Abstract
In this chapter, we examine many ways to expand ML research. First, we look at how ML directly influences someone’s motivation. We also look at how ML operates over time through adjustments to ML messages. We then provide ideas on how to translate ML from a leader -to-follower theory to a theory that can operate between any two people, including one-to-many settings, and even through non-oral communications. At its heart, MLT describes how language can be used to instigate some action or achieve some end. We also believe that motivating language should make organizations better for the people who work in them and the communities that surround them, and call for more research on ML ’s ethical role.
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Mayfield, M., Mayfield, J. (2018). Future Directions. In: Motivating Language Theory. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-66930-4_10
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DOI: https://doi.org/10.1007/978-3-319-66930-4_10
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