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Learning in the workplace: investigating perceived workload, work motivation, and choice independence in the construction industry
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Learning in the workplace: investigating perceived workload, work motivation, and choice independence in the construction industry
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Learning in the Workplace: Investigating Perceived Workload, Work Motivation, and
Choice Independence in the Construction Industry
by
Joshua Adam Johnson
Rossier School of Education
University of Southern California
A dissertation submitted to the faculty
in partial fulfillment of the requirements for the degree of
Doctor of Education
December 2024
© Copyright by Joshua Adam Johnson 2024
All Rights Reserved
The Committee for Joshua Adam Johnson certifies the approval of this Dissertation
Dr. Barry Savage
Dr. Anthony Maddox
Dr. Helena Seli, Committee Chair
Rossier School of Education
University of Southern California
2024
iv
Abstract
This study investigated the relationship between workplace factors, namely perceived workload,
work motivation, and choice independence, and employees’ approaches to learning at a midsized organization within the construction industry. The study applied deep, surface, and surfacedisorganized learning approaches and their cognitive outcomes as its foundational theoretical
frameworks. The workplace climate framework proposes workload, choice independence, and
supervision dimensions as influential in the learning approaches. A mixed-methods approach
was used to provide a holistic examination. Preliminary findings identified a tendency toward
surface-learning approaches among technicians. Quantitative results show a significant
correlation between perceptions of high workload and surface learning. On the other hand, the
qualitative outcomes described work obligations, leaving little time for deep engagement.
However, participants discussed motivations such as skill development opportunities as key in
promoting intrinsic drives for education. They also characterized supervision as hands-off
without guidance for self-directed growth. Insights emerged that compare technicians’
experiences to deep learning propositions. Theoretical findings also reveal additional
environmental influences that impact construction workers' learning and job satisfaction,
including supportive leadership, autonomy in task management, and collaborative workplace
cultures. Practically, recommendations aim to improve employees’ learning experiences by
optimizing workload balance, fostering independence, and providing supportive oversight.
Addressing barriers and enhancing facilitators of deep learning holds promise to strengthen
technician performance, retention, and career development at the company.
v
Acknowledgments
First and foremost, I would like to thank my heavenly father for His countless blessings
throughout my educational pursuits and for enabling me to conduct this study successfully.
My deep and sincere gratitude extends to my research supervisor and dissertation chair,
Dr. Helena Seli, for allowing me to conduct this research and also for providing invaluable
guidance through to its completion. Her dynamism, vision, sincerity, and motivation have deeply
inspired me. She taught me key methodological approaches that have enhanced my clarity in
presenting my findings. It was also a great privilege and honor to study under her colleagues,
most notably Dr. (Barry Savage) and Dr. (Anthony Maddox). I thank all of you for not only
serving on my committee but being my friends and mentors over the years.
I express extreme gratitude to my parents for their love, prayers, caring, sacrifices, and
educating and preparing me for my future, and to my siblings for their love and encouragement
throughout the years (SLY).
I am most indebted and thankful to my wife and our sons for their love, understanding,
prayers, and continued support in completing this research. Their sacrifices are the primary
reason this dissertation came to fruition. They are also my source of motivation to attain this
doctoral degree. I love you and dedicate this to you (SLY).
I wish to thank my friends and research colleagues for their support throughout this
study.
I want to thank the University of Southern California for the support to do this work.
Finally, my thanks go to all the people who have supported me in completing this study,
directly or indirectly.
vi
Table of Contents
Abstract.......................................................................................................................................... iv
Acknowledgments............................................................................................................................v
List of Tables ...................................................................................................................................x
List of Figures................................................................................................................................ xi
Chapter 1: Introduction to the Study................................................................................................1
Organizational Context and Mission ...............................................................................................2
Background of the Problem ...........................................................Error! Bookmark not defined.
Importance of Workplace Learning...................................Error! Bookmark not defined.
Technician Retention and Advancement Challenges ....................Error! Bookmark not defined.
Statement of the Problem.................................................................................................................6
Purpose of the Study ........................................................................................................................9
Research Questions..........................................................................................................................9
Significance of the Study...............................................................................................................10
Theoretical Framework..................................................................................................................11
Chapter 2: Literature Review.........................................................................................................13
Approaches to Learning.................................................................................................................13
Learning Approaches in the Academic Context ................................................................13
Deep Approach ..................................................................................................................14
Surface Approach...............................................................................................................15
Surface-Disorganized Approach........................................................................................16
Learning in the Workplace.............................................................................................................18
Learning in the Academic Context ....................................................................................19
Learning in the Business Context ......................................................................................21
Learning in the Medical Context .......................................................................................23
vii
Work Motivation............................................................................................................................25
Intrinsic Motivation ...........................................................................................................26
Extrinsic Motivation ..........................................................................................................28
Workload and Autonomy in Decision-Making..............................................................................31
Relationship Between Autonomy and Learning Approaches............................................32
Three Dimensions of Approaches to Learning ..............................................................................34
Workplace Climate ........................................................................................................................36
Good Supervision...............................................................................................................36
Workload............................................................................................................................39
Choice Independence .........................................................................................................41
Work Relationships............................................................................................................43
Recognition and Feedback.................................................................................................46
Growth Opportunities ........................................................................................................48
Work-Life Balance and Learning ......................................................................................50
Diversity and Inclusion for Learning.................................................................................51
Autonomy and Empowerment for Workplace Learning ...................................................53
Physical Work Environment and Learning........................................................................54
Summary........................................................................................................................................56
Chapter 3: Methodology ................................................................................................................58
Data Sources ..................................................................................................................................59
Survey ................................................................................................................................60
Participants........................................................................................................................62
Instrumentation..................................................................................................................63
Interviews...........................................................................................................................64
Participants........................................................................................................................65
viii
Instrumentation..................................................................................................................67
Data Analysis.................................................................................................................................72
Validity and Reliability..................................................................................................................73
Summary........................................................................................................................................75
Chapter 4: Results and Findings....................................................................................................76
Participants.....................................................................................................................................76
Survey Participants ............................................................................................................77
Interviewees.......................................................................................................................77
Research Question 1: What Are Construction Workers’ Approaches to Learning? .....................79
Deep Learning....................................................................................................................79
Surface Rational Learning .................................................................................................83
Surface Disorganized Learning..........................................................................................87
Summary............................................................................................................................91
Research Question 2: What are Construction Workers’ Perceptions of Workplace Climate? ......92
Good Supervision...............................................................................................................93
Perception of Workload .....................................................................................................96
Choice Independence .........................................................................................................99
Summary of Descriptive Statistics for Workplace Climate.............................................102
Research Question 3: What Effect, if Any, Does Workplace Climate Have on Construction
Workers’ Approaches to Learning? .............................................................................................103
Good Supervision.............................................................................................................103
Workload..........................................................................................................................104
Summary..........................................................................................................................106
Research Question 4: How Do Employees Experience Learning in the Workplace? .................107
Qualitative Data Theme 1: The Role of Social Interactions in Facilitating Learning .....107
Qualitative Data Theme 2: Technician Emotional Response to Learning Interactions...109
ix
Qualitative Data Theme 3: Effects of Learning Interactions on Vocational Pursuits......111
Qualitative Data Theme 4: The Effects of Learning Interactions on Attitudes Toward
Organizational Positions and Teams................................................................................112
Relationship Between Survey and Interview Data ..........................................................114
Chapter 5: Discussion and Recommendations.............................................................................115
Discussion of Key Findings for Research Question 1 .................................................................116
Discussion of Key Findings for Research Question 2 .................................................................117
Discussion of Key Findings for Research Question 3 .................................................................118
Recommendations for the Organization Studied .........................................................................118
Establish Clear Career Pathways.....................................................................................119
Enhance Autonomy and Engagement..............................................................................119
Provide Targeted Training Opportunities........................................................................120
Reinforce Learning Application ......................................................................................120
Reduce Perceived Workload Pressure .............................................................................121
Theoretical Contributions ............................................................................................................122
Limitations and Delimitations......................................................................................................122
Conclusions..................................................................................................................................125
References....................................................................................................................................129
Appendix A: Protocols.................................................................................................................142
Interview Protocol........................................................................................................................142
Survey Protocol............................................................................................................................145
Appendix B: Protocols.................................................................................................................151
Appendix C: Ethics........................................................................Error! Bookmark not defined.
Appendix D Limitations and Delimitations...................................Error! Bookmark not defined.
x
List of Tables
Table 1 Research Questions and Data Sources..............................................................................58
Table 2 Interview Participants’ Education Level...........................................................................78
Table 3 Descriptive Statistics for Items on the Deep Learning Scale............................................81
Table 4 Descriptive Statistics for Items on the Surface Rational Learning Scale .........................85
Table 5 Means, Standard Deviations, and Interpretations of the Mean for the Surface
Disorganized Scale.........................................................................................................................89
Table 6 Means, Standard Deviations, and Interpretations of the Mean for the Good Supervision
Scale...............................................................................................................................................95
Table 7 Means, Standard Deviations, and Interpretations of the Mean for the Workload Scale...97
Table 8 Means, Standard Deviations, and Interpretations of the Mean for the Choice
Independence Scale......................................................................................................................101
Table 9 Correlations Between Choice Independence and the Three Learning Approaches........104
Table 10 Correlations Between Workload and the Three Learning Approaches........................105
Table A1 Interview Protocol: Initial Prompt and Follow-Up Questions.....................................142
Table B2 Quantitative Analysis Matrix .......................................................................................153
Table B3 Data Analysis Matrix ...................................................................................................154
xi
List of Figures
Figure 1 Frequency Graph for Deep Learning Scale .....................................................................80
Figure 2 Frequency Graph for Surface Rational Learning Scale...................................................84
Figure 3 Histogram for Surface-Disorganized Scale .....................................................................88
Figure 4 Histogram for Good Supervision Scale...........................................................................94
Figure 5 Histogram for Workload Scale........................................................................................98
Figure 6 Histogram for Choice Independence Scale .....................................................................99
1
Chapter 1: Introduction to the Study
In today’s dynamic global economy, organizations recognize the critical role of
workplace learning in their success. Concepts such as learning organizations, learning climate,
and lifelong learning have emerged to reflect this understanding (Kyndt et al., 2009). However,
some factors can hinder employees’ learning, including perceived workload, work motivation,
and choice independence (Kirby et al., 2003). When an employee perceives a workload to be
overwhelming or lack motivation for the work they do, they may get discouraged and forfeit
learning. These factors impact employees’ learning approaches, such as deep, surface-rational,
and surface-disorganized approaches. Moreover, maladaptive approaches to organizational
learning also hinder growth opportunities for both individuals and corporations.
Continuous workplace training is necessary to catch up with technological evolutions
(Hager, 2005). The need for constant training is more so emphasized in the 2009 case example of
Toyota when their vehicles encountered significant safety issues, causing over nine million
recalls. This incident revealed how the corporation’s focus on becoming the world’s leading
automobile producer overshadowed its commitment to fostering a culture of continued
education, harming product quality. Similarly, when the Blockbuster movie industry refused to
grow with technological developments and consumer preferences, Netflix’s innovative approach
allowed it to grow rapidly to the extent that it challenged Blockbuster in its shares of the
audience (Davis & Higgins, 2013).
There is a shortage of research on employee learning approaches in the construction
industry. Kirby et al. (2003) identified three factors influencing employees’ approaches to work:
deep, surface-rational, and surface-disorganized. While their findings were consistent with the
literature on student learning, the relationship between perceived workload, work motivation,
2
and choice independence in commercial construction still needs to be addressed. These
constructs were chosen based on their ability to fit the type of work and structure utilized at the
study organization.
Background of the Problem
It is important for employees and organizations that workplace learning should promote
job satisfaction, productivity, and long-term business success. However, with limited
opportunities for growth and significant compensation inequities in many industrial settings, the
challenges of work retention and career management for technicians are real. Commitment to
skills training with equitable rewards and well-defined channels for career progression is a sure
way to maintain a motivated and capable workforce faced with such serious issues.
Importance of Workplace Learning
Workplace learning is a necessary process that benefits both employees and
organizations. Learning new skills and concepts opens up employee career growth and mobility
opportunities, enabling workers to take on more complex roles and responsibilities over time,
leading to higher job satisfaction and motivation (Kyndt et al., 2009). Learning also improves
productivity and problem-solving by keeping an individual’s competencies current. Competency
enhances their employability in their current organization and externally. As work evolves due to
technological advancements, the skills and expertise required to perform jobs change (Burke &
Ng, 2006). This realization makes ongoing learning essential for employees to maintain and
improve their job knowledge and performance.
From the organizational perspective, supporting workplace learning helps boost several
key performance indicators. For instance, it increases employee retention rates which saves costs
on recruiting and training new hires. Learning also drives innovation as the staff continuously
3
develop their perspective and critical thinking (Armstrong-Stassen & Schlosser, 2008; Aguinis &
Kraiger, 2009). Furthermore, learning equips an organization with a skilled workforce ready to
exploit emerging opportunities. It prepares them to meet shifting customer demands and remain
competitive (Avolio & Gardner, 2005). Lastly, workplace learning benefits the individual
employee and the organization by enhancing worker’s job skills which enables employees’
career mobility. Education improves retention, drives innovation, strengthens competitiveness,
and translates directly to more substantial business results for organizations (Chaudhry, 2007).
Therefore, fostering a strong culture of ongoing learning is essential for workers and companies
to succeed.
Technician Retention and Advancement Challenges
Technicians at industrial organizations often face significant retention and career
advancement challenges. Technicians ensure smooth operations as frontline workers perform
hands-on maintenance, troubleshooting, and repairs. However, high turnover in these positions is
a widespread problem. One factor hindering technicians’ retention is a lack of opportunities for
vertical promotion. Many remain doing similar work for years without developing new skills or
taking on leadership duties (Baeten et al., 2010). This lack of professional growth causes
dissatisfaction and makes external opportunities more attractive. Without clear pathways to
progress their careers, technicians seek other employers offering development programs.
Compensation also plays a role, as technical roles often do not receive wages comparable
to the level of responsibility carried out daily (Barley, 2012). When top-performing technicians
realize they are underpaid relative to their contribution, they start looking elsewhere for
improved compensation. This forces companies into a cycle of high recruitment and training
costs.
4
Job satisfaction among technicians tends to be associated with autonomy, various tasks,
and recognition from managers (Bensimon et al., 2006). However, micro-management styles and
menial fire-fighting tasks diminish workplace enjoyment. A lack of empowerment and
involvement in process improvements pushes technicians to consider other work settings, and
addressing these challenges requires an organizational commitment to ongoing skills training,
rewards for acquiring new qualifications, and career lattices that provide step-by-step
progression in responsibility and compensation. Fostering an engaging work culture with
opportunities to take on leadership roles will help cultivate a robust internal talent pipeline for
more specialized technician careers.
Organizational Context and Mission
The primary focus of this research was the Colorado Construction Company (CCC,
pseudonym), a mid-sized organization in the construction industry. With over a century’s
experience in engineering, installing, servicing, testing, and managing advanced systems, CCC
stands out as an established turnkey systems integrator. The organization strives to meet each
customer’s needs and budget by customizing fire alarm, detection, and suppression plans. This
vision aligns with CCC’s mission statement. The company creates high-quality installations and
offers routine inspections and related services. The company also believes this approach will
ensure long-lasting functionality for the installed system and those occupying the spaces in
which it was installed.
CCC has, for a long time, held a competitive advantage against newer market entrants.
However, as customer expectations and demands have increased, keeping up with such
evolutions has become challenging. Nevertheless, CCC recognizes that providing quality
5
assurance and employee growth is necessary for it to remain the best choice for clients as
competition grows.
Workplace learning is vital for organizational success in today’s rapidly changing
business environment. Nonetheless, many companies still do not prioritize learning and
development. According to CCC, workplace surveys, declining employee performance,
motivation, and retention have become serious problems that have lowered its profits. Since
2021, CCC has had experienced a 30% employee turnover which has led to the stalling of some
of its projects (Anonymous, 2019). The company president realized that having a clear definition
of a career path plus having viable avenues for employee development and training were some of
the reasons for higher employee turnover. Retaining experienced employees is challenging as
they are unhappy and disengaged from their roles. The company can invest heavily in promoting
workplace learning as a measure to improve employee motivation and additionally increase
employee retention.
The decline in employee performance, motivation, and retention coincides with
technological advances within the construction industry. New methods and materials resulting
from technological advancement require constant learning. However, interviews with CCC
managers suggest that employees do not apply knowledge from past training to expand their
abilities.
The company’s workforce is also aging, and many workers are nearing retirement. The
younger new hires have insufficient institutional knowledge compared to the more experienced
staff, therefore calling for a definitive knowledge transfer and exchange methodology. Without a
working strategy to facilitate knowledge transfer between generations, when senior staff
members leave, the company loses valuable expertise. To address these productivity, retention,
6
and skill gaps, CCC must understand how its employees approach learning. Failure to foster
intrinsic motivation and self-directed learning means workers will not adapt to changing job
demands.
This study evaluated learning approaches using Kirby’s change framework (Kirby et al.,
2003), providing insights to help CCC build a culture of continuous learning. The research
focused on CCC since it has observed a decline in employee career progression over the past
three years. The company employs approximately 120 male employees, all of whom have been
with CCC for at least a year. Employees’ racial and ethnic backgrounds are diverse, and their
ages range from 19 to 70. With advancements in technology, demands, and expectations, the
number of these employees progressing in their careers with the company has decreased.
Managers and other leadership personnel have begun questioning their workforce’s stagnation
and lack of motivation.
Therefore, this study explored why continuous learning and career development are
absent among CCC employees despite the rising standards in the company’s line of work. Doing
so can provide valuable insights into how CCC can better support its staff and encourage
continuous professional growth while maintaining high-performance levels. With the right
strategies in place, CCC can reengage workers and prepare them to propel the company into a
successful future (Kirby et al., 2003).
Statement of the Problem
Organizations must recognize that their capacity for growth directly correlates to their
employees’ learning experiences (Gino & Staats, 2015). Learning experiences help shape the
futures of both the employee and the organization. They are increasingly significant as
technology and innovations have led to intelligent machines capable of performing physical tasks
7
(Beke, 2005). Hyslop-Margison (1999) argued that conceptual and thinking skills would be
considered more valuable as jobs requiring physical skills become automated. Pearn et al. (1992)
asserted that trends such as participatory management, delayering, increased decision-making at
lower levels, and multi-skilling would place greater demands on average employees. Only a
malleable, deep-thinking workforce will be capable of producing more with scarce resources.
Deep approaches to learning are both necessary and desirable in the workplace and lead to a
more engaged workforce.
According to a workplace Gallup poll, in the United States, employees are more likely to
be engaged in their jobs than in any other country. However, with only 31% committed, there is
still significant room for improvement. An unengaged workforce damages productivity and
potential competitiveness (Thibault Landry et al., 2017). Competitiveness is a motivational factor
in driving employees to perform better, and for many organizations, it drives retention. Recent
research (Kyndt et al., 2009) reveals that a stimulating work environment, where employees have
more opportunities, makes them want to remain with their current employer. When employees
cannot have a learning experience at an organization or sense there is no choice, their
independence, learning outcomes, development, progression, and retention decline (Heriyati &
Ramadhan, 2012). Retention is an issue for industries that have large numbers of young trainees
(15–30 years of age), such as hairdressing (94%) and hospitality (77%), where employees tend to
change jobs often before they find their preferred employer or career (Curson, 2004). Starbucks
sought to overcome this barrier through training policies that encourage and reward commitment
to the job and enterprise (San et al., 2012). It achieved its goal by only permitting employees to
undertake training once employed with the enterprise for 12 months (Curson, 2004). This policy
8
helped staff retention by determining who was committed to working for the organization and
willing to invest their time and effort into developing their career.
First-year students in college often need to remember an estimated 60% of what they
learned in high school. Similarly, when employees study solely to pass a performance review or
to convey to managers that they learned a concept, they will often need to remember what they
learned (DeCamilla, 2020). German psychologist Hermann Ebbinghaus pioneered experimental
studies of memory and discovered what is known as the forgetting curve: humans tend to forget
roughly 75% of new information within 6 days (Murre & Dros, 2015). Although many human
resource departments are now actively engaged in moving the focus of their organizations
toward learning and development, few studies have examined how employees learn (Kyndt et al.,
2009).
Learning in the workplace is more likely to be successful when there is a commitment
from the key stakeholders. Watkins and Marsick (1993) argued that commitment must move past
solely providing job training to achieve learning in the workplace. Organizations must consider
factors such as the design and strategy management and how that strategy will foster employee
learning. Forecasting the organization’s external environment will help safeguard it from outside
sources that hinder the organization's ability to learn. Organizations must also understand their
governing policies to make sure that learning pursuits fit within the parameters (Watkins &
Marsick, 1993). Few studies have examined employees’ approaches to learning in the U.S.
construction industry workplace.
Researchers Kirby et al. (2003) argued that, due to the rapidly changing context in which
organizations operate, there is a need for employees to learn in a way that involves integrating
materials from different sources, relating new information to prior knowledge, and applying
9
knowledge differentially according to the situation. There is a need to further expand on Kirby et
al.’s research and investigation into the relationship between perceived workload, work
motivation, and choice independence with employees’ approaches to learning. This study sought
to validate the changes made at one commercial construction company. The final chapters
present the results and a strategic improvement plan.
Purpose of the Study
This study investigates the relationship between workplace factors of perceived
workload, work motivation, and choice independence with employees’ approaches to learning at
the CCC. I used a mixed-methods approach, building on the quantitative research of Kirby et al.
(2003) by incorporating qualitative data collection and analysis. Capturing employees’
perspectives and lived experiences in their own words provided valuable context and insights in
addition to the quantitative data.
Research Questions
1. What approaches to learning (deep, surface, disorganized) do construction workers
report using based on the AWQ?
2. Based on interviews, how do construction workers qualitatively characterize elements
of their workplace’s learning climate (supervision, workload, choice independence)?
3. What is the relationship between quantitatively measured dimensions of workplace
climate (as assessed by the WCQ) and qualitatively described approaches to learning?
4. How do construction workers’ learning experiences on the job, including motivations,
opportunities, challenges, and outcomes, compare to theoretical propositions
regarding deep versus surface approaches?
10
Significance of the Study
Recent research indicates a robust correlation between workplace variables such as
workload, motivation, and autonomy in decision-making and employees’ learning approaches
(Bensimon et al., 2006). High workload perception and low motivation lead employees toward
surface or disorganized learning approaches, potentially impacting retention rates adversely
(Bernsen et al., 2009). Organizations should foster an environment that promotes selfmanagement skills among employees to facilitate long-term learning and retention (Harrison &
Gordon, 2014). This resonates with Kirby et al.’s (2003) identification of limited career growth
opportunities as a major contributor to attrition.
Moreover, integrating qualitative data alongside quantitative measures can yield valuable
insights into employees’ perspectives and experiences concerning workplace factors influencing
their learning approaches (Bensimon et al., 2006; Kirby et al., 2003). Failure by CCC’s
management team to address these issues through comprehensive assessments, as recommended
by researchers, may result in diminished productivity, increased turnover rates, and reduced
employee morale, thereby jeopardizing the company’s profitability (Bernsen et al., 2009; Kirby
et al., 2003). This underscores the imperative for CCC to proactively tackle workplace factors
impacting employees’ learning approaches to sustain its competitiveness in the industry.
Technological innovations and globalization are increasing rapidly, and organizations
encounter adversities that coerce them to innovate and remain relevant (Burke & Ng., 2006).
Globalization has made organizations aware that learning at work is a crucial factor, as exhibited
in the concepts of lifelong learning, learning organizations, and the learning climate (Kyndt et
al., 2009). Kirby et al. (2003) found that organizations seeking to become learning organizations
need to ensure their employees are learning deeply and meaningfully. When employers need to
11
understand the approaches to learning that will help their employees, employees may either leave
or be fired for poor performance. While it may seem simple to replace one worker with another,
hiring someone can cost up to 30% of the job’s salary, which for an employee who makes
$40,000 a year could equal around $12,000.
The cost to organizations that must fully recognize the significance of learning in the
company’s overall success can be onerous (Glaveski, 2019). Organizations realize that success
for the organization and employees can be realized through a growth mindset when it comes to
approaches to learning and regarding mistakes as opportunities to learn and improve (Moser et
al., 2011). Organizations prioritizing employee development make a median revenue of
$169,100 per employee, while companies that fail to do so earn $82,800 (Admin, 2018).
Overworked employees need more time to learn new things or apply what they have learned.
Recent research found that handwashing in hospitals to prevent infections, which is known to be
critical, fell nine percent or greater during a 12-hour shift (Dai et al., 2015). Having identified
significantly declining worker performance and how it directly affects the morale and profit
margins at a decrease of almost 12% from last year at CCC and coupling that issue with the
rising turnover rates and numerous employees making the same mistakes continually has led to
CCC wanting to look at the approaches to learning at the organization instead of fixating on the
employees as the problem.
Theoretical Framework
The approaches to learning and workplace climate (Kirby et al., 2003) are the theories
used to explore the problem of practice. This theory posits three dimensions of approaches to
learning at work to observe: deep, surface-rational, and surface-disorganized. Individuals who
utilize a deep approach to learning have the intention to understand the learning task out of an
12
intrinsic interest in the task (Kirby et al., 2003). Deep learning is associated with strategies such
as collecting new information, relating new knowledge to previous knowledge, and searching for
underlying arguments (Biggs, 1987; Entwistle & Ramsden, 1983). On the other hand, a surface
approach to learning involves simply memorizing information without fully understanding it
(Pho, 2009). This learning type focuses on reproducing material rather than delving into its
deeper meaning. The goal here is to avoid failure rather than strive for mastery. As such,
individuals who take a surface approach often use rote learning techniques and only focus on the
most essential aspects of the subject (Biggs, 1987). Another term used to describe this mindset is
“achievement” or “surface-disorganized.” Those with an achievement-oriented mentality are
primarily motivated by competition and external validation from others regarding grades or
recognition (Kyndt et al., 2012). Their strategy centers around completing tasks efficiently
within set timeframes and organizing their study space effectively (Baeten et al., 2010). The
literature review explores different perspectives on learning approaches.
13
Chapter 2: Literature Review
The main objective of this literature review is to locate and synthesize literature about
workplace learning and how it relates to specific industries such as education, medicine, and
business. This review is purposeful and deliberately selective. It focuses primarily on
occupations closely aligned with industry training, such as academic, medical, and business. It
broadly outlines the environment within which approaches to locate learning in the workplace. It
provides examples from different bodies of literature and presents the advantages of learning in
the workplace. Lastly, it shows what is known and is not yet known about learning in the
workplace and alludes to the need for further research.
