Measurement & Evaluation/Human Resource Development

Toward a Theoretical Model of Dispositional Inﬂuences on Transfer of Learning: A Test of a Structural Model Bogdan Yamkovenko, Ed Holton This study explores the hypothesized relationship between dimensions of the Five Factor model of personality, goal orientation, self-efﬁcacy, and intent to transfer training on the job. This study attempts to model the complex rela- tionship between these characteristics and intent to transfer using structural equation modeling approach. A set of propositions is presented for each indi- vidual variable and its relationship to intent to transfer. Finally, a model of relationships is tested and the results are discussed. The ﬁndings revealed that conscientiousness was the only dispositional variable that had a signiﬁcant relationship to intent to transfer. In addition, control variables, learner readi- ness and motivation to transfer, were signiﬁcant in the model. The results sug- gest that dispositions may not be as important in the transfer system as other constructs like situational, motivational, and ability variables. Recommen- dations for further research and testing of the model are discussed. Introduction Training transfer is deﬁned as the degree to which trainees apply on their jobs the knowledge, skills, behaviors, and attitudes they learned in training (Baldwin & Ford, 1988).

Transfer of training is inﬂuenced by a large number of factors. Burke and Hutchins (2007) review all the factors that exert inﬂuence on transfer of train- ing. Among these are some that are well known and researched—training design, supervisor and peer support, utility, content relevance, and transfer cli- mate (e.g., Axtell & Maitlis, 1997; Chiaburu & Marinova, 2005; Clarke, 2002; Ruona, Leimbach, Holton, & Bates, 2002; Kontoghiorghes, 2002). All these fac- tors are important for training transfer. However, some factors are not getting HUMAN RESOURCE DEVELOPMENT QUARTERLY , vol. 21, no. 4, Winter 2010 © Wiley Periodicals, Inc.

Published online in Wiley Online Library (wileyonlinelibrary.com) • DOI: 10.1002/hrdq.20054 381 382 Yamkovenko and Holton the attention they deserve from researchers. For instance, dispositional charac- teristics are rarely examined in the transfer process. Dispositional characteris- tics of an individual are one’s personal differences and traits (Wiggins, 1996).

Traits usually stand alone because they are considered more stable than some other individual differences like goal-related constructs, self-esteem, self- efﬁcacy, values, and attitudes (Allport, 1937; Wiggins, 1996).

Various models have been proposed and tested in an attempt to identify an optimal set of inﬂuences that predetermine the transfer of knowledge on the job. Some combined factors of a different nature, including personal dif- ferences and organizational environment (Chiaburu & Marinova, 2005). Oth- ers are so broad and all encompassing that they factor in virtually every possible construct that could inﬂuence transfer (Holton, 1996). For instance, Holton’s model (1996) was designed to examine the whole system of inﬂu- ences on transfer of training (see Figure 1). The model is provided to visualize and understand how dispositions ﬁt into the overall system of factors that inﬂuence transfer.

HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq Linkage to organizational goals Organizational results Individual performance LearningReactionTransfer climateExternal events Motivation to learnMotivation to transferExpected utility/ROI Transfer design Ability Ability/ Enabling elements Outcomes Environmental elements Motivation elements Secondary influencesIntervention readinessIntervention fulfillment Job attitudes Personality characteristics Figure 1. HRD Evaluation Research and Measurement Model (Holton, 1996) The inﬂuences are divided into primary and secondary, according to their importance and role in the process. The overall model is referred to as a trans- fer system, and the signiﬁcance of each factor in the transfer system should not be underestimated. Ultimately, any of the factors can hinder transfer and reduce the effectiveness of a training program. In practice, examining a speciﬁc factor in the transfer system in isolation may not be effective. However, from a research standpoint, it is important to understand what mechanisms drive the inﬂuence of each of the factors individually. Unfortunately, some relationships remain unexamined in HRD literature. These relationships may be critical to organiza- tional performance outcomes and training transfer speciﬁcally.

In his transfer system model, Holton (1996) links dispositional differences as secondary inﬂuences on transfer through training motivation. The research on dispositional characteristics, even though abundant, is inconsistent in ﬁnd- ings and the speciﬁcation of variables. For instance, dispositional characteris- tics are rarely examined in a systemic fashion in their relation to transfer. This is a serious oversight because personality and dispositional characteristics research has produced very interesting and signiﬁcant ﬁndings in terms of inﬂuences and relationships to other types of organizational behavior.

Dispositional Characteristics and Transfer.The research on dispositional characteristics and organizational performance has been most proliﬁc and opti- mistic in the last 25 years. The increased attention to this area has shown that it is important to consider dispositional characteristics in organizations because these characteristics may affect life and work of an employee as a part of an organization. Hogan (2000) states that the most important claim of personal- ity psychology is that there are structures inside people that determine their behavior. These structures often are manifested in personal traits and individ- ual differences.

It is important to keep in mind the distinction between personality research, which focuses largely on traits, and research focused on other indi- vidual differences. All this work can be comprised under the umbrella of dis- positional characteristics.

The deﬁnition of personality research can be traced back to the work of Allport and Eysenck. Allport (1937) viewed personality theory as a trait the- ory. Trait theory argues that our behavior is consistent and it is rendered so by real neuropsychic structures, which exist inside of us. He posited that person- ality is the dynamic organization within the individual of those psychophysi- cal systems that determine one’s unique adjustment to the environment.

Allport deﬁned trait as a dynamic trend of behavior that results from the inte- gration of numerous speciﬁc habits of adjustment, and which expresses a char- acteristic mode of the individual’s reaction to his surroundings. Traits have been effectively classiﬁed into the widely used Five Factor model. The ﬁve dimen- sions of the model are a result of multiple factor-analytic studies and are an accepted approach to examining personality traits in behavioral research (e.g., McCrae, Zonderman, Costa, Bond, & Paunonen, 1996; and Wiggins & Trapnell, Dispositional Inﬂuences on Transfer of Learning 383 HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq 384 Yamkovenko and Holton 1997). Wiggins (1996) offers a detailed classiﬁcation of various personality dif- ferences. The Big Five traits take up only a small portion of the list. Dispositional characteristics include all other individual variables that, unlike traits, are less stable. These includegoal orientation, self-efﬁcacy, locus of control, and other motivational and sociocognitive differences (McAdams & Pals, 2006).

At the outset, it is important to understand how dispositional variables may be related to behavior in general. A majority of research links dispositional variables to performance through motivational constructs. Mitchell (as cited in Colquitt & LePine, 2000) suggests that psychological processes involved with arousal, direction, intensity, and persistence of voluntary actions are goal directed. In everyday life on a regular basis, people set different goals for them- selves. Some people do so more than others, and the goals vary according to different criteria (long-term vs. short-term goals, positive vs. negative goals, individual vs. organizational goals). Setting goals affects the behavior of the individuals and their job-related performance (Locke, 1968). Schneider and Smith (2004) surmise that broad goals of status, communion, and accomplish- ment-striving represent proximal motivational variables that can be used to explain why dispositional characteristics relate to job behaviors and job per- formance, because these proximal goals may be affected by more distal disposi- tions. Herold, Davis, Fedor, and Parsons (2002) also describe how dispositional differences may inﬂuence performance under the theory of motivation. They base their linkage of dispositional differences and performance on Campbell’s deﬁnition of motivation as the combined effects of three choices or decisions:

the decision to exert an effort (direction), the decision as to the level of effort (level), and the decision to persist at a given level of effort (persistence). Dis- positional differences inﬂuence these decisions by creating differences in self- set goals, assessments of situations, interpretations of situations, and reactions to these interpretations.

