Despite the popularity and high quality of machine-made products, handmade products have not disappeared, even in product categories in which machinal production is common. The authors present the first systematic set of studies exploring whether and how stated production mode (handmade vs. machine-made) affects product attractiveness. Four studies provide evidence for the existence of a positive handmade effect on product attractiveness. This effect is, to an important extent, driven by perceptions that handmade products symbolically "contain love." The authors validate this love account by controlling for alternative value drivers of handmade production (effort, product quality, uniqueness, authenticity, and pride). The handmade effect is moderated by two factors that affect the value of love. Specifically, consumers indicate stronger purchase intentions for handmade than machine-made products when buying gifts for their loved ones but not for more distant gift recipients, and they pay more for handmade gifts when purchased to convey love than simply to acquire the best-performing product.

Changing brand attitudes by pairing a brand with affectively laden stimuli such as celebrity endorsers or pleasant pictures is called evaluative conditioning. We show that this attitude change can occur in two ways, depending on how brands and affective stimuli are presented. Attitude change can result from establishing a memory link between brand and affective stimulus (indirect attitude change) or from direct "affect transfer" from affective stimulus to brand (direct attitude change). Direct attitude change is significantly more robust than indirect attitude change, for example, to changes in the valence of affective stimuli (unconditioned stimulus revaluation: e.g., endorsers falling from grace), to interference by subsequent information (e.g., advertising clutter), and to persuasion knowledge activation (e.g., consumer suspicion about being influenced). Indirect evaluative conditioning requires repeated presentations of a brand with the same affective stimulus. Direct evaluative conditioning requires simultaneous presentation of a brand with different affective stimuli. (c) 2010 by JOURNAL OF CONSUMER RESEARCH, Inc..

Consumers use brand names and product features to predict the performance of products. Various learning models offer hypotheses about the source of these predictive associations. Spreading-activation models hypothesize that cues acquire predictive value as a consequence of being present during the acquisition of product performance information. Least mean squares connectionist models hypothesize that any one cue acquires predictive value only to the extent that it can predict differences in performance that are not already predicted by other available cues. Five studies in the context of portfolio-branding strategies provide evidence supporting a least mean squares connectionist model. As predicted by this model, results show that subbranding and ingredient-branding strategies can protect brands from dilution in some situations but can promote dilution in other situations.

This research contributes to the current understanding of language effects in advertising by uncovering a previously ignored mechanism shaping consumer response to an increasingly globalized marketplace. We propose a language-specific episodic trace theory of language emotionality to explain how language influences the perceived emotionality of marketing communications. Five experiments with bilingual consumers show (1) that textual information (e.g., marketing slogans) expressed in consumers' native language tends to be perceived as more emotional than messages expressed in their second language, (2) that this effect is not uniquely due to the activation of stereotypes associated to specific languages or to a lack of comprehension, and (3) that the effect depends on the frequency with which words have been experienced in native- versus second-language contexts. (c) 2008 by JOURNAL OF CONSUMER RESEARCH, Inc..

As in other social sciences, published findings in consumer research tend to overestimate the size of the effect being investigated, due to both file drawer effects and abuse of researcher degrees of freedom, including opportunistic analysis decisions. Given that most effect sizes are substantially smaller than would be apparent from published research, there has been a widespread call to increase power by increasing sample size. We propose that, aside from increasing sample size, researchers can also increase power by boosting the effect size. If done correctly, removing participants, using covariates, and optimizing experimental designs, stimuli, and measures can boost effect size without inflating researcher degrees of freedom. In fact, careful planning of studies and analyses to maximize effect size is essential to be able to study many psychologically interesting phenomena when massive sample sizes are not feasible.