Abstract
Decision makers ought to adapt their information acquisition (IA) contingent to the task, which has not yet been investigated in the context of idea selection. Therefore, this paper suggests an operationalization of IA switching behavior using eye-tracking data. A first data analysis indicates that raters switch between modes of high and low IA in an idea selection task. These modes of IA could be associated with compensatory and non-compensatory information integration. The extent of switches between IA modes seems to stay stable between the first and the second half of the task with a slight decreasing trend towards the end. Future research will add cognitive load to explain occurring switches between different IA modes and may allow to deduce recommendations for more efficient IT designs, preserving rater’s cognitive resources.
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References
Jensen, M.B., Hienerth, C., Lettl, C.: Forecasting the commercial attractiveness of user-generated designs using online data : an empirical Study within the LEGO user community. J. Prod. Innov. Manag. 31, 75–93 (2014). https://doi.org/10.1111/jpim.12193
Beretta, M.: Idea selection in web-enabled ideation systems. J. Prod. Innov. Manag. 36, 5–23 (2019). https://doi.org/10.1111/jpim.12439
Einhorn, H.J., Hogarth, R.M.: Behavioral decision theory: processes of judgment and choice. J. Account. Res. 19, 1 (1981). https://doi.org/10.2307/2490959
Ferioli, M., Dekoninck, E., Culley, S., Roussel, B., Renaud, J.: Understanding the rapid evaluation of innovative ideas in the early stages of design. Int. J. Prod. Dev. 12, 67 (2010). https://doi.org/10.1504/IJPD.2010.034313
Bullinger, A.C.B., Moeslein, K.: Innovation Contests – Where are we ? AMCIS 2010 Proceedings, pp. 1–9 (2010)
Hoornaert, S., Ballings, M., Malthouse, E.C., Van Den Poel, D.: Identifying new product ideas: waiting for the wisdom of the crowd or screening ideas in real time. J. Prod. Innov. Manag. 34, 580–597 (2017). https://doi.org/10.1111/jpim.12396
Dawes, R.M.: The robust beauty of improper linear models in decision making. Am. Psychol. 34, 571–582 (1979). https://doi.org/10.1037/0003-066X.34.7.571
Tversky, A.: Intransitivity of preferences. Psychol. Rev. 76, 31–48 (1969). https://doi.org/10.1037/h0026750
Tversky, A.: Elimination by aspects: a theory of choice. Psychol. Rev. 79, 281–299 (1972). https://doi.org/10.1037/h0032955
Gigerenzer, G., Goldstein, D.G.: Reasoning the fast and frugal way: models of bounded rationality. Psychol. Rev. 103, 650–669 (1996). https://doi.org/10.1037/0033-295X.103.4.650
Patalano, A.L., Juhasz, B.J., Dicke, J.: The relationship between indecisiveness and eye movement patterns in a decision making informational search task. J. Behav. Decis. Mak. 23, 353–368 (2010). https://doi.org/10.1002/bdm.661
Russo, J.E., Dosher, B.A.: Strategies for multiattribute binary choice. J. Exp. Psychol. Learn. Mem. Cogn. 9, 676–696 (1983). https://doi.org/10.1037/0278-7393.9.4.676
Meißner, M., Oppewal, H., Huber, J.: Surprising adaptivity to set size changes in multi-attribute repeated choice tasks. J. Bus. Res. 111, 163–175 (2020). https://doi.org/10.1016/j.jbusres.2019.01.008
Zuschke, N.: An analysis of process-tracing research on consumer decision-making. J. Bus. Res. 111, 305–320 (2020). https://doi.org/10.1016/j.jbusres.2019.01.028
Payne, J.W., Bettman, J.R., Coupey, E., Johnson, E.J.: A constructive process view of decision making: multiple strategies in judgment and choice. Acta Psychol. (Amst) 80, 107–141 (1992). https://doi.org/10.1016/0001-6918(92)90043-D
Johnson, E.J., Payne, J.W.: Effort and accuracy in choice. Manag. Sci. 31, 395–414 (1985). https://doi.org/10.1287/mnsc.31.4.395
Payne, J.W., Bettman, J.R., Johnson, E.J.: Adaptive strategy selection in decision making. J. Exp. Psychol. Learn. Mem. Cogn. 14, 534–552 (1988). https://doi.org/10.1037/0278-7393.14.3.534
Johnson, E.J., Shu, S.B., Dellaert, B.G.C., Fox, C., Goldstein, D.G., Häubl, G., Larrick, R.P., Payne, J.W., Peters, E., Schkade, D., Wansink, B., Weber, E.U.: Beyond nudges: tools of a choice architecture. Mark. Lett. 23, 487–504 (2012). https://doi.org/10.1007/s11002-012-9186-1
Vom Brocke, J., Riedl, R., Léger, P.M.: Application strategies for neuroscience in information systems design science research. J. Comput. Inf. Syst. 53, 1–13 (2013). https://doi.org/10.1080/08874417.2013.11645627
Lohse, G.L., Johnson, E.J.: A comparison of two process tracing methods for choice tasks. Organ. Behav. Hum. Decis. Process. 68, 28–43 (1996). https://doi.org/10.1006/obhd.1996.0087
