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Exploring organizational transfer in self-directed, self-selected elearning courses

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Exploring organizational transfer in self-directed, self-selected elearning courses

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EXPLORING ORGANIZATIONAL TRANSFER IN SELF-DIRECTED,
SELF-SELECTED ELEARNING COURSES

by

Anjelica Wright Garcia

A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION

December 2011

Copyright 2011 Anjelica Wright Garcia
ii

ACKNOWLEDGEMENTS
When I began the journey to earn my doctoral degree, I was the new mom of a 3
month old; at graduation I am the mother of a happy, healthy 3 year old with another
coming (hopefully) just after this dissertation is submitted as fully completed. To say that
I am grateful for the love, support, and encouragement I have received during the process
as I balanced school, full time employment, and new motherhood seems like an extreme
understatement. My deepest thanks and love go to my family, especially my husband,
Gideon, my mother, Elle, and brother, Nate, who all rallied in the spirit of ‗it takes a
village‘ to keep me in classes and writing, and my son, Joaquin, who spent at least his first
year listening to me read peer reviewed articles out loud to him rather than Eric Carle and
learned to draw with highlighters rather than crayons.
Having a group of co-workers who value education and brilliantly contribute to the
design, editing, and analysis of this work has been beyond helpful. Encouragement from
Arienne McCracken, Lacey Leone McLaughlin, Susan Resnick West, and Theresa
Welbourne simultaneously challenged and supported me, ultimately making this work
much stronger. Special thanks to Beth Neilson, who served as my incomparable guide
through quantitative statistics and whose support and work can be measured in sacrificed
weekends, evenings, and lunches to overcome truncated deadlines.
I am privileged to have a network of extraordinary professional learning and
development contacts who guided me to make this research relevant and applicable.
Special thanks go to my contacts at this case‘s organization who were vital in the
implementation and supportive of doing something both academically and managerially
iii

vigorous. Finally, my committee of Guilbert Hentschke, Richard Clark, and John
Boudreau offered a brain trust that allowed me to bridge sectors and gave me access to
some of the most brilliant and cutting edge thinking in the space.

iv

TABLE OF CONTENTS
Acknowledgements ii
List of Tables vi
List of Figures viii
Abstract ix
Chapter 1: Introduction 1
Background 1
Conceptual Underpinnings for the Study 3
Transfer as a High-Performance Work Practice (HPWP) 3
Transfer 4
Self-Directed Learning 6
Statement of the Problem 7
Purpose of the Study 8
Limitations, Assumptions, and Design Controls 9
Definition of Key Terms 10
Summary 11

Chapter 2: Review of Related Literature 12
Learning and Training within the Organization 13
High-Performance Work Practices (HPWPs) 14
Changes in the Model 15
Elearning 16
Benefits to the Organization 16
Adoption and Acceptance of elearning 17
Disadvantages of elearning 18
Transfer within the Organization 21
Primary Factors that Influence Transfer 23
Learner/Trainee Characteristics 23
Work Environment Influences 24
Intervention Design and Delivery 24
Gaps in Transfer Research 25
Transfer within elearning 25
Transfer among Autonomous Workers and Open Skills Courses 26
Self-Directed Learning 31
SDL in the Workplace 32
Self-Directed elearning 33
Significance 34

v

Chapter 3: Research Design and Methodology
Introduction 36
Problem and Purposes Overview 36
Research Questions 37
Population and Sample 38
Data Collection and Instrumentation 40
Data Analysis 44
Validity and Reliability 47
Summary 48

Chapter 4: Analysis of Data 49
Introduction 49
Organization of Data Analysis 50
Presentation of Descriptive Characteristics of Respondents 51
Utilization Demographics 51
Respondent Demographics 52
Research Questions and Analysis of Data 56
Summary 85

Chapter 5: Findings, Conclusions, and Implications 86
Introduction 86
Summary of the Study 87
Summary of Findings 88
Motivation and Demographics 89
Breadth, Frequency, and Difficulty of Transfer 89
Participants who did not Attempt Transfer 90
Complete vs. Did Not Complete the Course 91
Closed Skills/Near Transfer (IT Courses) vs.
Open Skills/Far Transfer (Management Courses) 92
Central to the Organization 93
Conclusions 93
Implications 100
Future Research 104
Summary 106

References 108

Appendices 120
Appendix A: E-Mail Solicitation for Quantitative Survey 120
Appendix B: Quantitative Survey 123

vi

LIST OF TABLES

Table 1: IT and Management courses available and number of users
who accessed them during the research period 52

Table 2: Demographic characteristics of respondents 54

Table 3: Motivation and relationship to current job usage 57
Table 4: Relationship between current job use and demographics 58
Table 5: Relationship between motivation to engage and demographics 60
Table 6: Perceived frequency of use and difficulty of the overall course 61
Table 7: Perceived use, frequency, and difficulty of objectives 61
Table 8: Relationship between perceived use, frequency,
and difficulty of objectives 62

Table 9: Difference in means of frequency of use (course/objectives)
and difficulty of use (course/objectives) 62

Table 10: Percent indicating use of at least one course objective 63

Table 11: Correlation between dimensions of transfer and transfer index 64

Table 12: Participants who did not indicate transfer and their rank of
assigned as a requirement as their motivation for engaging in the course 65

Table 13: Significant differences between those who rated assigned as
requirement as the primary motivation for engaging and the general
population of those who did not attempt transfer 66

Table 14: Correlation between need to use content in 6 months
and training satisfaction 68

Table 15: Significant correlations between factors on the training
satisfaction survey and factors on the organizational climate survey 71

Table 16: Means and significant differences in motivations to engage
with the course for those who completed and those who did not
complete the course 72

vii

Table 17: Relationship between breadth, frequency and difficulty of use
as measured by construct for participants who completed the course and
participants who did not complete the course 74

Table 18: Differences in means on the training satisfaction survey
between participants who completed the course and those who did not 75

Table 19: Means and significant differences in motivations to engage
with the course for IT and management courses 76

Table 20: Organizational tenure for IT and Management
course participants 77

Table 21: Relationship between perceived use, frequency,
and difficulty of objectives by IT and management course participants 79

Table 22: Correlation between needing to use the course content
within 6 months and training satisfaction survey for IT and management
populations 80

Table 23: Means and significant differences in the organizational
climate survey for IT and management courses 81

Table 24: Relationship between perceived use, frequency, and difficulty
of objectives amongst participants in a course identified as central
to the organization 83

Table 25: Means and significant differences in the organizational climate
survey for participants in courses central to the organization 84

viii

LIST OF FIGURES

Figure 1: Study design 43

Figure 2: Response rates for management courses 53

Figure 3: Response rates for IT courses 54

Figure 4: Means and frequencies of training satisfaction indicators 67

Figure 5: Means and frequencies of organizational climate indicators 69

ix

ABSTRACT
Self-directed elearning courses have been implemented at a rapid rate by many
organizations and are perceived as having many organizational advantages such as cost
savings, wide dissemination, and maximum availability to learners in the organization, and
learner advantages, such as breadth and choice of learning opportunities. Management
employees in a large transportation company were surveyed to assess their perceptions of
transfer from self-directed elearning courses. This case showed that only 19% of
management employees who have access utilized the elearning platform. Of that, only
1% of the IT courses and 3% of management courses were accessed by 50 or more
people, or approximately 1% of the population who had access to them.
However, amongst those taking advantage of the program and responding, 62%
reported using the knowledge and skills presented in the course in their current job.
Several motivation variables showed significant positive correlations with perceived
transfer with the exceptions of supervisory suggestion and required assignment.
Demographic variables did not play a role in transfer of training with the exception of those
that had time devoted to learning and training, who reported greater transfer. Among
those who did indicate transfer, approximately 31% of the course content was applied in
the current job although 24% of the respondents in this group could not identify a single
course objective utilized. Among those who did not transfer, 82% indicated they did not
need to use the knowledge and skills within six months of taking the course and 69%
indicated that the primary reason for engaging in the course was that it was assigned as a
requirement. Responses were also sub-grouped by those who completed and did not
x

complete courses, management and information technology course participants, and
participants of courses identified as central to the organization. Among these sub-groups,
few significant differences from the general population were found, although IT
participants who did not transfer placed were more likely to blame utility value and
management participants who did not transfer were more likely to blame organizational
climate variables. Using a theoretical lens of transfer and self-directed learning theory,
this study provides evidence for the hypothesis that participation in self-directed elearning
courses may help educate the employee population and, thus, contribute to the bottom line
of the organization. However, hurdles have also been identified and organizations should
be aware that some choices, actions, or lack of actions may make self-directed elearning
courses less useful.
1

CHAPTER 1:
INTRODUCTION TO THE STUDY
Background
While there is a vast array of scholarly literature available on elearning, the
reference to elearning is usually to a program or class that is similar to or in replacement of
a face to face learning opportunity. For many companies though, elearning has been
implemented at a rapid rate as a resource for employees rather than a replacement of
traditional learning opportunities. In 2005, elearning grew by 25%, comprising 33% of all
workplace training (Bersin, 2005). Many of these courses are self-directed, meaning that
the learner acts alone to work through materials delivered through the internet and there is
not an instructor or group of students to interact with (Henderson, 2003). Although
self-directed elearning offers many advantages to the learner and the organization, little
research has been done as to the effectiveness of such courses. The result is the wide
implementation of a form of training with little knowledge as to what the training is
actually doing.
At a surface level, self-directed elearning courses have many advantages for the
organization; they are not tailored to a specific problem or even necessarily to a specific
organization and they don‘t require an instructor, making them low cost in comparison to a
traditional class or even a synchronous or asynchronous online course. The online
delivery allows for reach across large, multi-national corporations without employee
travel. Courses are self-directed; a method of learning that is thought to be effective with
adult populations and desired by organizations because of the limited amount of work time
2

allotted to learning. In essence, these courses attempt to fulfill online learning‘s promise
of ‗just enough‘ and ‗just on time‘…learners can pick what they want to learn, how much
of it they want to learn, and where they want to learn it at a low cost, widely implementable
way for the organization.
However, self-directed elearning has not been explored by the academic
community despite its popularity. Although somewhat controversial, it is fairly well
established that elearning can be as effective as traditional classes (Clark, Yates, Early, &
Moulton, 2010), which indicates that media deliver instruction, but do not influence
learning (Clark, 2001). As a form of elearning, self-directed courses may be grouped with
other types of elearning courses in scholarly research, but most of the research equating the
two types of delivery cites the use of active learning instructional methods. Instructional
methods beyond active learning, such as self-directed learning require further research to
determine their effect on transfer (Kirschner, Sweller, & Clark, 2006). Indeed, some of the
elements that make self-directed elearning courses appealing to the organization are
opposed to identified elements of effective online learning that utilize active learning
instructional methods such as trainer-trainee contact (Fredericksen, Pickett, Shea, Pelz, &
Swan, 2000; Powley, 1994) or peer to peer online community building (Hrastinski, 2008;
Solimeno, Mebane, Tomai, & Francescato, 2008).
In addition, little is known about the effectiveness of these courses within the
metrics most online libraries collect themselves. Tracking mechanisms give the
organization insight as to who is using the system and how often they are using it, but such
metrics do not give organizations adequate insight as to the use of the programs.
3

Presumably organizations provide learning opportunities to employees for two reasons:
(1) it is viewed as an employee benefit (Cappelli, 2004) or (2) employees are able to
transfer learning to their job and, thus, benefit the organization (Yelon & Ford, 1999).
Arguably, tracking statistics can be used to make the case that the courses are being utilized
as an employee benefit. As employees are not mandated to take self-directed elearning
courses, use indicates that employees are taking advantage of the benefit. Usage statistics,
however, do not give the organization insight as to if these programs transfer to employee
performance on the job and, thus, benefit the organization.
Conceptual Underpinnings for the Study
Training as a high-performance work practice (HPWP)
Human resource practices that are considered performance enhancing, such as
training, are known as high-performance work practices (HPWPs) (Huselid, 1995). These
practices are thought to increase employees‘ knowledge, skills, and abilities, empower
employees to leverage them for the organization‘s benefit, and increase their motivation to
do so (Delery & Shaw, 2001; Becker & Huselid, 1998). Although learning and training
were traditionally a function of academic institutions, the private sector has increasingly
taken on the role of educator as learning becomes more of a function of work (Meister,
1998). Effective training has the potential to increase knowledge, skills (Combs, Liu, Hall,
& Ketchen, 2006; Becker & Huselid, 1998), and has been employed as a strategic tool and a
method by which organizations can gain competitive advantage (Combs et al., 2006; Pfeffer,
1998; Huselid, 1995).

4

Elearning was incorporated as a means of continuing and expanding training and
learning in the organization. Although the notion of learning and development made sense
to business leaders (Harrison & Leitch, 2000) and began to show fiscal returns (Van Buren &
Erskine, 2002), two thirds of training costs were allotted to travel expenses (Bachman,
2000). Elearning provides scalability, access, and timeliness, principles that are often
lacking in traditional modes of delivery (Clark, 2001; Clarke & Hermens, 2001). The
purpose of offering courses online is to leverage the principles of elearning, but the purpose
of offering courses at all is to empower employees to leverage them for the organization‘s
benefit, and increase their motivation to do so (Delery & Shaw, 2001; Becker & Huselid,
1998;).
Transfer
Although the principles of elearning aid the organization‘s goals of providing
learning opportunities to their employees, the purpose of an organization funding and
implementing training is ultimately to benefit the organization. Positive transfer is the
purpose and concern of training (Goldstein & Ford, 2002) and is defined as the extent to
which the learning that results from a training experience transfers to the job and leads to
meaningful changes in work performance (Baldwin, Ford, & Blume, 2009; Goldstein &
Ford, 2002; Wexley & Latham, 1991). Despite the implementation of various learning
programs within the organization, investments in learning often yield deficient results and
training transfer has become a core issue for human resource development researchers and
practitioners (Yamnill & McLean, 2001).

5

Transfer consists of two dimensions: (1) generalization-the extent to which
knowledge and skills acquired in learning settings are applied to different settings, people,
and situations from those trained, and (2) maintenance-the extent to which changes resulting
from learning experiences persist over time (Blume, Ford, Baldwin, & Huang, 2009).
Transfer has also been distinguished as lateral and vertical transfer (Gagne, 1965) and near
and far transfer (Barnett & Ceci, 2002). Notable to self-directed elearning courses, vertical
transfer refers to a skill affecting a more complex or subordinate skill and is considered a
more difficult form of transfer. As self-directed courses are chosen by the learner, the
organization is not able to control who chooses courses designed to promote skills for lateral
or vertical transfer. Also notable, far transfer refers to situations that may be different from
the learning setting (Blume et al., 2009) and is also considered a more difficult form of
transfer. Save courses on computer skills, many courses on general business competencies
are likely to promote skills that will be used in a face to face context, indicating far transfer.
Burke and Hutchins (2007) identify three primary factors influencing transfer based
on influential conceptual models in the field (Alvarez, Salas, & Garofano, 2004; Salas,
Cannon-Bowers, Rhodenizer, & Bowers, 1999; Ford & Weissbein, 1997; Baldwin & Ford,
1988). The three primary factors influencing transfer are learning characteristics,
intervention design and delivery, and work environment influences. Blume et al. (2009)
similarly identify the three primary factors as trainee characteristics, work environment, and
training interventions in their meta-analytic review. Although several studies have been
conducted regarding transfer, few studies have been conducted from the elearning platform.
Also lacking are studies regarding transfer among autonomous workers performing open
6

skills. The relationship between several variables is contingent on whether the skills are
open or closed and a general pattern shows that predictor constructs tend to have a stronger
relationship with transfer of open skills than closed skills (Bloom et al., 2009). Yelon,
Sheppard, Sleight, and Ford (2004) have suggested that the dynamics of transfer may be
different for autonomous individuals than for heavily supervised workers because
autonomous workers have a choice about if and what to transfer, supported by Blume et al.‘s
(2009) estimated .34 relationship between voluntary participation and transfer.
Self-directed learning
The notion that there would be a strong relationship between voluntary
participation and transfer with adult learners is supported by self-directed learning theory.
Self-directed learning (SDL) has its roots in adult education in which it is viewed as a
particularly effective educational approach (Brockett & Hiemstra, 1991; Cross, 1981;
Knowles, 1975). Proponents of SDL maintain that adult learners desire self-directed
learning experiences (Knowles, Holton, & Swanson, 1998; Cross, 1981; Knowles, 1975).
SDL is often used to describe how adults learn in opposition to teacher-directed learning
which is usually associated with the traditional way of teaching children (Knowles, 1975).
Self-directed learning, therefore, is defined as learning initiated and controlled by the adult
(Clardy, 2000). SDL views learners as responsible owners and managers of their own
learning process, integrating self-management of context, social setting, resources, and
actions with self-monitoring through the evaluation and regulation of cognitive learning
strategies (Garrison, 1997; Bolhuis, 1996). Self-directed learners demonstrate a greater
awareness of making learning meaningful and monitoring themselves through the process
7

