Choice overload: A conceptual review and meta‐analysis

Chernev, Alexander; Böckenholt, Ulf; Goodman, Joseph K.

doi:10.1016/j.jcps.2014.08.002

Cited by 568 publications

(544 citation statements)

References 94 publications

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“…In a wellknown study, a tasting booth for jams attracted more people when it offered 24 different options than when it offered 6, but there were more purchases from the set with 6 options than from the set with 24 options [34]. This Bchoice overloadê ffect may however be restricted to situations, in which the consumers are unfamiliar with the options offered [18,54]. More generally, people overestimate the benefits of choice: in deciding how much time they should devote to selecting a better option, they do not incorporate the temporal, cognitive, and emotional costs of searching and of thinking.…”

Section: Costs Of Experiencing Autonomy In Consumer Choicementioning

confidence: 98%

Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data

André

Carmon

Wertenbroch

et al. 2017

Cust. Need. and Solut.

196

131

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Recent developments in the field of artificial intelligence and data analytics are facilitating the automation of some consumer chores (e.g., in smart homes and in self-driving cars) and allow the emergence of big-data-driven, micro-targeting marketing practices (e.g., personalized content recommendation algorithms). We contend that those developments can generate a tension for marketers, consumers, and policy makers: They can, on the one hand, contribute to consumer well-being by making consumer choices easier, more practical, and more efficient. On the other hand, they can also undermine consumers' sense of autonomy, the absence of which can be detrimental to consumer well-being. Drawing on diverse perspectives from marketing, economics, philosophy, neuroscience, and psychology, we explore how consumers' sense of autonomy in making choices affects their wellbeing. We discuss how new technologies may enhance or diminish consumers' perceptions of being in control of their choices and how either of those can, in turn, enhance of detract from consumer well-being. Building on this, we identify open research questions in the domain of choice, well-being, and consumer welfare, and suggest avenues for future research.

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Section: Costs Of Experiencing Autonomy In Consumer Choicementioning

confidence: 98%

Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data

André

Carmon

Wertenbroch

et al. 2017

Cust. Need. and Solut.

196

131

View full text Add to dashboard Cite

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“…Information overload in the consumer is triggered by a series of general and specific factors (Chernev, Böckenholt, & Goodman, ; Eppler & Mengis, ). Among the former, of particular importance are personal factors, which include: experience of particular tasks that deliver certain outcomes; skill indicators such as reading speed and speed of analysis; and a person's preferred ways of gathering, organizing, analyzing, or recalling information and experience (i.e., their cognitive style; Jackson & Farzaneh, ).…”

Section: Literature Reviewmentioning

confidence: 99%

The role of numeracy and information load in the tourist decision‐making process

Castañeda

Rodríguez‐Molina

Frías‐Jamilena

et al. 2019

Psychology and Marketing

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The aim of the present study is to merge two strands of research that have evolved in tandem (a) the consequences of numeracy; and (b) the effect of information load on the consumer decision‐making process. The study analyses the interaction effect between these two factors on decision accuracy and on a set of cognitive and affective variables of the decision‐making process (namely, confusion, calibration, and emotion). We measured participants' numeracy and used an experimental design to manipulate the information load. The results showed that the effect of numeracy on decision accuracy was entirely mediated by cognitive and affective processes involved in decision‐making. In particular, numeracy was found to have a negative relationship with the level of confusion, calibration, and emotions experienced during the decision‐making process. The results also showed that information load interacted with numeracy, such that individuals with high numeracy maintained lower levels of emotion when placed under conditions of low information load. However, under these same conditions, individuals with low numeracy maintained positive emotions. The findings help to identify approaches that can contribute to reducing the negative effects of low numeracy among consumers, in a context where the online channel predominates (the tourism sector).

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“…Complex choices can lead to information overload and ultimately demotivation (Bchoice overload^, (Chernev et al 2015). Under a Btoo many choices^condition, individuals become overwhelmed by the number of options and may accept even more the standard option.…”

Section: Limits In Information Processingmentioning

confidence: 99%

Consumers and their behavior: state of the art in behavioral science supporting use phase modeling in LCA and ecodesign

Sorrentino

Woelbert

Sala

2016

Int J Life Cycle Assess

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Purpose Life cycle thinking and assessment require a holistic approach to the evaluation of product supply chains. An assessment from raw material extraction to end of life of any products is based on modeling a wide number of aspects and options, e.g., at technological, geographical, and temporal levels. Since the use phase is one of the most contributing life cycle stages for some products (e.g., appliance, housing, cars), a robust modeling of this stage is fundamental. Several attempts to better modeling use-phase have been performed; however, so far no systematic study is available on how to integrate behavioral science (BS) insights into LCA. This is even more important when the impact of the product under consideration is strongly determined by the use phase relatively to other life cycle stages. The aim of this paper is to explore how behavioral science has been used to date and how BS can contribute towards more robust modeling of use phase in LCA and as basis for a behavior-driven ecodesign. Methods We identified the key areas in which LCA and ecodesign may benefit from integrating insights from behavioral science and developing a conceptual model. Both robust modeling and the design of behavior change interventions rest on a sound understanding of behavior in the specific context of interest though empirical investigation. Hence, we reviewed literature on behavioral science and introduce key drivers of human behavior that are relevant in the context of use phase modeling and ecodesign. We provide examples where these were applied to facilitate the integration of BS elements by practitioners. Results and discussion Consumer's behavior is increasingly recognized as one of the drivers of overall environmental impact of a product, and some examples of use of BS for LCA are available in literature. We suggest that behavioral science can be useful in the context of life cycle assessment in two ways: measuring behavior and assessing potential and means for changing behavior. Specifically, insights and methods from behavioral sciences could be applied for assessing variability of consumer behavior, understanding leverages for behavioral changes, and possible rebound effects. Conclusions This insight may help to model the use phase more accurately, to identify realistic scenarios, and to support behavior-driven eco-innovation.

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Choice overload: A conceptual review and meta‐analysis

Cited by 568 publications

References 94 publications

Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data

Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data

The role of numeracy and information load in the tourist decision‐making process

Consumers and their behavior: state of the art in behavioral science supporting use phase modeling in LCA and ecodesign

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