Ecosystem logic: from the localization model to the motivated-based one

Framing of the research. The present work is aimed to highlight a scientific neglect both in the ecosystem creation motivation and their aims. These elements are, already, contributing to developing an era that could be defined as “neo-mutualism” characterised by heterogeneous profit and no profit actors that, in the relationship between them and engaged by a social motivation, develop and stimulate an ecosystem aimed to answer social, environmental and economic needs. This effort aims to contribute to developing the scientific and practice fields on the relations between ecosystem, crowdsourcing processes and collaborative and collective intelligence phenomenon, supporting also the scientific growth of business for-profit model in social commitments and new governance forms. Therefore, in the past few decades, the literature in the ecosystem motivation building is prevalently focused on the structural elements and on the capacity of a territory to create a system of actors and infrastructures supporting the creation and development of innovative business projects (Alvedalen and Boschma, 2017; Spigel, 2017; Nicotra et al., 2018). In this regard, the most used example is Silicon Valley considered a place from which starting from the silicon raw material (structural element), was developed the modern ICT by means of the biggest players. The aim of the present work was born from the current consideration about the society that is facing many pressing and wide-ranging local and global health care, and environmental sustainability challenges, including climate change, biodiversity loss, and air and water pollution, to name but a few (Steffen et al., 2018; Tittensor et al., 2014). These issues are highly complex, frequently not context-dependent and often are not clear its focus, with their genesis and persistence involving multiple overlapping social and economic agents and drivers of change that operate within nested social-ecological systems (Sterner et al., 2019; Chávez-Ávila and Monzón-Campos, 2005). At the same time, there is the need to fight social exclusion and support an independent life, especially for the weakest social groups (Avelino et al., 2019). Regarding the last point, the cut of the welfare expenses, especially in the previous decades in capitalist countries, is stimulating the heterogeneous agents' involvement to develop processes of social innovation that may help answer the existing and the new social needs COVID-19 pandemic has brought. Therefore, the search for new solutions or improving existing ones, to manage the social and economic conditions caused by the pandemic crises provides unique opportunities for innovative, a cooperation approach, small businesses, social economy start-ups and NGOs at the local, regional, or national level. Concerning these needs, the World Economic Forum (WEF) uses its “Great Reset” initiative to support global stakeholders in cooperating to manage the direct consequences of the COVID-19 crisis (see: https://www.weforum.org/great-reset), and, at the same time, the European Union has created a stimulus package, the NextGenerationEU, to support research and innovation, and to help digital transitions (see: https://ec.europa.eu/info/strategy/recovery-plan-europe_en). In this scenario, the relationship between the actors involved in social, economic and environmental issues can be seen as an uncertain, often complex, and always collective endeavour involving both economic and social stakeholders, such as companies, scientists, NGOs, etc. As a collective or collaborative endeavour, all actors involved in these social innovation processes share the responsibilities and are co-responsible (Freeman et al., 2020; Blok et al., 2015). The Covid-19, considered a critical event, has stimulated the knowledge mobilization from many different places, advancing our learning and fostering our progress against the issue at hand (Chesbrough, 2014; Chesbrough, 2020) in an open innovation context characterised by the resource-sharing process (Chesbrough, 2003; West and Gallagher, 2006). The relationship becomes the source of an innovation and collaborative context, benefiting from engaging stakeholders to know their needs and interests, and helping create a mutual understanding between the innovation actors and favouring win-win solutions (Burchell and Cook, 2006; Grenwood, 2007). In this stream, in the first months of the COVID-19 pandemic, Italian hospitals had to face a severe shortage of ventilators engaging several stakeholders, including companies, NGOs, universities, and even individual inventors to mass-produce a new “open” machine model, designed using a scuba-diving mask and 3d printing technologies, and tested leveraging not-medical technologies such as and the wind tunnels previously used for creating cars and planes. At the same time, in other countries, some universities, like MIT, contributed to launching a competition for the best open-source ventilator design. In the authors' opinion, the pandemic taught that a social and/or economic ecosystem surfacing does not necessarily need a specific territory to support needs answer proposals. Therefore, these events bring the authors to consider the ecosystem not geographically or structurally driven but motivation-driven, related to a particular social, economic, and environmental need that stimulates, using ICT and AI evolutions, the ecosystem growth with a bottom-up process (Russell and Smorodinskaya, 2018). Regarding to this ecosystem view, in the oncology stream, some researchers use blockchain platforms to allow millions of patients to upload their data to help Artificial Intelligence-based software fight against cancer. These evolution forms were considered in the relevant literature, as in Free Innovation (2017), in