100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 1 
100 Years of Training and Development Research: What We Know and Where We Should Go 
 
 
Bradford S. Bell 
Cornell University 
 
Scott I. Tannenbaum 
The Group for Organizational Effectiveness (gOE), Inc. 
 
J. Kevin Ford 
Michigan State University 
 
Raymond A. Noe 
Ohio State University 
 
Kurt Kraiger 
Colorado State University 
 
Author Note 
 
 Bradford S. Bell, HR Studies Department, ILR School, Cornell University; Scott I. 
Tannenbaum, The Group for Organizational Effectiveness (gOE), Inc.; J. Kevin Ford, 
Department of Psychology, Michigan State University; Raymond A. Noe, Department of 
Management and Human Resources, Fisher College of Business, Ohio State University; Kurt 
Kraiger, Department of Psychology, Colorado State University.  
Correspondence concerning this article should be addressed to Bradford S. Bell, Cornell 
University, ILR School, 162 Ives Hall, Ithaca, NY 14853. E-mail: brad.bell@cornell.edu 
 
 
Citation 
 
Bell, B. S., Tannenbaum, S. I., Ford, J. K., Noe, R. A., & Kraiger, K. (2017). 100 years of 
training and development research: What we know and where we should go. 
Journal of Applied Psychology, 102 (3), 305-323. 
 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 2 
Abstract 
Training and development research has a long tradition within applied psychology dating back to 
the early 1900’s. Over the years, not only has interest in the topic grown but there have been 
dramatic changes in both the science and practice of training and development. In the current 
article, we examine the evolution of training and development research using articles published 
in the Journal of Applied Psychology (JAP) as a primary lens to analyze what we have learned 
and to identify where future research is needed. We begin by reviewing the timeline of training 
and development research in JAP from 1918 to the present in order to elucidate the critical trends 
and advances that define each decade. These trends include the emergence of more theory-driven 
training research, greater consideration of the role of the trainee and training context, 
examination of learning that occurs outside the classroom, and understanding training’s impact 
across different levels of analysis. We then examine in greater detail the evolution of four key 
research themes: training criteria, trainee characteristics, training design and delivery, and the 
training context. In each area, we describe how the focus of research has shifted over time and 
highlight important developments. We conclude by offering several ideas for future training and 
development research. 
    
 
 Keywords: training, employee development, learning, instruction, individual differences  
 
  
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 3 
100 Years of Training and Development Research: What We Know and Where We Should Go 
 Over the past century, training and development in organizations has undergone a 
remarkable transformation. As the nature of work has changed, training objectives have 
broadened beyond improving efficiency on simple manual tasks to providing employees with the 
skills needed to perform complex and dynamic jobs. Once conducted almost exclusively on-the-
job by supervisors (Kraiger & Ford, 2007), technological advances now allow learning to occur 
on-demand and virtually anywhere and at any time. The role of training and development has 
also expanded to where today it is viewed as not only a way to enhance individual capabilities 
but also as a valuable lever for improving team effectiveness and for organizations to gain 
competitive advantage (Noe, Clarke, & Klein, 2014).  
This transformation has been accompanied by a growing and constantly evolving body of 
research across a variety of disciplines, including industrial/organizational psychology, military 
psychology, human factors, and cognitive psychology, which has advanced our understanding of 
how to design and deliver training and development in organizations (Salas & Cannon-Bowers, 
2001). We examine this research evolution with particular attention on research published in the 
Journal of Applied Psychology (JAP). We also describe particularly important and relevant 
pieces published in other outlets. Our goal is to highlight what we have learned from training and 
development research published in JAP in the past 100 years and to provide recommendations 
about potentially fruitful areas for future research.     
The process we used to write this review included several steps. First, we created a 
database consisting of all articles on training, development, and learning published in JAP.1 We 
included articles on mentoring and rater training if they offered insights into the effectiveness of 
training and development. More comprehensive treatment of these topics can be found in the 
                                                          
