On the Adoption Paths and Competition of Network Applications

Soumya SenESE, UPenn

March 4, 2009

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Introduction

New network applications influence the popularity of the Internet.

How do we study the success or failure of these applications?– What factors influence the adoption of applications?– How to help a network application/technology take-off?

Prior works have underlined the important role that network externalities play in the diffusion process.

– Do these results hold in the context of all network applications?– How does the presence of network externality influence the competition

between two network applications?

How do individuals influence the adoption path of these new network applications?

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Identifying the Issues

Network Applications have certain different traits/features from generic goods with network effects.

– The user’s perception of relative benefits from network externality and stand-alone benefits matter.

– The results obtained from individual level decision models may be considerably altered when users are differentiated in their valuation of network benefits.

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Related Work (Single Technology Adoption)

Aggregate Decision models:

Adv: Simpler models, that allow the study of dynamic policies etc.

Disadv: Does not capture the mechanism that governs the adoption process and the impact of system parameters.

Fourt & Woodlock (constant hazard rate model) Bass (models with users as imitators and innovators) Tanny & Derzko Mahajan (overview)

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Related Work (contd..)

Individual Level Decision models:

Adv: Allows the study of user decision mechanism that drives the network application/technology adoption.

Disadv: Relatively more complex to analyze.

Horsky analyzed the purchase of a durable good based on individual level wage constraints

Cabral modeled the adoption of goods with network benefits where users are heterogeneous in their preference for stand-alone benefits, but are homogeneous in valuing the network benefits.

Both are applicable to only single technology settings.

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How are we different?

We model the utility functions that relate more closely with real network technologies

– Users may value stand-alone benefits only if sufficient network benefits are present

Eg: Skype

– User are not homogeneous in their valuation of network benefits.

Eg: Photo-sharing sites– Provide both stand-alone and network benefits– Users are differentiated on both factors.

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What questions are we asking?

Do these different utility functional forms for network applications still produce the same S-shaped adoption curves?

Can it used for addressing policy questions?– Discontinuity in Adoption path– Time of seeding, whom to seed etc.

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Related Work (Technology Competition)

Aggregate diffusion models– Norton & Bass (Substitution models)

Static models– Farrell and Saloner (impact of converters)– J.P.Choi (time of introduction of 2-way converters)– Joseph (converters in IPv4-IPv6 transition)

Individual level adoption models– Youngmi (adoption of incompatible network technologies)– Soumya (adoption of network technologies in presence of

converters).

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How are we different?

We provide the corresponding utility models for competing network applications.

We want to identify the kinds of interesting behaviors that may arise in the adoption process and the role played by converters

– Instability in adoption dynamics– Converters hurting technology’s adoption– Converters impact on overall market penetration.

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Models for Single Technology Adoption

Utility form 1: Cabral’s form

Utility form 2:

Users are heterogeneous over network benefits and value any stand-alone benefits only if network benefits are non-zero.

Utility form 3:

Users are heterogeneous over both stand-alone and network benefits.

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Analysis and Results (1)

Users learn about current adoption levels continuously and update their utility to realize the new equilibrium adoption levels (i.e. H(x)=x)

An exogenous change in parameter value (price) drives the system out of this equilibria to the closest realizable equilibria.

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Analysis and Results (2)

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Equilibrium Adoption Path

Pricing: Linearly decreasing price over time Catastrophe point (adoption level jumps to full market penetration) Policy implications: Seeding, time to seed etc. Approximation to S-shaped curve

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Equilibrium Adoption Path

Pricing: Slowly decreasing price New technology never takes off without seeding

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Analysis and Results (3)

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Equilibrium Adoption Path

Slowly decreasing price Shows the importance of seeding for technology to take off.

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Models for Technology Competition

Utility form 1:

Utility form 2:

Utility form 3:

Models consider fixed prices and qualities.

Technologies chooses converters.

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User decision process

Users are rational and make incentive compatible decisions:

Information on current adoption level proceeds through the population at a constant rate γ<1.

Users re-compute their utility and make decisions:

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Simulations:Adoption Behaviors, Instabilities

Analytical modeling is difficult. Simulation allows us to explore behaviors in

presence of converters for the competing technologies.

For a Cabral kind of a model analytical solutions are possible.

– Shows that unexpected behaviors may arise in these situations.

Converters creating Instability Better converters can hurt overall market penetration Improving converter efficiency may hurt its own adoption.

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Adoption Behavior (1)

Representative behaviors:– Converter of Technology 1 hurts its own market penetration– May result in a drop in overall market level– Create instabilities in the adoption dynamics.

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Adoption Behavior (2)

Technology/Application 2 improves its own converter– Hurts its own market penetration– Hurts the overall market penetration.

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Adoption Behavior (3)

Converter can hurt the overall market penetration Create instability in the adoption dynamics

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Conclusion

Study of network applications may need further treatment as their features may require the use of specific types of user utility functions.

However, we find that the S-shaped equilibrium adoption paths may be realized even for the alternative utility functions where users are differentiated over network benefits.

Competition between two technologies for all these utility functions show evidence of interesting behaviors about which application providers should be careful.