Conclusions
The purpose of this research is to evaluate e-learning courses for new employees of S Company based on Goodhue and Thompson (1995) task-technology fit model. E-learning is the on-going trend in industries worldwide even for traditional industries, in this case, the real estate companies who have put a lot of efforts in developing e-services in recent years.
The fast changing customer needs have urged agents to learn more effectively. Within such competitive environment, employees’ ability to turn what they learned into real skills on job is relatively critical.
Real estate agents of S Company, who generate most profits of the company, devote most of their time to contacting their customers and visiting potential real estate cases. It is hard to squeeze their limited time and ask them to go back to headquarter for training.
Additionally, the lost of opportunity cost will be a concern when they spend their time on training instead of being on their post. Furthermore, since the locations of agencies are scattered, the time and money spent on travelling to headquarter will make the training impractical to implement on a more frequent basis. As a result, a systematic training program is critical and helpful for urgent training needs. Literatures have pointed out that the fit between technology and tasks could lead to effectiveness of training (Yu & Yu, 2010). This study confirmed the effect of task-technology fit in e-learning context leading to better learning outcome and training transfer using data from 151 real estate agents. According to analyses in the previous chapter, the research questions can be discussed as follows.
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1. Characteristics of S Company’s e-learning courses fit those of agents’ tasks.
Since the mean of TTF scores is higher than the average score of 3 (mean= 3.99), the first research question is positively confirmed. However, the mean does not reach 4 and none of TTF questions were rated higher than 4.5. It may show the possibility that e-learning courses are only designed at an “acceptable” level. According to the analysis result, employees rated the item, “The e-learning courses maintain information at an appropriate level of detail for my purposes.” and “The information I use or need is accurate enough.”
lower. It suggests that in content dimension, the level of detail and accuracy of information may have been neglected in courses design. In technology and presentation dimension, there are rooms to improve the course such as software and hardware stability, the locatability and clearer operational functions as well as commands it provides for learners, and the timely update of data, etc. The research reflects the fact that e-learning courses should be designed with consideration of the fit to trainee’s job tasks in real situations.
2. Perception of task-technology fit of an e-learning course has a positive influence on employees’ learning outcome.
Based on the data analysis, hypothesis 1 is supported. Task technology fit does have a significant impact on employees’ learning outcome. The learning outcome refers to the score of the online test which was generated after employees finished the courses. Whether the courses are perceived fit directly reflects on the test results. Therefore, TTF may help improve e-learning course design as well as employees’ learning outcomes.
3. Perception of task-technology fit of an e-learning course has a positive influence on employees’ training transfer.
In Table 4.4, TTF of an e-learning course significantly influences training transfer both in the 2nd and the 3rd month test with p< .05 and p< .01 respectively. Therefore, among
various elements which affect employees’ training transfer, task technology fit can be considered as one of influencing factors.
4. Employees’ learning outcome mediates the relationship between task-technology fit of an e-learning course and training transfer.
In Table 4.5 and 4.6, while the result shows differently in the 2nd month and the 3rd month test, it does reveal the influential role learning outcome plays in the process when trainees apply the acquisition of knowledge and skills presented in training to their tasks on the job. Therefore, hypothesis 3 is supported.
5. The moderating effect of learning style on the relationship between task-technology fit of e-learning courses and employees’ learning outcomes is not significant.
This outcome may have resulted from the small variances of learners’ preferred learning styles as the data shows that most new employees are strong or moderate visual learners.
Although hypothesis 4 is not supported, it provides a possible direction for further research.
Practical Implications
In a meta-analytic review of training transfer, Blume et al. (2010) found that many existing studies obtained transfer outcomes by the same source in the same measurement, which consistently inflated transfer relationships. As both learning outcome and training transfer scores are provided by S Company from employees’ actual records, this research has fewer concerns of the common method variance (CMV) issue. Thus, the strength of relationships between TTF, learning outcome and training transfer shown in this study may be seen as the true level of relationships. S company can review the course design by content, technology and presentation dimensions. The e-learning courses for new recruited employees
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are expected to get improvement. The following sections present suggestions for real estate industry, managers and S Company.
For E-learning Design
Although researches of e-learning are abundant, the empirical study of real estate industry is relatively few. Since the distinctive characteristics of agents’ tasks make it hard to promoting e-learning courses, the e-learning course should be designed in more desirable form for employees. While e-leaning is applied for closing the gap of learning needs, communication based tasks need more realistic practice. Following the assumption of task technology fit model, the e-learning course, first of all, should be built in a stable system.
From the interview with employees in S Company, the researcher found that the web-based system seemed to be unstable when employees try to connect to courses but fail to do so. As the e-learning system is controlled by the main server located in S Company headquarter, it sometimes blocks the connection when too many computers in scattered distributors try to access to e-learning system. From statistical data in this study, the scores in technology dimension are not higher than 4, and items 18 and 19 related to system are even rated relatively low (mean= 3.78). To solve the problem S Company needs to take into consideration the large amount of distributors’ needs for accessing information.
