員工之任務科技適配度對於數位學習之學習成果與培訓遷移的影響

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(1)The Impact of Task Technology Fit on Employees’ Learning and Training Transfer of E-learning. by Iris Shu-Wei Ko. A Thesis Submitted to the Graduate Faculty in Partial Fulfillment of the Requirements for the Degree of MASTER OF BUSINESS ADMINISTRATION Major: International Human Resource Development. Advisor: Chu-Chen Rosa Yeh, Ph. D. National Taiwan Normal University Taipei, Taiwan December, 2011.

(2) ACKNOWLEDGEMENT First of all, I would like to appreciate my thesis advisor, Professor C. Rosa Yeh for all the supports and kind help she has been providing to me. I cannot go this far without Dr. Rosa’s advice as well as encouragement, and I am so grateful that I have learned so much from her. Also, I sincerely thank to committee members, Dr. Tsai and Dr. Chao who spent their valuable time coming along for my thesis and I appreciate all their precious advices during the research process. Second, I feel immense gratitude to all the professors, Dr. Lai, Dr. Lin, Dr. Shih, Dr. Chang and Dr. Lee, and I will never forget all your caring advices. For my dear friends, Rean, Molly, Lynn, Sandra, Mathew, Germaine, Grace, James, Williams and other classmates, I am grateful that I have your friendship during this period which helps me conquer all the difficulties. Thanks to alumni Kevin and Cassie’s help, I learn a lot about business and picture a map of human resource practice. Finally, thanks to my dear family for all your love and supports. Millions thanks again to Dr. Lai who always generously provides me so much support and gives me kind suggestions. I appreciate to be one of the members of International Human Resource and Development Graduate Institute..

(3) ABSTRACT The purpose of this study is to explore how task technology fit (TTF) of e-learning influences employees’ learning and training transfer. Through an empirical study of a case company’s e-learning program for new recruits, this research examines whether the fit between the e-learning technology and employees’ realistic tasks has an effect on employees’ learning effectiveness. To fulfill the study objectives, the researcher worked with one of the largest real estate agencies in Taiwan, which was nicknamed the S Company for anonymity, to collect data needed. Survey questionnaires containing demographics, measures of visual-verbal learning style and task technology fit of e-learning were administered to S Company’s 151 new real estate agents who completed the training for new-hires in the period between June and August of 2011. Data for the outcome variables of the study, the e-learning test scores and the training transfer scores of each trainee were provided by the S company. Exploratory factor analysis result showed the factor structure of the TTF measure to be composed of three distinct components: content, technology and presentation of e-learning. Hierarchical regressions were used to test the direct effect of TTF on learning outcome and training transfer, as well as the mediating effect of learning outcome between TTF and training transfer. These hypotheses were supported which implied the importance of ensuring TTF when using e-learning to train employees. The moderating effect of visual-verbal learning style on the TTF-learning outcome connection, however, was not supported, due to small variances in agents’ homogeneous learning style.. Keywords: E-learning, task-technology fit, learning outcome, training transfer, real estate agents I.

(4) TABLE OF CONTENTS ABSTRACT….. ................................................................................................... I TABLE OF CONTENTS .................................................................................... II LIST OF TABLES ............................................................................................ IV LIST OF FIGURES ............................................................................................V CHAPTER I.. INTRODUCTION .....................................................................1. Background of the Study .................................................................................................. 1 Purpose of Study ............................................................................................................... 4 Research Questions ........................................................................................................... 6 Delimitations and Limitations .......................................................................................... 7. CHAPTER II. LITERATURE REVIEW ..........................................................9 E-Learning ........................................................................................................................ 9 Task Technology Fit ....................................................................................................... 14 Learning Effectiveness ................................................................................................... 26 Learning Style ................................................................................................................. 38. CHAPTER III. RESEARCH METHODS ....................................................... 43 Research Framework ...................................................................................................... 43 Research Hypotheses ...................................................................................................... 44 Research Design ............................................................................................................. 44 Measurement ................................................................................................................... 47. II.

(5) CHAPTER IV. ANALYSIS AND RESULTS ................................................ 57 Descriptive Statistics of Task Technology Fit ................................................................ 57 Correlations ..................................................................................................................... 60 Regression for Hypothesis Testing ................................................................................. 62. CHAPTER V. CONCLUSIONS AND SUGGESTIONS .............................. 69 Conclusions ..................................................................................................................... 69 Practical Implications ..................................................................................................... 71 Limitations ...................................................................................................................... 74 Future Research Suggestions .......................................................................................... 75. REFERENCES.................................................................................................. 77 APPENDIX. RESEARCH QUESTIONNAIRE .............................................. 88. III.

(6) LIST OF TABLES Table 2.1. Types of E-learning Materials in S Company..................................................... 19 Table 2.2. Summary of Learner Characteristics—Transfer Link ........................................ 21 Table 2.3. Dimensions Items of Task Technology Fit ......................................................... 24 Table 2.4. Summary of Six Evaluation Models ................................................................... 27 Table 2.5. Summary of the Intervention Design—Transfer Link ........................................ 35 Table 2.6. Visual-verbal Learning Style Dimension ............................................................ 41 Table 2.7. Strength Level Scale ........................................................................................... 42 Table 3.1. Sample Description............................................................................................. 45 Table 3.2. Sample Item for Each Dimension of Task Technology Fit ................................. 47 Table 3.3. Reliabilities of Dimensions of Task Technology Fit Model ............................... 49 Table 3.4. Summary of Exploratory Factor Analysis Results for Task Technology Fit ...... 50 Table 3.5. Reliability ........................................................................................................... 52 Table 3.6. Sample Items for Learning Style: Visual-verbal Learners.................................. 54 Table 4.1. Descriptive Statistics－Question Items of TTF .................................................. 58 Table 4.2. Correlation Analysis Results .............................................................................. 61 Table 4.3. Results of Regression Analysis of Hypothesis 1 ................................................ 62 Table 4.4. Results of Regression Analysis of Hypothesis 2 (3rd months)............................ 63 Table 4.5. Results of Regression Analyses of Hypothesis 3 (3rd months) ........................... 64 Table 4.6. Results of Regression Analyses of Hypotheses 2, 3 (2nd months) ...................... 66 Table 4.7. Results of Regression Analyses of Hypothesis 4................................................ 67 Table 4.8. Descriptive Statistics—Distribution of Visual-verbal Preference ...................... 68. IV.

(7) LIST OF FIGURES Figure 2.1. Western model of e-learning implementation ................................................... 11 Figure 2.2. Task technology fit model. ................................................................................ 15 Figure 2.3. Learning in the workplace................................................................................. 32 Figure 3.1. Research framework ......................................................................................... 43. V.

