Influences of Formal Learning, Personal Characteristics, and Work Environment Characteristics on Informal Learning among Middle Managers in the Korean Banking Sector Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Woojae Choi, M.A. College of Education and Human Ecology The Ohio State University 2009 Dissertation Committee: Ronald L. Jacobs, Advisor Joshua Hawley Raymond Noe
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Influences of Formal Learning, Personal Characteristics, and Work Environment
Characteristics on Informal Learning among Middle Managers
in the Korean Banking Sector
Dissertation
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy
in the Graduate School of The Ohio State University
By
Woojae Choi, M.A.
College of Education and Human Ecology
The Ohio State University
2009
Dissertation Committee:
Ronald L. Jacobs, Advisor
Joshua Hawley
Raymond Noe
Copyright by
Woojae Choi
2009
ii
Abstract
The purpose of this study was to investigate the influences of formal learning,
personal characteristics, and work environment characteristics on informal learning
among middle managers in the Korean banking sector. The conceptual framework
identified three factors influencing informal learning. For this study, data collection was
conducted in the Korean Banking Institute (KBI) to prepare employees who were
working in the banking sector which has been characterized as one of the fastest changing
industries in Korea. Thus, middle managers as a population were selected due to their
various experiences in both formal and informal learning. The collected data was
analyzed using structural equation modeling, correlation analysis, descriptive analysis,
and thematic analysis to answer seven research questions.
The results of this study showed that the conceptual model representing three
factors as influencing factors on informal learning reasonably fit the data from middle
managers with a slightly modified structural equation model. Based on the modified
personal characteristics significantly affect informal learning, 3) work environments do
not directly affect informal learning, but they indirectly affect through formal learning,
and 4) both personal and work environment characteristics affect formal learning. The
results also showed that middle managers engage in various informal
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learning, even though self-experimentation is the most frequently used type of the three
types of informal learning. The consequences resulting from engaging in informal
learning were the acquisition of work-related knowledge and skills, individual
development, and the development of interpersonal skills. The results support the
conclusions that two forms of workplace learning are interrelated and, in particular,
informal learning is enhanced by managers who have well-organized knowledge and
skills. If managers perceive formal learning to be effective, they are more likely to utilize
it and to compromise with the two different learning practices. Therefore, it might be said
that the application of formal learning to work settings becomes a component of the
informal learning process. From a practical standpoint, the results support the conclusions
that formal learning is a reliable way to encourage managers’ informal learning, and also
that managers tend to synthesize their learning resulting from both formal and informal
learning experiences to meet the desirable levels of work requirements, to cope with
emerging problems, and to prepare for their future job and career. This study provides
implications for future research and practices in workplace learning and HRD.
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Dedication
Hyunjung, Joonyoung, and Joongu
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Acknowledgements
I would like to express my deepest gratitude to my advisor, Dr. Ronald Jacobs, for
his guidance and support during my study in human resource development. Without his
encouragement and invaluable advice, I could not finish this long journey. I will always
remember his endless support and friendship.
I express many thanks to Dr. Joshua Hawley who provided me with something
different from my perspectives on my study. He also shared his expertise in workforce
policy and data analysis with me. I am thankful to Dr. Raymond Noe for his helpful
advice and suggestions during the years of my research projects and graduate studies.
Also I thank Dr. Larry Miller for being my candidacy exam committee member.
My appreciation goes to Dr. Kibok Baik and Dr. Yongmin Kim at Kookmin
University. Since the years of my master’s degree, they have shown constant affection
and infinite support for my study. I appreciate Dr. Jegoo Shin who gave me long-distance
encouragement and endless affection during my graduate studies in the U.S. My memory
of his support will endure throughout my life.
My special thanks go to Dr. Seung-tae Moon and Dr. Eul-kyoo Bae for their
valuable comments and advice reflected in my study. I am grateful to my friend, Tommy
Park, who supported my dissertation research by helping with data gathering at the
Korean Banking Institute. In addition, I would like to express my gratitude to my current
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and former colleagues in my graduate program whose friendship enriched my life in
Columbus: Younghee Kong, Hyosun Kim, Sunok Hwang, Yoonhee Park, Joohee Chang,
Dongyeal Yoon, Bryan Wang, Edward Fletcher, Susan Johnston, and Charles Saunders.
They will be remembered for the wonderful times.
My gratitude extends to my Korean friends at The Ohio State University:
Kyeongyun Yeau, Chunjae Park, Kihwan Kim, Hakwoo Kim, Joohee Lee, and Yongchae
Jung for their friendship. And I would like to express my special thanks to Yusik Hwang,
who provided valuable advice and support. I would like to thank James Timothy Trout
and friends at Jungto Temple who provided invaluable support to me and my family in
Columbus.
Most importantly, I would like to express my sincere appreciation to my parents,
Ok-Suk Choi and Sam-Ja Joo, my wife’s parents, Hun-Young Kim and Sun-Ja Jee, and
all family members for their continuous support and love. I especially thank my wife,
Hyunjung Kim, who has always believed in me and provided immeasurable support
during my doctoral studies. Also I am grateful to my sons, Joonyoung and Joongu, for the
joy they have given me during my time as a doctoral student.
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Vita
April 1972 ………………… Born in Seoul, Korea
1998 ……………………… B.A. Business Administration, Kookmin University
2001 ………………………. M.A. Personnel Management, Kookmin University
2001 – 2005 ………………. Associate Researcher, Hyundai Research Institute, Seoul, Korea
2007 ………………………. Research Assistant The Center of Education for Training and Education The Ohio State University
2009 ……………………… Graduate Research Associate The School of Physical Activity and Educational Services The Ohio State University
Publications
Choi, W., & Jaocbs, R. (2008, February). Team transfer climate: Impacts of team leader support, compositional diversity, and task interdependence. Proceedings of the Academy of Human Resource Development. Panama City, Florida.
Choi, W., & Park, Y. (2007, November). Workplace Learning and Job Satisfaction in Korea. Proceedings of the 6th Asian AHRD Conference, Beijing, China.
Choi, W. & Jacobs, R. (2006, December). The relationships of team diversity, career commitment, and transfer of training. Proceedings of the 5th Asian AHRD Conference. (pp. 644-650). Putrajaya, Malaysia.
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Fields of Study
Major Field: Education Human Resource Development & Adult Learning Ronald L. Jacobs Workforce Development & Educational Policy Joshua Hawley Management and Human Resources Raymond Noe
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Table of Contents
Page
Abstract ………………………………………………………………………………….. ii
Dedication ………………………………………………………………………………. iv
Acknowledgements ………………………………………………………………………v
Vita ……………………………………………………………………………………. vii
List of Tables …………………………………………………………………………… xi
List of Figures …………………………………………………………………………xiii
Chapter 1: Introduction …………………………………………………………………..1 Statement of the Problem ………………………………………………………... 4 Research Questions ……………………………………………………………10 Definition of Terms …………………………………………………………….11 Significance of the Study...……………………………………………………....16 Limitations of the Study ……………………………………………………….18
Chapter 2: Review of Literature ……………………………………………………….. 19
Human Resource Development ………………………………………………. 19 Definition ………………………………………………………………..19 Components ……………………………………………………………. 22 Two Perspectives ………………………………………………………..27 Workplace Learning ……………………………………………………………..30 Definition ………………………………………………………………. 31 Components ……………………………………………………………. 35 Relationship between Formal and Informal Learning …………………. 44 Factors Influencing Informal Learning …..…………………………………….. 49 Personal Characteristics………………………………………………. 49 Work Environment Characteristics……………………………………... 53 Synthesis and Conceptual Framework …………………………………………. 56
Research Setting ………………………………………………………………... 63 Participant Selection..…………………………………………………………... 65 Instrument ……………………………………………………………………… 66 Design of the Instrument ……………………………………………….. 66 Operational Definitions of Variables …………………………………... 68 Instrument Development ……………………………………………….. 76 Translation of Instrument to Korean...………………………………….. 80 Research Procedures …………………………………………………………… 81 Data Collection ………………………………………………………… 81 Data Analysis …………………………………………………………... 82
Results for Research Questions ………………………………………………101 Research Question One ………………………………………………101 Research Question Two, Three, Four, Five, and Six …………………102 Research Question Seven ………………………………………………123
Chapter 5: Summary, Discussion, and Implications ……………………………...……129
Summary of the Results ………………………………………………………..129 Discussion ……………………………………………………………………...131
Preference of Engagement in Informal Learning ………………………131 The Consequences of Engagement in Informal Learning….…………..133 Influences of Formal Learning on Informal Learning …………………134
Influences of Personal and Work Environment Characteristics on Informal Learning ……………………………………………………...136
Implications …………………………………………………………………….140 Implications for Future Research ………………………………………140
Implications for Practices….…………………………………………...144
References ……………………………………………………………………………...146
Appendix ……………………………………………………………………………….162 Appendix A: English Version of Instrument …………………………………..162 Appendix B: Korean Version of Instrument ……………………………….…..174
Appendix C: AMOS Outputs for the Finally Modified Structural Model ……186
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List of Tables Table Page Table 3.1: Measures of formal learning ………………………………………………..69
Table 3.2: Measures of personal characteristics………………………………………72
Table 3.3: Measures of work environment characteristics……………………………..74
Table 3.4: Measures of informal learning ……………………………………………...76
Table 3.5: Internal consistency coefficients for pilot test survey instrument ………..79
Table 3.6: Data analysis strategies on each research question ………………………….88
Table 4.1: Demographic information about respondents (N= 274) ………………….91
Table 4.2: EFA results with principal components method and varimax rotation
(N=274) ………………………………………………………………………92
Table 4.3: Means and standard deviation formal learning activities according to satisfaction with and effectiveness of the learning activities (N=274) ……95
Table 4.4: Means and standard deviation for personal characteristics and work
Table 4.5: Correlation analysis and internal consistency coefficients (N=274) ………98
Table 4.6: Frequency of informal learning activity (N=274) ………………………….102
Table 4.7: Fit indices from CFA and fit guidelines ………………………………103
Table 4.8: Maximum likelihood estimates for CFA ………………………………..105 Table 4.9: Model fit indices for hypothesized model and alternative models ………109
Table 4.10: Model fit indices for hypothesized model and modified model ………112
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Table 4.11: Results of research questions with standardized path coefficients in the structural model……………………………………………………………114
Table 4.12: Standardized path coefficients regarding the relationship between formal
learning and informal Learning……………………………………………116 Table 4.13: Intercorrelations between observed variables of formal learning and informal
learning…………………………………………………………………….116
Table 4.14: Standardized path coefficients regarding the relationship between personal characteristics and informal learning………………………………………118
Table 4.15: Intercorrelations between observed variables of personal characteristics and informal learning………………………………………………...…………118 Table 4.16: Standardized path coefficients regarding the relationship between work environment characteristics and informal learning………………………...119 Table 4.17: Intercorrelations between observed variables of work Environment and informal learning……………………………….…………………………..120 Table 4.18: Standardized path coefficients regarding the relationship between personal characteristics and formal learning………………………………………...121 Table 4.19: Intercorrelations between observed variables of personal characteristics and formal learning……………………………………………………………..121 Table 4.20: Standardized path coefficients regarding the relationship between work environment characteristics and formal learning………………….……….123 Table 4.21: Intercorrelations between observed variables of work environment characteristics and formal learning………………………………………123
Table 4.22: The initiatives of informal learning (N=67) ……………………………..124
Table 4.23: The outputs of informal learning (N=57) ……………………………….128
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List of Figures
Figure Page Figure 2.1: Conceptual framework for investigating the influences of formal learning,
personal characteristics, and work environment characteristics on informal learning...…………………………………………………………………… 61
1995). However, the difference between these two perspectives may not be
distinguishable, and may be integrated into workplace learning activities. Thus, both
HRD and workplace learning disciplines address topics such as knowledge, expertise,
competence, organizational learning, and career issues, while the emphasis from the
themes shifts from formalized to experimental and individually controlled, from
discontinuous to continuous learning processes, and from skill acquisition to capability
building (Garavan et al., 2002).
The second section reviews the literature related to workplace learning in terms of
the definition, components, and relationship between formal and informal learning. In
this study, workplace learning is defined as either as a means to address employee
development that is consequently designed to enhance the likelihood of achieving
individual and organizational performance, or as an individual process designed to
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achieve learning toward personal and professional goals (Jacobs, 2001). Workplace
learning is comprised of both formal and informal learning (Marsick & Watkins, 1999).
Both forms of workplace leaning overlap each other, even though they used to be viewed
separately (Sevensson, Ellstrom, & Aberge, 2004). Like formal learning, “informal
learning does not occur for its own sake. It generally occurs as a means of achieving
organizational and individual goals” (Leslie et al., 1998. p. 14), though the processes and
outcomes of the learning are neither determined nor designed by the organization. The
relationships between the two forms of workplace learning can be integrated with two
rationales: (1) coexistence of formality and informality; (2) employee competence
achieved by both forms.
The third section discusses the personal and work environment characteristics that
influence informal learning. In this study, personal characteristics include motivation to
learn, self-efficacy, and learning goal orientation. A review of literature reveals that these
variables have been established as major personal characteristics affecting the learning
processes and outcomes in formal learning programs. However, these variables have not
been clearly examined in the research of informal learning, though some studies have
elicited the variables as encouraging factors affecting informal learning (e.g., Lohman,
2000; Lohman, 2005; van Woekrom, Nijhof, & Nieuwenhuis, 2002). Work environment
has been supported as a critical factor that is able to influence engagement in informal
learning because it is inherently experimental, social, and context-oriented. Based on the
review of literature, a conceptual framework is developed to guide this study as it
investigates the influencing factors, posing research questions to be answered with survey
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data gathered from middle managers in the Korean banking sector. As shown in Figure
2.1, the conceptual framework outlines the influences of formal learning, personal
characteristics, and work environment characteristics on informal learning. A primary
research question is, “what is the extent to which middle managers engage in informal
learning activities?” There are three different types of informal leaning. It is expected that
managers have different preferences for various informal learning activities. Lohman
(2000) argues that adult educators must be aware of the types of informal learning
activities as well as the ways in which individual and workplace influence participation in
these activities.
Another major research question is, “what is the relationship between formal and
informal learning activities?” It is also expected that variations in individuals’
experiences with formal learning activities will influence differently their informal
learning activities. In other words, the amount of participation and the judgment of
quality and work relevance by on-the-job and off-the-job formal learning influence
engagement in informal learning activities. Two forms of workplace learning are related
to each other (Rowden, 2002; Rowden & Connie, 2005). Formal learning stimulates the
occurrence of informal learning because the knowledge and skills learned from formal
learning increase the ability and desire of professionals to learn informally when they
face challenging work situations (Lohman, 2003; London & Mond, 1999). Informal
learning is more enhanced and initiated by individuals who have better organized formal
knowledge and skills (Leslie et al., 1998).
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The remaining research questions investigating the relationships among formal
learning, personal characteristics, work environment characteristics, and informal
learning are developed based on the review of literature, and examine extended
knowledge of informal learning and its influencing factors.
Figure 2.1. Conceptual framework for investigating the influences of formal learning, personal characteristics, and work environment characteristics on informal learning.
Informal Learning
Personal Characteristics
Formal Learning
Work Environment
Characteristics
On-the-Job Learning
Off-the-Job Learning
Organizational Support
Supervisor Support
Job Characteristics
Motivation to Learn
Self-efficacy
Learning Goal Orientation
Learning with Others Exchange Self-Experimenting
External Scanning
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CHAPTER 3
METHODOLOGY
This chapter is divided into five sections. The first section describes the type of
research used for this study. The second section depicts the research setting in which the
study was conducted. The third section indicates how the participants were selected for
the study. The fourth section identifies the instruments that were used for this study by
including instrument design, operationalization of variables, instrument development, and
translation into a Korean version from an English version. The last section describes
research procedures that were deployed to identify the method of data gathering, and
describes the procedures of data analysis related to the research questions.
