Managing Employee Retention - Report Example

Managing employee . This report gives is about the importance of increasing store level employee retention. The study involved a total of 75 stores located in different locations with different location factors such as population, pedestrian access and number of competitors. The report investigates the relationship between employee tenure and the level of store performance. The report also investigates the relationship between the employee retention and financial performance. Descriptive statics of variables From table 1 the descriptive statistics of all the variables used in this study has been given: the minimum scores, maximum scores the means scores and the standard deviation. From the table it can be observed that the maximum management tenure was 277.9877 and the lowest was zero. The maximum population was 26519 while the minimum was 1046 with the mean population being 9825.59. The mean crew skills score was 3.46 which was slightly lower than the management skills which had a mean value of 3.64. Table 1: Descriptive statistics of variables Descriptive Statistics N Minimum Maximum Mean Std. Deviation Management tenure 75 .0000 277.9877 45.296444 57.6715512 Crew tenure 75 .8871 114.1519 13.931499 17.6975171 Sales made 75 699306.0000 2.1131E6 1.205413E6 3.0453131E5 Population 75 1046.0000 26519.0000 9825.586667 5.9116738E3 Crew skills 75 2.0600 4.6400 3.456667 .4065854 Profit realized 75 122180.0000 518998.0000 2.763136E5 8.9404076E4 Competition 75 1.7 11.1 3.788 1.3114 Service quality 75 57.8955 100.0000 87.153844 12.6133920 Management skills 75 2.9567 4.6222 3.637976 .4084571 Pedestrian access 75 1.0 5.0 2.960 .9924 visibility 75 2.0 5.0 3.080 .7491 Resources 75 .0 1.0 .960 .1973 Store24 compliance 75 .0 1.0 .840 .3691 Valid N (listwise) 75 Financial performance The financial performance variables in this study were the amount of sales made and the profit realization. A correlation test on the two variables indicated that the two variables were strongly correlated with a Pearson correlation value of 0.923 as can be seen in table 2. This is a clear indication that the stores were making profits. It is also an indication that one of the variables can be used in relating financial performance to other variables. In this case profit will be used as the financial performance indicator. Table 2: Correlation between sales and profit Correlations Profit realized Sales made Profit realized Pearson Correlation 1 .923** Sig. (2-tailed) .000 N 75 75 Sales made Pearson Correlation .923** 1 Sig. (2-tailed) .000 N 75 75 **. Correlation is significant at the 0.01 level (2-tailed). Location factor variables and financial performance In order to find out the relationship between financial performance and location factors a correlation test was done between the profit variable and the location factor variables with the test of the results being as shown in table 3. The location factor variables were completion, pedestrian access, visibility and population. From the table it can be seen that all the location factor were significantly correlated to profit with the exception of visibility. It is also seen that competition was negatively correlated to profit with a correlation value of -0.334. The strongest correlation between the location factor variables and profit was that of pedestrian access which had a Pearson correlation of 0.449. The population variable had a correlation of 0.430 with the profit variable. From the table the correlation between the location factors is also given. The highest correlation between the location variables is that between pedestrian access and population with a value of 0.608. There is a negative significant correlation of -0.268 between competition and population meaning that in areas where there are high population completion is lower. Pedestrian access is also negatively correlated to completion but this is not statistically significant. Table 3: Correlation test between profit and location factor variables Correlations Profit realised Population Competition Visibility Pedestrian access Profit realised Pearson Correlation 1 .430** -.334** .136 .449** Sig. (2-tailed) .000 .003 .245 .000 N 75 75 75 75 75 Population Pearson Correlation .430** 1 -.268* -.050 .608** Sig. (2-tailed) .000 .020 .670 .000 N 75 75 75 75 75 Competition Pearson Correlation -.334** -.268* 1 .028 -.146 Sig. (2-tailed) .003 .020 .809 .210 N 75 75 75 75 75 Visibility Pearson Correlation .136 -.050 .028 1 -.141 Sig. (2-tailed) .245 .670 .809 .227 N 75 75 75 75 75 Pedestrian access Pearson Correlation .449** .608** -.146 -.141 1 Sig. (2-tailed) .000 .000 .210 .227 N 75 75 75 75 75 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). People factors and financial