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The role of social control in promoting healthy eating behavior among Chinese immigrants: an ecological approach

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The role of social control in promoting healthy eating behavior among Chinese immigrants: an ecological approach

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THE ROLE OF SOCIAL CONTROL IN PROMOTING HEALTHY EATING
BEHAVIOR AMONG CHINESE IMMIGRANTS: AN ECOLOGICAL APPROACH

By

Zheng An

!

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMMUNICATION)

!
!
May 2015

Copyright 2015 Zheng An

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ACKNOWLEDGEMENTS
I would like to express my deepest gratitude to my advisor, Dr. Margaret
McLaughlin, who strongly encouraged me to pursue my interests and supported my
dissertation ideas. I would never have been able to finish my dissertation without her
guidance and her support every step of the way. I also owe a debt of gratitude to Dr.
Sandra Ball-Rokeach, who inspired my interest in neighborhood storytelling, who
brought me to the Metamorphosis Team. I was very fortunate to work with a group of
brilliant people. I would also like to thank Dr. Chih-Ping Chou whose thoughtful
comments and suggestions have substantially contributed to the development of scales to
measure Chinese dietary beliefs. Thank you all for being patient with me.
Special thanks goes to Dr. Peter Monge, Anne Marie Campian, and the Annenberg
people who helped me along the way. I would also like to thank the Annenberg School
for financially supporting my dissertation research. Thank you to Dr. Susan Harris from
JEP, who provided me financial support while I was writing my dissertation in the San
Francisco Bay Area.
I would like to thank my husband, Wenbin Weng for always being there for me. I
would also like to thank my parents for their unconditional love and my parents-in-law
for their endless support. Thank you to my son, Andy Weng, who brings so much joy and
happiness to the family.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS ................................................................................................ ii
LIST OF TABLES .............................................................................................................. v
LIST OF FIGURES ........................................................................................................... vi
ABSTRACT ...................................................................................................................... vii
CHAPTER 1: INTRODUCTION ....................................................................................... 1
Overview ......................................................................................................................... 1
Chapter Summaries ......................................................................................................... 4
CHAPTER 2: THEORETICAL FRAMEWORKS ............................................................ 6
Computer-Mediated Social Support: The Interpersonal-Level ....................................... 6
Computer-Mediated Social Support from Strong Ties ............................................... 6
Computer-Mediated Social Support as a Dimension of Social Control ..................... 8
Computer-Mediated Social Support and Dietary Behavior ...................................... 11
The Neighborhood Storytelling Network: The Community-Level .............................. 13
Communication Infrastructure Theory ..................................................................... 13
The Social Control Function of the Neighborhood Storytelling Network ................ 16
Cultural beliefs about Dietary Behavior: The cultural-level ......................................... 22
Cultural Beliefs as Social Control ............................................................................. 22
Self-Control: Pathways Linking Social Control to Healthy Eating Behavior .............. 26
Planning .................................................................................................................... 27
Self-efficacy .............................................................................................................. 28
Moderating Effects of Social Control in Intention-Behavior Relationships ................. 30
The Intention-Behavior Gap ..................................................................................... 30
Comparisons between Foreign-Born and US-Born Immigrants ................................... 32
CHAPTER 3 DATA AND METHOD ............................................................................. 38
Participants .................................................................................................................... 38
Measures ....................................................................................................................... 43
Data Analysis ................................................................................................................ 50
CHAPTER 4 RESULTS ................................................................................................... 52
Preliminary Analysis ..................................................................................................... 52
Computer-Mediated Social Support ......................................................................... 52
Cultural beliefs about Dietary Behavior ................................................................... 55
Primary Results ............................................................................................................. 63
Descriptive Statistics ................................................................................................. 63
Social Control Hypotheses ........................................................................................ 65
Moderation Hypotheses ............................................................................................ 72
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Comparisons between Foreign-Born and US-Born Immigrants ............................... 78
CHAPTER 5 DISCUSSION AND CONCLUSIONS ...................................................... 80
Discussion of Results .................................................................................................... 80
Direct Influence of Social Control on Dietary Behavior .......................................... 80
Indirect Influence through Self-Regulation .............................................................. 82
Moderation Effects of Social Control in Intention-Behavior Relationships ............. 83
Comparisons between Foreign-Born and US-Born Chinese Immigrants ................. 85
Theoretical and Practical Implications .......................................................................... 86
Limitations and Suggestions for Future Directions ...................................................... 90
Conclusions ................................................................................................................... 93
REFERENCES ................................................................................................................. 95
APPENDIX: THE HEALTHY EATING SURVEY ...................................................... 122

v

LIST OF TABLES
Table 2-1: Summary of Hypotheses ..................................................................................37
Table 3-1: Sample Demographics ......................................................................................41
Table 4-1: Summary Statistics and Factor Loadings for Social Support ...........................54
Table 4-2: Summary Statistics for Items of Cultural Beliefs about Dietary Behavior ......55
Table 4-3: Correlations for CBDB Items .........................................................................56
Table 4-4: Summary of Goodness-of-Fit Statistics for CFA Models ..............................60
Table 4-5: Summary Statistics and Factor Loadings for CBDB Items ..............................61
Table 4-6: Correlations between CBDB, Acculturation, and Healthy Eating ...................62
Table 4-7: Descriptive Statistics ........................................................................................65
Table 4-8: Correlations between Variables ........................................................................67
Table 4-9: Direct, Indirect, and Total Effects of Social Control on Healthy Eating .........70
Table 4-10: Correlations between Variables ......................................................................72
Table 4-11: Summary Statistics of Regression Analyses Testing the Strength of
Behavior-Intention Relationships ......................................................................................74
Table 4-12: Summary Statistics of Regression Analyses for Variables Predicting Healthy
Eating Indicators among Intenders ....................................................................................78
Table 4-13: Means and Standard Deviations of Social Control Indicators .......................79

vi

LIST OF FIGURES
Figure 1-1. The conceptual model. ......................................................................................4
Figure 3-1. The hypothesized model. ................................................................................51
Figure 4-1. The two-factor model of social support. .........................................................53
Figure 4-2. Age-adjusted overweight rate by age group. ..................................................64
Figure 4-3. The structural model predicting healthy eating behavior. ..............................66
Figure 4-4. The moderating effect of computer-mediated social support on the
intention-behavior relationship. .........................................................................................75
Figure 4-5. The moderating effect of ICSN on the intention-behavior relationship. ........75
Figure 4-6. The moderating effect of cultural beliefs on the intention-behavior
relationship. ........................................................................................................................76

vii

ABSTRACT
This study examines the role of social control in promoting healthy eating behavior.
The escalating epidemic of overweight and obesity affects more than two-thirds of adults
in the United States. Unhealthy diets increase the risk of being overweight and obese.
Individuals’ desire to overeat unhealthy diets may be restrained by agents of social
integration. Guided by an ecological approach, this study focuses on the ways in which
social control is communicated at interpersonal, community, and cultural levels.
Specifically, this study applies and extends theoretical frameworks of social support,
Communication Infrastructure Theory, and dietary acculturation. It is proposed that social
support, connection to neighborhood storytelling, and cultural beliefs all have a
regulatory impact on dietary behavior. This study also examines the self-regulatory
mechanisms through which social control affects dietary behavior. Hypotheses were
tested using a cross-sectional sample of 505 Chinese immigrants living in the United
States. Immigrants may offer important analytical advantages for understanding the
changes in social environments. The results provide some evidence showing that social
control provides external regulation and facilitates self-regulation of healthy eating
behavior. The role of social control was also tested to explain two phenomena, including
the intention-behavior gap and the healthy immigrant effect. The results show that social
control may facilitate the realization of healthy eating behavior for individuals with the
viii

intention of eating healthy. The results also show that foreign-born Chinese had higher
levels of social control than US-born Chinese did at all levels. The results suggest that
decreased levels of social control experienced after immigration may in part contribute to
the loss of healthy eating habits and immigrants’ health advantages. The results have
important implications for promoting healthy diets and reducing the prevalence of
overweight and obesity.
Keywords: healthy eating behavior, social control, computer-mediated social support,
communication infrastructure theory, cultural beliefs about dietary behavior,
self-regulation, planning, self-efficacy, dietary acculturation, intention-behavior gap,
Chinese immigrants, overweight and obesity
1

CHAPTER 1: INTRODUCTION
Overview
The escalating epidemic of overweight and obesity affects more than two thirds of
adults in the United States (CDC, 2015). Approximately 69% of adults aged 20 years and
over are overweight and 35% are categorized as obese. The overweight prevalence has
nearly doubled since the 1970s and the obesity prevalence is four times that of the 1970s
(Singh, Siahpush, Hiatt, & Timsina, 2011). Overweight and obesity increase the health
risks of type 2 diabetes, hypertension, cardiovascular disease, fatty liver disease, and
some types of cancer (Kumanika et al., 2008). Overweight and obesity are also associated
with increased depression and anxiety as well as social and workforce discrimination
(Luppino et al., 2010; Puhl & Heuer, 2010). The estimated medical cost of treating
overweight and obesity is 5-10% of total U.S. healthcare spending (Tsai, Williamson, &
Glick, 2011).
Changes in dietary patterns and lifestyles are considered to increase the risks of
being overweight and obese (West, Caballero, & Black, 2006). The typical American diet,
also referred to as the Western pattern diet, is characterized as high intakes of sugar, salt,
saturated fat, red meat, and processed meat (Daniel, Cross, Koebnick, & Sinha, 2011;
Wells & Buzby, 2008). It has a high concentration of calories per serving. High
2

consumption of highly processed and energy-dense foods has been linked to an increased
risk of being overweight and obese (Sussner, Lindsay, Greaney, & Perterson, 2008).
Individuals are attracted to energy-dense foods for survival (Drewnowski &
Almiron-Roig, 2010). These foods are usually highly palatable and stimulate a desire to
eat. In a resource-constrained environment, limited access to food is a restraint in itself,
which protects individuals against overeating. Food availability has increased
dramatically in recent decades (Barnard, 2010; West et al., 2006). Individuals have more
resources than ever before to realize their desire to eat.
The desire for tasty but unhealthy foods can be constrained by the society. Social
control is the societal regulation of individuals’ thoughts, feelings, and behaviors in an
attempt to gain conformity (Umberson, 1987). The concept of social control is rooted in
Durkheim’s (1967) seminal work on social integration and suicide. He viewed
relationships of marriage and parenting as providing important role obligations and
meanings of life, which, in turn, protected individuals from committing suicide. The
health implication is that the society serves to regulate our behavior by engaging us in a
wide range of social activities and relationships in an attempt to promote good health.
Social control has been first studied in the area of delinquency and subsequently applied
to health and health behavior (Hirschi, 1969; Browning, Soller, & Jackson, 2015).
Umberson (1987) argued that social control of health behavior could be studied in the
same way as social control of nondeviant behavior. It is assumed that health is a
3

normative state and behaviors that contribute to morbidity and mortality are deviant. The
controlling influence of society may set limits on a person’s desire for diets that are high
in fat, sugar, and salt.
Although social control may not be immediately visible to an individual, it exists at
all levels and may consequently affect health and health behavior. Social control is an
abstract term and is communicated through various channels. Recent studies have used an
ecological approach to capture the communication process through which social and
cultural environments operate (Ball-Rokeach, Gonzalez, Son, & Kligler-Vilenchik, 2012;
Jeffres, Jian, & Yoon, 2013; Rojas, Shah, & Friedland, 2011; Wilkin, 2013). Guided by
the ecological approach, this study focuses on three indicators of social control that are
communicated at interpersonal, community, and cultural levels. The ecological approach
allows for a greater understanding of multiple levels of influences, as well as the
development of more comprehensive and effective interventions (Sallis, Owen & Fisher,
2008).
The main purpose of the present study is to examine the direct and indirect influence
of social control on dietary behavior. It is argued that social control provides external
regulation and affects self-regulation of dietary behavior. Figure 1-1 presents the
conceptual model of this study. In addition, this study explores the role of social control
in explaining two phenomena: namely, the intention-behavior gap and the healthy
immigrant effect. Hypotheses were tested using a cross-sectional sample of Chinese
4

immigrants living in the United States. Immigrants may offer some important analytical
advantages for understanding the changes in social environments, which, in turn,
potentially contribute to dietary changes.

Figure 1-1. The conceptual model.
Chapter Summaries
Chapter 2 presents the theoretical frameworks used in this study to describe the
social control function of multiple-level agents of social integration. It begins by
reviewing social support research and articulating the constraining function of social
support from strong ties (i.e., family and friends) at the interpersonal level. Second, it
reviews the Communication Infrastructure Theory and its current development in health
contexts, followed by a description of the social control function of the neighborhood
storytelling network at the community level. Third, it describes cultural beliefs about
dietary behavior as a social control system operating at the cultural level. Fourth, it
introduces the self-regulatory mechanisms through which social control affects dietary
5

behavior. Finally, it presents two phenomena: the intention-behavior gap and the healthy
immigrant effect, and describes the ways in which social control can be used to explain
them. This chapter ends with a summary of hypotheses tested in this study. Chapter 3
describes the sample, measures, and data analysis strategies. Chapter 4 reports the results
of the data analyses. It begins with preliminary results validating the use of social control
measures, and it follows by presenting descriptive statistics and results of hypothesis
testing using structural equation modeling, multiple regression, and ANOVA tests.
Chapter 5 interprets the findings and discusses the theoretical and practical implications
of promoting healthy eating behavior from a social control perspective. It also addresses
limitations and future research directions.

6

CHAPTER 2: THEORETICAL FRAMEWORKS
Computer-Mediated Social Support: The Interpersonal-Level
Computer-Mediated Social Support from Strong Ties
Computer-mediated social support broadly refers to any supportive interaction that
occurs through the use of communication technologies (e.g., discussion forums, social
networking sites, websites, chat rooms, instant messaging). Supportive interaction is the
“communication between recipients and providers that reduces uncertainty about the
situation, the self, and other or the relationship and functions to enhance a perception of
personal control” (Albrecht & Adelman, 1987, p.19). The majority of studies have
focused on online support groups and communities composed of a network of people
with similar problems or concerns. Despite its popularity, a recent study reports that
individuals desire more support of all forms from family and friends than that from online
groups and communities (High & Steuber, 2014). Computer-mediated social support
from traditional sources (i.e., family members and friends) has remained an unexplored
but important area for research (Wright, 2009). In this study, computer-mediated social
support specifically refers to supportive interactions exchanged between recipients and
their strong ties (i.e., family members and friends) through communication technologies.
Social support can take the forms of informational support (e.g., information and
7

advice), emotional support (e.g., empathy, love), instrumental support (e.g., tangible aid,
services), and appraisal support (e.g., affirmation, constructive feedback) (Heaney &
Israel, 2008). In computer-mediated supportive environments, family members and
friends may send a person a link to a news story, video, or website about dietary practices,
show concerns about his diet, or give suggestions on where to buy healthy produce.
Studying computer-mediated social support from strong ties is important for several
reasons. First, a considerable amount of computer-mediated social support with strong
ties occurs on a daily basis. According to the Pew Research Center (Smith, 2011), more
than two-thirds of US adults frequently use social media to stay in touch with family
members and friends. These social interactions provide a foundation for social support to
occur and play a pivotal role in regulating one’s affect, thought, and behavior (Lakey &
Orehek, 2011; Vega, Kolody, Valle, & Weir, 2008). Second, strong ties are important
drivers of news. In 2013, 21% of US adults received news from family and friends
through social media sites or e-mails (Enda & Mitchell, 2013). Family and friends may
spread news and information about dietary guidelines, healthy recipes, or food safety by
sharing it on social networking sites or by sharing a link to the news in an email or text
message. Third, social support from strong ties may exert more influences on individuals’
health and health behavior than that from other sources (Cutrona, 1990; Heaney & Israel,
2008).
8

Computer-Mediated Social Support as a Dimension of Social Control
The concept of social control at the interpersonal level has been developed from the
tradition of research on social relationships and health (Berkman, Glass, Brissette, &
Seeman, 2000; House, Umberson, & Landis, 1988; Lewis & Rook, 1999; Umberson,
1987). Social control and a number of closely related concepts, such as social integration
(i.e., the extent of participation in social relationships; Seeman, 1996), social networks
(i.e., the structural characteristics of social relationships), and social support (i.e., the
functional content or quality of social relationships; House et al., 1988), refer essentially
to the same phenomena—social relationships are protective for individuals’ health. Social
control is conceptualized as a function of social relationships that provide external
regulation of health behaviors (Umberson, 1987). Social control has direct and indirect
forms. Direct control takes place when members of social relationships take explicit
attempts to monitor, remind, or physically intervene in an effort to change a person’s
health behavior. For example, a father may limit sweets and cookies available to his
children, or a wife may remind her husband to eat green vegetables. Indirect social
control occurs when an individual internalizes norms of behavior for the relationship and
consequently avoids risky behaviors to fulfill his or her responsibilities to others. For
example, parents may eat at regular times throughout the day to set a good example for
their children.
9

