This chapter specifically presents, descriptively analyzes and summarizes the findings of the data gathered from the companies that have employed electronic human resource management in their systems. It is for the purpose of determining how effective is the e-hrm in modern human resource management practice? The findings of the study constitute the level of electronic processes, the score on the reduction of costs, reduction in human resource manager workload and the score on the overall effectiveness of electronic human resource management in organizations.

4.2: Data analysis methods

Descriptive statistical analysis was applied to the result findings. Deep analysis of the results was done to determine the cause-effect relationship between the adoption of E-HRM and the effectiveness of the company or organization in its operations (Johnson 2011). The figure presents the information that was given by the employees and the managers of various companies and organizations that were relevant for the study. The significance level among the selected variables in the study was set at 0.05 or 95% which was determined by Fisher's exact test.

4.3 Rate of correspondence.

Even though this was not part of the research study, there was a need to show how the employees and the managers responded to the questionnaires. Questionnaires and interviews were distributed to different employee and managers who belonged to different companies as determined by the sampling technique. Administration of the questionnaires and the direct interviews was carried in a way to ensure that the whole population was included. Also, the sample size had to be enough to use normal probability distribution. Some employees and managers from nearby companies were also interviewed, and their responses were documented in the response sheet. All the available responses were documented and the rate of responses was determined. The rate was as shown in the figure below.

Figure 4.1: rate of response to the questionnaire

A total of one hundred responses were collected as from different correspondences. From the hundred responses, 90.4% contained relevant information for the study while 9.6% of them had information that could not be used for the study. Some of the respondents ticked two answers in the same question and therefore one could not understand the answers they meant. Others had written nothing in the questionnaires while some just declined to answer the questionnaires. The respondents who did not have clear answers to their questionnaires were contacted for clarity purposes, and the one who corrected the mistakes were included in the relevant responses category. The information is as shown in figure 4.1 as showing the rate of response.

4.4: Effects of electronic human resource management on employee productivity

The null hypotheses stated that electronic human resource management increases employees' productivity, and their performance is improved. Therefore, rejection of the null hypothesis that would mean that adoption of the alternative hypothesis that stated that electronic human resource management does not improve employee productivity.

The null hypothesis meant that when electronic HRM is adopted in an organization, the results is that the employees' performance and productivity are improved. The theoretical part of this discussion stated that employee productivity would increase as a result of various reasons when the electronic human resource system is implemented. Employees would be more accurate with their tasks as they will be well trained through the e-training package of E-HRM. Productivity was also increased by a rise in the level of employees morale as they would feel appreciated and motivated to do their work (Kalleberg 1994). Constant challenges that would hinder maximum employee productivity would also be eliminated since there is smooth communication between the employees and their managers.

Responses on the level of electronic human resource management and the level of employees productivity were run on a linear regression using the Microsoft Excel, and the results from the regression are recorded and discussed as follows;

Coefficients. Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 0.166168 0.123603 1.344375 0.180056 -0.07728 0.409613 -0.07728 0.409613

X Variable 1 2.942105 0.055838 52.69005 1.1E-136 2.832128 3.052082 2.832128 3.052082

Table 4.1 shows the relationship between the level of automation and electronic processes as compared to the level of employee productivity.

The regression analysis gave the above results. A model was formed where a causal and effect relationship was found to exist between the level of electronic processes and the level of employee performance measured by their productivity. A positive relationship between the level of employees' productivity and the level of electronic human resource management was established. The model estimated was as follows. Employee productivity=-0.166168 + 2.942105 of the level of electronic human resource management. The model simply means that the level of employee productivity in any of the companies and organizations where the research study was undertaken would be traced back to the level of electronic HRM. The null hypothesis was accepted since the p-value was less than 0.05 for the x variable. The phenomenon meant that the changes in employee productivity were related to the level of electronic human resource management.

Most of the respondents associated the improvement in employee performance and productivity to the level of electronic human resource management due to the following reasons. Some stated that there elimination of barriers to achieve maximum employees' productivity through efficient communication with the management as the leading reason. Others attributed the improvement to the level of attention that the managers had for the employees as a result of reduced workload.

The above model accurately described the relationship between the level of E-HRM and the level of employee productivity since it has an adjusted R-squared of 0.917 which meant that 91.7% of the total variations in the level of employee productivity was explained by the level of electronic human resource management in the organization.

Regression Statistics

Multiple R 0.958121

R Square 0.917996

Adjusted R Square 0.917665

Standard Error 0.887813

Observations 250

Table4.2 show the relationship between the levels of adjusted r-square.

The model was also fit to explain the relationship between the two variables since it had an f-calculated greater than the f-critical.

4.5: Effects of electronic human resource on the expenses reduction in the human resource department.

