Examining Competing Entrepreneurial Concerns in a Social

Question and Answer (SQA) Platform

Irawan Nurhas

1,2 a

, Henri Pirkkalainen

, Stefan Geisler

and Jan Pawlowski

1,2 d

Institute of Positive Computing, Hochschule Ruhr West University of Applied Sciences, Bottrop, Germany

Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland

Faculty of Management and Business, Tampere University, Tampere, Finland

Keywords: Entrepreneurial Concerns, Challenges to Startups, Topic Modeling, Entrepreneurship, Sentiment Analysis.

Abstract: This study aims to determine the competing concerns of people interested in startup development and

entrepreneurship by using topic modeling and sentiment analysis on a social question-and-answer (SQA)

website. Understanding the underlying concerns of startup entrepreneurs is critical to society and economic

growth. Therefore, greater scientific support for entrepreneurship remains necessary, including data mining

from virtual social communities. In this study, an SQA platform was used to identify the sentiment of thirty

concerns of people interested in startup entrepreneurship. Based on topic modeling and sentiment analysis of

18819 inquiries in various forums on an SQA, we identified additional questions about founder figures, keys

to success, and the location of a startup. In addition, we found that general questions were rated more

positively, especially when it came to pitching, finding good sources, disruptive innovation, idea generation,

and marketing advice. On average, the identified concerns were considered 48.9 percent positive, 41 percent

neutral, and 10.1 percent negative. This research establishes a critical foundation for future research and

development of digital startups by outlining a variety of different concerns associated with startup

development in the digital age.

1 INTRODUCTION

This study provides a comprehensive insight into

people's concerns interested in entrepreneurship and

startup entrepreneurs (SEs). Entrepreneurship is a

powerful economic and social force (Harb & Shang,

2021; Schöning, 2013; van Stel et al., 2005). As a

result, several initiatives have been launched to

encourage SEs to establish their enterprises or

business models (Ratinho et al., 2020).

Meanwhile, in the knowledge economy and age

of virtual communities, the Social Question-and-

Answer (SQA) platform is rapidly moving

information-seeking behavior toward a more

collaborative and personalized question-and-answer

experience based on expertise (Choi et al., 2014).

Thus, SQA provides access to business networking

and open knowledge- and experience-based business

https://orcid.org/0000-0002-2211-8857

https://orcid.org/0000-0002-5389-7363

https://orcid.org/0000-0002-1976-0013

https://orcid.org/0000-0002-7711-1169

and entrepreneurial solutions to SEs with minimal

resources (Shneor & Flåten, 2015).

Although numerous studies on SEs have been

conducted (Chandra et al., 2016; Puhakka & Ojala,

2021; Saura et al., 2019), it is important to determine

the concerns of the discussed topic on a large scale

to capture better the different and more detailed

aspects (Saura et al., 2019). Therefore, topic

modeling (Chandra et al., 2016; Onan et al., 2016;

Rehurek & Sojka, 2010) and sentiment analysis

(Gao et al., 2013; Hutto & Gilbert, 2014; Onan et al.,

2016) methods were applied to the questions

collected from an SQA.

By investigating the substance of the issues in an

SQA, we set out to identify which competing

concerns are most salient and how these concerns are

perceived and evolve over time in the face of

digitalization. This study contributes twofold: First,

Nurhas, I., Pirkkalainen, H., Geisler, S. and Pawlowski, J.

Examining Competing Entrepreneurial Concerns in a Social Question and Answer (SQA) Platform.

DOI: 10.5220/0010661000003064

In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 3: KMIS, pages 145-152

ISBN: 978-989-758-533-3; ISSN: 2184-3228

145

we identify thirty areas of concern that emphasize the

most disputed issues involving positive and negative

emotions, thus paving the groundwork for future

scholarly investigation. Second, this study expands

the body of knowledge based on previous research by

emphasizing the use of SQA as a valuable element for

researching entrepreneurship and confirming the

identified SEs' dilemmas through the lens of SQA,

spanning a broader range of SE development topics

that are not limited by cultural, country-specific,

gender, or age boundaries.

