Towards Effective Ecosystems: A Framework for Mapping

Knowledge Governance and Management Activities of Innovation

Ecosystems Constituent Elements

Gustavo Simas da Silva

Graduate Program in Knowledge Engineering, Management and Media (EGC),

Federal University of Santa Catarina (UFSC), Florianópolis, Brazil

Keywords: Innovation Ecosystems, Knowledge Ecosystems, Framework, Knowledge Management, Knowledge

Governance.

Abstract: Innovation and knowledge ecosystems are integral parts of today’s fast-paced global economy. However, the

challenge of effectively governing and managing knowledge within these complex networks remains largely

unaddressed. Through a scoping literature review, focusing on existing frameworks, models and best practices

related to knowledge management and governance, this paper introduces the ARA (Actors, Resources,

Actions) Framework. The framework serve as tool for mapping knowledge management and governance

activities in operational, tactical and strategical levels with respect to the constituent elements of innovation

ecosystems. A conceptual Entity Relationship Diagram (ERD) is developed, providing a visual representation

of the relationships between actors, resources and actions, serving as a referential artifact for ecosystem

database modeling. The paper concludes by discussing the practical implications of the ARA Framework for

stakeholders and offering insights into future research and the combined utility with other models for

innovation and knowledge ecosystems, such as Open Innovation frameworks and the Triple or Quadruple

Helix models.

1 INTRODUCTION

Innovation ecosystems (IE) may be interpreted as

complex networks of entities that collaborate to

create and commercialize new ideas and technologies

(Adatto et al, 2023). Within this context, Knowledge

Management (KM) and Knowledge Governance

(KG) have the role of facilitating the effective

creation, sharing, and application of knowledge

(Foster et al, 2023). It also involves coordinating the

specialist knowledge of ecosystem members to foster

collaboration and innovation (Bhatt, 2001; Angrisani,

2023).

Knowledge governance in innovation ecosystems

involves the management and direction of innovation

efforts within a broader context. It encompasses

practices that align actors with roles and

responsibilities, leading to value creation and the

generation of innovations, technologies, and

solutions (Safadi & Watson, 2023). Governance in

innovation ecosystems is approached from different

https://orcid.org/0000-0003-3485-7910

theoretical lenses, with a focus on network

governance and the orchestration concept (Hoffmann

et al, 2022). It also involves addressing major

challenges in the management of uncertainty and

complexity by linking transformative innovation

policy with research perspectives from complex

adaptive systems, ecosystems, and adaptive and

participatory governance (Könnölä et al, 2021). Also,

anticipatory innovation governance, which aims to

create an enabling environment for innovation and

support anticipatory innovation practices, is another

aspect of knowledge governance in innovation

ecosystems (Minna & Trina, 2022).

Although the significance of knowledge

management and governance in innovation

ecosystems is widely acknowledged, the academic

and practical literature is yet to present a

comprehensive framework that delineates the

primary aspects and activities involved. Existing

work has primarily focused on the individual

components of governance, such as role alignment,

value creation, and managing uncertainty, among

Simas da Silva, G.

Towards Effective Ecosystems: A Framework for Mapping Knowledge Governance and Management Activities of Innovation Ecosystems Constituent Elements.

DOI: 10.5220/0012251900003598

In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) - Volume 3: KMIS, pages 99-106

ISBN: 978-989-758-671-2; ISSN: 2184-3228

others (Velu, 2015; Tang et al, 2020; de Vasconcelos

Gomes et al, 2021; de Freitas Nascimento et al, 2022;

Ferreira et al, 2023).

However, these fragmented approaches do not

offer an integrated view, leaving a gap in our

understanding of how these elements coalesce to

govern knowledge effectively within innovation

ecosystems. Given this, the current paper aims to

address this research problem by proposing a

cohesive framework that integrates these disparate

aspects. Through a scoping review, the framework

will endeavor to identify and align the key constituent

elements of innovation and knowledge ecosystems, in

order to map knowledge governance and

management activities, facilitating a more effective

intervention within IE. It seeks to bridge theoretical

contributions with practical implementations, also

providing actionable insights for researchers,

policymakers and industry practitioners alike.

