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UNDERSTANDING ORGANIZATIONAL CLIMATE AND OUTCOMES IN THE
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6/01 cJClHC/DabDuefiS-p. 15

UNDERSTANDING ORGANIZATIONAL CLIMATE AND OUTCOMES IN
THE MILITARY: KEYS TO THE FUTURE?

By

Charles George Capps

A DISSERTATION
Submitted to
Michigan State University

in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
School of Labor and Industrial Relations

2000

ABSTRACT

UNDERSTANDING ORGANIZATIONAL CLIMATES AND OUTCOMES IN
THE MILITARY: KEYS TO THE FUTURE?

By

Charles George Capps

Soon after the conclusion of the Gulf War, the United States (US) military no
longer faced viable threats from the Middle East and the Soviet Union. Therefore, in
1992, the US. Congress mandated that the Department of Defense reduce the size of
the military. Over the last eight years, military personnel were reduced by
approximately twenty-five percent while the number of obligations supported by the
US. military increased in number. The result is that there are fewer personnel to
perform more missions. Concurrently, the military’s recruiting and retention statistics
are lower than at any time in the previous twenty years.

This dissertation addresses two very important issues facing senior military
leaders: 1) performance and 2) people. The US. military cannot sacrifice quality in
its fighting force. Defending our national interests and those of our allies are of
utmost strategic importance. Therefore, the military must find a way to maximize its
performance while creating a work environment that allows military members to
improve their skills both personally and professionally. It is proposed that defining a
“military organizational climate” and understanding how key variables that comprise
the construct may facilitate positive organizational outcomes.

Organizational climate data were collected from 66 US. Air Force flying
squadrons located around the world. Additionally, two operational performance

measures, flying schedule effectiveness and mission capable rates were collected from

these squadrons. Analyses were conducted at the individual-, squadron-, and cross-
levels to determine if organizational climate is linked to outcome measures in a
military context.

The results indicate that organizational climate does have a predictive
capability with members’ job satisfaction and performance perceptions at each of the
three levels. At the individual level (N=7029), leadership, training, teamwork and job
characteristics are important in military members’ perceptions of the dependent
variables. Similarly, at the squadron-level (N=66), military members’ job satisfaction
can be predicted by the climate measures: training, supervision, resources, and job
characteristics. Teamwork and job characteristics have the largest effects on
perceptions of squadron-level performance. Finally, the cross-level analyses
(N=7029) show squadron-level climate effects have an additional predictive capacity,
over and above the individual-level effects, in members’ job satisfaction and
performance perceptions. Regression analyses failed to show a significant relationship
between climate and the operational performance indices. However, job satisfaction
and one of the flying metrics (flying schedule effectiveness) did reach statistical
significance in a correlation analysis.

In summary, there is conclusive evidence that military climate does have an
effect on organizational outcomes, most notably in members’ affective responses to
job satisfaction and performance perceptions. Limitations of this study and

implications for future research, theory and practice are presented.

DEDICATION

To my daddy. I told you I’d get it one day and here it is. You can tell all the
angels that your son has his Ph.D. If only you could be here to celebrate with Mom,

Kay, Kacey-Morgan, Ross, and I. Then everything would be OK. I miss you boy!

iv

ACKNOWLEDGEMENTS

Trying to think of everyone who helped me complete this dissertation and the
Ph.D. takes me back at least eight years. I’ve crossed paths with many good people
and I’ve certainly used more than my share of prayers. I would not be finished
without Philippians 4:13, family, friends, and green stamps. Fortunately, I never
exhausted the endless supply of blessings from each of these. At SLIR, my thanks go
out to Bill, Kristi, and Annette for taking great care of me from long-distance. I
appreciate your friendship.

My dissertation committee was fabulous. I won’t forget “Smokin’ Ed’s
question that kicked-off the dissertation defense meeting. Man! I thought they would
give me a few warm-up questions before going for the juggler — NOT!. John learned
that someone else has the comer on the market on climate consensus. I never did get
on the same train with Rich about filling tires or getting up in the morning. But, we
had fun. Overall, I owe most of my dissertation to the excellent guidance of my two
good friends, Kevin and Mike. Kevin played traffic cop when I committed major
statistical violations. Also, he encouraged, cajoled, and served as a confidant when I
needed it most. The chair (Mike) played his role extremely well. I know that nursing
me through this process was a task. However, Mike’s kind way made me feel like I
really could finish and to do it well. Mike, “Thank you!”

To the Slaters: WOW! You fed me, gave me a bed, provided a world-class
iron, listened to me whine, laughed at my jokes, and rubbed a knot. Next time I do a
dissertation, I know where I’m staying. Ken, I can’t possibly thank you enough for

the wisdom, walking, writing code, editing, and NOIR — I thank you for making a

stressful time as FUN as it could possible be. Donna, you really do run a good five-
star hotel.

Scott Hopkins. . .1 can never say enough about all you’ve done for me in pursuit
of MY dream. You delivered a data set that was perfect. You carved out the cases I
needed for the analyses. You made screen shots and you built tables. This degree is
partly yours. I’m here because of your genius and your friendship. DABAO!

Mom, you started telling me back in ’92 that, “it won’t be as long as it has
been.” It took a little longer than we all thought, but, it’s done. You taught me to
tackle mountains with confidence. I climbed this one and made it to the top, but, it
was sure a long, tough haul. Thanks for giving me what it takes to not be afraid of
challenges. I’m also grateful to Mr. and Mrs. Gillespie for support when my family
needed it most.

Ross and Kacey, my Ph.D. and this dissertation have been hanging around for
most of your life. I doubt you even remember when it WASN’T around. Well, let’s
give it a try and see how we do without it. I’ve got some catching-up to do and it’s
gonna be FUN. Both of you make me so proud. I love you.

The best is always saved for last. And I have the best. In fact, my best friend
in the whole world is my wife. Kay-Kay, this has been an incredibly long road.
You’ve been beside me every step and you allowed me to do it my way. I’m so, so,
so, thankful that I have you. Your help in completing this document is forever etched
in my mind. Your commas, periods, thats, rewrites, references, introduction, and love
embedded in the entire manuscript. If only I could have your name next to mine in the
diploma. You’ve certainly earned it. You deserve this degree as much as I do. I’m

thinking it’s time to put up a shelf or two. This chapter is closed. What’s next?

vi

TABLE OF CONTENTS

LIST OF TABLES
LIST OF FIGURES

CHAPTER 1
INTRODUCTION AND OVERVIEW
Importance of the Topic
Climate and Outcomes in a Military Context
Description of Chapters in the Dissertation
CHAPTER 2
LITERATURE REVIEW
Introduction
Historical Perspective of Climate
Theory Guiding Present Research
Individual-level (Psychological) Climate and
Outcome Measures
Organizational-level (Squadron) Climate and
Outcome Measures
Cross-level Climate and Outcome Measures
Summary

CHAPTER 3
RESEARCH METHODOLOGY
Data Collection Locations
Data Collection Procedures
Independent Variables
Dependent Variables-Operational
Performance Measures
The Sample of Subjects
Individual-level Sample
Organizational-level Sample
Operationalization of Variables
Overview
Independent Variables
The Job
Unit Resources
Core Values
Communication
Leadership
Supervision
Training and Development
Teamwork

vii

ix

12
12

23

28
41
46

47

49

62

63
64

66
67
67
68
68
69
69
70
70
71

Participation/Involvement
Recognition
Unit Flexibility
Dependent Variables
Mission-Capable Rate (MC R)
Flying Schedule Effectiveness (F SE)
Job Satisfaction
Performance Perceptions
General Organizational Climate
Data Analysis Procedures
Overview
Factor Analysis
Multiple Regressions
Individual-level Hypotheses
Squadron-level Hypotheses
Cross-level Hypotheses

CHAPTER 4
RESULTS
Factor Analysis
Individual and Cross-Level Descriptive
Statistics, Correlations, and Reliabilities
Squadron-Level Descriptive Statistics,
Correlations, and Reliabilities
Results of Analyses by Hypothesis
Individual-Level Climate and Outcome Measures
Squadron-Level Climate and Outcome Measures

Cross-Level Climate and Outcome Measures
CONCLUSIONS

CHAPTER 5
DISCUSSION
Summary
Limitations of This Study
Implications for Future Research and Theory
Implications for Practice

APPENDICES

REFERENCES

viii

71
72
72
72
73
74
75
75
75

76
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78
79
8O

82
83
84
92
94

100
103

104
113
114
118

123

145

3.1

3.2

4.1

4.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

4.10

4.11

4.12

LIST OF TABLES

Demographic Profile of Survey Respondents and Available
Workforce Between Rank/Grade Categories

Demographic Profile of Survey Respondents and Available
Workforce Within Rank/Grade Categories

Factors, Corresponding Questions, and Factor Loadings

Individual and Cross-level Variables: Descriptive Statistics,
Correlations, and Reliabilities

Squadron-level Variables: Descriptive Statistics, Correlations,
and Reliabilities

Regression Model: Dependent Variable — Individual-level Job
Satisfaction

Regression Model: Dependent Variable — Individual-level
Performance Perceptions

Regression Model: Dependent Variable — Flying Schedule
Effectiveness Rate

Regression Model: Dependent Variable — Mission Capable Rate

Regression Model: Dependent Variable — Squadron-level Job
Satisfaction

Regression Model: Dependent Variable — Squadron-level
Performance Perceptions

Regression Model: Dependent Variable — Individual-level
Job Satisfaction

Regression Model: Dependent Variable—Individual-level
Performance Perceptions

Summary of Results

ix

87

90

91

93

94

95

96

98

99

101

102

103

1.1

2.1

2.2

3.1

3.3

3.4

3.5

3.6

3.7

3.8

3.9

3.10

3.11

3.12

3.13

3.14

3.15

3.16

3.17

3.18

3.19

LIST OF FIGURES

Maximizing Organizational Climate and Performance
Hierarchical Model of Psychological Climate
Psychological Climate and Performance Outcomes Model
USAF Organizational Structure and Participating Levels
Introductory Survey Screen

Mandatory Language for All Air Force Surveys

Vice Chief of Staff Message to Survey Participants
Subject’s Location Conditions

Identify Squadron and Installation of Assignment
Verify Squadron and Installation of Assignment
Rank/Grade of Survey Participant

Introduction to Organizational Climate Section
Example of Question Presentation Format

The Job Questions

Resources Questions

Core Values Questions

Communication Questions

Leadership Questions

Supervision Questions

Training and Development Questions

Teamwork Questions

Participation/Involvement Questions

27

48

53

54

56

57

58

59

60

61

68

68

69

69

70

70

71

71

72

3.25

3.26

3.27

Recognition Questions
Unit Flexibility Questions

Job Satisfaction Questions

Perceptions of Performance Questions

General Organizational Climate Questions

Research Model for Hypothesis la.

Research Model for Hypothesis lb.

Research Model for Hypothesis 2a.

Research Model for Hypothesis 2b.

Research Model for Hypothesis 2c.

Research Model for Hypothesis 2d.

Research Model for Hypothesis 3a.

Research Model for Hypothesis 3b.

xi

76

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80

80

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80

81

81

CHAPTER ONE

INTRODUCTION AND OVERVIEW

Importance of the Topic

The termination of the Gulf War in 1991 brought about sweeping changes in the
force structure and fiscal strength of the United States (US) military. No longer facing
a potential war with the Soviet Union and with the defeat of Sadaam Hussein in the
Middle East, congressional and military leaders began considering strategic changes in
the Department of Defense’s (DoD) number of military personnel and operating budget.
Soon after our troops returned from the war with Iraq, the US. Congress issued several
mandates aimed at reducing the military personnel end strength. At that time, there
were 1,807,180 persons serving in the four DoD components: Air Force, Army, Navy

and Marines (Military Personnel Statistics; Directorate for Information Operations and

 

Reports, October 2000; [online]). Congress’ plan was to reduce these numbers by
approximately 25 percent over the next five years. Now, eight years later, the number

of persons wearing a military uniform stands at 1,385,703 (Military Personnel Statistics;

 

Directorate for Information Operations and Reports, October 2000; [online]). The

 

current numbers reflect a 23 percent reduction from the military population in 1992.
Thus, the right-sizing efforts closely approximated Congress’ directives.

While these initiatives were “downward-directed” by the top echelon of
leadership in this country, the same body of legislative forces allowed the mission of the
military to increase. Actually, the DoD is currently, “doing more with fewer people.”
Not only does the military maintain a warfighting capability in the Middle East and in

Europe, the forces are now being called upon frequently to perform counter terrorism,

peace keeping, and drug interdiction exercises.

Concurrently, the US. has witnessed a number of major socio-political changes
in the last eight years. The economy grew increasingly stronger, unemployment rates
steadily declined, and the era of the baby boomer generation meant there was a decrease
in the number of families that have relatives who served in the military. Traditionally.
the DoD has depended rather heavily upon these three contingencies as a source for
ensuring the accomplishment of its annual recruiting goals. When the economy is
suffering and jobs are difficult to find, the military serves as a viable alternative for
employment. Also, the number of active-duty service personnel with family members
who served previously has typically been very high over the years.

Yet, the number of new recruits is dwindling. During 1999, only one service
component, the US. Army, reached its annual recruiting goals. In order to accomplish
their objective, the Army reduced its entrance requirements for: 1) a high school or
equivalency diploma and 2) entrance scores on the Armed Services Vocational Aptitude
Battery (ASVAB). It is a very well known fact that, the services need more people.
However, making concessions on accessions policies to ensure enough “bodies” are
brought into basic military training may not be the best answer. The military must
maintain a quality force.

An equally disturbing trend currently occurring in the military is the fact that the
services are experiencing difficulties retaining active duty members, as well. High
attrition rates are occurring among the first- and second-term enlisted members. These
personnel comprise significant portions of the technical and mechanical specialties that
support each of the military services. Officers, in the pilot, navigator, engineering, and

scientific career fields, are opting-out of continuing to serve beyond their present

commitments.

The personnel reductions that occurred in the mid—19905 and the current attrition
rates are taking their toll on the remaining workforce. In the Air Force, which is the
focus of this dissertation, between 1998 and 1999. over 1,600 officers and 5.400
enlisted personnel left the service (Interactive Demographic Analysis System: Air Force
Personnel Center, [online]). These numbers are much higher than the expected attrition
rate. There simply are not enough people, including active duty, reserve, guard, and
civilians, to accomplish ever-increasing military areas of responsibility. Moreover,
there does not appear to be any major initiatives aimed at, “doing less with less.”
Senior military leaders must begin looking for ways to continue meeting their missions,
and doing them well, with fewer personnel.

The proposed topic for study is: What are the key factors that contribute to
performance in military work environments? Obviously, military members have little
impact on the social, economic, and political influences that often times capriciously
dictate where their work will be located and what they will be doing when they get
there. However, there is a key leverage point over which there is some control, the
climate of the organization. Thus, there are two areas that should be of critical
importance to the US. military: people and performance.

McGregor (1967) stated, “the performance (P) of an individual at work in an
industrial organization is a function of certain characteristics of the individual (I),
including his knowledge, skills, motivation, attitudes, and certain aspects of the
environmental situation (E), including the nature of his job, the rewards associated with
his performance and the leadership provided him {P = flIa,b,c,¢__, Emn,o,p)}” (p.5). The

performance of the industrial organization is dependent upon the collective

performances (activities) of the individuals who comprise that organization. An
accepted methodology for capturing the P =f(1 ,b,c,d,,,, Emmp) dynamic is through the
assessment of organizational climate.

Over the past eight years, climate surveys have become an annual event for the
entire US. Air Force. In 1996, approximately 130,000 military and civilian Air Force
members were administered some form of a climate survey. Then in 1997, the Chief of
Staff of the Air Force stopped the practice of multiple, independent surveys and stated
that he wanted only one survey to assess organizational climate and quality of life
issues. Over 216,000 Air Force personnel (39%) voluntarily participated in that survey.
The same survey was administered again in 1999, when approximately 209,000 Air
Force personnel (36%) volunteered to participate. The intended goal of these
organizational assessments was to provide senior leaders and commanders with
valuable information about their organizations. In turn, the data were supposed to show
where strengths and weaknesses in Air Force organizations (squadrons) existed.
Ultimately, commanders and senior leaders were expected to use the data to improve
areas over which they had some means for directing changes.

While the climate results were provided in a timely manner, and presented very
rich, actionable information, consultation with trained organizational development (OD)
personnel was almost non-existent. The biggest contributor to this void is the fact, the
Air Force simply does not have OD practitioners located at every Air Force installation.
Thousands of Air Force personnel are trained in group facilitation, problem solving and
teamwork principles. However, very few actually have the credentials to use the
organizational climate results to design organization-wide interventions. Most of the

Air Force OD professionals work as faculty members at the military service academies,

have key staff positions at the Pentagon, or are assigned commander duties at various
installations around the world. None of these assignments afford the officers and
civilians who have these skills, the opportunity to work as consultants for extended
periods of time.

Another key contributor to the lack of interventions is the fact, climate results
are not highly regarded as valid and actionable. The general impression is fairly bland
and if the results do not match the impressions commanders have for their various
organizations, the information is often dismissed as insignificant. Born and Mathieu
(1996) addressed the apathy some leaders feel toward a survey-guided feedback
approach (Beer, 1980; Lawler, Nadler, & Cammann, 1980; and Bowers & Franklin,
1977). Their results highlighted the ways in which climate survey information is
typically treated in a military organization. They found, the most common “users” of
survey results were those who received the highest scores at an intervention’s inception.
A one-year follow-up survey revealed that the “users” were also the ones who had
created significant positive changes in their work environment. Conversely, those who
received negative feedback from the first survey failed to use the data to enact changes.
In fact, the “non-users” received lower scores on the follow-up survey.

Weiner (1974, 1986) proposed that attributions (cognitive evaluations of
outcomes) influence future behavior. Attributions are formed by three cognitive
assessments: l) locus of causality (internal or external); 2) stability of outcomes (long
or short term); and 3) controllability (volitional) (Kanfer, 1990). Hence, one plausible
explanation for Born and Matheiu’s findings is related to “fundamental attribution
error.” This theory asserts that each of the three factors contribute to the ways in which

we perceive our environment. In the present example, perhaps, the commanders who

received positive feedback from the initial climate survey saw themselves as an action
agent (locus of control) with the ability and willingness (volitional) to make positive
changes to the climate in the squadrons (short term stability of outcomes). Conversely.
“non-users” may have believed less in their ability to create positive changes or perhaps
they deflected accountability by rationalizing that the results were beyond their control.
The major point is that commanders, who already had positive work climates, used the
results to continue to improve dynamics over which they had control. Those who
needed to make the most changes, typically did nothing with the survey results. Over
time, continuing to work in a negative work environment has a detrimental effect on
individuals. This study did not have the capability to assess the empirical relationship
between a military climate and turnover intentions. However, the literature review in
Chapter Two highlights the fact that workers intentions to leave an organization may be
predicted by their satisfaction with the work environment.

Commanders often voice their concerns about multiple competing priorities.
Because they are charged with preparing personnel, supplies, services, and weapon
systems for wartime contingencies, many commanders feel as though attention to
“organizational climate” is a low priority. A potential antecedent to the apathy might be
the fact that commanders are not typically held accountable for the “people-half of the
equation.” Instead they are evaluated on their abilities to fight wars, train for battles,
and support contingencies.

Therein lies the importance of this study. The theoretical basis for asserting that
climate is related to organizational outcomes is well documented in Chapter Two. A
very important criterion for making climate a strategic piece of information is to show

commanders how understanding and using these dynamics to their advantage can

actually help them achieve their missions. Figure 1.1 graphically demonstrates these

dynamics:

Figure 1.1: Maximizing Organizational Climate and Performance

 

 

 

 

 

 

 

Strong Desired State
Organizational
Climate
Poor
Organizational
Climate
Low High
Performance Performance

In the US. Air Force, there is no more important mission than launching
airplanes. Therefore, the present study is focused in an operational flying environment
that performs two basic missions: fighting wars and supporting the warfighter.
Warfighting squadrons actually go to war; whereas, those who support the warfighters
carry cargo and personnel, and provide surveillance information for their counterparts.

Finally, this study affords a unique opportunity to assess organizational
dynamics at multiple levels: 1) individual- (psychological) level; 2) organizational-
(squadron) level; and 3) cross-level. While the literature and theory are well articulated
for the individual and organizational levels, the literature describing cross-level
dynamics is rather sparse. Thus, this study will serve to advance the knowledge base in

the cross-level academic arena. The next section introduces the concept of climate and

outcomes in a military environment.

Climate and Outcomes in a Military Context

Before discussing climate in a military context, it is first important to recognize
that in today’s literature, “organizational climate” and “organizational culture” have
become almost synonymous. Denison (1996) provided a review of the two literatures.
Although the two constructs initially appeared in the literature as distinct, over the last
thirty years, researchers in both camps have used each other’s toolkits to conduct their
studies. Denison stated, “the analysis (in this article) has led me to conclude that these
two research traditions should be viewed as differences in interpretation rather than
differences in the phenomenon” (p. 645).

While recognizing differences may exist, for the purposes of this dissertation.
the two terms, “climate” and “culture,” shall be considered the same. Moreover,
climate will be defined as, “the shared perceptions of organizational policies, practices.
and procedures, both formal and informal” (Reichers & Schneider, 1990). Inherent in
this definition is a recognition that perceptions initiate at the individual level.
Moreover, the notion that these perceptions are shared connotes they are consensual
and form the basis for collective response tendencies from the individuals who comprise
an organization (Kozlowski and Hults, 1987).

Climate is generally accepted as a multi-dimensional construct that can be
defined at the individual or “psychological level” (James and Jones, 1974), and at the
organizational level (Ashforth, 1985; Glick, 1985; and Reichers, 1987). Outcomes are
considered to organizational measures of success that may be quantifiable

(efficiency/effectiveness) or affective (emotional/perceptual) in nature. Organizational

climate studies have been conducted in a large number of environments. Numerous
studies attempted to determine “climates” that were specific to an industry or a
commercial setting. Notable efforts addressed the following environments: safety
(Zohar, 1970); retail (Burke, Borucki, and Hurley, 1992; Wiley, 1991); technical
updating (Kozlowski and Hults, 1987); banking/financial management (Parkington and
Schneider, 1979; Schneider, Parkington, and Buxton, 1980; Schneider and Bowen.
1985); and schools (Ostroff; 1992, 1993).

James and Jones (1980) attempted to validate the construct of “psychological
climate” while collecting data aboard a US. Navy ship. Rousseau and Cooke (1988)
and Roberts, Rousseau, and La Porte (1994) published culture analyses that described
dynamics for high reliability organizations. Although they conducted their studies
onboard nuclear aircraft carriers in the US. Navy, their emphasis was on describing
processes that facilitate: 1) process reliability and 2) performance at high tempo for
extended periods of time. Their emphasis was not on describing a military climate.
Finally, Born and Mathieu (1996) published a study that included organizational climate
data from a US. Air Force installation in the Northeast. Their focus was on describing
an OD process that included a pre-survey, an OD intervention, and a follow-up survey.
Born and Mathieu were not focused on defining the specific dimensions that compose a
military climate.

As discussed earlier, climate is an organizational dimension over which there is
some leverage. Therefore in order for Air Force leaders to understand organizational
climate, the dimensions must be clearly articulated and leaders should have a clear
understanding of how it affects behavior and perceptions in an Air Force organization.

In the present context, performance is the critical link to getting leaders to pay attention

to organizational climate. No study has specifically addressed organizational climate in
a military setting with the purpose of instantiating a set of constructs that may be used
as the basis for organizational improvement. Therefore, this dissertation will attempt to:
1) validate the construct, “military climate” and 2) demonstrate that climate does have

an affect on outcomes at various levels of the US. Air Force “organization.”

Description of Chapters in the Dissertation
This section provides a brief overview of the chapters in this manuscript.

Chapter 2: Literature Review

 

This chapter reviews the literature that guided the formulation of this study. The
review begins with a look at climate in its early stages of discussion, with particular
emphasis on several seminal works. Then, the discussion changes to information
regarding climate at the individual-, organizational-, and cross- levels. Hypotheses that
support the ideas in this study are presented, as well.

Chapter 3: Research Methodology

 

Chapter Three addresses the where, how, who, and what of this dissertation.
The first half of the chapter describes the sites used in data collection, the methodology
for collecting data, and the sample of subjects. The last half of the chapter provides a
thorough discussion of the key variables used in this study and the data analysis
procedures used to arrive at the results.
Chapter 4: R_esu__lt_s_

The outputs from the data analyses are summarized in Chapter Four.
Specifically, tables from the factor, correlation, and regression analyses are displayed.

