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Spotlight on Women in Tech: Fostering an Inclusive Workforce when Exploring and Exploiting Digital Innovation Potentials

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In the digital industries, as elsewhere, an inclusive workforce is essential to reflect the diversity of society and reach different customer groups. However, women are particularly underrepresented in the digital technology sector. Even research on digital innovation processes and outcomes lacks a gender-aware perspective. Hence, our study focuses on the inclusion of women in tech. Drawing on a gender-aware framework and applying computational topic modelling to analyze 560 threads in a professional women online network, we identify the topics that preoccupy women involved in exploring and exploiting digital innovation potentials. We find that women are more concerned with topics related to the exploitation (67%) than the exploration (33%) of digital innovation potentials, suggesting that digital innovation potentials for women remain untapped. Our findings contribute to an understanding of how gender interferes with digital innovation processes, and informs measures to support companies seeking to address the underrepresentation of women in tech.
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Spotlight on Women in Tech: Fostering an Inclusive Workforce Spotlight on Women in Tech: Fostering an Inclusive Workforce
when Exploring and Exploiting Digital Innovation Potentials when Exploring and Exploiting Digital Innovation Potentials
Franziska Schmitt
Freie Universität Berlin
, fraenziska.schmitt@gmail.com
Janina Sundermeier
Freie Universität Berlin
, janina.sundermeier@fu-berlin.de
Nicolai Bohn
Hasso-Plattner-Institute
, nicolai.bohn@hpi.de
Ariane Morassi Sasso
Hasso Plattner Institute
, ariane.morassi-sasso@hpi.de
Follow this and additional works at: https://aisel.aisnet.org/icis2020
Schmitt, Franziska; Sundermeier, Janina; Bohn, Nicolai; and Morassi Sasso, Ariane, "Spotlight on Women
in Tech: Fostering an Inclusive Workforce when Exploring and Exploiting Digital Innovation Potentials"
(2020).
ICIS 2020 Proceedings
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Spotlight on Women in Tech
Forty-First International Conference on Information Systems, India 2020
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Spotlight on Women in Tech: Fostering an
Inclusive Workforce when Exploring and
Exploiting Digital Innovation Potentials
Completed Research Paper
Franziska Schmitt
Freie Universität Berlin
Garystraße 21, G -14195 Berlin
fraenziska.schmitt@gmail.com
Janina Sundermeier
Freie Universität Berlin
Garystraße 21, G-14195 Berlin
janina.sundermeier@fu-berlin.de
Nicolai Bohn
Hasso-Plattner-Institute Potsdam
Rudolf-Breitscheid-Str. 189, G -14482
Potsdam, nicolai.bohn@hpi.de
Ariane Morassi-Sasso
Hasso-Plattner-Institute Potsdam
Rudolf-Breitscheid-Str. 187, G -14482
Potsdam, ariane.morassi-sasso@hpi.de
Abstract
In the digital industries, as elsewhere, an inclusive workforce is essential to reflect the
diversity of society and reach different customer groups. However, women are
particularly underrepresented in the digital technology sector. Even research on digital
innovation processes and outcomes lacks a gender-aware perspective. Hence, our study
focuses on the inclusion of women in tech. Drawing on a gender-aware framework and
applying computational topic modelling to analyze 560 threads in a professional women
online network, we identify the topics that preoccupy women involved in exploring and
exploiting digital innovation potentials. We find that women are more concerned with
topics related to the exploitation (67%) than the exploration (33%) of digital innovation
potentials, suggesting that digital innovation potentials for women remain untapped.
Our findings contribute to an understanding of how gender interferes with digital
innovation processes, and informs measures to support companies seeking to address the
underrepresentation of women in tech.
Keywords: Digital Innovation, Women in Technology, Inclusion, Inclusive Workforce
Introduction
“I have a health tech startup and am desperately looking for a female developer. [..] We are making
women the experts on their body and we can't have a man build that!“ (member of Elpha.com)
The ongoing digitalization in many industries is fostering the emergence of an ever-increasing number of
novel business opportunities (Nambisan 2017; Nambisan et al. 2019; Yoo et al. 2012), which transform the
way we communicate (e.g., Whatsapp), work (e.g., Slack), and even plan our reproduction (e.g., Clue). The
exploitation of such novel business opportunities is driven by digital innovation processes that draw on
digital technologies, such as cloud computing, social media, 3D printing, and data analytics (Nambisan
2017). In particular, digital technologies are found to facilitate digital innovation processes (von Briel et al.
2018; Shaheer and Li 2020). Online platforms, such as Amazons Mechanical Turk, Kickstarter, Waze or 99
Designs, for example, enable crowdsourcing of mobile applications, financial resources, relevant data and
designs. These are just a few examples of how digital innovation extensively influences the nature and
structure of new products and services as well as their creation processes (Bharadwaj et al. 2013; Boudreau
and Lakhani 2013; Nambisan et al. 2017).
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The far-reaching implications of digital innovation on our private and professional lives require reflexivity
and caution in the creation and implementation of digital products and services in order to avoid the
(unintended) exclusion of certain user groups (McAdam et al. 2019; Olbrich et al. 2015). However, there
are numerous examples that illustrate how biased digital innovation processes and outcomes often are. For
instance, a machine learning-enabled recruiting engine that was only recently developed and applied by
Amazon Inc. to automatize the preprocessing of resumes for software developer jobs was found to
discriminate against female applicants (Paul 2019). The reason for the limited inclusiveness of the
algorithm was related to its supervised nature and its training set being based on past data, reflecting male
dominance in the tech industries (Houser 2019). Similarly, biased training data also accounts for flaws in
facial recognition software that has been shown to prevent people of color to use face recognition software
to unlock their iPhones (Rutkin 2016). The root cause for such shortcomings in products and services has
been related to homogeneous workforces whose very specificand narrowperspectives were unable to
determine flaws in digital innovation processes and outcomes (Trauth 2017; Olbrich et al. 2015). Although
it has been repeatedly shown that diversity is the key to unlocking innovation potentials (Dai et al. 2019),
workforces in our Western societies are still far from being inclusive (Houser 2019; Trauth et al. 2018).
