In a social network, adoption probability refers to the probability that a social entity will adopt a product, service, or opinion in the foreseeable future. Such probabilities are central to fundamental issues in social network analysis, including the influence maximization problem. In practice, adoption probabilities have significant implications for applications ranging from social network-based target marketing to political campaigns, yet predicting adoption probabilities has not received sufficient research attention. Building on relevant social network theories, we identify and operationalize key factors that affect adoption decisions: social influence, structural equivalence, entity similarity, and confounding factors. We then develop the locally weighted expectation-maximization method for Naïve Bayesian learning to predict adoption probabilities on the basis of these factors. The principal challenge addressed in this study is how to predict adoption probabilities in the presence of confounding factors that are generally unobserved. Using data from two large-scale social networks, we demonstrate the effectiveness of the proposed method. The empirical results also suggest that cascade methods primarily using social influence to predict adoption probabilities offer limited predictive power and that confounding factors are critical to adoption probability predictions.
Web site navigability refers to the degree to which a visitor can follow a Web site's hyperlink structure to successfully find information with efficiency and ease. In this study, we take a data-driven approach to measure Web site navigability using Web data readily available in organizations. Guided by information foraging and information-processing theories, we identify fundamental navigability dimensions that should be emphasized in metric development. Accordingly, we propose three data-driven metrics-namely, power, efficiency, and directness-that consider Web structure, usage, and content data to measure a Web site's navigability. We also develop a Web mining-based method that processes Web data to enable the calculation of the proposed metrics. We further implement a prototype system based on the Web mining-based method and use it to assess the navigability of two sizable, real-world Web sites with the metrics. To examine the analysis results by the metrics, we perform an evaluation study that involves these two sites and 248 voluntary participants. The evaluation results show that user performance and assessments are consistent with the analysis results revealed by our metrics. Our study demonstrates the viability and practical value of data-driven metrics for measuring Web site navigability, which can be used for evaluative, diagnostic, or predictive purposes.
The continuous growth of e-commerce makes it critical for firms to understand consumers' search behavior so that e-commerce Web sites and the underlying information systems can be designed to better cater to consumers' needs. This paper extends the classic search model to analyze online consumer search behavior. The analytical results suggest how consumers' search depth is influenced by a variety of factors such as search cost, individual consumer difference, and product characteristics. Evidence is provided using clickstream data of online searches and purchases of music CDs, computer hardware, and airline tickets during the period from July 2002 to December 2002 collected by an Internet marketing company, ComScore Inc. Compared with the search depth reported in previous works, this study finds that consumers are searching more intensely before purchasing online. This reflects the evolution of Internet users and the growth of online retail business.