Also, the statistical findings are interpreted for the hypotheses at the individual-,

10

organizational-, and cross- levels.
Chapter 5: Discussion

Chapter Five presents a summary of the findings that were presented in Chapter
Four. In addition, limitations of the study, implications for future research. and

implications for practice are listed.

11

CHAPTER TWO
LITERATURE REVIEW

Introduction

The purpose of this chapter is to present a review of the literature that serves as
the theoretical basis for the present study. The organizational climate topic has a rich
history in the academic study of work environments. First, the seminal research is
presented in a chronological fashion to demonstrate how the topic of organizational
climate matured from the mid-1960s to the mid—19805. Next, the sections are divided
into the key areas of research that are traditionally associated with understanding
organizational climate and outcomes: individual-, organizational-, and cross-levels.

The hypotheses that guide this dissertation are presented within each subsection.

Historical Perspective of Climate

The term, “climate,” initially appeared in academic circles as a summary term
that referred to a generalized condition in an environment where people interact.
Actually, the term went undefined for approximately 25 years, even though it was used
in several seminal articles. Little attention was initially devoted to understanding the
variables and levels (individual and organizational) that might comprise the construct
(Reichers & Schneider, 1990).

Lewin, Lippitt, and White (1939) first used the term “social climates” to refer to
differing treatment groups in an experimental study designed to assess group behavior.
Ten-year-old boys were assigned to autocratic, democratic, and laissez-fair group

leaders. The authors were primarily interested in evaluating leadership styles and how

12

the dynamics associated with each approach may affect aggressive behavior in
adolescent boys. The questions of interest, albeit of a social psychology nature. were
amazingly similar to phenomena in contemporary management literature. Here are the
issues that were of interest to Lewin, Lippitt, and White ): “1) What underlies such
differing patterns of group behavior as rebellion against authority, persecution of a
scapegoat, apathetic submissiveness to authoritarian domination, or attack upon an out
group? 2) How many differences in subgroup structure, group stratification, and
potency of ego-centered and group-centered goals can be utilized as criteria for
predicting the social resultants of different group atmospheres? and 3) Is not democratic
group life more pleasant, but authoritarianism more efficient? (p. 271).”

Since the goal of the study was to look at how leadership styles may create
differing “social climates,” the adolescent boys were assigned to the same leader for six
weeks and then they were moved to another group leader who portrayed a different
style of leadership. Lewin, Lippitt, and White found that autocratic climates produced
either aggressive or apathetic behavior in the boys. They indicated that the key to the
boys’ behavior rested in the behaviors modeled by the overly assertive leaders.
Conversely, the boys’ apathetic behavior was explained as the result of them repressing
their aggressiveness due primarily to fear of their autocratic leader. Democratic and
laissez-faire leaders were better liked than the autocratic leaders.

Lewin (1951) regarded the climate or atmosphere of the psychological field as a
characterization of salient environmental stimuli and an important determinant of
motivation and behavior (cited in Kozlowski & Doherty, 1989). Climate was conceived
as the key functional link between the person and the environment. In, The Human

Side of Enterprise, Douglas McGregor (1960) devoted an entire chapter to what he

 

13

called, The Managerial Climate. McGregor believed employees’ behavior was more
than a function of the leader’s style or the leader’s personality characteristics. He stated
that a great deal of what employees experience at work is due to the “psychological
climate” created by the managerial attitudes. Employees’ abilities to accomplish their
goals are highly dependent upon the attitudes and perceptions of their superiors. Bosses
form impressions about their subordinates and it is these perceptions that create the
climate in which the working relationship occurs. Both the supervisor and the
employee are aware of these dynamics and learn to respond appropriately. Thus, the
climate actually becomes more important than the style of the manager. McGregor
said, “The boss can be autocratic or democratic, warm and outgoing, or remote and
introverted, easy or tough, but these personal characteristics are of less significance than
the deeper attitudes to which his subordinates respond” (p. 134).

McGregor cited additional characteristics of the managerial climate such as:
trust, the expectation of fairness, and competence as key contributors to subordinates’
perceptions of their work environment. Finally, the author stated that the relationship
workers establish with their managers effectively determines their happiness at work,
because it is through the superior-subordinate communication that they are able to
determine whether or not their personal needs are being met. McGregor believed that
formal polices, programs, and procedures are interpreted through employees’
perceptions of the managerial climate. In fact, he perceived the managerial climate to
be of far more importance than the “machinery” of the administration (p. 144).

Additional studies in the early 19605 used the term “climate,” to differentiate
decentralized vs. centralized authority (Morse and Reimer, 1961), open vs. closed

systems (Barnes, 1960), and unstructured vs. structured environments (Leavitt, 1962).

14

Then in 1964, Forehand and Gilmer offered the first operational definition of the term.
“organizational climate.” Stating that some researchers have only alluded to the term,
they noted there was little consensus about what to call the phenomena (organizational
characteristics) that produce differences in organizational personalities. Hence, they
offered the following definition for organizational climate: “the set of characteristics
that describe an organization and that a) distinguish the organization from other
organizations, b) are relatively enduring over time, and c) influence the behavior of
people in the organization” (p. 362). They further stated that these organizational
properties were chosen in an effort to focus discussion upon features of organizational
variation that are amenable to specification, measurement, and incorporation into
empirical research.

Forehand and Gilmer (1964) stated that individual perceptions are an
appropriate mechanism by which climate can be studied. They cited Likert’s (1961),
“interaction-influence proposition” and quoted his discussion,

Organizational structure and the behavior of managers and
superiors, impinge upon the personalities of the organization.
These two, the causal variables and the personalities interact and
determine the perceptions. Thus, each member’s perception of the
behavior of his superior is determined by both the character of this
behavior and by the personality of the member. The individual’s
perceptions then lead to cognitive orientation. This refers to
dimensions of the individual’s intellectual understanding: his
concept of his job, what he thinks he is supposed to do and how he
is supposed to do it and his concept of the organization and its
objectives (pp. 197-198).

Until this point, the majority of data collected during organizational research
were primarily based upon “observations” of behavior in the work setting. Observers

catalogued workers’, supervisors’, managers’, and leaders’ behavior according to

protocols. However, as Forehand and Gilmer (1964) noted, assessing climate through

15

workers’ perceptions increases the likelihood of fidelity. These perceptions are “based
upon experience that is both more extensive and more involved than that of an outside
observer” (p. 364).

Over the course of the next ten years, climate researchers invested heavily in
attempting to identify the dimensions of climate that tended to distinguish one
organization from another. Largely through field studies and laboratory experiments.
Sells (1963); Woodward (1965); Hall, Haas, and Johnson (1967); Lawrence and Lorsch
(1967); Indik (1968); and Pugh, Hickson, Hinings, and Turner (1969) are credited with
spearheading the development of a taxonomy of climate factors that described
organizational contexts and structures. Then, in 1970, Campbell, Dunnette, Lawler, and
Weick compiled the first synthesis of organizational climate research. In their review,
they cited four common climate factors drawn from the following works: Kahn, Wolfe,
Quinn, Snoeck, & Rosenthal (1964); Litwin & Stringer (1968); Schneider & Bartlett,
1968). The list included: 1) Individual Autonomy; 2) Degree of Structure Imposed
upon the Position; 3) Reward Orientation; and 4) Consideration, Warmth, and Support.
Campbell, et a1. (1970) stated that organizational climate was a psychological process
that is determined by differing situations. Also, they proposed that the four climate
variables might be causative or serve as moderators for workers’ perceptions of their
work environment.

Schneider and Bartlett (1968, 1970) were interested in the extent to which
individuals’ climate expectations and preferences, when compared to organizational
climate realities could be used to predict the probability of success for life insurance
agents. Their research included six measures of life insurance climate: 1) Managerial

Support; 2) Managerial Structure; 3) Concern for New Employees; 4) Intra-agency

16

Conflict; 5) Agent Independence; and 6) General Satisfaction. Similarly, Campbell and
Beaty (1971) studied salaried personnel in a manufacturing plant. Their research
produced seven climate dimensions that were consistent at both the work group and at
the organizational levels: 1) Task Structure; 2) Reward/Recognition: 3) Decision
Centralization; 4) Achievement Emphasis; 5) Security vs. Risk: 6) Training and
Development Emphasis; and 7) Openness vs. Defensiveness.

As the list of climate variables increased, the literature began to focus on the
appropriate genesis for climate perceptions. Researchers began discussing the merits of
whether climate perceptions were based primarily on workers’ cognitive representation
of environmental events, or based on organizational events and characteristics.
Actually, organizational climate was seen as a summary perception that was the result
of both individual and organizational characteristics. Actions and processes occurring
in the work environment were interpreted through the experiences, values, levels of
satisfaction, and personality characteristics of the individual. Thus, work situations
could produce entirely different perspectives based upon the individuals’ cognitive
schemas.

This theme began to emerge in the literature. Schneider and Hall (1972) defined
climate as a set of summary or global perceptions held by individuals about their
organizational environment. These summary perceptions reflected, “an interaction
between personal and organizational characteristics, in which the individual, by forming
climate perceptions, acts as an information processor, using inputs from a) the objective
events in and characteristics for the organization, and b) characteristics of the
organization and the perceiver.” (cited in James and Jones, 1974; p. 1106). Also, James

and Jones (1974) stated, “interaction, intervention, and perception, while perhaps

17

distinct in a conceptual model, take place in the individual and therefore are individual
characteristics” (p. 1108). At this point, they recommended the differentiation between
climate as an organizational attribute vs. climate regarded as an individual attribute.
They proposed that the organizational attributes should be called “organizational
climate” and that the individual attributes should be labeled, “psychological climate."

While studying climate aboard a Navy aircraft carrier, Jones and James (1979)
found empirical evidence for their concept of the two levels of climate. About
psychological climate they stated:

Psychological climate: a) refers to the individual’s cognitively
based description of the situation; b) involves a psychological
processing of specific perceptions into more abstract depictions to
the psychologically meaningful influences in the situation; c) tends
to be more closely related to the situational characteristics that have
relatively direct and immediate ties to individual experience; and d)
is multidimensional, with a central core of dimensions that apply
across a variety of situations (though additional specific
dimensions might be needed to better describe particular
dimensions) (p. 205).

Similarly about organizational climate they wrote:

The argument for aggregating perceptually based climate scores
(i.e., psychological climate scores) appears to rest heavily on three
basic assumptions: first, that psychological climate scores describe
perceived situation; second, that individuals exposed to the same
set of situational conditions will describe these conditions in
similar ways, and third, that aggregation will emphasize perceptual
similarities and minimize individual differences. Based on this
logic, it is generally presumed that empirically demonstrated
agreement among different perceivers implies that these perceivers
have experienced common situational conditions (p. 206).

In their 1974 article, James and Jones hypothesized that the mean (average)
value of subunit, unit, division, organization, etc. would serve as an accurate
representation of the aggregate score as the desired levels of analysis progressed upward

in the organization hierarchy. Then in their 1979 study, they demonstrated that

18

significant differences in mean scores existed between functional divisions onboard a
Navy aircraft carrier. Twelve differing types of divisions were shown to be
homogeneous in their response patterns. Moreover, individuals assigned to these
divisions were shown to be similar in their personality characteristics and they
performed quite similar work tasks (e.g., navigation. deck maintenance. radio
communications, electronics).

James and Jones continued their research with particular interest in expanding
the construct of “psychological climate” (PC). In 1989, they presented a hierarchical
model (see Figure 2.1) of meaning that represented the lens through which individuals
perceive their work environments. They found a general or “g-factor” represented a
single latent psychological component that is used by individuals to assess their well-
being. Citing Lazarus (1982, 1984), they described the g-factor as a higher order
schema for appraising the degree to which the environment is personally beneficial
versus personally detrimental (damaging or painful) to the self and therefore to one’s
well-being. Underlying the general factor (PCg) were four first-order factors that were
derived from their previous research. These four major factors were: 1) Leader
Support and Facilitation; 2) Roll Stress and Lack of Harmony; 3) Job Challenge and

Autonomy; and 4) Workgroup Cooperation, Warmth and Friendliness.

l9

Figure 2.1: Hierarchical Model of Psychological Climate

   

     
   
 
     
   
 

General Factor
of Psychological
Climate (PCS)

 
 
      
   
 

   

     
   
   
 

Work Group

Leader Support RcciileLStrkessf Job Challenge Cooperation,

and Facilitation an ac o and Warmth and
Harmony Autonomy

F riendliness

 

The basis for James’ and Jones’ model lies in what they termed, “valuations.”
They believed personal values produce schemas that are in turn used to cognitively
assess work environments in terms of what is important to the individual. Obviously,
the extent of congruence between the individual and the work environment produces the
valence of the valuation. When the work environment reflects the individual’s value(s),
it is more likely to be perceived as more beneficial to the individual (James, James, &
Ashe, 1990; James, Hater, Gent, & Bruni, 1978). Valuation provides the link between
psychological climate and PCg. It is the g-factor that furnishes the information to
cognitively assess significance for well-being.

In the mid-19805, another perspective began to emerge. Schneider and Reichers
(1983), Ashforth (1985), Glick (1985), and Reichers (1987) challenged the notion that
climate was merely a function of individual perceptions. Instead, they introduced
hypotheses stating that climate is the result of, or at least includes, socialization

processes in the organization.

20

Glick (1985). in particular, did not believe the entire concept of organizational
climate rested at the individual level. He believed climate was a class of organizational
rather than psychological characteristics (p. 601). Also, he challenged the prevailing
view that the definition of organizational climate rested in an empirical decision as to
whether or not there was enough agreement between raters at the individual level to
aggregate to the next level (Jones and James, 1979). Glick noted that the practice of
assessing levels of perceptual agreement, in order to define organizational climate,
might rule out the existence of climate in some organizations. If inter-rater agreement
is low, then according to the Jones’ and James’ approach to defining organizational
climate, the construct does not exist. In other words, if the level of perceptual
agreement among raters from the same work environment is low, then the outcome of
the statistical analysis would indicate they were devoid of any organizational climate.
Glick believed this was impossible. Every organization has some type of climate.
Moreover, he stated, over reliance on composition rules negates the traditional criteria
of construct validity and measurement reliability. Finally, he said:

Organizational climate is the result of sociological and
organizational processes. Thus it should be
conceptualized as an organizational phenomenon not as
a simple aggregation of psychological climate. Neither
should it be abandoned in some organizations simply
because a hypothesis composition is disconfirrned (p.
605)

Schneider and Reichers (1983), Ashforth (1985), and Reichers (1987) proposed
what they called, “The Social Interactionist Perspective.” They asserted, individuals

evaluate their abilities and beliefs in light of the tasks they must perform and in light of

the people with whom they perform these tasks. Particularly for newcomers, this

21

evaluation process predestines them toward social influence. They pay attention to the
norms governing behavior and appearance, the status and power of organizational
structures, reward systems, communication patterns, etc. When certain dynamics are
unclear, they seek guidance and understanding from incumbents who then invoke their
values upon the newcomer. Most often, the newcomer seeks this information from
those with whom she believes she is most similar.

Workgroup members are likely to have similar backgrounds, demographic
characteristics, talent, interests, and current experiences (Schneider, 1983; Lawrence &
Lorsch, 1967). The work group is very likely to serve as the referent for social
' understanding. Hence, interaction does not take place simply because workers are
bounded physically, but also because of perceived interpersonal similarities (Ashforth,
1985). Generally speaking, the transfer of information between newcomers and referent
groups is accomplished more easily when the two entities are more similar. In time,
most newcomers assimilate the prevailing paradigms and perceptions of the work
environment, the organizational climate. Ultimately, as common perceptions and
understandings about the work environment are instantiated, individuals develop a
common stake in the perpetuation of their group (Ashforth, 1985).

Schneider and Reichers (1983) and Reichers (1987) said that the meaning of
organizational climate arises out of interactions between people (P), and individuals
transform their own perception of events in response to the interactions they have with
others in a situation (S). Thus, a person by situation (PxS) interaction occurs. Reichers
(1987) wrote, “Because individuals contribute to the meaning that arises in a setting,
individuals and situations mutually determine each other” (p. 279). Further, “meaning

that becomes the social reality is enacted by and shared by the social collective does not

22

exist merely in the experience of a single individual, but... exists in the behavioral
transaction itself ' (Schneider & Reichers, 1983; pp. 29-30). Thus, it is the interaction
or the episode that serves as the basis for the interpretation of climate (Ashforth, 1985).
The review presented above summarizes the early research that helped formulate
the meaning, etiology, and applications of organizational climate. Next. this chapter

focuses on the relevant research that guided this dissertation.

Theory Guiding Present Research

Individual-level (Psychological) Climate and Outcome Measures

Engaging, motivating, rewarding, and safe working environments should
increase employees’ levels of job satisfaction and cause them to perceive performance
outcomes in their organizations as high. Thus, it is important to study psychological
climate, because it is employees’ perceptions and valuations of the environment, rather
than the environment itself, that mediate attitudinal and behavioral responses (James &
Jones, 1974; James, et al., 1978; cited in Brown and Leigh, 1996). Therefore, climate
researchers began capitalizing on the utility of measuring employees’ perceptions about
their work environment. Initially, climate was assessed in light of individual
demographic variables. Then, the field of research moved in the direction of trying to
establish ways in which employees’ perceptions of climate may affect other variables.
At the individual level, this study focuses on two of these dimensions: Job Satisfaction
and Performance.

Schneider and Snyder (1975) and James and Jones (1980) published seminal
research articles that addressed these organizational dynamics. Schneider and Snyder

(1975) collected climate perceptions and organizational performance data from 50

23

insurance agencies. Their indices included six measures of climate (Schneider. 1972:
Schneider & Bartlett, 1970), five measures of job satisfaction, three measures of
individual needs, and three measures of agency-level performance. Their results showed
two of the climate dimensions were significantly (p 5 .05) correlated with indicators of
job satisfaction. Specifically, Managerial Support and Harmony in the Work Place
were related to overall satisfaction with supervisors and with co-workers. They also
found that climate perceptions differed according to the types of position held in the
insurance company. The climate - satisfaction relationship was strongest for staff
members, in-house trainees, and insurance brokers; the relationship was somewhat
weaker for managers and secretaries/stenographers.

Schneider and Snyder’s (1975) research failed to demonstrate that individuals’
perceptions of their work environment had an effect on their agency’s performance.
Generally, only the managers’ assessment of climate was correlated with agency
ratings, gross volume of insurance sales and how well the agency achieved its planned
appointments. It is worth noting that among the ten significant correlations, seven
showed an inverse relationship between climate and performance. This suggests that
some of the organizational processes that are intended to improve the work environment
actually serve to decrease performance.

James and Jones (1980) took a much more focused view of climate. They used
only one measure of climate, job attributes, to assess the extent to which climate
affected individual measures of job satisfaction. The job attributes factor was
composed of five subscales: 1) Job Complexity; 2) Job Pressure; 3) Boundary
Spanning; 4) Specialization; and 5) Standardization. These scales are very closely tied

to the job characteristics research that received a great deal of attention during that time

24

(Hackman and Lawler, 1971; Hackman and Oldharn, 1976).

Job structure variables differentiated among the sub-samples that included
individuals from information systems, production, and fire fighting professions. Also,
the results showed that the job attributes factor was a significant predictor of
individuals’ job satisfaction. In fact, each of the five subscales were significant
predictors (p 5 .01) of the dependent variable. Increased complexity, pressure, and
freedom to conduct business with others outside their work group had the largest
influences on individuals’ job satisfaction. Conversely, routine jobs that require
specialization and standardization had a detrimental effect on individuals’ job
satisfaction. The authors stated that these findings support the notion that individuals
respond to perceptions of environments, and not to the environments per se.

More recently, psychological climate research has witnessed an increase in the
number and types of climate variables that are considered to have an impact upon
outcomes experienced by the individual and by the organization. More attention is
being devoted to individuals’ perceptions of organizational processes and how such
things as training, work procedures, teamwork, promotion, and recognition affect the
work environment.

Yet, one dimension remains consistent as a psychological climate measure, job
characteristics. Gunter and Fumam (1996) found specific job-related components were
significantly correlated with individuals’ job satisfaction. Their research identified:
jobs that are clearly defined, provide immediate feedback, and are more challenging,
increase organizational members’ satisfaction. Brown and Leigh (1996) also found that
employees’ job satisfaction (job involvement) is highly correlated with the extent to

which their jobs are perceived to present them with challenging opportunities.

25

Additionally, Brown and Leigh used a structural equation modeling approach to
confirm that a higher-order psychological climate variable was a significant predictor of
job satisfaction (job involvement). The results demonstrated a significant path in the
expected direction, i.e., psychological climate —> job satisfaction.

Another construct recently receiving increased attention in the climate literature.
and shown to be a significant predictor of job satisfaction is, a participative work
environment. Tesluk, Vance, and Mathieu (1999) identified a relationship between job
satisfaction and a climate in which employees are involved in setting goals, deciding
how work gets accomplished, and allowed to voice their opinions about strategic
matters. Across four different organizations, Gunter and Fumam (1996) discovered a
very similar relationship. Their research indicated that job satisfaction is higher among
employees who have some influence over their job, partner with their managers in
decision-making scenarios, and feel like they are part of the information channels
throughout the organization.

Additional climate variables that appear to affect workers’ job satisfaction are:
training (Ritchie, 2000; Gunter & F umam, 1996), management support (Tesluk, Vance.
& Mathieu, 1999; Brown & Leigh, 1996), and rewards/recognition (Brown & Leigh,
1996). These will not be addressed in this section.

Earlier in this section, Schneider and Snyder ( 1975) were cited as a seminal
work that included measures of climate and organizational performance. The remainder
of the literature that describes the relationship between these two constructs at the
individual level is rather sparse. Individual performance measures are difficult to
collect. Therefore, surrogates are often used in lieu of these variables. In fact,

individual-level climate perceptions are often compared to organizational-level indices.

26

One exception is Brown and Leigh (1996). They were able to collect
performance data on 233 salespersons in the paper manufacturing and office supplies
industries. Available performance measures were 1) Achieving Sales Objective
(volume); 2) Extent of Technical Knowledge; and 3) Administrative Performance. One
of the climate measures. Supportive Management, which included such measures as the
manager’s flexibility with job-related tasks, willingness to listen to new ideas,
autonomy, and trust, was consistently correlated with all three performance measures.
Also, when the salespersons felt as though their work was important and that they, as
individuals, were highly valued by their company, these perceptions were significantly
(p301) correlated with increased levels of job satisfaction. Finally, Role Clarity
(understanding of job expectations and performance norms) was inversely correlated
with the three measures.

In a structural equation model, Brown and Leigh (1996) also identified a
significant path between Job Satisfaction (Job Involvement) and Performance. When
these findings are paired with the path between Psychological Climate and Job
Satisfaction (Job Involvement) that was described above, the entire effect of the linkage
between psychological (individual-level) climate and performance can be identified.
The full model also included workers’ level of effort as a mediator between job

involvement and performance. Figure 2.2 shows the full model.

Figure 2.2: Psychological Climate and Performance Outcomes Model

Psychological Job

Climate Involvement Performance

 

27

The studies reviewed in this chapter demonstrate that individual (psychological)
climate measures have been shown to be related to job satisfaction and to
organizational-level outcomes. The focus of this study is on determining whether or not
these same dynamics occur in the contemporary US. military.

Hypothesis 1a: Individual climate percgptions will explain a significant amount
of variance in squadron members’ job satisfaction.

 

Hypothesis 1b: Individual climate percgptions will explain a significant amount
of variance in squadron members’ perceptions of performance.
Organizational-leveljSquadron) Climate and Outcome Measures

An impressive series of studies were conducted in the banking industry from
1979-1985. Parkington and Schneider (1979); Schneider, Parkington, and Buxton
(1980); and Schneider and Bowen (1985) attempted to identify a number of outcome
measures that were related to what they called “service climates.” Actually, the data
were collected from financial services institutions.

Parkington and Schneider (1979) were two of the first researchers to
demonstrate the utility of employee attitude surveys in a contemporary work
environment. They stated:

Employees often are as close psychologically, organizationally,
(hierarchically and geographically), and physically to the
organization’s clients as they are to other employees of the
organization or perhaps even closer. Because of this boundary
position, service employees function as information processors
and filters (Aldrich & Herker, 1977), as representatives of the
organization, and as formal or informal agents of the organization
who influence the organization’s clients (Thompson, 1967; p
270).

