Allen Weiss

Many firm-sponsored websites rely on consumers to provide product reviews, answer each other’s questions, and generate new product ideas. Getting lurkers in these communities to contribute is one problem; for example, only 1% of Amazon’s users actually write reviews (Nielsen 2006). However, we focus on a related but separate problem, namely, how to encourage current contributors to stay engaged and continuing to contribute. Often these web sites use symbolic incentives, such as badges and points, to encourage contributions. By drawing on a volunteer workforce, such online communities have the potential to transform business models. However, little is known about how symbolic incentives affect the continuation of contribution behavior.

We propose that symbolic incentives have positive and negative effects on contribution behavior that vary by contributor characteristics. We develop novel, behaviorally observable, variables that characterize online contributors. We examine how these variables moderate the effects of symbolic incentives on contribution behavior. In addition to examining how symbolic incentives affect contribution behavior, and how these vary as a function of contributor characteristics, we add to the literature on contribution behavior by differentiating three types of contribution decisions: (a) whether to contribute in the first place, (b) how much to contribute, and (c) the quality of contributions.

Using data from an online community, in which members pose questions that other members answer, we demonstrate that an increase in the symbolic incentives associated with a question can have positive as well as negative effects on contribution likelihood, amount contributed, and contribution quality but that these effects depend on contributors’ prior behavior. Symbolic incentives have positive effects on the likelihood, amount, and quality of contributions as the depth and breadth of prior assistance increase. However, symbolic incentives reduce the amount contributed and the quality of contributions by those with a greater number of co-contributors who tend to contribute to popular topics. These results suggest that the effectiveness of symbolic incentives in increasing the likelihood, amount, and quality contributed depend on whether incentives reinforce or conflict with users’ reasons for participating.

Individuals often describe objects in their world in terms of perceptual dimensions that span a variety of modalities; the visual (e.g., brightness: dark-bright), the auditory (e.g., loudness: quiet-loud), the gustatory (e.g., taste; sour-sweet), the tactile (e.g., hardness: soft vs. hard) and the kinesthetic (e.g., speed: slow-fast). We ask whether individuals use perceptual dimensions to discriminate emotions from one another. Participants rated the extent to which features anchoring 29 perceptual dimensions (e.g., temperature, pressure, texture, taste) are associated with each of eight emotions (anger, fear, sadness, guilt, contentment, gratitude, pride and excitement). Results revealed that perceptual dimensions differentiate positive from negative emotions and high arousal from low arousal emotions. Most critically, they also differentiate among emotions that are similar in arousal and valence (e.g., high arousal negative emotions such as anger and fear). Specific features that anchor these dimensions (e.g., hot vs. cold) are also differentially associated with emotions. The implications of the results and novel research questions that stem from them are discussed.

Individuals often describe objects in their world in terms of perceptual dimensions that span a variety of modalities; the visual (e.g., brightness: dark-bright), the auditory (e.g., loudness: quiet-loud), the gustatory (e.g., taste; sour-sweet), the tactile (e.g., hardness: soft vs. hard) and the kinesthetic (e.g., speed: slow-fast). We ask whether individuals use perceptual dimensions to discriminate emotions from one another. Participants rated the extent to which features anchoring 29 perceptual dimensions (e.g., temperature, pressure, texture, taste) are associated with each of eight emotions (anger, fear, sadness, guilt, contentment, gratitude, pride and excitement). Results revealed that perceptual dimensions differentiate positive from negative emotions and high arousal from low arousal emotions. Most critically, they also differentiate among emotions that are similar in arousal and valence (e.g., high arousal negative emotions such as anger and fear). Specific features that anchor these dimensions (e.g., hot vs. cold) are also differentially associated with emotions. The implications of the results and novel research questions that stem from them are discussed.

Although marketing managers and customers often seek information to make decisions or gain knowledge, we know little about what affects individual judgments of the value of received information. We argue that, in settings where information comes from multiple information providers with whom the information seeker have had no prior interaction (i.e., no face-to-face interaction), aspects of information provider behavior influence seeker judgments of the value of received information. Analysis of a web forum for marketing professionals shows that an information provider’s current behavior, that is, how quickly, how frequently, and how elaborately they respond to an information query; and an information provider’s past behavior, that is, their reputation as an expert in a given domain and their reputation as a generalist, impact judgments of information value. Importantly, these effects are moderated by the information seeker’s goal orientation; in particular, whether they are trying to learn something new (a learning orientation) or whether they are seeking advice about what to do (a decision-making orientation).

Moving from free to 'free & fee' for any product or service represents a challenge to managers, especially when consumers have plenty of alternatives. For on an online content provider, this paper quantifies (1) the sources of long-run revenue loss (through attracting fewer free subscribers) and (2) how the firm’s marketing actions affect its revenue gains (through attracting paid subscribers). As to the former, the authors identify revenue loss from three sources: the direct effects of charging for part of the online content, the targeted free-to-fee conversion e-mails and the reduced effectiveness of search engine referrals and general emails. As to the latter, the authors find a differential response of monthly and yearly subscriptions to marketing actions. Price promotions are more effective at stimulating new monthly subscriptions, while marketing communication efforts are more effective at generating new yearly subscriptions.