PallotM_Alex_KrawczykP_HolzmannT Exploring ... - Semantic Scholar

This paper was presented at The XXV ISPIM Conference – Innovation for Sustainable Economy & Dublin, Ireland on 8-11 June 2014. The publication is available to ISPIM members at & This Society, paper was presented at The XXV ISPIM Conference – Innovation for Sustainable Economy www.ispim.org.

Exploring the Feasibility of an Online Serendipity Service in the Context of Open Innovation within the EU Horizon2020 Research Program Marc Pallot* MP CONEX 18 rue Sthrau, 75013 Paris, France. E-mail [email protected]

Alex Alishevskikh MP CONEX 18 rue Sthrau, 75013 Paris, France. E-mail [email protected]

Piotr Krawczyk JAMK University of Applied Sciences, 40200 Jyvaskyla, Finland. E-mail [email protected]

Thomas Holzmann University of Applied Sciences Munich, SCE, Heßstr.89, 80797 Munich, Germany. E-mail [email protected] * Corresponding author Abstract: This paper explores the feasibility and suitability of an online serendipity service allowing academic and industrial research organisations as well as individuals to quickly identify collaboration opportunities in the goal of replying to call-for-proposals. This work was carried out in the context of a Matchmaking workshop organised during the 4th Living Labs Summer School held in Manchester by the end of August 2013. It was intended to identify collaboration opportunities among participating Living Labs for collectively answering to the EU Horizon2020 first call-for-proposals. The software prototype used for simulating a serendipity service was developed within previous EU research project. This work addresses the problem of the systematisation of quickly identifying collaboration opportunities with relevant connections among Living Labs and Horizon2020 research themes in order to efficiently form Open Innovation ecosystems as recommended in the OI2 paradigm. Keywords: living lab; matchmaking; open innovation; serendipity.

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This paper was presented at The XXV ISPIM Conference – Innovation for Sustainable Economy & Society, Dublin, Ireland on 8-11 June 2014. The publication is available to ISPIM members at www.ispim.org.

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Introduction

On the one hand, the European Network of Living Labs (ENoLL) survey carried out in 2012-2013 showed that about 90% of the responding Living Lab managers expressed interest in international R&D collaboration and matchmaking initiative (Krawczyk, 2014). LLs are user-centred (Von Hippel, 1986) open innovation (Chesbrough, 2003) ecosystems operating on a local territory (city, agglomeration, region) and bringing together research and innovation (Bilgram et al. 2008) within a public-private-people partnership (Pallot 2009). However, there are several available LL descriptions and definitions (Corelabs, 2006; Niitamo et al., 2006; Pallot et al., 2008, 2009; Schumacher et al., 2007; Kusiak, 2007; European Commission, 2009). On the other hand, the European Commission was actively preparing the Horizon2020 (H2020) research program, successor of the famous 7th Research Framework Program, and Call-for-Proposals (CfP) through the elaboration of draft work programs. Hence, ENoLL decided to organise, during the LL Summer School held in Manchester by the end of August 2013, a matchmaking session among LLs interested for collaborating in the view of the coming H2020 research themes. In this context, we decided to explore the feasibility and suitability of an online serendipity service for helping LLs to easily explore and quickly identify collaboration opportunities that would be relevant to the Horizon2020 research themes. It is necessary to bear in mind that the European Commission launches many call-for-proposals every year and that the research program regularly evolves during a certain period of time. In this work, our main goal is to explore the feasibility and suitability of an online serendipity service operating as an LLs matchmaking artefact. Such a serendipity service would allow to quickly identifying collaboration opportunities with relevant connections among diverse entities in order to efficiently form Open Innovation ecosystems, as recommended in the OI2 paradigm (Curley and Salmelin, 2013). In order to simulate an online serendipity service, we have used a software prototype that was developed and experimented during two EU research projects, namely LABORANOVA and ELLIOT projects. This software prototype, currently named CONEX, implements the PeopleConcepts Networking (PCN) approach that was designed during the ECOSPACE and LABORANOVA projects (Pallot et al., 2013). It consists in scanning people collection of content-objects for a specific context (e.g. event, taskforce, project) and extracting the most significant concepts that become the machine tags associated to each content-object. These tags grouped at the collection of content-objects form a cloud of tags that characterise people’s profile for a specific context or globally in grouping all contexts. It is neither a declarative nor a static profile because the machine tags the content-objects and a user can add or remove content-objects in a collection that directly updates the user’s profile. This explains why we decided to name this evolving profile as being a dynamic profile. The above-mentioned survey (Krawczyk, 2014) captured the portfolio of active and past use-cases each LL has been involved in. It also included each LL interests in R&D collaboration across thematic domains identified in the past and validated by a number of researchers (Pallot et al. 2008; Salminen et al. 2011; Krawczyk et al. 2012 and Pallot et al. 2013). During this experiment, we have considered both LLs and H2020 as a same entity than people having a collection of content-objects leading to profile

