Trajectories of individual WWW usage: implications for electronic commerce

The number and variety of Web sites offering information, commerce and services has multiplied since 1995. The number of users making use of the Internet and the Web has also grown tremendously. Yet at the level of the individual, little is known about the trajectory of change over time in number of visits to Web sites. Drawing upon recent advances in semi-parametric, group-based statistical modeling, we examine whether there are distinctive clusters of such trajectories. Using longitudinal data from 1995-1998 on visits to distinctive Web sites, we provide answers to these questions. We find that WWW users can be clustered into four groups with distinct trajectories of use. These groups achieve saturation in their extent of Web usage as measured in the number of distinct Web sites they visit over time. We also develop demographic profiles of these different user groups. These results have important implications for Internet marketing.

[1]  D. Nagin,et al.  Trajectories of boys' physical aggression, opposition, and hyperactivity on the path to physically violent and nonviolent juvenile delinquency. , 1999, Child development.

[2]  Sheizaf Rafaeli,et al.  What do virtual "Tells" tell? Placing cybersociety research into a hierarchy of social explanation , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[3]  K. Land,et al.  AGE, CRIMINAL CAREERS, AND POPULATION HETEROGENEITY: SPECIFICATION AND ESTIMATION OF A NONPARAMETRIC, MIXED POISSON MODEL* , 1993 .

[4]  A. Cameron,et al.  Econometric models based on count data. Comparisons and applications of some estimators and tests , 1986 .

[5]  T. Moffitt,et al.  LIFE-COURSE TRAJECTORIES OF DIFFERENT TYPES OF OFFENDERS* , 1995 .

[6]  Daniel S. Nagin,et al.  Analyzing developmental trajectories: A semiparametric, group-based approach , 1999 .

[7]  Quentin Jones,et al.  Time to split, virtually: expanding virtual publics into vibrant virtual metropolises , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[8]  Kathryn Roeder,et al.  Modeling Uncertainty in Latent Class Membership: A Case Study in Criminology , 1999 .

[9]  G. A. Miller THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[10]  Robert E. Kraut,et al.  HomeNet: a field trial of residential Internet services , 1996, CHI.

[11]  K. Land,et al.  How Many Latent Classes of Delinquent/ Criminal Careers? Results from Mixed Poisson Regression Analyses1 , 1998, American Journal of Sociology.

[12]  K. Roeder,et al.  A SAS Procedure Based on Mixture Models for Estimating Developmental Trajectories , 2001 .

[13]  Sheizaf Rafaeli,et al.  Time to Split, Virtually: 'Discourse Architecture' and 'Community Building' Create Vibrant Virtual Publics , 2000, Electron. Mark..

[14]  Kenneth C. Land,et al.  Micro-models of criminal careers: A synthesis of the criminal careers and life course approaches via semiparametric mixed poisson regression models, with empirical applications , 1996 .