Formalizing the information requirements for decision-making of field managers during indoor construction activities
Bibliographic record
Abstract
Purpose Information technology provides important support for on-site decision-making of field personnel. Most literature focuses on the technological aspects of decision-support systems, without fully understanding the information required for effective decision-making. This study aimed to formalize decision-makers’ requirements in terms of the major goals, decisions and information. Design/methodology/approach The situation awareness (SA) approach was applied through the goal-directed task analysis (GDTA) method, narrowing the scope to field managers’ decision-making during indoor construction activities. This method was based on a series of interviews to define, revise and validate the decision-making requirements for the given scope. Findings The study yielded 1,056 highly interrelated elements. The results indicate that the field manager’s overall goal is to execute and handoff work within the established deadlines, with the required quality, maximizing profits, within a safe work environment. The overall goal construes into five main goals regarding work progress, quality, costs, safety and communication. These goals include subgoals, decisions, and the information necessary to attain them, depicted in diagrams. Practical implications The findings allow enhancing the design of decision-support solutions by identifying information required for future developments and showing the interrelations between goals and information requirements that need to be addressed to present interfaces for effectively assisting on-site decision-making. Moreover, the results allow for the assessment of solutions regarding the sufficiency of information. Originality/value This is the first effort to fully understand the information required by field managers for on-site decision-making during indoor construction activities.
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 | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".