Variations on anchoring: Sequential anchoring revisited

Bahník, Štěpán; Houdek, Petr; Vrbová, Lucie; Hájek, Jiřı́

doi:10.1017/s1930297500005428

Cited by 10 publications

(13 citation statements)

References 26 publications

(28 reference statements)

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“…Furthermore, a preceding anchor or the median absolute judgment of a preceding item did not show any effect on absolute judgment. While sequential anchoring has been shown to be replicable (Bahník et al, 2019), these results suggest that its size is negligible in comparison to the standard anchoring effect.…”

Section: Discussionmentioning

confidence: 78%

Anchoring without scale distortion

Bahník

2021

Judgm. decis. mak.

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The scale distortion theory of anchoring argues that people are influenced by a previously considered numeric value, an anchor, because the anchor distorts the scale on which a subsequent judgment is made. The distortion of the scale due to the anchor is a momentary effect that would be overridden if the scale was distorted again, for example, by consideration of a different value on the same scale. In the present study, participants compared thirteen random anchors on the same scale to thirteen different objects. Subsequent numeric estimates of objects’ attributes were influenced by the corresponding anchors even though the anchors were divided from the estimates by twelve questions pertaining to different values on the same scale. The numeric value considered immediately before the estimate did not have a considerable effect on the judgment. While the anchoring effect was robust, it cannot be easily explained by scale distortion. Other possible theories of the anchoring effect are compatible with the results.

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Section: Discussionmentioning

confidence: 78%

Anchoring without scale distortion

Bahník

2021

Judgm. decis. mak.

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“…For instance, previous evidence shows that the first evaluation (e.g., the assessment of a rabbit’s weight) works as an anchor and impacts the subsequent evaluation (e.g., the assessment of a giraffe’s weight). More recent research supports this theory by generalising it to other stimuli [ 21 ]. Focusing on one dimension of supplier performance, we expect that a high (or low) supplier score shifts managers’ perception of the scale on which supplier performance is assessed.…”

Section: Theoretical Background and Hypothesesmentioning

confidence: 84%

“…The second approach addresses the decision-maker by providing tools to mitigate the effects of bias [ 33 , 60 ]. This study contends that managers rely on heuristics to make decisions with System 1 and make corrections by effortful thinking with System 2 [ 21 , 61 , 62 ].…”

Section: Theoretical Background and Hypothesesmentioning

confidence: 99%

The power of past performance in multidimensional supplier evaluation and supplier selection: Debiasing anchoring bias and its knock-on effects

Wong

2024

PLoS ONE

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This research examines how anchoring bias affects managers’ multi-dimensional evaluations of supplier performance, supplier selection, and the effectiveness of two debiasing techniques. We consider the supplier past performance in one performance dimension as the anchor and investigate whether and how this anchor would have a knock-on effects on evaluating a supplier’s performance in other dimensions. We conducted two online experimental studies (Study 1, sample size = 104 and Study 2, sample size = 408). Study 1 adopts a 2 x 1 (high anchor vs. low anchor) between-subjects factorial experimental design, and Study 2 is a 3 (debiasing: no, consider-the-opposite, mental-mapping) x 2 (high anchor vs. low anchor) between-subjects factorial design. The results from Studies 1 and 2 suggest that when a supplier has received a low evaluation score in one dimension in the past, participants assign the same supplier lower scores in the other dimensions compared to a supplier that has obtained a high score in the past. We also find that anchoring has a knock-on effect on how likely participants are to choose the same supplier in the future. Our findings highlight the asymmetric effectiveness of ‘consider-the-opposite’ and ‘mental-mapping’ debiasing techniques. This research is the first study that examines how anchoring bias managers’ evaluations in a multi-dimensional setting and its knock-on effects. It also explores the effectiveness of two low-cost debiasing techniques. A crucial practical implication is that suppliers’ exceptionally good or disappointing past performance affects the judgement of supply managers. Hence, managers should use consider-the-opposite or mental-mapping techniques to debias the effect of high and low anchors, respectively.

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“…Weakening mechanism of bullwhip effect on digital supply chain 3.1 The effect of anchoring psychology on the bullwhip effect The anchoring effect is a mechanism by which people make judgments and decision strategies (Simmons et al, 2010). When making decision estimates, people spontaneously tend to start from an initial value (anchor point) and keep adjusting until they form a final estimate (Bahn ık et al, 2019). However, since the adjustment from the anchor point to the target value is often inadequate, different anchor points will lead to different estimates, thus generating estimation bias (Epley and Gilovich, 2001).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…, 2010). When making decision estimates, people spontaneously tend to start from an initial value (anchor point) and keep adjusting until they form a final estimate (Bahník et al. , 2019).…”

Section: Weakening Mechanism Of Bullwhip Effect On Digital Supply Chainmentioning

confidence: 99%

Inhibitory influence of supply chain digital transformation on bullwhip effect feedback difference

Gao,

Guan

et al. 2023

BPMJ

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PurposeThis paper breaks through the limitations of the research on bullwhip effect in the traditional supply chain, extends the research perspective to digital supply chain and discusses the weakening effect of digital supply chain on bullwhip effect by comparing the overall performance of the two.Design/methodology/approachThis paper starts with the weakening mechanism of supply chain digitization on bullwhip effect, builds bullwhip effect models of traditional supply chain and digital supply chain, respectively, simulates the influence of supply chain digitization transformation on bullwhip effect by using Matlab software and analyzes the causes of bullwhip effect in supply chain led by T company and the digitization process.FindingsFirstly, digitization can reduce bullwhip effect in multi-level supply chain by reducing information feedback deviation. Second, digital transformation is conducive to improving the overall performance of the supply chain. Third, government incentives can promote the digital transformation of supply chain and inhibit bullwhip effect.Research limitations/implicationsAlthough the study considers the heterogeneous subject -- the government's incentive effect on digital transformation and information sharing – it does not include the influence of the end node in the supply chain, that is the consumer. In addition, this paper only analyzes and discusses the bullwhip effect on the amplification of demand, without considering the situation that the market contraction will lead to the reduction of demand.Practical implicationsThis paper considers the distortion degree and delay degree of information feedback, carries out quantitative analysis of bullwhip effect, builds the bullwhip effect model of traditional supply chain and digital supply chain, uses Matlab software to analyze the difference of the influence of supply chain digital transformation on bullwhip effect suppression and puts forward the corresponding control strategy.Social implicationsThe research shows that digital transformation can reduce the bullwhip effect in multi-layer supply chain by reducing the information feedback deviation, which is conducive to improving the overall supply chain performance, and government support can accelerate the digital transformation of supply chain to a certain extent.Originality/valueFirst, break through the limitations of traditional supply chain research, expand the research perspective to digital supply chain and discuss the weakening effect of digital supply chain on bullwhip effect by comparing the overall performance of the two. Second, quantify the bullwhip effect through information feedback bias and provide an analysis method for the weakening of the bullwhip effect. Third, the driving role of the government in the digital transformation of the supply chain is considered in the study, so that the model is more close to the actual situation of enterprise operation.

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Variations on anchoring: Sequential anchoring revisited

Cited by 10 publications

References 26 publications

Anchoring without scale distortion

Anchoring without scale distortion

The power of past performance in multidimensional supplier evaluation and supplier selection: Debiasing anchoring bias and its knock-on effects

Inhibitory influence of supply chain digital transformation on bullwhip effect feedback difference

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