From Digital Integration to Performance: The Critical Role of Orchestration Capability and the Boundary Conditions of External Enterprise Systems Integration
Bibliographic record
Abstract
Although organizations have heavily invested in digital platforms to enhance supply chain visibility and partner connectivity, many still struggle to respond rapidly and coherently during disruptions. This study explains why enterprise systems integration does not automatically generate agility or performance and identifies the organizational capability that activates the value of digital systems. Drawing on dynamic capability theory, we conceptualize Supply Chain Orchestration Capability (SCOC) as the coordinated decision-activation mechanism through which digital integration is transformed into unified operational execution. We further propose that External Enterprise Systems Integration (EESI) follows a nonlinear inverted U-shaped pattern, whereby moderate external connectivity enhances orchestration, but excessive connectivity generates information overload and coordination friction, weakening execution rather than strengthening it. We position Supply Chain Agility (SCA) as the operational expression of orchestration and theorize that the value of orchestration and agility is amplified under Environmental Complexity (EC). Data were collected over three longitudinal waves from 268 manufacturing SMEs in the United States and Canada and analyzed using PLS-SEM, including nonlinear and moderated mediation effects. The study advances understanding of digital supply chain transformation by showing that enterprise systems create digital potential, but orchestration unlocks that potential, and agility expresses it operationally. The insights offer strategic guidance for firms seeking to convert digital investments into sustained performance advantages.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".