Empowered by Innovation: Unravelling Determinants of Idea
Implementation in Open Innovation Platforms
Frederik Situmeang
, Rob Loke
, Nelleke de Boer and Danielle de Boer
Centre of Market Insights, Amsterdam University of Applied Sciences, Wibautstraat 3b, Amsterdam, The Netherlands
Keywords: Crowdsourcing, Innovation, Unstructured Text.
Abstract: Companies use crowdsourcing to solve specific problems or to search for innovation. By using open
innovation platforms, where community members propose ideas, companies can better serve customer needs.
So far, it remains unclear which factors influence idea implementation in crowd sourcing context. With the
research idea that we present here, we aim to get a better understanding of the success and failure of ideas by
examining relationships between characteristics of ideators, characteristics of ideas and the likelihood of
implementation. In order to test the methodological approach that we propose in this paper in which we
investigate for business relevant innovativeness as well as sentiment based on text analytics, data including
unstructured text was mined from Dell IdeaStorm using webcrawling and scraping techniques. Some relevant
hypotheses that we define in this paper were confirmed on the Dell IdeaStorm dataset but in order to generalize
our findings we want to apply to the Lego dataset in our current work in progress. Possible implications of
our novel research idea can be used to fill theoretical gaps in marketing literature, help companies to better
structure their search for innovation and for ideators to better understand factors contributing to successful
idea generation.
The need for innovation is currently a top business
priority (Jaruzelski & Dehoff, 2010) and a key issue
in academic research (Hauser et al., 2006). On-going
technological advances and their enormous influence
and use in society have induced considerable changes
in people’s lifestyles (Romero and Molina, 2011).
Therefore, organizations need to adopt innovative
business models to engage customers and gain
competitive advantage in the marketplace (Zhang et
al., 2015). One way organizations can do this is by
online co-creation communities (OCC) (Zhang et al.,
2015) in the form of crowdsourcing. Majchrzak and
Malhotra (2013) define crowdsourcing for
innovations as “the public generation of innovative
solutions to a complex problem posed by the
company sponsoring the challenge call” (p. 258).
Companies are nowadays more often looking to
generate new ideas or solve specific problems with
the help of their customers (Erickson et al., 2012).
Companies hope to gain direct access to their
customers knowledge concerning user needs to
generate ideas for new products and use their
expertise to solve problems (Schemmann et al.,
2016). This is done in online communities, enabled
by companies, where customers are encouraged to
share their ideas and thoughts about specific topics
(Ye et al., 2012; Schemmann et al., 2016). Customers
are not passive targets of marketing action anymore.
They are perceived as more active operant resources
that determine and create value (Saarijarvi et al.,
Value co-creation has become a key concept
within marketing and business management. The
focus of value co-creation is to reinvent value in terms
of the value creating system itself where different
actors like suppliers, business partners, allies and
customers, work together to co-produce value. There
are a multitude of approaches to value co-creation
(Saarijarvi et al., 2013).) The example of Dell’s
Ideastorm illustrates how customers resources can be
engaged in the New Product Development (NPD)
(Saarijarvi et al., 2013). Over 50,000 ideas have been
generated in the online communities of Dell and Lego
Situmeang, F., Loke, R., de Boer, N. and de Boer, D.
Empowered by Innovation: Unravelling Determinants of Idea Implementation in Open Innovation Platforms.
DOI: 10.5220/0007948602890295
In Proceedings of the 8th International Conference on Data Science, Technology and Applications (DATA 2019), pages 289-295
ISBN: 978-989-758-377-3
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
through crowdsourcing.
Innovative solutions can include new sources of
revenue such as new product lines or services, or
adapt from existing processes and practices
(Dahlander and Gann, 2010). It is thought that a
diverse community will develop fundamentally
different innovations because they draw from a
different knowledge base (Von Hippel, 2005).
Diverse expertise may be derived from differences in
knowledge domains, context, product usage,
discipline or specialty work areas (Schenk and
Guittard, 2011). Crowdsourcing has a benefit for both
the crowdsourcing company and the user within the
online community. Customers’ needs are complex
and hard to measure (O'Hern and Rindfleisch, 2010).
