Analysis of labour turnover of the bankng industry in Singapore
An integrative turnover prediction model for investigating the determinants influencing the turnover intention of clerical bank employees in Singapore is proposed. To facilitate the data collection, a survey was conducted between August and October 1991. A total of 330 questionnaires was administ...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/64375 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | An integrative turnover prediction model for investigating the determinants influencing
the turnover intention of clerical bank employees in Singapore is proposed. To facilitate
the data collection, a survey was conducted between August and October 1991. A total
of 330 questionnaires was administered to the clerical employees of 11 randomly selected
commercial banks in Singapore comprising 8 full-licence banks, 2 offshore-licence banks
and 1 restricted-licence bank. The range of usable responses received was between 120
and 202 questionnaires, giving a usable response rate of between 36.36% and 61.21% .
An array of statistical methods including descriptive statistics, t-:tests of significant
differences, simple regression, stepwise regression and multiple regression were employed
to analyze the data. 16 independent variables consisting of 6 demographic factors, 8
specific job satisfaction factors and 2 job attitude factors, were measured. The results
are supportive of the existence of significant relationships between the set of independent
variables presented in the modelling framework and intention to turnover, but the results
do not support the hypothesis that all variables influence turnover behaviour.
Preliminary uni-variate analyses reveal that 10 of the variables proposed are significantly
related to the turnover intention of the subjects 'at a 0.10 level of significance. A
multiple· regression on the 16 variables, however, isolated only 3 of these 10 variables as
significant at a 0.10 level of significance. The results were confirmed through the use of the stepwise regression process and a final turnover prediction model, with a predictive
power of 57.41% (R^2 = 0.5741), was developed from the following variables: (1) kind
of work, (2) financial rewards, and (3) career future. |
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