This study examines how affect influences self-disclosure on social network (SN) websites. We test two competing models that build on direct causation theory and affect heuristic theory, respectively. In a direct effect model, affect steers self-disclosure, independent of cognitive costÐbenefit appraisals. The indirect effect model instead suggests that affect influences self-disclosure by adjusting perceptions of benefits and costs. The empirical comparison of the models relies on survey data from more than 500 university students. Overall, affect influences self-disclosure indirectly by adjusting the benefits people perceive. In particular, affect toward self-disclosure and toward SN websites relate positively to self-disclosure motivators; their perceived values appear amplified in the presence of positive affect. We also offer a plausible, alternative explanation of the observed positive relationship between privacy risk and self-disclosure according to an indirect effect model, in which self-disclosure is driven mainly by motivators, whereas the effects of inhibitors depend a posteriori on self-disclosure. These findings call for a reconsideration of any exclusive focus on the direct impacts of affect on technology use, as is common in previous research, and suggest the importance of affective factors for understanding social technology uses and managing customer relationships. > >
A successful enterprise resource planning (ERP) system ultimately requires loyal useÑproactive, extended use and willingness to recommend such uses to othersÑby employees. Building on interactional psychology literature and situational strength theory, we emphasize the importance of psychological commitment, in addition to behavioral manifestation, in a multilevel model of loyal use. Our empirical test of the model uses data from 485 employees and 166 information system professionals in 47 large Taiwanese organizations. Individual-level analyses suggest that perceived benefits and workload partially mediate the effects of perceived information quality (IQ) and system quality (SQ) on loyal use. Cross-level analyses show that IQ at the organizational level alleviates the negative effect of an employee's perceived workload on loyal use; organization-level SQ and service-oriented organizational citizenship behaviors (SOCBs) of internal information systems staff reduce the influence of employees' perceived benefits. Overall, our findings suggest that IQ, SQ, and SOCBs at the organizational level influence employees' loyal use in ways different from their effects at the individual level, and seem to affect individuals' costÐbenefit analyses. This study contributes to extant literature by considering the SOCBs of the internal information systems group that have been overlooked by most prior research. Our findings offer insights for managers who should find ways to create positive, salient, shared views of IQ, SQ, and SOCBs in the organization to nourish and foster employees' loyal use of an ERP system, including clearly demonstrating the system's utilities and devising viable means to reduce the associated workload. > >
Effective search support is an important tool for helping individuals deal with the problem of information overload. This is particularly true in the field of nanotechnology, where information from patents, grants, and research papers is growing rapidly. Guided by cognitive fit and cognitive load theories, we develop an advanced Web-based system, Nano Mapper, to support users' search and analysis of nanotechnology developments. We perform controlled experiments to evaluate the functions of Nano Mapper. We examine users' search effectiveness, efficiency, and evaluations of system usefulness, ease of use, and satisfaction. Our results demonstrate that Nano Mapper enables more effective and efficient searching, and users consider it to be more useful and easier to use than the benchmark systems. Users are also more satisfied with Nano Mapper and have higher intention to use it in the future. User evaluations of the analysis functions are equally positive.
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.
Word mismatch represents a fundamental information retrieval challenge that has become increasingly important as electronic document repositories (e.g., Web resources, digital libraries) grow in number and sheer volume. In general, word mismatch refers to the phenomenon in which a concept is described by different terms in user queries and in source documents. Query expansion represents a promising avenue to address such problems. Previous research predominantly approaches query expansion on the basis of global or local analysis. However, these approaches emphasize a global perspective rather than taking a topic-specific view of term associations. As a consequence, their effectiveness can be severely constrained when the document corpus spans a diverse set of topics. In this study, we propose a topic-based approach for query expansion and develop and empirically evaluate two novel methods--namely, nonfuzzy and fuzzy topic-based query expansion--to address word mismatch problems. According to our evaluation results, the proposed topic-based approach is more effective than a benchmark global analysis method, particularly when user queries consist of multiple query terms.