validateHOT is an R package that provides functions for preference measurement techniques such as (adaptive) choice-based conjoint analyses (hereafter CBC and ACBC, respectively) and maximum difference scaling (hereafter MaxDiff). Specifically, the package allows users to analyze validation tasks, perform market simulations, and rescale raw utility scores. It is compatible with data obtained using, for example, the ChoiceModelR package (Sermas, 2022) or Sawtooth Software’s Lighthouse Studio (Sawtooth Software Inc., 2024).

Maximum Difference Scaling (MaxDiff) is an essential method in marketing concerning forecasting consumer purchase decisions and general product demand. However, the usefulness of traditional MaxDiff studies suffers from two limitations. First, it measures relative preferences, which prevents predicting how many consumers would actually buy a product and impedes comparing results across respondents. Second, market researchers apply MaxDiff in hypothetical settings that might not reveal valid preferences due to hypothetical bias. The first limitation has been addressed by implementing anchored MaxDiff variants. In contrast, the latter limitation has only been targeted in other preference measurement procedures such as conjoint analysis by applying incentive alignment. By integrating anchored MaxDiff (i.e., direct vs. indirect anchoring) with incentive alignment (present vs. absent) in a 2 × 2 between-subjects preregistered online experiment (n = 448), the current study is the first to address both threats. The results show that incentive-aligning MaxDiff increases the predictive validity regarding consequential product choices—importantly—independently of the anchoring method. In contrast, hypothetical MaxDiff variants overestimate general product demand. The article concludes by showcasing how the managerial implications drawn from anchored MaxDiff differ depending on the four tested variants. In addition, we provide the first incentive-aligned MaxDiff benchmark dataset in the field.

Choice-based conjoint (CBC) analysis features prominently in market research to predict consumer purchases. This study focuses on two principles that seek to enhance CBC: incentive alignment and adaptive choice-based conjoint (ACBC) analysis. While these principles have individually demonstrated their ability to improve the forecasting accuracy of CBC, no research has yet evaluated both simultaneously. The present study fills this gap by drawing on two lab and two online experiments. On the one hand, results reveal that incentive-aligned CBC and hypothetical ACBC predict comparatively well. On the other hand, ACBC offers a more efficient cost-per-information ratio in studies with a high sample size. Moreover, the newly introduced incentive-aligned ACBC achieves the best predictions but has the longest interview time. Based on our studies, we help market researchers decide whether to apply incentive alignment, ACBC, or both. Finally, we provide a tutorial to analyze ACBC datasets using open-source software (R/Stan).

The aim of this chapter is to showcase the effectiveness of partial least squares structural equation modeling (PLS-SEM) in estimating choices based on data derived from discrete choice experiments. To achieve this aim, we employ a PLS-SEM-based discrete choice modelling approach to analyze data from a large study in the German healthcare sector. Our primary focus is to reveal distinct customer segments by exploring variations in their preferences. Our results demonstrate similarities to other segmentation techniques, such as latent class analysis in the context of multinomial logit analysis. Consequently, employing PLS-SEM to examine data from discrete choice experiments holds great promise in deepening our understanding of consumer choices.

Maximum Difference Scaling (MaxDiff) is an essential method in marketing concerning forecasting consumer purchase decisions and general product demand. However, the usefulness of traditional MaxDiff studies suffers from two limitations. First, it measures relative preferences, which prevents predicting how many consumers would actually buy a product and impedes comparing results across respondents. Second, market researchers apply MaxDiff in hypothetical settings that might not reveal valid preferences due to hypothetical bias. The first limitation has been addressed by implementing anchored MaxDiff variants. In contrast, the latter limitation has only been targeted in other preference measurement procedures such as conjoint analysis by applying incentive alignment. By integrating anchored MaxDiff (i.e., direct vs. indirect anchoring) with incentive alignment (present vs. absent) in a 2 × 2 between-subjects preregistered online experiment (n = 448), the current study is the first to address both threats. The results show that incentive-aligning MaxDiff increases the predictive validity regarding consequential product choices—importantly—independently of the anchoring method. In contrast, hypothetical MaxDiff variants overestimate general product demand. The article concludes by showcasing how the managerial implications drawn from anchored MaxDiff differ depending on the four tested variants. In addition, we provide the first incentive-aligned MaxDiff benchmark dataset in the field.