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Abstract

Despite its importance, our understanding of what entrepreneurial disappointment is, its attributions, and how it relates to depression is limited. Drawing on a corpus of 27,906 semi- anonymous online posts, we identified entrepreneurial disappointment, inductively uncovered its attributions and examined how depression differs between attributions. We found that posts with internal, stable, and global disappointment attributions (e.g., not fitting entrepreneurial norms) are, on average, higher in depression symptoms than posts with external, unstable, and specific disappointment attributions (e.g., firm performance). Our findings offer novel theoretical and methodological avenues for future research on entrepreneurs’ affective experiences and mental health.
ENTREPRENEURIAL DISAPPOINTMENT:
LET DOWN AND BREAKING DOWN, A MACHINE-LEARNING STUDY
Accepted for publication in Entrepreneurship Theory and Practice
--this is a preprint draft--
Amanda Jasmine Williamson
Lecturer in Innovation and Strategy
University of Waikato
Hillcrest Road, Hamilton 3240
New Zealand
Amanda.Williamson@waikato.ac.uk
Andreana Drencheva
Lecturer in Entrepreneurship
University of Sheffield
Management School
Conduit Road, Sheffield, S10 1FL
United Kingdom
andreana.drencheva@gmail.com
Martina Battisti
Professor of Entrepreneurship
Grenoble Ecole de Management
12 rue Pierre Sémard, 38000 Grenoble
France
martina.battisti@grenoble-em.com
Please cite as:
Williamson, A. J., Drencheva, A., & Battisti, M. (in press). Entrepreneurial disappointment: Let
down and breaking down, a machine-learning study. Entrepreneurship Theory and Practice.
https://doi.org/10.1177/1042258720964447
Acknowledgements: We are grateful to Editor Ute Stephan and the anonymous reviewers for their highly
constructive guidance throughout the publication process. We are indebted to Cesar Ferri, who performed
a pivotal role in preparing the data and contributed to a previous version of this manuscript. Our thanks go
to Anna Topakas, Juan Antonio Moriano Leon, Malcolm Patterson, Kamal Birdi, Timothy L. Michaelis
and participants at the 2019 Academy of Management conference who provided useful feedback on an
earlier version of this manuscript. Mistakes are our own.
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ENTREPRENEURIAL DISAPPOINTMENT: LET DOWN AND BREAKING DOWN, A
MACHINE-LEARNING STUDY
Despite its importance, our understanding of what entrepreneurial disappointment is, its
attributions, and how it relates to depression is limited. Drawing on a corpus of 27,906 semi-
anonymous online posts, we identified entrepreneurial disappointment, inductively uncovered its
attributions and examined how depression differs between attributions. We found that posts with
internal, stable, and global disappointment attributions (e.g., not fitting entrepreneurial norms) are,
on average, higher in depression symptoms than posts with external, unstable, and specific
disappointment attributions (e.g., firm performance). Our findings offer novel theoretical and
methodological avenues for future research on entrepreneurs’ affective experiences and mental
health.
INTRODUCTION
“I’ve been trying to raise a pre-seed round and the amount of people ignoring us and not
giving us decisive answers has thrown me into a pit of depression. Quote from our data
Because founding a venture involves setting hopes and expectations under conditions of
uncertainty, disappointment is a highly relevant topic for entrepreneurship scholarship.
Disappointment is prevalent (Schimmack & Diener, 1997), particularly in entrepreneurship, due
to the uncertain conditions in which expectations are formed (Goel & Karri, 2006; Norem, 2001)
and the limited control entrepreneurs have over outcomes (Kato & Wiklund, 2011). Researchers
have invoked disappointment to explain different empirical findings (such as the adverse effects
of entrepreneurs’ identification with their ventures; Lahti et al., 2019), yet disappointment has not
been defined and investigated in its own right in the literature on entrepreneurship (c.f., McGrath,
1995). Drawing on the extant psychology literature (Bell, 1985; van Dijk & Zeelenberg, 2002a,
2002b), we conceptualize entrepreneurial disappointment as an entrepreneurs negative emotions
and feelings of limited control concerning the unexpected disconfirmation of a desired condition.
Disappointment is particularly interesting to study because the same disappointment-
eliciting event can be attributed to different causes (Roseman & Smith, 2001), resulting in different
outcomes. For example, an entrepreneur could attribute disappointment resulting from a poor
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funding outcome (as per the introductory quote), to the ignorance of investors, or their own
shortcomings when pitching (among other things). According to Abramson et al. (1978), these
causal attributions differ meaningfully, such that investors’ ignorance is likely to trigger adaptive
responses, whereas personal shortcomings can trigger maladaptive responses and lead to
depression symptoms (Liu et al., 2015). Because there is no systematic investigation of the causes
entrepreneurs attribute their disappointment to, nor the disparate associations those attributions
might have with mental health, we do not know the nature of the link between entrepreneurial
disappointment and depression. In this study, we specifically focus on entrepreneurial
disappointment to examine what causes entrepreneurs attribute their disappointment to and how
different attributions of disappointment relate to depression.
To study disappointment attributions, we draw on a corpus of 27,906 semi-anonymous
online posts. We detect entrepreneurial disappointment within this big dataset, inductively identify
disappointment attributions, and uncover symptoms of depression from text using machine
learning. This article makes the following theoretical and methodological contributions to research
on entrepreneurs’ affective experiences and mental health.
First, we contribute to the literature on entrepreneurs’ affective experiences by offering an
initial nomological net of entrepreneurial disappointment. We define entrepreneurial
disappointment and draw on the concept of causal attributions (Weiner, 1985) to examine how
entrepreneurs explain their disappointment. These attributions challenge the taken-for-granted
assumption that entrepreneurs evaluate their disappointment only in relation to firm failure (i.e.,
Khelil, 2016) to offer a more nuanced perspective. This nuanced perspective also demonstrates
how disappointment attributions relate to depression differently, building on learned helplessness
(Abramson, Metalsky, & Alloy, 1989; Peterson & Seligman, 1987) as a theoretical framework.
Our findings indicate that, on average, depression symptoms are significantly more common in
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posts disclosing disappointment than those without disclosures of disappointment. However, we
find that, when entrepreneurs attribute disappointment to factors that are internal, global, and
stable, depression symptoms are significantly higher than when disappointment is attributed to
causes that are external, specific, and temporary.
Second, we offer an early example of the usefulness of machine learning in
entrepreneurship research. We developed a method to detect entrepreneurial disappointment as a
discrete emotion within unstructured textual data, which can help move research on entrepreneurs’
affective experience away from broad valence categories (i.e., positive and negative affect) and
toward specific emotions that provide nuanced insights for predicting behavior (Foo, Uy, &
Murnieks, 2015). Our study also highlights how future entrepreneurship research can leverage the
advances in machine learning techniques to explore other aspects of mental health, such as post-
traumatic stress disorder, bipolar disorder, and seasonal affective disorder (Coppersmith, Dredze,
& Harman, 2014; De Choudhury et al., 2013; Reece & Danforth, 2017).
THEORETICAL BACKGROUND
In this section, we first build on disappointment theory (Bell, 1985) to explain the
characteristics of entrepreneurial disappointment, why entrepreneurs might be particularly prone
to experiencing disappointment, and why research on this specific topic is warranted. We draw on
the wider body of literature on attributional styles and learned helplessness (Abramson et al., 1989;
Peterson & Seligman, 1987) to explain how entrepreneurs might explain the causes of
entrepreneurial disappointment and how variations in attributional style might relate to depression.
Entrepreneurial disappointment
Disappointment is a prevalent, discrete emotion that is neglected in entrepreneurship
research. Disappointment theory (Bell, 1985) states that disappointment is an emotional reaction
that arises from the discrepancy between an individual’s expectation and a realized outcome. The
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greater the perceived discrepancy between expectation and outcome, the greater the
disappointment. Unexpectedness is, therefore, a key dimension of disappointment. When
individuals do not expect an outcome, they are ill-prepared to deal with the situation (Frijda,
Kuipers, & ter Schure, 1989). Desirability and control potential over the outcome are also key
dimensions of disappointment. This means that when individuals experience disappointment, they
do not achieve something highly desirable, yet also feel that there is very little they can do to
change the situation (i.e., their control potential is limited: van Dijk & Zeelenberg, 2002a).
Entrepreneurs are poised to experience a discrepancy between their positive perceptions of
entrepreneurship and the realized outcomes, making them likely to experience disappointment.
Entrepreneurs tend to have highly positive expectations and are considered prone to wishful
thinking (Heger & Papageorge, 2018) and overconfidence (Wu, Matthews, & Dagher, 2007).
Individual optimism is also exacerbated by steadily increasing collective optimism about
entrepreneurship over the last decade (social contagion: Anglin, McKenny, & Short, 2018).
Optimistic perspectives toward entrepreneurship have been increasing at national levels (Suàrez
et al., 2020), and entrepreneurs are often represented as “victorious warriors” (Torrès & Thurik,
2019). Such positive representations make achieving entrepreneurial expectations highly
desirable. By ‘setting the bar too high’ (Baron, Hmieleski, & Henry, 2012; Graves & Ringuest,
2018; Norem, 2001), the resulting overly optimistic expectations about entrepreneurship are
unlikely to accurately reflect objective possibilities (Shepherd, Haynie, & McMullen, 2012).
Instead, a discrepancy may arise between the entrepreneur’s expectation and the realized outcome,
leading to disappointment
Lastly, the uncertainty under which entrepreneurial expectations are formed (Goel & Karri,
2006; Norem, 2001) is also likely to widen the aforementioned discrepancy by increasing the
unexpectedness of outcomes while, at the same time, reducing the control potential entrepreneurs
6
have over them. Entrepreneurial expectations are formed in the mind of the entrepreneur as
“future-focused subjective interpretations” (Wood, McKelvie, & Haynie, 2014, p. 253), yet
uncertainty is at the core of entrepreneurship (McMullen & Shepherd, 2006). Because the outcome
of the entrepreneurial experience cannot be known from the outset, expectations are built on
incomplete knowledge (Wennberg, Delmar, & Mckelvie, 2016), leaving entrepreneurs ill-prepared
to deal with any unexpected outcomes that may arise. While entrepreneurs are generally
considered to have a high degree of decisional freedom (Benz & Frey, 2008; Hundley, 2001), their
control over the outcome is limited due to constraints posed by customers, suppliers, advisors,
business partners, laws, and regulations (van Gelderen, 2016). Entrepreneurs can also be ill-
prepared themselves as a result of poor insight or planning (Lahti et al., 2019), lack of abilities and
knowledge (Wu et al., 2007), or lack of self-knowledge (Cubico et al., 2010).
The uncertain conditions in which expectations are formed (Goel & Karri, 2006; Norem,
2001) and the limited control entrepreneurs have over desirable outcomes (Kato & Wiklund, 2011;
Torrès & Thurik, 2019) suggest that disappointment is an important and potentially prevalent
emotional experience in entrepreneurship. Despite its relevance, disappointment has received
limited attention to date and we know little about how entrepreneurs explain their disappointment
to themselves. Next, we draw on attribution theory and explanatory response styles as conceptual
foundations to understand the perceived causes of entrepreneurial disappointment.
Causes of entrepreneurial disappointment
Disappointment is the subjective emotional experience of not meeting personal
expectations and potential. While the discrepancy between expectations and outcomes triggers
disappointment, people can attribute disappointment to different causes. Causal attributions
describe how individuals explain outcomes to themselves (Buchanan & Seligman, 1995). For
example, failing to negotiate a supply agreement with a large retailer may be disappointing for all
7
entrepreneurs. Yet, the causes entrepreneurs attribute this disappointment to can vary. Some
entrepreneurs might attribute the outcome to the “idiotic retailers”, whereas others might view
their own lack of entrepreneurial skills as the cause of disappointment.
To explain these kinds of variations in an individual’s responses to negative events,
Abramson et al. (1978) drew on attribution theory to postulate that individuals explain negative
events along three different dimensions. The personalization dimension describes the extent to
which people attribute an event to themselves or to external circumstances. The permanence
dimension describes the extent to which individuals attribute the cause of an event to stable and
persistent conditions or to unstable and changeable conditions. Lastly, the pervasiveness
dimension describes the extent to which individuals attribute the cause of an event to global
conditions that exist across contexts or to specific conditions that are relevant to distinct situations
only.
In the context of entrepreneurial disappointment, personalization can be interpreted as the
extent to which entrepreneurs attribute their disappointment to themselves; for example, their lack
of abilities and knowledge (Wu et al., 2007), or to external circumstances such as product or
service failures (Kato & Wiklund, 2011). Permanence can be interpreted as the extent to which the
cause of entrepreneurial disappointment has been ongoing ever since the entrepreneur started his
or her venture and its likelihood of continuing. For example, if entrepreneurs perceive themselves
as lacking aptitude (Cubico et al., 2010), this is likely to continue in future entrepreneurial
endeavors, whereas venture performance is changeable and may fluctuate (McGrath, 1995).
Pervasiveness can be interpreted as the extent to which the cause of entrepreneurial disappointment
is attributed to a specific entrepreneurial experience or to entrepreneurship in general. For example,
conflicts between co-founders are likely to apply to a specific venture experience, rather than to
entrepreneurship in general.
8
Because entrepreneurial disappointment has not been specifically examined in
entrepreneurship research (c.f., McGrath, 1995), our understanding of entrepreneurial
disappointment attributions is limited. While entrepreneurial disappointment has been mentioned
in research (e.g., Cubico et al., 2010; Kato & Wiklund, 2011), the range of different subjective
causes of disappointment has yet to be uncovered. We contend that it is crucial to examine the
perceived causes of disappointment among entrepreneurs to elucidate what entrepreneurial
disappointment is and the meaning it harbors. Thus, our first research question is: What causes do
entrepreneurs attribute their disappointment to?
