Leader-level influence on motivating language A two-level model investigation on worker performance and job satisfaction

Purpose - The purpose of this paper is to extend the motivating language (ML) theory conceptualization by examining the role of leader-level communication (as compared to the current dyadic level conceptualization) in employee performance and job satisfaction. Design/methodology/approach - Partial least squares (PLS) analysis is used to test how leader and dyadic-based ML effects employee outcomes. PLS analysis is applied in an incremental fashion, adding leader-level language after dyadic-level ML had been included in the model. Such an incremental approach shows the extent of added variance by leader-level ML. The sample is drawn from 151 health care workers in a Southeastern health facility. Findings - Results indicate that leader-level ML significantly and positively effects follower performance. In comparison, dyadic-level ML significantly and positively effects both employee performance and job satisfaction. Research limitations/implications - This research only examines a subset of the outcome variables that have been examined in ML research. As such, it is not clear how extensively leader-level ML effects related employee outcomes. Practical implications - The paper helps us to better understand how ML actually effects employee outcomes. As a result, this research contributes insights into improved organizational interventions that are designed to improve follower outcomes through leader communication. Originality/value - The paper extends our understanding of ML and leader communication. The paper adds a leader-level component to the original dyadic-level theory. This reconfiguration offers new avenues for research investigation and implications for leader training.