Nahl University of Hawaii

The purpose of this book is to establish a focus on affective and emotional dimensions in information behavior (IB) research, based upon recent theoretical developments and research findings in information science and the cognate fields of cognitive science, psychology, business, education, ethnomethodology, communication, neuroscience, and computer science. The affective paradigm established in this book traces its origins to early work in education and cognitive science. In 1950, Erik Erikson published his stillinfluential theory of socio-emotional development from birth to old age. In 1956, Benjamin Bloom, David Krathwohl, and Bertram Masia published the taxonomy of the affective domain that is still used to develop instruction according to the principle of internalization. In the late 1960s, Herbert Simon (1967) identified emotion as a major challenge to cognitive science due to emotion’s determining effect on cognition. This book introduces the emerging research areas of affective issues in situated information seeking and use, and the affective paradigm applied to IB in a variety of populations, cultures, and contexts. The book is primarily concerned with IB research findings on the user perspective, the user experience, and how emotional aspects can be interpreted, mitigated, or enhanced through design that is informed by use and by users who directly participate in information design. The chapters in the book present IB research relating to a variety of theories, Web technologies, everyday settings, populations, communities, ages, ethnicities, and cultures that are engaged in information seeking and use, and the research employed a variety of qualitative and quantitative methodologies that demonstrate how emotion can be measured in diverse settings. Over the past 35 years, social sciences research has developed ethnographic, observational, and self-reporting methodologies capable of examining the higher mental and emotional processes involved in naturalistic or situated behavior. This user-based methodological trend is driven by the need to reduce artificiality by obtaining concurrent process data that is collected as