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The adoption of technology is represented in the popular imagination as a straightforward process whereby the best technology wins in the marketplace, satisfying consumer needs. However, this superficial understanding neglects a number of important issues that shape technological adoption and the mechanisms of the process itself.

Technology And The Diffusion Model

The classic representation of technology adoption is Everett M. Rogers’s technological diffusion curve, which he outlines in his 2003 book The Diffusion of innovations. This s-shaped curve is broken up into three phases: innovation and early adoption, accelerating adoption, and saturation. In the first phase the technology is rare, unknown, and untested. First phase users are often very different from the general public or mass consumers,

whether in their general interest in novelties and new gadgets, or their specific organizational or occupational needs. For example, “Segway” personal transporters are not moving into mass circulation as fast as their inventor might hope, but airports and others with the need for moving security personnel have adopted them more readily. Similarly, being the first to adopt a fax machine or a cell phone was a risky proposition at first: There was no one to communicate with and it was not clear that the technology would be successful. In accelerating adoption more broad markets for innovations emerge, and new groups of potential adopters engage the technology, which rapidly spreads. At saturation the rate of new adoption slows and the market for a new technology is relatively stable, although economic activities such as replacement, repair, and accessorization may be quite dynamic. The diffusion model of technology adoption is a very effective model for describing successful consumer products, and it originated in Rogers’s studies of the communication of new ideas.

The origin of the diffusion model in studies of the dissemination of ideas leads to weaknesses in the model. The first is a pro-diffusion bias that is part of the core language of the model. For example those who are hesitant to adopt new technologies are described as “laggards,” which positions them as irrationally risk-averse and resisting new technologies adopted by risk-taking “early adopters,” rather than as hesitant to expend scarce resources on an untested new product.

The second problem with diffusion as a model of new technologies is that it leaves the design process for technologies unexamined, as well as the organizational contexts of technology adoption. Bruno Latour found in his 1996 study that when discussing contemporary corporate settings in particular, the designers and inventors interact with many different social groups (which may or may not be end users), and the earliest “adopters” of a new technology are the financiers, designers, managers, and suppliers and manufacturers who must be “enrolled” into supplying the resources for bringing the technology to a point of relative stability and maturity before it can move into the market. Within this process, asserts Robert J. Thomas in his 1994 book What Machines Can’t Do: Politics and Technology in Industrial Enterprise, the organizational contexts of technology’s design and implementation are most important, and internal politics, relationships with suppliers, employment markets and expertise, and other factors shape design and implementation or non-adoption of new technologies.

This points to the final issue with the mainstream dif-fusionist models of technology adoption: the assumption that the technologies that are moving into the marketplace are somehow the “best” in some unambiguous technical sense. But “best” can only be defined in relation to a particular group’s interests and activities. Any Apple™ user will argue that the near monopoly that Microsoft™ enjoys in the marketplace does not reflect that the “best” operating system has been adopted. Therefore, proposes Wiebe E. Bijker in his 1995 study, social and economic power dynamics shape the processes that produce new technologies to be adopted and defined as “working,” and the potential adoption patterns of those technologies across different user groups. Technology adoption discourses also assume that the technology is stable as a material and symbolic configuration: that there is closure of the technology. This assumption is not necessarily warranted: Sometimes in the process of the adoption a technology is adapted both materially changed, in terms of modifications or rebuilding, and certainly symbolically reconfigured, as the imagined and intended meanings and uses of the technology are reworked in different settings. In other cases, such as computers and cellular telephones, rapid changes in versions and models disrupt a smooth diffusion curve, as new features render technologies apparently obsolete, and closure and compatibility are not achieved.

Technology And Compatibility

The issue of compatibility points to the complexity of the adoption of technology in varying social contexts. In all contexts of technology adoption, a new technology must fit within existing manufacturing and use infrastructures, meet perceived needs, be nominally affordable, and be convergent with important cultural ideals. Each of these five elements presents different barriers to adoption in diverse cultural, particularly international, contexts. A technically functional technology that disrupts important social processes or relies on scarce resources will not be adopted. This is part of the reason that patent offices are full of descriptions of technologies that “work” in a technical sense, but are not adopted and are not seen as “working” by their intended users.

Bibliography:

Bijker, Wiebe E. 1995. Of Bicycles, Bakelites, and Bulbs: Toward a Theory of Sociotechnical Change. Cambridge, MA: MIT Press.
Latour, Bruno. 1996. Aramis, Or, The Love of Technology. Trans. Catherine Porter. Cambridge, MA: Harvard University Press.
Rogers, Everett M. 2003. Diffusion of Innovations. 5th ed. New York: Free Press.
Thomas, Robert J. 1994. What Machines Can’t Do: Politics and Technology in the Industrial Enterprise. Berkeley: University of California Press.