Developing a Destination Management Information System: A Case Study of Ottawa, Canada
Bibliographic record
Abstract
Over the last decade, the concept of smart destination management has been gaining momentum (Boes et al, 2016; Buhalis and Amaranggana, 2013, 2015; Del Vecchio et al, 2018a; Gretzel et al, 2015; Ivars-Baidal et al, 2019; Lamsfus and Alzua-Sorzabal, 2013; Xiang et al, 2015). As the tourism industry seeks recovery from the devastations of the COVID-19 pandemic, however, several authors have argued that it is more important than ever for destinations to become “smart” in efforts to build back in a more sustainable and regenerative way (Abbas et al, 2021; Assaf and Scuderi, 2020). Though called on globally to guide destinations through this era of change and adaptation, Destination Marketing Organizations (DMOs) continue to struggle to obtain adequate and reliable data. Specifically, those representing smaller regions often lack the internal capacity to perform the analyses required to become smart destinations (Dodds and Butler, 2019; Dredge, 2016; Gretzel et al, 2006). While the literature has pointed to Destination Management Information Systems (DMISs) as the solution to smart destination management, current applications have been limited and evidence remains primarily anecdotal. Therefore, guided by Höpken et al’s (2011) Knowledge Destination Framework Architecture, this study aimed to develop and empirically test a DMIS for Ottawa Tourism in its capacity to support smart destination management. Findings indicated that while it serves as a valid process in the development of a DMIS, a DMIS’s capacity to support smart destination management is limited by the quality of its inputs. Opportunities for future knowledge generation and knowledge application in the tourism industry are discussed along with areas for future research.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".