Energy Consumption Reduction with Low Computational Needs

in Multicore Systems with Energy-Performance Tradeoff

Sylvain Durand and Nicolas Marchand

Abstract-A two voltage level electronic device is interesting because the clock frequency and the supply voltage level could be reduced (respecting certain rules) in order to decrease the energy consumption. We proposed in a previous paper a robust control architecture to deal with this power-performance tradeoff and we are now interested in extending this principle for several devices which works together since they are all supplied with the same voltage and clock frequency. Thus, an intuitive multicore control strategy which duplicates the whole monocore architecture as much as devices is compared with a second strategy where the duplication is reduced as much as possible. It appears that the proposal clearly gives a low control computational needs with the same reduction of the energy consumption.

I. INTRODUCTION

An energy-performance tradeoff is required in many embedded electronic systems. Actually, three power consumption sources exist in CMOS circuits [START_REF] Chandrakasan | Minimizing power consumption in digital cmos circuits[END_REF], which could be sorted into a dynamic consumption from switching of electrical gates and a static consumption from short circuit and leakage currents:

P = P switching + P short circuit + P leakage P = K dyn f clk V 2 dd + K sc f clk V dd + K leak V dd (1)
It appears that the consumption could be reduced by decreasing V dd , i.e. the supply voltage, or f clk , i.e. the clock frequency. However, decreasing only the frequency will decrease the power consumption and results in a slower running task but the total energy consumption will remain unchanged [START_REF] Varma | A control-theoretic approach to dynamic voltage scheduling[END_REF]. The voltage has hence to be reduce in order to decrease the energy consumption. Furthermore, the supply voltage is the dominant term especially because the dynamic power is the most important part in [START_REF] Albea | Control and stability analysis for the vdd-hopping mechanism[END_REF]. In other words, decreasing the voltage will almost quadratically decrease the energy consumption. Unfortunately, this drop will decrease the computational speed (because of the propagation delay of transistors) and controlling the supply voltage is hence a power-delay tradeoff: the power consumption decreases while the delay increases. That is why the supply voltage and the clock frequency have to be controlled together to guarantee the critical path (the longest electrical path a signal can travel to go from a point to another of the circuit). Clearly, it is required to decrease the clock frequency before decreasing the supply voltage and, respectively, increase the supply voltage before increasing the clock frequency. S. Durand is with NeCS Project-Team, INRIA -GIPSA-lab -CNRS, Grenoble, France, sylvain.durand@inrialpes.fr N. Marchand is with NeCS Project-Team, INRIA -GIPSA-lab -CNRS, Grenoble, France, nicolas.marchand@gipsa-lab.inpg.fr A good energy-performance tradeoff could be achieved using a commonly used approach in embedded systems: the Dynamic Voltage and Frequency Scaling (DVFS). This method consists in adapting the voltage and the frequency to the computational load and leads up to an important energy consumption reduction (regarding the application) [START_REF] Pering | Voltage scheduling in the lparm microprocessor system[END_REF]. Furthermore, it seems that most of the applications could run with a reduced voltage [START_REF] Burd | Processor design for portable systems[END_REF], [START_REF] Burd | A dynamic voltage scaled microprocessor system[END_REF]. Thus, several behaviors are known to minimize the energy consumption. Firstly, each task has to be considered independently and its execution time has to fit with the deadline. Moreover, selecting some suitable voltage levels leads to a drastic energy reduction even if the number of levels is very small [START_REF] Ishihara | Voltage scheduling problem for dynamically variable voltage processors[END_REF]. The supply voltage has to be reduced as much as possible and the frequency clock adapted to the computational load to minimize the energy consumption [START_REF] Pouwelse | Dynamic voltage scaling on a low-power microprocessor[END_REF].

Based on these different rules, we proposed in [START_REF] Durand | Fast predictive control of micro controller's energy-performance tradeoff[END_REF] a robust strategy to control the clock frequency and the supply voltage level of an electronic device. The proposal leads to minimize the energy consumption while guaranteeing a good computational performance. We are now interested in extending this principle to several devices which works together (with the same voltage and frequency domain) but where each device has to deal with a different load. In the following section, we first propose to bring back the monocore system architecture and summarize its control strategy. In section III, the multicore architecture is then presented and two control strategies are detailed: a first intuitive one which duplicates the monocore principle as much as devices and a second strategy which reduces considerably the computational needs. Finally the two controllers are compared in section IV in term of energy consumption and control computational needs.

II. MONOCORE SYSTEM PRINCIPLE

The system architecture with only one device to control is shown on Figure 1. The Device is the system to control. It usually runs at nominal supply voltage and constant clock frequency but these quantities will now dynamically vary in order to reduce the energy consumption. That is possible introducing a closed-loop with a controller to monitor the activity of the device (its computational speed ω) and to adapt the supply voltage and the clock frequency regarding the computational load ref provided by the operating system for each task.

The Oscillator and the Vdd-hopping are the two actuators used in some DVFS systems. They respectively provide the clock frequency and the supply voltage to the device:

• The oscillator could be a ring oscillator [START_REF] Fairbanks | Analog micropipeline rings for high precision timing[END_REF].

• The Vdd-hopping principle is described in [START_REF] Albea | Control and stability analysis for the vdd-hopping mechanism[END_REF]. Two voltage levels are available (V low and V high ) and the one or the other could be achieved (with a certain transition time and dynamics that depends upon the internal controller of the Vdd-hopping) regarding the V level input signal: V level = level low to require the low voltage and respectively level high for the high voltage.

The Controller has to provide the control signals to the actuators. Actually, the controller can be divided into two parts, as depicted on Figure 2: • The computational speed controller (CSC) provides the computational speed set point ω sp . Thus, from some task informations -for each task T i the operating system provides the computational load (i.e. the number of instructions C i ) and the time before the task has to be executed (i.e. the deadline N i ) -a fast predictive control law permits to calculate the best speed set point in order to minimize the penalizing high voltage running time (and so the energy consumption) while guaranteeing the computational performance.

• Then the frequency and voltage level controller (FVC) fits the measured speed ω with the desired one ω sp , by adapting the frequency f and the voltage level V level . The whole monocore controller (CSC + FVC) leads to a robust control (see [START_REF] Durand | Fast predictive control of micro controller's energy-performance tradeoff[END_REF] for further details): for a given test bench, the device runs at the penalizing high supply voltage only during 30% of time and an energy consumption reduction of about 20% is achieved. We propose next to adapt this principle to a system with several devices.

III. MULTICORE SYSTEM PRINCIPLE

The system architecture with several devices to control is shown on Figure 3. In fact this system is not so different from the monocore one (presented in section II and shown on Figure 1). Indeed the bases remain the same, with a controller which sends the frequency f and the voltage level V level to the actuators, i.e. a ring oscillator and a Vdd-hopping which respectively provide the clock frequency and the supply voltage to the electronic devices. The main difference is that there are now N devices to control, which means as many references ref N given by the operating system (the number of instructions and the deadline for each task) and as many measured computational speeds ω N as devices. Therefore the controller has to control the whole system but devices do not work independently since they are all supplied with the same voltage V dd and the same clock frequency f clk . The only allowed dimension of freedom is to trigger a device with a ratio of the clock f clk because in fact in practice it is possible to add one or two NOPs (i.e. No OPeration) between each instruction in order that the device runs twice or three times slower. For this reason, now the energy controller has to provide the frequency ratios ρ N anymore. Notations: j (lower case denotes the signal ρ of the device j, whereas ρ N (upper case indice) means that there are N signals ρ, one for each device.