Because dispositional differences are related to organizational behavior, per the discussion above, and training transfer is a desirable organizational behavior, it seems likely that dispositional characteristics are related to success in transferring knowledge and skills from training to the job. Holton (1996) theorized that dispositional characteristics, as secondary inﬂuences, play an important part in the transfer system. Subsequent studies (e.g., Chiaburu & Marinova, 2005; Naquin & Holton, 2002) showed that some dispositional variables inﬂuence transfer-related behaviors. These studies have chosen to examine only a few dispositional variables, which either were relatively unsta- ble individual characteristics (self-efﬁcacy) or were extracted from the Big Five taxonomy of traits. The next step in linking transfer and dispositions is exam- ining a combination of dispositional variables in a theoretically logical system.

Such an examination may uncover a framework that could maximize the impact of individual differences on transfer behavior, not only in terms of variance explained, but also in practical application. This approach may increase our ability to identify those variables that practitioners can manipulate (self-efﬁcacy, HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq goals, attitudes, etc.) and by doing so bring out the best in an individual’s abil- ity to apply knowledge and skills to the job.

In addition, the existing studies on transfer and dispositional variables often include other variables in the models that do not relate to dispositions.

Some authors have built a few dispositional variables into comprehensive mod- els of transfer, like self-efﬁcacy and conscientiousness, but also included peer and supervisor support, thus modeling the relationship of constructs unrelated to individual differences (Chiaburu & Marinova, 2005). No study of training transfer has focused exclusively on dispositional inﬂuences on transfer. Most importantly, no study systematically examined what mechanisms drive trans- fer behaviors from the dispositional perspective. Barrick and Mount (2005) state that it is clear that dispositional variables matter, and now researchers must move on to examining issues that are more important. One of these issues is the process through which personality and other dispositional vari- ables affect behavior at work. Examining dispositional inﬂuences on transfer may mean solving one signiﬁcant piece of the transfer equation. In terms of HRD research, this would become a signiﬁcant addition to explanatory research on transfer and individual differences. The comprehensive modeling approach taken in this study could potentially specify the system of relation- ships and explain the nature, strength, and direction of the relationship between many important dispositional variables and training transfer. If the dispositional variables that facilitate transfer in some way could be determined, it might be easier to address factors related to training design and delivery, pro- vide a way for a more accurate selection of candidates, and develop those favorable dispositional variables that are malleable. In the ﬁeld, organizations often rely on personality-based instruments. Without having a clear picture of which dispositional variables matter most to transfer and training outcomes, choosing an appropriate instrument is like ﬁnding one’s way in the dark, hop- ing to stumble upon the right path. The ﬁndings of this study could not only provide valuable information to HRD research, but also provide a guide or a road map to HRD practitioners in terms of what dispositional characteristics to look for in trainees. Therefore, it is important to examine the relationship of dispositional characteristics and training transfer empirically.

Proposed Relationships Between Intent to Transfer and Personality Variables.

Transfer of training by itself is a variable that does not lend itself to easy oper- ationalization. In organizational settings, transfer measures are often subjec- tive self-reports or supervisor ratings not based on speciﬁc numerical performance measures (Bates, Holton, Seyler, & Carvalho, 2000). An alterna- tive approach to studying transfer may lie in the theory of planned behavior.

Individuals are likely to form an intention to behave in a certain way after their training is completed. This intention will be based on experiences with train- ing programs, perceptions of the work environment, goals, personal differ- ences, and so forth. Intent to transfer may be an effective approximation of training transfer measure without the measurement difﬁculties involved in Dispositional Inﬂuences on Transfer of Learning 385 HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq 386 Yamkovenko and Holton measuring actual transfer of training. In view of the measurement difﬁculties associated with transfer of training, the proximal variable—intent to transfer— will be used as a criterion. Intent is an immediate antecedent of an action or behavior (Ajzen & Fishbein, 1977). Because intention is an immediate antecedent of an action or behavior, all things being equal, the stronger the intention, the more likely an individual is to engage in behavior and achieve his or her behavioral goals (Ajzen, 2002).

Intent to transfer is a relatively new concept. Despite the fact that intent was linked to some behaviors, such as turnover and drug use (Ajzen, Timko, & White, 1982; Doran, Brief, Stone, & George, 1991; Price & Mueller, 1981), there is no research in which intent to transfer was used as a dependent vari- able. Given the novelty of intent to transfer as a dependent variable, this study becomes an important step in explanatory research and a methodological con- tribution to existing transfer literature. In the case of training transfer, intent to transfer knowledge from the training program onto the job may be one of the alternatives to other measures of transfer. Even if the transfer behavior is a novel behavior, the individuals may have deliberated and produced beliefs about the transfer behavior by the time they go back to work and attempt to transfer. In other words, by the end of the training program employees may have made the decision about the extent to which they will apply the knowl- edge at work.

Goal Orientation.Goal orientation has been a subject of research in var- ious settings and has been used to predict job performance (e.g., Arenas, Tabernero, & Briones, 2006; Sujan, Weitz, & Kumar, 1994). Goal orientation refers to the goals pursued by individuals in achievement situations (Dweck & Leggett, 1988). Learning- and performance-goal orientation, two broad cate- gories of the construct, have been shown to inﬂuence different types of job per- formance outcomes and to evoke various types of behaviors in performance settings (e.g., Arenas et al., 2006; Button, Mathieu, & Zajac, 1996; Sujan et al., 1994; VandeWalle, 2001). Because of the nature of this construct, it is specif- ically related to dispositional and situational goal preferences in achievement situations (Payne, Youngcourt, & Beaubien, 2007). Training transfer can be easily described as an achievement situation, where an individual attempts to utilize new skills and knowledge on the job to achieve a new level of perfor- mance. Therefore, the success of this achievement may be partially dependent on the type and the level of the goal an individual sets for himself.

Researchers proposed two types of goals, focused either on performance or on learning (Button, Mathieu, & Zajac 1996; Dweck & Leggett, 1988).

Speciﬁcally, individuals with high learning-goal orientations focus on increas- ing their learning and/or task competence, seeking challenges, and persisting in the case of failure (Dweck & Legget, 1988). In contrast, individuals with high performance-goal orientation are interested in demonstrating task com- petence through gaining positive and avoiding negative judgments of compe- tence. Such performance-oriented individuals tend to avoid challenges, HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq decrease their effort and persistence following failure, and fear negative eval- uation by others (Button et al., 1996).

Performance-goal orientation is essentially manifested in a shortsighted effort to “look good” to others (VandeWalle, 2001). Therefore, it can be sur- mised that goal orientation indicates whether an individual is interested in long-term success and development or short-term attainment of an objective.

People who focus on a failure-avoiding approach to training may be interested in a short-term performance of a certain task, receiving favorable judgments from others, and avoiding negative feedback. Such individuals select a task they know can be accomplished using what they know, because they tend to believe their skills are ﬁxed, and are discouraged by mistakes and failure (VandeWalle, 2001). Such individuals may not persevere when obstacles arise and will avoid challenging tasks. Because being discouraged by failure and crit- icism may curb development and lead to subpar performance, performance- goal orientation may not lead to long-term outcomes like transfer, persistent application of skills, and attempts to master new techniques on the job.

