Next Article in Journal
User Experience of 5G Video Services in Indonesia: Predictions Based on a Structural Equation Model
Next Article in Special Issue
A Novel Epidemic Model for the Interference Spread in the Internet of Things
Previous Article in Journal
RETRACTED: WDN: A One-Stage Detection Network for Wheat Heads with High Performance
Previous Article in Special Issue
Link Prediction in Time Varying Social Networks
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Autoethnography on Researcher Profile Cultivation

Małgorzata Pańkowska
Department of Informatics, University of Economics in Katowice, 40-287 Katowice, Poland
Information 2022, 13(3), 154;
Submission received: 7 February 2022 / Revised: 11 March 2022 / Accepted: 14 March 2022 / Published: 16 March 2022
(This article belongs to the Special Issue Information Spreading on Networks)


Information Communication Technology (ICT) and social networks have significant impact on everyday life. One the one hand, Internet users enjoy promoting themselves and feel free to disseminate information about themselves through websites and social networks, but on the other hand, people feel forced to reveal information about them on the Internet. Web technologies enable self-promotion for many reasons, i.e., social relations development, acquiring a new job, or research career support. This paper concerns autoethnography application for social science researcher profile cultivation. Autoethnography belongs to qualitative methods and focuses on deep analysis of experiences and competencies in a narrative way. In this study, autoethnography is self-reflection for personal development strategy. This study methodology includes the literature survey and case study. The Literature Survey (LS) on autoethnographic research is included to answer the question for what purposes autoethnography is applied. In the case study, the author proposes to expand autoethnography and presents that beyond stories, statistical data can be used to reveal researcher’s experiences and personality, and data anonymization is a solution for privacy protection in autoethnographic research. The results indicate that perception of individual profile is significantly influenced by ICT, Internet services, and social networks platforms and portals. Contemporary researchers are evaluated by Web statistical measures. The researcher’s profiling is much more complex and statistical measures and metrics provide a general view of the researcher. Application of statistical measures leads to concluding on general competencies of the researcher and precludes a deep focus on local scientific specificity of the researcher. This paper has added value because of presenting the academic community integration with the Internet social networks, e.g., Facebook, LinkedIn, or SciVal. The paper emphasizes transparency and visibility of researchers’ profiles, as well as the necessity to analyze their activities and publications in academic community context and in comparisons with others.

1. Introduction

Generally, autoethnography concerns a person, particularly an individual researcher, who observes themselves and monitors their capabilities. Researchers are located in a social community context, develop their personal identity, realize organizational processes, and communicate with other colleagues. Autoethnography belongs to qualitative research methods and it builds upon the ethnographic tradition. However, it focuses on researcher personal experiences and understanding of personal behavior in a social context [1]. The autoethnographic study methods include narrative introspection, observation, cultural analysis, but also could cover statistical data analysis. Denzin and Lincoln [2] argue that autoethnography aims to open discussions among researchers and privilege certain researcher interpretations. Researchers have the opportunity to read publications of other autoethnographers and conduct reflexive and critical analysis. In that way, they want to understand their own positions in comparison with the realm they investigate.
Autoethnography is becoming increasingly popular in social science, but also in medical science [2]. Marx et al. [3] argue that autoethnography is interdisciplinary and it relies on ethnography, phenomenology and critical identity theories. As any other qualitative research, autoethnography aims to gain a detailed understanding of underlying phenomena, reasons, beliefs, and motivations. It is a process of searching to answer the questions: Why? How? What is the activity? In what context or circumstances? Who, by Whom, and What are the influences on the course of actions? By definition, the autoethnographic analysis is interpretative and concerns a small number of research participants [4].
The literature survey allows for observation that autoethnography is a personal opinions’ description in natural language. This paper is to verify research question (RQ1), if it is possible to combine narrative with statistical data analysis in an autoethnographic research. Beyond that, there are two other questions: RQ2: Is it possible to apply autoethnographic research for personal profile cultivation? and RQ3: Is anonymization of research data in autoethnography a solution to an ethical problem?
Taking into account these three questions, the following paper structure was proposed. The Section 2 covers literature survey on autoethnography goals, weaknesses, validity, application domains, types, and benefits. The Section 3 includes discussion on publish-or-perish strategy and statistical measures analysis based on data from scientific research repositories. The Section 4 presents a model of individual researchers’ communication and explanation of particular stakeholders’ roles in researcher profile cultivation. Finally, the paper comprises conclusions.

