Ph D Projects' Performances

Quentin Plantec
email: quentin.plantec@mines-paristech.fr

Benjamin Cabanes

Pascal Le Masson

Benoit Weil

MARKET-PULL OR RESEARCH PUSH? EFFECTS OF RESEARCH ORIENTATIONS ON UNIVERSITY-INDUSTRY COLLABORATIVE

Keywords: University-Industry collaborations, Ph.D., Academic engagement; Doctoral education

There is abundant literature on the consequences of academic engagement with the industry, on overall scientists' commercialization and scientific performances. Nevertheless, the literature remains silent on how those performances are contingent on the research orientation's choice at the project level. This paper aims to fill this gap by exploring different research orientations in the context of university-industry collaborative Ph.D., a topic of interest as private companies are becoming more involved in Ph.D. training. To do so, we relied on a unique dataset comprising of 635 Collaborative Ph.D. projects through the CIFRE program in France. We classified the projects' ex-ante research directions: market-pull-oriented (MPO), research-push-oriented (RPO), and simultaneous-discovery-invention-oriented (SDI), and we observed their ex-post performances. First, as expected, an orientation towards industry needs conduct to higher commercialization performances. However, counter-intuitively, those projects are also prone to have similar scientific performances than those oriented towards scientific discoveries. Second, while SDI projects were considered over-performing other research orientations, they led to more significant scientific performances than traditional orientations but generated as many patents as MPO projects. Finally, we highlight that initial research orientation is a crucial determinant variable of scientific and commercialization performances, and our paper opens rooms for further research to the literature on academic engagement, university-industry collaborations, and Collaborative Ph.D.

INTRODUCTION

Collaborations between public research laboratories and private companies are of vital interest to favor new scientific knowledge, transfer, and technological innovation (e.g. [START_REF] Etzkowitz | The dynamics of innovation: From National Systems and "mode 2" to a Triple Helix of university-industry-government relations[END_REF][START_REF] Rothaermel | University entrepreneurship: A taxonomy of the literature[END_REF]. To support those, academic have tended to be more extensively engaged 1 with the industry (for a global overview, see [START_REF] Perkmann | Academic engagement and commercialisation: A review of the literature on university-industry relations[END_REF][START_REF] Perkmann | Academic engagement: A review of the literature 2011-2019[END_REF] through various forms of research collaborations.

Academic engagement is widely practiced across disciplines and is of substantial economic significance both for private companies and universities (e.g. [START_REF] Cohen | Links and Impacts: The Influence of Public Research on Industrial R and D[END_REF][START_REF] Hughes | The Changing State of Knowledge Exchange: UK Academic Interactions with External Organisations 2005-2015[END_REF]. Previous research puts great effort into identifying individuals (e.g. [START_REF] Tartari | Set them free: Scientists' evaluations of the benefits and costs of university-industry research collaboration[END_REF], organizational (e.g. [START_REF] Johnson | Follow the Leader or the Pack? Regulatory Focus and Academic Entrepreneurial Intentions[END_REF], relational (e.g. [START_REF] Tartari | In good company: The influence of peers on industry engagement by academic scientists[END_REF], and institutional characteristics (e.g. [START_REF] Abreu | The nature of academic entrepreneurship in the UK: Widening the focus on entrepreneurial activities[END_REF] conducting to greater academic engagement, as well as the consequences of such engagement on research productivity (e.g. [START_REF] Bikard | When Collaboration Bridges Institutions : The Impact of University -Industry Collaboration on Academic Productivity[END_REF], commercialization potential (e.g. [START_REF] Lavie | Collaborating for knowledge creation and application: The case of nanotechnology research programs[END_REF], and broader societal impact (e.g. [START_REF] Iorio | The importance of pro-social behaviour for the breadth and depth of knowledge transfer activities: An analysis of Italian academic scientists[END_REF].

Nevertheless, the consequences of academic engagement on research quality and intellectual property development's potential remain unclear [START_REF] Perkmann | Academic engagement: A review of the literature 2011-2019[END_REF], and are contingent to various research projects' characteristics (e.g. [START_REF] Callaert | Scientific yield from collaboration with industry: The relevance of researchers' strategic approaches[END_REF][START_REF] Rentocchini | The relationship between academic consulting and research performance: Evidence from five Spanish universities[END_REF]. While it has 1 To simplify, we will use the terminology "engage" always in the context of engage with the industry though research collaborations, in line with [START_REF] Perkmann | Academic engagement and commercialisation: A review of the literature on university-industry relations[END_REF][START_REF] Perkmann | Academic engagement: A review of the literature 2011-2019[END_REF] definition.

been shown that academic engagement conducts on average to higher academic productivity (e.g. [START_REF] Banal-Estañol | The double-edged sword of industry collaboration: Evidence from engineering academics in the UK[END_REF][START_REF] Bikard | When Collaboration Bridges Institutions : The Impact of University -Industry Collaboration on Academic Productivity[END_REF][START_REF] Gulbrandsen | Industry funding and university professors' research performance[END_REF][START_REF] Hottenrott | Fishing for complementarities: Research grants and research productivity[END_REF], engaged academics' scientific performances are heterogeneous at the individual level [START_REF] Van Looy | Publication and patent behavior of academic researchers: Conflicting, reinforcing or merely co-existing?[END_REF]. This latter phenomenon can be linked to the large variety of university-industry collaborative research projects' aims and settings [START_REF] Carayol | Objectives, agreements and matching in science-industry collaborations: Reassembling the pieces of the puzzle[END_REF][START_REF] Meyer-Krahmer | Science-based technologies: University-industry interactions in four fields[END_REF]. To go a step further in understanding academic engagement's effects on research projects' performances, management scholars have recently called to control for projects' research orientation (e.g. [START_REF] Tijssen | Anatomy of use-inspired researchers: From Pasteur's Quadrant to Pasteur's Cube model[END_REF], an issue that received less attention in the literature on academic engagement.

One the one hand, some scholars alleged that a more excellent orientation towards industry demands-ie. market-pull orientation (ie. MPO)-in university-industry collaborative research projects would lead to reducing academic contribution to open science and favoring the development of IP for private companies (e.g. [START_REF] Blumenthal | Relationships between Academic Institutions and Industry in the Life Sciences -An Industry Survey[END_REF][START_REF] David | Towards a new economics of science[END_REF], when compared with more research-push orientated projects (ie. RPO).

For example, [START_REF] Callaert | Scientific yield from collaboration with industry: The relevance of researchers' strategic approaches[END_REF], by analyzing three strategic approaches mainly used by scientists from two European Universities across their joint-research projects' portfolios, showed that when scientists do not participate in projects in response to industry's calls, they record higher scientific performances. On the other hand, even when supporting industry needs (ie. MPO), academics' research activities can be fostered by having access to companies' skills, equipment, or materials (e.g. D'Este and [START_REF] D'este | Why do academics engage with industry? The entrepreneurial university and individual motivations[END_REF][START_REF] Mansfield | Academic Research Underlying Industrial Innovations : Sources , Characteristics , and Financing[END_REF], by increasing the pool of available new ideas for scientists (e.g. [START_REF] Evans | Industry Induces Academic Science to Know Less about More[END_REF]Siegel et al., 2003), and by favoring more fruitful labor division [START_REF] Bikard | When Collaboration Bridges Institutions : The Impact of University -Industry Collaboration on Academic Productivity[END_REF]. Hence, [START_REF] Goldstein | Simultaneous pursuit of discovery and invention in the US Department of Energy[END_REF] explored another possible fruitful research orientation, based on the scientists' commitment to addressing fundamental research questions through applied research, referred to as Simultaneous Discovery-Invention (SDIO) research orientation (in echoes to [START_REF] Stokes | Pasteur's Quadrant. Basic science and technological innovation[END_REF].

As university-industry collaborations remain contentious [START_REF] Murray | The Oncomouse That Roared : Hybrid Exchange Strategies as a Source of Distinction at the Boundary of Overlapping Institutions[END_REF], there is a need to better assess the relevance of those results for academic engagement in diverse contexts (disciplines, universities, etc.), at project level, and with a fine-grained control variables' set due to, as previously mentioned, the heterogeneity of joint-research projects [START_REF] Callaert | Scientific yield from collaboration with industry: The relevance of researchers' strategic approaches[END_REF][START_REF] Tijssen | Anatomy of use-inspired researchers: From Pasteur's Quadrant to Pasteur's Cube model[END_REF]. Indeed, the effects of ex-ante research orientation on ex-post scientific and commercialization performances of joints research projects with the industry needs to be further assessed and still lack quantitative analysis. Our study aims to fill this gap by classifying joint research projects among different research orientations (ie. market-pull, research-push, or simultaneous-discovery-invention) at their launch, and reviewing their ex-post patenting and publication data.

