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Connecting Startups and Incumbents in University-driven Innovation Ecosystems A Look Behind the Curtain

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Entrepreneurship and its connection to the manifestations of innovation has been discussed in academia for quite some time. The last 15 years saw the increasing relevance of open innovation and a rising prominence of so-called ecosystems. As interactions between different stakeholders become more complex, innovation ecosystems are becoming of prime importance. There is a need for further researching how these ecosystems are formed and the factors that can render them successful or failed. The purpose of this article is to present the cases of three university-driven innovation ecosystems, i.e. ecosystems that are formed by established firms and startups under the coordination of a university. These ecosystems are created through an open innovation program aiming to create collaborative schemes by bringing together incumbent firms and startups to accelerate innovation and facilitate a co-creation process. These schemes involved important and discuss how their interactions shape the innovation process. The cases deal with different sectors airport, food, and finTech and provide insights on how an ecosystem is formed and how the innovation process is affected by the collaboration of different firms, funds, sponsors, and institutional partners. This work contributes to the current research on how to set up a university-driven scheme and identifies key factors that drive the actors to continue to operate within the ecosystem.
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Stratos Baloutsos, Angeliki Karagiannaki and Katerina Pramatari
ELTRUN Research Center, Dept. of Management Science and Technology, Athens
University of Economics and Business, Athens, Greece
sbaloutsos@aueb.gr
akaragianaki@aueb.gr
k.pramatari@aueb.gr
DOI: 10.34190/EIE.20.142
Abstract: Entrepreneurship and its connection to the manifestations of innovation has been discussed in academia for quite
some time. The last 15 years saw the increasing relevance of open innovation and a rising prominence of so-called
ecosystems. As interactions between different stakeholders become more complex, innovation ecosystems are becoming of
prime importance. There is a need for further researching how these ecosystems are formed and the factors that can render
them successful or failed. The purpose of this article is to present the cases of three university-driven innovation ecosystems,
i.e. ecosystems that are formed by established firms and startups under the coordination of a university. These ecosystems
are created through an open innovation program aiming to create collaborative schemes by bringing together incumbent
firms and startups to accelerate innovation and facilitate a co-creation process. These schemes involved important
and discuss how their interactions shape
the innovation process. The cases deal with different sectors airport, food, and finTech and provide insights on how an
ecosystem is formed and how the innovation process is affected by the collaboration of different firms, funds, sponsors, and
institutional partners. This work contributes to the current research on how to set up a university-driven scheme and
identifies key factors that drive the actors to continue to operate within the ecosystem.
Keywords: innovation ecosystems, university driven innovation, startups, case study
1. Introduction
Entrepreneurship and innovation are two of the most thoroughly researched concepts in business and
economics. Entrepreneurship involves recognizing opportunities, creating new value, assuming risks and
realising rewards, and may occur in a variety of setting s (Beliaeva et al., 2019). Innovation has been a driving
force for entrepreneurship whether as a simple invention, a closed innovation research lab, an open innovation
system, or an innovation cluster. As knowledge, and consequently innovation, spills over traditional organisation
boundaries, entrepreneurial ventures are increasingly affected and forced to adapt their innovation strategy to
a broader set of open innovation communities, the so called Innovation Ecosystems (IE) (Dahlander and Gann,
2010; Shaikh and Levina, 2019; Suominen, Seppänen and Dedehayir, 2019).
IEs have been a prominent issue of discussion both in academia and business (Oh et al., 2016; Ritala and
Almpanopoulou, 2017). The rise of open innovation practices, the success of the innovation communities,
combined with the rising complexity of business environments have shifted a lot of focus on how an IE can be
formed and be successful. While the importance of users in the innovation process was established more than
30 years ago by von Hippel (Hippel, 1988), the advent of open communities has highlighted the critical role of
communities in the innovation process. Considering the seminal work of Chesbrough on Open Innovation (2003)
and its relevance, the ability to identify sustainable and innovative ecosystems is becoming an issue of prime
importance for both established and growing companies.
