Investigating the impact of benefits and challenges of IOT adoption on supply chain performance and organizational performance: An empirical study in Malaysia
Why this work is in the frame
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Bibliographic record
Abstract
In Malaysia, manufacturing industry is a major contributor to the economic advancement. As a result, cutting-edge technology like the internet of things (IoT) is projected to have a significant impact on business operations and supply chain management (SCM). However, research into the influence of IoT deployment on supply chains and organizational performance is relatively sparse. Therefore, this study is to determine the relationship between benefits and challenges of IoT adoption and organizational performance. Furthermore, this study looks into the mediating role of supply chain performance in the relationship between IoT adoption benefits and challenges and organizational performance. The population of this study is comprised of 3019 manufacturing companies in Malaysia, while the minimum sample size needed is 43 manufacturing companies.1160 complete set of survey questionnaire were distributed through email and 63 responses received representing five per cent of response rate. Partial Least Square Structural Equation Modelling (PLS-SEM) is used to assess all of the study's hypotheses. The results of this paper support six out of the seven hypotheses tested. In conclusion, the manufacturing industry in Malaysia needs to be exposed more to the benefits of IoT rather than keep discussing its challenges. This study can be a guideline to the manufacturing companies in decision making for IoT adoption. The limitations and recommendation for future study is highlighted.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it