Countering the anchoring and adjustment bias with decision support systems

George, Joey F.; Duffy, Kevin; Ahuja, Manju

doi:10.1016/s0167-9236(00)00074-9

Cited by 84 publications

(58 citation statements)

References 11 publications

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“…In the field of information systems, researchers focused mostly on anchoring bias and reported that anchoring bias caused information systems professionals to develop erroneous systems [34,35] and make suboptimal decisions using decision support systems [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Seeking health information on the web: Positive hypothesis testing

Kayhan

2013

International Journal of Medical Informatics

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Section: Introductionmentioning

confidence: 99%

Seeking health information on the web: Positive hypothesis testing

Kayhan

2013

International Journal of Medical Informatics

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“…Prior studies demonstrated the effect of warning in diminishing the anchoring effect [11,29]. The results showed that the debiasing effect of warning appeared to decrease the anchoring bias.…”

Section: Warning Typementioning

confidence: 92%

“…However, the method of moderating or eliminating the bias depends on the particular bias and decision maker [5]. First, warning messages can moderate the online framing effect [11,29]. Health care is a very important issue in the United States.…”

Section: Introductionmentioning

confidence: 99%

The online framing effect: the moderating role of warning, brand familiarity, and product type

Chen

Chang

2015

Electron Commer Res

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This study examines the framing effect on online consumer purchase intention. Three framing effect moderators-warning type (text-based/picturebased), brand familiarity (familiar/unfamiliar), and product type (utilitarian/hedonic)-were considered. The results demonstrated that a positive frame message could create more favorable preferences toward the product than a negative frame message could. Further, consumers with text-based warnings who received positive framing messages had higher purchase intentions toward the target product than did those receiving negative framing messages. However, the online framing effect was not significant for consumers receiving picture-based warnings. Moreover, the online framing effect was significant for consumers familiar with the brand. Finally, positive messages resulted in higher purchase intention for hedonic rather than utilitarian products. This study further examined gender differences in framing effects and found women exhibited greater framing effects than men under the negative frame. The findings provide guidance for designing appropriate product strategy to induce online consumer purchase intentions that favor online retailers.

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“…The most relevant biases are availability, anchoring and overconfidence (George, Duffy, and Ahuja 2000). Experts with the availability bias overemphasise the effects of activities they easily recall or imagine.…”

Section: Proposed Approach For Estimating Non-product Related Investmentmentioning

confidence: 99%

Focused factories: a Bayesian framework for estimating non-product related investment

Wang

Chinnam

Doğan

et al. 2014

International Journal of Production Research

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Focused factories are one of the new manufacturing trends for automotive suppliers. A key requirement for these suppliers is the ability to accurately estimate both product and non-product related investment in these facilities to quote profitable business. We propose a systematic Bayesian framework to estimate non-product related investment in focused factories. Our approach incorporates uncertainty in the activity-based costing method and applies Monte Carlo simulation process to generate distributions of investment for the cost centres, and for the different project phases in setting up a facility. A Bayesian-updating procedure is introduced to improve parameter estimations as new information becomes available with experience in setting up these facilities. Our approach is deployed at a leading global automotive tier-one supplier, Visteon Corporation. The efficacy of the Visteon-focused factory cost model is validated using subject matter experts as well as by comparing the model results with estimates from the typical current process.

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Countering the anchoring and adjustment bias with decision support systems

Cited by 84 publications

References 11 publications

Seeking health information on the web: Positive hypothesis testing

Seeking health information on the web: Positive hypothesis testing

The online framing effect: the moderating role of warning, brand familiarity, and product type

Focused factories: a Bayesian framework for estimating non-product related investment

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