How a Firm’s Competitive Environment and Digital Strategic Posture Influence Digital Business Strategy1
Bibliographic record
Abstract
In this paper, we examine how the competitive industry environment shapes the way that digital strategic posture (defined as a focal firm’s degree of engagement in a particular class of digital business practices relative to the industry norm) influences firms’ realized digital business strategy. We focus on two forms of digital strategy: general IT investment and IT outsourcing investment. Drawing from prior literature on determinants of IT activity and competitive dynamics, we argue that three elements of the industry environment determine whether digital strategic posture has an increasingly convergent or divergent influence on digital business strategy. By divergent influence, we mean an influence that leads to spending substantially more or less on a particular strategic activity than industry norms. We predict that a digital strategic posture (difference from the industry mean) has an increasingly divergent effect on digital business strategy under higher industry turbulence, while having an increasingly convergent effect on digital business strategy under higher industry concentration and higher industry growth. The study uses archival data for 400 U.S.-based firms from 1999 to 2006. Our findings imply that digital business strategy is not solely a matter of optimizing firm operations internally or of responding to one or two focal competitors, but also arises strikingly from awareness and responsiveness to the digital business competitive environment. Collectively, the findings provide insights on how strategic posture and industry environment influence firms’ digital business strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".