As a reminder, the research presentation should

adhere to the following structure. In the next section,

we present studies on entrepreneurial challenges for

startups and the use of data mining techniques to

identify entrepreneurial insights. Subsequently, we

describe how we conducted our research using topic

modeling and sentiment analysis. We then highlight

the competing concerns of SEs in our findings

section. Next, we address the study's findings,

limitations, and recommendations for future research

in the following discussion section-and; finally, we

provide an overview of the study report.

2 RELATED WORKS

SEs face a variety of challenges in the early stages or

throughout their entrepreneurial journey (Giardino et

al., 2015; Nurhas et al., 2020; Saura et al., 2019;

Wang et al., 2016), including issues related to product

and team development, lean process, business model

design, and financing, scaling, partnerships, sales,

and product and market alignment (Giardino et al.,

2015; Wang et al., 2016). These challenges motivate

SEs to freely discuss, share, and communicate

attached with emotion in order to highlight the

elements of success, concerns, or motivations (Saura

et al., 2019) that spread across the Internet, the social

media community, including SQA (Chandra et al.,

2016; Puhakka & Ojala, 2021; Shneor & Flåten,

2015).

Giardino et al. (2015) highlighted that in addition

to the team and product-related challenges,

technology uncertainties and acquiring the first

customer are prominent challenges for software

startups that are also relevant to developing a

technology-based startup in the digital age. However,

in the study based on the analysis of entrepreneurial

posts on social media, these issues were not

mentioned as essential concerns for success factors

for SEs (Saura et al., 2019).

https://www.twitter.com/

The potential of identifying the topic of concerns

of SEs has already been investigated in a study of

shared thoughts on Twitter

, where topics were

categorized into business angels, business plans,

methodology, tools, projects, jobs, and founders

(Saura et al., 2019). Topic modeling and sentiment

analysis are two well-known tools (Gao et al., 2013;

Onan et al., 2016) for revealing the categories of

concern and emotions behind shared thoughts and

have also been used to study entrepreneurship and

startup based on a large amount of data collected

(Chandra et al., 2016; Harb & Shang, 2021; Puhakka

& Ojala, 2021; Saura et al., 2019). The methods have

already been used in various fields and SQA (Jiang et

al., 2018; Kumar et al., 2018).

Uncovering topics and sentiments from SQA is

important to identify motivations, areas of interest,

and expectations that will raise community awareness

of specific issues (Choi et al., 2014). Furthermore,

sentiment analysis has been demonstrated to reveal

feelings and perspectives that can influence decision-

making on particular concerns (Chen et al., 2018;

Choi et al., 2014; Johnson, 1990). Consequently, it is

critical for triggering discussion, directing, and

promoting the selection of policies, strategies, and

approaches to entrepreneurial development

(Blanchflower & Oswald, 1998; Gifford, 1992;

Nurhas et al., 2020; O’Shea et al., 2017).

3 METHODS

Two commonly accepted strategies for doing SQA

research are user-based and content-based research

(Choi et al., 2014). This research involved the use of

a content-based SQA method that focuses on content

patterns rather than user characteristics (Choi et al.,

2014). The Selenium Python package was used to

extract snapshot data at the beginning of the process

(Huber et al., 2011). The data was compiled from the

text of questions posted in the SQA Quora

forums

dedicated to startups and entrepreneurship.

We chose Quora as our study platform because it

appears to be a promising source for investigating

various social phenomena in other studies (Jiang et

al., 2018; Wang et al., 2013). Quora is an online

community founded in 2009 and headquartered in

California, United States of America. Quora focuses

on an online open-knowledge that is based on user-

generated content. Quora's primary content is a

question posed by a user to which other users

respond. Users of Quora are expected to use their real

https://www.quora.com/

KMIS 2021 - 13th International Conference on Knowledge Management and Information Systems

146

names, though they can choose to remain anonymous.