(8) CHAPTER I.. INTRODUCTION. In this chapter, the background, rationale, research questions, and research delimitations and limitations of this study are introduced. The background focuses on current e-learning implementation on workforce and that in realty industry. The rationale explains the reason why the study is conducted. Research questions and delimitations and limitations are addressed.. Background of the Study Training is an important issue in business for cultivating the company’s human resources. In order to facilitate employees’ learning of job-related competencies, organizations usually spend an immense amount of time and money on training. According to a recent American Society for Training and Development study, U.S. organizations spend more than $125 billion annually on employee training and development (Paradise, 2007). Facing the trend of e-commerce, more and more firms start to develop their internal operation system with more technological and highly connected network facilities for making work more efficient. Information and communication technology (ICT) has grown exponentially during the past two decades around the globe, which leads changes in nearly every field of practice including human resource development (Teng, Bonk, Bonk, Lin, & Michko, 2009). Masie (2008) provided a survey reported on how employees currently learn at work and how their learning preferences are changing; the result showed that e-learning was ranked as the second most frequently used learning tool/method next to reading in the workplace. In other words, the majority of employees today rely heavily on self-directed and asynchronous resources such as e-learning, to learn for work(Cho, Park, Jo, Jeung, & Lim, 2009). As one of the most technologically advanced societies, Taiwan provides a fertile ground to study the impact of ICT applications. By 2008, 68.1% of the population of Taiwan had accessed the 1.

(9) Internet (Taiwan. Network Information Center, 2008). According to a report from the. Economist Intelligence Unit (2010), Taiwan’s e-learning readiness was ranked 3rd in Asia and 12th in the world. It reflects a mature ICT environment for Taiwanese companies to reap the benefit and savings from implementing e-learning systems. In recent years, the challenge for companies is to improve the e-learning content and its deployment towards employees (Baudoin, 2010). Studies of e-learning effectiveness as a training method have focused more on educational field, for example, distance e-learning course at schools. In addition, studies of e-learning implementation on business usually face the conflicts of benefits between implementing e-learning and traditional instructions. As high-tech industries such as the electronic and computer manufacturers tend to train engineers or technicians through e-learning to cut down the learning process, e-learning of traditional industries and service industries are relatively neglected or underestimated. Since Taiwan develops into a post-industrialized society, the service industries have gained importance in serving as a major economic force, and thus deserve attention from learning technologists to look into the effectiveness of e-learning for training service personnel. Real estate industry, as one of most talent needed service industries, has extremely competitive market in Taiwan. The recent trend also shows an eagerness to exploit the internet to enhance visibility to more customers. Most real estate companies have developed an official website for customers to access real estate information. The booming real estate websites is an indication that the traditional transaction skill of face to face (FTF) communication has now started to blend with computer-mediated communication (CMC). For the increasing needs on competencies of using computer or web-based systems, real estate companies have begun to incorporate e-learning in their training. S Company, the case company in this study and a multinational company with branches in Asia and Japan, has been developing its e-learning program which is implemented on web-based platform since 2004. It is expected to save the spending of agents’ traffic time and learning period. 2.

(10) Four main functions built in the e-learning platform of S Company, which is called “E-School”, including “Learning Center”, “Learning Interaction Center”, “Unit Development Center” and “Learning Management Center”. For “Learning Center”, learners can check out the course list and see which course is interested to them, and they can directly register. Most information of e-learning courses is attained in this function. After the learners successfully register, the detailed information such as the duration or the instructor, etc., of each course will be shown on the screen. It benefits the real estate agents to check the required courses or those they need anytime as long as they have their computers connected to “E-School”. For “Learning Interaction Center”, learners can share their opinions, reflections, and other experiences through posting words on the discussion board or through chat room. “Unit Development Center” is for users who are supervisors to check their subordinates’ learning records. Learning Management Center” is for HR managers to see the class information. It is because they need to monitor if the figures of attending people are acceptable and take it as one of standards of rating a course. Xiao (1996) pointed out that policy makers tend to applied training and believe that the improvement of knowledge, skill, and attitude (KSA) after the training will make workers become more competent, and in turn, increase productivity. Blume, Ford, Baldwin, and Huang (2010) indicated that despite the large investments in and potential benefits of training, organizational decision makers are often not sure to what extent employees perform differently once back on the job. In other words, whether the training program improves employees’ learning effectiveness is the main focus when companies conduct training. Thence, the need to evaluate a training program becomes essential for realizing the learners’ learning effectiveness. Goldstein (1986) noted that well-developed programs can provide relevant learning experiences and improve trainees’ capacity, enabling them to work more efficiently. Since the utilization of a particular feature of technology tools relies on the distinctiveness of the 3.

(11) assigned task (Agnihotri & Troutt, 2009), the e-learning courses of S Company are expected to correlate to house agents’ tasks. Therefore, the “fit” between e-learning courses and employees’ tasks should be identified. This study conducts Goodhue and Thompson (1995) task technology fit (TTF) model to understand the employees’ perceived TTF of S Company’s e-learning courses. Since individual characteristic also forms the model of TTF, this study also intends to find out the role that individual differences play in the learning process. Research on trainee characteristics that influence training outcomes is various and relates to either personality or to motivation such as conscientiousness, self-efficacy, motivation to learn, learning goal orientation, performance goal orientation, and instrumentality of training (Tziner, Fisher, Senior, & Weisberg, 2007). As many studies of learning styles have been booming to explain individual differences on training, researchers found out that when instruction was matched to learner’s cognitive style, it leads to better performance of learning. Therefore, except some demographic factors such as age, gender, education background, and job experience, etc., this study implements Felder’s index of learning styles (ILS) as the measurement to examine the effects of individual characters on the relationship between e-learning and training effectiveness.. Purpose of Study The purpose of this research is to evaluate e-learning courses for new employees of S Company based on Goodhue and Thompson’s (1995) task technology fit model as well as Felder and Soloman’s index of learning style. Because of the high need of collecting sales data and the difficulties encountered in measuring the effects of sales training, it is important to make the determination of sales training effectiveness. Thus, a critical need is to determine if performance following the training, and to assess the performance with measurable organization results so that it contributes to the human resource development of the 4.

(12) organization (Honeycutt, Ford, & Rao, 1995). Gilley, Eggland, and Gilley (2002) indicated that “evaluation is a process, not an event, that involves all key decision-makers, stakeholders, and influencers, and should be influenced by a clear understanding of the organization’s performance and business needs, as well as its strategic goals and objectives.” The concept exhibits the importance of evaluating a company’s training system after it is being constructed. Since organizations have recognized the usefulness of e-learning and have had high expectations of its quality, e-learning evaluation enables organizations to check increased benefits from e-learning (Cho et al., 2009). MacDonald and Thompson (2005) stated that evaluation in e-learning has become a crucial issue, as evaluation plays an important role in improving the quality of e-learning and in justifying technology use in education. Since technology provides many useful tools to solve daily tasks, it is very important to understand the relationship between tasks and technology, which can have a significant impact on technology use and subsequent outcomes (cited by Yu & Yu, 2010). Also, organizations can consider e-learning as an organizational strategy that develops human resources and improves competitive advantages for overall business goals through systematic evaluation (Macpherson, Elliot, Harris, & Homan, 2004). Technology Fit (TTF) is a theory that describes performance impacts of an information system (Dwyer, 2007). Therefore, by examining employees’ perceived task technology fit, this research expects to analyze the effects of e-learning courses on learning effectiveness. Except of the fit between task and e-learning, the trainees’ individual differences have been studied in plenty amount of literature. As individual characteristics are included in TTF model, this study intends to explore the role of learners’ characteristics such as age, gender, and learning style, etc. that interacts in training process. Hosford and Siders (2010) indicated that research into individual learning style has been carried out for decades, most with a claims that understanding learning styles can facilitate and improve learning outcomes, many 5.