Although social control as a constraint on risky behavior is theoretically
distinguished from social support that focuses on the positive, affirming, and encouraging
aspects of social relationships, the two concepts are both functional contents of social
relationships and have considerable overlap in the ways they are measured (House et al.,
1988; Lewis et al., 2004). Social control has been assessed as members of social
relationships providing information, giving advice, reminding or pressuring an individual
to do something for his or her health, and rewarding or praising an individual for
performing a health enhancing behavior (Cohen & Lichtenstein, 1990; Lewis & Rook,
1999; Rook, Thuras, & Lewis, 1990; Westmaas, Wild, & Ferrence, 2002). Lewis et al.
(2004) explored a wide range of social control tactics in close relationships (e.g.,
marriage) and concluded that social control may be best conceptualized and measured as
a continuum of tactics ranging from direct coercion to subtle forms of influences (e.g.,
discussion, modeling, and making suggestions). The current literature suggests that social
control actions can be both unwelcome (e.g., nagging, policing) and positive (e.g.,
expressing support, praising). Positive social control has overlapped with some forms of
social support such as informational support (i.e., the provision of information and advice)
and emotional support (i.e., the provision of empathy, praise, and love). This study
focuses on the positive regulatory aspects of social support.
The tradition of research on regulatory functions of social relationships is rooted in
Durkheim’s (1967) seminal work on social integration. Durkheim viewed human desires
10

as unlimited, and believed that satisfaction stimulates rather than fulfills desires. The
desires can only be regulated by external controls. The society exerts the controlling
influence and puts limits on individuals’ behavior. When social control is weakened,
individuals are more prone to deviant behavior or committing suicide. Durkheim stressed
the importance of family relationships (e.g., parent-child, husband-wife) as sources of
social integration and found that family ties reduced suicide. Durkheim’s contribution
influenced major theoretical developments in linking social relationships with health
behavior through social control.
Umberson (1987) viewed social control as a dimension of social integration. She
proposed that the relationships of marriage and parenting provide external regulation and
facilitate the self-enforcement of norms of health behaviors. When individuals are
attached to family ties, they are likely to engage in health behaviors that promote health
and reduce mortality. House et al. (1988) explicitly defined the social regulation or
control function of social relationships. They articulated that the social regulation
function overlaps with that of social support but emphasizes the constraint on individuals’
behavior. They argued that social regulation and social support are important functional
contents of social relationships that promote individual or collective health-enhancing
behaviors. Berkman et al. (2000) published a review paper and proposed a conceptual
model for thinking about the mechanisms from social networks to health. Constraining
influences are one of the pathways through which social networks affect health behaviors
11

(e.g., diet, exercise). Thoits (2011) published another review paper that focused on the
mechanisms linking social ties and support to health. Social control is viewed as a direct
and active form of social influence. It is a key mechanism through which social ties affect
physical health outcomes. It is important to note that social control from strong ties does
not always produce desirable effects of regulating behavior. It may threaten one’s
autonomy and provoke psychological distress (Hughes & Gove, 1981; Lewis et al.,
2004).
Computer-Mediated Social Support and Dietary Behavior
Although little research has focused on the influence of computer-mediated social
support from strong ties on healthy eating behavior, relevant research examining the
effects of face-to-face social support from strong ties provides substantial empirical
evidence linking social support to healthy eating behavior. For example, Scholz, Ochsner,
Hornung, & Knoll (2013) conducted a longitudinal study of 252 participants who were
overweight. They found that male participants who received social support from their
partners significantly lowered their fat consumption 12 months later. Markey, Gomel, and
Markey (2008) surveyed 104 couples and found that the majority of participants
attempted to regulate their partners’ dietary behavior. They also found that regulatory
attempts (i.e., monitoring, pressuring, and restricting behaviors) were positively
associated with partners’ healthy eating behavior.
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The regulatory function of social support may migrate from face-to-face (FTF) to
computer-mediated contexts. The relationship between FTF and computer-mediated
support may be placed within a larger context that concerns the relationship between FTF
and computer-mediated communication (CMC). Early research proposed the
displacement hypothesis—FTF is negatively associated with CMC (Kraut et al., 2002;
Putnam, 1995). An individual’s total channel use is relatively constant; therefore, time
spent on using one channel will reduce time spent on using another. Recent research has
provided considerable evidence to support the complementary hypothesis, which posits a
positive relationship between FTF and CMC (Dutta-Bergman, 2004a; Dutta-Bergman,
2006; Ellison, Steinfield, & Lampe, 2007; Ognyanova et al., 2013). It is argued that the
functions of interactions affect how individuals use specific communication channels to
fulfill their goals (Bargh & McKenna, 2004). An individual may use both FTF and CMC
that serve a specific functional need, such as seeking or receiving support from family
and friends. Given that computer-mediated social support is positively associated with
FTF social support that regulates dietary behavior, it is proposed that:
H1: Computer-mediated social support will be positively associated with healthy
eating behavior.
!
13

The Neighborhood Storytelling Network: The Community-Level
The regulatory function of the society not only occurs at the interpersonal level, but
also at the community level. Classic work on the neighborhood effects suggests that
community contexts play an important role in regulating health-risk behavior (Browning
et al., 2015; Sampson, 2012; Sharkey, 2013; Wilson, 2012). The Communication
Infrastructure Theory (CIT) explores neighborhood storytelling and its impact on
problem solving and community building. Recent developments of CIT have
demonstrated its usefulness as a theoretical perspective on understanding neighborhood
health (Matsaganis, 2015; Wilkin, Moran, Ball-Rokeach, Gonzalez, & Kim, 2010; Wilkin,
2013). This chapter extends the CIT work by articulating the regulatory function of the
neighborhood storytelling network, which is thought to influence health behavior.
Communication Infrastructure Theory
Communication infrastructure theory (CIT) uses an ecological approach to examine
the communication infrastructure in which stories are created and disseminated in local
communities (Ball-Rokeach, Kim, & Matei, 2001; Kim & Ball-Rokeach, 2006a, 2006b).
The theory is developed based on the assumption that storytelling is a basic way in which
individuals develop their identity as a member of a community. A central concept of CIT
is the integrated connection to a storytelling network (ICSN), which consists of
multi-level storytellers, including residents, community organizations, and geo-ethnic
14

media (i.e., media outlets targeted to a particular geographic area and/or a particular
ethnic population) (Matsaganis, Katz, & Ball-Rokeach, 2010). Ideally, all levels of
storytellers are mutually dependent upon each other and form a neighborhood storytelling
network. Residents are embedded in the storytelling network and connected to various
communication resources to achieve their everyday goals. The triangle network of
neighborhood storytellers may interact with one another to produce synergistic effects.
As Kim, Moran, Wilkin, and Ball-Rokeach (2011) illustrate, a series of neighborhood
events such as a health story in the geo-ethnic media thus prompting dialogue among its
residents and connecting such a story to community organizations may have a greater
than the sum of the individual effects. A second component of CIT is communication
action context (CAC), which consists of characteristics of residential environments that
constrain or facilitate neighborhood storytelling. For example, residents living in an
unsafe neighborhood may be unwilling to chat frequently with other residents on streets
or in parks. Consequently, neighborhood safety issues may hinder the storytelling
process.
CIT posits that ICSN increases levels of civic engagement. There is evidence
supporting the link between ICSN to civic engagement indicators (i.e., political
participation, collective efficacy, and neighborhood belonging) conditioned on the
existence of the neighborhood storytelling network (Chen et al., 2013; Kim &
Ball-Rokeach, 2006a, 2006b; Ognyanova et al., 2013).
15

More recently, CIT has been applied to study health and health behavior with a focus
on reducing health disparities. A few studies have examined the influence of ICSN on
health care access and health literacy (Matsaganis, 2015; Wilkin, 2013; Wilkin &
Ball-Rokeach, 2011). For example, Wilkin and Ball-Rokeach (2011) surveyed 739
Latinos and found that Latinos who had higher levels of ICSN reported greater ease in
getting medical care. Similar results were found in another study that examined the
relationship between connections to health information and health care access outcomes
(Katz, Ang, & Suro, 2012). Katz et al. (2012) surveyed over 3800 Latinos and found that
those who were connected to health information from various sources (e.g., family and
friends, radio, Internet, television, newspapers or magazines, and community
organizations) had a regular place for health care, held continuous insurance coverage in
the past year, and had visited a doctor within the past year. Kim et al. (2011) found a
mediating effect of ICSN on the relationship between education and knowledge about
breast cancer and diabetes. They concluded that ICSN functions as a pathway through
which social inequality factors widen gaps in knowledge about chronic diseases.
Residents with higher socioeconomic status are more connected to communication
resources, which, in turn, increases their knowledge of chronic diseases.
The basic premise of CIT is that residents’ problem-solving capacities are part of the
function of their ICSN (Kim & Ball-Rokeach, 2006a; Kim at al., 2011). Being connected
to communication resources through storytelling is thought to increase the opportunities
16

for collectively identifying and solving problems in a local community context. The
problem-solving capacities range from political participation and health care access to
disaster preparedness.
The Social Control Function of the Neighborhood Storytelling Network
Social control in this study is broadly viewed as the constraining influence on
health-risk behaviors (e.g., unhealthy dietary behavior). The neighborhood storytelling
network sets the structural communication environment in which residents are exposed to
social control that regulates their health behavior. It is proposed that social control is the
functional content of the neighborhood storytelling network. In other words, the extent to
which residents are connected to the neighborhood storytelling network is considered as
an indicator of residents’ exposure to social control.
ICSN is thought to link with health behaviors in important ways. In particular, the
neighborhood storytellers may create, maintain, and modify social norms to control
behaviors. Social norms are perceptions of how members of a community should and
should not behave (Lapinski & Rimal, 2005). It serves as a guideline or standard for
behavior. Social norms can be formed and maintained both formally and informally.
Formal norms such as laws or rules, if broken, may result in legal punishments or
sanctions. Informal norms may be communicated through a variety of ways such as
interpersonal communication and mass media (Eveland, 2002; Friedkin, 2001). Social
17

norms can be seen as an integral part of the neighborhood communication environment
(Yanovitzky & Rimal, 2006). The extent to which norms are formed and maintained in a
community depends upon the relative importance of storytellers to community members
in their everyday life. Residents, geo-ethnic media, and community organizations,
identified as essential neighborhood storytellers by CIT, may individually and as a
cohesive whole enforce the adoption of social norms on healthy eating.
Interpersonal discussion. A number of theoretical frameworks have been used to
examine the role of interpersonal discussion in norm formation (Festinger, 1954; Friedkin,
2001; Hogg & Reid, 2006; Lapinski & Rimal, 2005). Social comparison theory
(Festinger, 1954) posits that individuals are motivated to compare themselves to others to
reduce uncertainty and evaluate the self. By comparing themselves to others, individuals
may normalize their experiences and reduce their sense of social isolation. Friedkin (2001)
advanced work on norm formation by investigating the process in which interpersonal
discussions produce consensus. Social norms are viewed as a special case of attitude. An
individual weighs the attitudes of influential members of his or her social network and
transforms his or her attitude to a normative evaluation that prescribes what thought,
feeling, and behavior should be. By talking to other people, individuals modify their
perceptions of what is appropriate and form a shared attitude. Interpersonal agreement
appears to be fundamental in norm formation.
18

In addition, individuals are motivated to be liked because they are dependent on
others to achieve their goals (Lapinski &Rimal, 2005). They adapt their behaviors to
conform to the socially accepted standards and thereby secure acceptance by others
(Baumeister, DeWall, Ciarocco, & Twenge, 2005). Conformity increases the likelihood
of being liked and helps build meaningful social relationships (Cialdini & Goldstein,
2004). Individuals are rewarded for their actions that are congruent with social norms
during interpersonal interactions. The homophily hypothesis – individuals tend to bond
with those who are similar – has received empirical support from many studies (e.g.,
McPherson, Smith-Lovin, & Cook, 2001).
Individuals who often exchange information with members of their social networks
are more likely to make decisions based on social norms than those with minimal
interactions with their social networks (Albarracín, Kumkale, & Johnson, 2004).
Research consistently shows that interpersonal discussion is the most important source
for obtaining local news and health information in culturally diverse communities (Chen
et al., 2013; Wilkin & Ball-Rokeach, 2006), thus providing opportunities for the diffusion
of social norms. There is evidence showing that interpersonal discussion increases the
prevalence of social norms (e.g., Frank et al., 2012).
Geo-ethnic media. Geo-ethnic media have the linguistic and cultural advantages of
delivering stories targeting ethnic minorities. According to the 2011 American
Community Survey, 21% of people aged 5 and over speak a language other than English
19

at home (Ryan, 2013). Around half of people who speak Korean, Chinese, or Vietnamese
at home do not speak English very well. Language barriers may prevent these individuals
from connecting to mainstream media. Despite that a substantial portion of new
immigrants speak English very well, difficulties in processing information in a non-native
language may make them reluctant to read or watch English news.
Furthermore, geo-ethnic media serving immigrant populations may help immigrants
negotiate and redefine their cultural identity. Food is central to an individual’s cultural
identity (Greene, 2011; Satia et al., 2000). There is constant negotiation over cultural
identities, manifested in food choices and cooking traditions. Members of minority
groups may not be able to make sense of food items or cooking traditions to which they
cannot relate. Nutrition knowledge could be best acquired in cultural and historical
contexts of community members (Pobocik, Montgomery, & Gemlo 1998).
Several studies provide evidence showing geo-ethnic media as promising ways of
reaching ethnic minorities (Literat & Chen, 2014; Matsaganis et al., 2010; Wilkin &
Ball-Rokeach, 2011). For example, Wilkin and Bokreach (2006) randomly surveyed 739
Latinos in Southeast Los Angeles and Pico Union and found that 32% of their
participants rated geo-ethnic television as the most important source of health
information, whereas only 8% rated mainstream television and 2% rated mainstream
newspapers as their most important sources for health information.
Community organizations. Community organizations range from grassroots to formal
20

nonprofit organizations. They initiate and facilitate neighborhood conversations (Wilson,
2012). Examples of the role of community organizations include stimulating
interpersonal discussions, organizing public events, and informing the community about
major public health issues. Such activities may increase individuals’ beliefs of their
ability to control behaviors (e.g., delinquent or health-risk behaviors) of individuals and
groups in the community (Kim & Ball-Rokeach, 2006b).
The social identity perspective may be used to explain why community organizations
play a role in norm formation and maintenance. According to social identity theory, part
of the self-concept derives from perceived membership in social groups (Tajfel, 1979).
Group membership is associated with norms that differentiate a social group from other
social groups (Hogg & Reid, 2006). Individuals who self-categorize themselves as
in-group members are likely to internalize and act in accordance with group norms. By
conforming to norms, they will experience positive affect and express their solidarity
with group members (Lapinski & Rimal, 2005). When the group membership is salient,
individuals are likely to decrease self-awareness and increase susceptibility to normative
influence. Therefore, those who are more identified with a social group will be more
likely to conform to group norms (Boer & Westhoff, 2006; Boldero, Sanitioso, & Brain,
1999; Hogg & Reid, 2006).
Community organizations often operate within a local community and bond people
to develop a place-based identity (Matsaganis, 2015). Individuals are not only attached to
21

the physical environment (e.g., architecture, landscape) that serves as place icons but also
to the ways they live (Hull, Lam, & Vigo, 1994). The nonmaterial properties of a place
such as social norms are a part of the community fabric that organizes and controls
lifestyle activities (e.g., exercising, grocery shopping, healthy eating). For example,
Hernández, MartÍn, Ruiz, and Hidalgo (2010) found that the place-based identity
indirectly prevented individuals from performing anti-ecological behaviors through
increasing norms of pro-environmental behaviors. Research shows that the normative
influence is strongest when norms are salient (Kallgren, Reno, & Cialdini, 2000). It may
be that connections to community organizations activate individuals’ place-based identity,
which, in turn, makes them conform to community norms.
Although little research has focused on the influence of connections to community
organizations on dietary behavior among immigrants, relevant research suggests that
connection to community organizations is linked to health behavior. Fothergill et al.
(2011) reported results from a longitudinal study over a 22-year period on the health
effects of community engagement and in particular engagement in secular and religious
organizations among African-American women in an urban setting. They found that
women who were involved in secular (e.g., civic organizations, social clubs) or religious
organizations to some degree reported better physical and mental health than
non-involved women. Women who persistently engaged in the community organizations
reported lower levels of anxiety and depressed mood than those who only participated
22

early in their life.
It is important to highlight that the neighborhood storytellers described in CIT do not
operate in isolation, but rather interact with each other in producing synergistic effects.
Research has shown that social norms can be enforced most effectively if formed and
maintained through multiple communication channels (Frank et al., 2012; Seo &
Matsagnis, 2013). Individuals are reminded, monitored, and pressured to adopt health
behaviors in structured ways. For example, Seo and Matsagnis (2013) found that
interpersonal discussion amplified the positive effects of being exposed to stories about
eating habits through the media. CIT not only evaluates the strength of each storyteller
but also the level of integration of the three storytellers (Kim & Rokeach, 2006b). ICSN
also has the advantage of providing an overall estimation of the extent to which
individuals are connected to community resources, especially when patterns of
connection to individual storytellers vary dramatically. Therefore, it is hypothesized that:
H2: Integrated connection to the storytelling network (ICSN) will be positively
associated with healthy eating behavior.
Cultural beliefs about Dietary Behavior: The cultural-level
Cultural Beliefs as Social Control
There is no consensus on the definition of culture, and it is a question that has been
debated for over a century in various disciplines (Unger et al., 2004). In general, culture
23

refers to socially learned and shared meanings, values, and beliefs that guide patterns of
behavior (Kreuter & McClure, 2004). Common proxies of culture include race, ethnicity,
national identity, and gender. These proxies are used to represent shared beliefs on health
within a particular cultural group. Other studies focus on the contextualized meanings of
beliefs on health and behaviors specific to local communities (Dutta, 2007).
In this study, culture is viewed as a social control system based on shared beliefs and
norms. Culture shapes individuals’ beliefs about health (Kreuter, Lukwago, Bucholtz,
Clark, & Sanders-Thompson, 2003). These shared beliefs and norms provide the basis for
social control to operate within a culture. Individuals learn the culture’s beliefs about
what constitutes healthy behavior through socialization, and they make behavioral
choices based on cultural beliefs about health, which lead to observed health behaviors
(Dressler, 1993). Cultural beliefs on dietary behavior reflect the internalization of eating
norms that are acceptable to cultural groups. Individuals internalize these norms and
ultimately treat them as moral codes (Godlin, Conner, & Sheeran, 2005; Kong & Hsieh,
2012; Li & Witteborn, 2012).
Social control comes from beliefs that are important to members of a culture
(O’Reilly & Chatman, 1996). Cultural beliefs about health behavior define what is
important and what is appropriate. When individuals identify more with a culture, they
will be more likely to conform to the behavioral expectations of that culture (Hogg &
Reid, 2006; Lapinski & Rimal, 2005). As illustrated in the previous section on the social
24

control function within local communities, the same logic can be applied to articulate the
social control function at the cultural level. Conformity is driven by individuals’
fundamental need to belong (Baumeister & Leary, 1995). Individuals are motivated to
conform to cultural norms in order to be liked by members of the culture (Cialdini &
Goldstein, 2004; Lapinski & Rimal, 2005). This may lead to a sense of belonging when
they form meaningful social relationships and practice shared behavior (Lambert et al.,
2013). In addition, individuals are also motivated to maintain a positive self-concept
(Cialdini & Goldstein, 2004). Performing behaviors that are congruent with cultural
expectations solidifies an individual’s self-concept. Thus, conformity serves the function
of maintaining and enhancing self-esteem.
The importance of cultural beliefs is evidenced in health-related research that tests
the effectiveness of culturally competent messages and interventions. A culturally
competent health intervention considers cultural influences on health beliefs and
behaviors (Betancourt, Green, Carrillo, & Ananeh-Firempong, 2003). There is evidence
linking the levels of cultural sensitiveness of interventions and individuals’ willingness to
adopt a health behavior (Kreuter & Wray, 2003; Roberto, Krieger, & Beam, 2009;
Enwald & Huotari, 2010). In a field experiment, Clarke, Evans, and Hovy (2011)
distributed tailored and generic recipes to individuals living in low-income households at
community food pantries. A tailored recipe was created baseing it on individuals’
preferences identified through a series of yes-no questions (e.g., “with Asian flavors?”).
25