To do so, we rely on an original dataset of a yet understudied category of joint-research projects: university-industry collaborative Ph.D. While Ph.D. students represent an essential part of academic research groups and are vital contributors to the development of new scientific knowledge (e.g. [START_REF] Baruffaldi | The productivity of science & engineering PhD students hired from supervisors' networks[END_REF][START_REF] Larivière | On the shoulders of students? The contribution of PhD students to the advancement of knowledge[END_REF], there is recent evidence that some of them are also involved in collaborations with the industry during their training, along with their supervisors (e.g. [START_REF] Buenstorf | When do firms get ideas from hiring PhDs[END_REF][START_REF] Gaughan | National science training policy and early scientific careers in France and the United States[END_REF]. In Europe, many countries have prescribed various government-funded programs to develop Collaborative Ph.D. with the industry, representing large number of Ph.D. students.

Nevertheless, in management science, research on Ph.D. students' contribution to science and innovation remains limited (Shibayama, 2019), and the specific study of Collaborative Ph.D. programs largely underexplored (Salimi et al., 2015a). It has to be highlighted that the managerial literature on Collaborative Ph.D., while scarce, obtains findings aligned with the broader literature on academic engagement, including an overall positive effect on scientific productivity compared to traditional Ph.D. [START_REF] Gaughan | National science training policy and early scientific careers in France and the United States[END_REF]Salimi, Bekkers, & Frenken, 2015a) despite heterogeneous settings of projects [START_REF] Butcher | A view from the coal face: UK research student perceptions of successful and unsuccessful collaborative projects[END_REF]. We also found evidence in qualitative studies of the three distinct research orientations previously mentioned (e.g. Grimm, 2018 for MPO;[START_REF] Harryson | Making innovative use of academic knowledge to enhance corporate technology innovation impact[END_REF] for SDI; Kerr and Ivey, 2003 for RPO), which make the case relevant for the review of research orientations' effects on joint projects' scientific and commercialization performances.

The paper is structured as follows: in the following paragraph, we define hypotheses derived from the literature; then we introduce the context of the empirical data, as well as the methodology used to analyze the different variables and indicators. A subsequent section covers the main results, and we then conclude by discussing theoretical implications, limitations, and room for further research.

THEORETICAL CONSIDERATIONS

Academic engagement: the case of Collaborative Ph.D.

While being subject to regional specificities, overall, Collaborative Ph.D. covers a research project that has "a typical duration of 3-4 years and which involves a university, a firm and a Ph.D. candidate, all working together to meet (common or individual) expectations" (Salimi, Bekkers, & Frenken, 2016: 813) and for which an "industry expert take part in the supervision committee, officially and informally" (Borrel-Damian, Morais, & Smith, 2015: 17).

Hence, they can be arranged directly by the company and a public laboratory on a discretionary basis (see examples from Italy, Switzerland, Norway, or Lithuania in Borrel-Damian et al., 2015, p. 67-69). However, in many cases, and particularly in Europe, policy-makers developed specific programs to support and develop Collaborative Ph.D. programs 2 . They sometime 2 Collaborative Ph.D. programs with the industry include BBSRC CASE or EPRSC CASE studentships in the UK, the Enterprise Partnership Scheme (IRCSET) in Ireland, the Leaders for Technological Industries scheme in Portugal, the Graduate-Cluster for Industrial Biotechnology in Germany, the Industrial doctoral student's Project in Sweden, or the Industrial Ph.D. program in Denmark, CIFRE case in France.

represent large numbers of Ph.D. students, such as in France where 10% of the total cohort of funded Ph.D. students each year is involved in such program3 . It is our understanding that Collaborative Ph.D. programs constitute then a modality for academic engagement [START_REF] D'este | Why do academics engage with industry? The entrepreneurial university and individual motivations[END_REF], that can be referred to as "knowledgerelated interactions by academic researchers with non-academic organisations, as distinct from teaching and commercialisation. These interactions include collaborative research, contract research […]" (Perkmann et al., 2021: 1). Indeed, Collaborative Ph.D. cannot be viewed as solely a practice to transfer or commercialize scientific knowledge, as they require overall similar scientific quality than traditional Ph.D. [START_REF] Kerr | The Engineering Doctorate model of consultant/researcher/innovator/entrepreneur for new product development -A gas turbine instrumentation case study[END_REF]. Furthermore, Collaborative Ph.D. is a way for both Ph.D. students and academic supervisors to be involved in collaborative and contractual research projects with companies. The latter constitutes one key reason universities engage in Collaborative Ph.D. projects [START_REF] Borrel-Damian | Collaborative Doctoral Education in Europe: Research Partnerships and Employability for Researchers[END_REF].

As previously noted, Ph.D. students involved in Collaborative Ph.D. do not seem to suffer, on average, from a negative effect on their scientific productivity in comparison with traditional Ph.D. programs. [START_REF] Kerr | The Engineering Doctorate model of consultant/researcher/innovator/entrepreneur for new product development -A gas turbine instrumentation case study[END_REF], through a case study analysis¸ concluded that the understanding of the scientific and technical issues in Collaborative Ph.D. projects is equivalent to traditional training programs. Hence Collaborative Ph.D. projects "entail the same high standards for scientific quality of research as that required of a doctorate in a traditional doctoral programme" (Borrel-Damian et al., 2015: 8). [START_REF] Gaughan | National science training policy and early scientific careers in France and the United States[END_REF], by conducting a survey and CV analysis on 807 Ph.D. in life sciences and physics concluded that being engaged in the CIFRE Collaborative Ph.D. program in France, does not affect student's publication productivity. Salimi et al. (2015), through an analysis of 448 Ph.D. students at Eindhoven University of Technology, showed that, on average, students involved in Collaborative Ph.D. research projects obtained better performances in terms of the number of scientific publications and number of publications' forward citations than other students4 .

Overall, those findings match with results from the academic engagement literature: one consequence of engagement with the industry is, on average, an increase of scientific productivity at individual level (e.g. [START_REF] Bikard | When Collaboration Bridges Institutions : The Impact of University -Industry Collaboration on Academic Productivity[END_REF][START_REF] Tijssen | Anatomy of use-inspired researchers: From Pasteur's Quadrant to Pasteur's Cube model[END_REF].

In terms of intellectual property generation and contribution to firms' innovation processes, Salimi et al. (2015) showed that Ph.D. students involved in Collaborative Ph.D. projects recorded better performances than those who do not, in terms of the number of filed patents and number of patents' forward. Furthermore, [START_REF] Buenstorf | When do firms get ideas from hiring PhDs[END_REF] analyzed the contribution to firms' innovation processes of recently graduated Ph.D. in the context of the German laser industry. Using topic-modeling techniques to compare Ph.D. dissertations and patents that Ph.D. holders filed for companies' accounts, they found out that some of them were filed while the inventor was still a Ph.D. student due to collaborative research projects. Focusing on those "early patent[s]" (Buenstorf & Heinisch, 2020: 4), they demonstrated that they were, on average, more exploratory regarding a given company's patent portfolio. This finding conducted them to claim that having an early access to new scientific knowledge developed by Ph.D. students is crucial for companies' innovation performances. Finally, those two studies are also in line with results from the academic engagement literature suggesting that engagement with industry increases academics' inventive prowess measured by patents (e.g. [START_REF] Lawson | Academic patenting: The importance of industry support[END_REF].

