Valérie Chanal
email: valerie.chanal@umanlab.eu

Marie-Laurence Caron-Fasan
email: marie-laurence.caron@iae-grenoble.fr

Marie-Laurence Caron Maitre

How to invent a new business model based on crowdsourcing: the Crowdspirit ® case

Keywords: Open innovation, crowdsourcing, business models How

Chesbrough's work on open innovation provides a theoretical framework to understand how firms can access external knowledge in order to support their R&D processes. The author defines open innovation as a paradigm that assumes that firms can and should use both external and internal ideas and internal and external paths to market. He considers that industrial R&D is undergoing a paradigm shift from the closed to the open model. Information and communication technologies and especially web 2.0 technologies accelerate this shift in so far they provide access to collective and distributed intelligence disseminated in the "crowd". This phenomenon named "crowdsourcing" is defined by Jeff Howe as "the act of a company or institution taking a function once performed by employees and outsourcing it to an undefined -and generally large -network of people in the form of an open call."

Though this approach may sound appealing to firms and R&D organizations, there is little research available about the strategic use of crowdsourcing for innovation processes. In this paper we develop the argument that crowdsourcing raises a certain number of strategic issues that we discuss on the basis of a real size crowdsourcing experiment.

We were associated in the project from the very outset up to the strategic analysis of a startup: Crowdspirit. The company's concept is based on the outsourcing of the entire R&D process to a community of designers and users, in the domain of consumer electronics. Our data is made up of the minutes of three strategic workshops with the managers that we completed step by step by additional theoretical study and some benchmarking of crowdsourcing experiments on the web. Although we started this collaboration mainly to help Crowdspirit team: (1) The emergence of the Crowdspirit business model; (2) The value creation process related to profiles of crowdspirit community of contributors (3) The challenging of the company's initial business model and (4) The creation of a new business model successively open and closed models. In the discussion we summarize the main strategic issues that emerged during the work on Crowdspirit's strategy with its managers, and interpret them on the basis of existing literature on open innovation. This leads us to complete Chesbrough's open innovation approach and Nambissan and Sawney network-centric innovation model by introducing new options for companies whose strategy is based on crowdsourcing.

the company design its optimal business model, this action research process has led us to address the following research questions: how can a firm create and capture value by means of a strategy based on crowdsourcing? What are the main strategic issues to be considered when a firm intends to open its innovation process through crowdsourcing? Due to the action research approach used, we do not dissociate the theoretical part from the empirical data, but rather to present our research process step by step. We therefore successively present four main phases of the strategic analysis carried out with the

INTRODUCTION

Chesbrough's work on open innovation (Chesbrough, 2006a;2006b); provides a theoretical framework to understand how firms can access external knowledge in order to support their R&D processes. The author defines open innovation as a paradigm that assumes that firms can and should use both external and internal ideas and internal and external paths to market. He considers that industrial R&D is undergoing a paradigm shift from the closed to the open model. Information and communication technologies and especially web 2.0 technologies accelerate this shift in so far they provide access to collective and distributed intelligence disseminated in the "crowd". This phenomenon emerged recently and has given birth to a new concept: "crowdsourcing". In 2006, Jeff Howe, contributing editor to Wired Magazine introduced this term and defined it as follows: " Crowdsourcing represents the act of a company or institution taking a function once performed by employees and outsourcing it to an undefined -and generally large -network of people in the form of an open call.". This concept of "crowdsourcing" appeared in the media and the web sphere and has not been studied as such in academic litterature. Nevertheless, new R&D practices consisting of seeking innovative ideas or solutions outside via the web, has been described by various authors with other concepts such as "open innovation" (Chesbrough, 2006a;2006b), "Connect and Develop" [START_REF] Sakkab | Connect and Develop complement Research and Develop at P&G[END_REF][START_REF] Huston | Connect and Develop. Inside Procter &Gamble's New Model for Innovation[END_REF] or, more recently, "The Global Brain" (Nambisan and Sawney, 2007). For us, the concept of open innovation is more general and refers to the opening up of the innovation process, both upstream, with the exploration of external sources for innovation opportunities, and downstream, with the use of those opportunities at different stages of the innovation process and through multiple channels. [START_REF] Sakkab | Connect and Develop complement Research and Develop at P&G[END_REF] and [START_REF] Huston | Connect and Develop. Inside Procter &Gamble's New Model for Innovation[END_REF] describe the new Procter and Gamble innovation model, based on the leveraging of the internal R&D competences by "tapping into the creative thinking of inventors and others on the outside" [START_REF] Huston | Connect and Develop. Inside Procter &Gamble's New Model for Innovation[END_REF]. The authors, who are both VPs in charge of innovation at P&G, argue: "As we studied outside sources of innovation, we estimated that for every P&G researcher, there were 200 scientists or engineers elsewhere in the world who where just as good -a total of perhaps 1.5 million people whose talents we could potentially use". Building their model on P&G and other similar examples such as IBM, Stapple, Ducati etc., Nambisan and Sawney proposed the concept of "Global Brain", referring to the creative potential or the pool of talent, that lies outside corporate boundaries.

In line with these approaches we will define "crowdsourcing" as: "the opening of the innovation process of a firm to integrate numerous and disseminated outside competencies through web facilities". These competencies can be those of individuals (for example creative people, scientists, engineers) or existing organized communities (for example open source software communities).

Though this idea of using outside knowledge and competencies may sound appealing to firms and R&D organizations, there is little research available about the strategic use in crowdsourcing for innovation processes. In this paper we develop the argument that crowdsourcing raises a certain number of strategic issues that we discuss on the basis of a real size crowdsourcing experiment. Our research team had the opportunity to support the Crowdspirit start-up from the beginning. The latter expresses its strategic vision as follows:

"Bringing the dreams of the Crowdspirit community to life". (see www.crowdspirit.com). The web platform of the company was launched in September 2007 for a beta test. The concept is ambitious: the idea being to create a mini revolution in consumer electronic products by outsourcing the entire R&D process to a community of users and designers. In the initial business model of Crowdspirit, users submit ideas for innovative electronic products that they would like to have, the community votes on the ideas, the best ideas are worked on by a community of designers who develop and draft the specifications, investors provide financing, developing partners make prototypes, the manufacturing is done by ad-hoc subcontractors and products are then sold via the web. All users involved in the product innovation cycle are to earn a share of the product revenue, according to the level of their contribution. To a certain extent Crowdspirit provides an example of an open innovation model pushed to its limit, where nearly all innovation activities are coordinated and carried out within the market rather than within an existing firm.

We were associated in the project from the very outset up to the strategic analysis of the company. Our data is made up of the minutes of four strategic workshops with the managers (May 2007, July 2007, November 2007, February 2008) that we completed step by step with additional theoretical study and some benchmarking of crowdsourcing experiments on the web. Although we started this collaboration with no other objectives than to help this company design its optimal business model, this action research process has led us to address the following research questions: how can a firm create and capture value by means of a strategy based on crowdsourcing? What are the main strategic issues to be considered when a firm intends to open its innovation process through crowdsourcing?

As the methodoloy is based on action-science research with a single case study, as described by [START_REF] Baskerville | Diversity in information systems action research methods[END_REF], we suggest that the theoretical part not be dissociated from the empirical data, but rather to present our longitudinal research process step by step.

We will therefore successively present the four main phases of the strategic analysis carried out with the Crowdspirit team:

1. The emergence of the original business model. At this stage we carried out the first formalized analysis of the Crowdspirit business model as thought up initially by the entrepreneurs. It revealed that crowdsourcing introduces specific strategic issues that cannot be addressed by a classical business model design approach.

2. The network construction. We contributed to the analysis of the value creation process by characterzing the profiles of the required contributors to the whole system and then defining hypotheses on their motivations for participating. The traditional view of value propositions for the end-customer is no longer relevant in this case, as customers are also contributors. They can pay for the products and also be paid for their talent and contributions. This highlights a new value creation process that is no longer linear but rather network based.

3. The challenging of the company's initial business model. The third meeting with the company took place after the manager decided to make his business model evolve following two months beta testing of the web platform. At this stage we provided the company with a theoretical framework on business models based on crowdsourcing, and identified two models considered here to be ideal-types. This framework should now help the company position itself between two models: one community-based and the other intermediary-based.

The invention of a hybrid business model with an open platform and a closed one.

We describe here the repositioning of the company's strategy with an alternative value creation model. The idea is to borrow from both the community-based model with principles close to those of open source platforms, and the intermediary-based model with a value proposition for firms wishing to use Crowdspirit's communities for their own R&D.

In the discussion we summarize the main strategic issues that emerged during this one year project on Crowdspirit's strategy with its managers, and interpret them on the basis of existing literature on open innovation. This leads us to complete Chesbrough's open innovation approach and Nambissan and Sawney network-centric innovation model by introducing new options for companies whose strategy is based on crowdsourcing.

THE EMERGENCE OF A NEW BUSINESS MODEL BASED ON

CROWDSOURCING

The first strategic workshop we had with the Crowdspirit team took place in May 2007, four months before the official launch of the web platform beta test, which was still under development. At this stage, the team needed help in challenging their business model.

In fact, the CEO of Crowdspirit found it difficult to explain his business model to potential investors and to convince them of its value creation potential. However, his vision was quite clear and he had already defined the value-sharing model for the different contributors of the community. The first meeting was therefore dedicated to challenging the Crowdspirit business model. For this we used a methodology, developed by our research team, which is based on developing business model scenarios. We briefly present the methodology in the first section below. In the following section, the main strategic points that were discussed during the meeting are summarized.

