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REGULATORY FOCUS, NUTRITION INVOLVEMENT, AND NUTRITION
KNOWLEDGE
Abstract
Few studies have examined the antecedents of nutrition involvement. Similarly,
conflicting results have been recorded on the relationship between nutrition involvement
and knowledge, knowledge and dietary behaviors, and nutrition involvement and dietary
behaviors. This paper addresses these research gaps by exploring the role of regulatory
focus as an antecedent of nutrition involvement. It also examines the effect of nutrition
involvement on nutrition knowledge and the effects of both involvement and knowledge
on diet adjustment. A large-scale study with 1125 Taiwanese consumers demonstrates a
positive effect of promotion focus and no significant effect of prevention focus, on
nutrition involvement. Sex and income moderate the effect of promotion focus on
nutrition involvement, which in turn has positive effects on nutrition knowledge and diet
adjustment. Nutrition knowledge also has a positive effect on diet adjustment. The study
clarifies these relationships and provides suggestions to policy making.
Keywords: regulatory focus; nutrition involvement; nutrition knowledge; promotion;
prevention.
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REGULATORY FOCUS, NUTRITION INVOLVEMENT, AND NUTRITION
KNOWLEDGE
1. Introduction
Consumer decisions regarding eating behaviors and nutrition lead to consequences such
as illness and obesity that have direct public policy implications (Andrews, Netemeyer &
Burton, 2009; Chandon & Wansink, 2007). An important construct that can inform the
stream of research on food selection/nutrition is the involvement construct (Zaichkowsky,
1985, 1986); more specifically, consumer involvement in nutrition (nutrition
involvement). This is so because obesity is preventable and enhancing consumer
involvement in nutrition enables achieving this goal. This study examines the effect of
nutrition involvement on nutrition knowledge and dietary behaviour as well as the effect
of regulatory focus on nutrition involvement.
Whereas a few studies have examined the consequences and moderating effects of
nutrition involvement (e.g., Mulders, Corneille, & Klein, 2018), little research has
examined its antecedents. This study addresses this gap by examining the effects of
regulatory focus on nutrition involvement based on the fundamental motivational
differences between promotion and prevention focus (Higgins, 1997). Regulatory focus
theory has been employed to examine food intake and nutrition issues (e.g. Sengupta &
Zhou, 2007). The theory proposes two types of foci – promotion (pursuit of positive
outcomes) and prevention (avoidance of negative outcomes). The present study finds
differential effects of promotion and prevention focus on nutrition involvement.
The study also examines the effect of (a) nutrition involvement and (b) nutrition
knowledge on diet adjustment following advice. The relationship between nutrition
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involvement and food selection and intake is unclear (Chandon & Wansink, 2007;
Moorman, 1990). Similarly, research has recorded a weak association between nutrition
knowledge and dietary intake (Sapp & Jensen, 1997). A possible reason for this
inconsistent association is that the assessment of nutrition knowledge was not reliable
(Parmenter & Wardle, 1999). The current study employs a reliable and valid scale of
nutrition knowledge and demonstrates a positive relationship between knowledge and
diet adjustment following advice. By documenting the effect of nutrition involvement and
nutrition knowledge on diet adjustment following advice, the study makes useful
contributions to the realm of nutrition and health policy making.
In sum, this study makes the following contributions to the literature. First, the
literature is scarce on the antecedents of consumer’s nutrition involvement. Second, it
extends the current understanding of the effects of regulatory focus on health and
nutritional issues. Third, the study provides empirical clarification on the relationship
between (a) nutrition involvement and consumer’s knowledge of nutrition, and (b)
nutrition involvement and nutrition related behavior, where ambiguous findings have
been reported. Finally, by demonstrating the effect of nutrition knowledge on dietary
behavior (diet adjustment following advice), the study seeks to clarify the inconsistencies
regarding this effect reported earlier and showcases the benefits of enhancing consumer’s
nutrition involvement and knowledge.
2. Literature review
2.1. Involvement
Involvement is a person’s perception of the relevance of the object based on needs,
values, and interests (Zaichkowsky, 1985). For the present study, the behavioral form of
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involvement was adopted, following the definition by Stone (1984): “Involvement shall
be defined as time and/or intensity of effort expended in the undertaking of behaviours”
(p. 210). Zaichkowsy (1986) proposed personal factors (e.g., interest and values), object
or stimulus factors (e.g., source/ content of communication) and situational factors (e.g.,
occasion) as antecedents of involvement. Further to the early studies, little research has
examined the antecedents of involvement. Also, given the domain specificity of
involvement (a person’s involvement with domain X may not be correlated with her
involvement with domain Y), it is important to understand the specific factors that lead to
nutrition involvement which will inform actionable strategies.
