The chapter presents the findings of the study; it reports the results of the various statistical analysis procedures that were conducted to test the proposed hypotheses.
Explanations and brief discussions are also provided to explain the results.
Influence of Treatment on Variables
The participants received two types of treatments, one with assumed low process virtualization requirements; change of personal profile information. The other one was performance appraisal, which was assumed to have higher process virtualization requirements. The comparisons of mean scores on all the research variables between groups “change of personal profile information” and “performance appraisal” are shown in the table below.
Table 4.1.
T-test between Different Treatments
Variable
Treatment (Mean value)
Mean difference t-value Change of profile
information
Performance appraisal Attitude toward
Technology 3.74 3.78 -.034 .676
Computer
Self-efficacy 3.54 3.49 .046 .575
Relationship
Requirements 3.71 3.90 -.192* .013*
56 identify different Relationship Requirements with different HR processes. It was also able to identify different Monitoring Capability of E-HR software between different processes. In Relationship Requirements, it was assumed that performance appraisal would have higher requirements. The performance appraisal process requires a lot of communication between the supervisor, the employee, and the HR. With so much interaction requirements, people would want to interact in person; this was confirmed with the experimental results. The Synchronism and the Identification Requirements were both not significant in this study. Since the two selected processes do not involve physical transactions or carry a sense of urgency, the participants may see both processes with equally moderate Synchronism Requirements. On the other hand, both processes require users to identify themselves and to enter user-specific information, therefore it is reasonable for the participants to see both processes with equally high
level of Identification Requirements. The significant difference of Monitoring Capability may implicate that the participants were more worried about privacy issues with the change of personal profile process. Therefore, they feel less confident with their privacy in the change of personal profile process, resulting in a perception of lower Monitoring Capability in the change of personal profile process in PeopleSoft software.
Relationship between Variables
Pearson‟s Correlations analysis was utilized for the eight variables of this study, plus four demographic variables. The correlations are shown in the following table
Table 4.2.
Means, Standard Deviations, and Correlation Coefficient
Note. Numbers in the parentheses represent the Cronbach‟s Alpha values of the variables.
*p<0.05, **p<0.01, ***p<0.001 Gender 0 = Male, 1 = Female
Education 0 = College student, 1 = Master student
Mean S.D. 1 2 3 4 5 6 7 8 9 10 11 12
1. Gender .66 .47
2. Age 22.39 4.90 -.264**
3. Education .12 .33 .121 .130
4. Computer experience (Years) 2.50 .73 -.099 .331** .124
5. Attitude toward Technology 3.76 .60 -.104 .264** .083 .175** (.91)
6. Computer Self-efficacy 3.52 .61 .054 .166* .063 .175** .590** (.87)
7. Sensory and Relationship Requirements 3.81 .57 .045 .090 .048 .037 .202** .288** (.91)
8. Synchronism Requirements 3.71 .61 .017 -.033 -.065 .021 .258** .312** .458** (.81)
9. Identification requirements 4.10 .61 .189** .115 .096 .109 .329** .408** .387** .429** (.85)
10. Representation Capability 3.71 .57 -.031 .202** .161* .066 .438** .436** .275** .270** .352** (.87)
11. Monitoring Capability 3.42 .63 -.013 .211** .192** .150* .389** .374** .240** .136* .183** .478** (.82)
12. Behavioral Intention 3.56 .55 -.043 .244** .257** .184** .482** .402** .239** .234** .342** .491** .453** (.78)
In demographic variables, gender is positively correlated with Identification Requirements (r=.189, p<.01); this suggests privacy is valued differently between males and females. Females have a generally higher privacy concern than males (Mean 4.19 vs. 3.94). Age shows positive correlations with Attitude toward Technology (r=.264, p<.01), Computer Self-efficacy (r=.166, p<.05), perceived Representation Capability (r=.202, p<.01) and Monitoring Capability (r=.211, p<.01), and Behavioral Intention (r=.244, p<.01). This shows that age makes a difference in all two Individual Attributes toward technology, their perceived IT Capability, and their Behavioral Intention to use E-HR software. Education has a positive correlation with perceived Representation Capability (r=.161, p<.05) and Monitoring Capability (r=.250, p<.01), also with Behavioral Intention (r=.257, p<.01). In computer experience, it is positively correlated with Attitude toward Technology (r=.175, p<.01), Computer Self-efficacy (r=.175, p<.01), perceived Monitoring Capability (r=.150, p<.05), and Behavioral Intention (r=.184, p<.01).
