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Evaluation of technostress in education

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Evaluation of technostress in education

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Content i
Evaluation of Technostress in Education
by
Ajit A. Marathe
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
May 2021
ii

Copyright by Ajit A. Marathe 2021
All Rights Reserved

iii

The Committee for Ajit A. Marathe certifies the approval of this Dissertation

Dr. Anthony Maddox
Dr. Carey Regur
Dr. Bryant Adibe, Committee Chair

Rossier School of Education
University of Southern California
2021

iv

Abstract
The field of education is increasingly reliant on the use of technology in the 21
st
century. The
focus is now on the quality of learning rather than the methods of delivery. Evidence highlights
that faculty face a continual need to keep up with technology to provide quality education. As a
result, employees experience technostress due to the increased use of technology. The problem
of technostress in education is essential to address because it may lead to disengagement,
turnover, and low employment performance. This study utilizes the Gap Analysis framework to
evaluate the knowledge, motivation, and organizational influences on the technostress
experienced by faculty and understand the triggers and inhibitors of technostress and find ways
to cope with it.
Keywords: technostress, gap analysis, KMO, technology, faculty, education,
disengagement, turnover, performance.

v

Dedication
To my mother, Suniti Marathe, and my wife Aarohi Padhye for their unconditional love and
support.

vi

Acknowledgments
I would like to acknowledge everyone who played a role in my academic
accomplishments. First of all, my family who supported me with love and understanding.
Without you, I could never have reached this current level of success.
Secondly, my committee chair and committee members have provided advice and
guidance throughout the research process. Thank you all for your unwavering support.

vii

TABLE OF CONTENTS
Abstract .......................................................................................................................................... iv
Dedication .......................................................................................................................................v
Acknowledgments ......................................................................................................................... vi
List of Tables ................................................................................................................................. xi
List of Figures ............................................................................................................................... xii
Chapter One: Introduction to the Study ...........................................................................................1
Context and Background of the Problem .............................................................................1
Technology and Stress: Technostress ..........................................................................2
Technostress and Increased Use of Technology in Education ....................................2
Technostress and Faculty Adaptation Challenges .......................................................3
Purpose of the Project ..........................................................................................................3
Importance of the Study .......................................................................................................3
Stakeholder Group of Focus ........................................................................................5
Overview of Theoretical Framework and Methodology .....................................................5
Research Questions ..............................................................................................................6
Organization of the Dissertation .........................................................................................6
Chapter Two: Literature Review .....................................................................................................7
Introduction ..........................................................................................................................7
Overview of Technostress ...........................................................................................7
Physical and Psychological Health Issues and Technostress ......................................7
Impact of Individual Characteristics and Technology on Technostress ......................8
Job Satisfaction Related to Technostress ....................................................................8
Organizational Role to Cope with Technostress ........................................................9
Education and Technology ........................................................................................10
Help-Seeking .............................................................................................................11
Organizational Training .............................................................................................11
Gap Analysis ......................................................................................................................12
Knowledge Focused Research and Literature ....................................................................12
Declarative Knowledge .............................................................................................12
Procedural Knowledge ..............................................................................................13
Help-Seeking: Faculty Knowledge ............................................................................15
Technostress and Knowledge ....................................................................................15
viii

Motivation Focused Research and Literature ....................................................................17
Self-Efficacy ..............................................................................................................17
Expectancy Value ......................................................................................................18
Technostress and Motivation .....................................................................................20
Organization Focused Research and Literature ................................................................22
Training .....................................................................................................................22
Help Desk Support ....................................................................................................23
Technostress and Organization ..................................................................................25
Conceptual Framework .....................................................................................................27
Chapter Three: Methodology .........................................................................................................30
Research Questions ............................................................................................................30
Overview of Design ...........................................................................................................30
Research Setting.................................................................................................................33
Participants .........................................................................................................................33
Data Sources ......................................................................................................................34
Cross-Sectional Survey Questions ............................................................................34
Retrospective Survey Questions ................................................................................35
Instrumentation ..........................................................................................................35
Data Collection Procedures .......................................................................................35
Data Analysis .............................................................................................................36
Reliability and Validity .....................................................................................................36
The Researcher...................................................................................................................38
Ethics .................................................................................................................................38
Chapter Four: Results and Findings ...............................................................................................39
Participating Stakeholders .................................................................................................40
Determination of Assets and Needs ...................................................................................42
Results and Findings for Knowledge Causes.....................................................................43
Declarative Knowledge .............................................................................................43
Procedural Knowledge ..............................................................................................49
Help-Seeking: Faculty Knowledge ............................................................................54
Results and Findings for Motivation Causes .....................................................................59
Self-Efficacy ..............................................................................................................60
Expectancy Value ......................................................................................................62
Choice ........................................................................................................................65
ix

Persistence .................................................................................................................67
Effort ..........................................................................................................................68
Performance ...............................................................................................................70
Job Satisfaction ..........................................................................................................74
Managerial Support ...................................................................................................75
Results and Findings for Organizational Causes ...............................................................77
Training .....................................................................................................................77
Proactive and Reactive Responses ............................................................................80
Positive Technologies ................................................................................................83
Help Desk Support ....................................................................................................85
Performance and Help Desk ......................................................................................87
Organizational Support and Help Desk .....................................................................91
Sentiment Analysis ...........................................................................................................92
Job Duties ..................................................................................................................93
Time Needs ...............................................................................................................93
Educational Needs ....................................................................................................93
Simplicity ..................................................................................................................93
Learning .....................................................................................................................93
Software Needs .........................................................................................................94
Convenience and Efficiency .....................................................................................94
Technical Systems ....................................................................................................94
Summary of Validated Influences ....................................................................................94
Knowledge ................................................................................................................94
Motivation ................................................................................................................97
Organization ..............................................................................................................99
Chapter Five: Recommendations and Discussion .......................................................................103
Discussion of Findings and Results .................................................................................103
Recommendations for Practice ........................................................................................103
Knowledge Influence Recommendations ................................................................104
Motivation Influence Recommendations ................................................................106
Organizational Influence Recommendations ..........................................................110
Integrated Knowledge, Motivation, Organizational Recommendations .............................114
KMO Influences and Mitigating Technostress ...........................................................114
Impact of Technostress on Faculty Performance and Job Satisfaction .......................115
x

Limitations and Delimitations .............................................................................................115
Recommendations for Future Research ...............................................................................116
Conclusion ..........................................................................................................................116
References ...................................................................................................................................118
Appendix A ..................................................................................................................................135
Appendix B ..................................................................................................................................141
Appendix C ..................................................................................................................................143

xi

LIST OF TABLES
Table 1: Data Sources ...................................................................................................................32
Table 2: Summary of Assumed Knowledge Influences ...............................................................95
Table 3: Summary of Assumed Motivation Influences ................................................................97
Table 4: Summary of Assumed Organizational Influences ........................................................100

xii

LIST OF FIGURES
Figure 1: KMO Influences on Technostress Experienced by Educators ......................................28
Figure 2: Distribution of Participants by the Number of Years of Teaching Experience..............41
Figure 3: Distribution of Participants' Age Ranges .......................................................................41
Figure 4: Distribution of Participants' Departments ......................................................................42

1

Chapter One: Introduction to the Study
The field of education is increasingly reliant on the use of technology in the 21
st
century.
The focus is now on the quality of learning rather than delivery methods (Bartos, 2013).
Evidence highlights that faculty face a continual need to keep up with technology to provide
quality education (Bartos, 2013). As Ayyagari et al. (2011) note, it is increasingly necessary for
employees and organizations to adapt to technology to accomplish work. As a result, employees
experience stress due to the increased use of technology (Tarafdar et al., 2015). Brod (1984)
identifies "technostress" as a disease caused due to the increasing use of technology and the
inability to cope with technology changes. The problem of technostress in education is essential
to address because it may lead to disengagement, turnover, and low employment performance
(Tarafdar et al., 2015). Without a solution, employees will experience physiological,
psychological, organizational, and societal consequences due to technostress (Salanova & Cifre,
2013). As such, educational institutions need to identify and understand the triggers, symptoms,
and mechanisms to cope with technostress.
Context and Background of the Problem
The field of education is experiencing an increased use of technology (Tondeur et al.,
2012). While the use of technology enables improvement in the quality of instruction and the
quality of learning (Mahapatra & Pillai, 2018), one of the side-effects of using technology is
technostress (Tarafdar et al., 2007). It is essential to understand the fundamentals of technostress
to evaluate its impact of technostress on faculty members (Tarafdar et al., 2015; Tondeur et al.,
2012). Additionally, it is crucial to understand the increased use of technology in education and
the challenges with faculty adaptation of technology (Robotham & Julian, 2006; Salanova &
Cifre, 2013).
2

Technology and Stress: Technostress
Employees may experience technostress due to the increased use of technology and their
inability to cope with technology advances (Tarafdar et al., 2007). The first mention of
technostress was by Brod (1984), and recent research asserts the existence of technostress
experienced by employees (Ayyagari et al., 2011; Ragu-Nathan et al., 2008; Salanova & Cifre,
2013; Tarafdar et al., 2007). Prior research focused on understanding triggers, symptoms, and
coping strategies for technostress on Information and Communication Technology (ICT)
professionals. While Fuglseth and Sorebo (2014) examined coping with the adverse effects of
technostress on ICT professionals, Salanova and Cifre (2013) notated technostress as the dark
side of technologies. The emphasis of this study is on the increased use of technology and faculty
adaptation. While technostress may have a physical or a psychological impact, this study focuses
on understanding its impact on job satisfaction (Ayyagari et al., 2011). Possessing the necessary
declarative and procedural knowledge, seeking help, self-efficacy, expectancy-value,
organizational training, and help desk support are aspects for analyzing the knowledge,
motivation, and organizational influences (Clark & Estes, 2008) on technostress. This
Dissertation focuses on faculty who use technology at their workplaces from six different
Southern California institutions. For anonymity, the six institutions will be referenced together as
Southern California University (SCU).
Technostress and Increased Use of Technology in Education
The use of technology in education has increased and evolved over the last twenty years
(Tondeur et al., 2012). At SCU, faculty use Learning Management Systems (LMS) to manage
the courses they teach. It is a requirement for faculty members to communicate with their
students through LMS and not external email (SCU organizational policies, 2019). Additionally,
3

it is an expectation for all tests and assignments to be set up in LMS to ensure ease of use and
access for the students (SCU organizational policies, 2019). Further, faculty must document any
meeting notes, action items, and follow-ups using Microsoft OneNote (A. Marathe, personal
communication, 02/22/2020).
Technostress and Faculty Adaptation Challenges
Most of the faculty at SCU commented on the challenges associated with adapting to
advances in technology. These faculty members have expressed difficulty in learning due to
limited time to practice and use technology. Their focus needed to be on the field of specialty
and not on learning new technology. Unfortunately, the only option for them is to continue to get
trained in the effective use of technology to ensure compliance with the institution's policies (A.
Marathe, personal communication, 02/10/2020). Such demands on faculty can produce
significant technostress (Ng, 2015).
Purpose of the Project
This study evaluates the technostress experienced by faculty at SCU. Technostress in
education leads to disengagement, job dissatisfaction, and low employment performance
(Tarafdar et al., 2015) and physiological, psychological, organizational, and societal
consequences for educational professionals (Salanova & Cifre, 2013).
Importance of the Study
Technology can make employees feel obsessed with staying connected, obligated to work
in real-time, and multi-task in a short amount of time, which may lead to technostress (Tarafdar
et al., 2011). Ayyagari et al. (2011) confirm the increasing use of technology in workplaces.
Most employees experience moderate to high degrees of technostress at some point in their
careers. They may face the consequences of not demonstrating an ability or willingness to use
4

technology (Tarafdar et al., 2011). Overall, technostress impacts an employee's ability to perform
in the workplace (Salanova & Cifre, 2013). Tarafdar et al. (2011) identify five technostress
creators: overload, invasion, complexity, insecurity, and technology uncertainty.
Technostress is prevalent in the ICT industry (Wang et al., 2008). With education
professionals having to use and adapt to technology frequently, technostress can become
prevalent in Education (Tondeur et al., 2012). Wang et al. (2008) note that technostress exists in
different organizational environments and impacts employees' physical and mental well-being.
The evidence highlights the potential effects of technostress, including decreased employee
satisfaction, lower commitment, and reduced productivity (Tarafdar et al., 2011). Zhao et al.
(2020) identify technostress as harmful to productivity, health, and happiness, which confirms
that it is one of the costliest problems in an organization. This problem is critical to address
because using technology for an extended period has become necessary at workplaces (Tarafdar
et al., 2007). Not taking steps to avoid and reduce technostress can lead to decreased employee
productivity and efficiency and adversely impact an organization regarding education and
learning (Tondeur et al., 2012).
Some faculty members at SCU experience technostress, which manifests itself through
various symptoms that they demonstrate (A. Marathe, personal communication, 02/10/2020).
While some faculty members may be able to cope with technostress in the short term and may
find methods that work for them, it is essential to understand the long-term impact of
technostress. Further research on technostress in education and its impact on employee
productivity and efficiency is necessary. This study attempts to understand the relationship
between triggers and inhibitors of technostress and the impact on employee job performance and
job satisfaction.
5

Stakeholder Group of Focus
The stakeholders for this study are faculty members teaching at various schools within
SCU. The faculty members may vary in their academic experience and maybe adjunct, assistant,
associate, or full professors at SCU. It is a requirement for faculty members to use and keep up
with technology regularly. Faculty members who encounter challenges in learning and using
technology may start demonstrating symptoms of technostress. The faculty members and leaders
at SCU need to recognize these symptoms to understand the creators and inhibitors of
technostress and resolve job performance gaps and job satisfaction.
Overview of Theoretical Framework and Methodology
Employees can experience technostress (Tarafdar et al., 2011) due to gaps in knowledge,
motivation, and organizational goals outlined by the Gap Analysis conceptual framework by
Clark and Estes (2008). This framework helps define performance gaps within an organization.
Specifically, three factors, knowledge, motivation, and organizational goals, help analyze the
stakeholders' impact and impact the organizational outcomes.
In this Dissertation, the knowledge influences focus on the technical knowledge that
education professionals currently possess or need to perform their work. The motivation of
education professionals to adapt to technology and advances in technology focus on motivational
influences. Finally, organizational influences play an essential role in determining how
effectively and consistently employees can learn to use and practice new technologies.
Understanding technostress in education will require working with employees at
educational institutions (Tarafdar et al., 2011). It is essential to understand that technostress is
real, and using practical ways to identify coping mechanisms aligns with the pragmatism model
6

(Kaushik, 2019; Tarafdar et al., 2007). Overall, this study aims at understanding the creators and
inhibitors of technostress and its impact on employee performance and job satisfaction.
Research Questions
Utilizing the Clark and Estes (2008) gap analytical framework, the following research
questions seek to determine the triggers, symptoms, and effects of technostress in addition to
identifying and understanding effective coping strategies.
1. What are the knowledge, motivation, and organizational influences associated with
recognizing technostress in educational environments?
2. How does technostress impact employee performance and job satisfaction?
3. What are the recommendations for mitigating technostress in educational
environments?
Organization of the Dissertation
This Dissertation will use five chapters to discuss the purpose and findings of the study
systematically. Chapter One will introduce the problem of technostress for education
professionals and discuss the importance of researching this problem. Chapter Two will review
existing literature and evidence for trends in the technology used in education and connects it to
the existing literature on technostress experienced in the IT industry. Chapter Three focuses on
the research methodology discussed using Gap Analysis and Quantitative Methods (Surveys)
related to the research of technostress experienced by faculty. Chapter Four will present the
findings of the study. Chapter Five will discuss the recommendations identified in the study.

7

Chapter Two: Literature Review
Introduction
Chapter Two begins with discussing the historical context of technostress and past
research (Overview of Technostress, Physical and Psychological Health Issues and Technostress,
Job Satisfaction Related to Technostress, Outcomes Related to Technostress, and Educational
Technology). Further, Chapter Two uses the Clark and Estes (2008) gap analytical framework to
understand the knowledge, motivation, and organizational (KMO) factors identified from the
literature to understand and mitigate technostress, including reviews of existing strategies to cope
with technostress and the approaches to the measurement of technostress experienced by
employees. Finally, it examines the KMO influences on employees' technostress and the
conceptual framework, which serves as the basis for a quantitative analysis within this study.
Overview of Technostress
Technology and computer-based applications are evasive within current business and
educational environments (Penzias, 1995). Cumo (2007) explains that the need for technology
arises from the needs of human behavior. Technology has changed how people operate and work
daily, and advances in technology have improved individuals' personal and professional lives
(Higgs et al., 2000). While the increased use of technology is beneficial, Tarafdar et al. (2007)
note that individuals may experience a negative impact from technology, including mental health
and physical symptoms (Brod, 1984).
Physical and Psychological Health Issues and Technostress
Technostress harms individuals' mental health due to the use of technology (Brod, 1984).
Individuals experience technostress due to the inability to use technology and difficulty adapting
to technology advances (Ragu-Nathan et al., 2008). Tarafdar et al. (2015) explain that some of
8

the triggers of technostress include the need to work more and faster, be available anywhere and
anytime, use sophisticated computer applications, and insecurity due to others who may learn
technology more quickly, and the frequent changes in technology.
Impact of Individual Characteristics and Technology on Technostress
Employees' attitudes and behavior and the complexity of technologies play an essential
role in the level of technostress experienced by employees (Ragu-Nathan et al., 2008). Marchiori
et al. (2019) describe that employees' level of technostress depends on their age, gender,
education, and professional experience. Older workers and employees with more extended work
experience exhibit higher technostress levels (Marchiori et al., 2019). In addition to the employee
attitude and demographics, technology's complexity also plays a vital role in employees'
technostress levels (Salanova & Cifre, 2013). Complex technologies that require increased effort
to learn and use may increase the level of technostress and decrease employee job satisfaction
(Brivio et al., 2018).
Job Satisfaction Related to Technostress
Individuals experiencing technostress may demonstrate symptoms like difficulty in
learning, confusion, lower job performance, and lower job satisfaction (Salanova & Cifre, 2013).
Eventually, individuals may burn out or switch jobs (Ayyagari et al., 2011). Job satisfaction
plays a vital role in determining employee productivity (Jena, 2015). While intrinsic job
satisfaction is determined by how much an employee likes the tasks they do and the skills they
learn, extrinsic job satisfaction refers to employee job satisfaction based on pay, co-workers, and
benefits (Oldknow & Knights, 2011). Ragu-Nathan et al. (2008) explain that technological
competencies, information overload, and attitude towards technology adoption significantly
impact employees' technostress levels. Oldknow and Knights (2011) identify that employees
9

may not see all the benefits of using technology and may not realize its full potential in
improving learning outcomes if they have trouble in adopting technology and adapting to
technology (Stallard & Cocker, 2014).
Organizational Role to Cope with Technostress
Organizations play an essential role in ensuring that employees invest time in learning
technology (Peters, 2009). Not having the knowledge and ability to use technology can result in
a lack of motivation to learn (Clark & Estes, 2008). Additionally, with limited support from their
organization to learn technology, employees may experience stress due to the use of technology
(Tarafdar et al., 2007). Increasing numbers of employees experiencing technostress and
increasing technostress levels impact how organizations perform (Tarafdar et al., 2007).
However, organizational leadership and focused efforts can help mitigate technology's
detrimental effects (Tarafdar et al., 2015).
Managers and organizations play an essential role in helping employees cope with
technostress (Tarafdar et al., 2015). Managers need to identify the triggers of technostress to
determine coping mechanisms (Brivio et al., 2018). Technostress triggers may arise from
insufficient training for employees, not enough support from the managers and organizations to
adapt to technology changes, and frequent technology changes (Tarafdar et al., 2007). Managers
need to communicate with employees to understand the levels of technostress they may be
experiencing, based on different triggers (Salanova & Cifre, 2013). Existing research focuses on
managers working with employees in the ICT industry to help them cope with technostress
(Brivio et al., 2018). Similarly, administrators in education need to work with faculty to help
them cope with technostress.

10

Education and Technology
Faculty use technology to improve the learning outcomes for students (Ivory, 2019).
Williams and Godwin (2007) identify several tools like learning management systems (LMS),
video conferencing, mobile computing, web 2.0, and podcasts as necessary when teaching with
technology. Peters (2009) identifies that organizations can support global education and social
learning (Brown & Bartee, 2007). The technological pedagogical content knowledge framework
(TPACK) developed by Koehler et al. (2004) and Koehler et al. (2007) helps education
professionals with the organization of content. TPACK allows employees to focus on challenges
with learning technology and implementing it. TPACK also highlights the need for support from
academic leadership to motivate employees to learn, in addition to instructor collaboration.
While pedagogy focuses on educating children, andragogy addresses how adults learn (Knowles
et al., 2005). Andragogy addresses that adults retain information when it is relevant and valuable
since their focus is on finding solutions for real-world problems (Youde, 2018). Additionally, it
is crucial to understand the levels of technostress experienced by faculty members due to
classroom technology (Keefe & Steiner, 2018). Further, Underwood and Luckin (2011) describe
the use of Artificial Intelligence in Education (AIED) to focus on Technology Enhanced
Learning (TEL) to help employees find better ways to communicate with technology. Oldknow
and Knights (2011) emphasize that it impacts effective instruction and learning practices when
employees encounter technology challenges.
This study's subsequent sections focus on evaluating the KMO influences to analyze
employees' technostress and coping mechanisms. Further, the conceptual framework based on
the gap analysis framework (Clark & Estes, 2008) discusses applying the theoretical framework
to the problem of practice and the research questions.
11

Help-Seeking
Employees need to recognize that they need help and know how to seek help or reach for
help when needed (Zimmerman, 2000). Employees may feel embarrassed, low on self-esteem,
and may experience personal inadequacy to seek help (Nelson-Le Gall & Brown, 1985).
Zimmerman (2000) clarifies that help-seeking is a part of the self-regulation process. Employees
need to understand if a problem exists and a need for help (Zimmerman, 2000). Further,
employees need to determine if they should seek help and choose the type of support required
(Gonida et al., 2019). Once the employee solicits help, they may likely obtain answers to their
questions (Zimmerman, 2000).
Organizational Training
Training helps employees work on their tasks effectively, improving their job
satisfaction, happiness, and loyalty towards the organization (Radi Afsouran et al., 2018;
Schmuck, 1970). Training supports essential learning in an organization and helps employees
understand gaps in knowledge, and provides valuable and alternative ways for employees to
perform their tasks (Radi Afsouran et al., 2018).
Overall, it is crucial to understand and evaluate the creators and inhibitors of technostress
(Tarafdar et al., 2007) and its impact on employee performance and job satisfaction (Radi
Afsouran et al., 2018). Faculty need to keep up with technology (Williams & Godwin, 2007) and
seek help when needed (Zimmerman, 2000). Organizations play an important role in helping
employees to recognize and cope with technostress (Tarafdar et al., 2007) by resolving
knowledge gaps and gaps in motivation (Clark & Estes, 2008).