Learning-goal orientation, on the other hand, affects achievement situa- tions differently. Individuals with learning-goal orientation are focused on developing new skills, attempting to understand new skills, and successfully achieving self-referenced standards for mastery (Ford, Weissbein, Smith, Salas, & Gully, 1998). They prefer challenging tasks, and therefore may aspire to achieve more than their counterparts with performance goals do. They believe that their efforts lead to success and exhibit greater persistence in face of difﬁ- culties. In uncertain and new situations like those found in transfer environ- ment, learning-goal orientation may help individuals deal with obstacles and view errors as learning opportunities. Learning-oriented individuals view neg- ative feedback as useful diagnostic information that can help facilitate skill development (Arenas et al., 2006; Ford et al., 1998).

Moreover, learning-goal orientation is a motivational mechanism that engages an individual in achievement situations and instigates action, effort, and determination to achieve a goal (Zweig & Webster, 2004). Payne et al.

(2007) suggest that goal orientation is a compound personality characteristic that results from a combination of the Big Five source traits. It is a proximal mid-level motivational construct positioned between more distal disposition and speciﬁc behaviors (Elliot & Church, 1997). Consequently, this leads to a hypothesis that the goal-orientation construct may have a focal inﬂuence on transfer process. More speciﬁcally, goal orientation may hold a central place in the system of dispositional inﬂuences on transfer of training. Therefore:

H YPOTHESIS 1:Learning-goal orientation will be positively related to intent to transfer.

Self-Efﬁcacy.Similarly to goal orientation, self-efﬁcacy is an important dispositional characteristic that inﬂuences individual behavior as a self- regulatory mechanism. Generalized self-efﬁcacy is an enduring belief in one’s Dispositional Inﬂuences on Transfer of Learning 387 HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq 388 Yamkovenko and Holton capacity to perform across a wide range of situations and tasks (Bandura, 1995; Chen, Gully, & Eden, 2001). Washington (2000) suggests that different peo- ple with similar skills or the same person under different circumstances may perform poorly, adequately, or extraordinarily, depending on ﬂuctuations in their beliefs of personal efﬁcacy. In other words, a person who does not believe in his or her capability to perform and apply new skills and knowledge on the job is not likely to succeed in doing so. Martocchio (1994) suggests that indi- viduals who have high self-efﬁcacy usually view ability as an acquirable skill and believe that gaining knowledge and building their competence can increase their capabilities. This belief, arguably, may be very important in the context of training transfer, because the transfer environment will likely pro- duce a number of obstacles to overcome. Chiaburu and Marinova (2005) posited that self-efﬁcacy was related to both learning-goal orientation and learning transfer. Locke and Latham (1990) found that self-efﬁcacy was related to self-set goals. Arenas et al. (2006) found that individuals with lower perfor- mance-goal orientation and a more positive attitude toward errors (typical for high-mastery goal orientation) reported higher beliefs in their own ability to perform the task (self-efﬁcacy).

Payne et al. (2007) examined the nomological net of goal orientation in their meta-analysis and discovered that self-efﬁcacy is one of several disposi- tional antecedents of goal orientation. As such, self-efﬁcacy facilitates individ- ual beliefs that performance can be improved through effort. Therefore, individuals that have beliefs of self-efﬁcacy in the ability to master new skills and apply them on the job are more likely to set the learning and mastery goals. The meta-analysis by Payne et al. (2007) provides ample support for this statement; the true correlation between general self-efﬁcacy and learning-goal orientation in their study was r 0.71. At the same time, the correlation with performance-goal orientation was strongly negative, indicating that individu- als with high self-efﬁcacy are likely to be learning and mastery oriented.

It is likely that self-efﬁcacy, along with other dispositional variables, may affect intent to transfer work through a more complex mechanism. Self- efﬁcacy, being an antecedent of goal orientation (Payne et al. 2007), may affect intent to transfer by facilitating the beliefs associated with learning- goal orientation. Baron and Kenny (as cited in Holmbeck, 1997) suggest that this type of relationship is indicative of a mediator, where a given inde- pendent variable (self-efﬁcacy) inﬂuences the mediator (goal orientation), which then inﬂuences the outcome variable (transfer). This leads to a sec- ond hypothesis. Self-efﬁcacy, as an antecedent of goal orientation (Payne et al., 2007), may affect intent to transfer by through learning-goal orienta- tion, by increasing the likelihood that individuals will set long-term goals of mastery.

H YPOTHESIS 2:Learning-goal orientation will mediate the relationship between self- efﬁcacy and intent to transfer. HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq Conscientiousness.Similar to the ﬁndings with self-efﬁcacy, Payne et al.

(2007) found that some dimensions of the Five Factor model of personality are also antecedents of goal orientation. They assert that goal orientation is a compound trait made up of various aspects of the Big Five. For instance, learn- ing- and performance-goal orientation may be grounded in achievement moti- vation, which is a component of conscientiousness. Payne et al. (2007) found that conscientiousness was positively related to learning-goal orientation, negatively related to avoidance performance orientation, and unrelated to performance- goal orientation. The latter two constructs are subdimensions of performance-goal orientation construct.

Many research studies have examined how conscientiousness relates to both transfer- and goal-related constructs. For instance, Herold et al. (2002) found that conscientiousness improved the transfer of learning from phase I to phase II of the training program and compensated for early learning difﬁ- culties. Trainees that were more conscientious were more persistent in the early stages of training because conscientiousness affected goal setting, beliefs about effort–performance contingencies, and attention-allocation decisions.

A positive relationship between learning-goal orientation and conscien- tiousness was empirically demonstrated by Zweig and Webster (2004), Chan and Tesluk (2000), Colquitt and Simmering (1998), Klein and Lee (2006), and others. Conscientiousness may therefore inﬂuence behavior directly and through a self-regulatory mechanism of goal-related constructs.

A direct relationship between conscientiousness and outcome variables was examined in several major studies (e.g., Barrick & Mount, 1991; Dean, Conte, & Blankenhorn, 2006; Salgado, 1997). Conscientiousness was found to be a valid predictor of performance for all occupational groups across three performance criteria, including training proﬁciency.

Day, Radosevich, and Chasteen (2003) state that conscientious individu- als are self-disciplined and motivated to see a task through to completion, per- severe in the face of difﬁculties, and do not avoid challenging tasks. Such characteristics may therefore explain intent to transfer as a desire to follow through and accomplish a training goal or a task, which leads to the third hypothesis:

H YPOTHESIS 3:Conscientiousness will be positively related to intent to transfer.

Making matters more complex, conscientiousness was positively related to learning-goal orientation (r 0.32) and negatively related to performance- goal orientation (r 0.18) (Payne et al., 2007). This may indicate that con- scientiousness may inﬂuence transfer through learning-goal orientation. We know that hardworking, conscientious individuals do not avoid difﬁcult tasks, and persevere (Barrick & Mount, 1991). However, along with properly set goals—learning/mastery goals—they may transfer more successfully because they will utilize hard work and perseverance properly. Instead of seeking the Dispositional Inﬂuences on Transfer of Learning 389 HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq 390 Yamkovenko and Holton positive feedback and positive evaluation of others, these individuals work hard for the purpose of learning and mastery of the concept. Therefore, con- scientiousness may inﬂuence intent to transfer through a more proximal mech- anism of learning-goal orientation, which indicates a partial mediation of conscientiousness–intent-to-transfer relationship by learning-goal orientation.

H YPOTHESIS 4:Learning-goal orientation will partially mediate the relationship between conscientiousness and intent to transfer.

