This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.
Transfer climate is hypothesized in Holton's (1996) model as a significant environmental part to influence motivation to transfer. According to Holton (1996), trainees who worked in conditions helpful of training transfer are more likely to transfer their learning to the job. Seyler et al. (1998) conceptualized transfer climate to demote to organizational climate that includes supervisor support, supervisorsanctions and peer support. This study demonstrated that peer support and supervisorsanctions were important predictors of motivation to transfer but supervisor supportwas not for the cause that small sole discrepancy was left to be explained by supervisor support after accounting for the power of other organizational climate variables. Although supervisor support was not important in Seyler et al.'s (1998) study, other studies have demonstrated its optimistic outcome (Clarke 2002; Clark et al. 1993; Kontoghiorghes 2001) and that of peer support (Clark et al. 1993; Ruona, Leimbach, Holton & Bates 2002) on motivation to transfer.
Transfer climate also conceptualized to consist of state and penalty that either slow down or help to make easy the transfer of learning into a job situation (Rouiller & Goldstein 1993). These authors suggested four types of ‘situational' cues: goal cues, social cues, task cues and self-control cues operate in the transfer process. These situational cues strike a chord to the trainees of what they have learned, or at least provide a chance for them to use what they have learned. In dissimilarity, ‘consequence' cues were described as on-the-job outcomes, which affect the extent to which training is transferred. The four ‘consequence' signs comprise positive feedback, negative feedback, punishment, and no feedback. Unfortunately, their study could not validate the suggested constructs because the sample size was inadequate. Later, Holton et al. (1997) continued the attempt to authenticate the transfer climate variables suggested by Rouiller and Goldstein (1993). Results of the later study suggested that trainees perceive the transfer climate according to referents to the organization (for example supervisor, peer/task, or self) rather than according to psychological cues (for example goal cues, social cues), as proposed by Rouiller and Goldstein (1993).
In addition peer support, supervisor support and supervisor sanctions, the study recognized other transfer climate constructs as well as openness to change (current group norms are apparent to discourage use of new skills); personal outcomes positive(request of training on the job leads to optimistic outcomes); personaloutcomes-negative (application of training on the job leads to unenthusiastic outcomes); and opportunity to use learning (trainees are provided with capital and tasks that allow them to use their new skills on the job).
A different study by the Holton team attempted to validate transfer climate constructs in an effort to develop a diagnostic tool to measure factors affecting transfer of training (Holton et al. 2000). In this study, feedback (formal and informal indicators from an organization about an individual's job performance) emerged as yet another dimension of transfer climate. A range of studies have confirmed the effect of feedback on training transfer (Clarke 2002; Tracey, Tannenbaum & Kavanagh 1995) and employees' performance (Reber & Wallin 1984). For example, Clarke (2002) found, in a study of training in the Social Services Department, UK, that an absence of feedback to the trainee on his or her performance impedes the transfer of training.
The study confirmed earlier research by Reber and Wallin (1984) who predicted that the performance of employees will be improved if they inward feedback about their own goads when connected to their department's performance. Research examining personal outcomes-positive on training outcomes was established in two studies (Bates & Holton 1999; Tracey et al. 1995). For example, in Tracey et al.'s study (1995) originates that extrinsic personal outcomes (for example, pay and promotion) and inherent personal outcomes (for example, praise and recognition) have a straight impact on post training behaviors'. In particular, extrinsic rewards exhibited very feeble associations with training preservation whereas inherent rewards proved to be more significant variable absolutely impacting on training preservation. Two other studies examined personal outcomes-negative (Kontoghiorghes 2001; Tracey et al. 1995). Tracey et al. (1995) establish that personal outcomes-negative such as punishment was an important predictor of post training behavior, mainly in inhibiting training preservation. An opposing finding was found in Kontoghiorghes's (2001) study when punishment for failing to use the new skills exhibited only weak associations with training preservation. Regardless of the opposing finding in Kontoghiorghes's (2001) study, this thesis examines transfer climate as a variable affecting motivation to transfer because it is categorized as a main variable in Holton's (1996) model and this has been confirmed in other studies showing the important influence of transfer climate on training transfer and motivation totransfer.