The authors hypothesized that discrepancies between the way employees act

toward clients (service orientation) and the way employees interpret management’s

28

approach to operationalizing a service-oriented climate will result in increased role
conflict and role ambiguity for the employees (Blau & Scott. 1962; Crozier, 1964:
Kahn, Wolfe, Quinn, Snoeck, & Rosenthal, 1964). In turn, employees will experience
job dissatisfaction, frustration, a lack of confidence in the organization. and desire to
leave the firm (Brief & Aldag, 1976; cited in Parkington and Schneider, 1979; p. 271).
To the extent that differences could be identified, changes could be implemented that
would either reduce negative outcomes for employees and/or increase customer
satisfaction with services offered by the bank. The authors also believed that employee
outcomes (organizational satisfaction, frustration, turnover intentions, and employee
service quality views) would be related to customer’s perceptions of service they
received in the bank.

Using hierarchical regression, Parkington and Schneider verified their
hypothesis. They found that discrepancies in employees’ perceptions of the service
climate and management’s practices aimed at creating a service climate, have a direct
effect on employees’ dissatisfaction, turnover intentions, and perceptions of the quality
of services offered to customers. Prior to looking at the effects of the discrepancy
variable, they entered measures of role conflict and role ambiguity into the equation. In
addition, they found that increased discrepancies in practice and policies have indirect
effects on employee outcomes. Specifically, they determined that role conflict and role
ambiguity act as “psychological mechanisms” which influence the ways in which
employees interpret discrepancies between their perceptions of the service climate and
management’s actions related to the service climate. As the differences in perception
increase, employees feel more role conflict and ambiguity, thus leading to

dissatisfaction, desire to leave, and more negative views of the service provided to

29

customers.

Parkington and Schneider (1979) were also interested in whether employee
outcomes were related to customers’ perceptions of service quality offered by the bank.
The results pointed out that when employees see their service quality as high. so do
customers. In addition, when employees are more satisfied with their work
environment, customers perceive that they receive better service.

Clearly, these results indicate a need for management and employees to be
aligned in their practices and beliefs about what constitutes a service-related climate.
Leaders and managers should attempt to determine what discrepancies exist, why they
are present, and how they might resolve the differences in a way that promotes effective
management and human resource policies. To the extent that these discrepancies can be
reduced, management in the banking industry may reduce the amount of role conflict
and role ambiguity experienced by its employees.

Schneider, Parkington, and Buxton (1980) were primarily interested in whether
a “service-oriented climate” has an effect on customer satisfaction. Schneider et a1.
asserted, employees’ interpretations of the events in the workplace can provide the
framework for understanding internal processes. Customers’ perceptions offer a
different perspective about the same events and services (Thompson, 1967; Blau and
Scott, 1962). Moreover, the dual approach to gathering data (assessing employees and
customers) “eliminates the potential contamination associated with data coming from a
single source by permitting the examination of relationships between the process
evaluation of branch practices and procedures by employees, and the outcome
evaluation by customers” (Campbell, 1974; Goldstein, 1978; cited in Schneider et al,

1980;p.263)

30

Their results showed a strong agreement between employee and customer
attitudes about the level of quality service offered, and customers’ perception of service
received. Traditionally, leaders and managers used customer inputs to make
changes/additions in services offered by the company. While external opinions remain
quite important, this study pointed to the importance of employee attitudes as an
additional source of information.

Schneider et al. also reported that employees’ attitudes about promoting a
service climate are highly correlated with branch customers’ attitudes about the same
processes. Essentially, if employees are focused on taking care of customers and not
concerned with or tasked with too many internal procedures, they can provide better
services. The outcome of such an environment is: customers perceive that the bank
operates much more efficiently on a daily basis.

In 1985, Schneider and Bowen replicated Parkington and Schneider (1979) and
Schneider et a1. (1980). They found that customer perceptions of efficient bank
operations and service climate were correlated with employees’ perceptions of the
bank’s human resource policies. In addition, Schneider and Bowen discovered that
employee and customer turnover intentions were significantly related. In sum, these
findings demonstrate the empirical relationship between an internal service climate in
banks and customers’ views of banking effectiveness. The authors assert that there is
indeed a preferred “service ideal” which both employees and customers desire. To the
extent that there is a difference between this ideal and the “organizational climate,”
employee and turnover behavior can be predicted.

Paradise-Tomow (1991) also validated employee attitudes as a legitimate factor

in helping bank leaders make data-based decisions regarding employee/customer

31

practices. Executives of a large retail-banking firm instituted several changes aimed at
creating a “service-quality culture” in an attempt to remain competitive in its market.
The author and the bank’s leaders conferred to develop a “Leadership/Management
Model for Community Banking.” The executive committee was interested in evaluating
bank presidents and senior bank teams against the criteria in the model. The leaders and
the author decided to proceed with an employee opinion survey assessing
leadership/management dimensions, customer-service orientation, and an employee
connectedness aspect intended to assess how well employees were relating to the bank’s
internal processes. The results of the survey showed that employees form clear
cognitive distinctions between leadership and management practices in the banking
climate. Each of these dimensions also has a different relationship with measures of
performance in the retail business.

Burke, Borucki, and Hurley (1992) provided an innovative synthesis of James
and James (1989), Schneider et al. (1980) and Schneider and Bowen (1985). However.
the context in which the new study was conducted was purposely shifted to a large US.
retail firm. Burke et al. used 18.000 respondents to test the existence of first- and
higher-order organizational climate models. Specifically, they sought to determine
whether employees in a retail environment reliably form “valuations” (James & James,
1989) about what is beneficial to them and to other constituencies, most notably, their
customers (Schneider et al., 1980; Schneider & Bowen, 1985). Their model took into
account “valuations” which are shaped not only by history and individual differences,
but also by key work environment ideals.

Burke et al.’s assumptions were based on previous research evidence that

demonstrated: 1) employees form cognitive appraisals about their work environment in

32

terms of what is beneficial/harmful to them as individuals (James & James. 1989): and
2) employees who have contact with significant stakeholders have a good idea of what
is important to the stakeholders and are aware of organizational processes which
support services to the stakeholders (Schneider & Bowen, 1985). Thus. Burke et al.
tested the hypothesis: employees perceive not only what is beneficial to themselves. but
to stakeholders as well.

Analyses across five regions of the company confirmed the existence of a first
order model composed of the following factors: 1) Goal Emphasis; 2) Management
Support; 3) Non-Monetary Reward Orientation; 4) Monetary Reward Orientation; 5)
Organizational Service Orientation; 6) Human Resource-Related Obstacles; and 7)
Merchandise-Related Obstacles. Burke, et al. stated, “our confirmed first-order factors
are viewed, in part, as personal value-based schemas (e.g., goal emphasis) reflecting
employees’ cognitive appraisals of the degree to which a personal value (e.g., clarity) is
represented in or by retail environmental attributes (e.g., the extent to which an
employee’s immediate manager sets clear cut performance standards with respect to
approaching customers quickly” (p. 727).

At the beginning of the 19905, organizational climate researchers showed an
intense interest in examining the utility of organizational climate surveys beyond
applications to internal organizational processes and with customer
satisfaction/expectations. Theses studies focused on whether climate has an empirical
relationship with the bottom line for the organization. . .perforrnance. Unfortunately, the
literature showed little overlap in the climate and performance measures used in the
respective studies. Comparisons are subjective, at best.

Tornow and Wiley (1991) provided one of the first contemporary studies

33

investigating empirical linkages between measures of organizational climate and
performance. The target environment for this study was the computer services industry.
The authors were interested in three linkages: 1) customer satisfaction and employees’
perceptions/attitudes; 2) customer satisfaction and organizational performance: 3)
employee perceptions/attitudes and organizational performance.

The study was performed for a multi-national corporation that provides payroll,
tax filing, accounting, and human resource management services to outside agencies.
Overall, 633 customers and 667 employees participated in the survey. Because the
authors were interested in finding linkages between the three key measures at the
organizational level, the district unit (aggregate of smaller units) served as the unit of
analysis. Thirty district units were included in the study and all data were collected
over a 12-month period.

Tomow and Wiley (1991) used correlational analyses to determine empirical
relationships among the variables of interest. Results indicated that employee
perceptions of the organizational climate were related to effectiveness measures in the
computer services corporation. Specifically, perceptions about their “Culture for
Success” were linked to the company’s ability to retain existing contracts (p501). The
authors’ findings showed, when the organization provided necessary resources for
employees to complete their work, an environment that based new product development
on customer needs was fostered. Hence the company was better able to retain existing
customers. Another employee attitude dimension that correlated with contract retention
was Satisfaction with Management Practices (p305). This finding indicated that when
managers do a good job of recognizing their employees, and when trust between

managers and employees is high; the company is better able to maintain contracts for

34

extended periods of time.

Overall Employee Satisfaction with the company and the performance
dimension, Right First Time, were also significantly correlated (p301). This
relationship revealed that when employees feel as though they have job security, are
positive about the company’s loyalty to them, and are satisfied with their employment,
they will in-turn, exert more effort to ensure products are successfully installed the first
time the product is placed in the customer’s workplace.

Tomow and Wiley (1991) provided rich evidence that organizational climate has
an impact on bottom line performance measures that are important to company leaders.
Their work marries nicely with Schneider, et a1. (1980) and Schneider and Bowen
(1985) who found that leadership and management practices, which promote a service
quality environment, also influence customers in a positive fashion. This dissertation
extends the previous work by demonstrating that positive dimensions of the work
environment influence outcome measures, as well.

Paradise-Tomow’s (1991) article addressed linkages between organizational
climate and measures of performance. She created a leadership/management model for
a community banking organization in an effort to “direct, evaluate, and reward the
people management behaviors of bank presidents” (p. 129). Key dimensions of the
model included: Leadership; Sales/Service Culture; Performance Management and
Development; Compensation Management; Staffing; and Employee Relations. Next,
Paradise-Tomow developed an employee attitude survey to assess these areas. The
results were extremely interesting. Paradise-Tomow found a significant relationship
(p505) between the leader’s ability to clearly communicate sales goals

(Communication) to the sales force, and the Bank’s Achievement Against Plan

35

(Actual/Plan) index. Although the results were slightly weaker (p510). Paradise-
Tornow also found that leaders’ ability to gain commitment from employees and to
promote a positive employee relations environment had an impact on the Achievement
Against Plan measure.

This study included measures of bank efficiency, in addition to the performance
statistics. When leader behaviors were compared to the efficiency dimensions,
Downward Communication, Sales Goals, Teamwork at Top and Change Management
were related to varying measures. The most significant relationship existed between
Communication and Non-Personal Expenditures (p305).

Paradoxically, factors representing the bank’s management culture revealed a
consistently negative pattern of relationships with the performance and efficiency
measures. Most striking were the negative effects of Communication, Service Climate,
Performance Management and Development, and Staffing. Each of these were
significant at the p501 level. Similarly, each of the management culture indicators was
negatively correlated with the unit efficiency measures.

Paradise-Tomow’s (1991) study points out a potential “three-way tradeoff
between people, performance, and quality” (p. 139). Clearly, bank employees
distinguish between leader and manager behaviors in the work environment. The
behaviors are found to differentially affect performance and efficiency measures.

When management behaviors are entered into the summary, the very same
activities that promote “service quality environments” appear to deter financial success.
The study included possible caveats to these counter-intuitive findings. First, financial
indicators may be too multidimensional to correlate with distinct management

behaviors. Next, the drive to generate short-term financial success may preclude

36

managers from paying attention to service quality and lead them to ignore sound human
resource (HR) practices. Finally, when the bottom line for the company is considered.
e.g. financial performance, success may be viewed more as a function of
outputs/outcomes in the form of services and products offered by the company. rather
than as a function of the service quality environment and sound HR activities promoted
by the organization.

Ostroff (1992) presented one of the classic studies in the climate-performance
literature. She asserted that satisfied employees would be more willing to work toward
fulfilling organizational objectives than dissatisfied employees. Also, she stated.
“organizational performance should not be the cause of employee satisfaction, but that
satisfaction and attitudes of employees are predictors of effectiveness” (p. 965).

Therefore, she designed a study aimed at demonstrating empirical linkages
between employee attitudes, job satisfaction, and performance. Three types of data were
collected from over 13,000 teachers in public school settings: 1) employee
satisfaction/attitudes; 2) school demographics; and 3) organizational performance
indices. The teacher satisfaction dimension included measures of satisfaction with: co-
workers, supervision, pay, administration, career advancement opportunities, student
discipline, school curriculum, community and parental support, physical facilities, and
communication. Teacher responses to theses scales were aggregated to the school-level
and a mean (average) score computed for each of the variables. Thus. each of the 352
schools that participated in the study received one score for each of the
satisfaction/attitude dimensions. Organizational outcomes were assessed by:
Academic Achievement, Student Behavior, Student Satisfaction, Teacher Turnover, and

Administrative Performance. The level of analysis was at the organizational (school)

37

level.

Ostroffs findings demonstrated the intended relationships among the key
variables. As expected, teacher satisfaction was strongly related to the organizational
performance scores. In fact, when teacher satisfaction was reported as high, students’
scores in reading, math, and social science were also high. The level of teacher
satisfaction also predicted students’ attendance, intentions to dropout and classroom
behavior. Finally, teachers’ intentions to quit were predicted by the valence of their
satisfaction. That is, when teachers were more satisfied, they intended to remain as an
employee in the public school system.

Prior to analyzing results at the school-level, Ostroff computed two intraclass
correlation coefficients: ICC(l) and ICC(2) (James, 1982). The first is intended to
represent the proportion of variance in individuals’ perceptions accounted for by
differences in organizations. ICC(2) provides a measure of proportional consistency of
variance. Ostroff s use of these coefficients is worth noting because, with the exception
of one article (Kozlowski & Hults, 1987), none of the articles reported in this review
has shown empirical evidence to justify aggregation to the organizational-level.
Kozlowski and Hults reported a measure of within group agreement, r“.g (James,
Demaree, & Wolf; 1984, 1993) to justify aggregation to the next higher level. The
intraclass correlation coefficients indicate the reliability of measures (consistency),
whereas rwg is a measure of agreement (consensus) (Kozlowski & Hattrup, 1992).

Another study that maintained academic rigor was Ryan, Schmit, and Johnson
(1996). In addition to ICC(I) and ICC(2), they reported rwg. They also attempted to
establish relationships between employee attitudes and organizational outcomes using a

sample of subjects from the financial management services industry. A key initiative

38

they addressed was the directionality of the relationship among the variables. Data
were collected on eight organizational climate dimensions. ten productivity indicators.
and one measure of customer satisfaction.

Correlation analyses showed a fairly consistent pattern of significant
relationships (p_<_.05) between the climate indicators: Job Satisfaction, Teamwork. and
Training, with the financial performance indicators. Ryan et al. then conducted
regression analyses using the financial indices as criterion variables and one overall
measure of climate that they called, “Satisfaction/Attitudes.” The results demonstrated
that Employee Satisfaction/Attitudes was a significant predictor of both Customer
Satisfaction (pf.001) and Customer Intentions to Change Financial Institutions (p501).
Among the ten “hard” measures of financial performance, only 60-day Delinquent
Payment could be predicted by Employee Satisfaction/Attitudes (p_<_.05).

These findings are important because they show that when employees report a
positive climate and they are satisfied, these dynamics have an important effect on
organizational outcomes. Specifically, higher overall satisfaction has a positive effect
on customer satisfaction, intentions to remain with the firm, and desire to pay their bills
on time.

The next two studies, Petty, Beadles, Lowery, Chapman, and Connell (1995)
and Schuster, Morden, Baker, and McKay (1997) were published within the past five
years. Neither of these reported using a criterion measure for justifying individual
scores to the organizational-level of analysis. Yet, the findings so closely measure the
intended approach to organizational-level analyses in this study, the results are worth
reporting.

Petty et al. (1995) studied organizational culture in the electric utility industry.

39

Despite the fact they called their effort a culture study, their assessment tools are very
much akin to the ones reported thus far. In fact, the Teamwork; Trust and Credibility;
Performance and Common Goals; and Organizational Functioning scales they used to
measure culture dynamics are the same as many mentioned in climate studies discussed
in this section. One summary performance variable was created from five objective
measures of performance (Operations; Customer Accounting; Support Services.
Marketing, and Employee Safety and Health). Using correlation analyses. they found
that Teamwork, Trust, and Performance and Common Goals were significantly related
to the overall performance dimension.

Finally, Schuster, Morden, Baker, and McKay (1997) studied the extent to
which an employee-centered management approach affected the financial performance
of a dairy products firm. Employee-centered management was defined as, “a strategy
for achieving high levels of employee motivation, commitment, and performance
through management practices such as participation and involvement that emphasize
paying attention to employee needs and goals” (p. 176). Fifteen “climate” dimensions
that assessed employee perceptions of the work environment were collected prior to the
implementation of an organizational change initiative that was based on training all
employees in the employee-centered approach. Financial measures were recorded at the
same time.

At the end of five years, the employees were once again asked to complete the
climate survey. Financial measures were collected at Time 2, as well. Both measures,
Employee-centered Climate and the performance index, increased significantly during
the course of the five-year OD dimension. The results of the statistical analyses showed

that approximately 75 percent of the changes in the company’s financial performance

40

were due to the accompanying changes in the employee-centered climate. Schuster. et
al., noted, “although it is true that the trend of all 15 factors has been upward over the 5-
year period, the two factors that have improved the most are Participation and
Communication; the two factors that are most directly the focus of the intervention to
implement employee-centered management” (p. 188).

The studies presented in this section provide theoretical justification for
identifying the organizational-level (squadron) as an appropriate level of analysis.
Therefore, four hypotheses will address the relationships between squadron-level

climate and squadron outcome measures:

 

 

 

HypothesisZa: Sguadron climate will explain a significant amount of
variance in the Squadron Flying Schedule Effectiveness
Rates.

Hypothesis 2b: Sguadron climate will explain a significant amount of

 

 

variance in the Squadron Mission Capable Rates.

Hypothesis 2c: Squadron climate will exriain a significant amount of
variance in the Squadron-level Job Satisfaction.

 

Hypothesis 2d: Squadron climate will explain a significant amount of
variance in the Sguadron-level Performance Perceptions.

Cross-level Climate and Outcome Measures

Rousseau (1985) advocated a multi-level approach to the study of organizations.
She stated, “the issue of levels is of paramount importance in the field of organizational
behavior... it sets the field apart from its parent disciplines in that most of what we
study in and about organizations are phenomena that are intrinsically. mixed level” (p.

2). Similarly, Klein, Dansereau. and Hall (1994) wrote:

41

By their very nature, organizations are multi-Ievel. Individuals
work in dyads, groups and teams within organizations that interact
with other organizations both inside and outside the industry.
Accordingly, levels issues pervade organizational theory and
research. No construct is level free. Every construct is tied to one
or more organizational levels or entities that is. individuals. dyads,
groups, organizations. industries. markets, and so on. To examine
organizational phenomena is thus to encounter levels issues (p.
198).

Researchers considering the use of multi-level phenomena must correctly
specify the appropriate level of theory. In the present study, constructs at one level are
assumed to be related to constructs at a different level. Thus, the correct specification
of theory is called, the “cross-level” (Klein, et al. 1994; Rousseau, 1985). One of the
benefits of this type of research is the ability to: 1) improve specification, that is, to
describe how variables at one level affect variables at a different level; and 2) increase
generalization, determine cause and effect relationships between independent and
dependent variables that exist at different levels of the organization.

Over the last decade, cross-level research has grown in popularity. The increase
in the number of studies is partly due to the advancement in empirical techniques which
support hypothesis testing for multi-level theory development. Two methodologies
currently exist to assess these multi-level dynamics: 1) the manipulation of antecedent
conditions; and 2) variance partitioning or “variance explained” (Hollenbeck, Colquitt,
& Gulley,(l995). Additionally, Hollenbeck, et al., stated, “we emphasize the notion of
variance (here at the outset) because of our belief that, in the end, researchers who claim
to be studying organizational phenomena must ultimately come to grips with questions

of not only variance and variance explained, but. variance explained at different levels

of analysis” (p. 3).

42

A major advantage to variance partitioning is that the analyses allow for
assessing the amount of variance that may be attributed to the key variables of interest
in the study. Moreover, variance can be determined for the differing levels at which
data are collected. By calculating the total variance explained in a variable at one level.
additional explainable variance could be determined for that same variable at ascending
levels.

For simplicity, assume that the total amount of variance for teamwork at the
individual-level is .30. Then, an aggregate measure for the teamwork variable is
computed at the next level, perhaps at the branch-level. If the variance in the branch
teamwork variable is .20, then the total variance attributable to the teamwork variable at
that level is computed by calculating the ratio of branch-level variance to total variance
(.20/.30 or .66). The interpretation is that 66% of the variance in teamwork may be
attributed to between-branch differences. Subtracting the between-branch variance
from the total variance yields the within-branch variance (1.00 - .66 = .34). Hence, 34%
of the variance in teamwork is due to within branch dynamics (Hollenbeck, Ilgen, &
Sego,l994)

Once this is accomplished, a repeated measures regression technique is used
(Cohen & Cohen, 1983). Specifically, the proposed independent variables are entered
hierarchically, with each of the steps arranged from the lowest-level indicator to the
highest—level indicator. In the example listed above, individual-level teamwork would
be entered first and branch-level teamwork would follow. The output is not only
important from a statistical/investigative view, it has tremendous practical implications,
as well. The combination of variance coefficients and beta weights derived from the

regression equations identify where (the appropriate level) in the organization,

43

improvement activities should be targeted.

Generally speaking, the literature on multi- and cross-level research addresses
primarily the theoretical and empirical issues. Very few studies have been published
that include true organizational dynamics and measures. Yet. when actual studies are
produced, the academic and practitioner communities are exposed to a “deeper. richer
portrait of organizational life, one that acknowledges the influence of the organizational
context on individuals’ actions and perceptions and the influence of individuals’ actions
and perceptions on the organizational context” (Klein, Tosi, & Carmella, 1999; p. 243).

A literature review targeted on cross-level studies that included the use of
climate and outcome measures revealed only one study. This finding alone highlights
the difficulty that researchers encounter while attempting to conduct these types of
studies.

Ostroff (1993) conducted a cross-level investigation to determine the extent to
which organizational-level climate and personal orientation may affect individual-level
outcome measures in an educational environment. She developed a survey that
assessed 12 dimensions of climate and 12 dimensions of teachers’ personal orientation.
Additionally, Ostroff collected data on the following individual-level outcome
measures: 1) Satisfaction with Climate; 2) Personal Commitment; 3) Personal
Involvement; 4) Personal Adjustment; 5) Stress; 6) Absenteeism; and 7) Perceptions of
Individual Performance.

Ostroff calculated the mean response on the individual-level climate
perceptions. That value was used to represent organizational-level climate in the
supporting analyses. For the Satisfaction with Climate outcome variables, a series of 12

hierarchical regressions was completed with the school-level climate variables entered

44

first. The results indicated that for each of the 12 satisfaction dimensions. school-level
climate was a significant predictor of satisfaction (p301). Moreover, the individual-
level personal orientation variables explained an additional amount of variance in the
individual-level satisfaction indices (p501). Specifically, school-level climate indices
had an influence upon individual-level satisfaction with the schools’ facilitation of: 1)
Participative Climates; 2) Cooperation; 3) Warmth; 4) Opportunities for Growth; 5)
Innovation; 6) Autonomy; 7) Achievement; 8) Levels of Hierarchy; 9) Structure; 10)
Social Reward; 11) Intrinsic Reward; and 12) Extrinsic Reward. School-level climate
measures accounted for an average of nine percent of the variance in satisfaction while
personal orientation accounted for an additional two percent. The regressions included
interaction terms (school-level climate dimension x personal orientation dimension),
however, none of these achieved statistical significance with the facet job satisfaction
measures.

Ostroffs results also showed that school-level climate variables were significant
predictors (p_<_.01) of all but two (Involvement with School Activities and Performance
Perceptions) of the additional outcome measures. Thus, organizational-level climate
does have an influence on commitment, adjustment, stress, turnover and absenteeism.
Ostroff also reported that each of the personal orientation variables were significant
predictors (p501) of the outcome measures, as well. Again, the school-level variables
were entered first in the regression equation while the individual-level variables were
entered second. Thus, once the variance associated with school-level climate is
removed, any remaining variance is attributable to the individual-level variables.
Finally, like the results reported for the interaction terms when satisfaction was the

dependent variable, none of the school-level climate x personal orientation interaction

45

terms was a significant predictor of the remaining outcome variables.

