Training enables a person to systematically acquire skills, knowledge and attitudes in order to improve his or her work performance (Goldstein & Ford, 2002). It is an important aspect in today’s workplace. With intense global competition, organizations must rely on its workforce to remain competitive (Salas & Cannon-Bowers, 2001). This scenario has led organizations to view training as one of its strategic components, thus placing training as a main agenda in many workplaces. HR academics also share the same view. Deadrick and Gibson (2007), who reviewed over 4300 articles published in four HR-related journals (i.e. Journal of Applied Psychology, Personnel Psychology, Human Resource Management and HR Magazine) over a period of 20 years, found that there is no research-practice gap in the area of human resource development. This finding indicates that both HR academics and HR practitioners agree that human resource development is a topic of growing concern.
Despite the extant organizational research addressing issues in human resource development (Chen & Klimoski, 2007; Salas & Cannon-Bowers, 2001), the growing interest has been on training transfer (Burke & Hutchins, 2007). Various models have specified that training transfer is a function of learning motivation (e.g. Holton, 2005; Noe, 1986), which in turn is influenced by individual and situational antecedents. However, a recent literature review indicates that there is still much to know about individual antecedents that affect learning process and training transfer (Chen & Klimoski, 2007).
According to Tracey, Hinkin, Tannenbaum, and Mathieu (2001), previous research has found exogenous variables to influence training preparation. One important aspect that has been subject to scrutiny is the influence of trainees’ characteristics on motivation to learn. In the training literature, two types of trainee’s characteristics have been identified, which are attributes and attitudes. However, studies have focused more on attributes (e.g. Colquitt & Simmering, 1998; Herold, Davis, Fedor, & Parsons, 2002; Major, Turner, & Fletcher, 2006) rather than attitudes. As noted by Colquitt, LePine and Noe (2000) and Holton (2005), the number of studies including job or career attitudes is somewhat limited. Therefore, more research is needed to examine how these attitudes influence motivation to learn.
To the researcher’s knowledge, only two research have been conducted to primarily study the influence of individual characteristics on motivation to learn (see Noe & Schmitt, 1986; Tracey et al., 2001). Hence, this research aims at examining the influence of individual characteristics on motivation to learn. Specifically, this research will examine the influence of job involvement on motivation to learn and the moderating effect of learning goal orientation on job involvement-motivation to learn relationship.
Justification as to choosing job involvement, learning motivation & LGO.
THEORETICAL FRAMEWORK AND HYPOTHESES
This research is a response to Tracey et al. (2001) who call for replication of their study on the influence of individual characteristics and the work environment on varying levels of training outcomes. Because their research is based on a sample of managers from a single corporation, they argued that the results are limited in generalizability. Therefore, “the findings should be replicated in other companies that employ different types of training programs and trainees (p. 21)”. However, this research differs from Tracey et al. (2001) by focusing only on trainee’s characteristics on motivation to learn. Specifically, this research focuses on the influence of job involvement and learning goal orientation on motivation to learn. Although Tracey et al. (2001) suggest the need to study the influence of work environment,
Motivation to Learn
There are various terms used to refer to motivation in training such as motivation to learn, motivation to transfer, pre-training motivation and post-training motivation (Burke & Hutchins, 2007; Cannon-Bowers, Salas, Tannenbaum, & Mathieu, 1995). However, the most commonly included in the training models is motivation to learn, which refers to trainee’s desire to learn the content of the training program (Colquitt et al., 2000; Noe, 1986; Noe, 2005). In this research, motivation to learn and pre-training motivation is defined such that the two constructs refer to the same meaning. In training literature, pre-training motivation refers to trainee’s motivation to learn before a particular training program commences, whereas motivation to learn refers to trainee’s motivation to learn in general. Because the difference between these two constructs lies on the specificity of the training program, it is argued that substantively there is no different in meaning. In other words, the two constructs are referring to trainee’s desire to learn the content of the training program before the training takes place. Using motivation to learn rather than pre-training motivation is also more advantageous because it minimizes the issue of generalizability. Moreover, using pre-training motivation construct entails the experimental design, which is not a design of interest in the current research.
