Advertisement

The Effect Of Structure On Convergence Activities Using Group Support Systems

  • Doug Vogel
  • John Coombes
Chapter
Part of the Advances in Group Decision and Negotiation book series (AGDN, volume 4)

Abstract

Facilitating group decision and negotiation is a broad topic and has received considerable attention (see chapters by Ackermann and Eden, and Richardson and Andersen, this volume). As research on Group Support Systems (GSS) has evolved over the past decades, a good deal of useful knowledge on group processes, idea generation and decision making has been found (see the chapter by Lewis, this volume).Particular attention has been given to aspects of decision making structure (for example, see chapters by Salo and Hamalainen, Hujala and Kurttila, this volume). See the chapter by Kolfshoten et al., this volume: they consider a number of aspects related to collaboration engineering.However, there are still areas that require in-depth research to remove uncertainties and move computer-mediated group process research forward into the second decade of the 21st century.For example, the ability to converge on the most worthy ideas to translate into knowledge has been generally neglected in this research literature.This is regrettable given its group process importance, especially given the volume of ideas generated by GSS.In this chapter, our focus is on the effects of structure on convergence activities using GSS.Experience shows that convergence dynamics are affected by facilitation (e.g., protocol) as well as specialized technological support (e.g., a “dashboard”).This chapter gives a brief overview of the research and presents an empirical study to illustrate the sort of issues that remain (and the possible solutions) plus directions for future study.

