Keywords

Start-up Ecosystems, Start-up Performance, Network Learn Capability, Resource, Open Innovation, Social Capital.

Introduction

Creating many start-ups can play an important part in the development and confidence of a nation (Berger & Kuckertz, 2016; Naudé, 2010; Wong et al., 2005). Successful start-ups provide employment creation and growth at local and regional levels (Tripathi et al., 2019) but many fail. While new venture dynamics capabilities are well recognised the odds for their success can be improved (Baron & Harima, 2019; Hasani & O'Reilly, 2020). In particular, understanding of the ecosystem in which start-ups gestate and emerge can improve and accelerate start-up development (Ahn et al., 2019). The start-up network-ecosystem offers embryonic start-ups support, resources and services (Sperber & Linder, 2019) that can add value to ways in which founders/co-founders of start-ups are able to work (Singh et al., 2019).

Start-up ecosystems have gained important recognition from public policymakers and industry actors because of their role in supporting business growth, innovation and creation of new venture (Kong, 2019; Pavlak & Petru, 2018; Singh et al., 2019; Sipola et al., 2016). Over recent years the number of start-up ecosystems have grown globally (Tripathi et al., 2019); for example, in the global start-up ecosystem report in 2020 (Genome, 2020), almost 70 ecosystems were analysed that generated more than $4 billion in ecosystem value. Changes have occurred in the ways that start-up ecosystems function (Hasani & O'Reilly, 2020). The first generation of start-up ecosystems focused fundamentally on providing physical and technological infrastructure, designing culture and linking to support services and universities (Brown & Mason, 2017; Roundy et al., 2017; Singh et al., 2019; Spigel & Harrison, 2018). Second-generation start-up ecosystems improved their way of working by facilitating networks, support programs and advice services (Cao & Shi, 2020; Singh et al., 2019). Recently, start-up ecosystems have expanded by generating network learning capabilities (Liu & Tang, 2020; Pustovrh et al., 2020; Weerawardena et al., 2015). These might include n venture access resources as well as capabilities such as information, legitimacy, knowledge, and investment (Tripathi et al., 2019). Focusing on capabilities significantly improves new ventures (Albourini et al., 2020).

Scholars have been researching the changing dynamics of start-up ecosystems (Feng et al., 2019; Singh et al., 2019). The new venture ecosystem-based entrepreneurial economy view of start-ups was has gained significant interest (Sipola et al., 2016). Significantly, the perspective asks whether and how ecosystem-based start-ups have an impact on new venture performance (Baraldi et al., 2019). The focus is very much on the start-up organisation’s development through internal and external networks (Baraldi et al., 2019), especially global networks (Sipola et al., 2016). The increased attention paid to start-up ecosystems has produced a rich and wide diversity of perspectives and approaches (Hasani & O'Reilly, 2020; Sperber & Linder, 2019). However, as the knowledge field grew, two notable deficiencies arose (Cao & Shi, 2020).

First, studies on the impact those start-up ecosystems as entrepreneurial micro-economies have on new venture performance led to opposite findings. Some researchers argued that the start-up ecosystem did not increase new venture performance (Phangestu et al., 2020; Seo et al., 2018). Other researchers showed that start-up ecosystem do lead to enhanced performance (Kee et al., 2019; Tripathi et al., 2019). These different findings may be the result of the diversity of start-up ecosystem in terms of network learning capabilities (Baraldi et al., 2019; Wang & Fang, 2012; Weerawardena et al., 2015), contextual differences (Tripathi et al., 2019) or because of a focus on specific measures (Marvel et al., 2019; Spender et al., 2017). Studied in isolation, it is not easy to assign causality. Likewise, start-up ecosystem aspects have previously been principally analysed individually, which makes it difficult to rank their importance in analysing new venture performance.

Second, start-up ecosystem research is very general in its orientation to practices (Cao & Shi, 2020) as are network learning capability perspectives (Pustovrh et al., 2020). While academics refer to relevant literature, according to some scholars, it has poor conceptual depth (Hasani & O'Reilly, 2020), as for example, in Baron and Harima (2019), discussion of high-growth companies’ creation and consolidation: the so-called new venture firms. To close the gap between the literature on the start-up ecosystem and the network learning capability perspective we will examine the impact of start-up ecosystems on new venture performance, using the three conceptual perspectives that have used most frequently in this knowledge field. These theories are the Resource-Based View (RBV), Open Innovation (OI) and Social Capital Theory (SCT). First, we will analyse and discuss research about start-up ecosystems and the impact they have on new venture performance. We will focus on how network learning capability influence new venture performance. Second, we will analyse start-up ecosystems in terms of what management and business perspectives can contribute, discovering gaps and inconsistencies about start-up ecosystems. Finally, we propose a research agenda that strengthens how the startup ecosystem supports the new venture.

Methods

Systematic literature review (SLR) consists of identifying, analysing, selecting, and critically appraising the main conceptual categories of research through a stage-by-stage procedure for content review and analysis (Mayring, 2004; Shapiro et al., 1998). We will describe a group of scientific articles that address the role and impact of new venture ecosystems on start-up performance. We define the start-up's ecosystem in terms of networks (Albourini et al., 2020; Ojaghi et al., 2019). The focus is on new ventures and their performance (Chatterji et al., 2019; Seet et al., 2020). The literature review encompasses leading journals in the management field (i.e., Strategic Management Journal, Organization Science, Academy of Management Review, Journal of Management, Academy of Management Journal, Administrative Science Quarterly, and Journal of Management Studies) as well as in the field of entrepreneurship (i.e., International Small Business Journal, Strategic Entrepreneurship Journal, Entrepreneurship and Regional Development, Entrepreneurship Theory and Practice and the Journal of Business Venturing) and technology and innovation (i.e., Research Policy, Technovation, Journal of Technology Transfer, Journal of cleaner production, Technological Forecasting and Social Change and Journal of Products Innovation Management). The period for the exploration was from 2010 to 2021. It led to a general set of 217 papers.

We subsequently read the articles closely to evaluate whether to incorporate into the analysis for subsequent classification. We had two criteria for exclusion. First, the name or abstract of the papers had to include “start-up”, ‘‘start-up ecosystem’’, "start-up performance", "new venture performance", "firm performance", "business performance" or "company performance" to assure that the paper focused on our subject of interest. Second, we searched for inclusion of a topic related to networks’ learning capacity, signified by including ‘‘network*’’, ‘‘learning*", "capacity*’’, "alliances*’’ or ‘‘support*’’. We restricted the search to English-language papers in the selected journals. These papers were then evaluated on their pertinence for the purpose of our research, leading to the exclusion of 91 papers that did not meet the criteria. We removed thirty-six papers because they did not fit the criterion of a start-up’s ecosystem. Likewise, forty-one papers did not focus on new venture performance and network learning capability or their connection. Finally, we excluded fourteen papers because they did not provide an important contribution, upon further inspection. The ultimate data set includes eighty-five papers, listed in the Appendix. All the papers were published between 2010 and 2021, with only five papers in print before 2015 and the majority published after 2010.

Forty-four of the papers considered were quantitative studies. Some were focused on the resources and supports of the start-up's ecosystem for a new venture (Assenova, 2020; Islam et al., 2018; Nair et al., 2017; Nair et al., 2020; Pustovrh et al., 2020); others looked at the difference between start-ups support and non-support ecosystems (Cao & Shi, 2020; Crespo et al., 2019; Tripathi et al., 2019), while some focused on the impacts of network learning capability on new venture performance (Anwar et al., 2018; Karami & Tang, 2019; Wu et al., 2020; Xue et al., 2019; Zheng et al., 2020). Thirty-nine of the papers were based on qualitative data, such as case studies of particular start-up ecosystems (Baron & Harima, 2019; De Groote & Backmann, 2020; Feng et al., 2019; McDonald & Eisenhardt, 2020); discussions of start-ups performance (Jin, 2017; Marvel et al., 2020; Shepherd et al., 2021); focusing on the role of the entrepreneur (Kee & Rahman, 2018; Vaznyte & Andries, 2019; Zaech & Baldegger, 2017), network learning capability (Cacciolatti et al., 2020; Garidis & Rossmann, 2019) and their impact on the start-up (Cole & Sokolyk, 2018; Jeong et al., 2020; Kozubikova et al., 2019). The remaining two research papers used mixed methodology to research start-ups ecosystem (Hallen & Pahnke, 2016) or used qualitative data to complement mostly quantitative research with other techniques and instruments (Garidis & Rossmann, 2019).

Considering the papers' academic perspectives, all but nineteen of the papers drew on three theories. First, the RBV perspective steered twenty-five articles. The RBV centres on how a new venture accesses and obtains diverse kinds of resources in the start-up ecosystem (Wu et al., 2020; Xue et al., 2019). Second, eighteen articles used an open innovation (OI) perspective centre on the sharing of knowledge and the collaborative processes of a new venture (Faridian & Neubaum, 2020; Michelino et al., 2017). The twenty-three articles that adopted Social Capital Theory (SCT) focussed on the company’s capacity to create, combine and reconfigure tangible and intangible knowledge produced between actors and networks (Smith et al., 2017; Zheng et al., 2020).