The starting point of this research is, therefore, to contribute to the ongoing discussion around IEs by presenting
three different cases of open innovation projects that created IEs. Specifically, this research focuses on
university-driven IEs; meaning IEs that are facilitated by a university and involve an established firm (incumbent)
and several startups. Within the academic community, the issue of the formation of an IE remains crucial
(Ceccagnoli et al., 2012; Dattee et al. 2018). In addition, there is a research gap regarding the factors and KPIs
that make an IE successful (Durst and Poutanen, 2013; Oh et al., 2016; Suominen, Seppänen and Dedehayir,
2019). While several strategies for gaining value from ecosystems have been proposed (Hannah and Eisenhardt,
2018), committing to a new ecosystem is still regarded a high risk endeavour (Dattee et al. 2018). To commit to
a new ecosystem is more difficult for established firms that may not identify this as a significant added value to
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their core business. Trying not only to describe the formation of a university-driven IE but also to
, the specific objectives are:
to present how a university-driven IE can be setup to bridge the different actors (i.e. incumbents and
startups) within an open innovation program;
to pinpoint key factors that drive the actors involved to form a continuous relationship and to continue to
operate within a university-driven IE, thus providing sustainability to the IE in question.
This paper is organized as follows. Section 2 provides a theoretical background on university-driven IEs. Section
3 provides a description of the research design. Section 4 describes the results and Section 5 provides the
conclusions, limitations, and future research directions.
2. The university-driven innovation ecosystem
While it is relatively simple to intuitively comprehend what constitutes an IE, many researchers have been
discussing IEs in general terms. The first and most widely used definition (Adner, 2006) is one of the most
abstract ones. Within this work, we adopt one of the most recent definitions proposed by Granstrand and
Holgersson (2020) as: the evolving set of actors, activities, and artifacts, and the institutions and relations,
including complementary and substitute relations, that are important for the innovative performance of an actor
or a population of actors (Granstrand and Holgersson, 2020). Based on this definition, we try to define the core
elements that constitute the IEs under study, that is an IE under the orchestration of a university as the
underlying institution.
Figure 1: Innovation ecosystem definition
The importance of universities in advancing the innovation process has been generally recognised in the
innovation literature (Cooke, Uranga and Etxebarria, 1997; Klofsten and Jones-Evans, 2000; Cohen, Nelson and
Walsh, 2002), especially in the context of the entrepreneurial university (Guerrero et al., 2016). Our work
expands on this notion. It proposes that utilising a universi
facilitate the initiation of an IE by helping to overcome some of the barriers common in such collaborative
schemes (e.g. the proverbial chicken and (2018)). It also helps
mitigating potential issues that arise when an actor inside an ecosystem has significantly more market power
than the rest, a de facto situation in incumbent-startup collaborations.
Based on the above definition, a university-driven IE:
is formed by an institution, specifically a university, that sets the rules and facilitates the innovation process
involves actors, specifically an incumbent and several startups
performs several activities such as the exchange of knowledge and data, the sharing of technological
equipment and any facilities
aims to create common artifacts that are co-developed products or services, to address the respective
markets.
An adaptation of Figure 1 that conceptualises the IEs in question is shown in Figure 2 (the figure shows only four
startups for the sake of brevity). This format is very similar to what is referred to as the hub-based ecosystem
where a central stakeholder (i.e. the hub firm) acts as a leader and dictates the governing rules and general
direction of the ecosystem (Nambisan and Baron, 2013). However, in these cases, the existence of the university
as an orchestrator acts as a differentiating factor that alters many of the qualities of the ecosystem and mitigates
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Stratos Baloutsos, Angeliki Karagiannaki and Katerina Pramatari
several problems. This is in line with the literature arguing the significance of universities in IEs (Etzkowitz and
Leydesdorff, 2000; Heaton, Siegel and Teece, 2019).
