Lengnick-Hall and Moritz (2003) indicates that survey data on e-HR practices are scattered and nonscientific. With the aim to reveal the usage of e-HR practices in Taiwan, this research presents the results of descriptive analysis on the usage of e-HR practices. The 182 companies, consequently, are used as a reference to understand the current e-HR trend.
The descriptive statistics of e-HR practices would follow Haines and Lafleur’s (2008) e-HR practices categorization. The findings and discussion of e-HR practices were thus organized by the following categories: survey, employee benefit, compensation and rewards, performance management, human resource planning and career development, staffing, training and development, and employee relationship.
Survey
Basically, 23.6% of the companies did not use electronic means to conduct employee surveys. The rest of the practices were evenly distributed: 19.8% used manual work, 24.2%
used office software, 17.6% used packaged software and 14.8% used integrated software. The mean for the e-HR practices on surveying is 2.8 and the standard deviation for the function is 1.37.
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Employee Benefit
Concerning the employee benefits, some practices were rated very low. Generally speaking, employee benefit did not adopt many electronic solutions; 23.6% of the companies used stand-along software packages and only 15.8% used integrated electronic solutions. The general pattern of e-HR practices in employee benefit function is shown in Figure 4.1.
Figure 4. 1. General pattern of e-HR practices in employee benefit function More specifically, at least 20% of the companies did not provide employees with the self-service for personal benefits, such as providing employees with retirement planning information, giving employees access to their own benefit information or letting employees make changes to their own benefits records, let alone the more advanced consulting services of employee benefits, such as assisting employees in their benefits selection. Practices used for disseminating the internal news were mostly through office software with 45.1% of usage.
For internal HR administrative work, the tasks were better organized and functioned, with at least 20% usage of packaged software in practices such as processing monthly benefits, invoices, and handing and processing claims. The information is organized in Table 4. 1.
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Table 4. 1.
The Overview of E-HR Practices for Employee Benefits
Question items in employee benefits
No practice Manual Office software Packaged software Integrated
software Mean SD
2. Posting relocation policies, procedures and handbook online.
1.1% 24.7% 45.1% 14.8% 14.3% 3.16 0.99 3. Providing employees with
retirement planning information
25.8% 24.2% 25.8% 11.5% 11.5% 2.58 1.31 4. Giving employees access to their
own benefit information
23.1% 15.9% 18.7% 23.6% 18.1% 2.98 1.44 5. Assisting employees in their
benefits selection
31.3% 23.1% 22.5% 14.3% 8.8% 2.46 1.30 6. Letting employees make changes to
their own benefits records
34.6% 25.3% 10.4% 18.7% 11.0% 2.46 1.41 7. Processing of monthly benefits
invoices
13.7% 29.7% 17.0% 23.1% 16.5% 3.00 1.32 8. Handling and processing claims 4.9% 26.9% 19.8% 32.4% 15.9% 3.29 1.16
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Compensation and Rewards
Concerning the compensation and rewards, the general pattern is generally skewed to the right side. That is, the companies generally adopted more packaged software, but not very integrative with other function. The general pattern of e-HR practices for compensation and rewards is presented in figure 4. 2.
Figure 4. 2. General pattern of e-HR practices in compensation and rewards function
Detailed discussion concerning the e-HR practices in compensation and rewards fell under three categories: payroll work, self-service and organizational compensation analysis.
The payroll work received relatively high ratings in e-HR adoption: almost half of the companies used packaged software and at least 30% of the companies used integrated software to facilitate processes, such as transferring employee data between HR and outside payroll systems, and processing employee compensation relevant data such as wages, vacation and sick time. As for the self-service, 33% of the companies did not allow employees to access pay data information; on the other hand, 23.1% used packaged software and 19.2% adopted integrated software. E-HR applications in the compensation and rewards focus greatly on the internal compensation analysis functions. The tasks of performing
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compensation structure and cost analysis adopted more advanced electronic practices, with 24.2% using packaged software and 18.7% using integrated software. On the other hand, some compensation and rewards E-HR practices are less used currently in Taiwanese companies, such as exporting external compensation survey data and performing analysis and comparison concerning compensation. The information is organized in Table 4. 2.
Table 4. 2.
