Despite the importance of decisions regarding international brand names, research on brand naming has focused primarily on English name creation. The authors conceptualize the local brand-name creation process in a multilingual international market. The authors present a framework that incorporates (1) a linguistic analysis of three translation methods—phonetic (i.e., by sound), semantic (i.e., by meaning), and phonosemantic (i.e., by sound plus meaning)—and (2) a cognitive analysis focusing on the impact of primes and expectations on consumer name evaluations. Using dual English-and-Chinese brand names, the authors show that the effectiveness of the translation depends on the emphasis of the original English name (versus the Chinese name) and the method of translation used previously for brand names within the same category.

Brand experience is conceptualized as sensations, feelings, cognitions, and behavioral responses evoked by brand-related stimuli that are part of a brand's design and identity, packaging, communications, and environments. The authors distinguish several experience dimensions and construct a brand experience scale that includes four dimensions: sensory, affective, intellectual, and behavioral. In six studies, the authors show that the scale is reliable, valid, and distinct from other brand measures, including brand evaluations, brand involvement, brand attachment, customer delight, and brand personality. Moreover, brand experience affects consumer satisfaction and loyalty directly and indirectly through brand personality associations.

Abstract Previous research has shown that consumers respond differently to decisions made by humans versus algorithms. Many tasks, however, are not performed by humans anymore but entirely by algorithms. In fact, consumers increasingly encounter algorithm-controlled products, such as robotic vacuum cleaners or smart refrigerators, which are steered by different types of algorithms. Building on insights from computer science and consumer research on algorithm perception, this research investigates how consumers respond to different types of algorithms within these products. This research compares high-adaptivity algorithms, which can learn and adapt, versus low-adaptivity algorithms, which are entirely pre-programmed, and explore their impact on consumers' product preferences. Six empirical studies show that, in general, consumers prefer products with high-adaptivity algorithms. However, this preference depends on the desired level of product outcome range—the number of solutions a product is expected to provide within a task or across tasks. The findings also demonstrate that perceived algorithm creativity and predictability drive the observed effects. This research highlights the distinctive role of algorithm types in the perception of consumer goods and reveals the consequences of unveiling the mind of the machine to consumers.

Using text-mining, the authors develop version 1.0 of the Relevance to Marketing (R2M) Index, a dynamic index that measures the topical and timely relevance of academic marketing articles to marketing practice. The index assesses topical relevance drawing on a dictionary of marketing terms derived from 50,000 marketing articles published in practitioner outlets from 1982 to 2019. Timely relevance is based on the prevalence of academic marketing topics in practitioner publications at a given time. The authors classify topics into four quadrants based on their low/high popularity in academia and practice —“Desert,” “Academic Island,” “Executive Fields,” and “Highlands”—and score academic articles and journals: Journal of Marketing has the highest R2M score, followed by Marketing Science, Journal of Marketing Research, and Journal of Consumer Research. The index correlates with practitioner judgments of practical relevance and other relevance measures. Because the index is a work in progress, the authors discuss how to overcome current limitations and suggest correlating the index with citation counts, altmetrics, and readability measures. Marketing practitioners, authors, and journal editors can use the index to assess article relevance, and academic administrators can use it for promotion and tenure decisions (see www.R2Mindex.com). The R2M Index is thus not only a measurement instrument but also a tool for change.