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Express yourself: Facial expression of happiness, anger, fear, and sadness in funding pitches

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We build upon theory from evolutionary psychology and emotional expression, including basic emotion theory and the dual threshold model of anger in organizations, to extend knowledge about the influence of facial expressions of emotion in entrepreneurial fundraising. First, we conduct a qualitative analysis to understand the objects of entrepreneurs' facial expressions of four basic emotions in their pitches: happiness, anger, fear, and sadness. This provides a base for our theorizing that the frequency of entrepreneurs' facial expression of each of these emotions exhibits an inverted U-shaped relationship with funding. We also argue that the frequency of changes in entrepreneurs' facial expressions is positively related to funding. We test our predictions with a sample of 489 funding pitches using computer-aided facial expression analysis. Results support inverted U-shaped relationships of the frequency of facial expression of happiness, anger, and fear with funding, but show a negative relationship of sadness with funding. Results further support that the frequency of change in entrepreneurs' facial expressions promotes funding.
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EXPRESS YOURSELF: FACIAL EXPRESSION
OF HAPPINESS, ANGER, FEAR, AND SADNESS IN FUNDING PITCHES
BENJAMIN J. WARNICK*
Department of Management, Information Systems, and Entrepreneurship
Carson College of Business
Washington State University Vancouver
BLAKLEY C. DAVIS*
Department of Management and Entrepreneurship
School of Business
Virginia Commonwealth University
THOMAS H. ALLISON*
Department of Entrepreneurship and Innovation
Neeley School of Business
Texas Christian University
AARON H. ANGLIN*
Department of Entrepreneurship and Innovation
Neeley School of Business
Texas Christian University
Highlights
Entrepreneurs express both positive and negative emotions in their funding pitches.
We perform a qualitative analysis examining the objects of entrepreneurs’ facial
expressions and the nature of changes between facial expressions.
We use facial expression analysis software to perform a quantitative analysis examining
the influence of entrepreneurs’ facial expressions of emotion on funding.
The frequency of entrepreneurs’ facial expression of happiness, anger, and fear exhibits
an inverted U-shaped relationship with funding.
The frequency of changes in entrepreneurs’ facial expressions increases funding.
*The authors contributed equally.
Corresponding author: Benjamin J. Warnick, 14204 NE Salmon Creek Ave., Vancouver, WA 98686
Email: b.warnick@wsu.edu
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EXPRESS YOURSELF: FACIAL EXPRESSION OF
HAPPINESS, ANGER, FEAR, AND SADNESS IN FUNDING PITCHES
ABSTRACT
We build upon theory from evolutionary psychology and emotional expression, including basic
emotion theory and the dual threshold model of anger in organizations, to extend knowledge about
the influence of facial expressions of emotion in entrepreneurial fundraising. First, we conduct a
qualitative analysis to understand the objects of entrepreneurs’ facial expressions of four basic
emotions in their pitches: happiness, anger, fear, and sadness. This provides a base for our theorizing
that the frequency of entrepreneurs’ facial expression of each of these emotions exhibits an inverted
U-shaped relationship with funding. We also argue that the frequency of changes in entrepreneurs’
facial expressions is positively related to funding. We test our predictions with a sample of 489
funding pitches using computer-aided facial expression analysis. Results support inverted U-shaped
relationships of the frequency of facial expression of happiness, anger, and fear with funding, but
show a negative relationship of sadness with funding. Results further support that the frequency of
changes in entrepreneurs’ facial expressions promote funding.
Keywords: entrepreneurial funding pitch; emotion; emotional expression; facial expression; basic
emotions; object of emotion; mixed methods; qualitative; happiness; anger; fear; sadness; passion
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EXPRESS YOURSELF: FACIAL EXPRESSION OF
HAPPINESS, ANGER, FEAR, AND SADNESS IN FUNDING PITCHES
Executive Summary
Entrepreneurs express emotion in many ways, but facial expressions are particularly
influential (Bonanno et al., 2002; Keltner et al., 2019). This may be especially true in visual
presentations, such as the funding pitch (Lee and Anderson, 2016). However, understanding of
entrepreneurs’ facial expressions of emotion in funding pitches remains limited in a few key ways.
To begin, it is well documented that people display a variety of facial expressions in social interaction
(Ekman, 1992; Rafaeli and Sutton, 1989; Russell et al., 2003), yet entrepreneurship scholars have
focused on positive expressions in funding pitches, overlooking the potential benefits of emotions
traditionally viewed as negative. In addition, people differ in how frequently they display facial
expressions of a given emotion (Ekman, 1982) and change, sometimes quickly, from one facial
expression to another (Ekman, 1982; Wehrle et al., 2000). However, entrepreneurship scholars have
given little attention to such differences in emotional expression. This is important, since research
suggests that negative effects become more likely when one expresses an emotion either more or less
frequently than is acceptable in a given situation (e.g., Shields, 2005) and changes in expressions
influence an audience’s perceptions of, and response to, the expresser (e.g., Eastwood et al., 2001;
Frischen et al., 2008). Accordingly, we ask: “How do entrepreneurs’ facial expression of emotions in
a funding pitch, both positive and negative, influence funding?”
Our study focuses on facial expressions of happiness, anger, fear, and sadness, since these are
the four “basic” emotions that research has shown can be reliably captured and distinguished via
facial analysis software (Jack et al., 2014). We first conduct a qualitative analysis of entrepreneurs’
facial expressions of these emotions to examine if they are displayed in funding pitches, to
understand the objects of each, and to provide insight into the nature of entrepreneurs’ changes from
one facial expression to another. With this enriched understanding, we proceed to theorize about the
influence of entrepreneurs’ facial expression of each emotion on funding by building upon research
in evolutionary psychology and emotional expression, including basic emotion theory (Ekman, 1992,
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1999; Keltner et al., 2019), theory surrounding change detection (Rensink, 2002), and the dual
threshold model of anger in organizations (Geddes and Callister, 2007). The dual threshold model of
anger in organizations suggests that the extent to which expression of an emotion results in either a
positive or negative social outcome is influenced by how frequently it is expressed (Geddes and
Callister, 2007; Geddes et al., 2020). We extend this model and propose that facial expression of each
emotion plays a role in increasing funding, but expressing an emotion too infrequently or too
frequently may violate display rules of appropriate expression, hindering funding. We thus expect
that each emotion exhibits an inverted-U relationship with funding. Research further suggests that
changes in facial expression increase observer attention (Eastwood et al., 2001; Frischen et al., 2008)
and influence perceptions of an expression’s authenticity (Cohn and Schmidt, 2003). Accordingly, we
argue that the frequency of changes in entrepreneurs’ facial expressions increases funding. We test
our hypotheses using a sample of crowdfunding pitch videos. The results provide broad support for
our hypotheses apart from sadness, which exhibited a negative relationship with funding.
We offer three main contributions. First, our qualitative analysis provides insight into the
objects of entrepreneurs’ expressions of happiness, anger, fear, and sadness in their funding pitches.
Our quantitative analysis then complements this study and shows that frequently expressing a given
emotion—including happiness—may prove counterproductive beyond a given point—a finding that
runs counter to the long-held assumption that expressing positive emotions is universally beneficial.
Second, we extend the dual threshold model’s principles regarding frequency of expression to
account for three emotions beyond anger, thereby opening a new line of research concerning the
influence of emotion display rules in entrepreneurship. Finally, we suggest a potential boundary
condition for emotional expressiveness in funding pitches. Whereas entrepreneurs benefit from
emotional expressiveness in terms of frequent changes in their facial expressions, emotional
expressiveness is detrimental if an entrepreneur expresses any single emotion too frequently.
1. Introduction
Entrepreneurs’ emotional expressions play an important role in convincing potential funders
to provide financial capital. To that end, research examining entrepreneurs’ emotional expressions
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has focused on how expressions of positive emotions, such as passion or enthusiasm, shape funding
outcomes (e.g., Cardon et al., 2017; Davis et al., 2017; Jiang et al., 2019; Murnieks et al., 2016). For
instance, recent work has found that resource providerssuch as angel investors, crowdfunders, and
informal investors—often respond more favorably to entrepreneurs who exhibit higher levels of
passion (Li et al., 2017; Mitteness et al., 2012; Shane et al., 2020; Warnick et al., 2018). Such studies
have laid the groundwork for understanding the importance of emotional expressions in
entrepreneurial funding by showing that positive emotional expressions often facilitate favorable
evaluations of entrepreneurs and their ventures.
Despite these developments surrounding the importance of emotional expressions in funding
pitches, knowledge remains limited concerning a key mechanism by which emotions are expressed:
facial expressions (Stroe et al., 2020). While emotions may be expressed in a number of ways, facial
expressions have special salience (Bonanno et al., 2002; Keltner et al., 2019), particularly in visual
presentations like funding pitches (Lee and Anderson, 2016). Knowledge about the influence of
entrepreneurs’ facial expressions in a funding pitch remains stunted in several ways. First,
people display a variety of facial expressions in social interaction (Ekman, 1992; Rafaeli and Sutton,
1989; Russell et al., 2003), yet research on funding pitches has focused on expression of positive
emotions, overlooking the potential benefits of expressing emotions traditionally viewed as negative.
This is noteworthy considering that management and social psychology literatures have begun to
acknowledge the persuasive benefits of expressing negative emotions, such as anger (e.g., Geddes
and Callister, 2007; Geddes et al., 2020; Van Kleef et al., 2015). Second, people differ in how
frequently they display facial expressions of a given emotion (Ekman, 1982), but entrepreneurship
scholars have yet to examine differences in how frequently an emotion is expressed during a pitch.
This omission is problematic because emotional expression research suggests that expressing an
emotion either more or less frequently than is socially acceptable for a given situation can cause
others to respond negatively (e.g., Shields, 2005). Finally, people change, sometimes quickly, from
one facial expression to another (Ekman, 1982; Wehrle et al., 2000). Entrepreneurship research has
yet to examine the influence of such changes, perhaps due to a tendency to focus on a single emotion
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per study. To address these limitations and further understanding of the influence of entrepreneurs’
facial expressions of emotion in funding pitches, we ask: “How do entrepreneurs’ facial expression of
emotions in a funding pitch, both positive and negative, influence funding?”
To answer these questions, we leverage computer-aided facial expression analysis to capture
entrepreneurs’ facial expressions in 489 funding pitch videos drawn from the Kickstarter
crowdfunding platform. We ground our work in research from evolutionary psychology and
emotional expression, including basic emotion theory (Ekman, 1992, 1999; Keltner et al., 2019),
theory surrounding change detection (Rensink, 2002), and the dual threshold model of anger in
organizations (Geddes and Callister, 2007; Geddes et al., 2020). Basic emotion theory explains that
there are certain “basic” emotions which are distinct and serve as the fundamental basis of more
complex emotions, and has emphasized the salience of facial expressions in influencing others
(Bonanno et al., 2002; Keltner et al., 2019). Recent work applying basic emotion theory has
employed facial analysis software to demonstrate that facial expressions can be reliably captured and
distinguished as indicative of four basic emotions: happiness, anger, fear, and sadness (Jack et al.,
2014). Accordingly, we focus on entrepreneurs’ facial expressions of these emotions.
To lay the groundwork for our theorizing, we first conduct an exploratory qualitative analysis
of entrepreneurs’ facial expressions of basic emotions in their pitches. Here, we examine
the words entrepreneurs say while expressing each emotion to provide insight into their objects and
the nature of changes from one expression to another. Building upon our qualitative work, we then
conduct a quantitative study by extending the dual threshold model of anger in organizations (Geddes
and Callister, 2007; Geddes et al., 2020) to explain how the frequency of facial expressions of
happiness, anger, fear, and sadness may shape funding outcomes. This model specifies that
expressions of anger in organizations can lead to positive outcomes if they meet an expression
threshold where one’s expressions of anger are visible to others. However, highly frequent
expressions of anger can exceed an impropriety threshold, rendering negative outcomes more likely
(Geddes and Callister, 2007; Geddes et al., 2020). We theorize that each of these basic emotions
exhibits a positive influence on funding provided it is not expressed so frequently that it exceeds an
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impropriety threshold beyond which expression is more likely to be viewed as inappropriate,
hindering funding. Accordingly, the frequency of facial expression of each emotion exhibits an
inverted U-shaped relationship with funding.
We further incorporate theory surrounding change detection to explain that how frequently
entrepreneurs change from one facial expression to another influences funding. This work argues that
people automatically detect changes in others’ facial expressions at a preattentive level (Ro et al.,
2001). Changes in expression increase observer attention (Eastwood et al., 2001; Frischen et al.,
2008). Moreover, the authenticity of emotions is revealed by changes in expressions over time,
whereas a lack of such changes is indicative of inauthenticity (Cohn and Schmidt, 2003). As a result,
we argue that frequency of changes in entrepreneurs’ facial expressions increases funding.
