Do online shops support customers' decision strategies by interactive information management tools? Results of an empirical analysis.

Thomas Groissberger, René Riedl

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Online shops provide a multitude of products with different attributes to their customers. This enormous information supply often leads to information overload, which may negatively affect customer satisfaction and conversion rates. Therefore, interactive decision aids, particularly interactive information management tools, can help customers to control the information supply by filtering, sorting, or comparing product attributes. Moreover, interactive information management tools support different decision strategies that customers apply in online shopping decision processes. For example, a customer applying the elimination-by-aspects strategy eliminates products that do not meet the cutoff value for the most important attribute. Customers repeat this elimination process for the second most important attribute, the third most important attribute and so on, and processing continues until a single product remains. An online shop with filtering tools supports application of this strategy. This study describes a set of well-known decision strategies, and examines whether online shops provide interactive information management tools that support the application of these decision strategies. We examined the 100 largest online shops in North America in order to analyze their provided interactive information management tools. Results show that the online shops support decision strategies which are frequently used by customers (e.g., elimination-by-aspects strategy). In general, the supported decision strategies are qualitative and noncompensatory in nature. That is, customers applying such strategies compare values in their decision process, but do not involve summing, subtracting, and/or multiplying attribute values, and customers do also not make trade-offs among attribute values. Hence, compensating for a bad value on one attribute with a good value on another attribute is uncommon. We discuss implications of our results for online shop companies and developers of online shop systems.

Original languageEnglish
Pages (from-to)131-151
Number of pages21
JournalElectronic Commerce Research and Applications
Issue number6
Publication statusPublished - Nov 2017


  • Decision making in online shops
  • Decision strategies
  • In-depth comparison phase
  • Interactive decision aids
  • Interactive information management tools
  • Product comparison matrices
  • Screening phase


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