A relational digital capability cascade: how sensing, agility, and innovation drive B2B performance
Bibliographic record
Abstract
Digital transformation reshapes how B2B firms sense market changes, respond to disruptions, and innovate with partners, yet many organizations struggle to convert digital investments into performance gains. This study develops and tests a relational digital capability cascade explaining how Information Technology Capability (ITC) enables Digital Market Orientation (DMO), how DMO drives Digital Agility (DA) and Supply Chain Innovation (SCI), and how these interdependent capabilities influence performance. Using survey data from 248 Canadian B2B firms and Structural Equation Modeling, the results show that ITC strongly enhances DMO, which significantly improves agility and collaborative innovation. Agility boosts both SCI and performance, while SCI emerges as the most powerful predictor of performance. A feedback learning mechanism is also validated, demonstrating that agility enhances subsequent sensing by refining how firms interpret digital signals. The findings position digital transformation as a cyclical capability system rather than a linear sequence, showing that sensing, acting, and innovating reinforce one another. This research offers a holistic framework for how B2B firms can integrate digital infrastructure, agile decision routines, and cross-partner innovation practices to achieve superior performance.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".