2. Autoethnography as Research Method

Autoethnography has many purposes, such as looking for interpretation of phenomena in difficult situations, exploring important personal issues within social context. Therefore, autoethnographic papers concern topics such as work activities, personal experiences, illness and injury, or family life [5]. Contemporary authors relate autoethnography research to different forms of self-expression, including personal musings, playing, singing, social networking, blogging, and speaking. They argue that all these forms provide a sociological contribution, enable the researcher to express their competencies, capabilities, preferences and in that way present individual profiles, which are constantly monitored and improved for further social acceptance [5]. Autoethnography emphasizes cultural analysis and ensures an interpretation of the researcher’s behaviors, thoughts and experiences in relation to others in society [6]. Legge [7] argues that autoethnographers present details of their own experience, their emotions, perception, and vulnerabilities. Those experiences have an impact and are meaningful to research interests of other readers and researchers. Ellis et al. [8] also emphasize the issue of understanding the cultural experience in autoethnography research, which is considered by them as a socio-political contribution act. The term “autoethnography” means to systematically analyze (graphy) personal experience (auto) in order to understand cultural and social context (ethno) [9,10]. In autoethnography, the principal investigator makes the decisions on who, what, when, where and how to research [8]. These decisions are connected with personal requirements, funding and personal circumstances. Chang [11] suggests application of autoethnography to search for current topics (e.g., COVID-19), exceptional occurrences, inclusions, exclusions and omissions, to connect the present situation with the past, widely contextualize, compare the autoethnographer with other people’s cases, and theories. Lee [12] argues that autoethnography is a qualitative research “attempt” to collect stories about the self as well as about the general culture practices embedded and represented in those self-narratives. Hence, autoethnographers should emphasize personal experiences in their research and writing, they can order their experiences in processes, show reflexivity and relations among social identities, offer knowledge of cultural phenomena and cultural norms and practices, and finally seek feedback reaction from the audience [12].
The literature survey allows identifying some types of autoethnography, i.e., reflexive, literary, critical, collaborative, and duoethnography. Reflexive autoethnography focuses on cultures and subcultures. Authors use their own experiences to analyze and learn about the self-other interactions [13]. In literary autoethnography, authors primarily identify themselves as autobiography’s writers rather than as social scientists [13]. Critical autoethnography is a process that involves a dialogue between the autoethnographer and study participants [14]. It allows for an examination of institutional and professional contexts, a better understanding of themselves in the self-analysis, but also for reflecting on the institutional processes in fields in which they operate. In collaborative autoethnography research, the researcher presents their life as well as the life of other colleagues, shares key experiences and social identities with the research participants [15,16]. Duoethnography is a cooperative writing including personal memories, artefacts, field observation, and theoretical reflexivity. In this approach, more than one researcher is writing in a collaborative way and communicating with each other. This method allows to reveal differences and conflicts in interpretations and interactions [17].
There are some arguments to support the thesis that autoethnography is a strong research method [18]. Autoethnography is accepted as a unique way to help researchers make sense of themselves, understand their circumstances, and reveal their problems in a context created by similar people. This method is supported through narratives, however, knowing this method inspires to apply other forms of self-expression. It is a positivist view of scientific research and it enables transition from qualitative to quantitative approach. Autoethnography is criticized by some research methodologists [18,19,20]. Costello et al. [18] notice the weaknesses of the autoethnographic approach—insufficient rigor, poor quantification, problem of generalization of results. In this method, the validity of research results depends on acceptance by other researchers; therefore, the autoethnographic method is rather the way of comparisons instead of generalizations. Authors treat as a shortcoming the difficulty in acquiring appropriate information to reveal the researcher experience and acceptance by other stakeholders. There is also an ethical challenge concerning the author’s privacy and autoethnographer’s surroundings privacy protection. In general, ethics is related to the justification of human activity according to some universal norms [21] (p. 16). An ethical framework for social science academicians covers for example honor, professional rigor and adequacy, justice, effective and efficient actions, plagiarism risk avoidance, privacy protection, intimacy respect, rationality and reasonability, intellectual rights protection, avoiding cyberbullying, social inclusion through open access publications, lifelong learning, and equal opportunities creation. According to Mitcham [22] (p. 64), university professionals are rational if they choose the most efficient means to achieving their goal, and reasonable if they are maintaining a certain equitable relationship between themselves and others. Although the study of ethics concerns morality, fairness, and natural justice, the social responsibility is based upon the conviction that university individuals and organizations should be engaged in activities that are beneficial to society. Tolich has formulated guidelines for ethics in autoethnography [23]. He highlights the necessary respect of research participants’ autonomy, voluntary participation and practice of participation consent at each stage of the research, anticipation of the author’s future vulnerability, recognition of possible conflicts of interests, minimization of the risk to harm anybody in the research, and data anonymization.
The Internet enables and encourages to exchange news, ideas, opinions, rumors, and critique information. Its accessibility and openness for discussions make it a comfortable communication medium. Therefore, Internet users must make ethical decisions on how to use such incredible freedom and power. Although the right to freedom of expression is the right for people everywhere, it is restricted because the written opinions must be true and cannot harm nor defame any other person. The harm need not to be of financial nature, but it can also destroy personal reputation, reduce the ability to learn or work in a profession. Anonymous writing is the expression of opinions by people who do not reveal their identity. However, collecting the anonymous opinions requires their authors’ consent. Gathering these permissions is not an easy task, particularly because people prefer to formulate and present anonymously negative opinions instead of commendations. Avoiding defamation or spamming is a challenge. In autoethnographic research, concealment of email addresses, photos and other personal data, as well as avoiding discussions on sensitive topics, i.e., national politics, religions may be required. A growing number of people creates their own private blogs relating to their personal experiences. Others provide comments on Twitter, photos on Instagram, or films on YouTube. All these forms are for sharing experiences and views, presenting local cultures and developing social relations; therefore they are autoethnographic research work. The Internet users find blogging a new way to gain recognition, to improve their personal image, and to attract new partners. People need blogs to ask others for help with life or health problems, to share professional information or build on a new idea. Social networks and blogs include the risk of defamation and theft of innovative ideas.
Social networks and Internet portals support professional and personal development. Personal profile cultivation is ensured through continuous self-recognition, self-monitoring, and self-realization. Autoethnographers on the Internet have the opportunity to receive immediate feedback from readers. Therefore, the quality of autoethnographic presentation is under continuous control on the Internet. The autoethnographers may worry about the risk of privacy revealing loss. Burkart [24] considers anonymity as comparable to the situation of “the stranger on a train”, when a strange passenger reveals personal information to others, because there is no fear of misuse and the vulnerability seems to be reduced. In an Internet community, self-presentation is democratized, evaluation and acceptance are received from incidentally met reviewers and advisors. Carefully chosen or recommended therapists and coaches lose influence. Social networks and Internet portals are opportunities for narcissism, which is an investment in oneself for personal satisfaction and professional career development, e.g., self-presentation on LinkedIn emerges as a compulsion for good job seekers. In autoethnography literature, Zaretsky [25] distinguishes narcissism from self-reflection in the result of self-presentation. Therefore, narcissism is based on a belief of unconditional perfection, while self-reflection arises out of unhappiness and shortcomings. Narcissism is an admiration of one’s idealized self-image, but self-reflection is communicative and oriented towards social relations’ development. Anyway, autoethnographer needs other interlocutors to reveal themselves, but must think to achieve an optimal degree of access and isolation, therefore anonymization and concealment of private data are required in autoethnographic research. Reed-Danahay [26] reinforces the notion that autoethnography permits to reveal the self-to-environment relations. The “self” is perceived as a performer, a process, performance as an act of becoming, a strategy for discovering oneself in a certain culture [26,27]. Reed-Danahay, Manning, and Adams add that there is no single way to do autoethnography and that these research results fall across the social science writing continuum [26,27].
In autoethnography, primary technique, i.e., narrative analysis is de facto story sequence analysis that includes the analysis of scripts and human interpretations of events, opinions, and interviews [28]. However, the story analysis can be combined with quantitative research covering survey, laboratory experiments, simulation, mathematical modelling, and analysis of tendencies and forecasting. For example, Internet of Things offers different mobile devices and wearables for monitoring the health status of individuals. Therefore, statistical analysis of the data collected through auto-monitoring can support autoethnographic textual report. Method triangulation is possible and applicable when autoethnographer wants to look at the same topic from different perspectives. Myers [28] argues that triangulation is a challenge when the applied research methods are fundamentally different, as, for example, in a situation where researchers try to combine quantitative and qualitative research methods. Statistical analysis is a supplement to contextualize qualitative micro-scale research [29].
Literature survey on autoethnography has been carried out in this journal paper just to present the background knowledge on discussed issues. Its main purpose is to summarize earlier research publications. In this literature survey, author presents the main domains of autoethnography research. The reviews have been carried out using the following publication repositories: Association of Information Systems electronic library (AIS eLib), IEEEXplore, SageJournals, Science Direct, Scopus, Web of Science, and Google Scholar. The literature survey covers papers published in years 2010–2022. The searching was conducted via the search term “autoethnography”. In general, 18,425 publications were found, however, 89% (i.e., 16,400) publications were stored in Google Scholar repository. The selection of research items has required significant analysis. After deduplication and rejection of too general papers, only the most idiosyncratic papers have been chosen in Table 1, Table 2, Table 3 and Table 4 to illustrate the most valuable autoethnographic research examples. Generally, there are four subdomains of application of autoethnography in social science. The subdomains in Table 1, Table 2, Table 3 and Table 4 cover societal problems, education, healthcare, and ICT usage.
Autoethnographic studies offer unique insights into healthcare experiences, particularly from patients’ perspectives, but also from the point of view of healthcare professionals and patients’ families [38]. Relatively huge number of publications concern teaching and sharing experience by teachers at different educational level, in different circumstances and countries. Some papers are on minority issues, LGBT people and their place in society [32,37].
Table 1 includes considerations on problems of co-existence within communities. There are problems of relations between leader and employees, challenges of resocialization, questions of LGBT people and others, who have adaptation problems within a community. Hence, studies in Table 1, Table 2 and Table 3 includes experiential autoethnographies.
Table 3 covers challenges of teachers, in particular situations, e.g., COVID-19 pandemic time. However, there is a separate category of autoethnography papers on the acceptance of Information Communication Technology (ICT) solutions, Internet of Things, or smart home devices. The author of this study argues that autoethnography is well suited to smart technologies and Internet of Things acceptance investigation. Understanding the usage, usability and usefulness of digital devices supports deep reflection on technology for end users. Findings in this category are included in Table 4. In each of these four subdomains, autoethnography requires reflexivity of research, recognitions of importance of considered topics, and representing social issues.