Market research conducted internally would only
provide companies a signal of their customers’
desires and needs, which still results in a lot of new
product failures (Ogawa and Piller, 2006; O'Hern and
Rindfleisch, 2010). Organizations have a problem
with anticipating what consumers actually need.
Through crowdsourcing, ideas come directly from the
customers, investing in solving problems that they
have and, as a consequence, new product
development would become less risky (Lüthje and
Herstatt, 2004). Knowledge about consumer
preference can contribute to the success of a product.
Past research shows that organizations substantially
benefit if they effectively manage and improve the
earlier stages of the new product development (NPD)
process (Verworn, 2009).
Despite being well-recognized in the industry,
limited academic research is done to study OCCs as a
technology driven innovation concept (Bugshan,
2014). It remains unclear how specific characteristics
of ideators and ideas might influence the likelihood
of idea implementation. Also, little is known about
long-term open idea calls. These idea calls can result
in thousands of ideas and detecting the ones to
implement can be difficult for companies. The
IdeaStorm platform operated by the organization Dell
has collected more than 23,000 ideas since 2007
(Schemmann et al., 2016). Empirical research in this
field is lacking (Schemmann et al., 2016). This study
addresses this research gap. The aim of this research
is to estimate whether the characteristics of the
ideator and comments provided by other ideators can
influence the success of an idea. The contributions of
this research are twofold. First, we contribute to the
literature about long-term online idea crowdsourcing.
Not much is known about which factors contribute to
the likelihood that an idea gets implemented. Our
second contribution is a methodological advancement
by providing a new approach to study this topic. We
provide a technique to disentangle unstructured idea
and comment texts to identify innovative efforts and
sentiment in comments. This is the first study to our
knowledge that uses text analysis on crowdsourcing
ideas and comments.
Idea crowdsourcing can be seen as a part of
Chesbrough’s Open Innovation Paradigm. This
assumes that organizations can and should use ideas
from external stakeholders to innovate (Chesbrough,
2006). In the case of crowdsourcing the process of
innovation builds upon the external ideas of
individuals. The crowdsourcing organization controls
the ideation process, observes and analyses the
communication and discussion of ideas and finally
decided which ideas will be implemented
(Schemmann et al., 2016). The online idea
crowdsourcing is likely to generate a large amount of
ideas. Therefore, the company needs to filter out with
tremendous effort to identify which ideas will be most
valuable. Previous research suggests that successful
ideators possess certain characteristics (Von Hippel,
2005). However, thus far little is known about the
factors determining which ideas are most likely to be
implemented (Schemmann et al., 2016). The first two
hypotheses of this research focus on the influence of
ideator related characteristics on the likelihood of
idea implementation. Not all users within a
community are able to generate the same quality
ideas. Characteristics that might influence the
likelihood of idea implementation are activity (Bayus,
2013) and popularity (Gangi and Wasko, 2009) of an
ideator. Shah (2003) found that more active ideators
produce more valuable ideas. However, Schemmann
et al., (2016) found that ideators who post more ideas
are not more likely to generate implemented ideas
than ideators who suggest only one idea. Due to the
mixed results we look once again into the
relationships between ideator characteristics (i.e. the
amount of comments given and received) and idea
Ideator Activity.
The starting point of our investigation is to create a
link between the activity of an ideator in the past and
the quality of the idea that (s)he generates. The
exposure to other creative ideas can enhance one’s
own creativity which leads to the production of more
creative ideas (Nijstad and Stroebe, 2006). By
commenting upon other ideas an ideator’s knowledge
DATA 2019 - 8th International Conference on Data Science, Technology and Applications
base will be more diverse. More alternative and
creatives ideas can be formulated by combining,
recycling, and further developing pieces of
information (Fleming and Szigety, 2006). Research
recognizes that interaction and idea exchange among
ideators will facilitate the retrieval of relevant and
diverse knowledge during idea generation (Kohn and
Smith, 2011). A fundamental believe of
brainstorming is that interactions with diverse others
can stimulate associations in the memory that lead to
higher quality ideas (Osborn, 1953). Interaction with
others help ideators to generate alternatives, upgrade
their own knowledge, get to know more diverse
customer needs and therefore create more innovative
and original ideas. Innovation is thought to play an
important role in idea implementation (Schemmann
et al., 2016). Original ideas are found to be more
attractive by company experts than less original ideas
(Witell et al., 2011). Schemmann et al., (2016) find
that the odds of implementation increase when an
idea is more innovative. Combining the insights of
ideator activity and innovative idea generation, we
propose the following hypothesis:
H1: The number of comments that an ideator had
given in the past has a positive effect on the extent to
which other customers find his/her idea to be
Giving comments is a signal that an author invests
in the community and spends more time reading other
ideas and commenting upon others. This will increase
the visibility of the ideator among other customers.