Figure 2: The university-driven ecosystem
3. Research design
This research relies on a multiple-cases design to build theory that is deeply informed by primary data gathered
through three qualitative, longitudinal case studies. Given the pre-mature level of a university-driven innovation
ecosystem, case research gained respect in this design as it is ideal
(Yin, 2003), allowing for a richer knowledge of non-conceptualized issues. When conducting qualitative research,
several stages take place as depicted in Figure 3 (adapted from Murphy et al., 2016): develop initial objective;
data collection; the coding process; searching for patterns.
Figure 3: Research design
Data collection. We used the following techniques:
Semi-structured interviews with members of the startups and the incumbent firms involved in the
innovation process. This technique is suited to case study data collection, and particularly for exploratory
research, as it allows expansive discussion to illuminate factors of importance (Albuam and Oppenheim,
1993; Dennehy and Conboy, 2019).
Personal observations and shadowing of the innovation process. Researchers spent a great deal of time and
effort to analyse underlying factors that drive the sustainability of the ecosystem and encourage further
collaboration between the actors. This was accomplished by interacting in a day-to-day manner with the
incumbent firms and the startups. Based on these observations, the research team was able to gather data
about their interactions, collaborations, and general progress throughout the course of the innovation
program.
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The coding process. To get insights on potential between-subject patterns in the data, which can be a boon for
theorizing, we used the principle of constant comparison. In this way, emerging primary data were always
viewed through the lens of what has been gathered from the personal observations during the stages of the
open innovation process, the semi-structured interviews and the progress reports, as well as existing literature.
the participants of the IE -
2006). Following the coding process, we moved to emerging categories, a higher level of data abstraction
(Glaser, 1978). T and thus we came across some factors that can drive
startups and established firms to form a continuous relationship within a university-driven Innovation
Ecosystem.
Searching for patterns. Every year that the open innovation programme was active, for each case, interviews
were conducted with two key personnel of the incumbent firm and four different startups. This led up to a total
of 18 interviews for Case A (3 years), 12 interviews for Case B (two years), and 6 interviews for Case C (one year).
These interviews lasted about 30 to 45 minutes. The interviews revolved around expected knowledge
about: (1) factors that hinder or accelerate the successful implementation of the innovation process and (2)
issues that drive or discourage continued collaboration. The questions were open-ended in order to allow more
freedom of expression in an effort to capture the complexities of operating within an IE more accurately. Over
the time frame of our study, we repeatedly reviewed
4. Describing the three open innovation programmes as university -driven IEs
Case A comprises a university-driven IE aiming to activate the startup community to propose a technological
innovation that will transform the airport industry. While the initial vision was to innovate the core airport
operations, the ecosystem evolved to include several products/services catered to the broader environment and
added complementary offerings. Some example artefacts are: an easily adjustable system for detecting foreign
objects at airfields; an IoT quantitative recycling performance technology & collection service to disrupt the
waste management sector, enabling visualization of measurement data and employee engagement.
Case B comprises a university-driven IE aiming to modernise the customer experience through fintech
technologies and help e-commerce and retail industry to stay up to date in a digital environment. Example
artefacts created are: an innovative
interactive virtual walkthroughs available for online users to browse and shop; a platform that offers coupons
to commuters whose daily route passes through specific points of sales.
Case C comprises a university-driven IE aiming to innovate in the food sector. Some example of artefacts are:
valorisation of the organic residues of the coffee in order to generate new products; an analytics platform that
gathers transactional data from retail environment.
All three cases represent open innovation programs that are set up using similar principles as university-driven
IEs. The university orchestrates the program to ensure trust and collaboration via establishing a transparent and
mutually beneficial process. The programs are constructed in consecutive phases: problem identification, idea
generation, business model innovation, new prototype design, and implementation support. This structure is
based on the steps proposed by West and Bogers (2014) for the open innovation process. This process is
important to ensure the alignment of expectations and setup of working channels across different actors in the
IE.
