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Demographic Diversity and Effective Message Design: How Behavioral Theory Can Help
Unformatted Document Text:  Diversity effects 3 segmentation strategy if these variables are related to the behavior of interest. The particular focus on demographic variables has indeed been fueled by epidemiological studies that found relations between demographic variables and unhealthy behaviors such as unsafe sex (e.g., Janssen, de Wit, Hospers & van Griensven, 2001), and illicit drug use (e.g., EMCDDA, 2002; Johnston, O’Malley & Bachman, 1999; Pedersen, Mastekaasa & Wichstrom, 2001). Such findings justify the use of demographic variables for segmentation purposes, but they do not imply an effective tool for selecting segmentation dimensions. One way to interpret these findings is that the best segmentation dimensions are those that are important determinants of the behavior. Following this reasoning, theories of behavioral prediction should be able to guide the selection of segmentation dimensions. This paper illustrates the usefulness of behavioral theory for examining the validity of demographic variables, i.e., ethnicity, gender and age, as segmentation dimensions. Our focus is on health messages that address adolescents’ regular marijuana use. One theoretical framework that can account for the relationship of ethnicity, gender and age with regular marijuana use is the Integrative Model of Behavioral Prediction (Fishbein, 2000; Fishbein, Cappella et al., 2002). The Integrative Model differentiates between a set of proximal determinants of intention and behavior and a set of distal variables. The difference between the two types of variables is that proximal determinants directly predict intention and behavior, while distal variables are indirectly related to intention and behavior. Ethnicity, gender and age are considered to be distal variables; there is no necessary relationship with marijuana use, but when there is, it is indirect. That is, differences in marijuana use cannot be caused by ethnicity, gender and age. Instead, demographic segments hold different beliefs about marijuana use, and these belief differences ultimately account for differences in marijuana use. Thus,

Authors: Fishbein, Martin., Cappella, Joseph. and Yzer, Marcus.
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Diversity effects 3
segmentation strategy if these variables are related to the behavior of interest. The particular
focus on demographic variables has indeed been fueled by epidemiological studies that found
relations between demographic variables and unhealthy behaviors such as unsafe sex (e.g.,
Janssen, de Wit, Hospers & van Griensven, 2001), and illicit drug use (e.g., EMCDDA, 2002;
Johnston, O’Malley & Bachman, 1999; Pedersen, Mastekaasa & Wichstrom, 2001). Such
findings justify the use of demographic variables for segmentation purposes, but they do not
imply an effective tool for selecting segmentation dimensions. One way to interpret these
findings is that the best segmentation dimensions are those that are important determinants of the
behavior. Following this reasoning, theories of behavioral prediction should be able to guide the
selection of segmentation dimensions. This paper illustrates the usefulness of behavioral theory
for examining the validity of demographic variables, i.e., ethnicity, gender and age, as
segmentation dimensions. Our focus is on health messages that address adolescents’ regular
marijuana use.
One theoretical framework that can account for the relationship of ethnicity, gender and
age with regular marijuana use is the Integrative Model of Behavioral Prediction (Fishbein,
2000; Fishbein, Cappella et al., 2002). The Integrative Model differentiates between a set of
proximal determinants of intention and behavior and a set of distal variables. The difference
between the two types of variables is that proximal determinants directly predict intention and
behavior, while distal variables are indirectly related to intention and behavior. Ethnicity, gender
and age are considered to be distal variables; there is no necessary relationship with marijuana
use, but when there is, it is indirect. That is, differences in marijuana use cannot be caused by
ethnicity, gender and age. Instead, demographic segments hold different beliefs about marijuana
use, and these belief differences ultimately account for differences in marijuana use. Thus,


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