This study examines the causal relationship between dietary knowledge and behavior by including self-efficacy in the models. Regression analyses supported the hypothesized relationships that self-efficacy mediates effects of dietary knowledge and social influences on dietary behavior. Self-efficacy also accounted for variance in eating behavior not explained by knowledge or demographic variables. The coefficients for the relationship between dietary behavior and knowledge decreased for each of the four dietary models when the influence of self efficacy was added. Interventions and health promotion campaigns should seek to directly address factors influencing diet related self-efficacy instead of focusing on disseminating information only.Efficacy, Mediation, Health Knowledge, Agricultural and Food Policy, Consumer/Household Economics, Health Economics and Policy,

Moon, Wanki

Rimal, Arbindra

English

Research Papers in Economics

1 
 
Self efficacy as a mediator of the relationship between dietary 
knowledge and behavior 
 
 
Arbindra Rimal 
Agribusiness-Department of Agriculture 
Missouri State University 
901 S. National Avenue 
Springfield, MO 65810 
Fax) 417 836 6979 
Tel) 417 836 5094 
Email) arbindrarimal@missouristate.edu 
 
 
Wanki Moon 
Associate Professor 
Department of Food and Resource Economics  
Korea University Seoul, Korea 
Email) wmoon@korea.ac.kr 
Tel) 02-3290-3031 
 
 
 
 
 
 
 
 
Selected Paper at Annual Meeting of Southern Agricultural Economics Association, Atlanata, 
Georgia, January 31 ~ February 3, 2009 
 
 
Abstract: This study examines the causal relationship between dietary knowledge and behavior 
by including self-efficacy in the models. Regression analyses supported the hypothesized 
relationships that self-efficacy mediates effects of dietary knowledge and social influences on 
dietary behavior. Self-efficacy also accounted for variance in eating behavior not explained by 
knowledge or demographic variables.  The coefficients for the relationship between dietary 
behavior and knowledge decreased for each of the four dietary models when the influence of self 
efficacy was added.  Interventions and health promotion campaigns should seek to directly 
address factors influencing diet related self-efficacy instead of focusing on disseminating 
information only. 
 
 2 
 
Self efficacy as a mediator of the relationship between dietary 
knowledge and behavior 
 
 
Introduction and objectives: 
Increased availability of nutritional information has been successful in enhancing public 
awareness of the importance of healthy diet and lifestyles.  The important issue is whether 
enhanced nutrition and health awareness has any significant impact on consumers‟ actual dietary 
behavior.  The date from the healthy eating index (HEI) show that although dietary quality has 
improved over the past years, the diets of most Americans need improvements in several aspects 
(Kennedy et al., 199; Guo et al., 2004).  Studies evaluating the relationship between nutrition 
knowledge and dietary behavior have found no direct correlation between the two (Putler and 
Frazao, 1994; Sapp, 1991).  Therefore, translating the dietary knowledge among individuals into 
healthy behavior remains a challenging task. This study examines the causal relationship 
between dietary knowledge and behavior by including self-efficacy in the models. 
Self-efficacy is defined as a person‟s ability of exerting self-control in changing his/her 
behavior with regard to, for example, food choices (Parcel et al., 1995; Steptoe, et al., 1995), 
smoking ( Schinke et al., 1985) and drug use (Hays and Ellickson, 1990).  A person‟s health 
related self efficacy is influenced by his/her health knowledge and other socio-demographic 
background.  Since self-efficacy itself is explained by the dietary knowledge of individuals 
(Slater, 1989), it is likely to play a mediating role in the relationship between healthy behaviors 
and dietary knowledge.  Consumers with higher level of self-efficacy are more likely to sustain a 
healthy behavior with regard to food choices compared to those with lower level of self-efficacy. 
 3 
 