Secondly, as the market information varies rapidly, data preserved in e-learning system should be frequently updated. Most of e-learning courses in S Company are knowledge based which means that the information is rarely changed such as laws of taxes, deeds making process, and company policy, etc. It lowers the sense of importance of e-learning when employees cannot get information that matters to current tasks. For example, the market is diverse regionally and strategy of selling should be different while it is hardly shown as an e-learning course. Consequently, tasks oriented e-learning needs to be developed with more flexible and intellectual design so that it can fit the learners’ task and individual diversity. The
optimal influence is therefore expected on employees’ job performance.
Thirdly, the platform of e-learning system should be designed in readable and friendly way. Although S Company makes efforts on designing vivid and animated e-learning course for increasing employees’ learning motivation, the result still shows that there are rooms for improvement of the course presentation.
For Mangers of S Company
Based on the result, the manager of human resource (HR) should be aware of the market trend of current real estate market. As an effective training should relate to employees’ tasks, HR supervisor needs to notice the currency of e-learning courses. Some employees revealed that the e-learning courses do not maintain information at an appropriate level of detail for their purposes (item 5). In addition, the result shows that the overall of satisfaction scores of content dimension are not higher than 4.5. In this case, the HR manager should reinforce the connection with the sales department to understand their needs. Considering the large number of distributors located nationally, the information can be categorized regionally which facilitate the course design. According to one of the HR managers, the format of e-learning system has limited room to change because it was designed with certain parameters when the system provider made it. As technology advances every day, the most desirable system may one day be created and adopted. It is also the reason that human resource practitioners should keep searching for the most suitable system provider to build customized systems.
For sales managers, it is necessary to support the subordinates to learn from e-learning.
Some sales managers indicated that it is too busy to attend courses when there is certain performance standard that each distributor needs to achieve. As the company tries to balance the time spent between agents development and sales performance, e-learning training is offered to save training time and time for transportation. Therefore, sales department should provide opinions regarding what the agents desire to learn in the training so that it can ensure
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the contents of e-learning courses provide what they need. In other words, the cooperation between sales and HR department benefits both sides as well as the company.
Since both learning outcome and training transfer scores were provided by S Company from employees’ actual records, this research has fewer concerns of the common method variance (CMV) issue. However, as training transfer score consists of different indicators including a large proportion of ratings from supervisors, there may be a concern of the “Hallo Effect.” In other words, the training transfer score each distributor’s supervisor rated may be biased and influenced by each agent’s number of sales, characteristics, etc.
Limitations
Although most study hypotheses were supported by the data, it is noted that R² of all regression models are considered small. The small R² is an indication that the independent variables cannot explain most of the variance in the dependent variables of learning effectiveness and that there may be other more significant predictors being left out of the regression model.
As shown in chapter two, there are other individual and organizational factors that might influence learning effectiveness. For instance, the literature has suggested that a supportive environment and many individual predictors (e.g., cognitive ability, conscientiousness, neuroticism, voluntary participation, pretraining self-efficacy, motivation to learn, a learning goal orientation, etc.) influence training transfer (Blume et al., 2010). These predictors were not included in this study because their effects have been established by extant research. This study focuses more on the system design effects specified in the TTF model on employees’
learning effectiveness. Thus, the study result can help the case company to improve its e-learning system.
The survey was taken two months after the employees attended the e-learning courses for new employees. During the period before they took the test on target e-learning courses, they were still required to attend other e-learning courses. Whether the other courses’ learning outcomes affect that of this study as well as supervisory rating is a concern in this study.
To be noted, the individual difference is not confirmed in this study. The insignificant result of learning style’s moderating hypothesis may stem from the homogeneity of employees. That is to say, S Company may have been consistently selecting similar characteristics in new recruits when they went through the recruitment process. The HR manager confirmed that the company asks job candidates to take an online personality test and selects only those who fit the high potential profile to proceed to job interviews. This practice seems to have constrained the individual differences in S Company.
Future Research Suggestions
Several suggestions for future research are offered in the following. First of all, since the training transfer data is provided by S Company using the 3-month performance of new real estate agents, future research can consider a longer term observation of continuous performance. For example, the return on investment (ROI) and turnover rate can be included in future study.
Secondly, this study can be replicated to samples in other types of jobs or another industry to further validate the effect of TTF on training transfer. Since the tasks characterized differently depend on different industries, it may result in different findings by expanding the scope of task technology fit.
Thirdly, as the comparatively low explanatory power of the regression models shows that there are other factors contributing to employees’ learning effectiveness, future research may expand the model to include some organizational and personal predictors and examine
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their moderating effect between TTF and training transfer. For example, the moderating effect of learning style is not significant in this study as the study found out that surprisingly most agents are visual learners. Though the study should not over attribute the tendency of learners to the company’s selection process, it is still useful information for the company to design e-learning courses for target employees to suit their unique learning style.
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