The choice of categories allows a systematic review and analysis of the papers. The conceptual variables drawn from the three approaches postulate diverse effects on the dependent (start-up performance) and independent constructs (start-up ecosystem and network learning capability) in terms of the conceptual perspectives' primary constructs. We organised the article according to the categories utilising a comprehensive approach (Mayring, 2004). We content analysed the papers and analysed them in turns of business and administration theories' descriptions, applying the same analysis to quantitative, qualitative and mixed papers. The structuration of categories was cross-checked by the three authors independently.

Findings

The results show six subparts. In the first three, we analyse the notion of the start-up ecosystem, start-up performance and network learning capability literature. We focus on the reviewed papers' methodological and conceptual considerations in these first three subparts. Figure 1 below displays the papers in terms of their links among the principal categories.

The fourth, fifth, and sixth subparts consider the network learning capability perspective across RBV, OI, and SCT theories. In these subparts, we consider the conceptual aspects, the network learns capability and the impact of the start-up ecosystem on new venture performance. All categories and descriptions are presented in Table 1 and will be explained in the results section of the paper.

Table 1 Categories, Dimensions And Descriptions Of Analysis
Categories	Dimensions	Descriptions
Start-up ecosystem (SE)	General objective measures	Governmental help, investor angels, venture capital (VC), crowd funding and business incubation.
	Ecosystem-specific objective measures	New venture support from the start-up ecosystem and several lifted obstacles.
Start-up performance (SP)	General objective measures (Enterprise)	Success, employment, total of funds obtained, profit, survival, firm size and firm growth in terms of sales.
	General subjective measures (Entrepreneur’s satisfaction).	Entrepreneurial success, market performance, firm performance, the achievement of enterprise goals, estimation of sales and growth, competitive performance, anticipated survival, performance compared to competitors, estimation of profit, and satisfaction with the return on assets.
Network learn capability	Network learn-specific objective measures	Advancement in the internal and externally focused network learning capability procedure from initiation to market scaling.
	Actors	Researchers, coaching, consultants, service providers, universities, incubators, mentors, informal financing and VC.
	Relationships	The content, formality, strength of the interactions, communication, and internal or external relationship.
Resource based-view (RBV)	Resources	Specific contacts, technical knowledge, shared knowledge, recommendation, a sense of belonging, funding, office space, a general network and credibility.
	Capabilities	Learn capabilities, absorption capabilities, innovative capabilities, adaptation capabilities, managerial capabilities, marketing capabilities, network capabilities.
Open innovation (OI)	Process	A Start-up firm is a potent engine of OI processes.
	Outcomes	Results have enabled us to classify them into two sub-categories: innovation performance and organizational performance.
Social capital theory (SCT)	Social capital	Structural dimension of social capital: the influence of structural diversity on innovation processes and network results.
		The relational dimension of social capital: the impact of qualitative variations in single company´s relationships with the other organisations on innovation processes and results.

All six subparts have been organised so that a formal association is made between the categories of the perspectives and network learning capability theory. Each subpart concludes with definitions and theoretical and conceptual assumptions on start-ups ecosystems and start-up performance research.

Start-up ecosystem

Definitions and conceptions:

The academic literature on start-up ecosystem dates back to the 90s’ when his term was popularized by James More (Moore, 1993), gaining real traction around 2010 (Mason & Brown, 2014; Singh et al., 2019). However, the publication of a systematic review in 2019 by Tripathi et al., 2019 in a specific issue in the Journal of Information and Software Technology, directed future research along three main lines of discussion. These were the concepts of a start-up ecosystem; the essential components involved in creating a start-up ecosystem as well as the functions that these components play in start-ups’ performance.

There is an interesting debate in the literature about actors, roles, functions, and relationships generate in the start-up ecosystems (Alvedalen & Boschma, 2017; Amedofu et al., 2019; Kuckertz, 2019; Kuckertz et al., 2020; Sperber & Linder, 2019; Tripathi et al., 2019). Some of these studies suggest that understanding of the concept of a start-up ecosystem has become quite clear but that it is necessary to distinguish between an ‘entrepreneurial ecosystem’ (Kuckertz, 2019; Stam et al., 2014; Stam & Spigel, 2015) and a ‘start-up ecosystem’ (Singh et al., 2019), two tightly associated ideas which, however, deal with distinct kinds of management activities.

The start-up ecosystem refers to a favourable environment where diverse actors collaborate to support the start-up (Acs et al., 2014). A start-up ecosystem's key factors are companies, infrastructure, accelerators, innovation centres and universities (Neck et al., 2004; Sharif & Tang, 2014; Spender et al., 2017). The start-up ecosystem provides favourable conditions for high-growth start-up firm creation, development and growth (Baron & Harima, 2019). The needs of start-ups differ from small firms, for example, in the investments, capabilities and pace of growth (Fraiberg, 2017; Haines, 2016).

Start-up performance

Start-ups are firms in the process of identifying, creating and scaling a viable and replicable business model to materialize market opportunities (Ehrenhard et al., 2017). Rompho (2018) defined them as being young firms, less than ten years old, with an innovative business model and that show a notable increase in employees or sales. Their continuance can be considerably improved by innovation (Feng et al., 2019). Their innovation and knowledge processes and performance notably vary from those of established companies (Centobelli et al., 2017; Criscuolo et al., 2012) because they are knowledge-based, solve contemporary problems, are flexible, agile, and have a high potential for growth and scalability.

Start-up performance is one of the most extensively applied dependent constructs in the management, entrepreneurship and innovation field in recent times. Start-up performance is related to a rise in their sales, market penetration and financial achievement (Rompho, 2018). New venture performance can be indicated by agility, efficiency and adaptability of the firms’ activities in different industry environments (Cantamessa et al., 2018; Rekarti & Doktoralina, 2017). In terms of measuring new venture performance, authors utilise a host of performance criteria (Marvel et al., 2019; Rompho, 2018). The usual dimensions are start-up performance growth, funding attracted, market share, employment, gross profit, business volume, survival, cost control, successful outcome, goal attainment, satisfied customers, evaluation of success by the founder, among others (Dutta & Folta, 2016; Hasani & O'Reilly, 2020; Sorooshian, 2017). Given the several notions and perspectives suggested in terms of measuring new venture performance, doing so consistently is not easy (Marvel et al., 2019; Rompho, 2018). The measures proposed can be either more objective when utilising financial records or more subjective when based on people’s perception (Stam et al., 2014).

Network learning capability

Network learning capability supports new ventures by improving their network as well as the firm (Baraldi et al., 2019). We define this capability as the company’s capacity to create, combine, and reconfigure tangible and intangible knowledge by internal and external links with organisations or actors in their environment (Weerawardena et al., 2015). Both types of network learning capabilities catch different knowledge’s required to offer valuable services that markets demand.

The external learning capabilities facilitate identifying the knowledge intensity you need for the new venture in its initial business activities (Zheng et al., 2020). Internally focused learning capabilities capture the company’s abilities in terms of the founders' vision and expertise in accessing current networks and generating new ones (Weerawardena et al., 2015). However, it is crucial to consider two essential components of a network; nodes and connects (Dickel et al., 2018; McGrath et al., 2019).

Nodes are generally situated at distinct enterprise levels, such as companies, areas, projects or individuals (Dickel et al., 2018). Nodes can also be actor networks (Anwar et al., 2018), including venture capital, government institutions, business accelerators, business incubators, large enterprises, small enterprises and universities (Baraldi et al., 2019). Ties typically describe relational aspects, including collaboration, communication, confidence and knowledge exchange, information and advice (Xue et al., 2019). Studies usually consider one particular kind of interaction but it multiple connections can be considered (Panetti et al., 2019).

Improving network learning capability calls for knowing and working with other companies and so is more than an internal ability of a new venture that is directed towards other different companies. These other companies also need to have access to specific means and context, establishing a network capability (McGrath et al., 2019). Most start-up companies begin outside of a network-ecosystem. The chief executive officers (CEO) need to identify those links that generate critical relationships for success. The lack of an established status for new ventures suggests that time is necessary for other companies to accept them as new actors. Network learning capability has long been linked with new venture performance (Zacca et al., 2015).

There is a consensus in the research studied that connections and network links do produce performance benefits for small business (Semrau & Sigmund, 2012), although extant studies are inconclusive due to the contradictory approaches used. Hence, it is not clear what types of network links help small business performance (Stam et al., 2014). In the context of start-ups and their externally focused network learning capability, the start-up ecosystem relationships with technological factors are useful for start-up performance.

Resource-based View

The resource-based view (RBV) holds that companies have tangible (financial, technological or physical) and intangible (human and knowledge) resources that can convert into unique capabilities that are the source of a firm’s competitive advantage (Barney, 1991; Guo et al., 2020). Likewise, the extent of information exchange influences resources acquisition's abundance and variety, having an impact on the community’s capability for exploring and relationships (Xue et al., 2019). The RBV sees companies as a set of resources and capabilities whose value generates benefits for enterprise, such as, for example, revenues (De Groote & Backmann, 2020; Grant, 1991).