Several studies have examined the connection between learning results and inspiration to transfer (Huczynski & Lewis 1980; Seyler et al. 1998; Tannenbaum et al. 1991). In Tannenbaum et al.'s (1991) study, learning was assessed using test performance following training. The authors hypothesized that test performance was related to motivation to transfer. Indeed, they found that test performance was positively related to motivation to transfer. In a similar study, Huczynski and Lewis (1980) also reported that motivation to transfer was influenced by the learning gained. An interesting, contrary finding to these two studies was that reported by Seyler et al. (1998) who set up that learning was not an important predictor of motivation to transfer. The authors reasoned that the lack of findings related to learning may be a function of the way learning was measured. In their particular study, learning was measured by averaging test scores recorded by a computer on tests taken by the trainees at the end of each lesson. The authors reported that they were not given the opportunity to audit the tests and therefore, there was no assurance that the tests were representative measures of learning that took place during training. In Holton's model, learning was categorized as a main variable affecting motivation to transfer. However, in this thesis, learning was not examined because the researcher did not have the opportunity to meet with the trainers to discuss how learning was measured.
2.5 Influences on Motivation to Learn
The earlier section detailed the five variables in Holton's (1996) HRD Evaluation Research and Measurement Model which influence motivation to transfer. This section now twist to the authority on a trainee's motivation to learn, which in twist, has been linked with increased transfer of training. Motivation to learn can be distinct as the exact need of a learner to learn the content of a training program (Noe 1986; Noe & Schmitt 1986). In Holton's (1996) model, motivation to learn is hypothesized to be influenced by personality characteristics, intervention readiness, and job attitudes. These essentials are the basis of variables used in this study and will now is discussed.
Baldwin and Ford (1988), in their model of the transfer process, suggested self efficacy as one of the trainee's characteristics that can affect training transfer. Self efficacy is a key element in Bandura's Social Learning Theory referring to the belief in one's own capability to perform a specific task (Bandura 1977). The concept of self efficacy can be understood from three dimensions: magnitude or level, strength and generality. Magnitude or level applies to the level of task difficulty. In other words, people may differ in their self belief of being capable of performing tasks of varying difficulty. Strength, the second dimension of self-efficacy, refers to whether the conviction regarding magnitude is strong or weak. This means that individuals may differ in their confidence in attaining a given level of performance. Finally, generality indicates the degree to which the expectation is generalized across situations (Bandura 1977:194). Psychologists have contributed greatly in the field of human HRD, especially in understanding of how self-efficacy affects the application of knowledge, skills and behavior learned in training on the job. This is evidenced by researchers who have found that self-efficacy has been shown to be related to motivation to learn (Colquitt, LePine & Noe 2000; Quinones 1995) and training outcomes: training and task performances (Gist 1989; Gist et al. 1989; Gist et al. 1991; Tannenbaum et al. 1991). For example, in Gist's (1989) study, self-efficacy was found to be positively related to training performance on an innovative problem solving task. Another study by Gist et al. (1989) also found self-efficacy played an important role in computer software training when trainees with high levels of self-efficacy performed better than trainees with lower levels. This finding was consistent with a later study by Gist et al. (1991) that examined the effects of self-efficacy in a two-stage training process on the acquisition and maintenance (retention) of complex interpersonal skills (negotiation skills, in this instance). The authors found that trainees with high self-efficacy negotiated significantly higher salaries than trainees with moderate or low self-efficacy. These studies have demonstrated that the effectiveness of training was dependent on the strength of trainees' self-efficacy. Whilst many studies had attempted to examine the effect of self efficacy on training transfer, no study has been located to examine the effect of self efficacy on motivation to transfer. For this reason, in this thesis, self-efficacy was examined as a secondary influence variable affecting motivation to transfer because it is likely that trainees with high self-efficacy are motivated to transfer training as well.