We offer three main contributions. First, we study “the nuances of multiple emotions and their
impact,” as called for by entrepreneurship scholars (Cardon et al., 2012: 6). We offer both qualitative
insight into entrepreneurs’ expressions of four basic emotions in funding pitches and a theory-driven
quantitative analysis concerning the influence of each expression on funding performance; we
thereby build knowledge concerning often overlooked emotions (e.g., anger, sadness, fear). Contrary
to the stereotype of the smiling, enthusiastic entrepreneur pitching their venture, we illustrate
that frequently expressing a single emotion—including happiness—may prove counterproductive
beyond a given point. Second, we extend the dual threshold model’s principles regarding frequency
of expression to account for three emotions beyond its focus on anger. We provide evidence that
principles of the dual threshold model about “too much” or “too little” expression of a given emotion
govern the influence of entrepreneurs’ emotional expressions in funding pitches. In doing so, we
answer calls to extend the scope of the dual threshold model of anger (Geddes and Callister, 2007;
Geddes et al., 2020), resulting in a dual threshold model of emotional expression in funding pitches.
Our contribution enhances the generalizability of the dual threshold model itself and opens a new line
of research concerning display rules in entrepreneurship. Finally, by incorporating insights from basic
emotion theory (e.g., Ekman, 1992; Keltner et al., 2019) and theory surrounding change detection
(Ro et al., 2001), we explain why the frequency of changes in entrepreneurs’ facial expressions of
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emotion promotes funding. Our theorizing and findings suggest potential boundary conditions for
emotional expressiveness in funding pitches, such that expressiveness is more likely to be beneficial
if an entrepreneur expresses a variety of emotions.
2. Facial expressions of emotion and funding pitches
Emotions are discrete affective states that enable people to respond to evolutionarily
significant threats and opportunities (Keltner et al., 2019). While it is well documented that emotions
reflect internal experiences, they also correspond with brief patterns of behavior that serve to
communicate the emotion to others (Keltner and Cordaro, 2017). These patterns are termed emotional
expressions, or expressions of emotion, because they outwardly display an emotion to others (Ekman
and Davidson, 1994; Matsumoto et al., 2008; Russell et al., 2003). Emotional expressions
communicate information to others (Van Kleef, 2009, 2016) from which observers to infer
information about the expresser, such as their assessment of their environment and their behavioral
intentions (Elfenbein, 2007).
Facial expressions are a particularly salient form of emotional expression (Bonanno et al.,
2002; Keltner et al., 2019). Seminal work on facial expressions of emotion began by examining how
individuals label static images of facial muscle configurations as discrete emotions (Ekman et al.,
1969; Matsumoto et al., 2008). This led to the development of basic emotion theory, which has
identified a set of basic emotions that are distinct in their antecedent stimuli, physiological response,
subjective experience, and expression (Ekman 1992; Tracy and Randles, 2011). Initially, Ekman and
colleagues’ (1972) development of basic emotion theory argued that facial expressions can be
reliably distinguished as indicative of six basic emotions. However, recent research has employed
computer-aided facial analysis algorithms, finding that facial expressions are reliably distinguished as
indicative of only four basic emotions: happiness, anger, fear, and sadness (Jack et al., 2014).
Facial expressions of emotion can be directed toward others in an attempt to influence them in
ways that benefit the expressor (Russell et al., 2003). As a result, entrepreneurs’ facial expressions of
emotion have the potential to influence potential funders when displayed in funding pitches. Funding
pitches are relatively complex visual presentations that may feature multiple individuals and an array
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of expressions. Humans are evolutionarily wired to efficiently process such complex visual
presentations (Whitney and Yamanashi Leib, 2018) and, in doing so, quickly evaluate others’
emotional expressions, often without conscious awareness (Ramachandran and Jalal, 2017; Tooby
and Cosmides, 2005, 1990). Because people use facial expressions to make inferences about others, it
follows that potential funders would be attuned to, and influenced by, entrepreneurs’ facial
expressions of emotion within a funding pitch.
Changes among facial expressions of basic emotions are also an influential source of
information for observers since they increase observer attention and provide insight into the
authenticity of expressions (Kovarski et al., 2017; Niedenthal et al., 2001). Changeable states of
facial expression include the basic emotions (happiness, anger, fear, sadness) and the rest state—
neutral. Reflecting this, lab experiments that examine change in emotional expression commonly
include changes from facial expression of one emotion to facial expression of another, and changes to
or from a neutral expression (e.g., Trautmann et al., 2009), such as “neutral‐to‐fear, neutral‐to‐anger,
and fear‐to‐anger,” for example (Thomas et al., 2007: 547). Evolutionary psychology research on
change detection suggests that noticing and reacting to changes involves different attentional
mechanisms depending on the emotional significance of the change (Palermo and Rhodes, 2007).
Changes without emotional significance require substantial attentional resources to be noticed
(Rensink, 2002), whereas those with emotional significance—like emotionally expressive human
faces—are processed quickly and preattentively.1 As such, people are generally well-equipped to
quickly detect changes in others’ facial expressions (Ro et al., 2001), which has been established in
multiple lab experiments (e.g., Kovarski et al., 2017; Niedenthal et al., 2001).
3. Objects of facial expressions of emotion in funding pitches: Qualitative analysis
3.1. Sample
There is no prior literature on facial expressions of anger, fear, or sadness in entrepreneurial
funding pitches (cf. Jiang et al., 2019). Accordingly, we first conducted a qualitative analysis of facial
1 Preattentive processing occurs before attention, independent of consciousness (Treisman, et al., 1992). Preattentive
processing can be distinguished from automatic processing in that while both are independent of attention, preattentive
mechanisms are acquired early in life (or innate).
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expressions of emotion in funding pitch videos to provide a basis for theorizing about how basic
emotions may shape funding outcomes. Our aim was to engage in theory elaboration (e.g., Fisher and
Aguinis, 2017) by exploring the objects of entrepreneurs’ facial expressions of happiness, anger, fear,
and sadness in funding pitch videos. By conducting this analysis, we expect to be better able to
contextualize our subsequent theorizing and hypothesis development.
We examined the increasingly prevalent context of crowdfunding (Pollack et al., 2021;
McKenny et al., 2017; Short et al., 2017). In this form of fundraising, entrepreneurs create campaigns
to solicit financial capital from the general public via an internet-based platform. The centerpiece of
most crowdfunding campaigns is the funding pitch video. Crowdfunding videos often prominently
feature the faces, and thus the facial expressions, of those delivering the pitch. Potential funders
viewing crowdfunding pitches tend to lack experience and expertise in evaluating new ventures,
invest little time in pitch evaluation, and make relatively small contributions to pitches they choose to
fund (Allison et al., 2017; Anglin et al., 2018a). As such, subjective information, such as emotional
expressions, is especially influential in crowdfunding (e.g., Davis et al., 2017; Li et al., 2017).
Our sampling frame includes all Kickstarter campaigns that launched between 2009 and 2016.
We drew an initial random sample of 1,000 campaigns. We screened these campaigns, retaining those
with a pitch video in which an entrepreneur’s face was visible. Thirty-two had no video. Of the 968
remaining, approximately half did not meet one or more parts of our inclusion criteria. For example,
some contained only a video or animation in which a face was not displayed (e.g., a voice-over).
Others were videos which were not a funding pitch (e.g., a movie trailer). Ultimately, a final sample
of 489 funding pitches remained.
3.2. Identifying facial expressions of emotion
Ekman and Friesen (1978) pioneered the measurement of facial movement by developing the
Facial Action Coding System (FACS) to capture and categorize facial expressions. Using 44 action
units, defined as the position of one or more facial muscles, FACS describes visual facial
expressions. Given that combinations of these action units are associated with certain emotions,
FACS enables the researcher to objectively measure facial expressions of emotions (Ekman et al.,
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2002). Figure 1 diagrams these action units. Facial expression analysis has historically relied upon
trained experts to manually code facial action units frame-by-frame based on the FACS framework.
However, this process is subject to bias due to coder fatigue and the affective state of the coder.
Computers, on the other hand, do not tire, aiding reliability (McKenny et al., 2018). As such, we
relied on computer-aided facial expression analysis for our coding.
---------------------------------------
Insert Figure 1 here
---------------------------------------
Computer-aided facial expression analysis has advanced in recent years (Loijens and Krips,
2018), with recent studies employing algorithms such as FaceReader (Jiang et al., 2019) and
Affectiva AFFDEX (Stroe et al., 2020). Our choice was Emotient FACET (iMotions, 2019) for three
key reasons: the software’s close alignment with our theoretical framework—basic emotion theory
(Ekman, 1992; Keltner and Cordaro, 2017; Tracy and Randles, 2011)—evidence of the software’s
strong reliability, and its incorporation of FACS. In addition to its use of facial action units to
measure emotional expressions, Emotient FACET analyzes patterns of wrinkles and crevices that are
created by facial expressions, an approach superior to pure facial-point-based architectures (iMotions,
2019). In a validation study, Emotient FACET measured facial expressions with 96% accuracy,
which is superior to other software (Bernin et al., 2017), including FaceReader (88%) and Affectiva
AFFDEX (68%; Stöckli et al., 2019). Emotient FACET has also been found to be reliable across
gender and race and has been validated against the Extended Cohn-Kanade (CK+) database of facial
expressions (iMotions A/S, 2019; Lucey et al., 2010).
Emotient FACET measures facial expressions frame-by-frame within a video (frames are
typically 1/30th or 1/25th of a second) (Montgomery, 2018). For each frame, the algorithm analyzes
the displayed facial expressions and calculates an evidence score for each emotion (iMotions A/S,
2019). Evidence scores represent the odds of an expression being present. An evidence score of 1.0
indicates that an expression is 10 times more likely than not to be categorized by an expert human
coder as indicative of the focal emotion. Using these values, we classified each frame of video as
displaying one of the four basic emotions, or none of them (i.e., neutral expression). This approach
mirrors prior video analysis work in which researchers capture the occurrence and frequency of target
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behaviors (cf. Waller and Kaplan, 2018). The cutoff for classification is an evidence score of 1
(Huang and Khan, 2016), which represents a minimum probability of the emotion’s presence of 0.91
(probability = 1 / (1 + 10evidence score). This level of confidence is comparable with the minimum
probabilities associated with measurement reliability norms employed in human-coded content
analysis (e.g., 0.80; and Krippendorff, 2007).2 This threshold ensured that few frames (< 0.01%) were
classified as displaying more than one emotion; of those that did, the emotion with the highest
evidence score was used as the emotion present. Before proceeding with our qualitative analysis, we
confirmed that facial expressions of each emotion are displayed in funding pitches. Of the 489
funding pitches in our sample, 487 (99.6%) displayed happiness, 338 (69.1%) displayed anger, 379
(77.5%) displayed fear, and 323 (66.1%) displayed sadness, at least once in the pitch.
3.3. Coding the objects of facial expressions of emotion
We began by calculating the percentage of the video in which each of the four emotions was
present (cf. Stroe et al., 2020) to measure entrepreneurs’ facial expression of happiness, anger, fear,
and sadness over the course of their video-based pitch. To narrow our sample of funding pitches for
our qualitative analysis, we identified four sets of 50 funding pitches that exhibited the highest
frequency of facial expressions of each of the four emotions (200 pitches in total). Facial expressions
of emotion in a pitch are often coincident with speech specific to a topic within the funding pitch
(e.g., customer problem/pain; product description; need for funding). These topics provide insight
into the object(s) of each expression. To understand these objects of entrepreneurs’ facial expressions
of each emotion, two authors analyzed the 50 pitches identified for each emotion (200 total),
transcribing the words spoken concurrent to each facial expression, as well as the words spoken
before and after the expression to aid contextual understanding.
We started with an open coding approach (Corbin and Strauss, 2015) in which the words
spoken concurrent to the facial expressions of each emotion were categorized and labeled as codes
for objects of the entrepreneurs’ facial expressions of that emotion. The authors progressed through
2 We repeated the analyses which follow using evidence scores corresponding to probabilities of 0.80 and 0.95. All results
hold at the 0.80 level. All results also hold at the 0.95 level, except for one predictor for a single dependent variable (fear
was no longer a statistically significant predictor of dichotomous performance (met goal).
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this process independently, meeting after every 10 pitches to compare codes and reach full consensus.
This process was recursive; codes were revised according to updated understanding of the objects of
the entrepreneurs’ facial expressions (Corbin and Strauss, 2015; Suddaby, 2006). Codes were
combined when they accounted for substantially similar concepts and were broken into separate
codes when they comprised multiple, distinct concepts (Corbin and Strauss, 2015; Gephart, 2004).
This culminated in a list of first-order codes for facial expression of each emotion.
The authors then proceeded to group first-order codes into second-order theoretical themes to
develop, relate, and differentiate codes (i.e., axial coding; Corbin and Strauss, 2015). The authors
repeatedly compared emerging themes with transcriptions of the words communicated concurrently
with the facial expressions (Glaser and Strauss, 1967). This iterative process continued until no new
themes were produced. These second-order themes reflect the four aggregate dimensions we sought
to understand: the objects of entrepreneurs’ facial expressions of happiness, anger, fear, and sadness
in their funding pitches. Figure 2 illustrates the data structure that emerged from this qualitative
analysis, consistent with Gioia, Corley, and Hamilton (2013). We now discuss our findings and
highlight example quotations, which represent the themes we identified as objects of entrepreneurs’
facial expressions of each emotion. Table 1 presents additional example quotes of identified themes.