Regarding the consequences of nutrition involvement, Moorman (1990) found
that involvement, measured as enduring motivation, enhanced self-assessed ability to
process nutritional information, but not comprehension of information. Chandon and
Wansink (2007) noted that consumer involvement leads to better calorie estimations.
2.2. Regulatory focus
Regulatory focus theory suggests that two types of foci - promotion and prevention -
guide people’s behaviors (Higgins, 1987, 1997). Individuals with a promotion focus are
concerned about the presence or absence of positive outcomes that lead to desired end
states, whereas those with a prevention focus are concerned about the absence or
presence of negative outcomes. Promotion focus is concerned with people’s wishes and
aspirations, whereas prevention focus is concerned with their duties and responsibilities.
The theory also suggests that the two foci are not always a stable, individual difference
variable (Higgins, 1998). Rather, the foci can be situationally induced.
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The theory has been employed by scholars studying health and nutrition (Keller,
2006; Sengupta & Zhou, 2007; Karnal et al. 2016). For example, Sengupta and Zhou
(2007) showed that impulsive eaters develop a promotion focus when they see food items
that are tempting, which in turn drive their choice behavior to consume the hedonically
tempting food.
2.3. Consumer knowledge of nutrition
Consumer knowledge refers to information, both conceptual and relational, regarding the
domain stored in consumer’s memory. Studies have examined consumer knowledge of
nutrition. For example, Moorman et al. (2004) found that subjective knowledge of
nutrition affects where consumers search and this in turn leads to better quality choices.
While some researchers found that nutrition knowledge does not have an effect on food
label use (Nayga 2000), Miller and Cassady (2015), in their review, report a positive
effect.
Studies have found that nutrition knowledge is correlated with greater weight loss
among low-income mothers who are either obese or overweight (Klohe-Lehman et al.,
2006). Men have been found to have poorer knowledge of nutrition compared to women,
and nutrition knowledge decreases among people with lower education level and those
who belong to lower socio-economic classes (Parmenter, Waller & Wardle, 2000). A
salient finding from this stream of research is the weak and inconsistent association
between nutrition knowledge and (a) dietary intake (Sapp & Jansen, 1997; Perlstein et al.
2017) and (b) nutrition related behaviors in general (Sapp, 1991). A possible reason for
this is the unreliable assessment of nutrition knowledge (Parmenter & Wardle, 1999).
3. Hypotheses
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3.1. Effect of regulatory focus on nutrition involvement
Promotion and prevention focus serve the purpose of goal pursuit, though the specific
strategies employed by individuals with different foci will differ (Lanaj et al., 2012).
Both serve the purpose of health pursuit, albeit through approach (e.g. engaging in
healthy behaviors) and avoidance (e.g. avoiding unhealthy behaviors) routes (Crowe &
Higgins, 1997). Therefore, both foci can be related to nutritional involvement.
Prior research has noted that promotion focused individuals engage in relational
processing of information while prevention focused individuals engage in item level
processing (Zhu, 2003). Relational elaboration involves “generating a wide range of
associations that pertain to the similarities, connections, or relationships among a
provided set of items, or to a given item” (Zhu, 2003; p. 4). But item specific elaboration
focuses on the specific details of each piece of information (Meyers-Levy, 1991).
Consequently, only limited and rather immediate associations are generated (Zhu, 2003).
People often encounter information related to diseases and health conditions
resulting from poor dietary habits. Promotion focused individuals, through relational
processing, are able to generate a variety of associations using this information, and
thereby connect it to the nutrition domain, which enhances saliency of this domain. It also
enhances the utilitarian value of nutritional practices, as such practices can prevent
diseases. In addition, relational processing enhances the risk probability of poor
nutritional choices by highlighting the connection between poor choices and diseases.
Following research findings, increased saliency of the domain, greater utilitarian value,
and greater risk probability enhances people’s nutrition involvement (Kapferer &
Laurent, 1993). Hence, promotion focused people are likely to exhibit greater levels of
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nutrition involvement. Prevention focused people engage in item level processing, focus
on the particularities of the given data and therefore generate only limited associations
(Zhu, 2003). When they encounter information related to diseases as well as health
conditions, they are unlikely to relate the information to nutrition. Consequently, for
prevention focused individuals, nutrition domain is not salient, utilitarian value of
nutrition is not brought to the fore, and perceived risk probability of poor nutritional
choices is not highlighted. Given the possible association of prevention focus with
nutrition involvement discussed earlier (since both serve the purpose of health pursuit),
we expect a weak positive effect between prevention focus and nutrition involvement.
H1a: Promotion focus will have a positive relationship with consumers’ nutrition
involvement.
H1b: Prevention focus will have a positive relationship with consumers’ nutrition
involvement; the effect will be significantly weaker than the effect of promotion focus on
nutrition involvement.
