Making Inferences About Missing Information: The Effects of Existing Information

Abstract

This article is concerned with the process by which inferences about missing information are formed. We propose that consumers use within-attribute (other-brand) processing first and, if that information is diagnostic, infer the value of the missing information with little consideration of additional information. If the within-attribute processing does not prove diagnostic, consumers then move to within-alternative (same-brand) processing. In two experiments, we find that high variation in other-brand information results in a statistically significant discount to the inferred value even with no variation in same-brand information, whereas the reverse is not true. This is supportive of an inference-formation process in which other-brand information is considered first. Further, we find that, if there is low variation in other-brand information, time and effort are reduced, attention to the attribute with the missing information is proportionately increased, and there is a shift away from alternative-based processing. These results, encompassing both traditional process measures and inference-based measures, support the process model that we advance.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 117 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.