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Leveraging social normative influence to design online platforms for healthy eating

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Leveraging social normative influence to design online platforms for healthy eating

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LEVERAGING SOCIAL NORMATIVE INFLUENCE TO DESIGN ONLINE

PLATFORMS FOR HEALTHY EATING

by

Jinghui Hou

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 Jinghui Hou
ii
Dedication
To my younger self.
To all scholars who are devoted to making this world a better place.

iii
Acknowledgments
Writing this dissertation would not have been possible without the support of
many people. The first person that deserves my forever gratitude is my advisor, Dr.
Margaret McLaughlin. Peggy, you have given me enormous freedom to wander
intellectually, meanwhile encouraged me to pursue research that serves the public
interest. You have given me constant guidance, personal attention, endless
encouragement and full support. Thank you for all of the meetings and chats over the
years. I am also greatly indebted to my committee members, Dr. Peter Monge, who has
always hold me to high standards as a social scientist. I have learned so much from your
methods classes about doing rigorous research. Your wisdom, knowledge, and
commitment inspired and motivated me. I am grateful to Dr. Wendy Wood for planting
the seed of this dissertation idea through your own work as well as your generous
support, your time and effort in service on my committee.
I wish to offer my heartfelt thanks to Dr. Kwan Min Lee. Kwan, you have been a
brilliant mentor, a wonderful collaborator, and a generous friend to me. I have learned a
lot from our conversations. Thank you for sharing your experience and thoughts with me.
I also want to thank Dr. Dimitri Williams, Dr. Michael Dawson, Dr. Michael Cody, and
Dr. Lynn Miller for their intellectual guidance during my doctoral program.
This dissertation would not have been possible without the support of the Walton
College Behavioral Business Research Lab at the University of Arkansas. My warm
appreciation is due to Nidhi Dahiya, Will Alfred, Dr. Cary Deck, and the Marketing
Department for allowing me to use a world class research facility. I also want to thank
Dr. Xiao Ma and Eric Hatch for letting me recruit your students. I am grateful to John
iv
Cagle, my research assistant, for your commitment to the project and your invaluable
help whenever I needed you.
I am grateful to my friends and colleagues at Annenberg: Lu Li, Zheng An, Nan
Zhao, Wenlin Liu, Rong Wang, Chih-Wei Hu, Mina Park, Ritesh Mehta, Neta Kligler
Vilenchik, Lin Zhang, Bei Yan, Jin Huang, Wei Wang, Chi Zhang, Yao Sun, Poong Oh. I
also want to thank Annenberg alumni: Jingbo Meng, Nancy Chen, Helen Hua Wang,
Cindy Cuihua Shen, Yujung Nam, Young Ji Kim, Joe Phua, Jingfang Liu, Shuya Pan,
Adam Kahn, Robby Ratan, Sandi Bangasser Evans, Scott Sanders, Katya Ognyanova,
Drew Margolin, Leo Xiong, and Wei Peng. You have provided me with wonderful
companionship and valuable feedback on my research and graduate student life over the
years at Annenberg. You have made my graduate school life special and full of love and
support.
I also gratefully acknowledge the institutional support that I have received while
working on this project. In particular, I thank the Annenberg Foundation, the Graduate
School, and the University of Southern California for supporting me with generous
fellowships and scholarships.
I have been blessed with a supportive family. I am enormously indebted to my
parents-in-law. Without your willingness to take care of my baby son Melvin, this
dissertation would have taken longer to complete. My deep appreciation to my parents for
understanding of my aspiration. And last but certainly not least, I am incredibly grateful
to my other half, Xiao. Thank you for your infallible love, wisdom, patience, sacrifice,
encouragement, humor, and support.

v
Table of Contents
Dedication ii
Acknowledgments iii
List of Tables vii
List of Figures viii
Abstract x
Chapter 1: Introduction 1
Overview 1
Understanding Eating Behavior 2
Chapter Summaries 17

Chapter 2: Focus of The Current Study 19
Context: Nudge through Communication Technologies 19
Consumption Quantity 21
Social Environment on Consumption Quantity 22

Chapter 3: Theories Adapted and Tested 34
Study I: Social Norms, Self-Regulation Resources and Consumption 34
The Focus Theory of Normative Conduct 34
Inferential Mechanisms: Descriptive Norms and Consumption 37
Self-Regulation Mechanisms: Injunctive Norms and Consumption 40
Study II: Anchor Level, Comparison Feedback, and Order Quantity 43
Anchors and First Rater Problem 44
Technology-Based Normative Feedback 47

Chapter 4: Method 50
Test Bed: SnackTime 50
Study I 51
Participants 51
Design and Procedure 52
Experimental Manipulations 55
Measures 58
Data Analysis 61
Study II 61
Participants 61
Design and Procedure 62
Manipulations 67
Measures 69
Data Analysis 70

vi
Chapter 5: Results 71
Study I 71
Participants Characteristics 71
Randomization Checks 71
Manipulation Checks 72
Order Quantity/Consumption Quantity 72
Hypotheses Testing 73
Study II 82
Participants Characteristics 82
Order Quantity 82
Hypotheses Testing 82
Post-hoc Analyses 86

Chapter 6: Discussion and Conclusion 90
Study I 90
Study II 96
Limitations and Future Directions 99
Conclusion 102

References 104

Appendices
Appendix A: Information about Cookies 122
Appendix B: Recruitment Messages of Study II 123
vii
List of Tables

Table 1: Five SnackTime Website Versions 63

Table 2: Numbers of Participants in Each Experimental Condition 71

Table 3: Order Quantity/Consumption Quantity in Each Condition 73

Table 4: Rated Influence on Order Quantity 77

Table 5: Conflict Feelings by Condition 80

Table 6: Testing Moderating Effect of Healthy Eating Goal in
Injunctive Norm Condition 81

Table 7: Descriptive Statistics for Order Quantity in All Conditions 82

Table 8: ANOVA Results for Ordering Quantity 84

Table 9: Deviations in Each Condition 86

Table 10: A summary of Hypotheses Testing Results 89

viii
List of Figures

Figure 1: Herman & Polivy’s Model of Consumption Quantity 29

Figure 2: Wansink & Chandon’s Model of Consumption Quantity 30

Figure 3: Screenshot of Homepage of the SnackTime Website 50

Figure 4: Screenshot of SnackTime Ordering Page in the Control
Condition 54

Figure 5: Study I Procedure 55

Figure 6: Screenshot of SnackTime Ordering Page in the Descriptive
Norms Condition 56

Figure 7: Screenshot of SnackTime Ordering Page in the Injunctive Norms
Condition 57

Figure 8: The Scales of the Self-Assessment-Manikin 60

Figure 9: Screenshot of Simple Instructions on SnackTime Home Page 64

Figure 10: Screenshot of SnackTime Sign Up Page 64

Figure 11: Screenshot of SnackTime Ordering Page in the Control
Condition 65

Figure 12: Screenshot of SnackTime Confirm Order Page without
Comparison Feedback 66

Figure 13: Summary of the Snack Ordering Procedure of Study II 66

Figure 14: Screenshot of SnackTime Ordering Page in the Low Anchor
Condition 68

Figure 15: Screenshot of SnackTime Ordering Page in the High Anchor
Condition 68

Figure 16: Screenshot of SnackTime Confirm Order Page with
Comparison Feedback 64

Figure 17: Rated Influence on Order Quantity Overall 78

Figure 18: Order Quantity by Condition 79

ix
Figure 19: Order Quantity by Participants 84

Figure 20: Rated Influence on Order Quantity 87

x
ABSTRACT
Recent literature suggests that the social normative approach is one of the most
influential yet underexplored areas on food consumption decisions. This dissertation
project advances this area by testing how two distinct types of social norms, information
cued through the direct decision-making environment that conveys norms about how
other people have behaved (descriptive norms) and what they think is appropriate to do
(injunctive norms), influence food intake decisions. Study I examined the intervening
psychological processes involved in responding to each type of social norms, and
assessed the differential effects of self-regulation resources on the likelihood of
conforming to the two norms in a controlled laboratory experiment (N = 139). Moreover,
following the nudge approach, this project probes how to implement cost-effective
nudges in technology-mediated environments to influence food consumption decisions.
Study II tested two technology-based nudges, anchor setting and normative feedback, in
an online food ordering website using a field experiment (N = 93).
Across our studies, our primary findings are that descriptive norms cued through
the immediate environment in which food consumption decisions are made function as
anchoring heuristics. In other words, people depend on others’ consumption volume as
anchors to which they match their own intake. Conformity to descriptive norms is
moderated by the level of self-regulation resource such that people are more responsive
to descriptive norms of others’ consumption quantity in the state of ego depletion.
Moreover, adherence to descriptive norms can be fostered using personalized comparison
feedback made saliently to the immediate technology environment. Employing
technology-based nudges of setting an initial low anchor combined with providing
xi
comparison feedback can potentially lead people to order and consume less food. Finally,
similar to other types of environmental factors, descriptive norms inferred from the
environment operate in nudging people’s intake decisions without people being aware of
their influence.

Keywords: Descriptive norms, Food consumption decisions, Environmental cues,
Anchoring heuristics, Ego depletion, Technology-based nudge, Personalized comparison
feedback