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characterisation through the extraction of the most significant concepts. At the end, we have got connections among LLs with H2020 clustered through the similarity of tags and associated collection of content-objects to be further explored in order to create new proposal ideas.

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Previous Work and Existing Concepts

Open Innovation Henry Chesbrough (2003) created the paradigm of Open Innovation (OI) where ideas flow among diverse organisations and generate multiple exploitation opportunities. According to Curley and Salmelin (2013), innovation success is characterized today by an efficient way of assembling diverse participants into an innovation ecosystem able to co-create novel products and services that are quickly adopted. Curley and Salmelin (2013) initiated the OI2 paradigm as an innovation model based on extensive networking and co-creative collaboration among all stakeholders, spanning organizational boundaries well beyond traditional licensing and collaboration schemes. They argue that with OI2, sharing and co-generating innovative options enable a significant competitive advantage and help achieve broader scale innovation benefits for larger numbers of stakeholders. They also propose to put the “focus on designing for network effects where new users, players or transactions reinforce existing activities”. Finally, they claim: “network effects accelerate growth in the number of users and in value creation. Networking is a socioeconomic process where people interact and share information to recognize, create and act upon business opportunities”. Matchmaking in the open innovation approach becomes an element of paramount importance (Galbraith et al. 2008) for identifying collaboration opportunities. According to Holzmann and colleagues (2014), matchmaking is more than searching the right partner and a subsequent market transaction. They argue that cooperation decision is a complex group decision-making process that may have direct impact on technology platform and/or business model alternative that determine the future innovation direction.

Serendipity According to Johnson (2010), Horace Walpole has coined the term “serendipity” as making discoveries by accident about things they were not looking for. Many scientists have carried out investigations on serendipity and came to the conclusion that indisputably it positively impacts discovery, creativity and innovation. André et al. (2009) argue that computer scientists have developed systems rather supporting chance encounters or serendipitous discoveries (Lieberman, 1995; Beale, 2007) than making valuable use of these discoveries. McCay-Peet and Toms (2010) have investigated the process of serendipity in knowledge work with a holistic view instead of focusing on unexpected triggers for serendipity. They found out that serendipity occurs during social networking and active learning as well as exploration activity. It also appears that there is a period of incubation sometimes necessary before the serendipitous nature of a latent trigger is attained. It would mean that serendipity is not always an instantaneous flash.