The RBV perspective demonstrates that the exploration and absorption of an initial resources base for creating new ventures will shape competitive advantage and thus, performance (Marullo et al., 2018). The RBV literature has mostly worked on new ventures, advising that the exploration and exploitation of a set of initial resources facilitates a firms' ability to comprehend and develop value?creating strategies (DeTienne & Cardon, 2008; Marullo et al., 2018).

The RBV?inspired entrepreneurship perspective has frequently studied the impact of distinct internal resources (human capital, technology and finance) on new venture performance. Still, it has insufficiently assessed the influence of teams' ‘openness’ to external resources in the startup initial phase as a factor that improves success (Marullo et al., 2018; Tedmanson et al., 2012). The RBV approach recommends that human capital helps generate a long-lasting competitive advantage. New ventures acquire the skills and the prior experience of their founders or allies; thus, entrepreneurs are among the most critical human resources present in the company in terms of value-creating (Agarwal et al., 2004; Marullo et al., 2018).

The RBV suggests that the survival of a new venture in a competitive context depends on its capability to harness resources; therefore, start-ups successful transfer of innovations depends on the availability of complementary assets, so start-ups' capability-based resources have a more significant impact on competitive advantage than other intangible and tangible assets (Hyytinen et al., 2015; Paradkar et al., 2015). For the new ventures, alliances with other actors are a vital asset, and so the ability to be able to form partnerships is a key dynamic capability. Successful new ventures leverage their available resources to attract allies to obtain complementary resources.

Open Innovation

The literature on Open Innovation (OI) in small and medium business has highlighted the influence of an open focus on start-up creation as a critical aspect driving new venture success (Eftekhari & Bogers, 2015; Marullo et al., 2018). We define IO as purposive inflows and outflows of knowledge to accelerate business and develop the market through externally sourced innovation (Chesbrough et al., 2006). Therefore, OI can play a crucial function in the absorption and exploiting of knowledge. Two supports sustain OI processes: technology exploration (inbound OI) and technology exploitation (outbound OI) (Pustovrh et al., 2020).

OI phenomenon have been on the growth in established companies, particularly in collaboration with new ventures (De Groote & Backmann, 2020), which suffer a fundamental lack of key resources and learning capabilities (Wymer & Regan, 2005). Their lack of financial resources and human capital limits the growth of innovation. New ventures adopt OI practices to overcome both their novelty and smallness (Bogers, 2011), such that the new venture phenomenon and OI are intimately linked (Spender et al., 2017).

The research in OI has developed diverse strands, displaying the multidimensional character of the notion of OI (Spender et al., 2017). Many types of research have explored the complex dimensions of OI (Aslesen & Freel, 2012; Huizingh, 2011), focussing on diverse aspects, such as innovation practices (Baldwin & Von Hippel, 2011; Galati et al., 2016; Saguy & Sirotinskaya, 2014); OI levels (Herrmann et al., 2007); OI modalities (Bigliardi et al., 2012; Dahlander & Gann, 2010); knowledge flows (Lichtenthaler & Ernst, 2009); effectiveness of OI activities and practices (Dahlander & Gann, 2010; Greco et al., 2015; Tomlinson, 2010) and internal and external contexts of OI (Harison & Koski, 2010; Huizingh, 2011). An OI approach to development of a start-up ecosystem typically promotes the development of four key aspects: (a) a coordinative and collaborative network of the principal firm and allies; (b) links based on cooperative vertical and horizontal relationship among network allies, with a particular focus on intra-network flows of knowledge; (c) value-capture with allies and (d), external connections of the network-ecosystem to other networks and enterprises (Pustovrh et al., 2020). From this theoretical perspective, an ‘open approach’ to start-up creation is key, i.e. intentionally leveraging external innovation?related and technological knowledge through the new venture’s enterprise boundaries (Eftekhari & Bogers, 2015; Marullo et al., 2018; Presutti et al., 2011) that can support the founding teams in overcoming internal resources constraints, providing new venture performance (Drechsler & Natter, 2012; Gruber et al., 2013; Ketchen et al., 2007; West & Bogers, 2014).

Social Capital Theory

The social capital perspective is mainly understood as an entrepreneurs' capacity to acquire and use resources from links to achieve expected results (Adler & Kwon, 2002; De Groote & Backmann, 2020). This theory has been applied widely in sociology (Smith et al., 2017), business and management (Ter Wal et al., 2016), including entrepreneurship (Packard, 2017) and the innovation literature (Feng et al., 2019).

The SCT perspective shows that innovation and entrepreneurship generate entrepreneur positive behaviour inside of the start-up ecosystem and increase access to knowledge, relationships, or benefits over other actors and a sense of solidarity or support (Engel et al., 2017; Nair et al., 2020). As such, the dependence on personal networks for a new venture can be described in terms of SCT, which stresses the importance of the social environment in which the new venture actors are established as a principal source for reducing risks and promoting innovation (De Groote & Backmann, 2020; Leyden et al., 2014). Therefore, we adopt a notion of social capital as the sum of real and possible resources set within, available and derived from the network of links maintained by people (Smith et al., 2017). This concept of social capital is consistent with its multidimensional nature. Despite conceptualization challenges and associated validity studies of the dimensions SCT (Gedajlovic et al., 2013), advancement has been made in the definition of how social capital is manifest. For Smith et al. (2017), two complementary viewpoints have emerged in the innovation and entrepreneurship literature: 1) bridging social capital and 2) bonding social capital.

The bridging social capital evolves across structural relationships with other actors and is essentially distinguished by information and knowledge sharing. Entrepreneurs are thought to generate networks that contribute access to the resources and capabilities they require to be successful by bridging the structural gaps in their collaboration networks, often via brokers, to achieve expected performance. Likewise, bonding social capital is accrued through an entrepreneur's network, deepening attitudes and behaviours, including the time-based relations of pacing, network keeping and embedding. However, when we study the features of the relationships that influence the level of social capital we need to consider three constructs (Smith et al., 2017).

First, the structural construct focuses on the high or limited position of the actor in the network, such as social support and access to unique information. Both relate to company performance but limited closure has a more substantial effect on start-ups. Second, the relational construct leads to the strength of the connection and interaction, such as friendships, gratitude and respect that influences a high level of trust and reciprocity but is costly to maintain. Likewise, relationally weak ties are important because they are moderately low to maintain and they usually connect actors in distinct contexts, holding diverse information (Stam et al., 2014). The third construct of SCT is homophily (Ruef et al., 2003), which refers to how related the two actors are in terms of what they know, think and have. For a new venture in high-tech businesses and innovation, a low level of homophily is more strongly related to start-up performance (Stam et al., 2014).

Synthesis and Discussion

We have studied individual contributions to the network learning capabilities literature across three management perspectives. Table 2 condenses these results. The matrix is organised in terms of the review's principal categories and expands the concepts. Preconceived categories are start-up ecosystem, start-up performance measures, network learning capability perspective and conceptual aspects. The concepts that arose from the papers are network learning capability practices, start-up performance outcomes, synthesis and research agenda. We debate the network learning capability approach through the conceptual perspectives to arrive at a general knowledge of the phenomena.