(Garrison, 1997). The overall goal of a SDL approach is to maximize autonomous
learning (Ellis, 2007).
Like its namesake theory, self-directed elearning courses make many of the same
assumptions about the people engaging in the courses. However, several barriers have
been identified within the literature on implementing a learning organization that have
implications for self-directed learning theory; learners may choose not to learn, choose the
wrong learning needs, place inappropriate priorities for learning, fail to effectively learn, or
stop learning for some reason; learners or organizations may fail to choose the correct
method to optimize learning or fail to evaluate learning effectively leading to repeat mistakes
(Robotham, 1995). Self-directed learning theorists may argue that although some variables
that play a role in transfer are not under student control (Hiemstra & Sisco, 1990) and that
freedom to define their own domains and functionality provides an advantage to many
learners without hindering any learners (Ellis, 2007).
Statement of the Problem
While organizations have implemented self-directed elearning courses at a rapid
rate, little is known about the effectiveness of such courses. Does the information
presented in such courses transfer to employee performance and, thus, benefit the
organization? As a high performance work practice, training may increase employees‘
knowledge, skills, and abilities, empower employees to leverage them for the organization‘s
benefit, and increase their motivation to do so (Delery & Shaw, 2001; Becker & Huselid,
1998). As a mode of elearning, media is not expected to influence learning, but rather the
instructional method would influence (Clark, 2001). Utilizing self-directed learning
8

theory as its instructional method, courses may be a particularly effective educational
approach (Brockett & Hiemstra, 1991; Cross, 1981; Knowles, 1975). But as the
organization‘s goal is not necessarily learning, but rather transfer (Goldstein & Ford, 2002),
is implementation of such courses enough? There are many confounding factors that hinder
transfer in the work environment and although organizations may have good intentions in
implementing self-directed online learning systems, it is unknown if employees are using
these courses to the benefit of the organization.
Purpose of the Study
The market for self-directed elearning is large, but little research has been
conducted as to the effectiveness of these courses within organizations. This study will
provide an opportunity to examine self-directed elearning courses, employees‘ perceptions
of their own transfer within such courses, how much they are attempting transfer, or why
they might not transfer. The goals of this research project are to a) provide data about the
perception of transfer, b) identify employee characteristics among commonly collected
organizational data that may be indicative of transfer, c) explore the breadth, depth, and
frequency of what is being transferred when employees indicate transfer, d) explore why
employees may not transfer, and e) identify possible differences in transfer amongst
sub-groups.
The research questions this study will examine are:
1. To what extents do employees who utilize on-demand, voluntary elearning courses
use the knowledge and skills from the courses in their job?
9

2. What employee characteristics, if any, among commonly collected organizational
data might be indicative of someone likely to attempt transfer?
3. If an employee reports transfer within their job, how much, how often, and how
difficult is it for the employee to transfer?
4. If an employee does not report transfer within the job, are there organizational
environment or intervention design and delivery characteristics that hinder
transfer?
5. To what extent is a course‘s status as completed, closed or open skills, or central to
the organization an indication of transfer amongst employees?
Limitations, Assumptions, and Design Controls
This study uses quantitative methods and research will be conducted in two steps;
1) the researcher will identify the most popular courses, courses which have a high ratio of
incomplete vs. complete employee participation, and courses identified as central to the
organization, and 2) an electronic survey is distributed to employees who have taken the
identified course(s) and individual responses are recorded.
Despite measures taken to ensure the reliability of this study, it is limited in a
number of ways. This study is being conducted within one organization and results from
other organizations may be different due to various factors such as transfer climate (Kiely,
2007). This study will not explore formal certifications or required courses, such as those
mandated by industries or state and federal law, a hallmark of many elearning companies
10

and courses. As self-directed learning theory advocates for learner choice, it is debatable
if such courses could truly be considered a product of self-directed instructional methods
and, therefore, this study will not examine such courses. Due to organizational
constraints, independent variables will be measured at the same time as transfer measures
despite Blume et al.‘s (2009) compelling evidence that when measured at the same,
transfer data is likely to be inflated. Finally, this study measures employee perception of
transfer and an individual‘s perception might not be fully congruous with actual behaviors.
Definition of Key Terms
Several terms will be used throughout this study; for clarification purposes, these terms
refer to the following definitions:
 High-performance work practices (HPWPs): Human resource practices that are
considered performance enhancing, such as training (Huselid, 1995).
 Learning organization: One in which employees know their learning needs and
those needs are facilitated by various processes to prioritize and meet those needs
(Popper & Lipshitz, 2000).
 Self-directed elearning: The learner acts alone to work through materials
delivered through the internet and there is not an instructor or group of students to
interact with (Henderson, 2003).
 Self-directed learning theory: Learning initiated and controlled by the adult
(Clardy, 2000).

11

 Transfer of training: The extent to which the learning that results from a training
experience transfers to the job and leads to meaningful changes in work performance
(Baldwin et al., 2009; Goldstein & Ford, 2002; Wexley & Latham, 1991).
Summary

Organizations have adopted self-directed elearning for its perceived benefits to the
organization, but little research has been done that explores the effectiveness of
self-directed elearning courses in the organization. Conflicting evidence as to the potential
effectiveness or non-effectiveness of such courses can be found in the theoretical
underpinnings of self-directed learning and elearning. This study contributes to the gap in
the literature by providing statistics on employee perceptions of transfer and providing
suggestions for further research.
Chapter 2 of this study includes a literature review of learning and training within
the organization, elearning, transfer within organizational learning and training, and
self-directed learning theory. Chapter 3 describes the research design and methodology and
includes Appendix A, the email introduction utilized in introducing potential participants
to the study and Appendix B, the survey instrument used in the study. Chapter 4 analyzes
the data and Chapter 5 discusses the study‘s findings, conclusions, and implications.
12

CHAPTER 2:
REVIEW OF RELATED LITERATURE
Self-directed elearning courses have been adopted by many organizations with little
research as to their effectiveness. Although they are viewed and sold under the auspices of
cost savings, wide dissemination, and maximum availability to learners in the organization,
these factors do not speak to the impact the courses may or may not have on the organization.
The purpose of this study is to examine the targets of transfer of training among self-directed
elearning courses, identify potential differences between types of courses, examine
commonly held employee data as a means of identifying potential learner differences, and
provide suggestions for future research.
This literature review is composed of four distinct but related parts. First, learning
and development within the organization is explored as background on the organization‘s
role as employee educator. Second, elearning within the business organization is examined
including advantages and disadvantages to both the learner and the organization. Third, the
vast literature on transfer of training is analyzed to reveal what is known about what helps
and hinders transfer of training. Much of the literature in this field focuses on transfer
within closed skill, face to face courses, amongst supervised workers responsible for
learning outcomes within training; for this reason, transfer in elearning courses and transfer
amongst autonomous workers and open skills is examined in greater detail. Finally,
self-directed learning as a theory is analyzed with self-directed learning within the
organization and within elearning receiving special consideration.

13

Learning and Training within the Organization
Traditionally, learning had been offered by academic institutions, but as learning
becomes more a function of work, the private sector has increasingly taken on the role of
educator (Meister, 1998). Education and training have taken an important role within the
organization as a means of growth and competitive advantage. Peter Senge‘s (1990)
seminal work on learning organizations encouraged organizations to shift to a more
interconnected way of thinking to foster employee commitment. In his model, employees
collectively and continually enhance capabilities to create results they care about; personal
development is as important as commitment and work for the organization. In a rapid
change environment, employee commitment and capacity to learn are imperative qualities
for the organization to harness in becoming flexible, adaptive, and productive.
The emphasis on the learning organization at this time culminated in the growth of
corporate universities from 400 to over 1,000 (Meister, 1998). Meister (1998) identifies
five broad forces that contributed to this growth: (1) the emergence of the flat, flexible
organization, (2) the transformation to a knowledge economy, (3) the shortened shelf-life
of knowledge, (4) focus on lifetime employability over lifetime employment, and (5) a
fundamental shift in the global education marketplace. The model of the learning
organization, one in which employees know their learning needs and those needs are
facilitated by various processes to prioritize and meet those needs (Popper & Lipshitz, 2000),
made sense to the market‘s knowledge workers and leaders; Harrison & Leitch (2000) note

14

the importance of increasing awareness of knowledge and learning as an intellectual
response to business and the rapidly changing environment. Training became an integral
part of high performance human resource practices (HPWPs) within the organization.
High-performance work practices (HPWPs)
Human resource practices that are considered performance enhancing, such as
training, are known as high-performance work practices (HPWPs) (Huselid, 1995). These
practices are thought to increase employees‘ knowledge, skills, and abilities, empower
employees to leverage them for the organization‘s benefit, and increase their motivation to
do so (Delery & Shaw, 2001; Becker & Huselid, 1998). HPWPs result in greater job
satisfaction, lower employee turnover, higher productivity, and better decision making
(Becker, Huselid, Pickus, & Spratt, 1997) and operate through the organizations‘ internal
social structure to increase flexibility and efficiency (Evans & Davis, 2005). In their
meta-analysis of all HPWPs, which includes other practices such as compensation,
employee participation, and flexible work arrangements, Combs et al. (2006) offer a
conservative estimate that 20% of the utility available from predicting performance
differences among organizations can be attributed to HPWPs, making HPWPs statistically
significant and managerially relevant. In particular, training has been employed as a
strategic tool and a method by which organizations can gain competitive advantage (Combs
et al., 2006; Pfeffer, 1998; Huselid, 1995). Effective training has the potential to increase
knowledge, skills, and abilities and enable employees to transfer skills to the organization‘s
benefit (Combs et al., 2006; Becker & Huselid, 1998).

15

Changes in the model
Following the acceptance of the idea of the learning organization, the rise of the
corporate university, and the continued evolution of HPWP research and implementation,
organizations and especially their learning approaches have since been shaped by (1) the
globalization of the economy and its demand for world-class products, (2) the emergence
of the post-industrial information age and the growth and distribution of knowledge, (3)
demand for greater access to lifelong learning created by rapid changes in the economy,
and (4) increasing costs of higher education (Cunningham, Ryan, Stedman, Tapsall,
Bagdon, Flew, & Coaldrake, 2000). However, the organization‘s new role of educator
was and is not without cost; in the early part of the decade, Merrill Lynch estimated global
expenditures of education and training at over $2 trillion (as cited in Clarke & Hermens,
2001). There is some evidence that investment in training benefited the organization, the
American Society of Training and Development (ASTD) and the Masie Center (2001)
reported that companies leading in training expenditure spent $1,655 per employee annually
while the industry average was $677. On average, companies who led in training
expenditures enjoyed a 24% higher gross profit margin. But two thirds of training costs at
that time were allotted to travel expenses (Bachman, 2000). As the decade continued, the
market became more volatile, and dramatic advances in technology of delivery emerged,
elearning was and continues to be used as a mode that can dramatically decrease the cost of
classroom or face to face courses primarily through saving on travel and living costs

16

(Allen, 2007; Henderson, 2003; Clark, 2001; Clarke & Hermens, 2001). American Society
for Training and Development recently reported that U.S. organizations spent more than
$125 billion annually on employee training and development (Paradise & Homer, 2007).
Elearning
Elearning is a nebulous term that is used in different contexts by different people
(Dublin, 2009). Even within the subject of business, vendors, researchers, and people
who implement solutions often use the term interchangeably to refer to different modes of
delivery ranging from synchronous, in which students and the instructor are online at the
same time and can interact with each other, to asynchronous, in which student and
instructor are not online at the same time, but interaction can take place, to self-directed
elearning, in which the learner acts alone to work through materials delivered through the
internet and there is not an instructor or group of students to interact with (Henderson,
2003). Although the current study seeks to explore the mode of self-directed elearning,
much of the literature on benefits and areas of improvement do not distinguish between the
different types of elearning. Characteristics and research findings on elearning are
important to the acceptance and implementation of all types of elearning and research on
elearning in the business context is also rare. Thus, despite the lack of differentiation, this
section explores elearning in all its definitions in the organizational context.
Benefits to the organization
In theory, elearning provides learning and development for the organization while
creating a host of organizational benefits that face to face learning cannot achieve.
Elearning principles include scalability, access, and timeliness while traditional modes of
17

learning usually lack all three characteristics (Clarke & Hermens, 2001). These principles
result in several benefits to the organization: no physical classroom space is needed,
immediate cost savings due to the elimination of travel expenses, timely access to
information, greater flexibility in the workplace, methods can increase learners‘ interest,
deliver content clearly, and provide instant feedback to students (Clark, 2001; Fry, 2001;
Pollard & Hillage, 2001; Schriver & Giles, 1999). Training magazine reports that
companies can save between 50 and 70% when they replace instructor led courses with
elearning (Fitz-Enz, 1994). In addition to cost savings, new learning opportunities that may
have been impossible because of face to face limitations may now be feasible with elearning
(Henderson, 2003). Elearning provides organizations with the ability to widely distribute
learning products (Allen, 2007), an asset to organizations that have found some of their
employees‘ skills becoming obsolete every two to three years (Kiely, 2007; Fry, 2001).
Elearning has the potential for global and continuous delivery as well as rapid and ‗just
enough‘ learning development (Masie, 2003).
Adoption and acceptance of elearning
Because of these advantages, elearning has been rapidly adopted by the business
community. Bersin (2005) surveyed 526 companies in the U.S. and Canada and estimates
that elearning grew in 2005 by 25%, comprising 33% of all workplace training. ASTD
reports an increase in training delivery via technology from 24% in 2003 to 27% in 2004 in
their broad sample and from 35% in 2003 to 38% in 2004 in their sample of large
organizations (Suqrue & Rivera, 2005). Broader access and use of technology has assisted
the implementation of elearning within business as learning online becomes more
18

commonplace and accepted outside of business. More than 91% of American homes have
a personal computer, with 80.6% enjoying internet access (Over 57 percent, 2009) and 63%
of adult Americans reporting a broadband connection in their home (Pew Research Center,
2009). Several online resources exist to access learning opportunities from these broadband
connections. Since 2002, MIT has published text and syllabi for 85 percent of its
curriculum for free on the web, Apple‘s iTunes U allows 170 schools to offer free content to
the public, and YouTube.edu offers free videos from more than 100 schools (Ford, 2009).
A new generation of workers born after 1980 have been deemed ―Generation G‖ and are
described as having the ability to organize without organizations (Shirky, 2008) and
operating at twitch speed with random access to learning rather than exposition and
step-by-step logic (Buckingham, 2008).
Disadvantages of elearning
However, the implementation of elearning within organizations has not been without
its noted disadvantages, both to the learner and the organization. For the learner, a
fundamental issue has arisen as to if elearning should be conducted on company or personal
time because elearning is less formal than face to face learning. Surveys suggest that
elearning seems to be pushed to personal time (Ellet & Naiman, 2003; ASTD and the Masie
Center, 2001). In addition, open office designs (i.e. cubicles) and the amount of programs
and work that is now conducted from the computer create a learning atmosphere that is likely
to include many interruptions (Ellet & Naiman, 2003; ASTD and the Masie Center, 2001).
Synchronous and asynchronous courses create problems in communication between the
instructor and fellow employee learners that are similar to those in a telephone meeting
19

versus a face to face meeting; people sometimes simply act differently when they are face to
face (Henderson, 2003). A shift to elearning also often shifts the organizational focus from
education innovation to technology solutions. In places where training was not done well,
this often eliminates the perceived ‗good part‘ of training, getting out of the office, but keeps
the same stale programs (Schank, 2007, p. 63). Technology is a means to development and
training, not the answer to development and training (Schank, 2007) and quality may be
sacrificed when too much attention is placed on the means.
For the organization, the promise of ‗anywhere‘ is not quite fulfilled as proprietary
competencies are difficult to relay to a global audience (Cohen, 2007). Accounting standards
and legal and compliance training also differ across countries (Cohen, 2007), negating some
of the cost savings because of the customization required for this type of training.
Successful courses also require advertisement and internal championing, a new or revised
role or roles within the organization. ASTD and The MASIE Center (2001) surveyed 30
courses at 16 companies in the United States with over 700 learners to analyze the
relationship between organizational efforts to market and motivate learner participation and
actual satisfaction with technology as a means of providing learning. The results revealed
that the most successful courses were well advertised and internally championed as well as
those for which completion time and support are provided during work hours. It should be
noted, however, that this study did not disaggregate data between voluntary and mandatory
courses. Finally, it is difficult to determine if the cost effectiveness and wide dissemination
of elearning has caused a role change for the organization or if that role change was
inevitable with the advent of the learning organization, but organizations are increasingly
20

funding general business education through elearning. Universal competencies include
non-proprietary business management, executive presence, and business acumen (Cohen,
2007). In the past, highly specialized skill development was often initially financed by
companies and the burden of costs transferred to the individual as the skill become more
general (Levenson, 2004). Self-directed elearning solutions change that model as elearning
provides employees the opportunity to develop general skills at the expense of the company.
Vendors cite savings benefits as a sales tool. For instance, it is reported that Cisco
Systems saves $240 million annually by cutting $12,000 per employee to send to four
courses a year with travel; Oracle estimates a savings of $100 million per year; Barclays
Bank estimates a savings of $1.5 million per year by moving its leadership development
program on line (WFC Resources, n.d.). Vendors also report that learners believe elearning
solutions to be just as effective as conventional training (Skillsoft, 2009). However, many
elearning courses offered by organizations are voluntary. In its company survey, ASTD
and The MASIE Center found that the average start rate for participation in voluntary
courses was 32% (2001). Many courses could also be considered general business skill
based training, which was not traditionally offered by organizations. It is unknown if
voluntary, general business skill building elearning courses benefit the organization.
Although cost savings have been noted and are used as a sales tool, if the organization is not
benefitting from the courses, it cannot truly be considered a cost savings alternative mode
unless the face to face courses they purportedly replace are also zero impact.