3. Publish-or-Perish Strategy and Internet Promotion Measures

In many countries, contemporary researchers are involved in so-called “publish-or-perish” strategy, which has many proponents as well as opponents. Proponents argue that publishing the research results is highly recommended as they are inputs to the knowledge repositories. Therefore, results of the researched financed by public donators should be revealed in open repositories. Open access publications available in Internet repositories undoubtedly create opportunities for academics to learn and recognize the latest scientific achievements, which are particularly important for less developed countries, where investments in research and development work are lower. However, opponents of that strategy doubt whether publications and high impact papers increase the quality of science [45]. They argue that scientists should focus on science and research instead on pursuing more and more publications, reporting citations and promoting their publications in Internet through social media. Tan et al. [45] emphasize that “publish-or-perish” strategy favor researchers who have many publications and high H index. For academic institutions, they stay relevant to receive grants for research. However, the academics’ evaluation policies should accept more holistic evaluation criteria, i.e., originality, novelty, idiosyncrasy of work. In addition, Eshchanov et al. [46] have noticed that for last few years, the number of publications is a common indicator for identification of academic competencies. Governments and universities apply this indicator as an important criterion for promotion and recruitment. Hence, academicians have no access to financial support due to lack of high rank publications. Researchers, who do not publish, are out of competition. Academicians, who have not published yet and have some years old publications, are in the similar situation. Therefore, researchers spend more time on promoting their research results to receive academic community acceptance. However, this attitude towards research is not omnipresent. Some governments and private institutions supporting research works need not to be interested in social media promotion, the Internet visibility, and quantities of reads, citations, and publications.
The “publish-or-perish” strategy results in publishing for publishing itself, as readers are not reading, but looking for citations and reads of their papers [47]. Cortegiani et al. [48] argue that predatory journals are ethical threat to the credibility of science. The author of this paper agrees that predatory journals and conferences are tolerant for poor scientific research works, but they create opportunities for academics to arrive on the scene. The selection of reviewers and their careful work are extremely important for publishing valuable papers. There are reviewers, who are not able to admit that they may not be qualified. Therefore, the good practice is to ask reviewer how competent they are in particular discipline as well as evaluate they competencies. It seems to be a natural human willingness to prefer and accept works that are prepared according to common paradigm. Reviewers may support papers, which are cohesive with their opinions and background knowledge. The classic example is Copernicus thesis, which had after its presenting so many opponents.
There is a question how many papers are written by conference paper reviewers, who are corrupt members of the organizational committees of those events. Nowadays, the Internet tools (i.e., SciVal) allow to monitor this phenomenon. Walker and da Silva [49] noticed that editors are able to influence the review process, to choose reviewers, who are the most suitable members from the point of view of their research interests. The random selection of reviewers is recommended good practice. The “publish-or-perish” strategy favor globalization of scientific work. Wati et al. [50] argue that globalization favors diminishing local cultures and languages, local values, and wisdom. They argue that ethnoscience needs to be developed. University teachers and researchers must be able to insert local cultural values in the science and learning processes.
In this paper, the “publish-or-perish” strategy is considered as temporal dependent determinant of academic career development. For those who realize this strategy the constant improvement, self-promotion, excellence of digital identity of a professional, struggle with personal enemies and competitors in academic community, improvement of social position and complacency are the subjects of special attention. The process of personal profile cultivation can be considered as autoethnographer research work and this process is under strong influence of this strategy.
In this study, the process of autoethnographic research for individual profile cultivation is proposed to cover two stages:
  • Defining the appropriate research communities, gathering data, and data visualization in charts and diagrams;
  • Collecting data from the communities’ data repositories, observing tendency, analyzing, and concluding on what to do to improve actual indicators’ values.
In this approach, there is an opportunity for further comparisons of this autoethnographic results with other academic personal profiles, but not–for generalizations. In this study, an academic personal profile data can be received from social media (e.g., Facebook, LinkedIn), open repositories (e.g., Research Gate, Scopus), as well as from conference reports and proceedings. Exemplar recognized sources of data for academic personal profile are presented in Figure 1. In those repositories of scientific publications, the academics leave their experiences, knowledge and opinions in form of papers, photos, comments, signs of preference, and reports.
Social media sources reveal personal data of autoethnographers and of their social communities. Researchers at contemporary universities are under pressure to be available on Internet, in social media, mainly Facebook and LinkedIn. For many of them it is an acceptable form of promotion and the individual profile cultivation support. Others may have dissenting judgment on this transparency; they feel uncomfortable to be transparent in social media. They may say that Internet photos as well as H index calculation decrease their personality. The discussion on self-promotion on social media has been provided in publications [51,52,53]. Sometimes, a researcher is involved in many different social networks, and can have slightly different profile in each of them. Presentation in social media and other repositories requires privacy protection and endeavor the anonymization of personal data. A photograph or video of a person cannot be disseminated without the person’s consent for public viewing by people on the Internet [54]. However, statistical measures concerning a particular researcher in publication repositories are anonymous, synthetic, comparable, and suitable to acquire academic community feedback and self-evaluation of actual achievements. That said, each academic profile is unique and the research work requires a non-random approach and individual specification of data sources. Autoethnographers have opportunity to analyze statistical data from Google Analytics dashboards and Google Search Console, as well as from the repositories, i.e., Scopus Author Feedback Wizard, SciVal, Research Gate, and Association for Information Systems eLibrary (AIS eLib). Google Analytics is a tool allowing for the analysis of the autoethnographer’s personal website. While this tool seems to be suitable for business and commercial organizations, because through web data monitoring, one can increase the return of investment, academic website is not maintained strictly for direct commercial purposes. Google Analytics measures and separates online traffic into channels, hence for the autoethnographer it may be interesting to know about traffic from search engines, from social media platforms, from other websites, direct email, paid search, display addressing, or custom campaigns. Google Analytics allows the autoethnographer to learn who are the users of their website. Users are classified by gender, country, city, interests, age, language, platform, and operating system. For academic career development, information that for example 61% of users prefer English is a valuable suggestion that the website should be maintained in English. Google Analytics offers some behavioral metrics, i.e., bounce rates, behavior flows, pages/session, average session duration, page views, exit rates, top pages and their performance, custom events, and conversions. The key benefit of behavioral metrics is that it provides valuable information on what pages are the most interesting, and hence the autoethnographer can enrich them for better users’ engagement. Information about new visitors and returning visitors helps to understand if the autoethnographer website users are coming back to the website or not. Not having recurrent visitors is wasteful. Possibly, the autoethnographer is not well known in academic community, otherwise people would come back for news. Figure 2 presents author’s website Google Analytics data. Number of users and new users are almost the same. In the author’s opinion the Google Analytics data are not critical for research profile development. The Google Analytics metrics may be stimulants of personal development; however, the other descriptive measures may have stronger impact.
