Adoption of IoT by telecommunication companies in GCC: The role of blockchain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Internet of Things (IoT) has become essential for business. The adoption rate of IoT has dropped recently and this could be due to security, privacy, and trust issues. Blockchain (BC) has the potential to mitigate the risk of security, privacy, and trust. However, few studies examined the integration between IoT and BC in the context of developing countries. The purpose of this study is to examine the predictors of IoT adoption by telecommunication companies in the Gulf Cooperation Council (GCC). In addition, the study aims to examine the moderating role of BC as well as the effect of using IoT and BC on the competitive advantage of companies. Based on technology acceptance model, social exchange theory, and resource-based view, the study proposed that security, privacy, trust, communication quality, perceived ease of use (PEOU), and perceived usefulness (PU) affect positively the adoption of IoT. BC is proposed as a moderating variable and expected with IoT to affect the competitive advantage of companies. The population includes all the telecommunication companies in GCC. Data was collected using purposive sampling from IT professionals. The results of data analysis using SmartPLS showed that security, privacy, trust, PU, and PEOU positively affected the adoption of IoT. BC and IoT adoption have a positive effect on competitive advantage. Further, BC moderated only the effect of security and privacy on the adoption of IoT. Services providers must enhance the security, privacy, and trust of IoT services by deploying BC technology. Effective integration of IoT and BC will lead to the achievement of competitive advantages.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 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