A Typology of Training Design

Human Resource Development Review / March 2002 Russ-Eft / TAXONOMY FOR WORKPLACE LEARNING

A Typology of Training Design and Work Environment Factors Affecting Workplace Learning and Transfer

DARLENE RUSS-EFT American Institutes for Research

The purpose of this article is to develop a typology of elements involved in the design of training as well as in the work environment that affect work- place learning and transfer. This typology focuses on elements that can be manipulated by the human resource development (HRD) researcher and practit ioner as part of the HRD implementation rather than on dispositional and personality characteristics of individuals participating in the intervention. It identifies elements within the work environment, as well as elements before, during, and after training. By presenting a typology, this article provides a first step in theory building or a “theory of the middle range.” Furthermore, it leads to implications for future theoret- ical development, research, and practice.

Human resource development (HRD) is defined as “a set of systematic and planned activities designed by an organization to provide its members with the opportunities to learn necessary skills to meet current and future job demands” (DeSimone, Werner, & Harris, 2002, p. 3). These authors sug- gested that learning forms the core of HRD efforts. They also stated that

a main goal of HRD is to ensure that employees perform their jobs effectively. In addition to learning and retaining new material, employees must also use it on the job to improve performance. The transfer of training to the job situation is criti- cally important to the success of HRD efforts. (p. 88)

Baldwin and Ford’s (1988) transfer of training model identified the training inputs as including trainee characteristics, training design, and work environ- ment. Given potential ethical issues involved in selecting or manipulating trainee characteristics, the present article focuses on elements in the training design and the work environment. (See Naquin & Holton, in press, for a contrary argument.) Its purpose is to present a typology of such elements from training situations that can be manipulated and used to enhance workplace learning and transfer. Furthermore, these elements are ones that appear in recent research on learning and transfer.

Human Resource Development Review Vol. 1, No. 1 March 2002 45-65 ©2002 Sage Publications

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Typologies as Theories of the Middle Range

Rather than present a theoretical model, the present article focuses on the development of a typology or taxonomy of training design and work envi- ronment factors that affect training transfer. Some argue that a theory involves a specification of the relationships among certain variables and that a typology or taxonomy does not constitute a theory (Bacharach, 1989; Blalock, 1969; Scott, 1981). Indeed, Dubin (1969) stated that a theoretical model includes four features. First, a theory begins with the identification of variables or units. This is followed by a specification of the “laws of interac- tion” governing these variables or units. Third, a theory describes the limits or boundaries for the theory or theoretical model. Finally, a theory identifies various “system states” and indicates the ways in which the variables or units interact within each of these different states.

By presenting a typology, this article identifies certain variables or units and represents the first step in Dubin’s (1969) approach to theory building. As such, it provides a “theory of the middle range” (Merton, 1968). Both Pinder and Moore (1979) and Doty and Glick (1994) argued for such theo- ries of the middle range in general and for typologies more specifically. Fur- thermore, Doty and Glick (1994) stated that “typologies are complex theo- retical statements” (p. 231).

Typologies enable the theorist to identify and put together similar or like entities. They offer an advantage by providing for chunking or grouping these entities, thus allowing “large amounts of information . . . [to] be col- lapsed into more convenient categories that would be easier to process, store and comprehend” (Carper & Snizek, 1980, p. 73). At the same time, because of smaller within-cell variance, “change can be better predicted” (Bobko & Russell, 1991, p. 295).

Typologies or taxonomies can be created empirically, through clustering or factor analysis (Pinder & Moore, 1979). Although empirically derived taxonomies may excel in their ability to predict differences, they lack generalizability to different organizations and situations (Bobko & Russell, 1991). To provide the basis for theory, research, and practice in a wide vari- ety of organizations, the present article focuses on the development of a typology of elements leading to workplace learning and performance through the use of theoretical derivation based on existing research (Bobko & Rus- sell, 1991; Doty & Glick, 1994).