While this has crucial implications for the types of products and services created (Urquhart and Underhill-
Sem 2009), recent statistics show that the proportion of women exploring and exploiting digital innovation
potentials (from here on referred to as women in tech) is still considerably low (Hewlett 2014; McAdam et
al. 2019). In particular, the number of women working in technical departments of established companies
in the US has averaged only 20% in recent years (Houser 2019; Peck 2015). Looking at the start-up context,
a similar picture emerges: the share of female founders in technology-related branches averages 10%
worldwide (GEM 2020). Like any other underrepresented groups in the workforce, the lack of women in
tech has fundamental implications for the value offerings created (McAdam et al. 2019; Olbrich et al. 2015)
and, consequently, for the development of a vibrant and growing economy in general, (Gatewood et al.
2009), and for the tech industries in particular (Trauth 2017).
Despite recent research efforts to investigate the determinants of the low proportion of women in tech (e.g.
Annabi and Lebovitz 2018; Armstrong et al. 2018; Brush et al. 2019; McAdam et al. 2019), digital innovation
processes and outcomes are still predominantly treated as gender-neutral in the IS literature. Drawing on
feminist theories, we argue that such a neutral perspective neglects the obstacles faced by women in tech,
perpetuating their underrepresentation in the respective workforces (Gatewood et al. 2008, Trauth 2017;
Brush et al. 2008). In particular, liberal and social feminist theories point to the fact that discriminatory
barriers, systematic biases, and women’s socialization processes account for their disproportionately low
numbers in the tech industries (Brush et al. 2009; Greene and Brush 2000). Since these obstacles have
already received considerable research attention (Adam et al. 2002; Adya 2008; McGee 2017), our research,
instead of considering the obstacles faced by women entering the digital innovation domain, adopts a
‘positive’ approach towards workforce diversity and inclusiveness (Joshi et al., 2018) by profiling women
already operating in tech workforces. On this basis, we are able to provide insights into how their gender
intersects with digital innovation processes.
To that end, we conceptualize women’s contributions towards the exploration and exploitation of digital
innovation potentials as entrepreneurial activities. Opportunities for digital innovation potential are first
explored through discovery, play, flexibility, risk-taking, search, and variation, and the identified
opportunities are then exploited, refined and extended through selection, production, implementation, and
execution (March 1991). IS research approaches the notions of exploring and exploiting digital innovation
potentials with two different foci: the first describes the technology-enabled processes through which novel
value offerings are identified and used, and the second refers to the digital outcomes of such processes
(Bharadwaj et al. 2013; Yoo et al. 2012; Nambisan et al. 2017). Digital innovation, then, refers to both the
digitalization of innovation processes and to the utilization of digital technologies for the creation of novel
products and services (Bharadwaj et al. 2013; Boudreau and Lakhani 2013; Nambisan et al. 2017).
To capture these activities from a gender-aware perspective, we draw on the so-called 5M framework of
Brush et al. (2009), who argue that the exploration and exploitation of innovation potential by women is
decisively influenced by the following five determinants: (1) access to market, (2) financial means (money),
(3) management (human capital), (4) motherhood (household), and (5) meso/macro-level contexts. By
drawing on this 5M framework, we seek answers to the question, What topics preoccupy women in tech
involved in the exploration and exploitation of digital innovation opportunities?”.
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To generate answers, we use computational topic modelling to analyze discussion threads in a professional
online network for women operating in tech industries. First, we identify the topics that women in tech are
concerned with and classify them in accordance with a gender-aware framework of factors that enable or
inhibit innovation processes. Our findings contribute to a more in-depth understanding of how gender
intersects with digital innovation processes, and build the basis for identifying suitable measures that can
support companies in creating a more inclusive tech workforce, at least as far as women are concerned. In
particular, the identification of topics allows to sensitize leaders about the needs and wants of women in
tech. Our findings indicate that while many topics that are relevant to women in tech are seemingly gender
neutral, women still seek out ‘safe spaces’ (i.e. open only to women) in which to exchange their ideas about
innovation potentials, and the obstacles they face when exploiting them. Companies could greatly benefit
from supporting such women-only spaces as these would create opportunities for knowledge flows within
organizations, raising greater awareness of women’s concerns and helping companies develop a more
heterogenous workforce. A variety of strategic programs focused on the topics we identified could provide
a firm foundation for empowering women in tech. To achieve these contributions, we structured our paper
as follows: In section 2, we outline the relevant literature to reflect the current status of digital innovation
and the need for inclusive workforces, particularly with regard to women in tech. In section 3, we present
the methodological design, and our data analysis in section 4. Section 5 presents our findings, including a
visual representation, and a discussion. In the conclusion we summarize our results, outline limitations and
contributions, as well as avenues for future research.
Theoretical Background
In this section, we outline the relevant literature to reflect the current status of digital innovation and the
need for inclusive workforces, particularly with regard to women in tech. We then focus our argument for
the ever-growing need to reflect on inclusiveness in today's workforces on women in tech, who are still
considerably underrepresented in the respective industries.
Digital Innovation and the Need for an Inclusive and Diverse Workforce
Over the last two decades, our society underwent remarkable changes in the wake of the digital innovation
that has decisively transformed our professional and personal lives (Nambisan et al. 2017). Our
communication, shopping habits, and work environments are only a few examples for how our everyday
lives have changed considerably since digital devices (laptops, tablets, and especially smart phones) have
started to connect the world (Saheer and Li 2020). These developments gave rise to a multitude of novel
business opportunities, the exploration and exploitation of which is enabled by digital innovation
(Bharadwaj et al. 2013; Boudreau and Lakhani 2013; Nambisan et al. 2017). How and what kind of values
are created through digital innovation is determined by the workforce involved, which is why a diverse and
inclusive workforce has been proven to be essential for the development of a growing and vital economy
that serves all groups in society (Bharadwaj et al. 2013; Boudreau and Lakhani 2013; Liff et al. 2008;
Nambisan et al. 2017). IS researchers have put forward four arguments that justify the need for inclusive
workforces (Trauth and Howcroft 2006; Trauth et al. 2007; Trauth 2011): First, the innovation argument
suggests that, as workforces become increasingly smarter with diversified knowledge, it is all the more
important to recruit talent from all sections of society in order to remain competitive. Second, the consumer
argument highlights that, the more diverse a workforce in terms of its backgrounds and perspectives, the
greater its capacity to create a broader range of products for a broader customer base, as it is better at
understanding the broader range of consumer wants and needs. Third, the equity argument is that fairness
and equality should be an underlying principle for all human beings as everybody should have equal
economic opportunities to work in innovative fields. Lastly, the policy argument highlights that, the more
governments proactively increase diversity in the workforce through supporting underrepresented groups,
the more companies and organizations are encouraged or required to show evidence for a diverse workforce.