(13) of these claims have been criticized for being based on little or no empirical evidence. However, as e-learning courses are usually ill-judged for lacking interaction with trainees, the researcher assumes that trainees’ learning preferences may affect their satisfaction with e-learning. Therefore, this study also explores the influence employees’ learning styles may have on learners’ learning outcomes. The result of this study will inform the S Company in order to improve its e-learning course and the researcher expects to find out whether the e-learning courses fulfill the new employees’ training needs and improve their learning outcomes and training transfer. Theoretically, this study will also empirically test the validity of the task technology fit model when e-learning is treated as a technology to improve human performance. Meanwhile, the reliability and validity of Felder and Soloman’s ILS instrument is also testified in this study.. Research Questions Based on the research purpose, the research questions are stated as following: 1. Do characteristics of S company’s e-learning system fit those of agents’ tasks? 2. Does task technology fit of e-learning have an influence on learning outcome? 3. Does task technology fit of e-learning have an influence on training transfer? 4. Does employees’ learning outcome mediate the relationship between task technology fit and training transfer? 5. Does employees’ learning style moderate the relationship between task technology fit and learning outcome?. 6.

(14) Delimitations and Limitations This study will specifically focus on the e-learning courses in the training system of S Company. The sample was limited to new real estate agents recruited from June to August of 2011 who participated in e-learning courses administered by S Company. Since the study used a quantitative method, the research instrument was a survey questionnaire distributed to real estate agents during new agent training. Additional data such as agent’s test scores and training transfer scores were provided by human resource managers of S company. The survey participants were asked to provide their names for tracking the courses they attended. For the agents’ learning outcome score, it came from a test held in the class. The test is designed from the contents of e-learning courses which are knowledge based including agent process, different contracts introduction, blueprint and floor plan mapping, house condition inspection, etiquette and customer service, standard operation procedure, and so on. The score is recorded and considered as a referential indicator of new agents’ performance during their probation. For the training transfer score, it is graded each month in S Company with different proportion of grades provided from supervisors, senior agents, and tests, etc. The supervisor and senior agents will judge and grade new agents by reviewing their actual performances on the job; for example, the attendance, reflection reports of running each case and the overall knowledge, skill and abilities (KSAs). The test results of e-learning and classroom courses, the behavior on training and so on are also taken into account to make sure the training transfer score is with objective sources. There are still potential limitations in this study. As S Company is one of few realty companies that implement e-learning, the study may not be necessarily comparable to other realty companies. Additionally, the results which are based on e-learning courses designed for new hired agents may not explain the learning effectiveness of all employees. Furthermore, the training transfer scores in this study only track the employees’ three months performance, 7.

(15) which may not predict long-term performance in the future. Moreover, instruments of learning style are diverse and ILS is only one of them, which may not explain all employees’ learning preference especially when this study specifically focuses on visual-verbal learning preference by considering the characteristics of e-learning courses and tasks.. 8.

(16) CHAPTER II. LITERATURE REVIEW In this chapter, some terminology are addressed such as e-learning, task technology fit, individual characters and learning effectiveness, which The evaluation on employees’ learning effectiveness will follow the rationale of learning and behavior phase on Kirkpatrick ’s (1987) four levels evaluation model. Also, hypotheses of the research will be stated following the explanation of terminology.. E-Learning E-learning has been trumpeted in the practitioner literature as one of the fastest growing (Martin, Massy, & Clarke, 2003). In 2009, the training industry was estimated to be worth $90 billion worldwide, with $20 billion spent on e-learning (ASTD, 2009; Lam, 2009) and the e-learning market will be worth $40 billion by 2012 (Allen & Seaman, 2007; Garavan, Carbery, O’Malley, & O’Donnell, 2010; Insight, 2009; Jones, Moeeni, & Ruby, 2005). Definitions of e-learning could be found in plenty of literature. Some of these definitions are listed as following: 1. Henry (2001) defined e-learning as the appropriate application of the Internet to support the delivery of learning, skills and knowledge in a holistic approach not limited to any particular courses, technologies, or infrastructures. 2. E-learning is defined as “the use of Internet technologies to deliver a broad array of solutions that enhance knowledge and performance” (Rosenberg, 2001, p. 28). 3. E-learning is Internet-enabled learning including the components of content delivery in multiple formats, management of the learning experience, and a networked community of learners, content developers and experts(Gunasekaran, McNeil, & Shaul, 2002).. 9.

(17) 4. E-learning can be defined as a combination of training methods (Welsh, Wanberg, & Brown, 2003). 5. E-learning is a set of methods which is designed for training in companies (Welle-Strand & Thune, 2003). 6. E-learning can be defined as a “media training” (DeRouin, Fritzsche, & Salas, 2005). 7. E-learning is “a combination of content and instructional methods delivered by media elements such as words and graphics on a computer intended to build job-transferable knowledge and skills linked to individual learning goals or organizational performance” (Mayer & Colvin, 2007). Wallace, Kupperman, Krajcik, and Soloway (2000) found that it is considered a complex and difficult process for students to seek online information; to develop content comprehension for students and teachers through the internet is challenging. Ligorio (2001) indicated that only when students well knew the technologies and tools associated with each communication style, online learning activities with the various communication styles are considered valuable. Therefore, researchers concluded that previous research on e-learning specifically focuses on several issues: (1) the implementation of e-learning as an optional learning tool, (2) the learning process of e-learning and (3) learners’ acceptance of the mode and technology of e-learning (Davis, Bagozzi, & Warshaw, 1989; Hung & Cho, 2008; Urquhart et al., 2005). Through experiences in the West, most companies have understood that e-learning cannot be seen as a separate tool or technique but has to be integrated as part of daily jobs of employees and managers (Ali & Magalhaes, 2008). Figure 2.1 shows the model modified from Ali and Magalhaes (2008) who integrated literature of e-learning implementation found in Western economies.. 10.

(18) Rapid changing competitive environments. ENVIRONMENTAL TRENDS. Increased HR development efforts. E-LEARNING ENABLERS. Rapid technological change. Increased KM capacity. KEY E-LEARNING DRIVER Capacity to respond to market needs. E-LEARNING DEVELOPMENT MODEL Plan. Design. Integrate. Improve. Barriers to implementation. Impact on business goals. Impact on job performance. Impact on organizational culture. Figure 2.1. Western model of e-learning implementation. Adopted from “Barriers to implementing e-learning: a Kuwaiti case study,” by Ali and Magalhaes (2008), International Journal of Training and Development, 12(1), p.40.. 11.