Openness to Experience.As an antecedent variable, openness to experi- ence is positively and strongly (r 0.44) related to learning-goal orientation and unrelated to performance-goal orientation (Payne et al., 2007). Barrick and Mount (1991) noted that individuals who score high on openness to experi- ence tend to engage more in learning activities and have a more positive atti- tude toward learning. Openness to experience was found to be predictive of training proﬁciency (r 0.31,p 0.05) (Barrick & Mount, 1991). People who were curious, broad-minded, intelligent, and cultured were more likely to have a positive attitude toward learning experiences.

Herold et al. (2002) found that openness to experience was positively and signiﬁcantly correlated with acquiring the necessary skills in a training environment. Dean et al. (2006) obtained even more optimistic results, with openness to experience being a signiﬁcant predictor of training performance in a simulation-based environment. Because individuals open to experiences usually have characteristics like being curious, broad-minded, and intelli- gent, which are associated with favorable attitudes toward learning experi- ences, these individuals may also be more proﬁcient in training and get more beneﬁts out of a training program (Barrick & Mount, 1991). Judge and Ilies (2002) also estimated a true score correlation of p 0.18 between openness to experience and goal-setting motivation, which indicates that individuals open to new experiences are motivated to set new and interesting goals for themselves.

At the same time, Costa and McCrae (1992) proposed that part of open- ness to experience is a willingness to entertain new ideas and to try new things, which may lead to covariation between openness to experience and learning- goal orientation. Because individuals high in learning-goal orientation embrace new experiences as learning opportunities and not threats, these individuals should logically score higher on openness to experience. This statement has been supported by empirical research (e.g., Chan & Tesluk, 2000; Connolly & Vieswesvaran, 2002; VandeWalle, 1997). Therefore, openness to experience may inﬂuence transfer through a more proximal mechanism of learning-goal orientation:

H YPOTHESIS 5:Learning-goal orientation will mediate the relationship between open- ness to experience and intent to transfer. HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq Emotional Stability.Martocchio (1994) suggests that emotional instabil- ity may have an impact on adaptation and learning in a new and changing environment. This is only a theoretical link. However, in support of this link, Colquitt and LePine (2000) found that anxiety had a signiﬁcant negative rela- tionship with motivation to learn and posttraining self-efﬁcacy. Emotionally stable people may cope more easily with stress and difﬁculties on the job and in training, remain calm, and address problematic issues more efﬁciently. For example, Oakes, Ferris, Martocchio, Buckley, and Broach (2001) used Cattell’s 16 Personality Factors (16PF Questionnaire) Questionnaire in predicting train- ing success for air trafﬁc controllers and identiﬁed emotional stability as a valid predictor of the criterion variable.

Judge, Higgins, Thoresen, and Barrick (1999) found a signiﬁcant negative correlation between negative affectivity, which is likened to neuroticism and job satisfaction. Similar relationships were found earlier in the literature (e.g., Levin & Stokes, 1989; Spector & O’Connell, 1994) Speculations on these ﬁnd- ings lead us to suggest that those individuals who are not satisﬁed with their jobs may be less successful in their attempts to transfer new skills and knowl- edge. This may be caused by lower morale and lack of commitment to the well-being of an organization. Individuals who have high anxiety, worry a lot, and are subject to high levels of stress may have a smaller chance of success during a complex process of transfer, which is usually accompanied by an uncertain and stressful environment. Because there is virtually no support for these speculations in the literature and only partial and inconsistent support for job-performance relationship, an attempt will be made to test the transfer–neuroticism relationship.

H YPOTHESIS 6:Emotional stability will have a signiﬁcant positive relationship with intent to transfer.

Extraversion.Extraversion is an interesting construct, and it comprises several traits that could be argued to relate to performance or training out- comes. For instance, assertiveness and ambitiousness are traits that may cor- relate with self-efﬁcacy, because ambitious and assertive people are more likely to achieve their goals and therefore may have more conﬁdence in their abili- ties (e.g., Epstein, Grifﬁn, & Botvin, 2000; Lee, 1984; Morokoff et al., 1997).

Extraversion predicted training proﬁciency relatively well (p 0.26) in the meta-analysis by Barrick and Mount (1991). In their more recent meta-analysis, Barrick, Mount, and Judge (2001) reported a moderate but positive estimated true correlation for extraversion and training proﬁciency (p 0.23). However, they posited that extraversion is mostly an important predictor in situations where inter- personal interactions are critical. Herold et al. (2002) suggested that agreeableness and extraversion reﬂect interpersonal orientations more so than task orientations.

However, some researchers (e.g., Chan & Tesluk, 2000; Zweig & Webster, 2004) hypothesize that extraversion may have an interesting relationship with goal Dispositional Inﬂuences on Transfer of Learning 391 HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq 392 Yamkovenko and Holton orientation. Zweig and Webster (2004) suggest that extraversion may be related to an individual’s positive affect and cognitions. Therefore, such people may be more optimistic and less susceptible to the stress of competition. Because one of the underlying traits of the extraversion dimension is ambition, it may affect mas- tery seeking and perseverance (Zweig & Webster, 2004). These two dimensions are key concepts in learning orientation.

Because learning-goal orientation is a central construct in the model and extraversion may correlate with learning-goal orientation according to the stud- ies cited above, it is important to include it in the model as an antecedent of goal orientation. Therefore, the hypothesis for the extraversion dimension is as follows:

H YPOTHESIS 7:Extraversion will be positively related to learning-goal orientation.

External Inﬂuences.Because transfer of learning is a complex process and does not occur in a vacuum, it is unwise to ignore the inﬂuences of other vari- ables outside of the dispositional realm. The Transfer System Model proposed by Holton (1996) depicts dispositional differences as secondary inﬂuences on transfer. Learning is a fundamental construct in Holton’s model; clearly, no transfer can occur without learning. In addition, a number of factors exist that determine the transfer climate in a given organization. The relationship between transfer and these factors has been cited in various studies (e.g., Burke & Hutchins, 2007; Chiaburu & Marinova, 2005; Ruona et al., 2002).

Because intent to engage a behavior is a predictor of behavior, the argument can be made that because transfer climate and learning explain variance in trans- fer proper, they may also explain variance in intent to transfer. This argument is upheld by the contention that attitudes and beliefs about a certain behavior are formed based on previous experiences and perceptions of the environment (Ajzen, 2002). Essentially, perceptions of transfer climate may inﬂuence individ- uals’ attitudes to transfer and therefore their intentions to engage in transfer behav- ior. A similar contention can be made about learning. Individuals who achieve high learning outcomes (i.e., gain new knowledge and skills) may be more likely to intend to apply them than those who did not gain sufﬁcient new knowledge.

Because we do not attempt to model the relationships of each construct subsumed under transfer climate and learning, no hypotheses are made regard- ing the nature of their relationships with variables of interest. These variables are included exclusively to control and account for variance explained by these variables. The Proposed Model of Dispositional Differences and Transfer As evident from the discussion on the relationship of transfer and dispositional variables, several crucial elements can be combined into one conceptual model.

HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq Figure 2 depicts the proposed model of the intent to transfer and personality variables. In the center of this model lies learning-goal orientation that inﬂu- ences intent to transfer directly and at the same time mediates a number of relationships between other variables. The learning-goal orientation may be considered a prerequisite for the intent to transfer from the individual perspec- tive. It is a motivational construct that is responsible for choosing, setting, and “sticking” to the goals that may foster transfer. This construct essentially insti- gates an individual to move toward learning-transfer behaviors. Other variables, although important, depend largely on the goal processes of an individual. Self- efﬁcacy, conscientiousness, openness to experience, neuroticism, and extraver- sion are all favorable characteristics, but if an individual sets goals other than those pertaining to learning/mastery or transfer, the inﬂuence of these charac- teristics may be misguided or diminished.

