Many firms are moving to make virtual interfaces their primary, or even sole, points of customer contact. In this environment, some traditional service quality dimensions that determine customer satisfaction, such as the physical appearance of facilities, employees, and equipment, and employees' responsiveness and empathy are unobservable. In contrast, trust may play a central role here in enhancing customer satisfaction. We model trust as an endogenously formed entity that ultimately impacts customer satisfaction, and we elucidate the linkages between trust and other factors related to the performance of the online service provider and to the service environment. The model is validated using two samples—one comprising 225 online investors of a large online broker, and the other comprising 203 members of the American Association of Individual Investors (AAII). The findings suggest that perceived trustworthiness of an online broker is a significant antecedent to investors' satisfaction, and that perceived environmental security and perceived operational competence impact the formation of trust. The results have important managerial implications.

There is an increasing attention in information systems to be able to predict outcomes using search frequency on popular portals. This growing literature focuses on revealing demand patterns of individual assets (e.g., products, stocks, services). However, users typically search many different assets together (e.g., correlated searches) and leave a digital footprint, which can help provide insights on the behaviors of a group of assets. Furthermore, such group behavior can be used to predict outcomes (e.g., demand, stock returns) in the future. We analyze the underlying behavior of distinct subnetworks formed by correlated user searches for multiple items in the stock market and use such information for return prediction. Using cosearch data for stocks from Yahoo! Finance, we find 50–79 search clusters representing 230–349 stocks among Russell 3000 stocks at different time periods. These clusters reveal interesting habitats where the returns of stocks within a cluster tend to comove after controlling for known determinants of comovement. When a stock enters (departs) a cluster, the focal stock return comoves (detaches) with the cluster returns. Thus, search cluster–based habitats reveal aggregate investment preferences and are more granular than fundamental-based habitats. We find that search-based habitats can also improve return predictability of related stocks. This paper was accepted by Chris Forman, information systems.