A Neuroscience Approach to Women Entrepreneurs' Pitch Performance: Impact of Inter-Brain Synchrony on Investment Decisions

Abstract

Making a successful pitch to investors is vital to the success of startups. Improving pitch performance in women entrepreneurs might be an effective mechanism to close gender disparity in entrepreneurship. Drawing on social neuroscience studies, we present our scientific approach to shedding light on the role of "inter-brain synchrony" between women entrepreneurs and investors in pitch performance. Using functional near-infrared spectroscopy (fNIRS) hyperscanning, we will scan 40 entrepreneur-investor dyads who engage in naturalistic pitch events. We will elucidate patterns of inter-brain synchrony that are associated with pitch performance. Additionally, we will assess whether the sex composition of an entrepreneur-investor dyad affects these associations. A better understanding of the inter-brain signatures underlying successful (and unsuccessful) pitches will generate insights into the design of novel and effective interventions that can help catalyze the success of women entrepreneurs. Potential Managerial Implications. Elucidating the underlying inter-brain mechanisms associated with strong pitch performance will generate practical implications for women entrepreneurs. Our empirical findings will help craft more effective behavioral interventions (e.g., belonging [Walton et al., 2020] and interpersonal trust interventions [Balters et al., 2022]) and pitch training (e.g., Clingingsmith et al., 2022) tailored to improve pitch performance of women entrepreneurs. The results of this study can also inform the development of fNIRS neurofeedback paradigms (e.g., Liu et al., 2016) that can provide entrepreneurs and investors with vital information to voluntarily regulate their behavior in real-time. Lastly, our neuroscience-based approach may offer a new, powerful way to better match startups with investors thereby enhancing the efficiency of the matching process.

Full text

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A Neuroscience Approach to Women Entrepreneurs’ Pitch Performance:
Impact of Inter-Brain Synchrony on Investment Decisions
Stephanie Balters1, Sohvi Heaton2, Allan L. Reiss1,3,4
1 Stanford University, Department of Psychiatry and Behavioral Sciences
2 Santa Clara University, Leavey School of Business
3 Stanford University, Department of Pediatrics
4 Stanford University, Department of Radiology
Abstract. Making a successful pitch to investors is vital to the success of startups. Improving pitch
performance in women entrepreneurs might be an effective mechanism to close gender disparity in
entrepreneurship. Drawing on social neuroscience studies, we present our scientific approach to
shedding light on the role of “inter-brain synchrony” between women entrepreneurs and investors in
pitch performance. Using functional near-infrared spectroscopy (fNIRS) hyperscanning, we will scan
40 entrepreneur-investor dyads who engage in naturalistic pitch events. We will elucidate patterns of
inter-brain synchrony that are associated with pitch performance. Additionally, we will assess whether
the sex composition of an entrepreneur-investor dyad affects these associations. A better
understanding of the inter-brain signatures underlying successful (and unsuccessful) pitches will
generate insights into the design of novel and effective interventions that can help catalyze the success
of women entrepreneurs.
Potential Managerial Implications. Elucidating the underlying inter-brain mechanisms associated with
strong pitch performance will generate practical implications for women entrepreneurs. Our empirical
findings will help craft more effective behavioral interventions (e.g., belonging [Walton et al., 2020]
and interpersonal trust interventions [Balters et al., 2022]) and pitch training (e.g., Clingingsmith et
al., 2022) tailored to improve pitch performance of women entrepreneurs. The results of this study
can also inform the development of fNIRS neurofeedback paradigms (e.g., Liu et al., 2016) that can
provide entrepreneurs and investors with vital information to voluntarily regulate their behavior in
real-time. Lastly, our neuroscience-based approach may offer a new, powerful way to better match
startups with investors thereby enhancing the efficiency of the matching process.
1. Introduction
Making a successful pitch to investors is vital for entrepreneurs across all business stages (Chen et al.,
2009; Drover et al., 2017). However, only a small number of business ventures successfully compete
for investor funding. These difficulties are magnified for women-led startups (Balachandra et al., 2021;
McSweeney et al., 2022; Sanchez-Ruiz et al., 2021). Only 2.3 % of venture capital investment went to
women founders in 2020 (Crunchbase, 2020), although companies founded by women deliver higher
returns on investment – more than twice as much per dollar invested as those by men (BCG, 2018).
Besides the missed opportunity of improving global gross domestic product by 2-3 % of global GDP or
$2 trillion (Miao, 2022), we are wasting half our genetic pool of innovative intelligence. Catalyzing
pitch performance is thus a vital mechanism in fostering the success of women entrepreneurs.

