Implementation of business intelligence framework for Malaysian halal food manufacturing industry towards initiate strategic financial performance management
Bibliographic record
Abstract
The utilization of Business Intelligence (BI) to yield financial management among manufactures has been one of the main advantages of managing businesses through strategized financial performance.The BI can be conceptualized as a decision-making process, which is an emerging topic within technology management and financial decision making.Furthermore, such factors influencing the adoption of this type of strategized decision-making process are under extensive investigation.The main objective of this research is to develop a BI framework to provide a data analytics and action plan to help Malaysian Halal Food Manufacturing Industry (MHFMI) strategize financial performance.The specific objective of the study is to assess and validate the relationship between the adoption of BI and MHFMI.The methodology of the study starts with the theory adoption of BI parameters and methods, followed by the development of the adopted and adapted theoretical models using the conceptual framework developed and MHFMI involvement into the strategic financial performance management.The managers of the MHFMI is requested to provide the necessary information about their companies, perception of BI and financial performances.Reliability analysis is used to validate the constructs and test measurements in variance.The study applies regression analysis to determine the effects of different decision making strategies on BI.The research output indicates that the BI data analytics and the action plan as well as the conceptual framework could improve the financial performance by the adoption and the adaptation of the conceptualized strategy among MHFMI.
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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".