Juan Antonio Lossio-Ventura

Clement Jonquet
email: jonquet@lirmm.fr

Mathieu Roche
email: mathieu.roche@cirad.frteisseire@teledetection.fr

Maguelonne Teisseire

Integration of Linguistic and Web Information to Improve Biomedical Terminology Extraction

Keywords: H.2.8 [Database Applications]: Data mining; I.2.7 [Natural Language Processing]: Text analysis BioNLP, Text Mining, Web Mining, Automatic Term Extraction

Comprehensive terminology is essential for a community to describe, exchange, and retrieve data. In multiple domain, the explosion of text data produced has reached a level for which automatic terminology extraction and enrichment is mandatory. Automatic Term Extraction (or Recognition) methods use natural language processing to do so. Methods featuring linguistic and statistical aspects as often proposed in the literature, solve some problems related to term extraction as low frequency, complexity of the multi-word term extraction, human effort to validate candidate terms. In contrast, we present two new measures for extracting and ranking muli-word terms from domain-specific corpora, covering the all mentioned problems. In addition we demonstrate how the use of the Web to evaluate the significance of a multi-word term candidate, helps us to outperform precision results obtain on the biomedical GENIA corpus with previous reported measures such as C-value.

INTRODUCTION

The amount of textual documents available on the web is always growing. Nowadays, analysis on such data for finding interesting knowledge is still a challenge. It is even more true when concepts or terms are domain-based (clinical trial description, adverse event report, electronic health records, customer complaint emails or engineers' repair notes [START_REF] Ittoo | Term extraction from sparse, ungrammatical domain-specific documents[END_REF]) as it needs more complex approaches for efficiency purpose. Recently, in the Natural Language Processing community, the automatic extraction of representative patterns from plain texts has been addressed to identify terms or expressions from a given corpus such as the Automatic Term Extraction (ATE), or Automatic Term Recognition methods. The associated applications are lexicon update, domain ontology construction, summarization. Term extraction methods usually involves two main steps. The first step extracts candidates by unithood calculation to qualify a string as a valid term. The second step verifies them through termhood measures to validate their domain specificity. Formally, unithood refers to the degree of strength or stability of syntagmatic combinations and collocations, and termhood is defined as the degree that a linguistic unit is related to domain-specific concepts [START_REF] Kageura | Methods of automatic term recognition: A review[END_REF]. Such terms may be: (i) single-word terms (usually simple to extract), or (ii) multi-word terms (hard to extract). In this paper, we focus on mutli-word term extraction.

There are some known issues with ATE such as: (i) extraction of non valid terms (noise) or missing of relevant terms with low frequency (silence), (ii) extraction of multi-word terms that inevitably have complex and various structures, (iii) human effort spent for validating the candidate terms, which is usually manually performed [START_REF] Conrado | Exploration of a rich feature set for automatic term extraction[END_REF], (iv) application to large-scale corpora. In response to the above problems, we propose two new measures, the first one called LIDFvalue, which is statistic-and linguistic-based measure, which deals with first, second and last previously mentioned issues. And the second one called WAHI, which is a web-based measure which deals with the three firsts issues. The novelty of the LIDF-value measure is an enhance consideration of term unithood, by computing a degree of quality for term unithood. The novelty of the WAHI measure is to be web-based which has never been applied within ATE approaches. In this paper, we compare the quality of the proposed methods with the most used baseline measures despite the difficulty in comparing the ATE measures due to the size of the used corpora, due to the absence of libraries implementing methodologies proposed before. We demonstrate that the use of these two measures improves the automatic extraction of domainspecific terms from text collections that do not offer reliable frequency statistical evidence.

The rest of the paper is organized as follow: We discuss related work in Section 2. The two new measures are described in Section 3. Evaluation of our approach is presented in Section 4 followed with conclusions in Section 5.

RELATED WORK

Several recent studies focused on multi-word (n-grams) and singleword (unigrams) term extraction; existing term extraction techniques can be divided in four broad categories: (i) linguistic, (ii) statistical, (iii) machine learning, and (iv) hybrid. All of these techniques are enclosed in text mining approaches. Existing web techniques have not been applied to ATE, but as we will see, such techniques can be adapted for such purpose.

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Text Mining approaches

Linguistic approaches: Linguistic approaches attempt to recover terms thanks to the formation of syntactic patterns. The main idea is to build rules in order to describe naming structures for different classes using orthographic, lexical, or morphosyntactic characteristics, for instance [START_REF] Gaizauskas | Term recognition and classification in biological science journal articles[END_REF]. As explained in [START_REF] Krauthammer | Term identification in the biomedical literature[END_REF], the main approach is to (typically manually) develop rules that describe common naming structures for certain term classes using either orthographic or lexical clues, or more complex morpho-syntactic features.

Statistical methods: The statistical techniques chiefly rely on external evidence presented through surrounding (contextual) information. Statistical approaches mainly address the recognition of general terms [START_REF] Van Eck | Automatic term identification for bibliometric mapping[END_REF]. The most basic measure is frequency. For instances, term frequency (tf) counts the frequency of a term in the corpus; document frequency (df) counts the number of documents where term occurs; average term frequency (atf), which is tf df . A comparable research topic, called Automatic Keyword Extraction (AKE), identifies the processes of extracting the most relevant words or phrases in a document. AKE measures are often use for automatic indexing. Keywords, which we define as a sequence of one or more words, provide a compact representation of a document content. IN previous publications, we demonstrated that AKE measures can be adapted for term extraction from a corpus and obtained significant results [START_REF] Lossio-Ventura | Combining c-value and keyword extraction methods for biomedical terms extraction[END_REF] [START_REF] Lossio-Ventura | Biomedical terminology extraction: A new combination of statistical and web mining approaches[END_REF]. We have taken two popular AKE measures, Okapi BM25 and TF-IDF (also called weighting measures) to extract biomedical terms. One might also mention residual inverse document frequency (RIDF), which compares df to another model of chance where terms with a particular term frequency are distributed randomly throughout the collection. In addition, Chi-square [START_REF] Matsuo | Keyword extraction from a single document using word co-occurrence statistical information[END_REF] computes how selectively words and phrases co-occur within the same sentences as a particular subset of frequent terms in the document text, it is applied to determine the bias of word co-occurrences in the document text which is then used to rank words and phrases as keywords of the document. Finally, RAKE [START_REF] Rose | Automatic keyword extraction from individual documents[END_REF] hypotheses that keywords usually consist of multiple words and usually do not contain punctuation or stop words and uses the information of word co-occurrences to determine the keywords.determine the keywords.

Machine Learning: Machine Learning systems are usually designed for a specific class of entities and integrate term recognition and term classification. Machine Learning systems use training data to learn useful features for term recognition and classification, but the existence of reliable training resources is one of the main problems as they are not widely available. Some proposed ATE approach uses machine learning [START_REF] Foo | Using machine learning to perform automatic term recognition[END_REF] [23] [START_REF] Newman | Bayesian text segmentation for index term identification and keyphrase extraction[END_REF]. Although machine learning may also generate noise and silence, it facilitates the use of a number of TCs and their features, but the main challenge is to select a set of discriminating features that can be used for accurate recognition (and classification) of term instances.