The researchers compared the consumption of fresh vegetables between the two groups
after six days of distribution. It was found that individuals who received the tailored
booklet for food recipes and tips consumed more vegetables than expected, while
individuals who received generic materials consumed fewer vegetables than expected.
Pobocik et al. (1998) described how a school-based nutrition education curriculum was
modified to accommodate needs of the Western Pacific Islanders. The health
professionals worked with twenty-one elementary school teachers and replaced
unfamiliar and expensive foods (e.g., strawberries) with locally available foods (e.g.,
mangos and papayas) in the worksheet. Food preparation activities were also adapted to
reflect cultural practices, such as adding pickled papaya, beef jerky, and salted fish. The
modified curriculum improved relevance and effectiveness for food education in the
cultural context of the students. These studies suggest that changes in health behavior are
most likely to occur when performing the behavior conforms to the shared beliefs and
norms of members of a given culture. It is important that a group’s beliefs and values
should be recognized when interpreting the meanings of health behaviors (Kreuter et al.,
2003).
The social control function of cultural beliefs may be weakened when individuals
loosely identify with a culture. This is evidenced in research on acculturation and dietary
behavior among immigrants. In general, immigrants who identify less with a culture are
less likely to practice dietary behavior that is specific to that culture (Satia et al., 2000;
26

Satia et al., 2001). Many studies show that immigrants who are more acculturated to the
Western culture gradually lose their dietary traditions and are more likely to consume
diets that are high in fat, sugar, and salt (Allen et al., 2014; Batis, Hernandez-Barrera,
Barquera, Rivera, & Popkin, 2011; Hubert, Snider & Winkleby, 2005; Rosenmöller,
Gasevic, Seidell, & Lear, 2011; Sussner et al., 2008; Unger et al., 2004). Therefore, the
extent to which an individual holds cultural beliefs about dietary behavior may reflect
levels of social control internalized by that individual. It is hypothesized that:
H3: Cultural beliefs about dietary behavior will be positively associated with healthy
eating behavior.
Self-Control: Pathways Linking Social Control to Healthy Eating Behavior
Numerous pathways exist by which social control may influence health behavior.
One possible pathway is through self-control. Self-control, also referred to as
self-regulation, “the capacity to control or alter one’s responses, is a vital mechanism for
producing adaptive and socially desirable behavior” (Baumeister et al., 2005, p.590).
Umberson (1987) claimed that social control might facilitate self-regulation of health
behaviors. Social control is effective only if it increases one’s beliefs to manage his or her
life circumstances (Bandura, 2004). Increasing self-regulation is particularly important
for maintaining health behavior in the long term or for adopting a difficult health
behavior after a relapse (Schwarzer, 2008). Research shows that some individuals may
27

not stick to a healthy behavior once constraining influences are removed from their social
environment (Rook et al., 1990).
Performing health behaviors requires self-regulation strategies. Schwarzer (2008)
proposed the Health Action Process Approach and identified two self-regulatory
strategies: namely, planning and self-efficacy, which are central in health behavioral
change, particularly when individuals have already set up a goal or formed an intention.
Planning
The notion of action planning can be traced back to the 1960s, when Leventhal,
Singer, and Jones (1965) claimed that fear appeals could facilitate behavioral change only
when people know where, when, and how to carry out the behavior. Schwarzer (2008)
stressed the importance of having specific instructions on when, where and how to act for
effective goal pursuit. When they encounter barriers, individuals need to have
pre-planned coping strategies to overcome those barriers. “Planning enhances
information processing in terms of increased accessibility, recall, detection, and
discrimination of critical cues (Reuter, Ziegelmann, Wiedemann, & Lippke, 2008, p.
196).” Previous research has found the links between planning and health behavior
(Anderson, Winett, & Wojcik, 2007; Lee et al., 2011; Lippke et al., 2004; Reuter et al.,
2008). For example, Anderson et al. (2007) found that planning, as part of self-regulation,
was the best predictor of adopting a low-fat diet, high in fruits and vegetables, and high in
28

fiber. Lee et al. (2011) described the implementation of an intervention to increase
women’s physical activity and the consumption of fruits and vegetables using an
ecological approach. Their participants selected themselves into teams, collectively
planned their behavioral goals (e.g. distance to walk), and exercised together in each
intervention session. In addition, participants surveyed their neighborhood for areas
favorable for walking or buying fruits and vegetables, and became more aware of
resources (e.g. parks) in their neighborhood, especially places that were not on their route
to work or school. The findings also suggest that agents of social control may teach
individuals planning skills by engaging them in collective activities or providing social
modeling.
Self-efficacy
Self-efficacy is individuals’ beliefs in their ability to perform a behavior (Bandura,
2004). Efficacy beliefs are fundamental to individuals’ motivation and action.
Self-efficacy is a common pathway through which social influences affect health
behavior. Chatterjee et al. (2011) found that interpersonal discussion significantly
predicted self-efficacy, which, in turn, predicted performing preventive health behaviors
in the HIV/AIDS context. Maibach, Flora, and Nass (1991) found that exposure to a
year-long community health campaign increased self-efficacy, which, in turn, contributed
to the adoption of healthy eating behavior and regular exercise. In a series of experiments,
29

Baumeister et al. (2005) found that social exclusion impairs individuals’ ability to
regulate dietary behavior. Individuals who were rejected by social groups or relationships
were more likely to eat cookies and less likely to consume a healthy but bad-tasting
beverage than those who were socially accepted by others. They concluded that social
exclusion may impair self-regulation, which, in turn, increases health-risk behaviors.
The present study conceptualizes a model in which planning and self-efficacy as
important self-regulatory strategies work as partial mediators between social control
indicators and healthy eating behavior. In other words, it is proposed that social control
works through self-regulation before having an influence on healthy eating behavior. The
pathways suggest that individuals may internalize constraining influences and acquire
necessary skills to adopt healthy eating behaviors. Therefore, it is hypothesized that:
H4: Planning will mediate the relationships between computer-mediated social
support (H4a), ICSN (H4b), and cultural beliefs about dietary behavior (H4c) with
healthy eating behavior.
H5: Self-efficacy will mediate the relationships between computer-mediated social
support (H5a), ICSN (H5b), and cultural beliefs about dietary behavior (H5c) with
healthy eating behavior.
30

Moderating Effects of Social Control in Intention-Behavior Relationships
The Intention-Behavior Gap
With the increased efforts in the fight against unhealthy eating, individuals are
becoming more aware of what they eat. Compared to 10 years ago, research shows that
individuals have a broader understanding and increased awareness of what constitutes a
healthy diet (Leatherhead Food Research, 2012). According to the Low Income Diet and
Nutrition Survey 2005, about half of the respondents mentioned eating more fruit and
vegetables as a healthy diet, followed by more fruit/juice, less fat, more fresh food, eating
a balanced diet, using less fat in cooking, eating smaller portions, less sugar, eating
regularly and more variety (National Obesity Observatory, 2011). The Health Survey for
England reveals that about 69% of respondents indicate that they would like to eat
healthier (National Obesity Observatory, 2011).
Despite that, research consistently shows an intention-behavior gap (Sheeran, 2002;
Schwarzer, 2008). In a meta-analysis of 47 experimental studies, Webb and Sheeran
(2006) found that a medium-to-large change in intention generated a small-to-medium
change in behavior. Sutton (1998) found that the constructs of the Theory of Planned
Behavior (TPB) typically explained a much larger variance in intention (40-50%) than in
behavior (19-38%). For example, Povey, Conner, Sparks, James, and Shepherd (2000)
found that TPB and social influence variables explained 42% of variance in individuals’
31

intention to eat healthy, but 15% of variance in the actual eating behavior. Applying TPB
to Dutch adults, Brug, Oenema, and Ferreira (2005) found that the majority of their
participants had positive attitudes, subjective norms and perceived control to eat a low-fat
diet and these constructs were strongly associated with intention. However, more than 80%
of the Dutch population were eating a diet higher in fat than the official recommendation.
Fila and Smith (2006) surveyed 139 Native American youth and found no association
between intention to eat healthy and their dietary behaviors. In a survey conducted by the
International Food Information Council (IFIC, 2011), most respondents recognized the
health benefits of certain foods or components, such as omega-3 fatty acids and B
vitamins, but only about half of them reported having these food components on a regular
basis.
To address the intention-behavior gap, a few studies have explored an inventory of
social influence factors as moderators that may explain the discrepancies between
intention and behavior (Ajzen & Fisbbein, 1974; Schwarzer, 2008; Sheeran, 2002; Webb
& Sheeran, 2006). The goal is to identify social environmental factors that facilitate or
hinder the translation of intention to behavior. Researchers operationalize the moderating
effect by testing an interaction term between a social environmental factor (e.g., social
norms) and behavioral intention. A positive interaction effect indicates that the
intention-behavior gap decreases when levels of social norms increase.
32

In this study, it is hypothesized that social control indicators will moderate the
intention-behavior relationship. It is expected that the association between intention and
behavior will vary depending on the level of social control being exposed or internalized.
A positive interaction between social control and intention would suggest that intention is
a stronger predictor of behavior when levels of social control are higher. For example,
previous research has found that perceived reinforcement as intervening events improved
the intention-behavior relationship (Ajzen & Fishbein, 1974).
However, a negative moderating effect – the association between intention and
behavior will be lower among those who are exposed to higher levels of social control –
will also be possible. In this case, it may be that social control stimulates resistance or
rebellion. Previous studies indicate that constraining influences may threaten individuals’
autonomy (Tilden & Galyen, 1987; Walen & Lachman, 2000). Individuals may adopt
behaviors that are against social norms as a way of showing resistance. It is hypothesized
that:
H6: Computer-mediated social support (H6a), ICSN (H6b), and cultural beliefs about
dietary behavior (H6c) will moderate the relationship between healthy eating
intention and behavior.
Comparisons between Foreign-Born and US-Born Immigrants
Research has repeatedly found a “healthy immigrant effect.” Immigrants in the
33

United States are generally healthier than the native-born (Acevedo-Garcia, Bates,
Osypuk & McArdle, 2010; Corlin, Woodin, Thanikachalam, Lowe, & Brugge, 2014;
McDonald & Kennedy, 1982; Newbold, 2005; Ng, 2014). Immigrants tend to lose their
health advantages within a decade of US residency. This effect is evidenced in
immigrants’ overweight and obesity rates that are significantly lower than that of the
native-born (Singh et al., 2011). Overweight and obesity rates of immigrants converge to
those of the native-born within a decade of U.S. residency (McDonald & Kennedy, 2005).
For example, the National Health Interview Surveys shows that the observed overweight
prevalence in 2003-2008 was 19.8 for recent Chinese immigrants and 40.1 for US-born
Chinese (Singh et al., 2011).
There are two predominant explanations for the healthy immigrant effect. The first
explanation is that individuals with a better health status are more likely to migrate to the
U.S. The health status distribution of the immigrants may look quite different from that of
a random sample of the native-born (Palloni & Ewbank, 2004). It is possible that
mandatory medical examination for immigrants coming to the U.S. screens out those with
inadmissible health conditions. In addition, the immigrant self-selection theory posits that
the healthier and wealthier individuals are more likely to migrate to developed countries
(Kennedy et al., accessed 2015). This theory is based on the assumption that income and
health are positively related (Lynch, Smith, Kaphan, & House, 2000). The
first-generation immigrants from developing countries are likely to have higher levels of
34

income that provide the financial means for them to migrate to the U.S. They may have
better a health status than US-born individuals who fall on a wide spectrum of the income
distribution. Overall, the health selection explanation has received substantial support
from empirical studies (Kennedy et al., accessed 2015).
The second explanation is that immigrants maintain favorable health behaviors and
habits they have acquired prior to immigration, such as having low-fat and low-calorie
diets. Dietary choices based on traditional eating habits and food practices are lost within
one generation of immigrants living in the U.S. (Batis et al., 2011). The native-born from
various ethnic groups are acculturated to diets high in fat, salt, and sugar, which in part
contribute to higher risks of being overweight and obese (Hubert et al., 2005; Sussner et
al. 2008; Oster & Yung, 2010; Perez-Escamilla & Putnik, 2007; Unger et al., 2004). Batis
and colleagues (2011) found that Mexican Americans had higher intakes of sugar, pizza,
fries, dessert, and salty snacks, and saturated fat than Mexicans did. Chinese immigrants
exhibited a similar pattern. They increased their consumptions of high-fat diets and
convenience foods after immigration (Rosenmöller et al., 2011; Satia et al., 2001). These
studies suggest that the constraining influence of traditional cultural beliefs on dietary
behavior may be weakened for immigrants who were born in the United States.
Earlier research on social support and immigration indicates that recent immigrants
may lack social ties that can provide various forms of social support after landing in a
foreign country (Choi & Thomas, 2009). Despite the fact that recent immigrants
35

gradually build their social ties over time, full integration to the host country may take
more than one generation (Jiménez, 2011). Research also shows that strong ties (i.e.,
family and friends) may be the most important source that provides a constraining
influence on health behavior (Lakey & Orehek, 2011; Umberson, 1987; Vega et al.,
2008). The first generation immigrants may receive limited social support from family
and friends who stay in their home country.
Recent research suggests that computer-mediated communication has become a part
of everyday life; social support is communicated through a variety of computer-mediated
channels (Wright, 2009). The primary advantage of CMC support from strong ties is
convenient access that overcomes time and space constraints. By sending text messages,
photos, videos, and emoticons online, individuals are connected to family and friends in
different time zones and locations. This feature is particularly useful in helping
individuals stay in touch with family and friends at a distance, such as family living
overseas or high school friends moving away from the geographic location where the ties
were formed (Ellison et al., 2007; Anderson & Rainie, 2010). Computer-mediated social
support provides opportunities for immigrants to be exposed to constraining influences
from their strong ties.
The foreign-born and US-born immigrants may also differ in the extent to which
they are connected to the neighborhood storytelling network. Although little research has
directly compared communication patterns between the two groups, relevant research
36

examining connections to ICSN and civic engagement patterns among individuals with
different ethnicities suggest that newer immigrants may be less connected to ICSN and
less integrated to the community than older immigrants. For example, Chen et al. (2012)
randomly surveyed Chinese, Latinos, and Anglos living in the same residential area in
Los Angeles. They found that the Chinese, who, on average, had a shorter immigration
history, had lower levels of connection to local storytellers and civic engagement than did
Anglos and Latinos. Chinese participants who had a longer immigration history reported
higher levels of a sense of neighborhood belonging—an important indicator of civic
engagement. The results in general suggest that immigrants who have a longer
immigration history are better integrated into the communication fabric of the local
community.
In this study, it is predicted that foreign-born and US-born immigrants will differ in
levels of computer-mediated social support, ICSN, and cultural beliefs about dietary
behavior.
H7: There will be differences in levels of computer-mediated social support (H7a),
ICSN (H7b), and cultural beliefs about dietary behavior (H7c) between
foreign-born and US-born immigrants.

37

Table 2-1
Summary of Hypotheses

Social Control Hypotheses

Hypothesis 1: Computer-mediated social support will be positively associated with
healthy eating behavior.

Hypothesis 2: Integrated connection to the storytelling network (ICSN) will be
positively associated with healthy eating behavior.

Hypothesis 3: Cultural beliefs about dietary behavior will be positively associated
with healthy eating behavior.

Mediation Hypotheses

Hypothesis 4: Planning will mediate the relationships between computer-mediated
social support (H4a), ICSN (H4b), and cultural beliefs about dietary
behavior (H4c) with healthy eating behavior.

