Playing with Perspectives and Unveiling the Autoethnographic Kaleidoscope in HCI – A Literature Review of Autoethnographies

Title of the Paper

Exploring Perspectives in HCI and Unveiling the Kaleidoscope of Autoethnography: A Literature Review on Autoethnography

Bibliographic Information

• Field of Study: Application of autoethnographic methods in Human-Computer Interaction (HCI)
• Keywords: Literature review, meta-review, Human-Computer Interaction, autoethnography, qualitative methods, first-person methods, HCI theory, design methodology, reporting standards

Research Background and Issues

• Identified Problems or Challenges:
  • Autoethnography, as a research method combining personal experience with academic inquiry, has gained increasing attention in HCI in recent years. However, there is still a lack of systematic analysis of its functions and roles in HCI research.
  • HCI methodologies often lack transparency and theoretical reflection, particularly regarding first-person research methods (e.g., autoethnography), where norms and best practices remain unclear.
• Significance of the Research Problem:
  • As the complexity of technology-human interaction increases, first-person methods (e.g., autoethnography) offer unique perspectives on subjective experiences. A systematic understanding of this research method can advance methodological development in the HCI field.
• Research Motivation and Related Work:
  • By conducting a literature review of 21 years (2005–2023) of autoethnographic research in HCI, this paper aims to address the lack of a systematic overview in this area, clarify the types of contributions, themes, and methods, and provide guidance for future researchers.

Proposed Solution

• Methods or Solutions:
  • This study conducted a Systematic Literature Review (SLR), analyzing 31 papers in the HCI field that employed autoethnographic methods.
  • Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, the review was divided into four stages: (1) identification, (2) screening, (3) exclusion criteria determination and exclusion, (4) inclusion.
  • The analysis focused on contribution types, methodological standards, technology types, and themes.
• Innovations:
  • Provides a clear distinction between different forms of autoethnographic methods (e.g., single researcher vs. collaborative multi-researcher approaches).
  • Proposes reporting recommendations for aspects such as data collection methods, analysis methods, and researcher positionality disclosure.
• Implementation Steps and Key Techniques:
  • Conducted keyword searches ("autoethno") in the ACM Digital Library, limiting the dataset to key HCI conferences and journals.
  • Used a coding framework combining qualitative and quantitative methods to classify and analyze the literature data (e.g., thematic analysis, author motivations, types of research contributions).

Research Findings

• Specific Outcomes:
  • Categorized and summarized seven major research themes of autoethnography in HCI (e.g., data and security, design and user experience, health and well-being, etc.).
  • Automatically coded the types of research contributions in 31 HCI papers, with the majority being empirical (94%).
  • Explained why HCI researchers choose autoethnographic methods, including diverse perspectives, personalized insights, longitudinal exploration, etc.
  • Found that over half of the studies combined multiple types of contributions (e.g., empirical combined with theoretical contributions).
• Advantages Compared to Existing Solutions:
  • Systematically distinguishes autoethnography from other first-person methods in HCI (e.g., autobiographical design), creating clearer definitions and classifications.
  • Emphasizes transparency in descriptions and methodological standards in HCI, laying a clear framework for subsequent research.
• Evaluation Results:
  • Identified that autoethnography is widely applicable to exploring complex issues in technology-human interaction, especially in non-traditional research contexts (e.g., pandemics, self-tracking).
  • The average time span of autoethnographic studies is approximately 1.2 years, indicating that it is not a "lightweight" method.
• Limitations and Future Directions:
  • Limitations:
    • Only based on ACM DL, without comprehensive coverage of other databases or conference literature.
    • Exclusion of other forms of academic outputs (e.g., posters, workshop proposals) might omit some important content.
  • Future Directions:
    • Expand coverage to other academic databases.
    • Propose more refined norms for addressing ambiguous terminology (e.g., autoethnography vs. autobiographical design).
    • Further explore the potential applications of autoethnography in different cultural contexts.

Conclusion:

This paper provides valuable insights into the transparency and refinement of HCI methodologies, advocating for standardized reporting mechanisms to facilitate the flexible application and understanding of research methods in academic discourse.