performance The relationship between the various people factors and financial performance was investigated through a correlation test. The people factors variables used in the test were; crew tenure, management tenure, management skills and crew skills. The service quality (servqual) was also included as people factor. The results of the correlation test was as in table 4. From the table it can be observed that there is a significant correlation between all the people factors and financial performance (profit) with the exception of the crew skill variable. The correlation values between profit and the people factors were 0.259, 0.323, 0.361 and 0.440 for crew tenure, management skills, service quality and management tenure respectively. This is an indication that management has the highest influence on financial performance; crew tenure has the lowest influence on financial performance. Table 4 also gives the correlation between the people factors as seen in table areas with light blue colour. Out of the ten pairs of correlations 4 are significantly correlated. The pairs which are correlated are: crew skills versus crew tenure; management tenure versus crew tenure; service quality versus management skills; and management skills versus management tenure with their respective significant Pearson values being 0.257, 0.243, 0.357 and 0.230 respectively. These results indicate that service quality is improved by the management having acquired the desired skills. Table 4: Correlations of people factors and financial performance Correlations Profit Crew ten. Management skills Service quality Crew skills Management tenure Profit realized Pearson Correlation 1 .259* .323** .361** .159 .440** Sig. (2-tailed) .025 .005 .001 .172 .000 N 75 75 75 75 75 75 Crew tenure Pearson Correlation .259* 1 .124 .081 .257* .243* Sig. (2-tailed) .025 .289 .489 .026 .035 N 75 75 75 75 75 75 Management skills Pearson Correlation .323** .124 1 .357** -.021 .230* Sig. (2-tailed) .005 .289 .002 .858 .048 N 75 75 75 75 75 75 Service quality Pearson Correlation .361** .081 .357** 1 -.034 .182 Sig. (2-tailed) .001 .489 .002 .775 .119 N 75 75 75 75 75 75 Crew skills Pearson Correlation .159 .257* -.021 -.034 1 .102 Sig. (2-tailed) .172 .026 .858 .775 .386 N 75 75 75 75 75 75 Manament tenure Pearson Correlation .440** .243* .230* .182 .102 1 Sig. (2-tailed) .000 .035 .048 .119 .386 N 75 75 75 75 75 75 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). Management tenure and financial performance In order to investigate the effect of management tenure and financial performance, the management tenure was used to generate management group variable while profit group was derived from profit variable. A chi-square analysis was then used in investigating the relationship between the two new variables with the results being as shown in table 5. From the table it can be seen that the group with the lowest profit margin of less than 200000 had a total of 15 managers serving in the group this being 20% of the total number of managers in the profit group. Out of the 15 managers 6 had management tenure of 20-40, the 6 being 40% of all the managers that belonged to shops with profit less than 200000 and 37.5% of the managers in the 20-40 management tenure. The other scores represented by a profit of less than 200000 are 5 for less than 20 tenure, 2 for both 40-60 tenure and 60 tenure this being a representation of 33.3%, and 13.3% within the profit group (highlighted in yellow). The highest management in the row for profit of 200000 -300000 was 20 which was 57.1% of the profit group and 62.5% within the management tenure of less than 20. Considering the 300000-400000 row it can be observed that the highest score for within the management tenure for this row was the greater than 60 management tenure with 5 managers’ score representing 31.3% within the management tenure. The figure 5 was above the expected value of 3.8. In the table it can also be seen that out the 7 total number of managers who managed stores that had profit of greater than 400000, 6 had a management tenure of greater than 60 while 1 belonged to management tenure group 40-60. The 6 managers represented 85.7% within the profit group (raw marked in light blue) and 37.5% within the management tenure group (Column marked green); while the 1 manager represented 14.3% within the profit group (raw marked in light blue) and 9.1% within the management tenure group (column marked in dark red). These results clearly indicate that there is a relationship between the management tenure group and the profit groups where high management tenure is associated with high profit group and vise versa. Table 5: Profit group and Management tenure group Cross-tabulation Profit group * Management