3.2. Moderating effects of sex, age, and income
In this section, we propose the moderating effects of sex, income, and age on the effect of
promotion focus on nutrition involvement. No hypotheses are offered regarding the effect
of prevention focus on involvement since our expectation, as shown above, is at best a
weak positive effect.
3.2.1. Sex: Prior research has recorded that females have greater knowledge of nutrition
and healthier habits compared to males (Yahia et al. 2016). von Bothmer and Fridlund
(2005), in a study among Swedish university students, found that female (vs. male)
students had healthier nutritional habits. Similar results were obtained in a study among
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older adults in the United Kingdom (Baker & Wardle, 2003) and athletes in a U. S.
university (Dunn, Turner, & Denny, 2007). Studies have also found that females (vs.
males) eat more fruits and vegetables (Wardle, Parmenter & Waller, 2000). Therefore,
the overwhelming evidence suggests that females have higher levels of nutritional
involvement. Hence, the effect of promotional focus on nutritional involvement will be
weaker among females compared to males. In other words, females are likely to have
nutritional involvement irrespective of promotion focus. But males have lower nutritional
involvement and consequently, their involvement levels will be more responsive to their
regulatory promotion focus. Hence we propose the following hypothesis:
H2: Sex moderates the effect of promotional focus on nutrition involvement such that the
effect is stronger among males, compared to females.
3.2.2. Income: Research has recorded a positive association between income and health
(Ecob & Smith, 1999). Low income people experience greater pressure to make ends
meet. Hence, they are less able to devote time to issues such as nutrition. It has also been
noted that low income consumers use fewer information cues and try to avoid “the cost of
thinking” (Shugan, 1980). They are also likely to avoid the cost of searching information
(Walsh, Evanschitzky, & Wunderlich, 2008). In addition, they will also not be able to
spend extra money on nutritious food. Consequently, even when they are more promotion
focused, the effect on nutritional involvement will be weak. Note that low-income
consumers experience constraints (time, money) that limit the resources they can devote
for nutritional issues; hence we argue this effect. But higher income people have more
time and money for issues concerning health. Research has noted that income and
education are correlated (Arnould, Plastina, & Ball, 2009) and hence higher income
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consumers process information better and are able to reason better, especially when
motivated to do so. Therefore, when their promotion focus increases, their nutritional
involvement is likely to show greater increase. Hence,
H3: Income positively moderates the effect of promotion focus on nutritional
involvement such that the effect is greater among high (vs. low) income consumers.
3.2.3. Age: Older consumers are more likely to have health problems and are more
concerned about health. Hence, they will be more concerned about nutrition related
issues, leading to greater levels of nutritional involvement. This is especially so as older
consumers have more free time (East et al., 2000), which enables them to devote
attention to nutrition and health issues. This effect will operate irrespective of promotion
focus. But younger consumers are less likely to be concerned about nutritional issues,
leading to lower levels of nutritional involvement. Promotion focus will likely enhance
nutritional involvement among younger consumers. In other words, the effect of
promotion focus is likely to manifest more strongly among younger compared to older
consumers. Therefore,
H4: Age negatively moderates the relationship between promotion focus and nutritional
involvement such that the effect is stronger among younger (vs. older) consumers.
3.3. Effect of nutrition involvement on knowledge of nutrition
As noted earlier, the relationship between nutrition involvement and nutrition knowledge
is unclear. Wansink (2005) suggested that consumers high in nutritional involvement are
likely to be more knowledgeable about nutritional issues than their low-involvement
counterparts are. Moorman (1990) found that nutrition involvement, measured as
enduring motivation, does not increase comprehension of nutrition information.
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Involvement is associated with (a) more time and effort that are spent on
searching the domain (Stone, 1984) and (b) cognitive elaboration and greater processing
of relevant information (Celsi & Olson, 1988). Highly involved, compared to uninvolved,
consumers are more motivated to form accurate judgements. Therefore, they engage in
more intensive search of relevant information (Pillai & Hofacker, 2007). These effects
will operate with nutrition involvement thereby resulting in the acquisition of greater
amounts of nutritional information by involved consumers. Therefore,
H5: Nutrition involvement is positively related to the level of nutrition knowledge.
3.4. Effect of nutrition knowledge on diet adjustment following advice
The effect of knowledge on the intention to perform the behavior and subsequent
behavior has been highlighted by the theory of planned behavior (Ajzen, 1985; Ajzen &
Fishbein, 1977). Research has documented the positive effects of diet adjustment and diet
modification. For example, it has been shown that diet modification, which implies a
move towards greater consumption of natural products, can help prevent cancer (Abdulla
& Gruber, 2000) and is critical in managing diabetes (vanWormer & Boucher, 2004).
