Sinan Aral

Computational Social Science

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

Identifying influential and susceptible members of social networks

Who Influences Who? A goal in social science is how to assess peoples influence over one. Aral and Walker (p. 337, published online 21 June) describe a generalized method for identifying influential and susceptible members of social networks based on large-scale in vivo randomized experimentation. The method was used to estimate peer effects in consumer demand for a commercial Facebook application in a representative sample of 12 million Facebook users. Older users were more influential than younger users, women were more influential over men than men over women, and married individuals were the least susceptible to influence in the decision to adopt the product studied. A randomized experiment based on product adoption among Facebook friends identified trend setters and followers. Identifying social influence in networks is critical to understanding how behaviors spread. We present a method that uses in vivo randomized experimentation to identify influence and susceptibility in networks while avoiding the biases inherent in traditional estimates of social contagion. Estimation in a representative sample of 1.3 million Facebook users showed that younger users are more susceptible to influence than older users, men are more influential than women, women influence men more than they influence other women, and married individuals are the least susceptible to influence in the decision to adopt the product offered. Analysis of influence and susceptibility together with network structure revealed that influential individuals are less susceptible to influence than noninfluential individuals and that they cluster in the network while susceptible individuals do not, which suggests that influential people with influential friends may be instrumental in the spread of this product in the network.

Life in the network: the coming age of computational social science

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

Social Influence Bias: A Randomized Experiment

Follow the Leader? The Internet has increased the likelihood that our decisions will be influenced by those being made around us. On the one hand, group decision-making can lead to better decisions, but it can also lead to “herding effects” that have resulted in financial disasters. Muchnik et al. (p. 647) examined the effect of collective information via a randomized experiment, which involved collaboration with a social news aggregation Web site on which readers could vote and comment on posted comments. Data were collected and analyzed after the Web site administrators arbitrarily voted positively or negatively (or not at all) as the first comment on more than 100,000 posts. False positive entries led to inflated subsequent scores, whereas false negative initial votes had small long-term effects. Both the topic being commented upon and the relationship between the poster and commenter were important. Future efforts will be needed to sort out how to correct for such effects in polls or other collective intelligence systems in order to counter social biases. A social news aggregation Web site was used to test whether prior ratings influence others to create bias in rating behavior. Our society is increasingly relying on the digitized, aggregated opinions of others to make decisions. We therefore designed and analyzed a large-scale randomized experiment on a social news aggregation Web site to investigate whether knowledge of such aggregates distorts decision-making. Prior ratings created significant bias in individual rating behavior, and positive and negative social influences created asymmetric herding effects. Whereas negative social influence inspired users to correct manipulated ratings, positive social influence increased the likelihood of positive ratings by 32% and created accumulating positive herding that increased final ratings by 25% on average. This positive herding was topic-dependent and affected by whether individuals were viewing the opinions of friends or enemies. A mixture of changing opinion and greater turnout under both manipulations together with a natural tendency to up-vote on the site combined to create the herding effects. Such findings will help interpret collective judgment accurately and avoid social influence bias in collective intelligence in the future.

Introduction to the Special Issue—Social Media and Business Transformation: A Framework for Research

Social media are fundamentally changing the way we communicate, collaborate, consume, and create. They represent one of the most transformative impacts of information technology on business, both within and outside firm boundaries. This special issue was designed to stimulate innovative investigations of the relationship between social media and business transformation. In this paper we outline a broad research agenda for understanding the relationships among social media, business, and society. We place the papers comprising the special issue within this research framework and identify areas where further research is needed. We hope that the flexible framework we outline will help guide future research and develop a cumulative research tradition in this area.

Social science. Computational social science.

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

The Diversity-Bandwidth Tradeoff

The authors propose that a trade-off between network diversity and communications bandwidth regulates access to novel information because a more diverse network structure increases novelty at a cost of reducing information flow. Received novelty then depends on whether (a) the information overlap is small enough, (b) alters’ topical knowledge is shallow enough, and (c) alters’ knowledge stocks refresh slowly enough to justify bridging structural holes. Social network and e-mail content from an executive recruiting firm show that bridging ties can actually offer less novelty for these reasons, suggesting that the strength of weak ties and structural holes depend on brokers’ information environments.