Recently, a new construct, namely motivation to improve work through learning (MTIWL) has been proposed to explain employee’s learning motivation. Because MTIWL is a function of motivation to train and motivation to transfer, Holton (2005) argued that the effects of MTIWL on learning outcomes is greater than motivation to learn. Therefore, its relevance is only when one is interested in studying the effectiveness of a training program (Alvarez, Salas, & Garofano, 2004). Hence, motivation to learn will be used in the current research as the researcher is interested only in studying trainee’s readiness.
Generally, existing training literature shows that motivation plays an important role in driving employees towards participating in training and transferring it to the workplace. For example, studies have consistently found that motivation to learn relates to completion of training program and learning outcomes (e.g. Ryman & Biersner, 1975; Mathieu, Tannenbaum, & Salas, 1992; Guerrero & Sire, 2001; Major et al., 2006). It is also generally agreed that identifying antecedents of motivation to learn is important (Goldstein & Ford, 2002). As training comes with cost, identifying individual factors that influence trainees’ degree of readiness to benefit from training program will help the HRD professionals to decide whether training programs are financially feasible to be executed. Furthermore, learning and transfer of training are unlikely to occur if trainees themselves are not motivated to learn (Noe, 2005). Based on this argument, examining how trainee characteristics influence motivation to learn is imperative.
Job Involvement as a Predictor Variable
According to Goldstein and Ford (2002), different individuals may have different amount of motivation to learn. A possible explanation is that individuals have different characteristics, both attributes and attitudes, which in turn influence motivation to learn differently. One of the most consistent trainees’ characteristics to be found in organizational research is job involvement (Colquitt et al., 2000; Holton, 2005).
Job involvement, which is originally conceptualized by Lodahl and Kejner (1965), is defined as the degree to which an individual identifies psychologically with work and the importance of work to a person’s total image (Brown, 1996; Colquitt et al., 2000; Lodahl & Kejner, 1965). It has become a prominent job attitude to be included as an antecedent to motivation to learn in various training models, such as Colquitt’s et al. (2000) Integrative Theory of Training Motivation, Noe’s (1982) Model of the Motivational Influences on Training Effectiveness, and Holton’s Evaluation Model (1996). Such inclusion could be due to the underlying assumption that involvement is the key to unlock the power of human motivation (Brown, 1996). Specifically, it is assumed that highly-involved people are more likely to be motivated to learn (Colquitt et al., 2000) because they believe that participation in training can increase their skill levels, and subsequently improve their job performance and increase feelings of self-worth (Noe, 1986). Additionally, Colquitt et al. (2000) argued that job involvement can explain when training should succeed. This ‘added value’ makes it an important variable to be studied in relation to motivation to learn.
According to Holton (2005), job involvement has been found to be a significant predictor of motivation. However, empirical evidence from previous research on the relationship between job involvement and motivation to learn shows that the results are mixed. Tracey et al. (2001) reported that there is a significant relationship between job involvement and pre-training motivation (Î² = .29, p<.01) in their study involving a sample of 420 managers from a private organization. Clark (1990) also found that job involvement significantly predicted training motivation (as cited in Mathieu, Tannenbaum and Salas, 1992, p.831). However, much empirical evidence suggests that job involvement does not predict motivation to learn. For example, Noe and Schmitt (1986) found that job involvement is not statistically related to pre-training motivation. Similarly, Mathieu et al. (1992) found no significant relationship between job involvement and training motivation. In both cases, the researchers provide explanation for their findings, which could be remedied in future research. As Noe and Schmitt (1986) noted, the non-significant finding is due to using a relatively small sample size (N = 44) to analyze the data, whereas Mathieu, Tannenbaum and Salas (1992) argued that the non-significant finding is due to trainees viewing the proofreading course as having little importance to their jobs. In a study involving a sample of MBA graduates, Cheng and Ho (2001) also reported that there is no significant relationship between job involvement and learning motivation. They argued that this finding reflects the pursuit of MBA degree among the graduates is influenced by their desire to enhance their employability rather than job performance.