Popular press and mass media constantly highlight the importance of diet modification
for better health. Nutrition knowledge enables better understanding and processing of the
messages which enables subsequent action. Consumers are also likely to receive several
pieces of advice regarding nutrition from multiple sources in their day-to-day lives.
Greater levels of nutrition knowledge enhance the processing of such messages, and
enables consumers to understand the implications of following such advice. Therefore,
greater levels of knowledge will lead to diet adjustment following advice.
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H6: Greater the level of nutritional knowledge, greater the incidence of diet adjustment
following advice.
3.5. Effect of nutrition involvement on diet adjustment following advice
As noted earlier, the effect of nutrition involvement on nutrition related behaviors is
unclear (Moorman, 1990; Chandon & Wansink 2007). A direct effect of involvement on
behavior is true by the definition of involvement. Nutrition involvement enhances the risk
importance and risk probability of nutritional domain (Kapferer & Laurent, 1993), which
will lead to individuals paying greater attention to advice regarding diet, and being more
likely to heed such advice. Hence we propose a direct effect of nutrition involvement on
diet adjustment following advice.
H7: Greater the level of nutrition involvement, greater the incidence of diet adjustment
following advice.
Hypotheses 5, 6, and 7 propose that nutrition involvement leads to both nutrition
knowledge and diet adjustment following advice, while nutrition knowledge leads to diet
adjustment. Taken together, the hypotheses propose a partial mediation effect of nutrition
knowledge on the relationship between nutrition involvement and diet adjustment. Hence,
H8: Nutrition knowledge partially mediates the relationship between nutrition
involvement and diet adjustment following advice.
Figure 1 shows the hypothesized model.
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Insert figure 1 here
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4. Method
The hypotheses were tested in a national level study conducted in Taiwan. We chose
Taiwan as a representative country for the region. The high per capita GDP is comparable
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to Hong Kong, Japan and South Korea, while the ethnic similarity with Chinese lead to
meaningful extrapolation of the findings to urban China. Dietary habits have been
changing in Taiwan. Increases in metabolic syndrome and diabetes were observed from
the 1990s to mid 2000s. Positive dietary behaviors were catching up during this period
(avoidance of animal fat, more intake of fruits, fish etc.) (Pan et al., 2011). All these
trends make Taiwan a useful context for this study by providing insights about nutrition-
related consumer attitudes and behaviors in East Asia.
Data were collected through stratified sampling from all the major geographical
regions in Taiwan – North, Middle, South and East in early 2009. Care was taken to
ensure that the geographical distribution of the sample corresponded with the census data.
The proportion of consumers aged 20-64 from each region in the national population
matched with the proportion of consumers from each region in the sample, within a
margin of 2%. Trained research assistants undertook the data collection. These research
assistants contacted adults from the general population and sought participation in the
study. 1176 questionnaires were completed. After rejecting incomplete ones, 1125 usable
questionnaires were obtained.
4.1. Measures
Nutrition knowledge was measured using a 21 item scale developed by the current
authors. Prior research has employed diverse methods to measure nutrition and health
knowledge. For example, Moorman (1990) employed a ten item scale to measure
consumers’ health knowledge. Nayga (2000) used an 8 item knowledge scale. In the
nutrition science domain too, various scales have been employed and concern has been
raised that some of these scales do not meet standards of reliability or construct validity
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(Parmenter & Wardle, 1999; Sapp & Jensen, 1997). Attempts have been made to remedy
the problem through the development of scales through accepted scale development
procedures (Dickson-Spillmann, Siegrist, & Keller, 2011). However, food being
culturally rooted, these scales are often based on the more popular food consumed in the
countries in which they were developed. Therefore, we sought to develop our own scale
suited to the context of study (Taiwan) to measure consumer’s nutrition knowledge.
The scale development followed standard psychometric procedure such as (a)
initial review of the literature and consultations with dieticians in Taiwan to develop an
initial pool of 179 items (b) paring down the number of items to 73 following interviews
with dieticians and masters students in food and nutrition regarding accuracy and clarity
of construction of the items, and (c) pretest among 60 Taiwanese students in the U.K to
understand the difficulty and discrimination of the items. Items with poor discrimination
scores were dropped and 21 items were selected.
Nutrition involvement was measured using the five-item scale proposed by
Chandon and Wansink (2007) (I pay close attention to nutrition information; Calorie
levels influence what I eat; It is important to me that nutrition information is available; I
ignore nutrition information (reverse coded); I actively seek out nutrition information).
Responses were obtained on a 7 item scale. Promotion and prevention focus were
measured using the Regulatory Focus Questionnaire (RFQ) (Higgins et al., 2001).
Promotion focus scale comprises six items, while the prevention focus scale comprises
five items. Responses were obtained on a 1 to 5 agree-disagree scale.