Google Search Console is Google’s free service that helps the autoethnographer to understand their website and the people who use it. The autoethnographer can see how many people are visiting their website, which pages are more popular and which—less. Through Google Search Console, the autoethnographer is able to learn what queries brought users to the website, and which pages on the website are the most valuable for them. For example, a great surprise is that people are interested in recognition references more that in full text in English. Google Search Console Analytics covers four basic parameters:
  • Total number of clicks as primary measure for website tracking;
  • Effectiveness measure, i.e., impressions referring to a single instance of a website’s link being displayed in search results to a unique users;
  • Click-Through-Rate (CTR) metrics showing percentage of people who liked the content enough to click on it. CTR is calculated by dividing the total number of clicks by the total number of impressions;
  • Position on the Google search results page.
Scopus Author Feedback Wizard provides the autoethnographer with information from the Scopus Author details page. Hence, the autoethnographer can know an overview of citations, list of cited publications, and view the h-graph. The quoted documents are classified by source, type, year, and subject. An analysis of citations in particular years allows the autoethnographer for the retrospective analysis of the research activities self-evaluations and concluding which publications were mostly needed. It is difficult to answer if the number of citations is an argument to develop research work on topics that are preferred and highly appreciated by readers. Probably the number of citations depends on the accessibility of publications, therefore the autoethnographer is encouraged to publish in high range and open access journals. Through the analysis of Scopus Author report, the autoethnographer has access to information on citations of comparable publication written by other authors. Comparison with others is to support the self-evaluation and self-improvement. The SciVal uses Scopus data and presents the interests of the whole research community. SciVal reveals the number and list of publications, classification of publications by subject area, and publications in the top 10% journals. SciVal allows for benchmarking analysis, which supports the autoethnographer’s self-evaluation through the opportunity to compare numbers of publications written by the autoethnographer with number of publications written by other selected authors. In the benchmarking analysis, the fundamental question is who is the best benchmark for a researcher under investigation. Therefore, the researchers can compare themselves to their colleagues, co-workers, reviewers or people having the same research interests. Assuming that author is interested in autoethnography on researcher’s profile the following similar researchers have been found in Scopus: Darci M. Graves [55], Austin Gerhard Oswald, Sarah Ross Bussey, Monica X. Thompson, Anna Ortega-Williams [56], Tania Rodriguez-Kaarto [57], Elmarie Sadler, Jacobus P.H. Wessels [58], and Robert Werner [59]. Figure 3 presents comparison of author’s publications with publications of similar researchers. They have other important metrics (i.e., citations, H-indices) almost on the similar level. Each of them has his/her own background knowledge, competencies, experiences and separate context of research. Their research works may be inspiring but the statistics are less useful for professional development and research profile cultivation. The statistical measures should always be considered in a context. Author of this study considers social media and publication repositories contents as more inspiring to professional development and more valuable for individual profiling than statistical metrics. Therefore, the LinkedIn portal is valuable as it covers information on employment opportunities, events, conferences, webinars, and seminars, or research publications. For authors this communication channel is a way to disseminates research results and to encourage people to joint research. Google Research Gate is a social networking repository for researchers, who provide their publications for open sharing, ask and answer questions, look for collaborators, find academic partners and volunteers for work. In Research Gate reports, autoethnographer has not only citations, but also reads, full text reads, and recommendations. For some discipline professionals and particularly for practitioners just reads are more important than citations.
AIS eLib provides its members statistics included in the Author Dashboard. Hence, the autoethnographer has the opportunity to monitor publication readership distribution by countries, institutions, and referrers. Beyond that, the author can observe downloads of publications generally and separately for each particular presentation.
The SciVal statistical metrics allow researchers for comparisons of their profiles with others. However, beyond that, the benchmarking statistics can be used for choosing the appropriate reviewers as well as for evaluations of the earlier selection of reviewers. In Figure 4, author of this study presents her own profile that covers specification of scientific disciplines, to which her publications belong. The author is able to compare her profile with SciVal profile of similar interests researcher (SIR) as well as with SciVal profile of her research work reviewer (REV). Similar interest researchers (SIRs) are already included in Figure 3. Although they are non-randomly selected, their profiles’ statistics are used to present low correlations although they also published on autoethnography (Figure 5).
Figure 5 includes values of the Pearson correlation coefficient (PCC) between the author’ profiles and profiles of 9 similar interest researchers (SIRs) and 9 reviewers (REVs). The profiles of reviewers are presented in SciVal, however anonymized in this research. For the further comparisons of SIR and REV profiles, the SciVal statistics were standardized and the Euclidian distance was calculated.
The standardization was realized in the following way:
xijs = xij/SDj
where i—discipline of science, jnumber of SIR or REV, SD—standard deviation.
The Euclidian distance (d) was calculated in the following ways:
d(x,y) = {∑(xi –yi)2}1/2
where i—discipline of science, x, y—percentage of publications in discipline for autoethnographer and a researcher included in the comparative study. Figure 6 includes an additional measure, which is calculated as follows:
dPCC = d (x,y)/PCC (x,y)
The statistics in Figure 5 and Figure 6 reveal that different discipline authors (i.e., SIR1-SIR9) and author of this paper have not similar research profiles although they apply the autoethnography and publish on application of this research method. They all have realized research in different domains, i.e., medicine, social science, where the qualitative methods are applicable. Beyond that, comparison of the author profile with research profiles of her past publications’ reviewers permits to conclude about strong correlations. Therefore, author can conclude that selection of reviewers was well carried out. Reviewers, whose profiles are similar to research profile of evaluate author can be expected to realize the task competently. The question of who can be a reviewer arrives at universities for evaluation of PhD works as well as on conferences, where paper reviewers are selected. The selection of reviewers is realized according to the subjective knowledge of conference organizational committee members. Although, in paper evaluation system (i.e., EasyChair) they may insert questions to potential reviewers, which papers they want to review and how competent they are. Figure 6 present also comparisons of the author’s profile with profiles of SIR and REV researchers. In Figure 6, the differences of dPPC values are also quite visible. Similarly, as in Figure 5, reviewers’ profiles are very similar, because of small value of dPCC.
The presented Internet services’ statistics allow the autoethnographer for self-discovery and self-creation. Particularly the personal profile self-cultivation is interesting, when the daily life is interrupted by a risky event, e.g., huge conference, or original research topic development. Then the autoethnographer usually has many newcomers. The autoethnographer’s visibility online permits to join communities of practice. Within these communities, the language and communication are vital components and therefore a number of studies considering the narrative, language and stories on social interactions arrived in autoethnography [60]. Lyytinen et al. [61] argue that in many cases, in academic communities of practice, the practical goals are pragmatic career needs. They name it publish-and-perish syndrome. For universities, the credits obtained from top journal publications should not be the only way to career advances. In this pejorative sense, the goal of creating knowledge is not an end value, but it becomes a means to survive [61]. Fortunately, beyond that, for most researchers, the online profile cultivation and open access publishing strategy are the ways to support knowledge creation, evaluation, and dissemination. Nowadays, the academic homepage development is not popular, although for years it was the basic way to be visible in the academic community. Davis [62] argues that through their personal homepage, the researcher has control over the way in which their publications are distributed and interpreted. However, nowadays the searching and positioning mechanisms provided by publishers ensure the Internet visibility of papers, the issue of personal website maintenance is still valid in the context of ethnoscience development, for promotion of local language, culture, or specific methods of research.