Method Measures.Personalitywas measured with the use of the Costa and McCrae (1992) Neuroticism–Extroversion–Openness Five Factor Inventory (NEO-FFI). This is an abbreviated measure of the longer NEP-PI-R and only measures ﬁve global domains of personality without measuring ﬁner facets Dispositional Inﬂuences on Transfer of Learning 393 HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq Neuroticism Extraversion Openness to experienceLearning goal orientationTransfer climate LearningIntent to transfer Self-Efficacy Conscientiousness Figure 2. A Model of Dispositional Differences and Intent to Transfer 394 Yamkovenko and Holton subsumed under each domain. NEO-FFI measures each dimension of the Five Factor Model with a 12-item scale. The scores for each item are then summed to provide a total score for each personality dimension. The scales of NEO-FFI have demonstrated good internal reliability and convergent validity with the full version, NEO-PI-R (Ferguson & Patterson, 1998; Naquin, 1999). Coefﬁcients alpha for the NEO-FFI scales were a 0.86 for Neuroticism, a 0.77 for Extraversion, a 0.73 for Openness to Experi- ence, and a 0.81 for Conscientiousness. A sample item for this scale is “I often feel inferior to others.” Generalized self-efﬁcacywas measured with the use of the Chen et al.

(2001) eight-item measure called the New Generalized Self-Efﬁcacy Inventory (NGSI). Factor analysis of NGSI yielded a single factor solution for the eight items, with reliabilities ranging from a 0.85 to a 0.88. The test–retest reli- ability coefﬁcients for the eight-item NGSI scale were high: rt1 t2 0.65, rt2 t3 0.66,rt1 t3 0.62. NGSI consists of eight statements describ- ing individual attitudes and feelings related to one’s belief in his/her ability. For example, “I will be able to achieve most of the goals that I have set for myself.” The respondents are then asked to indicate the level of agreement with such a statement on a 5-point Likert scale.

Learning-goal orientationwas measured with the use of the four-item Learning-Goal Orientation (LGO) scale from VandeWalle’s (1997) 13-item Goal-Orientation instrument. The LGO scale consists of four items and has an internal consistency coefﬁcient of a 0.89. A sample item from this measure is “I am willing to select a challenging work assignment that I can learn a lot from.” Intent to transferwas measured by the four-item Intent to Transfer scale designed for this study. This Likert-type scale asks respondents to what extent they intend to engage in transfer behaviors. The respondents are asked to indi- cate their degree of agreement with each statement. This measure was devel- oped based on the theory of planned behavior (Ajzen & Madden, 1986).

A sample item for this measure is, “I anticipate making every effort in the com- ing weeks to put into practice what I learned in this training.” TheTransfer Climatewas measured with the Learning Transfer System Inventory (LTSI) (Holton, Bates, & Ruona, 2000). The present version of the instrument contains 68 items; it was validated by Holton et al. (2000). The 68-item instrument is subdivided into two domains: Training in Speciﬁc and Training in General. The ﬁrst domain consists of 45 items and the second domain consists of 23 items. There are four sets of factors in the instrument:

Motivation, Work Environment, Ability, and Secondary Inﬂuences. Overall, factor analysis consistently revealed 16 factors. The reliability coefﬁcients for LTSI range from a 0.63 to a 0.91. Cronbach’s alpha for Learner Readiness scale is a 0.73 and for Motivation to Transfer a 0.83. A sample item is “Before the training, I had a good understanding of how it would ﬁt my job- related development.” HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq Only 60 respondents have returned the surveys with a measure of learn- ing. It is very uncommon to ﬁnd corporate training programs with tests of knowledge. Even if one exists, the trainer usually asks the class to answer together. Therefore, ﬁnal test grades are either not available or not meaning- ful. It was decided to exclude the measure of learning from the study.

Sample.Employees from a large organization in the southeastern United States were contacted. The total number of survey requests sent out was 450.

The returned and completed surveys came from 290 respondents for a response rate of 64%. After listwise deletion, the usable sample size was 252.

Because the survey consisted of 155 questions, no additional demographics were collected to minimize the chance of nonresponse. The sample size for the study is based on the Bentler and Chou (1987) recommendation for SEM stud- ies of at least ﬁve cases per parameter estimate. Including all indicator vari- ables, there are 45 parameters to be estimated for this study. Hair, Anderson, Tatham, and Black (1998) made similar recommendations of a minimum of 5 respondents per estimated parameter, whereas 10 respondents per parameter were considered most appropriate. Hair, Black, Babin, Anderson, and Tatham (2005) recommend that a sample size sufﬁcient for accurate estimation range from 100 to 400 respondents. A sample size that exceeds 400 respondents results in an excessively sensitive model.

Procedure.Subjects were identiﬁed through a large organization-wide training database, which identiﬁed the type of training completed, duration, completion date, ﬁnal course grade/rating, supervisor name, and location of the subject. Professional employees and craft workers of the same organization were used as respondents in the study. Surveys were collected within 5 days of the training completion to capture intent most accurately. Training programs included craft training, accounting practices, ﬁnancial training, ﬁrst aid and safety training, HR practices, hazardous materials, and software training. All classes were at least 4 hours long and ranged anywhere from 4 hours to 16 hours. The goal was to administer the measures as soon as possible after training completion. Too much variance in times of completion would intro- duce a possibility of some random event inﬂuencing the responses on the mea- sures. It was especially critical for the Intent to Transfer instrument, which was used as a surrogate measure of training transfer. Ajzen and Madden (1986) stated that intent to engage into certain behavior might be severely inﬂuenced by the time between the measurement of the intent and an action or behavior itself. To minimize this variance it was decided to administer the measures within 5 days of training completion.

As soon as the subjects completed training, they were contacted via phone or e-mail in order that the survey instrument could be administered. If the sub- jects were not reached within 5 days or the survey was not returned to the researchers within the acceptable period of 1 week, the cycle was repeated and the next group of subjects was identiﬁed and contacted. This process ensured that the intent to transfer was measured within the short window after training Dispositional Inﬂuences on Transfer of Learning 395 HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq 396 Yamkovenko and Holton program completion. All measures were administered through paper-and- pencil questionnaires.

Analysis.Structural equation modeling was used to test the model. Struc- tural equation modeling analyses in this study was performed using AMOS 17.

According to Anderson and Gerbing (1988), the model-building task can be thought of as the analysis of two conceptually distinct models. Conﬁrmatory factor analysis (CFA) is used to specify the relations of the observed measures or indicators to their posited underlying constructs. CFA is an inseparable part of the SEM technique because it provides a way to test a measurement model or the relationship of observed variables to underlying constructs. Next, the struc- tural model, based on theoretical framework, speciﬁes the causal relationships of the constructs to one another. The test of a structural model then constitutes a test of nomological validity (Campbell, 1960). The model ﬁt is then evalu- ated with a variety of available ﬁt indices. From here, the researcher can accept the ﬁndings about the model ﬁt, conﬁrm or disconﬁrm the model, and choose whether to modify the model or not.

We followed the recommendations of several authors, including Hair et al. (2005) and Bentler (2007), for selecting which ﬁt indices to examine and report. CFI, RMSEA, NNFI, GFI, and x 2will be reported to indicate model ﬁt.

Prior to the test of the model, all-subsets multiple regression was used to identify the Transfer Climate factors that explained the largest amount of vari- ance in the intent to transfer. Because the transfer climate factors were included in the model only for the control purposes and not to model actual relation- ships, this was an appropriate technique to minimize the number of variables while still controlling for the most important variables in the transfer climate domain (Pedhazur, 1997). Results and Findings Descriptive statistics for all variables included in the study are presented in Table 1. The reliabilities of each measure are also included in the table and are presented on the diagonal.