These four arguments indicate that the lack of diversity and the exclusion of certain societal groups in the
workforce have a decisive impact on the kinds of digital innovation potentials that are explored and
exploited, as well as the representativeness of the created products and services (Olbrich et al. 2015). There
are many examples for flaws in digital innovation that could have been avoided with a more diverse and
inclusive workforce, including AI-enabled speech recognition systems that struggle to identify female voices
(Reiley 2016); facial recognition technology with difficulties to identify darker-skinned faces (Singer 2019),
or image recognition services that entirely fail to recognize people with darker skin colors (Paul 2019). Over
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the past 20 years, IS scholars have intensified research endeavors on diversity and inclusion in a range of
technology-related issues (Trauth 2017). The focus is both on institutional practices and on the societal
impact of digital processes and structures in order to theorize about biases in digital innovation processes
(Olbrich et al. 2015; Trauth et al. 2008). In order to recognize these biases during innovation processes,
and to allow their minimization, it is widely recognized that an inclusive workforce is crucially important.
In fact, the involvement of diverse stakeholder groups (developers, users, managers, IS designers, members
outside organizations and individuals affected by implementation of innovation) are essential in the
exploration and exploitation of digital innovation potentials. An inclusive workforce as such allows to
benefit from different viewpoints, values, and concerns (Cordoba 2007).
Variables that are frequently examined in diversity and inclusion research within the IS discipline include
gender, age, race, nationality, ethnicity, sexual orientation, socio-economic status and disability. Especially
the underrepresentation of women in tech, and the barriers they face when entering relevant workforces,
have received considerable research attention (Armstrong et al. 2018; Hewlett 2014; McAdam et al. 2019;
Trauth et al. 2008). These studies provide the foundation for a still vibrant research stream that focuses on
“women in the IT workforce” (Adam et al. 2002; Ahuja 2002; McGee’s 2017; Trauth 2017), which is a topic
still actively discussed in IS associations, networks, special issues of journals, and conference tracks (see
Trauth 2013, for a detailed overview). Despite considerable interest in this research topic and the repeatedly
observed positive effects of gender equality on the innovative capability of workforces (Dai et al. 2019;
Olbrich et al. 2015; Tzabbar and Margolis 2017), progress has been modest (Houser 2019; Trauth et al.
2018). In particular, recent statistics indicate that the proportion of women that explore and exploit digital
innovation potentials - women in tech’- is still considerably low (Hewlett 2014, McAdam, Harrison and
Leitch, 2019). The findings of various studies indicate that women continue to face considerable obstacles
and barriers when it comes to the exploration and exploitation of digital innovation potentials (AbuJarour
et al. 2019; Liff et al. 2008). Despite these insights, digital innovation processes are mainly analyzed in a
gender-neutral way. We argue that an understanding into how gender interferes with digital innovation
processes, provides a foundation for companies to address the underrepresentation of women in tech. To
contribute to this understanding and prepare the ground for suitable interventions and support measures
for women in tech, we follow the work by Joshi et al. (2018) by pursuing a ’positive’ approach towards
fostering workforce diversity and inclusion. To that end, we profile women who are already operating in the
tech workforce to identify what topics they talk about when talking to peers about [their experiences of
involvement in] exploring and exploiting digital innovation opportunities.
Women in Tech
A new EU law adopted in 2014 requires companies with more than 500 employees to disclose their social
and environmental challenges (EU 2014). The resulting diversity reports that many companies published
highlight the particularly low number of women working in tech. Statistical data from 2020 indicates that
women make up only about 20% in the US tech industry, even though they comprise 46.8 % of the entire
workforce (Statista 2020). Tech companies such as Twitter and Facebook have actively introduced counter
measures and support programs aimed at attracting women to their workforce (Facebook 2014; Brand
2018), but despite these initiatives, the number of women in tech is further declining (Allen et al. 2006;
Peck 2015; Trauth 2002). Evidence also suggests that retention is an issue, as less than half of the women
who start their careers in tech industries stay in this field, compared with 83% of men (Houser 2019).
For nearly 20 years of gender research in the IS discipline (Ahuja 2002; Allen et al. 2006; Panko 2008;
Trauth, 2017), scholars have aimed to develop theories and frameworks to explain the underrepresentation
of women in tech. In fact, numerous studies have investigated the barriers faced by women operating in
different industries (e.g., Allen et. al 2006, Armstrong et al. 2018; Trauth 2017; Trauth et al. 2018). The
reasons for the considerable turnover of women in tech industries are manifold and can be mainly
attributed to (unconscious) gender bias (Ahuja 2002), a hostile environment and the glass ceiling effect
(Hewlett 2014). These challenges and obstacles not only inhibit women’s contributions to the exploration
and exploitation of digital innovation potentials (Hewlett 2014) but at the same time influence the overall
culture within tech industries (Trauth 2017) which in turn has fundamental implications for the type of
value offerings that are created (Brush et al. 2017). From a theoretical standpoint, liberal and social feminist
theories each offer different explanations for the determinants that account for the low proportion of
women in tech. Liberal feminist theory suggests that women are equal to men and therefore have the same
social and economic opportunities (Greene and Brush 2000). As a consequence, women are perceived as
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equally able to successfully explore and exploit digital innovation potentials. The existing
underrepresentation of women in tech industries is instead ascribed to external factors, including
systematic biases and discriminatory barriers that inhibit women to access crucial resources or professional
networks that are often dominated by men (Brush et al. 2017). Liberal feminist theorists argue that these
biases and barriers can be identified and overcome (McAdam et al. 2017). In comparison, social feminist
theory suggests that women and men are different due to their socialization, ascribed societal roles and
experiential backgrounds (Brush et al., 2009). Thus, women and men are perceived as having distinct
viewpoints and interpreting similar situations differently. This perspective, based on gendered social
construction, implies that women face different obstacles than men, including in the context of
entrepreneurial activities (Sundermeier et al. 2019). Drawing on social feminist theory, women’s societal
association with parenting responsibilities, for instance, could be an obstacle to exploiting digital
innovation processes due to its association with risks, uncertainties, and considerable time requirements
(Adya 2008). Hence, social feminist theory suggests that the exploration and exploitation of digital
innovation potentials differs between men and women.