21

Transfer within the Organization
Although cost savings in training are important to organizations, the purpose of an
organization funding and implementing training is ultimately to benefit the organization.
For this reason, learning is not the paramount concern of organizational training efforts;
rather, positive transfer of training is the purpose and concern (Goldstein & Ford, 2002).
Transfer of training is defined as the extent to which the learning that results from a training
experience transfers to the job and leads to meaningful changes in work performance
(Baldwin, Ford, & Blume, 2009; Goldstein & Ford, 2002; Wexley & Latham, 1991). ―The
goal of training is transfer: trainees are to apply on their jobs what they have learned during
instruction (Yelon & Ford, 1999, p. 58).‖ Investments in learning often yield deficient
results and training transfer has become a core issue for human resource development
researchers and practitioners (Yamnill & McLean, 2001). Implementation of skills is
intended to improve the employee‘s performance in an organization, but if the employee
does not apply the skills or if factors hinder the application, training has failed. Therefore,
courses must be evaluated by learning performance as the primal goal of training and
transfer performance as the ultimate goal (Lim, Lee, & Nam, 2007).
Transfer consists of two dimensions: (1) generalization-the extent to which
knowledge and skills acquired in learning settings are applied to different settings, people,
and situations from those trained, and (2) maintenance-the extent to which changes resulting
from learning experiences persist over time (Blume et al., 2009). Gagne (1965)
distinguishes between lateral and vertical transfer. Lateral transfer occurs when a skill
spreads over situations at the same level of complexity while vertical transfer occurs when a
22

skill affects a more complex or subordinate skill. These frameworks lead to the
development of near and far transfer; as discussed by Barnett and Ceci (2002), near and far
transfer can refer to the physical location of the learning context as well as the cognitive
dimension of the knowledge being acquired. Near transfer tasks, those that are highly
similar to the learning task, are more likely to transfer than far transfer, in which tasks and
situations are different from the transfer setting (Blume et al., 2009).
For transfer to occur, learned behavior must be generalized and maintained on the job
(Baldwin & Ford, 1988). There are several reasons training might not transfer: poor
selection of employees attending programs, lack of clear expectations from supervisors
about application, lack of support back on the job, lack of confidence to perform adequately,
lack of immediate post-training monitoring, or lack of incentives to apply new skills and
knowledge (Stolovitch & Keeps, 2002). Estimates on transfer of training vary widely as do
the costs associated with lack of transfer. Broad and Newstrom (1992) estimated that over
80% of training is not fully applied by employees back on the job. Robinson and Robinson
(1996) report that less than 30% of what people learn gets used on the job. Sevilla and
Wells (1988) report that U.S. based companies only saw 10-15% of training is applied to
work. Survey data suggest 40% of trainees fail to transfer immediately after training, 70%
fail one year after the program, and ultimately only 50% of training results in organizational
or individual improvement (Saks, 2002). Regarding costs, the most frequently repeated
statistic is that American industries spend more than $100 billion on training, but only about
10% of the expenditures results in transfer to the job (Ford & Weissbein, 1997; Baldwin &
Ford, 1988; Georgenson, 1982). This statistic, however, is largely anecdotal (Fitzpatrick,
23

2001; Georgenson, 1982). Curry, Caplan, and Knuppel (1994) estimated an 87-90% loss
of investment when reviewing training expenditure via transfer.
Primary factors that influence transfer
Statistics on transfer may vary widely because so many factors potentially play a role
in an employee‘s decision and ability to effectively transfer. In their integrative literature
review, Burke and Hutchins (2007) identify three primary factors influencing transfer based
on influential conceptual models in the field (Alvarez et al., 2004; Salas et al., 1999; Ford &
Weissbein, 1997; Baldwin & Ford, 1988). This is a unique contribution in that it considers
empirical work and the theories the work is based on, offering a comprehensive review of the
literature on training transfer. The three primary factors influencing transfer are learning
characteristics, intervention design and delivery, and work environment influences. Blume
et al. (2009) similarly identify the three primary factors as trainee characteristics, work
environment, and training interventions in their meta-analytic review.
Learner/Trainee characteristics. Among learner characteristics, cognitive ability,
self-efficacy, pre-training motivation, anxiety/negative affectivity, openness to experience,
perceived utility, career planning, and organizational commitment show a strong or
moderate relationship with transfer. Extrinsic vs. intrinsic motivation, conscientiousness,
and external vs. internal locus of control showed mixed support. Minimal empirical
research exists on motivation to learn, motivation to transfer, and extroversion (Burke &
Hutchins, 2007). Bloom et al. (2009) found cognitive ability (.37), conscientiousness (.28),
and voluntary participation (.34) to have moderate relationships with training transfer.

24

Work environment influences. Training in context (Ford, 1997) has been explored
by assessing variables independently and in aggregate; both approaches have found positive
effects for supporting transfer through cues, consequences, and support that exist in work
relationships and design (Burke & Hutchins, 2007). Transfer climate, supervisory support,
peer support, and opportunity to perform show a strong or moderate relationship with
transfer, while minimal empirical research exists on strategic link and accountability (Burke
& Hutchins, 2007). Inconsistent findings are rare in this factor although multiple measures
with different foci exist for measuring transfer climate. Blume et al. (2009) found a general
environment characteristic to have a moderate relationship with training transfer (.22) and
further stratified this characteristic to transfer climate (.27), support (.21), and constraints
(.05). Although based on small sample size, supervisor support (.31) may have a stronger
relationship than peer support (.14).
Intervention design and delivery. Of the three factors examined, intervention design
and delivery is identified as the area in which there is the greatest need for research to
establish or strengthen preliminary findings. Innovative and performance support
technologies have been found to be neglected in empirical transfer research (Burke &
Hutchins, 2007). Factors that have shown a strong or moderate relationship with transfer
include learning goals, content relevance, practice and feedback, behavioral modeling, and
error-based examples. Self-management strategies show mixed support and a host of
variables are identified with minimal or no empirical research including needs analysis,
over-learning, cognitive overload, active learning, and technological support. Blume et al.

25

(2009) found that the effects of training interventions to be small to moderate and also
cautioned these results as their analysis only yielded 3 to 6 studies that examined such
factors.
Gaps in transfer research
Transfer within elearning. Although several studies have been conducted regarding
transfer, few studies have been conducted from the elearning platform. Usability of the
system has been considered to relate to transfer in elearning settings. Research has been
done on design issues that help or hinder transfer; design of a program should be simple and
transparent and allow quick recovery from system and user errors (Ikegulu, 1998;
Jannasch-Pennell, 1996; Nielsen, 1993). Interaction has also been named as crucial to
ensuring the effectiveness of a distance education program (Fulford & Zhang, 1993;
Garrison, 1993; Moore, 1989). However, many elearning programs do not include
interaction or only include learner-computer interaction (Hillman, Willis, & Gunawardena,
1994), such as the courses included in this study.
Computer attitudes and computer self-efficacy have been shown to have a direct or
indirect relationship with training outcomes (Al-Jabri & Al-Khaldi, 1997; Ford, Quinones,
Sergo, & Sorra, 1992; Gist, 1989; Tannenbaum, Mathieu, Salas, & Cannon-Bowers, 1991;
Yaverbaum & Nosek, 1992). Despite advances and increased access in technology, recent
research continues to support these findings. Park and Wentling (2007) found that
computer anxiety and confidence had significant correlations with breadth, frequency, and
the overall transfer as did satisfaction, learnability, and efficiency of the elearning platform.
Beyond computer anxiety, Lim et al. (2007) identified five dimensions which affected
26

efficacy of online training: the trainee, training content, level of communication between
trainer and trainee, ease of use of online website resources, and organizational environment.
The only factor found to influence both learning performance and transfer achievement was
learning motivation, suggesting to the authors that trainees‘ learning motivation was the
most important factor in online educational training. Pham‘s (2008) thesis work in
Vietnamese organizations indicates that using IT tools did not significantly influence
intraorganizational knowledge transfer, instead indicating factors associated with traditional
transfer of training research such as organizational culture. Tatlanti, Poulymenakou, and
Paraskeva (2010) collected data from a large financial organization that suggested
motivation, validity of content, and supervisor support were related to transfer of training.
No further studies were identified that explored the relationship between transfer and
elearning systems within organizations. However, two variables that may affect transfer
within the organizational elearning context were identified. Imamoglu (2007) found that
perceived ease of use was positively related to perceived usefulness of elearning. Chyung
and Vachon (2005) found learning activities to be indicative of a satisfying elearning
context. The authors also explored instructor variables, which are not relevant to this study
as courses are entirely self-directed. The survey instrument used in this study to measure
intervention design and delivery has been modified slightly to reflect these two variables.
Transfer among autonomous workers and open skill courses. The setting and types
of workers researched or not researched in transfer studies also has implications for
self-directed elearning courses. For instance, Bates, Holton, and Seyler (1997) investigated
factors affecting the transfer of computer-based training in an industrial setting and found
27

that content validity and climate factors such as supervisor sanctions, resistance to change,
and peer support were significant indicators. However, an industrial setting is likely to have
very different workers and very different training needs than a knowledge-worker business
environment. Notably, open skills and autonomous workers are more likely to be found in
this type of environment. Objectives associated with learning specific skills to be
reproduced in the transfer environment are labeled closed skills, whereas objectives
associated with learning principles are labeled open skills (Yelon & Ford, 1999).
Autonomous workers decide what they will do and how they will do it and evaluate their
own performance, which is in contrast to supervised workers who are told when, how, and
what they perform and are checked by others for conformity (Yelon & Ford, 1999).
Yelon and Ford (1999) compared studies of transfer and hypothesized that under
different instructional and work conditions, both open and closed skills transferred.
However, at the time, there were no studies about autonomous workers performing open
skills save Yelon, Reznich, and Sleight‘s (1997) initial investigation regarding the complex
way in which medical professionals successfully applied what they learned from a yearlong
program on how to be successful academic physicians. Factors have been studied
individually and since that time, some work has been done that has implications for both
fields.
Open skills gives the learner more choice as to if, how, and when to transfer. Ford et
al. (1992) indicate that those who are more motivated to learn an open skill are also more
likely to seek opportunities to apply that skill in the workplace. The relationship between
several variables is contingent on whether the skills are open or closed (Blume et al. 2009).
28

For instance, pre-training self-efficacy shows a higher relationship with transfer for open
(.23) versus closed skills (.10). The environment-transfer relationship also shows a higher
correlation for open (.26) versus closed skills (.04). A general pattern shows that predictor
constructs tend to have a stronger relationship with transfer of open skills than closed skills.
Pre-training self-efficacy, motivation, and environmental context become more important
when training open skills. One intriguing exception, although only based on two studies, is
cognitive ability, which had a stronger relationship with closed skills.
Regarding worker autonomy, historically the research on transfer of training has
focused on training individuals who are held accountable for transferring skills or has not
differentiated between those who were and were not held accountable for transfer (Yelon,
1999). Yelon et al. (2004) have suggested that the dynamics of transfer may be different for
autonomous individuals than for heavily supervised workers because autonomous workers
have a choice about if and what to transfer. In their meta-analytic review, Blume et al.
(2009) estimated a .34 relationship between voluntary participation and transfer. There has
been further research that suggests autonomy does impact the transfer process through the
increase of motivation to learn or transfer (Baldwin, Magjuka, & Loher, 1991; Hicks &
Klimoski, 1987). As measured by pre-training motivation, motivated trainees have been
found to be more likely to transfer what they‘ve learned in training (Chiaburu & Marinova,
2005; Facteau, Dobbins, Russell, Ladd, & Kudisch, 1995; Quinones, 1995). Although
these studies focused on motivation as an outcome variable, Axtell, Maitlis, and Yearta
(1997) utilized motivation as a predictor and found a relationship with transfer one year
after training.
29

Yelon and Shepard (1999) also suggest that each learner performs a personal
cost-benefit analysis in deciding if transferring what they have learned is worth the effort.
In a follow up qualitative study, Yelon et al. (2004) found evidence that suggests trainees
actively engage in a conscious decision-making process about transfer before, during, and
after training based on credibility, practicality, and need or, otherwise titled, the utility
value of the course. Clark, Dobbins, and Ladd (1993) distinguish between job utility, the
perceived usefulness of the training to facilitate goals associated with the current job, and
career utility, the perceived usefulness of training to facilitate career goals and provide
evidence that both types of perceptions were significant predictors of training motivation.
Accordingly, the survey item in this study that references job utility has been split to reflect
current and future jobs. Ruona, Leimbach, Holton, and Bates (2002) found utility
reactions had a small, significant impact on the ability to predict motivation to transfer.
Alliger, Tannenbaum, Bennett, Traver, & Shotland (1997) found that utility reactions were
more strongly correlated with job performance than measures of learning or affective
reactions. Axtell et al. (1997) found that trainees‘ perceptions of relevance predicted
self-report transfer. Lim and Morris (2006) found that trainees‘ rating of immediate need
to use knowledge predicted perceived transfer. Finally, although only based on six studies
that do not have single source or method bias, Blume et al. (2009) estimates a .17
relationship with utility reactions and transfer, which indicates the possibility of a
significant positive relationship. Billington, Ford, & Yelon (2010) found evidence that
beliefs about utility related to positive self-report transfer rates above and beyond
motivation to learn and intent to transfer. Further, when utility beliefs were added to a
30

predictive model that included motivation to learn and intentions to transfer, intentions to
transfer were no longer predictive of transfer.
Research has also identified other relationships between more general motivations
to perform well at work like job identification and involvement, organizational
commitment, and interest in career development or advancement and positive transfer
behavior (Pidd, 2004; Colquitt et al., 2000; Mathieu, Tannenbaum, & Salas, 1992). Most
of this research has been done with required training; Billington et al. (2010) suggest that
when employees enter training by choice, motivation is likely to be high and the decision to
transfer may be linked to perceived utility value. Through the personal choice of deciding
what type of training to engage in and then what from that training the employee will
transfer, the individual truly customizes the learning experience to fit their needs and wants
(Baldwin et al., 2009).
Some research also suggests that individual factors can overcome a non-supportive
work environment. Enos, Kehrhahn, and Bell (2003) found experienced managers
persisted in gaining proficiency even when transfer climate factors did not have a significant
relationship to transfer, suggesting a mediating effect of metacognitive ability and allowing
them to achieve objectives by seeking informal learning opportunities. These findings put
the learner front and center in the decision to transfer, suggesting that self-directed elearning
might be a useful tool in promoting transfer of general business knowledge. Computer
based courses offer flexibility and a higher degree of self-directed learning, suggesting that
motivation to learn is stronger for the users of such courses and that this media can provide
expanded opportunities and facilitate motivation (Clardy, 2000; Tough, 1978).
31

Self-Directed Learning
Although elearning did not produce the instructional design theory of self-directed
learning, teaching methods and especially motivational strategies from this theory can be
used to support the justification and design of such courses. Self-directed learning (SDL)
has its roots in adult education in which it is viewed as a particularly effective educational
approach (Brockett & Hiemstra, 1991; Cross, 1981; Knowles, 1975). Proponents of SDL
maintain that adult learners desire self-directed learning experiences (Knowles et al., 1998;
Cross, 1981; Knowles, 1975). SDL is often used to describe how adults learn in
opposition to teacher-directed learning which is usually associated with the traditional way
of teaching children (Knowles, 1975). Self-directed learning, therefore, is defined as
learning initiated and controlled by the adult (Clardy, 2000). SDL views learners as
responsible owners and managers of their own learning process, integrating
self-management of context, social setting, resources, and actions with self-monitoring
through the evaluation and regulation of cognitive learning strategies (Garrison, 1997;
Bolhuis, 1996;). Self-directed learners demonstrate a greater awareness of making
learning meaningful and monitoring themselves through the process (Garrison, 1997).
The overall goal of a SDL approach is to maximize autonomous learning (Ellis, 2007).
Hiemstra and Sisco (1990) identified 9 learning variables that students can control:
1) identification of needs, 2) selection of topics and goals for learning, 3) identification of
expected outcomes, 4) determination of assessment methods, 5) selection of
documentation methods, 6) selection of learning experiences, 7) choice of learning
materials from a variety of sources, 8) structure of learning environment, 9) pace of
32

learning. Motivation and volition play a significant role in maintaining learner efforts by
driving the decision to participate and sustaining the will to see a task through to the end
(Garrison, 1997; Corno, 1992). Proponents of SDL emphasize the importance of allowing
learners to pursue their own interests (Abdullah, 2001). Corno (1992), for instance,
suggests allowing learners to pursue personal interests without the threat of formal
evaluation to sustain interest and transcend frustration. Ellis (2007) collected evidence
that indicates the freedom to define their own domains and functionality afforded advanced
students to explore beyond the boundaries of the course without impeding students with
less background. In this way, SDL is partly an empowerment issue as it gives choice to the
learner. Choices are mediated by the learner‘s context as well as the education and
motivation of the learner to decide the best methods by which to learn (Harrison & Leitch,
2000).
SDL in the workplace
Learning at work, for instance, can be hard and time-consuming. Several barriers
exist in the successful implementation of a learning organization and these barriers have
implications for self-directed learning within the organization. Learners may choose not to
learn, choose the wrong learning needs, place inappropriate priorities for learning, fail to
effectively learn, or stop learning for some reason; learners or organizations may fail to
choose the correct method to optimize learning or fail to evaluate learning effectively leading
to repeat mistakes (Robotham, 1995). Employees have been found to engage if the course
teaches something critical to the immediate job, the student is working towards a
certification or a degree, or if the student is required by management (Henderson, 2003). In
33

Carwile‘s (2009) qualitative exploration of women entrepreneurs, self-directed learning was
launched by weaknesses, challenges, and questions. Furthermore, learning was utilized
primarily as a just-in-time strategy, involved little pre-planning, and the women were
primarily concerned with what could be immediately practiced (Carwile, 2009).
Self-directed elearning
Sadler-Smith, Allinson, and Hayes (2000) noted that self-directed learning projects
are not as well recognized by HR managers as a valid learning mode, which has interesting
implications for elearning courses in the organization. In order to convince people to do
something new, they must believe that their extra efforts will directly or indirectly
contribute to what they need to feel successful and effective (Clark, 2003). Work norms
and work context mediate self-directed learning at work. Self-directed learners must be
able to understand how their learning fits back into their work context and environment.
They must be aware of their role and performance in relation to the company‘s mission,
strategies, and objectives and they must be able to show others how their learning will bring
better business and performance results (Popper & Lipshitz, 2000). As an HPWP, does
HR‘s lack of recognition play a role in self-directed learners‘ use of the medium? One of
the interesting implications about HPWPs is that all are not equal (Delery, 1998). For some
practices, the specific version of a practice may or may not make a difference and for some,
the mere implementation of some practices might affect organizational performance
(Huselid, Jackson, & Schuler, 1997). Is the mere implementation of elearning enough to
attract self-directed learners to the proper courses? Online learners perceive the learning

