INNOWEB: Gathering the context information of innovation processes with a collaborative social network platform

Abstract

This paper describes the development of a collaborative social platform to support innovation process management. The Drupal based platform accommodates different types of innovation processes (also called waves or idea contests), enhances collaboration and eases management. The main contribution lies on the gathering of context parameters which helps enterprises on the detection of critical success factors, enabling later reproductions. The development leverages open source social computing, real-time web and semantic web technologies adding new functionality in a modular way. Blogs, wikis, graphical tools and voting systems support collaboration and idea management in the early stages of the innovation process. A workflow module launches customized innovation campaigns where topics, participants, stages, selection criteria and communication methods are optimized to enterprise needs.

Full text

INNOWEB: Gathering the context information
of innovation processes with a collaborative
social network platform
Alain Perez1, Felix Larrinaga1, Osane Lizarralde1, Igor Santos2
1Mondragon Unibertsitatea, Goiru kalea 2, 20500 Arrasate-Mondragon, Spain,
{aperez,flarrinaga,olizarralde}@mondragon.edu
2 ISEA S.Coop, Goiru kalea 7, 20500 Arrasate-Mondragon, Spain, isantos@iseamcc.net
Abstract
This paper describes the development of a collaborative social platform to support innovation process management.
The Drupal based platform accommodates different types of innovation processes (also called waves or idea
contests), enhances collaboration and eases management. The main contribution lies on the gathering of context
parameters which helps enterprises on the detection of critical success factors, enabling later reproductions. The
development leverages open source social computing, real-time web and semantic web technologies adding new
functionality in a modular way. Blogs, wikis, graphical tools and voting systems support collaboration and idea
management in the early stages of the innovation process. A workflow module launches customized innovation
campaigns where topics, participants, stages, selection criteria and communication methods are optimized to
enterprise needs.
Keywords
Collaborative innovation, critical success factors, social web, semantic web, innovation context variables
1 Introduction
Innovation is extremely important for the growth strategy of most enterprises [Capozzi, et al.
2010]. With the rise of emerging economies, business is entering a new era of extreme
competition where the only way to survive is to innovate. Many companies and especially Small
and Medium Enterprises (SMEs) have problems applying innovation processes, due to the lack
of resources, appropriated tools or innovation culture. Without innovation those enterprises are
not able to grow and their competitors take advantage of that weakness. Innovation allows
enterprises to compete and evolve efficiently.
Many tools have been developed to support innovation processes. Idea Management Systems
(IMSs) are employed in the early stages of the process, where ideas are generated. IMSs are idea
centred. Thus they collect information about the ideas gathered in the platform. Existent IMSs
hardly collect information about the context. That is, the conditions on which those ideas have
been gathered are lost. Information such as the type of contest where the idea was conceived, the
actions taken during the different stages, the idea contributors or the timing between stages are
often forgotten since there is not a platform that gathers that information. Collecting that data is
essential to reproduce the conditions and the context for successful ideas. Thus, in most IMS
when an idea becomes successful there is no way to identify the context where that idea was
conceived.
The work presented in this paper describes the development of an open source community
platform for the front end of the innovation process focused on the innovation context. The
platform gathers and stores information about environment conditions for different types of
innovation processes. The importance of collecting those conditions lies on the possibility of

repeating those contexts where successful ideas have been created. Hypothetically, the re-
creation of those conditions will turn into new ideas with higher probability of becoming
successful.
The next section outlines the research context introducing the innovation framework for the
project and the state of the art on the technologies selected to create the platform. The third
section describes the objectives, context metrics, the evolutionary methodology and the structure
of the platform presenting the tools and technologies employed in its development. The
following section outlines a case study conducted and summarizes the results and contribution of
the platform within the research work. The last section presents the conclusions and the future
work withdrawn from the project.
2 Relation to Existing Theories and Work
The platform presented in this paper was conceived as the result of previous research. In [Errasti
2010] a thorough study on the innovation process, different innovation models and existent tools
and technologies applied to the different innovation stages was conducted.
The innovation process identified in the study is outlined next. The following subsection presents
a state of the art on the technologies selected for the development of the platform.
2.1 Innovation Process
Most innovation models show a similar baseline and the differences among them lay in the
particularities incorporated to the model in each particular case [Errasti 2009]. This baseline is
understood as a process with several stages. Four stages form the first part or front end of the
innovation process.
1. Idea Generation: creation and collection of new ideas and comments.
2. Idea Analysis: the study of created ideas and the search of relations among them, in
order to merge, split or complement with other ideas.
3. Idea Enrichment: experts add more valuable information into chosen ideas.
4. Idea Selection: select the best ideas for their development into projects, using custom
criteria and weights.
Figure 1: Full innovation process

