Studying Potentials and Barriers of Successful Implementation of Smart Contracts in Saudi Arabia’s Construction Industry
Bibliographic record
Abstract
Smart contracts have the ability to address a variety of important obstacles in the construction business, such as contract conflicts and payment issues. Likewise, it may facilitate the adoption of building information modeling (BIM) in the industry. Poor adoption of distributed ledger technology in the Arabian construction sector stems from a variety of impediments. The usage of smart technology is growing in popularity across the globe in the recent few years. Nonetheless, in the Arab world, there are not enough experts who are aware of how smart contract technology can make the construction sector more efficient, collaborative, and transparent in the digital era. With that in mind, the purpose of this study is to evaluate the benefits and drawbacks of using smart contracts in the construction industry. To accomplish its goals, this study mostly uses descriptive statistics to analyze the collected data from questionnaire surveys. This research utilizes an illustrative diagram to map the potentials and obstacles to the adoption of smart contracts from the respondents’ perspective. According to the conducted analysis, there are five main potentials and barriers to smart contract implementation, namely, the Internet of Things, data reliability, stakeholders, security for distributed systems, and finance and economics. Additionally, the questionnaire survey yielded 21 potentials such as better risk allocation, decreased transaction time and cost, and ease of understanding by multiple stakeholders. Moreover, the conducted survey obtained 19 barriers such as slow learning curve, high training and education costs, and cybersecurity concerns. It can be argued that the present research study can assist in endorsing the adoption of smart contracts in the Arabian construction market.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".