Depression as a correlate of entrepreneurial disappointment
In addition to postulating three dimensions of attributions, Abramson et al. (1978) also
argue that individuals develop a particular way in which they explain events to themselves. These
individually differing yet habitual ways of explaining bad events” are called explanatory or
attributional styles (Peterson & Seligman, 1987, p.241). As indicated in the quote, attributional
styles are consistent across events and stable over time (Peterson, Luborsky, & Seligman, 1983).
While some individuals explain negative events through causes that are external, unstable, and
specific, others explain negative events through causes that are internal, stable, and global.
These two causal explanations determine the extent to which individuals experience
feelings of helplessness and, subsequently, how they adapt to negative events (Abramson et al.,
1989; 1978). When individuals attribute a negative event to internal, stable, and global causes,
solutions to negative events might not seem possible. As a result, this style of attribution is
associated with feelings of helplessness, which can undermine subsequent behavior, leading to
passivity and withdrawal (Peterson & Seligman, 1987). Moreover, it can damage self-esteem
(Peterson & Seligman, 1987). Due to the psychologically debilitating nature of this attributional
style, it is associated with a maladaptive response (Peterson, Buchanan, & Seligman, 1995; Robins
9
& Hayes, 1995) which may make individuals more prone to developing depression (Liu et al.,
2015). On the other hand, if individuals attribute negative events to external, unstable, and specific
causes, the resulting helplessness is transient, circumscribed to a specific situation, and leaves the
individuals’ self-esteem intact (Peterson & Seligman, 1987). This attribution style is associated
with an adaptive response because it relates to higher levels of motivation, perseverance, and
achievement (Schulman, 1995), but lower levels of depressive symptoms (Robins & Hayes, 1995).
If entrepreneurs attribute their disappointment to external, unstable, and specific causes,
such as a specific decision by a specific group of angel investors in an investment round, they
might persevere and try harder when experiencing disappointment (Ucbasaran et al., 2013; van
Dijk & Zeelenberg, 2002b). They are more likely to still believe that their actions influence the
outcome, to be motivated, and to invest more energy. This way, disappointment has the potential
to be adaptive as individuals pay more attention, try to understand the experience, and adapt (Frijda
et al., 1989). As the introductory quote illustrates, an adaptive response might result in the
entrepreneur feeling depressed. However, the depressive symptoms are more likely to be mild and
temporary (Robins & Hayes, 1995), particularly if the negative outcome (i.e., not raising capital)
was related to a specific event with limited implications for the entrepreneurs wider life.
However, if entrepreneurs attribute their disappointment to internal, stable, and global
causes, for example, not possessing the necessary entrepreneurial characteristics, their response
may be maladaptive. As an individual cannot easily remedy perceived ineptitude, they may feel
helpless compared to other, successful entrepreneurs, which can result in lowered self-esteem
(Abramson et al., 1989). Due to the iterative nature of the entrepreneurial process, there is ample
opportunity for an entrepreneur to be reminded of and ruminate on a disappointment (Weinberger
et al., 2018). In fact, if individuals repeatedly perceive a lack of control, the resulting emotional
response may become maladaptive over time, resulting in depression (Roseman & Smith, 2001).
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Research in affective neuroscience has indeed shown that disappointment activates areas of the
brain that are also related to depression (Chua, Gonzalez, Taylor, Welsh, & Liberzon, 2009).
Due to different attributional styles, disappointment may relate heterogeneously to
psychological outcomes. Yet, it is currently unclear how various attributions of entrepreneurial
disappointment relate to depression. As such, our second research question seeks to probe the
association between disappointment and depression: How do different attributions of
entrepreneurial disappointment relate to depression?
METHOD
The limited prior theoretical and empirical development on entrepreneurial disappointment
warrants an exploratory approach, similar to other studies on different attributions in
entrepreneurship (e.g., Bullough & Renko, 2017). Our approach considers induction and deduction
as complementary processes in theory progress and combines them (Wright, 2017). Inductively,
we developed a foundation for conceptual clarity on entrepreneurial disappointment and, building
on this foundation, we tested and clarified relationships between entrepreneurial disappointment,
disappointment attributions, and entrepreneurs’ depression.
Sample
A total of 27,906 semi-anonymous posts in online forums for startups were extracted for
this research. The online forums included the Reddit group called “r/startups (Reddit herein) and
“Startups Anonymous (Anon herein). This data is considered semi-anonymous because in Reddit
“registered users can anonymously discuss various topics” (Sekulić, Gjurković, & Šnajder, 2018,
p. 73) and Anon posts are advertised as entirely anonymous. While other forums were examined
for inclusion in this research, no others were included because they lacked anonymity and/or self-
disclosures. Unlike other forms of social media where self-preservation and social desirability
biases and image concerns are high, such as Twitter and Facebook, the anonymity of Reddit and
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Anon enable the candid self-disclosures of disappointment and mental health issues essential for
this study. Numerous posts made on the forums confirmed the importance of utilizing semi-
anonymous data for the present study. For example, one entrepreneur stated: “I’m petrified and I
feel alone. Most days are spent with my stomach in knots wondering if this is going to work. God
forbid I say that out loud or express a negative thought on a social media page though. Thank you
guys so much for this place.” This sentiment was widely shared among the contributors to the
forums: “it is really great to see you are not alone struggling with a startup, in a world where
everyone *appears* to have success come easy”. Our level of analysis is individual posts. An
outline of the steps taken in analyzing the data is depicted in Appendix A and is described in the
following sections.
Measures
Disappointment. We captured the presence of disappointment with content analysis.
Content analysis is a method for categorizing text for quantitative analysis (Krippendorff, 2004),
common to the organizational sciences (Williams & Shepherd, 2017). We performed content
analysis on a random selection of 14,504 posts from our dataset to detect entrepreneurial
disappointment. We treated posts as units of meaning that harbor clues throughout the entirety of
its text (Krippendorff, 2004). Our very first step was to ensure that each unit of meaning
represented the experience of an entrepreneur. We examined whether the post under analysis was
written by an entrepreneur, the intended subject of this research. Clues that the text was written by
an entrepreneur were found in self-identifying statements such as “I am the solo founder of a
startup that…” and from the context the text described, e.g., “we were pitching our idea…”.
Next, we developed three theory-based criteria (Marcatto & Ferrante, 2008; van Dijk &
Zeelenberg, 2002b, 2002a) to detect the presence of disappointment in a post: 1) a negative
affective state, 2) unexpectedness of not achieving the desired condition, and 3) perceived low
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control over the condition. Each post written by an entrepreneur was examined in the following
way to determine the presence of disappointment. The first criterion was an unpleasant situational
state, which involved evaluating the text to determine whether it disclosed an event or situation
with a negative psychological impact on the entrepreneur. For example, the “hurt” mentioned in
the following statement is indicative of the negative situational state: “My family tells me to ‘get a
job’ because they think physical labor = work, I act like I’m taking it as a joke but I know they
really think that way and it hurts every time!”. The second criterion was unexpectedness, whereby
the event or situation fell short of expectations and desired outcomes in an unanticipated manner.
Often this was implied in the information provided in the post. For example, in the following
excerpt, the entrepreneur’s behavior (using credit) indicated that they had expected a positive
outcome, but that reality had unexpectedly fallen short of their previous financial expectations: “I
maxed out my credit cards… Now I’m deep in credit card debt and have no way to build my
prototype. My dream of changing the world is dying…. Third, the text had to indicate that the
author viewed the situation as being beyond their control. Perceptions of low control were
communicated as powerlessness over key events, such as when other people or situations may be
to blame for a negative experience. For example, in the following excerpt, the entrepreneur
expressed powerlessness over his own ability to focus on work due to circumstances outside of his
control (the things that “hold” him back): when you are an entrepreneur [sic], life does not cut
you any slack … I recently got diagnosed with a tumor … when I sit down to work, I just stare at
my computer screen, held back by all the things happening in my life.”
Each criterion received a dichotomous rating. Only posts that satisfied all three inclusion
criteria of disappointment and were posted by entrepreneurs were classified as disappointment-
related posts. To ensure reliability, a first coder (the first author) and a second coder (the second
author) independently analyzed a random selection of 150 posts (over 10% of the corpus of
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manually-labeled posts). The accuracy of the coding (Cohen’s k) was 73%, which is considered
an acceptable level of interrater agreement (Graffin, Haleblian, & Kiley, 2016). Coding
discrepancies were discussed and coding conflicts were resolved by refining and clarifying the
criteria. A total of 974 posts met all criteria and were classified as containing entrepreneurial
disappointment. Posts that did not meet all criteria are listed in Appendix B.
We detected the presence of entrepreneurial disappointment in the corpus by performing a
supervised machine learning classification task. As a first step, we created a training set with the
974 posts containing disappointment and the 13,530 posts without disappointment based on the
previously described content analysis. This corpus of 14,504 posts was employed to train a model
to detect posts containing entrepreneurial disappointment and those that do not. We used Python’s
Natural Language Tool Kit and Scikit-learn libraries (Bird, Klein, & Loper, 2009; Pedregosa et
al., 2012) to manipulate the text data, and employed a variety of standard machine learning
algorithms to calculate the level of accuracy different algorithms achieved (c.f., Li, 1987). To do
this, in accordance with best practice, we employed cross-validation techniques to select the best-
performing algorithms (c.f., Shao, 1993; Yang, 2007). Specifically, we used a 5-fold cross-
validation technique, which is a standard intensive resampling method that partitions the data into
sections and tests in a progressive manner (Bengio, 2003). In other words, we partitioned our
training set into five, then respectively trained and tested the algorithm on each partition. This
technique allowed us to assess how accurately different algorithms predicted disappointment-
related posts in relation to the pre-defined labels. Ultimately, the Logit Boost algorithm performed
best, detecting disappointment-related posts with 88% accuracy on a hold-out sample. We
therefore selected the Logit Boost algorithm (Friedman, Hastie, & Ribshirani, 2000), and later
used it to predict the labels of the 13,402 posts not previously labeled. This resulted in the
identification of a total of 2,381 posts containing disappointment.
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Disappointment attributions is a categorical variable with five discrete types related to:
self, norms (maladaptive categories), others, process, and performance (adaptive categories). To
arrive at these attribution types the following steps were taken. While we describe this process as
linear for readability purposes, the process was iterative, moving between and among the data and
relevant literatures to refine the conceptual codes and types (Eisenhardt, 1989). The first author
inductively analyzed 51% (n = 1,223) of the posts containing disclosures of disappointment to
identify causal statements for the arising disappointment. Coding started by reading each post and
assigning an initial conceptual code (Braun & Clarke, 2006) that captured how the entrepreneur
explained the cause for their disappointment, for example, the shortcoming of a co-founder,
discrimination by a venture capitalist, or financial performance below expectations. Each post was
compared to the previous one. Additionally, all posts with a specific conceptual code were
compared to each other to refine boundaries. When conceptual boundaries between codes became
more established, a second coder (the third author) analyzed 100 hold-out posts (posts not
previously viewed). Results of the two coders were compared, and coding differences were
discussed until agreement was reached. Another round of double coding on a new hold-out sample
(50 posts) followed, which resulted in excellent agreement between the two coders (92%). A single
coder (the first author) analyzed the remaining posts. This effort resulted in five emergent types of
entrepreneurial disappointment with ten codes: self (personal shortcomings), norms (societal
esteem of entrepreneurs, discrimination in entrepreneurship), others (team, personal others,
ecosystem others), entrepreneurial process (demands, lack of reward), and venture performance
(interest in business, the performance of the business). Codes, frequencies, and emergent types are
outlined in Table 1.
[Insert Table 1 About Here]
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Next, to distill disappointment attributions that could provide theoretical explanations,
emergent types were abstracted and consolidated into attributional response styles. To assign
attribution styles, we used analytical procedures common in research on attributions to extract
naturally-occurring attributions from diverse sources of data, such as interviews, essays, and
political speeches (e.g., Henry, 2005; Peterson, Luborsky & Seligman, 1983), which are coded
into abstract categories of theoretical interest. At this stage, as a team, we focused not on what
exactly the entrepreneurs attributed their disappointment to, but on how we could synthesize these
diverse attributions in theoretically-meaningful ways based on three core dimensions:
personalization, permanence, and pervasiveness (Abramson et al., 1989). As a team, we reflexively
considered how the data in each type described causal explanations that were, 1) attributed to the
entrepreneur themselves or to external circumstances, such as others (i.e., personalization); 2)
attributed to stable causes that were ongoing since starting this venture/becoming an entrepreneur
or unstable causes that had changed over time, fluctuated, or had the potential to change (i.e.,
permanence); and 3) attributed to causes just to this specific situation or to causes widely
applicable across entrepreneurial experiences and contexts (i.e., pervasiveness). Both permanence
and pervasiveness refer to the generality of the attribution; however, permanence refers to
generality across time, while pervasiveness refers to generality across situations.
We strictly examined all units of meaning within an attribution type to identify robust
patterns of how attribution types exhibited the three dimensions of personalization, permanence,
and pervasiveness with clear categorical boundaries. We paid careful attention to the language
used by the entrepreneurs to discern the dominant dimensions of attribution types (Henry, 2005;
Peterson et al., 1983). For example, at this stage, we clarified that some entrepreneurs express
disappointment with them not fitting social norms, thus internalizing the cause of their
disappointment, instead of being disappointed with social norms, which would indicate an external
16
attribution. At this stage, we also engaged with literatures relevant to the five attribution styles to
further clarify their dimensions. For example, research on social norms emphasizes that norms are
slow to change (Morris et al., 2015), indicating that norms-related attributions were stable.