Diet adjustment following advice was measured by a four item scale, adapted
from the cues about the importance of eating a quality diet scale employed by Sapp and
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Weng (2007). The items included (1) I have once been affected by mass media
presentations to adjust my diet type (2) I have once listened to my doctor’s
recommendations to adjust my diet type (3) I have once accepted my family member’s
advice to adjust my diet type and (4) I have once accepted my friend’s recommendation
to adjust my diet type. Responses were obtained on a yes-no format.
The scales in English were translated to traditional Chinese by three PhD level
English-traditional Chinese bilingual researchers in Taiwanese academia who were
employed in the health or health psychology fields. Six Masters students studying
Chinese literature, with English as their mother tongue, back-translated the questionnaires
to English. The versions were compared and required modifications were made, after
consultations with the researchers and the Masters students. The translated questionnaire
was pretested with 42 consumers from different socio-economic backgrounds to ensure
that it works well.
The moderating variables can have direct effects on the three dependent variables.
Literature provides unequivocal support only for the direct effect of sex on nutrition
involvement, nutrition knowledge and dietary behaviour. In order to account for any
potential direct effects and provide a more rigorous test of the proposed hypotheses, we
controlled for the effects of sex, income and age on nutrition involvement, nutrition
knowledge and diet adjustment following advice.
4.2. Scale reliability and validity
Nutrition involvement scale had reliability (coefficient alpha) of .83. The scale was also
found to be unidimensional as 60.2% of the variance was extracted by the first principal
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component. The coefficient alpha value of the six item promotion scale was .86, while
that of the five item prevention scale was .85.
The nutrition knowledge scale is a formative scale. Following the
recommendations of Diamantopoulos and Winklhofer (2001), we examined indicator
collinearity which could potentially lead to item redundancy. This was done through a
dummy variable regression analysis, with knowledge as the dependent variable and the
individual items as the independent variables. Maximum VIF obtained (1.39) was less
than the recommended cut-off value of 10 (Neter, Kutner, Nachtsheim, & Wasserman,
1996). Therefore, no significant redundancy was observed.
Reliability estimates of the knowledge scale and the diet adjustment scale were
also obtained using the Proportional Reduction of Loss method (Rust & Cooil, 1994).
This method can be used to compute the reliability of formative scales. The measure is
evaluated similar to the coefficient alpha, with .7 being the acceptable threshold of
reliability. The PRL method gave estimates of .93 for the nutrition knowledge scale and
.79 for the diet adjustment scale. These estimates can be considered as very conservative
as they are obtained from the table with a maximum number of 20 judges (Rust & Cooil,
1994, p. 7), whereas the sample size for the study is 1125. Note that reliability increases
with the number of judges for a given proportion of inter-rater agreement. Overall, both
the scales are deemed very reliable.
Confirmatory Factor Analysis (CFA) using maximum likelihood estimation was
employed to test the convergent and discriminant validities of the three multi-item
constructs. The fit indices indicated that the model had a good fit (χ2 (101) = 427, p <
.001; TLI = .95; CFI = .95; RMSEA = .05). We also computed construct reliabilities
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using standardized loadings. Construct reliabilities of nutrition involvement, promotion,
and prevention were .83, .86, and .85, all higher than the threshold value of .75 (Bagozzi
& Yi, 1988). Average variance extracted and factor loadings were examined to assess
convergent validity. AVE values of Nutrition Involvement, Promotion, and Prevention
were .50, .51, and .52, all of them being higher than the recommended cut-off value of .5.
Factor loadings of all the constructs were statistically significant at .05 level. In addition,
indicators load substantively on their respective constructs, as the standardized
coefficients are greater than .5. Together, these results indicate convergent validity. We
examined discriminant validity by comparing the AVE values with the square of the
correlation between the construct and each of the other constructs. Discriminant validity
is obtained if the square of the intercorrelation is less than the AVE values of the
corresponding constructs (Fornell & Larker, 1981). This was the case for all pairs of
constructs, establishing discriminant validity.
Common method variance is discounted for the study as the responses for the
variables were obtained through different operational procedures (Likert scale items for
regulatory focus and involvement, multiple choice items for knowledge, yes-no response
choices for diet adjustment scale; response items for different income, age, sex groups).
This conforms to the recommendation by Podsakoff et al. (2003, p. 888) that the
predictor and criterion variables should be measured using different response formats. A
Harman’s one factor test, run using the three multi-item scales (promotion, prevention,
nutrition involvement) yielded 28.2% of variance for the single factor, which is
acceptable. A common factor analysis was also run. The regression estimate between the
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common factor and each item was .00, denoting an extremely low common variance.
Overall, it can be concluded that common method variance is not an issue.
4.3. Descriptive statistics
The sample exhibited a high level of nutrition involvement with a mean of 5.29 ± 1.76.
