Information Technology, Network Structure, and Competitive Action
Bibliographic record
Abstract
Researchers in competitive dynamics have demonstrated that firms that carry out intense, complex, and heterogeneous competitive actions exhibit better performance. However, there is a need to understand factors that enable firms to undertake competitive actions. In this study, we focus on two antecedents of competitive behavior of firms: (1) access to network resources and (2) use of information technology (IT). We argue that while network structure provides firms with the opportunity to tap into external resources, the extent to which they are actually exploited depends on firms' IT-enabled capability. We develop a theoretical model that examines the relationships between IT-enabled capability, network structure, and competitive action. We test the model using secondary data, about 12 major automakers over 16 years from 1988 to 2003. We find that network structure rich in structural holes has a positive direct effect on firms' ability to introduce a greater number and a wider range of competitive actions. However, the effect of dense network structure is contingent on firms' IT-enabled capability. Firms benefit from dense network structure only when they develop a strong IT-enabled capability. Our results suggest that IT-enabled capability plays both a substitutive role, when firms do not have advantageous access to brokerage opportunities, and a complementary role, when firms are embedded in dense network structure, in the relationship between network structure and competitive actions.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".