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.
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.
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.
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.
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.
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.
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