Unifying the Role of IT in Hyperturbulence and Competitive Advantage Via a Multilevel Perspective of IS Strategy1
Bibliographic record
Abstract
While information technology (IT) serves as a new source of sustainable competitive advantage for firms, it also induces hyperturbulent environments (or hyperturbulence) that erode that sustainable competitive advantage. In this paper, we posit that these contradictions might be due to cross-level nonlinear causality between firm-level IT-based strategic actions and collective-level IT-induced hyperturbulence. We develop a multilevel perspective of IS strategy for theorizing this causality, and unifying novel with established research. Complex adaptive systems theory is employed as the overarching framework for its strength in formalizing cross-level nonlinear causal paths. Using literature-based theorization and agent-based modeling, we establish two bottom-up nonlinear causal paths by which IT drives hyperturbulence: IT can act as an external force (i.e., component IT innovation) to locally instigate firm strategic actions that aggregate to temporary hyper-turbulence or as an internal force (i.e., architectural IT innovation) to drive pervasive firm strategic interactions that aggregate to persistent hyperturbulence. Each causal path produces varied amounts of reducible and irreducible uncertainties and thereby renders a top-down nonlinear effect that reshapes the opportunity for IT to contribute to competitive advantage. This multilevel theorization paves the way for new, IS-specific theory regarding IT’s unique role in inducing nonlinear dynamics and in affording new business strategies in today’s competitive environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".