2 THEORETICAL

BACKGROUND

The current section offers precise definitions of key

terms related to knowledge governance and

innovation ecosystems. Establishing this common

vocabulary is essential for a focused analysis of the

ARA Framework's role in these contexts.

2.1 Definition of Innovation

Ecosystems and Knowledge

Ecosystems

Innovation ecosystems and knowledge ecosystems

are terms that have gained considerable prominence

in both academic and practitioner discourses over the

past few decades. While they share similarities, they

each bring distinct frameworks, objectives, and

historical developments to the table.

The term "innovation ecosystem" first gained

attention in the early 1990s, rooted in business and

management literature by Moore (2016). It

conceptualizes an interconnected set of actors—such

as startups, corporations, universities, and

policymakers—that collaborate to foster innovation.

Over time, multiple frameworks have been proposed

to analyze innovation ecosystems; prominent among

them is the Triple Helix model (Etzkowitz &

Leydesdorff, 1995), lately extended to a Quadruple

and Quintuple Helix model, which explores the

relationship among universities, industry and

government (Carayannis & Campbell, 2010).

Knowledge Ecosystems (KE), while sharing some

similarities with innovation ecosystems, mainly focus

on the flow, management and utilization of

knowledge. This term emerged in the early 2000s

within the field of information science and

technology (Valkorari, 2015). Frameworks like the

SECI model (Nonaka & Takeuchi, 2007), which

describes the conversion of tacit to explicit

knowledge, and the Cynefin (Snowden & Boone,

2007) have played a pivotal role in the mapping and

sense making of complex scenarios in the Knowledge

Society.

Both innovation and knowledge ecosystems have

evolved to accommodate the complex, fast-changing

nature of the digital era. The quadruple/quintuple

helix model, for instance, expands the triple helix to

include civil society and environmental perspectives

(Carayannis & Campbell, 2010) and the transition

from “stocks" to “flows’"of knowledge reflects the

influence of digitization and network theories

(Hustad & Teigland, 2008).

With time, innovation and knowledge ecosystems

have garnered scholarly attention across social

sciences, health sciences, engineering, and other

fields, exploring a wide range of perspectives about

their aspects. Papaioannou et al (2009) caution

against a reductive approach to KE devoid of

historical context, while others advocate for their

utility in grasping the collaborative and evolutionary

aspects of innovation. Mercier-Laurent (2018) and

Tejero et al (2020) explore technological enablers

like AI-based platforms and knowledge graphs,

which offer new avenues for advanced analysis and

insights about ecosystems. Tang et al (2020) and

Spena (2016) emphasize the efficiency of knowledge

networks and specific knowledge practices in

facilitating learning and innovation in IE.

Figure 1: Illustration of IE definition. Source: Adapted from

Granstrand and Holgersson (2020).

In essence, the literature collectively highlights

the complex nature of KE and IE, suggesting that they

KMIS 2023 - 15th International Conference on Knowledge Management and Information Systems

100

serve as collaborative, evolving networks that are

supported by a range of technological and managerial

practices.

More recently, Granstrand and Holgersson

(2020), through an extensive literature review and

conceptual analysis, aimed to bring a clear definition

of IE, being “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”. In their work, innovation

ecosystems are composed of three core entities that

interact mutually: actors, artifacts and activities. This

representation is presented graphically in Figure 1.

This conceptualization broadens the scope beyond

traditional definitions, which primarily emphasized

collaboration and knowledge sharing among actors.

In this updated definition, actors include a diverse

range of participants like producers, consumers, and

regulators; artifacts extend beyond mere products to

include intangible resources and various system

inputs like technology and intellectual property;

activities in the ecosystem encompass not only

collaboration but also competition, knowledge

sharing, and social and economic exchanges.

