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Burnout and Job Satisfaction in Online Student Support Staff

A dissertation submitted

by

Lisa Marie Haas

toBenedictine University

in partial fulfillmentof the requirements for the degree of

Doctor of Educationin

Higher Education and Organizational Change

Lisle, Illinois

Burnout and Job Satisfaction in Online Student Support Staff

A dissertation submitted

byLisa Marie Haas

toBenedictine University

in partial fulfillmentof the requirements for the degree of

Doctor of Educationin

Higher Education and Organizational Change

This dissertation has been accepted for the facultyof Benedictine University

____________________________ Eileen Kolich, Ph.D. __________Dissertation Committee Director & Chair Date

____________________________ Cassandra Sheffield, Ed.D. ___________Dissertation Committee Reader Date

____________________________ Seung Won Yoon, Ph.D. ________ Dissertation Committee Reader Date

____________________________ Sunil Chand, Ph.D. ________Program Director, Faculty Date

____________________________ Ethel Ragland, Ed.D., M.N., R.N. ________ Dean, College of Education and Health Services Date

ACKNOWLEDGEMENTS

I would like to express my deepest gratitude to everyone who supported me

throughout my doctoral journey. First, I want to thank Dr. Kolich for guiding and

mentoring me throughout the program and through my dissertation. Her continuous

support, advice, and knowledge were an invaluable resource. I would not have made it to

this point without her being a great director and chair.

I also want to thank the rest of my committee for their guidance and direction

throughout my study. Dr. Sheffield would always send an encouraging e-mail at the right

time to help me focus on the big picture. Dr. Yoon provided me with the statistical

expertise that I needed to explore my topic successfully. I cannot say thank you enough.

My sincerest thanks also go out to Dr. Chand, the Benedictine University Higher

Education and Organizational Change faculty, and my classmates who assisted me on my

journey. Your experience, advice, and feedback greatly influenced me. I have learned to

embrace my progressive thinking instead of shy away from it. I appreciate the thought-

provoking discussions and the ways you challenged me so I could strengthen my

arguments. You would not let me settle for less than I was capable of achieving. You

inspired me to set challenging goals and to continue my leadership journey in higher

education.

Last, but not least, I want to thank my family for supporting me throughout my

education. I learned my work ethic from them and know that anything worth working for

requires sacrifice. My time with them was more limited during my studies. I am also

iv

very grateful for the encouragement from my mom to have my own career and to tackle

any obstacles in my way. Yes, Dad, I am finished with my education—at least for the

time being. While my Nana and Poppy are not around to see me finish, I know that I

would make you proud.

v

DEDICATION

This dissertation is dedicated to all higher education professionals who were ever

burned out due to caring too much about the success of students. Thank you for your

perseverance and service.

TABLE OF CONTENTS

Acknowledgements............................................................................................................iv

List of Tables.......................................................................................................................x

List of Figures.....................................................................................................................xi

Abstract..............................................................................................................................xii

Chapter 1: Introduction........................................................................................................1

Burnout, Job Satisfaction, and Online Education..........................................................1

Statement of the Problem...............................................................................................5

Purpose of the Study......................................................................................................6

Research Questions........................................................................................................6

Significance of Study.....................................................................................................7

Definitions.....................................................................................................................7

Limitations.....................................................................................................................8

Delimitations..................................................................................................................8

Chapter 2: Literature Review...............................................................................................9

Burnout..........................................................................................................................9

Job Satisfaction............................................................................................................16

Online Education.........................................................................................................23

Student Support Staff...................................................................................................25

Chapter 3: Methodology....................................................................................................31

Research Design..........................................................................................................31

Population and Sample................................................................................................32

Instrumentation............................................................................................................33

vii

Demographic Information.....................................................................................33

Maslach Burnout Inventory...................................................................................33

Job Satisfaction Survey................................................................................................35

Data Collection............................................................................................................37

Data Analysis...............................................................................................................38

Research Question 1..............................................................................................38




Chapter 4: Findings............................................................................................................41

Restatement of Purpose and Research Questions........................................................41

Data Collection............................................................................................................42

Demographics of Respondents....................................................................................43

Research Questions......................................................................................................45





Summary......................................................................................................................53

Chapter 5: Conclusions and Recommendations................................................................55

Summary......................................................................................................................55

Conclusions..................................................................................................................57


viii




Implications for Practice..............................................................................................60

Recommendations for Future Research.......................................................................61

REFERENCES..................................................................................................................63

Appendix A Consent Form................................................................................................78

Appendix B Demographic Information.............................................................................79

Maslach Burnout Inventory.........................................................................................80

Questions from the Maslach Burnout Inventory-Human Services Edition.................80

Job Satisfaction Survey................................................................................................82

ix

LIST OF TABLES

Table 1. Instructions for Scoring the Job Satisfaction Survey...........................................36

Table 2. Norms for Higher Education in the United States...............................................37

Table 3. Ages of Participants.............................................................................................43

Table 4. Length of Time Worked at the Institution...........................................................44

Table 5. Highest Level of Education Participants Completed...........................................44

Table 6. Participants' MBI Scores.....................................................................................46

Table 7. Indication of Burnout per Category.....................................................................46

Table 8. Participants Who Scored in a Burned Out Range................................................47

Table 9. Functional Work Areas in the Burned-Out Group..............................................48

Table 10. Participants’ Job Satisfaction Scores.................................................................49

Table 11. Intercorrelations Between Burnout and Job Satisfaction..................................50

Table 12. Block-Entry Multiple Regression Analyses Predicting Job Satisfaction From

Demographic Information and Emotional Exhaustion, Depersonalization, and Inefficacy

...........................................................................................................................................52

Table 13. Block-Entry Multiple Regression Coefficients Predicting Job Satisfaction From

Demographic Information and Emotional Exhaustion, Depersonalization, and Inefficacy

...........................................................................................................................................53

x

LIST OF FIGURES

Figure 1. Student services web..........................................................................................26

Figure 2. Relationship among technology, student affairs, and distance learners.............30

xi

ABSTRACT

This study measured burnout and job satisfaction in online student support staff at higher

education institutions. Online education continues to grow, but the effects on staff

members have not been studied. Data were collected online and the Maslach Burnout

Inventory—Human Services Survey was used to measure burnout and its components

exhaustion, cynicism, and self-inefficacy; the Job Satisfaction Survey was used to

measure job satisfaction and nine facets of it; and a general demographics questionnaire

was used to gather background information. Burnout emerged as an emotional reaction,

while job satisfaction was an attitudinal response. The findings indicated that

approximately 57% of the participants showed indications of burnout and, in general, had

an ambivalent attitude toward job satisfaction. There was a strong relationship between

burnout and job satisfaction among the participants, and the strongest correlation was

among emotional exhaustion and job satisfaction. The Maslach Burnout Inventory

variables emotional exhaustion, depersonalization, and inefficacy, when combined with

demographic variables, predicted about 60% of the variance of job satisfaction.

xii

CHAPTER 1: INTRODUCTION

Burnout, Job Satisfaction, and Online Education

Professional burnout can be devastating and can lead to negative consequences

for individuals and their employers, coworkers, customers, and families. Burnout can

occur in any organization and across industries, but it occurs most often in service

organizations such as in the medical and educational fields (Maslach, Jackson, & Leiter,

2010). Burnout is a major concern because most of the individuals who experience job

burnout work with others in various capacities. Burnout is a three-dimensional problem

involving emotional exhaustion, depersonalization, and personal accomplishment

(Maslach, Jackson, et al., 2001). People who experience burnout will start detaching

themselves from others and cannot give all their attention to their work, which can be

detrimental for the individual and for any customers or clients whom they serve.

When burnout occurs in higher education, students are often the ones who do not

receive the best advice and answers. When student support staff, such as academic and

financial aid advisors, become burned out, they cannot deliver the best service, which can

negatively affect their information or satisfaction levels. Psychologically and physically,

the support staff detach themselves from their situation as a coping method (Maslach et

al, 2001). Everyone suffers as a result.

Over the past five years, there has been more emphasis and pressure on higher

education. Government and organizational leaders are placing more importance on

retention, graduation, and completion rates, and President Obama made it a goal to

increase the number of college graduates in the United States, with a goal of becoming

the country with the most college graduates by 2020 (U.S. Department of Education,

1

2011). Online education is one method students choose to complete their education.

According to I. E. Allen and Seaman (2011), “Online courses are those in which at least

80 percent of the course content is delivered online” (p. 7). According to a study by the

Sloan Consortium, which tracks online education trends in the United States by surveying

all higher education institutions, more than 6.1 million students, or about 31% of the

students pursuing higher education in 2010, are taking at least one online course (I. E.

Allen & Seaman, 2011). This rate of increase exceeds the growth of enrollment in higher

education (I. E. Allen & Seaman, 2011). Online education had become part of the long-

term strategy for 65% of the institutions that participated in the study, and the percentage

has been increasing for several years (I. E. Allen & Seaman, 2011).

In online higher education, student support staff who serve students on a full-time

basis are critical to student success. Some agencies such as the Commission on Colleges:

Southern Association of Colleges and Schools make support staff and support services a

requirement for accreditation, as the online programs need to be similar to traditional

programs (Commission on Colleges of the Southern Association of Colleges and Schools,

2010). Support staff provide students with the ancillary information they need tobe

successful in college such as planning classes, facilitating the financial aid process,

finding employment, and coaching students (Floyd & Casey-Powell, 2004). When staff

members are burned out, the quality of their work may be negatively affected. They

detach themselves from work and the students as a coping mechanism, which can

negatively affect the job satisfaction rate of employees and result in lower service and

higher turnover (Maslach & Leiter, 2008). If an institution or specific program has

limited staffing, one individual’s departure can put the service within the overall program

2

at risk (K. Betts, personal communication, July 16, 2012). Dalziel and Payne (2001)

noted, “Providing effective, efficient online student services is an enormous challenge for

higher education administrators” (p. 5). When coupled with burnout, quality service can

be even harder to provide.

Christina Maslach is one of the leading researchers on professional burnout.

Through several studies, she has shown that people in service professions such as

education or health care (Maslach, Schaufeli, & Leiter, 2010) have higher tendencies to

burn out. Student support staff fall into this category. The researcher has witnessed the

burnout of several support staff members over the past 10 years working as both a

support staff member and as an administrator. The researcher also had the opportunity to

talk with advising administrators at other institutions who expressed similar experiences

with their employees. The trend is alarming, and with the increase in online education,

research is needed to explore the burnout and satisfaction of online student support staff.

Brewer and Clippard (2002) inquired into the school support staff at higher

education institutions. They believed that burnout is higher in these staff members

because of the depth and the amount of contact with students. In several institutions,

customers (students) expect quick responses (Maslach, 2003). As a result, the

environment increases the pressure on the employees and can cause burnout. The chronic

stress and environmental factors also can increase the risk of burnout. When it increases,

engagement decreases, which can also lead to lower job satisfaction (Maslach & Leiter,

2008).

Job satisfaction and engagement are important to many organizations within and

outside of higher education, as an engaged and happy staff is often more productive

3

(Vance, 2006). Researchers for organizations such as the Society for Human Resource

Management (SHRM) and Gallup regularly conduct research on job satisfaction and

engagement to look at trends (Mendes, 2011; SHRM, 2011; Vance, 2006). Organizations

also hire consultants and consulting firms to survey their employees on job satisfaction.