1 The transformation of training in organizations has blurred the traditional distinctions between training and 
development (e.g., development is more future-oriented and focused on generalizable skills).  Accordingly, we use 
the terms interchangeably in the current article.        
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 4 
articles on mentoring and performance appraisal found in this special issue.  A list of the JAP 
articles included in our database, organized by decade, is provided in the online supplemental 
materials. Next, we used citation analysis and our own judgment to identify key themes and 
articles. Last, our review of the key articles served as the basis for the citations and article 
summaries included in each section of the paper.  
We begin by presenting a timeline of training and development research in JAP and 
identifying key advances and innovations. We then examine four prominent research themes – 
training criteria, trainee characteristics, training design and delivery, and the training context – 
each of which provides unique insight into our evolving understanding of training and 
development. Finally, we discuss important directions for future research. 
A Timeline of Training and Development Research in JAP 
Since the first training and development article appeared in JAP in 1918, there has been 
gradual yet steady growth in the volume of training-related research. This growth is illustrated in 
Figure 1 along with a summary of important research advances over the past century.  The figure 
also highlights several influential and integrative scholarly contributions that reflect what was 
happening in training at different points in time and spotlights a few key societal events that 
shaped research in the field. Below we discuss the evolution of training research within JAP over 
the past century, highlighting important developments and the factors that helped to shape them. 
The Early Years (1917-1959) 
 The first training article to appear in JAP outlined a plan for the uniform training of 
applied psychologists, a profession that had grown in terms of both numbers and importance 
during World War I (Geissler, 1918). The article detailed an undergraduate curriculum that could 
be used to train applied psychologists and described the types of positions that would be 
appropriate for graduates (e.g., commerce, law, education). Over the next few decades, much of 
the training research shared a similar focus on describing or evaluating specific training efforts. 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 5 
Articles, for example, described a sales training course at the American Steel & Wire Company 
(Sturdevant, 1918) and techniques for training clinical psychologists (Sibley & Stogdill, 1936). 
A number of learning studies also appeared during this period describing the elements involved 
in learning different skills (e.g., piloting a glider; Otis, 1934) and investigating rates of learning 
(e.g., Chapman, 1919; Renshaw, 1927). 
 The 1940’s witnessed significant growth in the number of published articles with a 
greater interest in identifying factors predicting training success, particularly in military settings 
(e.g., Frandsen & Hadley, 1943; Lawshe & Thornton, 1943). For the first time, articles appear 
that are focused primarily on methods and instruments for measuring progress. Kellogg (1946), 
for example, described how data gathered through a pilot-response recorder could be used to 
provide an objective analysis of progress in the development of flying skill.  
 The 1950’s saw continued growth, with more than twice as many articles published as 
compared to the previous decade. Much of the applied learning research during this period was 
supported by different military research agencies (Kraiger & Ford, 2007), a trend that continues 
today.  Many of the themes that characterized prior decades carry over into this period. However, 
consistent with the emphasis on “human relations” during this period (Kraiger & Ford, 2007), a 
number of articles emerge focused on the development of supervisory skills and attitudes (e.g., 
Canter, 1951; Lawshe, Bolda, & Brune, 1958, 1959). Also, the transfer of learning became more 
of a focus by examining in laboratory settings the extent to which learning of a response in one 
task or situation influenced the response in another task or situation.  For example, Ritchie and 
Michael (1955) experimented with the ordering of training pilots on two types of visual displays 
to determine which sequence should be used to enhance learning across the two types of 
displays. One study that moved beyond this approach was Benschoter and Charles (1957) who 
compared the retention of classroom and television learning, marking the first study published in 
JAP on technology-based, distance training. Overall, the early years of JAP focused on 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 6 
describing or evaluating training interventions with little attention to building a broader 
understanding of the factors that influence training effectiveness.  
The Emergence of Theory (1960’s & 1970’s) 
 In his Annual Review article, Campbell (1971) concluded, “By and large, the training and 
development literature is voluminous, nonempirical, nontheoretical, poorly written, and dull” (p. 
174). Indeed, many of the articles published during the 1960’s and 1970’s continued to test 
interventions rather than theory (Kraiger & Ford, 2007). For instance, studies compared the 
effects of different training methods and approaches (e.g., Mayo & Longo, 1966; Siegel, 
Richline, & Federman, 1960), including computer-assisted instruction (Schwartz & Haskell, 
1966). Articles continued to report on factors that predict training success, although there is a 
notable shift to a greater focus on training design variables, such as knowledge of results 
(Alexander, Kepner, & Tregoe, 1962; Wiener, 1963). 
 Perhaps in response to Campbell’s (1971) dire portrayal of training research, there 
emerged more theory-driven training research and systems thinking. One notable example is 
Latham and Saari’s (1979) application of social learning theory to the development of a 
behavioral modeling training program designed to improve supervisors’ interpersonal skills. 
Also, published research began to recognize that the effectiveness of a training program is 
shaped by the broader system in which it is embedded (e.g., Friedlander & Greenberger, 1971). It 
is also worth noting that some early studies on cross-cultural training (Fiedler, Mitchell, & 
Triandis, 1971) and team training (Naylor & Briggs, 1965) were published during this period. 
The Trainee is Alive (1980’s) 
 Although compared to the 1970’s fewer training articles were published in JAP during 
the 1980’s, this period marked some important milestones. In particular, a more learner-centered 
orientation began to emerge as studies exhibited greater interest in how trainees approach and 
respond to learning during training. Kanfer and Ackerman’s (1989) influential monograph, for 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 7 
example, spurred interest in the interactive effects of ability, motivation, and task demands 
during skill acquisition, and other studies highlighted the importance of trainee self-efficacy for 
both performance and transfer (Gist, Schwoerer, & Rosen, 1989; Latham & Frayne, 1989).  
 This period also saw an increase in interest in the study of transfer to improve the 
application of workplace training (Baldwin & Ford, 1988; Goldstein, 1980).  Transfer of training 
was defined as consisting of generalization, or the extent to which acquired knowledge and skills 
are applied to settings, people and/or situations different from those trained, and the maintenance 
of those learning experiences over time on the job.  The first training-related meta-analysis 
appeared in JAP with Burke and Day’s (1986) effort to resolve conflicting findings about the 
effects of managerial training on learning during training, behavior change after training, and 
tangible results such as reduced costs and promotions. In subsequent years, influential meta-
analyses would focus on training motivation, reactions, and effectiveness, helping to summarize 
and clarify what we have learned and shaping the future research agenda in these areas. 
Training in Context (1990’s) 
 During the 1990’s, research focused more on the role of the broader training context in 
shaping employees’ participation in development activities, learning, and transfer. Studies 
demonstrated that employees’ perceptions of the work environment (e.g., social support) 
influence their participation in continuous learning activities (Birdi, Allan, & Warr, 1997; 
Maurer & Tarulli, 1994; Noe & Wilk, 1993) and that pre-training contextual factors contribute to 
individual differences in learning (Quiñones, 1995; Smith-Jentsch, Jentsch, Payne, & Salas, 
1996). Research also showed that the application of trained skills to the job can be influenced by 
aspects of the work environment, such as the transfer climate (Tracey, Tannenbaum, & 
Kavanagh, 1995). Finally, we see evidence of the cognitive revolution occurring in psychology 
as Kraiger, Ford, and Salas’ (1993) monograph applied cognitive theory to broaden existing 
models of training evaluation to include cognitive, skill-based, and affective learning outcomes. 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 8 
Beyond the Individual and the Classroom (2000 - Present) 
 During the 2000’s more training articles were published than in any previous decade, 
stimulated in part by the development of new theoretical models, constructs, and evaluation tools 
(Salas & Cannon-Bowers, 2001). Research continued to feature a learner-centered approach as 
studies examined how different training methods, such as error training and mastery training, not 
only influence self-regulatory processes during learning (e.g., Keith & Frese, 2005; Kozlowski & 
Bell, 2006) but also interact with individual differences to impact training outcomes (Gully, 
Payne, Koles, & Whiteman, 2002). The burgeoning use of online training in organizations led to 
studies seeking to understand how such programs can be designed to improve trainee 
engagement and learning (e.g., Orvis, Fisher, & Wasserman, 2009; Sitzmann & Ely, 2010).  
 Another important development was a focus on the important benefits of learning for not 
only individuals but also teams and organizations (Aguinis & Kraiger, 2009). For example, 
studies examined the effectiveness of different team training methods (e.g., Marks, Sabella, 
Burke, & Zaccaro, 2002) as well as the impact of training and learning activities on unit 
performance and firm productivity (e.g., Kim & Ployhart, 2014; Van Iddekinge et al., 2009).  
 Recently, studies have increasingly focused on learning that occurs beyond the 
boundaries of formal training (e.g., Courtright, Colbert, & Choi, 2014; DeRue & Wellman, 2009; 
Dragoni, Oh et al., 2014). This research has demonstrated that the developmental value of an 
experience can be shaped by not only the nature of the experience but also aspects of the 
organizational context (e.g., supervisor support), interventions (e.g., structured reflection), and 
characteristics of the person (e.g., self-efficacy).  
The Evolution of Key Research Themes within JAP 
 In this section we examine four important research themes: training criteria, trainee 
characteristics, training design, and training context. We chose these themes because historically 
they have been the focus of most training research and they provide an organizing framework for 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 9 
a more detailed examination of how training has evolved over time. We first examine the criteria 
that have been used to understand training effectiveness and then discuss three sets of factors – 
trainee characteristics, training design, and training context – that influence these criteria.   
Theme 1: Training Criteria 
Given that training and development is done for a purpose it is important to examine the 
criteria that have been used to measure its effectiveness (Wallace, 1965). In this section, we first 
focus on the criteria of individual learning and transfer of learning (often to another type of task) 
as these are the two major criteria that have been the focus of training research in JAP over the 
last century.  Then, we describe research that is broadening the criterion space of interest by 
examining the effects of training on broader job performance, work attitudes, and transfer 
outcomes at multiple levels of analysis. 
 An expanding understanding of learning. Learning is an inferred process that is “seen” 
through changes in knowledge, skills, attitudes, and behaviors during training and generalization 
to the transfer context (Baldwin & Ford, 1988).  In one of the initial applied psychological 
studies on learning, Thorndike proposed principles of learning such as identical elements theory 
(Thorndike & Woodworth, 1901) with an emphasis on refined and sequenced content and 
appropriate reinforcement according to the law of effect (Thorndike, 1927). From this 
perspective, learning occurs when behaviors are followed by favorable consequences. Similarly, 
