some
EAA; agents can create value with the innovation if they adopt it earlier
than others, as in Equation 2. Since the VCP of the innovation depends on
the future diffusion levels in order to evaluate the innovation, agents need
to build expectations on diffusion. These expectations are built adaptively
as shown in Equation 1.
E(Nit) = Nit-1 + ai(100 - Nit-1)
E(VCPikt) = βk / [E(Nit) + 1]
(1)
(2)
Social networks in the model provide a simplistic representation of the
social class structure. Accordingly agents are heterogeneous with respect
to an attribute called accumulated characteristics. It is assumed that agents
make friends with others who are similar to themselves in the rank of
accumulated characteristics within society.
There are 10 institutions each of which has a root location in the rank of
accumulated characteristics. Institutions can be in conformity or in conflict
with the innovation (a random number between -1 and 1). All agents are
influenced by all the institutions but the magnitude of this impact is
inversely related to the sum of the distances between agents' friends and
the root location of the institution.
The results of the simulation experiments under Scenario 3 are shown
below. Although the W-pattern is still visible, it is not as evenly-shaped as in
Scenario 1 as a result of the heterogeneity introduced. In addition, the
pattern is less regular in time; diffusion level appears to be increasing in
time. When simulations are run for longer, this trend also leads to a lock-in
to the innovation.
Scenario 3
Scenario 4
Scenario 4: In Scenario 4 constant-sum EAA is studied with IC and the
results of the simulation experiments are shown above. Due to the element
of heterogeneity, some adoptions occur at βk values that did not yield any
adoptions in Scenario 2. In this range of βk , the cost of non-adoption is not
high enough so some agents cease adoption. At even higher βk values,
however, the cost of non-adoption increases, leading to more involuntary
adoptions and making the triangles of the W-pattern smaller and smaller,
and finally disappear. The society locks-in to the innovation.
CONCLUSIONS
Scenario 1
Scenario 2
Scenario 2: In Scenario 2, the EAA are constant-sum and so, the usevalue offered to the agents at the individual level does not correspond to
new and additional value at the societal level but instead to similar losses
by the non-adopters.
Constant-sum EAA also means that when all agents adopt and the diffusion
expectations are high, agents worry that cost of not adopting will be very
high. For this reason, the triangles in the W-shape disappear, meaning that
all agents adopt the innovation and the society locks-in to the innovation.
Scenario 3: This scenario studies the variable-sum EAA together with IC.
Regarding the latter, it may be useful to remember that institutions affects
all agents but their effect on each agent varies in strength and so, there is
now heterogeneity in agents' likelihood of adoption.
Findings presented in this paper show that individuals may have limited or
no control over diffusion of some technologies. This result can be seen as
technologically determinist, allowing little room for human agency. However,
it is essentially the human actions - in expectancy of other human actions that bring the outcomes in this story. That being said, technology also has
an active role in the story. By providing means of competition and
necessitating co-ordination on new social institutions; technology, like the
natural environment, defines the material setting and conditions of human
interaction. In this respect, what is told here is an abstract story about
interactions of human agents with technology and with each other.
The major implication of the study is the possibility that inefficient,
destructive or partially harmful technologies diffuse extensively given that
they entail some particular forms of consumer interdependencies. This
paper raises this possibility, not as an argument against technology but
rather as a call for open discussions of technological trajectories and their
socio-economic impacts. What is challenged here is the common-sense
view that market selection alone is adequate for controlling use of
technology in a way to increase social welfare.
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