In Table 1 and our reference to specific quotes in the sections that follow, we emphasize the words
coinciding with the facial expression of emotion in bold.
------------------------------------------
Insert Figure 2 and Table 1 here
------------------------------------------
3.3.1. Happiness
Facial expressions of happiness served multiple purposes in highlighting positive aspects of a
funding pitch. Our analysis revealed that entrepreneurs often displayed facial expressions of
happiness when discussing themselves and their team, their passion for the project, the value of their
proposed product/service to funders, when using humor, and when discussing communal topics.
First, entrepreneurs’ facial expressions of happiness were often manifest when introducing
themselves or their team members and when emphasizing pride in their team. For instance, one
entrepreneur who sought funding for a comedy film that she was directing expressed happiness when
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talking about others involved in the project and their skills: “I actually have two editors. One has
been to school for it and he's like a genius, he really is. Both of them being on this project with me,
is amazing. They're both so good at editing.
Second, entrepreneurs expressed happiness when discussing their passion, including the
personal importance of the project and their project-related accomplishments. For example, one
entrepreneur who sought funding to produce her TV show expressed happiness when highlighting her
progress: “I have been doing [the show] for 7 years basically on my own. I've edited the show,
produced the show, and now I'm starting a Kickstarter campaign to produce the show I want to
produce and give you guys a quality show. … [The show] has long been a passion of mine.”
Third, entrepreneurs expressed happiness when highlighting the value of their proposed
product/service to funders, with statements such as: “You can choose the color and the style and we’ll
send it to you so that you’re amongst the first to enjoy the safety and security that the wrist guardian
offers(smart watch for family security).
Fourth, happiness was also expressed when using humor. For instance, entrepreneurs
sometimes made jokes when telling potential funders that they should give money: “Hi! My name is
[name] and I want you to give me money” (spoken in a joking manner; short film about dancing).
Finally, entrepreneurs displayed facial expressions of happiness when emphasizing
communality with funders, including statements associated with appreciation for funders and
invitations to join in: “You can grow with us and you can be part of that voice in order to establish
what we want to do in the future. And that’s to share good stories … So right now, we’re at an
opportune time for people to be a part of us and be part of that voice and that mission(theater).
3.3.2. Anger
We identified three themes when analyzing entrepreneurs’ facial expressions of anger within
their pitch. Facial expressions of anger were manifest when entrepreneurs discussed the seriousness
of the problem they were solving, when highlighting their determination to execute the project, and
when asserting their competence.
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First, entrepreneurs expressed anger when discussing the importance of a problems they were
combatting, such as when emphasizing the importance of a problem being addressed, details
associated with their project, and specifics surrounding their need for funding. For example, an
entrepreneur making a film about child brides in Yemen expressed anger when discussing the
problem confronted by her project and the details of getting the film off the ground: “It's about a
woman who was given away by her family at 12 years old and she was married off to a man 22
years older. She was traded for a wad of cash and a goat on a rope. That was the value of her life. I
want to tell this story. We have to pay for locations, insurance, craft services, costumes, makeup,
food. Literally every dollar counts.”
Second, entrepreneurs displayed facial expressions of anger when discussing their
determination, including the personal importance of their project, their dedication, and obstacles they
had overcome. For instance, an entrepreneur who sought funding for a video game displayed facial
expressions of anger when discussing his team’s determination to see the project through: “I would
like to introduce the people who have spent the past 2.5 years researching and developing this
video game. Some have even moved across the nation to make this game happen.”
Finally, entrepreneurs displayed facial expressions of anger when asserting competence, as
illustrated by an entrepreneur pitching an art exhibit inspired by nature: “I entered the United States
Navy, serving my country in the field of illustration drafting. My combination of business and art
talents have given me the adventures of participating in art exhibits throughout this great country.”
3.3.3. Fear
Our analyses also uncovered three themes of the objects of entrepreneurs’ facial expressions
of fear. Entrepreneurs displayed facial expressions of fear when discussing their need for resources,
when highlighting issues that have hindered their progress (or might in the future), and when
referencing concepts related to their vision for the project.
First, entrepreneurs displayed facial expressions of fear when emphasizing their need for
resources and their concerns regarding what would happen if they do not raise adequate funding.
Reflecting this, one entrepreneur expressed fear when listing numerous expenses associated with
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successfully executing his team’s project, an art exhibition and accompanying catalog: “We need
your help ... The show itself is only one aspect of the overall project. Your donations will help cover
our rental fee, postage for all of the catalogs, and our printing costs.
Second, entrepreneurs displayed facial expressions of fear when noting hindrances to their
progress, including past, current, or anticipated future difficulties. One entrepreneur expressed fear
when explaining complexity in running a summer theater program due to its remote location in the
mountains of Greece: “The whole program is a remarkable achievement that has taken many years
and input from so many people to create, both in its crazy logistics—believe me it isn't easy
managing 35 theater artists in the mountains of Greece with no cell phones and very little email
access—but also in its creative and artistic design.”
Finally, entrepreneurs displayed facial expressions of fear when discussing their vision,
suggesting uncertainty surrounding long-term aspirations as a “dream,” or that realizing these
aspirations would be a “miracle.” A U.S.-based Scandinavian folk music group, for example,
explained: “It has long been a dream of ours to, at some point, travel to Scandinavia and meet up
with some of the master musicians.”
3.3.4. Sadness
We uncovered three themes of the objects of entrepreneurs’ facial expressions of sadness in
funding pitches. These expressions were displayed while communicating concerns, relating a
disappointment, or when engaging in supplication by asking for help in a sincere manner.
First, entrepreneurs displayed facial expressions of sadness when communicating concern,
including concerns related to people affected by a problem they are seeking to address, their personal
experience with the problem, or concern regarding a current, past, or anticipated future situation. For
example, an entrepreneur displayed facial expressions of sadness during his pitch for an online
substance abuse recovery program when discussing his concern for the difficulties that drug addicts
experience: “I want to dedicate this fight to anyone who’s struggling today, I love you all.
Second, entrepreneurs displayed facial expressions of sadness when discussing past failures or
anticipated disappointment if they are not able to pursue their project. Reflecting this, one filmmaker
16
expressed sadness when discussing how disappointed he would be if unable to make the film he was
pitching: “I need to make this movie. It's time. It's something I've wanted to do my entire life, and
if I don't do it now, I don't know when I will have the chance again. I wish I wasn't here having to
ask you for money. But we need this money to make this movie, if we don't get this $7,000, it's not
going to happen; we are going to have to shelve the project.
Finally, we found that entrepreneurs displayed facial expressions of sadness as supplication,
sincerely asking for help and communicating appreciation to potential funders. One entrepreneur, for
example, explained her team’s desire to produce a television series showcasing the talent of emerging
musicians to celebrate her region’s musical heritage, displaying facial expressions of sadness when
posing a call for financial support: “At this point, we are looking for your help. Simply put, we need
assistance to complete the program, and that's where you as an individual or your organization
can help to make this show a reality and promote our natural talent.
3.3.5. Changes in facial expression of emotion
Having ascertained that entrepreneurs express happiness, anger, fear, and sadness in their
pitches, and having identified the objects of these expressions, we proceeded to investigate the nature
of changes in entrepreneurs’ facial expressions of emotion. Examples of changes in facial expressions
include a change from facial expression of any of the four emotions to a neutral expression or vice
versa, and any of the four emotions to any of the three other emotions. We focused this aspect of our
qualitative analysis on the 25 pitches with the most changes in facial expressions of emotion (i.e.,
most frequent changes) and the 25 pitches with the fewest changes (i.e., most infrequent changes).
The same two authors examined these additional 50 pitches.
Pitches with frequent changes in facial expressions appeared to be subjectively more
engaging. Entrepreneurs in these pitches used changes in their facial expressions of emotion as
punctuation to emphasize key points. For example, when pitching for resources to tour her debut
album, one entrepreneur in our sample switched among different facial expressions of emotion. She
began by expressing happiness, saying: “I’m really excited to share my songs with you,” before
moving to a facial expression of fear when highlighting uncertainty regarding the potential for
17
success: “I am shooting for the moon on this one.” Later, she changed back to a facial expression of
happiness when talking about her bandmates, stating: “I just can’t wait for you to hear [my
drummer], he is just so, so good,” before expressing sadness, followed by fear when saying: “I
decided to give Kickstarter a try, because on a preschool teacher’s salary I do have time to record
in summer, but I’m not getting paid. It’s just something that’s not in my reach financially.” Finally,
she returned to a facial expression of happiness when concluding her pitch, discussing rewards for
funders: “So please check out the list of rewards available on the side!
Another entrepreneur similarly displayed frequent changes in her facial expressions of
emotion when pitching a comic book that she wrote, frequently changing to and from facial
expressions of happiness. She began with a facial expression of happiness when introducing herself:
“Hi, my name is [name]! I am a cartoonist and illustrator.” After explaining that her previous comic
books have been relatively short, she displayed a facial expression of anger when asserting her
determination to improve, stating, “This year, I decided to set my sights higher.” She again
displayed a facial expression of happiness when discussing her progress on the new comic and how it
reflects her passion: For the last 6 months, I’ve been writing an illustrating a 36-page comic …
and it’s finally ready to see the light of day … [This comic] is a story about having the guts to do
what you love.” She continued expressing happiness when discussing its inspiration, saying, “The
story is an homage to one of my mentors,” then changing to a facial expression of sadness when
discussing his untimely death: “He passed away last year due complication related to leukemia. His
influence on my comics has been enormous and this work is a testament to that influence.” She
returned to a facial expression of happiness when describing her progress, “The process of making
this comic has been extremely hands-on,” followed by a facial expression of sadness when
discussing her financial constraints, “Unfortunately, printing comics of this caliber costs more than
I can afford. Afterward, she changed back to a facial expression of happiness when inviting people
to contribute: “Which is where you come in,” continuing to express happiness when explaining
rewards offered to those who contribute. Finally, she briefly displayed fear when talking about the
project as a dream: “There’s a whole bunch of incentives to help (facial expression of happiness)
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make this dream a reality (facial expression of fear),” concluding with a facial expression of
happiness, stating, “I can’t wait to share this story with you. It’s going to be awesome.
In contrast, the pitches containing infrequent changes in facial expressions of emotions lacked
such emphasis and appeared less engaging. Some entrepreneurs displayed facial expressions of
happiness quite consistently, but did not express any other emotions and/or neutral facial expressions.
Alternatively, some entrepreneurs were quite neutral in their facial expressions throughout the pitch,
even when discussing emotionally laden content. One entrepreneur, for example, closed his pitch
with a neutral facial expression despite the positivity of his words: “I hope this has inspired you to
donate.” Similarly, another entrepreneur in our sample displayed a neutral facial expression
throughout much of his pitch for a book about his research on dementia. This neutral expression
remained even while he employed emotionally laden language, saying: “We might be on the brink of
something really big here. We might be able to change the lives of millions of people and perhaps at
least delay the onset of this terrible occurrence we call dementia.”
4. A dual threshold model of the frequency of facial expressions of emotion in funding pitches
Our qualitative analysis reveals that all four basic emotions are expressed in funding pitches.
Moreover, our axial coding revealed common themes of the objects of entrepreneurs’ facial
expressions of these emotions within funding pitches. These suggest that facial expression of these
emotions might prove beneficial in promoting funding. Aided by this contextualized understanding of
entrepreneurs’ facial expressions of happiness, anger, fear, and sadness, we proceed to theorize about
the influence of each on funding. Specifically, we build upon research which suggests that the
influence of emotional expressions on others depends on how frequently emotions are expressed.
Emotional expression research holds that how an expression is received by others is shaped
by display rules—social norms governing appropriate expression (Ekman, 1993). These influence the
extent to which an expression engenders positive or negative effects in social interaction. The dual
threshold model of anger suggests that display rules define (1) an expression threshold beyond which
an emotion is expressed to receivers in an acceptable manner, and (2) an impropriety threshold,
which defines the point at which the expression of an emotion violates social norms of
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appropriateness (Geddes and Callister, 2007). The model predicts that expressions of anger are more
likely to engender positive individual and organizational outcomes—such as resolving conflict and
promoting positive organizational change—when they cross the expression threshold such that they
are perceptible to observers (Geddes et al., 2020). The model further predicts that expressing anger so
frequently that it crosses the impropriety threshold increases the likelihood of negative outcomes
(Geddes and Callister, 2007). Beyond the impropriety threshold, the expression violates display rules
of acceptable behavior. Expressing an emotion more frequently than that deemed acceptable by a
situation’s social norms may lead one’s expressions to be interpreted by receivers as inappropriate,
undermining otherwise positive outcomes in social interactions. Shields (2005) highlights this point
in her work on social norms surrounding emotional expressions: “The ‘too emotional’ response is
read as the response of someone who is ‘merely’ emotional,” thereby detracting from the message at
hand; the ‘not emotional enough response’ is read as the response of someone who is not genuinely
engaged with others” (p. 12). These relationships are amplified for more extreme violations, where
the expressor may be perceived as emotionally disturbed (Thoits, 2003).