4. Researcher’s Communication Context

Autoethnographers do not work in isolation. They live in social communities that include friends, relatives, partners, children, co-workers, students, teachers, agency authorities, university officials, research facilitators, reviewers, and publication agency, commercial, governmental and non-profit organizations (see Figure 7). These people may have impact on the research projects and processes. The research results depend on the stakeholders around the autoethnographer. Sometimes, just one eminent person (i.e., father, teacher, supervisor) may determine the researchers’ career. In autoethnographic research, beyond the principal investigator narratives, there is also place for personal reflection of other participants of this research. Personal reflection of others, their opinions, attitudes, emotions, and behavior add a context to the research [7]. Context in autoethnography is provided by including conversations or connections with peers as well as by analyzing their documents, interviewing them and analyzing their personal stories and official opinions. Dependencies and communication with other partners within social communities for autoethnographer require to consider ethics and respect ethical norms.
Figure 7 present author’s original communication model. Autoethongrapher in central point maintains personal connections with many stakeholders, thus making relational ethics more complicated. The distance to particular stakeholder is subjective and as such depends on individual perception. The distance is private and changeable. Autoethnographers may be interested in revealing their identities and personalities through the research process [16]. Consequently, ethical issues associated with friendships or just work colleagues become an important part of the autoethnographic research process and product. Effective communication in research circumstances requires the acceptance of certain norms and standards. Communication is realized through a flow of messages among stakeholders in a process of knowledge development. Knowledge is subject of the researcher’s work. However, on the one hand, it is the knowledge on autoethnography and social network stability, on the other—it is social science subdomain knowledge, e.g., healthcare, social politics, technology knowledge. The more appropriate and widely disseminated the knowledge, the more accepted the researcher is as a community authority. Knowledge should be shared, acquired and deployed in practice within the academic community. Researchers belonging to the network of science and research expect the network stability to conduct the research work. Their roles can change within this community and the researchers as actors on scene can change, leave and return to the community, while in general, the research community is continuing its existence [18].
Research communication is necessary for interpretation of research findings as well as for establishing a certain consensus on what is truth, what is sensible, rational, and justified in research process. The communication involves network of stakeholders, i.e., researchers themselves, journalists, editors, intermediaries, donors, policy makers, governments, users’ organizations, or individual beneficiaries. The research team consists of researchers, who are involved in similar areas of study and research, who may use and develop research findings further. University representatives, i.e., teachers, examiners, internal reviewers, administrative staff can encourage autoethnographer to engage, analyze and discuss research issues, to undertake challenges, or to respect the relevance and rigor of the research. Intermediaries and Non-for-profit Organizations focus on knowledge sharing. Workshops, conferences, and seminars allow researchers to share findings with each other. However, the participants should have time and incentives for discussion and reflection on the shared knowledge. This interaction builds trust and confidence. There are also opportunities to expand perspectives in research. Applied methods include face-to-face meetings, exchange visits, peer reviews, exchange of report summaries, emailing, and virtual conferences. Communication can be limited because of linguistic and technological barriers, financial resource limitations, local cultural norms, lack of confidence, or lack of a sense of entitlement [63].

5. Discussion

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.

6. Conclusions

This paper presents a change of autoethnographic paradigm. So far, prevailing thoughts in autoethnography have concerned certain research subject, revealed through narrative and story on experiences. Finally, the autoethnographer and the author of this paper in one person argues that no personal data of any other persons has been revealed and formulates positive answers to the three questions presented in the Introduction. This paper has focused on another way to explain competencies and experiences, just through charts and statistical data. They are based on evidence, i.e., research papers written by this autoethnographer as well as on reasoning of other researchers, who through their reads and citations express their positive attitude. The Internet statistical measures are not able to ensure the full description of academic personality, however they can support the autoethnographic research and they enable comparisons by numbers to avoid personal data discussions. Social media and Internet repositories of publications make researchers’ activities visible and interpretable. Researchers can monitor their work metrics as well as enrich their activity through involvement in events organized by other professionals. There are still many opportunities to further improve the benchmarking tools, analyze trends and forecast researchers’ development. The future autoethnographic research should focus on application of software tools and Internet services for personal competencies’ enrichment.