The second analysis involved the process of determining how effective was the electronic human resource management on reducing the budgetary expenses in the human resource department. The theory stated that most of the processes that made the company incur huge budgetary expenses were reduced by the adoption of electronic human resource management in the organization (Boxal 2011). Most of the researchers agreed that electronic human resource management reduced the level budgetary allocations to the HR department through the elimination of most of the processes that required the HR managers to be present to supervise and conduct them. Such processes included the recruitment of new employees to the organization. Expenses were incurred to hire personnel to mail the responses and also to evaluate them. More workers in the human resource department were required to help the managers with the huge workload that was piled up on the HR managers. The traditional systems also presented a challenge to the performance evaluation of workers. The process involved a lot of time and resources as the workers were evaluated one at a time. On the other hand, the electronic human resource provided the solution through the provision of such services such as e-training and e-recruitment of personnel.

The null hypothesis was formed to find out whether electronic human resource management reduced the level of budgetary expenses in the human resource department. On the other hand, the alternative hypothesis stated that the adoption of electronic human resource management did not have the effect of reducing expenses in the human resource department. To investigate the relationship between the level of electronic human resource management and the effect on the human resource department expenses, a linear regression was carried out to the level of electronic human resource management and the score on the reduction of expenses in the human resource department.

The results from the regression were as follows where a linear model was predicted.

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept -0.26876 0.112062 -2.39833 0.01721 -0.48948 -0.04805 -0.48948 -0.04805

X Variable 1 3.130204 0.050624 61.8318 1.1E-152 3.030495 3.229912 3.030495 3.229912

Table 4.3: show the regression results of cost reduction against the level of E-HRM.

A model the represented the regression results information was established. The model suggested that cost reduction was positively attributed to the level of electronic human resource management. The model has stated as; cost reduction = -0.26876 + 3.130204 of the level of electronic human resource management. The model stated that a unit improvement in the level of electronic human resource management would reduce the cost allocation in the department by 3.130204 units. The phenomenon would be observed untill a certain point where the costs cannot be decreased any more. The phenomenon is observed since there are the basic allocations that are made to support the existent of the human resource function. The reasons behind the cutting down of costs in the human resource department included the e-recruitment, e-training and the improved employee performance that meant less operational expenses were required to run the HR department. The correspondents also attributed the reduction in costs to the increased level of efficiency in communications that meant fewer costs were incurred to pass information from the source to the intended recipient.

The model correctly predicted the relationship since the adjusted r-square of the equation was 0.9388, and it meant that 93.88% of the changes in the reduction in costs were attributable to the adoption of the electronic human resource management.

Regression Statistics

Multiple R 0.969063

R Square 0.939084

Adjusted R Square 0.938838

Standard Error 0.80492

Observations 250

Table 4.4 show the r-square of the regression

Only a total of less than 7% of the reduction in costs in the HR department was attributed to other factors apart from the adoption of E-HRM. On the other hand, the model was fit following the rejection of the null hypothesis that stated that the model does not correctly predict the relationship. Rejection of the null hypothesis is because the f-statistic is much higher than the critical f.

The model had a lower p-value than 0.05 for the x-coefficient and therefore the null hypothesis was accepted that reduction in costs in the human resource department was attributable to the adoption of electronic human resource management.

4.6: Effects of E-HRM on the workload of Human Resource managers.

The null hypothesis stated that the adoption of web-based human resource management practices led to the reduction in the workload of the HR managers. The hypothesis was formulated as a result of the information found in theory that adoption of practices such as online recruitment, online training, and web-based performance evaluation, internet based human resource planning processes had the direct effect of reducing the workload among the human resource managers.

The human resource managers have been known to be the most active in their management role since their activities means controlling most if not all activities of an organization. The traditional system makes the HR managers to be more concentrated on matters of how coordination of employees would be achieved and less insight into the performance and motivation of their employees (Dowling 2008). Most of the managers spent most of their hours in recruitment processes whenever an employee left work or quit and the process to replace them commenced.

The theory stated that the level of electronic processes in an organization had a direct impact on the workload borne by the managers. This study analyzed the responses on the level of human resource managers and the level of E-HRM in the company. Hypothesis testing was done where the null hypothesis would either be rejected or accepted depending on the level of the p-value. A linear regression was done to investigate the relationship between the two variables which were a level of E-HRM as the independent variable and the level of HR managers' workload reduction as the independent variable.

