University of Southern California Dissertations and Theses

Individual resistance to organizational change: The impact of personal control and job ambiguity

(USC Thesis Other)

Individual resistance to organizational change: The impact of personal control and job ambiguity

PDF

Download

Open document

Flip pages

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content INDIVIDUAL RESISTANCE TO ORGANIZATIONAL
CHANGE: THE IMPACT OF PERSONAL
CONTROL AND JOB AMBIGUITY
by
Martha Marie Jensen
A Dissertation Presented to the
FACULTY OF THE ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION
August 2003
Copyright 2003 Martha Marie Jensen
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
UMI Number: 3116720
Copyright 2003 by
Jensen, Martha Marie
All rights reserved.
INFORMATION TO USERS
The quality of this reproduction is dependent upon the quality of the copy
submitted. Broken or indistinct print, colored or poor quality illustrations and
photographs, print bleed-through, substandard margins, and improper
alignment can adversely affect reproduction.
In the unlikely event that the author did not send a complete manuscript
and there are missing pages, these will be noted. Also, if unauthorized
copyright material had to be removed, a note will indicate the deletion.
®
UMI
UMI Microform 3116720
Copyright 2004 by ProQuest Information and Learning Company.
All rights reserved. This microform edition is protected against
unauthorized copying under Title 17, United States Code.
ProQuest Information and Learning Company
300 North Zeeb Road
P.O. Box 1346
Ann Arbor, Ml 48106-1346
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
University of Southern California
Rossier School of Education
Los Angeles, C alifornia 90089-0031
This dissertation w ritten by
M A R T H A M A R IE J E N S E N
under the discretion of h ER D issertation C om m ittee,
and approved by all members o f the C om m ittee, has
been presented to and accepted by the Faculty of the
Rossier School of Education in partial fu lfillm e n t of the
requirem ents fo r the degree of
D octor of Education
Date
Dean
Dissertation- j2om ,m,ii
Chairperson
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
DEDICATION
This paper is dedicated to my father and mother, Thomas and Anne
Jensen, and my sisters, Maryanne, Elizabeth, Stephanie, and Kathy. My
parents taught me the importance of persistence, hard work, and the pursuit
of knowledge throughout life. They fostered in me a desire to improve con
tinually and to challenge myself. They built for me an environment of love,
understanding, and support. My sisters reinforce these ideals and values
every day. They remind me of the important things in life: family, friends,
love, and laughter. It is through their friendship, support, and love that I
have achieved all that I have in life.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
TABLE OF CONTENTS
Page
DEDICATION.............................................................................................. ii
LIST OF TABLES ...................................................................................... v
ABSTRACT ................................................................................................... vi
Chapter
1. INTRODUCTION .......................................................................... 1
Technology............................................................................. 1
Globalization .......................................................................... 4
Statement of the Problem..................................................... 7
Significance of the Study...................................................... 7
2. LITERATURE REVIEW ............................................................... 9
The Nature of Change in the 21 st Century.......................... 9
Anxiety in the Work Place .................................................... 14
Reactions to Change: Resistance ......................................... 15
Job Ambiguity......................................................................... 19
Personal Control.................................................................... 21
Commitment........................................................................... 26
Resilience to Change ........................................................ 27
Summary................................................................................ 30
Purpose of the Study............................................................. 30
Hypotheses ............................................................................ 31
3. METHODOLOGY ........................................................................ 32
Subjects ................................................................................. 33
Definition of Terms ................................................................ 34
Instrumentation...................................................................... 35
Job Ambiguity ................................................................. 35
Personal Control ............................................................. 36
Resistance ...................................................................... 37
Resilience ....................................................................... 38
Procedure............................................................................... 38
Data Analysis ......................................................................... 39
4. RESULTS ..................................................................................... 41
Descriptive Statistics ............................................................. 41
Resilience............................................................................... 43
5. DISCUSSION AND CONCLUSIONS.......................................... 53
Summary................................................................................ 54
Respondents................................................................... 54
Instruments ..................................................................... 55
iii
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Page
Procedure......................................................................... 55
Results............................................................................. 55
Qualitative Findings ......................................................... 57
Discussion.............................................................................. 59
Conclusions and Recommendations..................................... 63
REFERENCES ........................................................................................... 65
APPENDIX ................................................................................................. 73
iv
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
LIST OF TABLES
Table Page
1. Cost and Commitment M atrix............................................................. 34
2. Variable Means and Standard Deviations for All Independent,
Dependent, and Supplemental Variables....................................... 42
3. Correlation Matrix for All Variables.................................................... 44
4. Regression Analysis for Personal Control and Job Ambiguity
on Change (Specific) ..................................................................... 45
5. Regression Analysis for Personal Control and Job Ambiguity
on Change (General) ..................................................................... 46
6. Commitment to Change (General) and Perceived
Effectiveness .................................................................................. 46
7. Commitment to Change (Specific) Versus Perceived Cost