All the independent and dependent variables are positively related. The Process Virtualization Requirements showed different results from the hypotheses, which were assumed to have negative effects with Behavioral Intention. It is important to note that Attitude toward Technology has an especially high correlation with Computer Self-efficacy with coefficient value of .590.
Hypotheses Testing
Hierarchical regression was conducted in order to test the hypotheses in this study. Demographic factors of gender, age, education, and computer experience were included as control variables. The first model only included the control demographic variables, while Behavioral Intention acts as the dependent variable. The second
Self-efficacy), Process Virtualization Requirements (Relationship and Sensory Requirements, Synchronism Requirements, identification requirements), and IT Capability (Representation Capability and Monitoring Capability) added as the independent variables for testing. Hypotheses testing results are shown in the following table.
Table 4.3.
Results of Regression Analysis
Behavioral Intention
Independent Variable Standardized coefficients (Beta)
Model 1 Model 2
Gender -.010 -.037
Age .179* .043
Education .221*** .153**
Computer experience (Years) .100 .051
Attitude toward Technology .220**
Computer Self-efficacy .026
Sensory and Relationship Requirements
.012
Synchronism Requirements .047
Identification requirements .111
Representation Capability .196**
Monitoring Capability .187**
R2 .119 .410
Adjusted R2 .103 .378
△ R2 .119 .290***
F 7.139*** 12.867***
N 217 217
Note.*p<0.05, **p<0.01, ***p<0.001
In model 1, age (β=.179, p<0.05) and education (β=.221, p<0.001) show a significant effect on Behavioral Intention. The model explains 11.9% (R2=.119) of behavior intention to use E-HR technology. In demographics, age and education play significant role in the Behavioral Intention to use E-HR software.
Moving on to model 2, the model shows significant improvement over model 1 with 41% (R2=.410) of variance in Behavioral Intention explained and change in R squareof .290. Age is not significant anymore in this model, while the significance of education (β=.153, p<0.01) is also lowered. Attitude toward Technology is significant (β=.220, p<0.01), while Computer Self-efficacy is not. As seen in the correlation table, Attitude toward Technology is highly correlated with Computer Self-efficacy, therefore this result may be due to collinearity problems. All of the process virtualization requirements showed no significant effect on Behavioral Intention. On the other hand, both of the IT Capability variables showed significant effect, with Representation Capability (β=.196, p<0.01) and Monitoring Capability (β=.187, p<0.01) respectively having an influence on Behavioral Intention. This may implicate that the younger generation care about the interface and capability of E-HR software more than the process itself. As long as the software is easy to use and the user has a positive Attitude toward Technology, they are willing to try new electronic process.
In the end, three out of seven hypotheses were confirmed, in which both of the IT Capability factors were confirmed. The following table summarizes hypothesis testing results.
Table 4.4.
Summary of Hypotheses Testing Results
Hypothesis Result
Hypothesis 1: Perceived Relationship and Sensory Requirements are negatively related to Behavioral Intention to use E-HR system.
Not supported Hypothesis 2: Perceived Synchronism Requirements are negatively related
to Behavioral Intention to use E-HR system.
Not supported Hypothesis 3: Perceived Identification and Control Requirements are
negatively related to Behavioral Intention to use E-HR system.