There may be a plethora of reasons behind the trend, including the desire to decrease

absenteeism and turnover or to increase the well-being of their employees (Vance, 2006).

No matter the reason, job satisfaction is important to higher education.

A difficult aspect of research on job satisfaction is that there is not one, concrete

definition or description. Most descriptions classify job satisfaction as the way

employees feel about their job (Spector, 1997). Each theory branches in different

directions from the others.

Spector (1997) described job satisfaction as a collection of feelings toward a job.

He measured satisfaction based on nine facets: pay, promotion, supervision, benefits,

contingent rewards, operating procedures, coworkers, nature of work, and

communication (Spector, 1997). Spector created the Job Satisfaction Survey (JSS) to

evaluate job satisfaction in human service positions, and the instrument has since been

validated and normed across different types of organizations.

One of the most widely accepted theories is Locke’s range of affect theory, which

considers job satisfaction as what one values in a job compared to what one has in a job

(Locke, 1976). The focus of Locke’s theory was the value of specific facets of what an

individual enjoys (Locke, 1976). This theory is vastly different from Herzberg’s two-

factor theory, which is also known as the motivator–hygiene theory. According to the

two-factor theory, certain aspects motivate an employee to do well such as the work and

4

responsibility, and other factors lead to dissatisfaction, including supervision and

organizational policy (Herzberg, Mausner, & Snyderman, 1959).

Approximately 71% of employees are disengaged or not completely engaged in

their work (Blacksmith & Harter, 2011). As a result, these employees leave

organizations, which can be costly (Blacksmith & Harter, 2011). According to Mendes

(2011), only 87.5% of employees are satisfied with their employment, which had lowered

due to the downturn in the economic situation in the United States. While the job

satisfaction of professors trended on the higher end of the job satisfaction scale despite

the recent recession, research on support staff is limited (Bozeman & Gaughan, 2011;

Jump, 2010).

5

Statement of the Problem

There are several studies on burnout or job satisfaction in higher education

(Bozeman & Gaughan, 2011; Brewer & Clippard, 2002; Guthrie, Woods, Cusker, &

Gregory, 2005; Love, Tatman, & Chapman, 2010; Perrakis, Galloway, Hayes, &

Robinson-Galdo, 2011). The researchers of these studies focused on faculty, students,

presidents, and other leadership positions. Other studies are qualitative, and the

researchers explored the perceptions of burnout (Gross, Kmeic, Worell, & Crosby, 2001;

Simpson, 2001; Zhang, DeMichele, & Connaughton, 2004). There have also been

several studies on how the online environment affects faculty members (McCann & Holt,

2009; McLawhon & Cutright, 2012; Saterlee, 2010). However, no one had linked

burnout and job satisfaction in online support staff at online higher education institutions.

Spector (1997) linked the two variables: “where job satisfaction is an attitudinal response,

burnout is more of an emotional response to the job” (p. 65). Research was needed on

online student support staff, focusing on the link between burnout and job satisfaction.

By using the Maslach Burnout Inventory (MBI) to gauge the burnout of online

support staff and correlating it to job satisfaction measured by the JSS, higher education

leaders can address the problem of burnout for their institution and for individuals. This

study has the potential to benefit support staff and their well-being to increase the quality

of services students receive, which may lead to higher student retention, satisfaction, and

completion rates.

A survey to gather “attitudes, opinion, behaviors, or characteristics of the

population” (Creswell, 2008, p. 388) can be a useful step to understand better the

relationship between burnout and online staff members’ job satisfaction. Several

6

departments fall within the support staff realm, including Academic Advising, Career

Services, Student Accounts, Financial Aid, Registrar’s Office, Learning Center,

Academic Support, and Quality Assurance. The different departments mainly interact

with students and communication is completed at a distance.

Purpose of the Study

The purpose of this study was to apply Maslach’s theory of burnout and examine

the relationship between perceived exhaustion, cynicism, and self-inefficacy and job

satisfaction according to the JSS in online student support staff in higher education

institutions located in the United States.

Research Questions

The research questions and hypotheses for the research study were as follows:

RQ1: To what extent does support staff feel burned out in an online higher

education environment?

RQ2: How satisfied are support staff members who work in an online higher


RQ3: What is the relationship between burnout and job satisfaction in online

support staff?

RQ4: How well does burnout predict job satisfaction in online student support

staff?

Significance of Study

As online education continues to grow, administrators need research to help guide

their staffing decisions. Very little research has been conducted on staff members at

online higher education institutions. As a result, this research can set the foundation for

7

burnout and job satisfaction in higher education. Specifically, this study can provide

leaders in higher education the information to reduce burnout and improve the job

satisfaction of their employees.

Definitions

Burnout: Burnout is a “prolonged response to chronic emotional and interpersonal

stressors on the job, and is defined by the three dimensions of exhaustion, cynicism, and

inefficacy” (Maslach et al., 2001, p. 397).

Engagement: Engagement is “an energetic state of involvement with personally

fulfilling activities that enhance one’s sense of professional efficacy” (Maslach & Leiter,

2008, p. 498).

Job satisfaction: Job satisfaction “is the extent to which people like (satisfaction)

or dislike (dissatisfaction) their jobs” (Spector, 1997, p. 2).

Student support staff: Student support staff “includes all personnel whose primary

responsibility is to support the academic program of students. It includes all pedagogical

support staff, as well as other professional support staff employed” (Organisation for

Economic Co-operation and Development, 2002, p. 12) in higher education institutions.

Limitations

This research study was limited to the student support staff at institutions located

in the United States. It was limited to the feelings toward burnout and job satisfaction at

one moment in time. These feelings may have changed over time or with any

interventions that the institutional leaders may have implemented to increase employee

morale. The study was also limited to the staff members who participated in the study.

8

Delimitations

The study included several delimitations. The researcher used a convenience

sample to collect data from a large number of participants during a 1-month period. As a

result, the participants did not represent all types of institutions or programs. The

researcher chose to solicit participants through social media, e-mail, and organizational

electronic mailing lists that were available. The results were also limited to the

measurements of the MBI and the JSS.

9

CHAPTER 2: LITERATURE REVIEW

Burnout

Professional burnout occurs in many professionals, especially in the helping

professions. It is defined as “a prolonged response to chronic emotional and

interpersonal stressors on the job, and is defined by three dimensions of exhaustion,

cynicism, and inefficacy” (Maslach et al., 2001, p. 397). It can also be defined as

“overwhelming exhaustion, feelings of cynicism and detachment from the job, and a

sense of ineffectiveness and lack of accomplishment” (Maslach et al., 2001, p. 399).

Gorkin (n.d.) noted that a person who is burned out does not see the results, does not feel

adequately recognized for the work or rewarded properly, and does not see any end in

sight. Haddad (1998) noted that burnout makes employees irritable and increases apathy.

Historically, burnout has gone through many revisions and theories. In the 1960s,

people started gaining interest in burnout. The interest started with a pioneering phase,

where psychologists viewed burnout as emotional stress, and the coping strategies

included job identity (Maslach et al., 2001). Burnout was thought only to occur in social

services industries such as the medical field. In the pioneering phase, researchers found

that depersonalization occurred naturally in people who were burned out. It was a coping

strategy to help people distance themselves from the emotions of their jobs (Maslach et

al., 2001). In the helping professions, the constant negative feedback from clients made

this aspect worse.

After the pioneering phase, the empirical phase added new insights into

professional burnout. The revised theory added job fulfillment and commitment and their

effects on job turnover (Maslach et al., 2001). Around this time, psychologists were

10

starting to consider the psychological effects of job stress. They believed that only the

best and most idealistic employees ever experienced burnout after a long exposure to job

stressors (Maslach et al., 2001).

According to Maslach et al. (2001), psychologists also started to evaluate

situational factors, quantitative demands, and occupational characteristics and started to

look at the amount of workload that a person was supposed to accomplish and the

pressure that the person experienced daily. After this, the psychologists started to

evaluate job role and any conflicts within positions, which led them to investigate the

social support from coworkers and managers. They found that workers who did not have

support had fewer resources, which led to an increased burnout level.

No matter what theory anyone chooses, burnout has numerous negative effects on

individuals. The negative effects include job withdrawal, lower productivity,

absenteeism, tardiness, and employee turnover (Maslach et al., 2001). It also has

negative effects on one’s health, including increased stress, depression, and

gastrointestinal problems (Maslach et al., 2001).

According to recent studies, burnout is more likely to occur for people who fit

into a combination of demographic characteristics, including age, marital status,

educational level, and personality type (Cordes & Dougherty, 1993; Lee & Ashforth,

1996; Maslach et al., 2001). Employees who are in their 20s, are single, and have at least

a baccalaureate degree are more likely to get burned out because they want to do well and

have fewer obligations outside of the professional environment (Maslach et al., 2001).

Furthermore, these employees are more at risk to burn out if they have a Type A

personality, which indicates that they like to be in control and strive for perfectionism

11

(Maslach et al., 2001). Employees are also more likely to burn out if they are newcomers

to an organization or in their career (Dunford, Shipp, Boss, Angermeier, & Boss, 2012;

Tull 2006). Tull (2006) noted, “The culture of an organization, particularly in higher

education, has the potential to influence a new professional’s retention” (p. 465) and

burnout rates.

Personality may also play a role in burnout. Swider and Zimmerman (2010) have

explored the five personality traits and their relationship to burnout: neuroticism,

extraversion, agreeableness, openness, and conscientiousness. Neurotic individuals show

signs of being depressed, fearful, insecure, anxious, and nervous (Digman, 1990). They

often see the negative side of situations, which manifests in depersonalization (Swider &

Zimmerman, 2010). Extroverts are less likely to burn out compared to introverts due to

their optimism and hopefulness in situations (Layman & Guyden, 1997; Swider &

Zimmerman, 2010). People who are open tend to have lower levels of exhaustion and

depersonalization and higher levels of efficacy by working positively through ambiguity

(Swider & Zimmerman, 2010). Conscientious individuals typically have a strong work

ethic, persevere throughout obstacles, and are goal-orientated; as a result, they have lower

levels of burnout, since they do not detach themselves from the situation (Swider &

Zimmerman, 2010).

All past research and demographics have led to the newest theories on burnout.

Maslach’s theory has three phases and six dimensions to address professional burnout.

The three phases are exhaustion, cynicism, and inefficacy (Maslach et al., 2001; Angerer,

2003).

12

The first stage of burnout is exhaustion. This stage includes constant fatigue from

stressors in the workplace. Angerer (2003) indicated that multitasking has become

normal, which increases the amount of work that employees are expected to perform.

Multitasking also adds to the exhaustion phase because employees cannot complete the

required amount of work within the time allotted. Maslach (2003) stated that exhaustion

is the first outward symptom of too much stress in the professional environment.

Employees also feel that recovery is impossible. This starts the cycle of burnout.

When employees reach the second phase or the cynicism phase, they start to

detach themselves from the work environment. As noted in previous studies, this is the

body’s natural defense to burnout (Maslach et al., 2001). A person detaches from clients

or the work environment as a way to eliminate some of the chronic stressors. Most

employees do not realize that they are acting this way. After employees reach this phase,

they usually share their negative thoughts and actions with others. This can be

contagious to other employees who may start to feel burned out as well (Maslach et al.,

2001).

The third and final stage of Maslach’s theory of burnout is inefficacy. When

employees are in this phase, they are dissatisfied with their job and with their enjoyment

of it. The employees also suffer from a decrease in productivity (Maslach et al., 2001).