principles of behaviorism emphasized drill, practice, and reinforcement with the goal of mastery 
learning (Skinner, 1954). In JAP, Pressey (1963) argued that applications of behaviorist 
principles generated from animal studies to human learning were misguided. He particularly 
targeted the principles adopted with the “teaching machine” (programmed instruction).  
Cognitive-based theories of learning emerged with an emphasis on learning more 
complex tasks and skills. This focus led to the development of learning hierarchies (e.g., 
cognitive outcomes from factual/declarative knowledge to higher order outcomes such as 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 10 
synthesis and evaluation; Bloom, 1956). Conditions of learning (Gagne, 1965) targeted learning 
events and issues of attention, memory, retrieval and information processing. As noted by Gagne 
(1985), from a cognitive perspective, “learning is largely dependent on events in the environment 
with which the individual interacts” (p. 3). These conditions of learning were then synthesized 
into principles of instructional design to facilitate learning and transfer of learning (Gagne & 
Briggs, 1974). Based on this expanding understanding of learning, Kraiger et al. (1993) argued 
that there had been reliance in the training field on incomplete theories of what learning is and 
how learning should be measured. Drawing from a variety of research domains, they developed a 
multidimensional classification scheme of cognitive, skill, and affective outcomes that can guide 
the conceptualization and measurement of learning outcomes. For example, cognitive outcomes 
could include verbal knowledge (declarative), knowledge organization, and cognitive strategies, 
skill based outcomes could focus on compilation or automaticity, and affective outcomes could 
focus on motivational or attitudinal constructs. This construct oriented approach showed how to 
target a number of learning constructs that are interrelated in complex and interesting ways.  
The Kraiger et al. (1993) paper was the likely trigger to the more precise measurement of 
the effectiveness of training at the individual level. A thorough review of examples can be found 
in Ford, Kraiger, and Merritt (2010). However, examples specific to JAP include cognitive 
outcomes such as knowledge structures (Day, Arthur, & Gettman, 2001) and metacognitive 
activity (Ford, Smith, Weissbein, Gully, & Salas, 1998); affective outcomes such as self-efficacy 
(Bell & Kozlowski, 2002; Chen, Thomas, & Wallace, 2005); and skill-based outcomes such as 
skill retention (Arthur, Day, Bennett, McNelly & Jordan, 1997) and the generalization of 
learning to tasks that had not been practiced during training (Holladay & Quiñones, 2003). 
 Learning tasks and outcomes. To understand how learning has been conceptualized and 
measured in JAP, one must first examine the task to be learned. The learning task (e.g., the 
objectives of training) guides what was trained, which has important implications for what can 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 11 
and should be measured as learning and transfer outcomes.  
The research can be broken down into two main categories of learning tasks – 
operator/manual tasks and cognitive/mental tasks. Most articles on learning and transfer from 
1919 to 1972 focused on understanding learning rates for operator/motor skill tasks such as 
typewriting (Chapman, 1919), transcription (Henry & Wasson, 1939), knitting (Blankenship & 
Taylor, 1938), street car operators (Ghiselli & Brown, 1947), assembly tasks (Lawshe & Cary, 
1952; Smader & Smith, 1953), and steering and/or tracking tasks (e.g., Garvey 1960; Hinrichs, 
1970). The studies typically measured learning as the rate of improvement over time –with 
studies spanning many days, practice trials, and blocks of trials. Most studies presented learning 
curves to visually show the rate of improvement in terms of the reduction in time to perform a 
task, increase in productivity (Blankenship & Taylor, 1938, Chapman, 1919), increase in 
accuracy (Ehrlich, 1943; Hinrichs, 1970) or reduction in error rates or accidents (e.g., Ghiselli & 
Brown, 1947; Smith & Sussman, 1970). For example, Blankenship and Taylor (1938) examined 
improvements in output using weekly work samples and found the now familiar learning curve 
characteristics of an early sharp increase in output with negative acceleration, and the 
development of a learning plateau beyond which little improvement occurred. They also found 
that early proficiency level was not a good predictor of later proficiency and that there were 
individual differences in the learning curves. Interestingly, Wagner (1933) studied the cognitive 
strategies that participants used while attempting to complete paper mazes – an early forerunner 
to taking a cognitive perspective to understanding strategies that are linked to learning outcomes.  
The majority of studies in this time period that explicitly focused on transfer of learning 
of a response in one task or situation to another mainly examined the learning of operator/manual 
skills such as basic assembly (Woodward, 1943), aiming (Stockbridge & Chambers, 1958), 
operator tasks (Hammerton & Tickner, 1967) and tracking/radar control tasks (e.g., Briggs & 
Weiner, 1966; Lincoln & Smith, 1951). The measure of learning transfer included the number of 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 12 
transfer trials to reach a criterion level (Muckler & Matheny, 1954), error rates (Briggs & 
Weiner, 1966), time on target (Lincoln & Smith, 1951), efficiency indices such as reductions in 
time to perform (Von Trebra & Smith, 1952) and measures of both accuracy and commissive 
errors (Weiner, 1963). Many of these studies examined the generalizability of the training 
conditions for reducing negative transfer where the learning of a response in the learning context 
can interfere with the appropriate response in the transfer context.  
Since 1972 there has been an increase in the complexity of operator/manual skills in 
studies of learning and transfer such as vehicle maintenance safety (Komaki, Heinzmann & 
Lawson, 1980), navigation skills (Olea & Rea, 1994), computer software skills and video games 
(e.g., Day et al., 2001; Hughes et al., 2013), and more complex radar tracking studies that require 
accessing more information and making strategic decisions over time (Ackerman, 1992; Ford et 
al., 1998). These studies have tended to measure learning (or transfer) in terms of decision 
accuracy and skilled performance (total points scored given game/simulation rules).  
Studies that incorporated cognitive/mental tasks can be divided into two main categories 
– those employing simple learning tasks such as basic memory and paired association tasks and 
those investigating knowledge gained from courses/classes. The majority of studies employing 
basic cognitive tasks were conducted prior to 1980 and included learning tasks such as 
memorizing the position of cards in a deck through paired associations (Renshaw, 1927), making 
English translations (Thisted & Remmers, 1932), completing paper mazes (Wagner, 1933), and 
recalling words (Rubenstein & Aborn, 1958). The main measure of learning was often the rate of 
improvement or how long it took to reach a set criterion level (e.g., Renshaw, 1927). A number 
of studies (some before 1980, but most after 1980) have studied learning through examining 
knowledge acquisition. The typical measure of learning targeted declarative knowledge such as 
grades (Hollander, 1954; LePine, LePine, & Jackson, 2004) or achievement test scores (e.g., 
Chen, Gully, Whiteman & Kilcullen, 2000). But some studies also assessed procedural 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 13 
knowledge (Neal et al., 2006; Sitzmann & Johnson, 2012). A recent focus on decision making in 
more dynamic contexts (e.g., Goodman & Wood, 2004) has led to measuring learning in terms of 
strategic as well as declarative knowledge (Bell & Kozlowski, 2008; Hughes et al., 2013). 
Broadening the criteria. The first efforts to broaden training criteria beyond direct 
measures of learning and training transfer emerged in the 1950’s and 1960’s as research began to 
examine job performance, specifically performance that was not directly related to the training 
itself. Kraiger and Ford (2007) noted that during the 1940’s the responsibility for training shifted 
from supervisors to training experts. Since training was now emerging as a cost center, 
organizations began to consider their return on investment. Organizations moved beyond an 
interest in determining whether trainees learn faster or retain skills longer to a focus on improved 
job performance. One strategy for showing training benefits was to look at the impact of training 
on a standardized assessment related to the job. For example, Barthol and Zeigler (1956) 
examined the effectiveness of a 20-week supervisory training program on production, safety, 
budgeting, and human relations topics that had been given to 210 first- and second-line 
supervisors. The researchers compared group means on parallel forms of a previously validated 
assessment of knowledge of supervisory practices and found a significant increase in post-test 
scores. Interestingly, they also argued that reduced variability was an indicator of training 
effectiveness, a proposal made three decades later by Alliger and Katzman (1997).  
During the 1970’s and 1980’s, criteria further expanded to include trainee attitudes as an 
outcome of training, and, presumably, a benefit to the individual and to the organization. As one 
example, O’Brien and Plooij (1977) compared programmed instruction (PI) and “prose” training 
(standard text) to a control group in preparing nurses to work with Aboriginals in South 
Australia. Although there were positive effects for PI training on cultural knowledge, there was 
no effect on trainees’ interest in or apprehension about working in the Aboriginal community. 
Similarly, Zacker and Bard (1973) compared affective-experiential training and special cognitive 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 14 
(classroom) training to a no-training condition for 60 New York City police officers. The 
affective-experiential training, which included role plays and simulations of interpersonal 
conflict, showed stronger effects on both performance criteria and a danger-tension index (total 
arrests/total sick days * 100) which was operationalized as a measure of officer morale.  
More recently, several papers have used different forms of multilevel analyses to 
demonstrate the relationship of training to both team and organizational effectiveness. Multilevel 
modeling provides a powerful tool for examining both within-person and cross-level 
relationships (e.g., Kolozlowski & Klein, 2000; Ployhart & Hakel, 1998; Ployhart & 
Vandenberg, 2010). It has long been assumed that changes in individual knowledge or skills 
affect individual or team performance, which in turn affects unit-level or organizational-level 
indicators of effectiveness. But, prior to the development of advanced statistical analyses for 
treatment of hierarchical data, it had been difficult to show this. Several JAP papers offer 
examples of these effects. Van Iddekinge et al. (2009), for example, examined the effects of 
selection and training programs on customer service indicators and financial performance in 861 
fast-food restaurants on a monthly basis for one year. Latent growth curve modeling was used to 
examine the effects of changes in the use of training on change in the performance indicators. 
They reported that use of training was both directly related to financial performance and 
indirectly related through improved customer service. Kim and Ployhart (2014) used over 12 
years of longitudinal data to examine the effects of selective staffing and internal training on the 
financial performance of 359 firms during pre- and postrecessionary periods. Prior to the 
recession, they found that more internal training contributed to firm profit growth and this effect 
could be partially explained by the effects of training on firm productivity. In addition, they 
found a significant interaction between selective staffing and internal training, such that firms 
only achieved consistent profit growth when both were high. In contrast, they found that internal 
training conducted during and after the recession did not have an effect on postrecession profit. 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 15 
All this suggests that training may help firms develop slack resources during strong economic 
periods (i.e., prerecession) that help them to weather and recover from economic downturns.   
Theme 2: Trainee Characteristics 
Trainee characteristics can be broadly defined to include capabilities (e.g., general mental 
ability and intelligence, and specific aptitudes), personality traits (e.g., Big Five, locus of 
control), motivational constructs (e.g., self-efficacy, goal-orientation), values and interests (e.g., 
vocational and occupational interests), attitudes and emotions (e.g., motivation to learn) and 
perceptions (e.g., perceived managerial support, climate for training).  
