How have businesses been affected by nepotism?

 Data Collection Methodology and Data Analysis

In the research by (Büte, 2011) the data was analyzed using a statistical software package known as LISREL 8.54. Cross-sectional data were obtained via a previously developed research model and scale, and were analyzed by quantitative methods. For the reputation of the state bank the names of the executives were confidential and was not to be disclosed. The researcher found it very costly and time consuming to contact all the state bank branches in Ankara. Therefore a sample of 300 respondents from relevant state banks was developed taking into account the location, accessibility and financial factors and the questionnaire forms prepared were sent to the employees. Among these 250 questionnaires were returned and 7 questionnaires from that were not completely filled out. So a total of 243 questionnaires were used to collect data.

Table 1: Demographic Characteristics

The demographic characteristics were analyzed based on the respondent’s gender, their marital status, their age, educational level, the banks they used and their level of experience. According to the table 1 above majority of the respondents were female (61.73%). The breakdown of the banks they used are close to one another.  And a 65.02% of them were married and the same percent of them were with a bachelor’s degree. Nearly all the respondents were with a sector of experience.

Table 2: The Results of Confirmatory Factor Analysis

Notes: Each item is measured on a five-point Likert scale. All correlations are significant at 0.001 level.

In the research total 37 questions were generated in order to o identify the relationships between nepotism and favoritism and human resources practices, affective commitment, job satisfaction and turnover intention. Among them 10 questions were based on “nepotism and favoritism”, there Cronbach’s Alpha coefficient was found to be 0.89. 12 questions related to “Human resources practices” and there Cronbach’s Alpha coefficient was 0.93. Another 6 questions was for measuring “affective commitment”, its Cronbach’s Alpha coefficient was found to be 0.88. Again 6 questions on “job satisfaction” was taken and its Cronbach’s Alpha coefficient was found as 0.91. The last 3 questions were related to “intentions to quit” its Cronbach’s Alpha coefficient was found to as 0.81. In the factor analysis, 7 items were loaded below the expected variables, i.e. below the 0.60 load value, the acceptable load value in the literature.

Table 3: Means, Standard Deviations and Correlations Related to All Variables Used in the Model

In the questionnaire, 5-point Likert scale from 1=strongly disagree, 2=disagree, 3=neutral, 4=agree to 5=strongly agree was employed. More than half of the survey was conducted via face-to face interviews. The remaining were given to respondents to be filled out at home and were collected the next day.

According to the table from the research article, the highest correlation was found between human resource practices and job satisfaction (0.69). Thus the lowest correlation was between human resources practices and turnover intention (0.31). The mean and standard deviations of these factors were calculated to support their validity and the results shows that it supports the validity of the scale.

Conclusion

The study examined the effects of nepotism and favoritism in the state banks on human resources practices, job satisfaction, organizational commitment and intention to quit. It also revealed the importance of independent executives in the banking sector. They should keep their managerial decisions transparent and clear to the employees without nepotism or favoritism included. This will be the main reason for employee job satisfaction and thus will improve their commitment and efficiency. `

References