Keywords

Cognitive Load Knowledge Creation Time Structure Information Overload Human Information Processing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Adamson RE, Taylor DW (1954) Functional fixedness as related to elapsed time and to set. J Exp Psychol 47:122–126CrossRefGoogle Scholar
  2. Alavi MA, Leidner DE (2001) Review: knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Q 25(1):107–136CrossRefGoogle Scholar
  3. Allen RB (1983) Cognitive factors in the use of menus and trees: an experiment. IEEE J Selected Areas Commun 1:333–336CrossRefGoogle Scholar
  4. Chapanis A (1972) Studies in interactive communication: the effects of four communication modes on the behaviour of teams during cooperative problem solving. Hum Factors 14:487–509Google Scholar
  5. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46CrossRefGoogle Scholar
  6. Cook GJ (1993) An empirical investigation of information search strategies with implications for decision support system design. Decis Sci 24(3):683–697CrossRefGoogle Scholar
  7. Csíkszentmihályi M (1996) Creativity: flow and the psychology of discovery and invention. Harper Perennial, New York, NYGoogle Scholar
  8. Davenport T, Prusak L (1998) Working knowledge. Harvard Business School Press, Boston, MAGoogle Scholar
  9. Davis GB, Olson MH (1985) Management information systems: conceptual foundations, structure and development, 2nd edn. McGraw-Hill Series, New York, NYGoogle Scholar
  10. Deci L, Ryan RM (1985) Intrinsic motivation and self-determination in human behaviour. Plenum, New York, NYCrossRefGoogle Scholar
  11. Dennis A, George J, Jessup L, Nunamaker J, Vogel D (1988) Information technology to support electronic meetings. MIS Q 12(4):591–624CrossRefGoogle Scholar
  12. Dennis AR, Aronson JE, Heminger WG, Walker ED (1999) Structuring time and task in electronic brainstorming. MIS Q 23(1):95–108CrossRefGoogle Scholar
  13. DeSanctis G, Poole MS (1991) Understanding the differences in collaborative-systems through appropriation analysis. In: Proceedings of the 24th annual Hawaii international conference on system sciences, Hawaii, 3, pp 547–553Google Scholar
  14. Easton AC, Vogel DR, Nunamaker JF (1989) Stakeholder identification and assumption surfacing in small groups: an experimental study. In: Nunamaker JF (ed) Proceedings of the 22nd Hawaii international conference on system sciences, vol 3. IEEE Computer Society Press, Los Alamitos, CA, pp 344–352Google Scholar
  15. Eggemeier FT, Crabtree MS, LaPoint PA (1983) The effect of delayed report on subjective ratings of mental workload. In: Proceedings of the human factors society 27th annual meeting, Norfolk, VA, pp 139–143Google Scholar
  16. Fjermestad J (1998) In GSS research how many groups per treatment condition are enough? Am Conf Inf Syst 17(3): 115–159Google Scholar
  17. Gallupe RB, DeSanctis G, Dickson GW (1988) Computer-based support for group problem finding: an experimental investigation. MIS Q 12(2):277–296CrossRefGoogle Scholar
  18. Gilhooly KJ (1999) Creative thinking: myths and misconceptions. In: Sala SD (ed) Mind myths: exploring popular assumptions about the mind and brain. Wiley, New York, NY, pp 138–155Google Scholar
  19. Gopher D, Braune R (1984) On the psychophysics of workload: why bother with subjective measures? Hum Factors 26: 519–532Google Scholar
  20. Gray P (1983) Initial observations from the decision room project. In: Proceedings of the 3rd international conference on decision support system. Boston, Mass, pp 135–138Google Scholar
  21. Grise ML, Gallupe RB (2000) Information overload: addressing the productivity paradox in face-to-face electronic meetings. J Manage Inf Syst 16(3):157–185Google Scholar
  22. Grohowski R, McGoff C, Vogel D, Martz B, Nunamaker J (1990) Implementing electronic meeting systems at IBM: lessons learned and success factors. MIS Q 14(4):368–383CrossRefGoogle Scholar
  23. Gruber M, Davis GB (1988) Inching our way up Mount Olympus: the evolving systems approach to creative thinking. In: Sternberg RJ (ed) The nature of creativity. Contemporary psychological perspectives. Cambridge University Press, Cambridge, UK, pp 243–270Google Scholar
  24. Guilford JP (1984) Varieties of divergent production. J Creat Behav 18:1–10CrossRefGoogle Scholar
  25. Huff AS (1990) Mapping strategic thought. Wiley, New York, NYGoogle Scholar
  26. Ivanov A, Cyr D (2006) The concept plot: a concept mapping visualization tool for web-based asynchronous brainstorming sessions. Inf Vis 5(3):185–191CrossRefGoogle Scholar
  27. Jacko JA, Salvendy G (1996) Hierarchical menu design: breadth, depth and task complexity. Percept Mot Skills 82:1187–1201CrossRefGoogle Scholar
  28. Jessup LM, Connolly T, Galegher J (1990) The effects of anonymity on GDSS group process with an idea-generating task. MIS Q 14(3):312–321CrossRefGoogle Scholar
  29. Lamm H, Trommsdorff G (1973) Group versus individual performance on tasks requiring ideational proficiency (brainstorming): a review. Eur J Soc Psychol 3(4):361–387CrossRefGoogle Scholar
  30. Lim LH, Raman KS, Wei KK (1994) Interacting effects of GDSS and leadership. Source Decis Support Syst Arch 12(3):199–211CrossRefGoogle Scholar
  31. Kelly JR, Karau SJ (1993) Entrainment of creativity in small groups. Small Group Res 24(2):179–198CrossRefGoogle Scholar
  32. Kiger JL (1984) The depth/breadth trade off in the design of menu-driven user interface. Int J Man Mach Stud 20: 201–213CrossRefGoogle Scholar
  33. Kirton MJ (ed) (1989) Adaptors and innovators: styles of creativity and problem solving. Routledge, LondonGoogle Scholar
  34. Kreuger GP, Chapanis A (1976) Conferencing and teleconferencing in three communication modes as a function of the number of conferees. Ergonomics 23(2):103–122CrossRefGoogle Scholar
  35. Kwok R, Ma J, Vogel D (2002) Assessing GSS and facilitation effect on knowledge acquisition. J MIS 19(3):185–229Google Scholar
  36. Landauer TK, Nachbar DW (1985) Selection from alphabetic and numeric menu trees using a touch screen: breadth, depth, and width menu systems. In: Proceedings of ACM CHI’85 conference on human factors in computing systems. San Francisco, California, pp 73–78Google Scholar
  37. Locke EA, Latham GP (1990) A theory of goal setting and task performance. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  38. McGrath JE (1991) Time interaction and performance (TIP) a theory of groups. Small Group Res 22(2):147–174CrossRefGoogle Scholar
  39. McGrath JE, Hollingshead AB (1994) Groups interacting with technology. Sage, Thousand Oaks, CAGoogle Scholar
  40. Miller J (1978) Living systems. Wiley, New York, NYGoogle Scholar
  41. Newell A, Simon H (1972) Human problem solving. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  42. Nonaka I (1988) Toward middle-up-down management: accelerating information creation. Sloan Manage Rev 29(3):9–18Google Scholar
  43. Nonaka I (1994) A dynamic theory of organizational knowledge creation. Organ Sci 5(1):14–37CrossRefGoogle Scholar
  44. Nunamaker J, Dennis A, Valacich J, Vogel D, George J (1991) Electronic meeting systems to support group work. Commun ACM 34(7):40–61CrossRefGoogle Scholar
  45. Nunamaker JF, Briggs RO, Romano NC, Mittleman DD (1997a) The virtual office work-space: group systems web and case studies. In: Coleman D (ed) Groupware: collaborative strategies for corporate LANs and intranets. Prentice-Hall, New York, NY, pp 231–253Google Scholar
  46. Nunamaker JF, Briggs RO, Mittleman DD, Vogel DR, Balthazard PA (1997b) Lessons from a dozen years of group support systems research: a discussion of lab and field findings. J Manage Inf Syst 13(3):163–207Google Scholar
  47. Orlafi R, Harkey D, Edwards J (1996) The essential distributed objects survival guide, John Wiley and Sons Inc, New YorkGoogle Scholar
  48. Paas F (1992) Training strategies for attaining transfer of problem-solving skill in statistics: a cognitive-load approach. J Educ Psychol 84:429–434CrossRefGoogle Scholar
  49. Paas F, Merrienboer van JJG (1994) Variability of worked examples and transfer of geometrical problem-solving skills: a cognitive-load approach. J Educ Psychol 86(1):122–133CrossRefGoogle Scholar
  50. Salas E (1991) Productivity loss in brainstorming groups: a meta-analytic integration. Basic Appl Soc Psychol 12(1): 3–23CrossRefGoogle Scholar
  51. Smith CAP, Hayne SC (1997) Decision making under time pressure: an investigation of decision speed and decision quality of computer-supported groups management. Commun Q 11(1):97–126Google Scholar
  52. Stahl G (2006) Group cognition: computer support for building collaborative knowledge. MIT, Cambridge, MAGoogle Scholar
  53. Watson R, DeSanctis G, Poole MS (1988) Using a GDSS to facilitate group consensus: Some intended and unintended consequences. MIS Q 12(3):436–478CrossRefGoogle Scholar
  54. Williams E (1977) Experimental comparisons of face to face and mediated communications: a review. Psychol Bull 84: 963–967CrossRefGoogle Scholar
  55. Zigurs I, Buckland BK (1998) A theory of task/technology fit and group support systems effectiveness. MIS Q 22(3): 313–334CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.City University of Hong KongHong KongPeople’s Republic of China

Personalised recommendations