.
1
CHAPTER 1: INTRODUCTION
Overview
The conventional health interventions that heavily rely on providing information
or boosting motivation are only partially effective in promoting healthy eating behaviors
(Camerer & Loewenstein, 2004; Wansink, 2010; Wansink & Chandon, 2014). This is
probably due to the fact that people’s decisions around food are largely influenced by the
immediate decision environment (Rothman, Sheeran, & Wood, 2009; Wansink, Just, &
Payne, 2009; Wansink & Chandon, 2014). Drawing on this insight, the environmental
interventions, which focus on designing environmental cues to the decision context to
induce people to eat better, have been acclaimed as a promising approach, an approach
often referred to as “nudge” (Thaler & Sunstein, 2008)
Following this nudge approach, this project examines how the social environment
influences consumption decisions. Specifically, it focuses on social normative
information cued through the direct decision-making environment that conveys norms
about how other people have behaved (descriptive norms) and what they think is
appropriate to do (injunctive norms, Cialdini, Reno, & Kallgren, 1990). Recent literature
suggests that the social normative approach is one of the most influential yet
underexplored areas on food consumption decisions. The lack of understanding about the
influence of social norms on food consumption decisions has been driving a significant
amount of speculation about the mechanisms associated with such influence and an
extensive debate over the effectiveness of using the social normative approach to combat
poor eating behaviors. This dissertation project advances such understanding insofar as it
is among the first line of research to test predicted differences in how two distinct types
2
of social norms influence food intake decisions, and the intervening psychological
processes involved in responding to each norm type. Our first study is devoted to
examine these issues in a controlled laboratory experiment.
Meanwhile, we now inhabit an increasingly technologically mediated world; thus
much of our decision-making environment is presented to us through a screen. While
most of the nudge research is not situated in technology-mediated decision-making, we
argue that developing technology-based nudges, or nudges designed and implemented in
a technology-mediated environment, is a merited direction. Our second study focuses
exclusively on the influence of descriptive norms and proposes and tests two technology-
based nudges: anchor setting and normative feedback, through the use of randomized
field trials.
Understanding Eating Behaviors
The Deliberative Approach
Obesity – everyone knows it is bad and that it is everywhere. Nearly two-thirds of
Americans are overweight or obese (Doane, 2010). While the straightforward solution to
the obesity epidemic may sounds deceptively simple, take in fewer calories a day, it is
not just a matter of people deciding they are going to eat less.
Perhaps the most frustrating aspect of the obesity epidemic is the contrast between
our understanding of the detrimental consequences of poor eating behaviors and our
inability to restrain such behaviors. This contrast, along with the continued growth of the
obesity epidemic, makes us question the effectiveness of conventional health
interventions that are primarily information-centric and belief or motivation-related.
3
To date, numerous public health interventions have been conceived to help people
make informed eating decisions through such means as publicizing nutritional knowledge
of food options and educating them about food intake regulation (Berman & Lavizzo-
Mourey, 2008; Dietary Guidelines for Americans, 2005). For example, the Patient
Protection and Affordable Care Act mandated that calorie labels be added to menu boards
of chain restaurants with 20 or more locations (Nestle, 2010). A large survey among
1,156 low-income adults assessing the impact of menu labels found no significant change
in calories purchased after the introduction of the intervention in New York City (Elbel,
Kersh, Brescoll, & Dixon, 2009). Similar studies designed to assess the labeling policy
found reductions in calories purchased (Chu, Frongillo, Jones, & Kaye, 2009; Pulos &
Leng, 2010; Roberto, Larsen, Agnew, Baik, & Brownell, 2010), reductions at some
chains but not others (Dumanovsky, Huang, Bassett, & Silver, 2010), or no significant
effect on food-purchasing behaviors (Elbel, Gyamfi, Kersh, 2011; Finkelstein,
Strombotne, Chan, & Krieger, 2011; Harnack et al., 2008; Vadiveloo, Dixon, & Elbel,
2011). Later meta-analysis studies on menu labeling concluded that the intervention had
no significant effect on healthier food choices (Chaufan, Fox, & Hong, 2011), calories
ordering, or consumption (Swartz, Braxton, & Viera, 2011).
While decades of national educational effort have successfully increased
awareness of nutrition and healthy eating (American Dietetic Association, 2011), people
eat less well than they did 20 years ago (Center for Disease Control and Prevention,
2004; 2010). Individuals often engage in poor eating behaviors even if they understand
the risks and are motivated to eat a more healthful diet. This challenges the assumption
that people will behave in a self-interested fashion if they are well informed and are
4
sufficiently motivated. This is an assumption that conventional health behavior models
are based upon (Hou, 2013). Conventional health behavior models are primarily derived
from a deliberative approach, which contends that health-related behavior is principally
driven by behavioral intention to perform or not perform as a result of a deliberative
evaluation of anticipated outcomes (Hou, 2013). Therefore, standard interventions to
combat overweight and obesity have been grounded on the assumption that individuals’
eating behavior is carried out via rational decision making processes (Just & Payne,
2009).
Nevertheless, researchers maintained that models based on deliberative reasoned
action did not work well for poor eating behaviors (Read & Van Leeuwen, 1998). A
review of 53 studies on healthy eating (Michie, Abraham, Whittington, McAteer, &
Gupta, 2009) pointed out that behavioral interventions targeted at enhancing determinants
of behavioral intention (i.e., attitudes and self-efficacy) were found to have little effect on
outcomes associated with eating. Deliberative models, such as the Theory of Reasoned
Action and the Theory of Planned Behavior (Ajzen & Fishbein, 1980; Ajzen, 1991;
Fishbein & Ajzen, 1975), have come under criticism due to their low predictive validity
and strong theoretical bias to understand eating as a rational process (Köster, 2009).
Ogden (2003) in her book, The Psychology of Eating, expand upon this criticism and
argued that cognitive models fall short of explaining food-related decisions because they
assume that “eating behavior is a consequence of rational thought” (p. 7).
In addition, food consumption decisions may involve cognitive biases, or
systematic errors in judgment or decisions, which work against healthy eating. One such
cognitive bias is called present-biased preferences (Hou, 2013). Decision-making and
5
behaviors (e.g., over consumption, smoking, binge drinking) that undermine health often
involve immediate benefits (such as eating and a feeling of satiation) coupled with
delayed costs (such as obesity) or immediate costs (such as the inconvenience of taking a
drug) coupled with delayed, and often uncertain, benefits (such as early detection of a
disease). When facing such decision-making, people are inclined to place more weight on
immediate benefits and costs and less on future outcomes, a phenomenon that has been
labeled present-biased preferences (O’donoghue & Rabin, 1999). Present-biased
preferences typically work against healthy eating because the immediate temptation of
unhealthy foods likely eclipses considering future health consequences (Loewenstein,
Brennan, & Volpp, 2007), especially since weight gain from unhealthy food choices is
not immediate but rather delayed and gradual. People are often blind to the cumulative
effect at the moment of decision-making (Ratner et al., 2008), but a long-term
accumulation of unhealthy decisions eventually leads to obesity. This special nature of
eating behavior makes it hard to promote healthy eating through a deliberative approach
because people tend to value the joys of a supersized plate today but little of tomorrow’s
good health (Cash & Schroeter, 2010).
“Health halo” is another cognitive bias that works against healthy eating. Scholars
(Wansink & Chandon, 2006) found that labeling information may have unintended and
undesirable consequences, as consumers can be biased by the healthy impression attached
to the products, which lead them to believe that the food is healthier than they would
otherwise think. This phenomenon is called a “health halo” (Chandon & Wansink,
2007a). In both laboratory and natural filed experiments, Wansink and Chandon (2006)
found that foods falsely labeled to be “low fat” led participants to over-consume these
6
foods relative to control foods. Similarly, consumers ordered and ate more from fast-food
restaurants claiming to be healthy (e.g., Subway) compared to those that do not (e.g.,
McDonald’s); thus healthy claims can result in greater total caloric intake (Chandon &
Wansink, 2007).
Environmental Influences on Eating
Alternatively, a growing belief is that environment influences the amount of food
people consume (Cohen & Farley, 2008; Rothman, Sheeran, & Wood, 2009; Wansink,
Just, & Payne, 2009). Recent research has documented a wealth of evidence that
consumption volume is strongly influenced by factors such as portion size, visibility, and
availability of foods, while nutrition knowledge, motivation (Wansink, 2010), hunger,
and taste do not affect food intake as much as are commonly believed (Herman & Polivy,
2014; Wansink & Chandon, 2014).
For instance, food portion size has been found to be a robust determinant of how
much people eat. Individuals served larger portions consume more despite of the food
items, food taste, meal settings, timing of other meals, and eaters’ gender, body weight,
and usual intake amounts (Burger, Fisher, & Johnson, 2010; Diliberti, Bordi, Conklin,
Roe, & Rolls, 2004; Fisher, Rolls, & Birch, 2003; Flood, Roe, & Rolls, 2006; Rolls, Roe,
Kral, Meengs, & Wall, 2004; Wansink & Kim, 2005). For example, consumers who were
severed an increased size of a pasta entrée (377-g) at a restaurant ate 43% more than
consumers served the original portion (248-g) of the dish, increasing their caloric
consumption at the meal by 159 kcal (Diliberti et al., 2004). People given 170-g bags of
potato chips increased their intake volume by an average of 143 kcal on five separate
days compared to when they were given a 28-g bag size. And they did not adjust their
7
intake to compensate for the increased intake of the snack at the subsequent dinner meal
(Rolls et al., 2014). People drank more when served a larger portion of beverage
regardless of the type of the beverage (Flood et al., 2006). Moviegoers given a large
bucket of popcorn ate 33.6% more than those given a medium bucket of popcorn even
when the popcorn was 14 days old and they complained about the bad taste (Wansink &
Kim, 2005). Even food experts are not immune to the impact of portion size. Large sizes
lead professional bartenders to over-pour alcohol and nutritional scientists to over-serve
themselves ice cream (cf. Wansink, Just, & Payne, 2009).
Mindless Eating. Why do environmental cues influence how much we eat?
Perhaps two accounts explain the impact of environment on food consumption. The first
one is that people do not really know how much to eat. And secondly, they use
environmental cues as simple rules to help them decide the quantity.
People tend to believe that they know how much to eat because they know when
they are full. However, research suggested that people’s internal cues of satiation are
vague signals to monitor food consumption volume (Wansink, 2010; Wansink, Just, &
Payne, 2009). Despite an increase in intake, individuals given large portions generally do
not report increased levels of fullness (Ello-Martin, Ledikwe, & Rolls, 2005).
Experimental studies (Rolls, Roe, & Meengs, 2006) also have shown no differences in
fullness when people were served 50% or 100% more food than usual, although their
consumption had increased by 16% and 26%, respectively. Wansink (2006) described a
consumption range – a mindless margin – in which people can either under-eat or overeat
up to 15 – 20% more or less than they typically do without feeling either overly hungry
or overly full. That is, if a person needs 2000 calories per day to maintain an energy
8
balance, she could eat within the 1700 – 2300 calories without feeling she had eaten less
or more than was typical (Wansink, 2006). Yet, the median weight gain in the obesity
epidemic over the last two decades could be caused by a daily excess of just 100 to 150
calories (Cutler, Glaeser, & Shapiro, 2003).
People are not clearly aware of how much they eat based on internal cues.
Therefore, they tend to rely on environmental cues as reference points to help them
determine how much to consume. One study asked 145 Americans when they knew they
were “through eating dinner”. People responded that they knew they were through eating
dinner when their “plates were empty” or when the TV show they were watching while
eating “was over” (Wansink, Payne, & Chandon, 2007). Thus, instead of using internal
cues (fullness) to determine when to stop eating, people depend on environmental cues
(plate and TV show) as simple rules to determine the termination of eating. What if the
environmental cues are absent? In one study, participants who ate blindfolded decreased
the intake of food by 22%, while their subjective feelings of fullness were similar to the
ratings of those eating without a blindfold (Linné, Barkeling, Rössner, & Rooth; 2002).
The researchers suggested that eating blindfolded may force subjects to rely more on
internal signals. In another clever experiment, when participants ate from a bottomless
bowl of tomato soup that was secretly connected to a tube underneath a table which
automatically refilled soup to the bowls, they unknowingly consumed 73% more soup
(Wansink, Painter, & North, 2005). Moreover, despite the increased intake volume,
participants did not believe they had consumed more, nor did they perceive themselves as
more sated that those ate from a normal bowl. When individuals were interviewed if they
were full during the experiment, a common response was, “How can I be full, I still have
9
half a bowl left?” (Wansink, Painter, & North, 2005). Researchers concluded that people
rely on external cues (such as eating until the plates are clean) as points-of-reference to
determine the termination of eating, or if such cues are absent, they stop eating when they
eventually get too sated (Wansink et al., 2005).
Furthermore, while environmental cues strongly influence how much people eat,
people do not typically recognize these influences (Vartanian, Herman, & Wansink,
2008). When it is pointed out that they could be influenced by their environment, people
deny that influence (Bargh & Chartrand, 1999; Wansink & Cheney, 2005; Wansink &
Van Ittersum, 2005; Wansink & Sobal, 2007). Even after they have accepted the robust
evidence that environmental factors affect eating decisions, they claim that these factors
influence others but not themselves (Pronin, Berger, & Molouki, 2007). Instead, people
justify their eating decisions in terms of conscious goals and intentions, such as I decide
to eat because “I like it,” or “I was hungry” (Wansink, 2006).
On a macro level, food scientists have described the current food environment in
the United States as “toxic,” in which consumers get ubiquitous exposure to inexpensive,
readily accessible, heavily marketed, and large quantities of unhealthful albeit appealing
foods. They urged that this toxic food environment is the origin of the obesity epidemic
(French, Story, & Jeffery, 2001; Hill & Peters, 1998; Hill, Wyatt, Reed, & Peters, 2003;
Schwartz & Brownell, 2007). For instance, high-fat and high-sugar foods are often too
accessible: when fast-food restaurants offer free refills on sodas, consumption is
encouraged by eliminating the need to wait in line again and pay at the counter.
Accessibility of fast-food and full-service restaurants has been found to be the leading
predictor of local obesity trends (Chou, et al., 2004).
10
The Nudge Approach
What can be done then? If people are mindless to how much they eat and their
eating decisions are largely influenced by the environment, one implication is to change
people’s environment rather than to change their thinking (Wansink, 2006).
Beyond the domain of eating decisions, an alternative approach has emerged to
understand how to tap environment to our advantage – the nudge approach (Thaler &
Sunstein, 2003). Human behavior, such as people’s eating decisions, takes place in a
context such that many features, noticed or unnoticed, in the context influence people’s
decisions. Choice architecture is a term that is used to describe such contexts; it can be
broadly defined as the environments in which people make decisions. The term was
originally coined by Thaler and Sunstein (2008), who further proposed that choice
architecture can be intentionally designed as a means to influence people’s decision-
making and to induce them to do what is in their best interest. Additionally, the person
who creates, designs, and engineers the choice architecture is, in Thaler and Sunstein’s
(2003) terminology, a choice architect. An attribute of the choice architecture specifically
designed to influence decisions is called a nudge. One of the major contributions of the
nudge approach to behavioral change lies in the application of small “nudges” in the
design of the decision environment.
Conceptual Root: Bounded Rationality. The idea of designing choice architecture
to nudge decision-makers for the better draws on insights of bounded rationality (Simon,
1955; 1972). Classic economic theories posit that humans are perfectly rational actors
who make decisions in ways that best achieve their objectives (Becker, 1962; Simon,
1959). They assume that individuals integrate all information through some type of
11
calculus, i.e., a utility function, to arrive at a decision. Due to both their strong
assumptions and narrow explanatory scope, classic economic theories have been subject
to criticism for bearing no resemblance to reality (Bohman, 1992). Departing from the
premise of rational optimizing behavior, the concept of bounded rationality was
developed by Herbert Simon (1972) to account for the fact that people always act in ways
that are economically suboptimal because of various constraints (Gigerenzer & Selten,
2002; Simon, 1972). In other words, human rationality is bounded. Simon used the
metaphor of two blades of a pair of scissors to describe the two sources of bounds on
human rationality. One blade is the cognitive limitations of human minds, such as
restricted information-processing capabilities and computational skills, flawed memory,
selective attention, and lack of knowledge. The other blade is the structure of the
environment (Simon, 1990, p. 7; Todd & Gigerenzer, 2003). In Simon’s (1956) words, “a
great deal can be learned about rational decision making … by taking account of the fact
that the environments to which it must adapt possess properties that permit further
simplification of its choice mechanisms” (p. 129; italics added). From this perspective,
human behavior is shaped by the two types of bounds fitting together (Todd &
Gigerenzer, 2003): the cognitive capability of an individual and the environment in which
the individual behaves.
Taking one step further, Simon (1955) argued that because of bounded rationality,
an individual simply undertakes a decision based on “schemes of approximation.” In
other words, they use simplified procedures, or heuristics, that do not guarantee an
economically optimal decision, but allow a satisfactory decision that saves cognitive
resources (Simon, 1955). The term heuristic is of Greek origin, meaning “serving to find
12
out or discover.” Einstein (1905) used the term heuristic to indicate an idea that he
considered incomplete, given the limits of our knowledge, but useful (cf. Gigerenzer,
2015, p. 110). Newell and Simon (1972) used the term heuristic to describe simple
processes that replace complex calculation. Shah and Oppenheimer (2008) referred to
heuristics as methods that use rules of effort-reduction and simplification. Simon (1990)
argued that heuristics are “methods for arriving at satisfactory solutions with a modest
amount of computation” (p. 11), reasoning that people seek to reduce the effort
associated with decision processes.
The decisions individuals make around food are multifaceted and complex – we
“decide” what to eat, how much to eat, when to eat, where to eat, and with whom to eat
multiple times every day throughout our lives. It is estimated that people make 200 food-
related decisions every day (Wansink & Sobal, 2007). It would be unrealistic if our food
intake depended on continuous rational deliberation. Even if we were given nutrition
information, reading and interpreting it is confusing and complex. Human beings are
cognitive misers. Unless highly motivated, they tend to use as little cognitive effort as
necessary (Chaiken et al., 1989; Todorov, Chaiken, & Henderson, 2002a). In order to
reduce the cognitive requirements of so many decisions, individuals reply on heuristics or
simple principles to guide food consumption decisions (Wansink 2010). This capability is
in some essential respect what nature has designed us to do. We would probably not
survive very long if our food intake relied on continuous cognitive deliberation (Herman
& Polivy, 2014).
Altogether, bounded rationality theory’s emphasis on environment as a major
influence in the decision-making process complements present theories of food decision-
13
making. Food intake decisions based on rational deliberation is effortful and not
evolutionary adaptive, while intake decisions based on internal cues (sensation of
fullness) are vague, unreliable, and usually delayed (satiety sensations lag behind intake).
As a result, people’s food consumption is largely influenced by the eating environment
because people tend to rely on environmental cues as heuristics to guide their
consumption decisions.
The Nudge Approach to Promote Healthy Eating
As mentioned, the nudge approach highlights how aspects of the decision
environment and simple changes in the way environmental cues are presented or arranged
can have profound effects on decisions individuals ultimately make. The effectiveness of
nudge interventions for behavioral change has been documented by an increasing number
of studies in many domains of application including retirement savings, charitable giving,
voting behavior, organ donation, and eating behavior (Thaler & Sunstein, 2008). Based
on our discussion above, we argue that the nudge research provides valuable insights for
the design of health interventions regarding eating in particular, because it relies less on
education or information but more on environmental interventions that lead people into
making better decisions.
Public health scholars and government agencies have started to adopt nudge
interventions to improve healthy eating (e.g., Hanks et al., 2012; Rozin et al., 2011; ).
Most notably, the U.S. Department of Agriculture (USDA) established the Cornell Center
for Behavioral Economics in Child Nutrition Program with the goal of applying the
nudge approach to encourage children to make healthful food choices (USDA Blog,
2010). The Center’s Smarter Lunchrooms Initiative has helped redesign school
14
lunchrooms through strategic modifications of the food choice environment by means
such as reducing the size of bowls, moving baskets of fresh fruit next to the cashiers, and
putting chocolate milk behind the plain milk (Hanks et al., 2012; Hanks et al., 2013; Just
& Wansink, 2009; Wansink et al., 2013; Wansink et al., 2012). A recent review article
(Thapa & Lyford, 2014) identified eight field studies (Goto, Waite, Wolff, Chan, &
Ciovanni, 2013; Hakim & Meissen, 2013; Hanks, Just, Smith, & Wansink, 2012; Hanks,
Just, & Wansink, 2012; 2013a; 2013b; Wansink, Just, Payne, & Klinger, 2012; Wansink,
Just, Hanks, & Smith, 2013; six of them are conducted by researchers from the Cornell
Center) that implemented and empirically tested nudge interventions to promote healthy
food choice decisions in lunchrooms. The review article concluded that nudge
interventions have shown positive results in increasing sales and consumption of
healthier food items. As the same time, the authors pointed out that published empirical
research investigating the effectiveness of nudge interventions for healthy eating is still
very limited and called for more research effort in this area (Thapa & Lyford, 2014).
Beyond school lunchroom settings, nudge intervention has also been adopted and
tested in public hospitals (Thorndike, Sonnenberg, Riss, Barraclough, & Levy, 2012;
2014) and grocery stores (Thapa et al., 2014) to improve healthy food choices. For
example, a team of doctors at Massachusetts General Hospital rearranged the placement
of food items in the hospital cafeteria by displaying healthy foods at eye level or at
various points in the cafeteria line. They found such small changes of the environment
can increase consumer purchases of these food options (Thorndike, et al., 2012). A
follow-up longitudinal study over 24 months further demonstrated that such an
intervention resulted in sustained healthier choices, suggesting that nudge intervention
15
can potentially promote long-term behavioral changes toward a more healthful direction
(Thorndike, Riis, Sonnenberg, Levy, 2014).
In addition to improving food choice decisions, nudge interventions have also
been introduced to individuals who want to reduce food intake. For instance, a Self-
Assessment Scorecard developed by Wansink and colleagues (Wansink & Chandon,
2014) suggests 100 small, easy-to-implement changes to dieters’ personal environment
such that their eating environment works in their self-interest. These nudge interventions
primary focus on decreasing the accessibility, visibility, or quantities of unhealthy foods
to which people are exposed and increasing the cues in the environment that encourage
healthy eating. Example items on the scorecard are “plates are between 9 and 10-in. in
diameter,” “water glasses are 16 oz or larger,” and “fruit and vegetables are on the center
shelf in the refrigerator” (p. 423). Some empirical studies demonstrated that nudge
intervention did help people eat less. A six-month trial that giving portion-controlling
plates and bowls to Type 2 diabetics found participants lost 4.4 lb more than the control
condition (Pedersen, Kang, & Kline, 2007). A three-month NIH trial showed that
decreasing plate size led to reduced meat intake by 34% for adults (Robinson &
Matheson, 2014). However, innovative design of nudge interventions as well as empirical
evaluation of their effectiveness for reducing food intake has still been limited.
Moreover, from an intervention standpoint, the nudge approach has striking
advantages over conventional health campaigns in a cost-effectiveness framework. For
one thing, employing nudges or environmental cues costs very little compared to health
messages. For another, the intervention is made to the decision context, and thus is salient
directly before choice, while most health campaigns are separated both physically and
16
temporally from the context in which actual health decisions take place (Goldstein &
Cialdini, 2007). Furthermore, nudging interventions always gently, or sometimes even
unobtrusively, steer people toward better health behaviors without much reliance on their
willpower (Thaler & Sunstein, 2003). In other words, altering personal environments can
help people eat better without asking for much cognitive effort and willpower (Wansink
et al., 2009). This aspect of the nudge approach is of great value. On the one hand, many
scholars focus on the important role of self-control in engaging in healthy eating and
general food consumptions (Gerrits et al., 2010; Hoch & Loewenstein, 1991; Myrseth &
Fishbach, 2009). Michie et al. (2009) showed that willpower or self-control based
intervention tools (i.e., “self-monitoring” of food choices; “specific goal settings,”
“forming intention”) have an effect of medium magnitude on eating outcomes. On the
other hand, given that only one in twenty dieters successfully maintains weight loss (Hill,
2009), mindful self-governance can be difficult for many individuals as a means of
controlling calorie intake (Wansink, Just, & Payne, 2009). Scholars argued that relying
on strict, mindful regulation, cognitive control, and willpower for many individuals is
often disappointing (Wansink, 2006; Wansink, Just, & Payne, 2009).
While literature on the environmental influence on eating and the nudging
approach represents a starting point, much remains to be done in terms of incorporating
useful nudges to encourage healthy food consumption. Most healthy eating interventions
undertaken from a nudging approach are at the idea stage and little evaluative work has
been done. This dissertation project follows the nudge approach and aims to contribute to
it by developing technology-based nudges as well as empirically testing their
effectiveness.
17
Chapter Summaries
This dissertation is organized as follows. The current chapter provides the broad
background knowledge of this project. It argues that people’s eating behavior is largely
influenced by the environment because they rely on heuristics embedded in the
environment to guide their food intake decisions. It also argues that the nudge approach,
which focuses on environmental interventions for better eating decision, is merited.
Chapter 2 describes the focus of the current study. It aims to demonstrate how a
special type of nudge - social norms (independent variables of this project), embedded in
the immediate technologically-mediated environment (context of this project), can
potentially influence food consumption decisions (dependent variables of the study). It
elaborates why those specific foci are chosen, and from that, what the contributions are of
the current project.
Chapter 3 discusses theories and develops hypotheses in the two studies of this
dissertation project. Our theoretical argument and hypotheses are derived from
implications of the focus theory of normative conduct, the model of ego depletion, the
social modeling effects, as well as empirical research on social normative influence and
food consumption decisions.
Chapter 4 provides details of the methods employed in the two studies by
introducing the research test-bed, recruitment of participants, research design and
procedures, measurement, and data analysis methods for each study. Chapter 5 reports
descriptive statistics on research participants and measures, results of randomization
checks, manipulation checks, as well as hypotheses testing in both studies. Finally,
Chapter 6 discusses the findings of the two studies, addresses theoretical and practical
18
implications associated with this project, acknowledges its limitations, and proposes
future research directions.
19
CHAPTER 2: FOCUS OF THE CURRENT STUDY
Context: Nudge through Communication Technologies
We now inhabit an increasingly technologically mediated world; web platforms,
social networks, online communities constitute many of the contexts in which people
make decisions. Given that our decisions around foods are influenced by the decision
environment, and that much of our decision-making context is technologically mediated,
can we promote healthy eating behaviors through the design of communication
technologies? While the majority of the nudge research is not situated in technology-
mediated decision-making, this research project proposes that a promising way of
nudging is through technological innovations.
Using technology for behavioral change is not new. As computers and their
products permeate our lives, some scholars proposed a research area that examines
“Computers as Persuasive Technologies” (Fogg, 2002). They defined this kind of
persuasive technology as “interactive information technology designed for changing
users’ attitudes or behavior” (Fogg, 2003). Thus, instead of focusing on improving
usability of user-centered technology design (e.g., Norman, 2002), researchers and
engineers who develop persuasive technologies seek to steer users’ attitudes or behavior
in certain directions. To date, the majority of persuasive technologies developed to
promote better eating behaviors have followed an information-centric approach.
Grounded in deliberative models, efforts have been made to develop technologies that
communicate easy-to-understand, timely, tailored information to users. For instance, an
intelligent kitchen provides nutritional information about ingredients at the time of
cooking (Chi, Chen, Chu, & Chen, 2007); a weight management robot helps people keep
20
track of what they have eaten and calculates caloric information (Intuitive Automata,
2012); a barcode scanner on the PDA device provides tailored information for users at
exactly the time they make their shopping decisions on which kind of food to choose
(Intille, Kukla, Farzanfar, & Bakr, 2003). However, scholars maintain that technologies
using information-centric techniques may have limited effects in persuading people to
engage in healthy eating behaviors (Blumenthal & Volpp, 2010; Downs, Loewenstein, &
Wisdom, 2009; Wisdom, Downs, & Loewenstein, 2010).
Our research project also aims to explore how to use communication technologies
for behavioral change. However, as opposed to previous research, we examine
communication technologies as decision environments rather than information tools
(Hou, 2014). Today, more and more of the decision-making environment is presented to
us through communication technologies. In other words, essentially all technological
interfaces can be considered as choice architectures. For instance, we increasingly choose
what product to buy, or which restaurant to go for dinner via some form of
communication technology. Some product must appear first on a shopping site, and such
placements are not inconsequential. One could argue that the technological choice
architectures are never neutral (Oinas-Kukkonen, 2010). They always, intentionally or
unintentionally, nudge people in one way or another.
There is scant literature on technology-based nudges. In one study, Lee, Kiesler,
and Forlizzi (2011) utilized a convenience nudge such that healthy foods were offered as
the convenient options to participants, while they could take simple steps to choose
alternative options, i.e., unhealthy foods. In particular, the convenience nudge was
implemented on a snack shopping website, where healthy snacks (apples and bananas)
21
were presented on the first two pages; unhealthy foods (chocolate cookies) were
presented on the last two pages. And participants needed to click “next” and “previous”
buttons to browse those snack options. The results showed that a convenience nudge
applied via web-based technology was effective in influencing food choices. The authors
also compared the effectiveness of the convenience nudges implemented in different
media: one that was presented by a person who delivered snacks and another that was
implemented on the website. They found that both convenience nudges were equally
effective in terms of encouraging healthy food choices.
As illustrated in Lee et al.’s (2011) study, extremely simple changes in technology
interfaces may have substantial impact on people’s decision-making, and thus can be
potentially leveraged to promote self-beneficial behaviors, such as healthy food choices.
Nudging through technology design has striking advantages from an intervention
perspective. Because machines never get tired; they can work around the clock in active
efforts to nudge or wait for the right moment to intervene; nudges afforded by innovative
technologies can be customized to target different recipients; they can be implemented on
popular online platforms and be cost-effectively scaled to millions of users.
To our knowledge, no studies have looked at how technology-based nudges can
influence food consumption quantity (discussed subsequently). Given the striking
advantages, this topic is ripe for deeper investigation, and thus this is the focus of the
current project.
Consumption Quantity
Research on food decision-making is generally concerned with two types of
decisions: food choice decisions, or what we eat (salad or fries), and food consumption
22
quantity decisions, which determine how much we eat (half of the plate or all of it). The
present research project focuses on consumption quantity decisions due to two major
considerations.
First, while both types of food decisions are relevant to understand the continued
growth of obesity, most research to date has focused on food choice decisions (Hill,
2009; Wansink & Chandon, 2014) whereas less effort has been invested in understanding
decisions about food consumption volume (Wansink, 2010). Some food psychologists
(e.g., Herman & Polivy, 2014) argued that consumption quantity is more important since
excessive intake is a more serious problem than eating the wrong things, which is the
essential reason that leads to obesity. There is evidence showing that even if the food that
an individual eats is unhealthy, as long as that person eats less than usual, she will still
show improvements in cholesterol and triglyceride levels (Park, 2010).
Second, external factors have more of an influence on how much we eat than on
what we eat, while internal factors (hunger) affect food intake less than what is
commonly believed (Herman & Polivy, 2014; Wansink & Chandon, 2014). As
summarized in the previous chapter, most of the nudge interventions aim to promote
healthy food choices, i.e., to choose healthy foods over unhealthy ones. However, how
the nudge approach influences food consumption volume has been largely understudied.
Given these two considerations, the current project focuses on food consumption quantity
as the main outcome variable.

