Impact of Information Systems on Innovation (Product Innovation, Process Innovation) - Field Study on the Housing Bank in Jordon
Bibliographic record
Abstract
The use and activation of information systems is now considered an important measure of the extent of organizations growth and progress. Thus, many of organizations accelerated and rushed today to find, develop and benefit from such systems to achieve its objectives. Perhaps the successive technological developments have made access to information quickly and easily, especially in light of the low costs of obtaining and using such systems; and to meet the needs of organizations and support performing the administrative functions therein. The aim of the study was to demonstrate the effect of management information system (TPS, MIS, DSS, ESS) on innovation (Product and process). The study population consisted of the Housing Bank branches in Irbid Governorate in all senior, middle and lower administrative levels. Sample was taken from all employees in the branches of the Housing Bank in Irbid Governorat. Approximately 160 questionnaires were distributed. About 152 questionnaires were retrieved. About 13 questionnaires were excluded due to non-validity of the statistical analysis, so that the questionnaires valid for analysis would become 139 questionnaires to form the percentage of (91%). The study reached to conclusion of, Existence of positive significant correlation between each of the management information system, decision support system, Executive Information Systems and innovation (product and process). Lack of positive correlation between the Transaction Processing Systems and innovation (product and process). The researcher recommended, focusing on the use of decision support systems for the nature of the data which can be provided, as it depends on the internal and external data systems, which assist the organization in studying and analyzing the internal position of the organization that is reflected on its external position which contributes to increase its competitiveness through discovering new services and products increase the satisfaction of the customer’s desire.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".