Taking the lead in artificial intelligence (AI) forms part of the national agenda of several countries. Despite the investment volume of other countries, Germany and the United States are superior in implementing AI applications due to their high number of early adopters. Therefore, one area of interest refers to the adoption of machine learning, as a subfield of AI, from a cultural and organizational perspective. Through qualitative research, this study explores how culture affects the technological, organizational, and environmental (TOE) determinants of machine learning adoption by conducting a comparative case study between Germany and the United States. Based on Hofstede’s cultural dimensions and the TOE framework, the results of 21 expert interviews show that distinct cultural characteristics impact the TOE determinants. For instance, the varying extent of uncertainty avoidance results in different technological and environmental approaches. Germany tends to take preparatory actions for data management, while the low index of the United States is reflected in the absence of data protection regulations. By combining the TOE framework with a national culture construct, our study identifies cultural characteristics that influence machine learning adoption and, thus, could serve as a guideline for future cultural research and managerial decisions for machine learning adoption.
Eitle, Verena and Buxmann, Peter, "Cultural Differences in Machine Learning Adoption: An International Comparison between Germany and the United States" (2020). In Proceedings of the 28th European Conference on Information Systems (ECIS), An Online AIS Conference, June 15-17, 2020.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.