Abstract
Providing information online is pervasive in human-computer interactions. While providing information, people may deliberate their responses. However, organizations only receive the end-result of this deliberation and therefore have no contextual information surrounding the response. One type of contextual information includes knowing people's response uncertainty while providing information. Knowing uncertainty allows organizations to weigh responses, ask follow-up questions, provide assistance, or identify problematic instructions or responses. This paper explores how mouse cursor movements may indicate uncertainty in a human-computer interaction context. Specifically, it hypothesizes how uncertainty on multiple-choice questions influences a mouse-movement statistic called area-under-the-curve (AUC). We report the result of two studies that suggest that AUC is higher when people have moderate uncertainty about an answer than if people have high or low uncertainty. The results suggest a methodology for measuring uncertainty to facilitate multi-method research and to assess data in a pragmatic setting.
| Original language | English (US) |
|---|---|
| Title of host publication | 2015 Americas Conference on Information Systems, AMCIS 2015 |
| Publisher | Americas Conference on Information Systems |
| ISBN (Print) | 9780996683104 |
| State | Published - 2015 |
| Event | 21st Americas Conference on Information Systems, AMCIS 2015 - Fajardo, Puerto Rico Duration: Aug 13 2015 → Aug 15 2015 |
Other
| Other | 21st Americas Conference on Information Systems, AMCIS 2015 |
|---|---|
| Country | Puerto Rico |
| City | Fajardo |
| Period | 8/13/15 → 8/15/15 |
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Keywords
- Adaptive computers
- Area-under-the-curve
- Mouse cursor tracking
- Uncertainty
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
Cite this
A multi-experimental examination of analyzing mouse cursor trajectories to gauge subject uncertainty. / Jenkins, Jeffrey L.; Larsen, Ross; Bodily, Robert; Sandberg, Daniel; Williams, Parker; Stokes, Steve; Harris, Spencer; Valacich, Joseph S.
2015 Americas Conference on Information Systems, AMCIS 2015. Americas Conference on Information Systems, 2015.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A multi-experimental examination of analyzing mouse cursor trajectories to gauge subject uncertainty
AU - Jenkins, Jeffrey L.
AU - Larsen, Ross
AU - Bodily, Robert
AU - Sandberg, Daniel
AU - Williams, Parker
AU - Stokes, Steve
AU - Harris, Spencer
AU - Valacich, Joseph S
PY - 2015
Y1 - 2015
N2 - Providing information online is pervasive in human-computer interactions. While providing information, people may deliberate their responses. However, organizations only receive the end-result of this deliberation and therefore have no contextual information surrounding the response. One type of contextual information includes knowing people's response uncertainty while providing information. Knowing uncertainty allows organizations to weigh responses, ask follow-up questions, provide assistance, or identify problematic instructions or responses. This paper explores how mouse cursor movements may indicate uncertainty in a human-computer interaction context. Specifically, it hypothesizes how uncertainty on multiple-choice questions influences a mouse-movement statistic called area-under-the-curve (AUC). We report the result of two studies that suggest that AUC is higher when people have moderate uncertainty about an answer than if people have high or low uncertainty. The results suggest a methodology for measuring uncertainty to facilitate multi-method research and to assess data in a pragmatic setting.
AB - Providing information online is pervasive in human-computer interactions. While providing information, people may deliberate their responses. However, organizations only receive the end-result of this deliberation and therefore have no contextual information surrounding the response. One type of contextual information includes knowing people's response uncertainty while providing information. Knowing uncertainty allows organizations to weigh responses, ask follow-up questions, provide assistance, or identify problematic instructions or responses. This paper explores how mouse cursor movements may indicate uncertainty in a human-computer interaction context. Specifically, it hypothesizes how uncertainty on multiple-choice questions influences a mouse-movement statistic called area-under-the-curve (AUC). We report the result of two studies that suggest that AUC is higher when people have moderate uncertainty about an answer than if people have high or low uncertainty. The results suggest a methodology for measuring uncertainty to facilitate multi-method research and to assess data in a pragmatic setting.
KW - Adaptive computers
KW - Area-under-the-curve
KW - Mouse cursor tracking
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=84963625990&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963625990&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84963625990
SN - 9780996683104
BT - 2015 Americas Conference on Information Systems, AMCIS 2015
PB - Americas Conference on Information Systems
ER -