By including these interrelated elements, the

authors aim for a more nuanced understanding of how

IE function and can be effectively managed. And this

clear conceptual definition serve as a basis for the

proposed framework.

2.2 Knowledge Governance and

Management in Innovation

Ecosystems

Although interconnected, Knowledge Management

and Knowledge Governance serve different functions

within an organization or ecosystem. KM is primarily

concerned with the systematic processes for

capturing, storing, and sharing information and

expertise (Santos & Zattar, 2019). Its focus is

operational, aiming to optimize the day-to-day

handling of knowledge assets and facilitate their

seamless transfer among individuals and

departments. In contrast, Knowledge Governance

encompasses a broader set of activities that include

the formulation of policies, procedures and norms to

guide how knowledge is acquired, utilized, and

disseminated (Giebels et al, 2016). While KM

provides the tools and techniques for effective

knowledge flow, KG provides the strategic

framework that defines the “why” and “how” of

knowledge utilization, addressing issues such as

ownership, control, and ethical considerations.

Therefore, Knowledge Governance acts as an

overarching structure that sets the stage for

knowledge management activities, ensuring

alignment with organizational objectives and ethical

norms.

Regarding Innovation Ecosystems, Carayannis

and Campbell (2011) argue that the intricacies of KM

in IE necessitate a dedicated system for knowledge

production. Their framework emphasizes that the

central challenge is not merely the transfer of

knowledge among organizational actors but also its

translation into actionable innovation—whether in

products, services, or novel solutions. This sentiment

underscores the vital role of KG in forging symbiotic

relationships among diverse ecosystem participants,

ranging from academia to industry. Furthermore,

Carayannis (2014) suggests that such governance is

facilitated by the multi-organizational presence of

individuals, like academics, who can act as conduits

for knowledge between research settings and

practical applications.

Santos and Zattar (2019) delineate paths to

effectively implement KM within IE, indicating

several key strategies which must be adopted. First,

acquiring relevant information is critical to reducing

systemic uncertainty and enhancing the ecosystem's

capacity for knowledge absorption. Second, breaking

down complex bodies of knowledge into manageable

units can simplify the learning process and facilitate

its dissemination. Structuring circulating knowledge

also aids in diminishing ambiguity, providing a

clearer pathway for decision-making. Establishing a

robust knowledge production system is pivotal not

only for governing these complex knowledge flows

but also for augmenting the intellectual capital that

undergirds the ecosystem's development. The

collaborative integration of diverse stakeholders—

ranging from research centers and universities to

entrepreneurs and established corporations—fuels a

dynamic knowledge flow. This multi-agent

interaction not only enriches the ecosystem's

intellectual repository but also serves as a cornerstone

for its long-term success and adaptability.

Also, in support of KM and KG, knowledge

engineering serves as the bedrock for designing,

developing and managing the content, practices and

relationships that facilitate innovation. The primary

purpose here is to construct formal knowledge

representations, typically using ontologies or

semantic web technologies, to enable more efficient

discovery, sharing and recombination of innovative

ideas and technologies (O’Leary, 1998). Activities

might include knowledge extraction from multiple

sources, building intelligent systems capable of

Towards Effective Ecosystems: A Framework for Mapping Knowledge Governance and Management Activities of Innovation Ecosystems

Constituent Elements

101

problem-solving, and creating advanced algorithms

to analyze patterns in data to predict future

innovations. The implications are profound: well-

executed knowledge engineering can significantly

amplify the collective intelligence of an innovation

ecosystem, thereby accelerating the pace of

innovation and reducing redundancies (Kendal &

Creen, 2007; Mercier-Laurent, 2020; Tejero et al,

2020). However, it's crucial that this engineering be

conducted with an eye to ethical considerations and

the broader impacts on the ecosystem's stakeholders,

including the potential for unequal access to benefits

(de Kreuk et al., 2009; Bryan & Gezelius, 2017; Stahl,

2022) and in accordance with KG strategies.