While numerous studies deepened our understanding of the gender-specific issues that women in tech are
facing, we still lack profound knowledge on how women in tech can be encouraged and supported to explore
and exploit digital innovation potentials. To gain a systematic understanding of what is needed to
successfully pursue digital innovation processes, we draw on the so-called 5M framework by Brush et al.
(2009) that provides a gendered perspective on the exploration and exploitation of digital innovation
potentials. The authors suggest that there are five building blocks that decisively influence innovation
activities pursued by women: market, money, management, motherhood, and meso/macro
environment. Specifically, access to markets, money, and management (human capital) are needed to
explore and exploit digital innovation potentials. These building blocks are seemingly gender neutral, but
research findings clearly indicate that women face gender-specific obstacles in these regards. In terms of
access to money, it is found that women’s access to private equity money is limited (Greene et al. 2001).
According to Brush et al. (2008), female innovations are seen as risky investments because of company size,
choice of industry, growth expectations and ownership/control issues. In relation to management and
access to human and organizational capital, it is found that women are rarely seen in leadership positions
of venture backed startups (Brush et al. 2008). As they are much less encouraged to choose technology-
related study subjects (Ahuja 2002), fewer women have the opportunity to develop technical knowledge,
and hence, women are generally perceived as possessing less technical knowledge. Furthermore, Orhan
(2001) found that women often lack crucial expertise in areas such as finance and management, which
further limits their role in the exploration and exploitation of digital innovation. When relating the 5M’s to
exploration and exploitation of digital innovation, Brush et al. (2009) suggest that money and management
can be regarded as the main enablers of opportunity exploitation.
To gain a comprehensive understanding of the determinants that influence innovation undertaken by
women in tech, Brush et al. (2009) argue that societal values, norms and external expectations as
“motherhood” and “meso/macro environment” must also be considered. The first component describes the
household context. For instance, Dimova et al. (2006) found that gender differences in the workforce are
explained by household characterizations, rather than other topics like gender discrimination or different
individual characteristics. For women in tech, research has found that, especially when work-family
overlaps occur, motherhood tends to limit women’s ambitions for creating new ventures, and hence
likeliness to exploit innovation potentials (Hsu et al. 2016). Brush et al. (2009) describes the meso/macro
environment component as the expectations of society, such as national level policies, culture, and (macro)
economy, as well as institutions and structures. Macro structures frame gender roles and responsibilities
for women, leading to unequal gender relations within societies (Kantor, 2002) and, thus, ultimately
resulting in unequal gender relations in the exploration and exploiting of digital innovation. Both macro
and meso initiatives support women in tech to explore and exploit digital innovation potential. In fact,
political empowerment initiatives act on a macro level and female network initiatives on a meso level
(Baughn and Neupert 2006). We draw upon this 5M framework to gain a more in-depth understanding of
the topics that women in tech share amongst themselves in an online forum. We argue that this ‘positive’
approach allows us to derive knowledge about the extent to which digital innovation processes are actually
gendered and what suitable support measures to achieve greater inclusivity in the tech workforce could look
like.
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Methodological Design
In the following section, we introduce our methodological design, including the research setting and the
data collection process.
Research Setting
In order to gain a more comprehensive understanding of the experiences of women in tech during the
exploration and exploitation of digital innovation potentials through the topics they discuss, we draw on
data from a professional women-only online network, i.e., Elpha (www.elpha.com). Elpha is being
described as a “private community designed and built by women to provide opportunities, advice and
resources for women in tech” (Elpha 2020). The platform was initially developed as an internal women’s
networking group for participants in the Y Combinator accelerator program, a well-known seed accelerator
in Silicon Valley. It was then made available to the public as an independent spin-off in 2019. To date, Elpha
has raised USD 1.3 million in venture capital (Balasubramani 2020; Crunchbase 2020). The structure of
the platform is divided into subject areas, each containing posts and replies curated by registered and
authorized members. A personal account is needed to participate in the discussions, with participants
disclosing their real identities. Posts can either be made available for registered users only or to the general
public. As of now, Elpha offers a protected space to approximately 18,000 women in tech in which they can
discuss topics that concern both their private and professional lives. Topics range from founding and
investment stories, personal learnings experiences, politics, to the description of challenging tasks for which
support from the community is solicited (Elpha 2020; TechCrunch 2019). The success of the platform, as
indicated in its considerable user-growth rates, media attention, and venture capital funding, shows that
the three founders of Elpha have created a platform that reflects a value proposition that has responded to
a strong need in the women in tech community. The target population of this paper are women or non-
binary women that work in tech, including women who start a venture, hold tech leadership roles or other
tech roles, e.g. as software developers, tech product managers or software engineers.
Data Collection
To identify the topics that are discussed by women in tech, we made use of the platform data, limiting our
efforts to the discussion threads that were made available to the general public and that can be accessed
without creating a personalized Elpha account. By doing so, we neither violate the privacy of members nor
the terms and conditions of the platform. A total of 560 discussion threads were available to us, which we
crawled. Each discussion thread was split into the initial post and its individual replies, which were then
each extracted as individual documents. We removed documents which contained fewer than 30 characters,
as these were primarily expressions of gratitute (e.g., “thank you”) and appreciation (e.g. “great to hear your
story”), which do not contribute to our reseach objective. The final data set contains 5,106 individual
documents, adding up to a total of 136,649 words.
Data Analysis
In order to derive insights from the large amount of data collected, we applied computational topic
modelling through Latent Dirichlet Allocation (hereafter LDA) (Blei et al. 2003). LDA is a probabilistic topic
modelling technique based on an unsupervised machine learning approach that supports the inductive and
automated discovery of topics in large amounts of texts (Bogusz and Morisse, 2018). The essential idea
behind LDA is that authors compose documents D by first deciding about a discrete distribution of topics
T to cover, and then rely on words W from a discrete distribution of words that are common for their chosen
topic (Blei 2012; Gutt 2018). In other words, LDA predicts the topics inherent in a given document by
assessing the words it contains and producing a topic distribution on this basis (Debortoli et al. 2016). The
model treats documents as probability distributions over latent topics, which are in turn treated as
probability distributions over words (Blei et al. 2003). On the basis of these distributions, the algorithm
conducts a sampling of a topic for each word and ranks words in accordance with their estimated relevance
for each topic (Griffiths and Steyvers 2004). The LDA is hence a suitable technique for identifying relevant
topics and common conversations in our data set. The practical application of LDA is subject to several
challenges as the relevant data consists of natural language that is characterized as unstructured and full of
noise (Kurgan and Musilek 2006). To deal with these challenges, we conducted a careful pre-processing of
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the data, including a high-level exploratory analysis and a computation of summary statistics (word-
frequency plots) (Debortoli et al. 2016). On this basis, we were able to get a first overview of the range of
words, and to identify outliers and frequently used words. These steps supported us in gaining a better
understanding of the structure and potential flaws in our data, e.g. HTML tags, which we removed. We then
moved on to the pre-processing of our documents. Each post, including all its comments, were handled as
one document with a unique distribution of words and topics.