12

Gap Analysis
The gap analysis framework explained by Clark and Estes (2008) is suited to study
technostress experienced by employees. The KMO influences play a significant role in
evaluating faculty's technostress (Stallard & Cocker, 2014; Tarafdar et al., 2015). Gaps in
employee knowledge and motivation, in addition to insufficient support from their organizations,
are some of the critical factors that determine the symptoms and the level of technostress
experienced by employees (Ayyagari et al., 2011). The following section evaluates the literature
that focuses on the knowledge influences on technostress.
Knowledge Focused Research and Literature
Employees need to possess the knowledge and skills to perform well in their jobs (Clark
& Estes, 2008). To achieve job performance goals, employees need to understand how they can
achieve them (Clark & Estes, 2008). Further, employees need to access relevant information to
acquire the knowledge and skills required to perform their jobs (Oldknow & Knights, 2011).
When information is not available, employees need to seek help to gain the necessary
information (Garber, 2011). Overall, possessing essential knowledge or factual information like
principles, processes, procedures, and concepts for job-relevant tasks is essential for employees
(Tunison, 2016).
Declarative Knowledge
Declarative knowledge is the knowledge about essential elements that employees need to
possess to be familiar with their work or solve problems (Krathwohl, 1993). These discrete
pieces of information or declarative knowledge, when put together, can help employees relate to
things and events (Wignall, 2006). Declarative knowledge is the foundation for all other types of
knowledge and is essential for employees when they work on large and complex tasks
13

(Krathwohl, 2002). Employees may remember declarative knowledge through day-to-day use or
practice (Egbert, 2009). Declarative knowledge may change when new information becomes
available, and it cannot be associated with any specific context or action (Pezzulo, 2011).
Employees need to possess significant declarative knowledge to demonstrate excellent job
performance (Krathwohl, 2002). A high level of declarative knowledge indicates that employees
have the experience and aptitude for learning (Jex & Britt, 2014). Possessing strong declarative
knowledge is an indicator of the increased possibility to acquire procedural knowledge (Jex &
Britt, 2014).
Procedural Knowledge
Procedural knowledge is the knowledge about knowing how to perform a task (Mayer,
2011). The basis of procedural knowledge is the declarative knowledge that employees possess,
and it involves making connections between factual information (Krathwohl, 2002). Procedural
knowledge is illustrated through actions and involves sequential steps in performing a task. It can
demonstrate through job performance and requires much practice before an employee achieves
mastery (Fantl, 2017). Employees need to have declarative and procedural knowledge to achieve
high job performance (Mayer, 2011).
Employee job performance depends on the amount of declarative and procedural
knowledge they possess (Campbell, 1990; Khosrow-Pour, 1999). Employees possessing
knowledge know about procedures and practices at an organization and need to have the proper
knowledge at the right time to complete a task (Egbert, 2009). For employees to demonstrate
high performance, there should not be a significant gap between knowledge and action (Gorelick
et al., 2004). As knowledge changes due to advances in technology, employees can achieve
excellent job performance with the right attitude, skills, and learning ability (Jex & Britt, 2014).
14

Organizations play an important role in helping employees achieve performance goals by
enabling them to gain the necessary knowledge to perform in their jobs (Fantl, 2017).
For employees to achieve performance goals, acquiring declarative and procedural
knowledge is a primary requirement (Egbert, 2009). The research elements related to knowledge
influences include the role of employees in an organization, the tasks that they perform, and their
performance goals (Sturmer et al., 2013). Employees need to be aware and informed about their
roles in the organization and the tasks expected to complete. It will enable employees to
understand ways to succeed in the role (Clark & Estes, 2008). Employee performance goals need
to align with the team, department, and organization (Wilson, 2008). Employee performance
goals need to be attainable but challenging (Presslee et al., 2013). Additionally, employee
performance goals should also be specific, measurable, achievable, relevant, and time-based
(Leonard, 2016). Employees need to have access to appropriate training to understand their roles,
tasks, and performance goals (Merriënboer, 1997).
Training is one of the most important ways for employees to acquire declarative and
procedural knowledge (Merriënboer, 1997). Training improves employee performance,
engagement, and retention and improves their productivity and efficiency (Esteban-Lloret et al.,
2018). Employees learn new skills or develop existing skills through training (Cadman, 2013).
They need to receive training when they are new to an organization or a role, for annual
compliance, before releasing a new product, or before using a new service at the organization
(Cadman, 2013). Additionally, training needs to be made available to implement a new process
or a workflow (Garber, 2011). Employees need to seek help when they do not have enough
knowledge or training to perform a task (Zimmerman, 2000).

15

Help-Seeking: Faculty Knowledge
Help-seeking is essential for employees to bridge any gaps in their knowledge (Garber,
2011). The employee needs to process the help received and then determine how they will use
the help (Gonida et al., 2019; Hudesman et al., 2014). Aleven and Graesser (2013) describe that
the availability of self-help tools is an essential part of the help-seeking process.
Employees need to have access to self-service job aids to help answer questions at a
specific moment (Aleven & Graesser, 2013). Job aids are beneficial when employees may not
remember specific steps to a solution or need access to a particular workflow (Bodine, 2013).
For example, suppose it is expected for a professor to run a report on the adoption of the
organization's LMS on campus. In that case, they need to have access to job aids that may help
them navigate the location of the reports on the LMS (Aleven & Graesser, 2013). Job aids can
help improve the efficiency, productivity, and accuracy of a task an employee is working on
(Lindholm et al., 2012). Employees may be likely to experience technostress (Tarafdar et al.,
2007) if they do not have access to appropriate job aids or the knowledge to perform a task
(Clark & Estes, 2008).
Technostress and Knowledge
Employees need to have appropriate technical knowledge about the tasks they need to
perform (Clark & Estes, 2008). As technology continues to become more advanced with new
tools and features, employees continuously need to learn new technologies or updated versions
of existing technologies (Tarafdar et al., 2007). It may be essential for employees to use software
applications for video conferencing, telecommuting, and information sharing (Tarafdar et al.,
2019). Further, employees may need to use portable devices like iPads and laptops, in addition to
using several enterprise applications and tools for collaboration and connectivity (Ragu-Nathan
16

et al., 2008). In higher education, employees are encouraged to use software applications to
improve learning quality, regardless of the medium of instruction, in-person, or online (Queen
Booker et al., 2014). Due to the increased use of technical terms and technical steps needed to
use a software application, employees may be intimidated and may start showing technostress
symptoms (Fuglseth & Sorebo, 2014).
Employees need to recognize the signs and symptoms of technostress due to a lack of
knowledge (Fuglseth & Sorebo, 2014). The level of computer knowledge and related education,
and computer confidence level may determine the level of technostress experienced by
employees (Queen Booker et al., 2014). Employees need to understand the gaps in skills required
to perform a task and seek knowledge or training they may require (Tarafdar et al., 2015).
Regardless of the amount of information, training, and education available, employees need to
determine how to apply the knowledge gained (Tarafdar et al., 2019). When there is an overload
of information, employees may feel overwhelmed, resulting in decreased job performance
(Ayyagari et al., 2011). When employees learn to recognize the signs and symptoms of
technostress, the next step is to determine ways to cope with technostress caused due to the lack
of or overload of knowledge (Salanova & Cifre, 2013).
Managing knowledge-related technostress can be accomplished using technostress
inhibitors like adequate employee training and employee empowerment (Fuglseth & Sorebo,
2014). Employees need to possess sufficient declarative and procedural knowledge to perform
their tasks (Krathwohl, 2002). Additionally, they need to have access to self-help tools and the
helpdesk to get answers to any questions they may have (Gonida et al., 2019). Similarly, when
there is not enough information or training available, employees may start feeling disengaged
with the task they are working on (Fuglseth & Sorebo, 2014). Overall, recognizing knowledge
17

gaps, working towards acquiring knowledge, seeking help when needed, and knowing who to
reach for help, are critical steps for managing technostress and improving employee motivation
(Tarafdar et al., 2015).
Motivation Focused Research and Literature
Employees need to be motivated and driven about their work to accomplish quality work
(Ventrice, 2009). Motivation helps employees be persistent and energetic, inspiring them to
solve work problems (Clark & Estes, 2008). When employees are motivated at work, it improves
their commitment to work, job satisfaction, productivity, and efficiency (Thomas, 2009).
Motivated employees drive a team's success and, subsequently, the organization's success
(Ventrice, 2009). Trusting their own beliefs in performing specific tasks and achieving particular
performance goals is critical for employees to be motivated (Bandura, 1994). This section
provides an overview of motivation essential for individuals to mitigate technostress: self-
efficacy and expectancy-value theory.
Self-Efficacy
Self-efficacy explains that individuals need to believe in their ability to complete a task or
approach a challenge (Bandura, 1994). Employees may experience high self-efficacy due to past
successes, and it can boost their motivation to continue to learn and strive to attain their goals
(Mayer, 1998). When an employee has the drive to succeed, they will likely have positive
experiences, which will improve their self-efficacy (Bandura, 2000). Higher self-efficacy helps
increase an employee's confidence, while higher confidence helps improve self-efficacy
(Bandura, 1994). Similarly, employees that exhibit high self-efficacy demonstrate a willingness
to take on new challenges in addition to their ability to creatively seek ways to solve a problem
(Bandura & Schunk, 1981; Tierney & Farmer, 2011). The level of self-efficacy may depend on
18

the complexity of the tasks that employees are assigned (Karwowski & Kaufman, 2017).
Adopting innovation requires that employees have high self-efficacy and intrinsic motivation
(Hausberg et al., 2017). Similarly, high self-efficacy increases employee motivation and
increases persistence to find better ways to complete a task (Ventrice, 2009).
Persistence refers to how far employees are willing to stretch themselves to achieve a
goal (Clark & Estes, 2008). Employees may persevere through tasks to prove their beliefs and
point of view, thus demonstrating a higher motivation to learn and make progress (Skinner et al.,
1990). Employees who persist demonstrate the appropriate behavior and choices to complete a
task successfully (Wigfield & Eccles, 2000).
Expectancy Value
The expectancy-value theory explains that employees' beliefs or expectancies determine
their behavior and choices while performing a task (Wigfield & Eccles, 2000). Expectancies
determine an employee's confidence in their ability to succeed (Nagengast et al., 2011).
Employees with the ability to perform a task and realize the need to accomplish that task are
critical elements of the expectancy-value theory (Eccles, 2005). Expectancies and values can
help understand an employee's interest, focus, and engagement (Trautwein et al., 2012). Further,
expectancies and values significantly influence an employee's attitude, persistence, effort, and
performance (Bembenutty, 2008). When employees possess an expectancy to pursue a
performance goal actively, they will demonstrate increased motivation (Usher & Pajares, 2008).
Attitude
If employees are motivated, they will work smarter and develop more innovative ideas to
accomplish a task (Clark & Estes, 2008). Employees who demonstrate the right attitude exhibit a
willingness to take on new challenges and solve problems (Bembenutty, 2008).
19

Persistence
Actively pursuing a goal, even if the employee may not have chosen the goal for
themselves, helps increase employee motivation (Ventrice, 2009). The higher the belief to
complete a task, the more the employee will persist in completing the task (Bandura, 1997).
Effort
Highly motivated employees demonstrate the effort to achieve a specific goal (Clark &
Estes, 2008). If employees see the value or incentive to pursue a goal, they will present their best
effort to complete tasks needed to achieve the goal (Thomas, 2009). Employees may demonstrate
increased mental effort based on the incentive to achieve a goal (Venables & Fairclough, 2009).
Performance
The basis of the effort needed towards achieving a goal is the self-worth that the
employees may perceive (Usher & Pajares, 2008). If a task is complex and not worthwhile,
employees may not demonstrate the needed effort to perform better (Brehm et al., 1983).
Managers play an essential role in determining the goals and the corresponding effort required by
employees (Cadwallader et al., 2010).
Managers need to recognize the alignment of employee desires and organizational goals
(Kauppila, 2018). Employees may not always realize the value of achieving a goal, and the
managers need to guide their employees in understanding the significance of the goal
(Cadwallader et al., 2010). Managers play a significant role in increasing employee motivation
and need to possess excellent communication skills (Thomas, 2009). It helps employees
understand the value of their contribution to ensuring a positive work environment (Kauppila,
2018). If the employees are low on motivation and do not receive support from their managers,
they will likely experience technostress (Ayyagari et al., 2011).
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Technostress and Motivation
Employee job satisfaction depends on their motivation, attitude, and experiences at work
(Ayyagari et al., 2011). Maslow (1987) describes five needs for employee motivation:
physiological, safety, social, ego, and self-actualization. For employees to be satisfied with their
jobs, the lower level needs need to be satisfied before the higher-level needs (Maslow, 1987).
High-performance expectancy depends on the employee's beliefs and self-efficacy (Eccles &
Wigfield, 2002). Job satisfaction experienced by employees and the support they receive from
their managers helps determine the level of technostress caused due to motivation challenges
(Ayyagari et al., 2011).
Employee Job Satisfaction
Employees need to be empowered to provide input before implementing new workflows
or new technologies (Tarafdar et al., 2007). Employees need to have reasonable deadlines for
learning new workflows or new technologies, in addition to having enough time to learn. It will
improve employee motivation and, in turn, their job satisfaction (Fuglseth & Sorebo, 2014). To
continue to be motivated and improve their job satisfaction, employees need opportunities to
learn industry-standard technologies, besides learning on the job (Ayyagari et al., 2011). Further,
employees may need support from their peers to stay motivated and demonstrate high job
performance (Tarafdar et al., 2007). According to Bedeian (1976), improving employees'
motivation and ensuring they are satisfied with their job should be their managers' primary focus.
Managers Supporting Employees
Managers play an essential role in motivating employees (Cadwallader et al., 2010). They
need to empower employees to learn and use highly sophisticated technologies to perform on the
job (Tarafdar et al., 2007) effectively. Eventually, employees will reach their full potential to
21

learn new skills and be motivated to perform and help other employees acquire these skills
(Maslow, 1987). Offering praise and support to employees is an essential function of managers
to motivate employees (Salanova & Cifre, 2013). Managers need to understand that not all
employees are motivated by monetary incentives (Bedeian, 1976). Employees who have
managers that do not support them to increase their motivation are likely to experience
technostress (Tarafdar et al., 2015).
Managers need to understand the triggers of technostress caused due to low employee
motivation (Tarafdar et al., 2015). Employees experiencing technostress due to low motivation
may demonstrate low confidence, low job performance, and performance anxiety (Salanova &
Cifre, 2013). They may also show difficulty adapting to change and may not be flexible to learn
new skills (Ayyagari et al., 2011). Managers need to communicate with employees regularly and
clearly understand the technostress triggers (Tarafdar et al., 2007). Managers who work with
their employees effectively may have a higher chance of helping employees cope with
technostress experienced due to low motivation (Fuglseth & Sorebo, 2014).
Maintaining and increasing employee motivation is vital to improve their productivity
and efficiency (Cadwallader et al., 2010). High employee motivation may help reduce turnover
and enhances job satisfaction (Ayyagari et al., 2011). Employees demonstrating high job
performance are usually highly motivated (Bedeian, 1976). Highly motivated employees need to
see incentives for high performance (Kauppila, 2018). Providing incentives increase fairness,
employee competence, persistence, and motivation (Bedeian, 1976). Managers and organizations
may need to reward their employees using public recognition, increased pay, bonuses, or
promotions (Kauppila, 2018). With support from managers and the organization, self-intrinsic
22

motivation and self-enhancement motivation helps employees perform better (Kauppila, 2018;
Salanova & Cifre, 2013).
Organization Focused Research and Literature
Every organization exhibits behaviors based on its leaders' and employees' beliefs,
values, and work ethic (Smith & Vecchio, 1993). One of the significant ways for organizations to
demonstrate employee-centric behavior, specifically regarding information technology systems,
is by providing them access to appropriate job-related training and access to help desk resources
(Robbins, 2013; Smith 2012).
Training
Organizational training plays a vital role in its success and its employees (Biech, 2012).
When organizational training content aligns with the organizational goals and business
objectives, it helps employees understand the expectations and goals needed to perform in their
jobs (Biech, 2012). Organizational culture plays a vital role in determining an organization's
training strategy (Robbins, 2013).
A healthy organizational culture plays an essential role in defining employee success and
organizational success (Robbins, 2013). An influential organizational culture depends on its
behavior, employee beliefs, and its leaders' values (Smith & Vecchio, 1993). Effective decision-
making, high trust, strong training strategy, and increased cooperation are critical features of an
influential culture (Smith, 2012). Organizational cultures that value control, collaboration,
competence, training, or cultivation, demonstrate corresponding organizational strategy,
leadership, and epistemology (Suda, 2008). Organizations that promote employee training and
focus on employee readiness before deploying new technologies play an essential role in their
employees' success (Smith, 2012). Organizational culture determines its employees' behavior,
23

and the leaders play a crucial role in deciding how the organizational culture shapes with time
(Robbins, 2013). Leaders who drive the organizational culture towards adopting industry-
standard and advanced technologies are their backbone (Suda, 2008). Further, the leaders who
ensure adequate training and help desk assistance play a significant role in aligning
organizational strategy and the culture (Smith, 2012). Similarly, help desk support is a critical
sign of a healthy organizational culture (Biech, 2012).
Help Desk Support
Streamlined help desk support is the backbone of an organization (Middleton & Marcella,
1997). A dedicated help desk department within organizations supports employees with their
technical needs (Foo et al., 2000). The range of tasks that the help desk department supports
varies from simple day-to-day questions to complex technical support needs (Sun & Li, 2011).
Whenever there is a technology-related issue, employees are encouraged to reach the help desk
department to guide them to solve the problem and access any self-help documentation (Delic &
Hoellmer, 2000). Additionally, employees are encouraged to reach the help desk department to
resolve an issue regardless of its complexity (Foo et al., 2000). Some organizations may ask
employees to contact an intelligent help desk chatbot or an automated phone service as a first
step before reaching the help desk team (Foo et al., 2000; Wang et al., 2011). If the questions are
still unanswered, then a ticket is created and routed to a helpdesk analyst (Wang et al., 2011).
The help desk support team is at the frontline of helping employees, and organizations must
invest in staffing and training for the help desk department to ensure high-quality support (Hall
et al., 2014). Any organizational change results in an increase in the number of questions that the
help desk department receives from employees (Collins, 2001).

24

Performance and Help Desk
To ensure continuous performance improvement, organizations change their processes,
strategies, structure, and technologies from time to time (Clark & Estes, 2008; Robbins, 2013).
Suppose employees continue to use the same processes to accomplish tasks for several years. In
that case, organizations may not see the benefits of using advanced technologies and process
improvement strategies to complete these tasks efficiently (Collins, 2001). Successful
organizational change requires effective leadership and efficient change management strategies
and equipping the help desk team with the knowledge needed to support employees (Robbins,
2013).
Organizational Support and Help Desk
Organizational leaders need to communicate effectively, collaborate with employees, and
commit to change (Robbins, 2013). They need to review employees' workload to understand how
they may cope with managing organizational change while managing their workload (Collins,
2001). Employees need to be supported and encouraged to express their ability or inability to
learn new skills (Dewe & Cooper, 2017). Organizations may direct their employees to reach the
help desk team to ensure that they are thoroughly trained and proficient (Foo et al., 2000).
Further, employees need to understand their role in the organizational change, resulting in better
adoption of the change (Dewe & Cooper, 2017). Empowering employees to seek support using
self-help tools, help from peers, or help desk, is an integral part of an organizational change
(Middleton & Marcella, 1997). Employees who do not have access to these help-seeking avenues
are likely to experience technostress (Hall et al., 2014; Ayyagari et al., 2011).

25

Technostress and Organization
Organizational strategy plays a vital role in managing workplace stress and technostress
(Bond et al., 2010). Organizational cynicism, mistreatment of employees, and employee inability
to learn or adopt new technologies quickly are some of the critical challenges that organizations
need to address (Gorsline, 2016). Organizations need to understand the triggers of work
stressors, underlying challenges, or issues that teams and employees may be facing (Tarafdar et
al., 2007). Once triggers are identified, organizations need to evaluate the impact of technostress
based on employees' gender, age, personalities, and attitudes (Tarafdar et al., 2015). Employees
may feel threatened if organizations do not support them in coping with technostress, resulting in
uncertainty or job insecurity (Dewe & Cooper, 2017). How organizations react to employees
facing technostress depends on organizational beliefs and strategy (Bond et al., 2010).
Proactive and Reactive Response
Organizations may be proactive or reactive while dealing with employees experiencing
technostress (Jena, 2015). Proactive investment to deal with technostress can help with cost
reduction due to the implementation of strategies to avoid technostress (Jena & Mahanti, 2014).
One approach to prevent technostress is updating old processes and making them more efficient
and comfortable to follow (Tarafdar et al., 2007). Further, organizations need to ensure that
employees have enough time to adjust to any change and continue to be motivated (Ayyagari et
al., 2011). It requires that organizational change happens at an appropriate pace (Tarafdar et al.,
2007). Organizations need to be proactive in supporting employees who may be experiencing
technostress, besides stress caused by other professional or personal reasons (Ayyagari et al.,
2011). Organizations that tend to be reactive to the occurrence of technostress in employees need
to focus on knowing the hot spots of technostress and encouraging employees through problem-
26

solving recommendations (Jena & Mahanti, 2014). Organizations that assist employees with
managing technostress may achieve higher employee performance in addition to improved
organizational performance (Bond et al., 2010). Higher organizational performance is one of the
significant signs of using positive technologies and behaviors that it demonstrates (Bond et al.,
2010; Brivio et al., 2018).
Positive Technologies
The use of positive technologies can help with preventing technostress (Brivio et al.,
2018). To prevent technostress, organizations need to adopt positive technologies that are well-
designed and role appropriate for employees (Costabile & Spears, 2012). Positive technologies
are easy to learn and use (Calvo & Peters, 2014) and help reduce learning anxiety and positively
impact employee performance (Baños et al., 2019). Organizations with a good understanding of
employees' ability will introduce positive technologies and corresponding training to ensure high
employee adoption (Botella et al., 2012). When technologies are easy to use and learn, it can
help prevent technostress caused due to sophisticated technology (Brivio et al., 2018). Similarly,
employees may feel more secure and sure about adopting new skills using positive technologies
(Pawlowski et al., 2015). Overall, positive technologies help understand the triggers and
symptoms of technostress quicker and determine ways to cope with technostress (Brivio et al.,
2018). Additionally, organizational documents and policies may outline employees' guidelines
for the effective and positive use of technology (Botella et al., 2012). The review of existing
literature helps understand the triggers, symptoms, and mechanisms that are useful to cope with
technostress. By applying the theoretical framework, the following section discusses the
conceptual framework for this study. It will help understand the relationship between the
concepts in existing research and the problem of practice.
27

Conceptual Framework
A conceptual framework manifests from the theoretical framework (Maxwell, 2013).
Specifically, the conceptual framework provides a diagrammatic representation of the theoretical
framework (Merriam & Tisdell, 2016). The literature review, combined with the theoretical
research, helps the researchers identify the connections between the concepts and the problem of
practice (Wolcott, 2009). The conceptual framework determines the research methodology and
helps understand and evaluate the research results (Merriam and Tisdell, 2016). The conceptual
framework for this study focuses on applying the KMO influences on technostress experienced
by faculty.
The relationship between employee knowledge, motivation, and organizational influences
is critical to analyzing employee performance, engagement, and job satisfaction (Clark & Estes,
2008). Employees working at organizations with high centralization and a high innovation pace
may experience increased levels and symptoms of technostress (Wang et al., 2008). With the
increasing use of technology in education, organizations need to adopt technologies that are
easier to learn, easier to use, productive, and easier to deploy (Bond et al., 2010). Using the gap
analysis framework, the evaluation of coping mechanisms helps employees minimize knowledge
gaps, low motivation, and organizational challenges (Clark & Estes, 2008). Figure 1 provides a
diagrammatic representation of the conceptual framework used in this study.
Figure 1 demonstrates the interaction between the KMO influences on the technostress
experienced by employees. This figure explains that the employee knowledge and motivation
factors fall under the overall organizational influences. Employees need to be motivated to
decrease the level of technostress they may experience (Tarafdar et al., 2019). The amount of
28

knowledge that an employee needs to use technology depends on their motivation and
organizational expectations (Tarafdar et al., 2015).

Figure 1
KMO Influences on Technostress Experienced by Educators

Note. This figure demonstrates the knowledge, motivation, and organizational influences on
faculty technostress adapted from Clark, R. E., & Estes, F. (2008). Turning research into
results: A guide to selecting the right performance solutions. Information Age Pub Inc.

29

When there are knowledge gaps, employees need to use self-help tools, seek help from
peers, or reach the help desk department with questions (Foo et al., 2000). It will help employees
attain proficiency in technology use (Salanova & Cifre, 2013). Gaining knowledge may increase
employee motivation and eventually improve job satisfaction and performance (Ragu-Nathan et
al., 2008). Subsequently, it will impact how organizations will support employees experiencing
technostress, helping employees with motivation (Miner, 2005).
Understanding the relationship between KMO influences is an appropriate way to
examine the technostress experienced by faculty. Further, it will help evaluate technostress
related to job dissatisfaction and disengagement (Clark & Estes, 2008; Tarafdar et al., 2015). The
purpose of this study is to examine the triggers and symptoms of technostress experienced by
employees and determining ways to cope with it.