A focus on trainee ability and aptitudes for training success.  Munsterberg (1913) 
suggested that we cannot place enough emphasis on considering how differences in ability 
determine how much individuals benefit from training. Not surprisingly, most of the studies on 
trainee characteristics published in JAP prior to the 1960’s focused on the role of general mental 
ability and specific aptitudes for training success.  
  The first studies published in JAP on trainee characteristics found that IQ was related to 
success in learning mechanical, printing, garment, culinary, and farmwork trades (Cowdery, 
1922). Gilbert (1941) emphasized that “aptitude” (the relative ability of individuals to benefit 
from training) was an important factor in how much individuals will benefit from training. 
Research also showed that intelligence and aptitudes were related to vocational and occupational 
choice (e.g., Hillman, 1934). Studies helped the U.S. military predict who would succeed in pilot 
or soldier training during WWII. For example, Fiske (1947) found that the Wonderlic was useful 
for predicting Navy flight training candidate’s ground school failures and a combination of a 
mechanical comprehension test and biographical data was useful for predicting flight failures.  
The role of personality and attitudes in training received some, but notably less attention 
than aptitudes during the early years. For example, Geiger, Remmers, and Greenly (1938) 
developed a measure of attitudes toward training which included items related to fellow 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 16 
employees, instruction, and management. Wiener and Simon (1950) showed that embalmer 
trainees had a greater concern for their own personal health (hypochondriasis) as measured by 
the MMPI compared to a control group with different career objectives.  
Moving beyond ability and aptitude for predicting training success. In the 1960’s 
through the 1970’s research continued to focus on predicting training success, with increasing 
interest in trainee characteristics other than cognitive ability and aptitude. Several studies 
suggested that considering trainees’ cognitive ability as well as interests, personality, moods, 
values, biographical data, motivation, and physical and mental health might enhance prediction 
of training success. For example, Gordon and Alf (1962) showed that interest inventory scores 
contributed beyond aptitude test scores in the prediction of naval school training grades. Mobley, 
Hand, Baker and Meglino (1979) using a role choice model, a variant of expectancy theory, 
found that expectancies about completing training, a Marine’s role, and finding a civilian role 
predicted the turnover of Marine Corp recruits during training.  
Trainability and matching trainee and instructional characteristics. Studies on 
trainee characteristics published in JAP between 1970 and 1990 made two important 
contributions. First, based on the work of Brown and Ghiselli (1952), trainability was considered 
distinct from job success. Trainability testing, distinguished from cognitive ability tests by the 
inclusion of a period of structured instruction followed by an assessment of learning, was used to 
select employees for training rather than to choose employees who are already qualified for the 
job. Studies by Robertson and Downs (1979, 1989) emphasized the differences between work 
sample tests and trainability tests (e.g., a structured learning period, observation of how things 
are done and what is done, and including assessment of these observations, error correction, and 
perceptual and manual skills) and showed that trainability work sample tests predict short-term 
success more accurately than longer term success. Reilly and Israelski (1988) found that 
minicourses, a variant of trainability testing used for assessing job candidates’ ability to acquire 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 17 
knowledge related to training courses, had similar validities with job performance and training 
performance as work samples, trainability tests, and ability tests.  
A second contribution was the development and testing of multivariate models of how 
trainee characteristics influence motivation and subsequent learning (e.g., Noe, 1986), which 
answered calls for systematic and theoretical research which incorporates motivation theories 
and investigates how learning is influenced by trainee and instructional characteristics 
(Campbell, 1971; Goldstein, 1980). Kanfer and Ackerman (1989) integrated motivation and 
cognitive ability within an information processing framework. They showed that information 
processing and ability demands change as a function of practice, training paradigm, and timing 
of goal setting. Mumford, Weeks, Harding, and Fleishman (1989) found that individual attributes 
(e.g., aptitude, academic achievement motivation) are likely to have a greater impact on 
achievement and motivational outcomes than course content variables (e.g., practice, 
instructional aids). Also, research identified new trainee characteristics that can influence 
training success and learning, including managerial motivation (Butler, Lardent, & Miner, 1983), 
seniority (Gordon, Cofer, & McCullough, 1986), and self-efficacy (Gist et al., 1989).  
Expansion of the scope of trainee characteristics research. Compared to the previous 
forty years, during the 1990’s there was a noticeable increase in the number of studies of trainee 
characteristics published in JAP. This was likely due to Tannenbaum and Yukl’s (1992) 
observation that more studies were needed to understand how trainee characteristics interacted 
with program design features to affect learning outcomes rather than continuing to focus on 
identifying the factors responsible for trainees who would pass training programs. Also, the 
“War for Talent” (Chambers, Foulton, Handfield-Jones, Hankin & Michaels, 1998) emphasized 
the role of training and development for attracting, retaining, and motivating employees 
throughout their employment. Three new research areas emerged in the 1990’s. The first focused 
on understanding how trainee characteristics related to participation in development activities. 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 18 
Studies showed that person (e.g., learning motivation, self-efficacy, values) and environment 
(e.g., social support, organization policies, incentives) influence participation in development 
activities. Noe and Wilk (1993), Maurer and Tarulli (1994), and Birdi et al. (1997) identified 
some common predictors of participation in different types of development activities (e.g., work-
based on work time, career planning activities on own time) and found that job satisfaction and 
organizational commitment were related to participation in work-based development activity.  
The second research area focused on the role of training and learning in socializing new 
employees. Self-efficacy, prior experience, and the extent to which training meets or fulfills 
trainees expectations were shown to be related to new employees’ training motivation, 
commitment, and intentions to quit. For new employees with low levels of self-efficacy, training 
was more strongly related to post-training self-efficacy, ability to cope, job performance, and 
intentions to leave the profession (Saks, 1995). The extent to which training meets trainees’ 
expectations (training fulfillment) influences motivation, organizational commitment, and 
different types of self-efficacy (Tannenbaum, Mathieu, Salas, & Cannon-Bowers, 1991). The 
third area expanded our understanding of the role of self-efficacy, conscientiousness, goal 
orientation, climate and culture, and their interaction on motivation to learn, development of 
learning goals, task analysis, metacognitive activity, and learning and transfer of training (Brett 
& Vandewalle, 1999; Colquitt & Simmering, 1998; Ford, Smith, Sego, & Quiñones, 1993; Ford 
et al., 1998; Martocchio & Judge, 1997; Tracey et al., 1995). For example, Brett and Vandewalle 
(1999) found that goal orientation was related to the type of goals that trainees adopted for a 
training program. Trainees with a high learning goal orientation adopted skill improvement 
goals, which resulted in better training performance. In addition to these three areas, studies 
identified other important trainee characteristics such as proactive personality (Parker & Sprigg, 
1999) and continued to advance our understanding of the effects of cognitive ability and its 
relationship with other trainee characteristics. For example, Eyring, Johnson, and Francis (1993) 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 19 
used learning curve modeling with multilevel models to show that cognitive ability was more 
important earlier than later in skill acquisition and that practice resulted in reduced performance 
differences between individuals higher and lower in ability, self-efficacy, and task familiarity.   
New theoretical frameworks and consideration of instructional context. The number 
of published studies on trainee characteristics during the 2000’s continued at the same rate as in 
the 1990’s. These studies made several notable contributions. First, two important meta-analysis 
of trainee characteristics were published in JAP during the 2000’s. Colquitt, Lepine, and Noe 
(2000) summarized how trainee characteristics influence training motivation and learning 
outcomes and proposed and tested an integrative theory of training motivation. Training 
motivation explained incremental variance beyond the effects of cognitive ability. The effects of 
personality, climate, and age on training were only partially mediated by self-efficacy, valence, 
and job involvement. Payne, Youngcourt, and Beubien (2007) found that both learning goal 
orientation and prove goal orientation were positively related to learning, although the former 
had a larger effect. Avoid-goal orientation had close to a zero relationship with learning. Second, 
studies emphasized that how trainee ability and motivation influence learning is more complex 
and nuanced than shown in earlier studies. Gully et al. (2002), for example, found that trainees 
with high cognitive ability or openness to experience benefited more from error training than 
trainees with low cognitive ability or openness to experience. Several studies also added to our 
understanding of the relationship between cognitive ability, goal orientation, self-efficacy, and 
personality and how they influence self-regulation, learning, and skill performance. Bell and 
Kozlowski (2002) found that learning goal orientation was adaptive for high ability individuals 
but had no effect for low ability individuals. The effects of performance orientation depended on 
cognitive ability and the specific learning outcomes examined. Using multilevel analysis, Yeo 
and Neal (2004) showed that the relationship between effort and performance increased with 
practice and this rate of this increase was faster for trainees with high ability or low performance 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 20 
orientation. Vancouver and Kendall (2006) showed that the self-efficacy-learning relationship is 
likely more dynamic than suggested by prior studies using between subject designs. Using a 
within-person analysis they found that high self-efficacy may cause trainees to allocate fewer 
resources when planning, which results in a negative, rather than positive, relationship between 
self-efficacy and learning performance. 
Third, researchers moved beyond using expectancy theory to explain participation in 
development activities and instead relied on a broader array of theories and models, including the 
theory of planned behavior, proactive personality, social exchange, career motivation theory, 
person-environment fit, and deprivation theory (Kraimer, Siebert, Wayne, Liden, & Bravo, 2011; 
Major, Turner, & Fletcher, 2006; Simmering, Colquitt, Noe, & Porter, 2003). Research 
continued to show the importance of trainee characteristics as antecedents of motivation to learn 
and participation in development activities found in studies in the 1980’s and 1990’s were 
replicated. Maurer, Weiss, and Barbeitte (2003) found that age negatively affects individual and 
situational factors that predispose employees to participate in development. Emotional stability, 
self-efficacy, and learning orientation were positively related to self-perceived role knowledge, 
which is important for how new leaders acquire knowledge and learn behaviors (Dragoni, Park, 
Soltis, & Forte-Trammell, 2014). A new research area also showed that learning orientation and 
self-efficacy likely buffer trainees from the potentially negative effects of stress on learning from 
challenging job experiences (DeRue & Wellman, 2009; Courtright et al., 2014).  
Fourth, research moved away from the prevalent focus in the 1990’s on how trainee 
characteristics interacted with instructional design to consider instructional modality and 
instructor style. Towler and Dipboye (2001), for example, found during face-to-face instruction 
that trainees with a high mastery orientation had their poorest problem solving performance after 
listening to an organized and inexpressive instructor. Organization or inexpressiveness did not 