At this stage, if an intervention is not provided, employees will start to look for another

job. The only way to overcome burnout at this stage is to increase engagement, as

discussed later in the literature review (Maslach et al., 2001).

13

Along with the stages of professional burnout, six dimensions are the underlying

causes of burnout. Although there are six dimensions, it only takes imbalance in one or

two aspects for an employee to become burned out.

The first two dimensions include workload and control. If employees have too

much to do within their scheduled workday hours, they begin to feel that they cannot

accomplish anything. If employees feel that there is no end to the work, recovery

becomes impossible (Maslach et al., 2001). Technology has increased workloads, as

customers expect very quick turnarounds with e-mail and other forms of technology

(Leiter & Maslach, 2001). In higher education, this expectation is no different from

students or parents as the customer. The workload can be, and often is, the start of

burnout. The next area that often accompanies workload is control. Employees prefer to

make decisions about their work and the way they perform it. When employees feel they

have lost control, and their managers have more control, the employees lose balance.

Focus starts shifting away from individuals and onto teams, which results in a greater

need for organization and coordination, which can decrease the control individuals have

over their work (Leiter & Maslach, 2001). Additional factors such as operations and

technology have also decreased an individual’s control (Leiter & Maslach, 2001). When

employees have less control of their work, their chances of burnout increase.

The next two areas of professional burnout are reward and fairness. Employees

want to be rewarded fairly for their work. It varies for each individual, but a fair reward

usually includes fair pay, recognition from managers and peers, and any other benefits of

the profession (Maslach et al., 2001). A fast-paced environment can take away from the

time to enjoy personal rewards in the workplace (Leiter & Maslach, 2001). If this area is

14

out of balance, employees usually have a lower job satisfaction rate. If employees do not

feel that they are treated fairly, they will have an increased rate of cynicism. Employees

may feel that they are treated unfairly if other employees are treated better than they are

or if they feel they are being singled out in a negative way. The intense pace in work

environments increases the intensity of fairness; if an individual feels that the

environment is not fair, those feelings will create an “intense emotional impact” (Leiter &

Maslach, 2001, p. 50) for the individual, which will lead to burnout.

The last two areas include community and values. If employees feel that their

values do not match the values of an organization, or if they feel the services or products

are unethical, the employees will be more cynical and will start to detach from their work

(Maslach, Schaufeli, & Jackson, 2001). Technology has increased the pace of work and

can result in people losing their connection to their work (Leiter & Maslach, 2001).

Employees are also expected to increase their productivity, which can lead to a

compromise in their values (Leiter & Maslach, 2001).

The final area of burnout is community. Employees need to feel that they have a

social network at work with coworkers or management. If employees feel that there is no

one to talk to, they are more likely to feel exhausted. However, this area may also be

unbalanced if an employee is already burned out. The sense of community has increased

due to technology. Virtual communities such as Facebook and LinkedIn can increase a

sense of belonging but can compromise the connection with people locally (Leiter &

Maslach, 2001). Detachment from work will cause an employee to lose touch with the

local community.

15

After investigating and researching burnout for many years, Maslach and Jackson

created the MBI in 1976 (Maslach, Jackson, et al., 2001). The survey includes 22

questions relating to the three dimensions and six areas of burnout. The survey includes a

5-point Likert-type scale with ratings ranging from never to almost always (Maslach,

Jackson, et al., 2001). The survey underwent three revisions between 1976 and 2015.

There are also three different inventories. The first and original survey addressed burnout

in social service organizations such as in the medical industry. The second survey is the

Educational Survey, designed for professionals in education. The third inventory is

general and addresses burnout in any other industry (Maslach, Jackson, et al., 2001).

An individual dealing with burnout, like someone suffering from any negative,

long-term experience, needs to cope with the situation. Over the years, many suggestions

have emerged for coping with professional burnout. However, no longitudinal studies

have dealt with coping strategies for burnout. Almost all current coping strategies

include working with an individual on stress management or with the exhaustion phase of

burnout (Maslach et al., 2001). Almost none of the coping techniques have dealt with the

cynicism or inefficacy phases of burnout. The most effective coping strategy is working

individually with a burned out employee to cope with individual factors and to see what

management may be able to change about the working conditions.

The newest strategy is to engage employees in their position. According to

Maslach (2003), engagement is the “antithesis of burnout” (p. 190), as it includes creating

energy for work, involvement in the position and with the work, and increasing

effectiveness. Each company can work with individuals experiencing burnout by

16

combining educational resources, changes in the job environment, and managerial styles

(Maslach et al., 2001).

Job Satisfaction

Job satisfaction refers to the way employees feel about their jobs (Spector, 1997),

which can lead to employee behavior that affects organizational performance. Job

satisfaction has been assessed and researched for years and is often measured through

questionnaires (Spector, 1997). There is a plethora of research and differing perspectives

on job satisfaction.

Every few years, organizations and associations such as the Gallup Organization

survey American workers to see how they feel about their jobs (Spector, 1997). Like

burnout, job satisfaction is multidimensional. Some of the common facets include

“appreciation, communication, co-workers, fringe benefits, job conditions, nature of work

itself, organization itself, organizational policies and procedures, pay, personal growth,

promotion opportunities, recognition, security, supervision” (Spector, 1997, p. 3).

From a business or educational perspective, employees’ job satisfaction can affect

business goals. As a result, human resources staff and other organizational leaders study

job satisfaction. The Society of Human Resource Management (SHRM) conducts

periodic assessments of employees’ job satisfaction. SHRM (2011) reported, “The best-

performing employers know that taking their employees’ pulse and linking it to their

business goals will help companies succeed and put them at a competitive advantage” (p.

3).

A well-known job satisfaction theory is Locke’s theory of affectiveness, which

divides job satisfaction into four categories: rewards, other people, nature of work, and

17

organizational context. Locke noted, “Job satisfaction results from the perception that

one’s job fulfills or allows the fulfillment of one’s important job values” (as cited in

Henne & Locke, 1985, p. 222). According to Henne and Locke (1985), people want

fairness in regard to pay, to earn enough money to cover expenses, clarity, and

availability for promotions. They also want to work in a convenient and safe

environment (Henne & Locke, 1985).

According to the job characteristics theory, the “content and nature of job tasks

themselves” (Spector, 1997, p. 31) can lead to an increase or decrease in job satisfaction.

This knowledge led Herzberg (1974) to the theory that job design is one way to increase

job satisfaction. Hackman and Oldham (1976) found that people who have intrinsic

motivation are more likely to do the job well. This theory can also be traced back to the

Hawthorne studies when paying attention to employees caused them to perform better

(Spector, 1997). However, in 2010, only 53% of the population stated the work itself is

an important factor in their job satisfaction (SHRM, 2011).

The job characteristic theory shows that there are five characteristics of any job:

skill variety, task identity, task significance, autonomy, and job feedback (Spector, 1997).

The five characteristics cause three psychological states: meaningfulness of work,

responsibility, and knowledge of results of products (Hackman & Oldham, 1976). If all

three are met, an employee will be motivated to perform well. It also states that people

who prefer a challenge in their work will be happier in jobs that are more complex. This

theory was developed into the Job Descriptive Survey (Spector, 1997).

Herzberg offered another perspective of job satisfaction. Herzberg divided

factors into two groups: motivators and hygiene factors. Motivators describe the work

18

itself and lead to high job satisfaction (Herzberg, 1974). Motivators include

“achievement, recognition for achievement, interesting work, increased responsibility,

growth, and advancement” (Herzberg, 1974, p. 18). Contrarily, hygiene factors describe

the work context and when not present lead to job dissatisfaction; they describe how well

people are treated (Herzberg, 1974). Hygiene factors include “[company] policy and

administration practices, supervision, interpersonal relationships, working conditions,

salary, status, and security” (Herzberg, 1974, p. 18).

People in the United States are typically happy with the nature of the work that

they do, but are not satisfied with the rewards and pay (Spector, 1997). According to

SHRM (2011), 83% of U.S. employees are satisfied with their jobs. About 41% said that

they were very satisfied, and 42% said that they were somewhat satisfied. This is a

downward trend since 2009 (SHRM, 2011). Many employees rated the ability to use

their skills and abilities as one of the top reasons for their job satisfaction (SHRM, 2011).

Age is another demographic feature that may influence job satisfaction.

Generally, job satisfaction increases with age, which may be due to changed expectations

(Spector, 1997). According to SHRM (2011), employees over 67 were more likely to be

very satisfied than younger employees. Wright and Hamilton (1978) assumed that the

expectations become lower as a person ages, or older workers have better jobs and are

more skilled than their younger counterparts are.

Different countries have different levels of job satisfaction, which shows that

culture can influence job satisfaction. For example, Japan has lower job satisfaction than

the United States, and the Dominican Republic has a higher overall satisfaction (Spector,

1997). The differences fall within the different facets of job satisfaction. The United

19

States falls lower with pay and promotion. Spector (1997) noted, “Americans tend to

believe they should advance at work. When they do not, they are likely to be dissatisfied

with promotion opportunities” (p. 24). SHRM found the same information. Employees

who are under 30 or who are in middle-level management have the expectation that

promotional opportunities will be available to them (SHRM, 2011). The promotion or

lack of promotions affects their job satisfaction. People in the United States also feel that

pay is in the top five characteristics that lead to their job satisfaction (SHRM, 2011).

According to SHRM (2011), the direct supervisor rates among the top five

reasons for job satisfaction. It ranks higher than benefits and compensation (SHRM,

2011). Nearly three fourths of employees were satisfied with this aspect (SHRM, 2011).

Several antecedents may influence job satisfaction. In 2010, employees viewed job

security as the most important facet of their job satisfaction (SHRM, 2011). The

weakened economy or the aging population may influence this phenomenon. The

economy affects job turnover, which is a consequence of job satisfaction. Job turnover

and job satisfaction are highly correlated at a time when unemployment is low and less

correlated during times of higher unemployment (Spector, 1997).

Organizational constraints can also be antecedents to job satisfaction. There have

been higher relationships between job constraints, job performance, and job satisfaction

(Spector, 1997). Some of these aspects include the budget, tools, supervisors, training,

and work environment (Spector, 1997). The employee workload may also be an

antecedent for job satisfaction. Both quantitative and qualitative work may have an

impact, as well as the control that employees have on their day-to-day work functions.

Control can range from being able to make decisions to autonomy (Spector, 1997).

20

Role ambiguity and conflict can also affect job satisfaction (Singh, Goolsby, &

Rhoads, 1994; Spector, 1997). Role ambiguity means that individuals are unsure of the

demands of their supervisor (Spector, 1997). Role conflict occurs when there are

competing demands upon an employee (Spector, 1997). Conflict can also occur between

competing obligations such as work and family. These types of uncertainty can decrease

job satisfaction. Other items may increase job satisfaction such as flexible scheduling

(Thomas & Ganster, 1995). Pay did not have as large an influence on job satisfaction as

fairness in pay did (Henne & Locke, 1985; Spector, 1997).

Job stress can negatively influence job satisfaction. Job stressors are items that

require the employee to adapt (Jex & Beehr, 1991). For example, customers yelling at an

employee can cause long-term job dissatisfaction. Job strain is a response to a job

stressor, which manifests in behavioral reactions such as quitting a job, physical reactions

such as health concerns, or psychological reactions such as frustration (Jex & Beehr,

1991).

Staw and Ross (1985) indicated that job satisfaction is partly due to personality.

The locus of control is one of the determinates of satisfaction, which is how individuals

feel about various sectors of their life. Some people also have negative affectivity if they

tend to feel depressed and have negative emotions in other areas of their life, they may

also have lower job satisfaction. There is also person–job fit, where employees will be

less satisfied if they do not want or like some aspect of their job (Staw & Ross, 1985).

One of the potential effects of job satisfaction is organizational citizenship

behavior, which is a “behavior by an employee intended to help coworkers or the

organization” (Spector, 1997, p. 57). A few examples are being on time, wanting to

21

improve things, and helping others. Job satisfaction can increase organizational

citizenship behavior, which most supervisors do not tie into performance (Spector, 1997).

Another effect is hostility. When job satisfaction is low, hostility is increased (Chen &

Spector, 1992).

Job satisfaction is also related to burnout. The difference is that job satisfaction is

an attitudinal response and burnout is an emotional response. Spector (1997) noted,

“Burnout correlates significantly with job satisfaction in that dissatisfied employees are

likely to report high levels of burnout” (p. 66). Lee and Ashforth (1993) also found

stronger relationships between job satisfaction and emotional exhaustion than with

personalization.

Henne and Locke (1976) noted that employees can increase their satisfaction by

changing the way that they think about the job. It is possible that an individual has

misinterpreted a situation such as a disagreement with a manager, and it affected their

satisfaction towards their job. An employee would also be able to change job values for a

situation, which might lead to an increase in job satisfaction.

There are several different questionnaires to assess job satisfaction. Some are

based on different facets of job satisfaction, and others are based on overall job

satisfaction. There are positives and negatives about using the different scales.

The most common assessment is the Job Descriptive Index, which has 72

questions that assess five facets of job satisfaction: work, pay, promotion, supervision,

and coworkers (Spector, 1997). The scale lists a variety of adjectives about aspects of the

job, and the participants say if the word describes their job or not. The scale does not rate

22

job satisfaction overall but only individual aspects (Bowling Green State University,

1997).

The JSS is a survey that assesses nine different facets of job satisfaction and

measures more facets than the Job Descriptive Index (Spector, 1997). The facets are pay,

promotion, supervision, fringe benefits, contingent rewards, operating conditions,

coworkers, nature of work, and communication (Spector, 1997). Spector (1985) designed

it to evaluate job satisfaction in human services personnel, as other questionnaires were

not specific to that industry.

The Minnesota Satisfaction Questionnaire assesses 20 facets of job satisfaction.

The facets include “achievement, activity, advancement, authority, compensation,

creativity, security, social service, moral values, supervision, and variety” (University of

Minnesota Department of Psychology, n.d.). There is a long and a short version of the

scale. The long version has 100 questions with five questions for each facet. The shorter

version only has 20 questions (Spector, 1997). The negative to this scale is that the

different facets are highly correlated, which means that several of the questions assess the

same thing (Spector, 1997).

The Job Diagnostic Survey is another assessment that focuses more on the human

side of job satisfaction. The focus of the survey is on the nature of the job and the task,

the person’s motivation, personality, and psychological states (Spector, 1997). The Jobs

in General Assessment focuses on satisfaction overall. It does not focus on specific

facets within the survey.

23

Online Education

Online education has become a large influencing factor in higher education. It

was expected to be the largest form of education by 2015 (Ambient Insight, 2010).

Although online education is new, distance education is not. According to the U.S.

Distance Learning Association (2012), distance learning is “the acquisition of knowledge

and skills through mediated information and instruction, encompassing all technologies

and other forms of learning at a distance” (para. 3). Online education falls into this

category and is only one medium for delivering online education. In 2008,

approximately 4,277,000 people in the United States had taken at least one online course

(National Center for Education Statistics, 2011). This trend will continue as more

students advance their education, as online programs offer convenience, flexibility, and

access to postsecondary classes while not interfering with one’s outside obligations and

responsibilities (Taylor & Holley, 2009).

Distance learning in higher education took root in the 1800s as correspondence

classes and then television classes (Perry & Pilati, 2011). By the mid-1990s, distance

learning transformed into online learning. In 2002, approximately 1.6 million students

were enrolled in online courses, and this number tripled by 2008 (I. E. Allen & Seaman,

2010). Online learning offers several benefits that correspondence classes could not

offer, such as synchronous and asynchronous communications across different platforms

to bridge any gaps in the time zone (Perry & Pilati, 2011). Technology is continuing to

transform online learning. Part of the appeal for students is they can take courses when

they normally would not be able to attend in person (Perry & Pilati, 2011).

Administrators tend to like online learning because it can be cost effective if done

24

correctly (Perry & Pilati, 2011). To increase the success, online institutions should use a

business model instead of an education model (Cox, 2009).

Since 2012, the students taking online courses have been from different

generations. About 47% of students who take online courses are traditional-aged

students (18 to 25 years old), and 47% are non-traditional-aged students (Instructional

Technology Council, 2012). The students will need a variety of resources to be

successful in the environment, such as technology support (Hornak, Akweks, & Jeffs,

2010). The support that one student needs will be different from the support another

student needs.

Due to the self-directed nature of the online environment, attrition levels are 10-

20% higher than in traditional classrooms (Perry & Pilati, 2011). Nash (2005) found that

the main reason students stop attending online courses is time management. A few other

reasons include the classes being too difficult, not being able to get the help when they

needed it, or taking on too much (Nash, 2005). Other studies have reported that the lack

of family support, increased job demands, and curriculum relevance may be reasons why

students leave classes (Martinez, 2003; Park & Choi, 2009).

Students still need collaboration and support while taking online courses, since

satisfaction is also lower in an online environment compared to a face-to-face

environment (Hiltz, Coppola, Rotter, Toroff, & Benbunan-Fich, 2000; Levy, 2005). To

increase retention, institutions need to increase student satisfaction levels (M. Allen et al.,

2002). One of the main reasons for lowered satisfaction is the feeling of isolation and the

lack of a sense of belonging to institution, which may stem from the physical void of

attending a campus (Alston et al., 2005; Buchanan, Myers, & Hardin, 2005; Owens,

25

Hardcastle, & Richardson, 2009). Song, Singleton, Hill, and Koh (2004) found that 71%

of students who were not as satisfied with online learning felt a lack of community

support. Learners need to feel like they are part of a community and part of the

classroom, which will increase their engagement at the institution (Perry & Pilati, 2011).

Some of these resources include advising, library services, and areas for social activity,

such as learning communities (Angelino, Williams, & Natvig, 2007).

One way to prevent attrition in online environments is to provide the right

services, which student support staff often run. According to Hughes (as cited in

Angelino et al., 2007),

Information that should be available for students includes assessments, educational counseling, administrative process such as registration, technical support, study skills assistance, career counseling, library services, students’ rights and responsibilities, and governance. In order to maximize participation in student government, all meetings should be available in electronic format in an effort to engage distance learners. (p. 8)

Student retention is important for universities, as it is less expensive to retain a student

than to replace a student (Hu, 2012). Accrediting bodies, such as the Western

Association of Schools and Colleges and the Accrediting Council of Independent

Colleges and Schools, are also looking at student retention as one of the factors for

accreditation (Accrediting Council of Independent Colleges and Schools, 2010; Fain,

2012; Western Association of Schools and Colleges, 2012). One of the ways to improve

retention is to increase involvement with the help of student support staff.

Student Support Staff

According to the Western Interstate Commission for Higher Education

Cooperative for Educational Technologies (WCET, 2002), student services in an online

environment are all about the people. Students expect the same level and quality of

26

services as students who attend a traditional campus. Taylor and Holley (2009) noted,

“Effective student affairs practice in an online environment is oriented toward facilitating

student learning rather than simple service delivery” (p. 82). Additionally, a university

cannot achieve full student success in an online environment until student services are

fully implemented (WCET, 2002). Student support services, student services, and

student support staff are phrases used interchangeably and may mean something different

to each campus. Figure 1 shows what student services may look like for an online

student (WCET, 2002). A few of the departments that students regularly interact with are

advising, registrar, technical support, placement services, and the library (Pullan, 2010;

WCET, 2002).

Figure 1. Student services web.

From “Guidelines for Creating Student Services Online” (p. 3), by Western Interstate Commission for Higher Education Cooperative for Educational Technologies, 2002, http://www.acswasc.org/pdf_general/WASC _CriteriaSamplePrompts_Cat_C.pdf. Copyright 2002 by WCET.

27

Students who take online classes typically expect just-in-time services to get the

information that they want or need when they want it (Pullan, 2010; WCET, 2002). They

also want customized and personalized services to meet their needs (Cain, Marrara, Pitre,

& Armour, 2003; Shea, 2005). In 2009, Taylor and Holley found that students taking

online programs desired student support throughout their program. According to Astin

(1999), “The amount of student learning and personal development associated with any

educational program is directly proportional to the quality and quantity of student

involvement in that program” (p. 519). The student services that students receive directly

affects their success, as students are involved in the process.

Student support staff is critical to the student learning process. They help students

grow emotionally, personally, and cognitively while supporting the mission of the college

or university (Sandeen & Barr, 2006). The staff also fulfill the need for academic and

psychosocial support that students in online programs need, as the students feel isolated

(Cain et al., 2003). As distance learning grows, student services staff members are

becoming generalists in the institution, as students want a single point of contact (Hirt,

Cain, Bryant, & Williams, 2003; Schwitzer, Horton-Parker, & Jurgens, 2002). They are

also the link between the students, the faculty, other departments, and institutional leaders

(Hu, 2012; Schwitzer et al., 2002).

In 2011, a survey of colleges showed that student services is one area where

colleges have decreased services (Instructional Technology Council, 2012). Hu (2012)

noted, “At many universities, funding cuts, shrinking recruitment budgets, added

responsibilities and growing technology . . . have made their jobs increasingly

challenging” (p. 18). University leaders are trying to budget wisely with the funding that

28

they have. The area that college administrators find most challenging is how to provide

the best services for students taking classes online (Instructional Technology Council,

2012).

Support services are also becoming more important because regional accrediting

bodies are requiring distance learning programs to provide services equal to their on-

campus counterparts (Chau, 2012; Dare, Zapata, & Thomas, 2005). However, higher

education professionals are starting to find that equal services do not provide the same

level of support; therefore, the programs need to be designed for distance learners issues

and concerns (Dare et al., 2005).

Student support staff are directly responsible for student satisfaction from the time

students inquire about a program through graduation. The recruitment or enrollment

teams are the “gatekeepers of incoming” (Hu, 2012, p. 18) students heading up the

communication between the student and the university and between university

departments. Student support departments are also blending online programs to provide

the best support for learners. Enrollment services often include admissions, financial aid,

and registration, as distance learners do not want to talk to many different departments

(Hirt et al., 2003; Shea, 2005).

Student support services in an online environment can be stressful. Students want

services synchronously and asynchronously (Pullan, 2010). They also expect immediate

responses. Pullan (2010) stated, “Given the demand and the response, it is apparent that

education is becoming a commodity, and students are the consumers” (p. 242). This

phenomenon puts extra stress on student support staff to respond to e-mails quickly while

still providing a high-touch service so students feel as if they are connected to the

29

institution (Dare et al., 2005; SchWeber, 2008; Schwitzer et al., 2002). E-mail, social

media, and message boards are replacing face-to-face interaction; they are the standard in

distance education (National Center on Disability and Access to Education, 2007). The

staff members may also work varied hours, as students who attend classes online have

many responsibilities, live in various time zones, and are not always available during the

day (Cain et al., 2003). The staff members are also constantly multitasking, prioritizing,

and communicating to different constituents (Schwitzer et al., 2002). The fast-paced

environment can lead to added stress and burnout (Leiter & Maslach, 2001).

Shea (2005) outlined that student services in an online environment need to meet

student expectations. Students want on-demand services personalized for them, which

include the right messaging at the right time (Shea, 2005). They also want two-way

communication in multiple modalities. If the students are from a low socioeconomic

background, they may have additional needs, as they are more likely to be academically

unprepared for higher education, and they are more likely to succeed with extra guidance

and support from a community of their peers and support from the institution (Engstrom

& Tinto, 2008). One way to achieve this extra support is through learning communities

based on particular subjects by combining social integration with tutoring (Angelino et

al., 2007; Muilenburg & Berge, 2005).

In general, there is a lack of research on student affairs and student support

services in online and distance education (Dare et al., 2005). As shown in Figure 2, the

relationship between student affairs, distance education, and technology is still evolving.

30

Figure 1. Relationship among technology, student affairs, and distance learners.

From “Assessing the Needs of Distance Learners: A Student Affairs Perspective” by L. A. Dare, L. P. Zapata, and A. G. Thomas, 2005, New Directions for Student Service, 2005(112), p. 40.. Copyright 2005 by Wiley Periodicals, Inc.

Student support services can increase enrollment, decrease attrition, and provide

for a holistic educational experience (LaPadula, 2005). As a result, more research is

needed on student support services in distance education, as it can improve student

learning and increase graduation rates.

31

CHAPTER 3: METHODOLOGY

Research Design

The Maslach theory of burnout predicts that employees burn out due to several

factors with symptoms of exhaustion, depersonalization, and self-inefficacy, which leads

to disengagement in the workplace. As a result, employees may have lowered job

satisfaction in one category or with overall job satisfaction. Burnout and job satisfaction

have been studied in higher education and student support staff. However, no researchers

have shown how burnout or job satisfaction is correlated for student support staff who

work in an online environment. As online education continues to grow, it is important

that there is research to better support employees.

For this study, the research questions were as follows:

RQ1: To what extent do support staff feel burned out in an online higher





support staff?


staff?

To answer the research questions, this study included the correlational research

design with a survey as a data collection tool. As Creswell (2009) noted, “Survey

research provides a quantitative or numeric description of trends, attitudes, or opinions of

32

a population by studying a sample of that population. From the sample results, the

research generalizes or makes claims about the population” (p. 145).

The selected design was beneficial for several reasons. First, this study included

validated and reliable survey instruments: the MBI and the JSS. The study included a

cross-sectional, convenience sampling described in more detail later in this chapter.

This study used the Mind Garden Transform system to administer the survey and

to collect results online. Mind Garden is the company that owns the rights to the MBI—

Human Services Survey (HSS). There were several convenience factors, such as

automatic scoring for the MBI-HSS to avoid error. The system also protected the privacy

of the participants, as no personally identifying information was collected.

Population and Sample

For this study, participants primarily worked with students who were taking

online courses. The study included a cross-sectional sample of participants from two

institutions.

The goal was to collect at least 100 completed surveys. There is not a typical

response rate for online questionnaires. In recent years, response rates for online surveys

have been higher than paper-based surveys (Greenlaw & Brown-Welty, 2009; Kiernan,

Kiernan, Oyler, & Gilles, 2005). Online surveys reach a larger number of people in a

short amount of time. As of 2012, about 85% of Americans used the Internet and about

91% of those checked e-mail regularly (Pew Internet & American Life Project, 2012).

The aim of this study was to reach a large demographic of participants, so the online

survey was the best option for this type of research. The researcher sent the questionnaire

33

to different organizational electronic mailing lists such as the Distance Educational group

of the National Academic Advising Association and on social media.

Instrumentation

The survey was divided into three aspects. The first portion contained the MBI-

HSS, which was used to measure burnout. The second portion contained the JSS, which

measured job satisfaction. The third portion contained demographic questions.

Demographic Information

This study obtained demographic information by looking at contributing factors

of burnout and job satisfaction. Past studies reported that gender, age, and marital status

have larger correlations with burnout, whereas salary, age, and nationality have larger

correlations with job satisfaction (Spector, 1997). This study also asked for the main

service area of the employee. The researcher included a question to find out the length of

time the participant had been at the institution and the highest education level for

correlational and tracking purposes.

Maslach Burnout Inventory

As shown in Chapter 2, the MBI is the leading instrument to measure and assess

burnout in a variety of industries. This study used the MBI-HSS, as it was designed for

human services positions. The MBI-HSS has been validated and is reliable across several

subgroups, including postsecondary education (Maslach, Jackson, & Leiter, 2010). This

survey instrument was the most appropriate for this study, as this study looked at student

support staff, which was a human services field. It also complemented the JSS well since

both were designed for human services employees.

34

The MBI-HSS consisted of 22 statements that measured the subareas of burnout

including emotional exhaustion, depersonalization, and personal accomplishment (self-

efficacy) and the overall levels of burnout. The questions on the MBI-HSS asked about

feelings an employee had at work such as feeling depressed or that he/she felt worthwhile

at work (Maslach et al., 2010). The scale ranged from “0” meaning the participant never

felt that specific way to “6” that he/she felt that way every day (Maslach et al., 2010).

There were nine items to measure emotional exhaustion, five items to measure

depersonalization, and eight items to assess personal accomplishment (Maslach et al.,

2010). Burnout was categorized as scoring high on the emotional exhaustion and

depersonalizations subscales and low on the personal accomplishment subscale (Maslach

et al., 2010). The high range was considered to be in the top third of the range, normative

is the middle third, and low is the bottom third of the ranges (Maslach et al., 2010).

Several studies have shown the reliability and validity of the MBI-HSS.

“Internal consistency was estimated by Cronbach’s coefficient alpha from more than one

thousand respondents. The reliability coefficients for the subscales were the

following: .90 for Emotional Exhaustion, .79 for Depersonalization, and .71 for Personal

Accomplishment” (Maslach et al., 2010, p. 12). Nunnally (1978) stated that anything

above .7 is acceptable. Cortina (1993) suggests that any scale with more than 14 items

was reliable at .7. The test-retest also showed reliability significance (Maslach et al.,

2010).

The MBI-HSS has been validated in several ways. First, the scores were

“correlated with behavioral ratings made independently by a person who knew the

individual well” (Maslach et al., 2010, p. 12). It was also validated through the presence

35

of certain job characteristics and outcomes, such as emotional attitudes toward work or

dealing with others. The burnout study was also validated externally by coworkers and

spouses on an individual’s behavior at work and at home (Jackson & Maslach, 1982;

Maslach & Jackson, 1979).

Job Satisfaction Survey

The JSS was the third part of the survey. Spector designed the JSS to assess job

satisfaction in the human services professions. The JSS assessed nine categories of job

satisfaction: pay, promotion, supervision, fringe benefits, contingent rewards, operating

conditions, coworkers, nature of work, and communication (Spector, 1997). This survey

had 36 comprehensive questions to assess the nine components. The JSS uses a 6-point

Likert-type scale, where 1 = disagree very much, 2 = disagree moderately, 3 = disagree

slightly, 4 = agree slightly, 5 = agree moderately, and 6 = agree very much. There is no

neutral option, so participants had to choose one way or another.

Each question on the survey had up to 6 points (Spector, 1997). If the question

was worded positively, the chosen number represented the score for that question and

was added together to obtain an overall job satisfaction score. If the question was worded

negatively, the points were reversed (Spector, 1997). For example, if the participant

answered 5 for a negatively worded question, the participant would earn 2 points. Table

1 shows how the questions relate to each category of job satisfaction.

The negatively worded items were “2, 4, 6, 8, 10, 12, 14, 16, 18, 19, 21, 23, 24,

26, 29, 31, 32, 34, 36” (Spector, 1999, para. 4). If a participant did not answer a specific

question, Spector suggested that to preserve the accuracy of the data, the mean of the

36

remaining areas of that specific facet should be used to answer that question (Spector,

1999).

Table 1

Instructions for Scoring the Job Satisfaction Survey

Subscale Item numbersPay 1, 10, 19, 28Promotion 2, 11, 20, 33Supervision 3, 12, 21, 30Fringe benefits 4, 13, 22, 29Contingent rewards 5, 14, 23, 32Operating conditions 6, 15, 24, 31Coworkers 7, 16, 25, 34Nature of work 8, 17, 27, 35Communication 9, 18, 26, 36Total satisfaction 1-36Note. From Instructions for Scoring the Job Satisfaction Survey, JSS, by P. Spector, 2011, retrieved from http://shell.cas.usf.edu/~pspector/scales/jssscore.html. Copyright 2011 by Paul E. Spector. Reprinted with permission.

The JSS has been shown to be reliable on two fronts. It has a high internal

consistency. The coefficient alpha for the nine facets ranged from .62 to .91 (Spector,

1997). While two facets were less than acceptable, the other seven categories were in an

acceptable range to show reliability (Spector, 1997). The test–retest reliability “takes into

account errors produced by differences in conditions” (Aiken, 1994, p. 85) and showed

how reliable the scale was over time. The coefficients ranged from .37 to .74, which is a

lower range. However, the sample was small and occurred at an organization that had

several organizational changes over an 18-month period (Spector, 1997). Reliability

tends to be lower when measuring the affective domain compared to the cognitive

domain (Ravid, 2011).

The JSS has been validated against the Job Descriptive Index, which was the most

carefully validated scale of job satisfaction (Spector, 1997). The coefficients ranged from

37

.61-.80 for the nine categories. The JSS has been normed for higher education as

demonstrated in Table 2.

Table 2

Norms for Higher Education in the United States

Facet X Xw SDSalary 11.9 12.3 1.8Promotion 11.5 11.9 1.6Supervision 18.9 18.7 1.6Benefits 15.3 15.1 1.4Contingent rewards 14.1 14.2 1.4Conditions 13.6 13.7 1.1Coworkers 18.1 18.2 1.5Work itself 19.7 19.7 1.3Communication 14.6 14.6 2.1

Note. Number of samples =14; N = 3,764. From Job Satisfaction Survey Norms, by P. Spector, 2011, retrieved from http://shell.cas.usf.edu/~pspector/scales/jssnorms.html. Copyright 2011 by P. Spector. Adapted with permission.

Data Collection

The researcher used an online survey for a few reasons, including saving postage

costs and saving time stuffing envelopes and data entry (Greenlaw & Brown-Welty,

2009; Sue & Ritter, 2012). The online survey assisted in a quicker turnaround for data

collection. Greenlaw and Brown-Welty (2009) showed that most online surveys are

completed within a 2-week period.

When creating the consent form, the researcher alluded to researching the work

environment instead of studying burnout and job satisfaction. According to Maslach et

al. (2010), the MBI-HSS is labeled the Human Services Survey to avoid any bias that

participants may have when hearing about burnout phenomena. For the same reason, this

study did not mention burnout or job satisfaction in the name of the survey or the consent

form.