Hybrid methods: Several approaches combine different methods (typically linguistic and statistical) for term extraction. Glos-sEx [START_REF] Kozakov | Glossary extraction and knowledge in large organisations via semantic web technologies[END_REF] is a method that considers the probability of the word in a domain corpus divided by the probability of the same word in a general corpus, moreover, the importance of the word is increased according to its frequency in the domain corpus. Weirdness [START_REF] Ahmad | University of surrey participation in trec8: Weirdness indexing for logical document extrapolation and retrieval (wilder)[END_REF] considers that the distribution of words in a specific domain corpus is different from the word distribution in a general corpus. C/NCvalue [START_REF] Frantzi | Automatic recognition of multi-word terms: the c-value/nc-value method[END_REF], combines statistical and linguistic information for the extraction of multi-word and nested terms, it the most well known in the literature. While most studies address specific types of entities, C/NC-value is a domain-independent method. It was also used for recognizing terms from biomedical literature [START_REF] Hliaoutakis | The amtex approach in the medical document indexing and retrieval application[END_REF] [START_REF] Lossio-Ventura | Combining c-value and keyword extraction methods for biomedical terms extraction[END_REF]. In [START_REF] Zhang | A Comparative Evaluation of Term Recognition Algorithms[END_REF], authors show that C-value has the best results compared to other measures cited above. Another measure is F-TFIDF-C [START_REF] Lossio-Ventura | Biomedical terminology extraction: A new combination of statistical and web mining approaches[END_REF] that combines an ATE measure (C-value) and an AKE measure (TF-IDF) to extract terms obtaining better results than C-value. Also, the C-value measure was also applied to many different languages besides English, such as Japanese, Serbian, Slovenian, Polish, Chinese [START_REF] Ji | Chinese terminology extraction using window-based contextual information[END_REF], Spanish [START_REF] Barron-Cedeno | An improved automatic term recognition method for spanish[END_REF], Arabic, and French [START_REF] Lossio-Ventura | Combining c-value and keyword extraction methods for biomedical terms extraction[END_REF]. That is why we take C-value and F-TFIDF-C as baselines for comparison in our experiments.

Web Mining approaches

Different web mining studies focus on semantic similarity, semantic relatedness. It means to quantify the degree in which some words are related, considering not only similarity but any possible semantic relationship among them. The word association measures can be divided in three categories [START_REF] Chaudhari | Lexical co-occurrence, statistical significance, and word association[END_REF]: (i) Co-occurrence measures that rely on co-occurrence frequencies of both words in a corpus, (ii) Distributional similarity-based measures that characterize a word by the distribution of other words around it, and (iii) Knowledge-based measures that use knowledge-sources like thesauri, semantic networks, or taxonomies. In this paper, we focus on co-occurrence measures, because our goal is to extract multi-word terms and we suggest to compute a degree of association between words composing a term. Word association measures are used in several domains like ecology, psychology, medicine, and language processing and recently studied in [START_REF] Pantel | Web-scale distributional similarity and entity set expansion[END_REF] [START_REF] Zadeh | Dimension independent similarity computation[END_REF], such as Dice, Jaccard, Overlap, Cosine. Another measure to compute the association between words using web search engines results is the Normalized Google Distance [START_REF] Cilibrasi | The google similarity distance. Knowledge and Data Engineering[END_REF], which relies on the number of times words cooccur in the document indexed by an information retrieval system. In this study, we compare our results obtained with our web-based measure with basic measures (Dice, Jaccard, Overlap, Cosine).

OUR APPROACH

3.1 A new ranking measure based on linguistic and statistical information

Linguistic-based measure

The objective of this measure is to give a higher importance to the term unithood than the measures cited in the state-of-the-art, in order to detect terms that have low frequency. Part-of-Speech (POS) tagging is the process of assigning each word in a text to its grammatical category (e.g., noun, adjective). This process is performed based on the definition of the word or on the context which it appears in.

As previously cited work, we suppose terms of a domain have similar syntactic structure. Therefore, we build a list of the most common linguistic patterns according the syntactic structure of technical terms present in a dictionary, in our case, UMLS1 , which is a set of references biomedical terminologies. We do part-of-speech tagging of the domain dictionary using the Stanford CoreNLP API (POS tagging) 2 , then compute the frequency of syntactic structures. We then choose the 200 highest frequencies to build the list of patterns and we compute the following weight: the probability a candidate term may have of being a domain term if its syntactic structure appears in the linguistic pattern list . The number of terms used to build these lists of patterns was 2 300 000.

Statistical-based measure

The objective of our measure called LIDF-value (Linguisitic patterns, IDF, and C-value information) is to compute the termhood for each term, using the probability calculated previously, also with the idf , and the C-value of each term. The inverse document frequency (idf ) is a measure of whether the term is common or rare across all documents. It is obtained by dividing the total number of documents by the number of documents containing the term, and then taking the logarithm of that quotient. The probability and the idf improve the extraction of terms with low frequency. In addition, the C-value measure is based on term frequency of terms. The aim of the C-value is to improve the extraction of nested terms, i.e., this criteria favors a candidate term that not appears often in a longer term. For instance, in a specialized corpus (Ophthalmology), the Ananiadou et al. [START_REF] Frantzi | Automatic recognition of multi-word terms: the c-value/nc-value method[END_REF] found the irrelevant term soft contact while the frequent and longer term soft contact lens is relevant.

We combined these different statistical information (i.e., probability of linguisitic patterns, C-value, idf ) to propose a global ranking measure called LIDF-value (formula 1). A represents multi-word term; P(ALP ) is the probability of A which has the same linguistic structure of linguistic pattern LP , it means the weight of the linguistic pattern LP computed in Subsection 3.1.1.

LIDF -value(A)=P(ALP ) × idf (A) × C-value(A) (1)

So, to compute LIDF-value we execute three step:

(1) Part-of-Speech tagging: we apply part-of-speech to the whole corpus, after we take the lemma of each word. (2) Candidate terms extraction: before applying any measures we filter out the content of our input corpus using patterns previously computed. We select only the terms which syntactic structure is in the patterns list. (3) Ranking of candidate terms: finally we compute the value of LIDF-value for each term. In order to improve the ranking, we propose, in the following subsection, to take into account web information to highlight relevant terms.

A new Web ranking measure

Previous studies of web mining approaches query the Web via search engines to measure association of words. This enables to measure the association of words composing a term (e.g., 'soft', 'contact', and 'lens' that compose the relevant term 'soft contact lens'). To measure this association, our web-mining approach takes into account the number of pages provided by search engines (i.e., number of hits). Our Web-based measure has for objective to rerank the list obtained previously with the LIDF-value measure. We will show that it enables improving the precision of the k first terms extracted (see Section 4) and that it is specially appropriated for multi-word term extraction. The Dice's coefficient used in our approach computes some kind of relationship between two words called a co-occurrence. This measure is defined by the following formula:

D(x, y)= 2 × P (x, y) P (x)+P (y) (2)
Formula 2 leads directly to formula 3. 3 The nb function used in formula 3 represents the number of pages provided by search engines (i.e., Yahoo and Bing). With this measure, we compute a strict dependence (i.e., neighboring words by using the operator ' " ' of search engines). For instance, x might represent the word 'soft' and y the word 'contact' in order to calculate the association measure of 'soft contact' term.

Dice(x, y)= 2 × nb("xy") nb(x)+nb(y) (3)
In a natural way, we extend this approach to n elements as follows:

Dice(a1,...,an)= n × nb("a1 ... an") nb(a1)+... + nb(an)

This measure enables to calculate a score for all multi-word terms, such as 'soft contact lens'. Moreover, we associate Dice criteria with another association measure called WebR (see formula 5 and [START_REF] Lossio-Ventura | Biomedical terminology extraction: A new combination of statistical and web mining approaches[END_REF]). This one takes only into account the number of web pages containing all the words of the terms by using operators ""and AND.

