The global use of online communities has exploded to involve hundreds of millions of users. Despite the tremendous social impact and business opportunities afforded by these communities, little information systems (IS) research has addressed them – especially in a cross-cultural context. Our research proposes an online community self-disclosure model, tested in a cross-cultural setting using data provided by French and British working professionals. Our model is based on social exchange theory (SET) and social penetration theory (SPT), as well as on cross-cultural theory related to individualism-collectivism. SET explains that individuals engage in relationships when the perceived costs associated with the relationship are less than the expected benefits. SPT extends SET to explain that individuals participate in self-disclosure to foster relationships – reciprocation is the primary benefit of self-disclosure, whereas risk is the foundational cost of self-disclosure. Our study established several important findings: positive social influence to use an online community increases online community self-disclosure; reciprocity increases self-disclosure; online community trust increases self-disclosure; and privacy risk beliefs decrease self-disclosure. Meanwhile, a tendency toward collectivism increases self-disclosure. We further found that French participants had higher scores on horizontal individualism than British participants. Several other findings and their implications for practice are also discussed.

The efficient market hypothesis (EMH) is a cornerstone of financial economics. The EMH asserts that security prices fully reflect all available information and that the stock market prices securities at their fair values. Therefore, investors cannot consistently ldquobeat the marketrdquo because stocks reside in perpetual equilibrium, making research efforts futile. This flies in the face of the conventional nonacademic wisdom that astute analysts can beat the market using technical or fundamental stock analysis. The purpose of this research is to partially assess whether technical analysts, who predict future stock prices by analyzing past stock prices, can consistently achieve a trading return that outperforms the stock market average return. This is tested using knowlege engineering experimentation with one price history pattern - the ldquobull flag stock chartrdquo - which signals technical analysts of a future stock market price increase. A recognizer for the stock chart pattern is built using a template-matching technique from pattern recognition. The recognizer and associated trading rules are then tested by simulating trading on over 35 years of daily closing price data for the New York stock exchange composite index. The experiment is then replicated using the horizontal rotation or mirror image pattern of the ldquobull flagrdquo (or ldquobear flagrdquo stock chart) that signals a future stock market decrease. Results are systematic, statistically significant, and fail to confirm the null hypothesis based on a corollary to the EMH: that profit realized from trading determined by this heuristic method is no better than what would be realized from trading decisions based on random choice.