A Typology of Workplace Learning and Transfer

Figure 1 presents a typology of training design and work environment elements that affect workplace learning and transfer. Prior to discussing each of these in detail, it is important to describe the development and place- ment of these elements. The development of this typology and some of the

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included elements were initially identified in the work of Baldwin and Ford (1988). Later, Broad and Newstrom (1992) suggested that important trans- fer elements exist before, during, and after the training. Thus, the structure of the typology identifies pretraining elements, training design elements, and posttraining elements.

The typology also includes elements from the work environment as origi- nally suggested by Baldwin and Ford (1988). Later, Rouillier and Goldstein (1993) confirmed that these work environment elements may actually lead to greater transfer than various aspects of the training. More recently,

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Situational Elements (or the Transfer Environment)

Supervisor support

Supervisor sanction

Workload

Opportunity to use

Peer support Posttraining Elements

Pretraining Elements Training Design Elements (training initiated)

Persuasive message linking Advance organizers Relapse prevention mastery and job survival

Realistic training previews Guided discovery Self-management

Voluntary versus involuntary Error-based learning Goal setting: proximal versus distal

Metacognitive instruction Training in self-talk

Learner control Visualization

Mastery orientation Posttraining follow-up versus performance orientation

Practice:

behavioral practice versus symbolic practice;

spaced practice; variable examples/practice; random practice; overlearning

Coaching/feedback/ scaffolding

FIGURE 1: Elements to Enhance Transfer of Training

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Holton, Bates, Seyler, and Carvalho (1997) and Holton, Bates, and Ruona (2000) provided research on an instrument measuring transfer climate, or in other words, the work environment facilitating or inhibiting transfer of the training to the job.

Situational Elements

The following situational elements emerge from the work of Baldwin and Ford (1989), Rouillier and Goldstein (1993), and Holton et al. (1997, 2000). Note that several of the elements identified in Holton et al. (2000) could be considered personality or individual factors, such as motivation to transfer, personal capacity to transfer, perceived content validity, and others. These factors have been omitted because they may not be easily manipulated by the HRD researcher or practitioner. Other factors, such as transfer design and performance coaching, are discussed in other sections of the typology.

At this point, it seems appropriate to mention that the placement of ele- ments within this typology, or any typology, can be problematic. “The notion of taxonomies (or categories) implies that elements under analysis (individuals, tasks, etc.) can be placed in discrete, mutually exclusive cate- gories. However, it may be that there are underlying continuities involved” (Bobko & Russell, 1991, p. 304). For example, performance coaching, placed as a training design element in the present typology, could be included as an element in the work environment or as one of the posttraining elements. As another example, goal setting could be undertaken as a pretraining intervention, as part of the training design, or as part of the posttraining intervention. For the present article, these elements will be placed in accordance with the empirical studies cited in the various sections.

Supervisor Support

This element refers to situations in which supervisors provide reinforce- ment for the use of learning on the job. It includes working with trainees to set goals to use learning, giving trainees assistance, providing a model of the trained behaviors, and offering positive reinforcement for the use of skills. Baldwin and Ford (1988), in examining the influence of the work environ- ment on transfer of training, noted that supervisory support is considered a key environmental variable. Taylor (1992) found a positive correlation between ratings of support from immediate supervisors and training outcomes.

Supervisor Sanctions

This element, which can be viewed as the negative side of supervisor sup- port, emerged in Holton et al.’s (1997, 2000) work as an independent and

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separate factor. It includes supervisors’ indifference to trainees’ use of trained skills, negative feedback or no feedback, and active opposition to the use of skills. Indeed, Mathieu, Tannenbaum, and Salas (1992) found that trainees reporting situational constraints, such as lack of time, equipment, and resources, expressed lower motivation to learn at the beginning of training.

Workload

Time and energy are needed to facilitate learning and transfer; the indi- vidual’s workload may contribute to or hinder such learning and transfer. For example, Porras and Hargis (1982) found a negative correlation between on- the-job skill use and the factors of role conflict, overload, and job-generated stress. Decker and Nathan (1985) reported the individual’s workload as an important factor affecting training success. Russ-Eft (2001), however, reviewed some of the literature on workload and stress and indicated that further efforts are needed to untangle the complex relationships shown in previous research. Finally, The Holton et al. (1997, 2000) work confirmed this factor, providing a label of “personal capacity to transfer.”