WebR(A)= nb("A") nb(A) (5)
Where A = multi-word term, ai ∈ A and ai = {noun, adjective, foreign word}.

For the example of term 'soft contact lens', the numerator corresponds to the number of web pages with the query "soft contact lens", and for the denominator we consider the query soft AND contact AND lens.

Finally the global ranking approach combining Dice and WebR is given by WAHI measure (Web Association based on Hits Information):

WAHI(A)= n × nb("A") n X i=1 nb(ai) × nb("A") nb(A) (6)
We will show that open-domain (general) resources, such as web, can be exploited to support domain-specific term extraction. Thus, they can be used to compensate for the unavailability of domainspecific resources.

EXPERIMENTS AND RESULTS

Data, Protocol, and Validation

We use GENIA 4 corpus, which is made up of the 2 000 titles and abstracts of journal articles that have been taken from the Medline database, with more than 400 000 words. GENIA corpus contains linguistic expressions referring to entities of interest in molecular biology such as proteins, genes and cells. The GENIA technical term annotation covers the identification of physical biological entities as well as other important terms. Whereas the Medline indexes broad range of academic articles covering the general or specific domains of life sciences, GENIA is intended to cover a smaller 3 by writing P (x)= nb(x) nb_total , P (y)= nb(y) nb_total , P (x, y)= nb(x,y) nb_total subject domain: biological reactions concerning transcription factors in human blood cells. Then in order to automatically validate and to cover medical terms, we create a dictionary that contains every term of UMLS as well as all the GENIA technical term. We can now evaluate precision with a proper reference for valid terms.

Results

Results are evaluated in terms of precision obtained over the top k terms (P @k) for the two measures presented in previous section. The following sections show part of the experiment results done for multi-word terms and considering the top k extracted terms. In the following, we keep only the first 1000 extracted terms.

LIDF-value results

Table 2 compares the results of C-value, F-TFIDF-C, with our new measure LIDF-value. Best precision results were obtained with LIDF-value whatever k. This table proves that LIDF-value is better than the baseline measures with a gain in precision that reaches 11% for the first hundred extracted multi-word terms. This result is particularly positive because it is often more interesting to extract multi-word terms because is quite complex.

C-value

Web Mining results

Our web mining approach is applied at the end of the process, with only the first 1 000 terms extracted during the previous linguistic and statistic measures. The main reason for this limitation is the limited number of automatic queries one can make to search engines. At this step, the objective is to re-rank the 1 000 terms trying to improve the precision by intervals. Each measure listed in Table 3 and Table 4 shows the precision obtained after re-ranking. We experimented WAHI with Yahoo and Bing search engines. Table 3 and Table 4 prove that WAHI (either using Yahoo or Bing) is well adapted for ATE and this measure obtains better precision results than the baselines measures for word association.

Summary

LIDF-value obtains the best precision results for multi-word term extraction and for each index term extraction (n-gram) and for intervals. Table 5 presents the precision comparison of our two measures. In terms of overall precision, our results produce consistent results from GENIA corpus. WAHI based on Yahoo obtains best precision (i.e., 90%) for the first P @100. In comparison WAHI based on Bing obtains a precision of 80%. For the other interval,

CONCLUSIONS AND FUTURE WORK

The paper presents two measures for automatic multi-word term extraction. The first one, a linguistic-and statistic-measure, LIDFvalue, improves precision of automatic term extraction in comparison with the most popular term extraction measure. Our approach overcomes the lack of frequency information with the values of linguistic pattern probability and idf. The second one is a webbased measure. This measure called WAHI takes as input the list of terms obtained with LIDF-value. WAHI enables to reduce the huge human effort for validating candidate terms. Our experiments re-

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veal that LIDF-value outperforms a state-of-the-art reference measures for extracting terms in the biomedical domain, at least on the GENIA corpus. We experimentally show LIDF-value returns best results in comparison with baseline measures (i.e. C-value and F-TFIDF-C) performing as well for index term extraction (ngram). Moreover our experimental evaluations reveal that WAHI improves the results given with LIDF-value. A future extension of this work consists in using the Web to extract more terms than those extracted. Moreover, we project to test this general approach on other domains, such as ecology and agronomy. Finally we plan to experiment our proposals on French and Spanish corpora as well as on other English corpora.

Table 1 :

 1 Table 1 illustrates the computation of the linguistic pattern probability. Example of pattern construction (where NN is a noun, IN a preposition or subordinating conjunction, JJ an adjective, and CD a cardinal number)

	Pattern	Frequency	Probability
	NN IN JJ NN IN JJ NN	3006	3006/4113 = 0.73
	NN CD NN NN NN	1107	1107/4113 = 0.27
		4113	1.00

Table 2 :

 2 Precision comparison with baseline measures

			F -TFIDF-C LIDF-value
	P@100	0.730	0.770	0.840
	P@200	0.715	0.725	0.790
	P@300	0.730	0.723	0.780
	P@400	0.697	0.705	0.757
	P@500	0.674	0.694	0.752
	P@600	0.670	0.687	0.765
	P@700	0.661	0.686	0.761
	P@800	0.644	0.669	0.755
	P@900	0.641	0.649	0.757
	P@1000	0.635	0.637	0.746
	P@2000	0.601	0.582	0.708
	P@5000	0.530	0.513	0.625
	P@10000	0.459	0.439	0.574
	P@20000	0.382	0.335	0.416

Table 5

 5 shows that in general WAHI based on Bing has the best results. That is very encouraging, because it helps to alleviate the

		WAHI Dice Jaccard Cosine Overlap
	P@100	0.900	0.720	0.720	0.76	0.730
	P@200	0.800	0.775	0.770	0.740	0.765
	P@300	0.800	0.783	0.780	0.767	0.753
	P@400	0.800	0.770	0.765	0.770	0.740
	P@500	0.820	0.764	0.754	0.762	0.738
	P@600	0.767	0.748	0.740	0.765	0.748
	P@700	0.786	0.747	0.744	0.747	0.757
	P@800	0.775	0.752	0.7463	0.740	0.760
	P@900	0.756	0.749	0.747	0.749	0.747
	P@1000	0.746	0.746	0.746	0.746	0.746
	Table 3: Precision comparison of WAHI with YAHOO and word
	association measures
		WAHI	Dice	Jaccard Cosine Overlap
	P@100	0.800	0.740	0.730	0.680	0.650
	P@200	0.800	0.775	0.775	0.735	0.705
	P@300	0.800	0.770	0.763	0.740	0.713
	P@400	0.800	0.765	0.765	0.752	0.712
	P@500	0.800	0.760	0.762	0.758	0.726
	P@600	0.817	0.753	0.752	0.753	0.743
	P@700	0.814	0.7514	0.751	0.733	0.749
	P@800	0.775	0.745	0.747	0.741	0.754
	P@900	0.778	0.747	0.748	0.742	0.748
	P@1000	0.746	0.746	0.746	0.746	0.746

Table 4 :

 4 Precision comparison of WAHI with BING and word association measures manual validation of candidate terms. The performance of WAHI depends of search engine because the associated are different. Then the number of hits returned by these will be different for each case. Moreover, Table5highlights that re-ranking with WAHI enables to increase the precision of LIDF-value. The purpose for which this web-mining measure was created, has been reached.

