“knowing is not enough; we must apply. willing is …...v abstract for training to be effective it...
TRANSCRIPT
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“Knowing is not enough; we must apply. Willing is not enough; we must do.”
Johann Wolfgang von Goethe
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TABLE OF CONTENTS
1 INTRODUCTION .......................................................................................................... 1
1.1 PURPOSE AND OUTLINE OF THIS RESEARCH ............................................ 4 1.2 A WORD ABOUT WORDS ....................................................................... 5
2 TRAINING TRANSFER ................................................................................................ 9
2.1 CONCEPTUAL MODELS OF TRAINING TRANSFER ..................................... 10 2.2 EMPIRICAL RESEARCH INTO EFFECTIVE TRAINING TRANSFER .................. 17 2.3 THE ROLE OF MOTIVATION IN MODELS OF TRAINING AND TRANSFER ...... 20
2.3.1 Conceptualisation ............................................................................................... 22
2.3.2 Definition ............................................................................................................ 23
2.3.3 Dimensionality .................................................................................................... 23
2.3.4 Dynamism ........................................................................................................... 24
2.3.5 Operationalisation .............................................................................................. 25
2.4 CONCLUSION .................................................................................... 25
3 TRAINING-RELATED MOTIVATION ......................................................................... 27
3.1 MOTIVATIONAL STAGES ..................................................................... 30 3.1.1 Participation Motivation ..................................................................................... 31
3.1.2 Learning Motivation............................................................................................ 32
3.1.3 Transfer Motivation ............................................................................................ 33
3.2 MOTIVATIONAL MECHANISMS ............................................................ 34 3.2.1 Can-do Motivation .............................................................................................. 36
3.2.2 Reason-to Motivation ......................................................................................... 38
3.2.3 Energised-to Motivation ..................................................................................... 39
3.3 INTEGRATION AND CONCLUSION .......................................................... 41
4 PROACTIVITY AND TRAINING TRANSFER.............................................................. 45
4.1 PROACTIVE BEHAVIOUR AS GOAL REGULATION PROCESS ....................... 49 4.2 PROACTIVE TRANSFER BEHAVIOUR ..................................................... 49
4.2.1 Envisioning .......................................................................................................... 51
4.2.2 Planning .............................................................................................................. 52
4.2.3 Enacting .............................................................................................................. 52
4.2.4 Reflecting ............................................................................................................ 52
4.3 INTEGRATION AND CONCLUSION ......................................................... 53
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5 POSITIVE COGNITIVE STATES AS ANTECEDENTS OF TRANSFER MOTIVATION ... 57
5.1 HOPE, OPTIMISM, RESILIENCE, AND SELF-EFFICACY ............................. 59 5.1.1 Hope ..................................................................................................................... 59
5.1.2 Optimism ............................................................................................................. 60
5.1.3 Resilience ............................................................................................................. 62
5.1.4 Self-Efficacy .......................................................................................................... 63
5.1.5 Combined Hope, Optimism, Resilience & Self-Efficacy ........................................ 65
5.2 POSITIVE COGNITIVE STATES AS SOURCE OF TRANSFER MOTIVATION ...... 68 5.2.1 PsyCap and Training Effectiveness ....................................................................... 69
5.2.2 Hope, Optimism, Resilience & Self-Efficacy and Transfer Motivation ................. 71
5.3 INTEGRATION AND CONCLUSION .......................................................... 73
6 STUDY 1: MEASURE DEVELOPMENT AND CONSTRUCT VALIDATION FOR TRAINING-RELATED MOTIVATION .......................................................................... 77
6.1 AIM & HYPOTHESES ........................................................................... 77 6.2 SAMPLE & PROCEDURE ...................................................................... 80
6.2.1 Item Generation ................................................................................................... 80
6.2.2 Content Validation ............................................................................................... 82
6.2.3 Pilot Test .............................................................................................................. 83
6.2.4 Development Sample ........................................................................................... 84
6.2.5 Validation Sample ................................................................................................ 85
6.3 ANALYSIS & FINDINGS ....................................................................... 86 6.3.1 Exploratory Factor Analysis ................................................................................. 86
6.3.2 Confirmatory Factor Analysis ............................................................................... 92
6.3.3 Path Analysis ......................................................................................................100
6.4 DISCUSSION .....................................................................................102 6.5 LIMITATIONS .................................................................................. 104 6.6 CONCLUSION .................................................................................. 104
7 STUDY 2: POSITIVE COGNITIVE STATES, TRANSFER MOTIVATION, AND PROACTIVE TRANSFER BEHAVIOUR ..................................................................... 107
7.1 AIM & HYPOTHESES ......................................................................... 107 7.2 SAMPLE & PROCEDURE ...................................................................... 111 7.3 MEASURES ...................................................................................... 112 7.4 ANALYSES ....................................................................................... 114
7.4.1 Data Screening ...................................................................................................114
7.4.2 Measurement Models ........................................................................................116
7.4.3 Path Analysis ......................................................................................................123
7.5 RESULTS ......................................................................................... 127
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7.6 DISCUSSION .................................................................................... 127 7.6.1 Transfer Motivation .......................................................................................... 127
7.6.2 Proactivity ......................................................................................................... 128
7.6.3 Positive Cognitive States ................................................................................... 129
7.7 LIMITATIONS ................................................................................... 129 7.8 CONCLUSION ................................................................................... 131
8 STUDY 3: A LONGITUDINAL STUDY OF THE EFFECTS OF POSITIVE COGNITIVE STATES AND TRANSFER MOTIVATION ON PROACTIVE TRANSFER BEHAVIOUR AND TRAINING TRANSFER ..................................................................................... 133
8.1 AIM & HYPOTHESES ......................................................................... 133 8.2 SAMPLE & PROCEDURE ..................................................................... 136 8.3 MEASURES ...................................................................................... 137 8.4 ANALYSIS ........................................................................................ 138
8.4.1 Data Screening .................................................................................................. 138
8.4.2 Measurement Models ....................................................................................... 140
8.4.3 Path Analysis ..................................................................................................... 143
8.5 RESULTS ......................................................................................... 149 8.6 DISCUSSION .................................................................................... 149 8.7 LIMITATIONS ................................................................................... 151 8.8 CONCLUSION ................................................................................... 152
9 DISCUSSION & INTEGRATION ................................................................................ 153
9.1 SUMMARY & CONTRIBUTIONS ............................................................ 154 9.2 LIMITATIONS ................................................................................... 156 9.3 THEORETICAL IMPLICATIONS AND FUTURE RESEARCH .......................... 158 9.4 PRACTICAL IMPLICATIONS AND DIRECTIONS ........................................ 161 9.5 CONCLUSION ................................................................................... 165
REFERENCES ............................................................................................................. 167
APPENDICES ............................................................................................................. 207
11.1 MEASURES STUDY 2 & 3 ................................................................... 207
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v
ABSTRACT
For training to be effective it must be applied on the job. However,
transfer of training to the workplace is regularly not sufficiently realised. As a
result, individuals and organisations may not reach their performance
potential, and this also questions the rationale for investing costly resources
into professional development activities.
Training transfer is a function of a system of influences, of which research
identified only a few as consistent predictors. Most of these act through the
person being trained. The present research seeks to understand why some
people are more likely to transfer training than others by investigating
individual-level factors that can influence whether what gets learned in
training is applied at work.
This doctoral thesis is anchored in a goal regulatory framework and
examines the influence of three inter-related psychological processes on the
transfer of training. Specifically, it is argued that (1) positive cognitive states
relating to work determine (2) transfer motivation, which in turn influences
(3) proactive transfer behaviour, and training transfer.
To begin, the thesis first proposes a refined multi-stage, multi-
dimensional conceptualisation of training-related motivation. It is argued
that the training-related motivation takes different forms at different stages
in the training process (participation motivation, learning motivation, and
transfer motivation) and each of these forms of motivation contain
dimensions of can-do, reason-to, and energised-to psychological states which
shape goal setting and striving. Study 1 seeks to operationalise these training-
related motivational constructs using heterogeneous trainee cohorts
(N = 368). An independent sample (N = 353) established support for the
internal consistency, multi-dimensionality, and construct validity for the
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parsimonious set of measures. Longitudinal findings suggest sequential
interrelationships between participation, learning and transfer motivation.
The thesis then proposes a model whereby crucial transfer motivation
mediates the effect of positive cognitive states on proactive transfer
behaviours and training transfer. The concept of proactive transfer behaviour
is introduced to the literature as new competencies trained require a person
to self-initiate change in the environment and create opportunities for
oneself, as opposed to transfer being explicitly requested or thought to just
happen. Also, by understanding training as integral episode of the work
experience it is argued that the transfer of training is a function of
individuals’ positive psychological states as they relate to work. Namely,
hope, optimism, resilience, and self-efficacy collectively represent an
individual’s core confidence and transfer motivation is proposed to mediate
the effect of core confidence on proactive transfer behaviours and training
transfer.
Study 2 (N = 949) finds that positive cognitive states at work (core
confidence) relate to transfer motivation, proactive transfer behaviours, and
training transfer. Results also support the conclusion that proactive transfer
behaviours are relevant for realising the transfer of training, and that they
need to be motivated as the influence of a proactive personality is small.
Study 3 (N = 365) confirms these findings via a more robust longitudinal
research design.
The research is discussed in light of methodological strengths and
limitations as well as theoretical and practical implications and directions.
Overall, this thesis enriches psychological perspectives on training transfer
for further research and practice. It provides new constructs and measures of
training-related motivation; and suggests core confidence and proactive
transfer behaviour as fruitful factors that may be managed to improve
training transfer.
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Keywords
confidence; goals; hope; learning; measures; motivation; optimism;
positive psychology; proactivity; professional development; resilience;
self-efficacy; self-regulation; training transfer
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ix
Acknowledgements
Completing my PhD research and this thesis has probably been the most
challenging and interesting activity of my life. The best and worst moments of
my doctoral journey have been shared with many people who deserve to be
thanked.
My first debt of gratitude must go to my supervisor, Prof. John Cordery.
He patiently provided the vision and advice necessary for me to navigate this
intense learning process. John has to be thanked for the freedom he wisely
granted that allowed me to pursue different avenues, make mistakes, earn
success, and thereby grow as a junior scholar that researches, teaches, and
continuously seeks to do more. He humbly contributed exceptional amounts
of time, opportunity, funding, and kindness to make this a productive and
successful PhD endeavour.
Gratitude also goes to Prof. Kerrie Unsworth who co-supervised this
thesis. She was never short of an idea, method, or reference to tackle a
challenge and often it was that specific knowledge and feedback which
steered this research into the right directions.
I gratefully acknowledge the funding sources that made my PhD work
possible: the Australian Research Council and The University of Western
Australia including its Business School and Graduate Research School. In the
same vein, I wish to thank the Australian Institute of Management – Western
Australia for collaborating on this research project, contributing funding, and
supporting data collection efforts. In particular, I thank Dr. Shaun Ridley for
these opportunities as well as for the engaging conversations that sought to
bridge the research and practice of what makes training work.
It has been a great privilege to spend several years in the Management &
Organisation discipline at the Business School of The University of Western
Australia. To be around such an eclectic and collegial faculty has served me
well and I owe many my heartfelt appreciation, including Sharon Parker, Uta
Bindl, Chia-huei Wu, Sandra Kiffin-Petersen, Patrick Dunlop, Gillian Yeo,
Christina Gibson, Amy Tian, and David Day. Many more played a favourable
role over the years and also deserve my sincerest thanks, including Lee
Partridge, Phil Hancock, Mark Griffin, Geoff Soutar, Tracy Taylor, Elvira
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Kurejsepi, Siew Hong Wade, Robyn Oliver, and Mei Han. By the same token,
and for their friendship and assistance, this also includes my fellow PhD
mates Elisa, Tim, Andrea, and Daniel who shared many long hours of
working and several moments of joy and life. Lastly, thank you to the
students for which I had the opportunity to create learning experiences, as
they gave me the chance to develop as a teacher, and to comprehend why
research into training matters.
Who get often overlooked are those that might critique our work the most,
although this is what enables true scholarly progress. Thank you to all the
nameless reviewers who provided constructive feedback on my work. Special
thanks goes to Raymond Noe and Alex Stajkovic for their considerate
feedback and encouragement on this research. Equally, thank you to all those
colleagues who generously gave their time at the various doctoral consortia
around the world to mentor me on how to survive and thrive in academe.
I wish to thank my parents, Isolde and Michael Wenzel, who long ago set
me on the right path to succeed with whatever I set my mind on. Without
you, my educational history would have looked much different, I owe you a
lot. I also want to thank my brothers and sisters and in-laws for their
unconditional support. Deep appreciation goes to my friends in Down Under,
Germany, and elsewhere; in no particular order Ecke, Basti, Robert, Kathi
and all the others for their unflagging encouragement and intellectual
exchange.
Finally, my wife, Christiane, who inspired me to pursue this doctoral path,
and who deserves utmost respect for having gone it herself. She endured the
screams, shared the laughter, and understood the sense of awe and agony
that underpins this intense and rewarding time in life. Awaiting the arrival of
our first child, I wonder what nerdy conversations the little one(s) will have
to tolerate, and what life holds beyond PhD, both will be great. Foremost,
thank you for the love, patience, support, and encouragement that allowed
me to finish this journey.
Thank you.
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List of Figures
FIGURE 1. A MODEL OF THE TRANSFER PROCESS (BALDWIN & FORD, 1988) .......................... 11
FIGURE 2. CONCEPTUAL FRAMEWORK MANAGING LEARNING TRANSFER SYSTEMS
(HOLTON & BALDWIN, 2003) ........................................................................................... 15
FIGURE 3. A PROPOSED MODEL OF TRANSFER (BURKE & HUTCHINS, 2008) ........................... 16
FIGURE 4. FRAMEWORK GUIDING RESEARCH .............................................................................. 26
FIGURE 5. A MULTI-STAGE, MULTI-DIMENSIONAL MODEL OF TRAINING-RELATED
MOTIVATION..................................................................................................................... 42
FIGURE 6. PROACTIVE TRANSFER BEHAVIOUR AS IT RELATES TO TRANSFER MOTIVATION
AND TRAINING TRANSFER ............................................................................................. 55
FIGURE 7. POSITIVE COGNITIVE STATES (CORE CONFIDENCE) AS ANTECEDENTS OF
TRANSFER MOTIVATION AND SUBSEQUENT TRAINING TRANSFER ........................ 75
FIGURE 8. HYPOTHESISED PATH MODEL FOR EXPLORING TRAJECTORIES OF TRAINING-
RELATED MOTIVATION ................................................................................................... 79
FIGURE 9. THE TRAINING-RELATED MOTIVATION GRID ............................................................... 80
FIGURE 10. MEASUREMENT MODEL AND STANDARDISED LOADINGS FOR PARTICIPATION
MOTIVATION..................................................................................................................... 97
FIGURE 11. MEASUREMENT MODEL AND STANDARDISED LOADINGS FOR LEARNING
MOTIVATION..................................................................................................................... 97
FIGURE 12. MEASUREMENT MODEL AND STANDARDISED LOADINGS FOR TRANSFER
MOTIVATION..................................................................................................................... 98
FIGURE 13. ESTIMATED STRUCTURAL MODEL: TRAJECTORIES OF CAN-DO, REASON-TO, AND
ENERGISED-TO PARTICIPATION MOTIVATION, LEARNING MOTIVATION, AND
TRANSFER MOTIVATION. ............................................................................................. 101
FIGURE 14. HYPOTHESISED RELATIONSHIPS BETWEEN PREDICTORS AND MEDIATORS ON
TRAINING TRANSFER ................................................................................................... 110
FIGURE 15. ESTIMATED STRUCTURAL MODEL M1’: CORE CONFIDENCE, TRANSFER
MOTIVATION, PROACTIVE TRANSFER BEHAVIOUR, PROACTIVE PERSONALITY,
AND TRAINING TRANSFER. .......................................................................................... 126
FIGURE 16. HYPOTHESISED MODEL FOR REPLICATING RELATIONSHIPS BETWEEN
PREDICTORS AND MEDIATORS ON TRAINING TRANSFER ...................................... 135
FIGURE 17. ESTIMATED STRUCTURAL MODEL M1: CORE-CONFIDENCE, TRANSFER
MOTIVATION, PROACTIVE TRANSFER BEHAVIOUR, PROACTIVE PERSONALITY,
AND TRAINING TRANSFER ........................................................................................... 147
FIGURE 18. ESTIMATED STRUCTURAL MODEL M1P: USING COMPLETE CASES ONLY............ 148
xiii
List of Tables
TABLE 1. FACTORS AFFECTING TRAINING TRANSFER ............................................................. 19
TABLE 2. STUDY 1: CHARACTERISTICS OF INDEPENDENT DEVELOPMENT SAMPLES ......... 85
TABLE 3. STUDY 1: PATTERN MATRIX FOR PARTICIPATION MOTIVATION ............................. 89
TABLE 4. STUDY 1: PATTERN MATRIX FOR LEARNING MOTIVATION ....................................... 89
TABLE 5. STUDY 1: PATTERN MATRIX FOR TRANSFER MOTIVATION ...................................... 90
TABLE 6. STUDY 1: TRAINING-RELATED MOTIVATION QUESTIONNAIRE ITEMS .................... 91
TABLE 7. STUDY 1: MODEL COMPARISON FOR PARTICIPATION MOTIVATION ....................... 95
TABLE 8. STUDY 1: MODEL COMPARISON FOR LEARNING MOTIVATION ................................ 96
TABLE 9. STUDY 1: MODEL COMPARISON FOR TRANSFER MOTIVATION ............................... 96
TABLE 10. STUDY 1: DESCRIPTIVE STATISTICS ........................................................................... 99
TABLE 11. STUDY 2: ASSESSING DISCRIMINANT VALIDITY ...................................................... 121
TABLE 12. STUDY 2: DESCRIPTIVE STATISTICS, SCALE RELIABILITIES, CORRELATIONS .... 122
TABLE 13. STUDY 2: COMPARISON OF EXAMINED PATH MODELS .......................................... 125
TABLE 15. STUDY 3: DESCRIPTIVE STATISTICS, SCALE RELIABILITIES, CORRELATIONS .... 142
TABLE 16. STUDY 3: COMPARISON OF EXAMINED PATH MODELS .......................................... 146
INTRODUCTION 1
CHAPTER 1
INTRODUCTION
It is commonly asserted that training and development activities engaged
in by work organisations generate considerable benefits for individuals,
teams, organisations, and societies (Aguinis & Kraiger, 2009; Cedefop, 2011).
However, evidence on the degree to which such benefits are realised,
particularly within organisations, is surprisingly mixed (Alliger,
Tannenbaum, Bennett Jr., Traver, & Shotland, 1997; Colquitt, LePine, & Noe,
2000; Tharenou, Saks, & Moore, 2007). Thus, it comes as no surprise that
Kozlowski and Salas (2009) – both distinguished scholars in the field of
learning and training – have argued that researchers need to determine why,
when, and for whom training is effective.
Training effectiveness is more than just a function of learning during a
formal training experience. For training to be truly effective it must be
applied on the job or ‘transferred’ to the workplace (Baldwin, Ford, & Blume,
2009). However, training transfer “does not just happen” (Subedi, 2004) and
2
the failure to achieve effective transfer has implications for individuals,
organisations, and societies alike.
People today must deal with the demands of constant change arising from
rapid rates of scientific, technological, social, and economic development.
These demands affect private as well as professional lives, in which
continuous adaption and learning have become key virtues for survival and
well-being (e.g. Porath, Spreitzer, Gibson, & Garnett, 2011; Solomon, 1999).
In this world of lifelong learning (London, 2012), systematic training helps
equip citizens with the knowledge, skills, and attitudes they need to cope with
such demands (Eraut & Hirsh, 2007).
Training activities are also important for economic stability and growth.
In Europe, 70% of those attending a recent summit of business leaders felt
that increased investment in education and skills development was the
appropriate response in times of economic crisis (Accenture, 2012). However,
with regards to a country’s competitiveness and prosperity, it is also
recognised that “the causal link between higher level skills and increased
productivity is not simply about the acquisition of skills but more broadly
about both skills acquisition and their subsequent deployment” (DTWD,
2010, p. 155).
For organisations, workforce training is increasingly seen as a source of
competitive advantage as they “have shifted their views about employee
development from a separate, stand-alone affair to a fully integrated,
strategic component of the organization” (Salas & Cannon-Bowers, 2001, p.
472; see also Pfeffer, 1998). By the same token, firms are paying more
attention to the returns they receive from their investments in human capital,
seeking to maximise the value that is added by human resource activities (J.
G. Combs, Liu, Hall, & Ketchen, 2006; Huselid & Becker, 1997). This includes
costly training and development activities (De Grip & Sauermann, 2012;
Mayo, 2000), as investigations have shown that the economic utility of
corporate‐wide training can vary significantly (Morrow, Jarrett, & Rupinski,
1997).
In the USA it is estimated that employers spent about $171.5 billion on
employee learning and development in 2010, with an estimated average
INTRODUCTION 3
investment per employee of $1,228, or 2.27% direct expenditure of the
payroll (ASTD, 2011). In the United Kingdom, total employer expenditure on
work-related training is estimated to have been £49bn in 2011 (Vivian, Mark,
Jan, & Davies, 2011). Figures from the UK further show that, on average, an
employee devoted nine full working days annually to training, most of which
(84%) was job-specific.
Given the magnitude of these investments of time and resources, it is
clearly important that work-related training results in beneficial outcomes
within the workplace. Unfortunately, the available evidence suggests that
much of what is learned through structured training fails to have an impact
on how people subsequently perform their jobs. Although the claim that
trainees use only as little as 10% of their trained skills at work (Georgenson,
1982) should be considered “a cautionary tale” (Ford, Yelon, & Billington,
2011), concerns continue to be expressed about the extent to which training
interventions generally fall short of providing the benefits for which they
were designed (Cromwell & Kolb, 2004; Grossman & Salas, 2011). This
shortfall is commonly attributed to a failure of learned knowledge, skills and
abilities to transfer from the training environment back onto the job, and is
termed the ‘training transfer’ problem or challenge. In the context of the
strategic and economic significance that is placed on training by society, and
the large investments of time, money and energy in training activities made
by both employers and employees, failures in the transfer of training are an
important concern for practitioners and researchers alike.
This concern has led researchers to seek to identify factors that can
influence the effective application of knowledge, skills and abilities acquired
through training and development activities, thereby improving work
performance (Alliger et al., 1997; Burke & Hutchins, 2008; Ford & Weissbein,
1997; Kozlowski & Salas, 2009; Salas & Cannon-Bowers, 2001). Although the
literature on training effectiveness has proliferated since Baldwin and Ford’s
(1988) seminal paper on the transfer of training, a recent meta-analysis
revealed that only a few of those factors that have been identified as
potentially influencing training transfer have proved to be consistent
predictors (Blume, Ford, Baldwin, & Huang, 2010). This conclusion was
4
echoed recently by Grossman & Salas (2011, p. 4) who suggested that
“conclusions regarding the key components of transfer remain somewhat
ambivalent”.
1.1 Purpose and Outline of this Research
This doctoral thesis seeks to examine the influence of three inter-related
psychological processes on the transfer of training. Specifically, it will be
argued that (1) positive cognitive states relating to work determine (2) facets
of transfer motivation, which in turn influence (3) the proactive transfer
behaviours, all of which directly or indirectly affect training transfer. The
thesis is organised as follows.
Chapter 2 presents an overview of the literature on the transfer of
training, including key conceptual models and empirical studies. From this
review, motivational processes are identified as a key factor underpinning
training effectiveness. Particular emphasis is then given to a number of
concerns with existing approaches of motivation as it relates to training.
Chapter 3 reviews the concept of human motivation in relation to goal
regulatory theory. Ultimately, a refined conceptualisation of training-related
motivation is proposed that comprises the three interrelated stages
participation motivation, learning motivation, and transfer motivation; each
of which is based on the three psychological processes labelled as can-do
motivation, reason-to motivation, and energised-to motivation.
Chapter 4 reviews the literature on proactivity and applies it to the
training transfer phenomenon. Although individuals are often required to
exercise personal initiative and be proactive in order to bring about changes
to their work following training, such self-directed training transfer has not
been the subject of prior research. The concept of proactive transfer
behaviour is introduced as a means of explaining how transfer motivation
might convert into actual training transfer via self-initiation.
Chapter 5 examines one potentially important source for training
transfer. It reviews the literature on positive psychology and builds the case
for investigating positive cognitive states in order to usefully connect
INTRODUCTION 5
individual’s episodic training experiences with more general thoughts and
beliefs about their work. The constructs hope, optimism, resilience, and self-
efficacy are discussed and it is proposed that they complementary serve as
cognitive antecedents for transfer motivation.
Chapter 6 describes study 1, in which new questionnaire-based measures
of participation motivation, learning motivation and transfer motivation are
developed to validate the conceived training-related motivation construct.
Chapter 7 describes study 2, a large cross-sectional study designed to
test a range of hypotheses linking positive cognitive states, transfer
motivation, and proactivity to training transfer.
Chapter 8 describes study 3, a longitudinal study designed to replicate
findings of study 2; the effects of positive cognitive states and transfer
motivation on proactive transfer behaviour and training transfer.
Chapter 9 integrates and summarises the main findings of the three
studies. Contributions to theory and practice are considered along with
limitations associated with the research, and directions for future research
outlined.
1.2 A Word about Words
The conceptual terms adopted in this thesis are consistent with the
scholarly literature and some boundaries must be placed on this research.
The term competence is used as an aggregate label for any combination of
interrelated cognitive, affective, and behavioural capacities including factual
and procedural knowledge, metacognitions, action routines, and personal
qualities such as values, beliefs, attitudes, motivations, and emotions
(Boyatzis, 2008; Weinert, 2001). Training seeks to mobilise these
components to enable workers to meet the complex demands of a particular
professional position or to successfully carry out a complex work activity or
task (Arthur Jr., Bennett Jr., Edens, & Bell, 2003; Wickens, Hutchins,
Carolan, & Cumming, 2011). This view represents a demand-oriented or
functional approach, placing at the forefront the manifold challenges
6
individuals encounter in the context of work and everyday life (Rychen,
2004). Thus, the term competence (or plural: competencies) is most suitable
when discussing training effectiveness. It has an outcome focus, it does not
describe the learning process, rather it refers to what an individual shall
achieve in results, in an event, or in a way of behaving.
Training can be understood as a systematic approach to affecting
individuals’ competence in order to improve individual, team, and
organisational effectiveness (Baldwin & Ford, 1988; Goldstein & Ford, 2002).
This definition (in combination with the competence view described above)
spans a large number of potential learning-related activities that may be also
commonly categorised under the umbrella term development. In theory,
training activities seek to build specific competencies for, focus upon, and are
evaluated against the job that an individual currently holds. Conversely,
development activities seek to cultivate personal growth focusing on future
jobs and roles. However, as Aguinis and Kraiger (2009) remark, often it is
difficult to determine whether a specific intervention or research study
addresses training, development, or both. Therefore, in the remainder of this
thesis, all efforts that refer to training and development will be termed
training.
On the same note, when speaking about training I refer to learning
activities that are deliberate and organised (Litwinska, 2006). Deliberate
signifies the intention to search for competencies (knowledge, skills, attitudes
etc.) of lasting value; consciously formulated before starting the activity by
the learner or by another entity. Organised implies a planned pattern or
sequence with particular aims, involving a providing agency (person, body, or
institution) which sets up the learning experience. Together, these inclusion
criteria describe a vast range of formal learning activities that may be
provided in-house or by an external training provider, set up as an extensive
program or a condensed burst, delivered in a classroom or online, resulting in
a recognised qualification or not. Of course, a number of activities whose
main purpose is not learning may also produce learning. In fact, accidental,
random, or informal learning experiences occur frequently and can be
INTRODUCTION 7
extremely valuable to individuals and organisation (Watkins & Marsick,
1992), but they are not part of this investigation.
For the purposes of this research the terms transfer of training and
transfer of learning are differentiated, albeit I acknowledge that views differ
in this debate (e.g. Hager & Hodkinson, 2009; Haskell, 2000; Leberman,
McDonald, & Doyle, 2006). Transfer of learning has its origin in the
educational context and describes when previous learning supports new
learning or is rather a hindrance (e.g. Cree & Macaulay 2000; Thorndike,
1932). While this notion is indeed highly relevant to training effectiveness,
my research does not cover learning theory. It is assumed that trainees do
understand the competencies trained. Rather, the conception of transfer of
training is distinct: it is more than a function of the original learning during
training, it relates to the utilisation of work-oriented training (e.g. Baldwin &
Ford, 1988; Broad, 1992). This thesis focuses on training transfer (transfer
of training is used synonymously) and the concept is defined further in this
thesis.
From a human capital perspective, training transfer is – though crucial –
only one building block for training effectiveness. For work training to be
ultimately considered effective, it must develop competencies that are
strategically aligned with an organisation’s goals (Noe & Tews, 2012). For
instance, ideally a training intervention is determined by a thorough needs
analysis and the resulting training design and delivery convert these needs
into appropriate learning opportunities. My research acknowledges that
training alone may not be able to realise its benefits if it is ill prepared,
disconnected from human resource management functions, or if the
organisation is dysfunctional in other areas (Aguinis & Kraiger, 2009).
Likewise, the science of learning, quality instruction, and program evaluation
are also crucial building blocks for training effectiveness (Coultas, Grossman,
& Salas, 2012). These necessary operations should ideally be informed by
research about training transfer but are likely affected by various
organisational contingencies. The present research acknowledges those
realities and assumes respective qualities and features to be equally
distributed in affecting individual and organisational performance.
8
Finally, this research does not consider more distal outcomes such as
organisational or team performance because they arise as a function of a
broad range of factors; training transfer being only one of them. While there
is clearly a need for research about those links including the vertical transfer
of training (Kozlowski & Salas, 1997), the present research deals with
variables and processes that lead up to the horizontal, individual-level
transfer of training.
TRAINING TRANSFER 9
CHAPTER 2
TRAINING TRANSFER
Training transfer signifies that new competencies gained in a training
context are applied in a work environment. This transfer involves retention
and application as well as generalisation and maintenance of new knowledge,
skills, abilities, attitudes, and behaviours (Baldwin & Ford, 1988; Burke &
Hutchins, 2007; Salas, Tannenbaum, Kraiger, & Smith-Jentsch, 2012). That
is to say, training transfer is more than what was learned during training; it is
the evidence that competencies trained were used on the job for which they
were intended.
The importance of training transfer as a concept is illustrated by a
reported meta-analytic correlation between the transfer of training and job
performance of .59 (Colquitt et al., 2000). However, this must be set against
evidence of how little actually gets transferred.
There have been various attempts to estimate how much, on average, gets
transferred from training to the workplace. The figure of 10% as an average
10
transfer rate, originally a speculation by Georgenson (1982), has become the
“sticky idea” (Ford et al., 2011) in the scholarly literature. Cited in a seminal
paper by Baldwin and Ford (1988), the figure was not in fact based on
scientific data (Saks, 2001). Wexley and Latham (2001) and Saks (2002)
respectively estimated that 40 and 62 per cent of content is transferred
immediately after a training intervention, while 25 - 44 per cent remains
transferred after six months, falling to 15 - 34 per cent after one year.
Although these estimates suggest that average transfer rates are not as weak
as has been traditionally thought, they nevertheless reinforce the commonly
held belief that much training fails to result in full and sustained transfer of
new competencies to the workplace.
There has been tremendous growth in training research over the last forty
years, and the training field has grown exponentially in the past decade
(Blume et al. 2010; for comprehensive and historic reviews see Baldwin,
Ford, & Blume, 2009; Cheng & Hampson, 2008; Leberman, McDonald, &
Doyle, 2006). The rise in scholarly interest in training and development in
work organisations is reflected by new conceptualisations (Segers &
Gegenfurtner, 2013) and regular reviews of the training literature (Aguinis &
Kraiger, 2009; Baldwin & Ford, 1988; Burke & Hutchins, 2007; J. P.
Campbell, 1971; Cheng & Hampson, 2008; Cheng & Ho, 2001; Ford &
Weissbein, 1997; Grossman & Salas, 2011; Kozlowski & Salas, 2009; Latham,
1988; Merriam & Leahy, 2005; Salas & Cannon-Bowers, 2001; Salas,
Tannenbaum, et al., 2012; Salas, Weaver, & Shuffler, 2012; Tannenbaum &
Yukl, 1992; Wexley, 1984). A significant part of this literature is devoted to
research and theory relating to training transfer.
2.1 Conceptual Models of Training Transfer
Training transfer has been historically conceptualised in terms of two key
learning-related processes (Blume et al., 2010, p.1067-1068):
“(a) generalization—the extent to which the knowledge and skill acquired in a learning setting are applied to different settings, people, and/or situations from those trained, and (b) maintenance—the extent to which changes that result from a learning experience persist over time.”
