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Integrating anxiety and uncertainty management (AUM) theory and theory of organizing, this study aims to contribute to the knowledge management literature by examining the interdependent and bidirectional nature of knowledge transfer between expatriates and host country nationals (HCNs). Specifically, the authors investigate how receivers’ cognitive response to senders’ behaviors during their interactions becomes an important conduit between senders’ behaviors and the successful transfer of knowledge.

The authors used the actor partner interdependence model to analyze data from 107 expatriate-HCN dyads. The authors collected the responses of these expatriate-HCN dyads in Shanghai, Taipei, Hong Kong, Vietnam, South Korea, Malaysia, Thailand, Indonesia and India.

Receivers’ interaction anxiety and uncertainty, as a response to senders’ relationship building behaviors, mediate the relationship between senders’ relationship building behaviors and successful knowledge transfer. When senders are expatriates, senders’ communication patience and relationship building behaviors interact to reduce the direct and indirect effects of both receivers’ interaction anxiety and uncertainty. However, when senders are HCNs, the moderation and moderated mediation models are not supported.

The study contributes to the knowledge management literature by investigating knowledge transfer between expatriates and HCNs using an interpersonal cross-cultural communication lens. The authors make refinements to AUM theory by going beyond the sender role to highlighting the interdependence between senders and receivers in the management of anxiety and uncertainty which, in turn, influences the effectiveness of cross-cultural communication. The study is also unique in that the authors underscore an important yet understudied construct, communication patience, in the successful transfer of knowledge.

This paper aims to explore factors contributing to music popularity using machine learning approaches.

A dataset comprising 204,853 songs from Spotify was used for analysis. The popularity of a song was predicted using predictive machine learning models, with the results showing the superiority of the random forest model across key performance metrics.

The analysis identifies crucial genre and audio features influencing music popularity. Additionally, genre specific analysis reveals that the impact of music features on music popularity varies across different genres.

The findings offer valuable insights for music artists, digital marketers and music platform researchers to understand and focus on the most impactful music features that drive the success of digital music, to devise more targeted marketing strategies and tactics based on popularity predictions, and more effectively capitalize on popular songs in this digital streaming age.

While previous research has explored different factors that may contribute to the popularity of music, this study makes a pioneering effort as the first to consider the intricate interplay between genre and audio features in predicting digital music popularity.