They refer to Cunha’s process (2005) that explains while someone engaged in an activity inducing precipitating conditions, a bisociation – a surprising association is made between disparate unconnected pieces of information (e.g. terms or concepts) – is made, which results in an unexpected idea or solution valuable within another activity than the initial one. Koestler's fundamental idea (1964) is that any creative act is a bisociation of two or more incompatible frames of thought. Stankovic and Musacchio (2012) investigate the different types of relevance used by web systems to support and encourage serendipity instead of inhibiting it. Interestingly, they concluded that innovation is based on new combinations of knowledge, ideas and people with diverse backgrounds. Rubin et al. (2011) describe a conceptual model of serendipity in everyday chance encounters that has four facets, namely: Prepared Mind, Act of Noticing, Chance, and the Fortuitous Outcome. Eagle and Pentland (2005) demonstrated the coupling of mobile and social computing for fostering face-to-face interaction among nearby event participants that never met before while sharing similar interests. According to Dantonio (2010), most of existing literature has considered serendipity as individualistic while she found that the serendipity experience results from social effort. Dantonio (2010) mentions information encountering as a particular type of serendipity happening when people find relevant information while looking for something else (Erdelez, 1999). She cites also Campos and De Figueiredo (2002) arguing that it is possible to design for serendipity like in suggesting related links when browsing web pages (Toms and McCay-Peet, 2009). According to Dantonio (2010), there is no recognised way of testing serendipity so far meaning that it is not possible to compare which tools best support serendipity. Interestingly, she found that some of the experiment participants considered curiosity as an important element of the pleasing serendipity experience contributing to extend their knowledge and creativity. In contrast, Dantonio reports also about participants’ negative feelings, such as serendipity is a waste of time, serendipity diverts people from their original path or serendipity contributes to losing focus. More than 50% of the participants used social media web applications every day, such as Zotero, Cityulike, Mandeley, Wikipedia and Blogs, recognising that they encounter relevant content while it was not intended. Dantonio argues that the quality and quantity of online connections may lead to more serendipitous encounters. She reports about another study (Hagel, 2010) emphasising the importance of creating and managing a network of people in order to increase serendipitous occurrences. She also points out that tagging documents can push people towards unexpected discoveries. Finally, she argues that serendipity is a social phenomenon, especially in the case of academia, rather than an individual one. Most of the previously published papers, from 2000 up to 2014, related to inducing a certain form of serendipity through information systems, addressed ‘information encountering’ as the most frequent objective within specific contexts (see Table 1). The most usual type of serendipity appears to be ‘information place’ that is properly correlated to the objective of ‘information encountering’. This is based on natural constructs of serendipity, such as event (e.g. conference, meeting) and place (e.g. coffee area). Beside André and colleagues review of existing systems supporting serendipity in one form or another, we created the following table (see Table 1) in order to repertory all related published papers from 2000 up to 2014.

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Table 1 Characterisation of previous work on IS inducing a certain form of serendipity Objective

Context

Type

Information Encountering

Exploratory Search

Information Place

Serendipitous Social Media Connection Information Exploratory Encountering Search

Workplace Sagacity

Serendipitous Social Media Connection

Information Place

Serendipitous Encounters

Tool Design

Serendipitous Connection

Connection Maker

Serendipitous Encounters

Seeking Serendipity

Information Place Online, Event & Arenas Information Place

References (Toms, 2000); (Foster & Ford, 2003); (Erdelez, 1997, 1999, 2000, 2004); (Marchionini, 2006); (Gritton, 2007); (Palsdottir, 2010); (McCay-Peet & Toms, 2010); (Makri et al., 2011); (Makri & Blandford, 2012); (Snowden, 2005) (Jeffrey & McGrath, 2000); (Cunha, 2005); (Brown et al., 2014) (Campos & De Figueiredo, 2002); (Toms & McCay-Peet, 2009) (Thom-Santelli, 2007); (Rubin et al., 2010); (Dantonio, 2010); (Rubin et al., 2011); (Bordino et al., 2013); (Mejova et al., 2013); (Dimitrova et al., 2013); (Said et al., 2012); (Maxwell et al., 2012); (Sun et al., 2011); (Bental et al., 2012); (Thudt et al., 2012) (Newman et al., 2002); (Burkell et al., 2012); (André et al., 2009) (Eagle & Pentland, 2005); (Beale, 2007); (Bojic et al., 2011); (Pallot et al., 2013); (Chin et al., 2013); (Mayer, 2014) (McBirnie, 2008); (LeClerc, 2010)

The natural constructs supporting serendipity, such as places and events, provide opportunities of serendipitous (chance) encounters also named accidental or fortunate discoveries. This kind of natural construct is reflected in the ‘Type’ column (see Table 1) in order to characterise the previous experiences as related to the concept of place or event. However, in the context of Information Systems (IS), we consider an information (or virtual) place (e.g. global through the Internet or local through the use of databases) rather than a physical place. As for the concept of event, we consider the Ambient Intelligence aspect for identifying physical spaces or rooms where the event takes place and participants through the use of sensors (e.g. GPS, RFID).

Figure 1 Repartition of Table 1 published papers per objective


While most of the IS reported in Table 1 for supporting serendipity, address the ‘chance encounter’ aspect, only two address the ‘sagacity’ aspect of serendipity. We consider ‘Information Encountering’ as main objective for those that provide links with relevant information; ‘Serendipitous Connection’ for those that use social media tools (in the context of physical spaces or virtual spaces) or connection maker among people (e.g. matchmaking, speed dating, online dating).