Collaborative Ph.D. projects differ in terms of settings, goals, and characteristics (e.g. [START_REF] Butcher | A view from the coal face: UK research student perceptions of successful and unsuccessful collaborative projects[END_REF]Salimi et al., 2015b). While they are associated on average to greater scientific performances, performances at the project level are heterogeneous [START_REF] Tavares | Industry-university collaboration in industrial doctorates: A trouble-free marriage?[END_REF]. Similarly to more traditional university-industry collaborative projects, one key, but understudied, explanatory factor might be embedded in the choice of projects' research orientation [START_REF] Callaert | Scientific yield from collaboration with industry: The relevance of researchers' strategic approaches[END_REF][START_REF] Tijssen | Anatomy of use-inspired researchers: From Pasteur's Quadrant to Pasteur's Cube model[END_REF], that has to be analyzed by reflecting to the broader literature on academic engagement. We acknowledge that relying here on collaborative Ph.D. instead of traditional university-industry collaborative projects do not conduct to significant bias regarding research orientation, as in most collaborative Ph.D. program, the Ph.D. supervisor is highly involved in the project.

Collaborative Ph.D. and research orientations

This section discusses possible research orientations in Collaborative Ph.D. projects and their effects on subsequent scientific discoveries, commercialization potential, and possible joint-outcomes (ie. at least one publication and one patent) through a given project.

Market-pull (MPO) and Research-push (RPO) research orientations

Market-pull-oriented (ie. MPO) collaborative Ph.D. projects are based on conducting research on the companies' products, processes, or services in relation to companies' R&D short-term goals, needs, or issues5 . Conversely, research-push-oriented (ie. RPO) projects are more oriented towards creating novel basic scientific knowledge 6 . Those two research directions are mostly presented as opposed to one another.

In terms of performances, MPO research projects are mainly considered as less prone to favor new scientific discoveries but tend to lead more often to commercialization through patenting (e.g. [START_REF] Bush | Science, the endless frontier; a report to the President on a program for postwar scientific research[END_REF][START_REF] Calderini | If star scientists do not patent: The effect of productivity, basicness and impact on the decision to patent in the academic world[END_REF]. Some examples of both research directions were found when reviewing the literature on Collaborative Ph.D. (e.g. Grimm, 2018 for market-pull;Kerr and Ivey, 2003 for research-push). Overall, MPO projects are more frequent in university-industry interactions (Mansfield, 1998;Muscio & Pozzali, 2013) as it would be easier for firms to absorb more applied knowledge (Bruneel, D'Este, & Salter, 2010;Nooteboom, Van Haverbeke, Duysters, Gilsing, & van den Oord, 2007). This phenomenon might be amplified in Collaborative Ph.D. projects due to companies' bargaining power in providing financing for the project [START_REF] Slaughter | The " Traffic " in Graduate Students : Graduate Students as Tokens of Exchange between Academe and Industry[END_REF]. Here, we will detail factors explaining those projects' performances in relation to the two identified research directions in the context of Collaborative Ph.D.

First, when external funding requirements shape the content of a research project (ie.

MPO projects), it has been showed that scientists involved record lower scientific performances due to less incentive to explore novel lines of inquiry [START_REF] Azoulay | Incentives and creativity: Evidence from the academic life sciences[END_REF].

In the context of Ph.D. students, [START_REF] Broström | Academic breeding grounds: Home department conditions and early career performance of academic researchers[END_REF], analyzing survey results related to the 2006 cohort of Ph.D. students in Sweden, confirmed this result. Notably, he highlighted that "there exists a trade-off between two potential objectives: to "get what you want" in terms of research content and to foster academically successful PhDs" [START_REF] Broström | Academic breeding grounds: Home department conditions and early career performance of academic researchers[END_REF](Broström, : 1656)). As in MPO Collaborative Ph.D. projects, content and objectives are extensively shaped by the company, it supports the detrimental effect of Ph.D. student scientific performances compared to RPO Collaborative Ph.D. projects, which might be more extensively shaped by Ph.D. students and Ph.D. supervisors.

Second, Collaborative Ph.D. students may act as "gatekeepers" (Thune and Børing, 2015, p. 641) between universities and industries, particularly in MPO projects for which key objectives would be transferring and adapting public laboratory's knowledge to the industry context. Those Ph.D. students might have to spend time and effort for other purposes than research activities (problem-solving for the companies, business analyses, etc.), activities that have been shown to negatively impact scientists' production of new academic knowledge [START_REF] Toole | Commercializing science: Is there a university "brain drain" from academic entrepreneurship[END_REF]. Conversely, adapting public laboratory's knowledge to the industry context can boost patenting in those MPO research projects, compared to more RPO ones.

Third, Collaborative Ph.D. students are on average more prone to take a position in the industry at project completion [START_REF] Mangematin | PhD job market: Professional trajectories and incentives during the PhD[END_REF][START_REF] Thune | Industry PhD schemes: Developing innovation competencies in firms?[END_REF], or to engage in an entrepreneurial activity through start-up creation (Muscio and Ramaciotti, 2019). Hence, they might tend to self-select more in MPO projects as their commitment to producing breakthrough science might be lower than acquiring business knowledge, as shown by [START_REF] Grimm | Assessin the industrial PhD: Stakeholder insights[END_REF] in the context of the German automotive industry.

Four, in MPO, companies can be more prone to implement publication barriers restricting or delaying publications as it has a direct impact on their innovation portfolio [START_REF] Blumenthal | Relationships between Academic Institutions and Industry in the Life Sciences -An Industry Survey[END_REF][START_REF] Czarnitzki | Delay and secrecy: Does industry sponsorship jeopardize disclosure of academic research?[END_REF], which can ultimately impact the scientific productivity of involved Ph.D. students compared to RPO projects.

Based on those findings, we then derived the following:  Hypothesis H1. Ex-ante RPO research direction in Collaborative Ph.D. projects would result in ex-post greater scientific performances than MPO projects.

 Hypothesis H2. Ex-ante MPO research direction in Collaborative Ph.D. projects would result in ex-post greater patenting performances than RPO projects.

Simultaneous discovery-invention (SDI) research orientation

Another possible research orientation in collaborative Ph.D. projects lies with the Pasteur quadrant logic [START_REF] Stokes | Pasteur's Quadrant. Basic science and technological innovation[END_REF], by intersecting basic research and an interest in the discovery's usage. There are qualitative observations that the cycle of discovery-invention is not always linear [START_REF] Godin | The Linear model of innovation, the historial construction of an analytical framework[END_REF][START_REF] Narayanamurti | Cycles of Invention and Discovery: Rethinking the Endless Frontier[END_REF][START_REF] Narayanamurti | RIP : The Basic / Applied Research Dichotomy[END_REF]. Hence, [START_REF] Goldstein | Simultaneous pursuit of discovery and invention in the US Department of Energy[END_REF] studied the performances of research projects committed to address technological issues that are too novel to be considered as applied ones [START_REF] Goldstein | Simultaneous pursuit of discovery and invention in the US Department of Energy[END_REF]: 1507). To do so, they analyzed projects from a specific branch of the US Department of Energy, the Advanced Research Projects Agency-Energy (ARPA-E), which aims to support such projects. They compared performances of ARPA-E projects with those from other divisions: the Energy Efficiency and Renewable Energy (EERE) branch, which usually financed applied-oriented projects (ie. MPO), the Office of Science, which usually financed basic-research (ie. RPO). They showed that projects of ARPA-E record higher performances than projects from the other branches in terms of joint output of patents and publications. We found one example of this strategy in qualitative work on Collaborative Ph.D. in [START_REF] Harryson | Making innovative use of academic knowledge to enhance corporate technology innovation impact[END_REF]. We would push this line of research forward by testing how simultaneous discovery-invention (SDI) research direction would perform in the context of Collaborative Ph.D. projects. We made then the following hypothesis (Hypothesis

H3):

 Hypothesis H3. Ex-ante SDI research direction in Collaborative Ph.D. projects would result in ex-post greater joint output (scientific publication and filed patents) performances than MPO or RPO project's direction.