34

itself and the types of learning activities to be satisfying factors as opposed to rewards
associated with learning (Chyung & Vachon, 2005; Youn, 2007). Is this satisfaction
enough to promote transfer within the organization?
Significance
This literature review focused on learning within the organization, elearning,
transfer of training, self-directed learning theory, and where these constructs intersect.
Although each is largely distinct and has enough research and theory to warrant individual
literature reviews, they all play a role in defining self-directed elearning, transfer, and the
importance both have to the organization. Performance technologists often give
recommendations for one model of transfer, that of a standard procedure which employees
are to follow as prescribed and for which organizations will hold workers accountable for
(Yelon & Ford, 1999). But self-directed elearning as a mode of delivery runs counter to
the model most recommendations of transfer have been derived from, suggesting that the
mode of delivery may have implications for transfer as a field of study.
The terms used to define ―Generation G‖ (Shirky, 2008), operating at twitch speed
with random access to learning rather than exposition and step-by-step logic (Buckingham,
2008), could be used to describe self-directed learners and how they may be relating to
elearning courses. ASTD and The Masie Center (2001) reported that although companies
believe they are marketing elearning effectively, their users did not agree. To justify this
statement, they cited findings that indicated start rates of elearning were low, but
completion rates even lower. Does that statement have any significance in an elearning
environment? As self-directed learners who may be operating at twitch speed rather than
35

by traditional, step-by-step logic, are learners simply selecting and using what they need for
their job? Ellis (2007) collected evidence that indicates the freedom to define their own
domains and functionality afforded advanced students to explore beyond the boundaries of
the course without impeding students with less background. Could self-directed learning
work in the same way in the organization? Could self-directed learners be expanding their
knowledge base beyond what traditional training would normally allow them? This
research will attempt to add to the body of literature by intersecting self-directed learning,
elearning, transfer, and the organization. This intersection has implications for practice as
well as theory as organizations may be able to better leverage their online training courses
with a more thorough examination of what these courses are accomplishing.
36

CHAPTER 3:
RESEARCH DESIGN AND METHODOLOGY

Introduction
This chapter reviews the methodology and research design of the study of
self-directed elearning courses within business organizations. Business organizations have
embraced self-directed elearning courses, allowing employees access to a broad array of
business skill enhancing courses. However, the purpose of providing courses to
employees is for the employee to transfer learning to their job and, thus, benefit the
organization (Yelon & Ford, 1999). It is unknown if employees engage in these courses
and then transfer the skills and knowledge to their job. This study seeks to collect data on
perceived transfer from employees who have engaged in self-directed elearning courses
using an analytic lens of transfer of training and self-directed learning theory.
Problem and Purposes Overview
This applied research study will provide an opportunity to analyze perceived
transfer in self-directed elearning courses. Several best practices have been identified to
evaluate training programs; some of these recommendations are problematic for elearning
opportunities however, and evaluating elearning has become a difficult undertaking
(Turmel, 2003).
As evidenced by the lack of research studies exploring transfer in online platforms,
metrics that may indicate the usefulness of the platform have not been collected by the
systems or organizations in their implementation. Therefore, formal cost analyses, an
evaluation recommendation widely accepted as useful (Clark, 2000; Kirkpatrick, 1994), are
37

now being reported as cost savings marketed by elearning companies and do not accurately
reflect return on investment for the organization. Instead, these numbers simply represent
the bottom line cost savings of providing a course online versus face to face. Although
those numbers may be appealing to the organization, if employees are not accessing and
transferring the knowledge in a way that‘s effective to the organization, the organization is
not receiving a good return on its elearning investment.
Of course, it could be argued that learning systems can be used and viewed by the
organization as an employee benefit (Cappelli, 2004). One of the goals of this study is to
help inform that distinction for self-directed elearning courses. Evaluation can be most
effective when it informs future decisions (Hatton, 2003). The question of if it is being
used determines its positioning as an employee benefit; the question of if it is being
transferred determines its position as an effective training tool. The organization in this
study perceives that self-directed elearning courses are being used, but little is known as to
if the knowledge and skills are being transferred. This study will begin to provide
information as to perceived transfer rates of such courses.
Research Questions
The research questions this study will examine are:
1. To what extents do employees who utilize on-demand, voluntary elearning courses
use the knowledge and skills from the courses in their job?
2. What employee characteristics, if any, among commonly collected organizational
data might be indicative of someone likely to attempt transfer?
3. If an employee reports transfer within their job, how much, how often, and how
38

difficult is it for the employee to transfer?
4. If an employee does not report transfer within the job, are there organizational
environment or intervention design and delivery characteristics that hinder
transfer?
5. To what extent is a course‘s status as completed, closed or open skills, or central to
the organization an indication of transfer amongst employees?
Population and Sample

One company will be analyzed in this case study. The organization is a large, U.S.
based, multi-national, S&P 500 transportation company. Approximately 55,000
management employees have access to approximately 8,400 non-compulsory titles. Of
these 8,400 titles, 3,050 are labeled management courses and 3,250 are labeled information
technology courses. Of these, approximately 1,300 management courses and 1,200
information technology courses were accessed by approximately 10,400 individual
users. Management employees are salaried, knowledge workers ranging from supervisors
to the CEO; no hourly or administrative workers are identified as management. Elearning
courses are offered through a third party software as a service company that is widely
recognized in the industry, has received numerous awards including special honors for soft
skills training, and is widely popular amongst businesses.
The first step of the study was to examine the usage data of the available titles to
determine the most popular courses and courses in which ratios of non-completed sessions
were similar to completed sessions. Organizational representatives helped determine
39

courses considered central to the organization. Upon identification, each employee who
had accessed the course in the past 3 to 8 months received an electronic copy of the
quantitative survey. Although Baldwin and Ford (1988) criticized the design of utilizing
single-source data to assess transfer outcomes, confidentiality issues arise as these courses
are self-selected and can be taken without other‘s knowledge. For instance, interviewing
skills is the title of a course offered. It is likely that some participants in such a course would
object to supervisors and peers knowing that they took such a course. This study also relies
on self-reports, which may be distorted in the presence of high social desirability
(Podsakoff , MacKenzie, Lee, & Podsakoff, 2003), such as the desire to impress a
supervisor. Therefore, this study will examine perceived transfer using single source data
despite its limitations identified by previous researchers.
The course sample was limited to the most popular courses, taking care to include
courses within the popular courses identified as central to the organization and courses in
which ratios of non-completed sessions were similar to completed sessions. This
sampling strategy was ―purposely ‗biased‘, not to make the program(s) look good, but rather
to learn from those who were exemplars of good practice‖ (Patton, 2002, p. 234). Meaning,
for the purposes of this study, these courses may illustrate a difference in course use that may
be reflective of self-directed learning or transfer of training principles. Popular courses may
be indicative of course quality or course utility. Courses identified as central to the
organization were communicated to employees as central via department meetings, internal
communications, and referrals from supervisors, which may be indicative of course utility,
organizational pressure, or the role of managerial recommendation. Courses in which
40

ratios of non-completed sessions were similar to completed sessions may be indicative of
the role of elearning and its potential of ‗just enough, just in time.‘ In addition,
completion is a valued internal metric for the organization without support as to if this
metric is indicative of course or organizational usefulness.
Data Collection and Instrumentation
Instruments in this survey have largely been identified and adapted using the
Baldwin and Ford (1988) approach to the transfer construct. Baldwin and Ford‘s (1988)
approach is widely used and cited and is generally praised for its comprehensiveness.
Instruments from this approach usually measure individual constructs in the course and how
they transfer, but this survey has been adapted to expand targets, acknowledge online
learning‘s potential of ‗just enough‘, and consider organizational context.
A scale measuring user motivation begins the survey and was developed based on
past studies regarding transfer of training and motivation as well as internal organizational
factors. Factors based on past studies include self-efficacy and valence (Clark, 2004; Clark,
2003; Clark, Howard, & Early, 2006) while internal organizational factors include
supervisor/co-worker suggestion and indication of a required course. Although none of the
courses in the sample would be considered mandatory by the organization, it is possible that
a course may be assigned to the employee as mandatory through a supervisory relationship.
The next question asks the user to identify if the course was completed and, if not, why not.
This organization places particular importance on course completion, which may or may not
be relevant in the elearning context. A split logic question then differentiates between users
who indicate transfer within their current job versus users who indicate possible transfer in
41

other jobs.
Upon indication that the employee perceives transfer in the current job, the employee
will be directed to an adaptation of Wang‘s (2000) assessment tool of level of transfer.
Wang‘s (2000) adaptation is based on the Ford et al. (1992) and Quinones et al.‘s (1995)
conceptualization of the measurement of transfer. Ford et al. (1992) have conceptualized
measurement of transfer by breadth, the number of distinctive trained tasks used, frequency
of transfer, the number of times trained tasks are used, and difficulty of transfer, the
complexity or difficulty level of used tasks. Wang (2000) adopted this conceptualization to
assess the level of transfer and found that the three dimensions are somewhat compensatory
and, thus, it is meaningful to use all three to measure transfer of training.
If the employee does not indicate transfer within their current job, s/he will be
directed to adaptations of Lim and Morris‘ (2006) training satisfaction survey and
organizational climate survey to attempt to discover why the employee has not attempted
transfer within the current job. These instruments were also created using the Baldwin and
Ford (1988) approach, but were developed to be shorter than most existing instruments.
The organizational climate survey has been minimally changed with some word editing to
reflect face validity within the organization. This survey reflects items identified within
organizational climate that may be important to transfer in a shortened format with
reliabilities of .83 for responsiveness to change, .80 for educational support, .86 for transfer
opportunities, and .61 for peer/supervisor feedback. The training satisfaction survey
reflects constructs identified as important to transfer within learning intervention design and
delivery. The original survey found a reliability alpha of .83 for job helpfulness of learning
42

content, .72 for quality of learning content, and .73 for instructor effectiveness with an
overall reliability of .88. This instrument, however, has been modified to reflect the
elearning platform better. Items referring to instructor effectiveness have been removed
and three items have been added reflecting variables explored in the earlier literature review.
Those items attempt to measure perceived ease of use (Imamoglu, 2007), learning activities
(Chyung & Vachonm 2005), and career utility (Clark, Dobbins, & Ladd, 1993).
Some measurements of individual learner/trainee characteristics related to transfer
conclude the survey and include age, level of education, length of time with company,
length of time in current position, work time devoted to learning/training, and ease, access,
and comfort level with computers. Although many of these characteristics have not been
traditionally identified in the transfer of training research, they were selected because
courses are self-selected and organizations typically have little control as to the personal
characteristics of the employees who access such courses. Some of the identified variables
are commonly collected HR metrics and identification of possible significant correlations
may assist in the marketing and support for populations in which elearning may be
particularly useful, which seems to be lacking (ASTD and The Masie Center, 2001). Time
devoted to training and/or learning has been previously cited as a major problem in elearning
as employees are often not given dedicated time to complete courses unlike face to face
learning in which time is mandated by physically leaving one‘s workspace (Lim et al., 2007;
Schank, 2007). Access to and comfort level with computers is perhaps the most well
supported finding in the elearning space (Park & Wentling, 2007; Al-Jabri & Al-Khaldi,
1997; Ford et al., 1992; Yaverbaum & Nosek, 1992; Tannenbaum et al., 1991; Gist, 1989).
43

This factor had particular face validity within the organization as it is perceived that some
field management employees do not have regular access to a personal work computer despite
their role as knowledge workers.
This study collected data from 20 different courses and used one quantitative survey
instrument that was customized for each course. The data collection took place as follows;
first, all available courses were analyzed to determine course usage, taking care to include
courses identified as central to the organization and courses in which ratios of
non-completed sessions were similar to completed sessions. Second, a quantitative survey
was sent to all employees who accessed the identified courses within 3 to 8 months from
identification. The Qualtrics Web tool was used to develop a quantitative survey. A link
to the survey was sent via e-mail to each employee in the identified courses (See Appendix
A). An example of the full survey instrument including questions that will be modified for
each course can be found in Appendix B. Figure 1 represents the complete design of this
study including the split logic used in the survey instrument.
Figure 1: Study Design

44

Survey respondents remained completely anonymous at all times, were advised
that their participation is voluntary, and that individual responses will be kept completely
confidential, even from their organization.
Data Analysis
Usage data collected through the organization elearning system was used to
determine the courses in the sample. Popular courses were determined via the number of
unique user identifications logged within the six month period of analysis. Courses in
which ratios of non-completed sessions were similar to completed sessions were also
determined via the number of unique user identifications who began the course within the
six month period of analysis but did not finish the course during the period of analysis and
unique users who began and completed the course within that time. Courses identified as
central to the organization were determined via learning administrator and organizational
leadership direction.
Research Question 1: To what extents do employees who utilize on-demand, voluntary
elearning courses use the knowledge and skills from the courses in their job?
Descriptive statistics were used to determine the percentage of the population
intending transfer to their current job. Frequencies and means were calculated for each
motivation variable and a Pearson correlation was used to determine significance between
motivation to engage and usage of the course within the current job.

45

Research Question 2: What employee characteristics, if any, among commonly collected
organizational data might be indicative of someone likely to attempt transfer?
Frequencies and means were calculated for each demographic variable. Because a
standard scale could not be identified for tenure, participants were asked to write in both
organizational and position tenure. Histograms were plotted and quintiles were calculated
to determine the scale utilized to calculate results. A Pearson correlation was used to
determine significance between scale demographics and usage of the course within the
current job as well as all demographics and motivation to engage. For demographic
variables in which the responses were yes/no, a Spearman‘s rho was used to determine
significance with usage of the course within the current job.
Research Question 3: If an employee reports transfer within their job, how much, how
often, and how difficult is it for the employee to transfer?
Upon indication of transfer in the current job, transfer of training is further measured
by perceptions of frequency and difficulty of transfer based on the overall course and
dimensions of breadth, frequency, and difficulty based on course objectives. The sum of
distinctive knowledge/skill elements based on course objectives for each person was used to
measure the breadth of transfer. Frequencies and means were calculated for both depth
and difficulty within overall course perceptions and breadth, depth, and difficulty based on
course objectives and a Pearson correlation was used to determine if a significant correlation
existed between the variables. A t-test was used to determine if there was a significant
difference between perceptions based on overall course and when measured via course
objectives. Descriptive statistics were used to determine the percentage of participants who
46

indicated transfer within the current job, but could not identify a course objective that was
transferred. A z-score was calculated for each dimension and the sum of the three resulted
in a transfer index score. A Pearson correlation was used to determine if a significant
correlation existed between the dimensions of transfer and the overall transfer score (or
transfer index). As results did not match the original study from which this tool was
adapted, Pearson correlations were performed with the motivation and demographic
variables previously referenced.
Research Question 4: If an employee does not report transfer within the job, are there
organizational environment or intervention design and delivery characteristics that hinder
transfer?
Upon indication of lack of transfer in the current job, employees are directed to
identify why transfer may not have occurred utilizing scales designed to measure work
environment and intervention design and delivery characteristics. Descriptive analytics
were used to determine the percentage of this population who indicated that they did not
need to use the knowledge and skills within six months of taking the course. Frequencies,
means, and a Pearson correlation were used to determine motivation variables that may have
played a role with this group. Frequencies and means were calculated for both scales and a
Pearson correlation used to determine if a significant correlation existed between the
variables. A t-test was used to determine possible correlations between both scales and
needing to use the content within six months. Factor analysis with Varimax rotation and
principal axis factoring with oblique rotation was run on items to confirm validity of scales
and interrelationships were be explored using Spearman‘s rho correlations.
47

Research Question 5: To what extent is a course’s status as completed, closed or open
skills, or central to the organization an indication of transfer amongst employees?
Analysis as outlined in the previous research questions were run to compare the
groups outlined in research question five. Closed skills were defined as IT courses while
open skills were defined as management courses. Beyond the analyses outlined above,
t-tests were used to evaluate differences between each group.
Validity and Reliability
The validity and reliability of this research study will be supported in several ways.
The time period of 6 months has been purposefully chosen as reflective of the transfer
field. Measuring transfer too quickly after training is likely to reflect short-term retention
rather than transfer and the field has generally adopted a wider time interval of 3 to 8
months past training (Quinones et al., 1995; Warr & Bruce, 1995; Rouiller & Goldstein,
1993; Ford et al., 1992; Noe & Schmitt, 1986). Survey instruments were selected or
developed based on a unifying conceptualization of transfer (Baldwin & Ford, 1988) and
modified in accordance to an extensive literature review that accounted for transfer and
elearning platforms. Items in the survey have been checked and tested by two different
groups specifically for the purposes of this study and independent variables have been
identified by the same two groups for content and face validity: (1) research experts who
have knowledge of and experience in developing questionnaires, and (2) practitioners who
are responsible for the elearning systems within their organizations. Although content
validity is a given in most academic studies, Welbourne (2006) asserted the importance of
face validity, which asks if a question makes sense to the manager who is able to do
48

something with the data. If a question is deemed irrelevant by a manager, it is likely that
s/he will not take action. As the results of this study‘s goal to be managerially relevant,
face validity has been considered.
Summary
The research design and methodology of this study will use transfer and
self-directed learning theory as the analytical lens to examine self-directed elearning
courses, particularly those that are self-selected and offered in business organizations. Data
collection will take place through the learning system‘s usage statistics and a quantitative
survey developed for the study. This study will fill a gap in the literature by providing
insight into perceived employee transfer of self-directed elearning courses as well as
providing implications for practice for learning and development professionals who
manage these systems.
49

CHAPTER 4:
ANALYSIS OF DATA
Introduction
The purpose of this study was to analyze perceived transfer in self-directed
elearning courses. This research also examined usage data for self-directed elearning
courses.
Using a quantitative survey, this study showed that 62% of the overall population
estimated transfer of training in their current job. Among other key findings: a large
number of courses were not accessed or accessed by very few people, all motivation
variables showed significant positive correlations with perceived transfer except
supervisory suggestion and required assignment, and demographic variables did not play a
role in transfer of training except in the case of time devoted to learning and training.
Amongst those who did indicate transfer, approximately 31% of the course was used in the
current job. Amongst those who did not indicate transfer, various factors proved
significant regarding organizational climate and opportunity. Amongst sub-groups
explored in this research, few significant differences from the general population were
found. This chapter will present detailed data derived from the survey results.
The following research questions guided this study:
1. To what extents do employees who utilize on-demand, voluntary elearning courses
use the knowledge and skills from the courses in their job?
2. What employee characteristics, if any, among commonly collected organizational
data might be indicative of someone likely to attempt transfer?
50

3. If an employee reports transfer within their job, how much, how often, and how
difficult is it for the employee to transfer?
4. If an employee does not report transfer within the job, are there organizational
environment or intervention design and delivery characteristics that hinder
transfer?
5. To what extent is a course‘s status as completed, closed or open skills, or central to
the organization an indication of transfer amongst employees?
A quantitative survey was distributed in May 2011 to those employees who
accessed the selected courses between August 2010 and January 2011. The survey was
left open for three weeks with one reminder email was sent to the entire population while
the survey was open. It should be noted that the survey did not have a forced response
option, so response rates for questions varies although the vast majority of respondents
completed the entire survey.
Organization of Data Analysis
First, demographic data will be presented and distinguished by utilization
demographics, which determined the course sample, and respondent demographics, which
outlines the response rates for the quantitative survey. Second, data for each research
question will be addressed in order of the questions. For each question, descriptive and
inferential statistics will be provided.

