Dual Pathways of Value Creation from Digital Strategic Posture: Contingent Effects of Competitive Actions and Environmental Uncertainty
Bibliographic record
Abstract
Digital strategic posture (DSP) is defined as a firm’s overall strategic stance toward investing in information technology (IT) initiatives relative to that of rival firms. This study examines how a firm’s DSP affects firm performance. Drawing on the competitive dynamics perspective and contingency view, we demonstrate that DSP influences competitive actions through dual pathways. First, DSP enables firms to take competitive actions that are more appropriate given the level of environmental uncertainty (captured by industry dynamism). In particular, our findings suggest that a proactive DSP enables relatively more innovation-oriented actions in dynamic industries while enabling relatively more operations-oriented actions in less dynamic industries. Second, DSP plays a facilitating role in firms’ execution of competitive actions such that a firm’s value from its proactive DSP is enhanced when there is a fit between the type of the firm’s competitive actions and its level of environmental uncertainty. Specifically, we find that firms with a more proactive DSP achieve superior firm performance from innovation-oriented actions in dynamic industries and from operations-oriented actions in less dynamic industries. Taken together, our findings suggest that a proactive DSP not only allows firms to take appropriate competitive actions that fit their environmental conditions but also contributes to firms’ performance by facilitating the execution of these appropriate actions, thus enhancing their efficacy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".