Research paper presented at the International Conference for Information Systems in Austin in 2021.

1. Pick the Odd-Ones Out!
Conferring Legitimacy Of
Initial Coin Offerings By
Distinction
Authors: Shahid M.N. & Jungpil Hahn

2. Outline
2
Introduction
Motivation
Hypotheses and Measures
Data Sources
Methodology
Results
Contributions

3. Introduction
Let’s begin here.
1
3

4. Initial Coin Offerings (ICOs)
and Entrepreneurial Ventures
4
◉ Initial Coin Offerings are a popular new venture financing
method.
ICO
$
₿
○ In 2018, ICOs raised three times more money from
resource-providing audiences than venture capital
(CB Insights 2019).
○ ICOs are associated with high risks (Constable
2021; Hornuf et al. 2021) and information
asymmetry (Hornuf et al. 2021) that can pose
challenges to venture’s legitimacy (Chod and
Lyandres 2018; Fisch and Momtaz 2020).
■ In 2020, 80,000 cryptocurrency scams occurred in
USA (Constable 2021).
Introduction

5. “
Legitimacy is a generalized
perception or assumption that the
actions of an entity are desirable,
proper, or appropriate within
some socially constructed system
of norms, values, beliefs, and
definitions (Suchman 1995).
5

6. Legitimacy Indicators
6
Entrepreneuri
al ventures
Venture
Financing
Established
organizations
Crowdfunding
Institutional
Investors
Initial Coin
Offerings
(ICOs)
Initial Public
Offerings
(IPOs)
Angel Investors Venture Capital
◉ Legitimacy is
characterized by
legitimacy indicators
(Cho and Patten 2007;
Suddaby et al. 2017; Tang
2017).
○ Product track records
○ Legality
○ Access to costly media
◉ ICOs lack access to
traditional legitimacy
indicators.
Introduction

7. Legitimacy
7
◉ 
Why is legitimacy important?
Long – term
survival (Suchman
1995; Guo 2012)
Social
appropriateness
(Suchman 1995)
Access to
resources
(Deephouse and
Suchman 2012;
Suchman 1995)
Introduction

8. “
How can legitimacy of ICO ventures be
conferred?
8

9. Cultural Entrepreneurship
Theory and Signaling Theory
9
○ We employ two theories in our study:
■ Cultural entrepreneurship theory
■ Signaling theory
○ Entrepreneurial storytelling: Central driver for
legitimacy in the cultural entrepreneurship theory.
■ Source of organizational identity-building.
■ Antecedent for new ventures’ legitimation
and resource acquisition (Lounsbury and
Glynn 2001; Snihur 2016).
Introduction

10. Cultural Entrepreneurship
Theory and Signaling Theory
10
○ Distinctiveness of Entrepreneurial storytelling: The
degree to which an entrepreneurial story deviates
from the prototypical story of a market category.
■ Why we use distinctiveness?
● It can attract resources effectively (Lounsbury
and Glynn 2001, 2019; Navis and Glynn 2011).
○ In our study, distinctiveness is a property of
entrepreneurial storytelling that signals quality and
can serve as a legitimacy indicator.
Introduction

11. “
Can distinctive entrepreneurial stories
convey ICO venture’s legitimacy to the
resource-providing audiences?
11

12. Concepts Summary
12
Entrepreneurial ventures (a.k.a.
ICO ventures)
New ventures seeking resources
through ICOs.
Legitimacy
A generalized perception or
assumption that the actions of
an entity are appropriate.
Resource-providing audiences
Financially interested in the
venture and exist externally.
Entrepreneurial storytelling
Source of organizational
identity-building.
Distinctiveness of
entrepreneurial storytelling
The degree to which an
entrepreneurial story deviates
from the prototypical story of a
market category.
Introduction

13. Motivation
Let’s look into our motivation for this research.
2
13

14. Motivation (1)
14
Extant Theory/Assumption
(s)
Reason (s) Argument (s)
Distinctiveness counteracts
the attainment of legitimacy
(Powell and DiMaggio 2012).
Distinctiveness from a categorical
prototype reduces understandability
(Navis and Glynn 2011).
Categorical prototypes might not represent
novelty which resource providing
audiences seek in ICO ventures.
Early-stage entrepreneurial ventures are
more susceptible to lemons market
(Akerlof 1970) than established
organizations.
• Distinctive entrepreneurial story can be
associated with effort and time that
provides an “observable” signal to the
audiences reducing the information
asymmetry.
Motivation

