Understanding the relationship between IT capabilities and operational agility: a multi-method approach
Bibliographic record
Abstract
Purpose The system of information technology (IT)-oriented resources and processes that organizations need to develop to achieve operational agility remains unclear. The study research seeks to extend existing competency literature by incorporating the unique contextual nuances of the relationship between IT capabilities and operational agility. Design/methodology/approach Using a multi-method approach, this paper presents a theoretical framework of IT-enabled operations strategy that conceptualizes the role of IT capability in leveraging resources and processes for operational agility. Drawing on operations and information systems research, the authors advance that IT enables operational agility through two dimensions. From the perspective of a resource-based operations strategy, the authors explore the role of IT in resource-leveraging activities by investigating the nonlinear relationship between IT infrastructure and IT reconfiguration. From the perspective of a process-oriented operations strategy, the authors explore the role of IT in process-enhancing activities by investigating the nonlinear relationship between IT coordination and IT integration. Findings The study results, based on a sample of 113 organizations in Europe, Asia and North America, show that the interaction between IT infrastructure and IT reconfiguration positively influences operational agility, hence showing complementarity between the two constructs, while the interaction between IT coordination and IT integration negatively affects operational agility, hence indicating substitutability between the two constructs. A series of 62 interviews and a case study of Carrefour were further conducted to validate the field survey's results and to provide a finer grained explanation of the research model and quantitative findings. Originality/value The study findings offer an alternative explanation of the inconsistent relationship between IT capability and operational agility.
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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 | 0.035 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".