The results of the regression were as follows;

Regression Statistics

Multiple R 0.964628

R Square 0.930508

Adjusted R Square 0.930228

Standard Error 0.806853

Observations 250

ANOVA

df SS MS F Significance F

Regression 1 2161.845 2161.845 3320.747 1.3E-145

Residual 248 161.4509 0.651012

Total 249 2323.296

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 0.129302 0.112331 1.151076 0.25081 -0.09194 0.350547 -0.09194 0.350547

X Variable 1 2.924289 0.050746 57.62592 1.3E-145 2.824341 3.024237 2.824341 3.024237

Table 4.5 show the linear regression results on the effect of E-HRM on managers' workload reduction

The results established a positive relationship between the reduction in the HR managers, workload with the level of web-based human resource management. A model to predict the relationship was as follows. Workload reduction = 0.129302 + 2.924289 the level of electronic human resource management. The direct relationship meant that any substantial change in a company's level of web-based human resource management has a direct impact on the workload of the employees. The more digitalized a company's human resource function is, the lesser the workload borne by the human resource managers.

The model correctly predicted the relationship since the calculated f statistic was way much higher than the critical f. therefore the relationship could be relied upon. On the other hand, the adjusted R-square was 0.930228 which meant that 93.0228% of the reduction of the managers' workload was attributable to the level of digital human resource management. The objective of the research was to find out how effective in modern human resource practices is the EHRM, and therefore the reduction in managers' workload meant that they would be more effective in their roles.

Most of the correspondences attributed the reduction of work to the efficiency in most of the processes such as the recruitment and performance evaluation. They felt that the managers were left with lesser roles a claim that was supported by the reduction in the number of human resource personnel's in the human resource management department.

The use of online planning software to have scheduled for the employees enabled the managers to shift focus to other activities. Other roles such as supervising, training, recruitment, and motivating of employees are reduced by the use of web-based human resource management (Foot 2008). Period focused on such activities are reduced and this even triggers the managers to creatively and innovatively perform their tasks, and further workload reduction is realized.

4.7 Effectiveness of electronic human resource management on employee retaining.

Employee retaining in many companies is a constant challenge since employees are rational, and they can be performing less than desired by the company. They might also be involved in other activities which are against the company's policies such as corruption or embezzlement of funds. The employees might also be experiencing challenges at their workplace but the communication process between them and their seniors is poor, so they are unable to pass their grievances. The consequences of such behaviors and relationships are the willing exit of the employees in search of greener pastures or firing of the employees due to poor performance and poor conduct.

The focus of this study was to find out the effectiveness of the electronic human resource in organizations operations. Employee hiring and retaining being the focal point. The null hypothesis stated that the adoption of electronic human resource management is positively related to the retaining of employees in an organization (Luthans 2008). The research was to find out the level of employee turnover in organizations that already adopted the E-HRM. The null hypothesis is based on the theory which emphasized that adoption of electronic human resource management has the direct impact on employee turnover.

Theoretically, the employee turnover rate at any organization is low where electronic human resource management has been adopted. The reasons behind this were that the employee to human resource managers was greatly improved. All the employees' grievances were well communicated to the involved parties through the use of portals and other internally established electronic communication practices in an organization. The level of employee satisfaction is greatly improved with electronic human resource management since they are well motivated. Employee appraisal is also more efficient with web-based human resource management system since the working environment is more improved, and the employees' safety is well taken care of which makes them feel appreciated and valued in that company (Holley 2009). Employee satisfaction prevents them from being involved in practices that are against the company policies which might cause them to be fired. The rate at which employee willingly quit work is also reduced with high employee satisfaction.

To find out the relationship, a regression analysis using Excel was determined. The independent variable being the level of Electronic HRM in an organization and the dependent variable being the score on employee remaining in the organization. A model from the linear regression that shows the relationship is as follows The model predicted that employee retaining rate =0.02905 + 3.088717 of the level of EHRM. It, therefore, meant that the rate of which employees are retained in the organization was positively related to the level of EHRM. It specifically implied that a unit improvement in the level of HRM implicated an increase in the employee retaining with three units. The results from the regression were as follows.

Regression Statistics

Multiple R 0.967904

R Square 0.936837

Adjusted R Square 0.936583

Standard Error 0.809735

Observations 250

ANOVA

df SS MS F Significance F

Regression 1 2411.794 2411.794 3678.363 9.5E-151

Residual 248 162.6063 0.65567

Total 249 2574.4

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 0.02905 0.112732 0.257691 0.796859 -0.19298 0.251085 -0.19298 0.251085

X Variable 1 3.088717 0.050927 60.64951 9.5E-151 2.988412 3.189022 2.988412 3.189022

Table: 4.6 show the results of regression analysis.

