Entrepreneurial Ecosystems for Technology-based Ventures
Nowadays it is very common to be familiar with the term “startup success” or “satisfactory development” of a project through its different stages. However, from the perspective of an Entrepreneurial Ecosystem (EE), it has not yet been absolutely determined what the success of technology-based projects means or represents. Product development can often be considered as success, but also the development of entrepreneurs, or market growth. Several key processes have been identified as essential for the proper functioning of a Technology Innovation System (TIS) (Bergek et al. 2008). There are different theoretical bases that involve approaches of dynamism and interaction of different actors within an EE.
Successful technology-based ventures are seen to heavily depend on the outcomes of actions by entrepreneurs and their ability to not only combine resources but also tolerate a higher degree of uncertainty (Giones et al., 2013). Considering that the areas of study on the subject are very broad, for the purposes of this analysis six areas of focus were defined in the review: (1) Variables, (2) Resources, (3) Entrepreneurship programs, (4) Bases of entrepreneurship, (5) Technological development and (6) Measurement systems for startups and ecosystems.
Proposals or initiatives such as Multidimensional Entrepreneurial Ecosystem Scale (MEES) represent a great contribution to this research area given that it considers different approaches or ‘domains’ including technical focus, as well as business development and human-oriented approach. This view considers the dynamic interplay between macro-institutional factors and micro-individual actions (Van De Ven, 1993).
Theoretical underpinning
Ecosystems as well as networks are multi-level phenomena, which makes it difficult to manage them as a single entity. Valkokari, K., Seppänen, M., Mäntylä, M., & Jylhä-Ollila, S. (2017). This multi-level focus involves the consideration of several approaches to define a path of integral development for technology-based ventures.
Several actors intervene in an EE, each of them plays a role and their activities contribute to certain results. Nevertheless, sometimes those efforts work as isolated labors or in the best case, as clustered jobs. Although, this collaborations or links inside an EE are not yet completely measured. Science parks, or indeed any innovation policy and practice, should be subject to the criterion of ‘commensurability’. In other words, is there a reasonable balance between ultimate goals/ends and paths/means in science park development? (Etzkowitz, H., & Zhou, C., 2018).
Discussion
Identifying the relation between areas represents a high impact for the concept of ‘Technology-based EE”. The following knowledge map denotes a broad view from different disciplines to give a holistic view and provide the foundation to synthesize the research field across the disciplines (Pittaway et al. 2014). With this map the research pretends to define a niche or niches that enable the development of new research. Furthermore, the knowledge map shows the growing research trends and directions of the field (Tranfield et al. 2003).
For tech-based ventures, the innovation practices can be grouped according to the stage of development of the venture. These areas are innovation strategy, external business intelligence, idea management, product portfolio management, technology portfolio management, development and launch, post-launch, resource, and competence management. The role of each stage seeks to reach successful results for the venture in practice. However, there are many factors that influence the speed of economic progress (Van Stel et al, 2005). Entrepreneurship has been recognized to be one such factor (Acs and Szerb 2007, Audretsch and Thurik 2001, Marchesnay 2011). However, as proposed by Baumol (1968), entrepreneurship being hard to capture and due to lack of appropriate data, has not been given due attention in the growth literature (Romer 1990, 1994). Recently, data availability from the Global Entrepreneurship monitor has allowed the empirical investigation of the effect of entrepreneurship on economic development at both the individual country level and in a larger set of countries.
Sometimes small and startup firms in the high-tech industry usually engage in networking to overcome resource, knowledge, and competence constraints. This network capability is an ability to manage and benefit from external relationships. Nonetheless, the contribution of these efforts does not always examine their effects over the performance of the venture. Most of the times the network takes place because of a particular need that entrepreneurs have. The connections include physical or geographical access, knowledge resources, mentors, financial needs, and entrepreneurial projection. Inside an EE, links and network is needed.
Connecting goals, efforts, and resources leads to better results. However, entrepreneurship and institutions, in combination in an ecosystem, can be viewed as a “missing link” in an aggregate production function analysis of cross-country differences in economic growth. The system of entrepreneurship does not work perfectly, with system failure operationalized by recognizing bottlenecks (Miller 1986; Casadio Tarabusi and Guarini 2013).7 Hence, building pillars of entrepreneurial activity constitute a system where the outcome is moderated by the weakest performing pillar. National Systems of Entrepreneurship (NSE) represent resource allocation systems that combine institutions and human agency into an interdependent system of complementarities. The original notion of inputs generating outputs through an aggregate production function has been extended by more sophisticated measures of inputs, including human capital (Barro 1991), as well as more complex conceptualizations of the functional relationship and the factors underlying it (Barro and Sala-i-Martin 1995).
Emerging technologies such as artificial intelligence will enable re-imagining of physical-systems functionalities in health care, transportation, energy, and other sectors in ways that were not feasible before, and thereby create new needs for stakeholders. In other words, the expansion of both accessible resources and addressable needs has significantly increased the variety of possible resource configurations that a firm could design and enable in a digital age. Resource configuration of a firm depicts the ways in which it orchestrates and connects the resources it utilizes (Sirmon et al., 2011; Sirmon, Hitt, and Ireland, 2007). The way managers orchestrate resources within a single firm points the way to the development of capabilities and sources of competitive advantage that a venture could achieve in each period. There should be a balance between resource-configuration prototypes, value-creation sources, and the underlying resource-configuration processes.
According to an external approach of variability in an EE regarding tech-based ventures, the concept of Environmental Dynamism is hard. It involves the relationship of the entire global notion of the research field. At the same time, it is related to the connection with exogenous resources. A clear example of this kind of resources could be explained through the role description of an incubator or business accelerator in an EE. Since the 1980s, Technological Business Incubators (TBIs) have been considered by governments in both developed and developing economies to be an important mechanism for stimulating technology-based entrepreneurial activity (Phan, Siegel, and Wright 2005). They have become an accepted catalytic instrument of economic development, providing a range of business resources and services to nurture and support the growth of new technology-based ventures (NBIA 2007). The challenge to measure the impact of this kind of programs should examine the extent to which both the support services of TBIs and exogenous local factors facilitate the innovation activity of incubated new ventures.
Wrap-up
Measuring innovation opens a debate according to the EE actor we are analyzing. According to the literature review, the first measure is based on the number of approved intellectual property rights (AIPs) granted by industrial professional associations to firms within an incubator (e.g., for product designs, software copyright, printed circuit boards, or a new type of plant). This broad measure indicates innovations that may not be patentable but take the form of modifications of practical value to the development and competitiveness of the business. The second narrower measure is based on the number of patents granted to firms within an incubator from patenting offices/ authorities. The protection period provided by patents is generally longer than that for non-patentable intellectual property. The third measure is based on the number of national science and technology project grants (NSTPs) awarded to firms within an incubator. (Xiao, L., & North, D., 2018).
Effectuation is a concept that describes a causal logic in decision-making, organic phenomenon, and real interaction in an EE. Despite the great importance of measuring business model innovation for various purposes, a validated measurement scale is still not available. Thus, though effectuation we could investigate the decision-making logics used by new ventures to develop their business models according to their stage of development, resource availability, founders’ profiles, cultural and cognitive characteristics about them and their environment.
