Impact of using project management tools and techniques on project and firm performance
Bibliographic record
Abstract
Purpose This research aims to empirically examine perceived predictive relationships between the use of project management tools and techniques (PMTTs) and resulting project-level and firm-level performance. Design/methodology/approach This study used data gathered from 190 project management professionals engaged in strategic projects. Data were collected using a survey tool that drew on prior project management research studies and asked about the use of 49 PMTTs. The conceptual model hypothesized positive relationships between the use of PMTTs and the project management outcomes, project-level performance and firm-level performance. Hypotheses were tested using PMTT components extracted through an exploratory factor analysis of the PMTTs as independent variables and project and firm performance as dependent variables. Six factors that emerged as independent variables (subsets of PMTTs) were labeled per managerial focus (e.g. quality management and cost management). The extracted dependent variables were one factor representing project-level performance and one factor representing firm-level performance. The hypothesized relationships between independent and dependent variables were tested using linear regression analysis. Findings PMTTs were found to contribute positively to project management outcomes, both project-level performance and firm-level performance. For the sample of strategic projects considered, practices used for time and resource planning, quality management and supplier management were found to positively and significantly impact project-level performance. Practices used for time and resource planning and scope management of tasks and interdependencies were found to positively and significantly impact firm-level performance. Practices used for cost management and risk management were not found to have a significant impact on project management outcomes for the sample of strategic projects studied. Practical implications This study validates the positive relationship between the use of PMTTs and both project-level and firm-level performance. It also suggests that while there is a collective of PMTTs that are considered standard practice, some PMTTs may more significantly predict project management performance than others in specific project and organizational contexts. Originality/value The study used a survey tool that draws on prior research and collected a new dataset on strategic projects to contribute to understanding the importance of different subsets of PMTTs for both project success and a firm’s competitive advantage.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| 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".