An Empirical Investigation on the Influence of Temporal Distance on the Acceptance of Innovations - Using the Example of Urban Air Mobility Mikael Bagratuni (B.Sc.) Abstract Upon reviewing different approaches to explain the acceptance of innovations, it becomes apparent that the temporal proximity or distance of innovations, the possible effects of which could be neglected, are not considered sufficiently. The question is whether the influence on the acceptance can be observed by high or low temporal distance. This assumption is motivated by the implications of the Construal Level Theory, which suggests such an influence of temporal proximity or distance of an event on the evaluation. The concept of Urban Air Mobility was used as an example of application. To test this assumption, the participants (N = 369) of an online survey were confronted with a temporally close or distant condition and asked to complete a questionnaire designed on the basis of UTAUT2 and other measurement aspects. The results showed that the different conditions had an influence on the time estimation of the participants. Furthermore a significant influence of the moderating effect of the time distance could be determined for the factor safety concerns. 1 Introduction Acceptance research has become an increasingly relevant topic in recent years (Meyer, 2019; see also Schäfer & Keppler, 2013). Due to the great array of innovations that have managed to fundamentally change everyday life, whether it is the smartphone revolution or the advent of social media, it is in the interest of companies, but also of empirical social research, to discuss aspects that determine acceptance towards these new technologies. On the whole, no one saw these developments coming or could even guess whether such innovations would be technically feasible at all. It is, therefore, questionable at this point whether the knowledge of these innovations and thus the perceived temporal distance to them has an influence on their acceptance. Perceived temporal distance refers to a mental abstraction level that varies depending on the perceived distance of an event (Trope & Liberman, 2003). To vindicate the inference, it is no longer unusual to assume that in a few years, air taxis will also be commercially available as a common means of transport in urban areas. In September 2019, a research group from the Stuttgart University of Applied Sciences (Planing et al., under review; Planing & Pinar, 2019) conducted a field study during the event in front of the Mercedes- Benz Museum in Stuttgart where the first public flight of an air taxi on European soil was demonstrated. Taking advantage of this opportunity, the study measured acceptance towards air taxis before and after participants were able to view the flight. In addition, visitors had the opportunity to view and sit in a 1:1 model on the ground. The study had a high degree of internal validity owing to the accessibility of the innovation object to the participants (Planing & Pinar, 2019). The results of this study showed a fundamentally positive acceptance towards air taxis. This positive attitude is also in line with the results of Al Haddad and colleagues (2020), who investigated the acceptance of air taxis using a hypothetical scenario. However, due to the aforementioned high internal validity, the consideration of the temporal aspect gets out of focus. The participants did not receive any concrete information about the market launch of the air taxis, which raises the question of whether acceptance could also have been influenced by the accessibility of the innovation. Since acceptance research basically deals with innovations that already exist in draft form and we can already make certain concrete statements about these concepts (Kromrey, 1999), the concept of Urban Air Mobility (UAM) is very well suited for this investigation. In technology acceptance research, it is commonly accepted to use validated models when eliciting relevant factors that influence the acceptance of technologies among the population (Neudorfer, 2004). This approach is particularly common in Anglo-American information systems research. The TAM research models (e.g. Davis et al., 1989; Venkatesh et al., 2003) are among the most prominent representatives of this field. Depending on the model, the complexity varies, and so does the accompanying set of influencing factors that the respective model postulates, although this does not automatically mean that the number of predictors improves the