Opportunities to Use

In this case, trainees are provided with the resources and tasks that allow them to use the trained skills on the job. This includes needed time and resources to use the training. Baldwin and Ford (1988) included this element as part of their model. Later, Ford, Quinones, Sego, and Sorra (1992) found significant differences among air force technical trainees in the opportunity to apply what they had learned. Pentland (1989) found that attempts to prac- tice computer skills immediately upon returning to the job had a major impact on long-term retention. Supervisor and peer support was related to these opportunities to perform trained tasks. Indeed, the importance of supervisory support for training appears in the provision of opportunities to perform the skills learned during training (Quinones, Ford, Sego, & Smith, 1992).

Peer Support

This element, as described by Holton et al. (1997), involves peers’ pro- viding reinforcement for trainees’ use of learning on the job. It includes helping trainees set goals to use the training, giving trainees some assis- tance, and offering positive feedback for the use of skills. Note the relation- ship to the element of “opportunity to use.”

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Pretraining Interventions

Pretraining interventions tend to prepare trainees for the training experi- ence. Each of these may be related to the factor of learner readiness identi- fied by Holton et al. (2000).

Persuasive Message

Haccoun (1996) demonstrated the effectiveness of a persuasive message. Such a message draws the trainee’s attention to the links between mastery of the skill and job survival. Haccoun (1997) suggested other approaches, such as “pre-training pep talks, group discussions about the reasons for training, and emphasizing the marketability of the new skills” (p. 342).

Realistic Training Previews

Realistic training previews may lead to higher levels of motivation regarding the training. Hicks and Klimoski (1987), for example, compared a realistic preview with the traditional pretraining announcement. The realis- tic preview contained the following elements: (a) outcomes expected from the workshop, (b) content indicating specific topics and expected home- work, (c) expected evaluation procedures, (d) an indication of who should attend, and (e) workshop leaders, dates, times, and locations. The traditional announcement contained (a) outcomes expected from the workshop (very brief and positive), (b) content (very brief), and (c) workshop dates, times, and locations. The realistic preview resulted in significantly higher levels of commitment to attend the training and motivation to learn from the training. Further research is needed to determine whether such motivational factors enhance training outcomes.

Voluntary Versus Mandatory Training

The voluntary nature of training appears to result in better outcomes as compared with mandatory training (Martocchio, 1992; Mathieu et al., 1992). Nevertheless, such voluntary participation may be difficult to moti- vate, particularly if training requires some effort and is perceived as involv- ing some evaluation (Hesketh, 1997; McLean, 1998). For example, Hicks and Klimoski (1987) examined the issue of voluntary and mandatory train- ing. “Of those with a high degree of choice, 46 (17% [of the 263 managers and supervisors]) attended the training, whereas 55 (71% [of the 77 manag- ers and supervisors]) who had a low degree of choice attended” (p. 545). Nevertheless, these researchers found that “ . . . participants who had a high degree of choice received higher achievement test scores and reported that

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they learned more from the training than those who had a low degree of choice” (p. 548).

Training Design Strategies

These training design strategies relate to a variety of theoretical perspec- tives and will be discussed separately. Training design strategies refer to manipulations undertaken during training (Broad & Newstrom, 1992). Fur- thermore, they may be related to the factor of transfer design, defined as “the degree to which (1) training has been designed and delivered to give trainees the ability to transfer learning to the job, and (2) training instructions match job requirements” (Holton et al., 2000, p. 345).

Advance Organizers

Ausubel (1968) defined advance organizers as materials presented at the beginning of training that provide a framework for the training. As such, we might consider advance organizers to be related to notions included in cog- nitive theories and schema theories in which learners actively process infor- mation. Indeed, Mayer (1979) suggested that advance organizers allow trainees to organize and retain material to be learned. In his study, Mayer operationalized the advance organizer by using an analogy from the individ- ual’s previous experience (using scoreboards, ticket windows, and shopping lists to train people to program a computer). Such advance organizers should include a demonstration of the end goal (Glaser & Bassok, 1989). Note that advance organizers that are inconsistent with the information that follows can create “contextual interference.” This can force the learner to stop and think while trying to reconcile the inconsistency. Thus, with the inconsis- tency, immediate performance may be worse, but transfer performance is improved (as compared with advance organizers that are consistent with training). In addition, an advance organizer condition in which trainees cre- ate their own organization of the material can lead to more adaptive transfer (e.g., DiVesta & Peverly, 1984).