Examining each attribution type based on these dimensions resulted in two emergent
attribution styles. The first style included self-related and norms-related attributions because they
shared stable, internal, and global dimensions. Previous research suggests that this attribution style
is likely to be associated with a maladaptive attributional response (Peterson et al., 1995; Robins
& Hayes, 1995). The second attribution style that emerged from the analysis included others-
related, process-related, and performance-related attributions because they shared unstable,
external, and specific dimensions. Previous research suggests that this attribution style is likely to
be associated with an adaptive attributional response (Peterson et al., 1995; Robins & Hayes,
1995).
Depression. Depression was detected via machine learning techniques. Specifically, we
employed depression classifier software developed by Losada and Gamallo (2020). The classifier
detects depression in text automatically from the words used (lexica) and the parts of speech.
Traditionally, depression is diagnosed by mental health experts via symptom checking using
symptom lists (i.e., the Diagnostic and Statistical Manual of Mental Disorders). Such symptom
lists have limited application for the detection of depression from human text because a single
symptom can be expressed in a wide variety of ways. In order to understand the “whole spectrum
of the linguistic means ordinary people use to express depression” (Neuman et al., 2012, p. 20),
expanded linguistic representations of symptoms have been created for accurate automatic
depression detection in what is called a lexica approach. Losada and Gamallo’s (2020) combined
multiple depression-specific lexica and extended them by searching for related synonyms, and
complimented it with parts of speech tagging. This is an example of using machine learning to
17
detect depression symptoms from how individuals employ language. The comparison of text from
individuals with depression against controls indicates that depression presents in linguistically-
distinguishable ways. One example of this is if the text is broken down to its linguist components,
such as verbs, nouns, and adjectives, key statistical differences can be detected from speech. One
of the linguist features of depression includes a statistically significant increase in the use of
interpersonal pronouns, such as “I” (Rude, Gortner, & Pennebaker, 2004), “me”, and “my”
(Eichstaedt et al., 2018).
Losada and Gamallo (2020) built on recent advances in data science that allow researchers
to integrate lexica and linguist features for accurate detection of depression symptoms in small and
large text. By using a lexicon and machine learning approach, Losada and Gamallo’s (2020)
classifier achieved high accuracy in detecting signs of depression from Reddit posts (Losada &
Gamallo, 2020), which is also one of the data sources in this research, suggesting a comparable
context. Therefore, we applied Losada and Gamallo’s (2020) classifier directly to our data
1
to
detect depression symptoms in our dataset.
The level of depression in our dataset followed a right-skewed distribution with many posts
having no or little depression. To make inferences about the degree of depression in the data, we
performed a log transformation to correct for skewness (Frederiksen, Wennberg, & Balachandran,
2016; Schuster, Nicolai, & Covin, 2018). Posts with depression greater than zero were used in this
transformation (Draper & Cox, 1969) and observations without depression were not retained.
However, in the instance where we examine differences in the presence or absence of depression
(such as in the Chi-square test), we converted depression to a binary variable with zero signifying
no depression symptoms and one indicating depression symptoms.
1
Analyses probing the validity of the measure are included in Appendix D.
18
Control variables
We included control variables in our regressions. We controlled for the source of the post
(Anon and Reddit), and the word count as a precaution (c.f., Williams & Shepherd, 2017). Using
the Linguistic Inquiry Word Count automated text analysis program (LIWC; Pennebaker, Booth,
& Francis, 2007), which is a validated tool for aiding text analysis, we also controlled for the time
orientation in posts (past, present, and future focus) because time perspectives correlate with
mental health problems (van Beek et al., 2011).
RESULTS
Attributions of entrepreneurial disappointment
Our first research question asked about the attributions of entrepreneurial disappointment.
The analysis of the data revealed five distinct attributions of disappointment from the
entrepreneurs’ perspective, illustrating the diversity of entrepreneurs’ disappointment experiences.
The attributions that emerged from our analysis are: self-related, norms-related, others-related,
entrepreneurship-process-related, and venture-performance-related (see Table 1). Self-related
and norms-related attributions were internal, stable, and global in their dimensions, while others-
related, entrepreneurship-process-related, and venture-performance-related were external,
unstable, and specific in their dimensions. These attributions are next explained in detail.
Self-related attributions. Self-related attributions of disappointment represented the
entrepreneur’s personal ongoing shortcomings that they believed resulted in them not meeting
their expectations throughout the venturing process. These internal attributions could trigger
entrepreneurs questioning their identities, yet the entrepreneurs also perceived these behaviors,
personality traits, and character flaws to be out of their control. One entrepreneur described self-
related disappointment in relation to ongoing challenges with personal discipline and motivation
applicable to the entire venturing experience:
19
I started my business 3 years ago as a sole proprietor and despite challenges, things are
going well. One of my biggest challenges is my energy level - which I have to fight to keep
high. Despite being 27, I have big ups and downs day-to-day with my energy. I believe
this is a mix of some depression (never quite as busy as Id like, have had some big downs,
life in general), and discipline. I wish I could crank away at work from 8-5, but I often find
my self loosing [sic] motivation and feeling sluggish.
Entrepreneurs perceived their ongoing personal shortcomings as limiting their ability to
reach their objectives or to perform the role of entrepreneur under different circumstances, thus
driving disappointment:
Overcoming Perfection… This issue has stopped me many times from just releasing
something that is good enough. Eventually, Ill give up on the product idea and abandon
it (this has even happened with products I was making money on).
Norms-related attributions. Entrepreneurs reported feeling disappointment because they
did not fit with norms and, thus, experienced ongoing prejudice in their personal and professional
lives across settings. Their role as an entrepreneur created ongoing disappointing experiences in
their personal and romantic lives due to negative social perceptions of an entrepreneurial career
broadly:
… men and women proudly mention they are a lawyers, doctors, etc. [sic] but with startup
founders people have preconceived notions before getting to know you… it is best left to
only mention once you start hitting it off over messages with someone, so people dont jump
to conclusions and write you off before ever getting to that point.
Norm-related disappointment included not only the low societal esteem of entrepreneurship
in general but also not fitting the entrepreneur stereotype, both of which could lead to
disappointment because they related to entrepreneurs’ self-views. Indeed, norms-related
disappointment was particularly common amongst entrepreneurs from demographic groups who
did not fit the local norm of an entrepreneur, or who were not perceived to belong to the
mainstream community of entrepreneurs. For example, female entrepreneurs repeatedly expressed
grave disappointment in their romantic life because of the entrepreneurial stigma. As one
entrepreneur concluded: “dating is impossible because I intimidate most men I’m attracted to.”
20
These norms-related disappointments were experienced not only in entrepreneurs’ personal lives
but also in their entrepreneurial pursuits because discrimination in entrepreneurship activities also
triggered feelings of disappointment about not fitting the prototypical entrepreneurial description.
Entrepreneurs reported feeling disappointment because of discrimination and prejudice
experienced in entrepreneurial activities due to a range of factors, such as gender, age, ethnicity,
and physical appearance, that signaled “otherness”, yet “I can’t change my sex or my color”. For
example:
Being an Older Woman creating a Start-Up... In an industry where everyone expects
someone running a tech start-up to be a) a guy b) the age Mark Zuckerberg once was when
he came up with the idea for Facebook and c) living in the U.S… If you’re a 54-year-old
women, successful businessperson, who wants to enter a new sector, you can’t expect the
doors to open for you… why is it when it comes to funding, there’s only one winner?
Disappointment-inducing discrimination was also experienced by all entrepreneurs in
relation to experience, education, and social capital. As one entrepreneur lamented:
Why is Silicon Valley obsessed with top-tier schools? If you’re not from a top-tier school
you have no network, no brand, no interest from investors or anyone important, no respect
from startup recruiters, nothing… it seems impossible to make a connection or break into
this mafia.
Disappointment that was associated with not fitting norms was particularly noticeable in
specific geographical regions (i.e., Silicon Valley) and within technology entrepreneurship, as the
quotes above illustrate.
Others-related attributions. Others-related attributions of disappointment represented the
failure of individuals to meet the expectations of the entrepreneur. This category of external
attributions included disappointment resulting from the entrepreneurial team, personally
significant others, and individuals from the entrepreneurship ecosystem, who no longer met the
expectations of the entrepreneur and the needs of the specific venture.
Entrepreneurs were disappointed when team members, including potential team-members,
failed to join the venture. More commonly, they were disappointed when current team-members
21
fell short of the entrepreneurs’ expectations by violating trust and demonstrating poor behavior or
unsatisfactory effort. Numerous entrepreneurs expressed a longing for business failure due to high
levels of disappointment in their co-founders and shared details of their changing circumstances:
Have you had [a] situation when you started project, were working on it about 2 years and
some day [sic] your co-founder stopped to deliver anything valuable? It is [a] tough
decision for me, but for [the] last 6 month that guy made just several bug fixes.
Outside of the venture, entrepreneurs related disappointment to personally-significant
others. Close ties are usually portrayed as helping entrepreneurs in their entrepreneurial pursuits,
yet our results indicated that significant others were a notable source of entrepreneurial
disappointment. Entrepreneurs experienced discord with their close social ties in regards to
opportunity beliefs about the venture. The chasm was described as developing over time. For
example, one entrepreneur explained that their persistence and tolerance for risk progressively
exceeded what their parents deemed appropriate, leading to negative feelings of disappointment:
I have great parents … but they no longer believe in my start-up and think it is tearing me
apart and this makes me very upset and depressed… They don’t realize success takes a
while and many iterations.
Additionally, entrepreneurs related disappointment to the dissonance in others’ affective
commitment to their ventures. For example, one entrepreneur expressed disappointment in her
friends when they failed to celebrate her entrepreneurial milestones:
… launched my beta last week. My friends have barely acknowledged it. We’re all aspiring
business owners & I feel they’re so envious they can’t be happy for me… it really hurts.
Significant others also triggered feelings of disappointment by consciously or
unconsciously disrupting the entrepreneur’s venture efforts in unpredictable ways and generating
family-to-work conflict. One entrepreneur, for example, related the lack of venture growth to the
substance abuse of her significant other:
Trying to Run a Startup When Your Spouse is an Addict… The real primary reason I
haven’t spent so much as 1 day giving my business 100% is because I haven’t had 100%
22
to give. … I am stunned at the amount of emotional and physical energy that simply living
with an addict can bleed out of a person.
The final person-related disappointment attribution involved the actors in the
entrepreneurial ecosystem. These actors ranged from venture capitalists, to suppliers, government
bodies, and consumers. This pattern was primarily underscored by a difference in interests and
motivation between the entrepreneur and external agents, which led to what the entrepreneur
deemed as disappointing behaviors. For example, one entrepreneur expressed his disappointment
with the behavior of his customers by stating:
Most of my customers are idiots and I’d rather ignore them. But, I want that
recommendation, and payment, so badly.
Many entrepreneurs expressed disappointment due to less-than-satisfactory input from
mentors and failure to gain funding because of a fault on the part of a funding agent. For example,
one entrepreneur lamented that funding agents were unwilling to give him a “big break”, which
sent him into a disappointment-induced depression:
18 year old Cofounder of a startup, I’ve been trying to raise a pre-seed round and the
amount of people ignoring us and not giving us decisive answers has thrown me into a pit
of depression.
Entrepreneurship process-related attributions. Aspects of the entrepreneurial process and
the entrepreneurial role were also related to disappointment. The demands of entrepreneurship
included a difficult and iterative process where entrepreneurs frequently made personal sacrifices
and disappointment often resulted. Given this backdrop, the entrepreneurial process was viewed
as driving temporary conflict and isolation in the social lives of entrepreneurs, which was also
related to producing disappointment. The isolation-induced disappointment that entrepreneurs
reported was multifaceted. Some entrepreneurs felt physically and financially isolated from their
social groups during periods of their entrepreneurial pursuits. They were not able to socialize like
their peers or were physically absent from locations where occupational socializing would be easy.
23
Other entrepreneurs expressed not feeling as if they had the time or energy to tend to social
connections and contribute to meaningful relationships during their venture’s busy periods, which
made them feel disappointed with the entrepreneurial process. Entrepreneurs with families
sometimes felt disappointed with having to maintain both roles and perceived that family and
entrepreneurial success were mutually exclusive. Beyond disappointment with physical,
occupational, and social isolation due to the entrepreneurial process, posts also expressed
disappointment resulting from affective, interpersonal disconnection. Some entrepreneurs did not
feel able to share their authentic emotions with others and felt emotionally isolated, leading to
feelings of disappointment attributed to the entrepreneurial process. One entrepreneur described
his disappointment with having to engage in surface acting as a means to retain the support and
energy of others during the entrepreneurial process:
Am a struggling founder. Trying to screen my feelings and desperation from: my wife
(who is increasingly frustrated by lack of success, and concerned we don’t have enough
money for our new baby boy), my cofounder (who I keep pushing and staying ‘pretend
positive’ for),– my professional contacts…my family… myself.
The entrepreneurial process was also related to a surprising lack of personal rewards, which
entrepreneurs expressed with a sense of disappointment. This involved expressions of feeling
increasingly weary of the difficulties or demands of entrepreneurship in relation to its rewards:
surprise at the lack of fulfillment or loss of passion and motivation that occurred during the process.