Hypothesis 5: Self-efficacy will mediate the relationships between
computer-mediated social support (H5a), ICSN (H5b), and cultural
beliefs about dietary behavior (H5c) with healthy eating behavior.

Moderation Hypotheses

Hypothesis 6: Computer-mediated social support (H6a), ICSN (H6b), and cultural
beliefs about dietary behavior (H6c) will moderate the relationship
between healthy eating intention and behavior.

Comparisons between Foreign-Born and US-Born Immigrants

Hypothesis 7: There will be differences in levels of computer-mediated social
support (H7a), connections the ICSN (H7b), and cultural beliefs about
dietary behavior (H7c) between foreign-born and US-born immigrants.

38

CHAPTER 3 DATA AND METHOD
Participants
The Main Survey
In July 2014, a total of 505 Chinese living in the United States were recruited from an
online panel from Quatrics.com, a commercial online panel for social science and market
research. Chinese immigrants were selected in this study because they are the most
fast-growing immigrants in the U.S. in the past decade (Census, 2012). Dietary
acculturation and its health consequences have been consistently observed in Chinese
immigrants (e.g., Satia et al., 2001). Chinese immigrants may also offer important
analytical advantages for understanding changes in levels of social control in the social
environments.
Quatrics worked with panel providers (e.g., Survey Sampling International) who
recruited participants to join an online panel through various online tools, such as banners,
e-mails, text messages, and social networking sites. A multi-stage randomization process
was used in matching a participant with a survey. Participants were randomly selected
from the online panel. They were asked to respond to a set of randomly selected profiling
questions (e.g., lifestyles, media consumption, demographics). Based on their answers,
participants were matched with a survey they were likely to be able to take using a
randomization factor. Participants were invited to take the survey through e-mails, SMS
39

and text messages, telephone alerts, banners, and messaging on websites and online
communities. Specific project details were not included in the invitation.
Qualtrics sent the link to the web-based survey used in this study to the randomly
selected participants through its panel partners. Selected participants were asked to
respond to screening questions regarding their ethnicity and current state of residence.
Participants who self-identified as Chinese and lived in the United States were recruited
in this study.
The survey was developed in English and translated and back-translated into
Chinese by two bilingual speakers in English and Chinese. The survey appeared in the
language (English or Chinese) designated in the participant’s browser language setting.
Participants were able to switch between English and Chinese by clicking on the
language button on the top right of the survey at any time when they filled out the survey.
About 98% of participants completed the survey in English. Males were oversampled in
this study because they are less likely than females to participate in online panels for
survey research in the United States (Ipeirotis 2010). Participants were provided with
incentives of various kinds (e.g., redeemable points for merchandise, gift cards, or cash).
In the final sample, participants were 47% males and 53% females. The mean
participant age was 37.08 (SD = 14.21); median annual household income level was
$60,000-75,000. The majority of participants had at least some college-level education
(88.7%). About 43% were married or had domestic partnership; 34% had at least one
40

child. More than half of the participants (56%) spoke both English and Chinese at home,
followed by English only (31%), and Chinese only (6%). The majority of participants
(62%) were homeowners; 72% were employed. Table 3-1 reports the sample
characteristics.
The Follow-Up Survey
A follow-up survey was administered by Qualtrics a week following the date on
which the main survey was completed. A total of 52 participants were recruited from the
505 participants who successfully completed the main survey. The purpose of the
follow-up survey was to obtain reliability statistics on some measures used in this study.
Details were discussed later in this chapter.
41

Table 3-1
Sample Demographics
M or % (SD or N)
Age 37.08 (14.21)
Gender:
Male 47% (239)
Female 53% (265)
Education
Some high school or less 1.4% (7)
High school graduate 8.9% (45)
Some college or technical school 23.6% (119)
Bachelor’s degree 37.6% (190)
Master’s degree or higher 27.5% (139)
Income:
Less than 15,000 11% (56)
15,000-20,000 3% (17)
20,000-35,000 12% (58)
35,000-45,000 8% (40)
45,000-60,000 12% (59)
60,000-75,000 11% (53)
75,000-100,000 15% (74)
100,000 or more 28% (142)
Married or domestic partnership 43% (215)
Immigration Generation:
Foreign-born 49% (249)
US-born 50% (251)
Number of children:
0 65% (326)
1 14% (69)
2 15% (78)
3 or more 5% (25)
(continued)
42

Table 3-1
Continued

Total Sample
(N = 505)

M or % (SD or N)
Employment status:
Not employed or retired 27% (136)
Employed, working 1-20 hours per week 9% (46)
Employed, working 21-40 hours per week 15% (77)
Employed, working 40 or more hours per week 48% (244)
Language spoken at home:
Chinese only 6% (31)
Both English and Chinese 56% (281)
English only 31% (155)
Local residence:
Owner 62% (314)
Renter 29% (145)
Other 9% (43)

43

Measures
Body Mass Index (BMI). BMI is a simple index of weight-for-height that is used to
classify overweight and obesity for all ages of adults because it correlates with the
amount of body fat. BMI for each participant was calculated by self-reported weight in
kilograms divided by the square of the height in meters (kg/m
2
). According to the World
Health Organization (WHO) (2015), an adult is considered being overweight if his or her
BMI is between 25 and 29.9. A BMI of 30 or higher is defined as obesity. The cut-off
points for overweight and obesity have been questioned because the current criteria
underestimate the amount of body fat in Asians (Chiu, et al., 2011; WHO, 2004). Asians
have a higher percentage of body fat than Whites with the same BMI after controlling for
age and sex. Ethnic-specific BMI cut-off points for overweight (24 – 26.9) and obesity (≥
27) were used in this study (Asian American Diabetes Initiative, 2010). BMI was
dichotomized based on the overweight and obesity status (BMI ≥ 24). Of the 505
participants, 37% were overweight or obese.
Healthy eating scale. Nine 5-point Likert-type items from Dutta-Bergman (2004b)
were used to measure healthy eating behavior. Participants indicated their agreement (1 =
strongly disagree, 5 = strongly agree) for each statement (e.g., “I try to avoid foods that
are high in fat”; “I use a lot of low calorie or calorie reduced products”). Items were
averaged to form a scale of self-reported healthy eating behavior (α = .86, M = 3.61, SD
44

= .65).
Fruit and Vegetable Intake. Fruits and vegetable intake over the past month was
calculated using a two-item food frequency questionnaire (University of Leeds, accessed,
2015). The two-item questionnaire was validated by previous studies as a simple
assessment tool to estimate fruits and vegetable intake (Cappuccio et al., 2003).
Participants were asked to indicate how frequently they consume at least a portion of
fruits (item1) and vegetables (item 2), excluding potatoes, over the past month or so (1 =
rarely or never, 8 = 5 times or above a day). A portion was defined as a handful of grapes,
an orange, a serving of carrots, or a side salad. The two items were averaged to form a
scale of fruits and vegetables (α = .68, M = 4.91, SD = 1.40).
Dietary Acculturation Scales. Fifteen items from Satia et al. (2001) were used to
measure dietary acculturation. The Western Dietary Acculturation Scale had 10 items
(e.g., “eat sweets, cakes, or pies for dessert”; “Eat pizza or spaghetti with tomato sauce”),
and the Chinese Dietary Acculturation Scale had 5 items (e.g., “eat tofu”; “balance
yin/yang foods”). Participants were asked to report whether they performed a certain
dietary behavior in the past month (0 = no; 1 = yes). Summary scores were computed for
the Western Dietary Acculturation Scale (α = .76, M = .64, SD = .26), and the Chinese
Dietary Acculturation Scale (α = .66, M = .51, SD = .30).
Healthy Eating Intention. Healthy eating intention was measured by three items,
including “I intend to eat healthy in the forthcoming month”; “I plan to eat healthy in the
45

forthcoming month”; and “I will try to eat healthy in the forthcoming month”.
Participants rated their agreement with each statement on a five-point Likert-type scale (1
= strongly disagree, 5 = strongly agree). These items were averaged to create a
unidimensional scale for intention (α = .95, M = 3.86, SD = .77). Participants who scored
three or less were classified as nonintenders (n = 108, M = 2.74, SD = .05), whereas
participants who scored above three were classified as intenders (n = 396, M = 4.17, SD
= .02).
Planning. Planning was assessed using five items (Renner & Schwarzer, 2007).
Participants were asked to evaluate each statement on a four-point Likert-type scale (1 =
not at all true, 4 = exactly true). The original scale had two sub-dimensions including
action planning (e.g., “I already have concrete plans how to change my nutrition habits”)
and coping planning (e.g., “I already have concrete plans what to do in difficult situations
in order to stick to my intentions”). The exploratory factor analysis (EFA) detected a
single-factor structure. After a Varimax rotation, the factor loadings of all five items
exceeded .80 and together explained 75.55% of the variance. Therefore, the five items
were averaged to create a unidimensional scale for planning (α = .92, M = 2.64, SD
= .69).
Self-Efficacy. Self-efficacy was measured using six items (Renner & Schwarzer,
2007). Participants were asked how sure they were they could overcome obstacles when
certain barriers made it hard to change one’s nutrition habits (e.g., “I can stick to a
46

healthy diet even if I have to learn much about nutrition”; “I can stick to a healthy diet
even if initially food doesn’t taste as good”). Items were averaged to form a measure of
self-efficacy (α = .88, M = 2.66, SD = .61).
Social support variables
Face-to-Face social support. Face-to-face support was measured by six five-point
Likert-type items adapted from the social support scale specific to healthy eating
behavior (Sallis et al., 1987). Participants were instructed that there was a list of things
family and friends might do or say in face-to-face interactions. They were asked to
indicate how often their family (3 items) or friends (3 items) have said or done what was
described in the survey during the past 30 days (1 = none, 2 = rarely, 3 = a few times, 4 =
often, 5 = very often; e.g., “My family encouraged me not to eat high fat, high salt foods;”
“My friends gave suggestions on how to eat more healthy foods”).
Computer-medicated social support. Computer-medicated social support was
measured by six five-point Likert-type items modified from the social support scale
(Sallis et al., 1987) to make them applicable for computer-mediated settings.
Participants were asked to indicate the frequency for a list of things their family (3 items)
or friends (3 items) have said or done during the past 30 days (1 = none, 5 = very often;
e.g., “My family members sent me a link to a news story, video, or website about healthy
eating”; “My friends sent me a link to a healthy recipe, a healthy food blog, or a news
story about healthy eating.”).
47

CIT Variables
Interpersonal Discussion. Frequency of interpersonal discussion about healthy eating
was measured by a single item on a ten-point scale (1 = never, 10 = all the time), “How
often do you have discussions with other people about dietary practices?”
Connection to community organizations. Participants were asked to name up to two
neighborhood clubs, groups, or organizations from which they or others in their family
received information about healthy eating. For each organization they named, participants
were asked to indicate whether this organization was located within a 20-minute drive
from their home. Participants who responded to this question with a “Yes” indicated that
the organization was located in their residential neighborhood and thus was considered as
a community organization. Participants who named two community organizations were
coded as 2 (13%); participants who named one community organization were coded as 1
(16%); and those who did not name any local organizations were coded as 0 (71%).
Connection to geo-ethnic media. Participants were asked to name the two most
important media channels from which they obtained information about healthy eating for
themselves or their family, such as World Journal, CNN, or a local radio station. For each
media channel they named, participants were asked to indicate whether this media
channel targeted ethnic Chinese or their geographic area. Participants who responded to
this question with a “Yes” indicated that the media channel they named was considered to
be a geo-ethnic media channel. Participants who named two geo-media media channels
48

were coded as 2 (9%); participants who named one geo-ethnic media channel were coded
as 1 (3%); and those who did not name any geo-media media channels were coded as 0
(88%).
ICSN. ICSN was calculated as a summation of the three interaction terms between
intensity of interpersonal discussion, scope of connection to geo-ethnic media, and scope
of connection to community organizations (Kim & Ball-Rokeach, 2006b).
Cultural beliefs about dietary behavior. A pool of 12 possible items was generated
based on previous literature on Chinese dietary behavior (Satia et al., 2000; Satia et al.,
2001; Wang, 2006; Zhao, 2008). The central idea is to conceptualize healthy eating using
a relational approach. The relational approach is grounded in the traditional Chinese
philosophy positing that yin and yang as opposing forces coexist and strive for balance.
Health is the condition of harmonious interactions of food with nature. Foods can create
hot or cold energy on the body. In general, cold foods help remove toxins and clear
excess heat. Hot foods help warm the body and provide greater energy for activity. A
healthy diet should correspond to geographic locations (e.g., “It is healthy to eat local
foods”), seasonal changes (e.g., “People should avoid cold foods in winter”; “Eating too
many hot foods in summer can increase internal body heat”; “Eating seasonal foods is
good for health”), body types (e.g., “Individuals with a cold body type could eat more hot
foods.”), and the balance of energies and flavors (e.g., “It is better to eat crabs with hot
seasonings such as ginger and green onions”; “It’s better to eat lambs with cold side
49

dishes”; “It is healthy to balance hot and cold foods”; and “It is important to balance the
five flavors: sour, sweet, bitter, spicy, and salty”). A traditional Chinese medicine expert
and two Chinese communication scholars who specialize in health communication
reviewed the 12 items. One item (“Eating chili helps reduce dampness in the body in hot
and humid weather”) was dropped due to disagreement. The final item pool consisted of
11 items. Participants were asked to indicate their agreement with these statements (1 =
strongly disagree, 5 = strongly disagree).
Immigration generation. Participants were asked for their place of birth in an
open-ended question (“In which country were you born?”). Responses were coded into a
binary variable with foreign-born equal to one and US-born equal to two.
Control variables. Demographic variables included age, gender (1 = male, 2 =
female), education (1 = graduated from primary school, 6 = master’s degree or higher),
income (1 = less than 15,000, 8 = 100,000 or more), marital status (1 = married or
domestic partnership, 2 = else), number of children, language spoken at home (1 =
Chinese only, 2 = both English and Chinese, 3 = English only), local residence (1 =
owner, 2 = renter, 3 = other), and employment status (1 = not employed or retired, 4 =
employed, working 40 or more hours per week). Other control variables included levels
of physical activity (from 0 to 10), nutrition knowledge (8 items, M = 4.63, SD = 1.92;
Parmenter & Wardle, 1999, see appendix).
50

Data Analysis
The first goal of this study was to examine the direct and indirect impact of social
control on dietary behavior. Structural equation modeling (SEM) was used to test H1-H5
using LISREL 8.8. Figure 3-1 shows the hypothesized structural model. The endogenous
variables were BMI, healthy eating scale, intake of fruits and vegetables, planning, and
self-efficacy. The exogenous variables were computer-mediated social support, ICSN,
and cultural beliefs about dietary behavior. Control variables included age, gender,
education, income, levels of physical activity, and nutrition knowledge. An alpha level
of .05 was used to determine the level of significance for individual path coefficients.
Second, to test the moderation effects (H6), R 2.15.1 was used to construct two OLS
hierarchical regression models that predicted the healthy eating scale and fruits and
vegetables intake respectively. Demographic variables, predictors of interests (i.e.,
self-regulatory indicators, social control indicators, and intention to eat healthy), and
interaction terms were entered in sequences. Both models included three interaction terms:
(a) computer-mediated social support and intention; (b) ICSN and intention; and (c)
cultural beliefs about dietary behavior and intention. An alpha level of .05 was used to
determine significance.
Finally, three univariate general linear models were constructed to compare levels of
social control between foreign-born and US-born Chinese immigrants (H7). The
51

dependent variables were computer-mediated social support, ICSN and cultural beliefs
about dietary behavior. Control variables included age, gender, education, and income.
An alpha level of .05 was used to determine significance.

Figure 3-1. The hypothesized model.
52

CHAPTER 4 RESULTS
Preliminary Analysis
Computer-Mediated Social Support
The twelve items that measured FTF and CMC support were subject to exploratory
factor analysis (EFA). EFA detected a two-factor structure, with six items of FTF support
loading on the first factor (α = .90, M = 2.82, SD = .99) and six items of CMC support
loading on the second factor (α = .93, M = 2.40, SD = 1.05), explaining 38.26% and
32.62% of the variance respectively. Table 4-1 reports the summary statistics and factor
loadings for the social support items. Then, confirmatory factor analysis with a two-factor
oblique solution was conducted in LISREL 8.8. All item loadings were significant (see
Figure 4-1). The overall model showed an acceptable fit, with χ
2
= 201.63, p < .01, df =
50, RMSEA = .08, GFI = .93, CFI = .98, and IFI = .98 (Byrne, 1998). The measurement
model indicates that participants who received higher levels of face-to-face social support
also received higher levels of computer-mediated social support.
A multiple regression analysis was conducted to examine the characteristics of
participants who were more likely to receive computer-mediated social support. The
results show that younger (β = -.27, p < .001), male (β = -.11, p < .05), foreign-born (β =
-.18, p < .001), more educated (β = .13, p < .05) participants were more likely to receive
53

computer-mediated social support. Having children (β = .12, p < .05) was also a
significant predictor for computer-mediated social support. Income (β = -.02, p = ns) and
marital status (β = .07, p = ns) did not significantly predict computer-mediated social
support. These demographic variables explained 10.2% of the variance in
computer-mediated social support, with F(7,473) = 7.68, p < .001.
Figure 4-1. The two-factor model of social support.

