Response Times and Subjective Complexity of Food Choices: A Web-Based Experiment Across 3 Countries

Accurate collection of response times is one of the main advantages of web-administered stated choice experiments and it can be thought of as a behavioral indicator of cognitive effort. We use data from a food choice experiment administered across three countries and estimate a panel Mixed Multinomial Logit Model to obtain individual-specific utility weights. These are used to construct two utility-based measures of contextual choice complexity, which are combined with subjective measures of cognitive resources as well as indicators of opt-out selection. We first develop and then test hypothesized effects of complexity at the level of single choice task and choice sequence on response times. By using a log-linear random effects model with choice task response-time as dependent variable, we isolate these effects from other background variables. Results suggest that as our measures of complexity increase, so do response times and such effects are robust across the three countries. We argue that these results broadly support the validity of web-based choice surveys to measure food preference. We suggest that computers can help improve survey design by implementing algorithms to improve the overall efficiency of choice tasks design, for example, by using adaptive design algorithms that control cognitive challenges in accordance with the respondent's predicted ability to tackle cognitive effort.