One entrepreneur reflected this sentiment of entrepreneurial-process-related disappointment in
relation to the opportunity cost of the entrepreneurial endeavor:
Doubting if Startup life is worth the effort and mental pressure [] I loved the startup
life[] Now I have a team, product and small success. But now I’m looking at growing the
company and realizing it is lot more commitment then I realized. Not just that I now have
family, kids and other monthly expenses, but it seems like now I am on an emotional roller
coaster. One part of me says to pull the plug and go back to secure job lifestyle.
Venture-performance-related attribution. The final disappointment attribution was
related to the (subjective) performance of the specific venture, which could fluctuate over time.
24
On the one hand, these attributions related to a lack of product-market fit and thus potential for
failure. For example, one entrepreneur explained that they were “solving a big problem that no one
would pay for, which meant it was not sustainable in its current form. On the other hand,
disappointment related to venture performance also included financial characteristics of the
venture. For example, one entrepreneur explained that the financial return-on-investment from
selling her profitable venture was less than what she could have earned as an employee:
After developing a good quality product, I started getting some revenue… I … sadly
realized that, in the most plausible scenario, in 3 years from now… If I manage to sell the
company… I’m left, after taxes, with not enough money to retire and I’d be looking for a
job as I approach my 50’s.
However, these attributions to external factors were not only based on objective
performance characteristics but also to not meeting subjective indicators of performance: “I'm
making more money than ever but not growing … It’s not satisfying at all.”
In summary, the disappointment attributions expressed by entrepreneurs were
phenomenologically diverse. Entrepreneurs attributed disappointment as self-related, norms-
related, others-related, process-related, and venture-performance-related misalignments with
expectations. These five disappointment types differed in their dominant causal explanation
dimensions, representing two emergent attribution styles (outlined in the measures section). Our
coding indicated that self- and norms-related disappointment types predominantly featured
internal, stable, and global dimensions, which are characteristics of a maladaptive attributional
response. Others-related, process-related, and venture-performance-related misalignments
generally featured external, unstable, and specific causal explanation dimensions, which are
characteristics of an adaptive attributional response (Peterson et al., 1995; Robins & Hayes, 1995).
25
Disappointment and depression
Statistical analysis
The second research question asked how different attributions of entrepreneurial
disappointment related to depression
2
. We answered this question in two steps. First, we examined
if there were indeed systematic differences in the distribution of depression symptoms
(absence/presence) in posts that disclosed disappointment and other posts in our dataset
(disappointment/not-disappointment) to understand if disappointment attributions relate to
depression in the first place. Because these aforementioned variables include dichotomous
categories, we estimated distribution differences with chi-square analysis, using the gmodels
package in R. The 11,159 promotional and advertising-based posts were excluded from this
calculation to produce a fair comparison within the dataset.
Second, we analyzed differences between disappointment attributions in the extent of
depression symptoms they had by fitting a linear regression in R (OLS assumptions were met).
We entered depression as the dependent variable and five controls in our model (the null model).
Next, we included our independent variable so that each disappointment attribution was
represented with binary vectors and firm performance served as a reference category.
Third, building on the null model, we included attributional response style as the
independent variable. We empirically examined if the extent of depression symptoms differed
significantly between the two overarching attributional response styles: internal-stable-global
(associated with maladaptive response) and external-unstable-specific (associated with adaptive
response). Attributional response was entered as a dummy variable whereby adaptive response
was used as the reference category.
2
Descriptive statistics of study variables and a plot illustrating differences between disappointment attributions is provided in Appendix E and
Appendix F respectively.
26
Statistical results
The chi-squared test indicated that there was a significant association between the
presence/absence of depression symptoms and disappointment/not disappointment posts, χ2(1, N
= 14,336) = 29.19, p < .001. Based on the odds ratio, the odds of an anonymous post containing
symptoms of depression were 1.33(1.19,1.52) times higher when disappointment was disclosed.
This means that, on average, depression symptoms were significantly more common in posts
disclosing disappointment.
Regarding differences in the extent of depression between disappointment attributions, we
found that compared to the control variables alone (Model 1, Table 2), adding disappointment
significantly improved the fit of the model to the data and explained an additional 2.4% variance
in the extent of depression, F(4, 941) = 6.35, p < .001. Compared to the reference group
(performance), posts with disappointment attributed to norms (β = 0.37, p < 0.01) and to self (β =
0.18, p < 0.05), on average, had a greater extent of depression symptoms, F(9, 941) = 18.87, p <
0.00, Adj. R² = 0.14, as illustrated in Model 2. This indicates that the extent of depression differs
significantly between disappointment attributions.
[Insert Table 2 Here]
Finally, compared to posts with disappointment featuring an adaptive attributional
response style, posts with disappointment which featured a maladaptive attributional response
style were significantly higher in the extent of depression (β = 0.20, p < 0.01), F(6, 944) = 26.99,
p < 0.01, Adj. R² = 0.14. This model did not significantly worsen the fit from Model 2
(disappointment attributed to self, norms, others, process, and performance), indicating that our
pattern of findings, based on the attributions that were inductively derived from the experiences
shared in posts, is supported through the theoretical lens of attributions and attributional responses.
27
DISCUSSION
The results of the research suggest that entrepreneurs attribute their disappointment to the
self, norms, others, the entrepreneurship process, and venture performance. Entrepreneurial
disappointment was associated with a greater odds of presenting depression symptoms, and the
extent of depression symptoms varied between disappointment attributions. The extent of
depression symptoms was comparatively greater when entrepreneurs attributed the cause of
disappointment to broadly internal, global, and stable attributions (self and norms), which is
associated with a maladaptive response (Robins & Hayes, 1995). Depression symptoms were
comparatively lower when disappointment was attributed to causes that were generally external,
specific, and temporary (others, the process, or venture performance), which is associated with an
adaptive response (Robins & Hayes, 1995). Figure 1 presents an overview of our framework of
entrepreneurial disappointment attributions, relationships between attribution patterns and
depression, and theory regarding the mechanisms behind these relationships.
[Insert Figure 1 Here]
Implications for research
This study has conceptual implications for research on entrepreneurs’ affective experiences
and methodological implications for broader entrepreneurship research on mental health and
stigmatized topics.
Toward a nomological net of entrepreneurial disappointment
We extend research on entrepreneurs’ affective experiences by offering an initial
foundation for a nomological net of entrepreneurial disappointment, as a neglected discrete
affective experience. We do this by defining entrepreneurial disappointment and explicating how
it arises and with what it correlates. From this perspective, disappointment, disappointment
28
attributions, and the correlates of disappointment can help us to more accurately portray
entrepreneurs and entrepreneurship as a process.
Based on psychological research on disappointment, we define entrepreneurial
disappointment as an entrepreneurs negative emotions and feelings of limited control concerning
the unexpected disconfirmation of a desired condition. Despite its emergence in previous
entrepreneurship research (e.g., Wu et al., 2007; Lahti et al., 2019), disappointment has until now
been used without an explicit definition. Our definition allows scholars to distinguish
entrepreneurial disappointment from other negative affective experiences that may have different
effects on cognition and behavior. For example, grief arises from the discrete loss of something
valued and, thus, unlike disappointment, it can be an emotionally extreme experience that is
difficult to overcome psychologically or learn from (Shepherd, 2003). Consequently, by defining
entrepreneurial disappointment, we enable construct clarity, which is important for examining the
role and impact of different affective experiences in entrepreneurship moving forward.
This article offers a nuanced understanding of entrepreneurial disappointment’s causal
explanations, challenging assumptions of when disappointment occurs. Our study demonstrates
that disappointment can arise from a wide range of attributions, ranging from the shortcomings of
the self and others to discrepancies between expectations and experiences related to the
entrepreneurship process, venture performance, and norms. In this regard, we challenge the taken-
for-granted assumption that disappointment arises when ventures are not performing well (e.g.,
McGrath, 1995). Even when disappointment does relate to the performance of the venture, it is not
exclusively because of poor performance. In fact, disappointment can arise even under conditions
of objective financial success. For example, many entrepreneurs reported that their venture was
performing well, but that they held expectations of grandeur (i.e., “…to be the next Steve Jobs”).
Additionally, entrepreneurial disappointment arises not only at work but also in entrepreneurs’
29
personal lives, for example from the lack of support from personally significant others or role
conflict at work and in the personal domain.
These emergent disappointment attributions also challenge how entrepreneurs are
portrayed. Entrepreneurs are often portrayed as proactive (e.g., Glaub et al., 2014), overly
optimistic (Wu et al., 2007), wishful thinkers (Heger & Papageorge, 2018) or heroic figures
(Torrès & Thurik, 2019). Our findings demonstrate that entrepreneurs can also be aware of their
shortcomings as multidimensional human beings. Not only are they sometimes aware of their
deficiencies, but when they attribute unexpected disconfirmation of a desired condition to their
shortcomings, intense depression symptoms can arise because this challenges their self-views and
can catalyze maladaptive responses (Peterson et al., 1995; Robins & Hayes, 1995). Thus, a more
nuanced research approach could investigate entrepreneurs’ shortcomings and valuable
characteristics together to explore their interactions.
While previous research demonstrates the importance of norms that value and accept
entrepreneurship (entrepreneurship rates; Stephan & Uhlaner, 2010), our findings further extend
what we know about the role of norms by highlighting the challenges presented by norms for
individuals who do not embody the characteristics of a prototypical entrepreneur. Aligned with
research on norms and sanctions for not adhering to them (Morris et al., 2015), individuals in our
study who did not fit the stereotype, particularly women, shared instances of discrimination within
the entrepreneurship ecosystem, as well as in their personal lives in terms of establishing new
(romantic) relationships. Norms are stable and pervasive in their impact (Morris et al., 2015), while
individuals may have limited opportunities to change the aspects of the self that make fitting norms
possible, such as gender (see Table 1). This is aligned with attribution theories suggesting that
when individuals repeatedly perceive a lack of control, the resulting emotional response may
become maladaptive over time, leading to mental health issues, such as depression (Roseman &
30
Smith, 2001). Our findings indicate that to cope with and to prevent further disappointment,
entrepreneurs may detach socially and induce their own isolation. Ironically, entrepreneurs also
expressed disappointment in the loneliness of entrepreneurship and from the perceived need to
engage in surface acting around others to maintain personally and professionally important
relationships (e.g., impression management). While entrepreneurs may change their behavior to
cope with norms-related disappointment, this change is not necessarily productive for their mental
health.
In our data, perceptions of not fitting norms were often linked to technology
entrepreneurship and specific geographical regions (i.e., Silicon Valley). One potential explanation
for this finding is the role of tight social norms in these environments (Gelfand et al., 2011). This
type of entrepreneurship and these types of regions may have strong social norms in relation to
what it means to be an entrepreneur and, as such, are less welcoming to those who do not conform
to these norms, limiting diversity. The implication of this is that locations and types of
entrepreneurship with loose social norms might be more welcoming to entrepreneurs of diverse
backgrounds due to lower norms-related disappointment attributions.
We investigate depression as only one correlate of entrepreneurial disappointment. To
extend this nomological net, additional mechanisms need to be examined to explicate relationships
between entrepreneurial disappointment and other indicators of poor mental health, such as
sleeplessness or suicidal thoughts. Furthermore, while we examine the relationship between
entrepreneurial disappointment and depression through the lens of attributions and learned
helplessness (Abramson et al., 1978), other mechanisms can also explain how disappointment and
other indicators of poor mental health can be related. These include resource depletion and
hindered recovery. On the one hand, entrepreneurial disappointment can increase resource
depletion because entrepreneurs’ self-views are closely aligned with their ventures (Fauchart &
31
Gruber, 2011), thus disappointment can be a threat to self-views. According to models of stress
and coping (e.g., Lazarus, 1991), threats to self-views motivate individuals to suppress or regulate
the negative emotions associated with such threats (Avero et al., 2003; Skinner & Brewer, 2002),
which depletes resources (Baumeister et al., 1998). On the other hand, prolonged and repeated
experiences of disappointment are likely to impair recovery because recovery of normal
psychological and physiological resources occurs during respite from negative affective
experiences (Meijman & Mulder, 1998). Thus, disappointment can potentially enhance resource
expenditure while also hindering resource recovery, which jeopardizes health (Horwitz, 2015;
Meijman & Mulder, 1998). Exploring these mechanisms in the future would further extend the
nomological net of entrepreneurial disappointment.
Another way of extending our nomological net of entrepreneurial disappointment is to
explore person-level differences and temporal factors that influence the relationship between
entrepreneurial disappointment and depression. For example, dark personality traits may help
individuals avoid depression amidst episodes of entrepreneurial disappointment. Individuals with
narcissistic personality traits, for example, are more likely than others to attribute negative events
to unstable and external causes (Ladd et al., 1997). The results of our study indicate that this kind
of response style (adaptive) is related to fewer depressive symptoms amidst episodes of
entrepreneurial disappointment. Further, our initial nomological net of entrepreneurial
disappointment implies that investigating the factors that relate to more severe indicators of poor
mental health, by examining how and when some entrepreneurs lack the resources to recover from
cumulative episodes of disappointment (Vasumathi et al., 2003), would make a worthwhile
contribution.
Our nomological net of entrepreneurial disappointment may also be extended to include
learning. Arguably, the discrepancy between expectations and outcomes that catalyzes
32
disappointment provides feedback to entrepreneurs that challenges their assumptions, leads to new
insights, and highlights areas for improvement, thereby enabling learning (Carver & Scheier, 2001;
Lerner et al., 2015). While disappointment is a negative emotion that is experienced as unpleasant,
learning from the discrepancy that disappointment signals can enable adaptation and protect
against other negative affective and depressive experiences. Indeed, entrepreneurial
disappointment indicates that the entrepreneur has allowed their beliefs to be influenced by new
information that has the potential to counterbalance the escalation of commitment and to encourage
a change of direction, thus leading to better future performance (McGrath, 1995). These learning
and development experiences resulting from disappointment are at least partially different from
the learning associated with grief (e.g., Cope, 2011) because grief, in the entrepreneurship context,
usually occurs after firm failure (Cope, 2011; Mantere et al., 2013). Disappointment can enable
learning when the entrepreneur is still engaged in the process and can make changes based on
learning in the current venture, thus potentially reducing the risk of firm failure and the associated
grief.
