Performance Effects of Positive and Negative Affective States in a Collaborative Information Seeking Task

  • Roberto González-Ibáñez
  • Chirag Shah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8658)


Collaborative information seeking (CIS) is a common process carried out by groups in a wide variety of situations and contexts. From family activities to business tasks, people typically engage in collaborative search practices while working toward a common goal. In collaborative settings, various aspects of human behavior influence the way people interact with each other and make decisions. One of these aspects corresponds to emotions and related affective processes such as mood and feelings. Studies in social psychology have suggested that group dynamics and their performance may be affected by the interaction of affective processes, in particular positive and negative ones. Although such findings have been derived in different group situations, to the best of our knowledge none of them refer to the particular case of CIS. Based on previous studies, we investigate to what extent positive and negative affective states relate to group performance in CIS. To carry out this study, we designed an experiment with 45 dyads distributed in three configurations based on initial affective states: (1) positive-positive, (2) positive-negative, and (3) negative-negative. To achieve these initial conditions, members of each dyad were individually exposed to affective stimuli. Following, each dyad worked on a precision-oriented search task. Our results suggest that the three interactions of affective states have different implications on the performance of dyads. In particular, the negative-negative configuration performed significantly better than the other two configurations. Conversely, performance of the positive-negative condition was found to be significantly lower than the other two conditions. Findings from this work have practical implications for applications such as team design in tasks involving CIS.


