A Coevolution Model of Network Structure and User Behavior: The Case of Content Generation in Online Social Networks

2019 | Information Systems Research | Citations: 7

Authors: Bhattacharya, Prasanta; Phan, Tuan Q.; Bai, Xue; Airoldi, Edoardo M.

Abstract: With the rapid growth of online social network sites (SNSs), it has become imper ... Expand

Abstract: With the rapid growth of online social network sites (SNSs), it has become imperative for platform owners and online marketers to quantify what factors drive content production on these platforms. Previous research identified challenges in modeling these factors statistically using observational data, where the key difficulty is the inability of conventional methods to disentangle the effects of network formation and network influence on content generation from the subsequent feedback effect of newly generated content on network structure. In this paper, we adopt and enhance an actor-oriented continuous-time statistical model that enables the joint estimation of the coevolution of the users’ social network structure and of the amount of content they produce, using a Markov chain Monte Carlo–based simulation approach. Specifically, we offer a method to analyze nonstationary and continuous-time behavioral data, typically recorded in social media ecosystems, in the presence of network effects and other observable and unobservable user-specific covariates. The proposed method can help disentangle network effects of interest from feedback effects on the network. We apply our model to social network and public posting data over six months to find that (1) users tend to connect with others that have similar posting behavior; (2) however, after doing so, these users tend to diverge in their posting behavior, and (3) peer influence effects are sensitive to the strength of the posting behavior. More broadly, the proposed method provides researchers and practitioners with a statistically rigorous approach to analyze network effects in observational data. Our results lead to insights and recommendations for SNS platform owners on how to sustain an active and viable community.The online appendix is available at https://doi.org/10.1287/isre.2018.0790. Collapse

Topics: social network network effect social media user behavior social influence

Methods: simulation longitudinal research digital trace data robustness check quantitative observational study

Theories: co-evolution theory

Prescribing Response Strategies to Manage Customer Opinions: A Stochastic Differential Equation Approach

2019 | Information Systems Research | Citations: 16

Authors: Yang, Mingwen; Zheng, Zhiqiang (Eric); Mookerjee, Vijay

Abstract: Today, the reputation of a firm is profoundly influenced by user opinions expresse ... Expand

Abstract: Today, the reputation of a firm is profoundly influenced by user opinions expressed in online consumer reviews. Managing these opinions is, therefore, critical for the success of firms. We study the problem of devising an appropriate opinion management strategy (or response strategy) for a firm to respond to online customer reviews. To unravel the underlying mechanics of the problem, we develop a stochastic differential equation model that describes the evolution of review ratings over time for a given response strategy employed by the firm. This model is validated using data on online customer reviews and firm responses from two of the world’s largest online travel agents. When pitted against popular benchmark models, such as autoregressive moving average, generalized autoregressive conditional heteroscedasticity, moving average, exponential smoothing, and naive method, our approach not only achieves comparable (often better) predictive performance, it is also able to incorporate the response strategy into the data-generation process underlying the review ratings. Our approach, therefore, is not just predictive, but, more importantly, one that can be used in a prescriptive sense, namely to prescribe a response strategy that controls review ratings in a desired manner. We operationalize the theoretical response strategy in our stochastic model to an operational prescription that a firm can implement and show the applicability of our approach for different business objectives, such as mean control, meanvariance control, and service-level control. Finally, we demonstrate the flexibility of the stochastic differential equation model by extending it to encompass multiple state variables. Collapse

Topics: marketing management value creation accounting user-generated content online review

Methods: autoregressive moving average model generalized autoregressive conditional heteroskedasticity model longitudinal research experimental group descriptive statistic