The role of artificial intelligence, marketing strategies, and organizational capabilities in organizational performance: The moderating role of organizational behavior
Bibliographic record
Abstract
Currently, artificial intelligence and marketing strategies have become significant factors for improving the business capabilities that lead to improved business performance. Thus, the current study investigates the impacts of artificial intelligence and marketing strategies on the organizational performance of the tourism industry in the UAE. The present study also examines the mediating role of organizational capabilities in linking artificial intelligence, marketing strategies, and organizational performance of the tourism industry in the UAE. The analysis of the moderating impact of organizational behavior on the links between artificial intelligence, marketing strategies, and organizational performance are also part of the current study’s goals. This study used questionnaires to collect primary data from the respondents and analyzed it using Smart-PLS. The results indicated that artificial intelligence and marketing strategies have a positive association with the organizational performance of the tourism industry in the UAE. The outcomes also revealed that organizational capabilities positively mediate the links between artificial intelligence, marketing strategies, and organizational performance. The findings also demonstrated that organizational behavior significantly moderates the links between artificial intelligence, marketing strategies, and organizational performance of the tourism industry in the UAE. This study is relevant for regulators in supporting policy development related to artificial intelligence and marketing strategies for high organizational performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".