Article ID: | iaor201111629 |
Volume: | 12 |
Issue: | 4 |
Start Page Number: | 387 |
End Page Number: | 408 |
Publication Date: | Dec 2011 |
Journal: | Information Technology and Management |
Authors: | Westland Christopher |
Keywords: | statistics: general |
There is no agreement on how to formally incorporate affective data into statistical analysis and research conclusions. The information systems (IS) literature has recently published several position papers that have established a framework and perspective for using affective technology in IS research though. The frameworks have not been extensively tested, and are likely to evolve over time as empirical studies are conducted, and the validity of the methodologies is confirmed or disproved. A major goal of the current paper is to take the initial steps in translating the frameworks to usable methodologies, with application to improving our understanding of how to make effective empirical tests. This paper also investigates the adoption cycle of one of these technologies–electrodermal response (EDR) technologies–whose incarnation in the polygraph in forensic applications went through a complete adoption cycle in the twentieth century. The use of EDR response data in marketing research and surveys is nascent, but prior experience can help us to forecast and encourage its adoption in new research contexts. This research investigates three key questions: (1) What technology adoption model is appropriate for electrodermal response technology in forensic science? (2) What is the accuracy of affective electrodermal response readings? (3) What information is useful after superimposing affective EDR readings on contemporaneous survey data collection? Affective data acquisition technologies appear to add the most information when survey subjects are inclined to lie and have strong emotional feelings. Such data streams are informative, non‐invasive and cost‐effective. Informativeness is context‐dependent though, and it relies on a complex set of still poorly understood human factors. Survey protocols and statistical analysis methods need to be developed to address these challenges