Approaches to Learning
Approaches to learning include both congruent strategies persons use to fulfill a task as
well as the intentions they hold toward learning that task (Biggs et al., 2001). Biggs et al. (2001)
observed that students approach learning with certain expectations, and these expectations serve
as the motivation for being fully engaged in the learning process. Students’ consideration for a
learning experience’s outcome strongly correlates to its outcome. Conceptualizing approaches to
learning requires considering students’ intentions toward learning a task and the process they use
to satisfy that intention. Marton and Saljo (1976) pioneered much of the research into approaches
to understanding by investigating the relationship of perceived workload, work motivation, and
choice independence with employees’ approaches to learning. This research revealed three
distinct approaches to learning.
Learning Approaches in the Academic Context
Initial research concerning learning primarily took place in academia. Though there are
differences in the environment, many parallels with learning within the workplace aid in
14
understanding better approaches to learning across different industries and organizations.
Researchers Marton and Saljo (1976) found that approaches to learning collectively represent a
student’s intentions toward learning a task and the process they utilize to fulfill that intention.
Students who have different approaches to learning exhibit different behaviors. The following
sections discuss each.
Deep Approach
A deep approach involves learners intentionally seeking to comprehensively understand
new ideas by relating them to prior knowledge (Biggs, 1987), such as examining logic and
evidence (Biggs et al., 2001), and considering how concepts can be practically applied
(Richardson, 2000). Learners adopting a deep approach view knowledge as interlinked
(Entwistle & Peterson, 2004) and aim to discern fundamental theories, principles, and concepts
rather than simply memorizing isolated facts (Entwistle & Peterson, 2004). They place
importance on gaining learning for its inherent worth and satisfying their innate curiosity rather
than just achieving qualifications or other external rewards (Bolman & Deal, 2017). This
approach requires more effort than surface learning but leads to higher-quality outcomes such as
enhanced critical thinking abilities. Learners are more engaged in learning as they find intrinsic
meaning and satisfaction in delving deeper into conceptual understandings.
The deep approach to learning is associated with enhanced critical thinking, lifelong
learning skills, and the ability to apply knowledge flexibly and creatively to new problems
(Burke & Ng, 2006). Learners using this approach are more engaged and immersed in the
process, inhering meaning in comprehension at deeper levels (Christensen et al., 1995).
However, adopting a deep approach may require more exertion and time than surface learning, as
15
it demands thoroughly investigating and associating new concepts instead of mere memorization
(R. S. Clark & Plano Clark, 2019).
Motivation for learning plays a crucial role, as intrinsic drives such as curiosity and
interest tend to facilitate deeper cognitive processing compared to extrinsic motivating elements
like assessment scores (Creswell et al., 2003). Learners utilizing a deep approach demonstrate an
enhanced predisposition to self-regulate by adequately tracking their comprehension, seeking
assistance when necessary, and associating new ideas with past experiences (Fernandez &
Rainey, 2017). Environmental conditions that endorse a deep approach include autonomy over
defining learning aims and activities, suitable workload volume, and high-quality supervision
(Fidler, 2010). For instance, learners who are permitted some liberty in selecting research topics
or determining assignments experience augmented internal inspiration to explore subjects more
extensively (Jogulu & Pansiri, 2011). In addition, supervision that supports self-direction rather
than rigidity promotes investigation beyond memorizing for testing.
Surface Approach
A surface approach to learning prioritizes satisfying course obligations by reproducing
subject matter with small individual comprehension or linking it to other information (Kim et al.,
2021). Students who embrace this tactic endeavor to finish work with a restricted effort by
centering on separate parts of content instead of interconnecting or combining ideas (Kirby et al.,
2003). The motivation is external, concentrating on completing tasks and qualifying rather than
genuinely engaging with the notions (Kotter, 2007). This approach involves memorizing specific
facts to answer predicted test questions rather than internalizing the material. The aim is to
achieve high assessment scores with the lowest possible work expenditure (Kyndt et al., 2009).
While this approach demands less cognitive processing than deep learning, it frequently leads to
16
fragmented and transitory understanding and the inability to cope with novel circumstances
involving evaluation and problem-solving (Kyndt et al., 2009). However, under conditions like
tight deadlines and assessments prioritizing reproducing over applying knowledge, the
temptation of superficially covering content to attain qualifications surfaces (Kotter, 2007).
Studies suggest that the surface approach to learning relates to the superficial, rote
memorization of information that is rapidly forgotten and problematic to apply to fresh
circumstances, necessitating understanding or problem-solving skills (Lane & Kivisto, 2008).
Relying on this method can be impractical, specifically for multifaceted themes requiring higher
cognitive abilities (Sharples et al., 2014). However, the motivation to reduce exertion while
satisfying objectives may impel avoiding deeper examination, specifically when under deadlines
or assessment, placing significance on replication as opposed to application of ideas (Wetzel &
Farrow, 2023). The surface approach stores just fundamental facts with poor retention instead of
internalization. While expediting short-term assessment achievement, it fails to cultivate
adaptable expertise, given its dissociation of data from broader intellectual frameworks.
Environmental factors that can encourage a surface approach involve perceptions of large
workloads with unrealistic deadlines, assessment emphasis on reproducing content rather than
real comprehension, and inflexible supervision focusing more on following rules than freedom
(Dai et al., 2015). Motivation through rewards or penalties from external evaluation demands is
also ineffective for inspiring interest - instead, it promotes only strategic studying of probable
tests (Austin, 2012). These circumstances diminish deeper analysis as learners aspire to
accomplish the most outstanding amount with minimum effort assessment requirements permit.
Surface-Disorganized Approach
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Specific research has described a third surface approach to learning referred to as surfacedisorganized. Individuals taking on this method show insufficient lucid strategies or structure,
providing a perception of hurried, minute, and ineffective studying attempts. They battle to
recognize critical ideas and logically connect them. This demonstrates a condition of
bewilderment and incapability to dynamically engage with learning due to intellectual overload
from extreme workloads, direction deficiency through effective oversight, or inadequate
scholarly skills (Akinosho et al., 2020). The surface-disorganized approach reflects trouble in
self-managing research because of internal hindrances like too many tasks or external barriers,
including ambiguous requirements and weak guidance. It prevents comprehending on a deeper
level and forming a cohesive understanding.
The surface-disorganized approach has been depicted as the most deficient for any
genuine understanding, with fragmented and poorly retained information. Embracing this tactic
signifies an incapacity to self-administer research and administer time proficiently because of
internal and external barriers. The surface-disorganized learning method usually results from an
overburdened cognitive state and chaotic examination that precludes comprehending the material
on a deeper level (Kyndt et al., 2013). Addressing aspects that add to mental overload and
confusion may assist learners in transferring to more calculated deep or regular surface
techniques with more beneficial results (Delgado & Oyedele, 2022). For instance, reducing
workloads, offering organized direction, and nurturing time management abilities can help
release cognitive load. This may empower learners to coherently correlate ideas and commit to
more strategic studying, whether with an intention for complete grasp as in deep learning or
simply finishing demands, as is characteristic of many surface methods.
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In summary, while a deep approach appears most conducive to high-quality, long-lasting
learning, contextual and individual factors can impact whether learners adopt deep, surface, or
surface-disorganized methods in a given situation. Appreciating these dynamics is significant for
supporting optimal techniques. The deep approach cultivates critical thinking and lifelong
learning abilities but requires extensive effort. The regular surface approach aims to fulfill
necessities with minimum work, resulting in fragmented learning. Meanwhile, the surfacedisorganized approach reflects disarray and feeble scholarly skills. No particular style
consistently works best. Educators should recognize how motivations, environmental stressors,
workloads, supervision, and student preparedness could encourage ineffective, rushed studying.
Targeting influences proven to hinder deep processing or organized learning hold promise for
helping students capitalize on their strengths and abilities. The next portion explores the critical
consequences of learning approaches in more detail.
Learning in the Workplace
This section analyzes how learning approaches may vary based on context, focusing on
academic, business, and medical domains. Each area features distinctive demands, obstacles, and
motivators that could impact learners’ inclination toward deep, surface, or disorganized
processing when acquiring new information. Academia prioritizes content mastery and
assessment, yet educational design influences whether students adopt hurried memorization or
integrate knowledge meaningfully (Susilaningsih et al., 2021). Corporate training cultivates
practical, self-directed learning through experience, but heavy workloads risk surface habits.
Medicine relies on rigorous evidence-based education, yet assessment-heavy curricula and stress
can encourage superficial styles unless mitigated. Appreciating these contextual nuances is vital
for facilitating optimum adaptive approaches in varied learning environments.
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Learning in the Academic Context
Academic settings place a strong focus on thoroughly learning content, achieving high
assessment scores, and obtaining credentials. These priorities raise the likelihood of adopting
surface-learning approaches (Ackerman, 2020). However, characteristics inherent to the
educational context can either support or hinder the deeper processing of material. Factors like
class size, workload volume, and assessment structure influence whether students engage
through memorization-focused cramming or meaningfully relating and applying concepts. The
learning environment, if properly designed, can cultivate intrinsic motivation for comprehensive
understanding.
At the undergraduate level, certain institutional factors can encourage a surface approach.
Large class sizes make personalized learning difficult, while overscheduled assignments
simultaneously fail to allot time for thorough processing. Assessments emphasizing fact recall
over applied understanding can foster last-minute cramming instead of relating ideas cohesively.
These stresses lend themselves to disorganized, rushed surface studying that is inefficient for
learning. Students perceive workloads as unmanageable when classes are large, assessmentheavy, and scheduled tightly (Shaari et al., 2011). The perception of being overworked impedes
concentration on learning for the long term. Research suggests that giving undergraduates more
time to internalize concepts could help mitigate surface tendencies arising from institutional
stressors like courseloads (Alkin, 2011). Appropriate workload balance is essential to facilitate
deeper learning.
However, specific contextual characteristics foster intrinsic motivation and deep learning
approaches better. Higher engagement results from granting students autonomy in selecting
research topics of personal interest and quality supervision. Pedagogies emphasizing active
20
discussion, relating theories to practical examples, and self-directed project work inspire deeper
processing than passive lectures alone (Asghari et al., 2022). Choice, support, interaction, and
relevance to real-world problems enhance motivation to comprehend beyond a surface level.
These learner-centered elements cultivate a sense of control and material connection, countering
tendencies toward hasty memorization in more rigid environments with compressed assessment
timelines.
Curricula structured around problem-solving and inquiry-based learning methods foster
skills like active analysis and curiosity from students. Presenting educational material in the
context of problems or uncovered concepts that require questioning to understand better compels
learners to think deeply about topics and relate information to find solutions. Scaffolding support
provided consistently over long-term assignments also helps maintain learner involvement in the
material through to completion instead of sparking panicked last-minute efforts to understand
complex topics on a compressed timeline (Bertrand & Knapper, 1991). Regular formative
feedback to reinforce comprehension rather than just reporting grades alone guides students
toward an orientation centered around knowledge internalization rather than surface-level
memorization for tests. These attributes mutually shift study habits away from sporadic
cramming of disengaged facts. Instead, they cultivate a cohesive, meaningful, long-lasting
understanding of conceptual frameworks and their real-world usage. By empowering students as
active problem-solvers and focusing on mastery over marks, such curricular elements tend to
decrease surface-learning tendencies that fail to facilitate deeper information processing.
Students typically exhibit increased self-motivation and capability to independently
manage their studies at the graduate level compared to undergraduates (Bolman & Deal, 1994).
The highly specialized nature of graduate training also supports deep, exploratory learning
21
approaches as students delve more extensively into their narrowed fields or topics. However,
PhD candidates, in particular, face higher risks of surface-disorganized studying patterns if
coping with challenges like isolation, mental health issues, and unrealistic publishing pressures.
The solitary work required for extensive research projects and demands to optimize productivity
can overwhelm students’ capacity for organized, efficient learning. Instead, chaotic studying may
result from stress, lack of work-life balance, or inadequate guidance on establishing structured
work routines. While deep approaches remain facilitated by interest-driven learning at the
graduate level, additional social and wellness supports may help deter surface-disorganized
behaviors for candidates facing cognitively taxing circumstances.
Learning in the Business Context
Compared to student learning, employee learning has been the subject of less extensive
research. However, prior studies have revealed both similarities and differences between these
two types of learning. Knapper (2001) pointed out that university education tends to promote
generalized and isolated thought processes focused on finding advanced solutions through
individual competition. In contrast, workplace training is more specialized and emphasizes
practical approaches in a collaborative setting. With the increasing complexity brought about by
change in business organizations, employees will need to adopt adaptable strategies for
integrating new information with prior knowledge to solve emerging problems effectively
(Knapper, 2001). Building on Biggs’ (2012) framework for understanding different approaches
toward learning tasks, Kirby et al. (2003) identified three primary factors applicable specifically
in work settings: deep approach which reflects an eagerness among workers seeking
comprehensive comprehension; surface-rational method where individuals are motivated solely
by external incentives rather than genuine interest or concern; as well as surface-disorganized
22
style characterized mainly by confusion during job execution (Bernsen et al., 2009; Delva et al.,
2004).
The surface-rational approach reflects a preference for orderly, accurate, and detailed
work, thereby making use of surface strategies such as memorization and a methodical, step-bystep approach (Kirby et al., 2003). Dissimilar to a student’s surface approach, the surfacerational has little to do with fear of failure or extrinsic motives (Kirby et al., 2003). The surfacedisorganized approach reflects a nonacademic orientation in combination with surface motives. It
is associated with dissatisfaction with one’s work environment and incompetence when
executing tasks (Bernsen et al., 2009). Kirby et al. (2003) noted that the surface-disorganized
approach is rather a reaction to work than an actual approach.
Kyndt et al. (2009) findings suggest that the deep approach to learning in the workplace
is positively associated with supportive supervision that includes choice independence. In
contrast, the surface-disorganized approach in the same study was negatively associated with the
surface-rational approach and deep approach and positively associated with the workload. Kyndt
et al. (2009) lent support to the notion that the dimensions related to businesses are broadly
consistent with the student learning literature, with some salient differences. Research conducted
on learning in the workplace has found to support the existence of the deep factor, little support
for the achievement, and mixed results concerning the surface factor (Baeten et al., 2010;
Bernsen et al., 2009; Kyndt et al., 2009; Van Ruysseveldt & Van Dijke, 2011). Biggs et al.
(2001) found that although the fear of failure is usually associated with the surface-factor
approach to learning, there is an important distinction: almost nothing in surface-rational
approaches to learning describes this fear. However, surface disorganization is a combination of
surface motives and Entwistle and Ramsden’s (1983) nonacademic orientation; it is more a
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reaction to work than an approach to it. For example, an extrinsic motive for studying may be
common in technical subjects yet acts in combination with meaningful learning, whereas an
extrinsically motivated worker may not fear failure.
Learning in the Medical Context
The motivations and strategies seen in the business context of learning also manifest in
the approach to learning by medical students (Fernando et al., 2013). Teaching in the medical
field is a constantly evolving process that demands continuous expansion of knowledge from
both students and educators. The challenge lies in acquiring knowledge within a limited time
frame, retaining it effectively, recalling information when needed, and interpreting knowledge
proficiently. This challenge has prompted crucial changes in medical education, shifting from
teacher-focused and subject-based teaching to interactive, problem-based, and student-focused
learning. Many medical school curricula have embraced new teaching and learning methods to
varying extents (Koh et al., 2008).
The selected learning approach indicates a positive shift toward deep and strategic
learning in postgraduate students. While most preclinical undergraduates tend to have a
multimodal preference for auditory learning, approaches tend to evolve as students progress in
their medical education. Variances in learning approaches are evident between undergraduates
and postgraduates (Fernando et al., 2013). However, there is no similar distinction in
undergraduate students during their transition from the first to the final year. These differences in
learning approaches hold significant implications for the development of effective medical
curricula for both undergraduates and postgraduates (Fernando et al., 2013). Cook (2002),
emphasizing the student’s role in learning, highlights that successful teaching relies on effective
learning. Regardless of the well-intentioned nature of lessons, the effort is in vain if students do
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not grasp the material. While approaches to learning in the medical field, business, and education
are crucial, another essential aspect of learning is motivation.
Internship experiences in medical training programs necessitate even higher-order
cognitive skills like recognizing relationships between diverse phenomena to diagnose illnesses
accurately (Deci & Ryan, 2013). Moreover, the process of lifelong continuing education endures
as a career-long responsibility and necessity for medical professionals to shape and update their
knowledge responsibly. However, some studies have linked hour limitations increasingly
imposed on interns for compliance reasons with potential risks of insufficient preparation and
inconducive superficial learning among trainees. When work hours are severely constrained,
interns demonstrate hurried surface-level study methods like cursory reviewing materials just
before clinical encounters to compensate for the lack of engaged learning opportunities with
actual patients (Entwistle & Ramsden, 1983). While internships should facilitate the relating of
comprehensive understandings developed in preclinical years to diagnostic practice, specific
systemic stresses can undermine deep processing by rushing trainees and hindering meaningful
experiences essential for connecting theory and practice robustly in healthcare’s high-stakes
environment.
Trainee well-being initiatives in medical education that effectively help mitigate burnout,
stress, and mental exhaustion experienced by students and residents have positively impacted
their learning approaches. When fatigue and well-being needs are better supported, learners
demonstrate decreased tendencies toward harried, surface-level studying habits driven by
exhaustion (Fang et al., 2021). Whether medical training cultivates deep, meaningful knowledge
internalization through inquisitive habitual investigation or encourages short-term superficial
learning marked by inefficient memorization under time pressure depends on interplaying
25
systemic and individual factors. Medical trainers respect personal interests, and providing
supportive clinical environments that balance workloads can enable proper internalization of
extensive yet complicated material. Meanwhile, distributions of responsibilities that overburden
or disengage learners risk enabling rushed, detached habits at the expense of patients’ and
learners’ needs. Therefore, hospital climates and consideration for wellness facilitate education
that translates to enhanced patient care through doctors wielding professionally developed,
applicable comprehension.
In summary, surface-level learning strategies frequently arise when educational or
training systems over-emphasize outcomes like passing grades, job skills credentials, or exam
scores at the expense of meaningful understanding. However, each discussed context - academic,
corporate, or medical - also contains aspects conducive to cultivating deep learning habits
essential for long-term career success. Deeper approaches remain important, whether through
interests driving self-directed scholarly pursuits, real-world problem-solving promoting the
purposeful association of ideas, or clinical experiences requiring comprehension-based
application. Recognizing how structures within different domains variably influence tendencies
toward surface or deep processing is critical. Once contextual pressures are understood, targeted
facilitation of optimal conditions like decreased assessment weight, work-life balance initiatives,
or active knowledge application opportunities better safeguard against surface habits. Awareness
of impacts on learning practice allows tailored support provision across industries.
Work Motivation
Work motivation refers to the drivers that compel employees to perform their duties and
responsibilities to the best of their abilities. It is a complex topic with numerous influences, but at
its core resides two overarching categories: intrinsic motivation and extrinsic motivation.
26
Understanding what inspires and energizes an individual’s work ethic provides valuable insights
for employees desiring career fulfillment and organizations aiming to engage and retain talent.
The following sections will explore the key factors that compose intrinsic and extrinsic
motivation. They examine how autonomy, purpose, and social connections contribute to inherent
enthusiasm. Additionally, they present an analysis of external rewards, evaluations, and
advancement opportunities as extrinsic motivators. The goal is to shed light on optimizing work
design and motivation strategies for both employee well-being and optimal business outcomes.
Intrinsic Motivation
Learning is a mental phenomenon in which motivation plays a significant role (Pernu,
2017). In some contexts, student motivation can be intrinsic instead of extrinsic, which comes
from external sources such as a teacher (Delong, 2006). Intrinsic is more self-motivated since the
actors are actually interested in performing the task, while in extrinsic motivation, there is an
underlying external factor towards the motivation goal. However, in K–12 and higher education
settings, extrinsic motivation plays a significant role when it comes to vocational pursuits.
Intrinsic motivation is widely accepted as a sought-after educational goal (Ryan & Deci, 2000).
According to Pintrich and Schunk (2002), self-determination theory supports the idea that an
individual’s experience regarding competence, autonomy, and relatedness can foster high-quality
motivation, which is internal to the individual and, therefore, intrinsic motivation. SDT points to
extrinsic motivation as not being a fluid concept, but rather, through the internalization process,
extrinsic motivations can be molded into personal values that eventually become behavioral
norms (Ryan & Deci, 2000).
Ryan and Deci (2000) demonstrated that supporting autonomy and providing a rationale
for engaging in an uninteresting behavior promotes internalization and integration processes,
27
such as making correlations between new tasks and prior knowledge. In the review by Niemiec
and Ryan (2009) on SDT and educational practice, the conclusion was that how teachers
introduce learning tasks impacts students’ satisfaction with the basic psychological needs for
autonomy and competence. As an extension, this review demonstrated that if done correctly,
intrinsic motivation with students will thrive and cause a deeper level of learning (Niemiec &
Ryan, 2009).
Intrinsic motivation is also strongly driven by social and collaborative elements of work.
Feeling part of an impactful team effort, having opportunities to help or support others, and
drawing on coworkers’ combined skills all provide intrinsic value to employees (Fang et al.,
2021). Roles that are entirely isolated and siloed, lacking any engagement or connection with
other individuals, tend to undermine intrinsic enthusiasm since they do not fulfill humanity’s
inherently social and relational needs. Organizations have significant power to foster higher
levels of inherent work motivation by designing jobs and roles that facilitate key drivers like
autonomy, continuous skills growth through tailored challenges, a strong sense of purpose or
meaning, social connections and cooperation, and dynamic work that remains stimulating (Fu et
al., 2023). When job crafting allows employees to influence their responsibilities in a way that
optimizes these types of motivating factors, workers are more likely to find work engaging for its
own sake rather than solely for external incentives (Fu et al., 2023). This results in sustained
effort, creativity, commitment, and performance.
However, not all roles can maximally tap into intrinsic motivators. Certain job functions
involve inherently less appealing but essential responsibilities that are unavoidable for
operational or practical reasons. Cases where aspects of the work do not naturally drive intrinsic
enthusiasm may require alternative motivational approaches to engage staff fully (Fu et al.,
28
2023). While intrinsic motives stemming from the work provide inherently sustainable
inspiration, extrinsic factors complement specific job designs. Skillfully combining intrinsic
motivators tailored for individual tasks with carefully considered irrelevant elements like
rewards or recognition can optimize overall work motivation when incorporated sensitively
according to the specific role’s needs (Fu et al., 2023). A balanced, nuanced approach maintains
intrinsic passion while supplementing fewer appealing duties.
Extrinsic Motivation
Following their early years in education, most people fall into either intrinsic or extrinsic
motivation categories. As they seek out vocations, the freedom to be intrinsically motivated
becomes restrained by the roles that people assume and the requirements of those positions to
accept responsibility for non-intrinsically interesting tasks, such as work hobbies that have been
made to become paramount work structures (Kirby et al., 2003). Extrinsic motivation is a
construct that pertains to an activity done to attain some outcome independent of the learning
activity. Extrinsic motivation tends to contrast with intrinsic motivation in engaging an activity
or task for the pleasure of the activity itself, as opposed to its usefulness in terms of a future goal.
Deci and Ryan (1985) concluded that extrinsic motivation could vary considerably in terms of
autonomous behavior. For example, children who do their homework solely to avoid parental
punishments are extrinsically motivated because they are doing the work to secure the outcome
of avoiding punishment. Comparably, children who opt to complete work due to their innate
belief that the work is valuable for their vocational pursuits are also extrinsically motivated
because they do it for the instrumental value instead of necessarily finding the subject matter
interesting. One of the major differences between the two examples is that the second example
entails a feeling of choice. At the same time, the former involves mere adherence to external
29
control. This same scenario also exists in businesses throughout the world. For instance, extrinsic
rewards, such as commissions, bonuses, or prizes, are sometimes effective motivators in sparking
interest in certain tasks. While external rewards can motivate a team to undertake a new
challenge, achieve a certain goal for the quarter, or even learn a new skill, implementing a
rewards-based system has to be strategically thought out (Hayden, 2020).
Provided that many of the organizational tasks prescribed in businesses are not
intrinsically interesting, a question arises concerning how to motivate employees to value and
self-regulate such activities, and without external pressure, to carry them out on their own. This
problem, according to SDT, is conveyed in terms of fostering the internalization and integration
of values and behavioral regulations (Deci & Ryan, 1985). Thus, internalization is the process by
which individuals can transform their value into their own (Deci & Ryan, 2001). Motivation is a
very powerful personal factor that can influence approaches to learning. Still, contextual factors
like workload and choice independence can influence learning approaches (Baeten et al., 2010;
Kirby et al., 2003).
In addition to financial incentives, tangible drivers like formal performance evaluations,
public acknowledgment of achievements, prestigious special projects, and symbols of success
competitively energize extrinsic motivation for certain personalities. Some individuals are
motivated by irrelevant tools that fuel ambition or desire to outperform peers for status and
attention (Fu et al., 2023). However, organizations should exercise caution with overreliance on
such competitive extrinsic tactics, as an excess can produce heightened job-related stress and
even undermine intrinsic work passion. A similar yet typically more sustainably motivating
irrelevant factor is offering clear career growth prospects, such as the opportunity for promotions
to roles with greater responsibilities and authority. The promise of professional advancement
30
motivates efforts toward developing new competencies that qualify employees for senior
positions and salaries (Fu et al., 2023). At the same time, external progression pathways align
self-improvement with justifiable organizational rewards differently than monetary bonuses.
For some fraction of employees, threats of aversive organizational consequences can
extrinsically motivate lawful conduct and expected productivity levels through induced fear and
pressure to avoid punishment. The menace of termination, reduced compensation, or formal
disciplinary action may spur compliant behaviors and high output in the temporary interest of
self-preservation for these individuals. However, such predominantly externally controlling
extrinsic techniques are inherently unsustainable as primary motivators. Constantly operating
from fear and threat undermines intrinsic passions, commitment to employers, well-being,
creativity, and performance if habitually relied upon (Fu et al., 2023). While short-term
compliance can result, the long-term interests of organizations are better served through positive
motivation, encouraging growth, and discretionary effort.
In summary, extrinsic motivators, unlike intrinsic ones, do not stem from inherently
engaging qualities of the work itself or channel workers’ natural inclinations. Their motivating
influence hinges on continual reinforcement as they motivate through external contingencies
rather than internally fueled passion. Financial bonuses, public acknowledgment, or looming
threats cease functioning as drivers once discontinued since they do not connect to personal
interests or satisfaction. While extrinsic incentives are valuable for specific roles or tasks, relying
on them exclusively risks unsustainable outcomes. Intrinsic forces prove most dependable as
self-generated enthusiasm intrinsically fuels consistent effort and excellence over the long term.