Research Type
Correlational research was used to describe and explain the phenomena related to
engagement in informal learning activities among middle managers in a Korean bank.
The purpose of a correlational study is to determine whether or not relationships exist
among the variables (Ary, Jacobs, & Razavieh, 2002). A correlational study may be used
to gain insight into variables or factors, and is useful when the researcher “is trying to
understand a complex construct or is building a theory about some behavioral
phenomena” (Ary, et al., p.390).
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Research Setting
This study was conducted at the Korean Banking Institute (KBI) where HRD and
workplace learning programs have been developed and implemented for employees who
are working in the Korean banking sector. The programs provide knowledge and skills
that are required to perform the work and to meet the needs of the rapidly changing
banking business. Four criteria were applied to identify a suitable research setting for this
study. The criteria included the following:
1) The Korean banking sector has been characterized as one of the fastest changing
industries in Korea, which means that each bank requires employees to learn more
by themselves than did they before.
2) KBI has been providing middle managers with various HRD and workplace
learning programs that are planned to instruct a specific set of learning objectives.
3) The middle managers, as subjects for this study, participate in many formal and
informal learning activities. This means that middle managers can judge their
work and learning needs based on their experiences with both types of workplace
learning.
4) The middle managers are required to develop their knowledge and skills to deal
with daily challenges related to their job that they encounter at the workplace.
Korea is a high performing country in Asia (Ashton, Green, Sung, & James,
2002). However, following the financial and economic downturn of Korea in 1997, the
Korean commercial banking sector has undergone significant change resulting in the
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deterioration of the financial health of the Korean corporate sector. This has led to a sharp
increase in non-performing loans and a weakening of the capital structure of Korean
financial institutions. Moreover, the experience of the national financial crisis provided a
lesson to the Korean bank industry. The capability to survive in the business world
depends on persistent endeavors to sustain the comparative strengths and abilities to
detect and respond to the needs from markets and clients (Kookmin Bank, 2005). The
volatility of the global banking industry over the past decades as well as in Korea have
certainly influenced the awareness of globalization and technical innovation which, in
turn, require the development of human resources who are able to deal with the process.
KBI was established by a consortium of 11 Korean domestic banks and the Bank
of Korea in 1976. In that year, KBI provided in-class training and mentality education to
a total of 6,691 employees from financial institutions on an annual basis. In 2005, KBI
provided 85,592 employees with various training and learning programs such as in-class,
distance, and on-line courses. Most banks in Korea (e.g., Woori Bank, Kookmin Bank,
Shinhan Bank, Hana Bank, and Citibank) have been working in close partnership with
KBI, by sending employees to KBI and jointly developing diverse professional training
programs (e.g., Advanced Private Banker Program). Therefore, respondents could be
more effectively recruited for this study in KBI than in other institutions or even in each
bank (Korea Banking Institute, 2006).
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Participant Selection
Participants selected for this study attended training programs in the institute
during the period of time when the questionnaire was distributed through the employees
of the institute both at the beginning and the end of the learning and development
programs. There are four sources of potential error to be considered in the survey.
The first is frame error, which occurs when there is a discrepancy between target
population and actual population. However, the institute was selected because it
specializes in employees who are working in the Korean banking sector, and the
respondents were sponsored by their bank. The researcher contacted each training
program coordinator directly by phone or email to receive the list of all potential
attendants in the programs. Therefore, this error was not a factor in this study.
Second, selection error should be considered. It appears when certain sampling
units have a greater chance of being selected for the sample than other sampling units. It
could be controlled by the way in which the questionnaires were distributed to all middle
managers who were attending training programs during the period of data collection.
However, it should be noted that there was a possibility that a certain bank would have
proportionately greater numbers of attendants during the time period than other banks.
Third, sampling error can occur when a non-representative sample is used. This
was not a concern in this study because all participants were working in a bank as middle
managers. Thus, this study did not include middle managers who were working in other
industries (e.g., insurance), a characteristic that was recognized by a review of the
background information.
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Finally, non-response error could be an issue in this study when respondents fail
to respond, refuse to respond, or do not return the questionnaire. In order to control non-
response error, the respondents can be categorized into early and late response groups and
their responses on the questionnaire can be compared to identify whether or not any
significant differences appear (Ary, Jacobs, & Razavieh, 2002). However, this study
could not identify those who did not respond to the survey questionnaire or compare the
early with the late group because all data gathering procedures were conducted by staff
instead of the researcher due to limited authority to access the participants of the learning
and development programs provided by KBI. This fact should be recognized when the
study results are interpreted.
Instrument
Design of the Instrument
The instrument was designed to minimize the potential sources of common
method variance which refers to “variance that is attributable to the measurement method
rather than to the construct of interest” (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003,
p. 879). Biases resulting from methods cause the main sources of measurement error
including both a random and a systematic component that threatens the validity of the
conclusions about the relationships between measures. Especially, “systematic
measurement error is a particularly serious problem because it provides an alternative
explanation for the observed relationships between measures of different constructs that
is independent of the one hypothesized” (Podsakoff et al., p. 879). One of the main
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sources of systematic measurement error is method variance, which refers to variance
that is attributable to the measurement method rather than to the construct of interest
(Bagozzi & Yi, 1991). Potential sources of common method biases are such things as
common rater effect, item characteristic effect, and measurement context effect. First,
common rater effect refers to any artificial covariance between the predictor and criterion
variable due to the same respondent on both variables. Second, item characteristic effect
refers to any artificial covariance resulting from the influence or interpretation that a
respondent might ascribe to an item solely because of specific properties or
characteristics the item possesses. The final source is measurement context effect, which
refers to any artificial covariance resulting from the context in which the measures are
obtained.
Although behavioral and educational research is encouraged to control all
potential causes of common method biases, separating the measurement of the predictor
and criterion variables has been recommended when it is impossible to obtain data from
different raters or sources (Podsakoff et al., 2003). Therefore, in order to control common
method biases for this study, the predictor variables (formal learning, personal
characteristics, and work environment characteristics) and the criterion variable (informal
learning) were measured at different points in time with two separate sets of
questionnaires.
The first questionnaire was distributed at the beginning of a training program at
the research site, which constituted questions regarding the predictor variables (ten items
for formal learning; twenty-seven items for personal characteristics; twelve items for
68
work environment characteristics) and demographic information (four items). The second
questionnaire was distributed at the end of a training program with questions regarding
informal learning (twelve closed-ended items and three open-ended items). The total
number of items was sixty-eight.
Operational Definitions of Variables
The research consisted of four latent variables which are operationalized in the
following ways.
Formal Learning Activities
This construct was measured for two types of learning according to three
variables such as participation, satisfaction, and effectiveness. These variables refer to
middle managers’ previous participation in and their attitude toward formal learning
activities according to the two types as presented in Table 3.1.
Participation in formal learning activities. This variable refers to whether or not
the respondents participated in each formal learning activity during the past 12 months. In
this study, the participation in each of two types of formal learning activities is an
average score comprised of five items for on-the-job formal learning and five items for
off-the-job formal learning. This variable was assessed with ‘Yes’ or ‘No’.
Satisfaction with the learning activity. This variable refers to a reaction regarding
learners’ liking of or feelings for formal learning activity (Kirkpatrick, 1959). The
purpose of reaction evaluation is to support the quality of formal learning activity. In this
study, satisfaction with formal learning activity is an average score that measures five
69
items for formal on-the-job learning and five items for formal off-the-job learning. This
variable was assessed with a 5-point Likert scale that ranges from 1 (High) to 5 (Low).
Effectiveness of the learning activity. This variable refers to the perception on the
extent to what formal learning activities are effective to the intended learning objectives
regarding work performance. In this study, the effectiveness of formal learning activity is
an average score comprised of five items for formal on-the-job learning and five items for
formal off-the-job learning. This variable was assessed with a 5-point Likert scale that
ranges from 1 (High) to 5 (Low).
Variable Item
Formal on-the-job learning
• Coaching session from a peer or a supervisor to help improve on some aspect of one’s job.
• Mentoring session from a formally designated mentor to help plan one’s career options.
• Structured on-the-job training session that is conducted by a designated trainer to help learn a specific aspect of one’s job.
• Action learning project with a group of colleagues to improve a business process.
• Vendor-sponsored training program to learn more about a technology being adopted by the company.
Formal off-the-job learning
• Company-sponsored training program in the training center to learn some job-specific information.
• Company-sponsored training program in an outside facility to learn some job-specific information.
• Company-sponsored training program that is delivered through the computer.
• Company-sponsored training program that is delivered through a correspondence course.
• Tuition assistance from one’s company to attend a college or university course.
Table 3.1. Measures of formal learning
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Personal Characteristics
Motivation to learn. This variable refers to one’s desire to obtain learning
contents through workplace learning activities. The scale was originally developed by
Noe and Schmitt (1986). The original seventeen-item scale’s Cronbach alpha was .81.
Tharenou (2001) used a scale comprised of seven items by conducting a principal
component factor analysis (PCA), while including the items measuring expectancy,
instrumentality, and valence. The results of PCAs extracted four distinct factors. In an
that loaded on its factor. The reliability coefficient for the seven-item scale was .82,
which compares favorably with the original seventeen-item (a=.81). Therefore, this study
used the seven-item scale. This variable was assessed with a five-point Likert scale that
ranges from 1(Agree) to 5 (Disagree).
Self-efficacy. This variable refers to a general self-efficacy that was used to
measure respondents’ belief about themselves. The scale was originally developed by
Sherer and colleagues (1982) and later modified by Bosscher and Smit (1998). It consists
of three subscales, initiative, effort, and persistence. The reliability coefficient for the 12
items was 0.69. The coefficients for subscales, initiative, effort, and persistence, were
0.64, 0.63, and 0.64 respectively. Barnard (2005) reports that a Cronbach’s alpha of 0.87
for 12 items was obtained in her study regarding training effectiveness. The internal
consistency of general self-efficacy is considered acceptable if the value is greater
than .70 (Kline, 2005). In this study, the variable is measured by an average score
71
comprised of twelve items. This variable was assessed with a five-point Likert scale that
ranges from 1(Agree) to 5 (Disagree).
Learning goal orientation. This variable refers to the extent of one’s intention to
engage in challenging activities, an eagerness to improve oneself, and a tendency to use
one’s past performance as a standard to evaluate current performance (Button et al.,
1996). The scale was originally assessed with ten items by Button and his colleagues
(1996). The Cronbach’s alpha for the 10-item scale was .79. However, two items were
dropped after a confirmatory factor analysis that tested whether learning goal and
performance goal orientations were distinguished. The Cronbach’s alpha for 8-item
learning goal orientation was also .79. Therefore, this study uses the scale consisting of 8
items. This variable was assessed with a five-point Likert scale that ranges from 1(Agree)
to 5 (Diagree). Table 3.2 represents the items used to measure personal characteristics.
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Variable Item
General Self- Efficacy
• If something looks too complicated, I will not even bother to try it.
• I avoid trying to learn new things when they look too difficult. • When I try something new, I soon give up if I am not initially
successful. • When I make plans, I am certain I can make them work. • If I cannot do a job the first time, I keep trying until I can. • When I have something unpleasant to do, I stick to it until I finish
it. • When I decide to do something, I go right to work on it. • Failure just makes me try harder. • When I set important goals for myself, I rarely achieve them. • I do not seem to be capable of dealing with most problems that
come up in my life. • When unexpected problems occur, I do not handle them very
well. • I feel insecure about my ability to do things.
Learning Goal Orientation
• When I fail to complete a difficult task, I plan to try harder the next time I work on it.
• I prefer to work on tasks that force me to learn new things. • The opportunity to learn new things is important to me. • I do my best when I’m working on a fairly difficult task. • I try hard to improve on my past performance. • The opportunity to extend the range of my abilities is important to
me. • When I have difficulty solving a problem, I enjoy trying different
approaches to see which one will work.
Motivation To Learn
• I try to learn as much as I can from learning activities. • I believe I tend to learn more from learning activities than others. • I am usually motivated to learn knowledge and skills emphasized
in formal learning activities. • I would like to improve my skills through learning activities. • I am willing to exert effort in learning activities to improve my
skills. • I am willing to take learning activities even though they are not
high priority for me. • I am willing to invest effort to improve job skills and
competencies.
Table 3.2. Measures of personal characteristics
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Work Environment Characteristics
This construct is grounded in the construct of learning climate (Tracey & Tews
(2005). The items were originally developed to measure general workplace learning
climate. The results from the reliability analysis reported that Cronbach’s alpha was .85
for job characteristics, .87 for supervisor support, and .87 for organizational support.
However, the items used were newly designed by the researcher to align with the context
of this research.
Organizational support. This variable refers to the perception of the extent to
which an organization’s culture, policies, or systems facilitate employee learning
activities. In this study, the variable was measured by four items. This variable was
assessed with a five-point Likert scale that ranges from 1(Agree) to 5 (Disagree).
Supervisor support. This variable refers to the perception of the extent to which
supervisors encourage subordinates’ learning activities. The variable was measured by
four items. This variable was assessed with a five-point Likert scale that ranges from
1(Agree) to 5 (Disagree).
Job characteristics. This variable refers to the perception of the extent to which
the features embedded in a job facilitate learning activities that are required to perform a
job effectively. The variable was measured by four items. This variable was assessed
with a five-point Likert scale that ranges from 1(Agree) to 5(Disagree). Table 3.3
presents the items used to measure work environment characteristics.
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Variable Item
Organizational Support
• My company makes it possible for employees to participate in a wide range of learning activities.
• My company values the need for employees to learn on a continuous basis.
• My company rewards employees who attain advanced knowledge and skills.
• My company provides the resources that are used to support learning.
Supervisor Support
• My supervisor encourages me to participate in as many learning activities as possible.
• My supervisor assigns only those tasks that employees know how to perform.
• My supervisor provides me with information regarding learning activities.
• My supervisor adjusts my work schedule when I need to attend a learning activity.
Job Characteristics
• My job requires me to seek for better ways to deal with changes in the work.
• My job requires continuous learning to meet the customer’s expectations.
• My job does not allow me to spend much time in learning activities.
• My job performance depends on the extent of my knowledge and skills.
Table 3.3. Measures of work environment characteristics
Informal Learning Activities
Informal learning activities refer to learning activities which an individual
engages during his or her daily work in a self-initiated manner which are not sponsored
by the organization, when he or she needs to learn something at work or to deal with
some challenging work situation. The scale for this variable was originally developed by
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Lohman (2005). It consists of three types of activities: (1) knowledge exchange, (2)
experimenting, and (3) environmental scanning. Validity was established through both a
panel of experts including educational researchers and a field test. The reliability
coefficient was .63 for eight items.
In this study, the three types of informal learning activities were revised and
extended to align with the context of this research. Consequently, the scale consists of
twelve items for three types of informal learning activities: 1) four items for learning with
others, 2) four items for self-experimentation, and 3) four items for external scanning.
Table 3.4 presents the items used to measure informal learning.
Frequency of the learning activity. This variable refers to the perceived frequency
of the extent to which the respondents engage in each informal learning activity during
the past 12 months. In this study, the frequency of each of three types of informal
learning activities is an average score comprised of (1) four items for learning with others
(2) four items for self-experimentation, and (3) four items for environmental scanning.
This variable was assessed with a five-point Likert scale that ranges from 1 (Infrequent)
to 5 (Frequent).
Effectiveness of the learning activity. This variable refers to the perception of the
extent to which informal learning activities contribute to the learning that is intended. In
this study, the effectiveness of informal learning activity is an average score comprised of
(1) four items for learning with others, (2) four items for self-experimentation, and (3)
four items for environmental scanning. This variable was assessed with a 5-point Likert
scale that ranges from 1 (Frequent) to 5 (Infrequent).
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Variable Item
Learning With Others
• Informal one-on-one discussion with supervisor about some work situation.
• Idea exchange on how to solve a problem situation with peers during a break or lunch period.
• Observation of how other employees dealt with a challenging work situation.
