Two worlds of trust for potential e-commerce users : Humans as cognitive misers

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)1246-1262
Journal / PublicationInformation Systems Research
Issue number4
Publication statusPublished - Dec 2012


In this paper we consider the impact of trust on a new visitor's intention to revisit a website, but instead of using the typical expectancy-value theories as our conceptual basis, we look at the issue from the perspective of cognitive complexity and "humans as cognitive misers." Starting with the suggestion that it is cognitively taxing to distrust, we propose that in order to conserve on cognitive resources, once a new visitor has convinced him or herself that a website is "trustworthy enough," that user will drop trustworthiness from their concerns and only consider other characteristics of the website (e.g., task-technology fit, aesthetic appeal, etc.) in determining their revisit intention. This leads to what we call a "trust tipping point" and two different worlds of trust. Above the tipping point revisit intention is constructed in one way, and below the trust tipping point it is constructed in a quite different way. This perspective results in very different recommendations for website designers as to the likely payoff from improving task-technology fit, aesthetic appeal, or trustworthiness, depending upon where their existing website stands relative to the trust tipping point. To test our hypotheses we used data from 314 student website users, and expanded a technique called piecewise regression (Neter et al. Applied Linear Statistical Models, 4th ed.) to allow us to analyze data as two different linear surfaces, joined at the tipping point. We found good support for our assertions that users operate differently above and below a trust tipping point. © 2012 INFORMS.

Research Area(s)

  • Accessibility-diagnosticity, Bounded rationality, Humans as cognitive misers, Piecewise regression, Task-technology fit, Trust, Web aesthetics