The impact of business intelligence through knowledge management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Competition among companies has intensified during the last few decades and hence monitoring the organization’s environment has become a priority. Monitoring the internal and external environments involves collecting, retrieving, managing, and disseminating large volumes of data and information. Companies are able to handle these complex tasks very efficiently through knowledge management (KM). A valuable tool of KM is business intelligence (BI), that is, the set of coordinated actions of research, treatment, and distribution of information that can help support the company’s competitiveness. This study aims to evaluate BI and quantitatively demonstrate its impact on the competitiveness of an organization. It proposes a methodology and applies it to a multinational food processing company to determine the influencing elements in BI and measure their impacts on the organization’s competitiveness. This study identified four variables of BI that are likely to have an impact on the competitiveness of the company: the search for information, the treatment of information, the utility of information, and information security. To collect the required data, this study developed a survey with five categories, namely, research, utility, treatment, security, and competitiveness, and the collected data were analyzed using second-order partial least square-structural equation modeling in SmartPLS 3. This study found that research, utility, treatment, and security have positive correlations with BI, and that the strength of the relationship between BI and each variable is significant. Furthermore, the results show that the BI elements can explain over 38 percent of the variation in the competitiveness of the company.
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.
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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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 it