• Collaboration with others who shared the need to solve a particular problem.
Self- Experimentation
• Spending time to reflect back how I dealt with a challenging work situation.
• Trying to solve a challenging work situation through trial and error process by myself.
• Spending time to reflect on what I had learned in a classroom training program to apply that information to a challenging work situation.
• Reading a standard operations manual or other similar texts on my own to find an answer to a question.
External Scanning
• Searching the Internet for information to help solve a challenging work situation.
• Attendance at a non-mandatory professional conference or seminar that might provide useful information.
• Reading professional magazines or vender publications to be current in some topic.
• Having contact with someone outside the company who is able to help solve a challenging work situation.
Table 3.4. Measures of informal learning
Instrument Development
Instrument Validity
Validity reflects the extent to which the instrument measures the concept or
phenomenon that it is studied (Ary et al., 2002). To establish validity, the instruments
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must be developed based on an extensive literature review in human resource
development and workplace learning.
A panel of experts reviewed the questionnaires for content validity. The panel of
experts was composed of a group of experts including two scholars, three HRD
professionals, and six doctoral students. There are one HRD professor and one workforce
development and policy professor who have ample knowledge of research methodology,
research, and theory in HRD and workplace learning. Three HRD professionals working
in a Korean bank or KBI provided practical perspectives on the development of the
instrument when the instrument is applied to the population of this study. They have been
working in HRD fields for more than ten years. One professional earned a Ph.D in
organizational behavior and two professionals graduated from an MBA program. There
are six doctoral students who are majoring in HRD, vocational education or workplace
learning. Their working experience in the field of HRD or vocational education ranges
from two to eight years before starting their doctoral program. They have practical
experience and theoretical knowledge about HRD and workplace learning. Each member
of the panel was asked to evaluate the clarity of the questionnaires. If any member of the
panel found any unclear or inappropriate wording or expressions, he or she was asked to
indicate this and to suggest more desirable wording or expressions. The members were
also asked to provide suggestions to improve the questionnaires’ content validity.
The instrument can be field-tested with a population similar to the proposed
population to help assure content validity (Ary et al., 2002). Three MBA students were
invited to participate in a field test for this study. They were attending a short-term MBA
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program in a large university in the U.S. They have participated in various workplace
learning activities while performing their jobs as middle managers in a variety of
industries. This group was asked to answer the items and provide feedback to the
researcher on the instruments to clarify the items and eliminate any problems with the
questionnaires.
The comments about survey items from the panel of experts were used to modify
the early version of the instrument. After revising the instrument several times, the final
version of instrument was completed.
Testing for Instrument Reliability
The reliability of an instrument refers to the degree to which the instrument
consistently measures whatever it is measuring (Gay, Mills, & Airasian, 2005). Internal
consistency measures whether items of a construct are inter-correlated and produce the
same scores in the same construct (Gay et al., 2005). To establish the reliability of the
instrument, a pilot test was conducted using a convenient sampling procedure. Among the
52 responses, a set of 44 responses was finally used to examine the instrument’s
reliability after removing responses with many missing answers. That is, if more than five
question items were missed in a questionnaire, then the responses were not included in
the test.
Cronbach’s alpha coefficient indicates the degree of internal consistency across
items to measure one underlying construct. Although a value greater than .7 is desirable
(Ary et al., 2002; Kline, 2005), a value greater than .6 is accepted as a reliable level (Van
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de ven & Ferry, 1980). Table 3.1 presents the Cronbach’s alpha coefficients for the
survey responses.
Scale Number of Items Cronbach’s Alpha (n=44)
Personal characteristics General self-efficacy Learning goal orientation Motivation to learn Work environment characteristics Organization support Supervisor support Job characteristics Informal learning activities Learning with others Frequency Effectiveness Self-examination Frequency Effectiveness Environmental scanning Frequency Effectiveness
27 12 8 7 10 4 3 3 24 8 4 4 8 4 4 8 4 4
.888
.737
.807
.814
.854
.778
.801
.630
.902
.866
.801
.719
.829
.680
.652
.842
.703
.692
Table 3.5. Internal consistency coefficients for pilot test survey instrument
The measures of formal learning activities were not included for the reliability test.
The frequency of participations was not enough to test the reliability for the measure
because the learning activities were rated for satisfaction and effectiveness only if an
individual participated in each learning activity. Among the measures of work
environment characteristics, an item for the supervisor support and an item for the job
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characteristics were removed to avoid reducing the reliability of the measures. The other
variables were considered to be at acceptable level. Although two items for work
environment characteristics might be regarded as items that are not appropriate to
measure each variable, the final instrument also included the two items to ensure there
were no differences between the pilot test and the real test.
Translation of the Instrument to Korean
The English version of the survey was developed by the researcher. The
instrument was also translated into Korean. To validate the initial translation, three
Korean HRD professionals who were working in a bank or KBI confirmed the translation.
These were the same people who were asked to provide their practical perspectives on the
development of instrument. Six Korean doctoral students, in the Workforce Development
and Education program at The Ohio State University, who had practical experience in the
field of HRD and workplace learning in Korea, participated in the process of translation.
All participants are fluent in both English and Korean based on their educational
background. Thus, all participants have experience in translating documents or articles
written in English into Korean. Prior to the beginning of the procedure, explicit
information regarding the use and intent of the instrument was given to all panel
members. Panel members were asked to indicate any words and phases in the Korean
translation that are not appropriate or unclear compared to the English version. Following
discussion and agreement among participants, a consensus was reached on the translation.
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In order to translate backward from Korean to English, the Korean version was
presented to a Korean doctoral student pursuing a degree in linguistics at The Ohio State
University. This process is important to ensure that the words are consistent with the
meaning with minimal error between the English version and the translated-back English
version. Based on the initial and backward translation and some potential adjustment, the
instrument was confirmed by the researcher.
Research Procedures
Two steps were followed in this study. First, the data for this study were collected
using a self-developed instrument. Second, the collected data were analyzed to
investigate engagement in informal learning, to examine the relationship among the
variables, and to investigate the nature of informal learning activities among managers in
the Korean banking industry.
Data Collection
This research was approved by the Human Subject Review Committee at The
Ohio State University on March 31, 2009 (Protocol number: 2009E0261). The survey
consisted of two questionnaires. One questionnaire included questions regarding formal
learning, personal characteristics, work environment characteristics, and demographic
information. Another questionnaire included questions regarding informal learning. A
description and a brief consent to participate in the survey were presented on the first
page of the two questionnaires. In order to match the first questionnaire with the second,
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the instruction indicated on the second page of each questionnaire: “Please fill out the last
four-digit of your cell-phone number in the box below.”
The questionnaires were distributed in the KBI during April and May, 2009.
During that time, the questionnaires were collected from middle managers who are
participating in an HRD or workplace learning program. Staff who were responsible for
implementing and operating the programs explained the details of the survey, in lieu of
the researcher. Prior to distributing the questionnaire, the researcher provided the staff
with guidelines regarding confidentiality.
Before distributing and collecting the survey questionnaires, the researcher
contacted KBI in December 2008 to ask about the potential for data collection. In
December 2008, the researcher received a confirmation from a manager who was
responsible for planning HRD systems and for developing HRD and workplace learning
programs in KBI. Then, the researcher visited KBI to consult with the KBI staff and to
monitor the data gathering process.
Data Analysis
The collected data were analyzed using Statistical Package of the Social Sciences
(SPSS 17.0 Windows) and Analysis of Moment Structures (AMOS 17.0). First,
demographic characteristics of the respondents were analyzed to describe the frequencies,
percentages, means, and standard deviations. Demographic characteristics include job
position, education completed, and tenure at the current company. Second, the research
questions were answered by descriptive analysis for research question one, by structural
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equation modeling (SEM) for research questions two, three, four, five, and six, and by
thematic analysis for research question seven.
Specifically, SEM was selected to analyze the research questions as an
appropriate statistical technique which is best to explore an overall conceptual framework.
SEM is a statistical technique for testing a set of relationships representing multiple
equations by using a combination of statistical data and qualitative causal assumptions,
while other dependence techniques or regression analysis seek to explain relationships in
a single equation (Hair et al., 2006; Kline, 2005).
SEM provides two critical benefits relative to other statistical procedures. The
first is that this technique allows for the examination of latent and observed variables.
The use of latent variables allows instrument items to be aggregated into constructs that
may be difficult to measure. This allows the researcher to explicitly capture the
unreliability of measurement in the model, which in theory allows the structural relations
between latent variables to be accurately estimated (Kline, 2005). The second benefit is
that SEM explicitly estimates error variance whereas traditional multivariate approaches
do not. Parameters are interpreted in a manner of regression coefficients. While SEM is
often regarded as causal modeling, causal inferences are only possible when the data are
consistent with some conditions for causality such as covariance between the cause and
effect, the temporal sequence of events, and theoretical support (Hair et al., 2006).
Kline (1998) urges SEM researchers to test the pure measurement model
underlying a full structural equation model first, and if the fit of the measurement model
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is found acceptable, then to proceed to the second step of testing the structural model.
This study followed commonly accepted procedures for SEM (Kline, 2005. p. 64).
1) Specify the model. For this study, the researcher’s hypotheses should be
expressed in the form of a structural equation model which corresponds to
presumed relations among observed and latent variables.
2) Determine whether the model is identified. This means to theoretically ensure
whether or not a unique estimate of every model parameter is derived. If a
model does not meet the relevant requirements for identification, estimation
may be unsuccessful.
3) Select measures and collect, prepare, and screen the data. All variables
represented in the model should be operationalized based on research,
empirical results, and theory. The data should be carefully gathered and
screened to prevent the potential problems such as normality, missing data,
and outliers.
4) Evaluate model fit. This means to determine how well the full model
including measurement and structural models explains the data. If the model
does not fit the data, the model should be respecified. The revised model
should be evaluated for fit to the same data.
5) Interpret the parameter estimates. Once the model fits the sample data,
estimates of its parameters should be evaluated in terms of meaningfulness.
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6) Consider equivalent models. An equivalent model provides a competing
account of the data. There may be several equivalent models that should be
explained by the researcher.
The first three steps were completed through an extensive review of literature,
development of measures, and conceptual framework to be examined. Prior to evaluating
the structural model of informal learning activities and their influencing factors, it is
necessary to consider the results of the confirmatory factor analysis (CFA) of the multi-
item measures. These findings indicated whether all of the items have significant
loadings on their respective latent variables of interest. Inappropriate items of the
measures were adjusted or excluded in the subsequent analyses.
The last three steps were conducted by using multiple indexes. It is recommended
that the researcher should report one incremental index, one absolute index, chi-square
value and the associated degrees of freedom, and one badness-of-fit index (Hair et al.,
2006). A single index reflects only a particular aspect of model fit so that a minimal set of
fit indexes should be reported and evaluated for the overall fit of the model to the sample
data. Due to the current state of practice and recommendations (Kline, 2005), six
statistics will be included in this study as follows: (1) chi-square statistic, (2) normed chi-
square (NC), (3) root mean square residual index (RMR), (4) goodness-of-fit index (GFI),
(5) comparative fit index (CFI), and (6) root mean square error of approximation
(RMSEA).
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The most basic fit index is chi-square statistic, which is actually a badness-of-fit
index because the higher value, the worse the model’s fit to the data. The model chi-
square tests the null hypothesis that the data has fit in the population. The researchers’
model is supported when the null hypothesis fails to reject. However, the model chi-
square tends to be sensitive to the sample size. For an overidentified model, the rejection
of the model is possible only when sufficient sample size exists. When normal
distribution cannot be assumed, the value of chi-square tends to be too high. This means
that even though the model is fit to data, the model will be rejected by interpreting the
test statistic. These problems with the chi-square test have encouraged the use of different
fit statistics.
Normed chi-square (NC) is used to reduce the sensitivity of chi-square to
sample size. There are no clear-cut guidelines about what value of the NC is minimally
acceptable. However, values of the NC of 2.0 or 3.0 are recommended as indicating
reasonable fit (Bollen, 1989).
Root mean square residual (RMR) represents the average residual value derived
from the fitting of the variance-covariance matrix for the hypothesized model to the
variance-covariance matrix of the sample data. Values of the RMR less than .05 indicate
a good fit.
Goodness-of-fit index (GFI) is a measure of the relative amount of variance and
covariance in the sample data. Values close to 1.00 indicate a good fit.
Comparative fit index (CFI) tests the relative improvement in fit of the
researcher’s model compared with a null model which assumes zero population
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covariances among the observed variables. A good fit of the reseacher’s model is
indicated by a value roughly greater than .90.
Root Mean Square Error of Approximation (RMSEA) is a measure to correct for
the tendency of the chi-square test statistic to reject models with large samples or a large
number of observed variables. It represents how well a model fits a population, not just a
sample used for estimation. Lower RMSEA values indicate better fit. RMSEA values of
less than 0.80 indicate a good fit (Browne & Cudeck, 1993).
The research questions that are answered based on various statistical and thematic
analysis strategies are presented in Table 3.6.
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Research question Research methods used to answer Q 1: To what extent do middle managers engage in informal learning activities?
Descriptive analysis of informal learning activities in terms of frequency
Q 2: What is the relationship between formal learning activities and engagement in informal learning activities?
Path coefficient derived from SEM between two variables
Q 3: What is the relationship between personal characteristics and engagement on informal learning activities?
Path coefficient derived from SEM between two variables; correlation coefficients
Q 4: What is the relationship between work environment characteristics and engagement on informal learning activities?
Path derived from SEM between two variables; correlation coefficients
Q 5: What is the relationship between personal characteristics and formal learning activities?
Path coefficient derived from SEM between two variables; correlation coefficients
Q 6: What is the relationship between work environment characteristics and formal learning activities?
Standardized regression coefficient derived from SEM between two variables; correlation coefficients
Q 7: What is the nature of informal learning activities that respondents have engaged in?
Thematic analysis on three open-ended sub-questions in a basis of clustering and thematic coding procedures
Table 3.6. Data analysis strategies for each research question
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CHAPTER 4
RESULTS
This chapter presents the results of the study. The first section describes the
demographic information regarding job positions, tenure at the company, and education
levels. The second section presents the results of exploratory factor analysis (EFA) and
descriptive statistics of variables in terms of means, standard deviations, and internal
consistency. Intercorrelatons between variables are also presented. The third section
answers each research question. The primary purpose of this study was to investigate the
impact of influencing factors on informal learning after assessing the measurement model
validity and structural model validity. In order to achieve this research objective, research
questions 2, 3, 4, 5, and 6 were answered on the basis of the results of structural equation
modeling (SEM).
Demographic Information
This section contains demographic information about the respondents. Of 400
questionnaires distributed, 312 questionnaires were returned. The response rate was
seventy-eight percent. In order to eliminate respondents from non-banking industries, the
question “your organization?” was included in the demographic information. There were
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twenty-one respondents from finance and insurance industries. These responses were not
included in the subsequent procedures. On the basis of the initial data evaluation process,
the total number of respondents included 291 middle managers (general and deputy
general managers). From a subsequent process of data evaluation, twelve responses were
eliminated because eight respondents did not answer all questions about formal learning
and four respondents did not answer all questions regarding informal learning. These
non-responses on either formal or informal learning might be because of two separated
sets of questionnaires designed and implemented to control the potential sources of
common method biases (see Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Predictor
and criterion variables were measured at different points in time. The first questionnaire
was distributed at the beginning of the KBI training programs, while the second
questionnaire was distributed at the end of the KBI training programs. However, if
respondents left the designated program early rather than completing the program, they
could not answer the second questionnaire. It might be the reverse case that respondents
could not answer the first questionnaire if they arrived late in the program.
Finally, among the remaining responses, seven responses were considered
incomplete because the respondents answered less than six of the twelve informal
learning activity on frequency and did not answer the subsequent question on
effectiveness of informal learning activity. Consequently, a total of 274 responses were
used in the subsequent procedures.
