We seek explanatory models of how and why AI systems work in particular environments. We are not

Affect factors have gained researchers ’ attention in a number of fields. The Information Systems (IS) literature, however, shows some gaps and inconsistencies regarding the role of affect factors in human–computer interaction. Building upon prior research, this study aims at a better understanding of affect factors by clarifying their relationships with each other and with other primary user acceptance factors. Two affect variables that are different in nature were examined: computer playfulness (CP) and perceived enjoyment (PE). We theoretically clarified and methodologically verified their mediating effects and causal relationships with other primary factors influencing user technology acceptance, namely perceived ease of use (PEOU), perceived usefulness (PU), and behavioral intention (BI). Quantitative data were analyzed using R.M. Baron and D. Kenny’s (1986) method for mediating effects and P.R. Cohen, A. Carlsson, L. Ballesteros, and R.S. Amant’s (1993) path analysis method for causal relationships.These analyses largely supported our hypotheses. Results from this research indicate that a PE→PEOU causal direction is favored, and PEOU partially mediates PE’s impacts on PU whereas PE fully mediates CP’s impact on PEOU. With the increased interest in various affect factors in user technology acceptance and use, our study sheds light on the role of affect factors from both theoretical and methodological perspectives. Practical implications are discussed as well. Along with unprecedented advances in information systems (IS), user technology acceptance research remains a focal topic. After decades of research, this area is considered by some researchers to be one of the most mature areas in

Abstract. The discovery of causal relationships from empirical data is an important problem in machine learning. In this paper the attention is focused on the inference o fprobabilis tic causal relationships, for which two different approaches, namely Glymour et al.'s approach based on constraints on correlations and Pearl and Verma's approach based on conditional independencies, have been proposed. These methods differ both in the kind of constraints they consider while selecting a causal model and in the way they search the model which better fits to the sample data. Preliminary experiments show that they are complementary in several aspects. Moreover, the method of conditional independence can be easily extended to the case in which variables have a nominal or ordinal domain. In this case, symbohc learning algorithms can be exploited in order to derive the causal law from the causal model. 1