Using Design Thinking to Write and Publish Novel Teaching Cases: Tips From Experienced Case Authors
Bibliographic record
Abstract
With increasing calls for a greater connection between management education and practice, teaching cases play a vital role in the business curriculum. Cases not only allow instructors to expose students to practical problems but also let educators contribute to the scholarship of teaching and learning. An important reason why faculty members may refrain from writing cases is they perceive it is difficult to develop publishable cases that are also novel. Reviewers of the journals that publish teaching cases are increasingly asking authors to place the case in the extant literature and explain what makes their case unique. To overcome some of the challenges encountered when attempting to write and publish novel teaching cases, this article presents a useful framework—Design Thinking—for tackling the “wicked problem” of developing novel cases and provides experience-based tips to implement the framework. By introducing the concepts and language of design thinking, we provide case writers with an iterative approach that leads to the development of novel cases by identifying and innovatively addressing instructors’, students’, and editors’ demands. We argue that by applying a design-thinking approach, case writers can produce novel and publishable instructional cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.117 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".