In the beginning, the university searches the local entrepreneurial ecosystem to assess the level of maturity of
existing startups or research teams. Discussions are held with incumbent firms to involve them in the IE and
come up with sets of high-level concepts that would be of interest. The university acts as an intermediary for
broadcast search (Jeppesen and Lakhani, 2010). A token monetary prize is decided to attract more startups to
collaborate. Another important aspect of this stage is aligning the startups in understanding the business
context, identifying business opportunities, and establishing a collaboration context. This is achieved by
workshops and meetups which allow actors to familiarise themselves with each other and reinforce the feeling
of a shared culture. Team-building exercises support the formulation of teams with complementary skills,
including business vision and technical knowledge (Karagiannaki et al., 2018).
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As the programs moves forward, the university serves as a facilitator to knowledge transfer and communication
in terms of business model depending on how the co-creation evolves. This is done either by involving its own
experts or bringing into the ecosystem expertise from the broader startup ecosystem. This procedure is once
again easier to take place with a university present as it acts as a trusted mediator. This way the co-creation
process
Finally, as co-designed services start to appear, it is important to ensure a mutually beneficial collaboration by
helping actors commercialise their co-creations. Co-creation can lead to decline of interest for some startups
especially as the co-developed innovation can sometimes be acquired by the incumbent or allow startup
Irrespectively, concerning the sustainability of the IE, there is a need of a viable
number of startups active and willing to collaborate with each other and the incumbent.
5. Findings and discussion
By analysing the three IEs, a total of 17 factors that drive the sustainability of the ecosystem is identified and
consequently the actors willingness to continue. A summary of the findings can be found Table 1. The factors
are categorized based on the type of actor that identified them and their importance that is varied between the
different cases. /low or non-applicable (n/a). For the sake of brevity,
we opted to omit the interviews, but they are available upon request.
Table 1: Ecosystem sustainability factors by type of actor
5.1 Innovation ecosystem sustainability factors as seen by incumbents
The incumbents identified seven different factors as described in the following paragraphs.
Access to talent refers to the effort to find new people to employ in a firm. This was not explicitly mentioned by
the incumbents but in case B there was an effort to absorb talent from the startups to the firm itself. As this only
happened in Case B, it can be surmised that it was a firm specific behaviour. Similarly, the incumbent that was
more willing to absorb startup staff was also more willing to move forward with the acquisition of startups and
their solutions when they developed promising products. In the other two cases, both in terms of access to
talent and acquisition, the importance was low to non-existent.
Incumbents Case A
Case B
Case C
Access to Talent n/a High n/a
Acquisition n/a High n/a
Product Co-Creation High High Low
Outwards Exposure High High Low
Innovation Procurement High High High
Intrapreneurial Culture n/a Low High
New Market Expansion Low High High
Startups Case A Case B Case C
Access to Industry Experts Low High High
Customer Acquisition High High High
Outwards Exposure Low n/a n/a
Partnerships/ Joint-Venture Low High Low
Market Knowledge n/a High High
Networking High High Low
Prize Funding Low Low Low
Product/Service Development Low High High
(Pre)Seed Funding High High Low
Time to produce results Low Low Low
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Product co-creation refers to the effort to create new products together with the companies involved in the
ecosystem. This was high on the list of all the cases, as incumbents were eager early on to develop new
innovations by appealing to an innovative ecosystem. However, especially in the Case C as the system was
This evolution is possibly related to the fact that this sector, i.e. the
food industry, requires a lot of resources in terms of time and funds to be devoted to R&D. It also requires a lot
of technical expertise for products to be developed and be adopted into the incumbent s supply chain.
Outwards exposure was also important, and incumbents considered it a high priority to demonstrate social
responsibility. Case A demonstrated that that exposure was initially a very important driver. However, as the
ecosystem matured and the incumbent understood the business value that can be created, exposure became
of secondary importance.
Innovation procurement refers to buying new innovations outside of the firm. It is the effort of the established
firms that are characterized by incumbent inertia (Lieberman and Montgomery, 1988) to find innovative
solutions to their problems that they cannot develop in-house. This was a high priority from the moment the
incumbents committed to the IE and remained as such throughout their involvement. Especially when
considering complementary solutions that could be used to enhance their core offerings in innovative ways, this
was one of the main benefits that the incumbents identified in remaining in the ecosystem.