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Figure 1. Experimental setup of the fNIRS hyperscanning study. Entrepreneur-investor dyads engage in a
naturalistic pitch event.
Prior research on pitch performance has put forward various factors that may influence pitch
performance, including individual attributes (e.g., founders’ passion [Shane et al., 2020] and
confidence [Sanchez-Ruiz et al., 2021]) and team characteristics (e.g., team diversity [Foo et al., 2005]).
Several studies investigated entrepreneur-investor dyads and examined whether specific traits in
entrepreneurs (e.g., personality, experience, social capital) influence investor decision-making
(Murnieks et al., 2011; Franke et al., 2008). Socio-psychological phenomena such as similarity effect
(Murnieks et al., 2011) and opinion conformity (Sanchez-Ruiz et al., 2021) have been argued to
influence pitch performance. A growing number of empirical studies have further investigated the
effects of gender on pitch performance and demonstrated mixed findings. Some studies found
gendered differences in the level of assertiveness (McSweeney et al., 2022) and self-promotion
(Sanchez-Ruiz et al., 2021), as well as rhetorical strategies (Balachandra et al., 2021) that worked
against the pitch performance of women. On the contrary, other researchers showed no gender
difference in pitch performance (Balachandra et al., 2019; Hohl et al., 2021).
The existing studies highlight the social complexity of pitches that involve many conscious
and subconscious social variables. While emerging studies have utilized behavioral assessments to
evaluate gendered pitch performance, little is known about the inter-brain mechanisms underlying
entrepreneur-investor pitches. The current study fills this research gap. Specifically, we leverage the
emerging technology of fNIRS hyperscanning to measure the inter-brain dynamics of an entrepreneur-
investor dyad during a naturalistic pitch. Our aims are (1) to elucidate inter-brain mechanisms that are
associated with high (or low) levels of pitch performance and (2) to assess whether the sex
composition of an entrepreneur-investor dyad impacts these inter-brain mechanisms.
2. fNIRS hyperscanning to understand inter-brain mechanisms during entrepreneur-investor pitches
Functional NIRS is an optical brain imaging technology that measures changes in cortical oxygenation
as a proxy for neural activation (Cui et al., 2010; Strangman et al., 2002). In recent years, fNIRS systems
have become increasingly portable and affordable, allowing researchers to investigate neurocognitive
behavior in real-world settings (e.g., Bruno et al., 2018). In contrast to electroencephalography (EEG),
another portable brain imaging technology, fNIRS is relatively robust to motion artifacts and has a
Inter-brain synchrony

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relatively high spatial resolution (~1 cm; Cui et al., 2010). Researchers have extended fNIRS
measurements from single-brain to inter-brain applications (i.e., “fNIRS hyperscanning”) to investigate
shared brain functions underlying social interactions (Cui et al., 2012; Funane et al., 2011). A common
approach has been to study when and how neural processes become synchronized and how inter-
brain synchrony (“IBS”, i.e., correlation of cortical activity between brains) relates to behavioral
measures of interaction (Babiloni & Astolfi, 2014; Balters et al., 2020).
Numerous studies have shown the role of IBS in various behavioral and performance
outcomes. Studies have found that increased IBS is associated with synchronously executed activities
such as joint limb movement (Holper et al., 2012; Niu et al., 2019; Nozawa et al., 2019), coordinated
button presses (Cheng et al., 2015; Cui et al., 2012; Funane et al., 2011), singing (Osaka et al., 2014,
2015), drumming (Duan et al., 2015), and marching (Ikeda et al., 2017). Beyond the mere engagement
in the same activity, it appears that shared attention is essential for the occurrence of IBS. Studies
have found increased IBS when pairs completed a task together as opposed to completing the identical
task individually in parallel (Feng et al., 2020; Fishburn et al., 2018; Hu et al., 2017; N. Liu, Mok, et al.,
2016; Zhou et al., 2022). In addition to joint attention, research shows that joint goal-directed
intention is related to IBS (Kruse et al., 2021), and dyads demonstrate more IBS when cooperating
than when competing with one another (Cui et al., 2012; T. Liu et al., 2017; Lu et al., 2019). Increased
IBS has also been observed in other prosocial interaction contexts, such as in-group bonding (Yang et
al., 2020), dyadic empathy (Bembich et al., 2021), and after gift exchanges (Balconi et al., 2019).
The literature on IBS also provides clues as to whether the sex composition of the interacting
entrepreneur-investor dyad could influence pitch performance. For example, Baker et al. (2016) and
Cheng et al. (2015) conducted wavelet synchrony analyses on fNIRS hyperscanning data for dyadic
cooperation tasks and found that inter-brain synchrony is highly dependent on the sex composition of
the dyad. Differences in fNIRS neural signatures in association with the sex composition of a dyad also
emerged during spontaneous face-to-face deception (Zhang, Liu, Pelowski, & Yu, 2017), risky decision-
making during gambling games (Zhang et al., 2017), and group creative idea generation (Lu et al.,
2020). Given the combined findings, we expect that IBS is associated with pitch performance or its
correlates (e.g., cooperative behavior), and might alter depending on the sex composition of an
entrepreneur- investor dyad.
3. Dynamic inter-brain biomarkers of pitch performance
A pitch is a highly dynamic social interaction and likely requires a dynamic analysis approach to
uncover associations between IBS and pitch performance. Researchers have traditionally utilized task-
averaging approaches to assess mean levels of IBS (e.g., “over a task duration of 10 minutes”, Balters
et al. 2021). A limitation of these approaches is that they do not capture the dynamic nature of social
interaction. Our research group therefore developed a novel “dynamic IBS” approach (Li et al., 2021).
This approach allows the study of inter-brain synchrony patterns (i.e., “inter-brain states”) with higher
temporal resolution. Inter-brain synchrony can simultaneously occur between and across functional
regions at any given time, and the dynamic IBS approach accounts for this complexity. It derives inter-
brain states characterized by a “heatmap” of IBS values for all possible region of interest (ROI) pair
combinations. In our recent work (Balters et al., 2022), we linked these inter-brain states for the first
time to behavioral measures of cooperation (e.g., conversational turn-taking behavior). As shown in
Figure 2, we achieved the identification of inter-brain states that were significantly associated with
adverse

