In recent years, machine learning (ML) and artificial intelligence (AI) have attracted
considerable attention in different industry sectors, including marketing. ML and AI hold …

This review article provides reflections on the state of the art of research in conjoint analysis—
where we came from, where we are, and some directions as to where we might go. We …

We present a method that dynamically designs elicitation questions for estimating risk and
time preference parameters. Typically these parameters are elicited by presenting decision …

As technology advances, new products (eg, digital cameras, computer tablets, etc.) have
become increasingly more complex. Researchers often face considerable challenges in …

The opinions of seven respondent groups about the relative importance of different practices
pertaining to the welfare of Australian beef cattle, sheep and goats were surveyed …

Quantitative preference models are used to predict customer choices among design
alternatives by collecting prior purchase data or survey answers. This paper examines how …

Designers faced with the task of developing a new product model of a brand must balance
several considerations. The design must be novel and express attributes important to the …

The authors introduce hybrid individualized two-level choice-based conjoint (HIT-CBC),
which combines self-explicated preference measurement (SE) with choice-based conjoint …

One of the main tasks of conjoint analysis is to identify consumer preferences about potential
products or services. Accordingly, different estimation methods have been proposed to …

Preference learning has been a topic of research in many fields, including operations
research, marketing, machine learning, and behavioral economics. In this work, we strive to …