Therefore, organizations maximize work motivation by crafting a balanced blend of intrinsic and
carefully considered extrinsic elements tailored precisely for each job. This nuanced approach
31
maintains autonomy, skills development, and purpose while supplementing less inherently
engaging duties.
The research consistently shows that intrinsic motivation stemming from meaningful
work, a sense of impact, autonomy, continuous skills development, and mastery is self-generated
and, therefore, inherently sustainable over the long term. When jobs engage employees’ natural
interests and passions, the inner drive provides a reliable wellspring of motivation. However, not
all duties equally lend themselves to intrinsic appeal. In such cases, extrinsic motivators
judiciously incorporated to supplement less inherently engaging tasks can still play an essential
motivating role if thoughtfully administered (Wu et al., 2019). A balanced, situational approach
tailored to different positions maximizes employee engagement. Rather than relying solely on
intrinsic or extrinsic forces, the most influential organizations recognize that an integrated
combination of motivational elements optimized for specific job requirements results in the most
sustained effort, productivity, creativity, and satisfaction. Employers can consistently achieve
high levels of work motivation through a nuanced, multifaceted design that taps into intrinsic
passions while supplementing fewer motivating responsibilities.
Workload and Autonomy in Decision-Making
Armstrong-Stassen and Schlosser (2008) established the significance of a positive
organizational learning climate for fostering employee engagement. In the academic context,
high perceived workload positively correlates with a surface-learning approach and a negative
correlation with a deep approach to learning (Diseth et al., 2006). However, in the organizational
context, the relationship between workload and learning approaches appears more nuanced.
Limited studies on workplace learning suggest that high perceived workload links predominantly
to a surface-disorganized approach (Delva et al., 2004; Kirby et al., 2003; McManus et al.,
32
2004), a relationship supported by Frese and Zapf’s (1994) research. The workload is posited as
a potential cause of surface approaches to learning, demanding time that could otherwise go to
activities like reflection, experimentation, and exploration.
Employees grappling with high workloads often feel a need for more time to engage in
critical learning activities, resorting to automated behaviors (Van Ruysseveldt & Van Dijke,
2011). In contrast, Karasek’s (1979) job demands control model proposes a counterintuitive
relationship between workload and learning. This theoretical framework suggests that when high
expectations become challenging for employees to meet, it stimulates the need for effective
strategies and behaviors to achieve goals. Therefore, high job demands, such as a substantial
workload, can promote learning activity, with results sometimes exhibiting complexity (Van
Ruysseveldt & Van Dijke, 2011). Karasek (1979) noted that workload can be stimulating as long
as it remains manageable, emphasizing the need for balance. Additionally, this study considers
choice independence as the second perceived characteristic of the workplace.
Relationship Between Autonomy and Learning Approaches
Choice independence, as described by Kirby et al. (2003), is closely related to the
workplace, positively influencing learning approaches and prompting individuals to contemplate
their tasks more deeply. This concept can also be interpreted as the perceived ability to control
one’s actions and decision-making processes (Delva et al., 2004; McManus et al., 2004).
Research by Delva et al. (2004), Kirby et al. (2003), and McManus et al. (2004) indicates a
positive correlation between choice independence and a deep learning approach, while it
correlates negatively with a surface-disorganized approach. In alignment with these findings,
Vansteenkiste et al. (2004) reported a significant effect of autonomy-supportive work contexts
on the quality of self-reported depth of processing. Environments that support autonomy result in
33
higher levels of deep processing compared to controlling contexts where individuals experience
limited independence or choice. Moreover, Van Ruysseveldt and Van Dijke’s (2011) findings
suggest that job autonomy moderates the impact of workload, with jobs featuring both high
workloads and autonomy (within reasonable limits) fostering optimal learning. Conversely, a
mismatch between workload and autonomy can hinder the learning process.
Moreover, Kirby et al. (2003) conducted three studies examining employees’ approaches
to learning at work and their perceptions of the workplace environment. Building on prior
research with university students, two questionnaires—the AWQ and WCQ—were developed
and distributed to a random sample of 2,000 alums from a large Canadian university. The
participants, who had obtained a bachelor’s degree at least 5 but no more than 15 years earlier,
provided 339 responses. All participants completed the 64-item Approaches to Work and the 40-
item WCQ, using a Likert-type scale with response options ranging from “definitely agree” to
“disagree.” This study employs the theories of approaches to learning in the workplace and
workplace climate, applying self-determination theory to discern levels of autonomy and their
correlation with motivation in the workplace.
Approaches to learning, whether in a business, medical, or educational context, are not
isolated phenomena but are influenced by the environments in which individuals engage in
learning. The Course Perceptions Questionnaire (CPQ) devised by Ramsden and Entwistle
(1981) assessed how considerate and supportive instructors were perceived to be by employing
factor analysis, revealing multiple dimensions. The CPQ helped assess whether instructors
respond to individual interests and needs, whether individuals feel they have the freedom to
choose what and how they learn, and how demanding the workload is. The surface approach
connects with limited academic freedom (Ramsden & Entwistle, 1981), heavy workloads
34
(Bertrand & Knapper, 1991), instructor attitudes favoring knowledge transmission over
facilitation (Christensen et al., 1995; Gow & Kember, 1993), and curricula focused on
transmitting facts and details (Hattie et al., 1996). In contrast, research has linked deep
approaches to learning to lighter workloads (Bertrand & Knapper, 1991; Entwistle & Ramsden,
1983), greater academic freedom (Ramsden & Entwistle, 1981), and effective teaching (Bertrand
& Knapper, 1991; Ramsden & Entwistle, 1981).
Three Dimensions of Approaches to Learning
Findings by Kyndt et al. (2009) revealed that the three dimensions of approaches to
learning—deep, surface-rational, and surface-disorganized—were essentially uncorrelated. This
aligns with subsequent studies by Baeten et al. (2010) focusing on deep and surface approaches.
Both studies sought to understand how learners approach situations in general and in their
current workplace context. Deep and surface-rational approaches represent complementary
strategies suitable for different levels of responsibility or various tasks. In contrast, the surfacedisorganized approach is more associated with dissatisfaction with the work environment and a
sense of task incompetence (Kyndt et al., 2009). Students and employees who perceive
competition with their peers tend to adopt a more surface-disorganized approach to learning
(Kyndt et al., 2009). Employees responsible for collaborating with peers at work may view their
team or organization as competing with others. The research suggests that effective supervision,
choice independence in learning approaches, and workload quantity are three primary
dimensions shaping employees’ perceptions of their workplace.
Kyndt et al. (2012) identified a noteworthy positive correlation between choice
independence and supervision. This correlation suggests crucial associations between
employees’ perceptions of their work environment, the quality of supervision, and the learning
35
approaches they adopt. There was a positive association between the deep learning approach and
positive aspects of the work environment, such as effective supervision and choice
independence. This relationship is reciprocal, with deeper learning contributing to more
supportive supervision and choice, fostering a supportive and challenging environment that, in
turn, encourages deeper learning. Moreover, individuals with a propensity for deep learning are
attracted to positions offering more supportive supervision and choice independence.
In the academic context, deep learning approaches are crucial, as students using these
approaches tend to achieve higher grades and demonstrate superior integration, transfer, and
retention of information (Biggs, 1987; Entwistle & Ramsden, 1983). Deep approaches to
learning are also associated with greater enjoyment of learning, reading, relating content to prior
experiences, understanding diverse resources, collaborative idea communication, and the ability
to apply acquired knowledge to real-world situations. Deep approaches to learning enable
individuals to comprehend larger constructs, discern connections between pieces of information,
and apply knowledge to various scenarios (Entwistle, 1983; Tagg, 2003). Scholars draw a clear
distinction between approaches to learning and the resulting learning. Students' study activities
and behaviors define their approaches to learning, with a deep approach promoting profound
learning and a surface approach leading to more superficial learning. Integrating and
synthesizing information, along with connecting it to prior knowledge, signifies a deep approach,
allowing individuals to retain information in the long term (Baeten et al., 2010). Deep learning
approaches facilitate individuals in updating their ways of thinking, approaching new
phenomena, and comprehending problems from various perspectives throughout the learning
process (Ramsden, 2003; Tagg, 2003). Deep approaches to learning represent the standard most
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institutions and organizations aim to establish, serving as a fundamental pillar in the conceptual
framework of this research.
Workplace Climate
An organization’s climate sets the stage for how well learning and development
initiatives will thrive. Elements such as leadership, workload management, relationship building,
feedback practices, and work-life integration weave together to form an overall environment that
either empowers or discourages ongoing skill growth (Vanitha et al., 2019). When organizational
leaders handle these climate factors supportively, they foster motivation, engagement, and
satisfaction with learning. Conversely, harmful or toxic climates can undermine even the best
learning programs. This subsection explores several critical aspects of workplace culture and
how to optimize each to cultivate a setting where employees feel most able and excited to
enhance their abilities throughout their careers continuously.
Good Supervision
The relationship between managers and their teams is critically important for fostering
lifelong learning in the workplace. Supportive supervisors who prioritize employee development
create an environment where people can thrive. By regularly checking in, managers can guide
the learning process through collaborative goal-setting, constructive feedback, and thoughtful
coaching (Collins, 2001). Understanding each person’s strengths, interests, and aspirations
allows managers to help map out customized progression plans aligned with individual passions.
This supportive approach nurtures self-directed learning and helps motivated workers to expand
their skills over the long term.
Effective supervisors recognize that guidance and autonomy are essential for nurturing
workplace learning. Managers provide grounding and structure without micromanaging details
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by setting clear expectations and parameters for goals and performance. They empower
employees to self-direct development, allowing independent exploration of new topics aligned
with interests and strengths (San et al., 2011). This intrinsic motivation is critical. Accessible
managers who make time to answer questions, offer suggestions, and serve as informal mentors
strengthen an environment of self-guided learning. With support and empowerment from
supervisors, workers feel confident growing skills at their own pace while knowing managers
have an open-door policy. This approach cultivates independent, motivated learners while still
providing needed supervision.
When guiding employees’ learning and development, a compassionate, empathetic style
from managers is essential. Showing flexibility and understanding that mistakes will inevitably
occur along the journey helps people feel secure enough to experiment outside their comfort
zones. A supportive, “learning from errors” mindset frames occasional stumbles not as failures
but as valuable parts of the learning process (Rashid & Louis, 2019). With empathy from
supervisors, people are more willing to take measured risks and stretch assignments that may not
go perfectly the first time. A safe environment allows learners to reflect on mistakes without fear
of repercussions. This sympathetic approach cultivates a growth mindset that sees setbacks as
learning opportunities rather than judgments of ability.
Publicly acknowledging milestones drives continued learning motivation. Recognizing
employees’ mastery of new skills or completion of developmental goals boosts confidence and
encourages further reinvestment. Distributed credit for team achievements sets the tone for the
value of collaborative learning. Fostering connection, supervisors should periodically check that
their style encourages openness. Surveys or dedicated conversations provide space for feedback
on the guidance relationship itself and how to support better learners facing obstacles (Rashid &
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Louis, 2019). Ensuring the supervisor-employee dynamic cultivates transparency and two-way
dialogue keeps developmental supervision productive and tailored to individual needs. When
workers feel genuinely heard, they stay engaged with goal-setting and practicing new abilities.
Trust is the foundation of any successful supervisor-employee relationship centered on
learning. Consistency, treating all staff uniformly with fairness and respect, and cultivating
approachability over time build the confidence necessary to empower risk-taking in skills
development. Workers must feel confident that their leaders will handle mistakes or failures
supportively rather than punitively (Austin, 2012). With that security in place, trust allows for
respectful but honest feedback, including constructive criticism, to help people enhance
performance and meet high standards. Done judiciously, with care for the learner’s feelings and
self-esteem, critical feedback advances abilities (Rashid & Louis, 2019). Maintaining open twoway dialogue handles disagreements or issues respectfully before misunderstandings and
resentments have time to decay. Addressing concerns promptly through empathetic discussion
upholds the dignity of both parties. An equitable, caring approach grounded in established trust
empowers people to stretch beyond their comfort zones continuously, sustaining a commitment
to long-term learning and growth.
The most effective supervision approaches workers first and foremost as lifelong
learners, not just employees functioning solely at the direction of management. Framing the
employee-manager relationship this way instills dignity and nurtures internal drive rather than
Dependence. It fosters intrinsically motivated self-development rather than relying entirely on
external structure and direction. Such an environment cultivates workers who feel personally
responsible for continuously honing their skills to strengthen the organization’s capabilities
(Rashid & Louis, 2019). Through collaborative goal-setting, empowerment, and compassionate
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guidance, supportive managers build a culture where everyone understands lifelong learning as
necessary for individual evolution and workplace strengthening. This establishes an ethos of
collective responsibility for growth that endures even without oversight.
Workload
According to research, the volume and pacing of job responsibilities assigned by
management significantly influence workers’ ability and drive to learn (Newble & Entwistle,
1986). Unrealistic workloads that consistently overwhelm employees drain precious mental,
emotional, and temporal reserves. This leads to adverse outcomes like stress, burnout, and
disengaging from development activities or further education to avoid extra duties. However,
managers must also avoid the opposite problem—giving team members too little work or not
enough stimulating challenges (Frese & Zapf, 1994). Insufficient work fails to engage and
stretch employees optimally, and subsequent learning may stagnate without adequately tailored
job duties. The key is moderation—allocating a balanced workload that leaves capacity for
autonomous skill development within achievable timeframes.
According to the research, actively soliciting employees’ input on task prioritization
enables managers to gain insight into time demands that new responsibilities or learning projects
may place on workers (Newble & Entwistle, 1986). With this understanding, oversight can help
prevent overload by dividing significant work objectives or skill-building goals into reasonable,
clearly bounded stages that maintain steady momentum over time. Collaborative workstyles,
where teams spread duties among their members through cooperation and knowledge-sharing,
also promote balanced workloads. They distribute responsibilities appropriately to prevent
exceeding capacity limits for any person while cultivating peer-to-peer learning. Managers can
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use proactive strategies like seeking input and establishing cooperation to structure settings
conducive to on-the-job growth with maintainable work-life rhythms.
According to the research, supervisors setting practical job expectations is essential to
preserving employees’ psychological and cognitive flexibility (Newble & Entwistle, 1986).
Leaving room for exploratory research, skill experimentation, and learning from mistakes
sustainably better supports on-the-job growth. Unrealistic demands risk draining the mental
bandwidth needed. Managers can also foster learning by allowing for some workday elasticity.
Permitting flexible schedules enables staff to more seamlessly integrate self-guided study, online
coursework, and other independent development pursuits around shifting work rhythms on their
timelines (Gino & Staats, 2015). Not being rigidly confined to a one-size-fits-all structure
respects individual processes and protects the capacity for autonomy in planning goal-oriented
skill progression. Together, balanced expectations and scheduling versatility cultivate
psychological safety for lifelong learning.
According to the research, cultivating autonomy through entrusting workers with selfassessment of their capacity supports on-the-job learning sustainably rather than rigidly
assigning workloads, empowering employees to evaluate and report on their bandwidth, and
fostering responsibility periodically. They gain valuable experience planning and balancing their
schedule to complete duties while prioritizing development goals effectively (Avolio & Gardner,
2005). Long-term growth mindsets can thrive when objectives and scope of work are
collaboratively aligned with employees’ evolving capabilities, as reported through selfassessment. Workers avoid potential frustration and reduce intrinsic motivation from perpetually
exceeding limits. Meanwhile, supervisors maintain oversight to ensure work-life balance and
provide support when needed.
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Achieving the proper equilibrium between workload and capacity is fundamental to
sparking employees’ passion for continuous skill-building. An optimized balance prevents
burnout while regularly providing stimulating challenges that employees can steadily meet
through ongoing learning (Soelton, 2023). Finding this equilibrium cultivates high-performing
work environments where people are not overwhelmed but instead energized to engage deeply
with their work and constantly hone their abilities. It ensures exhaustion does not override
motivation and lays the groundwork for lifelong professional enrichment.
Choice Independence
Research shows that offering employees autonomy over directing their learning paths can
significantly boost motivation, engagement, and retention of new knowledge and skills on the
job (Malloy, 2011). When workers feel they have a degree of control and ownership over
steering the course of their career progression through self-guided learning opportunities, they
become far more invested in their development. Having flexibility and choice in customizing a
relevant plan for acquiring new competencies fosters a deeper level of commitment to
continuous skill-building in the workplace.
Research indicates that allowing employees to have meaningful input when crafting both
long-term professional goals and short-term objectives to achieve those aims enables them to
leverage their interests and strengths to guide their learning and development (Malloy, 2011),
empowering workers with choice and flexibility in determining specific skills to prioritize,
selecting from a range of learning delivery methods that best suit their preferences, or picking
applicable seminars and courses that engage diverse learning approaches. The flexibility to
autonomously explore knowledge domains and skills aligning with their motivations and
passions intrinsically sparks the curiosity-fueled, self-directed mastery essential for sustaining
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lifelong learning. This personalized approach to career planning fosters much higher levels of
ownership and commitment.
Supervisors play an essential mentoring role in helping employees align their
personalized long-term goals with current business needs while still respecting individuals’
strengths and passions. Finding the right balance of guidance and independence from
management is critical. It establishes expectations that challenge workers without overwhelming
them or leaving them under-stimulated. By collaborating on goal-setting that respects personal
comfort zones, supervisors can help craft plans to engage employees in continuous skill
expansion that matches their enthusiasm and readiness to learn. This cultivates an ideal
environment for sustained career progression.
According to research, providing employees opportunities to directly apply newly
acquired knowledge and skills through hands-on learning experiences like special projects or
assignments is vital for strengthening retention and building confidence to eventually expand
one’s scope of work (Malloy, 2011). Empowering workers to apply their learning in realistic onthe-job contexts cements the connections between theory and practice. It allows them to gain
proficiency that can later solidify into increased responsibility. Such application-focused
opportunities also provide space for calculated risk-taking, experimentation, and creative
problem-solving. Supervisors who foster an environment that allows prudent risk-taking
empower staff with the flexibility and confidence to innovate. This cultivation of innovation and
creativity is essential for sparking new ideas to help power organizational growth in the long
term.
Fostering a supportive culture that encourages experimentation provides learners with
invaluable lessons emerging from failures and successes in a low-risk setting. Access to relevant
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resources and the option to conveniently access subject matter experts on demand nurtures
independent problem-solving skills and self-reliance. Employees become better equipped to
solve future issues without turning upwards for every minor roadblock. This self-reliant capacity
lays the groundwork for lifelong learning resilience as professionals can progressively develop
the confidence to push the boundaries of their skills, aided but not dependent on others to
troubleshoot constantly.
Suitable autonomy and choice in determining their career progression paths and the skills
they wish to expand upon instill employees with a strong sense of control and commitment over
their lifelong learning and development. In turn, cultivating learners internally driven by their
passion and interests directly benefits the organization because those individuals are eagerly keen
to transfer back and apply newly acquired expertise consistently. They aim to steadily enhance
their contributions by innovatively utilizing emerging competencies that align with
organizational needs. Ultimately, a learning culture powered by autonomy stimulates employees
who enthusiastically return value to their work through specialized skills and knowledge pursued
through self-determined means.
Work Relationships
Research indicates that collegial relationships are the foundation for any thriving
collaborative work culture, significantly enhancing on-the-job learning outcomes for everyone
involved (Kyndt et al., 2013). A positive social environment and strong professional bonds
between coworkers foster an atmosphere where people feel at ease openly exchanging
knowledge, perspectives, resources, and honest feedback to help propel one another’s
advancement. Ideas can flow freely in a psychologically safe setting conducive to compiling and
distributing lessons learned from experiences. Such peer networks cultivate learning
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relationships where employees take a genuine interest in one another’s growth, offering advice,
platforms for discussion, and camaraderie for imparting and requesting fresh viewpoints (Austin,
2012). This collegial learning model embedded in social relationships effectively spreads
applicable skills throughout the organization to strengthen individual roles while positioning the
company for optimal performance.
Meaningful mentoring relationships that enhance on-the-job learning do not always stem
from formal partnerships but can develop spontaneously in workplaces that strategically foster
strong team cohesion and connections across roles, ranks, and disciplines. Intentional teambuilding activities that unite diverse workers, such as group projects, training, and social events,
can nurture rich informal mentoring networks (Kunnanatt, 2016). As colleagues from varied
backgrounds build rapport, they may turn to one another for guidance, feedback, and problemsolving help on an impromptu basis. These natural connections can prove highly influential
learning relationships and facilitate organic cross-pollination of knowledge throughout the
organization.
Seeing how peers address and solve problems differently helps employees gain new
perspectives that stretch their thinking patterns. Providing opportunities like Q&A sessions or
cooperative projects allows workers to safely learn from colleagues’ varied skills in a low-stakes
environment. These exchanges foster cognitive development. Additionally, encouraging online
or in-person communities of practice aids self-directed learners in locating resources. They can
tap into others’ expertise instead of independently trying to solve problems or continuously
reinventing solutions—the cross-pollination of perspectives and skills across such networks
seeds continuous organizational learning.
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While fostering social learning has benefits, open communication and fair conflict
resolution are necessary to promote an inclusive environment where all feel safe contributing.
Regular sensitivity training also mitigates unconscious biases that could disenfranchise some
groups from participating fully. Recognition programs highlighting instances where individuals
helped advance coworkers’ abilities showcase cross-pollination of learning. Communal
celebrations of milestones strengthen bonds by emphasizing cooperation, not just competition, in
developing new skills. This encourages knowledge-sharing and cultivates a culture where
employees enthusiastically help one another progress through challenges.
While positive social connections enhance learning, toxic workplace dynamics
undermine employee morale and drive. They discourage the natural intrinsic motivations that
fuel continuous growth. As such, anti-harassment policies aim to safeguard against behaviors
that might dampen passions or silence valuable voices from sharing ideas or feedback. When
issues inevitably arise between colleagues, access to mediators who facilitate confidential
resolutions helps repair damaged relationships and restore broken conduits for transferring
knowledge (Iacuone, 2005). This gets social learning networks flowing effectively again among
the entire organizational body.
In summary, cultivating an inclusive and positive social environment where all worker
differences are respected lays the critical foundation for collaborative learning. It fosters the
development of profound collegial relationships rooted in genuine care for one another’s
advancement. In a psychologically safe setting, individuals are empowered and motivated to
extend their abilities through self-determined means continuously. Their enlightenment then
efficiently circulates throughout the organization as they enthusiastically enrich its collective
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expertise. This accelerates the rate at which wisdom spreads, and the entire company body
progresses through communal thriving.
Recognition and Feedback
The research underlines how supplying purposeful recognition and direction is paramount
for motivating continued learning in organizational settings. Consistent appreciation reassures
workers that their investment in honing new skills and deepening existing talents fuels progress.
It validates the worth of enriching one’s competencies endlessly. Failing to acknowledge
accomplishments may discourage perseverance (San et al., 2012). Regular affirmation
emphatically conveys to employees that their lifelong commitment to growing capability affords
worth. This spurs innate passions to keep advancing as progress feels duly supported.
Meaningful feedback propels continual enrichment across teams to mutual benefit.
While acknowledgment is critical, the validation must be sincere and personalized to fuel
ongoing growth truly. Generic rewards lack authenticity and fail to convey real investment in a
coworker’s distinct achievements and goals. Public celebrations can provide a heartfelt
showcase, specifically honoring milestones that ignite others (Avolio & Gardner, 2005).
Thoughtfully customizing feedback demonstrates care about an employee’s contributions and
aspirations rather than treating successes as tasks to check off. This nurtures relationships where
workers feel seen and motivated to stretch in personally meaningful directions. Nuanced,
genuine recognition cultivates learning environments defined by authentic support.
Research shows that constructive feedback is equally essential as validation in propelling
ongoing development (Bolman & Deal, 2017). Regular check-ins provide opportunities for open
dialogues about performance, where supervisors can objectively assess progress toward recently
set goals while subjectively exploring their interests and passions with employees to refine future
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objectives. This two-pronged discussion helps individuals strengthen their capabilities while
nurturing intrinsically motivated growth in new directions aligned with personal motivation. By
facilitating respectful exchanges about strengths and areas still needing work, feedback cultivates
learning relationships where workers feel supported to continually challenge themselves,
supported by collaborative goal-setting with guidance from team members.
However, how feedback is delivered is just as important as the content for feedback to
facilitate progress. Comments need to strike a balance, complementing accomplishments with
considerate tips for enhancement, not harsh criticisms. A supportive, empathetic approach is
necessary to provide perspective and bolster motivation to surpass limits. Harshly worded
judgments risk damaging morale and dampening innate passion for persistently experimenting
beyond current abilities (Bernsen et al., 2009). Maintaining compassion and acknowledging the
personal dignity of each team member is paramount to retaining drive. Respectful feedback
coupled with validation respects workers as whole beings and nurtures an environment where
challenges feel surmountable through open collaboration, not isolation. This empowers the
continuous stretching of limitations.
While formalized performance evaluations provide a scheduled structure for evaluating
work against objectives over longer durations, casual coaching discussions promote a continual
emphasis on development journeys rather than final destinations. Regular check-ins prioritize
improvement efforts and maintain motivation along the way. Asking for worker self-assessments
demonstrates valuing their perspective as experts and can critically analyze strengths and growth
areas (Fernandez & Rainey, 2017). Likewise, inviting the sharing of career interests and
aspirations shows employees that their well-being and fulfillment matter beyond current duties.
This nurtures a psychologically safe environment where ambition is encouraged rather than seen
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as a deficiency. Together, an optimal balance of scheduled reviews and impromptu coaching
keeps the focus on learning as a lifelong, self-directed process (Biggs et al., 2001). Recognizing
employees as whole people fuels intrinsic motivation central to workplace success.
Meaningful recognition programs lay the foundation for organizational cultures devoted
to continuous learning. Timely acknowledgment of accomplishments, additional initiatives
undertaken, and versatile implementation of refined talents positively motivate persistent selfdevelopment. While small, personalized tokens may have low monetary costs, they profoundly
boost confidence to expand one’s impact (Hager, 2005). Coupled with regular opportunities for
respectful feedback and guidance tailored to individual strengths and objectives, employees feel
invested as whole people rather than human capital. Balanced frameworks incorporating sincere
and supportive dialogue create psychologically safe spaces where ambition grows. This nurtures
innate passions for challenging boundaries and reinforces companies’ commitment to cultivating
skill evolution from within through dedicated, lifelong learning. A cooperative culture empowers
employees as expert learners.
Growth Opportunities
Sustainable skill development relies on diverse pathways to enrich competencies in
alignment with personal interests and career aspirations over time. Offering a variety of learning
modalities—from formal training programs to informal mentoring and peer-to-peer learning—
allows employees the agency to shape customized development journeys (Burke & Ng, 2006).
This fosters intrinsic motivation that drives lifelong commitment to ambitious self-improvement.