In summary, Ostroff’s research demonstrated the cross-level effects of
organization-level climate and individual-level personal orientations when predicting
organizational outcomes. These results serve as a strong initiative for the cross-level
hypotheses generated in this study:

Hypothesis 3a: Squadron climate will explain a significant
amount of the variance in squadron members’ job

satisfaction over and above individual-level
climate perceptions.

 

 

 

 

 

Hypothesis 3b: Squadron climate will explain a significant
amount of the variance in squadron members’
perceptions of performance over and above
individual-level climate perceptions.

 

 

 

 

This concludes the literature review and presentation of the hypotheses that support the
present study. Chapter Three describes the research agenda and the methodology used

to test each of the hypotheses.

46

CHAPTER THREE
RESEARCH METHODOLOGY
This chapter discusses information about the locations for data collection.
procedures for data collection. the sample, Operationalization of the variables. and data

analysis procedures.

Data Collection Locations

Two sources were used in this study. First, all survey data were collected as part
of the “1997 Chief of Staff of the Air Force (CSAF) Survey.” The researcher, who
served as the project officer for the survey, was employed by the Air Force Center for
Quality and Management Innovation (AFCQMI). This Center was appointed as the
lead agency for executing the survey and reporting the results to the Air Force Chief of
Staff (the highest ranking general officer in the US. Air Force), and to commanders
throughout the service. For a brief history about how the survey was chartered see
Appendix A.

Over 235,000 Air Force members participated in the survey. These individuals
represented 8 major commands (MAJCOMs); 109 installations; and in excess of 16,000
organizations (squadrons). However, because performance data were available from
only a very limited number of Air Force organizations, this study focuses on data from
only 1 MAJCOM, 22 installations, and 66 squadrons. Figure 3.1 shows the
organizational structure and lists the installations from which data were obtained. The
researcher retained the Air Force (AF) master data file and the data for this study were

extracted from that file for analysis.

47

Figure 3.1: USAF Organizational Structure and Participating Levels

 

HQUSAIrForce

Washington Dc.

’ Pentagon

 

 

 

Pacific Air Form
Hawaii

US Air Forces Europe
Germany

I

 

Space CMD
Colorado Springs. CO

Randolph AFB. TX

]__I Air Education a. Training CMD

 

 

 

Air Mobility CMD
Scott AFB, IL

Special Operations CMD

Huflburt Field, FL

I

 

| Air Combat CMD ’__

Langley

 

AFB, VI

AF Material CMD

Wright-Patterson AF B, 0H

 

 

 

l MAFBJW

im““l

 

I smm,sc ]___+ mama?

 

Seymour‘lohnson
AFB. NJ.

Dlvb-Ibnthln
AFB. AZ

 

 

 

l BoaloAFB.CA l_

L1 Cannon AFB, NM |

 

 

 

Elhwonh AFB, so |_

Kolhvlk AFB
Icohnd

 

 

Pop. AFB, NC }_

_| Barksdalo AFB, LA I

 

 

immrwmmwfi

 

 

lwmwi4wewi

 

lmfl“i

_[ Holoman AFB, NM

 

 

rmmmr

_[aosomm,cnl

 

 

P“““I

 

_l Lanai-y AFB. VI l

 

 

l
Participating
MAJ COM

 

 

(ISCOs)

 

22 Participating
Installations

 

 

 

 

 

 

l Organ‘zation A Organization 8 I Squadrons
[Organization 0 Organization D I ( SSCOs)

 

 

66 Participating

 

 

48

 

Next, performance data were collected in much the same fashion. The
researcher visited Headquarters, Air Combat Command (Langley AFB, Virginia) and
briefed the Commander of the Quality and Management Innovation Squadron about the
proposed study. In essence, the researcher was looking for performance metrics that
were collected as part of normal monthly reporting processes and were available at
differing levels of the organization. The researcher knew data would be available from
the CSAF Survey at the individual-, squadron-, and wing- levels; hence, he was looking
for a similar structure of performance metrics. During the meeting, flying squadrons
were identified as the best candidate community in which all squadrons and wings
collected and reported the same performance metrics on a monthly basis. At that time,
the researcher also received permission from the commander to obtain the data from the
flying squadrons.

During each of the three months in which the CSAF Survey was administered,
officials at Langley AFB, Virginia, e-mailed performance data from each of the 66

squadrons that were identified to participate in the survey to the researchers at

AFCQMI.

Data Collection Procedures

Independent Variables

 

Twelve weeks prior to the survey initiation date, the researcher and an associate
began identifying survey control officers at each Air Force installation located around
the world. A memorandum was sent to all MAJCOM headquarters requesting the name

of one primary and one alternate installation survey control officer (ISCO) to be

49

responsible for survey administration at their respective installations. During
the next eight weeks, personnel were identified to oversee the entire survey process; that
is, loading survey files on all squadrons’ servers at their installations, monitoring the
process while the survey was active, ensuring all files were collected and backed-up,
and finally, ensuring all subjects’ (Ss’) data files were forwarded to the researcher.

Four weeks prior to the implementation date, the researcher and his associate
personally hosted nine worldwide teleconferences with ISCOs for the purpose of
training these individuals in proper electronic survey administration procedures. The
entire survey process was briefed, from the point at which the survey would be initially
delivered to the installations, down to the point at which survey results would be
returned to the commanders of the squadrons that participated in the survey. A
demonstration of the electronic survey was provided to teleconference attendees.

Despite the fact many of the traditional requirements for survey “proctors” are
removed when electronic media are employed, ISCOs were briefed about the
importance of ensuring all SS were provided information about voluntary participation,
conditions of anonymity, debriefing, and confidentiality of their surveys. Another
significant part of the training curricula focused on the technical requirements for
loading and downloading _S_s’ data files. During the teleconference, ISCOs were
instructed to enlist help from squadron-level survey control officers (SSCOs) to assist
with executing the survey in their respective squadrons.

One week prior to the survey implementation date, the researcher and his
associate e-mailed the survey file to 109 ISCOs around the world. The goal for the
mass pre-survey distribution of files was for the survey control officers to have an

opportunity to test the survey on their local electronic platforms prior to the survey

50

being made available for participation. Survey officials also wanted the ISCOs to
become proficient in the use of the survey so they could be the first line consultants to
their SSCOs. should administration problems arise. Detailed instructions were provided
about how to properly load and test the survey to ensure proper encryption of the data.
In essence, the survey file was a zipped file that. upon activation. guided ISC Os through
the process of installing the survey on a dedicated server/directory. This did not require
significant technical expertise. Also, ISCOs were instructed about how to properly
protect the servers/directories in which the survey files were located.

During the ensuing week, the researcher and his associate were available to
answer questions from ISCOs. Surprisingly few questions were raised. This can be
attributed to excellent software designed by a team of experts located at AF CQMI and
with the contractor who tailored existing software code to specifically execute the
CSAF survey.

Installation survey control officers were instructed to pass the survey file to each
of their SSCOS so they too could load the survey on their squadron’s servers/directories.
They were asked to follow the same process and to test the software to ensure the
survey file was working properly. Special emphasis was placed on validating that all
data would be properly encrypted and protected from potential users’ intrusion or
sabotage.

One day prior to survey initiation, all Air Force installations published a “must
run” article that described the survey, explained the intended uses of the results and
contained a message from the Vice Chief of Staff of the Air Force encouraging all
members to “let their voice be heard” (See Appendix B). Also, the Air Force

Homepage, “AF Link” contained the survey announcement as its lead article for

51

approximately four days (See Appendix C).

Then, on the date of survey initiation, SSCOs sent a squadron-wide memo
informing members that the C SAF Survey was now available. Also, the correct server.
directory, folder, and file were identified. At this point, survey participation was
entirely dependent upon _S_s’ willingness to voluntarily participate. If an S decided to
complete the questionnaire, she simply found the appropriate file in the identified
directory, double—clicked on the file name and began experiencing the sequence of
screens leading her through the survey. Figures 3.2 — 3.4 show the screen presentations
that were shown to all 3 upon entry into the electronic survey file.

Figure 3.2 shows the first screen, which contained information about viewing
the file, ways in which §s may exit the survey, the amount of time required to complete

the survey and assurance that Ss’ responses were encrypted.

52

Figure 3.2: Introductory Survey Screen

'- lfSAlTINN Data Entry New Record 5

 

Eb 2mm
Helpful Hints for Taking the Survey

[I Since monitor sizes very. all of the text may not fit on your screen. if
that is the case, please use “Zoom screen" on the top menu bar to adjust
the magnification.

0 Select or deselect items using your (1) mouse or Q) arrow key and
spacebar.

E1 You can exit the survey at any time by clicking on ”File,” then ”Save
and Exit.“

E1 The ONLY way to reenter your survey is by using the Usemame and
Password you just entered. (You may want to jot them down now!)

0 Estimated time to complete this survey is 30 to 45 minutes, depending
on how many comments you wish to enter.

0 All information you enter Into the survey is protected by encryption.

 

 

 

Current Air Force policy, Air Force Instruction (AFI) 36-2601, Air Force

Personnel Survey Program (February 1996), requires that all surveys administered to

 

Air Force personnel must be reviewed by the Air Force Personnel Center, Directorate of
Operations, Survey Branch. The survey branch ensures that questions do not violate
human subjects’ rights and that the purpose of the survey will provide some benefit to
the Air Force. Also, the survey branch mandates that specific information is presented
to all potential survey participants prior to initiating any survey. Figure 3.3 shows
Screen #2 from the electronic survey and the elements deemed necessary according to

AFI 36-2601. Specifically, §s were made aware of the purpose of the survey, that their

53

participation was voluntary, that their anonymity would be protected. and that adverse
action for their choice to not participate in the survey would not be tolerated. Finally.
AFI 36-2601 requires that the survey control number be published along with each

survey.

Figure 3.3: Mandatory Language for All Air Force Surveys

i 1 .Al ‘l/WN Hula I ntw New lit-run! 'i

 

Ele locum

in accordance with AFI 37-132, Air Force Privacy Act Program. paragraph 3.2.
and The Privacy Act of 1974, the following information Is provided:

a. Authority:
1. 5 U.S.C. 301. Departmental Regulations
2. 10 U.S.C. 8013, Secretary of the Air Force, Powers and Duties
3. Delegation by Executive Order (EO) 9397

b. Purpose: The purpose of this survey Is to obtain data on AF personnel
attitudes about Quality of Life and Organizational Climate.

c. Routine uses: None

d. Disclosure: Participation ls voluntary. No adverse action of any kind may
be taken against any individual who elects not to participate in any portion of
this survey.

e. IAw AFI 37-132, the official control number for this survey Is 97-73. (This Is
not a tracking number for your individual survey.)

 

 

 

The final introductory screen was a message from the Vice Chief of Staff of the
Air Force, General Ralph Eberhart, thanking §S for their participation in the survey.
The general also explained the purpose of the survey and assured §S their anonymity
would be protected. Further, General Eberhart told _S_s that he would share the survey
results at the beginning of 1998. Figure 3.4 shows the message that was displayed in

the electronic survey file.

Figure 3.4: Vice Chief of Staff Message to Survey Participants

5 CSAI‘JNN Data Entry New Record 5

 

Message from the Vice Chief of Staff

We went to thanlr you in advance for participating in this survey. This is a great
opportunity for AF leaders to receive feedback about how we’re currently doing in a
variety of "people program.” To reach the AF vision. “Global Engagement for the 21st
Century.“ we must ensure excellence in Quality of Life and the work environment for our
people. The “1997 CSAF Survey” is a convenient, anonymous way for you to provide
valuable information to leaders in your chain of command. all the way from your unit
commander to the CSAF.

The questions are hardhttttng; however. we need to capture your candid thoughts. There
are 130 questions addressing significant Quality of Life (001.) and Organizational Climate
(00 topics. You’ll also have an opportunity to provide written comments. Results from
the 00L section will be reported to your Installation commander and higher. The OC
remit: will be reported to your unit commander and higher.

Please don't be alarmed by questions asking for personal Information. Rest assmed. our
intention is to maintain anonymity for all who participate in the survey. We're counting on
you to let your voice be heard and in turn. we look forward to sharing the AFJevel results
with you early next year.

RALPH E. EBERHART
General, USAF
Vice Chief of Staff

 

 

 

After reviewing these three screens, §s were ready to begin answering questions.
The first screen in which §S actually entered data is shown in Figure 3.5. Survey
officials knew it was important to identify the circumstances (locations) upon which §S
were completing the survey. The survey developers knew most participants would
choose Option A because they would be at their home duty station, completing the
survey in their work environment. However, three additional conditions were important
to consider. First, if an S was deployed (located at a duty installation different than her
home installation), survey designers were interested in collecting data from him.
Anecdotal information predisposes military personnel to believe the Quality of Life and

Organizational Climate for deployed members is inferior to those who remain in the

55

U.S. Second, identifying students was an important condition because they were not
required to complete the organizational climate and MAJCOM sections of the survey.
“Students” are Air Force members currently not assigned to a typical Air Force job.
Instead, they are in some facet of training that ranges from basic training to senior
academic schools designed to enhance officer, enlisted, and civilian professional
military education. They are not exposed to the range of organizational dynamics
highlighted in the questions and, they are not truly assigned to an operational
MAJCOM. Third, if §s were not completing the survey at their home station and they
were in Temporary Duty status (TDY), thus requiring better tracking of their responses
to ensure their data were returned to their home location for aggregation and analysis.

Figure 3.5: Subject’s Location Conditions

h LSAl “NH Uni.) l. rriry New iicmrrd fr

 

Please select the item that best describes the condition(s) under
which you are completing this survey:

 

G [Atmyhome station and not In student status

P [Deployed or TDY and survey provided by home unit
r l in student m: (includes TDY and PCS)

r I TDY, not a student , and will nor be home by 31 Oct
P I Other

 

 

 

 

—l——--

- Ifyou are TDY and not a student, please take the survey at your
home unit, if possible.

- ifyou are deployed, please take a survey provided by your home

unit to ensure your responses are grouped with others in your unit.
- - Complete the survey with your home unit! installation in mind.

< Previous I warm]

 

 

 

 

Subjects simply used the pointing device (mouse or arrow) on their computer to

identify their chosen answer to the questions. Note that $3 were allowed the option to

56

exit the survey. Upon selection, §s‘ responses were recorded in two separate databases.
one for the Organizational Climate section (database used for this study) and one for the
Quality of Life section. Appendix A contains a brief discussion about the evolution of
the C SAF Survey and the mandate for two sections. Rather than combine databases and
work in collaboration, survey officials preferred to keep the two data bases. analyses.
and reporting mechanisms distinct processes.

After identifying their current status, §s were asked to identify the installation
and the squadron to which they were assigned. Survey officials compiled a master list
of all Air Force assignment locations/squadrons in the world. This list was made

available to all 85 for their entry. Figures 3.6 and 3.7 show how this was accomplished.

Figure 3.6: Identify Squadron and Installation of Assignment

Eie Zorneoeeen ._
Select Unit I Duty Location

Please select your unit and duty location from the list
and click on "Next"

If your unit name is not listed, please select Exit
and contact your unit survey representative.

 

 

AF RECRUITING SER SR. RANDOLPH_AFB

317 USAF RECRUITING SO. ANDREWS_AFB

332 USAF RECRUITING SO. NASHVILLE_CTY

362 USAF RECRUITING SO. MARCH_AFB

367 USAF RECRUITING SO. COLORADO SPRINGS_CTY
342 USAF RECRUITING SQ. FT SNELLING_AIN

372 USAF RECRUITING GP. HILL_AFB

368 USAF RECRUITING SO. HILL_AFB '

 

 

 

 

 

 

l<Previouel r Next> H Exitj

 

 

 

Because this was such an important variable. §s were asked to verify their

selection (see Figure 3.7).

57

Figure 3.7: Verify Squadron and Installation of Assignment

'- l SAI 'J/WN Unto i ntry New Iiccord tr

 

Verity Unit I Duty Location

Please confirm the unit you selected:
330 USAF RECRUITING SQ, INDIANAPOLIS_CTY

ifyou are assigned to the unit identified above, click on
"Next.“ Otherwise, click on “Previous" or "Exit"

“El—l

 

 

 

Next, _S_s were asked to identify their rank/grade status, e.g.. officer, enlisted, or
civilian (see Figure 3.8). Besides installation/squadron of assignment, this is the only
other demographic variable that was mapped into the Organizational Climate database.
Subjects’ responses also served the purpose of “triggering” additional demographic

variable questions designed for the Quality of Life section and concomitant analyses.

58

Figure 3.8: Rank/Grade of Survey Participant

i [THAI ‘i/WN “did i rrlry New iii-cord D

 

Which category best describes you:

~-
(‘-
"Clvlllm

 

 

 

Because there were two primary sections to this survey, survey designers were
cognizant of the need to counterbalance the order of administration. Thus, at this point
in the survey, the software was designed to search immediately for the last occurrence
of survey administration for each rank/grade category. If the previous S received the
Quality of Life section first, then the next S received the Organizational Climate section
first. This practice helps alleviate the temporal effects of participating in longer
surveys.

Assuming the current S was identified to receive the Organizational Climate
section initially, Figure 3.9 shows the introductory message that framed the way in

which _S_s were asked to cognitively approach completing questions in this section.

59

Figure 3.9: Introduction to Organizational Climate Section

- CSAF‘JMN Data f niry New Record 5

 

Organizational Climate Section

in this section, you will be asked to rate 14 key aspects about the organizational climate in
your unit. Answer questions using the 6-point scale, "Strongiy Disagree to Strongly Agree.”
with an option for "Don't Know.” At the end of each category. you can provide comments
phrased as strengths and improvement opportunities. Your unit commander/director will
receive a summary report for the entire unit (anonymity will be preserved).

Please apply the following definitions as you complete the survey:
UNIT: Your squadron or staff agency equivalent

UNIT LEADERSHIP: Leaders who are responsible for establishing the direction
and daily operations of your unit.

 

 

 

Immediately after this screen, §s began answering the 72 questions designed to
assess Organizational Climate. Figure 3.10 demonstrates how the questions were
presented to S5, with one question per screen and all potential responses established on

a six-point scale (Strongly Disagree to Strongly Agree) with a “Don’t Know” option.

60

Figure 3.10: Example of Question Presentation

- l,‘,Ai ‘J/WN Uni.) I ntry New Record 5

 

JOB CHARACTERISTICS

My job requires me to use a variety of skills.

5335‘3'3‘3

 

_-

 

 

 

Subjects completed the Organizational Climate and Quality of Life sections in
the counterbalanced fashion. Next, they answered ten MAJCOM—specific questions
(see Appendix A for explanation) and finally, the remainder of demographic questions
for the Quality of Life section. The total number of questions was 147. A copy of the
Organizational Climate Survey can be found in Appendix D.

At the conclusion of the survey, each participant was asked if he was sure he had
completed the questionnaire. Subjects were instructed to use the “Save and Exit” option
in the upper left hand comer of the screen. At this point _S_s’ responses were written to
the respective databases within an encrypted file that was stored separately on a
server/directory accessible to only the SSCO and her alternate. There was no residual
information remaining on computers used by 85 who completed the survey.

During their training sessions and via several e-mails from the researcher and his

61

associate, ISCOs were instructed to remind their SSCOs to make duplicate copies.
Thus, at the close of each business day. SSCOs saved Ss’ files into a different backup
directory.

At the mid-point in the survey process, ISCOs were instructed to collect all data
files from their SSCOs. Once this was accomplished, ISCOs aggregated all survey
responses from their locations and forwarded them to the researcher and his associate
for storage. These two individuals then created a master file containing the records for
all §s who had participated in the survey up to this point. This process was intended to
be a safety mechanism should anomalies occur during the remainder of the time the
survey was available for participation. The mid-point exercise also served as a trial run
for the final aggregation that would take place at the conclusion of the survey. There
were no major errors discovered during the mid-point exercise.

The final aggregation process went almost as smoothly. Only one of the 109
participating installations reported major problems. In fact, over 50 percent of the data
from that location was lost. After the researcher and his associate retrieved all data
files, a master file was constructed and sent to the contractor who, in turn, decrypted the
data file and returned the clean data in .dbf format. Several months later, survey
responses from the 66 participating squadrons was extracted from the master data file

and saved to the data file used for this study.

Dependent Variables- Operational Performance Measures

 

Reporting of the operational dependent measures began at the squadron level.
Squadron officers logged mission capable rates and flying schedule effectiveness on a

daily basis. At the end of each month, the performance indices were summarized into a

62

monthly report. The report was forwarded to a functional representative who was
responsible for collecting these data from all flying squadrons at that installation.
Installation-level officers computed installation statistics based on the aggregate
performance of the squadrons located at that installation. These data were next
forwarded to MAJCOM functional officers who prepared the information for MAJCOM
leaders to review. Major command leaders are presented with three levels of reports:
MAJCOM, installations, and squadrons. All reporting was accomplished in this “roll-
up” fashion.

Data for the 66 squadrons that participated in the survey were e-mailed to the
researcher and his associate at the conclusion of each month in which the survey was
available to AF personnel. These files were saved in MS EXCEL and MS WORD

formats.

The Sample of Subjects

Individual-level Sample

 

The original sample from the 66 squadrons was 7.635. However, 523
participants’ records were omitted due to: 1) missing data; 2) repeated consistent
response patterns (holding down the same key when responding); 3) “Christmas-tree”
response patterns; and 4) duplicate records. Thus, the sample for this study was initially
reduced to 7,112 usable surveys.

These 7,112 records are traceable to the specific installations and squadrons at
which _S_s are employed. However, squadron-level staffing data, such as numbers of
officer, enlisted, and civilian members assigned, were available for only 59 of the 66

squadrons that participated. Seven squadrons' staffing information is considered,

63

“Sensitive” and could not be used for comparison statistics in this study. Thus, §S from
these seven squadrons (121 officers; 527 enlisted; 3 civilians) were omitted when
calculating participation rates.

That leads to analyses based on what is known about the numbers and
rank/grade of participants. The population in the 59 organizations for which manpower
data are available is 18,900. There were 6,461 respondents, which represents a 34
percent response rate overall. There is no reason to expect that the seven organizations
for which manpower data are not available would yield abnormalities.

When the profile of respondents is compared to the profile of available
respondents, analyses show no difference between the overall compositions of the two
groups (X2 (2, N=6392) = .537; p>.05). Table 3.1 shows the breakouts between the
different categories of rank/grade. The composition of 18 percent officer; 81 percent
enlisted; and 1 percent civilian is consistent for the sample and for the population.
Further inspection of the data showed that 12 of the 83 civilian S5 (or 16 percent of the
authorized number of civilians for the study) were from one squadron when in fact, that
squadron had only two authorized civilian positions. Similar examples were noted for
three of the remaining squadrons. Therefore, a decision was made to eliminate the 83
civilians from this study. Thus, the final sample was composed of 7.029 officer and

enlisted personnel.

Squadron-level Sample
Data were collected from 22 Air Force installations, worldwide. Among the 22
installations, a total of 66 squadrons, whose charter was to prepare and to fly US. Air

Force airplanes, participated in the organizational climate survey. Participation rates

64

among these organizations ranged from five to 73 percent.

Table 3.]

Demographic Profile of Survey Respondents and Available Workforce
Between Rank/Grade Categories

 

 

Variable Survey Respondents Available Workforce
Employee Category:
Officer 1,157 (18%) 3.390 (18%)
Enlisted 5.272 (81%) 15.427 (81%)
Civilian Q ( 1%) 8_3 (1%)
6.461 18.900

 

 

 

 

 

Within groups analysis for the military members (officers and enlisted members)
shows an acceptable representation of the rank structure. Table 3.2 shows the breakouts
within the categories. Sample percentages of 34 percent for each of the two groups

provide a healthy replica of the officer and enlisted populations at large.

Table 3.2
Demographic Profile of Survey Respondents and Available Workforce
Within Rank/Grade Categories

 

 

Variable Survey Respondents Available Workforce
Employee Category:
Officer 1,157 (34%) 3,390
Enlisted 5.272 (34%) 15.427
6,429 18,817

 

 

 

 

 

65

Operationalization of Variables
Overview

The researcher commissioned a team of survey developers who were members
of the Air Force to construct the organizational climate survey used in the present study.
The group was composed of persons possessing doctorates in industrial/organizational
psychology, experimental psychology, and management/organizational behavior. The
researcher participated in the development of the questions, as well: Four additional
representatives from the MAJCOMs who had experience administering organizational
surveys were also part of the team.

