The Predictors Of The Service Climate In Chinese Context

The purpose of my study was to explore the predictors of service climate under Chinese circumstances. Furthermore, my second intention was to examine the differences of the predictors of service climate among different industries that were belonged to different skill level. In addition, I wanted to testify the relationship between service climate and service performance in Chinese context.The literature review, contained nine parts, was conducted to reveal the present studies related to this study. They are studies on human resource management in service work, Chinese context, service climate, service performance, perceived organizational support, perceived supervisor support, perceived coworker support, role model, and empowerment. Previous researches on service climate showed there was a positive relationship between service climate and service performance. I was interested in finding out where similar result can be found in China. As far as the service climate was such crucial for service organization, the predictors of service climate was significant to organization. Because the service organization could acknowledge which aspects can be improved to build a stronger climate for service by these predictors. In my study, after the literature work, there were five potential predictors were selected. They were perceived organizational support, perceived supervisor support, perceived coworker support, role model, and empowerment.Hospital industry, finance industry and retail industry were chosen to represent the frontline service industry, because of the important role they played in China’s GDP index. Meanwhile, the hospital industry was classified to the highest technique level, then the finance industry was classified to the medium, and the retail industry belonged to the lowest technique level. I was also interested in where the technique hierarchies affect the result for the predictors.According to my research aims, seven hypotheses were assumed before the field study. They were: hypothesis one, empowerment was related to service climate and empowerment could predict service climate; hypothesis two, perceived organizational support was related to service climate and perceived organizational support could predict service climate; hypothesis three, perceived supervisor support was related to service climate and perceived supervisor support could predict service climate; hypothesis four, perceived coworker support was related to service climate and perceived coworker support could predict service climate; hypothesis five, role model was related to service climate and role model could predict service climate; hypothesis six, service climate was related to service performance and service climate could affect service performance; hypothesis seven, differences of predictors among the three industries were existed.The quantitative research method was employed in my case by survey. I designed the questionnaire by literature analysis and conducted a pilot study to test the responsibility of the scale in Chinese context to make sure my research is responsible. After analyzed the results of pilot study,4items were deleted to increase the reliability of the scales. In formal field study, located in Chongqing,300questionnaires with the statistics variables and covers that demonstrated the purpose of the research were sent to hospitals, financing industries and retailing industries, and each of them got100copies. Two months later,62responded questionnaires were collected from financing industries,95responded questionnaires were collected from hospitals, and92responded questionnaires were collected from retailing industries. Therefore, the response rate for financing industries was62percent; the response rate for hospitals was95percent; the response rate for retailing industries was92percent. The response rate was83percent in total.The Pearson correlation test was utilized to find the relations among the seven variables. The results showed that there were significant correlations among all seven variables. Then, multiple regressions were conducted to explore the exact relationships among these variables. Finally, the model indicated the perceived manager support, perceived coworker support and perceived organizational support could predict the service climate was selected as the soundest one with a0.506adjusted R-Square in general. Then I conducted the multiple regressions separately according to the model by industries to identify the differences among them. The index indicated a significant difference existed. For the hospital, the empowerment seemed as the most important factor for service climate, while for the financing industry, the perceived manager support, perceived coworker support and perceived organizational support were crucial to the service climate; for the retail industry, the perceived manager support and perceived organizational support played important roles for the service climate. A casual relation between service climate and service performance was also indentified in my case. Consequently, I concluded that there was a difference among the’industries concerned the prediction of variables for service climate.