ω N ω N ref N fclk&Vdd ρ N f Vlevel Multicore system Controller Actuators Device 1 Device 2 Device 3 Device n ω 1 ω 2 ω 3 ω n ρ 1 ρ 2 ρ 3 ρ n
In the two following subsections we will detail two control strategies: a first intuitive one which duplicates the monocore principle as much as devices and a second one which tries to minimize the computational needs of the controller.

A. Multicore control based on full duplication of the monocore control strategy

A first way to control several devices is to duplicate the monocore control strategy (detailed in section II) as much as devices. The resulting multicore architecture is presented on Figure 4 and could be divided in three steps:

1) First, the computational speed controller (CSC) calculates the speed set points ω N sp for the whole devices. Thus the set point ω j sp is independently calculated for each device j, using the task information C j i and L j i (given by the operating system) and the measured speed ω j .

2) Then the frequency and voltage level controller (FVC) independently calculates the frequencies f N and the voltage levels V N level usually required to control a single device. 3) Finally a frequency ratio controller compares the calculated frequencies f N to deduce the critical device c, i.e. the device which needs the maximal frequency to fit with its load. Thus the frequency f and the voltage level V level sent to the actuators are those from the critical device, i.e. f c and V c level , and the frequency ratios ρ N are obtained by doing the ratio between the frequency of the current device f j and the one of the critical device f c .

ω N f N cor Vlevel ρ N ω N sp ω N N N i C N i ω N fcor V N level CSC N FVC N
Frequency ratio controller Multicore system Σ N Fig. 4. Multicore control architecture based on full duplication of the monocore control strategy: the computational speed controller (CSC) and the frequency and voltage level controller (FVC) are duplicated as much as devices and a frequency ratio controller calculates the critical frequency and voltage level to deduce the frequency ratios ρ N

This intuitive strategy guarantees that the tasks are correctly performed for all devices because each device is independently controlled using the monocore strategy. Indeed, the monocore strategy works for one device and we focus the frequency and the voltage level decision on the critical one, i.e. the device which has to treat the task with the highest computational needs. Thus, all the non-critical tasks will be executed with the critical voltage level and a frequency lower or equal to the critical frequency. Moreover, an non-critical device could become the critical one whereas its task requires more and more computational needs.

An improvement could be done for the non-critical devices. Actually, if a device runs at high level then it is forced to the maximal frequency in order to run the shortest possible time at the penalizing high supply voltage (see [START_REF] Durand | Fast predictive control of micro controller's energy-performance tradeoff[END_REF] for further details). A non-critical device -which a priori could run at V low -will hence have its frequency forced anyway when the critical device needs to run at V high . For this reason, we propose to force only the frequency of the critical device. However in practice the critical device is not known yet when the frequencies are calculated, i.e. in step 2, because the frequency ratio controller determined it in step 3. Fortunately, a solution consists in using the device which was critical during the previous sampling period, by using the assumption that the critical device does not often change.

This intuitive duplication of the whole monocore principle leads to reduce the energy consumption of several devices working together while guaranteeing their computational performance. Nevertheless, a consequence is that the control computational needs are multiplied as much as devices and the number of variables seriously increases too. That is why we propose next to duplicate only some parts of the monocore control strategy.

B. Multicore control based on partial duplication of the monocore control strategy

This second strategy tries to minimize the control computational needs by not intuitively duplicating all the monocore control strategy. In fact, the frequency ratios ρ N require to be calculated and so some parts have necessary to be duplicated in order to obtain the N signals. The aim is to repeat the least code as possible. The best solution would be to use the references ref N (given by the operating system) to deduce the ratios without duplicating any part of the monocore strategy, but these signals are not relevant enough. Indeed, the critical task could not be known only from the number of instructions and the deadline because the computational load which was already executed is necessary too. Therefore we propose to duplicate the computational speed controller (which seems to have to be repeated anyway). Thus the multicore architecture on Figure 5 is proposed: 1) First, the computational speed controller (CSC) provides the speed set points ω N sp , from which the frequency ratios ρ N could be obtained since they provide information on the remaining computational load.

2) Then the frequency ratio controller compares the whole speed set points ω N sp to deduce the critical task c, i.e. the task which needs the maximal speed to fit with its deadline. Thus the speed set point ω sp and the measured speed ω sent to the FVC are those calculated for the critical task, i.e. ω c sp and ω c , and the frequency ratios ρ N are obtained by doing the ratio between the speed set point of the current device ω j sp and the one of the critical task ω c sp . 3) Finally the frequency and voltage level controller (FVC) calculates the frequency f and the voltage level V level to send to the actuators only for the critical device, i.e. the device which has to compute the critical task.

With this proposal, only the CSC is repeated and not the FVC anymore. We so hope a reduction of the control computational needs without impacting the gain on the energy consumption.

ω N ωsp ω fcor Vlevel ρ N ω N sp ω N ω N N N i C N i CSC N FVC Multicore system

Σ N

Frequency ratio controller Fig. 5. Multicore control architecture based on partial duplication of the monocore control strategy: only the computational speed controller (CSC) is duplicated as much as devices and then a frequency ratio controller calculates the critical speed set point which will be used by the frequency and voltage level controller (FVC) and deduces the frequency ratios ρ N Though all the devices are not independently controlled using the monocore strategy, the computational performances are yet guaranteed for each device. Indeed, with this second architecture the monocore control strategy only guarantees that the critical task will fit with its deadline, since the monocore control strategy is only applied to the critical device. The frequency ratios for the non-critical devices are then calculated from the computational load of the task of each device which is finally adjusted thanks to the CSC. Thus all the non-critical tasks are executed until their deadline anyway, or a task becomes the critical one when its computational needs become the more important one.

C. Discret values of the frequency ratios

One could note that the control algorithms proposed in both previous subsections were developed with ideal continuous frequency ratios ρ N . However, as explained in introduction of the multicore principle, some devices could be triggered with a ratio of the clock frequency f clk by adding NOPs between instructions in order that the device runs slower. This is why the frequency ratios could only be a discrete value which correspond to the number of NOPs, i.e.

= {1; 1 2 ; 1 3 } for 0, 1 or 2 NOPs respectively added between each instruction (note that the discrete frequency ratios will be called and the continuous ones ρ).

In order to implement this behavior, we first have to calculate the continuous ratios ρ N (i.e. ρ j = f j cor /f c cor for the multicore control strategy based on full duplication of the monocore control strategy and ρ j = w j sp /w c sp for the one based on partial duplication). Then, iterations have to be done for each device j in order to deduce the discrete frequency ratio j just upper than the value of the continuous ratios ρ j , as depicted by the below algorithm:

j =              1 if 1 2 < ρ j ≤ 1 1 2 if 1 3 < ρ j ≤ 1 2 1 3 if 0 < ρ j ≤ 1 3 0 otherwise (2)
This discrete ratio behavior would lead to a less energy efficient system because the frequencies of the non-critical devices will be higher than required -thanks to (2)contrary to the continuous case where these frequencies correspond exactly to the desired ones. Moreover, the control computational needs would increase a little bit thanks to the added code required to calculate the discrete ratios N .