Arguably, when the ideator posts a new idea, other
customers would acknowledge the ideator as a
valuable member of the customer community and
would be more inclined to contribute to improve the
idea. Based on this mechanism, we propose the
second hypothesis:
H2: The number of comments that an ideator had
given in the past has a positive effect on the number
of comments that the ideator would receive.
Signalling Theory.
Next, we will investigate how innovativeness and
comments that an idea had received can be linked to
the likelihood of its implementation. An underlying
mechanism which explains the relationship between
the attributes of an idea and idea implementation can
be found within the Signaling theory by Spence
(1973). This theory explains how people make
decisions based on signals of quality, particularly
when quality is difficult to ascertain. The lower the
ability of the decision maker to evaluate all available
information, the more important the presence of
signals will be (Spence, 1973). Signaling theory has
been applied in customer research. Through these
signals people can predict the quality of for example
a product (Cheung et al., 2014). One could argue that
it is hard for companies to process all available ideas
because of an information overload. Furthermore,
since the ideas in crowdsourcing are still in ideation
phase, it is arguably difficult to ascertain the quality
of the idea. Therefore, the company has to rely on
signals to determine the quality of the idea.
The first signal that we propose is idea
innovativeness. The innovativeness of an idea can
possibly be found within the comments that other
customers give to the proposed idea. Previous studies
have not investigated the strength of the text of
comments upon idea implementation. But as previous
research found that innovative ideas are more likely
to be implemented (Schemmann et al., 2016), we
expect that comments which imply that an idea is
innovative will be seen as a signal for companies that
the idea is indeed innovative. Therefore, we expect:
H3: The extent to which other customers find an idea
to be innovative as expressed in the comments has a
positive effect on the likelihood of an idea being
Other Customers’ Interest.
The next signal that we propose is other customers’
interest towards the idea quantified from the number
of comments. When ideas receive more comments
this can be interpreted as a signal of high quality. The
Prospect theory adds to this logic by stating that
people are risk-averse (i.e. avoid uncertainty) and
therefore make decisions based on potential gains and
losses (Tversky and Kahneman, 1992). One could
argue that ideas with lots of comments are less risky
to implement, because managers already have an
indication that these ideas will be popular among
potential users.
H4: The number of comments that an idea receives
has a positive effect on the likelihood of the idea
being implemented.
Besides looking at comments we study the sentiment
in comments. To our knowledge no studies have
established the relationship between the sentiment of
the comments and the likelihood of implementation.
It is important to study sentiment because it expresses
the attitude of ideators about an idea from another
ideator. Attitude is defined as “the degree to which a
person has a favorable or unfavorable evaluation or
appraisal of the behavior in question” (Ajzen, 1991,
p. 188). A positive sentiment means a positive
Empowered by Innovation: Unravelling Determinants of Idea Implementation in Open Innovation Platforms
emotion. This research aims to identify the attitude of
fellow ideators towards ideas. Comments can give
useful insights into why certain ideas will be accepted
and others not. To improve the existing literature, we
propose that comments must be analyzed with a
sentiment analysis. It is not enough to look at the
amount of comments when sentiment in comments
could influence the likelihood of implementation.
Based on the underlying mechanisms explained in the
Signaling theory and Prospect theory, we propose the
H5: A positive sentiment within the comments given
to an idea has a positive effect on the likelihood of the
idea being implemented.