15. Motivation (2)
15
Extant Theory/Assumption
(s)
Reason (s) Argument (s)
Established organizations
remained the context of extant
research (e.g., Powell and
DiMaggio 2012; Navis and
Glynn 2011).
Established organizations have access to
costly or observable legitimacy indicators.
• Successful product track records.
• Lawful business and practices.
• Participation in activities that can gain
social acceptance (such as CSR).
• Engagement through traditional media.
Entrepreneurial ventures lack access to
traditional legitimacy indicators.
• Costly signal  involving financial costs.
• Observable signal  involving
observable signals staking effort and
time.
Distinctive entrepreneurial story can serve
both as an observable and costly signal
that the new venture intends to
differentiate itself from its competitors.
Motivation

16. Motivation (3)
16
Extant Theory/Assumption
(s)
Reason (s) Argument (s)
The extant literature
conceptualizes legitimacy
from two perspectives:
1. As an active endorsement
(Deephouse 1996).
2. As a judgment made by
individual evaluators (Tost
2011).
Active endorsement involves actual deal
between resource-providing audiences
and the venture.
We view legitimacy from a novel (third)
perspective.
• As an organizational attribute (e.g.,
Taeuscher et al. 2020) that may contain
both active endorsements and
judgements made by individual
evaluators.
• Example: Members of online
communities may not assess a venture's
legitimacy (not due to irrationality), but
still invest in it (herding behaviour).
Judgement might not involve an actual
deal between resource-providing
audiences and the venture.
Motivation

17. Motivation (4)
17
Extant Theory/Assumption
(s)
Reason (s) Argument (s)
Traditional media is
conceptualized as an agenda
setting device (McCombs et al.
2014) in accordance with
agenda setting theory
(Swanson et al. 1963).
There is a lack of research that examines
the role of online media in legitimacy realm
as both – an agenda setting and affect-
based device (towards legitimacy).
Our research identifies online media as a
dichotomous device.
• It is comparable to traditional media as
rather inexpensive but equally effective
source for reducing information
asymmetry through venture-managed
online-activity.
• It serves as a feedback system (Lenz
and Burilkov 2017) to the venture (or its
product line) which resides outside of
the strategic control of the venture.
• It can shift normative legitimacy by
influencing cognitive legitimacy.
Motivation

18. Motivation (5)
18
Social
Non-financial
• Ventures gain
legitimacy
(normative)
(Suchman 1995;
Aldrich and Fiol
1994), when
novelty-expecting
audiences perceive
them to engage in
"the right thing to
do”.
• Extant literature
highlights social
aspect of legitimacy
alongside "the right
thing to do”. In
other words, “right
thing to do is
treated as a single
non-financial
vector within the
social context.
Motivation

19. Motivation (5)
19
Economic
Ecological
Social
Non-financial
Non-financial
• Audiences set
expectations along
multiple vectors.
• The legitimacy of
an entrepreneurial
venture with a
focus on ecology
and social well-
being will represent
greater “right thing
to do” than one
with a lone focus
on social well-
being.
Motivation

20. Hypotheses and
Measures
Let’s talk about our hypotheses and measurement of variables.
3
20

21. Measure: Dependent Variable
◉ Legitimacy is a nebulous construct and
difficult to measure directly (Deephouse &
Suchman 2008; Deephouse et al. 2017;
Vergne 2011).
◉ We employ underpricing (e.g., Chanson et al.
2018) as a proxy to measure legitimacy.
◉ Underpricing for ICOs: The difference
between the issuance price of the coins and
the closing price after a workweek on a
publicly accessible exchange (Chanson et al.
2018).
21
t=0
Offer price
Share price
Hypotheses and Measures

22. Control Variables
22
Control
level
Variable Name Description
Project
Venture age, Raised
Amount, Valuation,
Oversubscribed, ICO
Duration, Min Cap, Max
Cap, Length, Rating
Oversubscribed: Dummy variable set to 1 if the ICO has reached its maximum cap.
Min cap: Minimum amount set for contribution, expressed in Ether.
Max cap: Set to 1 if maximum investment limit is applicable.
Length: Logged number of words in narratives (Calic and Mosakowski 2016).
Rating: Minimum grade of rating is 0, which indicates a low-quality ICO, and a maximum
grade of five indicates a high-quality ICO.
Creator
Team, Advisors, Projects
backed, Creator projects,
Insider, GitHub
Team: Number of team members reported prior to the ICO.
Advisors: Number of Advisors in the team.
Projects-backed: Projects-backed controls the number of projects backed by the ICO
creator.
Creator-projects: Creator-projects controls the projects launched by the same creator in
the past.
Insider: Tokens kept by the ICO team.
GitHub: Dummy variable set to 1 if a GitHub repository existed before the ICO.
Platform U-Token, S-Token, Bonus
U-Token: Utility token.
S-Token: Security token.
Bonus: Bonus take the value of 1 if an early investor bonus was offered and 0 otherwise.