Baldw
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2008); it
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TRAINING TRAN
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NSFER 11
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12
Laker (1990) proposed that the temporal dimension (maintenance in
Baldwin & Ford’s model) should include transfer initiation and transfer
maintenance. Transfer initiation refers to whether trainees attempt to apply
the competencies taught in training, whereas transfer maintenance refers to
continued application of trained competencies over time. Laker’s
generalisability dimension (generalisation in Baldwin & Ford’s model)
includes near and far transfer, and is closely linked to what Yelon and Ford
(1999) later classified as closed or open competencies which training
attempts to instil. Closed competencies are those which are established in the
training environment and that can be produced identically in the transfer
environment. In this ‘near transfer’, trainees take stepwise actions in one
particular way according to a set of rules that was implemented at the
training intervention in a precise fashion. Essentially, transfer occurs in work
situations that mirror those in training. For instance, a mechanic that was
trained to change a car tyre can do so immediately after training on the job by
mimicking the behaviours in essentially the same form as they were
presented in training. In contrast, open competencies are those that are
characterised by guidelines and principles that need to be interpreted within
the transfer environment. In this ‘far transfer’, trainees cannot rely on one
single correct way to take action but experience freedom to perform highly
variable competencies as actual situations at work are different from those in
training. For example, leadership development exposes trainees to schemas
that conceptually abstract reality with the intent to allow maximal flexibility
and utility.
Broad and Newstrom (1992) highlighted the role of key stakeholders and
time in affecting training transfer. Three key time periods were identified:
pre-training, training, and post-training. Key stakeholders included
executives, supervisors, performers, performance consultants, evaluators,
performance partners, co-workers, subject matter experts, etc. (Broad, 2005).
In a more actionable view, the stakeholders and time periods form a matrix
and the authors derive strategies that should be undertaken by each
stakeholder at each time point to ensure transfer of training occurs. In
addition to the explicit inclusion of time and stakeholders as a factor in
training transfer processes, Broad & Newstom’s framework is also more
TRAINING TRANSFER 13
systems-oriented than those developed earlier, acknowledging that training
alone does not ensure behavioural change as it does not take place in a
vacuum (see also Goldstein, 1993). A view also echoed by Baldwin and
Magjuka (1997) stating that training experiences should not be considered as
isolated events but episodes in peoples’ organisational and working lives. For
example, the degree of clarity about a training’s purpose before the training
has an impact on training transfer and is a function of a supervisors coaching
regarding desired objectives. A central premise of the model is that relevant
organisational stakeholders need to be involved from beginning to end in
order to maximise the likelihood that transfer will occur. However, while
transfer strategies are suggested, they largely focus on the environment, and
the explanatory psychological mechanisms underpinning these strategies are
little discussed.
Addressing the individual in the transfer process, Russ-Eft (2002)
suggested a training transfer typology that focuses on situational elements
that directly affect the trainee, rather than on a trainee’s dispositional and
personality characteristics. The author compiled and organised constructs
which can be manipulated by researchers or practitioners as part of a training
intervention or its implementation. Elements are categorised along the time-
dimension into pre-training elements, training design elements, and post-
training. This stage view is flanked by situational elements of the transfer
environment (i.e. work environment) which likely affect an individual
throughout the entirety of given learning experience. The contribution of this
typology is that it draws attention to malleable factors that reside in the
individual or the environment and discusses how these factors potentially
affect a trainee and the transfer of training.
Building on the systems view of Newstrom and Broad (1992),
Kontoghiorghes (2004) proposed the investigation of training effectiveness
via organisational factors as they relate to work performance, of which
training and its transfer are parts. The author argued that most factors
studied under the traditional conceptual transfer frameworks (e.g. Baldwin &
Ford, 1988) pertain to trainee characteristics and attributes that are directly
related to the training context or training-related outcomes. Under such view,
14
the work environment is defined in terms of characteristics that mainly
describe the training transfer climate and therefore treat training as a non-
systemic phenomenon, independent of the variables that affect work
performance. Kontoghiorghes (2004) asserted that organisational factors
that directly or indirectly influence performance, and likely a trainee’s belief
that training can actually result in enhanced performance, had been given
insufficient attention by researchers in the training transfer area.
Holton and colleagues (H.-C. Chen, Holton III, & Bates, 2005; Holton III,
Bates, & Ruona, 2000; Holton III, 1996) developed the Learning Transfer
System Inventory (LTSI) framework, which also stresses the importance of
adopting a systemic approach to training transfer. The LTSI framework
comprises 16 factors grouped into motivational, environmental, and ability
elements as well as secondary influences that jointly affect learning,
individual performance, and organisational results. The LTSI is meant as a
diagnostic tool administered post-training to assess individual trainees’
perceptions and the transfer environment (Holton, et al., 2000). Its strengths
are its extensive validation and the comprehensive set of variables that cover
the three training inputs identified by Baldwin and Ford (1988). As a result it
can provide information to organisations about what factors to target for
enhancing training transfer. Conversely, as the instrument is administered
after the training and the model neglects the time-dimension, it falls short of
covering sufficient details about factors that influence transfer before and
during training.
Over the last decade, two noteworthy attempts have been made to
synthesise extant knowledge and integrate elements of these earlier models.
The first by Holton and Baldwin (Holton III & Baldwin, 2003) sought to
integrate the three models generated by Baldwin and Ford (1988), Broad and
Newstrom (1992), and Holton et al. (2000). Aiming to reshape elements into
a more intervention-oriented framework, the authors reconceptualised
Baldwin and Ford’s original ‘trainee’ category to also include teams of
learners. Adopting a systems perspective, they further recognise “that the
learner or team is both an input to the process […] and a unit in the model
that may be shaped by interventions” (Holton & Baldwin, 2003, p. 9).
Illustrate
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TRAINING TRAN
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TRAINING TRANSFER 17
Summarising 25 years of theorising about the phenomenon, no particular
approach dominates contemporary research. The transfer of training might
be best understood as a function of a system of influences. Those influences
comprise stakeholders and processes nested in the work and learning
environment as well as the learner him- or herself. Each of these categories is
thought to carry a range of characteristics with interactions between them
taking place before, on entry, during, on exit, and/or after a training activity.
Accordingly, the transfer of training may be considered a complex and
challenging phenomenon. At the same time, all models reviewed suggest that
training transfer is a function of malleable factors which are susceptible to
deliberate interventions. Consequently, improving transfer can be addressed
by targeting features of the work organisation, the individual, and the
learning experience. Given that these factors are interrelated, a systems
perspective of training transfer is sensible. In other words, work training and
its transfer are not to be understood in isolation but are episodic and
fundamental work experiences for an individual. Ultimately, training
effectiveness is more than just learning new knowledge and skills but also
applying and converting those to competence and performance at work.
2.2 Empirical Research into Effective Training Transfer
The study of training transfer effectiveness focuses on “variables that
affect the impact of training on transfer of training as well as on interventions
intended to enhance transfer” (Aguinis & Kraiger, 2009, p. 465). Many
studies have examined the individual, situational and contextual influences
on multiple dimensions of training transfer (e.g. Chiaburu & Tekleab, 2005).
In fact, reviews by Cheng and Hampson (2008) and Grossman and Salas
(2011) conclude that empirical research into training transfer has produced a
wealth of information about the factors underlying transfer effectiveness. Yet,
both these sets of authors contend that there is also significant variability in
findings across those studies and counterintuitive results that are reported
but not explained.
18
A recent meta-analysis by Blume et al. (2010) provides the most
comprehensive overarching summary of the empirical findings in respect of
the variables linked to transfer effectiveness. This meta-analysis integrates
the results of 89 studies (total N=12,496) and is the only full-scale
quantitative review of its kind in the training transfer domain. A recent
qualitative review by Grossman & Salas (2011) serves as a valuable
complement to the Blume et al.’s (2010) meta-analysis. An overview of the
findings of these two papers is presented in Table 1, categorised in terms of
the contributions made by characteristics of the learning experience, the
work environment, and of the individual being trained.
The learning experience plays a central role, not merely for developing
new competencies, but also in transferring them. Blume et al (2010) found
that transfer was promoted to the extent that the training environment and
the transfer environment (i.e. the job or work setting) were similar. In other
words, a learning experience that is realistic and encompasses characteristics
of the work environment increases the probability that trained competencies
will transfer (Grossman & Salas 2011). Moreover, transfer is enhanced by
employing training design strategies such as behaviour modelling, error
management, adaptive training, error prevention, and scaffolding (Wickens
et al., 2011).
Concerning the work environment, studies have identified the transfer
climate as the most crucial factor influencing transfer (Blume et al., 2010;
Colquitt et al., 2000; Grossman & Salas, 2011). Transfer climate has no
agreed definition and is rather an umbrella concept (Burke & Hutchins,
2007; Holton III, Bates, Seyler, & Segerstrom, 1997). It includes support for
transfer from the supervisor and/or peers via recognition, feedback,
encouragement, rewards, and modelling. The transfer climate may also
comprise opportunities and adequate resources to apply new competencies,
as well as cues that remind of transfer, feedback, and rewards. Additionally,
empirical research has shown that the transfer of training is facilitated by
employing such organisational features at different time points before,
during, and after the training (Saks & Belcourt, 2006), as well as through
processes of training evaluation (Saks & Burke, 2012).
TRAINING TRANSFER 19
Table 1. Factors affecting Training Transfer
Factors identified through meta-analysis Factors identified through qualitative review
Blume, Ford, Baldwin & Huang (2010) Grossman & Salas (2011)
Characteristics of the learning experience (i.e. the setting and events that constitute the learning experience)
optimistic preview (.20)
goal-setting (.08)
behavioural modelling
error management
realistic training environment
Characteristics of the trainee (i.e. the protagonist expected to learn and apply new competencies)
utility reactions (.46*) utility reactions
job involvement (.38*)
cognitive ability (.37) cognitive ability
voluntary participation (.34)
motivation (.29) motivation
conscientiousness (.28)
post-training knowledge (.24)
pre-training self-efficacy (.22) pre-training self-efficacy
post-training self-efficacy (.20) post-training self-efficacy
neuroticism (-.19)
learning goal orientation (.16),
gender (male; .12)
avoid-performance goal orientation (-.12)
locus of control (-.12)
Characteristics of the work environment (i.e. the setting and events in which the trained competencies shall be used)
supervisor support (.31) supervisor support
transfer climate (.27) transfer climate
peer support (.14) peer support
opportunity to perform
follow-up
Note. Factors with meta-analytic effect sizes of <.1 have not been listed. Figures in brackets represent meta-analytic mean population correlation (corrected for unreliability in predictor and criterion).*denotes values that are substantially inflated through same-source and same-measurement-context (Blume et al., 2010)
20
For factors that reside with the individual, cognitive ability is the most
strongly associated with transfer (ρ for job involvement and utility reactions
is much smaller when corrected for bias of same-source and same-
measurement-context; Blume et al., 2010). Trained employees with higher
cognitive ability have more success in processing, retaining and generalising
the competencies learnt. Other stable individual characteristics which have
been found to have a consistent relationship with transfer are personality
constructs such as conscientiousness and neuroticism. The studies also
identified a number of more dynamic individual characteristics which can be
managed to influence transfer capacity. This is especially important when
considering organisational realities in which the capacity to select trainees on
the basis of dispositional characteristics might be constrained by logistic,
legal and political reasons (Blume et al., 2010; Yamkovenko & Holton III,
2010). For instance, voluntary participation in training was found to affect
subsequent training transfer. Also, self-efficacy and motivation were
identified as consistent mechanisms by which individuals are far more likely
to transfer training into the workplace (Blume et al., 2010; Colquitt et al.,
2000; Gegenfurtner, 2011; Grossman & Salas, 2011).
Lastly, the nature of the competence has been found to act as a moderator,
whereby some of the factors discussed predict transfer more strongly when
the training develops “open” competencies (e.g. leadership), rather than
“closed” competencies (e.g. operating particular computer software packages)
(Blume et al., 2010).
2.3 The Role of Motivation in Models of Training and Transfer
Table 1 suggests that many factors influence the transfer of training, and
that no single factor accounts for the majority of the variance in transfer. A
large amount of empirical research into transfer has focused on
characteristics of the employee/trainee (Blume et al., 2010). This is perhaps
not surprising, since the person trained is ultimately responsible for applying
trained competencies at work. Salas and Kozlowski (2009) note that person
and psychological processes are the central elements through which all other
factors act to deliver training outcomes:
TRAINING TRANSFER 21
“All training starts (or should start) by considering who the trainee is, that is, by identifying the individual characteristics, motivations, and skills the trainee brings to training. […] The person-the trainee and his or her characteristics-influences the way that training will be experienced, what will be salient, and how the processes of learning and motivation will unfold.” (p. 463).
The individual is accordingly the basic inflow in a system comprised of the
work and training environment (Broad, 2005; Holton III & Baldwin, 2003),
and can ‘‘elect’’ (or not) to use new knowledge and skills once returned to ‘life
as usual’ (Perkins & Salomon, 2012). The set of psychological processes
within this system which determine specific (transfer) intentions and actions
may be understood as motivation (Kanfer, Chen, & Pritchard, 2008).
Motivation has populated literally all models of the training transfer process
since Baldwin and Ford’s (1988) initial framework. It is also among those
constructs that show high meta-analytic correlation with training transfer
(ρ = .29; Blume et al. 2010; see also Table 1), making it a key explanatory
variable. Indeed, as one reviewer commented, “theories that assume either
the absence of, or a negligible role for, motivation are unlikely to be
scientifically productive” (Gegenfurtner, 2011, p. 163).
At the same time, scholars have also raised a number of concerns about
properties of motivational constructs, labels and the measures that have
evolved in the training domain. For example, in their review of transfer
motivation, Gegenfurtner et al. (2009) conclude that “prevailing theories and
methods of measuring transfer motivation are limited in scope” (p. 416).
Equally, Zaniboni and colleagues claim that: “there is still ambiguity in the
definition and measurement of training motivation” (Zaniboni, Fraccaroli,
Truxillo, Bertolino, & Bauer, 2011, p. 136).
Therefore it is argued that research needs to focus much more attention on
the psychology of motivation as it relates to training effectiveness. Next,
motivation as it relates to the training context is briefly introduced and
current conceptual and operational limitations summarised. The following
chapter 3 then develops a refined conceptualisation of training-related
motivation (TRM).
22
Wexley & Latham (1981) were among the first to highlight the important
role of motivation for work training. They argued that, even if trainees
possess the ability to comprehend and apply new competencies, training
outcomes will be poor if motivation is low. Noe (1986, p. 743) subsequently
identified two aspects of motivation as being particularly salient in the
training context. Motivation to learn was defined as “a specific desire on the
part of the trainee to learn the content of the training program” and
motivation to transfer as “the trainee’s desire to use the knowledge and skills
mastered in the training program on the job”. Because what is learned
influences what is available to transfer, both aspects of motivation are seen as
ultimately influencing behaviours linked to applying new competencies at
work (Colquitt et al., 2000; Naquin & Holton III, 2003). Researchers have
also pointed to pre-training motivational states and processes, for example
to understand training participation (Bates, 2001). Although these and other
TRM constructs and their measures have been a valuable stimulus for
research, five interrelated concerns have been identified about extant
research and theorising on TRM.
2.3.1 Conceptualisation
There has historically been a lack of precision in the conceptualisation of
TRM (Salas & Cannon-Bowers, 2001). Whilst theories of work motivation
and performance abound (Kanfer et al., 2008; Latham, 2007; Pinder, 2008),
for the most part TRM constructs are not well grounded in established
motivation theory (Gegenfurtner, 2011). Few researchers have clearly
articulated the underlying motivational theory when empirically testing a
TRM construct (e.g. Gegenfurtner, Festner, et al., 2009; Zaniboni et al.,
2011). Zaniboni et al. (2010) argue that the ambiguity and lack of
sophistication in conceptualisation results in disadvantageous effects on
training effectiveness theories, types of measures used to assess TRM, and
interpretation of findings.
Thus, research need to begin by providing sufficient theory which clearly
states how and why a given TRM construct should relate to other constructs.
For instance, the meta-analytic paths analysis by Colquitt et al. (2000) and
TRAINING TRANSFER 23
the theoretical review by Beier and Kanfer (2009) provide integrated
narratives on a range of processes and variables involved for understanding
and conceptualising TRM.
2.3.2 Definition
The problems with inadequate conceptualisation carry over to the
semantics and labels employed around TRM constructs. For example, there is
no agreed definition of pre-training motivation. While it signifies the time
before training, it is unclear whether it captures the motivation for attending
training (e.g. Bates, 2001), the motivation to learn during training, as
measured before the training (e.g. Machin & Treloar, 2004), or the
motivation to transfer what may be learned in the training (e.g. Warr, Allan,
& Birdi, 1999). Similar issues arise for the label training motivation, which
has been employed as the desire for training in general (e.g. Zaniboni et al.,
2011), the motivation to attend a particular training course (e.g. Mathieu,
Tannenbaum, & Salas, 1992), or the intent to learn during training (e.g.
Smith, Jayasuriya, Caputi, & Hammer, 2008).
2.3.3 Dimensionality
No grand theory of human motivation exists; instead it is considered a
complex and multi-faceted phenomenon (Ryan, 2012; Shah & Gardner,
2008). Interest in understanding the nature of motivation in relation to work
has led to the development of a range of models that better delineate the
different pathways by which environmental and individual factors influence
motivational processes and their outcomes (Kanfer, 2012; Latham, 2007;
Pinder, 2008). However, Gegenfurtner et al. (2009) cite numerous
publications that conceived and operationalised TRM uni-dimensionally (e.g.
Facteau, Dobbins, Russell, Ladd, & Kudisch, 1995; Major, Turner, & Fletcher,
2006; Noe, 1986). Yet, a model of learning motivation comprising
dimensions of self-eff cacy, goal setting, and goal commitment showed
superior predictive power over Noe and Schmitt’s (1986) uni-dimensional
measure which is often employed by researchers (K. Kim, Oh, Chiaburu, &
24
Brown, 2012); and Beier & Kanfer (2009) have also identified multiple
processes underlying motivation at various stages in the training process.
Only a few constructs and measures of TRM have been purposefully
crafted as multi-dimensional, and they themselves have limitations. The
motivation to improve work through learning (MTIWL; Naquin & Holton III,
2003) is described as a function of motivation to train and motivation to
transfer. However, the two dimensions define two different motivational
goals (to train and to transfer; see 2.3.2) rather than the multiple
psychological processes which would explain respective goal selection and
striving. The MTIWL further combines various subscales but only refers to
other original work from which measures were taken, not fully revealing the
underlying theory for this proprietary instrument (Naquin & Holton III,
2003). Another model of training motivation was based on dimensions of
valence, instrumentality, and expectancy, which correlated highly but showed
differential effects. Yet, the scale used was only empirically tested in Italian
(Zaniboni et al., 2011). Finally, promising attempts to link processes of self-
determination to predict autonomous and controlled motivation to transfer
resulted in mixed findings, and thus require more research (Gegenfurtner,
Festner, et al., 2009).
2.3.4 Dynamism
Motivation is a dynamic psychological state (Kane, 1986). Changes in
environmental characteristics and conditions can influence, both
immediately and over time, the motivational states within an individual.
However, Gegenfurtner et al. (2009) identify just one study out of 33 as
conceiving of TRM as dynamic. Consequently, measures are not adequately
equipped to capture TRM as a variable state: “Doing well in training
programs is important to me” (Facteau et al., 1995); “I work hard to do well
in training programs, even when I don’t like them.” (MTIWL Naquin &
Holton III, 2003); “While applying training at work, I can learn a lot.”
(Gegenfurtner, Festner, et al., 2009); “I think it is important to learn new
things from training activities.” (Zaniboni et al., 2011). Such
operationalisations reflect general views about training, and their latent
TRAINING TRANSFER 25
constructs would capture little motivational variance regarding a distinct
training courses or over time.
2.3.5 Operationalisation
The literature on employing TRM constructs generally provides little, if
any, information about how theoretical notions were translated into
measures and/or regarding their validation (e.g. Weissbein, Huang, Ford, &
Schmidt, 2010; exceptions are Gegenfurtner, Festner, et al., 2009; Zaniboni
et al., 2011). Often, items are taken from the original work of Noe (1986),
thereby passing on limitations of these measures, such as problems of
internal consistency (e.g. Egan, Yang, & Bartlett, 2004). Equally, questions
arise whether measures created specifically for one study, sample or context
can be equally used for another study sample in a different context (original
items relate to principal education in schools; Noe, 1986). Ad hoc adoption or
not reporting item selection and adaption criteria is common yet problematic
(MacKenzie, 2003).
2.4 Conclusion
The transfer of training poses a complex challenge for practitioners and
researchers alike. Various models, theories, and constructs have advanced
our understanding about dispositional and situational factors that can enable
or inhibit the effective application of trained competencies to the workplace.
Meta-analytic findings confirm that a range of those variables consistently
affect training effectiveness. Together, this body of literature suggests that
focussing research on the individual as central part of a larger system is
sensible. The literature review further highlights that much of successful
training transfer is a function of training-related motivation.
However, currently a gap exists between knowing that certain trainee
actions must be motivated and understanding the underlying psychological
processes, antecedents and consequences. Specific motivational constructs in
the realm of training are subject to one or more of the following critiques:
26
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TRAINING-RELATED MOTIVATION 27
CHAPTER 3
TRAINING-RELATED MOTIVATION
Motivation derives from the Latin verb movere, meaning “to be moved” to
a particular action (Ryan, 2012, p. 9). It determines how resources such as
time and energy are allocated to an array of tasks. This chapter develops a
refined conceptualisation of training-related motivation (TRM).
In relation to work, Pinder (1998) offers an encompassing definition for
motivation as “a set of energetic forces that originate both within as well as
beyond an individual’s being, to initiate work-related behaviour and to
determine its form, direction, and intensity, and duration” (p. 11). Various
theories focus on motivation as a precursor to human behaviour (e.g. Deci &
Ryan, 2004; Heckhausen & Heckhausen, 2010; Kanfer et al., 2008; Latham,
2007; Pinder, 2008; Porter, Bigley, & Steers, 2003). Motivational theories
that involve the setting and regulation of goals have become particularly
prevalent in recent times, and are advocated as useful organised system for
understanding and influencing thoughts, emotions, decisions, and behaviour
(Forgas, Baumeister, & Tice, 2013; Locke & Latham, 2002).
28
Goals can be understood as “mental representations of outcome states
that an individual seeks to realize” (Kanfer, 2012, p. 460). Based on the view
that human beings are purposeful by nature, such “desired states range from
biological set points for internal processes (e.g. body temperature) to
complex cognitive depictions of desired outcomes (e.g. career success).
Likewise, goals span from the moment to a life span and from the
neurological to the interpersonal” (Austin & Vancouver, 1996, p. 338). In all
domains of life, goals are believed to be pursued consciously at times and, at
other times, they might be pursued less consciously through impulses,
routines or the influence of external cues (Karoly, Boekaerts, & Maes, 2005;
Stajkovic, Locke, & Blair, 2006).
The literature further recognises two main processes associated with
goals: goal setting and goal striving. Goal setting pertains to processes by
which an individual allocates time or energy across behaviours or tasks, such
as analysing a scenario, selecting a set of goals, planning a strategy for
achievement, and making the choice to actively engage in the pursuit (Locke
& Latham, 2002).
Goal striving refers to processes by which individuals seek to accomplish
those established goals through reducing the discrepancy between a current
state and an envisioned outcome (Carver & Scheier, 1999). Accordingly,
much of human action involves continuous regulation of multiple goals by
controlling impulses, delaying gratification, prioritising etc. (Baumeister,
Vohs, & Tice, 2007). It has been suggested that this capacity to control and
self-regulate actions is “the quintessential characteristic of human beings”
(Forgas, Baumeister, & Tice, 2009, p. 1) and “their most essential asset”
(Porath & Bateman, 2006, p. 185), enabling individuals to function within
dynamic and complex human societies.
Self-regulation refers to the change of the self by the self in bringing
thinking, feeling, and behaving into accord with desired goals or preferred
standards (Forgas et al., 2013). This involves initiating action, putting forth
effort, controlling focus, trying different task strategies, seeking feedback,
evaluating progress, and persisting in the face of obstacles or setbacks
(Kanfer et al., 2008). Gollwitzer and Bayer (1999) propose a perspective on
TRAINING-RELATED MOTIVATION 29
self-regulatory processes which involves four phases: pre-decisional
(choosing among competing desires); pre-actional (forming implementation
intentions in the service of the desired goal); actional (bringing goal direct
actions to a successful end); and post-actional (evaluating whether and what
further action is necessary).
Self-regulatory mechanisms are, or can be made, conscious so as to guide
purposeful activities. That is, goals are argued to be mentally accessible by
the individual and so they do not represent mere impulses, unconscious
motives, or deeply held needs (Pintrich, 2000). As outcome states, goals
provide a reference to which the self modifies, alters, or otherwise changes
itself or its responses including the direction of attention, intensity, and
persistence (Kanfer, 1990). Direction reflects the choice, quality, and level of
the goal. Intensity represents the vigour with which an individual pursues the
associated goal. Persistence denotes the maintenance of the pursuit of the
goal. Taken together, these properties may be considered motivation.
Various incarnations of goal- and self-regulation theories have been used
to explain motivation and outcomes in domains such as sport, education, and
work (Karoly et al., 2005), including aspects of health, wellbeing, and job
performance (Vancouver & Day, 2005), and training (e.g. Brown & Warren,
2002; Johnson, Garrison, Hernez-Broome, Fleenor, & Steed, 2012; K. Kim et
al., 2012; Locke & Latham, 2002; Sitzmann & Ely, 2011; Wexley & Baldwin,
1986). For instance, research has shown that employees are motivated to
participate in developmental activities as a response to perceived gaps in
their competencies, relative to their peers (Zoogah, 2010). In total, these
studies support the notion that the setting of and striving for goals varies as a
result of various internal and external factors, and as a result those
motivational processes mediate the achievement of goals.
Considering that individuals generate and strive for goals over a training
episode it is beneficial to conceptually anchor training transfer research –
and this thesis – in a goal regulatory paradigm. In other words, the transfer
of training involves purposeful and self-regulated acts that must be
motivated.
30
The conceptualisation of training-related motivation proposed in this
dissertation is based on Wolters’ (2003) perspective of human motivation.
According to this perspective, TRM can be understood as either a product or
a process. Viewed as a product, motivation refers to an individual’s state or
willingness to set goals, engage in, or stay on a task. This gives rise to the
question: ‘Motivation for what?’ The motivational stage model of the training
process proposed by Beier and Kanfer (2009) addresses this question. It
provides specific targets or ‘umbrella’ goals: that is, trainees experience a
motivational level or state that influences their choice, effort, and persistence
regarding participating in, learning from, and transferring training.
Viewed as a process, motivation refers to the psychological mechanisms
that account for said choice, effort, and persistence (Schunk, Pintrich, &
Meece, 2007). This gives rise to the question: ‘What gives rise to this
motivation?’ The perspective about the means will be subsequently addressed
by referring to Parker, Bindl and Strauss’ (2010) who compiled a range of
theories relevant for goal setting and striving, categorised and coined can-do,
reason-to, and energised-to.
The stage model of TRM proposed by Beier & Kanfer (2009) is now
discussed in greater detail.
3.1 Motivational Stages
Beier and Kanfer (2009) proposed a heuristic 3-stage model of TRM which
considers the motivation for participating in training, the motivation during
the learning process, and the motivation to transfer the training to the work
situation. Their model has a number of features. First, each stage of the
model describes a distinct motivational domain: participation, learning,
transfer. This allows to discern discrete goals for the main resource allocation
and self-regulation processes involved in a training episode. Second, the
model explicitly incorporates the motivation to participate as key antecedent
for training effectiveness; arguably a variable that has been under-
investigated. Third, the model depicts a temporal ordering (see Broad &
Newstrom, 1992) where the outcomes at each stage influence the other stages
TRAINING-RELATED MOTIVATION 31
of the process. Specifically, the desire to participate in a training activity is
portrayed as having a significant flow-on effect onto the motivation to learn
during training, which subsequently affects the motivation to transfer. Given
these contributions, it is surprising that the work by Beier and Kanfer (2009)
has not received more attention. Interestingly, while scholars have
investigated one or two of these motivational domains (e.g. Holton III et al.,
2000), no empirical research has examined the relationship between all
three: participation motivation, learning motivation, and transfer motivation.
3.1.1 Participation Motivation
No agreed definition exists for participation motivation, it is characterised
by how individuals approach a training opportunity (Beier & Kanfer, 2009).
Trainees enter training activities with varying aspirations, experiences, and
assumptions (Quiñones, 1995; Steele-Johnson, Narayan, Delgado, & Cole,
2010; Yelon, Sheppard, Sleight, & Ford, 2004). For instance, attitudes,
subjective norms, and perceived control as well as reactions to past activities
and perceptions of supportiveness have been found to determine future
training intentions (Bates, 2001; Hurtz & Williams, 2009).
Participation motivation may be viewed as a precondition to training
effectiveness. Empirical research suggests that an individual’s motivation to
participate in a training activity influences subsequent attendance (Fishbein
& Stasson, 1990; Tharenou, 2001), often a prerequisite to learning new
competencies. Indeed, some studies have found measures of pre-training
motivation to predict post-training test performance (Martocchio, 1992;
Webster & Martocchio, 1993) and training transfer (Facteau et al., 1995).
However, others have reported a negative relationship between pre-training
motivation and post-training test performance (Tannenbaum, Matthieu,
Salas, & Cannon-Bowers, 1991).
Understanding the motivational states which precede the actual learning
experience is important, not solely because of the potential impact on
learning and transfer, but also because of increasing expectations that
individuals take ownership for and self-direct their regular competence
32
development (Carbery & Garavan, 2011). Yet, the motivation to participate in
training is arguably the least investigated aspect of training-related
motivation. In the present research participation motivation is defined as an
individual’s desire to join and attend a training opportunity.
3.1.2 Learning Motivation
Learning motivation refers to the desire of the trainee to learn the content
of a training program (Noe, 1986), it determines the choices individuals make
to attend to and persist in learning activities (Klein, Noe, & Wang, 2006). For
instance, trainees motivated to learn have been found to be more likely to
seek out practice opportunities for their learning via rehearsal (Ford,
Quiñones, Sego, & Sorra, 1992). Motivation to learn also influences
knowledge comprehension and competence mastery, independently of ability
to learn (Wexley & Latham, 1981). For example, learners who invested more
time and effort into completing online materials also learned more (K. G.
Brown, 2001).
The motivation to learn construct has been extensively discussed and
researched over the years (e.g. Ainley, 2006; Colquitt & Simmering, 1998;
Kanfer, 1990; Kinman & Kinman, 2001; Noe, Tews, & McConnell Dachner,
2010; Schunk, Pintrich, & Meece, 2007; Wlodkowski, 2011). Empirical
studies support the conclusion that motivation to learn is a robust predictor
of a variety of training outcomes, that include both learning and transfer
(Kontoghiorghes, 2004; Quiñones, 1995; Tannenbaum & Yukl, 1992;
Weissbein et al., 2010).
A meta-analysis of 104 studies (Colquitt et al., 2000) found that
motivation to learn explained meaningful amounts of variance in learning
outcomes, over and above that accounted for by cognitive ability, for
declarative knowledge, skill acquisition, post-training self-efficacy, and
affective reactions to training. Particularly relevant for the present study,
motivation to learn was also a strong correlate of transfer (rc = .58), with path
analyses showing that it mediated between individual and situational
antecedents such as pre-training self-efficacy, valence, job involvement, locus
TRAINING-RELATED MOTIVATION 33
of control, anxiety, and training climate, and a range of learning outcomes
that affect transfer. A decade later, Blume et al.’s (2010) meta-analysis of
training transfer studies also found that motivation to learn exhibited a
moderately strong (.23) correlation with transfer. More recently,
Gegenfurtner (2011)’s meta-analysis also reported a moderate sized
relationship between motivation to learn and transfer (ρ =.28). Both Blume et
al. (2010) and Gergenfurtner (2011) concluded that motivation to learn is
among a select number of malleable trainee characteristics that are important
for subsequent training transfer.
3.1.3 Transfer Motivation
Transfer motivation refers to the desire of the trainee to apply the trained
competencies at work (Noe, 1986). Distinguishing transfer motivation from
learning motivation acknowledges the possibility that individuals might have
a strong wish for acquiring knowledge and stimulating learning experiences
but experience less of a desire to put these competencies to use at work. This
might be due to a lack of compelling reasons to do so, or because the
associated challenges or risks are seen as too daunting. Thus, the transfer
motivation potentially plays a key role in the training process, affecting
whether or not a trainee chooses to apply newly learned competencies at the
workplace (Holton III et al., 2000).