Intervention readiness is hypothesized in the model to influence motivation to learn. According to Holton (1996) intervention readiness includes such variables as the degree to which trainees are involved in assessing training needs; involved in planning the training; the degree to which their expectations are clarified; the degree of choice; and other unexplored influences. Research examining trainees' readiness to participate in training was found in four studies (Baldwin et al. 1991; Hicks & Klimoski 1987; Ryman & Biersner 1975; Tannenbaum et al. 1991). In Hicks andKlimoski's (1987) study, intervention readiness was conceptualized as referring to prior information that trainees receive about their training program and the amount of freedom they have to take the program. The authors found that trainees who received a realistic training preview (containing some positive, neutral and unfavorable statements) and those who had a high degree of choice were more likely to believe the training program was appropriate for them to take and were better able to benefit from the training. These trainees also showed more commitment to their decision to attend training than others who received the traditional announcement (containing brief overly positive statements) and those who had a low degree of choice. In a similar study, Baldwin et al. (1991) examined the degree of choice of training content rather than simply the choice to attend training in general as was done in Hicks and Klimoski's (1987) study. Baldwin et al. (1991) found that trainees who received their choice had a higher level of motivation to learn prior to entering the training session than those who were not provided the choice. In this thesis, intervention readiness was examined as a secondary influence variable affecting motivation to transfer because it is likely that trainees who are ready to participate in training are motivated to transfer training as well.
As described earlier, job attitudes are said influence motivation to transfer. In addition, job attitudes are also hypothesized to influence motivation to learn as well. Four studies have examined the relationship between job attitudes and motivation to learn but findings are mixed (Cheng & Ho 2001; Facteau, Dobbins, Russell, Ladd & Kudisch 1995; Mathieu et al. 1992; Noe & Schmitt 1986). For example, Noe and Schmitt (1986) found that job involvement was strongly related to the acquisition of the key behaviors' emphasized in the training program while in Cheng and Ho's (2001) study, job involvement was not significantly related to learning motivation and learning transfer for the reason that the trainees tested (who were pursuing an MBA degree) may have represented their desires to enhance their employability rather than job performance. In this thesis, job attitudes was not examined as a variable affecting motivation to transfer and the justification for this has been described previously in section 2.4.1.
2.6 Influences on Learning Outcomes
In the third part of this discussion on elements affecting motivation, this chapter turns
to the influences on learning outcomes. Specifically, learning outcomes are hypothesized in Holton's (1996) model as being influenced by ability, reaction totraining and motivation to learn. These variables will now be discussed.
Ability refers to the general capacities related to performance of a set of tasks (Fleishman 1972). Psychologists have demonstrated that general cognitive ability has been shown to have substantial positive relationship with job performance (Ford et al. 1992; Kanfer & Ackerman 1989; Robertson & Downs 1979). For example, Robertson and Downs (1979) in their review of trainability studies (the degree to which trainees are able to learn and apply the material emphasized in the training program) have suggested that approximately 16 percent of the variance in trainee performance may be attributable to ability. Research on ability was also done to see how it interacts with motivation to enhance outcomes. For example, Kanfer and Ackerman (1989) found that individual differences in cognitive abilities clearly exerted an effect on performance. In another study, Ford et al. (1992) found that those trainees with high abilities obtained a higher number of the trained tasks when performed on the job. In this thesis, ability was examined as a variable affecting motivation to transfer because it is likely that trainees with high ability are more motivated to transfer training to the job.
As described in section 2.3.1 above, Kirkpatrick's (1994) four level training evaluation model (reaction, learning, behavior change, outcome) defined reaction as trainees' ‘liking of' and ‘feelings for' a training program. In his model, he viewed reaction as an outcome that could lead to learning. However, he also stressed that a favorable reaction to a training program does not assure learning. Several studies examined the relationship between reaction and learning and the findings showed no support for this relationship. For example, a Meta analytic review of the literature based on Kirkpatrick's model conducted by Alliger and Janak (1989) found that only eight studies reported the correlation between reaction and learning and the correlation reported was weak. Therefore, they concluded that reactions had no significant relationship with learning. Similarly, Noe and Schmitt (1986) also found no support for a direct link between reaction and learning while in Dixon's (1990) study, reaction was found to have a little correlation with learning.