38

The researcher worked with Mind Garden to send out the surveys. The company

holds the rights to several surveys, including the MBI (Mind Garden, 2015). The

researcher collaborated with Mind Garden to add a demographic questionnaire and the

JSS (see Appendices A and B). The company did not collect any identifying information

to protect anonymity. Mind Garden scored the categories of burnout, overall job

satisfaction, and the facets of satisfaction. This step reduced the potential for error from

data entry and on the scoring piece. The researcher was able to download an Excel

document with all the survey responses and calculated ranges.

Data Analysis

When the results came in, the researcher uploaded the data into SPSS for

statistical analysis. Afterward, the researcher checked the SPSS file for missing data and

excluded any data where there was too much information missing to get an accurate

result. If more than one question per subscale was not answered for either the JSS or the

MBI-HSS, the researcher would exclude the data for the corresponding survey prior to

statistical analysis.

Research Question 1

RQ1 was as follows: To what extent do support staff feel burned out in an online

higher education environment? To answer the first research question on the frequency of

burnout in the online environment, the researcher used descriptive statistics to state “what

the data shows” (Trochim, 2006, para. 2). The researcher used a frequency and tendency

distribution to look at the burnout rates and for different demographic information such

as age range and ethnicity. The researcher analyzed the frequency of each subscale

(exhaustion, depersonalization, and self-inefficacy) of burnout to describe the data. The

39

mean, standard deviation, and relationship between burnout and job satisfaction were

analyzed and compared to the norms of the higher education field.

Research Question 2

RQ2 was as follows: How satisfied are support staff members who work in an

online higher education environment? Similar to the previous research question, the

researcher used descriptive statistics to analyze job satisfaction for the same population.

This study used frequency distribution to analyze the facets of job satisfaction compared

to the norms for higher education.

Research Question 3

RQ3 was as follows: What is the relationship between burnout and job

satisfaction in online support staff? The focus of the third research question was on the

relationship between burnout and job satisfaction. A correlational analysis was used to

assess the mean, standard deviation, and relationship between burnout and job

satisfaction. The researcher explored the individual categories of burnout, including

exhaustion, cynicism, and self-inefficacy, and the relationships between the facets

(Maslach, Jackson, et al., 2001) of the JSS. The correlational coefficients showed the

strength of the relationships between the variables. Only relationships with a significance

level of .05, which meant that there was a 95% chance that the relationship exists outside

of chance, were discussed (Ravid, 2011). The results are displayed in an

intercorrelational matrix.

Research Question 4

RQ4 was as follows: How well does burnout predict job satisfaction in online

student support staff? The researcher performed a multiple regression to investigate how

40

well burnout predicted job satisfaction. A block entry model was used with the

demographic variables entered in the first block and the burnout categories entered in the

second block. Overall job satisfaction was the dependent variable. The researcher

evaluated the significance of the relationships and calculated the variance explained by

the independent variables.

41

CHAPTER 4: FINDINGS

Restatement of Purpose and Research Questions

The purpose of this quantitative study was to apply Maslach’s theory of burnout

and to examine the relationship between perceived exhaustion, cynicism, self-inefficacy,

and job satisfaction per the JSS to online student support staff in higher education

institutions located in the United States.

This chapter includes a description of the data analysis of the four research

questions, which were as follows:






support staff?


staff?

The researcher used the Mind Garden Transform survey system to collect the

survey results. No identifying information was gathered during the survey. The

questionnaire was programmed where each respondent had to answer every question.

There was an option to select “Choose not to answer.” It was also arranged that each IP

address could only take the questionnaire once. As a result, each click on the survey

counted for one of the licenses.

42

Data Collection

The researcher used social media sites Facebook and LinkedIn and electronic

mailing lists to solicit participation in the survey. The researcher posted a status update

on Facebook and LinkedIn asking for higher education student support professionals to

help with research and a link to a web page outlining the full description of the study and

qualifications to participate in the study. The web page included an informed consent

section that if the person qualified and agreed to the study, they would click on the

hyperlink to begin the questionnaire.

The researcher posted similar information in the Online Learning Consortium, the

Association for the Study of Higher Education, and Higher Education Online LinkedIn

groups, which are areas where members voluntarily join based on particular topics. The

Online Learning Consortium group had over 5,000 members and is designed to support

the quality of online learning through research and development (Online Learning

Consortium LinkedIn Group, 2014). The Association for the Study of Higher Education

group had over 5,000 members who support higher education research as a field of study

(Association for the Study of Higher Education LinkedIn Group, 2014). The Higher

Education Online group had over 2,500 members who work in online education (Higher

Education Online LinkedIn Group, 2014).

The researcher sent individual messages to professional connections through

LinkedIn, asking them to participate in the research study if they qualified. The message

was sent to over 400 contacts. The message also asked them to share the study with their

colleagues. The researcher also messaged the National Academic Advising Association

Distance Learning electronic mailing list, which consists of academic advisors who work

43

with online students. The message is located in Appendix A. The survey was left open

for 1 month to collect responses. In that month, 130 people clicked on the survey, and

107 participants completed it. No respondents started the questionnaire and did not finish

it, and all questions were answered.

Demographics of Respondents

After the close of the survey, the researcher used descriptive statistics to calculate

the demographics of the participants. Sixty-five percent of the respondents were female

(n = 70) and 32% were male (n = 34). Three respondents chose not to answer. The

respondents fell into multiple age ranges, as outlined below in Table 3.

Table 3

Ages of Participants

Age f %20-30 25 23.4031-40 40 37.4041-50 18 16.8051-60 20 18.7061-70 4 3.70

About 79% (n = 85) of the respondents had been in higher education less than 15

years. A majority of the respondents had been at their institutions between 1 and 10

years. About 33.6% (n = 36) of the respondents had been at their institution for 1-5

years, and 32.7% (n = 35) had been at their institutions for 6-10 years. About 18.7% (n =

18.7) had been with their institution for 11-15 years. Very few respondents were at their

institutions more than 15 years, as shown in Table 4.

44

Table 4

Length of Time Worked at the Institution

Length of time at the institution f %<1 year 10 9.301-5 years 36 33.606-10 years 35 32.7011-15 years 20 18.7016-20 years 2 1.9020+ 3 2.80Prefer not to answer 1 0.90Total 107 100.00

The respondents worked in a wide variety of areas in higher education. The three

areas with the most representation were in academic/student advising (n = 39), academic

support (n = 21), and other. Participants were able to choose “other” as an option.

Being in higher education, a majority of the participants had higher levels of

education. Only seven participants did not have at least a bachelor’s degree. About 57%

of the respondents had a master’s degree. The complete breakdown appears in Table 5.

Table 5

Highest Level of Education Participants Completed

Highest education completed f %Some college 2 5.71Bachelor's 14 40.00Master's 15 42.86Doctoral 4 11.43Total 35 100.00

The income of the respondents ranged from under $24,000 to over $70,000. The

range with the highest frequency was $30,000-$40,000 with 23 participants. A higher

percentage of participants chose not to answer the income question (n = 16; 15%).

The participants were not highly diverse with regard to ethnicity and marital

status. About 81% were White. The other ethnicity groups (i.e., Asian, Black, Hispanic)

45

all had fewer than 5% of the respondents. About 72% of the respondents were married or

living with another. About 15% were single, and the rest identified were divorced

(7.5%), separated (1.9%), or prefer not to answer (3.7%).

Research Questions

Research Question 1

RQ1 was as follows: To what extent does support staff feel burned out in an

online higher education environment? To answer the question, the researcher used

descriptive statistics to analyze the data.

Burnout was categorized by having scores high in emotional exhaustion and

depersonalization, and low scores in personal accomplishment or self-inefficacy

(Maslach, Schaufeli, & Leiter, 2010). The survey did not measure an overall burnout

score. According to the results for postsecondary education, the top third of the

normative scale represents burnout, which is an emotional exhaustion (EE) total score

≥24, a depersonalization (DP) score ≥9, and a personal accomplish (PA) score ≤35

(Maslach, Schaufeli, et al., 2010).

The higher education norms were calculated by cumulative results from 635

postsecondary educators and personnel in 1990 (Maslach, Schaufeli, et al., 2010). The

norms might have changed since they were calculated almost 20 years ago. Little

information was known about the personnel. Additionally, the higher education

environment has changed.

The means from this study were all in the normal range, as shown in Table 6,

which suggested the participants in this study were not burned out compared to the

previous postsecondary norms of burnout. However, the standard deviation was large

46

enough in all categories to suggest that people were burned out. Furthermore, emotional

exhaustion was the category that was most correlated to burnout and had the largest

variance requiring additional analysis (Maslach, Schaufeli, et al., 2010).

Table 6

Participants' MBI Scores

Burnout category N Min. Max. X SD Higher education normsEmotional exhaustion 10

71 49 19.991 11.2287 ≥24

Depersonalization 107

0 24 7.523 6.1894 ≥9

Self-inefficacy 107

7 48 36.86 8.0041 ≤35

When looking at the data on a more granular level, 38% of the participants scored

high or in the burnout range on the emotional exhaustion subcategory, 36% scored high

on the depersonalization subcategory, and 36% scored low on the personal

accomplishment scale, as shown in Table 7.

Table 7

Indication of Burnout per Category

No indication of burnout Indication of burnoutCategory n % n %

Emotional exhaustion 66 62 41 38Depersonalization 69 64 38 36Self-inefficacy 69 64 38 36

Although the scores for the subscales could not be combined into a single score,

the count of participants who scored in different categories could be calculated. As such,

42.06% of the participants did not score high in any subscales, whereas 16.82% scored

high on one subscale, 14.02% scored high on two subscales, and 27.10% scored high on

all three subscales as shown in Table 8.

47

Table 8

Participants Who Scored in a Burned Out Range

Number of categories f % Cum. %No burnout 45 42.06 42.061 category 18 16.82 58.882 category 15 14.02 72.903 category 29 27.10 100.00

According to the data, 57.94% (n = 62) of the participants were within the range

indicating burnout in at least one category, which indicated that burnout might be a

problem in online education for student support staff.

The researcher looked at the demographics of the participants who had burnout

indicators in two or three categories to look for trends. This group size consisted of 35

participants and is referred to as the burned-out group. The vast majority of the

participants in the burned-out group were White (82.8%). About 65.7% of the people in

the group were female. A majority of the participants in the burned-out group (88.5%)

were between the ages of 20 and 40. Approximately 82% of the burned-out group had a

bachelor’s (n = 14, 40%) or master’s (n = 15, 42.86%) degree. Sixty percent of the

burned-out group was married, compared to 25.7% who identified themselves single.

Regarding demographic information about their employment, just under 50% of

the participants who were burned out had worked at their institution between 6 and 10

years. Only 5% of the burned-out group had been with the institution between 16 and 20

years. Participants in the burned-out group worked in a variety of functional areas,

including advising (n = 9, 25.7%) and academic support (n = 6, 17.1%). Table 9 shows

the breakdown in all areas. Incomes were dispersed among all ranges.

48

Table 9

Functional Work Areas in the Burned-Out Group

Functional work area f %Advising 9 25.71Academic support 6 17.14Other 5 14.29Career services 3 8.57Financial aid 3 8.57Registrar 3 8.57Prefer not to answer 3 8.57Learning services 2 5.71Quality assurance 1 2.86Grand total 35 100.00

Research Question 2


online higher education environment? Unlike the MBI, the JSS measured an overall

satisfaction and subscale satisfaction. According to Spector (2007),

Translated into the summed scores, for the 4-item subscales with a range from 4 to 24, scores of 4 to 12 are dissatisfied, 16 to 24 are satisfied, and between 12 and 16 are ambivalent. For the 36-item total where possible scores range from 36 to 216, the ranges are 36 to 108 for dissatisfaction, 144 to 216 for satisfaction, and between 108 and 144 for ambivalent. (para. 3)

The larger scale data showed that the job satisfaction for participants ranges from

79, which demonstrated there were dissatisfied employees to very satisfied employees

who scored 198. However, the mean was 138, which fell within the ambivalent range.