(19) The model combines current perspectives through literature review including environmental trends, enablers of e-learning, the key drivers to e-learning (the capacity to respond to market needs), the e-learning development model and key impacts of e-learning on the organization and business (Ali & Magalhaes, 2008). The environmental trends are made up of the same factors which are the cause and the consequence of the globalized economy; for example, rapid technological change and rapidly changing competitive environments. Distinguished from macro trends, two micro trends have developed in an intertwined manner: the ability of companies to manage their information and knowledge resources and increasing pressures on companies to develop their HR. These four types of trends form pressure on companies to use IT to improve their capacity to respond to market needs by implementing e-learning. Looking precisely to the model, the barrier to e-learning implementation can be found between the decision to implement e-learning (represented by the e-learning development model) and the impacts on the organization. Therefore, whether e-learning is considered effective is determined on the actual evidence. Rosenberg (2006) indicated that the goal of e-learning in the workplace is to enhance individual and organizational performance. However, research showed that e-learning might not be necessary effective and efficient for learners to improve their performance. For example, technological problems such as anxiety regarding using computers for learning, difficulties in using email as well as completing homework through the Internet, and the difficulty of solving system problems—the sudden system shut down (Tsai, 2009). Thus, research based on technological theories emerged and tried to close the gap between learners and system usage. Eddy and Tannenbaum (2003) indicated that e-learning refers to training with contents, communication and learning materials which is provided to learners through the use of technology. Other information technology concepts including computer self-efficacy, normative technology fit, compatibility, computer-based tutorials, which are stemming from social psychology and cognitive fit theories, also explain the electronic 12.

(20) learning system adoption and usage (Yu & Yu, 2010). E-learning in this study refers to the courses performed by multimedia means. The employees’ learning effectiveness is evaluated by examining learners’ acceptance of the mode and technology of e-learning. Thus, an understanding of the task-technology relationship is critical since this relationship has a crucial impact on technology usage and its succeeding outcomes (Yu & Yu, 2010); these outcomes lead to improve group performance (Carswell & Venkatesh, 2002), modify user perceptions (Wenger & Carlson, 1995) or expand user utilization (Kim, Malhotra, & Narasimhan, 2005; Ngai, Poon, & Chan, 2007; Venkatesh, Morris, Davis, & Davis, 2003). Technology acceptance model (TAM, Davis, 1989) is widely applied in many researches as the theoretical basis for many empirical studies of users’ technology acceptance (Behrens, Jamieson, Jones, & Cranston, 2005; Loukis, Georgiou, & Pazalos, 2007; Ngai et al., 2007). According to TAM, the acceptance of an e-learning system can be assessed by testing users’ perceived usefulness and ease of use (Tselios, Daskalakis, & Papadopoulou, 2011). Although studies related to e-learning acceptance are increasing, research which was conducted in the context of blended learning are neglected (Keller, Hrastinski & Carlsson, 2007; Park, 2009; Pituch & Lee, 2006; Tselio et al., 2011). Task technology fit, a construct that is conceptually related to user-perceived usefulness of the TAM model, is concerned with the extent to which technology meets task-related requirements and was used to explore how individual task and technology fit profiles improve user’s performance and technology utilization (Yu & Yu, 2010).. 13.

(21) Task Technology Fit In the era of knowledge economy, organizations’ major sources of competitive advantage for maximizing profits are their human resources which have already become organizations’ most precious asset (Lin, 2005). As applying informational system is expected to save the training cost and make training more effectively and efficiently, Goodhue and Thompson (1995) suggested that information systems (systems, policy, IS staff, etc.) have a positive impact on performance only when there is correspondence between their functionalities and the task requirements of users. Task technology fit was created for providing greater understanding of the interrelationship between individual and task characteristic fitness, which affects user’s choice regarding whether to utilize an information system or not (Yu & Yu, 2010). Goodhue takes into account not only technology and user, but he also considers the complexity of the clinical tasks which have to be supported by an IT system (Ammenwerth, Iller, & Mahler, 2006). Task technology fit (TTF) is the degree to which a technology assists an individual in performing his or her portfolio of tasks (Goodhue & Thompson, 1995). It investigates the link between technology distinctiveness, task characteristics and an individual’s performance, and assumes that “performance impacts will depend increasingly upon task-technology fit rather than utilization” (Goodhue & Thompson, 1995, p. 216). Therefore, Task technology fit evaluates the impact of information systems on performance improvement (Dwyer, 2007). TTF model has four key constructs: task characteristics, technology characteristics, which together affect the third construct task technology fit, which in turn affects the outcome variable, either performance or utilization (Dishaw, Strong, & Bandy, 2002). The essential parts of the task performance chain are individual and task characteristics. The technology-to-performance chain combines insights from research on user attitudes as predictors of utilization and insights from research on task technology fit as a predictor of 14.

(22) performance (McGill & Hobbs, 2007). Figure 2.1 shows the rationale of TTF model.. Task characteristics. Technology characteristics. Task-technology fit. Performance impacts. Individual characteristics. Figure 2.2. Task technology fit model. Adopted from “Task-technology fit and individual performance,” by Goodhue, D. L. & Thompson, R. L., 1995, MIS Quarterly, 19(2), p.220.. Two largely independent theories were emerged from TTF. One focuses on information system (IS) use by individuals and establishes TTF as an important concept in assessing and explaining IS success (Goodhue & Thompson, 1995). It has its origin in a technology-to-performance model that includes characteristics of IT, tasks, and individual users as explanatory variables for IS use and individual performance (Gebauer, Shaw, & Gribbins, 2010). Related studies support the relevance of the TTF construct to assess the value of an IS (Goodhue, 1995) and to predict IS usage (Dishaw & Strong, 1998) and individual performance (Goodhue, Klein, & March, 2000). The second stream of TTF research emphasizes the development and deployment of group support systems (GSS) to support group tasks (Zigurs & Buckland, 1998). It assumed 15.

(23) that a fit between tasks and IT supports group performance and fit is developed as “ideal profiles composed of an internally consistent set of task contingencies and GSS elements that affect group performance” (Gebauer et al., 2010). Following early empirical support, the group-oriented stream of TTF has been applied to various collaborative forms of IT with the intent to improve group tasks (Barkhi, 2001; Dennis, Wixom, & Vandenberg, 2001; Massey, Montoya-Weiss, Hung, & Ramesh, 2001; Murthy & Kerr, 2004; Susman, Gray, Perry, & Blair, 2003). The concept has also found support in the area of electronic commerce (Jahng, Jain, & Ramamurthy, 2000). Since the researcher tries to find out the relationship between TTF and learning as well as training transfer of e-learning, the first theory better describes the contexts of this study. In the following sections,. task. characteristics,. technology characteristics,. individual. characteristics and the concepts of fit are identified.. Task Characteristics Goodhue and Thompson (1995) defined tasks as the actions carried out by individuals in turning inputs into outputs. Goodhue (1998) explained that the essence of the task technology fit model is the assumption that information systems give value by being instrumental in some tasks or collection of tasks and that users will reflect this in their evaluations of the systems. Goodhue (1998) then signified that the strongest link between information systems and performance impacts will be due to a correspondence between task needs and system functionality (task technology fit). Potter and Balthazard (2000) also believed that the technology must ‘‘fit’’ the task for prospective users and therefore make users perceive that it is useful for accomplishing the task and that it is easy to use. The tasks as well as knowledge, skills and abilities (KSAs) needed for various jobs are typically presented in the form of a job description, which summarizes the job duties, needed KSAs, and working conditions for a particular job (Aguinis & Kraiger, 2009). The tasks in 16.

