A network analysis using metadata to investigate innovation in clean-tech – Implications for energy policy

Marra, Alessandro; Antonelli, Paola; Dell’Anna, Luca; Pozzi, Cesare

doi:10.1016/j.enpol.2015.06.025

Cited by 56 publications

(41 citation statements)

References 42 publications

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“…For the relevance cycle, we compiled a database of firms that use blockchain technology as an integral part of their business model. We used CrunchBase as the world's largest database for new ventures (Marra et al 2015). Because blockchain is a novel technology, the focus on new ventures enables us to analyze a breeding ground of emerging business models.…”

Section: Iteration 1: Develop Taxonomymentioning

confidence: 99%

The impact of blockchain technology on business models – a taxonomy and archetypal patterns

et al. 2019

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Blockchain technology enables new ways of organizing economic activities, reduces costs and time associated with intermediaries, and strengthens the trust in an ecosystem of actors. The impact of this seminal technology is reflected by an upcoming research stream and various firms that examine the potential uses of blockchain technology. While there are promising use cases of this new technology, research and practice are still in their infancy about altering existing and creating new business models. We develop a taxonomy of blockchain business models based on 99 blockchain ventures to explore the impact of blockchain technology on business models. As a result, we identify five archetypal patterns, which enhance our understanding of how blockchain technology affects existing and creates new business models. We propose to use these results to discover further patterns fueled by blockchain technology and illustrate how firms can use blockchain technology to innovate their business models.

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Section: Iteration 1: Develop Taxonomymentioning

confidence: 99%

The impact of blockchain technology on business models – a taxonomy and archetypal patterns

et al. 2019

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“…In this study, the database developed by CrunchBase is exploited to overcome those limitations. A classification methodology using the keywords in the CrunchBase database for startups was utilized while selecting the sample data, mirroring Marra, Antonelli, Dell'Anna, and Pozzi (), who also used the keywords in the CrunchBase database.…”

Section: Resultsmentioning

confidence: 99%

Acquiring startups in the energy sector: a study of firm value and environmental policy

2018

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Mergers and acquisitions are important activities for the growth of firms. In the energy sector, acquiring firms can obtain eco‐friendly technology and premiums, as well as tangible assets, through the acquisition of energy firms. Among many energy firms, an acquired startup is more effective since the acquisition price is relatively inexpensive and the acquiring firm is also exposed to entrepreneurial culture. In this study, to test the acquisition effect, datasets from Worldscope and CrunchBase are used. It is observed that, despite the advantages of an acquisition, the effect of a startup acquisition is negative. However, firms in environmentally sensitive industries benefit from such acquisition activity. The acquisition price of energy startups is overestimated due to increasing demand for acquisitions. On the other hand, the eco‐premiums also help dirty firms make a profit.

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“…Metadata are proxies of firms' products, services, technologies and, more generally, refer to the know‐how, capabilities and knowledge on which firms build their own specializations. Such information, bottom‐up (generated by companies' owners and employees, and other contributors) and up‐to‐date, is at a very detailed level and much more informative than SIC codes, as showed by the relevant and recent literature (Marra et al, ; Marra, Antonelli, & Pozzi, ; Nathan & Rosso, ; Nathan, Rosso, & Bouet, ; Papagiannidis, See‐To, Assimakopoulos, & Yang, ; Tech City, ; Tech City, ).…”

Section: Datasetmentioning

confidence: 99%

“…CrunchBase is increasingly used in research (Cassetta, Marra, Pozzi, & Antonelli, 2017;Dalle et al, 2017;Homburg, Hahn, Bornemann, & Sandner, 2014;Marra, Antonelli, Dell'anna, & Pozzi, 2015;Moschner, Fink, Kurpjuweit, Wagner, & Herstatt, 2019;Ratzinger, Amess, Greenman, & Mosey, 2018;Spiekermann, 2019;Sussan, Sloboda, & Hall, 2018;Waldner, Zsifkovits, & Heidenberger, 2012;Werth & Boert, 2013). Dalle et al (2017) compare the coverage of CrunchBase comparing it with the OECD Entrepreneurship Financing Database and find that the pattern across years and countries are substantially similar across the two data sources.…”

Section: Datasetmentioning

confidence: 99%

Exploring networks of proximity for partner selection, firms' collaboration and knowledge exchange. The case of clean‐tech industry

Marra

Carlei

Baldassari³

2019

Bus Strat Env

Self Cite

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Nowadays scholars widely recognize that know‐how, capabilities and knowledge needed to generate innovations often reside outside the firm, start‐ups are a valuable source, and collaborative networks are a fundamental strategy for innovation. This is true especially for the clean‐tech sector, which is characterized by the continuous search for innovative solutions and technological advancements. The purpose of the paper is to provide a methodological support for the screening of potential partners based on network analysis and, then, help firms to select them for collaboration and knowledge exchange. The methodology can be easily adopted by managers and executives to identify firms to monitor with greater attention for future investments. The analysis is on a dataset of 4,782 clean‐tech companies operating worldwide. Results highlight that energy companies looking for external sources could investigate their network of business proximity if they intend to specialize in a defined field and/or collaborate with similar partners, while they could explore their network of strategic proximity if they intend to diversify their businesses, that is cooperating and exchanging knowledge with firms with distant but complementary capabilities and resources.

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A network analysis using metadata to investigate innovation in clean-tech – Implications for energy policy

Cited by 56 publications

References 42 publications

The impact of blockchain technology on business models – a taxonomy and archetypal patterns

The impact of blockchain technology on business models – a taxonomy and archetypal patterns

Acquiring startups in the energy sector: a study of firm value and environmental policy

Exploring networks of proximity for partner selection, firms' collaboration and knowledge exchange. The case of clean‐tech industry

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