Which Came First, IT or Productivity? The Virtuous Cycle of Investment and Use In Enterprise Systems

While it is now well established that IT intensive firms are more productive, a critical question remains: Does IT cause productivity or are productive firms simply willing to spend more on IT? We address this question by examining the productivity and performance effects of enterprise systems investments in a uniquely detailed and comprehensive data set of 623 large, public U.S. firms. The data represent all U.S. customers of a large vendor during 1998–2005 and include the vendor’s three main enterprise system suites: Enterprise Resource Planning (ERP), Supply Chain Management (SCM), and Customer Relationship Management (CRM). A particular benefit of our data is that they distinguish the purchase of enterprise systems from their installation and use. Since enterprise systems often take years to implement, firm performance at the time of purchase often differs markedly from performance after the systems “go live.” Specifically, in our ERP data, we find that purchase events are uncorrelated with performance while go-live events are positively correlated. This indicates that the use of ERP systems actually causes performance gains rather than strong performance driving the purchase of ERP. In contrast, for SCM and CRM, we find that performance is correlated with both purchase and golive events. Because SCM and CRM are installed after ERP, these results imply that firms that experience performance gains from ERP go on to purchase SCM and CRM. Our results are robust against several alternative explanations and specifications and suggest that a causal relationship between ERP and performance triggers additional IT adoption in firms that derive value from their initial investment. These results provide an explanation of simultaneity in IT value research that fits with rational economic decision-making: Firms that successfully implement IT, react by investing in more IT. Our work suggests replacing “either-or” views of causality with a positive feedback loop conceptualization in which successful IT investments initiate a “virtuous cycle” of investment and gain. Our work also reveals other important estimation issues that can help researchers identify relationships between IT and business value.

Commentary---Identifying Social Influence: A Comment on Opinion Leadership and Social Contagion in New Product Diffusion

Isuggest five broad directions for future research on social influence and opinion leadership that could, if appropriately addressed, dramatically improve how we conceptualize and manage social contagions in a variety of domains.

Mining Face-to-Face Interaction Networks using Sociometric Badges: Predicting Productivity in an IT Configuration Task

Social network theories (e.g. Granovetter 1973, Burt 1992) and information richness theory (Daft & Lengel 1987) have both been used independently to understand knowledge transfer in information intensive work settings. Social network theories explain how network structures covary with the diffusion and distribution of information, but largely ignore characteristics of the communication channels (or media) through which information and knowledge are transferred. Information richness theory on the other hand focuses explicitly on the communication channel requirements for different types of knowledge transfer but ignores the population level topology through which information is transferred in a network. This paper aims to bridge these two sets of theories to understand what types of social structures are most conducive to transferring knowledge and improving work performance in face-to-face communication networks. Using a novel set of data collection tools, techniques and methodologies, we were able to record precise data on the face-to-face interaction networks, tonal conversational variation and physical proximity of a group of IT configuration specialists over a one month period while they conducted their work. Linking these data to detailed performance and productivity metrics, we find four main results. First, the face-to-face communication networks of productive workers display very different topological structures compared to those discovered for email networks in previous research. In face-to-face networks, network cohesion is positively correlated with higher worker productivity, while the opposite is true in email communication. Second, network cohesion in face-to-face networks is associated with even higher work performance when executing complex tasks. This result suggests that network cohesion may complement information-rich communication media for transferring the complex or tacit knowledge needed to complete complex tasks. Third, the most effective network structures for latent social networks (those that characterize the network of available communication partners) differ from in-task social networks (those that characterize the network of communication partners that are actualized during the execution of a particular task). Finally, the effect of cohesion is much stronger in face-to-face networks than in physical proximity networks, demonstrating that information flows in actual conversations (rather than mere physical proximity) are driving our results. Our work bridges two influential bodies of research in order to contrast face-to-face network structure with network structure in electronic communication. We also contribute a novel set of tools and techniques for discovering and recording precise face-to-face interaction data in real world work settings.