IV. PERFORMANCE EVALUATION

This section presents some simulation results. The benchmark test is the same for all the simulations, where four devices with a different reference (number of instructions and deadline shown on Figure 6) have to be controlled: device 1 → three tasks to execute: the first task starts with 5 instructions to do in 0.5µs, then a 75 instruction task has to be executed in 2.5µs and the last one has to compute 10 instructions in 1µs. device 2 → three tasks also: a 15 instruction task to execute in 1.25µs, a task with 50 instructions to do in 2.25µs and then 5 instructions to execute in 0.5µs. device 3 → a single task of 40 instructions to do in 4µs. device 4 → three tasks again: 10 instructions to compute in 0.75µs, a task with 20 instructions to do in 0.75µs and a last 40 instruction task to execute in 2.5µs.

First, the simulation results for both control strategies (with ideal continuous frequency ratios) are shown on Figures 7 and 8. The top plots show the average speed set point (for guideline), the speed set point ω sp , the measured speed ω and the critical speed ω c (for guideline) for each device. One could verify that ω = ω c when the device is the critical one (highlighted by the gray areas on plots). Moreover, the supply voltage V dd (which is the same for the whole devices because of the multicore architecture) is shown on the bottom plot. Note that the calculated frequency f or the clock frequency f clk and the voltage level V level are not plotted because they do not provide relevant information: the frequencies are proportional to the speed and the level can be deduced from the voltage. The results are quantified in term of energy consumption and computational needs: Energy consumption of the system: The energy consumption is calculated in order to have an idea of the reduction achieved thanks to our proposal. Thus, the relation ( 1) is used and a ratio of this power consumption is added due to the Vdd-hopping principle: 20% more during the voltage transition time and 3% more during the steady state [START_REF] Miermont | A power supply selector for energy-and area -efficient local dynamic voltage scaling[END_REF]. Finally, an integration during the whole running time gives the total energy consumption. Computational needs of the controller: The control laws are compared in term of computational needs, i.e. the number of instructions required to calculated the computational speed set points, the frequencies, the voltage levels and the frequency ratios. To do that, we use the Lightspeed Matlab toolbox proposed by T. Minka [START_REF] Minka | The lightspeed matlab toolbox v2.2[END_REF], which provides a number of flops for each instruction. Moreover, the strategies are compared with a system using the intuitive control strategy (by duplicating the whole monocore control strategy) but without Dynamic Voltage Scaling (DVS): in this case the measured speed tracks the average speed set point and the supply voltage is fixed to the penalizing high voltage, i.e. V level = level high .

In both cases, the system runs during more than 50% of the simulation time at low voltage and a reduction of the energy consumption of about 20% is achieved in comparison with a system without DVS. The differences between the two Fig. 7. Simulation results of the multicore controller based on full duplication of the monocore control strategy (with ideal continuous frequency ratios): energy consumption of 3.976 • 10 -5 J and computational needs of 5.8 • 10 5 f lops, that is 82.2% of energy consumption and 94% of computational needs compared to a controller without DVS control strategies are during the voltage transitions and come from the choice of the critical device:

A) For the multicore control strategy based on full duplication of the monocore control strategy, one could see on Figure 7 that the measured speed ω is continuous for all the devices. This is because the ratios ρ N are obtained from the frequencies f N independently calculated for each device.

B) For the strategy based on partial duplication, one could see on Figure 8 a discontinuity of the measured speed ω as soon as the critical device changes, such as at time 2.35µs on device 2. Indeed, the frequency ratios ρ N are obtained from the speed set points ω N sp which are switching variables due to their construction (see [START_REF] Durand | Fast predictive control of micro controller's energy-performance tradeoff[END_REF] for further details). Thus the speed set point value of a device could suddenly change and so are the ratios. Nevertheless, the critical frequency -Fig. 8. Simulation results of the multicore controller based on partial duplication of the monocore control strategy (with ideal continuous frequency ratios): energy consumption of 3.98 • 10 -5 J and computational needs of 3.8 • 10 5 f lops, that is 82.4% of energy consumption and 62% of computational needs compared to a controller without DVS and so the critical speed -remains continuous.

While the energy consumption is very similar for both strategies, the computational needs is considerably reduced for the second one with a drop of 35% of the number of flops. For this reason, it would be the strategy to use.

Finally we propose to compare the simulation results of the control strategy with low computational cost, on a first hand when the frequency ratios are the ideal continuous variables ρ N and on an other hand when they are the discrete variables N described by the algorithm (2). One could immediately remarks than the results, respectively shown on Figures 8 and9, are quite similar. The main difference is that the measured speed ω does not track the speed set point ω sp in the discrete case as well as in the continuous case. However, the algorithm assures that the speed will be at least upper Fig. 9. Simulation results of the multicore controller based on partial duplication of the monocore control strategy (with discrete frequency ratios): energy consumption of 4•10 -5 J and computational needs of 4.3•10 5 f lops, that is an increase of 1% of energy consumption and 11% of computational needs compared to the controller with ideal continuous frequency ratios than the desired one and so the computational load will be correctly computed. This is why this principle is interesting since it only leads to an increase of less than 1% of the energy consumption and 11% of the control computational needs in comparison with the continuous frequency ratio case (which could not be implemented in practice anyway).

V. CONCLUSIONS AND FUTURE WORKS

This paper proposes architectures to control several devices which work together since they are all supplied with the same voltage V dd and the same clock frequency f clk (or a ratio of this clock). The multicore control strategies are based on the monocore control strategy depicted in [START_REF] Durand | Fast predictive control of micro controller's energy-performance tradeoff[END_REF], where a fast predictive control technique gives a computational speed set point to track in order to minimize the energy consumption while guaranteeing the computational performance.

While the first multicore control strategy intuitively duplicates the whole monocore architecture as much as devices, the second strategy -the contribution of this paper -tries to minimize as much as possible the duplication in order to decrease the control computational needs. Both architectures lead to a similar gain of energy consumption (compared to a system without DVS mechanism) but an important reduction of the number of flops is achieved with the second one. We finally propose to use discrete frequency ratios which are the only way to implement our controller in practice.

Next steps in this research is to test these control strategies in practice.

Fig. 1 .

 1 Fig. 1. Monocore system architecture

Fig. 2 .

 2 Fig. 2. Monocore controller architecture: a computational speed controller (CSC) plus a frequency and voltage level controller (FVC)

Fig. 3 .

 3 Fig. 3. Multicore system architecture

Fig. 6 .