The survey development team responded to a number of political scenarios
during the instrument construction phase. Several high-ranking officers mandated that
the survey include specific scales and specific questions. However, the general officer
who chartered the development team provided tremendous support in telling the
development team, “design the best survey you can and ignore all the political pressure
to keep others“ surveys intact.” This statement is quite telling in that the pressure was
largely due to the fact several major commands had at least three years of data from the
surveys they were administering at that time. Senior leaders of these commands were
quite satisfied with the content of their surveys. Moreover, they were concerned with
losing their three years of “trend data” and being forced to start the data collection effort
from the outset one more time.

The survey development team reviewed a number of existing surveys: The

Department of Defense Climate Survey, Officer of Personnel Management Survey, Ajg

 

Combat Command Culture and Leadership Survey, United States Army Climate

 

Survey. Federal Express Management Evaluation Survey. and an instrument from the

 

66

Mayflower Group. The goal for this exercise was to gain an understanding of how
organizational climate is operationally defined in existing surveys. Team members
compared this information to organizational measurement theory in order to achieve the
final charter of developing an instrument that would provide a valid assessment of key
Air Force organizational climate indicators. There was no predetermined number of
scales prior to the team convening.

The survey development and approval process was completed under a very tight
timeframe. The development team was allowed only one week to construct the
organizational climate scales and questions. Once that was accomplished, the survey
was pilot tested and revised so that an empirically sound instrument could be presented
to the CSAF for final approval. The pilot survey consisted of 82 items and 15 scales.
Each of the team members asked workers from their home organization to complete the
survey and provide responses to the researcher. A sample size of 184 Air Force
personnel completed the pilot instrument. Principal components factor analyses with
varimax rotation were conducted on the data. A moderately stable factor structure was

present with 72 questions and 14 constructs (eigen values 3 .93).

Independent Variables

 

In this section, the 11 independent variables (scales) are identified along with a
brief discussion about the intended measurement topics, means, standard deviations,
and corresponding alpha coefficients. Finally. questions comprising the scales are
presented.

The Job. Assesses the extent to which the S’s job is motivating, important,

interesting and challenging. Questions address skill variety, task identity, task

67

significance, autonomy, and feedback from the job. Figure 3.11 contains the five
questions used in this scale. Reliability analysis of the five items yielded a coefficient
alpha of.82 (mean=5.63; s.d.=1.1).

Figure 3.11: The Job Questions

 

My job requires me to use a variety of skills.

My job allows me to see the finished products of my work
Doing my job well affects others in some important way.

My job is designed so that I know when I have performed well.
My job allows me freedom to work with minimum supervision.

.U‘F‘P’Nt"

 

 

 

Unit Resources. Assesses management‘s effective use of the unit‘s resources
(time, personnel, funds, and equipment) to accomplish its mission. Respondents rate
their ability to accomplish tasks in a typical workday, availability of supplies, and the
appropriateness of the size of the work group needed to complete the tasks. Figure 3.12
contains the four questions used in this scale. Reliability analysis yielded a coefficient
alpha of .82 (mean=4.53; s.d.=1.45).

Figure 3.12: Resources Questions

 

6. I have adequate time to do my job well.

7. We have enough people in my work group to accomplish the job.

8. I have the right tools, equipment, and materials to accomplish the job.

9. I have enough time to accomplish my daily workload during my duty hours.

 

 

 

Core Values. Assesses the extent to which the Air Force Core Values are
understood and demonstrated by squadron personnel. Behavioral dimensions of the Air
Force’s Core Values: 1) “Integrity First,” 2) “Service Before Self,” and 3) “Excellence
in All We D0” are evaluated. Figure 3.13 contains the seven questions used in this

scale. Reliability analysis of the five interval scale items yielded a coefficient alpha of

68

.78 (mean=5.45; s.d.=.94).

Figure 3.13: Core Values Questions

 

10. I received a copy of, United States Air Force Core Values.

11. I have been briefed on the contents of, United States Air Force Core Values.
*** Questions 10 and 11 were dichotomous questions. "Yes" or “No"

12. I am able to do my job without compromising my integrity.

13. Overall, people in my unit uphold high standards of excellence.

14. I understand how the AF core values apply to my job and daily activities.

15. Overall, people in my unit demonstrate that duty takes precedence over desires.

16. Overall, people in my unit are held accountable for behavior that contradicts the
core values.

 

 

 

 

 

Communication. Assesses how well information flows throughout the unit and

 

whether workers are well informed about important issues. Questions tap leaders‘
communication of the squadron’s mission, goals, and objectives. Figure 3.14 contains
the five questions used in this scale. Reliability analysis yielded a coefficient alpha of
.83 (mean=5.39; s.d.=1.22).

Figure 3.14: Communication Questions

 

17. Leadership in this unit listens to my ideas.

18. My unit makes me aware of important events and situations.

19. I am comfortable discussing my ideas with the leadership in this unit.

20. Leaders in my unit are easily accessible.

21. Leadership in this unit effectively communicates the mission, goals. and
objectives.

 

 

 

Leadership. Assesses the extent to which leadership positively influences the
direction. people, and culture of the squadron. Transformational/charismatic leadership
traits are evaluated. §S also report whether squadron leaders are exemplifying the same
behaviors they openly espouse. Figure 3.15 contains the ten questions used in this

scale. Reliability analysis yielded a coefficient alpha of .95 (mean=5.24; s.d.=1.1).

69

Figure 3.15: Leadership Questions

 

 

22.
23.
24.
25.
26.
27.
28.
29.
30.
31.

I trust the leadership in my unit.

My unit leadership is inspirational.

I am proud of the leadership in my unit.

My unit leadership sets challenging goals.

My unit leadership provides a clear vision.

My unit leadership motivates me to do my best.

My unit leadership makes decisions based on facts.

My unit leadership motivates me to achieve our goals.

My unit leadership encourages me to look for ways to cut costs in my job.
I see my unit leaders doing the same things they publicly promote.

 

Supervision. Assesses the extent to which _S_‘s supervisor is perceived to be

skilled at planning. organizing, directing and providing feedback. Also. questions

address how well supervisors care for workers, establish challenging goals, and serve as

role models for workers. Figure 3.16 contains the nine questions used in this scale.

Reliability analysis yielded a coefficient alpha of .94 (mean =5 .46; s.d.=1.1).

Figure 3.16: Supervision Questions

 

 

32.
33.
34.
35.
36.
37.
38.
39.
40.
mission.

My supervisor is good at planning my work.
My supervisor sets high performance standards.
My supervisor is concerned with my development.
My supervisor models the core values of the Air Force.
My supervisor corrects poor performers in my work group.
My supervisor looks out for the best interests of my work group.
My supervisor keeps me up-to-date on what is happening in my unit.
My supervisor provides instructions that help me meet his/her expectations.
My supervisor helps me understand how my job contributes to my unit’s

 

 

Training and Development. Assesses the extent to which workers have the

necessary training required to do their jobs and are provided opportunities and support

for professional development. Figure 3.17 contains the four questions used in this scale.

70

 

Reliability analysis yielded a coefficient alpha of .83 (mean =5.27; s.d.=1.21).

Figure 3.17: Training and Development Questions

 

 

41.
42.
43.
44.

1 am given opportunities to improve my skills.

I am encouraged by my unit leadership to learn new things.

I have been adequately trained for the job I am expected to do.
I am allowed to attend continuing professional training.

 

 

Teamwork. Assesses the extent to which team members work together to

accomplish the squadron’s mission. Sharing information, resolving conflicts, respect

for team members, and cooperation among teams in the squadron are evaluated. Figure

3.18 contains the five questions used in this scale. Reliability analysis yielded a

coefficient alpha of .87 (mean=5.49; s.d.= 94).

Figure 3.18: Teamwork Questions

 

 

45.
46.
47.
48.
49.

People in my work group respect each other.

My work group adequately resolves conflicts.

There is cooperation among work groups in my unit.
Members of my work group willingly share information.
People in my work group cooperate to get the work done.

 

Participation/Involvement. Assesses the extent to which squadron personnel

have a voice in defining what work gets done and how it is accomplished. _S_s rate their

comfort level in recommending innovative ways to complete work and the efficacy of

organizational practices that elicit novel ideas. Figure 3.19 contains the four questions

used in this scale. Reliability analysis yielded a coefficient alpha of .90 (mean =5.37;

s.d.=1.19).

71

 

Figure 3.19: Participation/Involvement Questions

 

 

50. I feel free to suggest new and better ways of doing things

51. I am asked how we can improve the way my work group operates.

52. Sufficient effort is made to get the opinions and ideas of people in this work unit.
53. Suggestions made by unit personnel are implemented in our daily work activities.

 

 

Recognition. Assesses the extent to which leaders provide public recognition
for exceptional performance. Individual and team acknowledgement as well as equity
of rewards are evaluated. Figure 3.20 contains the three questions used in this scale.

Reliability analysis yielded a coefficient alpha of .93 (mean =4.97; s.d.=1 .51).

Figure 3.20: Recognition Questions

 

54. My unit leadership rewards team performance fairly.

55. My unit leadership rewards individual performance fairly.

56. My unit leadership does a good job of recognizing people in all grades and
types of jobs.

 

 

 

Unit Flexibility. Assesses the extent to which the unit successfully responds to

 

changes in the environment. Risk taking. trying new ways of doing business, and ability
to respond to environmental influences are assessed. Figure 3.21 contains the three
questions used in this scale. Reliability analysis yielded a coefficient alpha of .85
(mean =5.01; s.d.=1.27).

Figure 3.21: Unit Flexibility Questions

 

57. My unit adapts to changes quickly.
58. My unit encourages appropriate risk taking.
59. My unit challenges old ways of doing business.

 

 

 

Dependent Variables

 

In this section, five variables are presented. The first two are flying squadron

72

operational measures collected from the 66 squadrons independent of the CSAF Survey.
A definition, brief discussion, mean, standard deviation, and reliability coefficient is
available for both measures. The remaining three variables were classified as outcomes
of organizational climate and were included in the CSAF Survey. For each of these. the
scale is identified along with a brief discussion about the intended measurement t0pic.
mean, standard deviation, and corresponding alpha coefficient. F inally, questions

comprising the scales are presented.

Mission-Capable Rate (MCR). For this variable, a difference score will be

 

calculated: MCR — MAJCOM standard for the specific aircraft flown. The MCR is
defined as the ratio of actual to projected number of hours that aircraft are available to
fly missions during a month. This value is multiplied by 100 to calculate the percentage

99

of time a squadron’s aircraft are “fully mission capable. Highly skilled maintenance
workers are employed primarily to ensure aircraft are available for use by pilots and
navigators. These personnel work independently and in teams to guarantee routine
aircraft maintenance. They also work diligently to repair anomalies that occur with the
aircraft. When flying hours are lost due to non-mission capable aircraft, leaders become
upset.

The MCR — MCR MAJCOM standard calculation allows for standardization
across the different types of aircraft. Because each weapon system experiences
differing anomalies and because some are more aged than others, maintenance leaders
establish a MAJCOM standard for each aircraft. This statistic provides a relative

comparison to the MCR. That is, an MCR of 70 percent for both an F-l6 and a B-52

has different implications about performance. The MAJCOM MCR standard for an F-

73

16 may be 80 percent and for the B-52 it may be 60 percent. Hence, the F-16 squadron
had a MCR 10 percentage points below the standard; the B-52 squadron was 10
percentage points above the standard. These results would cause installation leaders to
praise the leaders of the B-52 squadron. Conversely, installation leaders would ask the
F-16 squadron commander about the reasons he did not meet his monthly performance
objectives. Descriptive statistics for MCR showed mean = 79.90 and standard
deviation = 6.62. Reliability analysis for the MCR statistic yielded a coefficient alpha

of.66 (mean =-5.73; s.d.=10.78).

F lLing Schedule Effectiveness (FSE). For this variable, a difference score will
be calculated: FSE — FSE MAJCOM standard for the specific aircraft flown. FSE is
defined as the squadron’s ability to respond to the monthly flying schedule. The
performance criterion is calculated: (number of scheduled flights — number of
deviations from the schedule due to maintenance. weather, supply, air traffic control.
commander-directed, etc.) / number of scheduled flights. This value is multiplied by
100 to determine the F SER. Operations officers are responsible for launching airplanes.
Thus, they work very closely with maintenance, training, and scheduling officers to
ensure all missions are flown each month. Pilots and navigators are closely monitored
with regard to the numbers and types of missions they are required to fly each month.
Thus, the schedule plays a critical role in ensuring each of these officers is prepared to
go to war. A great deal of time is invested in designing a flying schedule that
maximizes mission capable aircraft and the availability of pilots and navigators to fly
intended missions.

Like the MCR, a F SE — MAJCOM standard calculation allows for a comparison

74

of the squadron‘s monthly performance relative to an established MAJCOM standard
for all aircraft. Descriptive statistics for FSE mean = 73.71 and standard deviation =
8.52. Reliability analysis yielded an alpha coefficient of .78 (mean=-8.23; s.d =7.89)
Job Satisfaction. Assesses S_‘s sense of personal accomplishment and fulfillment
from work related tasks and from the work environment. A general measure of job
satisfaction is also included. Figure 3.22 contains the three questions used in this scale.

Reliability analysis yielded a coefficient alpha of .93 (mean=5.09; s.d.=1 .43).

Figure 3.22: Job Satisfaction Questions

 

60. In general. I am satisfied with my job.
61. I have a sense of personal fiilfillment at the end of the day.
62. The tasks I perform provide me with a sense of accomplishment.

 

 

 

Performance Perceptions. Assesses the extent to which the squadron is
perceived to be satisfying its mission. Subjects rate quality and quantity of output, cost
effectiveness, and external perceptions of squadron performance. Figure 3.23 contains
the five questions used in this scale. Reliability analysis yielded a coefficient alpha of

.88 (mean =5.52; s.d.=1.02).

Figure 3.23: Perceptions of Performance Questions

 

63. The quality of work in my unit is high.

64. The quantity of work accomplished in my unit is high.

65. My unit is known as one that gets the job done.

66. My unit is successfully accomplishing its mission.

67. My unit accomplishes its mission in a cost-effective manner.

 

 

 

75

General Organizational Climate. Assesses generalized issues related to the

 

squadron. A collage of questions addressing morale, organizational citizenship
behaviors, perceived value of worker to unit. and expectation about feedback is
presented. Figure 3.24 contains the five questions used in this scale. Reliability analysis
yielded a coefficient alpha of .82 (mean =5.04; s.d.=1.23).

Figure 3.24: General Organizational Climate Questions

 

68. Morale is high in my unit.

69. I am a valued member of my unit.

70. I would recommend an assignment in my unit to a friend.

71. People in my unit are charitable with their time, talents, or money.
72. My unit will be briefed about the results of this survey.

 

 

 

Data Analysis Procedures
In this section, the methods of data analysis are outlined for each of the

hypotheses presented in Chapter 2.

Overview
Testing the hypotheses in this study requires that the variables of interest are
identified and analyzed correctly with respect to the appropriate levels of interest. This
must be accomplished with care because, “levels issues create particular problems when
the level of theory, the level of measurement, and statistical analysis are incongruent.”
(Klein, Dansereau, & Hall, 1994; p. 198). This statement is particularly germane
because the hypothesis testing strategy in this study includes individual-, squadron-, and

cross-levels analyses. In order to test the hypotheses articulated in Chapter 2. a

76

prescribed order was followed. First, factor analysis was used to reduce the number of

constructs (scales) that were included in the "1997 C SAF Survey.” Second. multiple

regression was used to test the each of the hypotheses.

Factor Analysis

At the outset, there were 14 independent variables included in the study. Thus.
in an attempt to gain a more parsimonious set of constructs and to establish a set of
factors that were more independent of each other (Kerlinger, 1992; Tabachnick &
Fidell, 1989), a Principal Components Analysis with a Varimax Rotation was
performed on the data. Factor analysis yielded a mathematical solution that must be
inspected carefully. Actually, the solution makes no reference to construct integrity.

Therefore, careful attention was taken in reviewing and validating the “interpretability”

of the revised scales after the factor analysis was completed.

Multiple Regressions

Hierarchical regression analysis was used to test each of the hypotheses. This
technique is a very useful tool for determining significant effects primarily because the
researcher may specify the independent variables in the expected causal priority. Such
an approach affords the opportunity to “control” for variables that may result in
spurious relationships between key variables of interest. In fact, hierarchical regression
also allows for sets of factors to be entered simultaneously, as we shall see in the
procedures used in this study. The real value of this technique is in the interpretability
of the output. At each step in the process, “the R2 and the partial coefficients are

determined as each independent variable joins the others” (Cohen & Cohen, 1983).

77

 

 

Individual-level Hypotheses

Hypothesis 1a: Individual climate perceptions will explain a significant
amount of variance in squadron members' job
satisfaction.

 

Hypothesis 1b: Individual climate perceptions will explain a significant
amount of variance in squadron members‘ perceptions of

wformance.

 

Because the level of analysis that supports these hypotheses is at the individual
level, all 7.029 cases were used. Two equations were established to correspond with the
two dependent variables. Here, rank (the only appropriate control variable) was entered

on the first block followed by the squadron climate measures on the second block.

 

Figures 3.25 and 3.26 depict the process.

Figure 3.25: Research Model for Hypothesis 1a.

 

 

 

 

 

 

 

 

 

Block 1 Block 2
Control Climate
Variable Variables Depepdent
Rank + Factor Analysis = J b___Var.rafble.
(Officer, Enlisted) Results 0 58118 action
N=7029

Figure 3.26: Research Model for Hypothesis lb.

 

 

 

 

 

 

 

 

Block 1 Block 2
Control Climate Dependent
Variable + Variables = Variable
Rank Factor Analysis Performance
(Officer, Enlisted) Results Perceptions
N=7029
78

Squadron-level Hypotheses

HYPOTHESIS 2a. Squadron climate will explain a significant amount of
variance in the Squadron Flying Schedule Effectiveness

(FSE) Rates.

HYPOTHESIS 2b. Squadron climate will explain a significant amount of
variance in the Squadron Mission Capable Rates (MC R).

HYPOTHESIS 2c. Squadron climate will explain a significant amount of
variance in the Squadron-level Job Satisfaction.

 

HYPOTHESIS 2d. Squadron climate will explain a significant amount of
variance in the Squadron-level Performance Perceptions.
Because these hypotheses assess squadron-level phenomena, the level of
analysis is appropriately fixed at the squadron-level. Therefore, mean scores were
calculated on each of the variables for each squadron that participated in the study.
Four equations were established to correspond with the hypotheses. In all cases, the
control variables (Squadron Size, Type of Mission, and Percent of Officers Assigned)
were entered in the first block followed by the squadron climate measures on the second

block. Figures 3.27 — 3.30 depict the process.

Figure 3.27: Research Model for Hypothesis 2a.

 

 

 

 

 

 

 

 

Block 1 Block 2
Control Climate
Variables Variables Dependent
Squadron Size + Factor Analysis ___Varrable
Squadron Mission Results FSE
% Officer
N=66

79

 

 

Figure 3.28: Research Model for Hypothesis 2b.

Block 1

Block 2

 

 

Control
Variables
Squadron Size
Squadron Mission
% Officer

 

 

 

Climate
Variables
Factor Analysis
Results

 

 

 

N=66

Dependent
Variable
MC R

Figure 3.29: Research Model for Hypothesis 2c.

Block 1

 

Control
Variables
Squadron Size
Squadron Mission
% Officer

 

M

 

 

 

Climate
Variables
Factor Analysis
Results

 

 

 

N=66

Dependent
Variable
Squadron-level
Job Satisfaction

Figure 3.30: Research Model for Hypothesis 2d.

Block 1

Block 2

 

 

 

Control
Variables
Squadron Size
Squadron Mission
% Officer

 

 

Climate
Variables
Factor Analysis
Results

 

 

 

Cross-level Hypotheses

 

N=66

Dependent
Variable
Squadron-level
Performance
Perceptions

These hypotheses represent the need for a cross-level hierarchical regression.

80

Following Ostroff (1993), interactions were computed (individual-level climate scores

 

X squadron-level climate scores), thus producing a unique cross-level score for each

climate variable and each of the 2,069 cases. Blocks 1 and 2 represent the individual

level effects are removed, therefore remaining significant effects are attributable to

squadron-level effects.

Block 1

Block 2

Figure 3.31: Research Model for Hypothesis 3a.

Block 3

 

 

 

Control
Variable
Rank
(Officer,
Enlisted)

 

 

 

Individual
Climate
Variables
Factor Analysis
Results

 

Interaction
Variables
Individual
Climate X
Squadron
Climate

 

 

 

 

 

Block 1

N=7,029

Block 2

Figure 3.32: Research Model for Hypothesis 3b.

Block 3

 

 

 

Control
Variable
Rank
(Officer,
Enlisted)

 

 

 

Individual
Climate
Variables
Factor Analysis
Results

Interaction
Variables
Individual
Climate X
Squadron
Climate

 

 

 

 

N=7,029

 

 

Dependent
Variable
Job
Satisfaction

Dependent
Variable
Performance
Perceptions

This concludes the Data Analysis section. Chapter Four contains the results

from each of the analyses that were conducted for the Hypotheses articulated above.

 

CHAPTER FOUR
RESULTS
In Chapter Four, the first section addresses the results of the factor analysis. The

second section corresponds to the analyses that supported the testing of the eight

hypotheses.

Factor Analysis
The factor analysis achieved the goal of reducing the original 14 scales to a
more parsimonious number of germane constructs. In fact. the principal components
analysis yielded eight scales. The original Leadership, Communication, Participation.
and Recognition factors loaded most heavily on the first component that was

reproduced in the factor analysis. Therefore, one scale (ten questions) was derived from

the items that loaded on this factor. Because each of these questions essentially

addressed leadership behaviors, the title, “Leadership” was retained. In addition to the
four scales mentioned above, Core Values and Unit Flexibility were removed from
further investigation in this study because in most cases, the variables comprising these

scales loaded on the other constructs. The independent variables: Job Characteristics.

Resources, Training. and Teamwork remained as strong factors. Similarly, Job

Satisfaction and Performance Perceptions held up well in the factor analysis and were

retained as dependent variables. Table 4.1 shows the eight remaining factors (six

independent variables and two dependent variables), questions, and corresponding

factor/item loadings. Two tables of means, standard deviations, correlations, and

reliability coefficients were prepared for the analyses. Table 4.2 cites data used while

testing both individual-level effects (Hypothesis 1) and cross-level effects (Hypothesis

82

 

 

3). Table 4.3 shows the information used while testing squadron—level effects

(Hypothesis 2).

Individual and Cross-level Descriptive Statistics, Correlations, and Reliabilities
Table 4.2 shows the information used in support of testing Hypothesis 1
(individual-level effects) and Hypothesis 3 (cross-level effects). At the individual level,
correlations among the climate variables were smaller than at the squadron-level. The

range of Pierson r’s was from .23 to .62 (avg = .45), thus showing less mulitcollinearity

than the squadron-level variables. All values were significant at the p301 level.

Reliabilities ranged between .73 and .95 (avg. = .87). The pattern of correlations is
quite similar to those described for the squadron-level variables. Resources was least
correlated with the other climate variables.

At the individual-level, military rank (whether or not a person was officer or

enlisted) was included in the analyses. This variable was significantly (p_<_.01)

correlated with all climate variables. The variables with which the magnitude of the

correlations was highest were Leadership and Teamwork. Thus, officers very clearly

rate their leaders’ behavior and teamwork activities within their units more favorably

than enlisted members. In fact, that pattern holds true for each of the climate variables

except Resources in which case, enlisted members responded more favorably about the

equipment, number of personnel, and amount of time they have to complete their tasks.
The remainder of the information in Table 4.2 shows correlations among the
interactions that were computed by creating cross products (individual-level x
squadron-level) for each of the climate variables. Analyses and variables used in this

case were the same as those used while testing Hypothesis 1. Operational performance

83

 

 

values were not available for individuals, therefore Job Satisfaction and Performance
Perceptions were the only two dependent variables used in the cross-level analyses.
Here the entire data set (N=7029) was used. The alpha coefficients remained consistent
and the absolute values of the intercorrelations averaged : .04 points with the data used

for the N=66 analyses. In the cross-level analysis, the role of military rank remained

essentially the same as in the individual analyses. However, the magnitude of the

correlations increased slightly.