A business model scenario perspective

The concept of business model (BM), used in professional circles since the development of companies on the Internet, is only beginning to be defined more precisely in management literature. The most cited authors in BM literature in strategy are [START_REF] Timmers | Business Models for Electronic Commerce[END_REF], [START_REF] Amit | Value Creation in E-Business[END_REF] and [START_REF] Magretta | Why Business Models matter[END_REF]. Their definitions include the following elements: the architecture of the offer and the resources implemented, the value proposition for the client, the position of the company in the value network and the revenue model. These elements are to be found in the definition of [START_REF] Van Der Heijden | The Wheel of Business Model Reinvention: How to Reshape your Business Model to leapfrog Competitors[END_REF] which we have retained for the rest of our work: "The term business model can be defined as the particular business concept (or way of doing business) as reflected by the business's core value proposition for customers; its configurated value network(s) to provide that value, consisting of own strategic capabilities as well as other (e.g. outsourced/allianced) value networks and capabilities to continually sustain and reinvent itself to satisfy the multiple objectives of its various stakeholders." In line with the BM concept and in order to take into account the crowdsourcing activity, we identify the specific BM dimension related to the Internet context. The work of [START_REF] Rappa | Business Models on the Web[END_REF] enables us to identify the community model that could be useful in helping to understand the role of internaut communities and the economic logic of e-business models based on crowdsourcing.

At this stage, these different research elements lead us to retain the following elements in order to analyse the Crowdspirit business model:

-The value proposition for the clients; -The resources and capacities necessary to develop the concept and its solution;

-The structure of the value network; -The economic model (economic logic for revenue generation).

As the business model of Crowdspirit is not fully stabilized, due both to uncertainties concerning value creation within the community and value capture by the company, we decided to implement a scenario-based approach. Several authors have highlighted the interest of this approach [START_REF] Chesbrough | The role of the business model in capturing value from innovation: evidence from Xerox Corporation's technology spin-off companies[END_REF][START_REF] Van Der Heijden | The Wheel of Business Model Reinvention: How to Reshape your Business Model to leapfrog Competitors[END_REF][START_REF] Pateli | Technology innovation-induced business model change: a contingency approach[END_REF]. Designing scenarios about future BMs implies identifying uncertainty factors in order to build breakthrough scenarios. The factors considered are those that we postulate will have a strong impact (either positive or negative) on the strategy or the project (Strauss and Radnor, 2004). The scenarios aim to modify the view that the actors have of their environment by highlighting the contradictions and anomalies that result in questioning old and existing paradigms.

Based on this perspective, the scenarios are built as being possible futures. They should therefore increase creativity, improve focus and help managers take into account the uncertainty and complexity in their environment more effectively.

The first strategic workshop: the formalization of Crowdspirit strategic alternatives around two scenarios

This strategic meeting took place in May 2007 and brought together the two coauthors of this paper and the two managers of Crowdspirit (Lionel, CEO, and Jean-François VP R&D and manufacturing). The meeting lasted for approximately 4 hours. We present hereafter our methodology and the main issues that were raised during the workshop.

Our methodology consists of following a systematic step-by-step challenging of the various elements of the BM. The first stage consists of asking and discussing a number of questions, whilst posting the provisional answers on different coloured sheets on the wall directly in front of the participants in order to enhance the sense-building process. These questions are listed below:

-What is the concept? -Who are the customers and what is the value proposition that Crowdspirit can provide? -What resources and capacities are necessary to develop the concept and the technological platform? -How can the value network be organised? -What is the economical logic for revenue generation?

The second stage involves scenario building by grouping different answers and hypotheses on the BM dimensions, as they are posted on the wall. This is done to build contrasting but coherent BM scenarios.

We started by trying to determine the concept of the Crowdspirit activity precisely, in other terms, the company's strategic vision. This task was particularly difficult as in May 2007 the crowdsourcing concept was not that well known. Lionel, the CEO, who had already presented his project in a number of professional trade fairs, had formalized the following sentence to explain the concept: "selling innovative consumer electronics products, under the Crowdspirit brand, which perfectly fit end-user needs, due to their involvement throughout the innovation process". This statement rapidly appeared inadequate: the term "end-user" appeared ambiguous, the crowdsourcing community at the core of the concept, was not referred to, nor the value brought to the contributors through the incentive model. After long discussion, we came up with the following concept statement: "To be the first platform and worldwide community allowing the design, industrialization and selling of consumer electronic products, and ensuring a fair payment of all contributors". This new formulation appeared satisfactory to the Crowdspirit team, as they considered that each key word of their business was present: platform, community, fair payment of the contributors.

When we asked the question « who are the customers », it appeared that Crowdspirit had different targets in mind which included a number of contradictions. People fond of new technologies, also called « geeks », appear to be the main target of Crowdspirit who need them to make the community function. But are they also potential buyers? Can the platform reach a mass market for the final products, not only composed of « geeks »? In other words, would "ordinary" end-users buy the high tech products designed by the "geeks"? Furthermore, could consumer goods companies also be clients of the web platform, paying for innovative ideas or design specifications, and importing Crowdspirit products into their own business model as described in Chesbrough's open innovation paradigm? During the first meeting, all these issues remained open, and we decided to focus our analysis on the value proposition for the future community participants, the first target defined by Crowdspirit, for the launching of its beta test.

When we came to the value proposition step, we realized that it would also be necessary to better characterize the value potential for a community of "geeks". For Lionel, there is an intrinsic benefit in participating in a crowdsourcing enterprise: the satisfaction of participating in an economic revolution by rethinking the boundaries of the traditional firm. This could be consistent with previous research results on the motivation of open source contributors (West and Gallagher, 2006;[START_REF] Von Hippel | Free revealing and the private-collective model for innovation incentives[END_REF]. Nevertheless, and although Lionel can be considered a "geek" himself, little was known by the team of the "geeks" motivation: would they have the same motivation as the open source contributors for software development, would they be motivated by significant financial rewards, as is the practice in for example in the Innocentive platform1 ? Another issue, related to the "geeks" motivation is the following: how would the community members react if Crowdspirit captured their contributions to the benefit of private companies (as is done by Innocentive)? Is this compatible with the community spirit that Lionel wants to create? It therefore appeared necessary to undertake a complementary study about contributors' profiles and values to answer these questions and to better define the value creation model. This will be the central focus of the second strategic workshop.

We then turned to the competencies required to develop a BM covering all the R&D processes as well as the manufacturing and the selling of products on the web. The main observation is that Crowdspirit lacks marketing competencies and identified distribution channels for its products. Crowdspirit has not identified the right channel to the endcustomers. For the moment they intend to build partnerships with well-referenced web sites and with e-business companies having some supply chain experience. Through this discussion the Crowdspirit managers identified that this would be a key issue, and that building access to the mass market would require huge investments that the start-up was unable to provide at that time. The economic viability of the BM appears therefore uncertain. On one hand, value capture must be ensured by the selling of designed products, but the selling model is not yet stabilized. On the other hand, and on the basis of the open innovation paradigm, we identified that the BM could find other paths of value capture, for example through the selling of semifinished products generated by the work of the community: ideas, testing of concepts, detailed specifications, or even patents. These by-products could generate revenue as long as Crowdspirit can find the right business partners to valorise them on the end-market.

At this stage, we identified that Crowdspirit's initial concept could create and capture value through two types of BM: one is based on the management of the entire innovation process from idea generation to the end market, the second based on the valorisation of intangible assets produced by the community. We then decided to study these two scenarios in more detail and firstly to identify and define the profiles of the different types of contributors and their potential motivation to collaborate to the community. We put this subject on the agenda of the second workshop, which is presented in the next section. [START_REF] Raymond | Understanding the Motivations, Participation and Performance of Open Source Software Developer: A Longitudinal Study of The Apache Projects[END_REF]. The collective action model assumes that innovators relinquish control of knowledge, or other assets they have developed during the project, and so create public goods. According to Von Hippel and Von Krogh (2006), one major problem of the collective action model is how to reward real contributors and how to avoid free riding (downloading of open source software without contribution)?

The literature on collective action argues that the creation and deployment of selective incentives to reward contributors is essential to the success of collective action projects. The importance of selective incentives suggests that small groups will be most successful at executing collective action projects because selective incentives can be carefully tailored to each group member, and individual contributions can be more effectively monitored [START_REF] Olson | The Logic of Collective Action[END_REF][START_REF] Ostrom | A behavioral approach to the rational choice theory of collective action[END_REF].

Research on open source communities reveals that financial rewards are not necessary to recruit and motivate community members. Open source communities are not motivated by profit and don't propose financial compensation to contributors [START_REF] Raymond | Understanding the Motivations, Participation and Performance of Open Source Software Developer: A Longitudinal Study of The Apache Projects[END_REF]. Open source projects are based on "bazaar" governance with open licence contracts where control and incentives are very low [START_REF] Demil | Neither Market nor Hierarchy nor Network: The Emergence of Bazaar Governance[END_REF]Lecocq, 2006, Nambissan andSawney, 2007). Open source community members tend to be more motivated by symbolic rewards such as recognition, knowledge sharing, increasing their own knowledge, the feeling of solidarity, fairness and altruism [START_REF] Raymond | Understanding the Motivations, Participation and Performance of Open Source Software Developer: A Longitudinal Study of The Apache Projects[END_REF]Von Hippel andVon Krogh, 2006, West andGallagher, 2006). Von [START_REF] Von Hippel | Free revealing and the private-collective model for innovation incentives[END_REF] propose a new incentive model that combines elements of the private and collective models: the "Private-collective" model.