Table 2 Synthesis Of Network Learning Capability Perspective
Categories	Resource-based view (25 papers)	Open innovation (18 papers)	Social capital theory (23 papers)
Start-ups ecosystem (SE)	The ecosystem provides resources to start-ups (ten papers), such as funding, physical spaces, universities, investors, business experts, accelerators, consultants, general and specific networks, knowledge flow, managerial knowledge and mentoring.	The ecosystem offers collaboration processes that include dimensions, roles and impact of specific relationships (18 papers), such as support governmental, business angels, venture capital (VC), crowdfunding support and business incubation help.	The network is generally developed and kept going by the start-up ecosystem managers (nine papers). Hence, when a new venture connects to actors, it adds to the number of interactions in the external network, also improving business closure. As a result, network learning capabilities attach to the ‘closure’ of the network, increasing the feeling of belonging and confidence for the start-up's ecosystem.
Start-ups performance (SP)	Objective measures (12 papers) are size and company growth, employment, market sales, investments and profit. Subjective measures (seven papers) are entrepreneur’s satisfaction, survival, success, estimation of growth, competitive performance, outcomes and performance compared to competitors.	Objective measures (seven papers) companies’ growth in sales, total funds obtained profit and employment. Subjective metrics (six papers) are early-stage, estimation of profit, satisfaction, survival and success, while other measures (five articles) do not focus on the new venture performance.	Objective metrics (three papers) are market sales, profit, funds obtained, growth and employment. Subjective measures (two papers) are satisfaction costumers, survival, capability building, competitive advantage and success.
Network learning capability (NLC) conceptual aspects	NCL provides new venture resources and capabilities across the relationship with several actors. Special network-related learning capabilities are required for absorption resources and capabilities. The network learning capabilities are conducive to increased performance.	Open innovation plays a crucial role in exploring and exploiting knowledge collaboratively for start-ups development and drive. New ventures suffer the structural lack of tangible and intangible resources that can be overcome with new open innovation processes.	Social capital theory in innovation and entrepreneurship postulates that others' positive behaviour towards the entrepreneur can improve access to knowledge, relationships or impact on other actors and provide a sense of solidarity or support.
NLC research approach	Ten papers offer a theoretical perspective on researching network learning capability while fifteen are empirical.	Most research approaches network learning capability empirically (11 articles) and the remainder theoretically (seven articles).	The social capital theory articles approach the network learning capability empirically (14 papers) and the remainder theoretically (nine papers).
Network learning capability (NLC) practices	NLC offers access to resources in different ways to start-ups, such as spaces, support, investors and mentors, across relationships with other new ventures, and by actively including new ventures in relation to external actors or quietly supporting them to reach out to external actors in the ecosystem. Network learning-related capabilities, such as learning capabilities, absorptive capabilities, network capabilities and innovation capabilities, affect the resources and capabilities.	The OI practices enable the new venture to overcome both novelty and smallness. NLC provides a coordinate network of the principal company and allies, links based on the collaborative vertical and horizontal relationships between network allies, value-capture with allies and the network ecosystems external connections to other networks and enterprise.	SCT provides, on the one hand, physical proximity, linkages between network partners, social events, facilitates connection, training and shared spaces between the actors in the internal network; for another, it involves an external network that increases or decreases the probability of business closure, such as, by motivating entrepreneurs to terminate some relationships.
Start-ups Performance impact	Resources and capabilities related to new venture performance are the university connection, support programs, scaling capability and internal and external networking. These are linked to survival, satisfaction with new venture performance and competitive advantage. Internal and external networking has not been related to growth in employment, funds obtained and revenue.	The start-up seems to perform more critically in terms of market performance when relying heavily on the network. Therefore, the successful start-up has to be autonomous; by relying too heavily on their network, their performance reduces as they start copying others. Network structure and Innovation processes reciprocally shape other actors in the network and determine the conditions for new products or services, create new artefacts, accept or reject them and in the process, change their interactions and modify their relations.	Network capabilities affect the structural construct of social capital by supporting and stimulating the new venture to form relationships with other actors. The NLC literature has described two key aspects (network physical and cognitive proximity) where both improve network capability and positively affect the start-up’s innovative and market performance.
Reflections and further research	The start-up network-ecosystem offers a more detailed perspective on which resources and learning capabilities affect new venture performance constructs. It concentrates on the resources and capabilities most likely to influence new venture performance. Finally, capability dimensions such as absorptive capabilities, innovation capabilities, network capabilities and learning capabilities go beyond recognising what resources are key in leading to new venture performance.	A more detailed view of the mutual impact among the new venture making decisions and OI processes can help understand how the absorption of knowledge from one actor inside the start-up ecosystem can simultaneously balance innovative and market performance. Also, network learning capabilities are very varied; therefore, potential contextual aspects to consider are the type of business, features of the support activities, institutional contextual and features of the relationships.	How social capital has been incorporated into the different relationships and interactions with the network is clear. However, its effect on the performance of the start-up is not so apparent. This literature gap can be ascribed to the greater interest of current studies with the positive impacts of social capital rather than its adverse impacts. For further studies, we propose the incorporation of a broader view of possible consequences.

Research utilizes a wide diversity of start-up performance metrics but they are similar between the theoretical perspectives. Some studies integrate theories such as RBV and OI, as in Rompho (2018). Nevertheless, over all the research most start-up performance metrics are applied just once or twice, making generalization and comparison difficult through variance in the specific empirical circumstance and contexts. The dearth of research applying similar start-up performance measures hinders the configuration of a clearer view of network learning capabilities. In the network learning capabilities perspective, network capability is principally empirically approached through distinct conceptual perspectives. About half the papers using RBV provide more empirical studies but for SCT and OI, the empirical support is greatly lessened. A theoretical approach can be beneficial in a qualitative environment, as it helps discover conceptual aspects across which network learning capabilities have an impact on new venture performance. However, an empirical perspective is necessary for understanding the value of these aspects and generalising outcomes. The conceptual aspects differ between the theories. One emphasis is on the benefits that a start-up receives. For RBV, these are resources and learning capabilities; for OI, they are shared and collaborative; for SCT, it is social and relationship capital.

The conceptual aspects from different theorists’ approaches feed into each other. Papers assuming an SCT perspective centre on how new ventures generate interactions and exchange of resources and learning capabilities (RBV), knowledge sharing and collaboration (OI). Likewise, the new venture requires network learning capabilities (RBV) to generate links and interactions with other actors and located itself structurally in the internal and external network (SCT). Moreover, network learning capability develops strong ties between actors (SCT), where knowledge is shared between the new venture and other actors (KBV). Furthermore, the homophily construct of interactions is influenced by network learning capability across new venture candidates chosen (SCT). It feeds into the absorptive, learning and innovation capacities, which affect the collaboration process (OI) and transfer of knowledge between actors (KBV).

The studies describe three network learning capabilities improving practices. First, network learning capabilities offer relationship opportunities with fellow start-up’s ecosystem. Second, network learning capabilities entail developing interactions with the coach, mentors and consultant of the start-up’s ecosystem. Third, the start-up's ecosystem provides a support system and opportunities for network learning capabilities development with actors outside the ecosystem. Finally, the impact on new venture performance, from a network learning capabilities perspective, generates benefits for the new venture: resources, learning capabilities, network capabilities, absorption capabilities, innovation capabilities, shared knowledge, and social capital. However, empirical evidence shows that the use of start-up performance metrics is too different.

We can make suggestions about further research from the three theoretical perspectives. Summarising these recommendations, the network learning capabilities approach be assisted in the power of its theorizing by drawing from social and management approaches, such as the RBV; from the OI approach it can take a concern with the environmental factors assisting collaboration and relationship processes; finally, from SCT it could consider the impact and benefits of social capital. These are the likely fields that further research should explore. Moreover, the three perspectives hold many other promising ideas from which the network learning capabilities perspective can potentially have an impact on organisational ambidexterity, dynamic capabilities, learning analytics and experiential learning. Nevertheless, because these ideas have not been used in the network learning capabilities perspective, they could not be debated in this review.

Likewise, we detected different but partially used performance metrics and evidence that research implications are dependent on metrics for performance that were utilised. Moreover, the network learning capabilities perspective assumes a broad and recurring set of metrics. We propose an integration of objective (e.g. growth, sales, revenue) and subjective (e.g. estimation of profit, satisfaction) start-up performance metrics. Further measures of start-ups’ performance may also be important, such as entrepreneur welfare. Additionally, we propose that further study should use more analytical network capability perspectives. Finally, further studies should generate a more detailed approach to the effect of network learning capabilities on start-up performance. This model requires to going beyond the impact that this network has on new venture performance. Further studies assessing the effects of influence on start-up performance metrics are required.

Conclusion and Implications

The present systematic literature review (SLR) aimed to analyse the empirical and theoretical studies of network learning capabilities on new venture performance through three management perspectives. We examined different network learning capabilities and practices and debated the conceptual aspects of how the practice leads to benefits for new ventures (Champenois et al., 2020). We identified that network learning capabilities have an impact on new ventures although the importance of these practices for network learning capabilities is imprecise. Moreover, the effect of benefits on new venture performance is complex. The benefits are resources, learning capabilities, collaboration process, knowledge shared and social capital, improving or worsening new venture performance, depending on the start-up’s performance metrics.

We proposed a research agenda to address the gaps and limitations identified and further the knowledge field. In this way, we have answered two key problems in the network learning capabilities perspective. First, the SLR shows the papers that find a positive impact between network learning capabilities and start-up performance (Garidis & Rossmann, 2019; Marvel et al., 2020; Rompho, 2018; Seet et al., 2020). Hence, we propose a more detailed model of the influence of network learning capabilities as a moderator variable to advance the knowledge area. We started our analysis by discovering and defining the impact of benefits on new venture performance to achieve this model. Second, by examining the three management perspectives, we propose to expand the theoretical and conceptual depth of the network learning capabilities approach (Cao & Shi, 2020; Shepherd et al., 2021; Tripathi et al., 2019). Theories and insights from the RBV, the OI, and SCT can increase the power of the network learning capabilities perspective.

The SLR has restricted itself to conceptual approaches, most often in the network learning capabilities perspectives. We argue that with the addition of other management theories, a greater understanding of the networked learning capabilities approach can be achieved (Cao & Shi, 2020; Weerawardena et al., 2015). Nevertheless, currently, there are too few studies that apply these theories to incorporate these concepts or categories in this SLR. Other important theoretical approaches that have been recommended are dynamics capabilities (Feng et al., 2019; Jeong et al., 2020; Paradkar et al., 2015), organisational ambidexterity (Faridian & Neubaum, 2020), institutional theory (Acs et al., 2018; Islam et al., 2018; Kuratko et al., 2017), entrepreneurial ecosystems (Berger & Kuckertz, 2016; Brown & Mason, 2017; Pustovrh et al., 2020), innovation systems literature (Panetti et al., 2019; Sperber & Linder, 2019) and Actor-Network Theory (Baraldi et al., 2019; Fraiberg, 2017). These alternative perspectives open up opportunities to evaluate the impact of networked learning capabilities on start-ups performance metrics at the territorial level, such as enterprise development, employment, innovation and growth.