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Figure 2. Using fNIRS hyperscanning, we studied the differences in IBS states between in-person and virtual
interaction during a problem-solving task (Balters et al. 2022). Dyads interacted either in person or over Zoom®
video conferencing. Results showed that the occurrence rate of inter-brain state 1 was associated with a
reduction in behavioral cooperation (r = -0.52). In contrast, the occurrence rate of inter-brain state 2 was
associated with an increase in behavioral cooperation (r = 0.37). Due to the dynamic character of the analytical
approach, we can observe the occurrence of each inter-brain state over time, and identify temporal differences
between groups (left).
behavioral cooperation (i.e., inter-brain state 1) and positive behavioral cooperation (i.e., inter-brain
state 2). Notably, the dynamic IBS analytical approach allows researchers to observe the dynamics
within and between brain states over time. It is thus possible to study the functional characteristics of
each brain state and identify “critical events” that can be linked to changes in behavior through video
coding, for example. Due to dynamic characteristics, we could elucidate differences in the occurrence
of inter-brain states between dyads that interacted in person and over Zoom® during a problem-
solving task (Figure 2; Balters et al., 2022). We leverage this cutting-edge analytical approach in the
present study to understand the impact of inter-brain states on investment decisions.
2. Methodology
2.1 Participants
A total of N = 40 female entrepreneurs and N = 40 investors (N = 20 females, N = 20 males) will
participate in the study. All participants will be right-handed, healthy, with normal or corrected to
normal hearing and vision. The age range will be limited to 18 – 50 years to avoid aging-related chronic
diseases. We will recruit participants through our collaborating entrepreneurship centers and VC
organizations at Stanford and the broader Silicon Valley. For this first proof-of-concept study, we will
focus on informal investors and student entrepreneurs due to the cost and challenges of obtaining
experts. It is common for exploratory studies to utilize naive subjects (Shane et al., 2020; Chen et al.,
2009; Hsu et al., 2017). We will randomly match female entrepreneurs with either a female investor
(group 1) or a male investor (group 2). Each group includes twenty dyads (Table 1). Age and
race/ethnicity will not be matched across participants. We will therefore utilize both variables as
covariates in statistical analyses. Participants of the same dyad will be previously unacquainted. We
will recruit participants through email lists and social media and obtain written consent before the
study. Stanford’s Institutional Review Board has approved the experimental methodology (IRB
#18160).
Inter-brain state 1
Time (8 min)
Occurrence rate
1
0
In-person group
Virtual group
1.51
-1.51
Inter-brain synchrony
Inter-brain state 2
Time (8 min)
Occurrence rate
1
0
In-person group
Virtual group
1.51
-1.51
Inter-brain synchrony