The data source of this study is the Dell IdeaStorm
website (ideastorm.com) that is commonly applied in
this research context (Bayus, 2013; Gangi and
Wasko, 2009). The study uses data mined through
web crawling and scraping with scrapy (Kouzis-
Loukas, 2016) in Python. The database consists of
844 ideas that were available online at the moment of
data mining (46% implemented/partially
implemented), in overall as well as 24 categories of
product ideas, Dell ideas and topic ideas, posted
between 2007 and 2018, by 622 ideators. The ideators
received 277 votes and 70 comments on average. The
ideas received 98 votes and 14 posts on average (193
and 25, respectively, for implemented/partially
implemented ideas). We process the texts of the ideas
and comments using text-mining techniques with
library ‘tm’ in R, version 3.4.3. This library contains
a procedure to identify frequently mentioned terms in
texts. For pre-processing, we clean the review texts
from punctuations, numbers, multiple blank spaces
and stop words.
Dependent Variable.
We measure the effect of the independent variables
on the idea implementation, our dependent variable.
Per idea the website indicated whether an idea is
implemented or rejected. The dependent variable is
operationalized with the use of the idea status
indicated on the platform (Schemmann, et al., 2016).
This information was also scraped.
Independent Variables.
The first independent variable in this research is
ideator activity that is measured by the number of
comments that authors receive and give to ideas of
The second independent variable, sentiment, was
operationalized with sentiment score for each
comment using SentiStrength software, a tool for
processing different types of information contained in
text. SentiStrength estimates the strength of positive
and negative sentiment in a text by using a predefined
sentiment word list (Thelwall et al., 2010). This
software is free for academic research and has been
tested and validated in previous research (Thelwall et
al., 2010; Thelwall and Buckley, 2013). SentiStrength
analyses text based on a 1-5 scale.
For the third independent variable, innovation, we
created a document text matrix using the tm library in
R to determine the most frequently mentioned
innovative words. We asked two linguistic experts in
the field of communication science to indicate, out of
a list of words, the degree in which these words were
good synonyms for both innovative and not
innovative. With these words we are able to quantify
the level of innovativeness of the idea by the number
of associated words expressed in the comments. In
total 73 words were selected. After forming the lists,
the experts assigned scores to these words based on
the degree of innovativeness. The score assignment
process is similar to the sentiment analysis which is
based on a 1-5 scale. 1 means the word has little
innovativeness and 5 means that the words has a high
degree of innovativeness. To quantify innovativeness
the authors replaced the sentiment word list with the
innovativeness word list in SentiStrength. This
provided the authors with the innovativeness score of
the comments.
Control Variables.
In the text analysis and modelling, we add the word
length of the idea title and the idea text and the
number of votes that the idea received as control
Hypotheses 1 and 2 will be tested through an OLS
regression in SPSS with the innovativeness of the
comment and the number of comments received as
dependent variables; while the number of comments
that the authors had given in the past as the
independent variable.
Binary logistic regression will be performed in
SPSS to test Hypotheses 3, 4, and 5 because whether
an idea is implemented is a binary variable. The
formula for the binary logistic regression is:
DATA 2019 - 8th International Conference on Data Science, Technology and Applications
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The results show that the number of comments that an
ideator had given in the past has a positive effect on
the extent to which other customers find his/her idea
to be innovative (β = .136, t = 3.981, p < .00). H1 is
confirmed. The number of comments that an ideator
had given in the past has a positive effect on the
number of comments that the ideator received (β =
.015, t = 2.285, p < .05). H2 is also confirmed.
Table 1: Logistic regression results.
Coefficient S.E.
-1,174 .207 ***
. of Past Comments
.002 .000 ***
. of Comments
.039 .008 ***
Average Innovation
.621 .166 ***
Average Positive Sentiment
.391 .127 *
ame Length
.016 .014
Elaboration Length
.-002 .001
.001 .001
*is significant ant the .050 level (2-tailed).
** is significant at the .010 level (2-tailed).
*** is significant at the .001 level (2-tailed).
The logistic regression model for H3, H4, and H5
was significant (χ
(8) = 243.831, p < .005) in
explaining the likelihood of an idea getting accepted.