Unexpected connections leading to serendipitous discoveries According to Pallot et al (2013), it is the unplanned, unlimited and continuously growing size of the network connecting users and their salient concepts extracted from their content-objects forming diverse nodes of knowledge that provide chance encounters and serendipitous connections. This approach is named People-Concepts Networking (PCN) and was designed during the LABORANOVA and ECOSPACE EU research projects. Concurrently, the PCN approach was implemented into a software prototype (Alishevskikh et al., 2009). More recently, a new version of the prototyped application, named CONEX, was used for carrying out experiments on innovative IoT based services within six use cases during the ELLIOT (Experiential Living Labs for the Internet of Things) EU research project. CONEX provides machine-generated connections among individuals (e.g. researchers, practitioners), organisations (research labs, businesses) and targets (CfP from diverse research and innovation programs).

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CONEX Platform

People-Concepts Networking The “People-Concepts Networking” (PCN) approach (Pallot et al., 2005, 2006) is intended to successfully stimulate creativity and innovativeness through the capacity to explore potential knowledge connections among people through the concepts they use in their content-objects. Embedded into this PCN design, there is a vision of creating a virtual connection space interconnecting individuals through a Group Forming Network (Reed, 1999) that has the capacity to grow exponentially. It is expected that PCN would stimulate the emergence of a new form of socio-intellectual networking - supporting explicit and tacit knowledge connection – thus providing a fortunate discovery of encountering a collaboration opportunity.

Implementation of the PCN Approach It consists in creating a machine networking tool that links people through the extraction of their most relevant concepts, according to a specific context (e.g. project, event), in order that users can make serendipitous discoveries of content objects (explicit knowledge) and people (tacit knowledge) that are relevant to their current interests. PCN main goal is to provide “knowledge connection” among people through the use of their most salient concepts that appear in the information they produce. These concepts, also named tags, form a kind of user’s conceptual profile represented by a cloud of tags. They also act as links to retrieve users’ content objects. PCN clusters People with Concepts

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(tags) and Content-Objects (see Figure 2-A). This clustering is based on an ontology model (see Figure 2-B) and allows People (PCN users) retrieving content-objects that could have some sort of valuable relevant knowledge.

Figure 2-A: PCN Cluster

Figure 2-B: PCN Ontology

The PCN ontology implementation (Alishevskikh et al., 2009) is based on a combination of existing ontology vocabularies, namely: SCOT (DERI, 2008) as a skeleton conceptualization framework, SIOC (DERI, 2009) for social networking information and description of the content resources, and FOAF (Brickley and Miller, 2007) for personal (static profile) data, as well as a proprietary vocabulary for contextualizing other types of relationships. In modelling the Person-Concept and Resource-Concept relationships, the PCN ontology follows the SCOT paradigm based on a tripartite ‘Actor-Concept-Instance’ model, proposed for aligning the semantic models with a social dimension (Mika, 2005). The SCOT approach puts the concept of Tagging at the centre of the conceptualisation model. Tagging represents a single personalised act of conceptualisation and defines the person who performed it, the resource and what concepts (tags) have been used. It can also carry auxiliary information about the action, such as the time of tagging. By the context information, Tagging represents the cornerstone of the PCN ontology, effectively relating together all classes of the PCN model and providing the full set of PCN relationships, both explicit and implicit (inferred). This leads to a kind of “dynamic” (in contrast with more “static” or declarative user profile) user profiling approach as it is based on people production rather than on a declarative profile. This allows Knowledge Connection among users through scanning, tagging and sharing relevant diversified and multi-format content assets within their communities. CONEX monitors the content locations that are duly specified by the users and incrementally update their “dynamic” tag-cloud (conceptual profile) on a server as content objects are added, modified or deleted. The concepts in the dynamic profile can be grouped into specified “contexts”, which help to organize them in accordance with different user’s activities. The context provides also a social dimension of users’ profiles, in representing working or social activities (e.g. projects, communities) shared by different users. The dynamic profiles of different users are aggregated into a single cluster (socio-semantic network), or network of concepts, where users are connected to each other via the shared area defined by concepts they have in common. The clustering


also enables to compare users’ tag-clouds (conceptual profiles) through a percentage of similar concepts that allows identifying similar interests. This development was carried out in the context of the Laboranova (Pallot et al., 2007, 2008) and Ecospace EU research FP6 projects (Pallot et al., 2006) as well as later on during the ELLIOT FP7 project.

