Information flows and business process integration
Bibliographic record
Abstract
Purpose Many business process improvement efforts emphasize better integration, yet process integration can mean many things. The purpose of this paper is to emphasize the importance of information flows to modern business processes, and draw upon recent organizational and information systems literature to characterize process integration and to derive four principles of process integration: accessibility, timeliness, transparency, and granularity of information flows. Design/methodology/approach Using a field study, the four principles of process integration are applied to analyze ten different business processes across five organizations. Findings In total, 18 generalized activities are identified that describe non‐integrated behavior, and “keying in known data” was found to be the most common. Among other findings, analysis highlights the importance of documentation to modern business processes, especially for coordination roles, and the paper describes three different purposes for documentation found in the data: content, process validation, and posterity. Research limitations/implications The articulation of “business process integration” offers a foundation for future research in this area. Findings are limited in generalizability to various levels of processes, as well as possible instrument‐related biases. Practical implications The principles of process integration provide a lens through which practitioners can analyze processes. Empirical findings stress the role of documentation, forms of documentation, and types of non‐integrated work. Originality/value The paper characterizes process integration in relation to other commonly‐used constructs such as organizational integration, data integration, and application integration. Principles are derived from the literature that can guide future inquiry and practice associated with business process improvement.
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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".