This article presents a classification method that makes possible the detection of important decision strategies that people use in nonrisky multiattribute decision tasks. The classification method incorporates a multimethod approach that combines elements of both structural modeling and process tracing. We begin with a discussion of nine common characteristics of decision strategies and then classify 13 decision strategies on the basis of these characteristics. Next, we demonstrate that six of the characteristics can be used to develop one outcome-based and three process-based metrics. In addition, we present an algorithm that shows how to use the metrics. We demonstrate that using our metrics in conjunction with a measure for direction of search allows identification of most of the 13 decision strategies. Finally, we outline the potential of our research for the design of decision support systems such as online shops.

Business and information systems engineering (BISE) is currently undergoing a phase of increasing internationalization and the U.S.-based Information Systems (IS) discipline is often considered as an ideal. Using language-statistical evaluations of articles in German-and Englishspeaking journals, the authors highlight the thematic trends and differences between the BISE and the IS discipline. BISE more consistently dwells on recent developments than IS research does, but is also subject to a greater risk of placing too much emphasis on fads. BISE is topically more diverse and more specific than IS research. Buzzwords in IS usually show a higher abstraction level than in BISE articles. This may be interpreted as a reduced orientation towards practice, but may also be considered as a strength of IS research in terms of developing universal theories.

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Pedagogical agents (PAs) are virtual characters in computer-based learning environments. PAs can train humans in various domains. Here, a PA cues subjects to learn vocabulary items through enactment, i.e., to perform an illustrative gesture while learning a word. It is well known that enactment impacts memory. Also, imitation is a natural mechanism driving learning. Combining both enactment and imitation could improve memory even more. In a within-subjects study, 44 school children learned 45 vocabulary items according to three conditions: an audio-visual baseline, an observation condition (participants watched the PA during enactment) and an imitation condition (participants imitated the PA's gestures). We documented learning progress by cued recall tests. Over four days, we found that, compared to the baseline and to mere observation, imitation of enactment significantly enhanced memory for words in the foreign language.

In this paper we introduce a new generation of language trainers: intelligent virtual agents (IVAs) with human appearance and the capability to teach foreign language vocabulary. We report results from studies that we have conducted with Billie, an IVA employed as a vocabulary trainer, as well as research findings on the acceptance of the agent as a trainer by adults and children. The results show that Billie can train humans as well as a human teacher can and that both adults and children accept the IVA as a trainer. The advantages of IVAs are multiple. First, their teaching methods can be based on neuropsychological research findings concerning memory and learning practice. Second, virtual teachers can provide individualized training. Third, they coach users during training, are always supportive, and motivate learners to train. Fourth, agents will reside in the user's mobile devices and thus be at the user's disposal everywhere and anytime. Agents in apps will make foreign language training accessible to anybody at low cost. This will enable people around the world, including physically, financially, and geographically disadvantaged persons, to learn a foreign language and help to facilitate multilingualism.