The role of information management systems and electronic-HRM on company performance: A study on Indonesian manufacturing
Bibliographic record
Abstract
This study aims to analyze the relationship between e-HRM variables and company performance and the relationship between Management Information System (MIS) variables and company performance. This study is a quantitative study with an explanatory method that aims to explain the relationship between symptoms. The data used in this study are primary data through an online questionnaire method distributed using social media. The respondents who became the sample were 389 manufacturing managers determined through simple random sampling. Data processing in this study uses the Partial Least Square (PLS) method with the help of SmartPLS software. The stages of data processing include evaluating the measurement model (outer model) and the structural model (inner model). Evaluation of the measurement model consists of validity testing and reliability testing. Validity testing can be seen from the standardized loading factor value. An indicator is valid when the loading factor value is greater than or equal to 0.7. The reliability test is seen from Cronbach's Alpha and Average Variance Extracted (AVE) values. A construct is declared reliable when Cronbach's Alpha value is greater than or equal to 0.7, and the minimum AVE value is 0.5. The next reliability test is to evaluate discriminant validity. Discriminant validity is evaluated through cross-loading and comparing the AVE root value with the correlation between constructs. If the correlation between the indicator and its construct is higher than the correlation with other block constructs, it indicates that the construct predicts the size of its block better than other blocks. After evaluating the measurement model, an evaluation of the structural model obtained is carried out based on the last model that has been declared valid and reliable. Hypothesis testing of the t-statistic and p-value values generated from calculations using SmartPLS. Path coefficients that have a t-statistic value ≥ 1.96 or a p-value ≤ 0.05 are declared significant. The results of this study show that e-HRM has a positive and significant relationship with Performance, and the Management Information System (MIS) has a positive and significant relationship with Performance.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".