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, non-adoption becomes an even worse state, hence the agents adopt, the innovation albeit not happily. 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) (1) E(VCPikt) = βk / [E(Nit) + 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. 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. Scenario 2: In Scenario 2, the EAA are constant-sum and so, the use-value 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. Scenario 1 Scenario 2 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 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. e-mail: o.dilaverkalkan@lancaster.ac.uk