The who and how of commercializing emerging technologies: A technology-focused review

Haessler, Philipp; Giones, Ferran; Brem, Alexander

doi:10.1016/j.technovation.2022.102637

Cited by 14 publications

(4 citation statements)

References 115 publications

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“…Scientific publications on the commercialization of knowledge focus on the study and description of the mechanisms of cooperation agreements between participants in the transfer process. In their review of the knowledge commercialization literature, Haessler et al (2023) concluded that a high level of technology variability and a large number of different participants (scientists, management of a scientific institution, manufacturer, service companies, etc.) influence the process of commercialization of technologies.…”

Section: Entrepreneurship and Sustainability Issuesmentioning

confidence: 99%

Commercialization of Ukrainian & Slovak scientific research: facets and implementation algorithm

Yermachenko,

Hrebennyk,

Litvinov

et al. 2023

JESI

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The article emphasizes the need to develop entrepreneurship in the academic environment to commercialize the scientific research results as one of the possible directions. The problems that prevent Ukrainian and Slovak scientists from commercializing their scientific developments are highlighted, classified, and described in detail, and recommendations are proposed for solving these problems. The commercialization of scientific research is based on transforming scientific developments into profitable commercial products or services that can be successfully introduced to the market. The study examines various aspects of commercialization, including evaluating the commercial potential of scientific research, developing business models, finding investors and partners, intellectual property, marketing innovations, and sales. The elements of innovative ecosystems of different countries and Ukraine are described. Various algorithms for commercializing scientific research are suggested and scrutinized, including specific steps scientists must perform to introduce their developments to the market successfully. The difference in commercialization processes in Ukraine and economically developed countries is demonstrated. A business commercialization algorithm is proposed, which assumes that the first step is to analyse the demand for innovation. It has been proven that projects developed under the market's needs today and in the future are more effective for commercial use. Hence, this approach provides better opportunities for attracting investments, creating strategic alliances with the industrial sector, and increasing effective commercial projects. Recommendations suggested by the authors can help Ukrainian and Slovak scientists commercialize their scientific works more effectively, which will positively impact economic development.

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Section: Entrepreneurship and Sustainability Issuesmentioning

confidence: 99%

Commercialization of Ukrainian & Slovak scientific research: facets and implementation algorithm

Yermachenko,

Hrebennyk,

Litvinov

et al. 2023

JESI

View full text Add to dashboard Cite

show abstract

“…In this regard, Rotolo [10] clarified the core elements involved in the concept of emerging technology through a literature review, i.e., the key attributes which qualify a technology as emerging, including radical novelty, coherence, relatively fast growth, prominent impact, uncertainty, and ambiguity. This provides a comprehensive conceptual framework of emerging technology that has been recognized and applied by other scholars in related fields [11][12][13][14][15]. On this basis, this paper will also draw on some key attributes of emerging technology proposed by Rotolo for subsequent research.…”

Section: Introductionmentioning

confidence: 99%

Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective

Liu

Chen

et al. 2023

Systems

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With the cyclical development of emerging technologies, in reality, the evolution dynamics of their innovation networks will inevitably show obvious time attributes. Numerous network analyses of real complex systems usually focus on static networks; however, it is difficult to describe that most real networks undergo topological evolutions over time. Temporal networks, which incorporate time attributes into traditional static network models, can more accurately depict the temporal features of network evolution. Here, we introduced the time attribute of the life cycle of emerging technology into the evolution dynamics of its innovation network, constructed an emerging technology temporal innovation network from a temporal network perspective, and established its evolution model in combination with the life cycle and key attributes of emerging technology. Based on this model, we took 5G technology as an example to conduct network evolution simulation, verified the rationality of the above model building, and analyzed the cyclical evolution dynamics of this network in various topological structures. The results show that the life cycle of emerging technology, as well as multiple knowledge attributes based on the key attributes of emerging technology, are important factors that affect network evolution by acting on node behaviors. Within this study, we provide a more realistic framework to describe the internal mechanism of the cyclical evolution of emerging technology innovation network, which can extend the research on innovation network evolution from the single topological dynamics to the topological–temporal dynamics containing time attributes and enrich the research dimensions of innovation network evolution from the perspective of temporal evolution.

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“…Despite the due diligence processes of screening, evaluating, and selecting promising startups, VCs often lack insights into the startups' businesses, such as their operations, managerial capabilities, and market potential [6]. Navigating the informational challenges is even more crucial when assessing startups pursuing highly novel, unproven technologies where uncertainty abounds [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Overcoming Uncertainty in Novel Technologies: The Role of Venture Capital Syndication Networks in Artificial Intelligence (AI) Startup Investments in Korea and Japan

Hyun,

Kim

2024

Systems

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This paper investigates how historical inter-firm syndication networks influence venture capitalists’ (VCs) propensity to invest in startups pursuing novel, uncertain technologies, with a focus on artificial intelligence (AI). We theorize that VCs’ positional attributes within cumulative syndication networks determine their access to external expertise and intelligence that aid AI investment decisions amidst informational opacity. Specifically, reachability to prior AI investors provides referrals and insights transmitted across short network paths to reduce ambiguity. Additionally, VC brokerage between disconnected industry clusters furnishes expansive, non-redundant information that is pivotal for discovering and assessing AI opportunities. Through hypotheses grounded in social network theory, we posit network-based mechanisms that equip VCs to navigate uncertainty when engaging with ambiguous innovations like AI. We test our framework, utilizing comprehensive historical records of global venture capital investments. Analyzing the location information of VC firms in this database, we uncovered a history of 14,751 investments made by Korean and Japanese firms. Using these data, we assembled an imbalanced panel dataset from 1984 to 2022 spanning 230 Korean and 413 Japanese VCs, with 4508 firm-year observations. Negative binomial regression analysis of this dataset reveals how historical relational patterns among venture capital firms foster readiness to evaluate unfamiliar innovations.

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The who and how of commercializing emerging technologies: A technology-focused review

Cited by 14 publications

References 115 publications

Commercialization of Ukrainian & Slovak scientific research: facets and implementation algorithm

Commercialization of Ukrainian & Slovak scientific research: facets and implementation algorithm

Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective

Overcoming Uncertainty in Novel Technologies: The Role of Venture Capital Syndication Networks in Artificial Intelligence (AI) Startup Investments in Korea and Japan

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