This article proposes that compared with a promotion regulatory focus, a prevention focus increases sensitivity to the advertiser's manipulative intent. Specifically, when message cues make manipulative intent moderately salient, prevention-focused people are more likely to activate persuasion knowledge and give less favorable brand evaluations than promotion-focused people. When message cues make manipulative intent highly salient or when manipulative intent is not salient, brand evaluations do not differ across regulatory foci. In addition, externally priming suspicion of manipulative intent makes promotion-focused people react similarly to prevention-focused people, suggesting that regulatory focus affects vigilance against persuasion.

No abstract

This article demonstrates that variations in ceiling height can prime concepts that, in turn, affect how consumers process information. We theorized that when reasonably salient, a high versus low ceiling can prime the concepts of freedom versus confinement, respectively. These concepts, in turn, can prompt consumers' use of predominately relational versus item-specific processing. Three studies found support for this theorizing. On a variety of measures, ceiling height-induced relational or item-specific processing was indicated by people's reliance on integrated and abstract versus discrete and concrete ideation. Hence, this research sheds light on when and how ceiling height can affect consumers' responses.T here appears to be widespread belief that ceiling height can affect the quality of indoor consumption experiences. Fischl and Gärling (2004) found that ceiling height ranked among the top three architectural details that influenced consumers' psychological well-being. Much anecdotal evidence also supports this view. A home development company that uses design ideas inspired by the guru of transcendental meditation maintains that homes with higher ceilings induce clearer and improved thinking, more energy, and better health among residents (Bivins 1997). Airplane manufacturers seem to concur that higher ceilings can enhance consumers' consumption experience, even if the increased height is only illusory. Such manufacturers use numerous techniques to engender the illusion of increased vertical space or volume in plane interiors, including repositioning overhead baggage bins, installing gently arched illuminated ceiling panels, and affixing wavy mirrors on the bulkheads beneath overhead storage bins (Lunsford and Michaels 2002).Despite such anecdotal evidence that ceiling height exerts *Joan Meyers-Levy is professor of marketing at the Carlson School of Management, University of Minnesota, Minneapolis, MN 55455 ( jmeyerslevy@csom.umn.edu). Rui (Juliet) Zhu is assistant professor of marketing at the Sauder School of Business, University of British Columbia, Vancouver, BC, Canada V6G 3J3 ( juliet.zhu@sauder.ubc.ca). Both authors contributed equally to this work. Financial support from the Social Sciences and Humanities Research Council of Canada is gratefully acknowledged. Corresponding author: jmeyerslevy@csom.umn.edu. John Deighton served as editor and Gavan Fitzsimons served as associate editor for this article. Electronically published June 1, 2007a critical influence on consumers, we were unable to uncover any theory or research that explains how, when, and why ceiling height might exert an effect. This article seeks to address this issue by investigating the thesis that ceiling height may affect the very manner in which consumers process information and thus how they respond to products. To illustrate, suppose that you were shopping for a sleek new coffee-table and paused to evaluate how sleek one of the contenders truly appeared to be. We propose that different types of concepts might be activated or pr...

Second authorship is shared equally by Chen and Dasgupta. The authors thank the Power Information Network, an affiliate of J.D. Power and Associates, for the data used in this study. They are also grateful to Akshay Rao for his helpful comments. Financial support from the Social Sciences and Humanities Research Council of Canada is gratefully acknowledged. Dilip Soman served as associate editor for this article. RUI (JULIET) ZHU, XINLEI (JACK) CHEN, and SRABANA DASGUPTA*When consumers decide to upgrade to a new or better product, they often trade in their currently owned or used product for the new one. The authors examine whether such trade-in behavior, in which consumers must negotiate the price for both the new and the used product, affects their willingness-to-pay price for the new good. Drawing on research on mental accounting, the authors reason that when consumers engage in a transaction involving a trade-in (i.e., when they act as both buyer and seller simultaneously), they place more importance on getting a good value for the used product than on getting a good price for the new product. As a result, such consumers exhibit a higher willingness-to-pay price for the new product than consumers who just buy the new product alone. The results from a series of laboratory experiments provide systematic support for this hypothesis. Finally, the authors lend external validity to their results by confirming the hypothesis using real-world transaction data from the automobile market.

Traffic forecasting is a challenging problem due to the complexity of jointly modeling spatio‐temporal dependencies at different scales. Recently, several hybrid deep learning models have been developed to capture such dependencies. These approaches typically utilize convolutional neural networks or graph neural networks (GNNs) to model spatial dependency and leverage recurrent neural networks (RNNs) to learn temporal dependency. However, RNNs are only able to capture sequential information in the time series, while being incapable of modeling their periodicity (e.g., weekly patterns). Moreover, RNNs are difficult to parallelize, making training and prediction less efficient. In this work we propose a novel deep learning architecture called Traffic Transformer to capture the continuity and periodicity of time series and to model spatial dependency. Our work takes inspiration from Google’s Transformer framework for machine translation. We conduct extensive experiments on two real‐world traffic data sets, and the results demonstrate that our model outperforms baseline models by a substantial margin.