Demographic information was collected on the participant’s current position,
education level, and tenure in their current bank. In terms of job position, the sample of
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middle managers was established from 134 general managers (48.9 %) and 140 deputy
general managers (51.1 %). Many middle managers (77.9 %) had a four-year university
diploma (68.91 %) or graduate degree (8.99 %). For respondent’s tenure, the median
value was calculated from the year1993, and the mean value was 15.88 years (SD=5.87).
Table 4.1 presents the number and the percentage of respondents by their job position,
education, and tenure.
n % Job position Manager Deputy general manager
274 134 140
100 48.9 51.1
Education High school Community college 4-year University Graduate school
267 39 20
184 24
100 14.61 7.49
68.91 8.99
Tenure(year) 1-7 8-14 15-21 22-
267 12 95
121 39
100 4.49
35.58 45.32 14.61
Table 4.1. Demographic information about respondents (N= 274).
Descriptive Analysis
To describe the properties of the variables, exploratory factor analysis (EFA) was
conducted first because descriptive statistics were used to describe the data with a small
number of indices (Gay et al. 2006). The results of EFA suggested that some items of
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variables should be adjusted or deleted. After applying the results of EFA, the means,
standard deviations, reliabilities, and intercorrelations for the variables are presented.
Exploratory Factor Analysis (EFA)
Exploratory factor analysis is the starting point for other multivariate analysis
(Hair et al., 2006). Prior to proceeding with subsequent analyses, EFAs were conducted
to indentify the underlying structure of variables and assess the overall fit of the variables.
Mulaik and Millsap (2000) suggest that specification of an unrestricted measurement
model begins with conducting an exploratory factor analysis (EFA) to determine the
number of factors. Table 4.2 presents the results of EFAs.
Factors (the number of items) Items Factor Loading
Table 4.3. Frequency, means, and standard deviations for formal learning (N=274).
For the effectiveness of formal learning activity, the mean values ranged from
3.61 to 4.08. The most effective formal learning activity was tuition assistance (M=4.08,
SD=.812), followed by in-class training in company (M=3.93, SD=.624). The least
effective formal learning activity was CBT (M=3.61, SD=.751).
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Taken together, it was found that formal off-the-job learning activities were more
pervasive than formal on-the-job learning among middle managers. Among formal off-
the-job learning, tuition assistance, in-class training in company, and in-class training out
of company were more satisfying and effective than other learning activities. For formal
on-the-job training, coaching, mentoring, and OJT were perceived better than action
learning and vendor training in both satisfaction and effectiveness.
Personal Characteristics and Work Environment Characteristics
Personal characteristics as a latent variable were measured with three variables:
general self-efficacy, learning goal orientation, and motivation to learn. The instruments
of all three variables were based on the question items that were previously validated
with empirical evidence. Work environment as a latent variable was measured with three
variables: organizational support, supervisor support, and job characteristics. Even
though the variables were developed on the basis of a study by Tracey et al. (2001), the
items for the variables were adjusted to align with the context of the study.
The means of general self-efficacy, learning goal orientation, and motivation to
learn were 3.77 (SD=.614), 4.10 (SD=.488), and 4.17 (SD=.513) respectively. The means
of organization support, supervisor support, and job characteristics were 3.75 (SD=.581),
3.37 (SD=.679), and 3.72 (SD=.558) respectively. Consequently, middle mangers’
learning orientation, motivation to learn, and self-efficacy pertaining to personal
characteristics were rated relatively higher than the perceptions of organization support,
supervisor support, and job characteristics pertaining to work environment characteristics
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supporting learning. Table 4.4 presents the means and standard deviations for the
observed variables of personal characteristics and work environment characteristics.
Construct/Variable Number of items M(SD) Personal Characteristics Self-efficacy Learning goal orientation Motivation to learn Work Environment Characteristics Organization support Supervisor support Job characteristics
Table 4.4. Means and standard deviations for personal characteristics and work environment characteristics (N=274). Note: Items used for descriptive analysis were determined through the exploratory factor analysis (EFA).
Correlations and Internal Consistency Analysis
Correlation analyses were conducted with all variables based on the results of
EFAs. Table 4.5 presents the results of correlation analysis.
Thus, the reliabilities were sufficiently high for all variables, except two category
variables, formal on-the-job learning frequency and formal off-the-job frequency, which
were indicated in parentheses in Table 4.5.
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Continued
Table 4.5: Correlation analysis and internal consistency coefficients. Note: Values in the parentheses are internal consistency coefficients. ** p<.01, * p< .05
Research question 1: To what extent do middle managers engage in informal learning
activities?
In order to answer this research question, the frequency of engagement in each
informal learning activity was analyzed, as presented in Table 4.8. Learning activities
were grouped according to three variables of informal learning: (1) learning with others
(Items 1, 2, 3, and 4), (2) self-experimentation (Items 5, 6, 7, 8, and 9), and (3) external
scanning (Items 10, 11, and 12). The results suggested that self-experimentation is the
most frequently used learning type (M=3.58, SD=.63) among the three types of informal
learning. Learning with others (M=3.37, SD=.66) was the second most preferred type
among informal learning. External scanning (M=2.90, SD=.88) was the least selected
informal learning type.
In terms of each learning activity, the most frequently used informal learning
activity among middle managers was reading manuals or other texts (M=3.85, SD=.81),
followed by using the Internet (M=3.79, SD=.85) and reflecting on the past (M=3.73,
SD=.81). In contrast, attending non-mandatory professional conferences or seminars
(M=2.63, SD=1.06) and contacting someone outside the company (M=2.82, SD=1.13)
were rated as least frequently used informal learning activities.
As a supplementary analysis, an independent t-test was conducted to investigate
whether the difference between two groups (manager and deputy general manager) of job
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positions exists. The results suggested that learning activities between two groups do not
make a significant difference at the level of 0.05. Table 4.6 presents means and standard
deviations of informal learning activities.
Informal learning activity M SD Learning with others 3.37 .663 1. Informal one-on-one discussion with my supervisor 3.03 1.051 2. Ideas exchange on a problem situation with peers 3.30 .914 3. Observation on others’ work 3.48 .790 4. Collaboration with others 3.70 .759 Self-experimentation 3.58 .629 5. Reflecting on the past 3.73 .813 6. Trial and error process by myself. 3.41 .923 7. Reflecting on past learning 3.10 .982 8. Reading manual or other texts 3.85 .809 9. Using the Internet to search for information 3.79 .851 External scanning 2.90 .883 10. Attending non-mandatory professional conferences or seminars 2.63 1.062 11. Reading a professional magazine or vendor publication 3.25 .956 12. Contacting someone outside the company 2.82 1.130
Table 4.6. Frequency of informal learning activity (N=274). Note: a 5-point scale.
Research Questions 2, 3, 4, 5, and 6
A specific interest in this study was the extent of influences of formal learning,
personal characteristics, and work environment characteristics on informal learning. The
extents of the influences were measured by path coefficients yielded from the results of
structural equation modeling (SEM) so that measurement model and structure model
were assessed prior to answering the research questions. In addition, correlation analysis
103
was reviewed to investigate paired relationships between observed variables under latent
variables.
Assessment of measurement model and structural model
Confirmatory factor analysis. Confirmatory factor analysis (CFA) was
conducted to estimate the quality of the structural reliabilities and designated factor
loadings by testing the model fit between the proposed measurement models and the
collected data. CFA could be adapted to verify the adequacy of the item to factor
associations and the number of dimensions underlying the construct. Therefore, CFA was
conducted to verify a full measurement model derived from each modified measurement
model. Based on the results of EFAs, items were analyzed in CFA. Consequently, there
was little difference from EFAs. Table 4.7 presents the results of CFAs with the fit
indices, which are recommended (Byrne, 2001; Hair et al., 2006; Kline, 2005).
Fit index Attribute of fit index
Good Fit Guidelines
Measurement Model’s Output
χ 2 NC RMR GFI CFI RMSEA
Absolute fit Absolute fit Absolute fit Goodness-of-fit Incremental fit Badness-of-fit
Table 4.8. Maximum likelihood estimates for CFA. Note: PC-Personal characteristic (SELF: Self-efficacy; Lo: Learning orientation; MOT: Motivation to learn), WE-Work environment (ORG: Organization support; SUP: Supervisor support: JOB: Job characteristic), FO-Formal learning (F1: Effectiveness of Formal on the job learning; F2: Effectiveness of Formal off the job learning), and IN-Informal learning (IF1: Effectiveness of Learning with others; IF2: Effectiveness of Self examination; IF3: Effectiveness of External scanning)
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In addition, squared multiple correlation represents how much variation in an
observed variable is explained by the latent variable, which is calculated by squaring the
standardized factor loading. For instance, personal characteristics accounted for 80.6% of
the variation in learning orientation, 53.8% of the variation of motivation to learn, and
36.9% of the variation of self-efficacy. In this way, work environment characteristics
explained 47.8% of the variation of supervisor support, 41.4 % of the variance of job
characteristics, and 36.0 % of the variation of organization support.
Formal off-the-job learning (53.9%) was accounted more by formal learning than
was formal on-the-job learning (39.5%). Informal learning explained 53.9% of the
variation of self experimentation, followed by learning with others (33.2%) and external
scanning (27.4%).
Common method variance. The instrument was designed and developed to
minimize the potential sources of common method variance which refers to “variance
that is attributable to the measurement method rather than to the construct of interest”
(Podsakoff et al., 2003, p.879). In order to control common method biases, the predictor
variables (personal characteristics, work environment characteristics, and formal
learning) and the criterion variable (informal learning) were measured at different points
in time with two separate sets of questionnaires. Separating the measurement of the
predictor and criterion variables has been recommended when it is impossible to obtain
data from different raters or sources (Podsakoff et al., 2003).
To diagnose whether separating the measures is appropriate, Harman’s single
factor test was implemented. A worse fit for the single factor model than for the
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measurement model indicates that common method variance does not occur. The single
factor model yielded a χ 2=199.98 with df=44. All fit indices were worse (NC=4.454;
RMR=.029; GFI=.847; CFI=.764; and RESEA=.132) than those of the measurement
model, indicating that common method variance does not pose a problem in this study.
Structural equation modeling (SEM). The purpose of SEM was to determine
whether the theoretical relationships specific at the conceptualization stage are supported
by the collected data.
The hypothesized structural model yielded an overall χ 2 value of 98.465, with 38
degrees of freedom. Even though a non-significant model in the chi-square statistic can
be considered as representative of a good fit, use of the chi-square index provides little
guidance in determining the extent to which the model does not fit. Therefore, other
indices of fit should be used. Primary among the fit indices are the GFI, CFI, and
RMSEA (Byrne, 2001). The CFI (.907) and the NC (2.591) suggested that model fit was
only marginally adequate, while the GFI (.919) and the RMR (.020) suggested that it was
relatively well-fitting. However, the RMSEA value of .089 was not within the
recommended range of acceptability (<.05 to .08).
In order to determine which model the data fit best, the hypothesized model
should be compared with a limited number of theoretically different alternative models
(Schumacker & Lomax, 2004). The alternative approach used a chi-square difference test
to compare each of the alternative models. The first alternative model was the model that
informal learning influences formal learning because most research which has
investigated the relationship between formal and informal learning supported the positive
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association between the two learning forms while they have not designated any specific
direction for the association. The first alternative model yielded an overall χ2 value of
114.866 (df=39, p=.000), with NC=2.945, RMR=.041, GFI=.911, CFI=.878, and
RMSEA=.098. Compared with the results of the hypothesized model presented in Table
4.9, the first alternative model was poorly fit to the data. Thus, the χ2 difference between
the hypothesized model and alternative model 2 was statistically significant
(∆χ2(hypothesized-alternative 2) = 16.401) so it was concluded that the hypothesized model was
better fit to the data than was the alternative model 1.
The second alternative model was the model that addressed only the relationship
between formal learning and informal learning while it excluded any other relationships
among latent variables because the relationship between two forms of workplace learning
was of particular interesting in this study. In comparison, the chi-square difference test
was not appropriate because the hypothesized model and the alternative model 1 were not
nested so parsimonious fit indices were used to compare the models (Byrne, 2001;
Schumacker & Lomax, 2004). The hypothesized model yielded the PGFI of .529, the
PNFI of .594, the PCFI of .626, and the AIC of 154.465, while the alternative model 2
produced the PGFI of .552, the PNFI of .547, the PCFI of .581, and the AIC of 249.049.
Although the PGFI of the alternative model 2 was lower than that of the hypothesized
model, the hypothesized model yielded lower values of fit indices for PNFI and PCFI
than did the alternative model 2. Also, the AIC of the hypothesized model was lower than
that of the alternative model 2. These results indicated that the hypothesized model was
better fit to the data that was the alternative model 2.
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Taken together, these results suggested that the hypothesized model was better-
fitting to the data than were two alternative models. The results of the comparisons are
presented in Table 4.9.
Fit index Hypothesized Model
Alternative Model 1
Alternative Model 2
Comparison
χ2
d.f. p
98.465 38
.000
114.866 39
.000
203.049 43
.000
Hypothesized model and Alternative model 1
PGFI PNFI PCFI AIC
.529
.594
.626 154.465
.539
.589
.623 168.866
.552
.547
.581 249.049
Hypothesized model and Alternative model 2
Table 4.9. Model fit indices for hypothesized model and alternative models
Given the results of the comparison of the hypothesized model with the
alternative models, the next procedure of model evaluation was to identify any area of
misfit in the hypothesized model (Joreskog, 1993). There are two types of information
that can be helpful in detecting model misspecification, such as the standardized residuals,
and the modification indices (Byrne, 2001). On the one hand, the standardized residuals
are fitted residuals divided by their asymptotically standard errors, which are analogous
to Z scores. Values greater than 2.58 are considered to be large. In examining the
standardized residual values, no large value appeared while all values yielded no greater
than 1.81 (job characteristics and self-efficacy). It is therefore concluded that all
relationships between variables are well accounted for by the model.
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On the other hand, the modification indices are the expected values that the chi-
square would decrease by if such a parameter were to be included. A series of
modifications was conducted to produce the most appropriate model by using
modification indices produced in AMOS outputs. The misspecified error covariances
may be representative of systematic measurement error derived from either the items or
the respondents (Aish & Joreskog, 1990). From the hypothesized model, four covariances
between error terms were added to produce a finally modified model (Modified Model 4),
as presented in Figure 4. 2.
The modified model 1 yielded an overall χ2 value of 87.176, with NC=2.356,
RMR=.019, GFI=.930, CFI=.922, and RMSEA=.082, after adding a covariance (e4-e8).
In addition to the indication from modification index, the relationship between formal
off- the-job learning (e4) and organizational support (e8) has been supported due the fact
that if individuals perceive that their organization encourages formal learning as a means
of developing employee competence, they are more likely to be enthusiastic about the
learning activity and to be effectively learned and trained (Baldwin & Magujuka, 1997;
Lee et al., 2004; Tracey et al., 2001). Although the improvement in model fit for the
modified model 1, compared with the originally hypothesized model, would appear to be
trivial on the basis of the NC, RMR, GFI, CFI, and RMSEA values, the model difference
nonetheless was significant (∆χ2(1) = 11.289). However, the χ2 value was still significant
and the RMESA was not within the recommended range.
Therefore, the modified model 2 was produced by adding a covariance (e10-e11),
which yielded an overall χ2 value of 75.919, with NC=2.109, RMR=.018, GFI=.940,
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CFI=.938, and RMSEA=.074. The covariance between self-experimentation and external
scanning seem reasonable because although each type of informal learning represents
different aspects of informal learning, all types of workplace learning are interrelated
(Rowden, 2002). In particular, the two types of informal learning in this study were
significantly associated (r=.389; p<.01). Again, the χ2 difference between the modified
model 1 and the modified model 2 was statistically significant (∆χ2(1) = 11.257).
