Behavioral and procedural consequences of structural variation in value trees

Our experiment shows that the division of attributes in value trees can either increase or decrease the weight of an attribute. The structural variation of value trees may also change the rank of attributes. We propose that our new findings related to the splitting bias, some other phenomena appearing with attribute weighting in value trees, and the number-of-attribute-levels effect in conjoint analysis may have the same origins. One origin for these phenomena is that decision makers' responses mainly reflect the rank of attributes and not to the full extent the strength of their preferences as the value theory assumes. We call this the unadjustment phenomenon. A procedural source of biases is the normalization of attribute weights. One consequence of these two factors is that attribute weights change if attributes are divided in a value tree. We also discuss how the biases in attribute weighting could be avoided in practice.