 6 Fig. 6. References used for the simulations: the number of instructions, the deadline and the laxity (the remaining available time to execute the task) for each device

VI. ACKNOWLEDGMENTS

This research has been supported by the NeCS Project-Team (INRIA, GIPSA-lab, CNRS) in the ARAVIS project context. ARAVIS project is a Minalogic project gathering ST Microelectonics with academic partners of different fields, namely TIMA and CEA-LETI for micro-electronics and INRIA for operating system and control. The aim of the project is to overcome the barrier of subscale technologies (45nm and smaller).


Clarice Bertin
email: clarice.bertin@icn-artem.com

Véronique Schaeffer
email: schaeffer@unistra.fr

Local Ecosystem Open Innovation Intermediaries as Key Enablers for the Development of Incumbents' Digital Technology Partnerships

Keywords: Open innovation intermediaries, Partnership, Entrepreneur, Startup, Fintech, Banking sector, Dynamic capabilities, Digitalization, Ecosystem

Partnerships between incumbents and digital technology entrepreneurs are prone to develop innovative solutions needed by the coming "new normal" society. This paper focuses on the banking sector, which is currently facing huge challenges related to open innovation and digitalization. The qualitative approach adopted is based on observations and interviews involving actors from a given ecosystem: Luxembourg. The objective of this article is to explore the roles of innovation intermediaries in the capability development of incumbent firms' to establish new collaborations with innovative digital technology startups. We employ the dynamic capabilities framework to tackle this issue. Our findings show the crucial complementary roles of both external and internal innovation intermediaries in enabling digital technology partnerships and developing incumbents' relational capabilities. This paper contributes to knowledge in the field of strategic management by showing how traditional, highly structured organizations such as banks can regenerate their dynamic capabilities by being strongly engaged in their open innovation ecosystem and connected to a variety of actors from their ecosystem, especially open innovation intermediaries. For policy makers, this research suggests the deployment of means to empower these key interfaces to act in favor of their ecosystems.

I. INTRODUCTION

The digital era gives rise to many challenges for incumbents urged to innovate in services [START_REF] Barrett | Service innovation in the digital age: key contributions and future directions[END_REF]. The rise of digital technologies over a wide range of industries leads to the emergence of plenty of entrepreneurial opportunities. The advent of impacting new technologies has already occurred in the past after the 1980s with for example the development of advanced materials, biotechnologies or technologies of information and communication [START_REF] Maine | Commercializing generic technology: The case of advanced materials ventures[END_REF]- [START_REF] Oliver | The Coming Biotech Age. The Business of Bio-Materials[END_REF]. However, the impact of digital technologies presents today specificities due to a perpetual change both in the environment of firms and in the behavior of actors who are increasingly connected [START_REF] Nambisan | Digital Innovation Management: Reinventing Innovation Management in a Digital World[END_REF], [START_REF] Nambisan | Digital Entrepreneurship: Toward a Digital Technology Perspective of Entrepreneurship[END_REF]. The continuous adaptation requires strong dynamic capabilities from incumbent firms which were previously evolving in more stable environments [START_REF] Ambrosini | Dynamic capabilities: An exploration of how firms renew their resource base[END_REF], [START_REF] Teece | Business models and dynamic capabilities[END_REF]. In fast-moving technological and business environments, firms need to develop multiple networks in order to avoid the rigidity associated with stability and to open up their innovation process [START_REF] Chesbrough | Open innovation: The new imperative for creating and profiting from technology[END_REF]. Partnerships integrating digital technologies are a way for incumbents to adapt, innovate and perform. Firms have to develop their relational dynamic capabilities (RDC) in order to develop cooperation with new partners and promote the combination of internal and external resources. Some innovation intermediaries within their ecosystem might be able to help incumbent firms to develop new technology partnerships. This study investigates to what extent these innovation intermediaries from a given ecosystem play significant roles throughout the development of these technology partnerships between traditional industries and technology startups. The aim of this research is to show the role of open innovation intermediaries in the development of incumbent firms RDC. This research focuses on the banking sector which is under particularly strong pressure due to exponential technological change and needs. As well as other industries, this sector is increasingly impacted by the emergence of new digital technology competitors and by new demands from their hyper-connected customers. The stakeholders involved in the local innovation ecosystem studied in this paper are organizationally independent [START_REF] Adner | Match Your Innovation Strategy to Your Innovation Ecosystem[END_REF], [START_REF] Adner | Value creation in innovation ecosystems: How the structure of technological interdependence affects firm performance in new technology generations[END_REF] and their collaborations are based on an ad hoc coordination of actors and a diffuse, thus not powerbased governance. In addition, the current era of collaboration gave rise over the last decade to the expansion of open innovation [START_REF] Chesbrough | Open Innovation -The new imperative for creating and profiting from technology[END_REF]- [START_REF] West | Open innovation: The next decade[END_REF]. This open mode of collaboration to innovate has become a must for banks [START_REF] Fasnacht | Open innovation in the financial services[END_REF]. It can also be considered as a means to accelerate organizational change for a better understanding of organizational changes to help firms to develop this transition and the associated capabilities in order to capture value [START_REF] Gianiodis | Open service innovation in the global banking industry: Inside-out versus outside-in strategies[END_REF].

In their near ecosystem, innovation intermediaries play a crucial role for the stakeholders they serve. A recent study [START_REF] Bertin | Proximity and organizational factors for startup -large firm collaboration in an open innovation context[END_REF] showed that these intermediaries represent the keystone of the collaboration between startups and large firms by facilitating the construction and then the project progress thanks to their regular exchanges with the partners, their understanding and knowledge of the partners and the ecosystem network. According to [START_REF] Howells | Intermediation and the role of intermediaries in innovation[END_REF], the field of research on intermediaries is quite fragmented, with a lack of studies focusing on the holistic role of intermediaries in the firms' innovation process. Furthermore, [START_REF] Agogué | Rethinking the role of intermediaries as an architect of collective exploration and creation of knowledge in open innovation[END_REF] argue that "the industries studied are limited, although several argue that an important function for the intermediary is to establish connections between industries." More recently, Keinz & Marhold (2021) also invite scholars to investigate a diversity of organizations and intermediaries. [START_REF] Randhawa | Open service innovation: The role of intermediary capabilities[END_REF] invite "to investigate the 'open service innovation capabilities', focusing on the capabilities by clients as opposed to the intermediary". The various roles of innovation intermediaries in the ability of incumbent firm to establish digital technology partnerships in a given fertile ecosystem have not been studied yet.

In line with these calls, this paper aims to investigate this issue in the banking sector and is based on the study of the dynamic open innovation ecosystem of Luxembourg. We used the framework of dynamic capabilities as a process to explore innovation intermediaries' roles and highlight their influence in new partnerships' development of traditional industries with digital technology startups or Fintech. The second part presents the theoretical background focusing on incumbents' technological challenges and open innovation needs, innovation intermediaries types and roles in the era of digitalization, and the specific case of the banking sector and the third explains the methodology employed. Results are detailed in the fourth section, followed by a discussion on the enabling roles of the four types of innovation intermediaries. A sixth section suggests perspectives for future research and conclude.

