The role and perception of the Web in its various usage contexts is rapidly changing – from an early focus on “Web-only” interaction with customers, information seekers, and other users, to the Web becoming one central component in a multi-channel information and communication strategy. In fact, multi-channel retailers increased their online market share from 52% in 1999 to 67% in 2001 – in contrast to Internet-only retailers, who lost market share respectively [Silverstein, et al., 2002]. Incumbent companies with a traditional store network seem to dominate the online market currently. With the increasing online competition, measuring success has become crucial for both Web-only and multi-channel retailers.
Web site owners have the opportunity to collect, analyze and use an increasing amount of online consumer information. On the Internet users transmit personal information, either actively by sending customer data (e.g. a shipping address for books), or passively, by leaving traces that are registered with the server side (in the so-called Web server log). In a multi-channel context, Web sites can also collect information about online consumers’ use of offline channels. Despite the increasing flow of consumer data, Web sites often lack the ability to utilize the information for measuring e-business success. Multi-channel retailers in particular lack a measurement system to analyze online success in a complex multi-channel information, communication and distribution strategy.
While yielding benefits to the companies, the analysis and use of consumer data increases privacy concerns significantly, which has become a primary impediment for successful e-commerce. Online shoppers claim they would buy considerably more if they were less concerned about their online privacy [Cyber Dialogue, 2001 Department for Trade and Industry, 2001 Forrester, 2001]. Privacy legislation and industry-driven initiatives aim at alleviating some of these concerns. As a consequence, a Web site that aims at analyzing and using online consumer data must include privacy requirements in its analysis practices. Moreover, it must efficiently communicate these privacy standards to its users in order to increase consumer trust.
With regard to Web retailing, we will address the following questions:
With regard to online privacy, we will focus on the following questions:
This thesis will propose concrete solutions for the questions raised above.
The thesis’ specific contributions are the following:
The thesis concludes with a summary and an outlook on further research in Chapter 7.
A sketch of the thesis structure is captured in Figure 1-1:
|Figure 1-1: Thesis structure|
This thesis chooses exploratory and confirmatory research approaches that aim at balancing advantages and disadvantages of both theory-building and theory-testing methodologies.
Chapter 2 takes a confirmatory approach to data analysis. Hypotheses are developed and tested on data from 1048 online consumers. Multi-causal relationships have been observed using LISREL 8 [Jöreskog and Sörbom, 2003]. Chapter 3 uses an exploratory research approach. Techniques from data and Web mining are applied on Web user and usage data. The data sample includes customer information from 13,653 customers, 92,467 sessions from a multi-channel retailer’s Web logs and external information. Confirmatory elements have been integrated into the Web mining approach in Section 3.7, where background knowledge is used as guidance for the mining process. Preconceptions about the data are tested against a reference set of 27,647 user sessions from a non-commercial site. Chapter 4 develops a prototype, which integrates the exploratory analysis techniques of Chapter 3. Chapter 5 is based on a comparative literature review. Chapter 6 concentrates on an experimental approach. A between-subjects design has been chosen to explore the impact of privacy and personalization communication on users’ data disclosure behavior.
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