In turn, such dedication to cultivating growth sustains high job satisfaction and strengthens
retention, as workers feel their long-term success and happiness are priorities (Barley, 2012). A
multifaceted approach to skills progression is critical.
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Formal training curricula are meaningful in continuously advancing workforce
knowledge and capabilities. Investing in certifications and degree programs, especially by
subsidizing tuition, communicates belief in employees’ future potential contributions. Rotating
talented individuals through varied departments over time cultivates multi-skilled dexterity
beneficial to dynamic business needs. From sponsoring technical workshops to funding industry
credentials, strategically supporting related qualifications keeps proficiency on the cutting edge.
It equips workers to take on enhanced roles while maintaining relevance. A blend of learning
activities maximizes versatility and long-term career marketability within and outside the
organization.
Informal mentoring pairings permit less experienced personnel to glean sage advice from
esteemed role models. Shadowing various colleagues exposes junior employees to diverse
methods and points of view, stretching their thinking. Taking on unique short-term assignments
marries freshly acquired knowledge with hands-on experience, allowing workers to test-drive
skills in low-risk ways—these enriched responsibilities prompt interconnectivity across
functions, culture sharing, and innovative problem-solving through cross-pollination. Various
developmental activities, including mentoring, observation, and project-based practical
application, fuel rounded professional maturation over the long term (Avolio & Gardner, 2005).
Vast arrays of flexible learning avenues cater to all lifestyles and meet employees where
they are. On-demand digital pieces of training and self-paced online courses respect schedule
constraints. Professional organization memberships spark ideas and best practices from the field.
Conference attendance interacts with thought leaders—second, offer immersive skill enrichment
options. Part-time further education pursued externally while working demonstrates clear
organizational championing of lifelong learning. Together, these diverse pathways motivate
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taking the initiative to develop continuously, gaining new outlooks to enhance performance,
career options, and company value.
For development to prove sustaining, opportunities must align closely with intrinsic
passions and not feel like burdensome obligations. Practical guidance involves unveiling
potential through performance evaluation insights and advising optimal pathways catered to
revealed talents and interests. Growth demands iterative stretching just beyond comfort zones
balanced with confidence-boosting wins. According to research, by linking learning activities to
purpose via regular check-ins, workers remain energized over the long haul. Nurturing engaged
self-direction through holistic consultation motivates ambition tied distinctly to core motivations
and aptitudes.
When organizations foster a culture where career development stems strategically from
interest-led opportunities, the results empower employees and company competitiveness—
networks for peer learning exchange skills. Teaching roles reinforce mastery while aiding others.
Innovation programs breed new ideas. Entrepreneurial ventures inspire risk-taking. This
develops skilled, invested workers aware of the optimization of their talents through continual
enrichment. Employees are motivated to deepen their expertise through diverse, passion-aligned
avenues. The organization gains a competitive edge as an employer with top human capital
dedicated to advanced capabilities that strengthen long-term success.
Work-Life Balance and Learning
Enabling flexible working arrangements makes learning and development more
attainable for staff. Options like remote work, modified hours, and compressed schedules help
integrate career-boosting activities such as courses, conferences, mentoring, and independent
study seamlessly around other life demands (Soelton, 2023). This reduces the likelihood of rigid
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workplace cultures causing learning activities to conflict with critical personal responsibilities.
Reasonable workload expectations likewise preserve crucial headspace for employees to center
sufficient energy on setting and achieving development goals and challenges. People learn most
effectively when relaxed rather than frazzled from being overworked or overcommitted.
Allowing staff reasonable autonomy and work-life integration supports motivation to learn and
challenge oneself continuously. Employees are in a better position to dedicate focused effort to
studying, researching, and experimenting hands-on with new skills.
In addition to flexible arrangements, organizational support for employee wellness
ensures people remain physically and mentally fit to take on future challenges that facilitate
expertise growth. Wellness programs promote conditions conducive to long-term learning
ambitions. Regular breaks replenish motivation and perseverance essential for the dedication
learning necessitates (Bucea-Manea-Țoniş et al., 2020). Leave policies also significantly
influence skills currency maintenance despite life circumstances like parenting or family
caregiving duties that may require time away from regular duties. For example, generous family
leave allows smooth integration of developmental activities before and after an absence through
options like part-time return, learning credits, or backup resources. This facility assists learners’
reintegration into their tailored career path objectives upon rejoining. Prioritizing work-life
harmony yields employees best equipped to sustain an intrinsic commitment to ongoing learning.
It prevents burnout risks that could undermine an organization’s learning-focused culture.
Diversity and Inclusion for Learning
Building an inclusive culture where all staff feel respected and appreciated lays the
crucial groundwork for unleashing the highest potential of continuous on-the-job learning. When
organizations embrace workforce diversity and foster a sense of belonging, they facilitate robust
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intellectual capital development. A diverse makeup allows tapping into a wealth of perspectives
and ideas that can spark innovation by exposing learners to solutions beyond their lens.
Interacting regularly with colleagues from varied backgrounds broadens individual thinking by
challenging assumptions and preconceptions (Avolio & Gardner, 2005). This opens minds to
new problem-solving approaches and technical strategies that promote skills evolution.
Furthermore, when psychologically safe spaces allow open sharing of views across
demographic differences, it expands collaborative learning possibilities. Employees feel
comfortable seeking and providing feedback, which enriches developmental discussions. An
inclusive environment that celebrates uniqueness optimizes networking relationships and peerto-peer learning exchanges (Cook, 2002). With support for diversity as a driver of creative
approaches and comfort contributing authentically, a culture unleashes the full benefit of on-thejob continual learning to maximize workforce potential.
For continuous learning programs to achieve their most benefit, consideration of diverse
needs across the workforce proves equally vital. Sensitivities like accommodating religious
obligations, flexible scheduling, or digitized materials for employees facing disabilities help
ensure all staff can fully access training initiatives. This inclusion maximizes engagement by
removing hindrances to participation. Unconscious or implicit bias training further mitigates
preconceptions that may cause some groups to feel alienated from growth support or networking
chances. Employees feel motivated to competitively expand competencies when all groups
perceive fair treatment and impartial access to new responsibilities and promotion opportunities
regardless of attributes (Bernsen et al., 2009). No one wishes to hone skills in an environment
where one’s chances feel limited by factors external to ability and work ethic. With proactive
sensitization efforts reducing subtle prejudices through awareness, the organization cultivates the
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belief that the refinement of talents benefits individuals based purely on merit. As a result,
continuous learning programs achieve their aim of strengthening the entire workforce and
allowing the business to reap the rewards of top, diverse talent, maximizing potential through
skilling initiatives.
Celebrating the diverse cultures, experiences, and abilities in the workforce shows that
differences are valued as a path to progression, setting an example of esteeming each member’s
varied contributions to learning. Equitable representation across staffing and promotional
opportunities gives underrepresented minorities visibility that their dedication to mastering new
skills makes a difference and more closely identifies their perspectives and expertise as respected
organizational assets crucial for continuous evolution. When all demographic groups feel the
learning culture moves conscientiously toward fairness and inclusion at all levels, comfort, and
motivation to refine competencies from all areas of talent increase continuously. No longer do
any personas perceive barriers to engagement or recognition, unleashing fuller potential.
Harnessing ideas from a more complete range of lived realities strengthens any organization.
Diversity bolsters the scope and quality of perspectives to spark insight, challenges
preconceptions through exposure, and generates role models showing mastery opportunities
irrespective of attributes. This inspires collective dedication to tackling new frontiers through
learning from differences.
Autonomy and Empowerment for Workplace Learning
Cultivating employee autonomy is paramount in nurturing an organizational culture
where continuous skills development thrives. Empowering individuals with control over their
work, responsibilities, and learning opportunities tap into the intrinsic motivation that fuels
mastery of new abilities (Gomes et al., 2021). When workers can independently structure job
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functions according to preferred strengths and focus, it permits dedicated concentration on
customized objectives at an optimized pace. Likewise, calibrating challenges according to
differing skill thresholds prevents learned helplessness that hinders growth—granting input into
defining learning aims, timelines, and action plans through self-directed goal-setting fosters a
sense of ownership over career progression that nourishes self-motivation critical to lifelong
learning. Employees are also more willing to experiment when errors pose less risk to
performance assessments or professional standing. This supportive, low-stakes environment
where consequences are minimal cultivates confidence for independent pushing of boundaries
and maximizes learning from mistakes.
Participatory decision-making that allows employee skills to influence operational
enhancements through direct application motivates further competency development. Putting
abilities to use in problem-solving concrete workplace issues through contributory decisionmaking cultivates an internal drive to refine talents (Berry & Hughes, 2020). Autonomy and
empowerment to shape career trajectories and confidently take on stretch assignments without
micro-management nurtures self-reliance and intrinsic motivation for continuous skills
extension. Workers feel responsibility and ownership over independently driving their lifelong
learning rather than relying solely on external mandates or direction. This fosters a flexible,
adaptable mindset that is better prepared to take the initiative when new challenges arise without
prior planning. The resulting nimble, self-starting workforce ready to rise to the occasion
strengthens business preparedness for changing realities and market surprises.
Physical Work Environment and Learning
The physical work environment plays a notable role in employees’ abilities and drives for
continuous on-the-job learning. Beyond general ergonomics, practical considerations such as
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floor plan structure, collaborative tools provisioned, and aesthetics should facilitate growthfocused behaviors and interactions. Dedicated breakout zones furnished for group study sessions,
hands-on skills practice, and informal cross-training project work help spark valuable peer-topeer coaching and information exchange (Gomes et al., 2021). Providing a variety of everyday
setups, from quiet nooks to open counseling areas, accommodates differing social and solo
learning approaches. Networked computers, multimedia displays, and collaboration software
optimize real-time sharing of best practices, training resources, and brainstorming across
locations. Ubiquitous digital access keeps staff training and development agile and current by
enabling self-paced modules that integrate seamlessly around responsibilities (Bauwens et al.,
2020). A learning-conducive physical workspace broadcasts organizational prioritization of skills
evolution, continuously motivating workers to sharpen talents through infrastructure-supported
formal and informal learning streams.
To fully support continuous learning, practical workspace elements should also enable
focused solo study, which is essential for skills development. Quiet nooks and private meeting
enclosures remove social and ambient noise distractions to optimize concentration levels
required for deliberate online coursework, research reading, and video tutorial consumption.
Ergonomic furniture options and adjustable workstation components that prioritize physical wellbeing, such as adjustable desks, comfortable seating, and anti-fatigue floor mats, help mitigate
risks of overuse injuries or eye strain over long self-learning hours. Keeping learners healthy
preserves stamina to take on challenges necessary for competency expansion. Beyond
functionality, biophilic designs utilizing natural lighting, indoor plants, and other nature-inspired
aesthetics in work areas foster restorative, engaged states of mind that elevate information
retention and productivity when studying (Bensimon et al., 2006).
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Small company libraries or learning centers stocked with an array of reference materials
such as technical manuals, industry journals, newsletters, and other periodicals related to key
competency domains help inspire ongoing self-education through easy reference access on
workplace premises. Browsing topical resources sparks discovering new interests, skills trends,
and related areas worth exploring to strengthen one’s qualifications profile. An optimized
physical workspace that facilitates focused solo or group study protects well-being, minimizes
distractions, and reinforces learning as value broadcasts the organizational priority placed on
continuous education to employees. Beyond conveying importance, a learning-centric work
environment motivates staff by cultivating growth mindsets and bolstering self-drive and
ownership over competency expansion (Baeten et al., 2010). Workers feel empowered to
independently extend their skills repertoire through formal and informal channels supported by
the infrastructure. The optimized facilities cement an organizational culture that lifts barriers and
nurtures intrinsic motivation for lifelong learning through practical accommodations that
facilitate environments conducive to learning, collaboration, and skills evolution.
Summary
This chapter comprehensively reviewed the literature surrounding approaches to learning
in various contexts. Research demonstrates three primary learning approaches: deep, surface, and
surface-disorganized. A deep process involves actively relating new concepts to prior knowledge
through critical analysis, leading to robust comprehension. In contrast, a surface approach
focuses on minimum effort memorization to fulfill assessment needs, resulting in shallow
retention. A surface-disorganized approach reflects disorganized studying without clear
strategies, preventing meaningful understanding.
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The approach adopted depends on individual factors like motivation and environmental
influences. Academic settings can encourage surface habits due to assessment pressures, large
class sizes, and tight schedules. However, autonomy, quality supervision, active learning
pedagogies, and problem-based curricula foster intrinsic motivation for deep analysis. In
business, on-the-job learning and results-driven culture cultivate practical skill development
through associating ideas purposefully. However, heavy workloads risk enabling surface
tendencies without sufficient detachment opportunities.
Medicine requires consistently applying extensive knowledge through lifelong deep
learning. While clinical experiences support this, assessment-heavy curricula can prompt
superficial study. Work-hour limits also threaten intentional skill-building if binding preparation.
You are defending trainee well-being guards against harried habits potentially arising from
systemic stressors.
Contextual attributes like autonomy, manageable workloads, supervision, relevance, and
support for wellness shape whether environments catalyze surface shortcuts or nourish deep,
comprehensive understanding through autonomous investigation. Optimizing influences shown
to deter shallow memorization in favor of cohesive knowledge assimilation remains important
across varied learning settings for education to achieve its objectives.
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Chapter 3: Methodology
This chapter will focus largely on the methodology applied within this study in regard to
the research design and data collection approach. The research design used was a mixed-methods
approach. A pragmatist worldview informed the convergent parallel design of the study, which
comprised a qualitative strand involving interviews and a quantitative strand involving surveys. I
tested the validity of the research through the convergence of information from these two
sources. Triangulation took place through the development of a comprehensive understanding of
the phenomenon regarding employee motivation at the CCC.
Table 1
Research Questions and Data Sources
Research questions Survey: AWQ and
WCQ
Interview
What learning approaches (deep, surface,
disorganized) do construction workers report
using, according to the AWQ?
X
What is the correlation between quantitatively
measured workplace climate dimensions (via
the WCQ) and qualitatively described learning
approaches?
X
How do construction workers’ on-the-job
learning experiences, including motivations,
opportunities, challenges, and outcomes,
X
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compare to theoretical propositions on deep
versus surface approaches?
Data Sources
The study used a mixed-methods approach to produce results that were more robust and
compelling than single-method studies (Davis et al., 2011). This integration allowed for a more
synergistic data utilization than separate qualitative and quantitative analysis. In this mixedmethods design, the study’s purpose and research questions determined the sequence of
quantitative and qualitative data collection. The qualitative phase came first in the sequence
because the study aimed to seek an in-depth explanation of the results from the qualitative
measures. This mixed-methods approach, known as the sequential explanatory design, consisted
of two distinct phases: quantitative followed by qualitative (Creswell et al., 2003). In this study, I
first collected and analyzed the quantitative (numeric) data. The qualitative (text) data were
collected and analyzed second in the sequence, helping to explain or elaborate on the quantitative
results obtained in the first phase. The second qualitative phase is built on the quantitative phase,
and the study’s intermediate stage connects the two phases.
The rationale for using this approach was that the quantitative data and their subsequent
analysis provided a general understanding of the research problem. Then, the qualitative data and
their analysis refined and explained those statistical results by exploring participants’ views in
more depth (Creswell et al., 2003). Jogulu and Pansiri (2011) created a graphical representation
of the mixed-methods sequential explanatory design procedures used for an illustrative study
such as this. The model portrayed the sequence of the research activities in this study, indicated
the priority of the qualitative phase by capitalizing the term QUALITATIVE, specified all the
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data collection and analysis procedures, and listed the products or outcomes from each of the
stages of the study (Ivankova et al., 2006). It also showed the connecting points between the
quantitative and qualitative phases and the related products, as well as specified the place in the
research process where the integration or mixing of the results of both quantitative and
qualitative phases occurred.
Survey
The main goal of the initial study phase was to quantitatively examine how certain
workplace factors might predict technicians’ tendencies toward deep, surface, or disorganized
learning approaches. To uncover any predictive relationships, I administered a survey utilizing
validated scales to measure technicians’ self-reported learning approaches and their perceptions
of work environment influences such as workload and supervision. Statistical analysis revealed
whether and how strongly selected climate factors could forecast learning method preferences.
The quantitative phase utilized two validated questionnaires to gather survey data from
technicians. The AWQ developed by Kirby et al. (2003) employed Likert scales to measure
tendencies toward deep, surface, or disorganized learning approaches based on three subscales.
Concurrently, the WCQ from Kirby et al. (2003) gauged perceptions of critical environmental
factors such as workload, autonomy, and supervision quality across three subscales. These
standardized scales that demonstrated reliability and validity allowed a description, rather than
an experimental manipulation, of technicians' natural behaviors. It also facilitated correlating
learning approaches with environmental views to understand relationships among the technician
population better.
I invited technicians to participate through a convenience sample. I administered the
survey digitally through emails, where participants provided informed consent. In addition to
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collecting AWQ and WCQ scale responses, brief demographic questions characterized the
sample—the study aimed to minimize the burden, taking approximately 45 minutes. To increase
motivation and response rates, participants had the option to enter a raffle for one of two $20 gift
cards upon survey submission while still maintaining response anonymity. The survey data
collection methodology prioritized participant privacy, confidentiality, and comfort in sharing
views honestly. Encrypted servers meet strict standards and securely store all information
gathered via the Qualtrics online platform. No personally identifiable information such as names,
employee numbers, or IP addresses was collected, attached to, or traceable within individual
responses to ensure anonymity. Participants could not be linked back to their answers. This
approach removed the risk of disclosure or ramifications for technicians, aiming to allow open
and truthful reflections on learning tendencies and environmental perspectives without fear of
identification or consequences.
Upon closing the survey collection period, I downloaded responses from Qualtrics into
IBM SPSS statistical analysis software. I employed several techniques to analyze the quantitative
data, both descriptive and inferential. Descriptive statistics involving frequencies, percentages,
means, and standard deviations provided an overview of critical characteristics such as
participant demographics and scale scores measuring learning approaches and perceptions of the
work environment. Inferential analyses, specifically bivariate correlational methods such as
Pearson’s r, examined the connections between variables. This evaluated whether more
incredible endorsements of certain climate elements correlated with increased tendencies toward
deep or surface-learning approaches. This quantitative analysis revealed relationships and laid
the groundwork for subsequent qualitative investigation to explain initial findings in richer
human terms. Statistical results guided focused interview inquiries.
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Participants
The target population for the quantitative phase was approximately 60 technicians
employed at various local companies that serviced medical devices. Due to the feasibility
constraints of contacting all technicians across different departments, I used a non-probability
convenience sampling method to recruit available participants. I invited technicians with whom I
had existing contacts to participate via email. While this approach enabled prompt data
gathering, it resulted in a sample based on self-selection rather than random selection. Findings
thus may not generalize beyond the technician characteristics of those who chose to respond to
the survey.
The participants' demographic characteristics reflected the overall technician population
at the company. Specifically, I found that 100% of the sample identified as male, as the field
remained predominantly represented by men. Additionally, 100% of participants were full-time
employees. Less than 5% of the participants had a high school diploma or equivalent education
level. Similarly, under 5% of the participants had only a secondary degree as their highest
qualification. Age and tenure with the company provided further context about participants'
experiences. Ages ranged from 18 to 65, encompassing life and career stages. Company tenure
among the technicians varied considerably, from only a year to a maximum of 15 years. This
wide range of exposure to the workplace culture offered diverse viewpoints. By participating in
the survey, technicians helped inform understanding of approaches to learning and perceptions of
the work environment. While generalizability was limited, survey findings provided initial
insights to shape the subsequent interview phase of the mixed-methods research design.
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Instrumentation
This study aimed to assess technicians’ approaches to learning and their views of critical
environmental factors. Validated survey instruments were employed to collect quantitative data
to achieve these goals. Specifically, the AWQ and WCQ developed by Kirby et al. (2003) were
well-suited for the research context and objectives. The AWQ used a 30-item Likert scale format
with three subscales demonstrating reliability and validity. The deep learning approach subscale
contained 10 items measuring active knowledge construction. A surface-disorganized subscale
comprised 10 things related to a random, unfocused study method. Finally, 10 items gauged a
surface-rational strategy of rote memorization through a third subscale.
The original scale alpha shows the internal consistency reliability of the survey
instrument measured by Cronbach's alpha coefficient. In this study, AWQ developed by Kirby et
al. (2003) was employed. In contrast, the original scale alpha for the AWQ, as reported in the
literature, showed the degree to which items within a given subscale, that is, deep learning
approach, surface-disorganized, and surface-rational strategies, correlated with each other.
Meanwhile, Cronbach's alpha was calculated for the corresponding subscales whose items this
study considered worked together to measure the internal consistency reliability in the context of
the sample.
The WCQ also employed a 15-item Likert scale design. It tapped into technicians’
perceptions of workload, autonomy, and supervision quality through five-item subscales. The
workload dimension assessed staff perceptions of duties being overly burdensome. Choice
independence examines the level of choice and control in work responsibilities and methods.
Additionally, a supervision quality subscale evaluated guidance and support from supervisors.
These multidimensional survey tools allowed for statistical testing of the predictive relationships
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between variables. Specifically, the AWQ elucidated how technicians currently approached onthe-job learning, while the WCQ shed light on their views of the surrounding environmental or
climate factors. Together, these insights laid the groundwork for follow-up qualitative
exploration. By administering the standardized, previously validated AWQ and WCQ, reliable
and valid quantitative data could be collected to address key research questions. Comparable
scoring procedures also facilitated mixed-methods merging and results interpretation.
The Qualtrics online survey software facilitated survey distribution and response
collection. I distributed an anonymous link allowing direct access to the AWQ and WCQ
instruments to participants via their work emails. Technicians could complete the 15- to 20-
minute questionnaires on any personal device during their own time. To encourage participation,
all individuals who completed the survey were entered into a raffle to win one of two $20 gift
cards to a local restaurant. While incentivizing response rates, this raffle process kept survey
responses anonymous by collecting names separately from answer data. Encrypted servers that
met the highest industry standards for privacy and security securely stored all information
gathered through Qualtrics. No personally identifiable information, such as names or employee
numbers, was associated with responses. IP addresses and other digital identifiers were also not
recorded. This online administration approach streamlined data gathering while prioritizing
participant confidentiality and comfort in disclosing opinions freely. Technicians could honestly
share learning behaviors and workplace opinions without fear that the research linked their
responses to them.
Interviews
Following the collection and statistical analysis of survey response data, I conducted
interviews to gather richer, more explanatory contextual information regarding any quantitative
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relationships discovered between variables. Recruiting for the qualitative interview phase
involved purposefully selecting 10 technician participants directly from those who completed the
initial survey portion of the research. Their contributions enhanced understanding of workplace
factors, learning approach tendencies, and informant experiences beyond what surface-level
survey figures alone could provide.
Participants
I invited survey participants to participate in follow-up interviews to explore the
quantitative results further. At the end of the online survey, participants could provide their
names and contact information through a discreet link, keeping this data separate from survey
responses. This ensured that participant anonymity was protected. I selected 10 technicians for
interviews from the pool of interested individuals. Gathering qualitative insights directly from
frontline staff who completed the survey added nuanced context to any relationships uncovered
in the statistical analysis. The targeted interview sample aimed to demographically mirror the
technicians who received the initial poll, linking the qualitative findings directly to the
characterized population quantitatively analyzed. Only those at least 18 years old could
participate and provide informed consent. This focus on eliciting experiences from a group
representative of the adult technician staff surveyed helped ensure the interview data remained
well-grounded within the scope and context of the research parameters.
While not required for participation, I offered a $5 digital gift card to each technician
participating in follow-up interviews as a gesture of appreciation for their additional commitment
of time and insights. While this small compensation may have helped increase recruitment
success rates, interview involvement remained strictly voluntary without any obligation or
pressure to participate. The research team recognized the value of directly gathering qualitative
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perceptions from members of the studied population who completed the initial survey. However,
consent for interviews was informed, and technicians could choose whether they wished to
contribute further or not. Upholding ethical standards of voluntary participation without coercion
or undue incentives was paramount. The offer of a nominal gift aimed to express thanks should
technicians freely elect to share more of their experiences, perspectives, and realities working on
the job.
Once recruited for interviews, I assigned each technician a unique coded identifier known
only to me to ensure participant confidentiality. Rather than any personal names or identifying
information, I used this code on all subsequent audio recordings of interviews, transcripts created
from the tapes, and in any reporting or discussion of qualitative findings. This protected the
identity of individuals who shared more of their experiences through voluntary participation in
follow-up interviews.
Achieving diversity in the interview participant demographics allowed for more wellrounded insights to emerge from the qualitative research phase. When recruiting the targeted
sample of 10 technicians for follow-up interviews, I selected individuals representing a balanced
cross-section of characteristics like age, years of work experience in various roles, technical
specializations, and other relevant factors. This strategic recruitment aimed to avoid an interview
pool comprised solely of technicians sharing similar profiles. Instead, the goal was a
heterogeneous mix of variables to enhance the breadth and depth of perspectives shared.
Technicians from various backgrounds lent different viewpoints to a more holistic understanding
of workplace factors, learning tendencies, challenges, and opportunities among the population
surveyed. Ensuring approximately equal representation of demographic profiles added
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multidimensionality to the qualitative findings beyond what could result from interviews with
just one narrowly defined group.
Instrumentation
A semi-structured interview approach using an interview protocol facilitated gaining
richer, more in-depth insight into technicians’ viewpoints and realities on the job. The protocol
consisted of open-ended questions centered around on-the-job learning experiences, workplace
factors that shaped these, and the perceived impacts on work performance and career
progression. However, I was flexible in following up organically on participants’ responses with
additional probing questions as needed. I aimed to engage in meaningful discussions and allow
full exploration of perspectives beyond rigid adherence to a script. The goal was to gather rich
qualitative data through a naturalistic conversation.
With technicians’ time and schedules in mind, interviews lasted approximately 30
minutes. This duration aimed to respect volunteers’ availability while still permitting exploration
of topics in sufficient depth. To further accommodate technicians’ needs and the realities of
conducting research amid an ongoing pandemic, I scheduled interviews via the Zoom
videoconferencing platform at participants’ convenience. This provided a flexible, sociallydistanced option for technicians to engage virtually from any location. Online video calls made
participation straightforward without requiring travel or in-person meetings. The 30-minute
timeframe alongside virtual interview scheduling helped optimize technician engagement in
qualitatively enriching the study without undue burden on their daily lives and responsibilities
during a challenging time. Ultimately, prioritizing technicians’ consideration, comfort, and input
throughout this process hopefully encouraged the sharing of meaningful experiences and
perspectives to deepen understanding qualitatively.
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With participants’ informed consent, interviews were audio recorded and later transcribed
to capture the full richness and nuances of the discussions for in-depth qualitative analysis.