influence participants with a low mastery orientation. In the late 2000’s to 2015, new training 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 21 
delivery and instructional methods (e.g., e-learning, games, and simulations) have stimulated the 
examination of how trainee characteristics including cognitive ability, goal orientation, and self-
efficacy, influence the learner’s engagement (practice difficulty, cognitive involvement) and 
anxiety in training, and in turn, knowledge acquisition and task performance (Hughes et al., 
2013; Orvis et al., 2009; Watson et al., 2013). For example, mastery orientation was positively 
related to pre-training motivation to learn, trainee satisfaction, and learning in an e-learning 
program. Learner control had a positive relationship with trainee satisfaction, but unlike 
performance orientation, was unrelated to off-task attention (Orvis et al., 2009).  In one of the 
few original studies attempting to understand how trainee characteristics influence trainee 
reactions, Harman, Ellington, Surface, and Thompson (2015) found that interest in the topic, 
satisfaction, and entity beliefs influenced open-ended comments provided by trainees.  
Theme 3: Training Design and Delivery 
 An important step in designing any training program involves choosing methods and 
techniques appropriate for delivering content to trainees, identifying and sequencing instructional 
objectives, and determining the instructional tools and strategies necessary to support learning 
and transfer (Salas & Cannon-Bowers, 1997). In this section, we discuss the evolution of 
research on training design and delivery. We then turn attention to three topics - active learning, 
technology-based learning, and team training - which have captured significant attention over the 
past few decades and are likely to remain influential in the years ahead.  
 Designing training to support learning. Much of the training research published during 
the early years of JAP did not focus on issues of training design and delivery. This may be due, 
in part, to the fact that during this time there was very little variability in training methods 
(Kraiger & Ford, 2007). Training was typically conducted on the job by either a supervisor or 
more experienced worker and involved demonstrating and explaining the steps until the trainee 
could perform them correctly. Most training design articles published in JAP during this period 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 22 
provided descriptions of successful training programs. For example, Lindahl (1945) provided a 
detailed description of a method designed to teach workers the proper hand and foot movements 
for operating machines used to cut tungsten rods into small discs.  
 By the 1960’s, new techniques, such as case studies, role playing, programmed 
instruction, and television, were introduced, leading to greater variability in training methods. In 
an effort to sort through this variety, the focus of research over the next two decades shifted 
largely to comparing the utility of different methods. For example, Barbee and Keil (1973) 
compared videotape-feedback and behavior modification techniques for training job-
interviewing skills and O’Brien and Plooij (1977) compared the effectiveness of programmed 
instruction and essays for training nurses. The value of such studies, however, was limited since 
there were few attempts to explain why a particular method yielded superior outcomes. As 
Goldstein (1980, p. 261) concluded, “a continuation of studies which only compare techniques 
and are not concerned with the particular behaviors being taught is probably not very fruitful for 
computer-assisted instruction, programmed instruction, or any technique, for that matter.” 
 Over time, the field began to shift from an emphasis on hardware and techniques to 
understanding how training systems should be designed to support the process of learning. 
However, research on traditional learning principles, such as stimulus variability and conditions 
of practice, was criticized as having limited utility for training the complex skills required by 
organizations (Baldwin & Ford, 1988; Goldstein, 1974). These concerns coupled with 
developments in the fields of cognitive psychology (Anderson, 1983) and instructional 
psychology (Pintrich, Cross, & Kozma, 1986) helped spur research on training approaches that 
leverage mental processes and motivational factors to develop more complex skills. Within JAP, 
theory-driven research examined goal-directed learning (e.g., Kanfer & Ackerman, 1989), 
overlearning (Driskell, Willis, & Copper, 1992), and learner control (Simon & Werner, 1996). In 
contrast to earlier trends, these studies often investigated the learning mechanisms through which 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 23 
the approaches have their effects. For example, Eden and Aviram (1993) showed that a 
behavioral-modeling workshop delivered to unemployed individuals boosted their general self-
efficacy, which in turn increased job-search activity and led to a higher rate of reemployment 
among individuals who were low in initial general self-efficacy. In addition, research recognized 
the importance of aligning training delivery methods with learning objectives. Arthur, Bennett, 
Edens, and Bell’s (2003) meta-analysis found that the effectiveness of specific training delivery 
methods often varied considerably depending on the skill or task being trained (e.g., cognitive 
skills, interpersonal skills) and the criteria used to evaluate training (e.g., learning, behavior).  
 A shift from passive to active learning. Over the years, research and practice has often 
treated the learner as a passive recipient of instruction (Ford & Kraiger, 1995). This is due 
largely to the dominance of the behavioral paradigm, which emphasized tightly structured 
training designed to walk trainees through the steps to be learned and to eliminate errors and 
increase efficiency (Smith, Ford, & Kozlowski, 1997). In contrast, more recent research has 
adopted an active learning approach in which the trainee is seen an active participant in the 
learning experience (Bell & Kozlowski, 2009). This evolution has occurred as the result of 
changes in both the nature of work and training. For example, research suggests that although 
behavioral approaches may be effective for developing routine expertise, they are ill-suited for 
developing the flexible and adaptable skills (i.e., adaptive expertise) needed in today’s more 
complex and dynamic work environments (Ford, Santoro, & Showler, in press; Kozlowski et al., 
2001). In addition, the increased reliance on technology-based training and informal learning 
within organizations has led to more learner-centered training and has given trainees 
unprecedented control over their learning (Noe et al., 2014). 
 Research published in JAP has been instrumental in not only developing models of active 
learning but also advancing our understanding of how to design and support more learner-
centered training (Ford et al., 1998). For example, studies have demonstrated that active, self-
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 24 
regulated learning can be supported by providing guidance on the sequencing of practice tasks 
(i.e., guided exploration; Debowski, Wood, & Bandura, 2001), encouraging trainees to actively 
explore and to make and learn from errors (i.e., error management training; Gully et al., 2002; 
Keith & Frese, 2005; 2008), and using goals, situational cues, and instructions to encourage 
trainees to adopt a mastery orientation (i.e., mastery training; Kozlowski & Bell, 2006). A study 
by Bell and Kozlowski (2008) integrated this and other work into a model of active learning 
consisting of three core training design elements – exploratory learning, error-encouragement 
framing, and emotion control – that interact with individual differences (e.g., cognitive ability, 
goal orientation) to influence cognitive, motivational, and emotional self-regulatory processes 
during learning and, as a result, impact both proximal (e.g., skill acquisition) and distal (e.g., 
generalization) training outcomes.  
 Growth in technology-based training. The proliferation of technology-based training in 
the workplace (Sitzmann, Kraiger, Stewart, & Wisher, 2006) has sparked significant research 
interest. Examination of this research reveals an evolutionary pattern similar to the one described 
earlier for more traditional training methods. For the first few decades, studies compared 
technology-based methods (e.g., computer-assisted instruction) to more conventional methods 
(e.g., classroom) (e.g., Schwartz & Haskell, 1966; Schwartz & Long, 1967). These studies found 
that although technology often reduced training time, trainee attitudes and achievement were 
generally comparable to more conventional methods. Researchers now largely agree that efforts 
to evaluate the comparative effectiveness of different technologies are of limited value (Brown, 
Charlier, & Pierotti, 2012; Clark, 1994). Rather, what is more important is understanding the 
pedagogical features that influence the effectiveness of technology-based training and the 
conditions under which it is likely to be most effective (Bell & Federman, 2013). In the late 
1980’s, research in JAP began to move in this direction as Gist et al. (1989) examined different 
approaches (modeling vs. tutorial) to computer-based training as well as their interaction with 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 25 
trainees’ computer efficacy. They found that the modeling approach led to better training 
outcomes, including higher performance and more positive affective responses. In addition, 
trainees in the modeling condition developed higher levels of software self-efficacy, particularly 
if they had low pre-training computer self-efficacy. More recently, research has focused on the 
effects of learner control in technology-based training (Hughes et al., 2013; Orvis et al., 2009) 
and strategies designed to help learners make better use of this control (Sitzmann & Ely, 2010; 
Sitzmann & Johnson, 2012) or to monitor their behavior during technology-based training 
(Watson et al, 2013). This research suggests that a variety of factors, including individual 
differences (e.g., cognitive ability, self-efficacy), training design features (e.g., error framing), 
and the provision of support (e.g., self-regulatory prompts), shape the decisions individuals make 
during learner-controlled training and ultimately how much they learn. 
 Using training to improve team functioning. While a number of studies through the 
1970’s examined the role of training on team effectiveness (e.g., Briggs & Johnston, 1966; 
Naylor & Briggs, 1965), it was not until the late 1980’s and 1990’s that team training became 
driven by both theory and research. Integral in the maturation of team training research was the 
distinction between taskwork and teamwork skills (e.g., Salas, Stagl, Burke, & Goodwin, 2007). 
The former are those skills team members need to perform the work (often with coordination), 
and the latter are those required to perform more effectively as a team. This distinction enabled 
researchers to identify and test more general strategies for team skills as opposed to testing more 
task-specific team training interventions. Marks et al. (2002), for example, examined the impact 
of cross-training on shared mental-models among team members. Cross-training involves 
training team members in the role behaviors of other team members, and shared mental models 
have been found to be determinants of team performance (Mathieu, Heffner, Goodwin, Salas, & 
Cannon-Bowers, 2000). Marks et al. (2002) found across two studies and nearly 100 trained 
teams that cross-training enhanced the development of shared team-interaction models. 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 26 
Finally, other research has shown that leader briefings (Marks, Zaccaro, & Matheiu, 
2000) and after-event reviews (S. Ellis & Davidi, 2005; Villado & Arthur, 2013) can also 
improve team functioning. Leader briefings help team members focus on priorities before 
accomplishing tasks, while after-event reviews or team debriefs can be considered “mini-
trainings” in which the leader reviews with the team examples of successful and unsuccessful 
performance with respect to tasks and teamwork. 
Theme 4: Training in Context: Conditions Before and After Training 
In the last 30 years, training research in JAP evolved, in part, to reflect an awareness that 
organizational training and development does not occur in a vacuum. Proper instructional design 
is necessary but insufficient because learning and transfer of learning to the job involves people 
who work in complex and varied environments. The work context, including what happens 
before and after formal learning experiences (Tannenbaum & Yukl, 1992), can affect how 
employees think, feel, learn, and behave with important implications for understanding and 
ensuring training effectiveness.  
A systems perspective. In the original Handbook of Industrial and Organizational 
Psychology, Hinrichs (1976) offered an initial view of training as a system. By the late 1980’s, 