CONEX Software System The CONEX system (see Figure 3) includes the following four components (Alishevskikh et al., 2009): The “Indexing-Processing Module” accesses document storages to retrieve full-text content and metadata (basic facts about documents and their contexts). It contains “Crawlers” providing access to different types of content locations, such as web sites or BSCW server sources and “Parsers” processing documents of different types (e.g. PDF, HTML) to represent them in the form of token streams and RDF graphs of metadata properties. For every indexed document, it produces output in the form of RDF graphs containing metadata and token streams representing the fulltext content of documents. The “Storage Module” stores metadata and full-text in a persistent storage facility. It is composed of “Document metadata repository”, a high-efficient persistent graph store to keep documents metadata represented as graphs of RDF triples, and “Full-text index” that is an inverted text index to keep the parsed token streams. The index is used for full-text search and as a source of data for text analysis functions. A SPARQL API allows other components to access the stored metadata through the graph store.

Figure 3 Simplified CONEX Architecture

The “Concept Extraction Module” integrates algorithms and techniques of statistical text analysis to identify the concepts represented in the documents content. The module retrieves the textual data from the full-text index component of the storage module and generates RDF graphs of conceptual data, represented by SCOT Tag ontology objects. The relationships between Tag objects and document metadata or user profile graphs are the basis of PCN Logics functionality. The “PCN Logics Module” manages user profile, identification of conceptual similarities between profiles of different users, generation of recommendations and other logics of the PCN conceptual model. It integrates the “PCN data repository”, a persistent graph stored to keep the data comprising the PCN Ontology Model (user profiles, concepts, contexts and resource descriptions). Importing the resource metadata from the document

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repository and the conceptual data provided by the Concept Extraction Module populates this ontology model. Stored PCN data is available via SPARQL API that is used by algorithms of this module as a source of data for its tasks.

BSCW Context Folders CONEX extracts data from BSCW content-location objects through open-source software named “Aperture”. It is a Java framework for extracting and querying full-text content and metadata from various information systems (e.g. file systems, web sites, mail boxes) through a crawler. A BSCW parser was developed for accessing content objects uploaded within BSCW shared workspaces.

Figure 4 BSCW LLs and H2020 folders and content-objects

As a preparation of the CONEX experiment, each participating LL had a specific private workspace within BSCW that includes context folders holding the content-objects (see Figure 4).

Connective Experience While this paper reports about an investigation on the degree to which autonomously generated connections among LLs are relevant or not, our secondary goal is to study, later on, the connective experience and its impact on the potential adoption of such technology. The PCN approach was implemented in a semantic based Connective Technology platform that became a Connective Experience (CONEX) platform. CONEX is intended to provide serendipitous connections among its users, currently knowledge workers.


Figure 5 – HumanTech LL Clustering Map with Connections to other LLs

The LL’s network of automatically generated connections (see Figure 5) provides an overall view that could be filtered by context and zoomed in and out for exploring/browsing connections. Hence, the contextualisation of resulting connections allows simplifying the graphic cluster according to a smaller single contextual tag-cloud.

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Research Approach

CONEX Experiment among living labs and H2020 In order to explore the feasibility of an online serendipity service for supporting the matchmaking among LLs and H2020 research themes, a CONEX experiment took place during August 2013 with 18 LLs (see Figure 4). Regarding the suitability, a previous experiment was designed to: 1) Evaluate the user perceived relevance of machine created connections in assessing, through a rating from 1 to 5, the relevance of people, tags and content-objects; 2) Evaluate the nature of connectedness in assessing if in the reality entities are not already connected, somehow connected or actively connected.