Although the dual threshold model of anger is built around expressions of anger within
organizations, it has been suggested that the model may also apply to expressions of happiness, fear,
and sadness (Geddes and Callister, 2007). For example, research consistent with the dual threshold
model in a customer service context suggests that highly frequent expressions of happiness or sadness
are more likely to be rated as inappropriate and inauthentic, reducing trust and satisfaction (Cheshin
et al., 2020). We view this as suggestive of the applicability of the dual threshold model to emotional
expressions in contexts outside the organization. Focusing on the frequency of emotional expressions
during a funding pitch, we build upon the dual threshold model of anger in organizations to extend
the dual threshold model to expressions of happiness, anger, fear, and sadness in funding pitches. We
begin our theoretical development regarding facial expression of each emotion with the expectation
of inverted U-shaped effects of expressions of an emotion, as this is the nonlinear relationship
implied by the model’s general prediction that expressions of an emotion have positive effects until
they exceed an impropriety threshold, beyond which negative effects become increasingly likely.
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Recognizing that the reasons for these positive and negative effects likely differ by emotion, we
develop explanations regarding the frequency of facial expression of happiness, anger, fear, and
sadness in funding pitches as separate hypotheses, which follow.
4.1. Happiness
Emotions scholars have conceptualized happiness as the primary, if not sole, positive basic
emotion (e.g., Ekman, 1992). Happiness is often experienced and expressed when making reasonable
progress toward the realization of one’s goals (Lazarus, 1991). Consistent with this notion, our
qualitative analysis found that entrepreneurs in our sample expressed happiness when highlighting
their progress as well as factors that enabled their progress, such as their team’s ability, their passion,
or funders’ support.
A significant body of work provides evidence of the benefits of experiencing and expressing
positive emotions and moods such as happiness, including successful outcomes in work and life, as
well as more favorable assessments of the expresser (Barsade and Gibson, 2007; Lyubomirsky et al.,
2005). Entrepreneurship scholars have found that entrepreneurs’ expressions of positive emotions
such as enthusiasm and excitement promote funding, often construing such expressions as indicative
of passion (e.g., Cardon et al., 2017; Davis et al., 2017; Li et al., 2017; Murnieks et al., 2016). In a
similar vein, employees who frequently express positive emotions are evaluated more favorably by
their managers, are paid more, and receive more social support from their coworkers and supervisors
(Staw et al., 1994; Wright and Staw, 1999). People who are express happiness are judged by others as
more competent and assertive, suggesting an ability for efficacious action (Lyubomirsky et al., 2005).
Research on funding pitches provides evidence that such qualities are important to funders, finding
that displays of both confidence and a propensity to act are positively related to funding (e.g., Anglin
et al., 2018a). As such, expressions of happiness are consistent with funders’ expectations that
entrepreneurs display confidence and propensity to act, particularly given that the necessity to act in
the face of the uncertainty is inherent to the early stages of entrepreneurship (McMullen and
Shepherd, 2006). Entrepreneurship scholars have further lauded the benefits of entrepreneurs’
experience of positive affect—which is displayed to others via expressions of happiness—including
21
its role in facilitating opportunity recognition, responding to dynamic environments, expanding one’s
skills and social networks, and persuasion (Baron, 2008). Taken together, this suggests that
entrepreneurs’ facial expression of happiness promotes funding.
However, expressing happiness too frequently in a funding pitch could be detrimental.
Despite its general positive influence, scholars have found that spending too much time expressing
happiness at a peak level is negatively related to funding (Jiang et al., 2019). Moreover,
entrepreneurship scholars have found that, beyond a given point, entrepreneurs’ dispositional positive
affect—which reflects their frequent experience of positive emotions such as happiness—is
associated with declines in firm performance (Baron et al., 2011). Baron and colleagues (2011)
explain these findings by noting that experiencing very high positive affect may lead entrepreneurs to
make unrealistically favorable estimates of their success, perhaps reducing motivation to continue to
innovate and reducing their attentiveness to negative information. In that people who express
happiness more frequently are perceived to be happier, entrepreneurs’ frequent facial expression of
happiness in a pitch may communicate such an overly positive outlook to others, engendering
negative inferences about them. Indeed, people who express that they are very happy are perceived
by others as naïve (i.e., lower competence) and as more likely to avoid or ignore negative information
(Barasch et al., 2016). Such qualities conflict with funders’ expectations that entrepreneurs
demonstrate competence (e.g., through their experience) and openness to feedback (Warnick et al.,
2018). Considering the positive influence of facial expression of happiness together with the potential
negative influence of highly frequent facial expression of happiness, we hypothesize:
Hypothesis 1: The frequency of entrepreneurs’ facial expression of happiness in a funding
pitch has an inverted U-shaped relationship with funding.
4.2. Anger
Anger is experienced and expressed when goal pursuit has been blocked (Carver and Harmon-
Jones, 2009), motivating action to remove problematic elements of a situation (Lerner and Tiedens,
2006; Parkinson, 2017) or to address an appraised wrong (Geddes et al., 2020). The experience of
anger is associated with optimistic risk estimates and risk-prone behavior (Fessler et al., 2004; Lerner
and Kelter, 2000, 2001), including that of entrepreneurs in their opportunity evaluation and
22
exploitation (e.g., Foo, 2011; Welpe et al., 2012). Consistent with this, our qualitative analysis
revealed that entrepreneurs express anger when discussing the seriousness of the problem being
addressed as well as factors related to overcoming a problem, such as their determination to succeed
and their competence. Entrepreneurs’ facial expression of anger may thus encourage funders to
provide financial capital. Indeed, entrepreneurship is inherently based in identification, evaluation,
and exploitation of opportunities (Shane and Venkataraman, 2000), which scholars have also
described in terms of problem solving (e.g., Hsieh et al., 2007). Moreover, facial expression of anger
may lead to favorable perceptions regarding ambition (Van Kleef et al., 2010), as well as perceptions
of competence, determination, toughness, power, and status (Hareli and Hess, 2010; Harmon-Jones et
al., 2011; Tiedens, 2001; Veling et al., 2012). Such traits are valued by potential funders (e.g., Alsos
and Ljunggren, 2017).
Despite the potential benefits of expressing anger in social interaction, highly frequent
expression of anger renders negative outcomes more likely (Geddes and Callister, 2007; Geddes et
al., 2020). Expressing anger too frequently relative to the display rules for the situation crosses the
impropriety threshold, leading to negative inferences about the expresser (Geddes and Callister,
2007). For instance, highly frequent expression of anger within negotiation setting tends to be
perceived as inappropriate by the other party, reducing cooperation and damaging the relationship
(Côté, 2005; Van Kleef et al., 2004a, 2004b; Van Kleef and De Dreu, 2012). Similarly, employees
who express anger more frequently in the workplace than is acceptable may perceived as “volatile,
out of control, aggressive, or unprofessional” (Geddes and Callister, 2007: 733), overconfident,
reckless, or irrational (cf. Frijda, 1986; Geddes and Callister, 2007). Such qualities are theorized to
lead to poor entrepreneurial performance (Hmieleski and Baron, 2008), with research suggesting they
reduce funding (Forbes, 2005; Warnick et al., 2018). Thus, taking the positive influence of facial
expression of anger together with the negative influence of highly frequent facial expressions of
anger, we hypothesize:
Hypothesis 2: The frequency of entrepreneurs’ facial expression of anger in a funding pitch
has an inverted U-shaped relationship with funding.
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4.3. Fear
Fear is often experienced and expressed when encountering a stimulus that is perceived as
unpredictable and uncontrollable (Foo, 2011; Lerner and Keltner, 2000, 2001; Ortony and Turner,
1990; Smith and Ellsworth, 1985). The experience of fear reflects a threat appraisal (Cacciotti and
Hayton, 2015), with its expression signaling to others that a threat is present in the environment
(Marsh et al., 2005). For this reason, people are highly attuned to others’ facial expressions of fear,
increasing their attention when such cues are displayed (LaBar, 2016; Öhman, 2002). As a result, the
expression of fear can lead others to form positive attitudes regarding proposed behaviors that are
meant to remedy the threat at hand (cf. Das et al., 2003; de Hoog et al., 2005; Nabi, 2002),
particularly when it is perceived that specific threat-reducing actions can be taken (DeSteno et al.,
2004). Entrepreneurship is inherently uncertain (McMullen and Shepherd, 2006), entailing threats
such as those associated with financial constraints. This suggests that some acknowledgement of this
uncertainty and potential threats via one’s facial expressions of fear may prove beneficial in a funding
pitch. Along these lines, our qualitative analysis shows that entrepreneurs sometimes display facial
expressions of fear when discussing their vision, perhaps indicating some degree of uncertainty
related to the achievement of such long-term goals without funders’ help.
Expressing fear can also encourage receivers to join with others and work to avoid or
overcome a common threat (cf. Griskevicius et al., 2006, 2009). Crowdfunding is an example of this
in that it requires collective funder support (e.g., Kuppuswamy and Bayus, 2017; Lin et al., 2014).
Here, communicating the presence of a threat to be feared is a technique used to marshal resources
for entrepreneurial ventures (e.g., Ruebottom, 2013). For instance, our qualitative analysis found that
entrepreneurs expressed fear when discussing issues that have hindered their progress in the past or
might in the future and when discussing their need for resources. Facial expression of some fear may
prove valuable in funding pitches as a means by which entrepreneurs convey the salience of a
problem and awareness of potential obstacles, thereby promoting funding.
Despite the potential benefits of displaying facial expressions of fear in a funding pitch,
highly frequent facial expression of fear may hinder funding. Indeed, beyond the impropriety
24
threshold, expressions of fear communicate a high degree of anxiety, uncertainty, or uncontrollability
of the situation, making disengagement with the expressor’s message more likely (cf. Henthorne et
al., 1993). Consistent with this notion, scholars have found that persuasion is most effective when a
speaker highlights not only a threat but also a solution to reduce the threat (Dillard et al., 2017). This
pattern suggests that, beyond a given point, entrepreneurs’ expression of fear in a funding pitch is
counterproductive, which is consistent with research demonstrating that entrepreneurs’ experience of
fear increases risk estimates in opportunity evaluation (Foo, 2011) and discourages intentions to
pursue an entrepreneurial opportunity (Welpe et al., 2012). Reflecting this, entrepreneurs’ highly
frequent expression of fear may convey such uncertainty and uncontrollability that it raises doubts
regarding their ability to proactively address such threats upon receipt of funding. Taking the positive
influence of facial expression of fear together with the negative influence of highly frequent facial
expression of fear, we hypothesize:
Hypothesis 3: The frequency of entrepreneurs’ facial expression of fear in a funding pitch has
an inverted U-shaped relationship with funding.
4.4. Sadness
Sadness reflects an appraisal of loss or prolonged adverse circumstances (Lazarus, 1991;
Frijda, 2007) and can indicate that a situation is problematic (DeSteno et al., 2004; Schwarz et al.,
1991; Tiedens and Linton, 2001). For instance, our qualitative analysis found that entrepreneurs
display facial expressions of sadness when communicating concern related to the problem they seek
to address with their venture. Further, sadness is also often expressed when soliciting help to achieve
a goal that is meant to prevent such a loss (Hackenbracht and Tamir, 2010). Within entrepreneurship
in particular, sadness is often associated with loss in terms of venture failure (e.g., Jenkins et al.,
2014; Mantere et al., 2013; Patzelt and Shepherd, 2011). Such was the case with the entrepreneurs in
our sample, who expressed sadness when discussing anticipated disappointments if they are unable to
proceed or when engaging in supplication by asking funders for help in a sincere manner. Expressing
sadness can also increase the extent to which one is perceived as warm, nice, and likeable (Keltner
and Buswell, 1997; Keltner et al., 1997), and in the face of adverse circumstances, may engender
perceptions of the expresser’s sincerity (e.g., Stephens et al., 2019). Expressing sadness in a
25
collaborative setting may also lead receivers to feel concern for the expressor (Sinaceur et al., 2015).
In this way, expression of sadness can encourage prosocial behaviors (Lazarus, 1991) and increase
persuasion (DeSteno et al., 2004; Dillard and Peck, 2000; Nabi, 2002). As a result, entrepreneurs’
facial expression of sadness, when displayed with appropriate frequency, may promote funding.
However, Entrepreneurs who display facial expressions of sadness in their funding pitches too
frequently may be perceived as lacking necessary motivation and/or competence. Indeed, funders
value tenacity (Murnieks et al., 2016). In contrast, sadness is a relatively passive emotion that, when
expressed, can engender perceptions of weakness, submissiveness, and incompetence (Tiedens,
2001). As such, entrepreneurs’ expression of sadness may raise concerns given that the
entrepreneurial process requires entrepreneurs to persevere through challenges and remain motivated
in pursuit of their goals to promote venture success (Cardon and Kirk, 2015; Mueller et al., 2017).
The negative perceptions associated with highly frequent expression of sadness likely hinder funding
given that highly frequent expressions of sadness may lead potential funders to perceive that
entrepreneurs are helpless and lack other valued attributes (e.g., tenacity, competence). Taking the
positive influence of facial expression of sadness, up to a certain frequency of expression, alongside
the negative influence of highly frequent facial expression of sadness, we hypothesize:
Hypothesis 4: The frequency of entrepreneurs’ facial expression of sadness in a funding pitch
has an inverted U-shaped relationship with funding.
