Are social incubators different from other incubators? Evidence from Italy

In the aftermath of the 2008 financial crisis and the Great Recession, the conventional paradigms of political leadership in the U.S. have been increasingly challenged.  America’s post-New Deal hierarchical policymaking pyramid with the Federal government at the top, followed by State governments setting the direction and leading innovation in the public and private sectors seem increasingly mired in partisan rancor and incapable of responding to new challenges.

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The Metropolitan Revolution: How Cities and Metros Are Fixing Our Broken Politics and Fragile Economy

University business incubators (UBIs) are an important part of the ecosystem that supports entrepreneurial activities and economic development. Extant research has focused on examining UBI activities at a single point in time, but there is a paucity of theoretical and empirical work aimed at understanding the forces that explain why and how UBIs change over time. This is an important gap because establishing a university business incubator does not assure its development and growth. We address this issue by drawing upon the Fisher et al. Academy of Management Review, 41(3), 383-409 (2016) Identity-Legitimacy-Life Cycle model to explain how the pursuit of resources and organizational legitimacy shapes the development of UBIs along key strategic and operational dimensions, which has implications for performance evaluation over time. We illustrate with a case study about the creation and evolution of the DMZ, a leading UBI at Ryerson University in Canada. This case provides new insights about the dynamics of UBIs and their relationships with the entrepreneurial ecosystems in which they are embedded. Implications for future research, management practice, and public policy are discussed.

Entrepreneurial ecosystems and the lifecycle of university business incubators: An integrative case study

Sustainable development startups (SDSs) are important to help overcome societal challenges. However, starting an SDS or investing in them is a high-risk endeavor. Hence, policymakers are trying to make entrepreneurial ecosystems (EEs) more favorable for SDSs. A critical component of any EE is a financial support network, through which startups receive investments and business knowledge most importantly from private venture capitalists (VCs), among other finance providers. To be successful, SDSs thus need to become embedded in the financial support network. This embeddedness also allows SDSs to serve as network brokers between VCs and other startups, which is beneficial for the entire EE. Entrepreneurial support organizations (ESOs) can help build a sufficiently dense financial support network by introducing startups to other actors. However, there are often not enough promising SDSs in an EE to meaningfully influence the financial support network. This places ESOs that promote SDSs in the dilemma of which startups to admit: they can either focus their efforts exclusively on SDSs or give their unfilled spots to non-SDSs, with the latter facilitating network brokering among startups. Therefore, this paper answers the following research question: What is the effect from ESOs’ support mechanisms and admission regimes on the number of investments in SDSs? Using an agent-based model, I demonstrate that ESOs are a necessity for EEs with many constrained SDSs, particularly when the constraints are technology-based. Without ESOs, the presence of such SDSs negatively influences the entire EE due to a loss of brokering in the financial support network. ESOs can help repair this damage by having the right admission regimes and helping tenant SDSs overcome some of their constraints. Ultimately, the most effective way to do this is to have an admission regime under which only SDSs are accepted and receive twice as much support from the ESO. 

Intermediaries for the greater good: How entrepreneurial support organizations can embed constrained sustainable development startups in entrepreneurial ecosystems

Evaluation of startup companies using multicriteria decision making based on hesitant fuzzy linguistic information envelopment analysis models

The evolution of the incubator is retraced from the foundational research in the 1950s and 1960s of Research-on-Research —that explored the R&D process in a number of contexts to derive and test theories of organizational behavior. In this paper, we review the next phase of this evolution, starting with seminal incubation research in the early 1970s and, we reflect on the prolific growth of incubation research and practice, as well as new challenges that lie ahead for the next phase of incubation. The incubation field has expanded to include a wide range of models and a plethora of spin-off terminology that have been used interchangeably, with the emergence of the most recent model— the accelerator. Our historical critical review of the evolution of the incubation field is used to ask: in this current era of technological change, what can we learn from the past? We reflect on this question in an attempt to help direct researchers in their endeavors to contribute to the next generation of incubation research. This paper presents lessons learned from prior research and provides a range of indicative areas for further empirical studies at four levels of analysis.

The Evolution of the Incubator: Past, Present, and Future

Successful organizations anticipate the need to change. They do so with minimal effort and a focus on improving outcomes. This paper describes a systems-based approach by which organizations can become more adaptive and thus more agile, proactive, and innovative. The key idea is the alignment of vision and strategy, culture and beliefs, processes, plans, people, and desired outcomes. Such organizations have senior leadership that consistently and effectively communicate the vision and strategy (i.e., why the organization does what it does). The organization has a shared set of beliefs about why the vision and strategy are important. The organizational culture embraces innovative thought rather than rejecting such behavior. The resultant model is a codification of best practices based on a thorough review of the innovation, strategic management, leadership, and adaptive organization literature. The model was applied successfully in the transformation of a large, applied research organization.

A model to guide organizational adaptation

This paper introduces a disciplined, system-focused approach to systematic innovation and product enhancement. This approach addresses a paradox facing multinational companies that must quickly adapt to new market opportunities and enhance products to sustain market competitiveness but must also assure their products perform as specified with predictable development and support cost. Thus, the engineering process must be able to incorporate innovative concepts that may be high risk while ensuring such concepts translate into product enhancements with low risk. The approach described in this paper is based on a visionary system description that suggests possible enhancements to an existing system. The system is decomposed into subsystem components. Opportunities to enhance each component are explored and each is assessed based on the technical risk of the proposed enhancement. Each risk is then reduced to an acceptable level through a combination of prototyping, experimentation, and/or open innovation methods. The work described is based on a collaboration between thyssenkrupp AG, a multinational company headquartered in Essen (Germany) and its innovation centre at a leading U.S. research university, the Georgia Institute of Technology in Atlanta, Georgia (USA). This collaboration is an example of how corporations are increasingly seeking strategic university partners to augment their own technical expertise, to search for and acquire new technologies, to influence university research programmes, and to explore potential game changing innovations. The source for the visionary system description in this work is the Industry 4.0 vision for advanced manufacturing as first articulated in Germany. A case study is provided to illustrate the systems-focused innovation approach. Based on the case study, observations are made about lessons learned as well as future areas of research.

A systems engineering approach to systematic innovation in an industry-university collaboration

The Georgia Institute of Technology has been a catalyst for economic growth in the Southeast United States since its founding in 1885. Over the past 30 years, it has become known as one of the top technological universities in the world. As part of a strategic planning effort commenced in mid-2009, it sought to strengthen its thought leadership and impact through the development and implementation of an innovation ecosystem strategy. The Institute serves as the integrating focus within its region to promote (and provoke) disruptive thought, use-inspired research, experimentation, and accelerated implementation through novel educational, research, and industry partnership programs. Since 2009, there has been a marked increase in economic development impact. This paper describes the guiding principles, strategy, innovative programs, benefits, and lessons learned associated with a regional innovation ecosystem.

Strategic Considerations in Leading an Innovation Ecosystem

Realizing the Value of Industry-University Innovation Alliances: Universities and Corporate Partners Should Implement Five Key Practices to Get the Most from Industry-University Innovation Alliances

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