The Formation of Market‐Level Expectations and Its Covariates

A formal model of market‐level expectations is developed and used to identify testable hypotheses. The empirical findings indicate that market‐level expectations are more adaptive in nature than previously thought. The study also provides the first systematic investigation of cross‐industry variatio...

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Bibliographische Detailangaben
Veröffentlicht in:Journal of Consumer Research. - University of Chicago Press. - 30(2003), 1, Seite 115-124
1. Verfasser: Anderson, Eugene W. (VerfasserIn)
Weitere Verfasser: Salisbury, Linda Court
Format: Online-Aufsatz
Veröffentlicht: 2003
Zugriff auf das übergeordnete Werk:Journal of Consumer Research
Schlagworte:Economic Theories and Analysis Economic Psychology Macro Consumer Behavior Bayesian Inference Survey Research Mathematics Social sciences Business Economics Behavioral sciences Eugene W.
Beschreibung
Zusammenfassung:A formal model of market‐level expectations is developed and used to identify testable hypotheses. The empirical findings indicate that market‐level expectations are more adaptive in nature than previously thought. The study also provides the first systematic investigation of cross‐industry variation in the formation of market‐level expectations. Several factors, including advertising, word‐of‐mouth, market growth, and purchase frequency, are found to have a significant moderating influence on the adaptation rate. Finally, we find that market‐level expectations adjust faster when perceived quality declines, suggesting that negativity biases manifest at a macrolevel—a phenomenon that has not been previously observed.
Beschreibung:* Eugene W. Anderson is professor of marketing and associate dean for degree programs at the University of Michigan Business School, 701 Tappan Street, Ann Arbor, MI 48109‐1234 ( geneaumich.edu ). Linda Court Salisbury is a doctoral candidate at the University of Michigan Business School, 701 Tappan Street, Ann Arbor, MI 48109‐1234 ( lsalisbu@umich.edu ). The authors gratefully acknowledge the support of both the National Quality Research Center at the University of Michigan Business School and the University of Michigan Business School. In addition, this research has benefited from the helpful comments of a number of friendly reviewers. The authors would also like to thank Jae Cha, Lopo Rego, and Sanal Mazvancheryl for their work in assembling various portions of the data set.
ISSN:15375277
DOI:10.1086/374694