ACM Home Page
Please provide us with feedback. Feedback
Price issues in delivering E-content on-demand
Full text PdfPdf (200 KB)
Source ACM SIGecom Exchanges archive
Volume 3 ,  Issue 2  (Spring, 2002) table of contents
Pages: 18 - 27  
Year of Publication: 2002
Authors
Srinivasan Jagannathan  Department of Computer Science, University of California, Santa Barbara, CA
Kevin C. Almeroth  Department of Computer Science, University of California, Santa Barbara, CA
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 0,   Downloads (12 Months): 12,   Citation Count: 1
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/844340.844348
What is a DOI?

ABSTRACT

The explosive increase in Internet bandwidth and usage opens a vista of opportunities to sell multimedia-rich software and services using the Internet. Once e-content is created, the cost of replication is negligible. Customers can download the e-content immediately after online transactions. Alternately, the content provider can stream the content to the customers. A sound business model is necessary for the success of such an enterprise. In this paper, we examine the determinants of revenue for an Internet based on-demand content delivery service. The determinants of revenue are: transaction model, pricing strategy, customer behavior, distribution resources, and competition. We briefly describe each of these factors and discuss how they relate to revenue. Our belief is that by better understanding how these factors affect revenue, content providers can develop services that generate more revenue while also being more compelling to users.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
ALMEROTH, K., DAN, A., SITARAM, D., AND TETZLAFF, W. 1997. Long term channel allocation strategies for video applications. In IEEE Infocom.
 
2
 
3
BRESLAU, L., CAO, P., FAN, L., PHILLIPS, G., AND SHENKER, S. 1999. Web caching and zipf-like distributions: Evidence and implications. In Infocom. 126-134.
 
4
CHAN, S. AND TOBAGI, F. 1999. On achieving profit in providing near video-on-demand services. In Proceedings of the 1999 IEEE International Conference on Communications (ICC'99).
 
5
CHAVEZ, A. AND MAES, P. 1996. Kasbah: an agent marketplace for buying and selling goods. In Proceedings of the First International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology.
6
 
7
 
8
JAGANNATHAN, S. AND ALMEROTH, K. C. 2001a. An adaptive pricing scheme for content delivery systems. In Global Internet Symposium. San Antonio, Texas, USA.
 
9
 
10
JAGANNATHAN, S., NAYAK, J., ALMEROTH, K., AND HOFMANN, M. 2001. E-content pricing: Analysis and simulation. Tech. rep., University of California Santa Barbara. available at http://www.nmsl.cs.ucsb.edu/papers/ECONTENTPRC.ps.gz.
 
11
JAGANNATHAN, S., NAYAK, J., ALMEROTH, K., AND HOFMANN, M. 2002. On pricing algorithms for batched content delivery systems. Tech. rep., University of California Santa Barbara. available at http://www.nmsl.cs.ucsb.edu/papers/BatchingPrc.ps.gz.
 
12
ODLYZKO, A. 2000. The history of communications and its implications for the Internet. available at http://www.research.att.com/-amo/doc/networks.html.
 
13
TSVETOVATY, M., GINI, M., MOBASHER, B., AND WIECKOWSKI, Z. 1997. Magma: an agent-based virtual market for electronic commerce. Applied Artificial Intelligence.


Collaborative Colleagues:
Srinivasan Jagannathan: colleagues
Kevin C. Almeroth: colleagues