When algorithms help cross-cultural management educators: a proposed supportive tool

This study explores the conceptualization and design of a supportive tool aiming at assessing the student Cross- Cultural Competence (CCC) within the context of higher education. Employing Principal Component Analysis (PCA) and non-hierarchical Cluster Analysis (non-HCA), the research aims to discern patterns and similarities in student data to aid in the conceptualization of the supportive tool for cross-cultural management educators. It coherently to the relevant literature discourse examines the impact of several factors, including experiences in foreign countries and participation in academic courses with a cross-cultural emphasis. This algorithm is also designed to assist in creating tailored strategies for enhancing students’ CCC by exploiting identified cluster compositions. Data was gathered through a specifically designed survey based on literary research orientations and distributed to students across bachelor’ s and master’ s degree programs at the University of Salerno. The PCA identified three principal components exerting a significant influence on students’ capability to navigate diverse cultural environments. Subsequent non-HCA defined distinct student traits, thereby providing deeper insights into the assessment of CCC. The algorithm, based upon these statistical methodologies, can enhances the automation of future survey analyses across diverse contexts. This advancement provides cross-cultural management educators with a supportive tool for fostering CCC through the deployment of customized educational strategies, tailored to the unique attributes of the students.