Canadian Small and Medium Enterprises Use of Social Media to Improve Brand Awareness
Bibliographic record
Abstract
Small and medium enterprises (SMEs) must grow their brand awareness to attract and sustain business. This study delves into the social media marketing strategies SME managers use to improve brand awareness. Guided by Aaker’s brand equity model as the conceptual framework, the study was to identify key elements SMEs can leverage to strengthen their social media presence. This qualitative multiple case study across sectors included six participants who were managers of SMEs in Ontario, Canada, who had proven their ability to leverage key elements of social media marketing for brand awareness. Data analysis relied on Yin’s five-step process and the qualitative data analysis tool NVivo was used to organize, transcribe, code, and break out the data thematically. Four major themes emerged from the data, which participants associated with successful social media-driven brand awareness: (a) engagement, (b) visuals, (c) community alignment, and (d) tailored messaging. The key recommendations for SMEs would be to focus on authenticity and community-driven engagement to enhance brand recall, recognition, and top-of-mind awareness that, according to the conceptual framework, will drive brand awareness. The implications for positive social change include the potential for SMEs to enhance brand awareness through social media marketing, thereby contributing to business sustainability and employment opportunities.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".