Therefore, based on the above argument and given that the current research uses a sample of lecturers where training provided by the university is related specifically to their jobs, the researcher poses the following:
Hypothesis 1. There is a significant positive relationship between job involvement and motivation to learn
Learning Goal Orientation as a Moderator
Although most prior research shows that job involvement does not predict motivation to learn, the correlation between the two constructs is found to be positive and significant. For example, Noe and Schmitt (1986) reported that the correlation between the two constructs is positive and significant (r = .23, p < .01). Similarly, Tracey et al. (2001) found positive and significant correlation between job involvement and pre-training motivation (r = .45, p < .05). Based on their meta-analysis study, Colquitt et al. (2000) reported that motivation to learn was strongly to moderately related to job involvement (rc = .20) although job involvement did not significantly explained the variance in motivation to learn. This implies that there could be other variables that influence the relationship between job involvement and motivation to learn. One possible variable to influence the relationship is goal orientation. This proposition is based on DeShon and Gillespie (2005) contention that 'goal orientation has been used to understand and predict learning and adaptive behavior in a wide variety of context, including training' (p. 1096).
There are two notable issues that require attention in relation to goal orientation. First, there is no common definition available for goal orientation. DeShon and Gillespie (2005) noted that varied ways have been used to define goal orientation, including defining it as a trait or a state. Definition also varies in terms of its stability. For example, while other authors used different definitional approach (see DeShon and Gillespie, 2005), VandeWalle (1997) has defined goal orientation as a trait but only within work-domain. Hence, researchers are presented with choices to select appropriate definition to suit their objectives. Second, the type of goal orientation has been expanded from its original formulation to include approach-avoidance motivation (Pintrich, 2003). As a result, goal orientation has been classified as performance approach orientation, performance avoidance orientation, mastery approach orientation and mastery avoidance orientation. However, previous research has shown that their influence varied except for learning goal orientation (Grant & Dweck, 2003; Pintrich, 2003). In view of these two issues, VandeWalle’s (1997) learning goal orientation will be used to represent goal orientation construct in the current research.
Learning goal orientation can be defined as a desire to develop the self by acquiring new skills, mastering new situations and improving one’s competence (VandeWalle, 1997). It has become an important study variable in human resource development research. For example, goal orientation has been used to predict mentoring relationship (Egan, 2005; Kim, 2007), employees’ continuous development decision (Garofano & Salas, 2005), and employees’ learning process (Colquitt & Simmering, 1998). In a study examining the role of conscientiousness and goal orientation as predictors and moderators to motivation to learn, Colquitt and Simmering (1998) found that learning orientation was related to motivation-to-learn and it moderated the relationship between performance and expectancy/valence. However, the role of learning goal orientation as a moderator variable involving job attitudes and motivation to learn has yet to be tested.
Most empirical findings in previous research (e.g. Cheng & Ho, 2001; Mathieu et al., 1992; Noe & Schmitt, 1986) show that there is no direct link between job involvement and motivation to learn. Perhaps this link will be significant when learning goal is included as a moderator variable. The arguments follow that people who are learning oriented view learning or acquiring new skills as the mean of improving their competency. They also enjoy challenging task and are more persistent when facing the obstacles. According to Grant and Dweck (2003), these behaviours are apparent although the current ability is low. Therefore, it could be expected that people who are high on job involvement are more motivated to learn the training content if they are high rather than low in learning goal orientation. Hence, the researcher poses the following:
Hypothesis 2: The relationship between job involvement and motivation to learn will be moderated by learning goal orientation, such that higher job involvement will be more associated with motivation to learn for highly learning goal oriented trainees.
Learning Goal Orientation
Motivation to Learn
Figure 1: Conceptual framework
Figure 1 shows the conceptual framework of this research. Three variables are included in this research, which are job involvement, motivation to learn and learning goal orientation. Job involvement is hypothesized to influence motivation to learn, and it is also hypothesized that the job involvement-motivation to learn relationship is moderated by learning goal orientation.