This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Hokkanen, S. Analyzing personal embodied experiences: Autoethnography, feelings, and fieldwork. Int. J. Transl. Interpret. Res. 2017, 9, 24–35. [Google Scholar] [CrossRef]
  2. Denzin, N.K.; Lincoln, Y.S. Introduction: The discipline and practice of qualitative research. In Handbook of Qualitative Research, 2nd ed.; Denzin, N.K., Lincoln, Y.S., Eds.; Sage Publications: London, UK, 2000; pp. 1–29. [Google Scholar]
  3. Marx, S.; Pennington, J.L.; Chang, H. Critical autoethnography in pursuit of educational equity: Introduction to the IJME special issue. Int. J. Multicult. Educ. 2017, 19, 1–6. [Google Scholar] [CrossRef] [Green Version]
  4. Hennink, M.; Hutter, I.; Biley, A. Qualitative Research Methods; Sage: Los Angeles, CA, USA, 2011. [Google Scholar]
  5. Wall, S.S. Toward a moderate autoethnography. Int. J. Qual. Methods 2016, 15, 1–9. [Google Scholar] [CrossRef] [Green Version]
  6. Vone‘che, J. Identity and narrative in Piaget’s autobiographies. In Narrative and Identity: Studies in Autoethnography, Self, and Culture; Brockmeier, J., Carbaugh, D., Eds.; John Benjamins: Amsterdam, The Netherlands, 2001; pp. 219–246. [Google Scholar]
  7. Legge, M.F. Autoethnography and teacher education: Snapshot stories of cultural encounter. Aust. J. Teach. Educ. 2014, 39, 8. [Google Scholar] [CrossRef] [Green Version]
  8. Ellis, C.; Adams, T.E.; Bochner, A.P. Autoethnography: An Overview. FQS Forum Qualitative Sozialforschung 2011, Volume 12, No 1, Art. 10. Available online: (accessed on 6 February 2022).
  9. Ellis, C. The Ethnographic I: A Methodological Novel about Autoethnography; AltaMira Press: Walnut Creek, CA, USA, 2004. [Google Scholar]
  10. Holman Jones, S. Autoethnography: Making the personal political. In Handbook of Qualitative Research, 3rd ed.; Denzin, N.K., Lincoln, Y.S., Eds.; Sage: Thousand Oaks, CA, USA, 2005; pp. 763–791. [Google Scholar]
  11. Chang, H. Autoethnography as Method; Left Coast Press: Walnut Creek, CA, USA, 2008. [Google Scholar]
  12. Lee, K. Autoethnography as an authentic learning activity in online doctoral education: An integrated approach to authentic learning. Tech Trends 2020, 64, 570–580. [Google Scholar] [CrossRef]
  13. Ellis, C.; Bochner, A.P. Autoethnography, personal narrative, reflexivity. In Handbook of Qualitative Research, 2nd ed.; Denzin, N.K., Lincoln, Y.S., Eds.; Sage: Thousand Oaks, CA, USA, 2000; pp. 733–768. [Google Scholar]
  14. Reed-Danahay, D. Bourdieu and cricial autoethnography: Implications for research, writing, and teaching. Int. J. Multicult. Educ. 2017, 19, 144–154. [Google Scholar] [CrossRef] [Green Version]
  15. Laterza, V.; Evans, D.; Davies, R.; Donald, C.; Rice, C. What’s in a “research passport”? A collaborative autoethnography of institutional approvals in public involvement in research. Res. Involv. Engag. 2016, 2, 24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. O’Riordan, N. Autoethnography: Proposing a new research method for information systems research. In Proceedings of the European Conference on Information Systems (ECIS) 2014, Tel Aviv, Israel, 9–11 June 2014; Available online: (accessed on 17 May 2021).
  17. Formenti, L.; Luraschi, S.; Del Negro, G. Relational aesthetics: A duoethnographic research on feminism. Eur. J. Res. Educ. Learn. Adults 2019, 10, 123–141. [Google Scholar] [CrossRef] [Green Version]
  18. Costello, J.; Feller, J.; Sammon, D. On the road to trusted data: An autoethnography of community governance and decision-making. J. Decis. Syst. 2016, 25, 182–197. [Google Scholar] [CrossRef] [Green Version]
  19. Buzard, J. On auto-ethnographic authority. Yale J. Crit. 2003, 16, 61–91. [Google Scholar] [CrossRef]
  20. Delamont, S. The only honest thing: Autoethnography, reflexivity and small crises in fieldwork. Ethnogr. Educ. 2009, 4, 51–63. [Google Scholar] [CrossRef] [Green Version]
  21. Gonzalez, W.J. On the role of values in the configuration of technology: From axiology to ethics. In New Perspectives on Technology, Values, and Ethics, Theoretical and Practical; Gonzalez, W.J., Ed.; Springer: Cham, Switzerland, 2015; pp. 3–29. [Google Scholar]
  22. Mitcham, C. Rationality in technology and in ethics. In New Perspectives on Technology, Values, and Ethics, Theoretical and Practical; Gonzalez, W.J., Ed.; Springer: Cham, Switzerland, 2015; pp. 63–89. [Google Scholar]
  23. Tolich, M. A critique of current practice: Ten foundational guidelines for autoethnographers. Qual. Health Res. 2010, 20, 1599–1610. [Google Scholar] [CrossRef]
  24. Burkart, G. When privacy goes public: New media and the transformation of the culture of confession. In Modern Privacy, Shifting Boundaries, New Forms; Blatterer, H., Johnson, P., Markus, M., Eds.; Palgrave Macmillan: Basingstoke, UK, 2010; pp. 23–38. [Google Scholar]
  25. Zaretsky, E. Narcissim and the emergence of the network society. In Modern Privacy, Shifting Boundaries, New Forms; Blatterer, H., Johnson, P., Markus, M., Eds.; Palgrave Macmillan: Basingstoke, UK, 2010; pp. 149–166. [Google Scholar]
  26. Reed-Danahay, D. Auto/Ethnography: Rewriting the Self and the Social; Berg Publishers: Oxford, UK, 1997. [Google Scholar]
  27. Manning, J.; Adams, T.E. Popular culture studies and autoethnography, an essay on method. Pop. Cult. Stud. J. 2015, 3, 187–222. Available online: (accessed on 16 May 2021).
  28. Myers, M.D. Overview of qualitative research. In Research Methods in Management; Frenz, M., Nielsen, K., Walters, G., Eds.; Sage: Los Angeles, CA, USA, 2014; pp. 87–98. [Google Scholar]
  29. Tomaselli, K.G.; Dyll, L.; Francis, M. “Self” and “other”: Auto-reflexive and indigenous ethnography. In Handbook of Critical and Indigenous Methodologies; Denzin, N.K., Lincoln, Y.S., Smith, T., Eds.; Sage: London, UK, 2008; pp. 347–371. [Google Scholar]
  30. Deckers, J. The value of autoethnography in leadership studies and its pitfalls. Philos. Manag. 2021, 20, 75–91. [Google Scholar] [CrossRef]
  31. Zubriski, S.; Norman, M.; Shimmell, L.; Gewurtz, R.; Letts, L. Professional identity and emerging occupational therapy practice: An autoethnography. Can. J. Occup. Ther. 2020, 87, 63–72. [Google Scholar] [CrossRef] [PubMed]
  32. Lee, C. Culture, consent and confidentiality in workplace autoethnography. J. Organ. Ethnogr. 2018, 7, 302–319. [Google Scholar] [CrossRef]