		LIDF -value WAHI WAHI
			(Bing) (Yahoo)
	P@100	0.840	0.800	0.900
	P@200	0.790	0.800	0.800
	P@300	0.780	0.800	0.800
	P@400	0.757	0.800	0.800
	P@500	0.752	0.800	0.820
	P@600	0.765	0.817	0.767
	P@700	0.761	0.814	0.786
	P@800	0.755	0.775	0.775
	P@900	0.757	0.778	0.756
	P@1000	0.746	0.746	0.746

Table 5 :

 5 Precision comparison LIDF-value and WAHI

http://www.nlm.nih.gov/research/umls

http://nlp.stanford.edu/software/corenlp. shtml IDEAS14

http://www.nactem.ac.uk/genia/ genia-corpus/term-corpus IDEAS14

ACKNOWLEDGMENTS

This work was supported in part by the French National Research Agency under JCJC program, grant ANR-12-JS02-01001, as well as by University Montpellier 2, CNRS and IBC project.

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Nathalie Lazaric

Jun Jin

Ali Douai

Cécile Ayerbe

Role of Users in the Developing Eco-Innovation: Comparative case research in China and France

Keywords: eco-innovation, user, e-bike, France, China

This article proposes a model of eco-innovation that emphasizes the role of users and regulation in the development and diffusion of eco-innovation products, by comparing the diffusion of two e-bike companies, CEP and Lvyan, from China and France. These cases show that diffusion of eco-innovation in China and France is strongly linked to the institutional context and specific consumer needs, highlighting the importance of involving users in the development and diffusion of eco-innovation in order to satisfy market demand, and increase profit and competitiveness in niche markets. It also shows that, to achieve a comprehensive picture, institutions and policy makers should adopt a coevolutionary approach to regulation that includes consideration of technology, uses and practices. The case of CEP reveals that regulation appropriate to the market fosters companies" eco-innovation; compared to the case of Lvyan which shows that irrelevant regulation can become a barrier to the diffusion of ecoinnovations such as the e-bikes. The superior "snob effects" of the French market are discussed and compared with the "bandwagons effects" noted in the Chinese market.

INTRODUCTION

How can we spur eco-innovation in order to improve its adoption and diffusion?

Underlying this question, is the continual balance that policy makers try to achieve between promoting eco-innovations and supporting the companies that are part of this process, and satisfying users. Policy makers, eco-innovation firms and users are the key actors in the dynamic described in [START_REF] Rennings | Redefining innovation: Eco-innovation research and the contribution from ecological economics[END_REF], and the main problem is predicting ex ante elements in the national system of innovation (NSI) that coevolve and may be mutually reinforcing [Foxon, T. 2011]. For instance, in the field of transportation, the visions of China and Western countries differ in relation to governance of the ecosystem, based on prior institutional contexts (i.e. transportation behavior history, user practices, biking behavior, attitudes to energy use, individual values and leisure activities, market size, etc.). Beyond historical differences in institutional contexts, a main driver of eco-innovation is the potential increasing returns of adoption that accompany increased demand [START_REF] Arthur | Competing technologies, Increasing Returns and Lock in by Historical Events[END_REF]]. However, the diffusion of eco-innovation is not driven automatically, and users play an important role. We argue that the role of the user in eco-innovation has been underinvestigated despite its significance. We are interested in the role of users in ecoinnovation in different countries, and the institutions and others mechanisms that shape users" practices.

We apply user innovation theories, and apply the case of diffusion of e-bikes in China and France to observe and compare the role of user practices in eco-innovation in these two countries [START_REF] Hippel (von | Lead Users: A Source of Novel Product Concepts[END_REF][START_REF] Nelson | Technology, Institutions and Economic Growth[END_REF]Witt, U.2001;[START_REF] Maréchal | Overcoming inertia: insights from evolutionary economics into improved energy and climate policies[END_REF]. Both countries consume in different ways, engage in different leisure activities, have distinct environmental values, and different transportation histories. The consumption of goods was discussed by [START_REF] Leibenstein | Bandwagon, Snob, and Veblen Effects in the Theory of Consumers' Demand[END_REF] who outlined potential "snob effects" that may confine some goods and services to being luxury products, and "bandwagons effects" that cause demand to rise with increasing consumption of a product. As consumption increases, even initially reluctant followers will begin to espouse new behaviors and new forms of consumption thereby enforcing some kind of in-group cohesion, mirroring the psychological theory of normative conformity [START_REF] Leibenstein | Bandwagon, Snob, and Veblen Effects in the Theory of Consumers' Demand[END_REF]Rogers, E. 1962;Kahle and Kim, 2006].

The case of the development of e-bikes provides a good example of this dynamic; electromobility shows a distinct development path which is linked to transportation, the presence of infrastructures, an also values and practices related to biking.

Comparing France and China could be informative for the governance of ecoinnovation. Despite some clear divergences between these two countries mainly related to market size, both nations adopt some common means to push technologies, and in both the role of policy makers and important public-private partnerships to promote technologies and uses are crucial [START_REF] Altenburg | Sustainability-oriented innovation in the automobile industry: advancing electromobility in China, France, Germany and India[END_REF].

The paper is organized in five sections. Section 2 provides a framework for the research on eco-innovation based on a review of the literature. Section 3 describes the historical context promoting eco-innovation and presents the research method used to observe the development of two business strategies related to e-bikes, shaped by users and institutions. The role of users is analyzed in Section 4 and Section 5 discusses some implications of this research.

SECTION 2. RESEARCH FRAMEWORK FOR OBSERVING ECO-INNOVATION

Three Pillars of Eco Innovations

Rennings"s insights might suggest that eco-innovation is similar to classical innovations in that they "can be developed by firms or non-profit organizations, they can be traded on markets or not, their nature can be technological, organizational, social or institutional" [Rennings, K. 2000: p.322]. From this perspective, ecoinnovation should not be viewed as isolated but as the product of social innovation and institutional innovation because "effective environmental policy requires understanding not only technological but also lifestyle dynamics'" [Duchin, 1999cited in Rennings K., 2000: p.323]. Thus in the context of sustainable development, eco-innovation should not be reduced to a solely Schumpeterian dynamic in which technological factors are prominent, but should integrate demand -such as lifestyles, values, preferences -and the way it is mediated via social institutions. In this context, the path to adoption of eco-innovation, and its wide diffusion may be more complicated compared to "other kinds of innovation" because "eco-innovations are, in contrast to such technologies as microelectronics and telecommunications, normally not self-enforcing. Because factors of technology push and market pull alone do not seem to be strong enough, eco-innovations need specific regulatory support." [Rennings, 2000: p. 326].

Eco-innovators must also be aware of regulation and grasp potential opportunities enabled by the introduction of new rules. Eco-innovation has three main dimensions, technological, market, and regulatory, which coevolve and promote its diffusion. The technological and demand components of eco-innovation are important but not always sufficient; for example a large contribution to emissions reduction was triggered by environmental regulations that affected the decisions of households and their practices [START_REF] Jorgenson | Cap and trade climate policy and U.S. economic adjustments[END_REF][START_REF] Popp | The effect of new technology on energy consumption[END_REF][START_REF] Bergh (van Den | Environmental innovation and societal transitions: Introduction and overview[END_REF].

These considerations led to the definition of an eco-innovation in the context of the European Project MEI (Measuring Eco-Innovation): "The production assimilation or exploitation of a product, production process, service or management or business methods that is novel to the organization ( developing or adopting it) and which results, throughout its life cycle , in a reduction of environmental risk pollution another negative impacts of resources use (including energy use) compared to relevant alternatives" [Kemp K. and Pearson, P., 2008: p.7]. This definition embraces all those innovations that enable a firm to reduce its negative environmental impact through products, services or methods. Given the numerous environmental criteria, it is difficult to assess the global environmental impact, and because of potential rebound effects, use of an eco-innovation may not lead to an absolute reduction in environmental harm. Thus, eco-innovations cannot be considered in relation to their absolute environmental impact, but must be considered more broadly in terms of their relative impact compared to alternative technologies.