54

Table 4-1
Summary Statistics and Factor Loadings for Social Support

Items M S.D. EFA Factor Loadings
CMSS FTFSS
Computer-Mediated Social Support (CMSS)
My family members sent me a link to a news
story, video, or website about healthy eating.
2.45 1.18 .77 -.32
My family reminded me not to eat high fat,
high salt foods using online chat applications.
2.51 1.32 .76 -.20
My family showed concerns about my diet in
emails or text messages.
2.27 1.23 .80 -.31
My friends chatted with me on social network
sites about eating more healthy foods.
2.29 1.21 .82 -.35
My friends sent me a link to a healthy recipe,
a healthy food blog, or a news story about
healthy eating.
2.44 1.19 .82 -.31
My friends gave suggestions on where to buy
healthy produce through emails, text
messages or social network sites.
2.41 1.20 .84 -.29
Face-To-Face Social Support (FTFSS)
My family encouraged me not to eat high fat,
high salt foods.
3.03 1.31 .70 .48
My family offered me low-fat snacks. 2.70 1.15 .77 .35
My family talked with me about eating more
healthy foods.
3.03 1.21 .73 .48
My friends gave suggestions on how to eat
more healthy foods.
2.74 1.18 .81 .23
My friends said nice things about what I eat. 2.81 1.17 .60 .22
My friends reminded me not to eat high fat,
high salt foods.
2.62 1.24 .80 .18
Note. Exploratory factor analysis with a Varimax rotation.
55

Cultural beliefs about Dietary Behavior
Table 4-2 presents the summary statistics for the cultural beliefs about dietary
behavior (CBDB).
Table 4-2
Summary Statistics for Items of Cultural Beliefs about Dietary Behavior

No. Item M SD
1 It is healthy to eat local foods. 3.73 .84
2 It is healthy to balance hot and cold foods. 4.02 .81
3 People should avoid cold foods in winter. 2.85 1.02
4 Eating too many hot foods in summer can increase internal
body heat.
3.29 1.00
5 Eating seasonal foods is good for health. 3.89 .81
6 Individuals with a cold body type could eat more hot foods. 3.41 .87
7 Individuals with a hot body type could eat more cold foods. 3.39 .86
8 It is important to balance the five flavors: sour, sweet, bitter,
spicy and salty.
3.81 .91
9 It’s better to eat crabs with hot seasonings such as ginger and
green onions.
3.53 .94
10 It’s better to eat lambs with cold side dishes. 3.18 .89
11 Women should avoid cold foods during their period. 3.36 1.08

56

Table 4-3
Correlations for CBDB Items
Item 1 2 3 4 5 6 7 8 9 10 11
1 1
2 .41
**
1
3 .10
*
.12
**
1
4 .19
**
.25
**
.45
**
1
5 .43
**
.34
**
.10
*
.28
**
1
6 .20
**
.33
**
.36
**
.44
**
.34
**
1
7 .19
**
.31
**
.32
**
.43
**
.31
**
.80
**
1
8 .30
**
.46
**
.14
**
.24
**
.36
**
.39
**
.40
**
1
9 .16
**
.26
**
.17
**
.24
**
.35
**
.37
**
.33
**
.36
**
1
10 .05 .18
**
.22
**
.36
**
.22
**
.30
**
.32
**
.25
**
.39
**
1
11 .04 .25
**
.41
**
.37
**
.19
**
.42
**
.35
**
.21
**
.37
**
.34
**
1
Note.
*
p < .05,
**
p < .01. CBDB = cultural beliefs about dietary behavior.
Two-factor model. The 11 items of the cultural beliefs about dietary behavior (CBDB)
were subjected to exploratory factor analysis (EFA). Table 4-3 presents the correlations
between variables. Prior to performing EFA, the suitability of data for factor analysis was
assessed. Inspection of the correlation matrix revealed the presence of many coefficients
of .3 and above. The Kaiser-Meyer-Oklin value was .82, exceeding the recommended
values of .6 (Kaiser, 1974) and the Barlett’s Test of Sphericity (Bartlett, 1954) reached
statistical significance, supporting the factorability of the correlation matrix.
Exploratory factor analysis revealed the presence of two components with
eigenvalues exceeding 1, explaining 37.08% and 13.4% of the variance respectively. The
two-component model was retained for further analysis. Using a Varimax rotation, the
57

solution explained a total of 50.49% of the variance, with Component 1 contributing
28.12% and Component 2 contributing 22.37%. Item 3, 4, 6, 7, 9, 10, and 11 were
strongly loaded on Component 1 (factor loadings ranged from .47 to .72), and item 1, 2, 5,
and 8 were strongly loaded on Component 2 (factor loadings ranged from .66 to .75).
The two-factor model was then analyzed using confirmatory factor analysis (CFA).
The two-factor model (Model 1) did not fit the data sufficiently well, with λ
2
= 456.90, p
< .05, df = 44, RMSEA = .14, GFI = .86, CFI = .86, and IFI = .86. The modification
indices suggested the correlation between the error terms of item 6 and 7 (MI = 173.06),
and the correlation between factor 1 and factor 2 (MI = 113.61). The revised model
(Model 2) showed a slightly better fit, with λ
2
= 204.48, p < .05, df = 42, RMSEA = .09,
GFI = .93, CFI = .95, and IFI = .95.
The two-factor model was not used for further analysis because the model did not fit
the data sufficiently well (Clark & Watson, 1995). In addition, the modification indices of
Model 2 suggested that a few items should cross-load on the two factors, which was
evidence of nonspecific relationships with latent variables (Brown & Moore, 2012).
One-factor model. The 11 items assessing CBDB were then analyzed using EFA
with a one-factor solution. Factor loadings ranged from .42 to .79. All items were
included in the subsequent analysis because loadings above .40 in EFA suggest
acceptable candidates for convergent validity (Pallant, 2013). A confirmatory factor
analysis (CFA) was conducted to examine the fit of the one-factor model. The one-factor
58

model (Model 3) did not fit the data sufficiently well, with λ
2
= 487.19, p < .05, df = 44,
RMSEA = .15, GFI = .83, CFI = .82, and IFI = .82.
A series of steps were performed to eliminate items from the original 11-item scale
to improve convergence validity. First, item 7 was excluded from the following analysis
to reduce redundancy. An examination of the zero-order interitem correlations revealed
that item 6 and 7 were strongly correlated (Pearson’s r = .80), suggesting that the two
items were redundant (Clark & Watson, 1995). It was further evidenced in the
modification indices of Model 3 where the highest degree of misfit represents correlated
errors between items 6 and 7 (MI = 215.64). The content of item 7 was a slightly
reworded version of item 6 or vice versa. Therefore, item 7 was temporarily excluded in
subsequent analyses until final item candidates were selected.
Second, item 1 was not correlated with a few items and thus was removed from the
scale. Clark and Watson (1995) suggested that all the interitem correlations should be
moderate in magnitude with Pearson’s r between .15 to .50 to ensure unidimensionality.
Third, items 8, 5, and 11 were removed sequentially to improve convergent and
discriminant validity. Table 4-4 reports the goodness-of-fit statistics. The modification
indices of Model 4 suggests that the misfit of the constrained parameters lay in the error
terms of item 8 with item 2 (MI = 44.34), item 3 (MI = 13.95), item 4 (MI = 10.66), and
item 5 (MI = 10.54). This information suggested that some of the covariance of item 8
with other items were not explained by the latent variable. Item 8 thus was a candidate
59

for deletion because dropping this item would eliminate the non-random exogenous cause.
This same procedure was applied to evaluate other items in subsequent models. Items 5
and 11 were also deleted.
The remaining items (items 2, 3, 4, 6, 9, and 10) were included in Model 6, with λ
2
=
68.03, p < .05, df = 9, RMSEA = .11, GFI = .96, CFI = .92, and IFI = .92. Although the
goodness-of-fit statistics were substantially improved, there was still some evidence of
misfit. The modification indices suggested correlated error terms between item 3 and 4,
and between items 9 an 10. A plausible reason for correlating the two pairs of items was
that the hot and cold components were reverse-worded in each pair. The revised (Model 7)
model showed a satisfactory fit, with λ
2
= 25.16, p < .05, df = 7, RMSEA = .07, GFI = .98,
CFI = .97, and IFI = .98.
An alternative model (Model 8) was evaluated with item 6 being replaced by item 7.
The model contained 6 items (items 2, 3, 4, 7, 9, and 10) with two pairs of specified
correlated errors. The model fit the data adequately well (Hooper, Coughlan, & Mullen,
2008), with λ
2
= 16.06, p < .05, df = 7, RMSEA = .05, GFI = .99, CFI = .99, and IFI = .99.
The goodness-of-fit statistics were slightly improved compared with Model 7. The final
index was selected based on Model 8. Table 4-5 presents summary statistics and factor
loadings for the selected items.

60

Table 4-4
Summary of Goodness-of-Fit Statistics for CFA Models

Model Items and Model
Specification
λ
2
df p RMSEA GFI CFI IFI
Two-factor
model
1 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11

456.90 44 <.05 .14 .86 .86 .86
2 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11
Correlated error terms
between items 6 and 7

204.48 42 <.05 .09 .93 .95 .95
One-factor
model
3 1, 2, 3, 4, 5, 6, 8, 9, 10,
11

489.19 44 <.05 .15 .83 .82 .82
4 2, 3, 4, 5, 6, 9, 10, 11

104.02 19 <.05 .10 .95 .94 .94
5 2, 3, 4, 6, 9, 10, 11

81.95 14 <.05 .10 .96 .94 .94
6 2, 3, 4, 6, 9, 10

68.03 9 <.05 .11 .96 .92 .92
7 2, 3, 4, 6, 9, 10
Correlated error terms
between items 3 and 4,
and between items 9
and 10

25.16 7 <.05 .07 .98 .97 .98
8 2, 3, 4, 7, 9, 10
Correlated error terms
between items 3 and 4,
and between items 9
and 10

16.06 7 <.05 .05 .99 .99 .99

61

Table 4-5
Summary Statistics and Factor Loadings for CBDB Items

Items Mean S.D. CFA
Factor
Loadings
It is healthy to balance hot and cold foods. 4.02 .81 .42
People should avoid cold foods in winter. 2.85 1.02 .42
Eating too many hot foods in summer can increase
internal body heat.
3.29 1.00 .62
Individuals with a hot body type could eat more cold
foods.
3.39 .86 .71
It’s better to eat crabs with hot seasonings such as
ginger and green onions.
3.53 .94 .45
It’s better to eat lambs with cold side dishes. 3.18 .89 .49
Reliability tests. The CBDB had a moderate average interitem correlation of
approximately .29 (suggested in the range of .15 to .50 by Clark et al., 1995). The
Cronbach’s alpha was .72. The test-retest reliability was obtained by correlating
responses across Time 1 and Time 2, and had an acceptable value of .74 (Kline, 1999).
Results of the reliability tests suggest that CBDB has good reliability.
Validity tests. To test construct validity, Person’s correlation coefficients were
calculated between CBDB and other healthy eating indicators (see Table 4-6). CBDB
was positively correlated with the healthy eating scale (r = .30, p < .01), fruits and
vegetable intake (r = .09, p < .05), and the healthy eating intention scale (r = .23, p < .
01).
CBDB was negatively correlated with two of the three indicators that measured
62

participants’ general level of acculturation. Specifically, CBDB was negatively correlated
with years in the U.S. (r = -.13, p < .01) and immigration generation (r = -.19, p < .01).
CBDB was not significantly correlated with language spoken at home (r = -.07, p = n.s.).
In addition, CBDB was positively correlated with the Chinese food acculturation scale (r
= .24, p < .01), but not with the Western food acculturation scale (r = -.00, p = n.s.).
In summary, results of the present study suggest that the CBDB is a valid and useful
way to conceptualize and measure perceptions of healthy eating from a relational
perspective.
Table 4-6
Correlations between CBDB, Acculturation, and Healthy Eating

Measures Correlations
Acculturation Scales
Years in the U.S. (Foreign-Born only) -.13
*

Immigration Generation (1=Foreign-Born,
2=US-born)
-.19
**

Language Spoken at Home -.07
Chinese Food Acculturation Scale .24
**

Western Food Acculturation Scale -.00
Healthy Eating Indicators
Healthy Eating Scale .30
**

Healthy Eating Intention .23
**

Fruits and Vegetables Intake .09
*

Note.
*
p < .05,
**
p < .01.
A multiple regression analysis was conducted to examine the characteristics of
participants who had high scores on CBDB. The results show that older (β = -.12, p < .05)
63

and foreign-born (β = -.20, p < .001) participants had higher scores on CBDB. Having
children (β = .10, p < .001) was also a significant predictor for CBDB. Gender (β = -.02,
p = ns), education, (β = .03, p = ns), income (β = -.02, p = ns) and marital status (β = -.10,
p = ns) did not significantly predict CBDB. These demographic variables explained 5.9%
of the variance in CBDB, with F(7,473) = 4.25, p < .001.
Primary Results
Descriptive Statistics
Figure 4-2 shows the age-adjusted overweight rate for the foreign-born and US-born
Chinese participants. Age-adjusted overweight rate was 35.37% for foreign-born Chinese
participants and 39.51% for US-born Chinese participants. Table 4-7 provides basic
descriptive statistics for the variables used in this study.

64

Figure 4-2. Age-adjusted overweight rate by age group.
0!
10!
20!
30!
40!
50!
60!
70!
18+24! 25+34! 35+44! 45+54! 55+64!
Foreign+Born!
US+Born!
65

Table 4-7
Descriptive Statistics

Total US-Born Foreign-Born
M SD M SD M SD
Healthy Eating Scale 3.60 .65 3.52 .66 3.68 .63
Fruits & Vegetables 4.90 1.40 4.71 1.41 5.10 1.37
Planning 2.63 .69 2.62 .70 2.67 .68
Self-Efficacy 2.66 .60 2.63 .63 2.70 .58
Computer-Mediated Social Support 2.39 1.04 2.19 1.04 2.60 1.01
Connections to ICSN 8.75 2.36 8.48 2.25 9.07 2.44
Cultural beliefs about dietary
behavior
3.38 .59 3.26 .56 3.49 .60

Social Control Hypotheses
Global Fit of the Model. Table 4-8 reports the zero-order correlations of variables.
The hypothesized model showed a moderate fit (Byrne, 1998), with χ
2
= 177.78, df = 49,
RMSEA = .07, GFI = .96, AGFI = .91, CFI = .93, and IFI = .94. An examination of the
modification indices indicated that the model fit would be improved if a path between
planning and self-efficacy was added.
A revised model was estimated by correlating the error terms between planning and
self-efficacy, with χ
2
= 3.90, df = 48, RMSEA < .01, GFI = 1.00, AGFI = 1.0, CFI = 1.00,
and IFI = 1.00. The revised model explained 13% of the variance in BMI, 42% of the
variance in the healthy eating scale, 13% of the variance in the consumption of fruits and
vegetables, 20% of the variance in planning, and 14% of the variance in self-efficacy.
66

Figure 4-3 presents the coefficients for the revised structural model.
A parsimonious model was estimated by deleting nonsignificant paths from the
revised model, with χ
2
= 64.12, df = 85, RMSEA < .01, GFI = .98, AGFI = .98, CFI =
1.00, and IFI = .96. The coefficients for the remaining paths were all significant.

Figure 4-3. The structural model predicting healthy eating behavior.

67

Table 4-8
Correlations between Variables

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1.Overweight 1
2.Healthy Eating
Scale -.07 1
3.Fruits & Vegetables -.07 .24
**
1
4.Planning -.05 .54
**
.24
**
1
5.Self-Efficacy -.11
*
.48
**
.20
**
.60
**
1
6.Age .21
**
.08 .18
**
.05 -.06 1
7.Gender -.22
**
.07 -.01 .03 .04 -.16
**
1
8.Immigration
Generation .04 -.12
**
-.12
**
-.04 -.08 -.21
**
.06 1
9.Education .00 .06 .06 .04 .00 .37
**
-.25
**
-.23
**
1
10.Income .03 .04 .11
*
.02 .01 .37
**
-.24
**
-.17
**
.36
**
1
11.Physical Activity -.06 .34
**
.18
**
.30
**
.21
**
.07 -.12
**
-.03 .12
**
.18
**
1
12.Knowledge .03 .12
*
.17
**
.12
*
.05 .18
**
-.02 -.01 .18
**
.12
**
.03 1
13.CMSS .03 .39
**
.14
**
.33
**
.30
**
-.07 -.12
*
-.20
**
.12
**
.00 .32
**
-.02 1
14.ICSN .00 .32
**
.14
**
.27
**
.18
**
-.03 .02 -.14
**
.10
*
.11
*
.29
**
.09
*
.49
**
1
15.CBDB -.02 .29
**
.08 .22
**
.26
**
-.05 -.02 -.19
**
.02 -.01 .11
*
.01 .35
**
.19
**
1
Note.
*
p < .05,
**
p < .01. CMSS = Computer-Mediated Social Support, ICSN = Integrated Connection to a Storytelling
Network, CBDB = Cultural beliefs about dietary behavior.

