Direct network effects, small-world networks, and industry formation

Funk, Jeffrey L.

doi:10.1016/j.telpol.2009.02.003

Cited by 4 publications

(3 citation statements)

References 52 publications

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“…A distinction is made between the direct and indirect network effect. The direct network effect was described in 1986 by Katz and Shapiro and states that a product's value changes with the total number of consumers of that product (Farrell & Saloner, 1992;Funk, 2009;Katz & Shapiro, 1986). Often referred examples for this effect are telephones and fax machines.…”

Section: The Disruptive Potential Of Digital Platformsmentioning

confidence: 99%

Platform Patterns—Using Proven Principles to Develop Digital Platforms

et al. 2021

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Hardly any other area has as much disruptive potential as digital platforms in the course of digitalization. After serious changes have already taken place in the B2C sector with platforms such as Amazon and Airbnb, the B2B sector is on the threshold to the so-called platform economy. In mechanical engineering, pioneers like GE (PREDIX) and Claas (365FarmNet) are trying to get their hands on the act. This is hardly a promising option for small and medium-sized companies, as only a few large companies will survive. Small and medium-sized enterprises (SMEs) are already facing the threat of losing direct consumer contact and becoming exchangeable executers. In order to prevent this, it is important to anticipate at an early stage which strategic options exist for the future platform economy and which adjustments to the product program should already be initiated today. Basically, medium-sized companies in particular lack a strategy for an advantageous entry into the future platform economy.The paper presents different approaches to master the challenges of participating in the platform economy by using platform patterns. Platform patterns represent proven principles of already existing platforms. We show how we derived a catalogue with 37 identified platform patterns. The catalogue has a generic design and can be customized for a specific use case. The versatility of the catalogue is underlined by three possible applications: (1) platform ideation, (2) platform development, and (3) platform characterization.

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Section: The Disruptive Potential Of Digital Platformsmentioning

confidence: 99%

Platform Patterns—Using Proven Principles to Develop Digital Platforms

et al. 2021

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“…We envision an exponential correlation between privacy and trust due to observed direct network effect [9] in social networks. The network effect (a.k.a network externality) is a theory in economics and business stating that for certain products (e.g.…”

Section: Privacy Inference Modelmentioning

confidence: 99%

“…social networks) the willingness to pay arises as the quantity of consumers or users rises, provided that: 1) there is zero utility in a network of zero size (i.e. zero utility for the first user) 2) there is immediate benefit in the expansion of a small network for a user [9]. On-line social networks work in the same way, being more useful as more users join.…”

Section: Privacy Inference Modelmentioning

confidence: 99%

Exploiting Dynamic Privacy in Socially Regularized Recommenders

Bunea¹,

Mokarizadeh²,

Dokoohaki³

et al. 2012

2012 IEEE 12th International Conference on Data Mining Workshops

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Users of social networks have shown an increasing concern for exposing their personal data to untrusted entities in order to receive recommendations. In this work, we describe the components of a privacy-aware collaborative filtering based recommender framework which targets two important issues in recommender systems operating in a social network: privacy concern of profile owners and sparsity of social trust among users in a social network. Assuming an initial global privacy in the social network, the framework employs a probabilistic matrix factorization technique to estimate the quality of the missing trust relation between each pair of users. Because of the latent features inferred by matrix factorization, the resulting trust is an augmentation of both social relation and user similarity driven trust. We introduce a privacy inference model which exploits the underlying inter-entity trust information to obtain a personalized privacy view for each individual in the social network. Using this personalized privacy view, we employ an off-the-shelf collaborative filtering recommender system to make predictions. Experimental results show that the proposed approach obtains better accuracy than similar non-privacy aware recommender systems, while at the same time meeting profile privacy concerns.

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