3 THE ARA FRAMEWORK

Rooted in the foundational work by Granstrand and

Holgerssson (2020), who initially conceptualized the

relationships among Actors, Artifacts and Activities

within IE, the Actors-Resources-Actions (ARA)

framework adapts and expands on these original

constructs, aligning the structure more closely with

the specific requirements of KG and KM. Through

this engineering, the ARA framework aims to offer a

closer approach to KG and KM, accommodating the

complexities and demands of IE and KE constituent

elements.

Figure 2: ARA Framework with indicated KG and KM

activities. Source: the authors.

The framework, with indicated KG and KM

demands for each aspect in Figure 2, serve as a tool

for orchestrating the various elements in IE, spanning

operational, tactical and strategic layers —layers

original from military doctrine but adapted for use in

business management and other fields (McNair &

Vangermeersch, 2020). On the operational end, it

encapsulates foundational activities such as mapping

actors and profiling their skills and motivations,

creating a comprehensive inventory of resources and

laying down the essential technology infrastructure.

These activities set the stage for tactical

interventions, where talent management comes into

play, roles and accountabilities are clearly defined,

and intellectual property (IP) is safeguarded.

Capacity building through a network of mentors and

careful actor-resource matching ensures optimal

utilization of available assets. The tactical layer also

involves asset valuation, making sure that all

resources, whether tangible or intangible, are

appropriately valued for actions, whenever necessary.

These tactical considerations prepare the ground

for strategic maneuvers in the ecosystem, including

meticulous action-resource matching and long-term

governance of innovation and knowledge. It is, also,

at this level that actions such as cultural facilitation

and knowledge curation may be concentrated, aiming

to sustain an ecosystem that is not only innovative but

also resilient.

With a keen focus on aligning actions with

resources and actors, the ARA framework aims to

create a self-sustaining loop of continuous

improvement and value creation, thereby ensuring the

ecosystem's long-term viability and impact.

3.1 Actors

Actors in innovation ecosystems refer to the diverse

human or non-human entities that participate in the

ecosystem, including companies, educational

organizations, policy makers and third-party actors.

Here, the Triple/Quadruple/Quintuple Helix

models (Carayannis & Campbell, 2010) may come

into play in order to categorize into a more specified

ontology the different existing actors within the IE.

Matt et al. (2021) emphasize the role of three

ecosystem actors - companies, educational

organizations, and regional policy makers - in

enabling and fostering the adoption of new industry

standards. These actors bring different assets to the

ecosystem, such as technological expertise, research

capabilities and policy support.

Also, the innovation ecosystem can be viewed as

a multilevel structure formed by different layers of

actors. Beliaeva et al. (2019) propose a four-layer

structure, including the focal company, a community

of innovation, an innovation habitat, and an

innovation ecosystem. Each layer represents different

types of actors and their relationships within the

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ecosystem. The diversity of actors within the

ecosystem is paramount for its support of digital

entrepreneurship and innovation. The more diverse

the actors and their assets, the more prolific the

ecosystem becomes.

3.2 Resources

Resources in IE refer to the assets, capabilities, and

knowledge that actors bring to the ecosystem. The

resources (tangible or intangible) brought by actors in

IE contribute to the overall ecosystemic innovation

potential. These resources may include financial

capital, human skills, intellectual property, and

technological capabilities. The diversification and

quality of these resources can significantly affect the

ecosystem’s overall ability to innovate and adapt. For

example, intangible resources like social capital and

tacit knowledge can be just as critical as financial

resources in promoting ecosystemic collaboration

and co-creation (Adner, 2017).

3.3 Actions

Actions in innovation ecosystems refer to the

activities, interactions and interventions undertaken

by actors to drive innovation and ecosystem

development. The actions of actors in innovation

ecosystems can range from R&D activities to

training, networking, startups incubation and

acceleration programs, policy interventions and other

innovative initiatives. To foster the adoption of

emerging economic approaches, such as “Industry

4.0” or “5.0”, it's crucial not only to focus on R&D

activities but also to engage in interorganizational

actions like training and networking that involve all

ecosystem actors. Further, the development and

success of innovation ecosystems hinge on the

effective alignment and coordination between various

participating entities in integrated actions (Matt et al.,

2021; Santos et al., 2021).