Scrutinizing the Role of Demand and Lead Users

Economic analysis of eco-innovation is based mainly on evolutionary theory, especially the bounded rationality of agents, system failures such as lock-in and unpredictability, and network interactions among agents [START_REF] Bergh (van Den | Environmental innovation and societal transitions: Introduction and overview[END_REF]. We draw also on sociological theories of consumption, and classical views of demand such as the insights developed by [START_REF] Leibenstein | Bandwagon, Snob, and Veblen Effects in the Theory of Consumers' Demand[END_REF], to enable a richer understanding of the development of eco-innovation and its diffusion. Eco-innovation should not be confined to niche markets and use. In order to increase adoption, social mechanisms are critical, notably to the presence of a critical mass of specific users to increase this process.

The sociologic literature on demand emphasizes the role of social norms and status seeking behaviors. The literature on status-seeking consumption originated with Veblen"s [1994Veblen"s [ , 1899] ] work on "conspicuous consumption" and Duesenberry"s [1949] "relative income hypothesis". In contrast to the individual maximization of utility according to exogenous preferences, as posited by the neoclassical approach, Veblen developed an evolutionary framework where preferences are generated and shaped in interaction with the social structure. According to Veblen"s theories, individuals emulate the consumption patterns of other individuals at higher levels in the social hierarchy [START_REF] Trigg | Veblen, Bourdieu and Conspicuous Consumption[END_REF]. Social norms appear, or what Veblen called conspicuous consumption, are important for explaining how consumption is used to gain and signal social status. More recently, the importance of social norms for environmentally responsible behaviors has been documented, and social context and personal norms appear to be relevant for transport issues [START_REF] Bamberg | Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour[END_REF]].

Veblen"s insights have been applied in economics to demonstrate inertia in demand and consumption [START_REF] Duesenberry | Income Saving and the theory of consumer Behaviour[END_REF] and the existence of externalities and the bandwagon effect described by [START_REF] Leibenstein | Bandwagon, Snob, and Veblen Effects in the Theory of Consumers' Demand[END_REF]. Increasing returns from adoption are necessary for the development of these externalities. As has been shown in the case of innovation, the scale of adoption is determined largely by the success of a novelty, while positive externalities are generated as soon as the newness extends beyond a restricted community of users [Arthur, B. 1988;[START_REF] Maréchal | Overcoming inertia: insights from evolutionary economics into improved energy and climate policies[END_REF]. Network externalities are generated by increasing returns from information [START_REF] Katz | Network Externalities, Competition, and Compatibility[END_REF]: being surrounded by people who have adopted new behaviors and new practices reinforces the willingness to adopt similar behaviors, through a contagion effect. From a different standpoint but producing rather similar outcomes, network externalities have been discussed in sociology [e.g. Campbell, 1997 andBourdieu, 1984] to explain importance of "the social mirror" [see also [START_REF] Trigg | Veblen, Bourdieu and Conspicuous Consumption[END_REF]. Also, [START_REF] Witt | Can Sustainable Consumption Be Learned[END_REF] suggests that consumers acquire new behavioral repertoires either by individual trial-and-error learning, or by communicating with and imitating others (social learning process). The latter is very important for ecoinnovation which is characterized by uncertainty compared to "normal innovation".

Consequently, the dynamic of imitation and adoption is extremely dependent on the "early adopters" who may trigger a larger dynamic depending on their positions in the social structure [Wörsdorfer and Kaus, 2011]. Early adopters are those consumers who play the role of opinion leaders and contribute to the dissemination of information concerning the new product. They have the potential to mobilize other groups of buyers who may react by delaying adoption or "jump onto the bandwagon" and become part of an "emergent majority" [Wörsdorfer and Kaus, 2011]. Early adopters play a critical role by enrolling "laggards" who are more skeptical and more reluctant to adopt novelty because of its unfamiliarity compared to their current consumption. Early adopters in Rogers"s (DATE) terminology or "lead users" according to von Hippel [1986,2005], are decisive for diffusing information and enhancing imitation. Von Hippel describes them as highly motivated adopters with significant intrinsic motivation, able to decrease the level of doubt inherent in novelty.

Indeed, under-used innovation requires lead users to reduce risk aversion and diffuse information on the characteristics of the good or service. However, as specific actors who have vested interests, their enrolment is not neutral. Indeed, "lead users" are far in advance of the general market and they expect direct personal benefit from the innovation are positioned to benefit significantly by obtaining a solution to those needs" [von Hippel, E. 1986: p. 796]. Consequently they can orient technical solutions according to their specific and personal needs which may be in harmony with or detrimental to consumers" requirements. For instance, leads users in France and China may support different visions of the product and may push their design towards some technological trajectories that are decisive for design, perception, etc.

For this reason, in the context of biking, [von Hippel, E. 2005: p. 957] notes that: "users developing innovations reported that they gained a high personal benefit from using their innovations in their own mountain biking activities". If lead users anticipate large benefits from a solution that fits their specific and local needs, producers may be trapped by these needs into supplying niches thus impeding the wider diffusion of eco-innovation. In short, lead users are decisive actors that can constrain the development of the product according their own vision of ecoinnovation. Thus they may create potential snob effects rather than the bandwagons effects needed for broader diffusion.

Users, institutions and beyond

User practices appear to be critical for eco-innovation especially in the emerging market phase when an understanding of users" needs is required to shape the future product or service. Users are rarely isolated; their needs are intertwined within institutions, business strategies, technologies, and ecosystems. These factors coevolve to produce a model for eco-innovation, defining its boundaries and content. For instance, regulation such as the maximum speed authorized for e-bikes (between 20km/h and 26km/h) may have a critical impact on business strategies and the path of this eco-innovation because users in China could switch to another option such as scooters or some other two wheeled vehicle. Institutions can both constrain and enable human interactions. Nelson [Nelson R.R., 2005] defines institutions as "social technologies", i.e. ways of structuring and organizing human interactions. In this perspective, institutions express socially acceptable ways of acting, and enable behavior by providing social contexts for acting which do not need to be continuously negotiated. [START_REF] Foxon | A coevolutionary framework for analysing a transition to a sustainable low carbon economy[END_REF] argues-after [START_REF] Beinhocker | The Origin of Wealth: Evolution, Complexity and the Radical Remaking of Economics[END_REF]that "the coevolution of physical technologies, social technologies and business plans has driven the creation of wealth in Western industrialized countries, crucially through the development of property-right based market economies which encourage the innovation of physical and social technologies for more efficiently and effectively meeting (and creating) consumer demands " [Foxon, T. 2011[Foxon, T. , p. 2261]].

Coevolution is defined by Murmann [Murmann, J.P. 2003: p.22] as a process of causal and cumulative influence that has an impact on the final system, i.e." two evolving populations coevolve if and only if they both have a significant causal impact on each other's ability to persist". This societal embedding is reflected in the capacity of the institutional framework to influence user practices relating to the use of the new technology but also, more generally, the ability of each NSI to define key technological trajectories that have an impact on business strategies. E-bike products and services are linked to position of e-mobility in the NSI which delineates the interactions between vehicles and energy systems, and the interface between the production of innovative electric vehicles and urban mobility planning [START_REF] Altenburg | Sustainability-oriented innovation in the automobile industry: advancing electromobility in China, France, Germany and India[END_REF]. The coevolution of these various systems is summarized in Figure 1. In this perspective, France and China have some similarities related to spurring increasing returns from adoption of e-vehicles; both have created large public-private partnerships to promote their e-mobility innovation policies: "the distinctive feature of the French strategy for electric vehicles is the key role of the state and national programmes. The orientations and selected players are defined by administration" [OECD, 2011, p.208]. China"s strategy related to e-vehicles seems to be in the direction of developing indigenous technology. However both countries have in common long term government involvement to promote battery technology and lower battery costs in order to achieve mass production and lower overall costs. China has a much larger market than France which has shaped its user practices and technologies.

Indeed "China has the largest market for e-bikes in the world. This market is supplied by Chinese firms based on mainly indigenous technology. It also accustomed Chinese consumers of the idea of e-mobility" [Altenburg T. et al., 2012, p. 11]. We study this divergence at the macro level to observe how institutions shape business strategies and user practices, and at the meso level to show how two firms, in France and China, coevolved according to various causal influences.

SECTION 3. USERS PRACTICES AND INSTITUTIONS AS SEEDS FOR ECO-INNOVATIONS

E-bike market in China and France

The institutional contexts for the development if e-bikes in China and France are completely different. Perception and use of e-bikes rely on historical attitudes to bicycling. China is the largest producer and the largest consumer of bicycles in the world. But the adoption of bicycles has followed different development trajectories.

The rapid growth in use of bicycles occurred during the Maoist period, and increased with improvements in living conditions. This growth was disrupted during the 1990s when use of automobiles increased [Allaire, G., 2007] In the 2000s, the increased automobile traffic in big cities, and congestions problems, have promoted renewed interest in bicycles and opened opportunities for e-bikes in various provinces and cities. In 1998, 40,000 e-bikes were sold in China, this rose to 10 million in 2005 with the number of users calculated as 22 million (with 1 million in Shanghai alone). In 2011, the size of the e-bike market in China was predicted to be 23 million compared to Europe at 1.35million [Presto report, 2010].

During the 1990s, e-bikes were promoted as a clean and cheap mode of transportation by China"s local governments in a bid to reduce congestion in its urban areas. The price of e-bikes is much lower and more affordable than the price of a car, and the speed that can be achieved by an e-bike is much faster than that of an ordinary bicycle.

Thus e-bikes were welcomed by rural-urban immigrants and citizens and gained an increasing share of two-wheeled transportation in China. In some cities, such as Chengdu and Suzhou, e-bikes have even exceeded the share of traditional bicycles [START_REF] Weinert | The transition to electric bikes in China: history and key reasons for rapid growth[END_REF]. The national standard for e-bikes was enforced in 1999.

Approved in 2004, the Road Transportation Safety Law describes e-bikes as nonmotorized vehicle, giving their riders the same rights as bicyclists [START_REF] Weinert | The transition to electric bikes in China: history and key reasons for rapid growth[END_REF]. However, in practice this law is applied differently according to the regulations applying to individual cities, and the willingness to promote (or not) this type of transport. Beijing"s local government does not support this eco-innovation while Shanghai and Chengdu are pro-e-bikes. E-bikes call into two categories: scooter style electric bikes and bicycles style electric bikes. The latter use smaller batteries and have lower powered motors, can achieve speeds of 30km/h on average, and weigh between 40kg and 60kg [START_REF] Cherry | Use Characteristics and mode Choice behavior of electric bike users in China[END_REF]. Where e-bikes and traditional bikes use the same cycle lanes, safety is an important issues for e-bike growth. They are much faster but make no sound. In China, many e-bikes and e-scooters can achieve top speeds of around 50km/h and can carry heavy loads. This has led many to associate the e-bike with accidents, crime (theft of both bikes and batteries), and even congestion. Some local regulations prohibit e-bikes or regulate their specification in their cities; e.g. Beijing in 2002, Fuzhou, Shenzhen, and Guangzhou. The recycling of batteries is also a barrier to the development of e-bikes in China. Speed is a focus of new Chinese regulation1 on ebike currently being discussed.

In France, the development of e-bikes is in its infancy and the technology is not well diffused. France has limited development of this eco-innovation with only 47,000 ebikes sold in 2012, compared to sales in the Netherlands of 300,000 in the same period [Figaro Le, 2013]. However the rate of growth of this novelty has been significant in France at more than 20% per year since 2005.

Traditional bicycles have been used for leisure activities in France since the Second World War. Bicycles were considered to be exclusive to weekend and sport activities.

However, there has been a shift to their use in cities and municipalities have introduced policies to promote the reintroduction of bicycles in city centers. Along with production firms they have been the key actors in the new vision of transport and provision of rental systems such as "velo bleu' in Paris and Nice and "Velos v" in Lyon [START_REF] Bouf | The dark side of making transit irresistible: The example of France[END_REF]. Municipalities have been decisive also in providing bike lanes to protect cyclists (especially important in Paris) and promoting new kinds of bicycles (traditional or e-bikes). "Velo v" in Lyon exemplifies this scheme for renting bikes inside cities and promoting new transport services based around cars and bikes.

In line with this "green policy", habits and practices are changing and use of e-bikes in cities is receiving more interest. Employers and municipalities are promoting their use e.g. in Sceaux near Paris and in Monaco and the South of France). Experience is confined mostly to small communities of users and lead users. Despite some interest from users, and the incentives provided by some municipalities, take-off of e-bikes is confined to specific uses. Table 1

compares the development of e-bikes in China and

France.

Table 1 Triggers and Enablers for the Development of E-bikes in China and

Research methods

Case study is considered a suitable methodology for explorative and comparative research [START_REF] Yin | Case study research: Design and methods[END_REF]]. Our aim is to conduct an in-depth investigation and comparison of the role of users in the eco-innovation industries in China and France.

A case study provides an in-depth understanding of a specific context. The case of ebikes was selected, first, the cases illustrate the business strategies related to ecomobility. Second, e-vehicles and thus e-bikes are embedded in the NSI and promote developments related to batteries and infrastructures. Third, user practices shaped by social technologies are decisive for the industry dynamics. Our cases are two producers of e-bikes, Lvyuan in China and CEP in France, which are used to explain the development of e-bikes in these countries, and contribute to a framework for studying ecological innovations. The data were collected during site visits, from semistructured interviews, and in informal meetings with company managers and CEOs.

Secondary information sources include annual reports, press releases, presentations to customers and stakeholders, media material, etc.

Two contrasting paths for e-bikes: co-evolution of users and institutions for shaping e-bike opportunities

Despite quite divergent trends related to market size and diffusion of e-bikes, France and China share similar policy making to promote coordinated investments at central and regional levels. State intervention includes subsidies for the diffusion of evehicles and provision of infrastructures [START_REF] Altenburg | Sustainability-oriented innovation in the automobile industry: advancing electromobility in China, France, Germany and India[END_REF]. China has clear economies of scale advantage and benefits from complementarities between evehicles and e-bikes. In France, the complementarity between e-vehicles and e-bikes is less significant with the result that policy is promoting e-bikes at the regional level but is not being accompanied by local infrastructure initiatives. CEP is an active member of several associations keen on electric transport developments (e.g. AVEM: Association for the Future of the Mediterranean Batterydriven vehicle). These partnerships are critical for actors in the field of electric mobility such as manufacturers of electric vehicles, providers of services and electrical energy (batteries, loading systems…), operators of mobility (suppliers of public transports, car sharing, renters, taxis…) and public institutions (universities, associations, municipalities…). CEP"s business strategy relies on the open model described in [START_REF] Chesbrough | Open Innovation -The New Imperative for Creating and Profiting from Technology[END_REF] which includes key technological partnerships and protection of core competencies via patents. Balancing internal and external activities is critical for innovative firms [START_REF] Berchicci | Towards an open R&D system: internal R&D investment, external knowledge acquisition and innovative performance[END_REF] and is particularly important in emerging markets such as e-bikes. CEP"s strategy also includes interactions with users related to customized docking stations according to specific needs, and for obtaining tacit knowledge for the development of future generations of e-bikes. Thus, its business strategy is co-shaped by permanent feedback from users and lead users used to design new products and to leapfrog technologies for integration in future production.