51

Presentation of Descriptive Characteristics of Respondents
Utilization Demographics
Approximately 55,000 management employees have access to approximately 8,400
non-compulsory titles. Of these 8,400 titles, 3,050 are labeled management courses and
3,250 are labeled information technology courses. Of these, approximately 1,300
management courses and 1,200 information technology courses were accessed by
approximately 10,400 individual users. Management employees are salaried, knowledge
workers ranging from supervisors to the CEO; no hourly or administrative workers are
identified as management. (See Table 1).
Twenty courses were identified for the sample, 12 IT and 8 management courses.
Amongst the IT courses, all courses were amongst the top 17 most popular and had over 70
users with a range of 77 to 688. Three courses were identified as popular only, 5
identified as central, and 4 in which ratios of non-completed sessions were similar to
completed sessions. Amongst the management courses, all courses were amongst the top
22 most popular and had over 160 users with a range of 163 to 670. Four courses were
identified as popular only and 4 in which ratios of non-completed sessions were similar to
completed sessions. No courses were identified as central to the organization in this
category as the organization determined that pivotal management skills depended on the
sector and role in which the manager was engaging.

52

Table 1: IT and Management courses available and number of users who accessed
them during the research period

IT courses: 1495 courses accessed; 3250 available
0 users: 1,755 (54%)
1 user: 2, 278 (70%)
Less than 5 users: 2,821 (87%)
Less than 10 users: 3,042 (94%)
Less than 25 users: 3,181 (98%)
Less than 50 users: 3,224 (99%)

Management courses: 1388 courses accessed; 3050 available
0 users: 1,662 (54%)
1 user: 1,901 (62%) 239 (17%)
Less than 5 users: 2,237 (73%)
Less than 10 users: 2,528 (83%)
Less than 25 users: 2,849 (93%)
Less than 50 users: 2,964 (97%)

Respondent Demographics
Online surveys were sent to all participants in 20 courses. Approximately 5,000
employees received the survey; 2,900 enrolled in management courses and 2,100 enrolled
in IT courses. Overall response rate averaged 10%, with IT course response rates ranging
53

from .5-11% and management course response rates ranging from 1-21%. Respondent
demographics indicate that while the most popular management courses received the
highest response rates (14-21%), the most popular IT courses received the lowest response
rates (.5-2%). Higher response rates in IT courses were found in those that were either
identified as central (5-11%) or those in which ratios of non-completed sessions were
similar to completed sessions (4-8%), while low response rates were recorded for
management courses in which ratios of non-completed sessions were similar to completed
sessions (1-4%). No courses were identified as central within management courses.
(See Figures 2 and 3).
Figure 2: Response rates for management courses

0%
5%
10%
15%
20%
25%
54

Figure 3: Response rates for IT courses

Amongst respondents, age, level of education, position tenure, organizational
tenure, work time devoted to learning, regular and easy access to a computer at home and at
work, and comfort level with computers were recorded. The majority of respondents were
aged 40-59, possess a bachelor‘s degree, have work time devoted to learning or training,
have regular and easy access to a computer at home and at work, and rate themselves as
very comfortable with computers. (See Table 2).
Table 2: Demographic characteristics of respondents
Age N %
18-29 5 1.2
30-39 59 13.7
40-49 201 46.6
50-59 147 34.1
60+ 19 4.4
0%
2%
4%
6%
8%
10%
12%
55

Table 2: Demographic characteristics of respondents (continued)

Highest level of education N %
High School Degree 88 20.1
Bachelor‘s Degree 258 59.0
Master‘s Degree 85 19.5
Doctoral Degree 6 1.4

Work time devoted to training and/or learning N %
No 175 39.8
Yes 265 60.2

Regular and easy access to a computer at home N %
No 12 2.7
Yes 432 97.3

Regular and easy access to a computer at work N %
No 1 .2
Yes 445 99.8

Comfort level with computers N %
1-Not at all comfortable 1 .2
2 4 .9
3 11 2.5
4 52 11.8
5-Very comfortable 374 84.6

Years at the organization N %
Less than 10 years 56 12.9
10-14.9 years 115 26.4
15-17.9 years 73 16.8
18-20 years 37 8.5
More than 20 years 154 35.4

Years in current position N %
Less than 3 years 63 14.6
3-4.9 years 80 18.5
5-6.9 years 87 20.1
7-10 years 78 18.1
More than 10 years 124 28.7

56

Research Questions and Analysis of Data
Research Question 1: To what extents do employees who utilize on-demand, voluntary
elearning courses use the knowledge and skills from the courses in their job?
Survey results indicate that 62% of all respondents have used the knowledge and
skills presented in the course in their current job while 38% indicate they have not used the
knowledge and skills in their current job. Motivation for engaging in a course ranges with
all factors positively significantly correlating to usage in the current job except supervisory
suggestion (r=.090) and assigned as a requirement (r=-.093). (See Table 3).
Research Question 2: What employee characteristics, if any, among commonly collected
organizational data might be indicative of someone likely to attempt transfer?
Amongst demographic variables measured, the only variable that correlates with
using the knowledge and skills in a person‘s current job is a positive relationship with
―work time specifically devoted to training and/or learning” (ρ=.198, p<.001). (See Table
4).
Exploratory data regarding demographics and motivation to engage. Although
not associated with transfer data, various factors correlated with motivation for engaging in
a course and with demographic variables. Results suggest that age (r=-.179, p<.001) and
comfort level with computers (r=-.187, p<.001) were significantly negatively correlated
with co-worker support. Organizational tenure and supervisor suggestion (r=-.127,
p<.01) were also significantly negatively correlated. Significant positive correlations
include higher levels of education with expected proficiency in the topic (r=.097, p<.05)
and organizational tenure with course required as an assignment (r=.096, p<.05).
57

Table 3: Motivation and relationship to current job usage

To what extent / did the following play a role in your
decision to take the course:
Mean
Standard
deviation
Usage in current
job (Pearson
Correlation)
The class was interesting to me 2.93 1.398 .426
***

I expected to become very proficient in the
topic
2.83 1.265 .429
***

It was suggested to me by a supervisor 2.69 1.717 .090

It was suggested to me by a co-worker 1.54 1.059 .145
**

It was assigned to me as a requirement 3.98 1.513 -.093

I thought the class would help me achieve goal(s) in
my current job
2.98 1.366 .464
***

I thought the class would help me achieve goal(s) in
future jobs at this organization
2.96 1.386 .429
***

I thought the class would help me achieve goal(s) in
future jobs at other organizations
2.58 1.439 .292
***

***. Correlation is significant to the .001 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).

58

Table 4: Relationship between current job use and demographics

Have / you used the
knowledge and skills
presented in the course in
your current job?
Please indicate your highest level of education Pearson Correlation .091
Sig. (2-tailed) .061
N 430
Please rate your comfort level with computers Pearson Correlation .017
Sig. (2-tailed) .723
N 434
How many years have you been with the
organization?
Pearson Correlation -.002
Sig. (2-tailed) .965
N 427
How many years have you been in your current
position?
Pearson Correlation -.020
Sig. (2-tailed) .675
N 424
Please indicate your age Spearman's rho
Correlation Coefficient
-.038
Sig. (2-tailed) .430
N 423
Do you have work time specifically devoted to
training and/or learning?
Spearman's rho
Correlation Coefficient
.198
***

Sig. (2-tailed) .000
N 432
Do you have regular and easy access to a
computer at home?
Spearman's rho
Correlation Coefficient
-.076
Sig. (2-tailed) .113
N 437
Do you have regular and easy access to a
computer at work?
Spearman's rho
Correlation Coefficient
-.038
Sig. (2-tailed) .431
N 438
***. Correlation is significant at the 0.001 level (2-tailed).
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
59

Work time devoted to training and/or learning positively correlated with skills value
(r=.153, p<.01), self-efficacy (r=.127, p<.01), goals in current jobs (r=.192, p<.001), and
goals in future jobs within the organization (r=.144, p<.01). (See Table 5).
Research Question 3: If an employee reports transfer within their job, how much, how
often, and how difficult is it for the employee to transfer?
Of the 62% of employees who indicated transfer within their current job, their
perceptions of what they transferred, how often they transferred, and how difficult it was to
transfer was further examined. Participants were asked to evaluate these measures of
transfer by overall perception of the course and through course objectives. Overall course
ratings indicate a mean of 3.42 regarding how frequently they transferred knowledge and
skills from the course on a scale of 1 to 5 with 1 being ―never used the knowledge and
skills‖ and 5 being ―always use the knowledge and skills‖ and 2.52 regarding how difficult
it was to transfer knowledge and skills from the course on a scale of 1 to 5 with 1 being a
perception of ―very easy to use the course knowledge and skills‖ and 5 being ―very difficult
to use the course skills.‖ (See Table 6). There was not a significant correlation between
the two elements.

60

61

Table 6: Perceived frequency of use and difficulty of the overall course
Please rate how / often you have
used the knowledge and skills
presented in the
course...-Never-Always
Valid
Percent
Please rate how difficult you perceive
it was / to perform the knowledge and
skills presented in the course...-Very
easy-Very difficult
Valid
Percent
1 Never .7 1 Very easy 14.9
2 16.2 2 29.1
3 36.4 3 45.5
4 34.2 4 9.7
5 Always 12.5 5 Very difficult .7
Mean 3.42 Mean 2.52

When measuring via course objectives instead of overall course perception, participants
indicated that, on average, 31% of the course was used with a mean of 3.09 regarding
frequency of course use and 2.69 regarding difficulty of course use using the same 5 point
scales as overall course frequency and difficulty. (See Table 7).
Table 7: Perceived use, frequency, and difficulty of objectives

Used the
objective
Frequency of use
of objective
Difficulty of use of
objective
N Valid 5296 2461 2136
Missing 0 2835 3160
Mean .31 3.09 2.69

Further, three significant correlations were indicated amongst objective use, frequency of
use, and difficulty of use. Use of the objective was positively correlated with frequency of
use (r=.461, p<.001) and a significant negative correlation was found with use of the
objective and difficulty (r=-.189, p<.001) and frequency of use and difficulty (r=-.204,
p<.001). (See Table 8).
62

Table 8: Relationship between perceived use, frequency, and difficulty of
objectives

Used the
objective
Frequency of
use of
objective
Difficulty of use
of objective
Used the objective Pearson Correlation 1 .461
**
-.189
***

Sig. (2-tailed)

.000 .000
N 5296 2461 2136
Frequency of use of objective Pearson Correlation 1 -.204
***

Sig. (2-tailed)

.000
N 2461 2079
Difficulty of use of objective Pearson Correlation 1
Sig. (2-tailed)

N 2136
***. Correlation is significant at the 0.001 level (2-tailed).

The difference in means between overall course frequency of use and frequency of use as
measured by course objective is positively significant (t=.349, p<.05) and the difference in
means between overall course difficulty and course difficulty as measured by course
objective is negatively significant (t=-.149, p<.05). (See Table 9).
Table 9: Difference in means of frequency of use (course/objectives) and difficulty
of use (course/objectives)

Mean
Std.
Deviation
Std.
Error
Mean t df Sig. (2-tailed)
Frequency of use of
(course/objectives)
.349 1.234 .025 13.904 2413 .000
Difficulty of use of
(course/objectives)
-.149 .930 .020 -7.375 2120 .000
63

Although all survey participants who had access to this part of the survey indicated transfer
in their current job, when asked via individual course objectives which had been used, 24%
indicated they did not use any course objectives. (See Table 10).
Table 10: Percent indicating use of at least one course objective

Frequency Percent Valid Percent
Valid 0 No 62 23.8 23.8
1 Yes 199 76.2 76.2
Total 261 100.0 100.0

Like Wang‘s (2000) original study, a transfer index score was formulated by summing the
z-scores of the three dimensions and correlating the overall transfer score with each
dimension revealing positive significant correlations between overall transfer and breadth
(r=.748, p<.001) and overall transfer and frequency (r=.665, p<.001). Unlike Wang
(2000), a positive significant correlation was also found between breadth and frequency of
transfer (r=.320, p<.001) and overall transfer and difficulty (r=.553, p<.001). (See Table
11). Because results differed from Wang‘s, dimensions were correlated against the
motivation and demographic variables previously referenced. Dimensions were all
significantly correlated with each motivation variable and not significantly correlated with
any demographic variable.

64

Table 11: Correlation between dimensions of transfer and transfer index

Breadth Frequency Difficulty
Transfer
index
Breadth Pearson Correlation 1 .320
***
.070 .748
***

Sig. (2-tailed)

.000 .329 .000
N 208 202 196 208
Frequency Pearson Correlation 1 -.056 .665
***

Sig. (2-tailed)

.399 .000
N 248 226 248
Difficulty Pearson Correlation 1 .553
***

Sig. (2-tailed)

.000
N 227 227
Transfer index Pearson Correlation 1
Sig. (2-tailed)

N 255
***. Correlation is significant at the 0.001 level (2-tailed).

Research Question 4: If an employee does not report transfer within the job, are there
organizational environment or intervention design and delivery characteristics that hinder
transfer?
Of the 38% of employees who indicated they did not use the knowledge and skills
presented in the course in their current job, 82% indicated that they did not need to use the
knowledge and skills within six months of taking the course. Because the percentage was
high, frequencies were run between the 38% of the employees who did not indicate transfer
and motivation to engage in training. While almost all factors had a mean less than 2.5,
―assigned as a requirement‖ had a mean of 4.15. Frequencies reveal that 69% of the
population directed to this part of the survey rated ―assigned as a requirement‖ as playing a
primary reason for engaging in the course, ranking it 5 on a scale of 1 to 5. (See Table 12).
65

Table 12: Participants who did not indicate transfer and their rank of assigned as a
requirement as their motivation for engaging in the course

Frequency Percent Valid Percent
Valid 1 Did not play a role 24 14.1 14.2
2 / 4 2.4 2.4
3 / 13 7.6 7.7
4 / 10 5.9 5.9
5 Played a primary role 118 69.4 69.8
Total 169 99.4 100.0
Missing System 1 .6

Total 170 100.0

However, when this population was parsed from the general population answering
this section of the survey, only a few differences were found to be significant. Responses
to needing to use the course content within 6 months was similar, with 78% of those
required to take the course answering negatively and 78.4% of the general population
answering negatively. Correlations with items in the training satisfaction survey reveal
significant differences in the items ―this course provided the training content I need for my
job‖ and ―this course provided the training content I need for jobs I‘d like to hold.‖ (See
Table 13). Correlations with items in the organizational climate survey reveal a
significant difference in the item ―organization‘s role in developing role models.‖ (See
Table 13). No other correlations were found to be significantly different for this
population than the general population as outlined below.

66

Table 13: Significant differences between those who rated assigned as requirement
as the primary motivation for engaging and the general population of those who did
not attempt transfer

Levene's Test for Equality
of Variances t-test for Equality of Means
F Sig. t df
Sig.
(2-tailed)
Mean
Difference
This course provided the training content I
need for my job.
Equal variances assumed .277 .600 -2.051 161 .042 -.312
Equal variances not assumed

-2.095 98.700 .039 -.312
This course provided the training content I
need for jobs I’d like to hold.
Equal variances assumed 1.405 .238 -2.803 164 .006 -.499
Equal variances not assumed

-2.644 84.166 .010 -.499
Organization’s role in developing role
models
Equal variances assumed .384 .536 -2.287 160 .024 -.469
Equal variances not assumed

-2.283 93.830 .025 -.469

Amongst the entire population indicating no transfer, means in the training
satisfaction survey varied from 1.80 to 3.39, with participants most disagreeing with
statements regarding course utility for their current job and most agreeing with facilities of
the online platform being easy to use and at appropriate learning levels. (See Figure 4).
Significant negative correlations were also found between the need to use the course
content within 6 months and utility value of the course in the current job. (See Table 14).
Amongst the entire population indicating no transfer, means in the organizational
climate survey varied from 1.75 to 3.05 with participants giving the lowest influence in
their decision not to transfer to peer and supervisor feedback on training and application
and the highest influence in opportunity to use in and match between training content and
current job. (See Figure 5). No significant correlations were found between the need to
use the course content within 6 months and any of the items on the organizational climate
survey.
67

68

69

70

Like Lim and Morris (2006) a factor analysis was run on both scales to determine
construct validity; first, factor analysis with Varimax rotation and then with principal axis
factoring with oblique rotation. For the training satisfaction survey, items loaded on to
two factors in both analyses. These factors reflected utility value and course/platform
efficacy. All items had factor loadings above .7 with the exception of ―this course
provided the training and content I need for jobs I‘d like to hold,‖ which had a factor
loading of .370. For the organization climate survey, all items loaded on to three factors
in both analyses with the exception of the ―organization‘s role in developing role models to
provide job help and mentoring.‖ In the Varimax rotation, this item loaded on to the peer
and supervisor feedback factor, but when further examined in the oblique rotation, this
item loaded on to the organizational support factor. Factors represent as organizational
support, transfer opportunities, and peer and supervisor feedback. All items had a factor
load greater than .4.
After factor analysis, some significant correlations were found between factors
represented on the training satisfaction survey and factors represented on the
organizational climate survey. Significant positive correlations were between
organizational support and utility value (ρ=.232, p<.01), transfer opportunity and
course/platform efficacy (ρ =.213, p <.01), and between peer and supervisor feedback and
utility value (ρ =.245, p <.01). (See Table 15).