In comparison to the research into learning motivation, there have been
relatively few studies of the motivation to transfer. Gegenfurtner, Veermans,
Festner, and Gruber (2009) identify 31 empirical studies (e.g. Egan, 2008;
Egan, Yang, & Bartlett, 2004; Foxon, 1993, 1997; Gegenfurtner, Festner,
Gallenberger, Lehtinen, & Gruber, 2009; Machin & Fogarty, 2004), and their
review investigates the proposition that motivation to transfer acts as a
mediator between individual and situational characteristics and training
transfer. Interestingly, the authors report that only a third of the studies they
reviewed examined the relationship between transfer motivation and training
transfer. Of these, only three found that transfer motivation precedes the
subsequent use of new competencies training at work (Axtell, Maitlis, &
34
Yearta, 1997; Chiaburu & Lindsay, 2008; Machin & Fogarty, 1997).
Gegenfurtner et al. (2009) remark that this finding may be a function of
variability in criteria, methods, sources, and time points used to measure
training transfer across studies. Indeed, valid and reliable measurement of
training transfer and effectiveness is considered one of the field’s biggest
challenges (Blume et al., 2010; Ford et al., 2011). More recently,
Gegenfurtner’s (2011) meta-analysis found that the population correlation
estimate between transfer motivation and training transfer indicated a strong
relationship (ρ =.44), greater than that for learning motivation (ρ =.28).
In summary, participation motivation, learning motivation, and transfer
motivation are temporally distinct motivational states relating to training; yet
research also suggests that they are sequentially interrelated in their impact
upon training transfer. The review above describes them as motivational
products or goals related to a given training episode. Beier and Kanfer (2009)
also discuss a range of recent advances in motivational theory (i.e. process)
that might underpin the three motivational stages; yet they declare “our goal
is not to provide a unified, comprehensive theory” (p. 66). Moreover, they
discuss a broad array of concepts including goal choice, goal striving, goal
type, goal orientation, valence-instrumentality-expectancy, self-regulation,
self-monitoring, self-evaluation, reaction, person characteristics, and
environmental influences; simply too many to conceive a single useful TRM
construct from. Yet, on the basis of goal regulation theory, the next section
addresses the question “What gives rise to this motivation?’ by
conceptualising the underlying psychological processes.
3.2 Motivational Mechanisms
Facteau et al. (1995) and Chiaburu and Lindsay (2008) noted that two
elements need to be present for transfer to occur: trainees have to believe
that they are 1) capable of mastering challenges associated with the training,
and 2) the extra efforts associated with the training lead to valued outcomes.
Meta-analytic research strongly supports the conclusion that these cognitive
processes are strong predictors for training transfer (Gegenfurtner, 2011).
TRAINING-RELATED MOTIVATION 35
However, literature in other domains would suggest that there is value in
researching motivation mechanisms beyond the cognitive realm (Barsade,
Brief, & Spataro, 2003; Brief & Weiss, 2002; Mowday & Sutton, 1993).
Izard, Ackerman, Schoff and Fine (2000) argue that affect, in addition to
cognition, plays a crucial role in complex human behaviour. Pekrun, Goetz,
Titz, and Perry (2003) emphasise the functional structure of the affect
system, where emotions are defined as coordinated sets of interrelated
psychological processes, including affective, cognitive, physiological, and
motivational components. Affect is inherent to the human condition
whenever an individual interacts with its context, such as in the work
environment (Barsade & Gibson, 2007). Kanfer (2012) explains that for the
most part of the last century affect has been viewed as a rather static
influence on performance, either in terms of valence or attitudes. However,
more recent approaches to motivation portray affect as dynamic, distinct,
biologically driven, and occasionally non-conscious processes that influence
goal choice, goal pursuit, and that activate action (Beal & Weiss, 2005;
Diamond & Aspinwall, 2003; Lyubomirsky, King, & Diener, 2005; Yeo,
Frederiks, Kiewitz, & Neal, 2013).
Although interest in the role of affect in organisational behaviour has
grown substantially over recent decades (Brief & Weiss, 2002; Hartel,
Ashkanasy, & Zerbe, 2005) such affective mechanisms have received little
attention in existing TRM approaches (excepions include Kehr, Bles, &
Rosenstiel, 1999; Machin & Fogarty, 2004). The majority of transfer research
that has examined affect in a training context has conceptualised and
operationalised it as a liking for or satisfaction with the training (e.g. Alliger
et al., 1997; Colquitt et al., 2000; Sitzmann, Brown, Casper, Ely, &
Zimmerman, 2008). Despite this lack of direct focus on affective aspects of
TRM, Noe’s (1986) early work did suggest that motivation related to training
is comprised of an energising component, “a force that influences enthusiasm
about the program” (p, 737). Noe, Tews and McConnell Dachner (2010) have
recently restated the view that affect plays a significant role in TRM and
training effectiveness. They refer to the seminal work of Kahn (1990) and
36
argue for a stronger focus on learner engagement in which “people express
themselves physically, cognitively, and emotionally” (p. 282).
Recent work by Parker, Bindl and Strauss’ (2010) addresses both
cognitive and affective processes associated with motivation. The authors
reviewed a range of theories relevant for goal setting and striving, and
characterised these in terms of three fundamental underlying mechanisms or
motivational processes. They described these as can-do, reason-to, and
energised-to motivation. In the next section, these motivational forms are
described, and their relevance for TRM discussed.
3.2.1 Can-do Motivation
Can-do motivation arises from one’s efficacious beliefs, perceived costs of
action, and control appraisals and attributions (Parker et al., 2010).
Individuals need to believe they can be successful when engaging in an
activity, such as when attempting to master a new competence. Self-efficacy
is based in social cognitive theory and refers to a person’s belief that one is
able to organise and execute necessary resources and courses of action in
order to perform well in a particular situation (Bandura, 1997). Self-efficacy
contributes to better performance by reinforcing an individual’s judgement
that better performance is possible and by generating superior commitment
to such performance goals (Locke, Frederick, Lee, & Bobko, 1984).
Furthermore, efficacy beliefs have been shown to enhance persistence and
increase individuals’ willingness to overcome obstacles (Bandura, 1997). The
phenomenon has been widely studied with relative consistent findings: the
higher one’s certainty about handling something, the better the performance
outcomes (Bandura, 2000; Stajkovic & Luthans, 1998). In the training
literature efficacy beliefs have been found to be strongly positively related to
learning outcomes and training transfer (Blume et al., 2010; Colquitt et al.,
2000). In sum, trainees need to feel confident they can set and strive for
goals relevant for training.
According to Parker et al. (2010), the perceived cost of behaviour or
engaging in a task also characterises can-do motivation (Eccles & Wigfield,
TRAINING-RELATED MOTIVATION 37
2002). That is, goal striving requires resources that are finite (Kanfer &
Ackerman, 1989) and individuals may not engage in actions if they perceive
the effort involved as too costly in terms of time, money, energy, or other
resources relative to the gain they may provide (Aspinwall, 2005). Costs also
involve negative aspects of engaging in a task, such as fear of failure or
opportunities lost by focusing on one action rather than another (Parker et
al., 2010). Thus, trainees might judge the costs of actions needed for training
effectiveness as too high, thereby revising or even failing to set goals vital for
learning or transfer. For example, a trainee with high mental or physical
energy can fully engage in a process of trialling new competencies on the job.
Correspondingly, a trainee with little available time may not sense sufficient
resources to engage in such disruptive activities at work. In consequence,
trainees must consider required efforts as feasible.
Can-do motivation further involves processes of control appraisal and
attribution (Parker et al., 2010). Individuals with high control appraisals
maintain a strong sense of responsibility, do not give up easily, and search for
opportunities to act (Frese & Fay, 2001). Thus, if trainees perceive low
control over processes vital for training effectiveness, then difficulties might
be interpreted as signalling that the goal is not attainable and thus lead to
goal disengagement. For example, if trainees sense little available time or job
autonomy, they may belief that there are no opportunities for use of new
competencies at work. As a result, trainees may not be motivated to
participate in training in the first place. Therefore, trainees need to sense
control over the challenges inherent in acquiring and applying new
competencies.
All in all, can-do motivation signifies trainees’ perceptions about: the
ability to organise and execute necessary resources, the costs of this resource
allocation, and the control over this process. Accordingly, before a training
course commences, individuals should believe they have the capacity to
partake by controlling the process of organising and executing necessary
resources. That is, individuals can invest only finite amounts of time,
presence, attention, or money for learning activities, and they will use self-
referenced information regarding the availability and cost of such resources
38
(Baldwin & Magjuka, 1997; Guthrie & Schwoerer, 1994). Efficacious beliefs
about learning have been consistently related to individuals actively engaging
in developmental activities (Schunk & Ertmer, 2000), learning outcomes
(Schunk, 1990; Zimmerman, 2000) and performance (Burke & Hutchins,
2007). Also, if trainees perceive low control over their learning processes
then difficulties can be interpreted as signalling that the goal is not attainable
and thus lead to goal disengagement (K. G. Brown, 2001). Initial attempts at
using new competencies at work can be difficult, awkward, uncomfortable,
and unsuccessful (Rackman, 1979 in Laker, 1990). Thus, when trainees
transfer training they must believe they have the resources for change and are
in control of associated challenges (Chiaburu & Lindsay, 2008; Ford et al.,
1992; Frese & Fay, 2001; Machin & Fogarty, 1997).
3.2.2 Reason-to Motivation
Trainees might feel able to participate, learn, or transfer but have no
compelling reason to do so. Reason-to motivation characterises why someone
generates and strives for goals. One dominant concept in this area refers to
task value beliefs which describe perceptions of the relevance, utility, and
importance of the task (Eccles & Wigfield, 2002). Based on Vroom’s (1964)
expectancy theory, reason-to motivation is understood as a future-oriented
belief in that individuals anticipate the amount of need satisfaction that will
occur when outcomes are achieved. This motivational mechanism can be
understood as a resource-allocation process where time and energy are
distributed between and allocated to arrays of tasks in order to satisfy needs
(Kanfer & Ackerman, 1989). In other words, people pursue goals when they
recognise that change toward an envisioned future outcome is important.
Beier and Kanfer (2009) state that utility judgments are well recognised in
existing motivation theory and are also useful for understanding goal choice
and pursuit in the training domain. Yet, so they argue, this theory might have
somewhat waned over the years because of “untenable assumptions about
rationality of decision-making processes and the mental calculations that
precede goal choices” (p. 69). And research has indeed shown that rational
reasoning can be trumped by irrational decision making (e.g. Ariely, 2009;
TRAINING-RELATED MOTIVATION 39
Manktelow, 2012). Nevertheless, training effectiveness research has brought
about constructs originating in expectancy theory that meaningfully explain
and show positive relationships with different forms of training effectiveness:
perceived utility (Ruona, Leimbach, Holton III, & Bates, 2002), perceived
relevance (Axtell et al., 1997), training usefulness (Tai, 2006), and utility
reactions (Blume et al., 2010).
In essence, trainees must appraise particular goals as valuable and so
there is a strong case for reason-to motivation. To desire training
participation, employees must expect and appreciate relevant outcomes
(Cannon-Bowers, Rhodenizer, Salas, & Bowers, 1998; Cohen, 1990; Guthrie
& Schwoerer, 1994; Tsai & Tai, 2003). During the learning process,
individuals must sense they will receive a meaningful return on exerted
efforts (Noe et al., 2010; Roszkowski & Soven, 2010; Shechter, Durik,
Miyamoto, & Harackiewicz, 2011; Simons, Dewitte, & Lens, 2003). Ultimately
there must be sufficient conviction about the need satisfaction resulting from
applying and maintaining new competencies at work (Axtell et al., 1997;
Kanfer & Ackerman, 1989; Velada & Caetano, 2007).
3.2.3 Energised-to Motivation
A trainee might be confident about and appreciative of activities reflecting
training effectiveness but not feel stimulated to do anything. Energised-to
motivation refers to activated positive affective states that can influence the
setting of and striving for goals (Seo, Barret, & Bartunek, 2004). From a
neuropsychological perspective, positive affect is associated with increased
brain dopamine levels, which in turn have been found to improves cognitive
flexibility, and to favourably influence episodic memories, working memory,
and creative problem solving (Ashby, Isen, & Turken, 1999). Such affect may
be understood as momentary and elementary feelings that combine both
valence and activation (Russell, 2003). They inf uence the selection and
pursuit of goals and are thus thought to play a beneficial, multifaceted, and
flexible role in self-regulatory processes that cannot be explained by other
current theories (Aspinwall, 1998; Lord, Klimoski, & Kanfer, 2002; Russell,
2003). That is, the experience of energy leads to a high degree of activation
40
which increases the amount of effort put into behaviour (Brehm, 1999).
Hence, activated positive affect is thought to be particularly important for
facilitating proficient and adaptive behaviours where the situation may be
ambiguous and requires persistence (Bindl & Parker, 2010).
Positive arousal or energy have been found to regulate goal processes
(Isen, 2000), including work behaviour (Isen & Reeve, 2005) as well as
learning (Ainley, 2006), and learning (Erez & Isen, 2002). Positive mood
appears to facilitate careful processing of goal-relevant information, even
negative information (Aspinwall, 1998). Very limited research considered
positive affect in relation to training transfer but beneficial effects were found
in relation to motivation to transfer (Machin & Fogarty, 2003; 2004) and
individual’s motivation to improve work through learning (Naquin & Holton,
2003). More generally, it has been found that positive affect fosters the
setting of more challenging goals, helps individuals engage with a more
problematic future (Oettingen, Mayer, & Thorpe, 2005), promotes taking
charge behaviours (Fritz & Sonnentag, 2009), and enhances problem solving
and decision making that is thorough and efficient (Ashby, Isen, & Turken,
1999). Positive moods were associated with higher proactive goal-regulation
(Bindl & Parker, 2011) and using a greater number of informational cues to
make more accurate judgments (Djamasbi, 2007). Positive feelings can
facilitate coping processes (Aspinwall, 1998), creativity, cognitive flexibility,
innovative responding, and openness to information (Isen, 2001), and
prompt change-oriented behaviours (Bindl et al., 2012).
In consequence, energised-to motivation appears a useful dimension for
training-related goals. Research supports that mechanisms associated with
positive affect influences training participation decisions (Karl &
Ungsrithong, 1992; Quiñones, 1995). Positive affective experiences linked to
learning are associated with higher levels of energy and enthusiasm towards
new information (Macey & Schneider, 2008), aroused states of interest that
make it more likely to continue with difficult learning tasks (Ainley, 2006),
and the total and content knowledge acquired (Konradt, Filip, & Hoffmann,
2003). Transfer goals need active striving on the part of the trainee in order
to be realised (Burke, 1997) because good intentions do not automatically
TRAINING-RELATED MOTIVATION 41
lead to goal accomplishment (Gollwitzer, 1999). Positive affect can fuel such
efforts for instigating actions that differ from mental and behavioural
routines (Bindl et al., 2012). Research shows that positive affect activates an
approach action tendency (Seo et al., 2004), broadens individuals’
momentary action-thought repertoires (Isen, 1999), allows people to see
tasks as richer and more varied (Kraiger, Billings, & Isen, 1989), and prompts
more responsible behaviours, such as when completing uninteresting tasks
that need to be done (Isen & Reeve, 2005).
In summary, three separate, meaningful, and complementary pathways,
known to shape individual’s goal setting and striving have been reviewed.
These psychological processes, labelled can-do, reason-to, and energised-to
motivation, are proposed to underpin training-related motivation. Next,
elements from Beier & Kanfer’s (2009) stage theory of training motivation
and Parker et al’s (2010) process approach to goal setting and striving are
combined in order to develop a comprehensive theory of TRM.
3.3 Integration and Conclusion
For training to be effective, trainees must be motivated. This chapter has
drawn on two recent complementary perspectives on motivation as a basis
for proposing a new conceptualisation of TRM, one that recognises the three
key inter-related stages at which motivation arises with respect to a training
episode and the psychological processes that underlie motivation at each of
those stages.
Theory and empirical research support the premise that trainees will
establish sensible goals, initiate action, pursue it, and sustain persistence
when they are confident, see value, and feel activated to do what they desire
to do or needs to be done. Before training, those motivational processes are
concerned with the decision to participate in training. This is followed by
motivational processes during the training that influence learning. Finally,
motivational processes are involved in the transfer of new competencies to
the work environment. These three stages form a sequence: from
participation, through learning, to transfer. Consequently, motivation at each
42
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TRAINING-RELATED MOTIVATION 43
Next, attention must be given to the consequences of training-related
motivation. As discussed in Chapter 2, researchers have examined the
concept of training transfer in many different ways (e.g. Barnett & Ceci,
2002; Haskell, 2000b; Leberman, McDonald, & Doyle, 2006b; Smith, Ford,
& Kozlowski, 1997). Despite the breadth of these perspectives, for the most
part these are taxonomies that distinguish different types of transfer (e.g.
near and far transfer) and are less concerned with the actual means of
transfer. That is, they explain little of what trainees actually do when they are
motivated to transfer learned competencies. It has also been discussed that
things learned in training are not consequentially used at work, the person
trained is ultimately responsible for transferring the training. The following
chapter argues that individuals must frequently create the opportunities and
initiate the means to apply newly trained competencies. In other words, they
must be proactive.
44
PROACTIVITY AND TRAINING TRANSFER 45
CHAPTER 4
PROACTIVITY AND TRAINING TRANSFER
Instead of merely reacting to and being shaped by their work
environment, individuals are also able to be proactive: to seek opportunities,
show initiative, take action, and thus directly and intentionally influence their
dealings and environments (Bateman & Crant, 1993). The notion of
proactivity at work is thought to “make things happen” (Frese, Garst, & Fay,
2007; Parker, Bindl, & Strauss, 2010). In this Chapter, I review the literature
on proactivity at work, explore its potential application to training transfer,
and introduce the concept of proactive transfer behaviour.
In today’s world, job descriptions and task instructions are unlikely to be
able to capture or pre-empt all responsibilities and situations at work. In such
dynamic and complex environments, organisations need engaged employees
who actively perform as part of their role and beyond (Frese & Fay, 2001).
46
Equally, managers rely heavily on employees “to adapt to and introduce
changes in the nature of work and the methods used to carry it out” (Grant &
Parker, 2009, p. 341), for example by setting goals for themselves and
creating their own rewards (Crant, 2000; Frese & Fay, 2001), or by changing
the complexity of and control over their workplaces even when they do not
change jobs (Frese, Krauss, et al., 2007).
A proactive approach to individual and organisational challenges not only
corrects problematic procedures (Frese & Fay, 2001), but also alters
situations in ways that improve performance (Crant, 1995). Proactive
employees are described as being agentic and anticipatory in their actions
(Grant & Ashford, 2008), and as having a designated intention to change
something about the self and/or the environment (Bateman & Crant, 1993;
Grant & Parker, 2009). They are also described as being mindful (Weick &
Roberts, 1993), and adopting a long-term perspective to develop personal
goals that go beyond explicitly assigned tasks (Frese & Fay, 2001).
Conceptually, proactivity is characterised by a) an action orientation,
which involves self-initiating activities opposed to passivity or reaction; b) an
orientation to inf uence and change situations or procedures constructively
and meaningfully, as opposed to waiting for those changes to occur; and c) an
orientation towards the future to anticipate problems and seek opportunities
(Parker et al., 2010; Tornau & Frese, 2013).
Being proactive was identified as one of ‘Seven Habits of Highly Effective
People’ (Covey, 1990). Meta-analytic studies show sizeable positive
relationships between proactivity and commitment, satisfaction, social
networking (Thomas, Whitman, & Viswesvaran, 2010), and supervisor-rated
overall job performance (Fuller & Marler, 2009; Tornau & Frese, 2013) based
on indicators such as sales (Crant, 1995), customer satisfaction (Raub & Liao,
2012), network building (Morrison, 2002), creativity (T. Kim, Hon, & Crant,
2009), entrepreneurial behaviours (Becherer & Maurer, 1999), and
information or feedback seeking (Morrison, 1993a, 1993b). Proactivity has
further been shown to generate positive outcomes for the individual such as
obtaining employment (Kanfer, Wanberg, & Kantrowitz, 2001), career
PROACTIVITY AND TRAINING TRANSFER 47
satisfaction (Seibert, Kraimer, & Crant, 2001), and career success (De Vos,
Clippeleer, & Dewilde, 2009).
Proactivity has been studied under a variety of different labels (Belschak,
Den Hartog, & Fay, 2010), including proactive personality (Bateman & Crant,
1993), personal initiative (Frese, 2001), taking charge (Morrison & Phelps,
1999), and voice (Dyne & LePine, 1998). Recent meta-analytic findings by
Tornau and Frese (2013) show high convergence between the more
dispositional constructs of personal initiative/personality and proactive
personality as well as between the more dynamic constructs of personal
initiative/behaviour, voice and taking charge. A distinction may thus be made
between (1) proactivity as personality, and (2) proactivity as behaviour.
Proactive personality signifies an individual difference, a general and
enduring tendency to take self-initiated action for changing oneself and/or
the environment. It is a broad concept, which encompasses a variety of
general behaviours (e.g. “I am always looking for better ways to do things.”;
Bateman & Crant, 1993). This more stable orientation may be considered
especially important in cases where situational factors exert little positive
influence on the willingness and determination to pursue a self-initiated
course of meaningful action. The strength of proactive personality is
predictive of job performance (Fuller & Marler, 2009; Thomas et al., 2010;
Tornau & Frese, 2013). Research also shows that people with a proactive
personality are more likely to demonstrate a learning goal orientation and
career self-efficacy (Fuller & Marler, 2009).
Proactive behaviours may be understood as self-initiated actions which
are more specific to tasks, situations, or contexts. For example, asking for
feedback is proactive behaviour, as it is instrumental for gaining information
about performance at work that otherwise would not be available (Wu,
Parker, & de Jong, 2013). Parker, Williams and Turner (2006) identified two
dimensions of proactive work behaviours. The first, proactive idea
implementation, involves taking charge of articulating or self-implementing
new knowledge or an idea for improving work aspects. The second, proactive
problem solving, concerns future-oriented responses which seek to prevent
the reoccurrence of a typical work problem by solving it in a new or
48
nonstandard way. Frese & Faye (2001) argue such a classification is
influenced by the context, because what is a new idea or nonstandard in one
environment may be a routine approach in another context. Parker and
Collins (2010) therefore eventually identified three categories of proactive
behaviour at work, that are differentiated by the intended target of the
behaviour: the internal organisation environment (work), the organisation’s
fit with the external environment (strategic), and the individual’s fit within
the organisational environment (person-environment fit).
In general, the antecedents of proactive behaviour are not well understood
(Parker et al., 2006). Factors that have been found to promote proactive
behaviour include individuals’ curiosity, core self-evaluations, and future
orientation (Wu & Parker, 2011), values and affect (Grant, Parker, & Collins,
2009), as well as organisational conditions such as job autonomy and co-
worker trust (Parker et al., 2006), and transformational leadership and team
and organisational commitment (Strauss, Griffin, & Rafferty, 2009).
Proactive behaviour has also been found to be affected by individual
differences, including personality, demographics, and cognitive states (Bindl
& Parker, 2010; Parker et al., 2006). Ohly and Fritz (2007) examined
multiple motivational predictors of proactive behaviour and found that role
orientation and role breadth self-efficacy showed significant relationships,
whereas intrinsic motivation and job self-efficacy did not. Parker et al. (2006)
tested a model in which personality and work environment factors acted as
antecedents of proactive work behaviour. They found significant direct and
indirect effects for personality, role breadth self-efficacy, flexible role
orientation, job autonomy, and co-worker trust on proactive work behaviour.
Finally, the effects of proactive personality on outcome variables such as
job performance have been found to occur mainly via (proactive) behaviours
(Parker et al., 2006; Tornau & Frese, 2013). Hence, Tornau and Frese (2013)
suggest concentrating future research on the behavioural goal-directed side
of proactivity.
PROACTIVITY AND TRAINING TRANSFER 49
4.1 Proactive Behaviour as Goal Regulation Process
Scholars have conceptualised proactive behaviours in different, yet
similar, ways. Frese and Fay (2001) identified several key stages of behaving
proactively, comprising (1) redefinition of tasks, (2) information collection
and prognosis, (3) planning and execution, and (4) monitoring and feedback.
Grant and Ashford (2008) suggested that proactive action involves the
phases of anticipation, planning, and action towards impact. More recently,
Bindl et al. (2012) have developed a model of goal-directed proactive
behaviour that encompasses four behavioural elements or phases:
envisioning (setting goals), planning (preparing implementation), enacting
(performing actions), and reflecting (considering adjustments). Two studies
reported by Bindl et al. (2012) investigate the effect of motivational variables
similar to the can-do, reason-to, energised-to aspects discussed previously on
work-related proactivity and career-related proactivity. They found that high-
activated positive mood (e.g. being inspired, energised, and enthused)
predicted all four elements of proactive goal regulation whereas low-activated
negative mood was positively associated with envisioning only. That is,
dispirited feelings at work might lead to thoughts about changing a situation
but appear not to convert into planning or action.
The conceptualisations of proactive behaviours provided by Frese & Fay
(2001), Grant & Ashford (2008), and Bindl et al. (2012) are strikingly similar
to the self-regulation phases for goal implementation proposed by Gollwitzer
and Bayer (1999), which were discussed in the previous chapter. Given that
prior research suggests favourable effects of can-do, reason-to, and
energised-to motivational states on proactive behaviours (Bindl et al., 2012),
this suggests that proactive behaviour in respect of transferring training may
constitute an important link between training-related motivational states and
transfer outcomes.
4.2 Proactive Transfer Behaviour
It has been said that training transfer “does not just happen” (Subedi,
2004, p. 592). Although a formal training event may be seen as a natural
50
prompt for change, awareness of new knowledge and comprehension of new
concepts trained do not automatically convert to different behaviours at
work. Instead, I argue that transferring new competencies frequently requires
a person to behave proactively – for example, to take active steps to adapt
their learned competencies to fit within the organisational environment
(person-environment fit), to change to the internal organisation environment
(work) to suit the application of those new competencies, or even to change
the organisation’s fit with the external environment (strategic) in order to
provide a better opportunity to make use of what has been learned (see
Parker & Collins, 2010). In other words, training transfer would appear to
require the trainee to behave proactively, at least to some extent.
While contemporary perspectives on effective learning in the training
domain tend to portray the individual as being active and self-directed (e.g.
Bell & Kozlowski, 2008), surprisingly this has not tended to be the case for
training transfer. Griffin et al. (2007) state that proactive behaviours are
often important in ‘weak’ situations, in which individuals have high levels of
discretion, where goals are not tightly specified and the means for achieving
them are uncertain, and where goal attainment is not clearly linked to
rewards. These are characteristics that likely apply to many training transfer
scenarios.
Of course, training transfer behaviours are not always self-initiated; it
likely depends on the context. That is, situational and environmental cues at
work can clearly trigger the application of newly trained competencies; their
use may be officially requested or implicit in a rigid formalisation of work
processes. Therefore, training transfer is thought to be both reactive and
proactive.
Research on proactivity in the context of training is scant and limited to
the personality dimension. Major et al. (2006) found that proactive
personality had significant incremental validity in the prediction of
motivation to learn and significant indirect links to subsequent development
activity, measured as the number of training courses registered for and hours
spent in training during a six months period. This is in line with findings of
prior studies suggesting that proactive personality is related to career
PROACTIVITY AND TRAINING TRANSFER 51
management behaviours including development activities (e.g. Bryson, Pajo,
Ward, & Mallon, 2006; Chiaburu, Baker, & Pitariu, 2006; Orvis & Leffler,
2011). Similarly, Bertolino, Truxillo and Fraccaroli (2011) showed that higher
levels of proactive personality relate positively to the perceived
instrumentality of training opportunities, future participation intentions, and
perceived career development, and those relationships are moderated by age.
In sum, both studies show that proactivity favourably relates to professional
development but do not offer any insights about the relationship between
proactivity and training transfer.
It is therefore argued that researching proactivity in the context of
training transfer is timely. The literature reviewed previously would suggest
focusing on proactivity as goal-directed behaviour. While the self-regulation
perspective has featured strongly in training research for more than three
decades, this has been almost exclusively concerned with the process of
learning and mastering new competencies (Sitzmann & Ely, 2011). However,
Sitzmann and Ely (2011) conclude their recent review by arguing that: “we
must begin to examine how self-regulation after trainees leave the training
environment influences training transfer” (p. 435).
Consequently, building on the earlier work of Gollwitzer and Bayer (1999)
and Bindl et al. (2012), the present research examines the self-initiated
transfer of newly trained competencies at work as a form of self-regulated
goal-directed behaviour, subsequently referred to as proactive transfer
behaviour. Following Bindl et al. (2012), four components of proactive
transfer behaviour are proposed:
4.2.1 Envisioning
When envisioning training transfer, individuals set and decide on goals
that are self-initiated, anticipatory, and change-oriented. That is, the learner
imagines what it would be like to apply what was learned. Decisions are made
among competing desires based on the anticipation how work might be
different as a result of employing new knowledge, skills, attitudes, routines,
or beliefs. For instance, leadership responsibilities may be realised via
principles of inspirational communication that one has recently learned in a
52
training. The employee/leader may imagine future endeavours in which the
deployment of such inspirational communication ultimately results in
meaningful outcomes at work.
4.2.2 Planning
In planning training transfer, individuals form implementation intentions
by preparing to engage in concrete behaviours that are related to their
proactive goal. Independent of environmental cues, a trainee considers
different scenarios and steps to best implement new competencies so the
envisioned outcomes can be produced. For instance, opportunities to apply
inspirational communication are considered with regards to when, where,
how, and with whom.
4.2.3 Enacting
Enacting training transfer consists of overt and/or mental proactive
behaviours that execute prepared plans to actually bring about the envisioned
outcome state. That is, actions are self-initiated and change how the trainee
performs work-related tasks based on new knowledge, skills, attitudes,
routines, or beliefs. For instance, to align subordinates’ with strategic
priorities, inspirational communication is used as trained during group
meetings. This may involves mental preparation in a manner consistent with
trained principles.
4.2.4 Reflecting
In reflecting on training transfer, individuals engage in efforts that seek to
understand the consequences and implications of engaged proactive transfer
activities. Such monitoring efforts evaluate whether and what further action
is necessary, and lead the trainee to sustain or alter cognitive, affective, or
behavioural elements until the initially envisioned outcome state is attained.
For instance, deliberately observing subordinates’ reactions and their
subsequent behaviours or seeking extra feedback from peers about one’s
PROACTIVITY AND TRAINING TRANSFER 53
inspirational communication attempts provides information about success or
failure.
Taken together, proactive transfer behaviour comprises both observable
actions and cognitions. The enacting phase is more outward-focused and
noticeable, provided trained competencies involve behavioural outcome
aspects. The other three phases – envisioning, planning, reflecting – are
mostly internalised, although for example the reflection phase may include
active observable feedback-seeking behaviour.
An important question arises as to whether proactive transfer behaviour is
more appropriately conceptualised as a multifaceted or integrated construct.
That is, regulatory processes are by definition cyclical and iterative (Bindl et
al., 2012; Gollwitzer, 1999; Karoly et al., 2005). For instance, if outcomes do
not appear satisfactory, one might go back and re-think alternative ways to
improve transfer and next seek feedback and confirmation on those revised
plans before eventually attempting to enact them. The exact engagement with
a particular self-regulation element may also play out non-consciously albeit
the overall engagement with a goal is consciously intended (Boekaerts &
Cascallar, 2006; Fitzsimons & Bargh, 2004; Karoly et al., 2005). In other
words, the four phases, while logically articulated, may not always be fully
sequential and consciously distinguishable in an applied context. Thus, future
research needs to explore how proactive transfer behaviour may be best
modelled – as four distinct behavioural elements, or as a cluster of closely
related behaviours reflecting a single higher-order proactive transfer
behaviour construct.
4.3 Integration and Conclusion
This chapter has proposed that proactive transfer behaviour, via goal self-
regulation, will be an important determinant of successful self-initiated
training transfer.
On the whole, examining proactivity is timely because in a highly dynamic
world organisations increasingly rely on self-directed employees who
anticipate and initiate change. In the last two decades a large literature on
54
proactivity has emerged, suggesting that it is “a high-leverage concept rather
than just another management fad” (Crant, 2000, p. 435), a conclusion that
is reinforced by recent meta-analyses on proactivity and its consequences
within organisational settings.
Empirical evidence shows that proactivity promotes individual and
organisational performance, and that it is particularly pertinent whenever
aspects of work and its performance cannot be strictly formalised. In spite of
this, no previous studies have examined the role of proactive behaviour in
promoting training effectiveness.