The advice for public policymakers, start-ups ecosystem practitioners, new venture founders, and other actors is that network learning capabilities are not instruments for solving many new venture problems. While network learning capabilities can facilitate some benefits, these can lead to both positive and adverse impacts. The results of this SLR propose that the match between the needs and goals of the new venture and the offerings of network learning capabilities need to be considered for start-up performance improvement. We only made a theoretical selection of start-up performance metrics to assess the influence of network learning capabilities. Thus, start-up ecosystem practitioners and public policymakers should keep track of a broad range of start-up performance metrics and compare them.

Acknowledgements

We thank UTS Start-up Community and iNNpulsa Colombia for their valuable support and assistance. We would also like to thank the University of Technology Sydney and CEIPA Business School by supported this work was under project number ETH20-5325.

Appendix

Appendix in Table 3

Table 3 Overview Of Reviewed Articles
No	Reference	Title	Theoretical perspective	Ultimate performance measures	Contributions
1	Shepherd et al. (2021)	Creating New Ventures: A Review and Research Agenda	Management theories, organizational behaviour, strategic management	Create new ventures (theoretical)	The study develops ten subtopics (i.e., lead founder, founding team, social relationships, organizational emergence, entrepreneurial environment, etc.) are then organized into three major stages of the entrepreneurial process: co-creating, organizing, and performing.