researchers began taking more of a systems perspective in their attempts to understand training 
and development. A training “system” includes three major components that can influence 
overall effectiveness (Baldwin & Ford, 1988): a) instructional design and training methods–
including the attributes of the experience designed to promote learning; b) trainee or personal 
characteristics– the makeup and mindset of the learners; and c) organizational conditions or work 
environment– the context in which employees work. In earlier sections we examined the first 
two components, instructional design and trainee factors. Here, we examine how conditions 
before and after a learning event influence trainee motivation, training transfer, and ultimately, 
the organizational effectiveness of training and development.  
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 27 
Work conditions that can influence the effectiveness of training include supervisor and 
peer support; the amount of time and opportunity learners have to apply new skills, and the 
challenges and constraints they face on the job; organizational policies and practices; the broader 
organizational climate and culture in which they work; and previous work and training 
experiences they have encountered (Ford, Quiñones, Sego, & Sorra, 1992; Mathieu, 
Tannenbaum, & Salas, 1992; Noe, 1986; Tracey et al., 1995). Prior to the 1980’s, few studies 
considered how the work environment affects training effectiveness, and almost none appeared 
in JAP. However, a few JAP studies presaged subsequent research themes. For example, 
Alderfer (1971) showed that employees who were more satisfied with their career prospects were 
more likely to view a management development program positively. This study hinted at a future 
research theme – how the work environment affects employees’ motivations and perceptions 
about training. Similarly, Bowen and Kilmann (1975) developed an early measure of learning 
climate for use in an academic environment which was a precursor to later research efforts 
examining learning climate in work settings  
Conditions affect participation in developmental activities. As noted earlier, 
employees should be considered active participants in the training system, possessing differing 
levels of motivation to participate in developmental activities, to learn when they are 
participating, and to use what they have learned back on the job. Employees often have some 
choice about whether to participate in development, so it is important to understand why some 
employees are more active than others (Kozlowski & Hults, 1987). Several studies in JAP 
confirmed that work conditions and prior experiences can influence motivation to participate. 
Birdi et al. (1997) conducted a study of 1,700 manufacturing employees. They examined 
formal and informal, as well as voluntary and required developmental experiences and 
discovered that employees who received greater management support were more likely to 
participate in voluntary developmental activities. Noe and Wilk (1993) also found that 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 28 
employees’ perceptions of social support and work conditions influenced their participation in 
developmental activities. Hurtz and Williams (2009) gathered data from four organizations 
across two time periods. They reported that participation in developmental activities was greater 
when employees were aware of available learning opportunities, and perceived support for 
employee development, particularly from “important people.” Similarly, Facteau, Dobbins, 
Russell, Ladd, and Kudisch (1995) reported a strong correlation between training reputation and 
pre-training motivation. So while trainee reactions are not necessarily a strong predictor of 
learning (Alliger, Tannenbaum, Bennett, Traver, & Shotland, 1997), reactions become more 
important when viewed as part of the larger training system. A positive learning experience may 
contribute to future participation and motivation, beginning a virtuous learning cycle. In sum, it 
appears that positive experiences, ample opportunities, and a supportive environment can 
increase employee participation in developmental activities.  
The role of supervisor and peer support and organizational practices. Colquitt et 
al.’s (2000) meta-analysis examined how the work environment influences trainee motivation. 
They concluded that supervisor support, peer support, and positive climate are all positively 
related to motivation to learn and motivation to apply learned skills on the job. Maurer and 
Tarulli (1994) found that manager support is particularly influential for those employees who 
value such support.  
Team leader support and team climate are also positively related to post-training 
behaviors. When a leader behaves in a manner that supports what was taught in training, trainees 
are more likely to apply what they learned. This was true even when leader support was 
demonstrated during an interaction with someone other than the trainee (Smith-Jentsch, Salas, & 
Brannick, 2001). In addition to behavioral outcomes, trainees are more likely to respond 
positively to training when they perceive that learning is supported (Sitzmann, Brown, Casper, 
Ely, & Zimmerman, 2008). One way that support for development can be shown is through 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 29 
mentoring. When employees receive career mentoring they are more likely to perceive greater 
organizational support for development (Kraimer et al., 2011). Managers can also support the 
development of others through both “showing” (e.g., modeling appropriate behaviors) and 
“telling” (e.g., providing job related information) (Dragoni, Park et al., 2014).  
A trainee’s supervisor plays a clear role in transfer of knowledge and skills learned in 
training to the job. Interestingly, Taylor, Ross-Eft, and Chan’s (2005) meta-analysis of behavior 
modeling training found that subsequent trainee behavioral changes on the job was far greater 
when the trainee’s supervisor also received the training. That may be because the supervisor was 
fully aware of the training and was better equipped to support and encourage the trainee to apply 
what they learned. Tracey et al. (1995) studied 500+ supermarket managers from 52 stores. They 
found that both workplace climate (e.g., supervisors and peers encouraging use of trained skills) 
and continuous learning culture (i.e., more general cues about learning, such as being given 
stretch assignments) significantly influenced post training behaviors.  
Organizational practices and policies may also be important, including how training 
opportunities are communicated. As noted above, employees need to know about the 
opportunities that are available (Hurtz & Williams, 2009). The message provided about the 
training also matters. Quiñones (1995) found that whether training was described as “remedial” 
vs. “advanced” influenced trainees’ expectations and in turn their motivation to learn and 
subsequent learning and behavioral outcomes. Expectation setting has other implications, as 
Tannenbaum et al. (1991) found that the extent to which training meets a trainee’s expectations 
influences post-training commitment and self-efficacy. 
The role of work experiences. Prior training experiences can influence trainee 
motivation about and reactions to subsequent training and so can prior work experiences. In a 
study of assertiveness training for pilots, Smith-Jentsch et al. (1996) found that pilots who had 
previously experienced work events where assertiveness was needed (e.g., being pressured to fly 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 30 
in bad weather) were more motivated to learn and more likely to demonstrate trained 
assertiveness skills. 
Several studies in JAP have examined how work experiences, including job challenges, 
can contribute to or hinder development. In a study involving over 660 managers, McCauley, 
Ruderman, Ohlott, and Morrow (1994) designed and validated a developmental challenge profile 
for managerial jobs. The profile identified on-the-job challenges that promote leadership 
development (e.g., situations where a leader had to manage business diversity or influence 
without authority). Morrison and Brantner (1992) found that employees learn faster on the job 
when they have greater role clarity and face fewer job challenges, suggesting that ambiguity and 
stress can in some instances hinder learning. But the influence of job stress and challenge on 
learning may be complex. LePine et al. (2004) found that stress associated with “challenge” 
stressors (e.g., working on multiple projects) were positively related to learning motivation while 
“hindrance” stressors (e.g., a lot of busy work) were negatively related. DeRue and Wellman 
(2009) concluded that to optimize learning there needs to be ample challenge. In a study of 
leader development, they found that developmental challenge (e.g., unfamiliar responsibilities, 
working across boundaries) was a plus for development, but only up to a point. It also appears 
that teams learn more when their workload is distributed evenly (Ellis et al., 2003). Perhaps 
when team members are overloaded, it creates challenges for learning throughout the team.  
Collectively, these studies indicate that to fully understand training and development, it is 
critical to investigate the context within which training occurs. Research in JAP has examined 
and helped clarify the interrelationship between work conditions, prior experiences, employee 
motivation, learning, and reactions to training experiences.  
Future Research Directions 
As noted throughout this article, there have been many advances in training and 
development research and JAP has published many seminal articles.  A summary of key 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 31 
conclusions derived from the past 100 years of training and development research is provided in 
Table 1.  These research findings have important practical implications, although there is a need 
for more translations of what we know so as to bridge the science-practice gap.  As the field of 
training and development continues to evolve there are several areas where additional research is 
needed. Below we highlight future research needs for each of the four themes we have discussed. 
Training Criteria: Learning, Transfer, and Effectiveness 
A critical step in developing and implementing a job-relevant training program that meets 
trainee needs is identifying what needs to be learned. Training needs analysis is the process used 
to determine individual, team, and organizational training needs. Based on this information, 
appropriate decisions can be made regarding the objectives of the program and the content to be 
covered in training (Goldstein & Ford, 2002). Although widely considered one of the most 
important steps in developing training, needs analysis has received little research attention and 
only two articles in our database specifically studied the process (Ford et al., 1993; Trabue & 
Dvorak, 1934). This area also warrants greater consideration because of the emerging challenges 
organizations face when conducting training needs analysis. As organizations seek to become 
more agile, for example, the focus is shifting toward more rapid development and deployment of 
training solutions, which means they no longer have the luxury of taking months to conduct a 
detailed needs assessment. Thus, research is needed to understand how to identify training needs 
more rapidly without sacrificing the quality of the information that is gathered. One area to look 
to for guidance is research on large scale societal interventions such as emergency responses by 
the U.N. to a humanitarian crisis or community based interventions such as AIDS prevention. 
Decision makers in these domains want to know what interventions can work and real time 
information on whether the chosen intervention is succeeding or failing. This need has led to the 
development of the rapid evaluation and assessment method (REAM) (McNall & Foster-
Fishman, 2007; McNall, Welch, Ruh, Mildner, & Soto, 2004), which provides the feedback link 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 32 
in the instructional system design models of training that we often talk about (but seldom 
research) between evaluation and reassessment of needs.  
We also need to continue to evolve how we think about and measure learning. The 
Kraiger et al (1993) paper on training evaluation is often cited by researchers as helping to drive 
their conceptualization and measurement of learning. While research has improved our 
understanding of training criteria, many studies continue to measure learning only in terms of 
declarative knowledge. In addition, there is a need to better understand the nomological network 
connecting the different training criteria of learning, transfer, and performance. For example, 
Warr, Allan, and Birdi (1999) employed a longitudinal design of three types of training 
evaluation measures – reactions (enjoyment, usefulness, perceived difficulty of learning and 
motivation to learn), learning (confidence, knowledge level and learning strategy), and 
