Search (3 results, page 1 of 1)

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Abstract

This representative study of German search engine users (N?=?1,000) focuses on the ability of users to distinguish between organic results and advertisements on Google results pages. We combine questions about Google's business with task-based studies in which users were asked to distinguish between ads and organic results in screenshots of results pages. We find that only a small percentage of users can reliably distinguish between ads and organic results, and that user knowledge of Google's business model is very limited. We conclude that ads are insufficiently labelled as such, and that many users may click on ads assuming that they are selecting organic results.

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Abstract

In an online experiment using a representative sample of the German online population (n = 1.000), we compare users' selection behaviour on two versions of the same Google search engine results page (SERP), one showing advertisements and organic results, the other showing organic results only. Selection behaviour is analyzed in relation to users' knowledge on Google's business model, on SERP design, and on these users' actual performance in marking advertisements on SERPs correctly. We find that users who were not able to mark ads correctly selected ads significantly more often. This leads to the conclusion that ads need to be labeled more clearly, and that there is a need for more information literacy in search engine users.

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Abstract

Sprachsteuerungssysteme, die den Nutzer auf Zuruf unterstützen, werden im Zuge der Verbreitung von Smartphones und Lautsprechersystemen wie Amazon Echo oder Google Home zunehmend populär. Eine der zentralen Anwendungen dabei stellt die Suche in Websuchmaschinen dar. Wie aber funktioniert "googlen", wenn der Nutzer seine Suchanfrage nicht schreibt, sondern spricht? Dieser Frage ist ein Projektteam der HAW Hamburg nachgegangen und hat im Auftrag der Deutschen Telekom untersucht, wie effektiv, effizient und zufriedenstellend Google Now, Apple Siri, Microsoft Cortana sowie das Amazon Fire OS arbeiten. Ermittelt wurden Stärken und Schwächen der Systeme sowie Erfolgskriterien für eine hohe Gebrauchstauglichkeit. Diese Erkenntnisse mündeten in dem Prototyp einer optimalen Voice Web Search.