Here, innovators in projects use their own resources to privately invest in the creation of new knowledge and in product or service innovation. Private investment and collective action can coexist and enable the production of public goods through private funding.

This literature provides us with some guidelines to think about the incentive model Crowdspirit should adopt. On one hand, private investment appears to be relevant, as the final developed product will not be in the public domain. It is therefore legitimate than individual contributors are paid for their activities. On the other hand, we can expect than in this type of community contributors may have other motivations than purely financial ones, such as: participating in an economic revolution, obtaining products they can't find on the market or even creating or enhancing their reputations. In this case, we would have an incentive model closer to that of the collective action model, rather than the "Private-collective" model of Von Hippel and Von Krogh, as the final products will remain private and will be sold on the market. These questions require that we work on the profiles and motivations of the different types of contributors. Although, as noted by Von Hippel and Von Krogh ( 2006

The second strategic workshop: characterizing contributors profiles

The objective of the second workshop was to define the value brought by Crowdspirit to the different contributors. This was vital in order to develop appropriate communication to convince them to participate. This had to be ready for the launching of the platform, scheduled at the end of August 2007. Thus, we tried to identify the different profiles of the contributors, and for each of them, to make the hypotheses concerning the following: their expected role in the community, their motivations to participate and the competencies each type of contributor should have. We identified eight contributor profiles: the idea initiator, the design team member, the investor, the tester, the ambassador, the project leader, the reseller, the customer. As an example, we provide a view of the type of description we produced for each of the eight identified profiles:

The idea initiator Role: He/she submits new ideas, concepts or problems Motivation: He/she, is motivated by sharing his/her ideas or acting as a lead-user and bringing them to being with the help of the community Competencies: He/she is interested in high tech products, has imagination, creativity. He/she is also able to describe his/her ideas in a pragmatic way.

We gave particular attention to the criteria of motivation and competencies. This was done in order to provide a better view of the value brought by the system to the contributors in relation to the level and the value of their contribution.

The description of the customer, which appeared to be a key issue in the preceding workshop, also required specific work. We identified a segment of "ordinary customers", those who buy MP3 or mobile phones through traditional distribution channels. Another segment is the "geeks" who can play different roles: they can be initiators, members of the design teams but also resellers, or customers. The problem is that the initial BM of Crowdspirit is based on a volume strategy: products with a relatively low price (the target is under 150$) but sold to a mass market. With little investment in marketing and advertising, it is particularly crucial to rely on a network of prescriptors and create increasing adoption returns. Then the question appears of how to build the link with "ordinary customers" who do not belong to any geek community. Can the Internet "buzz"2 , that already started around the Crowdspirit project, be sufficient to reach this segment?

Following the identification of the different types of contributors, we worked on the value distribution model amongst the community members. The original model of Lionel consists of allocating points according to the level of contribution. For example an initiator will gain a certain amount of points when submitting an idea. These points can be converted to cash only if and when the product is commercialized and generates revenue. A percentage of the margin (that we cannot publish for confidentiality reasons) is a priori devoted to paying the community. This model raises two problems. The first is that it doesn't take into account other types of incentives that have been identified in previous research (Von [START_REF] Von Hippel | Free revealing and the private-collective model for innovation incentives[END_REF][START_REF] West | Does Appropriability Enable or Retard Open Innovation?[END_REF]Gallagher, 2006, Roberts et al, 2006). So far, for example, there is no formal model for the symbolic payment of talented contributors. The second problem that we have not identified as such in the literature is the fact that the incentive model is individually based and can introduce competition within the community and therefore may be counterproductive in terms of motivation. A second incentive model emerged from our analysis: this time it is based on the payment of the community engaged in the design of the product, which Lionel calls "the core-team". At this point we also raised the question of IP management, which is a central issue in open innovation models (Chesbrough, 2006a). At this point in time, the decision of Lionel was to ask the community to transfer all IP generated by the team work, to Crowdspirit. This is particularly important, as some members of the core-team could also be employees of dominant firms in the consumer electronics market 3 . The principle of a relatively closed core-team dedicated to the design of the product, after the idea generation and idea selection phases is therefore adopted. Only people with a sufficient number of points gained in the previous phases can belong to this core-team. The core-team would then be responsible for the innovation process up to commercialization. This of course requires the identification of a system of competencies so that all the needed competencies (design, engineering, marketing, commercial) could be represented in the team.

Finally, this second workshop led us to identify the main elements of the value creation model within the community, and to define the basis for the beta test: an open model for the idea generation and idea selection phases, a more closed system for the following innovation process, with a core-team that could be more interested and motivated by the success of the product on the market. At this stage only a real experiment with a real product can help refine the economic and the incentive models as the financial returns for the contributors will be proportional to the success of the product. If the first products are successful, motivation for further contributions will be re-enforced.

Nevertheless, we still lacked a clear view of the Crowdspirit value capture model required to ensure that the company can be profitable and be able to raise funds for its development. This will be the purpose of the third strategic workshop that we decided should take place after the launching of the platform.

THE CHALLENGING OF THE INITIAL BM AND THE IDENTIFICATION OF TWO MODELS: THE COMMUNITY MODEL AND THE INTERMEDIARY MODEL

The third workshop in November 2007 took place after three months of real experimentation of the web platform. This experimentation provided us with real results and raised new strategic issues for the Crowdspirit team. In this section therefore, we will start with the main events of this experiment followed by the new questions posed by the CEO. We then present a theoretical framework that we discussed with the team, in order to help them to refocus their BM and finally change their initial concept.

The main milestones of the web platform launch

The first important step in the life of the company was the opening of a beta test of the web platform, at the end of August 2007. This test provided feedback on the functioning of the community and enabled the technical side of the platform to be tested. This launch was done on a limited basis with invited members who had only one day to register before the formed community was closed. This first experimentation phase produced positive results with more than 500 people registered and more than 50 ideas generated.

The second major event was the official launch of the web platform during the TechCrunch 40 conference (a major Web 2.0 conference) in San Francisco on the 17 th and 18 th September 2007. Crowdspirit was selected among 700 applications worldwide to present the web site to potential investors.

In October, Lionel was quite disappointed by the number of visits to the website, which had not increased since the beta test. On the whole, the site has had 6000 visitors but only a core group of people is ensuring the life of the community. The number of ideas produced is still quite low (around 100 in November). Different reasons can explain these results: difficulty in understanding what steps to carry out following the idea generation phase and the lack of tool-kits to support the design activity. Nevertheless, one product idea stands out and obtained a large number of positive votes (70%): the digital wall calendar. The concept involves centralising and co-ordinating, on a wall-mounted electronic device all the activities of family members. A closed core group of around 20 people has been formed to define the detailed product specifications. In December 2007, the Crowdspirit team decided to proceed to the industrialization and commercialization phases of this promising product.

As we stated, despite the emergence of a real innovative product after a few months of functioning, the Crowdspirit team remains disappointed by the results. Therefore, back from the TechCrunch Conference, Lionel thought it was time to make his business model evolve.

The first months of experimentation, from September to November 2007 confirmed the difficulty in recruiting enough participants into the community. Furthermore it appeared, as we previously identified, that it would be very difficult to address a mass market for the first product developed by the Crowdspirit community (the digital wall calendar). Lionel then decided to study the opportunity of finding additional partners to address the final market, such as established firms in the consumer electronics industry. The objective of November's workshop was to discuss this issue on the basis of some preliminary theoretical work that we prepared before the meeting, and that we present hereafter.

A framework to characterize different types of crowdsourcing-based strategies

Chesbrough's recent work on open innovation (Chesbrough, 2006a(Chesbrough, , 2006b) ) describes two modes of managing innovation and R&D processes: the so-called closed model and the open model. The closed innovation system is called closed in so far the firm is seen as being a closed pipe where projects enter one way, at the beginning, and can only exit one way, by going into the market. This model describes a traditional vertical integration model where internal R&D activities lead to internally developed products that are then distributed by the firm. In the view of Chesbrough and colleagues, the open innovation model involves the use of deliberate inflows and outflows of knowledge to accelerate internal innovation and to expand the markets for the external use of innovation. Chesbrough (2006aChesbrough ( , 2006b) ) considers that the business model of the firm acts as a filter to select the projects that fit the business model. Other projects can be valorised through other paths such as licensing or patent selling.

In his book on open innovation business models, Chesbrough (2006a) also underlines that the open innovation paradigm has given birth to a new type company called "innovation intermediaries". These companies act as market places whose function is either to help innovators use external ideas more rapidly, or help inventors find more markets where others can use their own ideas to mutual benefit. InnoCentive, which we mentioned previously, is one example of this type of innovation intermediary. [START_REF] Nambissan | The Global Brain[END_REF] recently completed the open innovation perspective. They take it further by identifying four types of what they call network-centric innovation:

1. The "orchestra" model is controlled by a set of partners and addresses a defined innovation space. This can be a R&D consortium with different partners being responsible for different aspects of the innovation.

2. The "creative bazaar" model is controlled by one dominant firm who sources emergent innovative ideas from a diverse network of inventors. This form appears to us to be typical of the open innovation model as defined by Chesbrough and colleagues, and also typical of what has been defined as crowdsourcing.