collaboration information seeking affective states performance 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baeza-Yates, R., Pino, J.A.: A first step to formally evaluate collaborative work. In: ACM Group Conference, pp. 56–60. Phoenix, AR (1997)Google Scholar
  2. 2.
    Bechara, A., Damasio, A.R.: The somatic marker hypothesis: A Neural Theory of Economic Decision. Games and Economic Behavior 52, 336–372 (2005)CrossRefzbMATHGoogle Scholar
  3. 3.
    Bradley, M.M., Lang, P.J.: Measuring emotion: The Self-Assessment Manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry 25(1), 49–59 (1994)CrossRefGoogle Scholar
  4. 4.
    Clark, H.H., Brennan, S.E.: Grounding in communication. In: Resnick, L.B., Levine, R.M., Teasley, S.D. (eds.) Perspectives on Socially Shared Cognition, pp. 127–149. The American Psychological Association (1991)Google Scholar
  5. 5.
    Coan, J.A., Allen, J.J.B. (eds.): Handbook of emotion elicitation and assessment. Oxford University Press (2007)Google Scholar
  6. 6.
    Forgas, J.P.: Affective influences on partner choice: Role of mood in social decisions. Journal of Personality and Social Psychology 61, 708–720 (1991)CrossRefGoogle Scholar
  7. 7.
    Forgas, J.P.: The Affect Infusion Model (AIM): An integrative theory of mood effects on cognition and judgments. In: Martin, L.L., Clore, G.L. (eds.) Theories of Mood and Cognition: A User’s Guidebook, pp. 101–136. Lawrence Erlbaum Associates Publishers, Mahwah (2009)Google Scholar
  8. 8.
    Fox, S., Karnawat, K., Mydland, M., Dumais, S., White, T.: Evaluating implicit measures to improve Web search. ACM Transactions on Information Systems (TOIS) 23(2), 147–168 (2005)CrossRefGoogle Scholar
  9. 9.
    Fredrickson, B.L., Losada, M.F.: The positive affect and the complex dynamics of human flourishing. American Psychologist 60(7), 678–686 (2005)CrossRefGoogle Scholar
  10. 10.
    González-Ibáñez, R., Shah, C.: Group’s Affective Relevance: A proposal for studying affective relevance in collaborative information seeking. In: ACM Group Conference, Sanibel Island, FL, USA, November 6-10, pp. 317–318 (2010)Google Scholar
  11. 11.
    González-Ibáñez, R., Shah, C.: Coagmento: A system for supporting collaborative information seeking. In: ASIS&T 2011, New Orleans, LA, USA, October 9-13 (2011)Google Scholar
  12. 12.
    González-Ibáñez, R., Shah, C.: Investigating positive and negative affects in collaborative information seeking: A pilot study report. In: ASIS&T 2012, Baltimore, MD, USA, October 26-30 (2012)Google Scholar
  13. 13.
    González-Ibáñez, R., Shah, C., White, R.: Pseudo-collaboration as a method to perform selective algorithmic mediation in collaborative IR systems. In: ASIS&T 2012, Baltimore, MD, USA, October 26-30 (2012)Google Scholar
  14. 14.
    Hyldegard, J.: Collaborative information behaviour - Exploring Kuhlthau’s information search process model in a group-based educational setting. In: Information Processing and Management, vol. 42(1), pp. 276–298. Pergamon Press, Inc., Tarrytown (2006)Google Scholar
  15. 15.
    Kent, A., Berry, M.M., Luehrs Jr., F.U., Perry, W.: Operational criteria for designing information retrieval systems. American Documentation 6(2) (1955)Google Scholar
  16. 16.
    Kirkland, M.R., Saunders, M.A.P.: Maximizing student performance in summary writing: Managing cognitive load. TESOL Quarterly 25(1), 105–121 (1991)CrossRefGoogle Scholar
  17. 17.
    Kuhlthau, C.: Inside the search process: Information seeking from the user’s perspective. Journal of the American Society for Information Science 42(5), 361–371 (1991)CrossRefGoogle Scholar
  18. 18.
    Losada, M., Heaphy, E.: The role of positivity and connectivity in the performance of business teams: A nonlinear dynamics model. American Behavioral Scientist 47(6), 740–765 (2004)CrossRefGoogle Scholar
  19. 19.
    Martin, M.: On the induction of mood. Clinical Psychology Review 10, 669–697 (1990)CrossRefGoogle Scholar
  20. 20.
    Palmero, F., Guerrero, C., Gómez, C., Carpi, A.: Certezas y controversia en el estudio de la emoción. Revista electrónica de motivación y emoción (R.E.M.E) 9, 23–24 (2006)Google Scholar
  21. 21.
    Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic inquiry and word count (LIWC): LIWC2001. Erlbaum Publishers, Mahwah (2001)Google Scholar
  22. 22.
    Sinclair, R.C., Mark, M.M.: The effects of mood state on judgemental accuracy: Processing strategy as a mechanism. Cognition and Emotion 9(5), 417–438 (1995)CrossRefGoogle Scholar
  23. 23.
    Shah, C.: Collaborative information seeking: A literature review. Advances in Librarianship 32, 3–33 (2010)CrossRefGoogle Scholar
  24. 24.
    Shah, C.: Coagmento - A collaborative information seeking, synthesis and sense-making framework. Integrated demo at CSCW 2010 (2010)Google Scholar
  25. 25.
    Shah, C., González-Ibáñez, R.: Exploring information seeking processes in collaborative search tasks. In: American Society of Information Science and Technology (ASIST), Pittsburgh, PA, October 22-27 (2010)Google Scholar
  26. 26.
    Shah, C., González-Ibáñez, R.: Evaluating the synergic effect of collaboration in information seeking. In: Annual ACM Conference on Research and Development in Information Retrieval (SIGIR 2011), Beijing, China, pp. 913–922 (2011)Google Scholar
  27. 27.
    Tang, A., Pahud, M., Inkpen, K., Benko, H., Tang, J.C., Buxton, V.: Three’s company: Understanding communication channels in three-way distributed collaboration. In: Proceedings of CSCW 2010 (2010)Google Scholar
  28. 28.
    Watson, D., Clark, L.A., Tellegen, A.: Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology 54(6), 1063–1070 (1988)CrossRefGoogle Scholar
  29. 29.
    Wilson, M.L., Wilson, M.: Social anxieties and collaborative information seeking. In: Collaborative Information Seeking Workshop at GROUP 2010, Sanibel Island, FL, USA (November 7, 2010)Google Scholar
  30. 30.
    White, R.W., Huang, J.: Assessing the scenic route: Measuring the value of search trails in Web logs. In: Annual ACM Conference on Research and Development in Information Retrieval (SIGIR), Geneva, Switzerland (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Roberto González-Ibáñez
    • 1
  • Chirag Shah
    • 2
  1. 1.Departamento de Ingeniería InformáticaUniversidad de Santiago de ChileSantiagoChile
  2. 2.School of Communication and Information (SC&I)RutgersThe State University of New JerseyNew BrunswickUSA

Personalised recommendations