30

Chapter Three: Methodology
The purpose of this study is to evaluate the technostress experienced by faculty members
at SCU. It aims to identify the KMO influences affecting the SCU faculty members, contributing
to job dissatisfaction and low job performance (Bond et al., 2010). With the large number of
faculty members needing to use educational technology, this study is relevant to education
trends. Chapter Two discussed the gap analysis framework (Clark & Estes, 2008) based on this
study's conceptual framework. The conceptual framework addressed the anticipated KMO
influences on technostress experienced by employees. This study's research strategy focuses on
using quantitative research and the survey methodology to evaluate the impact of technostress
and identify ways to cope with it. The following questions guide the research methodology, data
collection, and data analysis based on the study's design.
Research Questions
1. What are the knowledge, motivation, and organizational influences associated with
recognizing technostress in educational environments?
2. How does technostress impact employee performance and job satisfaction?
3. What are the recommendations for mitigating technostress in educational
environments?
Overview of Design
The use of the quantitative research method guides this study to find answers to the
research questions (Plonsky et al., 2014). Quantitative research methods focus on measurements
and data analysis through one or more tools (Babbie, 2010). In this study, the data gathered will
be generalized to explain the frequency and severity of employees' technostress (Creswell, 2013;
Tarafdar et al., 2007). Because the purpose of this study is to evaluate the relationships between
31

technostress experienced by employees and technostress triggers and inhibitors, the quantitative
approach was the appropriate choice (Creswell, 2013). The necessary data for this study will be
collected using surveys.
It is essential to use the suitable survey research method for a quantitative study
(Creswell, 2013). This study will utilize cross-sectional and retrospective surveys to help
understand the impact of technostress and discover ways to cope with technostress. While a
cross-sectional survey is an observational survey that focuses on determining the data at one
specific point in time (Pazzaglia et al., 2016), a retrospective survey is a longitudinal survey that
focuses on information from past events (Beckett, 2001).
To be able to analyze data at a single point in time, a cross-sectional survey is the
appropriate survey method to evaluate the outcomes and the experience of the study participants
(Setia, 2016). A cross-sectional survey will help acquire information from participants regarding
their technostress experience at a given point in time. Additionally, to understand the potential
relationships between the outcomes and study participants' experience, a retrospective survey is
the appropriate survey method (Salkind, 2010). A retrospective survey will help obtain
information from participants to determine the potential relationship between technostress
creators and inhibitors. Further, this study will use the exploration approach (Maxwell, 2013) to
understand the impact of technostress on different individuals and how other methods work to
cope with technostress. The exploratory process is the methodology of trial and error and
experimentation to explore the various ways to solve a problem (Maxwell, 2013). It is essential
to understand that technostress is real, and using practical ways to identify coping mechanisms
aligns with the pragmatism model (Kaushik, 2019; Tarafdar et al., 2007).
32

Table 1
Data Sources
Research Questions
Cross-sectional
Survey
Retrospective
Survey
RQ1: What are the knowledge, motivation, and organizational
influences associated with recognizing technostress in
educational environments?
X
RQ2: How does technostress impact employee performance
and job satisfaction?
X X
RQ3: What are the recommendations for mitigating
technostress in educational environments?
X X

Note. This table lists the data sources for the study's research questions.

The survey research method will be used in this study to understand the traits and
behaviors of employees experiencing different levels of technostress (Ayyagari et al., 2011;
Robinson & Firth, 2019). Further, it will help describe the population's characteristics based on
the relationship between the KMO influences and the impact of technostress and ways to cope
with it (Bickman et al., 2009; Tarafdar et al., 2011). Table 1 mentions the data sources for the
research questions in this study. The survey employs cross-sectional survey questions to give a
snapshot of employee behavior at one point in time (Patwardhan, 2017). Additionally, the survey
includes retrospective questions to determine specific examples from faculty experiences (Rubin
& Bellamy, 2012).
33

Research Setting
This study focuses on faculty technostress experienced at SCU. For this study, the desire
is to include approximately five hundred faculty at SCU (n = 500) out of the population of over
5,000 faculty members (N = 5000). The participants are faculty members within various schools
at SCU and demonstrate a wide range of technology use, with some being novice while others
being seasoned users of technology. Similarly, the participants may be experiencing various
technostress levels, depending on their familiarity and experience using technology.
The problem of technostress in education is essential to address because it leads to
disengagement, job dissatisfaction, and low employment performance (Tarafdar et al., 2015).
Without a solution, the faculty will experience physiological, psychological, organizational, and
societal consequences due to technostress (Salanova & Cifre, 2013). As such, educational
institutions need to identify and understand the triggers, symptoms, and mechanisms to cope
with technostress (Tarafdar et al., 2007).
Participants
Almost eight hundred faculty members teaching at various schools within SCU
responded to the survey. The participants varied in their academic experience and maybe
adjunct, assistant, associate, or full professors at SCU. Additionally, the survey was sent to the
participants regardless of their age, gender, and instruction medium. All faculty members at SCU
received the survey. As seen in Appendix B, the researcher contacted the faculty administrator at
SCU to seek permission for faculty members to receive the survey. The identity of participants
was not known to the researcher.
34

Data Sources
The data was collected from the participants using the survey method. In a single survey
sent to the participants one time, the participants need to answer cross-sectional and retrospective
questions. The survey begins with demographic questions to understand the participant's tenure,
age, gender, department, and education level. Further, it groups the questions based on the KMO
influences on technostress experienced by employees. The survey includes closed questions, and
the participants need to answer them by choosing options based on the Likert scale. The detailed
survey protocol is in Appendix A. The survey protocol is adapted from the existing research on
technostress by Ragu-Nathan et al. (2008) and Suh and Lee (2017). The survey responses helped
the researcher determine the triggers and inhibitors of technostress related to the research
questions and the conceptual framework.
Cross-Sectional Survey Questions
A cross-sectional survey is an observational survey that focuses on determining the data
at one specific point in time (Pazzaglia et al., 2016). The survey sent to the participants includes
questions that ask about a moment in time when the participant experienced technostress
(Ayyagari et al., 2011). Additionally, the survey consists of questions to understand triggers that
resulted in technostress and how the participants coped with it (Alexander, 2018; Ayyagari et al.,
2011). Cross-sectional survey questions helped understand the snapshot of prevailing
characteristics seen in the cohort (Connelly, 2016). The goal is to find co-relations that existed at
the point in time when the participant experienced technostress (Bowling, 2014; Tarafdar et al.,
2007). In addition to the cross-sectional questions, the survey includes questions that ask the
participants to look back in time to examine the triggers of technostress and methods used to
cope with it (Bhat & Koppelman, 1999; Salanova & Cifre, 2013).
35

Retrospective Survey Questions
A retrospective survey is a longitudinal survey that focuses on information from past
events (Beckett, 2001). The use of retrospective survey questions helped understand participant
behavior and work ethic before and after experiencing technostress (Ayyagari et al., 2011). The
goal is to find groups of participants who have experienced severe technostress and those with
mild technostress levels during a specific time frame (Tarafdar et al., 2007; Thigpen, 2019).
Comparing the data collected from the two participant groups helped understand the triggers and
inhibitors of technostress (Gordis, 2009; Tarafdar et al., 2011).
Instrumentation
The survey was made available to the participants using Qualtrics based on the
researcher's access to this survey tool by the University of Southern California (USC). The
researcher sent the informed consent form, as shown in Appendix C, to the faculty administrator.
Additionally, the faculty administrator at SCU approved the survey protocol before it was sent to
faculty members. The survey questionnaire included questions to understand the experience of
the participant with technology. The subsequent questions focused on understanding the KMO
influences on the technostress experienced by the participants. The participants needed less than
fifteen minutes to complete the survey.
Data Collection Procedures
The researcher sought approval from the Institutional Review Board (IRB) at USC to
collect data from the participants. Additionally, the researcher sought approval from the faculty
administrator at SCU. The researcher contacted the faculty administrator at SCU to seek
permission to make the survey questionnaire available to the faculty members. Based on this
communication, the participants were aware of an upcoming survey and the need for
36

approximately fifteen minutes to complete the survey. The survey responses were anonymously
stored using Qualtrics. Once submitted, participants do not have access to survey responses and
will not analyze data and interpret results. Additionally, since each institution is different, the
data collected may be analyzed collectively and individually.
Data Analysis
The primary tools for quantitative data analysis were Qualtrics, SPSS, and Tableau. In
some scenarios, the KMO influences are independent variables, and the technostress experienced
by individuals is the dependent variable. Subsequently, in other scenarios, employees'
technostress is an independent variable, and the resulting low employee performance, job
dissatisfaction, and turnover are dependent variables. Mann-Whitney U ANOVA, Kruskal-
Wallis H ANOVA, and regression analysis were used to analyze the data from the survey
responses. The Likert scale used for survey questions helped quantify the responses. Specifically,
the quantitative study used histograms, means rank, and median functionality of SPSS.
Additionally, data analysis from Tableau and the data analysis from Qualtrics helped analyze the
survey responses.
Reliability and Validity
As a significant step of the research methodology, it is essential to strengthen the study's
data collection and analysis (Litwin, 1995). The researcher made the survey questionnaire
available to the participants and subsequently analyzed the collected data. This quantitative
research design is one way to improve the study's reliability and validity (Merriam & Tisdell,
2016).
Measuring the correct items and reusing the measurement tool are essential criteria to
determine reliability and validity (Creswell, 2013; Salkind, 2014). Validated research by Ragu-
37

Nathan et al. (2008) and Suh and Lee (2017) provided the appropriate design and structure for
this study. The basis of this study's reliability is the consistency of the responses to the survey
protocol (Heale & Twycross, 2015). Using the Split-Halves test and the Cronbach's Alpha test
helped determine the survey protocol's internal consistency (Allen, 2017). The measurement of
reliability using these methods strengthened the survey responses' consistency (Bartholomew,
2004). The survey protocol, along with the gap analysis framework (Clark & Estes, 2008),
helped understand the triggers, inhibitors, and the level of technostress experienced by faculty
members.
The survey protocol aimed to measure the level of technostress experienced by faculty
and the corresponding impact on job satisfaction and performance. The protocol was determined
to be valid based on the concept of construct validity by evaluating the survey responses and the
corresponding degree of technostress experienced by faculty (Heale & Twycross, 2015). The
existing research regarding technostress (Ayyagari et al., 2011; Ragu-Nathan et al., 2008;
Salanova & Cifre, 2013; Tarafdar et al., 2007) guided the determination of gaps in knowledge,
motivation, and organizational influences that faculty demonstrate (Clark & Estes, 2008). In
addition to the survey protocol's validity, it is crucial to evaluate its accuracy and consistency
(Korb, 2012).
The survey protocol used in this study had twenty-five questions that focused on the
KMO influences on the technostress experienced by faculty members. The nine questions that
focused on the knowledge influences helped identify the procedural and declarative knowledge
that faculty members possess, in addition to the level of help-seeking behavior they may
demonstrate. Eight questions on motivation influences pertained to the self-efficacy theory and
38

the expectancy-value theory. The remaining eight questions focused on training and helpdesk
support as the vital organizational influences on technostress.
The Researcher
The researcher has a faculty position, but none of the study participants have a direct
relationship with the researcher regarding being a direct report or being under a contract. The
researcher shared the purpose of the study with the participants, and the sole interest of data
collection was to perform a gap analysis of the technostress experienced by participants. There
was no monetary incentive for the researcher or the participants to contribute to this study.
Additionally, the researcher had the necessary skills to frame survey questions and analyze the
surveys' data.
Ethics
To be able to limit the researcher bias, this study is based on the research design and
ethical data collection and analysis, as explained by Creswell (2013). Further, this study protects
the participants by not collecting information regarding their identity (Glesne, 1999). To ensure
the participants' protection, the data collection step was approved by the IRB at USC and the
faculty administrator at SCU. The informed consent form and the request for approval by IRB
were submitted based on the recommendation by Glesne (1999) and Rubin (2005). Additionally,
participation was voluntary, and the data's confidentiality was in line with ethical research
principles (Merriam & Tisdell, 2016). The researcher did not share the data with anyone and
separated the research requirements and professional duties.

39

Chapter Four: Results and Findings
The focus of this study is on the technostress experienced by faculty members at SCU. It
aims to identify the KMO influences affecting the SCU faculty members, contributing to job
dissatisfaction and low job performance (Bond et al., 2010). Chapter Three discussed the
methodology to evaluate triggers and inhibitors of technostress among faculty members. This
study's research strategy focuses on using quantitative research and the survey methodology to
assess the impact of technostress and identify ways to cope with it. The following questions
guide the research methodology, data collection, and data analysis based on the study's design.
1. What are the knowledge, motivation, and organizational influences associated with
recognizing technostress in educational environments?
2. How does technostress impact employee performance and job satisfaction?
3. What are the recommendations for mitigating technostress in educational
environments?
The data was collected from the participants using the survey method. The link to the
survey created using Qualtrics was sent to the participants via email. The survey begins with
demographic questions to understand the participant's tenure, age, gender, education level, and
years of teaching experience. Further, it groups the questions based on the KMO influences on
technostress experienced by employees. The survey includes closed questions, and the
participants need to answer them by choosing options based on the Likert scale. The survey's
final question is open-ended, and the participants can choose to enter any additional information
regarding technostress. The detailed survey protocol is in Appendix A. The survey protocol is
adapted from the existing research on technostress by Ragu-Nathan et al. (2008) and Suh and
Lee (2017). The survey responses helped the researcher determine the triggers and inhibitors of
40

technostress related to the research questions. Once the given deadline in Qualtrics automatically
submitted the survey responses, the researcher made observations and conducted data analysis.
The findings presented using the knowledge, motivation, and organizational influences align
with the conceptual framework from Chapter 3. Gaps in employee knowledge, motivation, and
organizational influences are critical factors that determine the symptoms and the level of
technostress experienced by employees (Ayyagari et al., 2011).
Participating Stakeholders
The participants for this study included faculty members teaching at various schools
within SCU. The participants varied in their academic experience and in their knowledge of
using technology. Additionally, the survey was sent to the participants regardless of their age,
gender, education level, and instruction medium. All faculty members at SCU received the
survey. As seen in Appendix B, the researcher contacted the faculty administrator at SCU to seek
faculty members' permission to receive the survey. Once the survey responses were received, the
identity of participants was not known to the researcher.
Over five thousand faculty members at SCU received the survey, and seven hundred and
ninety-six faculty members responded to the survey. Participants' teaching experience ranged
from 0 - 49 years, and most participants had around 0 - 19 years of experience in using
technology. The highest level of education for 75% of the participants was either Doctorate or
post-doctorate. 60% of the participants were under 59 years of age, and there was an almost
equal distribution of male and female participants. Most of the survey respondents were from the
Social Sciences and Humanities, Science, Business, or Education departments at SCU. The three
figures below describe the demographic information of participants. Figure 2 depicts the
distribution of participants by the number of years of their teaching experience. While Figure 3
41

provides the distribution of the participants' age ranges, Figure 4 represents the departments to
which the participants belong.

Figure 2
Distribution of participants by the number of years of teaching experience

Note. This figure describes the distribution of survey participants by the number of years of their
teaching experience.

Figure 3
Distribution of participants' age ranges

Note. This figure describes the distribution of survey participants' age ranges.
42

Figure 4
Distribution of participants' departments

Note. This figure describes the distribution of survey participants' departments.

Determination of Assets and Needs
The data was collected from the participants using the survey method. The survey
questions were grouped based on the KMO influences on technostress experienced by
employees. The survey responses helped the researcher determine the triggers and inhibitors of
technostress related to the research questions and the conceptual framework. The final question
on the survey asked participants to provide any additional information regarding technostress
voluntarily. 37% of the total participants responded to the last question of the survey.
By analyzing the survey responses and comparing participant responses with the KMO
influences discussed in Chapter Two, it was possible to determine the assets and needs. If the
43

effect size of a factor impacting the influence is greater than zero, the factor impacting the
influence is a need. All other evaluated factors impacting the influence are an asset, as discussed
in Chapter Two. Validation of the survey data analysis findings is possible by comparing them
with the existing research results that focus on ICT professionals. Additionally, using smart data
analytical tools like SPSS and Qualtrics provide insights into the story behind the gathered data
using survey responses. Through this triangulation process, the evaluation of the creators and
inhibitors of technostress in education is possible. Further, since the data is obtained using the
Likert scale, it is not normally distributed, and the use of a non-parametric statistic like the
Mann-Whitney U Test is needed. Based on the Mann-Whitney U Test outcome, additional tests
like Kruskal-Wallis Test and Ordinal Regression are required, as discussed in Chapter 3.
Results and Findings for Knowledge Causes
The survey sent to the faculty at SCU included nine questions focused on assessing the
knowledge influences on technostress creators and inhibitors. Specifically, the emphasis is on the
declarative knowledge, procedural knowledge, and help-seeking behavior of faculty members.
The following section discusses the finding associated with the knowledge influences on
technostress.
Declarative Knowledge
The survey sent to faculty members was used to assess the essential technical knowledge
they need to teach students. The results of the survey are used to understand the gaps in
declarative knowledge experienced by faculty members.
Influence 1
Finding it difficult and complicated to use new technologies.
44

Survey results. Since the data is obtained using the Likert scale, it is not normally
distributed, and the use of a non-parametric statistic like the Mann-Whitney U Test is needed.
Also, because we are comparing only two groups, male faculty and female faculty, Mann-
Whitney U Test is used instead of the Kruskal-Wallis H Test. Based on the data analysis output
from SPSS, male faculty have a Means Rank of 398.93, and female faculty have a Means Rank
of 393.91. The z-value is -0.322, and the p-value is 0.748. Since the p-value is greater than 0.05,
we accept the null hypothesis of equal Mean Ranks. Therefore, we cannot confirm if either male
or female faculty find it difficult or complicated to use new technologies. The histogram
representation of the two groups proves that male faculty do not have a significantly different
distribution than female faculty when analyzing the difficulty and complexity of using new
technologies.
Kruskal-Wallis H Test is used to analyze the non-normally distributed data since we
compare multiple age groups next. By comparing the Mean Rank values, there is a difference in
how faculty from different age groups find it difficult to use new technologies. The Chi-Square
value is 41.312, and the p-value is 0.000. Since the p-value is less than 0.05, we reject the null
hypothesis of no differences between Mean Ranks. Further, by calculating the effect size and
using the ordinal regression analysis, we see a 5.18% of the mean Rank scores' variability is
accounted for by the age groups. The median for analyzing the difficulty and complexity of using
new technologies varies by age groups. While age groups 20 – 29 years, 30 – 39 years, 40 – 49
years, 50 – 59 years, and 60 – 69 years reported a median value of "somewhat disagree," age
group 70 – 79 years reported a median value of "neither agree nor disagree" and age group 80 –
89 years reported a median value of "somewhat agree."
45

Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels find it difficult to use new technologies, the p-value of 0.283 is greater
than 0.05. Therefore, we accept the null hypothesis of no differences between Mean Ranks.
Thus, we cannot determine the impact of education level on how faculty find it difficult to use
new technologies. Further, using the Kruskal-Wallis H Test and ordinal regression analysis, we
see the effect size of 2.93% that confirms the impact of the number of years of teaching
experience on how faculty find it difficult to use new technologies. When faculty have 0 – 9
years, 10 – 19 years, 20 – 29 years, and 30 – 39 years of teaching experience, we see a median
value of "somewhat disagree." When faculty have 40 – 49 years of teaching experience, we see a
median value of "neither agree nor disagree.". Additionally, we cannot determine the
department's impact in which the faculty teaches on how they find it difficult to use new
technologies.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for declarative knowledge.
Summary. The assumed influence was that the faculty find it difficult to use new
technologies. Since the effect size for the faculty age groups and the number of years of their
teaching experience is greater than zero, we accept these factors of the influence as a need.
Further, since there is no impact of faculty gender, their level of education, and their department
on the influence, we accept these factors of the influence as an asset.
Influence 2
Frequent updates in the technologies used in the organization.
46

Survey results. Using the data analysis output from SPSS, it is seen that the Means Rank
for male faculty is 390.67 and for female faculty is 402.72. The z-value is -0.794, and the p-value
is 0.427. Since the p-value is greater than 0.05, we accept the null hypothesis of equal Mean
Ranks. Therefore, we cannot confirm what the male or female faculty feel about frequent updates
in the technologies used in their organization. The histogram representation of the two groups
confirms that male faculty do not have a significantly different distribution than female faculty
when evaluating how the faculty feel about the frequent updates in the technologies used at their
organizations.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty are impacted due to frequent updates in technologies used at their
organizations. The Chi-Square value is 22.294, and the p-value is 0.001. Since the p-value is less
than 0.05, we reject the null hypothesis of no differences between Mean Ranks. Further, we see
that 2.80% of the mean Rank scores' variability is accounted for by the age groups by calculating
the effect size and using the ordinal regression analysis. The median for analyzing the impact of
frequent updates in technologies varies by age groups. While age groups 20 – 29 years, 30 – 39
years, 40 – 49 years, 50 – 59 years, 60 – 69 years, and 70 – 79 years reported a median value of
"somewhat agree," age group 80 – 89 years reported a median value of "strongly agree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels are impacted due to frequent updates in technologies used at their
organizations, the p-value of 0.601 is greater than 0.05. Therefore, we accept the null hypothesis
of no differences between Mean Ranks. Thus, we cannot determine the impact of education level
on the faculty experience of new developments in the technologies used in their organization.
Further, using the Kruskal-Wallis H Test, we see the effect size of 2.63% that confirms the
47

impact of the number of years of teaching experience on how faculty know about new
development in their organization's technologies. For all faculty, regardless of the years of their
teaching experience, we see a median value of "somewhat agree.". Additionally, we cannot
determine the department's impact in which the faculty teaches on how they know about new
development in their organization's technologies.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for declarative knowledge.
Summary. The assumed influence was that frequent organizational technology updates
impact the faculty. Since the effect size for the faculty age groups and the number of years of
their teaching experience is greater than zero, we accept these factors of the influence as a need.
Further, since there is no impact of faculty gender, their level of education, and their department
on the influence, we accept these factors of the influence as an asset.
Influence 3
Technologies create more problems, requests, or complaints while performing job duties.
Survey results. Based on the data analysis output from SPSS, male faculty have a Means
Rank of 406.12, and female faculty have a Means Rank of 386.23. The z-value is -1.259, and the
p-value is 0.208. Since the p-value is greater than 0.05, we accept the null hypothesis of equal
Mean Ranks. Therefore, we cannot confirm if either male or female faculty feel that technologies
create more problems, requests, or complaints in their jobs. The two groups' histogram
representation confirms that male faculty do not have a significantly different distribution than
female faculty when evaluating technology problems.
48

By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty from different age groups feel that technologies create problems. The
Chi-Square value is 18.555, and the p-value is 0.005. Since the p-value is less than 0.05, we
reject the null hypothesis of no differences between Mean Ranks. Further, by calculating the
effect size, we see 2.33% of the mean Rank scores' variability is accounted for by the age groups.
The median for analyzing how technologies create problems varies by age groups. While age
groups 20 – 29 years, 30 – 39 years, 50 – 59 years, 60 – 69 years, 70 – 79 years, and 80 – 89
years reported a median value of "neither agree nor disagree," age group 40 – 49 years, reported
a median value of "somewhat disagree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels are impacted due to increased problems in technologies used at their
organizations, the p-value of 0.978 is greater than 0.05. Therefore, we accept the null hypothesis
of no differences between Mean Ranks. Thus, we cannot determine the impact of education level
on the increased problems that faculty experience in their organizations' technologies. Further,
using the Kruskal-Wallis H Test, we see the effect size of 3.80% that confirms the impact of the
number of years of teaching experience on how faculty are impacted due to increased technology
problems in their organization. When faculty have 0 – 9 years of teaching experience, we see a
median value of "somewhat disagree." When faculty have 10 – 19 years, 20 – 29 years, 30 – 39
years, and 40 – 49 years of teaching experience, we see a median value of "neither agree nor
disagree.". Additionally, we cannot determine the department's impact in which the faculty
teaches on how they are impacted due to increased technology problems in their organization.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
49

Document Analysis. No documents were analyzed for declarative knowledge.
Summary. The assumed influence was that technologies create more problems for
faculty. Since the effect size for the faculty age groups and the number of years of their teaching
experience is greater than zero, we accept these factors of the influence as a need. Further, since
there is no impact of faculty gender, their level of education, and their department on the
influence, we accept these factors of the influence as an asset.
Procedural Knowledge
The survey sent to faculty members was used to assess the essential technical knowledge
they need to teach students. The results of the survey are used to understand the gaps in
procedural knowledge experienced by faculty members.
Influence 1
Knowing how to update skills for using technology regularly.
Survey results. Using the data analysis output from SPSS, it is seen that the Means Rank
for male faculty is 386.79 and for female faculty is 406.87. The z-value is -1.302, and the p-value
is 0.193. Since the p-value is greater than 0.05, we accept the null hypothesis of equal Mean
Ranks. Therefore, we cannot confirm if either male or female faculty have a higher likelihood of
knowing how to update technology skills. The histogram representation of the two groups
confirms that male faculty do not have a significantly different distribution than female faculty
when analyzing their knowledge of how to update their technology skills.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty from different age groups know how to update their technology skills
regularly. The Chi-Square value is 19.532, and the p-value is 0.003. Since the p-value is less than
0.05, we reject the null hypothesis of no differences between Mean Ranks. Further, we see a
50

2.45% of the mean Rank scores' variability is accounted for by the age groups by calculating the
effect size. The median for analyzing the faculty knowledge to update technologies' skills varies
by age groups. While age groups 20 – 29 years, 30 – 39 years, 40 – 49 years, 50 – 59 years, 60 –
69 years, and 70 – 79 years reported a median value of "somewhat agree," age group 80 – 89
years, reported a median value of "neither agree nor disagree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels are impacted by the need to update their technology skills regularly,
the p-value of 0.386 is greater than 0.05. Therefore, we accept the null hypothesis of no
differences between Mean Ranks. Thus, we cannot determine the impact of education level on
the faculty understanding of how to update technology skills regularly. Further, using the
Kruskal-Wallis H test, we cannot determine the impact of the number of years of teaching
experience on faculty understanding about how to update their technology skills regularly.
Additionally, we cannot determine the department's impact in which the faculty teaches on
knowing how to update their technical skills.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for procedural knowledge.
Summary. The assumed influence was that faculty know how to update their technical
skills. Since the effect size for the faculty age groups is greater than zero, we accept this factor of
the influence as a need. Further, since there is no impact of faculty gender, the number of years
of their teaching experience, their level of education, and their department on the influence, we
accept these factors of the influence as an asset.

51

Influence 2
Knowing how to be a part of technology change and implementation.
Survey results. Based on the data analysis output from SPSS, male faculty have a Means
Rank of 395.91, and female faculty have a Means Rank of 397.13. The z-value is -0.079, and the
p-value is 0.937. Since the p-value is greater than 0.05, we accept the null hypothesis of equal
Mean Ranks. Therefore, we cannot confirm if either male or female faculty know how to
participate in technology change and implementation. The histogram representation of the two
groups confirms that male faculty do not have a significantly different distribution than female
faculty when analyzing the knowledge about participating in technology change and
implementation.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty from different age groups know how to be a part of technology
change and implementation. The Chi-Square value is 49.304, and the p-value is 0.000. Since the
p-value is less than 0.05, we reject the null hypothesis of no differences between Mean Ranks.
Further, we see that 6.19% of the mean Rank scores' variability is accounted for by the age
groups by calculating the effect size. The median for analyzing the faculty knowledge of
knowing how to be part of technology change and implementation varies by age groups. While
age groups 20 – 29 years, 30 – 39 years, 40 – 49 years, and 50 – 59 years reported a median
value of "neither agree nor disagree," age groups 60 – 69 years, 70 – 79 years, and 80 – 89 years
reported a median value of "somewhat agree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels know to be a part of technology change or implementation at their
organization, the p-value of 0.770 is greater than 0.05. Therefore, we accept the null hypothesis
52

of no differences between Mean Ranks. Thus, we cannot determine the impact of education level
on how the faculty with different education levels know how to be a part of technology change
or implementation at their organization. Further, using the Kruskal-Wallis H Test, we see the
effect size of 5.36% that confirms the impact of the number of years of teaching experience on
how faculty with different education levels know to be a part of technology change or
implementation at their organization. When faculty have 0 – 9 years and 10 – 19 years of
teaching experience, we see a median value of "neither agree nor disagree." When faculty have
20 – 29 years, 30 – 39 years, and 40 – 49 years of teaching experience, we see a median value of
"somewhat agree.". Additionally, we cannot determine the department's impact in which the
faculty teaches on knowing how to be a part of technical change and implementation at their
organization.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for procedural knowledge.
Summary. The assumed influence was that the faculty know how to participate in
technology change and implementation. Since the effect size for the faculty age groups and the
number of years of their teaching experience is greater than zero, we accept these factors of the
influence as a need. Further, since there is no impact of faculty gender, their level of education,
and their department on the influence, we accept these factors of the influence as an asset.
Influence 3
Knowing how to deal with problems while using technology for job duties.
Survey results. Using the data analysis output from SPSS, it is seen that the Means Rank
for male faculty is 402.56 and for female faculty is 390.03. The z-value is -0.835, and the p-value
53

is 0.404. Since the p-value is greater than 0.05, we accept the null hypothesis of equal Mean
Ranks. Therefore, we cannot confirm if either male or female faculty have a higher likelihood of
knowing how to deal with problems while using technology for job duties. The histogram
representation of the two groups confirms that male faculty do not have a significantly different
distribution than female faculty when analyzing the knowledge about dealing with problems
while using technology.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty from different age groups know how to deal with problems while
using technology for job duties. The Chi-Square value is 33.146, and the p-value is 0.000. Since
the p-value is less than 0.05, we reject the null hypothesis of no differences between Mean
Ranks. Further, we see that 4.16% of the mean Rank scores' variability is accounted for by the
age groups by calculating the effect size. The median for analyzing the knowledge to deal with
technical problems does not vary by age groups. All age groups reported a median value of
"somewhat agree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels know to deal with technical problems at their organization, the p-value
of 0.069 is greater than 0.05. Therefore, we accept the null hypothesis of no differences between
Mean Ranks. Thus, we cannot determine the impact of education level on how the faculty know
to deal with technology problems at their organization. Further, using the Kruskal-Wallis H Test,
we see the effect size of 1.93% that confirms the impact of the number of years of teaching
experience on how faculty know how to deal with problems while using technology for job
duties. For all faculty, regardless of the years of their teaching experience, we see a median value
54

of "somewhat agree.". Additionally, we cannot determine the department's impact in which the
faculty teaches on knowing how to deal with technical problems in their job duties.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for procedural knowledge.
Summary. The assumed influence was that the faculty know how to deal with technical
problems in their job duties. Since the effect size for the faculty age groups and the number of
years of their teaching experience is greater than zero, we accept these factors of the influence as
a need. Further, since there is no impact of faculty gender, their level of education, and their
department on the influence, we accept these factors of the influence as an asset.
Help-Seeking: Faculty Knowledge
The survey sent to faculty members was used to assess the essential technical knowledge
they need to teach students. The results of the survey are used to understand the gaps in faculty
knowledge about help-seeking.
Influence 1
Encouragement from the organization for knowledge sharing and using new technology.
Survey results. Based on the data analysis output from SPSS, male faculty have a Means
Rank of 385.66, and female faculty have a Means Rank of 408.07. The z-value is -1.459, and the
p-value is 0.145. Since the p-value is greater than 0.05, we accept the null hypothesis of equal
Mean Ranks. Therefore, we cannot confirm if either male or female faculty receive
encouragement from their organization for knowledge sharing and using new technology. The
histogram representation of the two groups confirms that male faculty do not have a significantly
55

different distribution than female faculty when analyzing support from the organization for
knowledge sharing and using technology.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in understanding that faculty from different age groups receive encouragement from
their organization to share and use new technologies. The Chi-Square value is 10.258, and the p-
value is 0.114. Since the p-value is greater than 0.05, we accept the null hypothesis of no
differences between Mean Ranks. Therefore, we cannot confirm if the faculty age group impacts
determining if they receive encouragement from their organization to share and use new
technologies.
Using the Kruskal-Wallis H test for comparing the difference in understanding if the
faculty with different education levels receive encouragement from their organization for
knowledge sharing to help deal with new technology, the p-value of 0.032 is less than 0.05.
Therefore, we reject the null hypothesis of no differences between Mean Ranks. By calculating
the effect size, we see 1.10% of the Mean Rank scores' variability is accounted for by different
education levels of faculty. While faculty with a Bachelor's degree and Post Doctorate as their
highest education reported a median value of "neither agree nor disagree," faculty with a
Master's degree and Doctorate as their highest education reported a median value of "somewhat
agree." Further, using the Kruskal-Wallis H test, we cannot determine the impact of the number
of years of teaching experience on the faculty understanding of receiving encouragement from
their organization for knowledge sharing to help deal with new technology. Additionally, we
cannot determine the department's impact in which the faculty teaches on receiving
encouragement from their organization for knowledge sharing to help deal with new technology.
Interview findings. No interviews were used for this study.
56

Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for faculty knowledge regarding
help-seeking.
Summary. The assumed influence was that the faculty know if their organization
encourages knowledge sharing to help deal with new technology. Since the effect size for the
faculty level of education is greater than zero, we accept this factor of the influence as a need.
Further, since there is no impact of age groups, the number of years of their teaching experience,
faculty gender, and their department on the influence, we accept these factors of the influence as
an asset.
Influence 2
Organizational emphasis on teamwork in dealing with technology problems.
Survey results. Using the data analysis output from SPSS, it is seen that the Means Rank
for male faculty is 384.57 and for female faculty is 409.24. The z-value is -1.562, and the p-value
is 0.118. Since the p-value is greater than 0.05, we accept the null hypothesis of equal Mean
Ranks. Therefore, we cannot confirm if either male or female faculty have a higher likelihood of
confirming if their organization emphasizes teamwork in dealing with technology problems. The
histogram representation of the two groups confirms that male faculty do not have a significantly
different distribution than female faculty when analyzing organizational emphasis on teamwork
in dealing with technology problems.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty from different age groups feel that their organization emphasizes
teamwork in dealing with technology problems. The Chi-Square value is 16.703, and the p-value
is 0.010. Since the p-value is less than 0.05, we reject the null hypothesis of no differences
57

between Mean Ranks. Further, by calculating the effect size, we see 2.10% of the mean Rank
scores' variability is accounted for by the age groups. The median for analyzing how different
faculty feel that their organization emphasizes teamwork in dealing with technology problems
varies by age groups. While age group 50 – 59 years reported a median value of "neither agree
nor disagree," age groups 20 – 29 years, 30 – 39 years, 40 – 49 years, 60 – 69 years, 70 – 79
years, and 80 – 89 years reported a median value of "somewhat agree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels know if their organization encourages teamwork on how to deal with
technical problems, the p-value of 0.123 is greater than 0.05. Therefore, we accept the null
hypothesis of no differences between Mean Ranks. Thus, we cannot determine the impact of
education level on how the faculty knows if their organization encourages teamwork to deal with
technical problems. Further, using the Kruskal-Wallis H test, we cannot determine the impact of
the number of years of teaching experience on how faculty know if their organization encourages
teamwork to deal with technical problems. Additionally, we cannot determine the department's
impact in which the faculty teaches on how the faculty knows if their organization encourages
teamwork to deal with technology.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for faculty knowledge regarding
help-seeking.
Summary. The assumed influence was that the faculty know that their organization
encourages teamwork to deal with technology. Since the effect size for the faculty age groups is
greater than zero, we accept this factor of the influence as a need. Further, since there is no
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impact of faculty gender, the number of years of their teaching experience, their level of
education, and their department on the influence, we accept these factors of the influence as an
asset.
Influence 3
Ability to reach other employees in the organization using technology.
Survey results. Based on the data analysis output from SPSS, male faculty have a Means
Rank of 383.87, and female faculty have a Means Rank of 409.99. The z-value is -1.698, and the
p-value is 0.090. Since the p-value is greater than 0.05, we accept the null hypothesis of equal
Mean Ranks. Therefore, we cannot confirm if either male or female faculty know their ability to
reach other employees in their organizations using technology. The histogram representation of
the two groups confirms that male faculty do not have a significantly different distribution than
female faculty when analyzing their ability to reach other employees in their organizations using
technology.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty from different age groups feel about their ability to reach other
organization employees using technology. The Chi-Square value is 8.791, and the p-value is
0.186. Since the p-value is greater than 0.05, we accept the null hypothesis of no differences
between Mean Ranks. Therefore, we cannot confirm if the faculty age group impacts their ability
to reach other organization employees using technology.
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels know if technologies enable them to reach other employees, the p-
value of 0.123 is greater than 0.05. Therefore, we accept the null hypothesis of no differences
between Mean Ranks. Thus, we cannot determine the impact of education level on how the
59

faculty know if technologies enable them to reach other employees. Further, using the Kruskal-
Wallis H test, we cannot determine the impact of the number of years of teaching experience on
how faculty know if technologies enable them to reach other employees. Additionally, we cannot
determine the department's impact in which the faculty teaches on how the faculty knows if their
organization encourages teamwork to deal with technology.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for faculty knowledge regarding
help-seeking.
Summary. The assumed influence was that the faculty know their ability to reach other
organization employees using technology. None of the factors of the influence can be accepted
as a need. Further, since there is no impact of faculty gender, age groups, the number of years of
their teaching experience, their level of education, and their department on the influence, we
accept all the factors of the influence as an asset.
Results and Findings for Motivation Causes
The survey sent to the faculty at SCU included ten questions focused on assessing the
motivation influences on technostress creators and inhibitors. Specifically, the emphasis is on
self-efficacy, expectancy-value, choice, persistence, effort, performance, job satisfaction, and
managerial support for faculty members. The following section discusses the finding associated
with the motivation influences on technostress.

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Self-Efficacy
The survey sent to faculty members was used to assess the importance of self-efficacy for
faculty members' motivation. The results of the survey are used to understand the gaps in self-
efficacy experienced by faculty members.
Influence 1
Ability to learn technology quickly.
Survey results. Using the Mann-Whitney U Test, we compare two groups, male faculty
and female faculty. Based on the data analysis output from SPSS, male faculty have a Means
Rank of 397.88, and female faculty have a Means Rank of 395.02. The z-value is -1.188, and the
p-value is 0.851. Since the p-value is greater than 0.05, we accept the null hypothesis of equal
Mean Ranks. Therefore, we cannot confirm if either male or female faculty have a significantly
different ability to learn technology quickly. The histogram representation of the two groups
confirms that male faculty do not have a significantly different distribution than female faculty
when analyzing their ability to learn technology quickly.
Kruskal-Wallis H Test is used to analyze the non-normally distributed data since we
compare multiple age groups next. By comparing the Mean Rank values, there is a difference in
how faculty from different age groups can quickly learn technology. The Chi-Square value is
97.503, and the p-value is 0.000. Since the p-value is less than 0.05, we reject the null hypothesis
of no differences between Mean Ranks. Further, by calculating the effect size and using the
ordinal regression analysis, we see a 12.23% of the mean Rank scores' variability is accounted
for by the age groups. The median for analyzing the ability to learn technologies quickly varies
by age group. Faculty in the age group of 20 – 29 years reported a median value of "strongly
agree." While age groups 30 – 39 years, 40 – 49 years, 50 – 59 years, 60 – 69 years, and 70 – 79
61

years reported a median value of "somewhat agree," age group 80 – 89 years reported a median
value of "neither agree nor disagree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels have a different ability to learn technologies quickly, the p-value of
0.031 is less than 0.05. Therefore, we reject the null hypothesis of no differences between Mean
Ranks. By calculating the effect size, we see 1.10% of the Mean Rank scores' variability is
accounted for by different education levels of faculty. Regardless of their level of education, all
faculty reported a median value of "somewhat agree." Further, using the Kruskal-Wallis H Test
and ordinal regression analysis, we see the effect size of 7.94% that confirms the impact of the
number of years of teaching experience on how faculty have a different ability to learn
technology quickly. When faculty have 0 – 9 years, 10 – 19 years, 20 – 29 years, and 30 – 39
years of teaching experience, we see a median value of "somewhat agree." When faculty have 40
– 49 years of teaching experience, we see a median value of "neither agree nor disagree.".
Additionally, we cannot determine the department's impact in which the faculty teaches on their
ability to learn technology quickly.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for declarative knowledge.
Summary. The assumed influence was that the faculty find it challenging to learn
technology quickly. Since the effect size for the faculty age groups, the number of years of their
teaching experience, and education level, is greater than zero, we accept these factors of the
influence as a need. Further, since there is no impact of faculty gender and their department on
the influence, we accept these factors of the influence as an asset.
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Expectancy Value
The survey sent to faculty members was used to assess faculty motivation's dependence
on their expectancies and values. The survey results are used to understand the gaps in
expectancies and values experienced by faculty members.
Influence 1
Ability to be better at doing work duties.
Survey results. Using the Mann-Whitney U Test, we compare two groups, male faculty
and female faculty. Based on the data analysis output from SPSS, male faculty have a Means
Rank of 385.90, and female faculty have a Means Rank of 407.82. The z-value is -1.542, and the
p-value is 0.123. Since the p-value is greater than 0.05, we accept the null hypothesis of equal
Mean Ranks. Therefore, we cannot confirm if either male or female faculty have a different
ability to try to be better at doing their work duties. The histogram representation of the two
groups confirms that male faculty do not have a significantly different distribution than female
faculty when analyzing their ability to perform their work duties better.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in faculty ability to be better at performing work duties. The Chi-Square value is
12.652, and the p-value is 0.049. Since the p-value is less than 0.05, we reject the null hypothesis
of no differences between Mean Ranks. Further, we see that 1.59% of the mean Rank scores'
variability is accounted for by the age groups by calculating the effect size and using the ordinal
regression analysis. The median for analyzing the faculty's ability to perform work duties does
not vary by age group. All age groups reported a median value of "strongly agree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels have a different ability to be better at performing work duties, the p-
63

value of 0.146 is greater than 0.05. Therefore, we accept the null hypothesis of no differences
between Mean Ranks. Thus, we cannot determine the impact of education level on the faculty's
ability to perform work duties better. Further, using the Kruskal-Wallis H test, we cannot
determine the impact of the number of years of teaching experience on the faculty's ability to
perform work duties. For all faculty, regardless of the years of their teaching experience, we see
a median value of "somewhat agree." Additionally, we cannot determine the department's impact
in which the faculty teaches on the faculty's ability to be better at performing work duties.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for declarative knowledge.
Summary. The assumed influence was that faculty face challenges to perform their work
duties. Since the effect size for the faculty age groups is greater than zero, we accept this factor
of the influence as a need. Further, since there is no impact of faculty gender, their level of
education, the number of years of teaching experience, and their department on the influence, we
accept these factors of the influence as an asset.
Influence 2
Using computer skills to help with job duties.
Survey results. Using the data analysis output from SPSS, it is seen that the Means Rank
for male faculty is 386.62 and for female faculty is 407.05. The z-value is -1.501, and the p-value
is 0.133. Since the p-value is greater than 0.05, we accept the null hypothesis of equal Mean
Ranks. Therefore, we cannot confirm if either male or female faculty have a different ability to
use computer skills to help with their work duties. The histogram representation of the two
64

groups confirms that male faculty do not have a significantly different distribution than female
faculty when analyzing their computer skills that aid their work duties.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in faculty ability to use computer skills to help with their work duties. The Chi-
Square value is 13.157, and the p-value is 0.041. Since the p-value is less than 0.05, we reject the
null hypothesis of no differences between Mean Ranks. Further, we see that 1.65% of the mean
Rank scores' variability is accounted for by the age groups by calculating the effect size and
using the ordinal regression analysis. The median for analyzing the faculty's ability to use
computer skills to help with their job duties does not vary by age group. All age groups reported
a median value of "strongly agree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels have a different ability to use computer skills to help with their work
duties, the p-value of 0.384 is greater than 0.05. Therefore, we accept the null hypothesis of no
differences between Mean Ranks. Thus, we cannot determine the impact of education level on
the faculty's ability to use computer skills to help with their work duties. Further, using the
Kruskal-Wallis H test, we cannot determine the impact of the number of years of teaching
experience on the faculty's ability to use computer skills to help with their work duties. For all
faculty, regardless of the years of their teaching experience, we see a median value of "somewhat
agree." Additionally, all faculty except nursing faculty reported a median value of "strongly
agree" when determining the department's impact in which the faculty teaches on their ability to
use computer skills to help with their work duties. Nursing faculty reported a median value of
"somewhat agree."
Interview findings. No interviews were used for this study.
65

Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for declarative knowledge.
Summary. The assumed influence was the difficulty of using computer skills to perform
work duties. Since the effect size for the faculty age groups, the department in which they teach,
and the number of years of their teaching experience is greater than zero, we accept these factors
of the influence as a need. Further, since there is no impact of faculty gender and their level of
education on the influence, we accept these factors of the influence as an asset.
Choice
The survey sent to faculty members was used to assess faculty motivation's dependence
on their choices. The survey results are used to understand the gaps in choices opted by faculty
members.
Influence 1
Willingness to change work habits to adapt to new technologies.
Survey results. Based on the data analysis output from SPSS, male faculty have a Means
Rank of 365.19, and female faculty have a Means Rank of 429.94. The z-value is -4.369, and the
p-value is 0.000. Since the p-value is less than 0.05, we reject the null hypothesis of equal Mean
Ranks. Therefore, we can confirm that female faculty have a higher willingness to change work
habits to adapt to new technologies than male faculty. Using ANOVA for the Mann-Whitney U
Test, the eta squared effect size is 0.024, which means that 2.41% of the Mean Ranks' variability
is accounted for by the gender of the faculty.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in faculty willingness to change work habits to adapt to new technologies. The Chi-
Square value is 19.055, and the p-value is 0.004. Since the p-value is less than 0.05, we reject the
66

null hypothesis of no differences between Mean Ranks. Further, by calculating the effect size,
we see 2.39% of the mean Rank scores' variability is accounted for by the age groups. The
median for analyzing faculty willingness to change work habits to adapt to new technologies
varies by age groups. While the age group 20 – 29 years reported a median value of "strongly
agree," age groups 30 – 39 years, 40 – 49 years, 50 – 59 years, 60 – 69 years, 70 – 79 years, and
80 – 89 years reported a median value of "somewhat agree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels have a different willingness to change work habits to adapt to new
technologies, the p-value of 0.000 is less than 0.05. Therefore, we reject the null hypothesis of no
differences between Mean Ranks. By calculating the effect size, we see 0.38% of the Mean Rank
scores' variability is accounted for by different education levels of faculty. While faculty with a
Bachelor's degree as their highest education level reported a median value of "strongly agree,"
faculty with Masters, Doctorate, and Post Doctorate as their highest level of education reported a
median value of "somewhat agree." Further, using the Kruskal-Wallis H Test, we see the effect
size of 2.54% that confirms the impact of the number of years of teaching experience on the
faculty willingness to change work habits to adapt to new technologies. Regardless of the
number of years of teaching experience, we see a median value of "somewhat agree" for all
faculty. Additionally, we cannot determine the department's impact in which the faculty teaches
on their willingness to change work habits to adapt to new technologies.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for declarative knowledge.
67

Summary. The assumed influence was that faculty might not be willing to adapt to new
technologies. Since the effect size for the faculty gender, age groups, education level, and the
number of years of their teaching experience is greater than zero, we accept these factors of the
influence as a need. Further, since there is no impact of their department on the influence, we
accept these factors of the influence as an asset.
Persistence
The survey sent to faculty members was used to assess the persistence demonstrated by
faculty. The survey results are used to understand the gaps in the persistence factor for faculty
motivation.
Influence 1
Striving to make work enjoyable.
Survey results. Using the data analysis output from SPSS, it is seen that the Means Rank
for male faculty is 372.81 and for female faculty is 421.80. The z-value is -3.482, and the p-value
is 0.000. Since the p-value is less than 0.05, we reject the null hypothesis of equal Mean Ranks.
Therefore, we can confirm that female faculty have a higher ability to strive to make work
enjoyable. Using ANOVA for the Mann-Whitney U Test, the eta squared effect size is 0.015,
which means that 1.53% of the Mean Ranks' variability is accounted for by the gender of the
faculty.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty strive to make work enjoyable. The Chi-Square value is 7.611, and
the p-value is 0.268. Since the p-value is greater than 0.05, we accept the null hypothesis of no
differences between Mean Ranks. The median for analyzing the faculty's ability to strive to make
68

work enjoyable does not vary by age groups. All age groups reported a median value of "strongly
agree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels strive to make work enjoyable, the p-value of 0.002 is less than 0.05.
Therefore, we reject the null hypothesis of no differences between Mean Ranks. By calculating
the effect size, we see 1.92% of the Mean Rank scores' variability is accounted for by different
faculty education levels. Regardless of their level of education, all faculty reported a median
value of "strongly agree." Further, using the Kruskal-Wallis H test, we cannot determine the
impact of the number of years of teaching experience on the faculty's ability to strive to make
work enjoyable. Additionally, we cannot determine the department's impact in which the faculty
teaches on their ability to strive to make work enjoyable.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for procedural knowledge.
Summary. The assumed influence was that faculty strive to make their work enjoyable.
Since the effect size for the faculty gender and their education level is greater than zero, we
accept these factors of the influence as a need. Further, since there is no impact of faculty gender,
the number of years of their teaching experience, age group, and their department on the
influence, we accept these factors of the influence as an asset.
Effort
The survey sent to faculty members was used to assess the effort demonstrated by
faculty. The survey results are used to understand the gaps in the effort factor for faculty
motivation.
69