The model correctly predicted the relationship, and it could be adopted. The standard deviation from the mean was 0.8097 while the adjusted R-square recorded 0.9365 which was translated to 93.65% of the changes in employee retaining was attributable to the level of EHRM. Implications were that the higher the level of EHRM, the lower the level of employee turnover. The F statistic from the ANOVA table was much higher than the significant f and therefore it meant that the overall goodness of fit of the model was acceptable. The x variable was also significant since the p-value was much lower than 0.05. Most of the employees attributed the scenario to the improved communication between the human resource managers and the employees.

4.7: overall effectiveness of Electronic HRM in an organization.

Adoption of Electronic human resource management in the modern human resource management practices has been major to deal with efficiency in the organizations which is vital for their survival. There is a high level of competition in all industries, and therefore inefficiency means that an organization can be easily surpassed by others and it may exit the industry due to making losses.

Theory states that efficiency in the human resource department is equivalent to efficiency in the whole organization. Efficiency in the human resource department is translated to efficiency in the whole organization since the human resource department coordinates all the other factors of production. Cutting down of cost and high productivity of employees are used as the major benchmarks for efficiency in human resource department (Redman 2008). Other qualities of an effective human resource department is low employees turnover and highly motivated employees in the organization.

The major goal of this study was to investigate the effectiveness of electronic human capital management on modern HR practices, and therefore, in order to test the validity of the null hypothesis which stated that the adoption of EHRM leads to efficiency in an organization a regression analysis was done with the score on the effectiveness of EHRM being the dependent variable while the level of EHRM being the independent variable. A prediction model was formed, and its general goodness of fit was established. The significance of the coefficients was also investigated by using the p values benchmarked by a significance level of 0.5.

The results from the regression analysis were as follows.

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 0.032373 0.11948 0.270949 0.786655 -0.20295 0.267697 -0.20295 0.267697

X Variable 1 3.030237 0.053975 56.14113 5.4E-143 2.923928 3.136545 2.923928 3.136545

Table 4.7: show the results of the regression analysis of the intercept.

A linear relationship between the overall effectiveness of EHRM and the level of EHRM was established. The overall efficiency of a company = 0.032373 + 3.030237 the level of RHRM. The intercept was ruled out since it was insignificance. It had a p-value of 0.7867 which was way much higher than the significant p-value of 0.05. The independent variable coefficient which is the level of EHRM was significant since its p-value was way much lower than 0.05. Therefore, the model collapsed and was rearranged as follows. Overall efficiency = 3.030237 the level of RHRM. A directly proportional relationship between the effectiveness of EHRM and the level of EHRM was established. It, therefore, predicted that a unit improvement in the level of EHRM had the direct impact of increasing the overall efficiency of the company by 3.03 units.

The overall goodness of fit for the model was tested using the f statistic. An examination of the overall goodness of fit was to determine whether the model correctly predicted the relationship between the two variables. It was used to determine whether to accept the model or to reject it which meant rejection of the null hypothesis. Calculations of the f statistic were as follows i.e. the results of the ANOVA table.

ANOVA

df SS MS F Significance F

Regression 1 2321.331 2321.331 3151.826 5.4E-143

Residual 248 182.6529 0.736504

Total 249 2503.984

Table 4.8: show the results of the ANOVA table.

The null hypothesis tested by the ANOVA table stated that the model was insignificant and therefore not fit to predict the relationship. The alternative hypothesis, on the other hand, stated that the model was significant and could be adopted to predict the relationship. The calculated F statistic was 3151.826 which was greater than the significance F or the critical F. Therefore, the null hypothesis was rejected, and this led to the adoption of the alternative hypothesis that stated that the model was significant to predict the relationship. It, therefore, meant that the model was fit to determine the relationship between the effectiveness of the EHRM and the level of EHRM in modern HRM practices today.

To determine the percentage of the changes in the overall effectiveness of EHRM in a company was attributable to the changes in the level of EHRM, the r-squared and the adjusted r-squared were put into consideration. The results from the linear regression analysis the adjusted r-squared were as follows.

Regression Statistics

Multiple R 0.962837

R Square 0.927055

Adjusted R Square 0.926761

Standard Error 0.858198

Observations 250

Table 4.9: Regression results for the adjusted r-squared

The multiple r shows the correlation between the two variables. A strong correlation between cause and effect relationship existed between the two variables. The r-squared was at 0.927 which meant that 92.7% of the overall changes in the overall effectiveness of the modern HRM practices was attributable to EHRM. The adjusted r-square that takes into account the degrees of freedom was also high. Therefore, only a small proportion of the changes in the effectiveness of a company was attributable to other factors. The results of the regression model, therefore, showed that an improvement in the level of electronic human resource management had a direct and positive impact on the overall level of efficiency in the company. It also meant that since control of the human capital is a major cause of effectiveness in a company, therefore, the adoption of the electronic human resource is one way of achieving a high level of efficiency in a company.

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