Guided Discovery

In contrast to traditional learning methods that provide trainees with the task, its concepts, rules, and strategies, discovery learning forces trainees to explore and experiment with the task to infer and learn the concepts, rules, and strategies (e.g., Hermann, 1969). Such ideas seem related to the notion of having authentic training, as incorporated into situated cognition. Early research indicated that such discovery learning led to greater transfer, par- ticularly for complex tasks. Such learning can result in higher levels of moti- vation (Singer & Pease, 1976), greater attention to the application of strate-

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gies (McDaniel & Schlager, 1990; Veenman, Elshout, & Busato, 1994), and better integration with existing knowledge structures (Egan & Greeno, 1973; Frese et al., 1988). More recent formulations (Frese et al., 1988; Smith, 1995; Smith, Ford, & Kozlowski, 1997) suggest that providing guid- ance to learners in forming hypotheses about the material and in testing ideas can be beneficial in reducing the burden on the trainee and the instruc- tional time required for such learning. Note that more research can be used to confirm such speculations.

Error-Based Learning

Although behaviorists recommended minimizing incorrect responses (e.g., Skinner, 1987), researchers from the cognitive perspective, including both cognitive and situated cognitive theorists, have argued that making errors can be beneficial. Specifically, errors elicit attention, alert trainees to incorrect assumptions, and force increasing mental processing (e.g., Frese et al., 1988; Ivancic & Hesketh, 1995). Providing error management strate- gies can enable trainees to overcome negative motivational consequences. Such strategies include (a) focusing trainees on the beneficial aspects of error for learning, (b) identifying the information that such errors can pro- vide, and (c) helping trainees determine what caused an error and how it can be avoided in the future.

Metacognitive Instruction

Metacognition refers to an awareness of one’s cognitive processes and a monitoring and evaluating of selected strategies while performing tasks (Etelapelto, 1993). Instruction in metacognitive processing comes from schema theory. Such metacognitive processing can be enhanced by encour- aging trainees to (a) identify learning and skill goals, (b) generate new ideas, (c) elaborate on existing ideas, and (d) strive for better understanding (Scardamalia, Bereiter, & Steinbach, 1984). Increasing learning control and encouraging a mastery orientation appear to promote the development of metacognitive skills (Smith et al., 1997). It should be noted that this instruc- tion can be used as a pretraining intervention.

Learner Control

Steinberg (1989) argued that giving learners control over instructional elements facilitates metacognitive processing, again stemming from schema theory. Such control can include choice over the content, sequence, and pacing of instruction. This learner control can allow trainees to focus more time on material that is difficult for them (Wilson & Cole, 1991).

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Mastery Orientation Versus Performance Orientation

Trainees can be encouraged to adopt either of these orientations toward training (Stevens & Gist, 1996). A mastery orientation leads trainees to believe that training will result in improved outcomes, development of new skills, and use of self-referenced standards. In contrast, a performance ori- entation leads trainees to believe that ability is demonstrated by performing better than others perform and to use normative-based standards. Research on classroom settings emphasizing mastery or performance has shown sig- nificant differences. Mastery goals lead students to (a) use more effective learning strategies, (b) prefer challenging tasks, (c) express more positive attitudes about the class, and (d) indicate a stronger belief that success will come from effort (Ames & Archer, 1988). Performance goals lead students to (a) focus on their abilities, (b) evaluate their abilities negatively, and (c) attribute failure to their lack of ability. In addition, when facing challeng- ing tasks, those with mastery goals tend to persistence, whereas those with performance goals tend to avoid such tasks (Dweck, 1986; Dweck & Leggett, 1988; Elliott & Dweck, 1988). Although much of the research has examined training orientation as part of the training, Stevens and Gist (1996) incorporated it as part of a posttraining intervention.