2	Zheng et al. (2020)	Entrepreneurial networking during early stages of opportunity exploitation: Agency of novice and experienced new venture leaders (NVL)	Entrepreneurial networking agency	NVLs’ experience influences their networking behaviours in three ways: time orientation, desired benefits, and networking actions.	The study contributes to the literature by clarifying a critical source of systematic differences between the networking behaviours of different NVLs. We find that NVLs’ motives and the unknowns they face, vary based on their experiences, and this consequently affects networking behaviours.
3	Wu, W., et al. (2020)	Incubator networks and new venture performance: the roles of entrepreneurial orientation and environmental dynamism.	Theories networks and Resource-based view (RBV)	Entrepreneurial Orientation, Internal Network, External Network, Environmental Dynamism and New venture Performance	The research identifies both the internal and external networks of BIs positively affect new venture performance and entrepreneurial orientation (EO) has a mediating effect in this relationship. Environmental dynamism plays a positive moderating role in the relationship between BIs’ internal and external networks and EO.
4	Seet et al. (2020)	Understanding early-stage firm performance: the explanatory role of individual and firm level factors	Social learning theory and the perspective psychological factors	Firm’s entrepreneurial orientation, market orientation, and firm performance	Examines the inter-relationships among key constructs: values, entrepreneurial attitude and entrepreneurial self- efficacy (ESE) as antecedents to entrepreneurial orientation (EO), market orientation (MO) and performance.
5	Pustovrh et al. (2020)	The role of open innovation in developing an entrepreneurial support ecosystem.	Open innovation (OI)	The role of a business accelerator in an emerging entrepreneurial ecosystem	An OI approach development an entrepreneurial ecosystem should facilitate the emergence of the following pillars: (a) a coordinative and cooperative network of the focal organization and partners; (b) relationships based on collaborative vertical and horizontal linkages among network partners, with a specific focus on the intra-network flow of knowledge; (c) value-capture in partnerships; and (d) external linkages of the ecosystem network to other networks and firms.
6	Nair et al. (2020)	Toward the Emergence of Entrepreneurial Opportunities: Organizing Early-phase New-venture Creation Support Systems	Complexity and Systems theory, Industrial organization/economics, Social capital theory	Spatial Dimensions, Interventions, and organizing early-phase support systems (Openness and Self-Selection, and visibility and connectivity).	The focus on early-phase support systems, defined as organizations facilitating the emergence of venture concepts as gateways to entrepreneurial efforts, invites attention to a wide range of empirical phenomena such as startup cafes, startup weekends, startup campuses, innovation boot camps, and hackathons, which remain undertheorized.
7	McDonald and Eisenhardt (2020)	Parallel play: Startups, nascent markets, and effective business-model design.	Blended organization theory with a fresh theoretical lens—business-model processes—.	The entrepreneurs borrow from peers and focus on established substitutes for their services or products; test assumptions, then commit to a broad BM template; and pause before elaborating the activity system.	The insights from our framework contribute to research on optimal distinctiveness and to the learning and evolutionary-adjustment literatures. Parallel play is apt to be best suited to circumstances similar to those we studied—settings characterized by technological innovations, new products, and high uncertainty, and by multiple peers, pathways (e.g., several technologies), and product markets.
8	Marvel et al. (2020)	Examining entrepreneurial experience in relation to pre-launch and post-launch learning activities affecting venture performance.	Theory of entrepreneurial learning	Founder Start-Up Experience, Pre-Launch Customer Learning, Pre-Launch Technology Learning, Post-Launch Market Pivots, Post-Launch Product Pivots, and Early Venture Performance.	The study sheds light on the antecedents and consequences of prelaunch and postlaunch learning while advancing the concept of pivots as a way to better understand entrepreneurial learning and new venture performance. The findings underscore and illustrate entrepreneurial experience, prelaunch learning investments, and postlaunch pivots as important to venture performance.
9	Kuckertz  et al. (2020).	Startups in times of crisis–A rapid response to the COVID-19 pandemic.	Grounded theory	Crisis induced adversity, bricolage response, and resilience.	The lockdown measures as a response to the spread of the new coronavirus threaten the existence of many innovative startups. First, illustrates the challenges entrepreneurs face as a consequence of the crisis. Second, illustrates how entrepreneurs are dealing with the effects of the crisis and what they are doing to protect their ventures. Finally, present measures that could be utilized by policymakers to assist entrepreneurs facing challenges.
10	Jeong et al. (2020)	The Role of Venture Capital Investment in Start-ups’ Sustainable Growth and Performance: Focusing on Absorptive Capacity and Venture Capitalists’ Reputation.	Theory and information asymmetry, and Learning capability (absorptive capacity).	Initial invested redound, absorptive capacity, venture capital´s and company´s performance.	This study demonstrated that startups are sustained and perform better as they receive their VC investment at the initial stage. The level of potential absorptive capacity positively moderated this association, unlike realized absorptive capacity, which did not show significant moderating effects.
11	Hasani and O'Reilly (2020)	Analyzing antecedents affecting the organizational performance of start-up businesses.	Theory of Diffusion of innovation, technology acceptance model and technology, organization and environment.	Technological Characteristics, Organisational Characteristics, Environmental Characteristics, Managerial Characteristics, and Marketing Performance.	The findings suggest positive effects of technological and environmental characteristics on the organizational performance of start-up businesses. The managerial characteristics do not have any positive effect on the organizational performance of start-up businesses.
12	Guo et al. (2020)	Technology Push or Market Pull? Strategic Orientation in Business Model Design and Digital Startup Performance.	Resource-based view (RBV) and Demand-side perspective.	Business model desing (value creation, value proposition and value capture), and start-up performance.	The empirical results show that both technology and consumer orientations are beneficial to the performance of start-ups. However, it would be counterproductive for a digital start-up to seek a balance between both strategic orientations in business model design. Furthermore, the positive relationship between consumer orientation and firm performance becomes more prominent in a highly open technological environment, but is weakened in environments characterized by high user interactivity.
13	Faridian and Neubaum (2020).	Ambidexterity in the age of asset sharing: Development of dynamic capabilities in open source ecosystems.	Dynamic capabilities, Ambidexterity, Open innovation, and theory of network	Explorations-oriented ties, asset positions andexploitation-orientedties.	The research on intrapreneurial capabilities has four contributions. Firts, the role of different types of network ties in recognizing and leveraging opportunities. Second, it integrates dynamic capabilities literature a network perspective to reconceptualize asset positioning in the context of value co-creation and co-capture. Third, we address the complexities associated with intellectual property rights in sharing assets through different forms of network ties. Lastly, we link the dynamic capabilities with ambidexterity by offering insights on how asset sharing can facilitate the simultaneous development of capabilities.
14	De Groote and Backmann (2020)	Initiating open innovation collaborations between incumbents and startups: How can David and Goliath get along?	Resource-based view (RBV), Open innovation, Institutional theory, and Social network theory	Partner selection, collaboration; collaborative innovation and startups.	This study found that startups and incumbents agree on the most important partner-related selection criteria, while they differ in their professionalism when sourcing for partners and the project-related selection criteria. Startups seem to rely much more on personal networks when searching for the right partner than established organisations, which utilise a much wider range of screening sources.
15	Champenois et al. (2020)	Entrepreneurship as practice: systematic literature review of a nascent field.	Structuration Theory, Social Practice Theory, Entrepreneuring and ANT-Approach.	The emerging of entrepreneurship-as-practice	To identifies a spectrum of seven theoretical frameworks underlying EaP studies and puts forward examples of research and empirical contexts to provoke the entrepreneurship research community to study actual practices in their diversity.
16	Cao and Shi (2020)	A systematic literature review of entrepreneurial ecosystems in advanced and emerging economies.	Complex adaptive system theory, Social network theory, and Sponsorship governance theory.	Resource logic, interction logic, Governance logic and System of entrepreneurial opportunity discovery, pursuit, and scaleup.	To reveal three key findings that challenge the direct application of the model vis-à-vis advanced economy entrepreneurial ecosystems to emerging economy entrepreneurial ecosystems: resource scarcities, structural gaps, and institutional voids. The findings contribute to entrepreneurial ecosystem literature in terms of ecosystem dynamics and contextualizing entrepreneurial ecosystems in emerging economies.
17	Cacciolatti et al. (2020)	Strategic alliances and firm performance in startups with a social mission.	International business theory, Open innovation and Strategic alliances.	Stage of development, strategic alliances and firm performance.	Startups can improve their business performance by leveraging on equity and non-equity based strategic alliances, so to pursue growth. However, sustainable growth requires attracting the right investments at the right stage of development of the startup.
18	Assenova (2020)	Institutional Change and Early-Stage Start-Up Selection: Evidence from Applicants to Venture Accelerators.	Institutional theory, Open innovation and Entrepreneurship theory	Institutional change, early-stage start-up selection, and venture accelerators	The institutional changes could influence entrepreneurs’ perceptions of the value of partnering with venture accelerators and potentially improve these sponsors’ capacity to select high-growth start- ups to fund and develop.
19	Albourini et al. (2020)	The effect of networking behaviours on the success of entrepreneurial startups.	Social network theory	Networking behaviours and success of entrepreneurial startups.	The results confirm that the better entrepreneurs are at practicing these networking behaviours, the more influence they have on the success of their startups. Those behaviours that carry most influence is cultivating internal contacts, cultivating external contacts, and getting involved in professional activities.
20	Xue et al. (2019)	Information Sharing and Investment Performance in the Venture Capital Network Community: An Empirical Study of Environmental-Social-Governance Start-Ups.	Resource-based theory and Network theory	Breadth of information sharing, Depth of information sharing, Scouting capability, Coaching capability, and Community investment performance.	Empirical results show that venture capital network community information sharing, from both the prospective of breadth and depth, has a significant positive impact on investment performance of ESG start-ups. We also find that the investment capability, such as scouting and coaching, plays a partial intermediary role in affecting investment performance by community information sharing.
21	Vaznyte and Andries (2019)	Entrepreneurial orientation and start-ups' external financing.	Contingency theory, Pecking order theory and Static trade-off theory	Entrepreneurial finance, Entrepreneurial orientation, Industry-level risk, Development stage of a Start-ups	Proposes that a start-up's entrepreneurial orientation differently affects the costs and benefits associated with external debt and equity financing, and thereby its use of the respective financing forms; with the strength of these relationships depending on industry-level risk and venture development stage.
22	Tripathi et al. (2019)	Insights into startup ecosystems through exploration of multi-vocal literature.	Startup ecosystem perspective, Open innovation and Product development	Eight elements of a startup ecosystem: finance, demography, market, education, human capital, technology, entrepreneur, and support factors.	The results show four definitions of a startup ecosystem. These definitions used common terms, such as stakeholders, supporting organization, infrastructure, network, and region.
23	Tripathi et al. (2019)	Startup ecosystem effect on minimum viable product development in software startups.	Startup ecosystem perspective, Open innovation and Product development	Elements in a startup ecosystem and minimum viable product development.	We conclude from this study of a regional startup ecosystem that the MVP development process is most affected by founding team members’ experiences and skill sets and by advanced technologies.
24	Sperber and Linder (2019)	Gender-specifics in start-up strategies and the role of the entrepreneurial ecosystem.	Expectancy theory, Network theory and Startup ecosystem perspective	Financial support, non-supportive environment, personality factors, social support and status.	The findings show that while women tend to mobilize more resources than men in order to overcome support constraints, men are more confident of their capabilities. We highlight that the start-up strategies chosen reflect the perceived support from the ecosystem, the entrepreneurs’ current life situation, and the intended goals.
25	Singh et al. (2019)	Analyzing the startup ecosystem of India: A Twitter analytics perspective.	Social media analytics Startup ecosystem perspective	Positive, neutral and negative categories	The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative.
