A Study to Find out the Influencing Factors for Non-Usage of Management Information System in Selected Small Scale Industries
Bibliographic record
Abstract
In this information age, data has become one of the most important resources to organizations. The effective and efficient management of large quantities of data is a common problem found in many industries. In this study, initially it was decided to carry out a survey to verify the implementation, usage and acceptance of MIS in 200 small scale industries in and around Karnataka state. The study is exclusively based on the primary data collected through a sample survey is conducted on the respondents such as Chief Executives, line supervisors, Engineers, Managers and Supervisors of the respective organizations by supplying questionnaires prepared on the basis of review of literature and many discussions with experienced academicians, consultants and professionals. The study reveals that only 96 (46%) out of 200 small scale industries were making use of MIS in their organization. The study is extended to find out the possible reasons for non usage of MIS in the remaining104 small scale, so the respondents were requested to provide a feedback on various factors which could be possible reason for non-use of MIS in their respective units. These respondents were supplied with the questionnaires and interviewed for cross verification. After collecting the feedback, the average and standard deviation is calculated for each reason for non-use of MIS. It is noticed that the calculated value of standard deviation for each responses is not much, i.e. less than one. So the collected feedback from the respondents for possible reason for non-usage of MIS is appropriate. The study also found out the most influencing factors i.e. possible reasons for non-usage of MIS in selected small scale To validate these results different analysis such as Normality test and Onesample t-Tests were carried out with the help of Minitab statistical software.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".