By lowering the costs of gathering and sharing information and offering new ways to learn about products before purchase, the Internet reduces traditional distinctions between search and experience goods. At the same time, differences in the type of information sought for search and experience goods can precipitate differences in the process through which consumers gather information and make decisions online. A preliminary experiment shows that though there are significant differences in consumers' perceived ability to evaluate product quality before purchase between search and experience goods in traditional retail environments, these differences are blurred in online environments. An analysis of the online behavior of a representative sample of U.S. consumers shows that consumers spend similar amounts of time online gathering information for both search and experience goods, but there are important differences in the browsing and purchase behavior of consumers for these two types of goods. In particular, experience goods involve greater depth (time per page) and lower breadth (total number of pages) of search than search goods. In addition, free riding (purchasing from a retailer other than the primary source of product information) is less frequent for experience than for search goods. Finally, the presence of product reviews from other consumers and multimedia that enable consumers to interact with products before purchase has a greater effect on consumer search and purchase behavior for experience than for search goods. Building on this idea, Nelson (1970Nelson ( , 1974 classifies products into search and experience goods according to consumers' ability to obtain product quality information before purchase. Nelson argues that consumers conduct minimal prepurchase information search for experience goods but perform extensive search for search goods. However, several authors (e.g., Alba et al. 1997;Klein 1998;Peterson, Balasubramanian, and Bronnenberg 1997) have suggested that because the Internet enables consumers to learn from the experiences of others and to gather product information that is often difficult to obtain in offline settings (Klein 1998;Lynch and Ariely 2000), it makes all attributes searchable and erases differences between search and experience goods.An alternative perspective on information search is provided by research on how consumers acquire and process information to make decisions (Bettman et al. 1993;Ha and Hoch 1989;Lurie 2004;Lynch and Ariely 2000;Shugan 1980). This research shows that different types of information are associated with different cognitive processes that affect the way information is acquired, the amount of information acquired, and the time spent processing each piece of information (Johnson, Bellman, and Lohse 2003;Johnson and Payne 1985;Payne, Bettman, and Johnson 1988). If consumers seek different information for search and experience goods, this perspective implies that online search and purchase behavior may be different for these two types of goods.This art...

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link Gamification and Mobile Marketing Effectiveness AbstractA variety of business sectors have been buffeted by the diffusion of mobile technology, a trend that presents a variety of difficult challenges but interesting opportunities to marketers. One such opportunity is gamification, which, one hopes, will enhance appeal to mobile consumers.Our sense from both personal experience and the literature is that the gamified mobile apps currently offered by firms mostly miss the mark. We provide a systematic overview of game design and note how principles derived from that field are highly applicable to gamification in mobile marketing settings. We are aided by the work of Schell (2008), whose Elemental GameTetrad Model allows us to offer a coherent look at how gamification should affect mobile marketing outcomes.

Prior research shows that positive online reviews are less valued than negative reviews. The authors argue that this is due to differences in causal attributions for positive versus negative information such that positive reviews tend to be relatively more attributed to the reviewer (vs. product experience) than negative reviews. The presence of temporal contiguity cues, which indicate that review writing closely follows consumption, reduces the relative extent to which positive reviews are attributed to the reviewer and mitigates the negativity bias. An examination of 65,531 Yelp.com restaurant reviews shows that review value is negatively related to review valence but that this negative relationship is absent for reviews that contain temporal contiguity cues. A series of lab studies replicates these findings and suggests that temporal contiguity cues enhance the value of a positive review and increase the likelihood of choosing a product with a positive review by changing reader beliefs about the cause of the review.

A large number of visualization tools have been created to help decision makers understand increasingly rich databases of product, customer, sales force, and other types of marketing information. This article presents a framework for thinking about how visual representations are likely to affect the decision processes or tasks that marketing managers and consumers commonly face, particularly those that involve the analysis or synthesis of substantial amounts of data. From this framework, the authors derive a set of testable propositions that serve as an agenda for further research. Although visual representations are likely to improve marketing manager efficiency, offer new insights, and increase customer satisfaction and loyalty, they may also bias decisions by focusing attention on a limited set of alternatives, increasing the salience and evaluability of less diagnostic information, and encouraging inaccurate comparisons. Given this, marketing managers are advised to subject insights from visual representations to more formal analysis.

The tailoring of a firm's marketing mix to the individual customer is the essence of one-to-one marketing. In this paper, we distinguish between two forms of one-to-one marketing: personalization and customization. Personalization occurs when the firm decides what marketing mix is suitable for the individual. It is usually based on previously collected customer data. Customization occurs when the customer proactively specifies one or more elements of his or her marketing mix. We summarize key challenges and knowledge gaps in understanding both firm and customer choices in one-to-one markets. We conclude with a summary of research opportunities.