The case was made in this chapter that investigating the role of proactivity
in training transfer is relevant, as organisations require employees that are
not just highly skilled but who can also self-direct in putting trained
competencies to use. Proactive training transfer implies seizing opportunities
and creating situations to apply newly acquired competencies as opposed to
wait and react for transfer to be explicitly requested or just happen.
Research suggests that proactivity is a function of both a more durable
and generalised proactive personality and situational factors that can
motivate more specific proactive behaviours. Theory and empirical evidence
would further suggest that proactive personality exerts its influence through
proactive behaviours. In the event that proactive behaviour is found to affect
training transfer, organisational and managerial interventions could
potentially be employed for enhancing training transfer via this means. Given
that proactive behaviours need to be motivated (Bindl et al., 2012), “transfer
motivation is [..] likely to provide the proactive component needed for actual
transfer” (Chiaburu & Lindsay, 2008, p. 201).
Proactive transfer behaviour is understood as a form of goal self-
regulation that is self-initiated, change oriented, and future focused. It is
characterised by acts of envisioning, planning, enacting, and reflecting, which
themselves need to be motivated. However, training transfer will not be self-
initiated each time; it can also be a direct response to situational and
environmental cues or demands. Thus, proactive training transfer processes
will account only partially for training transfer (Figure 6).
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POSITIVE COGNITIVE STATES AS ANTECEDENTS OF TRANSFER MOTIVATION 57
Chapter 5
POSITIVE COGNITIVE STATES AS
ANTECEDENTS OF TRANSFER
MOTIVATION
The transfer of new competencies learned can occur only after a training
experience. Yet, training experiences should not be considered as isolated
events but episodes in peoples’ organisational and working lives (Baldwin &
Magjuka, 1997). Even so, Naquin and Baldwin (2003) argue there is little
research about what constitutes “transfer-ready” employees or how they may
be managed. How favourably people appraise their overall personal situation
at work would appear to be an important consideration in determining how
they approach any training experience and, in particular, how motivated they
are to transfer the results of training. In this chapter, I first review the
literature relating to a set of work-related cognitions and then examine how
58
these may be related to motivational states relevant for the transfer of
training.
In recent times, much attention has been devoted to the study of
positively-framed psychological constructs as they impact on people, work,
and organisations (Christopher, Richardson, & Slife, 2008; Gable & Haidt,
2005; Linley & Harrington, 2010). The so-called ‘positive psychology’
movement developed as a reaction to what was seen by some as psychology’s
preoccupation with deficit phenomena (C. Peterson, 2006), seeking instead
to study the conditions and processes contributing to human f ourishing and
optimal functioning. The positive psychology movement has grown massively
with hundreds of empirical studies, new interventions, and theories spanning
emotional, cognitive, biological, societal, clinical and more perspectives
(Seligman & Steen, 2005; Sheldon, Kashdan, & Stege, 2011).
Over the past decade, there has been considerable interest from
organisational scholars in the application of the positive psychology
paradigm to the study and management of behaviour in organisations. Based
on the premise that factors that bring about a negative state (e.g. stress) are
not necessarily the same factors that cause a positive state (e.g. thriving),
several schools of thought have evolved that focus on the application of a
positive psychology paradigm in organisational settings. One body of
research (labelled Positive Organisational Scholarship) has focused primarily
on the generative dynamics in institutions and organisational constructs that
then lead to the development of human strength (Cameron, Dutton, & Quinn,
2003; Cameron & Spreitzer, 2011; Roberts, 2006; Spreitzer & Cameron,
2012). Another body of work (labelled Positive Organisational Behaviour) has
been more concerned with individual level positive psychological concepts
that are amenable to management (Bakker & Schaufeli, 2008; Luthans &
Avolio, 2009; Luthans & Youssef, 2007; Luthans, 2002). A distinct focus of
this latter body of research has been on malleable factors that can act as
internal resources to an individual when faced with the demands and
responsibilities inherent in work roles and settings. For example, constructs
like authenticity, mindfulness, positive emotions, or creativity have been
found to meaningfully relate to measures of performance, retention,
POSITIVE COGNITIVE STATES AS ANTECEDENTS OF TRANSFER MOTIVATION 59
sustainability, innovation, or well-being (Linley & Harrington, 2010; Lopez,
2011).
One particular set of positive psychological constructs has been the
subject of considerable attention from organisational behaviour researchers.
This set comprises the psychological states known as hope, optimism,
resilience and self-efficacy.
5.1 Hope, Optimism, Resilience, and Self-Efficacy
Stajkovic (2003, 2006) describes hope, optimism, resilience, and self-
efficacy as manifestations of a person’s core confidence, pointing out that
“employee’s concerns over their work are typically linked to a perceived lack
of confidence to handle work demands rather than to the objective difficulty
to executing such demands” (p. 1209). Luthans and colleagues (2004;
Luthans & Youssef, 2004) argue that these four constructs collectively
amount to psychological capital, explained as a resource that can be drawn
on by the individual in response to the opportunities and challenges
presented by work, and to the benefit of both themselves and the
organisation.
The constructs hope, optimism, resilience, and self-efficacy are now
individually described before recent literature on their potential combined
functioning is reviewed. The section that follows will propose that these
positive cognitive states serve as antecedents for transfer motivation.
5.1.1 Hope
Hope has been defined as “the perceived capability to derive pathways to
desired goals, and motivate oneself via agency thinking to use those
pathways” (Snyder, 2002, p. 249). Viewed thus, the hope construct is
comprised of three elements: goals, pathways, and agency. Goals provide the
targets for mental action sequences. Yet, they remain unanswered calls
without thoughts that identify usable pathways to those goals. Lastly, agency
is the driving component in hope, manifesting in the perceived capacity to
60
use one’s pathways to reach desired goals (Snyder, 2002). The duality of
‘willpower’ (agency) and ‘waypower’ (pathways) sets the construct apart from
the common usage of the word hope, as in ‘hoping for the best’. Therefore,
hope can be understood as a cognitive condition necessary to bring about
determined goal pursuit (Oettingen & Gollwitzer, 2002). In short, hopeful
individuals select specific goals they want to achieve, and figure out how to go
about doing that.
Research has shown that hopeful thinking predicts eventual goal
attainment (Feldman, Rand, & Kahle-Wrobleski, 2009). Higher hope has
been found to be related to better academic performance, athletic
performance, psychological adjustment, coping with physical illness, more
sense of life meaning, and finding benefit in adversity (Rand & Cheavens,
2009). For example, findings from a 3-year longitudinal study suggest that
hope uniquely predicts objective academic achievement above intelligence,
personality, and previous academic achievement (Day, Hanson, Maltby,
Proctor, & Wood, 2010).
Organisational research has found relationships between hope and valued
outcomes such as commitment and job satisfaction (Adams et al., 2002).
Through its waypower and willpower components, hope has also been shown
to predict different dimensions of creativity (Rego, Machado, Leal, & Cunha,
2009). More hopeful management executives produced more, and better
quality solutions to a work-related problem (S. J. Peterson & Byron, 2008),
and had more profitable work units along with better employee satisfaction
and retention rates (S. J. Peterson, Gerhardt, & Rode, 2006). Overall, hope
plays an important role for work performance and employee well-being
(Reichard, Avey, Lopez, & Dollwet, 2013). One recent study found that hope
was positively associated with the development of vocational competencies at
work (Wandeler, Lopez, & Baeriswyla, 2011).
5.1.2 Optimism
Optimism has been conceptualised and operationalised in two principal
ways. One is based on expectancy-value theory (Vroom, 1964); describing the
POSITIVE COGNITIVE STATES AS ANTECEDENTS OF TRANSFER MOTIVATION 61
expectations people hold about the future. Regardless of present
circumstances, optimists anticipate that future events will be positive in
nature and negative events scarce (C. Peterson, 2000). Another way
optimism has been conceptualised involves people’s explanatory style; how
individuals explain the causes of events that happen to them. This attribution
style deems positive events as personal, permanent, and pervasive, and
negative events as external, temporary, and situation-specific (Seligman &
Schulman, 1986). Both approaches are conceptually distinct (Carver, Scheier,
& Segerstrom, 2010), yet they shape the nature and level of goals individuals
set and associated striving behaviour (Forgeard & Seligman, 2012).
Being optimistic is described as allowing individuals “to acquire resources
to pursue goals, be persistent, and be open to opportunities” (p. 115; Forgeard
& Seligman, 2012). Some have argued that high levels of optimism can be too
much of a good thing (Armor & Taylor, 1998; Schneider, 2001; Weinstein &
Klein, 1996) as for example a strong positive bias would lead to greater
persistence which could cause extra goal conflict (Segerstrom & Nes, 2006)
or an unwillingness to disengage from impossible tasks (Aspinwall & Richter,
1999). However, people high in optimism were actually found to disengage
more quickly from (truly) unsolvable tasks in order to allocate effort to
potentially solvable tasks. As a result, optimists had a higher overall
performance (Aspinwall & Richter, 1999). Yet, researchers have also
identified scenarios in which too much optimism can have drawbacks, such
as in gambling behaviour (Gibson & Sanbonmatsu, 2004) or entrepreneurs
decision making (Hmieleski & Baron, 2009). This form of optimism is mainly
associated with people being unrealistic as a result of their comfortable status
quo (Heine & Lehman, 1995). Optimism in the service of higher efforts and
standards likely leads to good outcomes (Forgeard & Seligman, 2012; Kirk &
Koeske, 1995).
Empirical research links higher optimism to higher emotional well-being,
more effective coping strategies, better physical health, greater success in
school and on the playing field, as well as likeableness and other
advantageous aspects of interpersonal relationships (Carver et al., 2010;
Forgeard & Seligman, 2012). Research in optimism’s application to the
62
workplace has shown desirable effects, relating to higher productivity and
lower turnover in insurance sales agents (Schulman, 1999; Seligman &
Schulman, 1986), less distress symptoms and burnout, more affective
commitment and job satisfaction (Kluemper, 2009), together with increased
individual and organisational performance (Green Jr, Medlin, & Whitten,
2004).
5.1.3 Resilience
Resilience can be understood as reduced vulnerability to environmental
risk experiences, the overcoming of a stress or adversity, or a relatively good
outcome despite risk experiences (Rutter, 2006). Although there are varying
conceptions (Luthar, Cicchetti, & Becker, 2000; Rutter, 2012), resilience at
the individual level has been mainly characterised as the capability to cope
successfully in the face of significant change, adversity, or risk (Masten,
2001). It was also described as “a capacity to rebound or bounce back from
adversity, uncertainty, conflict failure or even positive change, progress and
increased responsibility” (Luthans, 2002, p. 702). In addition to these
conceptualisations describing means of recovery, resilience is equally noted
to reflect the quality of sustainability (Reich, Zautra, & Hall, 2010), which
portrays the capacity to absorb disturbances before straining experiences
transpire and to continue on in this face of adversity (Bonanno, 2004).
Therefore, resilience may also be conceived as the capacity to endure stress
(Staal, Bolton, Yaroush, & Bourne, 2008).
While it is known that the capacity of resilience often develops through
exposure to adversity (i.e. “people rise stronger from the ashes”, Luthar et al.,
2000; Rutter, 2006), the mechanisms of action that actually serve to account
for this ability are not well understood (Staal et al., 2008). Concepts
associated with resilience include processes of forgiveness, resourcefulness,
locus of control, adaptive distancing, mindfulness, humour (Benard, 2004),
temperament, meaning of life, parenting quality, or effective schooling
(Masten, Cutuli, Herbers, & Reed, 2009). Whether these concepts promote
resilience, describe resilient behaviour, are resilience, or are outcomes of
resilience, depends on the theoretical lens applied (Luthar et al., 2000;
POSITIVE COGNITIVE STATES AS ANTECEDENTS OF TRANSFER MOTIVATION 63
Rutter, 2006). All included, after disruptive and demanding events, people
with high resilience display a greater capacity to quickly regain psychological,
physiological, and social equilibrium (Zautra, Hall, & Murray, 2010).
In general, although resilient people face no less of a challenge than others
when they confront difficult change, research has found they tend to regain
their balance faster, maintain a higher level of productivity, are physically
and emotionally healthier, and generally rebound from the demands of
change even stronger than before (Diehl & Hay, 2010; Friborg, Barlaug,
Martinussen, Rosenvinge, & Hjemdal, 2005; Metzl, 2009). The bulk of
empirical knowledge about resilience comes from developmental psychology
and research on vulnerable children. For instance, resilient adaptation
among at-risk children during infancy and toddlerhood was found to predict
competent functioning during the elementary school years (Egelanda,
Carlsona, & Sroufe, 1993). Similarly, children found to have greater resilience
maintained high functioning in everyday life across a period of over 30 years
(Werner, 1995, 2013). Albeit less researched, resilience beyond childhood is
also found to lead to positive adaptation by helping adults sustain access to
daily positive emotions that lead to recovery from stress (Ong, Bergeman, &
Boker, 2009). Individual resilience is little explored in the context of
organisations and work, and so far has been found to relate favourably to job
dissatisfaction, turnover intention, burnout, coping skills, and performance
(Beckett, 2011; Everly, Smith, & Lating, 2009; Luthans & Avolio, 2005). Only
one study was identified that links resilience to aspects of learning,
correlating it positively with persistence towards goal achievement (Kemp,
2001).
5.1.4 Self-Efficacy
Self-efficacy refers to a person’s belief that one can generate necessary
means in order to perform well in a particular situation (Bandura, 1998).
More specifically, self-efficacious thoughts appraise the ability to accomplish
goals via existing resources such as knowledge, skills, or energy to
successfully execute required actions (Bandura, 2011). Individuals with low
self-efficacy conceive challenges more arduous than they really are and
64
subsequently reside in the status quo. Alternatively, people who perceive
themselves as highly efficacious activate attention and energy to set and
pursue goals (Bandura, 2001). Moreover, according to Bandura (1997) high
self-efficacy leads to heightened goal striving when attainment of the goal is
challenged. Essentially, those believing in their own abilities do better.
Self-efficacy is arguably the most prominent construct derived from
social-cognitive theory and has been widely studied. Cumulative empirical
evidence suggests positive causal relationship between efficacy expectations
and goal attainment in arrays such as sport performance (Klassen & Chiu,
2010), computer usage (Compeau, Gravill, Nicole Haggerty, & Kelley, 2006),
exercise behaviour (McAuley, 1993), and academic attainment (Zimmerman,
Bandura, & Martinez-Pons, 1992). Efficacy beliefs were also found to have
extensive favourable impact on various work-related aspects including
leadership (G. Chen & Bliese, 2002), team or group performances (Gully &
Incalcaterra, 2002; Stajkovic, Lee, & Nyberg, 2009), job satisfaction (Klassen
& Chiu, 2010), career satisfaction and salary (Abele & Spurk, 2009), and
stress and burnout (Schwarzer & Hallum, 2008). Finally, two meta-analytic
studies concur that the higher the self-efficacy, the better the work and
performance outcomes (Sadri & Robertson, 1993; Stajkovic & Luthans, 1998).
However, the most recent meta-analysis notes that this effect is substantially
reduced when more distal variables such as general mental ability,
personality, or prior experience are entered (Judge, Jackson, Shaw, Scott, &
Rich, 2007).
As identified in chapter 2, out of the four constructs reviewed above, self-
efficacy is the only one that has received considerable attention in learning
and training research. Whether acquired before or during training, higher
self-efficacy leads to greater participation, more motivation to learn, and
better learning outcomes (Baldwin & Magjuka, 1997; G. Chen, Gully,
Whiteman, & Kilcullen, 2000; Colquitt et al., 2000; Matthieu et al., 1992;
Quiñones, 1995). In line with theory, individuals reporting higher self-
efficacy work harder and persist longer during learning activities (Phan,
2011), are more likely to perform the tasks they were trained for, including
more complex and difficult tasks (Ford et al., 1992). Self-efficacy has been
POSITIVE COGNITIVE STATES AS ANTECEDENTS OF TRANSFER MOTIVATION 65
linked to different posttraining behaviours (Gaudine & Saks, 2004; Matthieu
et al., 1992), including skill maintenance (Stevens & Gist, 1997), and has also
been found to be important for the transfer of training (Gegenfurtner, 2011;
Tai, 2006).
5.1.5 Combined Hope, Optimism, Resilience & Self-Efficacy
The review to date has treated hope, optimism, resilience, and self-efficacy
as conceptually representing discrete constructs, and many scholars concur
with this view (Aspinwall & Leaf, 2002; Luthans, 2006; Shorey, Snyder,
Rand, Hockemeyer, & Feldman, 2009; Snyder, 2002). In fact, in the
literature, the constructs originated independently from each other,
describing different psychological mechanisms. For instance, individuals can
believe that a certain behaviour will produce a particular outcome (i.e.
optimism; Scheier & Carver, 1985), but may not believe they can perform that
behaviour (i.e. self-efficacy; Bandura, 1977).
Research has also demonstrated empirically that the constructs have
discriminant validity with studies differentiating hope from optimism
(Bailey, Eng, Frisch, & Snyder, 2007; Bruininks & Malle, 2006; Bryant &
Cvengros, 2004; Carvajal, Clair, Nash, & Evans, 1998; Huprich & Frisch,
2004; Rand, Martin, & Shea, 2011; Rand, 2009; Shorey, Little, Snyder, Kluck,
& Robitschek, 2007), the above from self-efficacy (Carifio & Rhodes, 2002;
Davidson, Feldman, & Margalit, 2012; Magaletta & Oliver, 1999), and the
above from resilience (Luthans, Avolio, & Norman, 2007).
In spite of this, scholars have also argued that all four constructs share an
underlying agentic capacity and therefore have sought to either combine
them into aggregate constructs or treat them as first-order manifestations of
a higher-order construct. For example, Stajkovic (2003, 2006) used the term
core confidence to describe a latent or second-order construct with hope,
optimism, resilience, and self-efficacy as its first-order manifestations. About
the same time, Luthans et al. (2004; Luthans & Youssef, 2004) assembled the
same constructs into a composite variable they termed psychological capital
(later PsyCap). While core confidence has received very little attention as a
66
construct, PsyCap has amassed a growing body of literature which is now
discussed.
After about one decade of accumulated PsyCap research, the authors of
the only meta-analysis in this area note “significant positive relationships
between PsyCap and desirable employee attitudes (job satisfaction,
organisational commitment, psychological well-being), desirable employee
behaviors (citizenship), and multiple measures of performance (self,
supervisor evaluations, and objective). There was also a significant negative
relationship reported between PsyCap and undesirable employee attitudes
(cynicism, turnover intentions, job stress, and anxiety) and undesirable
employee behaviors (deviance)” (Avey, Reichard, Luthans, & Mhatre, 2011, p.
127).
On the one hand, these findings encourage continued research about the
combined influence of hope, optimism, resilience, and self-efficacy in relation
to work-related phenomena, such as the transfer of training. On the other
hand, the PsyCap literature offers little analysis of the function of the
individual component constructs (hope, optimism, self-efficacy, resilience)
for variables of interest. Rather, it is assumed that each of the component
states influence outcomes in the same manner and to a similar degree.
This may be the result of how the designated standard measure of PsyCap,
derived from the PsyCap Questionnaire (PCQ), was constructed and
introduced into the literature. Published procedures for analysis and scoring
involve calculating PsyCap as an average score (Luthans et al., 2007). A
rationale for why PsyCap should be analysed as an aggregated average of
items tapping the four psychological states instead of as a factor score
weighted composite (as suggested by Stajkovic, 2006) has not been
articulated. Yet, the benefits of higher-order latent constructs have been
demonstrated elsewhere, for example when researching emotional
intelligence as multi-factorial phenomena (Brackett & Mayer, 2003;
Ciarrochi, Chan, & Caputi, 2000; Law, Wong, & Song, 2004; Petrides &
Furnham, 2000). Such an approach would also benefit research about
combined hope, optimism, resilience, and self-efficacy; for instance, when
using the imbalanced PCQ-12 scale (Luthans et al., 2007; Luthans, Youssef, &
POSITIVE COGNITIVE STATES AS ANTECEDENTS OF TRANSFER MOTIVATION 67
Avolio, 2006a) in which optimism is represented by only two items whereas
resilience and self-efficacy are reflected by three items each, and hope by four
items.
One review found that only 13 of 24 studies employed PsyCap as a latent
variable, with just one that had attempted to confirm the construct validity of
the four PsyCap dimensions (Dawkins, Martin, Scott, & Sanderson, 2013).
Indeed, this study (Rego, Marques, Leal, Sousa, & Cunha, 2010) showed that
individual job performance was better predicted when the PsyCap
components were considered separately, than when aggregated into an
overall PsyCap factor. Moreover, the authors also found that the hope
construct with its facets of agency and pathways loaded on two factors and a
five factor model (with hope’s two dimensions considered separately)
produced higher validity than a uni-dimensional PsyCap variable or the
presumed four factor model.
Adding to the confusion, Stajkovic, Lee, Greenwald, and Raffiee (2013)
have recently argued that the constructs hope, optimism, resilience, and self-
efficacy “may all be influenced by the same, common core belief that simply
manifests itself in life in linguistically different ways” (p. 3). In three steps the
authors examined convergent, discriminant, and incremental validities
among the four variables. First, by meta-analysing bivariate correlations from
133 studies they found that the average correlation between hope, optimism,
resilience, and self-efficacy was r = .60 (ranging from r = .55 to .67.). Second,
using confirmatory factor analysis for data collected from three independent
samples they found convergent validity among the four indicator measures of
r= .51, .62, and .60. Third, by employing multi-trait-multi-method
approaches they found discriminant validity among the four manifest
variables was low, as was their incremental validity. Individually, hope,
optimism, resilience, and self-efficacy accounted for an average incremental
variance of 11% when the common core factor was controlled for, ranging
from .00 to a maximum of .05 on any of the outcome variables for any of the
four positive-framed constructs. Accordingly, Stajkovic et al. (2013) reason
that hope, optimism, resilience, and self-efficacy appear to be manifest
variables of a higher order latent construct.
68
In summary, it appears that scholars have not yet fully understood how to
best model and measure positive thoughts and beliefs that let humans set
goals, be certain, cope, and strive for achievement. The different approaches
as summarised above can certainly be helpful in advancing scientific inquiry.
However, it is not the purpose of the present research to find definitive
answers to these controversies. What is important is to be aware that
currently there is no agreed conceptualisation and operationalisation for the
combined investigation of hope, optimism, resilience, and self-efficacy. Based
on reviewed theory, the present research consequently argues that these four
constructs are distinct, and at the same time acknowledges that this must be
empirically supported in subsequent studies. It is now important to
understand how the positive cognitive states relate to the transfer of training.
5.2 Positive Cognitive States as Source of Transfer Motivation
Research on the role played by positive psychological constructs in
determining training effectiveness is very limited, yet promising. Kim and
colleagues (2012) investigated whether a learner’s positive perception of self
could boost learning motivation and performance. They found that
fundamental evaluations of one’s self-worth, competence, and capabilities –
or simply core self-evaluations – affect learning motivation and performance
beyond general mental ability and conscientiousness. The authors argued
that their findings implied that training effectiveness could be improved by
boosting learners’ core self-evaluations. Yet, they also suggested that this
could only happen over the very long term because core self-evaluations are
considered fairly stable.
In contrast, hope, optimism, resilience, and self-efficacy are considered
open to development (Luthans & Youssef, 2004; S. J. Peterson, Luthans,
Avolio, Walumbwa, & Zhang, 2011; Stajkovic, 2006). The literature shows
that described cognitions are characterised by both more general or trait-like
orientations and more specific or state-like thoughts and beliefs that affect
how people appraise and shape their experiences. Dynamic properties are
attributed to hope (S. J. Peterson et al., 2006; Snyder et al., 1991; Snyder,
Sympson, & Ybasco, 1996), optimism (Carver et al., 2010; Green Jr et al.,
POSITIVE COGNITIVE STATES AS ANTECEDENTS OF TRANSFER MOTIVATION 69
2004; Kluemper, 2009), resilience (Jacelon, 1997; Kim-Cohen, Moffitt,
Caspi, & Taylor, 2004; Ong et al., 2009), and self-efficacy (Bandura, 1997,
2006; G. Chen et al., 2000; Scholz, Doña, Sud, & Schwarzer, 2002).
Scholars have recently suggested that hope, optimism, resilience, and self-
efficacy could help facilitate human resource development (Ardichvili, 2011;
G. M. Combs, Luthans, & Griffith, 2009) via their influence on learning and
transfer. Two recent unpublished doctoral dissertations represent the only
empirical accounts in this domain. They examine relationships between
PsyCap and training effectiveness and are reviewed next. Following this, the
potential for work-related hope, optimism, resilience, and self-efficacy to act
as antecedents of transfer motivation will be discussed.
5.2.1 PsyCap and Training Effectiveness
Griffith (2010) conducted two studies and found significant positive
correlations between PsyCap and both pre-training motivation (r=.42 to .54)
and training transfer motivation (r=.28 to .44). Pre-training motivation was
found to partially mediate the relationship between PsyCap and training
transfer motivation, with the latter predicting overall job performance (.55),
job engagement (.33), and job satisfaction (.42). One of the studies also
employed an intervention aiming to increase levels of PsyCap in a treatment
group via a standardised computer-based activity of 37 minutes length. This
intervention drew on previous PsyCap development activities (Luthans,
Avolio, Norman, & Combs, 2006) and intended to make training goals more
salient by introducing PsyCap concepts to demonstrate how each could be
utilised to reach those goals. The study claims that this intervention provided
participants with the opportunity to focus on work-related goals in the
company, which simultaneously requires their cognitive integration of the
key concepts presented in the training. However, this PsyCap development
did not noticeably influence motivational levels relating to training when
compared with a control group. According to Griffith, contextual and
motivational factors alongside extra-role demands may have interacted with
experimental conditions so that the intervention was rendered ineffective.
70
West (2012) found a positive significant correlation between PsyCap and
training transfer motivation for individuals trained in skills relating to
computer applications, administration, management, and communication.
What is of most interest however, is that optimism (r=.53) had a stronger
association with training transfer motivation than did the composite PsyCap
(r=.49). That is, the composite construct was not found to explain more
training transfer motivation than its component constructs. This suggests the
value of examining the four positive constructs individually, and not simply
as an aggregate.
Both scholars have to be commended for commencing empirical research
on the relationship between the positive cognitive states and training
effectiveness. However, despite their usefulness in highlighting the potential
role of positive psychological states in a training context, both these studies
have significant limitations. First, neither study provides sufficient clarity
about the psychological mechanisms by which PsyCap has its effects on
outcome variables. This leaves the need for clearer specification about
the function of hope, optimism, resilience, and self-efficacy for training
transfer. Second, the cross-sectional studies in both Griffith (2010) and
West (2012) rely on small samples (N = 54; N = 74) and both authors argue
that relationships are thus assessed via basic correlation analysis only instead
of potentially more meaningful regression analysis involving variables of
interest, and albeit hypotheses and figures provided suggest directionality.
Third, in his longitudinal study Griffith (2010) tested his hypotheses via a
path model, which did not appear to provide an acceptable fit to the data (e.g.
CFI = .775; RMSEA = .207). Fourth, both authors made no attempt to
examine the construct validity of PsyCap.
Taken together, those studies provide some initial empirical evidence to
suggest that positive cognitive states may serve as determinants of transfer
motivation and subsequent transfer behaviours. Next the case is made for
positive cognitive states representing antecedents for transfer motivation.
POSITIVE COGNITIVE STATES AS ANTECEDENTS OF TRANSFER MOTIVATION 71
5.2.2 Hope, Optimism, Resilience & Self-Efficacy and Transfer Motivation
Cognition is inherent in motivation (Locke, 1991) and so the basic premise
is that state-like hope, optimism, resilience, and self-efficacy embody
thoughts and beliefs regarding one’s work experience that reciprocally
influence effort expenditures on goals related to work. In other words,
individuals do not have a direct read on reality, but their information about
the world of work is appraised through their cognitive system, which may be
more or less accurate (Ellis & Grieger, 1977 in Shatte & Seligman, 2005).
Therefore, individuals can be considered self-regulating agents who are not
only products but also producers of their environment and experiences.
Given that the transfer of training represents an integral work experience,
the accurate appraisal of opportunities and challenges at work has
considerable functional value for training transfer. Accordingly, low levels of
hope, optimism, resilience, and self-efficacy in relation to work may offset the
existing potential to transfer and perform on the job the competencies that
were learned via training. Conversely, high levels of these positive
psychological states may lead to favourable outcomes such as the successful
initiation and use of new competencies at work.
The consequences of hope, optimism, resilience, and self-efficacy have
cognitive as well as affective qualities, giving rise to renewed thoughts and
affect (Benetti & Kambouropoulos, 2006; Isen, 2000; Lackaye et al., 2006;
Marshall, Wortman, Kusulas, Hervig, & Vickers, 1992; Ong et al., 2006; S. C.
Segerstrom, Taylor, Kemeny, & Fahey, 1998). In other words, people think
and feel about work-related matters. Accordingly, it is argued that the more
distal positive psychological states hope, optimism, resilience, and self-
efficacy have the potential to affect the more proximal cognitive and affective
qualities of transfer motivation, which, as prior theorised, determines
transfer goal setting and striving.
Hope would appear to have considerable potential to affect transfer
motivation. The interplay of pathway and agency explains why employees
with high levels of hope have been found to persist in their particular goal
strivings and finding alternative routes to attain work-related goals when
72
usual routes were blocked (Adams et al., 2002). Accordingly, people with
high levels of hope engage in increased pathway thinking at work, thereby
generating multiple avenues for realising transfer goals which increases
transfer motivation. People with higher levels of hope have also been shown
to be more creative at work, conceiving more novel and more useful ideas as
well as championing their ideas (Rego et al., 2009; Rego, Sousa, Marques, &
Cunha, 2012). Such increased idea generation may lead to improved schemes
about when and where to apply new knowledge and skills, essentially more
specific transfer goals from which to choose from, thereby increasing the
motivation to transfer. Hope has also been related to emotional reactivity,
whereby low levels of hope lead to apathy, a state in which people give up all
goal pursuits (Rodriguez-Hanley & Snyder, 2000). Conversely, those people
with high levels of hope experience less negative emotions when their goals
are blocked (Snyder, LaPointe, Crowson, & Early, 1998). A high-hope person
should thus have a sense of positive zest and thus feel more energised to
transfer training.
Optimism is defined in terms of favourable expectancies for the future
(Carver et al., 2010), whereby those with higher levels of optimism think and
also feel positively about their prospects at work (Erez & Isen, 2002; C.
Peterson, 2000). Optimism is thus a goal-directed construct with
motivational qualities. Accordingly, if individuals believe that achieving goals
at work is feasible then specific goals that may be valued are indeed pursued.
Empirical research has also shown that more optimistic people experience
less stress and burnout as well as greater emotional well-being and affective
commitment (Carver et al., 2010; Forgeard & Seligman, 2012; Kluemper,
2009). This is particularly relevant when the achievement of work goals is
uncertain, such as when attempting to apply a new competence until it
becomes a routine. If optimistic expectancy is low, people at work are less
likely to feel positive about or committed to transfer goals. As a result,
particular transfer goals may never be set and efforts towards achieving those
goals may be reduced. In contrast, people high in optimism at work believe
good things are bound to happen, setting free the transfer motivation needed
to pursue transfer goals, which is more likely to lead to the realisation of
those goals.
POSITIVE COGNITIVE STATES AS ANTECEDENTS OF TRANSFER MOTIVATION 73
In the present research, resilience is understood as the capacity to endure
stress and overcome setbacks (Masten, 2001; Reich et al., 2010). This is
important because perceived failures often result in negative affect which
hinders goal striving (Schwarz, 2000; Subramaniam, Kounios, Parrish, &
Jung-Beeman, 2009). Resilience at work thus provides the mechanisms by
which individuals sustain their focus on established goals in the face of
ongoing or potential adversity and refocus on meaningful goals if that focus
was lost. For instance, initiating and maintaining newly trained competencies
at work generates mental and physical demands in addition to the usual work
load. Moreover, failure and setbacks are likely when attempting something
new at work, such as in the transfer process when executing a new
competence. Resilient people think but also feel positively through adverse
situations at work (Forgeard & Seligman, 2012) and thereby can sustain
transfer motivation until transfer goals are achieved.
Finally, the effect of self-efficacy on transfer motivation is based on the
judgements about the ability and controllability to execute courses of action
for achieving goals at work (Bandura, 1991). This involves more general
appraisal of personal resources as they relate to the work experience as well
as anticipatory estimates of efforts involved in attaining transfer goals. The
degree of consistency then affects goal setting and subsequent effects on
persistence in goal striving for goal achievement (Gist, Mitchell, & Mitchell,
2010). Accordingly, transfer motivation is a result of the broader conviction
that one is generally able to attain work goals. Therefore, people with low
levels of self-efficacy at work likely exert little effort when given the
opportunity for applying new skills and knowledge. Conversely, when people
view their work experience as controllable, then this will mobilise transfer
motivation for applying new skills and knowledge.