Promotion (Mean = 3.34 ± .86) and prevention (Mean = 3.28 ± .93) were just above
average. Nutrition knowledge was moderate with a mean of 11 ± 2.91. Diet adjustment
following advice too was above average (Mean = 2.47 ± 1.48). Table 1 provides the
descriptive statistics. We compared the distribution of age with the data for Taiwan
obtained from U. S. census bureau for 2008 and found that there is a broad
correspondence with the national figures on age distribution.
-----------------------------
Insert tables 1 and 2 here
-----------------------------
4.4. Test of hypotheses
The hypotheses were tested using a structural model. The model obtained an acceptable
fit (χ2 (215) = 612, p < .001; IFI = .95; CFI = .95; RMSEA = .041). The supplementary
table shows the parameter estimates. It can be seen that promotion focus is positively
related to nutrition involvement but prevention has no relationship with involvement;
these results provide support for H1a but not H1b. Sex tended to have a directional
negative moderating effect on the effect of promotion on nutrition involvement (p =
.052); since males were coded as 1 and females as 2, the directional negative moderating
effect indicates that the effect is likely to be stronger among males; thus there is some
evidence of directional support to H2. The moderating effect of income on the
promotion- nutrition involvement relationship is supported; the effect of promotion is
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greater among high income people, thus supporting H3. The moderating effect of age is
not significant; thus H4 is not supported. Nutrition involvement is found to have a
positive effect on nutrition knowledge, supporting H5. As hypothesized by H6, nutrition
knowledge has a positive effect on diet adjustment following advice. According to H7,
nutrition involvement has a positive effect on diet adjustment following advice. It can be
seen that this hypothesis is also supported.
The significance of paths in hypotheses 5, 6, and 7 together seem to indicate a
partial mediation effect. In order to provide a more rigorous test for the effect, we
conducted the Sobel test (Sobel, 1982). Note that Sobel test is powerful when the sample
size is large, as is the case with this study. The test was significant (Test statistic = 3.567;
S.E. = .008; p = .00). Therefore, H8 is supported.
It is possible to argue that nutrition knowledge leads to nutrition involvement. To
rule out this possibility, we ran another model with nutrition knowledge leading to
nutrition involvement and the other constructs remaining the same. It was found that the
chi-square value for the second model went up from 612.4 to 651.6. The AIC for the first
model was 782.4, whereas the same for the second model was 821.6. This indicates that
the specified model is superior to the alternative model.
5. Discussion
Regulatory focus theory, proposes the existence of fundamental motivational differences
among people (Higgins, 1997, 1998). The very existence of such differences, and their
influence on consumer behaviors, make them very relevant to the study of issues that
affects public policy. Not many studies have examined the effects of regulatory focus on
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such issues (e.g., Dholakia et al., 2006). The results of this study point to the applicability
of regulatory focus theory to the study of diet and nutrition.
The study found that promotion focus leads to consumer’s nutrition involvement.
Prevention focus had no effect on nutrition involvement. The former result is in line with
theoretical expectations. It is important to note that promotion focused people are ideal
focused (pursuing their own goals) whereas prevention focused people are ought focused
(pursuing responsibilities). Expectedly, it is the former who are likely to devote attention
to their nutrition needs. The higher aspirational levels of promotion focused consumers
will lead to greater involvement with nutrition to enhance their well-being. More
interesting are the moderating effects of sex and income. Sex tended to have a
moderating effect such that the effect is likely to be greater among males (vs. females).
The study found that the effect of promotion focus on nutritional involvement is greater
among high (vs. low) income consumers.
Both regulatory foci serve the purpose of goal pursuit, albeit through different
strategies (Lanaj et al., 2012). Thus, while both promotion and prevention focused
individuals will be motivated to maintain good health, the former are more likely to
employ approach strategies such as nutritional involvement, while the latter will employ
avoidance strategies (e.g., avoiding risky behaviors). The findings of the study indicate
that involvement is essentially an approach behavior.
The study found that nutrition involvement leads to nutrition knowledge. While
the theory of involvement leads to such an expectation, prior findings have been rather
equivocal (e.g., Moorman, 1990). The current study provides useful empirical
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clarification regarding this effect. The finding was obtained using a rigorously developed
scale, following recommended psychometric procedures, to assess nutrition knowledge.
The study also contributes to the literature by demonstrating a pertinent
behavioral consequence of nutrition knowledge. In this context too, the study provides
empirical clarification regarding an important effect where conflicting findings have been
recorded (Nayga, 2000; Sapp, 1991; Sapp & Jensen, 1997). However, as noted by
previous researchers, knowledge was often measured poorly, using scales comprising
only a few items. This study reexamines the effect using a reliable 21 item scale and
confirms the effect. In so doing, it provides valuable guidance to public policy efforts in
enhancing consumer’s nutrition involvement and knowledge.