When squadron type was correlated with the dependent variables, the data
showed significant correlations (p301) with both job satisfaction and performance
perceptions. This implies that among those squadrons that actually go to war, their

members appear to be more satisfied with their jobs and they rate their squadrons‘

 

performance more favorably than those squadrons whose members support the

warfighters.

Squadron-level Descriptive Statistics, Correlations, and Reliabilities

Table 4.3 shows the information used in support of testing Hypothesis 2
(squadron-level effects. Squadron members’ responses were aggregated and averaged

to determine a squadron score for each of the climate variables. Thus, the sample size

for testing squadron-level effects was 66.

The decision to use squadron-level scores was based on Hollenbeck, Ilgen, and
Sego’s (1994) and Hollenbeck, Colquitt and Gulley's (1995) recommended approach
for partitioning variance. Total variance (individual-level) was calculated for each of

the survey scales. Next, the amount of variance in the each of the squadron-level scores

84

 

was derived. Variance in the squadron-level variables ranged from .003 to .13. Only
two of the scales achieved an acceptable threshold for aggregation, Resources and
Leadership.

However, as a matter of practical convenience, the remaining six constructs
were used in the study. Internal consistency reliability for each of the constructs was
computed. Alpha coefficients ranged from .83 to .98 (avg. = .93). Twenty-three of the
possible 28 relationships were significant at the p501 level. Three additional
correlations were significant at the p305 level. Yet, three of the relationships appear to
be relatively independent of each other: Resources and Job Characteristics, Resources
and Supervision, and Resources and Teamwork. Conversely, Job Characteristics,
Leadership, Teamwork, and Training produced the highest correlations with the other
variables. Among all possible relationships, the correlation coefficients ranged from .17
(Job Characteristics-Resources) to .74 (Job Satisfaction-Training). The average
correlation between the squadron-level climate variables was .53. Thus, there was a

moderately high degree of mulitcollinearity among the constructs.

Inspection of correlations among the climate and squadron-level operational
performance indicators, flying schedule effectiveness and mission capability, yielded no
significant relationships. However, flying schedule effectiveness was significantly
correlated with both of the dependent measures, Job Satisfaction (p305) and
Performance Perceptions (p305). Thus, it appears as though, at the squadron-level,
squadrons that report the highest levels of job satisfaction are also the squadrons that do
better at meeting their monthly flying schedules. The Performance Perceptions-Flying
Schedule Effectiveness correlation implies that squadrons are quite accurate at judging

how well their units are meeting their monthly flying schedule.

85

 

 

When the climate variables are viewed in concert with the dependent measures
included in the CSAF Survey, the data showed all possible correlations significant at
p_<_.01 level. The strongest correlations with Job Satisfaction were Training. Job
Characteristics, and Leadership. Performance Perceptions was correlated most highly
with Job Characteristics, Training, and Leadership. Another relationship was
uncovered between these dependent measures and the control variables. Squadron
Mission Type (warfighting vs support mission) was significantly correlated with both
Job Satisfaction and with Performance Perceptions. Therefore, it appears as though
squadrons that have a warfighting mission, report higher job satisfaction. Also, these
squadrons see the quality, quantity, and efficacy of their outputs more positively than
squadrons that fly support missions. Because all of the reported significant
relationships are correlations, it cannot be determined whether one variable in the

relationship may have a predictive capability with the other.

86

Table 4.]

Factors, Corresponding Questions, and Factor Loadings

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

SQUADRON LEADERSHIP (factor 1) l 2 3 4 5 6 7 8
[My unit leadership is inspirational. .80 .23 .15 .15 .13 .08 .18 .06
[I trust the leadership in my unit. .79 .22 .19 .17 .1 1 .09 .16 .08
[My unit leadership makes decisions based on facts. .78 .22 .18 .20 .15 .06 .09 .06
[My unit leadership motivates me to do my best. .78 .26 .16 .18 .13 .09 .16 .l i
see my unit leaders doing the same things they
ublicly promote. .76 .23 .22 .18 .l 1 .07 .09 .07
[Leadership in this unit listens to my ideas. .74 .19 .22 .07 .10 .14 .18 .05
[Leaders in my unit are easily accessible. .72 .19 .17 .12 .1 1 I3 .08 I2
eadership in this unit effectively communicates the
ission, goals, and objectives. .72 .21 .15 .19 .15 .l l .08 .12
My unit leadership does a good job of recognizing
eople in all grades and types of jobs. .65 .22 .26 .11 .12 .03 .14 .17
[My unit leadership sets challenging goals. .61 .26 .08 .30 ~01 .13 .04 .l l
[SUPERVISOR SUPPORT (factor 2)
y supervisor provides instructions that help me
eet his/her expectations. .20 .83 .13 .09 .09 .08 .07 .12
[My supervisor is concerned with my development. .20 .82 .12 .09 .07 .07 .10 .14
y supervisor looks out for the best interests of my
ork group. .24 .82 .18 .ll .10 .06 .10 .07
y supervisor models the core values of the Air
Force. .25 .79 .18 . 12 .07 .06 .IO .07
y supervisor keeps me up-to-date on what is
appening in my unit .27 .77 .17 .09 .08 .08 .10 .12
My supervisor helps me understand how my job
ontributes to my unit’s mission. .27 .76 .16 .11 .05 .10 .12 .15
[My supervisor sets high performance standards. .17 .75 .10 .21 .01 .14 .03 .04
y supervisor is good at planning my work. .15 .74 .l 1 .l 1 .15 .04 .13 .09
y supervisor corrects poor performers in my work
group. .16 .74 .17 . 13 .10 .09 .04 .09
[I‘EAMWORK (factor 3)
[My work group adequately resolves conflicts. .27 .25 .79 .14 .05 .08 .14 .06
[People in my work group respect each other. .28 .20 .79 .10 .03 .10 .17 .02
embers of my work group willingly share
information. .24 .25 .77 .16 .07 .1 1 .07 .13
[People in my work group cooperate to get the work
one. .21 .26 .75 .23 .10 .1 l .06 .13
IThere is cooperation among work groups in my unit. .34 .20 .73 .19 .1 l .07 .10 .10

 

 

 

 

 

 

 

 

 

 

87

 

Table 4.1
Factors, Corresponding Questions, and Factor Loadings (cont.)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

[PERFORMANCE PERCEPTIONS (factor 4) 1 2 3 4 5 6 7 8
fly unit is known as one that gets the job done. 25 .18 .14 .79 .06 .10 .08 .05
[My unit is successfully accomplishing its mission. 24 .17 12 .78 .14 .08 .IO .10
[The quantity of work accomplished in my unit is high. .17 .17 .18 .73 -.O3 .12 .1 l .07
[The quality of work in my unit is high. .31 .22 .26 .62 .15 .12 .17 .12
y unit accomplishes its mission in a cost-effective
anner. .41 .19 .16 .53 .20 .04 .14 .13
[RESOURCES (factor 5)
I have enough time to accomplish my daily workload
uring my duty hours. .08 .10 .02 .06 .86 -.03 .05 .08
[I have adequate time to do my job well. .12 .13 .06 .05 .80 .08 .17 .06
We have enough people in my work group to
ccomplish the job. .17 .07 .10 .04 .76 -.02 .05 .06
l have the right tools, equipment, and materials to
ccomplish the job. .21 . 10 .05 .14 .65 .06 .01 .17
[JOB CHARACTERISTICS (factor 6)
oing my job well affects others in some important
ay. . 10 .1 1 .06 .15 .01 .79 .03 .02
y job allows me to see the finished products of my
ork. .11 .12 .10 .10 .12 .75 .17 .07
[My job requires me to use a variety of skills. .06 .05 .07 .07 -. 15 .72 .04 .13
y job is designed so that I know when I have
erfonned well. .29 .18 .12 .02 .20 .56 .25 .09
y job allows me freedom to work with minimum
upervision. .30 .14 13 -.03 .23 .33 .34 -.23
[JOB SATISFACTION (factor 7)
I have a sense of personal fulfillment at the end of the
ay. .29 .20 .18 .19 .13 .18 .79 .18
[In general, I am satisfied with my job. .26 .20 .16 .18 .15 .15 .77 .17
he tasks I perform provide me with a sense of
ccomplishment. .28 .19 . 16 .22 .08 .21 .74 .21

 

 

 

 

 

 

 

 

 

88

 

Table 4.]
Factors, Corresponding Questions, and Factor Loadings (cont.)

 

 

[TRAINING (factor 8) I 2 3 4 5 6
have been adequately trained for the job I am
xpected to do. .09 23 13 .14 I9 15

 

I am allowed to attend continuing professional training

conferences. workshops. etc.). .33 .04

 

b) Is)
Ix) I0
1:.
O
00
id
Id

[1 am given Opportunities to improve my skills. .3.) .12 .15 .14 .18

 

I am encouraged by my unit leadership to Ieam new
hings. .50 .33 .13 .18 .06 .15

 

 

 

 

 

 

 

 

 

 

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91

Results of Analyses by Hypothesis

Individual-Level Climate and Outcome Measures

 

Hypothesis la. Hypothesis 1a stated that individual climate perceptions would

 

explain a significant amount of variance in squadron members‘ job satisfaction.

Table 4.4 shows the results of the regression analyses. In the first block, military rank
was entered as a control variable. Although the results indicate officers are more
satisfied than enlisted members (p3001), this variable only accounted for one percent
of the total variance. The sheer sample size (N=7029) enhances the power to detect
very small effects.

As predicted, the largest amount of variance is clearly associated with the
climate variables. In fact, these variables accounted for an additional 45 percent of the
variance in individual-level job satisfaction. The most significant climate variables in
the regression equation were Job Characteristics (B = .27), Training (B = .23),
Leadership
(B = .18), and Teamwork (B = .12). Each of these is significant at the p§.001 level.
Thus, individual-level climate does play a major role in determining flying squadron
members’ job satisfaction. When individuals perceive their jobs as enriching, they have
the necessary training to do their jobs, their leaders are good leaders, and teamwork is

high; their job satisfaction is likely to be high as well. Hypothesis 1a was supported.

92

Table 4.4
Regression Model: Dependent Variable — Individual-level Job Satisfaction

 

 

 

 

 

VARIABLE MODEL 1 a MODEL 2 a
1. Rank .11**** -.02
(Enlisted/Officer)
2. Job Characteristics .27****
3. Resources .05****
4. Leadership .18****
5. Supervision .O4****
6. Training .23****
7. Teamwork .12****
- Overall ModelF 83.72"” 858.15****
- Ad'usted R2 .01 .46
- (R Change) ---- (.45)
*prO, **p_<_.05, ***p§.01
*akakatPEDOl
a Standardized reg. coef.

 

 

 

 

Hypothesis 1b. Hypothesis 1b stated that individual climate perceptions would
explain a significant amount of variance in squadron members’ perceptions of
performance. Table 4.5 shows the results of the regression analysis. Once again.
military rank was entered as a control variable. The results were similar to the analysis
using Job Satisfaction as the dependent variable. The R2 value for military rank
doubled to .02.

As predicted, the climate variables explained a significant amount of the
variance in individual-level performance perceptions. In fact, climate explained an
additional 41 percent of the total variance in the dependent variable. The most
significant climate variables in the regression equation were Leadership (B = .31),
Teamwork (B = .20), and Training (B = .15). Each of these is significant at the p300]

level. The magnitude of the beta weights show that Leadership is the strongest

93

predictor of the climate factors. Hence, as leaders effectively communicate the
squadron’s mission, goals, and objectives, elicit trust, and model their own rhetoric.
squadron members are more likely to perceive their squadron’s performance in a better
light. Similarly, when squadron members perceive that teamwork is high and they have
the necessary training to complete their jobs, they are more likely to perceive that their

squadrons are performing highly. Hypothesis 1b was supported.

 

 

 

 

Table 4.5
Regression Model: Dependent Variable - Individual-level Performance
Perceptions

VARIABLE MODEL 1 3 MODEL 2 a
1. Rank .03**** -.01

(Enlisted/Officer)
2. Job Characteristics .06****
3. Resources .05****
4. Leadership .31****
5. Supervision .05****
6. Training .15****
7. Teamwork .20****

- Overall Model F 142.88**** 748.93****

- Adjusted R2 .02 .41

- (R2 Change) (.43)

*prO, “p305, **p§.01

*IHHI: p500]

a Standardized reg. coef.

 

 

 

 

 

fiuadron-level Climate and Outcome Measures

HYPOTHESIS 2a. Hypothesis 2a stated that squadron climate would explain a

significant amount of variance in the squadrons’ Flying Schedule Effectiveness Rates.

94

Table 4.6 shows the results of the regression analyses. Neither of the two
models revealed statistical significance. The control variables, which were entered in
the first block, were not significant predictors of the dependent variable. Similarly, the
climate variables also failed to show a significant relationship in the second block. The
total amount of variance accounted for by the complete model (Model 2) only reached
approximately six percent. Hypothesis 2a was not supported.

Because the correlation analyses showed Job Satisfaction and Performance
Perceptions were significantly related to flying schedule effectiveness. a post-hoc
regression analysis was conducted to see if either of these variables have a predictive
relationship with the dependent variable. Neither of the two independent variables
reached statistical significance.

Table 4.6
Regression Model: Dependent Variable - Flying Schedule Effectiveness Rate

 

 

 

 

 

VARIABLE MODEL 1 MODEL 2
1. Squadron % Officer .01 -.O3
2. Squadron Size .06 .05
3. Squadron Mission .20 .14
(Warfighter Y/N)
4. Job Characteristics -.11
5. Resources -.04
6. Leadership -.07
7. Supervision . l 3
8. Training .27
9. Teamwork .12
- Overall Model F .78 .57
- Ad'usted R2 -.01 -.O6
- (R Change) ---— (-.05)
a Standardized reg. coef.

 

 

 

 

9S

HYPOTHESIS 2b.
a significant amount of variance in the squadrons’ Mission Capable Rate. Table 4.7
shows the results of the regression analysis. Consistent with Hypothesis 2a, neither the
control nor climate factors proved to be significant predictors of the dependent variable.
In fact, the control variables only consumed two percent of the total variance in
predicting squadrons’ Mission Capable Rate.

contributed marginally by consuming only an additional one percent of the variance.

Thus, Hypothesis 2b was not supported.

Hypothesis 2b stated that squadron climate would explain

Similarly, the climate variables

 

 

 

 

Table 4.7
Regression Model: Dependent Variable — Mission Capable Rate

VARIABLE MODEL 1 3 MODEL 2 a
1. Squadron % Officer .10 .13

2. Squadron Size .15 .16

3. Squadron Mission .01 .17

(Warfighter Y/N)

4. Job Characteristics -.14

5. Resources .24

6. Leadership -.26

7. Supervision .09

8. Training -.10

9. Teamwork .23

- Overall Model F .57 .80

- Ad'usted R2 -.02 -.03

- (R Change) (-.01)

 

a Standardized reg. coef.

 

 

 

Hypothesis 2c.

 

significant amount of variance in squadron-level Job Satisfaction. Table 4.8 shows the

results of the regression analysis.

satisfaction was due to the control variables. However, only one of these variables,

96

Hypothesis 2c stated that squadron climate would explain a

Approximately six percent of the variance in job

 

squadron mission, appears to be a significant predictor of Job Satisfaction. The same
relationship that was described earlier in the correlation analysis was confirmed in the
regression analysis. Thus, squadrons that have warfighting missions may be predicted
to also have higher rates of Job Satisfaction.

The climate variables account for approximately 83 percent of the total variance
associated with Job Satisfaction. While controlling for the squadron mission effects.
Training (B = .61), Job Characteristics (B = . 42), and Resources (B = .19) proved to be
significant predictors of Job Satisfaction. Each of these variables is significant at the
p_<_.001 level. Thus, those squadrons providing their members with the training required
to do their jobs and providing enriched work environments (skill variety, task identify,
task significance, autonomy and feedback) are affecting their members in a very
positive way. Similarly, squadron-level job satisfaction is higher when the unit provides
its members with the necessary people, time, equipment, and training to perform their
tasks.

The next finding presents some cognitive dissonance. That is, the results
indicate Supervision (B = -.14) is also a strong predictor of squadron members’ Job
Satisfaction (p305). However, the uncertainty arises from the fact that the data indicate
these two variables are negatively related to each other. These items are not reverse
scored, thus, one would expect that as one score increased, so would the other.
However, flying squadrons reported just the opposite. Supervisory behaviors described
in the survey actually decreased job satisfaction. The items are intended to measure
characteristics that typify good supervisors. One possible interpretation may be that the
components measured in the Supervision scale tend to migrate toward the “micro-

management” or “too personal” behaviors and the resulting perception is a negative

97

connotation. In other words, survey participants may actually feel that the behaviors
measured in the Supervisor scale border on activities that somehow take away from
their freedom and autonomy.

The final model accounted for a respectable 89 percent of the variance

associated with Job Satisfaction. Therefore, Hypothesis 2c was supported.

 

 

 

 

Table 4.8
Regression Model: Dependent Variable - Squadron-level Job Satisfaction
VARIABLE MODEL 1T MODEL 2 a
1. Squadron % Officer -.01 -.O9*
2. Squadron Size .11 .00
3. Squadron Mission .34*** .08
(Warfighter Y/N)

4. Job Characteristics .42****
5. Resources .19****
6. Leadership -.09
7. Supervision -.14**
8. Training .61 ****
9. Teamwork .06
- Overall Model F 2.46* 57.28”"

- Adjusted R2 .06 .89
- (R2 Change) --— (.83)****
*prO, **p_<_.05, ***p§.01,
##1## 135-001
a Standardized reg. coef.

 

 

 

 

 

Hypothesis 2d. Hypothesis 2d stated that squadron climate would explain a significant
amount of variance in the squadron-level Performance Perceptions. Table 4.9 shows
the results of the regression analysis. Once again the control variable, Squadron
Mission, was a predictor of the dependent variable. Here, the data indicate that
warfighting squadrons typically report higher perceived levels of performance. These

results accounted for approximately three percent of the variance in the dependent

98

variable.

Concomitantly, the climate variables explained an additional 52 percent
(p§.001) of the variance. The number of climate variables found to be significant
predictors of the dependent variable was reduced to two in the case of Performance
Perceptions. Here, Job Characteristics (B = .35, p305) and Teamwork (B = .26, p510)
were the significant variables in the equation. Thus, at the squadron level. it appears as
if squadrons that rate their jobs and teamwork in their units favorably, may be predicted
to also rate the performance of their squadrons strongly.

The climate variables did, in fact, explain a significant amount of the variance in

Performance Perceptions. Therefore, Hypothesis 2d was supported.

 

 

 

 

Table 4.9
Regression Model: Dependent Variable - Squadron-level Performance
Perceptions
VARIABLE MODEL l"r MODEL 2T
1. Squadron % Officer -.03 -.O8
2. Squadron Size -.06 -.15
3. Squadron Mission .25* .12
(Warfighter Y/N)

4. Job Characteristics .35“
5. Resources .16
6. Leadership .03
7. Supervision -.05
8. Training .19
9. Teamwork .26*
- Overall Model F 1.68 9.82””

- Adjusted R2 .03 .55
- (R2 Change) (.52)****
*p_<_.lO, ”p305, ***p_<_.01,
’ Standardized reg. coef.

 

 

 

 

 

Cross-Level Climate and Outcome Measures

 

Hypothesis 3a. Hypothesis 3a stated that squadron climate would explain a

 

significant amount of variance in squadron members’ job satisfaction over and above
individual-level climate effects. Results of the regression analysis are presented in
Table 4.10. Blocks 1 and 2 are the same as Hypothesis 2a.

Consistent with Ostroff (1993), cross-level interactions were computed using
individual-level scores x squadron-level scores (mean values). This process was
repeated for each of the climate variables in the present study. The cross-level climate
variables were entered in Block 3. This step was intended to prove that squadron-level
climate possesses additional explanatory power in squadron members’ job satisfaction
that is beyond that of the individual-level climate effects. The amount of variance for
which the interaction terms accounted was significant, but minimal (R2 change = .01).
Also, the mixed directions of the beta weights are somewhat confusing (Training, B =
.34; Supervision, B = -.18; Job Characteristics, B = .18; and Leadership, B = -.16). This
dynamic makes interpretation somewhat difficult. When these interaction terms are
plotted using Job Satisfaction as the dependent variable, there are indications that
behaviors typically thought to produce positive effects on Job Satisfaction actually do
not have the intended effect.

Hypothesis 2c demonstrated that with data aggregated to the squadron-level
(N=66), Squadron Mission Type was a significant predictor of Job Satisfaction. Thus, a
post-hoe analysis that included this variable in the regression equation for this
hypothesis was conducted. Squadron Mission Type was entered in Block 1 along with
military rank. The results of adding the additional control variable were very similar to

the effects of the cross-level variables. The R2 value increased by one percent and

100

squadron mission type was significant at the p301 level. Hypothesis 3a was supported.

 

 

 

 

 

Table 4.10
Regression Model: Dependent Variable — Individual-level Job Satisfaction
VARIABLE MODEL 1 MODEL 2 3 MODEL 3 a
1. Rank .11*** -.02 -.01
(Enlisted/Officer)

2. Ind Job Characteristics .27**** .10

3. Ind Resources .05**** .02

4. Ind Leadership .l8**** 32*“:1:
5. Ind Supervision .04**** .2244"
6. Ind Training 23“" -J]

7. Ind Teamwork .12**** 25“"
8. Incl x Sq Job

Characteristics _1 8“: all

9. Ind x Sq Resource _03

10. Ind x Sq Leadership -.16***
11. Ind x Sq Supervision _Jgtu
12. Ind x Sq Training 34*":
13. Ind x Sq Teamwork -.13*

- Overall Model F 83.72"” 858.15**** 469.51***
- Ad'usted R2 .01 .46 .47

- (R Change) ---- (.45) (.Ol)

*p_<_.10, "p305, ***p§.01
a Standardized reg. coef.

 

 

 

 

 

 

Hypothesis 3b. Hypothesis 3b stated that squadron climate would explain a significant

 

amount of variance in squadron members’ performance perceptions over and above
individual-level climate effects. Table 4.1 1 shows the results of the regression analysis.
Blocks 1 and 2 are identical to Hypothesis 2b. Cross-level climate variables
(interaction terms) were entered into the regression on Block 3. Consistent with results
in Hypothesis 3a, the cross-level variables explained only an additional one percent of

variance in the dependent variable. Two interactions were identified as significant

10]

predictors of members’ performance perceptions: Job Characteristics (B = .32. p301)
and Training (B = .18, p_<_.05).

The same post-hoc analysis that was performed while testing Hypothesis 3a was
performed here. That is, Squadron Mission Type was included as a control variable and
the regression was executed again. The results of this analysis were the same as the
post-hoe analysis for Hypothesis 3a. The R2 value increased by one percent and was

significant at p501. Hypothesis 3b was supported.

 

 

 

 

 

Table 4.11
Regression Model: Dependent Variable-Individual-level Performance Perceptions

VARIABLE MODEL 1 MODEL 2‘r MODEL 3 3’

l.Rank .14*** -.01 -.01
(Enlisted/Officer)
2. Ind Job Characteristics .06**** -.26****
3. Ind Resources .05**"‘* .03
4. 1nd Leadership .31**** .42****
5. Ind Supervision .05**** .18***
6. Ind Training .15**** -.02
7. Incl Teamwork .20**** .08
8. Ind x Sq Job
Characteristics .32** *

9. Ind x Sq Resources .02
10. 1nd x Sq Leadership -. 1 3
11. Ind x Sq Supervision -.12
12. 1nd x Sq Training .18"
13.1ndeq Teamwork .13
- Overall Model F l42.89**** 748.93**** 4]0.88****
- Ad'usted R2 .02 .43 .44
- (R Change) ---- (.41) (.01)
*prO, **p§.05,***p_<_.01
****pf.001
a Standardized reg. coef.

 

 

 

 

 

102

 

Table 4.12 summarizes the findings shown in this chapter.

Table 4.12
Summary of Results

 

Supported/Not Dependent

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Hypothesis Supported Variable

Individual-Level

la Supported Job
Satisfaction

1 b Supported Performance
Perceptions

Squadron-Level

2a Not Supported Flying
Schedule
Effectiveness

2b Not Supported Mission
Capable Rate

2c Supported Job
Satisfaction

2d Supported Performance
Perceptions

Cross-Level

3a Supported Job
Satisfaction

3b Supported Performance
Perceptions

CONCLUSIONS

Six of the eight hypotheses were supported. The dependent variables for all
supported hypotheses were either Job Satisfaction or Performance Perceptions. In both
cases, data were collected along with the climate variables from the CSAF Survey. The
first two hypotheses, which included the Operational performance measures, Flying
Schedule Effectiveness and Mission Capable Rates, were not supported. Explanations

for all findings will be discussed in Chapter Five.

