**4. Complementary and competitive interactions**

Research findings support a representation of the Academies as a knowledge ecosystem that engages global players for inclusive, and experiential learning suitable for highly qualified jobs' demand in the region. Academies are schools of innovation aimed at filling the regional digital skills gap through an active labor market initiative for hi-tech job creation. Digital training partnerships have stemmed from the university's collaboration with technology and advanced manufacturing multinationals, leading, between 2016 and 2022, to 12 yearly training courses listed in **Table 3**. Rooted in engineering studies, digital academies have followed a separate track from both undergraduate and graduate courses. The offerings integrate advanced research and teaching experiences in Information and Communications Technology (ICT) design and management and involve university faculty, PhDs, and graduate students who can find opportunities for lifelong learning, teaching, and collaboration with companies. Although academies share digital and soft upskilling curriculums, each school presents its scope and specificity by intersecting Information Technology (IT) instructions with industry applications (see **Table 3** and [17]).


#### **Table 3.**

*The academies, their scope, partners, and launch year\*.*

Global hi-tech partner companies gravitate around the university campus, where the university manages logistics, organization, and knowledge-based exchanges. This proximity assures being part of a dense knowledge-based value creation strategy that also benefits from global networks affecting the local innovative output. In Järvi et al.'s words, this knowledge ecosystem works as an "organization that includes several actors linked by a joint search for knowledge while having an independent agency beyond the ecosystem of knowledge" ([7], p. 1524).

The knowledge-based value creation strategy of the university and its partners builds on sharing resources and capabilities that are not always quantifiable in monetary terms. Although university competencies are not sellable in the marketplace, the exploitation of knowledge created by the Academies occurs through job placement, as soon as trainees complete their training program and enter the labor market. Unlike its partners, the university cannot assure job placement to all trainees, because it is not an actor operating in the labor market. And, precisely for this reason, the university has undergone several pressures to adapt education to partners' fast-changing priorities and diversified participants' needs.

The university has learned to manage the growing complexity of nontraditionalstudent training as scalable operations that have expanded over time. For instance, following the first year, the Apple Academy has targeted the best performers with The Pier advanced program. Participant selection and the design of the second-year syllabus have involved international nongovernmental organizations (NGOs) working in social impact projects throughout the world. The Digita Academy jointly with Deloitte and Q8 has opened a classroom in the local neighborhood for participants to involve teens at risk in digital learning applications. The FS Smart Mobility Academy and the 5G Academy have implemented a separate track for midcareer professionals, scaling up upstream and downstream training services. All research laboratories localized in the campus have also grown collaborations with local companies (see **Figure 1**).

By conferring scientific and academic legitimacy to the knowledge produced through the Academies, the university has leveraged its influence to perform more efficiently and capitalize on its value-added advantages. In line with Stam [4], the study findings show that partners share skills, activities, organizational and financial resources that are mobilized for the creation and exploitation of knowledge outside the ecosystem.

Traditionally, the university faculties have taken the initiative for building collaborations with industry. Not all potential firm-entrants have got access to the campus. Companies have become affiliates on a case-by-case assessment leading to joint laboratory setup or other types of coproduction training (see **Table 4**). Yet, for the Academies, this sequencing has reversed. Globally reputed hi-tech multinationals have approached the university to explore the potential for collaboration and have localized within the campus with the expectation of additional investments and significant employment effects in the region. In such a configuration, partner alignment mirrors the agreements reached by each partner with the university, including the financial contribution, the flow of activities based on the number of trainees served each year, and the expectations that each actor holds regarding the role of all other partners in the ecosystem. Based on these conditions, as shown in **Figure 2**, Apple is the focal actor among business partners while Deloitte and Cisco—and all other latecomers—are the complementors [42]. As the university has leveled the playing field, opening up the access for collaborations to traditional competitors, this alignment has guaranteed ecosystem growth because all partners have acknowledged roles assigned and performed [30].

*Assessing Interdependencies in Innovation Ecosystems: Evidence from the Training Partnerships… DOI: http://dx.doi.org/10.5772/intechopen.112558*

#### **Figure 1.**

*Cumulative share of participants by academy and financial contribution by partner (2016–2023).*


#### **Table 4.**

*Types of collaboration, co-innovation mechanisms, and interdependencies.*

In line with Rybnicek and Königsgruber [14], organizational skills play an important role in knowledge ecosystems, and monitoring administrative processes is key in keeping up with partners' choices and unexpected market opportunities and shocks. The study findings show that partners' cooperation gets assessed on multiple levels. Training offerings undergo a yearly review process to gauge participants' learning outcomes and job placement performance. Space allocation rewards partners who efficiently use the space assigned. If underutilized, the university redistributes the premises to meet newly emerging needs. Thus, the exchange of resources depends

#### **Figure 2.**

*The network of big tech, industrial manufacturing, and regional companies sampled in the study. Source: Marra et al. [17].*

not only on trust and network relations, but also on the relative efficiency to adapt to contexts ([4]; see also [17]).

The university decision-making roles have rested on a results-driven management approach rather than the formal governance hierarchy to effectively address co-innovation risks, including the gaps and inconsistencies in role expectations and performance. A co-innovation risk has first comprised completing both facilities and procedures to host training activities to assure highly interactive learning. The open classrooms with modular tables, screens, blackboards, and coaches that furnish all soundproofed environments within the premises accommodated learning needs, social interactions, and networking. The recruitment of mentors and tutors tested for their technical competence also assured interpersonal skills for team and group management. Another co-innovation risk has involved addressing the priorities for some partners that others did not share. Collaboration with Apple, for instance, was conditional on the confidentiality of all teaching materials, whereas Deloitte's priority was to introduce as many class tutors as possible to manage peer interaction.