Compared to researcher notes alone, recordings retained all details of the exchanges beyond just
surface-level content. At the start of each interview, I also reminded technicians that their
participation was entirely voluntary. They need not answer any question or could stop the
interview at any time to reinforce their agency and frame the discussion as a respectful exchange
rather than an interrogation.
Complete verbatim transcription of the audio recordings from the interviews generated
comprehensive written records of the discussions suitable for meticulous analysis during the
coding and theme development stages of research. Rather than depending only on a researcher’s
memory or partial notes taken during fast-paced live exchanges, verbatim transcripts captured
subtle nuances in communication, such as emphasis, tone of voice, pauses, and more. Having a
written account of each interview directly from the audio recording allowed findings to be firmly
grounded in technicians’ own words and viewpoints without the potential for interpreter
influence or bias. The transcripts constituted objective data reflecting precisely what was said,
permitting multiple reviews and evaluations to deeply analyze interview content and derive
themes most authentically representative of participants’ lived experiences and intended
meanings.
Transcribing interviews verbatim required significant time but was essential for fully
honoring participants’ contributions and maintaining the integrity of the qualitative research
process. By producing word-for-word transcripts, I and any future audiences could thoroughly
immerse themselves in the perspectives and realities conveyed directly by technicians during
discussions. Rather than being limited by summary interpretations, verbatim transcription kept
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the rich qualitative data in its original form to allow for understanding meanings, realities, and
phenomena on the technicians’ terms. It prevented premature inferences by allowing themes,
insights, and conclusions to emerge authentically from meticulous analysis of what was said,
minimizing the potential for researcher bias. Preserving interviews in this comprehensive written
form through complete transcription respected the technicians’ voluntary input. It also ensured
that findings accurately reflected participants’ lived experiences, intentions, and meanings rather
than risk having third-party abstractions affect them.
I used a mixed-methods approach to gain a broad and deep understanding of the
approaches to learning, motivation, and workplace climate gaps that were impacting CCC’s
ability to provide a sound learning environment by better understanding the different approaches
to the education of their technicians. In the quantitative phase of the study, I sent a survey to all
technicians who had been with the organization for more than a year. The qualitative phase was
conducted in the form of interviews with survey participants who volunteered to participate in
the interview phase of the study. The interviews enabled me to gain a deeper understanding of
the approaches to learning, motivation, and workplace climate gaps.
For both the quantitative and qualitative phases of the study, I ensured that participants
had an opportunity to provide informed consent. Informed consent included making participants
aware that their participation was voluntary, that they could stop participating at any time, and
that they had been informed of any elements of the study that might impact their well-being
(Glesne, 2011). Study participants also had a right to privacy and confidentiality (Glesne, 2011).
The survey was conducted anonymously to protect participants’ identities. The interviews were
recorded via the Zoom platform, and all data gathered was kept strictly confidential. Any
personal or identifying information collected in the survey or interviews was removed prior to
70
publication. Both survey and interview data were stored in password-protected files on a secure,
password-protected device.
Study participants received full disclosure regarding the purpose of the study and how the
results would be used and distributed. The purpose of the study was aligned with the CCC
organizational goal of improving learning within the organization. In addition to satisfying the
requirements of my dissertation, I shared the study’s findings and recommendations with the
president and owner of CCC for his consideration and potential action. Glesne (2011) described
an intervener as a researcher who discovers and attempts to correct a situation that they
determine to be wrong. I hoped to intervene on behalf of the CCC technicians by identifying
research-based recommendations that could improve their approaches to learning, skills,
motivation, organizational resources, and the workplace climate in relation to their goal of
learning and progressing within the company.
I provided all survey and interview participants with a disclosure statement that included
the purpose of the study, how the study would be used and distributed, an assurance of
confidentiality, a promise to remove any identifying information from the final report, and
instructions to stop at any point they decided they no longer wanted to continue. In addition,
survey participants were informed that their responses would be anonymous. To indicate
consent, survey participants were asked to click on a link to proceed to the survey after reading
the disclosure statement. Rubin and Rubin (2011) recommended showing the utmost respect to
interviewees by being honest rather than deceitful, by gaining permission to record, and by being
punctual and polite throughout the interview. I asked interviewees for permission to record their
interviews. Interviewees were provided with a copy of the disclosure statement. I reminded them
verbally that they could skip any question or stop the interview at any time.
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As a manager at CCC, my interest in this study was both academic and professional. My
position as a leader in the organization could have created a potential power issue for study
participants, making them feel coerced and impacting the validity of the survey or interview data
(Creswell & Creswell, 2014). To reduce the impact of the power dynamic, the quantitative data
was collected using an anonymous survey. Surveys were sent, and responses were collected
using Qualtrics so that reactions could not be tied to specific individuals. Rubin and Rubin
(2011) advised researchers not to pressure interviewees to participate or to answer any questions
that made them uncomfortable. Interviewees were invited, not coerced, to participate. Glesne
(2011) suggested that researchers show gratitude for the time and value provided by the study
participants, especially for interviews. I provided $5 Chick-fil-A gift cards to interviewees in
appreciation for their time. I listened respectfully without imposing my own opinions.
My disciplinary orientation was the lens through which I viewed my problem of practice
and was a compilation of all my education and experience (Merriam & Tisdell, 2016). I selfidentified as part of the commercial construction industry even though I worked within only one
sector of it. The last 15 years of my career were spent at this company, which also ingrained a
business mindset that had persisted through all my time in commercial construction. As a
manager within the organization who had been a part of it since it opened, I had assumptions
about the approaches to learning, workplace environment, and motivation of technicians. Using
the mixed-methods approach helped to mitigate the inherent bias and weakness of both
quantitative and qualitative approaches by canceling each other out (Creswell & Creswell, 2014).
When analyzing and reporting the data, I used multiple perspectives and unbiased language, as
recommended by Creswell and Creswell (2014). I also requested peer reviews of my findings to
identify any biases or assumptions I might have overlooked.
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Data Analysis
Once the survey response period concluded, I downloaded and analyzed data collected
through Qualtrics using IBM SPSS statistical software. I employed both descriptive and
inferential statistical techniques to address the research questions. Descriptive analyses included
calculating frequencies, percentages, means, and standard deviations. These summarized key
sample characteristics like technicians’ attributes and scale scores on learning approaches and
environmental perceptions. Inferential analyses focused on relationships. Specifically, bivariate
correlational analyses examined connections between variables through techniques such as
Pearson’s r. This assessed whether, for instance, greater perceptions of autonomy related to
increased deep learning tendencies.
I also ran multivariate regression analyses to evaluate predictive abilities. For example,
models could show whether workload, choice independence, and supervision together forecasted
technicians' likelihood to adopt deep versus surface study behaviors. Chapter 4 presents the
results through detailed written interpretations accompanied by tables and figures as appropriate.
Correlation matrices and descriptive statistics tables condensed large amounts of data for
straightforward interpretation. Quantitative survey findings laid the foundation for sequential
qualitative interviews to explain initial results in richer human terms. Statistical significance
values clarified when relationships or predictions rose above chance levels.
The qualitative analysis involved a multi-step coding process using a constant
comparative approach. In the first stage of open coding, I closely read each interview transcript
line by line. I labeled discrete phrases, exchanges, or passages that were meaningful and related
to the research questions with a descriptive code summarizing their core meaning. The code
could be a word or a short phrase. As coding progressed through additional transcripts, I grouped
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some codes under a new higher-order code or renamed specific codes to capture emerging
phenomena in the data accurately.
The second analytical phase involved axial coding, as I methodically assembled specific
open codes derived from the initial line-by-line review beneath broader axial or thematic
categories. I clustered codes exhibiting similarities together under overarching conceptual
themes. I constantly compared relationships between principles and evolving themes with each
new transcript evaluated. This questioning process aimed to refine the thematic categories and
organization on an ongoing basis as analysis continued, permitting new understandings to
reshape the coding scheme as more perspectives from additional transcripts came to the
forefront.
The final stage was selective coding, where I synthesized the interrelationship between
axial themes to construct a narrative accounting for the core perspectives arising in the
participant responses. The presentation of findings emphasized themes supported by robust
examples across interviews. This multi-step process allowed concepts to emerge organically
from the raw data through iterative categorization rather than imposing a pre-existing framework
or biases.
Validity and Reliability
It was essential to establish the credibility of research methods and outcomes. The AWQ
and WCQ surveys chosen had undergone extensive examination in prior work, providing
evidence that they produced valid and reliable results. Specifically, the tools demonstrated
content validity by measuring the targeted abstract constructs, construct validity by correlating
with related variables as expected, and criterion validity by predicting real-world criteria.
Additionally, using multi-item scales enhanced reliability, as indicated by high internal
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consistency reliability in previous deployments of the instruments. This psychometric testing
helped ensure the quality and trustworthiness of findings derived from the assessment tools.
To further increase validity, educational experts knowledgeable about mixed-methods
research and the technician industry environment reviewed the survey questions and directions.
They provided feedback to refine the wording for clear understanding and to reduce the chances
of misconceptions. Their evaluation aimed to enhance comprehensibility, thereby strengthening
the meaningfulness and reliability of the data collected through the quantitative and qualitative
aspects of the study. The open-ended interview questions allowed for capturing genuine
perspectives without directional prompting. To achieve this, I adopted a neutral, non-evaluative
position and utilized reflective listening skills to understand the opinions expressed from diverse
stances. This approach aimed to gather insights untainted by biased intervention.
Verbatim transcription through careful reproduction of exact words, phrasing, and
vocalizations prevented distortion and misinterpretation of participants’ perspectives when
engaging with the raw data. Blind coding separately by two researchers shielded the analysis
from pre-existing biases skewing identified themes. Additionally, having participants engage in
member reflections ensured that the researcher understood the intended significance of emergent
themes derived from each technician’s contributions. This validated the credibility and
trustworthiness of findings stemming from the coding process.
Merged results delivered thick, rich descriptions conveying the findings’ contextual
details and multi-angle understanding. Readers could thus judge transferability based on shared
characteristics between the research setting and their interest situations. An audit trail further
documented every research step and decision to enhance dependability and confirmability for
future replication or extension.
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Full disclosure invited scrutiny that reinforced the data analysis in readiness for making
informed decisions from the relationship hypothesis. The nature of the data analysis is only a
huge representation of the conclusion within this study of how workplace motivation relates to
other variables tested. Therefore, a combination of both quantitative and qualitative data analysis
is a means of boosting the data analysis's validity and reliability so that finer results can be
obtained conclusively.
Summary
This chapter presents a robust research method clearly grounded in a pragmatist
worldview and a mixed-methods technique. In a concurrent parallel design, the research
instrument combined qualitative and quantitative strands in search of validity in the convergence
of data. This triangulation ensured that questions related to employee motivation at CCC were
well understood. The embedded explanatory design was sequential in that the qualitative insights
were used to interpret and explain the quantitative findings of this study. The surveys were
anonymous and administered online to assure privacy and confidentiality. Statistical analyses
informed takeaway findings and directed specific interview probe questions. A nonprobability
convenience sample technique was used to sample technicians, with a will to be demographically
representative. Interviews then served to enrich comprehension, made easier by a semi-structured
process. Validity was assured via extensive psychometric load testing, expert consultation, and
blinded coding. Summary findings integrated quantitative and qualitative findings for more
robust credibility and reliability of this study.
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Chapter 4: Results and Findings
This study utilized surveys and interviews to explore learning in the construction
workplace. Specifically, it investigated perceived workload, work motivation, and choice
independence among construction workers. The overarching goal was to understand better
approaches to learning and perceptions of workplace climate in this industry. Four questions
guided the research:
1. What are construction workers’ approaches to learning?
2. How do construction workers perceive the workplace climate?
3. What effect does workplace climate have on construction workers’ approaches to
learning?
4. How do employees experience learning in the workplace?
This chapter outlines the key results and findings from the research project. It begins by
reporting the findings of surveys completed by 71 construction industry employees and
interviews with a smaller sample of 10 workers. The chapter first presents an overview of the
participant's demographic characteristics to help contextualize the results. The following section
illustrates the quantitative and qualitative analytical techniques for investigating the four study
aims. Specifically, it details how descriptive and inferential statistics via Qualtrics software
addressed the research questions and how semi-structured interviews with open-ended questions
complemented the quantitative data collection and analysis.
Participants
This study's participants were technicians with diverse expertise. Authorization from the
human resource department allowed for access to their demographic data. These data guided the
sampling of these participants.
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Survey Participants
In all, I invited 125 technicians from varied backgrounds to participate in the survey
portion of the study. This resulted in a census sample of 71 participants, representing a selfselecting sample. Of the 71 surveys collected, 26 were excluded or incomplete, leaving 45 valid
responses for analysis. As the researcher for this study, I had access to demographic information
about the target population through company human resources records. The population was
100% male and 100% employed full-time. Less than 5% did not obtain a high school diploma,
while a comparable proportion secured a secondary degree. Ages in the population ranged from
18 to 65 years, with tenure at the company spanning from one to 20 years.
Interviewees
The qualitative interview portion of the research commenced after participants completed
the initial quantitative survey. Consistent with typical practice in qualitative studies, the
interview sample of 10 individuals was intentionally more diminutive than the 71 participants
who responded to the study. I recruited individual technicians willing to participate in a followup discussion after the study through a provided link. Given that over 10 technicians opted in, I
deliberately selected a subset sample of 10 interviewees from this larger pool of volunteers.
Seven of the 10 interviews initially scheduled were entirely conducted, with the technicians
providing full informed consent for the study’s findings to incorporate their responses.
These seven interviewees helped provide contextual insights into the perception of work
motivation at Company CCC and allowed for a deeper exploration of initial survey trends.
Mirroring the broader population and survey sample, the interview participant demographics
included only male technicians working full-time. Educational backgrounds also aligned, with
less than five percent lacking a high school diploma/K–12 education and a comparable
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proportion possessing a secondary degree. Ages ranged from 18 to 65 years, spanning the one to
20-year tenure period represented company-wide. Collectively, the interview sample
recapitulated the characteristics of the larger piece and workforce.
Table 2
Interview Participants’ Education Level
Interviewees Education Years with company
Participant 1 High school 15
Participant 2 High school 18
Participant 3 Some college 3
Participant 4 Middle school 13
Participant 5 Some high school 10
Participant 6 Some college 7
Participant 7 High school 16
Participant 8 High school 4
Participant 9 High school 12
Participant 10 Some college 15
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Research Question 1: What Are Construction Workers’ Approaches to Learning?
I applied the deep learning scale, surface rational learning scale, and surface disorganized
scale to explore the construction workers' approaches to learning.
Deep Learning
The deep learning scale is a self-reporting questionnaire about knowledge and experience
about deep learning concepts and techniques. It has 10-items, assessing the architecture of the
neural network and optimization algorithms to practical applicability in other domains of
different deep learning techniques. I evaluated the internal reliability of the 10-item Deep
Learning Scale (DLS) using Cronbach’s alpha. The calculated alpha coefficient of 0.71 indicates
adequate internal consistency among the items to justify aggregating them into a single DLS
score, as Norusis (1993) supported. This scale score was the average rating across the 10
constituent items. The DLS’s overall mean and standard deviation were M =3.14 and SD = 0.39.
With response options ranging from 1 to 5, a mean close to 3 suggests the technicians tended
toward somewhat agreeing they employ a deep learning approach in their work, on average.
Examination of the standard deviation and frequency graph (Figure 1) shows the data moderately
dispersed and approximating a normal distribution. This meets the assumptions for subsequent
inferential analyses. In summary, the DLS exhibited sufficient reliability, and the participant
responses were adequately normally distributed to interpret results involving this scale and
permit planned statistical comparisons.
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Figure 1
Frequency Graph for Deep Learning Scale
Table 3 shows the means and standard deviations for the individual items on the DLS.
Eight item means were close to a score of 3 (somewhat agree) and ranged between 2 and 4 on
the 5-point scale. The highest item mean was for Item 1, with a mean close to 4 (definitely
agree).
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Table 3
Descriptive Statistics for Items on the Deep Learning Scale
M SD N
Interpretation
of mean*
DS1: The work I am doing in my present job
will be good preparation for other jobs I may
have in the future.
3.58 .84 45 Agree
DS2: In trying to understand a puzzling idea, I
let my imagination wander freely, even if I
don’t seem much nearer to a solution.
2.62 .94 45 Somewhat
agree
DS3: In trying to understand new ideas, I often
try to relate them to real-life situations to
which they may apply.
3.51 .73 45 Agree
DS4: I like to play around with my ideas even if
they don’t get me far.
3.16 .77 45 Somewhat
agree
DS5: If conditions aren’t right for me at work, I
generally manage to do something to change
them.
3.24 .74 45 Somewhat
agree
DS6: In my job, one of my main attractions is
learning new things.
3.47 .63 45 Somewhat
agree
DS7: I find studying for new tasks can often be
exciting and gripping.
3.18 .78 45 Somewhat
agree
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DS8: I spend much of my spare time learning
about things related to my work.
2.84 .98 45 Somewhat
agree
DS9: I find it helpful to ‘map out’ a new topic by
seeing how the ideas fit together.
3.13 .69 45 Somewhat
agree
DS10: Some issues that crop up at work are so
interesting that I pursue them, though they are
not part of my job.
3.00 .80 45 Somewhat
agree
The mean for Item 3 was between 3 and 4 but slightly closer to 4, at 3.51. In summary,
the typical worker somewhat agreed that they used deep learning strategies at work on average.
However, it is worth noting that the typical worker decided that they tried to relate new ideas to
real-life situations. The standard deviations of all item scores were large for this scale, ranging
from 0.63 to 0.98. This indicates that the scores were relatively widely dispersed across the 5-
point response scale. Therefore, while most workers agreed with using deep learning approaches,
there was significant variability in individual responses.
I measured the DLS items on a 5-point Likert-type scale. Participants could select values
ranging from 1 to 5, representing the following response options: 1 was disagree, 2 was
somewhat disagree, 3 was somewhat agree, 4 was agree, and 5 was strongly agree. When
interpreting the item’s means, means below 2 indicated more disagreement than agreement,
means close to 3 were considered most comparable to somewhat agree, and means above 3.5
trended closer to agree definitely. This provides context that higher mean values represented
more substantial agreement, while means closer to the midpoint of 3 reflected more ambivalence
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or mild consensus. The standard deviation values helped assess the variability or dispersion in
responses across the scale choices. Collectively, this information aids in understanding how
participants engaged with the different learning approach prompts.
Surface Rational Learning
The surface rationale is commonly used in psychology and decision-making. It refers to
an immediate, superficial way of thinking or deciding, as opposed to deep analysis or thoughtful
considerations of the fundamental, underlying issues. I examined the internal consistency of the
10-item Surface Rational Learning Scale (SRS) using Cronbach’s alpha. The very high
coefficient of 0.83 indicated excellent reliability among the variables. The SRS’s overall mean
and standard deviation were M =2.85 and SD = 0.50, based on 71 participants. With response
choices ranging from 1 to 5, a mean of 2.85 aligned most closely with somewhat agree as the
interpretation for the scale midpoint (3). This suggests that the technicians had a mild tendency
toward strategic, organized surface-learning approaches in their work. Additional analysis of the
data distribution found that the standard deviation represented a moderate dispersion of scores
across workers. Figure 2 depicts the frequency graph showing that the data approximated a
normal distribution. Therefore, the normality assumption for planned inferential analyses was
satisfied.
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Figure 2
Frequency Graph for Surface Rational Learning Scale
As shown in Table 4, nine of the 10-item means on the SRS fell between 2.0 and 4.0 on
the 5-point scale, closely aligning with a score of 3 (somewhat agree). The sole exception was
Item 7, which had a mean of 2.71, indicating more disagreement than agreement. By definition, a
mean close to 3 represents only mild agreement with the item statements. Therefore, based on
the overall scale score mean of 2.85 and consistency across nine of the 10 SRS item means, on
average, the participants somewhat agreed they utilized organized, strategic surface-learning
approaches in their work.
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Table 4
Descriptive Statistics for Items on the Surface Rational Learning Scale
M SD N
Interpretation
of mean
SRS1: I like to be told precisely what is
expected When I am given a job to do at
work.
3.00 1.02 55 Somewhat
agree
SRS2: I prefer to tackle each part of a task or
problem in order, working out one at a time.
3.11 .79 55 Somewhat
agree
SRS3: When doing a piece of work, I follow
instructions strictly, even if they conflict
with my ideas.
2.64 .87 55 Somewhat
agree
SRS4: I prefer the work I am given to be
clearly structured and highly organized.
3.04 .90 55 Somewhat
agree
SRS5: I prefer to follow well-tried approaches
to problems rather than anything too
adventurous.
2.60 .81 55 Somewhat
agree
SRS6: When I learn something new at work, I
put a lot of effort into memorizing essential
facts.
2.85 .89 55 Somewhat
agree
SRS7: I find it better to start with the details
of a new task and build an overall picture
that way.
2.71 .88 55 Somewhat
agree
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SRS8: The best way for me to understand
what technical terms mean is to remember
the textbook definitions.
2.35 .87 55 Somewhat
disagree
SRS9: I think looking at problems rationally
and logically is essential without making
intuitive leaps.
3.09 .73 55 Somewhat
agree
SRS10: I remember things best if I
concentrate on the order in which they are
presented.
2.78 .76 55 Somewhat
agree
The standard deviations ranged from 0.73 to 1.02, which are relatively large for a 5-
choice scale. This indicates scores widely dispersed across the response options for each item,
with variability in how strongly individuals agreed or disagreed. Notably, although an SRS scale
score of 2.85 implies some inclination toward this learning approach, the sizable standard
deviations signify this was far from a uniform effect across all technicians. Their responses
spread broadly across levels of (dis)agreement with each prompt. While there was an overall
tendency, there was substantial diversity in individuals’ endorsements of surface-rational
techniques.
The SRS items utilized a 5-point Likert response scale where participants could select
from 1 (disagree), 2 (somewhat disagree), 3 (somewhat agree), 4 (agree), and 5 (strongly agree).
When interpreting the item means, means below 2 indicated more substantial disagreement than
agreement, a mean of 3 represented the closest proximity to somewhat agree as the midpoint
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response option, and means over 3.5 trended closer to agree or higher levels of agreement
definitely.
This scale structure provides essential context for understanding the mean values
regarding participant endorsement. Means closer to 3 reflected mild or modest agreement,
whereas higher averages suggested more pronounced concurrence with item content.
The standard deviations further qualified the strength of effects, revealing a more comprehensive
range of individual opinions distributed across the full-scale range rather than uniform
consensus. Collectively, these statistical results assist in fully comprehending how participants
subjectively related to different statements about their learning processes.
Surface Disorganized Learning
The surface Disorganized Scale is part of the Approaches to Work Questionnaire
developed by Kirby et al. in 2003. It is a 10-item scale measuring the tendency to use random
and unfocused study methods on an individual. These scales help evaluate, at the surface level,
the approach to learning problems that are disorganized and lack clear structure. The Cronbach’s
alpha value of 0.71 for the SDS items indicates adequate internal reliability to statistically
aggregate them into an overall SDS scale score, according to Narsis (1993). The overall SDS
mean was 1.97, with a standard deviation of 0.42 based on 71 participants. In contrast to the
other scales, higher values on the 5-point SDS response scale reflected more robust disagreement
rather than agreement. Therefore, a mean close to 2 most aligned with somewhat agree regarding
disorganized learning behaviors. Figure 3 displays the frequency distribution of SDS scores.
Both the standard deviation and graph indicate moderate dispersion of responses across the scale
values. While the data were slightly leptokurtic, the large sample (N = 71) renders the SDS
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robust to normality assumptions for planned statistical tests, according to the central limit
theorem principles.
Figure 2
Histogram for Surface-Disorganized Scale
Table 5 details the means and standard deviations for each item on the SDS. After
interpreting the item values based on the scale structure (higher numbers indicating less
agreement), conclusions emerged: The typical technician response for most items aligned with
somewhat agree or definitely agree, suggesting acknowledged tendencies toward disorganized
learning behaviors. Specifically, the data imply technicians often feel rushed and stressed at
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work. They also perceived that supervisors sometimes overcomplicate things unnecessarily or
are content just scraping by with minimal effort. Some participants somewhat or concurred they
may jump to conclusions or find it difficult to see the big picture in their work. A few agreed
they occasionally wonder why they work for the company.
Table 5
Means, Standard Deviations, and Interpretations of the Items of the Surface Disorganized Scale
M SD N Interpretation of the mean*
SDS1: At work, I find it
challenging to organize my
time effectively.
2.02 .790 41 Somewhat agree
SDS2: I prefer to have a good
overview rather than focus on
the details.
2.71 .873 41 Somewhat disagree
SDS3: The continual pressure at
work—tasks to do, deadlines,
and competition—often
makes me tense and
depressed.
2.10 .800 41 Somewhat agree
SDS4: My habit of putting off
work leaves me with far too
much catching up to do.
1.78 .822 41 Somewhat agree
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SDS5: Managers seem to
delight in making the simple
truth unnecessarily
complicated.
1.83 .892 41 Somewhat agree
SDS6: Often, I find I have to
read things without having a
chance to understand them.
2.07 .818 41 Somewhat agree
SDS7: I want a good
performance appraisal, but it
doesn’t matter if I scrape
through.
1.44 .709 41 Agree
SDS8: Although I generally
remember facts and details, I
find fitting them into an
overall picture difficult.
1.49 .637 41 Agree
SDS9: I seem to be a bit too
ready to jump to conclusions
without waiting for all the
evidence.
2.05 .921 41 Somewhat agree
SDS10: When I look back, I
sometimes wonder why I
decided to work here.
1.49 .840 41 Agree
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Notably, the standard deviations were moderate primarily in size, indicating responses
were dispersed across the scale rather than clustered at one end. This reinforces those technicians
varied in the intensity of their agreement or disagreement with each statement. The SDS Scale
utilized a 5-point Likert response format ranging from 1 to 5. In contrast to the previous scales,
higher numbers on the SDS indicated as follows: 1 (agree), 2 (somewhat agree), 3 (somewhat
disagree), 4 (disagree), and 5 (strongly disagree). When considering the item means, means
below 2.5 reflected more agreement than disagreement, a mean close to 3 aligned closest with
somewhat disagree, and standards above 3.5 suggested a more substantial dispute. It is essential
to outline this scale structure, as it is reverse-scored compared to the DLS and SRS. Means
toward the lower end represented endorsement of disorganized tendencies, whereas higher
averages signaled a rejection of those behaviors. Together, the response scale, item means,
standard deviations, and distributional qualities help fully understand technicians’ subjective
attitudes as measured through this survey instrument exploring their diverse approaches to
workplace learning.
