Building Manager Communication Competencies in Canadian MBA Programs
Bibliographic record
Abstract
Organizations today face many persistent challenges, including stubbornly low employee engagement, declining public trust, and growing risk of internal and external activism. Strong managerial communication competencies are vital to addressing these risks, as they are closely tied to building trust, engagement, and resiliency. Globally employers prioritize these communication competencies over other soft skills and technical competencies, yet they have long expressed concern that graduate management education is not adequately meeting this need. To better understand this persistent gap, the research question was “How can Canadian MBA programs, equip managers with advanced communication competencies that will enable them to build trust, engagement and drive change in a complex business context?” The two objectives for this study were to: (1) identify which communication competencies are most important for MBA graduates, students, faculty, program staff and employers; and, (2) identify changes to MBA structure that can support these priorities.The research uses a sequential exploratory qualitative approach to identify key communication competencies sought by Canadian organizations and compare them with the course content from Canada’s MBA programs. A total of 49 interviews were conducted with Canadian executives, recent MBA graduates, current MBA students, and faculty and staff from Canadian MBA programs. This research finds there is a misalignment between how employers define a communicative manager and the competencies MBA programs develop in their core and elective courses. Finally, this research proposes a way to help MBA programs better include these key competencies in their curricula, thereby improving outcomes for the post-secondary institutions that deliver these programs and to the organizations that hire their graduates.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".