4.5. Frequency of change in expressions of emotion
When pitching their business or product, entrepreneurs face the challenge of holding the
attention of potential funders to maintain their interest in the venture and, in turn, obtain needed
funding (Anglin et al., 2018a; Shane et al., 2020). Potential funders’ attention can wander in any
pitch; this may be particularly true in crowdfunding, where funders can end the pitch with the click of
a mouse, denying entrepreneurs the opportunity to regain funders’ attention, interest, and money.
Here, changes in facial expression of emotion may prove useful in promoting funding, given their
ability to increase observer attention (Eastwood et al., 2001; Frischen et al., 2008) and cognitive
engagement (Krumhuber and Kappas, 2005; Trautmann et al., 2009; Schmidt and Cohn, 2001).
26
In addition, the authenticity of emotions is revealed by the extent to which expressions change
over time, such that people who change their expression infrequently are more likely to be perceived
as inauthentic (Cohn and Schmidt, 2003). Extant work holds that funders value entrepreneurs’
authenticity (Cardon et al., 2017). In consequence, pitches delivered with frequent changes in facial
expressions are more likely to increase potential funders’ attention and to reflect authentic
expressions, thus promoting more favorable impressions of entrepreneurs and their ventures.
Consistent with this, the pitches analyzed in our qualitative analysis with very frequent changes in
facial expression appeared more engaging and dynamic, whereas those delivered with the fewest
changes in facial expression appeared stiff, forced, or even disengaged. We thus expect that funding
pitches in which entrepreneurs frequently change their facial expressions of emotion from one
expression to another wield greater persuasive influence on potential funders compared to those
delivered with relatively infrequent changes in facial expression.
Hypothesis 5: The frequency of change in entrepreneurs’ facial expressions of emotion in a
funding pitch—from one facial expression to another—is positively related to funding.
4.6. Measures
Using the Emotient FACET software and procedures described in section 3.2, we measured
the frequency of facial expressions of each emotion (H1–H4) (i.e., the percentage of the video in
which each of the four emotions) and the frequency of change in facial expressions of emotion (H5),
both at the pitch level. We chose to measure at the pitch level for a few key reasons. Like traditional
funding pitches, video-based funding pitches often feature more than one speaker. However, unlike
traditional funding pitches, where speakers often self-identify their roles in the venture, making it
possible to identify a leader, this is not common in crowdfunding. Examining the videos in our
sample, it was often unclear what roles each speaker played. We thus opted to focus on the pitch as a
whole rather than any specific speaker. This level of aggregation is appropriate as research on
ensemble perception demonstrates that when presented with multiple different faces, images, or
angles of the same faceas is the case in a video-based funding pitch—people extract summary
information, such as the average of each facial expression of emotion in a pitch, rather than being
influenced by an individual face or frame in particular (cf. Neumann et al., 2013; Whitney and
27
Yamanashi Leib, 2018). This suggests that funders process entrepreneurs’ facial expressions at the
pitch level, rather than forming a separate perception of each visible face. In addition, most other
crowdfunding studies focus on pitch-level influences and outcomes (e.g., Anglin et al., 2018a; 2018b;
Chan et al., 2020; Davis et al., 2017; Oo et al., 2020). Our approach also mirrors the broader financial
resource acquisition literature, which is primarily conducted at the pitch level (e.g., Balachandra et
al., 2019; Shane et al., 2020). Given these advantages, we focus on the pitch as our level of analysis
and account for any speaker effects through a set of control variables explained below.
Our five independent variables each exhibited skewness with a right tail. Accordingly, to
restrict the influence of these extreme observations on the normality of our measures, we used the
natural log of each of the five measures.
4.6.1. Funding performance measures
Crowdfunding outcomes are multidimensional (Ahlers et al., 2015; Anglin et al., 2018b).
Therefore, we operationalize funding with three different measures (e.g., Anglin et al., 2018b). Our
first measure captures the total amount of funds raised for the campaign (e.g., Li et al., 2017). This
measure is important because there is no limit to the amount of funds that can be raised, paralleling
the broader entrepreneurial finance literature (e.g., Kanze et al., 2018). Second, we measure the total
number of funders that contributed to the campaign. Indeed, the crux of crowdfunding depends on
gaining many funders to support a project. The number of funders provides insight into the potential
market for a campaign’s offering (Anglin et al., 2018b). Finally, we measured whether a campaign
met its target funding goal via a dichotomous measure (e.g., Oo et al., 2019), since entrepreneurs on
Kickstarter do not receive any funds raised unless the funding goal is met.
4.6.2. Control variables
We looked to extant research in areas such as emotional expression, funding pitches,
entrepreneurial finance, and crowdfunding research for potential alternative explanations. Three
sources were used in the development of control variables for the study: readily available platform-
based information, information gathered from the pitch itself by trained human coders, and
information gathered from the pitch by computer-based analysis.
28
Looking first to platform-based data, we began by controlling for platform category (15
product categories) using random effects within category; we also controlled for the tangibility of the
product being pitched (1 = tangible; 0 = not tangible; see Allison et al., 2017) and the word count of
the written project description (Anglin et al., 2018). Following extant research, we further controlled
for fundraising duration (days) and the campaign’s funding goal in U.S. dollars (USD). Some
platform categories are more popular and more competitive and this changes over time. Thus, we
controlled for the total funding (USD) in each category during the year of each campaign.
We then developed a series of controls from information gathered by a team of three trained
coders to account for visible characteristics of the entrepreneur(s) who delivered the pitch. First
impressions often matter more, so we controlled for characteristics of the first speaker (Lesko and
Schneider, 1978; Scharioth, 2012). Specifically, we controlled for race (1 if non-white; 0 otherwise),
gender (1 if woman; 0 otherwise), age (1 = under 18; 2 = 18 to 24; 3 = 25 to 44; 4 = 45 to 64; 5 = 65
or older), and attractiveness (1 = very unattractive to 5 = very attractive; see Baron et al., 2006; Rule
and Ambady, 2008). In some videos there was more than one entrepreneur visible (32.7% of our
sample). As such, we also included controls for the percentage of women on the team and the
percentage of non-white entrepreneurs on the team. We measured these percentages in decimal form
between 0 and 1, and take the natural log of each to alleviate skewness. We also included controls for
average attractiveness and average age of the team members. Finally, we accounted for whether the
pitch was made by an individual or team (0 = individual, 1 = team; Calic and Mosakowski, 2016).
We followed established practice for funding pitch research by developing specific
procedures and conducting training prior to the start of coding (Pollack et al., 2012; Scheaf et al.,
2018). Each coder independently coded the first 50 pitches to establish initial interrater reliability.
Krippendorff’s alpha for speaker age was 0.85 prior to reconciliation.3 Cohen’s kappa was strong as
well (k = 0.83). Krippendorff’s alpha for speaker attractiveness was 0.83 prior to reconciliation (k =
0.77). After completing initial coding, the coders met to discuss and reconcile all discrepancies,
achieving full agreement. The remaining funding pitches were evenly split among the three coders.
3 Krippendorff (2004) suggests an alpha value of at least 0.67.
29
Finally, we developed a series of controls through information derived from our computer-
based analyses. To account for the entrepreneurs’ facial orientation within the video-based pitch, we
measured facial yaw (left versus right facial position), roll (tilt of the head), and proximity (face size
in the video), since such nonverbal factors play a role in interpersonal communication (Bonaccio et
al., 2016; Burgoon et al., 2016). Each was measured as average value across all the frames of the
pitch in which a face was present. Last, we controlled for video length, or more specifically, video
length in which a face is present. We include this control because more than pure video length, the
amount of time a face is present in a video may be influential. We measure this as the number of
frames of video where a face was present.
4.7. Estimation procedures
We use multilevel modeling to test our hypotheses. Past work has noted that individual
crowdfunding campaigns may not represent independent observations. Specifically, campaigns are
likely not independent of their given project category in which the campaign was launched (Anglin et
al., 2018b; Devaraj and Patel, 2016). Crowdfunding scholars have employed multilevel modeling to
account for the lack of independence due to individual campaigns being nested within their project
category (e.g., Anglin et al., 2018b; Devaraj and Patel, 2016). As such, our controls and independent
variables make up level 1 of our models and categories make up level 2 of our models.
Our funds raised and number of funders variables are right-skewed, with long right tails,
suggesting a gamma distribution. Such distributions often result in non-normal residuals, violating a
key assumption of linear models. Past work has addressed this issue by using generalized linear
models (GLMs) (e.g., Anglin et al., 2018a) because such approaches enable researchers to account
for non-normality concerns (McCullagh, 1984). To model funds raised and number of funders, we
employ GLMs that utilize the gamma distribution with a log-link function. This approach is robust to
heteroscedastic errors common to non-negative data with long right tails (Ng and Cribbie, 2017).
We used multilevel logistic regression for our dichotomous dependent variable—meeting the
fundraising goal (cf. Oo et al., 2019)—as is common when modeling relationships between a
dichotomous dependent variable and a set of explanatory variables. All models were fit with
30
heteroscedasticity-consistent standard errors, which are unbiased and consistent in the presence of
heteroscedasticity, to guard against model misspecification.
4.8. Results
---------------------------------------------------------------------------
Insert Tables 2 through 5 about here
---------------------------------------------------------------------------
Table 2 provides the means, standard deviations, and correlations for our variables. Tables 3,
4, and 5 report the results for the total funds raised, number of funders, and the likelihood of meeting
the funding goal variables, respectively. Hypothesis 1 proposed a curvilinear, inverted U-shaped
relationship between the frequency of facial expression of happiness and funding. Each of the
quadratic terms indicate a curvilinear effect of the frequency of happiness on funding (funds raised,
happiness squared = -12.24, p = .013; funders, happiness squared = -7.45, p = .000; met goal,
happiness squared = -13.21, p = .000). Figures 3A through 3C plot the relationship between the
frequency of expressed happiness and each dependent variable. Each figure suggests an inverted U-
shaped relationship. Ceteris paribus, the relationship turns negative when happiness was expressed
for more than 36% of the pitch (ln of happiness = 0.31) for funds raised and number of funders, and
32% of the pitch (ln of happiness = 0.28) for met goal. Thus, we find support for Hypothesis 1.
---------------------------------------------------------------------------
Insert Figures 3A through 5C about here
---------------------------------------------------------------------------
Hypothesis 2 proposed a curvilinear, inverted U-shaped relationship between the frequency of
facial expressions of anger and funding. The quadratic terms in the funds raised model (anger squared
= -10.02, p = .026) and number of funders model (anger squared = -7.29, p = .047) are consistent
with the hypothesis. Figures 4A and 4B plot these relationships, indicating an inverted U-shaped
relationship. Ceteris paribus, the relationship turns negative when anger was expressed for
approximately more than 30% of the pitch (ln of anger = 0.26) for funds raised and 34% of the pitch
(ln of anger = 0.29) for number of funders. Thus, we find support for Hypothesis 2 on two of our
performance variables.
Hypothesis 3 proposed a curvilinear, inverted U-shaped relationship between the frequency of
facial expressions of fear and funding. Each of the quadratic terms indicate a curvilinear effect of fear
31
on funding (funds raised, fear squared = -5.59, p = .000; funders, fear squared = -5.01, p = .000; met
goal, fear squared = -13.57, p = .000). Figures 5A–5C plot these relationships. Each figure suggests
an inverted U-shaped relationship. Ceteris paribus, the relationship turns negative when fear was
expressed for approximately more than 52% of the pitch (ln of fear = 0.42) for both funds raised and
number of funders and approximately 20% of the pitch (ln of fear = 0.18) for met goal. Thus, we find
support for Hypothesis 3.
Hypothesis 4 proposed a curvilinear, inverted U-shaped relationship between the frequency of
facial expressions of sadness and funding. None of the quadratic terms for sadness were statistically
significant. Instead, we found a negative direct relationship between frequency of expressed sadness
and funding performance (p < .01 for all three funding dependent variables), but not the hypothesized
inverted-U relationship. Thus, we fail to find support for Hypothesis 4.
Hypothesis 5 proposed that the frequency of changes in facial expressions from one emotion
to another is positively related to funding. All three of our models provide evidence for a positive
relationship between frequency of change in facial expression and funding (funds raised, b = 0.16, p
= .000; number of funders, b = 0.06, p = .001; met goal, b = 0.12, p = .010). Thus, we find support for
Hypothesis 5.
Summarizing our results, we find a positive relationship for frequency of change in facial
expression on emotion on funding performance as well as an inverted U-shaped relationship for the
influence of frequency of happiness, anger, and fear on funding performance.
5. Discussion
Emotions and their expression play a central role in entrepreneurship (Cardon et al., 2012).