The data are displayed in Table 10.

Looking at the mean of the facets, participants were satisfied with supervision, the

nature of work, coworkers, and fringe benefits, since the mean was greater than 16 and

less than 24. Participants were ambivalent toward contingent rewards, operating

conditions, and communication due to a mean between 12 and 16. They were dissatisfied

49

with pay and promotional opportunities since the mean was lower than 12. The mean

among employees in higher education institutions in previous studies also showed that

employees were satisfied with the work itself and supervision.

Table 10

Participants’ Job Satisfaction Scores

JSS subscale Min. Max. X SDX -Postsecondary

educationTotal satisfaction 79.0 198.0 138.682 27.7283 137.2Pay 4.0 22.0 11.850 4.7718 11.9Promotion 4.0 22.0 11.290 4.3762 11.5Supervision 4.0 24.0 19.757 4.7995 18.9Fringe benefits 5.0 24.0 16.215 4.7285 15.3Contingent rewards 5.0 24.0 13.935 4.8976 14.1Operating conditions 6.0 24.0 14.047 3.9295 13.6Coworkers 6.0 24.0 17.710 4.3589 18.1Nature of work 6.0 24.0 19.103 4.2713 19.7Communication 4.0 24.0 14.776 4.9341 14.6Note. N = 107.

Research Question 3


satisfaction in online student support staff? The researcher answered this question by

correlating the relationship between emotional exhaustion, depersonalization, and self-

efficacy to total job satisfaction and the facets of job satisfaction. Table 11 depicts the

intercorrelation among all MBI and Job Satisfaction variables.

Emotional exhaustion, depersonalization, and self-efficacy had a significant

relationship to other MBI variables to a .01 significance level. Emotional exhaustion and

depersonalization both had significant relationships to all facets of job satisfaction and

total job satisfaction. Self-efficacy had a significant relationship to all facets of job

satisfaction except operating conditions.

50

Table 11

Intercorrelations Between Burnout and Job Satisfaction

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13Emotional exhaustion 1 .673** -.299** -.370** -.367** -.602** -.226* -.576** -.567** -.627** -.544** -.543** -.725**

Depersonalization 1 -.462** -.296** -.298** -.467** -.206* -.407** -.310** -.566** -.645** -.470** -.602**

Inefficacy 1 .309** .206* .239* .195* .193* .052 .355** .707** .231* .408**

Pay 1 .581** .203* .414** .575** .266** .193* .374** .369** .662**

Promotion 1 .340** .242* .633** .331** .299** .363** .473** .704**

Supervision 1 .134 .455** .300** .554** .406** .423** .632**

Fringe benefits 1 .413** .183 .133 .323** .270** .521**

Contingent rewards 1 .423** .513** .381** .591** .829**

Operating conditions 1 .360** .162 .513** .570**

Coworkers 1 .571** .547** .683**

Nature of work 1 .516** .673**

Communication 1 .778**

Total satisfaction 1*p < 0.05 level (2-tailed). **. p < 0.01 level (2-tailed).

There was a very strong, negative, and statistically significant relationship

between emotional exhaustion and total job satisfaction (r = -.725, p <.05). This

correlation was the strongest relationship among the MBI and JSS variables. Emotional

exhaustion was the variable that had the strongest relationships with JSS variables. The

only variable that did not have a strong positive or negative relationship was fringe

benefits, which had a weak negative relationship (r = -.226, p < .05).

There was a strong, negative relationship between depersonalization and job

satisfaction (r = -.602, p < .01). There were also strong, negative relationships between

depersonalization and supervision, contingent rewards, coworkers, the nature of work,

and communication. There was a weak, negative relationship between depersonalization

and pay, promotion, and fringe benefits. A number of significant correlations implied

that depersonalization was an important variable to consider when assessing the

relationships among all facets.

There was a strong, positive relationship that was statistically significant between

self-inefficacy and total job satisfaction (r = .408, p <.01). All relationships among self-

inefficacy and job satisfaction variables were positively correlated. However, they were

51

not as strong as the relationships among emotional exhaustion and depersonalization and

the variables. The only very strong positive relationship with self-inefficacy was with the

nature of work (r = .707, p < .05). The moderately strong relationships included pay and

promotion, whereas the weak relationships included promotion, supervision, and

communication. There was a negligible relationship with fringe benefits, contingent

rewards, and operating conditions. These relationships indicated that relationships with

self-inefficacy were not as strong with job satisfaction as other MBI variables.

There were strong to very strong relationships between the independent variables

emotional exhaustion, depersonalization, and self-inefficacy and the variable job

satisfaction. All these variables were significant predictors of total job satisfaction.

Research Question 4


student support staff? A block-entry, multiple regression analysis was conducted to

examine the predictors or independent variables of burnout and the dependent variable

job satisfaction. Demographic variables (income, marital status, work area, age, gender,

time at the institution, ethnicity, education, and time in higher education) were entered

into the first block and the MBI variables (emotional exhaustion, depersonalization, and

self-inefficacy) were entered into the second block. Together these variables made a very

strong impact on total job satisfaction.

The first model with just the demographic information only accounted for 14.6%

of the variance in job satisfaction (R2 = .146), whereas the second model, which included

the demographic and MBI variables, accounted for 60.2% of the variance (R2 = .602).

The Durbin-Watson value (d = 1.915) concluded that there were no first-order auto-

52

correlations among the independent variables (University of Texas, n.d.). Model 2, as

shown in Table 12, is the better model for predicting burnout, since it explains a higher

percentage of the variance.

Table 12

Block-Entry Multiple Regression Analyses Predicting Job Satisfaction From

Demographic Information and Emotional Exhaustion, Depersonalization, and Inefficacya

Model R R2 R2 (Adj.) Standard error estimate1b .383a .146 .067 26.78042c .776b .602 .551 18.5784

a Dependent variable: JSS total satisfaction. b Predictors: (constant), income, marital status, work area, age, gender, time at the intuition, ethnicity, education, time in higher education. c Predictors: (constant), income, marital status, work area, age, gender, time at the institution, ethnicity, education, time in higher education, emotional exhaustion, personal accomplishment, depersonalization.

Table 13 displayed the multiple regression coefficients predicting job satisfaction

from burnout and demographic characteristics. The F ratio F(3, 94) = 11.83, p = .000

showed the model was statistically significant to predict job satisfaction. Emotional

exhaustion (β = -1.611) was negatively associated with job satisfaction and was the only

significant predictor of it. The other variables had an impact on job satisfaction but alone

were not significant enough to predict job satisfaction.

53

Table 13

Block-Entry Multiple Regression Coefficients Predicting Job Satisfaction From

Demographic Information and Emotional Exhaustion, Depersonalization, and Inefficacya

Model

Unstandardized coefficients

Standardized coefficients

t p

Correlations

β E β Zero-order Partial Part1 (Constant) 111.668 19.235 5.806 .000

Gender -5.977 5.435 -.111 -1.100 .274 -.104 -.111 -.103Age 6.202 2.976 .257 2.084 .040 .314 .207 .195Time at institution 1.136 2.696 .048 .421 .674 .133 .043 .040Time in higher education .474 2.942 .021 .161 .872 .235 .016 .015Education 5.562 3.282 .190 1.695 .093 .221 .170 .159Work area -.335 .804 -.041 -.417 .678 -.060 -.042 -.039Ethnicity -.253 1.674 -.017 -.151 .880 -.025 -.015 -.014Marital status -.005 2.685 .000 -.002 .999 -.113 .000 .000Income -1.490 1.563 -.106 -.953 .343 .019 -.096 -.089

2 (Constant) 150.906 17.465 8.641 .000Gender .105 3.928 .002 .027 .979 -.104 .003 .002Age -.310 2.229 -.013 -.139 .890 .314 -.014 -.009Time at institution -2.697 1.950 -.113 -1.384 .170 .133 -.141 -.090Time in higher education 1.559 2.088 .070 .747 .457 .235 .077 .049Education 1.566 2.433 .053 .643 .521 .221 .066 .042Work area -.051 .576 -.006 -.089 .929 -.060 -.009 -.006Ethnicity -.373 1.178 -.025 -.317 .752 -.025 -.033 -.021Marital status -.638 1.876 -.027 -.340 .734 -.113 -.035 -.022Income 1.478 1.131 .105 1.307 .194 .019 .134 .085Emotional exhaustion -1.611 .246 -.652 -6.554 .000 -.725 -.560 -.427Depersonalization -.438 .488 -.098 -.897 .372 -.602 -.092 -.058Personal accomplishment .466 .288 .134 1.618 .109 .408 .165 .105

a Dependent variable: JSS total satisfaction.

Summary

This study found evidence that employees working at online higher education

institutions in the United States were burned out. Most of the group that was burned out

was between the ages of 20 and 40 and had a bachelor’s or master’s degree. Employees

were satisfied with their careers, were dissatisfied with their pay and promotional

opportunities, and satisfied with the work itself and supervision. In general, employees

were satisfied with their careers, the work itself, and supervision; however, they were

dissatisfied with their salary and promotional opportunities. All independent variables

showed a strong correlation between total job satisfaction with the strongest, negative

54

correlation among emotional exhaustion and job satisfaction. The study also showed that

60% of the variance in job satisfaction can be explained by demographic information and

the MBI variables emotional exhaustion, depersonalization, and self-inefficacy.

55

CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS

Summary

The growth of online education has resulted in an increase of student support staff

to support students taking online courses. The purpose of this quantitative research study

was to apply Maslach’s theory of burnout and to examine the relationship between

burnout and job satisfaction measured by the JSS among online student support staff in

higher education institutions in the United States. In general, researchers have measured

burnout and job satisfaction in higher education and for specific populations within

higher education. However, research measuring student support staff in online

environments was lacking.

The overall research question examined the perceived exhaustion, cynicism, and

self-inefficacy and job satisfaction as measured by online student support staff in higher

education institutions. The following research questions guided the research study:






support staff?

RQ4: How well does burn out predict job satisfaction in online support staff?

The study included a quantitative methodology and a correlational research design

to investigate the relationship between the independent variables perceived exhaustion,

depersonalization, and self-inefficacy and the dependent variables job satisfaction and its

56

subcategories. The researcher used a questionnaire consisting of three parts to collect

data: the MBI-HSS, the JSS, and a demographic section. The survey was deployed using

the Mind Garden Transform system, which was a web-based tool. Both the MBI and the

JSS were preexisting surveys with established reliability and validity measures. The

researcher chose participants based on convenience by sending messages to electronic

mailing lists, social media groups, and colleagues. The survey was available for one

month. One hundred seven responses were collected.

The researcher used SPSS for statistical analysis. Descriptive statistics was used

to describe the demographic information about the participants and to answer RQ1 and

RQ2. The results were described using the mean and standard deviation for continuous

variables and frequency for categorical variables. The researcher used Pearson’s

correlation coefficient (r) to answer RQ3 and a block-entry multiple regression analysis

to investigate RQ4. The analysis was two-tailed to a .05 significance level.