3.4 Conceptual ERD

A conceptual Entity-Relationship Diagram (ERD)

was developed to visually represent the possible

relationships between the Actors, Resources and

Actions entities, serving as a graphical foundation for

database modeling in alignment with the ARA

framework, as presented in Figure 3. The attributes in

the ERD are merely suggestions and can be expanded

or refined as per the specific requirements. An ERD

is a conceptual blueprint that graphically depicts the

structure of a database, illustrating how entities are

related to one another (Frantiska, 2017). It outlines

how different entities (such as tables) relate to each

other, specifying relationships through primary keys

(PK) and foreign keys (FK). A primary key is a

unique identifier within a table, ensuring that each

record is distinct. A foreign key, on the other hand, is

a field in one table that matches the primary key in

another table, establishing a linkage between them.

The use of PKs and FKs helps maintain data integrity

and enables complex queries and operations.

In this design, an actor can engage in multiple

actions, and reciprocally, an action can involve

numerous actors (many-to-many relationship). This

ability to accommodate multiplicity reflects the real-

world complexity of innovation and knowledge

ecosystems, where collaborative actions often

involve multiple stakeholders. The relational

structure extends to actions and resources as well,

allowing an action to be associated with multiple

resources and vice versa. This is particularly

important for understanding how diverse resources—

be they tangible or intangible—can be leveraged

across various initiatives within an innovation

ecosystem. Moreover, a resource can be linked to

multiple actors and vice versa, which enables the

model to capture scenarios of commons (Hess &

Ostrom, 2007), resource sharing, co-ownership or

even competition among various entities.

Figure 3: ARA conceptual ERD. Source: the authors.

The framework also incorporates self-referencing

hierarchies within each of the three entities. For

instance, an actor at an institutional level could

function as a parent entity that envelopes several

individual-level actors, thus representing an

organizational hierarchy. The same principle applies

to resources, where a high-level resource like an

infrastructure could encompass other, more

specialized resources. As for actions, a macro-level

action could serve as an umbrella for multiple,

interconnected sub-actions. This hierarchical

representation is critical for modeling the often nested

Towards Effective Ecosystems: A Framework for Mapping Knowledge Governance and Management Activities of Innovation Ecosystems

Constituent Elements

103

structures encountered in IE, thereby enhancing the

granularity and depth of knowledge governance

studies.

4 CONCLUSIONS AND

PERSPECTIVES

Effective knowledge management in innovation

ecosystems hinges on a synergistic blend of various

actions, such as knowledge creation, validation, and

dissemination, necessitating shifts in organizational

culture and technology adoption (Bhatt, 2001; Spena

et al, 2016). Central to the ecosystems are diverse

actors—ranging from firms and educational

institutions to individuals—who not only facilitate

the knowledge and other tangible or intangible

resources flow but also take on specialized roles, like

universities serving as regional innovation leaders

(Pucci et al., 2018; Yalcin, 2022). Knowledge

frameworks aid in the streamlined flow of knowledge

among these actors, enhancing both exploration and

exploitation stages of innovation (Secundo et al.,

2018). Lead firms, equipped with both open and

closed action strategies, are vital in this milieu for

managing knowledge and accelerating the rate of

innovation, thus stimulating the entire ecosystem

(Velu, 2015).

The proposed framework's principal limitation is

its generic nature, which might overlook specific,

nuanced attributes of particular ecosystems or

sectors. Also, the framework is built to be more

oriented towards business relationships and may

require adaptations to fully capture the intricacies of

social, environmental or public policy dynamics.

Furthermore, the framework may be less effective in

rapidly changing environments where the

identification of stable actors, resources or actions

becomes challenging.