23. Independent Variables:
Entrepreneurial Storytelling
Attributes
23
Distinctiveness
Degree to which an
entrepreneurial story
deviates from market
category’s prototypical
story.
Contribution claims
Convey the venture’s
congruence with an
audience’s normative
expectations (Lounsbury &
Glynn 2001).
Founder’s beliefs: We value our users
because they trust our platform for
safekeeping of their money.
Business Model: Our business model
combines the principle of
"commission" fees and "free
marketplace.
Product’s technology: Our product
uses Ethereum's Plasma Cash
Protocol as its underlying technology.
Product’s sustainability: Our product
reduces the harmful environmental
impact created by typical crypto-
mining services.
Example of
entrepreneurial
storytelling
Hypotheses and Measures

24. Theoretical Model
24
Figure 2a. 
Research model (H1 & H2)
Entrepreneurial
Storytelling
Distinctiveness
Contribution
Claims
Judgements about
Legitimacy
ICO Underpricing
H1 (+)
H2 (+)
• H1: Distinctiveness
positively affects the
judgements about
legitimacy of an ICO
venture.
• H2: Contributions claims
positively affect the
judgements about
legitimacy of an ICO
venture.
Hypotheses and Measures

25. Theoretical Model
25
• The positive effects of
legitimacy are bounded, so
ventures will benefit from any
source of legitimacy until they
reach the 'range of acceptability'
of their audience.
• H3: Contribution claims
negatively affect the positive
relationship between
distinctiveness and the
judgements about legitimacy of
an ICO venture.
Entrepreneurial
Storytelling
Distinctiveness
Contribution
Claims
Judgements about
Legitimacy
ICO Underpricing
H1 (+)
H2 (+)
H3 (-)
Figure 2b. 
Research model (H3)
Hypotheses and Measures

26. Measure: Independent
Variables
◉ Distinctiveness
○ Identify deviation between prototypical content and entrepreneurial story.
○ 𝑇=1
100
𝑎𝑏𝑠(𝜗𝑇,𝑖 − 𝜗𝑇,𝑀
−
) 𝑤ℎ𝑒𝑟𝑒, 𝜗𝑇,𝑖 means venture i weight for topic T and
𝜗𝑇,𝑀
−
means market category M’s average weight for topic T.
◉ Contribution claims
○ 
We use Computer-Aided-Text-Analysis (CATA) to measure the number of
contribution claims in each entrepreneurial story.
○ CATA is reliable because it provides a structured, systematic, and easily
replicable approach for measuring theoretically derived constructs in large
amounts of text documents (Short et al. 2010).
26
Hypotheses and Measures

27. Online Media
◉ Entrepreneurial ventures strategically manage online media by
keeping a volume and frequency of online engagements.
◉ Ordinary citizens express and negotiate judgments about the
organizations online (Castelló et al. 2013).
○ Approximately 20% of all online media content is about organizations
(Jansen et al. 2009).
◉ Measure: We use NLTK to determine sentiment polarity in the text.
27
Hypotheses and Measures

28. Theoretical Model
28
Figure 2c. 
Research model (H4 & H5)
Entrepreneurial
Storytelling
Distinctiveness
Contribution
Claims
Judgements about
Legitimacy
ICO Underpricing
H1 (+)
H2 (+)
H3 (-)
Online Media
Activity Volume
Sentiment Polarity
Positive
Negative
H4 (+)
H5 (-)
H4 (+)
• H4: Online media activity
volume and positive
sentiment about the
distinctiveness and
contribution claims prior to
ICO positively mediates the
judgements about legitimacy
of the ICO venture.
• H5: Online media’s negative
sentiment has a negative
moderating effect on the
relationship between
contribution claims and the
judgements about legitimacy
of an ICO venture.
Hypotheses and Measures

29. Market Category Viability
29
Zone of
Viability
Risk of
isolation
Risk of
absorption
Risk of
attenuation
Risk of
fragmentation
Low High
Low
High
Distinctiveness
Coherence
Figure 4. Zone of viability
• Degree to which a market category is
balanced along coherence and
distinctiveness dimensions to become
and remain viable (Lo et al. 2020).
• Measure: Logged cumulative volume
of text about a given market category
in the online media up until the month
in which a given venture’s ICO
campaign has ended.
• 
H6: Perceived market category
viability has a positive moderating
effect on the relationship between
distinctiveness and the judgements
about legitimacy of an ICO venture.
Hypotheses and Measures

30. Theoretical Model
30
Figure 2d. 
Research model
Entrepreneurial
Storytelling
Distinctiveness
Contribution
Claims
Market Category
Perceived Viability
Judgements about
Legitimacy
ICO Underpricing
H1 (+)
H2 (+)
H6 (+)
H3 (-)
Online Media
Activity Volume
Sentiment Polarity
Positive
Negative
H4 (+)
H5 (-)
H4 (+)
• 
H6: Perceived market category
viability has a positive
moderating effect on the
relationship between
distinctiveness and the
judgements about legitimacy of
an ICO venture.
Hypotheses and Measures