In some cases, the incumbents were also interested in cultivating entrepreneurial mindset amongst their
employees, hence promoting intrapreneurial culture. This element was discussed in all cases however it turned
out as most relevant in Case C. In all cases the responsible departments wanted to motivate employees to think
out of the box and search for new and innovative solutions. Especially in Case C we observed an active effort
from the firm to involve as many different departments as possible. Also, while the ecosystem was starting
to provide results, they involved employees in th We theorise that this happened in part
due to the specific personalities that were involved in each case and in part to the industry itself. Actors in the
food industry, irrespective of their technological readiness, run traditional operations and are more focused to
incremental innovation (Baregheh et al., 2012). Contact with new technologies and disruptive innovation is
something out of the ordinary that can be very exciting for people involved in this industry. It is also a harder for
them to come in contact with the entrepreneurial ecosystem if they are not actively trying to do so. This, on the
other hand, is not the case in fintech where a lot of disruption is happening, and employees are moving across
companies sharing knowledge and expertise.
Finally, new market expansion was a factor that, while not evident in the beginning and not initially planned,
was an indirect result of the involvement in the ecosystem. This finding is also supported by the
open innovation literature (Chesbrough, 2003) but what we observed in practice was that as new products were
developed, that were on the peripheral of the company's main offerings, the company was forced to make the
managerial decision to enter a new market, either directly or via a collaborative scheme with a startup, or to
abandon the opportunity which usually meant a decline in the interest for future collaborations from the startup
side. In some cases, this decision was more complex but in all cases the IE started a discussion about how to
address needs in markets that were not the core business of the incumbent.
5.2 Innovation ecosystem sustainability factors as seen by startups
One of the most important elements for startups going into the ecosystem was access to industry experts. Their
involvement in an ecosystem was deemed crucial to collaborate with people that had unique insights and
knowledge. Knowledge that would otherwise be unattainable to an up-and-coming startup. However, the
impact these experts had on the startups value offering largely depended on the sector. For example, in the
fintech case this ecosystem provided a lot of knowledge that was crucial for product development and market
entry while in the case of airport operations the experts offered a more narrow scope.
an opportunity to gain insights, data, and in-depth market knowledge. This possibility constituted a big motivator
for startups to join an ecosystem. Especially in the fintech case which involves a complex sector both in terms of
share with the startups either highly motivated or demotivated them to remain active in the ecosystem. Contrary
to the other two cases, in the airport operations case, as the ecosystem evolved this became increasingly
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irrelevant. Most startups were indifferent to learning more about the specific market and rather focussed their
attention on other issues.
Another important aspect that retained its relevance throughout the involvement in the ecosystem
was customer acquisition. Essentially, startups were hoping that their involvement in a broader system would
help them attract new customers either in the incumbents themselves, their partner network, or other SMEs
that were involved in the process. This factor remained very important and in some cases was a game changer
for the startups since they had direct access and exposure to a network of potential customers.
Outwards exposure and publicity outside the ecosystem were not important for both the incumbents and the
startups. However, while initially startups considered it a very important factor as time went by this became less
relevant for them. A two-sided reason was given for this. On the one hand as incumbents realized that exposure
was secondary to the actual value generation, they reduced their effort towards that direction. Since they were
the most capable actors to drive publicity, the ecosystem was not exposed as a whole. On the other hand, while
this might seem like a loss for the startups, at the same time startups realised that more value could be found
inside the ecosystem and through the collaboration they already built and hence outwards exposure became
secondary.
The possibility of a joint venture or partnership was always a very important factor for the startups. As the
ecosystem evolved, we observed several partnerships taking place mainly between an incumbent and a startup.
Market relevance was crucial in this process. While specific joint ventures have been discussed, one has yet to
come to full fruition. As discussion often take place this is somethi ng that startups look forward to and the
incumbents have also been positive towards, under the right circumstances.
