Understanding social innovation leadership in universities: empirical insights from a group concept mapping study
Bibliographic record
Abstract
Purpose Recently, social innovation (SI) has captured the imagination of a range of actors globally, including in the higher education (HE) sector. This study explores the conceptual domain of SI leadership in HE. Drawing on the insights of 22 experienced practitioners in Canada, it provides a concept map to help guide leadership practice and research. Design/methodology/approach The study adopted Group Concept Mapping (GCM), a mixed methods approach that provides a structured way to map the “conceptual domain” of a topic from the perspectives of those with close knowledge of it. Using online GCM software, one group of participants generated statements in response to a prompting question about SI leadership. Another group sorted statements into conceptual groups, rating them for importance. The authors then produced a preliminary map using cluster analysis. A third group interpreted that map. The authors analyzed interpretation data to produce a final concept map, which is discussed in light of relevant literature. Findings GCM methodology resulted in 108 statements about SI leadership, with the vast majority ranked as highly important. The analysis produced a map consisting of nine “clusters” of conceptually related ideas about SI leadership, encapsulated under three interacting areas of focus: individual, relational, institutional/system. Participants confirmed the map reflected key dimensions of practice, noting it indicated important tensions and paradoxes core to their practice. Their interpretations highlighted how the map represented iterative dynamics of leadership over time and across levels; and how different theoretical and practice traditions in SI communities affect conceptualizations of leadership. Research limitations/implications The study suggests that an area ripe for study is the navigation of micro-level systems in pursuit of meso-level and macro-level systems change. The map provides an empirically derived set of dimensions for instrument development in future SI leadership studies. The context of conventional academic institutions bounds the results but helps to surface key considerations for researching SI processes in other mature institutions. Comparison of findings with extant leadership theories suggests that, to advance theoretical research on the topic of leadership in SI, bricolage or new theory development will be needed. Practical implications The map paints broad themes with concrete, practice-level ideas. It can serve as a touchstone for reflection by experienced practitioners and offers a reference point to orient those new to leading SI. Originality/value The scholarly knowledge base about SI has been growing rapidly, but it is still an emerging field of study. There are only a handful of published studies offering perspectives on SI in HE, and none with respect to SI leadership in HE. This study offers original conceptual insights grounded in empirical data gathered through a method aligned with its exploratory purpose.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".