II. THEORETICAL BACKGROUND

A. Incumbents' technological challenges and open innovation needs

Digitalization is a driving factor pushing organizations to more openness, externally but also internally [START_REF] Bertin | Organizational impact of digital open innovation in retail banks: Managing external and internal pressure[END_REF]. It also represents technological challenges for incumbents. Many organizations are indeed at the same time users of digital innovations and providers of innovative services integrating brand new technologies. Internally, digital technologies provoke a deep modification of current entrepreneurial and innovation processes [START_REF] Nambisan | Digital Innovation Management: Reinventing Innovation Management in a Digital World[END_REF], [START_REF] Nambisan | Digital Entrepreneurship: Toward a Digital Technology Perspective of Entrepreneurship[END_REF] and have consequences on strategic management process insofar as they imply a perpetual change both in the environment and actors' behavior.

A major change to be considered is the disappearance of boundaries structuring the entrepreneurial and innovation outcomes and processes. Boundaries between industries and markets evolve in a fluid and continuous process. The concepts of spatial boundaries and location loose of relevance because of the dematerialization of business activities [START_REF] Dahlander | How open is innovation?[END_REF], [START_REF] Palmié | The evolution of the financial technology ecosystem: An introduction and agenda for future research on disruptive innovations in ecosystems[END_REF]. Business models are in a perpetual redefinition and the boundaries between different stages of the entrepreneurial and innovation process are fuzzier [START_REF] Ries | The lean startup[END_REF]. The components constituting a business model are "the firm's value proposition and market segments, the structure of the value chain required for realizing the value proposition, the mechanisms of value capture that the firm deploys, and how these elements are linked together in an architecture" [START_REF] Saebi | What Drives Business Model Adaptation? The Impact of Opportunities, Threats and Strategic Orientation[END_REF]. The two-way and complex link between technologies and business model choice has been shown by scholars [START_REF] Baden-Fuller | Business Models and Technological Innovation[END_REF] who consider it as having received little attention. Furthermore, business models are closely linked to organizations' dynamic capabilities [START_REF] Teece | Business models and dynamic capabilities[END_REF], which can be considered as "the firm's ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments" [START_REF] Teece | Dynamic capabilities and strategic management[END_REF]. Following [START_REF] Teece | Business models and dynamic capabilities[END_REF], "the highest-order capabilities are those on which top management is (or should be) most focused. They are the most relevant for the innovation and selection of business models that address the problems and opportunities the company is endeavoring to solve/exploit."

Another major change induced by the development of digital technologies is the increasingly distributed nature of the innovation process [START_REF] Bogers | Managing Distributed Innovation: Strategic Utilization of Open and User Innovation[END_REF]. It leads to collective, open innovation processes that involve many actors having different goals and behaviors. In this moving, open environment where new business opportunities emerge, mature firms have to develop their ability to benefit from external innovation capacities and to renew their partnership. In a fast-moving environment, a firm's ability to renew and incorporate external competences is indeed crucial if it is to maintain its competitive edge [START_REF] Teece | Explicating dynamic capabilities: The nature and micro foundations of (sustainable) enterprise performance[END_REF], [START_REF] Ahmadjian | Keiretsu, Governance, and Learning: Case Studies in Change from the Japanese Automotive Industry[END_REF]. In addition to long-term stable relations, firms also need to develop multiple networks [START_REF] Chesbrough | Open Innovation -The new imperative for creating and profiting from technology[END_REF]. Relational capabilities have the status of distinctive capabilities that encourage innovation and corporate growth [START_REF] Lorenzoni | The leveraging of interfirm relationships as a distinctive organizational capability: a longitudinal study[END_REF] and help to develop dynamic capabilities over time [START_REF] Teece | Dynamic capabilities and strategic management[END_REF], [START_REF] Eisenhardt | Dynamic capabilities: What are they?[END_REF], [START_REF] Zollo | The impact of knowledge codification, experience trajectories and integration strategies on the performance of corporate acquisitions[END_REF].

B. The case of the banking sector

This paper investigates organization types which have been scarcely studied in digital open innovation context, namely banks. The banking sector is a particularly good example of a mature sector, deeply anchored in laws, in regulatory authorities' systems and where large organizations behave as central actors. Digitalization and open innovation have changed the traditional business model of banks and provoked the entrance of many new actors within the ecosystem in which banks evolve. Indeed, the new digitalized, openness context urges banks to find new ways, namely new sustainable business models, to cope with the huge changes occurring in their environment. These new ways are closely linked to the development of their innovation capacity, to their openness level to external actors from the ecosystem, and to their capability to manage internal and external actors and knowledge [START_REF] Bertin | Organizational impact of digital open innovation in retail banks: Managing external and internal pressure[END_REF].

Digitalization has changed the whole banking landscape and ecosystems: market itself and competition game rules, banking activities and internal processes including the new roles of bankers [START_REF] Palmié | The evolution of the financial technology ecosystem: An introduction and agenda for future research on disruptive innovations in ecosystems[END_REF]. Indeed, the huge potential in terms of new services offered by digital technologies gave birth to new needs from hyperconnected customers, and therefore to a need from banks to transform their activities to be able to bring the appropriate value added requested by their customers [START_REF] Fasnacht | Open Innovation Ecosystems: Creating New Value Constellations in the Financial Services[END_REF]. Furthermore, competition in the banking sector is harsh due to online banking developed by some traditional competitors, but mostly by non-traditional ones such as neo-banks, insurance companies or other organizations [START_REF] Lee | Fintech: Ecosystem, business models, investment decisions, and challenges[END_REF]. The latter are not known from the start for their banking activities, but they decided to develop this service offering for their existing customers. These new, increasingly numerous players can divert customers from their usual bank. In addition, the threat of potential new big entrants -the GAFAM-in banking activities is growing. Hence, for banks, digitalization can be considered both as a threat and as an opportunity. The worst threat is to disappear if unable to compete by digitalizing existing and new activities. From another perspective, digitalization is also an opportunity [START_REF] Marshall | Open innovation: digital technology creates new opportunities[END_REF] for banks to improve their own activities and processes and to restructure them by finding new business models based on digital technologies, and that will allow them both to create and to capture value. Hence, banks need to engage in relevant partnerships with technology startups, including startups providing innovative solution but also Fintech, on which research work is in its early stages [START_REF] Cai | Disruption of financial intermediation by FinTech: a review on crowdfunding and blockchain[END_REF]. To what extent do open innovation intermediaries from their ecosystem have a role to play in this development of new partnerships?