Summary
The results provide insight into the predominant learning approaches the construction
technician participants exhibited. Across the three Approaches to Learning Scales measuring
distinct constructs, several key findings emerged. On average, technicians agreed that they
employ deep and surface-rational learning approaches in their work. The DLS indicates that
technicians somewhat endorse efforts to relate new ideas to real-world examples and see work as
preparation for future roles. The comparable SRS average suggested a modest inclination toward
organized, strategic studying. However, standard deviations on these scales also revealed
substantial variability in individuals’ scale responses. While overall tendencies surfaced,
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technicians significantly differed in their agreement with specific dimension prompts. Regarding
surface-disorganized learning measured by the SDS, technicians agreed somewhat with
characterized behaviors like feeling rushed or questioning job value - though varying extents
depending on the item. These quantitative findings demonstrate that technicians draw from
multiple learning orientations rather than exclusively preferring one style. Their use of deep,
surface-rational, and surface-disorganized tendencies is moderate on average but diverse
between individuals. The results characterize a multifaceted approach to workplace learning
among this technician population.
Research Question 2: What are Construction Workers’ Perceptions of Workplace
Climate?
I assessed the participants’ perceptions of workplace climate using three established
scales (Williams & Anderson, 1991). In this study, the 5-item scale for GS (α = 0.89) measured
perceptions of supervisor competence, fairness, and support (e.g., “My supervisor is quite
competent in doing his/her job”). The 5-item Workload Scale (α = 0.86) captured feelings of
being overworked or unable to complete tasks (e.g., “I am given enough time to do what is
expected of me on my job”). This 5-item Choice Independence scale (α = 0.75) evaluated
perceptions of autonomy and control (e.g., “I have a lot to say about what happens on my job”).
Each scale used a 5-point Likert response format to indicate the level of agreement. As Williams
and Anderson (1991) reported, the scales demonstrated good internal consistency reliability,
permitting aggregation of items into composite scores. This provided three distinct yet related
measures of workers’ subjective workplace climate experiences.
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Good Supervision
The high Cronbach’s alpha of 0.95 in this study for the GS Scale indicates excellent
reliability among the five items. This warranted aggregating the items into a single composite
score represented by the mean rating. The overall GS mean was 3.07, with a standard deviation
of 0.83 based on 66 participants. On the original 5-point scale where higher values reflected
more substantial agreement, a mean close to 3 aligned most closely with “somewhat agree.”
However, the sizable standard deviation of 0.83 showed responses widely dispersed across the
full-scale range.
As shown in Figure 4, the variable distribution notably deviated from normality. There
was a pronounced ceiling effect where most scores clustered at the upper end of the scale,
resulting in a negative skew. This non-normality may suggest that social desirability bias
regarding supervision perceptions has possibly influenced responses. While the violation of
normality is noteworthy, the central limit theorem principles suggest the large sample of 66
renders the GS variable sufficiently robust for the planned inferential analyses to proceed
reasonably as proposed.
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Figure 3
Histogram for Good Supervision Scale
As presented in Table 6, all five item means on the GS scale fell between 3.0 and 4.0,
aligning most closely with some agreement on the original 5-point response scale. Specifically,
the data imply that, on average, workers somewhat agreed their supervisors make efforts to get to
know employees personally and take their ideas and input seriously. Participants also partially
concurred supervisors generally make themselves available to help or assist when needed.
Notably, while the item expressed lukewarm agreement, inspection of the standard deviations
reveals scores widely dispersed across response options for each statement. This qualifies that
while there was an overall tendency, the strength of individual opinions varied substantially. To
summarize, workers acknowledged to a modest degree that their supervision demonstrated
positive supervisory behaviors like competence, approachability, and valuing workers’
contributions based on the item means. However, substantial diversity existed between
individuals’ assessments, represented by the broad standard deviations.
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Table 6
Means, Standard Deviations, and Interpretations of the Items of the Good Supervision Scale
N M SD Interpretation of the
mean*
GSS1 Most supervisors try hard to get to
know employees.
65 2.97 .93 Somewhat agree
GSS2 Supervisors here make a real effort
to understand employees’ difficulties
with their work.
65 3.00 .93 Somewhat agree
GSS3 Supervisors in this organization
seem to go out of their way to be
friendly towards employees.
64 3.27 .84 Somewhat agree
GSS4 The supervisors in this
organization always seem ready to give
help and advice on the best way to
learn something new.
64 3.11 .87 Somewhat agree
GSS5 Supervisors in this organization
generally take employees’ ideas and
interests seriously.
63 3.08 .95 Somewhat agree
The GS scale items utilized a 5-point Likert response format ranging from 1 to 5.
Specifically, the response options were 1 (disagree), 2 (somewhat disagree), 3 (somewhat
agree), 4 (agree), and 5 (strongly agree). When considering the item means, means below 2.5
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reflected more disagreement than agreement, means close to 3 aligned closest with somewhat
agree,” and those above 3.5 suggested a more robust understanding.
This scale structure is essential for appropriately qualifying the average ratings regarding
the workers’ level of endorsement. Means toward the lower end expressed mild disagreement,
around 3 indicated modest agreement and higher averages signaled more pronounced
concurrence. Understanding the nuanced Likert scale scoring aids in accurately interpreting how
participants subjectively related to prompts about their supervision’s behaviors and
competencies. Combined with standard deviations, this contextualizes variation in individual
viewpoints.
Perception of Workload
The Workload Scale (WS) demonstrated outstanding reliability with a Cronbach’s alpha
of 0.82, permitting aggregation of items into an overall mean score. However, unlike the other
scales, higher WS values reflected increased disagreement rather than agreement with statements
about workload perceptions. The overall WS mean was 2.56, with a standard deviation of 0.71
based on 68 participants. According to the 5-point scale structure (1 = definitely agree to 4 =
definitely disagree), a mean closest to 3 signified an average somewhat disagreement. Despite
this overall interpretation, inspection of individual item means in Table 7 revealed a mixed
pattern of responses. While the typical worker somewhat agreed that their workload was too
heavy, they disagreed that their job required too many tasks or imposed undue pressure or selfdirected learning demands. Notably, while mean differences were modest, qualitative differences
in item wording suggested diverse perceptions of workload-specific factors. Responses
demonstrated a general tendency against perceptions of excessive requirements but
acknowledged workload amounts as somewhat taxing.
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Table 7
Means, Standard Deviations, and Interpretations of the Items of the Workload Scale
n M SD Interpretation of the
mean
WS1: The workload here is too heavy. 63 2.17 .94 Somewhat agree
WS2: Sometimes, my job requires me to do
too many different things.
64 2.73 1.01 Somewhat disagree
WS3: In this organization, you must spend a
lot of time learning things independently.
65 2.98 .91 Somewhat disagree
WS4: There seems to be too much work to get
through here.
63 2.27 .94 Somewhat agree
WS5: There’s a lot of pressure on you as an
employee here.
60 2.63 .94 Somewhat disagree
The WS items used a 5-point Likert response format ranging from 1 to 5: 1 (agree), 2
(somewhat agree), 3 (somewhat disagree), 4 (disagree), 5 (strongly disagree). Higher values
reflected more robust disagreement rather than agreement. Therefore, a mean closest to 3 aligned
with somewhat disagree.
The standard deviation of 0.71 shows responses widely dispersed across the full-scale
range for the overall score. This dispersion is also evident in Figure 5, which reveals a nonnormal leptokurtic distribution where most scores clustered in the mid-range rather than
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following a standard bell curve pattern. While deviations from normality were present, the
sample of 68 respondents, according to the central limit theorem, means inferential tests can still
be reasonably interpreted, as large samples mitigate the impacts of non-normal data.
Figure 4
Histogram for Workload Scale
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Choice Independence
The Cronbach’s alpha of 0.92 for the 5-item Choice Independence Scale (CIS)
demonstrated excellent internal reliability in the sample of 54 workers. This permitted
aggregating items into a single CIS score. The overall CIS mean was 2.83, with a standard
deviation of 0.74 based on 66 responses. On the original 5-point scale, a compromise closest to 3
aligned with “somewhat agree.” Therefore, on average, workers expressed modest agreement
with autonomy and control over their work. As shown in Figure 6, the standard deviation of 0.74
and distribution histogram indicate scores reasonably dispersed across the full-scale range, albeit
with a slightly leptokurtic clustering in the mid-section versus a standard curve. While some nonnormality was evident, the sample size of 66 participants means the variable is robust enough for
planned inferential analyses according to the central limit theorem.
Figure 6
Histogram for Choice Independence Scale
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Table 6 presents a more descriptive look at workers’ responses to the individual CIS
items. All five item means fell between 2.5 and 3.5, indicating modest agreement tendencies.
Specifically, the data imply that, on average, workers somewhat agreed their employer affords
choices in how to complete tasks and how learning occurs on the job.
Notably, while the overall item means implied lukewarm concurrence, inspection of the
standard deviations (not shown) reveals scores dispersed across the full response scale for each
statement. This distribution qualifies that while there was a general leaning, the intensity of
individual opinions spanned the spectrum from definite disagreement to agreement. No item
elicited uniform solid endorsement. The item means signal workers perceived their roles as
providing a reasonable degree of control and discretion based on the scale midpoint
interpretation. However, looking solely at averages risks overlooking the substantial diversity
between viewpoints exposed through analyzing each item’s variability. A holistic view is
needed.
The CIS utilized a 5-point Likert response format ranging from 1 to 5. Specifically, the
response options were 1 (disagree), 2 (somewhat disagree), 3 (somewhat agree), 4 (agree), and 5
(strongly agree). When considering the item means, means below 2.5 reflected more
disagreement than agreement, a mean close to 3 aligned closest with “somewhat agree,” and
standards above 3.5 suggested more substantial agreement.
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Table 8
Means, Standard Deviations, and Interpretations of the Items of the Choice Independence Scale
n M SD Interpretation
of the mean
CIS1: There is a real opportunity in this
organization for people to choose their
tasks.
60 2.62 .86 Somewhat
agree
CIS2: The organization seems to encourage
us to develop work-related interests as far
as possible.
62 2.84 .93 Somewhat
agree
CIS3: We seem to be given a lot of choices
here in the work we have to do.
63 2.68 .89 Somewhat
agree
CIS4: This organization allows you to go
about your work in ways that suit your way
of learning.
65 3.03 .83 Somewhat
agree
CIS5: Employees here have a great deal of
choice over how they learn new tasks.
64 2.92 .84 Somewhat
agree
Understanding this scale structure is essential for accurately qualifying what the average
ratings represent in terms of workers’ levels of endorsement. Means toward the lower end
expressed mild disagreement, around 3 indicated modest agreement and higher averages signaled
more pronounced concurrence. Providing the nuanced scoring aids interpretation beyond simply
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labeling means as agree or disagree. It contextualizes where responses feel relative to the
midpoint and full spectrum of options. Combined with variability measures, this paints a
complete picture of response patterns. The scaled nature and how it links to qualitative labels is
critical information for correctly comprehending participants’ subjective attitudes, as revealed
through Likert scale survey results.
Summary of Descriptive Statistics for Workplace Climate
The GS scale revealed that technicians, on average, expressed modest agreement with
positive statements about their supervisors’ competency, approachability, and value of worker
input. Specifically, the item suggests that supervisors generally try to understand employees and
take their contributions seriously. For the CIS, the average response indicated workers perceived
their roles as offering a reasonable degree of autonomy and control over tasks and learning.
However, inspection of variability showed viewpoints extensively varied across the entire
spectrum. The WS results were more nuanced. While agreeing on work amounts felt heavy,
workers disagreed with statements about excessive job requirements, self-directed studying, or
undue pressures. This demonstrated a multifaceted evaluation rather than uniform positive or
negative perceptions of workload factors. Substantial dispersion and non-normal distributions
necessitated consideration beyond sole reliance on mean interpretations for all scales. Individual
perceptions spanned scales, signifying averages obscured valuable diversity. A comprehensive
understanding emerged from fully contextualizing central tendencies and response variability
across measurements.
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Research Question 3: What Effect, if Any, Does Workplace Climate Have on Construction
Workers’ Approaches to Learning?
The research question implied a causal relationship. However, as a non-experimental
study, the design cannot definitively answer causal questions. Rather than attempting to establish
effects, the best approach is to explore potential associations between climate and style of
learning constructs. Significant correlations indicate patterns worthy of further investigation but
cannot prove causality. Specifically, this study examined relationships among the three climate
scales and three learning approach dimensions. Finding correlations does not mean one factor
causes changes in the other.
Good Supervision
To explore potential relationships, Table 9 presents the correlations between the GS scale
and the three learning approach dimensions: active, reflective, and pragmatic approaches.
Specifically, the coefficients reveal the strength and direction of linear relationships between
supervision perceptions and preferences for learning via experimentation versus observation,
problem-centered versus theory-centered processing, and applying versus accumulating
knowledge. There was a statistically significant positive correlation between GS and the active
learning approach (r = .28, p <. 05), indicating a higher endorsement of competent supervision
associated with a tendency to learn through active experimentation and engagement. The
correlations for reflective (r = .16) and pragmatic styles (r = .07) did not reach significance.
Therefore, while preliminary evidence hints that competent supervision may facilitate active
learning, no clear pattern was seen for theoretical or application-based processing preferences.
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Table 9
Correlations Between Choice Independence and the Three Learning Approaches
Deep learning Surface
rational
Surface disorganized
Choice Independence 0.46 -0.05* 0.42
*p <.05
This table reports the correlations of choice independence with each learning approach
dimension: Deep Learning, Surface-Rational, and Surface-Disorganized. There is a medium
effect size and statistically significant correlation between workload and Deep Learning, r =
0.46, p < 0.006 but in contrast more varied for Surface-Rational, r = -0.05, p < 0.64 and
Surface-Disorganized, r = 0.42, p < 0.0001 approaches. According to Cohen (1969), this effect
size was considered medium. Surface-Rational Learning approach is an approach where the
learner focuses on memorizing facts to pass exams without comprehensively relating ideas or
understanding concepts. The positive direction means that participants who report having a
workplace climate that supports choice independence also register using a surface-rational
learning approach characterized by memorization without integration. Conversely, participants
who report a work climate that does not support choice independence are much less likely to use
the surface-rational approach.
Workload
To explore potential relationships with workload perceptions, Table 10 presents the
correlations between the WS and each learning approach dimension: active, reflective, and
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pragmatic approaches. The coefficients reveal the strength and direction of linear relationships
between views of job demands and preferences for experimentation-centered, theory-centered, or
application-centered information processing when learning. There was a statistically significant
negative correlation between workload and the active learning approach (r=-.27, p<.05),
suggesting higher perceived demands associated with reduced tendencies toward hands-on
engagement. Meanwhile, the correlations for reflective (r=-.15) and pragmatic (r=-.11) styles
were not significant at the p<.05 level. While exploratory, this initial evidence tentatively
indicates that workload levels may inhibit active learning orientations. Further research is
warranted to understand the role of job demands on learning approach inclinations.
Table 10
Correlations Between Workload and the Three Learning Approaches
Deep learning Surface
rational
Surface disorganized
Choice Independence 0.33 -0.06* 0.42
* p<.05
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This table reports the correlations of workload with each learning approach dimension:
Deep Learning, Surface-Rational, and Surface-Disorganized. There is a medium effect size and
statistically significant correlation between workload and Deep Learning, r = 0.33, p < 0.006, but
in contrast, more varied for Surface-Rational, r = -0.06, p < 0.64 and Surface-Disorganized, r =
0.42, p < 0.0001 approaches. Only the latter has a significant positive effect. This was a
significant positive effect. Both variables were reverse-scored. Thus, participants agreeing with
higher workloads tended to report more surface-disorganized learning, while those disagreeing
with high workloads reported less use of this surface style.
Summary
The results reveal several significant correlations between perceptions of workplace
climate and learning approaches. A sizable negative relationship was observed between good
supervision and surface-disorganized learning, indicating that workers who feel supported by
competent supervisors are less likely to approach learning disorganized without structure or
planning. Perceptions of autonomy through choice independence showed a medium-sized
positive link to surface-rational learning, a style focused on memorization without
comprehension. Higher workload correlated with less reported use of deep understanding, which
emphasizes integrating and relating concepts. Interestingly, workload displayed a large positive
tie to surface-disorganized education, suggesting increased demands connected to a more
haphazard study approach. These exploratory findings point to climate-influencing tendencies
toward deep, meaningful learning versus surface styles focused on memorization and suggest
addressing workplace factors that may impact how employees approach current information and
skills development.
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Research Question 4: How Do Employees Experience Learning in the Workplace?
I interviewed seven participants to obtain qualitative data about times they felt
exceptional learning occurred at a past or current job, with an initial open-ended prompt asking
them to describe what happened and follow-up questions from Table 10 to gain further
contextual details. Responses were analyzed for common themes in the reported experiences and
surrounding conditions during periods perceived as especially effective workplace learning,
aiming to complement and aid the interpretation of quantitative survey findings showing
relationships between organizational climate factors like supervision, workload, autonomy, and
learning approaches. The qualitative approach provided richer narrative data that could help
explain observed trends and generate new hypotheses about how aspects of the occupational
environment intersect with on-the-job skills acquisition according to retrospective employee
accounts.
Qualitative Data Theme 1: The Role of Social Interactions in Facilitating Learning
The interviews revealed that positive social interactions and hands-on, collaborative
experiences at work, which allowed technicians to engage directly with colleagues through
problem-solving situations, observation, and feedback, were closely associated with enhanced
learning and acquisition of essential skills such as understanding team roles, customer service
techniques, and management tools. In contrast, limited opportunities to practice and apply new
knowledge interactively, such as only observing others without participation and insufficient
guidance, resources, and feedback, appeared to inhibit learning among technicians, according to
their accounts. Participants also described workplace relationships and collaborative cultures that
facilitated learning as yielding additional productive outcomes, including improved teamwork,
higher project completion rates, and increased customer satisfaction.
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Participant 8 described a formative learning experience involving workplace interactions.
He shared his experience with struggling to navigate an unfamiliar software system when
receiving training in a new role, and his trainer grew frustrated with the amount of time it took
for him to learn the program. However, during this process, he eventually "had an epiphany" as
he realized that the capabilities of this particular software could streamline their inefficient
paper-based processes by creating electronic job folders. These folders incorporated all forms,
such as work orders, technician notes, invoices, and bills, allowing technicians to fill out/upload
reports via WiFi electronically. This meant delays were no longer occurring while handwritten
forms were waiting to be processed. Even though Participant 3 saw some merit in these changes,
he was disappointed, "My supervisor did not think we would have enough WiFi connection
capability where the teams are located." He felt confident these would add value to their projects
but needed to be allowed to move forward with implementing these changes.
Participant 8 shared how, undeterred, in his own time, he investigated other systems. He
discovered one that could handle all of his envisioned workflow and even integrate accounting.
Wanting to test his theory, he paid to be trained on the new software. Armed with self-assurance,
he presented the system to the owner, who promoted him to start implementation and training.
He self-reported being highly proud of solving problems surrounding inefficiencies and getting
recognition for innovative learning. Although the supervisor did not support choice
independence, the participant took responsibility for his education through self-study. In this
regard, the owner supported choice independence by rewarding his efforts. This shows that the
ability to meet challenges or obstacles through deep learning may trigger career progression even
when there is no early work-based support. This experience has illustrated this person's resilience
in using negative interactions to positive advantage.
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Participant 2 shared just one formative learning experience: Frustratingly, this started
with learning to use a new software system in training that was unfamiliar to him, and his
supervisor was getting impatient. He said, "At first, it was difficult, and I thought there was no
hope for me, but my determination kicked in." However, reflecting deeply on workflow
inefficiencies, he developed an idea to computerize their paper-based process - using a more
appropriate software program. Though his trainer had eliminated that as a possibility, he
followed up on the problem with some self-directed study, researching other systems and
training on one himself. This participant in the interview expressed being confident about his
solution that he presented the enhanced technology to the owner despite the discouragement of
others who told him, "It will never work." He declared, "The owner recognized my initiative and
thinking independently to do something new; they promoted me on the spot." Though there was
no support in the early employment workplace, this man's persistence worked because thinking
out of the box was appreciated. Deep learning strategies benefited him in his career under nonsupportive environmental conditions. This clearly demonstrates how resilience can empower
employees to turn challenges into opportunities, advancing their skills, solutions, and careers.
Qualitative Data Theme 2: Technician Emotional Response to Learning Interactions
The interview findings from seven participants showed that positive learning experiences
where technicians could effectively develop new skills made them feel happier at work, more
valued and respected by their organization, and more confident in their abilities. On the other
hand, hostile learning environments that inhibited skill growth were linked to detrimental effects
such as feelings of frustration, isolation, and decreased confidence among technicians.
Workplace cultures that promoted learning appeared to enhance professional knowledge and
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worker well-being. Conversely, those without a supportive learning environment detract from
education and fundamental employee outcomes, such as job satisfaction and motivation.
As an illustration of how technician emotional responses to learning interactions affect
the workplace, Participant 6, a lead technician, expressed that he felt tension and separation
between him and the technicians under him, "Brother, at one point, I got so frustrated that I made
some negative comments towards their efforts." Instead, it counterproductively made them frown
on having assistance or problem-solving together because they felt reprimanded by his words.
The participant shared that eventually learning that respect is always essential, even in the
presence of difficulty, to ensure a positive and constructive experience of learning through
challenges: "I wish at the time that I had been more respectful because it would have helped us
get through our challenge faster with better results." His analysis brings to light the way
interactions at work have a significant impact on psychological safety, collaboration, line
learning opportunities, and the degree to which workers feel comfortable taking risks or asking
questions - conditions that are essential and pivotal for completing tasks involved in successfully
functioning teams while intervening in an organization's capacity for growth.
Participant 6 discussed this one day when "the lead technician degraded the technicians'
work instead of helping" as unexpected problems interrupted the work schedule and caused them
to disconnect. This aversive exchange discouraged potential collaboration and feedback; hence,
learning was restrained by undermining psychological safety. For example, the participant said,
"It felt like no matter what we tried, it was wrong and not worth our effort or time," which ended
up inhibiting development for the participant's team on this job site because of its discouraging
nature. The experience serves as a great example of the need for communication during hardship
to aid growth rather than inhibit it.
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Qualitative Data Theme 3: Effects of Learning Interactions on Vocational Pursuits
The interviews with seven participants revealed that positive skill-forming learning
experiences enabled technicians to develop new skills and boosted some to seek more advanced
roles where they could apply expanding competencies. This signaled how supportive workplace
learning cultures fostered feelings of progression and willingness to take on new organizational
challenges. Conversely, workplaces with hostile learning environments that impeded skill
formation discouraged technicians from taking up roles requiring those abilities. The starkest of
such instances, where learning was not taking place because resources were not being adequately
supplied or cultures receptive to learning, saw technicians stepping back from the learning
process or considering moving to another company or out of the profession altogether, which
strongly suggests occupational learning links to employee retention, engagement, and motivation
in that disappointing learning is likely to negate feelings of future trajectory and growth potential
within an organization.
Participant 4, with over ten years of experience working in the construction industry,
provided insight into learning and workplace interaction as factors that determined technicians'
careers in his company. He mentioned, for example, that anytime project managers and leads
related well and directed their subordinates in a supportive manner, with affirmation, they ended
up rearing successful teams: "They would be so charismatic [and] collaborative.". … [They had]
high profits for the team," which most of the time led to extra work being taken on willingly by
all members. On the other hand, those employing dictatorial styles, enforcing errors, saw
demotivation from technicians just going through the motions; these left quite frequently due
largely to discontentment. Participant 4 shared, "Demotivation from technicians just going
through the motions." Those working within the group, however, under positive working
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environments, continued to grow in skills and confidence and gain promotions within different
teams, ailing where problems could still arise concerning workload roadblocks to further
development and learning other than those already mastered. Participant 5 shared that "working
within the group under positive working environments allowed them to grow in skills and
confidence and gain promotions." This, in turn, brings us full circle back to a becoming need for
supportive cultures that emphasize deep skill acquisition through problem-solving based on
collaboration instead of criticism or punishment, which hurts motivation as well as retention
possibilities in the process.
Participant 10 observed that project managers who had good social skills and who could
thus lead in a humble, supportive style tended to build great teams over the ten years.
Charismatic technicians made a tremendous profit, confidence, and promotion, as illustrated in
this quote: "Understanding leaders created charismatic technicians, which in turn was a
concomitant cause for our exceptional profits, boosted confidence and frequent promotions."
However, authoritative directions intolerant of mistakes gave way to downtrodden technicians
merely working for the pay and often leaving: "Authoritative directions intolerant of mistakes
gave way to downtrodden technicians merely working for the pay." Workload-inhibited learning
was highlighted by experienced staff as a reason for not doing so at swaggering firms:
"Interactive learning cultures deepened skills instead of critical styles savaging motivation."
Bringing out the relation of new ideas to practical application, interactive learning cultures
deepened skills instead of critical styles savaging motivation.
Qualitative Data Theme 4: The Effects of Learning Interactions on Attitudes Toward
Organizational Positions and Teams
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Participants explained activities that generated positive learning and competency
development through social contact, which included more positive attitudes to cross-functional
cooperation and a reduction in stereotyping. Negative experiences hindered competency
development and confirmed or formed adverse stereotypes about other jobs held by the
technicians. The interviews revealed that learning at-work interactions impact attitudes to
internal cooperation and a culture of internal support because fulfilling the inherent needs of
employees is more likely to lessen occupational silos. Specifically, Participant 7 mentioned:
I hated my job because my supervisor never answered questions or demonstrated the
tasks, yet he always rushed to reprimand different approaches. I was the scapegoat for the
team's poor performance, and there was no way out of it other than transferring teams.
This was contrary to what Participant 6 said about having had a good experience under a
different project manager who made him "thrive under this mentality of support, embracing
competence, and autonomy." Participant 5 claimed to have received after-hours classes teaching
techniques away from pressure, which gave him a "shared vision" and goals that made the
workplace conducive by giving choice independence and leading learning through failure. The
predominating factors of these two contrasting accounts underscore how managerial styles can
make a difference in employees' experience. In this respect, how managers motivate their
employees may be rather influential in determining the level of commitment an employee
demonstrates towards the organization over time and how well their basic psychological needs
are served within the working context. All these elements shape employees' experiences and
devotion to the organization simultaneously.
Participant 5 also described having had a negative experience in an earlier job where he
was working under a supervisor who was not willing to teach but was the first person to
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reprimand him. At the same time, he felt scapegoated among the favorites of the supervisor.
According to Participant 5, this destroyed teamwork and learning, so many left work with
negative attitudes. Participant 5 spoke about understanding project management after-hour
classes that worked through difficulties in a safe environment. Participant 5 emphasized, "We
learned about understanding project management after-hour classes that worked through
difficulties in a safe environment," which strongly points to the relevance of supportive learning
environments to develop competence and resilience. Technicians thrived and then learned more,
some even coming back again due to a shared vision of psychological safety by embracing
competence through attempts that failed. Therefore, these experiences have revealed that
different leadership styles have affected motivation, careers, and loyalty over time.