5.3 Integration and Conclusion
This chapter proposed that four positive-framed cognitive constructs
(hope, optimism, resilience, and self-efficacy) act as antecedents for transfer
motivation and subsequent training transfer (Figure 7).
74
To begin, the positive psychology paradigm was reviewed as this body of
research seeks to identify factors that constitute human flourishing and
optimal functioning. Although interest is increasing for exploring the role of
positive psychology for work outcomes, such research in relation to training
effectiveness has been quite limited. Yet, theory and evidence suggest that
investigating positive-laden constructs in this context is warranted.
Four cognitive mechanisms were identified that affect how people
appraise their overall personal experience at work and that determine goal
setting, striving, and attainment. Empirical studies show that hope,
optimism, resilience, and self-efficacy each can play a favourable role for
work-related outcomes. Given the integral nature of training as work
episodes, this would suggest these four constructs may also represent
important antecedents of training transfer, describing an individual’s
‘transfer readiness’. Hope, optimism, resilience, and self-efficacy relating to
work help generate the motivation to transfer learned competencies, which in
turn determines subsequent training transfer.
Furthermore, the schemes of thought and beliefs these constructs
represent are considered malleable and therefore hope, optimism, resilience,
and self-efficacy would allow “managing transfer before learning begins”
(Naquin & Baldwin, 2003). Moreover, given the evidence on the positive
effects these positive cognitive states have on work-related variables,
practically it appears more efficient to act upon and manage those personal
capacities that impact several positive outcomes at work than upon another
one that potentially impacts training transfer only.
As the collective role of hope, optimism, resilience, and self-efficacy has
gained considerable attention, the promising and conflicting developments of
the unifying constructs PsyCap and Core Confidence were also discussed.
However, evidence on how to treat the four cognitive states remains
inconclusive. Consequently, the present research understands the four
positive cognitive states as conceptually distinct; yet, for good scientific and
pragmatic reasons, hope, optimism, resilience, and self-efficacy are modelled
as first-order manifestations of an individual’s overall core confidence.
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STUDY 1: MEASURE DEVELOPMENT AND CONSTRUCT VALIDATION FOR TRAINING-RELATED MOTIVATION 77
CHAPTER 6
STUDY 1: MEASURE DEVELOPMENT AND
CONSTRUCT VALIDATION FOR
TRAINING-RELATED MOTIVATION
6.1 Aim & Hypotheses
Chapter 3 conceptualised training-related motivation (TRM) as a multi-
dimensional and multi-stage construct. This chapter presents the findings of
a study designed to test a series of hypotheses derived from this
conceptualisation. In doing so, new measures are developed that capture
these motivational elements at different stages in the training process.
78
It was earlier argued that three interrelated motivational stages are
relevant in the training process: participation motivation, learning
motivation, and transfer motivation. Each represents, in turn, a motivational
product of three motivational processes that shape individual’s goal setting
and striving: can-do, reason-to, and energised-to motivation.
H1: Can-do, reason-to, and energised-to emerge as distinct
elements of a) participation motivation, b) learning
motivation, c) transfer motivation.
It was also proposed that outcomes at each motivational stage influence
subsequent stages of the process: that is to say, participation motivation (PM)
before training begins affects learning motivation (LM) during training,
which in turn shapes transfer motivation (TM) after training. Therefore, PM
represents the independent variable, LM the mediator, and TM the
dependent variable. No empirical research has examined these
interrelationships. Moreover, the discreteness of can-do, reason-to, and
energised-to dimensions within each stage would suggest that the
motivational trajectories over time are distinct as well.
H2: Can-do learning motivation mediates the relationship
between can-do participation motivation and can-do transfer
motivation.
H3: Reason-to learning motivation mediates the relationship
between reason-to participation motivation and reason-to
transfer motivation.
H4: Energised-to learning motivation mediates the relationship
between energised-to participation motivation and
energised-to transfer motivation.
Next, Figure 8 summarises and illustrates all these hypotheses.
Figure 8. Hy
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STUDY 1: MEASURE D
odel for exploring tra
DEVELOPMENT AND CO
ajectories of training
ONSTRUCT VALIDATIO
-related motivation
ON FOR TRAINING-RELLATED MOTIVATION 779
6.2
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STUDY 1: MEASURE DEVELOPMENT AND CONSTRUCT VALIDATION FOR TRAINING-RELATED MOTIVATION 81
I generated the initial item pool as follows. I first systematically reviewed
the literature to identify existing measures as they pertain to training and
development motivation (Carlson & Bozeman, 2000; Chiaburu & Lindsay,
2008; Chiu & Wang, 2008; Elliott & Church, 1997; Facteau et al., 1995;
Gegenfurtner, Festner, et al., 2009; Kossek & Roberts, 1998; Machin &
Fogarty, 1997; Matthieu et al., 1992; Naquin & Holton III, 2003, 2002; Noe &
Schmitt, 1986; Noe & Wilk, 1993; Ruona et al., 2002; R. Smith et al., 2008;
Warr & Bunce, 1995; Weinstein & Klein, 1996; Wigfield, Cambria, & Eccles,
2012; Zaniboni et al., 2011). Items were included if they were judged to be
consistent with any of the latent constructs defined above. To reduce
conceptual redundancies, similar items were grouped and merged into a
single item. Following this, I developed additional items consistent with the
conceptual definitions to ensure that the initial item pool fully covered the
content domain theorised.
With the majority of all items positively worded, each content domain also
included a few negatively worded items to “keep the respondent honest”
(Netemeyer et al., 2003), and to enable possible response bias, in the form of
acquiescence or affirmation during data collection, to be identified (Price &
Mueller, 1986).
All items were designed as general measures to maximise their relevance
for a wide variety of learners and training experiences. To illustrate, the
scales would need to be applicable to assessing training-related motivation
for a one-year leadership development program, for a brief eLearning
training about software functions, as well as for cohorts with varying degrees
of cognitive ability and education level.
In accordance with that reality I crafted consistent item stems, discarded
context- or domain-limiting terminology, and applied uniform and simple
vocabulary (e.g. training program, course, development session, intervention
etc. became training). The quality of item wording and framing was
enhanced by making use of the TACT framework (Fishbein & Ajzen, 1975). It
suggests writing items that are composed of a Target, an Action, a Context,
and a Time. Conceptual and semantic ambiguity was further refined as a
result of discussions with two senior scholars and two professional training
82
experts. A readability test (Flesch-Kincaid Grade Level; Kincaid, 1975)
available via Microsoft Word 2010 approximated the comprehension
difficulty across all items with a score of 7.6, which suggests that 8th grade
students can fully understand the items.
All of the above occurred under the premise of retaining as many
theoretical facets of the content domain as possible. Care was taken to ensure
that each content area had an adequate sampling of items. The resulting
initial pool consisted of 180 items in three sets of 60 items aligned for PM,
LM, and TM.
6.2.2 Content Validation
To maximise content validity in the initial item pool, I adapted an item-
definition matching process employed by MacKenzie, Podsakoff and Fetter
(1991). 10 naive respondents (i.e. English-speaking graduate students with
varying nationalities, mother languages, and discipline associations) were
asked to match each item with one of the provided corresponding construct
definitions. Items that respondents determined not to fit any of the definition
were labelled unclassified.
Specifically, respondents physically sorted one item at a time into one of
ten possible baskets made up of the 3x3 definition grid plus an unclassified
basket. The first step involved sorting items along the psychological states
(vertical axis), containing can-do, reason-to, and energised-to motivation.
The second step included sorting items along the motivational training stages
(horizontal axis), comprising stages of participation motivation, learning
motivation, and transfer motivation. Each participant processed the full item
set in random order, further increasing the efficacy of the sorting process
technique. Individual item allocations for both dimensions were captured
and the agreement between respondents calculated.
Hinkin (2005) suggests an agreement index (i.e. the percentage of
respondents who correctly classify an item) of 75% or more for further
inclusion of an item. Given that items had to be sorted twice within a two
dimensional framework, I adopted a decision rule to retain only those items
STUDY 1: MEASURE DEVELOPMENT AND CONSTRUCT VALIDATION FOR TRAINING-RELATED MOTIVATION 83
that had a total agreement index of 56% (= .75 x .75) or higher. This cut-off is
in line with methodological suggestions by Netermeyer et al. (2003) to “err
on the high side” in the early stage of scale development so as to have a larger
rather than a smaller item pool to be carried over for subsequent
developmental samples. As a result of the above procedures, 43 items were
deemed conceptually inconsistent and consequently discarded, leaving 137
items.
6.2.3 Pilot Test
A pilot administration of the remaining 137 items to a purposeful target
audience (adult trainees, N=25, 2 days of teaching-related software training)
gave further indication about the adequacy and clarity of the items and the
feasibility of the self-report survey format (Iarossi, 2006). In other words,
how able and comfortable do people feel responding to these items on a
Likert scale: (1) Strongly disagree, (2) Disagree, (3) Neither disagree or agree,
(4) Agree, (5) Strongly agree.
I chose a Likert-type scale as it is the most frequently used in survey
questionnaire research (Cook et al., 1981) and was found the most useful in
behavioural research (Kerlinger, 1986). I decided to use a 5-point Likert-type
scale as Coefficient alpha reliability with Likert scales has been shown to
increase up to the use of five points before levelling off (Lissitz & Green,
1975).
The generic lead-in phrase is: “How you may think about yourself right
now in regards to this training: Please indicate your level of agreement or
disagreement with each statement to most accurately represent your view.”
The trainees in a classroom setting were asked to respond to three printed
questionnaires before (PM), during (LM), and after (TM) the training and
invited to flag problematic items or highlight response difficulties. Additional
written comments and brief follow-up interviews resulted in 3 items being
reworded and 8 items being rejected.
84
6.2.4 Development Sample
The remaining 129 items were now tested on a development sample for
subsequent exploratory factor analysis and to refine the scales. Given the
large number of 43 items each for PM, LM, and TM, three independent but
similar samples were used to pre-empt survey fatigue. The samples were
deliberately constructed to comprise heterogeneous trainee cohorts.
Participants were trainees attending one of a number of work training
courses offered by a large Australian training provider. The training courses
lasted between 1 and 5 days and covered a wide range of competencies,
including leadership, human resources, project management, sales,
marketing, creativity, communication, accounting, legal matters,
occupational health and safety, office software, and specialist IT
competencies. The courses offered training in both open and closed
competencies (see Yelon & Ford, 1999) at varying levels of sophistication to
working age individuals employed in a range of occupations, organisations
and industries.
To minimise response set bias, items were randomly arranged on a
printed questionnaire which was administered to trainees in a classroom
setting either at the beginning (PM), mid-point (LM), or end (TM) of a
training course. In each case, the trainer read a description of the research
project with instructions for participation; questionnaires were then
presented as part of the training program. Responses were completely
anonymous, and participants were allowed to withdraw if they wished.
This resulted in response sets of N = 393 for PM, N = 338 for LM, and
N = 374 for TM. These large sample sizes produce good item-to-response
ratios of 1:9.1, 1:7.9, and 1:8.7 respectively (Hinkin, 1998). The three samples
showed comparable demographic characteristics (Table 2). An exploratory
factor analysis and item reduction resulted in 9 items each for PM, LM, and
TM (described next in 6.3.1).
STUDY 1: MEASURE DEVELOPMENT AND CONSTRUCT VALIDATION FOR TRAINING-RELATED MOTIVATION 85
6.2.5 Validation Sample
An independent sample was subsequently used to confirm factor structure
and test the hypotheses. Thus, a longitudinal study was designed whereby
learners were asked to complete a printed questionnaire and respond to the
remaining PM items before course beginning at arrival (T1), to the LM items
at the mid-point of the course (T2), and to the TM items at the very end of the
course (T3).
The 17 courses selected were offered by a major Australian training
provider and had to have an instruction period of two full days or more to
ensure data efficacy through sensible distribution of survey load.
Competencies trained related to leadership, finance, negotiation,
communication, people and performance management, and human
resources.
At the beginning of each course the trainer read a description of the
research project with instructions including the option to withdraw at any
time if anyone had objections to the study. While complete confidentiality
was assured, responses had to be matched over time, and so participants
Table 2. Study 1: Characteristics of Independent Development Samples
Characteristics Sample 1 (PM) Sample 2 (LM) Sample 3 (TM) Mean
N 393 338 374 368.3 Distinct training courses 32 22 34 29 Gender (male) 50.6 % 52.9 % 54.8 % 52.8 % Age (in years) 37.0 (11.1) 36.2 (10.4) 35.1 (9.3) 36.1 Total work experience (in years) 17.4 (10.8) 17.2 (10.3) 16.2 (10.5) 16.9 Organisational tenure (in years) 3.0 (5.1) 4.5 (5.8) 5.1 (6.1) 4.2 Job tenure (in years) 2.7 (3.6) 2.3 (3.1) 2.9 (4.2) 2.7 Full-time employment 93.0 % 96.1 % 93.1 % 94.1 % Education level No High School Graduation 11.1 % 9.5 % 9.4 % 10.0 % High School Graduation 28.0 % 24.5 % 40.6 % 31.0 % University Diploma/TAFE 23.8 % 31.2 % 12.8 % 22.6 % Bachelor Degree 20.6 % 20.5 % 25.3 % 22.1 % Honours Degree 7.4 % 6.7 % 5.7 % 6.6 % Master Degree 8.5 % 7.3 % 6.0 % 7.3 % Doctoral Degree 0.5 % 0.3 % 0.3 % 0.4 % Note. Standard deviations are given in brackets.
86
were asked to provide their first and last name on each printed questionnaire.
Trainers and administrators who assisted the data collection confirmed very
few objections to survey participation, suggesting an overall response rate of
more than 90% at the onset of each course.
Participants’ age ranged from 21 to 68 with a mean of 38.1 years, 62.1%
respondents were male, and 95.0% worked full-time. Complete cases
collected were: N1 = 343, N2 = 341, N3 = 335, with total N = 353. Trainers
explained the slight fluctuation in response numbers with trainees not
attending all sessions, being late, or having to exit the class-room early.
6.3 Analysis & Findings
Factor analysis to investigate latent variables has become common for
such areas as development, refinement, and evaluation of tests, scales, and
measures (T. A. Brown, 2006). Consequently, an exploratory factor analysis
is used to select items that best reflect the theory. This is followed by a
confirmatory factor analysis to verify the theoretical structure of the
constructs PM, LM, and TM, therefore testing hypothesis H1. A structural
model then tests the remaining hypotheses H2-H4 via path analysis.
6.3.1 Exploratory Factor Analysis
The software SPSS 19 (SPSS Inc., 2010) was used to conduct exploratory
factor analysis (EFA) on the development samples. The overarching goal of
the EFA was identifying the underlying relationships between measured
variables, including the actual magnitudes of each item’s cross loadings. The
analytical steps involved were identical for each item set and findings are
reported in the usual sequence: PM, LM, TM.
Respondents used the entire response range (1 to 5) for every item and
thus generated sufficient variance for subsequent statistical analyses (Hinkin,
2005). A total of 8 respondents did not correctly follow procedures (e.g.
multiple or invalid item responses); 2, 2, and 4 cases were thus removed from
the sample sets. Data were screened to test for normality and
STUDY 1: MEASURE DEVELOPMENT AND CONSTRUCT VALIDATION FOR TRAINING-RELATED MOTIVATION 87
multicollinearity (Field, 2013). Mahalanobis’ distance revealed 4, 3, and 3
multivariate outliers and these were subsequently removed. Kaiser-Meyer-
Olkin tests of sampling adequacy (all above .93) and Bartlett tests of
sphericity (all .000) further indicated that the data were appropriate for
factor analysis.
EFA using Maximum Likelihood (ML) estimation extracted 7 factors for
PM, 8 factors for LM, and 6 factors for TM with eigenvalues greater than 1. In
all cases the scree plots showed a noticeable break after the first four factors,
which accounted for the majority of the variance.
As often has often been found to be the case (e.g. Harrison & McLaughlin,
1993; Hazlett-Stevens, Ullman, & Craske, 2004), the negatively worded items
loaded highly onto the fourth “methods factor” (Herche & Engelland, 1996).
Given that domain content sampling would not be affected, I concluded that
all negatively worded items should be omitted in the further scale
construction.
As the number of retained items at this stage was still impractically large,
I next eliminated items that would substantially improve the reliability of the
measures and not diminish the sampling domain. This analysis was
conducted independently for items conceptually associated with can-do,
reason-to, or energised-to (i.e. 3 x 3 = 9 subscales). Items with inter-item
correlations of less than .4 were omitted from further analysis (J.-O. Kim &
Mueller, 1978). Items with corrected item-to-total correlations below .50
(Obermiller & Spangenberg, 1998) were also omitted at this stage.
Given the intention to develop multidimensional scales based on discrete
yet conceptually related dimensions, EFA using ML estimation with oblique
rotation (PROMAX) forcing three factors was then used. Oblique rotation
allows factors to correlate, produces estimates of correlations among factors,
and makes the solution more interpretable, assisting to decide what items to
delete or retain. Items that substantially cross-loaded (> .30) were
subsequently excluded (Ford, MacMCallum, & Tait, 1986). The EFA was
rerun each time after an item was deleted until the criteria were satisfied for a
three factor matrix. To this point an identical item configuration resulted for
88
PM, LM, and TM across the three independent samples, adding support to
the process of item development and selection.
At this stage, each scale’s ‘energised-to’ dimension comprised 3 items,
whereas the ‘can-do’ dimension contained 5 items, and ‘reason-to’ dimension
contained 6 items. In the interest of parsimony and practicality, further
trimming to 3 items per factor appeared sensible.
The first ‘can-do’ item was omitted across all three scales based on the fact
that it had the smallest factor loading but highest cross-loading when
compared to its conceptually related items. The second ‘can-do’ item was
omitted as it overlapped conceptually with another can-do item that showed
comparable factor loadings. A similar approach was used to reduce the
numbers of items tapping the ‘reason to’ dimension in each of the three
measures from 6 items to 3 items. The EFA was rerun each time after an item
was deleted, and the factors can-do, reason-to, and energised-to now
comprised three items each. The total of 9 items explains a cumulative
variance of 61% for PM, 53% for LM, and 56% for TM.
Internal consistency values (Coefficient Alpha; α) for the sub-scales
exceeded the conventional level of acceptance of .70 (Nunnally & Bernstein,
1994). They range from .71 to .84 and are shown alongside factor loadings in
Table 3, Table 4, and Table 5. The final selection of questionnaire items for
PM, LM, and TM is presented in Table 6.
STUDY 1: MEASURE DEVELOPMENT AND CONSTRUCT VALIDATION FOR TRAINING-RELATED MOTIVATION 89
Table 3. Study 1: Pattern Matrix for Participation Motivation
Item ID Factor
1 2 3 PMCan05 .091 .484 .014
PMCan10 -.008 .655 -.115
PMCan11 -.053 .934 .056
PMRea03 .848 -.007 .020
PMRea05 .704 .068 -.008
PMRea10 .750 -.032 -.046
PMEnr01 .000 .003 -.828
PMEnr04 -.005 -.002 -.837
PMEnr05 .133 .115 -.598
Note: α for factor 1 (reason-to) = .78, α for factor 2 (can-do) = .76,
α for factor 3 (energised-to) = .84
Boldface values that the item was developed to assess this factor.
Table 4. Study 1: Pattern Matrix for Learning Motivation
Item ID Factor
1 2 3 LMCan05 .120 .494 -.093
LMCan10 -.036 .848 .022
LMCan11 .141 .572 -.086
LMRea03 .727 -.039 .008
LMRea05 .515 .182 -.075
LMRea10 .617 .098 -.011
LMEnr01 -.097 .024 -.820
LMEnr04 .090 .022 -.674
LMEnr05 .265 -.035 -.569
Note: α for factor 1 (reason-to) = .73, α for factor 2 (can-do) = .72,
α for factor 3 (energised-to) = .78
Boldface values that the item was developed to assess this factor.
90
Table 5. Study 1: Pattern Matrix for Transfer Motivation
Item ID Factor
1 2 3 TMCan05 .023 .605 -.050
TMCan10 .011 .777 .032
TMCan11 -.069 .648 .010
TMRea03 -.835 -.048 .044
TMRea05 -.518 .171 -.098
TMRea10 -.685 .025 -.061
TMEnr01 -.057 .030 -.851
TMEnr04 .020 -.035 -.891
TMEnr05 -.106 .161 -.522
Note: α for factor 1 (reason-to) = .71, α for factor 2 (can-do) = .73,
α for factor 3 (energised-to) = .83
Boldface values that the item was developed to assess this factor.
STUDY 1: MEASURE DEVELOPMENT AND CONSTRUCT VALIDATION FOR TRAINING-RELATED MOTIVATION 91
Table 6. Study 1: Training-related Motivation Questionnaire Items
Dimension Item ID Items
Participation Motivation
Can-do PMCAN05 I can overcome challenges related to participating in this training.
PMCAN10 I am able to invest the necessary time into this training.
PMCAN11 I can put forth the energy required for being part of this training.
Reason-to PMREA03 Participating in this training is important for my professional development.
PMREA05 Going through this training will improve my work performance.
PMREA11 Taking part in this training is of great practical value to me for my job.
Energised-to PMENR01 I feel enthusiastic about this training.
PMENR04 I feel excited about participating in this training.
PMENR05 I feel inspired by the opportunity this training offers.
Learning Motivation
Can-do LMCAN05 I can overcome challenges related to attaining new knowledge and skills from this training.
LMCAN10 I am able to invest the necessary time to learn new skills and knowledge from this training.
LMCAN11 I can put forth the energy required to master the new skills and knowledge from this training.
Reason-to LMREA03 Mastering this training is important for my professional development.
LMREA05 Learning new knowledge and skills from this training will improve my work performance.
LMREA11 Acquiring new knowledge and skills from this training is of great practical value to me for my job.
Energised-to LMENR01 I feel enthusiastic about learning from this training.
LMENR04 I feel excited about acquiring new knowledge and skills from this training.
LMENR05 I feel inspired by the learning opportunity this training offers.
Transfer Motivation
Can-do TMCAN05 I can overcome challenges related to applying the new knowledge and skills to my work.
TMCAN10 I am able to invest the necessary time to apply new knowledge and skills from this training at work.
TMCAN11 I can put forth the energy required to apply new knowledge and skills from this training at work.
Reason-to TMREA03 Using this training at work is important for my professional development.
TMREA05 Applying the new knowledge and skills will improve my work performance.
TMREA11 Using the new knowledge and skills will be of great practical value to me for my job.
Energised-to TMENR01 I feel enthusiastic about using this training on the job.
TMENR04 I feel excited about using new knowledge and skills from this training at work.
TMENR05 I feel inspired by the opportunity to apply new skills and knowledge from this training at work.
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6.3.2 Confirmatory Factor Analysis
Using data from the independent sample I then carried out a confirmatory
factor analysis (CFA) to estimate the degree of correspondence between the
theorised dimensional structure of PM, LM, and TM, and the observed
covariances among measured items selected.
A CFA is realised within the structural equation modelling (SEM)
framework, specifically via the software Mplus 6.1 (Muthén & Muthén, 2011),
and using maximum likelihood (ML) estimation. Multiple fit indices were
used to assess model fit. The Chi-square (χ2) statistic was evaluated via a Chi-
square difference test (Δχ2) across each of the nested models for significance.
As preliminary tests indicated the multivariate normality of the data, the chi-
square differences in these procedures were based on the usual chi-square
values under the ML estimation. In addition, a chi-square divided by degrees
of freedom (χ2/df) is a more informal test of model fit and a ratio of < 3 may
be interpreted as a good fit (Kline, 2011). The root mean square error of
approximation (RMSEA) is a measure of average standardised residuals per
degree of freedom. Values just below < 0.10 may be considered admissible yet
mediocre, a value of < 0.08 indicates an acceptable fit, values of < 0.05 are
good, and < 0.01 is considered excellent (Browne & Cudeck, 1993;
MacCallum, Browne, & Sugawara, 1996). The standardised root mean square
residual (SRMR) provides information about the total average deviation in
the variance/covariance matrix. While values < .08 are considered fair, a
value of < .05 is considered a good fit (Hu & Bentler, 1999). The comparative
fit index (CFI) and Tucker–Lewis Index (TLI; the same as the Non-Normed
Fit Index or NNFI) are comparative indices that contrast a hypothesized
model against an absolute null model that proposes that all indicators are
uncorrelated. If other indices suggest acceptable fit, values for the CFI and
TLI > .90 may be considered admissible, albeit more recent
recommendations consider > .95 as representing good fit (Kline, 2011).
The process of testing Hypothesis 1 involved comparing a range of
alternative models and was the same for each of the scales. To begin with, five
models were examined. Model 1 represents the hypothesised three-factor
model, specified so that the can-do, reason-to, and energised-to items load on
STUDY 1: MEASURE DEVELOPMENT AND CONSTRUCT VALIDATION FOR TRAINING-RELATED MOTIVATION 93
separate latent factors. Model 2, model 3, and model 4 were two-factor
solutions in which the 6 items thought to be associated with two of the three
dimensions were merged as if representing one factor, the second factor
contained the remaining 3 items. Model 5 was a one-factor model, containing
all 9 items. The measurement models contained no double-loading indicators
and all latent factors were allowed to correlate, but the items’ error terms
were not. The fit statistics for all models described are shown in Table 7 for
PM, Table 8 for LM, and Table 9 for TM.
The results illustrate that the best-fitting solution is the hypothesised
three-factor model (M1) for PM, LM, and TM. The goodness-of-fit indices
show a considerable improvement in the χ2, χ2/df, RMSEA, CFI, TLI, and
SRMR over the competing one- and two-factor models (M2-M5). All factor
loadings were also statistically significant (p < .001), item loadings ranged
from .62 to .89 (median = .79). Inspection of modification indices suggested
that no item sought to strongly associate with any factor other than the one
for which it was intended. The measurement models are shown for PM in
Figure 10, for LM in Figure 11, and for TM in Figure 12.
Construct validity was further assessed via a range of means. All following
descriptive statistics are summarised in Table 10. To begin, internal
consistency was examined via Coefficient Alpha (α) and Composite reliability
(CR). CR assesses the internal consistency of a latent measure, analogous to
that of α, yet with more accurate estimates based on loadings and residuals. A
CR value of >.7 suggests good reliability (Bagozzi & Yi, 1988). CR was
calculated individually for can-do, reason-to, and energised-to factors as they
relate to PM, LM, and TM. In regards to α all scales but can-do participation
motivation (α = .63) exceeded the >.70 threshold (Nunnally & Bernstein,
1994). However, the value for CR was .81, and so this was considered
permissible given that α is a lower bound on true reliability (e.g. Novick &
Lewis, 1967; Sijtsma, 2009), it “underestimates the true reliability of a
measure that may not be fully tau equivalent” (Osburn, 2000, p. 344), and
CR as “’better’ estimate of true reliability” (R. A. Peterson & Kim, 2013, p.
194) clearly exceeded the suggested threshold. Given that each of the 9 scales
is comprised of 3 items only, these are respectable reliability estimates
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(Kopalle & Lehmann, 1997; Stanton, Sinar, Balzer, & Smith, 2002; Voss, Jr, &
Fotopoulos, 2000).
Convergent validity was assessed by calculating the average variance
extracted (AVE). The AVE reflects the average amount of variance in
observed variables that a latent construct is able to explain due to random
measurement error. It is suggested that a latent construct has an AVE > .5,
indicating that the majority of the variances is shared through this factor
(Fornell & Larcker, 1981). The latent factors can-do, reason-to, and
energised-to met this criterion for PM, LM, and TM.
Although the three-factor solutions were found to fit the data well for the
PM, LM and TM measures, a more rigorous tests for divergent validity was
conducted. Fornell and Larcker (1981) argue that a latent construct is
sufficiently discriminant when its AVE is larger than the shared variance of
given construct with another construct. Accordingly, the square root of a
constructs' AVE should be greater than that factor’s correlation with other
factor’s to support sufficient discriminant validity between them. Most of the
latent factors can-do, reason-to, and energised-to met these criteria in their
respective configurations for PM, LM, and TM. However, in several
configurations the differences between the correlation and the square root of
the AVE are marginal and the relationship between PM’s reason-to and
energised-to dimension is even characterised by a correlation that is .014
higher than the respective AVE square root of the energised-to factor.
Closer examination further revealed high correlations between the factors
can-do, reason-to, and energised-to for PM, LM, and TM (up to .76), giving
rise to the question whether treating can-do, reason-to, and energised-to as
distinct constructs in studies and for practical application is indeed sensible
(see T. A. Brown, 2006).
Based on the above, the following was concluded: First, findings relating
to convergent validity and internal consistency support the psychometric
adequacy of the developed latent constructs and their measures. Second, tests
of divergent validity support that can-do, reason-to, and energised-to can be
understood as distinct, albeit highly correlated dimensions of PM, LM, and
STUDY 1: MEASURE DEVELOPMENT AND CONSTRUCT VALIDATION FOR TRAINING-RELATED MOTIVATION 95
TM, thereby supporting H1a,b,c. Third, these findings permit the testing of
H2-H4 to proceed.
Table 7. Study 1: Model Comparison for Participation Motivation
Model χ2 df χ2/df ∆χ2 RMSEA CFI TLI SRMR
PM1 3-Factor Model Can<>Reason<>Energised 38.99 24 1.62 0.00 1.00 1.01 0.02
PM2 2-Factor Model CanReason<>Energised 112.65 26 4.33 73.66* 0.10 0.91 0.88 0.06
PM3 2-Factor Model CanEnergised<>Reason 96.77 26 3.72 57.78* 0.09 0.93 0.90 0.05
PM4 2-Factor Model Can<>ReasonEnergised 97.91 26 3.77 58.92* 0.09 0.93 0.90 0.05
PM5 1-Factor Model CanReasonEnergised 154.94 27 5.74 115.95* 0.12 0.87 0.83 0.06
Note. ∆χ2 relates to comparison with the hypothesised PM1. * denotes ∆χ2 is significant at p<.001 (two-tailed). <> denotes correlation. No space between factor labels (e.g. CanReason) denotes associated items represent single latent factor.
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Table 8. Study 1: Model Comparison for Learning Motivation
Model χ2 df χ2/df ∆χ2 RMSEA CFI TLI SRMR
LM1 3-Factor Model Can<>Reason<>Energised 65.26 24 2.72 0.07 0.97 0.96 0.03
LM2 2-Factor Model CanReason<>Energised 110.89 26 4.27 45.63* 0.10 0.95 0.92 0.04
LM3 2-Factor Model CanEnergised<>Reason 200.12 26 7.70 134.86* 0.14 0.89 0.84 0.07
LM4 2-Factor Model Can<>ReasonEnergised 214.86 26 8.26 149.60* 0.15 0.88 0.83 0.06
LM5 1-Factor Model CanReasonEnergised 273.44 27 10.13 208.18* 0.16 0.84 0.79 0.07
Note. ∆χ2 relates to comparison with the hypothesised LM1. * denotes ∆χ2 is significant at p<.001 (two-tailed). <> denotes correlation. No space between factor labels (e.g. CanReason) denotes associated items represent single latent factor.
Table 9. Study 1: Model Comparison for Transfer Motivation
Model χ2 df χ2/df ∆χ2 RMSEA CFI TLI SRMR
TM1 3-Factor Model (hypothesised) Can<>Reason<>Energised
64.78 24 2.70 0.07 0.98 0.97 0.03
TM2 2-Factor Model CanReason<>Energised 141.46 26 5.44 76.68* 0.12 0.94 0.91 0.05
TM3 2-Factor Model CanEnergised<>Reason 195.34 26 7.51 130.56* 0.14 0.91 0.87 0.06
TM4 2-Factor Model Can<>ReasonEnergised 128.04 26 4.92 63.26* 0.11 0.94 0.92 0.05
TM5 1-Factor Model CanReasonEnergised 227.97 27 8.44 163.19* 0.15 0.89 0.85 0.06
Note. ∆χ2 relates to comparison with the hypothesised TM1. * denotes ∆χ2 is significant at p<.001 (two-tailed). <> denotes correlation. No space between factor labels (e.g. CanReason) denotes associated items represent single latent factor.