The Dynamic Capabilities (Teece et al, 1997) and

the concept of Absorptive Capacity (Camisón &

Forés, 2010) offer lenses through which an

organization’s ability to adapt, learn, and innovate can

be understood. These theories can guide the Actions

and Actors elements in the ARA Framework, helping

to pinpoint where capacity building or training may

be needed to maximize the potential for innovation.

Social paradigms like Communities of Practice

(Wenger, 1998) and Social Network Analysis offer

social and relational perspectives. They particularly

inform the Actors element, illustrating how tacit

knowledge and social capital flow within and

between organizations and can be mapped for more

effective KG.

Future research can focus on customizing the

ARA framework for specific ecosystem types, such

as platform ecosystems, local or regional innovation

hubs, and smart city initiatives. Investigating how the

ARA framework interacts with other innovation and

knowledge ecosystem frameworks can also provide

valuable insights. For example, connecting the ARA

model with frameworks of Open Innovation (Enkel et

al, 2011), Triple or Quadruple Helix (Schütz et al,

2019) and Sustainability models (Liu & Stephens,

2019) could offer a more comprehensive view of

ecosystem dynamics. Empirical studies are

encouraged to test these integrations across diverse

sectors.

The current scenario indication is that innovative

frameworks for knowledge governance have the

potential to serve as catalysts in the evolution of a

Knowledge Society that continually adapts, learns

and thrives, fostering sustainable, innovative and

knowledge-rich ecosystems.

REFERENCES

Adatto, L., Aouinaït, C., Le, S. T. K., & Mongo, M.

(2023). Innovation Ecosystems in the New Economic

Era (No. emse-04099899).

Adner, R. (2017). Ecosystem as structure: An actionable

construct for strategy. Journal of management, 43(1),

39-58.

Angrisani, M., Cannavacciuolo, L., & Rippa, P. (2023).

Framing the main patterns of an academic innovation

ecosystem. Evidence from a knowledge-intensive case

study. International Journal of Entrepreneurial

Behavior & Research, 29(11), 109-131.

Beliaeva, T., Ferasso, M., Kraus, S., & Damke, E. J. (2019).

Dynamics of digital entrepreneurship and

the innovation ecosystem: A multilevel

perspective. International Journal of Entrepreneurial

Behavior & Research, 26(2), 266-284.

Bhatt, G. D. (2001). Knowledge management in

organizations: examining the interaction between

technologies, techniques, and people. Journal of

knowledge management, 5(1), 68-75.

Bryden, J., & Gezelius, S. S. (2017). Innovation as if people

mattered: The ethics of innovation for sustainable

development. Innovation and development, 7(1), 101-

118.

Camisón, C., & Forés, B. (2010). Knowledge absorptive

capacity: New insights for its conceptualization and

measurement. Journal of Business Research, 63(7),

707-715.

Carayannis, E. G. (2014). Strategic knowledge arbitrage

and serendipity (SKARSE™) in action. Journal of the

Knowledge Economy, 5, 203-211.

KMIS 2023 - 15th International Conference on Knowledge Management and Information Systems

104

Carayannis, E. G., & Campbell, D. F. (2010). Triple Helix,

Quadruple Helix and Quintuple Helix and how do

knowledge, innovation and the environment relate to

each other?: a proposed framework for a trans-

disciplinary analysis of sustainable development and

social ecology. International Journal of Social Ecology

and Sustainable Development (IJSESD), 1(1), 41-69.

Carayannis, E. G., & Campbell, D. F. (2011). Open

innovation diplomacy and a 21st century fractal

research, education and innovation (FREIE) ecosystem:

building on the quadruple and quintuple helix

innovation concepts and the “mode 3” knowledge

production system. Journal of the Knowledge

Economy, 2, 327-372.

de Freitas Nascimento, S., Lima, M. C., & Gondim, I. J. C.