C. Open innovation intermediaries: Types and roles in the era of digitalization

Innovation intermediaries have been the object of many studies that have brought to light their different roles, in particular their role as brokers [START_REF] Howells | Intermediation and the role of intermediaries in innovation[END_REF], [START_REF] Hargadon | Technology brokering and innovation in a product development firm[END_REF], [START_REF] Mina | Open service innovation and the firm's search for external knowledge[END_REF], networkers, architects of collective exploration and knowledge creation [START_REF] Agogué | Rethinking the role of intermediaries as an architect of collective exploration and creation of knowledge in open innovation[END_REF], trust builders [START_REF] Gómez | Trust builders as open Innovation intermediaries[END_REF], or absorptive capability facilitators [START_REF] Kokshagina | Fast-connecting search practices: On the role of open innovation intermediary to accelerate the absorptive capacity[END_REF]. Other scholars underscored the roles of open innovation intermediaries in overcoming cultural barriers in open innovation processes [START_REF] Aquilani | Overcoming cultural barriers in open innovation processes through intermediaries: a theoretical framework[END_REF]. Online technological platforms have been flourishing last years and can be considered as new substitutes for traditional intermediaries [START_REF] Palmié | The evolution of the financial technology ecosystem: An introduction and agenda for future research on disruptive innovations in ecosystems[END_REF] especially in the financial sector with crowdfunding platforms [START_REF] Cai | Disruption of financial intermediation by FinTech: a review on crowdfunding and blockchain[END_REF]. Blockchain technology represents also a new intermediary which provides "the capability for new digital services and platforms to be created" [START_REF] Ali | The state of play of blockchain technology in the financial services sector: A systematic literature review[END_REF]. Despite the extensive literature related to innovation intermediaries, definitions of some roles sometimes overlap, such as those of boundary spanners, gatekeepers, and knowledge brokers [START_REF] Haas | Crowding at the frontier: Boundary spanners, gatekeepers and knowledge brokers[END_REF].

Hernández-Chea et al. ( 2021) have recently studied how incubators as intermediary organizations shape collaboration dynamics in entrepreneurial ecosystems. This paper replies to the call for research study on more diverse intermediaries [START_REF] Keinz | Technological competence leveraging projects via intermediaries: Viable means to outbound open innovation and mediated capability building?[END_REF] by investigating the roles of innovation intermediaries from an ecosystem, namely: accelerators, digital management consulting agency, and open innovation club. Recent studies can be found on the role of accelerators in startups' performance [START_REF] Sarto | Startup accelerators as an open environment: The impact on startups' innovative performance[END_REF], [START_REF] Battistella | Open accelerators for start-ups success: a case study[END_REF] and of accelerator programs in corporate-startup collaborations [START_REF] Kurpjuweit | Startup Supplier Programs: A New Model for Managing Corporate-Startup Partnerships[END_REF], [START_REF] Kohler | Corporate accelerators: Building bridges between corporations and startups[END_REF]. However, little is known on the role of external independent accelerators, thus not integrated to corporate programs, in the development of new collaborations between incumbents and technology providers such as innovative and Tech startups. In addition, digital management consultants seem to be completely forgotten by scholars in this research context. Open Innovation (LOI) as a means for public policy to increase collaboration for innovation in SMEs. The authors define LOI as "an intermediated network approach for collaborative problem-solving using the broadcast search mechanism to reach out to potential solution providers emphasizing relationships of trust through personal contact and the deliberate restriction to a defined geographical region to mitigate issues stemming from resource limits and cognitive constraints on the side of solution seekers, while also increasing intrinsic motivation on the solver-side". This study aims to investigate the propensity of innovation intermediaries as LOI actors prone to enhance startup-large firm collaborations.

Hence, beyond the value created by innovation intermediaries for themselves by collaborating with their client firms [START_REF] Silva | Innovation intermediaries and collaboration: Knowledge-based practices and internal value creation[END_REF] and the contribution of boundary spanners internal to organizations to detect opportunities for the latter [START_REF] Haas | How do boundary spanners contribute to the seizing of opportunities? Insights on a dynamic capability[END_REF], this paper aims to highlight the value and specific and roles of external innovation intermediaries for incumbents in the context of new partnership building between Tech startups and large firms. Scholars have not investigated the value created by local open innovation intermediaries (such as public or private accelerators, consultants in digital management and open innovation clubs) for organizations in this specific context. The objective of this article is to characterize the roles of these four types of innovation intermediaries throughout the building process of new collaborations between Tech startups and incumbents in a local open innovation ecosystem and within the context of digitalization needs. To approach this issue, the dynamic capabilities framework was employed (see section III), in line with recent research showing that "digital transformation is an ongoing process of using new digital technologies in everyday organizational life, which recognizes agility as the core mechanism for the strategic renewal of an organization's (1) business model, (2) collaborative approach, and eventually the (3) culture." [START_REF] Warner | Building dynamic capabilities for digital transformation: an ongoing process of strategic renewal[END_REF] III. METHODOLOGY A. Sample

Actors from the ecosystem of Luxembourg

We chose the single case study of the ecosystem of Luxembourg because it shows a series of changes in coherence with the existing theories on business model innovation and open innovation [START_REF] Tsujimoto | A review of the ecosystem concept -Towards coherent ecosystem design[END_REF]- [START_REF] Usman | How startups successfully organize and manage open innovation with large companies[END_REF]. This country expanded these last years to develop the adaptability and capacity of local organizations to innovate, among which banks, and to foster entrepreneurship through the support of technology startups and Fintech via incubators and accelerators. Several specific characteristics of the Luxembourger ecosystem led us to choose it as field of study. Firstly Luxembourg is clearly focused on technology and innovation, and digitalization is a priority for the Luxembourger government. Secondly, Luxembourg has built a growing technology startup and Fintech ecosystem [START_REF] Mothe | The impact of non-technological innovation on technological innovation : do services differ from manufacturing ? An empirical analysis of Luxembourg firms[END_REF], [START_REF]OECD Reviews of Innovation Policy: Luxembourg 12016[END_REF]. Thirdly, as a gateway to European markets, communication is facilitated inside the ecosystem itself, but also with the outside because of the generalized use of the English language, what makes Luxembourg naturally prone to open innovation. Finally, stakeholders from the ecosystem are geographically close to each other because of the country size, which allows to reduce distances between people. Many data such as publicly available reports, videos and press releases present these aspects of the development and activities of this ecosystem.

We conducted interviews to collect data from a diversity of stakeholders involved in the phenomena studied (see Table 1). The aim of these interviews was to collect descriptions of the world in which respondents are involved and to share their interpretation of the meaning of the phenomenon studied [START_REF] Kvale | The qualitative research interview: A phenomenological and a hermeneutical mode of understanding[END_REF], [START_REF] King | Using interviews in qualitative research[END_REF]. Then, the aim of the first series of interviews internal to banks was to understand through interviewees' description the challenges and changes introduced to adapt the banking system in the digital era, and also to collect their perception of the difficulties associated to this transition. The second series of interviews was dedicated to the various roles of external innovation intermediaries in the ecosystem as a whole and more specifically in the capability of banks to build new partnerships with startups and Fintech. We did not impose an overly rigid structure in the conduct of the interview but favored exchange, in order to perceive the interviewee's point of view [START_REF] Kvale | The qualitative research interview: A phenomenological and a hermeneutical mode of understanding[END_REF]. We conducted 12 semi-structured interviews in order to bring out in the discussion an understanding of the levers at stake in the evolution of banks' and intermediaries' organization. Their duration was from 30 to 90 minutes.