All-Subsets Regression.The model with learner readiness and motivation to transfer explained 37% of variance in the intent to transfer. The Fvalue was signiﬁcant,F 72.4,p 0.001. Motivation to Transfer and Learner Readi- ness explained the highest percent of variance in the intent to transfer out of all possible combinations of 16 transfer climate factors. Table 2 includes the results of the all-subsets regression analysis for this particular set of predictors.

Test of the Measurement Model.Because NEO-FFI is a well-established and validated instrument, it was decided that it would not be tested in the measurement model (Hair et al., 1998). Therefore, the measurement model consisted of ﬁve latent variables with their indicators and error terms. The mea- sures include the Intent to Transfer scale with four indicators, the Learning-Goal Orientation scale with four indicators, Self-Efﬁcacy scale with eight indicators, HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq Table 1. Descriptive Statistics for the Composite Scores of the Scales Used in the Study MSD12 3456789 1. Neuroticism 2.28 0.47 (0.86) 2. Conscientiousness 4.00 0.41 0.47* (0.81) 3. Extraversion 3.58 0.43 0.37* 0.41* (0.77) 4. Openness to experience 3.19 0.41 0.09 0.06 0.17 (0.81) 5. Learning-goal orientation 0.45 0.32 0.25* 0.53* 0.41* 0.21 (0.89) 6. Self-efﬁcacy 4.09 0.41 0.35* 0.58* 0.43* 0.05 0.69* (0.88) 7. Learner readiness 3.62 0.65 0.16 0.19 0.17 0.13 0.21 0.19 (0.73) 8. Motivation to transfer 3.98 0.58 0.06 0.32* 0.35* 0.06 0.21 0.16 0.19 (0.83) 9. Intent to transfer 6.21 0.97 0.10 0.29* 0.18 0.03 0.13 0.15 0.24 0.59* (0.92) Note: N 252. Reliability coefﬁcients are along diagonal.

*Correlations signiﬁcant at a level of 0.05. 398 Yamkovenko and Holton and the Motivation to Transfer and Learner Readiness scales from the LTSI with four indicator variables each.

Following the Hair et al. (2005) criteria for factor loadings, the factor analysis conﬁrmed the ﬁve hypothesized factors. Factor loadings ranged from 0.35 to 0.92 and all items loaded on the appropriate factors. One indicator variable of Learning-Goal Orientation (LGO) and one indicator variable of Learner Readiness had loadings below the recommended level. One item belonging to Learning-Goal Orientation scale had a loading of 0.35, which is at the minimum acceptable level. An item belonging to the Learner Readiness scale had a loading of 0.45, which is within the acceptable range but is signif- icantly lower than the other three indicators in the scale. It was decided to remove a low loading item from the LGO scale only because the other item was within an acceptable range. The measurement model obtained acceptable goodness of ﬁt (x 2 242 441.9,p 0.01, CFI 0.94, RMSEA 0.063, GFI 0.869, NNFI 0.929, SRMR 0.057).

According to Hair et al. (2005), for sample sizes of more than 250 the acceptable CFI is 0.92 or higher and RMSEA below 0.08.

Test of the Structural Model.Once the measurement model was con- ﬁrmed, the next step was to test the structural model. The test of the structural model involves a model of the hypothesized relationships among the latent constructs (Anderson & Gerbing, 1988).

The structural model included nine hypothesized paths. These are asso- ciated with hypotheses listed earlier. Additional paths from Climate variables to Intent to Transfer were not included as hypothesized relationships, but were included in the model as control variables. The Climate variables Learner Readiness and Motivation to Transfer add two more paths to the model.

The NEO-FFI constructs were included as single indicator constructs.

This is an acceptable practice with valid measures, because it allows minimiz- ing the number of parameters to be estimated and helps model parsimony (Hair et al., 1998). The indicators were obtained as a composite from all items belonging to a given scale. The item responses were averaged and the mean was used as a single indicator. The NEO-FFI parameters were set as free.

According to Hair et al. (2005), when a single indicator is used for a latent con- struct, the error variance is set to 1 minus the reliability of the scale times the variance. This is one of the best ways of estimating the measurement error. HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq Table 2. All-Subsets Regression Results for the Model with Learner Readiness and Motivation to Transfer Model Variable Adjusted R 2 F Signiﬁcance 1. a* 2. b** 0.363 72.425 0.001 *Constant.

**Motivation to transfer, learner readiness. The initial ﬁt of the structural model was less than desired and several paths were not signiﬁcant. The insigniﬁcant paths included LGO to Intent to Transfer (p 0.420), Extraversion to Intent to Transfer (p 0.095), and Neu- roticism to Intent to Transfer (p 0.777).

Modiﬁcation indices indicated signiﬁcant changes possible in the chi- square statistic if additional paths were included. Addition of covariance paths from Self-Efﬁcacy to Conscientiousness and from Self-Efﬁcacy to Extraversion would result in the chi-square drop of 47.023. In addition, such modiﬁcation is supported by the literature. For example, Hartman and Betz (2007) found a strong relationship between conscientiousness, extraversion, and a wide range of self-efﬁcacy domains. Therefore, two covariance paths were added.

Adding the covariance paths between Self-Efﬁcacy and Conscientiousness and Self-Efﬁcacy and Extraversion improved the model ﬁt. CFI increased from 0.89 to 0.926. In addition, error variance was reduced as indicated by the RMSEA, which decreased from 0.072 to 0.062. This indicates an acceptable and satisfactory ﬁt (x 2 309 605.8,p 0.001, CFI 0.926, RMSEA 0.062, NNFI 0.916, SRMR 0.104, GFI 0.850) (Hair et al., 2005). The param- eter estimates for the modiﬁed structural model are presented in Table 3.

The proposed model, therefore, was only partially supported. However, the main hypothesis suggesting that goal orientation is the central construct in the model of dispositional inﬂuences on intent to transfer was not supported.

Figure 3 shows the ﬁnal model with parameter estimates. Only the parameter estimates for the signiﬁcant paths are shown.

Overall, 48% of variance was explained in the dependent variable by con- scientiousness, motivation to transfer, and learner readiness, leaving 52% to error variance. Interestingly, conscientiousness explained only 10% of variance in the intent. Approximately 38% of variance was explained by the control factors—motivation to transfer and learner readiness. Motivation to transfer by itself explained 35%, with learner readiness adding another 3% in explanatory Dispositional Inﬂuences on Transfer of Learning 399 HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq Table 3. Standardized Parameter Estimates for the Modiﬁed Structural Model Path Estimate p value Learning-goal orientation←Self-efﬁcacy 0.638 * Learning-goal orientation←Openness to experience 0.128 0.017 Learning-goal orientation←Conscientiousness 0.223 0.003 Intent to transfer←Motivation to transfer 0.614 * Learning-goal orientation←Extraversion 0.026 0.698 Intent to transfer←Conscientiousness 0.182 0.05 Intent to transfer←Neuroticism 0.011 0.861 Intent to transfer←Learner readiness 0.131 0.046 Intent to transfer←Learning-goal orientation 0.099 0.234 *The probability of getting a given critical ratio in absolute value is less than 0.001. 400 Yamkovenko and Holton power. Signiﬁcance of conscientiousness is a consistent ﬁnding with other research (e.g., Barrick & Mount 1991; Chiaburu & Marinova, 2005), which found a signiﬁcant relationship between transfer and conscientiousness.