  33. Campbell, E. “Apparently being a self-obsessed C**T in now academically lauded”: Experiencing twitter trolling of au-toethnograhers. Forum Qual. Sozialforschung 2017, 18, 16. [Google Scholar]
  34. Jung, I.; Omori, S.; Dawson, W.P.; Yamaguchi, T.; Lee, S.J. Faculty as reflective practitioners in emergency online teaching: An autoethnography. Int. J. Educ. Technol. High. Educ. 2021, 18, 1–17. [Google Scholar] [CrossRef]
  35. Hunter, A. Snapshots of selfhood: Curating academic identity through visual autoethnography. Int. J. Acad. Dev. 2020, 25, 310–323. [Google Scholar] [CrossRef]
  36. Paukova, A.; Khachaturova, M.; Safronov, P. Authoethnography of tutoring in the Russian university: From theoretical knowledge to practical implementation. Mentor. Tutor. 2019, 27, 213–230. [Google Scholar] [CrossRef]
  37. Mayer, C.-H.; May, M. Of being a container through role definitions: Voices from women leaders in organisational autoethnography. J. Organ. Ethnogr. 2018, 7, 373–387. [Google Scholar] [CrossRef]
  38. Mann, C. ‘We might have a conversation once a week but the quality is high’: Research and consultancy in primary care multidisciplinary teams. In Professional Communication. Communicating in Professions and Organizations; Mullany, L., Ed.; Palgrave Macmillan: Cham, Switzerland, 2020; pp. 211–223. [Google Scholar]
  39. Ramalho-De-Oliveira, D. Overview and prospect of autoethnography in pharmacy education and practice. Am. J. Pharm. Educ. 2020, 84, 7127. [Google Scholar] [CrossRef]
  40. Shankar, S. An autoethnography about recovering awareness following brain injury: Is my truth valid? Qual. Inq. 2018, 24, 56–69. [Google Scholar] [CrossRef]
  41. Dirndorfer Anderson, T.; Fourie, I. Collaborative autoethnography as a way of seeing the experience of care giving as an information practice. In Proceedings of the ISIC, the Information Behaviour Conference, Leeds, UK, 2–5 September 2015; Part 2, (paper isic33). Available online: (accessed on 14 September 2021).
  42. Hine, C. Strategies for reflexive ethnography in the smart home: Autoethnography of silence and emotion. Sociology 2020, 54, 22–36. [Google Scholar] [CrossRef]
  43. Neil, J. Creating spaces for reflection with digital autoethnography: Students as researchers into their own practices. Int. J. Art Des. Educ. 2019, 38, 823–831. [Google Scholar] [CrossRef]
  44. Iacono, V.L.; Symonds, P.; Brown, D.H. Skype as a tool for qualitative research interviews. Sociol. Res. Online 2016, 21, 103–117. [Google Scholar] [CrossRef]
  45. Tan, S.Z.K.; Lim, L.W. An existentialist approach to authentic science. IBRO Neurosci. Rep. 2021, 11, 52–55. [Google Scholar] [CrossRef]
  46. Eshchanov, B.; Abduraimov, K.; Ibragimova, M.; Eshchanov, R. Efficiency of “publish or perish” policy—Some considerations based on the Uzbekistan experience. Publications 2021, 9, 33. [Google Scholar] [CrossRef]
  47. Fernandez-Cano, A. Letter to the editor: Publish, publish…cursed! Scientometrics 2021, 126, 3673–3682. [Google Scholar] [CrossRef] [PubMed]
  48. Cortegiani, A.; Manca, A.; Giarratano, A. Predatory journals and conferences: Why fake counts. Curr. Opin. Anaesthesiol. 2020, 33, 192–197. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Walker, R.; da Silva, P.R. Emerging trends in peer review—A survey. Front. Neurosci. 2015, 9, 169. Available online: (accessed on 6 February 2022). [CrossRef] [PubMed] [Green Version]
  50. Wati, E.; Yuberti Saregar, A.; Fasa, M.I.; Aziz, A. Literature research: Ethnoscience in science learning. IOPConference Ser. Earth Environ. Sci. 2021, 1796, 012087. Available online: (accessed on 6 February 2022). [CrossRef]
  51. Brownlee, D. Is Self-Promotion on Social Media Savvy or Arrogant? Forbes 27 February 2019. Available online: (accessed on 6 February 2022).
  52. Poretsky, S. How to Use LinkedIn for Self-Promotion. Available online: (accessed on 6 February 2022).
  53. van Dijck, J. ‘You have one identity’: Performing the self on Facebook and LinkedIn. Media Cult. Soc. 2013, 35, 199–215. [Google Scholar] [CrossRef] [Green Version]
  54. Harris, A.L.; Lang, M.; Yates, D.; Kruck, S.E. Incorporating ethics and social responsibility in IS education. J. Inform. Syst. Educ. 2011, 22, 183–189. Available online: (accessed on 1 June 2021).
  55. Graves, D.M. Guns on campus: An autoethnography of “concealed carry” policies. Fem. Psychol. 2022, in press. [Google Scholar] [CrossRef]
  56. Oswald, A.G.; Bussey, S.; Thompson, M.; Ortega-Williams, A. Disrupting hegemony in social work doctoral education and research: Using autoethnography to uncover possibilities for radical transformation. Qual. Soc. Work 2020, 21, 112–128. [Google Scholar] [CrossRef]
  57. Rodriguez-Kaarto, T. Autoethnographic account of 2L Finnish learning. Disambiguation of language. In Proceedings of the 10th International Conference on Design and Emotion—Celebration and Contemplation, Amsterdam, The Netherlands, 7–30 September 2016; pp. 567–571. [Google Scholar]
  58. Sadler, E.; Wessels, J.S. Transformation of the accounting profession: An autoethnographical reflection of identity and intersectionality. Meditari Account. Res. 2019, 27, 448–471. [Google Scholar] [CrossRef]
  59. Werner, R.J. “Learning this way is like piecing a puzzle together”: Bringing my autoethnography as a self-directed learner of french to a conclusion. SiSal J. 2021, 12, 92–117. [Google Scholar] [CrossRef]
  60. Kozinets, R.V. Netnography: Redefined, 2nd ed.; Sage: Beverley Hills, CA, USA, 2015. [Google Scholar]
  61. Lyytinen, K.; Baskerville, R.; Iivari, J.; Te’Eni, D. Why the old world cannot publish? Overcoming challenges in publishing high-impact IS research. Eur. J. Inf. Syst. 2007, 16, 317–326. [Google Scholar] [CrossRef]
  62. Davis, J. Architecture of the personal interactive homepage: Constructing the self through MySpace. New Media Soc. 2010, 12, 1103–1119. [Google Scholar] [CrossRef]
  63. Neuman, Y. Computational Personality Analysis, Introduction, Practical Applications and Novel Directions; Springer: Cham, Switzerland, 2016. [Google Scholar]
  64. Anderson, L. Analytic autoethnography. J. Contemp. Ethnogr. 2006, 35, 373–395. [Google Scholar] [CrossRef]
  65. Ellis, C.S.; Bochner, A.P. Analyzing analytic autoethnography: An autopsy. J. Contemp. Ethnogr. 2006, 35, 429–449. [Google Scholar] [CrossRef]
  66. Pillay, D.; Naicker, I.; Pithouse-Morgan, K. Academic Autoethnographies Inside Teaching in Higher Education; Sense Publishers: Rotterdam, The Netherlands, 2016. [Google Scholar]