3. The "MOD station" model (MOD standing for "modifications") is one form where innovation is based on an existing well-defined architecture (incremental innovation) and a community of users and experts carries out the innovation. An example of this model can be found in the video game industry where, by offering the source of the software to the community, a company can facilitate the creation of variations of the game.

4. The "Jam Central" model is when members of an innovation network get together to innovate, the structure of the innovation evolves through the continuous interactions of the members, and the leadership of the network is diffused amongst a group of members. We think that this model is typical of some open source communities.

Together, Chesbrough's work on open innovation and Nambissan and Sawhney's approach on network-centric innovation, provide us with a framework to position Crowdspirit's business model. Here below are our conclusions. They must be currently considered to be provisional as the Crowdspirit business model is still evolving.

What are the strategic alternatives for Crowdspirit?

We first tried to characterize strategic alternatives for Crowdspirit inside the open innovation paradigm. According to Chesbrough (2006b), two dimensions of the business model have to be taken into account: the value creation process and the value capture process.

What in our opinion, distinguishes the closed innovation model from the open innovation model, is that, for the latter, the business model utilises both external and internal ideas to create value. In the open innovation paradigm, the business model acts as a filter to select the innovations that will be carried out within the firm, so that the firm can capture value from the innovation inside its BM.

FIGURE 1 Crowdspirit BM: between the open innovation and open source model

Currently we suggest that the Crowdspirit business model shares the fact that value creation is outsourced to the community with the open source models and shares the fact that some value capture should occur inside Crowdspirit's BM with the open innovation models. This confirms the importance of the issue raised in the previous workshops, namely the capability of Crowdspirit to capture enough value from the whole process to be profitable.

We then tried to position Crowdspirit within the framework of network-centric innovation developed by Nambisan and Sawhney (2007). Again, it appears that the Crowdspirit model doesn't fit into one of the four types and could in fact borrow from two types namely the "Creative bazaar" and the "Jam Central". It could be a "creative bazaar" model on the condition that the company could act as a dominant firm sourcing ideas from inventors. This supposes that Crowdspirit have full control over the innovation processes. It could also be a "jam central" model if Crowdspirit gave up the leadership of the innovation strategy to the community, thus coming closer to an open source model.

Finally, from this analysis, two major types of business model emerged that Crowdspirit could adopt to ensure its future development: one that we call the community model and the other the intermediary model. We summarize hereafter these two types and define for each of them the main strategic issues as they appeared in the workshops with Crowdspirit.

The community model

The community model is the one initially adopted by Crowdspirit. It is close to the open source model where nearly all the value creation process is outsourced to a community in which inventors submit ideas for innovative products. In this model, members of the community send ideas or describe a problem, contribute to the product specifications, vote and test the concept. They can also promote the product and invest money to finance its development. Once the product development stage has been completed, as with the digital wall calendar product, customers can purchase the product by means of the Crowdspirit supply chain and network of potential manufacturers. Community members can also purchase the product thank to a "pre-sales" system. The main strategic issues related to this model that emerged during our strategic analysis with Crowdspirit are as follows:

-How to motivate members in order to ensure their contribution and to provide value.

What is the right incentive model? -How to ensure value capture by Crowdspirit if the community controls the entire innovation process (which is not the case in the present business model).

-How to develop the path to market with a sufficient volume of sales to finance both the community work and the Crowdspirit activity.

FIGURE 2 VALUE SHARING HYPOTHESES WITHIN THE NETWORK

The intermediary model

The intermediary model can be considered as a strategic alternative for Crowdspirit. This would not be the end-users but rather companies willing to access crowdsourcing facilities. In his book on Open Business Models published in 2006, Chesbrough (2006a) dedicated a chapter to innovation intermediaries (Chap 6). He identified two main models of intermediaries: the agent, representing one side of a transaction, and the broker or market place, bringing parties together to achieve a transaction. He argued that an innovation intermediary should be able to help its clients define the problem that needs to be solved, and manage the problem of identifying both parties (companies and inventors). However, a recent study about innovation intermediaries and marketplaces [START_REF] Lichtenthaler | Innovation Intermediaries: Why Internet Marketplaces for Technology Have Not Yet Met the Expectations[END_REF] concludes that Internet marketplaces for technology transactions (such as Innocentive, NineSigma or Yet2.com) have not yet met the expectations of both technology suppliers and seekers and still generate a relatively small flow of transactions. The authors observe that those marketplaces have recently made their business model evolve by offering additional services to facilitate technology exchanges. In addition, they note that there is a challenge involved in identifying high potential applications before actively commercializing technology that is not yet present in existing marketplaces. Crowdspirit could thus act as an intermediary between innovation communities and firms, but not especially for technology or patent transfer, but rather to help firms invent new concepts, test them, or identify application domains for a given technology they have in portfolio. In other terms the company could become a "market oriented" rather than a "technology oriented" innovation intermediary.

Adopting this type of model would be a major strategic shift for Crowdspirit, which would then need to resolve the following:

1. Define the scope of its activity as intermediary (only broker, or broker with addedvalue services) 2. Define the target customers among all the actors of the consumer electronics industrial value chain (cf. [START_REF] Christensen | The Industrial Dynamics of Open Innovation -Evidence from the transformation of consumer electronics[END_REF] 3. Define the value proposition for these customers 4. Invent a new incentive model for the community 5. Check that the existing community would remain involved even if they loose control of the innovation network.

6. Managing the IP issues with the contributors and the customers, according to the nature of the knowledge transferred.

The emergence of a hybrid model

Between December 2007 and February 2008 Lionel David started to redefine the CrowdSpirit's web platform to fit the newly defined strategic model. His reflection led him to present us his new business model in a fourth workshop in Febrary 2008. He decided to borrow from the two models that we presented in November and design a hybrid model, combining the community model and the intermediary model. The main reason for this is that the CS community is considered as a strategic asset for the company for value creation, but cannot ensure value capture to the benefit of the company. In Lionel's view, the community model is based on two categories of contributors. The first one is made up of anonymous individuals from the "crowd" providing ideas, problems or solutions and testing innovative ideas. The other category includes members of "core-teams" acting as a project team to define user requirements and prepare the product for industrialization. The services offered by Crowdspirit as an intermediary are therefore based on both types of contribution. A client firm could use the open web platform of Crowdspirit to test concepts, find applications for his technology, or look for innovative solutions. It could also negotiate with a core-team to obtain the rights for the ideas, concepts, scenarios, design or detailed product specifications produced by the team. As shown in figure 3, CrowdSpirit will manage communities of contributors and play an intermediary role by offering firms an access to the community members. This hybrid model again raises some key issues: (1) the role of CrowdSpirit as intermediary: either a broker (able to advise the client on how best to approach the market) or an agent (able to bring parties together to achieve a transaction); (2) the value proposition for community members. On the latter topic, research on open source communities provides useful insights. According to [START_REF] Shah | Motivation, governance and the viability of hibrid formes in open source software development[END_REF], the open source community is made up of two types of members: the need driven participants and the "hobbyist" participants. The first ones interact with the community in order to satisfy their needs, whereas the second ones participate to the community for fun and enjoyment. [START_REF] Raymond | Understanding the Motivations, Participation and Performance of Open Source Software Developer: A Longitudinal Study of The Apache Projects[END_REF] research suggests that motivation, participation and performance of open source communities require the implementation of a feedback system and a status motivation system. Further study on the motivation of Crowdspirit's community members should help us define a relevant incentive system for both of the categories presented previously. So far, we can assume that we will find need-driven participants and "hobbyist" participants in both categories.

Others issues should be studied: How to demonstrate the value of the service to the clients (measure the contribution of the intermediary), how to access a two-sided market and how to establish a strong positive reputation. open source practices rely to a certain extent on crowdsourcing. We had the opportunity to follow the launching of the Crowdspirit start-up from the outset. Through a step-by-step action research process, we discovered a number of strategic key-issues, not addressed in literature to date. These will provide a first base on which to carry out future research on crowdsourcing. The limits of this study remain those inherent to exploratory research, and especially in this business case, which is continuously evolving, with no obvious development paths for the future.

Our main results can be summarized as follows:

1. Crowdsourcing practices can lead to a renewed vision of the function of a business model.

Traditionally the business model perspective deals with the value creation model of a firm.

The scenario-based approach, used to challenge the Crowdspirit business model, revealed that in a context of crowdsourcing it is necessary to clearly distinguish between value creation and value capture. Indeed, for a company like Crowdspirit, whose entire innovation activity is

Crowdsourcing practices question the incentive model to enhance community members participation

The literature on the incentives for innovation activities, and especially the recent work of Von [START_REF] Von Hippel | Free revealing and the private-collective model for innovation incentives[END_REF] suggests three models to reward innovation efforts: the private investment model (where the knowledge produced is paid by private investors), the collective action model (where the knowledge produced is revealed freely to the benefit of all) and the intermediate "private-collective" model, which theoretically takes the best of the former two models, and especially avoids free-riding behaviour. Crowdspirit is trying to invent a fourth form that we could call "collective-private". Interestingly enough, Crowdspirit is managing this issue by separating the different phases of value creation. The first phase, idea generation and idea selection, could function as a collective action model, with quite low financial rewards, if other forms of reward are developed, as discussed in the literature (such as reputation),. The second phase, product specifications and industrialization, will be carried out by a closed "core-team" with a more private investment model. We can make the hypothesis that experts (engineers, designers, marketing people etc.) would expect returns their investment of time, proportional to the success of the product. As there has been no product commercialized so far, this remains to be checked with future empirical data on the case. Another point is the recruitment of team members, according to their profile and competencies. As Von [START_REF] Von Hippel | Free revealing and the private-collective model for innovation incentives[END_REF] suggested, this requires focusing on relatively small teams that the company can control and motivate. Thus, with crowdsourcing practices we see new business relations emerge, namely in this case between the market and hierarchy. Pure market coordination would appear to be unviable because the firm has to be able to control the innovation activities for its own benefit. This raises very interesting question on new hybrid organizational forms that should be addressed by further research.