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Female investor
Male investor
Female entrepreneur
Group 1 (20)
Group 2 (20)
Table 1 | Study design will follow a between-subject design with 20 dyads per group.
2.2 Experimental Procedure
The dyad partners will interact face-to-face in a presentation setting (Figure 1). We will attach fNIRS
caps while participants are in the same room and instruct participants not to talk to one another during
that time. Before starting the experiment, participants will have three minutes to introduce
themselves. During the pitch, participants will be alone in the room and receive task instructions via
audio prompts. We will collect audio and video recordings of the participants, with four portable video
cameras capturing front and side views of both participants. After the experiment, each participant
will complete a post-experimental survey in a separate room to assess investment interest, pitch
performance, subjective experiences during the pitch, and demographical information.
2.3 Experimental Tasks
Each entrepreneur will have 45 minutes to prepare for their pitch immediately prior to the study.
Entrepreneurs will make a 10-minute pitch based on their pitch script followed by a 5-minute Q&A
session. The pitch will be entirely oral, with no presentation tools allowed. Entrepreneurs and
investors will receive the same task instruction.
2.4 Functional NIRS Hyperscanning Data Acquisition and Processing
Data Acquisition. We will record the cortical hemodynamic activity of each participant using a
continuous wave fNIRS system (NIRSport2 System, NIRX, Germany) with two wavelengths (760 and
850 mm) and a sampling frequency of 10.2 Hz. We will divide a total of 128 optodes (64 sources x 64
detectors) between the two participants resulting in 100 measurement channels per participant.
Optodes will spread over the entire cortex according to the international 10-20 EEG placement system.
Additionally, we will place sixteen short channels per participant across the cortex to capture and
correct background physiological noise (e.g., cardiac, respiratory, and blood pressure fluctuations).
We will utilize plastic connectors between each source/detector channel pair to maintain an estimated
3 cm distance.
Data Preprocessing. We will analyze the raw fNIRS data using the NIRS Brain AnalyzIR Toolbox
(Santosa et al., 2018) in Matlab version R2021a (MathWorks, Inc.). We will assess data quality via the
scalp coupling index or “SCI” (Pollonini et al., 2016) and exclude the channels with excessive noise (i.e.,
SCI ≤ 0.8) from subsequent analyses to ensure good data quality. We will convert the remaining raw
data to optical density data and apply motion artifacts correction using a wavelet motion correction
procedure (Molavi & Dumont, 2012). Subsequently, we will transform data into concentration changes
of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) according to the Modified
Beer-Lambert Law (Wyatt et al., 1986). Since HbO and HbR data are relative values, we will convert
resulting data to z-scores. We will create 32 ROIs via source localization (Huppert et al., 2017) by
averaging all channels that shared a common fNIRS source (Balters, Miller, et al., 2022). We will project
all ROIs onto the cortical surface using an automatic anatomical labeling method (Lancaster et al.,

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2000; Singh et al., 2005). Because HbO measures are known to be more robust and sensitive to task-
associated changes compared to HbR measures (Ferrari & Quaresima, 2012; Plichta et al., 2006), we
will only use HbO data for further analyses as common in fNIRS hyperscanning research (Balters et al.,
2020).
Dynamic Inter-brain State Analyses. We will use Wavelet Transform Synchrony or “WTC” (Cui et al.,
2012) analysis to assess averaged inter-brain synchrony (IBS), i.e., the similarity between NIRS signals
of dyad partners. For a more in-depth explanation of WTC, please see (Grinsted et al., 2004).
Specifically, we will calculate IBS between each ROI and the rest of the ROIs on the converted HbO
time series (a total of 1024 combinations: 32 ROIs x 32 ROIs). We will then average the IBS between
the same ROI pairings resulting in 528 ROI pairings. We will calculate the average synchrony value
between 0.15 and 0.02 Hz. This frequency band will allow us to exclude noise associated with cardiac
pulsation (about 1 Hz) and respiration (0.2–0.3 Hz; [Molavi & Dumont, 2012]). Finally, we will apply
the dynamic IBS approach (Li et al., 2021) following the procedures described in Balters et al. (2022).
2.5 Additional Assessments during the Experiment
Investment Interest. Following Shane et al. (2020, p.8), we will measure investment interest as pitch
performance. Investors rate five sub-scores on a 7-point Likert scale: (1) “I would be interested in
seeing more information about this venture”, ranging from “strongly disagree” to “strongly agree”;
(2) “Based on the information at hand, I would be interested in investing in this company”, ranging
from “strongly disagree” to “strongly agree”; (3) “This company represents a good investment
opportunity for me”, ranging from “strongly disagree” to “strongly agree”; (4) “I would expect higher
financial returns from investing in this company than in other startup companies”, ranging from
“strongly disagree” to “strongly agree”; (5) “The content of this elevator pitch was, ranging from “very
poor” to “excellent”. We will calculate the final score by averaging the five sub-scores.
Pitch Performance. Entrepreneurs and investors will rate their subjective experience of pitch
performance on a 9-point Likert scale ranging from “extremely poor” to “extremely good.”
Level of Cooperation. Participants will rate the overall cooperation of the dyad on a 9-point Likert scale
ranging from “not at all cooperative” to “extremely cooperative”.
Affective Responses. Participants will complete the Affect Grid Survey (Russell, 1980) to inquire about
their level of arousal and level of valence on a 9-point Likert scale ranging from “sleepy” to “energized”
and “unpleasant” to “pleasant” respectively. We will also obtain self-reported levels of stress via the
Perceived Stress Scale (Cohen et al., 1994), using a 9-point Likert scale ranging from “low” to “high”.
Interpersonal Closeness Index. Participants will rate their subjective sense of closeness towards their
dyad partner (i.e., “Interpersonal Closeness”]) on five 7-point Likert sub-scales, including questions
about connectedness and trust (Wiltermuth & Heath, 2009), an adapted version of the inclusion of
other in self scale (Aron et al., 1992), likeability (Hove & Risen, 2009) and similarity in personality
(Valdesolo & DeSteno, 2011). We will calculate the final score by averaging the sub-scores.
Coded Behavioral Metrics. We will capture videos of the dyadic interaction to derive additional
behavioral metrics, including the number of conversational turn-taking and conversational

