The Choice Is Yours: The Role of Cognitive Processes for IT-Supported Idea Selection

  • Isabella SeeberEmail author
  • Barbara Weber
  • Ronald Maier
  • Gert-Jan de Vreede
Conference paper
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 25)


The selection of good ideas out of hundreds or even thousands has proven to be the next big challenge for organizations that conduct open idea contests for innovation. Cognitive load and attention loss hinder crowds to effectively run their idea selection process. Facilitation techniques for the reduction and clarification of ideas could help with such problems, but have not yet been researched in crowd settings that are prevalent in idea contests. This research-in-progress paper aims to contribute to this research gap by investigating IT-supported selection techniques that differ in terms of selection direction and selection type. A laboratory experiment using eye-tracking will investigate variations in selection type and selection direction. Moreover, the experiment will test the effects on the decision-making process and the number and quality of ideas in a filtered set. Findings will provide explanations why certain mechanisms work for idea selection. Potential implications for theory and practice are discussed.


Idea contest Idea quality Idea selection Open innovation Screening rules 



The research was partially funded by the Austrian Science Foundation (FWF): P 29765-GBL.


  1. 1.
    Bullinger, A.C., Neyer, A.K., Rass, M., Moeslein, K.M.: Community-based innovation contests: where competition meets cooperation. Creativity Innov. Manage. 19, 290–303 (2010)CrossRefGoogle Scholar
  2. 2.
    Bjelland, O.M., Wood, R.C.: An inside view of IBM’s Innovation Jam. MIT Sloan Manage. Rev. 50, 32–40 (2008)Google Scholar
  3. 3.
    Bayus, B.L.: Crowdsourcing new product ideas over time: an analysis of the Dell IdeaStorm community. Manage. Sci. 59, 226–244 (2013)CrossRefGoogle Scholar
  4. 4.
    Jouret, G.: Inside cisco’s search for the next big idea. Harvard Bus. Rev. (2009)Google Scholar
  5. 5.
    Boudreau, K.J., Lakhani, K.R.: Using the crowd as an innovation partner. Harvard Bus. Rev. 91, 60–69 (2013)Google Scholar
  6. 6.
    Dean, D.L., Hender, J.M., Rodgers, T.L., Santanen, E.L.: Identifying quality, novel, and creative ideas: constructs and scales for idea evaluation. J. Assoc. Inf. Syst. 7, 30 (2006)Google Scholar
  7. 7.
    Girotra, K., Terwiesch, C., Ulrich, K.T.: Idea generation and the quality of the best idea. Manage. Sci. 56, 591–605 (2010)CrossRefGoogle Scholar
  8. 8.
    Velamuri, V.K., Schneckenberg, D., Haller, J., Moeslein, K.M.: Open evaluation of new product concepts at the front end of innovation: objectives and contingency factors. R&D Manag. (2015)Google Scholar
  9. 9.
    Merz, A., Seeber, I., Maier, R., Richter, A., Schimpf, R., Füller, J., Schwabe, G.: Exploring the effects of contest mechanisms on idea shortlisting in an open idea competition. In: 37th International Conference on Information Systems, Dublin, Ireland (2016)Google Scholar
  10. 10.
    Magnusson, P.R., Wästlund, E., Netz, J.: Exploring users’ appropriateness as a proxy for experts when screening new product/service ideas. J. Prod. Innov. Manag. 33, 4–18 (2016)CrossRefGoogle Scholar
  11. 11.
    Yoon, P.K., Hwang, C.-L.: Multiple attribute decision making: an introduction. Thousand Oaks, CA: Sage (2011)Google Scholar
  12. 12.
    Fadel, K.J., Meservy, T.O., Jensen, M.L.: Exploring knowledge filtering processes in electronic networks of practice. J. Manage. Inf. Syst. 31, 158–181 (2015)CrossRefGoogle Scholar
  13. 13.
    Pilli, L.E., Mazzon, J.A.: Information overload, choice deferral, and moderating role of need for cognition: empirical evidence. Rev. Administração 51, 36–55 (2016)CrossRefGoogle Scholar
  14. 14.
    Petty, R.E., Cacioppo, J.T.: The elaboration likelihood model of persuasion. Adv. Exp. Soc. Psychol. 19, 123–205 (1986)Google Scholar
  15. 15.
    Klein, M., Garcia, A.C.B.: High-speed idea filtering with the bag of lemons. Decis. Support Syst. 78, 39–50 (2015)CrossRefGoogle Scholar
  16. 16.
    Svenson, O.: Process descriptions of decision making. Organ. Behav. Hum. Perform. 23, 86–112 (1979)CrossRefGoogle Scholar
  17. 17.
    Gilbride, T.J., Allenby, G.M.: A choice model with conjunctive, disjunctive, and compensatory screening rules. Mark. Sci. 23, 391–406 (2004)CrossRefGoogle Scholar
  18. 18.
    Schulte-Mecklenbeck, M., Kühberger, A., Ranyard, R.: A handbook of process tracing methods for decision research: a critical review and user’s guide. New York, NY: Taylor & Francis (2011)Google Scholar
  19. 19.
    Kolfschoten, G.L., Brazier, F.M.: Cognitive load in collaboration: convergence. Group Decis. Negot. 22, 975–996 (2013)CrossRefGoogle Scholar
  20. 20.
    De Vreede, G.J., Briggs, R.O., Massey, A.P.: Collaboration engineering: foundations and opportunities: editorial to the special issue on the journal of the association of information systems. J. Assoc. Inf. Syst. 10, 121–137 (2009)Google Scholar
  21. 21.
    Levine, J.M., Moreland, R.L.: A history of small group research. In: Kruglanski A.W., Stroebe, W. (eds) Handbook of the history of social psychology, New York, NY: Psychology Press (2012)Google Scholar
  22. 22.
    Stibel, J.M., Dror, I.E., Ben-Zeev, T.: The collapsing choice theory: dissociating choice and judgment in decision making. Theor. Decis. 66, 149–179 (2009)CrossRefGoogle Scholar
  23. 23.
    Chandler, P., Sweller, J.: Cognitive load theory and the format of instruction. Cogn. Instr. 8, 293–332 (1991)CrossRefGoogle Scholar
  24. 24.
    Sweller, J., Van Merrienboer, J.J., Paas, F.G.: Cognitive architecture and instructional design. Educ. Psychol. Rev. 10, 251–296 (1998)CrossRefGoogle Scholar
  25. 25.
    Kornish, L.J., Ulrich, K.T.: Opportunity spaces in innovation: empirical analysis of large samples of ideas. Manage. Sci. 57, 107–128 (2011)CrossRefGoogle Scholar
  26. 26.
    Wang, W., Benbasat, I.: Interactive decision aids for consumer decision making in e-commerce: the influence of perceived strategy restrictiveness. MISQ 33, 293 (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Isabella Seeber
    • 1
    Email author
  • Barbara Weber
    • 2
  • Ronald Maier
    • 1
  • Gert-Jan de Vreede
    • 3
  1. 1.Department of Information Systems, Production and Logistics ManagementUniversity of InnsbruckInnsbruckAustria
  2. 2.Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkCopenhagenDenmark
  3. 3.Information Systems and Decision Sciences DepartmentUniversity of South FloridaFloridaUSA

Personalised recommendations