31. Hypotheses Summary
Hypotheses
H1 Distinctiveness positively affects the judgements about legitimacy of an ICO venture.
H2 Contributions claims positively affect the judgements about legitimacy of an ICO venture.
H3
Contribution claims negatively affect the positive relationship between distinctiveness and the judgements about legitimacy of an ICO
venture.
H4
Online media activity volume and positive sentiment about the distinctiveness and contribution claims prior to ICO positively mediates the
judgements about legitimacy of the ICO venture.
H5
Online media’s negative sentiment has a negative moderating effect on the relationship between contribution claims and the judgements
about legitimacy of an ICO venture.
H6
Perceived market category viability has a positive moderating effect on the relationship between distinctiveness and the judgements
about legitimacy of an ICO venture.
31
Hypotheses and Measures

32. Data Sources
This section briefly explains data sources.
4
32

33. Data Sources
◉ Data on 306 (272 after cleaning) ICOs is collected across 29
categories.
○ Project descriptions, Categories, Historical pricing, Tweets,
Discussions forums content
◉ Icobench.com (Laatikainen 2020; Fisch and Momtaz 2020);
Coin market cap, Twitter, Bitcointalk, Reddit (Chanson 2018;
Fisch 2019; Kranz 2019).
33
Data Sources

34. Method
In this section we discuss operationalization of constructs, dependent
and independent variables.
6
34

35. Method
◉ 
Several OLS regressions using Python packages that includes Pandas,
NumPy, StatsModels, and MatPlotLib.
◉ We used VIF to determine multicollinearity. Our models have VIF
between 1.3 and 2.7 (the maximum individual VIF is 6.69) which shows
there is no multicollinearity.
◉ Latent Dirichlet Allocation (Hannigan et al. 2019; Schwarz 2018) to
identify and represent each narrative as a probabilistic representation
of topics (DiMaggio 2015).
35
Method

36. Descriptive Statistics
36
*After
logarithmic
transformation,
**After
orthogonalization.
Variable Mean Median Min Max Std
ICO underpricing 133.46 55.18 -88.41 2433.00 276.36
Distinctiveness 0.91 0.42 0.34 1.89 0.23
Contribution claims 4.31 2.47 0.00 53.00 3.34
Category coverage* 9.69 3.46 0.00 22.00 4.46
Length** 0.00 6.61 -4.00 11.00 1.62
Tweets 64.76 44.31 0.00 276.00 59.90
Followers 14.63 5.34 0.00 77.00 16.23
Threads 8.81 7.35 22.00 32.00 3.34
Venture age 22.21 13.01 0.00 148.00 23.25
Rating 2.66 1.14 0.200 5.00 0.743
Team 7.44 18.00 1.00 50.00 6.368
Advisors 2.65 4.00 0.00 20.00 3.51
Insider 0.246 0.34 0.00 0.991 0.251
Projects-backed 2.06 7.66 0.00 654.00 13.19
Creator-projects 1.06 1.02 0.00 96.00 1.97
U-Token 0.57 0.31 0.00 1.00 0.41
S-Token 0.08 0.14 0.00 1.00 0.27
C-Token 0.164 0.110 0.00 1.00 0.368
Raised amount 18.032 10.760 0.50 218.00 26.703
Oversubscribed 0.84 1.00 0.00 1.00 0.44
Bonus 0.339 0.148 0.00 1.00 0.467
ICO duration 16.33 8.00 1.00 88.00 19.48
Valuation 289.46 131.44 6.00 9734.00 1456.63
Min cap 0.22 0.00 0.00 2.65 0.45
Max cap 0.22 0.00 0.00 1.45 0.41
Method

37. Results
This section shows initial results.
7
37

38. Preliminary Results
38
N=272;
*p<0.05,
**
p<0.01,
***
p<0.001
Model 1 (H1, H2) Model 2 (H3) Model 3 (H4) Model 4 (H5) Model 5 (H6)
         