STUDY 1
Figur
Figur
1: MEASURE DE
re 10. Measurem
re 11. Measurem
EVELOPMENT AN
ment model an
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ND CONSTRUCT
nd standardise
nd standardise
T VALIDATION FO
d loadings for
d loadings for
OR TRAINING-R
Participation M
Learning Motiv
RELATED MOTIV
Motivation
vation
VATION 97
98
Fig
gure 12. Measuurement model
l and standardised loadings for Transfer M
otivation
STUDY 1: MEASURE DEVELOPMENT AND CONSTRUCT VALIDATION FOR TRAINING-RELATED MOTIVATION 99
Table 10. Study 1: Descriptive Statistics, Scale Reliabilities, Correlations for Validation Sample
Variables Mean SD α CR Participation Motivation Learning Motivation Transfer Motivation Can-do Reason-to Energised-to Can-do Reason-to Energised-to Can-do Reason-to Energised-to
PM Can-do 4.01 0.39 0.63 0.81 .774
PM Reason-to 4.25 0.49 0.79 0.81 .606** .769
PM Energised-to 4.05 0.52 0.76 0.79 .643** .763** .749
LM Can-do 4.06 0.38 0.76 0.81 .570** .445** .519** .768
LM Reason-to 4.14 0.48 0.83 0.83 .406** .669** .549** .707** .791
LM Energised-to 4.10 0.53 0.87 0.87 .402** .479** .688** .684** .729** .836
TM Can-do 4.12 0.39 0.79 0.81 .550** .450** .494** .763** .616** .517** .773
TM Reason-to 4.16 0.46 0.79 0.82 .408** .619** .568** .646** .841** .663** .750** .781
TM Energised-to 4.13 0.55 0.89 0.89 .357** .513** .679** .648** .696** .797** .718** .758** .852
Note. N = 335-343. Standard Deviation (SD); Cronbach Alpha (α); Composite Reliability (CR). Figures underlined present square root of Average Variance Extracted (AVE). Figures underlined present the square root of the Average Variance Extracted (AVE) whereby a value >.707 equals an AVE above the suggested threshold of .5. Correlation is significant at * p<.05, ** p<.01 (two-tailed).
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6.3.3 Path Analysis
Further structural equation modelling via Mplus 6.11 and ML estimation
was used to test H2-H4. An initial CFA for the measurement model
comprising 9 latent variables – can-do, reason-to, and energised-to states for
PM, LM, and TM – showed good fit (χ2 = 519.276(288); RMSEA = 0.048;
CFI = 0.96; TLI = 0.95; SRMR = 0.036).
Next, the structural model based on H2-H4 as shown in Figure 8 was
estimated and showed good fit (χ2 = 569.999(309); RMSEA = 0.049;
CFI = 0.95; TLI = 0.94; SRMR = 0.056). The model and standardised path
coefficients are described graphically in Figure 13.
All three dimensions significantly (p = < .001) related to their respective
equivalent in the subsequent stage. That is, can-do PM related to can-do LM
(.66), and the latter to can-do TM (.75). Likewise, reason-to PM related to
reason-to LM (.67), and the latter to reason-to TM (.83). At last, energised-to
PM related to energised-to LM (.67), and the latter to energised-to TM (.81).
In addition, there was ample support for LM mediating the relationship
between PM and TM with significant indirect effects (p = < .001) for the
respective dimensions: can-do (.49), reason-to (.55), and energised-to (.55).
Taken together this fully supports H2, H3 and H4.
Figure
e 13. Estimated Stru uctural Model: Trajec
Solid lines
ST
tories of Can-do, Res represent paths tha
TUDY 1: MEASURE DE
ason-to, and Energisat were significant (**
EVELOPMENT AND CON
sed-to Participation M** p < .001). All facto
NSTRUCT VALIDATION
Motivation, Learningr loadings significan
FOR TRAINING-RELAT
g Motivation, and Trant at p < .001.
TED MOTIVATION 10
ansfer Motivation.
01
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6.4 Discussion
The results of this study support the central proposition, namely that can-
do, reason-to, and energised-to may be viewed as distinct dimensions for the
constructs LM, PM, and TM. The study also provides support for the
proposed sequential interrelationships between motivational processes of
different stages of training.
Levels of can-do, reason-to, and energised-to PM were correlated with
their respective LM dimensions, which in turn were associated with the
corresponding TM dimensions. This suggests that some degree of motivation
for each of these facets is brought into the training and carries through until
the end of training, albeit to varying degrees.
This would suggest that the degree to which individuals believe at the start
of a course they have the resources to partake affects their appraisals about
their capacity to learn and subsequently transfer. At the same time,
heightening the crucial can-do TM prior to returning to work may be best
achieved by bolstering can-do beliefs during class (LM) about mastering new
competencies as this appears to have a more sizeable flow-on effect.
A comparable pattern emerged for the relationships associated with the
reason-to dimension: PM to LM was moderate, and LM to TM was high.
Thus, individuals carry a reason-to PM into the training which affects utility
judgements for LM and TM. Strikingly, the effect size of the reason-to LM to
TM path would suggest trainees’ judgements about why they might consider
making use of trained competencies at work, is little different to the
perceived relevance and utility of the learning efforts. This is in line with
expectancy value appraisals which are future oriented; those trainees
understanding the potential of new competencies in class keep this view until
course end. In other words, unless the training course itself provides
sufficient reason-to master new competencies, little transfer may be realised.
The findings relating to the energised-to dimension are similar. Levels of
energised-to PM on entry shape activation on exit for transfer. An
explanation may be that the learning experience during training was largely
STUDY 1: MEASURE DEVELOPMENT AND CONSTRUCT VALIDATION FOR TRAINING-RELATED MOTIVATION 103
cognitive and individuals were not really exposed to affect-changing stimuli.
Consequently, many would have remained with their initial level of affect
during these short courses. An alternative explanation may be the effect of
dispositional affect (Diener, Nickerson, Lucas, & Sandvik, 2002; Judge &
Larsen, 2001), a more general degree of cheerfulness. Future research on
training transfer may investigate both of these avenues. Finally, positive
affective states during learning (energised-to LM) directly shape energised-to
transfer motivational states on exit, suggesting to facilitate positive affect
during training.
Taken as a whole, the detailed examination of training-related motivation
leads to two arguments. First, levels of PM by which people enter a training
episode affect the levels of TM with which those people return to work. While
further antecedents have not been investigated here, this finding has
implications for training providers and work organisations alike. It suggests
that motivating individuals for training effectiveness should begin before
training because initial motivational states have the potential for downstream
effects on goal setting and striving for learning and transfer. Future research
should seek to identify which factors of the work environment affect PM.
Second, the study suggests that PM, LM, and TM each encompass three
distinct motivational processes. However, the high factor correlations
indicate that can-do, reason-to, and energised-to share considerable amounts
of variance. On the one hand, this is not necessarily a problematic finding. As
has been discussed, these processes may be seen as complementary
mechanisms that in reality likely arise together and jointly operate (Freeman,
2000; Seo et al., 2004). On the other hand, considerable amounts of shared
variance between dimensions can be impractical in applied research (T. A.
Brown, 2006).
Accordingly, on pragmatic grounds, future studies may need to consider
respecifying PM, LM, and TM in terms of superordinate or higher-order
models. The advantage of a superordinate model is that it allows for
covariation among first-order factors through the second-order factor.
Substantive theoretical reasons exist to arrive at such a model: can-do,
reason-to, and energised-to are conceptually distinct but nevertheless linked
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by a broader motivational goal (i.e. participation, learning, transfer). In the
psychological literature high correlations are regularly reported between
conceptually discrete factors (e.g. Fernet, 2010; Gagne et al., 2010), leading
researchers to treat them as manifestations of a higher-order construct (e.g.
Walumbwa, Avolio, Gardner, Wernsing, & Peterson, 2007).
6.5 Limitations
One obvious limitation of this study is the absence of measures of other
training-related constructs in the data that could be used to provide further
evidence of convergent and discriminant validity. DeVellis (2011) stresses
that at an early stage it is paramount to collect clean data for scale
development and validation which is not affected by survey fatigue or other
construct bias. Future research thus needs to incrementally position the new
conceptualisation of training-related motivation within a nomological net.
Another limitation may relate to the timing of the three stages of data
collection. If the measurement waves were spread further apart, or more
evenly distributed, for example, it is possible that the observed effect sizes
would change.
Finally, all data used to develop and validate the measures originated from
a single training provider. While this institution offers a diversified course
range and trainers have diverse backgrounds and profiles, a bias due to
organisational specifics (e.g. marketing approach, venue quality, cost
structure) may represent a limitation, albeit a small one.
6.6 Conclusion
The purpose of this chapter was to comprehensively operationalise
training-related motivation as it was conceptualised in chapter 3. To this end,
I developed a total of 180 items representing can-do, reason-to, and
energised-to aspects of participation, learning, and transfer motivation. They
were tested for face and content validity, piloted, and refined.
STUDY 1: MEASURE DEVELOPMENT AND CONSTRUCT VALIDATION FOR TRAINING-RELATED MOTIVATION 105
Three comparable development samples responded to a 43 item
questionnaire either before, during, or after a professional training activity.
These samples were heterogeneous with respect to respondents’ work
organisations as well as the training courses attended. The resulting data
were then subjected to factor analyses. Through EFAs and a range of
selection criteria the measures were narrowed down to sets of three items
each for the can-do, reason-to, and energised-to dimensions of motivation at
each training stage. In addition to the integrated conceptualisation of
training-related motivation, the present research thus contributes to the
literature through these three 9-item scales for PM, LM, and TM.
The scales are conceptually grounded, defined upon a training-related
goal, equally balanced in dimensional composition, and comprise a
parsimonious set of psychometrically adequate items that may be used to
capture dynamic motivational states. The universal measures provide a
platform for extending training research at the inter- and intra-individual
level.
CFA on an independent sample was used to confirm that can-do, reason-
to, and energised-to may be viewed as distinct dimensions for the constructs
LM, PM, and TM. Nevertheless it was suggested that conceptualising LM,
PM, and TM as higher-order constructs may be warranted in future, given the
high intercorrelation between the component factors at all stages in the
training process and the fact that the elements are linked at each stage by a
common focal goal (i.e. participation, learning, transfer).
The present research also provided an initial test of what Beier and Kanfer
(2009) proposed as stage model, suggesting that substantial variance in
transfer motivation is explained by preceding levels of learning motivation
and participation motivation. In other words, people already enter a course
with certain amounts of confidence beliefs, appreciation thoughts, and
positive feelings, which ultimately affect the direction, intensity, and
persistence of transfer goals. More research is needed that examines the
potentially relevant organisational conditions and individual factors
contributing to these motivational states, as well as their impact on training
transfer. Accordingly, and given the crucial role of transfer motivation, the
106
present research continues by examining positive cognitive states as potential
antecedents of transfer motivation, and proactive transfer behaviour and
training transfer as potential consequences.
STUDY 2: POSITIVE COGNITIVE STATES, TRANSFER MOTIVATION, AND PROACTIVE TRANSFER BEHAVIOUR 107
CHAPTER 7
STUDY 2: POSITIVE COGNITIVE STATES,
TRANSFER MOTIVATION, AND
PROACTIVE TRANSFER BEHAVIOUR
7.1 Aim & Hypotheses
In this chapter I describe a study conducted as an initial test of
propositions developed in Chapters 3, 4 and 5. As the theory connecting the
focal constructs has already been greatly discussed in these chapters, the
central premises are summarised briefly before specifying the hypotheses and
integrating those relationships in an illustrative model (Figure 14).
The transfer of training must be motivated. The direction, intensity, and
persistence to apply new competencies at work is argued to be a function of
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confidence beliefs, appreciation thoughts, and positive activating feelings
with regards to the training received.
This transfer motivation is further argued to fuel proactive transfer
behaviours, whereby individuals self-initiate the envisioning, planning,
enacting, and reflecting of training transfer. Given that training transfer may
also be directly solicited, proactive transfer behaviours will only partially
account for the effect of transfer motivation on training transfer.
Dispositional proactivity is also thought to affect training transfer, although
this effect is argued to be fully mediated by more proximal proactive transfer
behaviours.
Hope, optimism, resilience, and self-efficacy represent thoughts and
beliefs about ability, controllability, and probability of achieving work goals.
As a result this core confidence is likely to influence a transfer motivational
state that determines the setting, pursuit, and achievement of transfer goals.
H1: Transfer motivation is positively related to training transfer.
H2: Transfer motivation is positively related to proactive transfer
behaviour.
H3a: Proactive transfer behaviour is positively related to training
transfer.
H3b: Proactive transfer behaviour partially mediates the
relationship between transfer motivation and training
transfer.
H4a: Proactive personality is positively related to proactive
transfer behaviour.
H4b: Proactive transfer behaviour fully mediates the relationship
between proactive personality and training transfer.
STUDY 2: POSITIVE COGNITIVE STATES, TRANSFER MOTIVATION, AND PROACTIVE TRANSFER BEHAVIOUR 109
H5a: Core confidence, a higher-order construct comprised of hope,
optimism, resilience, and self-efficacy, is positively related to
transfer motivation.
H5b: The relationship between core confidence and training
transfer is mediated by transfer motivation.
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Figure 14. Hypotheesised relationshipss between predictors and mediators on trraining transfer
STUDY 2: POSITIVE COGNITIVE STATES, TRANSFER MOTIVATION, AND PROACTIVE TRANSFER BEHAVIOUR 111
7.2 Sample & Procedure
4668 individuals who had participated in one of 181 different formal work
training courses offered by a major Australian training provider in the last
12 months, were invited by email to participate in the study by completing a
questionnaire online. To incentivise participants, they were offered the
opportunity to enter a draw for winning a tablet (e.g. iPad) when completing
the survey.
The first page of the online survey reminded each respondent about the
specific training course s/he participated in, the start date, and the training
location. For example: “You participated in the training program ‘Applied
Project Management’ in June 2011 at XYZ training institute. Please think
about this training now when responding to the survey.” This was done in
order to prime the respondent about their personal learning experience and
the particular competencies associated with it.
Given the cross-sectional design of the study, I employed procedural
remedies suggested by Podsakoff, MacKenzie, Lee, and Podsakoff (2003) to
minimise question order effects and common source measurement biases
through separating measures and wording of the criterion variable, so that
items of the criterion variable were mixed with other items, as opposed to
items representing predictors, which were clearly separated. Questionnaire
items of multi-dimensional constructs were randomly scrambled within their
block so that related items did not appear near each other. In addition, to
generate some psychological separation, respondents were asked to complete
some demographic and organisational data mid-survey before completing the
remaining foci constructs’ measures. Research indicates that even small
division in time or context can reduce consistency motifs and demand
characteristics (Bethlehem & Biffignandi, 2012).
949 (20.3%) valid responses were collected. The respondents were
associated with 148 training courses covering a broad range of job levels,
closed and open competencies, and again domains (e.g. administration
assistance, software proficiency, strategic thinking, people skills).
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Participants’ age ranged from 18 to 69 with a mean of 38.9 years, 47.6%
respondents were male, and 93.3% worked full-time.
7.3 Measures
In order to test the hypotheses of interest, measures of 8 latent constructs
were included in the questionnaire. They are described next and listed in
Appendix 11.1. Unless otherwise stated, respondents were asked how much
they agree or disagree with certain statements on a 5-point response scale:
(1) Strongly disagree, (2) Disagree, (3) Neither, (4) Agree, (5) Strongly agree.
Training transfer was measured using 4 items focusing on generic
implications of training transfer. This was necessary due to the highly
heterogeneous sample covering various training programs, competencies,
and work contexts. Three items were based on Xiao (1996), a sample item
reads “I have changed parts of my behaviour/activities at work in order to be
consistent with material taught in the training.”. The fourth item asked “How
much of the new knowledge/skills covered by the training do you estimate
you actually use at work?”, and required a response on a 0-100% range via a
5-point scale (i.e. 20% increments).
Transfer motivation was measured by the 9-item TM scale, specifically
developed in the present research and described in chapter 7.
Proactive training transfer was assessed by adapting the 12-item
instrument developed by Bindl et al. (2012) for work- and career-related
proactive goal self-regulation that showed good psychometric properties
(α > .90). The items were adapted to capture the training transfer context.
The resultant 12-item scale is comprised of three items each capturing the
proactive transfer-goal self-regulation elements envisioning, planning,
enacting, and reflecting. For each item, respondents were asked to indicate
how much time and effort they have expended since the training program,
e.g.: “envisioning how my job might be different if I began to use what I have
learned.” (envisioning); “getting myself prepared to use new skills or
knowledge acquired through this training.” (planning); “self-initiating ways
to use trained skills at work?” (enacting); “seeking extra feedback from
STUDY 2: POSITIVE COGNITIVE STATES, TRANSFER MOTIVATION, AND PROACTIVE TRANSFER BEHAVIOUR 113
someone about any training-related actions I engaged in?” (reflecting).
Responses were captured on a 5-point scale ranging from (1) not at all to (5) a
great deal.
Proactive personality was measured by the six highest-loading items from
the measure developed by Bateman and Crant (1993). The same items were
used in Parker (1998; α = .85) and validated as one of the shortest best
working scales to assess personal disposition toward being proactive (Claes,
Beheydt, & Lemmens, 2005; α = .86). A sample item reads: “I am always
looking for better ways to do things.”
For the four positive cognitive states, items were taken from the original
scales and adapted to fit the work context, if necessary. Hope was measured
using 6 items adapted from the State Hope Scale (SHS) developed by Snyder
et al. (1996). A sample item reads: “There are lots of ways around the sorts of
problems I encounter right now at work.” Optimism was measured with 6
items from the Life Orientation Test-Revised (LOT-R) developed by Scheier,
Carver, and Bridges (1994) and three items are reversed. A sample item
reads: “When things are uncertain for me at work, I usually expect the best.”
Resilience was measured using 6 items from the Resilience Scale (RS-14)
developed by Wagnild (2009), with one item being reversed. A sample item
reads: “At work, I can get through difficult times because I’ve experienced
difficulty before.” Self-efficacy was measured with 6 items from the New
General Self-Efficacy Scale (NGSE) developed by Chen, Gully, and Eden
(2001). A sample item of the NGSE reads: “I am confident that I can perform
effectively on many different job tasks.” Taken together, 15 out of the 24
items chosen match those comprising the Psychological Capital
Questionnaire (PCQ; Luthans, Youssef, & Avolio, 2006). The full set of
measures associated with the PCQ was considered inappropriate as some
items target a managerial work context/tasks (e.g. “I feel confident
contributing to discussions about the company's strategy.”) which relates
only to a sub-population of the sample in this study.
Control Measures. Training transfer can be affected by a number of
individual and contextual factors (Blume et al., 2010; Chiaburu, Sawyer, &
Thoroughgood, 2010), and so respondents were asked to indicate their age
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(in years), gender (male = 1, female = 2), organisational tenure (in years),
job tenure (in years), and total work experience (in years). Respondents were
also asked to indicate their educational level (i.e. highest degree attained: no
High School Graduation = 1, High School Graduation = 2, University
Diploma/TAFE = 3, Bachelor Degree = 4, Honours Degree = 5, Master
Degree = 6, Doctoral Degree/PhD = 7). The date of the actual survey
completion was additionally captured to calculate the elapsed transfer
opportunity, it simply describes the time in days between the last training
day and the actual survey response.
7.4 Analyses
7.4.1 Data Screening
Data were screened for multicollinearity and normality with items
showing skewness between -1.59 and 0.53 and kurtosis between -0.89 and
2.82, indicating no severe violation of the principal normality assumption
(Kline, 2011).
Missing data analysis revealed two noticeable patterns by which items
were not completed for the four positive cognitive states (7.6%; hope,
optimism, self-efficacy, resilience) as well as proactive personality (13.3%).
The most likely explanation is survey fatigue as these measures were
captured in the last two sections of the questionnaire; some people simply did
not fully complete the online survey. Another reason may be that to some
people these constructs’ measures felt more personal than items about e.g.
training transfer; they decided to not respond. Most importantly, results of
an ANOVA between those cases with complete records and those cases with
missing observations on one or both of the associated question blocks showed
no significant differences on any other variables measured. Further
examination of missing patterns revealed no particular dependencies or
clusters other than the ones discussed. The remaining items show no
systematic patterns at all as they have no or very few (max. 0.02%) missing
observations. Thus, there was no association between the propensity for
STUDY 2: POSITIVE COGNITIVE STATES, TRANSFER MOTIVATION, AND PROACTIVE TRANSFER BEHAVIOUR 115
missing data and the values of the foci variables or demographics in the data
set. It can be concluded that the data can be treated as missing completely at
random (MCAR; Roderick & Rubin, 2002).
To conduct SEM by using Mplus 6.11 (Muthén & Muthén, 2011), and given
established support for assuming data normality and MCAR, no particular
data handling was required for the following analysis. Specifically, using
Maximum Likelihood (ML) estimation handles the missing data and
parameter estimation in a single step. In brief, ML in Mplus uses all data that
is available to estimate the model using full information maximum likelihood
(FIML); each parameter is estimated directly without first filling in missing
data values for each individual. Simulation studies suggest that this provides
accurate estimates of model parameters and standard errors (Allison, 2009;
Buhi, Goodson, & Neilands, 2008; Enders & Bandalos, 2001; Enders, 2001),
even when data loss is much larger than in the present study.
Data were also screened for any response bias effects; i.e. whether any
particular characteristic inherent in the data collection affected the data.
First, there was no recognisable response pattern based on training course
association. In fact, the initial distribution within the data set used to send
the survey invitations was well reflected in the actual response data: the
ratios associated with a given course correlated .82 (p = .000). Second, the
elapsed time between the last training day and the survey invitation
(mean = 214 days, SD = 104 days) did have a decreasing effect on the
response rate, albeit a very small one (skew = .014).
Third, to test for the presence of common method effect underlying the
data I first conducted a Harman’s one-factor test (Podsakoff, MacKenzie &
Lee, 2003). All latent construct items were entered into an EFA using
principal axis factoring, unrotated and with promax rotation. In both cases
the EFA revealed 6 distinct factors (eigenvalue > 1.0) and not a single factor
that would account for the majority of the covariance among the variables.
Those results would suggest no substantial amount of common method
variance is present. This was followed by a more rigorous test for the
presence and equality of method effects by employing a latent marker
variable (Lindell & Whitney, 2001). It involves identifying an item that is
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theoretically most unrelated to the focal variables of the model. The
conceptually most unrelated item available was “My supervisor makes
difficult decisions based on high standards of ethical conduct“. Given its post-
hoc selection, the item may be considered a ‘nonideal’ marker (Richardson,
Simmering, & Sturman, 2009). Nonetheless, using CFA procedures
(Williams, Hartman, & Cavazotte, 2010) two models were compared. In the
baseline model the marker latent factor loaded only onto the marker item
while all latent first-order constructs correlated with each other but not with
the marker latent factor (χ2 = 2051.38(1170); CFI = 0.96). The constrained
model adds method factor loadings, which are constrained to be equal, onto
all reflective indicators (χ2 = 2046.89(1168); CFI = 0.96). The chi-square
difference test between the constrained model and the baseline model was
not significant (Δχ2 = 4.49; Δdf = 2). Also, no loadings between the reflective
indicators and the latent marker factor were significant. Together this
suggests that there is no common method variance equally affecting all
variables.
The findings resulting from the Harman’s one-factor test and the latent
marker variable test suggested that it was reasonable to assume that no
substantial response bias effects are present in the data.
7.4.2 Measurement Models
SEM was chosen for path analysis to test for the hypothesised
relationships (Figure 14). SEM is considered more rigorous than more typical
regression techniques as all mediation paths are measured simultaneously
rather than step by step. The major advantage of SEM is that it models the
relationships at the item level, thus explicitly accounting for measurement
error. However, the validity of results through a structural model is
dependent on capturing and establishing the reliability of the underlying
constructs. For instance, one must assure that a potential bad model fit can
be attributed to incorrectly specified paths, and is not caused by the involved
measures. Thus, the full benefit of SEM can only be achieved through a two-
step approach, the first step of which is establishing the soundness of
STUDY 2: POSITIVE COGNITIVE STATES, TRANSFER MOTIVATION, AND PROACTIVE TRANSFER BEHAVIOUR 117
involved latent variables, which are then used in the subsequent structural
model (Anderson & Gerbing, 1988).
As recommended by Kline (2011), I separately analysed each latent
variable before estimating a full measurement model with all latent variables
that would then constitute the path model. Following steps for congeneric
testing outlined by Brown (2006), I conducted CFA with MPlus 6.11 using ML
estimation. The same model fit indices criteria were applied as discussed in
study 1. This examination also involved published scales as previous evidence
of scale validity does not necessarily ensure validity in subsequent uses
(Levine, Hullett, Turner, & Lapinski, 2006). As part of the process, internal
consistency of scales was assessed via Cronbach's alpha (α) and composite
reliability (CR), the suggested standard for both being a value > .70
(Nunnally & Bernstein 1994). Each latent variable’s convergent validity was
also assessed via the average variance extracted (AVE), whereby a value > .50
is suggested (Fornell & Larcker, 1981). Unless mentioned otherwise, all
constructs met these criteria.
First, all items showed significant (p = .001) loadings on their a priori
factor. Training transfer showed good model fit (χ2 = 4.99(2);
RMSEA = 0.040; CFI = 0.99; TLI = 0.99; SRMR = 0.010).
Corresponding to proposed theory and findings from study 1, transfer
motivation exhibited the best fit with can-do, reason-to, and energised-to
representing distinct factors (χ2 = 77.18(24); RMSEA = 0.048; CFI = 0.99;
TLI = 0.98; SRMR = 0.022). Given the high correlations (.79-.85) among the
three factors, they were loaded onto a second-order factor with identical
model fit.
Proactive Transfer Behaviour had good model fit (χ2 = 93.58(48);
RMSEA = 0.032; CFI = 0.99; TLI = 0.98; SRMR = 0.014). However, the
factors envision, plan, enact, and reflect correlated very highly (.90-.96). As
discussed in chapter 4, potentially this reflects the iterative nature of self-
regulation processes, especially when captured in retrospect. Thus, proactive
transfer behaviours were conceptualised as an integrated construct and the
four regulatory factors formed a second-order model which also showed a
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good fit (χ2 = 110.37(50); RMSEA = 0.036; CFI = 0.99; TLI = 0.98;
SRMR = 0.016).
Proactive personality showed good model fit (χ2 = 41.97(9);
RMSEA = 0.067; CFI = 0.97; TLI = 0.95; SRMR = 0.027). The variable met
the reliability criteria with α = .78 and CR = .78, but its AVE = .35 was below
the suggested value of .5, and further item omission could not improve this
situation. Given the construct’s main role in this study - to ‘control for’ a
potential dispositional proactive orientation – alongside its sufficient
divergent validity to correlates including proactive transfer, it was considered
permissible.
As discussed prior, the latent variables representing the four positive
psychological states hope, optimism, resilience, and self-efficacy have been
treated differently in the literature: as a one-factor model, as four correlating
factors, or as four constructs comprising a higher-order factor. I examined
these alternatives as an intermediate step to the full measurement model.
The one-factor model by which all reflective indicators load onto the same
latent construct was rejected due to unacceptable model fit
(χ2 = 262.378(252); RMSEA = 0.105; CFI = 0.69; TLI = 0.66; SRMR = 0.116).
I then assessed the four constructs individually before estimating the most
sensible way to represent them out of the remaining two alternatives.
Hope showed acceptable model fit (χ2 = 58.82(9); RMSEA = 0.076;
CFI = 0.97; TLI = 0.95; SRMR = 0.033), and model fit could not be
significantly improved when separating the construct’s ‘pathways’ and ‘goals’
dimensions.
Initially, optimism did not exhibit acceptable model fit (χ2 = 228.17(9);
RMSEA = 0.167; CFI = 0.86; TLI = 0.76; SRMR = 0.090), but closer
examination revealed effects through a ‘method factor’ caused by the three
reversed (i.e. negatively worded) items. Given that the literature regularly
reports these effects (see Carver et al., 2010), a second-order model was
formed by which the three reversed items loaded onto a first-order factor
(OptiRev), the three positively worded items loaded onto another first-order
factor (OptiPos), and OptiRev and OptiPos respectively loaded onto a second-
order factor called Optimism. The construct’s model fit improved
STUDY 2: POSITIVE COGNITIVE STATES, TRANSFER MOTIVATION, AND PROACTIVE TRANSFER BEHAVIOUR 119
dramatically and was good (χ2 = 37.82(9); RMSEA = 0.065; CFI = 0.98;
TLI = 0.96; SRMR = 0.037).
Resilience demonstrated mediocre model fit (χ2 = 54.04(9);
RMSEA = 0.076; CFI = 0.94; TLI = 0.91; SRMR = 0.032) and the
modification index clearly identified one item as problematic, which when
omitted, improved model fit to good (χ2 = 16(5); RMSEA = 0.051; CFI = 0.98;
TLI = 0.97; SRMR = 0.021). Potentially the meaning of the item (“At work, I
can be ‘on my own’, so to speak, if I have to.“) was not well understood by the
respondents and consequently not consistently addressing the same
underlying construct as the remaining five items.
Similarly, self-efficacy showed mediocre model fit (χ2 = 56.66(9);
RMSEA = 0.076; CFI = 0.94; TLI = 0.91; SRMR = 0.032) and the
Modification Index pinpointed one item as problematic, which when omitted,
improved model fit to good (χ2 = 54.04(9); RMSEA = 0.076; CFI = 0.94;
TLI = 0.91; SRMR = 0.032). Semantically the item does not appear to deviate
much from the remaining five, albeit it is conceivable that it rather addresses
adversity recovery (i.e. resilience; “Even when things are tough at work, I can
perform quite well”).
Next, when estimating the competing models that involved all four
positive cognitive constructs as specified above, the fit indices suggested poor
solutions: modification indices consistently identified one item each for
resilience and self-efficacy that showed substantial signs of cross-loading.
With respect to improving model fit, attempting to omit only one item was
not successful, but excluding both resulted in acceptable model fit. Then,
while the four factor model provided the best fit (χ2 = 466.12(162);
RMSEA = 0.46; CFI = 0.95; TLI = 0.94; SRMR = 0.040), correlations ranged
between .61-.75, indicating potentially disputable discriminant validity in this
model.
I thus assessed discriminant validity for the four positive cognitive factors
with each other. In less than half of the cases (5 out of 12), the values of the
square root AVE for hope, optimism, resilience, and self-efficacy did exceed
the values of the constructs’ correlations with each other. According to
Fornell and Larcker (1981) this suggests insufficient divergence between the
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four variables. As discussed in chapter 5, this situation reflects the ongoing
debate about how to best conceptualise, measure, and model these four
positive cognitive constructs. However, consistent with the hypotheses, a
higher-order factor comprised of hope, optimism, resilience, and self-efficacy
was formed. In line with Stajkovic (2006), this variable arguably represents a
higher-order construct called ‘core-confidence’. Fit for this higher-order core-
confidence model was good (χ2 = 496.47(164); RMSEA = 0.48; CFI = 0.95;
TLI = 0.94; SRMR = 0.041).
Next, after having evaluated the measurement model for each latent
variable, all measures as specified above were then entered into a CFA, and fit
was good for this measurement model (χ2 = 2275.267(1202);
RMSEA = 0.031; CFI = 0.95; TLI = 0.95; SRMR = 0.045). Aside from the
discussed, all latent factors met the criteria for divergent validity considering
the relationships between factor correlation and AVE as suggested by Fornell
& Larcker (1981; see Table 12). Divergent validity for all latent factors was
further supported through a test proposed by Anderson and Gerbing (1988).
The unconstrained measurement model (M0) estimated above was compared
to a series of constrained models (M1-M10) in which the relationship
between each possible pair of variable was set to 1.00. A chi-square difference
test subsequently compared the unconstrained and the ten constrained
measurement models. A higher χ2 for a constrained model that is significant
indicates that the factors that have been constrained to be equal are not
extremely correlated because they carry sufficient amounts of unique
variance. The Δχ2 for all comparisons was significant at the .001 probability
level (Table 11). According to Anderson and Gerbing (1988) this would
suggest that discriminant validity exists between all latent variables.
Descriptive statistics and correlations are summarised Table 12. All in all,
both the construct validity and the good model fit of the post-hoc modified
measurement solution presented adequate conditions to further test the
hypothesized relationships in a structural model via path analysis.
