Browsing by Author "Yang, Haiyang"

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    How mortality salience hurts brands with different personalities
    (Elsevier B.V., 2024) Landgraf, Polina; Yang, Haiyang; Stamatogiannakis, Antonios; Agencia Estatal de Investigación; Ivey Business School; Western University; https://ror.org/02jjdwm75
    From deadly disease outbreaks to crimes and terrorism,consumers often experience mortality salience (MS). This research examines how MS-inducing events impact brand evaluations. We propose that under MS,consumers avoid experiencing change. Because consumers perceive brands with an exciting personality to be more closely associated with the notion of change than brands with other types of personality,the onset of MS is more likely to hurt the evaluations of exciting brands than those of other brands. Study 1,a large-scale secondary data study,showed that the 9/11 terror attacks degraded consumers’ evaluations of exciting brands but not of other types of brands. Subsequent studies demonstrated causality and the underlying mechanism. In Study 2,experimentally inducing MS decreased evaluations of an exciting brand but not of a control brand. Using a process-by-moderation approach,Study 3 showed that manipulating consumers’ perception of the extent to which an exciting brand was associated with the notion of change moderated the negative impact of MS on brand evaluations. Studies 4a-4b demonstrated that consumers’ tendency to avoid experiencing change mediated the detrimental effect of MS on the evaluations of an exciting brand but not of a control brand. These findings add to the literature on branding and offer practical insights for brand management during crises. © 2024 The Authors
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    Publication
    Tensor Approximation for Multidimensional and Multivariate Data
    (Springer Science and Business Media Deutschland GmbH, 2021) Pajarola, Renato; Suter, Susanne; Yang, Haiyang; Ballester, Rafael; Seventh Framework Programme; Swiss National Science Foundation; https://ror.org/02jjdwm75
    Tensor decomposition methods and multilinear algebra are powerful tools to cope with challenges around multidimensional and multivariate data in computer graphics,image processing and data visualization,in particular with respect to compact representation and processing of increasingly large-scale data sets. Initially proposed as an extension of the concept of matrix rank for 3 and more dimensions,tensor decomposition methods have found applications in a remarkably wide range of disciplines. We briefly review the main concepts of tensor decompositions and their application to multidimensional visual data. Furthermore,we will include a first outlook on porting these techniques to multivariate data such as vector and tensor fields.