A Mixed Review of Cash Flow Modeling: Potential of Blockchain for Modular Construction
Bibliographic record
Abstract
Cash is considered the most critical resource in construction projects. However, many contractors fail to obtain adequate liquidity due to a lack of proper cash flow management. Therefore, numerous research studies have been conducted to address cash flow-related issues in the construction industry. However, the literature still lacks a comprehensive review of cash flow management, methods and topics, in the construction industry. This study contributes by providing a holistic, up-to-date, and thorough review of 172 journal articles on construction cash flow. To achieve this primary objective, the study applies a mixed review methodology using scientometric and systematic reviews. The scientometric analysis provides the most contributing scholars, the timeline of cash flow research attention, and keywords clustering. On the other hand, the systematic analysis categorizes the cash flow themes, identifies current literature gaps, and highlights future research areas in the cash flow domain. The results show that cash flow analysis gained more research attention in the last two decades, cash flow-based schedule is the most frequent topic in the literature, and optimization techniques are predominant in the literature. Consequently, the study highlights five potential research frontiers. Further, an automated payment framework for modular construction projects using Blockchain-based smart contracts is developed to address some of the literature limitations. This study provides a guideline for future research efforts and raises researchers’ awareness of the latest trends and methods of construction cash flow analysis.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".