Following the pre-processing of the documents, we started to fit a suitable topic model using a popular
cloud-based LDA service (MineMyText.com). To ensure coherence in our approach, we followed the
recommendations by Debortoli et al. (2016) by iterating several preparation-modeling-evaluation cycles
which supported us in iteratively assessing a suitable number of topicsthe most crucial paramter for LDA
(Boyd-Graber et al. 2014). We started broadly with 75 topics and qualitatively evaluated their cohesiveness.
The initial set of topics contained numerous uninformative words that are part of speech (e.g. ‘the’, ‘and’,
pronouns, adverbs, etc.), which we defined as stop words. Following this aproach gradually reducing the
number of topics in steps of 5, manually evaluating the cohesiveness of topics, defining stop words we
eventually fine tuned the number of topics between 20 to 30 in steps of 1 and determined 21 to be the most
fine-grained number of topics the model could achieve. This number of topics lies in the range of 10 to 50
topics, proposed as suitable for human interpretation of the final topic model (Debortoli et al. 2016). To
further refine these topics, we used n-gram tokenizing to split documents into 1-3 successive words and
lemmatizing to reduce words to their dictionary form. In order to fine-tune our model and achieve the best
semantic coherence possible, we experimented with different data preparation options and iteratively
refined the LDA algorithm until we reached a high correlation with human jugdment semantic coherence
(Blei et al. 2003; Debortoli et al. 2016).
The following Table 1 contains a detailed list of the most probable words that we identified for each of the
21 topics.
Topic
Probable Words
1
day,time, feel, kid, family, sleep, week, life, hour, year
2
product, brand, build, market, launch, business, start, content, customer, love
3
student, school, program, college, teach, university, study, grow, year, graduate
4
company, interview, job, offer, role, candidate, remote, hire, apply, negotiate
5
team, people, build, design, product, company, process, culture, goal, hire
6
company, startup, product, growth, market, customer, build, scale, sale, early
7
investor, founder, startup, fund, company, raise, vc, invest, build, fundraise,
8
offer, token, issue, activism, local, climate, political, security, climate_change, law
9
technology, healthcare, health, company, science, medical, patient, study, include,
opportunity
10
story, life, woman, inspire, love, success, support, career, challenge, community
11
great, love, question, story, advice, time, insight, answer, community, happy
12
question, yc, founder, time_answer, answer, application, apply, time, cofounder, company
13
event, meet, community, group, love, woman, join, start, network, space
14
firm, cost, pay, lawyer, legal, report, client, train, service, rate
15
company, people, technology, understand, team, crypto, space, finance, money, community
16
company, start, pay, people, job, cofounder, business, ceo, hire, option
17
career, role, engineer, tech, experience, company, job, time, startup, skill
18
people, feel, time, learn, experience, start, hard, great, talk, change
19
project, learn, code, developer, build, app, website, program, web, start,
20
woman, company, group, tech, community, diversity, create, people, culture, engineer
21
data, security, background, access, cybersecurity, privacy, provide, location, service, phone
Table 1. Most Probable Words for Each Topic
The last step of the data analysis comprises the interpretation and sense-making of the topics that the LDA
revealed and subsequent data refinements. For the purpose of unpicking the meaning of each topic, it is
necessary to analyze the most probable terms used in relation to this topic in combination with the most
probable documents that contain respective topics. We integrated Figure 2, a word cloud for the word
distribution of topic 1 (size = probability of a term), and Table 2, providing examples of quotes from posts
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and replies on the same topic, in order to illustrate the consecutive steps of the data analysis that have been
conducted for each topic. All topics were independently coded by two scientists who discussed their
interpretations. Apart from three topics, the results differed only in minor wording differences that were
again discussed with a native English speaker until common agreement was achieved. The remaining three
topics were again analyzed by two additional scientists and discussed with all four coders involved, which
helped to resolve any open discussions.
Figure 1. Most Probable Words for Topic 10.
Probability
Examples of Quotes
80.06%
The woman that has the biggest impact on my life is Skillcrush founder Adda Birnir.
Adda is one of my favorite role models and continues to inspire me today as a woman
in tech. She particularly has a huge impact on my life because her story inspired me to
learn how to code and become the confident woman I always wanted to be.
76.03%
What was one of those challenges and what did you do to overcome it? In general,
what is your advice to entrepreneurs who are going through a low point and aren't
sure whether they should give up (because they simply can't afford to keep doing this
without some income) or keep pushing?
71.11%
Personally, I am challenged by becoming less and less able to confidently define my
own success and achievements (and describe the path that helped me get there).
Sometimes, I will hear my husband describe my background to someone, and I think
to myself, "why can't I say it as plainly and confidently as that!" I realize it's
something that is holding me back from future growth, and I am eager to find a
solution to overcome it. You mentioned you worked with a coach. Would you mind
sharing how you found this coach and if this coach has any particular focus?
Table 2. Examples of Quotes for Topic 10.
Results
In the following section, we present a detailed interpretation of our topic model, followed by a discussion
of findings.
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Interpretation of Topic Model
Table 3 contains a detailed overview of the topics that we identified, illustrated with examples of quotes that
were derived from our data set. In a first step, we mapped the 21 topics with the 5M framework to gain a
gender-aware perspective on the topics mentioned by women in tech. In a second step, we refined our
interpretation by mapping the topics according to whether they are referring to the exploration or the
exploitation of digital innovation potentials.
