Abstract
Systematic Science Mapping (SSM) is a novel mixed methods research (MMR) design for literature reviews of large scale, thousands of publications, including entire scientific fields. SSM establishes a “big picture” view of a field’s evolution, a thematic analysis of the research in a field, and synthesizes findings even in the presence of conceptual overlaps or inconsistencies. An overview of its roots in systematic literature reviews (SLRs) and science mapping is presented first before integrating them in a sequential mixed models design. Then, the application of SSM is illustrated in the field of responsible artificial intelligence (RAI). Evolutionary maps are presented as a tool for visualising the semantic drift of ethical principles over time. Based on “thick data”, SSM shows a way of emphasising commonalities over differences for reducing the academic-to-practice gap in RAI. Guiding notes are provided to those who may wish to employ this MMR design.
| Original language | English |
|---|---|
| Title of host publication | Handbook of Mixed Methods Research in Business and Management |
| Editors | Roslyn Cameron, Xanthe Golenko |
| Publisher | Edward Elgar Publishing Ltd. |
| Pages | 381-393 |
| Publication status | Published - 2023 |
