Project Performance and the Enabling Role of Information Technology: An Exploratory Study on the Role of Alignment
Bibliographic record
Abstract
As firms focus on new product, process, and service innovations, improving the performance and productivity of projects that help deliver these innovations assumes greater importance. Information technology (IT) has been an enabler of manufacturing productivity improvement, but its effect on improving the productivity of innovation-intensive operational activities has been mixed. In this paper, we explore the pathways through which IT impacts project-level performance measured in terms of speed, quality, and cost. Specifically, in this exploratory study we seek to present a theory of how the fit between enabling IT and the core characteristics of the project impacts project performance. We test our research hypotheses empirically, using a relatively large, cross-sectional sample of project data. The central contribution is the development and testing of a research model to improve our understanding of the relationship between enabling IT-project alignment, project competencies, and project performance. In doing so, our study clarifies the role of information technologies in project management, providing insights into how to integrate IT into innovation-intensive operational activities for improving project execution competence and productivity.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".