The Future of Weak Ties

“The Strength of Weak Ties” (Granovetter 1973) arguably contains the most influential sociological theory of networks. Granovetter’s subtle, nuanced theory has spawned countless follow-on ideas, many of which are immortalized in the 35,000 manuscripts that cite the original work. Among these are notable theories in their own right, such as Ron Burt’s structural holes theory (Burt 1992), which itself has generated a sizable body of knowledge about the social structure of competition

IT Assets, Organizational Capabilities, and Firm Performance: How Resource Allocations and Organizational Differences Explain Performance Variation

Despite evidence of a positive relationship between information technology (IT) investments and firm performance, results still vary across firms and performance measures. We explore two organizational explanations for this variation: differences in firms’ IT investment allocations and their IT capabilities. We develop a theoretical model of IT resources, defined as the combination of specific IT assets and organizational IT capabilities. We argue that investments into different IT assets are guided by firms’ strategies (e.g., cost leadership or innovation) and deliver value along performance dimensions consistent with their strategic purpose. We hypothesize that firms derive additional value per IT dollar through a mutually reinforcing system of organizational IT capabilities built on complementary practices and competencies. Empirically, we test the impact of IT assets, IT capabilities, and their combination on four dimensions of firm performance: market valuation, profitability, cost, and innovation. Our results—based on data on IT investment allocations and IT capabilities in 147 U.S. firms from 1999 to 2002—demonstrate that IT investment allocations and organizational IT capabilities drive differences in firm performance. Firms’ total IT investment is not associated with performance, but investments in specific IT assets explain performance differences along dimensions consistent with their strategic purpose. In addition, a system of organizational IT capabilities strengthens the performance effects of IT assets and broadens their impact beyond their intended purpose. The results help explain variance in returns to IT capital across firms and expand our understanding of alignment between IT and organizations. We illustrate our findings with examples from a case study of 7-Eleven JapanNYU, Stern School of Business, IOMS department, Center for Digital Economy Researc

Identifying Social Influence in Networks Using Randomized Experiments

The recent availability of massive amounts of networked data generated by email, instant messaging, mobile phone communications, micro blogs, and online social networks is enabling studies of population-level human interaction on scales orders of magnitude greater than what was previously possible.1\u272 One important goal of applying statistical inference techniques to large networked datasets is to understand how behavioral contagions spread in human social networks. More precisely, understanding how people influence or are influenced by their peers can help us understand the ebb and flow of market trends, product adoption and diffusion, the spread of health behaviors such as smoking and exercise, the productivity of information workers, and whether particular individuals in a social network have a disproportion ate amount of influence on the system

Designing Viral Product Features for Broader Reach

Companies increasingly rely on “network” and “viral” marketing within their communication strategies. This study showed that providing viral products with specific features can increase their diffusion substantially. Products that were enabled to send automated notifications within a user’s local Facebook network upon adoption generated a 450 % higher adoption rate among Facebook friends compared with products without any viral features. Products that enabled adopters to send personal invitations to install the app generated an increase in the adoption rate by friends by 750 % more than in the control group. Although each personalized referral had a much stronger impact, notifications outperformed invitations in overall adoption. Automated notifications require no effort, and therefore substantially more messages were generated. The number of users who took the effort to send out personalized invitations was much smaller. A simulation of adoption beyond immediate individual networks showed that the passive-broadcast app experienced a 246 % increase in the rate of adoption, whereas adding active-personalized viral messaging capabilities generated only an additional 98 % increase, compared with the group without viral features

Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

In recent years, there has been significant interest in understanding users' online content consumption patterns. But, the unstructured, high-dimensional, and dynamic nature of such data makes extracting valuable insights challenging. Here we propose a model that combines the simplicity of matrix factorization with the flexibility of neural networks to efficiently extract nonlinear patterns from massive text data collections relevant to consumers' online consumption patterns. Our model decomposes a user's content consumption journey into nonlinear user and content factors that are used to model their dynamic interests. This natural decomposition allows us to summarize each user's content consumption journey with a dynamic probabilistic weighting over a set of underlying content attributes. The model is fast to estimate, easy to interpret and can harness external data sources as an empirical prior. These advantages make our method well suited to the challenges posed by modern datasets. We use our model to understand the dynamic news consumption interests of Boston Globe readers over five years. Thorough qualitative studies, including a crowdsourced evaluation, highlight our model's ability to accurately identify nuanced and coherent consumption patterns. These results are supported by our model's superior and robust predictive performance over several competitive baseline methods

Creating Social Contagion through Viral Product Design: A Randomized Trial of Peer Influence in Networks

We examine how firms can create word-of-mouth peer influence and social contagion by designing viral features into their products and marketing campaigns. To econometrically identify the effectiveness of different viral features in creating social contagion, we designed and conducted a randomized field experiment involving the 1.4 million friends of 9,687 experimental users on Facebook.com. We find that viral features generate econometrically identifiable peer influence and social contagion effects. More surprisingly, we find that passive-broadcast viral features generate a 246% increase in peer influence and social contagion, whereas adding active-personalized viral features generate only an additional 98% increase. Although active-personalized viral messages are more effective in encouraging adoption per message and are correlated with more user engagement and sustained product use, passive-broadcast messaging is used more often, generating more total peer adoption in the network. Our work provides a model for how randomized trials can identify peer influence in social networks

Creating Social Contagion through Viral Product Design: A Randomized Trial of Peer Influence in Networks

We examine how firms can create word of mouth peer influence and social contagion by incorporating viral features into their products. Word of mouth is generally considered to more effectively promote peer influence and contagion when it is personalized and active. Unfortunately, econometric identification of peer influence is non-trivial. We therefore use a randomized field experiment to test the effectiveness of passive-broadcast and active-personalized viral messaging capabilities in creating peer influence and social contagion among the 1.4 million friends of 9,687 experimental users. Surprisingly, we find that passive-broadcast viral messaging generates a 246% increase in local peer influence and social contagion, while adding active-personalized viral messaging only generates an additional 98% increase in contagion. Although active-personalized messaging is more effective per message and is correlated with more user engagement and product use, it is used less often and therefore generates less total peer adoption in the network than passive-broadcast messaging

Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment

We leverage the newly emerging business analytical capability to rapidly deploy and iterate large-scale, microlevel, in vivo randomized experiments to understand how social influence in networks impacts consumer demand. Understanding peer influence is critical to estimating product demand and diffusion, creating effective viral marketing, and designing “network interventions” to promote positive social change. But several statistical challenges make it difficult to econometrically identify peer influence in networks. Though some recent studies use experiments to identify influence, they have not investigated the social or structural conditions under which influence is strongest. By randomly manipulating messages sent by adopters of a Facebook application to their 1.3 million peers, we identify the moderating effect of tie strength and structural embeddedness on the strength of peer influence. We find that both embeddedness and tie strength increase influence. However, the amount of physical interaction between friends, measured by coappearance in photos, does not have an effect. This work presents some of the first large-scale in vivo experimental evidence investigating the social and structural moderators of peer influence in networks. The methods and results could enable more effective marketing strategies and social policy built around a new understanding of how social structure and peer influence spread behaviors in society