Critical variables and constructs in the context of project management: importance-performance analysis
Bibliographic record
Abstract
Purpose Relying on the importance-performance theory first established by Martilla and James (1977), this research paper utilizes a unique statistical analysis instrument embedded into the SmartPLS software. It explores the importance and performance of key project management constructs and indicators with a purpose to make practical and actionable recommendations for project managers to identify and improve project management practices. Design/methodology/approach The data used were derived from 3,130 system delivery projects in the facilities management industry. The data was analyzed with Partial Least Squares Modelling (PLS) software SmartPLS, using its embedded importance-performance functionality. Findings The findings indicate the importance and performance of the project management constructs and their respective indicator variables in an importance-performance (IPMA) map. All three project management phases (constructs); proposal, installation and commissioning, were significantly related to satisfaction. The installation phase (construct) showed the highest potential for performance improvement in project management. With regard to the specific indicator variables, the variable “Coordinating their work with other contractors (or the owner's staff)” received a strong “Do better” recommendation. Originality/value The approach and results provide an easy to use and visual tool for project managers to assess the importance and performance of the various elements of project management. The instrument provides a project management direction for the identification of strategic enhancement areas as it is essential to recognize what facets of project management contribute most to the improvement of project management performance over a longer period of time (Cronin and Taylor, 1992; Palmer, 1998).
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".