In the next stage (Idea Development) idea developing planning is approached. Studies are
conducted on many issues; market, technology, business plans, risks, possible collaborations or
competitors. The last stage is concerned with the implementation of the idea (Idea
Implementation stage). This is the actual materialization of an idea where it turns, among other
things, into a product, process, services, patent, strategy or company.
The front end of the innovation process where idea management is developed is one of the most
critical stages.
Thus, the main issue is to manage the innovation process and more specifically the front end of
the innovation process, providing an efficient platform for the innovation process. The following
requirements have been identified:
 A common space to represent and gather all the information related to innovation
processes is needed. This common platform will be used to collect ideas, identify experts,
introduce comments and follow idea progress or search for similar ideas.
 Idea context gathering is essential in order to reproduce successful idea contests.
 The advantage of using social networks in organizations is clear; employees could
develop contacts, share knowledge, improve communication between experts, gather interest
in new projects or ideas, enrich ideas using incremental collaborative contributions and
identify professional opportunities.
 The platform has to be flexible enough to accommodate different types of innovation
campaign or waves simultaneously; firm-centric innovation process or crowd sourcing
contest with hundreds of participants.
2.2 Technology
Our requirement research presents a state of the art where many technologies and tools used to
enhance the innovation process are studied. A description of those technologies, classified in
three groups; social software and innovation platforms, semantic web and real time web is
presented next.
2.2.1 Social software and innovation platforms
Social software is the term used to refer to the different applications and technologies associated
to the Web 2.0 term, widely introduced and depicted in a seminal article by Tim O'Reilly
[O’Reilly 2005]. James Surowiecki demonstrated [Surowiecki 2005] that complex tasks can be
solved more effectively by group collaboration than by any individual of the group.
On the other hand, the term enterprise social software (Enterprise 2.0) refers to the application of
Social Web applications to enterprise environments. Most demanded functionalities for these
applications are similar to those of Facebook or LinkedIn but with more control and governance.
Besides, the increasing interest in IMSs [Conry-Murray 2010] has pushed community platform
vendors to integrate idea management and community software into a single type of platform
that includes idea management functionalities and social technologies.
As social networking started to grow in popularity a new breed of Web applications took on the
market among enterprises; community platforms. Among the core features, community
platforms offer all the functionalities inherited from social Web technologies like blogging, wikis
or social networking [Parrish 2010].
The last issue of the series of McKinsey reports on Web 2.0 adoption shows very positive results
on the use of social technologies and a majority of respondents say their companies enjoy
measurable business benefits from using Web 2.0 [Bughin J, Chui M 2010]. The use of social
webs in the context of enterprise is very incipient. Many barriers are detected, above all
organizational barriers such as no implication of managers, need of a cultural change or
hierarchical structure. However, initial data show very quantifiable benefits and there is no doubt

