Does Digitalization Impact Tourism Ecosystem in Cameroon?
Bibliographic record
Abstract
The advancement of digitalization, particularly through information systems, has significantly reshaped the tourism industry by altering both operational practices and structural organization, thereby establishing digital infrastructure as a pivotal component of the sector. This transformation has prompted economic stakeholders to increase investments in digital infrastructure to improve connectivity and facilitate access to digital services. The primary objective of this study is to evaluate the impact of public digital infrastructure investment on tourism development in Cameroon. To this end, we employed the Autoregressive Distributed Lag (ARDL) model using annual data spanning the period from 2000 to 2021. Tourism development is proxied by tourism receipts, while digitalization is captured through public sector investments in digital technologies. The empirical findings indicate that public digital investment, inflation and foreign direct investment (FDI) are statistically significant and positively associated with tourism receipts in Cameroon. Exchange rate and public investment in security shows a negative effect on tourism receipts, although this effect is not statistically significant. However, the analysis also reveals a temporal asymmetry: in the short term, only foreign direct investment exerts a statistically significant effect on tourism revenues in Cameroon. These findings therefore indicate that the studied variables contribute positively to tourism revenues only over the long term.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".