68

Hypothesis 1. H1 proposed that computer-mediated support would be positively
associated with healthy eating behavior. The results show that computer-mediated social
support was positively associated with the healthy eating scale (γ = .14, p < .001), but not
with the consumption of fruits and vegetables (γ = .04, p = ns). H1 was partially
supported.
Hypothesis 2. H2 predicted that integrated connection to a storytelling network
(ICSN) would be positively associated with healthy eating behavior. The results indicate
that ICSN was not significantly associated with either the healthy eating scale (γ = .08, p
= ns) or the consumption of fruits and vegetables (γ = .04, p = ns). H2 was not supported.
Hypothesis 3. H3 proposed that cultural beliefs about dietary behavior would be
positively associated with healthy eating behavior. The results show that cultural beliefs
about dietary behavior was positively associated with the healthy eating scale (γ = .10, p
< .01), but not with fruits and vegetables (γ = .00, p = ns). H3 was partially supported.
The Mediating Hypotheses
Hypothesis 4. H4 predicted that planning would mediate the relationships between
computer-mediated social support (H4a), ICSN (H4b), and cultural beliefs about dietary
behavior (H4c) with healthy eating behavior. The results indicate that social support had
an indirect effect on healthy eating scores through planning (Estimate = .06, p < .05).
The indirect effect was also found for cultural beliefs about dietary behavior (Estimate
69

= .03, p < .05). The indirect effect was not found for ICSN (Estimate = .02, p = ns).
When predicting the consumption of fruits and vegetables, the indirect effects were found
for social support (Estimate = .02, p < .05) and cultural beliefs (Estimate = .01, p < .05).
The indirect effect was not found for ICSN. H4a and H4c were supported. H4b was not
supported.
Hypothesis 5. H5 predicted that self-efficacy would mediate the relationships
between computer-mediated social support (H5a), ICSN (H5b), and cultural beliefs about
dietary behavior (H5c) with healthy eating behavior. The results show that
computer-mediated social support (Estimate = .04, p < .05) and cultural beliefs about
dietary behavior (Estimate = .03, p < .05) had indirect effects on the healthy eating scale
through self-efficacy. The indirect effect was not found for ICSN (Estimate = .00, p = ns).
When predicting the consumption of fruits and vegetables, indirect effects were not found
for computer-mediated support (Estimate = .02, p = ns), ICSN (Estimate = .00, p = ns), or
cultural beliefs about dietary behavior (Estimate = .02, p = ns). H5a and hypothesis 5c
were partially supported. H5b was not supported. Table 4-9 shows direct, indirect, and
total effects for social control indicators on healthy eating.

70

Table 4-9
Direct, Indirect, and Total Effects of Social Control on Healthy Eating

Health Eating Scale Fruits & Vegetables
Direct
Effect
Indirect
Effect
Total
Effect
Direct
Effect
Indirect
Effect
Total
Effect
CMSS .14
**
.10
***
.24
***
.04 .04
**
.08
ICSN .08 .02 .10
*
.04 .01 .05
CBDB .10
**
.07
***
.17
***
.00 .03
**
.03
Note.
*
p < .05,
**
p < .01,
***
p < .001. CMSS = Computer-Mediated Social Support,
ICSN = Integrated Connection to a Storytelling Network, CBDB = Cultural beliefs about
dietary behavior.
Additional tests. Kang, Chung, and Chung (2014) found that storytelling indicators
(i.e., interpersonal discussion, connection to community organizations, and media
connectedness) were strongly correlated with social support. In the present study, strong
correlations were also found for computer-mediated social support with interpersonal
discussion (r = .50, p < .01), connection to community organizations (r = .27, p < .01),
and connection to geo-ethnic media (r = .16, p < . 01). It may be that the estimate of the
impact of ICSN on healthy eating was less precise while controlling for
computer-mediated social support. In addition, a few studies have shown differences in
the predictive power among connections to individual storytellers (i.e., interpersonal
discussion, connection to local organizations, and connection to geo-ethnic media) (Chen
et al., 2013; Ognyanova et al., 2013).
71

Using LISREL 8.8, a new structural equation model was estimated to examine the
direct and indirect effects of connections to individual neighborhood storytellers on
healthy eating behavior. The exogenous variables were interpersonal discussion,
connection to local organizations, and connection to geo-ethnic media. The endogenous
variables were planning, self-efficacy, and healthy eating indicators. To test the direct
effect, the model included paths from exogenous variables to healthy eating indicators.
To test the indirect effect, the model included paths from exogenous variables to
self-regulation indicators (i.e., planning and self-efficacy) as well as paths between
self-regulation and healthy eating indicators. This model also included paths from
controls to endogenous variables. As suggested by previous analyses, a path between the
error terms of planning and self-efficacy was added to this model.
Table 4-10 presents the correlations. The global fit of the model was satisfactory,
with χ
2
= 4.51, df = 48, RMSEA < .01, GFI = 1.0, AGFI = 1.0, CFI = 1.0, and IFI = 1.0.
The model explained 12.3% of the variance in BMI, 40.4% of the variance in healthy
eating scale, 14.3% of the variance in fruits and vegetables, 18.0% of the variance in
planning, and 11.6% of the variance in self-efficacy.
The results show that interpersonal discussion (γ = .14, p < .001) and connection to
local organizations (γ = .07, p < .05) had a direct effect on the healthy eating scale.
Connection to geo-ethnic media had a direct effect on the consumption of fruits and
vegetables (γ = .10, p < .05). Interpersonal discussion also had an indirect effect on the
72

healthy eating scale through planning (Estimate = .08, p < .05) and self-efficacy
(Estimate = .06, p < .05).
Table 4-10
Correlations between Variables
1 2 3 4 5 6 7 8
1.Overweight 1
2.Healthy Eating Scale -.07 1
3.Fruits & Vegetables -.07 .24
**
1
4.Planning -.05 .54
**
.24
**
1
5.Self-Efficacy -.11
*
.48
**
.20
**
.60
**
1
6. Interpersonal Discussion -.04 .38
**
.19
*
.36
**
.30
**
1
7.Connection to Local Organizations .00 .16
**
.03 .10
*
.04 .23
**
1
8.Connection to Geo-Ethnic Media -.03 .02 .11
*
.08 .02 .17
**
.25
**
1
Note.
*
p < .05,
**
p < .01
Moderation Hypotheses
Hypothesis 6. H6 stated that computer-mediated social support (H6a), ICSN (H6b),
and cultural beliefs about dietary behavior (H6c) would moderate the relationship
between healthy eating intention and behavior. Table 4-11 presents the coefficients of the
regression analyses. When predicting the healthy eating scale, the overall model was
significant, with F(16,455) = 29.744, p < .001. This model explained 51.1% of the
variance in the healthy eating scale. Participants with higher levels of computer-mediated
social support reported higher scores on the healthy eating scale than did those with lower
levels of computer-mediated social support (β = .087, p < .05). The results suggest that
the intention-behavior gap was smaller when the interpersonal-level social control was
73

higher. The model also showed a moderating effect of ICSN on the intention-behavior
relationship (β = -.13, p < .01). However, contrary to expectation, the intention-behavior
gap was smaller when the community-level social control was lower. The strength of the
relationship between healthy eating intention and behavior did not depend on levels of
cultural beliefs about dietary behavior (β = .061, p = ns).
When predicting the consumption of fruits and vegetables, the overall model was
significant, with F(16,455) = 4.702, p < .001. This model explained 14.2% of the
variance in the consumption of fruits and vegetables. A moderating effect was found for
the cultural beliefs about dietary behavior on the intention-behavior relationship (β =
-.102). However, contrary to expectation, the intention-behavior gap was smaller when
the cultural-level social control variable was lower. The strength of the intention-behavior
did not depend on levels of computer-mediated social support (β = -.055, p = ns) or
connections to the integrated neighborhood storytelling network (β = .027, p = ns).
Therefore, hypothesis 6a was partially supported (Figure 4-4, Figure 4-5, Figure 4-6).
H6b and H6c were not supported.

74

Table 4-11
Summary Statistics of Regression Analyses Testing the Strength of Behavior-Intention
Relationships

Healthy Eating Scale Fruits & Vegetables
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Block 1 – Control Variables
Age .055 .085
*
.090
*
.141
**
.154
**
.151
**

Gender .121
**
.076
*
.074
*
.041 .026 .027
Education -.004 .002 .001 -.058 -.058 -.045
Income -.044 -.022 -.016 .022 .025 .021
Immigration
Generation
-.114
*
-.028 -.018 -.103
*
-.079 -.085
Physical Activity .357
***
.112
**
.113
**
.172
***
.092 .087
Knowledge .108
*
.026 .022 .140
**
.115
*
.116
*

Block 2 - Predictors
Planning .177
***
.170
***
.092 .090
Self-Efficacy .153
***
.158
***
.082 .081
CMSS .148
***
.124
**
.038 .054
ICSN .044 .063 .033 .032
CBDB .075
*
.072 -.008 .006
Intention .314
***
.334
***
.047 .023
Block 3 - Interactions
Intention × CMSS .087
*
-.055
Intention × ICSN -.130
**
.027
Intention × CBDB .061 -.102
*

ΔR
2
(%) 16.2
***
33.3
***
1.6
**
9.0
***
3.7
**
1.4
Total R
2
(%) 16.2
***
49.5
***
51.1
***
9.0
***
12.8
***
14.2
***

Note. Entries are standardized regression coefficients.
*
p < .05,
**
p < .01,
***
p < .001.
CMSS = Computer-Mediated Social Support, ICSN = Integrated Connection to a
Storytelling Network, CBDB = Cultural beliefs about dietary behavior.

75

Figure 4-4. The moderating effect of computer-mediated social support on the
intention-behavior relationship.

Figure 4-5. The moderating effect of ICSN on the intention-behavior relationship.

2
2.5
3
3.5
4
Low Intention High Intention
Healthy Eating Scale
Low CMSS
High CMSS
2
2.2
2.4
2.6
2.8
3
3.2
3.4
3.6
3.8
4
Low Intention High Intention
Healthy Eating Scale
Low ICSN
High ICSN
76

Figure 4-6. The moderating effect of cultural beliefs on the intention-behavior
relationship.

Additional tests. To determine if healthy eating indicators could be predicted as a
function of social control variables among individuals with an intention to eat healthy,
hierarchical regression analyses were performed utilizing healthy eating indicators as
outcomes, and control variables, self-regulation indicators, and social control indicators
as predictors. As shown in Table 4-12, the social control variables (ΔR
2
= 6.6%) were
significant predictors for the healthy eating scale after controlling for demographic
variables, levels of physical activity, nutrition knowledge, and self-regulation variables.
Specifically, computer-mediated social support (β = .201, p < .01) and cultural beliefs
about dietary behavior (β = .129, p < .05) significantly predicted the healthy eating scale.
ICSN did not significantly predict the healthy eating scale (β = .044, p = ns). The model
2
2.2
2.4
2.6
2.8
3
3.2
3.4
3.6
3.8
4
Low Intention High Intention
Fruits & Vegetables
Low Cultural
Beliefs
High Cultural
Beliefs
77

explained 33.8% of the variance in the healthy eating scale, with F(12,311) = 13.206, p
< .001.
The second hierarchical regression analysis was conducted to predict the
consumption of fruits and vegetables. The social control variables were not significant
predictors for predicting the consumption of fruits and vegetables after controlling for
demographic variables, levels of physical activity, nutrition knowledge, and
self-regulation variables. The model explained 8.0% of the variance in the consumption
of fruits and vegetables, with F(12,311) = 2.26, p < .01.

78

Table 4-12
Summary Statistics of Regression Analyses for Variables Predicting Healthy Eating
Indicators among Intenders

Healthy Eating Scale Fruits & Vegetables
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Block 1 – Control Variables
Age .067 .084 .139
*
.146
*
.152
*
.164
*

Gender .150
**
.128
*
.151
**
.083 .079 .079
Education .004 .007 -.032 -.060 -.060 -.066
Income -.006 -.008 -.005 .056 .055 .046
Immigration
Generation
-.120
*
-.121
*
-.047 -.054 -.053 -.050
Physical Activity .262
***
.176
**
.112
*
.095 .078 .049
Knowledge -.001 -.029 -.012 .121
*
.117
*
.118
*

Block 2 – Self-Regulation
Planning .252
***
.196
**
.034 .018
Self-Efficacy .235
***
.194
**
.064 .068
Block 3 – Social Control
CMSS .201
**
.052
ICSN .044 .081
CBDB .129
*
-.068
ΔR
2
(%) 9.9
***
17.3
***
6.6
**
6.1
**
.7 1.2
Total R
2
(%) 9.9
***
27.2
***
33.8
***
6.1
**
6.8 8.0
Note. Entries are standardized regression coefficients.
*
p < .05,
**
p < .01,
***
p < .001.
CMSS = Computer-Mediated Social Support, ICSN = Integrated Connection to a
Storytelling Network, CBDB = Cultural beliefs about dietary behavior.
Comparisons between Foreign-Born and US-Born Immigrants
Hypothesis 7. A series of univariate general linear models were estimated to compare
differences in social control indicators between foreign-born and US-born Chinese
participants. The results show that the foreign-born Chinese participants had significantly
higher score on computer-mediated social support, with F(1,478) = 18.22, p < .001, η
2
=
79

3.7%, ICSN, with F(1, 474) = 6.23, p < .05, η
2
= 1.3%, and cultural beliefs about dietary
behavior, with F(1,478) = 18.75, p < .001, η
2
= 3.8%, after controlling for age, gender,
education, and income. H7a, H7b, and H7c were supported. Table 4-13 presents the
means and standard deviations.
Table 4-13
Means and Standard Deviations of Social Control Indicators
Foreign-Born
M (SD)
US-Born
M (SD)
Computer-Mediated Social Support 2.59 (1.02) *** 2.18 (1.04)
Connections to ICSN 9.08 (2.45) * 8.49 (2.25)
Cultural beliefs about dietary behavior 3.49 (.61) * 3.27 (.56)
Note.
*
p < .05,
**
p < .01,
***
p < .001.

80

CHAPTER 5 DISCUSSION AND CONCLUSIONS
Discussion of Results
The present study aimed to examine the impact of social control on dietary behavior
among Chinese immigrants living in the United States. Specifically, this study a) tested
the direct influence of three social control indicators, including computer-mediated social
support, connection to the integrated neighborhood storytelling network, and cultural
beliefs about dietary behavior representing levels of influences from individual-,
community-, and cultural-levels, on dietary behavior; b) examined the self-regulatory
mechanisms through which social control exerted influences on dietary behavior; c)
investigated the moderating role of social control in the intention-behavior relationship;
and d) compared levels of social control among foreign-born and US-born Chinese
immigrants. This study hoped to advance our understanding of regulating dietary
behavior from theoretical frameworks related to the communication of social control and
discuss implications for reducing the overweight and obesity epidemic.
Direct Influence of Social Control on Dietary Behavior
Results of this study provide some support for linking social control to dietary
behavior. Computer-mediated social support and cultural beliefs about dietary behavior
significantly predicted self-reported healthy eating behavior. Individuals who received
81

higher levels of computer-mediated social support and had higher levels of cultural
beliefs reported higher levels of healthy eating behavior. Regarding the role of social
support, the finding is consistent with previous research suggesting that social support as
a regulatory function of social relationships provides a constraining influence on health
behavior (Chan, Prendergast, Grønhøj, & Bech-Larsen, 2013; House et al., 1988; Markey
et al., 2008; Scholz et al., 2013; Umberson, 1987). It is also important to note that the
results show a positive relationship between face-to-face and computer-mediated social
support, suggesting that the constraining influence can migrate from offline to online
communication environments. It provides support for the complementary hypothesis that
proposes a positive relationship between face-to-face and computer-mediated
communication in interpersonal interactions (Dutta-Bergman, 2004a; Dutta-Bergman,
2006; Ellison et al.; Ognyanova et al., 2013). It is evidenced in this study that specific
functions of social relationships (e.g., the regulatory function) can transcend methods of
communication.
The relationships between ICSN and healthy eating indicators were not significant.
The results show that computer-mediated social support and ICSN were highly correlated
(r = .49). When both variables were entered in a model that predicted healthy eating
behavior, computer-mediated social support might be sufficient to explain the variance in
healthy eating behavior. The correlations between social support and ICSN were
evidenced in a recent study conducted by Kang, Chung, and Chung (2014). The
82

correlation was strongest between interpersonal discussion and social support. This
correlation is not surprising because interpersonal interactions provide the foundation for
giving and obtaining social support. It may be that participants frequently discussed
diet-related issues with their family and friends and were exposed to constraining
influences from their strong ties. When computer-mediated social support was removed
from the model, post-hoc analyses revealed that interpersonal discussion and connection
to community organizations had a direct effect on self-reported healthy eating behavior.
Connection to community organizations was also found to be linked to fruit and
vegetable intakes. Therefore, the findings provide some support for the claim that
connections to neighborhood storytelling networks regulate individuals’ dietary
behavior.
Indirect Influence through Self-Regulation
Results of this study also provide support for the indirect influence of social control
on dietary behavior through self-regulation. This study tested the mediating effects of two
self-regulation skills including planning and self-efficacy. The mediating effect was
found for the influence of computer-mediated social support and cultural beliefs on
self-reported healthy eating scale and consumption of fruits and vegetables through
planning. The mediating effect was also found for the influence of computer-mediated
social support and cultural beliefs on self-reported healthy eating scale through
83

self-efficacy. The results suggest that social control facilitates self-regulation, which, in
turn, affects dietary behavior. The findings are consistent with previous studies
supporting the self-regulatory mechanism through which social control affects dietary
behavior (Baumeister et al., 2005; Chatterjee et al., 2011; Maibach et al., 1991;
Schwarzer, 2008; Umberson, 1987).
No mediating effect was found for the relationships between ICSN and healthy
eating indicators. ICSN was not significantly associated with either planning or
self-efficacy. This finding does not support the notion that individuals increase their
problem-solving capacities by connecting to neighborhood storytelling (Kim &
Ball-Rokeach, 2006a; Kim et al., 2011). Previous research shows that Chinese
immigrants on average have low levels of connection to community storytellers (Chen et
al., 2013). It is possible that there is not enough variation in ICSN that can show an effect
on self-regulatory indicators.
Moderation Effects of Social Control in Intention-Behavior Relationships
Results of the moderation effects are mixed. A positive moderating effect was found
for computer-mediated social support on the intention-behavior relationship. When
predicting levels of the healthy eating scale, the strength of the intention-behavior
relationship increased when levels of computer-mediated social support increased.
84

