The FunCaps Framework: Reconceptualizing Operational Alignment
Bibliographic record
Abstract
Abstract Background and purpose: Operational alignment, the alignment between business processes (BPs) and information systems (ISs), is a well-acknowledged requirement for improving business efficiency. However, a lack of sound foundation for the practical implementation of operational alignment remains in the existing literature. This is, in part, because previously developed coarse-grained strategic alignment models for operational alignment have overlooked the differences between strategic and operational levels of alignment. Additionally, while some studies have recognized these differences, they remain limited. This is partly due to their negligence of the IS’s socio-technical nature or their focus on identifying the social antecedents and their effect on operational alignment, without considering how ISs meet the business requirements in achieving operational alignment. To overcome this potential lack of applicability, the purpose of this paper is to determine the right level of abstraction for describing BPs and ISs and reconceptualizing operational alignment. Methodology: This paper conducts empirical research using a grounded theory (GT), centering on semi-structured interviews with 28 experts involved in the Iranian top public universities. Data were analyzed by using MAXQDA software. Results: The resulting FunCaps framework specifies the required combinations of BP functions and IS capabilities for operational alignment. Conclusion: FunCaps reconceptualizes operational alignment based on operational planning and reciprocal integration and establishes the broader picture by considering an IS as a socio-technical system.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".