tenure group Crosstabulation Management tenure group Total 60 Profit group 400000 Count 0 0 1 6 7 Expected Count 3.0 1.5 1.0 1.5 7.0 % within Profit group .0% .0% 14.3% 85.7% 100.0% % within Management tenure group .0% .0% 9.1% 37.5% 9.3% % of Total .0% .0% 1.3% 8.0% 9.3% Total Count 32 16 11 16 75 Expected Count 32.0 16.0 11.0 16.0 75.0 % within Profit group 42.7% 21.3% 14.7% 21.3% 100.0% % within Management tenure group 100.0% 100.0% 100.0% 100.0% 100.0% % of Total 42.7% 21.3% 14.7% 21.3% 100.0% Chi-square test results From the cross tabulation results it was revealed that there was a relationship between the management tenure groups and the profit groups. The chi-square results given in table 6 clearly reveal these findings. From the table; there is a significant relationship between the variables with a chi-square value being 26.373. Table 6: Chi-square test results between management tenure groups and profit groups Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 26.373a 9 .002 Likelihood Ratio 25.125 9 .003 Linear-by-Linear Association 9.985 1 .002 N of Valid Cases 75 a. 10 cells (62.5%) have expected count less than 5. The minimum expected count is 1.03. Regression analysis Having established that there was correlation between location factors versus profit and also there being a correlation between people factors and profit it is possible to use regression analysis to determine how the variables contribute to financial performance. The regression test was performed where the profit was taken as the dependant variable and the people factor and location factor variables were taken as the independent factors. When the regression test was run the results was as seen in table 7 and table 8. Anova From table 7 the anava test results for the regression has been given with F value being given as 15.793 the sum of square value is 4.059x1011 and the mean square is 4.510x1010. Table 7: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 4.059E11 9 4.510E10 15.793 .000a Residual 1.856E11 65 2.856E9 Total 5.916E11 74 a. Predictors: (Constant), Crew skills, Management skills, Store24 compliance, Competition, Crew tenure, Service quality, Pedestrian access, Manament tenure, Population b. Dependent Variable: Profit realised Coefficients The coefficient of the regression test are as seen in table 8. The table gives both the unstandardized and standardized coefficients. The table gives the t values and the p-values. The p-values of 0.05 or less indicate that the variable is statistically significant and thus can be used on calculating the profit. From the table it can be seen that the variables which are statistically significant are: Management tenure (x1), population (x2), competition (x3), pedestrian access (x4), store 24 compliance(x5) , management skills (x6), service quality (x7) Using the standardized coefficient of the statistically significant variables the equation for profit (P) is given as P = 0.441 x1+0.19 X2-0.388 X3+0.318 X4+0.223 X5+0.197 X6+0.171 X7 equation (1) From equation one it can be seen that completion contributes a lot in determining the profit i.e the higher the competition the lower the profit 9due to the negative sign preceding the competition coefficient). Management tenure is the highest positive contributors to profit as it has the highest positive coefficient of 0.441. Among the significant variable that contribute to profit service quality contributes the least. Table 8: Coefficients Coefficients Model Un-standardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -33320.778 88000.231 -.379 .706 Management tenure 684.132 121.273 .441 5.641 .000 Crew tenure 573.242 385.613 .113 1.487 .142 Population 2.872 1.441 .190 1.992 .051 Competition -26425.472 5235.804 -.388 -5.047 .000 Pedestrian access 28663.247 8224.681 .318 3.485 .001 Store24 compliance 54033.932 18552.838 .223 2.912 .005 Management skills 43110.381 17243.514 .197 2.500 .015 Service quality 1213.824 546.243 .171 2.222 .030 Crew skills -14596.543 17156.827 -.066 -.851 .398 a. Dependent Variable: Profit realized Conclusions From the analysis it has been found that both people factors and location factors play a role in determination of financial performance. Profit and sales have been for to be strongly correlated which is an indication that the stores are running at a profit and also profit can be used as an indicator of financial performance. From the findings it is very important to increase the management tenure in order to increase financial performance. It is also important to put into consideration the competition in a particular location when judging the performance of a manager. Where the competition is high it is advisable to have vigorous marketing so as to counter the competition. Read More