Alliances, Rivalry, and Firm Performance in Enterprise Systems Software Markets: A Social Network Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Enterprise systems software (ESS) is a multibillion dollar industry that produces systems components to support a variety of business functions for a widerange of vertical industry segments. Even if it forms the core of an organization's information systems (IS) infrastructure, there is little prior IS research on the competitive dynamics in this industry. Whereas economic modeling has generally provided the methodological framework for studying standards-driven industries, our research employs social network methods to empirically examine ESS firm competition. Although component compatibility is critical to organizational end users, there is an absence of industry-wide ESS standards and compatibility is ensured through interfirm alliances. First, our research observes that this alliance network does not conform to the equilibrium structures predicted by economics of network evolution supporting the view that it is difficult to identify dominant standards and leaders in this industry. This state of flux combined with the multifirm multicomponent nature of the industry limits the direct applicability of extant analytical models. Instead, we propose that the relative structural position acquired by a firm in its alliance network is a reasonable proxy for its standards dominance and is an indicator of its performance. In lieu of structural measures developed mainly for interpersonal networks, we develop a measure of relative firm prominence specifically for the business software network where benefits of alliances may accrue through indirect connections even if attenuated. Panel data analyses of ESS firms that account for over 95% of the industry revenues, show that our measure provides a superior model fit to extant social network measures. Two interesting counterintuitive findings emerge from our research. First, unlike other software industries compatibility considerations can trump rivalry concerns. We employ quadratic assignment procedure to show that firms freely form alliances even with their rivals. Second, we find that smaller firms enjoy a greater value from acquiring a higher structural position as compared to larger firms.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.013 |
| 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