Incorporating preference uncertainty in best worst scaling

Area of Science:

• Consumer Behavior
• Marketing Science
• Psychometrics

Background:

• Traditional Best-Worst Scaling (BWS) elicits preferences but may overlook respondent uncertainty.
• Understanding consumer preferences for product attributes is crucial for market strategy.
• Preference uncertainty can influence decision-making and requires further investigation.

Purpose of the Study:

• To enhance the Best-Worst Scaling (BWS) method by incorporating preference uncertainty.
• To elicit more informative relative preferences for wine attributes among consumers.
• To identify and analyze uncertainty levels associated with wine purchasing decisions.

Main Methods:

• Developed an extended Best-Worst Scaling (BWS) method incorporating preference uncertainty.
• Applied the novel method to 342 Argentinian wine consumers evaluating 16 wine attributes.
• Compared results from the extended BWS with the standard BWS approach.

Main Results:

• Significant variability in uncertainty levels was observed across and within wine attributes.
• Incorporating preference uncertainty altered the attribute preference rankings compared to standard BWS.
• The choice of uncertainty indicator influenced the observed preference alterations.

Conclusions:

• The extended BWS method provides a more nuanced understanding of consumer preferences by accounting for uncertainty.
• Preference uncertainty is a critical factor that can modify traditional preference rankings.
• Further research should explore diverse indicators to investigate preference uncertainty heterogeneity.