Other co-innovation risks have involved inconsistent expectations of partner alignment. The challenge of coordinating multilateral relations has entailed not only managing cooperation with and among the partners but also their competitive pressures. Both complementary and competitive relations have affected value creation for end-users. Competition also involves the closer knowledge ecosystems of training partnerships. For instance, the network of companies linked to the Digita Academy cooperates in the training delivery but competes to attract the best participants in applied research activities. With a hundred companies involved in the training, the

#### *Assessing Interdependencies in Innovation Ecosystems: Evidence from the Training Partnerships… DOI: http://dx.doi.org/10.5772/intechopen.112558*

Digita Academy benefits from Deloitte's established reputation within the proximity and global marketplace. This network supports Deloitte's alignment in the ecosystem and contributes to Digita's role and activities and the collaborations learners develop with businesses for their project work. The access to these networks makes it possible to mobilize resources and capabilities for local innovative performance.

Competition within Digita's network feeds back on the ecosystem partners. Over 2019, Accenture, being an external competitor that was hiring Apple and Digita trainees, has become a new partner by creating the Cyber HackAdemy. This transformation shows—according to Adner [30]—that competition operates on two levels. Within the ecosystem, to secure activities, positions, and roles, competition affects the distribution and capture of value among partners. Through the ecosystem, competition between actors influences the creation and acquisition of value compared to rivals' constellations both inside and outside the ecosystem [30]. Although these levels are separated, they interact: the study findings show that partners' competitiveness grows with new entrants, but their participation in creating knowledge, the value of this knowledge, the rarity, and inimitability of these resources and products contribute to their complementarity.

The latter also confirms that an ecosystem is a community in constant transformation that has to create new value through collaboration over competing alternatives [44]. It is not individual firms but entire networks of companies that compete with one another in what is called the networked economy [45]. This nested nature of ecosystems and the fact that they consistently need to evolve and adapt underline the fact that transformation is a key concern in understanding the success and failure of ecosystems [46, 47].

Considering the collaborations through a single-layer network analysis between the university and the sampled local companies, interdependencies emerge within processes of (i) recruiting trainees who complete their digital training; (ii) taking part in the yearly initiatives (such as business fairs, hackathons, boot camps, and roadshows) associated with digital instruction; (iii) partnering to deliver digital training offerings outside traditional university courses; and (iv) cofinancing joint laboratories for measurement, quality assessment, certification, and concept-proofing [17].

For each type of collaboration mentioned, **Table 4** epitomizes the sources of interdependencies and the learning and innovation mechanisms that can get strengthened in the region. In learning by hiring, crucial are tech skill availability, labor mobility, and the labor market's efficiency in the region. In learning by partnering, essential is the existence of an entrepreneurial ecosystem interfirm links that share business and commercialize their products, besides knowledge-based connections with the university. The study findings highlight that although university-business collaborations have increased, competitive pressures persist at the territorial level because of the limited experiences of cooperation among the firms located in the region [17].

Thus, the academy system has created several partnerships operating at the local and global levels, but business cooperation networks remain modest among regional companies [17]. **Figure 1** represents the network that includes all sampled companies and their relationships with University of Naples, besides global partners. Since the degree of relatedness among the sampled firms is low, this networked representation features bilateral alliances rather than a multilateral configuration of a knowledge ecosystem.

To recap: The case study examined has presented a co-innovation process involving multinational technology companies and the university operating in a less innovative European region. The evidence confirms the first hypothesis of this study concerning the alignment between partners. The study findings show that

both the University of Naples and Apple were focal actors capable of generating a knowledge-based value creation strategy that has attracted other global players as complementors. A knowledge ecosystem has emerged showing both complementary and competitive relationships in knowledge exploration. Competitive pressures have also led players to focus on short-term benefits rather than the sustainability of the value created [4, 30, 48]. Thus, the future of cooperation requires identifying and performing new critical functions, such as the scouting of potential new entrants, the interaction with new affiliates, the change in resource allocation priorities, and further scaling up training offerings facing demand saturation.

The second hypothesis concerning the strengthening of university-industry collaborations is only partially confirmed. As examined elsewhere [17], the knowledge ecosystem includes all multinationals partnering with the university. This finding suggests that when it comes to cooperation networks, knowledge stemming from nonlocal sources is crucial for innovation [49] and extra-cluster linkages are important for innovation [50] and might even be crucial for cluster firms to avoid lock-ins [51]. However, although the academy system has created several partnerships operating at the local and global levels, interfirm links in cooperation networks with regional companies are still modest [17]. A clear sign of persistent competitive pressures, lacking networked relationships among local firms, may lead to knowledge exploitation at the expense of long-term knowledge creation and sharing [4, 52]. This finding confirms that spatial interaction per se does not automatically induce knowledge spillovers or innovation diffusion and warns that even short-term favorable results may turn into development traps [13, 53]. Finally, in assessing the university societal impact, the recursive experiences of collaborations question linear causal inference models, paving the way for multilayer network analyses to explore the multilateral and multilevel logic of cooperation in knowledge ecosystems. The multilevel network analysis makes it possible not only to take a snapshot of existing complementarities but also to simulate potential connections to be activated through meso-level policy interventions.