Theoretical and Empirical Models 
The preceding discussion points to a causal flow from dietary knowledge (hereafter, we 
call these predictor variables) and socio-demographic characteristics to self-efficacy and/or 
dietary behavior.  At this point, an empirical question that remains to be determined is whether 
the predictor variables affect only self-efficacy, or dietary behavior, or both.  We propose a 
mediation model here.  More specifically, we hypothesize that (a) the predictor and socio-
demographic variables influence both self-efficacy and dietary behavior, and (b) these variables 
influence dietary behavior primarily via their link to risk perceptions.  For example, when 
consumers possess a high level of dietary knowledge, they are predisposed to exert a greater 
control over their diets and lifestyle, thereby adopting a healthy dietary behavior.   
The hypotheses above underscore the notion of mediation.  In other words, the mediation 
approach recognizes that consumers‟ self-control (efficacy) over diet and lifestyle can mediate 
the effects of the predictor variables (dietary knowledge) on the dietary behavior (Baron and 
Kenny 1986).  Figure 1 (as adapted from Baron and Kenny 1986) illustrates this modeling 
approach using self-efficacy as mediators of the relationship between dietary behavior and 
predictor variables.  The figure depicts three causal paths in a model of how overall dietary 
behavior is formed: (i) the direct impact of the predictors on dietary behavior (path a); (ii) the 
path from the predictors to the mediators (path b) and (ii) the impact of mediators on attitude 
(path c).   
In this study, the mediating hypothesis is tested using the following four criteria adopted 
from Judd and Kenny (1981) and Baron and Kenny (1986): a) the self-efficacy of individuals 
(mediator) has statistically significant impact on dietary behavior; b) dietary knowledge and 
socio-demographic variables (predictors) have significant influence on dietary behaviors; c) 4 
 
dietary knowledge exert a significant influence on diet related self-efficacy of individuals; and d) 
the effects of dietary knowledge is either diminished or no longer significant when self-efficacy 
is controlled for the dietary behavior equations.   
Following Baron and Kenny (1986) and Judd and Kenny (1981), a series of regression 
models were developed to assess whether risk and benefit perceptions mediated the link between 
the predictor variables and attitude toward agrobiotechnology: 
Model 1: BEHAVIOR = b10 + b11 DIETARY KNOWLEDGE + e 
 
Model 2: BEHAVIOR = b20 + b21 DIETARY KNOWLEDGE +b22 FFICACY + e 
 
Model 3: BEHAVIOR = b30 + b31 DIETARY KNOWLEDGE + b32 FFICACY +b33 AGE + b34 GENDER 
+ b35 INCOME+ b36 EDUC+ b37 RACE+ b38 HOUSEHOLD SIZE + e  
 
Model 4: EFFICACY  = b40 + b41 DIETARY KNOWLEDGE + b42 AGE + b43 GENDER + b44 
INCOME+ b45 EDUC+ b46 RACE+ b47 HOUSEHOLD SIZE + e  
 
Comparing estimated coefficients across Models 1 - 4 allows us to assess whether self-
efficacy mediates the effects of the predictor variables on dietary behavior.  To illustrate, assume 
that dietary knowledge exerts a statistically significant influence on behavior in Model 1.  If 
dietary knowledge in the Model 2 has a negligible effect on behavior, it indicates that the effect 
of dietary knowledge is largely transmitted via the degree of self-control consumers can exercise 
on their diet and lifestyle.  Second, if the effect of self-efficacy in Model 3 differs little from that 
in Model 2, it suggests that impacts of efficacy on diet behavior remain stable despite the 
presence of other predictors (socio-economic profile) in the model.  The last case is a 
combination of the previous two: although the effects of efficacy in Model 3 are smaller to those 
in Model 2, they remain statistically significant.  This indicates that the effects of dietary 
knowledge are partially mediated by risk perceptions. 
 5 
 
The empirical model posits that a participant‟s dietary behavior is a function of dietary 
knowledge, self control (efficacy) in changing health behavior with regard to food choices and 
life-style and various socio-economic characteristics of individuals. The model, therefore, can be 
formally written as  
(1)  Uj =  ‟Zj +  j, 
where Uj is the actual dietary behavior of the jth household and Zj is a vector of explanatory 
variables  including  participant‟s  socio-economic  profile.    While  Uj  is  unobserved,  what  is 
observed is the reported dietary behavior represented by the rank-ordered dependent variables, R, 
where 
(2)  R = 0  if Uj  0 
  R = 1  if 0 < Uj  1 
  R = 2  if  1 < Uj  2 
. 
  . 
  R = w  if  w-2 < Uj  
 
where the  ‟s are the threshold variables or cut-off points which provide the ratings of  five 
different  responses.  The  lowest  ranked  outcome,  R  =  0,    represents  the  situation  when  the 
specific  diet  was  considered  almost  never;  highest  ranked  outcome,  R  =  w,  represents  the 
situation when the consumer considers the specific diet nearly all the time. 
 
