Ain’t misbehavin’ – on the evolution of Internet-related behavior studies

Internet Research

ISSN: 1066-2243

Article publication date: 1 April 2004

350

Citation

Schwartz, D.G. (2004), "Ain’t misbehavin’ – on the evolution of Internet-related behavior studies", Internet Research, Vol. 14 No. 2. https://doi.org/10.1108/intr.2004.17214baa.001

Publisher

:

Emerald Group Publishing Limited

Copyright © 2004, Emerald Group Publishing Limited


Ain’t misbehavin’ – on the evolution of Internet-related behavior studies

  • I don’t stay out late,Don’t care to go.I’m home about eight,Just me and my [Internet] radio.Ain’t misbehavin’,I’m savin’ my love for you(Fats Waller, 1929)

We seem to spend an inordinate amount of time trying to understand behavior. The underlying proposition being that if we can understand past behavior, we can use that understanding to both predict and influence future behavior. It is a reasonable approach successfully applied in fields ranging from child psychology to stock market investing to name but two.

However, what both those examples (and most other behavioral studies) have in common is a long history of experience and model development from which behavioral models can be derived, and behavior can be learned. When it comes to the Internet we are not quite so lucky. Not only is the medium young resulting in limited historical data, it is also dynamic, meaning that the interaction factors that influence behavior today, may be superceded by functionality released tomorrow. This makes the study of Internet-related behavior fraught with difficulty.

This issue of Internet Research contains four prime examples of the challenges involved in studying Internet-related behavior, providing insights into this rapidly evolving field of study.

Constantinides, in “Influencing the online consumer’s behavior: the Web experience”, identifies key components of a consumer’s Web experience specifically related to the buying process. In an attempt to overcome some of the limitations discussed above, Constantinides constructs a meta-study based on seven years of prior research into various aspects of online behavior. Analyzing 48 research papers from 33 different publishing venues, this article not only presents the building blocks for new behavioral models, but also provides us with a much-needed “historical” perspective on how the study of online behavior is evolving.

An important element in studying behavior is the impact of personal values. The dimensions of personal values take center stage in Jayawardhena’s article “Personal values influence on e-shopping attitude and behavior” which applies a values-attitude-behavior model.

Studying the behavior of organizations requires an altogether different approach as illustrated by Büyüközkan, in “Multi criteria decision making for e-marketplace selection”. She combines the analytic hierarchy process, a multi-criteria decision-making method, with the Delphi method to help capture the criteria organizations use in determining e-marketplace participation.

There are clear differences between studying the behavior of an organization versus studying the behavior of an individual. When it comes to studying Internet behavior, we have a common element to consider – the behavior of the Internet itself as a growing, evolving network of information systems. While individuals and organizations may be influenced by a variety of factors, it is the basic diffusion of the Internet itself that has had the most significant impact on user behavior over the past decade. In “Toward a diffusion model of Internet systems”, Kim and Galliers present a conceptual diffusion model that helps us understand how both external technical and market factors, and internal organizational and system factors influence the worldwide growth and adoption of Internet-based systems.

Two specific external elements that may influence the diffusion of Internet systems are quality of service (QoS) and pricing, where in general a higher price is expected for higher QoS. Bouras and Sevasti discuss pricing models for network services with multiple levels of quality in “Pricing QoS over transport networks”. In addition to providing a detailed introduction to the issues surrounding the role of service-differentiated pricing, they develop and propose an innovative pricing model.

We conclude this issue with an extensive case study of a major Canadian e-learning initiative. Sivakumar and Robertson, in “Developing an integrated Web engine for online Internetworking education: a case study”, document the design, development and deployment of an integrated Web engine environment used to deliver remote Internetworking education to students at geographically remote sites.

David G. SchwartzEditor

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