STUDY 2: POSITIVE COGNITIVE STATES, TRANSFER MOTIVATION, AND PROACTIVE TRANSFER BEHAVIOUR 121
Table 11. Study 2: Assessing discriminant validity for specified latent variables by comparing measurement models for all possible
combinations of constrained correlations (based on Anderson & Gerbing, 1988)
Model χ2 df Comparison ∆χ2 ∆df CFI
M0 Unconstrained Model 2275.27 1202 0.95
M1 Training Transfer<1>Transfer Motivation 2596.79 1203 M0-M1 -321.53 * -1 0.93
M2 Training Transfer<1>Proactive Transfer B. 2612.27 1203 M0-M2 -337.00 * -1 0.93
M3 Training Transfer<1>Proactive Personality 3067.72 1203 M0-M3 -792.45 * -1 0.92
M4 Training Transfer<1>Core Confidence 3055.11 1203 M0-M4 -779.84 * -1 0.92
M5 Transfer Motivation<1>Proactive Transfer B. 2816.96 1203 M0-M5 -541.69 * -1 0.93
M6 Transfer Motivation<1>Proactive Personality 3125.99 1203 M0-M6 -850.73 * -1 0.92
M7 Transfer Motivation<1>Core Confidence 3204.27 1203 M0-M6 -929.01 * -1 0.91
M8 Proactive Transfer B.<1>Proactive Personality 3160.29 1203 M0-M6 -885.02 * -1 0.91
M9 Proactive Transfer B.<1>Core Confidence 3222.21 1203 M0-M6 -946.94 * -1 0.91
M10 Proactive Personality<1>Core Confidence 2704.77 1203 M0-M6 -429.50 * -1 0.93
Note. ∆χ2 relates to comparison with the hypothesised M0. * denotes ∆χ2 is significant at p<.001 (two-tailed). <1> denotes correlation is constrained to 1.00.
122
Table 12. Study 2: Descriptive Statistics, Scale Reliabilities, Correlations
Mean SD α CR
1 Training Transfer 3.58 0.64 0.78 0.83 .7452 Transfer Motivation 3.94 0.51 0.89 0.94 .728 ** .829
3 Proactive Transfer Behaviour 3.03 0.80 0.95 0.98 .737 ** .709 ** .8654 Proactive Personality 3.78 0.48 0.78 0.78 .296 ** .330 ** .344 ** .611
5 Core Confidence 3.90 0.39 0.88 0.89 .369 ** .377 ** .349 ** .588 ** .682
6 Hope 3.89 0.50 0.81 0.87 .389 ** .370 ** .347 ** .497 ** - .7137 Optimism 3.75 0.55 0.76 0.86 .230 ** .241 ** .254 ** .495 ** - .745 ** .712
8 Resilience 4.12 0.44 0.71 0.71 .215 ** .237 ** .207 ** .472 ** - .613 ** .722 ** .6259 Self-Efficacy 3.91 0.48 0.82 0.82 .295 ** .324 ** .286 ** .498 ** - .747 ** .609 ** .641 ** .734
10 Age 38.91 10.24 - - -.043 -.049 -.010 .115 .011 .044 .197 .034 .004 -
11 Gender 1.53 0.50 - - .041 .062 -.011 .005 .010 -.022 .037 -.065 -.041 -.129 ** -12 Educational Level 3.30 1.31 - - -.177 -.155 -.119 -.113 -.087 -.074 -.098 -.022 -.086 -.101 ** -.019 -
13 Work Experience (years) 18.23 10.59 - - -.040 -.022 -.002 .128 .012 .037 .168 .042 .000 .863 ** -.150 ** -.176 ** -14 Organisational Tenure (years) 6.04 6.48 - - -.041 -.067 -.054 -.415 -.064 -.012 .028 -.010 .011 .387 ** -.148 ** -.144 ** .435 ** -
15 Job Tenure (years) 2.79 3.17 - - -.034 -.064 -.016 .113 .036 -.004 .039 .041 .023 .312 ** -.075 * -.071 * .303 ** .421 ** -
16 Training Program Length (days) 1.99 1.12 - - .020 -.016 .017 .005 .005 -.038 -.009 .007 -.007 -.057 -.017 .036 -.069 * -.020 -.032 -17 Elapsed Transfer Opportunity (days) 213.99 103.86 - - -.008 -.129 ** -.088 * -.018 -.018 .014 .051 .020 .038 .071 * .035 .028 .045 .132 ** .112 ** .056
6 7 8 9 12 13 14 15 16
Note . N = 949. Standard Deviation (SD); Cronbach Alpha (α); Composite Reliability (CR). Figures underlined present square root of Average Variance Extracted (AVE). Figures underlined present the square root of the Average Variance Extracted (AVE); a value >.707 equals an AVE above the suggested threshold of .5.Table contains descriptives for Hope, Optimism, Resilience, and Self-Eff icacy as independent f irst-order variables and as second-order factor Core Confidence.Coding for control variables: Gender w as coded as 1 for male and 2 for female; Educational Level w as coded from 1 for no High School degree to 7 P.hD. degree.Correlation is signif icant at * p<.05, ** p<.01 (tw o-tailed).
Variables 1 2 3 4 5 10 11
STUDY 2: POSITIVE COGNITIVE STATES, TRANSFER MOTIVATION, AND PROACTIVE TRANSFER BEHAVIOUR 123
7.4.3 Path Analysis
The first model tested was the hypothesised model (M1) presented in
Figure 14. It provided a good fit to the data (χ2 = 2284.99(1206);
RMSEA = 0.031; CFI = 0.95; TLI = 0.95; SRMR = 0.046), and explained
0.898 variance (R2) of training transfer. I then tested two alternative models
by which the higher-order core-confidence factor directly related respectively
to training transfer (M2) or proactive transfer behaviour (M3).
These added paths would represent cognitive-motivational processes
unaccounted for by the present transfer motivation construct. Both models fit
the data almost equally well when compared to the hypothesised model (M2:
χ2 = 2284.97(1205); RMSEA = 0.031; CFI = 0.95; TLI = 0.95; SRMR = 0.046;
M3: χ2 = 2284.69(1205); RMSEA = 0.032; CFI = 0.95; TLI = 0.95;
SRMR = 0.047). Given that the chi-square difference tests were statistically
not significant, the new paths added were not significant, and no other paths
estimates change in magnitude or significance, those additional parameters
can be eliminated, and the smaller model (M1) can be accepted (Kline, 2011).
No other competing model was substantively sensible, including those
suggested through modification indices. Accordingly, alternative models did
not offer a better or more meaningful solution and so the hypothesised model
(M1) was accepted.
Next, I sought to ensure that the findings of this study are not biased as a
result of the congeneric testing and associated measure alteration. That is,
the prior omitted items (two each for self-efficacy and resilience) were re-
entered and, as expected, model fit decreased. Importantly, significance
levels remained and effect sizes changed only marginally, if at all, when
compared with the considerably better fitting post-hoc model. This brief test
simply assured that study’s findings would not be affected by the
measurement post-hoc modification.
I then entered the demographic and organisational membership
characteristics as control variables for the accepted structural model; they
were added as exogenous factors to the regression paths. The path model
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estimation showed no new significant relationships and modification indices
also did not suggest any sensible new paths. Most importantly, regardless of
entering any of the control variables, relationships between the focal
variables did not change with respect to significance, while effects sizes
differed only marginally, if at all. Consistent with the recommendation from
Kline (2011), auxiliary parameters that are not significant should not be part
of the model as they merely introduce noise, make the structural models less
parsimonious, and decrease model fit (M1c: χ2 = 2753.87(1542);
RMSEA = 0.033; CFI = 0.94; TLI = 0.93; SRMR = 0.046). However, to
account for potentially confounding effects of the study design, the elapsed
transfer opportunity was entered as control measure (M1’:
χ2 = 2331.62(1254); RMSEA = 0.030; CFI = 0.95; TLI = 0.95;
SRMR = 0.046).
The fit indices of all discussed models are summarised in Table 13. In
sum, the hypothesised model (M1’) indeed represented the best solution, it is
described graphically in Figure 15 alongside standardised path coefficients.
STUDY 2: POSITIVE COGNITIVE STATES, TRANSFER MOTIVATION, AND PROACTIVE TRANSFER BEHAVIOUR 125
Table 13. Study 2: Comparison of examined Path Models
Model χ2 df χ2/df Comparison ∆χ2 RMSEA CFI TLI SRMR M1
Hypothesised model 2284.99 1206 1.89 0.03 0.95 0.95 0.046
M2 as M1 plus direct paths from
core-confidence to training transfer
2284.97 1205 1.90 M1-M2 0.02 0.03 0.95 0.95 0.046
M3 as M1 plus direct paths from
core-confidence to proactive transfer behaviour
2284.69 1205 1.90 M1-M3 0.30 0.03 0.95 0.95 0.047
M1c as M1 plus all demographic and
organisational control variables 2753.87 1542 1.79
0.03 0.94 0.93 0.046 M1' as M1 plus 'elapsed time' as
control measure 2331.62 1254 1.86
0.03 0.95 0.95 0.046
1
26
Figure 15. S
Estimated StructuraSolid lines represent
al Model M1’: Core cot paths that were sig
onfidence, Transfer Mnificant (all *** p < .0
Motivation, Proactive01). All factor loadin
e Transfer Behaviourgs significant at p <
r, Proactive Persona.001.
lity, and Training Traansfer.
STUDY 2: POSITIVE COGNITIVE STATES, TRANSFER MOTIVATION, AND PROACTIVE TRANSFER BEHAVIOUR 127
7.5 Results
The significant effects (unless otherwise stated all p = < .001) for the
hypothesised model M1’ were as follows: Transfer motivation was strongly
predictive of training transfer (.86), supporting hypothesis H1.
Transfer motivation also exhibited a substantial relationship with
proactive transfer behaviour (.67), with the latter in sequence having a small
association with training transfer (.14), supporting H2 and H3a. The results
also support H3b, suggesting that proactive transfer behaviour partially
mediates the relationship between transfer motivation and training transfer
(indirect effect = .09).
Proactive personality showed a small relationship with proactive transfer
behaviour (.14), supporting hypothesis H4a. Support was also found for H4b,
as the indirect effect (p = <.01) of proactive personality onto training transfer
was significant yet very small (.02).
Core-confidence was moderately positively related to transfer motivation
(.39), supporting H5a. Also, core-confidence had a total indirect effect on
training transfer (.37), via transfer motivation (.34), suggesting a full
mediation and supporting H5b; and to a lesser extent via transfer motivation
and proactive transfer behaviours (.04). There were no other significant
direct or indirect paths.
7.6 Discussion
This discussion focuses on the interpretation of study 2 findings.
Integrated theoretical and practical implications spanning the entire research
are discussed in chapter 9.
7.6.1 Transfer Motivation
The study provides support for the proposition that being motivated for
transferring what one has learned through training is associated with higher
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levels of training transfer. Trainees that appreciate the utility of applying the
training, believe they have the ability and resources to do so, and feel
positively activated about making use of it, are more likely to report increased
levels of training transfer.
According to the results, transfer motivation acts as a mediator of the
influence of more distal factors (i.e. core confidence) on training transfer and
also drives more proximal influences on transfer (i.e. proactive transfer
behaviour). This finding of the centrality of transfer motivation in the
training transfer process is consistent with prior findings (e.g. Colquitt et al.,
2000; Facteau et al., 1995).
7.6.2 Proactivity
A second major finding of this study was that proactive behaviour is
significantly related to the transfer of training. The relationships with
training transfer were small, yet significant.
This relationship remains significant even when accounting for the effect
of more dispositional characteristics in form of proactive personality. While
proactive personality had a small significant relationship with proactive
transfer behaviours, it was not directly related to training transfer, and its
indirect effect on training transfer was marginal. In line with arguments
made by Tornau and Frese (2013), the behavioural goal-directed side of
proactivity appears particularly relevant for training transfer.
The findings support the conclusion that such proactive transfer
behaviours need to be motivated. Individuals who reported higher transfer
motivation appear to seize more opportunities and create situations for
applying trained competencies at work.
As theorised, proactive training transfer activities did not exclusively
explain training transfer. Instead, they accounted for a small but significant
proportion. This mediation role would suggest that training transfer is to a
substantial degree a reactive function, but at the same time it involves
degrees of freedom for self-initiation. Importantly, the study provides the
STUDY 2: POSITIVE COGNITIVE STATES, TRANSFER MOTIVATION, AND PROACTIVE TRANSFER BEHAVIOUR 129
first empirical evidence that proactive goal self-regulatory behaviours are
relevant for effective training transfer.
Lastly, it was proposed that proactive transfer behaviour comprises
cognitive and behavioural elements that are organised in a self-regulation
framework of envisioning, planning, enacting, and reflecting. While the
construct appears conceptually sound, as supported by similar work by Bindl
et al. (2012), the data of this study did not support the multi-facet structure.
It remains to be seen whether this would change in future research. For
example, tracking these factors over time or manipulating them under
controlled conditions would allow isolating and understanding better the
individual elements of proactive self-regulated training transfer. The high
factor correlations may also be a function of the measures employed. More
research needs to look at the way in which these behaviours are measured,
and deliberately developed and validated measures would then lead to the
differentiation of proactive transfer behaviours.
7.6.3 Positive Cognitive States
The study also supported the proposition that positive cognitive states in
the form of core confidence are relevant for training transfer. As theorised,
these cognitions about work do not affect training transfer directly but via
transfer motivation. In other words, individuals’ thoughts and beliefs about
their capacity for achieving work goals appear integral for psychological
states and decisions about transferring from training. This suggests that how
people generally view their abilities and experiences at work also affects how
effective a training intervention can be.
7.7 Limitations
This study has several limitations, including potential common method
bias due to its cross-sectional design. To remedy against such bias, temporal
separation in capturing focal construct data is desirable. A longitudinal
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research design is therefore used to test the model in the study that is
described in the next chapter.
Although procedural remedies in this study involved ensuring respondent
anonymity and carefully designed scale items, the use of self-report data for
both independent and dependent variables involves the possibility that
common method variance represents an alternative explanation of observed
relationships. That is, the relationships between the variables may be
artificially increased, for instance the large effect size between transfer
motivation and training transfer. Statistically, the effects of such
measurement error were examined. Two tests for the presence of equal
method variance returned no concerning results.
Practically, there seems no other plausible or effective way to measure
individuals’ cognitive or motivational states other than by asking a person, as
they represent intra-psychic mechanisms. The same applies for the self-
regulation of proactive transfer, especially since observing them via third
party judgement would also involve substantial error, in addition to being
labour intensive. Yet, capturing independent measures relating to proactive
transfer behaviours may be a suggestion for future research that involves
more knowledge and control over the transfer environment. By the same
token, there may also be a retrospective bias in regards to the amount
transferred (Blume et al., 2010), or even the quality of what resulted from
training (Chiaburu et al., 2010). Future research consequently should assess
training transfer through a different source such as a supervisor or even
objectively by means of job performance indicators.
However, it is a strength of this study that it made use of a highly
heterogeneous sample comprised of working age population individuals
associated with a broad range of trained competencies, training courses,
organisational types and levels, etc. A large number of studies in the training
literature employ samples comprised of e.g. tertiary students or sales
personnel; potentially a too limited approach to study a phenomenon that
applies to highly heterogeneous population. So while the present study design
and sample configuration did not allow for the implementation of specific
STUDY 2: POSITIVE COGNITIVE STATES, TRANSFER MOTIVATION, AND PROACTIVE TRANSFER BEHAVIOUR 131
training transfer/job performance observations, conclusions of this study,
albeit limited, may be considered more generalizable.
7.8 Conclusion
This study has provided an initial empirical test of the full set of
propositions encompassed by Chapters 2 to 5. These suggested that (1)
positive psychological states arising in respect of work are relevant for
training transfer; (2) training transfer is in part the result of proactive
transfer behaviours; and (3) transfer motivation is a key factor and central
mediator of the relationships between work-related positive psychological
states and training transfer.
In this chapter, a model summarising these proposed relationships was
presented (Figure 14). The findings of the subsequent empirical investigation
were supportive of all the hypotheses derived from these proposed
relationships.
In the next chapter, a further empirical test of this model is reported, one
that employs a longitudinal research design.
132
STUDY 3: A LONGITUDINAL STUDY 133
CHAPTER 8
STUDY 3: A LONGITUDINAL STUDY OF
THE EFFECTS OF POSITIVE COGNITIVE
STATES AND TRANSFER MOTIVATION ON
PROACTIVE TRANSFER BEHAVIOUR AND
TRAINING TRANSFER
8.1 Aim & Hypotheses
Replication is imperative in scientific inquiry (Bauernfeind, 1968; Schafer,
2001) and while study 2 supported the proposed model and associated
hypotheses, its cross-sectional design means that it is not possible to fully
rule out common method bias. The purpose of the present study is to provide
134
a more robust test for the proposed relationships among the focal variables.
Utilising a longitudinal design helps diminish potential effect size inflation,
reduce method bias concerns, and permit stronger inferences to be made
about the direction of causality (Conway & Lance, 2010; Little, Card,
Preacher, & McConnell, 2009).
Accordingly, employees carry into a given training their core confidence
about work which shapes the transfer motivation to eventually apply the
competencies learned. This transfer motivation subsequently determines the
extent of training being transferred at work. The process of putting new
competencies to use at work involves varying degrees of proactivity, mainly
behaviours which need to be brought about by higher levels of transfer
motivation. The study model (Figure 16) and hypotheses are shown next.
H1: Transfer motivation is positively related to training transfer.
H2: Transfer motivation is positively related to proactive transfer
behaviour.
H3a: Proactive transfer behaviour is positively related to training
transfer.
H3b: Proactive transfer behaviour partially mediates the
relationship between transfer motivation and training
transfer.
H4a: Proactive personality is positively related to proactive
transfer behaviour.
H4b: Proactive transfer behaviour mediates the relationship
between proactive personality and training transfer.
H5a: Core-confidence (hope, optimism, resilience, self-efficacy) is
positively related to training transfer.
H5b: The relationship between core confidence and training
transfer is mediated by transfer motivation.
Figure 16. Hypothesisedd model for replicating relationships betwween predictors andd mediators on trainin
STUDY 3: A LONG
ng transfer
GITUDINAL STUDY 1335
136
8.2 Sample & Procedure
The longitudinal survey design was implemented in the following way. A
set of suitable training courses was identified from a pool of open training
programs offered by a major Australian training provider. The 17 courses
selected had instruction periods between 2-7 full days, took place in a typical
class-room environment, were not restricted in access by particular criteria or
organisational association, and covered a broad range of more open
competencies: leadership, people and performance management, human
resources, project management, negotiation, communication, and finance.
The study involved three waves of data collection. Each participant was
asked to respond to a printed questionnaire on arrival, before the course
began (T1); at the end of the course on exit (T2); and to an online survey four
weeks after the last training day (T3). This follow-up period was determined
on the basis of Baldwin and Ford (1988) arguing that a critical period for
transfer occurs immediately following the training. It would appear that after
about four weeks there has been, on average, substantial opportunity to apply
at work what was learned in the respective courses.
The dual means of data collection prompted no concerns given that prior
research consistently finds paper-based and web-based surveys to generate
data of equivalent quality (e.g. Greenlaw & Brown-Welty, 2009; Lonsdale,
Hodge, & Rose, 2006; Riva, Teruzzi, & Anolli, 2003). Moreover, participants
in the present study could not choose a particular survey mode; instead all
responded under the same survey conditions at a given measurement wave.
At the beginning of each course the trainer read a description of the
research project with instructions including the option to withdraw at any
time if anyone had objections to the study. While complete confidentiality
was assured, responses had to be matched over time, and so participants
were asked to provide their first and last name alongside a valid email
address on each printed questionnaire. Trainers and administrators who
assisted the data collection confirmed very few explicit objections to survey
participation, suggesting an overall response rate of more than 90% at the
onset of each course. Participants were invited by email to the follow-up
STUDY 3: A LONGITUDINAL STUDY 137
survey online and reminded twice over the duration of two weeks if no
response had been recorded.
Participants’ age ranged from 21 to 68 with a mean of 38.1 years, 62.1%
respondents were male, and 95.0% worked full-time. A total of 365
individuals participated over the course of the three data collection waves by
completing at least one questionnaire. Sample sizes (response rates) are:
N1 = 343 (94%), N2 = 335 (92%), N3= 113 (31%), and N = 94 (26%)
respondents completed all three questionnaires. Trainers explained the slight
fluctuation in printed questionnaire completion (N1, N2) with trainees not
attending all sessions, being late, or having to exit the class-room early. The
response attrition for N3 appears typical for longitudinal survey research,
especially when compared to the very high response rates resulting from the
in-class survey environment in which the first two survey waves may have
been perceived as integral to the class room experience.
8.3 Measures
The longitudinal nature of this study required participants to complete
two paper-based questionnaires within the constraints of a professional
training activity, plus responding to an additional follow-up survey online. In
agreement with the training provider, it was important to not impede the
actual learning experience, and also to pre-empt survey fatigue in the interest
of maximising completion rates. Thus, questionnaire length for the three
measurement waves had to be kept to a minimum, and so for some variables
shorter measures were used based on those employed in study 2 (see
Appendix 11.1).
Core-confidence was measured before the training course began (T1).
Items were chosen from those employed in study 2 on the basis of exhibiting
the strongest factor loadings and no cross-loadings: 3 items were chosen each
for optimism, resilience, and self-efficacy; 4 items were selected for hope, two
each equally representing the construct’s goals and pathways dimension. Of
the total 13 core-confidence items selected for the present study, 7 match
those employed in the 12 item Psychological Capital questionnaire (PCQ-12),
138
arguably resulting from extensive psychometric evaluation (Luthans et al.,
2007; Luthans, Youssef, et al., 2006a). Transfer motivation was measured at
the end of given training course (T2) using the TM scale developed in the
present research and described in chapter 7. Proactive personality was
measured (T2) using the same 6 items as in study 2. Proactive Transfer goal
self-regulation was captured in the follow-up questionnaire (T3) with the
8 strongest loading items identified through study 2, whereby 2 items each
represented the dimensions envision, plan, enact, and reflect. Training
transfer was captured in the follow-up questionnaire (T3) and involved the
same measures as in study 2. Control Measures involved the same as in
study 2: Age, gender, organisational tenure, job tenure, total work
experience, educational level, and elapsed transfer opportunity.
8.4 Analysis
8.4.1 Data Screening
Data were screened for multicollinearity and normality with items
showing skewness between -1.30 and 0.47 and kurtosis between -0.43 and
4.68. This indicated no severe violation of the principal normality
assumption (Kline, 2011) and thereby allowed maximum-likelihood (ML)
estimation in Mplus to conduct SEM.
Missing data analysis revealed one noticeable pattern, namely the
response attrition for measurement wave 3, resulting in 69% of cases
missing. Further missing data examination finds that between 6.1% and 8.2%
of observations at T1 to T3 are missing. Missing data pattern analysis via
SPSS and Mplus suggest no systematic dependencies. This likely reflects the
reported reality of few participants randomly not being present in the
classroom during surveying or responses on printed questionnaires being
ambiguous and thus coded as missing. Most importantly, results of an
ANOVA between those cases who completed the follow-up questionnaire and
those who did not showed no significant differences on any variables
measured. Accordingly, there was no association between the propensity for
STUDY 3: A LONGITUDINAL STUDY 139
missing data and the values of the foci variables or demographics in the data
set. It can be concluded that the data can be treated as missing completely at
random (MCAR; Little & Rubin, 2002) and for the employed SEM analysis is
“ignorable” (Graham, 2009).
As mentioned prior, ML estimation in Mplus handles missing data and
parameter estimation in a single step; the imputation uses all available data,
or simply full information maximum-likelihood estimation (FIML).
Notwithstanding, especially with regards to missing cases at T3, this handling
of the missing data might require some explanation to assure confidence in
estimated findings. Graham (2009) conceptually and practically discusses
and dismisses potential missing data handling concerns, two of which are
relevant here. First, missing data imputation is not making up data. While
imputation “is the process of plugging in plausible values where none exist
[..] the point of this process is not to obtain the individual values themselves.
Rather, the point is to plug in these values (multiple times) in order to
preserve important characteristics of the data set as a whole.” (p. 559).
Second, including the dependent variable (DV) in the imputation process is
decidedly warranted because then “all relevant parameter estimates are
unbiased, but excluding the DV from the imputation model for the IVs and
covariates can be shown to produce biased estimates” (p. 559). Moreover,
simulation studies using FIML produce correct estimates with more than
50% missing data in the DV (Allison, 2009; Buhi et al., 2008; Enders &
Bandalos, 2001; Enders, 2001). In this vein, Graham (2009) explains that the
main consequence of MCAR missingness may be loss of statistical power,
whereby model estimation may be depleted. Accordingly, as long as SEM
model fit criteria are met, analyses based on data that can be treated as
MCAR will yield unbiased parameter estimates that are close to population
values.
Taken together this means that the relationships of interest can be
estimated from the available data. However, to make a strong case for
eventual findings a supporting analyses will be conducted using only
complete cases. That is, in addition to estimating the structural model with
all available data (N = 365), the same model will be estimated again by using
140
only those cases that completed all three measurement waves (N = 94; 26%).
Should the significance, direction, and effect size of findings from the two
models be approximate congruent, any missing data bias concerns can be
considered less relevant. Overall, the data screening allowed proceeding with
the analysis.
8.4.2 Measurement Models
Congeneric testing via CFA for each latent variable was based on the same
model fit indices criteria as in study 1 and study 2. All items showed
significant (P = .001) loadings on their intended factor.
Training transfer showed good model fit (χ2 = 3.39(2); RMSEA = 0.069;
CFI = 0.99; TLI = 0.96; SRMR = 0.030). Transfer motivation exhibited the
best fit with can-do, reason-to, and energised-to representing distinct factors
(χ2 = 77.18(24); RMSEA = 0.048; CFI = 0.99; TLI = 0.98; SRMR = 0.022).
Given the high correlations (.72-.76) among the three factors, they were
loaded onto a second-order factor with identical model fit. Proactive transfer
behaviour also did exhibit poor dimensional separation between envision,
plan, enact, and reflect (correlations between .93-.96; χ2 = 15.77(14);
RMSEA = 0.033; CFI = 0.99; TLI = 0.96; SRMR = 0.018). A one-factor
solution was thus considered appropriate for this study and had a good fit
(χ2 = 28.93(20); RMSEA = 0.063; CFI = 0.99; TLI = 0.98; SRMR = 0.025).
Proactive personality initially showed poor model fit (χ2 = 31.535(9);
RMSEA = 0.086; CFI = 0.93; TLI = 0.88; SRMR = 0.041), whereby
modification indices identified one item as problematic (“If I believe in an
idea, no obstacle will prevent me from making it happen.“). Omitting this
item lead to good model fit (χ2 = 10.536(5); RMSEA = 0.057; CFI = 0.98;
TLI = 0.96; SRMR = 0.026).
Hope showed good model fit (χ2 = 2.37(2); RMSEA = 0.023; CFI = 0.99;
TLI = 0.99; SRMR = 0.015). Individually, the solutions for optimism,
resilience, and self-efficacy were ‘just identified’ (i.e. parameter estimates
with 3 items have a unique solution that perfectly reproduces the observed
matrix). An intermediate four factor measurement model revealed that the
STUDY 3: A LONGITUDINAL STUDY 141
four latent variables for hope, optimism, resilience, and self-efficacy
correlated strongly (.74-.89; χ2 = 88.43(59); RMSEA = 0.038; CFI = 0.96;
TLI = 0.94; SRMR = 0.041), and higher than their respective squared AVE
(.60-68), suggesting insufficient overall discriminant validity among the
positive psychological states. Given a one-factor model was a poor fit
(χ2 = 165.46(65); RMSEA = 0.067; CFI = 0.86; TLI = 0.84; SRMR = 0.060),
the four factors again formed a higher-order core-confidence construct with
acceptable fit (χ2 = 107.19(61); RMSEA = 0.047; CFI = 0.94; TLI = 0.93;
SRMR = 0.047).
The full measurement model comprising five latent variables had a good
fit (χ2 = 946.053(685); RMSEA = 0.032; CFI = 0.94; TLI = 0.93;
SRMR = 0.075). Other than discussed, all variables met suggested criteria for
convergent and divergent validity (Fornell & Larcker, 1981). Values for
internal consistency also met suggested standards (> .70; Nunnally &
Bernstein, 1994). Descriptive statistics and correlations are summarised in
Table 14. In summary, these were adequate conditions to test the
hypothesised relationships via a structural model.
142
y p , , f yp ( ) g p
Mean SD α CR
1 Training Transfer 3.47 0.59 0.72 0.82 .731
2 Transfer Motivation 4.14 0.42 0.91 0.92 .460 ** .742
3 Proactive Transfer Behaviour 3.00 0.80 0.96 0.96 .645 ** .485 ** .827
4 Proactive Personality 3.73 0.48 0.70 0.71 .159 .250 ** .273 ** .615
5 Core Confidence 3.91 0.37 0.77 0.89 .165 .304 ** .312 ** .628 ** .755
6 Age 38.10 9.24 - - .042 -.019 -.053 .004 .041 -
7 Gender 1.39 0.49 - - .024 .161 -.028 -.051 .027 -.181 ** -
8 Educational Level 2.77 1.67 - - -.071 .022 .024 .143 -.031 -.115 * .071 -
9 Work Experience (years) 18.09 0.54 - - .078 -.008 .000 .010 .117 .894 ** -.162 ** -.250 ** -
10 Organisational Tenure (years) 5.52 0.29 - - .087 -.042 .071 -.125 -.077 .318 ** -.108 * -.111 * .339 ** -
11 Job Tenure (years) 2.39 2.61 - - -.004 .057 .015 .016 .072 .249 ** -.024 .060 .241 ** .307 ** -
12 Elapsed Transfer Opportunity (days) 39.53 15.93 - - -.027 .086 .177 .080 .139 .082 -.011 .101 .122 .020 .137
8 9 10 11
Note. N = 113-343. Standard Deviation (SD); Cronbach Alpha (α); Composite Reliability (CR).Figures underlined present the square root of the Average Variance Extracted (AVE); a value >.707 equals an AVE above the suggested threshold of .5.Coding for control variables: Gender was coded as 1 for male and 2 for female; Educational Level was coded from 1 for no High School degree to 7 P.hD. degree.Correlation is significant at * p<.05, ** p<.01 (two-tailed).
Variables 1 2 3 4 5 6 7
Table 14. Study 3: Descriptive Statistics, Scale Reliabilities, Correlations
STUDY 3: A LONGITUDINAL STUDY 143
8.4.3 Path Analysis
A structural model (M1) was specified as shown in Figure 16. It provided
acceptable fit to the data (χ2 = 945.299(689); RMSEA = 0.032; CFI = 0.94;
TLI = 0.93; SRMR = 0.075), and explained 0.478 variance (R2) of training
transfer. Similar to Study 2, I then tested two alternative models by which the
higher-order core-confidence factor directly related respectively to training
transfer (M2) or proactive transfer (M3), representing cognitive-motivational
pathways unaccounted for by transfer motivation. Both models fitted the data
slightly less well when compared to the hypothesised model (M2:
χ2 = 947.077(688); RMSEA = 0.032; CFI = 0.93; TLI = 0.93; SRMR = 0.075;
M3: χ2 = 944.885(688); RMSEA = 0.032; CFI = 0.93; TLI = 0.93;
SRMR = 0.076). The new paths added were not significant, no other paths
estimates changed in magnitude or significance, and chi-square difference
tests were not significant Table 15. Further models were not substantively
sensible and the hypothesised model was thus supported (Figure 17).
In view of the discussed missing data scenario, the next step involved re-
estimating the model by using data from only those cases that completed all
three measurement waves. Given that N = 94 would not provide sufficient
statistical power to identify a model using latent variables with a total of
39 indicators (Matsunaga, 2008), a parcelling approach was taken that
improved the ratio of sample size to estimated parameters (Bentler & Chou,
1987).
Parcelling involves the creation of aggregate-level indicators of several
individual items. These composite scale scores are then submitted to analyses
with SEM, rather than the raw score from the individual questionnaire
responses (Coffman & MacCallum, 2005). The literature on parcelling
remains somewhat controversial (Little, Cunningham, Shahar, & Widaman,
2002), whereby those arguing against it claim parcelling camouflages
measurement error (Marsh, Lüdtke, Nagengast, Morin, & Von Davier, 2013),
while proponents demonstrate its utility (Graham & Tatterson, 2000).
However, the present research can be agnostic about this debate because it
employs parcelling for the sole purpose of reducing concerns about potential
imputation bias. Here, parcelling is used to estimate a solution for
144
comparison with an arguably more precise structural model that is based on
individual items.
Using the arithmetic mean, parcels were created according to the latent
constructs, some of which then formed higher-order factors (Landis, Beal, &
Tesluk, 2000; Little et al., 2002). Specifically, the four parcels hope,
optimism, resilience, and self-efficacy loaded onto core-confidence. The three
parcels can-do, reason-to, and energised-to loaded onto transfer motivation.
The four parcels envision, plan, enact, and reflect loaded onto proactive
transfer behaviour. To preserve some measurement error for proactive
personality and training transfer, both constructs were randomly parcelled
into two composites each, based on their scales odd and even item numbering
(Landis et al., 2000).