The Data 
In 2007, a national on-line survey among 3,458 US household was conducted.  
Households were randomly selected from the database of 400,000 households who make up 
Ipsos-NPD marketing research panel.  The selection process was appropriately stratified to 
ensure that the demographic characteristics of the sample households corresponded with the 6 
 
latest U.S. census.  Sample households were sent e-mails soliciting information regarding their 
soy-consumption pattern and household characteristics.  Each e-mail included a unique URL 
(keyed to the respondent‟s ID) to direct the respondent to the survey website.  In addition to 
socio-economic characteristics of sample households, survey instruments included questions 
relating to three key components in the mediating model: dietary knowledge, dietary behavior 
and diet related self-efficacy. 
Respondents were asked dietary behavior questions about fresh fruits, fresh vegetables, 
fat and cholesterol (Table 1).  They were asked to respond as to how well the statements 
described their dietary behavior using a scale of one to five where one represented “not at all” 
and five represented “extremely well.”  Four statements to measure diet related self-efficacy 
were read to the participants in the survey. Respondents‟ reported self-efficacy were recorded on 
a 5-point scale.  All responses were first coded such that the higher values represented high level 
of self-efficacy.  Respondents were asked to respond as to how well the statements described the 
self-control (efficacy) in changing health behavior with regard to food choices and life-style 
(Table 2). The lowest degree of self-control was represented by the response “extremely 
unlikely” and the highest degree of self control was represented by „the response “extremely 
likely.” The total self-efficacy score was divided by total number of statements to construct an 
efficacy index. The higher the index value the higher the overall level of self control. A dietary 
knowledge construct (Moorman and Matulich, 1993) was calculated as the total number of 
nutrition related questions respondents answered correctly.  Respondents were asked to link or 
match each of the eleven nutrients with appropriate health consequences from a list.  
 
 7 
 
Preliminary results and conclusion: 
Regression models for each of the four dietary behaviors: fresh fruits, fresh vegetables, 
fat and cholesterol were run and reported in Tables 4 to 7.  Initially, only knowledge was used as 
the explanatory variable. Self-efficacy and socio-demographic variables were added in 
subsequent runs. 
Regression analyses supported the hypothesized relationships that self-efficacy mediates 
effects of dietary knowledge and social influences on dietary behavior for each of the four 
dietary items. Self-efficacy also accounted for variance in eating behavior not explained by 
knowledge or demographic variables. The pseudo R-squared for each of the four dietary items 
increased by a huge magnitude when self-efficacy was added to the models. The coefficients for 
the relationship between dietary behavior and knowledge decreased or became statistically 
insignificant for each of the four dietary models when the influence of self efficacy was added.  
Interventions and health promotion campaigns should seek to directly address factors influencing 
diet related self-efficacy instead of focusing on disseminating information only. 
 
   8 
 
Figure 1.  Conceptual model depicting the mediating role of self-efficacy between dietary 
behavior and predictor variables (adapted from Baron and Kenny, 1986). 
   
 
Self-Efficacy 
-Nutritional labels 
-Change diet to reduce the risk of 
diseases 
-Exercise three times a week 
-Prevent health problems before 
feeling symptoms 
 
(Mediators) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Dietary Knowledge 
Socio-demographics 
 
(Predictor Variables) 
 
Dietary Behavior 
 
(Dependent 
Variable) 
b 
a 
c 9 
 
Table 1: Food Consumption Behavior of US households (n=3056). 
 