The research study showed that burnout is a phenomenon in online student

support staff in online environments. About 57% of the participants showed indications

of burnout in at least one subcategory. Employees who were burned out were typically

between the ages of 20 and 40 with a master’s degree. In general, employees were

satisfied with their jobs in higher education. However, there was a high correlation

between the facets of burnout and job satisfaction among staff members who were burned

out. The data also showed that about 60% of the variance of job satisfaction can be

explained by demographic data and the subcategories of burnout, with emotional

exhaustion being the most significant.

57

Conclusions

Research Question 1

RQ1 was as follows: To what extent does support staff feel burned out in an

online higher education environment? The results of this research study supported

previous research studies. Just less than 60% of the total sample rated themselves as

burned out in at least one indicator. About 88.5% of the burned-out group (those who

showed indications of burnout in at least two categories) in this study were between the

ages of 20 and 40, and 65.7% of them were female. According to Maslach et al. (2001),

people who are typically in their 20s, females, and single people are more likely to burn

out. This research study also supported that females and people who are younger were

more burned out, as were people who had not worked in the field or their institution as

long. However, this study differed from previous studies in that 60% of the burned out

group were married.

The mean scores of emotional exhaustion, depersonalization, and inefficacy were

below the normative ranges of other higher education professionals. This result was not

surprising, as Leiter and Maslach (2001) noted that technology may increase burnout

level, as recovery is not possible. Staff members working in online higher education

constantly use technology to interact with their students. As there have not been any

studies regarding burnout in online student support staff at online institutions, the data

cannot be compared to other studies with a similar population.

Research Question 2


online higher education environment? In general, the findings for this research question

58

corroborated the results from previous studies. The results showed that the participants

were ambivalent in their job satisfaction range. Participants were satisfied with the

nature of their work and supervision and dissatisfied with pay and promotional

opportunities, which was similar to Spector’s (1997) research.

The total job satisfaction mean for this survey was 138.6, which was close to the

mean of postsecondary education of 137.2 (Spector, 2011). The standard deviation was

27.7, which showed that the range of satisfaction varied among the participants. Factors

not measured in this survey may have an influence on job satisfaction, such as the survey

being deployed during a recession in the United States, cultural backgrounds, and type of

work environment.

Research Question 3


satisfaction in online student support staff? The data showed a strong relationship

between burnout and job satisfaction. Several previous studies also found similar results

(Lee & Ashforth, 1993; Spector, 1997). This study showed similar relationships among

the MBI variables and job satisfaction that Lee and Ashforth (1993) found in a prior

educational study.

This study showed a very strong negative relationship between emotional

exhaustion and job satisfaction. This result was not surprising, as mentally tired

individuals would be less satisfied until they were able to recover (Maslach & Leiter,

2011). The role of technology could make it harder for an employee to recover from

burnout, which may have been the case in this study, but it was measured.

59

There was a positive correlation among all facets of job satisfaction and total

satisfaction in this research. The lowest correlation was among pay and total satisfaction.

According to Spector (1997), workers in the United States feel that they deserve higher

salaries and promotions. This study indicated that pay among online student support staff

does not relate to job satisfaction as much as other variables do.

There was also a strong relationship between depersonalization and self-

inefficacy and total job satisfaction. The relationship was stronger with depersonalization

than with inefficacy. Based on previous research, there is a stronger relationship

between depersonalization and emotional exhaustion, which would indicate a stronger

relationship to job satisfaction than to inefficacy (Maslach et al, 2010).

Research Question 4


student support staff? The results of this survey showed that approximately 60% of the

variance in job satisfaction could be explained by demographic factors, emotional

exhaustion, depersonalization, and inefficacy. Emotional exhaustion had the strongest

beta weight among the variables for predicting job satisfaction. The factors were not

surprising, as previous research studies had shown that people with a combination of

personality and demographic characteristics were more likely to be burned out, and

burnout may influence job satisfaction (Cordes & Dougherty, 1993; Lee & Ashforth,

1996; Maslach et al., 2001; Spector, 1997). The data in this research study showed that

an employee who had worked in a support role in online education for less than 10 years,

earned a bachelor’s or master’s degree, and was emotionally exhausted had a lower job

satisfaction rating.

60

Implications for Practice

The researcher felt that burnout and job satisfaction were important elements to

research, as more online programs and classes are moving online. As a result, more

students are taking classes online. Most research studies focused on students or faculty

members, but not the support staff. In some institutions, the support staff work later

hours or on weekends to meet the needs of the students. The online environment also

creates a need for increased technology and increased demands on the support staff. This

shift in education creates a different work environment than in the past.

These data indicated that almost 60% of the sample showed burnout in at least

one category and that the overall job satisfaction ratings showed that employees were

ambivalent about their work. These findings indicated that burnout may be a larger issue

in online education than in other areas of higher education.

Managers and higher education institutions should consider the outcomes of this

study. If their support staff members are burned out, they may not be providing the best

service to students, as distancing themselves from students is a coping mechanism.

Burnout also leads to higher absenteeism, more tardiness, and higher turnover (Vance,

2006). Job satisfaction was also ranked in the ambivalent range. The combined factors

can lead to turnover, which can cost an institution time and money along with providing

less than satisfactory service to its students.

Managers can monitor the emotional exhaustion levels of their employees and can

implement interventions to help them recover. Emotional exhaustion can be assessed

through conversations and observations. If managers discover environmental factors

61

(work situation, hours, caseload, etc.) are contributing to burnout, they may be able to

implement changes to help their employees.

Recommendations for Future Research

The data showed that a relationship exists between burnout and job satisfaction in

higher education, student support staff, who work in the online environment. However,

there are opportunities for further research to develop the knowledge. The focus of this

study was online student support staff and the study included a convenience sample.

Further research should include larger studies based on specific populations and the type

of institution (i.e., proprietary, 2-year colleges, 4-year colleges), the age range of

participants, role within the organization, and environmental factors. It would also be

beneficial to research burnout and job satisfaction based on the level of student that a

support staff member primarily works with, such as first-time students, undergraduate

students, or transfer students. This population is important, as organizational leaders may

be able to minimize burnout by changing the internal structures within the organization.

As forms of online education are continuing to grow (Lokken & Mullins, 2015), a

qualitative research study would provide more insights into the burnout phenomenon.

Such areas would include an in-depth analysis of the characteristics of staff members

who are burned out compared to those who are not burned out. The study should also

explore how environmental factors affect employees and their burnout levels.

Future researchers can investigate how burned out employees in online education

recover. Such a study would include what coping mechanisms they use or what changes

they made to ease their burnout. The literature shows that time off can help an employee

recover, but that is not always an option (Leiter & Maslach, 2001). This research could

62

provide benefits to higher education institutions, as the employees would care about the

work they are doing and thus the students of the institution.

This study was limited to participants’ feelings at one point in time. A

longitudinal study would provide insight into how staff’s burnout and job satisfaction

changes over a longer period. Qualitative data added to the longitudinal study can show

how intended or unintended interventions and events influence the variables. Events

would include raises, changes in employment duties, and changes in leadership.

63

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78

APPENDIX A

CONSENT FORM

Dear Participant,

I am a doctoral candidate at Benedictine University in the Higher Education and Organizational Change program. As online education continues to grow, it is important to research the effects that the environment has on staff that support online students. If you support an online educational program, you can participate in this study. Follow the link below to start the questionnaire assessing your feelings and attitudes about working with students in the online environment. Your participation will help provide insight on the topic.

There are three parts to this survey and should take about 25-30 minutes to complete. Participation is voluntary and responses are collected anonymously. The risks to your physical, emotional, professional, or financial well-being are considered minimal. Submission of the completed survey will be interpreted as your informed consent to participate and that you are at least 18 years of age.

The link will provide more information about the study and a link to the questionnaire. http://bit.ly/1cT58sj

This research project is overseen by the Higher Education and Organizational Change Department at Benedictine University. If you have any questions about the research, please contact Lisa Haas at [email protected] or Dr. Eileen Kolich at [email protected]. If you would like a summary of the findings, please contact Lisa Haas at the email address above.

Sincerely,

Lisa Haas

Doctoral Candidate

Benedictine University

79

APPENDIX B

DEMOGRAPHIC INFORMATION

Gender:

____Male ____Female

Age:

____<20 ____20-30 ____31-40 ____41-50 ____51-60 ____61-70 ____70+

Length of time at the institution:

____<1 year ____1-5 ____6-10 years ____11-15 years ____16-20 years ____20+

Length of time in higher education:

_____0-5 years _____6-10 years _____11-15 years _____16-20 years _____20+

What is the highest level of education you have completed?

____High school or equivalent ____Some college ____Bachelor’s degree ____Master’s Degree ____Doctoral degree ____Professional degree (MD, JD, etc) ____Other

In which area are your main work responsibilities?

____Academic/Student Advising ____Academic Support ____ Bursar/Accounts ____Career Services ____Financial Aid ____Learning Services ____Registrar Services ____Quality Assurance ____Other

How would you classify yourself?

___Asian/Pacific Islander ____Caucasian/White ____Hispanic ____Black ____Middle Eastern

____Latino ____Multiracial ____Would rather not say ____Other

What is your current marital status?

____Divorced ___Married ____Single ___Living with another ___Separated ____Widowed

What is your income for your current position?

____<$24,000 ____$24,001-30,000 ____$30,001-$40,000 ____$40,001-$50,000 ____$50,001-60,000 ____$60,001-$70,000 ____$>70,001

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Maslach Burnout Inventory

Questions from the Maslach Burnout Inventory-Human Services Edition

81

JOB SATISFACTION SURVEY

Paul E. Spector

Department of Psychology

University of South Florida

Copyright Paul E. Spector 1994, All rights reserved.

PLEASE CIRCLE THE ONE NUMBER FOR EACH QUESTION THAT COMES CLOSEST

TO REFLECTING YOUR OPINIONABOUT IT. D

isag

ree

very

m

uch

Dis

agre

e m

oder

atel

y

Dis

agre

e sl

ight

ly

Agr

ee sl

ight

ly

Agr

ee

mod

erat

ely

1 I feel I am being paid a fair amount for the work I do.

1 2 3 4 5 6

2 There is really too little chance for promotion on my job.

1 2 3 4 5 6

3 My supervisor is quite competent in doing his/her job.

1 2 3 4 5 6

4 I am not satisfied with the benefits I receive. 1 2 3 4 5 6

5 When I do a good job, I receive the recognition for it that I should receive.

1 2 3 4 5 6

6 Many of our rules and procedures make doing a good job difficult.

1 2 3 4 5 6

7 I like the people I work with. 1 2 3 4 5 6

8 I sometimes feel my job is meaningless. 1 2 3 4 5 6

9 Communications seem good within this organization.

1 2 3 4 5 6

10 Raises are too few and far between. 1 2 3 4 5 6

11 Those who do well on the job stand a fair chance of being promoted.

1 2 3 4 5 6

12 My supervisor is unfair to me. 1 2 3 4 5 6

83

13 The benefits we receive are as good as most other organizations offer.

1 2 3 4 5 6

14 I do not feel that the work I do is appreciated. 1 2 3 4 5 6

15 My efforts to do a good job are seldom blocked by red tape.

1 2 3 4 5 6

16 I find I have to work harder at my job because of the incompetence of people I work with.

1 2 3 4 5 6

17 I like doing the things I do at work. 1 2 3 4 5 6

18 The goals of this organization are not clear to me. 1 2 3 4 5 6

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