Language in international business: a review and agenda for future research

A fast growing number of studies demonstrates that language diversity influences almost all management decisions in modern multinational corporations. Whereas no doubt remains about the practical importance of language, the empirical investigation and theoretical conceptualization of its complex and multifaceted effects still presents a substantial challenge. To summarize and evaluate the current state of the literature in a coherent picture informing future research, we systematically review 264 articles on language in international business. We scrutinize the geographic distributions of data, evaluate the field’s achievements to date in terms of theories and methodologies, and summarize core findings by individual, group, firm, and country levels of analysis. For each of these dimensions, we then put forward a future research agenda. We encourage scholars to transcend disciplinary boundaries and to draw on, integrate, and test a variety of theories from disciplines such as psychology, linguistics, and neuroscience to gain a more profound understanding of language in international business. We advocate more multi-level studies and cross-national research collaborations and suggest greater attention to potential new data sources and means of analysis

A machine learning approach to predict perceptual decisions: an insight into face pareidolia

The perception of an external stimulus not only depends upon the characteristics of the stimulus but is also influenced by the ongoing brain activity prior to its presentation. In this work, we directly tested whether spontaneous electrical brain activities in prestimulus period could predict perceptual outcome in face pareidolia (visualizing face in noise images) on a trial-by-trial basis. Participants were presented with only noise images but with the prior information that some faces would be hidden in these images, while their electrical brain activities were recorded; participants reported their perceptual decision, face or no-face, on each trial. Using differential hemispheric asymmetry features based on large-scale neural oscillations in a machine learning classifier, we demonstrated that prestimulus brain activities could achieve a classification accuracy, discriminating face from no-face perception, of 75% across trials. The time–frequency features representing hemispheric asymmetry yielded the best classification performance, and prestimulus alpha oscillations were found to be mostly involved in predicting perceptual decision. These findings suggest a mechanism of how prior expectations in the prestimulus period may affect post-stimulus decision making