The Interrelationships Between Organizational Communication, Organizational Climate, and Job Satisfaction in a Local Government Agency.

Abstract

The interrelationships between three multivariate concepts: Organizational communication, organizational climate, and job satisfaction were studied in a local government agency. Demographic data were also studied as they, as a group, related to the three multivariate concepts. Questionnaires were collected from 175 of the 220 employees. Canonical analysis was utilized to analyze the respondents' perceptions of the three concepts and to determine their relationships to the demographic data set. Organizational climate significantly correlated (p < 0.001) with job satisfaction. Only very small, but significant (p < 0.01) redundancy was found between the two concepts. Organizational communication and organizational climate were significantly correlated (p < 0.001). Redundancy analysis indicated that small to moderate redundancies are shared between the concepts for the sample under study. Organizational communication and job satisfaction also exhibited significant correlation (p < 0.001). Redundancy analysis indicated that communication explains a small but significant (p < 0.05) amount of the variance in job satisfaction. However, job satisfaction did not not explain a significant amount of the variance in the communication variable set. For each of the canonical analyses studied, some of the values of the loadings on the components possessed positive signs while others possessed negative signs. Thus, the direction of the relationships between the three concepts remains unclear. Canonical analysis of demographic data and each of the three multivariate concepts was undertaken to determine whether the demographic makeup of the sample was affecting the relationships between the concepts. No significant relationship was found between demographics and organizational climate at the p < 0.05 level. Canonical correlations between the demographic data set and the communication data set were significant beyond p < 0.01. Redundancy analysis indicated that demographics did not explain a significant amount of the variance in communication (p < 0.05). The reverse relationship was significant at p < 0.01, thus clouding the question of the importance of demographics in explaining variance in the communication set. Canonical analysis of the demographic/satisfaction relationship was also significant (p < 0.01). Redundancies yielded similar mixed findings as in the demographics/communication relationship. Conclusions from the analysis were drawn. Limitations to the usefulness of the findings were discussed. Finally, suggestions for further research were made.

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