Exploring the Renewal of IT-enabled Resources from a Structural Perspective
Bibliographic record
Abstract
Organizations are exposed to ever-increasing dynamic environments, making sustaining the derivation of IT benefits critical. However, researchers have observed that IT benefits are short-lived and have called for studies on how organizations can sustain the derivation of IT benefits, especially in dynamic environments. Research shows that the integration of IT assets and other organizational resources needed to form IT-enabled resources from which organizations derive IT benefits can also constrain the renewal of IT-enabled resources to sustain the derivation of IT benefits. In this study, we draw on relevant theories, published empirical cases, and a primary case study to explore, from a structural perspective, the renewal of IT-enabled resources to sustain the derivation of IT benefits. We find that certain structural properties (i.e., component flexibility, component centrality, and component coupling) emerge during the formation and modification of IT-enabled resources and influence the renewal of IT-enabled resources. We extend Nevo and Wade’s model on the formation of IT-enabled resources with the structural properties and offer eight propositions on how the structural properties and organizational capabilities influence the renewal of IT-enabled resources. We discuss the theoretical and managerial implications and identify areas for future research.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".