Key Success Domains for Business-IT Alignment in Cross-Governmental Partnerships
Bibliographic record
Abstract
Business-IT alignment is a crucial concept in the understanding of how profit-and-loss organizations use Information Technology (IT) to support their business requirements. This alignment concept becomes tangled when it is addressed in a socio-political context with non-financial goals and political agendas between independent organizations, i.e., in governmental settings. Collaborative problem-solving and coordination mechanisms are enabling government agencies to deal with such a complex alignment. In this chapter, the authors propose to consider four key domains for successful business-IT alignment in cross-governmental partnerships: partnering structure, IS architecture, process architecture, and coordination. Their choice of domains is based on three case studies carried out in cross-governmental partnerships, in Mexico, The Netherlands, and Canada, respectively. The business-IT alignment domains presented in this chapter can guide cross-governmental partnerships in their efforts to achieve alignment. Those domains are still open to further empirical confirmation or refutation. Although much more research is required on this important topic for governments, the authors hope that their study contributes to the pool of knowledge in this relevant research stream.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".