which von Hippel further developed the idea of interacting with “large crowds” by highlighting the open and spontaneous nature of innovation-related activities among heterogeneous stakeholders (Gault, 2018). On the same page, Surowiecki (2004), in the "wisdom of crowds", argues that the agents in interconnection represent the collective intelligence that arises when our imperfect judgments are aggregated. Regarding the collective term, Leimeister (2010) argued that ‘collective’ describes a group of heterogeneous individuals or stakeholders, who are not necessarily required to have the same attitudes or viewpoints but work together to find solutions to a given social and economic problem using the ability to learn, to understand and to adapt themselves. This collective sharing of knowledge aimed to solve communal social and economic problems is increasingly characterising the thousands of individuals’ involvement with the ultimate goal of solving or reducing a complex problem felt by numerous individuals. The Massachusetts Institute of Technology (MIT) provided the following definition: “Collective intelligence is a group of individuals doing things collectively creating a communal intelligence that can be compared to a system genome” (Malone et al., 2010). Therefore, Malone et al. (2010) metaphorically compared the ecosystem component to a gene that, with its specificity, combined with other genes, creates a collective intelligence that represents the “genome” of the social and economic initiative. In this regard, we can think Wikipedia experience, in which different and anonymous individuals write and edit an article; or another case showed by one of the most famous Italian automotive luxury brands, Lamborghini, that in the pandemic era adapted a part of its production chain to produce sanitary masks designed by young engineers and 3D printer industry entrepreneurs. In this regard, Jacobides et al. (2018) argue that the ecosystem has not to be considered network synonymous, because in it the end-users can choose the goods and services on offer among those supplied by each inner or external participant and can also, in some cases, choose how to combine them. Such modularity allows the no-standard production of interdependent system components by different suppliers, with only limited coordination required across the production (or production and consumption) chain. For example, an end-user of the Linux software can decide which part or version to use and support to improve performance by sharing new developments with other companies, programmers and end-users. Therefore, organisations within an ecosystem have a significant degree of autonomy in how they design, price, and operate their respective modules, as long as they interconnect with others in agreed and predefined ways (Baldwin, 2008; Kuan and West, 2021). These kinds of collaborations show crowd members, represented both producers and end-users, working together to create something with dependencies existing between their contributions (Secundo et al., 2021). At the same time, crowdsourcing allows a company to gain access to skills that are far removed from its core business activities reducing the innovation risks developed according to a stand-alone strategy. The work object is to present the ecosystem motivation based as a virtual and/or real context fostered by a crowdsourcing process characterised by knowledge and intelligence sharing to answer, new ideas or adapt existing goods or services, to social and economic needs. Purpose of the paper. Our paper aims to reduce a neglect regarding the ecosystem structural view, by proposing a conceptual framework according to which the ecosystem rises from social and economic needs that will be satisfied by heterogeneous agents (profit, no profit business, public institutions, end-users, etc.) applying models based on collaborative and collective efforts. This trend also shows a business model evolution that balances the social and economic entrepreneurial commitments. Therefore, the authors argue that the innovation scenario, particularly in a complex moment, is characterised by numerous and heterogeneous social and economic agents, in relations between them, that, share their knowledge by means of stakeholder engagement, and creating a crowdsourcing virtual or real processes, contribute to answering to social, environmental and economic needs. Crowdsourcing can therefore be defined as a distributed, collective process aimed at problem‐solving and pursuing innovation, in which members of communities, that compose an ecosystem, contribute to spillover knowledge and intelligence in a collaborative and collective way. Methodology. The present work design is based on the assimilation and combination of evidence in the form of previously developed concepts and theories on the ecosystem, selected, acquired and analysed in the more relevant literature, considering the development and application of this approach in the pandemic era (Hirschheim, 2008). So, the ecosystem development, and its application and emergent figures, is the focal theory (focus) by the authors argue that the ecosystem concept, in the pandemic era, developed considering the different applications that have been achieved in the last two years. These studies are aimed to fulfil a conceptual framework to contextualize and match the ecosystem scientific knowledge with the socio-economic changing. On these bases, the literature, dealing with policies to facilitate competitiveness and innovation-led growth, describes innovation ecosystems through the lens of their crucial function - to provide a smooth and continual exchange of knowledge flows in bringing innovations to answer social, environmental and economic needs. This approach focuses on what stimulates and produces the system rather than on the system's structure (Bergek et al., 2008; Dahlke et al., 2021). Results The paper has wanted to focus on the innovation ecosystems building and dynamics, highlighting that it is not necessarily built in a top-down way and by means of the existence of structural elements but, can develop spontaneously from deliberate, collaborative activities of social, economic and institutional agents, based on social and economic motivations. In this regard, Powell et al. (2013) argued that an ecosystem is indeed a bridge between the social and the economic and that such a result is achieved through processes that were initially less structured. Therefore, it is possible to say that an ecosystem is based on processes of co-creation of activities, shared strategies, responsibilities and goals, and collective intelligence. These considerations bring the authors to explain different conclusive cues about business and governance model changes. Regarding the business model matter, the work shows how the traditional business models, influenced by the increasing social and environmental needs, are changing the company life more and more contextualised in planned or spontaneous ecosystems where they satisfy both their social commitment and their needs of know-how and knowledge. These changes show a company as a "bifacial Giano" involved, on the one hand, to answer, in a recursive way, social needs within a social open innovation context and, on the other hand, to fight in a competitive scenario. These behaviours could belong to the same face or different faces but be functional between them. This scenario highlights the existence of a trend moving toward an open, not localisable and democratic practice for the production of innovation that allows almost anyone to exploit their creative potential in pursuing new solutions in a collaborative context by means of the contribution of different stakeholders to create value both at individual and at context level. Let’s agree with the old saying, “necessity is the mother of invention”. It becomes natural to read the ecosystem theory as methodological support for organisations in creating a steady set of stakeholder engagement practices, providing them tools aimed at facilitating access to external know-how sources, and identifying the actors that can be more effective in delivering new solutions (Porter and Kramer, 2011; von Hippel and Suddendorf, 2018). Furthermore, the emerging crowdsourcing process trend, brought by Covid-19 and before the hurricane Katrina events, is further affecting business models and strategies and is changing the role and behaviour of end-users/consumers. This new condition, adequately supported by technological innovation, could represent the scenario in which for-profit and non-profit organisations, public institutions, consumers and communities will find solutions to their social, environmental and economic needs. About the governance model issue in the “crowded” context, Ball (2009) argued that it is a growing modification trend from the hierarchical to heterarchical model. In which, it is replacing bureaucracy and administrative structures and relationships with a system of organisation replete with overlap, multiplicity, mixed ascendancy and/or divergent-but-coexistent patterns of relation. In this scenario, the governance term modifies its significance from reaching leadership and control (Moore, 1998) to show abilities to conduct the ecosystems to react, adapt and answer to its social and economic environmental needs (Powell and Giannella, 2010; Mäkinen and Dedehayir, 2012). On this page, the authors argue that the ecosystem governance could be considered as “the amount of decision making and control (or coordination) that ecosystem creator should recognise to the members of the same ecosystem on the basis of their capabilities aimed to reach the mutual objective” (Tiwana et al., 2010; Mäkinen and Dedehayir, 2012, p.5). This condition shows the ecosystem governance in moving from a hierarchical to a heterarchical model. Research limitations. The work represents a theoretical view of conceptual papers that can strive to advance understanding of a concept or phenomenon in big leaps rather than incremental steps. To be taken seriously, any such leap must be grounded in thorough consideration and justification of an empirical appropriate research design. Managerial implications. In the term of future research implications, in the authors’ opinion, one of the essential considerations that emerge from the ecosystem trend is that will develop different forms of decentralised and heterarchical governance models, increasingly diffused and dispersed along the innovative chain, and capable of managing social and economic relationships, norms, and trust values, fundamental for the innovative products, mainly when they don’t occur in a specific environment. Furthermore, when the business for profit participates spontaneously in the ecosystem could show social and environmental behaviours without reputational aims. If this trend was correct could empty the CSR planning role and to change the CSR role in the marketing and communication. Finally, the collaborative, open innovation, context could also change the risk assessment. Therefore, this variable moves to the concepts of interdependence risk, the uncertainties of coordinating with complementary innovators, and integration risk, the uncertainties presented by the adoption process across the value chain. Originality of the paper. The work shows itself as a patchwork that focuses on a current matter, tracing the relationships between ecosystem, crowdsourcing and phenomenon as collective and collaborative intelligence, that have scarce attention in the scientific stream but could drive a new “knowledge economy” that we could define “care economy” characterising the “neo mutualism” era (Venturi and Zandonai, 2022).