Given the results, adding a covariance (e1-e8) produced the modified model 3,
which yielded an overall χ2 value of 71.403, with NC=2.040, RMR=.017, GFI=.944,
CFI=.944, and RMSEA=.072. It is reasonable to include the covariance because self-
efficacy has been supported as a predictor for learning and performance (Machin &
Fogrty, 2003; Tannenbaum et al. 1991). Similar to the modified model 2, the χ2 difference
was statistically significant (∆χ2(1) = 4.516).
Another covariance (e8-e10) was added into the modified model 3. Including the
covariance between formal off-the-job learning (e8) and self-experimentation (e10) is
reasonable because both forms of workplace learning comprise each other (Sevensson et
al., 2004). Individuals who participate more in formal learning tend to dedicate more time
to self-initiated learning (Westbrook & Veale, 2001). Estimation of the modified model 4
yielded an overall χ2 value of 64.670, with NC=1.902, RMR=.017, GFI=.949, CFI=.953,
and RMSEA=.067. The χ2 difference was statistically significant (∆χ2(1) = 6.733). Even
though the RMESA did not appear very close to the rigorously recommended range
(<.05) and the χ2 statistic did not meet the recommended p-value (<.05), all other fit
indices represented a good model fit to the data.
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In order to decide whether or not the subsequent modification was necessary, an
additional review was employed. The information from the modification index provided
by AMOS Outputs suggested some negative relationships between error terms, which
were not consistent with this study. Therefore, the subsequent suggestions were ignored
because modification indices identified by AMOS as belonging in a model are based on
statistical criteria only. The inclusion of some covariances must be substantively
meaningful for the study (Byrne, 2001, p. 157).
The modified model 4 represented the best fit to the data and no further
consideration was made on the modification (see Appendix C). Table 4.9 presents fit
indices of a hypothesized structural equation model and modified structural equation
models. The next sections for the research questions are discussed using the results from
the modified model 4.
Model χ 2 (df ) p NC RMR GFI CFI RMSEA Hypothesized Model
98.465(38) .000 2.591 .020 .919 .907 .089
Modified Model 1
87.176(37) .000 2.356 .019 .930 .922 .082
Modified Model 2
75.919(36) .000 2.109 .018 .940 .938 .074
Modified Model 3
71.403(35) .000 2.040 .017 .944 .944 .072
Modified Model 4
64.670(34) .001 1.902 .017 .949 .953 .067
Table 4.10: Model fit indices for hypothesized model and modified models
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Figure 4.1: Hypothesized structural model
Figure 4.2: Modified structural model
Personal Characteristi
c
Work Environment
Formal Learnin
g
Informal Learning
SELF
LO
MOT
ORG SUP JOB
IL1 IL2 IL3
FL1 FL2
.71
.64
.92
.59
.72
.71
.56
.66
.61
.64
.41
.54 .39
.02
.62
.39
.29
Personal Characteristi
c
Work Environment
Formal Learnin
g
Informal Learning
SELF
LO
MOT
ORG SUP JOB
IL1 IL2 IL3
FL1 FL2
.63
.73
.90
.61
.73
.69
.60
.64
.73
.58
.52
.56 .50
-.18
.59
.45
.27
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The estimated parameters (standardized path coefficients) were examined, which
provided information on the strength and the direction of the proposed relationship.
Furthermore, straight arrows depicted in Figure 4.2 represent dependence relationships
that mean the impact of one construct on another construct or variable. The significance
of the estimated parameters between the proposed latent variables was also considered
because “statistically significant estimated parameters in the structural model provide
evidence that covariation is present” (Hair et al., 2006, p. 721). Each research question
was answered with path coefficients derived from the final modified model. Table 4.10
presents the path coefficients between latent variables in terms of total, direct, and
indirect effect as well as statistical significance.
RQ Path coefficient Total effect
Direct effect
Indirect effect
p- value
2.
Formal Learning Effectiveness
→ Informal Learning Effectiveness
.392 .392 - .006
3.
Personal Characteristics
→ Informal Learning Effectiveness
.731
.619 .112 .000
4. Work Environment Characteristics
→ Informal Learning Effectiveness
.175
.024 .151 .849
5. Personal Characteristics →
Formal Learning Effectiveness
.285 .285 - .014
6. Work Environment Characteristics
→ Formal Learning Effectiveness
.386 .386 - .004
Table 4.11. Results of research questions with standardized path coefficients in the structural model
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Research question 2: What is the relationship between formal learning and informal
learning?
The relationship between formal and informal learning was examined by using
standardized path coefficients derived from the final modified structural model. As
shown in Table 4.12, examination of the standardized path coefficients indicates that
formal learning has a significant impact on informal learning in terms of effectiveness.
The direct effect from formal learning to informal learning is .392, with a t value of 2.752
(p=.006). There is no indirect effect. The significance of standardized path coefficient in
the structural model provides evidence that covariation between formal and informal
learning is present.
Intercorrelation analysis was conducted to investigate the relationships between
observed variables within two latent variables such as formal and informal learning, as
presented in Table 4.13. The results show that both formal on-the-job learning and formal
off-the-job learning are significantly and positively related with all three types of
informal learning. In particular, formal on-the-job learning is most strongly related with
learning with others (r=.368; p <.01) among the three types of informal learning, while
formal off-the-job learning is most strongly associated with self-experimentation (r=.409;
p<.01).
The results of standardized path coefficients and intercorrelation analyses provide
empirical information about the relationship between the two forms of workplace
learning. Taken together, middle managers who receive effective formal learning
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perceive that their informal learning activities are effective for dealing with novel and
challenging situations or problems.
Path coefficient Total effect
Direct effect
Indirect effect
p- value
Formal Learning Effectiveness
→ Informal Learning Effectiveness
.392 .392 .006
Table 4.12. Standardized path coefficients regarding the relationship between formal learning and informal Learning
Formal on-the-job Learning
Formal off-the-job Learning
Learning with Others .368** .228**
Self-experimentation .262** .409**
External Scanning .179* .246**
Table 4.13. Intercorrelations between observed variables of formal learning and informal learning. Note: * p<.05; **p<.01
Research question 3: What is the relationship between personal characteristics and
informal learning?
The relationship between personal characteristics and informal learning was
examined by assessing standardized path coefficients derived from the final modified
structural model, as presented in Figure 4.2. Also, as shown in Table 4.14, examination of
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standardized path coefficients indicates that personal characteristics have a significant
impact on informal learning in terms of effectiveness. The direct effect from formal
learning to informal learning is .619, with a t value of 4.557 (p=.000). The total effect
comprised of direct and indirect effects is .731. As presented in Figure 4.2, the indirect
effect (.112=.287×.392) of personal characteristics on informal learning is generated
through formal learning. The significance of standardized path coefficients provides
evidence that informal leaning effective is influenced by personal characteristics.
To examine the relationship between observed variables within two latent
variables, intercorrelation analyses were conducted, as presented in Table 4.15. The
results show that the three observed variables pertaining to personal characteristics are
significantly and positively associated with all three types of informal learning. Among
the three variables of personal characteristics, learning goal orientation has the stronges
relationship with all three types of informal learning at the level of p<.01: r=.486 with
learning with other; r=.440 with self-experimentation; and r=.305 with external scanning.
The results of path coefficients and intercorrelation analyses show that each
observed variable in personal characteristics is significantly and positively related with
each type of informal learning, and informal learning effectiveness is influenced by
managers’ personal characteristics.
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Path coefficient Total effect
Direct effect
Indirect effect
p- value
Personal Characteristics →
Informal Learning Effectiveness
.731 .619 .112 .000
Table 4.14. Standardized path coefficients regarding the relationship between personal characteristics and informal learning
Self-efficacy Learning goal orientation
Motivation to learn
Learning with Others .282** .486** .340**
Self-experimentation .279** .440** .401**
External Scanning .227** .305** .233**
Table 4.15. Intercorrelations between observed variables of personal characteristics and informal learning. Note: * p<.05; **p<.01
Research question 4: What is the relationship between work environment characteristics
and informal learning?
The relationship between work environment characteristics and informal learning
was investigated by using standardized path coefficients derived from the structural
model, as presented in Figure 4.2. Table 4.16 shows the result that work environment
characteristics do not have a direct effect (.024, p=.849) on informal learning, even
though there is a weak indirect effect (.151=.386×.392) to informal learning mediated
through formal learning, as presented in Figure 4.2.
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To investigate how each observed variable is associated with another beyond the
relationship between two latent variables, intercorrelation coefficients are reviewed. The
results of intercorrelation analysis are presented in Table 4.17. The results suggest that
among paired correlations (3 × 3), six correlations are significant at p<.01 or p<.05.
However, the relationships between organizational support and external scanning,
supervisor support and self-experimentation, and supervisor support and external
scanning were not significant at the level of p<.05, which may cause the non-significant
weak impact of work environment characteristics on informal learning.
The results of path coefficients and intercorrelation analyses show that although
some correlations exist among the observed variables of work environment
characteristics and informal learning, and an indirect effect through formal learning is
present, work environment characteristics do not directly affect informal learning.
Path coefficient Total effect
Direct effect
Indirect effect
p- value
Work Environment Characteristics
→ Informal Learning Effectiveness
.175 .024 .151 .849
Table 4.16. Standardized path coefficients regarding the relationship between work environment characteristics and informal learning
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Organizational support
Supervisor Support
Job Characteristics
Learning with others .236** .279** .298**
Self-experimentation .146* .106 .141**
External scanning .118 .061 .207**
Table 4.17. Intercorrelations between observed variables of work Environment and informal learning. Note: * p<.05; **p<.01
Research question 5: What is the relationship between personal characteristics and
formal learning?
To investigate the relationship between personal characteristics and formal
learning, standardized path coefficients were assessed, as presented in Figure 4.2.
Examination of standardized path coefficients indicates that personal characteristics have
a significant and positive impact on formal learning at the level of p<.05, as presented in
Table 4.18. The direct effect from personal characteristics to formal learning is 2.85, with
a a t value of 2.464 (p=.014). There is no indirect effect. The significance of standardized
path coefficients provides evidence that formal learning effective is influenced by
personal characteristics.
To address the relationships between observed variables under two latent
variables, intercorrelation analyses were conducted. The results show that all
relationships among the paired variables (3 × 2) are significant at least at the level of
p<.05, with correlation coefficients ranging from .303 for the relationship between
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motivation to learn and formal off-the-job learning to .163 for the relationship between
self-efficacy and formal on-the-job learning, as presented in Table 4.19.
The results of standardized path coefficients and intercorrelation analyses provide
the empirical evidence about the relationship between personal characteristics and formal
learning effectiveness. Taken together, each observed variable in personal characteristics
is significantly and positively related with each type of formal learning, and formal
learning effectiveness is influenced by managers’ personal characteristics.
Path coefficient Total effect
Direct effect
Indirect effect
p- value
Personal Characteristics
→ Formal Learning Effectiveness
.285 .285 .014
Table 4.18. Standardized path coefficients regarding the relationship between personal characteristics and formal learning
Formal on-the-job Learning
Formal off-the-job Learning
Self-efficacy .163** .229**
Learning goal orientation .289** .276**
Motivation to learn .208** .303**
Table 4.19. Intercorrelations between observed variables of personal characteristics and formal learning. Note: * p<.05; **p<.01
122
Research question 6: What is the relationship between work environment characteristics
and formal learning?
To examine the relationship between work environment characteristics and formal
learning, standardized path coefficients were assessed, as presented in Figure 4.2.
Examination of standardized path coefficients indicates that work environment
characteristics have a significant and positive impact on formal learning at the level of
p<.01. The direct effect is .386, with a t value of 2.909 (p=.004), as presented in Table
4.20. There is no indirect effect. The significance of standardized path coefficients
provides evidence that formal leaning effective is influenced by work environment
characteristics.
To address the relationships between observed variables under two latent
variables, intercorrelation analyses were conducted. The results show that all
relationships are significant and positive at p<.01, with correlation coefficients ranging
from .353 for the relationship between organizational support and formal off-the-job
learning to .189 for the relationship between organizational support and formal on-the-job
learning, as presented in Table 4.21.
The results of standardized path coefficients and intercorrelation analyses provide
empirical evidence of the relationship between work environment characteristics and
formal learning effectiveness. Taken together, each observed variable in personal
characteristics is significantly and positively related to each type of formal learning, and
work environment characteristics affect the effectiveness of formal learning.
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Path coefficient Total effect
Direct effect
Indirect effect
p- value
Work Environment Characteristics
→ Formal Learning Effectiveness
.386 .386 .004
Table 4.20. Standardized path coefficients regarding the relationship between work environment characteristics and formal learning
Formal on-the-job Learning
Formal off-the-job Learning
Organizational support .189** .353**
Supervisor support .264** .266**
Job characteristics .205** .241**
Table 4.21. Intercorrelations between observed variables of work environment characteristics and formal learning. Note: **p<.01
.
Research question 7
Research question 7: What is the nature of informal learning activities that respondents
have engaged in?
Three open-ended questions were included in the survey questionnaire to
investigate the nature of informal learning activities among respondents. The participants
were asked to consider 1) the reason why they engage in an informal learning activity, 2)
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what they engaged in the informal learning activity, and 3) what they learned and how
they used the information learned through engagement in a informal learning activity.
Compared to the number of completed survey questionnaires (n=274), fewer
open-ended responses were provided, with 67 responses (24.5%) for the first, 64
responses (23.4%) for the second, and 57 responses (20.8%) for the third short question.
The collected responses were analyzed by theme coding and thematic analysis (Strauss &
Corbin, 1998).
Reasons why they engage in an informal learning activity. One way of
understanding informal learning is investigating the situations which informal learning
activity is needed. There were sixty-seven responses that reported why participants
engaged in an informal learning activity; these were grouped into five categories, as
presented in Table 4.22.
Initiatives of informal learning N (%) 1. Preparing new role or assignment (project or rotation) 23 (31.1) 2. Individual development (Preparing new career or retirement, etc.) 18 (24.3) 3. Developing knowledge on current work or duty 13 (17.6) 4. Improving the skills to counsel with client 8 (10.8) 5. Lack of formal learning opportunity 5 (6.8) 6. Others (i.e. “always”) 7 (9.5)
Table 4.22. The Initiatives of Informal Learning (N=67)
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The results of the thematic coding showed that preparing for a new role or
assignment was ranked as the first reason that middle managers encouraged engagement
in informal learning (23 responses; 31.1%). This reflected the fact that middle managers
in the banking sector are required to prepare for their next role or assignment while they
performing their current tasks because their place to work has been rotated through their
career in the bank. The second ranked initiative related more to their career after job or
retirement, which is similar to the concept of lifelong learning. As Marsick and Watkins
(1999) suggested, “informal learning takes place as people go about their daily activities
at work or in other spheres of life” (p.4).
In particular, the expansion of performance related pay in organizations and of
flexibility in the labor market in Korea are threatening managers’ job security. A survey
reported that most current employees anticipate leaving their company before their 50’s
so that they recognize the importance of self-development for their next career (Lee,
2009).
Finally, five respondents (6.8%) reported that when they felt a lack of formal
learning on a certain issue or when formal learning did not prepare them to be competent
for the desired level of work requirements, they decided to engage in informal learning.
Taken together, informal learning was catalyzed by looking around the
environment by individual learners, which means that individuals used to reassess their
personal interests (i.e. individual development) as well as evaluate external changes (i.e.
preparing for a new job or assignment) regarding their organization or their jobs (Marsick
& Watkins, 1999).
126
What they engaged in the informal learning activity. The engagement in informal
learning was categorized with three types: learning with others, self-experimentation, and
external scanning. Among the three types of informal learning, self-experimentation
activity was the most frequent type (37 responses; 55.22%). In the self-experimentation
activity, reading books, journals, or magazines, self-study to prepare for certificates with
texts, and searching the Internet were the most frequent informal activities (15 responses
-i.e., reading books on leadership and self-development, 12 responses- i.e., preparing the
certificate exam for CFA, and 10 responses- i.e., putting key words into searching engine
such as Google).