However, knowledge remains limited concerning a key mechanism by which emotions are expressed:
the face (e.g., Jiang et al., 2019; Stroe et al., 2020). Drawing on work in evolutionary psychology and
emotional expression, including basic emotion theory (Ekman, 1992, 1999; Keltner et al., 2019),
theory surrounding change detection (Rensink, 2002), and the dual threshold model (Geddes and
Callister, 2007), we seek to demystify the role of entrepreneurs’ facial expressions of emotion. Our
study offers several important contributions.
32
5.1. Theoretical contributions
We examine the nuances of multiple emotions and their influence on funding to further
knowledge concerning emotional expression in entrepreneurship. In research on funding pitches,
positive emotions are often the focus. However, naturalistic facial expression often includes a variety
of expressions beyond those that are positive, with each expression serving a distinct role in social
interaction. As such, neglecting emotions that are deemed “negative” impedes our understanding of
how expression of emotions common to the human experience relate to entrepreneurship. Moreover,
the limited entrepreneurship research studying negative emotions has focused on their experience
rather than their expression. To overcome these limitations, our study contributes a view of how
entrepreneurs’ facial expression of emotions, both positive and negative, influence funding
performance.
Regarding fear and sadness, entrepreneurship scholars have often focused on potential venture
failure as the object of entrepreneurs’ fear (Cacciotti et al., 2016; Patzelt and Shepherd, 2011; Stroe et
al., 2020; Welpe et al., 2012) and the loss of a venture as the object of entrepreneurs’ sadness
(Jenkins et al., 2014; Mantere et al., 2013; Patzelt and Shepherd, 2011). We extend these literatures
by showing that concerns of failure and venture loss also relate to how entrepreneurs express fear and
sadness in funding pitches. In addition to showing that fear may imply uncertainty in relation to an
entrepreneur’s vision and their ability to push forward if the call for resources is not met, our study
suggests that expressing some fear is acceptableor even preferred—in crowdfunding. This appears
to run counter to popularized conceptions of the overconfident, dominant, or brash entrepreneur that
are often perpetuated by the media (cf. Anglin et al., 2018b), instead providing evidence that funders
favor entrepreneurs who show some semblance of uncertainty or trepidation concerning their venture.
Expression of sadness, however, had a negative relationship to funding. It may be that the negative
relationship of facial expressions of sadness with funding is due to the low activation (i.e., energy,
intensity, or arousal) inherent to sadness relative to happiness, anger, or fear (Russell and Barrett,
1999).
33
Our theorizing and findings also provide evidence that expression of anger has previously
unimagined roles to play in entrepreneurship. Scholars have found that experienced anger encourages
entrepreneurs to exploit opportunities (Welpe et al., 2012) and reduces their risk perception in
opportunity evaluation (Foo, 2011). We extend this work by illustrating that expressed anger is also
associated with opportunity evaluation and exploitation. Specifically, entrepreneurs displayed facial
expressions of anger to communicate the salience of the problem they were addressing, which may
help others understand why an opportunity should be exploited. We also found that entrepreneurs
expressed anger when communicating determination and competence to solve a problem, implying
that the entrepreneur’s evaluation of the opportunity is favorable.
Next, extant research has often construed entrepreneurs’ positive expressions as being
indicative of their passion (Cardon et al., 2017; Chen et al., 2009; Li et al., 2017; Mitteness et al.,
2012; Shane et al., 2020). Our study builds on this, demonstrating that entrepreneurs may convey
their passion(s) through a variety of emotional expressions, including negative expressions. For
instance, entrepreneurs displayed facial expressions of anger when highlighting motivational aspects
of their passion (e.g., determination), displayed facial expressions of fear when highlighting their
desire to pursue their passion and their vision, and displayed facial expressions of sadness when
communicating the depth of their concern related to the problem they were solving and how
disappointed they would be if not able to pursue their project. We contribute to the passion literature
by suggesting that entrepreneurs can productively communicate their passion by displaying highly
activated negative facial expressions (e.g., anger and/or fear, but not sadness) and that these
expressions exhibit a positive influence on funders provided they are not expressed too frequently.
Another contribution of our work is that we extend the dual threshold model’s principles
regarding frequency of expression to account for three emotions beyond anger. This resulted in a dual
threshold model of basic emotions in funding pitches. We thus answer calls to extend the scope of the
dual threshold model of anger beyond intraorganizational interaction (Geddes and Callister, 2007;
Geddes et al., 2020). This model suggests two key implications for entrepreneurship. First, our study
complements research showing that the benefits of experiencing positive emotions on venture
34
performance outcomes might reverse beyond a given point (Baron et al., 2011) by showing that this
pattern is also mirrored by the influence of entrepreneurs’ facial expression of happiness on funding.
Simply put, our theorizing and findings demonstrate that expressing positive emotions such as
happiness does not lead to universally positive outcomes. Second, we join management research in
building theory on why expressing negative emotions can facilitate favorable outcomes (Geddes et
al., 2020). Here, we believe it is important to realize that expressing negative emotions does not mean
an entrepreneur will have poorer funding prospects. Overall, our study suggests that positive and
negative emotional expressions may have both positive or negative influences on important outcomes
for entrepreneurs that are determined by the frequency with which they are expressed.
Finally, we show that the frequency of change in entrepreneurs’ facial expressions of emotion
promotes funding. People display a variety of facial expressions in social interaction and may
frequently change from one facial expression to another (Kuppens and Verduyn, 2017).
Entrepreneurship scholars, however, have not yet considered how changes in entrepreneurs’ facial
expressions influence funding. This is a critical oversight because changes in facial expressions
encourage observers’ engagement by increasing attention (Eastwood et al., 2001; Frischen et al.,
2008) and by influencing perceptions of the expressor’s authenticity (Cohn and Schmidt, 2003),
which is an emerging research area within entrepreneurship (e.g., Cardon et al., 2017). We further
extend research on emotional expressiveness, which suggests that a “natural,” open style of
expression is an important social skill that promotes entrepreneurs’ success (Baron and Markman,
2000, 2003; Baron and Tang, 2009) by illustrating that varying emotional expressions in funding
pitches leads to increased funding performance. More broadly, our quantitative and qualitative
studies together suggest that entrepreneurs’ facial expressions of emotion, as well as changes in these
expressions, serve as a form of emphatic punctuation, enabling entrepreneurs to convey meaning
(e.g., sincerity, concern, determination, passion) and emphasis to certain moments of their pitch.
5.2. Practical implications
Our results offer several practical implications. First, our findings provide insight into the
limitations of expressing of positive emotions and the potential benefits of expressing negative
35
emotions. In addition to expressing happiness, we find that expressing anger and fear can prove
beneficial in a funding pitch. However, we caution against highly frequent facial expression of
happiness, anger, or fear, as doing so may violate the display rules of the funding pitch setting,
hindering funding. Second, our findings suggest that entrepreneurs should not express sadness in their
funding pitches, since doing so reduces funding. These implications should be considered in light of
the fact that we studied lay funders, providing the most direct implications for those seeking funding
via online, reward-based crowdfunding platforms. Our findings also suggest that entrepreneurs
should change-up their displayed facial expressions while pitching. Our study suggests such changes
serve as emotional punctuation, likely increasing funder attention and aiding perceptions of
authenticity. These findings extend previous work, which suggests that nonverbal communication
enables entrepreneurs to deliver their pitches in a more persuasive manner (e.g., Clarke et al., 2019).
This suggests that entrepreneurs may benefit from being aware of their nonverbal expressions,
including their facial expressions, as well as contextual display rules governing what constitutes
appropriate emotional expression.
5.3. Limitations and future research directions
Our contributions should also be understood considering the limitations of our work. First, we
do not examine the experience of emotion, only its expression. Whereas some entrepreneurs may
express an emotion in an authentic manner such that it mirrors their experience of the emotion, others
may express emotion as a means of impression management or deliberate deception (Baron, 1989;
Baron and Markman, 2003). Strategic displays of emotion have been found to be ineffective, even
counterproductive, when detected (Groth et al., 2009; Côte et al., 2013). To understand such
possibilities, future research might consider the degree of alignment between experienced and
expressed emotion, and the influence of this on the affect and cognition of receivers. Given the
interplay between emotion and cognition and calls for joint examination of affective and cognitive
processes (e.g., Foo et al., 2014), such efforts are important.
Second, differences in entrepreneurs’ social skills (Baron and Markman, 2003; Baron and
Tang, 2009) likely shape the effectiveness of their interactions with key stakeholders. For example,
36
some entrepreneurs lack the expressiveness needed to effectively communicate their emotions and
present themselves in a favorable manner, consistent with contextual display rules—holding
implications for their ability to influence others (Baron and Markman, 2003; Baron and Tang, 2009).
For example, this may influence subjective ratings of project quality. We did not control for the
potential influence of perceived quality and call for future research to examine whether subjective
ratings of quality change depending on the emotions expressed. Likewise, some people are more
adept at social perception and more attuned to others’ emotional expressions (e.g., emotional
intelligence). As a result, such people may be able to infer others’ emotions with greater accuracy,
particularly in terms of drawing inferences regarding motivations, intentions, and confidence (Baron
and Markman, 2003; Baron and Tang, 2009). Future research should consider how such differences
in both the entrepreneur and receiver influence the nature of their interaction.
Third, although facial expressions are a particularly salient channel of communication
(Bonanno et al., 2002; Russell et al., 2003), emotions may be communicated through a variety of
verbal and nonverbal mechanisms. Nonverbal expressions such as gestures (Clarke et al., 2019),
physical touch, eye contact (Burgoon et al., 2016), and vocal expressions (Scherer, 2003) are
important in influencing others’ perceptions and behavior. Future research could study these different
means of expressing emotion in entrepreneurship, their relative importance, and their interaction.
Likewise, environmental attributes that may shape what emotions are deemed appropriate to express.
For example, differences in the pitch setting or the use of symbols, music (e.g., Keeler and Cortina, in
press), lighting, or images can influence communication (e.g., Bitner, 1992).
Finally, whereas we found that the frequency of facial expressions of emotions matters, we
urge scholars to examine other characteristics of facial expressions. For example, we did not focus on
the intensity of expressions. While intensity may be highly correlated with the evidence scores
produced by our facial analysis algorithm (Gutiérrez-García et al., 2019), the intensity of emotional
expression may vary over the course of a pitch. Examining intensity could shed further light on the
results of our study because dual thresholds likely exist not only for frequency, but also intensity
(e.g., Geddes and Callister, 2007). Examining emotional expressions over the course of a pitch, future
37
research might find common patterns or configurations of expression. Along the same lines, our
emphasis on changing from one expression to another is useful for understanding how frequency in
changes may shape funding, but our scope does not include the duration elapsed between changes in
expressions. Effects of duration between changes in expressions on funding could be studied
experimentally, using pre-defined time spans. Finally, basic emotions serve as the building blocks for
more complex emotions that future research might consider as influences on funding. These could
include shame, guilt, jealousy, and pride, for example (Levenson, 2011; Tracy and Randles, 2011).
6. Conclusion
Emotional expressions are varied, encompassing both the positive and the negative, with
facial expressions playing a primary and highly salient role in social interaction. Despite this, the
literature studying entrepreneurs’ expressions of emotion in funding pitches has focused on positive
expressions to the neglect of negative expressions, and has only scratched the surface in considering
the frequency with which entrepreneurs change facial expression while seeking to influence others.
Our theory and findings suggest that by staying attuned to display rules for the situation and
frequently changing their expressions, entrepreneurs are better positioned to influence potential
funders, with implications for their interactions with others throughout the entrepreneurial process.
38
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Table 1
Qualitative analysis of entrepreneurs’ facial expressions of emotion in funding pitches: Example quotations.
Expressed
Emotion
(Aggregate
Dimension)
Object of
Emotional
Expression (2nd-
Order Theme)
Example Quotations (Words Communicated Concurrently with Facial Expression of Emotion in Bold)
Happiness
Team
“I actually have two editors. One has been to school for it and he's like a genius, he really is. Both of them being on this project with me, is amazing. They're
both so good at editing.” (Comedy film)
We made a video to introduce ourselves. Here's a little clip from it. Take a look.” (Art education program)
Passion
We rallied the kids We created a design and a plan. The kids were coming to meetings, they were coming every week, and the kids started having
fundraisers and we raised a good chunk of change.” (Community skate park)
“I have been doing [the show] for 7 years basically on my own. I've edited the show, produced the show, and now I'm starting a Kickstarter campaign to
produce the show I want to produce and give you guys a quality show. ... [The show] has long been a passion of mine.” (TV show highlighting different cultures)
I'm excited to build a cocktail program that captures the authentic spirit of Argentina.” (Argentina-inspired bar)
Value to Funders
You can choose the color and the style and we’ll send it to you so that you’re amongst the first to enjoy the safety and security that the wrist guardian
offers. (Smart watch for family security)
“The [bike] is really amazing .... Climbing is very easy now with the [bike. Try speed mode 3, I’m sure you’ll love it.” (Battery-powered bike)
“Even if you're not a gamer, there are some really cool ways you can benefit from this technology. [Our video game controller] is much easier to press than a
cluster of keys on a smartphone or small device. Our supporters will get a kit that allows them to experiment with different way to use the [controller].