B. Data analysis

We adopted the approach of the single case study [START_REF] Yin | Case study research: Design and methods[END_REF], [START_REF] Yin | The Case Study Crisis: Some Answers[END_REF] of an ecosystem to explore the foundations of a complex organizational phenomenon. This case study has an instrumental value, in the sense that the intention of the research is not to describe all aspects of the case and then mobilize a set of theories to explain the situations observed. This case is used as a field of observation to study the influence of innovation intermediaries on the capability of mature firms to build innovation partnerships with agile startups and/or Fintech. This capability is considered as a dynamic capability. This phenomenon could have been studied in other contexts with similar characteristics [START_REF] Stake | Case studies[END_REF]. We asked questions to banks representatives about the new challenges for banks in the digital era, their open innovation strategy, the internal change supporting the evolution of the activities of the bank, the difficulties associated to these changes and the role of actors from the ecosystem in the transformation of banks. We then asked to innovation intermediaries about their activities, about their roles in direction to firms, especially banks having integrated in their strategy the building of new partnerships with startups and Fintech. The interviews have been recorded and transcribed into text. Data coding of interview verbatim was undertaken means NVIVO software which was insightful to organize and examine data [START_REF] King | Using interviews in qualitative research[END_REF].

We conducted a qualitative analysis of these data through a three-steps process leading to identify firstorder, and second order codes [START_REF] Gioia | Seeking Qualitative Rigor in Inductive Research : Notes on the Gioia Methodology[END_REF] that we integrated to third-order codes issuing from the dynamic capabilities framework. The contribution of dynamic capabilities as a theoretical foundation to study digital transformation has been underscored in a recent review [START_REF] Vial | Managing Digital Transformation: A Review and Research Agenda[END_REF]. The research focus on relational dynamic capabilities (RDC) and explore the role of open innovation intermediaries on the three components of the dynamic capabilities framework: sensing, seizing, transforming [START_REF] Teece | Business models and dynamic capabilities[END_REF]. These three components served as analytical grid. Sensing capacities of firms relate to "a scanning, creation, learning, and interpretive activity." (Teece, 2009, p.9). We define sensing RDC as the activities related to the identification and the evaluation of opportunities of new partnerships. Seizing RDC relate to the design of "strong relationships [that] must also be forged externally with suppliers, complementors, and customers" [START_REF] Teece | Achieving integration of the business school curriculum using the dynamic capabilities framework[END_REF]. Finally, "a key to sustained profitable growth is the ability to recombine and to reconfigure assets and organizational structure" [START_REF] Teece | Explicating dynamic capabilities: The nature and micro foundations of (sustainable) enterprise performance[END_REF]. We define Transformation/reconfiguring RDC as the ability to transform the organization in a way that favor sensing and seizing RDC.

IV. RESULTS

A. Involvement of open innovation intermediaries in the regeneration of banks' dynamic capabilities

Innovation intermediaries have a real impact on banks relational dynamic capabilities as shown in table 2. Intermediaries contribute to increase the ability of banks to sense opportunities. They provide quickly highquality information on innovation opportunities, and for some of them (digital consultant, and Fintech accelerator) knowledge related to emergent technologies (Table 2). The result table below shows intermediaries' roles in searching for innovation opportunities and keystakeholders in the local open innovation ecosystem and abroad. The role of the digital consultant and of accelerators oriented towards business is important in the development of sensing relational dynamic capabilities (Table3).

The digital consultants and accelerators also play a role to develop the ability of incumbent banks to seize new partnerships opportunities. They help to the coordination between the actors and also provide their competences or the competences of experts from their network to make the cooperation possible.

"When we work with a client, we give them knowledge about Fintech, help them structure a partnership, and then we withdraw. We are not part of the relationship that will be established between the bank and Fintech. We want to remain independent, because when we give advice, well, there's no conflict of interest... we remain totally independent." (Digit_Consult)

The digital consultant is the only one to contribute to transforming relational dynamic capabilities of the incumbent banks. The others are less present than in the sensing and seizing activities. Once the partnership with a Fintech or a startup has been established, most intermediaries withdraw, except the digital consultant who accompanies the bank.

However, some intermediaries contribute to develop the transforming RDC of banks through their role in the development of the innovation ecosystem. The ability of an incumbent firm does not rely only on its internal capabilities but also on the capability of its ecosystem. By playing their role of networkers, they increase the ability of actors to meet and then favor the development of networks, they develop the ecosystem's open innovation culture, and they support the entrepreneurial dynamic, necessary to the sustainability and the evolution of the ecosystem. There is a diversity of role of innovation intermediaries regarding the development of RDC of banks and these intermediaries are specialized. The development of networks of innovation intermediaries then appears to be crucial. In the world of FinTech, these networks are local and are connected to global networks of innovation intermediaries. The literature about innovation intermediaries underlines the role they play to reduce the cognitive or social distance between two or several actors [START_REF] Howells | Intermediation and the role of intermediaries in innovation[END_REF], [START_REF] Agogué | Rethinking the role of intermediaries as an architect of collective exploration and creation of knowledge in open innovation[END_REF]. We show that innovation intermediaries also contribute to increase the sensing, seizing, and transforming capacities of incumbent firms, which must develop strong relational dynamic capabilities [START_REF] Warner | Building dynamic capabilities for digital transformation: an ongoing process of strategic renewal[END_REF], [START_REF] Donada | Understanding the relational dynamic capability-building process[END_REF], to be able to combine internal and external resources and adapt to fast-moving digital technologies. We bring evidence about the role that local open innovation intermediaries play in the development of these relational dynamic capabilities. We show firstly that these innovation intermediaries compensate for a lack of connection between stakeholders, in this case banks and Fintech or startups, who do not know each other a priori. They also help reducing the uncertainty level -and thus the associated reluctance to engage-for stakeholders willing to collaborate for innovation purposes. Indeed, by creating connections between potential partners, local open innovation intermediaries foster their mutual understanding, necessary to collaborate. Some intermediaries also provide technology translation to banks that means knowledge on what a given new technology can bring to business lines on one hand, and to banks' clients on the other hand. The above results reinforce thus conclusions of a previous research work [START_REF] Stewart | Intermediaries, users and social learning in technological innovation[END_REF]. Furthermore, in the extension of a recent research work [START_REF] Kivimaa | Towards a typology of intermediaries in sustainability transitions: A systematic review and a research agenda[END_REF], our study indicates that innovation intermediaries bridge between originally distant, asymmetric partners. Moreover, their role as "architects" [START_REF] Agogué | Rethinking the role of intermediaries as an architect of collective exploration and creation of knowledge in open innovation[END_REF] is crucial for several dimensions of partnerships between banks and Fintech or startups, and especially the regeneration of dynamic capabilities of banks.

