In the context of the wars between the upstart Internet retailers and the existing bricks-and-mortar retailers, many e-marketing techniques were invented. This article develops a single unifying and theoretically based taxonomy for e-marketing techniques: the e-marketing mix. Drawing on the paradigms of exchange, relationships, and digital interactions in networks, 11 e-marketing functions are identified that form the elements of the e-marketing mix. Nine of the 11 e-marketing functions are considered basic, while 7 functions moderate the effects of others and are termed overlapping. The 11 e-marketing functions provide a categorization of the e-marketing techniques. Compared to the conventional marketing mix, the e-marketing mix has more overlapping elements and directly represents personalization, an aspect of segmentation, as a basic function. The existence of multiple elements that are basic and overlapping in the e-marketing mix indicates that integration across elements should be more commonplace compared to the traditional marketing mix.

The creation and sharing of user-generated content such as product reviews has become increasingly “social,” particularly in online communities where members are connected. While some online communities have used monetary rewards to motivate product review contributions, empirical evidence regarding the effectiveness of such rewards remains limited. We examine the possible moderating effect of social connectedness (measured as the number of friends) on publicly offered monetary rewards using field data from an online review community. This community saw an (unexpected) overall decrease in total contributions after introducing monetary rewards for posting reviews. Further examination across members finds a strong moderating effect of social connectedness. Specifically, contributions from less-connected members increased by 1,400%, while contributions from more-connected members declined by 90%. To corroborate this effect, we rule out multiple alternative explanations and conduct robustness checks. Our findings suggest that token-sized monetary rewards, when offered publicly, can undermine contribution rates among the most connected community members. Data and the online appendix are available at https://doi.org/10.1287/mksc.2016.1022

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The impact of a decision calculus model on decision quality is assessed in a laboratory setting. An experimental design assesses the effects of: (a) the size of the problem (i.e., the number of control units over which allocations are to be made), (b) the noise-to-signal ratio in the market and (c) individual differences among the model users. The aspects of individual differences studied are: (1) time taken on the task, (2) mathematical ability and (3) cognitive style. The main experiment involved 96 subjects in a full factorial design. In general, the decision calculus model had a beneficial impact on a broad range of decision quality measures, particularly profit achievement.marketing