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).

基于选择的联合 (CBC) 分析在预测消费者购买的市场研究中占有重要地位。本研究重点关注旨在增强 CBC 的两个原则：激励调整和基于自适应选择的联合 (ACBC) 分析。虽然这些原则已分别证明了它们提高 CBC 预测准确性的能力，但尚未有研究同时评估这两者。本研究通过两个实验室和两个在线实验填补了这一空白。一方面，结果表明，激励协调 CBC 和假设 ACBC 的预测效果相对较好。另一方面，ACBC 在大样本量的研究中提供了更有效的每信息成本比。此外，新引入的激励协调型 ACBC 实现了最佳预测，但访谈时间最长。根据我们的研究，我们帮助市场研究人员决定是否应用激励调整、ACBC 或两者兼而有之。最后，我们提供了使用开源软件 (R/Stan) 分析 ACBC 数据集的教程。