Quora is ranked #356 globally by AlexaPage Rank

(as of June 15, 2021) and is available in many

countries. Even though Quora's content is open, the

company does not offer an official API for retrieving

data from its websites.

In the following instance, data collection was

done by taking all the data in Quoran SQA on

September 21, 2018, removing duplicate questions

(N_initial:24802 questions to N_final: 18819

questions), cleaning the data by removing

punctuations and stop words, developing labels for

different topics as concerns, and identifying the

sentiment of each topic to identify competing

concerns. The Latent Dirichlet Allocation (LDA)

algorithm of Gensim was used as a Python package

for clustering topics because it is considered to

outperform other known topic models (Harb &

Shang, 2021).

The LDA class of statistical language models is

used in generative probability computing, which is a

subset of statistical computing. LDA makes Topics

using word clusters instead of text clusters to

understand data better (specifically, in this study, the

SQA's questions). In LDA, each topic is a model of a

mixture of words. Each word is represented by a

Dirichlet distribution coefficient. The model in each

topic has the capability to forecast significantly

related topics in a document (Blei et al., 2003). For

LDA, a predictive likelihood-based approach was

utilized to pick out the most optimal number of topics

(Chang et al., 2009). Then, the concern was labeled

based on the given keywords and the coefficient of

each identified topic from the Gensim LDA model.

Three to ten relevant keywords of word clusters were

used to designate each topic.

Following the identification of each cluster's

pertinent topics, the next step is to ascertain the

sentiment for each question posed within each cluster.

The Vader algorithm (Hutto & Gilbert, 2014) was

used to identify sentiment for each issue, which

shows not only sentiments and viewpoints from

written language but also emojis or spoken emotions

(Hutto & Gilbert, 2014; Onan et al., 2016) that are

relevant and frequently contained in a text (Gao et al.,

2013). All queries that have the same identified topic

were grouped individually. The Vader algorithm

determines each question's negative, positive, and

neutral trends on the same topic. The percentage of

sentiments for each issue was derived by comparing

the three tendencies to the total number of questions

on a specific topic of concern.

Following that, each topic's popularity ratio was

determined and organized in descending order

(1 to 30, where 30 is the most popular topic in a

given year; and 0 for topics not found in a given

year, this may happen, for example, if a topic

appears after 2010). The popularity ratio value is the

percentage of questions asked about a specific topic

in a given year compared to the total number of

inquiries from all topics. It is compared to the

percentage of each topic to detect dynamic changes

in topic popularity.

4 RESULT

Based on the log-likelihood value of various learning

decay scenarios (0.5, 0.7, & 0.9). Thirty topics with a

consistent value amongst diverse scenarios of

concern were taken into account. As shown in Figure

1, thirty topics were chosen as the number that can

yield the greatest number of topics for the dataset.

The selection is based on the maximum feasible log-

likelihood value under specific conditions before the

log-likelihood value for a larger number of topics

drops significantly.

Figure 1: log-likelihood value for a different number of

topics.

Table 1 presents the thirty labeled concerns as a

result of defining the cluster from LDA. This

includes, for example, lean startup, the

entrepreneurial process, pitching the idea, scaling the

business, and managing company resources, such as

founders and co-founders, investments, and location,

partnerships, and emerging trends (Giardino et al.,

2015; Ratinho et al., 2020; Shneor & Flåten, 2015;

Wang et al., 2016).

Overall, sentiment for each concern was positive,

with an average percentage of 48.9 percent. All

competing concerns for positive and negative were

outlined in gray in Table 1. For example, concerns

about Pitching and finding good sources with 66%

and concern about success keys (64.8%) were

Examining Competing Entrepreneurial Concerns in a Social Question and Answer (SQA) Platform

147

Table 1: List of concerns of SE and the identified sentiment.

