Trust in Technology: A Status Value Approach to Understanding Technology Adoption

Trust in Technology: A Status Value Approach to Understanding Technology Adoption

Celeste Campos-Castillo
DOI: 10.4018/978-1-61520-901-9.ch009
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The notion of trust in technology has recently flourished through translating what researchers know about interpersonal trust into the realm of technology. What has been missing from this movement is a sociological perspective on trust in technology, an understanding of how the social and cultural framework in which one is embedded can shape outcomes like trust. To fill this void, the author develops a framework for understanding how these macrostructures can become imported into the local context (e.g., the workplace) to influence trust in technology. Specifically, this framework takes a status value approach (Berger & Fisek, 2006) to explain how the status of social actors (e.g., people, organizations) can transfer to the technologies to which they are associated and be used as a basis for trust. The author focuses the discussion of this theory around implications for technology adoption and offers suggestions for future applications of the theory in other domains.
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A recent observation about two popular social networking sites – MySpace and Facebook – is that their customers are divided along social class lines, with the working class individuals concentrated in MySpace and the middle class individuals gathering on Facebook (boyd, 2007). What might account for this differential adoption of technologies that mediate social networking?

In a world increasingly reliant on technologies to mediate tasks like social networking, there has been a growing interest regarding trust in technology (Lee & See, 2004), which I am defining here as the belief that the technology will perform a set of delegated tasks with minimal risk. Essentially, the probability that a person will adopt a technology depends on the person’s trust in that technology (Lee & Moray, 1992, 1994; Lewandowsky, Mundy, & Tan, 2000; Muir, 1987; Parasuraman & Riley, 1997). Weber (1947) saw technology as a means to move beyond finite human capacity and rationalize a process; if a human controller does not trust technology, then how can technology increase productivity? Can you trust that the directions from your car’s GPS unit will lead you to the correct destination and not a dead end road? How do you determine if you will spend your money, for instance, on the books that recommends while you peruse around their website? Or how about trusting websites like to match you and the person with whom you will be presumably spending the rest of your life?

A number of explanations for the extent to which technologies are trusted exist in the literature. Some have focused on how stable individual differences in the propensity to trust (Rotter 1971) influence trust in technology (e.g., Parasuraman, Singh, Molloy, & Parasuraman, 1992). Others have looked at how the design of the technology might alter trust (e.g., Lee & Moray, 1994). While much has been written on the role of trust in technology, there is a missing piece to the picture – a sociological perspective. I build on Lee and See’s (2004) suggestion that macrostructures shape the evolution of trust in technology by providing a theory of how larger macrostructures can become imported into the local context (e.g., workplace) and have influence over trust in technology. A primary emphasis in sociology is that the local context is influenced by the larger social and cultural framework in which it is embedded. The social structure and personality perspective within sociology (McLeod & Lively, 2003), for instance, focuses on the multiple, interconnected layers of social milieu that shape individual outcomes such as which social networking site to use. Of course, a sociological understanding of technology is not new. For example, sociology has provided explanations for the digital divide, which is a term used to describe differential access to and ownership of technology. A variable often used in sociological models – socioeconomic status – has been shown to be a primary correlate of access to computer technology (Bimber, 2000; Calvert et al., 2005; Wilson, Wallin, & Reister, 2003).

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