The emergence of two models for innovation activities relying heavily on crowdsourcing

A question not addressed in literature on open innovation is the proportion of innovation activities of a given firm outsourced via crowdsourcing. If this proportion is high, as it is the case for Crowdspirit, we see only two possible viable business models. The first is the community model, similar to the "jam central" model of Nambisan and Sawney. In this configuration, the community should gain the leadership and the control on the innovation network, which is not completely the case in Crowdspirit at the current time. This would also mean that Crowdspirit would have to focus its business model on industrialization and marketing activities. It may sound paradoxical however that the company controls lower added value activities and outsources the higher added value ones. An alternative for Crowdspirit would therefore be to develop an innovation intermediary business model, close to the "creative bazaar" of Nambisan and Sawhney. In this case, Crowdspirit would maintain its leadership in the value added innovation activities. Innovation intermediaries are companies whose activity is specialized in crowdsourcing for the benefit of their clients. This would suppose that Crowdspirit develop additional competencies such as the capacity to understand the innovation problem of the client and to develop the right crowdsourcing strategy for the client. In addition, Crowdspirit could reinforce its capacity to manage a

  THE NETWORK CONSTRUCTION: HOW TO PROVIDE VALUE FOR THE VARIOUS CONTRIBUTORS Defining a value creation model based on a crowdsourcing strategy requires a good understanding of how to motivate contributors to ensure they actively participate in community work. The first section provides a review of several theoretical incentives models in open innovation and open source contexts. The second section describes the second strategic workshop we had with the managers of Crowdspirit during which we discussed the profiles and competencies of potential contributors and then defined the main reward principles. Incentives and motivation in open innovation and open source communities Von Hippel and Von Krogh (2003) define two main ways by which innovation efforts can be rewarded both in industry and society in general. The first is the private investment model where contributors are individually rewarded for the knowledge they provide by private investors who expect to make profits. Intellectual propriety is transferred from individual contributors to private investors via patent or copyrights. InnoCentive is an illustration of this model: individual contributors are paid for the ideas, or technical solutions, they provide to the seekers. The second major model through which innovation efforts can be rewarded is the collective action model. This model is based on the revealing of findings, discoveries and knowledge freely. It characterises open innovation, and above all open source software projects, in so far as it makes collaborative design possible

  ), it is difficult in an open innovation community to recruit, and then motivate contributors, the open source literature still provides little advice on this issue. It is precisely what we wanted to address in the second meeting with the company.

  Open source software models (OSS) can somehow be assimilated to the open innovation model, but one major difference is that open innovation explicitly incorporates the business model as the source of both value creation and value capture, while open source focus on value creation throughout an industry value chain, its proponents usually downplay the importance of value capture. These distinctions allow us to define the Crowdspirit business model as being somewhere in between open innovation and open source models, as shown in figure 1.

  The reward system is based on the selling price as shown on the figure n°2. Community members are remunerated according to their contribution by receiving a percentage of the final revenue. This model is close to the "Jam central" model of Nambisan and Sawhney where the community owns the leadership of the innovation network, and the innovation goals evolve from the continuous interactions between community members

  would be a model closer to the open innovation model, where value creation would still rely upon the work of a community, but value capture would be ensured by the valorisation of the community developments towards established firms. In this model, the clients of Crowdspirit

  FIGURE 3 REPRESENTATION OF CROWDSPIRIT'S HYBRID MODEL SOURCE: CROWDSPIRIT ®

  based on crowdsourcing, value creation is performed by the community (as in the case of open source software), whereas value capture should be made within the business model (as in many open innovation experiments). The first business model as designed by Crowdspirit appears problematic, as the competencies owned by the firm do not guarantee a clear path to the mass market. It then appears than the company has to manage the right balance between an open source business model and an open innovation business model. Adopting a strategy to develop their products, like those adopted by software firms relying upon established open source communities, would be one option. The other option would involve playing the role of innovation intermediary to the benefit of established firms in the domain of consumer electronics products.

InnoCentive is an open innovation marketplace whose principle is the following: scientific or technical challenges are published on the Innocentive web platform by seekers (corporations, agencies, not-for-profit organizations) who are looking for solutions to help with product development or other science problems. Each challenge has a significant associated cash award. People whose solution is chosen by the seeker win the bonus ( www.innocentive.com)

The reader can type « Crowdspirit » on Google to get an idea of the « buzz » around the project.

The identification of the geographical origin of contributors will later confirm this hypothesis, as we will see that the regions of dominant firms in consumer electronics are over-represented.

community of experts in the domain of consumer electronics and invent a relevant incentive model that we think should be close to the private investment model. Crowdspirit has found a strategic position between the community model and the intermediary model. Although we cannot currently assess the relevance of this choice, it suggests that practices of innovative companies in the realm of web 2.0 can renew the theoretical models of open innovation and open source by introducing new approaches. Future research will be necessary to consolidate our understanding of these emergent business models.


Madani Hedidi

William Erb
email: william.erb@univ-rennes1.fr

Frédéric Lassagne

Yury S Halauko

Oleg A Ivashkevich

Vadim E Matulis

Thierry Roisnel

Ghenia Bentabed-Ababsa

Florence Mongin

Functionalization of pyridyl ketones using deprotolithiation-in situ zincation

Keywords: procedures, experimental procedures and compound characterizations, 1

The metallation of aryl ketones was achieved by using LiTMP in the presence of ZnCl2•TMEDA, as evidenced by subsequent interception with iodine or by palladium-catalysed cross-coupling reaction. One of the synthesized iodo ketones has been further elaborated to reach derivatives of biological interest.

Pyridines play a large part in biological processes (nicotine, niacin, NADP, vitamin B6…), in pharmaceuticals and agrochemicals, [START_REF] Eicher | The Chemistry of Heterocycles[END_REF] as well as in organic materials. [START_REF] Kim | [END_REF] In addition, pyridyl ketones bearing a halogen at the position adjacent to the carbonyl function are key intermediates to access heterocyclic scaffolds of interest such as azafluorenones, 3 azaxanthones, 4 naphthyridones, 5 and thieno-, 6 pyrazolo-7 and isoxazolo-pyridines. 8 Even if deprotonative lithiation 9 has been largely used to regioselectively functionalize pyridines, [START_REF] Queguiner | [END_REF] the method has never been extended to pyridyl ketones due to their low compatibility with organolithiums. Mixed lithium-nonalkali metal combinations have been developed to achieve chemoselective deprotometallation of aromatics. 11 In this context, the 1:1 mixture of homometallic amides LiTMP (TMP = 2,2,6,6-tetramethylpiperidino) and Zn(TMP)2, 12 obtained by mixing in a 3:1 ratio LiTMP and ZnCl2•TMEDA (TMEDA = N,N,N',N'-tetramethylethylenediamine) in THF (THF = tetrahydrofuran), was successfully employed with a large range of sensitive substrates. 13 Such a synergy, attributed to reversible deprotolithiation shifted by zinc-mediated transmetallation 12 (or 'trans-metal trapping' 14 ), has since been extended to the use of LiTMP in the presence of ZnCl2•2LiCl, MgCl2 or CuCN•2LiCl. 15 Herein, we report the efficiency of aryl ketones as directing groups for LiTMP-mediated deprotometallation of pyridines and other arenes in the presence of ZnCl2•TMEDA as in situ trap (Scheme 1, Table 1).

Scheme 1

Zincation of aryl ketones 1 using LiTMP in the presence of ZnCl2•TMEDA followed by iodolysis or palladium-catalysed cross-coupling to afford ketones 2 and 2'.

Thus, after optimization of the reaction conditions (using four different reaction temperatures from -70 to -10 °C and different amounts of LiTMP from 1 to 3 equiv), treatment of 2benzoylpyridine (1a) in THF containing ZnCl2•TMEDA (1 equiv) with LiTMP (1.5 equiv) at -30 °C for 15 min and then iodine led to the 3-iodo derivative 2a in 50% yield (entry 1). Similarly, 4benzoylpyridine (1b) was converted to the 3-iodo derivative 2b by using LiTMP (2 equiv); with this substrate benefiting from two free positions adjacent to the benzoyl group, a second deprotonation was observed to some extent, as evidenced by the competitive formation of the diiodo 2b' (entry 2).

3-Benzoylpyridine (1c) is more prone to nucleophilic attack onto the ring than its 2-and 4-isomers. [START_REF] Chen | For Lewis acid-catalysed nucleophilic additions at C4 onto 3substituted pyridines[END_REF] As a consequence, deprotolithiation-zincation could only be evidenced by subsequent iodolysis at temperatures below -50 °C. Using LiTMP (1.5 equiv) at -55 or -70 °C for 15 min and then iodine provided the 4-iodo derivative 2c in 30 or 37% yield, respectively (entry 3).