Influence 1
Willingness to spend more time to study and upgrade technology skills.
Survey results. Based on the data analysis output from SPSS, male faculty have a Means
Rank of 369.32, and female faculty have a Means Rank of 425.53. The z-value is -3.696, and the
p-value is 0.000. Since the p-value is less than 0.05, we reject the null hypothesis of equal Mean
Ranks. Therefore, we can confirm that female faculty have a higher willingness to spend more
time studying and upgrading their technical skills. Using ANOVA for the Mann-Whitney U Test,
the eta squared effect size is 0.017, which means that 1.73% of the Mean Ranks' variability is
accounted for by the gender of the faculty.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in faculty willingness to spend more time to study and upgrade their technical skills.
The Chi-Square value is 13.614, and the p-value is 0.034. Since the p-value is less than 0.05, we
reject the null hypothesis of no differences between Mean Ranks. Further, we see that 1.71% of
the mean Rank scores' variability is accounted for by the age groups by calculating the effect size
and using the ordinal regression analysis. The median for analyzing the faculty willingness to
spend more time studying and upgrading their technical skills does not vary by age groups. All
age groups reported a median value of "somewhat agree."
Using the Kruskal-Wallis H test to compare how faculty with different education levels
demonstrate different willingness to spend time studying and upgrading their technical skills, the
p-value of 0.000 is less than 0.05. Therefore, we reject the null hypothesis of no differences
between Mean Ranks. By calculating the effect size, we see 3.15% of the Mean Rank scores'
variability is accounted for by different faculty education levels. While faculty with a Bachelor's
degree as their highest education level reported a median value of "strongly agree," faculty with
70

Masters, Doctorate, and Post Doctorate as their highest level of education reported a median
value of "somewhat agree." Further, using the Kruskal-Wallis H test, we cannot determine the
impact of the number of years of teaching experience on the faculty's willingness to spend time
studying and upgrading their technical skills. Additionally, we cannot determine the department's
impact in which the faculty teaches on their desire to spend time researching and upgrading their
technical skills.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for procedural knowledge.
Summary. The assumed influence was that the faculty might not be willing to study and
upgrade their technical skills. Since the effect size for the faculty age groups, gender, and
education level is greater than zero, we accept these factors of the influence as a need. Further,
since there is no impact of the number of years of faculty teaching experience, and their
department on the influence, we accept these factors of the influence as an asset.
Performance
The survey sent to faculty members was used to assess the relationship between faculty
motivation and job performance. The survey results are used to understand the gaps in job
performance demonstrated by faculty.
Influence 1
Higher workload due to increased technical complexity.
Survey results. Using the data analysis output from SPSS, it is seen that the Means Rank
for male faculty is 374.38 and for female faculty is 420.12. The z-value is -2.902, and the p-value
is 0.004. Since the p-value is less than 0.05, we reject the null hypothesis of equal Mean Ranks.
71

Therefore, we can confirm that female faculty experience a higher workload due to increased
technical complexity. Using ANOVA for the Mann-Whitney U Test, the eta squared effect size
is 0.010, which means that 1.06% of the Mean Ranks' variability is accounted for by the gender
of the faculty.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty experience a higher workload due to increased technical complexity.
The Chi-Square value is 10.594, and the p-value is 0.102. Since the p-value is greater than 0.05,
we accept the null hypothesis of no differences between Mean Ranks. The median for analyzing
the faculty experience of higher workload due to increased technical complexity varies by age
groups. While age groups 20 – 29 years, 40 – 49 years, 50 – 59 years, 60 – 69 years, 70 – 70
years, and 80 – 89 years reported a median value of "somewhat agree," age group, 30 – 39 years
reported a median value of "neither agree nor disagree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels may experience a higher workload due to increased technical
complexity, the p-value of 0.147 is greater than 0.05. Therefore, we accept the null hypothesis of
no differences between Mean Ranks. Thus, we cannot determine the impact of education level on
how faculty experience a higher workload due to increased technical complexity. Further, using
the Kruskal-Wallis H Test, we see the effect size of 1.97% that confirms the impact of the
number of years of teaching experience on the faculty's higher workload due to increased
technical complexity. For all faculty, regardless of the years of their teaching experience, we see
a median value of "somewhat agree.". Additionally, faculty from Engineering, Library and
Information Science and Public Affairs reported a median value of "neither agree nor disagree"
when evaluating the impact of the department in which the faculty teaches on their experience of
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a higher workload due to technical complexity. Faculty from other departments reported a
median value of "somewhat agree."
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for procedural knowledge.
Summary. The assumed influence was that the faculty might experience a higher
workload due to technical complexity. Since the effect size for the faculty gender, their
department, and the number of years of their teaching experience is greater than zero, we accept
these factors of the influence as a need. Further, since there is no impact of faculty age groups
and their level of education on the influence, we accept these factors of the influence as an asset.
Influence 2
Faculty need to work faster due to technology.
Survey results. Using the data analysis output from SPSS, it is seen that the Means Rank
for male faculty is 375.07 and for female faculty is 419.39. The z-value is -2.804, and the p-value
is 0.005. Since the p-value is less than 0.05, we reject the null hypothesis of equal Mean Ranks.
Therefore, we can confirm that female faculty have a higher likelihood of working faster due to
technology than male faculty. Using ANOVA for the Mann-Whitney U Test, the eta squared
effect size is 0.009, which means that 0.99% of the Mean Ranks' variability is accounted for by
the gender of the faculty.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in the faculty need to work faster due to technology. The Chi-Square value is
14.680, and the p-value is 0.023. Since the p-value is less than 0.05, we reject the null hypothesis
of no differences between Mean Ranks. Further, by calculating the effect size, we see 1.84% of
73

the mean Rank scores' variability is accounted for by the age groups. The median for analyzing
the faculty need to work faster due to technology does not vary by age group. All age groups
reported a median value of "neither agree nor disagree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels know if they are forced to work faster due to the use of technology, the
p-value of 0.604 is greater than 0.05. Therefore, we accept the null hypothesis of no differences
between Mean Ranks. Thus, we cannot determine the impact of education level on how the
faculty know if they are forced to work faster due to technology use. Further, using the Kruskal-
Wallis H test, we cannot determine the impact of the number of years of teaching experience on
the faculty's need to work faster due to technology use. Additionally, we cannot determine the
department's impact in which the faculty teaches on the need to work faster due to technology
use.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for faculty knowledge regarding
help-seeking.
Summary. The assumed influence was that the faculty need to work faster due to the use
of technology. Since the effect size for the faculty gender and age groups is greater than zero, we
accept this factor of the influence as a need. Further, since there is no impact of the number of
years of teaching experience, faculty level of education, and their department on the influence,
we accept these factors of the influence as an asset.

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Job Satisfaction
The survey sent to faculty members was used to assess the relationship between faculty
motivation and job satisfaction. The survey results are used to understand the gaps in job
satisfaction demonstrated by faculty.
Influence 1
Faculty are enjoying their job.
Survey results. Based on the data analysis output from SPSS, male faculty have a Means
Rank of 400.92, and female faculty have a Means Rank of 391.78. The z-value is -0.657, and the
p-value is 0.511. Since the p-value is greater than 0.05, we accept the null hypothesis of equal
Mean Ranks. Therefore, we cannot confirm if either male or female faculty are more likely to
feel that they enjoy their jobs. The histogram representation of the two groups confirms that male
faculty do not have a significantly different distribution than female faculty when analyzing the
survey response for faculty enjoying their job.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty enjoy their job. The Chi-Square value is 16.729, and the p-value is
0.010. Since the p-value is less than 0.05, we reject the null hypothesis of no differences between
Mean Ranks. Further, by calculating the effect size, we see 2.10% of the mean Rank scores'
variability is accounted for by the age groups. The median for analyzing how faculty enjoy their
job does not vary by age group. All age groups reported a median value of "strongly agree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels know if they enjoy their job, the p-value of 0.319 is greater than 0.05.
Therefore, we accept the null hypothesis of no differences between Mean Ranks. Thus, we
cannot determine the impact of education level on how the faculty knows if they enjoy their job.
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Further, using the Kruskal-Wallis H Test and ordinal regression analysis, we see the effect size
of 2.78% that confirms the impact of the number of years of teaching experience on how faculty
know if they enjoy their job. For all faculty, regardless of the years of their teaching experience,
we see a median value of "strongly agree.". Additionally, we cannot determine the department's
impact in which the faculty teaches on their ability to enjoy their job.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for faculty knowledge regarding
help-seeking.
Summary. The assumed influence was that the faculty know how to enjoy their job.
Since the effect size for the faculty age group and the number of years of their teaching
experience is greater than zero, we accept these factors of the influence as a need. Further, since
there is no impact of faculty gender, their level of education, and their department on the
influence, we accept these factors of the influence as an asset.
Managerial Support
The survey sent to faculty members was used to assess the relationship between faculty
motivation and managerial support. The survey results are used to understand the gaps in
managerial support experienced by faculty.
Influence 1
Feeling supported by managers to perform at the highest level.
Survey results. Based on the data analysis output from SPSS, male faculty have a Means
Rank of 391.31, and female faculty have a Means Rank of 402.04. The z-value is -0.687, and the
p-value is 0.492. Since the p-value is greater than 0.05, we accept the null hypothesis of equal
76

Mean Ranks. Therefore, we cannot confirm if either male or female faculty feel more supported
by their managers to perform at the highest level. The two groups' histogram representation
confirms that male faculty do not have a significantly different distribution than female faculty
when analyzing the survey response for managerial support.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty feel supported by their managers to perform at the highest level. The
Chi-Square value is 15.048, and the p-value is 0.020. Since the p-value is less than 0.05, we
reject the null hypothesis of no differences between Mean Ranks. Further, by calculating the
effect size, we see 1.89% of the mean Rank scores' variability is accounted for by the age groups.
The median for analyzing how faculty feel supported by their managers to perform at the highest
level does not vary by age group. All age groups reported a median value of "somewhat agree."
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels feel supported by their managers to perform at the highest level, the p-
value of 0.179 is greater than 0.05. Therefore, we accept the null hypothesis of no differences
between Mean Ranks. Thus, we cannot determine the impact of education level on whether the
faculty feel supported by their managers to perform at the highest level. Further, using the
Kruskal-Wallis H test, we cannot determine the impact of the number of years of teaching
experience on how faculty feel supported by their managers to perform at the highest level.
Additionally, we cannot determine the department's impact in which the faculty teaches on how
faculty feel supported by their managers to perform at the highest level.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
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Document Analysis. No documents were analyzed for faculty knowledge regarding
help-seeking.
Summary. The assumed influence was that the faculty do not feel supported by their
managers to perform at their highest level. Since the effect size for the faculty age group is
greater than zero, we accept this factor of the influence as a need. Further, since there is no
impact of faculty gender, the number of years of their teaching experience, level of education,
and their department on the influence, we accept these factors of the influence as an asset.
Results and Findings for Organizational Causes
The survey sent to the faculty at SCU included nine questions focused on assessing the
organizational influences on technostress creators and inhibitors. Specifically, the emphasis is on
the training, proactive and reactive responses, positive technologies, performance, help desk
support, and organizational support for faculty members. The following section discusses the
finding associated with the organizational influences on technostress.
Training
The survey sent to faculty members was used to assess the training needs of faculty. The
results of the survey are used to understand the gaps in the training needed for faculty members.
Influence 1
End-user training before the introduction of new technology.
Survey results. Since the data is obtained using the Likert scale, it is not normally
distributed, and the use of a non-parametric statistic like the Mann-Whitney U Test is needed.
Also, because we are comparing only two groups, male faculty and female faculty, Mann-
Whitney U Test is used instead of the Kruskal-Wallis H Test. Based on the data analysis output
from SPSS, male faculty have a Means Rank of 382.67, and female faculty have a Means Rank
78

of 411.27. The z-value is -1.844, and the p-value is 0.065. Since the p-value is greater than 0.05,
we accept the null hypothesis of equal Mean Ranks. Therefore, we cannot confirm if either male
or female faculty feel that their organization provides end-user training before introducing new
technologies. The two groups' histogram representation confirms that male faculty do not have a
significantly different distribution than female faculty when analyzing organizational training
availability before introducing new technologies.
Kruskal-Wallis H Test is used to analyze the non-normally distributed data since we
compare multiple age groups next. By comparing the Mean Rank values, there is a difference in
how faculty from different age groups understand organizational training availability before
introducing new technologies. The Chi-Square value is 8.954, and the p-value is 0.176. Since the
p-value is greater than 0.05, we accept the null hypothesis of no differences between Mean
Ranks. Thus, we cannot determine the impact of faculty age groups on a different understanding
of organizational training availability.
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels have a different understanding of organizational training availability,
the p-value of 0.587 is greater than 0.05. Therefore, we accept the null hypothesis of no
differences between Mean Ranks. Thus, we cannot determine the impact of education level on
how faculty may have a different understanding of organizational training availability. Further,
using the Kruskal-Wallis H Test and ordinal regression analysis, we cannot determine the impact
of the number of years of teaching experience on faculty understanding of organizational training
availability. Additionally, we cannot determine the department's impact in which the faculty
teaches on faculty understanding of organizational training availability.
Interview findings. No interviews were used for this study.
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Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for declarative knowledge.
Summary. The assumed influence was that the faculty might not have a good
understanding of organizational training availability. Since the effect size for none of the factors
of influence is greater than zero, we cannot determine any factors of the influence as a need.
Further, since there is no impact of faculty age group, gender, their level of education, their
department, and the number of years of teaching experience on the influence, we accept these
factors of the influence as an asset.
Influence 2
Setting a schedule for completing assigned training.
Survey results. Using the data analysis output from SPSS, it is seen that the Means Rank
for male faculty is 382.09 and for female faculty is 411.89. The z-value is -1.967, and the p-value
is 0.049. Since the p-value is less than 0.05, we reject the null hypothesis of equal Mean Ranks.
Therefore, we can confirm that female faculty have a higher likelihood of setting their schedule
for completing assigned training than male faculty. Using ANOVA for the Mann-Whitney U
Test, the eta squared effect size is 0.007, which means that 0.73% of the Mean Ranks' variability
is accounted for by the gender of the faculty.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty can set their schedule for completing assigned training. The Chi-
Square value is 6.689, and the p-value is 0.351. Since the p-value is greater than 0.05, we accept
the null hypothesis of no differences between Mean Ranks. Thus, we cannot determine the
impact of faculty age groups on their ability to set their schedule for completing assigned
training.
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Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels have a different ability to set their schedule for completing assigned
training, the p-value of 0.209 is greater than 0.05. Therefore, we accept the null hypothesis of no
differences between Mean Ranks. Thus, we cannot determine the impact of education level on
the faculty's ability to set their schedule for completing assigned training. Further, using the
Kruskal-Wallis H test, we cannot determine the impact of the number of years on teaching
experience on faculty ability to set their schedule for completing assigned training. Additionally,
we cannot determine the department's impact in which the faculty teaches on how the faculty's
ability to set their schedule for completing assigned training.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for declarative knowledge.
Summary. The assumed influence was that faculty find it difficult to set their schedule
for completing assigned training. Since the effect size for the faculty gender is greater than zero,
we accept this factor of the influence as a need. Further, since there is no impact of faculty age
group, the number of years of teaching experience, their level of education, and their department
on the influence, we accept these factors of the influence as an asset.
Proactive and Reactive Responses
The survey sent to faculty members was used to assess the proactive and reactive
responses of faculty. The results of the survey are used to understand the gaps in proactive and
reactive responses of faculty.
Influence 1
The organization provides clear guidelines to end-users for using technology.
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Survey results. Based on the data analysis output from SPSS, male faculty have a Means
Rank of 378.37, and female faculty have a Means Rank of 415.86. The z-value is -2.395, and the
p-value is 0.017. Since the p-value is less than 0.05, we reject the null hypothesis of equal Mean
Ranks. Therefore, we can confirm that female faculty have a higher likelihood of recognizing
that their organization provides clear guidelines to end-users for using technology. Using
ANOVA for the Mann-Whitney U Test, the eta squared effect size is 0.007, which means that
0.73% of the Mean Ranks' variability is accounted for by the gender of the faculty.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty from different age groups recognize that their organization provides
clear guidelines to end-users for using technology. The Chi-Square value is 6.557, and the p-
value is 0.364. Since the p-value is greater than 0.05, we accept the null hypothesis of no
differences between Mean Ranks. Thus, we cannot determine the impact of faculty age groups
on recognizing that their organization provides clear guidelines for using technology.
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels have a different understanding of organizational guidelines to use
technology, the p-value of 0.174 is greater than 0.05. Therefore, we accept the null hypothesis of
no differences between Mean Ranks. Thus, we cannot determine the impact of education level on
the faculty understanding of the availability of organizational guidelines for using technology.
Further, using the Kruskal-Wallis H test, we cannot determine the impact of the number of years
of teaching experience on faculty understanding of organizational guidelines to use technology.
Additionally, we cannot determine the department's impact in which the faculty teaches on
faculty understanding of organizational guidelines to use technology.
Interview findings. No interviews were used for this study.
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Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for declarative knowledge.
Summary. The assumed influence was that faculty might find it challenging to
understand organizational guidelines to use technology. Since the effect size for the faculty
gender is greater than zero, we accept this factor of the influence as a need. Further, since there is
no impact of faculty age groups, the number of years of their teaching experience, their level of
education, and their department on the influence, we accept these factors of the influence as an
asset.
Influence 2
Faculty are encouraged to try out new technologies.
Survey results. Using the data analysis output from SPSS, it is seen that the Means Rank
for male faculty is 385.48 and for female faculty is 408.27. The z-value is -1.486, and the p-value
is 0.137. Since the p-value is greater than 0.05, we accept the null hypothesis of equal Mean
Ranks. Therefore, we cannot confirm if either male or female faculty have a higher likelihood of
feeling encouraged by their organization to try new technologies. The histogram representation
of the two groups confirms that male faculty do not have a significantly different distribution
than female faculty when analyzing their understanding of feeling encouraged to try new
technologies.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty from different age groups feel encouraged to try new technologies.
The Chi-Square value is 4.176, and the p-value is 0.653. Since the p-value is greater than 0.05,
we accept the null hypothesis of no differences between Mean Ranks. Thus, we cannot determine
the impact of faculty age groups on whether they feel encouraged to try new technologies.
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Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels feel encouraged to try new technologies, the p-value of 0.271 is greater
than 0.05. Therefore, we accept the null hypothesis of no differences between Mean Ranks.
Thus, we cannot determine the impact of education level on whether they feel encouraged to try
new technologies. Further, using the Kruskal-Wallis H test, we cannot determine the impact of
the number of years of teaching experience on faculty engagement to try new technologies.
Additionally, we cannot determine the department's impact in which the faculty teaches on
faculty engagement to try new technologies.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for procedural knowledge.
Summary. The assumed influence was the difficulty of faculty engagement to try new
technology. Since the effect size for none of the factors of influence is greater than zero, we
cannot determine any factors of the influence as a need. Further, since there is no impact of
faculty gender, age group, the number of years of their teaching experience, their level of
education, and their department on the influence, we accept these factors of the influence as an
asset.
Positive Technologies
The survey sent to faculty members was used to assess the use of positive technologies in
education. The results of the survey are used to understand the gaps in the use of positive
technologies.
Influence 1
Organization commitment to adopt technologies that are easy to use and learn.
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Survey results. Based on the data analysis output from SPSS, male faculty have a Means
Rank of 388.63, and female faculty have a Means Rank of 404.90. The z-value is -1.040, and the
p-value is 0.298. Since the p-value is greater than 0.05, we accept the null hypothesis of equal
Mean Ranks. Therefore, we cannot confirm if either male or female faculty understand the
organization’s commitment to adopt technologies that are easy to use and learn. The histogram
representation of the two groups confirms that male faculty do not have a significantly different
distribution than female faculty when analyzing the adoption of technologies that are easy to use
and learn.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty from different age groups recognize their organization’s commitment
to adopt technologies that are easy to use and learn. The Chi-Square value is 6.294, and the p-
value is 0.391. Since the p-value is greater than 0.05, we accept the null hypothesis of no
differences between Mean Ranks. Thus, we cannot determine the faculty age group's impact on
whether they recognize their organization’s commitment to adopt technologies that are easy to
use and learn.
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels know about their organization’s commitment to adopt technologies
that are easy to use and learn, the p-value of 0.070 is greater than 0.05. Therefore, we accept the
null hypothesis of no differences between Mean Ranks. Thus, we cannot determine the impact of
education level on how the faculty with different education levels know about their
organization’s commitment to adopt technologies that are easy to use and learn. Further, using
the Kruskal-Wallis H test, we cannot determine the impact of the number of years of teaching
experience on faculty understanding of organizational commitment to adopt new technologies
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that are easy to use and learn. Additionally, we cannot determine the department's impact in
which the faculty teaches on their understanding of organizational commitment to adopt new
technologies that are easy to use and learn.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for procedural knowledge.
Summary. The assumed influence was that the faculty might not understand
organizational commitment to adopt new technologies that are easy to use and learn. Since the
effect size for none of the factors of influence is greater than zero, we cannot determine any
factors of the influence as a need. Further, since there is no impact of faculty gender, age group,
the number of years of their teaching experience, their level of education, and their department
on the influence, we accept these factors of the influence as an asset.
Help Desk Support
The survey sent to faculty members was used to assess the use of helpdesk support by
faculty. The results of the survey are used to understand the gaps in the use of helpdesk support.
Influence 1
Help Desk answers questions about the technology used in the organization.
Survey results. Using the data analysis output from SPSS, it is seen that the Means Rank
for male faculty is 381.59 and for female faculty is 412.42. The z-value is -1.998, and the p-value
is 0.046. Since the p-value is less than 0.05, we reject the null hypothesis of equal Mean Ranks.
Therefore, we can confirm that female faculty feel that their organization’s help desk answers
questions about the technology used at their organization. Using ANOVA for the Mann-Whitney
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U Test, the eta squared effect size is 0.005, which means that 0.50% of the Mean Ranks'
variability is accounted for by the gender of the faculty.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty from different age groups know that their organization’s help desk
can answer questions about technology. The Chi-Square value is 22.064, and the p-value is
0.001. Since the p-value is less than 0.05, we reject the null hypothesis of no differences between
Mean Ranks. Further, we see that 2.77% of the mean Rank scores' variability is accounted for by
the age groups by calculating the effect size. The median for analyzing how different faculty feel
that their organization’s help desk answers questions about the organizational technology varies
by age groups. While faculty from age groups 20 – 29 years, 30 – 39 years, 40 – 49 years, 50 –
59 years, 60 – 69 years, and 70 – 79 years reported a median value of “somewhat agree,” faculty
from age group 80 – 89 years reported a median value of “strongly agree.”
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels know that their organization’s help desk can answer questions about
technology, the p-value of 0.126 is greater than 0.05. Therefore, we accept the null hypothesis of
no differences between Mean Ranks. Thus, we cannot determine the impact of education level on
whether faculty know that their organization’s help desk can answer technical questions. Further,
using the Kruskal-Wallis H test, we cannot determine the impact of the number of years of
teaching experience on faculty understanding of the help desk’s ability to answer questions about
technology. Additionally, we cannot determine the department's impact in which the faculty
teaches on their understanding of the help desk’s ability to answer questions about technology.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
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Document Analysis. No documents were analyzed for procedural knowledge.
Summary. The assumed influence was that the faculty might not understand the help
desk’s ability to answer technical questions. Since the effect size for the faculty age groups and
gender is greater than zero, we accept these factors of the influence as a need. Further, since
there is no impact of faculty gender, their level of education, the number of years of their
teaching experience, and their department on the influence, we accept these factors of the
influence as an asset.
Performance and Help Desk
The survey sent to faculty members was used to assess the role of help desk support in
organizational performance improvement. The survey results are used to understand the gaps in
help desk support for organizational change.
Influence 1
Help desk being responsive to requests.
Survey results. Based on the data analysis output from SPSS, male faculty have a Means
Rank of 382.94, and female faculty have a Means Rank of 410.98. The z-value is -1.829, and the
p-value is 0.067. Since the p-value is greater than 0.05, we accept the null hypothesis of equal
Mean Ranks. Therefore, we cannot confirm if either male or female faculty have a likelihood of
knowing that the organization’s help desk is responsive to their requests. The histogram
representation of the two groups confirms that male faculty do not have a significantly different
distribution than female faculty when analyzing the help desk being responsive to support
requests.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty from different age groups feel that their organization’s help desk
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support is responsive to requests. The Chi-Square value is 16.256, and the p-value is 0.012. Since
the p-value is less than 0.05, we reject the null hypothesis of no differences between Mean
Ranks. Further, by calculating the effect size, we see 2.04% of the mean Rank scores' variability
is accounted for by the age groups. The median for analyzing how different faculty feel that their
organization’s help desk is responsive to requests varies by age groups. While age groups 30 –
39 years, 40 – 49 years, 50 – 59 years, and 60 – 69 years reported a mean value of “somewhat
agree,” age groups 20 – 29 years, 70 – 79 years, and 80 – 89 years reported a median value of
“strongly agree.”
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels know if their organization’s help desk is responsive to requests, the p-
value of 0.131 is greater than 0.05. Therefore, we accept the null hypothesis of no differences
between Mean Ranks. Thus, we cannot determine the impact of faculty education level on
whether the faculty know if their organization’s help desk is responsive to requests. Further,
using the Kruskal-Wallis H test, we see the effect size of 1.29% that confirms the impact of the
number of years of teaching experience on faculty understanding of the help desk’s
responsiveness to requests. When faculty have 0 – 9 years, 10 – 19 years, 20 – 29 years, and 30 –
39 years of teaching experience, we see a median value of "somewhat agree." When faculty have
40 – 49 years of teaching experience, we see a median value of "strongly agree." Additionally,
we cannot determine the department's impact in which the faculty teaches on whether the faculty
know if their organization’s help desk is responsive to requests.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
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Document Analysis. No documents were analyzed for faculty knowledge regarding
help-seeking.
Summary. The assumed influence was that the faculty might not understand that their
organization’s help desk is responsive to requests. Since the effect size for the faculty age group
and their education level is greater than zero, we accept this factor of the influence as a need.
Further, since there is no impact of faculty gender, the number of years of their teaching
experience, and their department on the influence, we accept these factors of the influence as an
asset.
Influence 2
When contacted, the help desk provides help quickly.
Survey results. Based on the data analysis output from SPSS, male faculty have a Means
Rank of 388.38, and female faculty have a Means Rank of 405.17. The z-value is -1.083, and the
p-value is 0.279. Since the p-value is greater than 0.05, we accept the null hypothesis of equal
Mean Ranks. Therefore, we cannot confirm if either male or female faculty have a likelihood of
knowing that the organization’s help desk provides help quickly. The two groups' histogram
representation confirms that male faculty do not have a significantly different distribution than
female faculty when analyzing the help desk response time.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty from different age groups feel that their organization’s help desk
support is responsive to requests. The Chi-Square value is 13.709, and the p-value is 0.033. Since
the p-value is less than 0.05, we reject the null hypothesis of no differences between Mean
Ranks. Further, by calculating the effect size, we see 1.72% of the mean Rank scores' variability
is accounted for by the age groups. The median for analyzing how different faculty feel that their
90