about the upward trend of adoption of these technologies. A high percentage of companies have
planned to increase the investment in 2.0 technologies.
Several architectures of participation have been analysed including Facebook, Digg, Wikipedia,
IBM Idea Factory, IdeaScale Innocentive, SalesFoce Idea Management, Hominex, Mindmeister,
LaboraNova, IBM Lotus Connections 2.5, Microsoft Office Sharepoint, Brightidea, Imaginatik
Idea Central, Jive SBS, Elgg, Drupal, Liferay, Joomla and Plone. Over 20 factors were
considered as the comparison criteria for the different platforms. The most relevant criteria was;
context gathering, type of license, ease of use, developing language, operative system,
integration with social networks, integration with real time web, integration with semantic web,
blogs, wikis, RSS, email, etc.
The main conclusions extracted from the analysis are:
 All analysed IMSs are idea centred (such as Idea Scale, Mindmeister or Ideatorrent),
gathering little context information in the best cases.
 There are open source platforms with similar characteristics to proprietary software.
Proprietary platforms are discarded. The inclination to select open source is reinforced.
 Any existent or new innovation support platform must consider the integration of its
mechanism with most popular social web platforms, such as Facebook or Twitter in order to
be successful by means of participation. Share ideas among those platforms guarantees
reaching collaborators and in some cases open the process to new participants.
 Semantic Web technologies can support information management and consequently
contribute into better idea generation tasks. Especially, Semantic Web can help in keeping all
the information structured in electronic files accessible by anyone from anywhere. Moreover,
intelligent agents can then deal with this structured data, to avoid direct human interaction,
for instance when searching for new ideas. It can also strengthen ideas by complementing
proposed ideas with content found automatically in other sites or repositories.
 Drupal and Liferay have obtained the highest score, but the dimension of actual and
potential community of users and the effort made by the community of users to integrate
semantic technology or web 3.0 currently favours Drupal.
2.2.2 Semantic Web
Semantic Web consists on transforming plain text found in internet´s content into another one
with sense and meaning. It is defined as the web of data that can be processed directly and
indirectly by machines. The objective is to build a context around information by adding
categories, metadata and relations between things that add sense to that data.
In order to add semantic meaning to data, ontologies are used. Two are the relevant innovation
ontologies encountered in the literature; the innovation management ontology presented by
Christopher Riedl [Riedl, et al. 2009] and the GI2MO ontology presented by Adam Westerski
[Westerski 2009]. The developers of GI2MO ontology have also created a RDF metadata
publishing module for Drupal called RDFme.
3 Research Approach
The state of the art shows that although many idea management tools are available, community
software platforms found are idea. They do not register the context where ideas are gathered.
Thus, a platform that collects ideas and registers the context where they are created is needed.
Additionally the platform must enhance integrability [Riedl, et al. 2009] using semantics.
In this section, firstly, the main objectives of the research can be read. Secondly, the metrics and
data to be collected by the platform are described. Next, the methodology followed in the

development of the platform is detailed. Finally, the platform itself is presented with all the
functionalities.
3.1 Objectives
The main objective is to develop a baseline social networking platform to support the front end
of the innovation process, gather context information and enhance integrability of data.
The technological objectives of the research addressed in this paper are the following:
 Deploy the baseline platform on Drupal representing each of the stages for the innovation
process and gathering all the context related data (ideas, contests, participants and their skills,
outcome, companies, events, etc.). The platform will be made flexible enough to
accommodate and adapt to different scenarios.
 Provide a set of tools for each of the four stages identified.
 Be able to articulate a successful architecture of participation around the platform using
the possibilities brought by social web and real time web technologies.
 Prepare information collected with semantic meaning enhancing integrability.
The methodological objectives are the following:
 Apply the deployed platform launching idea generation campaigns within a set of
companies.
 Gradually establish a cooperative culture in all aspects of innovation through the baseline
platform.
 Measure those case studies with previously defined metrics. This will enable a better
understanding of the issues related with the innovation process.
 Analyze the success factors of these idea generation campaigns considering the defined
metrics.
3.2 Metrics
In order to identify innovation success factors, metrics have to be defined. Environment
conditions or context information is an important issue that needs to be addressed. Data and
metrics about the innovation process, innovation campaigns, the activity and outcome has been
identified and classified according to the following criteria.
3.2.1 General Wave Characteristics
Enterprises usually launch innovation contests (waves) in their innovation processes. Wave
information shows the framework environment on which ideas are generated.
 Innovation type: stores the level of innovation of the wave (radical, incremental…).
 Stages: the stages the wave will follow on the Idea Management life cycle.
 Status: describes the current stage.
 Target: indicates the objective or target aimed by a wave.
 Topic: how the wave has been classified.
 Contest type: indicates the type of innovation searched by the wave.
 Fields of the idea: the fields will the user have to fill in order to submit an idea.
 Duration: the time the wave will last.
 Situation: environment in which the wave is created (relaxed, time or condition
pressure…).
 Selection criteria: indicates the criteria used for idea ratings (set by experts).

