Unlocking the potential of big data through open innovation on strategic foresight: An empirical analysis
Bibliographic record
Abstract
The aim of this study is to examine the impact of Big Data on strategic foresight in the presence of open innovation as a (mediating variable) in the companies of the therapeutic industry and the medical supplies sector in Amman the capital city of Jordan. Out of the (121) companies making up the total of such companies, the study focused on (18) industrial companies that had more than (100) employees in total, however only (11) of those have consented to take part in the study. The study population consisted of (271) employees occupying different jobs (general manager, deputy general manager, unit manager, department manager). Due to the limited size of the population, the entire number of employees were included as participants using the comprehensive survey technique, and the number of returned and validated questionnaires for analysis were (259), representing (95.5%) of the total. In order to determine the study problem, pilot structured interviews were used in a sample of the mentioned companies. The questionnaire was employed as the key tool to measure the study variables through data collection, and the descriptive and inferential statistics methods were used to analyze the collected data, through calculations of the arithmetic mean, standard deviation, t-test and half-segmentation, exploratory and confirmatory factor analysis and the structured equation model using SMART PLS3 for hypothesis testing. The study culminated in several results, the most important of which was evidence that open innovation played a partial mediating role in the relationship between big data and strategic foresight in the companies of the therapeutic industries and medical supplies sector. Accordingly, a set of recommendations were put forward, the most important of which is to increase investment in big data in the companies due to its importance in foreseeing the future. As well as applying open innovation practices and strengthening strategic foresight practices due to its important role in avoiding extensive losses and seizing new opportunities. Highlighting the need to pay attention to open innovation practices that generate ideas and help to have strategic foresight.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".