A negative moderating effect was found for ICSN for the relationship between
intention and the healthy eating scale. A negative moderating effect was also found for
the relationship between intention and consumption of fruits and vegetables. The two
negative moderating effects suggest that the strength of the intention-behavior
relationship increased when individuals were less connected to ICSN and when they held
fewer cultural beliefs about dietary behavior. It is possible that exposure to social control
may stimulate nonconforming behavior, which is a way of showing resistance to social
control (Tilden & Galyen, 1987; Walen & Lachman, 2000). Chinese immigrants may
assimilate to individualistic orientations as they stay in the United States. The need for
being autonomous may make them less susceptible to social control (Bond & Smith,
1996).
In addition, it was assumed that intention was a precursor of behavior (Ajzen &
Fishbein, 1974). Information on participants’ intention and behavior was collected at one
specific point in time in this cross-sectional study. It may be that some individuals had
healthy eating habits without recognizing them. It is also possible that some participants
did not have an intention to eat healthy but practiced healthy eating because of social
influence. In these cases, the assumption that intention occurs before the actual behavior
may not be met, thus yielding mixed results when testing moderating effects for the
intention-behavior relationship.
85

To eliminate these possibilities described above, a sub-sample of individuals who
had a clear intention to eat healthy was selected. Post hoc regression analyses show that
computer-mediated social support and cultural beliefs about dietary behavior were
significant predictors of the healthy eating scale after controlling for demographics and
self-control. In other words, the intenders were more likely to eat healthy when they had
higher levels of computer-mediated social support and higher levels of cultural beliefs.
The results suggest the possible role of social control in closing the gap between intention
and behavior.
Comparisons between Foreign-Born and US-Born Chinese Immigrants
Results of this study show that foreign-born Chinese scored significantly higher on
all social control indicators than did the US-born Chinese. Specifically, the foreign-born
Chinese reported higher levels of computer-mediated social support than did the US-born
Chinese. This finding may mirror differences in cultural orientations with foreign-born
Chinese being more likely to utilize and conform to social control (Cialdini & Goldstein,
2004). Communication technologies enable social ties to provide the regulatory function
that transcends geographic and temporal constraints.
It was anticipated that foreign-born Chinese would have lower scores on ICSN
compared to US-born Chinese because previous research shows that newer immigrants
may be less integrated and attached to local communities (Chen et al., 2013). Contrary to
86

expectations, foreign-born Chinese were more connected to the neighborhood storytelling
network than the US-born Chinese. This difference was significant after controlling for
age, gender, education, and income. It may be that, compared to foreign-born Chinese,
US-born Chinese may have fewer cultural and linguistic barriers and thus are more likely
to connect to mainstream outlets for diet-related information.
As predicted, foreign-born Chinese had higher levels of cultural beliefs on dietary
behavior than did the US-born. It is consistent with previous research showing that
traditional eating habits and dietary practices may be lost within one generation of
immigrants living in the United States (Batis et al., 2011; Satia et al., 2010). Individuals
acquire health-related beliefs and norms through socialization (Dressler, 1993). US-born
Chinese may not be exposed to traditional cultural beliefs about dietary behavior when
they integrate to the mainstream U.S. culture.
Theoretical and Practical Implications
The present study has important theoretical and practical implications for advancing
health behavior research from a social control perspective, with a focus on the
communication of constraining influences from multiple stakeholders.
First, this study showed that social control affected dietary behavior. Higher levels of
social control were linked to higher levels of self-reported healthy eating behavior.
Differences in levels of social control were found between foreign-born and US-born
87

Chinese, with foreign-born Chinese having higher levels of social control and healthy
eating behavior than the US-born. This observation provides preliminary evidence for a
third explanation on the healthy immigrant effect besides self-selection (Kennedy et al.,
assessed 2015; Lynch et al., 2000) and acculturation (Batis et al., 2011; Hubert et al.,
2005; Satia et al., 2001; Unger et al., 2004). That is, immigrants may slowly lose their
exposure and conformity to social control as they assimilate to the American culture,
which places high values on self-reliance and independence (Gudykunst, 1996). The lack
of constraining influences on one’s dietary behavior may in part contribute to the
overweight and obesity epidemic. The social control argument may have broader
implications in addressing dietary problems nationwide. It is important to note that social
control may compromise one’s autonomy and induce psychological stress at a certain
level, but it does produce protective effects on regulating individuals’ behavior. Previous
research suggests that social control tactics range from direct coercion to subtle forms of
influence (Lewis et al., 2004). If direct coercion is considered as an act against the core
values of the American culture, subtle forms of social control may be used by various
stakeholders or be incorporated in dietary behavior interventions. The key is to provide
individuals a structured social environment in which they are reminded and pressured to
eat a healthy diet by agents at multiple levels (e.g., family, friends, neighbors, geo-ethnic
media, community organizations, cultural rituals).
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Second, this study examined computer-mediated social support from strong ties (i.e.,
family and friends), which expands previous work that has mainly focused on
computer-mediated social support from weak ties. Wright (2009) noted that
computer-mediated social support from traditional sources has remained an unexplored
area. Just as online support groups provide new ways of organizing and exchanging
resources for individuals in need of aid, computer-mediated social support from strong
ties migrate from offline to online and can significantly increase individuals’ access to
resources (both tangible and intangible), especially for those whose family and friends
live at geographically dispersed locations. Of particular importance with respect to
support from strong ties is that they are central in individuals’ everyday lives and may
exert more influence on behavior change compared to influence from weak ties. Family
interventions with a computer-mediated component may be used to increase family
engagement and effectiveness in promoting healthy eating behavior.
Third, this study advanced CIT research by exploring the underlying social and
psychological mechanisms through which structural factors of the neighborhood
storytelling network influence behavioral outcomes. CIT has been developed from a
sociological tradition that examines structural barriers to civic engagement and health
care in multiethnic communities. This study contributes to CIT research by articulating
an important function of the communication structure. It is argued that social control is
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the functional content of the neighborhood storytelling network and may directly affect
health behavior or facilitate self-regulation of health behavior.
Forth, this study created and validated a scale of cultural beliefs about dietary
behavior for Chinese immigrants. This scale was developed to reflect the influence of
traditional Chinese philosophy on conceptions of healthy eating (Wang, 2006; Zhao,
2008). The items were created based on the relational view of health—yin and yang as
opposing forces that coexist and strive for balance. A breakdown of the balance may lead
to diseases. This dialectic way of thinking about health provides a foundation for
conceptualizing healthy eating in the Chinese culture. This study expanded the work of
Satia and her colleagues (2000) on dietary acculturation by applying the relational view
of health to develop diet-related items at the cognitive level.
Fifth, this study examined self-control as a pathway linking social control to healthy
eating behavior. Relapse may occur if the constraining influence is removed from one’s
social environment (Schwarzer 2008). Bandura (2004) also argued that social influence is
effective only if it increases self-efficacy. In other words, the ultimate goal is to transform
social control into self-regulation skills that empower individuals for behavioral change
and translate intention to actual behavior. This study provided some evidence supporting
self-regulation as the mechanism through which social control affected dietary behavior.
Interventions to promote healthy eating habits may assist individuals to develop concrete
plans for performing a certain behavior (e.g., eating two servings of fruits per day), and
90

increase their beliefs that they can perform that behavior.
Limitations and Suggestions for Future Directions
The present study has several limitations. First, the cross-sectional design limits the
ability to control for community-level factors and their impact on communication
patterns. Differences in ICSN may be due to the variance in communication resources
available in local communities or due to individual differences in their capacities or
preferences of connecting to local storytellers. Future studies could control for possible
confounding effects of community-level factors by sampling individuals living in a
certain geographic area (e.g., Chen et al., 2013) or by conducting multilevel models that
control community variances (e.g., Carroll-Scott et al., 2013).
Second, this study used self-reported measures on assessing healthy eating behavior.
Self-reported measures may be problematic because individuals may overestimate their
consumption of healthy foods, which is known as the optimistic bias. This bias makes
individuals believe that they are less at risk of a negative event compared to others. It is
possible that individuals overestimated their consumption of fruits and vegetables. Given
that the healthy eating scale (Dutta-Bergman, 2004b) used in this study included
subjective evaluations of the extent to which individuals had healthy diets, it is possible
that some individuals who reported their diets to be healthy did not actually eat healthy
diets. The overestimation of healthy eating behavior may be also due to the lack of
91

nutrition knowledge. Some individuals might perceive their dietary behavior to be
healthy without realizing the risk factors. In addition, the self-reported healthy eating
scale may also suffer from social desirability bias. Individuals may over-reported healthy
eating behavior because it is socially desirable. Future studies could use better measures
to estimate dietary behavior such as food-frequency questionnaires and 24-hour diet
recalls (National Obesity Observatory, 2011).
Third, the sub-sample of US-born Chinese immigrants slightly differed from the
sub-sample of foreign-born Chinese immigrants in that it contained a substantial number
of young adults aged between 18-24 years. Research shows that young adults may not
pay attention to dietary quality as much as adults in other age groups (McCabe-Seller et
al., 2007). Therefore, the results of comparisons of social control and dietary behavior
between US-born and foreign-born immigrants should be interpreted with caution. The
right-skewed distribution of age in the US-born sub-sample may limit the study’s ability
to generalize the findings. A better sampling strategy or weighting may be used in future
studies to increase the prevision of the estimates on social control and dietary behavior.
Fourth, this study integrated insights from theoretical frameworks of social
comparison, social identity, and social norms to infer the social control function of ICSN.
Future studies could develop and use measures of community-level informal social
control, and examine the social and psychological mechanisms through which ICSN
affects health behavior. In addition, previous studies on geo-ethnic media have focused
92

on its role in providing resources and assisting immigrants in adjusting to the host
country (Matsaganis et al., 2010). Future research could investigate the role of geo-ethnic
media in exerting constraining influences on risky behaviors among immigrants.
Fifth, it appears that there was some confusion in the conceptualizations of social
support and social control because the two concepts have considerable overlap in the
ways they are defined and measured (House et al., 1988; Lewis et al., 2004). This study
focused on a particular aspect of social support that provides regulatory functions derived
from social relationships. It may be that the content of social support has multiple
functions and social control is one of these functions. For example, advice on food
quality from one’s mother (informational support) may be considered as a subtle form of
social control within the family. The advice may be intended to stimulate conformity
although not being explicitly expressed. The regulatory aspect of social support has been
also found in other areas such as emotions. Lakey and Orehek (2011) found that
ordinarily affective conversations and shared activities help individuals regulate emotions
when dealing with stress. Future research could focus on clarifying conditions in which
each concept should be used.
Sixth, this study provides some preliminary evidence for the validity and reliability
of the cultural beliefs about dietary behavior scale. Items were derived from the Chinese
tradition of viewing health and healthy eating using a dialectic approach. Cultural beliefs
about dietary behavior in this study were considered as an indicator of social control at an
93

abstract level. The scale should be used with caution when being applied to designing
cultural sensitive interventions. Future research could examine the ways in which these
cultural beliefs are translated into concrete dietary practices.
Conclusions
Social control is an essential part of social integration (Berkman et al., 2000;
Durkheim, 1967, Umberson, 1987). The results of the present study provide some
evidence showing that social control directly and indirectly affects dietary behavior.
Constraining influences can come from interpersonal, community, and cultural levels.
Multiple-level agents of social integration may directly monitor and pressure individuals
to eat healthy or indirectly affect their dietary behavior through self-regulatory
mechanisms. This study also illustrates the importance of social control in facilitating the
realization of healthy eating behavior when individuals have already formed an intention
to eat healthy. Finally, it is suggested that the erosion of social control may, in part,
contribute to the healthy immigrant effect; that is, new immigrants may gradually lose
their health advantages because of decreased levels of social control. It is important to
note that social control may threaten one’s autonomy and evoke psychological distress.
Future research may explore social control tactics that are effective in restraining
individuals from engaging in health-risk behavior while minimizing the negative effects.
94

This study may have broader implications for reducing the overweight and obesity
epidemic nationwide.
95

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APPENDIX: THE HEALTHY EATING SURVEY
!
Q1. INTRODUCTION!!
Dear!Participant,!
!
If!you!are!18!years!of!age!or!older,!we!would!like!to!invite!you!to!participate!in!an!online!survey.!The!
information!gathered!from!this!survey!will!help!academic!researchers!and!practitioners!better!understand!
how!to!eat!healthier.!Th e!survey!will!take!you!about!10 G 15!minutes!to!complete. !

Please!do!the!best!you!can!to!answer!all!questions.!There!is!no!right!or!wrong!answer.!Please!be!honest.!
We!value!your!opinion.!The!survey!will!remain!completely!confidential!and!anonymous.!Thank!y ou!for!
helping!with!this!study!
!
Based!on!the!information!you!provided,!you!do!not!qualify!for!this!study. !!Thank!you!for!your!time.!Have!a!
great!day!!!
!
HOT!AND!COLD!FOODS!INDEX!!
Q2. In!the!Chinese!diet,!foods!can!create!hot!or!cold!energy!on!the!body.!In!gene ral,!cold!foods!help!
remove!toxins!and!clear!excess!heat.!Hot!foods!help!warm!the!body!and!provide!greater!energy!for!
activity.!Neutral!foods!are!somewhere!in!between!hot!and!cold!foods,!mostly!for!general!nutrition.!
Please!select!the!appropriate!category! for!each!food!item.! !!
!
! ! Hot/Warm!Foods! Neutral!!
Foods!
Cold/Cool!Foods!
! Apple! ! ! ! ! ! !
! Ginger! ! ! ! ! ! !
! Lamb! ! ! ! ! ! !
! Cucumber! ! ! ! ! ! !
! Crab! ! ! ! ! ! !
! Carrot! ! ! ! ! ! !
! Green!Tea! ! ! ! ! ! !
! Cherry! ! ! ! ! ! !
! Rice! ! ! ! ! ! !
! Banana!! ! ! ! ! ! !
!
!
!
123

!
CULTURAL!BELIEFS!ABOUT!DIATARY!BEHAVIOR!
Q3. Please!rate!your!agreement!with!the!following!statements.!
! Strongly!
Disagree!
Disagree! ! Neutral! ! Agree! ! Strongly!
Agree!
It!is!healthy!to!eat!local!foods.! ! ! ! ! ! ! ! ! ! !
It!is!healthy!to!balance!hot!and!cold!foods.! ! ! ! ! ! ! ! ! ! ! !
People!should!avoid!cold!foods!in!winter.! ! ! ! ! ! ! ! ! ! ! !
Eating!too!many!hot!foods!in!summer!can!increase!
internal!body!heat.! !
! ! ! ! ! ! ! ! ! !
Eating!seasonal!foods!is!good!for!health.! ! ! ! ! ! ! ! ! ! ! ! !
Individuals!with!a!cold!body!type!could!eat!more!hot!
foods.! !
! ! ! ! ! ! ! ! ! !
Individuals!with!a!hot!body!type!could!eat!more!cold!
foods.! !
! ! ! ! ! ! ! ! ! !
It!is!important!to!balance!the!five!flavors:!sour,!
sweet,!bitter,!spicy!and!salty.! !
! ! ! ! ! ! ! ! ! !
It’s!better!to!eat!crabs!with!hot!seasonings!such!as!
ginger!and!green!onions.! !
! ! ! ! ! ! ! ! ! !
It’s!better!to!eat!lambs!with!cold!side!dishes.! ! ! ! ! ! ! ! ! ! ! !
Women!should!avoid!cold!foods!during!their!period.! ! ! ! ! ! ! ! ! ! ! !
!
COMPUTERGMEDIATED!SUPPORT!FROM!FAMILY!AND!FRIENDS!
Q4. Below!is!a!list!of!things!your!family!or!friends!might!do!or!say!to!you!in!computerGmediated!
interactions.!Please!rate!how!often!your!family!or!friends!said!or!done!what!is!described!during!the!
past!30!days.!!
! None! Rarely! A!Few!
Times!
Often! Very!
Often!
My!family!members!sent!me!a!link!to!a!news!story,!
video,!or!website!about!healthy!eating.!!
! ! ! ! ! ! ! ! ! !
My!family!reminded!me!not!to!eat!high!fat,!high!salt!
foods!using!online!chat!applications.!
! ! ! ! ! ! ! ! ! !
My!family!showed!concerns!about!my!diet!in!emails!
or!text!messages.!!
! ! ! ! ! ! ! ! ! !
My!friends!chatted!with!me!on!social!network!sites!
about!eating!more!healthy!foods.!!
! ! ! ! ! ! ! ! ! !
My!friends!sent!me!a!link!to!a!healthy!recipe,!a!
healthy!food!blog,!or!a!news!story!about!healthy!
eating.!!
! ! ! ! ! ! ! ! ! !
124