How well each of 
the statements 
describes you? 
 I eat a lot of 
fresh fruits 
 
I eat a lot of 
fresh vegetables  
 
I am actively 
trying to 
consume less fat 
in my diet  
I am actively 
trying to 
consume less 
cholesterol in my 
diet  
 1 = Not at all  5.9%  5.5%  8.1%  12.2% 
 2 = Slightly  19.8  17.0  13.4  16.1 
 3 = Somewhat  33.8  33.0  31.8  31.0 
 4 = Very well  25.9  29.2  31.8  26.7 
 5 = Extremely well  14.5  15.2  14.9  13.9 
 
 
   10 
 
Table 2: Reported level of self-control (Efficacy) in changing health behavior with regard to 
food choices and life-style (n=3056). 
  
How likely are you to:  
1 =  
“Extremely 
Unlikely” 
2 = 
Slightly 
3 = 
Somewhat 
4 =  
Very 
much 
5 =  
Extremely 
Likely 
Read nutritional Labels 
on food packages very 
carefully  12.5  19.5  27.8  24.6  15.5 
Change Diet to reduce 
the risk of certain 
diseases  23.3  18.9  28.9  20.1  8.7 
Exercise at least three 
times per week  25.9  20.2  19.6  17.3  16.9 
Prevent health problems 
before feeling any 
symptoms  9.0  17.9  35.2  27.5  10.4 
 
   11 
 
 
Table 3:  Description of other explanatory variables used in the analysis. 
 
VARIABLES  DESCRIPTION  Mean  Std. 
Deviation 
Dietary Knowledge  Total number of dietary questions answered 
correctly (0 to 11). 
6.085  3.142 
Sociodemographics       
   Gender  1 = female; 0 = male  0.501  0.500 
   Age  Respondents‟ age in years  49.722  14.754 
   Income  1 = less than $5,000; 25 = $250,000 or more   67,377  38,292 
   Education  1 = college or more than college education; 
0= otherwise 
0.649  0.477 
   Household Size  Number of household member  2.612  1.399 
   Ethnic background  1 if white; 0 otherwise  0.734  0.442 
 
   12 
 
 
Table 4: Mediation by efficacy in the relationship between dietary knowledge and fruit 
consumption behavior: An Ordered Probit Model 
 
Variables 
Model1  Model2  Model3 
Coeff.  P-value  Coeff.  P-value  Coeff.  P-value 
ONE  1.399  0.000  0.265  0.000  -0.154  0.142 
KNOW  0.028  0.000  0.002  0.706  -0.006  0.317 
EFFICACY 
   
0.563  0.000  0.551  0.000 
AGE 
       
0.005  0.000 
GENDER 
       
0.215  0.000 
INCOME 
       
0.001  0.005 
EDUCA 
       
0.090  0.036 
RACE 
       
-0.125  0.003 
HHSIZE 
       
0.043  0.001 
Mu( 1)  0.914  0.000  1.012  0.000  1.025  0.000 
Mu( 2)  1.812  0.000  2.006  0.000  2.031  0.000 
Mu( 3)  2.628  0.000  2.910  0.000  2.945  0.000 
Pseudo-R-Squared
*  0.01  0.32  0.35 
*R
2
ML = 1 – exp(-G
2/N), where G
2 = -2 ln [L(Mα)/L(Mβ)]; Mα = restricted likelihood, Mβ = Unrestricted Likelihood, 
and N=Number of observation (Maddala, 1983) 
   13 
 
Table 5: Mediation by efficacy in the relationship between dietary knowledge and vegetable 
consumption behavior: An Ordered Probit Model 
 
Variables 
Model1  Model2  Model3 
Coeff.  P-value  Coeff.  P-value  Coeff.  P-value 
ONE  1.361  0.000  0.171  0.008  -0.415  0.000 
KNOW  0.041  0.000  0.016  0.005  0.007  0.286 
EFFICACY 
   