26	Panetti et al. (2019)	Exploring the relational dimension in a smart innovation ecosystem: a comprehensive framework to define the network structure and the network portfolio.	Social network theory and Network theory	Network structure, network portfolio¡ and performance	The findings show that a smart innovation ecosystem presents an open network structure with structural holes, a high level of modularity and a portfolio of relationships that privileges informal and non-redundant ties within small communities focused on specific themes.
27	McGrath et al. (2019)	A process-based model of network capability development by a start-up firm.	Resource based view and Network theory	Network of relationships, through relationships in the network, problem solving processes and social-cognitive processes	The model highlights the role of the start-up manager in sensemaking with managers across a number of firms to resolve commercial problems. Second, managers' temporal horizons and the specific temporal profile of events and activities inside the involved business relationships are important in understanding and developing, with other firms, network capability.
28	Marvel et al. (2019)	Accelerating sales in start?ups: A domain planning, network reliance, and resource complementary perspective.	Network theory	Time to Sales, Market Planning and Technology Planning, and Network Reliance for Market Planning and Technology Planning	The findings illustrate domain planning, network reliance, and resource complementarities as important to venture development.
29	Kuckertz (2019).	Let's take the entrepreneurial ecosystem metaphor seriously!	Complexity theory	Natural ecosystem management and entrepreneurial ecosystems	The outcome is a novel, service-based definition of EEs and five suggested principles for the management of EEs that might advance theorizing on them and future empirical analysis.
30	Kozubikova et al. (2019)	The impact of political factors' perception on suitability of international business environment: the case of startups.	International business theory	Starting a Business, Dealing with Construction Permits, Getting Electricity, Registering Property, Getting Credit, Protecting Minority, Investors, Trading across, and Resolving Insolvency	The governments should continue facilitating start-ups’ development by reducing and eliminating administrative barriers, improving access to finance, and by adapting educational programs involving entrepreneurial education at different educational levels.
31	Karami and Tang (2019)	Entrepreneurial orientation and SME international performance: The mediating role of networking capability and experiential learning.	Learning theory, Social exchange theory and Effectuation theory	Entrepreneurial orientation, networking capability and international performance	Understanding of foreign market entry of SMEs by considering the mediating mechanisms (i.e. networking capability and experiential learning) through which entrepreneurial orientation leads to superior performance across borders.
32	Giraudo et al. (2019)	Entrepreneurship policy and the financing of young innovative companies: Evidence from the Italian Startup Act.	Entrepreneurial finance theory	Human Capital-specific, Technical Education Economic Education, Managerial Experience Sector, Work Experience and Generic Work Experience	Results suggest that the two mechanisms (crowding-in or crowding-out and R&D subsidies) appear to be functional to different typologies of young innovative companies, and the venture Capital investments significantly reduce the probability to access bank loans.
33	Garidis and Rossmann (2019)	A framework for cooperation behaviour of start-ups.	Behaviour theory and Organizational theory	Cooperation intensity, Cooperation quality, Intention of cooperation and performance	The research was able to build a multi-item scale for start-ups cooperation behaviour. Also provides a causal analysis on the impact of cooperation behaviour on start-up performance. The research finds, that the found dimensions are suitable for measuring cooperation behaviour. It also shows a minor positive effect on start-up’s performance.
34	Feng et al. (2019)	The key role of dynamic capabilities in the evolutionary process for a startup to develop into an innovation ecosystem leader: An in-depth case study.	Resource based view and Social capital theory	Development processes start-up, outcomes and innovation ecosystem	The results reveal that the dynamic capabilities of the case company play a key role. They help the company acquire, renew and reconfigure resources to conquer its own development puzzles.
35	Crespo et al. (2019)	The adoption of management control systems by start-ups: Internal factors and context as determinants.	Contingency theory and Management control systems theory	Country-based cultural context, strategy, organizational structure, and performance.	He results show that different causal paths lead to high degrees of MCS adoption. These paths vary according to the type of MCS. This study complements previous research and provides support to practitioners' decisions and policymakers' influence on MCS adoption.
36	Chatterji, A., et al. (2019).	When does advice impact startup performance?	Resource based view and Theory of entrepreneurial learning	Advice and Formal Training, An Executive Retreat for Entrepreneurs and Firm´s performance	The find that entrepreneurs who received advice from peers with an active approach to managing people grew 28% larger and were 10 percentage points less likely to fail than those who got advice from peers with a passive people-management approach two years after our intervention.
37	Caseiro and Coelho (2019)	The influence of Business Intelligence capacity, network learning and innovativeness on startups performance.	Resource based view, Network theory, and Business intelligence perspective	Business Intelligence capacities, network learning and start-up performance	The results of this study point to positive effects among the different variables and we can conclude that Business Intelligence capacities have an impact on network learning, innovativeness and performance. It seems that proposing business intelligence practices is a new challenge to overcame, but as information is a key resource for better decision making it can payoff.
38	Baron and Harima (2019)	The role of diaspora entrepreneurs in start-up ecosystem development-a Berlin case study.	Austrian capital theory	Capital cultural, Capital humano y Capital social	The empirical findings show that diasporas are an auspicious co-creator for ecosystem development as they enrich the supportive environment with diverse resources that local entrepreneurs cannot provide.
39	Baraldi et al. (2019)	Start-ups and networks: Interactive perspectives and a research agenda.	Social Network theory, the Industrial Marketing & Purchasing view, and Actor-Network Theory	Network and start-ups	The conclude with a research agenda for further research: (1) tracing start-ups' process of network embedding, (2) mapping the connections between the different networks affecting a start-up, (3) grasping the negative effects of networks on start-ups, (4) making longitudinal case studies on start-ups and networks more comparable, and (5) investigating how policy influences the complex interplay between start-ups and networks.
40	Van Weele et al. (2018)	Start?up Communities as Communities of Practice (CoP): Shining a Light on Geographical Scale and Membership.	Communities of practice theory	Social breadth, Geographical scope, entrepreneur and support.	The results show that start-up communities that are confined to a particular workspace strongly resemble a CoP. Furthermore, many elements of CoPs can also be found in regional start-up communities. Finally, we find that workspace communities have more direct and top-down facilitation activities, while regional start-up communities have more indirect and bottom-up facilitation activities.
41	Symeonidou and Nicolaou (2018)	Resource orchestration in start?ups: Synchronizing human capital investment, leveraging strategy, and founder start?up experience.	Contingency theory, Organizational theory and Resource based viefw	Human capital investment, leveraging strategy, and founder start-up experience	Find that deviating from rivals' resource investments negatively affects performance, while conforming to the norms set by rivals positively affects performance. However, we also find that a higher investment in human capital relative to rivals is less detrimental when aligned with a leveraging strategy focused on innovation.
42	Rompho (2018)	Operational performance measures for startups.	Contingency theory	Performance measures by type of startup	The results show that there is a positive relationship between the perceived importance and the performance of each metric. However, no significant differences are found in the importance and performance of each metric among the various stages of startups.
43	Park and Bae (2018)	When are ‘sharks’ beneficial? Corporate venture capital investment and startup innovation performance.	Entrepreneurial finance theory and Resource based view	Performance startup, corporate venture capital and investment	Finded that CVC funding is beneficial for startup innovativeness when CVC investment is established after initial independent venture capital funding. Moreover, a start-up’s patent stock before CVC funding also influences on that effect.
44	Marullo et al. (2018)	‘Ready for Take?off’: How Open Innovation influences startup success.	Resource?based view and Open innovation	Internal resources (finance, technology, human capital)	Results provide weak support for the effect of financial resources on startup success: among the financial resources indicators, the only significant predictor of ‘Take?off’ is the link to venture capital investors.
45	Kee and Rahman (2018)	Effects of entrepreneurial orientation on start-up success: A gender perspective.	Competitive advantage and Gender perspective	Entrepreneurial Orientation, Innovativeness, Proactiveness, Risk-Taking and Start-up success	The results show that EO was statistically related to Start-up Success and unveiled the magnitude of change that gender possesses in improving the relationship between EO practice and Start-up Success.
46	Kato and Zhou (2018)	Numerical labour flexibility and innovation outcomes of start-up firms: A panel data analysis.	Numerical labour flexibility and Innovation activities perspectives	Numerical labor flexibility, innovation outcomes, and start-up firms	The estimation results of a random-effects probity model indicate that an in- verted U-shaped relationship exists between the external labour turnover of regular employees and the probability of patent applications. A similar U-shaped relationship exists between the proportion of non-regular employees and the probability of product innovation.
47	Islam et al. (2018)	Signalling by early stage startups: US government research grants and venture capital funding.	Theory of entrepreneurial resource acquisition and institutional theory	Early stage startups, Government research grants and Venture capital funding	Early stage startups seeking to acquire resources struggle to demonstrate the legitimacy they need to transition from conceptualization to commercialization. They must efficiently cross thresholds over the organizational life cycle to assure their survival and growth.
48	Dickel et al. (2018)	Networking for the environment: The impact of environmental orientation on start-ups’ networking frequency and network size.	Network theory	External and internal environmental orientation, Networking frequency and Network size	The results highlight the relevance of differentiating between the external and internal environmental orientation of start-ups because both concepts can have very different effects.
49	Cole and Sokolyk (2018)	Debt financing, survival, and growth of start-up firms.	Theories of financial intermediation, Capital structure and Bank loan demand	Debt financing, Survival, and Growth of start-up	The results distinguish between business debt, obtained in the name of the firm, and personal debt, obtained in the name of the firm's owner and used to finance the start-up firm. Start-up firms with better performance prospects are more likely to use debt and, in particular, business debt.
50	Cantamessa et al. (2018)	Start-ups’ roads to failure.	Management and marketing theory	Environment, Customer/user, Organization, Product and Business model	Descriptive statistics show how the lack of a structured Business Development strategy emerges as a key determinant of startup failure in the majority of cases.
51	Bandera and Thomas (2018)	The role of innovation ecosystems and social capital in startup survival.	Theory of entrepreneurship	Social capital availability, Social capital utilization and Startup Survival	The results find that those startups that use social capital by collaborating with other agents significantly outperform startups that do not. However, we also observe that the amount of social capital available in an ecosystem counterintuitively does not correlate with a tenant’s use of it, and correlates weakly with longer life expectancy.
52	Anwar et al. (2018)	Networking and new venture’s performance: Mediating role of competitive advantage.	Social networking theory and Resource-based view theory	Networking, New venture’s performance: and Competitive advantage	Results of the study indicate that business networking, financial networking and political networking significantly and positively contribute to new ventures performance and competitive advantage. Results also show that competitive advantage is a strong mediator between financial networking and new venture performance, as well as between business networking and new venture performance, respectively.
53	Adomako et al. (2018)	Entrepreneurial alertness and new venture performance: Facilitating roles of networking capability.	Theory of entrepreneurship and Resource-based view theory	Entrepreneurial alertness, Networking capability and new venture performance	The results show that increases in the levels of entrepreneurial alertness are related to increases in new venture performance. However, under conditions of increased use of social and business networking capabilities, the potency of entrepreneurial alertness as a driver of new venture success is amplified.
54	Acs et al. (2018)	Entrepreneurship, institutional economics, and economic growth: an ecosystem perspective.	Growth theory and National systems of entrepreneurship perspective	Entrepreneurship, Institutional economics, and Economic growth	The results show that the support and role of the entrepreneurial ecosystem in economic growth is so important.
