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Enregistrement W2051983460 · doi:10.1287/isre.1100.0300

Competing in Crowded Markets: Multimarket Contact and the Nature of Competition in the Enterprise Systems Software Industry

2010· article· en· W2051983460 sur OpenAlexafffund
Ramnath K. Chellappa, V. Sambamurthy, Nilesh Saraf

Notice bibliographique

RevueInformation Systems Research · 2010
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueDigital Platforms and Economics
Établissements canadiensSimon Fraser University
Organismes subventionnairesSocial Sciences and Humanities Research Council of Canada
Mots-clésCompetition (biology)Industrial organizationRivalryExternalityBusinessDigitizationNetwork effectMarketingEconomicsCommerceMicroeconomicsTelecommunications

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,032

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,011
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0030,010
Communication savante0,0100,018
Science ouverte0,0010,005
Intégrité de la recherche0,0040,002
Charge utile insuffisante (le modèle a refusé de juger)0,0100,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,265
Écart entre enseignants0,246 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations49
Publié2010
Routes d'admission2
Résumé présentoui

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