(Video game controller)
Humor
Hi! My name is [name] and I want you to give me money (said in a joking manner).” (Short film about dancing)
“Here in California, we wear sandals and flip flops year-round, and as a result, many of us have problem feet [pointing to his foot and laughing]. We've tested all
kinds of foot exfoliators and decided there has to be a better solution.” (Skin exfoliator)
Communality
“We need to raise $5,500 and to get there we need your love. You’ll not only be supporting creativity in the world, but for your donation you will be handsomely
rewarded. So, show us some love and please give us a pledge. And tell your friendsall of themand your neighbors!” (Stage production)
“You can grow with us and you can be part of that voice in order to establish what we want to do in the future. And that’s to share good stories … So right now
we’re at an opportune time for people to be a part of us and be part of that voice and that mission.” (Theater)
Help me start this production. Help me make this film. I can’t do it without you.” (Dance film)
Anger
Problem
Salience
“There is economic instability, there is political instability, and there is emotional instability. A handful of people brought thousands of people to their knees. And
that's the pain, that's the hard part to deal with, I think. (Documentary about citizens working for positive political change in Iceland)
“The images haunted me for weeks and I couldn't get them out of my head. I'm directing my first project, and it's called silk. It’s about a woman who was given
away by her family at 12 years old and she was married off to a man 22 years older. She was traded for a wad of cash and a goat on a rope. That was the value
of her life. I want to tell this story. We have to pay for locations, insurance, craft services, costumes, makeup, food. Literally every dollar counts.” (Short
film about child brides in Yemen)
I need your help in order to procure the steel, the earth, the sod, needed to create more berms, and to create sun shades that act as water catchment systems.”
(Public art and agriculture park)
Determination
I would like to introduce the people who have spent the past 2.5 years researching and developing this video game. Some have even moved across the
nation to make this game happen. (Video game)
“So why am I doing this? I have long had an interest in what occurs at the intersection of disciplines. It's that accidental collaboration, the inspiration that comes
from proximity, these are the things that interest me. Furthermore, I've been bothered by the lack of adequate tools and adequate space, or even community, to
drive the work of creative people. Even now, with the rise of maker spaces, tech incubators, and art schools, something is missing.” (Pop-up workshop and design
studio)
Competence
“I entered the United States Navy, serving my country in the field of illustration drafting. My combination of business and art talents have given me the
adventures of participating in art exhibits throughout this great country.” (Nature-inspired art exhibition)
47
Fear
Need for
Resources
“I got this new red white and blue paint and this one here is about 13 dollars and that is a lot of money for someone with limited means. I have a friend who
loaned me an easel, and I’ve got brushes from back like almost 20 years ago. (Art exhibition)
“To achieve this goal, I will need to raise $70,000. That's ambitious but I believe it can be done with your help.” (Pottery business)
We need your help ... The show itself is only one aspect of the overall project. Your donations will help cover our rental fee, postage for all of the catalogs,
and our printing costs.(Art show and accompanying catalog)
Hindrances to
Progress
“The whole program is a remarkable achievement that has taken many years and input from so many people to create, both in its crazy logisticsbelieve me it
isn't easy managing 35 theater artists in the mountains of Greece with no cell phones and very little email accessbut also in its creative and artistic design.”
(Summer theater program in Greece)
Vision
It has long been a dream of ours to, at some point, travel to Scandinavia and meet up with some of the master musicians. (U.S.-based Scandinavian folk music
group)
We really, really need some support to make this miracle happen. We’re really looking forward to updating everyone on our progress as we go. (Public art
installation)
That’s why I’ve chosen to be a performer, not to fight for the spotlight, but to inspire people through my passion and my music. Choosing to do this at this time
is incredibly hard, but you have to find the strength inside yourself to continue with your passion, continue with what you love, and with your purpose.
[Opera singer]
This is my dream. It is the most important thing to me that we do not get cut off halfway through. (Immersive website for young adult fiction novel)
Sadness
Concern
“Right now, we're seeing, in the United States, youth pastors' salary going down. It’s something that every youth pastor goes through. But often times, when they
go through it, they feel alone, they feel like maybe they're the only ones that have gone through it, or I don’t know, maybe kind of lost with what to do.
(Book about problems youth pastors face)
I want to dedicate this fight to anyone who’s struggling today, I love you all. Our Kickstarter project is to build the [program]. This digital aid assists in the
spiritual, physical, and mental aspects of recovery.” (Online substance abuse recovery community/program)
“By having somebody be able to come up and have a conversation about books, about what they like to read, I think it's refreshing to them to be able to talk about
something else. To be able to get a copy of a book that might help them escape their reality if that's what they need. (Bicycle-powered mobile library for
homeless people)
Disappointment
I need to make this movie. It's time. It's something I've wanted to do my entire life, and if I don't do it now, I don't know when I will have the chance
again. I wish I wasn't here having to ask you for money. But we need this money to make this movie, if we don't get this $7,000, it's not going to happen;
we are going to have to shelve the project.” [Film]
“We started work on [our game] in 2004 and in 2008 we had a publisher for it, who sadly wasn't able to follow up on their promise to produce it.” (Board
game)
Supplication
“Any help, anything that you can do would be so generous if you could just even pass this along to different people we would be so appreciative. Thank
you so much for your time and have a wonderful day.” (Film about children with special needs)
“As the writer and performer of this work, I know you’re investing in me and I’m sincerely grateful.” (Theater production)
“At this point, we are looking for your help. Simply put, we need assistance to complete the program, and that's where you as an individual or your
organization can help to make this show a reality and promote our natural talent.” (TV series showcasing emerging musicians and celebrating regional
musical heritage)
48
Table 2
Descriptive statistics.
Variables
Mean
SD
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Funds Raised
12688.65
49744.05
Number of Funders
87.18
136.42
0.19
Success
0.58
0.49
0.17
0.34
Category Pledges (ln)
17.02
1.39
0.19
0.12
-0.17
Goal
17023.95
39442.72
0.41
0.09
-0.19
0.24
Duration (ln)
3.43
0.33
0.02
0.09
-0.17
0.00
0.14
Team (0/1)
0.33
0.47
0.09
0.10
0.17
0.06
0.13
0.02
Tangible
0.83
0.37
0.07
0.07
-0.04
0.17
0.01
0.04
-0.12
1st speaker gender
0.28
0.45
-0.06
0.00
0.09
-0.07
-0.05
0.03
0.01
-0.01
1st speaker age
3.06
0.76
0.14
0.11
0.02
-0.05
0.13
0.02
0.07
0.01
-0.04
1st speaker race
0.25
0.66
-0.04
-0.06
-0.05
0.08
0.03
-0.02
0.02
-0.04
-0.01
-0.13
1st speaker attractiveness
2.97
0.78
-0.04
0.05
0.06
0.02
-0.08
0.03
-0.05
0.02
0.03
-0.06
-0.05
Pct. Women on Team
0.24
0.24
0.00
-0.01
0.04
-0.04
0.01
0.04
-0.02
0.03
0.05
0.01
0.04
0.09
Avg. Age
3.06
0.67
0.07
0.04
-0.06
0.06
0.07
0.08
-0.06
-0.02
0.01
0.06
-0.02
-0.13
-0.10
Pct. Black/Latino on Team
0.05
0.12
-0.04
-0.10
-0.14
-0.03
0.04
0.06
0.11
-0.05
-0.05
0.01
0.01
-0.03
0.01
-0.12
Avg. Attractiveness
2.96
0.80
0.01
0.08
0.08
0.02
-0.01
-0.04
0.01
0.06
0.03
0.00
-0.03
0.50
0.19
-0.24
Speaker Face Size
9.28
0.77
-0.01
-0.08
-0.07
-0.07
-0.04
-0.05
-0.12
-0.06
0.00
-0.05
-0.02
-0.08
-0.02
-0.07
Facial Yaw
-0.82
4.86
0.03
0.00
-0.08
0.02
0.04
0.07
-0.01
0.00
0.04
0.02
-0.06
0.00
-0.10
0.07
Facial roll
0.29
3.95
-0.02
-0.05
0.07
-0.04
-0.07
-0.07
0.07
-0.01
0.05
0.04
0.09
-0.02
-0.07
0.00
Video Length
1461.59
1319.26
-0.08
-0.12
-0.10
-0.06
0.06
0.06
0.14
-0.06
-0.03
-0.03
0.10
0.01
-0.03
0.02
Word Length
5.88
0.71
0.06
0.06
0.01
0.05
0.12
0.09
0.22
-0.06
0.03
0.05
0.07
-0.03
-0.06
0.08
Happiness
0.12
0.13
0.04
0.04
0.07
0.00
0.05
-0.03
0.05
0.01
-0.02
-0.01
0.03
0.05
0.36
-0.13
Anger
0.02
0.05
-0.02
0.01
0.03
0.08
0.04
0.01
-0.01
0.09
0.06
-0.03
0.10
-0.01
0.01
0.03
Sadness
0.01
0.04
0.01
-0.02
-0.06
-0.03
0.03
0.01
-0.03
0.02
0.05
0.01
-0.03
-0.04
-0.08
0.09
Fear
0.02
0.05
-0.02
0.01
-0.03
-0.09
0.00
0.02
-0.06
-0.05
0.00
0.00
0.06
0.02
0.05
0.05
Changes in Expression
4.37
1.27
-0.01
-0.05
0.02
-0.06
0.03
0.04
0.30
-0.07
-0.02
0.03
0.07
0.01
0.12
0.05
N = 489; correlations with an absolute value exceeding 0.08 are significant at p < .05
Table 2
Descriptive Statistics (continued).
Variables
15
16
17
18
19
20
21
22
23
24
25
16
Avg. Attractiveness
0.05
17
Speaker Face Size
0.15
-0.07
18
Facial Yaw
0.04
-0.04
0.05
19
Facial roll
-0.03
-0.05
-0.02
-0.17
20
Video Length
0.07
-0.07
-0.02
0.00
0.03
21
Word Length
0.00
-0.05
-0.04
-0.01
0.01
0.56
22
Happiness
0.07
0.18
-0.04
-0.03
-0.06
-0.03
-0.05
23
Anger
-0.06
-0.03
-0.10
-0.01
-0.01
-0.11
-0.04
-0.15
24
Sadness
0.01
-0.05
0.11
-0.01
0.06
0.00
0.01
-0.15
0.00
25
Fear
-0.02
-0.05
0.05
-0.08
0.05
0.03
-0.02
-0.07
-0.06
0.08
26
Changes in Expression
0.16
0.04
0.03
0.00
0.04
0.55
0.37
0.24
-0.07
0.19
0.15
N = 489; correlations with an absolute value exceeding 0.08 are significant at p < .05
49
Table 3
The influence of facial expressions of emotion on total funds raised.
Controls Only Direct Effects Emotions
Changes in
Expression
Variable Coeff. SE
p-
value
Coeff. SE
p-
value
Coeff. SE
p-
value
Coeff. SE
p-
value
Category Pledges (ln)
-0.02
0.03
0.543
0.00
0.02
0.998
-0.01
0.02
0.508
-0.01
0.03
0.725
Goal
0.00
0.00
0.000
0.00
0.00
0.000
0.00
0.00
0.000
0.00
0.00
0.000
Duration (ln)
0.24
0.04
0.000
0.29
0.03
0.000
0.26
0.01
0.000
0.25
0.07
0.000
Team (0/1)
0.57
0.13
0.000
0.59
0.11
0.000
0.48
0.08
0.000
0.50
0.17
0.002
Tangible
0.14
0.15
0.335
0.18
0.18
0.320
0.20
0.21
0.340
0.13
0.12
0.304
1st speaker gender
0.01
0.19
0.954
0.07
0.16
0.656
0.11
0.10
0.292
0.02
0.17
0.923
1st speaker age
0.18
0.19
0.346
0.15
0.20
0.439
0.13
0.14
0.341
0.19
0.18
0.309
1st speaker race
-0.05
0.04
0.232
-0.08
0.06
0.197
-0.11
0.06
0.082
-0.06
0.04
0.133
1st speaker attractiveness
0.06
0.06
0.368
0.07
0.09
0.398
0.01
0.11
0.921
0.04
0.06
0.472
Pct. Women on Team
0.17
0.26
0.529
-0.31
0.08
0.000
-0.43
0.07
0.000
0.10
0.31
0.758
Avg. Age
0.23
0.01
0.000
0.28
0.04
0.000
0.27
0.03
0.000
0.19
0.01
0.000
Pct. of Black/Latino
-0.80
0.03
0.000
-0.77
0.13
0.000
-0.92
0.22
0.000
-0.94
0.11
0.000
Avg. Attractiveness
0.27
0.16
0.095
0.26
0.16
0.112
0.28
0.17
0.106
0.26
0.17
0.116
Speaker Face Size
-0.16
0.00
0.000
-0.17
0.01
0.000
-0.19
0.02
0.000
-0.18
0.03
0.000
Facial Yaw
-0.01
0.01
0.142
-0.01
0.00
0.003
-0.01
0.00
0.002
-0.01
0.00
0.075
Facial roll
0.01
0.03
0.770
0.01
0.02
0.598
0.01
0.02
0.640
0.01
0.03
0.801
Video Length
0.00
0.00
0.000
0.00
0.00
0.000
0.00
0.00
0.000
0.00
0.00
0.000
Word Length
0.74
0.02
0.000
0.74
0.13
0.000
0.66
0.04
0.000
0.67
0.03
0.000
Happiness
2.34
0.77
0.002
7.57
2.88
0.009
Happiness Sq.