55	Zaech and Baldegger (2017)	Leadership in start-ups.	Leadership perspective and Theory of entrepreneurship	Leadership behaviour, Start-up performance, and Start- up size.	Results indicate that for start-ups and their performance, leadership behaviour is as important as their context. Today, leadership is neglected by most entrepreneurs and is not considered a motivation for founding a business. The results show that founders should focus more on leadership behaviour.
56	Wright et al. (2017).	An emerging ecosystem for student start-ups.	Theory of entrepreneurship	Start-up ecosystem	The elements emerging ecosystem are facilitated student entrepreneurship, involvement from pre-accelerators through to accelerators; the involvement of a variety of entrepreneurs, support actors and investors; the particular nature of the university environment and the external context.
57	Van Rijnsoever et al. (2017)	Network brokers or hit makers? Analysing the influence of incubation on start-up investments.	Theory of entrepreneurship and Network theory	Start-up investments, Network brokers and Incubation	Incubators have a positive effect on (1) the amount of funding that start-ups attract and (2) the ability of start-ups to attract funding from formal investors and banks.
58	Spender et al. (2017)	Startups and open innovation: a review of the literature.	Open innovation perspective and Network theory	Start-ups’ networks, Actors interacting, Start-up ecosystem, Financing institutions, Entrepreneurial dimension, Start-up performance, Knowledge stocks and flows	The analysed literature has been synthesized in seven sub-topics, which have been evaluated as the most relevant in explaining the phenomenon of startups in relation to Open Innovation (OI).
59	Smith et al. (2017)	Embracing digital networks: Entrepreneurs' social capital online.	Capital social theory and Network theory	Digital User Profile, Digital Search, Digital Relations and Network Transparency.	Development a conceptual framework with 12 research propositions that specify how the unique technical capabilities of social network sites impact entrepreneurs' bridging and bonding social capital online.
60	Roundy et al. (2017)	"The resilience of entrepreneurial ecosystems."	Organizational theory and Theory of entrepreneurship	Resilience and Entrepreneurial ecosystems	To contend that entrepreneurial ecosystems differ in both the diversity of participants, ventures, business models, and support organizations, and their coherence around shared values and activities.
61	Packard (2017).	Where did interpretivism go in the theory of entrepreneurship?	Theory of entrepreneurship	Interpretivism and theory of entrepreneurship	The results show that process theories of entrepreneurship are aligned with interpretivist meta-theory, and that their explicit adoption of an interpretivist foundation may better facilitate theoretical progress.
62	Michelino et al. (2017)	Open innovation for start-ups.	Open innovation	Open innovation collaborations, Specialization on knowledge domains and Quality of innovation output	On average, the openness level in the start-up phase is higher than the consolidation one. Furthermore, the higher the level of openness during the start-up phases, the higher the propensity to collaborate with scientific organizations.
63	Men et al. (2017)	Dialogues with entrepreneurs in China: How start-up companies cultivate relationships with strategic publics.	Theory of entrepreneurship	Relationships and strategic publics	Findings suggest that employees and customers are the most important strategic publics for start-ups, followed by investors, the media, and the government.
64	Kuratko et al. (2017)	The paradox of new venture legitimation within an entrepreneurial ecosystem.	Institutional theory, Social psychology and Theory of entrepreneurship	New venture legitimation and entrepreneurial ecosystem	To address this paradox, integrate ideas from the entrepreneurship and innovation literature with insights from the legitimacy literature to describe how different types of venture newness employ different legitimation strategies which results in different levels of legitimacy diffusion beyond an ecosystem.
65	Jin (2017)	The effect of psychological capital on start-up intention among young start-up entrepreneurs.	Theory of psychological capital, Innovative growth theory and Theory of reasoned action	Psychological capital, Start-up intention and Hofstede`s cultural dimensions	The results show that sub-factors that comprise positive psychological capital, namely, hope, resilience and self-efficacy, were found to have positive effects on start-up intention. However, another sub-factor, optimism, did not have a significant effect on start-up intention.
66	Fraiberg (2017)	Start-up nation: Studying transnational entrepreneurial practices in Israel’s start-up ecosystem.	Actor-network theory, Cultural–historical activity theory, Transnational entrepreneurship	Transnational Entrepreneurial Practices, Multilingualism, Mobility, Innovation systems, Networking	Dubbed the Start-Up Nation, Israel contains more start-ups per capita than any other country in the world, with its high-tech industry made up of a dense eco- system of conferences, accelerators, meetups, social media, and coworking spaces.
67	Engel et al. (2017)	Toward a dynamic process model of entrepreneurial networking under uncertainty.	Network theory and Entrepreneurship theory	Dynamic process en Entrepreneurial networking	Taking a novel perspective on entrepreneurial networking and theorize about how entrepreneurs act when desired ties cannot be identified in advance, networking outcomes cannot be predicted, and ongoing social interactions fuel the emergence of new objectives.
68	De Lange (2017)	Start-up sustainability: An insurmountable cost or a life-giving investment?	Sustainable entrepreneurship theory and Institutional theory	Sustainable start-ups, Sustainable national contexts, Reputations, and Expected Marketplace value	Results show that investors avoid sustainable firms, particularly those that are environmentally sustainable. Moreover, investors enjoy national contexts that are socially responsible, but pay no attention to those that are environmentally conscious. In addition, firms that are sustainable in a sustainable national context are not better off for attracting investment.
69	Centobelli et al. (2017)	Knowledge management in startups: Systematic literature review and future research agenda.	Theory of economic growth and Knowledge management perspective	Knowledge management (KM); Performance; Start-up firms, Scalability, ad Factors affecting KM,	The main findings highlight that, even though there is an increasing number of papers on the topic of KM in startups, several issues are still neglected (environmental and socio-political factors, taxonomy of knowledge management systems, level of alignment between start-up’s strategies and technologies adopted, and start-up’s performances).
70	Brown and Mason (2017)	Looking inside the spiky bits: a critical review and conceptualisation of entrepreneurial ecosystems.	Resource Dependency Theory and Entrepreneurship theory	Ecosystem dynamics, Embryonic ecosystem and Scale-up ecosystem	The study provides a critical review and conceptualisation of the ecosystems concept: it unpacks the dynamics of the concept; outlines its theoretical limitations; measurement approaches and use in policy-making.
71	Ter Wal et al. (2016)	The best of both worlds: The benefits of open-specialized and closed-diverse syndication networks for new ventures’ success.	Network theory	Open-specialized and Closed-diverse Syndication Networks and New Ventures’ Success	Testing the effect of investors’ social capital on the success of their portfolio ventures. Finding that ventures have the highest chances of success if their syndicating investors have either open-specialized or closed-diverse networks.
72	Hallen and Pahnke (2016)	When do entrepreneurs accurately evaluate venture capital firms’ track records? A bounded rationality perspective.	Organizational theories and Organizational learning	Venture capital and Accelerators	Accelerators are organizations that aim to accelerate early venture gestation by providing cohorts of ventures with formal education and mentorship connections during intensive, temporally- compressed programs –.
73	Colombelli et al. (2016)	To be born is not enough: the key role of innovative start-ups.	Knowledge Spillover Theory and Entrepreneurship theory	Product Innovator, Process Innovator and Survival time	Our own empirical analysis shows that greater survival is achieved when start-ups engage successfully in both product innovation and process innovation, with a key role of the latter.
74	Berger and Kuckertz (2016)	Female entrepreneurship in startup ecosystems worldwide.	Entrepreneurship theory, Open innovation and Startup ecosystems perspective	Meso (market, money and management) and Macro environment	The results suggest two different configurations explaining a high proportion of female founders and reveal which issues require attention on a metropolitan level and which issues might require national policy makers to become involved.
75	Zacca et al. (2015)	Impact of network capability on small business performance.	Resource-advantage theory and Network theory	Network capability, small enterprise performance and EO (competitive aggressiveness and innovativeness)	The results show that NC is positively related to knowledge creation and that competitive aggressiveness and innovativeness are key mediators between knowledge creation and firm performance.
76	Weerawardena et al. (2015)	The role of the market sub-system and the socio-technical sub-system in innovation and firm performance: A dynamic capabilities approach.	Organizational learning	International vision and Early, internally learning capability and Network learning capability	Moving beyond the past emphasis on market learning, we develop a more complete explanation of learning, its relationship to innovation, and their joint effect on early internationalization.
77	Soetanto and Van Geenhuizen (2015)	Getting the right balance: University networks’ influence on spin-offs’ attraction of funding for innovation.	Network theory and Evolutionary perspective	Network characteristics and Attract funding	Found that all four university network characteristics (size, density, strength of ties, and multiplexity) have a positive relationship with the spin-offs’ ability to attract funding.
78	Paradkar et al. (2015)	Innovation in start-ups: Ideas filling the void or ideas devoid of resources and capabilities?	Dynamic capabilities perspective and Resource based view	Early stage, capability, resource and Start-up performance	Find that successful commercialization of innovations depends on the availability of complementary assets, and that capability-based resources, especially dynamic capabilities, have a greater impact on competitive advantage of start-ups than other intangible and tangible assets.
79	Hyytinen et al. (2015)	Does innovativeness reduce startup survival rates?	Resource-based theory and entrepreneurship theory	Interaction of innovativeness and Startup survival rates	Find that entrepreneurs' greater appetite for risk magnifies this negative association.
80	Stam et al. (2015)	Social capital of entrepreneurs and small firm performance: A meta-analysis of contextual and methodological moderators.	Social capital theory and Contingencial theory	Social capital and small firm performance:	Analyses indicated that the social capital–performance link was positive and significant. Effect sizes of weak ties were smaller than those of structural holes, while network diversity had the largest positive effect on performance.
81	Schott and Sedaghat (2014)	Innovation embedded in entrepreneurs’ networks and national educational systems.	Network theory and Entrepreneurship theory	Entrepreneurial outcomes, Entrepreneur’ s behaviour and Society’ s institutions	Hierarchical linear modelling shows that, while overall networking benefits innovation, innovation is decreased by private sphere networking and increased by networking in the public sphere.
82	Acs et al. (2014)	National systems of entrepreneurship: Measurement issues and policy implications.	Configuration theory and Entrepreneurship theory	National Systems of Entrepreneurship and Global Entrepreneurship Monitor	National Systems of Entrepreneurship are fundamentally resource allocation systems that are driven by individual-level opportunity pursuit, through the creation of new ventures, with this activity and its outcomes regulated by country-specific institutional characteristics.
83	Wang and Fang (2012)	The moderating effect of environmental uncertainty on the relationship between network structures and the innovative performance of a new venture.	Network theory and Contingency theory	Environmental uncertainty, Network structures, Innovative performance and New venture	Find that innovative performance is impacted by different aspects of the network structure, and that environmental uncertainty contributes to this impact. Overall, find that network structure, innovative performance and environmental uncertainty together contribute to a contingent view of the conditions under which network boundary conditions impact innovative performance.
84	Semrau and Sigmund (2012)	Networking ability and the financial performance of new ventures: A mediation analysis among younger and more mature firms.	Network theory and Entrepreneurship theory	Network ability, Financial performance, New venture and Mature and young firm	The results show a significant relationship between entrepreneurs’ networking ability and their new ventures’ financial performance, which is mediated by new ventures’ network size and the strength of network relationships. Additionally, the relationships observed are salient for younger but not for more mature firms.
85	Naudé (2010)	Entrepreneurship, developing countries, and development economics: new approaches and insights.	Neoclassical growth theory	Entrepreneurship, Developing countries, and Development economics	These contributions model and explore the role of the entrepreneur in key areas of concern for development economics, such as structural change and economic growth, income and wealth inequalities, welfare, poverty traps, and market failures.

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Received: 09-Dec-2021, Manuscript No. IJE-21-10375; Editor assigned: 13-Dec-2021, PreQC No.  IJE-21-10375 (PQ); Reviewed: 03-Jan-2022, QC No.  IJE-21-10375; Revised: 22-Feb-2022, Manuscript No. IJE-21-10375 (R); Published: 01-Mar-2022