(24) this study are communication based for real estate agents. For example, agents need to make phone calls to customers discussing the price of real estate products, accompany customers visiting different dwellings, and promote real estate products by all means. A managerial practitioner in S Company indicated that the tasks of real estate agents are distinguished with several rules. First of all, during the process of selling the company’s product, the agents need to be very familiar with the company’s vision and convey their honesty to customers. Secondly, the policy of transparent finance transaction will be promoted through the face to face communication. Besides, in the process of transaction, showing professional knowledge to customers can fast build the trust among buyers, sellers and agents. That is to say, real estate agents have to study relative knowledge including laws, taxes, stocks, and the latest market information. Therefore, the real estate agents need to turn what they learned including cognitive knowledge, communication skills and other required skills into process of transaction with customers. Despite of large efforts put on e-learning of S Company, the features of realty industry make it hard to successfully pursue company’s profits as well as benefits of talent development at the same time. Goodhue and Thompson (1995) suggested that the strongest effect of task characteristics on TTF was from non-routine tasks. In other words, it is believed that users who are involved in non-routine jobs are consequently use IS to address new problems; for example, seeking new data and combining it in different ways. Therefore, individuals engaged in more non-routine tasks tended to rate their information systems lower on data quality, data compatibility, data locatability, and difficulty of getting authorization to access data (Norzaidi, Chong, & Salwani, 2008). Since the tasks of agents are challenging and change every day, to realize the link between their task and e-learning courses becomes urgent.. 17.

(25) Technology Characteristics Information and communications technology has been increasingly influencing higher education in recent decades (McGill & Hobbs, 2007). Metcalfe (1995) considered technology as “an ability to act, a competence to perform translating materials, energy and information in one set of states into another more highly valued set of states.” Technologies in organization are perceived as tools which are used by individuals for their tasks. In the context of information systems research, technology refers to computer systems (hardware, software and data) and user support services (training, help lines, etc.) provided to assist users in their tasks (Goodhue & Thompson, 1995). Goodhue and Thompson (1995) developed the instrument of task-technology fit (TTF) model, which looks at the correspondence between the functionality of information systems and task requirements. A survey is conducted by Goodhue and Thompson (1995) with 600 users, employing 25 different technologies, working in 26 different non-IS departments in two very different organizations. There are eight out of eighteen factors examining technology utilization distinguished: quality, locatability, authorisation, compatibility, ease of use/training, production timeliness, system reliability, and relationship with users. These factors were adapted to thirteen dimensions (Goodhue, 1998). The e-learning system built in S Company is web-based network with not only training but administration functions. In this study, e-learning courses for new-hired agents are deemed as the role of technology in TTF model. The instructional method of e-learning courses is blended by using multimedia and texts information and all materials are performed through e-learning system. There are basically three types of learning materials shown in Table 2.1.. 18.

(26) Table 2.1. Types of E-learning Materials in S Company Types of E-learning Materials in S Company Type. Description. Texts with Video. Animation Materials. Texts with Audio/Picture. Texts information with instructors’ image shown on video. The learning contents that are important and are expertise will be likely performed by video. For example, knowledge of laws or tax, etc. For example, some attitude perspective topics or values will be designed in this way. Text information with only pictures and audio. The information here would be relatively simple value of company or regulations of company’s policy. For example, the information the agents need to know if they want to be the agency head in the future.. Source: Compiled by this study.. Individual Characteristics Some researchers noted that it has to take into account the nature of learners in the organization when any assessment of a generalized theory that specifies a relationship between an organization’s capacities for acquiring, transforming and exploiting e-learning and achieving key strategic outcomes (Martin et al., 2003). One of the more enduring conceptualizations in the psychology literature is that an individual’s ability and motivation affect performance (Sackett, Gruys, & Ellingson, 1998). It constructed the idea that a learner’s characteristics may influence training outcomes. Schmidt (2009) indicated that training programs that take into consideration an organization’s diversity of human resources can result in positive outcomes for both the employee and the organization. Despite of the psychological characteristics that associate with transfer mentioned before, Schmidt (2009) also pointed out that many of diversity dimensions which may be important in examining 19.

(27) workplace-related variables including job status, tenure and educational background, relate directly to the workplace. According to. Judge, Cable, Boudreau, and Bretz (1995), demographic variables. influence many behavior patterns. It follows that such demographics need to be considered when examining predictors of participation in e-learning. Age, gender and level of education explain participation in e-learning (Ashton & Sung, 2002; Digilio, 1998; J. Jones & Madden, 2002). General-person characteristics may have more relevance in e-learning situations given that learners have to assume more responsibility, engage in more self-directed behavior and work independently (Garavan et al., 2010). Many literatures that consider individual diversity as critical variables are plenty. These individual differences include gender (Ford and Miller, 1996), system experience (Holscherl & Strubel, 2000), prior knowledge and spatial ability (Mayer & Gallini, 1990), occupational experience (Durling, Cross, & Johnson, 1996) and cognitive styles (Shih & Gamon, 1999). Buchel (2002) found that employees who are considered overqualified for their positions (based on their levels of formal education) were more likely to participate in job training and also more likely to have longer periods of job tenure with the same organization than their correctly qualified colleagues. Manning, Everett, and Roberts (2004) found that older learners were much more likely to be satisfied with online learning than younger learners. Pergamit and Veum (1999) link promotion receipt to a variety of variables, including training and job satisfaction, and point to the role of training in enhancing the likelihood of promotion within the workplace, especially for women. Ong and Lai (2006) also observed that men’s rating of perceptions with respect to computer self-efficacy, perceived usefulness, perceived ease of use, and behavioral intention to use e-learning were higher than women’s. Schnotz and Kurschner (2007) mentioned how prior knowledge can interact with instructional design with cognitive load theory (CLT). There are some other literature that support the effects of prior knowledge on learning and e-learning (Johnson-Glenberg, 2010). 20.

(28) Kalyuga (2008) assumes that experts falter when they compare back and forth between new and old information, which reduces the efficacy of the positive load. Although it is not necessary that e-learning experience positively effects training, it can still tale an interacted role in the e-learning training context. According to literature review of Hutchins and Burke (2007), there are other learner characteristics strongly link to training transfer including cognitive ability, self-efficacy, pretraining motivation, anxiety/ negative affectivity, openness to experience, perceived utility, career planning, and organizational commitment (shown in Table 2.2).. Table 2.2. Summary of Learner Characteristics—Transfer Link Summary of Learner Characteristics—Transfer Link Strong or Variable. Moderate Mixed Relationship with Support Transfer. Research Is Minimal Needed to Clarify Empirical Research Exists or Build Findings. Cognitive Ability Self-efficacy Pretraining Motivation Motivation to Learn Motivation to Transfer Extrinsic vs. Intrinsic Motivation Anxiety/ Negative Affectivity Conscientiousness Openness to Experience (table continues). 21.