23
Social Environment on Consumption Quantity
The current project aims to understand the influence of the social environment on
food intake, and how to translate it into interventions. We choose a social normative
approach for three major reasons.
Social Modeling Effects and Social Norms
First, while there has been robust empirical evidence showing that people’s social
environment strongly influences their consumption volume, there has also been a
significant amount of (indecisive) speculation on the influence mechanisms that need
further investigation.
Abundant studies have demonstrated the pervasiveness of social context in
steering people’s eating behavior. People eat more when an eating companion eats more,
and eat less when the companion eats less (Herman, Roth, & Polivy, 2013). This effect
has been observed with snack foods (Robinson, Tobias, Shaw, Freeman, & Higgs, 2011)
and during meals (Hermans, Larsen, Herman, & Engels, 2012), among dieters and non-
dieters (Polivy, Herman, Younger, & Erskine, 1979), among children (Salvy, Vartanian,
Coelho, Jarrin, & Pliner, 2008) and young women (Vartanian et al., 2013), and even
when participants have been food-deprived for up to 24 hours (Goldman, Herman, &
Polivy, 1991). When a person eats in social groups, a light eater eats much more in a
group of heavy eaters, and a heavy eater shows more restraint in a light-eating group. As
the group size increases, no one wants to stand out, and people increasingly conform to
the group average (Bell & Pliner, 2003). The influence of the social environment on food
intake is observed even when the other person is fictional or not physically present (Roth
et al., 2001). In fact, a direct comparison demonstrated that live and remote confederates
24
are equally effective at influencing participants’ consumption quantity (Feeney, Polivy,
Pliner, & Sullivan, 2011).
This robust phenomenon is called social modeling effects, or people’s tendency to
adjust their intake toward the amount modeled by their eating companion(s) (Herman,
Roth, & Polivy, 2003). Matching food intake with a social model is also consistent with
macro-level studies showing that obesity spreads across social networks (Christakis &
Fowler, 2007). Nevertheless, the exact mechanisms underlying social modeling effects
are not entirely clear. Scholars have proposed three possible sources of this phenomenon:
social desirability, social mimicry, and social norms (Herman & Polivy, 2005; Leary &
Kowalski, 1990; Robinson, Blissett, & Higgs, 2013; Roth, Herman, Polivy, & Pliner,
2001).
Robinson et al., (2013) suggested that the social modeling phenomenon is
primarily driven by social desirability. In other words, people eat the same amounts as
their eating partner to be liked. The social desirability account suggests that the extent to
which an eater is keen to be liked and to make a good impression will increase the
likelihood of modeling (Robinson, Tobias, & Shaw, 2011). Herman et al. (2003)
suggested that people may increase or decrease intake in the presence of others,
depending on how much the others eat and the extent to which one is eager to impress his
or her co-eater(s) (Herman, Roth, & Polivy, 2003).
Automatic mimicry is another explanation for social modeling effects. Individuals
unconsciously mimic postures, mannerisms, and behaviors of others in social interaction.
Mimicry has benefits for people as it supports harmonious social interaction (Chartrand
& Bargh, 1998). It is possible that people copy others’ eating behaviors due to
25
unconscious mimicry (Herman, et al., 2012). By videotaping how eating dyads of young
females interplay, researchers found that they tend to take a bite of their meal at similar
times, which provides evidence that social mimicry is at work for food intake modeling
phenomena (Herman et al., 2012).
However, as noted, social modeling has also been found in studies using a
remote-confederate design. In these studies, participants were led to believe that non-
present others had eaten very little or large amounts during earlier sessions. They still
adjusted their own intake depending on the (fictional) information to the same extent as
they did when a live confederate was present (Feeney, Polivy, Pliner, & Sullivan, 2011;
Pliner & Mann, 2004; Robinson et al., 2013; Roth et al., 2001). These findings
undermined the social desirability and mimicry accounts as it is doubtful that participants
tried to be liked by people whose identity they are unaware and whom they were unlikely
to ever meet (Herman & Polivy, 2005). As such, these findings could be perceived as
evidence that modeling effects are not driven solely by social desirability concerns or
unconscious mimicry.
The third account for social modeling - social norms – is perhaps the most well
received explanation. Researchers generally agree that social models influence people’s
food intake by providing a norm (Herman et al., 2003; Herman & Polivy, 2014; 2005;
Leary & Kowalski, 1990; Robinson, et al., 2013; Roth, Herman, Polivy, & Pliner, 2001;
Wansink & Chandon, 2014). In other words, other people’s eating behavior provides
information about how much they themselves may eat. Nevertheless, there have been
significant debates about the mechanisms of how social norms influence food intake.
These debates can be categorized into two main suggestions as to why people’s food
26
consumption is impacted by other people’s eating behavior. The first one centers around
the idea of social acceptability of behavior. The second speculation postulates that social
norms work as behavioral guidance.
Herman et al. (2003) suggested that people, out of social acceptability concerns,
look outward to cues from the environment to determine how much to eat. In particular,
they use other people’s intake as a way of determining an upper boundary of an
appropriate intake amount. Herman et al.’s (2003) explanation was based on laboratory
observations that participants tend to eat somewhat less than does the confederate while
social modeling effects still hold (i.e., eating more when the confederate eats more and
eating less when the confederate eats less). They postulated that people do not want to be
regarded as an excessive eater or greedy because there are negative stereotypes regarding
overeating (Vartanian, Herman, & Polivy, 2007). Therefore, inhibitory norms derived
from the social environment constitute an permissible amount of food to eat that should
not be exceeded (Herman, Roth, & Polivy, 2003). The social acceptability account is in
line with the phenomenon of injunctive norms (Cialdini, Reno, & Kallgren, 1990).
The second account for social norms influence is that they act as useful guidance
about what to do (Prinsen, Ridder, & de Vet, 2013). For example, studies showed that
participants exposed to a fictitious list showing how much ‘other participants’ ate
adjusted their intake toward the amount listed (Feeny, Polivy, Pliner, & Sullivan, 2011).
Prinsen et al. (2013) found that simply leaving chocolate wrappers on a counter made
people eat more compared to when such wrappers were absent without their being aware
that they regulated their intake in response to the apparent consumption or non-
consumption by other people. In combination, these studies demonstrated that the
27
environment conveys cues about how other people have behaved, which is used by
individuals as simple principles or heuristics to guide their own behavior. This type of
social information has been labeled descriptive norms (Cialdini, Reno, & Kallgren,
1990). We argue that the behavioral heuristics account is in line with the aforementioned
nudge research.
Nevertheless, research investigating the influence of social norms on food
consumption has been producing inconsistent results. We suggest that this may be
because the conceptualization of what constitutes a social norm of eating varies across
studies. In majority of the eating literature, social norms are used to refer to different
types of normative information, which have distinctive influence mechanisms. For
example, an abundance of studies equate “perceived eating norms” (e.g., Robinson,
Blissett, & Higgs, 2013), “peer eating norms” (e.g., Stok, de Ridder, de Vet, & de Wit,
2014), or “perceived peer norms” (e.g., Lally, Bartle, Wardle, 2011) with descriptive
norms. However, we argue that these studies did not establish a descriptive norm in the
sense of behavioral heuristics following the nudge approach (or Cialdini’s focus theory of
normative conduct, which will be discussed in the next chapter). Instead, they focused on
norm perceptions, which are antecedents of behavioral intentions that shape actual
behavior. In these studies, the norm perceptions construct is essentially a derivative of the
subjective norms construct in the Theory of Planned Behavior (Robinson et al., 2013).
The rationale for the perceived social norms research is that (a) a common misperception
exists that overestimates the prevalence of unhealthy eating behaviors among peers, and
(b) individuals use this perception to form their own behavioral intention. Thus, these
studies seek to reduce overeating by correcting such misperceptions as a means to
28
influence behavioral intention, which are rooted in a deliberative approach to eating as
discussed earlier.
Moreover, the lack of differentiating between the influence mechanisms of
different types of norms has brought about an extensive debate over the effectiveness of
social-norms-based interventions. Many scholars contend that a high level of self-
regulation is a necessary condition for making healthy eating decisions (Gerrits et al.,
2010; Hoch & Loewenstein, 1991; Hofmann, Friese, & Wiers, 2008; Myrseth &
Fishbach, 2009; Schwarzer, 2008). Yet effortful self-regulation can be difficult for many
individuals as a means of controlling caloric intake (Wansink, Just, & Payne, 2009).
Herman and Polivy (2012) criticized the normative approach to combating obesity by
arguing that adherence to social norms (in their sense the injunctive norms for social
acceptability) places a burden on individual decision-making and draws on scarce self-
regulation resources. Their criticism was counter-argued by de Ridder and colleagues
(2013) who listed empirical evidence of the non-conscious influence of social norms (i.e.,
descriptive norms in the sense of behavioral heuristics) on people’s food consumption.
We suggest that both groups of scholars pointed out valid arguments in their
debate; however, the key to their disagreement is the lack of distinction between two
types of social norms (i.e., descriptive and injunctive norms) and the distinct underlying
mechanisms involved in responding to each norm. We argue that a clear distinction
between these two norms is crucial to understand the unique effects of each norm type on
food decision-making. The current research, thus, focuses on the social normative
approach.

29
Theoretical Models of Food Intake
The second reason why we focus on social normative influence is that recent
theoretical models on food intake could benefit from refinement and extension. In
particular, while researchers (e.g., Herman & Polivy, 2014; 2005; Wansink & Chandon,
2014) have generally agreed that normative factors are crucial in governing food intake,
research would benefit from a further explication of this important construct. For
example, both Herman and Polivy’s (2014, p. 433, Figure 1) model and Wansink and
Chandon’s (2014, p. 414, Figure 2) model of food intake spotlight the role of normative
influences on consumption quantity. In Herman and Polivy’s (2014) model, three factors
are identified to influence food intake – namely, hunger, taste, and appropriateness. They
further argued that the role of hunger and the taste of food in the determination of food
intake is relatively weak, and that to a significant extent, food intake is guided by norms
of appropriateness (Herman & Polivy, 2005; 2014; see Figure 1 for relative weights as
represented by the size of the factor).
Figure 1. Herman & Polivy’s Model of Consumption Quantity