organization’s help desks’ response time varies by age groups. While age groups 20 – 29 years,
30 – 39 years, 40 – 49 years, 50 – 59 years, 60 – 69 years, and 70 – 79 years reported a mean
value of “somewhat agree,” age group 80 – 89 years reported a median value of “strongly agree.”
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels know if their organization’s help desk responds quickly, the p-value of
0.056 is greater than 0.05. Therefore, we accept the null hypothesis of no differences between
Mean Ranks. Thus, we cannot determine the impact of faculty education level on their
knowledge of the help desk’s response time. Further, using the Kruskal-Wallis H test, we cannot
determine the impact of the number of years of teaching experience on faculty understanding of
whether the help desk responds quickly. Additionally, we cannot determine the department's
impact in which the faculty teaches on their knowledge of whether the help desk responds
quickly.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for faculty knowledge regarding
help-seeking.
Summary. The assumed influence was that the faculty might not know whether the help
desk responds quickly. Since the effect size for the faculty age group is greater than zero, we
accept this factor of the influence as a need. Further, since there is no impact of faculty gender,
their education level, the number of years of their teaching experience, and their department on
the influence, we accept these factors of the influence as an asset.

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Organizational Support and Help Desk
The survey sent to faculty members was used to evaluate the accessibility of the
organization’s help desk. The survey results are used to understand the gaps in accessing the
organization’s help desk.
Influence 1
The organizational help desk is easily accessible.
Survey results. Using the data analysis output from SPSS, it is seen that the Means Rank
for male faculty is 391.71 and for female faculty is 401.62. The z-value is -0.644, and the p-value
is 0.520. Since the p-value is greater than 0.05, we accept the null hypothesis of equal Mean
Ranks. Therefore, we cannot confirm if either male or female faculty have a higher likelihood of
confirming if their organizational help desk is easily accessible. The histogram representation of
the two groups confirms that male faculty do not have a significantly different distribution than
female faculty when analyzing the accessibility of the organization’s help desk.
By comparing the Mean Rank values calculated using the Kruskal-Wallis H Test, there is
a difference in how faculty from different age groups feel that their organization’s help desk is
easily accessible. The Chi-Square value is 7.126, and the p-value is 0.309. Since the p-value is
greater than 0.05, we accept the null hypothesis of no differences between Mean Ranks.
Therefore, we cannot determine the impact of age groups on how faculty from different age
groups feel that their organization’s help desk is easily accessible.
Using the Kruskal-Wallis H test for comparing the difference in how faculty with
different education levels know if their organization’s help desk is easily accessible, the p-value
of 0.289 is greater than 0.05. Therefore, we accept the null hypothesis of no differences between
Mean Ranks. Thus, we cannot determine the impact of education level on whether the faculty
92

know if their organization’s help desk is easily accessible. Further, using the Kruskal-Wallis H
test, we cannot determine the impact of the number of years of teaching experience on faculty
understanding of the help desk being easily accessible. Additionally, we cannot determine the
department's impact in which the faculty teaches on their knowledge of the help desk being
easily accessible.
Interview findings. No interviews were used for this study.
Observation. No observations were conducted for this study.
Document Analysis. No documents were analyzed for faculty knowledge regarding
help-seeking.
Summary. The assumed influence was that the faculty might not understand that their
help desk is easily accessible. Since the effect size for none of the factors of influence is greater
than zero, we cannot determine any factors of the influence as a need. Further, since there is no
impact of faculty gender, age group, the number of years of their teaching experience, their level
of education, and their department on the influence, we accept these factors of the influence as an
asset.
Sentiment Analysis
The survey sent to faculty includes an open-ended question as to the final question. In
this optional question, the survey respondents were asked to provide any additional feedback that
may not have been covered from their perspective. Two ninety-four faculty members responded
to this question. Sentiment analysis was performed on these text responses using Qualtrics and
Tableau, and the emerging themes are discussed below.

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Job duties
Faculty responses discussed work challenges specific to technical job duties and training
needed. Their workload makes it challenging to manage the problematic task of learning
technology tools. Overall, using technology is an issue since it creates technostress in addition to
physical stress.
Time needs
Faculty responses mentioned that they wish to learn technology; however, a shortage of
time is a significant factor that impacts learning.
Educational needs
In addition to learning tools needed for providing quality education, faculty explain the
difficulty of using technology in a classroom or a virtual classroom in their survey responses.
Further, the faculty responses discuss that classroom and virtual technology challenges make it
difficult for them to focus on teaching.
Simplicity
In their survey responses, several faculty members lamented their lack of technical skills
as a critical trigger for technostress. Further, we see that faculty hope to learn technological tools
that are simple and solve technical problems.
Learning
From the survey responses, it appears that the faculty understand the need to embrace
technology and changes in technology. While they may often be frustrated due to technology,
they will need to address the challenges of understanding the benefits of technology use and
transition to new technology tools.

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Software needs
Based on the survey responses, faculty need to understand the hardware needs and
software needs for effective instruction. Different platforms have a different look and feel, and
faculty experience technostress to challenges with interfacing with students and being on camera
in a virtual classroom.
Convenience and Efficiency
While responding to the survey's final question, several faculty discuss being
overwhelmed and frustrated, which impacts their ability to function efficiently and reduces the
quality of education they provide.
Technical systems
In their survey response, faculty discuss the need to use personal computers over work
laptops due to ease of use. Additionally, some faculty members explain the importance of a good
internet connection to ensure high-quality instruction.
Summary of Validated Influences
Tables 2, 3, and 4 show the knowledge, motivation, and organizational influences for this
study. Further, the tables include the determination of the influences as an asset or a need.
Knowledge
Gaps were determined for some of the factors of the knowledge influences. Table 2
provides an overview of the assessment results for each factor of the knowledge influence.
Recommendations to integrate each identified gap are discussed in Chapter Five.

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Table 2
Summary of Assumed Knowledge Influences
Assumed Knowledge Influences Faculty Factors of Influence Asset or
Need
Declarative knowledge
Finding it difficult and complicated to use
new technologies.
Gender, Education level,
Department
Asset
Age group, Number of years of
teaching experience
Need
Frequent updates in the technologies used in
the organization.
Gender, Education level,
Department
Asset
Age group, Number of years of
teaching experience
Need
Technologies create more problems, requests,
or complaints while performing job duties.
Gender, Education level,
Department
Asset
Age group, Number of years of
teaching experience
Need
Procedural knowledge
Knowing how to update skills for using
technology regularly.
Gender, Education level, Number
of years of teaching experience,
Department
Asset
Age group Need
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Knowing how to be a part of technology
change and implementation.
Gender, Education Level,
Department
Asset
Age group, Number of years of
teaching experience
Need
Knowing how to deal with problems while
using technology for job duties.
Gender, Education level,
Department
Asset

Age group, Number of years of
teaching experience
Need
Help-seeking: Faculty knowledge
Encouragement from the organization for
knowledge sharing and using new technology.
Gender, Age group, Number of
years of teaching experience,
Department
Asset
Education level Need
Organizational emphasis on teamwork in
dealing with technology problems.
Gender, Education Level, Number
of years of teaching experience,
Department
Asset
Age group Need
Ability to reach other employees in the
organization using technology.
Gender, Age group, Education
level, Number of years of teaching
experience, Department
Asset
None Need

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Note. This table lists the assumed knowledge influences on technostress experienced by faculty.

Motivation
Gaps were determined for some of the factors of the motivation influences. Table 3
provides an overview of the assessment results for each factor of the motivation influence.
Recommendations to integrate each identified gap are discussed in Chapter Five.

Table 3
Summary of Assumed Motivation Influences
Assumed Motivation Influences Faculty Factors of Influence Asset or
Need
Self-efficacy
Ability to learn technology quickly. Gender, Department Asset
Age group, Education level,
Number of years of teaching
experience
Need
Expectancy value
Ability to be better at doing work duties. Gender, Education level, Number
of years of teaching experience
Department
Asset
Age group Need
Using computer skills to help with job duties. Gender, Education level Asset
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Age group, Number of years of
teaching experience, Department
Need
Choice
Willingness to change work habits to adapt to
new technologies.
Department Asset
Gender, Age group, Education
level. Number of years of teaching
experience
Need
Persistence
Striving to make work enjoyable. Age group, Number of years of
teaching experience, Department
Asset
Gender, Education Level Need
Effort
Willingness to spend more time to study and
upgrade technology skills.
Number of years of teaching
experience, Department
Asset

Gender, Age group, Education
level
Need
Performance
Higher workload due to increased technical
complexity.
Age group, Education level Asset
Gender, Number of years of
teaching experience, Department
Need
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Faculty need to work faster due to
technology.
Education Level, Number of years
of teaching experience,
Department
Asset
Gender, Age group Need
Job satisfaction
Faculty are enjoying their job. Gender, Education level,
Department
Asset
Age group, Number of years of
teaching experience
Need
Managerial support
Feeling supported by managers to perform at
the highest level.
Gender, Education level, Number
of years of teaching experience,
Department
Asset
Age group Need

Note. This table lists the assumed motivation influences on technostress experienced by faculty.

Organization
Gaps were determined for some of the factors of the organizational influences. Table 4
provides an overview of the assessment results for each factor of the organizational influence.
Recommendations to integrate each identified gap are discussed in Chapter Five.

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Table 4
Summary of Assumed Organizational Influences
Assumed Organizational Influences Faculty Factors of Influence Asset or
Need
Training
End-user training before the introduction of
new technology.
Gender, Education level,
Department, Age group, Number
of years of teaching experience
Asset
None Need
Setting a schedule for completing assigned
training.
Education level, Department, Age
group, Number of years of
teaching experience
Asset
Gender Need
Proactive and reactive responses
The organization provides clear guidelines to
end-users for using technology.
Age group, Education level,
Number of years of teaching
experience, Department
Asset
Gender Need
Faculty are encouraged to try out new
technologies.
Gender, Education Level,
Department, Age group, Number
of years of teaching experience
Asset
None Need
Positive technologies
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Organization’s commitment to adopt
technologies that are easy to use and learn.
Gender, Education level,
Department, Age group, Number
of years of teaching experience
Asset

None Need
Help desk support
The help desk answers questions about the
technology used in the organization.
Education level, Number of years
of teaching experience,
Department
Asset
Gender, Age group Need
Performance and help desk
Help desk being responsive to requests. Gender, Number of years of
teaching experience, Department
Asset
Age group, Education Level Need
When contacted, the help desk provides help
quickly.
Gender, Education level, Number
of years of teaching experience,
Department
Asset
Age group Need
Organizational support and help desk
The organization’s help desk is easily
accessible.
Gender, Education level,
Department, Age group, Number
of years of teaching experience
Asset
None Need

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Note. This table lists the assumed organizational influences on technostress experienced by
faculty.

In Chapter Five, proposed solutions are provided for each demonstrated cause. The
proposed solutions align with evidence-based recommendations identified from existing
literature.

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Chapter Five: Recommendations and Discussion
This study evaluates the technostress experienced by faculty at SCU. Technostress in
education leads to disengagement, job dissatisfaction, and low employment performance
(Tarafdar et al., 2015) and physiological, psychological, organizational, and societal
consequences for educational professionals (Salanova & Cifre, 2013). This study's conceptual
framework guided the research questions, research problem, and purpose (Maxwell, 2013). The
conceptual framework explained the relationship between the knowledge, motivation, and
organizational influences impacting the creators and inhibitors of technostress experienced by
faculty. Data collection and analysis guide the recommendations discussed in this chapter. The
recommendations and evaluation plans are inter-related and based on understanding the gaps in
knowledge, motivation, and organizational influences on faculty's technostress.
Discussion of Findings and Results
Chapter Five discusses recommendations based on the gaps identified in knowledge,
motivation, and organizational influences resulting in faculty's technostress. Each
recommendation is guided by the literature discussed in Chapter Two to achieve expected results
to help faculty cope with technostress. In many instances, the recommendations are transferrable
to other educational institutions and are not unique to SCU. The New World Kirkpatrick model
(Kirkpatrick & Kirkpatrick, 2016) provides the foundation for the recommendations presented in
this chapter.
Recommendations for Practice
The usage of the New World Kirkpatrick model (Kirkpatrick & Kirkpatrick, 2016) is the
continuation of the conceptual framework created using the gap analytical framework (Clark &
Estes, 2008). As a result, each recommendation is followed by discussing the indications,
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behaviors, learning, outcomes, and reactions. Knowledge, motivation, and organizational
recommendations are presented in the same sequence as previous chapters.
Knowledge Influence Recommendations
This section discusses the recommendations for the identified gaps emerging from the
knowledge influences on technostress. While there are factors of the knowledge influences that
are assets, addressing the knowledge possessed by older faculty and faculty with a higher
teaching experience is an integral part of the recommendations below.
Developing Declarative Knowledge
Faculty need to possess appropriate declarative knowledge about instruction by optimal
technology use (Aleven & Graesser, 2013). Technology continues to advance, and faculty must
learn to use technologies to provide effective education (Hudesman et al., 2014). Notably, older
faculty who have been providing education for several years need support to acquire technology
skills and for the ability to learn tools and devices effectively. According to Clark and Estes
(2008), it is critical to provide job aids and necessary information even when a task is familiar to
the employee.
Facilitation of knowledge transfer and reinforcement learning can help older faculty
retain declarative knowledge (Lindholm et al., 2012). Creating training content in a manner that
is easy to use and follow is an essential aspect of the andragogy theory that can help faculty
improve learning and knowledge retention (Queen Booker et al., 2014). Repetition is one of the
most critical aspects of adult learning. Similarly, providing outlines, rubrics, and goals as part of
the training improve collaborative help-seeking to address declarative knowledge gaps. Overall,
simple task aids and interactive instruction help develop and retain declarative knowledge,
eventually providing faculty the tools to inhibit or cope with technostress. Availability of
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adequate faculty training can improve both their declarative and procedural knowledge (Egbert,
2009).
Developing Procedural Knowledge
To help faculty cope with technostress symptoms, they need to prepare, apply, practice,
and integrate skills learned through training (Fernandes, 2017). Lack of time and empowerment
to learn and the lack of practical integration are some of the problems that need to be alleviated
to help faculty develop their procedural knowledge (Jex & Britt, 2014). Faculty can learn
technology by first understanding the theory and having access to essential knowledge
(Fernandes, 2017). As a next step, according to Shell et al. (2010), when faculty receive
demonstration and practice for the technology needed for instruction, it can help with the
retention of procedural knowledge. Further, practicing the skills learned with a trial class or in a
test environment can solidify faculty confidence and expertise in using the technology needed for
instruction. Finally, when faculty can integrate the technical skills with their instruction routines,
it provides a structure to use technology effectively (Shell et al., 2010). Overall, following the
learning path through preparing, applying, practicing, and integrating technical skills can provide
a foundation for developing procedural knowledge for all faculty to use technology effectively
(Fernandes, 2017).
Faculty members' performance depends on the amount and quality of the declarative and
procedural knowledge (Campbell, 1990; Khosrow-Pour, 1999). For faculty to demonstrate high
performance, allowing them to develop their procedural and declarative knowledge is a salient
factor in coping with technostress (Esteban-Lloret et al., 2018). Availability of quality training
will help faculty learn new skills and develop existing skills, thus helping reduce the level of
technostress and finding ways to cope with it (Leonard, 2016). In addition to developing
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declarative and procedural knowledge, help-seeking is a vital behavior that faculty need to
demonstrate to cope with technostress (Garber, 2011).
Help-Seeking
Regardless of the faculty education level and age, increasing faculty awareness for
effective help-seeking is one lesson learned from this study. While faculty need to have access to
job aids and self-service tools (Aleven & Graesser, 2013; Clark & Estes, 2008), faculty need to
feel empowered to seek help and be aware of help-seeking to improve their knowledge (Bodine,
2013). Managing knowledge-related technostress can be accomplished using technostress
inhibitors like adequate employee training and employee empowerment (Fuglseth & Sorebo,
2014). Employees need to possess sufficient declarative and procedural knowledge to perform
their tasks (Krathwohl, 2002). Additionally, they need to have access to self-help tools and the
helpdesk to get answers to any questions they may have (Gonida et al., 2019). Similarly, when
there is not enough information or training available, employees may start feeling disengaged
with the task they are working on (Fuglseth & Sorebo, 2014). Overall, recognizing knowledge
gaps, working towards acquiring knowledge, seeking help when needed, and knowing who to
reach for help, are critical steps for managing technostress and improving employee motivation
(Tarafdar et al., 2015).
Motivation Influence Recommendations
This section discusses the recommendations for the identified gaps emerging from the
motivational influences on technostress. While there are factors of the motivation influences that
are assets, addressing the motivation possessed by older faculty is an integral part of the
recommendations below. The recommendations also consider the number of years of teaching
experience, education level, department, and gender of the faculty.
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Self-Efficacy
To help faculty improve their self-efficacy, they need to learn techniques that enhance
their self-control, personal and professional obligations, and social communication (Bandura,
1994; Hausberg et al., 2017). One of the primary methods to improve faculty self-efficacy would
be setting stretch goals to learn and use technology, be more willing to take chances, and be
resilient to setbacks (Mayer, 1998). Approaching technology slowly and trying new things at an
individual pace will help reduce the faculty's technostress level (Bandura, 1994; Tarafdar et al.,
2007). While the goals need to be simpler, the faculty need to understand the big picture by
understanding priorities, planning, focusing on learning, and using technology more efficiently
(Karwowski & Kaufman, 2017). Further, restructuring how faculty look at learning setbacks can
help them cope with technostress (Ventrice, 2009). High self-efficacy will assist faculty with
increasing motivation and persistence to find better ways to learn and use technology, thus
providing them the ability to finding ways to cope with technostress (Skinner et al., 1990,
Tarafdar et al., 2007).
Expectancy Value
Older faculty and faculty within departments that have traditionally not used technology
for instruction need support to improve their beliefs and pursue their learning goal and actively
use technology (Tarafdar et al., 2007; Wigfield & Eccles, 2000). Faculty need to set several
goals and ensure that these goals are meaningful, challenging, and achievable. It will enhance
their ability to process and achieve the goal of using technology effectively and efficiently
(Nagengast et al., 20111). Being able to participate in focus groups at their organization to ensure
peer support and learning from each other is one way to improve expectancy (Trautwien et al.,
2012). Increased effort and confidence can help faculty be on a path to reducing the level of
108

technostress and finding ways to cope with it (Bembenutty, 2008). When faculty possess an
expectancy to pursue a performance goal actively, they will demonstrate increased motivation
(Usher & Pajares, 2008).
Choice
Faculty need to be willing to change work habits to adapt to new technologies based on
their intrinsic motivation (Glasser & Glasser, 1998). Since improving the technical skills at work
is necessary, faculty need to be aware of this need and improve technical skills (Naderi et al.,
2015). According to data collected in this study, older male faculty with a terminal degree need
to learn technology by feeling capable, confident, and happy with their choice of work habits
(Glasser & Glasser, 1998). It will help reduce the symptoms of technostress and reducing
technostress's severity (Tarafdar et al., 2011).
Persistence
When faculty strive to make work enjoyable, they will demonstrate persistence, thus
improving their motivation (Ventrice, 2009). The higher the belief to learn and use technology,
the more the faculty will persist (Bandura, 1997). To improve persistence, faculty need to first
identify the instruction needs for using technology (Naderi et al., 2015). According to the data
collected in this study, male faculty need to develop a discipline and habit to improve their
technical skills, create an action plan and seek help (Ventrice, 2009).
Effort
Faculty need to demonstrate the effort to achieve the goal of learning and use technology
effectively and efficiently (Clark & Estes, 2008; Thomas, 2009). Moreover, faculty need to
understand this effort's value towards achieving high job performance (Thomas, 2009).
According to the data collected in this study, male faculty with a terminal degree need to
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demonstrate increased effort based on the incentive to achieve their learning goals (Venables &
Fairclough, 2009). It will help with coping with technostress effectively (Tarafdar et al., 2011).
Performance
Faculty who demonstrate higher motivation will show improvement in their performance
(Fuglseth & Sorebo, 2014). It is essential for faculty to receive feedback from peers and
administrators and clear expectations for using technology for instruction (Ayyagari et al., 2011).
Overall, achieving employee engagement and job satisfaction is possible when faculty
demonstrate a high motivation to excel in their jobs (Usher & Pajares, 2008). According to the
data collected in this study, older female faculty teaching in Engineering departments may need
additional support and guidance to improve their job performance related to finding ways to cope
with technostress (Tarafdar et al., 2007).
Job Satisfaction
Faculty need to feel empowered to provide input before implementing new workflows or
new technologies (Tarafdar et al., 2007). Further, faculty need to have reasonable deadlines for
learning new workflows or new technologies, in addition to having enough time to learn. It will
improve faculty motivation and, in turn, their job satisfaction (Fuglseth & Sorebo, 2014). To
continue to be motivated and improve their job satisfaction, faculty need opportunities to learn
industry-standard technologies, besides learning on the job (Ayyagari et al., 2011). Further,
faculty may need support from their peers to stay motivated and demonstrate high job
performance (Tarafdar et al., 2007).

