Path Dependence of Dynamic Information Technology Capability: An Empirical Investigation
Why this work is in the frame
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Bibliographic record
Abstract
Organizations seek to differentiate themselves in the marketplace by deploying information technology (IT) to develop dynamic IT capabilities and resist competitors’ attempts to imitate or improve these capabilities. While this strategy has been justified on the grounds that dynamic IT capabilities are durably heterogeneous, there does not seem to be empirical evidence supporting or refuting this assumption. This study empirically validates the assumption of durable heterogeneity of dynamic organizational IT capability (ITC) due to path dependence. We capture ITC heterogeneity by introducing a framework in which firms try to achieve ITC leadership in their industry and we propose that durable ITC heterogeneity can be attributed to path dependence, and, hence, it can be tested using Heckman’s true state dependence of ITC leadership status. Using random and fixed effect dynamic logit models, we investigate true state dependence of ITC leadership on a sample of large US firms. Results, which are robust to alternative sample, dependent, and control variable specifications, show that achieving ITC leadership is a true state-dependent process suggesting durable heterogeneity of ITC due to path dependence. The study contributes to the dynamic capabilities literature and has important managerial implications. The proposed framework for conceptualizing durable resource heterogeneity due to path dependence is general and versatile thus providing a foundation for future research on dynamic capabilities. The findings provide empirical evidence to confirm that ITC is durably heterogeneous and should be managed as a potential source of competitive advantage.
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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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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 it