30

Figure 2. Wansink & Chandon’s Model of Consumption Quantity

Wansink and Chandon’s model (2014) of consumption quantity includes three
factors that drive food intake: sensory drivers, emotional drivers, and normative drivers.
Each of these drivers exerts a direct impact on consumption quantity, and an indirect
influence through its effect on consumption monitoring, which also affects intake
volume. Consumption monitoring here is loosely defined as simply being aware of how
much one ate, noticing whether one ate more than usual, more than what others ate, or
just remembering whether one ate or not, as well as strictly setting a calorie target and
estimating the consumption volume (Wansink & Chandon, 2014). Sensory, emotional,
and normative drivers can all interfere with or facilitate accurate consumption monitoring
(Wansink & Chandon, 2014).
31
One of the principal virtues of Wansink and Chandon’s (2014) model is that it
pays ample attention to environmental heuristics (e.g., portion size cues) and social
influences (e.g., social facilitation and matching) on food intake. Such factors have been
given short shrift in most models of food intake (Herman & Polivy, 2014), even though
numerous studies amass evidence showing they robustly operate in various conditions as
discussed earlier. Notably, in this model, portion size cues, social facilitation (i.e., people
eat more when they eat with others compared with eating alone) and matching (i.e., social
modeling effects) are listed as examples of a normative driver.
Although using different terminologies, the two models share a certain similarity
in conception. Appropriateness is more or less identical to normative drivers (Herman &
Polivy, 2014). In both models, appropriateness or normative drivers are particularly
emphasized, as these authors pointed out: “appropriateness corresponds to Normative
drivers. …this construct is extremely important and powerful” (Herman & Polivy, p.
434). Yet “inadequate attention has been paid to this construct heretofore” (p. 434), and
“scientists … tend to downplay or ignore the important role of social influence such as
appropriateness norms” (p. 434).
We are in strong agreement with those authors that social normative influence,
whatever it may be called, is of particular importance but has been largely understudied.
And, therefore, we respond to their call for research on how social normative influence
impacts food intake. However, what we found to be problematic is again, in both models,
what appropriateness or normative drivers actually constitute is elusive. In Wansink and
Chandon’s (2014) model, for instance, the construct “normative driver” is used to include
a broad range of sub-constructs, including environmental cues such as cues signaling
32
portion size, and social influences such as social norms. According to Wansink and
Chandon (2014), consumption norms can be internally and externally established. Portion
size and social norms are all grouped under the same category because all those
influences have a normative nature. That is, people can infer how much is appropriate,
normal, typical, and reasonable to eat from the portion size of the food, or how much
other people eat (Wansink & Chandon, 2014).
In Herman and Polivy’s (2014) model, appropriateness refers to norms or
“indicators of how people should behave or how people do behave” (p. 433). As the
authors contended, “norms tell people how much it is appropriate to eat, which brings us
directly to our Appropriateness construct” (p. 433). While this construct is important, it
does not distinguish among the various types of normative effects. After all, one could
always argue that a person ate a certain amount because she thought that amount was
appropriate. As Wansink and Chandon (2014) commented: “there is a risk that
appropriateness becomes a tautological “catch-all” category” (p. 447). Thus, the current
research aims to examine one of most influential yet still underexplored (Herman &
Polivy, 2005; 2014; Wansink & Chandon, 2014) sub-constructs of the normative factors
on food intake: social norms.
Potential for Interventions
The third reason why we are particularly enthusiastic about the social normative
approach is for practical concerns regarding translational research. Compared to other
social influences, social norms can be potentially translated into cost-effective, scalable,
and enduring solutions to nudge individuals to eat more healthfully. On one hand, for
instance, while research shows that social facilitation and social desirability concerns can
33
influence food intake, it is not clear what we can do about these findings (e.g.,
encouraging us to eat in isolation or with opposite-sex companions who are evaluating
us). Their effects are sometimes either individually specific or otherwise difficult to
change in a way that is scalable and cost-effective for public health.
On the other hand, social communication technologies have amassed massive
real-time social information and made social sharing and comparison possible. Thus,
such technologies can potentially constitute the direct environment that conveys social
normative information. For instance, it is now a common practice for web-based social
communication technologies to provide information about what users of a website think
or do, and such information has tremendous impact on individuals’ decisions about the
opinions, products, or services on the website (e.g., Chevalier & Goolsbee, 2003;
Chevalier & Mayzlin, 2006; Clemons, Gao, & Hitt, 2006; Godes & Mayzlin, 2004; Li &
Hitt, 2008; Liu, 2006). Thus, our project aims to develop and test nudges, as guided by
the social normative approach, in technology-mediated environment to induce better food
intake decisions.
34
CHAPTER 3: THEORIES ADAPTED AND TESTED
Study I: Social Norms, Self-Regulation Resources, and Consumption
In the previous chapter, we introduce the focus on the current project. In this
chapter, we will discuss our theoretical argument and hypotheses, which are derived from
implications of the focus theory of normative conduct, the model of ego depletion as well
as empirical research on food consumption decisions.
The Focus Theory of Normative Conduct
The focus theory of normative conduct (Cialdini, Reno, & Kallgren, 1990;
Cialdini et al., 1991) posits that there are two distinct types of social information –
descriptive norms and injunctive norms. The first type of social information, descriptive
norms, specify how other people typically behave in a given context, and the second type
the injunctive norms refer to what is commonly considered right versus wrong based on
morals or beliefs (Cialdini et al., 1991; Cialdini, 2012). The focus theory further holds
that the two types of norms differ with respect to the two fundamental human goals and
the underlying psychological mechanisms involved in responding to each norm type.
Descriptive norms function as heuristics for behavior; they are simplifications of decision
making, and thus are relevant for the intrapersonal goal of behaving effectively or
accurately. Injunctive norms, on the other hand, are particularly linked with the
interpersonal goal of building and maintaining social relationships; people follow
injunctive norms in order to be liked and accepted (Cialdini & Trost, 1988; Deutsch &
Gerard, 1955). Therefore, the two types of norms do not always influence behavior in the
same direction and to the same extent (Jacobson, Mortensen, & Cialdini, 2011; Schultz et
al., 2007).
35
As noted, social modeling effects – people’s tendency to adjust their intake
toward the amount other people consume – are a powerful and robust phenomenon,
which is best interpreted in terms of social norms. Nevertheless, the underlying
mechanisms of social normative influence on consumption quantity are not fully
understood, particularly due to the lack of differentiation of the two types of social
norms: descriptive and injunctive norms. As a result, there has been a continuous debate
over the effectiveness of social-norms-based interventions, which primarily revolved
around the issue of whether conformity to social norms depends on individuals’ self-
regulation resources (de Ridder, et al., 2013; Herman & Polivy, 2012).
The current investigation targets this gap. We argue that descriptive and
injunctive norms impact consumption quantity through two distinct mechanisms.
Specifically, we expect descriptive social norms to influence consumption through an
inferential mechanism, whereas injunctive social norms influence consumption through a
self-regulation mechanism. The two mechanisms produce distinct hypotheses that could
help us understand when and how social normative information influences consumption
quantity, and by whom. In addition, we propose that self-regulation resources moderate
conformity to social norms on food intake. Self-regulation resources increase conformity
to injunctive norms but decrease conformity to descriptive norms (Jacobson, Mortensen,
& Cialdini, 2011).
Self-Regulation Resources and Social Norms
Ego depletion, or self-regulation resource depletion, refers to “a temporary
reduction in the self’s capacity or willingness to engage in volitional action (including
controlling the environment, controlling the self, making choices, and initiating action)
36
caused by prior exercise of volition” (Baumeister, Bratlavasky, Muraven, & Tice, 1998,
p. 1253). The model of ego depletion suggests that one’s capacity for volitional action
relies on limited but renewable cognitive resources (Baumeister, Bratlavasky, Muraven,
& Tice, 1998). The nature of such capacity is akin to a muscle that may become
“fatigued” over the course of using it and then regenerated through “rest” (Baumeister et
al., 1998; Muraven & Baumeister, 2000). When a self-regulation resource is used up as
an individual exerts volitional action, the person is in a state of mental exhaustion or
depleted (Baumeister, Bratslavsky, Muraven, & Tice, 1998; Gailliot & Baumeister,
2007). The state of ego depletion can be counteracted by replenishing the resource
through rest (Gailliot et al., 2007). The model of ego depletion also proposes that self-
regulation resource is global (Baumeister et al., 1998; Muraven et al., 1998); exerting
self-regulation in one domain would impair subsequent self-regulation attempts in
another domain as this global resource is taxed (Baumeister et al., 1998).
Ego depletion drains cognitive resources, and thus affects cognitive processes and
decision making (Mauraven, in press). For example, research has shown that depleted
individuals fail to consider all alternatives as carefully in decision making tasks as non-
depleted individual; they also rely to a greater extent than non-depleted counterparts on
heuristics (Masicampo & Baumeister, 2008; Pocheptsova, Amir, Dhar, & Baumeister,
2009). As noted earlier, heuristics, due to their simplicity, require relatively little energy
and time, and are particularly useful when cognitive capacities are low (Simon, 1955). As
such, ego depletion results in fewer cognitive resources available for deliberation and
thus fosters greater reliance on heuristics in decision making.
37
In Jacobson, Mortensen, and Cialdini’s (2011) study, ego depletion was
investigated as a boundary condition that accounts for differentiated responses for
injunctive and descriptive social norms. The researchers manipulated participants’ self-
regulation recourses and measured their conformity to a descriptive or an injunctive norm
for completing extra surveys. They found ego depletion increases conformity to
descriptive norms while it decreases conformity to injunctive norms despite the fact that
the two types of norms advocated identical behaviors.
Inferential Mechanisms: Descriptive Norms and Consumption
By providing information about what other people do, descriptive norms serve as
simple heuristics for behavior (Cialdini, 2009). Following descriptive norms allow people
to make social inferences rapidly and with reduced cognitive effort. Numerous studies in
recent decades have demonstrated that descriptive norms cued through the environment
can guide behaviors (e.g., Burger, Bell, & Harvey, 2010; Cialdini, Reno, & Kallgren,
1990; Donaldson, Grahman, & Hansen, 1994; Larimer & Neighbors, 2003; Prinsen et al.,
2013; Schultz, 1999). For example, a series of classic studies by Cialdini, Reno, and
Kallgren (1990) have examined the effects of descriptive norms on littering behaviors.
The researchers manipulated the descriptive norms in the direct environment that was
either clean (an anti-littering descriptive norm) or littered (a pro-littering descriptive
norm). Participants were observed to litter significantly more into a littered environment
than into a clean environment. These studies demonstrated that descriptive norms
conveyed in the environment can elicit norm-consistent behavior.
We propose that environment could signal descriptive norms regarding
consumption quantity (Burger et al., 2010; Prinsen, et al., 2013). Especially in
38
technology-mediated environments, social computing techniques enable individuals to
observe information about how other people have behaved, and norms can be developed
out of observation of such information. It is worth noting that the focus theory
emphasizes that norms need to be made salient in order to effectively influence behavior.
As such, cues conveying descriptive norms can be embedded directly into the interface of
social communication technologies, and thus are salient directly at the moment of
decision making.
We argue that descriptive norms of other people’s consumption quantities
function as anchors that individuals use to guide their own consumption decisions. As
noted, one major explanation to the social modeling effects contended that other people’s
food intake provides behavior guidelines for an eater to decide her own amount
(McFerran, Dahl, Fitzsimons, & Morales, 2013; Vartanian et al., 2013). Classical anchor
effects studies (Tverseky & Kahneman, 1974) showed that people are influenced by the
numeric information in tasks where they have to judge values. They anchored on this
information, orienting themselves toward the provided guidelines in their judgments.
Therefore, anchors set up in the form of descriptive norms can serve as reference points
or guidelines that represent a relatively simple way to model individuals’ decisions about
how much to consume. In this regard, descriptive norms function as nudges which are
cued through the direct environment to influence people’s consumption decisions.
Furthermore, people in a state of ego depletion have been found to be more
susceptible to contextual influences (Bruyneel, Dewitte, Vohs, & Warlop, 2006;
Hofmann, Strack, & Deutsh, 2008; Salmon, Fennis, de Ridder, Adriaanse, & de Vet,
2013). As mentioned, ego depletion drains self-regulation resources and facilitates greater
39
reliance on heuristics in decision making. Thus, individuals tend to use descriptive norms
to guide behavior when their self-regulation resources are low (Jacobson, Mortensen, &
Cialdini, 2011). Salmon et al. (2013) showed that individuals whose self-regulation
resources have been depleted were more likely to behave in line with a food choice
descriptive norm. Thus, we predict that individuals will be more inclined to act upon
descriptive norms signaling consumption quantity of other people in a state of self-
regulation depletion.

H1. Individuals with low self-regulation resources will be more likely to conform
to descriptive norms cuing the consumption quantities of others than those with
high self-regulation resources.

Descriptive Norms as Underdetected Influence
Individuals fall prey to an introspection illusion when explaining the cause of
their own decisions (Pronin, Molouki, & Berger, 2007). As mentioned earlier, people
typically deny environmental influences on how much they eat (Vartanian, Herman, &
Wansink, 2008); instead they tend to place greater value on introspective thoughts and
beliefs related to their consumption decisions, such as liking of the food, taste of the food,
or being hungry.
It has been documented in the social psychological literature that individuals
generally deny the impact of the actions of others on their judgments or decisions
(Cialdini, 2005; Nolan et al., 2008; Sherif, 1937). For instance, Nolan and colleagues’
(2008) studied California residents’ energy conservation behaviors. They found that
40
descriptive norm of energy conservation of participants’ neighbors produced the greatest
effect on their own energy saving behaviors. However, descriptive norms were rated as
the least important reason in participants’ conservation decisions among other reasons to
conserve, such as protecting the environment, being socially responsible, or saving
money. Anchoring effects research also showed that anchors affect people’s judgments or
decisions even though people typically believe they are uninfluenced (Wilson, Houston,
Etling, & Brekke, 1996). Following these studies, we expect that the influence of
descriptive norms on individuals’ consumption decisions will be underdetected. It other
words, descriptive norms will be perceived as less important than other factors in driving
how much people consume.

H2. Individuals will rate descriptive norms cuing the consumption quantities of
others as less of an influence factor on their consumption decisions than other
introspective factors.

Self-Regulation Mechanisms: Injunctive Norms and Consumption
Humans have a desire to conform to the expectations of others because deviating
from normative expectations can lead to disapproval, resentment, and sanctions (Schultz,
Tabanico, & Rendon, 2008; Sugden, 2000). This interpersonal motivation underlies the
effectiveness of injunctive norms regulating behaviors. It has been argued that
punishment and negative emotions (e.g., shame and guilt) are two enforcers of injunctive
norm compliance (Fehr & Fischbacher, 2004). There are negative stereotypes regarding
overeating (Vartanian, Herman, & Polivy, 2007), and the obese in most cultures represent
41
an undesirable or even inferior group (Shapiro, King, & Quinones, 2007). It can be
argued that negative judgments of others on excessive food consumption may invoke a
state of psychological tension, which facilitates the influence of injunctive norms. As
mentioned early, the social acceptability explanation for the social modeling effects
postulated that people tend to consider the viewpoint of others in terms of how much to
eat, and they regulate their consumption to avoid being inappropriate or greedy (Herman
et al., 2003; Leary & Kowalski, 1990; Roth et al., 2001). In this regard, the use of
injunctive norms to promote healthy eating is in line with the deliberative approach we
discussed at the beginning of chapter two. Injunctive norms operate through activating or
enhancing behavioral intentions (i.e., to be socially accepted), which then shape actual
consumption decisions.
Furthermore, in contrast to descriptive norms, injunctive norms are most
influential under conditions of highly effortful cognitive activity (Jacobson, et al., 2011;
Mollen et al., 2013). Individuals often experience conflicting motives when responding to
injunctive norms (Bicchieri, 2006; Jacobson, et al., 2011). In other words, processing a
injunctive norm may lead individuals to experience conflict over decisions to conform or
not conform in situations that social standards (e.g., do not overeat) may not be aligned
with immediate self-interests (e.g., taste enjoyment). Thus, responding to injunctive
norms would evoke thoughts about competing motives, and require effortful self-
regulation to restrain intrapersonal impulsive desires (Hofmann et al., 2007). On the other
hand, descriptive norms should not trigger conflict thoughts or motives because they
primarily serve as heuristics for efficient decision-making. We hypothesize that
injunctive norms will be more influential in guiding consumption decisions when self-
42
regulation resources are high, because injunctive norms will evoke that conflict
experience that demands sufficient self-regulation to follow.

H3. Individuals with high self-regulation resources will be more likely to conform
to injunctive norms cuing others’ disapproval of overeating than those with low
self-regulation resources.

H4. Individuals exposed to injunctive norms will experience greater conflicting
feelings than those exposed to descriptive norms.

Injunctive Norms and Personal Healthy Eating Goals
Many studies (Mollen et al., 2013; Robinson, Fleming, & Higgs, 2013; Stok, de
Ridder, de Vet, & de Wit, 2014) showed that injunctive norms promoting healthy foods
failed to increase people’s consumption of those foods. Stok et al. (2014) found that
injunctive norms encouraging fruit consumption in adolescents not only had no positive
effects on fruit intake but actually led to a decrease in fruit intake intentions. When they
were told how much fruit their peers thought they ought to eat (i.e., injunctive norms),
adolescents did not eat more fruit but actually reported lower fruit intake intentions than a
control group. They argued that injunctive norms may evoke reactance as people might
feel as if they were being pushed in a certain direction and their freedom of thinking and
acting were threatened (Silvia, 2006). Reactance may lead people to focus mostly on
counterarguments, to suppress thoughts in favor of the proposed behavior and to think
negatively about the credibility of the message source (Silvia, 2006; Tormala & Petty,
43
2004). Thus, the injunctive norms can backfire (Melnyk et al., 2011; Stok et al., 2014),
especially when that norm is not in line with one’s personal goals (Jacobson, Mortensen,
& Cialdini, 2011; Melnyk, Van Herpen, Fischer, & Van Trijp, 2011). Researchers
suggested that the potential negative and reactance-inducing effects of injunctive norms
“should be addressed in future research” (Stok et al., 2014, p. 60).
We postulate that injunctive norms can influence the motivation to comply with
social standards (i.e., eat healthy foods or do not overeat) via activating one’s personal
goals. To put it differently, injunctive norms have positive effects for people whose
personal goal is in line with the stated norms. As mentioned earlier, focus theory
proposes that only when made salient or focused upon can norms affect behaviors. The
state of being salient can be affected by dispositional factors, such as a strong personal
belief or motivation. In this case, it is the subjective salience of an injunctive norm rather
than its objective availability that’s more important in influencing behaviors. Thus, we
expect that injunctive norms are effective on individuals who aim to eat more healthfully.

H5. Individuals’ personal healthy eating goal will moderate their conformity to
injunctive norms cuing others’ disapproval of overeating. More specifically, such
injunctive norms will curb consumption quantity of individuals who score high on
the personal goal of healthy eating but not those who score low on it.