Practice

One of the basic tenets of behaviorism and connectionism is “practice makes perfect,” which could be referred to as Thorndike’s law of frequency. Some recent (and not so recent) research suggests that certain forms of prac- tice may prove more effective than others. The following subsections will explore some of these: behavioral versus symbolic practice, variable exam- ples or practice, spaced practice, random practice, and overlearning.

Behavioral versus symbolic practice. Behavioral practice refers to some reproduction of the skills, and suggestions for such behavioral practice would seem to come from behaviorist and connectionist theories. In behavior modeling training, this tends to take the form of structured role-playing exercises. This type of practice has been compared with symbolic rehearsal or practice, in which trainees practice by mentally manipulating symbols representing certain behaviors. Such mental practice might be considered as related to cognitive or schema theories. Some studies showed superior reproduction of trained behav- iors with behavioral practice (e.g., Decker, 1983; Stone & Vance, 1976). Other studies found superior retention and generalization with symbolic practice (Bandura, Jeffery, & Bachicha, 1974; Decker, 1980, 1982).

Perhaps reproduction can be enhanced with behavioral practice, whereas retention and generalization can be enhanced with symbolic practice. More

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research is needed to determine which practice condition leads to the spe- cific desired outcome.

Variable examples or practice. Research in the area of motor performance has shown the effectiveness of variable practice as suggested by some cognitive theorists. For example, Kerr and Booth (1978) asked 8-year-old children to toss miniature beanbags at a target. In the variable practice group, the children prac- ticed throwing at the target from 2 feet to 4 feet, whereas in the constant practice group, the children always practiced using a target that was 3 feet away. Those in the variable practiced performed with fewer errors, even though the criterion distance was set at 3 feet. Other research has shown that variable practice leads to improved generalization to novel situations (e.g., Lee, Magill, & Weeks, 1985; Van Rossum, 1990).

Similar findings have been shown in the use of variable models in behav- ior modeling training. The use of either positive models only or a mixture of positive and negative models resulted in the highest levels of behavior change and cognitive gain (e.g., Mills, 1985; Newman & Fuqua, 1988; Russ-Eft & Zuchelli, 1987; Trimble, Decker, & Nathan, 1991). Baldwin (1994), for example, examined the effects of positive and negative models on retention and generalization. He found that the mixed-model condition significantly improved the behavior on the generalization task.

Spaced practice. Research over the past century confirms the general superi- ority of spaced practice (Dempster, 1988; Ebbinghaus, 1885/1964; Jost, 1897). Spaced practice refers to situations in which training or practice is interspersed with periods of no training or practice. Such spaced training can be contrasted with massed training and practice, in which these take place at one time. The per- formance gains obtained from massed training and practice yield superior per- formance during training or in short intervals after training. Spaced training and practice yields lower levels of performance during training. But, as shown in more recent study of learning foreign language vocabulary, improved retention occurred with increasing amounts of spacing (Bahrick, Bahrick, Bahrick, & Bahrick, 1993). The last section of this article provides more discussion of some of the issues related to spaced practice.

Random practice. Random practice refers to practice in which trainees switch from task to task during practice. This can be contrasted with blocked practice, in which the trainees practice several trials of one task (a block) before proceeding to the next task. Hall, Domingues, and Cavazos (1994) showed the superiority of random practice.

Overlearning. Overlearning refers to deliberate training and practice beyond a set criterion performance. For example, a criterion may be set at one errorless performance. If it takes 10 trials to reach the criterion, overlearning would con- sist of additional trials. Five additional trials would represent 50% overlearning; 10 additional trials would represent 100% overlearning, and so forth.

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Ebbinghaus (1885/1964) and Krueger (1929, 1930) provided the earliest examination of the effects of overlearning. More recently, Schendel and Hagman (1982) showed that 100% overlearning resulted in 65% fewer errors than a control group when retested after 8 weeks (with the task being the disassembly and assembly of an M60 machine gun).