3.2.2 Structural Metrics
These metrics measure the structural properties of the contest and their impact.
 People: groups, dedication, number of participants, active users, roles…
 Enterprise: time and resources assigned to R+D+i by companies.
 Resources: locations (e.g. meeting rooms), amount of resources spent on awards and
prices…
3.2.3 Activity Metrics
These metrics measure the activity in the platform.
 Traffic measures: number of views, unique visitants, average time spent, repeat
visitors…
3.2.4 Stimulation Metrics
These metrics register actions or stimuli provided in the wave to boost participation and improve
quality of ideas.
 Events: the number of events, participants, type, duration, location…
 Awards: the number of awards and the amount of money earned in each…
3.2.5 Outcome Metrics
The outcome metrics measure the result of the contests.
 Wave level: number of ideas that fit the target, become a product, create a spin off…
 Idea level: innovative ideas, innovation level, number of innovations introduced,
generated sales...
3.3 Methodology
The agreed methodology approach followed in Innoweb is based on an incremental development
cycle, where requirements guide implementation. The experience collected in a cycle will help to
improve the next one. The results and conclusions obtained with the developed prototypes can
generate new requirements.
Next the phases that summarize each cycle in the development are presented.
1. Requirements: a field study is performed through a set of interviews to different
representatives of the involved organizations in order to assess the use of Social Web
technologies. This input, together with the state of the practice research, is used to depict
the case studies.
2. Implementation: the necessary prototypes and the methodology are developed. The
methodology will provide a stepwise approach for the adoption of the prototypes within
an organization.
3. Validation: a set of piloting activities are carried out within real production scenarios
and are based on the depicted case studies. A set of indicators are set up in order to
evaluate the result and the success of the contest.

Figure 2: Evolutionary life cycle methodology
3.4 Platform
The innovation platform presented in this paper has been developed in Drupal. Drupal is a
powerful open source Content Management System (CMS). One of Drupal´s main assets is its
flexibility and modularity. Drupal is like a Lego kit. Skilled developers have already made the
blocks or modules that Drupal users need to create their sites; news site, an online store, a social
network, blog, wiki, or something else (in our case, an innovation platform).
Drupal’s core includes basic community features like blogging, forums, and contact forms, and
can be easily extended by downloading other contributed modules and themes. Drupal also
provides a set of APIs (Application Programming Interfaces) that bring the possibility of creating
new functionalities programmatically and has a very active community that develops and offers a
wide variety of modules.
Figure 3: Front-end of the innovation process stages
Innoweb platform not only adds some modules to Drupal in order to transform this CMS into a
IMS but the addition of Wave module also allows administrators the personalization of idea
campaigns and manage the workflow of the innovation process. Other modules with specific
functionalities were also constructed. Next a description of the technological solutions, tools and

modules employed in each of the stages of the innovation process is presented. Finally the
ontology and tools used to store data in semantic format is outlined.
3.4.1 Idea Generation
For the first stage of the process a module that collects ideas has been developed. Ideas are
collected in blog format and their content stored in database (MySQL). Ideas can be commented
by other users contributing or enhancing idea quality at this early stage. Users can also vote upon
ideas and comments using community provided votingapi and voteupdown modules. Comments
are stored in databases along with related ideas. Innoweb also offers the option of voting upon
ideas. Number of votes is saved providing administrators with valuable information for further
stages (analysis or selection). All ideas are linked to the wave they belong.
3.4.2 Idea Analysis
The second stage consists in analysing ideas and preparing them for the next stage. This is,
convert ideas in blog format into wikis. For this stage Innoweb provides a set of graphical tools
that help managers in the search and comparison of ideas. The following relations are explored;
ideas using the same tags, ideas from the same user, most voted/readed/commented ideas…
These tools are especially useful when administrators deal with a large amount of ideas and the
relation among ideas is not clear. Finding which ideas are well considered in the community and
the existent relations among ideas-users-companies make the filtering of ideas an easier task.
This turns into an increase on productivity and a better coverage.
Two types of graphical tools are provided in Innoweb; Animated Charts using Google Chart
Tools JavaScript API and Blazegraph charts using Blazegraph Flash dynamic graph layout
engine. To develop these visual aids new modules were built.
Finally another module has been developed at this stage to convert automatically ideas in blog
format to ideas in wiki format. It offers a table for experts to select the best ideas and transform
them into wikis.
3.4.3 Idea Enrichment
At this stage a wiki is provided for each filtered idea. Once ideas are in wiki format, experts add
valuable information creating richer ideas. All contributions are saved in different revisions in
order to identify contributors and idea evolution, and also give the possibility of restoring
previous versions. A custom module has been developed to offer wikis linked to the wave and
the original idea.
3.4.4 Idea Selection
At this stage of the process Innoweb provides tools to carry out the selection of ideas and to
establish customize criteria that support the selection process. A new module has been created to
help in this task. This module has been called Innoselect.
Administrators configure the selection framework by adding conditions or questions that will be
considered when ideas are evaluated (selection criteria). Each of those conditions can be
weighted. That is, different weights can be established depending on the relevance of the
criterion.
Once criteria are established, authorized users will have the possibility of rating ideas. There is a
box for each criterion-idea relation where the user introduces his grade or rating.
The module offers three rating possibilities:
1. Normal weighting: The user has to enter a value between 1 and 10.
2. Weighted selection: The user has to order ideas from the worse to the best.
3. Criteria matrix: The user can only enter predefined values in the ratings.

