Encouraging intelligent failure in an MBA class

Abstract

Failure has an important role to play in learning how to navigate highly uncertain organizational environments. But “failing fast” just for its own sake may in fact undermine learning if not set up or handled correctly. Using failure‐based pedagogy, including generative failure, whole‐person learning, and entrepreneurial thinking, an MBA course was designed and experienced by 48 students in three instances. Structured around a novel guiding framework of “brains, bravery, and belief,” the course has resulted in highly impactful learning for students. Student experiments are typically based around either exploring an entrepreneurial idea, developing or enhancing a particular skill, or applying skills and knowledge to help improve a societal problem. In each case, students are supported but also challenged to go beyond their comfort zones and encounter some intelligent failure in the journey. Regular reflection on their experiences, both from a cognitive and an affective perspective, is an essential element built into the course experience. The course, which itself was an experiment and not without its own instructive failures, is now an essential part of the MBA experience.

Collaboration for work: Assessing the correlates of three types of collaboration perceptions that alumni associated with an MBA capstone course

“…Future research would benefit from improvements in the design and measures, including: rearranging the order of the survey items; using confirmatory factor analysis on the three new collaboration scales; including greater distinctions between levels of client engagement; supplementing rater-based client engagement perceptions with student-based perceptions; collecting measures of collaboration at several points in time, perhaps during the class (including peer ratings) and at several points after graduation (Sroufe & Ramos, 2015); and adding measures of collaboration behaviors rather than the perception of collaboration. Similarly, the regression models could be made more robust by adding additional variables that might explain collaboration, such as more precise measures of cross-functional integration (collaboration, coordination and communication -see D 'Souza, Bement, & Cory, 2022); conflict resolution (Behfar, Mannix, Pererson & Trochim, 2011); students' emotions during the interactions (collected through reflection papers -see Walsh, 2023); and insights into the client-project executive relationship.…”