(29) Table 2.2. (continued). Variable. Strong or Moderate Mixed Relationship with Support Transfer. Minimal Research Is Empirical Needed to Clarify Research Exists or Build Findings. Extroversion Perceived Utility Career Planning Organizational Commitment External vs. Internal Locus of Control Sources: Adopted from “Training transfer: An integrative literature review,” by Burke, L. A., & Hutchins, H. M., 2007, Human Resource Development Review, 6(3), p.271.. Accordingly, individual factors like job experience, e-learning experience, gender, age, education background, subject majored may relate directly to the workplace and are considered as control variables in this study. Among these individual characteristics, learning style which is derived from cognitive style will be clarified later. Fit Goodhue and Thompson (1995) defined fit as the degree to which a technology provides features that support the requirements of a task and the abilities of an individual. The “fit” focus has been most evident in research on the impact of graphs versus tables on individual decision-making performance (Goodhue & Thompson, 1995). A series of laboratory experiments showed that the impact of data representation on performance seemed to depend on fit with the task (Benbasat, Dexter, & Todd, 1986; Dickson, 1986). Another study proposes that mismatches between data representations (a technology characteristic) and tasks would slow decision-making performance by requiring additional translations between data representations or decision processes (Vessey, 1991). Other research found strong 22.

(30) support for this linkage between “cognitive fit” and performance in laboratory experiments (Jarvenpaa, 1989; Vessey, 1991). There have been links suggested between fit and utilization (Goodhue & Thompson, 1995); for example, “fit” and utilization or adoption have been linked at the organizational level (Cooper & Zmud, 1990; Tornatzky & Klein, 1982), while at the individual level, a “system/work fit” construct has been found to be a strong predictor of managerial electronic workstation use (Floyd, 1988).Thirteen dimensions that measure the fit between task and technology include: the right data, level of detail, accuracy, compatibility, locatability, accessibility, meaning, assistance, ease of use, systems reliability, currency, presentation and confusion. “The right data” asks whether the system maintains the needed basic fields or elements of data. “Level of detail” refers to the right level of detail of the data that the system maintains. “Accuracy” means whether the system provides correct data. “Compatibility” refers to the system’s ease with which data from different sources can be aggregated or compared without inconsistencies. “Locatability” indicates the level of clarity of what data are available in the system and where the user can easily find the information needed. “Accessibility” measures the ease of access to desired data that the system offers to users. “Meaning” measures whether system provides clear definition of data element, or if the user knows exactly what the data means. “Assistance” refers to how easy it is for the users to acquire help from the system when there are problems with the data. “Ease of use” measures if the users perceive it easy to do what they want to do using the system hardware or software for accessing and analyzing data. “System reliability” measures if the system is stable and provides dependability of access. “Currency” measures whether the system provides up-to-date data that meets users’ needs. “Presentation” measures whether the system provides a satisfying format for users. “Confusion” measures whether the system provides clear and specific direction for users to find or access the data. Question items of thirteen dimensions 23.

(31) developed by Goodhue (1998) are shown in Table 2.3.. Table 2.3. Dimensions Items of Task Technology Fit Dimensions Items of Task Technology Fit Dimension Right Data. Items 1. The computer systems available to me are missing critical data that would be very useful to me in my job.. 2. The data maintained by the corporation or division is exactly what I need to carry out my tasks.. 3. It is more difficult to do my job effectively because some of the data I need is not available.. Sufficiently detailed data is maintained by the corporation or division. The company maintains data at an appropriate level of detail for 2 my purposes.. Level of Detail. 1. Accuracy. 1. The data that I use or would like to use is accurate enough for my purposes.. 2 There are accuracy problems in the data I use or need. Compatibility. When it’s necessary to compare or aggregate data from two or 1 more different sources, there may be unexpected or difficult inconsistencies. There are times when supposedly equivalent data from two 2 different sources is inconsistent. Sometimes it is difficult or impossible to compare or aggregate 3 data from two different sources because the data is defined differently.. Locatability. Accessibility. 1. It is easy to locate corporate or divisional data on a particular issue, even if I haven’t used that data before.. 2. It is easy to find out what data the corporation maintains on a given subject.. 1 I can get data quickly and easily when I need it. 2 It is easy to get access to data that I need. (table continues) 24.

(32) Table 2.3. (continued) Dimension Meaning. Items 1 2. Assistance. 1. 2. Ease of Use. 1. 2 System Reliability. On the reports or systems I deal with, the exact meaning of data element is either obvious, or easy to find out. The exact definition of data fields relating to my tasks is easy to find out. I am getting the help I need in accessing and understanding the data. It is easy to get assistance when I am having trouble finding or using data. It is easy to learn how to use the computer systems that give me access to data. The computer systems that give me access to data are convenient and easy to use.. 1 The data is subject to frequent system problems and crashes. 2 I can count on the system to be “up” and available when I need it.. Currency. 1 I can’t get data that is current enough to meet my needs. 2. I need some data on the up-to-the-minute status of operations or events but cannot get it.. 3 The data is up-to-date enough for my purposes. Presentation. 1. The data that I need is displayed in a readable and understandable form.. 2 The data is presented in a readable and useful format.. Confusion. There are so many different systems or files, each with slightly 1 different data, that it is hard to understand which one to use in a given situation. 2. The data is stored in so many different places and in so many forms, it is hard to know how to use it effectively.. Sources: Task-technology fit questions. Adopted from “Development and measurement validity of a task-technology fit instrument for user evaluations of information systems,” by Goodhue, D. L., 1998, Decision Sciences, 29(1), p.131. 25.

(33) Learning Effectiveness Training Evaluation The ultimate goal of training is to enhance the knowledge, skills, and abilities of an individual, which will lead to an increase in performance in the work setting (Holladay & Quiňones, 2003). To understand the employees’ learning effectiveness, the training program or courses should be evaluated first. Tanke (1999) indicated that the short-term need for organizations to conduct evaluation of their training program is to ensure that they provide employees sufficient knowledge and skill to perform their job, or change their behaviors or attitudes in order to improve their productivity and/or efficiency. Therefore, evaluating training program could lead to understanding if the program helps employees learn the critical knowledge, skills and abilities (KSAs). Based on an educational context, Tyler (1991) identified the six purposes of evaluation: (1) to monitor current programs; (2) to select a better program to replace the previous one; (3) to assist in developing a new program; (4) to identify the effects of a program; (5) to estimate the costs and effects of a program; and (6) to test the relevance and validity of a program. Horton (2001) pointed out six purposes of e-learning evaluation based on practical needs: 1. To justify investments in e-learning: An effective evaluation can play a role in proving that e-learning contributes to organizational profits. These proofs can encourage top executives to increase investments in e-learning for the future. 2. To make better decisions about e-learning: An appropriate evaluation provides information to decide regarding analysis, development, and implementation of e-learning. 3. To encourage stakeholders to be accountable: Evaluation encourages stakeholders in e-learning, including departments, developers, instructors, facilitators and suppliers to take expected responsibilities. 4. To demonstrate financial responsibility: Evaluation demonstrates that e-learning focuses 26.

