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

Jean Marie Spinks, Duncan Stuart Mortimer

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Background: 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. Methods: 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.Results: We find that complexity is the strongest predictor of ANA when other possible influences, such as timepressure, 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.Conclusions: 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.
Original languageEnglish
Article number14
Pages (from-to)1 - 13
Number of pages13
JournalBMC Medical Informatics and Decision Making
Volume16
Issue number1
DOIs
Publication statusPublished - 2016

Keywords

  • Attribute non-attendance
  • Complexity
  • Information processing
  • Eye tracking
  • Complementary medicine

Cite this

@article{c41da3d6cd9e4e79b92134e957bf7ace,
title = "Lost in the crowd? Using eye-tracking to investigate the effect of complexity on attribute non-attendance in discrete choice experiments",
abstract = "Background: 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. Methods: 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.Results: We find that complexity is the strongest predictor of ANA when other possible influences, such as timepressure, 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.Conclusions: 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.",
keywords = "Attribute non-attendance, Complexity, Information processing, Eye tracking, Complementary medicine",
author = "Spinks, {Jean Marie} and Mortimer, {Duncan Stuart}",
year = "2016",
doi = "10.1186/s12911-016-0251-1",
language = "English",
volume = "16",
pages = "1 -- 13",
journal = "BMC Medical Informatics and Decision Making",
issn = "1472-6947",
publisher = "BioMed Central",
number = "1",

}

Lost in the crowd? Using eye-tracking to investigate the effect of complexity on attribute non-attendance in discrete choice experiments. / Spinks, Jean Marie; Mortimer, Duncan Stuart.

In: BMC Medical Informatics and Decision Making, Vol. 16, No. 1, 14, 2016, p. 1 - 13.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Lost in the crowd? Using eye-tracking to investigate the effect of complexity on attribute non-attendance in discrete choice experiments

AU - Spinks, Jean Marie

AU - Mortimer, Duncan Stuart

PY - 2016

Y1 - 2016

N2 - Background: 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. Methods: 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.Results: We find that complexity is the strongest predictor of ANA when other possible influences, such as timepressure, 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.Conclusions: 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.

AB - Background: 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. Methods: 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.Results: We find that complexity is the strongest predictor of ANA when other possible influences, such as timepressure, 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.Conclusions: 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.

KW - Attribute non-attendance

KW - Complexity

KW - Information processing

KW - Eye tracking

KW - Complementary medicine

UR - http://goo.gl/iFXdjW

U2 - 10.1186/s12911-016-0251-1

DO - 10.1186/s12911-016-0251-1

M3 - Article

VL - 16

SP - 1

EP - 13

JO - BMC Medical Informatics and Decision Making

JF - BMC Medical Informatics and Decision Making

SN - 1472-6947

IS - 1

M1 - 14

ER -