Overall, by providing an initial nomological net of entrepreneurial disappointment as an
emotional response that is prevalent during the entrepreneurial journey, we hope that future
research can more accurately reflect entrepreneurs’ experiences and allow them to balance the
intense positive feelings of entrepreneurial passion and the experience of grief after a business
failure. By focusing on entrepreneurial disappointment as a discrete emotion, we hope to stimulate
future research that examines the outcomes of affect in more nuanced ways than possible with the
current dominant approach. Research tends to focus on broad categories exploring “positive” or
“negative” valence (e.g., Foo et al., 2015), yet affective experiences of the same valence do not
drive entrepreneurial behavior in the same way (e.g., Williamson et al., 2019). For example, future
research can examine how entrepreneurial disappointment relates to diminishing entrepreneurial
33
passion through the increase of other negative emotions, such as sadness and anger (Levine, 1996;
van Dijk & Zeelenberg, 2002a) and decrease of intense positive emotions (Collewaert et al., 2016),
or limited creativity through depleted resources (Williamson et al., 2019). Future research can also
build on our conceptual clarification of entrepreneurial disappointment as a discrete affective
experience to examine when entrepreneurial disappointment occurs, what sequences of
entrepreneurial disappointments lead to entrepreneurs exiting the venture creation process, and
how entrepreneurs learn while still engaged in the process (Cope, 2011; Mantere et al., 2013).
Toward novel methods for studying mental health and stigmatized topics in entrepreneurship
We contribute to the broader entrepreneurship literature by opening up new avenues for
future research on mental health and other stigmatized topics through novel techniques and data
sources. As an iterative process (Bhave, 1994; Dimov, 2007), entrepreneurship is a difficult
phenomenon to study due to the importance of social desirability and image protection for
developing and maintaining legitimacy and accessing resources (Suchman, 1995). Thus, new and
innovative research methods are required to investigate the entrepreneurship process, particularly
in relation to mental health (Hill & Wright, 2001; Stephan, 2018) and other stigmatized topics
because of the challenges “in gaining access to the empirical setting” (Biniari, 2012, p. 164).
This research detects entrepreneurial disappointment in textual data (see Appendix C for
list of unique words for disappointment disclosure in textual data) and how it relates to depression
through machine learning techniques from online posts that include candid self-disclosures on
stigmatized topics (Saha & De Choudhury, 2017), which would otherwise be difficult to capture.
As machine learning is only emerging in entrepreneurship research, we offer an early example of
the usefulness of this novel method, particularly in relation to mental health. While our focus is on
depression as one indicator of poor mental health, future entrepreneurship studies can leverage
advances in data science to explore multiple other aspects of mental health. For example,
34
established research in data science has demonstrated not only how depression, but also post-
traumatic stress disorder, bipolar disorder, and seasonal affective disorder, can be accurately
detected with data from social networks (Coppersmith et al., 2014; De Choudhury et al., 2013;
Reece & Danforth, 2017). Instead of relying on disclosure of sensitive and stigmatized information
through traditional methods, such as questionnaires, established machine learning techniques can
analyze patterns of language use, user engagement, color, metadata components, and algorithmic
face detection. Such techniques can be applied to data from online textual and image posts on
diverse social networks, including Twitter, Facebook, Instagram, and Reddit, to detect emotions
and mental health indicators. Thus, machine learning allows entrepreneurship scholars to examine
mental health in new ways that are closer to the experiences of entrepreneurs.
Our findings indicate that online forums can also be used for interventions. Forums can
serve as psychologically safe spaces and social support to enhance wellbeing due to their
anonymity and the reduction of stigma. The online posts we analyzed suggest that entrepreneurs
find it helpful to have a supportive online community to safely share mental health issues without
the need to protect their image, to fit certain norms, or to protect relationships. This can include
safely sharing issues related to depression, anxiety, and addiction to receive emotional support as
well as access relevant information, as demonstrated by #DisabilityTwitter and other online
communities (Hemsley & Palmer, 2016). Indeed, such interventions may be particularly relevant
for social contexts with tight social norms that, as discussed above, may be less welcoming of
diversity and thus enhance the severity of stigma and norms-related disappointment (e.g.,
Airhihenbuwa, Ford, & Iwelunmor, 2014). The impact of online forums on improving
entrepreneurs’ mental health across different contexts should be tested with future research to
“reduce the suffering associated with” entrepreneurship (Shepherd, 2019, p. 217).
35
Implications for practice
The practical implications of this research are pertinent to media practitioners, educators,
and entrepreneur role models who are responsible for transmitting the realities of the
entrepreneurial career because beliefs about entrepreneurship held at the collective level influence
entrepreneurs’ expectations (Anglin et al., 2018). These professionals could help entrepreneurs to
craft more realistic expectations, better manage their expectations, as well as offer accessible
mental health support. While scholars have made progress in transmitting the low probability of
entrepreneurship success, our research indicates that disappointment and poor mental health can
be reduced by bridging the expectations-outcomes gap (van Dijk & Zeelenberg, 2002b) in relation
to other aspects of entrepreneurship. This may be achieved by conferring the affective realities of
entrepreneurship to entrepreneurship students (Jones & Underwood, 2017) and nascent
entrepreneurs, and by engaging in affective preparation for the entrepreneurial experience
(Shepherd, 2004). The above suggestions might help address the large majority of disappointment
related to self, others, process and performance. To address norms-related disappointment, which
occurred less frequently in our sample but with a significantly negative effect on entrepreneurs’
mental health, the explicit and implicit rules about what is normal, desirable, and acceptable
(Green, 2016) in entrepreneurship need to be challenged. Researchers and educators can contribute
to this change by embracing the diversity among entrepreneurs (Davidsson, 2016) while also
highlighting the darker sides of entrepreneurship. The findings of this research also imply that an
adaptive response style may be beneficial for mental health when experiencing entrepreneurial
disappointment. Interventions aimed at helping entrepreneurs reframe disappointment and identify
external, unstable, and specific causes for disappointment (instead of internal, stable, and global
causes) could be useful for reducing depression in entrepreneurship. Such efforts can contribute to
36
preventing future mental health issues and reducing the global burden of mental health (WHO,
2014).
Limitations and future research
The findings of our exploratory study and its limitations offer a platform for fruitful future
research in entrepreneurship. Regarding the link with disappointment and depression symptoms,
the research design does not provide insight on causation. While entrepreneurs have been shown
to be up to 30% more likely to experience depression than comparison groups (Freeman et al.,
2018), the causal relation is not yet clear. Entrepreneurship may drive mental health issues
(Stephan, 2018), but it is also possible that individuals with poor mental health are more likely to
select into entrepreneurship (Johnson, Madole, & Freeman, 2018). Thus, the association between
disappointment and depression could be in the opposite direction of what is proposed in this
research, i.e., entrepreneurs experiencing depression may be more susceptible to disappointment.
Future research is required to specifically test this relationship as well as the potential positive
effects of disappointment for entrepreneurs’ mental health. For example, future research is needed
to explore how entrepreneurs may engage in sense-making, constructively grow (Funken, Gielnik,
& Foo, 2018), and build resilience following disappointment in a self-curative manner.
While we offer an early example of the usefulness of machine learning for entrepreneurship
research, we also acknowledge the limitations of the data and analyses used in this study. First,
machine learning insights cannot yet replace analyses by clinical psychologists, and thus the
depression measure employed in this research must be considered exploratory. Moreover, Reddit
users are not representative of the general population and the data corpus does not allow us to
describe the participants in detail. Pew Research Center report shows that, at least in the USA,
Reddit users are predominantly male (67% of users), young (64% of users are under 29 years old),
white (70% of users) and, by default, have high levels of digital literacy and internet access (Pew
37
Research Center, 2016). Thus, the experiences of entrepreneurs with different characteristics from
Reddit users may not be fully captured in our analysis. At the same time, dominant discourses
often describe entrepreneurship in masculine and youthful terms where women, minorities, and
older individuals may not fit in (e.g., Gupta et al., 2009; Kautonen, Tornikoski, & Kibler, 2011).
Our data potentially represents the experiences of individuals who more closely align with
dominant norms of who is an entrepreneur. The link between norms-related disappointment and
indicators of poor mental health may be stronger when investigating more representative samples.
The potential that our data may represent the experiences of individuals who more closely align
with dominant norms of who is an entrepreneur also explains why norms-related attributions
appear less frequently in our corpus, compared to the other emergent attributions.
Second, while the anonymity provided by online forums, such as Reddit and Startups
Anonymous, makes it possible to research stigmatized topics with potentially less social
desirability and image protection bias, it also means that we cannot describe the participants in
detail in relation to their industry, previous startup experience, and other background information
nor can we gather self-reported insights that are theory-driven. This limitation is partially related
to ethical challenges in internet research and how we addressed them. This research sought to
minimize the risk of identity capture and disclosure, informed by the ethical considerations of
respect (Wiles et al., 2008), harm, and valid ethical consent with data in the public domain (Snee,
2013; Stanford Encyclopedia of Philosophy, 2019). To protect the privacy of participants in this
study, we anonymized the primary identity of participants at source. This means that at the point
when data was obtained, the only information collected was the text excerpt in the post (i.e., not
the pseudo-usernames, nor other activity made by the author that could reveal their identity).
Therefore, no other pieces of data were collected (i.e., the source or date of the data). This conforms
38
to the ethical suggestions put forward for best practice in internet research and scraping of
personally identifiable information (c.f., Stanford Encyclopedia of Philosophy, 2019).
Additionally, we did not explicitly measure all attribution styles with their associated
dimensions but, instead, synthesized emergent attributions based on three core dimensions as they
were presented in the posts that entrepreneurs had shared for non-research purposes. Thus, not all
attribution styles that are theoretically possible emerged robustly in our data. Instead, only two
attribution styles emerged with theoretical saturation to be presented as robust patterns with
theoretically significant meaning (Strauss & Corbin, 1997). For example, while it is theoretically
possible for entrepreneurs to attribute their disappointment to internal, specific, and temporary
causes, such as a night of poor sleep due to a cold, this attribution type emerged very rarely in our
corpus of data, while internal, global, and stable attributions dominated disappointment related to
the self. Thus, future research with more traditional methods can be useful to replicate and extend
our findings by probing further into causal attribution styles with different dimensions.
Additionally, future research can test the theorized explanatory mechanisms for the relationship
between disappointment and mental health and their boundary conditions, and identify differences
between entrepreneurs.
This research examined disappointment experience by an individual entrepreneur, yet
disappointment can be felt within a team, or even by stakeholders. Investigating how
disappointment attributions differ from different vantage points and how they interact is an
interesting area for future research. For example, Garud et al. (2014) highlight that entrepreneurs
are required to project the potential of their new venture idea and develop public expectations to
build legitimacy and acquire resources. Yet, collective disappointment can form in the minds of
stakeholders due to the entrepreneur’s (perceived or objective) failure to meet these public
expectations, leading to the withholding of resources, creating a negative spiral effect of
39
entrepreneurial and stakeholder disappointment (Garud et al., 2014). An interesting extension of
the present research in concert with that of Garud et al.’s (2014) is to explore the interactive effects
in the manifestation of disappointment between entrepreneurs and different stakeholder groups.
We hope that our foundation for a nomological net of entrepreneurial disappointment will
ignite nuanced research on the role of disappointment in the entrepreneurship process across levels
of analysis. Future research is required to investigate the relationships between different
attributions and individual plus venture correlates to extend the nomological net. For example,
future research can further our understanding of entrepreneurial exit and failure by examining what
series of disappointments with different attributions trigger loss of affective commitment and
business exit. Ultimately, more research focusing on disappointment is required to examine the
effects of different disappointment attributions across levels of analysis, recognizing that
disappointment might have conflicting effects for the individual entrepreneur and the venture. For
example, when does disappointment (and different disappointment attributions) positively
influence the venture but negatively impact the life of the individual entrepreneur and vice versa?
Finally, we hope that our findings spark more research on the role of norm-related
entrepreneurial disappointment and norms more generally in entrepreneurship not only within
entrepreneurship ecosystems but also in entrepreneurs’ personal lives. Norms-related
entrepreneurial disappointment was most strongly linked with indicators of depression in posts
3
,
potentially due to the stable and global dimension of the attribution in a way that challenges self-
views. Additionally, the entrepreneurs in our study shared instances of perceived discrimination
based on their age, nationality, gender, and education within entrepreneurship ecosystems with
potentially tight social norms (Gelfand et al., 2011). This finding calls for more research on the
3
For example, the regression weight of norms is significantly higher than the weight of self, in relation to depression
(F1,941 = 4.76, p < 0.05).
40
strength of social norms in entrepreneurship ecosystems and the potential solutions for individuals
who do not fit the stereotype of who is an entrepreneur. For example, different ecosystems and
types of entrepreneurship with loose social norms and more diverse entrepreneurs (e.g., social
entrepreneurship; Estrin, Mickiewicz, & Stephan, 2016) may offer healthier and more positive
pathways for entrepreneurial pursuits. More broadly, these disappointing experiences of
discrimination call for broader research on the inclusiveness, diversity, and stigma within
entrepreneurship ecosystems. Our findings that experiences of perceived discrimination are not
confined to entrepreneurship ecosystems, but also occur in entrepreneurs’ personal lives also call
for more research on the personal implications of social norms and stereotypes in relation to
entrepreneurship.
