How to elicit and cease herding behaviour? On the effectiveness of a warning message as a debiasing decision support system

Compen, Boukje; Pitthan, Francisco; Schelfhout, Wouter; Witte, Kristof De

doi:10.1016/j.dss.2021.113652

Cited by 16 publications

(10 citation statements)

References 58 publications

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“…Besides the sales-based information, HM also has a strong social influence, which may trigger herd behavior. That is, people tend to be consistent with others and are more willing to trust the information provided by others to make their evaluation of the product more accurate (Compen et al ., 2022).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Effects of intrinsic and extrinsic cues on customer behavior in live streaming: evidence from an eye-tracking experiment

Chen,

Tong,

Yang

et al. 2023

IMDS

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PurposeThis study aims to explore how specific cues with new manifestations (i.e. herding message and price discount information) and customer cognitive style influence attention allocation and purchase intention.Design/methodology/approachTo empirically validate the research hypotheses, an eye-tracking experiment with a 2 × 2 × 2 mixed design was conducted on a sample of 44 participants recruited from a university in China. Repeated measures analysis of variance was employed for data analysis.FindingsThe results show that herding message and price discount information play different roles in viewers' attention and have an interactive effect on attention. Moreover, individual cognitive styles moderate the impact of herding message on attention allocation. Still, two cues positively affect customer purchase intention.Originality/valueThis study guides future research by applying cue utilization theory to investigate the effects of two cues in live streaming. Findings offer practical implications for how live streaming cues affect viewers' attention allocation and purchase intention.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Effects of intrinsic and extrinsic cues on customer behavior in live streaming: evidence from an eye-tracking experiment

Chen,

Tong,

Yang

et al. 2023

IMDS

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“…While the agent-based models have successfully been repeated observed empirical features (Witkam, 2013) and connect the micro-level norms of investor behavior to the macro-behavior of asset prices (Wolfram, 1994;Wang et al, 2018;Dhesi et al, 2021). Compen et al (2022) indicated that herding occurs when individuals are aware of the decision of at least 50% of other individuals. The results also indicated that individuals are easily influenced by the wrong information from peers, which is very effective in market anomalies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Analyzing herd behavior could explain excess volatility and bubbles (Manahov & Hudson, 2014;Steinbacher et al, 2022). Herding behavior occurs when individuals change their investment decision by awareness of others' decisions (Manahov & Hudson, 2014); thus, investors tend to herd by avoiding private information that would price deviate from fundamental values (Compen et al, 2022). Therefore, the volatility (disability) in financial markets, changes in the valuation, and trading activity magnitude might arise due to herding behavior bias (Alfarano et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Analyze the Behavioral Foundation of Stylized Facts Using Agent-Based Simulation and STGP Algorithm

Sarkamaryan¹,

Jafari²,

Pooraghajan³

2022

IJFIFSA

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Although theoretical and empirical literature regarding the stylized facts shows evidence of their correlations to herding behavior in financial markets, the causes of such phenomena are still unknown. Using an agent-based model strengthened by the competition co-evolution algorithm (STGP) technique, this study provides laboratory evidence on capital market dynamics and analyses the behavioral foundations of stylized facts such as fat tails, leverage effects, 2

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“…Debiasing is a means of reducing or managing the detrimental effects of bias (Cheng & Wu, 2010; Compen et al, 2022). Debiasing activities can therefore potentially help reduce the occurrence of cognitive biases within decision making (Kreilkamp et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Towards an integrated debiasing framework for consumer financial decisions: A reflection on debiasing research

Jugnandan

Willows

2023

Int J Consumer Studies

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Consumers are subject to cognitive biases, which impede the rationality of their financial decisions. This is problematic, given the onus on the individual to make investment and savings decisions. Thus, there is an impetus for research to identify mitigation strategies. This qualitative review surveys the debiasing literature to identify the prevalent debiasing approaches and proposes an integrated model towards debiasing. The identified core debiasing strategies (education and training, decision support systems, information aspects, experience, and financial advice) are organized and integrated into a single model using the 'Antecedents, Decisions, Outcomes' format developed by Paul and Benito. We also propose an agenda for future debiasing research.

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How to elicit and cease herding behaviour? On the effectiveness of a warning message as a debiasing decision support system

Cited by 16 publications

References 58 publications

Effects of intrinsic and extrinsic cues on customer behavior in live streaming: evidence from an eye-tracking experiment

Effects of intrinsic and extrinsic cues on customer behavior in live streaming: evidence from an eye-tracking experiment

Analyze the Behavioral Foundation of Stylized Facts Using Agent-Based Simulation and STGP Algorithm

Towards an integrated debiasing framework for consumer financial decisions: A reflection on debiasing research

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