Finally, the study showed the effect of nutrition involvement on diet adjustment.
The relationship between nutrition involvement and food selection is unclear in the
literature (Moorman, 1990; Chandon & Wansink 2007). The finding of this study
supports the conclusion that nutrition involvement has a positive effect on diet selection.
The direct effect of nutrition involvement on diet adjustment is stronger than the indirect
effect through nutrition knowledge. This underscores the importance of investing efforts
to promote nutrition involvement.
The study employed data collected through a rigorous procedure, which broadly
corresponded to the national distribution regarding relevant demographic variables. This
is a key strength of the study and adds to the external validity of the findings.
5.1. Theoretical implications and future research
As mentioned earlier, few studies have examined the determinants of nutrition
involvement. This is a major contribution of the study and furthers the research agenda in
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this direction. Given the problems of obesity and illnesses directly linked to unhealthy
eating habits and the direct effect of nutrition involvement on dietary behaviors
demonstrated in this study, the construct of nutrition involvement merits greater scholarly
attention. Future research can examine the role of other antecedents on nutrition
involvement as well as the boundary conditions of these relationships. Future research
can also examine the boundary conditions of the relationships between (a) nutrition
involvement and nutrition knowledge (b) nutrition knowledge and dietary behavior and
(c) nutrition involvement and dietary behavior that will help develop a more fine-grained
understanding of these effects.
In trying to understand the effect of regulatory focus on nutrition involvement
and thereby dietary behaviour, the study complements research in areas such as
neuroeconomics that seeks to decipher the mechanism behind nutrition choices (Bruce,
Krespi & Lusk 2015; Muller & Prevost 2016). Future research can investigate the
neurological bases and neural responses of regulatory foci and the paths through which
they influence dietary behavior.
5.2. Managerial implications
Prior research has recorded that regulatory focus can be induced (Roney et al., 1995).
Situational factors that can prime a person’s aspirations (duties) can induce promotion
(prevention) focus. Therefore, in addition to being an individual difference variable,
regulatory focus can also be considered as a motivational state (Zhu, 2003). The
implication is that public agencies can induce promotional focus through appropriate
communication, which should enhance their nutritional involvement. The direct effect of
promotion focus on nutritional involvement would call for such a course of action. But,
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as the results of this study indicate, the efficacy of this intervention will vary between
high and low income groups and is likely to vary between males and females. The
demonstrated positive effects of nutrition involvement on nutrition knowledge and
dietary adjustment would call for such an intervention.
5.3. Limitations
The study has some limitations. The hypotheses have been tested using correlational
design. Field experiments can further examine the hypothesized relationships. Further
research can also replicate the study in western contexts.
To conclude, the study makes useful contributions to the streams of research on
regulatory focus, nutrition involvement, and nutrition knowledge. It is hoped that the
reported findings will (a) fuel further research and (b) inform public policy formulation.
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Figure 1
Hypothesized model
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Table 1
Descriptive Statistics
Mean ± SD
Nutrition involvement
5.29 ± 1.76
Promotion
3.34 ± .86
Prevention
3.28 ± .93
Nutrition knowledge
11 ± 2.91
Diet adjustment following advice
2.47 ± 1.48
Percentage
Sex (Males)
42.1
Sex (Females)
57.9
Age (20-30)
32.5
Age (31-40)
27.9
Age (41-50)
21.1
Age (51-60)
14
Age (above 60)
4.5
Income (No income)
10.5
Income (Less than NT$ 17280)
13.2
Income (NT$ 17281-25000)
21.2
Income (NT$ 25001-35000)
22.2
Income (NT$ 35001-45000)
16.9
Income (NT$ 45001-55000)
11.7
Income (NT$ 55001-65000)
3.7
Income (Above NT$ 65000)
.5
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Table 2
Correlation Matrix
N.I.
Promo.
Preven.
N.K.
D.A.
N.I.
1.00
Promotion
.161**
(.00)
1.00
Prevention
.019
(.52)
.309**
(.00)
1.00
N.K.
.233**
(.00)
.065*
(.03)
.009
(.75)
1.00
D.A.
.215**
(.00)
.096**
(.00)
.068*
(.02)
.171**
(.00)
1.00
** significant at < .01 level.
* significant at < .05 level.
( ) indicates p values.
N.I - nutrition involvement; N.K. - nutrition knowledge; D.A. - diet adjustment
following advice
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Supplementary Table
SEM Parameter estimates
S.P.C
S.E.
C.R.
P value
Promotion -> N.I.
.384
.077
5.002
.00
Prevention -> N.I.
-.095
.068
-1.394
.163
Prom.XSex -> N.I.