The last model tested in this study manifested a relatively good ﬁt to data.

It is therefore unreasonable and unnecessary to make other model modiﬁca- tions within the limits of this study for the sole purpose of improving ﬁt. All other modiﬁcations should be based on theoretically supported hypotheses in order to test and ﬁnd the “correct” model, and therefore should be done as sep- arate studies.

Discussion The goal of this study was to test empirically a hypothesized model of relation- ships between personality traits and characteristics and intent to transfer train- ing. This study set out several hypotheses attempting to describe the complex system of dispositional inﬂuences on intent to transfer. Learning-goal orienta- tion was hypothesized as the central construct that fully or partially mediated the relationship between other dispositional inﬂuences, including self-efﬁcacy, the Big Five personality constructs, and the intent to transfer learning on the job.

A causal model was hypothesized and presented for these constructs.

HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq ConSEff LRead 0.640.13 0.21 Op Neur LGoal Transfer 0.63 1 0.13 Motiv TL Extr r1 1 0.23r2 Figure 3. Final Structure Model with Coefﬁcients The most interesting ﬁnding that came from the test of the model revealed that learning-goal orientation has no relationship with intent to transfer. This ﬁnding is contrary to some recent studies that identiﬁed learning-goal orien- tation as a predictor of the transfer of training (e.g., Chiaburu & Marinova, 2005; Tziner, Fisher, Senior, & Weisberg, 2007). This ﬁnding does not negate the inﬂuences of dispositional variables of the construct because the path from conscientiousness to intent to transfer was signiﬁcant and explained 10% vari- ance in the intent to transfer. However, these ﬁndings may suggest that the complex system of dispositional differences and learning-goal orientation as a central mechanism, which inﬂuences the intent to transfer, is not the appro- priate model.

One possible explanation is that the actual transfer of training was not a dependent variable. Instead, its proxy—intent to transfer—was used as a dependent variable. Other studies discussed earlier have often self-reported transfer several months after training. It is possible that the psychological nature of behavioral intent is such that dispositional differences play a much smaller role in the system of inﬂuences than constructs like social norms and biodata-type constructs. Ultimately, intent to transfer is a variable of behavioral intent, and it is entirely possible that intent variables are predicted best with the use of the Ajzen et al. (2000) model, which only included behavioral con- trol as a dispositional construct and therefore was not congruent with the goals of this study.

Mediation Hypotheses.The nomological net of the learning-goal orien- tation proposed by Payne et al. (2007) held up well in the model. The paths from several variables of the Five Factor model, including conscientiousness and openness to experience, to learning-goal orientation were signiﬁcant. It is possible that the mediation occurs not between the personality traits and intent, but learning-goal orientation mediates the personality traits relation- ship to some other construct, which then inﬂuences intent to transfer. Holton (1996) showed that motivation to transfer is an important motivational con- struct in the transfer system. In addition, this construct was related positively to the intent to transfer in this study. In fact, motivation to transfer explained the most variance in the intent to transfer of all variables included in the model. Motivation to transfer may be the motivational construct that is inﬂu- enced in some way by learning-goal orientation, which therefore mediates the relationship of personality traits to transfer.

Conscientiousness.Because conscientiousness was related signiﬁcantly to the intent to transfer in terms of behavioral intent, whereas learning-goal ori- entation was unrelated to the dependent variable, conscientiousness may be the driver of transfer behavior. This supports the third hypothesis of this study.

Conscientious individuals are generally described as persistent, dependable, and hardworking. Perhaps this trait inﬂuences how well an individual copes with the environmental obstacles to transfer. The relationship between conscientious- ness and the intent to transfer was not partially mediated by learning-goal Dispositional Inﬂuences on Transfer of Learning 401 HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq 402 Yamkovenko and Holton orientation. Interestingly, conscientiousness related positively to both learning- goal orientation and intent to transfer. However, because learning-goal orien- tation did not relate signiﬁcantly to intent, partial mediation did not occur.

This is another indicator that learning-goal orientation is not the central con- struct in the system as was hypothesized. Because conscientiousness showed signiﬁcant parameter estimates for all its paths, it may arguably be one of the most important individual characteristics when it comes to intent to transfer.

This ﬁnding is supported by other studies that investigated personality inﬂu- ences on training outcomes (Chiaburu & Marinova, 2005). Of all the disposi- tional variables included in the model, conscientiousness is the only signiﬁcant construct related to intent to transfer.

Neuroticism.Another interesting ﬁnding was the lack of signiﬁcance for the neuroticism path to intent to transfer. Neurotic individuals respond poorly to environmental stress, are prone to more anxiety, and are more likely to inter- pret ordinary situations as threatening. On the opposite side of the spectrum, emotionally stable individuals are more likely to remain composed and calm in threatening situations and cope with stress much more effectively than neu- rotic individuals. In certain circumstances, organizations may have conducive and favorable environments for learning and transfer of new skills. More often than not, training transfer involves change in the way an individual does the job, change in the surroundings, and resistance from others not just because of an existing mental schema, but also because of the peers’ and supervisors’ potential negative attitudes to the training. It would seem that the intention to transfer training might be signiﬁcantly hindered by the anxiety and fear that are prevalent in neurotic individuals.

However, the lack of a signiﬁcant relationship between neuroticism and intent indicates quite the opposite. At least when it comes to the intent to transfer, neuroticism does not seem to play an important part in the system of dispositional inﬂuences. One explanation may stem from the signiﬁcant skew found in the distribution of the intent to transfer. It is possible that the respon- dents felt obligated to indicate that they all intended to transfer what they learned to avoid possible perceived negative consequences. Social desirability may have outweighed any possible anxiety or fear, which may have affected the posttraining intentions.

Another reason could be that—just like in the case with learning-goal orientation—conscientiousness is a much more important construct in the sys- tem of dispositional inﬂuences. In other words, intent to transfer comes from work ethic and is inﬂuenced by individual persistence much more than it is inﬂuenced by anxiety or stress.

Limitations There were several limitations in the study. First, the literature on structural equation modeling is not clear about the recommended sample sizes. Some HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq studies recommend collecting data from over 100 respondents, and others rec- ommend samples nearing 1,000 and above. Chi-square and parameter esti- mates are known to be very sensitive to sample size. Perhaps, the sample size of 252 respondents was not sufﬁcient to detect signiﬁcance in a large model like the one proposed in this study.

Second, certain deviations from normality, both univariate and multivari- ate, were detected. Such deviations, although common in behavioral sciences research, may inﬂuence results by inﬂating chi square and deﬂating parame- ter estimates. Sample size also plays a role when such deviations are detected, because other estimation techniques like generalized least squares or— especially recommended—asymptotically distribution-free estimation is not possible with a sample size of several hundred.

Common-method variance may be another limitation in the study. Some constructs in the instrument, such as intent to transfer, self-efﬁcacy, and goal orientation, and some LTSI variables may have been subject to the effects of social desirability or other response biases. Such effects may explain higher means on intent to transfer and conscientiousness scales.

Finally, we were unable to collect the learning measure, which prevented us from controlling for learning in the test of the model. Therefore, it is impos- sible to attribute all effects to the dispositional variables entirely without test- ing the model with learning measure as a control variable.

Recommendations for Future Research The recommendations for future research can be grouped in two major cate- gories based on the outcomes of this study. First, there are several research directions for the model of dispositional variables including the existing vari- ables. Second, the alternative model research can lead to the test of similar models but with alternate central constructs.