Study II: Anchor Level, Comparison Feedback, and Order Quantity
Study I examined the distinct psychological mechanisms of two types of social
norms on consumption quantity decisions. In our view, the next critical step was to probe
44
how this research might inform solutions to the problem of overeating. Following the
vision of nudge research, our second study focuses exclusively on the influence of
descriptive norms and proposes and tests two technology relevant nudges: anchor setting
and normative feedback.
Anchors and First Rater Problem
The mechanism of descriptive norms demonstrates how an anchor set up by other
people impacts individuals’ decisions about consumption quantity. People anchor on the
consumption quantities of others, eating more if the other consumer sets up a high versus
a low anchor (McFerran, Dahl, Fitzsimons, & Morales, 2010; Vartanian et al., 2013).
Thus, when using descriptive norms to promote desired behaviors, it is important
to recognize that the level at which the anchor is set is critical to achieving a positive
impact. In charitable giving research, for example, knowing the amount that other people
donate can encourage donors to conform to the group norm, and give more than they
might have done without such an anchor (Smith, Windmeijer, & Wright, 2012). When
visitors to a Costa Rican national park were told about previous high donations ($10),
they increased their donations. But when they were told about a low donation ($2), their
donations decreased significantly (Alpizar, Carlsson, & Johansson-Stenman, 2008). With
respect to food consumption, although it is true that people are powerfully influenced by
norms, which potentially guide or limit food intake, the limit dictated by the norms is
often well above the nutritionally advisable amount (Hermans et al., 2010). If norms
reflects excessive eating, people who abide by such norms will still eat more than
desirable amount.

45
The First Rater Problem
In the digital age, we are increasingly relying on collaboratively shared
information from others to arrive at a decision. Aggregation of user opinions in the form
of online ratings and reviews are considered as a popularity indicator and an objective
recommendation that is unbiased by commercial motives. A 2012 Nielsen report
(Nielsen, 2012) surveying more than 28,000 Internet users in 56 countries found that
online consumer reviews are the second most-trusted source of brand information (after
recommendations from friends and family). Communication and social media scholars
also consistently show that information extracted by computational systems from records
of users that an individual has little control over have the best perceived source credibility
(Walther & Jang, 2012).
While users generally have faith in online ratings and reviews, researchers
recently suggested that there could be social biases hidden in those collective preferences
and judgments (Muchnik, Aral, & Taylor, 2013; Salganik, Dodds, & Watts, 2006).
Salganik, Dodds, and Watts (2006) conducted a web-based experiment exploring how
social influence impacts music download. In the control condition, participants were
asked to listen to song snippets and then were given the opportunity to download songs
they liked. In the eight experimental conditions, in addition to the standard fashion
participants could also see how many downloads were made by previous participants in
their particular subgroups. The eight subgroups evolved independently utilizing a
“multiple worlds” design. By sorting people into groups of several thousands, the
researchers were able to observe who in each “world” downloaded which songs.
Assuming that quality would win, the popularity of the music in the eight “worlds”
46
should be similar to each other and to that in the control condition. In other words, the
songs that did well or badly in the control condition would also did well or badly in the
social influence conditions. However, what actually happened was that while the “best”
songs were never flops and the “worst” songs were never best, aside from that “any other
result is possible” (p. 855). Such unpredictable outcomes, importantly, resulted from their
frequent dependence on the serendipitous movements of early participants in the action.
In all eight worlds, participants were more likely to download songs that had been
already downloaded by many people before them. The identical song could be popular or
unpopular simply because previous participants were seen to choose to have downloaded
it or not. The serendipitous fact of who were the early downloaders and what they were
downloading mattered significantly.
Where there is unpredictability, there is also malleability. If truthfully some songs
are getting popular early, or if experimentally the researchers inflate their perceived
popularity, then winners can be created. This conjecture is in line with another large-scale
web experiment (Muchnik et al., 2013). Muchnik and his colleagues (2013) studied a
social news aggregation website where users contribute news articles and post comments
to these articles, and other users can then vote on these comments. The experimenters
arbitrarily voted positively or negatively as the first vote to over ten thousand comments.
By observing more than thirty thousand votes submitted by subsequent users, they found
that prior voting (in this case, a single up or down vote) significantly influences following
voting behavior. A false positive upvote led to an inflated amount of positive feedback
afterwards, whereas a downvote inspired users to correct manipulated ratings.
47
A potential lesson from the above mentioned studies is that who the first raters are
and what their judgments and decisions are can be critical on technology platforms since
the information from first raters is likely to steer the subsequent users and encourage
them to follow suit. If the starting points or anchor set a “healthy” tone for a technology
platform, an environment might be engineered in which beneficial behaviors are
encouraged. We hypothesize that descriptive norms set up by the first few users or
arbitrarily by the technology designer will influence subsequent ordering quantity
decisions.

H6. Individuals will order more food when the initial descriptive norms cuing
others’ ordering quantity are set at a high level than when they are set at a low
level.

Technology-Based Normative Feedback
A common means to activate social norms is through the use of feedback.
Researchers suggested that normative feedback that is personalized to the individual
targets can enhance norm impacts due to the increased saliency of the norms (Cialdini,
Reno, & Kallgren, 1990; Kallgren, Reno, & Cialdini, 2000; Neighbors, Larimer, Lewis,
2004). For example, some early studies showed that personalized feedback
communicating information about a student’s drinking pattern and the actual alcohol
consumption of average college students was effective in influencing drinking behavior
even when the feedback was delivered by mail (Agostineilli et al., 1995; Walters, 2000).
48
As mentioned earlier, social communication technologies have provided a rich
space of opportunities for various types of feedback that can be leveraged to influence
decision-making. Computerized normative feedback has gained much interest as a
technique to promote self-beneficial and pro-social behaviors among computer scientists,
engineers, platform designers, and social scientists. For instance, in the area of home
energy consumption, a meta-analysis of 20 studies (Fisher, 2008) has demonstrated that
feedback resulted in 5% to 12% electricity savings. Additionally, although only 3 out of
the 20 studies reviewed used computerized feedback (in contrast, for example, to
redesigned bills), those studies resulted in the greatest change in energy consumption. In
particular, Fisher (2008) found that the most effective computerized feedback interfaces
contained multiple feedback options, comparison or normative information being one of
them. A UK-based energy saving program (Harris, Rettoe. & Studley, 2011) specifically
examined using digital technologies to deliver social normative feedback. Qualitative
interviews and focus groups reported that social normative feedback generated
considerable engagement of participants in the program.
Feedback may work especially well for food intake behavior. As theorized in
Wansink and Chandon’s (2014) consumption quantity model (Figure 1), “consumption
monitoring” is an important mediator of the relationship between intake and its normative
drivers (which include social normative influence). The authors argued that “simply
monitoring whether we ate … more than what others ate … can already have a large
impact on… subsequent intake (p. 447)”. Social communication technologies have the
potential to provide real-time, personalized comparison feedback to users. We expect to
find that providing personalized normative feedback, which incorporates social
49
comparisons, can promote norm-consistent decision-making. Thus, we hypothesize that
providing comparison feedback signaling discrepancies between one’s own food order
quantity and others’ order quantity (i.e., descriptive norms) will improve the likelihood of
conforming to descriptive norms.

H7. Providing personalized comparison feedback will enhance conformity to
descriptive norms cuing others’ food order quantity.

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CHAPTER 4: METHOD
This research project comprised a laboratory experiment (Study I) and a field
experiment (Study II). Both studies were approved by the University of Southern
California Institutional Review Board (IRB) and the University of Arkansas IRB. Both
studies were conducted at the University of Arkansas.
Test Bed: SnackTime
For both Study I and Study II, we developed a snack ordering website called
“SnackTime.” A screenshot is provided below that shows the homepage of the website.
The “SnackTime” website has basic functionalities of a typical e-commerce platform
including a user registration system, a shopping cart, and a checkout page. All treatment
webpages were created on this website, keeping an identical style and layout throughout,
differing only in the technological cues designed on the webpage interface that constitute
the manipulations in each study.
Figure 3. Screenshot of Homepage of the SnackTime Website

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Materials. Two types of mini cookies (chocolate and oatmeal) were offered on
“SnackTime” in both studies. These cookies were purchased from the market and were
unpacked before the studies to prevent the possible effects of brand or product
familiarity. On average, the size of each mini cookie was similar to a U.S. quarter coin
(approximately 2.5 cm in diameter), and each weighed 3.00 to 3.70 grams. In the
laboratory experiment, the same five sample cookies as well as some information about
the cookies (see Appendix A) were shown to all participants at the beginning of the
experiment so that they had a clear idea of what they could expect to eat. This was
intentionally done to control participants’ perception of the size of the cookies so that
perceived cookie size would not interfere with their decisions of how many cookies to
order and eat. In the field experiment, information about the two cookies, including their
sizes and ingredients, were displayed on the snack ordering website.
Study I
The main purpose of Study I was to examine the influence mechanisms of social
norms on food consumption, especially through testing the moderating role of self-
regulation resource level in responding to descriptive and injunctive social norms. This
study was conducted using a controlled experimental design in a laboratory setting.
Participants
139 undergraduate students were recruited from a large introductory information
systems class for partial credit toward a class research requirement. They were told that
they would be evaluating a snack ordering web system as well as tasting and rating some
foods ordered from that system. A pre-screening was conducted and individuals who had
eating disorders (e.g., diabulimia, anorexia, bulimia, compulsive overeaters, etc.),
52
obesity-related health problems (e.g., diabetes, high blood pressure, abnormal blood fats,
heart disease), and/or food allergies or any dietary restrictions which prevent them from
eating snacks, or elected not to eat anything were not eligible for this experiment.
Additionally, all participants had to be 18 years of age and above.
Design and Procedure
Study I consisted of two sections: an online survey and a laboratory experiment.
For the online survey section, all participants received a URL link to an online
questionnaire hosted on Qualtrics.com. The questionnaire was the same for all
participants. The first page of the questionnaire contained an Information Sheet; by
clicking on a link at the end of the page, participants indicated that they read the
Information Sheet and agreed to participate. Participants were then directed to the online
questionnaire, in which healthy eating goal and demographics were assessed. After
completed the online survey, participants were automatically directed to an online
scheduling system – SONA – and were invited to sign up for the laboratory experiment.
On average, participants finished the online survey 3 to 7 days before their lab
experiment appointment. The survey and the experiment were intentionally separated
temporally so that the survey questions would not activate participants’ personal motives
or goals that might interfere with the results of the experiment.
The laboratory experiment used a 3 (norms: descriptive norm, injunctive norm, or
control)  2 (self-regulation resource: high vs. low) between-subjects factorial design.
The experiment was open between the following hours of a day: 9:00 a.m. to 10:00 a.m.,
2:00 p.m. to 5:00 p.m., and 7:00 p.m. to 8:00 p.m. The lab hours were set up as such to
minimize the situations where participants were extremely hungry or full and thus
53
influence their consumption decisions during the experiment. Upon arrival at the
laboratory, participants were seated at a table in a private room. The lab study was
conducted in two parts that were disguised as unrelated studies. The first part was
designed to manipulate level of ego depletion/self-regulation resource. Participants were
randomly assigned to the high ego depletion (i.e., low self-regulation resource) condition
or the low ego depletion (i.e., high self-regulation resource or control) condition, and
completed an “e-crossing task” adopted from Baumeister et al. (1998; see Experimental
Manipulations). This task has been employed in previous research to manipulate ego
depletion (Baumeister et al., 1998; Tice, Baumeister, Shmueli, & Muraven, 2007;
Wheeler, Brinol, & Hermann, 2007; Salmon et al., 2014). The cover story for the ego
depletion task told participants that it was about written media.
Next, participants were instructed to rate their current level of hunger, mood, and
arousal using a paper-and-pencil questionnaire. After completing the questionnaire, they
were introduced to the snack ordering website “SnackTime”. The cover story for this task
was about evaluating the website, and ordering and tasting some foods as part of an
information systems research study. Participants were randomly assigned to one of the
three website conditions (i.e., descriptive norm, injunctive norm, or control) and were
instructed to explore the food ordering website for 3 minutes. Afterwards, they were told
that they would use the website to order some snacks that they were going to taste and
rate. The experimenter left the room, and let the participants explore and order snacks
alone in a private room. Figure 4 shows a snapshot of the Ordering Page in the control
condition.

54
Figure 4. Screenshot of SnackTime Ordering Page in the Control Condition

On the Ordering Page, participants could input how many chocolate cookies and
oatmeal cookies they would like to order and place that order. Once a participant placed
an order, the experimenter immediately got notified and observed that order from the
website’s MySQL database. The experimenter then prepared the cookies ordered in a
plate, and delivered them to the participant seated in a private room. The participant was
instructed to taste the cookies and fill out a paper-and-pencil questionnaire which
evaluating the cookies on several dimensions (e.g., how sweet, how salty). The
experimenter then left the room again, and let the participant eat cookies alone.
Participants were given enough time to eat cookies. Sufficient water was provided during
the task so that thirst would not influence cookie consumption.
After the tasting and rating task, participants completed a post-experiment
questionnaire, which included a number of dummy questions about the food ordering
55
website, manipulation checks, and a suspicion probe of whether the participant had any
ideas about the true nature of the study. Questions about self-reported reasons for
participants’ decision on ordering quantity and conflict feelings associated with such
decision were also included in the questionnaire. Finally, participants were debriefed,
thanked, and dismissed.
A brief summary of the procedure of study I can be found in Figure 5.
Figure 5. Study I Procedure

Experimental Manipulations
Normative Information. The experimental manipulation was designed into the
interface of the “SnackTime” Ordering Page, which presents either descriptive normative
information, injunctive normative information, or no information (i.e., the control
condition; see Figure 4). There was no interpersonal interaction involved in any of the
conditions.
In the descriptive norm condition, participants received the messages stating “on
average, the number of this cookie our users ordered is: **” displayed right underneath
the pictures of each cookie. The “**” were the mean values of numbers of cookies
previous participants had ordered through the website; the calculation of means was
conducted using PHP programming. In other words, the mean values presented on the
webpage were not static; they got recalculated each time a new user made an order. Thus,
56
each user would see different mean values on the ordering page. Figure 6 shows a
snapshot of the ordering webpage in the descriptive norm condition. In this example, the
participant saw that previous users had ordered 3.5556 chocolate cookies and 3.222
oatmeal cookies on average. The experimenter made the first order on “SnackTime,” and
ordered 4 chocolate cookies and 4 oatmeal cookies.
Figure 6. Screenshot of SnackTime Ordering Page in the Descriptive Norms Condition

In the injunctive norm condition, participants were presented with the messages of
“We want you to enjoy your cookies. But do not overeat!” on the Ordering Page.
Message texts in both descriptive norm and injunctive norm conditions were displayed
using the same font, font-size, and color, and were placed at the same position on the
page, i.e., right underneath the pictures of the cookies. Figure 7 shows a snapshot of the
ordering webpage in the injunctive norm condition.

57
Figure 7. Screenshot of SnackTime Ordering Page in the Injunctive Norms Condition

Ego Depletion/Self-regulation Resource. To manipulate ego depletion/self-
regulation resource, participants completed an “e-crossing task.” This manipulation was
adopted from an established ego depletion task initially developed by Baumeister et al.
(1998), and was used by numerous subsequent research as an effective manipulation of
ego depletion (e.g., Salmon et al., 2014; Hagger et al., 2010). The “e-crossing task”
consisted of two sections. First, all participants were given a pencil and two pages of
typewritten text, and they were instructed to cross out every letter “e” on a first page of
text. Second, participants in the low ego depletion (i.e., high self-regulation resources or
control) condition were instructed to continue crossing out every “e” on a second page of
text. Participants in the high ego depletion (i.e., low self-regulation resources) condition
were required to follow complex rules when crossing out “es.” The first section lasts for 5
58
minutes; the second section last for 5 minutes in the low depletion condition and for 12
minutes in the high depletion condition.
To be specific, participants read an article about China’s Internet censorship
excerpted from an article on Science Magazine. This article was selected because its
content is not related to eating behaviors, healthy eating goals, or body concerns that
might prime thoughts or concerns that could influence the results of the main experiment.
The article contained two pages. The first page of text contained a high number (231) of
“es” and therefore, after the first section of the task, participants established a well-
practiced habit of crossing out “es”. For the second page of text, participants in the
depletion condition were asked to cross out all the letters “e” (n = 239) except for “es”
that were followed by a vowel (e.g., reach) or was embedded in a word with a vowel
appearing two letters before the “e” (e.g., listen). Such task required participants to
inhibit the previously established habitual response formed during the first section, which
caused ego depletion.
Measures
Ordering and Consumption Quantity. The main dependent variables of interest
were how much food participants ordered and consumed. The number of cookies that
each participant ordered was recorded in the website’s backend database. To assess how
many cookies participants actually ate, they were told not to take uneaten cookies with
them. After each participant left, the experimenter counted the number of cookies
remaining to determine the number of cookies consumed.