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Managerial Support
Administrators play an essential role in motivating faculty (Cadwallader et al., 2010).
They need to empower faculty to learn and use highly sophisticated technologies to perform on
the job (Tarafdar et al., 2007) effectively. Eventually, faculty will reach their full potential to
learn new skills and be motivated to perform and help other faculty acquire these skills
(Cadwallader et al., 2010; Maslow, 1987). Faculty experiencing technostress due to low
motivation may demonstrate low confidence, low job performance, and performance anxiety
(Salanova & Cifre, 2013). They may also demonstrate difficulty adapting to change and may not
be flexible to learn new skills (Ayyagari et al., 2011). Administrators must regularly
communicate with faculty and clearly understand the technostress triggers (Tarafdar et al., 2007).
Administrators who work with their faculty effectively may have a higher chance of helping
them cope with technostress experienced due to low motivation (Fuglseth & Sorebo, 2014).
Organizational Influence Recommendations
This section discusses the recommendations for the identified gaps emerging from the
organizational influences on technostress. While there are factors of the organizational influences
that are assets, the recommendations' focus is on the other factors identified as needs. The
recommendations also consider the number of years of teaching experience, education level,
department, and gender of the faculty.
Training and Culture
Organizational training plays a vital role in its success and its employees (Biech, 2012).
Organizational training content needs to align with organizational goals and business objectives
(Biech, 2012). Similarly, organizational culture plays a vital role in determining an organization's
training strategy (Robbins, 2013). A healthy organizational culture helps in defining faculty
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success and organizational success (Robbins, 2013). Organizational cultures need to value
control, collaboration, competence, training, and epistemology (Suda, 2008). Organizations need
to promote faculty training focusing on faculty readiness before deploying new technologies
(Smith, 2012). Additionally, organizations need to ensure adequate training and help desk
assistance to faculty (Biech, 2012; Smith, 2012).
Help Desk Support
A dedicated help desk department within organizations needs to support faculty with their
technical needs (Foo et al., 2000). It could include simple day-to-day questions or complex
technical support needs (Sun & Li, 2011). Whenever there is a technology-related issue, faculty
need to be encouraged to reach the help desk department to guide them to solve the problem and
access any self-help documentation (Delic & Hoellmer, 2000). Additionally, faculty need to be
encouraged to reach the help desk department to resolve an issue regardless of its complexity
(Foo et al., 2000). The help desk support team is at the frontline of helping faculty, and
organizations must invest in staffing and training for the help desk department to ensure high-
quality support (Hall et al., 2014).
Performance and Help Desk
To ensure continuous performance improvement, organizations need to change their
processes, strategies, structure, and technologies from time to time (Clark & Estes, 2008;
Robbins, 2013). Suppose faculty continue to use the same processes to accomplish tasks for
several years. In that case, organizations may not see the benefits of using advanced technologies
and process improvement strategies to complete these tasks efficiently (Collins, 2001).
Successful organizational change requires effective leadership and efficient change management
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strategies and equipping the help desk team with the knowledge needed to support employees
(Robbins, 2013).
Organizational Support and Help Desk
Organizational leaders need to communicate effectively, collaborate with faculty, and
commit to change (Robbins, 2013). They need to review faculty workload to understand how
they may cope with managing organizational change while managing their workload (Collins,
2001). Faculty need to be supported and encouraged to express their ability or inability to learn
new skills (Dewe & Cooper, 2017). Organizations may direct their faculty to reach the help desk
team to ensure that they are thoroughly trained and proficient (Foo et al., 2000). Further, faculty
need to understand their role in the organizational change, resulting in better adoption of the
change (Dewe & Cooper, 2017). Empowering faculty to seek support using self-help tools, help
from peers, or help desk, is an integral part of an organizational change (Middleton & Marcella,
1997). Faculty who do not have access to these help-seeking avenues are likely to experience
technostress (Hall et al., 2014; Ayyagari et al., 2011).
Proactive and Reactive Response
Organizations may be proactive or reactive while dealing with employees experiencing
technostress (Jena, 2015). Proactive investment to deal with technostress can help with cost
reduction due to the implementation of strategies to avoid technostress (Jena & Mahanti, 2014).
One approach to prevent technostress is updating old processes and making them more efficient
and comfortable to follow (Tarafdar et al., 2007). Further, organizations need to ensure that
faculty have enough time to adjust to any change and continue to be motivated (Ayyagari et al.,
2011). It requires that organizational change happens at an appropriate pace (Tarafdar et al.,
2007). Organizations need to be proactive in supporting faculty who may be experiencing
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technostress, besides stress caused by other professional or personal reasons (Ayyagari et al.,
2011). Organizations that tend to be reactive to the occurrence of technostress in faculty need to
focus on knowing the hot spots of technostress and encouraging faculty through problem-solving
recommendations (Jena & Mahanti, 2014). Organizations that assist faculty with managing
technostress may achieve higher faculty performance in addition to improved organizational
performance (Bond et al., 2010). Higher organizational performance is one of the significant
signs of using positive technologies and behaviors that it demonstrates (Bond et al., 2010; Brivio
et al., 2018).
Positive Technologies
The use of positive technologies can help with preventing technostress (Brivio et al.,
2018). To prevent technostress, organizations need to adopt positive technologies that are well-
designed and role appropriate for faculty (Costabile & Spears, 2012). Positive technologies are
easy to learn and use (Calvo & Peters, 2014) and help reduce learning anxiety and positively
impact faculty performance (Baños et al., 2019). Organizations with a good understanding of
faculty ability need to introduce positive technologies and corresponding training to ensure high
faculty adoption (Botella et al., 2012). When technologies are easy to use and learn, it can help
prevent technostress caused due to sophisticated technology (Brivio et al., 2018). Similarly,
faculty may feel more secure and sure about adopting new skills using positive technologies
(Pawlowski et al., 2015). Overall, positive technologies help understand the triggers and
symptoms of technostress quicker and determine ways to cope with technostress (Brivio et al.,
2018).

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Integrated Knowledge, Motivation, Organizational Recommendations
The KMO influences play a significant role in evaluating faculty's technostress (Stallard
& Cocker, 2014; Tarafdar et al., 2015). Gaps in faculty knowledge and motivation, in addition to
insufficient support from their organizations, are some of the critical factors that determine the
symptoms and the level of technostress experienced by faculty (Ayyagari et al., 2011; Clark &
Estes, 2008).
KMO Influences and Mitigating Technostress
Faculty need to possess the knowledge and skills to perform well in their jobs (Clark &
Estes, 2008; Hudesman et al., 2014). To achieve job performance goals, faculty need to
understand how they can achieve them (Clark & Estes, 2008, Lindholm et al., 2012). Further,
faculty need to access relevant information to acquire the knowledge and skills required to
perform their jobs (Oldknow & Knights, 2011). When information is not available, faculty need
to seek help to gain the necessary information (Garber, 2011). Overall, possessing essential
knowledge or factual information like principles, processes, procedures, and concepts for job-
relevant tasks is necessary for faculty (Tunison, 2016). Additionally, faculty job satisfaction
depends on their motivation, attitude, and experiences at work (Aleven & Graesser, 2013;
Ayyagari et al., 2011). High-performance expectancy depends on the faculty’s beliefs and self-
efficacy (Eccles & Wigfield, 2002). Job satisfaction experienced by faculty and the support they
receive from their organization helps determine the level of technostress caused due to
motivation challenges (Ayyagari et al., 2011; Hudesman et al., 2014). Further, organizational
strategy plays a vital role in managing workplace stress and technostress (Bond et al., 2010).
Organizational cynicism, mistreatment of employees, and employee inability to learn or adopt
new technologies quickly are some of the critical challenges that organizations need to address
115

(Gorsline, 2016). Organizations need to understand the triggers of work stressors and the
underlying difficulties that faculty may face (Lindholm et al., 2012; Tarafdar et al., 2007). Once
triggers are identified, organizations need to evaluate technostress's impact based on faculty
gender, age, personalities, and attitudes (Queen Booker et al., 2014; Tarafdar et al., 2015).
Faculty may feel threatened if organizations do not support them in coping with technostress,
resulting in uncertainty or job insecurity (Dewe & Cooper, 2017). How organizations react to
faculty facing technostress depends on organizational beliefs and strategy (Bond et al., 2010).
Impact of Technostress on Faculty Performance and Job Satisfaction
Faculty experiencing technostress may demonstrate symptoms like difficulty in learning,
confusion, lower job performance, and lower job satisfaction (Egbert, 2009; Salanova & Cifre,
2013). Job satisfaction plays a vital role in determining faculty productivity (Jena, 2015). Ragu-
Nathan et al. (2008) explain that technological competencies, information overload, and attitude
towards technology adoption significantly impact faculty technostress levels. Oldknow and
Knights (2011) identify that faculty may not see all the benefits of using technology and may not
realize its full potential in improving learning outcomes if they have trouble in adopting
technology and adapting to technology (Stallard & Cocker, 2014).
Limitations and Delimitations
Each research study faces a risk of limitations and delimitations for the study outcomes
(Merriam & Tisdell, 2016). It is important to understand the extent to which the study can be
generalized and understand the potential weaknesses that may be out of the researcher's control
(Shipman, 1997).
This study's faculty population is from SCU, and faculty members may have chosen not
to answer the survey protocol. Since the study's focus is on the KMO influences on triggers and
116

inhibitors of technostress leading to job dissatisfaction, there is a risk of not receiving honest
responses since the faculty members currently work at SCU. In addition to these limitations,
delimitations exist for this study.
In addition to advances in technology, it is an expectation for faculty members to
effectively teach online classes (Oldknow & Knights, 2011). As such, there might be an
increasing willingness amongst faculty members to complete the technostress survey protocol.
While this study focuses on evaluating faculty's technostress, analyzing the KMO influences
helped understand their job satisfaction, motivation, and job performance.
Recommendations for Future Research
This study evaluates the knowledge, motivation, and organizational influences on
technostress. Further research could consider the impact of faculty personality on the severity of
technostress that they experience. Further research to consider includes the impact of
presenteeism and social support on technostress. Organizations need to ensure faculty
mindfulness and understand the impact of the COVID-19 pandemic on education changes.
Conclusion
The purpose of this study was to evaluate the technostress experienced by faculty
members. It aimed to identify the KMO influences affecting the faculty members, contributing to
job dissatisfaction and low job performance (Bond et al., 2010). With the large number of faculty
members needing to use educational technology, this study was relevant to education trends. The
foundation of this study was the gap analysis framework (Clark & Estes, 2008). The study’s
conceptual framework focused on understanding the gaps in faculty knowledge, motivation, and
organizational influences leading to technostress, lowering employee performance, and job
satisfaction. This study's research strategy focuses on using quantitative research and the survey
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methodology to evaluate the impact of technostress and identify ways to cope with it. The survey
questions were grouped based on the KMO influences on technostress experienced by
employees. The survey responses helped determine the triggers and inhibitors of technostress
related to the research questions and the conceptual framework. The study’s recommendations
are based on the gaps identified in knowledge, motivation, and organizational influences
resulting in faculty's technostress. Each recommendation is guided by existing literature to
achieve expected results to help faculty cope with technostress.

118

References
Aleven, V., Beal, C., & Graesser, A. (2013). Introduction to the special issue on advanced
learning technologies. Journal of Educational Psychology, 105(4), 931.
https://doi.org/10.1037/a0034155
Alexander, P. A. (2018). Engagement and literacy: Reading between the lines. Journal of
Research in Reading, 41(4), 732–739. https://doi.org/10.1111/1467-9817.12262
Allen, M. (2017). Reliability, Split-Half. The SAGE Encyclopedia of Communication Research
Methods. SAGE Publications, Inc. https://doi.org/10.4135/9781483381411.n494
Ayyagari, R., Grover, V., & Purvis, R. (2011). TECHNOSTRESS: TECHNOLOGICAL
ANTECEDENTS AND IMPLICATIONS. MIS Quarterly, 35(4), 831–858.
https://doi.org/10.2307/41409963
Babbie, E. R. (2010). The practice of social research (12th ed.). Wadsworth Cengage.
Bandura, A., & Schunk, D. H. (1981). Cultivating competence, self-efficacy, and intrinsic
interest through proximal self-motivation. Journal of Personality and Social Psychology,
41(3), 586–598. https://doi.org/10.1037/0022-3514.41.3.586
Bandura, A. (1994). Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human
behavior (Vol. 4, pp. 71-81). New York: Academic Press.
Bandura, A. (1997). Self-efficacy: The exercise of control. WHFreeman.
Bandura, A. (2000). Cultivate self-efficacy for personal and organizational effectiveness. In E.
A. Locke (Ed.), Handbook of principles of organization behavior. (pp. 120-136). Oxford,
UK: Blackwell.
Baños, R., Baena-Extremera, A., & Granero-Gallegos, A. (2019). The Relationships between
High School Subjects in terms of School Satisfaction and Academic Performance in
119

Mexican Adolescents. International Journal of Environmental Research and Public Health,
16(18), 3494-. https://doi.org/10.3390/ijerph16183494
Bartholomew, D. J. (2004). Measuring Intelligence: Facts and Fallacies. University Press.
https://doi.org/10.1017/CBO9780511490019
Bartos, J. (2013). What is the role of technology in education. Greenhaven Press.
Beckett, M. K. (2001). Accountability for after-school care devising standards and measuring
adherence to them. Rand, Labor & Population.
Bedeian, A. G. (1976). Relationship of need achievement to self-esteem: Evidence for validity of
Form B of Coopersmith's Self-Esteem Inventory. Perceptual and Motor Skills, 43(3 Pt 2),
1219–1220. https://doi.org/10.2466/pms.1976.43.3f.1219
Bembenutty, H. (2008). The First Word: A Letter From the Guest Editor on Self-Regulation of
Learning. Journal of Advanced Academics, 20(1), 5–16. https://doi.org/10.4219/jaa-2008-
884
Bhat, C. R., & Koppelman, F. S. (1999). A retrospective and prospective survey of time-use
research. Transportation, 26(2), 119–139. https://doi.org/10.1023/A:1005196331393
Bickman, L., Rog, D. J., & Best, S. J. (2009). The SAGE handbook of applied social research
methods (2nd ed.). SAGE.
Biech, E. (2012). Developing Talent for Organizational Results: Training Tools From the Best in
the Field. Pfeiffer.
Bodine, C. (2013). Assistive Technology and Science (pp. 1–270). SAGE Publications, Inc.
https://doi.org/10.4135/9781452218434
Bowling, A. (2014). Research Methods in Health: Investigating Health and Health Services
(Fourth edition.). Open University Press, McGraw-Hill Education.
120

Bond, F., Cooper, C., & Sutherland, V. J. (2010). Organizational Stress Management: A
Strategic Approach (pp. xiii, 296). Palgrave Macmillan.
https://doi.org/10.1057/9780230203938
Botella, C., Riva, G., Gaggioli, A., Wiederhold, B. K., Alcaniz, M., & Baños, R. M. (2012). The
Present and Future of Positive Technologies. Cyberpsychology, Behavior, and Social
Networking, 15(2), 78–84. https://doi.org/10.1089/cyber.2011.0140
Brehm, J. W., Wright, R. A., Solomon, S., Silka, L., & Greenberg, J. (1983). Perceived
difficulty, energization, and the magnitude of goal valence. Journal of Experimental Social
Psychology, 19(1), 21–48. https://doi.org/10.1016/0022-1031(83)90003-3
Brivio, E., Gaudioso, F., Vergine, I., Mirizzi, C. R., Reina, C., Stellari, A., & Galimberti, C.
(2018). Preventing Technostress Through Positive Technology. (Report). Frontiers in
Psychology, 9, 2569. https://doi.org/10.3389/fpsyg.2018.02569
Brod, C. (1984) Technostress: the human cost of the computer revolution. Reading, Mass:
Addison-Wesley.
Brown, M. C., & Bartee, R. D. (2007). Still Not Equal: Expanding Educational Opportunity in
Society (pp. 1–465).
Cadman, C. (2013). Employee training: Ticking the box or building business competence?
Industrial and Commercial Training, 45(1), 32–35.
https://doi.org/10.1108/00197851311296683
Cadwallader, S., Jarvis, C., Bitner, M., & Ostrom, A. (2010). Frontline employee motivation to
participate in service innovation implementation. Journal of the Academy of Marketing
Science, 38(2), 251–251. https://doi.org/10.1007/s11747-009-0160-2
121

Calvo, R. A., & Peters, D. (2014). Positive Computing: Technology for Wellbeing and Human
Potential. MIT Press, The MIT Press.
Campbell, J. P. (1990). Modeling the performance prediction problem in industrial and
organizational psychology. In Handbook of industrial and organizational psychology, Vol.
1, 2nd ed (pp. 687–732). Consulting Psychologists Press.
Clark, R. E., & Estes, F. (2008). Turning research into results: A guide to selecting the right
performance solutions. Information Age Pub Inc.
Collins, J. C. (2001). Good to great: Why some companies make the leap--and others don't (1st
ed.). HarperBusiness.
Connelly, L. M. (2016). Cross-sectional survey research. (Understanding Research). Medsurg
Nursing, 25(5), 369-.
Costabile, A., & Spears, B. A. (2012). The Impact of Technology on Relationships in
Educational Settings: International Perspectives. Taylor & Francis Group.
Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five
approaches (3rd ed.). SAGE Publications.
Cumo, C. (2007). Science and technology in 20th-century American life. Greenwood Press.
Delic, K. A., & Hoellmer, B. (2000). Knowledge-based support in helpdesk environments. IT
Professional, 2(1), 44–48. https://doi.org/10.1109/6294.819939
Dewe, P. J., & Cooper, C. L. (2017). Work Stress and Coping: Forces of Change and
Challenges. SAGE Publications Ltd. https://doi.org/10.4135/9781526421678
Eccles, J. S. (2005). Subjective Task Value and the Eccles et al. Model of Achievement-Related
Choices. In Handbook of competence and motivation (pp. 105–121). Guilford Publications.
122

Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of
Psychology, 53(1), 109–132. https://doi.org/10.1146/annurev.psych.53.100901.135153
Egbert, J. (2009). Supporting learning with technology: Essentials of classroom practice.
Pearson, Merrill, Prentice Hall.
Esteban-Lloret, N. N., Aragón-Sánchez, A., & Carrasco-Hernández, A. (2018). Determinants of
employee training: Impact on organizational legitimacy and organizational performance.
The International Journal of Human Resource Management, 29(6), 1208–1229.
https://doi.org/10.1080/09585192.2016.1256337
Fantl, J. (2017). Knowledge How. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy
(Fall 2017). Metaphysics Research Lab, Stanford University.
https://plato.stanford.edu/archives/fall2017/entries/knowledge-how/
Fernandes, B. (2017). Upper Elementary Teachers’ Self-Efficacy and Spelling Instruction: A
Qualitative Study. ProQuest Dissertations Publishing.
Foo, S., Hui, S. C., Leong, P. C., & Liu, S. (2000). An integrated help desk support for customer
services over the World Wide Web—A case study. Computers in Industry, 41(2), 129–145.
https://doi.org/10.1016/S0166-3615(99)00037-8
Fuglseth, A. M., & Sørebø, Ø. (2014). The effects of technostress within the context of employee
use of ICT. Computers in Human Behavior, 40, 161–170.
https://doi.org/10.1016/j.chb.2014.07.040
Garber, P. R. (2011). Coaching employee engagement training. ASTD Press.
Glesne, C. (1999). Becoming qualitative researchers: An introduction (2nd ed.). Longman.
Glasser, W., & Glasser, W. (1998). Choice theory in the classroom (Rev. ed.). Harper Perennial.
123

Gonida, E. N., Karabenick, S. A., Stamovlasis, D., Metallidou, P., & Greece, the C. (2019).
Help-seeking as a self-regulated learning strategy and achievement goals: The case of
academically talented adolescents. High Ability Studies: Self-Regulated Learning in Gifted,
Talented, and High-Achieving Learners, 30(1–2), 147–166.
https://doi.org/10.1080/13598139.2018.1535244
Gordis, L. (2009). Epidemiology (4th ed.). Elsevier/Saunders.
Gorelick C., Milton N., & April K. (2004). Performance Through Learning. Routledge Ltd,
Routledge. https://doi.org/10.4324/9780080479927
Gorsline, K. (2016). Facing organizational challenges from within. Canadian HR Reporter,
29(13), 9.
Hall, J. A., Verghis, P., Stockton, W., & Goh, J. X. (2014). It Takes Just 120 Seconds: Predicting
Satisfaction in Technical Support Calls. Psychology & Marketing, 31(7), 500–508.
https://doi.org/10.1002/mar.20711
Hausberg, J., Hülsdau, M., Moysidou, K., & Teuteberg, F. (2017, September 28). Employees'
Adoption of Workplace Innovations: An Investigation of Self-Efficacy, Motivation, Trust
and Risk Propensity. https://doi.org/10.18420/in2017_140
Heale, R., & Twycross, A. (2015). Validity and reliability in quantitative studies. Evidence
Based Nursing, 18(3), 66–67. https://doi.org/10.1136/eb-2015-102129
Higgs E., Light A., & Strong D. (2000). Technology and the Good Life? University of Chicago
Press, University of Chicago Press.
Hudesman, J., Crosby, S., Ziehmke, N., Everson, H., Issac, S., Flugman, B., Zimmerman, B., &
Moylan, A. (2014). Using Formative Assessment and Self-Regulated Learning to Help
124

Developmental Mathematics Students Achieve: A Multi-Campus Program. Journal on
Excellence in College Teaching, 25(2), 107–130.
Ingalls, J. (1973). A trainer’s guide to andragogy, its concepts, experience, and application. U.S.
Social and Rehabilitation Service; [For sale by the Supt. of Docs., U.S. Govt. Print. Off.]
1973.
Ivory, G. (2019). Technologies to lead schools: Key concepts to enhance student success.
Rowman & Littlefield.
Jena, R., & Mahanti, P. (2014). An Empirical study of Technostress among Indian
Academicians. International Journal of Education and Learning, 3, 1–10.
https://doi.org/10.14257/ijel.2014.3.2.01
Jena, R. K. (2015). Impact of Technostress on Job Satisfaction: An Empirical Study among
Indian Academicians. The International Technology Management Review, 5(3), 117-.
https://doi.org/10.2991/itmr.2015.5.3.1
Jex, S. M. & Britt, T. W. (2014). Organizational Psychology (Third edition.). Wiley.
Karwowski, M., & Kaufman, J. C. (2017). The Creative Self: Effect of Beliefs, Self-Efficacy,
Mindset, and Identity. Academic Press, Elsevier Science & Technology.
Kauppila, O.-P. (2018). How does it feel, and how does it look? The role of employee motivation
in organizational learning type. Journal of Organizational Behavior, 39(8), 941–955.
https://doi.org/10.1002/job.2270
Keefe, E., & Steiner, A. (2018). Remixing the curriculum: the teacher’s guide to technology in
the classroom. In Remixing the curriculum. Rowman & Littlefield Publishers.
Khosrow-Pour, M. (1999). Success and pitfalls of information technology management. Idea
Group Publishing.
125

Kirkpatrick, J., & Kayser Kirkpatrick, W. (2016). Kirkpatrick’s four levels of training
evaluation. atd press.
Knowles, M., Holton III, E., & Swanson, R. (2005). The Adult Learner: The Definitive Classic
in Adult Education and Human Resource Development. In The Adult Learner. Taylor &
Francis Group.
Koehler, M. J., Mishra, P., Hershey, K., & Peruski, L. (2004). With a little help from your
students: A new model for faculty development and online course design. Journal of
Technology and Teacher Education, 12(1), 25–55.
Koehler, M. J., Mishra, P., & Yahya, K. (2007). Tracing the development of teacher knowledge
in a design seminar: Integrating content, pedagogy and technology. (Author abstract).
Computers & Education, 49(3), 740–762. https://doi.org/10.1016/j.compedu.2005.11.012
Korb, R. (2012). Motivating Defiant and Disruptive Students to Learn: Positive Classroom
Management Strategies. Corwin Press, Corwin, SAGE Publications.
Krathwohl, D. R. (1993). Methods of educational and social science research: An integrated
approach. Longman.
Krathwohl, D. R. (2002). A Revision of Bloom's Taxonomy: An Overview. Theory Into
Practice: Revising Bloom's Taxonomy, 41(4), 212–218.
https://doi.org/10.1207/s15430421tip4104_2
Leonard, K. M. (2016). Performance leadership (First edition.). Business Expert Press.
Lindholm, J., Yarrish, K. K., & Zaballero, A. G. (2012). HR forms and job aids. Pfeiffer.
Litwin, M. S. (1995). How to Measure Survey Reliability and Validity. SAGE Publications, Sage
Publications.
126

