Gerald L. Lohse

Predictors of online buying behavior

Consumers worldwide can shop online 24 hours a day, seven days a week, 365 days a year. Some market sectors, including insurance, financial services, computer hardware and software, travel, books, music, video, flowers, and automobiles, are experiencing rapid growth in online sales. For example, in Jan. 1999, Dell Computer Corp. was selling an average of

Electronic shopping

14 million of equipment online per day, and Amazon.com has become the third largest bookseller in the U.S., despite being in business only since 1995. With projections that the Internet will generate consumer and business-to-business sales in excess of

Consumer buying behavior on the Internet: Findings from panel data

294 billion by 2002, online retailing raises many questions about how to market on the Net.

Cognitive Lock-In and the Power Law of Practice

he global electronic market will have a profound impact on commerce in the 21st Century. While current U.S. sales in cyberspace (

Internet retail store design: How the user interface influences traffic and sales

1 billion in 1995) are small in comparison to total U.S. retail sales (

Consumer Eye Movement Patterns on Yellow Pages Advertising

1.7 trillion in 1995) [3], U.S. cybersales projections for the year 2000 range from

A classification of internet retail stores

7 to

A cognitive model for understanding graphical perception

117 billion [9]. More importantly, most experts predict a radical shift in how business will be conducted in the next century. This shift not only has businesses scrambling to meet this new marketing reality, but also raises many important research questions about business strategy, technical infrastructure, government policies, the electronic market demographics as well as how people will use the technology. Issues of technology usage become critical as businesses and retailers attempt to exploit the boom in electronic marketing. There are large differences between a physical store and its electronic counterpart. A help button on the home page of the Web shopping site replaces the sales clerk’s friendly advice and service. The familiar layout of the physical store becomes a maze of pull-down menus, product indices, and search features. Now more than ever, the promise of electronic commerce and online shopping will depend to a great extent upon the interface and how people interact with the computer. Account managers, production staff, and merchant partners should not assume customers do not want an item in an online retail store if it is not selling. Nor should they conclude that a poor response to a given store design is due to the merchandising mix. It is important to look at the relationship between sales and user interface design. Limited menus, poorly designed navigation, and the difficulty in comparing multiple products on the same screen all have adverse effects on electronic shopping [2]. Can customers find what they want in the stores? Are customers aware of what products are available? After all, diligence in browsing a store is not a virtue retailers should expect from its online customers. We review online retail store attributes such as the number of links into the store, image sizes, number of products, and store navigation features. By reviewing the user interface features in the context of electronic shopping, we hope to facilitate the process of designing and evaluating alternative storefronts by identifying key features that impact traffic and sales. While this article specifically addresses user interface design issues for electronic shopping, user interface design is an important component of all software development [10] with important implications for productivity [5]. Unfortunately, Web information systems (WIS) designers sometimes do not heed the importance of the message advocated by user interface design pundits such as Jakob Nielsen [11] and Vincent Flanders (www.webpagesthatsuck.com). Many of the lessons learned from online stores also apply to WIS applications.

Defaults, Framing and Privacy: Why Opting In-Opting Out

Presents the findings from a panel data on consumer buying behavior on the Internet. Advantages and disadvantages of panel data for survey research; Demographics of online consumers; Dollar amount that consumers are spending online; Total online spending projections in the United States.

Towards a texture naming system: Identifying relevant dimensions of texture

The authors suggest that learning is an important factor in electronic environments and that efficiency resulting from learning can be modeled with the power law of practice. They show that most Web sites can be characterized by decreasing visit times and that generally those sites with the fastest learning curves show the highest rates of purchasing.