Relationship Between Survey and Interview Data
Semi-structured interview data with seven participants provided insight into technicians'
perceptions of workplace factors affecting performance. The central theme that emerged was that
a supportive climate embracing competency development through varying learning approaches
best motivated the individuals and teams. Survey data supported these perceptions. Interviews
strengthened this relationship by showing how cooperation better facilitates problem-solving. A
predictor of good outcomes was clear leadership that cultivates psychosocial safety through
guidance, feedback, and addressing challenges. Overwhelmingly, technicians reported
empathetic supervision increased camaraderie by giving responsibilities as incentives for selfimprovement and paid lessons forward. It means that variables such as psychological needs
satisfaction have been observed to have implications on retention, organizational citizenship
behaviors, and long-term organizational effectiveness since individuals model leadership and
shape careers, which are influenced by the style adopted in leadership.
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Chapter 5: Discussion and Recommendations
This closing chapter synthesizes and discusses significant findings emerging from the
research. The broader scope of this mixed-methods study was to gain insights into how
workplace factors, learning strategies, and work careers are longitudinally related to each other,
as reported by technicians currently working at Colorado Construction Company (CCC, a
pseudonym), a medium-sized company operating within the construction sector. Quantitative
survey data were collected using the Approaches to Work and WCQ to address these. Qualitative
interviews then explored reported perceptions in more depth. Therefore, this design provided a
nuanced examination linking behaviors, attitudes, and lived experiences regarding organizational
learning.
This chapter will analyze and integrate critical takeaways. First, findings about each
research question will be condensed, examining learning approaches, climate perceptions, and
how experiences aligned or diverged from theory. The chapter will then present an assessment of
the relationships between measured climate factors and described styles. It will also situate
discoveries within Kirby and colleague's (2003) conceptual frameworks guiding the study.
Conclusions will identify common threads uniting workplace influences, behaviors, and
developmental support at CCC. Results will shed light on optimizing learning through
understanding technicians' perspectives. Finally, strategic and operational recommendations for
CCC will be suggested based on integrated results and findings. The study's theoretical and
practical implications, limitations and delimitations, and prospects for additional exploration will
round out the discussion. In summarizing cross-checked quantitative and qualitative discoveries,
resolving discrepancies, and translating mixed insights, this chapter aims to fulfill the original
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objectives of comprehending organizational learning thoroughly through multiple lenses. The
generated guidance may help address CCC's pressing technician retention problem.
Discussion of Key Findings for Research Question 1
The approaches to work questionnaire provided several insights about technicians'
significant ways of learning. The majority of workers reported using a blended style, elements of
which involved the use of deep and surface techniques according to appropriateness. However,
on many tasks, surface learning seemed the dominant style, especially on those that were less
intrinsically interesting, more routine, or less challenging. Technicians discussed in individual
interview situations are likely to encourage the use of deep as opposed to surface approaches.
They stated that they would adopt a problem-solving attitude while learning any new technical
skill or trying to resolve complex problems involving related concepts. However, compliance
mandatory online modules received more perfunctory engagement than was aimed at merely
completing them.
Some interviewees practiced enterprises such as process diagramming or making detailed
notes to assist in developing a deeper understanding of new systems. Others described less
organized procedures when the workload was perceived as excessive, seeking direction from
coworkers rather than through their inspection. When contextual factors fostered self-direction,
interest, and applicability, technicians shared attacks on the material by analytically reflective
techniques that promoted deeper processing. Participants reported that factors such as lack of
time created the conditions in which cramming or memorization of content seemed sensible.
Quantitative and qualitative results were supportive of this observation, especially in regards to
the factors that affected leaning approaches.
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Discussion of Key Findings for Research Question 2
The WCQ measured technicians' perceptions of workload, choice independence, and
supervision at CCC. Results from this survey broadly supported the descriptions supplied during
the interview. Technicians with high perceived workloads via the WCQ reported a lack of time
and pressure. This supported a shift to hurried surface approaches when perceived demands were
felt to be excessive. Those reporting little freedom of choice in quantitative ratings also
described a sense of assigned tasks, which permitted little room for an individualized search.
This discouraged deep, interest-based exploration in favor of expeditious task completion.
Finally, a relationship emerged between quantitative ratings of adequate supervision and
qualitative descriptions of supportive guidance, allowing applying to be easier than passively
receiving. Such factors tie into the greater use of analytical, comparative deep techniques. The
quantitative workplace dimension ratings closely matched subsequent descriptions of contextual
prompts and barriers to adopting intrinsic deep engagement versus expediency-focused surface
strategies. Survey and interview results complemented each other in terms of influences on the
selection of learning approaches.
These findings were consistent with Kirby and colleagues' (2003) Workplace Climate
framework, which proposes workload, choice independence, and supervision as key influencing
dimensions of perceptions of high workload and low autonomy linked to surface methods,
aligning with predictions. Survey data revealed that those who reported adequate control were
correlated with deep approaches and also reported matched theorized relationships. It also
elicited differentiated indicators of deep and surface approaches, as posited in Approaches to
Learning by Kirby et al. (2003). The technician descriptions showed patterns of linking concepts
to existing knowledge and intrinsic motivation to understand instead of memorization and
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rewards as external motivators. It also elicited situational cues to choose a strategy that
corresponds to theorized antecedents, such as workload pressure taking time negatively related to
reflection. Qualitative insight, together with quantitative results, could confer and elaborate
theoretical models.
Discussion of Key Findings for Research Question 3
Technician descriptions of work-based learning experiences resonated with suggestions
that contrast deep and surface approaches. This was closer to the characteristics of a deep
system; externally imposed computer modules were described as rushed and unforgettable,
reflecting surface characteristics. Results also indicated that high workload perceptions inhibited
critical reflection in favor of speedier completion - again in concert with theories of surface
learning under pressure. Experiences that supported a problem-solving approach via applicationsupported propositions linking deep learning to developing analytical, lifetime skills over
practical memorization. In contrast, constraints left little room for self-exploration, paralleled
surface engagement that was primarily aimed at output. The qualitative findings supported
strategic and contextual factors that Kirby and colleagues, among others, have identified as
influencing the quality of learning. Technician motivation and strategy selection resonated with
theoretically proposed influences on deeper versus more surface processing. Their experiences
reinforced framework distinctions.
Recommendations for the Organization Studied
There are several recommendations based on the findings of this study, including
establishing vertical promotion within CCC, enhancing the autonomy and engagement of the
technicians, providing more specific training opportunities for the individual needs of
employees, the reinforcement learning applications from supervisors to see how implementation
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of newly acquired skills is going, reduce perceived workload pressure while continuing to be
productive. There are also theoretical contributions from the study’s findings of technicians
primarily using surface-learning strategies characterized by memorizing facts without deep
comprehension (Asghari et al., 2022). Though these recommendations offer new insights into
how CCC can become a thriving organization, there is still room for further research into other
factors not addressed in this study, such as demographics, early learning, learning styles, etc.
Establish Clear Career Pathways
The interviews with technicians revealed that a lack of opportunities for vertical
promotion is a crucial turnover driver. In order to address this issue effectively, CCC should
develop formal career pathways that provide step-by-step skills, responsibilities, and
compensation progression as technicians gain experience. Clearly defining the skills and
qualifications needed at each level will give employees tangible goals to work toward.
Recognizing and rewarding technicians who achieve additional certifications or take on
leadership roles will motivate ongoing learning to advance their careers. Potential pathways
could include Technician I-III levels with a Chief Technician or Crew Leader function for highly
experienced staff. Additionally, this form of recognition will offer a platform through which
CCC management will gather feedback on improvement areas (Asghari et al., 2022). The noted
improvement areas will then need to have more attractive incentives for technicians who are
working towards overcoming the set targets. This will eventually enhance the definition of clear
pathways as CCC management will identify growth areas that technicians need to focus on.
Enhance Autonomy and Engagement
The quantitative survey found that technicians perceiving less choice independence were
more likely to use surface-learning approaches. CCC can address this by empowering
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technicians to make decisions related to their work. Forming employee resource teams where
technicians help evaluate new equipment and processes will increase their interest and
investment in learning about changes (Bauwens et al., 2020). Soliciting input from technicians
on safety standards and operating procedures will also engage them in continuous improvement
efforts. This will, in turn, create confidence and recognition of the technician’s expertise as their
feedback will be taken into consideration, creating a balance between their intrinsic and extrinsic
motivation values. Supervisors should strive to enable technicians to resolve problems
independently rather than micro-manage tasks.
Provide Targeted Training Opportunities
While the CCC offers various training programs, interviewees often see these as “onesize-fits-all” and do not focus on individual development needs. The CCC could administer skills
assessments to identify each employee’s specific technical or soft skill gaps. Customized training
plans outlining clear learning objectives and a completion timeline would clarify responsibilities.
Pairing less experienced technicians with mentors and allowing time for mentor-mentee skills
transfer during the workday can also boost learning (Bauwens et al., 2020). Recognizing
technicians who complete extended development programs with bonuses or awards would
underscore CCC’s commitment to long-term employee development. This will have a positive
impact and provide a definitive career path identified within the early stage of career
progression. Training and development should also be commensurate with the changing
technology landscape to ensure that the technicians keep abreast of industry updates. Targeted
training opportunities will enhance the growth of industry captains in certain departments and, in
the long run, create autonomous training channels through these talents.
Reinforce Application of Learning on the Job
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The qualitative findings highlighted that technicians perceived limited opportunities to
apply learning on the job. Supervisors should check in regularly with technicians after training to
discuss how they implement the newly discovered skills. Site visits, observations, and hands-on
demonstrations can help supervisors identify specific needs for coaching or additional practice.
Periodic refresher sessions recapping core competencies at team meetings will also reinforce the
retention of concepts over the long term (Vanitha et al., 2019). This means that supervision
strategies also need to conduct constant follow-ups on team development to ensure that
practicability and skill application are being replicated on the field and what challenges are being
encountered. Partnering with local community colleges to develop certificate programs
acknowledging on-the-job learning progress could further motivate technicians toward deeper
learning approaches. Reinforcing learning applications will also enhance general employee
motivation through standardized supervision that portrays the company’s interest in ensuring
credible follow-up activities are instituted.
Reduce Perceived Workload Pressure
Results from the quantitative survey showed that a higher perceived workload predicted
surface-learning tendencies. While productivity is crucial, CCC must ensure technicians have
sufficient time to apply new skills and collaborate with colleagues. Introducing flex-time or
allowing technicians to voluntarily take on extra responsibilities they are comfortable with (e.g.,
training peers) will boost morale. Supervisors can also perform administrative duties to lessen
technicians’ cognitive load (Vanitha et al., 2019). Outsourcing less complex maintenance tasks
to trade partners where possible would free up capacity for technicians to focus on more
advanced responsibilities requiring deeper comprehension. Striving for a sustainable pace of
work through ongoing scheduling adjustments is essential to support deep learning approaches
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among staff over the long term. This will ensure that there is also autonomy creation in that
technicians do not have to be overburdened with constant workload as the company tries to save
on resources. Remember that the technicians are also seeking other better opportunities that will
offer autonomy through striking a work-life balance.
Theoretical Contributions
This research investigated the relationship between workplace factors and learning
approaches among technicians at CCC. Applying established theoretical frameworks through a
mixed-methods case study design to address CCC’s challenges with employee retention and
career advancement generated valuable insights. The study found technicians reported primarily
using surface-learning strategies, characterized by memorizing facts without deep
comprehension. Qualitative interviews revealed perceptions of high workload, lack of autonomy,
and limited opportunities to apply new skills, which contributed to surface tendencies.
Quantitative survey results confirmed a significant relationship between greater perceived
workload and less choice independence in predicting surface approach usage. These findings
provided important implications for how contextual stressors discouraged deeper engagement
necessary for the industry, and the recommendations focused on establishing supportive career
development strategies to foster an ongoing learning culture.
Limitations and Delimitations
Limitations are matters and occurrences that arose in a study that were out of the
researcher’s control. They limited the extent to which a study could go and sometimes affected
the results and conclusions that could be drawn (Simon & Goes, 2013). All studies have
limitations regardless of how well they were constructed. This rationale is due to the fact that
future research can cast doubt on the validity of any hypothesis or conclusion from a study. This
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study had access to only certain individuals within a single organization, certain documents, and
certain data. The majority of limitations to this study may be overcome through subsequent
studies.
The delimitations of a study are those characteristics that occur from limitations in the
scope of the research and the conscious exclusionary and inclusionary decisions made during the
development of the study plan (Simon & Goes, 2013). Delimitations differ from limitations in
that they result from specific choices by the researcher, whereas limitations flow from implicit
characteristics of method and design (Simon & Goes, 2013). Among these are the choice of
questions, variables of interest, the choice of theoretical perspectives that were adopted, the
paradigm, the methodology, the theoretical framework, and the selection of participants (Simon
& Goes, 2013).
This mixed-methods study included a survey sent to 60 technicians that collected both
quantitative and explanatory qualitative data, followed by interviews with 12 volunteers from
among the survey participants. The study sought to understand technicians’ perceptions and
descriptions of their experiences in relation to the goal of improving learning outcomes by at
least 20% by 2022. There were limitations and weaknesses in the study that were beyond the
scope of my control.
There were limitations based on the number of actual participants in the survey, which
reflected the response rate percentage. To account for factors within my control, I attempted to
time the study when the technicians and the organization were not at their busiest. I made sure
that forecasting with management and owners transpired to find the most amicable time for all
parties involved. My position in the CCC organization also may have created a power dynamic
that influenced whether or in what way technicians responded. Those who participated may have
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chosen to leave out information or skew their responses either positively or negatively. An
additional factor could be the low trust within the CCC culture, which had shown in previous
data to be a remaining organizational need. If there was a low response rate, the generalizability
of the findings and results to the entire target population or other similar populations would be
limited.
The study was delimited to a short timeframe; a longer study might have yielded a higher
response rate and more detailed or different results, but it was something that upper management
and ownership were not willing to entertain at that time. The study was delimited to the
approaches to learning, motivation, and workplace climate influences I had chosen to evaluate,
while other factors may have been involved. The interview protocol was developed for this type
of study and, therefore, was not validated in multiple studies. My choice of questions to explore
each influence may have delimited the data I was able to collect and the applicability of the study
to other groups. Data were delimited to the survey responses and information shared during the
interviews. In contrast, observations and document reviews might have provided more
information about technicians’ approaches to learning in relation to their goals. The results will
serve to inform best practices within CCC better but cannot be broadly applied to larger
populations.
In conclusion, more research applying mixed methods across multiple organizations’ job
types and over time would help further verify and expand on the current study findings.
Continued investigation of workplace factors influencing varied learning approaches remains
essential for building a comprehensive theory to optimize employee development in industrial
sectors undergoing technological disruption like construction. Addressing the limitations above
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through longitudinal, multilevel, and observational designs represents a promising direction for
future work.
Conclusions
The study concluded that a primary explanation of technicians’ selection of learning
approaches and descriptions of related experiences could use the frameworks developed by
Kirby and colleagues (2003). The findings demonstrated that contextual prompts in the
workplace, such as perceptions of high demands, inflexible job duties, and unsupportive
supervision, surfaced as significant influencers on approach tendencies. Specifically, such factors
connect to more expedient surface tactics aimed at quick output. In contrast, perceptions of
adequate support, reasonable workload, and autonomy facilitated deeper reflective engagement
encouraged by the frameworks.
A primary conclusion of the study was that the workload, independence, and supervision
dimensions proposed in theoretical frameworks played a significant role in molding technicians’
reported learning strategies. In particular, perceptions of high workload with insufficient time,
limited independence through mandatory obligations, and inadequate guidance from supervisors
meaningfully constrained intrinsic interest in learning. This chained engagement to external
mandates and compliance, fostering more expedient surface styles focused on outputs over
understanding. Contextual prompts around these climate factors emerged as highly influential.
In contrast, the study concluded that where workload permitted deep processing through
adequate time, duties provided sufficient autonomy and variety between tasks, and managers
emphasized comprehension over simple compliance, technicians reported engaging with learning
materials using comparative analysis and linking new ideas to prior knowledge. These reflective
strategies reflected the critical characteristics of a deep approach proposed in frameworks,
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suggesting such supportive contextual conditions in job duties and supervision fostered improved
understanding over superficial retention of information.
This study contributes meaningfully to the theoretical literature on workplace learning. It
empirically validates influential contextual dimensions proposed initially but not yet tested, such
as workload, independence, and supervision. Additionally, the study expands existing
frameworks by demonstrating their utility for thoroughly interpreting mixed-method findings
regarding organizational influences on technicians’ reported approach selection. By integrating
quantitative survey responses with rich qualitative interview accounts, the research leveraged
frameworks to generate more complete interpretations than prior single-method studies allowed.
From a practical perspective, the study concluded that optimizing technicians’ on-the-job
learning experiences requires organizations to address the underlying contextual prompts that
constrain full, deep involvement and intrinsic motivation. Specifically, workload relief through
adequate time, increased flexibility in job duties, and improvements in the quality of supervision
should be priorities. Methodologically, the convergence of quantitative survey data and rich
qualitative interview content provided a complete picture of the interwoven influences beyond
what either single-method approach could offer alone. This hybrid design yielded interpretations
that were not evident through separate strands.
This research investigated the relationship between workplace factors and learning
approaches among technicians at CCC. The study found technicians reported primarily using
surface-learning strategies, characterized by memorizing facts without deep comprehension.
Qualitative interviews revealed perceptions of high workload, lack of autonomy, and limited
opportunities to apply new skills, which contributed to surface tendencies. Quantitative survey
results confirmed a significant relationship between greater perceived workload and less choice
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independence in predicting surface approach usage. These findings provide important
implications. Surface learning habits alone are insufficient to maintain expertise in construction’s
rapidly changing technological environment. However, contextual stressors like compressed
work schedules leave technicians feeling overwhelmed, discouraging deeper engagement with
continual skills development required by the industry. Without targeted interventions, retention
issues and skills gaps will persist.
By implementing recommendations around clear career pathways, autonomy
enhancements, tailored training programs, application reinforcement mechanisms, and workload
control strategies, CCC can cultivate an organizational culture optimized for ongoing,
exploratory learning. Establishing sustainable work rhythms that are supportive of
comprehension rather than just output is critical. Empowering self-direction and investment in
individual growth journeys via customized development plans, mentorship, and recognition of
progress also emerged as promising leverage points. The study highlights the importance of
capturing employee perspectives to understand perceived barriers and opportunities for
improvement from “the ground up.” A mixed-methods design triangulating survey perceptions
with interview experiences generated more complete, actionable insights than quantitative data
alone. Future longitudinal research could assess the recommended strategies’ effectiveness over
time. In conclusion, recognizing specific contextual influences on varied learning approaches
remains crucial as industrial sectors actively reinvent work models to survive continuous
disruption. This study advances theoretical frameworks and offers practical lessons to strengthen
workforce capabilities through supportive, skills-focused workplace climates where learning
thrives as a strategic priority.
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In conclusion, this study’s findings demonstrate that an individual’s approach to learning
is not a fixed personal trait but rather proves highly molded and responsive to environmental
cues within the organizational context. Technicians adapted their strategies based on perceptions
of workload pressures, independence in job duties, and the quality of supervision provided.
Therefore, rather than make assumptions about learning approaches, organizations should
comprehensively understand the influences of the workplace climate identified in this research.
With such insights, organizational leaders can adjust and optimize conditions for nurturing deep,
analytic learning conducive to lifelong development. By aligning learning opportunities with
workers’ diverse motivations and abilities through supportive climate factors, organizations can
cultivate a more engaged and skilled workforce well-equipped for modern industry demands.
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References
Ackerman, C. E. (2020). Self-determination theory of motivation: Why intrinsic motivation
matters. Positive Psychology.
Aguinis, H., & Kraiger, K. (2009). Benefits of training and development for individuals and
teams, organizations, and society. Annual Review of Psychology, 60(1), 451–474.
https://doi.org/10.1146/annurev.psych.60.110707.163505
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., &
Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present
status and future innovations. Journal of Building Engineering, 32, Article 101827.
https://doi.org/10.1016/j.jobe.2020.101827
Alkin, M. C. (2011). Evaluation Essentials: From A to Z. Guilford Press.
Armstrong-Stassen, M., & Schlosser, F. (2008). Benefits of a supportive development climate for
older workers. Journal of Managerial Psychology, 23(4), 419–437.
https://doi.org/10.1108/02683940810869033
Asghari, V., Wang, Y., Biglari, A. J., Hsu, S.-C., & Tang, P. (2022). Reinforcement learning in
construction engineering and management: A review. Journal of Construction
Engineering and Management, 148(11), 03122009.
https://doi.org/10.1061/(ASCE)CO.1943-7862.0002386
Ashworth, P. (1996). Presuppose nothing! The suspension of assumptions in phenomenological
psychological methodology. Journal of Phenomenological Psychology, 27(1), 1–25.
Austin, J. (2012). Organization change and development: In practice and in theory. In N. Schmitt
& S. Highhouse (Eds.), Handbook of psychology (Vol. 12, pp. 390–410). Wiley.
130
Avolio, B. J., & Gardner, W. L. (2005). Authentic leadership development: Getting to the root of
positive forms of leadership. The Leadership Quarterly, 16(3), 315–338.
https://doi.org/10.1016/j.leaqua.2005.03.001
Baeten, M., Kyndt, E., Struyven, K., & Dochy, F. (2010). Using student-centered learning
environments to stimulate deep approaches to learning: Factors encouraging or
discouraging their effectiveness. Educational Research Review, 5(3), 243–260.
https://doi.org/10.1016/j.edurev.2010.06.001
Barley, M. (2012). Learning from reflective practice and metacognition – an anaesthetist’s
perspective. Reflective Practice, 13(2), 271–280.
https://doi.org/10.1080/14623943.2012.657792
Bauwens, R., Muylaert, J., Clarysse, E., Audenaert, M., & Decramer, A. (2020). Teachers’
acceptance and use of digital learning environments after hours: Implications for worklife balance and the role of integration preference. Computers in Human Behavior, 112,
106479. https://doi.org/10.1016/j.chb.2020.106479
Beke, K. (2005). Outsourcing and Innovation: To what extent does outsourcing manufacturing or
research and development influence the product innovation performance of organizations
in the Dutch production industry? Master Thesis.
https://thesis.eur.nl/pub/4827/Thesis%20Koos%20Beke%20Final.pdf
Bensimon, E. M., Hao, L., & Bustillos, L. T. (2006). Measuring the state of equity in public
higher education. Expanding Opportunity in Higher Education: Leveraging Promise,
143–165.
Bernsen, P., Segers, M., & Tillema, H. H. (2009). Learning under pressure: Learning strategies,
workplace climate, and leadership style in the hospitality industry. International Journal
131
of Human Resources Development and Management, 9(4), 358–373.
https://doi.org/10.1504/IJHRDM.2009.025069
Berry, G. R., & Hughes, H. (2020). Integrating work-life balance with 24/7 information and
communication technologies: The experience of adult students with online learning.
American Journal of Distance Education, 34(2), 91–105.
https://doi.org/10.1080/08923647.2020.1701301
Bertrand, D., & Knapper, C. (1991). Contextual influences on students’ approaches to learning
in academic departments (Unpublished paper). University of Waterloo.
Biggs, J. (2012). What the student does: Teaching for enhanced learning. Higher education
research & development, 31(1), 39-55.
Biggs, J., Kember, D., & Leung, D. Y. P. (2001). The revised two-factor Study Process
Questionnaire: R-SPQ-2F. The British Journal of Educational Psychology, 71(1), 133–
149. https://doi.org/10.1348/000709901158433
Biggs, J. B. (1987). Student approaches to learning and studying (Research monograph). ERIC.
https://eric.ed.gov/?id=eD308201
Bolman, L. G., & Deal, T. E. (1994). Looking for leadership: Another search party’s report.
Educational Administration Quarterly, 30(1), 77–96.
https://doi.org/10.1177/0013161X94030001006
Bolman, L. G., & Deal, T. E. (2017). Reframing organizations: Artistry, choice, and leadership.
John Wiley & Sons.
Bucea-Manea-Țoniş, R., Bucea-Manea-Țoniş, R., Simion, V. E., Ilic, D., Braicu, C., & Manea,
N. (2020). Sustainability in higher education: The relationship between work-life balance
132
and XR e-learning facilities. Sustainability, 12(14), Article 5872.
https://doi.org/10.3390/su12145872
Burke, R. J., & Ng, E. (2006). The changing nature of work and organizations: Implications for
human resource management. Human Resource Management Review, 16(2), 86–94.
https://doi.org/10.1016/j.hrmr.2006.03.006
Caprino, K. (2018, February 3). Transformational leaders: The top trait that separates them from
the rest. Forbes.
https://www.forbes.com/sites/kathycaprino/2018/02/03/transformational-leaders-the-toptrait-that-separates-them-from-the-rest/?sh=2ca5dc7352cc
Chaudhry, P. E. (2007). Developing a process to enhance customer relationship management for
small entrepreneurial businesses in the service sector. Journal of Research in Marketing
and Entrepreneurship, 9(1), 4–23. https://doi.org/10.1108/14715200780001337
Christensen, C. A., Massey, D. R., Isaacs, P. J., & Synott, J. (1995). Beginning teacher
education: Students’ conceptions of teaching and approaches to learning. The Australian
Journal of Teacher Education, 20(1), 19–29. https://doi.org/10.14221/ajte.1995v20n1.3
Clark, R. E., & Estes, F. (2008). Turning research into results: A guide to selecting the right
performance solutions. Information Age.
Clark, R. S., & Plano Clark, V. L. (2019). Grit within the context of career success: A mixed
methods study. International Journal of Applied Positive Psychology, 4(3), 91–111.
https://doi.org/10.1007/s41042-019-00020-9
Cohen, P. J. (1969). Decision procedures for real and p‐Adic fields. Communications on Pure
and Applied Mathematics, 22(2), 131-151.
133
Collins, J. (2001). Good to great: Why some companies make the leap ... and others don’t.
HarperBusiness.
Cook, B. G. (2002). Inclusive attitudes, strengths, and weaknesses of pre-service general
educators enrolled in a curriculum infusion teacher preparation program. Teacher
Education and Special Education, 25(3), 262-277.
Creswell, J. W., Plano Clark, V. L., Gutmann, M. L., & Hanson, W. E. (2003). Advanced mixed
methods research designs. In A. Tashakkori, & C. Teddlie (Eds.), Handbook Of Mixed
Methods in Social and Behavioral Research (pp. 209–240). Sage.