Mahapatra, M., & Pillai, R. (2018). Technostress in organizations: A review of literature.
Research Papers. https://aisel.aisnet.org/ecis2018_rp/99
Marchiori, D. M., Mainardes, E. W., & Rodrigues, R. G. (2019). Do Individual Characteristics
Influence the Types of Technostress Reported by Workers? International Journal of
Human–Computer Interaction, 35(3), 218–230.
https://doi.org/10.1080/10447318.2018.1449713
Maslow, A. H. (1987). Motivation and personality (3rd ed. / rev. by Robert Frager ... [et al.].).
Harper and Row.
Maxwell, J. A. (2013). Qualitative research design: An interactive approach (3rd ed.). SAGE
Publications.
Mayer, R. (1998). Cognitive, metacognitive, and motivational aspects of problem solving.
Instructional Science, 26(1), 49–63. https://doi.org/10.1023/A:1003088013286
Mayer, J. G. (2011). As Bad as They Say?: Three Decades of Teaching in the Bronx. Oxford
University Press, Fordham University Press, Empire State Editions.
Merriam, S. B. & Tisdell, E. J. (2016). Qualitative research: A guide to design and
implementation (Fourth edition.). Jossey-Bass.
Merriënboer, J. J. G. van. (1997). Training complex cognitive skills: A Four-Component
Instructional Design model for technical training. Educational Technology Publications.
Middleton, I. A., & Marcella, R. (1997). In need of support: The academic help desk. Campus-
Wide Information Systems, 14(4), 120–127. https://doi.org/10.1108/10650749710194510
Miner, J. B. (2005). Organizational behavior I. Essential theories of motivation and leadership.
MESharpe.
127

Naderi, H., Baezzat, F., & Motaghedifard, M. (2015). Effectiveness of quality education based
on Glasser’s choice theory on the student’s academic self-efficacy. European Journal of
Psychology & Educational Studies, 2(2). http://dx.doi.org.libproxy2.usc.edu/10.4103/2395-
2555.170720
Nagengast, B., Marsh, H. W., Scalas, L. F., Xu, M. K., Hau, K.-T., & Trautwein, U. (2011). Who
Took the "×" out of Expectancy-Value Theory?: A Psychological Mystery, a Substantive-
Methodological Synergy, and a Cross-National Generalization. Psychological Science,
22(8), 1058–1066. https://doi.org/10.1177/0956797611415540
Nelson-Le Gall, S., & Brown, A. (1985). Help-Seeking Behavior in Learning. Review of
Research in Education, 12, 55.
Ng, W. (2015). New Digital Technology in Education: Conceptualizing Professional Learning
for Educators (2015th ed.). Springer, Springer International Publishing.
https://doi.org/10.1007/978-3-319-05822-1
Oldknow, A. J., & Knights, C. (2011). Mathematics education with digital technology.
Continuum International PubGroup.
O'Reilly, T. (2007). What Is Web 2.0: Design Patterns and Business Models for The Next
Generation of Software. University Library of Munich, Germany, MPRA Paper, 65.
Patwardhan, A. (2017). Prevalence trends analysis of retrospective cross-sectional data: Use of
yoga for specific health conditions and corresponding providers referral. SAGE
Publications Ltd.
Pawlowski, J. M., Jansen, M., and Koch, O. (2015) Positive computing: a new trend in business
and information system engineering? Business Information Systems Engineering, 57(6),
405-408.
128

Pazzaglia, A. M., Stafford, E. T., & Rodriguez, S. M. (2016). Survey Methods for Educators:
Selecting Samples and Administering Surveys (Part 2 of 3). REL 2016-160. In Regional
Educational Laboratory Northeast & Islands. Regional Educational Laboratory Northeast
& Islands. https://eric.ed.gov/?id=ED567752
Penzias, A. A. (1995). Harmony: Business, technology & life after paperwork (1st ed.).
HarperBusiness.
Peters, L. (2009). Global education: Using technology to bring the world to your students (First
edition.). International Society for Technology in Education.
Pezzulo, G. (2011). Grounding Procedural and Declarative Knowledge in Sensorimotor
Anticipation. Mind & Language, 26(1), 78–114. https://doi.org/10.1111/j.1468-
0017.2010.01411.x
Plonsky, L. (2014). Study Quality in Quantitative L2 Research (1990-2010): A Methodological
Synthesis and Call for Reform: Study Quality in Quantitative L2 Research (1990-2010).
The Modern Language Journal, 98(1), 450–470. https://doi.org/10.1111/j.1540-
4781.2014.12058.x
Presslee, A., Vance, T., & Webb, R. (2013). The effects of reward type on employee goal setting,
goal commitment, and performance. Accounting Review, 88(5), 1805–1832.
https://doi.org/10.2308/accr-50480
Queen Booker, Carl Rebman, & Fred Kitchens. (2014). A MODEL FOR TESTING
TECHNOSTRESS IN THE ONLINE EDUCATION ENVIRONMENT: AN
EXPLORATORY STUDY. Issues in Information Systems, 15(2), 214–222.
Radi Afsouran, N., Charkhabi, M., Siadat, S. A., Hoveida, R., Oreyzi, H. R., & Thornton III, G.
C. (2018). Case-method teaching: Advantages and disadvantages in organizational training.
129

Journal of Management Development, 37(9/10), 711–720. https://doi.org/10.1108/JMD-10-
2017-0324
Ragu-Nathan, T. S., Monideepa Tarafdar, Bhanu S. Ragu-Nathan, & Qiang Tu. (2008). The
Consequences of Technostress for End Users in Organizations: Conceptual Development
and Empirical Validation. Information Systems Research, 19(4), 417–433.
https://doi.org/10.1287/isre.1070.0165
Robbins, S. P. (2013). Organizational behavior (15th ed.). Pearson.
Robinson, S. B., & Firth Leonard, K. (2019). Designing Quality Survey Questions. Los Angeles,
CA: Sage
Robotham, D., & Julian, C. (2006). Stress and the higher education student: A critical review of
the literature. Journal of Further and Higher Education, 30(2), 107–117.
https://doi.org/10.1080/03098770600617513
Rubin, A. & Bellamy, J. (2012). Practitioner's Guide to Using Research for Evidence-Based
Practice (2. Aufl., 2nd ed., 2nd ed). Wiley, John Wiley & Sons, Incorporated, Wiley-
Blackwell.
Rubin, H. J. (2005). Qualitative interviewing the art of hearing data (2nd ed.). Sage
Publications.
Salanova, M., Llorens, S., & Cifre, E. (2013). The dark side of technologies: Technostress
among users of information and communication technologies. International Journal of
Psychology, 48(3), 422–436. https://doi.org/10.1080/00207594.2012.680460
Salkind, N. (2010) Retrospective Study in Encyclopedia of Research Design. SAGE
Publications, Inc. https://doi.org/10.4135/9781412961288.n390
Salkind, N. J. (2014). Statistics for people who (think they) hate statistics (5 edition.). SAGE.
130

Setia, M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian Journal of
Dermatology, 61(3), 261–264. https://doi.org/10.4103/0019-5154.182410
Schmuck, R. A. (1970). Organizational training for a school faculty. Center for the Advanced
Study of Educational Administration, University of Oregon.
Shell, D., Brooks, D., Trainin, G., Wilson, K., Kauffman, D., & Herr, L. (2010). The unified
learning model: How motivational, cognitive, and neurobiological sciences inform best
teaching practices. Springer Science+Business Media. https://doi.org/10.1007/978-90-481-
3215-7
Shipman, M. D. (1997). The limitations of social research (4th ed.). Longman.
Skinner, E. A., Wellborn, J. G., & Connell, J. P. (1990). What it takes to do well in school and
whether I've got it: A process model of perceived control and children's engagement and
achievement in school. Journal of Educational Psychology, 82(1), 22–32.
https://doi.org/10.1037/0022-0663.82.1.22
Smith, C., & Vecchio, R. (1993). Organizational Culture And Strategic Management: Issues In
The Management Of Strategic Change. Journal of Managerial Issues, 5(1), 53–70.
Smith, A. (2012). Organizational culture and identity sport, symbols, and success. Nova Science
Publishers.
Stallard, C. K., & Cocker, J. (2014). Education Technology and the Failure of American Schools.
Rowman & Littlefield.
Sturmer, K., Konings, K., & Seidel, T. (2013). Declarative knowledge and professional vision in
teacher education: Effect of courses in teaching and learning. British Journal of Educational
Psychology, 83(3), 467–483.
131

Suda, L. V. (2008). Aligning strategy, leadership, and culture for project success. Paper
presented at PMI® Global Congress 2008—EMEA, St. Julian's, Malta. Newtown Square,
PA: Project Management Institute.
Suh, A., & Lee, J. (2017). Understanding teleworkers' technostress and its influence on job
satisfaction. Internet Research, 27(1), 140–159. https://doi.org/10.1108/IntR-06-2015-0181
Sun, B., & Li, S. (2011). Learning and acting on customer information: A simulation-based
demonstration on service allocations with offshore centers. (Report). Journal of Marketing
Research, 48(1), 72–86. https://doi.org/10.1509/jmkr.48.1.72
Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2007). The Impact of
Technostress on Role Stress and Productivity. Journal of Management Information Systems,
24(1), 301–328. https://doi.org/10.2753/MIS0742-1222240109
Tarafdar, M., Tu, Q., Ragu-Nathan, T. S., & Ragu-Nathan, B. S. (2011). Crossing to the dark
side: Examining creators, outcomes, and inhibitors of technostress. Communications of the
ACM, 54(9), 113. https://doi.org/10.1145/1995376.1995403
Tarafdar, M., Gupta, A., Turel, O., Srivastava, S. C., Chandra, S., & Shirish, A. (2015).
Technostress creators and job outcomes: Theorising the moderating influence of
personality traits. (Report). 25(4), 355–401. https://doi.org/10.1111/isj.12067
Tarafdar, M., Cooper, C. L., & Stich, J.-F. (2019). The technostress trifecta ‐ techno eustress,
techno distress and design: Theoretical directions and an agenda for research. Information
Systems Journal, 29(1), 6–42. https://doi.org/10.1111/isj.12169
Thigpen, C. (2019). Do bicycling experiences and exposure influence bicycling skills and
attitudes? Evidence from a bicycle-friendly university. Transportation Research Part A,
123, 68–79. https://doi.org/10.1016/j.tra.2018.05.017
132

Thomas, K. W. (2009). Intrinsic motivation at work what really drives employee engagement
(2nd ed.). Berrett-Koehler Publishers.
Tierney, P., & Farmer, S. M. (2011). Creative self-efficacy development and creative
performance over time. Journal of Applied Psychology, 96(2), 277–293.
https://doi.org/10.1037/a0020952
Tondeur, J., van Braak, J., Sang, G., Voogt, J., Fisser, P., & Ottenbreit-Leftwich, A. (2012).
Preparing pre-service teachers to integrate technology in education: A synthesis of
qualitative evidence. Computers & Education, 59(1), 134–144.
https://doi.org/10.1016/j.compedu.2011.10.009
Trautwein, U., Marsh, H. W., Nagengast, B., Ludtke, O., Nagy, G., & Jonkmann, K. (2012).
Probing for the Multiplicative Term in Modern Expectancy-Value Theory: A Latent
Interaction Modeling Study. Journal of Educational Psychology, 104(3), 763–777.
https://doi.org/10.1037/a0027470
Tunison, S. (2016). Cultivating Knowledge: Promoting Research to Enrich Everyday Practice.
Brill | Sense, Birkhäuser Boston, Sense Publishers, SensePublishers.
https://doi.org/10.1007/978-94-6300-603-3
Underwood, J., & Luckin, R. (2011). What is AIED, and why does Education need it?
Usher, E. & Pajares, F. (2008). Sources of Self-Efficacy in School: Critical Review of the
Literature and Future Directions. Review of Educational Research, 78(4), 751–796.
https://doi.org/10.3102/0034654308321456
Venables, L., & Fairclough, S. (2009). The influence of performance feedback on goal-setting
and mental effort regulation. Motivation and Emotion, 33(1), 63–74.
https://doi.org/10.1007/s11031-008-9116-y
133

Ventrice, C. (2009). Make their day! Employee recognition that works ; proven ways to boost
morale, productivity, and profits (2nd ed.). Berrett-Koehler Publishers.
Wang, D., Li, T., Zhu, S., & Gong, Y. (2011). iHelp: An intelligent online helpdesk system.
(Technical report). IEEE Transactions on Systems, Man, and Cybernetics--Part B:
Cybernetics, 41(1), 173.
Wang, K., Shu, Q., & Tu, Q. (2008). Technostress under different organizational environments:
An empirical investigation. Computers in Human Behavior, 24(6), 3002–3013.
https://doi.org/10.1016/j.chb.2008.05.007
Weinert, C., Maier, C., Laumer, S., & Weitzel, T. (2020). Technostress mitigation: an
experimental study of social support during a computer freeze. Zeitschrift Für
Betriebswirtschaft, 90(8), 1199–. https://doi.org/10.1007/s11573-020-00986-y
Wigfield, A., & Eccles, J. S. (2000). Expectancy–Value Theory of Achievement Motivation.
Contemporary Educational Psychology, 25(1), 68–81.
https://doi.org/10.1006/ceps.1999.1015
Wignall, E. (2006). Media, minds, methods: Linking online instruction and media for maximum
effect. Paper presented at the 21st annual conference on Distance Teaching and Learning,
May, 2006. Available: www.uwex.edu/disted/conference/Resource_library/proceedings/05-
1898.pdf.
Williams, J. M., & Goodwin, S. P. (2007). Teaching with technology: An academic librarian's
guide. Chandos.
Wilson, S. B. (2008). Goal setting how to create an action plan and achieve your goals (2nd
ed.). American Management Association.
Wolcott, H. F. (2009). Writing up qualitative research (3rd ed.). SAGE.
134

Youde, A. (2018). Andragogy in blended learning contexts: Effective tutoring of adult learners
studying part-time, vocationally relevant degrees at a distance. International Journal of
Lifelong Education, 37(2), 255–272. https://doi.org/10.1080/02601370.2018.1450303
Zhao, X., Xia, Q., & Huang, W. (2020). Impact of technostress on productivity from the
theoretical perspective of appraisal and coping processes. Information & Management,
103265-. https://doi.org/10.1016/j.im.2020.103265
Zimmerman, B. J. (2000). Chapter 2 - Attaining Self-Regulation: A Social Cognitive Perspective
(pp. 13–39). Elsevier Inc. https://doi.org/10.1016/B978-012109890-2/50031-7

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Appendix A: Survey Protocol
Table 1
Demographics Survey Questions
Demographic Survey Question Responses
Age What is your age group? - 20 -29 years
- 30 – 39 years
- 40 – 49 years
- 50 – 59 years
- 60 – 69 years
- 70 – 79 years
- 80 – 89 years
- None of the above
Gender To which gender identity do you
most identify?
- Male
- Female
- Transgender Male
- Transgender Female
- Gender Variant/Non-
Conforming
- None of the above
Education What is the highest level of
education you have completed?
- Bachelors
- Masters
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- Doctorate
- Post-Doctorate
- None of the above
Teaching
Experience
How many years of teaching
experience do you have?
- 0 – 9 years
- 10 – 19 years
- 20 – 29 years
- 30 – 39 years
- 40 – 49 years
- None of the above
Technology
Experience
How many years of experience do
you have in using technology in
your professional life?
- 0 – 9 years
- 10 – 19 years
- 20 – 29 years
- 30 – 39 years
- 40 – 49 years
- None of the above

Note. This table lists the demographic survey questions used to determine the demographic
information of the sample population.

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Table 2
KMO Influences on Technostress
KMO Influence Influences on
Technostress
Survey Question
(6-pt Likert scale response options: strongly disagree,
disagree, neutral, agree, strongly agree)
Knowledge Declarative
Knowledge
- I often find it too complex for me to use new
technologies (K-DK).
- There are always new developments in the
technologies we use in our organization (K-DK).
- Technologies create many more requests,
problems, or complaints in my job that I would
otherwise experience (K-DK).
Procedural
Knowledge
- I know how to regularly update my skills to
avoid being replaced (K-PK).
- Our faculty know how to be involved in
technology change and/or implementation (K-
PK).
- I know how to deal with technical problems and
my work activities (K-PK).
138

Help-Seeking:
Faculty
Knowledge
- Our organization encourages knowledge sharing
to help deal with new technology (K-DK).
- Our organization emphasizes teamwork in
dealing with new technology-related problems
(K-PK).
- Technologies enable me to reach other
employees (K-PK).
Motivation Self-Efficacy - I learn how to use technology quickly (M-SE).
Expectancy-
Value
- I like to be better at the things I do at work (M-
EV).
- My computer skills help me with my job (M-
EV).
Choice - I am willing to change my work habits to adapt
to new technologies (M-SE).
Persistence - I strive to make my job enjoyable (M-SE).
Effort - I am willing to spend more time studying and
upgrading my technology skills (M-SE).
Performance - I have a higher workload because of increased
technology complexity (M-EV).
- I am forced by technology to work much faster
(M-EV).
Job Satisfaction - I enjoy my job (M-AT).
139

Managerial
Support
- I often feel supported by my manager to perform
at the highest level (M-AT).
Organization Training - Our organization provides faculty training before
the introduction of new technology (O-S).
- I set my own schedule for completing assigned
training (O-C).
Proactive and
Reactive
Responses
- Our organization provides clear instructions or
guidelines to faculty on using new technologies
(O-S).
- Our faculty are encouraged to try out new
technologies (O-C).
Positive
Technologies
- Our organization is committed to adopting
technologies that are easy to use and learn (O-S).
Help Desk
Support
- Our help desk often does a good job of
answering questions about technology (O-S).
Performance
and Help Desk
- Our help desk is responsive to end-user requests
(O-S).
- When I contact our help desk, I am helped
quickly (O-S).
140

Organizational
Support and
Help Desk
- Our help desk is easily accessible (O-S).

Note. This table lists the survey questions used to determine the gaps in knowledge, motivation,
and organizational influences for the technostress experienced by faculty.

Table 3
Open-ended Survey Question
KMO Influence Influences on
Technostress
Survey Question
Knowledge,
Motivation,
Organization
Overall
feedback
- Is there anything else that you would like to
share regarding your experience in technology
and any stress that you may have experienced as
a result?

Note. This table lists the final survey question used to determine any other responses the
participant wishes to provide.

141

Appendix B: Seeking Permission
Electronic Communication 1
Dear Faculty Administrator,
This is Ajit Marathe, and I am currently a doctoral student at the Rossier School of
Education at the University of Southern California (USC).
As part of the dissertation, I am working on a quantitative study to evaluate technostress in
education. I am hoping to send a survey to all SCU faculty members to collect data. This survey
aims to collect de-identified data (survey instrument attached) from the faculty members. I do
not plan to gather any information that will identify the organization or the faculty members.
Additionally, I will identify the organization using a pseudonym (SCU).
I already have IRB approval from USC. Please let me know if you approve or if you may have
any questions.
I am attaching my resume for reference, and I look forward to hearing from you.
Thanks,
Ajit.
Electronic Communication 2
Dear Faculty:
My name is Ajit Marathe, and I am currently a doctoral student at the Rossier School of
Education at the University of Southern California (USC). The USC IRB reviewed my study and
142

certified it exempt from IRB review, and that the SCU IRB office determined that
additional SCU IRB review/exemption is not required.
As part of the dissertation, I am working on a quantitative study to evaluate technostress in
education. Your response to the survey link below will help collect data.
https://usc.qualtrics.com/jfe/form/SV_5dsN8tXrLOcPOx7
This survey aims to collect de-identified data. I do not plan to gather any information that will
identify the organization or a faculty member.
You will need ~4 minutes to complete the survey. Participation in this survey is voluntary, and I
appreciate your time in completing this survey and supporting my research.
Thanks,
Ajit.

143

Appendix C: Information Consent
University of Southern California
Rossier School of Education
Los Angeles, CA 90089

Evaluation of Technostress in Education
You are invited to participate in a research study conducted by Ajit Marathe, principal
investigator Dr. Bryant Adibe, Faculty Advisor at the University of Southern California, because
you are a current faculty at SCU. Your participation is voluntary. You should read the
information below and ask questions about anything you do not understand before deciding
whether to participate. Please take as much time as you need to read the consent form. You may
also choose to discuss participation with your family and friends. If you decide to participate,
you will be asked to sign this form. You will be given a copy of this form.
Purpose of the Study
The purpose of this study is to examine the knowledge, motivation, and organizational influences
on the technostress experienced by faculty.
Study Procedures
If you volunteer to participate in this study, you will be asked to respond to an online survey
created using Qualtrics. You will be afforded to review your responses before submitting them.
You may need 10-15 minutes to complete this survey.
Potential Risks and Discomforts
No risks are foreseen resulting from your participation in this study
Potential Benefits to Participants and/or to Society
Your participation will help find methods to cope with technostress for all faculty. Honest and
insightful responses, regardless of content, will be beneficial for all faculty.
Confidentiality
We will keep your records for this study confidential as far as permitted by law. However, if we
are required to do so by law, we will disclose confidential information about you. The research
team members and the University of Southern California's Human Subjects Protection Program
Informed Consent for Non-Medical Research

144

(HSPP) may access the data. The HSPP reviews and monitors research studies to protect the
rights and welfare of research subjects.
The data will be stored in Qualtrics, which can be accessed by the researcher when they login
into the Qualtrics account provided by the University of Southern California. You will be able to
access your survey responses. A pseudonym will be automatically assigned to you in Qualtrics
when you respond to the survey for inclusion purposes in this dissertation.
Certificate of Confidentiality
(If a Certificate of Confidentiality is used or anticipated to be issued, please use the following
language, otherwise, remove)
Any identifiable information obtained in connection with this study will remain confidential,
except if necessary, to protect your rights or welfare (for example, if you are injured and need
emergency care). The Certificate of Confidentiality will not be used to prevent disclosure to local
authorities of child abuse and neglect or harm to self or others.
When the research results are published or discussed in conferences, no identifiable information
will be used.
Participation and Withdrawal
Your participation is voluntary. Your refusal to participate will involve no penalty or loss of
benefits to which you are otherwise entitled. You may withdraw your consent at any time and
discontinue participation without penalty. You are not waiving any legal claims, rights, or
remedies because of your participation in this research study.
Investigator's Contact Information
If you have any questions or concerns about the research, please feel free to contact Ajit
Marathe: Principal Investigator.
Rights of Research Participant – IRB Contact Information
If you have questions, concerns, or complaints about your rights as a research participant or the
research in general and are unable to contact the research team, or if you want to talk to someone
independent of the research team, please contact the University Park Institutional Review Board
(UPIRB), 3720 South Flower Street #301, Los Angeles, CA 90089-0702, (213) 821-5272 or
upirb@usc.edu

I have read the information provided above, and I agree to participate in this study. I have seen a
copy of this form.
Signature of Research Participant

145

______________________________________
Name of Participant

______________________________________ ________________
Signature of Participant Date

I have explained the research to the participant. I believe that s/he understands the information
described in this document and freely consent to participate.

_____________________________________
Name of Person Obtaining Consent

______________________________________ ______________
Signature of Person Obtaining Consent Date
Signature of Investigator

Abstract (if available)

Abstract The field of education is increasingly reliant on the use of technology in the 21st century. The focus is now on the quality of learning rather than the methods of delivery. Evidence highlights that faculty face a continual need to keep up with technology to provide quality education. As a result, employees experience technostress due to the increased use of technology. The problem of technostress in education is essential to address because it may lead to disengagement, turnover, and low employment performance. This study utilizes the Gap Analysis framework to evaluate the knowledge, motivation, and organizational influences on the technostress experienced by faculty and understand the triggers and inhibitors of technostress and find ways to cope with it.

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Asset Metadata

Creator Marathe, Ajit Ashok (author)

Core Title Evaluation of technostress in education

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Publication Date 04/19/2021

Defense Date 03/24/2021

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

Tag disengagement,education,faculty,gap analysis,KMO,OAI-PMH Harvest,performance,Technology,technostress,turnover

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Adibe, Bryant (committee chair), Maddox, Anthony (committee member), Regur, Carey (committee member)

Creator Email ajitmara@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-446647

Unique identifier UC11667604

Identifier etd-MaratheAji-9497.pdf (filename),usctheses-c89-446647 (legacy record id)

Legacy Identifier etd-MaratheAji-9497.pdf

Dmrecord 446647

Document Type Dissertation

Rights Marathe, Ajit Ashok

Type texts

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

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

Repository Name University of Southern California Digital Library

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