Cultural Intelligence and Job Performance in International Transportation Business Settings
MetadataShow full item record
Motivating language (ML) is a technique used by managers to motivate subordinates using a three-part method aimed at reducing uncertainty, creating meaning, and establishing empathy. Preliminary research has shown that ML can help to improve such worker outcome variables as satisfaction, intent to stay, innovativeness, and performance. This study posits that a supervisor’s use of ML will significantly improve a subordinate’s cultural intelligence (CQ), which identifies an individual’s ability to adapt to new cultural situations through another multi-part method involving a person’s cognitive, metacognitive, motivational, and behavioral dynamics. Furthermore, CQ is examined against the same worker outcome variables as ML, which will help practitioners identify whether CQ development will result in meaningful financial figures and benchmarks. An examination of the employee outcome variables was conducted to help identify and confirm existing relationships in the literature. ML research on mediating effects is relatively scant, so this study contributes to the literature by examining ML’s effects on the employee outcome variables through CQ. This study examined a market leader in the motorcoach transportation industry by surveying employees from one of its largest divisions. Of the returned surveys, 257 were usable, with responses from all departments and each level of the organizational hierarchy. This industry has a large number of non-traditional workers who do not report to a traditional office setting but complete tasks outside the workplace. Hypotheses were examined using a linear model initially. However, an exploratory nonlinear test was also conducted to identify relationships that may have the shape of a U and J curve. Initial results indicated significant relationships between ML and the employee outcome variables, CQ and the employee outcome variables, and interrelationships among the employee outcome variables. Results of tests on the ML and CQ relationship, CQ as a mediator, and model differences were non-significant. Further examination of the scatter plots revealed the presence of a few select outliers possibly skewing the model results in a negative way. Thus, outliers were then controlled for by ranking the data. The results for the ML and CQ relationship and CQ as a mediator both proved to be significant and positive.