Mapping with the 5M framework indicates that the majority of topics (N=14) concern management, i.e.
the exchange of knowledge required to explore and exploit digital innovation potentials. Topics that are
discussed in this context range from job negotiation and interview techniques (T4), to team building and
maintenance measures (T5), IP protection and legal advice (T14), transition from employment to starting
own tech ventures (T16), career transition and advice (T17), the handling of confidential data (T21), and
learning how to code (T19). The second most frequently discussed cluster of topics (N=3) concerns the
meso/macro environment, in particular role models for women in tech (T10), creation of offline networks
(T13), and the development of a diverse workforce (T20). Comparably less frequently discussed are topics
on money (N=2) and motherhood (N=2), that are concerned for instance with raising money (T7), working
with and investing in crypto currencies (T15), and working in the context of motherhood (2). The latter
encompasses topics such as the balancing of professional and private life (T1), or dealing with the imposter
syndrome (T18). Interestingly, we have not found any indications for topics related to assessing market
opportunities, which are the very essence of digital innovation.
The mapping of the topics to the exploration and exploitation of digital innovation potentials indicates that
only 6 of the identified 21 topics concern the exploration of novel business opportunities. Topics that are
discussed in this context concern raising money (T7), launching a startup, or working for one (T6),
experiences with accelerator programs (T12), and developing a diverse workforce (T20). Topics that
concern the exploitation of once identified innovation potentials, including their refinement and extension,
encompass the majority of identified topics, such as skillset enhancement and development (T3), job
negotiations and interview techniques (T4), advice for efficient marketing channels (T2), and learning
coding (T19).
Topic
E
Examples of Quotes
7. Raising
money
Exploit.
“Why should female founders raise VC money for some businesses vs.
angel funding/social impact investment vs. making money? What are
the main distinctions for those who should or should not try pitching to
VCs for capital vs those who are looking for smaller funds or different
types of funds to help grow their business?” (91.75%)
15. Working
with and
investing into
crypto
currencies
Exploit.
“When I decide to support a crypto team I first look at why they are
using blockchain technology. Not everything needs to go on the
blockchain as it's really just an inefficient and expensive database. I'm
looking for things that truly need censorship resistance rather than
using blockchain as a marketing play.” (97.73%)
2. Advice for
marketing
channels
Exploit.
“Are there any examples you can give of a brand that has grown their
voice in the last few years that really stand out. And what advice would
you give to a new brand to make their voice authentic and unique?”
(91.30%)
3. Skillset
enhancement
and
development
Exploit.
“Google recently opened their worldwide college scholarship program
for those studying computer science, technology, and gaming. I won the
Women Techmakers one 2 years ago and wanted to offer to help people
any way I can, whether it's reading an essay or talking about my
experience”. (56.94%)
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4. Job
negotiation
and interview
techniques
Exploit.
“I'd love [it] if you'd be open to answering a few questions:1. Looking
back, what do you wish you had known during the conversations /
negotiations with your company (or boss)? 2. If anything, what would
you have done differently, hindsight 20/20? 3. Did you take a pay cut? If
so, how did you react and/or how was that negotiated or discussed? Are
you satisfied with the outcome?” (83.32%)
5. Team
building and
maintenace
measures
Exploit.
“Do you think there is a point where managers can over measure their
employee’s performance? With things like KPIs, OKRs, task boards,
etc.Yes absolutely, data is great because it has no bias and can help you
make decisions.[..]. Do you have any advice for managing up when you
interact with 2 or 3 levels of managers?” (93.92%)
6. Startup
launch and
experiences
working in it
Explor.
“What were your biggest challenges when starting (anonymzied)?
Wondering if there are any overlap between the task of creating a
company to build product vs. creating a company to invest in product
companies.” (77.65%)
8. Cyber
Security
Exploit.
“We are are community of people who are passionate about all things
Blockstack. Blockstack is a decentralized computing network and app
ecosystem. Blockstack apps protect your digital rights and are powered
by the Stacks blockchain.” (58.41%)
9. Technology
for digital
health appl.
Exploit
.
“We are aware of this study from 2017 and a lot has moved forward
since, including being able to detect depression from audio signals.”
(75.60%).
11. Sharing best
practices
experiences
Exploit
.
“This is the first of our new series highlighting members in the Elpha
community. We're curious who you are, what you're working on and
what inspires you.” (83.75%)
12. Accelorator
programs
experiences
Exploit.
“I’m (anonymized), excited to host a special AMA, (anonymized) at YC
for those of you who are wrapping up Startup School and are looking to
apply to YC. Ask us anything about YC from admissions to the batch
itself to Demo Day, being a woman founder or something else!”
(85.86%)
14. IP
protection
and legal
advice
Exploit.
“Hi Chess, good question, but I want to flag that patents are not the only
way to protect your intellectual property (IP). Copyright, trade secrets,
trademarks, all of these systems also help to protect your IP and are
often less expensive. [..] That said, if you think there is something truly
novel about your app and you want to pursue the patent process.”
(59.24%)
16. Transition
from
employment
to own
venture
Explor.
I started (anonymized) as a side project while employed full-time at
Dropbox. My cofounder, (anonymized), and I both had full-time jobs for
the first nine or ten months of (anonymized)'s existence. I didn't quit my
job to start a company, but rather waited until the idea and team came
to me before quitting my job. I highly recommend this strategy because
it's way less stressful!“ (84.03%)
17. Career
transition
and advice
Explor.
“What advice would you have for prospective engineers from
unconventional backgrounds (coding bootcamps, self-taught, career
switchers, etc.)? In your career, have you worked with any that really
stood out? What made them awesome? For context: I used to work in
tech (at (anonymized)) in a nontechnical role, am going through a 9
month bootcamp now, and am so intimidated by the job hunt.” (82%)
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19. Learn
coding
Exploit.
“I've done at least 2 tracks on Free Code Camp. I've gotten the responsive
web design certification and JavaScript Algorithms and Data Structures
certification. I'm almost done with the Front End Libraries certification.”
(87.65%)
21. Handling of
confidential
data
Exploit.
“I'm the technical co-founder of a cloud data analytics vendor. We host
in our own cloud. We need access to our clients' cloud hosted or data
center database servers. Most of these database servers are network
private (rightfully so). Here's a roundup of what I see as requirements
[..]. I know this is a lot to ask, but any reading materials to follow up
on?” (72.64%)
1. Balancing of
professional
and priv. life
Exploit
.
“I'm turning 31 next week and my husband and I are thinking about kids
in the next 1-2 years and I would be lying if I said I was somewhat
scared about how I'm going to balance everything.” (82.68%)
18. Imposter
Syndrome
Exploit.