Driskell, Willis, and Cooper (1992) in a meta-analytic study, showed that overlearning is an effective method for enhancing retention for both physi- cal and cognitive tasks. They showed that greater overlearning led to greater retention, and they suggested 100% and 150% overlearning. Finally, they found that the performance benefit of overlearning was reduced by one half after 19 days and to zero after 5 to 6 weeks. As a result, they recommended that “maintaining optimal performance requires that additional training take place after approximately 3 weeks” (p. 621).

Note that such overlearning may enhance retention and reproduction. It is uncertain whether it would lead to improved or reduced generalization and transfer.

Coaching, Feedback, and Scaffolding

Feedback on performance can lead to improvements in performance and would seem consistent with behaviorism (e.g., Ammons, 1956; Ashford & Cummings, 1983; Ilgen, Fisher, & Taylor, 1979; Komaki, Barwick, & Scott, 1978; Payne & Hauty, 1955). Much of this work showed improvements in production quantity. Ilgen and Moore (1987), however, demonstrated per- formance improvements along the separate dimensions of quantity and quality.

Several studies have shown the effectiveness of feedback combined with behavior modeling training (Decker, 1983; Fyffe & Oei, 1979; Wallace, Horan, Baker, & Hudson, 1975). Certainly, the use of feedback is consistent with social learning theory (Bandura & Cervone, 1983). Feedback provides information to the learner that allows a comparison of current and desired behavior. This comparison motivates the person to invest further effort to change his or her behavior or performance standard.

More feedback is not necessarily better, however, and may be related to the notion of scaffolding in situated cognition. Research on motor learning has shown that those who receive feedback after every attempt during train- ing tend to perform more poorly on subsequent tests of retention than do those receiving less frequent feedback (Salmoni, Schmidt, & Walter, 1984; Schmidt, 1991; Winstein & Schmidt, 1990). Schooler and Anderson (1990) showed that reduced feedback during learning the computer language LISP facilitated retention. In addition, Brophy (1999) recommended scaffolding to gradually transfer responsibility for learning to the learners. Because no studies are reported in the area of interpersonal and communications skills

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training, some investigation of the beneficial effects of faded feedback (Hesketh, 1997) would be warranted.

Posttraining Intervention Elements

As with the pretraining interventions and the training design elements, the concept of posttraining interventions were included as part of the trans- fer framework presented by Broad and Newstrom (1992).

Relapse Prevention

Relapse prevention originated in clinical psychology to enhance mainte- nance behaviors in treating addiction (Marlatt & Gordon, 1980, 1985) and may be related to some of the ideas included in cognitive theories or even social perspective theories. Marx (1982) suggested that an adaptation of these relapse prevention procedures might address problems that trainees experienced in transferring skills to the job. Marx (1982, 1986) and Burke (1997) described the full relapse prevention method as including the follow- ing actions by trainees: (a) choosing a specific skill that they want to main- tain, along with a specific, measurable skill-maintenance goal; (b) defining what constitutes a slip and a relapse; (c) identifying the positive and nega- tive consequences of using the new skill; (d) reviewing cognitive and behav- ioral transfer strategies; (e) predicting the situation for the first slip, as well as strategies to deal with it; and (f) reviewing a chart to track their progress on their skill-maintenance goal. (Note that limited research has been under- taken on the use of this method in corporate settings and that that research has been limited by design and conceptual issues.)

Self-Management

Self-management as a posttraining intervention can be viewed as similar to relapse prevention (Gist, 1997) and may also be related to both cognitive and social perspective theories. It consists of the following steps: (a) encour- age by not requiring trainees to set goals, (b) ask trainees to identify obsta- cles to success, (c) have them plan how to overcome the identified obstacles, (d) encourage self-monitoring of progress, and (e) suggest trainees use self- reinforcement to motivate accomplishments. Gist, Stevens, and Bavetta (1991) found that the effects of the posttraining intervention interacted with the self-efficacy of the trainees. Self-management training led to superior maintenance performance of learned interpersonal skills for low self-efficacy individuals. In contrast, having trainees set goals appeared better for high self-efficacy trainees. Bavetta’s (1992) research suggests that low self- efficacy trainees performed better under the directive conditions of relapse

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prevention or self-management, whereas high self-efficacy trainees did better with a less directive approach.