(2022). Level of collaboration and knowledge transfer

among actors of the innovation ecosystem: the

proposition of an analytical model. International

Journal of Innovation: IJI Journal, 10(3), 434-460.

de Kreuk, M., van de Poel, I., Zwart, S. D., & van

Loosdrecht, M. C. (2010). Ethics in innovation:

Cooperation and tension (pp. 215-226). Springer

Netherlands.

de Vasconcelos Gomes, L. A., de Faria, A. M., Borini, F.

M., Chaparro, X. A. F., Dos Santos, M. G., & Amaral,

G. S. G. (2021). Dispersed knowledge management in

ecosystems. Journal of Knowledge

Management, 25(4), 796-825.

Dos Santos, D. A. G., Zen, A., & Bittencourt, B. A. (2021).

From governance to choreography: coordination of

innovation ecosystems. Innovation & Management

Review, 19(1), 26-38.

Enkel, E., Bell, J., & Hogenkamp, H. (2011). Open

innovation maturity framework. International Journal

of Innovation Management, 15(06), 1161-1189.

Etzkowitz, H., & Leydesdorff, L. (1995). The Triple Helix-

-University-industry-government relations: A

laboratory for knowledge based economic

development. EASST review, 14(1), 14-19.

Ferreira, J. J., Fernandes, C. I., Veiga, P. M., & Dooley, L.

(2023). The effects of entrepreneurial ecosystems,

knowledge management capabilities, and knowledge

spillovers on international open innovation. R&D

Management, 53(2), 322-338.

Foster, L., Wiewiora, A., & Donnet, T. (2023). Integrating

Knowledge Management and Governance for

Innovation Outcomes: A New Framework for

Managing Innovation in a Project

Environment. Journal of the Knowledge Economy, 1-

28.

Frantiska, J. (2018). Entity-relationship

diagrams. Visualization Tools for Learning

Environment Development, 21-30.

Giebels, D., van Buuren, A., & Edelenbos, J. (2016).

Knowledge governance for ecosystem-based

management: Understanding its context-

dependency. Environmental Science & Policy, 55, 424-

435.

Granstrand, O., & Holgersson, M. (2020). Innovation

ecosystems: A conceptual review and a new

definition. Technovation, 90, 102098.

Hess, C., & Ostrom, E. (2007). Introduction: An overview

of the knowledge commons. In Understanding

Knowledge as a Commons: From Theory to Practice

(pp. 3-26). MIT Press.

Hoffmann, M. G., Murad, E. P., Lemos, D. D. C., Farias, J.

S., & Sanches, B. L. (2022). Characteristics of

Innovation Ecosystem’s Governance: an Integrative

Literature Review. International Journal of Innovation

Management, 26(08), 2250062.

Hustad, E., & Teigland, R. (2008). Implementing social

networking media and web 2.0 in multinationals:

Implications for knowledge management.

In Proceedings of the 9th European conference on

knowledge management (Vol. 323331).

Kendal, S. L., & Creen, M. (2007). An introduction to

knowledge engineering (pp. 1-25). Springer London.

Könnölä, T., Eloranta, V., Turunen, T., & Salo, A. (2021).

Transformative governance of innovation

ecosystems. Technological Forecasting and Social

Change, 173, 121106.

Liu, Z., & Stephens, V. (2019). Exploring innovation

ecosystem from the perspective of sustainability:

Towards a conceptual framework. Journal of Open

Innovation: Technology, Market, and

Complexity, 5(3), 48.

Matt, D. T., Molinaro, M., Orzes, G., & Pedrini, G. (2021).

The role of innovation ecosystems in Industry 4.0

adoption. Journal of Manufacturing Technology

Management, 32(9), 369-395.

McNair, C. J., & Vangermeersch, R. (2020). Total capacity

management: Optimizing at the operational, tactical,

and strategic levels. CRC Press.

