Table 1 Strategies to improve the quality of the research

Objectivity/confirmability

Reflexivity/participant objectivation (researcher's biography, values, a priori assumptions, perspectives, theoretical biases, etc.)

Particularity (doing justice to the integrity of each case)

Case study protocol (detailed description of methods and procedures)

Project logbook (decisions, procedures, communications, meetings, etc.)

Case study database (including case study notes, case study documents, tabular materials, analyses, etc.)

Chain of evidence (explicit links between the questions asked, the evidence, and the conclusions drawn)

Consideration of competing hypotheses or rival conclusions

Internal validity/credibility

Methodological triangulation

Data source triangulation

Investigator/analyst triangulation

Theory/perspective triangulation

Specification of the unit of analysis

Rich and thick description of context (settings, participants, procedures, etc.)

Rigorous and systematic fieldwork procedures

Reliability of coding and pattern analyses (using multiple coders)

Establish a chain of evidence

Integrity in analysis (search for and analysis of alternative themes, divergent patterns, rival explanations and negative cases)

Member checking (respondent validation)

External validity/transferability

Rich and thick description of context

Rich description of findings

Keeping methods and data in context (when communicating findings)

Use replication logic

Audience review (primary intended users of the report)

Generation of theoretical statements

Reliability/dependability

Strategic design congruent with research questions

Paradigm specified

Case study protocol, case study database, chain of evidence and project logbook available for review

Final coding cross-checked and verified with a second analyst (researcher)

Standardized data collection

Expert audit review (doctoral committee, peer reviewers for scientific publications and presentations)