METHODOLOGY

Context: the CIFRE program

To test our hypotheses, we relied on an original dataset regarding collaborative Ph.D. In the CIFRE Ph.D. program, the company, the public laboratory, and the Ph.D. student are all contractually tied (Figure 1). First, the company-from large companies of over 5,000 employees to SMEs of less than 250 employees-has to hire the Ph.D. student through a 3-year employment contract (or a permanent contract) at a minimum annual gross wage of 23,484€ (2015 figure). Second, the Ph.D. student has to be enrolled in the University 8 from which the public laboratory belongs. The Ph.D. student and the public laboratory are tied with a Ph.D. agreement, which details the conditions of the research, the registration conditions, rights, and duties of the Ph.D. students to obtain the Ph.D. diploma in the University. It has to be noted that the program also attracted 21% of foreign Ph.D. students in 2015. Third, the public laboratory and the company are tied with a 3-year Contractual Agreement. While there is no specific framework, usually, the following sections are included in the Contractual Agreement: characteristics of the parties, workplace arrangements for the Ph.D. student (including time to be spent in each facility), financial arrangements, intellectual property conditions, and litigation and termination arrangements. Usually, the company contribute financially to the supervision of the Ph.D. student by the public laboratory. Moreover, a specific Appendix in the Contractual 7 Translation from French to English by the authors. 8 We used the general term "University" here, but due to the French higher education system's specificities, it also includes Grandes Ecoles and other Higher Education institutions such as business or engineering schools.

Agreement contains a research project summary at the beginning of the joint research project.

ANRT supports the research by providing the company with a 14,000€ subsidy each year during the project's three-year duration, on a budget made available by the MESRI. One key element of the CIFRE program is that the parties are set free to define the joint research project goals, orientations, content, field of research9 and expected outcomes as long as the research work serves the Ph.D. dissertation and conduct the student to be graduated with the Ph.D. diploma at completion. Hence, the public laboratory and the company are able to negotiate the Contractual Agreement that is the most suitable for the project. For example, the time to be spent by the Ph.D. student in a given facility during the three years can be divided from 0% to 100%, intellectual property can be fully appropriated by one of the parties or shared between them. Nevertheless, to be eligible for the CIFRE program, the parties have to complete an application including details on their characteristics, conditions of the project (e.g. time spent by the Ph.D. student in each facility), and the research project's summary. ANRT evaluates the joint-research project eligibility to the CIFRE program, in particular based on an expert assessment of the project. The latter, which is typically made by a university professor in the field of the research project, focuses on the "scientific value of the project" and "the selected Ph.D. student's adequacy with the project's goals" (ANRT, 2019a: 4) The acceptance rate for a CIFRE Ph.D. project by ANRT is 90%.

ANRT is also performing an ex-post evaluation of the projects through dedicated surveys that are separately sent to the Ph.D. student, to the Ph.D. supervisor in the public laboratory, and to the Ph.D. supervisor in the company upon project completion. Those surveys include elements regarding the project management (e.g. number of meetings between the parties, potential conflict regarding the research orientation), and the performances (e.g. number of top journal publications, number of filed patents by the company related to the project).

Regarding our research question, the CIFRE Ph.D. program is highly suitable to test our hypotheses as the program is not oriented towards a particular field or scientific discipline, involved diverse universities and companies, and does not favor a specific research direction. Indeed, the CIFRE Ph.D. program supports projects oriented towards a "fundamental approach" (ANRT, 2020: 9) (ie. research-push); the development of "technological disruption" (ANRT, 2019b: 2) or aiming at "anticipating future technology" (ANRT, 2020: 9) (ie. simultaneous discovery-invention), or the "development of an competency to solve [company's] complex problems" (ANRT, 2020: 10) and "transfer and value [public laboratory] research" (ANRT, 2020: 9) (ie. market-pull). We also highlight that due to the specific settings of Collaborative Ph.D. in the CIFRE program (e.g. contractual agreement between the public laboratory and the company), those projects are representative of traditional university-industry research collaborations and do not appear biased by the fact that it also implies the Ph.D. dissertation completion.

Data

In this study, we relied on a unique dataset of Collaborative Ph.D. projects launched in 2015 through the CIFRE Ph.D. scheme in France. For each of those projects, the data includes: The sample was restricted to projects for which sufficient data were available (particularly, three completed post-CIFRE surveys and a comprehensive project summary), representing 635 Ph.D. projects.

The key independent variables of interest, related to the ex-ante research orientation, were computed following a qualitative analysis of the research project summaries included in the Contractual Agreement's Appendix (see next subsection for dedicated procedure). To control for characteristics of the different projects, we extracted 25 control variables from the Application Forms, Contractual Agreement, and the post-CIFRE surveys. Dependent variables were extracted from the post-CIFRE surveys based on self-reported (1) number of scientific publications, (2) number of scientific publications in top-journal, (3) number of filed patents by the company with the project, (4) Ph.D. Prize received as an award by Ph.D. student at project completion. Besides, to measure performances, we also explore joint outcomes [START_REF] Goldstein | Simultaneous pursuit of discovery and invention in the US Department of Energy[END_REF].

Procedure to derive research orientation

Exploring the role of research directions on projects' performances is a challenging task.

Scholars mainly relied, as a proxy for orientation towards MPO (or applied-research), to an expost evaluation of the project's outcomes. For example, scholars reviewed to what extent scientific papers published in relation with a given project was cited in any patent (see the usage of scientific non-patent literature, for example in [START_REF] Marx | Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles[END_REF], the type of journal in which scientific papers were published classified by the number of scientists affiliated to a private company that are publishing in that specific journal (e.g. [START_REF] Tijssen | Co-authored research publications and strategic analysis of public-private collaboration[END_REF], or the number of patents filed in the project's context (e.g. [START_REF] Bikard | When Collaboration Bridges Institutions : The Impact of University -Industry Collaboration on Academic Productivity[END_REF]. Others relied on proxy closer from an ex-ante analysis of the research direction. For example, [START_REF] Callaert | Scientific yield from collaboration with industry: The relevance of researchers' strategic approaches[END_REF] used survey data to distinguish between different research orientations in the portfolio of scientist's projects. [START_REF] Goldstein | Simultaneous pursuit of discovery and invention in the US Department of Energy[END_REF] relied on the projects' belonging towards the US Department of Energy's different departments to derive research orientations, [START_REF] Azoulay | Incentives and creativity: Evidence from the academic life sciences[END_REF] to projects' belonging towards either Howard Hughes Medical Institute or National Institutes of Health. However, having ex-ante information on projects' research orientations would be critical to assess the effect on projects' performances. Therefore, our original dataset contains projects' summaries at project launch that can be qualitatively analyzed to derive the research direction of the project.

To do so, we classified research projects' summaries enclosed in the Contractual Agreement according to three distinct research directions: (1) research-push, (2) market-pull and (3) SDI, by using a qualitative approach. The methodological process is detailed below and summarized in a flow chart (Figure 2). to make a decision. Finally, we were able to classify 748 projects (82% of the dataset), as some missing or incomplete Appendix were discarded (e.g. only recto pages scans while the Contractual Agreement was printed on recto-verso). We also discarded the projects for which the three ex-post surveys were not available (15%), and we ended up with 635 projects in our final dataset.

The detailed criteria for each research direction were defined iteratively in relation to the CIFRE project context in order to operationalize the three identified research directions.

There is no particular framework for the research summary enclosed in the Contractual Agreement, and the Parties are free to include the document that most suits them. On average, it is a document written in French, in most cases of around 3 to 5 pages that present the context of the project (discipline, key challenges, etc.), the key expertise of the public laboratory and the company for the project, a brief literature review, the research questions, the envisioned methodology and the workload of the Ph.D. student. The final coding criteria and some examples are provided in Table 1 10 .

RESULTS

Descriptive statistics

Variables' description is presented in Table 2 and we also provide a descriptive statistics analysis in Table 3. Looking at our sample, first we identify that the three categories of research orientations are not equally represented. While it is mainly assumed that Collaborative Ph.D.

projects would be prone to favor MPO projects due to companies' bargaining powers (e.g. [START_REF] Grimm | Assessin the industrial PhD: Stakeholder insights[END_REF][START_REF] Slaughter | The " Traffic " in Graduate Students : Graduate Students as Tokens of Exchange between Academe and Industry[END_REF], it is not the case in our sample. Hence 60% of the projects are RPO, and 27% MPO, while 13% only are SDI. When focusing on projects' performances, first, Collaborative Ph.D. projects with an orientation towards SDI, on average, better perform than MPO or RPO projects. They lead to a higher number of output per project and are also more likely to obtain at least a publication (86.3% of projects) or a joint-output (21.3% projects), even when considering top-journal publications only (16.3%). By using a t-test analysis with unequal variance, we show that the difference in means between SDI projects and projects with other research orientations projects are greater on all measured outputs at 99% confidence (Appendix A1). Second, we also observe that MPO projects better perform than RPO in terms of IP production, but lead to similar results in terms of scientific output (including for top-journal publications).