Mercier-Laurent, E. (2020). Platform for knowledge

society and innovation ecosystems. In Artificial

Intelligence for Knowledge Management: 6th IFIP WG

12.6 International Workshop, AI4KM 2018, Held at

IJCAI 2018, Stockholm, Sweden, July 15, 2018,

Revised and Extended Selected Papers 6 (pp. 34-47).

Springer International Publishing.

Minna, T., & Taina, T. (2022). Anticipatory Innovation

Governance Model and Regional Innovation

Ecosystems Supporting Sustainable Development.

AHFE international.

Moore, J. F. (2006). Business ecosystems and the view

from the firm. The antitrust bulletin, 51(1), 31-75.

Nonaka, I., & Takeuchi, H. (2007). The knowledge-

creating company. Harvard business review, 85(7/8),

162.

O'Leary, D. E. (1998). Using AI in knowledge

management: Knowledge bases and ontologies. IEEE

Intelligent Systems and Their Applications, 13(3), 34-

39.

Papaioannou, T., Wield, D., & Chataway, J. (2009).

Knowledge ecologies and ecosystems? An empirically

grounded reflection on recent developments in

innovation systems theory. Environment and Planning

C: Government and Policy, 27(2), 319-339.

Towards Effective Ecosystems: A Framework for Mapping Knowledge Governance and Management Activities of Innovation Ecosystems

Constituent Elements

105

Pucci, T., Runfola, A., Guercini, S., & Zanni, L. (2018).

The role of actors in interactions between “innovation

ecosystems”: drivers and implications. IMP

Journal, 12(2), 333-345.

Safadi, H., & Watson, R. T. (2023). Knowledge monopolies

and the innovation divide: A governance

perspective. Information and Organization, 33(2),

100466.

Santos, M. L. B., & Zattar, I. C. (2019). The importance of

knowledge management for the functioning of

innovation ecosystems. Journal on Innovation and

Sustainability, 10(1), 48-56.

Schütz, F., Heidingsfelder, M. L., & Schraudner, M.

(2019). Co-shaping the future in quadruple helix

innovation systems: uncovering public preferences

toward participatory research and innovation. She Ji:

The Journal of Design, Economics, and

Innovation, 5(2), 128-146.

Secundo, G., Toma, A., Schiuma, G., & Passiante, G.

(2019). Knowledge transfer in open innovation: A

classification framework for healthcare

ecosystems. Business Process Management

Journal, 25(1), 144-163.

Snowden, D. J., & Boone, M. E. (2007). A leader's

framework for decision making. Harvard business

review, 85(11), 68.

Spena, T. R., Trequa, M., & Bifulco, F. (2016). Knowledge

practices for an emerging innovation

ecosystem. International Journal of Innovation and

Technology Management, 13(05), 1640013.

Stahl, B. C. (2022). Responsible innovation ecosystems:

Ethical implications of the application of the ecosystem

concept to artificial intelligence. International Journal

of Information Management, 62, 102441.

Tang, H., Ma, Z., Xiao, J., & Xiao, L. (2020). Toward a

more efficient knowledge network in innovation

ecosystems: A simulated study on knowledge

management. Sustainability, 12(16), 6328.

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic

capabilities and strategic management. Strategic

management journal, 18(7), 509-533.

Tejero, A., Rodriguez-Doncel, V., & Pau, I. (2020).

Knowledge graphs for innovation ecosystems. arXiv

preprint arXiv:2001.08615.

Valkokari, K. (2015). Business, innovation, and knowledge

ecosystems: How they differ and how to survive and

thrive within them. Technology innovation

management review, 5(8).

Velu, C. (2015). Knowledge management capabilities of

lead firms in innovation ecosystems. AMS review, 5,

123-141.

Wenger, E. (1998). Communities of practice: Learning as a

social system. Systems thinker, 9(5), 2-3.

Yalcin, E. (2022, November). Universities of Applied

Sciences ’Leadership Role in Regional Innovation

Ecosystems. In European Conference on Management

Leadership and Governance (Vol. 18, No. 1, pp. 439-

446).

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