Method: An autoregressive model using three waves of longitudinal data was used to examine study hypotheses in Mplus statistical software. Data were collected at three time points 6-months apart from a nonprofit healthcare department (Time 1 n = 213, Time 2 n = 245, Time 3 n = 239).
Results: In regards to the sample, 41% of respondents self-reported as Asian, 21% as white, 20% as mixed race or other, 14% as Latinx, and 4% as Black. The model fit the data well (χ2 = 10.667; df = 5; p > .05; CFI = .97; TLI = .92; RMSEA = .07, 90% CI = .00, .14). After controlling for employee race/ethnicity, gender, age, education, job position, and job tenure, result suggested leader inclusiveness at Time 1 positively influenced climate of inclusion at Time 2 (β = .19, SE = .08, p < .05) and leader inclusiveness at Time 2 positively influenced climate of inclusion at Time 3 (β = .27, SE = .09, p < .01). These results were significant above and beyond controlling for previous time point leader inclusiveness and inclusion. In addition, the reverse pathways were not found to be statistically significant (climate of inclusion at Time 1 was not significantly associated with leader inclusiveness at Time 2 and climate for inclusion at Time 2 was not significantly associated with leader inclusiveness at Time 3).
Conclusions: Results support the argument that leaders are climate instigators and can set the tone for what the organizational climate will be. In other words, leaders are key to creating an inclusive and equitable workplace. Human service organizations are increasingly tasked with implementing DEI efforts and creating inclusive work environments to solve complex problems around inequity in the workplace. This study provides evidence-based information critical for designing leadership interventions aimed at increasing a climate of inclusion.