Lvyuan

CEP"s innovation capacity has been recognized and it has received several national prices in France for innovation and ecological innovation in particular, and has technology patents in the areas of securing and charging e-bikes. It has several patents in a system for recharging and automatic locking by electrically assisted jacks on the front wheels of bikes. CEP"s users are private and public organizations and schools keen to reduce CO 2 emissions and to improve transport for their inhabitants. Most recharging stations are bought by municipalities or firms for either private or public use in particular areas (e.g. for municipal workers).

In 2012, CEP entered in collaboration with Peugeot Scooters to launch a charging station for bikes and scooters. Customers can now choose to use a scooter or an ebike. Table 2 compares the business strategies of Lvyuan and CEP. Based on the cases of Lvyuan and CEP, in this section we discuss the role of users in the adoption and diffusion of eco-innovation, and in companies" competition strategies.

Role of users in the adoption and diffusion of eco-innovation

We use the cases of Lvyuan and CEP to describe the adoption and diffusion of an ecoinnovation. The technological dimension is critical to both Lvyuan"s and CEP"s Concerning the market side, there is growing interest among consumers in China and

France for more environmentally-friendly products. Both individuals and groups in

France are worried about the damage to the environment and are trying to change their ways of consuming [START_REF] Belin | Imitation and social norms in sustainable consumption: Theory and evidence from consumer"s green behaviour in France[END_REF]. Transport is at the center of these efforts which has provided a window of opportunity especially for biking and emobility. Thus, e-bikes benefit from positive social norms [START_REF] Veblen | The Theory of the Leisure Class[END_REF][START_REF] Veblen | The Theory of the Leisure Class[END_REF] which may create externalities in the future and potential bandwagon effects especially in France where electric vehicles leave a smaller carbon footprint than nuclear energy [START_REF] Altenburg | Sustainability-oriented innovation in the automobile industry: advancing electromobility in China, France, Germany and India[END_REF].

In the Chinese case, the success of Lvyuan mirrors the widespread growth of e-bikes in this country as an important alternative to classical vehicles such as automobiles. users who want to hire an e-bike must present their customer card to a card reader.

The bike is then released, and can be returned to any hiring station, where it placed on a vacant stand where it is locked and recharged. Note that in some areas, the same card provides access to bus, electric car-sharing, and electric cycle transport. The combination of design and high technology creates novelty for CEP e-bikes and new inter modality. Each fleet of e-bikes is designed to provide the best "local" solution for the customer. The docking in Monaco can be moved (thus, accommodating to the Formula One Monaco Grand Prix held every May). Thus, the users are the source of technological improvements In France, price is a problem for CEP and other e-bikes companies because the size of the market precludes them from being cheap, and the combination of a small market and new technology developments increases their costs impeding the creation of increasing returns from adoption and economies of scale [Figaro Le, 2013]. The sophistication of its product may confine CEP to a niche strategy and potential snob effects rather than the bandwagons effects required for large adoption.

Unlike the case of CEP, Lvyuan"s customers are individuals rather than organizations.

Therefore, it is not easy to find lead users in the market. Lvyuan defines the market according to user types -ladies, rural-urban immigrants, etc.. For example, Lvyuan develops types of e-bikes for rural-urban immigrants to carry heavy goods, and types of e-bikes that can carry small packages for express couriers. In order to attract ladies, Lvyuan has introduced smaller e-bikes painted in bright colors and attractive patterns.

Although the e-bike target markets of Lvyuan and CEP differ, the latter"s designs for niche markets within their market confirms von Hippel"s [2005] theory of using tacit and local consumer knowledge in the design of eco-products.

Role of users in the companies' strategies

growth of Lvyuan and CEP reveals that users have had an impact not only on the development, adoption, and diffusion of new eco-innovation products but also on these companies" strategies. Lvyuan"s strategy is aimed at individual users, for example. Lvyuan"s lead users might seem difficult to identify compared to CEP"s.

The companies apply different user innovation strategies. For instance, Lvyuan has set up agencies and distributors around China that provide comprehensive after-sales services including maintenance, inspection, repair, and 24-hour telephone customer service, as well as roadside assistance and home repair.

Figure 1 :

 1 Figure 1: Eco-innovations in a coevolutionary framework adapted from Foxon [2011: p.2262]

France

  Lvyuan (meaning "green energy" in Chinese) is one of the oldest and strongest competitors in the Chinese e-bike market. In 1996, Mr. Jie NI recognized e-bikes could be a potentially profitable market after visiting an institute in Beijing that was working on electric vehicles; he founded Lvyuan in 1997 in Jinhua, Zhejiang Province on China"s east coast. In 2012, Lvyuan had six manufacturing bases in China employing more than 4,000, offering a range of e-transport, from scooters to bikes, and producing over 300 parts and accessories for the Chinese market for distribution via individuals or agencies. Lvyuan has the capacity for annual production of 3 million e-bikes in over 300 models, and 5 million batteries for e-bikes. The company"s innovation system is based on several R&D centers in its headquarters, the Fuzhou subsidiary, and other bases. Lvyuan conducts research on electric motors, paints, batteries, etc. Up to March 2011, the firm had 70 Chinese patents and 29 patent applications. In order to improve the quality of its process line, the firm operates also a Just-In-Time system, and uses robots on the production line. Lvyuan publishes technical information manuals as part of its effort to diffuse professional information on e-bikes and to train its employees. The firm has participated in professional committees to formulate a set of e-bike product standards and regulations for the manufacture and use of e-bikes in China[START_REF] Tyfiled | Game Changing China: Lessons from China about Disruptive Low Carbon Innovation[END_REF] and has experienced strong and stable growth since 2001. The brand "Lvyuan " is recognized as a famous Zhejiang brand which was in the top 500 Chinese brands in 2007, and one of the most reliable low carbon brands in 2010. The firm is in the list of the top 50 "fastest-growing" companies published by Fast Company magazine in 2007 and was awarded the "SEE-TNC Enterprise Ecology Award". 2 Lvyuan"s business strategy includes competing in the electric car industry based on its experience and technology in batteries and e-bikes and it has entered a partnership with Beijing University for battery development. CEP Clean Energy Planet or CEP, is an innovative French company committed to sustainable mobility and specialized in the development and implementation of charging stations for e-bikes. This innovative small firm was founded in 2006 and is located in Sophia-Antipolis in the South of France, and is the French leader in twowheeled electric vehicles. In 2007, CEP set up a resellers' network and implemented the first fleets of e-bikes for self-service use in Monaco. In 2008 the company introduced the first electric docking station at the Sophia-Antipolis Technology Park. Currently the firm has more than 15 docking stations located mostly in the South of France, Paris, and Monaco. CEP has forged partnerships with leading technological firms to share knowledge for the development and industrialization of its products.

  development. For instance, CEP"s patented technology has made the company the main actor in electric mobility in France. Similarly, the patents filed by Lvyuan reinforce its place as leader in the Chinese e-bike market. However this technological input is only one facet of the process, namely the technology push dimension. There are other important factors such as the role played by context (regulatory side) and users (market side) in the development and acceptance of the product. Users play an important role in the development, adoption and diffusion of eco-innovation products, e.g. e-bikes.