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For this purpose, a set of information was collected from the above-mentioned ENoLL survey for 18 participating LLs. These LLs information included the portfolio of usecases each LL has been involved in (of expertise) as well as each LL interests in R&D collaboration across themes. As for the H2020 work-program, a set of concepts was extracted for each research theme. The generated content-objects were uploaded in 19 folders of an online-shared workspace (BSCW) specifically created for this CONEX experiment. Two sub-folders respectively named LL domain and LL interest were created in each LL folder and two other sub-folders respectively named LEIT (Leadership in Enabling and Industrial Technologies – for Information and Communication Technologies) challenges and Societal (Health, Energy, Environment, Inclusion) challenges were created in the H2020 folder. Each sub-folder represents a specific context that is then used within CONEX as a content-location for accessing a collection of content-objects. Within CONEX, LLs and H2020 were entered as normal entities even if they are representing a more complex structure than individuals because at the end they include people that have activities/projects and generate content-objects. There were 4 contexts created in CONEX, namely: LL domain and LL interest for characterising LLs, and LEIT challenges and Societal challenges for characterising H2020. Then, CONEX has automatically scanned the content-objects found in the content-locations and extracted the most significant concepts. The concepts are then used for tagging each contentobject. All tags of a same collection of content-objects constitute a tag-cloud for each LL or for H2020. Each tag-cloud represents the dynamic profile of each Living Lab. CONEX provides also a graphic representation of the generated connections among LLs and H2020 for exploring sources of innovative ideas and potential collaboration.

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Findings

It appears that CONEX allows moving from scarce number of chance encounters towards a more systematic discovery of a higher number of machine-generated connections (chance encounters). It also provides links to knowledge resources and collaboration opportunities while staying human centred even if there is an autonomous way of connecting entities (people, organisations, targets). The dynamic profiling of entities, based on a graph of concepts, seems to be relevant as confirmed by 25% only of machine-generated tags that are not relevant (see Figure 6). It also enables instant awareness among stakeholders involved in a matchmaking process as revealed with H2020 CfP targets. Due to the context feature, CONEX is applicable to individuals and organisations in linking tags while concept maps facilitate an alignment with prospect business and funding opportunities (see Figure 6).


Figure 6 H2020 Clustering Map with Tags Connecting LLs

Furthermore, the proven discovery of relevant knowledge resources and potential collaboration opportunities position CONEX as an experimental platform for designing with stakeholders, especially users, Future Internet serendipity and matchmaking services. Other interesting features are the discovery of emerging concepts, the enabling of instant learning and faster way to reach a common understanding; facilitating group consciousness; evaluating the impact of Knowledge Connection on creativity and innovativeness as well as the level of diversity within groups or communities. It was expected that human perception of text semantics would be radically different from machine analysis. Though, we have not seen yet any quantitative research to support this claim. Nearly even distribution between relevant/non-relevant categories (see Figure 7) suggests that perception of different human experts may differ greatly, which in itself is thought provoking. Given a relatively homogenous social group (knowledge workers) with supposedly similar cultural/professional backgrounds and knowledge contexts, we would expect some kind of consensus here. Upon closer inspection it may look like a good case for the PCN approach, as yet another temporary triumph of computer supported human intellect over the automated tagging results which in turn may seem as a challenge to the whole PCN theory based on automated document classification.

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Figure 7 – Relevance of tags as perceived by users

The research outcomes provide some insights into individual and organisations dynamic profiles for (autonomously generated) serendipitous connections as well as research interests for collaboration opportunities across the LLs network in the broad context of the EU Horizon2020 Research Program. Participating individuals and organisations have learnt a new matchmaking way to systematically and efficiently identify collaboration opportunities. Such an approach could be used in the context of Horizon2020 information days as well as in the broader context of open innovation initiatives.

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Conclusions

As compiled during the literature review on IS inducing serendipity, many of them address information connection rather than people connection. However, it should be noticed that connection makers are implemented within events like conferences or meetings. Beside serendipitous introduction (Mayer, 2014), CONEX appears as the only connection maker operating from an online place gathering people, organisations and targets that can be used concurrently before, during and after an event for matchmaking purpose. It could be used with other serendipity tool connecting event participants through physical proximity and physical resources at a conference (Chin, 2013). Our prototype of serendipity service has allowed us to demonstrate the possibility to quickly explore collaboration opportunities within targeted CfPs in simply visualising tags generated connections among entities. Interestingly, participants perceived that most of the machine-generated tags were accurately relevant (18%), relevant (26%) and somehow relevant (33%). As a follow-up, we decided to include a ‘tag editing’ feature within CONEX for replacing the 24% of not relevant machine-generated tags by usergenerated relevant tags. This new feature allows users to replace the ‘somehow relevant’ tags (33%) by more accurate tags. However, it will have a direct impact on generated connections among entities that need to be assessed in the next experiment. The next planned CONEX experiment will be held in conjunction with the ICE’2014 conference for assessing the capacity to automatically group more than 100 papers within the conference themes and sessions.


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