Distinctiveness 0.67*** 0.000 0.89*** 0.000 0.39* 0.000 0.12 0.000 -0.24 0.221
Contribution claims 0.08*** 0.000 0.16*** 0.000 0.08** 0.001 0.22*** 0.0003 0.04*** 0.000
Category coverage -0.06*** 0.000 -0.06*** 0.000 0.11** 0.000 0.017 0.000 -0.14*** 0.000
Length 0.21*** 0.000 0.21*** 0.000 0.06 0.000 0.055 0.000 0.21*** 0.000
Tweets 0.57* 0.000 0.35* 0.100 -0.001* 0.002 0.0161* 0.000 0.34* 0.000
Followers 0.66 0.002 0.336 0.000 0.026* 0.012 0.034** 0.000 0.045 0.010
Threads 0.056* 0.013 0.564 0.000 0.079*** 0.027 0.003 0.000 0.004 0.100
Venture age 0.14*** 0.000 0.14*** 0.000 -0.007 0.0071 0.654 0.000 0.14*** 0.000
Rating 0.544 0.000 0.636 0.000 0.064** 0.000 0.671 0.000 0.356 0.000
Team 0.026 0.000 0.0266 0.000 0.034 0.000 0.034* 0.000 0.036* 0.000
Advisors 0.059 0.000 0.554 0.000 0.055 0.000 0.441** 0.000 0.573** 0.000
Insider 0.600 0.000 0.631 0.000 0.698 0.000 0.573 0.000 0.451 0.000
Projects-backed 0.01*** 0.000 0.01*** 0.000 0.01*** 0.000 0.01** 0.000 0.01** 0.000
Creator-projects -0.00 0.428 -0.00 0.543 -0.00 0.643 -0.00 0.254 -0.00 0.131
U-Token 0.562 0.000 0.573 0.000 0.324 0.000 0.652 0.000 0.651 0.000
S-Token 0.866 0.000 0.781 0.000 0.799 0.000 0.901 0.000 0.931 0.000
Raised amount -2.356** 0.000 0.454 0.000 0.001 0.001 0.556 0.000 0.021** 0.034
Oversubscribed -0.265 0.000 0.665 0.000 1.000*** 0.428 0.054* 0.000 -0.045 0.000
Bonus 0.076* 0.000 0.061* 0.000 0.076* 0.000 0.581** 0.000 0.551** 0.000
ICO duration 0.115 0.000 0.432 0.000 0.030** 0.0091 0.1121 0.000 0.001*** 0.000
Valuation 0.665** 0.000 0.012 0.000 -0.000 0.000 0.543 0.000 0.565*** 0.0173
GitHub 0.255 0.000 0.371 0.000 0.169 0.000 0.191 0.000 0.197 0.000
Min cap 0.432 0.000 0.212 0.000 -0.035 0.138 0.003 0.000 0.021 0.0016
Max cap 0.212* 0.000 0.657 0.000 0.424** 0.237 0.554 0.000 0.542** 0.065
R2 0.616 0.616 0.617 0.105 0.491 0.0001 0.616 0.0065
P 0.000 0.000 0.000 0.000 0.000
Results

39. Preliminary Results
39
N=272;
*p<0.05,
**
p<0.01,
***
p<0.001
Model 1 (H1, H2) Model 2 (H3) Model 3 (H4) Model 4 (H5) Model 5 (H6)
         
Distinctiveness 0.67*** 0.000 0.89*** 0.000 0.39* 0.000 0.12 0.000 -0.24 0.221
Contribution claims 0.08*** 0.000 0.16*** 0.000 0.08** 0.001 0.22*** 0.0003 0.04*** 0.000
Category coverage -0.06*** 0.000 -0.06*** 0.000 0.11** 0.000 0.017 0.000 -0.14*** 0.000
Length 0.21*** 0.000 0.21*** 0.000 0.06 0.000 0.055 0.000 0.21*** 0.000
Tweets 0.57* 0.000 0.35* 0.100 -0.001* 0.002 0.0161* 0.000 0.34* 0.000
Followers 0.66 0.002 0.336 0.000 0.026* 0.012 0.034** 0.000 0.045 0.010
Threads 0.056* 0.013 0.564 0.000 0.079*** 0.027 0.003 0.000 0.004 0.100
Venture age 0.14*** 0.000 0.14*** 0.000 -0.007 0.0071 0.654 0.000 0.14*** 0.000
Rating 0.544 0.000 0.636 0.000 0.064** 0.000 0.671 0.000 0.356 0.000
Team 0.026 0.000 0.0266 0.000 0.034 0.000 0.034* 0.000 0.036* 0.000
Advisors 0.059 0.000 0.554 0.000 0.055 0.000 0.441** 0.000 0.573** 0.000
Insider 0.600 0.000 0.631 0.000 0.698 0.000 0.573 0.000 0.451 0.000
Projects-backed 0.01*** 0.000 0.01*** 0.000 0.01*** 0.000 0.01** 0.000 0.01** 0.000
Creator-projects -0.00 0.428 -0.00 0.543 -0.00 0.643 -0.00 0.254 -0.00 0.131
U-Token 0.562 0.000 0.573 0.000 0.324 0.000 0.652 0.000 0.651 0.000
S-Token 0.866 0.000 0.781 0.000 0.799 0.000 0.901 0.000 0.931 0.000
Raised amount -2.356** 0.000 0.454 0.000 0.001 0.001 0.556 0.000 0.021** 0.034
Oversubscribed -0.265 0.000 0.665 0.000 1.000*** 0.428 0.054* 0.000 -0.045 0.000
Bonus 0.076* 0.000 0.061* 0.000 0.076* 0.000 0.581** 0.000 0.551** 0.000
ICO duration 0.115 0.000 0.432 0.000 0.030** 0.0091 0.1121 0.000 0.001*** 0.000
Valuation 0.665** 0.000 0.012 0.000 -0.000 0.000 0.543 0.000 0.565*** 0.0173
GitHub 0.255 0.000 0.371 0.000 0.169 0.000 0.191 0.000 0.197 0.000
Min cap 0.432 0.000 0.212 0.000 -0.035 0.138 0.003 0.000 0.021 0.0016
Max cap 0.212* 0.000 0.657 0.000 0.424** 0.237 0.554 0.000 0.542** 0.065
R2 0.616 0.616 0.617 0.105 0.491 0.0001 0.616 0.0065
P 0.000 0.000 0.000 0.000 0.000
Results