As in the Idea Analysis stage, graphical tools have been developed using Google Charts. These
tools compare ideas visually depending on the ratings for each criterion and showing the total
score. The tools are used to identify selected ideas that will be transformed in future projects.
3.4.5 Wave
One of the objectives for this research work was to create a platform flexible enough to
accommodate and adapt different innovation campaigns or waves. Each wave has to be
customized according to the requirements suitable for that campaign and yet the baseline
platform has to be the same for different innovation experiments. Issues such as stages involved
in the process, users allowed, topics, tag vocabularies or idea fields to be collected have to be
fully configurable. Campaign owners must be able to configure that process before the campaign
is launched and once it is activated they need tools to manage its progress. This module must
gather most of the innovation process context information.
A new module that addresses these issues has been developed for the platform. The module,
called Wave, allows the creation of different customized idea contests and enables their
management simultaneously over the same baseline platform. The customized options included
are the following:
 Stages of the innovation process. Depending on the wave the process can omit some
stages.
 Dictionaries and tags. To customize vocabularies and tags to be used in a wave.
 Events. Specific organizational actions can be register in order to determine which
management actions help in the innovation process.
 Permissions. Users are allowed the possibility of viewing/editing/creating content
depending on their role.
 Idea fields. Administrators define the fields to be collected in each idea.
 Real time notifications. Represent the communication networks the platform will use to
communicate with users when something relevant happens.
3.4.6 Semantic web
The platform brings the possibility of serving the ideas in RDF format, making semantically
stored data interoperable. Not only ideas are stored in RDF, but also idea campaign metadata.
In order to represent the innovation process domain, GI2MO ontology was selected for the
platform. Additional classes and properties were added to fulfil the requirements innovation
campaigns introduced into the domain.
To present data in RDF format a community module called RDFme has been used. RDFme was
developed by the group that created GI2MO. The module allows the mapping of idea fields onto
the innovation ontology, converting automatically all ideas in RDF format. The platform also
works as a SPARQL endpoint so third parties could send idea or innovation process related
queries.
4 Findings
In 2009-2010 [Larrinaga, et al. 2011] a case study was conducted using an early development of
the platform. Some structural and traffic metrics were collected. Following the methodology
described in section 3.3, a new case study has been conducted with the aim to gather more
innovation context parameters.
It has been carried out in Mondragon Corporation (http://www.mcc.es), which is divided into
four main areas; Finance, Industry, Retail and Knowledge, and is today the top Basque business
group and the seventh biggest in Spain. Mondragon Corporation has a total of 256 companies

