Advertisement

All Users Are (Not) Equal - The Influence of User Characteristics on Perceived Quality, Modality Choice and Performance

  • Ina Wechsung
  • Matthias Schulz
  • Klaus-Peter Engelbrecht
  • Julia Niemann
  • Sebastian Möller
Conference paper

Abstract

This study investigated if cognitive skills, mood, attitudes and personality traits influence quality perceptions, modality choice (speech vs. touch), and performance. It was shown that attitudes and mood are related to quality perceptions while performance is linked to personality traits. Modality choice is influenced by attitudes and personality. Cognitive abilities had no effect.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wolters M, Engelbrecht K-P, Gdde F, Möller S, Naumann A, Schleicher R (2010) Making it easier for older people to talk to smart homes: Using help prompts to shape users’ speech. Universal Access in the Information Society, 9(4):311–325CrossRefGoogle Scholar
  2. 2.
    Chalmers PA (2003) The role of cognitive theory in human-computer interface. Computers in Human Behavior 19(5):593–607CrossRefGoogle Scholar
  3. 3.
    Carmichael A (1999) Style guide for the design of interactive television services for elderly viewers. Independent Television Commission, 1: 1–100Google Scholar
  4. 4.
    Wechsung I, Engelbrecht K-P, Naumann A, Schaffer S, Seebode J, Metze F, Möller S (2009) Predicting the quality of multimodal systems based on judgments of single modalities. In Proc. Interspeech 2009, Brighton, pp 1827–1830Google Scholar
  5. 5.
    Wechsung I, Naumann A (2009) Evaluating a multimodal remote control: The interplay between user experience and usability. In Proc. IEEE QOMEX 2009, San Diego, pp 19–22Google Scholar
  6. 6.
    Naumann A, Wechsung I, Hurtienne J (2010) Multimodal interaction: A suitable strategy for including older users? Interacing with Computers, 2010.Google Scholar
  7. 7.
    Hornbaek K, Law EL (2007) Meta-analysis of correlations among usability measures, In Proc. CHI 2007, pp 617–626Google Scholar
  8. 8.
    Oviatt SL, Coulston R, Lunsford R (2004) When do we interact multimodally? Cognitive load and multimodal communication patterns. In Proc. ICMI 2004, pp. 129–136Google Scholar
  9. 9.
    Wechsung I, Engelbrecht K-P, Naumann A,Möller S, Schaffer S, Schleicher R (2010) Investigating modality selection strategies, In: Proc. of IEEE workshop on spoken language technology (SLT 2010), pp 31–36Google Scholar
  10. 10.
    Chen C, Czerwinski M, Macredie R (2000) Individual differences in virtual environments, Journ of the American Society for Inform. Science, 51(6):499–507Google Scholar
  11. 11.
    Dillon A,Watson C (1996) User analysis in HCI -the historical lessons from individual differences research. International Journal of Human-Computer Studies 45(6) 619–637CrossRefGoogle Scholar
  12. 12.
    Wolters M, Georgila K, Moore JD, Logie RH, MacPherson SE, Watson M (2009) Reducing working memory load in spoken dialogue systems. Interacing. with Computers, 21(4):276–287CrossRefGoogle Scholar
  13. 13.
    Doolittle PE, Terry KP, Mariano GJ (2009) The effects of working memory capacity on learning and performance in multimedia learning environments. In R. Zheng (Ed.), Cognitive effects of multimedia learning, pp 17–33Google Scholar
  14. 14.
    Jawahar IM, Elango B (2001) The effect of attitudes, goal setting and selfefficacy on end user performance. Journal of End User Computing, 13(2):40–45CrossRefGoogle Scholar
  15. 15.
    Burnett GE, Ditsikas D (2006) Personality as a criterion for selecting usability testing participants. In In proceedings of IEEE 4th International conference on Information and Communications TechnologiesGoogle Scholar
  16. 16.
    Matilla M, Karjaluoto H, Pento T (2003) Internet banking adoption among mature customers: early majority or laggards? Journal of Services Marketing, 17(5): 514528Google Scholar
  17. 17.
    Angel A, Hartmann J, Sutcliffe A (2009) The effect of brand on the evaluation of websites. In Proc. INTERACT 09, pp. 638–651Google Scholar
  18. 18.
    Bless H, Schwarz N, Clore GL, Golisano V, Rabe C, WlkM(1996) Mood and the use of scripts: Does being in a happy mood really lead to mindlessness? Journal of Personality and Social Psychology, 71: 665679Google Scholar
  19. 19.
    Kahneman D (1999) Objective happiness. In: Kahneman D, Diener E, Schwarz N (Eds.) Well-being: Foundations of hedonic psychology, Russell Sage Foundation, New York, pp 3–25Google Scholar
  20. 20.
    Schupp J, Gerlitz J-Y (2008) BFI-S: Big Five Inventory-SOEP. Zusammenstellung sozialwissenschaftlicher Items und Skalen [collection of socioscientific items and scales]. GESIS, BonnGoogle Scholar
  21. 21.
    Borkenau P, Ostendorf F (1994) Das NEO Fnf-Faktoren-Inventar (NEO-FFI): Handanweisung [The NEO Five-Factor-Inventory: Manual]. Hogrefe, Gttingen.Google Scholar
  22. 22.
    Karrer K,Glaser C, Clemens C, Bruder C (2009) Technikaffinitt erfassen der Fragebogen TA-EG [Assessing technical affinity - the questionnaire TA-EG]. In Lichtenstein A, Stel C, Clemens C (Eds) Der Mensch als Mittelpunkt technischer Systeme. 8. Berliner Werkstatt Mensch-Maschine-Systeme, VDI Verlag, Dsseldorf, pp 196–201Google Scholar
  23. 23.
    Andrews FM, Withey SB, (1976). Social indicators of well-being.Plenum, New York.Google Scholar
  24. 24.
    Hassenzahl M, Monk A (2010) The inference of perceived usability from beauty. Human-Computer Interaction, 25(3):235–260CrossRefGoogle Scholar
  25. 25.
    Hassenzahl,M(2004) The thing and I: understanding the relationship between user and product. In: Blythe MA, Overbeeke K, Monk AF, and Wright PC (Eds.) Funology: From Usability To Enjoyment, Kluwer Academic, Norwell, pp 31–42Google Scholar
  26. 26.
    Eilers K, Nachreiner F, Ha¨necke K (1986) Entwicklung und U¨ berpru¨fung einer Skala zur Erfassung subjektiv erlebter Antrengung [Development and evaluation of a scale to assess subjectively perceived effort]. Zeitschrift f¨ır Arbeitswissenschaft, 40: 215224Google Scholar
  27. 27.
    De Waard D (1996) The measurement of drivers’ mental workload. PhD thesis, University of Groningen.Google Scholar
  28. 28.
    Tewes U (1991) HAWIE-R. Hamburg-Wechsler-Intelligenztest für Erwachsene [Hamburg-Wechsler-intelligence test for adults, Huber, BernGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Ina Wechsung
    • 1
  • Matthias Schulz
    • 1
  • Klaus-Peter Engelbrecht
    • 1
  • Julia Niemann
    • 1
  • Sebastian Möller
    • 1
  1. 1.Quality and Usability Lab, Deutsche Telekom Laboratories, TU BerlinBerlinGermany

Personalised recommendations