40. Preliminary Results
40
N=272;
*p<0.05,
**
p<0.01,
***
p<0.001
Model 1 (H1, H2) Model 2 (H3) Model 3 (H4) Model 4 (H5) Model 5 (H6)
         
Distinctiveness 0.67*** 0.000 0.89*** 0.000 0.39* 0.000 0.12 0.000 -0.24 0.221
Contribution claims 0.08*** 0.000 0.16*** 0.000 0.08** 0.001 0.22*** 0.0003 0.04*** 0.000
Category coverage -0.06*** 0.000 -0.06*** 0.000 0.11** 0.000 0.017 0.000 -0.14*** 0.000
Length 0.21*** 0.000 0.21*** 0.000 0.06 0.000 0.055 0.000 0.21*** 0.000
Tweets 0.57* 0.000 0.35* 0.100 -0.001* 0.002 0.0161* 0.000 0.34* 0.000
Followers 0.66 0.002 0.336 0.000 0.026* 0.012 0.034** 0.000 0.045 0.010
Threads 0.056* 0.013 0.564 0.000 0.079*** 0.027 0.003 0.000 0.004 0.100
Venture age 0.14*** 0.000 0.14*** 0.000 -0.007 0.0071 0.654 0.000 0.14*** 0.000
Rating 0.544 0.000 0.636 0.000 0.064** 0.000 0.671 0.000 0.356 0.000
Team 0.026 0.000 0.0266 0.000 0.034 0.000 0.034* 0.000 0.036* 0.000
Advisors 0.059 0.000 0.554 0.000 0.055 0.000 0.441** 0.000 0.573** 0.000
Insider 0.600 0.000 0.631 0.000 0.698 0.000 0.573 0.000 0.451 0.000
Projects-backed 0.01*** 0.000 0.01*** 0.000 0.01*** 0.000 0.01** 0.000 0.01** 0.000
Creator-projects -0.00 0.428 -0.00 0.543 -0.00 0.643 -0.00 0.254 -0.00 0.131
U-Token 0.562 0.000 0.573 0.000 0.324 0.000 0.652 0.000 0.651 0.000
S-Token 0.866 0.000 0.781 0.000 0.799 0.000 0.901 0.000 0.931 0.000
Raised amount -2.356** 0.000 0.454 0.000 0.001 0.001 0.556 0.000 0.021** 0.034
Oversubscribed -0.265 0.000 0.665 0.000 1.000*** 0.428 0.054* 0.000 -0.045 0.000
Bonus 0.076* 0.000 0.061* 0.000 0.076* 0.000 0.581** 0.000 0.551** 0.000
ICO duration 0.115 0.000 0.432 0.000 0.030** 0.0091 0.1121 0.000 0.001*** 0.000
Valuation 0.665** 0.000 0.012 0.000 -0.000 0.000 0.543 0.000 0.565*** 0.0173
GitHub 0.255 0.000 0.371 0.000 0.169 0.000 0.191 0.000 0.197 0.000
Min cap 0.432 0.000 0.212 0.000 -0.035 0.138 0.003 0.000 0.021 0.0016
Max cap 0.212* 0.000 0.657 0.000 0.424** 0.237 0.554 0.000 0.542** 0.065
R2 0.616 0.616 0.617 0.105 0.491 0.0001 0.616 0.0065
P 0.000 0.000 0.000 0.000 0.000
Results

41. Preliminary Results
41
N=272;
*p<0.05,
**
p<0.01,
***
p<0.001
Model 1 (H1, H2) Model 2 (H3) Model 3 (H4) Model 4 (H5) Model 5 (H6)
         
