The main purpose of the research is to show that organizational justice leads to better public service. We define organizational justice as the perception of the staff and managers of the company that the organization they are part of is treating them fairly. Fair treatment is measured by effective leadership, employment capability, and costumer focus. The indicators of these three factors are enumerated in the Amoco Renewal Survey. This questionnaire was developed in 1997 and covered questions pertaining to corporate roles and shared services.
The way to measure the participants’ perception was to conduct a person to person structured interview with 1,644 respondents selected from a 20,000 population through random sampling. The respondents are all full-time employees of a local government agency in North Carolina. In the survey instrument used, the questions were grouped into 12 categories, and 10 of these categories were subdivided into customer-focus index, employee capability index, and leadership capability index. For each question, respondents would either choose between Agree (A) /Totally Agree (TA) or Disagree (D) / Totally Disagree (TD).
The method used to generate results is the multivariate analysis, particularly factor analysis. Factors according to Bernard (2000) are considered “supervariables,” variables that incorporate a lot of variables” (p.635). The statistical software used for data analysis is SPSS. The variables included are leadership capability, employee commitment, empowerment, career management, diversity, teamwork, customer satisfaction, measurement, improving business process, special topics, and strategic communication. A total of 1,662 observations (N=1,662) were used. The first step was to get the descriptive statistics. The mean and standard deviation scores for each variable were produced. The variable with the lowest mean was Special topics with mean of 3.11 while teamwork (mean=3.69) and customer satisfaction (mean=3.68) have the highest means. As regards the standard deviation or SD, the variables with the highest SD are career management (SD=1.10) and strategic communication (SD=1.04). Of the total 1,662 (N) observations, there were 1471 valid observations while 191 were excluded. Reliability statistics generated a cronbach’s alpha of 0.9 showing that the different variables are highly correlated with each other.
Factor analysis is used to identify the relationships with the different variables in this study. Using SPSS, the variables named a19, a30, a68a, and a68b were analyzed. The SPSS output presented the Communalities and Total Variance Explained tables. The communalities provide information about the percentage that of variance that each variable shares with the generated factors. All the figures in the Initial column of the Communalities table is 1.000. Since this is a high value then these variables are useful for the analysis as these are good contributors to defining of the factor.
The table on Total Variable Explained shows the Eigenvalues of the conducted factor analysis. The table shows four factors under the Component column. The first factor captured a variance of 65.11%; the second factor has 19.10% variance; the third has 10.09% of variance; and the fourth factorhas5.68% of variance. The extraction sums column shows the same percentages with the first factor having 65.11% of variance. Principal component analysis was used for the extraction method and there was 1 component extracted. Results also show that the solution cannot be rotated.
The Scree plot presents in a graph the components and the eigenvalues. Here, components 1,2,3,4 are in the x-axis while the y-axis show the eigenvalues. It is shown here that the eigenvalues tend to decrease when the movement is towards the later components.
Bernard, H. R. (2000). Social Research Methods. California: Sage Publications, Inc.