My!friends!gave!suggestions!on!where!to!buy!healthy!
produce!through!emails,!text!messages!or!social!
network!sites.!!
! ! ! ! ! ! ! ! ! !
!
SOCIAL!SUPPORT!FROM!FAMILY!AND!FRIENDS!FOR!HEALTHY!EATING !
!
Q5. Below!is!a!list!of!things!your!family!or!friends!might!do!or!say!to!you!in!faceGtoGface!interactions.!
Please!rate!how!often!your!family!or!friends!have!said!or!done!what!is!described!during!the!past!30!
days.!!
!
! None! Rarely! A!Few!
Times!
Often! Very!
Often!
My!family!encouraged!me!not!to!eat!high!fat,!high!
salt!foods.! !
! ! ! ! ! ! ! ! ! !
My!family!offered!me!lowGfat!snacks.! ! ! ! ! ! ! ! ! ! ! !
My!family!talked!with!me!about!eating!more!healthy!
foods.! ! !
! ! ! ! ! ! ! ! ! !
My!friends!gave!suggestions!on!how!to!eat!more!
healthy!foods.!
! ! ! ! ! ! ! ! ! !
My!friends!said!nice!things!about!what!I!eat.! ! ! ! ! ! ! ! ! ! ! !
My!friends!reminded!me!not!to!eat!high!fat,!high!salt!
foods.!
! ! ! ! ! ! ! ! ! !
!
COMMUNICATIVE!NETWORKS!
Connections)to)Community)Organizations) )
Q6. We’d!like!to!ask!you!about!any!neighborhood!clubs,!groups!or!organizations!you!or!others!in!your!
family!receive!information!about!healthy!eating.!Please!give!us!the!names!of!up!to!two!
organizations.! ! )
)
1!_____________________________________!
!
Do!you!get!information!from!this!organization!online,!offline,!or!both?! !
! Online!
! Offline!
! Both! !
)
Is!this!organization!located!within!a!20!minute!drive!from!your!home?! ! !
! Yes!
! No!
125

)
2!_____________________________________!
!
Do!you!get!information!from!this!organization!online,!offline,!or!both?! !
! Online!
! Offline!
! Both! !
)
Is!this!organization!located!within!a!20!minute!drive!from!your!home?! ! !
! Yes!
! No!
)
Connections)to)Geo6Ethnic)Media) )
!
Q7. ! We’d!like!to!know!the!two!most!important!media!channels!you!get!information!about!healthy!
eating!for!yourself!or!for!your!family.!Could!you!tell!us!the!name!of!up!to!two!media!channels?!(e.g.!
World!Journal,!Sina!Weibo,!CNN,!your!local!radio!station)!
!
!! 1
st
!most!important!_____________________________________!
!
Do!you!get!information!from!this!channel!online,!offline,!or!both?!
! Online!
! Offline!
! Both! !
!
Does!this!channel!target!ethnic!Chinese!or!your!geographic!area?! !
! Yes!
! No!
!
!! 2
nd
!most!important! ! _____________________________________!
!
Do!you!get!information!from!this!channel!online,!offline,!or!both?!
! Online!
! Offline!
! Both! !
!
!
Does!this!channel!target!ethnic!Chinese!or!your!geographic!area?! !
! Yes!
126

! No!
!
Q8. How!often!do!you!have!discussions!with!other!people!about!dietary!practices?! !
!
Never! ! ! ! ! ! ! All! the! time!
1! 2! 3! 4! 5! 6! 7! 8! 9! 10!
!
HEALTHY!DIETS! !
!
Q9. Please!rate!your!agreement!with!the!following!statements.!
! Strongly!
Disagree!
Disagree! ! Neutral! ! Agree! ! Strongly!
Agree!
I!try!to!avoid!foods!that!are!high!in!fat.! ! ! ! ! ! ! ! ! ! !
I!try!to!avoid!foods!that!are!high!in!cholesterol.! ! ! ! ! ! ! ! ! ! !
I!try!to!avoid!foods!with!a!high!salt!content.! ! ! ! ! ! ! ! ! ! !
I!am!concerned!about!how!much!sugar!I!eat.! ! ! ! ! ! ! ! ! ! ! !
I!use!a!lot!of!low!calorie!or!calorie!reduced!products.! ! ! ! ! ! ! ! ! ! !
I!try!to!select!foods!that!are!fortified!with!vitamins!
and!minerals.!
! ! ! ! ! ! ! ! ! !
I!am!careful!about!what!I!eat!in!order!to!keep!my!
weight!under!control.!
! ! ! ! ! ! ! ! ! !
I!try!to!avoid!foods!that!have!additives!in!them.! ! ! ! ! ! ! ! ! ! !
I!am!concerned!about!getting!enough!calcium!in!my!
diet.! ! !
! ! ! ! ! ! ! ! ! !
!
! !
127

Q10. The!following!questions!ask!about!some!foods!&!drinks!you!might!have!during!a!“typical”!week.!
Please!indicate!how!often!you!eat!at!least!ONE!portion!of!the!following!foods!&!drinks:!(a!portion!
includes:!a!handful!of!grapes,!an!orange,!a!side!salad,!a!slice!of!bread,!a!glass!of!soft!drink).! !
! Rarely!
or!
never!
Less!
than!
1!a!
week!
Once!
a!
week!
2G3!
times!
a!
week!
4G6!
times!
a!
week!
1G2!
times!
a!day!
3G4!
times!
a!day!
5+!a!
day!
Fruit!(tinned!/!fresh)! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Fruit!juice!(not!cordial!or!squash)! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Salad!(not!garnish!added!to!
sandwiches)!
! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Vegetables!(tinned!/!frozen!/!fresh!but!
not!potatoes)!
! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Chips!/!fried!potatoes! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Beans! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
FibreGrich!breakfast!cereal! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Wholemeal!bread! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Cheese!/!yoghurt! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Crisps!/!savoury!snacks! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
!
Q11. Continued…!…!
! Rarely!
or!
never!
Less!
than!
1!a!
week!
Once!
a!
week!
2G3!
times!
a!
week!
4G6!
times!
a!
week!
1G2!
times!
a!day!
3G4!
times!
a!day!
5+!a!
day!
Sweet!biscuits,!cakes,!chocolate,!sweets! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Ice!cream!/!cream! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Soft!drinks!(not!sugar!free!or!diet)! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Beef,!lamb,!pork,!ham! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Chicken!or!turkey! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Processed!meats!/!meat!products! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Sausages,!bacon,!corned!beef,!meat!
pies,!burgers!
! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Chicken!/!turkey!nuggets,!turkey!
burgers,!chicken!pies!
! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
White!fish!–!like!cod,!flatfish,!plaice! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
Oily!fish!–!like!herrings,!sardines,!
salmon,!trout,!mackerel,!fresh!tuna!(not!
tinned!tuna)!
! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
128

DIETARY!ACCULTURATION! !
Q12. Could!you!tell!us!whether!you!have!the!following!foods!during!a!typical!week?! !
! ! No! ! Yes!
Eat!tofu! ! ! ! ! !
Eat!a!ChineseG style!breakfast! ! ! ! !
Balance!cold!and!hot!foods! ! ! ! !
Eat!traditionally!preserved!foods! ! ! ! !
Avoid!cold!foods!and!drinks! ! ! ! !
Eat!bread,!rolls,!or!bagels! ! ! ! !
Eat!sweets,!cakes,!or!pies!for!dessert! ! ! ! !
Drink!milk!products! ! ! ! !
Eat!between!meals! ! ! ! !
Eat!at!Western!fastG food!restaurants! ! ! ! !
Eat!pizza!or!spaghetti!with!tomato!sauce! ! ! ! !
Eat!ground!beef!and!hamburgers! ! ! ! !
Eat!packaged!or!prepared!foods!(e.g.!TV!dinners)! ! ! ! !
Drink!carbonated!beverages! ! ! ! !
Eat!any!kind!of!cheese! ! ! ! ! !
!
HEALTHYG DIET!KNOWLEDGE! !
Q13. Do!you!think!the!following!food!items!are!high!or!low!in!added!sugar?! !
! !! High! !! Low! ! Not!sure!
Bananas! ! ! ! ! ! !
Tomato!ketchup! ! ! ! ! ! !
!
Q14. Do!you!think!the!following!food!items!are!high!or!low!in!fat?!
! !! High! !! Low! ! Not!sure!
Nuts! ! ! ! ! ! !
Honey! ! ! ! ! ! !
!
Q15. Do!you!think!the!following!food!items!are!high!or!low!in!fiber/roughage?!
! !! High! !! Low! ! Not!sure!
Eggs! ! ! ! ! ! !
Broccoli! ! ! ! ! ! !
!
! !
129

!
Q16. Saturated!fats!are!mainly!found!in:! !
! Vegetable!oils!
! Dairy!products!
! Both!
! Not!sure! !
!
Q17. Brown!sugar!is!a!healthy!alternative!to!white!sugar.! !
! Agree!
! Disagree!
! Not!sure! !
!
BEHAVIROAL!INTENTION! !
!
Q18. Please!rate!your!agreement!with!the!following!statements.!
!
! Strongly!
Disagree!
Disagree! ! Neutral! ! Agree! ! Strongly!
Agree!
I!intend!to!eat!healthy!in!the!forthcoming!month.! ! ! ! ! ! ! ! ! ! !
I!plan!to!eat!healthy!in!the!forthcoming!month.! ! ! ! ! ! ! ! ! ! !
I!will!try!to!eat!healthy!in!the!forthcoming!month.! ! ! ! ! ! ! ! ! ! !
!
!
DESCRIPTIVE!NORMS! !
Please!rate!your!agreement!with!the!following!statements.!
! Strongly!
Disagree!
Disagree! ! Neutral! ! Agree! ! Strongly!
Agree!
My!family!eats!healthy!on!most!days.! ! ! ! ! ! ! ! ! ! !
My!friends!eat!healthy!on!most!days.! ! ! ! ! ! ! ! ! ! ! !
!
! !
130

SELFG EFFICACY! !
Q19. Certain!barriers!make!it!hard!to!change!one’s!nutrition!habits.!How!sure!are!you!that!you!can!
overcome!the!following!obstacles?!I!can!stick!to!a!healthy!(lowG fat!or!lowG salt)!diet!even…!
! Not!at!
all!true!
Barely!
true!
Mostly!
true!
Exactly!
true!
...if!I!have!to!learn!much!about!nutrition.! ! ! ! ! ! ! ! !
…if!I!initially!have!to!watch!out!in!many!situations.! ! ! ! ! ! ! ! !
..if!my!blood!pressure!doesn’t!improve!immediately.! ! ! ! ! ! ! ! !
…if!initially!food!doesn’t!taste!as!good.! ! ! ! ! ! ! ! ! !
…if!it!takes!a!long!time!to!get!used!to!it.! ! ! ! ! ! ! ! !
…if!my!partner/my!family!don’t!change!their!nutrition!
habits.! !
! ! ! ! ! ! ! !
!
!
PLANNING!STRATEGIES! ! !
Q20. Most!people!would!like!to!further!improve!their!nutrition!by!taking!in!less!salt!and!fat.!How!about!
you?!I!already!have!concrete!plans..!
!
! Not!at!
all!true!
Barely!
true!
Mostly!
true!
Exactly!
true! !
how!to!change!my!nutrition!habits.! ! ! ! ! ! ! ! !
when!to!change!my!nutrition!habits.! ! ! ! ! ! ! ! !
when!to!especially!watch!out!in!order!to!maintain!my!new!
nutrition!habits.! !
! ! ! ! ! ! ! !
what!to!do!in!difficult!situations!in!order!to!stick!to!my!
intentions.!
! ! ! ! ! ! ! !
how!to!deal!with!relapses.! ! ! ! ! ! ! ! ! !
!
PHYSICAL!ACTIVITY! !
Q21. Please!indicate!the!level!of!your!physical!activity!during!a!typical!week!on!a!scale!from!very!low!to!
very!high.!By!physical!activity!we!mean!both!work!in!and!outside!the!home,!as!well!as!
training/exercise!and!other!physical!activity,!such!as!walking,!swimming!and!running.!Mark!the!
number!that!best!describes!your!level!of!physical!activity!during!a!typical!week.!
!
Very!Low! ! ! ! ! ! ! Very!High!
1! 2! 3! 4! 5! 6! 7! 8! 9! 10!
!
!
!
131

SELFG RATED!HEALTH!
Q22. How!satisfied!are!you!with!your!health?!
! Very!satisfied!
! Satisfied!
! Somewhat!satisfied! !
! Neither!satisfied!nor!dissatisfied!
! Somewhat!dissatisfied! !
! Dissatisfied!
! Very!dissatisfied!
!
Q23. What!is!your!height!in!feet!and!inches?! !
_______!feet!and!________!inches! !
Q24. What!is!your!weight!in!pounds?! !
________!pounds! !
!
DEMOGRAPHICS!
Q25. What!language!or!languages!are!usually!spoken!in!your!home?!
! English!only!
! Mandarin/!Cantonese!only!
! Both!English!and!Mandarin/Cantonese!
! Another!language!only!
! English!and!other!languages!not!listed!above!
Q26. Who!in!your!family!first!came!to!the!United!States?!
! Child/niece/nephew!
! Me/spouse/sibling!
! Parents/aunt/uncle!
! Grandparents! !
! Great!grand!parents!or!earlier! !
! Other,!please!specify___________!
! Don’t!know! !
Q27. How!many!years!have!you!lived!in!the!United!States?!
___________________!
Q28. What!is!your!age!in!years?!
___________________!
Q29. What!is!your!gender?!
! Male!
! Female!
Q30. How!do!you!describe!your!ethnicity?!(Check!all!that!apply)!
! Caucasian!or!NonG Hispanic!White!
132

! Latino!or!Hispanic! !
! African!American! !
! Asian! !
! Others! !
!
IF!SELFG IDENTFIED!ETHNICITY!IS!ASIAN,!THEN!CONTINUE!TO!Q2.!
IF!SELFG IDENTIFIED!ETHNICITY!IS!NOT!ASIAN,!THEN!GO!TO!THANK!AND!END!SURVEY.!
!
Q31. Are!you….?!(Check!all!that!apply)!
! Chinese!
! Filipino!
! Japanese!
! Korean! !
! Vietnamese! !
! Others! !
Q32. What!is!the!highest!degree!or!level!of!school!you!have!completed?! !
! Some!high!school!or!less!
! High!school!graduate! !
! Some!college!or!technical!school!
! Bachelor’s!degree!
! Master’s!degree!or!higher!
! Unknown! !
Q33. What!is!your!marital!status?!
! Single,!never!married!
! Married!or!domestic!partnership!
! Widowed! !
! Divorced! !
! Separated! !
Q34. Which!of!the!following!categories!best!describes!your!employment!status?!
! Employed,!working!40!or!more!hours!per!week!
! Employed,!working!21G 40!hours!per!week!
! Employed,!working!1G 20!hours!per!week! !
! Not!employed,!looking!for!work!
! Not!employed,!NOT!looking!for!work!
! Retired! !
! Other,!please!specify_______________!
Q35. What!is!your!annual!household!income?!
! Less!than!15,000!dollars!
! 15,000!to!less!than!20,000!dollars!
133

! 20,000!to!less!than!35,000!dollars!
! 35,000!to!less!than!45,000!dollars!
! 45,000!to!less!than!60,000!dollars!
! 60,000!to!less!than!75,000!dollars!
! 75,000!to!less!than!100,000!dollars!
! 100,000!dollars!or!more!
Q36. How!many!children!do!you!have?!
________________!
Q37. Do!you!own!or!rent!your!current!residence?! !
! Own!
! Rent!
! Other,!please!specify!__________!
Q38. In!what!country!were!you!born?!
________________________!
Q39. What!state!in!the!US!do!you!live!in?! !
________________________!

Abstract (if available)

Abstract This study examines the role of social control in promoting healthy eating behavior. The escalating epidemic of overweight and obesity affects more than two-thirds of adults in the United States. Unhealthy diets increase the risk of being overweight and obese. Individuals’ desire to overeat unhealthy diets may be restrained by agents of social integration. Guided by an ecological approach, this study focuses on the ways in which social control is communicated at interpersonal, community, and cultural levels. Specifically, this study applies and extends theoretical frameworks of social support, Communication Infrastructure Theory, and dietary acculturation. It is proposed that social support, connection to neighborhood storytelling, and cultural beliefs all have a regulatory impact on dietary behavior. This study also examines the self-regulatory mechanisms through which social control affects dietary behavior. Hypotheses were tested using a cross-sectional sample of 505 Chinese immigrants living in the United States. Immigrants may offer important analytical advantages for understanding the changes in social environments. The results provide some evidence showing that social control provides external regulation and facilitates self-regulation of healthy eating behavior. The role of social control was also tested to explain two phenomena, including the intention-behavior gap and the healthy immigrant effect. The results show that social control may facilitate the realization of healthy eating behavior for individuals with the intention of eating healthy. The results also show that foreign-born Chinese had higher levels of social control than US-born Chinese did at all levels. The results suggest that decreased levels of social control experienced after immigration may in part contribute to the loss of healthy eating habits and immigrants’ health advantages. The results have important implications for promoting healthy diets and reducing the prevalence of overweight and obesity.

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator An, Zheng (author)

Core Title The role of social control in promoting healthy eating behavior among Chinese immigrants: an ecological approach

School Annenberg School for Communication

Degree Doctor of Philosophy

Degree Program Communication

Publication Date 04/08/2015

Defense Date 03/24/2015

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Chinese immigrants,communication infrastructure theory,computer-mediated social support,cultural beliefs about dietary behavior,dietary acculturation,healthy eating behavior,intention-behavior gap,OAI-PMH Harvest,overweight and obesity,planning,self-efficacy,self-regulation,social control

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor McLaughlin, Margaret L. (committee chair), Ball-Rokeach, Sandra J. (committee member), Chou, Chih-Ping (committee member)

Creator Email peaceicean@gmail.com,zan@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-543319

Unique identifier UC11297791

Identifier etd-AnZheng-3255.pdf (filename),usctheses-c3-543319 (legacy record id)

Legacy Identifier etd-AnZheng-3255.pdf

Dmrecord 543319

Document Type Dissertation

Format application/pdf (imt)

Rights An, Zheng

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA