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Lost in the crowd? Using eye-tracking to investigate the effect of complexity on attribute non-attendance in discrete choice experiments

Overview of attention for article published in BMC Medical Informatics and Decision Making, February 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

blogs
1 blog
twitter
2 tweeters

Citations

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39 Dimensions

Readers on

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38 Mendeley
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Title
Lost in the crowd? Using eye-tracking to investigate the effect of complexity on attribute non-attendance in discrete choice experiments
Published in
BMC Medical Informatics and Decision Making, February 2016
DOI 10.1186/s12911-016-0251-1
Pubmed ID
Authors

Jean Spinks, Duncan Mortimer

Abstract

The provision of additional information is often assumed to improve consumption decisions, allowing consumers to more accurately weigh the costs and benefits of alternatives. However, increasing the complexity of decision problems may prompt changes in information processing. This is particularly relevant for experimental methods such as discrete choice experiments (DCEs) where the researcher can manipulate the complexity of the decision problem. The primary aims of this study are (i) to test whether consumers actually process additional information in an already complex decision problem, and (ii) consider the implications of any such 'complexity-driven' changes in information processing for design and analysis of DCEs. A discrete choice experiment (DCE) is used to simulate a complex decision problem; here, the choice between complementary and conventional medicine for different health conditions. Eye-tracking technology is used to capture the number of times and the duration that a participant looks at any part of a computer screen during completion of DCE choice sets. From this we can analyse what has become known in the DCE literature as 'attribute non-attendance' (ANA). Using data from 32 participants, we model the likelihood of ANA as a function of choice set complexity and respondent characteristics using fixed and random effects models to account for repeated choice set completion. We also model whether participants are consistent with regard to which characteristics (attributes) they consider across choice sets. We find that complexity is the strongest predictor of ANA when other possible influences, such as time pressure, ordering effects, survey specific effects and socio-demographic variables (including proxies for prior experience with the decision problem) are considered. We also find that most participants do not apply a consistent information processing strategy across choice sets. Eye-tracking technology shows promise as a way of obtaining additional information from consumer research, improving DCE design, and informing the design of policy measures. With regards to DCE design, results from the present study suggest that eye-tracking data can identify the point at which adding complexity (and realism) to DCE choice scenarios becomes self-defeating due to unacceptable increases in ANA. Eye-tracking data therefore has clear application in the construction of guidelines for DCE design and during piloting of DCE choice scenarios. With regards to design of policy measures such as labelling requirements for CAM and conventional medicines, the provision of additional information has the potential to make difficult decisions even harder and may not have the desired effect on decision-making.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 18%
Student > Doctoral Student 6 16%
Researcher 5 13%
Student > Ph. D. Student 5 13%
Student > Bachelor 3 8%
Other 7 18%
Unknown 5 13%
Readers by discipline Count As %
Business, Management and Accounting 5 13%
Psychology 5 13%
Economics, Econometrics and Finance 4 11%
Decision Sciences 3 8%
Nursing and Health Professions 2 5%
Other 11 29%
Unknown 8 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 April 2018.
All research outputs
#1,767,392
of 12,852,852 outputs
Outputs from BMC Medical Informatics and Decision Making
#164
of 1,162 outputs
Outputs of similar age
#47,436
of 264,024 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 5 outputs
Altmetric has tracked 12,852,852 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,162 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,024 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them