An Empirical Analysis of Cellular Voice and Data services

Cellular telephony and associated data services has been a major social phenomena for well over a decade now. It has changed the way - in some countries more than others - in which people communicate. In many countries in Northern Europe and Asia, its penetration rates are very high and in others less so but in all cases it has engendered change at multiple levels - socially as noted and in terms of market structure and competition with the established Incumbent Local Exchange and Inter Exchange service providers. However, there has been little work published in the academic literature on user consumption of cellular voice and data services. This has been due to the unavailability of longitudinal data at the individual user level on their consumption of voice and data services. We have such data from a large cellular service provider in Asia. Demand for voice and data services is influenced by the tariffs or 'service plans' offered by firms. In our analysis we empirically estimate the drivers for cellular services how demographic and plan characteristics affect the user choices. We first provide a theoretical model and then provide insight into consumption patterns over a one year period of cellular voice and data services and relate it to service plan design

Cell Phone Demand and Consumer Learning - An Empirical Analysis

A structural model is used in this paper to analyze the demand and learning behavior in cell phone market. We assume that the cell phone consumption can be divided into a high-value part and a low-value part. The consumers are assumed to be uncertain about the exogenous shock of the need for high-value usage and also their preferences over the low-value usage. Meanwhile, we assume that the consumers' knowledge improves over time. As a result, the match between their plan choice and consumption pattern becomes better. Such a learning behavior is supported by the data set. Bayesian updating is used to represent the learning. The estimates of the parameters are obtained and compared to the benchmarks from previous research

Interest-Based Self-Organizing Peer-to-Peer Networks: A Club Economics Approach

Improving the information retrieval (IR) performance of peer-to-peer networks is an important and challenging problem. Recently, the computer science literature has attempted to address this problem by improving IR search algorithms. However, in peer-to-peer networks, IR performance is determined by both technology and user behavior, and very little attention has been paid in the literature to improving IR performance through incentives to change user behavior. We address this gap by combining the club goods economics literature and the IR literature to propose a next generation file sharing architecture. Using the popular Gnutella 0.6 architecture as context, we conceptualize a Gnutella ultrapeer and its local network of leaf nodes as a "club" (in economic terms). We specify an information retrieval-based utility model for a peer to determine which clubs to join, for a club to manage its membership, and for a club to determine to which other clubs they should connect. We simulate the performance of our model using a unique real-world dataset collected from the Gnutella 0.6 network. These simulations show that our club model accomplishes both performance goals. First, peers are self-organized into communities of interest - in our club model peers are 85% more likely to be able to obtain content from their local club than they are in the current Gnutella 0.6 architecture. Second, peers have increased incentives to share content - our model shows that peers who share can increase their recall performance by nearly five times over the performance offered to free-riders. We also show that the benefits provided by our club model outweigh the added protocol overhead imposed on the network for the most valuable peers

Cell Phone Demand and Consumer Learning - An Empirical Analysis

A structural model is used in this paper to analyze the demand and learning behavior in cell phone market. We assume that the cell phone consumption can be divided into a high-value part and a low-value part. The consumers are assumed to be uncertain about the exogenous shock of the need for high-value usage and also their preferences over the low-value usage. Meanwhile, we assume that the consumers' knowledge improves over time. As a result, the match between their plan choice and consumption pattern becomes better. Such a learning behavior is supported by the data set. Bayesian updating is used to represent the learning. The estimates of the parameters are obtained and compared to the benchmarks from previous research

Interest-Based Self-Organizing Peer-to-Peer Networks: A Club Economics Approach

Improving the information retrieval (IR) performance of peer-to-peer networks is an important and challenging problem. Recently, the computer science literature has attempted to address this problem by improving IR search algorithms. However, in peer-to-peer networks, IR performance is determined by both technology and user behavior, and very little attention has been paid in the literature to improving IR performance through incentives to change user behavior. We address this gap by combining the club goods economics literature and the IR literature to propose a next generation file sharing architecture. Using the popular Gnutella 0.6 architecture as context, we conceptualize a Gnutella ultrapeer and its local network of leaf nodes as a "club" (in economic terms). We specify an information retrieval-based utility model for a peer to determine which clubs to join, for a club to manage its membership, and for a club to determine to which other clubs they should connect. We simulate the performance of our model using a unique real-world dataset collected from the Gnutella 0.6 network. These simulations show that our club model accomplishes both performance goals. First, peers are self-organized into communities of interest - in our club model peers are 85% more likely to be able to obtain content from their local club than they are in the current Gnutella 0.6 architecture. Second, peers have increased incentives to share content - our model shows that peers who share can increase their recall performance by nearly five times over the performance offered to free-riders. We also show that the benefits provided by our club model outweigh the added protocol overhead imposed on the network for the most valuable peers

The Effect of P2P File Sharing on Music Markets: A SurvivalAnalysisofAlbums on Ranking Charts

Recent technological and market forces have profoundly impacted the music industry. Emphasizing threats from peer-to-peer (P2P) technologies, the industry continues to seek sanctions against individuals who offer significant number of songs for others to copy. Yet there is little rigorous empirical analysis of the impacts of online sharing on the success of music products. Combining data on the performance of music albums on the Billboard charts with file sharing data from a popular network, we: 1) assess the impact of recent developments related to the music industry on survival of music albums on the charts, and 2) evaluate the specific impact of P2P sharing on an album's survival on the charts. In the post P2P era, we find significantly reduced chart survival. The second phase of our study isolates the impact of file sharing on album survival. We find that sharing does not seem to hurt the survival of albums

The Effect of P2P File Sharing on Music Markets: A SurvivalAnalysisofAlbums on Ranking Charts

Recent technological and market forces have profoundly impacted the music industry. Emphasizing threats from peer-to-peer (P2P) technologies, the industry continues to seek sanctions against individuals who offer significant number of songs for others to copy. Yet there is little rigorous empirical analysis of the impacts of online sharing on the success of music products. Combining data on the performance of music albums on the Billboard charts with file sharing data from a popular network, we: 1) assess the impact of recent developments related to the music industry on survival of music albums on the charts, and 2) evaluate the specific impact of P2P sharing on an album's survival on the charts. In the post P2P era, we find significantly reduced chart survival. The second phase of our study isolates the impact of file sharing on album survival. We find that sharing does not seem to hurt the survival of albums