CONCLUSION
While entrepreneurship can bring satisfaction, it can also negatively influence
entrepreneurs’ mental health due to discrepancies between expectations and outcomes. In this
study, we used 27,906 semi-anonymous online posts and machine learning techniques to define
entrepreneurial disappointment, explicate its attributions, and examine the relationship between
entrepreneurial disappointment and depression. We found that, on average, depression symptoms
are significantly more common in posts disclosing disappointment than those without disclosures
of disappointment. However, the extent of depression differs significantly according to what
causes disappointment is attributed to. When disappointment is attributed to internal, global and
stable causes (related to the self and norms), depression is significantly higher than when
disappointment is attributed to causes that are external, specific and temporary (related to others,
process, and performance). Building on our findings and novel use of data sources and machine
learning techniques, we offer novel directions for future research on entrepreneurs’ affective
experience and mental health.
41
REFERENCES
Abramson, L. Y., Metalsky, G. I., & Alloy, L. B. (1989). Hopelessness depression: A theory-based
subtype of depression. Psychological Review, 96, 358372.
Abramson, L. Y., Seligman, M. E., & Teasdale, J. D. (1978). Learned helplessness in humans:
Critique and reformulation. Journal of Abnormal Psychology, 87, 4974.
Airhihenbuwa, C. O., Ford, C. L., & Iwelunmor, J. I. (2014). Why culture matters in health
interventions. Health Education & Behavior, 41, 7884.
Anglin, A. H., McKenny, A. F., & Short, J. C. (2018). The impact of collective optimism on new
venture creation and growth: A social contagion perspective. Entrepreneurship Theory and
Practice, 42, 390425.
Avero, P., Corace, K. M., Endler, N. S., & Calvo, M. G. (2003). Coping styles and threat
processing. Personality and Individual Differences, 35, 843861.
Baron, R. A. (2007). Behavioral and cognitive factors in entrepreneurship: Entrepreneurs as the
active element in new venture creation. Strategic Entrepreneurship Journal, 1, 167182.
Baron, R. A., Hmieleski, K. M., & Henry, R. A. (2012). Entrepreneurs’ dispositional positive
affect: The potential benefits and potential costs of being “up.” Journal of Business
Venturing, 27, 310324.
Baumeister, R. F., Bratslavsky, E., Muraven, M., & Tice, D. M. (1998). Ego depletion: Is the active
self a limited resource? Journal of Personality and Social Psychology, 74, 12521265.
Bell, D. E. (1985). Disappointment in decision making under uncertainty. Operations Research,
33, 127.
Bénabou, R. (2013). Groupthink: Collective delusions in organizations and markets. Review of
Economic Studies, 80, 429462.
Bengio, Y. (2003). No unbiased estimator of the variance of k-fold cross-validation. Journal of
Machine Learning Research, 5, 10891105.
Benz, M., & Frey, B. S. (2008). The value of doing what you like: Evidence from the self-
employed in 23 countries. Journal of Economic Behavior and Organization, 68, 445455.
Bhave, M. P. (1994). A process model of entrepreneurial venture creation. Journal of Business
Venturing, 9, 223242.
Biniari, M. G. (2012). The emotional embeddedness of corporate entrepreneurship: The case of
envy. Entrepreneurship: Theory and Practice, 36, 141170.
Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python (1st ed.).
Sebastopol, CA: O’Reilly Media, Inc.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in
Psychology, 3, 77101.
Buchanan, G. M., & Seligman, M. E. P. (1995). Explanatory style. In Explanatory style. New
York, NY: Routledge.
Bullough, A., & Renko, M. (2017). A different frame of reference: Entrepreneurship and gender
differences in the perception of danger. Academy of Management Discoveries, 3, 2141.
Carver, C. S., & Scheier, M. F. (2001). On the self-regulation of behavior. Cambridge, UK:
Cambridge University Press.
Chua, H. F., Gonzalez, R., Taylor, S. F., Welsh, R. C., & Liberzon, I. (2009). Decision-related
loss: Regret and disappointment. NeuroImage, 47, 20312040.
Churchill, N., & Bygrave, W. D. (1989). The entrepreneurship paradigm (I): A philosophical look
at its research methodologies. Entrepreneurship Theory and Practice, 14, 726.
Collewaert, V., Anseel, F., Crommelinck, M., De Beuckelaer, A., & Vermeire, J. (2016). When
passion fades: disentangling the temporal dynamics of entrepreneurial passion for founding.
42
Journal of Management Studies, 53, 966995.
Cope, J. (2011). Entrepreneurial learning from failure: An interpretative phenomenological
analysis. Journal of Business Venturing, 26, 604623.
Coppersmith, G., Dredze, M., & Harman, C. (2014). Quantifying mental health signals in twitter.
Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From
Linguistic Signal to Clinical Reality, 5160. Stroudsburg, PA, USA: Association for
Computational Linguistics.
Cubico, S., Bortolani, E., Favretto, G., & Sartori, R. (2010). Describing the entrepreneurial profile:
the entrepreneurial aptitude test (TAI). Int. J. Entrepreneurship and Small Business, 11, 424
435.
Davidsson, P. (2016). A “business researcher” view on opportunities for psychology in
entrepreneurship research. Applied Psychology, 65, 628636.
De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social
media. 7th International AAAI Conference on Weblogs and Social Media.
https://doi.org/10.1109/IRI.2012.6302998
Dimov, D. (2007). Beyond the single‐person, single‐insight attribution in understanding
entrepreneurial opportunities. Entrepreneurship Theory & Practice, 31, 713731.
Draper, N. R., & Cox, D. R. (1969). On Distributions and Their Transformation to Normality.
Journal of the Royal Statistical Society. Series B (Methodological), 31, 472476.
Eichstaedt, J. C., Smith, R. J., Merchant, R. M., Ungar, L. H., Crutchley, P., Preoţiuc-Pietro, D.,
… Schwartz, H. A. (2018). Facebook language predicts depression in medical records.
Proceedings of the National Academy of Sciences, 115, 1120311208.
Eisenhardt, K. M. (1989). Making fast strategic decisions in high-velocity environments. Academy
of Management Journal, 32, 543576.
Estrin, S., Mickiewicz, T., & Stephan, U. (2016). Human capital in social and commercial
entrepreneurship. Journal of Business Venturing, 31, 449467.
Fauchart, E., & Gruber, M. (2011). Darwinians, communitarians, and missionaries: the role of
founder identity in entrepreneurship. Academy of Management Journal, 54, 935957.
Foo, M.-D., Uy, M. A., & Murnieks, C. Y. (2015). Beyond affective valence: Untangling valence
and activation influences on opportunity identification. Entrepreneurship Theory & Practice,
39, 407431.
Frederiksen, L., Wennberg, K., & Balachandran, C. (2016). Mobility and entrepreneurship:
evaluating the scope of knowledge-based theories of entrepreneurship. Entrepreneurship
Theory and Practice, 40, 359380.
Freeman, M. A., Staudenmaier, P. J., Zisser, M. R., & Andresen, L. A. (2018). The prevalence and
co-occurrence of psychiatric conditions among entrepreneurs and their families. Small
Business Economics, 120.
Friedman, B. Y. J., Hastie, T., & Robert Ribshirani. (2000). additive logistic regression: a
statistical view of boosting. The Annals of Statistics, 28, 337374.
Frijda, N. H., Kuipers, P., & ter Schure, E. (1989). Relations among emotion, appraisal, and
emotional action readiness. Journal of Personality and Social Psychology, Vol. 57, pp. 212
228. US: American Psychological Association.
Funken, R., Gielnik, M. M., & Foo, M.-D. (2018). How can problems be turned into something
good? The role of entrepreneurial learning and error mastery orientation. Entrepreneurship
Theory and Practice, 00, 124.
Garud, R., Schildt, H. A., & Lant, T. K. (2014). Entrepreneurial storytelling, future expectations,
and the paradox of legitimacy. Organization Science, 25, 14791492.
Gelfand, M. J., Raver, J. L., Nishii, L., Leslie, L. M., Lun, J., Lim, B. C., … Yamaguchi, S. (2011).
43
Differences between tight and loose cultures: A 33-Nation Study. Science, 332, 11001104.
Glaub, M. E., Frese, M., Fischer, S., & Hoppe, M. (2014). Increasing personal initiative in small
business managers or owners leads to entrepreneurial success: a theory-based controlled
randomized field intervention for evidence-based management. Academy of Management
Learning & Education, 13, 354379.
Goel, S., & Karri, R. (2006). Entrepreneurs, effectual logic, and over-trust. Entrepreneurship
Theory and Practice, 30, 477493.
Graffin, S. D., Haleblian, J. (John), & Kiley, J. T. (2016). Ready, AIM, acquire: Impression
offsetting and acquisitions. Academy of Management Journal, 59, 232252.
Graves, S. B., & Ringuest, J. (2018). Overconfidence and disappointment in venture capital
decision making: An empirical examination. Managerial and Decision Economics, 39, 592
600.
Green, D. (2016). Shifts in social norms often underpin change. In how change happens (Vol. 15,
pp. 4768). Oxford University Press.
Gupta, V. K., Turban, D. B., Wasti, S. A., & Sikdar, A. (2009). The role of gender stereotypes in
perceptions of entrepreneurs and intentions to become an entrepreneur. Entrepreneurship
Theory and Practice, 33, 397417.
Heger, S. A., & Papageorge, N. W. (2018). We should totally open a restaurant: How optimism
and overconfidence affect beliefs. Journal of Economic Psychology, 67, 177190.
Hemsley, B., & Palmer, S. (2016). Two studies on twitter networks and tweet content in relation
to amyotrophic lateral sclerosis (ALS): Conversation, Information, and “Diary of a Daily
Life”. In A. Georgiou, L. K. Schaper, & S. Whetton (Eds.), Studies in health technology and
informatics (Vol. 227, pp. 4147). Amsterdam, Netherlands: IOS Press.
Henry, P. C. (2005). Life stresses, explanatory style, hopelessness, and occupational class.
International Journal of Stress Management, 12(3), 241256. https://doi.org/10.1037/1072-
5245.12.3.241
Hill, J., & Wright, L. T. (2001). A qualitative research agenda for small to medium‐sized
enterprises. Marketing Intelligence & Planning, 19, 432443.
Horwitz, A. V. (2015). The DSM-5 and the continuing transformation of normal sadness into
depressive disorder. Emotion Review, 7, 209215.
Hundley, G. (2001). Why and when are the self-employed more satisfied with their work?
Industrial Relations, 40, 293316.
Johnson, S. L., Madole, J. W., & Freeman, M. A. (2018). Mania risk and entrepreneurship:
overlapping personality traits. Academy of Management Perspectives, 32, 207227.
Jones, S., & Underwood, S. (2017). Understanding students’ emotional reactions to
entrepreneurship education. Education + Training, 59, 657671.
Kato, S., & Wiklund, J. (2011). Doing good to feel good A theory of entrepreneurial action based
in hedonic psychology. Frontiers of Entrepreneurship Research, 31, 123137.
Kautonen, T., Tornikoski, E. T., & Kibler, E. (2011). Entrepreneurial intentions in the third age:
the impact of perceived age norms. Small Business Economics, 37, 219234.
Khelil, N. (2016). The many faces of entrepreneurial failure: Insights from an empirical taxonomy.
Journal of Business Venturing, 31, 7294.
Krippendorff, K. (2004). Content analysis. Thousand Oaks, CA: Sage Publications Inc.
Ladd, E. R., Welsh, M. C., Vitulli, W. F., Labbé, E. E., & Law, J. G. (1997). Narcissism and causal
attribution. Psychological Reports, 80(1), 171178.
Lahti, T., Halko, M.-L., Karagozoglu, N., & Wincent, J. (2019). Why and how do founding
entrepreneurs bond with their ventures? Neural correlates of entrepreneurial and parental
bonding. Journal of Business Venturing, 34(2), 368388.
44
Lazarus, R. S. (1991). Progress on a cognitive-motivational-relational theory of emotion.
American Psychologist, 46, 819834.
Lerner, J. S., Li, Y., Valdesolo, P., & Kassam, K. S. (2015). Emotion and decision making. Annual
Review of Psychology, 66, 799823.
Li, K. (1987). Asymptotic Optimality for Cp, CL, cross-validation and generalized cross-
validation: Discrete index set. The Annals of Statistics, 15, 958975.
Liu, R. T., Kleiman, E. M., Nestor, B. A., & Cheek, S. M. (2015). The hopelessness theory of
depression: A quarter-century in review. Clinical Psychology: Science and Practice, 22, 345
365.
Losada, D. E., & Gamallo, P. (2020). Evaluating and improving lexical resources for detecting
signs of depression in text. Language Resources and Evaluation, 54, 1-124.
Mantere, S., Aula, P., Schildt, H., & Vaara, E. (2013). Narrative attributions of entrepreneurial
failure. Journal of Business Venturing, 28, 459473.
Marcatto, F., & Ferrante, D. (2008). The regret and disappointment Scale: An instrument for
assessing regret and disappointment in decision making. Judgment and Decision Making, 3,
8799.
McGrath, R. G. (1995). Advantage from adversity: Learning from disappointment in internal
corporate ventures. Journal of Business Venturing, 10, 121142.
McMullen, J. S., & Shepherd, D. A. (2006). Entrepreneurial action and the role of uncertainty in
the theory of the entrepreneur. The Academy of Management Review, 31, 132152.
