Skip to main content
SpringerLink
Log in

The Effect of Task Allocation Strategy on Knowledge Intensive Team Performance Based on Computational Experiment

  • Conference paper
  • First Online:
Knowledge and Systems Sciences (KSS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 780))

Included in the following conference series:

  • 827 Accesses

Abstract

The purpose of this study is to research the task allocation problem of the knowledge intensive team (abbreviated as KIT), which is different from the traditional task assignment. We built a KIT system model, designed task allocation strategies and team performance measurement scale, based on complex adaptive system (abbreviated as CAS) theory with regarding the knowledge requirement of tasks as a primer mover, additionally, took into consideration that knowledge exchange behaviors and processes would be contingent when different team members deal with different tasks. The computational experimental method was used to analyze how different allocation strategies impact KIT performance. The experimental results show that different allocation strategies variously influence KIT performance when the team members, team structures, and tasks to be assigned are different. We would be appreciated to help the decision maker, before the real tasks are executed, to apply the computational experiment method proposed in this paper to carry out the task allocation to provide with decision support.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Yu, Y., Hao, J.X., Dong, X.Y., et al.: A multilevel model for effects of social capital and knowledge sharing in knowledge-intensive work teams. Int. J. Inf. Manag. 33(5), 780–790 (2013)

    Article  Google Scholar 

  2. Chung, Y., Jackson, S.E.: The internal and external networks of knowledge-intensive teams the role of task routineness. J. Manag. 39(2), 442–468 (2013)

    Google Scholar 

  3. Faraj, S., Yan, A.: Boundary work in knowledge teams. J. Appl. Psychol. 94(3), 604 (2009)

    Article  Google Scholar 

  4. Wildman, J.L., Thayer, A.L., Pavlas, D., et al.: Team knowledge research emerging trends and critical needs. Hum. Factors: J. Hum. Factors Ergon. Soc. 54(1), 84–111 (2012)

    Article  Google Scholar 

  5. Sun, R., Chen, G.Q.: Knowledge work, knowledge team, knowledge workers and their effective management tactics-enlightenment from drucker. Sci. Sci. Manag. S. T. 31(2), 189–195 (2010)

    Google Scholar 

  6. Ma, A.M.J.: The tao of complex adaptive systems (CAS). Chin. Manag. Stud. 5(1), 94–110 (2011)

    Article  Google Scholar 

  7. Mittal, S.: Emergence in stigmergic and complex adaptive systems: a formal discrete event systems perspective. Cogn. Syst. Res. 21(1), 22–39 (2013)

    Article  Google Scholar 

  8. Polacek, G.A., Gianetto, D.A., Khashanah, K., et al.: On principles and rules in complex adaptive systems: a financial system case study. Syst. Eng. 15(4), 433–447 (2012)

    Article  Google Scholar 

  9. Argote, L., Ingram, P., Levine, J.M., et al.: Knowledge transfer in organizations: learning from the experience of others. Organ. Behav. Hum. Decis. Process. 82(1), 1–8 (2000)

    Article  Google Scholar 

  10. Levine, S.S., Prietula, M.J.: How knowledge transfer impacts performance: a multilevel model of benefits and liabilities. Soc. Sci. Electron. Publishing 23(23), 1748–1766 (2012)

    Google Scholar 

  11. Weerdt, M.M., Zhang, Y., Klos, T.: Multiagent Task Allocation in Social Networks. Kluwer Academic Publishers, Netherlands (2012)

    Google Scholar 

  12. Singh, A.J., Dalapati, P., Dutta, A.: Multi agent based dynamic task allocation. In: Jezic, G., Kusek, M., Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) Agent and Multi-agent Systems: Technologies and Applications. AISC, vol. 296, pp. 171–182. Springer, Cham (2014). doi:10.1007/978-3-319-07650-8_18

    Chapter  Google Scholar 

  13. Wu, S.B., Liu, M.T.: Assignment of tasks and resources for distributed processing. In: Proceedings of Distributed Computing, Twenty-First IEEE Computer Society International Conference, COMPCON 1980, pp. 655–662 (1980)