Although there are various kinds of training available in higher education targeted at improving its members’ performance, the most important is the one targeted at lecturers, namely research-related training. Due to the publish-or-perish principle, writing and publishing peer-reviewed articles have become a norm among lecturers in higher education. In fact, a lecturer’s appointment, promotion, scholarship and tenure are primarily based on the number of his or her published articles (Calabrese & Roberts, 2004; de Rond & Miller, 2005; Universiti Teknologi MARA, 2005). The higher the impact factor of the journal in which the article is published, the more chances will a lecturer to succeed in his or her career as an academician. The pressure to produce and publish articles of quality is endured more by junior members (de Rond & Miller, 2005). Because some of them may not have the necessary competence in writing and publishing peer-reviewed articles, higher education institutions have designed various research-related training programs to help them in improving their competency. More often than not, these training programs are made compulsory to junior members. Therefore, the issue whether they are motivated to learn remains unanswered.
The population of interest consists of junior lecturers who are on probation period and subject to attending a compulsory 3-day research methodology workshop from a large local university. Using the name list of junior lecturers taken from the university’s training centre, a convenience sample is used to select the respondents.
Measurement scales are adopted from previous research. For all measures, the respondents will indicate their agreement on a five-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree. Ratings on items for each measure will be averaged to form an overall score, where higher score indicates higher level of agreement with the variable being measured.
Job Involvement. Job involvement is measured using Kanungo’s (1982) Job Involvement Scale. This measurement scale is chosen based on Brown’s (1996) recommendation that Kanungo’s scale is greater in conceptual clarity and internal consistency than the commonly used 20-item scale developed by Lodahl and Kejner (1965). Although there are two versions of Kanungo’s Job Involvement Scale, the shorter version will be used in this research. As Noe (1986) noted, this 3-item scale is ‘uncontaminated’ and specifically assesses an individual’s identification with work. A sample item is ‘My jobs means a lot more to me than just money’. Boon, Arumugam, Mohammad Samaun Safa, & Nooh Abu Bakar (2007) reported that the reliability of this scale used in their study is Î± = .76.
Motivation to learn. Motivation to learn is measured using Tharenou’s (2001) motivation to learn scale, which was adapted from Noe and Wilk’s (1993) 17-item scale. A sample item is ‘I am usually motivated to learn the skills emphasized in the training program’. However, items will be modified to fit the research study objective by replacing the general term training program with specific training program as assigned by the university’s training centre. The reliability of this scale is Î± = .82 (Tharenou, 2001).
Learning goal orientation. Learning goal orientation is measured using VandeWalle’s (1997) goal orientation scale. This scale is chosen because it was developed to measure domain-specific goal orientation, which is work-domain. Because this research will use a sample of working adults, VandeWalle’s (1997) scale is appropriate for measuring respondents’ learning goal orientation. However, only one part of the scale will be adopted, which is learning goal orientation scale that consists five items. A sample item is ‘I often look for opportunities to develop new skills and knowledge’. The reliability of this scale is Î± = .89 (VandeWalle, 1997).
Control variables. The researcher includes age, gender, education level and work experience as control variables. Previous research has reported that motivation to learn is explained by these demographic variables (Tharenou, 2001). Therefore, including these variables will help in controlling spurious associations. Respondents are required to indicate their age by filling in the blank. Gender is dummy coded, such that 1 = female and 2 = male. Education level is measured using ordinal scale ranging, such that 1 = bachelor degree, 2 = master degree, 3 = doctoral degree, and 4 = other qualification. For work experience, the respondents are required to indicate the number of refereed article published during the respondent’s job tenure in the university.
A name list of trainees who are required to attend a research methodology workshop in the current year is obtained from the university’s training centre. Because the university is utilizing its internal network system (i.e. Intranet) as its main medium of communication, a survey questionnaire will be attached together with an invitation email to each of the respondents before the training starts. An electronic letter will also be attached with the email to inform the respondents of their voluntary participation and confidentiality of data obtained from them. In order to increase the response rate, a follow up email will be sent to the respondents a week later. All completed questionnaires will be returned via email addressing to the researcher.
