Overview of the papers and data sources

The dissertation includes four research articles, connected by the overall topic of innovation and transformation in German biotechnology. Table 2 below presents an overview of all papers, including research questions, subject of analysis, data and methodology used. The order of the papers in the table reflects the order of the dissertation. Chapter 1 is the present introduction. Chapter 2 unveils the main trajectories of biotechnology development, based on the analysis of the technology space. Chapter 3 adds science space to the analysis via examination of actor-inventors – persons, active both in patenting and publishing, and their role across both science and technology. Chapters 4 and 5 focus on a specific group of firms within biotechnology – small and medium enterprises (SMEs). Thus, chapter 4 deals with the

30 contractual partners of radical SMEs and looks, how these cooperations influence their innovative performance. Chapter 5 in its turn deals with the process of knowledge diffusion via patent citations and estimates, how different proximity measures (geographical, cognitive, organizational and social) influence the success of such diffusion.

As can be seen, the first two papers put a filter on a sub-field inside biotechnology focusing on plant biotechnology, whereas the two latter papers deal with a specific group of firms – SMEs.

The first limitation is explained by the disputable character of plant biotechnology, described in section 2.5 above. This fact makes it especially interesting to analyze. How does this field develop, taking into account the German legislative and funding situation? The second limitation is explained by the special role of SMEs inside German biotechnology. These firms possess high innovative potential and may break the routines in the field.

Tab. 2 Overview of dissertation papers

Chap ter

Title Research question/

hypotheses

Field Subject of

analysis Data sources used for dataset creation

Methodological approach

2 From biotech to bioeconomy.

New empirical evidence on the technological transition to plant-based bioeconomy based on patent data

Is there a transition from plant biotech to broader and sustainable bioeconomy?

Only plant biotech

Technology space (patents)

PATSTAT

2017b Main path

analysis;

Text mining analysis

3 Who shapes plant biotechnology in Germany? Joint analysis of the evolution of authors´ and co-inventors´

networks

How does the interaction of science and technology develop over time? What role do author-inventors play?

Only plant biotech

Co-inventors and co-authors

PATSTAT 2017b;

Web of Science

Network analysis;

Text mining analysis

4 Who benefits from radical innovations of SMEs? – Empirical evidence from the German Biotechnology

Do direct partners of radical

innovators profit from partnership?

Do partners from different regions and technological fields profit more?

Biotech Firms PATSTAT 2017b/2016b;

Förderkatalog;

BIOCOM AG;

Amadeus;

WISO-Net

Ego-networks creation;

negative binomial regression

5 Diffusion of radical innovation among biotechnology SMEs:

Does proximity matter?

How do different proximity measures influence the innovative performance of SMEs, citing radical innovators?

Biotech Firms PATSTAT 2017b;

Förderkatalog;

BIOCOM AG;

Orbis

Negative binomial regression

31 The following main data sources were used in the dissertation. First, the main source of patent data is PATSTAT – the data set for worldwide patent statistics, presented by the European Patent Office (EPO). This data set provides a “snapshot” of patent statistics from different patent offices (EPO 2017). In this dissertation the PATSTAT 2017 – Autumn Edition (PATSTAT 2017b) and PATSTAT 2016 – Autumn Edition (PATSTAT 2016b) was used. These data were collected in July (EPO 2017). Here the lag between patent application and patent publication, which takes 18 months according to the Patent Cooperation Treaty's (PCT) application process of the World Intellectual Property Organization (WIPO), needs to be taken into account (Walter and Schnittker 2016). This means that not all patents from 2016 can be available in the PATSTAT 2017 and 2016 – Autumn Edition. Thus, additional checks for 2016 patents with PATSTAT 2019 – Spring Edition were performed where necessary.

Chapter 3 examines the data for research articles, which complete existing patent data. This information is taken from the 'Web of Science' database, which is commonly used for such kind of research (for example Dornbusch et al. 2013; Tan et al. 2014). 'Web of Science' allows identifying articles belonging to a specific field based on keywords, research area or 'Web of Science'-category. Apart from that, information on authors, including affiliations, and on articles’ citations is available.

Apart from that, for Chapter 4 it was necessary to gather information about the funding SMEs received and about their project partners. This was done with the help of the 'Förderkatalog' – a database for more than 110000 on-going and finished federally-funded projects. The information available in this catalog, includes applicant name and contact data, as well as details of the project (e.g. funding start and end, funding sum, funding initiative).

Chapters 4 and 5 rely on the identification of SMEs as a sub-sample of the overall population of biotechnology firms in Germany. Hence, as a first step, dedicated biotechnology firms need to be identified. To do so, I decided to use an external source of data and not to rely entirely on patents. The reason for this is that not all firms, which patent in biotech-related technology classes, are dedicated biotechnology firms, as these classes can sometimes also be related to the chemical or pharmaceutical industry. Thus, I used firm lists from BIOCOM AG in order to create the data set. BIOCOM AG gathers and analyzes statistics in life science fields.

Furthermore, BIOCOM AG distinguishes between different firm categories, which allows to separate dedicated biotechnology firms from the others.

The information, provided by BIOCOM AG, needed to be complemented with data on the firm’s corporate structure and different events, such as mergers and acquisitions. For that, several other sources were used to complete this firm-level information. Depending on

32 availability of data, Orbis and Amadeus databases from Bureau van Dijk were used. Both databases provide comprehensive information on private sector firms, including their ownership and financial reports, which is consolidated from different sources. Orbis provides data on firms worldwide whereas Amadeus focuses on European firms. In order to complete missing entries of the data on events, the WISO-Net database was used, which provides access to several sources of firm-level information. Each of the next sub-sections introduces a particular dissertation paper in greater detail.