## 3 Methodology
### Analytic Process  
The analytic process employed in this study is grounded in the LDA algorithm. LDA is a generative probabilistic model designed to uncover latent thematic structures within text corpora. This model operates under the assumption of a Dirichlet distribution, which serves as a prior over potential topic mixtures and word distributions, ensuring a smooth allocation of topics across documents. By representing documents as mixtures of topics, where each topic is characterized by a distribution over words, LDA allows for the discovery of underlying themes in unstructured textual data. The generative process of LDA involves selecting a topic from a document-specific distribution and then generating words based on the selected topic's word distribution. This probabilistic approach enables the capture of latent patterns by identifying co-occurring words associated with specific topics, thus unveiling hidden semantic structures within the dataset. Bayesian inference is employed to infer the topic distribution of each document and the word distribution of each topic, allowing researchers to extract meaningful insights from complex textual data without requiring supervised labels. To proceed with the extraction of the main challenges (topics) of cybersecurity in the automotive sector, we proceed as follows:  
Figure 1: PRISMA 2020 flow diagram. Note: Source [PERSON] et al. [57]. First, we collect data by conducting an extensive literature review of documents, including academic papers, practitioner papers, and conference proceedings. We utilize data from Web of Science (WoS) and Scopus for our analysis. Both WoS and Scopus are among the most commonly used databases in bibliometric analyses in the social sciences. WoS and Scopus keep a complete record of the main information on the publication: title, author's information (including affiliations), abstract, and keywords. In this context, we determine the key search criteria based on our research model. In October 2022, we ran the search query using the following combinations to retrieve all relevant papers related to the topic of interest:  
TS: (Automotive AND/OR Vehicle AND/OR Industry AND/OR Cybers security AND/OR Challenges AND/OR Supply chain AND/OR Dealers AND/OR Adoption AND/OR Diffusion.) Furthermore, the content was revised to discard those papers with highly technical or mathematical components particularly. A total of 326 items from the WoS (Web of Science) and 236 from Scopus fulfilled the criteria and were included for further analysis. We combined the results from the two databases and excluded 25 duplicates for the next step, which resulted in a total of 537 unique documents. Furthermore, the titles, abstracts and keywords of the selected articles were manually reviewed, and those sources not in the field of research were removed. The result was 517 publications: 193 were proceedings; 187 were journal articles; 125 were conference papers; eight were books (or book chapters); and 4 were working papers. The examined literature spans from 2006 to 2023, indicating that research in this field is still in its early stages. This observation is consistent with the findings of [PERSON] et al. and [PERSON] (2018); Thing (IoT), who have identified areas such as electric vehicles, the Internet of Things (IoT), and autonomous vehicles as relatively new research domains. Regarding the geographical distribution of publications, the majority of the literature originates from the United States, which accounts for 187 publications. This is followed by the United Kingdom (80 publications), Austria (78), China (68), Germany (62), Italy (50), South Korea (32), and India (28). The distribution of research appears to correlate with the development level of automotive-related services, components, and manufacturing industries in these countries. An analysis of international collaborations reveals networks of co-authorship that connect researchers from different institutions and countries.