59
Online Survey Measures
Demographics. Self-reported age, gender, height, and weight were collected
through the online survey. Participants’ body mass index (BMI) was calculated, i.e., BMI
= (weight in pounds * 703)/height in inches
2
. According to the guideline of the Center for
Disease Control and the World Health Organization (World Health Organization, 1998),
participants were classified as underweight (BMI ≤ 18.5 kg/m
2
), normal weight (18.5 <
BMI < 25 kg/m
2
), overweight (25 ≤ BMI < 30 kg/m
2
), and obese (BMI ≥ 30 kg/m
2
).
Healthy Eating Goal. Healthy eating goal was measured by asking participants to
indicate their agreement with five statements: “I intent to eat more healthfully,” “I watch
how much I eat,” “Eating healthfully is important to me,” “Looking fit is very important
to me,” and “To me, eating a healthful diet is a goal.” on a 7-point scale (1 = strongly
disagree to 9 = strongly agree). The five items were averaged to create the scale (M =
5.29, SD = 1.04, Max = 1.20, Min = 7.00); higher scores on this scale denoted higher
level of personal goal toward healthy eating. Cronbach’s α was 0.85, indicating a highly
reliable measure.
Pre-experiment Questionnaire Measures
Hunger. Participants were asked to report how much time had passed since they
last ate (in minutes) and how hungry they currently felt by marking a number anchored
by 1 = not at all hungry and 7 = extremely hungry. Hunger level was included as a
control variable.
Mood and Arousal. The Self-Assessment-Manikin scales (SAM; Lang, 1980)
were used to assess participants’ subjective mood or pleasure (valence; 1 = very
unpleasant to 9 = very pleasant) and arousal (1 = very active to 9 = very calm) level at the
60
moment. Mood and arousal state have been documented to influence food consumption,
and thus were included in statistical analyses as control variables. SAM is a graphical
assessment technique (see Figure 8) that has been extensively applied.
Figure 8. The Scales of the Self-Assessment-Manikin

Post-experiment Questionnaire Measures
Factors Influencing Food Order. In the post-experiment questionnaire,
participants responded to a number of questions assessing their self-reported reasons for
the number of cookies that they ordered during the experiment session. Participants were
asked to what extent the amount they ordered was based on (a) “the taste”, (b) their
“liking”, (c) “being in the mood” for that kind of food, (d) “their level of hunger”, and (e)
“the amount that other people ordered” on a 7-point scale (1 = not at all an influence and
7 = very much an influence).
Conflict Feelings. Participants were asked whether they felt conflicted when
deciding how many cookies to order. Two questions were used to measure conflict
feelings on a 7-point scale (1 = not at all and 7 = very much). Specifically, participants
61
were asked to what extent did they “have mixed feelings” (Q1) and “feel conflicted” (Q2)
when deciding whether or not to order more cookies.” The two items were averaged to
create the scale scores (M = 2.79, SD = 1.56, Max = 1.00, Min = 7.00). Cronbach’s α was
0.86. Higher scores on this scale denoted more conflict feeling when ordering cookies.
Data Analysis
Data collected from the online survey and the lab experiment were combined
together by assigning each participant a unique ID; all date collected were strictly
anonymous.
Data were analyzed using IBM SPSS 22. For inferential statistical analysis,
independent sample t-tests, one-way analysis of variance (ANOVA), ANCOVA, linear
regression were carried out. Results with a p-value lower than .05 were considered
statistically significant.

Study II
Study II extended Study I by focusing on the impact of descriptive norms on food
ordering decisions in a more naturalistic environment. Specifically, we conducted Study
II among employees in office settings. Workplace snacking is pervasive and often highly
caloric, and high-calorie snacks have been shown to contribute to obesity (McCrory et al.,
1999). Meanwhile, it has become common practice for people to use online food ordering
and delivery services. Industry experts predicted a huge shift toward online shopping for
food and groceries. Our second study tested the impact of two technology nudges
implemented in a food ordering website on office employees’ ordering decisions through
the use of randomized field trials.
62
Participants
Participants in the field experiment were administrative staff, doctoral students
and postdoctoral scholars at the University of Arkansas. A total of 275 potential
participants were selected from the University’s online directory; each of them had a
university email address and an office located on campus. A recruitment advertisement
was sent to the potential participants through email. They were told that the research was
about testing a new online snack ordering and delivery service on campus. Additionally,
they had to be 18 years of age and above. As incentives, participants were told that they
would receive some free snacks of their choice, as well as be entered into a random
drawing for winning a $100 gift certificate from Amazon.com. Eventually, 91
participants (response rate = 33.1%) took part in this study and ordered snacks from
SnackTime.
Design and Procedure
This study employed a 2 (anchor: low vs. high)  2 (descriptive norm: with
feedback vs. without) + 1 (control: no descriptive norm) between-subjects factorial
design. Participants were randomly assigned to one of the five conditions. Specifically,
participants received the recruitment message which contained the contact information
for the researcher, as well as a description of the study – to test an online snack ordering
and delivery website. They were told that they would receive free snacks delivered to
their offices on campus, and were informed that their personal information was collected
for the purpose of snack delivery only and would be permanently destroyed after the
delivery (See Appendix B).
63
All participants received the same recruitment message, except that a URL was
also provided in the message that randomly directed participants to one of the five
“SnackTime” website versions (Table 1 shows the five website versions).
Table 1. Five SnackTime Website Versions
Versions Domain URL Treatments
1 snacktime.uark.edu/beta Low Anchor with Comparison Feedback
2 snacktime.uark.edu/test Low Anchor
3 snacktime.uark.edu/trial High Anchor with Comparison Feedback
4 snacktime.uark.edu/demo High Anchor
5 snacktime.uark.edu/freetest Control

While all the recruitment messages were sent out between 1:00 p.m. to 2:00 p.m.
on weekdays, when participants ordered snacks was not controlled. Participants could
access the “SnackTime” website at any place on campus as long as they were connected
to the University network. Participants received no instructions on how they should use
“SnackTime,” except for the simple information displayed on the home page of the
website (see Figure 9). This design intentionally prioritized external validity at the
expense of control and internal validity (Creswell, 2008). Since the design of the ordering
website was made simple and straightforward, none of our participants reported any
issues with using the website.
By clicking on the “get started” link on the Home Page, participants were directed
to the Sign Up Page (see Figure 10), on which a signup form was to be filled out.
Information about participants’ name, email, delivery address, and gender were collected.

64
Figure 9. Screenshot of Simple Instructions on SnackTime Home Page

Figure 10. Screenshot of SnackTime Sign Up Page

65
After participants had signed up, they were directed to the Ordering Page (see
Figure 11), where they could see the two types of mini cookies offered as well as some
information about the cookies. Participants could then input how many cookies of each
type they would like to order.
Figure 11. Screenshot of SnackTime Ordering Page in the Control Condition

After entering the numbers of cookies they wanted to order and clicking “add to
cart” and “ready to cart,” participants were then directed to the Confirm Order Page (see
Figure 12). On this page, they could review their order as well as modify it if needed. In
the end, by clicking “confirm order,” participants finished the entire snack ordering
process. A brief summary of the snack ordering procedure of study II can be found in
Figure 13.

66

Figure 12. Screenshot of SnackTime Confirm Order Page without Comparison Feedback

Figure 13. Summary of the Snack Ordering Procedure of Study II

67
Once participants placed an order, the experimenter got notified from the
website’s backend database. The experimenter then prepared the cookies and delivered
them to participants’ offices on campus. All the snacks were delivered between 2:00 p.m.
and 4:00 p.m. on weekdays.
One day later, participants were thanked through a follow-up email. In the same
email, they were invited to “give some feedback for the delivery service” by filling out a
short questionnaire hosted on Qualtrics.com. Participants were told that the questionnaire
would take 1 minute to complete, and giving “feedback” was strictly voluntary. This
follow-up questionnaire measured participants’ age, how many cookies they actually
consumed, and the factors that drove their decisions on ordering quantity.
Manipulations
Anchor Level Setting. The same descriptive normative information used in Study I
was employed. Participants saw “on average, the number of this cookie our users ordered
is: **,” where ** indicated the mean values of the number of cookies that previous users
had ordered. As in Study I, the mean values were dynamic, getting updated each time
when a new order was made.
The anchor was set at two points: low vs. high. In the low anchor level condition,
the initial number of cookies ordered was set at 2 cookies in total – 1 chocolate cookie
and 1 oatmeal cookie (see Figure 14). To be more specific, the experimenter signed up to
“SnackTime” and placed the first 5 orders, i.e., 1 chocolate cookie and 1 oatmeal cookie
were ordered each time, and the same procedure was repeated 5 times. In the high anchor
level condition, a total of 10 cookies – 5 chocolate cookies and 5 oatmeal cookies – were
ordered each time by the experimenter for 5 times (see Figure 15). The suggested serving
68
size of our cookies is about 5 – 6 pieces. Thus, the low anchor level was set at less than
half of the serving size, while the high anchor level was set at double of the serving size.
Figure 14. Screenshot of SnackTime Ordering Page in the Low Anchor Condition

Figure 15. Screenshot of SnackTime Ordering Page in the High Anchor Condition

69
Comparison Feedback. Comparison feedback was displayed on the Confirm
Order Page of the “SnackTime” website. In the feedback condition, an “order quantity
comparison” table containing the ordering quantity of average users and that of the
participant was presented. In Figure 16, for example, previous users ordered a total of 10
cookies on average, and the participant ordered a total of 2 cookies. This comparison
feedback was displayed right above the “confirm order” button, thus, was salient before
participants placed an order. Participants could go back to the Ordering Page to modify
their orders (i.e., number of cookies to order) as many times as they needed until they
finalized their decisions. Participants in other conditions (without feedback condition and
control condition) did not receive this feedback (see Figure 12), but they could also go
back to the Ordering Page to make modifications to their orders.
Figure 16. Screenshot of SnackTime Confirm Order Page with Comparison Feedback

70
Measures
Ordering Quantity. As was in Study I, the main dependent variable of interest was
how much food participants ordered. The number of cookies that each participant ordered
was recorded in the website’s backend database.
Factors Influencing Food Order. As in Study I, participants self-reported reasons
for the number of cookies that they ordered in the follow-up survey. They were asked to
what extent the amount they ordered was based on (a) “the taste”, (b) their “liking”, (c)
“being in the mood” for that kind of food, (d) “their level of hunger”, and (e) “the amount
that other people ordered” on a 7-point scale (1 = not at all an influence and 7 = very
much an influence).
Consumption Quantity. The number of cookies that participants actually ate were
assessed using a self-report measure in the follow-up survey. Participants were asked
what percent of cookies ordered they had eaten by choosing of one of following options:
(1) all, (2) about 80%, (3) about 50%, (4) about 30%, (5) less than 30%, and (6) none.
Demographics. Participants’ self-reported age and gender were collected.
Data Analysis
Data collected from the website’s backend database and the follow-up online
survey were combined together by assigning each participant a unique ID; all data
collected were strictly anonymous.
Data were analyzed using IBM SPSS 22. For inferential statistical analysis,
independent sample t-tests and analysis of variance (ANOVA) were carried out. Results
with a p-value lower than .05 were considered statistically significant.
71
CHAPTER 5: RESULTS
Study I
Participants Characteristics
A total of 139 participants completed both the online survey and the lab
experiment. Table 2 presents the number of participants in each experimental condition.
64% of the participants were male and 36% were female. The average age was 20.65 (SD
= 2.6, with a minimum of 18 and maximum of 36). Among all the participants, 71.9%
were White or Caucasians, 8.6% were Hispanics or Latinos, 8.6% were Black or African
Americans, 7.2% were Asians, 1.4% were Native Americans or India, and 2.2% were of
other racial or ethnic origins.
Mean BMI calculated from self-reported weight and height was 24.34 kg/m
2
(SD
= 4.01, with a minimum of 17.33 kg/m
2
and maximum of 34.86 kg/m
2
). According to the
guideline of the World Health Organization (World Health Organization, 1998), 4.3% of
the participants were classified as underweight, 60.4% were of normal weight, 28.8%
were overweight, and 6.5% were obese.
Table 2. Numbers of Participants in Each Experimental Condition
Conditions High Ego Depletion Low Ego Depletion Total
Descriptive Norms 23 23 46
Injunctive Norms 23 24 47
Control/No Norms 23 23 46
Total 69 70 139

72
Randomization Checks
An ANOVA with conditions as the independent variables and age, BMI, mood,
arousal, hunger level, and healthy eating goal as dependent variables showed that there
were no significant differences between conditions on these variables (all p’s > .30).
Also, the male to female participant ratio did not differ between conditions Pearson 
2
(2,
N = 139) = .014, p = .993.
Manipulation Checks
In the post-experiment questionnaire, participants answered manipulation check
questions. Participants in the descriptive norm condition were asked if they saw the
message “On average, the number of this cookie our users ordered is” on the ordering
webpage and were required to recall the numbers they saw. 100% of the participants
responded that they had seen the descriptive norm messages and recalled the numbers.
Participants in the injunctive norm condition were asked “Did you see ‘we want you to
enjoy your cookies. But do not overeat!’ on the ordering webpage?” 100% checked
“Yes.” Thus, the normative information manipulation was satisfactory.
To ensure the effectiveness of the ego depletion manipulation, immediately after
the “e-crossing” task, the experimenter collected the article and examined whether each
participant followed the rules required by the task. All participants followed the rules in
terms of crossing out the letters “e.” In addition, the experimenter kept track of time each
participants spent on the “e-crossing” task. Participants in the high ego depletion
condition spent significantly more time (about 18 minutes) on the task than those in the
low ego depletion (control) condition (about 10 minutes).

73
Order Quantity/Consumption Quantity
As mentioned in the Method chapter, both order quantity (i.e., how many cookies
participants ordered through the website) and consumption quantity (i.e., how many
cookies participants actually ate) were measured. It turned out that all participants ate all
the cookies they ordered
1
. Thus, the scores on the two measures were identical. We used
consumption quantity for all the analyses, and the two terms –consumption quantity and
order quantity – were used interchangeably to discuss the results of Study I. Table 3
presents consumption quantity in each experimental condition.
Table 3. Order Quantity/Consumption Quantity in Each Condition
Conditions High Ego Depletion
M (SD)
Low Ego Depletion
M (SD)
Total
M (SD)
Descriptive Norms 4.22 (1.62) 3.89 (3.79) 4.20 (2.83)
Injunctive Norms 3.30 (1.87) 3.83 (2.28) 3.57 (2.08)
Control/No Norms 4.43 (3.84) 4.95 (4.37) 4.70 (4.08)

Hypotheses Testing
H1 predicted that individual with low self-regulation resource would be more
likely to conform to descriptive norms cuing the consumption quantities of others than
those with high self-regulation resource. We tested this hypothesis from three different
angles.
First, following previous anchoring effects research (Mussweiler, Englich, &
Strack, 2004), we used the anchor-consumption gap to assess conformity to the anchor,
which was measured by taking the absolute value of the difference between participants’

1
The correlation between ordering quantity and consumption quantity was r = .993. This was because one
participant left some cookie residuals on the plate.
74
consumption quantity and the anchor value provided on the website. In other words, we
calculated the deviation between how many cookies participants ate and how many
cookies they saw other people had ordered (i.e., the value of the anchor). Smaller
deviations indicated stronger conformity or anchoring effects, and larger deviations
indicated weaker conformity or anchoring effects (Simmons et al., 2010). Therefore,
participant i’s deviation (Di) was calculated by

where was the number of cookies participant i consumed, and was the
average number of cookies the previous i - 1 participants had ordered, which was also the
descriptive norm displayed on the ordering website.
An independent sample t test with deviation as the dependent measure and self-
regulation resource condition as the independent treatment revealed that participants with
low self-regulation resource (M = 2.07, SD = 1.29) showed significantly lower deviations
from anchors than participants with high self-regulation resource (M = 3.42, SD = 2.85),
t(44) = 2.076, p = .022. In other words, depleted participants showed significantly higher
conformity to the anchor cued by descriptive norms than non-depleted participants.
Next, an ANCOVA analysis was conducted on deviation with self-regulation
resource level as the independent variable. We also included participants’ hunger level,
their state of mood and arousal, and their total order quantity as control variables. The
analysis showed that self-regulation resource level was a significant predictor of
deviation, F(1, 39) = 5.489, p = .024, partial 
2
= .123, such that depleted participants
showed significantly smaller deviations from anchors than non-depleted participants.
75
In addition, an F-test were conducted to compare the standard deviations of order
quantity between the low ego depletion (M = 3.89; SD = 3.79; n1 = 23) and high ego
depletion (M = 4.22; SD = 1.62; n2 =23) conditions. F score was calculated by
F = / = / = 5.47

The degrees of freedom are (n – 1) = 22 in both conditions. The tabulated value for F22,20
at 99% confidence level is F22,20 = 2.83. In this case, F > F22,20, thus we rejected the null
hypothesis of equal variances. In other words, there was a significant difference between
the standard deviations of order quantity in the two conditions. Participants’ consumption
quantities in the high depletion condition were more centered around their mean than
those in low depletion condition. Furthermore, we also examined the anchor values in the
high depletion and the low depletion conditions. The standard deviations of anchor values
in the high depletion (M = 5.70; SD = 1.15) and the low depletion conditions (M = 5.92;
SD = .75), were not significantly different at the 95% confidence level, F=2.34, p > .05.
Thus, anchor values provided to participants in the two conditions were not significantly
different in terms of means and standard deviations. However, while the mean values of
consumption quantity did not differ in the high depletion and the low depletion
conditions, consumption quantities in the low depletion condition were more spread apart
than those in the high depletion condition. Based on the above analyses, H1 was
supported.
H2 hypothesized that individuals would rate descriptive norms cuing the
consumption quantities of others as less of an influence factor on their consumption
decisions than other introspective factors. A one-way within-subject ANOVA was
76
conducted to examine participants’ ratings of the extent to which their order quantity was
influenced by (1) “the taste” of the food, (2) their “liking” of the food, (3) “being in the
mood” for that kind of food, (4) “their level of hunger”, and (5) “the amount that other
people ordered.” Overall ratings of the importance of these influences vary significantly
2
,
F(4, 180) = 9.39, p < .001, partial 
2
= .173. A follow-up pairwise comparison analysis
showed that other people’s order quantity (M = 3.15, SD =2.712) was rated as
significantly less of an influence than all the other four factors, i.e., liking of the food (M
= 5.23, SD =1.79; p < .001), taste of the food (M = 4.61, SD =1.95; p < .001), being in the
mood for that kind of food (M = 4.54, SD =1.91; p < .001), and ratings on hunger level
(M = 4.39, SD =2.06; p = .005). In addition, hunger level was rated as less of an influence
than liking of the food (p = .023). The remaining pairs of factors did not differ in terms of
the extent to which they were rated as an influence on order quantity. There was no
difference between the high depletion and low depletion conditions in terms of
participants’ ratings of others’ order quantity as an influence on their own order quantity
(p = .631).
A one-way within-subject ANOVA was also conducted on all participants (N =
139). The same pattern of results was obtained (see Table 4). Overall ratings of the
importance of these influences vary significantly
3
, F(4, 548) = 49.49, p < .001, partial 
2
= .265 (see Figure 17). Other people’s order quantity (M = 3.15, SD =2.712) was rated as
significantly less of an influence than all the other four factors, i.e., liking of the food (p <
.001), taste of the food (p < .001), being in the mood for that kind of food (p < .001), and