Distinctiveness 0.67*** 0.000 0.89*** 0.000 0.39* 0.000 0.12 0.000 -0.24 0.221
Contribution claims 0.08*** 0.000 0.16*** 0.000 0.08** 0.001 0.22*** 0.0003 0.04*** 0.000
Category coverage -0.06*** 0.000 -0.06*** 0.000 0.11** 0.000 0.017 0.000 -0.14*** 0.000
Length 0.21*** 0.000 0.21*** 0.000 0.06 0.000 0.055 0.000 0.21*** 0.000
Tweets 0.57* 0.000 0.35* 0.100 -0.001* 0.002 0.0161* 0.000 0.34* 0.000
Followers 0.66 0.002 0.336 0.000 0.026* 0.012 0.034** 0.000 0.045 0.010
Threads 0.056* 0.013 0.564 0.000 0.079*** 0.027 0.003 0.000 0.004 0.100
Venture age 0.14*** 0.000 0.14*** 0.000 -0.007 0.0071 0.654 0.000 0.14*** 0.000
Rating 0.544 0.000 0.636 0.000 0.064** 0.000 0.671 0.000 0.356 0.000
Team 0.026 0.000 0.0266 0.000 0.034 0.000 0.034* 0.000 0.036* 0.000
Advisors 0.059 0.000 0.554 0.000 0.055 0.000 0.441** 0.000 0.573** 0.000
Insider 0.600 0.000 0.631 0.000 0.698 0.000 0.573 0.000 0.451 0.000
Projects-backed 0.01*** 0.000 0.01*** 0.000 0.01*** 0.000 0.01** 0.000 0.01** 0.000
Creator-projects -0.00 0.428 -0.00 0.543 -0.00 0.643 -0.00 0.254 -0.00 0.131
U-Token 0.562 0.000 0.573 0.000 0.324 0.000 0.652 0.000 0.651 0.000
S-Token 0.866 0.000 0.781 0.000 0.799 0.000 0.901 0.000 0.931 0.000
Raised amount -2.356** 0.000 0.454 0.000 0.001 0.001 0.556 0.000 0.021** 0.034
Oversubscribed -0.265 0.000 0.665 0.000 1.000*** 0.428 0.054* 0.000 -0.045 0.000
Bonus 0.076* 0.000 0.061* 0.000 0.076* 0.000 0.581** 0.000 0.551** 0.000
ICO duration 0.115 0.000 0.432 0.000 0.030** 0.0091 0.1121 0.000 0.001*** 0.000
Valuation 0.665** 0.000 0.012 0.000 -0.000 0.000 0.543 0.000 0.565*** 0.0173
GitHub 0.255 0.000 0.371 0.000 0.169 0.000 0.191 0.000 0.197 0.000
Min cap 0.432 0.000 0.212 0.000 -0.035 0.138 0.003 0.000 0.021 0.0016
Max cap 0.212* 0.000 0.657 0.000 0.424** 0.237 0.554 0.000 0.542** 0.065
R2 0.616 0.616 0.617 0.105 0.491 0.0001 0.616 0.0065
P 0.000 0.000 0.000 0.000 0.000
Results

42. Preliminary Results
42
N=272;
*p<0.05,
**
p<0.01,
***
p<0.001
Model 1 (H1, H2) Model 2 (H3) Model 3 (H4) Model 4 (H5) Model 5 (H6)
         
Distinctiveness 0.67*** 0.000 0.89*** 0.000 0.39* 0.000 0.12 0.000 -0.24 0.221
Contribution claims 0.08*** 0.000 0.16*** 0.000 0.08** 0.001 0.22*** 0.0003 0.04*** 0.000
Category coverage -0.06*** 0.000 -0.06*** 0.000 0.11** 0.000 0.017 0.000 -0.14*** 0.000
Length 0.21*** 0.000 0.21*** 0.000 0.06 0.000 0.055 0.000 0.21*** 0.000
Tweets 0.57* 0.000 0.35* 0.100 -0.001* 0.002 0.0161* 0.000 0.34* 0.000
Followers 0.66 0.002 0.336 0.000 0.026* 0.012 0.034** 0.000 0.045 0.010
Threads 0.056* 0.013 0.564 0.000 0.079*** 0.027 0.003 0.000 0.004 0.100
Venture age 0.14*** 0.000 0.14*** 0.000 -0.007 0.0071 0.654 0.000 0.14*** 0.000
Rating 0.544 0.000 0.636 0.000 0.064** 0.000 0.671 0.000 0.356 0.000
Team 0.026 0.000 0.0266 0.000 0.034 0.000 0.034* 0.000 0.036* 0.000
Advisors 0.059 0.000 0.554 0.000 0.055 0.000 0.441** 0.000 0.573** 0.000
Insider 0.600 0.000 0.631 0.000 0.698 0.000 0.573 0.000 0.451 0.000
Projects-backed 0.01*** 0.000 0.01*** 0.000 0.01*** 0.000 0.01** 0.000 0.01** 0.000
Creator-projects -0.00 0.428 -0.00 0.543 -0.00 0.643 -0.00 0.254 -0.00 0.131
U-Token 0.562 0.000 0.573 0.000 0.324 0.000 0.652 0.000 0.651 0.000
S-Token 0.866 0.000 0.781 0.000 0.799 0.000 0.901 0.000 0.931 0.000
Raised amount -2.356** 0.000 0.454 0.000 0.001 0.001 0.556 0.000 0.021** 0.034
Oversubscribed -0.265 0.000 0.665 0.000 1.000*** 0.428 0.054* 0.000 -0.045 0.000
Bonus 0.076* 0.000 0.061* 0.000 0.076* 0.000 0.581** 0.000 0.551** 0.000
ICO duration 0.115 0.000 0.432 0.000 0.030** 0.0091 0.1121 0.000 0.001*** 0.000
Valuation 0.665** 0.000 0.012 0.000 -0.000 0.000 0.543 0.000 0.565*** 0.0173
GitHub 0.255 0.000 0.371 0.000 0.169 0.000 0.191 0.000 0.197 0.000
Min cap 0.432 0.000 0.212 0.000 -0.035 0.138 0.003 0.000 0.021 0.0016
Max cap 0.212* 0.000 0.657 0.000 0.424** 0.237 0.554 0.000 0.542** 0.065
R2 0.616 0.616 0.617 0.105 0.491 0.0001 0.616 0.0065
P 0.000 0.000 0.000 0.000 0.000
Results

