Bibliographic record
Abstract
Over the last three decades, much IS research has focused on information systems development (ISD) risk and its impacts on ISD success. While these studies have greatly advanced the understanding of the nomological network of ISD risk and success, the literature is still not sufficiently clear on the firm performance impacts of these concepts. Linking ISD risk and success to firm performance is important so as to better understand whether ISD projects can have broader firm-level implications, for example, in terms of providing firms with a competitive advantage. To address this research need, the present research note advances propositions regarding the linkage between ISD risk, success, and firm-level performance (conceptualized as competitive advantage). This linkage sheds light on the broader effects of ISD risk, and it helps ISD research overcome the isolation in which it is often conducted. Using the concept of residual risk (i.e., the risk present in the later stages of a project that remains after appropriate actions have been taken to mitigate initial risks in the early stages of a project), the authors propose that ISD risk impacts firm performance by reducing ISD success and that the value arising from ISD projects is higher when IT and business plans are synchronized (i.e., when they are in alignment).
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 | 0.028 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".