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A new design can be compared with its contemporaries or older designs. In this study, we argue that the temporal distance between the new design and its comparison play an important role in understanding how a new design’s similarity with other designs contributes to its valuation. Construing the value of designs as a combination of their informational value and their expressive value, we propose the “anchored differentiation” hypothesis. Specifically, we argue that expressive value (which is enhanced by how much the new design appears different from others) is emphasized more than informational value (which is enhanced by how much the new design appears similar to others) compared with contemporary designs. Informational value, however, is emphasized more than expressive value when compared against designs from the past. Therefore, both difference from other contemporary designs (contemporary differentiation) and similarity to other past designs (past anchoring) help increase the value of a new design. We find consistent evidence for our theory across both a field study and an experimental study. Furthermore, we show that this is because temporal distance changes the relative emphasis on expressive and informational values. We discuss our contribution to the growing literature on optimal distinctiveness and design innovation by offering a dynamic perspective that helps resolve the tension between similarities and differences in evaluating new designs.

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BackgroundNoninvasive prenatal testing (NIPT) using massively parallel sequencing of cell-free DNA (cfDNA) is increasingly being used to predict fetal chromosomal abnormalities. However, concerns over erroneous predictions which occur while performing NIPT still exist in pregnant women at high risk for fetal aneuploidy. We performed the largest-scale clinical NIPT study in Korea to date to assess the risk of false negatives and false positives using next-generation sequencing.MethodsA total of 447 pregnant women at high risk for fetal aneuploidy were enrolled at 12 hospitals in Korea. They underwent definitive diagnoses by full karyotyping by blind analysis and received aneuploidy screening at 11–22 weeks of gestation. Three steps were employed for cfDNA analyses. First, cfDNA was sequenced. Second, the effect of GC bias was corrected using normalization of samples as well as LOESS and linear regressions. Finally, statistical analysis was performed after selecting a set of reference samples optimally adapted to a test sample from the whole reference samples. We evaluated our approach by performing cfDNA testing to assess the risk of trisomies 13, 18, and 21 using the sets of extracted reference samples.ResultsThe adaptive selection algorithm presented here was used to choose a more optimized reference sample, which was evaluated by the coefficient of variation (CV), demonstrated a lower CV and higher sensitivity than standard approaches. Our adaptive approach also showed that fetal aneuploidies could be detected correctly by clearly splitting the z scores obtained for positive and negative samples.ConclusionsWe show that our adaptive reference selection algorithm for optimizing trisomy detection showed improved reliability and will further support practitioners in reducing both false negative and positive results.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-016-0222-5) contains supplementary material, which is available to authorized users.