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dominance. At a sampling frequency of roughly 1 Hz, we will code who is talking at a given moment.
We will count the number of turn-taking between two dyad partners and calculate conversational
dominance as the ratio of the total talk duration of the less dominant partner and the total talk
duration of the more dominant partner. Thus, a talk duration of one will indicate that both partners
had an equal share of talking.
2.6 Post Experimental Assessments
Personality Traits. Participants will complete the NEO-FFI-3 survey (McCrae & Costa, 2007), the Adult
Attachment Scale Survey (Collins & Read, 1990), and Wong and Law’s Emotional Intelligence Survey
(Wong & Law, 2002) to capture personality traits. For the NEO-FFI-3 survey, we will calculate T-scores
for all five subscales, including Neuroticism, Extraversion, Openness, Agreeableness, and
Conscientiousness. For the Adult Attachment Scale, we will calculate the Avoidant and Anxious
subscales. For the Emotional Intelligence score, we will calculate the average of the four sub-scores:
self-emotional appraisal, other’s emotion appraisal, use of emotion, and emotion regulation. We will
utilize the personality trait scores as potential covariates in statistical analyses.
Level of Professional Experience. We will measure professional experience as the number of years of
work experience reported by participants.
Level of Self-Efficacy. As a measure of self-efficacy, both entrepreneurs and investors will rate their
degree of certainty in performing the role from “completely unsure” to “completely sure” (Chen et al.
1998).
3. Analytical Methods and Planned Research
We are currently establishing data collection and present preliminary results at the conference. For
data analysis, we will characterize the properties of inter-brain states by the occurrence rate of each
inter-brain state following our previous work (Balters et al., 2022; Li et al., 2021). The occurrence rate
of a state is defined as the percentage of total duration an inter-brain state occurs within the entire
task period (Allen et al., 2014). We will utilize independent t-tests to assess whether there are
differences in the occurrence rate between the two groups (same sex versus opposite sex dyads). We
will calculate Pearson’s correlation between occurrence rate and pitch performance metrics. As an
additional exploratory analysis, we will assess whether the dynamics of inter-brain states over time
differ between the groups. We will also explore if we can identify other moderating factors that impact
pitch performance in women entrepreneurs (e.g., personality traits, experience, self-efficacy).
Building on the current study, we plan to extend the methods and measures presented in this
paper to real-life pitch events. In this context, we aim to utilize fNIRS hyperscanning to develop
interventions and training that can effectively improve the pitch performance of women
entrepreneurs. We will also explore the opportunity of using real-time feedback to help women
entrepreneurs modify IBS more successfully during the dyadic interaction. Lastly, we propose to
extend two-person hyperscanning to hyperscanning of three persons simultaneously. Such a design
will allow us to investigate the effects of inter-brain synchrony in a more realistic pitch setting that
usually involves multiple actors. Related findings might allow us to derive and understand inter-brain
pattern that are associated with the efficiency of the entrepreneur-investor matching process. We
hope this work will provide novel and valuable insights into key factors of successful pitches and foster
the success of women entrepreneurs.

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Funding
This work is supported by a Hasso Plattner Design Thinking Research Program grant.
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