The information contained in the spillover of sentiment across markets that has been the focus of recent studies has been reflected in the future stock returns. Chan et al. (2002) and Pan and Poteshman (2006) have indicated that the spillover of trading volume across stock and options markets is informative. The significant results presented by Han (2008) indicate that sentiment results in index options exhibiting a volatility smile.
Wide employment of agents in human–computer interaction (HCI) design has proven to be an effective way to construct robust yet flexible software architecture, in which information communication between the user and the technical system is mediated by many kinds of agents. The new interaction paradigm, evolved from traditional HCI, can be called human–agent interaction (HAI). In HAI, users are provided with a novel social collaborator during their tasks: the software agent (Wooldridge & Jennings, 1995). Obviously, this new interaction element opens a series of design considerations. At its core, HAI invites a more consequent evaluation and application of social psychological concepts to guide the agent’s behaviors during interaction.
Hence, a key question is what we can learn from social interaction research in the human context in order to design user-friendly, adaptive, and effective HAI (e.g., Nass & Moon 2000; Reeves & Nass, 1996). This exploitation of social psychological concepts in interaction design is a logical extension of the user psychological approach to human–technology research (Moran, 1981; Oulasvirta & Saariluoma, 2004)—a paradigm approach that is especially effective in projects where the product or technology is new or where the audience characteristics and habits are not yet well defined (Goschnick & Sterling, 2002; Murray, Schell, & Willis, 1997). Thus, it is ideal for contemporary HAI research pursuing psychologically-based, integral agent architectures (Pasquier, Rahwan, Dignum, & Sonenberg, 2006; Rahwan, 2005). In this vein, it is essential to evaluate core issues such as interpersonal communication, influence, persuasion, and decision making in interaction (e.g., Cialdini, 1984; Eagly & Chaiken, 1984; McGuire, 1969, 1985; Petty & Cacioppo, 1981; Sewell, 1989; Zimbardo & Leippe, 1991).
One of the factors that are thought to be important to make some firms more productive than others is exporting. Bartelsman and Doms (2000) survey of the literature on productivity that uses longitudinal micro-level data sets points out to the link between productivity and exporting as one of the factors this literature has focused on (the rest of factors are regulation, management/ownership, technology and human capital). Studies by Aw and Hwang (1995) on Taiwan; Bernard and Jensen (1995) (1999) on the US; Bernard and Wanger (1997) on Germany; Clerides, Lach and Tybout (1998) on Colombia, Mexico and Marocco; Aw, Chung and Roberts (2000) on Taiwan and South Korea; Girma, Greenaway and Kneller (2002) on the UK, provide evidence on the fact that export oriented firms are more productive than non-exporters.
Sunk costs are the main argument outlined to explain why exporters are more efficient than non-exporters, in particular the existence of higher sunk entry costs for exporters with respect to non-exporters. The argument comes from models of industry dynamics Jovanovic (1982) and Hopenhayn (1992)- and applies also to entry and exit to export markets as suggested by Aw, Chen and Roberts (1997). According to this argument, differences in sunk entry costs can explain productivity differences between exporters and domestic-oriented firms. Building on these ideas Roberts and Tybout (1997), Clerides, Lach and Tybout (1998) and Bernard and Jensen (2001) have developed models of the decision to export. The result that firm’s previous export status is a determinant of the decision to export is interpreted, in term of these models, as a favorable evidence to the existence of sunk entry cost in the export market.
