Competing in Crowded Markets: Multimarket Contact and the Nature of Competition in the Enterprise Systems Software Industry
Why this work is in the frame
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Bibliographic record
Abstract
As more and more firms seek to digitize their business processes and develop new digital capabilities, the enterprise systems software (ESS) has emerged as a significant industry. ESS firms offer software components (e.g., ERP, CRM, Marketing analytics) to shape their clients' digitization strategies. With rapid rates of technological and market innovation, the ESS industry consists of several horizontal markets that form around these components. As numerous vendors compete with each other within and across these markets, many of these horizontal markets appear to be crowded with rivals. In fact, multimarket contact and presence in crowded markets appear to be the pathways through which a majority of the ESS firms compete. Though the strategy literature has demonstrated the virtues of multimarket contact, paradoxically, the same literature argues that operating in crowded markets is not wise. In particular, crowded markets increase a firm's exposure to the whirlwinds of intense competition and have deleterious consequences for financial performance. Thus, the behavior of ESS firms raises an interesting anomaly and research question: Why do ESS firms continue to compete in crowded markets if they are deemed to be bad for financial performance? We argue that the effects of rivalry in crowded markets are counteracted by a different force, in the form of the economics of demand externalities. Demand externalities occur because the customers of ESS firms expect that software components from one market will be easily integrated with those that they buy from other markets. However, with rapid rates of technological innovation and market formation and dissolution, customers experience significant ambiguity in deciding which markets and components suit their needs. Therefore, they look at crowded markets as an important signal about the legitimacy and viability of specific components for their needs. Through their presence in crowded markets, ESS firms can signal their commitment to many of the components that customers might need for their digital platforms. Customers might find that such firms are attractive because their commitments to crowded markets can mitigate concerns about compatibilities between the components purchased across several markets. This unique potential for demand externality across markets suggests that ESS vendors might, in fact, benefit from competing in many crowded markets. We test our explanations through data across three time periods from a set of ESS firms that account for more than 95% of the revenue in this market. We find that ESS firms do reap performance benefits by competing in crowded markets. More importantly, we find that they can enhance their benefits from crowded markets if they face the same competitors in multiple markets, thereby increasing their multimarket contact with rivals. These results have interesting implications not just for understanding competitive conduct in the ESS industry but also in many of the emerging digital goods industries where the markets have similar competitive characteristics to the ESS industry. Our ideas complement emerging ideas about platform models of competition in the digital goods industry and provide important directions for future research.
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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 | 0.007 | 0.001 |
| 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 | 0.002 | 0.005 |
| 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