2
Another commonly used test also revealed significant results, Wilks’s  = .53, F(4, 42) = 9.15, p < .001,
partial 
2
= .466.
3
Wilks’s  = .423, F(4, 134) = 45.64, p < .001, partial 
2
= .577.
77
ratings on hunger level (p < .001). Furthermore, only ratings of the influence of other
people’s order quantity varied by condition, F(2, 135) = 8.58, p < .001, partial 
2
= .113.
Participants in the descriptive norms condition rated the order quantity of others as more
influential than did participants in the injunctive norms condition (p = .009) and
participants in the control condition (p < .001); there was no difference between the
injunctive norm and control conditions (p = .592). Based on our analyses, H2 was
supported.
Table 4. Rated Influence on Order Quantity
Descriptive
Norm Condition
Injunctive Norm
Condition
Control
Condition

Overall
M (SD) M (SD) M (SD) M (SD)
Liking 5.23 (1.79) 4.70 (1.67) 4.70 (1.81) 4.87 (1.77)
Taste 4.61 (1.95) 4.15 (1.65) 3.85 (2.04) 4.20 (1.91)
Liking 5.23 (1.79) 4.70 (1.67) 4.70 (1.81) 4.87 (1.77)
Mood 4.54 (1.91) 4.43 (1.70) 4.72 (1.85) 4.56 (1.81)
Hunger Level 4.39 (2.06) 4.26 (2.02) 4.52 (2.09) 4.39 (2.05)
Other People 3.15 (2.12) 2.09 (1.61) 1.74 (1.27) 2.33 (1.80)

78
Figure 17. Rated Influence on Order Quantity Overall

H3 hypothesized that individuals with high self-regulation resources would be
more likely to conform to injunctive norms cuing others’ disapproval of overeating than
those with low self-regulation resource.
An ANCOVA analysis was conducted on consumption quantity with self-
regulation resource level and normative informative (injunctive norm vs. control) as
independent variables. We also included participants’ mood, arousal, and hunger level as
control variables. Only the main effect for injunctive norm was marginally significant,
F(1, 85) = 3.77, p = .055, partial 
2
= .042. Figure 18 presents order quantity by
condition.
79
Figure 18. Order Quantity by Condition

Thus, our results revealed that participants exposed to injunctive norms (M =
3.61, SD = 2.09) indicating disapproval of overeating ordered and ate fewer cookies than
participants in the control condition (M = 4.70, SD = 4.08). However, the difference was
only marginally significant. The main effect for self-regulation resource level was not
significant, F(1, 85) = .51, p = .477. Another ANCOVA analysis was conducted with the
same control variables among participants in the injunctive norm condition. The results
revealed that there was no significant difference for consumption quantity between
participants in the high self-regulation resource condition (M = 3.83, SD = 2.28) and
80
those in the low self-regulation resource condition (M = 3.36, SD = 1.89), F(1, 41) =
.255, p = .617. Thus, H3 was not supported.
H4 hypothesized that individuals exposed to injunctive norms would experience
greater conflict feelings than those exposed to descriptive norms. An ANCOVA analysis
was conducted on conflict feelings with normative information (injunctive norm vs.
descriptive norm) as independent variables. Participants’ mood, arousal, hunger level,
and their total order quantity were included as control variables. We found the main
effect of normative information on conflict feelings was marginally significant, F(1, 85)
= 3.16, p = .079, partial 
2
= .036, such that participants in the injunctive norm condition
experienced more conflict feelings when deciding how many cookies to order than
participants in the descriptive norm condition. Table 5 presents conflict feelings measure
in the two conditions. Thus, H4 was partially supported.
Table 5. Conflict Feelings by Condition
Descriptive
Norm Condition
Injunctive Norm
Condition
M (SD) M (SD)
Conflict Feelings 2.54 (1.35) 2.98 (1.65)

H5 aimed to explore how individuals’ healthy eating goal would moderate their
conformity to injunctive norms cuing others’ disapproval of overeating. Specifically, H5
hypothesized that injunctive norms would curb consumption quantity of individuals who
scored high on the personal goal of healthy eating but not those who scored low on it. A
regression analysis failed to detect a moderating effect of healthy eating goal in the
injunctive norm condition. The interaction term between healthy eating goal and self-
81
regulation condition was not a significant predictor of order quantity, Beta = -.018, p =
.984. Table 6 presents the model of moderating effect testing.
Table 6. Testing Moderating Effect of Healthy Eating Goal in Injunctive Norm Condition
Order Quantity
Predictors R
2
Beta p
Block 1 (Controls) .13
Arousal .053 .745
Mood .027 .859
Hunger Level .361 .024*
Block 2: .01
Self-regulation Resource -.072 .930
Healthy Eating Goal -.062 .894
Block 3: .00
Health Eating Goal  Self-
regulation Resource
-.018 .984
Total R
2
.14
F 2.09
n 47

Additionally, our analyses showed that participants’ healthy eating goal was not
significantly correlated with their consumption quantity controlling for mood, arousal,
hunger level, and perceived healthfulness of the food, regardless of whether they were in
the injunctive norm condition (partial r = -.061, p = .697), the descriptive norm condition
(partial r = .208, p = .192), or the control condition (partial r = .205, p = .188).
82
Study II
Participants Characteristics
93 administrative staff and graduate students ordered cookies from the snack
ordering website (Low anchor plus feedback: N = 15; High anchor plus feedback: N =
18; Low anchor without feedback: N = 21; High anchor without feedback: N = 19;
Control: N = 20). 77% were female (N = 72) and 23% were male (N = 21) participants.
73 participants completed the follow-up online survey. The reported average age was
39.75 years (SD = 12.88).
Order Quantity
Overall, participants ordered a total of 6.46 cookies (SD = 6.33) on average across
all conditions. Table 7 presents descriptive statistics for order quantity in all conditions.
Table 7. Descriptive Statistics for Order Quantity in All Conditions
Conditions M SD Min Max N
Low Anchor with Feedback 2.60 1.12 1 4 15
Low Anchor 4.57 3.49 1 12 21
High Anchor with Feedback 11.33 6.73 1 25 18
High Anchor 4.47 2.87 1 10 19
Control 8.85 9.17 2 40 20

Hypotheses Testing
H6 hypothesized that individuals would order more food when the initial
descriptive norms cuing others’ order quantity were set at a high level than when they
were set at a low level. To test our hypotheses, a homogeneity of variance test was first
conducted. It revealed that the assumption of “equal variances” was violated, Levene’s
83
statistics = 6.46. p = .002. Thus, a Welch’s test, which deals with unequal variances and
sample size, was employed to compare means instead of the regular ANOVA test. The
results showed that there were significant differences in participants’ order quantity
among the low anchor, the high anchor, and the control conditions, Welch = 8.59, p =
.001. A follow-up pairwise comparison revealed that participants in the low anchor (M =
3.75, SD = 2.90) condition ordered a significantly smaller number of cookies than
participants in the high anchor (M = 7.81, SD = 6.13) condition, t(51.67) = -3.63, p <
.001, and participants in the control (M = 8.85, SD = 9.17) condition, t(21.14) = -2.42, p =
.013. The high anchor condition did not differ significantly from the control condition in
terms of participants’ order quantity, t(55) = -.51, p = .31. Thus, H6 was supported.
H7 predicted that providing personalized comparison feedback would enhance
conformity to descriptive norms cuing others’ food order quantity. This hypothesis was
tested through different approaches.
First, an ANOVA with order quantity as the dependent measure revealed a
significant interaction effect between anchor level and feedback, F(1, 69) = 20.51, p <
.000, partial 
2
= .23 (see Table 8). When feedback was not provided, participants in the
low anchor condition (M = 4.57, SD = 3.49) and high anchor condition (M = 4.47, SD =
2.87) ordered a similar amount of cookies. However, when feedback was provided,
participants in the high anchor condition (M = 11.33, SD = 6.73) ordered more than 4
times as many cookies as participants in the low anchor condition (M = 2.60, SD = 1.12,
see Figure 19).

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Table 8. ANOVA Results for Ordering Quantity
Source of variation Sum of squares df F p Partial 
2

Anchor 335.20 1 19.61 .000 .083
Feedback 107.41 1 6.28 .015 .221
Anchor  Feedback 350.55 1 20.51 .000 .229

Figure 19. Order Quantity by Participants

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A follow-up pairwise comparison revealed that participants in the low anchor plus
feedback condition ordered significantly fewer cookies than participants in all the other
four conditions, i.e., the low anchor without feedback condition (M = 4.57, SD = 3.49),
t(25.45) = -2.42, 2-tailed p = .023, the high anchor plus feedback condition, t(18.13) = -
5.42, p < .000, the high anchor without feedback condition (M = 4.47, SD = 2.87),
t(24.44) = -2.60, 2-tailed p = .016, and the control condition (M = 8.85, SD = 9.17),
t(19.76) = -3.02, 2-tailed p = .007. On the other hand, participants in the high anchor plus
feedback condition ordered significantly more cookies than participants in the high
anchor without feedback condition, t(22.74) = 3.99, 2-tailed p = .001, and the low anchor
without feedback condition, t(24.61) = 3.84, 2-tailed p = .001. The difference between the
high anchor plus feedback condition and the control condition was not significant, t(36) =
.94, 2-tailed p = .352. The differences between the high anchor without feedback
condition and the control condition, t(22.88) = -2.03, 2-tailed p = .054., and between the
low anchor without feedback condition and the control condition, t(24.16) = -1.86, 2-
tailed p = .062, were both marginally significant. Overall, these results showed that when
feedback was provided, participants’ order quantities were closer to the value of the
anchors.
Next, the same deviation scores, or the absolute difference between order quantity
and anchor value, were calculated to measure conformity as we did in Study I. This time,
we adjusted the deviation scores by dividing it by the anchor value. We could think of the
adjusted deviation score as a standardized measure, which equaled to the percentage
change (either increase or decrease) from the anchor value.
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Table 9 presents adjusted deviation scores in each experimental condition. An
ANOVA with deviations as dependent measure revealed a significant main effect for
feedback, F(1, 69) = 4.18, p = .045, partial 
2
= .06. The main effect for anchor level was
not significant, F(1, 69) = 2.70, p = .105, nor was the interaction between anchor and
feedback, F(1, 69) = 2.65, p = .108. Thus, H 7 was supported.
Table 9. Deviations in Each Condition
Conditions With Feedback
M (SD)
Without Feedback
M (SD)
Total
M (SD)
Low Anchor .43 (.33) .89 (.78) .70 (.67)
High Anchor .43 (.48) .48 (.29) .45 (.39)
Total .43 (.41) .69 (.63) .57 (.55)

Post-hoc Analyses
In a follow-up questionnaire, we again asked participants in Study II to rate how
different factors (i.e., (1) “the taste” of the food, (2) their “liking” of the food, (3) “being
in the mood” for that kind of food, (4) “their level of hunger”, and (5) “the amount that
other people ordered”) influenced on their decisions on order quantity. A one-way
within-subject ANOVA revealed a similar pattern of those ratings as we found in Study I.
Overall ratings of the importance of these influences vary significantly
4
, F(4, 224) =
27.89, p < .001, partial 
2
= .332. A follow-up pairwise comparison analysis showed that
other people’s order quantity (M = 2.72, SD =2.31) was rated as significantly less of an
influence than all the other four factors, i.e., liking of the food (M = 5.53, SD =1.71; p <
.001), taste of the food (M = 5.16, SD =1.84; p < .001), being in the mood for that kind of

4
Wilks’s  = .45, F(4, 53) =16.27, p < .001, partial 
2
= .551.
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food (M = 4.61, SD =1.82; p < .001), and ratings of hunger level (M = 3.54, SD =2.03; p
= .02). Thus, the results were in consistent with H2.
Figure 20. Rated Influence on Order Quantity

Our post-hoc analysis also looked at whether descriptive norm functions as an
inhibitory norm. A paired-sample t test among participants in Study I indicated that the
mean anchor value (M = 5.81, SD = .97) was significantly greater than the mean value of
participants’ consumption quantity (M = 4.01, SD = 2.89), p < .001. The 95% confidence
interval for the mean difference ranged from .83 to 2.69. The same results were revealed
among participants in the high anchor without feedback condition in Study II, in which
mean anchor value (M = 6.85, SD = 1.11) was also significantly higher than the mean
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value of participants’ order quantity (M = 4.47, SD = 2.87), p = .003, and the 95%
confidence interval for the mean difference was from .81 to 3.95. These results supported
the inhibitory norm proposition that participants tend to order no more than other people
did on average; they used descriptive norms as an informative guide for the maximum
amount that they themselves may order.
However, the inhibitory norm proposition was not supported in the other three
descriptive norm conditions in Study II. In the low anchor without feedback condition,
participants’ mean order quantity (M = 4.57, SD = 3.49) was even higher than the mean
anchor value (M = 2.89, SD = .70), 2-tailed p = .033. In the two feedback conditions, the
mean anchor value and participants’ mean order quantity were not significantly different.
Finally, in our follow-up questionnaire, participants self-reported how many of
the ordered cookies they actually ate. 72.6% reported that they ate all of the cookies they
ordered. 78% ate more than 80% of the cookies. There was no difference in terms of the
percentage of cookies participants ate between the low anchor (M = 2.03, SD = 1.76) and
the high anchor (M = 1.76, SD = 1.43) conditions, t(58), p = .513, although participants in
the high anchor condition ordered significantly more cookies than participants in the low
anchor condition.

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As a summary, Table 10 presents the results of hypotheses testing.
Table 10. A Summary of Hypotheses Testing Results
Hypotheses Supported?
Study I
H1 Individuals with low self-regulation resources will be more likely
to conform to descriptive norms cuing the consumption quantities
of others than those with high self-regulation resources.
Yes
H2 Individuals will rate descriptive norms cuing the consumption
quantities of others as less of an influence factor on their
consumption decisions than other introspective factors.
Yes
H3 Individuals with high self-regulation resources will be more likely
to conform to injunctive norms cuing others’ disapproval of
overeating than those with low self-regulation resources.
No
H4 Individuals who received injunctive norms will experience greater
conflicting feelings than those who received descriptive norms.
Partial
H5 Individuals’ personal healthy eating goal will moderate their
conformity to injunctive norms cuing others’ disapproval of
overeating. More specifically, such injunctive norms will curb
consumption quantity of individuals who score high on personal
healthy eating goal but not those who score low on it.
No
Study II
H6 Individuals will order more food when the initial descriptive
norms cuing others’ order quantity are set at a high level than
when they are set at a low level.
Yes
H7 Providing personalized comparison feedback will enhance
conformity to descriptive norms cuing others’ food order quantity.
Yes