  67. Allen-Collinson, J. Running the routes together: Co-running and knowledge in action. J. Contemp. Ethnogr. 2008, 37, 38–61. [Google Scholar] [CrossRef]
  68. Wigg-Stevenson, N. You don’t look like a baptist minister: An autoethnographic retrieval of „women’s feminist theology. Fem. Theol. 2017, 25, 182–197. [Google Scholar] [CrossRef]
  69. Vicary, A.M.; Jones, K. The implications of contractual terms of employement for women and leadership: An autoethnographic study in UK higher education. Adm. Sci. 2017, 7, 20. [Google Scholar] [CrossRef] [Green Version]
Figure 1. Potential sources of data for academic personal profile.
Figure 1. Potential sources of data for academic personal profile.
Information 13 00154 g001
Figure 2. Google Analytics selected metrics for author’s website (accessed on 6 February 2022).
Figure 2. Google Analytics selected metrics for author’s website (accessed on 6 February 2022).
Information 13 00154 g002
Figure 3. SciVal benchmarking comparison of Author’s publications with others.
Figure 3. SciVal benchmarking comparison of Author’s publications with others.
Information 13 00154 g003
Figure 4. SciVal classification of the author’s publication by subject area.
Figure 4. SciVal classification of the author’s publication by subject area.
Information 13 00154 g004
Figure 5. Pearson Correlation of Researchers’ Profiles.
Figure 5. Pearson Correlation of Researchers’ Profiles.
Information 13 00154 g005
Figure 6. dPCC based similarity of Researchers’ Profiles.
Figure 6. dPCC based similarity of Researchers’ Profiles.
Information 13 00154 g006
Figure 7. Autoethnographer in the center of communication with other stakeholders.
Figure 7. Autoethnographer in the center of communication with other stakeholders.
Information 13 00154 g007
Table 1. Autoethnography research publications on societal problems.
Table 1. Autoethnography research publications on societal problems.
Reference NumberResearch Findings
[30]Author argues that leadership studies are dominated by the perspective of leaders. The paper covers some examples of bad leadership as well as a solution to tackle bad leadership.
[31]The paper includes a critical autoethnography investigation about emerging occupational therapy practice with people coming to community post-imprisonment. The research has permitted to expand current occupational therapy theories.
[32]The study explores the impact of homophobic and heteronormative discursive practices on health, well-being and identity in the workplace. The research includes experiences of one lesbian teacher in a rural environment.
[33]Author shares her experiences on trolling of autoethnographers and encourages others to write about their experiences.
Table 2. Autoethnography research publications on education problems.
Table 2. Autoethnography research publications on education problems.
Reference NumberResearch Findings
[34]The study presents online teaching experiences of teachers in Tokyo during the COVID-19 pandemic in 2020. The findings suggest effective ways for competencies development and solving problems in emergent online teaching situations.
[35]Author uses photography as a medium through which she presents her own practice and professional identity within higher education. Findings highlight visual narratology as form of visual autoethnography.
[36]The article examines tutoring experiences in a blended learning environment. Authors argue that different roles and strategies across learning context make them more productive and less vulnerable towards conflicting messages.
[37]Autoethnographies reflect the roles of women leaders in higher education institution in South Africa. The paper presents fears and insecurities, segregation and inclusion, national belonging, gendered roles, marginalization, authority, and decision-making.
Table 3. Autoethnography research publications on healthcare problems.
Table 3. Autoethnography research publications on healthcare problems.
Reference NumberResearch Findings
[38]The research includes immersing in the primary treatment context to understand the patients’ health status, healthcare conditions, and general medical practices.
[39]Discussion on autoethnography as a valuable qualitative methodology in health science and pharmacy. This paper explains that pharmacists should participate in the healthcare system as care providers instead of only as drug sellers.
[40]This research concerns the treatment of patients who have had a traumatic brain injury that has an impact on the patient’s self-awareness.
[41]Dual analysis of experiences of caregivers and information practice researchers reveal nature of health-related experiences and needs. This sensitive information was accessible only in this autoethnographic way.
Table 4. Autoethnography research publications on ICT solutions usage.
Table 4. Autoethnography research publications on ICT solutions usage.
Reference NumberResearch Findings
[42]The paper explores autoethnography as a method of smart technology analysis. This approach entails the emotional dimension discussion.
[43]The paper examines how digital technologies give students an opportunity to create new space for their reflective activity. Research participants have taken part in interviews, and the autoethnographic method has permitted to identify patterns of experience and interpretations.
[44]The paper concerns usage of Skype to conduct qualitative interviews and contact with research participants in non-verbal forms. Ethical issues are emphasized because they create limitations, but also new opportunities. Authors conclude that Internet communication cannot completely replace face-to-face interaction, but it is a complimentary data collection tool for autoethnography research.
[16]Author argues that information system (IS) community ignores ethnographic methods in this field. This paper fills this gap and focuses on how IS researchers could evaluate autoethnography research.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Pańkowska, M. Autoethnography on Researcher Profile Cultivation. Information 2022, 13, 154.

AMA Style

Pańkowska M. Autoethnography on Researcher Profile Cultivation. Information. 2022; 13(3):154.

Chicago/Turabian Style

Pańkowska, Małgorzata. 2022. "Autoethnography on Researcher Profile Cultivation" Information 13, no. 3: 154.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop