Learning in Multi-Agent Information Systems - A Survey from IS Perspective

Multiagent systems (MAS), long studied in artificial intelligence, have recently become popular in mainstream IS research. This resurgence in MAS research can be attributed to two phenomena: the spread of concurrent and distributed computing with the advent of the web; and a deeper integration of computing into organizations and the lives of people, which has led to increasing collaborations among large collections of interacting people and large groups of interacting machines. However, it is next to impossible to correctly and completely specify these systems a priori, especially in complex environments. The only feasible way of coping with this problem is to endow the agents with learning, i.e., an ability to improve their individual and/or system performance with time. Learning in MAS has therefore become one of the important areas of research within MAS. In this paper we present a survey of important contributions made by IS researchers to the field of learning in MAS, and present directions for future research in this area

Determinants of online leisure travel planning decision processes :a segmented approach

D.B.A. ThesisThere is an abundance of information sources on the Internet that consumers use to plan and book their travel. This information reflects the fact that travel comprises a significant part of the business conducted through the web. Consumers are sometimes faced with a complex task of making purchasing decisions in the dynamic and fast-paced medium of the Internet. In spite of the importance of travel and the intricacies of the decision process, an integrated framework that identifies the various determinants of the online leisure travel planning decision process and how they interact, is largely absent in travel literature. This study aims to make a contribution by extracting from relevant literature useful elements that could comprise such a framework. It also uses several phases of qualitative research to refine the framework, and then a quantitative assessment of data collected from an online questionnaire completed by 1,198 respondents to test specific components of the framework that deal with online travel booking intention. In the final model building stage, three logistic regression models were compared. The first is a parsimonious one containing key determinants that lead to online travel booking intention. These determinants emerged from theoretical frameworks of the theory of reasoned action and innovation adoption theory. The second Model used strictly involvement, motivation, and knowledge variables that are thought to influence online booking intention. The third Model included a combination of relevant predictor variables from the other two Models. The relationship between various demographics and online travel booking intention was investigated yielding some interesting insights. Consequently, this study recommends these demographic variables be considered in segmenting travelers to find those more likely to book online. The determinants of online leisure travel booking decision processes could be used in conjunction with demographic variables to more accurately predict leisure travel website usage