71

Table 15: Significant correlations between factors on the training satisfaction
survey and factors on the organizational climate survey

Organizational Climate Survey
Training Satisfaction Survey
Utility Value
Course/platform
efficacy
Spearman's
rho
Organizational support Correlation Coefficient .232
**
-.030
Sig. (2-tailed) .003 .707
N 163 164
Transfer opportunity Correlation Coefficient .090 .213
**

Sig. (2-tailed) .251 .006
N 164 165
Peer & Supervisor feedback Correlation Coefficient .245
**
-.120
Sig. (2-tailed) .002 .130
N 162 162
**. Correlation is significant at the 0.01 level (2-tailed).

Research Question 5: To what extent is a course’s status as completed, closed or open
skills, or central to the organization an indication of transfer amongst employees?
This research question focused on various sub-groups within the sample. Analysis
methods for each group was identical to analysis with the general sample as outlined
above. The results for each group outlined below focuses on differences either between
groups or from the general sample as previously outlined.
Complete vs. did not complete the course. Among the general sample, 6% (N=25)
were identified as having not completed the course and 94% (N=424) as having completed
the course. Of those who had not completed the course, 52% indicated they had not used
the knowledge and skills from the course in their current job while 48% indicated they had
used the knowledge and skills in their current job; participants who completed split with
72

38% indicating no use and 62% indicating use. Responses between the two groups were
not significantly different from one another. Amongst those who did not complete the
course, 20% cited they found the knowledge/skills they were looking for and stopped, 28%
that they were not finding the course useful and stopped, and 52% were interrupted while
taking the course.
One significant difference was found between the two groups in their motivation to
engage with the course. ―Assigned as a requirement‖ (p=.003) significantly differed with
those who did complete reporting a higher mean in assigned as a requirement. (See Table
16).
Table 16: Means and significant differences in motivations to engage with the
course for those who completed and those who did not complete the course

Group 1 mean:
Completed the
course
Group 2 mean:
Did not complete
the course
t p
Assigned as a
requirement
4.05 2.83 3.313 .003

Regarding demographics, one significant difference was found between the two
groups regarding regular and easy access to a computer at work (p=.328), although it
should be noted the group features one outlier that reports not having any access to a
computer, which skewed results. Without this outlier, no significant differences were
found. No demographic variable significantly correlated with use of the knowledge and
skills presented in the course.

73

Amongst those who indicated they used the knowledge and skills in their current
job, the two groups did not differ significantly in their report of frequency of transfer or
difficulty of transfer when measured by the overall course. However, when measuring via
course objectives, the group who did not complete the course did not report a significant
correlation between frequency of use and difficulty of use of construct, which differs from
the group that did complete the course and the general sample. Other significant
correlations between these factors mirrored that of the general population. (See Table 17).
Results mirrored the general sample with 24% of those who completed the course and 33%
of those who did not complete the course reporting not using a single construct despite
indicating transfer of the knowledge and skills in their current job.
Amongst those who indicated they did not use the knowledge and skills in their
current job, 100% of participants who did not complete and 81% of participants who did
complete said they did not need to use the knowledge and skills within six months of taking
the course. The two groups reported significant differences in the training satisfaction
survey in all variables related to the course/platform efficacy factor with participants who
did not complete the course rating course/platform efficacy lower than those who had
completed the course. (See Table 18). The two groups did not significantly differ in
their responses to the organizational climate survey. Because there were not any positive
responses amongst those who did not complete the course to the item regarding need to use
within 6 months, correlations cannot be run against the two surveys.

74

Table 17: Relationship between breadth, frequency and difficulty of use as
measured by construct for participants who completed the course and participants
who did not complete the course

Did you complete this course?
Used the
construct
Frequency of
use of
construct
Difficulty of
use of
construct
Yes Used the construct Pearson Correlation 1 .455
***
-.206
***

Sig. (2-tailed)

.000 .000
N 4906 2297 1993
Frequency of use of
construct
Pearson Correlation 1 -.214
***

Sig. (2-tailed)

.000
N 2297 1938
Difficulty of use of
construct
Pearson Correlation 1
Sig. (2-tailed)

N 1993
No Used the construct Pearson Correlation 1 .558
***
-.027
Sig. (2-tailed)

.000 .814
N 280 92 79
Frequency of use of
construct
Pearson Correlation 1 -.090
Sig. (2-tailed)

.431
N 92 78
Difficulty of use of
construct
Pearson Correlation 1
Sig. (2-tailed)

N 79
**. Correlation is significant at the 0.001 level (2-tailed).

75

Table 18: Differences in means on the training satisfaction survey between
participants who completed the course and those who did not

Levene's Test
for Equality of
Variances t-test for Equality of Means
F Sig. t df
Sig.
(2-tailed)
Mean
Difference
Std. Error
Difference
This course provided the training content I
need for my job.
Equal variances assumed .552 .458 .819 162 .414 .215 .262
Equal variances not
assumed

.788 13.950 .444 .215 .273
This course provided the training content I
need for jobs I’d like to hold.
Equal variances assumed .019 .891 .517 165 .606 .161 .312
Equal variances not
assumed

.579 14.778 .572 .161 .279
This course was helpful in conducting certain
tasks I do right now.
Equal variances assumed .793 .375 .722 165 .471 .203 .281
Equal variances not
assumed

.597 13.324 .560 .203 .340
The level of instruction was appropriate for
me.
Equal variances assumed .219 .640 2.440 165 .016 .879 .360
Equal variances not
assumed

2.500 14.232 .025 .879 .351
The online platform (i.e. Skillport) the course
was hosted on was easy to use.
Equal variances assumed .000 .987 2.265 166 .025 .836 .369
Equal variances not
assumed

2.294 14.157 .038 .836 .365
The examples, activities, and exercises
clearly demonstrated how to apply new
knowledge and skills.
Equal variances assumed .014 .905 1.987 164 .049 .717 .361
Equal variances not
assumed

2.244 14.857 .041 .717 .320

Closed skills/Near transfer (IT courses) vs. Open skills/Far transfer (Management
courses). Among the general sample, 15% (N=68) were identified as participating in IT
courses and 85% (N=385) were identified as participating in management courses.
Amongst the IT participants, 24% indicated they had not used the knowledge and skills
from the course in their current job while 76% indicated that they did use the knowledge
and skills from the course in their current job; management participants split with 41%
76

indicating no use and 59% indicating use. Responses between the two groups were not
significantly different from one another. 4% of the IT participants and 6% of the
management participants did not complete the course. Amongst those who did not
complete the course, 67% of the IT participants stated that they did not finish because they
found the knowledge and skills they were looking for and stopped, while 59% of the
management participants stated that they did not finish because they were interrupted
while taking the course.
Some significant differences were found between IT and management course
participants in their motivation to engage with the course. Course interest (p=.001),
expected to become proficient in the topic (p=.013), assigned as a requirement (p=.000),
goals in current job (p=.001), and goals in future jobs at the organization (p=.043) all
significantly differed with IT reporting a higher mean in each factor except assigned as a
requirement. (See Table 19).
Table 19: Means and significant differences in motivations to engage with the
course for IT and management courses

Group 1 mean:
IT courses
Group 2 mean:
Management
courses
t p
The class was
interesting to me
3.45 2.84 3.330 .001
Expected to
become very
proficient in the
topic
3.18 2.76 2.491 .013
Assigned as a
requirement
3.14 4.13 -4.181 .000
Achieve goal(s)
in my current job
3.48 2.89 3.270 .001
Achieve goal(s)
in future jobs at
this organization
3.25 2.90 2.050 .043
77

Regarding demographics, one significant difference was found between the two
groups regarding organizational tenure (p=.000). While 17% of IT participants reported
having more than 20 years‘ experience, 39% of management participants reported over 20
years‘ experience. (See Table 20). It should be noted that age was not found to be
significantly different between the two groups.
Table 20: Organizational tenure for IT and Management course participants

Frequency Percent
Valid
Percent
IT Valid 1.00 Less than 10
years
14 20.6 21.2
2.00 10-15 years 34 50.0 51.5
3.00 15-18 years 6 8.8 9.1
4.00 18-20 years 1 1.5 1.5
5.00 More than 20
years
11 16.2 16.7
Total 66 97.1 100.0
Missing System 2 2.9

Total 68 100.0

Management Valid 1.00 Less than 10
years
42 10.9 11.4
2.00 10-15 years 81 21.0 22.0
3.00 15-18 years 67 17.4 18.2
4.00 18-20 years 36 9.4 9.8
5.00 More than 20
years
143 37.1 38.8
Total 369 95.8 100.0
Missing System 16 4.2

Total 385 100.0

78

As demographics correlate with use of the knowledge and skills presented in the course,
significant positive correlations were found for both groups in work time devoted to
learning and development; IT (ρ=.522, p<.01) and management (ρ=.143, .p<.01). A
significant positive correlation between work time devoted to learning and development
and attempt at transfer of training was also noted amongst the general sample. A
significant negative correlation was also found in the management group with regular and
easy access to a computer (ρ=-.106, p<.05), but it should be noted that this group features
one outlier that reports not having any access to a computer, which skewed results.
Amongst those who indicated they used the knowledge and skills in their current
job, IT and management participants did not differ significantly in their report of frequency
of transfer when asked to assess the course overall, but did differ significantly in their
report of difficulty of transfer (p=.050). However, when measuring via course objectives,
no differences were found between the two groups regarding breadth, frequency, or
difficulty of transfer with results mirroring those of the general sample. (See Table 21).
Results also mirrored the general sample with 13% of IT users and 26% of management
users reporting not using a single construct despite indicating transfer of the knowledge
and skills in their current job.

79

Table 21: Relationship between perceived use, frequency, and difficulty of
objectives by IT and management course participants

Used the
construct
Frequency
of use of
construct
Difficulty
of use of
construct
IT Used the construct Pearson Correlation 1 .533
***
-.132
*

Sig. (2-tailed)

.000 .047
N 408 259 226
Frequency of use of
construct
Pearson Correlation 1 -.164
*

Sig. (2-tailed)

.015
N 259 218
Difficulty of use of
construct
Pearson Correlation 1
Sig. (2-tailed)

N 226
Management Used the construct Pearson Correlation 1 .454
***
-.192
***

Sig. (2-tailed)

.000 .000
N 4888 2202 1910
Frequency of use of
construct
Pearson Correlation 1 -.222
***

Sig. (2-tailed)

.000
N 2202 1861
Difficulty of use of
construct
Pearson Correlation 1
Sig. (2-tailed)

N 1910
***. Correlation is significant at the 0.001 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Amongst those who indicated they did not use the knowledge and skills in their
current job, 87% of IT and 82% of management participants said they did not need to use
the knowledge and skills within six months of taking the course. Responses between the
two groups were not found to be significantly different from one another. The two groups
80

also did not differ significantly in any responses to the training satisfaction survey,
although two significant differences were found between the groups in the correlation
between needing to use the course content within six months of taking the course and the
training satisfaction survey. The management group reports a significant negative
correlation between needing to use the content within six months and the course providing
content needed and utility value of course in the current job. However, when considering
the coefficients, these differences are likely due to the discrepancy in sample size. (See
Table 22).
Table 22: Correlation between needing to use the course content within 6 months
and training satisfaction survey for IT and management populations

This course
provided the
training content
I need for my
job.
This course
provided the
training
content I
need for jobs
I’d like to
hold.
This course
was helpful in
conducting
certain tasks I
do right now.
The level of
instruction
was
appropriate
for me.
The online
platform the
course was
hosted on
was easy to
use.
The examples,
activities, and
exercises
clearly
demonstrated
how to apply
new
knowledge and
skills.
IT I needed to use the
course content in my
job within 6 months of
taking the course:
Correlation
Coefficient
-.263 -.266 -.117 -.118 .024 -.024
Sig. (2-tailed) .344 .339 .677 .677 .932 .932
N 15 15 15 15 15 15
Management I/ needed to use the
course content in my
job within 6 months of
taking the course:
Correlation
Coefficient
-.283
**
.010 -.195
*
-.119 .045 -.045
Sig. (2-tailed) .001 .910 .019 .158 .591 .595
N 140 143 143 143 144 142
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

81

Responses did differ between the two groups on the organizational climate survey
with management participants giving greater influence to organizational climate factors
and IT participants to transfer opportunity factors in their decision not to transfer the
knowledge and skills to their current jobs. (See Table 23). No significant differences
were found between the two groups in the reporting of the relationship between need to use
the course within 6 months and the organizational climate survey.
Table 23: Means and significant differences in the organizational climate survey for
IT and management courses

Group 1 mean:
IT courses
Group 2 mean:
Management
courses
t p
Overall
organizational
climate to adopt
change
1.50 2.38 -2.443 .027
Organizational
flexibility to apply
new processes
1.57 2.41 -2.189 .030
Organizational
support for
employees’
participation in
training programs
1.50 2.22 -2.214 .042
Match between
training content
and job tasks
3.86 2.75 2.654 .009
Opportunity to
use training
content
4.14 2.95 2.993 .003
Peer feedback on
training
application
1.31 1.79 -2.116 .050

82

Central to the organization. Among the general sample, 9% (N=39) were identified
as participating in courses considered central to the organization. It should be noted that
IT courses were the only courses identified as central to the organization, so these
participants are essentially a sub-group of the IT sub-group noted above. In this group,
72% indicated they did use the knowledge and skills in their current job, which was not
significantly different from those who did not participate in a course considered central.
All participants completed the courses.
No significant differences were found between this group and participants who did
not participate in a course considered central in their motivation to engage with the course.
Regarding demographics, significant differences were found between the two groups
regarding level of education (p=.018), comfort level with computers (p =.000), and
organizational tenure (p =.000). Participants who engaged in courses central to the
organization were educated at a higher level, more comfortable with computers, and had
lower organizational tenure than those who did not. As demographics correlate with use
of the knowledge and skills presented in the course, a significant negative correlation was
found with age (ρ=-.357, p<.05) and a significant positive correlation with ―work time
devoted to learning and development‖ (ρ=.744, .p<.001). A significant positive
correlation between ―work time devoted to learning and development‖ and attempt at
transfer of training was also noted amongst the general sample.
Amongst those who indicated they used the knowledge and skills in their current job, no
significant differences were found in the report of frequency of transfer or difficulty of
transfer when measuring via course. However, when measuring via construct, the
83

relationship between frequency and difficulty did not rise to significance as it did with the
general population. (See Table 24). Results also mirrored the general sample with 19%
reporting not using a single construct despite indicating transfer of the knowledge and
skills in their current job.
Table 24: Relationship between perceived use, frequency, and difficulty of
objectives amongst participants in a course identified as central to the organization

Course is central to organization
Used the
construct
Frequency of
use of
construct
Difficulty of
use of
construct
Used the construct Pearson Correlation 1 .411
***
.064
Sig. (2-tailed)

.000 .535
N 158 109 95
Frequency of use of
construct
Pearson Correlation 1 -.138
Sig. (2-tailed)

.190
N 109 92
Difficulty of use of
construct
Pearson Correlation 1
Sig. (2-tailed)

N 95
***. Correlation is significant at the 0.01 level (2-tailed).

Amongst those who indicated they did not use the knowledge and skills in their
current job, 90% of participants said they did not need to use the knowledge and skills
within six months of taking the course. Responses between this group and the group who
did not participate in a course considered central to the organization were not significantly
different from one another. The two groups differed significantly in responses regarding
the ease of use of the online platform (p=.035) and the appropriateness of examples
84

(p=.042) with those participating in a course central to the organization reporting a higher
level of satisfaction on both variables. A significant difference was found between the
groups in the correlation between needing to use the course content within six months of
taking the course and the training satisfaction survey. For participants in courses
considered central to the organization, the relationship between needing to use the course
within the next six months and the course being helpful with current tasks did not rise to
significance. Responses also differed between the two groups on the organizational
climate survey with participants in courses considered central to the organization giving
greater influence to the opportunity to use the training content factor and match between
training content and job tasks and less influence to peer feedback. (See Table 25). No
significant differences were found between the two groups in the reporting of the
relationship between need to use the course within 6 months and the organizational climate
survey.
Table 25: Means and significant differences in the organizational climate survey for
participants in courses central to the organization

Group 1 mean:
Courses not
considered
central to the
organization
Group 2 mean:
Courses
considered
central to the
organization
t p
Match between
training content
and job tasks
2.76 4.09 -2.878 .005
Opportunity to
use training
content
2.97 4.18 -2.707 .008
Peer feedback on
training
application
1.79 1.18 3.007 .009

85

Summary
The purpose of this chapter was to share findings from a web survey designed to
analyze perceived transfer in self-directed elearning courses. This research also examined
usage data for self-directed elearning courses and found that a large number of courses
were not accessed or accessed by very few people. Using a quantitative survey, this study
showed that 62% of the overall population estimated transfer of training in their current
job. Time devoted to learning and training was significantly and positively related to
transfer. Few differences were found amongst sub-groups although IT participants who
did not transfer placed more emphasis on utility value and management participants who
did not transfer placed more emphasis on organizational climate variables. Amongst
those who did indicate transfer, approximately 31% of the course was used in the current
job although 24% of the respondents in this group could not identify a single course
objective utilized. Chapter Five will discuss and analyze the study‘s findings, offer some
conclusions based on the data, and provide implications and suggestions for future
research.

