Modulators of choice overload in Google search engine

Nowadays, analyzing user behavior on search engines is highly regarded. Lots of options in retrieved results in the search engines makes users to choose. Once the selection is extremely complex, people will think the simplest decision rule: not to decide and select. Psychologists know this situation the consequence of “choice overload”. This study has been done with the goal to identify modulators of Google search engine. The present study is a survey and an applied research. Using stratified random non-relativistic sampling, 72 MS and PhD students of faculty of Psychology and Educational Sciences and Engineering of Ferdowsi University were selected as a sample. The tools used for collecting data were standard questionnaire and HCI browser. The results showed that regardless of the personality types (maximizer and satisfier) and only by regarding the type of search, you cannot say a definitive statement about choice overload theory. Considering two variables of personality and search type, a significant difference between satisfactions of two types personality from the number of retrieved results were observed. This means that maximizers are, in both special and general search, more satisfied from small number of results than large number of results (choice overload). But in special search, the number of retrieved results had no effect on the satisfaction of satisfiers (the nonoccurrence of choice overload). Also in satisfiers, in general search results, there was a significant difference between satisfaction from the small and large number of retrieved results. However, according to their personality and serendipity theory, with increasing the retrieved results, not only choice overload did not occur, but satisfaction of them was more. Hence we can understand the importance of two factors: the type of search and personality as Modulators of Choice Overload in Google Search Engine Fahimeh Mansouri PhD Candidate in Knowledge and Information Science; Ferdowsi University of Mashhad mansoori_fahimeh@yahoo.com Mohsen Nowkarizi PhD in Knowledge and Information Science; Associate Professor; Ferdowsi University of Mashhad Corresponding Author mnowkarizi@um.ac.ir Rahmatollah Fattahi PhD in Knowledge and Information Science; Professor; Ferdowsi University of Mashhad fattahirahmat@gmail.com Iranian Research Institute for Science and Technology

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