Our research also replies to a recent call [START_REF] Keinz | Technological competence leveraging projects via intermediaries: Viable means to outbound open innovation and mediated capability building?[END_REF] by showing how a diversity of innovation intermediaries contribute to the dynamic of a digital innovation ecosystem that support the digital transformation of incumbent firms. The various types of public and private intermediaries have specific role within the ecosystem, regarding the development of the relational dynamic capabilities of incumbent firms. In the case of banks, consultants in digital management, specialized in financial services, and Fintech accelerators play a crucial role in the development of the sensing capacities of incumbents. They possess specific technological expertise and develop local and worldwide networks in order to detect and evaluate technological opportunities. Fintech and generalist accelerators support local entrepreneurial dynamics that are fundamental in the internal dynamic of an innovation ecosystem [START_REF] Palmié | The evolution of the financial technology ecosystem: An introduction and agenda for future research on disruptive innovations in ecosystems[END_REF], [START_REF] Leckel | Local Open Innovation: A means for public policy to increase collaboration for innovation in SMEs[END_REF]. This dynamic of startups creation is necessary to the ecosystem's survival and to its adaptation to technological evolution. The accelerators and the corporate club play an important role in the development of the social network of actors, increasing their ability to meet new partners. A specific mission of the corporate club is the organization of events putting together a diversity of actors. The aim is to develop knowledge sharing and trust between actors, and an open innovation culture within the local ecosystem, which also influences the open innovation culture within incumbent organizations, thus internally. This structuration of the ecosystem is supported by the case we studied from the Chamber of Commerce. As a network orchestrator it contributes to structure the ecosystem and to give a consistency making the system understandable for incumbent firms and hence favoring their involvement.

If the four local open innovation intermediaries studied possess each specific roles, some of them overlap. We interpret these overlapping items as necessary prior knowledge [START_REF] Cohen | Absorptive Capacity: A New Perspective on and Innovation Learning[END_REF], even a need for intermediaries to understand each other and be able to collaborate on common collaboration projects for their clients. We hypothesize that this activity (and knowledge) overlapping strengthens the complementary of intermediaries for the benefit of their client firms and startups and their local ecosystem.

While innovation ecosystems mainly consider the local dynamic of innovation and are assimilated by some authors to clusters or regional systems of innovation, we show the role of worldwide networks of innovation intermediaries in supporting digital transformation of incumbent firms. Because the diffusion of digital technologies leads to fuzzier organizational and spatial boundaries, we show that innovation intermediaries develop both local and global networks, to explore technological opportunities. Locally, open innovation intermediaries support digital transformation of incumbent firms, and globally, they develop structured worldwide networks to provide these incumbents with a wide range of technological opportunities. They support the ability of banks to benefit quickly from potential solutions developed far from their geographical territory.

If external open innovation intermediaries play a crucial role as catalysts of technology partnerships in their ecosystem, relationships between banks and Fintech or startups can only occur if banks deploy the right internal innovation intermediary [START_REF] Ben | Construction of relations with distant suppliers on the cognitive and relational dimensions to coexplore discontinuous innovations[END_REF], the one who will be able to interact with local open innovation intermediaries, Fintech or startups, and then diffuse innovation culture within banks. These relationships between internal and external intermediaries allow both mutual understanding and co-construction of innovative solutions.

VI. CONCLUSION

The objective of this research paper was to explore the roles of external local open innovation intermediaries in technology partnerships' development of banks with Fintech and startups within the open innovation ecosystem of Luxembourg. We chose to look at local open innovation intermediaries' roles from a dynamic capabilities perspective and contribute thus to knowledge crossing between open innovation and dynamic capabilities which is currently scarce [START_REF] Teece | Hand in Glove: Open Innovation and the Dynamic Capabilities Framework[END_REF]. This framework choice allowed structuring chronologically the nature of the various activities of local open innovation intermediaries influencing the development process of new technology partnership between technology startups and incumbents (banks). It has implications for practice: incumbent firms can identify who among open innovation intermediaries is/are able to help them, when they will need them during the process and how they can be of help. It also has implications for policy makers supporting entrepreneurship and innovation within their local ecosystem. Finally, the open innovation ecosystem dynamics results from a real collective contribution to its development, including intermediaries, organizations, startups, Fintech, experts from various fields, and public authorities. This collective intelligence makes the community of the local open innovation ecosystem a dynamic interconnected one.

  Given the huge challenges associated to digitalization, it appears relevant to investigate their role in new partnerships development where technological issues are at the core of collaborations. A recent research from Amann et al. (2021) showed how Corporate Innovation Hubs can mitigate NIH and NSH problems in knowledge transfer. However, this intermediary organization has not been studied yet in the context of technology-oriented collaborations between startups and large firms. Furthermore, there is no research linking local open innovation intermediaries and collaboration between incumbent firms and innovative and Tech startups from a given ecosystem. Leckel et al. (2020) consider Local

TABLE 1 -

 1 INTERVIEWEES

	Codes	Interviewees	Functions	Type
		Digital
	Digit_C	consulting	Country Head	Intermed
	ons	agency	Luxembourg	iary
		Open	Corporate
	Corpo_	innovation	Innovation	Intermed
	Club	corporate club	Manager	iary
		Startup
		Accelerator of	Director of the	Intermed
	AccBk2	Bank2 (private)	accelerator	iary
		Startup
		Accelerator for
		international
		developement	Chief
	Int_Acc	deployed by	Executive	Intermed
	Bk2	Bank2 (private)	Officer	iary
		Fintech	Head of
		Accelerator	Partnerships	Intermed
	FintAcc	(public)	and Ecosystem	iary
		Chamber of
		Commerce -
		Community		Policy
		working space	Chief	and
	Chamb	(building and	Executive	financial
	Comm	organization)	Officer	support
				Actor
				from the
			Chief	OI
	RegIncu		Executive	ecosyste
	b	Incubator	Officer	m
			Human
	Bk1-		Resources
	HRM	Bank1	Manager	Bank
	Bk1-		Head of
	Inno	Bank1	Innovation	Bank
	Bk1-Strat	Bank1	Head of Strategy	Bank

TABLE 2 -

 2 ROLE OF OPEN INNOVATION INTERMEDIARIES IN THE DEVELOPMENT OF BANKS RELATIONAL DYNAMIC CAPABILITIES

				Effect
				of OI
				interme
	Actors involved	First order level	Second order level	diaries on RDC of
				incumb
				ent
				banks
	Digit _Consult Fintech_Acc	Exploration of technological opportunities for banks	Exploration
		Watch on	of
	Digit_Consult	startup or	opportunitie
	AccBk2	fintech	s
	Int_AccBk2	projects to be
	Fintech_Acc	deployed in
		banks
	Digit _Consult Digit_Consult Evaluation of Evaluation of technological opportunities start-ups	Evaluation of opportunitie s	Sensing
		Sourcing of
		solutions to
	Digit_Consult	specific
		problems identify by banks	Identificatio n of needs
	Digit_Consult	Understanding of banks
		needs
	Digit_Consult AccBk2 Int_AccBk2 Fintech_Acc	Help to design partnerships and to develop links with fintech Sharing and	Building of partnerships
	Digit_Consult Fintech_Acc	opening of a network of		Seizing
		expert
	Digit_Consult Fintech_Acc	Design of projects to develop specific solutions	Provider of technologic al knowledge

TABLE 3 -

 3 SUMMARY OF OPEN INNOVATION INTERMEDIARIES' SPECIFIC IN THE DEVELOPMENT OF BANKS RELATIONAL DYNAMIC CAPABILITIES

	Capacit y	2 nd order level	Digit _Con sult	Finte ch_A cc	Ac cB k2	Int_ Acc Bk2	Corp o_Cl ub
		Explo
		ration
	NG SENSI	s tunitie of oppor	**	**	*	*
		Evalu ation	**
		of