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

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.

Sample size determination for interval estimation of the prevalence of a sensitive attribute under non-randomized response models.

A sufficient number of participants should be included to adequately address the research interest in the surveys with sensitive questions. In this paper, sample size formulas/iterative algorithms are developed from the perspective of controlling the confidence interval width of the prevalence of a sensitive attribute under four non-randomized response models: the crosswise model, parallel model, Poisson item count technique model and negative binomial item count technique model. In contrast to the conventional approach for sample size determination, our sample size formulas/algorithms explicitly incorporate an assurance probability of controlling the width of a confidence interval within the pre-specified range. The performance of the proposed methods is evaluated with respect to the empirical coverage probability, empirical assurance probability and confidence width. Simulation results show that all formulas/algorithms are effective and hence are recommended for practical applications. A real example is used to illustrate the proposed methods.

Sample Size Determination for Interval Estimation of the Prevalence of a Sensitive Attribute Under Randomized Response Models.

Studies with sensitive questions should include a sufficient number of respondents to adequately address the research interest. While studies with an inadequate number of respondents may not yield significant conclusions, studies with an excess of respondents become wasteful of investigators' budget. Therefore, it is an important step in survey sampling to determine the required number of participants. In this article, we derive sample size formulas based on confidence interval estimation of prevalence for four randomized response models, namely, the Warner's randomized response model, unrelated question model, item count technique model and cheater detection model. Specifically, our sample size formulas control, with a given assurance probability, the width of a confidence interval within the planned range. Simulation results demonstrate that all formulas are accurate in terms of empirical coverage probabilities and empirical assurance probabilities. All formulas are illustrated using a real-life application about the use of unethical tactics in negotiation.

Saving for a Better Retirement: How Risk Attitudes Affect Choice of Retirement Scheme.

Over 3 million people in Hong Kong and 21 million people in the UK are saving for retirement under the mandatory provident fund and individual savings account schemes, respectively. Yet, we know little about how individual preferences, such as risk attitudes (risk-seeking and risk-averse) that are known to impact highly consequential decisions in a variety of real-world contexts, impact retirement investment choices. In two experimental studies (Study 1-Hong Kong sample and Study 2-United Kingdom sample), we show that personal risk attitudes were a strong predictor of the profile of retirement investment portfolios. Specially, risk-averse people allocated more of their savings to low-risk funds than risk-seeking people. The pattern of findings is consistent in both Hong Kong mandatory and the UK voluntary retirement investment schemes. These findings are considered in light of policy decisions made in Hong Kong retirement and UK pension schemes.