When present at pyridine 2-position, halogens are known to acidify the 4-position, as shown by pKa values calculated in THF. 13a The 2-halogeno 3-benzoylpyridines 1d-h were thus prepared. 17 Accordingly, when similarly reacted by using LiTMP at -55 °C, the iodo derivatives 2d-g were isolated in improved yields, in line with the long range effects of fluorine and chlorine (entries 4-7). In contrast, the reaction from 1h proved more complex, giving the iodide 2h in a modest yield (entry 8).

The presence of a methoxy group at 2-position of 3benzoylpyridine also had a positive impact on the course of the reaction since involving 1i in the sequence furnished the iodo derivative 2i in 88% yield (entry 9). Whereas this group does not acidify remote pyridine 4-position, 13b it acts by making the pyridine ring less sensitive towards competitive nucleophilic attack. Indeed, based on the 1 H NMR chemical shifts in CDCl3 of 1c (8.12 and 8.82 ppm for H4 and H6, respectively) and 1i (7.72 and 8.32 ppm for H4 and H6, respectively), one can predict as shown by Handy and Zhang 18 that the partial positive charges at C4 and C6 will be reduced for 1i. The behaviour of the ketones 1j-l, possessing reduced flexibility on carbonyl direction, was similarly examined. 4azafluorenone (1j) was converted to the 1-iodo derivative 2j in a moderate yield, similar to that obtained from 3benzoylpyridine (1c). In the case of 1j, the phenyl ring was also attacked to a lesser extent at a position facing the pyridine nitrogen to afford the diiodide 2j' (entry 10, Figure 1). In contrast with the reaction from 1j, the sequence using 4azaxanthone (1k) and 4-azathioxanthone (1l) provided the iodides 2k and 2l in higher 60% yields (entries 11 and 12). In order to evaluate the scope of the method, we chose other aromatic substrates. [START_REF] Snégaroff | Concerning the deprotonation of benzophenone using a lithium-cadmium base[END_REF] When compared with its 4-aza analogue 1k, xanthone (1m) similarly led to the 1-iodo 2m in 72% yield. On the contrary, as previously noted between 1j

Please do not adjust margins Please do not adjust margins and 1k, the reaction from 1n was less efficient than from 1m. Indeed, 1-iodofluorenone (2n) was obtained in 52% yield, but together with the ketone 2n' (35% yield) resulting from an addition of the metallated product to 1n (Scheme 1, Table 2). As organozincs hardly react with ketones, the product 2n' could rather result from an addition of 1-lithiofluorenone to 1n more rapid than its trapping by the zinc species. These results suggest that the carbonyl direction in the metallated derivative coming from 1n is less prone to stabilize a 1-lithio compound than that from 1m.

Entry

Substrate/n/temperature Product/yield a (%) Interestingly, the deprotolithiation-in situ zincation could be combined with a subsequent Negishi cross-coupling reaction. 20 Thus, using catalytic amounts of PdCl2 as palladium source and 1,1'-diphenylphosphinoferrocene (dppf) as ligand with 2-chloropyridine 12a allowed 2'm to be formed from 9xanthone (1m) in 76% yield (Scheme 1).

CH acidities are in general useful data to better understand deprotometallation outcomes, in particular regarding regioselectivity issues. We thus calculated selected pKa values in THF solution by means of quantum chemistry at the DFT B3LYP level of theory. 13 After geometry optimization and calculation of the vibrational frequencies by using the 6-31G(d) basis set, the single point energies were obtained using the 6-311+G(d,p) basis set. The solvent influence was treated through the polarized continuum model (PCM) with the default parameters for THF. Finally, the pKa values were reached from the Gibbs energy of the homodesmic reaction between the studied and probe heterocycles.

That both sets of CH acidity values for 1m and 1n are rather similar (Table 2) supports the role of the carbonyl direction on the course of the reaction through coordination.

Analogously, the difference observed between the pKa values of 1j and 1k is not significant enough to allow for a rationalization of the distinct yields noted, rather suggesting more efficient stabilization of the metallated compound by the carbonyl group in the case of 1k at the origin of the higher yield. Besides, the formation of dimetallated products from 1j and 1k, as demonstrated by isolation of 2j' and 2k' (Figure 1), could be favoured for the former by a nitrogen assistance and, for the latter, by a relatively low pKa value (35.6) at the 6position (Table 1).

Finally, when 2-benzoylthiophene (1o) was submitted to LiTMP in the presence of ZnCl2•TMEDA as before, the reaction took place next to sulphur to afford after iodolysis the iodo derivative 2o in 80% yield (Scheme 2). To move towards nitrogen-containing derivatives of biological interest, 2-benzoyl-3-iodopyridine (2a) was involved in further reactions (Scheme 3). A catalyst-base system was first optimized to perform guanidine copper-catalysed Narylation. 21 Upon treatment with K3PO4 as base and CuI as catalyst source in the presence of DMSO, the iodide 2a was converted to the pyrido [3,2-d]pyrimidine 3a in 77% yield. Besides, cyclizing Suzuki coupling [START_REF] Miyaura | For reviews on the topic[END_REF] was performed from the ketone 2a and 2-aminophenylboronic acid under palladium catalysis to provide 5-phenylbenzo [f][1,7] In summary, we have reported a short and simple access to iodinated aryl ketones. Additionally, transition metal-mediated reactions occurring with cyclization led to elaborated scaffolds. Applications towards the synthesis of libraries of compounds for biological evaluation are currently under investigation in our laboratory.

We thank the Ministère de l'Enseignement supérieur et de la Recherche scientifique Algérien (M. H.), the Centre national de la recherche scientifique and the Institut Universitaire de France (F. M.). We acknowledge FEDER founds (D8 VENTURE Bruker AXS diffractometer) and Thermo Fisher (generous gift of 2,2,6,6-tetramethylpiperidine). This research has been partly performed as part of the CNRS PICS project "Bimetallic synergy for the functionalization of heteroaromatics".

Notes and references

a

  Yield after purification by column chromatography. b 4-Benzoyl-3,5diiodopyridine (2b') was also obtained in 10% yield.c 1,5-Diiodo-4-azafluorenone (2j') was also obtained in 20% yield.d 1, was also obtained in 10% yield.

Figure 1

 1 Figure 1 ORTEP diagrams (30% probability) of compounds 2j' and 2k'.

a

  Yield after purification by column chromatography.

  Scheme 2Zincation of phenyl 2-thienyl ketone (1o) using LiTMP in the presence of ZnCl2•TMEDA followed by iodolysis to afford the iodinated 2-thienyl ketone 2o.

Table 1

 1 Substrates 1a-l, calculated pKa(THF) values for 1j and 1k, conditions used for the deprotolithiation-zincation, and iodinated aryl ketones 2a-l obtained.

	Entry	Substrate/n/temperature	Product/yield a (%)
		O	I	O
	1	N	N
		1a: 1.5 equiv, -30 °C	2a: 50%
		O	I	O
	2	N	N
		1b: 2 equiv, -30 °C	2b: 45% b

Table 2

 2 Substrates 1m and 1n and their calculated pKa(THF) values, conditions used for the deprotolithiation-zincation, iodinated aryl ketones 2m and 2n obtained, and ORTEP diagram (30% probability) of compound 2n'.

  naphthyridine (4a)23 in 68% yield.

							(H	2 N) 2 C=NH•HCl	(2	equiv)
						I			N	NH
									2
									K 3 PO	4 (2	equiv)
									CuI	(10	mol.%)
							O		N
				N				N
						Ph			Ph
									DMSO,	110	°C,	24	h
					2a				3a
									77%	yield
							K	2 CO	3 (8	equiv)
								EtOH,	H	2 O
	(HO)	2 B			Pd(PPh	3 )	4 (5	mol.%)
	(2	equiv)	NH	2 •HCl	toluene,	reflux,	24	h
							N
				N
						Ph
					4a
				68%	yield
	Scheme 3	Conversion of 2-benzoyl-3-iodopyridine (2a) to derivatives of biological
	interest.


Oualid Mouhoubi

Abdel Charbel

Amer Nour

Baghdadi

Low Latency Architecture Design for Decoding 5G NR Polar Codes

Keywords: 5G, polar codes, successive-cancellation decoding, list decoding, low latency, hardware design

Polar codes have been adopted as the coding scheme in the control channel of the 3rd Generation Partnership Project (3GPP) New Radio (NR) standard for 5G. However, the challenging requirements introduced by the 5G control channel in terms of block length and code rate flexibility render unsuitable most of the published hardware polar decoder implementations. Indeed, these latter focused mainly on successive cancellation decoders with ultra-high throughput, limited flexibility and error correction capabilities. With stringent constraints on end-toend delay and error correction, the 5G NR context steers towards lowlatency list-based decoder architectures. In this context, we propose an original flexible list-based hardware architecture for decoding 5G NR polar codes that can support all the frame sizes and code rates defined in 3GPP with a worst-case decoding latency below 24 µs.