Meijman, T. F., & Mulder, G. (1998). Psychological aspects of workload. In P. J. D. Drenth, H.
Thierry, & C. J. de Wolff (Eds.), A Handbook of Work and Organizational Psychology: Work
Psychology (2nd ed., pp. 534). Sussex, England: Psychology press.
Miceli, M., Castelfranchi, C., & Ortony, A. (2015). Expectancy and emotion. Cambridge, UK:
Oxford University Press.
Morris, M. W., Hong, Y., Chiu, C., & Liu, Z. (2015). Normology: Integrating insights about social
norms to understand cultural dynamics. Organizational Behavior and Human Decision
Processes, 129, 113.
Neuman, Y., Cohen, Y., Assaf, D., & Kedma, G. (2012). Proactive screening for depression
through metaphorical and automatic text analysis. Artificial Intelligence in Medicine, 56, 19
25.
Norem, J. K. (2001). The positive power of negative thinking. New York, NY: Basic Books.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, É.
(2012). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12,
28252830.
Peterson, C., Buchanan, G. M., & Seligman, M. E. P. (1995). Explanatory style: history and
evolution of the field. In Explanatory style (pp. 120). New York, NY: Routledge.
Peterson, C., Luborsky, L., & Seligman, M. E. (1983). Attributions and depressive mood shifts: A
case study using the symptomcontext model. Journal of Abnormal Psychology, 92, 96103.
Peterson, C., & Seligman, M. E. P. (1987). Explanatory style and illness. Journal of Personality,
55, 237265.
Pew Research Center, Barthel, M., Stocking, G., Holcomb, J., & Mitchell, A. (2016). “Nearly
eight-in-ten Reddit users get news on the site.” Washington, D.C: Pew Research Center.
Reece, A. G., & Danforth, C. M. (2017). Instagram photos reveal predictive markers of depression.
EPJ Data Science, 6, 15.
Ringuest, J. L., & Graves, S. B. (2017). Overconfidence and disappointment in decision-making
under risk: The triumph of hope over experience. Managerial and Decision Economics, 38,
409422.
45
Robins, C. J., & Hayes, A. M. (1995). The role of causal attributions in the prediction of
depression. In Explanatory style (pp. 7199). New York, NY: Routledge.
Roseman, I. J., & Smith, C. A. (2001). Appraisal theory: Overview, assumptions, varieties,
controversies. In K. R. Scherer, A. Schorr, & T. Johnstone (Eds.), Appraisal Processes in
Emotion: Theory, Methods, Research (pp. 319). New York, NY: Oxford University Press.
Rude, S. S., Gortner, E. M., & Pennebaker, J. W. (2004). Language use of depressed and
depression-vulnerable college students. Cognition and Emotion, 18, 11211133.
Saha, K., & De Choudhury, M. (2017). Modeling stress with social media around incidents of gun
violence on college campuses. Proceedings of the ACM on Human-Computer Interaction, 1,
127.
Schimmack, U., & Diener, E. (1997). Affect intensity: Separating intensity and frequency in
repeatedly measured affect. Journal of Personality and Social Psychology, 73, 13131329.
Schulman, P. (1995). Explanatory style and achievement in school and work. New York, NY:
Routledge.
Schuster, C. L., Nicolai, A. T., & Covin, J. G. (2018). Are founder-led firms less susceptible to
managerial myopia? Entrepreneurship Theory and Practice, 104225871880662.
Schwarz, N. (1990). Feelings as information: Informational and motivational functions of affective
states. In E. T. Higgins & R. Sorrentino (Eds.), Handbook of motivation and cognition:
Foundations for social behavior (Vol. 2). New York, NY: Guilford Press.
Sekulic, I., Gjurković, M., & Šnajder, J. (2018). Not just depressed: Bipolar disorder prediction on
Reddit. Proceedings of the 9th Workshop on Computational Approaches to Subjectivity,
Sentiment and Social Media Analysis, 7278. https://doi.org/10.18653/v1/W18-6211.
Shao, J. (1993). Linear model selection by cross-validation. Journal of the American Statistical
Association, 88, 486.
Shepherd, D. A. (2003). Learning from business failure: Propositions of grief recovery for the self-
employed. Academy of Management Review, 28, 318328.
Shepherd, D. A. (2004). Educating entrepreneurship students about emotion and learning from
failure. Academy of Management Learning & Education, 3, 274287.
Shepherd, D. A. (2019). Researching the dark side, downside, and destructive side of
entrepreneurship: It is the compassionate thing to do! Academy of Management Discoveries,
5, 217220.
Shepherd, D. A., Haynie, J. M., & McMullen, J. S. (2012). Confirmatory search as a useful
heuristic? testing the veracity of entrepreneurial conjectures. Journal of Business Venturing,
27, 637651.
Skinner, N., & Brewer, N. (2002). The dynamics of threat and challenge appraisals prior to
stressful achievement events. Journal of Personality and Social Psychology, 83, 678692.
Snee, H. (2013). Making ethical decisions in an online context: Reflections on using blogs to
explore narratives of experience. Methodological Innovations Online, 8, 5267.
Stanford Encyclopedia of Philosophy. (2019). Internet research ethics. Retrieved February 2, 2019,
from https://plato.stanford.edu/entries/ethics-internet-research/
Stephan, U. (2018). Entrepreneurs’ mental health and well-being: a review and research agenda.
The Academy of Management Perspectives, 32, amp.2017.0001.
Stephan, U., & Uhlaner, L. M. (2010). Performance-based vs socially supportive culture: A cross-
national study of descriptive norms and entrepreneurship. Journal of International Business
Studies, 41, 13471364.
Suàrez, J.-L., White, R. E., Parker, S., & Jimenez-Mavillard, A. (2020). Entrepreneurship and the
mass media: Evidence from big data. Academy of Management Discoveries, amd.2018.0177.
Suchman, M. C. (1995). Managing legitimacy: Strategic and institutional approaches. Academy
46
of Management Review, 20, 571610.
Tonidandel, S., King, E. B., & Cortina, J. M. (2018). Big data methods. Organizational Research
Methods, 21, 525547.
Torrès, O., & Thurik, R. (2019). Small business owners and health. Small Business Economics,
53, 311321.
Ucbasaran, D., Shepherd, D. A., Lockett, A., John Lyon, S., Lyon, S. J., & John Lyon, S. (2013).
Life after business failure: The process and consequences of business failure for
entrepreneurs. Journal of Management, 39, 163202.
van Beek, W., Berghuis, H., Kerkhof, A., & Beekman, A. (2011). Time perspective, personality
and psychopathology: Zimbardo’s time perspective inventory in psychiatry. Time & Society,
20, 364374.
van Dijk, W. W., & Zeelenberg, M. (2002a). Investigating the appraisal patterns of regret and
disappointment. Motivation and Emotion, 26, 321331.
van Dijk, W. W., & Zeelenberg, M. (2002b). What do we talk about when we talk about
disappointment? Distinguishing outcome-related disappointment from person-related
disappointment. Cognition and Emotion, 16, 787807.
van Gelderen, M. (2016). Entrepreneurial autonomy and its dynamics. Applied Psychology, 65,
541567.
Vasumathi, A., Govindarajalu, S., Anuratha, E. K., & Amudha, R. (2003). stress and coping styles
of an entrepreneur: An empirical study. Journal of Management Research, 3, 4351.
Weinberger, E., Wach, D., Stephan, U., & Wegge, J. (2018). Having a creative day: Understanding
entrepreneurs’ daily idea generation through a recovery lens. Journal of Business Venturing,
33, 119.
Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological
Review, 92, 548573.
Wennberg, K., Delmar, F., & Mckelvie, A. (2016). Variable risk preferences in new firm growth
and survival. Journal of Business Venturing, 31, 408427.
Wiklund, J., Nikolaev, B., Shir, N., Foo, M.-D., & Bradley, S. (2019). Entrepreneurship and well-
being: Past, present, and future. Journal of Business Venturing, 34, 579588.
Wiles, R., Crow, G., Heath, S., & Charles, V. (2008). The management of confidentiality and
anonymity in social research. International Journal of Social Research Methodology, 11, 417
428.
Williams, T. A., & Shepherd, D. A. (2017). Mixed method social network analysis: Combining
inductive concept development, content analysis, and secondary data for quantitative
analysis. Organizational Research Methods, 20, 268298.
Williamson, A. J., Battisti, M., Leatherbee, M., & Gish, J. J. (2019). Rest, zest, and my innovative
best: Sleep and mood as drivers of entrepreneurs’ innovative behavior. Entrepreneurship
Theory and Practice, 43, 582-610.
Wood, M. S., McKelvie, A., & Haynie, J. M. (2014). Making it personal: Opportunity
individuation and the shaping of opportunity beliefs. Journal of Business Venturing, 29, 252
272.
World Health Organization. (2014). Mental health: A state of well-being. Retrieved January 1,
2019, from https://www.who.int/features/factfiles/mental_health/en/
Wright, P. M. (2017). Making great theories. Journal of Management Studies, 54, 384390.
Wu, S., Matthews, L., & Dagher, G. K. (2007). Need for achievement, business goals, and
entrepreneurial persistence. Management Research News, 30, 928941.
Yang, Y. (2007). Consistency of cross validation for comparing regression procedures. The Annals
of Statistics, 35, 24502473.
47
TABLE 1
Emergent disappointment attribution types and dimensions
Causal attribution of entrepreneurial disappointment
Associated
attributional
response
Types
Conceptual
code
Description of code
Example quote
Pervasiveness
(specific vs
global)
Permanence
(stable vs
unstable)
Personalization
(internal vs
external)
Type of
attributional
response
Self
n = 131
Personal
shortcomings
n = 131
I realized that I have
personal shortcomings
that I did not expect, but
that I cannot change
either (e.g., personality
traits)
“My start-up is going well,
but the more progress we
make the more I learn my
weaknesses and limitations as
a human and a solo founder. I
am not sure I’ll be cut out for
this for the long term…I am
too flawed.”
Global
Stable
Internal
Maladaptive
Norms
n = 70
Societal
esteem
n = 9
The ongoing prejudice I
felt toward entrepreneurs
(e.g., after telling
someone I am an
entrepreneur) was worse
than I expected
“Why are guys no longer
interested in potentially
dating you when they find out
you are a startup founder?
I’m a female founder… when
interested guys find out that
I’m a founder… it is like a
light switch flicked off, and
the interest is gone.”
Global
Stable
Internal
Maladaptive
Discrimination
n = 61
The ongoing
discrimination I
experienced while doing
business
(e.g., discrimination
toward my gender,
ethnicity, physical
appearance etc.) was
worse than I expected
“ I am a sole non-technical
female founder. I could be
shitting rainbows and
unicorns out my ass (which I
do, daily) and investors still
won’t touch me with a stick.”
Global
Stable
Internal
48
Causal attribution of entrepreneurial disappointment
Associated
attributional
response
Types
Conceptual
code
Description of code
Example quote
Pervasiveness
(specific vs
global)
Permanence
(stable vs
unstable)
Personalization
(internal vs
external)
Type of
attributional
response
Others
n = 497
Team
n = 319
My team
(e.g.,
cofounders/employees)
is no longer meeting my
expectations
“… boy does it pain me to fire
people! Why do people
bother to come work if they
are going to treat it like an
after school club?”
Specific
Unstable
External
Adaptive
Personal
others
n = 28
My family, close-friends
and loved ones no longer
support me as I expected
“I have great parents … but
they no longer believe in my
start-up and think it is tearing
me apart and this makes me
very upset and depressed…
They don’t realize success
takes a while and many
iterations.”
Specific
Unstable
External
Ecosystem
others
n = 150
Agents in the business
ecosystem
(e.g., accountants,
mentors, competitors,
the government) no
longer meet my
expectations
“If you’re going to say you’re
going to help with
fundraising, but don’t reach
out to any of your contacts…
then you’re just an asshole.”
Specific
Unstable
External
49
Causal attribution of entrepreneurial disappointment
Associated
attributional
response
Types
Conceptual
code
Description of code
Example quote
Pervasiveness
(specific vs
global)
Permanence
(stable vs
unstable)
Personalization
(internal vs
external)
Type of
attributional
response
Entrepreneurial
process
n = 235
Demands
n = 184
The work demands of
entrepreneurship
(e.g., the workload, pace,
working hours) were at
times worse than I
expected
“My startup is killing my
marriage.. we have a new son
too.. and I am completely and
utterly consumed by my
startup. Every waking
moment, every single thought,
every single amount of brain
space is occupied by how we
can be successful.”
Specific
Unstable
External
Adaptive
Lack of
reward
n = 51
The reward I get from
entrepreneurial activities
(e.g., satisfaction from
being an entrepreneur) is
no longer what I
expected
“What would I get out of it?
Daily stress and sitting in
front of a computer every
day? Pride and reputation?
Money? … Is this really how I
want to spend the rest of my
life?!?? How is that at all
fulfilling??”
Specific
Unstable
External
Venture
performance
n = 290
Interest in
business
n = 259
The interest in my
business (e.g., customer
demand for my product)
is less than I expected
“I raised a round 6 months
ago and my startup is not
picking up. The New York
Fucking Times wrote
favourably about it and users
are still only trickling in… go
crawl under a rock and die?”
Specific
Unstable
External
Adaptive
Performance
of business
n = 31
The financial
performance of my
business
(e.g., turnover) is worse
than I expected
“A company I built went from
making 1-2 million a year for
eight years to 30k this year.
I'm letting the company
fold….”