The next frequent type of informal learning was external scanning activity (20
responses; 29.85%). The examples were “enrolling in a learning institution for
developing English skills (12 responses) and attending local conference for state-of-art
knowledge (4 responses).” The least frequent type of informal learning was learning with
others (10 responses; 14.93%). Attending communities of practices or study group (5
responses) were examples of the type, such as collaborating with people from a
knowledge sharing on-line community. Consequently, the results of the open-ended
question on what they engaged in informal learning were similar to those of research
question one. On research question one, reading manuals or other texts (M=3.85;
SD=.809) was the most frequent informal learning activity.
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What they learned and how they used the information earned through engagement
in informal learning activity. This question asked about outputs or results from
applications of learning. After thematic analysis of answers, the contents were
categorized into five groups, as presented in Table 4.23.
The first ranked response was working knowledge and skills (19 responses,
33.3%) as outputs of informal learning engagement. There were some examples such as
hands-on knowledge of product development through reading and talking with others,
and knowledge and skills applied to my job. The second ranked output was manifest
evidence or outcomes of learning. In other words, middle managers said that they earned
a certificate related to their job (e.g., Chartered Financial Analyst (CFA)) or higher scores
on English tests through self-study after work or on weekends. As well as the second
ranked output, a similar number of middle managers (12 responses, 22.8%) responded
that they experienced improvement in interpersonal skills such as problem solving with
others or communication skills. Four responses (7.0%) were related to the recognition of
the meaning of life, and confidence in themselves, and another four responses (7.0%)
were related to their leisure.
Approximately 77 percent of the outputs resulting from informal learning were
related to the development of professional knowledge and skills. The results support the
conclusion that learning at the workplace prepares managers to perform better by
developing competence as a combination of knowledge, skills, and attitudes. In addition
to professional development, engagement in informal learning tends to result in
increasing managers’ confidence and experiencing the sense of achievement in concerned
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leisure. These outputs may be attributable to the fact that all managers are adult learners
who are motivated to seek out learning experience only when they learn what they want
to learn and the learning is meaningful to them (Illeris, 2003). Consequently, informal
learning functions as a means of achieving individual goals as well as meeting
organizational needs.
Outputs (or results from application of learning) N (%) 1. Working knowledge and skills (job performance) 19 (33.3) 2. Certificate (or language skills) 13 (22.8) 3. Interpersonal skills (problem solving, communication, leadership, etc.) 12 (21.1) 4. Recognition of meaning of learning (confidence on self) 4 (7.0) 5. Leisure development (golf, sign language, baduk, etc.) 4 (7.0) 6. Others (not applied; not too much applied) 5 (8.8)
Table 4.23. The outputs of informal learning (N=57)
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CHAPTER 5
SUMMARY, DISCUSSION, AND IMPLICATIONS This chapter consists of three sections. The first section provides a summary of
the results derived from descriptive, relational, and thematic analyses which were
employed to answer the research questions. The second section discusses the results. The
final section considers implications for researchers, practitioners, and individual learners.
Summary of the Results
The purposes of this study were to investigate how middle managers in the
Korean banking sector engage in informal learning, to examine how formal learning,
personal characteristics, and work environment characteristics influence informal
learning, and to explore the nature of informal learning activities for respondents. Data
gathering for this study was conducted in the Korean Banking Institute (KBI). Two
hundred-seventy-four middle managers who worked in the Korean banking sector
responded to two separate questionnaires. Seven research questions were answered using
their responses. The following is the summary of the results.
• Formal learning has a positive and significant impact on informal learning. In
terms of observed variables, both formal on-the-job and off-the-job learning are
significantly and positively related to three types of informal learning.
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• Personal characteristics have a significant and positive impact on both formal and
informal learning. In terms of observed variables, three variables of personal
characteristics are significantly and positively related to all types of workplace
learning.
• Work environment characteristics do not have a direct significant effect on
informal learning, although they have a weak indirect effect on informal learning
through formal learning. With regard to observed variables, job characteristics are
positively and significantly related to all three types of informal learning.
However, organizational support is not significantly related to external scanning,
and supervisor support is not significantly associated with self-experimentation or
external scanning.
• The most pervasive informal learning type among the three informal learning
types is self-experimentation. In this type, reading manuals or other texts is the
most frequently used informal learning activity.
• Major consequences resulting from informal learning are the acquisition of hands-
on knowledge and skills that can be applied to the work, individual achievement
(i.e., CFA certificate and foreign language skills), and the development of
interpersonal skills (i.e., leadership and communication skills).
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Discussion
Preference on engagement in informal learning
The study results show that managers in the Korean banking sector engage in
various informal learning activities. Despite the variety of informal learning activities, the
descriptive data on frequency of informal learning activity indicate that the most common
informal learning activities are reading manuals or other texts, using the Internet to search
for information, and reflecting on past experiences that pertain to the type of self-
experimentation. Furthermore, the results of thematic analysis derived from the open-
ended questions also identified self-experimentation as the most frequently used type of
informal learning. These results can be interpreted in at least two ways.
The first interpretation is that middle managers tend to try something new at the
workplace for coping with challenging situations as an intentional effort. That is,
individual learners become researchers when they engage in the self-experimentation
learning type. They are not only dependent on established knowledge and skills, but also
construct a new theory for the new situation (Schon, 1983). Self-experimentation is
related to critical thinking and consists of two central activities, identifying and
challenging assumptions, and exploring alternatives (Brookfield, 1987).
Thus, one’s estimate of his or her desire to learn (Noe & Schimitt, 1986) and
orientation toward learning (Dweck, 1989) determine which activities are selected for
learning as well as how much he or she engages in learning activities. The results of
descriptive analyses show that middle managers are highly motivated to learn and highly
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oriented to a learning goal, but they perceive that organizational and supervisor support
are not favorable in comparison to their motivation to pursue informal learning. These
situations lead them to engage more in self-experimentation that results in an interactive
process of action and reflection driven by individual learners.
Another interpretation may be linked to the accessibility to learning partners or
learning resources because self-experimentation, the most common out of three informal
learning types, can be easily used compared to other types. It suggests that managers tend
to prefer more personalized learning resources. This may be related to the fact that
managers in a branch bank are assigned as a representative or a chief of a section or
department so that they are usually working with their subordinates who have fewer
career experiences and lower level of expertise. This situation makes managers more
dependent on their own learning strategies. Moreover, the opportunities to attend
conferences or seminars which are not planned or supported by their organization must
be limited to managers because if they decide to participate in some learning
opportunities, they have to allocate their own time for searching for the information,
arrange their work schedule, and pay attendance costs themselves.
Previous research showed various results regarding the choice by individual
learners of informal learning activities. For example, Lohman (2005) found that HRD
professionals tend to rely to a greater extent on independent learning activities such as
searching the Internet and scanning magazines and journals. Unlike HRD professionals,
public school teachers prefer interactive learning activities such as talking and sharing
materials with others rather than independent learning activities. According to the study
133
by Doornbos, Simons, and Denessen (2008), police officers preferred collaborative work-
related learning types such as learning from peers and learning together rather than
individual learning types, such as individual preparation and application of something
new.
Consequently, the results of this study lead to the conclusion that middle
managers in the Korean banking sector prefer the self-experimentation learning type to
other learning types. It is worthwhile to note that the selection of informal learning
activities may vary according to the features of the job and the work context where
informal learning activities take place.
The Consequences of Engagement in Informal Learning
Informal learning activities made middle managers achieve some intended
learning objectives that were used for developmental purposes. For middle managers,
major outputs or consequences from informal learning activities were working
knowledge and skills, specific abilities (i.e., foreign language skills), and job-related
certificates (i.e., CFA) which can be applied to their current jobs and future careers. It is
interpreted that informal learning activities are used to deal with specific and challenging
work problems and then produce new learning regarding job contents. Thus, informal
learning activities are also likely to enhance individual job performance because job
performance is determined by a combination of knowledge, skills, and motivation
(Campbell, McCoy, Oppler, & Sager, 1993), and informal learning activities are
important ways to obtain the determinants of job performance. Thus, experiences of
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solving work problems through challenging experience, action, reflection, and evaluation
are accumulated and transformed into individual competence or capability, while
building metacognitive knowledge and skills (Enos et al., 2003) and extending personal
development and competence development (Illeris, 2003).
Another consequence is related to reflection on learning itself. As presented in the
results of thematic analysis, the more managers spend their time to learn something new,
the more they are able to recognize the importance of learning. They also witness
improvement in personal learning objectives, such as leisure or sports, which they are
determined to engage. They experience psychological and emotional changes through the
learning process. Adult learning theories guide the interpretation of these findings. Adult
learners learn what they want to learn and what is meaningful for them to learn so that
learning for adults is a desire-based function (Illeris, 2003). Managers do not exert their
efforts to learn something if they are not interested in it or cannot recognize the meaning
or importance.
Influence of Formal Learning on Informal Learning
Results show that formal learning positively influences informal learning. This
result can be interpreted in four ways. First, the positive relationship between formal and
informal learning is consistent with the notion of Malcolm et al (2003) that all learning
situations include two attributes, formality and informality. Although the balance
between them varies according to the situation, it may be impossible that a learning
activity includes only formality or informality. The two attributes are also interrelated,
135
which influences the effectiveness of learning in both types. Thus, learning tools or
methods obtained from formal learning programs prepare managers to learn informally
because they are able to utilize these learning tools or methods when they need to learn,
they perceive how to go about learning in informal ways (Marsick & Volpe, 1999).
Second, informal learning may be enhanced by managers who have well-
organized knowledge and skills because informal learning is initiated by the learners
themselves. From this perspective, managers’ judgment of the usefulness and relevance
of formal learning to their job influences managers’ intentions to apply the learning to
work through the process of problem solving. In other words, if they perceive formal
learning as effective, they are more likely to utilize it and to compromise the two
different learning practices. Thus, the application of formal learning to work settings may
be a component of the informal learning process (Enos et al., 2003), so that knowledge
and skills furnished by formal learning are used for future informal learning.
Third, in terms of the direction of the relationship between formal and informal
learning, an alternative model which assumed the impact of informal learning on formal
learning was tested. However, the alternative model’s fit indices were worse than those of
the hypothesized model. The results showed that formal learning is not influenced by
informal learning, but that formal learning affects informal learning. It contends that
formal learning is a major source to provide managers with the ability to learn informally.
It may be concluded that the research model provides the best explanation for
relationships among formal learning, personal characteristics, work environment
characteristics, and informal learning.
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Finally, it is worthwhile to note that managerial competence is not formulated by
either formal or informal learning, but is accumulated through both learning activities
(Enos et al., 2003; Svesson et al., 2004). From this perspective, managers may be less
interested in selecting any form of workplace learning, whereas they pay more attention
to the achievement of desirable levels of competence. Therefore, participation in formal
learning practices or opportunities provided by an organization and engagement in
informal learning activities may not make a difference to managers and their competence.
Rather, managers tend to synthesize their learning and desire its effectiveness from
various opportunities for formal and informal learning activities. Managers utilize
learning experiences to cope with emerging problems, to meet work requirements
established by the organization, and to prepare for future jobs and careers.
Consequently, the results of this study can be interpreted as indicating that formal
learning and informal learning should be integrated into a comprehensive view,
addressing such perspectives as the coexistence of formality and informality embedded in
all learning activities, managers’ cognitive ability to learn, and formulation of managerial
competence through formal and informal ways of learning.
Influences of Personal and Work Environment Characteristics on Informal Learning
The results show that personal characteristics influence informal learning
effectiveness. Managers with high levels of variables pertaining to personal
characteristics perceive their informal learning activities as more effective than do
managers with low levels. The results can be interpreted in the following ways. On the
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one hand, managers’ desire to learn, orientation toward learning, and judgment of their
capability determine the extent to which they engage in informal learning and the
effectiveness resulting from their engagement. In more detail, managers with a high
motivation to learn may seek to learn more when they engage in any learning activities
that are selected by them compared to those with low motivation to learn. Managers who
have a high learning goal orientation may seek to understand something new or to
enhance their competence through informal learning. They may more actively engage in
a problem solving process or challenging task with confidence in their ability to cope
with these situations.
On the other hand, it may also be worth noting that learning goal orientation and
motivation to learn were more related to informal learning than general self-efficacy with
regard to correlation coefficients. Thus, the path coefficients suggest that the latent
variable of personal characteristics was more explained by learning goal orientation and
motivation to learn than by self-efficacy. The results of this study might be different from
those of previous studies examining the factors influencing informal learning have shown
that self-efficacy is a major personal characteristic likely to enhance engagement in
informal learning activities (Lohman, 2005; Lohman, 2006) and the most important
predictor of critical reflective working behavior (van Woerkom et al., 2002). This may be
interpreted by the fact that personality variables linked closely and directly to learning
itself, such as learning goal orientation and motivation to learn, have stronger impacts on
the learning process and its effectiveness. Self-efficacy in this study was measured as
general self-efficacy, which is not specific to learning or development activities.
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In terms of work environment characteristics, the results of this study showed that
work environment characteristics do not significantly affect informal learning. This
finding can be interpreted in three ways. First, it may be related to the notion about
statistical standpoint. In other words, a non-significant effect of work environment on
informal learning can be attributed to the fact that if work environment and personal
factors are included in the analysis at the same time, the effect of work environment may
be diminished (Kwakman, 2003). Berg and Chyung (2008) found that organizational
factors did not significantly influence the engagement in informal learning. Among ten
rank-ordered factors affecting informal learning, work environment was ranked seventh.
They contended that an individual might find a way to learn when he or she wants to gain
new knowledge regardless of whether the organization has an effective structure to
encourage informal learning. Although the conditions inhibiting learning have received
little attention, some studies focused that if management support and organizational
culture are not favorable for individual learning, they can be major inhibitors to informal
learning (Ellinger, 2005; Lohman, 2000; Sambrook & Stewart, 2000). For example,
Ellinger (2005) identified eight themes of negative organizational contextual factors.
Among the themes, unsupportive and disrespectful leaders were regarded as a major
inhibitor of informal learning.
Second, the effect of work environment characteristics on informal learning may
be related to how managers perceive their competence or capability. As Enos and his
colleagues (2003) found, organizational support and informal learning have a moderate
inverse relationship among managers. It can be interpreted that, in comparison to novices,
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managers might perceive their learning abilities high and, in turn, depend more on their
own learning mechanisms built through previous learning experiences than on supports
or resources given by work environment factors. This explanation also can be supported
by the results indicating that the means of rating on personal characteristics variables
were higher than those of work environment characteristics variables.
Finally, an interesting finding was that work environment did not have a direct
impact on informal learning, but had an indirect impact through formal learning. It can be
interpreted by the notion that formal learning is regarded as one of the work environment
factors that enable, encourage, or discourage managers’ informal learning. As Baldwin
and Magujuka (1997) mentioned, “there is no reason to anticipate a tidy separation of
training and organizational experience” (p.121). The mandatory participation on formal
learning may not be recognized separately from work requirements related to learning
and development.
The results show that personal characteristics and work environment
characteristics have a different impact on the two forms of workplace learning. Informal
learning is influenced more by personal characteristics because the learning is usually
initiated by learners themselves regardless of whether the organization encourages it,
while formal learning is more dependent on work environment characteristics because the
managers’ participation in formal learning is usually planned and implemented by the
organization’s policies or decisions.
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Implications
The implications of this study relate to the theory and practices of workplace
learning and HRD. The following are some implications from this study.
Implications for future research
Future research needs to be conducted by considering the potential to help
understand and explain learning in the workplace. There are four implications for future
research.
First, the examination of the impact of formal learning on informal learning
extends our understanding on how two types of workplace learning interact and influence
each other. However, the integration of formal and informal learning leads to a
challenging theme regarding workplace learning theory. In other words, future research
should pay attention to which aspects of workplace learning are related and not related to
each other. In this study, formal learning was referred to according to the location where
the learning takes place, and informal learning was categorized by the process and
sources of learning. Although the aspects of workplace learning may be appropriate to
investigate the relationship between formal and informal learning, other aspects can be
used to better understand workplace learning. For example, Jacobs and Park (2009)
introduced three dimensions: location, the degree of facilitator involvement, and the
degree of planning. If different aspects are used for indentifying workplace learning, and
if they are used to investigate the relationship between formal and informal learning, then
the results may vary according to these aspects. It is important that various attributes of
141
workplace learning such as process and content are investigated to understand the nature
beyond the relationship between formal and informal leaning.