Introduction

Proposed in the last few years, Polar codes have been shown to achieve channel capacity in binary discrete memoryless channels, when the codeword length N tends to infinity, using the low complexity successive-cancellation (SC) algorithm [START_REF] Arikan | Channel polarization: A method for constructing capacity-achieving codes for symmetric binary-input memoryless channels[END_REF]. However, performance starts to degrade at practical code lengths. Hence, list-augmented SC decoding (SCL), where a list of L candidate codewords are considered during decoding [START_REF] Tal | List decoding of polar codes[END_REF] and the most reliable codeword is chosen at the end, improves the block error rate (BLER). Nonetheless, this comes at the cost of additional latency, chip area occupation and reduction in throughput for hardware implementations. In addition, the BLER of the SCL decoder can be further improved by appending a cyclic redundancy check (CRC) to select the most reliable codeword [START_REF] Niu | Crc-aided decoding of polar codes[END_REF].

Being the cornerstone for all polar decoder types, several works have targeted the improvement of throughput and latency of the SC decoder through the proposal of solutions at both hardware design and algorithmic levels [START_REF] Zhang | Low-latency sequential and overlapped architectures for successive cancellation polar decoder[END_REF][START_REF] Bian | A low-latency SC polar decoder based on the sequential logic optimization[END_REF][START_REF] Yuan | Low-latency successive-cancellation polar decoder architectures using 2-bit decoding[END_REF][START_REF] Roy | High speed architecture for successive cancellation decoder with split-g node block[END_REF][START_REF] Shrestha | High-speed and hardware-efficient successive cancellation polar-decoder[END_REF]. Through the introduction of specific decoders for constituent codes, simplified successive cancellation decoding (SSC) was proposed in [START_REF] Alamdar-Yazdi | A simplified successive-cancellation decoder for polar codes[END_REF] as an alternative solution to reduce the latency. This latest work was then extended to the SCL decoder in [START_REF] Hashemi | Fast simplified successive-cancellation list decoding of polar codes[END_REF][START_REF] Sarkis | Fast list decoders for polar codes[END_REF].

Polar codes have been adopted for the control channel of the enhanced mobile broadband (eMBB) service of the 3GPP's 5G new radio (NR) standard. The control channel imposes block length and code rate flexibility levels far beyond previously published polar code designs. Moreover, low BLER and low hardware complexity added to a processing throughput and latency of 10s of Mbps and 10s of µs respectively are required [START_REF] Egilmez | The development, operation and performance of the 5g polar codes[END_REF].

Motivated by the need to provide a hardware-efficient polar decoder that supports the required flexibility and latency levels for 5G NR, we propose in this paper a novel hardware architecture targeting FPGA devices and offering the following features:

-Downlink and uplink 5G NR control channel compliance with full rate and frame size support ranging from N = 32 to N = 1024 bits. -CRC-aided SCL decoder with a semi-parallel architecture [START_REF] Leroux | Hardware architectures for successive cancellation decoding of polar codes[END_REF] for best performance. -Dedicated specific constituent-code decoders for reduced latency.

-Measured FPGA-based worst-case decoding latency of 23.91 µs, in compliance with target 5G NR constraints. -Favourable comparison with previously-published designs.

The above-mentioned features were obtained thanks to the following hardware architecture-related contributions:

-Proposal and implementation of on-the-fly identifier for the number of constituent codes in addition to their type and length. -Introduction of an original special node list decoder capable of decoding all identified constituent-code types.

The remainder of this paper is organized as follows. Section 2 provides a brief overview on polar codes and their decoding algorithms including fast decoding techniques used by our decoder. The proposed on-the-fly identifier and special node list decoder architecture are described in Section 3. Section 4 presents the implementation results and comparisons. Finally, Section 5 concludes the paper.

Preliminaries

Polar codes

Polar codes apply the channel polarization transform that divides the bit-channels to either perfect or completely noisy channels. Then the polar code allocates information bits to the K most reliable bit-channels while the remaining bits are frozen, i.e., they are all set to a known value, usually '0'. For a codeword length N = 2 n , n ≥ 1, a (N, K) polar code is a block code with K input bits and N output bits whose generator matrix G is the n-th Kronecker power of matrix F = 1 0 1 1 , i.e., G N = F ⊗n . The encoding process is performed by the matrix multiplication x = u.G where u = (u 0 , u 1 , . . . , u N -1 ) stands for the sequence input vector consisting of information bits and frozen bits and x = (x 0 , x 1 , . . . , x N -1 ) stands for the encoded vector.

algorithm was proposed to avoid resorting to hard decisions when computing partial sums during the sequential decoding phase. Hard decisions are replaced by soft hypotheses for the error-prone bits identified by low reliability values. This leads to the simultaneous exploration of several codeword candidates or equivalently paths in the graph of Fig. 1a, each corresponding to one or more varying bit-hypotheses. Hence, for each bit u i decoding step, both its possible values 0 and 1 are considered and 2L new candidate paths are explored. However, in order to break the exponential growth of the number of candidate paths, a subset L of the most likely paths is set to survive. The choice is made by selecting the L lowest path metric (PM) values. In terms of complexity, the SCL decoder can be seen as the concatenation of L competing SC decoders. Assuming that a path selection can be performed in one clock cycle, the latency of the SCL decoder becomes T SCL (N, K) = 2N -2 + K.

A simplified SC-based decoding algorithm (SSC) was presented in [START_REF] Alamdar-Yazdi | A simplified successive-cancellation decoder for polar codes[END_REF] in which the tree search is pruned. In fact, a tree with only frozen bits at leaves does not need to be traversed since its output is already known and is equal to an all-zero vector. This type of node is referred to as Rate-0. Moreover, a tree with only information bits can directly be decoded by applying a threshold decision at the root node. This type of node is referred to as Rate-1. Furthermore, the authors in [START_REF] Sarkis | Fast polar decoders: Algorithm and implementation[END_REF] have identified two new types of nodes among the constituent codes of rate 0 ≤ R ≤ 1. Hence, a repetition node (Rep) is a constituent code where all the bits are frozen except for the last one and the single parity check (SPC) node is a constituent code where at the exception of the first bit, all the bits are information. The pruned tree of the Fast-SSC decoder is shown in Fig. 1b where the four types of constituent codes are colored differently. This pruning technique was then extended to the SCL decoder. A hardware-friendly PM computation for Rate-0, Rep and Rate-1 nodes and for SPC node is presented in [START_REF] Hashemi | Simplified successive-cancellation list decoding of polar codes[END_REF] and [START_REF] Hashemi | A fast polar code list decoder architecture based on sphere decoding[END_REF], respectively.

3 Proposed Decoder Architecture

Proposed special nodes list decoding module

Instead of decoding every constituent code type individually, a special node list decoder which federates the common operations performed by the different special nodes is proposed. Considering that the tree should be traversed sequentially with a node by node processing schedule, special node decoders are able to share most of the memory and the computational resources. Hence, hardware duplication is avoided. The architecture of the special node list decoder is portrayed in Fig. 2.

Since the Rate-0 nodes comprise only frozen bits, no path splitting is needed to estimate the N Rate-0 bits. Only summing up N Rate-0 values is needed. A treestructure fully parallel adder is preferred to this purpose in order to guarantee no extra decoding latency. The complexity and the critical path of this adder structure are high especially when the maximum size M defined for special nodes is large. As long as P ≤ M , where P refers to the number of processing elements of the SPC node tree. An accumulation-minimum-finder tree-based structure similar to the one used for the adder units of Rate-0 and Rep nodes with P comparators is used in this regard. The even-parity check evaluation, in turn, is performed through an accumulator-XOR tree-based structure using P XOR gates, while the evolving even-parity constraint is performed for each of the existing L paths after each bit estimate through L XOR gates and stored in dedicated LRB registers. The final value of the latter is retained to preserve the even-parity constraint and inserted within the estimated bit vectors directly in its appropriate location provided by the minimum value index found in the first step (green arrow Fig. 2).

Except for a classical bit decoding case, more than one LLR are admitted by the special node decoders. To avoid complexity due to write/read operation, they are stored in a bank of parallel-load shift registers, as illustrated in Fig. 3. Assuming P ≤ M , they are seen as an array of M Q i -bits registers grouped in M/P independent columns which can be accessed independently thanks to M/P enable signals. Q i is the quantization level of the internal LLRs. LLR's nodes are stored in the same order they are produced, starting from the rightmost column to the leftmost one during 2 j P clock cycles. During the special node decoding phase, these registers are shifted both horizontally and vertically during the decoding phase of Rate-1 and SPC nodes as follows:

-Vertical shift: LLRs are accessed one-by-one by shifting the rightmost P registers from up to down. -Horizontal shift: After P -1 vertical shift, the following P LLRs to access, when they exist, are obtained by shifting registers of the different columns horizontally from left to right.

Excluding the last loaded registers column, the remaining ones can accept their LLRs from either their adjacent one or from input. This is made possible thanks to P • M P -1 2-to-1 MUX. Since the least reliable bit in an SPC node is decoded first, its LLR value should be skipped whenever it is encountered. To do this, the LLR bank register in Fig. 3 is designed to output two adjacent LLR values instead of one. A MUX is used to select the output based on the least reliable bit index. When the PC bits are used in uplink, they are decoded using the length-5 cyclic shift register (Fig. 2). To support path competition, the generated intermediate paths read their values, i.e., LLRs, even-parity check and minimums by means of crossbars. Newly estimated bits are temporarily stored in dedicated registers. A straightforward copy operation allows, by means of crossbars, to move all the contents of each of the L surviving paths from a register to another after each path selection. The process of PM sorting and bit estimation is repeated N Rate-1 or N SPC -1 times. The PM register is updated directly from the 2L sorter in the same clock cycle. When all bits are estimated at their top tree, the source word bits are obtained through a polar encoder, the PMU updates the pointer memory and the search procedure of the next nodes in the polar code tree resumes. The partial sums needed to update LLRs are computed and the CRC check process is resumed. In order to avoid extra latency, PS and LLR computations are performed in parallel.