43. Summary
Hypotheses OLS Model
Supported/Not
Supported
H1: Distinctiveness positively affects the judgements about legitimacy of an ICO venture. Model 1 Supported
H2: Contributions claims positively affect the judgements about legitimacy of an ICO
venture.
Model 1 Supported
H3: Contribution claims negatively affect the positive relationship between
distinctiveness and the judgements about legitimacy of an ICO venture.
Model 2 Supported
H4: Online media activity volume and positive sentiment about the distinctiveness and
contribution claims prior to ICO positively mediates the judgements about legitimacy of
the ICO venture.
Model 3 Supported
H5: Online media’s negative sentiment has a negative moderating effect on the
relationship between contribution claims and the judgements about legitimacy of an ICO
venture.
Model 4 Supported

H6: Perceived market category viability has a positive moderating effect on the
relationship between distinctiveness and the judgements about legitimacy of an ICO
venture.
Model 5 Supported
43
Results

44. Robustness Checks
◉ Adding a squared term to model 1 showed a significant negative effect
on underpricing for a u-shaped relationship.
◉ Test for joint hypothesis that the relationship between distinctiveness
and underpricing is positive at low values of distinctiveness and
negative at high distinctiveness values suggests rejecting a curvilinear
relationship (p=0.229) and consequently add further support to our
Hypothesis 1.
◉ We also repeated our checks using funding as the dependent variable
following the approach of Calic and Mosakowski (2016). In this, we
measure funding as the log amount of dollars pledged to a given ICO.
The results of these analyses are significant (p<0.001) and confirm the
predicted relationships.
44
Results

45. Contributions
Here is a list of contributions.
8
45

46. Contributions
◉ Our study advances the “optimal distinctiveness” proposition to the
ICOs context and characterizes it as a source of legitimacy.
◉ Isolate cognitive and normative dimensions of organizational
legitimacy as a function of distinctiveness.
◉ Cultural entrepreneurship theory (Aldrich and Fiol 1994; Suchman
1995).
○ Optimal distinctiveness centred on the cognitively legitimating effect of
entrepreneurial stories (Martens et al. 2007; Navis and Glynn 2011). We
add to this body of knowledge by emphasizing the normatively
legitimating effect of entrepreneurial stories.
◉ Contrary to u–shaped affect, we postulate that distinctiveness has a
positive affect on resource acquisition.
46
Contributions

47. Contributions
◉ We introduce the dichotomous role of online media.
○ Can act as a strategic device similar to traditional media (Pollock and
Rindova 2003).
○ Can act as an affect-based feedback device.
◉ Contrary to the assumptions that cognitive legitimacy is fixed (Suchman 1995),
we show that online media can shift normative legitimacy by altering cognitive
legitimacy.
◉ Membership in a viable market category can strengthen financial and non-
financial audiences' perceptions of novelty, providing an additional source of
legitimacy for crowdfunders.
◉ By highlighting how market categories affect normative legitimacy judgements,
we complement earlier research that has focused on market categories'
cognitive legitimizing function (Glynn and Navis 2013; Kennedy et al. 2010; Navis
and Glynn 2010). 47
Contributions

48. Any questions ?
You can find me at
◉ nauman@u.nus.edu
Thanks!
48