What moderates the too‐much‐choice effect?

Abstract

Core theories in economics, psychology, and marketing suggest that decision makers benefit from having more choice. In contrast, according to the too-much-choice effect, having too many options to choose from may ultimately decrease the motivation to choose and the satisfaction with the chosen option. To reconcile these two positions, we tested whether there are specific conditions in which the too-much-choice effect is more or less likely to occur. In three studies with a total of 598 participants, we systematically investigated the moderating impact of choice set sizes, option attractiveness, and whether participants had to justify their choices. We also tested the moderating role of search behavior, domain-specific expertise, and participants' tendency to maximize, in a within-subject design. Overall, only choice justification proved to be an effective moderator, calling the extent of the too-much-choice effect into question. We provide a theoretical account for our findings and discuss possible pathways for future research.

Size, entropy, and density: What is the difference that makes the difference between small and large real‐world assortments?

“…Therefore, the plethora of choices that the prodigious abundance in goods and service offers can carry a substantial price: People may choose not to participate, not to make a purchase, not to consume. Several attempts have been made to explain this too-much-choice effect (Reutskaja & Hogarth, 2009;Scheibehenne, Greifeneder, & Todd, 2009;White & Hoffrage, 2009). Our contribution to these explanations is that sheer size is not the only culprit.…”

Under Pressure: An Integrative Perspective of Time Pressure Impact on Consumer Decision-Making

“…Such evidence seems to explain previous findings about the consequences for decision-making of focusing participants in the task completion. When participants are warned, previously to decision-making, that they will have to justify their decisions, they engage in easier to explain, more rational decisions (Sela et al, 2009), and report that it is harder to decide (Scheibehenne, Greifeneder, & Todd, 2009).…”

Using microtasks to crowdsource DBpedia entity classification: A study in workflow design

“…It can be achieved through auto-matic tools addressing a similar task (as listed in section 2.1), however, they impose clear bounds on the accuracy of the crowd experiments, as not all input can be processed by the automatic tools (recall) and their output can only be as precise as the task input allows (precision). Additionally, the choice of an appropriate threshold to accept or reject produced results is also a subject of debate in related literature [41,42]. Another option is to provide the crowd all possible choices in a finite domain -in our case all classes of the DBpedia ontology.…”