Accessing the Web from mobile handheld devices has become increasingly common. However, accomplishing that task remains challenging mainly due to the physical constraints of handheld devices and the static presentation of Web pages. Adapting the presentation of Web pages is, therefore, critical to enabling effective mobile Web browsing and information searching. Based on cognitive fit theory and information foraging theory, we propose a novel hybrid approach to adapting Web page presentation that integrates three types of adaptation techniques, namely tree-view, hierarchical text summarization, and colored keyword highlighting. By following the design science research framework, we implemented the proposed approach on handheld devices and empirically evaluated the effects of presentation adaptation on mobile Web browsing. The results show that presentation adaptation significantly improves user performance and perception of mobile Web browsing. We also discover that the positive impact of presentation adaptation is moderated by the complexity of an information search task. The findings have significant theoretical and practical implications for the design and implementation of mobile Web applications.
Majority influence is the attempt by a majority of group members to impose their common position on group dissenters during group decision making. Because of globalization, the use of cross-cultural teams in group tasks is becoming increasingly common. The objective of this study was to investigate how national culture, social presence, and group diversity may affect majority influence in a group decision-making context. A total of 183 groups participated in a large-scale empirical experiment at multiple sites. The results show that the national culture of group minorities has a significant impact on majority influence and that the use of computer-mediated communication can reduce majority influence. The findings have both theoretical and practical implications for improving the outcome and the effectiveness of group decision making in cross-cultural environments.
The great potential of speech recognition systems in freeing users' hands while interacting with computers has inspired a variety of promising applications. However, given the performance of the state-of-the-art speech recognition technology today, widespread acceptance of speech recognition technology would not be realistic without designing and developing new approaches to detecting and correcting recognition errors effectively. In seeking solutions to the above problem, identifying cues to error detection (CERD) is central. Our survey of the extant literature on the detection and correction of speech recognition errors reveals that the system-initiated, data-driven approach is dominant, but that heuristics from human users have been largely overlooked. This may have hindered the advance of speech technology. In this research, we propose a user-centered approach to discovering CERD. User studies are carried out to implement the approach. Content analysis of the collected verbal protocols lends itself to a taxonomy of CERD. The CERD discovered in this study can improve our knowledge on CERD by not only validating CERD from a user's perspective but also suggesting promising new CERD for detecting speech recognition errors. Moreover, the analysis of CERD in relation to error types and other CERD provides new insights into the context where specific CERD are effective. The findings of this study can be used to not only improve speech recognition output but also to provide context-aware support for error detection. This will help break the barrier for mainstream adoption of speech technology in a variety of information systems and applications.
The increased chance of deception in computer-mediated communication and the potential risk of taking action based on deceptive information calls for automatic detection of deception. To achieve the ultimate goal of automatic prediction of deception, we selected four common classification methods and empirically compared their performance in predicting deception. The deception and truth data were collected during two experimental studies. The results suggest that all of the four methods were promising for predicting deception with cues to deception. Among them, neural networks exhibited consistent performance and were robust across test settings. The comparisons also highlighted the importance of selecting important input variables and removing noise in an attempt to enhance the performance of classification methods. The selected cues offer both methodological and theoretical contributions to the body of deception and information systems research.