Memory structure LLR Memory:

The SC decoding relies on dedicated memories to keep the LLR values available for the computation units as long as they are needed. Therefore, channel input LLRs are stored in a N × Q c bits register, where Q c is the quantization level of the channel LLRs. However, internal LLRs are stored in a dual-port RAM-based memory configured with one write port and one read port. The total number of LLR updates at decoding stage j is equal to 2 j . However, only P LLR updates are allowed to be performed at once. Thereby, for stages j where 2 j > 2P , a total of 2 j /(2P ) time steps are needed before proceeding to the lower stage while only one time step is needed for the other stages. Given that the computed LLRs in PEs have to follow the bit-reversed indexing scheme, the produced LLRs need a certain reordering before being mapped back to PEs. To avoid the need for multiplexing them, the equivalent operation is directly embodied in the control unit and two RAMs instead of one, each of width P Q i , are implemented for each path l. For higher stages, LLRs are first stored in one RAM during half of the 2 j /(2P ) time period required for computation before storing the remaining produced LLRs during the second half of the time period in the other one. However, for lower stages, where LLRs are produced during one time period, LLRs contiguity is ensured by adding a specific permutation network. In addition, a L × P Q i -bit buffer is used in order to process these generated LLRs directly during the following clock cycle.

Pointer Memory: The SCL decoder can also be seen as L SC decoder cores working in parallel with their own LLRs and memory resources. Nonetheless, the cores may share the same LLRs at some stages due to path competition. Thereby, to avoid copying the shared LLRs, a memory pointer is introduced and stored instead. Hence, each SC decoder core can read its inputs from one of the L RAM memories thanks to a permutation network. The used memory pointer is a (log 2 N -1) × L register array, where register at row j and column l stores a pointer of log 2 L bits indicating the index of the RAM where LLRs of path l at decoding stage j are stored. The pointers are updated in two different situations. First during path duplication/discarding. Second, to reset them to their initial values when the stages at which they are processed is activated.

Path Memory: A LN array register is dedicated to store the values of the L codeword bits after the PMs have been sorted and the surviving paths identified. Being mutually independent, all registers can be accessed simultaneously. This by merging a Rep and Rate-1 nodes of length two (highlighted in red) while an SPC node of length greater than four is obtained by merging another SPC and Rate-1 nodes, hence the presence of the MUX. Therefore, the two structures of Fig. 4a and Fig. 4b represent the building blocks of the identifier intended to determine the different supported special node types of any length.

The special and non-special nodes identified are stored together in register arrays in the same order as they are searched during the decoding process. This reduces the complexity of multiplexing the pattern compared to the case where only valid ones are retained. Since the SC decoder is sequential, the special node vectors V (Fig. 4c) are read from the register one at a time through a N M •(M -1) to-1 MUX. A log 2 N -bit counter is used to generate the register-based memory addresses. At each special node iteration search, the next pattern to read from the registers array depends on the size and address of the previous one. The complexity of the proposed architecture increases linearly with code length N . But since the special nodes are searched only upon request, a low complexity serial implementation is favoured. To do this, a N -to-M multiplexer is used to process serially the frozen set bits in groups of M . The architecture of a serial search of special nodes is depicted in Fig. 4c.

Synthesis results and comparisons

As a proof of concept, a decoder architecture fully compliant with the 5G NR polar code has been designed. The proposed architecture is generic with respect to the parameters P , L and M . In addition to the proposed special node list decoding and special node identification unit, the architecture includes: L × P Processing Elements (PE) to compute and update the LLRs, Multi-bits Partial Sum Network to produce PS, CRC bits calculation unit to perform CRC check operations in the case of multi-bit decoding and which operates in parallel to the decoding process. The Control unit integrates a finite state machine designed to generate all the control signals needed for the different decoder components. In order to manage candidates competition, crossbars were also designed whether for copying data to some freed memory locations or to ensure a correct routing of data as a result of the use of pointer memories.

The devised architecture has been described in VHDL. The FPGA used for synthesis was a Xilinx Virtex 7-xc7vx485t device. The number of bits used to represent internal LLR, PM and channel LLR values is Q i = 6, Q p = 7 and Q c = 4, respectively. Eight PEs are instantiated in the architecture for each of the L paths, where L = 8. The maximum size of special nodes M is set to 32. Internal LLRs and partial sum bits are stored in the FPGA dual-port Block RAMs (BRAMs) while input LLRs and partial sum memory addresses are stored using look-up tables (LUTs). Decoding latency is the main critical performance metric when considering the 5G NR polar codes that protect the control channel. A target end-to-end latency of 0.5 ms implies a physical layer latency of 50 µs [START_REF] Fettweis | The Tactile Internet: Applications and Challenges[END_REF][START_REF] Maunder | The 5G channel code contenders[END_REF]. Flexibility and low latency are the driving priority for the design of this channel decoder, which is the main demanding component of the physical layer. The latency for decoding one codeword is not constant and is highly affected by the choice of the operating code length and code rate. In order to give a full analysis of the latency, all combinations of code lengths and code rates ranging from R = 1/8 to R = 5/6 are evaluated through simulations. Table 1 provides the average and the worst-case latency for decoding one codeword. The latency is measured in number of clock cycles (cc) and in µs considering the maximum clock frequency f max = 108 MHz. The results show that the maximum latency recorded by the decoder is 23.91 µs which is 2.1 times lower than the physical layer latency constraint. This worst-case latency appears in the uplink scenario, whereas it is only 7.22 µs in the downlink.

Logic synthesis and performance results are summarized in Table 2. Implementations that are fully compatible with 5G code specifications, in terms of code structure and flexibility, allow for direct and fair comparisons. Therefore, the recently available Xilinx polar decoder [START_REF]Xilinx: IP Polar Encoder/Decoder[END_REF] is the most relevant reference with respect to the available decoder designs in the literature. Performance results of Xilinx decoder are available, yet with no published details on the architecture. Compared to this decoder, our proposed architecture has 60% and 70% less decoding latency for the uplink (1024,512) and the downlink (512,40) polar codes, respectively. Our design consumes 38% less Flip-Flops (FFs), but 11% more LUTs and 25% more BRAMs. The throughput, however, does not compare favorably, yet still compliant with the 5G NR requirement for this code used for the control channel.

In order to extend the comparison, we have considered recent designs that targeted FPGA implementation with similar code length, yet not compliant with 5G NR polar codes. For that, a second configuration of our proposed architecture has been designed and synthesized with L = 4 while keeping the number of PEs per list unchanged. Compared to the folding polar decoder of [START_REF] Liang | Hardware efficient and lowlatency ca-scl decoder based on distributed sorting[END_REF], our decoder uses 74% less LUTs and 6% less FFs while decoding the (1024,512) code in less than half the time (converted in clock cycles). However, without any reported clock frequency, the latency comparison is not complete. Compared to the stochastic SCL decoder of [START_REF] Liang | Efficient stochastic successive cancellation list decoder for polar codes[END_REF] targeting wearable and IoT devices with strict hardware constraints, our decoder supporting rate and frame size flexibility exhibits 109 times less latency while requiring 3.2 times and 4.4 times the numbers of LUTs and FFs, respectively.

Conclusion

In this paper we proposed an original hardware architecture for decoding the 5G NR polar codes of the uplink and the downlink control information channel. Thanks to a special node identifier, the proposed decoder continues to benefit from tree pruning techniques to speed-up the decoding whilst maintaining compliance with the 5G NR and the various defined combinations of code rate and code length. Hence, measured throughput and latency values of the proposed decoder obtained with FPGA target are able to meet 5G requirements. Moreover, synthesis results have shown a hardware efficiency that compares favourably with state-of-the-art FPGA implementations of polar decoders. Finally, the designed architecture reduces the decoding latency compared to the recently available Xilinx 5G polar decoder.

Fig. 3 :

 3 Fig. 3: Parallel-load shift registers of the top-node LLRs.

Table 1 :

 1 Average and maximum latency measured by the proposed decoder.

	Worst-case latency	Average Latency

Table 2 :

 2 Comparison with several FPGA-based SCL Architectures. Stochastic SCL with 2-level decoding 2 Simplified SCL with four constituent codes 3 Number of clock cycles

	Decoder	[12]	[11]	This work	Xilinx [20]	This work
	List size		4			8
	Algorithm	SCL	SSCL 1	SSCL 2	NA		SSCL 2
	Flexibility	Limited	Limited	High	High	High
	Quantization	NA	NA	(4,6,7)	8		(4,6,7)
	# PE	64	64	8	NA		8
	fmax (MHz)	N/A	445.2	108	223		108
	FPGA Device Stratix V	Stratix V	xc7vx485t	xc7vx485t	xc7vx485t
	ALMs/LUTs	101160	8146	26049	45569	51262
	FFs	13544	2862	12603	33063	20270
	BRAMs	0	0	34	51.5	68
	(N,K)	(1024,512)	(1024,512)	(1024,512) (512,40) (1024,512) (512,40) (1024,512)
	Latency (µs)	4064 3	1177	10.73	11.35	46.41	4.76	18.37
	TP (Mbps)	N/A	0.452	47.7	28.68	88.4	8.4	34.4

1