86

CHAPTER 5:
FINDINGS, CONCLUSIONS, AND IMPLICATIONS
Introduction
Self-directed elearning courses have been implemented at a rapid rate by many
organizations comprising a noteworthy percentage of workplace training. Such courses
are perceived as having many organizational advantages such as cost savings, wide
dissemination, and maximum availability to learners in the organization, and learner
advantages, such as breadth and choice of learning opportunities. However, few
organizations attempt to measure the effect such courses and programs have in their
organizations and the result is the wide implementation of a form of training with little
knowledge as to what the training is actually doing.
This study found that few courses were accessed by few people in the organization,
but when courses were accessed, the majority of employees perceived they transferred the
knowledge and skills to their current jobs. As many organizations have already
implemented such programs, it seems that increasing usage in strategic ways may only
help educate the employee population and, thus, contribute to the bottom line of the
organization. However, hurdles have also been identified and organizations should be
aware that some choices, actions, or lack of actions may make self-directed elearning
courses less useful as a tool. This chapter includes a brief discussion of the findings,
practical suggestions for addressing the various issues raised by the study, suggestions for
future areas of research, implications of the study, and conclusions.

87

Summary of the Study
This study sought to explore transfer of training within self-selected, self-directed
elearning courses through the following research questions:
1. To what extents do employees who utilize on-demand, voluntary elearning courses
use the knowledge and skills from the courses in their job?
2. What employee characteristics, if any, among commonly collected organizational
data might be indicative of someone likely to attempt transfer?
3. If an employee reports transfer within their job, how much, how often, and how
difficult is it for the employee to transfer?
4. If an employee does not report transfer within the job, are there organizational
environment or intervention design and delivery characteristics that hinder
transfer?
5. To what extent is a course‘s status as completed, closed or open skills, or central to
the organization an indication of transfer amongst employees?
A large, U.S. based, multi-national, S&P 500 transportation company was analyzed
for this study. Management employees who engaged in voluntary elearning courses
within a six month timeframe were targeted to receive an online survey. From the 2,500
course titles utilized during the timeframe, 20 courses were selected which enrolled about
5,000 management employees. Overall response rate averaged 10%, or approximately

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500 respondents. Information collected included motivation to engage, course
completion, perceived transfer of training, perceived frequency and difficulty of transfer,
perceived deterrents of transfer, and demographic variables.
The literature review used for this study focused on learning and development
within the organization, elearning within the business organization, transfer of training, and
self-directed learning theory. Instruments in the survey were largely identified using the
Baldwin and Ford (1988) approach to the transfer construct. Where needed, instruments
were adapted or developed based on the review of the literature and face validity with the
organization.
Summary of Findings

This case showed that only 19% of management employees who have access
utilized the program. Of that, only 1% of the IT courses and 3% of management
courses were accessed by 50 or more people, or approximately 1% of the population
who had access to them. Popular management courses received a relatively high response
rate (14-21%), while management courses in which ratios of non-completed sessions were
similar to completed sessions received a relatively low response rate (1-4%). Response
rates for IT courses were opposite; popular IT courses received a relatively low response
rate (.5-2%) while courses in which ratios of non-completed sessions were similar to
completed sessions and courses identified as central received a relatively high response
rate (4-8%; 5-11%). However, amongst those taking advantage of the program and
responding, 62% reported using the knowledge and skills presented in the course in their
current job.
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Motivation and demographics
Skills value, self-efficacy, co-worker suggestion, and goals in current and future
jobs in and out of the organization all significantly correlate with transfer of training while
supervisory suggestion and required assignment did not. Several demographic variables
significantly correlated with an employee‘s choice to engage in a course, but only one
variable significantly correlated with using transfer in one‘s current job: work time
specifically devoted to training and/or learning was significantly positively correlated with
transfer of training. Amongst demographic variables and employee motivation to engage
in a course, organizational tenure and supervisor suggestion were significantly negatively
correlated; significant positive correlations include age and comfort level with computers
and co-worker support, level of education and self-efficacy, organizational tenure and
requirement, and devoted work time with skills value, self-efficacy, goals in current job,
and goals in future jobs within the organization. It is notable that devoted work time,
while positively correlated with jobs within the current organization, was not correlated
with jobs outside of the organization.
Breadth, frequency, and difficulty of transfer
Of the 62% of employees who indicated transfer within their current job, the
average rating for frequency of course use was 3.42 and the average rating of difficulty of
course use was 2.52 with no significant correlations found between the perceived difficulty
of the course and perceived frequency of use. However, when measured via course
objectives, the average rating for frequency of course use was 3.09 and the average rating
of difficulty of course use was 2.62 with a significant positive correlation found between
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use of construct and frequency of use and significant negative correlations found between
the use of construct and difficulty and frequency of use and difficulty. The difference in
means between these disparate findings was significant in both cases. Further, when
measured via course objectives, participants reported that 31% of the course was used;
24% of participants reported not using any of the course objectives.
In comparison to Wang‘s original study, from which the instrument used in this
study was adapted, significant positive correlations were found between overall transfer
and breadth, frequency, and difficulty as well as breadth and frequency. Dimensions were
all significantly correlated with each motivation variable measured in this study, but not
correlated with and demographic variables measured in this study.
Participants who did not attempt transfer
Of the 38% of employees who indicated they did not use the knowledge and skills
presented in the course in their current job, 82% indicated that they did not need to use the
knowledge and skills within six months of taking the course. In the training satisfaction
survey, most participants indicated that they did not think that the course was useful for
their current job, but were relatively satisfied with the ease of use of the online platform
and the appropriateness of the course‘s learning level. Perhaps not surprisingly, those
who indicated they did not need to use the course in the next six months also indicated they
did not find the course useful to their current job. In the organization climate survey,
participants indicated that peer and supervisor feedback had little to do with if they
attempted transfer while the opportunity to use and match between the training content and
current job had the most influence on if they attempted transfer. 69% of this population
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indicated that the primary reason they were taking the course was because it was required
of them, but this population largely had similar responses to why they did not use the
course content; exceptions seemed to center around course appropriateness and the
organization‘s role in developing role models.
Factor analysis revealed that the training satisfaction survey items loaded on to two
factors identified as utility value and course/platform efficacy. Survey items on the
organizational climate survey loaded on to three factors identified as organizational
support, transfer opportunity, and peer and supervisor feedback. Correlations suggest
three significant correlations amongst utility value and organizational support,
course/platform efficacy and transfer opportunity, and utility value and peer and supervisor
feedback. In other words, the more the employee finds the course useful, the greater the
impact organizational support and feedback will have on his/her decision to attempt
transfer and the more the employee finds the course and platform easy to use, the greater
the impact opportunities to transfer will be on his/her decision to attempt transfer.
Complete vs. did not complete the course
Among the general sample, 6% were identified as not having completed the course
and 48% of those identified indicated they had used the knowledge and skills in their
current job, a number not significantly different than the general population. The majority
of respondents indicated they did not complete the course because they were interrupted.
This group also did not report a relationship between difficulty of using a construct and

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frequency of using a construct. For those in this group that did not indicate transfer, more
reported that they did not need to use the knowledge and skills within six months of taking
the course and rated the course/platform efficacy factors lower.
Closed skills/Near transfer (IT courses) vs. Open skills/Far transfer (Management
courses)
Among the general sample, 15% were identified as participating in IT courses and
85% were identified as participating in management courses. Not significantly different
from one another, 76% of IT participants and 59% of management participants indicated
they had used the knowledge and skills in their current job. Amongst those who did not
complete the course, IT participants were more likely to state they did not finish because
they found the knowledge and skills they were looking for and stopped while management
participants were more likely to report they were interrupted. IT participants were more
likely to engage in a course due to value, expected proficiency, and goals within the
organization while management participants were more likely to engage in a course due to
it being assigned as a requirement. Management participants were more likely to have
longer organizational tenure despite a lack of significant difference in age. Management
participants were also more likely to report a higher difficulty level of transfer when
measured by overall course, but were not significantly different than IT when asked to
measure via course objectives. For those in this group that did not indicate transfer, the IT
participants were more likely to cite transfer opportunity factors and management
participants more likely to cite organizational climate factors as factors in their decision not
to transfer the knowledge and skills to their current jobs.
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Central to the organization
Among the general sample, 9% were identified as participating in courses
considered central to the organization. Not significantly different from the general
sample, 72% indicated they did use the knowledge and skills in their current job. All
participants had completed the course. Participants were likely to be educated at a higher
level, more comfortable with computers, and have lower organizational tenure. Younger
participants were more likely to attempt transfer. This group did not report a relationship
between difficulty of using a construct and frequency of using a construct. For those in
this group who did not indicate transfer, a higher level of satisfaction was recorded with the
ease of use of the online platform and the appropriateness of examples. These participants
also gave greater influence to the opportunity to use training content.
Conclusions
Although it is assumed that a 62% overall estimate of transfer of training is slightly
inflated due to issues of self-report (Aronson, 2008) and the measurement of predictor
variables and transfer at the same time (Blume et al., 2009), this number is higher than
previous estimates of transfer of training (Saks, 2002) and supportive of the effects of
self-directed learning theory within such courses. Autonomy and voluntary participation
have been suggested as playing a role in transfer due to motivation, perceived utility value,
and learner choice (Billington at al., 2010; Blume et al., 2009; Yelon et al., 2004; Baldwin et
al., 1991; Hicks & Klimoski, 1987). Voluntary participation is further supported through
motivation data in this study with all variables showing a significant positive correlation with
perceived transfer except supervisory suggestion and required assignment, the two variables
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that most suggest a lack of choice in course engagement. These variables showed no
correlation with perceived transfer, calling to question the issue of under which
circumstances choice may be most motivating in transfer issues.
However, while transfer of training estimates look relatively strong for the courses
that are accessed, the large scope of the courses offered juxtaposed with the limited reach
of the program calls to question the program‘s efficacy. ASTD and The MASIE Center
found that the average start rate for participation in voluntary courses was 32% (2001); this
study suggests a much lower start rate. Huselid et al. (1997) suggest that the mere
implementation of some HPWPs may make a difference for some and self-directed
elearning courses are often sold under the auspices of wide dissemination, but this research
suggests that just because courses are offered does not mean employees will access such
courses.
ASTD and The MASIE Center (2001) found that the most successful courses were
well advertised and internally championed as well as those for which completion time and
support are provided during work hours. This research certainly supports the notion of
work time devoted to courses for successful training. Amongst other variables not
measured in this study, Burke & Hutchins (2007) found a strong or moderate relationship
with transfer in cognitive ability, self-efficacy, anxiety/negative affectivity, perceived utility,
career planning, supervisory suggestion, and peer support. While this study shows support
for some of these variables playing an important role for some employee‘s decision to
engage, it does not support a relationship with transfer. Henderson (2003) found that
employees were more likely to engage in a course if required by management. This
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research suggests that the longer the organizational tenure, the less likely an employee is to
engage in a course if suggested by a supervisor, again calling to mind the importance of
employee choice when deciding to engage and transfer.
Broad and Newstrom (1992) estimated that over 80% of training is not fully applied
by employees back on the job. Robinson and Robinson (1996) report that less than 30% of
what people learn gets used on the job. Sevilla and Wells (1988) report that U.S. based
companies only saw 10-15% of training is applied to work. This research suggests that
amongst participants who do transfer, approximately 31% of the course was transferred,
which most closely mirrors Robinson and Robinson‘s estimate. However, this estimate is
complicated by the fact that 24% of participants who initially indicated transfer could not
identify one course objective for which they transferred. Clark et al. (2010) warned that
because all learning is novel, it could be as harmful as it is beneficial. The results bring to
question what some participants are attempting to transfer if their knowledge and skills
cannot be defined by course objectives or points to issues regarding misnamed or misleading
course objectives.
In addition, when measuring via course objectives, it was found that the more
course objectives an employee reported using, the more frequently the employee used
those objectives. Further, the more difficult the employee found the objectives, the less
likely s/he was to use as many or to use them as frequently. This may be an indication of
fit between learner and course with learners who had difficulty with the course reporting
less transfer and learners who, for whatever reason, found more value in the course
reporting more transfer.
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Wang (2000) found that dimensions of transfer were not significantly correlated
with one another and concluded that her finding was consistent with Ford et al.‘s (1992)
theoretical perspective to consider that the three dimensions of breadth, frequency, and
transfer were independent. This research does not support that finding, instead finding a
significant positive correlation between breadth and frequency. Other measures collected
in this study (motivation and demographics) do not provide further guidance on this
discrepancy as all dimensions were correlated with each motivation variable and no
dimensions were correlated with any demographic variable. There are some important
differences between Wang‘s study and this study that may assist in future research efforts
to determine probable cause. Wang‘s study featured an intervention in the form of a post
program coach, while this study was reflective of a completely self-directed format of
learning. This study had a large sample size of approximately 500 while Wang‘s sample
size was approximately 20. This study featured multiple classes (20) while Wang‘s
featured one class. Finally, this study looked at an online platform while Wang‘s looked
at a traditional face to face platform. Further research is needed to determine possible
significant correlations amongst dimensions of transfer.
As design factors seem to be some of the most influential factors affecting transfer
(Brinkerhoff & Gill, 1992), it is perhaps comforting to determine that participants were
relatively satisfied with the ease of use of the platform and appropriateness of the course.
Content match and task similarity between learning and transfer setting have been shown
to be influential in transfer (Kontoghiorghes, 2002; Lim, 2000; Axtell, Maitlis, &Yearta,
1997; Rouiller & Goldstein, 1993; Baldwin & Ford, 1988) as has content relevance and
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examples (Burke & Hutchins, 2007) and this research supports those findings. Work
system factors such as opportunity transfer climate and opportunity to perform (Burke &
Hutchins, 2007) have also been shown to be influential in transfer and this research
supports that as well, although it does not seem to support supervisory suggestion or peer
suggestion as having a strong relationship with transfer (Burke & Hutchins, 2007). The
opportunity to immediately apply knowledge and skills has been emphasized in many
studies (Lim & Morris, 2006; Lim, 2001, 2000; Ford et al., 1992) and this research
suggests that the emphasis is warranted although the recommendations that often follow of
assigning work assignments that have to do with the training (Lim, 2001) prove
problematic for self-directed courses and given the non-significant relationship with
assigned as a requirement and lack of transfer.
Lending support to Lim and Morris‘ (2006) factor analysis of their training
satisfaction survey, this research also found that items aligned with two factors. These
factors differed, but it is likely a reflection of the adjustments made to the survey to reflect
the elearning platform and lack of instructors. This research also adds support for their
original characterization of the organizational climate survey rather than their factor
analysis, which suggested that all items fell on to one scale. Factor analysis in this
research found that the items fell on to three scales as originally hypothesized by Lim and
Morris (2006).
Comfort level with computers is perhaps the most cited variable in the elearning field
(Lim et al., 2007; Park & Wentling, 2007; Al-Jabri & Al-Khaldi, 1997; Yaverbaum &
Nosek, 1992; Ford et al., 1992; Tannenbaum et al., 1991; Gist, 1989). However, this
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research suggests amongst autonomous employees, very few people did not have regular and
easy access to a computer or would rate themselves as uncomfortable with computers.
While it‘s hard to disagree with the idea that computer familiarity or confidence relates to
elearning, it is also difficult to imagine a knowledge worker who does not regularly access a
computer. Managers in this sample had various roles, including several field roles in which
the organization was not sure about access to or comfort level with computers. Despite the
variety and lack of organizational insight, very few people indicated that computer issues
were issues in any way.
Perhaps the most significant finding in assessing various sub-groups is that there
were not many significant differences. The primary research question, if people
perceived transfer within their current jobs, did not differ amongst any sub-group. This
finding supports Yelon and Ford‘s (1999) hypothesis that both open and closed skills
transfer, but also suggests that both skills transfer at similar rates under similar
instructional and organizational conditions. This research supports earlier suggestions
that interruptions are problematic for elearning (ASTD and the Masie Center, 2001; Ellet
& Naiman, 2003). Some populations also partially support Wang‘s (2000) finding that
dimensions of transfer were not significantly correlated with one another with participants
who did not complete courses and participants taking part in courses considered central to
the organization not citing a relationship between difficulty and frequency regarding use of
course objectives.