Specific
Unstable
External
50
TABLE 2
Regression results for the extent of depression contrasted
between disappointment attributions
Dependent variable
Depression extent
Model number
Model 1
Model 2
Model 3
Estimate
SE
Estimate
SE
Estimate
SE
Intercept
-4.64 ***
(0.09)
-4.79 ***
(0.10)
-4.71 ***
(0.09)
Control variables
Source (anon)a
0.17 ***
(0.04)
0.16 ***
(0.04)
0.17 ***
(0.04)
Word Count
-0.00 ***
(0.00)
-0.00 ***
(0.00)
-0.00 ***
(0.00)
Past focus
0.02 **
(0.01)
0.03 ***
(0.01)
0.03 ***
(0.01)
Present focus
0.04 ***
(0.01)
0.04 ***
(0.01)
0.04 ***
(0.01)
Future focus
-0.02
(0.02)
-0.01
(0.02)
-0.01
(0.02)
Disappointment attributionb
Self
0.18 **
(0.07)
Norms
0.37 ***
(0.08)
Others
0.07
(0.05)
Process
0.06
(0.05)
Performance
Attributional response stylec
Maladaptive response
0.20 ***
(0.05)
Adaptive response
df (N)
945 (951)
941 (951)
944 (951)
F-statistic
28.25***
18.87***
26.99***
Adj. R2
0.12
0.14
0.14
This sample is comprised of human-classified disappointment posts only.
Note.
,
∗∗
, and
∗∗∗
indicate statistical significance at the 5%, 1% and 0.1% level, respectively.
Estimates represent unstandardized regression weights.
a Reference category is Reddit.
b Reference category is performance.
c Reference category is adaptive response.
51
FIGURE 1
Model of entrepreneurial disappointment, attributions and depression
-- APPENDICES --
53
APPENDIX A
Model of data analysis steps
1. Obtain data
Semi-anonymous online posts are
sourced and downloaded. n=
27,906
Posts are stripped of identifying
metadata (e.g., timestamp,
username) for ethical reasons.
2. Create an "entrepreneurial
disappointment" training set
Topics are uncovered in 14,504 posts.
Ensure posts are made by entrepreneurs.
Entreprepreneurs disclose
"disappointment" when expressing a
negative affective state, unexpectedness of
not achieving a desired condition, and
perceived low control over the condition.
974 posts containing entrepreneurial
disappointment are used as the affirmative
label in a disappointment training set.
3. Identify entrepreneurial
disappointment
A machine learning algorithm is
trained from the training set to
detect disappointment.
A total of 2,381 posts related to
disappointment are identified with
88% accuracy.
4. Analyze disappointment
attributions
Content analysis is conducted on
half the entrepreneurial
disappointment posts to identify
what causes entrepreneurs attribute
their disappointment to.
Codes are collapsed into five
themes: self, others, norms, process,
performance. n= 1,223.
5. Detect depression
Depression symptoms are detected
using a validated machine learning
classifier.
The validity of the measure is
further probed via a comparison of
depression scores with mental
health vocabulary.
8,822 of all posts contain no
depression.
6. Make statistical inferences
Depression and entrepreneurial
disappointment attributions are
entered into a linear regression.
In the regression, posts with
depression scores of zero are
removed (pairwise deletion removes
272 appraisals), and depression is
log transformed. n= 951.
Research Question 1
What causes do entrepreneurs attribute
their disappointment to?
Research Question 2
How do different attributions of
entrepreneurial disappointment relate
to depression?
54
APPENDIX B
Topics in corpus
Codes for non-disappointment posts related to three themes
4
. The first theme, most similar
to disappointment, is “disclosing experiences”, which accounts for 1,630 posts. “Disclosing
experiences” contains some of the attributes of the disappointment posts (but do not meet all
criteria). For example, posts in this theme were made by non-entrepreneurs (such as by
entrepreneurs’ loved ones, employees sharing a start-up experience), included the sharing of
feelings that were not necessarily disappointment (e.g., regret), and a collection of other personal
topics, such as broadcasting small wins. The second theme is promoting and advertising products
and services, which related to a total of 11,159 posts. These posts often include links to external
websites and clear marketing information. Finally, 741 posts related to basic feedback seeking
around entrepreneurial activities and events. In these posts, entrepreneurs or aspiring entrepreneurs
asked for feedback or advice from the community about entrepreneurial activities, rarely disclosing
anything personal or emotional. Examples of these themes, together with an example of disclosing
disappointment, are provided in Table B1.
4
Posts that were not written by entrepreneurs and did not meet the criteria of disappointment were classified as not
containing entrepreneurial disappointment. The topics of these posts were identified using open coding techniques
(Braun & Clarke, 2006) which involves freely coding text according to the themes that arise.
55
Table Appendix B1: Topics in corpus
High-level theme
Number
of posts
Example post
Disclosing
disappointment
974
First hire: close friend, turns out to be useless. What to do?
After a long search we finally found what looked to be the
perfect hire. A friend, (and the bf of my girlfriend’s best
friend), with a great resume. MBA and several AM-positions
in tech…. he has pretty much failed in every task he has
been given… Every single time me and my cofounder has
have to step in and do what he was supposed to do, putting
enormous pressure on us, … for our first hire we absolutely
needed someone to take some load off us so that we could
have room to grow… Had high hopes, turns out to be
disaster….”
Disclosing
experiences
1,630
“An angel investor approached me out of the blue and asked
to invest in my bootstrapped startup… Through the grape
vine, I guess an angel investor who has a significant
background in the industry heard about the project/startup
I'm working on and wants to invest. All my plans had
revolved around bootstrapping the startup and working on it
nights/evenings until it could comfortably sustain me. I
now find myself in unfamiliar territory: thinking about
investors, ownership and all that entails. The potential
investor is generally very highly regarded in the industry and
wants to invest $250K so that I can work on the project full
time. He also said that he would use his connections to help
me get some facetime with industry players that I might not
otherwise have access to…. I'm a little lost.
Promoting and
advertising
11,159
“Awesome deals at [site].com for startups. (Free $1000 in
[Brand] payment processing…”.
Seeking business
feedback and advice
741
“Tips for Launching? I've been working on a subscription
based tea service for the past couple of months and I'm ready
to launch it but I've never launched a company before. What
kind of things should I do to market it, get traffic and look
out for?”.
56
APPENDIX C
Keywords in disappointment posts
Disappointment keywords
Fail*
Hustler*
Minor*
Ringer*
Kill*
Tire*
Negoti*
Satisfi*
Gonna*
Deliveri*
Space*
Chore *
Shop*
Mentor*
Date*
Procrastin*
Failur*
Loner*
Uber*
Reject*
Firm*
Acceler*
Hindsight*
Patent*
Note. Top words were identified with a term frequency-inverse document frequency calculation
(TF-IDF). TF-IDF calculates how often a word appears in disappointment posts, controlling for
how common it is among the entire corpus of non-disappointment posts. Put another way, these
top words are rare in the corpus while being common to disappointment attribution posts.
57
APPENDIX D
Mental health measure probing: Pattern of change
Background
To ascertain whether the depression score accurately follows the same pattern of change
as related variables, we examined the relationship between the depression scores and sadness, the
use of first-person singular pronouns, and similarity with posts found in depression-related forums.
Sadness and first-person singular pronouns should have a positive relationship with depression
because sadness often accompanies depression and depressed individuals use slightly more first-
person singular pronouns (i.e., “I” and “me”) than non-depressed individuals. Similarly, posts
written by depressed individuals should share vocabulary that is not found in posts written by non-
depressed individuals. To examine the relationship between the depression score with the first two
variables, we ran the data through the Linguistic Inquiry Word Count automated text analysis
program (LIWC; Pennebaker, Booth, & Francis, 2007). LIWC is a validated tool for aiding text
analysis. First-person singular pronouns and sadness words are counted and divided by the total
number of words, resulting in an output between 0 and 1.
To compare the relationship between the depression score and depressed vocabulary, we
created a classifier to identify vocabulary in Reddit posts related to poor mental health.
Analytical Procedure
We downloaded 7,962 posts from eight subreddits related to mental health disclosures
(identified as “mental health subreddits” in research by Choudhury & De, 2014), and controlled
for words found in subreddits that are not related to mental health, with 10,768 posts (full list found
in Table D1). From these 18,730 posts, we were able to determine if a given post came from a
mental health subedit or not with 80% accuracy using a Multinomial Naive Bayes Classifier. We
58
applied this classifier to our data, and assigned a probability of the post to contain vocabulary
similar to the mental health-related subreddits.
Table Appendix D1: Mental health vocabulary builder
SubReddits
Mental health-related
Not mental health-related
Alcoholism
Anxiety
Bipolarreddit
Depression
Mentalhealth
MMFB
Socialanxiety
SuicideWatch
Askscience
Relationships
Healthanxiety
Writingprompts
Teaching
Socialanxiety
Writing
Parenting
Panicparty
Atheism
Christianity
Showerthoughts
Jokes
Lifeprotips
Writing
Personalfinance
Talesfromretail
Theoryofreddit
Talesfromtechsupport
Randomkindness
Talesfromcallcenters
Books
Fitness
Askdocs
Frugal
Legaladvice
Youshouldknow
Nostupidquestions
These three variables were entered into a regression with the depression score as the
dependent variable, as illustrated in Table D2. To correct for the right skew of sadness, we
conducted a log transformation on sadness before running the regression. The pairwise removal of
sadness variables with zero values from the dataset resulted in a reduced sample size for this
analysis (n = 4,970).
59
Results
The coefficients of vocabulary similarity, sadness, and first-person pronouns were all
significant, F(3,4966) = 184.93, p = 0.00. This indicates that as depression-forum vocabulary
similarity, expressed sadness, or first-person pronoun percentage increase, the depression score
also increases, thus suggesting the depression score follows a similar pattern of change to related
variables.
Table Appendix D2: Regression results for depression measure pattern consistency test
Depression measure
β
95% CI
[LL, UL]
r
Fit
(Intercept)
-4.42**
[-4.47, -4.37]
Vocabulary similarity
0.27**
[0.18, 0.37]
.01
Sadness words
0.18**
[0.16, 0.20]
.28**
First person singular pronoun
0.02**
[0.02, 0.03]
.20**
Adj. R2 = .100
95% CI[.09,.12]
Note. Dependent Variable: Degree of depression, with a log transformation, only retaining non-
zero values. n = 4970. LL and UL = lower and upper confidence interval limits. Significance
denoted by * and ** at the < .05 and < .01 level. r signifies the zero-order correlation.
60
60
APPENDIX E
Descriptive statistics and correlations
M
SD
min
max
1
2
3
4
5
6
1
Depression
-4.07
0.57
-6.17
-2.01
2
Word count
398.70
490.36
13
4919
-0.24***
3
Past focus
3.56
2.61
0
18.75
-0.03
0.08*
4
Present focus
12.52
3.90
0
30.77
0.23***
-0.24***
-0.51***
5
Future focus
1.18
1.04
0
9.09
-0.03
0.07*
-0.16***
0.12**
6
Source: Reddit
0.50
0.50
0
1
-0.24***
0.30***
-0.07*
-0.13**
0.02
7
Maladaptive disappointment attributions
0.16
0.37
0
1
0.13**
-0.05
-0.12***
0.01
-0.08*
-0.01
Represents dummy coded variable. Past, present, and future focus, and word count is estimated from LIWC calculations. Total number
of words in the dataset is 5,374,432, or approximately 7,000 pages of A4 single-spaced text.
*, **, *** signify significance at the 10%, 5%, and <1% level. Data-subset used for correlation matrix, n = 951.
61
61
APPENDIX F
Differences in means of depression extent, by causal attribution groupings
Entrepreneurial disappointment causal attributions
Note. Disappointment attributions are discrete categories, such that each post (n = 951) is assigned only one causal attribution.
The figure presents the mean scores in the extent of depression among the respective disappointment causal attributions with 95%
confidence intervals. The left plot shows differences in the extent of depression between disappointment attribution types, and the right
plot illustrates differences according to attributional responses. Depression is log-transformed. Non-positive scores of depression were
removed during log transformation, accounting for 272 posts balanced over the different attributions.
62
APPENDIX G
Detailed regression results for the extent of depression contrasted between disappointment attributions
Predictor
b
b
95% CI
[LL, UL]
sr2
sr2
95% CI
[LL, UL]
Tolerance
VIF
Fit
Intercept
-4.79**
[-4.98, -4.60]
Source (anon)
0.16**
[0.09, 0.23]
.02
[.00, .03]
.88
1.14
Word count
-0.00**
[-0.00, -0.00]
.01
[.00, .03]
.86
1.17
Past focus
0.03**
[0.02, 0.05]
.01
[-.00, .03]
.69
1.45
Present focus
0.04**
[0.03, 0.05]
.05
[.03, .08]
.67
1.49
Future focus
-0.01
[-0.04, 0.02]
.00
[-.00, .00]
.95
1.05
Disappointment attributionsa
Self
0.18**
[0.05, 0.31]
.01
[-.00, .02]
.76
1.31
Norms
0.37**
[0.22, 0.53]
.02
[.00, .04]
.81
1.24
Others
0.07
[-0.02, 0.16]
.00
[-.00, .01]
.6
1.67
Process
0.06
[-0.04, 0.17]
.00
[-.00, .01]
.65
1.53
Adj. R2 = .145
95% CI[.11,.19]
Note. A significant b-weight indicates the semi-partial correlation is also significant. b represents unstandardized regression weights.
sr2 represents the semi-partial correlation squared. LL and UL indicate the lower and upper limits of a confidence interval,
respectively. a indicates reference category is performance.
* indicates p < .05. ** indicates p < .01.
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