People need to move from one place to another for different purposes, from education and work to leisure activities. Transportation means are a way to facilitate these movements. Nevertheless, the transportation sector generates negative externalities, the major ones being safety incidents, environmental impact, and traffic congestion (van Wee et al., 2013). Innovations in transportation can reduce the sector’s negative externalities (Wiesenthal et al., 2015). Innovations can also benefit companies by accelerating growth and increasing profits (van der Panne et al., 2003). Urban Air Mobility (UAM) is a transport innovation that can be attractive from a societal and business perspective. UAM refers to an aerial transportation system operating within or traversing an urban area, with different applications, including passenger and goods movement, as well as emergency and surveillance services (Ragbir et al., 2020; Reiche et al., 2021; Straubinger et al., 2020; Winter et al., 2020). UAM can help to reduce traffic congestion, especially in large cities (Balac et al., 2019; Liu et al., 2017; Straubinger et al., 2020), can be safer than existing transport modes (Peksa & Bogenberger, 2020) and thanks to advances in battery technologies and in distributed electrical propulsion systems, UAM vehicles can be fully electric, generating zero local emissions (Straubinger et al., 2020). However, UAM faces significant challenges, including achieving a level of noise acceptable to society (Straubinger et al., 2020) as well as safety concerns that could hamper user adoption (Winter et al., 2020). As explained above, materializing UAM innovations can bring benefits to society and companies, but it is challenging because UAM creates negative externalities. Innovation projects are risky and have a high probability of failure (van der Panne et al., 2003). A literature review process of what could make innovations succeed or fail showed that early stage innovation indicators are lacking. A better understanding of indicators for early stage innovations is still needed (Dziallas & Blind, 2019). Thus, a knowledge gap was identified, namely a lack of early stage innovation indicators. The early stage of an innovation starts with the initial innovation idea and ends before the formal development process begins (Dziallas & Blind, 2019; Eling & Herstatt, 2017). The early stage is important because the most critical decisions are made there (Cooper & Kleinschmidt, 1987). The use of indicators can improve decision making processes by ensuring decisions are made based on established criteria (Cooper, 1990). Assessing an innovation project before it enters the development phase can help companies identify projects that will likely fail and thus avoid investing resources in them (Dziallas, 2020; Martinsuo & Poskela, 2011). In order to address the knowledge gap, the research question answered in this master thesis is: “What indicators are important to support decision making at the early stage of the innovation process for Urban Air Mobility innovations?”. The following methodology was pursued to answer the research question. The first step was to obtain scientific literature from Scopus, which was coded using the qualitative content analysis software Atlas.ti, producing a code set of indicators. Secondly, gray literature was obtained via snowballing from scientific literature and with search queries in Google, which was coded to produce another code set of indicators. The independently generated code sets from scientific and gray literature were compared, serving as an initial validation, and used to produce the Indicator Set V1. Thirdly, case studies provided by the external thesis supervisor were coded to extract indicators, using the Indicator Set V1 as coding scheme. Fourthly, an interview protocol and an interview guide were created, and eight semi-structured interviews with experts from industry and academia were conducted. Interview transcripts were coded to elicit indicators. The indicators found at interviews were compared and merged with the Indicator Set V1, producing the Indicator Set V2. Additionally, a framework was created, showing how indicators can be used to make decisions at early stages of the innovation process. Finally, a workshop was conducted with experts from a company manufacturing an UAM aircraft. The feedback obtained from the workshop was used to validate and revise the Indicator Set V2 and the framework, answering the research question. The results of this thesis show that the actions that can be taken regarding early stage indicators fall into three types. Firstly, some indicators can be determined at the early stage, such as the fulfillment of key requirements, alignment with company strategy, synergy with company capabilities, and the value proposition. Secondly, there are indicators whose value can be influenced at the early stage, although these indicators are also dependent on later stages. Examples are production and operation costs, operational downtime, sustainability (which includes emissions and recyclability at the end of life), and user comfort. Thirdly, there are indicators that although they cannot be impacted at the early stage, they should still be identified early to assess if they could become showstoppers at later stages. Examples are the perception of UAM as visually polluting, and its perception as a service only benefiting people from higher income classes. The thesis findings were compared with previous research, namely with Dziallas (2020), who investigated early stage indicators for incremental innovations in the automotive industry, with Cooper (2008), who listed a set of criteria to assess whether a new product should continue to the development phase, and with Feitelson & Salomon (2004), who proposed a model of how transport innovations are adopted. This comparison resulted in the following insights. Firstly, there are many indicators in common between this thesis and Dziallas (2020) and Cooper (2008), suggesting there are early stage indicators applicable to innovations in any industry. Secondly, some indicators found by Dziallas (2020) that were not elicited in this thesis seem to be relevant only for incremental innovations, while indicators found in this research not elicited by Dziallas (2020) seem to be only useful for radical innovations. Indicators related to the societal and political acceptance of the innovation are an example of those that are likely applicable to radical innovations only. Acceptance indicators are also absent at Gate 3 in the research of Cooper (2008). Finally, while the results of this thesis roughly confirm the Political Economy model proposed by Feitelson & Salomon (2004), namely that the factors that influence the adoption of an innovation are its technical feasibility, social and political feasibility and financial feasibility, this thesis contributes to the model in the following ways. It specifies that indicators belonging to the social and political feasibility should be identified at the early stage and it identifies early stage indicators belonging to the categories of user adoption and timing feasibility, which were not conceptualized in the Political Economy model.The main takeaways resulting from this thesis are the following. Firstly, the early stage of the innovation process is critical because there are several indicators whose value is already determined at this stage, as well as indicators that although they are dependent on implementation, their value can already be influenced at the early stage. Even for indicators that are fully dependent on later stages, it is important to identify them early to assess the likelihood that these indicators become showstoppers, which aids companies to assess the risk of innovation projects and decide whether to continue them or not. Secondly, user adoption, which is critical for the innovation’s success, can be addressed by involving potential users early in the design process. Ways to do this include employing surveys, interviews and simulators. Finally, societal and political acceptance are paramount for innovation success. The indicators relevant for them can be identified at the early stage in order to assess if they are likely to become showstoppers or if they are enablers that can be leveraged to boost the acceptance of the innovation. Then, companies can assess if they can be influenced early in the design process. For UAM, noise is an example of such an indicator. An UAM aircraft could comply with noise requirements from certification perspective and still not being allowed to fly in its target markets because the local regulators or the citizens consider the noise level generated as unacceptable. To avoid this, acceptable noise levels can be investigated at the early stage and translated into design requirements. Investigating at the early stage what indicators drive societal and political acceptance can be useful to any transport innovation, and not only to UAM. Further research is needed to conclude about this and to better understand how to address social and political acceptance at the early stage of innovation. Possible avenues are researching how previous successful and failed innovations have dealt with acceptance, and how current candidate innovations in transportation or in other sectors are addressing these issues at the early stage. Other opportunities for future research are the following. Firstly, given that most of the indicators found in this thesis can only be evaluated qualitatively (soft indicators), future researchers can continue looking for early stage innovation indicators that can be evaluated quantitatively (hard indicators). Secondly, to investigate the differences between early stage indicators for incremental versus radical innovations. Thirdly, to determine which early stage indicators are applicable across different industries, and which are specific to an industry or to a specific innovation. Finally, to investigate decision making processes at the early stage of innovation in order to better understand them.