Curson, R. (2004). Completion issues in industry training and effective learning in the
workplace. Industry Training Federation. https://www.voced.edu.au/content/ngv:37199
Dai, H., Milkman, K. L., Hofmann, D. A., & Staats, B. R. (2015). The impact of time at work
and time off from work on rule compliance: The case of hand hygiene in health care. The
Journal of Applied Psychology, 100(3), 846–862. https://doi.org/10.1037/a0038067
Davis, D. F., Golicic, S. L., & Boerstler, C. N. (2011). Benefits and challenges of conducting
various methods of research in marketing. Journal of the Academy of Marketing Science,
39(3), 467–479. https://doi.org/10.1007/s11747-010-0204-7
Davis, T., & Higgins, J. (2013). A blockbuster failure: how an outdated business model
destroyed a giant. https://ir.law.utk.edu/utk_studlawbankruptcy/11
DeCamilla, E. A. (2020). Predictive validity of standards-based and curriculum-embedded
assessments for predicting readiness at kindergarten entry. University of South Florida
ProQuest Dissertations & Theses, 2020. 27828665.
Deci, E. L., & Ryan, R. M. (2013). Intrinsic motivation and self-determination in human
behavior. Springer Science & Business Media.
134
Delgado, J. M. D., & Oyedele, L. (2022). Robotics in construction: A critical review of the
reinforcement learning and imitation learning paradigms. Advanced Engineering
Informatics, 54, Article 101787. https://doi.org/10.1016/j.aei.2022.101787
Delva, M. D., Kirby, J., Schultz, K., & Godwin, M. (2004). Assessing the relationship of
learning approaches to workplace climate in clerkship and residency. Academic
Medicine, 79(11), 1120-1126.
DeLong, L. L. (2006). College student's motivation for physical activity.
Diseth, Å., Pallesen, S., Hovland, A., & Larsen, S. (2006). Course experience, approaches to
learning, and academic achievement. Education+ Training, 48(2/3), 156-169.
Entwistle, N. J., & Ramsden, P. (1983). Understanding Student Learning. Social Science
Research Council.
Entwistle, N. J., & Peterson, E. R. (2004). Conceptions of learning and knowledge in higher
education: Relationships with study behaviour and influences of learning
environments. International Journal of Educational Research, 41(6), 407-428.
Fang, W., Love, P. E., Ding, L., Xu, S., Kong, T., & Li, H. (2021). Computer vision and deep
learning to manage safety in construction: Matching images of unsafe behavior and
semantic rules. IEEE Transactions on Engineering Management, 70(12), 4120–4132.
https://doi.org/10.1109/TEM.2021.3093166
Fernando, R. J., Sultan, A. H., Kettle, C., & Thakar, R. (2013). Methods of repair for obstetric
anal sphincter injury. Cochrane Database of Systematic Reviews, (12), 200-218.
https://doi.org/10.1002/14651858.CD002866.pub3
135
Fernandez, S., & Rainey, H. G. (2017). Managing successful organizational change in the public
sector. In R. F. Durant & J. R. S. Durant (Eds.), Debating Public Administration:
Management Challenges, Choices, and Opportunities (pp. 7–26). Routledge.
Fidler, F. (2010). The American Psychological Association publication manual sixth edition:
Implications for statistics education. Data and Context in Statistics Education: Towards
an Evidence-Based Society.
https://icots.info/icots/8/cd/pdfs/contributed/ICOTS8_C156_FIDLER.pdf
Fransson, A. (1977). On qualitative differences in learning: IV. Effects of intrinsic motivation
and extrinsic test anxiety on process and outcome. British Journal of Educational
Psychology, 47(3), 244–257. https://doi.org/10.1111/j.2044-8279.1977.tb02353.x
Frese, M., & Zapf, D. (1994). Action as the core of work psychology: A German
approach. Handbook of Industrial and Organizational Psychology, 4(2), 271-340.
Fu, X., Wu, M., Ponnarasu, S., & Zhang, L. (2023). A hybrid deep learning approach for
dynamic attitude and position prediction in tunnel construction considering spatiotemporal patterns. Expert Systems with Applications, 212, Article 118721.
https://doi.org/10.1016/j.eswa.2022.118721
Gino, F., & Staats, B. (2015). Why organizations don’t learn. Harvard Business Review.
https://hbr.org/2015/11/why-organizations-dont-learn
Glaveski, S. (2019). Where companies go wrong with learning and development. Harvard
Business Review, 2.
Gomes, G., Seman, L. O., & De Montreuil Carmona, L. J. (2021). Service innovation through
transformational leadership, work-life balance, and organizational learning capability.
136
Technology Analysis and Strategic Management, 33(4), 365–378.
https://doi.org/10.1080/09537325.2020.1814953
Gow, L., & Kember, D. (1993). Conceptions of teaching and their relationship to student
learning. British Journal of Educational Psychology, 63(1), 20-23.
Hayden, S. C. (2020). Career development and planning: A comprehensive approach by Robert
C. Reardon, Janet G. Lenz, Gary W. Peterson et al. Journal of College Student
Development, 61(3), 400-402.
Hager, P. (2005). Current theories of workplace learning: A critical assessment. In
N. Basica, A. Cumming, A. Datnow, K. Leithwood, & D. Livingstone (Eds.),
International Handbook of Educational Policy (pp. 829–846). London: Springer.
Harrison, S., & Gordon, P. A. (2014). Misconceptions of employee turnover: Evidence-based
information for the retail grocery industry. Journal of Business & Economics Research
(JBER), 12(2), 145-152.
Heriyati, P., & Ramadhan, A. S. (2012). The influence of employee satisfaction in supporting
employee work performance and retention is moderated by the employee engagement
factor of an institution. International Journal of Economics and Management, 6(1), 191-
200.
Hyslop-Margison, E. J. (1999). The employability skills discourse: A conceptual analysis of the
career and personal planning curriculum.
Iacuone, D. (2005). “Real men are tough guys”: Hegemonic masculinity and safety in the
construction industry. Journal of Men’s Studies, 13(2), 247–266.
https://doi.org/10.3149/jms.1302.247
137
Jogulu, U. D., & Pansiri, J. (2011). Mixed methods: A research design for management doctoral
dissertations. Management Research Review, 34(6), 687–701.
https://doi.org/10.1108/01409171111136211
Karasek Jr, R. A. (1979). Job demands, job decision latitude, and mental strain: Implications for
job redesign. Administrative Science Quarterly, 285-308.
Kim, J., Youm, S., Shan, Y., & Kim, J. (2021). Analysis of fire accident factors on construction
sites using web crawling and deep learning approach. Sustainability, 13(21), 11694.
https://doi.org/10.3390/su132111694
Kirby, J. R., Knapper, C. K., Evans, C. J., Carty, A. E., & Gadula, C. (2003). Approaches to
learning at work and workplace climate. International Journal of Training and
Development, 7(1), 31–52. https://doi.org/10.1111/1468-2419.00169
Knapper, C. (2001). Broadening our approach to teaching evaluation. New Directions for
Teaching and Learning, 88, 3-9.
Koh, G. C. H., Khoo, H. E., Wong, M. L., & Koh, D. (2008). The effects of problem-based
learning during medical school on physician competency: A systematic
review. Cmaj, 178(1), 34-41.
Kotter, J. P. (2007). Leading change: Why transformation efforts fail. Harvard Business Review,
85(1), 96–103.
Kunnanatt, J. T. (2016). 3D leadership–strategy-linked leadership framework for managing
teams. Economics, Management, and Financial Markets, 11(3), 30–55.
Kyndt, E., Dochy, F., & Nijs, H. (2009). Learning conditions for non-formal and informal
workplace learning. Journal of Workplace Learning, 21(5), 369–383.
https://doi.org/10.1108/13665620910966785
138
Kyndt, E., Raes, E., Dochy, F., & Janssens, E. (2013). Approaches to learning at work:
Investigating work motivation, perceived workload, and choice independence. Journal of
Career Development, 40(4), 271–291. https://doi.org/10.1177/0894845312450776
Kyndt, E., Cascallar, E., & Dochy, F. (2012). Individual differences in working memory capacity
and attention and their relationship with students’ approaches to learning. Higher
Education, 64, 285-297.
Lane, J. E., & Kivisto, J. A. (2008). Interests, information, and incentives in higher education:
Principal-agent theory and its potential applications to the study of higher education
governance. In J. C. Smart (Ed.), Higher education (Vol. 23, pp. 141–179). Springer
Netherlands., https://doi.org/10.1007/978-1-4020-6959-8_5
Malloy, C. (2011). Moving beyond data: Practitioner-led inquiry fosters change. Edge, (6), 4.
Marton, F., & Säljö, R. (1976). On qualitative differences in learning: I—Outcome and
process. British Journal of Educational Psychology, 46(1), 4-11.
McManus, I. C., Keeling, A., & Paice, E. (2004). Stress, burnout and doctors' attitudes to work
are determined by personality and learning style: a twelve-year longitudinal study of UK
medical graduates. BMC Medicine, 2, 1-12.
Moser, J. S., Schroder, H. S., Heeter, C., Moran, T. P., & Lee, Y. H. (2011). Mind your errors:
Evidence for a neural mechanism linking growth mindset to adaptive post-error
adjustments. Psychological Science, 22(12), 1484-1489.
Murre, J. M., & Dros, J. (2015). Replication and analysis of Ebbinghaus’ forgetting curve. PloS
One, 10(7), e0120644.
139
Newble, D. I., & Entwistle, N. J. (1986). Learning approaches and approaches: Implications for
medical education. Medical Education, 20(3), 162–175. https://doi.org/10.1111/j.1365-
2923.1986.tb01163.x
Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom:
Applying self-determination theory to educational practice. Theory and Research in
Education, 7(2), 133-144.
Norusis, M. J. (1993). Advanced statistics. SPSS for Windows, Release 6.0, 578.
Pernu, T. K. (2017). The five marks of the mental. Frontiers in Psychology, 8, 248519.
Pho, P. D. (2009). An evaluation of three different approaches to the analysis of research article
abstracts. Monash University Linguistics Papers, 6(2), 11-26.
Rashid, K. M., & Louis, J. (2019). Times-series data augmentation and deep learning for
construction equipment activity recognition. Advanced Engineering Informatics, 42,
Article 100944. https://doi.org/10.1016/j.aei.2019.100944
Ramsden, P., & Entwistle, N. J. (1981). Effects of academic departments on students' approaches
to studying. British Journal of Educational Psychology, 51(3), 368-383.
Richardson, L. (2000). Evaluating ethnography. Qualitative Inquiry, 6(2), 253-255.
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and
new directions. Contemporary Educational Psychology, 25(1), 54-67.
San, O. T., Theen, Y. M., & Heng, T. B. (2012). The reward strategy and performance
measurement (evidence from Malaysian insurance companies). International Journal of
Business, Humanities, and Technology, 2(1), 211-223.
Sharples, M., Adams, A., Ferguson, R., Mark, G., McAndrew, P., Rienties, B., Weller, M., &
Whitelock, D. (2014). Innovating Pedagogy 2014: Exploring new forms of teaching,
140
learning, and assessment to guide educators and policymakers. The Open University.
https://eduq.info/xmlui/bitstream/handle/11515/19657/2014-innovative-pedagogy-openuniversity.pdf?sequence=1
Shaari, R., Mahmud, N., Wahab, S. R. A., Rahim, K. A., Rajab, A., Saat, M. M., ... & Yusoff, R.
M. (2011). A study on learning approaches used among post-graduate students in
research university. International Journal of Social Sciences and Humanity Studies, 3(2),
411-420.
Soelton, M. (2023). How did it happen: Organizational commitment and work-life balance affect
organizational citizenship behavior. Jurnal Dinamika Manajemen, 14(1), 149–164.
Susilaningsih, F. S., Komariah, M., Mediawati, A. S., & Lumbantobing, V. B. (2021). Quality of
work-life among lecturers during online learning in COVID-19 pandemic period: A
scoping review. Malaysian Journal of Medicine and Health Sciences, 17, 163–166.
Tagg, J. (2003). The learning paradigm. Bolton. Anker.
Thibault Landry, A., Schweyer, A., & Whillans, A. (2017). Winning the war for talent: Modern
motivational methods for attracting and retaining employees. Compensation & Benefits
Review, 49(4), 230-246.
Van Ruysseveldt, J., & van Dijke, M. (2011). When are workload and workplace learning
opportunities related in a curvilinear manner? The moderating role of autonomy. Journal
of Vocational Behavior, 79(2), 470-483.
Vanitha, V., Krishnan, P., & Elakkiya, R. (2019). Collaborative optimization algorithm for
learning path construction in E-learning. Computers & Electrical Engineering, 77, 325–
338. https://doi.org/10.1016/j.compeleceng.2019.06.016
141
Vansteenkiste, M., Simons, J., Lens, W., Sheldon, K. M., & Deci, E. L. (2004). Motivating
learning, performance, and persistence: the synergistic effects of intrinsic goal contents
and autonomy-supportive contexts. Journal of Personality and Social Psychology, 87(2),
246.
Watkins, K. E., & Marsick, V. J. (1993). Sculpting the learning organization: Lessons in the art
and science of systemic change. Jossey-Bass Inc.
Williams, L. J., & Anderson, S. E. (1991). Job satisfaction and organizational commitment as
predictors of organizational citizenship and in-role behaviors. Journal of
Management, 17(3), 601-617.
Wetzel, E. M., & Farrow, C. B. (2023). Active learning in construction management education:
Faculty perceptions of engagement and learning. International Journal of Construction
Management, 23(8), 1417–1425. https://doi.org/10.1080/15623599.2021.1974684
Wu, J., Cai, N., Chen, W., Wang, H., & Wang, G. (2019). Automatic detection of hardhats worn
by construction personnel: A deep learning approach and benchmark dataset. Automation
in Construction, 106, Article 102894. https://doi.org/10.1016/j.autcon.2019.102894
142
Appendix A: Protocols
The following sections present the protocols used in this study.
Interview Protocol
Table A1 presents the interview protocol, including the initial prompt and the follow-up
questions.
Table A1
Interview Protocol: Initial Prompt and Follow-Up Questions
Interview question: “Please think of
a time when you felt exceptionally
good or naughty about your job,
either a job you have had or your
present job. Tell me what
happened.”
Potential probes RQ Concept
How long ago did this happen? 2 Learning
opportunities
How long did the feeling last? Can you describe what
made the change in
feelings begin? When
did it end?
3 Perceptions/choice
independence
Was what happened typical of what
was happening at the time?
1 Approaches to
learning
143
Can you tell me more precisely why
you felt the way you did at the
time?
3 Perceptions
What did these events mean to you? Why? 3 Perceptions
How did these feelings affect the
way you did your job?
How long did this go on? 3 Perceptions/choice
independence
Can you give me a specific example
of how your performance on the
job was affected?
How long? 2 Learning outcomes
8. How did what happened to affect
you personally in any way?
How long? Did it change
how you got along with
people or your family?
Did it affect your sleep,
appetite, digestion,
general health?
2 Learning outcomes
How did what happened affect how
you felt about working at that
company, or did it merely make
you feel good or bad about the
occurrence?
Why? 3 Perceptions/choice
independence
How did the consequences of what
happened at this time affect your
career?
How? 1 Approaches to
learning
144
How did what happened change
how you felt about your
profession?
3 Perceptions
How seriously were your feelings
(good or bad) about your job
affected by what happened? Pick
a spot on the line below to
indicate how strong you think the
good or bad feelings were. Circle
that position. Least 1 … Average
12–13 … Greatest 21.
Can you describe your
thoughts or rationale for
the spot you chose?
2 Real learning
opportunities
Could the situation you described
happen again for the same
reasons and with the same
effects?
If not, describe the changes
that have taken place
which would make your
feelings and actions
were different today than
they were then.
1 Approaches to
learning
Is there anything else you would
like to say about the sequence of
events you have described?
What did you think of the
interview? Do you have
any other comments on
the discussion or the
research?
3 Perceptions/choice
independence
145
Survey Protocol
Survey Email Invitation
You are invited to complete a brief survey as part of a doctoral study on learning in the
workplace.
The study aims to understand the current state of learning within the organization. Results
will be aggregated and used to continuously identify recommendations for improving learning in
the workplace.
The survey should take about 5–10 minutes to complete. There are no right or wrong
answers. Your honest responses will be the most helpful to the study. You may skip any question
you do not wish to answer and stop the survey anytime. All responses are anonymous. If you
agree to participate in the survey, please follow this link to the study:
Survey Instructions
The survey should take about 5–10 minutes to complete. There are no right or wrong
answers. Your honest responses will be the most helpful to the study. You may skip any question
you do not wish to answer, and you may stop the survey at any time. All responses are
anonymous. If you agree to participate in the survey, please click “Continue” below:
Survey Questions
All responses need to be provided on a five-point Likert-type scale with the following
choices: (5) definitely agree, (4) somewhat agree, (3) doesn’t apply or find it impossible to give a
definite answer, (2) somewhat disagree, and (1) definitely disagree.
Approaches to Learning at Work Questionnaire
Deep Scale
146
1. The work I am doing in my present job will be good preparation for other jobs I may
have in the future.
2. In trying to understand a puzzling idea, I let my imagination wander freely to begin
with, even if I don’t seem to be much nearer a solution.
3. In trying to understand new ideas, I often try to relate them to real-life situations to
which they may apply.
4. I like to play around with ideas of my own, even if they don’t get me very far.
5. If conditions aren’t right for me at work, I generally manage to do something to
change them.
6. In my job one of the main attractions for me is to learn new things.
7. I find that studying for new tasks can often be really exciting and gripping.
8. I spend a good deal of my spare time learning about things related to my work.
9. I find it helpful to ‘map out’ a new topic for myself by seeing how the ideas fit
together.
10. Some of the issues that crop up at work are so interesting that I pursue them though
they are not part of my job.
Surface-Disorganized Scale
1. At work I find it difficult to organize my time effectively.
2. I prefer to have a good overview rather than focus on the details.
3. The continual pressure at work—tasks to do, deadlines, and competition—often
makes me tense and depressed.
4. My habit of putting off work leaves me with far too much catching up to do.
5. Managers seem to delight in making the simple truth unnecessarily complicated.
147
6. Often I find I have to read things without having a chance to really understand them.
7. I certainly want to get a good performance appraisal, but it doesn’t really matter if I
only scrape through.
8. Although I generally remember facts and details, I find it difficult to fit them together
into an overall picture.
9. I seem to be a bit too ready to jump to conclusions without waiting for all the
evidence.
10. When I look back, I sometimes wonder why I ever decided to work here.
Surface-Rational Scale
1. When I am given a job to do at work, I like to be told precisely what is expected.
2. I generally prefer to tackle each part of a task or problem in order, working out one at
a time.
3. When I am doing a piece of work, I try to follow instructions exactly, even if they
conflict with my ideas.
4. I prefer the work that I am given to be clearly structured and highly organized.
5. I prefer to follow well-tried approaches to problems rather than anything too
adventurous.
6. When I learn something new at work, I put a lot of effort into memorizing important
facts.
7. I find it better to start straight away with the details of a new task and build up an
overall picture in that way.
8. The best way for me to understand what technical terms mean is to remember the
textbook definitions.
148
9. I think it is important to look at problems rationally and logically without making
intuitive leaps.
10. I find I tend to remember things best if I concentrate on the order in which they are
presented.
149
Workplace Climate Questionnaire
Good Supervision Scale
1. Most of the supervisors really try hard to get to know employees.
2. Supervisors here make a real effort to understand difficulties employees may be having
with their work.
3. Supervisors in this organization seem to go out of their way to be friendly towards
employees.
4. The supervisors in this organization always seem ready to give help and advice on the
best way to learn something new.
5. Supervisors in this organization generally take employees’ ideas and interests seriously.
Workload Scale
1. The workload here is too heavy.
2. It sometimes seems to me that my job requires me to do too many different things.
3. In this organization, you’re expected to spend a lot of time learning things on your own.
4. There seems to be too much work to get through here.
5. There’s a lot of pressure on you as an employee here.
Choice Independence Scale
1. There is a real opportunity in this organization for people to choose the particular tasks
they work on.
2. The organization really seems to encourage us to develop our work-related interests as far
as possible.
3. We seem to be given a lot of choices here in the work we have to do.
150
4. This organization gives you a chance to go about your work in ways that suit your way of
learning.
5. Employees here have a great deal of choice over how they learn new tasks.
Survey Wrap-Up
Thank you very much for taking the time to complete this survey. If you would be willing
to participate in a confidential 30-minute interview to explore this topic more deeply, please
click on the link below to provide your name and email address. This information will not be
linked in any way to your survey responses, which will remain anonymous. Interview
participants will receive a small gift of appreciation for their time.
151
Appendix B: Protocols
The following sections present the interview protocol.
Interview Opening Remarks
Thank you very much for agreeing to participate in this study. I am conducting research
as part of my EdD program in organizational change and leadership with the Rossier School of
Education. The interview will take around 30 minutes and consist of 14 questions. There are no
right or wrong answers. You can skip any question you don’t want to answer, and you can stop
the interview at any time.
Your responses will be kept confidential and shared only in summary form, with no
identifying information. As a result of this study, specific recommendations will be made to CCC
leadership related to improving technician satisfaction with learning. Again, your answers will be
kept confidential and summarized with other interview responses so that no individual
participant can be identified.
I want to record the interview to help me remember your responses. This recording will
be on a secure server and platform and will not be saved to my device. Within a week, I will
transcribe the session and permanently delete the recording. The transcription will be stored
under a pseudonym, so your responses cannot be connected to you.
Do I have your permission to record the interview?
Do you mind if I also jot down a few notes to jog my memory?
Do you have any questions for me before we get started?
Please review the Interview Information sheet that I had previously sent via email and
have placed in the chat box for your review. Remember that you can skip any question or stop
the interview at any time.
152
OK, let’s get started.
Table B1
Interview Protocol
Interview question: “Please think of a time when you felt exceptionally good about how much
you were learning at your job, one you have had in the past or your present job. Tell me what
happened.”
How long ago did this happen?
How long did the feeling last?
How typical would you say the experience was for you?
Can you tell me more precisely why you felt the way you did at the time?
How would you describe what this event meant to you at the time?
How did these feelings affect the way you performed your job?
Can you give me a specific example of how your performance on the job was affected?
How did what happened to affect you personally in any way?
Please describe how what happened affected your feelings about working or made you feel
good or bad about the occurrence itself.
How did the consequences of what happened at this time affect your career?
How did what happened change the way you felt about your profession?
How seriously were your feelings (good or bad) about your job affected by what happened?
Pick a number from 1 to 21 to describe how good or bad the feelings were, with 1 being the
least, 12-13 being average, and 21 being the greatest.
153
How could the situation you described happen again for the same reasons and with the same
effects?
Is there anything else you would like to say about the sequence of events you have described?
Table B2
Quantitative Analysis Matrix
Research
question
Independent variable(s)/
level of measurement
Dependent variable/
level of measurement
Test
What are
construction
workers’
approaches to
learning?
Approaches to
learning
subscales:
deep, surfacedisorganized,
surfacerational
Interval None n/a Descriptive
statistics –
M/SD
What effect
does workplace
climate have on
employees’
approaches to
learning?
Workplace
climate
subscales:
Good
Supervision
Scale,
Interval
(Likerttype)
Approaches
to learning
subscales:
deep,
surfacedisorganized,
Interval/ordinal
(Likert-type)
Correlationa
l analysis
154
Workload
Scale, Choice
Independence
Scale
surfacerational
Table B3
Data Analysis Matrix
Interview question: “Please think
of a time when you felt
exceptionally good about how
much you were learning at your
job, one you have had in the past or
your present job. Tell me what
happened.”
Potential probes RQ Concept
How long ago did this happen? 2 Learning
opportunities
How long did the feeling last? Can you describe what
made the change in
feelings begin? When
did it end?
3 Perceptions/choice
independence
155
How typical would you say this
experience was to your position
and job?
Why? 1 Approaches to
learning
Can you tell me more precisely
why you felt the way you did at
the time?
3 Perceptions
What did this event mean to you in
terms of being able to learn?
Why? 3 Perceptions
How did these feelings affect the
way you performed your job?
How long did this go
on?
3 Perceptions/choice
independence
Can you give me a specific
example of how your
performance on the job was
affected?
How long? 2 Learning
outcomes
How did what happened to affect
you personally in any way?
2 Learning
outcomes
How did what happened basically
affect the way you felt about
working?
Why? 3 Perceptions/choice
independence
How did the consequences of what
happened at this time affect your
career?
How? 1 Approaches to
learning
156
How did what happened change
the way you felt about your
profession?
3 Perceptions
How seriously were your feelings
(good or bad) about your job
affected by what happened?
Please indicate how strong you
think the good or bad feelings
were by choosing a number from
(1) being least, (12–13) being
average, and (21) being greatest.
Can you describe your
thoughts or rationale
for the spot you
chose?
2 Real learning
opportunities
How could the situation you
described possibly happen again
for the same reasons and with
the same effects?
If not, describe the
changes that have
taken place which
would make your
feelings and actions are
different today than
they were then.
1 Approaches to
learning
Is there anything else you would
like to say about the sequence of
events you have described?
What did you think of
the interview? Do
you have any other
comments on the
3 Perceptions/choice
independence
157
interview or the
research?
Note. Respondent type: individuals who are in the workplace.
Abstract (if available)
Abstract
This study investigated the relationship between workplace factors, namely perceived workload, work motivation, and choice independence, and employees’ approaches to learning at a mid-sized organization within the construction industry. The study applied deep, surface, and surface-disorganized learning approaches and their cognitive outcomes as its foundational theoretical frameworks. The workplace climate framework proposes workload, choice independence, and supervision dimensions as influential in the learning approaches. A mixed-methods approach was used to provide a holistic examination. Preliminary findings identified a tendency toward surface-learning approaches among technicians. Quantitative results show a significant correlation between perceptions of high workload and surface learning. On the other hand, the qualitative outcomes described work obligations, leaving little time for deep engagement. However, participants discussed motivations such as skill development opportunities as key in promoting intrinsic drives for education. They also characterized supervision as hands-off without guidance for self-directed growth. Insights emerged that compare technicians’ experiences to deep learning propositions. Theoretical findings also reveal additional environmental influences that impact construction workers' learning and job satisfaction, including supportive leadership, autonomy in task management, and collaborative workplace cultures. Practically, recommendations aim to improve employees’ learning experiences by optimizing workload balance, fostering independence, and providing supportive oversight. Addressing barriers and enhancing facilitators of deep learning holds promise to strengthen technician performance, retention, and career development at the company.
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Asset Metadata
Creator
Johnson, Joshua Adam
(author)
Core Title
Learning in the workplace: investigating perceived workload, work motivation, and choice independence in the construction industry
School
Rossier School of Education
Degree
Doctor of Education
Degree Program
Education (Leadership)
Degree Conferral Date
2024-12
Publication Date
09/16/2024
Defense Date
08/06/2024
Publisher
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Tags
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