behavioral change (frequency of use of equipment). They found that reaction measures were 
predictive of changes in learning outcomes but not changes in job behavior. The study 
demonstrates the importance of examining change scores as well as the typical post-training 
assessments for a greater understanding of outcome interrelationships. Blume, Ford, Baldwin and 
Huang (2010) distinguished use from effectiveness in their meta-analysis of factors impacting 
transfer. They found that factors had differential effects depending on the criteria examined. 
Thus, while our understanding of what we mean by learning has advanced, more empirical 
studies examining multiple dimensions of learning, transfer, and performance are needed to build 
up our foundational knowledge around criteria of training effectiveness. 
Kozlowski, Brown, Weissbein, Cannon-Bowers, and Salas (2000) emphasize that taking 
a levels approach to understanding training effectiveness presents both key opportunities and 
conceptual and statistical challenges. A large scale educational research study by Zvoch, 
Letourneau, and Parker (2007) reflects the move to more complex designs that incorporate a 
multilevel and multi-site approach. They evaluated the impact of an early childhood literacy 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 33 
program for over 1200 students in 49 classroom sites distributed across a number of schools. 
Their criterion was changes in literacy performance as assessed by growth curves. A three level 
model was adopted and results analyzed by hierarchical linear modeling for estimating growth 
trajectories. We are in need of similar large scale training designs to examine the impact of 
different amounts or types of training across multiple groups in order to help increase our 
understanding of team and multilevel effects on learning and training effectiveness.  
Finally, the research on learning and transfer has focused almost exclusively on between-
person effects.  Recent efforts that have begun to examine within-person changes in learners over 
time.  For example, Sitzmann and Johnson (2014) examined within-person effects of instructor 
use of jokes, stories, and video clips on self-regulated learning over time (e.g., time on task, 
attention, affect).  Such research is long overdue as it can complement the between-person level 
of analysis studies and lead to better understanding of how differences in instructional and 
contextual factors over time impact changes in learning processes and outcomes.  
Trainee Characteristics 
 Although we have learned a great deal about how trainee characteristics influence 
learning and training success, we need to continue to identify trainee characteristics that are 
relevant to new and emerging modes of learning. For instance, research is needed to identify how 
trainee characteristics interact with informal learning experiences to shape important outcomes. 
Recent research suggests that self-efficacy (Courtright et al., 2014), goal orientation (DeRue & 
Wellman, 2009), and personality (DeRue, Nahrgang, Hollenbeck, & Workman, 2012) play an 
important role in informal learning, but cognitive ability and other motivational factors (e.g., 
core self-evaluations; Stanhope, Pond, & Surface, 2013) likely matter as well. Similarly, future 
research is needed to advance our understanding of the trainee characteristics that are critical 
during technology-based training. Sitzmann and Johnson (2012), for instance, note that planning 
is critical in online training because individuals must often decide where and when to complete 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 34 
the course and how much time to dedicate to learning. Trainees who are highly proactive may be 
more likely to develop plans for how they will successfully complete the training and to also 
formulate alternative strategies in the event they encounter obstacles (Grant & Ashford, 2008). In 
this sense, proactivity may also be important for helping trainees avoid or overcome the technical 
difficulties and other interruptions that often occur during technology-based training and have 
been shown to impair learning and increase attrition (Sitzmann, Ely, Bell, & Bauer, 2010).     
Growth in knowledge management practices has also sparked research into the role of 
individual characteristics in knowledge sharing. Kim and Yun (2015), for example, argued that 
individual characteristics play an important role in determining whether recipients are likely to 
accept and use the knowledge shared by a coworker. In support of this contention, they found 
that coworker knowledge sharing had a stronger positive effect on the task performance of 
individuals who were low in general self-efficacy. In addition to understanding the factors that 
influence whether individuals utilize the knowledge received from coworkers, future research 
needs to identify individual characteristics (e.g., impression management motives, self-esteem) 
that determine whether employees share their knowledge with others (Wang & Noe, 2010).  
Finally, it is important to recognize that the decision to learn and transfer learning to the 
job ultimately resides within the trainee. From an active learning perspective, we can view 
trainees as making personal choices as to what aspects in training to learn and attempt to transfer 
and what aspects to leave behind. Yelon, Sheppard, Sleight, and Ford (2004) developed a model 
of the decision making process leading to the intention to transfer. The key decision criteria were 
(1) how credible the information was; (2) how practical the skills were; and (3) to what extent 
was the knowledge or skill needed. Research is needed that focuses on within person changes in 
perceptions of credibility, practicality, and need as trainees move through a training program.  
Training Design and Delivery 
 Recent research has moved us closer to a theory of learner-centered training design, but 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 35 
more work is needed. Studies should focus on decomposing active learning strategies to better 
understand how and when different instructional, motivational, and emotional design elements 
should be implemented. For instance, prior research has suggested that the timing of emotion-
control strategies during training may be important, although recommendations differ as to 
whether emotion-control should be phased in or out during training (Bell & Kozlowski, 2008). 
Research also needs to elaborate the mechanisms through which active learning interventions 
have their effects. We know quite a bit about how to design training to influence trainees’ self-
monitoring of cognitive functions (i.e., metacognition; Keith and Frese, 2005; Schmidt & Ford, 
2003), but much less about how to influence other potentially important cognitive mechanisms, 
such as the coherence and breadth of trainees’ knowledge structures (i.e., mental models; Bell & 
Kozlowski, 2009). Keith and Wolfe (2014) also note that most studies have examined active 
training and call for research that examines other forms of active learning. A good example is 
recent research exploring interventions, such as structured reflection, that can be used to enhance 
the value of developmental experiences (DeRue et al., 2012). 
 There are also a number of important issues to be addressed regarding the design and 
delivery of technology-based training. First, as noted earlier, future research on technology-based 
training needs to move beyond comparing different media to a focus on understanding the 
features and conditions that influence the effectiveness of these programs (Bell & Federman, 
2013). In particular, one critical issue is to better understand the unique features offered by social 
media, mobile technologies, and other emerging platforms and their impact on learning and 
transfer. Also, in emphasizing comparisons between technology-based and traditional training, 
the field has directed very little attention toward understanding how to leverage blended learning 
methods or flipped classrooms – two increasingly common learning solutions in organizations. 
How does one determine the optimal mix of different formats for a program and does the 
sequencing of technology-based and in-person instruction within a program make a difference?  
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 36 
  Moreover, organizations are increasingly leveraging user-generated content (e.g., videos 
created by employees) in their technology-based training. How do trainees react to this content 
(e.g., perceived credibility) and what are the implications for learning? What guidelines, if any, 
can future research provide to ensure user-generated content enhances learning? Finally, 
employees’ participation in technology-based training is often interwoven with their other work 
and family activities. Thus, future research needs to consider how job demands, work-life 
conflict, and other stressors influence participation in technology-based training during non-work 
hours as well as important outcomes, such as learning and attrition, and investigate the factors 
(e.g., manager and peer support) that may mitigate their impact. 
In recent years, a number of meta-analyses have provided evidence for the effectiveness 
of different team training techniques (e.g., Salas et al., 2008). Thus, as noted by Kozlowski and 
Bell (2013), future research needs center on refining these tools and examining their effects 
across different team contexts. Recent work by Tannenbaum, Mathieu, Salas, and Cohen (2012) 
argues that the fundamental nature of teams is changing. For instance, team composition is 
increasingly dynamic and advances in communication and collaboration technologies make it 
possible for teams to operate with few, if any, face-to-face interactions. These changes suggest 
that our approaches to training and developing teams may also need to evolve. Approaches 
designed to develop generic or transportable teamwork skills, for example, may be particularly 
valuable as team membership becomes more fluid and employees find themselves working on 
multiple teams simultaneously (Ellis, Bell, Ployhart, Hollenbeck, & Ilgen, 2005).  
Training in Context 
 Although recent research has provided valuable insight into learning that occurs in the 
context of work experiences, more attention is warranted given growing organizational efforts to 
exploit the potential of informal learning (Tannenbaum, Beard, McNall, & Salas, 2010). As a 
starting point, research needs to develop a clear conceptualization of informal learning and its 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 37 
defining elements. This is important for not only distinguishing informal and formal learning but 
also for identifying meaningful differences between informal learning and related concepts, such 
as continuous learning (Sessa & London, 2006) and self-development (Orvis & Leffler, 2011). 
Here too, research is needed that provides insight into how to determine the ideal mix of 
informal and formal learning (Noe et al., 2014) and to decompose the elements of work 
experiences that are critical for developing different types of skills. For instance, the 
developmental challenge profile developed by McCauley et al. (1994) was important because it 
identified the features of jobs that contribute to the development of managers. Similar work is 
needed to advance our understanding of how to craft work experiences for developing expertise 
in more technical and service-oriented fields (Ford, Santoro, & Showler, in press).   
 In their discussion of environmental predictors of transfer, Blume et al. (2010) note that 
some factors, such as support, have received considerable attention, while others, such as 
constraints (e.g., level of autonomy, situational constraints), have been relatively neglected. In 
addition, they highlight that relatively few studies have examined the effects of pre- and post-
training transfer interventions (e.g., goal-setting, relapse prevention) and their results fail to 
provide compelling support for their effectiveness. These modest effects may be due to the fact 
that in most studies these interventions lasted 2 hours or less. Thus, future research is needed to 
explore other, more intensive pre- and post-training transfer interventions.    
Conclusion 
Private and public organizations spend vast amounts of money on training and 
development and almost every working adult will spend hours of their lives participating in 
learning experiences. There is both a business and personal imperative to better understand how 
humans learn at work and how best to design, implement, and support training and development 
activities. The state of knowledge regarding training and development has come a long way in 
the last 100 years, with research yielding many practical insights that can help guide practice 
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 38 
(Salas, Tannenbaum, Kraiger, & Smith-Jentsch, 2012). The over 450 related articles published in 
JAP are a rich legacy, contributing to that growth in knowledge. We hope that by examining that 
legacy, researchers can continue to produce meaningful studies, further promoting effective 
learning in the workplace.  
  