Second, a considerable finding is a weak indirect impact of work environment on
informal learning through formal learning. Although work environment has been
regarded as an important factor for informal learning, the work environment itself may
not be powerful in practice (Kwakman, 2003). Rather, the impact of work environment
tends to occur through other factors such as formal learning and personality variables.
Therefore, it is necessary to investigate how work environment influences informal
learning by other mediating variables. In addition, personal characteristics and work
environment characteristics had different impacts on formal and informal learning. The
results require a more careful interpretation because different types or activities of
workplace learning and their effectiveness can be encouraged or discouraged differently
by various factors. Future research should be conducted to examine which factors have
more impact on one form of workplace learning than on another form.
Third, other variables which are likely to influence informal learning need to be
identified. This study included a number of influencing variables that have been
considered as major variables. Nevertheless, the limited set of variables restricts our
understanding of what determines engagement in informal learning activities and the
effectiveness that results from this engagement. A non-significant impact of work
environment on informal learning may contribute to the exclusion of influential variables.
Contextual or situational characteristics have been examined from various perspectives in
the workplace and HRD literature (Rouiller & Goldstein, 1993). From this perspective, it
142
is worthwhile to consider other variables such as learning culture, work pressure, and task
autonomy in future research.
Finally, issues regarding research design need to be considered for future research
in the fields of HRD and workplace learning. This study measured predictor variables and
criterion variables at different points in time for controlling common method biases.
Although two questionnaires were measured separately, the interval between the first and
the second was not long enough to control the biases because respondents to the survey
had only participated in a short term (i.e., 3-day leadership development training
program) in the KBI. Therefore, future research needs to be implemented with a more
extended interval to ensure the causality between influencing factors and informal
learning. The study results are also limited to managers in the Korean banking sector.
Future research should replicate this study for different job levels and in different
industries.
In conclusion, further research is needed to investigate whether the results of this
study are replicated in different research settings, by considering other variables that may
affect informal learning. A revised theoretical framework is presented in Figure 5.1.
Straight lines with a one-way arrow mean direct impacts that are supported by the results
of this study. In contrast, a dotted line with one-way arrow indicates the impact that is not
supported by the results of this study, but which should be investigated in future research.
Figure 5.1. A Revised Theoretical Framework for Future Research
Work Environment • Learning culture • Organization structure • Work autonomy • Work pressures • Management support • HRD policies
Personal Characteristics • Self-efficacy for learning • Motivation to learn • Learning & Performance
goal orientation • Work commitment • Cognitive knowledge &
There are two major implications for HRD and workplace learning practices. The
first and most important practical implication of the research findings suggests that the
organizational efforts toward formal learning are not only limited into the designated
learning objectives or goals, but they also affect informal learning. The results of this
study suggest that organizations that provide more effective formal learning are also
likely to support informal learning because formal learning provides managers with the
knowledge and skills that instruct learning tools and methods to be used for other work-
related learning activities. Subsequently, advanced knowledge and skills can be utilized
by managers to deal with challenging situations or to learn something new actions that
are necessary to meet organizational requirements as well as individuals needs.
This study, as it focuses on the effect of formal learning on informal learning,
provides clear evidence that organizations have the means to foster informal learning.
One way to maximize managers’ informal learning and its effectiveness is to design and
implement formal learning programs effectively, and to ensure that managers are ready
and motivated for informal learning. This finding may be a guideline for organizations or
HRD practitioners who are facing challenging assignments such as how to enhance
informal learning in the workplace and how to shift the emphasis of HRD activities from
training to learning. Although organizations do not always control all individual learning
taking place in various facets of daily work, when effective formal learning occurs, the
investment in formal learning becomes a reliable way to encourage managers’ informal
learning and consequently results in the development of managerial competence.
145
Another implication derived from the results of this study is that although the
work environment was not favorable for managers to engage in informal learning, the
environment still encouraged managers to attend formal learning programs or activities
and their effectiveness. This results suggest that organizations or HRD professionals must
remember that the majority of what managers need to know for their work requirements
is established by informal learning (Enos et al., 2003; Marsick & Watkins, 1990). This
does not claim that formal learning is useless. Rather, this suggests that formal and
informal learning in the workplace must be integrated to maximize the benefits of
organizational investment on employee development. This also suggests that
organizations and HRD professionals consider creating a learning environment where
employees continuously learn informally as well as formally. Nevertheless, work
environment was not found to be favorable for informal learning, although it was just
suitable for formal learning. The results implicate that organizational policies for HRD
should be formulated in a way that empowers managers to plan, design, take action, and
evaluate their learning because excessively strict HRD policies and practices that
exclusively focus on formal learning programs may inhibit managers’ engagement in
informal learning.
146
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APPENDIX A
ENGLISH VERSION OF SURVEY QUESTIONNAIRE
Workplace Learning Survey (1) This survey seeks to investigate workplace learning which includes formal learning supported by the organization and informal learning initiated by individuals that you engage in as part of your job. The survey will be used for planning the most effective workplace learning. It takes approximately 20 minutes of your time to complete. All responses will be kept confidential and only aggregate data will be reported. Your participation is voluntary. You may skip some questions in case that you honestly do not have sufficient information to respond or you do not want to respond. If you have any questions about the survey, please contact Park, Ho-Jin at [email protected] or 82-2-3700-1519 Thank you in advance for your participation. Ronald L. Jacobs, Ph. D. The Ohio State University 1-614-292-0589 [email protected] Woojae Choi The Ohio State University 1-614-378-6568 [email protected]
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This survey is composed of by two sets of instrument. It is necessary that you are identified into the two sets of instrument. Therefore, it is encouraged that you provide your identification number. This is the first set of instrument. Once again, all response that you provide will be kept confidential and only aggregate date will be reported. Please fill out the last four-digit of your cell-phone number on the box below.
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The following are questions regarding 1) your participation, 2) your satisfaction, and 3) the effectiveness of the following formal learning activities. Please check (√) at the most appropriate ones.
Participation
My satisfaction with the learning activity was:
The effectiveness of the learning activity was:
In the past year, I engaged in the following learning activities: Yes No 1=Very Dissatisfied, To
5=Very Satisfied 1=Very ineffective, To 5=Very Effective
1. Received formal coaching from a peer or supervisor to help me improve on some aspect of my job
1 2 1 2 3 4 5 1 2 3 4 5
2. Received formal mentoring from a designated mentor to help me plan my career options 1 2 1 2 3 4 5 1 2 3 4 5
3. Participated in one-on-one training session that was conducted by a designated trainer to help me learn a specific aspect of my job
1 2 1 2 3 4 5 1 2 3 4 5
4. Engaged in an action learning project with a group of colleagues to improve a business process.
1 2 1 2 3 4 5 1 2 3 4 5
5. Attended a vendor-sponsored training program to learn more about some technology being adopted by the company.
1 2 1 2 3 4 5 1 2 3 4 5
6. Attended a company-sponsored training program in the training center to improve some job-specific competence.
1 2 1 2 3 4 5 1 2 3 4 5
7. Attended a company-sponsored training program in an outside facility to improve some job-specific competence.
1 2 1 2 3 4 5 1 2 3 4 5
8. Engaged in a company-sponsored training program that was delivered through the computer 1 2 1 2 3 4 5 1 2 3 4 5
9. Engaged in a company-sponsored training program that was delivered through a correspondence course
1 2 1 2 3 4 5 1 2 3 4 5
10. Received tuition assistance from my company to attend a college or university course 1 2 1 2 3 4 5 1 2 3 4 5
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II. The following are questions regarding your own ways of dealing with challenging situations. Please check (√) at the most appropriate ones.
Disagree Tend to
Disagree Don’t Know
Tend to Agree Agree
1. If something looks too complicated, I will not even bother to try it. 1 2 3 4 5
2. I avoid trying to learn new things when they look to difficult. 1 2 3 4 5
3. When I try something new, I soon give up if I am not initially successful. 1 2 3 4 5
4. When I make plans, I am certain I can make them work. 1 2 3 4 5
5. If I cannot do a job the first time, I keep trying until I can. 1 2 3 4 5
6. When I have something unpleasant to do, I stick to it until I finish it. 1 2 3 4 5
7. When I decide to do something, I go right to work on it. 1 2 3 4 5
8. Failure just makes me try harder. 1 2 3 4 5
9. When I set important goals for myself, I rarely achieve them. 1 2 3 4 5
10. I do not seem to be capable of dealing with most problems that come up in my life. 1 2 3 4 5
11. When unexpected problems occur, I do not handle them very well. 1 2 3 4 5
12. I feel insecure about my ability to do things. 1 2 3 4 5
13. The opportunity to do challenging work is important to me. 1 2 3 4 5
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Disagree Tend to Disagree
Don’t Know
Tend to Agree Agree
14. When I fail to complete a difficult task, I plan to try harder the next time I work on it. 1 2 3 4 5
15. I prefer to work on tasks that force me to learn new things. 1 2 3 4 5
16. The opportunity to learn new things is important to me. 1 2 3 4 5
17. I do my best when I’m working on a fairly difficult task. 1 2 3 4 5
18. I try hard to improve on my past performance. 1 2 3 4 5
19. The opportunity to extend the range of my abilities is important to me. 1 2 3 4 5
20. When I have difficulty solving a problem, I enjoy trying different approaches to see
which one will work. 1 2 3 4 5
21. I try to learn as much as I can from learning activities. 1 2 3 4 5
22. I believe I tend to learn more from learning activities than others. 1 2 3 4 5
23. I am usually motivated to learn knowledge and skills emphasized in formal learning
activities. 1 2 3 4 5
24. I would like to improve my skills through learning activities. 1 2 3 4 5
25. I am willing to exert effort in learning activities to improve my skills. 1 2 3 4 5
26. I am willing to take learning activities even though they are not high priority for me. 1 2 3 4 5
27. I am willing to invest effort to improve job skills and competencies. 1 2 3 4 5
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III. The following are questions regarding your work environment . Please check (√) at the most appropriate ones.
Disagree
Tend to
Disagree
Don’t Know
Tend to
Agree Agree
1. My company makes it possible for employees to participate in a wide range of learning activities. 1 2 3 4 5
2. My company values the need for employees to learn on a continuous basis. 1 2 3 4 5 3. My company rewards employees who attain advanced knowledge and skills. 1 2 3 4 5
4. My company provides the resources that are used to support learning. 1 2 3 4 5 \ 5. My supervisor encourages me to participate in as many learning activities as possible. 1 2 3 4 5
6. My supervisor assigns only those tasks that employees know how to perform. 1 2 3 4 5
7. My supervisor provides me with information regarding learning activities. 1 2 3 4 5
8. My supervisor adjusts my work schedule when I need to attend a learning activity. 1 2 3 4 5
9. My job requires me to seek for the better ways to deal with changes of the work. 1 2 3 4 5
10. My job requires continuous learning to meet the customer’s expectations. 1 2 3 4 5 11. My job does not allow me to spend much time in learning activities. 1 2 3 4 5 12. My job performance depends on the extent of my knowledge and skills. 1 2 3 4 5
168
IV. Please complete or check (√) the most appropriate ones for each question. 1. Tenure at the company? From ( ) year
2. Education completed? □ High school □ Community college □ 4 year university □ Graduate school
3. Your current position? □ Staff □ Assistant manager □ Manager □ Deputy general manager □ Director □ Executive
4. Your organization? □ Bank □ Security □ Insurance □ Manufacture □ Other ( )
Thank you for your responses!
169
Workplace Learning Survey (2) This survey seeks to investigate workplace learning which includes formal learning supported by the organization and informal learning initiated by individuals that you engage in as part of your job. The survey will be used for planning the most effective workplace learning. It takes approximately 20 minutes of your time to complete. All responses will be kept confidential and only aggregate data will be reported. Your participation is voluntary. You may skip some questions in case that you honestly do not have sufficient information to respond or you do not want to respond. If you have any questions about the survey, please contact Park, Ho-Jin at [email protected] or 82-2-3700-1519 Thank you in advance for your participation. Ronald L. Jacobs, Ph. D. The Ohio State University 1-614-292-0589 [email protected] Woojae Choi The Ohio State University 1-614-378-6568 [email protected]
170
This survey is composed of by two sets of instrument. It is necessary that you are identified into the two sets of instrument. Therefore, it is encouraged that you provide your identification number. This is the second set of instrument. Once again, all response that you provide will be kept confidential and only aggregate date will be reported. Please fill out the last four-digit of your cell-phone number on the box below.
171
I. The following are questions regarding 1) your frequency and 2) the effectiveness of the following informal learning activities. Please check (√) at the most appropriate ones.
Frequency The effectiveness of the learning activity was:
In the past year, the frequency in which I engaged in these learning activities was:
1=Very Infrequent To 5=Very Frequent
1=Very ineffective To 5=Very Effective
1. Had an informal one-on-one discussion with my supervisor about some work situation. 1 2 3 4 5 1 2 3 4 5
2. Exchanged ideas on how to solve a problem situation with peers during a break or after work. 1 2 3 4 5 1 2 3 4 5
3. Observed how some other employee dealt with a challenging work situation 1 2 3 4 5 1 2 3 4 5
4. Collaborated with others who shared the need to solve a particular problem situation 1 2 3 4 5 1 2 3 4 5
5. Spent time to reflect back how I dealt with a challenging work situation 1 2 3 4 5 1 2 3 4 5
6. Tried to solve a challenging work situation through trial and error process by myself. 1 2 3 4 5 1 2 3 4 5
7. Spent time to reflect on what I had learned in a classroom training program to apply that information to a challenging work situation 1 2 3 4 5 1 2 3 4 5
8. Read a standard operations manual or other similar texts on my own to find an answer to a question 1 2 3 4 5 1 2 3 4 5
9. Used the Internet to search for information to help me solve a challenging work situation 1 2 3 4 5 1 2 3 4 5
10. Attended a non-mandatory professional conference or seminar that might provide useful information for the future 1 2 3 4 5 1 2 3 4 5
11. Read a professional magazine or vendor publication to be current in some topic 1 2 3 4 5 1 2 3 4 5
12. Contacted someone outside the company to help solve a challenging work situation 1 2 3 4 5 1 2 3 4 5
172
II. The following are open-ended questions regarding your learning activities. Please describe your thoughts.
1. When you have engaged in an informal learning activity, please describe the reason why you did this.
2. Please describe what you did when you engaged in the informal learning activity.
3. Please describe what you learned and how you used the information.
Thank you for your responses!
173
APPENDIX B
KOREAN VERSION OF SURVEY QUESTIONNAIRE
174
업무 현장 학습 설문지(1)
이 설문지는 여러분이 업무를 수행하는 과정에서 참여하는 공식적 교육훈련과 회사의 지원이나 계획과 관계없이 스스로
하는 비공식적 학습 활동에 대한 연구를 위해 설계되었습니다. 여러분의 설문 참여는 가장 효과적이고 효율적인 공식 및
비공식 학습 활동을 위해 중요한 자료가 될 것입니다.
설문지 작성은 약 15-20분 정도 소요될 것입니다. 여러분의 모든 응답은 비밀이 보장되며, 연구 결과는 단지 합산된
형태로만 보고될 것입니다. 여러분의 설문 참여는 자발적이며, 응답하고 싶지 않은 질문은 하지 않을 수도 있습니다.