Goal Setting: Proximal Versus Distal, Performance Versus Learning

Goal setting appears to lead to increased effort and improved perfor- mance (Locke & Latham, 1990; Wexley & Latham, 1991; Werner, O’Leary- Kelly, Baldwin, & Wexley, 1994) and may be related to both cognitive and social perspective theories. Such improvements occur whether goals are assigned or set participatively (Wexley & Baldwin, 1986). In addition, hav- ing trainees monitor such goals after training through the use of checklists leads to better application of training (Wexley & Nemeroff, 1975).

Hesketh (1997) recommended setting proximal goals as “a way of dealing with the conflict between long-term and short-term outcomes in training” (p. 333). In contrast, Locke and Latham (1990), Wexley and Latham (1991), and Werner et al. (1994) recommended setting proximal in addition to distal goals. They suggested that

proximal goals should be set for knowledge and skill acquisition during the train- ing program, and distal goals should be set for maintenance and enhancement after training. The distal goal for continuous learning regarding skill acquisition could be 1-3 years after training. (Locke & Lathem, 1990, p. 372)

Goal setting can, however, have a negative influence. Kanfer and Ackerman (1989) showed that assigning goals in the early stages of learning led to lower performance when compared with assigning goals at a later time. Difficult, spe- cific goals can impair performance, particularly for novel and complex tasks (Earley, Connolly, & Ekegren, 1989). Dweck (1986) contrasted performance goals and learning goals. Performance goals tend to lead to defensive strategies and to interpreting failures or errors as lack of ability. In contrast, learning goals tend to encourage individuals to increase their efforts when encountering obsta- cles. H. J. Klein and Thoms (1995) provided additional empirical evidence sup- porting the use of learning goals. But, frankly, further research in actual training settings is needed.

Self-Talk

Millman and Latham (1996) found that self-talk was a source of persua- sion for increasing self-efficacy, and increases in self-efficacy can aid in dealing with environmental obstacles. This element appears related to cog- nitive theories. The Millman and Latham study involved training unem- ployed managers to monitor their functional and dysfunctional self-talk regarding reemployment. The training involved 2-hour sessions conduced over a 2½-week period. Within 9 months, 50% of those trained to increase

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their functional self-talk found jobs, whereas only 1% of those in the control group were reemployed. Presumably, training in self-talk may help trainees overcome barriers in the work environment.

Visualization

Mental imaging techniques can enhance performance on cognitive tasks (Bennett, Wheatley, Maddox, & Anthony, 1994; Driskell, Cooper, & Moran, 1994; Neck & Manz, 1992). Latham and Seijts (1997) stated that “mental practice as a post-training intervention should lead to transfer of training through an increase in self-efficacy” (p. 373). Indeed, Neck and Manz (1992) suggested that when trainees mentally rehearse a task, they see themselves performing the task. This provides trainees with another exam- ple of a positive model.

Posttraining Follow-Up

Baldwin and Ford (1988) mentioned booster sessions as a means of creat- ing conducive transfer environments, and such recommendations appear related to behaviorist theories. Furthermore, trainees who expected some form of posttraining follow-up left training with stronger intentions to trans- fer the training to the job (Baldwin & Magjuka, 1991). Marx and Karren (1990) found that trainees were more likely to apply time management when follow-up occurred 3 weeks after a time-management course. This result corroborated the recommendation from Driskell et al. (1992).

Conclusion

This article has presented a typology of elements affecting workplace learning and transfer. Although appearing in a journal devoted to theory building, the greatest value of this taxonomy may be as a tool for enhancing training. HRD practitioners can, potentially, use one or more of these ele- ments with some assurance that there will be increases in training transfer among trainees. Unlike personality or motivational factors characteristic of individual trainees, each of the elements in this taxonomy can be manipu- lated or influenced by the HRD practitioners. When undertaking such manipulations, HRD practitioners can partner with HRD researchers to examine the impact of these manipulations.