In Holton's (1996) model, reaction was not viewed as an outcome but as having a moderating role between motivation to learn and learning. Two studies were located to test this notion but the findings were mixed. Support for the hypothesized relationship was found in Mathieu et al.'s (1992) study. In their study, reaction was found as having a moderating role between motivation to learn and learning as well as acting as a mediator of other relationships. However, a contrary finding was found in Seyler et al.'s (1998) study but the authors gave reason that it was due to learning measurement problems. The authors explained that although the learning measure was based on tests created by subject matter experts, there was no assurance that the tests were comprehensive or representative measures of the learning took place during the training. In this thesis, reaction was not examined because it has no direct effect on motivationto transfer (Holton 1996). Further, as described earlier, several other studies have shown the non significant effect of reaction on learning (Alliger & Janak 1989; Noe & Schmitt 1986).
2.7Motivation to Learn
Motivation to learn can be defined as the specific desire of a learner to learn the content of a training program (Noe 1986; Noe & Schmitt 1986). Motivation to learn is hypothesized in Holton's (1996) model to positively influence learning outcomes. Two studies examined this relationship but the findings were mixed. For example, Quinones (1995) found that whilst motivation to learn had a positive relationship with learning and behavior it was not related to task performance. Contrary to the findings of Quinones (1995), Noe & Schmitt (1986) in their model of motivational influences on training effectiveness, found no significant relationship between motivation to learn and learning. In this thesis, motivation to learn was not examined because despite being linked to learning outcomes in Holton's (1996) study it demonstrated no direct effect on motivation to transfer in the Holton model.
2.8 Knowledge Sharing and Its Benefits
Connelly and Kelloway (2003:294) defined knowledge sharing as a set of behaviors' that involves the exchange of information or assistance to others. One proposed definition consists of donating and collecting knowledge, where knowledge donating refers to communicating to others what one's personal intellectual capital is, and knowledge collecting refers to consulting colleagues in order to get them to share their intellectual capital (Van Den Hoof & Ridder 2004:118). It has been recognized that knowledge is valuable to organizations, particularly when it is shared (Noe et. al. 2004). As more organizations move towards achieving the status of a ‘learning organization', the sharing of knowledge among employees becomes crucial because it is the key and indeed, defining element in a learning organization (Gephatt, Marsick, Van Buren & Spiro 1996). A learning organization is a company that has an enhanced capacity to learn, adapt and change (Gephatt et al.1996). In a learning organization, the people are the essential ingredients and therefore, in order to become a learning organization, the people must be committed to learning and willing to share what they have learned (Noe et. al. 2004).
There has been a growing interest in the impact of knowledge sharing in a training and development context as a mechanism to meet business challenges and provide competitive advantage (Noe 2005; Noe et. al. 2004). According to Noe (2005:433), employees are expected to acquire new skills and knowledge in training, apply them on the job and share this information with fellow workers. This has led the changing role of training from a focus on programs to a broader focus on learning and creating and sharing knowledge (Martocchio & Baldwin 1997; Noe 2005; Noe et. al. 2004). The changing of training's role according to Noe (2005) is depicted in Figure 2.5 below.
Figure 2.5 demonstrates that to some extent, training will continue to focus on developing programs to teach specific skills. However, Noe (2005) argued, to improve employees' performance and help meet business needs and challenges means that the role of training must necessarily evolve to emphasize learning, creating and sharing knowledge. The importance of knowledge sharing behavior in understanding transfer of training has been acknowledged by several researchers through the concept of continuous learning (Noe & Ford 1992; Rosow & Zager 1988; Tracey, Tannenbaum & Kavanagh 1995). However, only one study appears to have empirically examined the relationship between continuous learning and post-training behavior (Tracey et. al. 1995). In Tracey et al.'s (1995) study, continuous learning was conceptualized as the acquisition, application and sharing of knowledge, behaviors' and skills not only from training but also from a variety of other sources. The findings of that study indicated that continuous learning (often conceptualized as knowledge sharing) has a positive effect on post-training behavior. In other words, the more trainees share their knowledge, the greater the transfer of training. Tracey et al. (1995) called for future research to examine how knowledge sharing may affect post-training behavior by influencing self-efficacy, motivation and trainees expectations about training experiences (Tracey et al. 1995). Clearly, it follows then, that if sharing behavior does have a direct effect on self-efficacy and motivation, then trainees in a less supportive transfer climate will probably be less likely to transfer training to the job.