Modiﬁcations of the Existing Model.The proposed model included a number of paths that were mediated by learning-goal orientation and some that were directly linked to the intent to transfer. It would be interesting to examine the same variables but with a different sample. In view of the normal- ity issue, it would be interesting to replicate this study with a sample that does not violate the assumptions. In addition, a larger sample size may produce much more conclusive and interesting results.

Most importantly, it is critical to attempt to ﬁt this model to transfer of training, and not its surrogate variable, intent to transfer. Such a modiﬁcation may produce completely different ﬁndings, since the actual behavior is mea- sured. Intent is not a lasting phenomenon and it is subject to inﬂuences from many sources. It may disappear or change in a very short time frame. Instead, transfer of training is an actual observable behavior, which is more likely to be maintained over time. It is entirely possible that intent is not driven by learning- goal orientation, while the actual behavior is.

Dispositional Inﬂuences on Transfer of Learning 403 HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq 404 Yamkovenko and Holton With these modiﬁcations in mind, future research should examine direct paths to transfer, instead of being mediated by another variable. Self-efﬁcacy has been shown to relate to perceived transfer in other studies (e.g., Chiaburu & Marinova, 2005; Switzer, Nagy, & Mullins, 2005). Barrick and Mount (1991) presented a number of studies in their meta-analysis linking extraversion, neu- roticism, and agreeableness to training proﬁciency. Perhaps removing goal ori- entation and leaving only direct paths from dispositions to intent to transfer will reveal stronger relationships.

Another alternative would be to examine a much simpler model with self- efﬁcacy, conscientiousness, openness to experience, and goal orientation, while using actual transfer as a dependent variable. Structural equation modeling is ideal for identifying the most parsimonious model to support a given theory.

A test of a simpler model may reveal stronger relationships and expose a sim- pler framework for the system of dispositional inﬂuences on transfer.

In light of the previous discussion about the role a motivational construct may play in relation to learning-goal orientation, it is important to research the relationship of learning-goal orientation to other constructs that may inﬂuence transfer. For instance, the control variable motivation to transfer may just be that missing link. An alternative model could then explore the paths from all dispositional variables of the Five Factor model to learning-goal orientation, with a hypothesized path from learning-goal orientation to motivation to trans- fer. The justiﬁcation for such modiﬁcation becomes clear when one considers the link between the goal-related constructs and motivation. Herold et al.

(2002) discussed motivation in terms of different goals individuals set and how persistent they are in achieving those goals. Learning-goal–oriented individu- als will set mastery-related goals and will stick to them due to the psycholog- ical nature of the construct. This may in turn result in higher motivation to transfer learning on the job.

Research with Alternative Models.The most obvious research direction is to explore the alternative dispositional constructs in the center of the model.

Learning-goal orientation is only one facet of the goal-orientation construct.

Additional models with other goal-orientation facets can be tested to identify whether performance-goal orientation is more important in achievement situ- ations. Because transfer is an achievement situation and can be rewarded or bring about negative outcomes, performance-goal orientation may be inﬂuen- tial in this context.

Because the dependent variable of interest in this study is intent to trans- fer, it is possible to resort to the test of the model with predictors based on the theory of planned behavior (Ajzen & Fishbein, 1977). The theory is based on several important factors: attitudes toward behavior; behavioral beliefs, which are consequences of previous experiences; subjective norms; and perceived behavioral control. Whereas investigating this alternative model takes the researcher further away from dispositions, dispositions may still have a role in this model. Ultimately, perceived behavioral control can be expressed as locus HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq of control, which is a dispositional characteristic that has not been explored in this study. Attitudes toward behaviors may be inﬂuenced in part by personal- ity differences. For example, neurotic individuals may exhibit more negative attitudes to certain work behaviors than emotionally stable individuals.

In other words, dispositions still remain in the system of inﬂuences but become more distal, whereas the planned-behavior factors take a more proximal posi- tion to transfer.

One other important direction in transfer research, which involves dispo- sitions, may be to look at the interaction of the environment in an organiza- tion and dispositional differences. In other words, dispositional characteristics may inﬂuence transfer and intent to transfer in situations where the environ- ment is negatively inﬂuencing training outcomes. In situations where resis- tance to change and various barriers to transfer exist, such dispositions like conscientiousness and emotional stability may manifest themselves as signiﬁ- cant predictors of transfer. In positive environments that are conducive to transfer, the signiﬁcance of dispositions may be suppressed. Research that examines the interaction of personality and organizational environment is often referred to as research on ﬁt. This research focuses on the way individuals may pick organizations based on their speciﬁc personality types and dispositional characteristics. “Lack of ﬁt” results in poor performance and dissatisfaction, whereas “perfect ﬁt” leads to positive individual and organizational outcomes (Pervin, 1968). Whereas most research on ﬁt focuses on organizational level variables, the recommendation is to focus the ﬁt research speciﬁcally on trans- fer. The application of these theories to training outcomes and transfer may lead to ﬁndings that are more fruitful.

Conclusions In this study, the focus is only on one job-related construct—intent to trans- fer training. With caveats mentioned earlier in mind, it is entirely possible, and mandated by the data, to suggest that personality inﬂuences on intent to trans- fer may be limited exclusively to conscientiousness. Adding other dispositional variables to the system may be overemphasizing the role of personality based on our assumptions. It is logical to think, for example, that neurotic individ- uals will be much more responsive to stressful organizational environments, negative feedback from peers and supervisors, and change in general. This study’s ﬁndings suggest that it is largely irrelevant in the transfer system. The same can be said for openness to experience, extraversion, and even self- efﬁcacy. Overall, all these variables explained about 10% of variance in the intent to transfer, with conscientiousness being the only signiﬁcant variable.

The logical extrapolation then becomes that as long as training participants are hardworking and have a high work ethic, from the personality standpoint they should have equal intent to transfer knowledge on the job. However, the ﬁnd- ings in this study indicate that environmental variables explain much more Dispositional Inﬂuences on Transfer of Learning 405 HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq 406 Yamkovenko and Holton variance in the intent than dispositional variables. This then leads to the con- clusion that personality differences are largely overshadowed by environmen- tal, motivational, and organizational variables. Speciﬁcally, in the tug of war between dispositions and situation, the latter clearly comes out a winner. The importance of organizational culture, climate, and training design outweighs the importance of individual traits and characteristics.

The test of the model shows that the Payne et al. (2007) model of goal ori- entation and its antecedents holds. The paths from conscientiousness and openness to experience, and self-efﬁcacy to learning-goal orientation were all signiﬁcant. In other words, the argument that conscientious individuals who are open to new experiences and believe in their capabilities are likely to be learning-goal oriented is plausible and is supported by the data in this study.

However, the link between this dispositional characteristic and intent to trans- fer is not supported. As was suggested earlier, we may be missing another important construct that would connect the pieces of the puzzle. It is possible that learning-goal orientation is important but does not predict intent. Instead, it may be an antecedent of another motivational variable like motivation to transfer or motivation to improve work through learning (MTIWL). Naquin and Holton (2002) successfully modeled the latter.

Clearly, the search for the proper dispositional variables in the transfer sys- tem is far from complete. It is possible that dispositions exert very little inﬂu- ence on transfer and intent. It is also possible that after careful examination of other constructs, be they other facets of goal orientation or motivational vari- ables driven by dispositions, we will be able to understand the role of person- ality in transfer situations.

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Bogdan Yamkovenko is with the School of Human Resource Education and Workforce Development, Louisiana State University and the Shaw Power Group.

Ed Holton is with Louisiana State University.

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