103

CHAPTER FIVE
DISCUSSION
This chapter presents a review of the most important findings in this dissertation
research. The discussion presents a summary of the results identified in Chapter Four.
Also included in this chapter are sections that address the limitations of this study.

implications for future research and theory, and implications for practice.

Summary

Over the past ten years, the academic literature that addressed organizational
climate and culture evolved beyond descriptive treatises and case studies. The focus
shifted toward understanding how various climate dimensions may be linked to both
performance and members’ affective measures in organizations. This paradigm shift
stimulated a research agenda that led to attempts to link climate and performance in the
service, technical, and health-care industries. The US. States military has traditionally
served as a vast proving ground for basic and practical research topics. However, a
thorough review of the organizational climate and culture literatures revealed there is no
current research that addresses organizational climate either in a descriptive format or in
a research design that attempts to establish a climate-performance linkage in military
work environments.

This dissertation addressed a very important issue facing the American military.
During the 19905, social, political, and economic changes occurred in the United States.
The Cold War ended and competition with other military superpowers was reduced
significantly. Reductions in fiscal strength and numbers of personnel in the military

followed. Also, the baby boomer era ushered in a generation of Americans who no

104

longer had personal ties to the military. Parents, who throughout history have proven to
be a very significant impetus for military service, did not serve in the military. Interest
in and a passion for military service waned in American minds. At the same time, the
economy and employment rates have grown increasingly stronger. These dynamics
contributed to the military experiencing declines in recruiting success as well as
increased attrition.

No longer can the US. military depend upon duty, honor, and country as a set of
core values that energizes American women and men to choose military service as an
occupation. Additionally, the reduction of military personnel and budget strength led to
increased numbers of work hours, time away from home (deployed to foreign
locations), decreased predictability in military members’ lives, fewer spare parts to
repair equipment, and salaries generally not thought to be competitive with American
market wages. Stated simply, the American military is having difficulties recruiting and
maintaining enough people to answer the call for protecting the security of this country.

There is an urgent need to identify and to understand the major issues that affect
military members’ decisions about serving their country. At the same time, it is the
stated mission of the military to deter war and to protect our national interests. Thus,
the present study was intended to look at both the people (organizational climate) and
outcome (performance and affective) measures in one context of the American military,
flying squadrons in the US Air Force.

A factor analysis was conducted to identify the most relevant military climate
constructs in the flying community. The results yielded a reduced number of
dimensions from the original CSAF Survey. Also, the output showed significant

correlations among several key variables of interest. Finally, regression analyses at the

105

individual-, squadron-, and cross-levels produced results that generally supported the
proposition, understanding military climate and outcome measures are key ingredients
to a successful future for the military.
The key findings of this study are a follows:

1. The factor analysis yielded a very stable set of six climate dimensions
(independent variables) and two outcome measures (dependent variables) that describe
Air Force flying squadrons. The six climate variables were Job Characteristics,
Resources, Leadership, Supervision, Training, and Teamwork. The two outcome
measures that resulted from the factor analysis were Job Satisfaction and Performance
Perceptions. These variables can be used to provide accurate descriptions of a military

climate and its effects on affective responses of squadron members.

2. The correlation analyses showed a moderately high degree of mulitcollinearity
among the climate variables and outcome measures that were measured in the CSAF
Survey. This finding is typical of organizational climate research (Lindell & Brandt,
2000; Ryan, et al., 1996; Ostroff, 1992; Kozlowski & Hults, 1987). While there were
no significant correlations between the climate variables and the performance measures
obtained from the squadrons, Flying Schedule Effectiveness was significantly correlated
with both Job Satisfaction (Ostroff, 1992; Tomow & Wiley, 1991) and Performance
Perceptions. Thus, squadrons that reported higher levels of job satisfaction and
performance perceptions were also doing well at meeting their monthly flying

schedules.

106

3. Another finding that supports the approach taken in this study is the significant
relationship between Flying Schedule Effectiveness and Performance Perceptions. This
demonstrates that, in lieu of pure performance data. organization members’ perceptions
of performance may serve as an acceptable surrogate. The implications of these findings
shows that flying squadron members were accurate judges of how well their squadrons

were meeting their flying schedule objectives (Schneider et al., 1996).

4. The correlation analyses also identified one contextual and one demographic
dimension associated with both climate and performance measures. Squadron-level
analyses showed that warfighting squadrons report higher levels of Job Satisfaction and
see their squadrons’ performance in a more favorable light than flying squadrons that
perform refueling, reconnaissance, and cargo missions for the Air Force. Also,
individual-level analyses showed that with the exception of one dimension, officers rate
climate much more favorably than enlisted members. This may be an artifact of the
“leadership” role that officers play in the squadron. Moreover, officers are responsible
for setting the direction of the squadron, ensuring that training agendas are met, and
establishing the framework for how teams will be employed in the work setting.
Finally, officers serve primarily in supervisory roles. Therefore, it is expected that they
would rate the programs over which they have responsibility more favorably than
enlisted members (Johnson, 2000). Yet, enlisted members were sending a message as
well. That is, their perception of “how good things are around here” was not in concert

with the officers’ perceptions.

107

5. At the individual-level, predictions of squadron members’ Job Satisfaction and
Performance Perceptions are tied to four climate variables. Regression analyses showed
two job-related and two process-related variables appear to be the largest influences
upon squadron members’ Job Satisfaction. With reference to the job, workers prefer
tasks that permit them to use different types of skills, allow for autonomy. and offer
feedback mechanisms. They also want to possess the requisite knowledge to do their
jobs or to be allowed and encouraged to attend training to reach acceptable levels of
proficiency. It appears flying squadron members prefer that their leaders are out in
front leading the unit by example, articulating a clear vision for success, inspiring trust,
and rewarding excellent performance. Finally, the results indicated that Teamwork is a
predictor of squadron members’ Job Satisfaction. There is something appealing and
reinforcing to squadron members who see themselves working in teams where the

members have respect for one another and c00perate to accomplish the unit’s mission.

6. Flying squadron members’ Performance Perceptions can also be predicted by
the military climate variables. The results indicate that Leadership, as described above,
had a rather large influence upon the ways in which squadron members view their units’
performance. Similarly, it was observed that squadron members who rate their units’

training and teamwork programs high. also rate their squadrons’ products favorably.

7. Prior to assessing the squadron-level effects, the researcher considered the
extent to which the climate and dependent variables passed an acceptable aggregation
criterion measure. Variance partitioning was the technique that was used. The decision

to aggregate rests on the extent to which climate scores at the squadron level can be

108

explained by the amount of variance in the scores at that level. In this dissertation. two
of the climate variables passed the test. Approximately 12 percent of the observed
effects in Leadership and 13 percent of the observed effects in Resources can be
attributed to the squadron level.

The immediate implication of these findings is that only these two variables
should be aggregated to the next level and included in the squadron-level analyses.
However, as the remaining sections in this chapter will show, this study did produce
significant findings at the squadron level, even with the climate variables that did not
pass the threshold for acceptable aggregation. In regression analyses, squadron-level
climate was a significant predictor for two of the dependent measures.

One potential explanation for these findings is that the climate variables may be
operative at a level between the individual and squadron. If this is true, residual effects
may be captured at the squadron level. In fact, there is an intermediate level in each of
the squadrons used in this study, the flight level. Unfortunately, flight level data were

not available.

8. Squadron-level regression analyses failed to show military climate as a
significant predictor of the operational performance measures, Flying Schedule
Effectiveness and Mission Capable Rate. Four plausible explanations are offered.
First, is the issue of power. The sample size (N=66) may have been too small to detect
significant effects with these two performance metrics. Second, range restriction in the
dependent variables may have impinged upon the ability to detect significances. Both
of these measures are highly visible to commanders and senior leaders in the flying

community. Commanders manage these numbers very carefully to 1) ensure they meet

109

their flying missions and 2) avoid being chastised for poor leadership. That is. if either
of these measures is not at an acceptable rate, then the perception is that the squadron
cannot possibly be performing its mission. Third, the measures themselves may be
confounded by other factors. In both cases, the metric is based not only on an absolute
sum of the number of missions flown or the number of hours aircraft are available to
fly; but, several types of “deviations” such as weather, control tower problems, and
unavailable spare parts are subtracted from the top-line counts. It is possible that
different measures less laden with “controls” would yield significant effects. Finally,
the direction of causality is germane. Ryan et al. (1996) found that performance
measures in a financial institution setting actually caused morale to increase rather than
the converse. A post-hoe analysis was conducted to test for these relations in the
present data set. Flying Schedule Effectiveness only predicted one of the climate
variables, Training. The implication is that when the squadron is meeting/exceeding its
flying goals, one would predict that squadron members’ perceptions of their training

would be high as well.

9. Squadron-level regression analyses did show that military climate explains a
significant amount of the variance in both squadron-level Job Satisfaction and
Performance Perceptions. This relationship holds true, while controlling for differences
in squadron missions (warfighter vs support mission). Among flying squadrons, the
best way to improve job satisfaction is through training, improving job designs and
providing enough time, people, and equipment for members to complete their work
tasks.

As stated in Chapter 4, the squadron-level analysis revealed an unexpected

110

finding. Supervisory behaviors typically thought to be positive actions (setting goals.
explaining the mission, taking care of workers’ personal needs) are actually a deterrent
to job satisfaction at the squadron-level. A general officer responded to these results in
the following way, “Remember, crew members and single seat weapon system pilots
think of their primary work setting as the cockpit, not the squadron building or the
office/desk where they spend their time when they’re not flying... their mentality leads
to a thought process that less supervision is good.” Because the primary mission of
these squadrons is performed in the air and most of the social exchange that occurs
between squadron members is completed while flying, there is little time for mentoring
and supervisory behaviors to occur. The majority of communications between pilots,
navigators, and crewmembers revolve around the mission itself. There is little time for,
nor will Air Force regulations permit, professional counseling to occur while squadron
members are flying.

Another potential explanation for the Supervisory factor possessing a negative
beta weight may be in the self-reinforcing roles of single-seat fighter aircraft. Pilots
learn to rely on their own judgments to help them succeed and to mature as professional
military members. They may perceive the supervisory behaviors measured in the CSAF
Survey as an insult to their individualistic nature and thus intrusive. When asked about
this finding, one fighter pilot responded, “we pilots tend to be very independent, we
would indicate more job satisfaction when the boss keeps his nose out of our business...
in other words, give me the desired results and 1 will perform the mission, but, don’t tell
me how to suck eggs.” It is a well-known fact that many pilots simply love to fly and

the tasks they perform provide high levels of intrinsic motivation.

111

10 The results from the squadron-level analysis showed that military climate
factors can be used to predict how flying squadron members will judge their squadrons’
performance, as well. When squadrons provide enriching jobs for their members and
when teamwork is perceived to be high, squadron members see their organizations
performing at very high efficiency and effectiveness levels. Moreover. they believe

others judge their units as, “a squadron that gets the job done.”

11. Cross-level analyses demonstrated that over and above individual-level climate
effects, squadron-level climate does have additional explanatory power in squadron
members’ job satisfaction and perceptions of performance. The regression analysis
with Job Satisfaction as the dependent variable (military rank was used as a control
variable) showed the amount of variance associated with the interaction terms was
minimal, but significant (R2 change = .01). Four of the interaction terms: Training.
Supervision, Job Characteristics and Leadership. These interactions were plotted. The
graphs show there are no real interpretable effects despite the fact statistical

significance was achieved

12. Very similar results were produced by the cross-level analysis when
Performance Perceptions was regressed on member’s rank (control), individual-level
climate variables and the climate interaction terms. The interaction terms only
accounted for an additional one percent of the variance in the dependent variable. Yet,
the Job Characteristics and Training interaction terms were significant predictors of
Performance Perceptions. However, once again the plots showed there is no easily

discernible interpretation of these interactions.

112

Limitations of This Study

There are a few limitations associated with this study. First, although there were
7,029 usable surveys at the individual-level, when the focus was shifted to identifying
squadron-level effects on organizational outcomes. all analyses were conducted with a
much smaller sample size (N = 66). There may be actual squadron-level effects that
exist, but went unrecognized in the present analyses due to the small sample size.

Second, for the findings that were identified, we must recognize that the 7.029
participants were from a very homogeneous sector of the US. Air Force. Civilian
records were eliminated from consideration; therefore, the only records that were
available for analysis were from officers and enlisted members of the service. Also, the
entire sample was taken from flying squadrons that belong to one major command, Air
Combat Command. While the Air Force certainly exists to fly airplanes, there are
several other communities (medical, logistics, support, and training) that perform
critical missions in concert with the flying community. Therefore, we should be
conservative in our attempts to generalize the findings reported here to all communities
in the military and the Air Force.

A third limitation is based upon the method by which the CSAF Survey data
were collected. Prior to the initiation of the survey, a rather aggressive media effort was
launched to announce the dates when the questionnaire would be available. Air Force
members were informed that while the CSAF wanted everyone to have the opportunity
to participate, members’ agreement to do so was entirely voluntary. Anecdotes made
available to the researcher made him aware that some Air Force members took the
position, “if it isn’t mandatory, then I won’t take the survey.” Therefore, unless senior

leaders intervened and asked squadron members to participate, it is possible the sample

113

was mostly composed of persons who typically participate in survey activities, thus
biasing the survey in favor of more sophisticated respondents. However, the fact that
34 percent of the population participated in the survey indicates this consideration may
be of minimal concern.

A fourth limitation lies in the collection of the operational performance
measures. The researcher had no ability to control the methods by which the data were
collected. Moreover, the researcher did not have an opportunity to train those who
collected and reported the performance measures in good data collection techniques.
There may be some contamination in the performance measures. Yet, there was no
available means to detect this artifact if it existed in the data. The researcher was too far
removed from this portion of the research to ensure the purity of the performance
metrics. Finally, common method variance (CMV) may be somewhat of a limitation.
When all responses are collected at the same time and through the same response
medium, it is possible that bias may be entered as a measurement consideration.
(Podsakoff & Organ, 1986; Williams & Brown, 1994). However, the fact that the
findings consistently showed differential effects on the dependent variables provides
evidence that CMV may not have interfered with the findings reported in this

dissertation.

Implications for Future Research and Theory

This dissertation contributes a number of relevant findings that advance the
knowledge in the climate research field. Moreover, given the findings mentioned in this

study, there are a number of potential areas for continued research.

114

First, the present study extends the organizational climate literature by validating that
the construct, military climate, exists in the US. military. In fact, the analyses produced
six very stable climate dimensions and two organizational outcome measures that
accurately provide a thorough description of military organizational processes. Similar
studies by Roberts, Rousseau, & La Porte (1994) and Rousseau and Cooke (1988)
identified differing “cultures” on board US. Navy aircraft carriers. Rather than
describing the organizational dynamics through a list of organizational processes, these
researchers completed “culture profiles” that described what they termed, “high
reliability organizations.” Their studies showed the extent to which the resulting
profiles were correlated with such outcome measures as Job Satisfaction, Career
Intentions, Behavior Incompatibility, and whether or not the job incumbent would
recommend a job on the aircraft carrier to another person. Officers and enlisted
members of the US. Navy served as subjects.

In the purest sense, the correct typology for this dissertation is in the “climate”
domain, while the studies conducted onboard naval aircraft carriers are classified as
“culture” studies. Yet, a review of the predominant characteristics and dimensions
studied in these works shows there is a significant amount of overlap among the
constructs. Dennison (1996) stated, “these two research traditions should be viewed as
differences in interpretation rather than differences in phenomenon” (p. 645).

Future climate and culture studies should explore ways to synthesize the
knowledge from these initiatives. The research should focus on deriving a more
parsimonious and empirically sound method for assessing military work environments.
Findings from the naval studies show there are three primary work styles (Satisfaction.

Task Security, and Self Protection). These dimensions are rooted in the traditional

115

approach used in studying organizational culture. They assess assumptions and values
that organizations impose upon workers. Thus. the results of the studies address deeper,
more fundamental organizational dynamics (Schein, 1992; Schneider, 1990).
Conversely, the variables of interest in this dissertation attempt to describe surface-
level, active processes that are visible to organizational members. This approach is
very much in line with traditional climate studies.

Combining the climate and culture knowledge bases in both public and private
sector studies would facilitate a much better understanding of diverse social contexts in
work environments. Also, combined research efforts would allow for better
specification of phenomena acting at differing levels of organizations. For example,
this study used three different levels analyses: individual-, organizational-, and cross-
levels. Defining organizational climate and culture in the military is still a concept.
Researchers are encouraged to use this proposition as an opportunity to bridge the gap
between the organizational climate and culture literatures.

This study added to the current literature by demonstrating that organizational
climate is related to organizational outcomes in the military. Each of the six military
climate dimensions was a significant predictor of individual-, organizational-
(squadron), and cross-levels of members’ Job Satisfaction and Performance
Perceptions. However, this study failed to produce evidence that military climate has
direct linkages to operational flying squadron measures in the U. S. Air Force. More
research is needed to identify true performance measures at the work unit and ascending
levels of organizations so that we may continue to explore the possibility that
organizational climate has an effect upon organizations’ productivity measures.

Researchers are encouraged to explore the conceptual reasons for choosing the variables

116

of interest. This will require a much greater understanding of the measures that are
selected for analysis.

This study did produce one finding that perhaps additional research will help to
explain. That is, while the military climate variables were not related to the operational
performance measures, one of the affective outcome measures, Job Satisfaction, was
significantly correlated with Flying Schedule Effectiveness. Yet, regression analyses
showed military climate variables were predictors of Job Satisfaction. Empirical tests
using Job Satisfaction as a mediator between military climate and Flying Schedule
Effectiveness produced no empirical evidence for this effect. However, the existence of
the triad of constructs (Military Climate, Job Satisfaction, and Flying Schedule
Effectiveness) presents an interesting topic for further study. The seeds for producing
evidence that military climate does affect performance outcomes are present. Perhaps a
better explanation of the theory (to include mediating and moderating variables) and the
potential performance indicators could lead to findings that validate the existence of the
linkages.

Appropriateness for aggregation often appears in climate research. Here, only
two of the climate variables passed the criterion for analysis at the squadron level. For
investigative purposes, the remaining variables were retained. Surprisingly, correlation
and regression analyses yielded significant findings for all the climate variables. Some
of these effects may be related to an intermediate level of analysis, the flight level.
Another potential explanation may be in the amount of consensus about the variables at
the squadron-level. Variance partitioning can produce an entirely different estimate
than consensus.

Therefore, one interesting research study may be to calculate climate consensus

117

(rwg) for each of the climate dimensions and to use that index as an independent measure
in predicting organizational outcomes. Lindell and Brandt (2000) provide an excellent
model for this approach. They found relatively small significant correlations between
climate consensus and organizational-level outcomes. Regression analyses failed to
show that consensus is a predictor of the same outcome measures. However. this is an
area that warrants further investigation. The present data set lends itself nicely to such a
study.

Finally, this dissertation adds to the field of multi-level research. Individual-.
organizational- (squadron), and cross-levels were addressed. There is clear evidence
that differing effects occur at each of the levels studied and that the differing climate
variables had significant effects on two organizational outcomes: Job Satisfaction and
Performance Perceptions. Klein et al. (1999) said, “multi-level theories may illuminate
the steps organizational actors may take, individually and collectively to yield
organizational benefits” (p. 243). That is precisely the theory tested by Hypotheses 3a
and 3b. The results of the cross-level analysis, while minimal and difficult to interpret,
demonstrated that the squadron-level climate does have effects on its members that are
over and above the effects that are present at the individual level. However, more effort

needs to be focused on specifying the theories that address multi-level constructs.

Implications for Practice
The findings in this dissertation advance practitioners’ knowledge about the
operationalization of the construct, “military climate. However, the implications are
certainly not limited to the military work environment. Rather, the findings generalize

to other public institutions as well as the private sector.

118

The six climate dimensions (Job Characteristics, Resources, Leadership.
Supervision, Training, and Teamwork) and two organizational outcome measures (Job
Satisfaction and Performance Perceptions) that were identified serve as reliable
indicators of the processes that are active in military organizations. These eight
dimensions provide a robust description of the work environment. Also, they provide
practitioners with a set of parsimonious, understandable, and actionable dimensions
upon which organizational change efforts may be initiated. Thus, practitioners seeking
to conduct organizational assessment and change activities are encouraged to continue
using the climate variables identified in this study.

This suggestion may have tremendous impact upon the current CSAF Survey, as
well. Since the inception of the survey in 1997, Air Force leaders have commented
repeatedly that the survey contains too many questions and takes far too long to
complete. Yet, they have defaulted to internal organizational consultants’ expert
opinion that the survey should contain the 13 factors used previously. This study may
provide the impetus for survey officials to consider reducing the number of questions
and factors to only the ones used here. These eight dimensions provide a thorough
picture of organizational processes and outcomes, particularly as they relate to the
“people” who comprise the organization. Refining the survey, while continuing to
ensure both theoretical and practical concerns are met, will pay benefits for the survey’s
longevity and ultimately its inclusion as a key component in the Air Force culture.

Another important implication is that practitioners must be cognizant of climate
effects at different levels of the organization. This is particularly germane for
organizational change efforts. The present study showed military climate’s differing

effects on the individual-, squadron-, and cross-levels. For example, approximately

119

twelve percent of the variance associated with both Leadership and Resources was
attributed to the squadron-level. The variance in the remaining variables was mostly
due to individual-level effects. If the practitioner’s goal is organizational-level
improvement (vs. individual-level climate improvements), then change activities should
be designed to address organizational-level phenomena.

However, using the current study as an example, most of the variance in the
climate variables exists at the individual level. Thus, the best opportunity to effect
positive change rests in individuals. Given the opportunity to work with the lowest
performing individuals or the 20 lowest performing squadrons, the best intervention
would be at the individual level. Change efforts designed around job enrichment or
teamwork would have far less effect on the organization as a whole. Most likely, the
outcome of the intervention would not be nearly as dramatic as if the focus remained on
the variables that appear to have the most organizational-level dependency.
Practitioners would be wise to consider how levels’ effects are operationalized in the
work environment and address the issues where the most improvements can be
achieved.

Also, this dissertation showed that flying squadron members perceive military
climate and its affect on outcomes differently depending upon the mission of their
squadron and depending upon their military rank. Warfighting squadrons responded
more favorably to the military climate and outcome variables than squadrons that fly
support missions. Also, this study indicated that officers were more positive about
these dimensions than enlisted personnel. Therefore, it is important to take these
contextual and demographic conditions into account when reporting results to senior

leaders and when designing follow-on change efforts. Knowledge about all these

120

"contingencies” may be somewhat overwhelming and to a certain extent, confusing to
the practitioner and the sponsors. However, awareness of all the relevant issues will
enable wise choices about how change efforts may be implemented such that
organization leaders’ desired end state might be achieved.

Finally, this dissertation indicates that practitioners should encourage clients to
describe the intentions for conducting organizational climate surveys and interventions.
If the goal is to use climate assessments to affect an organizational product or members’
affective responses, we need to ensure we collect data that helps achieve that objective.
The military conducts thousands of surveys every year. However, the end product is
usually the final statistical report. Typically, there is no other goal for the output than
simply reporting univariate and very minor multivariate results. The military should
use these surveys in much the same fashion as other public and corporate institutions.
That is, the data can be linked to intentions to turnover, quality of life, absenteeism, and
performance results. The academic literature certainly supports the assertion that
organizational climate affects these variables. Perhaps the military should consider
looking at this possibility within its squadrons/units.

In this study, the researcher attempted to link organizational climate dimensions
to four organizational outcomes from flying squadrons in the US. Air Force. The topic,
“military climate,” was chosen in an effort to the military climate up to date with the
current literature. The Air Force and other services can serve as a proving ground to
demonstrate ways in which climate dimensions affect performance and affect military
members’ perceptions about their work environment. Practitioners should capitalize on
every opportunity to use performance data that are collected entirely independent of the

climate instrument. Often, senior leaders are reluctant to provide this data because they

121

are uncertain about how it may be used. However, the real payoff is in demonstrating
ways in which climate variables may influence these performance data. When
practitioners are providing this type of information to clients, they are helping

organizations with their bottom line and ultimately their continued success.

