Mobilizing change in a business school using appreciative inquiry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how appreciative inquiry (AI) as a pedagogical tool can be generative in nature creating opportunities for development and change in a business school context. Design/methodology/approach Using a qualitative approach this research involved data collection and analysis in three stages of AI with a group of undergraduate students enrolled in strategic management and organizational change courses. Initial data collection occurred over a three‐hour period with a larger group of students, followed by two sessions with a smaller group of organizational change students. Findings The experiential nature of the AI process was a success in promoting inquiry and dialogue, encouraging collaboration and team building, and empowering individuals toward a collection vision. Through an iterative process, four possibility statements were developed including: meaningful relationships with professors and peers; leadership opportunities; experiential learning; and creativity and flexibility in program design. These statements serve as a starting point for future planning to the business school under study. Practical implications The process offered a number of insights for both faculty and students regarding the symbiotic relationships between learning and change as fundamental to moving a business school from a place of learning to a learning organization. The inquiry process of AI opens the system up to learning about itself as a prelude to change. By intentionally ignoring the traditional deficit approach to change, AI encourages the system to seek its point of light, its achievements, and in so doing, inhibits the dissipative nature of problem‐centred methodologies. Originality/value The use of AI in this context demonstrates the potential for AI as a pedagogical tool, as well as the usefulness of AI as a bridge to creating partnerships with multiple stakeholders in developing business schools into learning organizations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it