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Although IT participants were a relatively small sample size, their responses may
be indicative of the types of knowledge and skills sought from self-directed elearning
courses and the learner‘s goal for such skills. IT participants were more likely to not
finish a course if they found the knowledge and skills they were looking for. They were
also more likely to engage in a course due to value, expected proficiency, and goals within
the organization. They were more likely to have less organizational tenure and more
likely to cite transfer opportunity factors in their decisions not to transfer knowledge and
skills. Specific to those participating in courses considered central to the organization,
which were particular to IT management professionals, participants were educated at a
higher level, more comfortable with computers, more satisfied with the online platform,
and younger participants were more likely to attempt transfer. As far as higher levels of
education are related to cognitive ability, this finding may support Blume et al.‘s (2009)
research indicating cognitive ability had a stronger relationship with closed skills. The
differences between the two IT groups may not be surprising given the likely profile of an
IT management employee, but the similarities are striking in that they suggest those
participating in IT courses are likely to expect something specific and look for direct
application to tasks after engaging in the course.
In opposition, management participants were more likely to cite organizational
climate factors in their decision not to transfer which is consistent with findings that
suggest that those learning open skills are more likely to seek opportunities to apply that
skill in the workplace (Blume et al., 2009; Ford et al., 1992). Management participants
were also more likely to report a higher level of perceived difficulty in transferring skills
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learned when measured by overall course, but not when measured by construct, suggesting
that perception of difficulty of transfer in open skills courses may be different than the
reality.
The response rates for management courses differed from IT courses based on the
type of course queried. It is unknown as to why response rates differed so greatly for
different types of courses, but a possible explanation lies in the perceived value of time
devoted to survey taking for different types of courses. Value and cost have regularly
been cited as motivation to engage in surveys (Loosveldt & Storms, 2008; Dillman, 2007)
and the types of courses that are most popular in IT differ greatly from the types of courses
that are most popular in management. For instance, in this sample, popular IT courses
included ―Getting Started with Access 2007‖ and ―Microsoft Windows 7.‖ These courses
may be considered basic computer skill courses and it seems plausible that participants in
such courses may not perceive as much value in taking time to fill out a survey, especially
given the results and conclusions amongst the IT group.
Implications
The model of the learning organization, one in which employees know their learning
needs and those needs are facilitated by various processes to prioritize and meet those needs
(Popper & Lipshitz, 2000) can be supported by autonomous employees engaging in
self-directed elearning courses. These self-directed, elearning courses seem to transfer at
similar rates to face to face learning with a number of caveats. First, employee perception
of choice in selecting such courses is an important factor in their motivation to engage in
such courses. The impression that something is a requirement negatively impacts an
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employee‘s decision to engage in such a course and, thus, limits opportunities for transfer in
self-selected courses. This research also suggests a discrepancy with past research on
supervisor support, with this research indicating that supervisor suggestion also impedes an
employee‘s decision to attempt transfer. While this may be due to organizational climate,
there seems to be an innate divergence between the importance of choice in self-directed
learning and the impression some types of supervisor suggestion may create. Interpreted as
truly supportive, supervisor suggestion may indeed be helpful in engagement or transfer.
Interpreted in other ways, such as mediating employee choice, supervisor suggestion may
actually hinder engagement and transfer. Further research defining supervisor support and
its interpretation is needed.
Second, employers should allot time in an employee‘s work day to encourage
learning and development in an elearning platform as elearning does not have a specific time
or date associated with it like face to face learning. Surveys suggest that elearning seems to
be pushed to personal time (ASTD and the Masie Center, 2001; Ellet & Naiman, 2003).
This research suggests that if employers want to encourage use of elearning technology and
transfer of such knowledge, devoting time on the job to learning is imperative. Beyond
positively effecting transfer, it does not appear to encourage training for the sake of moving
to another organization, further enhancing the benefits to the organization.
Third, organizations need to be involved in the implementation, distribution,
maintenance, design, and championing of such courses without requiring usage and
impeding employee choice. ASTD and The MASIE Center (2001) found that the most
successful courses were well advertised and internally championed as well as those for
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which completion time and support are provided during work hours. Clark (2003)
suggests that in order to convince people to do something new, they must believe that their
extra efforts will directly or indirectly contribute to what they need to feel successful and
effective. Schank (2007) warns that technology is a means to development and training,
not the answer to development and training. This research champions these suggestions
as low usage data implies that employees are not utilizing elearning systems just because
they are offered.
Corporate universities, one of the most recent steps in the evolution of learning and
development initiatives in the organization, spawned recommendations of curriculum
having a strategic plan that intertwined with the organization‘s larger goals and objectives
(Barley, 2007) and using initiatives to expand organizational capabilities and culture
(Barley, 2007; Kiely, 2007). Organizations may use the recommendations that came out
of the last major learning and development initiative to help guide the elearning initiative,
tailoring or championing programs to align with organizational capabilities and culture.
Organizations should review usage data regularly to help determine the cost effectiveness
of elearning programs, balancing the scope with the reach of such programs. Like most
learning and development initiatives, it is not enough to simply offer such courses without
appropriate context.
As organizations become more adept at elearning technology and monitoring
usage, monitoring transfer to assess how effective courses are, what courses might need to
be emphasized within the organization, and what kinds of knowledge and skills employees
are seeking would be useful in continued efforts to stay involved. In this, measuring by
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construct or objective gives a significantly different measurement of transfer than asking
more general questions about the class and should be considered as organizations employ
assessment techniques. Organizations should consider the importance of course
objectives in achieving a more accurate measurement of transfer.
The difference in transfer when measured between overall course impressions and
course objectives as well as significant correlations found between breadth, frequency, and
difficulty also suggests that course designers and the organization may want to consider the
importance of course introductions and clear course objectives for the learner to encourage
appropriate fit between learner and course as well as a means of assisting in the employee‘s
navigation of the course. Organizations may need to be involved in these course
introductions to help guide learners as to how to best use the knowledge and skills learners
will encounter in the organization.
IT courses, and perhaps other courses that are indicative of closed skills, should be
specific in their introductions as to what skill elements are where in the course and exactly
how to use such skill elements in the workplace. As one of elearning‘s perceived benefits
is timeliness, specificity should increase use and transfer in these courses as users will be
able to gage exactly what they will learn and where they can apply it before fully engaging.
It is also worthwhile to explore policies regarding completion within these courses as skills
found within one course may not be dependent on each other and participants may find a
course more useful if able to view partial courses as applicable to their job instead of
partaking in the entire course for credit.

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While specificity in management or other courses indicative of open skills is also
important, it should likely be presented in a different way as participants in such courses
are likely to find their own opportunities to transfer. More important to this group,
organizational climate factors should be addressed in the course to help insure transfer.
For this reason, standard courses may be more than adequate for many closed skills, but it
is possible that open skill courses may require some customization or guidance as to how
one might possibly use the knowledge and skills in the person‘s current organization.
Suggesting and incorporating transfer-enabling interventions during and after the training
as suggested by Lim and Morris (2006), may be helpful in promoting transfer if done in a
way that does not imply a formal work assignment, which may affect employee impression
of choice. Further, organizational education regarding course competencies and
availability may be needed to help educate managers and co-workers of employees who
take such courses and attempt transfer to assist in an organizationally supportive
environment.
Future Research

Limitations in this study allow for numerous opportunities for future research
regarding transfer of training and organizational implementation of self-directed elearning
courses. For generalization purposes, future studies could involve broader populations of
both employees and organizations. Other variables in motivation, organizational reward
systems, or work ethics may also reveal meaningful findings. Verification of transfer
results could be collected in post-transfer data such as work performance, measurements of
learning, or qualitative interviews with employees themselves. The measurement of
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independent variables at a separate time than the measurement of transfer would lead to data
that is less likely to be inflated. This study also used single source data; although data from
multiple sources may be difficult to collect in self-directed elearning courses, such a study
would be useful to the field.
Specific to this research and findings that differed from or added to previous
research, further research should be conducted regarding Wang‘s (2000) tool and, more
broadly, Ford et al.‘s (1992) theoretical perspective that the three dimensions of breadth,
frequency, difficulty, and transfer were independent. Although the general populations
reported significant correlations between the dimensions, two sub-groups in this study did
not report one of the correlations. This research could also be extended through
perceptions of dimensions as measured via course and then course objectives. Such
research may be important as research on open skills moves forward; participants in such
courses may be attempting transfer at a greater level than initially perceived.
Beyond the call for further research between open and closed skills, further
differentiation may also be needed in closed skills research dependent on the type of closed
skills, the person engaging, and the perceived value. It is also possible that assessing
certain types of courses with certain populations may take different methods or incentives
to achieve desirable results.
This research, while supporting the notion that computer self-efficacy may be
important in considering the design of such programs, also suggests that it is likely no longer
as applicable in exploring transfer in such programs amongst some populations. Course
design, while important to transfer, is relatively standard in many off the shelf elearning
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programs. Technology is in such a place and many employees have enough comfort level
with computers to easily maneuver in such courses. While it is perhaps the only variable
that has a rich history with elearning, it seems to be an outdated variable amongst some
populations.
Finally, like Lim and Morris (2006), this researcher found few integrated
approaches that assessed transfer and that many survey instruments related to transfer of
training were long and, thus, undesirable for use within organizations. It is recommended
that future research incorporate integrated approaches and that shorter surveys continue to
be developed and refined to encourage further research within organizational training.
Summary
This study sought to inform organizational decisions and fill gaps in the literature
regarding self-directed elearning courses and transfer of training by providing data about
the perception of transfer, employee characteristics that may be indicative of transfer, the
breadth, depth, and frequency of transfer, why employees may not transfer, and possible
differences in transfer amongst sub-groups. Results reveal that when used, a majority of
the population indicated transfer of training in their current job. Work time devoted to
learning most helped facilitate transfer while impressions of the course being required and
organizational tenure most hindered in the engagement of such courses. Other findings
imply the importance of the role the organization might play in the implementation,
distribution, maintenance, design, and championing of such courses
Many studies have investigated transfer of training issues across a wide variety of
employee populations, but this study provided a unique opportunity to investigate the
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generalization of such principals within elearning courses. Elearning courses have been
revolutionary in their growth and effect they have had in organizational learning, but
programs have been implemented largely without questioning their benefits or
effectiveness as a training tool. The purpose of this study was to assess such programs
effectiveness as a training tool, but perhaps one of the most surprising findings in this study
was the low usage rates of such courses, which instead questions the benefits of such
programs in their claims of cost savings and reach.
As organizations move forward with self-directed elearning programs, it is perhaps
reassuring that this study indicates such courses can be as effective or more effective as
traditional, face to face courses. Coupled with the fact that, in theory, benefits to the
organization such as cost savings and reach are realistic when a program is used and
self-directed elearning courses seem to be an appealing alternative or supplement to
educating an organization‘s workforce. However, like other learning and development
initiatives, self-directed elearning courses cannot be introduced without some
organizational context and involvement. Past research on transfer of training coupled
with implications derived from this research should help practitioners in implementing
such courses to not only achieve the benefits promised from an elearning platform, but also
to insure the best results within their organization.

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APPENDIX A
EMAIL SOLICITATION FOR QUANTITATIVE SURVEY

My name is Anjelica Wright Garcia and I am a doctoral student in the Rossier School of
Education at USC. Your organization is partaking in a research project I am conducting on
elearning systems. The goal of this research project is to learn how you as an employee
may use the courses you access in your job. You have been selected as a potential
participant because of your participation in X course.

Participation in this study is entirely voluntary. Your identity as a participant will remain
confidential, even from your employer, at all times during and after the study. Your
relationship with your organization will not be affected whether or not you participate in
this study.

If you have questions, please contact me.

Thank you for your participation,

Anjelica Wright Garcia
213-740-7857
anjelicw@marshall.usc.edu

121

University of Southern California
Rossier School of Education
3470 Trousdale Parkway
Los Angeles, CA 90089

INFORMATION/FACTS SHEET FOR NON-MEDICAL RESEARCH

TITLE OF THE STUDY

Exploring Organizational Transfer in Self-Directed, Self-Selected elearning Courses

PURPOSE OF THE STUDY

The goal of this research project is to learn how employees may use voluntary elearning
courses in their job.

PARTICIPANT INVOLVEMENT

This web survey will take approximately 2-10 minutes to complete. You will be asked
questions regarding your experience with the course and your use of the knowledge and
skills taught in the course.

CONFIDENTIALITY

122

Your participation is voluntary and your responses will be kept completely confidential,
even from your employer. Your name, address or other identifiable information will not
be collected.

INVESTIGATOR CONTACT INFORMATION

Anjelica Wright Garcia, University of Southern California, 3415 S. Figueroa St., DCC
200, Los Angeles, CA 90089-0871, 213-740-7857, anjelicw@marshall.usc.edu

IRB CONTACT INFORMATION

University Park IRB, Office of the Vice Provost for Research Advancement, Stonier Hall,
Room 224a, Los Angeles, CA 90089-1146, (213) 821-5272 or upirb@usc.edu

123

APPENDIX B
QUANTITATIVE SURVEY

1. To what extent did the following play a role in your decision to take X:

Did not play a role 1 2 3 4 5 Played a primary role
The class was interesting to me
I expected to become very proficient in the topic
It was suggested to me by a supervisor
It was suggested to me by a co-worker
It was assigned to me as a requirement
I thought the class would help me achieve goals(s) in my current job
I thought the class would help me achieve goal(s) in future jobs at this organization
I thought the class would help me achieve goal(s) in future jobs at other organizations

2. Did you complete this course? Yes/No

Split logic: If the participant answers no to question #2, will be directed to the following
question:
3. What reason best explains why you did not complete the course?
I found the knowledge/skills I was looking for and stopped
I was not finding the course useful and stopped
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I was interrupted while taking the course
4. Have you used the knowledge and skills presented in the course in your
current job? Yes/No

Split logic: If the participant answers yes to question #4, will be directed to the following
questions:
5. Please rate how often you used the knowledge and skills presented in X:
Never 1 2 3 4 5 Always
6. Please rate how difficult you perceive it was to perform the knowledge and
skills presented in the course:
Very easy 1 2 3 4 5 Very difficult
The following questions refer to individual constructs presented in X.
Note: Constructs were taken from course objectives and addressed with the following
three questions.
7. Check if you used the construct in your job. Yes
8. Please rate how often you perform this construct:
Never 1 2 3 4 5 Always
9. Please rate how difficult you perceived it was to perform this construct:
Very easy 1 2 3 4 5 Very difficult

125

Split logic: If the participant answers no to question #4, will be directed to the following
questions:
The purpose of the following questions is to help assess why you may not have used the
knowledge/skills presented in X course in your current job. Your responses will be kept
completely confidential, even from your employer.
10. I needed to use the course content in my job within 6 months of taking the
course: Yes/No
11. Not at all----Very much so
1-2-3-4-5
This course provided the training content I need for my job.
This course provided the training content I need for jobs I‘d like to hold.
This course was helpful in conducting certain tasks I do right now.
The level of instruction was appropriate for me.
The online platform (i.e. name of platform) the course was hosted on was easy to
use.
The examples, activities, and exercises clearly demonstrated how to apply new
knowledge and skills.

12. What kind of influence did the following factors play in your decision not to
use the knowledge and skills presented in the course? No influence 1 2 3 4 5 A
great deal of influence
Overall organizational climate to adopt change
126

Organizational flexibility to apply new processes
Organizational interest in employee development
Organizational support for employees‘ participation in training programs
Organization‘s role in developing role models to provide job help and mentoring
Match between training content and job tasks
Opportunity to use training content
Supervisor feedback on training application
Peer feedback on training application

13. Please indicate your age
18-29, 30-39, 40-49, 50-59, 60+

14. Please indicate your highest level of education
High school degree, Bachelor‘s degree, Master‘s degree, Doctoral degree

15. How many years have you been with the organization? Fill in the blank

16. How many years have you been in your current position? Fill in the blank

17. Do you have work time specifically devoted to training and/or learning?

18. Do you have regular and easy access to a computer at home? Yes/No
127

19. Do you have regular and easy access to a computer at work? Yes/No

20. Please rate your comfort level with computers. Not at all comfortable 1 2 3 4 5
Very comfortable

Thank you for taking the survey! If you would like more details about the study, please
contact Anjelica Wright Garcia at anjelicw@marshall.usc.edu

Abstract (if available)

Abstract Self-directed elearning courses have been implemented at a rapid rate by many organizations and are perceived as having many organizational advantages such as cost savings, wide dissemination, and maximum availability to learners in the organization, and learner advantages, such as breadth and choice of learning opportunities. Management employees in a large transportation company were surveyed to assess their perceptions of transfer from self-directed elearning courses. This case showed that only 19% of management employees who have access utilized the elearning platform. Of that, only 1% of the IT courses and 3% of management courses were accessed by 50 or more people, or approximately 1% of the population who had access to them. ❧ However, amongst those taking advantage of the program and responding, 62% reported using the knowledge and skills presented in the course in their current job. Several motivation variables showed significant positive correlations with perceived transfer with the exceptions of supervisory suggestion and required assignment. Demographic variables did not play a role in transfer of training with the exception of those that had time devoted to learning and training, who reported greater transfer. Among those who did indicate transfer, approximately 31% of the course content was applied in the current job although 24% of the respondents in this group could not identify a single course objective utilized. Among those who did not transfer, 82% indicated they did not need to use the knowledge and skills within six months of taking the course and 69% indicated that the primary reason for engaging in the course was that it was assigned as a requirement. Responses were also sub-grouped by those who completed and did not complete courses, management and information technology course participants, and participants of courses identified as central to the organization. Among these sub-groups, few significant differences from the general population were found, although IT participants who did not transfer placed were more likely to blame utility value and management participants who did not transfer were more likely to blame organizational climate variables. Using a theoretical lens of transfer and self-directed learning theory, this study provides evidence for the hypothesis that participation in self-directed elearning courses may help educate the employee population and, thus, contribute to the bottom line of the organization. However, hurdles have also been identified and organizations should be aware that some choices, actions, or lack of actions may make self-directed elearning courses less useful.

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Garcia, Anjelica Wright (author)

Core Title Exploring organizational transfer in self-directed, self-selected elearning courses

School Rossier School of Education

Degree Doctor of Philosophy

Degree Program Education (Leadership)

Publication Date 10/14/2011

Defense Date 10/13/2011

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag business education,elearning,high performance work practices,learning organization,OAI-PMH Harvest,self directed learning,Training,transfer

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Hentschke, Guilbert C. (committee chair), Boudreau, John (committee member), Clark, Richard (committee member)

Creator Email anjelicagarcia@gmail.com,anjelicw@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c127-660881

Unique identifier UC1388571

Identifier usctheses-c127-660881 (legacy record id)

Legacy Identifier etd-GarciaAnje-337-0.pdf

Dmrecord 660881

Document Type Dissertation

Rights Garcia, Anjelica Wright

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu