The idea that consumers use brands to express their identities has led many companies to reposition their products from focusing on functional attributes to focusing on how they fit into a consumer's lifestyle. This repositioning is welcomed by managers who believe that by positioning their brands as means for self-expression, they are less likely to go head-to-head with their direct competitors. However, the authors argue that by doing so, these companies expose themselves to much broader, cross-category competition for a share of a consumer's identity. Thus, they propose that consumers' need for self-expression through brands is finite and can be satiated when consumers are exposed to self-expressive brands. Moreover, they argue that consumers' need for self-expression can be satiated not only by a brand's direct competitors but also by brands from unrelated product categories, nonbrand means of self-expression, and self-expressive behavioral acts. The authors examine these propositions in a series of five empirical studies that provide converging evidence in support of the notion that the need for selfexpression can be satiated, thus weakening preferences for lifestyle brands.

When customers journey from a need to a purchase decision and beyond, they rarely do so alone. This article introduces the social customer journey, which extends prior perspectives on the path to purchase by explicitly integrating the important role that social others play throughout the journey. The authors highlight the importance of “traveling companions,” who interact with the decision maker through one or more phases of the journey, and they argue that the social distance between the companion(s) and the decision maker is an important factor in how social influence affects that journey. They also consider customer journeys made by decision-making units consisting of multiple individuals and increasingly including artificial intelligence agents that can serve as surrogates for social others. The social customer journey concept integrates prior findings on social influences and customer journeys and highlights opportunities for new research within and across the various stages. Finally, the authors discuss several actionable marketing implications relevant to organizations’ engagement in the social customer journey, including managing influencers, shaping social interactions, and deploying technologies.

Recent managerial evidence and academic research has suggested that consumer decisions are influenced not only by the prices of individual items but also by a retailer's price image, which reflects a consumer's impression of the overall price level of a retailer. Despite the increasing importance of price image in marketing theory and practice, existing research has not provided a clear picture of how price images are formed and how they influence consumer behavior. This article addresses this discrepancy by offering a comprehensive framework delineating the key drivers of price image formation and their consequences for consumer behavior. Contrary to conventional wisdom that assumes price image is mainly a function of a retailer's average price level, this research identifies several price-related and nonprice factors that contribute to price image formation. The authors further identify conditions in which these factors can overcome the impact of the average level of prices, resulting in a low price image despite the retailer's relatively high prices, as well as conditions in which people perceive a retailer to have a high price image despite its relatively low average price level.

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We present our experience with QUIC, an encrypted, multiplexed, and low-latency transport protocol designed from the ground up to improve transport performance for HTTPS traffic and to enable rapid deployment and continued evolution of transport mechanisms. QUIC has been globally deployed at Google on thousands of servers and is used to serve traffic to a range of clients including a widely-used web browser (Chrome) and a popular mobile video streaming app (YouTube). We estimate that 7% of Internet traffic is now QUIC. We describe our motivations for developing a new transport, the principles that guided our design, the Internet-scale process that we used to perform iterative experiments on QUIC, performance improvements seen by our various services, and our experience deploying QUIC globally. We also share lessons about transport design and the Internet ecosystem that we learned from our deployment.