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Deciphering the choice overload phenomenon – Exploring the role of consideration sets and anticipated regret
- Source :
- IndraStra Global.
- Publication Year :
- 2017
- Publisher :
- Indian Institute of Management Ahmedabad, 2017.
-
Abstract
- Studies on choice proliferation and its negative effects, popularly referred to as ‘Choice Overload (CO)’, have built on insights from the seminal ‘Jam Study’ (Iyengar & Lepper, 2000), which highlighted the paradoxical finding that variety can be detrimental to choice. However, contradictory results from the extant literature indicate reservations about the generalizability of CO (Chernev, Böckenholt, & Goodman, 2015). Evidence contradicting the CO phenomenon is also corroborated by anecdotal evidence which indicates that consumers try to overcome CO by pre‐screening assortments and choosing from the shortlisted options. The strategies adopted by consumers to circumvent ‘overload’ might be impacted by the anticipated negative emotions, as indicated by recent literature on regret (Su, Chen, & Zhao, 2008). In this study, we investigate the role of consideration sets and anticipated regret in decision scenarios involving large assortments. This enquiry combines research on Regret (Zeelenberg, 1999), Consideration Sets (Goodman, Broniarczyk, & McAlister, 2013) and CO (Chernev, Böckenholt, & Goodman, 2015) to study the decision processes when choosing from large assortments. The research uses Regret Regulation theory (Zeelenberg & Pieters, 2007) to suggest that consumers anticipate regret from both decision process (process regret) and final outcome (outcome regret). This leads to consumer adoption of strategies to minimise the overall regret (Connolly & Zeelenberg, 2002). Further, the study proposes that a phased decision process (First shortlist and then choose) is the dominant operationalization of this regret minimising strategy. It is hypothesised that consumers try to avoid overload by forming consideration sets and that the size of consideration set is affected by the type of anticipation of regret and the focus on either outcome regret or process regret. A between-subjects experimental design was used to examine the role of consideration set size and anticipated regret (AR) on the choice process. Three experiments were conducted to 1) validate the CO phenomenon, 2) explore the impact of assortment size on consideration set size and 3) study the impact of anticipated regret on consideration set size and CO indicators. A fourth experiment analysed the differential impact of anticipated outcome regret on consideration set size and post-decision indicators of CO. Results of the experiments indicated that the presence of larger assortment sizes might fuel the expectations of the consumer and lead them to look at more products, subsequently increasing the cognitive overload, thus validating the CO phenomenon. The results indicate that larger assortments lead to bigger consideration sets and subsequently, CO. The studies also validated that the hypothesis that higher anticipated regret leads to bigger consideration sets and this impacts CO. It is found that while higher anticipated outcome regret when choosing from larger assortments can lead to bigger consideration sets, they might not have negatively impact all post purchase indicators of overload. This thesis contributes to theory by exploring the pro-active role of anticipated emotions when choosing from large assortments. The study provides insights into the conditions which can trigger overload and techniques adopted to consumers to avoid overload without compromising on the overall goals. The study has several important managerial implications, both offline and online. Offline systems can benefit from trained sales person who can help customers focus on possible anticipated regret prior to the final decision, leading to reduced choice deferrals. Online systems can similarly leverage anticipated regret triggers through systems designs to help consumers avoid overload.
- Subjects :
- Choice overload
Anticipated regret
Subjects
Details
- Language :
- English
- ISSN :
- 23813652
- Database :
- OpenAIRE
- Journal :
- IndraStra Global
- Accession number :
- edsair.issn23813652..985c017635e693f6c24a6d1c56ee18f4