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CHAPTER 6: DISCUSSION AND CONCLUSION
The current dissertation project has two fundamental purposes. The first is
concerned with filling research gaps and gaining a better understanding of how social
normative cues transmitted through the direct decision environment influence people’s
food consumption decisions (Study I), whereas the second probes to apply these findings
in designing nudges in an technology-mediated environment as an effort to provide
insights to combat overeating (Study II).
Study I
The first study examined the underlying mechanisms of two distinct types of
social norms: descriptive norms and injunctive norms, and assessed the differential
effects of self-regulation resource on the likelihood of conforming to the two norms.
Specifically, this study looked at descriptive norms cuing other people’s consumption
quantity and injunctive norms cuing other people’s disapproval of overeating. We
hypothesized that descriptive norms conveyed through the direct decision environment
function as an anchoring heuristic, which would be more effective when individuals have
low self-regulation resources (H1). Heuristic responding to descriptive norms also would
make it hard for individuals to detect their influence (H2). Conversely, we predicted that
injunctive norms would be associated with concerns of social acceptability, which
operated through motivating behavioral intentions to perform norm-consistent behaviors.
Processing injunctive norms would lead individuals to experience greater conflict when
deliberating over decisions to conform or not conform, which required sufficient self-
regulation resources to follow (H3 & H4).
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Among the findings in this study, the most important one is that descriptive norms
were significantly more effective when participants had been depleted by the
experimental manipulation than when they had not. Through different applications of
data analysis, we found that participants’ food consumption volumes were more closely
anchored on the consumption quantities of others under the condition of self-regulation
depletion. This finding is in line with the behavioral heuristics account of social modeling
effects (Prinsen, Ridder, & de Vet, 2013), which contends that people tend to match their
food intake with that of other people because other people’s intake serves as a simple and
efficient guideline for their decisions on consumption quantity. This finding also supports
the postulation of the functions of descriptive norms proposed by the focus theory of
normative conduct (Jacobson, Mortensen, & Cialdini, 2011), which states the influence
of descriptive norms is enhanced in situations in which self-regulation capacity is
impaired, because when individuals’ cognitive resources are low, they are more likely to
depend on heuristics for decision making.
Consistent with the above finding, our results also showed that participants
underestimated the extent to which their consumption decisions were influenced by the
consumption quantities of others. Instead, introspective factors, such as liking of the food,
taste of the food, being in the mood for the food, and hunger level of the eater were
believed to drive how much people consumed. Additionally, an identical pattern of
results was also found in our post-hoc analyses, whereby participants in a field
experiment (Study II) rated other people’s order quantity as a significantly less important
reason for their own order quantity decisions compared to the other factors. These
findings are in congruent with Wansink’s work (Wansink & Chandon, 2014; Wansink,
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2010), which suggests that people adamantly deny the influence of environment or
external cues on their decisions related to food intake. Taken together, it seems that
people do not acknowledge that descriptive norms conveyed in the environment impact
their food consumption and food-ordering/purchasing decisions. Meanwhile, our results
might imply that descriptive norms implicitly influence consumption decisions, and the
influence of descriptive norms could occur at a relatively peripheral level of which
people are not aware.
Contrary to descriptive norms, our findings concerned with injunctive norms were
largely unsupported. Our results only showed a marginally significant trend that
participants exposed to the injunctive norms signaling other people’s disapproval of
overeating ordered and ate less food than participants in a control condition. We failed to
find that a high level of self-regulation resources leads to increased conformity to
injunctive norms, although participants in the injunctive norms condition reported
experiencing marginally greater conflict in deciding their consumption volume than
participants in the descriptive norms condition. These findings were not in consistent
with the results found in the studies by Jacobson et al. (2011). In their studies, the authors
reasoned that the interpersonal goal of gaining social approval (or avoiding social
disapproval) underlies the effectiveness of injunctive norms regulating behaviors. And
capacity for effortful self-regulation is required to resolve the competing motives
between obtaining social acceptance and following one’s immediate and personal
interests. As a result, self-regulation resources facilitate the influence of injunctive norms.
Our speculation is that it is unclear to what extent individuals would take a more
interpersonal goal of social approval into consideration when making food-related
93
decisions. Perhaps our everyday experience tells us that two particularly salient and
conflicting goals we are facing when making food intake decisions are: a hedonic and
immediate goal of taste enjoyment and a more utilitarian goal of maintaining good health
(Dhar & Simonson, 1999; Fishbach, Friedman, & Kruglanski, 2003; Chandon &
Wansink, 2007). Both the hedonic and the utilitarian goals are intrapersonal goals of
benefiting a present or future self. But how salient and important the goal of adhering to
social standards is when individuals make consumption decisions remains a question.
In addition, the findings from our post-hoc analyses also challenge the social
acceptance account, particularly the inhibitory norms proposition by Herman and
colleagues (Herman, Roth, & Polivy, 2003; Herman & Polivy, 2005), who maintained
that people tend to eat less than other eaters because they use others’ intake as the upper
boundary of a permissible amount. Our findings showed that participants did not always
use other people’s consumption quantity as a limit. Our participants consumed less food
than the average of others when other people’s intake were relative high, but they ordered
more food than the average amount of others when other people’s order quantities were
relative low, which is consistent with studies that observed upward adjustments of intake
from low anchors (Wansink et al., 1998). Altogether, we demonstrate that it is not simply
people modeling food intake of others to look for an appropriate (usually an upper limit)
amount of food to eat, it depends on how much other people in the same situations
purchase and/or consume.
Our findings failed to demonstrate that participants’ personal healthy eating goal
moderated their conformity to injunctive norms conveying other people’s disapproval of
overeating (H5). Even for our participants who thought eating healthy was important to
94
them as well as had a high self-regulation capacity, they did not exhibit more norm-
consistent behaviors by taking and eating less food. As a matter of fact, we failed to find
that personal healthy eating goal impacted food consumption in any of our experimental
conditions. One possible explanation is that, as indicated by previous research, healthy
eating is typically not very high on young people’s list of personal goals (Croll et al.,
2001; Stead et al., 2011), although participants (i.e., college students) in our sample did
self-report a somewhat high level of healthy eating goal on average (M = 5.29, SD = 1.04,
the scale ranged from 1 to 7). Another possible explanation is in line with the general
criticism of the deliberative approach to promoting healthy eating. Individuals struggle
with excessive consumption on a day-to-day basis even if they are sufficiently motivated
to stop it.
Overall, Study I contributes to the literature on social normative influence on
consumption decisions (e.g., Herman et al., 2003) insofar as it is the first line of research
to differentiate the underlying mechanisms of how two distinct types of social norms (i.e.,
descriptive and injunctive norms) impact food intake decisions. Descriptive norms, in
particular, can be communicated through the immediate decision environment (Burger et
al., 2010; Prinsen et al., 2013) to cue the eating behaviors of others in the same situation.
Such environmental cues can serve as behavioral heuristics in the form of anchors that
help people to make their own intake decisions with reduced effort. As such, people are
more likely to depend on descriptive norms of others’ intake in the state of ego depletion,
because ego depletion reduces people’s capability to deliberate over their own behaviors
(Baumeister et al., 1994; Hofmann et al., 2007). Moreover, descriptive norms inferred
from the environment operate in nudging people’s intake without people being aware of
95
their influence; this finding aligns with previous research showing that people are poor at
explaining their intake decisions in terms of environmental factors (Wansink & Chandon,
2014). Our research to some extent reflects the notion that processing environmental
factors, descriptive norms being one example, may be relatively peripheral and may
occur outside of conscious awareness (Wansink, 2010).
The obtained results associated with injunctive norms are not unique. They have
some resemblance to previous research showing no effect of injunctive norms on
regulating food-related decisions (Mollen et al., 2013; Robinson, Fleming, & Higgs,
2013; Stok, de Ridder, de Vet, & de Wit, 2014). Some scholars (Mollen et al., 2013)
postulate that injunctive norms require more cognitive activity to influence eating
behaviors. Our findings, however, undermine this explanation by showing that even when
people have high self-regulation capacity, they are no more responsive to injunctive
norms compared to people under conditions of low self-regulation resources. Some other
scholars highlight norm saliency as a possible explanation for the lack of effect of
injunctive norms on food-related decisions (Lally et al, 2011). In our study, injunctive
norms signaling disapproval of overeating were made directly to the decision
environment (the same way as we did to descriptive norms), and thus can be argued as
being objectively salient. Following some scholars’ speculation that injunctive norms
likely affect behavior by making salient already existing beliefs (Fishbein & Cappella,
2006), we also tested subjective saliency of the injunctive norms in the form of existing
personal goals of healthy eating. Yet even for people who were highly motivated to eat
healthfully, they showed no more injunctive norm-consistent behaviors (e.g., not
overeating) than people who did not place a high value on healthy eating.
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Injunctive norms may operate through a deliberative processing of socio-
cognitive antecedents, particularly in terms of social standards on how to behave, prior to
behavioral intention and actual decisions. We question to what extent people consider
eating healthfully as a social obligations. Overweight and obese people are no longer a
minority or normatively deviant group, at least in the United Sates; they account for two-
thirds of the population. Exhorting people to change their eating behaviors through guilt-
inducing or self-presentational appeals may not be a credible solution.
Study II
Our second study extended the research insights from Study I, and focused our
investigation on descriptive norms in a more naturalistic setting. In this study, we
extended Wansink and colleagues’ work (Wansink & Chandon, 2014) on environmental
influence on consumption quantity by examining one type of social environmental cue:
descriptive norms, an area of research that to date has received only slight attention
(Herman & Polivy, 2005; Herman & Polivy, 2014; Wansink & Chandon, 2014). The
study also fits within the current trend to promote healthy eating behaviors with the use
of nudges (Thaler & Sunstein, 2008). We contribute to this trend (Thaler & Sunstein,
2008) by proposing that cost-effective nudges can be implemented in technology-
mediated environments (in our case, a website) to influence food-related decisions. In
particular, we tested two technology-based nudges on an online food ordering platform.
The first technology-based nudge tested how people use descriptive norms, or a
quantity anchor set up by others, to guide how much food they should order. Setting the
initial anchor at a low level on the ordering website was hypothesized to nudge people to
order less food but more when the initial anchor was set at a high level on the website
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(H6). The second technology-based nudge employed normative feedback and predicted
that providing personalized normative feedback, which simply displayed the comparison
information of the anchor and one’s own order quantity, would amplify conformity to the
anchor (H7).
We found that setting the initial anchor of the order quantity at a low level on the
website did make participants order less than participants in the high anchor condition
and participants in a control condition in which no anchor was provided. This finding
supports studies (Feeney et al., 2011; Pliner & Mann, 2004; Robinson et al., 2013; Roth
et al., 2001) that demonstrated social modeling effects with a remote-confederate design.
In these studies, exposing participants to bogus information of the amount of food
consumed by previous non-present others could make them eat more or less when
participants were led to believe that others had eaten a lot or very little. We provide
evidence relevant to this line of research and extend it by showing that social modeling
effects may occur through cuing descriptive norms in a technology-mediated
environment. Setting the initial order amount on the ordering website at a low level or a
high level can lead people to order less or more, respectively.
Moreover, our study found that when personalized normative feedback was
provided on the ordering website, participants ordered over 4 times more food in the high
anchor condition than participants in the low anchor condition! Combining a low initial
anchor and a normative feedback made our participants order only one-third the amount
of food as did participants in a control condition in which no anchor nor feedback was
provided. Through data analyses from different angles, we found that providing
normative feedback, which simply showed one’s own order volume and other’s average
98
order quantity on the website, enhanced conformity to descriptive norms. People’s order
quantities were more closely matched to the order quantity of others when they were
exposed to salient personalized normative feedback. Our finding is in line with research
on digitized normative feedback showing that such feedback fosters norm adherence in
other behavior domains such as energy saving (Fisher, 2008) because feedback enables
people to compare their own behavior to that of the others. It should also be noted that in
the present study normative feedback was presented in a salient way: embedded in the
immediate environment at the moment right before decision.
Across the two studies, we provide evidence that descriptive norms transmitted
through the immediate environment in which food consumption decisions take place
function as anchoring heuristics. In other words, people use others’ consumption volume
as anchors to which they match their own intake. Early studies on anchoring processes
and effects use a judgmental-task paradigm, which primarily concerns how people’s
estimations in judgment tasks are influenced by anchor numbers (Tversky & Kahneman,
1974). Our studies extend this research paradigm by showing that anchoring processes
may also occur and affect people’s behavior.
Interestingly, we found participants in the high anchor plus feedback condition
did not eat significantly more than did participants in the control condition. It may be that
high order anchors simply communicate to participants not to worry about watching their
caloric intake, which allows them to order and eat as much as they want. Leone et al.,
(2007) speculated that in the absence of clear norms, people tended to overeat. They
reasoned that in situations where a clear norm is absent, “one might feel more at liberty to
do as one pleases” (Leone et al., 2007, p. 62). The authors referred to this phenomena as
99
“the liberating effect.” It is possible that exposing people to a high anchor may also lead
to this liberating effect. In our study, the high anchor was set at 10 cookies (300 kcals),
which is 8 cookies more than the low anchor. This amount could be well beyond what
most people can comfortably eat. Therefore, the presence of a norm of an excessive
amount of food intake may lead people to take and eat as much food as they want.
Admittedly, the direct outcome measure in this field study was how much food
people ordered, not how much food people actually consume. A follow-up analysis on
our study 2 data demonstrated that the majority of the participants ate all the food they
ordered online. In addition, although participants in the high anchor level condition
ordered much more food than participants in the low anchor condition, the percentage of
food participants actual consumed was not significantly different between those
conditions. In other words, our findings implied that participants in the high anchor
condition ordered and actually consumed more food than participants in the low anchor
condition. This finding aligns with what we observed in the first study in which
participants ate all food delivered to them. Both results are in keeping with the claim that
people appear to be “hardwired” to clean their plate (Wansink, 2010, p. 455). In this
regard, the decision environment at both the time of food-purchasing and the time of
consumption should be carefully considered and leveraged to nudge for better eating
behaviors (Thaler & Sunstein, 2008; Wansink & Chandon, 2014).
Limitations and Future Directions
The present research was limited in a number of ways. One limitation of this
research project is the relatively small sample size of Study II. While we found that an
initial anchor set at a low level led participants to order significantly less food, this
100
anchor could adjust itself upward to a high or even an excessive level as more
participants get involved in. Meanwhile, the design of our research employing dynamic
web platform enables us to track the development of descriptive norms over time, and
thus can provide opportunities for researchers to explore interesting research topics such
as norm formation, norm shifting, and possible anchoring-and-adjusting processes of
norm development. In natural settings, it has been difficult to observe and forecast at
what level a social norm will emerge. For example, both Axelrod (1986) and Bicchieri
(2006) proposed models of norm emergence, but these models are based on strong
assumptions including pre-existence of some norms already in the population. Explaining
how norms come to exist remains an open problem. Nor is there yet a general account of
how populations can be shifted away from harmful norms that are already entrenched in a
population and toward more socially beneficial norms. These issues are critical for
people’s eating behavior. Leveraging technology-based nudges, can we guide the norms
of food consumption so that they emerge and develop at a desirable level? Future
research could seek to enrich our understanding of social-norms-based nudges through
innovative social communication technologies.
Another limitation of our project is that in our studies the decision of how much
food to order was a one-shot decision. Participants were unable to order again nor
consume more food than this initial order amount. It would be valuable for future work to
examine similar but repeated decisions over time to see if observed effects in this project
still hold. In addition, while we aimed to advance prior research by decoupling the
purchasing/ordering and consumption decisions, we did not really achieve this goal
particularly with our field data which were collected through an online questionnaire. The
101
reliability of self-reported eating behaviors is always a major limitation. Future research
could benefit from more objective measures. Additionally, our project studied snack
ordering and consumption. It would be useful to look at consumption decisions around
regular meals or other (un)healthy foods.
Our studies suggest a number of valuable avenues for future research. Exploring
the potential moderating roles of personality constructs like social approval motivation,
self-monitoring, and trait-level self-regulation tendencies is likely to enhance researchers’
understanding of the differences in response tendencies for injunctive and descriptive
norms. Further investigating the potential situational moderators would also be
worthwhile. Given the data obtained, descriptive norms may be particularly successful
when delivered at the end of a long workday, toward the end of a stressful task, or in
other situations in which self-regulation capacity is impaired. Future studies could
empirically test these propositions and develop creative nudges that translate findings
into effective interventions.
Our research does not involve social interaction. However, in addition to what the
norm is, who the norm setters are also matters hugely. Researchers have long recognized
that people are more likely to follow the norms of others whom they perceive to be
similar (Baron et al., 1996; Festinger, 1954). Forms of similarity that have been studied
included age (Murray et al., 1984), gender (White, Hogg, & Terry, 2002), race,
personality (Carli, Ganley, & Pierce-Otay, 1991), and attitudes (Suedfeld, Bocher, &
Matas, 1971). For food-related decisions in particular, studies have shown that the social
modeling effects are moderated by whether the social model is a member from a
desirable or undesirable group (Berger & Heath, 2007; White & Dahl, 2006; 2007).
102
Social network sites and online community technologies allow people to interact with
specific groups of people. It would be interesting for scholars to see how the identity of
norm setters moderates anchoring effects proposed in this project.
Finally, we think this research project may have important practical implications.
We showed that small nudges implemented in a technology-mediated environment can be
leveraged to potentially curb overeating. Beyond healthy eating behaviors, leveraging
pervasive social communication technologies to design innovative nudges can be a
promising avenue for translational research concerned with promoting a wide range of
self-beneficial behaviors. We hope our project can inspire synergy between behavioral
scientists and technology designers to create cost-effective interventions that benefit
humankind more broadly. One last comment about our research is that if our decisions
and choices are influenced by technology-based nudges, does this put a tremendous
amount of moral pressure and responsibly on the intermediate institutions who are
designing technology environments? This ethical issue obviously deserves more
discussion.
Conclusion
Recent literature suggests that the social normative approach is one of the most
influential yet underexplored areas of food consumption decisions. The lack of
understanding about the influence of social norms on food consumption decisions has
been driving a significant amount of speculation about the mechanisms associated with
such influence as well as an extensive debate over the effectiveness of using the social
normative approach to combat poor eating behaviors such as overeating. This dissertation
project advances such understanding insofar as it is among the first line of research to test
103
predicted differences in how two distinct types of social norms (i.e., descriptive and
injunctive norms) influence food intake decisions, and the intervening psychological
processes involved in responding to each norm type.
Across both studies, our primary findings are that descriptive norms cued through
the direct environment in which food consumption decisions are made function as
anchoring heuristics. In other words, people depend on others’ consumption volume as
anchors to which they match their own intake. Conformity to descriptive norms is
moderated by the level of self-regulation resources such that people are more responsive
to descriptive norms of others’ consumption volume in the state of ego depletion.
Moreover, adherence to descriptive norms can be fostered using personalized comparison
feedback made salient in the immediate environment. Employing technology-based
nudges of setting an initial low anchor combined with providing comparison feedback
could potentially lead people to order and consume less food. Finally, similar to other
types of environmental factors, descriptive norms inferred from the environment operate
in nudging people’s intake without people being aware of their influence.
104
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APPENDIX A: INFORMATION ABOUT COOKIES
In the laboratory experiment, information about the cookies that participants were going
to order and eat were presented to them at the beginning of the study.

123
APPENDIX B: RECRUITMENT MESSAGE OF STUDY II

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Creator Hou, Jinghui (author)

Core Title Leveraging social normative influence to design online platforms for healthy eating

School Annenberg School for Communication

Degree Doctor of Philosophy

Degree Program Communication

Publication Date 10/22/2015

Defense Date 03/24/2015

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

Tag anchoring heuristics,descriptive norms,ego depletion,environmental cues,food consumption decisions,OAI-PMH Harvest,personalized comparison feedback,technology-based nudge

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Advisor McLaughlin, Margaret L. (committee chair), Monge, Peter R. (committee member), Wood, Wendy (committee member)

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