Not supported Hypothesis 4: Perceived Representation Capability is positively related to
Behavioral Intention to use E-HR system.
Supported Hypothesis 5: Perceived Monitoring Capability is positively related to
Behavioral Intention to use E-HR system.
Supported Hypothesis 6: Attitude toward Technology is positively related to
Behavioral Intention to use E-HR system.
Supported Hypothesis 7: Computer Self-efficacy is positively related to Behavioral
Intention to use E-HR system.
Not supported
CHAPTER V CONCLUSIONS AND SUGGESTIONS
Conclusions
This particular study intended to test three competing theories that affect the use of E-HR systems by experimentally testing the Process Virtualization Theory, IT Capability, and Individual Attributes towards the use of technology under the HR context. The Process Virtualization Theory included four process virtualization requirements, sensory, relationship, synchronism, and identification requirements, however when used under HR context, Sensory and Relationship Requirements merged into one. IT Capability included two factors, representation and monitoring capabilities. Finally Individual Attributes includes Attitude toward Technology and Computer Self-efficacy. The study was a brand new experiment requiring the development of new scales, instruments, and experimental procedures to test the theories. It was also an informative test of the Oracle PeopleSoft E-HR software.
For the Process Virtualization Theory, the developed experiment was able to detect differences in Relationship and Sensory Requirements between different HR processes. However, negative relationship did not show between process virtualization requirements and Behavioral Intention as seen in the literature. The reason may be that to the younger Net generation, the virtualizability of a process may not be a key issue in their intention to use E-HR technology. Their main concern is shown in their Individual Attributes, their Attitude toward Technology, and their perception of the IT Capability, how they felt about the E-HR software. This was shown in the study because Attitude toward Technology showed the most effect on Behavioral Intention to use E-HR technology, for the Net generation. The IT capabilities, representation and Monitoring Capability were also significant in
appears that the Net generation is more concerned with their privacy and the software interface, along with their Attitude toward Technology; these were the main factors that affected their intention to use E-HR software.
Research Implications
In empirical test of the Process Virtualization Theory, it is important to note that out of the four process virtualization requirements, Sensory and Relationship Requirements merged into one when it was tested under HR context. It is vital to know that when used for interaction with people, instead of products, Sensory Requirements are less relevant. The Process Virtualization Theory did not show significant results for the Net generation under the HR context. Another reason that the Process Virtualization Theory was not confirmed may have to do with the lack of a pre-test and a post-test of behavior intention to use E-HR in the experiment procedure. The assessment of Behavioral Intention at the end of the procedure might have been the result of a direct reaction toward the E-HR software which mimics the PeopleSoft E-HR system. To conduct more timely and thus more accurate assessment of the effect of Process Virtualization Requirements, another assessment of Behavioral Intention should have been added after an HR process was introduced and before the hands-on experience on PeopleSoft. The suggested new experimental procedure is shown in the following Figure 5.1.
Student Subjects
Part A Questionnaire Attitude toward Technology
Computer Self-efficacy
Group (A) Process Briefing
Part B Questionnaire
Group (B) Process Briefing
Group (A)
As for IT Capability of representation and monitoring, they were significant when tested on the Net generation to predict behavior intention, thus IT Capability predictors appear to be useful tools in determining the acceptance of current E-HR products. Attitude toward Technology was also a significant factor in this particular study; it was shown to be positive in affecting the Behavioral Intention to use E-HR technology. The particular research also developed a way of carrying out theoretical evaluation tests by introducing “mock-up” software for maximum experiment control.
It proved to be very effective in letting the participants experience the E-HR technology in a controlled manner to contain unwanted effects. All in all, this study set a start in how E-HR software experiments can be conducted. The experimental procedures appeared to be effective in testing the capability of E-HR software.
Practical Implications
This research may not only be useful for helping the company decide which HR processes to go electronic, but also help test the capability of current E-HR software.