Label of

concern

Top 3 words and the

coefficient

Percentage of

sentiment (%)

(-) (n) (+)

Lean

startup

0.34*"startup"+

0.18*"lean"+

0.05*"book"

751 7.1 53.5 39.4

Inspiring

Company

0.11*"company"+

0.12*"grow"+

0.06*"learn"

743 10.2 42.8 47.0

Founder

figure

0.17*"elon_musk"+

0.05*"time"+0.04*"t

esla"

895

24.4 40.2 35.4

Find co-

founder

0.18*"startup"+

0.11*"founder"+0.0

5*"hire"

756 10.7 44.3 45.0

Success

keys

0.19*"entrepreneur"

0.15*"successful"+

0.03*"key"

881 10.4 24.7 64.8

User-

centric

app

0.12*"build"+

0.08*"app"+0.06*"u

ser"'

658 5.6 43.6 50.8

A year of

remote

wor

0.18*"work"+

0.07*"year"+0.06*"j

ob"

701

17.1 43.5 39.4

Payment

service

0.13*"service" +

0.06*"pay"+0.05*"p

roblem"

672

16.4 32.9 50.7

Pitching

0.12*"innovative"+

0.09*"idea"+0.06*"s

ell"

653 6.9 27.1 66.0

Investmen

t capital

0.27*"start"+

0.11*"business"+

0.07*"investment"

623 10.1 49.9 40.0

Scaling

the

usiness

0.21*"small"+

0.19*"business"+

0.06*"scale"

521 8.3 49.7 42.0

Breakeven

point

0.25*"make"+

0.14*"people"+

0.11*"mone

716

15.6 42.0 42.3

Small

business

owne

0.22*"business"+

0.18*"small"+

0.08*"owner"

540 11.7 45.4 43.0

Disruptive

innovation

0.18*"innovation"+

0.04*"change"+

0.03*"life"

787 8.8 30.9

60.4

Important

items

0.09*"thing"+

0.07*"important" +

0.06*"open"

647 7.4 41.7 50.9

Find good

sources

0.28*"good"+

0.14*"find"+

0.07*"software"

447 5.8 28.2

66.0

Funding

0.20*"startup"+

0.06*"investor"+

0.05*"invest"

608 9.7 46.4 43.9

Startup

location

0.32*"startup"+

0.32*"tech"+

836 8.4 45.2 46.4

Label of

concern

Top 3 words and the

coefficient

Percentage of

sentiment

(

)

(

)

(

)

(

)

0.02*"silicon_valley

Product

launch

0.25*"product"+

0.11*"market"+

0.08*"launch"

672 8.2 38.5 53.3

Initate

new

usiness

0.36*"business"+

0.08*"run" +

0.08*"start"

513 5.3 35.5 59.3

Risk

manageme

0.27*"strategy"+

0.04*"fail"+

0.03*"corporate"

684 13.0 45.2 41.8

Business

plan

0.40*"business"+

0.15*"plan"+

0.12*"good"

486 5.8 50.6 43.6

Business

model

0.12*"business" +

0.10*"model"+

0.08*"strategic"

591 6.4 50.3 43.3

Marketing

advise

0.08*"marketing"+

0.07*"give"+

0.07*"website"

531 7.3 32.4

60.3

Validating

idea

0.12*"customer"+

0.08*"development"

+ 0.05*"idea"

497 10.1 40.2 49.7

Digital

business

0.31*"business"+

0.25*"start"+

0.10*"online"

431 7.7 45.0 47.3

Emerging

markets

0.16*"start"+

0.13*"business"+

0.10*"india"

496 9.3 56.7 34.1

Partnershi

0.37*"company"+

0.04*"partner"+

0.03*" competitive_

advantage"

447

13.9 36.7 49.4

Technolog

y trends

0.11*"technology"+

0.09*"big"+

0.08*"industry"

509 9.4 41.3 49.3

Idea

invention

0.15*"idea"+

0.11*"invention"+

0.10*"good"

527 12.0 24.7

63.4

N: total questions; (-): negative sentiment; (+): positive

sentiment; (n): neutral sentiment;

positively perceived. Conversely, some issues are

perceived as challenges to SEs development, with

concerns about founder figure (24.4%), remote work

(17.1%), payment services, and break events (at

around 16% each) being perceived as more negative.