The Overview of E-HR Practices for Compensation and Rewards
Question items in compensation and rewards
No practice Manual Office software Packaged software Integrated
software Mean SD
9. Transferring employee data between HR and outside payroll systems
2.2% 4.4% 12.1% 50.5% 30.8% 4.03 0.90 10. Allowing employees to access pay
data information
33.0% 15.4% 9.3% 23.1% 19.2% 2.80 1.56 11. Performing compensation structure
and cost analysis
13.7% 18.1% 24.7% 24.2% 18.7% 3.16 1.31 12. Conducting job evaluations 25.8% 23.1% 28.0% 12.1% 11.0% 2.59 1.29 13. Exporting external compensation
survey data and perform analysis and comparison concerning compensation
23.1% 19.8% 25.8% 18.7% 12.1% 2.77 1.33
14. Processing employee compensation relevant data such as wages, vacation and sick time
1.1% 6.0% 12.1% 48.4% 32.4% 4.05 0.89
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Performance Management
Concerning the performance management, companies mainly adopted office software to assist HR administration. The general pattern of e-HR practice in performance management function is shown in Figure 4. 3.
Figure 4. 3. General pattern of e-HR practices in performance management
Almost all practices in performance management category displayed the same tendency as shown in Figure 4.3, except the task on “Collecting employee time and attendance data”, which was reported to adopt the most advanced technology with 44.5% using packaged software and 29.7% using integrated software. As mentioned above, the majority of performance management tasks adopted the office software as their e-HR practice. For example, 40.1% of the organizations used office software for “notifying supervisors of the need to conduct scheduled employee performance reviews”. That is, relatively low percentages of companies had implemented packaged or integrated software systems. The information is organized in Table 4. 3.
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Table 4. 3.
The Overview of E-HR Practices for Performance Management
Question items in performance management
No practice Manual Office software Packaged software Integrated
software Mean SD
18. Tracking progress toward goal attainment
7.1% 25.8% 34.6% 17.6% 14.3% 3.06 1.14 19. Collecting employee time and
attendance data
0.5% 9.3% 15.9% 44.5% 29.7% 3.93 0.94 20. Producing performance
documentation and employee evaluations
12.1% 15.9% 35.2% 20.3% 15.9% 3.12 1.22
Human Resource Planning and Career Development
Concerning the human resource planning and career development, almost all practices showed the same tendency in e-HR adoption; the majority of the tasks used the office software to facilitate the process. The general pattern of e-HR practice in human resource planning and career development function is shown in Figure 4. 4.
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Figure 4. 4. General pattern of e-HR practices in human resource planning and career development
Specifically, 61.1% of the organizations adopted the office software for “generating organizational charts”, and 53.8% for “building schedules based on both staffing requirements and employee requests”. Relatively low percentages of companies had implemented packaged or integrated software systems for this function. It is noteworthy that 36.3% of organizations reported not having the practices of succession planning or summarizing individual development plan for employees. In addition, 28% of the organizations did not provide self-assessment or career development guidance to employees.
The information is organized in Table 4. 4.
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Table 4. 4.
The Overview of E-HR Practices for Human Resource Planning and Career Development
Question items in human resource planning and career development
No practice Manual Office software Packaged software Integrated
software Mean SD
21. Forecasting staffing levels 14.3% 25.8% 38.5% 14.3% 6.6% 2.73 1.08 22. Generating organizational charts 4.4% 17.0% 61.0% 12.6% 4.9% 2.97 0.82 23. Building schedules based on both
staffing requirements and employee requests
7.7% 25.3% 53.8% 8.8% 4.4% 2.77 0.88
24. Tracking labor costs 11.0% 15.9% 43.4% 19.2% 10.4% 3.02 1.10 25. Tracking and optimizing the
distribution of human resources and their productivity
19.2% 19.8% 43.4% 11.5% 5.5% 2.64 1.09
26. Conducting succession planning 36.3% 23.1% 29.1% 6.6% 4.4% 2.19 1.14 27. Providing employees with
self-assessment and career development guidance
28.0% 21.4% 30.8% 12.1% 7.1% 2.49 1.22
28. Helping employees assess their performance and set goals
15.4% 19.8% 37.9% 17.0% 9.9% 2.86 1.17 29. Providing employees information
about internal job openings
13.2% 21.4% 32.4% 19.8% 13.2% 2.98 1.21 30. Summarizing information from
individual development plans
36.3% 13.2% 23.1% 17.6% 8.8% 2.49 1.37
Staffing
Concerning the staffing, almost all practices showed the same tendency in e-HR adoption. It appeared that the majority of the tasks in staffing were performed manually. The general pattern of e-HR practice in staffing function is shown in Figure 4.5.
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Figure 4. 5. General pattern of e-HR practices in staffing
The practices in staffing are by nature inclined to involve more human interactions, and hence the recruiters may feel obliged to perform the tasks in person. Thus, the highest percentage of companies reported to perform all the tasks in staffing manually. For example, 66.5% of the organizations performed the task “conducting employment interviews”
manually. Other more frequently reported electronic usages were office software and packaged software. The information is organized in Table 4. 5.