Data will be analyzed using SPSS version 15.0. Mean, standard deviation and correlation will be computed and reported in a tabular form. In order to test the hypotheses, hierarchical multiple regression analysis will be used. This analysis technique allows the researcher to specify the order in which the variables are entered (de Vaus, 2002). Hence, using this technique the researcher is able to control the influence of demographic variables first, and subsequently test both the main effect and the interaction effect.
According to Tracey et al. (2001), trainees who are highly involved in their job will be motivated to learn if the training is specific and relevant to their jobs. In this research, the training program refers to research methodology workshop, which is specific and related to lecturers’ job (i.e. conducting and writing research). Therefore, it is expected that there will be a positive and significant relationship between job involvement and motivation to learn.
It is also expected that the job involvement-motivation to learn relationship will be moderated by trainee’s learning goal orientation. Specifically, trainees with higher job involvement will be more motivated to learn for if they were high rather than low on learning goal orientation (see Figure 2).
Motivation to Learn
Figure 2: Predicted patterns of Job Involvement x Learning Goal Orientation interaction for motivation to learn.
The methodological contribution of this research is in using a sample of junior lecturers of a university. Because previous research has largely used non-academic samples, little is known how lecturers’ job involvement influences their training motivation. To the researcher’s knowledge, only one study on training motivation has been conducted using a sample of educators. However, the study conducted by Noe and Schmitt (1986) is limited in generalizability. They noted that their sample of ‘educators have unique attitudes that restrict the applicability of these findings to individuals employed only in educational settings’ (p.519).
Theoretically, this research contributes to training literature by adding more empirical evidence showing the importance of assessing pre-training trainees’ job attitudes. By adding an important emergent construct, learning goal orientation, this research theoretically addresses the potential role of learning goal orientation as both a predictor and a moderator to training motivation.
Practically, the expected results of the research will help HRD professionals to select trainees who are high in job involvement and learning goal orientation for costly training programs as they are more likely to be motivated to learn than those who are low in job involvement and learning goal orientation. It also helps the HRD professionals to include job involvement and learning goal orientation as criteria in assessing person needs analysis. Furthermore, they can also take necessary actions to change employees’ job attitude so that they will be motivated to learn the content of the training programs.
First, because learning goal orientation is viewed as a domain-specific trait in this research, its generalizability is limited to work settings involving university lecturers. Second, only learning goal orientation is used as a moderator in this research. Previous researchers, such as Pintrich (2003), have argued that an individual could simultaneously display performance and learning goal orientations. Therefore, it is interesting for future research to include performance goal orientation in examining how both goal orientations moderate the relationship between job attitudes and motivation to learn. The simplicity of the conceptual model involving only three variables is another notable limitation. However, given that this model is adopted from the existing training models in which the relationship between job involvement and motivation to learn is hypothesized, this research is theoretically sound.
Kanungo’s (1982) Job Involvement Scale
My job means a lot more to me than just money.
I am really interested in my work.
Work is something people should get involved in most of the time.
Source: Boon, O. K., Arumugam, V., Mohammad Samaun Safa, & Nooh Abu Bakar. (2007). HRM and TQM: Association with job involvement. Personnel Review, 36, 939-962.
Tharenou’s (2001) Motivation to Lean Scale
I try to learn as much as I can from the training program.
I believe I tend to learn more from the training program than others.
I am usually motivated to learn skills emphasized in the training program.
I would like to improve my skills.
I am willing to exert effort in the training program to improve my skills.
Taking the training program is not a high priority for me.
I am willing to invest effort to improve my job skills and competencies.
Source: Tharenou, P. (2001). The relationship of training motivation to participation in training and development. Journal of Occupational and Organizational Psychology, 74, 599-621.
VandeWalle’s (1997) Learning Goal Orientation Scale
I am willing to select a challenging work assignment that I can learn a lot from.
I often look for opportunities to develop new skills and knowledge.
I enjoy challenging and difficult tasks at work where I’ll learn new skills.
For me, development of my work ability is important enough to take risks.
I prefer to work in situations that require a high level of ability and talent.
Source: VandeWalle, D. (1997). Development and validation of a work domain goal orientation instrument. Educational and Psychological Measurement, 57, 995-1015).
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