(34) on business goals for financial outcomes. 5. To improve e-learning quality: Evaluation can measure the quality and effectiveness of e-learning and can identify areas that need improvement. 6. To encourage learning activities and participation in e-learning: The evaluation process encourages participants to be involved in learning activities and experiences by applying knowledge and sharing opinions.. Therefore, the need of measuring employees’ learning outcomes of a training program is crucial and fundamental to a company as it could improve the program itself and benefit the employees as well as companies. The training program in this study refers to courses for new-recruited employees carried out by S Company’s e-learning system. The needs of evaluating e-learning are justified in the following section. Table 2.4 is adopted from Cho et al. (2009) showing the rationale of six major and widely cited evaluation models: Stufflebeam’s CIPP model, Kirkpatrick’s four levels, Phillips’ ROI evaluation, Holton’s evaluation model, Khan’s evaluation issues, and Rosenberg’s e-learning success criteria.. Table 2.4. Summary of Six Evaluation Models Summary of Six Evaluation Models Evaluation Model. Summary Context: Are the mission and program goals being met?. Stufflebeam’s CIPP Model. Kirkpatrick’s Four Levels. Input: Does the quality and quantity of resources meet the needs? Process: To what extent were the program components implemented? Product: What impacts and outcomes have resulted from this program? Level 1 (Reaction): Trainee satisfaction Level 2 (Learning): Acquisition of knowledge and skills Level 3 (Behavior or Performance): Improvement of behavior Level 4 (Results): Business results achieved by trainees (table continues) 27.

(35) Table 2.4. (continued) Evaluation Model. Summary. Phillips’ ROI Evaluation. Return on Investment. Holton’s Evaluation Model. Cost-benefit ratio of the training Motivation elements: Motivation to learn and transfer learning Environmental elements: Culture and climate Ability/enabling elements: Transfer design linked to organizational goals. Khan’s Evaluation Issues. Content development process: Planning, design, production & evaluation Elements: People, process, and product Cost: Financial aspect. Rosenberg’s E-Learning Success Criteria. Quality: Kirkpatrick’s Four Levels Service : Customer needs Speed: Responsive system. Sources: Summary of six evaluation models. Adopted from “Developing an integrated evaluation framework for e-learning,” by Cho, Y., Park, S., Jo, S. J., Jeung, C., & Lim, D. H., 2009, Annual Review of Psychology, 60, p.701.. Kirkpatrick’s Four Levels of Evaluation This study adopts Kirkpatrick’s four levels of evaluation to evaluate the effectiveness of the e-learning course. It is considered for the reason that this model has been the most influential antecedent of existing training evaluation frameworks (Horton, 2001). Haupt and Blignaut (2007) indicated that the model is significant at following dimensions: (1) It contributes to focusing evaluation practice on learning outcomes. (2) It recognizes that single-outcome measures do not adequately reflect the complexity of instructional programs. (3) It underscores the importance of examining multiple measures of instructional effectiveness. (4) It differentiates between learning and behavior. (5) It emphasizes the importance of learning-transfer processes in making learning interventions effective. Kirkpatrick’s (1976, 1994) training evaluation model delineates four levels of training 28.

(36) outcomes: reaction, learning, behavior, and results. Level one, evaluating participants’ reaction, examines how well learners like the instruction and instructional interventions (Haupt & Blignaut, 2007). In other words, general satisfaction with the learning material is measured in this phase. Such evaluation does not measure what participants have learned, but gauges the interest, motivation, and attention levels of participants (Smidt, Balandin, Sigafoos, & Reed, 2009). Therefore, it is also called “happy sheet” because it usually reflects the participants’ direct feeling toward the course or program. Level two, learning, measures change on an intellectual level: increased knowledge, improved skills and changed attitudes (Haupt & Blignaut, 2007). That is to say, the second level involves measuring what participants have learned in terms of both knowledge and/or skills (Smidt et al., 2009). Increased knowledge is measured by content learned, the principles absorbed and mastered, the improved performance techniques and a positive attitude towards the learning (Clementz, 2002; Haupt & Blignaut, 2007; Kirkpatrick, 1994). Alliger, Tannenbaum, Bennett, Traver, and Shotland (1997) refined this phase to both immediate knowledge and knowledge retention. Learning can be assessed in a variety of ways; it could be determined by raters’ scores on a test of the training content and, less directly, by examining rating accuracy (Noonan & Sulsky, 2001). Level three, behavior, addresses either the extent to which knowledge and skills gained in training are applied on the job or result in exceptional job-related performance (Bates, 2004).. Level three refers to the changed behavior by transferring knowledge gained from. level two, it includes the application of trained strategies within a different context from the result of learning (Haupt & Blignaut, 2007). This can be assessed either directly or indirectly through self-report data potentially provided by the rater, ratee, or both Noonan and Sulsky (2001). Finally, level four, results, refers to achieved goals of training in terms of reduced costs, increased quality, improved production, and a decreased rate of employee turnover and 29.

(37) absenteeism (Kirkpatrick, 1994). Noonan and Sulsky (2001) identified results as measures of long-term payoff in organizational terms (i.e., the goal of training may have been to improve managerial skills, reduce the number of accidents, or increase profits).. Learning In this study, the researcher defines learning effectiveness as learning and behavior outcomes for the reason that the two levels can be quantified with objective data provided by S company. In Kirkpatrick’s four levels evaluation (Kirkpatrick & Kirkpatrick, 2006), Level two is content evaluation that examines whether or not employees change attitudes, improve knowledge, and/or increase skills as a result of participating in the program. In other words, the main purpose in learning phase is to evaluate the training and see if it provides knowledge and improves learners’ skill and attitude. It could be measured with formative assessment like a test or exercises. Learning at work is defined as observed changes in workplace behavior attributable to new knowledge and skills (Ivergard & Hunt, 2004) and takes places in the context of use and application (Päivi & Päivi, 2005). Park and Wentling (2007) suggested that learning in the workplace cannot be separated from the context where the knowledge and skills are used and is expected to result in positive changes in workplace behaviors and job performance. Workplace learning refers to learning or training activities which are undertaken in the workplace, with the goal of enhancing individual and organizational performance (Rosenberg, 2006). There are mainly three theories categorized to workplace learning: (a) adult learning, (b) organizational learning, and (c) knowledge management (KM). The implication of adult learning theory in the workplace context is that learners would be motivated once learning objectives have been rationally set that would meet their needs (Knowles, Holton, & Swanson, 1998). Organizational learning concerns both the ways individuals learn in an organizational context and the ways in which organizations themselves can be said to learn 30.

(38) (Easterby-Smith, Araujo, & Burgoyne, 1999). Knowledge management (KM) refers to a range of approaches and practices used by organizations to identify, create, represent, and distribute knowledge for reuse, awareness, and learning (Nonaka & Takeuchi, 1995). Illeris (2003) addressed several fundamental elements of a learning environment in workplace learning: (a) learners, (b) learning content, for example, the knowledge and expertise required in work practice; (c) social context, which considers groups and teams in the workplace; and (d) other learning stakeholders, such as the organization, society, or parent. Wang, Ran, Liao, and Yang (2010) indicated that the development of workplace e-learning applications should consider the alignment of individual and organizational learning needs, as shown in Figure 2.3, the connection between learning and work performance, and the interaction between individual learners.. 31.

(39) Organization. Learner. Other Learner. Work. Figure 2.3. Learning in the workplace. Adopted from “A performance-oriented approach to e-learning in the workplace,” by Wang et al. (2010), Educational Technology & Society, 13(4), 169.. According to the analysis above, when adult learners perceive the learning as related to job tasks and beneficial to work performance, they will be more motivated to learn and consequently produce better learning outcome. Therefore, to achieve the objective of evaluating learning, S Company conducts a formative assessment to make sure if learners really understand the e-learning courses. The test is held in classroom when all new agents return to headquarters taking a series training. The questions are designed in multiple choices. The participants need to finish the test and answer questions as correctly as possible. The scores are ranged from 0 to 100. It is expected that the more employees perceived the fit of the e-learning courses with their tasks in real situation, the better learning outcomes they will have. Hypothesis 1 is based on this 32.