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Table 4. 5.
The Overview of E-HR Practices for Staffing
Question items in staffing
No practice Manual Office software Packaged software Integrated
software Mean SD
31. Submitting job requisition information
4.4% 42.9% 26.9% 15.9% 9.9% 2.84 1.07 32. Handling internal referrals 8.8% 45.6% 29.7% 9.3% 6.6% 2.59 1.00 33. Recruiting outside the organization 1.6% 23.1% 28.0% 33.0% 14.3% 3.35 1.04 34. Tracking applicants through the
staffing process
5.5% 30.8% 29.7% 24.2% 9.9% 3.02 1.08 35. Processing résumés 1.1% 39.6% 24.7% 26.9% 7.7% 3.01 1.01 36. Prescreening job applicants 0.5% 44.0% 24.2% 23.6% 7.7% 2.94 1.00 37. Skill-testing candidates 3.3% 43.4% 24.7% 20.9% 7.7% 2.86 1.03 38. Conducting employment interviews 0.5% 66.5% 17.0% 11.0% 4.4% 2.52 0.87
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Training and Development
As for the e-HR practices of training and development in Taiwan, the majority of companies reported performing the tasks manually or using office software. The general pattern of e-HR practice in training and development function is shown in Figure 4.6.
Figure 4. 6. General pattern of e-HR practices in training and development
For instance, 50.5% of the organizations used office software for “assessing the training effectiveness”, while 22.5% adopted manual work; 49.5% used office software for
“conducting the assessment of training need for departments and organization”, while 28.6%
chose manual work.. Similar patterns applied to the other practices in training and development. The information is organized in Table 4. 6.
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Table 4. 6.
The Overview of E-HR Practices for Training and Development
Question items in training and development
No practice Manual Office software Packaged software Integrated
software Mean SD 43. Providing self-directed training
sessions 47. Constructing the training blueprint
according to different positions and needs
12.6% 22.0% 47.3% 13.7% 4.4% 2.75 0.99 analysis, such as skill inventory and the linkage behind the course
13.7% 25.3% 45.1% 8.8% 6.6% 2.69 1.03
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Employee Relationship
As for the e-HR practices of employee relationship in Taiwan, the practices mainly adopted manual work and office software. The general pattern of e-HR practice in employee relationship function is shown in Figure 4.7.
Figure 4. 7. General pattern of e-HR practices in employee relationship
‘Handling grievances” and “providing access to collective agreements, rules and regulations” had different patterns. On “handling grievances”, 42.3% of the companies chose to manually respond to the employees, and only 34.6% of the organizations adopted office software. As for providing access to collective agreements, rules and regulations, 21.4% of the companies did not implement the practice, 26.9% of the companies used manual work and 32.4% of the companies used office software. This finding showed that, in Taiwan, the companies did not encourage labors to form unions, and therefore did not provide access to the rules, agreements and regulation. The information is organized in Table 4.7.
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Table 4. 7.
The Overview of E-HR Practices for Employee Relationship
Question items in employee relationship\
No practice Manual Office software Packaged software Integrated
software Mean SD
50. Handling grievances 9.9% 42.3% 34.6% 7.1% 6.0% 2.57 0.98 51. Providing access to collective
agreements, rules and regulations
21.4% 26.9% 32.4% 9.3% 9.9% 2.59 1.21
Summary of the Descriptive Analysis
To sum up the descriptive analysis, office software was the most used in e-HR practices of all functions except the compensation. Office software was chosen as the dominant tool to facilitate the following HR processes in Taiwan: showing the information on employee benefit, and distributing the electronic employee handbook.
On the other hand, the three practices that received the highest rating of IT automation were all compensation related administrative work: 1) transferring employee data between HR and outside payroll systems; 2) Collecting employee time and attendance data. Similar to the findings of Lengnick-Hall and Moritz (2003), payroll administration in Taiwan is also one of the most automated and integrated e-HR practices.
Most of the Taiwanese firms chose to use the office software as the main solution to store and process employee data in human resource functions. Hence, human resource professionals were required to spend lots of time dealing with the operation and calculation in office software. The majority of practices adopted the office software and manual work. For example, almost all the practices in recruiting function are facilitated by office software. As a result, the benefit of a fully integrated e-HR system is not fully realized in Taiwan.