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 39 
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100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 64 
Table 1: Summary of Key Conclusions from 100 Years of Training and Development Research 
Training Criteria 
Learning is multidimensional, consisting of cognitive outcomes and strategies, skill-based outcomes, and 
affective outcomes.        
 
Training can be used to influence a broad array of criteria at the individual, team and organizational levels. 
 
Within-person changes in learners over time provide important information for diagnosing and maximizing 
training effectiveness. 
 
Trainee Characteristics 
Cognitive ability is an important predictor of motivation to learn and learning outcomes, but personality traits, 
motivational constructs, values, interests, attitudes, emotions, and perceptions should also be considered.  
 
Trainee characteristics, instructional design, modality, and instructor style influence motivation to learn and 
learning outcomes. 
 
Trainability, which focuses on a person’s capacity to learn a job, is distinct from job success and can be 
measured through tests that include a period of structured instruction followed by an assessment of learning.    
 
Age, motivation to learn, self-efficacy, learning goal orientation, values, and perceptions of social support, 
organizational policies, and incentives predict participation in and/or learning from development activities.  
 
Trainee characteristics which have been found to influence learner motivation and outcomes in face-to-face 
training are relevant for new training delivery methods such as e-learning, games, and simulations.  
 
The strength and direction of the relationships between learning outcomes and ability, self-efficacy, and goal 
orientation can vary over time depending on practice and self-regulatory processes. 
 
Training Design and Delivery 
The effectiveness of a particular training delivery method depends on the skills or tasks being trained and the 
desired learning outcomes. 
 
Training designs that encourage and support more active, self-regulated learning can facilitate the development 
of complex skills and adaptive performance. 
 
The effectiveness of technology-based training is determined not by the media but by the design of the 
instruction and the support provided to learners. 
 
Team training interventions, such as cross-training, leader briefings, and after-event reviews, can be effective 
tools for improving team functioning. 
 
Training in Context 
What happens prior to and after training can greatly influence training effectiveness, so it is critical to take a 
systems perspective that goes beyond instructional design and trainee characteristics and considers the context 
within which training occurs. 
 
Supervisor and peer support, opportunities to apply learned skills on the job, and organizational culture and 
practices can determine the extent to which newly acquired competencies are applied on the job. 
 
Contextual factors such as managerial support and prior training experiences can influence whether employees 
choose to participate in non-mandatory developmental activities. 
 
The work context influences participation in developmental activities and the effectiveness of those activities in 
part because it can increase or decrease employees’ motivation to learn and to apply what they have learned in 
training.               
100 YEARS OF TRAINING AND DEVELOPMENT RESEARCH 65 
 
 
 
Figure 1. Timeline of training and development research in JAP. 458 training articles were published through 2015. Number of cumulative articles 
and articles per decade shown reflect projections for 2010’s based on rate of publication from 2010 through 2015.