The benefits accruing to a purchaser of a product due to the existing base of consumers of the same or compatible products are known as network externalities. This paper studies Katz and Shapiro's (1986) model of network externalities in an experimental setting. Two sellers choose prices for competing technologies sold to two groups of four buyers purchasing sequentially in two stages. The results are qualitatively consistent with Katz and Shapiro's equilibrium predictions. In certain sessions over three-quarters of first stage buyers purchase the more expensive technology anticipating that later arriving buyers will also buy this technology. In periods where a strong network has been established for a technology in the first stage, over 80 percent of second stage buyers buy that technology, even though in most cases it is priced higher. The data, however, differ from the point predictions of the model.
Review of Industrial Organization – Springer Journals
Published: Oct 4, 2004
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera