## 3 Methodology
### First Phase: Delphi Method Adapted within a COVID-19 Context  
The study was structured into six phases, as can be seen in Figure 2. This methodology was planned and implemented during the COVID-19 pandemic when face-to-face individual or group interviews were not able to be carried out. Hence, a decision was made to adapt this methodology to the new reality at the time of study. Therefore, we were inspired by [PERSON] et al. [33], who already used an adaptation of the Delphi method in their research because of the COVID-19 pandemic, to conduct our research method. The Delphi method is a process used to arrive at a common group opinion or decision by surveying a panel of experts. Those experts respond to several rounds of questions, and the responses are aggregated and shared with the group after each round. The experts can adjust their answers each round, based on how they interpret other group members' responses. The process concludes with a normal consensus about what the group of experts thinks [34]. Since the Delphi method was launched in the COVID-19 period, the authors have used virtual meetings through Zoom, emails, and phone calls to facilitate the dialogue with experts. The following six phases of Figure 2, explained below, were pursued:  
A group of 25 experts, in different fields, were selected to explore what determinants would have the greatest effects on their car expenditures from different perspectives (e.g., engineers, behavioral scientists, market analyzers, etc.). The experts were selected from different fields of the automobile world, digital transformation, insurance, garages, and digital marketing. The criteria for selecting the experts considered the idea of covering as wide a range of knowledge as possible. This was possible through collaboration with Grupo Next, a mobility company. Its CEO, a co-author of this paper, assisted in the selection of the experts based on his long experience in the mobility industry (more than 25 years). Thus, of the 25 experts, four are experts in digital marketing, three in the insurance industry, three in the world of connected mobility, two in customer services, three are university members who are experts in digital transformation, three are experts in data processing, two are experts from the OEM aftermarket world, three are experts from the automotive industry and two are experts from the financial world and digital payment services. All are high-ranking executives, i.e., high-ranking executives in their organizations. A determinant, in the context of this research, is defined as a factor or construct with a set of characteristics that defines the spending behavior of a driver around the car. In this sense, a personalised email was sent to each expert. Each expert was asked to indicate the determinants that they believe are the most relevant in terms of influencing a driver's behavior reactions involving car expenses. They were asked to give a minimum of five determinants. The format of providing the information was open and unstructured. Thus, they were asked to reply to  
Figure 2: Delphi method description along with its six phases. the email by only listing their top selection of determinants associated with a description of them. Through an on-line Google Form, experts were provided with Table 1 (see the results section) and asked to rank the list of determinants according to their criteria (see Table 2). To avoid any bias in the responses, the determinants were presented in a random order to each expert. It was explained that the higher a determinant was placed in the list, the more relevant that determinant was for the expert in terms of implying the highest impact on drivers' expenses, according to the expert's opinion. On the other hand, assigning a ranking of 10 meant that such a determinant had the lowest impact on car expenses. Once they completed this initial ranking, another set of instructions was sent to the experts to find a consensus on the most influential determinants.