Exploring how business improvement area (BIA) organizations engage customers
Bibliographic record
Abstract
Business Improvement Area (BIA) organizations have evolved over the past 40 years and are now prevalent in Canada and worldwide. These organizations bring community stakeholders, partners, and members together to enhance a designated area in a specific region (TABIA, 2020). BIA organizations can range in size, budget, and focus depending on the community's needs (BIABC, 2020). BIA organizations pursue various initiatives, including capital improvements, enhancing the appearance, safety, and facilitating events that highlight the unique attributes of an area and bring people to the community (Giraldi, 2009). This study was conducted during the COVID-19 pandemic. An exploratory sequential mixed-method approach was used to explore how BIA organizations use events to enhance engage customers through hosting events and use social media to interact with customers. The events studied were produced prior to the pandemic. The thematic analysis provided insight into the BIA organization's perspective on how events are used to interact with customers and how social media is used during event production. The study used descriptive statistics, social network analysis, and content analysis to conduct social media analysis to validate the responses found in the interviews (Creswell, 2012). The findings confirmed that BIA organizations believe events and social media engagement can help foster relationships with customers. The research displays the importance of creating a partnership with community stakeholders and governments during event production. The findings suggest BIA organizations may not be using social media to it’s fullest potential and may have a simplistic view of customer engagement.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".