0.592  0.000  0.579  0.000 
AGE 
       
0.008  0.000 
GENDER 
       
0.246  0.000 
INCOME 
       
0.002  0.000 
EDUCA 
       
0.050  0.243 
RACE 
       
-0.115  0.007 
HHSIZE 
       
0.041  0.002 
Mu( 1)  0.853  0.000  0.955  0.000  0.976  0.000 
Mu( 2)  1.756  0.000  1.962  0.000  2.002  0.000 
Mu( 3)  2.649  0.000  2.960  0.000  3.016  0.000 
Pseudo-R-Squared
*  0.03  0.36  0.39 
*R
2
ML = 1 – exp(-G
2/N), where G
2 = -2 ln [L(Mα)/L(Mβ)]; Mα = restricted likelihood, Mβ = Unrestricted Likelihood, 
and N=Number of observation (Maddala, 1983) 
 
   14 
 
Table 6: Mediation by efficacy in the relationship between dietary knowledge and fat 
consumption behavior: An Ordered Probit Model 
 
Variables 
Model1  Model2  Model3 
Coeff.  P-value  Coeff.  P-value  Coeff.  P-value 
ONE  1.087  0.000  -0.617  0.000  0.524  0.000 
KNOW  0.055  0.000  0.019  0.002  0.025  0.000 
EFFICACY 
   
0.889  0.000  0.128  0.000 
AGE 
       
0.006  0.000 
GENDER 
       
0.034  0.368 
INCOME 
       
0.001  0.125 
EDUCA 
       
0.012  0.773 
RACE 
       
-0.003  0.942 
HHSIZE 
       
0.006  0.656 
Mu( 1)  0.621  0.000  0.782  0.000  0.607  0.000 
Mu( 2)  1.507  0.000  1.902  0.000  1.481  0.000 
Mu( 3)  2.475  0.000  3.120  0.000  2.439  0.000 
Pseudo-R-Squared
*  0.05  0.60  0.26 
*R
2
ML = 1 – exp(-G
2/N), where G
2 = -2 ln [L(Mα)/L(Mβ)]; Mα = restricted likelihood, Mβ = Unrestricted Likelihood, 
and N=Number of observation (Maddala, 1983) 
 
   15 
 
Table 7: Mediation by efficacy in the relationship between dietary knowledge and cholesterol 
consumption behavior: An Ordered Probit Model 
 
Variables 
Model1  Model2  Model3 
Coeff.  P-value  Coeff.  P-value  Coeff.  P-value 
ONE  0.932  0.000  -0.662  0.000  0.384  0.000 
KNOW  0.040  0.000  -0.009  0.147  0.004  0.542 
EFFICACY 
   
0.846  0.000  0.333  0.000 
AGE 
       
0.002  0.077 
GENDER 
       
-0.023  0.556 
INCOME 
       
0.000  0.382 
EDUCA 
       
-0.012  0.773 
RACE 
       
-0.054  0.201 
HHSIZE 
       
-0.038  0.004 
Mu( 1)  0.598  0.000  0.748  0.000  0.590  0.000 
Mu( 2)  1.413  0.000  1.768  0.000  1.416  0.000 
Mu( 3)  2.265  0.000  2.839  0.000  2.279  0.000 
Pseudo-R-Squared
*  0.03  0.60  0.29 
*R
2
ML = 1 – exp(-G
2/N), where G
2 = -2 ln [L(Mα)/L(Mβ)]; Mα = restricted likelihood, Mβ = Unrestricted Likelihood, 
and N=Number of observation (Maddala, 1983) 
 
   16 
 
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A model of consumers‟ preventive health behaviors: The role of health motivation and health ability.

Consumer Awareness of Diet-Disease Relationhsips and Dietary Behavior: The Case of Dietary Fat,”

Healthy eating index and obesity”

How generalizable are adolescents' beliefs about pro-drug pressures and resistance self-efficacy?”

Impact of Nutrition Knowledge withjin Expanded Rational Expectations Model of Beef Consumption,”

Skills intervention to prevent cigarette smoking among adolescents.”

Stability and Changes in Health Behaviors in Young Adults over a One Year Period.”

The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations.

Self efficacy as a mediator of the relationship between dietary knowledge and behavior

Self efficacy as a mediator of the relationship between dietary knowledge and behavior

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