Staff loyalty has always been a matter of concern for organizations. Employee turnover is highly detrimental both the organization as well as the employees. Loyalty has an impact over the organization’s costs relating to recruitment, training of new personnel and others. Private companies have the problem to keep talent and having more employee loyalty. Therefore, the study objective is to find out the determinants affecting the staffs’ loyalty at private companies in Can Tho City (CTC). The researchers surveyed 450 staffs working at private companies and answered 23 questions but sample size of 413 staffs processed. The data collected from August 2018 to March 2019 in CTC. Simple random sampling technique. Cronbach’s Alpha and the exploratory factor analysis (EFA) analyzed and used for Structural Equation Modelling (SEM) technique. Finally, the findings of the study have five factors affecting the staffs’ loyalty at private companies in CTC with significance level 0.01.
Job Satisfaction to Enhance a Commitment of Employees’ Organization at Dream Tour and Travel Company (Published)
A commitment of employees in an organization plays a very important role to bring the company successfull. Therefore, this research aims to analyze a job satisfaction to increase employee’s organization commitment at the Dream Tour and Travel Company. Variables are job satisfaction and organization commitment. Data was collected from 70 employees using organization commitment and job satisfaction scale, then analyzed by multiple regression analysis technique. The result of multiple regression analysis shows the value of significancy (p-value) was 0,000 at significant level p<0,05 means that job satisfaction can increase organization commitment. The categorization found out that job satisfaction and organization commitment were on medium level. The analysis reveals that job satisfaction can increase commitment organization. This paper may benefit staff of the company by encouraging more their jobs and may help them in their personal growth and development.
Quantitative Analysis of Factors Affecting Manufacturing Workers Performance in Industry: Case Study of Plastic Companies in Eastern Nigeria, Using SPSS (Published)
This work investigates the quantitative analysis of some factors that affect the performance of manufacturing workers in industries in southern Eastern Nigeria. Experiments were designed, and conducted through the use of work measurements technique and Tests Studies. These selected specific factors (maintenance, equipment, Power/Energy, technology, safety and Training.) used for the study, were investigated using eighty-two manufacturing workers from thirteen manufacturing companies. Data were collected for analyses. The software used was SPSS. Software tools used for various analyses in the study are: statistics, correlation, multi linear regression, response surface regression and multi- co linearity diagnoses, while t – value, F- ratio, p – values and variance of inflation factors (VIF) were used to test the hypotheses. The various statistical analyses performed were presented, studied and interpreted. The correlation coefficients are positive and in descending order of maintenance, equipment, Power/Energy, technology, safety and Training. The coefficient of determination, R2 and the variance ratio (VR); and F- value and t –coefficient values were also determined for strong inference. Curves were generated to observe the behavioral patterns of the relationship between manufacturing workers’ factors influence on performance. Results showed that the identified factors affected the performance of manufacturing workers in the manufacturing industries, in such a manner that the Companies productivity is affected positively by some factors and negatively by some others. Therefore, in the general consideration, the factorial indices that predicted the manufacturing workers performance of the selected factors: motivation, power, safety, maintenance, training, equipment and technology are found to be 0.877, 0.48, 0.614, -1.36, 0.789, 1.421 and – 0.495 respectively. These factorial indices are valid in controlling problems arising from manufacturing industries.