Peter H. Reingen

Bio: Peter H. Reingen is an academic researcher. The author has contributed to research in topics: Social relation & Interpersonal ties. The author has an hindex of 3, co-authored 3 publications receiving 2599 citations.

Social Ties and Word-of-Mouth Referral Behavior

Abstract: This article presents a network analysis of word-of-mouth referral behavior in a natural environment. The relational properties of tie strength and homophily were employed to examine referral behavior at micro and macro levels of inquiry. The study demonstrates different roles played by weak and strong social ties. At the macro level, weak ties displayed an important bridging function, allowing information to travel from one distinct subgroup of referral actors to another subgroup in the broader social system. At the micro level, strong and homophilous ties were more likely to be activated for the flow of referral information. Strong ties were also perceived as more influential than weak ties, and they were more likely to be utilized as sources of information for related goods.

Brand Congruence in Interpersonal Relations: A Social Network Analysis

Abstract: Previous studies dealing with the notion of brand congruence suffer from questionable methods of group determination, suspect demonstrations of brand congruence effects, and inadequate attention paid to types of social relation. To overcome these shortcomings, the present study uses graph-theoretic social network techniques to examine interpersonal relationships and brand choice behavior in natural environments. The brand choices of individuals in a social relationship were compared to those of unrelated individuals across various products, types of social relation, and types of basic sociological structure (dyad, clique, and 2-plex). While significant brand congruence effects were obtained, they were clustered in a few products mediated by types of social relation. Conspicuousness of the product, as traditionally defined, was found to be insufficient to account for these findings.

On Inducing Compliance With Requests

Abstract: Six behavioral influence strategies of inducing people to comply with a request to donate money were investigated in a field experiment. The findings, replicated with a different subject population, demonstrate the efficacy of several alternatives to a direct request for compliance. Possible processes that could explain the results are discussed.

Maximizing the spread of influence through a social network

Abstract: Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target?We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.

Maximizing the Spread of Influence through a Social Network

Abstract: Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target?We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.

The dynamics of viral marketing

Abstract: We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a ‘long tail’ where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective.

Using Online Conversations to Study Word-of-Mouth Communication

Abstract: Managers are very interested in word-of-mouth communication because they believe that a product's success is related to the word of mouth that it generates. However, there are at least three significant challenges associated with measuring word of mouth. First, how does one gather the data? Because the information is exchanged in private conversations, direct observation traditionally has been difficult. Second, what aspect of these conversations should one measure? The third challenge comes from the fact that word of mouth is not exogenous. While the mapping from word of mouth to future sales is of great interest to the firm, we must also recognize that word of mouth is an outcome of past sales. Our primary objective is to address these challenges. As a context for our study, we have chosen new television (TV) shows during the 1999-2000 seasons. Our source of word-of-mouth conversations is Usenet, a collection of thousands of newsgroups with diverse topics. We find that online conversations may offer an easy and cost-effective opportunity to measure word of mouth. We show that a measure of the dispersion of conversations across communities has explanatory power in a dynamic model of TV ratings.

Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective

Abstract: The effects of word-of-mouth (WOM) communications and specific attribute information on product evaluations were investigated. A face-to-face WOM communication was more persuasive than a printed format (experiment 1). Although a strong WOM effect was found, this effect was reduced or eliminated when a prior impression of the target brand was available from memory or when extremely negative attribute information was presented (experiment 2). The results suggest that diverse, seemingly unrelated judgmental phenomena—such as the vividness effect, the perseverance effect, and the negativity effect—can be explained through the accessibility-diagnosticity model.