-12.24
4.91
0.013
Anger
1.52
2.25
0.500
5.25
3.37
0.120
Anger Sq.
-10.02
4.49
0.026
Sadness
-2.01
0.71
0.005
-2.39
1.10
0.030
Sadness Sq.
0.35
7.34
0.961
Fear
2.50
0.33
0.000
4.61
0.39
0.000
Fear Sq.
-5.49
0.12
0.000
Changes in Expression
0.16
0.05
0.000
Constant
3.60
0.05
0.000
2.75
0.63
0.000
3.49
0.53
0.000
3.56
0.13
0.000
Category Variance
0.33
0.13
0.30
0.16
0.27
0.14
0.32
0.13
Log pseudolikelihood
-4651.21
-4642.69
-4633.21
-4648.29
50
Table 4
The influence of emotional expressions on the number of backers.
Controls Only Direct Effects Emotions Changes in Expression
Variable Coeff. SE
p-
value
Coeff. SE
p-
value
Coeff. SE
p-
value
Coeff. SE
p-
value
Category Pledges (ln)
-0.06
0.03
0.049
-0.05
0.02
0.003
-0.05
0.02
0.032
-0.06
0.03
0.052
Goal
0.00
0.00
0.977
0.00
0.00
0.865
0.00
0.00
0.946
0.00
0.00
0.976
Duration (ln)
0.32
0.18
0.087
0.31
0.13
0.019
0.28
0.13
0.040
0.31
0.16
0.058
Team (0/1)
0.60
0.03
0.000
0.60
0.03
0.000
0.51
0.01
0.000
0.57
0.01
0.000
Tangible
0.14
0.13
0.258
0.15
0.14
0.296
0.15
0.15
0.339
0.14
0.13
0.250
1st speaker gender
0.05
0.04
0.250
0.07
0.06
0.255
0.08
0.09
0.369
0.06
0.05
0.250
1st speaker age
0.14
0.17
0.414
0.13
0.18
0.482
0.12
0.16
0.454
0.14
0.16
0.402
1st speaker race
-0.06
0.07
0.369
-0.08
0.05
0.162
-0.09
0.05
0.065
-0.07
0.07
0.328
1st speaker attractiveness
0.05
0.07
0.507
0.06
0.08
0.439
0.00
0.09
0.965
0.05
0.08
0.555
Pct. Women on Team
-0.03
0.03
0.298
-0.28
0.16
0.078
-0.33
0.20
0.094
-0.06
0.07
0.396
Avg. Age
0.16
0.09
0.063
0.20
0.10
0.047
0.20
0.11
0.052
0.16
0.09
0.069
Pct. of Black/Latino
-1.42
0.44
0.001
-1.35
0.51
0.008
-1.43
0.48
0.003
-1.47
0.44
0.001
Avg. Attractiveness
0.19
0.11
0.080
0.19
0.11
0.077
0.21
0.12
0.083
0.19
0.12
0.103
Speaker Face Size
-0.07
0.02
0.006
-0.07
0.01
0.000
-0.10
0.01
0.000
-0.07
0.02
0.000
Facial Yaw
-0.01
0.01
0.450
-0.01
0.01
0.352
-0.01
0.01
0.289
-0.01
0.01
0.390
Facial roll
-0.01
0.01
0.503
0.00
0.01
0.479
0.00
0.01
0.658
-0.01
0.01
0.464
Video Length
0.00
0.00
0.000
0.00
0.00
0.000
0.00
0.00
0.000
0.00
0.00
0.000
Word Length
0.54
0.03
0.000
0.56
0.03
0.000
0.50
0.00
0.000
0.52
0.01
0.000
Happiness
1.29
0.81
0.110
4.54
0.81
0.000
Happiness Sq.
-7.45
0.14
0.000
Anger
1.35
2.17
0.534
4.26
3.39
0.209
Anger Sq.
-7.29
3.66
0.047
Sadness
-1.30
0.48
0.006
-4.69
3.37
0.164
Sadness Sq.
10.08
12.21
0.409
Fear
2.05
0.67
0.002
4.17
0.82
0.000
Fear Sq.
-5.01
0.99
0.000
Changes in Expression
0.33
0.06
0.02
0.001
Constant
0.50
0.76
0.509
-0.02
0.44
0.971
0.72
0.08
0.031
0.45
0.68
0.509
Category Variance
0.17
0.04
0.19
0.06
0.18
0.02
0.18
0.04
Log pseudolikelihood
-2560.81
-2555.37
-2548.18
-2560.1348
51
Table 5
The influence of emotions on meeting the fundraising goal.
Controls Only Direct Effects Emotions
Changes in
Expression
Variable Coeff. SE
p-
value
Coeff. SE
p-
value
Coeff. SE
p-
value
Coeff. SE
p-
value
Category Pledges (ln)
-0.24
0.03
0.000
-0.25
0.03
0.000
-0.26
0.05
0.000
-0.24
0.03
0.000
Goal
0.00
0.00
0.466
0.00
0.00
0.460
0.00
0.00
0.474
0.00
0.00
0.474
Duration (ln)
-1.08
0.58
0.061
-1.07
0.58
0.065
-1.11
0.57
0.049
-1.07
0.59
0.068
Team (0/1)
1.10
0.19
0.000
1.08
0.17
0.000
0.95
0.13
0.000
1.03
0.17
0.000
Tangible
0.06
0.10
0.517
0.06
0.13
0.656
0.11
0.14
0.432
0.06
0.11
0.590
1st speaker gender
0.30
0.09
0.001
0.31
0.09
0.000
0.37
0.08
0.000
0.31
0.09
0.001
1st speaker age
0.09
0.10
0.373
0.10
0.11
0.362
0.08
0.05
0.128
0.09
0.10
0.366
1st speaker race
-0.11
0.22
0.620
-0.13
0.20
0.536
-0.16
0.19
0.409
-0.11
0.21
0.596
1st speaker attractiveness
0.09
0.04
0.018
0.10
0.02
0.000
0.03
0.01
0.017
0.10
0.04
0.008
Pct. Women on Team
0.23
0.05
0.000
0.00
0.01
0.772
-0.19
0.01
0.000
0.14
0.11
0.183
Avg. Age
-0.06
0.13
0.631
-0.03
0.12
0.789
-0.04
0.11
0.689
-0.08
0.12
0.497
Pct. of Black/Latino
-2.61
0.00
0.000
-2.69
0.09
0.000
-2.94
0.11
0.000
-2.72
0.01
0.000
Avg. Attractiveness
0.14
0.16
0.365
0.12
0.17
0.463
0.13
0.17
0.445
0.13
0.15
0.387
Speaker Face Size
-0.14
0.01
0.000
-0.11
0.04
0.009
-0.16
0.02
0.000
-0.15
0.01
0.000
Facial Yaw
-0.02
0.01
0.000
-0.02
0.00
0.000
-0.03
0.00
0.000
-0.02
0.01
0.000
Facial roll
0.02
0.03
0.505
0.02
0.03
0.358
0.02
0.03
0.464
0.02
0.03
0.494
Video Length
0.00
0.00
0.000
0.00
0.00
0.000
0.00
0.00
0.000
0.00
0.00
0.000
Word Length
0.35
0.05
0.000
0.34
0.08
0.000
0.32
0.08
0.000
0.35
0.07
0.000
Happiness
1.27
0.01
0.000
7.30
0.40
0.000
Happiness Sq.
-13.21
0.73
0.000
Anger
2.05
4.40
0.641
2.92
7.77
0.707
Anger Sq.
-0.89
9.25
0.924
Sadness
-2.36
0.89
0.008
-3.00
2.83
0.289
Sadness Sq.
2.04
10.63
0.848
Fear
-0.52
1.20
0.668
4.83
0.11
0.000
Fear Sq.
-13.57
2.01
0.000
Changes in Expression
2.32
0.12
0.05
0.010
Constant
6.82
2.34
0.004
6.55
2.48
0.008
7.25
0.05
0.002
6.56
2.39
0.006
Category Variance
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Log pseudolikelihood
-283.69
-281.54
-275.59
0.000
-282.99
52
AU
Description
Facial Muscle
1
Inner brow raiser
Frontalis, pars medialis
2
Outer Brow Raiser (unilateral, right side)
Frontalis, pars lateralis
4
Brow Lowerer
Depressor Glabellae, Depressor Supercilli, Currugator
5
Upper Lid Raiser
Levator palpebrae superioris
6
Cheek Raiser
Orbicularis oculi, pars orbitalis
7
Lid Tightener
Orbicularis oculi, pars palpebralis
12
Lip Corner Puller
Zygomatic Major
15
Lip Corner Depressor
Depressor anguli oris (Triangularis)
20
Lip stretcher
Risorius
23
Lip Tightener
Orbicularis oris
26
Jaw Drop
Masetter; Temporal and Internal Pterygoid relaxed
Fig. 1. Action units (AU; adapted from the Facial Action Coding System; Ekman, Friesen, and Hager,
2002).
Note: The measurement of each basic emotion is comprised of the following AUs: happiness (AU6 +
AU12), anger (AU4 + AU5 + AU7 + AU23), fear (AU1 + AU2 + AU4 + AU5 + AU7 + AU20 +
AU26), and sadness (AU1 + AU4 + AU15).
53
Fig. 2. Qualitative study of entrepreneurs’ facial expressions of emotion in funding pitches: Data
structure.
54
Fig. 3A. Happiness (ln) on funds raised.
Fig. 3B. Happiness (ln) on number of funders.
Fig. 3C.
Happiness (ln) on success in meeting goal.
Fig. 4A. Anger (ln) on funds raised.
55
Fig. 4B. Anger (ln) on number of funders.
Fig. 5A. Fear (ln) on funds raised.
Fig. 5B. Fear (ln) on number of funders. Fig. 5C. Fear (ln) on success in meeting goal.
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Based on a sample of 11,019 daily observations of 333 Kickstarter projects, we find general support for our hypotheses. Our post hoc experiment further supports the mechanisms underlying the U-shaped relationship. This paper extends our understanding of the relationship between past actions and subsequent behavior, and contributes to the literatures of observational learning, crowdfunding, and visual information. Executive summary We challenge the extant literature that has proposed, but inconsistently documented, a positive effect of prior funding on subsequent crowdfunding contributions. We argue that such inconsistency is due to the lack of integration of insights from other social influence research, such as observational learning, threshold models, and bystander effects. This research has been limited when postulating that crowdfunding backers simply imitate others' actions in order to mitigate uncertainty producing a linear effect, yet such an effect depends on the intensity of others' actions and may be curvilinear. This research overlooks the fact that backers do not merely passively imitate others' behaviors, but incorporate quality signals sent by entrepreneurs to make such decisions. Thus, it is important to account for potential moderators that may influence the complex relationship between prior funding and subsequent contributions. Based on observational learning research and related models, accrued prior funding may be perceived differently by potential backers: individuals may view accrued funding as a sign of a decreased funding need on the part of a campaign's creators, because others are already backing the campaign or, alternatively, they may see it as a cue of high product quality. These conflicting effects may lead to a U-shaped relationship between prior funding and subsequent contributions at a collective level. At low funding levels, prior funding may be negatively associated with subsequent contributions because such levels indicate there is already support, but that the campaign may not be of high quality. At medium levels, prior funding may have no effect on contributions, aside from halfhearted interest of other backers. At high levels, prior funding may be positively related to subsequent contributions as this sends a robust cue of project quality and convinces prospective donors to contribute. Further, we derive a U-shaped relationship which may be moderated by visual media-based quality signals and situational urgency. To test these hypotheses, we conducted two studies. One was a field study of 333 Kickstarter projects, with 11,019 daily observations of crowdfunding activities from mid-February 2013 through mid-April 2013. We used the Kicktraq website to track Kickstarter projects and recruited 390 participants via Amazon Mechanical Turk (MTurk) to evaluate the video pitches on Kickstarter. Our results suggest a negative relationship between prior funding and subsequent contributions when funding amounts are small. This relationship becomes positive when prior funding amounts are large. Thus, we find a U-shaped relationship between prior funding and subsequent contributions, with an inflection point when prior funding amounts reach between 72% and 80% of the funding goal. This is strengthened in the presence of visual media-based quality signals, such as video quality, as well as the preparedness and passion of the entrepreneur. However, indicators of situational urgency may weaken this U-shaped relationship. We followed up with a post hoc experiment to document the two mechanisms by which prior funding influence subsequent contributions. We found that increases in the prior funding amount increased perceived product quality but decreased perceived urgency. These perceptions were found to jointly influence participants' proposed pledged amounts, supporting an explanation of a complex relationship between prior funding and subsequent contributions. This study is among the first to offer a more complete picture of the relationship between past actions and subsequent behavior under market and institutional constraints (i.e., when market actors observe the censored behaviors of other actors on crowdfunding platforms) and contributes to the literatures of observational learning, crowdfunding, and visual information.