설문과 관련된 질문이 있으시면, 언제든지 박 XX 연구위원 (82-2-3700-1519) 에게 연락을 주십시오
귀하의 설문지는 각각 다른 문항들을 포함한 1차와 2차로 구성되어 있습니다. 귀하의 1차와 2차 설문지를 대응시키기 위해
귀하의 고유 번호 확인이 필요합니다. 본 설문은 1차 설문지 입니다
다시 한번, 귀하의 고유번호는 절대 연구 이외의 다른 목적으로 사용되지 않을 것이며, 모든 응답은 비밀이
보장됨을 약속합니다. 귀하의 휴대폰 뒷 4자리를 기재해 주시길 바랍니다.
176
I�다음은 귀하가 재직하고 있는 회사에서 제공되는 공식적 교육훈련 및 학습 활동에 대한 1) 참여 여부, 2) 만족도, 및 3) 효과성에 질문입니다. 적절한 곳에 √ 표시를 해 주십시오.
참여여부 학습 활동에 대한 만족도
(참여한 경우만 해당)
학습 활동의 효과성
(참여한 경우만 해당)
지난 1년 동안, 나는 --- 예 아니
오
1= 매우 불만족 부터
5= 매우 만족
1= 매우 비효과적부터
5= 매우효과적
1. 직무와 관련된 능력을 향상시키기 위해 동료나 상사로부터 공식적으로
코칭(Coaching)을 받은 적이 있다. 1 2 1 2 3 4 5 1 2 3 4 5
2. 회사에서 지정한 멘토(Mentor)로부터 나의 경력개발에 도움이 되는
멘토링(Mentoring)을 받은 적이 있다. 1 2 1 2 3 4 5 1 2 3 4 5
3. 업무에 필요한 특정 지식이나 기술과 관련하여 상사 또는 동료로부터
일대일(1:1)로 직무현장학습(OJT 등)을 받은 적이 있다. 1 2 1 2 3 4 5 1 2 3 4 5
4. 회사의 업무 개선을 위한 액션러닝 프로젝트(Action Learning Project)에
동료들과 함께 참여한 적이 있다. 1 2 1 2 3 4 5 1 2 3 4 5
5. 회사에서 도입한 기술 및 장비에 대해 공급업체에서 제공하는 교육을
받은 적이 있다. 1 2 1 2 3 4 5 1 2 3 4 5
6. 직무 역량 향상을 목적으로 회사가 제공하는 교육훈련을 사내 연수원
(또는 사내 교육장)에서 받은 적이 있다. 1 2 1 2 3 4 5 1 2 3 4 5
7. 직무 역량 향상을 목적으로 회사가 지원하는 교육훈련을 사외
연수시설에서 받은 적이 있다. 1 2 1 2 3 4 5 1 2 3 4 5
8. 회사의 지원 또는 계획 하에 컴퓨터 또는 인터넷을 활용한 교육훈련을
받은 적이 있다. 1 2 1 2 3 4 5 1 2 3 4 5
9. 회사의 지원 또는 계획 하에 독서통신을 활용한 교육훈련을 받은 적이
있다. 1 2 1 2 3 4 5 1 2 3 4 5
10. 회사로부터 대학(또는 교육기관)에서의 수업료 등을 지원 받은 적이
있다. 1 2 1 2 3 4 5 1 2 3 4 5
177
I I. 다음은 귀하의 개인적 성향 및 특성에 대한 질문입니다. 적절한 곳에 √ 표시를 해 주십시오 .
나는 ---
전혀
그렇지
않다
그렇지
않다
그저
그렇다 그렇다
전적으
로
그렇다
1. 만약 어떤 일이 복잡해 보이면, 그것을 시도하지도 않을 것이다. 1 2 3 4 5
2. 어려워 보이는 일이 있어도, 이를 해결하기 위해 새로운 것을 배우려 하지 않는다. 1 2 3 4 5
3. 새로운 일을 시도할 때, 만약 초기에 성공하지 못하면, 나는 쉽게 포기한다. 1 2 3 4 5
4. 어떤 계획을 세울 때, 나는 그 계획을 완성할 수 있다고 확신한다. 1 2 3 4 5
5. 처음에 주어진 업무를 완수하지 못하면, 나는 그것을 할 수 있을 때까지 계속 한다. 1 2 3 4 5
6. 좋아하지 않은 업무가 주어졌을 때에도, 나는 그것을 완수할 때까지 매달린다. 1 2 3 4 5
7. 하기로 결정한 일이 있으면, 나는 바로 실행에 옮긴다. 1 2 3 4 5
8. 실패는 내가 그 일을 더욱 열심히 하도록 만든다. 1 2 3 4 5
9. 내 자신을 위해 중요한 계획을 세웠을 때, 그것들을 달성하는 경우가 적다. 1 2 3 4 5
10. 내 인생에서 직면하는 문제들을 처리하는 데에 능력이 없는 것 같다. 1 2 3 4 5
11. 예상하지 못한 문제가 발생했을 때, 나는 그것들을 잘 처리하지 못한다. 1 2 3 4 5
12. 일을 처리하는 나의 능력을 확신하지 못한다. 1 2 3 4 5
13. 도전적인 일을 할 수 있는 기회를 중요하게 생각한다. 1 2 3 4 5
178
나는 ---
전혀
그렇지
않다
그렇지
않다
그저
그렇다 그렇다
전적으
로
그렇다
14. 어려운 업무를 만족스럽게 처리하지 못하면, 다음에 그 업무를 할 때 더욱
열심히 하려고 한다. 1 2 3 4 5
15. 새로운 것을 배울 수 있는 업무를 선호한다. 1 2 3 4 5
16. 새로운 것을 배울 수 있는 기회를 중요하게 생각한다. 1 2 3 4 5
17. 매우 어려운 업무를 하면서도 최선을 다한다. 1 2 3 4 5
18. 과거의 업무 성과를 뛰어 넘기 위해 노력한다. 1 2 3 4 5
19. 능력의 범위를 확장시킬 수 있는 기회를 중요하게 생각한다. 1 2 3 4 5
20. 문제 해결에 어려움이 있을 때, 최선의 방식을 선택하기 위해 여러 다른
접근 방식을시도한다. 1 2 3 4 5
21. 교육훈련 및 학습 활동으로부터 내가 배울 수 있는 최대한을 배우려고 한다. 1 2 3 4 5
22. 내가 다른 사람보다 교육훈련 및 학습 활동에서 더 많은 것을 배운다고 믿는다. 1 2 3 4 5
23. 교육훈련 및 학습 활동에서 제공하는 지식과 기술을 배우고자 한다. 1 2 3 4 5
24. 학습 활동을 통해 나의 기술을 향상시키고 싶다. 1 2 3 4 5
25. 지식 및 기술을 향상시키기 위해 학습 활동에 기꺼이 참여할 것이다. 1 2 3 4 5
26. 비록 업무상 우선 순위가 아닐지라도, 나는 학습활동에 기꺼이 참여할 것이다. 1 2 3 4 5
27. 직무 기술과 역량을 향상시키기 위해 기꺼이 나의 노력을 투자할 것이다. 1 2 3 4 5
179
I I I . 다음은 귀하의 업무 환경에 대한 질문입니다. 적절한 곳에 √ 표시를 해 주십시오.
전혀 그렇지 않다
그렇지 않다
그저 그렇다
그렇다 전적으 로
그렇다 1. 우리 회사는 직원들이 다양한 학습 활동에 참여하도록 지원한다. 1 2 3 4 5 2. 우리 회사는 직원들의 지속적인 학습 요구를 중요시 한다. 1 2 3 4 5 3. 우리 회사는 새로운 지식과 기술을 습득한 직원들에게 상응하는 보상을 제공한다. 1 2 3 4 5 4. 우리 회사는 직원들의 학습 활동에 필요한 자원들(시간, 공간, 장비 등)을 제공한다. 1 2 3 4 5 5. 나의 상사는 내가 가능한 한 많은 학습 활동에 참여하도록 지원한다. 1 2 3 4 5 6. 나의 상사는 내게 반복적이고 새롭지 않은 업무만을 할당한다. 1 2 3 4 5 7. 나의 상사는 나에게 학습 활동과 관련된 정보를 제공한다. 1 2 3 4 5 8. 나의 상사는 내가 학습 활동에 참여해야 할 때, 나의 업무 일정상의 편의를 제공한다. 1 2 3 4 5 9. 나의 직무는 내가 업무상의 변화를 처리할 수 있는 더 나은 방식을 찾도록 만든다. 1 2 3 4 5 10. 나의 직무는 내가 고객의 기대에 부응하기 위해 지속적인 학습을 하도록 만든다. 1 2 3 4 5 11. 나의 직무는 내가 학습 활동에 더 많은 시간을 할애하기 어렵게 만든다. 1 2 3 4 5 12. 나의 직무 성과는 나의 업무 지식 및 기술의 수준에 의해 결정된다. 1 2 3 4 5
180
IV. 다음은 통계처리를 위한 기본적인 문항입니다. 1. 현 직장 (회사) 에서의 근무경력은 언제부터 입니까? ( ) 년 부터
2. 최종 학력? □ 고졸 □ 전문대 졸 □ 4년제 대학교 졸 □ 대학원 졸
3. 직위는? □ 사(행)원 □ 대리 □ 과장 □ 차(팀)장 □ 부장 □ 임원
4. 회사가 속한 산업은? □ 은행업 □ 증권업 □ 보험업 □ 제조업 □ 이 외 산업
( )
끝까지 성의껏 답변해 주셔서 대단히 감사합니다 !
181
업무 현장 학습 설문지(2)
이 설문지는 여러분이 업무를 수행하는 과정에서 참여하는 공식적 교육훈련과 회사의 지원이나 계획과 관계없이 스스로
하는 비공식적 학습 활동에 대한 연구를 위해 설계되었습니다. 여러분의 설문 참여는 가장 효과적이고 효율적인 공식 및
비공식 학습 활동을 위해 중요한 자료가 될 것입니다.
설문지 작성은 약 15-20분 정도 소요될 것입니다. 여러분의 모든 응답은 비밀이 보장되며, 연구 결과는 단지 합산된
형태로만 보고될 것입니다. 여러분의 설문 참여는 자발적이며, 응답하고 싶지 않은 질문은 하지 않을 수도 있습니다.
설문과 관련된 질문이 있으시면, 언제든지 박 XX 연구위원 (82-2-3700-1519) 에게 연락을 주십시오
귀하의 설문지는 각각 다른 문항들을 포함한 1차와 2차로 구성되어 있습니다. 귀하의 1차와 2차 설문지를 대응시키기 위해 귀하의 고유 번호 확인이 필요합니다. 본 설문은 2차 설문지 입니다 다시 한번, 귀하의 고유번호는 절대 연구 이외의 다른 목적으로 사용되지 않을 것이며, 모든 응답은 비밀이 보장됨을 약속합니다. 귀하의 휴대폰 뒷 4자리를 기재해 주시길 바랍니다.
183
I . 다음은 귀하의 비공식 학습 활동(회사의 지원이나 계획 없이 스스로 하는 학습) 에 대한 1) 참여 빈도와 2)효과성에 관한 질문입니다. 적절한 곳에 √ 표시를 해 주십시오.
학습 활동 빈도 학습 활동 효과성
지난 1 년 동안, 나는 --- 전혀
안함
드물
게함
종종
함
자주
함
매주
자주
함
매우
비효
과적
비효
과적 보통
효과
적
매우
효과
적
1. 특정 업무 상황과 관련하여 상사와 비공식적인 토론을 했다. 1 2 3 4 5 1 2 3 4 5
2. 휴식 시간 또는 일과 후에 동료들과 업무 상의 문제를 해결하기
위해 아이디어를 교환했다. 1 2 3 4 5 1 2 3 4 5
3. 어려운 업무 상황에 대해 다른 사람들이 어떻게 처리하는 지를
관찰하였다. 1 2 3 4 5 1 2 3 4 5
4. 업무상의 문제를 해결하기 위해 다른 사람들과 협력하였다. 1 2 3 4 5 1 2 3 4 5
5. 새로운 상황에 처했을 때, 과거의 어려운 업무 상황을 처리했던
나의 경험을 되돌아보았다. 1 2 3 4 5 1 2 3 4 5
6. 스스로의 시행착오를 통해 어려운 업무 상황을 해결하려 했다. 1 2 3 4 5 1 2 3 4 5
7. 어려운 업무 상황을 해결하기 위해 공식 교육훈련 프로그램
(집합교육 등)에서 배운 것을 되돌아 보았다. 1 2 3 4 5 1 2 3 4 5
8. 어려운 업무 상황에 대한 해답을 찾기 위해 스스로 업무 메뉴얼
(또는 사내 작업 지침서) 등을 참조하였다. 1 2 3 4 5 1 2 3 4 5
9. 어려운 업무 상황에 대한 해답을 찾기 위해 인터넷을 통해
정보를 얻었다. 1 2 3 4 5 1 2 3 4 5
10. 유용한 정보를 얻기 위해 자발적으로 컨퍼런스 또는 세미나에
참석하였다. 1 2 3 4 5 1 2 3 4 5
11. 업무 정보 및 지식 습득을 위해 전문 잡지 또는 관련 서적을
읽었다. 1 2 3 4 5 1 2 3 4 5
12. 어려운 업무 상황 해결을 위한 정보 및 지식을 얻기 위해 회사
밖의 관련 전문자들에게 연락하였다. 1 2 3 4 5 1 2 3 4 5
184
I I . 다음은 귀하의 비공식 학습 활동 (회사의 지원이나 계획 없이 스스로 하는 학습) 과 관련된 서술형 질문입니다.
1. 귀하가 업무 현장에서 회사의 지원이나 계획과 관계 없는 비공식 학습활동이 필요한 때는 언제입니까?
2. 귀하가 업무 현장에서 회사의 지원이나 계획과 관계없이 실행하는 비공식 학습활동에는 어떤 것들이 있습니까?
3. 귀하가 업무 현장에서 회사의 지원이나 계획과 관계없이 비공식 학습활동을 통해 배운 것들은 무엇이고, 어떻게 그것들을 활용하였습니까?
끝까지 성의껏 답변해 주셔서 대단히 감사합니다 !
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APPENDIX C
AMOS Outputs for the Finally Modified Structural Model
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Notes for Model (Default model)
Computation of degrees of freedom (Default model)
Number of distinct sample moments: 66 Number of distinct parameters to be estimated: 32
Degrees of freedom (66 - 32): 34
Result (Default model)
Minimum was achieved Chi-square = 64.670 Degrees of freedom = 34
Probability level = .001
Model Fit Summary
CMIN
Model NPAR CMIN DF P CMIN/DF Default model 32 64.670 34 .001 1.902 Saturated model 66 .000 0 Independence model 11 701.832 55 .000 12.761
RMR, GFI
Model RMR GFI AGFI PGFI Default model .017 .949 .902 .489 Saturated model .000 1.000 Independence model .092 .478 .374 .399
Baseline Comparisons
Model NFI Delta1
RFI rho1
IFI Delta2
TLI rho2 CFI
Default model .908 .851 .954 .923 .953 Saturated model 1.000 1.000 1.000 Independence model .000 .000 .000 .000 .000
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Parsimony-Adjusted Measures
Model PRATIO PNFI PCFI Default model .618 .561 .589 Saturated model .000 .000 .000 Independence model 1.000 .000 .000
NCP
Model NCP LO 90 HI 90 Default model 30.670 11.762 57.380 Saturated model .000 .000 .000 Independence model 646.832 565.086 736.017
FMIN
Model FMIN F0 LO 90 HI 90 Default model .320 .152 .058 .284 Saturated model .000 .000 .000 .000 Independence model 3.474 3.202 2.797 3.644
RMSEA
Model RMSEA LO 90 HI 90 PCLOSE Default model .067 .041 .091 .127 Independence model .241 .226 .257 .000
AIC
Model AIC BCC BIC CAIC Default model 128.670 132.712 234.692 266.692 Saturated model 132.000 140.337 350.672 416.672 Independence model 723.832 725.221 760.277 771.277
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ECVI
Model ECVI LO 90 HI 90 MECVI Default model .637 .543 .769 .657 Saturated model .653 .653 .653 .695 Independence model 3.583 3.179 4.025 3.590