Table 3 -Summary statistics

Nevertheless, those results are only descriptive. We need to control both the projects' characteristics (e.g. type and size of companies) and conditions of completion (e.g. conflict occurrence, frequency of meetings) of the Collaborative Ph.D. projects as they might influence project's performances, and the three types of research orientations are dissimilar regarding control variables. For example, MPO projects are more conducted by start-up companies, universities non-Paris area-based, universities with more significant experience working with industry, candidate less science-oriented and, by a higher share of foreign Ph.D. candidate.

Estimation

Estimation strategy

To measure the influence of ex-ante research directions on scientific discoveries and invention, we adopt a similar estimation strategy to [START_REF] Goldstein | Simultaneous pursuit of discovery and invention in the US Department of Energy[END_REF].

Hence, we measure first the influence of research directions on the likelihood to obtain both outputs jointly, and we then analyze in more depth each scientific and innovation outputs separately. To consider the characteristics and conditions of projects' completion, we rely on a set of 25 unique control variables. We model the probability of binary output variables using both logistic regression and linear regression (Equation 1). 𝑌 𝑖𝑗 is the outcome of the project i;

𝑋 𝑖 the research orientation (SDI, MPO, RPO); 𝜑 𝑖 a vector of control including the 25 control variables, 𝜔 𝑗 a fixed effect for the research orientation type.

𝑙𝑜𝑔𝑖𝑡 (𝑃(𝑌

𝑖𝑗 )) = 𝛽 0 + 𝛽 1 𝑋 𝑖 + 𝛽 2 𝜑 𝑖 + 𝜔 𝑗 Equation 1
Figures from logit regressions in tables are exponentiated coefficients that correspond to the odds ratio for obtaining a giving outcome (e.g. at least one top-journal publication), compared to the two other types of research orientations (except for linear regression models used for control purposes). To consider count variables (e.g. number of top-journal publications per project), we model the expected value by using negative binomial regression. In those models, exponentiated coefficients are incidence rate ratios compared to the two other types of research orientations. Due to the considerable heterogeneity in project performances per discipline (see Appendix A2), robust standard errors are clustered at discipline level. In the table below, we test three model specifications: a first with no control variables, a second with only control variables highly and significantly correlated to one of the project direction types, a third one with the full set of control variables.

Joint output performances

As Collaborative Ph.D. programs mainly claim that they favor both scientific and innovative impacts, it is interesting to beginning our analysis by reviewing the probability of getting a joint output through the CIFRE program (ie. at least one scientific publication, and one patent). Descriptive statistics indicate that obtaining both a publication and a patent remains a rare event, on average roughly in one project out of ten. Nevertheless, RPO projects, on average, underperform when looking at this criteria (9.9% of the projects), while MPO and SDI projects lead to approximately similar results (20.0%).

First, estimations' results (Table 4) show that SDI projects have odds ratios closer to one when compared to MPO projects, and the ratios are non-statistically significant (at p < 0.1 level). We interpret the latter result as that MPO projects are as likely as SDI projects to generate at least one journal publication and at least one patent. Those results hold when taking into account top journal publications only.

Table 4 -Joint output in CIFRE Ph.D. collaborative projects

Regression analysis also show that RPO projects are less likely to generate at least one journal publication and one patent than MPO projects (Table 4). Hence, the odds ratios are significantly lower than 0, which matches the linear model's sign. The latter OLS model indicates that RPO projects have a 7.1% lower probability of generating both outputs compared to MPO projects. Again, those results hold when considering top journal publications only (see

Appendix A3).

Therefore, we reject Hypothesis H3 as MPO projects are as likely as SDI ones to lead to joint outputs, and the letter research orientation is not strictly over performing other project types when considering this criteria. As we only take into account the likelihood to obtain at least both output, we need to analyze in more depth each type of output separately.

Scientific performances

One of the key goals is to review the performances in terms of scientific outputs of Collaborative Ph.D. projects according to their research orientation. In particular, many projects ended up with at least one publication at completion (77.5%). However, SDI projects are more likely to produce at least one scientific publication, and the number of publications per project is higher than for other strategies (2.9 vs. 2.2). Those differences are still at play when focusing on top-journal publications only (1.3 vs. 1.8) or Ph.D. prize. The difference in terms of scientific performances between MPO and RPO projects appear limited in the descriptive statistics.

Regression analyses confirm those results. SDI projects are more likely to generate at least one publication than other projects at the highest confidence level (Table 5). Hence, the odds ratio of producing at least one publication for SDI projects is strictly superior to one with all model specifications. In those projects, Ph.D. candidates also tend to publish more scientific papers than for any other research direction, as shown in the Negative Binomial estimations.

Those results also hold for top-journal publications, with even a greater and more significant effect on the number of published papers (see Appendix A4). As a robustness check, we also used as a dependent variable the probability of the Ph.D. student to receive an award at project completion. We found similar results than for publications (see Appendix A5).

Table 5 -Scientific publication output in CIFRE Ph.D. collaborative projects

On the opposite, the comparison between RPO and MPO projects is less straightforward. When focusing on all scientific publications only, RPO projects seem to be more likely to generate at least one publication than MPO ones, but with a low level of statistical significance, and the result does not hold for top-journal publications nor the probability to obtain a Prize at project completion. Indeed, the odds are roughly equal to one and are nonstatistically significant (at p<0.1) for both top journal publications and awards. When focusing on the number of published papers, performances between both types of projects' directions are similar as the odds ratios are also roughly equal to 1, and the difference is non-statistically significant, with similar results for top-journal publications. Hence, we reject Hypothesis H1 as MPO projects have overall similar scientific performances than RPO ones.

Patenting performances

In this section, we review the performances in terms of filed patents by the company in relation to the Collaborative Ph.D. projects, depending on the research directions. We adopt a similar approach to scientific outputs.

First, descriptive statistics show that, on average, filling a patent during a RPO project is a rare event. The company file at least one patent in only one project out of ten, a much lower number than for other projects' orientations. Estimations confirm this result (Table 6). Both odds ratios and incidence rate ratios are significantly lower than zero when considering both the probability of filing at least one patent or even the number of filed patents, at the highest level of significance.

Table 6 -Patent output in CIFRE Ph.D. collaborative projects

In comparison, descriptive statistics showed that, on average, SDI and MPO projects lead roughly to a similar number of filed patents: around one project out of four filed at least one patent. Results from estimation showed that SDI projects have roughly the same odds of generating at least one patent as the MPO projects, and results are not statistically significant. Furthermore, we showed that SDI projects tend to generate more patents on average than the others type of projects according to the incidence rate ratios. However, results are only statistically significant at p<0.05 level. Hence, we accept Hypothesis H2 as MPO projects are likely to lead to higher patenting performances than RPO ones.

DISCUSSION

In this paper, we review how university-industry research projects' scientific and commercialization performances are contingent on their initial research orientation (MPO, RPO, or SDI) in the context of the CIFRE Collaborative Ph.D. program in France. We claim that research orientation is a crucial variable, highly significant to determine a project's performances despite a large control variables' set. First, our analysis confirms the straightforward hypothesis that when a university-industry research project has an orientation towards industry needs, it develops more IP than projects oriented towards research push objectives. However, orientation towards industry needs leads to similar scientific performances as in projects oriented towards scientific discovery. Third, while projects with an orientation towards simultaneous discovery-invention were claimed to be more performant than others, we show that in the CIFRE program context, they record higher scientific performances but similar commercialization performances than projects oriented towards industry needs. As a result, similar performances between those two research orientations are observed in terms of the likelihood to generate joint-output.

Our findings contribute to the literature on academic engagement in two main ways.