"A friend I trust has told me that imposter syndrome is simply where
insecurity and privilege intersect - it is the privilege of being in the room
plus the insecurity of not belonging in the room. [..] It feels like we're all
so focused on our own thought bubbles that we never see or notice other
people's insecure thoughts. It's so amazingly reassuring to know other
people feel the same way.” (94.56%)
10. Role models
for tech
women
Exploit
.
“Your insight about climbing that ladder is such an important one. As a
leader, how did that impact the ladders you created for your teams?”
(83.79%)
13. Creation of
offline
networks
Explor.
“The most useful events are those which are structured to help strangers
connect with one another. I love breakfasts and small events where you
can go around the table, introduce yourself, and hear what everyone has
to say versus ‘all comers’ events where its just open networking.”
(90.81%)
20. Developing
a diverse
workforce
Explor.
I have a health tech startup and am desperately looking for a female
developer. Do you have any recommendations? We are making women
the experts on their body and health and we can’t have a man build that!
Everyone who has applied is male and I just can’t find someone.“
(88.68%)
Table 3. Interpretation of Topic Model.
Discussion
This paper contributes to the scholarly literature on digital innovation and inclusive workforces in various
ways. Applying LDA, this study identifies topics that are discussed by women in tech on a professional,
women-only platform, and hence provides first valuable insights that can inform measures aimed at
addressing the underrepresentation of women’s participation in the exploration and exploitation of digital
innovation.
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Figure 2: A Gendered Perspective on Digital Innovation.
First, we provide a gendered perspective on digital innovation by classifying the topics and issues raised
and discussed by women in tech on a peer forum, in accordance with the gender-aware 5M framework
which covers key aspects of innovation processes. While the framework suggests that money, market and
management are equally important factors for successfully exploring and exploit digital innovation
potentials, we found that women in tech are primarily concerned with topics that relate to accessing human
capital (management). Access to human and organizational capital is considered to be the key enabler of
exploitation innovation potentials (Brush et al. 2009). Our data clearly indicates that women in tech are
particularly looking for adviceand keen to share their knowledgeon matters related to organizational
and human capital, private and professional experiences, and the skills required for digital innovation.
While the majority of the discussions on these matters are seemingly gender-neutral, the success of this
women-only platform indicates that such a safe space for women in tech is a key measure to foster
knowledge exchange among women, which in turn can help tackle women’s underrepresentation in
respective industries. The women’s primary interest in exchanging knowledge and experiences could be
explained by the nature of professional online networks that are primarily designed to foster connections
and knowledge exchange. Nevertheless, it also indicates that women in tech are keen to learn from the
experiences of other women, who they perceive to be like-minded and suitable to providing relevant and
helpful advice on professional development and enhancement. Although prior research suggests that
women lack crucial technological knowledge (Orhan 2001), we could not find any evidence that the topics
discussed by women already operating in tech industries are notably different from those discussed by their
male counterparts. Nevertheless, the platform seems to be an important source of information for women
who want to increase their tech knowledge and expertise, e.g. learning how to code. In contrast to the
extensive knowledge exchange on management topics, we were surprised not to find any evidence for
discussions on the exploration of market opportunities, especially since prior research clearly indicates that
women face considerable obstacles in this area (Bates 2002). One possible explanation might be that the
women using Elpha are already one step ahead and in the midst of exploring and exploiting digital
innovation potentials. Alternatively, it could be another indicator that women in tech are still largely
underrepresented in positions that contribute to the exploration of novel business opportunities, which
would certainly imply that many digital innovation potentials still remain untapped as their perspectives
are missing in respective positions (Orhan 2001). Similarly, topics related to motherhood did not
particularly feature in the discussions by the women in tech that are active on the platform we analyzed.
Although previous research found that motherhood duties do limit women’s growth orientation and
likeliness to pursue innovation processes (Hsu et al. 2016), only 2 out of 21 topics focus on motherhood
duties. Nevertheless, the discussion of related topics indicates that women still worry about balancing
family and work duties, which indicates that they still face obstacles in these regards. These findings support
the studies of Hsu et al. (2016), indicating that motherhood limits women’s likeliness to exploit innovation
potentials. Another obstacle that has been determined in previous research are (unconscious) gender biases
that inhibit women from accessing financial resources (Greene et al. 2001). Our data indicates that women
Managem ent
T2 Adv ice for efficie nt marketing chan nels
T3 Skill set enhancement and development
T4 Job n egotiation and intervie w technique s
T5 Team buildin g and mainten ance measu res
T8 Cyb er Secu rity
T9 Tech nology req uireme nts for digital hea lth applic ations
T11 Sharing best practices experiences
T 12 A ccele rator programs exp erienc es
T14 IP protection an d legal advi ce
T19 Le arn coding
T21 Handli ng of confid ential data
Exploitation of
Digi tal Inn ovation
Money
T7 Rai sing Money
T15 Working with and investing i n crypto c urrenc ies
Motherhood
T1 Bal ancing of professional a nd private life
T18 Im poster Syn drome
Meso/Mac ro
T10 Role models for tech women
Meso/Mac ro
T13 Creation of offline networks
T20 Compili ng a div erse workforce
Managem ent
T16 S tartup launc h and expe rience s working in it
T16 Tran sition from e mployme nt to own venture
T17 Caree r transition a nd ad vice
Exploration of
Digi tal Inn ovation
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in tech are well aware of this challenge, with the role of female VCs in overcoming gender biases being
explicitly discussed on Elpha. These findings support the growing recognition of the positive relationship
between women’s networking activities and their access to financial capital (Brush et al., 2008). In
accordance with Brush et al. (2009), we also found that topics related to the “meso/macro environment”
are important to women’s participation in digital innovation processes. In particular, we found that women
actively discuss their role models, the importance of joining offline networks that connect women in tech
across regions, and the general importance of diversity in workforces. Our results are aligned with previous
findings stating that women feel more empowered when engaging in networks and benefiting from political
empowerment (Baughn and Neupert 2006).
Second, we follow a ‘positive’ approach by highlighting the topics that concern women who are already
exploring and exploiting digital innovation potentials. The comparison between exploration and
exploitation reveals that women are more concerned with topics that are leading to the exploitation (67%)
of digital innovation than with the exploration (33%) of identified innovation potentials. This finding
indicates that women in tech are less concerned with the identification of and experimentation with new
opportunities, but more with their exploitation. Looking at both categories - exploration and exploitation
- a few observations appear especially striking. While innovation processes pursued by women are seen as
risky investments (Brush et al. 2008), raising money is a topic that is actively discussed by women in tech.