Horstmann, N., Ahlgrimm, A., Glöckner, A.: How distinct are intuition and deliberation? An eye-tracking analysis of instruction-induced decision modes. Judgm. Decis. Mak. 4, 335–354 (2009). https://doi.org/10.2139/ssrn.1393729
Ryan, M., Krucien, N., Hermens, F.: The eyes have it: using eye tracking to inform information processing strategies in multi-attributes choices. Heal. Econ. (U. K.) 27, 709–721 (2018). https://doi.org/10.1002/hec.3626
Glaholt, M.G., Reingold, E.M.: Eye movement monitoring as a process tracing methodology in decision making research. J. Neurosci. Psychol. Econ. 4, 125–146 (2011). https://doi.org/10.1037/a0020692
Riedl, R., Brandstätter, E., Roithmayr, F.: Identifying decision strategies: a process- and outcome-based classification method. Behav. Res. Methods 40, 795–807 (2008). https://doi.org/10.3758/BRM.40.3.795
Rosen, L.D., Rosenkoetter, P.: An eye fixation analysis of choice and judgment with multiattribute stimuli. Mem. Cogn. 4, 747–752 (1976). https://doi.org/10.3758/BF03213243
Balcombe, K., Fraser, I., McSorley, E.: Visual attention and attribute attendance in multi-attribute choice experiments. J. Appl. Econ. 30, 447–467 (2015). https://doi.org/10.1002/jae.2383
Payne, J.W.: Task complexity and contingent processing in decision making: an information search and protocol analysis. Organ. Behav. Hum. Perform. 16, 366–387 (1976). https://doi.org/10.1016/0030-5073(76)90022-2
Glöckner, A., Herbold, A.-K.: An eye-tracking study on information processing in risky decisions: evidence for compensatory strategies based on automatic processes. J. Behav. Decis. Mak. 24, 71–98 (2011). https://doi.org/10.1002/bdm.684
Velichkovsky, B.M., Rothert, A., Kopf, M., Dornhöfer, S.M., Joos, M.: Towards an express-diagnostics for level of processing and hazard perception. Transp. Res. Part F Traffic Psychol. Behav. 5, 145–156 (2002). https://doi.org/10.1016/S1369-8478(02)00013-X
Ball, C.: A comparison of single-step and multiple-step transition analyses of multiattribute decision strategies. Organ. Behav. Hum. Decis. Process. 69, 195–204 (1997). https://doi.org/10.1006/obhd.1997.2681
Payne, J.W., Braunstein, M.L.: Risky choice: an examination of information acquisition behavior. Mem. Cogn. 6, 554–561 (1978). https://doi.org/10.3758/BF03198244
Wibmer, A., Wiedmann, F.M., Seeber, I., Maier, R.: Why less is more: an eye tracking study on idea presentation and attribute attendance in idea selection. In: 27th European Conference on Information Systems, pp. 1–14 (2019)
Shi, S.W., Wedel, M., Pieters, F.G.M.: (Rik): information acquisition during online decision making: a model-based exploration using eye-tracking data. Manag. Sci. 59, 1009–1026 (2013). https://doi.org/10.1287/mnsc.1120.1625
Chernev, A.: Goal-attribute compatibility in consumer choice. J. Consum. Psychol. 14, 141–150 (2004). https://doi.org/10.1207/s15327663jcp1401
Toubia, O., Netzer, O.: Idea generation, creativity, and prototypicality. Mark. Sci., 1–20 (2017). https://doi.org/10.1287/mksc.2016.0994
Blascheck, T., Kurzhals, K., Raschke, M., Burch, M., Weiskopf, D., Ertl, T.: State-of-the-art of visualization for eye tracking data. In: Borgo, R., Maciejewski, R., and Viola, I. (eds.) Proceedings of the Eurographics Conference on Visualization (EuroVis). The Eurographics Association (2014)
Rayner, K.: Eye movements and attention in reading, scene perception, and visual search. Q. J. Exp. Psychol. 62, 1457–1506 (2009). https://doi.org/10.1080/17470210902816461
Sambandam, R.: Cluster analysis gets complicated. Mark. Res. 15, 16–21 (2003)
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E.: Multivariate Data Analysis. Pearson Education Limited, Harlow (2014). https://doi.org/10.1038/259433b0
Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244 (1963). https://doi.org/10.1080/01621459.1963.10500845
Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. 3, 1–27 (1974). https://doi.org/10.1080/03610927408827101
Pfeiffer, J., Duzevik, D., Rothlauf, F., Bonabeau, E., Yamamoto, K.: An optimized design of choice experiments: a new approach for studying decision behavior in choice task experiments. J. Behav. Decis. Mak. 28, 262–280 (2015). https://doi.org/10.1002/bdm.1847
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Wibmer, A., Wiedmann, F., Seeber, I., Maier, R. (2020). Operationalization of Information Acquisition Switching Behavior in the Context of Idea Selection. In: Davis, F.D., Riedl, R., vom Brocke, J., Léger, PM., Randolph, A.B., Fischer, T. (eds) Information Systems and Neuroscience. NeuroIS 2020. Lecture Notes in Information Systems and Organisation, vol 43. Springer, Cham. https://doi.org/10.1007/978-3-030-60073-0_4
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