Supporting Deep Tech Startups to Streamline their Financial Marketing to Different Investor Types

Deep technology (DT) startups develop physical products based on cutting-edge technologies to create entirely new markets. Consequently, they have a comparably high demand for specialized infrastructure, expert knowledge and extended development cycles which result in large capital expenditures. However, especially early-stage (pre-seed/seed) DT startups often fail to raise sufficient funding from investors due to their large capital needs, severe technical challenges often not fully understood by investors, and long time to market. Therefore, this paper analyses the underlying issues by developing a model to support early-stage DT startups by assessing their fit with different investor types (e.g., business angels, venture capital, or other investment opportunities) in order to streamline and focus their funding process. This is achieved by applying the principal-agent-framework to model the information asymmetry between different investor types and DT startups. Relevant signals between startups and investors are derived from literature, adapted to the requirements set by the signaling theory, as an approach to counteract the information asymmetry, and included into the model

Concept for the Identification of Applications for Paradigm-Shifting Technologies on the Example of Quantum Computing

In this paper, the challenge of identifying high-value applications of quantum computing is examined. Although quantum computing holds enormous potential, it requires significant investments and development efforts. Therefore, it is crucial to define precise applications that can guide its development for an efficient industrialization process. To accomplish this goal, a methodology that systematically identifies and evaluates potential applications of quantum is developed. The methodology is designed for a strong alignment between tasks and technology, identification of problem and solution types, a systematic process for identifying problems, and a focus on socioeconomic challenges. It is structured according to the TRIZ methodology and comprises five submodels to determine socioeconomic applications for quantum computing

Case study on technological applications for production planning and control in the context of industry 4.0

In the course of the fourth industrial revolution, a rapid technological change proceeds in the manufacturing industry. Numerous new technologies enable multiple opportunities for industrial applications. In order to keep pace with this development, companies are forced to cope with a high amount of new technologies and arising application trends. For a successful positioning, knowledge of the industrial relevance of possible applications and the technologies associated with their implementation is required in particular. In this context, the Fraunhofer Institute for Production Technology IPT and the Centre of Excellence in Production Informatics and Control (EPIC CoE) conducted a two-stage case study to identify and evaluate promising industry 4.0 based application fields, such as self-optimizing production scheduling. The case study is proceeded as part of the European Union's Horizon 2020 research project under grant No. 739592. Within the first stage of this project a systematic screening for industrial application fields was conducted. Several potential application fields were identified and their advantages and disadvantages outlined. Furthermore, the application fields were evaluated according to their potential industrial impact and maturity level. In the second stage, technologies for the implementation of the most promising application fields were identified. At this, technologies were investigated and evaluated according to their readiness level for the identified application fields. In this paper, the methodology as well as the results of the first stage of the study are presented

Reference-phase Model for the Transfer Process of Deep Tech Innovations

The term Deep Tech is receiving major attention from start-ups, venture capitalists, and governmental decision makers as this special group of technology does have a strong impact on societies and national innovation systems. In European countries, commercialization and industrialization of Deep Tech-related products lacks behind in international comparison. Nevertheless, academic research about the reasons and circumstances in this field is scarce. To fill this gap in research, a comprehensive Deep Tech transfer reference-phase model is developed based on the current state of knowledge that incorporates the entirety of the technology transfer process from science to industry. Taking Deep Tech characteristics into account, four reference phases are set up and described along three descriptive characteristics (TRL, focus, target state) and four requirement categories (knowledge, resources and infrastructure, financial requirements, actors in focus). The analysis and synthesis show that the requirements within the single phases do highly change due to an adapted focus and target state over the technology transfer process. With the present work, a sound understanding of the technology transfer process for Deep Tech is established which enables future researchers to derive phase-specific key success factors and valid governmental recommendations for the technology transfer of Deep Tech

Description Approach for the Transfer of Competencies and Resources in Collaborations Between Corporates and Deep Tech Startups

Deep tech innovations and emerging competitors are putting increasing pressure on established companies to defend their competitive position in globalized markets. With the aim of efficiently generating deep tech innovations through access to deep technologies and thus ensuring growth, corporates are increasingly entering into collaborations with deep tech startups. For their part, deep tech startups are seeking access to complementary competencies in collaborations with corporates. However, due to their differences in practice both partners often lack an understanding of transferable competencies and resources, e.g., deep technologies, competencies and resources. In the context of this work, a model to characterize and identify the potentials for complementary transfer of competencies from corporates and startups within a collaboration is elaborated. Based on an organization-theoretical delimitation of the collaboration partners, a morphology is developed that characterizes suitable groups and dimensions for the identification of competencies and resources. For this purpose, existing approaches for the exchange of competencies in collaborations are analysed and the deficits in relation to deep tech startups are discussed. Based on this, superordinate groups are derived that consider the specific characteristics of corporates and startups. The morphology enables the description of the competencies and resources within a collaboration between corporates and deep tech startups

Transferring Innovation From Corporate Incubators To Its Parent Company: Derivation Of Requirements For The Interfaces

The transfer of innovations into the parent company is one of the major challenges that separate innovation paths, such as corporate incubators, are facing these days. So far there is no specific design model for the transfer of innovation from corporate incubators. This research paper therefore focusses on the development of requirements for the configuration of the interfaces between these two entities. Based on an intensive literature study as well as interviews within a German automotive supplier, requirements for the transfer process between corporate incubator and its parent company are derived and discussed

Development of a Life Cycle Model for Deep Tech Startups

Startups with a technological focus usually pursue the overarching goal of scaling and establishing themselves as a corporate with a product portfolio as quickly as possible. Deep tech startups in particular identify market niches in which no established players are present and aim to disrupt existing or create new markets with deep technology innovations. In this context, deep tech startups face the challenge of not only developing their organization, but also developing their technology and building a market in parallel. Here, a collaboration with a corporate could be helpful, but, yet often fails due to the insufficient knowledge about life cycle stages and associated goals. To date the goals of deep tech startups in their specific life cycle stages are not precisely formulated in literature. Consequently, the aim of this paper is to develop a life cycle stage model for deep tech startups that enables the explication and pursuit of tangible goals for the individual stages. In the context of a literature review, existing approaches for the description of life cycles of startups as well as goals in the development of organizations are examined. Deficits are then discussed and suitable life cycle stages as well as goal dimensions for the development of a deep tech startup are derived. The life cycle model is based on five distinct development dimensions of startups, and, thereby, enables the derivation of generic goals