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.

1 INTRODUCTION

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

https://orcid.org/0000-0002-2156-2083

https://orcid.org/0000-0002-7168-090X

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.,

2013).

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

289

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.

2 THEORETICAL DISCUSSION

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

implementation.

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

290

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

innovative.

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.

Innovativeness.

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

implemented.

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.

Sentiment.

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

291

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

following:

H5: A positive sentiment within the comments given

to an idea has a positive effect on the likelihood of the

idea being implemented.

3 METHODOLOGY

Data.

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.

Variables.

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

others.

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

variables.

Analysis.

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

292

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4 RESULTS

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.

Variables

Coefficient S.E.

Constant

-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

Votes

.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.



5 CONCLUSIONS

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,

1992).

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

293

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

comments.

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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