Teaching marketing to non-marketing majors: tools to enhance their engagement and academic performance
Bibliographic record
Abstract
Purpose While there has been a significant amount of work involving marketing education, it is unclear how faculty members can increase the engagement and achievement of non-subject specialists. Accordingly, guided by Bloom's Taxonomy, this current study examines the ways that academics can teach marketing to non-marketing undergraduate majors, with a focus on enhancing their engagement and academic performance. Design/methodology/approach Survey responses (and related archival information) were collected from 181 non-marketing majors in the United Kingdom (studying marketing modules as part of their undergraduate degrees). Such data passed a series of key robustness checks. The hypothesized and control paths were tested via covariance-based structural equation modeling. In addition, 20 semi-structured interviews were used to explore the underlying issues behind the statistical results. Findings Two variables were positive drivers of engaging non-marketing students, namely, discussion-oriented interactions and relating marketing to non-marketing subjects. However, integrating theory with practice produced a negative, but non-significant relationship with engaging non-marketing students. In turn, engaging non-marketing students yielded a positive and significant association with academic performance. The follow-up interviews suggested that to best-engage non-marketing majors, educators should consider hosting guest speakers (e.g. owner-managers) to demonstrate how their university-level studies are applicable to “real-world” subject contexts, like sports management and engineering when they graduate. Originality/value This current article strengthens the extant literature by identifying some actionable tools that can be employed to enhance the engagement and academic performance of non-subject specialists. This is important, since faculty members are under increased pressure to become effective teachers and facilitate student satisfaction (alongside their other duties, including research and administration). Hence, this paper assists such individuals to cope with the rapidly changing landscape of the higher education sector. In fact, Bloom's Taxonomy was a relevant pedagogical theory for unpacking how educators can teach marketing to non-marketing majors.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".