Based on Table 1 and in light of previous literature on

the dimensions of startup challenges, including

product, financial, team, and market (Giardino et al.,

2015; Ratinho et al., 2020; Shneor & Flåten, 2015;

Wang et al., 2016), we identified the following as

additional concerns of SEs that need to be considered.

As illustrated in Table 2, international startups and

role models are two additional dimensions of

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challenges that have been overlooked previously and

may arise due to Quora's global nature. As a result,

people from various countries use the platform to

inquire about opportunities, challenges, and success

factors associated with developing startups in other

locations and require an example of successful

development.

The "role models" category is an example of how

digital technology can help small and medium-sized

enterprises (SMEs) find inspiring stories of role

models who are either individuals or organizations to

gain insights into how startups in situations similar to

their own are evolving.

Table 2: Further concern dimension for SEs.

(Challenge

category):

definition

Label of

concern

Example question from

Quora

(internationa

l startup):

lack of

knowledge

regarding

place

developing

international

startup

Startup

location

“Why has Sub-Saharan

Africa failed to produce tech

giants like Twitter,

Facebook, Apple, Google,

Arm (acquired by SoftBank),

etc.?”

“Is Silicon Valley really the

best tech startu

location?”

Emerging

market

“What are the ways to start

a tech startup in India while

bein

located in the USA?

“

(Role

model): lack

of inspiring

role model

Founder

Figure

“What Elon Musk Gets

Wrong About Leadership?”

“Why did Steve Jobs push

technology forward, even

risking his company to

fail?”

“Why are there relatively

few female tech startup

ounders?”

Inspiring

company

“I’m 16, and I want to start

my own software company

like Apple and Microsoft.

How and where should I

start with?”

“What do you learn in a

small company that you can't

learn in a bi

one?”

SQA provides a place for direct engagement with

other successful SEs when it comes to using SQA to

look for role models. They may have a comparable

background and can serve as inspiring role models.

As seen in Figure 2, the number of queries for all

topics climbed dramatically year after year. Based on

the percentage of the ratio value of each year's

calculation. We discovered some intriguing patterns

of concerns that can help us better grasp how the

popularity of a specific issue competes in the SQA.

Figure 2: Number of questions per year of topic concerns.

First, we found that some topics were not queried

in the first year, 2010. The topics that appeared for the

first time in 2011 are pitching an idea, investment

capital, scaling business, small business, important

resources, finding good sources, startup location,

well-prepared business, business model, digital

business, emerging markets, partnership, and idea

generation. However, some of the concerns receive

more attention from SEs to discuss in the SQA.

Figure 3: Dynamic changes in popularity over time of

positive concerns relative to other concerns.

Next, we present the dynamic changes in

popularity of the concerns over time (for Figure 3 to

6, on the x-axis, the years, and the y-axis, the ranking

of the concerns relative to each other based on the

number of questions asked in that year, the higher the

ranking, the more questions asked relative to other

concerns in that year). Figure 3 shows the top positive

concerns based on Table 1 (pitching, finding good

resources, idea generation, marketing advice,

Examining Competing Entrepreneurial Concerns in a Social Question and Answer (SQA) Platform

149

disruptive innovation, and keys to success), and

Figure 4 shows the top negative concerns and those

that have changed significantly over time (Figures 5

and 6).

Figure 4: Dynamic changes in popularity over time of

negative concerns relative to other concerns.

As shown in Figure 3, the topic of concern about

disruptive innovation has received more attention

from 2015 to 2018 than other concerns that show

more positive sentiment. Meanwhile, the topics of

marketing advice and finding good sources fall from

the top in 2017.

Figure 5: Concerns that show increasing trends in

popularity compared to other concerns.

