Abstract
BackgroundThe 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.MethodsA 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.ResultsWe 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.ConclusionsEye-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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0251-1) contains supplementary material, which is available to authorized users.
Highlights
The provision of additional information is often assumed to improve consumption decisions, allowing consumers to more accurately weigh the costs and benefits of alternatives
With regards to discrete choice experiments (DCEs) design, results from the present study suggest that eye-tracking data can identify the point at which adding complexity to DCE choice scenarios becomes self-defeating due to unacceptable increases in ANA
Arising from the disciplines of psychology and economics, the theoretical basis for DCEs can be found in random utility theory (RUT), developed by McFadden [5] and later Hanemann [6]
Summary
The provision of additional information is often assumed to improve consumption decisions, allowing consumers to more accurately weigh the costs and benefits of alternatives. Increasing the complexity of decision problems may prompt changes in information processing. This is 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 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. The use of discrete choice experiments (DCEs) in health care has increased dramatically over the past decade [1,2,3,4]. There is increasing evidence suggesting that decision making of the type emulated by DCEs is prone to diversions from the underlying theory [7, 8], which assumes that consumers are both fully informed and make ‘rational’ (optimising) decisions. In the presence of ANA, DCE data may not characterise the preferences of affected individuals and standard approaches to analysis may produce biased estimates of the relative importance of product attributes [19]
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