Using the resulting 15 parcel indicators for the 5 latent variables, the fit of
the measurement model was good (χ2 = 119.886(80); RMSEA = 0.072;
CFI = 0.95; TLI = 0.94; SRMR = 0.055). Specifying regression paths
identically to model M1 above, the partially disaggregated structural model
M1p had a good fit (χ2 = 121.423(84); RMSEA = 0.068; CFI = 0.95;
TLI = 0.94; SRMR = 0.056). Importantly, when compared to the structural
model using imputation, the existing paths remained significant (see Figure
18). Equally, the alternative models (M2p and M3p) did not have a
significantly better fit and the additional paths were also not significant
(Table 15). Therefore, the Mplus FIML processing did not overly distort the
findings and it can be concluded that concerns relating to biased findings due
to missing data can be dismissed.
As a last step, and using all available data and cases, participants’
demographic and organisational membership characteristics and elapsed
transfer opportunity were entered as control variables to the regression paths
of model M1. None of these auxiliary variables had a significant effect or
noticeably changed estimated relationships between the focal variables;
however, they resulted in poor model fit (χ2 = 1575.992(977);
RMSEA = 0.087; CFI = 0.69; TLI = 0.66; SRMR = 0.095). In line with Kline
(2011), these auxiliary parameters were thus eliminated, and the
hypothesised model M1 was accepted for interpretation.
STUDY 3: A LONGITUDINAL STUDY 145
The accepted structural models are described graphically next in Figure 17
and Figure 18. In the interest of clarity, only significant relationships
(p < 0.05) are shown alongside standardised path coefficients.
146
Table 15. Study 3: Comparison of examined Path Models
Model χ2 df χ2/df Comparison ∆χ2 RMSEA CFI TLI SRMR
M1 Hypothesised model 945.30 689 1.37 0.032 0.93 0.93 0.08
M2 Additional path from core-confidence to training transfer 947.08 688 1.38 M1-M2 -1.78 0.032 0.93 0.93 0.08
M3 Additional path from core-confidence to proactive training transfer
947.08 688 1.38 M1-M3 -1.78 0.03 0.93 0.39 0.08
M1p as M1 using complete cases parcel estimation 121.42 84 1.45 0.07 0.95 0.94 0.06
M2p as M2 using complete cases parcel estimation 120.51 83 1.45 M1'-M2' 0.92 0.07 0.95 0.94 0.06
M3p as M3 using complete cases parcel estimation 121.36 83 1.46 M1'-M3' 0.06 0.07 0.95 0.94 0.06
Figure 17Solid line
7. Estimated Structures represent paths th
ral Model M1: Core-chat were significant (
confidence, Transfer (* p < .05, ** p < .01, *
Motivation, Proactiv*** p < .001). All facto
ve Transfer Behaviouor loadings significan
ur, Proactive Personant at p < .001.
STUDY 3: A LONG
ality, and Training Tr
GITUDINAL STUDY 14
ransfer.
47
1
48
Figure 18. Esto dismiss poof FIML perm
stimated Structural Motential distortion as
missible.
Model M1p: using coms the result of missin
mplete cases only (Ng data (69%) imputa
N = 94) via item parcetion processes. No s
els to re-estimate Pasevere differences em
th Model A1 and commerged, rendering fi
mpare findings so asndings on the basis
s
STUDY 3: A LONGITUDINAL STUDY 149
8.5 Results
Unless otherwise stated, all p = < .001. The direct relationship from
transfer motivation to training transfer was not significant, not supporting
hypothesis H1. However, transfer motivation significantly predicted eventual
proactive transfer behaviours (.46), supporting H2. Proactive transfer
behaviours in turn significantly related to training transfer (.54), supporting
H3a. Together this means that H3b was not supported by means of being a
partial mediation. Instead proactive transfer behaviours fully mediated the
transfer motivation and training transfer relationship (indirect effect = .27).
Proactive personality showed a small relationship with proactive transfer
behaviour (.18; p = < .05), supporting hypothesis H4a. No support was found
for H4b, the indirect effect of proactive personality onto training transfer was
not significant.
Core confidence was moderately positively related to transfer motivation
(.31), supporting hypothesis H5a. Given there was no direct path from
transfer motivation to training transfer, H5b could not be assessed. Core
confidence had a total indirect effect on training transfer (.15) via transfer
motivation and proactive transfer behaviours. There were no other significant
direct or indirect paths.
8.6 Discussion
Theoretical and practical considerations spanning the entireness of the
present research are discussed integrated in chapter 9 following. The focus
here was re-assessing findings derived prior through study 2. Using a more
robust longitudinal research design, the present Study 3 by and large
confirms prior found relationships.
The data show that people who at the end of training are motivated to
apply what they have learned report higher levels of training transfer about
40 days later. While this further supports transfer motivation as a key
concept, present findings however suggest that this relationship is fully
mediated by proactive transfer behaviour. In contrast, findings from study 2
150
would suggest that proactive transfer behaviour only partially mediate the
transfer motivation to training transfer relationship.
An explanation may be the elapsed time after exiting the training, which
differs substantially between the two samples. That is, participants of the
present study 3 reporting higher levels of training transfer potentially did so
as a function of substantial self-initiative to apply what they learned in the
early stages of returning from training. This would give support to Baldwin
and Ford (1988) suggesting that a critical period for transfer occurs
immediately following the training. Accordingly, individual proactivity
appears to be a key mechanism for realising the successful implementation of
new competencies. Future research could employ a more controlled transfer
environment and investigate what factors and amount of time facilitate or
inhibit such proactive transfer attempts.
An alternative explanation relates to the study design by which training
transfer and proactive transfer behaviours had to be measured at the last and
thus same measurement wave. Future research consequently might involve
tracking individuals over longer and multiple times into the post-training
period, for example via diary studies, thereby reducing potential method
effect inflation while capturing qualitative information of how and why self-
initiated transfer comes about.
The last finding associated with proactivity relates to the role of proactive
personality. Again, proactive personality had a small direct effect with
proactive transfer behaviour but conversely not an indirect effect onto
subsequent training transfer. However, although study 2 found support for
such an indirect effect, it was marginal. For the transfer of training this
indicates the importance of motivating proactive behaviour, while proactive
dispositions are less relevant (Tornau & Frese, 2013).
The study also replicated the finding that positive cognitive states in the
form of core-confidence are relevant for training transfer. What is important
to note is that those thoughts and beliefs about work with which people enter
a training course determine their transfer motivation on exit, with discussed
effect on training transfer later. Accordingly, positive cognitions that other
research found to shape the setting and striving for work goals, also relate to
STUDY 3: A LONGITUDINAL STUDY 151
the setting and striving for goals associated with training transfer. This
supports the notion that formal training is an integral episode of individuals’
work experiences and not an isolated act. Henceforth, how people appraise
their work scenario and abilities influences the degree of training transferred
and training effectiveness should be increasingly viewed as a systemic
function.
In summary, the present replication confirms study 2 and gives support to
the proposition that individual psychological states about work at course
beginning shape transfer approaches and outcomes after training.
8.7 Limitations
Although this study employed a more robust longitudinal design, it has
limitations. First, while temporal separation of the focal measures was
implemented, it could not be achieved for the proactive transfer behaviour
and training transfer variables, which had to be captured in retrospect. The
experienced sample attrition post training is a realistic indicator that
continuous surveying requires an entirely different study implementation
which is more suited for managing survey fatigue. Diary studies embedded
into a particular organisation or training course may allow for this level of
control, whereby this likely would offset a strength of this study: a sample
comprised associated with a heterogeneous group of employees and training
courses etc.
By the same token, independent measures relating to training transfer are
clearly the next step and could include supervisory or peer ratings or
objective organisational meta-data on e.g. sales or costs. This might be best
achieved via a training evaluation study by which a quasi-experimental
design rules out alternative explanations, so that observed changes can be
attributed to and differentiated between training and individual features.
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8.8 Conclusion
This study employed a longitudinal design for testing the model outlined
in Figure 16. It showed that positive psychological states and proactivity are
relevant for training transfer. A significant amount of variance in transfer
motivation can be attributed to positive thoughts and beliefs about work that
likely arise on the job. In line with extant research the study showed that the
level of transfer motivation with which individuals exit a course predicts the
amount of subsequent training transfer at work. To varying degrees it may be
inferred that this motivation converts into proactive transfer behaviours by
which individuals self-initiate the application of new competencies at work.
While design and measurement challenges in this study do not allow
definitive inferences about the degree of proactivity involved, it appears that
motivated behaviour is the most crucial component while dispositional
proactive personality plays a minor role, if at all. The positive cognitions
about work, or core confidence, indirectly shaped those proactive transfer
behaviours. Together this strengthens the argued notion that training
transfer is not an isolated act but rather a function of a number of factors,
some of which people carry from the work environment into training and
back.
DISCUSSION & INTEGRATION 153
CHAPTER 9
DISCUSSION & INTEGRATION
The aim of the present research was to examine the influence of three
inter-related psychological processes on the transfer of training. It was
argued that (1) positive cognitive states relating to work determine
(2) transfer motivation, which in turn influences (3) the proactive self-
regulation of training transfer behaviours, and training transfer. By and large,
the structural equation models in study 2 and 3 provided support for these
propositions, also in using measures of training-related motivation that were
developed and tested in study 1. This chapter integrates the research findings
and their contributions, discusses potential limitations, and considers the
theoretical and practical implications.
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9.1 Summary & Contributions
It was hypothesised that the positive psychological states of hope,
optimism, resilience, and self-efficacy on the subject of work act as
antecedents to transfer motivation, and subsequently affect training transfer.
Results from studies 2 and 3 support this argument on the basis of the four
positive-laden constructs forming a higher-order ‘core confidence’ factor.
Study 3 showed that an individuals’ core confidence about setting and
achieving work goals ultimately predicted the transfer of training about 40
days later via transfer motivation at training end. This adds to the noti0n that
the transfer of training should not be seen as an isolated concept but as an
integral part of work (Baldwin & Magjuka, 1997; Kontoghiorghes, 2002,
2004). That is, the transfer of training begins even before learners are
entering the classroom. It also suggests that certainty beliefs about whether
one can handle what one needs to do at work (Stajkovic, 2003, 2006) are an
important factor for eventually achieving transfer goals.
Proactive transfer behaviours were hypothesised to affect training
transfer. Results from both study 2 and 3 support the premise that self-
initiated acts of envisioning, planning, enacting, and reflecting about the
application of new competencies do indeed relate to training transfer. Both
studies further found that individuals’ disposition or personality of being
more proactive had a negligible influence on transfer. This supports
arguments made by Tornau & Frese (2013) concerning the need to
concentrate research and practice on the behavioural side of proactivity.
Study 2 also showed that proactive transfer behaviours need to be motivated,
and study 3 replicated this finding, whereby those reporting higher levels of
transfer motivation at training end reported 40 days later that they had
engaged in more proactive transfer behaviours.
Another contribution of the present research is thus the introduction of
the proactivity concept to the training transfer literature. The findings
suggest that proactivity represents a significant element in the transfer of
training, and proactive transfer behaviours need to be encouraged.
DISCUSSION & INTEGRATION 155
Findings discussed above further confirm the importance of transfer
motivation for training effectiveness, it is a key construct for the transfer of
training (Gegenfurtner, Vauras, Gruber, & Festner, 2010). Results from
study 2 and 3 show that transfer motivation represents a central mediating
mechanism, shaped by thoughts and beliefs about work, and fuelling training
transfer. However, the direct relationship between transfer motivation and
training transfer was only significant in study 2, in study 3 this was an
indirect relationship which was fully mediated via proactive behaviours. It is
thus an important issue for future research to better understand the
importance and change of motivational trajectories over the time
immediately after the training.
The transfer motivation construct is part of a larger framework of
training-related motivation which has been conceptually developed in
response to shortcomings associated with existing motivational constructs as
they relate to training effectiveness. Study 1 therefore systematically
developed measures that relate to three interrelated stages in the training
process: participation motivation, learning motivation, and transfer
motivation. Each motivational stage comprises psychological dimensions of
can-do, reason-to, and energised-to motivation which shape goal setting and
striving. Psychometric properties of the resulting scales met suggested
criteria for item adequacy and internal consistency.
Results by and large further support propositions about can-do, reason-to,
and energised-to being distinct motivational mechanisms or processes
(Parker et al., 2010). In spite of this, high dimensional correlations in all
studies suggest that it is sensible to specify participation motivation, learning
motivation, and transfer motivation as higher-order factors. The motivational
theory employed supports such superordinate models and they may be
favoured in applied research. Additionally it was hypothesised that levels of
participation motivation before training would predict subsequent levels of
learning motivation and ultimately levels of transfer motivation at course
exit. Results from studying a longitudinal sample corroborate propositions
about downstream trajectories (Beier & Kanfer, 2009), whereby the levels of
156
earlier can-do, reason-to, and energised-to motivation predict subsequent
levels of respective dimensions over time.
Refining training-related motivation in combining complementary
motivational mechanisms is thereby another conceptual and operational
contribution. The multi-stage, multi-dimensional framework and its
measures are parsimonious, universal, and practical, and may be considered
a step forward for the research and facilitation of training transfer.
9.2 Limitations
The present research is not without limitations. To begin, common
method bias may have affected the results and although this has been noted
in prior chapters, it needs to be discussed. First, the cross-sectional design of
study 2. Although study 3 made a much stronger case for common method
problems not overly affecting investigated relationships than study 2, several
key variables (proactive transfer behaviour and training transfer) were still
measured at the same point in time.
Also, both studies did rely on self-report estimates of positive cognition,
motivation, behaviour, and outcomes. As noted, it is challenging to imagine
how intra-psychic mechanisms such as states of hope or can-do motivation
may be assessed other than by the self. Nevertheless, the extent of training
transfer and proactive behaviours at work could be assessed by someone
other than the trainee/employee, for example peers or supervisors. Future
research should seek to replicate the present findings by employing as many
measures from independent sources as possible.
Additionally, this research adopted a generic measure of training transfer
(Xiao, 1996), and ideally there would have been a closer match between the
nature of the competencies trained and the measures to assess their
associated transfer. One option is to use course-specific measures, which
requires to properly develop and test them, this is resource intensive. In the
same vein, generating large enough sample sizes via a single and consistent
training course offering might prevent or impractically increase study
completion time. Arguably researchers need to strike a balance between
DISCUSSION & INTEGRATION 157
heterogenising the sample for the sake of increasing observed cases and
specifying customised measures to assess the transfer and effect of trained
competencies. Nevertheless, future research should test the same
relationships using a more homogenous trainee cohort and more specific
outcomes measures, potentially even within one organisation.
As has already been discussed, measuring and modelling the positive
psychological states has been challenging. To date it remains unknown
whether this is the result of hope, optimism, resilience, and self-efficacy being
conceptually very similar or whether extant measures insufficiently reflect
the underlying constructs. The convergence of hope, optimism, resilience,
and self-efficacy may indeed suggest they are manifestations of an
individual’s core confidence and future research needs to address conceptual
clarity and psychometric properties (Dawkins et al., 2013; see Stajkovic et al.,
2013).
In a related vein, the measures of proactive personality displayed
insufficient internal consistency and extracted too little average variance.
While this was just permissible for the present research, no plausible
explanation reduces concerns. The measures based on Bateman and Crant
(1993), not altered, and even re-validated in other research (Claes, Beheydt,
& Lemmens, 2005; α = .86; Parker, 1998; α = .85). Questions remain
whether this has been a function of the population, survey mode, or else.
Additionally, the present research was based on a mediational model
whereby distal variables had their effect through more proximal variables and
processes. This was based on a sound theoretical rationale; yet, alternative
moderated relationships are conceivable. For instance, facets of personality
(e.g. conscientiousness) may not just have their effect through more proximal
variables, but actually moderate the relationship of these proximal variables
and their outcomes. This is an angle that will need to be further investigated
in future research.
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9.3 Theoretical Implications and Future Research
An overall contribution of the present research is conceptually anchoring
it in a common goal regulatory framework. This has given theoretical clarity
for hypothesising the relationships between the focal constructs, and when
explaining the pursuit of goals (Carver & Scheier, 2001; Forgas et al., 2013;
Wood, 2005), including those relating to training transfer. While not entirely
new, this has not always been the case in training transfer literature, for
example when considering existing constructs of training-related motivation.
It is thus suggested that future research increases the use of goal and self-
regulation theory in understanding the transfer of training (Sitzmann & Ely,
2011).
One reflection of this call to action in the present research has been the
construct of proactive transfer behaviour. Although the four elements of
envisioning, planning, enacting, and reflecting could not be sufficiently
statistically differentiated, making self-regulation more visible and
interpretable is beneficial (Bindl et al., 2012) and warranted for training
effectiveness research (Sitzmann & Ely, 2011) to discern the antecedents of
goal setting and striving acts that realise training transfer. Developing
adequate measures which better support the factorial differentiation is an
option and short scales of self-regulation are desirable and have been
developed in other domains (e.g. Yeo & Frederiks, 2011). Alternatively,
operationalising the more complex process model by Foxon (1993, 1994) may
be a promising avenue to unpack the self-regulation of transfer. While the
model exists only in theory, diary studies for instance could capture trainee’s
intention to transfer, which are then followed by subsequent attempts and
partial transfer. Diary studies may also be particularly suitable for examining
the role of self-regulation in proactive and conscious maintenance of new
competencies that lead to unconscious work routines.
In the same vein, longitudinal studies would be helpful to understand the
proactive transfer of training. For example, when are proactive transfer
behaviours most crucial after returning from a training to work? Also, while
the present research gives initial support to the proactivity proposition,
further work is required to establish this. Future studies need to include third
DISCUSSION & INTEGRATION 159
party observations of proactive transfer, for instance via supervisors ratings,
and also involve control measures of ‘reactive’ transfer. Taking these
measures repeatedly over time then would allow discerning at what stage and
under what conditions self-initiated change arises as the result of a training
experience. Given that training transfer is integral to performing at work,
proactive transfer behaviours may be prompted by the same factors as
proactive work behaviours. However, this need to be researched, and a broad
body of literature exists to inform this theorising and testing (Aspinwall,
2005; D. T. Campbell, 2000; Fritz & Sonnentag, 2009; Grant & Parker, 2009;
Ohly & Fritz, 2007; Parker & Collins, 2010; Parker et al., 2006; Tornau &
Frese, 2013).
While construct validity has been established, the developed constructs of
training-related motivation require further attention so they are properly
situated in the wider nomological net of individual and environmental
variables for training effectiveness. This should begin by examining the
relationships of participation motivation’s facets to different forms of training
framing (Quiñones, 1995; Tai, 2006). The nature and means by which a
formal learning opportunity is communicated conceivably differentially
affects can-do, reason-to, and energised-to participation motivation (Hurtz &
Williams, 2009). This likely affects the decision to enrol in/voluntary
(Baldwin, Magjuka, & Loher, 1991; Baldwin & Magjuka, 1997) and the
subsequent motivation to learn (Baldwin, Ford, & Naquin, 2000). All of this
could be tested hypothetically or in a real work learning scenario.
Equally, distinct training design and delivery and learning environments
would have different effects on can-do, reason-to, and energised-to learning
motivation (Ainley, 2006; Weissbein et al., 2010). While learning during
training is a necessity for transfer, these motivational mechanisms likely have
downstream effects on eventual training transfer beyond the actual
attainment of new knowledge and skills. Lab studies and educational settings
might be best suited to test what can be done in the classroom to not just
create competence mastery but also improve their subsequent transfer.
Lastly, investigating the effects of deliberately influencing transfer
motivational states is warranted. Although research into transfer motivation
160
is on the rise (Gegenfurtner, Festner, et al., 2009; Weissbein et al., 2010;
Zaniboni et al., 2011), little, if any, research is occupied with how learners can
be energised via positive affect so that it transfer initiation and maintenance
are increased. Research into transfer motivation should also engage in latent
growth modelling. It has been argued that motivation is dynamic and
understanding what builds and maintains or even reduces it over time can
help establish causation for antecedents and consequences.
There are a number of possible future research avenues associated with
positive psychological states as they relate to the work situation. Research
suggests that strengthening hope, optimism, resilience, and self-efficacy
favourably affects work-related outcomes including performance (Avolio et
al., 2011; Kirk & Koeske, 1995; e.g. S. J. Peterson & Byron, 2008; S. J.
Peterson & Luthans, 2003; Reichard et al., 2013; Stajkovic & Luthans, 1998;
Wandeler & Bundick, 2011; Zunz, 1998). The present studies suggest that
employees’ core confidence at the workplace impacts the transfer of training.
Thus, developing positive thoughts and beliefs in employees would allegedly
lead to more training transfer. Yet, the present findings shed no light on this
premise and interventions studies involving control groups would have to
prove that building employees core confidence is a viable option for
enhancing training transfer. When compared to the large body of literature
on self-efficacy, the knowledge on building hope, optimism, and resilience at
work may be considered in its infancy. Qualitative studies that would
interview employees on the subject of work hope, optimism, resilience, and
training transfer appear constructive to advance our understanding. This
would further determine which conditions in the work environment can
prompt higher levels of core confidence and correspondingly improve
training outcomes.
What is more, the present research is testament to the conceptual and
psychometric ambiguity concerning the constructs. On the one hand, they are
theoretically distinct; on the other hand, insufficient, if any, research exists
that would clearly show how all four constructs provide incremental and
differential explanation to predict relevant outcomes (Dawkins et al., 2013).
Further research ought to clarify the construct validity profile of hope,
DISCUSSION & INTEGRATION 161
optimism, resilience, and self-efficacy. Given that associated sets of measures
have arisen individually through different bodies of literatures, a
concentrated effort to develop measure that better reflect and discriminate
the underlying psychological mechanisms may help remedy this dilemma.
9.4 Practical Implications and Directions
A central objective of the present research was to advance theory and
research about factors that are of managerial relevance by potentially being
open to intervention. From a practical perspective, findings of the present
research would suggest that managers who facilitate a work environment
which develops core confidence, facilitates training-related motivation, and
encourages proactive behaviours can have a great impact on many important
job-related outcomes including training transfer.
Theories and the academic literature can sometimes seem difficult for
practitioners to understand as a result of its enormousness, obscure terms,
and complex conceptualisations. Yet, from the onset the present research
desired to make a great deal of sense for the managerial reality so as to
enhance training transfer and work performance. This section seeks to be
straightforward and illustrates what can be done about the findings.
Promoting core confidence at the workplace would involve addressing its
four underlying dimensions hope, optimism, resilience, and self-efficacy.
Luthans and colleagues (Luthans, Avolio, et al., 2006) discuss possible
avenues to promote associated thoughts and beliefs, and also report that even
short interventions can increase levels of respective positive cognitive states
(Luthans, Avolio, & Peterson, 2010). For instance, demonstrating to
employees that others have achieved challenging goals at work may build
positive expectancies about one’s own future in this context (optimism). At
the same time explaining and modelling the behaviours that lead to such goal
attainment allow employees to assimilate and gain assurance with the
involved goal striving processes (self-efficacy). Creating awareness for
possible obstacles and illustrating how to mentally frame setbacks can pre-
empt goal disengagement (resilience). To help people get better in knowing
162
what to do at work, how to do it, and having the will to do it (hope), a range of
interventions are described next (Adams et al., 2002; Juntunen &
Wettersten, 2006; Snyder & Lopez, 2002). Simply for the sake of brevity,
only the core confidence dimension hope is chosen for this more elaborate
illustration.
Hope is fostered when people are allowed the freedom to set their own
goals. For instance, there should be an array of goals, including those which
seem to stretch the abilities of the individual. Consequently, the individual
can then be sensitised to the decision making processes that surround the
identification of important goals. These goals should be prioritised from least
to most important in order to focus effort and resources available. Next,
adequate goals must be explicitly set. Employees further need to learn how to
break large goals into smaller steps. This stepping process begins with the
most reachable goal to elicit positive mastery experiences for continued
pursuit. However, given that not all goals are readily attained, mental road
maps may be discussed that include alternate routes to the goal in case of
blockages or stressors. Ultimately, in an iterative way, a main path and
potential alternatives should derive which allow flexible and sustained goal
pursuit. If goal pursuit is hindered it is important to prevent constant
frustration as it will lead to lower agency. Instead, obstacles need to be
understood as important opportunities to learn and determine new
pathways. Encouraging thinking of problems as challenges allows people to
enjoy the process of goal attainment, and not just focus on the goal itself. This
can involve the patterns of positive self-talk and mindful reflection.
The above ultimately leads to more sustainable routines of setting and
striving for goals and the means to achieve them, and thereby more core
confidence. Those behaviours and their more cognitive underpinnings are
further fostered by social environments in which this positive thinking is
encouraged. Supervisors, peers, and mentors can serve as role models and
help individuals to set goals, encourage realistic beliefs about the future,
provide resources, and assist with the correction of erroneous pathways.
Equally, transfer attempts often involve unexpected obstacles and making
errors. Hence, a learning culture with active error management can
DISCUSSION & INTEGRATION 163
constructively deal with mistakes in goal setting and pursuit, and so foster
training transfer by developing core confidence.
Practitioners can use the framework of training-related motivation to
elicit desirable training and transfer behaviours. So employees think they
have the personal resources and can participate, managers can provide
absence management which balances usual work responsibilities with those
of the training episode, including re-direction of emails and the reduction of
work load. It should be paramount that employees fully understand what a
given training will be about, why it is relevant, how it is supposed to change
themselves and/or work outcomes. This goes in hand with defining clear
goals that arise out of a discussion involving expectations and skill gaps that
training shall fill. To foster reason for participation, managers can set up
work scenarios and responsibilities that require the new competencies to be
used immediately. Managers can energise employees for participation by
creating an environment in which the learning opportunity is meaningful and
perceived as an opportunity which is not to be missed. Creating such
ownership requires that training participation is minimally enforced and not
a result of mere compliance or even fear. It is beneficial to communicate
widely and excite for instance via emotional teaser about the nature and
features of the upcoming training. Diffusing positive testimonials from past
trainees can enthuse about the new competencies making a difference.
Increasing individuals’ belief in their ability to learn during the learning
stage can be increased through scaffolding, whereby a number of ‘quick wins’
lead to mastery experiences. This encourages active participation and making
mistakes in a safe environment. It further helps to articulate that
performance during training does not necessarily reflect the ability to apply
what was learned. Learners benefit from ongoing, meaningful, and diagnostic
feedback about their performance, challenges, and success. In order to
maintain a reason to learn it helps to illustrate the potential future that arises
through mastering the new competencies. Learners may be energised by
making learning inspiring, intriguing, fun, and enjoyable in addressing all
senses via visuals, audio, aesthetics, and relying on physical activity and
humour. Further stimulation of positive affect may involve more subtle but
164
deliberate creation of experiences such as socialising and networking, and
simply the realisation of having a change from daily work routines.
To facilitate employees can-do transfer motivation, managers should
provide ample time and control to those trained so they are able to
consolidate and convert training into work actions. This includes offering
plenty of resources and opportunities to transfer, for instance via immediate
goals tied to valued rewards. To add further reason for transfer, it should be
clearly communicated how the successful application of competencies trained
will contribute to organisational priorities. High positive arousal may be
generated by publicly and frequently recognising employees’ training
attendance, skill attainment, knowledge diffusion, and certification, for
instance via incentives and tokens that create pride and make people subject
champions.
Lastly, other than through promoting training-related motivational states,
the literature would suggest a range of factors that might encourage proactive
transfer behaviours. A challenge of deliberately promoting proactive training
transfer is that employees are expected to use independent judgment and
initiative, and are simultaneously supposed to think and act like their bosses
(D. T. Campbell, 2000). Therefore, employees may be provided with high
amounts of autonomy and control with regards to accomplishing their work
and transfer goals, which is flanked by high-quality communication (Parker,
1998) and co-worker trust (Parker et al., 2006). These appear to be necessary
conditions because if an employee perceives that engaging in proactive
transfer behaviours risks harming his or her image in the eyes of peers or
supervisors at work, he or she will be less likely to engage in such proactive
transfer behaviours. Instead, the employee remains reactive, waiting for
training transfer to be called for or simply doing nothing about changing the
self or work as the result of training.
Overall, interventions that combine these cognitive, motivational, and
behavioural antecedents to training transfer represent largely untapped
levers for practically improving training transfer.
DISCUSSION & INTEGRATION 165
9.5 Conclusion
Almost three decades have passed since the transfer of training began to
be proposed as a paradigm by which to research and facilitate the
effectiveness of work training. This concept has broadened the research
agenda beyond the focus on learning new knowledge and skills in a classroom
and highlighted the role of factors relating to the learning environment, the
work experience, and ultimately the individual being trained. Research has
identified several largely consistent variables and proposed models by which
to organise and understand their role. Motivation is such a key factor because
it represents central mediating mechanisms through which many other
influences affect training success. Much empirical research has demonstrated
this role albeit motivational theories and constructs as they relate to training
have not progressed in the same quality as in other bodies of literatures.
Additionally, a more systemic view of training effectiveness is increasingly
proposed whereby training transfer is integral to work performance.
The present research thus sought to address some of these important
conceptual and operational limitations and was anchored in a goal regulatory
framework. This thesis conceptually refined training-related motivation and
further systematically developed multi-stage and multi-dimensional
measures. This thesis also introduced the concept of proactive transfer
behaviour to the literature and began to operationalise it as a consequence of
transfer motivation. This thesis further made some headway in elucidating
why positive psychological states at work might be an antecedent for transfer
motivation and thus associated with higher training transfer. In conclusion,
the present research found support for those hypotheses.
It is my hope that researchers and practitioners build on those virtues and
continue to pursue this line of inquiry to further establish the mechanisms by
which core confidence at work, training-related motivation, and proactive
transfer behaviours can improve the transfer of training.
166
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APPENDICES 11.1 Measures Study 2 & 3 ID Construct Item
Training Transfer1 * I have changed parts of my behaviour/activities at work in order to be consistent with material taught in the training.2 * My actual job performance has improved due to the skills that I learned in this training course.3 * Supervisors, peers, or subordinates have told me that my work performance/quality has improved following the training.4 * How much of the new knowledge/skills covered by the training do you estimate you actually use at work?
Proactive Transfer Behaviour1 * Envisioning … imagining what it would be like to use what I have learned.2 Envisioning … thinking about ways to apply this training at work.3 * Envisioning … envisioning how my job might be different if I were to use what I have learned.4 Planning … going through different scenarios in my head about when and where to apply this training.5 * Planning … planning changes to a work situation based on things I learned.6 * Planning … getting myself prepared to use new skills or knowledge acquired through this training.7 Enacting … making changes to how I do my work based on this training.8 * Enacting … self-initiating ways to use trained skills at work.9 * Enacting … starting to use trained skills or knowledge to improve my job performance.
10 Reflecting … considering the outcomes of my training-related efforts in regards to my work performance.11 * Reflecting … extracting lessons for the future from my attempts to apply this training at work.12 * Reflecting … seeking extra feedback from someone about any training-related actions I engaged in.
Proactive Personality1 * If I see something I don’t like, I fix it.2 * No matter what the odds, if I believe in something I will make it.3 * I love being a champion for my ideas, even against others’ opposition.4 * I excel at identifying opportunities.5 * I am always looking for better ways to do things.6 * If I believe in an idea, no obstacle will prevent me from making it happen.
Positive Cognitive States1 * Hope If I should find myself in a jam at work, I could think of many ways to get out of it.2 Hope At the present time, I am energetically pursuing my work goals.3 Hope There are lots of ways around the sorts of problems I encounter right now at work.4 * Hope Right now I see myself as being pretty successful at work.5 * Hope I can think of many ways to reach my current work goals.6 * Hope At this time, I am meeting the work goals that I have set for myself.1 * Optimism When things are uncertain for me at work, I usually expect the best.2 Optimism If something can go wrong for me at work, it will.3 * Optimism I always look on the bright side of things regarding my job.4 Optimism In my job I hardly ever expect things to go my way.5 Optimism At work, I rarely count on good things happening to me.6 * Optimism Regarding work, I expect more good things to happen to me than bad.1 * Resilience At work, I usually manage one way or another.2 Resilience I usually take stressful things in stride at work.3 * Resilience I try to absorb difficulties at work quickly, so they do not disturb me too much.4 * Resilience At work, I can get through difficult times because I’ve experienced difficulty before.5 Resilience At work, I can be "on my own", so to speak, if I have to.6 Resilience When I have a setback at work, I have trouble recovering from it, moving on.1 * Self-Efficacy I am able to achieve most of my goals at work.2 Self-Efficacy When facing difficult tasks at work, I am certain that I will accomplish them.3 Self-Efficacy I think that I can achieve outcomes that are important to my organisation.4 * Self-Efficacy I will be able to successfully overcome challenges at work.5 * Self-Efficacy I am confident that I can perform effectively on many different job tasks.6 Self-Efficacy Even when things are tough at work, I can perform quite well.
Note. *denotes item was selected for Study 3.