-.096
.049
-1.946
.052
Prom.XIncome -> N.I.
.145
.048
3.035
.002
Prom.XAge -> N.I.
.002
.050
.046
.963
N.I. -> N.K.
.457
.062
7.395
.000
N.K. -> D.A.
.054
.015
3.508
.000
N.I. -> D.A.
.187
.032
5.819
.000
Sex -> N.I.
.342
.101
3.393
.000
Income -> N.I.
.006
.030
.198
.843
Age -> N.I.
.013
.042
.320
.749
Sex -> N.K.
.693
.171
4.045
.000
Income -> N.K.
.081
.051
1.587
.113
Age -> N.K.
-.012
.070
-.174
.861
Sex -> D.A.
.250
.088
2.852
.004
Income -> D.A.
.026
.026
.994
.320
Age -> D.A.
.016
.036
.445
.657
S.P.C - Standardized path coefficient; S.E. – Standard error; C.R. – Critical ratio
N.I - Nutrition involvement; N.K. - Nutrition knowledge; D.A. - Diet adjustment following advice
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Supplementary file
Appendix
NUTRITION KNOWLEDGE SCALE
1. Which is a safer method for long-term weight-loss?
(1) Surgeries + Slimming products (2) High-protein diets + Slimming products (3) Balanced low calorie
diets + Physical activities (4) Very low calorie diets + Physical activities
2. Which of the following is classified as acidic pH food?
(1) Milk (2) Citrus fruits (3) Green leafy vegetables (4) Meat products
3. Which vitamin is water-soluble?
(1) A (2) B (3) E (4) K
4. What vitamin can be added to milk to prevent rickets?
(1) A (2) B (3) C (4) D
5. Which nutrient helps heal wounds and scar tissue?
(1) Vitamin A (2)Vitamin C (3) Zink (4) Calcium
6. Your friend believes that consuming eggs is not healthy; how would you reply?
(1) Yes, eggs can provide good quality protein. (2) No, eggs contain high-cholesterol. (3) No, it causes
heart disease. (4) Eggs can be healthy or unhealthy, depending on the individual's daily diet.
7. Which statement presents the healthiest way to judge food in daily diets?
(1) To judge from the viewpoint of whether or not that food can reduce the incidence of diseases (2) Just
read the nutritional content to decide whether or not the food is good (3) Listen to most people's comments
on that food (4) Understand its role in all sorts of balanced meals and understand how foods are paired well
with each other
8. Which of the following oils contains the richness of omega-3 fatty acid?
(1) Olive oil (2) Sun-flower seed oil (3) Fish oil (4) Corn oil
9. Which food is an alkaline?
(1) Plum (2) Lemon (3) Pork (4) Lamb
10. A hamburger meal special (Hamburger, coke, fries, and apple pie) is low in what nutrient?
(1) Calcium (2) Sodium (3) Iron (4)Phosphorus
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11. Which is not a good source of calcium?
(1) Fresh milk (2) Yogurt (3) Skimmed milk (4) Butter
12. Which of the following menus contains the least amount of Vitamin C?
(1) One barbecued steak, carrots, noodles and Coke (2) One hot dog, lettuce salad, fries and milk (3) One
spaghetti with meat and tomato sauce, garlic bread and wine (4) One barbecued steak, broccoli, noodles
and green tea
13. Vitamin K is found chiefly in:
(1) Leafy green vegetables (2) Beef (3) Oats (4) Skimmed milk
14. Which oil is not conducive to the prevention of cardiovascular diseases?
(1) Peanut oil (2) Coconut oil (3) Soybean oil (4) Sunflower oil
15. Which nutrients are related to the maintenance of a person’s basic taste?
(1) Magnesium (2) Chromium (3) Copper (4) Zinc
16. Which of these is not the main function of protein in human body?
(1) Acts as an acid-base equilibrium (2) Provides energy (3) Promotes growth and repairs the tissues (4)
Maintains the balance between water and electrolytes
17. Which of the following factors is not a criterion taken into account by DRI (Dietary Reference Intake)?
(1) Age (2) Gender (3) Body weight (4) Have special diseases
18. Vitamin C can help body to absorb what mineral?
(1) Copper (2) Zinc (3) Iron (4) Magnesium
19. The material that cannot be decomposed by human digestive organs is known as:
(1) Crude fiber (2) Fiber (3) Dietary fiber (4) Residue
20. When the body needs energy, which carbohydrates are known as the fastest source of energy?
(1) Glycogen (2) Fructose (3) Sucrose (4) Glucose
21. Which group is the best source for calcium intake?
(1) Icy milk, butter, cheese (2) Milk, cheese, yoghurt (3) Sardines, spinach, cabbage (4) Chocolate milk,
dried fish, butter
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