Standardization, Compatibility, and Innovation

Farrell, Joseph; Saloner, Garth

doi:10.2307/2555589

Cited by 2,075 publications

(938 citation statements)

References 4 publications

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“…In traditional theoretical models of network externalities (Katz and Shapiro, 1985;Farrell and Saloner, 1985;Economides, 1996), network participants are assumed to be symmetrical in size and consequently the issue of network user size is not discussed. 7 However, later empirical papers such as Gowrisankaran and Stavins (2004) argue that user size itself can be used to detect the presence of network externalities.…”

Section: Conceptual Frameworkmentioning

confidence: 99%

Health information exchange, system size and information silos

Miller

Tucker²

2014

Journal of Health Economics

137

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There are many technology platforms which only bring benefits when users share data. In healthcare, this is a key policy issue, because of the potential cost savings and quality improvements from sharing patient data across medical providers. We find empirically that larger hospital systems are more likely to exchange electronic patient information internally, but are less likely to exchange patient information externally with other hospitals. This pattern is driven by instances where there may be a commercial cost to sharing data with other hospitals. Our results suggest that the common strategy of using 'marquee' large users to kick-start a platform technology has an important drawback of potentially creating information silos.

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Section: Conceptual Frameworkmentioning

confidence: 99%

Health information exchange, system size and information silos

Miller

Tucker²

2014

Journal of Health Economics

137

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“…The question of how system size affects network technology use is related to more general questions of how user size affects participation in information networks. In traditional theoretical models of network externalities (Katz and Shapiro, 1985;Farrell and Saloner, 1985;Economides, 1996), network participants are assumed to be symmetrical in size and consequently the issue of network user size is not discussed. 7 Later empirical papers, such as 6 We follow Ho (2009), who studies networks in healthcare, and focus on hospital systems rather than hospital networks, because a hospital system is the closest analog to a profit-maximizing unit.…”

Section: System Size and Data Exchangementioning

confidence: 99%

Health Information Exchange, System Size and Information Silos

Miller

Tucker

2011

SSRN Journal

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There are many technology platforms that bring benefits only when users share data. In healthcare, this is a key policy issue, because of the potential cost savings and quality improvements from 'big data' in the form of sharing electronic patient data across medical providers. Indeed, one criterion used for federal subsidies for healthcare information technology is whether the software has the capability to share data. We find empirically that larger hospital systems are more likely to exchange electronic patient information internally, but are less likely to exchange patient information externally with other hospitals. This pattern is driven by instances where there may be a commercial cost to sharing data with other hospitals. Our results suggest that the common strategy of using 'marquee' large users to kick-start a platform technology has an important drawback of potentially creating information silos. This suggests that federal subsidies for health data technologies based on 'meaningful use' criteria, that are based simply on the capability to share data rather than actual sharing of data, may be misplaced.

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“…This causes delays in building a network on a new standard and also creates 'excess inertia' (i.e. reluctance to switch to a superior new technology and thereby cementing a status quo bias towards the existing technology that has a large installed base) (Farrell and Saloner, 1985;. Suarez (2004) parsed the process of a battle for dominance between standards into five phases (R&D build-up, technical feasibility, creating the market, decisive battle and post-dominance) and identified the installed base, complementary assets and network effects as key success factors in the stage of a decisive battle between heterogeneous standards.…”

Section: Literature Review: Standards Network Effects and Legitimacymentioning

confidence: 99%