    Google Scholar 

  14. Garey, M.R., Graham, R.L., Johnson, D.S.: Performance guarantees for scheduling algorithms. Oper. Res. 26(1), 3–21 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  15. Sagar, G., Sarje, A.K., Ahmed, K.U.: Task allocation techniques for distributed computing systems - a review. J. Microcomput. Appl. 12(2), 97–105 (1989)

    Article  Google Scholar 

  16. Kim, Y.C., Hong, Y.S.: A task allocation using a genetic algorithm in multicomputer systems. In: Computer, Communication, Control and Power Engineering Proceedings, TENCON 1993 (1993)

    Google Scholar 

  17. Protzel, P.W.: Artificial neural network for real-time task allocation in fault-tolerant, distributed-processing system. In: Parallel Processing in Neural Systems and Computers. North-Holland (1990)

    Google Scholar 

  18. Andersson, M.R., Sandholm, T.W., Andersson, M.R., et al.: Contract types for optimal task allocation: II experimental results. In: Aaai Spring Symposium (1997)

    Google Scholar 

  19. Malville, E., Bourdon, F.: Task allocation: a group self-design approach. In: International Conference on Multi agent Systems. IEEE Computer Society (1998)

    Google Scholar 

  20. Wang, X., Yang, J.: Self-adaptive agent model for task allocation in a manufacturing system. Comput. Integr. Manuf. Syst. 7(8), 17–58 (2001)

    Google Scholar 

  21. Ma, Q.Y.: Research on dynamic task allocation based on MAS. Huazhong University of Science and Technology (2006)

    Google Scholar 

  22. Zhang, J., Li, X.W.: Artificial societies—agent based social simulation. Syst. Eng. 23(1), 13–20 (2005)

    Google Scholar 

  23. Sheng, Z.H., Zhang, W.: Computational experiments in management science and research. J. Manag. Sci. China 14(5), 1–10 (2011)

    Google Scholar 

  24. Sheng, Z.H.: Case Studies of Computational Experiment in Social Science. Shanghai Sanlian Bookstore, Shanghai (2009)

    Google Scholar 

  25. Robbins, S.P.: Organizational Behavior. People’s University Publication House, Beijing (2005)

    Google Scholar 

  26. Gainforth, H.L., Latimercheung, A.E., Athanasopoulos, P., et al.: The role of interpersonal communication in the process of knowledge mobilization within a community-based organization: a network analysis. Implementation Sci. 9(1), 1–8 (2014)

    Article  Google Scholar 

  27. Caimo, A., Lomi, A.: Knowledge sharing in organizations: a bayesian analysis of the role of reciprocity and formal structure. J. Manag. 41(2), 665–691 (2014)

    Google Scholar 

Download references

Acknowledgement

This work is partly supported by the National Natural Science Foundation of China under Grant No. 71471028.

Author information

Authors and Affiliations

  1. Institute of System Engineering, Dalian University of Technology, Dalian, 116024, China

    Shaoni Wang, Yanzhong Dang & Jiangning Wu

Authors
  1. Shaoni Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanzhong Dang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiangning Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaoni Wang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jian Chen

  2. Thammasat University, Bangkadi Muang Pathumthani, Thailand

    Thanaruk Theeramunkong

  3. National Electronics and Computer Technology Center, Pathumthani, Thailand

    Thepachai Supnithi

  4. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Xijin Tang

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Wang, S., Dang, Y., Wu, J. (2017). The Effect of Task Allocation Strategy on Knowledge Intensive Team Performance Based on Computational Experiment. In: Chen, J., Theeramunkong, T., Supnithi, T., Tang, X. (eds) Knowledge and Systems Sciences. KSS 2017. Communications in Computer and Information Science, vol 780. Springer, Singapore. https://doi.org/10.1007/978-981-10-6989-5_19

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6989-5_19

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6988-8

  • Online ISBN: 978-981-10-6989-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation