Factors causing mismanagement in public/private contracts: An Indonesian perspective
Bibliographic record
Abstract
The objective of this study is to examine the impact of client, consultant and contractor related factors on mismanagement in public and private contracts in the region of Indonesia.A structural questionnaire is developed for the selected items after detailed investigation of present literature.A final sample of 137 respondents associated with various contracts in the region of Indonesia is collected with demographic details and regression analysis.It is observed that factors like lack of strategy, failure to compile the documentary requirements, poor planning, delay in decision making, financial issues, late payments, difficulty in getting work permits and lack of management expertise are core issues, creating mismanagement in public and private contracts.In consultant related factors, role of inexperienced consultant, poor planning, late of instructions from architects, poor contract management, and poor-quality assurance are the key determinants of mismanagement in contracts.While contractor related factors like poor planning and scheduling, late or improper submission of contract, inadequate site supervision and inspection, poor construction methods, weak leadership, and lack of communication between the parties are the key indicators of mismanagement in contracts.As per significance, this study is found to be a reasonable addition in the present literature from the context of contract management.Originality of the study covers the significant findings for the policy makers in the field of public and private contracts.Study can be reworked in future through better sampling, and addition of more factors related to materials, equipment and labor causing for the poor delivery of the contracts.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".