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Model Testing
SmartPLS was used as the statistical tool to test the hypotheses in the research framework. The suggested minimum sample size for appropriate PLS operation is calculated by multiplying the number of 10 by the number of parameters in the variable with the most items or dimensions (Barclay et al., 1995; Gefen et al., 2000). In this research, the e-HR practices variable was composed of 7 dimensions, and was the variable with the most dimensions in this research. Therefore, the sample size should exceed the number of seventy (7 times 10). The sample size of this research is 182, which complied with the scholars’
suggestion. In addition, PLS is considered best suited to deal with formative scales (Chin, 1998; Pavlou & Fygenson, 2006). This study contained two formative scales, e-HR practices and IT capability, and hence the researcher chose PLS to test the research model.
After the measurement model has been successfully validated, the structural model can further be analyzed. The model validity can be evaluated by coefficient of determination (R2), and path coefficients.
As illustrated in Figure 4.8., the effects from complexities of HR role expectation and task complexities to e-HR practices explain 22.8% of model variances (R2=.228); the effects from e-HR practices to strategic focus and competence explain respectively 20.7% and 12.1%
of model variances (R2=.207 and .121).
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Figure 4. 8. The hypotheses testing with path coefficient and R square
The path coefficient is basically used to judge the relationship between an outcome variable and its predictor variables. The first hypothesis indicates that the complexity of HR role expectation shall influence the e-HR practices. However, the role complexity hypothesis is not supported (β＝.144; t<1.95). The second hypothesis indicates that the task complexity shall influence the e-HR practices. However, the task complexity hypothesis is not supported either (β＝.177; t<1.95). IT capability, as the moderator between perceived complexity of HR role and e-HR practices, and between task complexity and e-HR practices, does not prove to be significant. The path coefficient value is .248 with t-value less than 1.95 for HR role complexity, and -.229 with t-value less than 1.95 for task complexity. E-HR practices, on the other hand, are proven valid as the predictor of HR competency and strategic focus. The path coefficient between the adoption of e-HR practices and competency is .455 with a t-value of 5.50; the path coefficient between the adoption of e-HR practices and strategic focus is .348 with a t-value of 7.75. The overview of hypothesis testing is summarized in Table 4. 8.
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H1：Complexity of HR role expectation imposes a positive
influence on the departmental usage of e-HR practices. .144 0.49 Not supported H2：Complexity of tasks performed by HR staff has a
positive influence on the staff’s usage of e-HR practices. .177 0.72 Not supported H3：IT capability positively moderates the relationship
between the complexity of role expectation on HR staff and staff’s usage of e-HR practices.
.248 0.63 Not
supported H4：IT capability positively moderates the relationship
between the complexity of tasks performed by HR staff and staff’s usage of e-HR practices.
-.229 0.72 Not
supported H5：The increase of e-HR practice usage will positively
contribute to HR staff’s strategic focus. .455 5.50**
* Supported H6：The increase of e-HR practice usage will positively
contribute to HR competence. .348 7.75**
* Supported
* t-value> 1.65; ** t -value> 1.96; *** t -value> 2.58
Overall, the antecedents of e-HR practices were not supported. The literature suggests that the need for the HR to adopt a variety of e-HR practices indeed exists. The empirical data, however, suggests a different list of factors that explain the need for e-HR practices.
Tenenhaus et al. (2005) states that the Gof could be calculated as thesquareroot of average AVE multiplying average R2. Using Cohen (1988) R2 values, the Gof values would be as follows: taking the square root of the average variance extracted of all constructs (.93) and multiplying the average R2 of all endogenous constructs (.18) equals to the Gof value of .17, which exceeds the small values suggested (Tenenhaus, Amato, & Esposito Vinzi, 2000; Wetzels et al., 2009). To sum up, the data have a relative small fit to the model.
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Alternative Model and Model Testing
Since the moderator effect does not exist, the researcher refined the research model in order to provide a better understanding of the relationships among study variables. The alternative model shows that the causal relationship may indeed exist among the variables which were tested non-significant in the original research model. The comparison of the original and alternative analysis reveals that IT capability may not be a moderating effect but rather a direct effect on the usage of e-HR practices. Furthermore, the complexities of role expectation may have direct impact on strategic focus of human resource professionals and competence as well. The increasing role expectation toward human resource professionals may bring about some major changes in human resource department. More comprehensive e-HR systems may hence be implemented to facilitate the administrative work. The competence and strategic focus of the human resource professionals may hence be influenced not only by the changes in e-HR system, but also the pressure to perform HR jobs well. As a result, the alternative analysis provides rationale to further explore the causal effect between e-HR system and other factors. The Gof value is the square root of the average variance extracted of all constructs (.91) multiplied by the average R square of all endogenous constructs (.35), which equals to .32. The data fit the alternative model much better. The alternative analysis on e-HR adoption model is shown in Figure 4. 9.
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Figure 4. 9. The alternative analysis on e-HR adoption model
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