(40) assumption: Hypothesis 1: Perception of task-technology fit of an e-learning course will have a positive influence on employees’ learning outcome.. Training Transfer In behavior phase of Kirkpatrick four levels evaluation, it measures employees’ job performance by determining the extent to which employees apply their newly acquired knowledge and skills on the jobs. That is to say, it suggested that after a participant has learned something from a course, the knowledge, skill and ability will be revealed on their behavior on the job. This level is critical, as it addresses the issue of learning transfer. Transfer of training is the application of knowledge, skills and attitudes learned from training to the job and the subsequent maintenance of the learning over a certain period of time (Baldwin & Ford, 1988; Cheng & Ho, 2001). Broad and Newstrom (1992) pointed out that positive transfer of training concerns the effective and continuing application, by trainees to their jobs, of the knowledge and skills gained in training. Also, intended return on investments in training programs will only be achieved to the extent that training is transferred (Nijman, Nijhof, Wognum, & Veldkamp, 2006). Transfer was originally defined as the extent to which learning of a response in one task or situation influences the response in another task or situation (e.g., see Adams, 1987). Researchers found support for the generalization of responses when there was similarity in the stimuli and responses in the learning and transfer environments (Blume et al., 2010) and separated transfer into two subcategories: (a) near transfer, in which the stimulus in the transfer condition is similar to the stimulus in the original learning condition and (b) far transfer, in which the stimulus in the transfer condition is to some degree different from the stimulus in the original learning condition (Royer, 1979). Near transfer can occur and be studied during the same session as the learning, and far transfer can be studied months or 33.

(41) years later (e.g., see Blume et al., 2010). This generalization process allows people to react appropriately to new situations because of similarities with familiar ones (Bass & Vaughan, 1966). To be consistent with historical perspectives, Blume et al. (2010) defined transfer as consisting of two major dimensions: (a) generalization—the extent to which the knowledge and skill acquired in a learning setting are applied to different settings, people, and/or situations from those trained, and (b) maintenance—the extent to which changes that result from a learning experience persist over time. Compared to maintenance, generalization relates to the ability learners have to transform their KSA to real job application. Gagne (1965) distinguished between two types of generalization processes—lateral and vertical transfer. Lateral transfer occurs when a skill spreads over a broad set of situations at the same level of complexity or difficulty; in contrast, vertical transfer occurs when an acquired skill affects the acquisition of a more complex or superordinate skill (Blume et al., 2010). Empirical researches of training transfer are plenty and articles were ultimately categorized for discussion using the taxonomy of three long-standing factors affecting transfer: learner characteristics, intervention design, and work environment (Burke & Hutchins, 2007). As illustrated in Table 2.5, Burke and Hutchins (2007) noted that intervention design and delivery includes numerous established variables influencing transfer, mostly via their impact on learning, including learning goals, content relevance, practice and feedback, and behavioral modeling. However, research which discusses the linkage between technological support and transfer is not abundant. Therefore, an empirical test of the relationship between e-learning and training transfer may enhance e-learning implementation and improvement.. 34.

(42) Table 2.5. Summary of the Intervention Design—Transfer Link Summary of the Intervention Design—Transfer Link. Variable. Strong or Moderate Mixed Relationship with Support Transfer. Research Is Minimal Needed to Clarify Empirical Research Exists or Build Findings. Need Analysis Learning Goals Content Relevance Practice & Feedback Over-learning Cognitive Overload Active Learning Behavioral Modeling Error-based Examples Self-management Strategies Technological Support Sources: Adopted from “Training Transfer: An Integrative Literature Review,” by Burke, L. A., & Hutchins, H. M., 2007, Human Resource Development Review, 6(3), p.279.. For house agents, the knowledge of real estate industry should be required so that they make sure customers to understand the process and regulation, etc. In this case, near transfer is identified when the agents simply articulate the knowledge as it is. However, far transfer which refers to the tasks and situations in the learning situation are quite different from the transfer setting is the more critical for agents. In realty industry, the communication with customers is important and their skills when promoting products vary depend on various situations. It is a higher level for agents to transform what is learned from e-learning course to the actual selling skills. 35.

(43) According to Bates (2003), “assessment of transfer makes trainees, trainers, and others accountable for transfer success and helps create a culture that values learning and its application to the job” (p. 264). It is because employees only seem to use knowledge, skills and attitudes from corporate training programs to a very limited extent at their workplaces (Nijman et al., 2006). While it has often been argued that the workplace itself could be a major force in hindering or enhancing transfer (e.g. see Rouiller & Goldstein, 1993), and especially managers and supervisors might play a crucial role in the achievement of transfer of training, support from supervisors has been suggested to be one of the most powerful tools of enhancing transfer of training (e.g. Baldwin & Ford, 1988; Elangovan & Karakowsky, 1999). The training transfer in this study performs as scores derived from supervisory reports, agents’ case reports and some training tests, each is in certain proportion. Thus, it is logical to assume that if the training fit the job tasks of employees, they may be able to apply the new knowledge and skills better and have higher transfer when they work on real tasks. The hypothesis 2 is based on the assumption and is stated below: Hypothesis 2: Perception of task-technology fit of an e-learning course will have a positive influence on employees’ training transfer.. It is suggested that transfer of training first requires a trainee to learn new job-related competencies (Velada & Caetano, 2007). After learning and retaining the training content, trainees should transfer the knowledge and skills according to the work context with the intention of improving job performance over time (Noe, Hollenbeck, Gerhart, & Wright, 2006). To accomplish organizational tasks and improve employee performance, training programs should be designed in such a way that they create a win-win situation for both organizations and employees (Bhatti & Kaur, 2010).. 36.

(44) Researchers identified three primary factors influencing transfer—including learning characteristics, intervention design and delivery and work environment influences – as based upon influential conceptual models in the field (Baldwin & Ford, 1988; J. K. Ford & Weissbein, 1997; Salas, Milham, & Bowers, 2003). Nijman et al. (2006) believed that the design and delivery of training programs exert significant influence on trainees’ learning and transfer outcomes, and several specific training characteristics have been proposed as affecting training effectiveness. Empirical results have shown that trainees who perceive to have learned general principles regarding the content of training also perceive to have attained better learning outcomes, as well as that they show greater intentions to transfer what has been learned (Machin & Fogarty, 2003). The extent of identical elements between training and transfer setting, the teaching of general principles, the variation in both extent and variability of practice conditions, the extent of over-learning included in the training program, and both relapse prevention and goal-setting procedures were all expected to have a positive direct effect on transfer of training (Nijman et al., 2006). Therefore, it is logical to infer the effect of TTF on training transfer as mediated by learning outcome in the e-learning context. For the e-learning course to be tested as effective, learning must occur before trainees can begin to apply these newly acquired knowledge and skills on their job. Hypothesis 3 is proposed due to the idea: Hypothesis 3: Employees’ learning outcome will mediate the relationship between task-technology fit of an e-learning course and training transfer.. 37.