In every business system, networking is a very powerful tool (Gay, 2014). Throughout their involvement in the
ecosystem, startups considered networking as one of the most important factors to remain. The more active the
ecosystem was the more networking, making the startups more committed. However, this was sector specific
as startups not interested in the core industry of the incumbent were less inclined to pursue networking.
One of the mechanisms to initially engage startups in the ecosystem was prize funding. As these ecosystems
were built upon an open innovation programme, they were also running in parallel to a competition. The idea
was to offer some token prize funding to the startups that showed engagement and progress. This funding was
relatively small (up to 5,000) but was seen as an extra incentive. While initially this was considered as an
important factor after the continuous collaboration of startups and incumbents the price funding was one of the
less motivating factors for a company to continue to operate in the ecosystem.
The IEs offered a unique opportunity to startups to develop and test their products and services. These
ecosystems allowed startups a chance to test their products, come in contact with the market stakeholders, and
validate their offerings. Furthermore, they could develop their products using the facilities and help offered by
the other organisations involved (incumbents, startups, and the university itself). This remains a factor of prime
importance for them to remain to the ecosystem.
Seed funding is always of central importance for a startup. Startups considered their involvement in the IEs as a
way to demonstrate the value of their products and attract VCs. The role of the institution (i.e. the university)
here was essential to connect the IE with potential investors to make it more attractive for actors. Several
startups remaining over year received venture capital funding afterwards. In hindsight, their involvement
allowed them to accelerate their growth and demonstrate that they were capable of navigating a network to
create value.
Finally, an unexpected factor that came into play, was the time to produce results. Startups were reluctant to
continue to operate in the ecosystem if they did not see results for their product quickly enough. The term
This however meant that if the actors in the
ecosystem were not as motivated to work and collaborate it created a demotivation for some startups that
wanted to accelerate their innovation. This was not an issue that was reported across startups but rather
stressed in certain cases.
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6. Conclusion
This study aims to describe a university-driven IE and analyse how actors operating inside it are driven to
continue collaborating, thus ensuring the sustainability of the ecosystem. The actors involved identified several
factors as critical to continue pursuing the ecosystem activities and creating value adding artifacts. Essentially,
the ecosystems proved to be a method of accelerating the maturity of startups and providing incumbents with
innovations that would be otherwise unattainable. While these artificial ecosystems were focused on helping
internal actors, they eventually drew the attention of stakeholders from the broader startup ecosystem such as
venture capitalists and other investors. This is also an important issue for further research as university-driven
ecosystems can become the nucleoli around which broader intrapreneurial innovation ecosystems can be
formed. Using a university as an orchestrating institution proved important in forming the ecosystems and
setting the field for the eventual collaborations between incumbents and startups. It is clear that depending on
their sector and size, companies search for different drivers to stay committed in an ecosystem, drivers that
while different are not mutually exclusive and are attainable under the right circumstances.
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Article
We provide an overview of the grounded theory approach, a methodology with significant (and largely untapped) potential for human resources (HR) research. Grounded theory is an abductive, data-driven, theory-building approach that can serve as a conceptual link between inductive and deductive research approaches. We begin by explaining the grounded theory approach in detail and outlining two versions of the method that have been used in high-impact management publications—the Gioia approach and the Tabula Geminus (twin slate) approach. We then provide an overview of the similarities and differences between grounded theory and other inductive and/or qualitative methodologies, namely, ethnography, discourse analysis, rhetorical analysis, and content analysis. Following this discussion, we offer a step-by-step guide to using grounded theory in human resources research, illustrating these principles with data and processes from extant research. Finally, we conclude by discussing best practices for achieving rigor with the grounded theory approach.
  • A Suominen
  • M Seppänen
  • O Dedehayir
Suominen, A., Seppänen, M. and Dedehayir, O. (2019) European Journal of Innovation Management. Vol.22,No.2, pp. 335-360