Abstract:The rise of electronic commerce and the Internet have created new technologies and capabilities, which increase concern for privacy online. This study reports on the results of an investigation of Internet users attitudes towards concern for privacy online, online behaviours adopted under varying levels of concern for privacy (high, moderate and low) and the types of information Internet users are protective of. Methodological triangulation was used, whereby both quantitative and qualitative research was conducted. A questionnaire and semi-structured individual interviews were used as the data collection methods. The results of a cross-sectional survey of 104 Internet users suggest a lack of control over personal information online, a lack of privacy rights online, a dislike of government regulation and other privacy concerns. Concern was decreased if personal information was provided for customisation or if there was the ability to opt-out. Anonymity also decreased concern. The qualitative results reveal five areas of concern for privacy, namely the role of Internet Service Providers, online businesses, Internet shopping, government regulation and general Internet privacy problems. This study attempts to add something of value to the body of knowledge regarding concern for privacy on the Internet in the context of South African environment.

Abstract:The purpose of this paper is to investigate the application of logical tools such as inference trees and columnar data flow diagrams in the information system (IS) analysis and design context. Seventeen students at an institution of higher education were observed during the design and analysis of information systems and their experiences were evaluated through a focus group interview, observations and documents analysis. This research was based on a qualitative, action research approach (Yin 1994;Merriam 1998). The most important findings were: the columnar method empowers students' motivational and cognitive skills enhancing their vision and system design skills; inference trees improve detection and correction of reasoning errors overcoming students' limited information processing capacity.