Author(s), Title and Publication
Mayfield, J., & Mayfield, M. (2016). The diffusion process of strategic motivating language: An examination of the internal organizational environment and emergent properties. International Journal of Business Communication, 56(3), 368-392. doi: 10.1177/2329488416629093
Leaders can use three major forms of oral communication to enhance employees’ work motivation. The three speech act categories include meaning-making language, empathetic language, and direction-giving language. More specifically, meaning-making language refers to a leader’s transmission of organizational purpose, vision, and culture to followers. Empathetic language reflects a leader’s care and concern for employees’ well-being. Examples are recognition of employees’ accomplishment and commiseration with employees on their task setbacks. Direction-giving language from a leader clarifies task ambiguity and reduces job uncertainty. Use of the three forms of motivating language (ML) has been linked to beneficial outcomes for employees and organizations, such as job performance, job satisfaction, organizational commitment, higher self-efficacy, perceived leader competence, and higher communication satisfaction toward a leader. The goal of the current study is to examine how motivating language is actually adopted and diffused by leaders’ speech throughout an organization. To do so, the authors grouped factors affecting the diffusion of ML into three categories: the communication culture (i.e., CEO ML strength, the extent to which an organization’s culture nurtures oral communication competence as evidenced in selection criteria, rewards, and formal training), institutional factors (i.e., formal hierarchical layers, turnover rate), and influence variables (i.e., top-leaders, immediate supervisor, peers).
The authors used an agent-based simulation model (ABM) to address the research problem. ABM methods allow the researchers to create a set of axioms (conceptual foundations and beliefs) and test the results using these axioms aided by computer simulation. The results showed that top-leader ML use and organizational culture had the strongest influence on high-ML diffusion. It also showed that when top leaders communicate low ML, rewards are the key factor in the diffusion of high ML as seen in a 52% increase in ML talk by lower-level leaders. However, even when rewards are neutral or negative, training can offset the bleak reward system by increasing the high-ML diffusion rates to 41%. When ML training is absent, high-ML diffusion decreases with the passage of time. Finally, when there is a high-ML top leader, higher turnover rates can obstruct high-ML diffusion.
Implications for Practice
Based on the findings, the authors provided practical recommendations for companies: (1) Companies should carefully select CEOs with high-ML competence, if high ML is desired; (2) When a top leader does not possess high-ML competence, ML training and rewards would help the diffusion; (3) The passage of time (over 2.25 years) is needed for a high level of ML diffusion; (4) High turnover rate among leaders defers the ML diffusion rate.
Location of Article
This article is available online at: https://journals.sagepub.com/doi/full/10.1177/2329488416629093 (abstract free, purchase full article)