The model predicts 33.5 percent of the likelihood that
an idea will be implemented. The results show that
the extent to which other customers find an idea to be
innovative expressed in the comments (β =.621, p
<.000) has a positive effect on the likelihood that an
idea gets implemented. H3 is confirmed. The number
of past comments (β =.002, p <.000) and positive
sentiment (β =.391, p <.000) within the comments
have a positive effect on the likelihood of an idea
being implemented. H4 and H5 are also confirmed.
For the model details see Table 1.
Recently, online idea crowdsourcing for new product
ideas has become widely used by companies (Bayus,
2013; Schemmann, 2016). Companies are nowadays
more often looking to generate new ideas or solve
specific problems with the help of their customers. It
is thought that a diverse community will develop
fundamentally different innovations because they
draw from a different knowledge base (Von Hippel,
2005). Companies aim to develop and produce
exactly what consumers want, but this is become
increasingly difficult to attain, since customers’
quickly changing preferences and the heterogeneity
of their demands. Newly launched products suffer
from high failure rates. The main problem is the
faulty understanding of customer needs (Ogawa and
Piller, 2006). Only customers know their specific
needs and problems. Companies can adapt their
products based on this knowledge (Bogers et al.,
2010; Schemmann et al., 2016). By integrating
customers in the innovation process, ideas come
directly from customers (Ogawa and Piller, 2006).
Therefore, new product development would become
less risky (Lüthje and Herstatt, 2004). On the other
end of the spectrum, the customers who generated the
idea benefit by receiving economic incentives,
gaining self-worth, or obtaining the solution for their
problem (Estellés Arolas and González Ladrón-de-
Guevara, 2012).
The results of this study show that the ideator and
idea related characteristics influence the likelihood of
an idea being implemented. There is a significant
effect of the number of past comments of an ideator
on the extent to which others in the community find
his/her idea to be innovative (H1) and the number of
comments that the ideator would receive in the
community (H2). Furthermore, we found a significant
effect of the extent to which other customers in the
community find an idea to be innovative (H3), the
number of comments that an idea receives in the
community (H4) and the extent to which the
comments have a positive sentiment on the likelihood
than an idea will be implemented (H5). We explained
these effects by the Signaling Theory (Spence, 1973)
and the Prospect Theory (Tversky & Kahneman,
The findings of this study have several
implications for the existing literature. It contributes
to the literature by researching the effect of ideator
and idea characteristics on the likelihood that an idea
will be implemented. It also has a methodological
contribution by applying advanced techniques for text
mining and text analytics that provide the opportunity
to extract innovativeness and sentiment from
comments that are placed by ideas.
The results have practical implications that
provide useful insights for management. First, some
characteristics of ideators and ideas have a positive
Empowered by Innovation: Unravelling Determinants of Idea Implementation in Open Innovation Platforms
effect on the likelihood of an idea being implemented.
Management could use these characteristics to search
for more promising ideas on a crowdsourcing
website. Online crowdsourcing via long-term open
idea calls can result in thousands of ideas (Blohm, et
al., 2013; Schemmann et al., 2016). For an
organization it can be problematic to detect the ones
it wants to implement (Schemmann et al., 2016). This
research makes the crowdsourcing process for
companies more effective and less demanding.
Second, the failure rate of newly introduced products
is still about 40% (Castellion and Markham, 2013).
One problem for an organization is to anticipate what
the customers actually need and want (Schemmann et
al., 2016). This research helps companies to better
understand and serve the needs of their customers.
This makes new product implementation less risky.
However, as with any other studies, this research
has some limitations and raises suggestions for
further research. First, this research is solely based on
publicly available data generated for a single
crowdsourcing platform from a specific company.
Therefore, our findings may not be completely
applicable to crowdsourcing in other industries.
Future studies could research other platforms from
companies from different industries. Second, this
study uses data from a publicly available platform.
This provides interesting insights, however more
refined measures of ideator related characteristics (for
example, gender, age and location) or idea related
characteristics (for instance, the quality of an idea)
might benefit further research. Finally, future
research could also get insights from the interaction
between ideators which can be displayed in the
Regardless of these limitations, this preliminary
study contributes to the understanding of user
involvement via online idea crowdsourcing and helps
companies to get a better understanding of which
ideator and idea characteristics will influence the
likelihood of idea implementation.
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