Although sharing behavior has not been fully explored in the training transfer literature, the benefits of knowledge sharing have been documented in other settings. For example, knowledge sharing has been found to: promote better learning by individuals (Collison & Cook 2004), provide an important mechanism to achieve better decision making (Tschannen-Moran 2001); and could lead to project effectiveness (Eisenhardt & Tabrizi 1995; Henderson & Cockburn 1994; Leanord- Barton & Sinha 1993). Further, the benefits of knowledge sharing have been reported in studies of firms such as Buckman Laboratories and Texas Instruments, which claimed significant gains in revenue (Chua 2003) while Dow Chemicals and Chevron reported savings (Stewart 2001). One important point raised by researchers and practitioners in this field is that the success of knowledge sharing strategies in organizations is dependent on the extent to which individuals are willing to share their knowledge (Connelly & Kelloway 2003; Lin & Lee 2004; Sveiby & Simons 2002; Van Den Hoof & De Ridder 2004). Without the willingness to share, the benefits of knowledge sharing described earlier are less achievable. Therefore, it may be illuminating to examine the attitude towards knowledge sharing behavior in order to gain a greater understanding of why individuals decide to engage or not to engage in knowledge sharing. This issue is taken up in the following section which details the Theory of Planned Behavior (Ajzen 1991), a theory which has its roots in the field of social psychology and which has been widely used to predict and explain the phenomena of behavioral intention and actual behavior.
This chapter described the concept of training, transfer of training and motivation to transfer training through an international search of the research literature. While training was defined as a planned learning experience designed to bring about permanent change in an individual's knowledge, attitudes, or skills, transfer of training was described as the degree to which trainees apply the knowledge, skills and attitudes to their job. A trainee's motivation to transfer, on the other hand, was described as a trainee's desire to use the training on the job. Although, over time, researchers have expressed different views about transfer of training by proposing variously: concepts of positive, negative and zero transfer; general and specific transfer; and far and near transfer, it was argued in this chapter, that there was general agreement that transfer of training will occur only when trainees have the desire (motivation) to use the knowledge and skills learned in training on the job.
The two dominant evaluation models found in the literature were discussed in order to paint a picture of the factors which may influence a trainee's motivation to transferraining: the Kirkpatrick (1994) evaluation model and the LTSI (Holton et. al. 2000). The Kirkpatrick (1994) evaluation model provided a starting point in HRD evaluation but ultimately did not provide a strong guide to the whole training process as it focused only on training outcomes (reaction, learning, behavior and results) and did not account for the impact of other variables such as motivation and workenvironment on behavior change. The LTSI (Holton et. al. 2000), on the other hand, provided some indication that motivation to transfer is influenced by secondary influence variables, that is, performance-self efficacy and learner readiness. The chapter then moved to the key model outlining motivation to transfer described the Human Resource Development (HRD) Evaluation Research and Measurement Model (Holton 1996). Based on this model, motivation to transfer is hypothesized to be influenced by intervention fulfillment, job attitudes, learning, expected utility and transfer climate. Other variables such as intervention readiness, personalitycharacteristics, ability, and motivation to learn and transfer design were hypothesized to have an indirect influence on motivation to transfer. These factors were supported by several studies discussed in the chapter.
Arguably, a key omission in the models presented in this chapter was their failure to include knowledge sharing as a potential variable influencing motivation to transfer training. This chapter introduced the concept of knowledge sharing and reported its benefits. The chapter indicated that the theory of planned behavior may be used to provide an insight into an individual's intention to share learned knowledge and skills with others in the workplace, providing a set of variables to test the relationship of knowledge sharing with motivation to transfer training.
In the next chapter, the development of conceptual framework and the methodology chosen for the present thesis is detailed.