JM Insights in the Classroom
Teaching Insight:
A new study in the Journal of Marketing shows that the design of websites used for buying health insurance combined with high-quality big data can produce big savings for consumers. When health insurance products are ordered such that the best options for the consumer appear first in the presented list, this strongly improves consumers’ decisions. If the list is also partitioned to show a small number of options first (with an easy option to click through to see all options), this further improves consumers’ decisions. However, importantly, if the best options are not at the top of the list, partitioning can harm consumer decision quality.
Making good health insurance decisions is important to health outcomes and longevity, but consumers’ errors are well documented. This paper examines the interaction of two choice architecture tools: ordering the options from best to worst based on a high-quality user model and partitioning the total set of options. While ordering and partitioning do not always improve choices separately, the results of one field study and three experiments identify the conditions that allow the combination to greatly improve health insurance decisions. Process data shows that these effects are achieved by focusing consumers’ limited attention on higher quality options.
Thus, ordering and partitioning health insurance options based on a high-quality predictive user model is an inexpensive and efficient way to apply firm-level knowledge to improve social welfare. Firms in healthcare can use this knowledge to develop new business models that deliver long-term value for consumers, firms, and other stakeholders.
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Related Marketing Courses:
Advertising and Promotion; Consumer Behavior; Digital Marketing; Marketing Analytics; Marketing Communications; Retail Marketing
Full Citation:
Dellaert, Benedict G.C., Eric J. Johnson, Shannon Duncan, and Tom Baker (2022), “Choice Architecture for Healthier Insurance Decisions: Ordering and Partitioning Together Can Improve Consumer Choice.” Journal of Marketing. doi:10.1177/00222429221119086
Abstract:
Making good health insurance decisions is important to health outcomes and longevity, but consumers’ errors are well documented. The authors examine if targeted choice architecture interventions can reduce these mistakes. The paper examines the interaction of two choice architecture tools on improving consumer insurance decisions in online healthcare exchanges: ordering the options from best to worst based on a high-quality user model and partitioning the total set of options. While ordering and partitioning do not always improve choices separately, the authors use one field study and three experiments to identify the conditions that allow the combination to greatly improve health insurance decisions. Findings indicate that when options are ordered such that the best options appear at the beginning of the presented list, partitioning nudges consumers to focus on the best options. However, if the best options are not at the top of the list, partitioning discourages search and can impair consumers’ discovery of the best options. Process data shows that these effects are achieved by focusing consumers’ limited attention on higher quality options. These results suggest that wise choice architecture interventions need to consider the joint effect of choice architecture tools as well as the quality of the firm’s user model.
Special thanks to Demi Oba, Ph.D. candidate at Duke University, for support in working with authors on submissions to this program.
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Benedict G.C. Dellaert is Professor of Marketing, Erasmus University Rotterdam, The Netherlands.
Eric J. Johnson is Norman Eig Professor of Business, Columbia University, USA.
Shannon Duncan is a doctoral student in marketing, University of Pennsylvania, USA.
Tom Baker is William Maul Measey Professor of Law, University of Pennsylvania, USA.