Treffer: Experimental evaluation of five methods for collecting emotions in field settings with mobile applications

Title:
Experimental evaluation of five methods for collecting emotions in field settings with mobile applications
Source:
Evaluating affective interactionsInternational journal of human-computer studies. 65(4):404-418
Publisher Information:
London: Elsevier, 2007.
Publication Year:
2007
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
University of Oulu, P.O. Box 3000, University of Ouh, 90014, Finland
ISSN:
1071-5819
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.18551656
Database:
PASCAL Archive

Weitere Informationen

This paper presents experiences on using five different self-report methods, two adopted from literature and three self-created, for collecting information about emotional responses to mobile applications. These methods were used in nine separate field experiments done in naturalistic settings. Based on our experiments, we can argue that all of these methods can be successfully used for collecting emotional responses to evaluate mobile applications in mobile settings. However, differences can be identified in the suitability of the methods for different research setups. Even though the self-report instruments provide a feasible alternative for evaluating emotions evoked by mobile applications, several challenges were identified, for example, in capturing the dynamic nature of mobile interaction usage situations and contexts. To summarise our results, we propose a framework for selecting and comparing these methods for different usage purposes.