Empirical analysis of intellectual capital, potential absorptive capacity, realized absorptive capacity and cultural intelligence on innovation
Bibliographic record
Abstract
The purpose of this research is to reveal the influence of intellectual capital, potential absorptive capacity, realized absorptive capacity, and cultural intelligence on innovation in the tourism sector in Batam City. Batam has geopolitical and geographic advantages, located in the Malacca Strait, adjacent to and directly facing Singapore and Malaysia. It has not been optimal in exploring the potential for innovation in the tourism sector. Thus, this sector does not yet have a significant contribution to economic development. The research was conducted on the management of companies that are members of the Association of the Indonesian Tours and Travel Agencies in Batam City, totaling 54 people. By using the analysis of Structural Equation Model (SEM) with SmartPLS version 3.0, the results show that 1) intellectual capital has no effect on innovation; 2) potential absorptive capacity has no effect on innovation; 3) Realized absorptive capacity has a significant positive effect on innovation, and 4) Cultural Intelligence has a significant positive effect on innovation. The results of this study will give some insight to company managers in the tourism sector for developing innovation to maintain business continuity.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".