Moreover, we can observe in Figure 3 that there

are significant shifts in all concerns in 2014. On the

other hand, in Figure 4, in 2015, there were no

concerns about the "founder figure," Three years

later, the concern was the highest in the group of

negative concerns. Thus, overall, the negative

concerns group did not show a consistent significant

trend over time.

An interesting pattern that shows improvement

over time is presented in Figure 5 regarding the

concerns of the inspiring company, investment

capital, scaling the business, small business owner,

and startup location. Those concerns consistently

show positive tendencies and can be a signal for

providing content learning materials in that regard.

Also, four topics of concern show negative

popularity among the graphics: lean startup, user-

centered app, product launch, and technology trends.

Many reasons might push the concern popularity to

be reduced, for instance, the availability of learning

sources outside the SQA.

Figure 6: Concerns that show decreasing trends in

popularity compared to other concerns.

The following section will discuss the study's

implications, including limitations and

recommendations for further study.

5 DISCUSSION

Based on the identified concerns from the SQA,

further research can use the proposed designation and

validate it empirically with SEs based on different

types and sizes of businesses, SEs characteristics

(Chandra et al., 2016), or the current entrepreneurial

journey process. To fit the context of the SEs study,

the list of competing concerns may be prioritized,

modified, combined, or, if necessary, expanded to

include new concerns.

In comparison to earlier research (Giardino et al.,

2015; Wang et al., 2016), this study discovered two

new dimensions: role model and international startup

development. Pitching and product launch were also

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discussed in the SQA, which shows the importance of

startups in the digital age in terms of customer

acquisition (Giardino et al., 2015). Technology trends

and digital business also shows that the challenges

were also prevalent for SEs in the digital age, which

was previously discovered only from the social media

data of SE status on Twitter (Saura et al., 2019), but

from the direct interview and observation study

(Giardino et al., 2015; Wang et al., 2016).

Furthermore, the study demonstrates how topic

modeling and sentiment analysis may be used to

uncover SE concerns based on questions posted on an

SQA. We also confirmed SEs' usage of SQA in an

open virtual community forum to find preferences

and responses to their bewilderment, challenges, and

queries as concerns, which had previously been

generated by qualitative methods such as surveys,

literature studies, and personal interviews with SEs

(Giardino et al., 2015; Ratinho et al., 2020; Shneor &

Flåten, 2015; Wang et al., 2016).

Nonetheless, this study has a few limitations.

First, we only studied one SQA that uses English.

Further research can be conducted with other SQA

platforms and combine multilingual data collections.

Current research on language translation enables

topic modeling to mature in English by translating the

other language into English before applying the

English-based topic modeling. Also, we only looked

at the questions and excluded the answers. By

supplementing the dataset with responses, additional

information can be gathered that will aid in

determining the size of the issue at hand.

Furthermore, topics may overlap in meaning or

have almost identical meanings; since we only use the

log-likelihood value to select the number of topics,

other criteria (e.g., coherence value) can also be used.

In further studies, the related questions can be

included in the analysis to form the label and validate

the label with experts and SE directly. Besides, while

the study was limited to 2018, based on the identified

topics, the LDA model presented in Table 1 with

words and the associated coefficient can predict a

topic in new documents or questions. Future research

may indicate dynamic shifts in the popularity of

concern about a particular event, such as before and

after the Covid 19 pandemic.

6 CONCLUSION

Our research explores the SEs concern pattern and

shows that SQA can be reliable knowledge

management and entrepreneurship study tool. We

have identified thirty competing concerns that are

coupled with SEs' emotional preferences. In the

future, it is possible to conduct more empirical

explorations based on the thirty issues raised.

ACKNOWLEDGEMENTS

The first author has received a grant from the KPM

Committee of the Hochschule Ruhr West to publish

the paper with the ID: KPM321024. Additionally,

thanks to the Ministry of Culture and Science of the

State of North Rhine-Westphalia for the financial

support of the Institute of Positive Computing at the

Hochschule Ruhr West.

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