Such a structuring of variables can lead to future research and theory- building efforts. Each of the elements listed in the following model— before, during, and after training—have been shown to affect training out- comes in one or more studies. Nevertheless, some additional research and theoretical questions still remain, and some of these, already mentioned ear- lier in the article, will be highlighted in the following paragraphs.

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As mentioned in the previous section on realistic training previews, research has shown that such previews lead to higher levels of motivation. Whether these higher levels of motivation lead to improved performance and transfer needs further investigation. Hesketh (1997), in her review of the training research literature, suggested that training methods yielding better transfer may require greater levels of effort and active processing than train- ees prefer. Such a suggestion indicates that there may be a disconnect between trainee motivation and later learning and performance.

Similarly, the notion of guided discovery needs further research. Cer- tainly, discovery learning has been shown to be effective (Frese et al., 1988; McDaniel and Schlager, 1990; Veenman et al., 1994). Research can deter- mine whether providing some guidance to learners reduces the cognitive burden and the instructional time for such interventions as suggested by Frese et al. (1988), Smith (1995), and Smith et al. (1997). Furthermore, by providing such guidance, one can examine whether the benefits of discovery learning have been reduced.

Another area needing further research involves comparisons of behav- ioral practice and symbolic practice. Previous work has suggested that behavioral practice yields better performance in reproducing the learned skills (e.g., Decker, 1983). In contrast, symbolic practice appears to lead to better retention and generalization (e.g., Decker, 1980, 1983). A direct com- parison of these two types of practice may help to untangle the advantages of each separately and in combination.

Overlearning is yet another area requiring further investigation. Indeed, such overlearning enhances retention (e.g., Driskell et al., 1992) and may lead to a certain level of expertise and some level of automaticity of response. However, some specific features of expertise seem to lead to a decline in the ability to generalize the skills and to transfer those skills to other situations (Marchant, Robinson, Anderson, and Schadewald, 1991). According to Anderson (1982), learning a new skill requires some effort and that knowledge exists in a declarative form. With practice and repetition, that skill becomes proceduralized or automated and requires fewer attentional resources. It also leads to domain specificity and tends to rely on using previous experiences and examples rather than a set of rules (Ericsson & Kintsch, 1995; Ericsson & Pennington, 1993; G. A. Klein & Calderwood, 1991). Both of these conditions lead to reductions in the generalizability and transfer of skills.

Several of the elements—coaching, feedback and scaffolding, relapse prevention, and goal setting—need further investigation within the organi- zational training setting and with a variety of interventions. Such techniques may show positive results in laboratory settings or with selected topics. Whether these positive findings can generalize to various settings and topics needs additional testing.

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In addition, this taxonomy—or any taxonomy—fails to recognize that some of these elements can exist on a continuum. Thus, another line of research can focus on determining the place and relationship among these various elements. One example is the element of goal setting. Based on Kanfer and Ackermam’s (1989) work, it may be that the timing of any goal setting yields different results. Similarly, other elements, such as metacognitive instruction, within this taxonomy may prove more or less effective depending on the timing and placement of that element.

A final phase for future research can be undertaken to determine whether these elements are additive or multiplicative in their impact on learning and performance. This future work can help to determine the laws of interaction among the presented elements. Then, additional work can identify the limits of the theory as well as the system states. At that point, this will become a theoretical model of workplace learning and performance.

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Darlene Russ-Eft, Ph.D., is director of research services at AchieveGlobal, Inc. She has authored several books and articles about leadership, human resource development, research, and evaluation. Her most recent books include Everyone a Leader: A Grassroots Model for the New Workplace (John Wiley and Sons) and Evaluation in Organizations: A Systematic Approach to Enhancing Learning, Performance, and Change (Perseus). Dr. Russ-Eft is past chair of the Research Committee of the American Society for Training & Development and a past member of the Board of the American Evaluation Association. She received the 1996 Times Mirror Editor of the Year Award for her research work and the Year 2000 Outstanding Scholar Award from the Academy of Human Resource Development. She is the editor of Human Resource Development Quarterly.

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