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Artificial intelligence (AI) has far-reaching implications for education. Within organizations, especially companies, human resource development (HRD) enables and supports learning processes among employees. In a similar way to teachers and lecturers, HRD professionals play an important role in implementing AI in HRD. However, there is a lack of quantitative empirical evidence about this process. The aim of this paper is to shed light on how HRD professionals position themselves with respect to AI. The concept of Davenport and Kirby's augmentation strategies, adapted to HRD, act as the theoretical background. The core idea of augmentation lies in human-AI collaboration. In our study, we empirically validate this concept of augmentation strategies and predict the extent to which HRD professionals pursue the five strategies: step in, step up, step forward, step aside, and step narrowly. The predictors are grouped into three areas: attitudes, competence beliefs, and goal orientation. HRD professionals (N = 330) from German-speaking countries act as the sample. Covariance based structural equation modeling (CB-SEM) and partial least squares structural equation modeling (PLS-SEM) act as the method for data analysis. The findings reveal the crucial impact of cognitive attitudes towards digitalization and AI anxiety when pursuing the augmentation strategies. AI competence beliefs are an important predictor for collaboration with AI. General digital competence beliefs can only indirectly predict the augmentation strategies. Implications for theory and practice are discussed. |