122

APPENDICES

123

APPENDIX A

124

APPENDIX A
Survey Background

The 1997 Chief of Staff of the Air Force (CSAF) Survey was the inaugural
organizational climate survey completed for the highest-ranking general officer in the
United States Air Force. During the mid-19905. there were several independent
organizational climate surveys being administered across the Air Force. A review of
these instruments was conducted in 1996. While there was significant overlap among
the major constructs (themes) measured by these instruments, there was also a
concomitant list of approximately twenty-five variables that were not common across
the surveys. Along with the measurement inconsistencies, there was also a great deal of
disparity in: I) the reliabilities and validities of the instruments of choice; 2) reasons for
administering the surveys; 3) uses of the survey results, such as action planning for
leaders, debriefing survey participants, etc., and 4) the protection of individuals’
anonymity and confidentiality.

With this information as the basis for a decision, the CSAF requested the
development of one survey for the purpose of assessing organizational climate at all
levels of the Air Force. Also, he wanted survey results distributed to the lowest
possible levels in the organization. Finally, he specified the administration platform
would be via computer (electronic media) and that the instrument would be available
for voluntary participation by all active duty enlisted and officer personnel, as well as
all civilians currently employed by the Air Force. Along with these policy statements,
the CSAF named the Air Force Center for Quality and Management Innovation
(AFCQMI). Randolph Air Force Base (AFB) as the lead agency for this survey.

Guidance was also provided that the organizational climate survey should be

125

combined with the annual Air Force Quality of Life (QoL) Survey and that both would
be administered through the same medium. Each year, the Air Force Personnel Center
issues the QoL Survey in an effort to gain feedback from Air Force members about six
strategically important issues associated with military life: 1) Community Programs; 2)
Pay and Benefits; 3) Working Hours/Days Away from Home (PERSTEMPO); 4)
Housing; 5) Educational Opportunities; and 6) Health Care. The results of the QoL
survey are often used to address Congress about ways to improve the lives of Air Force
members. The information is also used to support initiatives that enhance recruiting and
retention of military personnel.

Finally, in an effort to ensure that general officers had an opportunity to collect
answers to questions germane to their major commands (MAJCOMs), the CSAF
allowed each of his subordinate four-star generals to ask ten questions addressing issues
most important to them. Therefore, each of the MAJCOM commanders appointed a
point of contact to coordinate with personnel at AFCQMI to ensure their questions were
included in the 1997 CSAF Survey. Consolidation of the three independent sections of
the surveys was accomplished by AFCQMI personnel and then included in the final

electronic data file.

126

APPENDIX B

127

APPENDIX B

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128

APPENDIX C

129

APPENDIX C

1997 chief of staff survey launches in October
Released: Oct. 2, 1997

WASHINGTON (AFNS) — An Air Force survey unlike any before arrives in October at
bases worldwide for all active-duty and civilian members.

Base points of contact will administer an entirely new Chief of Staff of the Air Force
Survey <http://www.afcqmi.randolp.af.mil/survey/survey.htm> assessing both quality
of life and organizational climate in a single, combined format.

 

 

“To reach the Air Force vision, ‘Global Engagement: A Vision for the 21“ Century Air
Force <http://www.xp.hq.af.mil/xpx/2l/nuvis.htm>’, we must ensure excellence in
quality of life and the work environment for out people,” said Air force Vice Chief of
Staff Gen. Ralph E. Eberharthttp://www.af.mil/news/biographies/eberhart re.html. “the
survey is a convenient, anonymous way for members to provide valuable information to
leaders in the chain of command — all the way from unit commanders to the chief of
staff.” Participation by active duty AF members and civilians is entirely voluntary.

 

 

 

 

The survey is composed of two sections: quality of life and organizational climate,
which total about 130 questions. Separate versions are tailored for civilian and active-
duty members. The Pentagon Quality of Live Office, along with functional
representatives responsible for planning and executing major programs at the Air Force
level, prepared the quality-of-life section. Key areas addressed are base-level
community and educational programs personnel tempo, compensation, medical care,
housing and retirement programs. Survey results will be reported to all wing, numbered
air force and major command commanders.

The organizational climate section addresses )4 major themes such as leadership,
communication, supervision, teamwork, job satisfaction and general climate.
Organizational climate results will be delivered to all unit-level commanders to debrief
their organizations and design appropriate actions for addressing major issues.
Safeguards are in place to protect individual identities.

“All active-duty and civilian members must have an opportunity to participate,”
Eberhart said in a letter to all commanders. “We are seeking maximum participation in
order for the results to be most effective.”

The Air Force Center for Quality and Management Innovation
<htga://www.afchmi.randolph.af.mil> will compile the quality-of-life and organizational
climate results in cooperation with the Air Force Personnel Center
<lmp:=‘r’xmnx'afpc.afmi1 ’> and US. Air force Academy <http://www.usafa.af.mil/>
Office of Institutional Research. Commanders at all levels will receive survey results in
January.

 

 

 

 

In a message to survey participants, Eberhart emphasized the influence the survey will
have on key decisions that will directly impact the future of the Air Force.

130

APPENDIX D

131

APPENDIX D
1997

Chief of Staff
of the Air Force

Survey

(Military Form)

 

In accordance with AF 1 37-l32, Air Force Privacy Act Program, paragraph 3.2, and The Privacy Act
of 1974, the following information is provided:

a. Authority:
1. 5 U.S.C. 301, Departmental Regulations
2. IO U.S.C. 8013, Secretary of the Air Force, Powers and Duties
3. Delegation by Executive Order (E0) 9397

b. Purpose: The purpose of this survey is to obtain data on AF personnel attitudes about Quality of
Life and Organizational Climate.

c. Routine uses: None

d. Disclosure: Participation is voluntary. No adverse action of any kind may be taken against any
individual who elects not to participate in any portion of this survey.

e. lAW AFI 37-132, the official AF Control Number for this survey is 97-73. (This is not a tracking
number for your individual survey data.)

 

 

 

OPRs
1997 CSAF Survey/Organizational Climate Section Quality of Life Section
Major Chuck Capps
Air Force Center for Quality and Management lnnovation Air Force Quality of Life Office
DSN: 487-2833 DSN: 225-8418

132

DEPARTMENT OF THE AIR FORCE
OFFICE or THE CHIEF or STAFF
WASHINGTON DC

25 Sep 97
MEMORANDUM FOR ALL AIR FORCE PERSONNEL

FROM: HQ USAF/CV
1670 Air Force Pentagon
Washington, DC 20330-1670

SUBJECT: Chief of Staff of the Air Force (CASF) Organizational Climate and Quality of Life
Survey

1 want to thank you in advance for participating in this survey. This is a great opportunity for
your leaders to receive feedback about how we’re currently doing in a variety of “people
programs.” To reach the AF vision, “Global Engagement for the 21St Century,” we must ensure
excellence in Quality of Life and the work environment for our people. The “1997 CSAF
Survey” is a convenient, anonymous way for you to provide valuable information to leaders in
your chain of command, all the way from your unit commander to the C SAF.

The questions are hard-hitting: however, we need to capture your candid thoughts. There are
130 questions addressing significant Quality of Life (QoL) and Organizational Climate (OC)
topics. You’ll also have an opportunity to provide written comments. Results from the QoL
section will be reported to your wing commander and higher. The OC results will be reported to
your unit commander and higher.

Please don’t be alarmed by questions asking for personal information. We aren’t looking to
target individuals. We’re far more interested in continually improving areas where we can have
significant impact. l’m counting on you to let your voice be heard and in turn, I look forward to
sharing the AF-level results early next year.

//SIGNED//
RALPH E. EBERHART

General, USAF
Vice Chief of Staff

Golden Legacy, Boundless Future. . . Your Nation ’5 Air F orce

133

Organizational Climate Section
(Military Form)

The survey is designed to reflect a system-wide analysis of your unit’s organizational climate. You will
see indicators for inputs (things about the job, unit-level resources, and core values). organizational
processes (leadership, communication, supervision, teamwork, training and development. recognition.
participation/involvement, and unit flexibility), and outcomes which result from a combination of these
factors.

You will be asked to rate each of these on a 6-point scale (strongly disagree to strongly agree) with an
option for, “Don’t Know." Throughout the survey, you will be asked to answer questions which address
differing groups of people in the hierarchy of your unit. Please use the definitions presented below as
your reference points for these questions.

Supervisor: The person to whom you report directly. Typically. this is the person who
writes

your performance report / appraisal.

Work Group: All persons who report to the same supervisor you do.
Unit Leadership: A reference to the leaders who are responsible for the direction and the

 

daily operations of your unit.

Unit: Your squadron- or your staff agency- equivalent

Thanks again for your participation. Your answers and comments will be extremely heflful!

134

1. Please select the item that best describes the condition(s) under which you are completing this

survey

a) At my home station and not in student status

b) Deployed or TDY and survey provided by home unit (Complete survey with your home

unit/installation in mind)

c) In student status. includes TDY and PCS (After answering questions 2. and 3, please skip to

question 73 for the QoL Section.)

d) TDY, not a student, and will NOT be home 31 Oct

e) Other

- If you are TDY and not a student, please take the survey at your home unit if possible
- If you are deployed, please take a survey provided by your home unit to ensure your responses are

grouped with others in your unit

2. Assignment Location

2a. To which MAJCOM/DRU/FOA are you assigned?

 

a) Air Combat Command (ACC)

b) Air Education and Training Command
(AETC)

 

c) Air Force Materiel Command (AFMC)

d) Air Force Space Command (AFSC)

 

e) Air Force Special Operations Command
(AFSOC)

f) Air Mobility Command (AMC)

 

g) Headquarters Air Force Reserve

h) Headquarters United States Air Force

 

i) Office of the Secretary of Defense (OSD)

j) Pacific Air Forces (PACAF)

 

k) United States Air Forces. Europe (USAFE)

1) United States Strategic Command

 

m) I 1th Wing

n) Air Force Communications and Information
Center

 

0) Air Force Doctrine Center

p) Air Force Operational Test and Evaluation
Center

 

q) Air Force Security Forces Center

r) United States Air Force Academy

 

5) Air Force Agency for Modeling and Simulation

t) Air Force Base Conversion Agency

 

u) Air Force Audit Agency

v) Air Force Base Disposal Agency

 

w) Air Force Center for Environmental Excellence

x) Air Force Civil Engineer Support Agency

 

y) Air Force Studies and Analysis Agency

2) Air Force Communications Agency

 

a) Air Force Cost Analysis Agency

bb) Air Force Flight Standards Agency

 

cc) Air Force Historical Research Agency

dd) Air Force History Support Office

 

ee) Air Force Inspection Agency

ft) Air Force Legal Services Agency

 

gg) Air Force Logistics Management Agency

hh) Air Force Medical Operations Agency

 

ii) Air Force Medical Support Agency

jj) Air Force News Agency

 

kk) Air Force Operations Group

II) Air Force Pentagon Communications
Agency

 

mm) Air Force Personnel Center

nn) Air Force Personnel Operations Agency

 

00) Air Force Program Executive Office

pp) Air Force Real Estate Agency

 

qq) Air Force Review Boards Agency

rr) Air Force Safety Center

 

55) Air Force Security Police Agency

tt) Air Force Technical Applications Center

 

uu) Air Intelligence Agency

vv) Air Reserve Personnel Agency

 

ww)Air Weather Service

xx) Combat Rescue Agency

 

yy) Center for Air Force History

22) Center for Quality and Management
lnnovation

 

aaa) HQ Air Forces Services Agency

bbb) Joint Services SERE

 

 

ccc) Technical Operations

 

ddd) Other

 

135

 

2b. To which installation are you assigned?

a)

b) AFDW PENTAGON WASHINGTON DC

c) ALTUS AFB OK

d) ANDERSEN AFB GUAM

e) ANDREWS AFB MD

0 ARNOLD

2 AVIANO AB ITALY

h) BARKSDALE AFB LA

i) BATTLECREEK

j) BEALE AFB CA

k) BOLLING AFB DC (AFOSI)

l) BOLLING AFB DC (AFDW)

m) BROOKS AFB TX

n) BUCKLEY AGB CO

0) CANNON AFB NM

p) CHARLESTON AFB SC

q) COLUMBUS AFB MS

r) DAVIS-MONTHAN AFB AZ

5) DOVER AFB DE

t) DYESS AFB TX

u) EDWARDS AFB CA

v) EGLIN AFB FL

w) EIELSON AFB AK

x) ELLSWORTH AFB SD

y) ELMENDORF AFB AK

2) FAIRCHILD AFB WA

aa) FALCON AFB CO

bb) FRANCIS E. WARREN AFB WY

cc) FT GEORGE G. MEADE MD 694 MSN SPT

dd) GOODFELLOW AFB TX

ee) GRAND FORKS AFB ND

ff) HANSCOM AFB MA

gg HICKAM AFB HI

hh) HILL AFB UT

ii) HOLLOMAN AFB NM

jj) HOWARD AFB PANAMA

kk) HURLBURT FIELD FL

ll) INCIRLIK TURKEY

mm) KADENA AB OKINAWA

nn) KEESLER AFB MS

00) KEFLAVIK NYI ICELAND

PP) KELLY AFB TX

‘19) KIRTLAND AFB NM AFMC

rr) KIRTLAND AFB NM OSB

ss) KUNSAN AB KOREA

tt) LACKLAND AFB TX (ATC)

uu) LACKLAND AFB TX
(ATC-PIPELINE)

vv) LAJES F LD AZORES

ww) LAKENHEATH RAF STA UK

xx) LANGLEY AFB VA

W) LAUGHLIN AFB TX

22) LITTLE ROCK AFB AR

136

aaa)
bbb)
ccc)
ddd)
eee)

fff)
ggg)
hhh)
ifi)
iii)
kkk)
ND
mmm)
nnn)
ooo)

PPP)
QQQ)
rrr)
555)
m)
uuu)
vvv)
“AN“O
xxx)
YYY)
ZZZ)
aaaa)
bbbb)
cccc)
dddd)
eeee)
fifif)
gggg)
hhhh)
fifi)

MAXWELL AFB AL
MCCHORD AFB WA
MCCLELLAN AFB CA
MCCONNELL AFB KS
MCGUIRE AFB NJ
MILDENHALL RAF STA UK
MINOT AFB ND

MISAWA ABS JAPAN
MOODYAFBGA

MT HOME AFB ID

NELLIS AFB NV

OFFUTT AFB NE

OSAN AB KOREA

PATRICK AFB FL

PETERSON AFB CO

POPE AFB NC

RAMSTEIN AB GERMANY
RANDOLPH AFB TX

REESE AFB TX

ROBINS AFB GA

SCOTT AFB IL

SEYMOUR JOHNSON AFB NC
SHAW AFB SC

SHEPPARD AFB TX
SPANGDAHLEM AB GERMANY
STUTTGART GERMANY (1 I41)
TINKER AFB OK

TRAVIS AFB CA

TYNDALL AFB FL

USAF ACADEMY CO
VANCEAFBOK
VANDENBERG AFB CA
WHITEMAN AFB MO
WRIGHT-PATTERSON AFB OH
YOKOTA AB JAPAN

2c. Please write in your unit name.

3. Which category best describes you?

a) Officer
b) Enlisted

The Job

Extent (0 which your job is motivating, important, interesting, and challenging.

Strongly Slightly
Disagree Disagree Disagree Agree Agree Agree

I. My job requires me to use a variety
of skills.

2. My job allows me to see the finished
products of my work.

137

b)

3. Doing my job well affects others in some

important way. I 2 3 4 5 6 7
4. My job is designed so that I know when

I have performed well. I 2 3 4 5 6 7
5. My job allows me freedom to work with

minimum supervision. l 2 3 4 5 6 7
Resources

Eflective management of your unit '5 resources (time, personnel. funds, and equipment) to accomplish the
mission.

 

Strongly Slightly Slightl Strongly
Disagree Disagree Disagree Agree Agree Agree DK
6. l have adequate time to do my job well. I 2 3 4 5 6 7
7. We have enough people in my work
group to accomplish the job I 2 3 4 5 6 7
8. I have the right tools, equipment, and
materials to accomplish the job. I 2 3 4 5 6 7
9. I have enough time to accomplish my
daily workload during my duty hours. I 2 3 4 5 6 7

Core Values
Extent to which the Air F orce core values are understood & demonstrated by unit personnel. The AF core
values are ”Integrity First “Service Before Self ', and ”Etcellence in All We Do. "

10. I received a copy of, United States Air Force Core Values. (“The Little Blue Book”).

a. Yes
b. No
c

I l. I have been briefed on the contents of. United States Air Force Core Values, (“The Little Blue Book”).

a. Yes
b. No

Strongly Slightly Slightly Strongly
Disagree Disagree Disagree Agree Agree Agree DK

12. I am able to do my job without

compromising my integrity. 1 2 3 4 5 6 7
13. Overall, people in my unit uphold high

standards of excellence. l 2 3 4 5 6 7
I4. I understand how the AF core values

apply to my job and daily activities. I 2 3 4 5 6 7

138

IS. Overall, people in my unit demonstrate
that duty takes precedence over personal

desires. l 2 3 4 5 6 7
I6. Overall, people in my unit are held

accountable for behavior which

contradicts the AF core values. I 2 3 4 S 6 7
Communication

Extent to which information flows throughout the unit & workers are well-informed about impt issues.

 

Strongly Slightly Slightly Strongly
Disagree Disagree Disagree Agree Agree Agree OK
17. Leadership in this unit listens to my ideas. I 2 3 4 5 6 7
18. My unit makes me aware of important
events and situations. I 2 3 4 5 6 7
I9 I am comfortable discussing my ideas
with the leadership in this unit. I 2 3 4 5 6 7
20. Leaders in my unit are easily accessible. I 2 3 4 5 6 7
2 I. Leadership in this unit effectively
communicates the mission, goals,
and objectives. 1 2 3 4 5 6 7

Leadership
Extent to which leadership is influencing the direction, people, and culture of the unit.

A reference to the leaders who are responsible for the direction and the daily operations of your unit.

 

Strongly Slightly Slightly Strongly
Disagree Diggree Disagree Agree Agree Agree DK
22. I trust the leadership in my unit. I 2 3 4 5 6 7
23. My unit leadership is inspirational. l 2 3 4 5 6 7
24. I am proud of the leadership in my unit. I 2 3 4 5 6 7
25. My unit leadership sets challenging goals. I 2 3 4 5 6 7
26. My unit leadership provides a clear vision. I 2 3 4 5 6 7
27. My unit leadership motivates me
to do my best. I 2 3 4 5 6 7

28. My unit leadership makes decisions based

on facts. I 2 3 4 5 6 7
29. My unit leadership motivates me to
achieve our goals. I 2 3 4 5 6 7

139

30 My unit leadership encourages me to look

for ways to cut costs in my job. I 2 3 4 5 6 7
31 I see my unit leaders doing the same things

they publicly promote. I 2 3 4 5 6 7
Supervision

Extent to which your supervisor is perceived to be skilled at planning. organi:ing, directing. and providing
feedback.

The person to whom you report directly. Typically, this is the person who writes your
perfonnancereport.appraisal.

 

Strongly Slightly Slightly Strongly
Disagree Disagree Disagree Agree Agree Agree DK
32. My supervisor is good at planning my work. I 2 3 4 5 6 7
33. My supervisor sets high performance
standards. I 2 3 4 5 6 7
34. My supervisor is concerned with my
development. I 2 3 4 5 6 7
35. My supervisor models the core values
of the Air Force. I 2 3 4 5 6 7

36. My supervisor corrects poor performers
in my work group. I 2

b)
A
U.
as
\)

Supervision (continued)
Extent to which your supervisor is perceived to be skilled at planning, organizing, directing, and providing

feedback.

The person to whom you report directly. Typically, this is the person who writes your performance report
appraisal.

Strongly Slightly Slightly Strongly
Disagree Disagree Disagree Agree Agree Agree DK

37. My supervisor looks out for the best

interests of my work group. I 2 3 4 5 6 7
38. My supervisor keeps me up-to-date on. I 2 3 4 5 6 7

what is happening in my unit
39. My supervisor provides instructions

that help me meet his/her expectations. I 2 3 4 5 6 7
40. My supervisor helps me understand

how my job contributes to my unit’s

mission. I 2 3 4 5 6 7

I40

Training and Development
Extent to which you have the training required to do your job and you are provided opportunities and
support for advancement.

Strongly Slightly Slightly Strongly
Disagree Disagree Disagree Agree Agree Agree DK

 

41. I am given opportunities to improve

my skills. I 2 3 4 5 6 7
42. I am encouraged by my unit leadership

to learn new things. I 2 3 4 5 6 7
43. I have been adequately trained for the

job I am expected to do. I 2 3 4 5 6 7
44. I am allowed to attend continuing

professional training I 2 3 4 5 6 7
Teamwork

Extent to which people cooperate to accomplish the mission of your unit.
All persons who report to the same supervisor you do.

Strongly Slightly Slightly Strongly
Disagree Disagree Dismee Agree Agree Agree DK

45. People in my work group respect

each other. I 2 3 4 5 6 7
46. My work group adequately resolves
conflicts. I 2 3 4 S 6 7
47. There is cooperation among work groups
in my unit. I 2 3 4 5 6 7
48. Members of my work group willingly

share information. I 2 3 4 5 6 7
49. People in my work group cooperate to

get the work done. I 2 3 4 5 6 7

Participation / Involvement
Extent to which unit personnel participate in defining what work gets done and how it is accomplished.

Strongly Slightly Slightly Strongly
Disagree Disagree Disagree Agree Agree Agree DK

50. I feel free to suggest new and better ways

of doing things. I 2 3 4 5 6 7
5 I. I am asked how we can improve the way
my work group operates. l 2 3 4 5 6 7

I41

52. Sufficient effort is made to get the opinions

and ideas of people in this work unit. I 2 3 4 5 6 7
53. Suggestions made by unit personnel are

implemented in our daily work activities. I 2 3 4 5 6 7
Recognition

Extent to which leadership provides public/private acknowledgment for exceptional performance.
A reference to the leaders who are responsible for the direction and the daily operations of your unit.

Strongly Slightly Slightly Strongly
Disagree DisagLee Disagree Agree Agree Agree OK

54. My unit leadership rewards team

 

performance fairly. l 2 3 4 5 6 7
55. My unit leadership rewards individual
performance fairly. I 2 3 4 5 6 7
56. My unit leadership does a goodjob of
recognizing people in all grades
and types of jobs. I 2 3 4 S 6 7
Unit Flexibility
Extent to which the unit responds to changes in the environment and is willing to try new things.
Strongly Slightly Slightly Strongly
Disagree Disagree Disagree Agree Agree Agree DK
57. My unit adapts to changes quickly. I 2 3 4 5 6 7
58. My unit encourages appropriate risk taking. I 2 3 4 5 6 7

59. My unit challenges old ways of doing
business. l

to
b)
A
kit
0‘
\l

Job Satisfaction
Sense of accomplishment and personal fulfillment you receive fiom the work you do and from the
environment that surrounds you.

 

Strongly Slightly Slightly Strongly
Disagree Disagree Disagree Agree Agree Agree DK
60. In general. I am satisfied with my job. I 2 3 4 5 6 7
61. I have a sense of personal fulfillment at the
end of the day. l 2 3 4 5 6 7
62. The tasks I perform provide me with a sense
of accomplishment. I 2 3 4 5 6 7

142

Unit Performance Outcomes
Extent to which your unit is satisfying its mission, goals, and objectives.

Strongly Slightly Slightly Strongly
Disagree Disagree Disagree Agree Agree Agree DK

63. The quality ofwork in my unit is high. I 2 3 4 S 6 7

64. The quantity of work accomplished

 

in my unit is high. I 2 3 4 5 6 7
65. My unit is known as one that gets
the job done. I 2 3 4 5 6 7
66. My unit is successfully accomplishing
its mission. I 2 3 4 5 6 7
67. My unit accomplishes its mission in a
cost-effective manner. I 2 3 4 5 6 7
General Climate
General perceptions about your unit.
Strongly Slightly Slightly Strongly
Disagree Disagree Disagree Agree Agree Agree DK
68. Morale is high in my unit. I 2 3 4 5 6 7
69. I am a valued member of my unit. I 2 3 4 5 6 7

70. I would recommend an assignment

in my unit to a friend. I 2 3 4 5 6 7
71. People in my unit are charitable with

their time. talents, or money. I 2 3 4 5 6 7
72. My Unit will be briefed about the

results of this survey. I 2 3 4 5 6 7

I43

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144

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