From the results of this study, it can be implicated that developing the current IT Capability is more important than deciding what processes to go virtual. Most of the younger generations are willing to try new things. They generally have a positive attitude toward new technology.
For companies with a lot of young employees, the intention to use E-HR software would not be a big problem. As long as the company has enough budgets to purchase E-HR software, the employee will be willing to use it. It would be very important to fully assess an E-HR software system before purchasing it, because how well the software was designed immensely affects the intention to use the software.
For HR professionals, promoting the E-HR system would be good for the company. The HR doesn‟t have to worry about losing their jobs because of E-HR
systems. In a previous study the researcher conducted, it concluded that E-HR systems actually increase the presence and credibility of HR professionals in the company. E-HR systems help move jobs of the HR more towards strategic positions and away from routine tasks (Yeh & Hsiao, 2013). Since the capability of IT is very important, the HR should work with the E-HR software providers to develop and improve on the capability of current E-HR software.
For IT vendors and E-HR product developers, one of the main issues learned from this study is the importance of privacy. The current generation has immense concerns for their personal information. Mainly the Monitoring Capability of IT, it is important to design software that protects precious personal information, to make the users feel safe about using the software, in order to achieve higher usage. Also, the user interface has to be friendly and convenient to use. Therefore, what‟s important is to make the software interface more user-friendly since one of their main concerns was Representation Capability.
Contribution of the Study
This study took E-HR study a step further by exploring a new field of technology adoption, the actual human requirements of HR processes. The study was a start for the exploration of process virtualization requirements under HR context. It is important to know the requirements for the HR processes because it would be wiser to invest in processes with more virtualizability at first. Although the hypothesized relations between these requirements and behavior intentions were not supported using data from the Net generation in a controlled experimental setting, it is still a good practice to examine virtualizability of a process before making automation decisions. Also, by examining the IT Capability of E-HR technology, it may be
virtualized. The study also found that one Individual Attributes, Attitude toward Technology, has the highest influence on the acceptance of E-HR technology.
The acceptance of employees is fundamental for the successful implementation of any new systems. By getting to know what influence acceptance, managers can assess these factors before starting the implementation process of a system (Razali &
Vrontis, 2010). This may be of help on companies that are trying to get their employees to use the system they invested heavily in.
Most importantly, this study set a standard of E-HR software experiments. No known studies have conducted empirical E-HR software experiments. The procedures appeared to be effective in testing the capability of E-HR software. The procedures may be replicated for future studies.
Limitations
Due to resources and funding issues, the study could not examine the process virtualization requirements for each HR process one by one. Only two extreme examples were selected to test the virtualization requirements. The measurement scales and experimental procedures used in this particular study are all newly developed. This makes the study lean towards the exploratory side. Since all the scales are based on a Likert scale of 1-5, common method variance may be another issue. In addition, convenient student samples were used. Samples were collected from the Net generation because of easier access. Student samples may be different from the actual workforce in the fact that they may not have access to company E-HR systems on a daily basis. Despite the criteria that the researcher has set for the student samples of being in business major, the age of the participants may also be too young to fully understand HR processes. They may not have been in a company and experienced HR processes to care for it. Therefore, caution should be used when
generalizing the findings of the study to other populations. In order to save time, most of the experiments were done on entire classes of students. The researcher was only able to find 1 or 2 assistants for each incidence of the experimental procedure; it is difficult to fully control classes of 30 to 40 people at a time.
Suggestions for Future Research
A standardized and efficient experimental research procedure for testing HR virtualization requirements and the capability of E-HR software has been developed and tested in this particular research. The use of “mock-up” software also proved to be useful in controlling the experiment. Future researches may follow the pattern of revised experimental procedures with the addition of pre-test/post-test Behavioral Intention measures. However, because the current measurements have only been tested on students of Net generation, this set of measurements should best be validated again before testing on other HR processes. Moreover, future research should bring the experiment to actual industry settings instead of schools in order to make maximum contribution
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