Data collected through multi-item Likert scales that contain reversed items often exhibit problems, such as unexpected factor structures and diminished scale reliabilities. These problems arise when respondents select responses on the same side of the scale neutral point for both reversed and nonreversed items, a phenomenon the authors call "misresponse." Across four experiments and an exploratory study using published data, the authors find that misresponse to reversed Likert items averaged approximately 20%, twice the level identified as problematic in previous simulation studies. Counter to prevailing thought, the patterns of misresponse and response latency across manipulated items could not be attributed to respondent inattention or acquiescence. Instead, the pattern supports an item verification difficulty explanation, which holds that task complexity, and thus misresponse and response latency, increases with the number of cognitive operations required for a respondent to compare a scale item with his or her belief. The observed results are well explained by the constituent comparison model.

No abstract

No abstract

No abstract

No abstract

No abstract

To construct a price judgment, consumers compare a focal price with one or more reference prices. However, reference price operationalizations in the brand choice literature use single-point summary measures that cannot account for several distributional effects. To account for effects beyond the first moment of the reference price distribution, the authors specify price judgments in models of brand choice in accordance with range–frequency theory. The findings indicate that range–frequency price judgments provide a more complete specification of reference price effects and become more important with an increase in the second and third moments of a reference price distribution. The data also indicate that range effects are stronger for coupon users and frequency effects are stronger for consumers exposed to a trend of prices. The results have several implications for choice modeling, pricing theory, and pricing strategy.

The probability of e-tail success may be related to the products sold and the service that online shoppers receive. We investigate these possibilities using data collected on 755 e-tailers from BizRate.com in 2000 and 2004. The results indicate that the likelihood of success is negatively related to the e-tailer's reliance on selling experience goods and positively related to the number of ordering methods and e-tailer prominence, while the relationship between e-tail success and the number of product categories sold is moderated by the presence of features that allowshoppers to search for products. Further, we find that while successful e-tailers typically added service-related features between 2000 and 2004, these e-tailers reduced shoppers’ contact ability over these years. This research has implications for marketing strategy pertaining to the products e-tailers should sell and the site features and services that e-tailers should provide.