User-Centered Service Design for Multi-language Knowledge Communication

Chapter

Abstract

With the rapid expansion of globalization, knowledge communication becomes more and more important among people from different nations. Information and communication technology (ICT) is expected to play an essential role in such kind of multi-language knowledge communication activities. However, the major problem of supporting multi-language knowledge communication using ICT is the variety of user requirements considering different types of communication fields, languages, and stakeholders. Therefore, the design process should be user-centered. Moreover, combining human activities and machine-supported services is becoming an important issue in such types of service design, which makes it necessary to test the environments for human-computer interaction and study human behaviors. In this chapter, we address the above issues by proposing a user-centered service design approach for multi-language knowledge communication. To achieve this goal, we first use a motivating example of multi-language knowledge communication between Japanese agricultural experts and Vietnamese illiterate farmers to illustrate our problem. Then, we propose the user-centered service design process. Finally, we use a field study proposed in the motivating example to show the effectiveness of our proposed design approach.

Keywords

Multi-language knowledge communication Quality of service Service design methodology User-centered design 

References

  1. 1.
    Eppler M (2007) Knowledge communication problems between experts and decision makers: an overview and classification. Electron J Knowl Manag 5(3):291–300Google Scholar
  2. 2.
    Bischof N, Eppler M (2011) Caring for clarity in knowledge communication. J Univers Comput Sci 17(10):1455–1473Google Scholar
  3. 3.
    Aiken M, Martin J, Shirani A, Singleton T (1994) A group decision support system for multicultural and multilingual communication. Decis Support Syst 12(2):93–96CrossRefGoogle Scholar
  4. 4.
    Ishida T (2006) Language grid: an infrastructure for intercultural collaboration. In: IEEE/IPSJ symposium on applications and the internet (SAINT-06), IEEE Computer Society, Phoenix, AZ, USA, pp 96–100Google Scholar
  5. 5.
    Ishida T (ed) (2011) The language grid: service-oriented collective intelligence for language resource interoperability. Springer, Berlin Heidelberg. ISBN 978-3-642-21177-5Google Scholar
  6. 6.
    Zeng L, Benatallah B, Ngu A, Dumas M, Kalagnanam J, Chang H (2004) QoS-aware middleware for web services composition. IEEE Trans Softw Eng 30(5):311–327CrossRefGoogle Scholar
  7. 7.
    Kita K, Takasaki T, Lin D, Nakajima Y, Ishida T (2012) Case study on analyzing multi-language knowledge communication. In: Proceedings of the international conference on culture and computing (culture and computing 2012), Hangzhou, ChinaGoogle Scholar
  8. 8.
    Lin D, Shi C, Ishida T (2012) Dynamic service selection based on context-aware QoS. In: IEEE international conference on services computing (IEEE SCC 2012), IEEE Computer Society 2012,Honolulu, HI, USA, pp 641–648Google Scholar
  9. 9.
    Ishida T, Nakajima Y, Murakami Y, Nakanishi H (2007) Augmented experiment: participatory design with multiagent simulation. In: Proceedings of the 20th international joint conference on artificial intelligence. Morgan Kaufmann Publishers, Hyderabad, India, pp 1341–1346Google Scholar
  10. 10.
    Ishida T (2002) Q: a scenario description language for interactive agents. IEEE Comput 35(11):42–47CrossRefMathSciNetGoogle Scholar
  11. 11.
    Lin D, Murakami Y, Ishida T, Murakami Y, Tanaka M (2010) Composing human and machine translation services: language grid for improving localization processes. In: The 7th international conference on language resources and evaluation (LREC 2010), European Language Resources Association 2010, Valletta, Malta, pp 500–506Google Scholar

Copyright information

© Springer Japan 2014

Authors and Affiliations

  1. 1.Department of Social InformaticsKyoto UniversityKyotoJapan

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