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Linear and Nonlinear Valence Functions: A Behavioral Decision‐Making Assessment
Author(s) -
Ravichandran Ramarathnam,
Baker Douglas D.,
Randall Donna M.
Publication year - 1989
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1989.tb01413.x
Subject(s) - expectancy theory , valence (chemistry) , nonlinear system , predictive power , psychology , econometrics , affect (linguistics) , linear relationship , power function , mathematics , function (biology) , social psychology , cognitive psychology , computer science , statistics , mathematical analysis , communication , physics , quantum mechanics , evolutionary biology , biology
In Vroom's [45] original formulation of expectancy theory, the relationship between affect and perceived instrumentality was assumed to be linear. Others have suggested that such a relationship may be better modeled by a nonlinear, utility‐type function [30]. The current research contrasts the predictive ability of two linear and four nonlinear functions. Using four levels of McClelland's [26] needs for achievement, affiliation, and power as instrumentalities, 101 subjects provided more than 12,900 decisions on the valences of jobs in a behavioral decision‐making experiment. Nearly 40 percent of the subjects exhibited nonlinear valence functions. The results emphasize the need to specify the appropriate functional form of the valence component to enhance predictive accuracy and to prevent misspecification problems.