First, while engagement with the industry is considered important in various disciplines, contexts, and geographies, there was very little evidence on engagement at the Ph.D. training stage. Indeed, senior scientists were considered to be more prone to engage as they are able to share expertise that had been cumulated after years of research (e.g. [START_REF] Levin | Research Productivity Over the Life Cycle: Evidence for Academic Scientists[END_REF][START_REF] Schuelke-Leech | Resources and research : An empirical study of the influence of departmental research resources on individual STEM researchers involvement with industry[END_REF]. Hence, in the context of the CIFRE program, we showed that some Ph.D. students might be very successful in producing novel scientific knowledge, with potential for commercialization through academic engagement. While it is now clearly established that there is a positive relationship between academic engagement and scientific productivity (see [START_REF] Perkmann | Academic engagement: A review of the literature 2011-2019[END_REF][START_REF] Perkmann | Academic engagement and commercialisation: A review of the literature on university-industry relations[END_REF], and because engagement is engendered by previous engagement, early positive exposure to the industry can be considered as promising for those (future) academics and open rooms for further studies. In particular, reviewing how the particular set of those types of collaboration could favor transfer of specific competencies from the Ph.D.

supervisor to the Ph.D. students regarding how to scientifically manage those collaborations could be of interest. Second, our results complement those from [START_REF] Callaert | Scientific yield from collaboration with industry: The relevance of researchers' strategic approaches[END_REF] who showed that the scientific performance of scientists engaged with industry was contingent on the scientists strategies. Furthermore, while [START_REF] Goldstein | Simultaneous pursuit of discovery and invention in the US Department of Energy[END_REF] showed that SDI research orientation was very effective in the US Department of Energy's particular institutional context, we extend those results by showing its effectivity into a broader range of university-industry projects. We hope that our contribution will encourage others to investigate this understudied area, in other geographical or institutional contexts or through qualitative studies.

Our paper also contributed to review the performances of MPO projects from a broader university-industry collaboration perspectives. Those projects are often viewed as a risky strategy for the global scientific ecosystem as applied research is often not considered as fruitful as basic research (e.g. [START_REF] Toole | Commercializing science: Is there a university "brain drain" from academic entrepreneurship[END_REF]. But our results strengthen theoretical views that focusing on a company's product, process, or service as a starting point of the research activity can leverage access to original knowledge components that could be combined with more traditional research elements to favor breakthrough discoveries [START_REF] Fleming | Science as a map in technological search[END_REF]. Learning form the industry is mainly the primary motivation of academic engagement [START_REF] D'este | Why do academics engage with industry? The entrepreneurial university and individual motivations[END_REF][START_REF] Lee | The sustainability of university-industry research collaboration: An empirical assessment[END_REF]Siegel, Waldman, & A. Link, 2003) and focusing on companies' products, processes or services can conduct the company to be more involved in the research and then favor a significant labor division with academics [START_REF] Bikard | When Collaboration Bridges Institutions : The Impact of University -Industry Collaboration on Academic Productivity[END_REF].

Further studies may focus on disentangling those effects and exploring why MPO projects lead to such scientific outcomes.

Our findings also contributed to the literature on Ph.D. students' training. Indeed, we extend [START_REF] Buenstorf | When do firms get ideas from hiring PhDs[END_REF] finding that the early access to Ph.D. research can be of importance for firms. Indeed, companies involved in Collaborative Ph.D. projects, and particularly, when relying on SDI or MPO research directions, can filed patents directly related to the project. As some projects led to joint-output, it may favor inventions based on cutting edge research. Our contribution also echoes [START_REF] Butcher | A view from the coal face: UK research student perceptions of successful and unsuccessful collaborative projects[END_REF] as results obtained from the analysis of the control variables call for additional research on how to favor interactions with start-up companies in Collaborative Ph.D. projects without harming the Ph.D. student scientific productivity.

Finally, our study is subjected to some limitations. First, it relies on a specific Collaborative Ph.D. program, involving a company's financial subsidy for the research, and an in-depth review of the project's content that ensures a certain level of selectiveness, a variable associated with more significant research performances [START_REF] Callaert | Scientific yield from collaboration with industry: The relevance of researchers' strategic approaches[END_REF]. Studying other Collaborative Ph.D. programs, which do not imply such a level of research commitment, would be of interest. Furthermore, some Ph.D. students might be engaged with the industry without being involved in such a program and it would be interesting to compare their scientific and inventive outputs. Second, our approach to compute strategy is based on a careful, at least double-blinded analysis following criteria that we set for the study. Further research may be conducted to define more fine-grained strategies at play in such university-industry collaborations. Third, data regarding performances were self-reported, and are based on a limited proxy of scientific and inventive contributions: patents and scientific publications (e.g. [START_REF] Jaffe | Patent Citation Data in Social Science Research: Overview and Best Practices[END_REF]. Additional studies can be conducted to review how new scientific knowledge was absorbed by the company and how a given Collaborative Ph.D. project contribute to the company's innovation process. Fourth, while we control (based on the survey), on whether or not the company or the university was already involved in the CIFRE program, we were not able to control for the complete picture regarding to what extent their innovation process relies on the CIFRE program. Indeed, companies more largely involved may adopt a portfolio approach with projects covering the three types of strategies. The research project is dedicated to developing new fundamental scientific knowledge in a strictly defined disciplinary area and that could aim to strengthen the company's scientific competencies.

FIGURES & TABLES

-There is only one scientific discipline mentioned in the project (eg. material chemistry, organic chemistry, computer science) with detailed existing literature and an identified precise scientific gap; -There is no existing precise product, process or service of the company that constituted the core of the research project; -It mainly includes projects focusing on (1) producing new scientific modeling and simulations, (2) establishing new scientific methodologies and chemical processes, (3) gathering data in the context of the company for social science projects but not directed towards implementation; -The project goal can be to develop a prototype only if is constituting a proof of concept of a fundamental research experiment.

Project #0421 between Company A, operating in the defense area, and INSA (an engineering school) focuses on a new family of explosive called "insensible". The scientific gap is that the mechanical deformation and rupture modes during the explosion are yet unknown. The scientific goal is to define a new scientific methodology to model mechanical deformation. Here, we acknowledge that the project is not focusing on a particular product but a generic one.

Market-pulloriented direction (ie. MPO)

The research project is dedicated to developing new academic knowledge directly applied to a pre-existing product, process, or service of the organization.

-The research project focuses on a particular product, process, or service already existing (or at the final development stage) in the company and is explicitly mentioned in the project's summary. Research focusing on generic products are not considered here (e.g. airplane motors in general vs. the airplane motor X2589 for A321 aircraft); -Or, the research project is focusing on developing a new product that can be directly commercialized at the end of the project.

-The project goal can be to develop a prototype, except if the prototype aims at being a proof of concept of a fundamental research experiment; -Social sciences projects for which the core objective is to design a new process directly implemented in the company and scientific literature is abundant according to the research summary would be considered in this category.

Project #0456 between Company B, producing tools for the beekeeper'sindustry, and Tours University focuses on developing a new trap for Vespa Velutina Nigrithorax, a sort of hornet. The company already conducted a project to develop scientific knowledge on this particular hornet. The project's objective is first to leverage scientific techniques to identify biotic parameters that would attract or be a repulsive for the hornet. Second, the project aims at designing a trap to capture the hornet that can be directly commercialized at project competition.

Simultaneousdiscoveryinventionoriented direction (ie. SDI)

The research project is dedicated to developing new scientific knowledge regarding the investigation of radically new scientific areas, transdisciplinary contexts-, or very disruptive industrial contexts.

-The research project can be transdisciplinary as more than one scientific discipline is mentioned in the project (e.g.: philosophy and medicine) or there is at least two public laboratories focusing on different areas involved; -The research project can be embedded in a radically innovative context which requires the development of new scientific methodology or new knowledge to tackle a new level of constraints (ie. the scientific gap includes to tackle many large challenges); -The research project can focus on the development of new knowledge in a very new scientific disciplines; -The project is not a follow-up of a previous company exploration (in particular with commercial objectives);

Project #0593 between Company C, producing numerical and video systems for the media industry, and focuses on developing innovative paths related to the display of images enhanced with tactile sensations. There are multiple scientific challenges to be tackle in the novel discipline called haptic. In particular, one objective is to be able to feel the sensation of a blue jeans by touching a screen.

  projects, started in 2015, and conducted through the Industrial Convention to Research Training (CIFRE) Collaborative Ph.D. program (then called "CIFRE program") in France. The CIFRE program was established in France in 1981 and aims at "financially support a company operating through the French legislation, to recruit a Ph.D. student that will be at the heart of a joint research project with a public laboratory. The research works of the joint-research project have to support the preparation of the Ph.D. student's defense" 7 (ANRT, 2018: 25). The National Agency for Research and Technology (ANRT), a not-for-profit organization that acts on behalf of the French Ministry of Higher Education, Research, and Innovation (MESRI), supervises the CIFRE program. In 2015, after 35-years of operation, the CIFRE Ph.D. program trained 25,400 Ph.D. students in 8,500 different companies and involved 4,000 different public laboratories. The CIFRE program has become more and more attractive each year since its creation. In 2000, 750 CIFRE Ph.D. projects were launched while 1,383 started in 2015, the latter representing 10% of all the Ph.D. students' cohort in France that year.