Moreover, accelerator programs, career transitions to launching one’s own venture, startup launches in
general, and the recruitment of a diverse workforce are all topics related to the exploitation of novel business
opportunities. Hence, women show an interest in topics related to the exploration of digital innovations,
although the interest is comparably lower concerning exploitation matters. Nevertheless, our data remains
silent whether systematic biases and discrimination (liberal feminist theory) or women’s socialization
processes (social feminist theory) account for their less pronounced interest in the identification of and
experimentation with novel digital innovation potentials. By taking a closer look at the topics that are
discussed in relation to the exploitation of innovation potentials, the topics range from role models, learn
coding, balancing professional and private life, to sharing best practices and experiences. The exchange
about these topics indicates that women are actively striving towards equality in these regards through
accessing networks that support them to contribute and participate in tech workforces. Fostering the
exchange about the topics that concern the exploitation of digital innovation potentials within companies
could hence contribute to increasing the number of women in tech.
Third, our insights provide a basis for inspiring ideas for suitable support measures that companies could
implement to create a more diverse workforce, particularly with regard to women in tech. For instance,
workshops, seminars, accelerators, learning journeys, online courses and other measures could focus on
empowering women with knowledge and skills about market and money topics, to encourage and support
women in their journey of exploring and exploiting digital innovation potentials. This could also involve a
diverse range of meso measures, such as offline networks, workshops, mentorship programs and flexible
working structures. While women on elpha.com frequently discuss issues pertaining to management and
human capital, companies could create an online resource pool around these topics to equip women and
other employees with the necessary tools to systematically explore and exploit novel market opportunities.
Such a resource pool could be augmented by offline events, such as accelerator programs, ideation sessions,
mastermind classes, skill sharing events, offline networks, and mentoring programs. While women are
noticeably less concerned with experimentation leading to the development of new opportunities
(exploration), companies could develop incentives and workshop formats that make women aware of the
importance and potential of their role in identifying digital innovation potentials that might otherwise not
be discovered, given the currently still prevailing male bias. Such workshops should encompass measures
that enable women to creatively discover novel market potentials. Additionally, companies could focus on
supporting women in balancing work and life (e.g. by offering childcare, maternity leave or flexible
working), and dealing with imposter syndrome, topics that were also raised by women, and which seem to
hold them back in their journey towards exploration of digital innovation processes.
In addition, we are also able to generate some practical implications for companies to strive towards
becoming more inclusive in terms of gender in their workforce. Our findings indicate a variety of topics that
women in tech are concerned with. Thus, companies should implement strategies with a focus on these
topics which empower women to exploit their existing potential and removes obstacles in the long term.
Measures could comprise, for instance, promotions and training in the topic areas, workshops, mentorship
programs, interdisciplinary networks, or personal coaching. To attract new female talent, companies could
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start initiatives and collaborations with universities and mentorship programs involving female employees
and students. In fact, companies will greatly benefit from creating women-only spaces as these foster
knowledge flows within organizations. Thus, greater awareness of women in tech and the topics relevant to
them allows companies to transition towards a more heterogeneous work environment.
Conclusion
The purpose of this paper is to identify and evaluate the topics and issues that are relevant and important
to women in tech who are involved in exploring and exploiting digital innovation potentials. Despite
existing research determined reasons for the low proportion of women in tech, we still lack profound
knowledge on how this underrepresentation can be addressed to form more inclusive workforces in tech
industries. Our first contribution is to show that women are primarily concerned with topics related to
assessing organizational and human capital that can help them advance their careers and exploit digital
innovation potentials. While prior research has identified several obstacles concerning women’s access to
money and described the influence of meso and macro environments, we found that related topics are
comparably less present on the online network at the center of our analysis. While all of the topics discussed
are seemingly gender-neutral, the success of elpha.comthe basis of our dataset indicates that women
need a safe spaceto exchange knowledge. In addition, we found that the majority of topics is concerned
with the exploitation of identified digital innovation potentials, indicating that women are still
underrepresented in positions concerned with the exploration of novel market opportunities. Topics related
to the exploitation of novel business opportunities for women include, for example, raising money,
accelerators programs, learn coding, job negotiation, interview techniques and cyber security. Our research
findings provide a basis for companies to derive suitable measures for empowering women already working
in tech and for creating a more diverse workforce. Organizations could, for instance, create a resource pool
on management and human capital topics. Additionally, women-only workshops, seminars, accelerators,
online courses and learning journeys could serve to enhance women’s exploration and exploitation of digital
innovation processes. While our research is at the exploratory stage, it provides an indication that women
in tech are more involved in the exploitation of innovation potentials. To gain a better understanding of
how more women can be encouraged to leverage their creative potentials and explore digital innovation
opportunities, future research needs undertake qualitative and quantitative studies, surveys and interviews
with women in order to determine suitable support measures. One of the limitations of our study remain
around the fact that gender intersects with other diversity dimensions such as race, disability, sexuality.
While we contribute to the stream of research exploring the underrepresentation of women in tech, the
‘positive’ approach we adopted by examining the experiences and concerns of women already operating in
tech industries allows to examine workplace inclusion with regard to other diversity identity characteristics,
such as ethnicity, race, nationality, geography, sexual orientation, socio-economic status, and disability
(Trauth 2017). Further limitations arise from the fact that Elpha was originally built to support mentorship,
allowing women in tech to talk candidly online. While members come for professional and personal
support, to find new jobs and opportunities, or make friends and join communities, users could also be
looking for membership-related topics, such as advice. This limitation could explain the low number of
motherhood topics. For women in tech, research has found that, especially when work-family overlaps
occur, motherhood tends to limit women’s ambitions for creating new ventures (Hsu et al. 2016). In
addition, women’s parenting responsibilities, for instance, could be an obstacle to exploring opportunities
in tech (Adya 2008), and if they left the tech industry for this reason, they would not be represented in the
data sample of our study - which would explain why we find few discussions on motherhood-related topics.
In regards to our contributions, a more diverse workforce could be realized through several measures like
mentorship programs, flexible working hours and offline networks for women. Based on our research data
drawn from a female forum, we would ideally need a comparative study to indicate to what extent the same
topics are also discussed by men or indeed other underrepresented groups. Another limitation is that the
data set for this type of study is relatively small. This is because we have looked at the terms of the forum
and only crawled accessible data. Despite the differences and the robust results we have obtained, an in-
depth study seems necessary to further explore this research topic.
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