  Since end November 2008, Lvyuan has emphasized the ecological benefits of e-bikes and e-scooters and their contribution to climate change. The authorities assume that in the near future, the ecological benefits of e-bikes will emerge in the market, especially in cities where there is serious air pollution (e.g. in January 2013 in Beijing). In addition, e-bikes are seen as a solution to traffic congestion in Chinese cities. Despite the effective presence of increasing returns from adoption in China in contrast to France, the local authorities continue to advertise these products heavily. The recent promotion of e-bikes by the Chinese Minister of Technology Wang Gang during President Obama"s visit to China in 2009, shows how much local governments concentrate on may create events by acting on the increasing returns of information and by trying to enroll laggards(Presto, 2010).The cases of CEP and Lvyuan show that users are sources of product/technology innovations and development of eco-innovation products in line with theories of user innovation[START_REF] Hippel (von | Lead Users: A Source of Novel Product Concepts[END_REF][START_REF] Hippel (von | User-innovators and "local" information: the case of mountain biking[END_REF]. For example, lead users in Monaco have pushed the design of e-bikes along a specific luxury goods and services trajectory. These lead users, who were very active in the earlier stages of development, have influenced e-bike design and promoted establishment of docking stations as sophisticated and secure commodities. For this reason, CEP"s products and services remain relatively costly confining their development to small niches such as municipalities, business schools, and luxury hotels, hampering the wider diffusion of eco-innovation. CEP customers are mostly private or public organizations that decide to install charging stations in order to improve transport for their local inhabitants and workers. E-bikes developed by CEP can be locked and recharged in one step. This very simple use and practice was a specific requirement of CEP"s lead users. This concept is unique in offering protection against theft thanks to the drive cylinders, enabling automatic charging when the bicycle is locked on the docking point, and adaptation of to any electric bicycle model. The hiring stations function as follow:

3

  In addition, Lvyuan advertises in the media.In contrast, CEP provides a customized service. It is not confined to development of classic eco-innovation goods such as e-bikes; much of its added value comes from the design of services related to renting bikes. For example, CEP offers automated collection and return of bikes by means of a RFID tag or swipe card. The interface is available in several languages (and use of extranet to manage the fleet remotely). The extranet allows private or public customers to obtain comprehensive monitoring and analysis of their fleet to observe its routes and history. It provides statistics related to use of the bikes and status of each docking point allowing the company to adjust to users" needs. As already noted, CEP not only develops products it also provides services (e.g. extranet) that clearly respond to demand. Users huge benefits from a customized solution which also acts as input to future designs and sophisticated products. In line with von Hippel[2005] eco-innovation in the CEP case is triggered by recurrent and repeated personal experience that allows the accumulation of local knowledge for the design of future products.Users of e-bikes in China enjoy their speed and weight bearing capacity. However, their speed is becoming one of the main causes of traffic accidents and some cities ate banning e-bikes. There is also a central government law that limits e-bikes to a maximum speed of 20km/h and classifies e-bikes exceeding these speeds as emotorcycles, which is introducing uncertainty among users about driving license requirements. Regulation announced in 2009 supposed to come into force in 2010, has so far not been implemented opening a window of opportunity for this eco-innovation at a new authorized speed of 26km/h. To anticipate future problems related to speed, Lvyuan has developed new technology to restrict the maximum the speed, improve the safety of e-bike transport, and reduce the possibility of illegal reequipping of ebikes[Tyfield, D. et al., 2010]. Lvyuan publishes manuals on e-bike standards, and the environmental benefits of e-bikes, and is developing new products for new niche markets, such as e-patrols the local police; it has plans to develop electric cars. It would seem that user behavior can be a barrier to company development, as well as the source of eco-innovation and influence on strategy.SECTION 5. CONCLUSIONSThis paper examined the role of users in the development and diffusion of ecoinnovation and firms" eco-innovation strategies, comparing the cases of Lvyuan and Our cases show that diffusion of eco-innovation in China and France is strongly linked to the context and specific needs of consumers. Market expansion is based on expanding niche markets rather than band wagon effects. To benefit innovative firms such as Lvyuan and CEP have to cope with sticky local information[START_REF] Hippel (von | User-innovators and "local" information: the case of mountain biking[END_REF]. This implies that new product development will rely on the needs of local users which are strong linked to the use environment. This study indicates that it is crucial to involve users during the development and diffusion of eco-innovation in order to match market demand and increase profit and competitiveness in niche markets. It has revealed also that government should consider context, uses, and practices when formulating regulation. The behavior of users can be a barrier to developments (e.g. un-enforced regulation in China) and a driver of eco-innovation.Companies should consider the impact on users of their eco-innovation strategies.Different people have different practices and the interplay with users is essential for a nascent technology to mature. Thus "lead users" who are quick to adopt a new technology (i.e. who look for novelty), are important. As shown in Buenstorf and Cordes[2008], social groups play an important role in introducing change given the tendency for imitation of prestigious individuals. Policy-makers should take account of the role of these groups which create favorable conditions for the emergence of new niches and may facilitate the transition to new eco-innovations. In this context, policy-makers should create conditions that favor the emergence of lead users because these highly, intrinsically motivated individuals can play a decisive role in technological development. Public authorities also play a part in modifying intrinsic and extrinsic motivations through the development of large and visible public investments that may instill new values in social groups[START_REF] Maréchal | Overcoming inertia: insights from evolutionary economics into improved energy and climate policies[END_REF]]. The use of only two comparative cases limits this research. Future work should include more cases, and more field work to test the framework proposed in this paper.

Table 2 : Comparison between business strategies in two institutional contexts

 2

	Comparative Items		Lvyuan (China)		CEP (France)
	Firm"s foundation	1997			2006
	and technological alliances	Strong	partnerships	with	Collaborative agreements with
		Universities or research institute	suppliers and others private
					firms
	Location	Jinhua, China		Sophia Antipolis, France
	Market Demand	A normal transportation method		Sport or a new vision of
					transportation
					Sophisticated	product	and
					service
	Customers	Individual customers		Organizations such as firms,
					schools or municipalities
	Providing Service	Thousands agencies in 29 provinces	Fifteen docking stations in
		in China
		Fleet of bikes but no services
		concerning e-bikes
		Product or services of bikes but no
		renting of e-bikes

The regulation was due to be published in

but has been postponed. The key change in this regulation is to limit e-bikes to a maximum speed of 20km/h and maximum weight of 40kg. E-bikes exceeding these limits will be reclassified as e-motorcycles and will require a driving license.

Data and information available at the official website of Lvyuan (inChinese) :http://www.luyuan.cn/page.php?mod=introduction, accessed 2013-02-12.

Information available from the official website of Lvyuan (in Chinese): http://www.luyuan.cn/page.php?mod=introduction, accessed 2013-02-12.Code de champ modifié

We want to thank participants of the Globalics Conference held in Hangzhou ( 2012). This research is supported by CNSF project: The Research of Reverse Innovation in China (CNSF: 71172111), and a collaborative CNRS project between France and China.