Generally, autoethnography arises from a combination of autobiography and ethnography methodology and focuses on self-consciousness and reflexivity [64,65]. Autoethnography locates the Self in the central point of considerations of all social phenomena. Despite the emphasis on the Self (or Auto), autoethnography is not a narcissistic autobiographical research methodology, but it should be developed as complex and changeable methodology for understanding the socio-cultural context. The Self (i.e., the central point, autoethnographer) is always considered in relation to others (i.e., stakeholders) in historical and social contexts that facilitate the experience expression [66]. Chang [11] suggests collecting observational data as well as reflective data to understand the Self. Personal narratives on experiences are basic sources of empirical data to conceptualize social phenomena. Narratives refer to texts presented in form of stories that cover personal experiences, motivations, knowledge, and emotional reactions. Therefore, personal data protection and privacy control are fundamental in the research data storing process. Another ethical issue concerns the validity of the research data. Validity in autoethnography means that experiences presented in autoethnographic essays are realistic and believable, as well as the opinions and feelings are true. The research results should be coherent. The qualitative research does not ensure the generalizability of results in the same way as it is in quantitative research. In autoethnography, the focus of generalizability moves from the principal investigator to story readers. Therefore, readers are responsible for comparisons to similar research results. Readers ensure validation by comparisons and by thinking about the social context of the research.

Myers [28] argues that qualitative methods are treated as unscientific, because they are based on personal impressions. Lack of reproductability or repeatability of autoethnographic events is a problem disenabling the generalizability of results. This inconvenience can be omitted through a method of triangulation, which allows for combining narratives with statistical data analysis and data visualization through charts and diagrams. In autoethnography, the primary role belongs to narratives, so in the same situation two or more autoethnographers can come to different conclusions, which can only be compared but not generalized. However, triangulation allows for statistical data comparisons and generalizations basing on empirical data, as well as for data collection process repetition and its controlling.

In applications of qualitative methods, the internal and external validities are considered. Myers [28] as well as Denzin and Lincoln [2] argue that internal validity concerns the question of how the constructs provided by the autoethnographer are grounded in the constructions of those being researched. It is known as the self-reflective criticality that is validated by repetitive researcher’s interpretations, faithfulness of interpretations, and checking the data accuracy. Secondly, external validity is the extent to which the generalization is possible from one case data to other cases and situations. The generalization is rationalistic, but still intuitive and based on empirical approach and direct experiences.

Autoethnography results are always evaluated in a certain time and culture context of investigating. In some autoethnographies, social science methods, such as statistics, surveys or structured interviews are the most useful and appropriate means of research, but even there the statistical measures are located in particular culture context. For example, H index for a researcher’s work evaluation is located in particular time, disciplines of science, and culture, and it cannot be treated as objective measure of scientific achievements.

In ethnography, as well as in autoethnography, many qualitative and quantitative research methods drawn from social science are applied. They include interviews, collective discussions, questionnaires and observations. However, the collection of statistical methods can be expanded because of the application of Internet analytics, automatic registration of personal data on mobile devices, through sensors, drones, or software agents.

Through that case study, author argues that it is possible to combine traditional narrative approach with statistical analysis in an autoethnography, as it was asked in RQ1. However, statistical measures are supplementary, because the researcher’s context, i.e., research competencies revealed through publications and activities in different events are valuable for individual and research community development. Taking into account the RQ2, author argues that autoethnographic method is applicable and needed to each researcher as it encourages to auto-reflection on his/her place in research community in comparison with others, e.g., co-workers, friends, competitors. Nowadays, social media and publications repositories in Internet make research processes and research results dissemination transparent. Hence, researchers are visible in Internet and they are encouraged to be visible through their publications. Therefore, answering the RQ3, author argues that anonymization of data in autoethnography process is difficult and in the benchmarking process even impossible.

This study emphasizes the value of Internet visibility statistical measures for academic profile development. As autoethnographers participate in various social events and situations everywhere, they leave footprints of their stay and their activity. Autoethnographer, through monitoring their data, is able to see themselves as a part of research context. Denzin and Lincoln [2] considered field notes, interviews, conversations, photographs, recordings and memos in qualitative research, but they do not reject quantitative data analysis. Autoethnographer is immersed in their writing and the work of others and with others through the practice, the stories, as it is usually in the case study method, which can be supported by numerical data, charts, and photos as other means of storytelling. The statistical charts include the historical measures of their academic work results, registration of contact with stakeholders, academic work evaluation metrics. They are anonymised and synthetic expressions of autoethnographer’s knowledge and experiences.

Allen-Collinson [67] has noticed that autoethnographers describe what they have carried out, or what their collaborators have carried out. They can discuss what one might do, but they are never authorized to say what one must do in a particular context. There is a retrospective analysis, but not prospective. Autoethnographers focus on their personal experiences to reveal wider cultural trend for respecting the tendency of a phenomenon in future decision-making. Wigg-Stevenson [68] notices that autoethnographers have a constant problem of representing the lives of others in their research and writing. They are able to look inside their questions and problems, but they are not able to analyze the problems of others. They do not participate as others. There is no change of roles. Autoethnographers are able to reveal opportunities for others to participate in a community of practice [69]. Therefore, this study aims to present autoethnographer-research in a community of academic practices.