and bodies, of which approximately half are co-operatives. The average number of employees at
Mondragon Corporation is 83.859 and approximately 9000 students course their studies at
Mondragon University.
The case study, named Ekiten, is an idea contest driven by the Engineering, Business and
Humanities faculties of Mondragon Unibertsitatea (MU) and the sponsorship of MONDRAGON
Corporation itself, SAIOLAN entrepreneurship development centre, Debagoiena commercial
development centre, Gazteempresa foundation and Athlon enterprise. The objective of the
contest is to promote entrepreneurship among students by collecting their ideas on the creation of
new enterprises or business models. Information about Ekiten has been collected using Innoweb
from 2010. Wave module has been used since 2011. Each year three main topics were
withdrawn; rural development, youth-leisure-sports and innovation enterprise. A wave was
launched for each main topic.
Every wave had a sponsor from outside the university as the owner or manager for that wave. An
external selection committee was appointed by each sponsor company. Each committee was
formed by five external experts that established the selection criteria for each wave and made the
actual selection of ideas. The criteria considered in most of the waves was related to the level of
innovation, definition and maturity of the idea, the technical and economic feasibility, the level
of alignment of the idea with the strategy and the priorities set for the topics dealt with in each
wave, and finally, the confluence and leverage of the proposal with the capacities and
competences available in Mondragon Corporation´s companies.
Wave administrators set the general parameters for the wave and configured participants, stages,
permissions, vocabularies and timelines prior to opening Innoweb idea management tools to
users. Students were allowed to introduce their proposals including their description and the title
of the idea, the outcome expected (become a new product, process, service or spin-off), the
issues addressed with the proposal, the type of innovation and the objective market or customer.
Experts used Innoselect to rate and select best ideas. Events were registered in the platform
during the whole process. The events registered were of 4 types: success stories, workshops,
information bulletins and coaching sessions. The amount of participants, events, experts, etc. can
be found on the first table (cf. Table 1). The outcome values, such as amount of ideas, average or
promoted ideas, can be found on the second table (cf. Table 2).
Context parameter
2010
2011
2012
Groups
10
27
46
Multidisciplinary groups
0
0
2
Participants
40
92
155
Experts
10
10
10
Evaluators
9
9
9
Companies
6
6
6
Ideas
10
27
49
Promoted ideas
3
2
3
Spin-offs
0
1
1
Events (success stories)
2
5
8
Event (workshops)
1
3
5
Events systematic for each group
8
8
8
Event (boletines)
0
0
3
Events total
11
16
24
Table 1: Inputs.

Outcome
Ekiten 2010
Ekiten 2011
Ekiten 2012
Ideas
10
27
49
Promoted ideas
3
2
3
Spin-offs
0
1
1
Table 2: Outcomes.
As an example of the type of information that can be gathered with the platform, an extract for
the three waves hold in 2012 is presented (cf. Table 3). The influence of events in the quality of
ideas can be observed. Further tracking of those events can be done in the platform. Thus, while
9 events were common to all campaigns, 4 were specific to the Youth-Leisure-Sports wave that
obtained better grades and where all ideas were online with the contest objectives.
Context parameter
Rural
Development
Innovation
Enterprise
Youth-Leisure-
Sports
Ideas
4
33
12
Average grade of ideas
3,17
2,78
6,68
Ideas online contest
2
15
12
Promoted ideas
0
1
2
Spin-offs
0
0
1
Events (success stories)
6
6
8
Event (workshops)
3
3
5
Table 3: 2012 wave comparison.
5 Conclusions
Having presented in this paper the developed platform and the case study where it has been
tested, some conclusions have been drawn:
 According to the requirements a platform to support the innovation process has been
built. The platform gathers context data that can be further analysed to determine the
influence in the outcome and detect success factors.
 The platform offers data in semantic format. This enables interoperability, machine
automatic search, exploitation of semantic meaning and the possibility to incorporate
ideas to the Linked Data.
 Although the volume of the waves analysed is small it can be concluded that the platform
presents not only activity or traffic metrics, but also quality measures such as grades or
relation between ideas collected and ideas on target among other.
 The impact of management decisions can also be measured. That is, campaigns can
determine if a workshop or brainstorming session translates into more (quantity) and
better (quality) ideas.
 The platform provides a better campaign control. Campaigns can be easily stopped,
paused, shorten, expanded or re-launched depending on activity or environment
conditions.
 Previous examples and experience recorded in the platform will allow a better design of
new campaigns.
 The platform permits the identification of active participants and allows co-creation
traceability. This is, if multiple users collaborate in the creation of an idea, their inputs
can be traced in the platform.

The aim now is to enhance the platform with new functionality taking into consideration the
incremental development cycle approach. Thus next cases studies will be conducted at
Mondragon Corporation.
The next steps on the research are the following:
 Enhance the platform to contemplate other aspects of the innovation process;
technological surveillance, decision making or outcome traceability
 Exploit the semantic possibilities already available.
 Keep on collecting ideas and measuring the performance of the platform.
 Keep on analysing the success factors for the innovation process in case studies.
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