Figure 1 -

 1 Figure 1 -Parties and institutional ties of a CIFRE collaborative Ph.D.



  Details from the Application Form completed by the parties to apply for the CIFRE program (e.g. number of employees of the companies, Nationality of the Ph.D. student, geographical location of the facility);  The Contractual Agreement (and its Appendix related to the research project summary) negotiated between the public laboratory and the company before the beginning of the project (e.g. intellectual property terms, a summary of the research projects);  The three post-project surveys completed by (1) the Ph.D. supervisor in the public laboratory, (2) the Ph.D. supervisor in the company and (3) the Ph.D. student (e.g. number of patents filed by the company along with the project, number of academic publications, number of inter-parties meetings, potential conflicts occurring during the project).

Figure 2 -

 2 Figure 2 -Research strategies methodology implementation.

Figure 1 -

 1 Figure 1 -Parties and institutional ties of a CIFRE Collaborative Ph.D.

Figure

  Figure 2 -Research strategies methodology implementation

Table 1 -

 1 Research direction coding criteria and examples.

Table 2 -

 2 Summary of variables used.

10 Due to confidentiality issues, company names are anonymized.

Table 1 -

 1 Research directions coding criteria and example

Table 3 -

 3 Summary statistics Robust standard errors in parentheses, clustered by discipline. Results are exponentiated coefficients (odds ratios). Base project type is MPO, COMPEQUIP, UNIVEQUIP. * p<0.1. ** p<0.05. *** p<0.01.

		Market-pull-	Research-
		oriented	push-oriented
		Ph.D.	Ph.D.	SDI Ph.D.
		(N = 171)	(N = 384)	(N = 80)
			Mean		Min	Max
	VARIABLES
	Control
	Comp. nbr. employees	34,584.9	28,090.1	26,809.7	0.0	364,795.0
	Comp. nbr. Ph.D. employees	39.1	39.0	41.7	0.0	100.0
	Cand. wage	28,467.5	28,988.0	29,932.4	23,360.0 57,660.0
	Cand. effective time with comp.	0.5	0.4	0.5	0.0	1.0
	Cand. contractual time comp.	0.5	0.4	0.5	0.0	1.0
	Dependent
	Nbr. publications	2.3	2.2	2.9	0.0	20.0
	Nbr. top journals publications	0.8	0.8	1.3	0.0	9.0
	Nbr. patents	0.5	0.2	0.7	0.0	14.0
	PERCENT
	Control
	Comp. R&D dpt.	88.3%	82.3%	91.3%	0%	100%
	Comp. Start-up	16.4%	9.4%	8.8%	0%	100%
	Comp. Paris area	38.0%	44.0%	37.5%	0%	100%
	Comp. exclusive IP	18.7%	13.8%	21.3%	0%	100%
	Comp. first CIFRE	7.6%	7.0%	21.3%	0%	100%
	COMPMASTERED	29.8%	28.1%	28.8%	0%	100%
	COMPNEW	59.6%	62.8%	63.8%	0%	100%
	COMPEQUIP	10.5%	9.1%	7.5%	0%	100%
	Univ. Paris area	22.8%	28.1%	31.3%	0%	100%
	Univ. exp. with industry	64.9%	62.0%	56.3%	0%	100%
	Univ. exclusive IP	1.8%	0.5%	0.0%	0%	100%
	UNIVEQUIP	11.1%	7.8%	11.3%	0%	100%
	UNIVMASTERED	28.1%	37.2%	37.5%	0%	100%
	UNIVNEW	60.8%	54.9%	51.3%	0%	100%
	Cand. Science-oriented	8.8%	16.9%	25.0%	0%	100%
	Cand. French nation.	76.0%	79.7%	83.8%	0%	100%
	Colab. new	28.7%	29.7%	31.3%	0%	100%
	Colab. multi. Lab.	11.7%	15.1%	13.8%	0%	100%
	Colab. conflict	36.8%	35.9%	27.5%	0%	100%
	Colab. cand. research diverted	54.4%	58.6%	47.5%	0%	100%
	Colab. quarter meetings	72.5%	65.6%	65.0%	0%	100%
	Colab. annual meetings	97.1%	93.0%	93.8%	0%	100%
	Discipline -Mathematics	4.7%	5.2%	10.0%	0%	100%
	Discipline -Physics	3.5%	0.8%	2.5%	0%	100%
	Discipline -Life Sciences	1.2%	1.0%	1.3%	0%	100%
	Discipline -Chemistry	12.9%	16.1%	11.3%	0%	100%
	Discipline -Health	5.3%	10.2%	10.0%	0%	100%
	Discipline -Human sciences	13.5%	7.6%	12.5%	0%	100%
	Discipline -Social sciences	0.6%	10.4%	3.8%	0%	100%
	Discipline -Engineering	14.6%	25.5%	8.8%	0%	100%
	Discipline -Agronomy	3.5%	4.4%	6.3%	0%	100%
	Discipline -TIC	40.4%	18.8%	33.8%	0%	100%
	Dependent
	At least 1 publication	72.5%	77.9%	86.3%	0%	100%
	At least 1 top journal publication	48.5%	51.6%	63.8%	0%	100%
	At least 1 patent	25.7%	11.5%	22.5%	0%	100%
	At least 1 patent and 1 publication	19.3%	9.9%	21.3%	0%	100%
	At least 1 patent and 1 top journal pub.	12.9%	7.6%	16.3%	0%	100%
	Ph.D. Prize	15.2%	12.8%	25.0%	0%	100%
	Notes: comp.: company, nbr: number, cand.: Ph.D. candidate, Univ.: University, Dpt.: department, IP: Intellectual Property, multi.
	lab.: more than one University laboratory involved, TIC: Technology of Information & Communication

Table 4 -

 4 Joint output in CIFRE Ph.D. collaborative projects Robust standard errors in parentheses, clustered by discipline. Results are exponentiated coefficients (odds ratios, incidence rate ratios). Base project type is MPO, COMPEQUIP, UNIVEQUIP. * p<0.1. ** p<0.05. *** p<0.01.

	Dependent Variable: At least 1 publication, number of journal publications
		(1)	(2)	(3)	(4)	(5)	(6)
	Model:	Logit	Logit	Logit	Negative	Negative	Negative
					Binomial	Binomial	Binomial
	SDI Ph.D.	2.768***	2.378***	3.368***	1.294*	1.316**	1.381**
		(0.842)	(0.771)	(0.982)	(0.178)	(0.160)	(0.182)
	Research-push-oriented Ph.D.	1.644*	1.333	1.807**	0.970	1.014	1.013
		(0.438)	(0.376)	(0.505)	(0.197)	(0.200)	(0.196)
	Comp. nbr. employees			1.000**			1.000*
				(0.00000243)			(0.00000046)
	Comp. R&D dpt.	1.838		1.788		1.225	1.194
		(0.877)		(0.707)		(0.266)	(0.213)
	Comp. Start up	0.775		0.884		0.844	0.910
		(0.250)		(0.343)		(0.128)	(0.153)
	Comp. nbr. Ph.D.			1.001			0.999
				(0.00296)			(0.00118)
	Comp. Paris area			1.087			1.207
				(0.448)			(0.197)
	Comp. excl. IP	1.750		1.661		1.024	0.948
		(0.602)		(0.612)		(0.110)	(0.0979)
	Comp. first CIFRE	0.422*		0.491		0.623**	0.671*
		(0.219)		(0.239)		(0.128)	(0.138)
	COMPMASTERED			1.433			1.227
				(0.622)			(0.170)
	COMPNEW			1.277			1.139
				(0.278)			(0.151)
	Univ. Paris area			0.827			0.996
				(0.219)			(0.102)
	Univ. exp. with indus.			1.610***			1.261**
				(0.255)			(0.147)
	Univ. excl. IP	0.767		0.601		0.532**	0.479**
		(0.664)		(0.564)		(0.142)	(0.142)
	UNIVNEW			1.152			0.906
				(0.408)			(0.168)
	UNIVMASTERED	0.936		1.019		0.895*	0.798
		(0.268)		(0.248)		(0.0550)	(0.121)
	Ph.D. Science oriented	1.094		1.098		1.226**	1.199**
		(0.255)		(0.276)		(0.0994)	(0.0962)
	Ph.D. French nationality			0.712			0.781***
				(0.232)			(0.0591)
	Ph.D. wage			1.000*			1.000
				(0.0000290)			(0.00000956)
	Ph.D. contractual time with comp.	3.320**		3.669***		1.327	1.262
		(1.639)		(1.450)		(0.306)	(0.251)
	Ph.D. effective time with comp.	1.194		1.173		1.260	1.213
		(0.481)		(0.528)		(0.178)	(0.178)
	Ph.D. diverted from research	0.679**		0.678**		0.981	1.033
		(0.113)		(0.117)		(0.0760)	(0.0882)
	Colab. multi. lab.			0.932			0.960
				(0.188)			(0.0832)
	Colab. conflict			0.693*			0.829***
				(0.146)			(0.0533)
	Colab. new			1.331			0.988
				(0.507)			(0.135)
	Colab. quarter meetings	1.636*		1.614**		1.093	1.056
		(0.415)		(0.378)		(0.131)	(0.109)
	Colab. annual meetings	1.161		0.964		1.239	1.121
		(0.629)		(0.467)		(0.213)	(0.191)
	Observations	635	635	635	635	635	635
	(Pseudo) R2	0.071	0.009	0.103	0.003	0.018	0.031
	Notes:

Table 5 -

 5 Scientific publication output in CIFRE Ph.D. collaborative projects Robust standard errors in parentheses, clustered by discipline. Results are exponentiated coefficients (odds ratios, incidence rate ratios). Base project type is MPO, COMPEQUIP, UNIVEQUIP. * p<0.1. ** p<0.05. *** p<0.01.

	Dependent Variable: At least 1 patent, number of patents
		(1)	(2)	(3)	(4)	(5)	(6)
	Model:	Logit	Logit	Logit	Negative	Negative	Negative
					Binomial	Binomial	Binomial
	SDI Ph.D.	0.838	0.994	0.977	1.555	1.773	1.736*
		(0.367)	(0.426)	(0.365)	(0.652)	(0.803)	(0.523)
	Research-push-oriented Ph.D.	0.374***	0.442***	0.426***	0.474***	0.581***	0.541***
		(0.0588)	(0.0746)	(0.0732)	(0.0671)	(0.0993)	(0.0844)
	Comp. nbr. employees			1.000			1.000
				(0.00000305)			(0.00000195)
	Comp. R&D dpt.		4.556**	3.443		4.194**	3.233**
			(3.357)	(2.614)		(2.793)	(1.533)
	Comp. Start up		0.689	0.759		0.701	0.911
			(0.254)	(0.301)		(0.238)	(0.323)
	Comp. nbr. Ph.D.			1.002			1.002
				(0.00271)			(0.00282)
	Comp. Paris area			0.632**			0.575***
				(0.113)			(0.115)
	Comp. excl. IP		1.831**	1.754**		1.634**	1.499
			(0.482)	(0.495)		(0.347)	(0.397)
	Comp. first CIFRE		0.268**	0.323*		0.436	0.587
			(0.147)	(0.206)		(0.238)	(0.314)
	COMPMASTERED			0.688			0.490***
				(0.279)			(0.118)
	COMPNEW			0.692			0.424**
				(0.324)			(0.175)
	Univ. Paris area			1.304			1.233
				(0.337)			(0.310)
	Univ. exp. with indus.			1.275			2.012**
				(0.500)			(0.707)
	Univ. excl. IP		6.342**	5.661**		3.041**	1.186
			(5.586)	(3.818)		(1.664)	(0.503)
	UNIVNEW			0.885			0.876
				(0.271)			(0.263)
	UNIVMASTERED		1.005	0.852		0.839	0.689
			(0.239)	(0.280)		(0.204)	(0.190)
	Ph.D. Science oriented		0.376**	0.376**		0.437**	0.478**
			(0.160)	(0.159)		(0.171)	(0.139)
	Ph.D. French nationality			0.551***			0.605**
				(0.120)			(0.120)
	Ph.D. wage			1.000			1.000*
				(0.0000296)			(0.0000370)
	Ph.D. contractual time with comp.		1.521	1.543		1.230	1.605
			(1.160)	(1.294)		(0.698)	(1.031)
	Ph.D. effective time with comp.		1.692	1.530		3.827**	2.727*
			(1.128)	(1.025)		(2.440)	(1.577)
	Ph.D. diverted from research		1.089	1.251		0.996	1.059
			(0.257)	(0.352)		(0.240)	(0.295)
	Colab. multi. lab.			0.813			1.089
				(0.264)			(0.394)
	Colab. conflict			0.878			0.933
				(0.195)			(0.184)
	Colab. new			0.616**			0.690**
				(0.139)			(0.119)
	Colab. quarter meetings		1.270	1.099		1.244	1.036
			(0.395)	(0.336)		(0.228)	(0.190)
	Colab. annual meetings		0.889	0.733		1.237	1.001
			(0.614)	(0.529)		(0.760)	(0.415)
	Observations	635	635	635	635	635	635
	(Pseudo) R2	0.033	0.115	0.141	0.019	0.075	0.106
	Notes:

Table 6 -

 6 Patent output in CIFRE Ph.D. collaborative projects Robust standard errors in parentheses, clustered by discipline. Results are exponentiated coefficients (odds ratios). Base project type is MPO, COMPEQUIP, UNIVEQUIP. * p<0.1. ** p<0.05. *** p<0.01. Appendix A3 -Joint output with top-journal publications only Dependent Variable: At least 1 top journal publication, number of top journal publications Robust standard errors in parentheses, clustered by discipline. Results are exponentiated coefficients (odds ratios, incidence rate ratios). Base project type is MPO, COMPEQUIP, UNIVEQUIP. * p<0.1. ** p<0.05. *** p<0.01. Appendix A4 -Scientific publication in top-journal in CIFRE Ph.D. collaborative projects Robust standard errors in parentheses, clustered by discipline. Results are exponentiated coefficients (odds ratios). Base project type is MPO, COMPEQUIP, UNIVEQUIP. * p<0.1. ** p<0.05. *** p<0.01. Appendix A5 -CIFRE Ph.D. collaborative projects that received an Award

	8. APPENDIX
		SDI projects average	Non SDI projects average	t-test 99% confidence interval.
	Nbr. publications	2.91	2.21	2.42**
	Nbr. top journals publications	1.29	0.83	2.67***
	Nbr. patents	0.70	0.29	1.70*
	Ph.D. Prize	0.25	0.14	2.26**
	Legend: * p<0.1. ** p<0.05. *** p<0.01.

Appendix A1t-test analysis with unequal variance

Appendix A2 -Output performances per discipline

French National Audit Court Data (2018), Report on the Investment Plan for the Future (PIA)

It has to be highlighted that the study was performed in a particular context as the University was extensively relying on this particular scheme (ie. 1/3 of its Ph.D. students). Moreover, lots of projects were conducted with a unique large regional industrial private company.

Those projects are equally called applied-oriented[START_REF] Tijssen | Anatomy of use-inspired researchers: From Pasteur's Quadrant to Pasteur's Cube model[END_REF] or non-proactive strategies[START_REF] Callaert | Scientific yield from collaboration with industry: The relevance of researchers' strategic approaches[END_REF].

Those projects are equally called discovery-oriented[START_REF] Tijssen | Anatomy of use-inspired researchers: From Pasteur's Quadrant to Pasteur's Cube model[END_REF] or proactive strategies[START_REF] Callaert | Scientific yield from collaboration with industry: The relevance of researchers' strategic approaches[END_REF]

The program is funding projects in any scientific disciplines (engineering, social sciences, mathematics, etc.), but in 2015, Information and Technological Science Technologies (22%), Engineering Sciences (20%), and Chemistry & Materials (12%) are the top-3 scientific disciplines among the CIFRE project.

We would like to thank C. Angelier (ANRT), P. Giat (ANRT), N. Sennequier