of Change ........................................................................................ 47
8. Cost and Commitment to Change (General) and Perceived
Cost of Change............................................................................... 49
9. Means and Standard Deviations: Personal Control, Job
Ambiguity, and Expectations for Resilient, Resistant, Doer,
and Slacker Types .......................................................................... 50
10. One-Way Analysis of Variance: Job Ambiguity, Control, and
Expectations ................................................................................... 51
11. Discriminant Function Analysis Using Personal Control, Job
Ambiguity, and Expectations to Predict the Membership of
Resilient and Resistant Groups .................................................... 51
v
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
ABSTRACT
Organizational change and how it is managed is an important factor
in organizational performance, impacting people, processes, and ultimately
the success or failure of the organization. This study examined the impact
of change on individuals within organizations and how factors such as per
sonal control and job ambiguity affect resistance or resilience to change.
Specifically, this study attempted to determine whether personal control or
job ambiguity predicts commitment to organizational change. One hundred
twenty-five managers in a Fortune 500 company, representing 12 countries,
participated in this study. Through surveys and focus groups, participants
were asked questions in three areas: (a) the levels of ambiguity in their
jobs, (b) the level of personal control that they held over their work, and (c)
their commitment to the changes that they were currently facing in the com
pany. The results of this study suggest that personal control can be a pre
dictor of commitment to organizational change.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
CHAPTER 1
INTRODUCTION
Technology and globalization are two “shock waves” (Albrecht, 1991)
that are impacting American business and producing massive changes in
all types of organizations. According to Plunkett (2001), four global trends
are forecasted for the coming years: continued globalization of production,
a shift to contract manufacturing, fierce global competition, and a converg
ence of information technology. The main trends framing the domestic
scene include increased merger and acquisition activity, continued diversifi
cation, and increased technology partnerships.
One survey (Romano, 1995) found that 84% of U.S. companies un
derwent at least one major business change in 1994. Respondents identi
fied adapting to and managing change as the biggest problem that they
faced in the business environment.
Technology
The past 30 years have been marked by major technological ad
vances, shifts in job requirements to more information technology sectors,
and changes in the global economic environment (Slater, 1998). The
1970s and the 1980s are forever associated with the “Information Age.”
The 1990s were the “Internet Age.” While both of these ages were revolu
tionary, the current decade is proving to take these leaps in technology and
converge them, creating even more changes in personal and professional
lives with rapidly evolving personal and Internet appliances. Through the
use of fiber optics, satellite, and cable modems, people are always on and
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
globally accessible in the “Convergence Age” (Plunkett, 2001). These ad
vances have brought many changes in how people live and work.
Computers and technology are a fact of life today. In 1960 there
were only 5,000 computers in the United States, and all of these were
mainframes. In 1965 there was only one computer for every 10,000 people
in the United States. With the advent of the personal computer (PC), com
puter distribution grew to 90 for every 1,000 people by 1989. The PC mar
ket continued to experience exceptional growth into the 1990s. Computers
per capita grew 306%, to 365 per 1,000 people. In 1995 PC sales ex
ceeded the total number of cars and trucks sold in the United States for the
first time (Computer Industry Almanac Inc., 1996). In 1995 dollar sales of
PCs were higher than those for all television sets. By early 2001 approxi
mately 69% of all homes in the United States contained a PC, with many
having more than one (Plunkett, 2001). Most homes with computers had
Internet access. It took 7 years for 30% of the general U.S. public to have
the Internet at home. By comparison, it took television 17 years, the tele
phone 38 years, and electricity 46 years to reach the same point of use
(Plunkett).
These technology changes are not limited to homes, influencing how
we live. They are just as profound in the workplace, influencing how we do
our work. In 1994 information technology accounted for approximately 85%
of the top programs that companies implemented to change the way in
which they did business (Romano, 1995). The GartnerGroup (Maglee &
Schiller, 1998) estimated that worldwide information technology spending
will total $1.7 trillion by 2003. Today, 63% of the U.S. work force uses a
2
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
computer daily at work (Plunkett, 2001). This is congruent with employment
and degree award trends of the past decade and projections for the future.
Between 1971 and 1995, degrees awarded in the area of computer and
information technology (including communications and engineering) experi
enced some of the fastest growth, growing from approximately 63,000 to
over 150,000, an increase of approximately 150%. By comparison, de
grees in business management grew approximately 100%, and the number
of degrees in mathematics, physical sciences, education, and social sci
ences decreased (U.S. Bureau of the Census, 1998).
Most of the growth in employment over the next 10 years will be in
the service-producing industries. The top three occupations with increases
over 100%—database administrators, computer engineers, and systems
analysts—are all directly concerned with computers (Handbook of U.S.
Labor Statistics, 1998; U.S. Bureau of the Census, 1998). This growth is
projected to continue, with total employment and job openings in U.S. in
formation technology occupations projected to increase dramatically from
1998 to 2008 (Kestler, 2002).
This is borne out by recent Department of Labor forecasts: The
services sector is the largest and fastest-growing major industry group, ex
pecting to add 11.8 million new jobs by 2008, with nearly 75% of them in
business services. Employment in computer services is projected to grow
117% between 1998 and 2008, with the top five fastest-growing occupa
tions projected to be in computing (U.S. Department of Labor, 2000).
3
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Globalization
Along with technology, globalization is a genuine driver of change
and a major characteristic of corporate life and success. It is no longer a
“buzz word” used as a catchall for any overseas ventures. Firms with inter
national activities grow faster in every industry than their isolated domestic
counterparts. As early as 1967, U.S. companies were turning into globally
focused multinationals to compete with the Japanese and other economic
powerhouses. In recent years, large numbers of American corporations
have deployed many of their assets overseas. Some (e.g., Citicorp, Digital
Equipment, Xerox Corporation, Manpower, and many of the large oil com
panies) expended more than half of their assets overseas (Weidenbaum,
1995).
The Internet is facilitating the ease and speed of globalization.
Technology companies are doing particularly well overseas. Ingram Micro,
Compaq Computer, Lucent Technologies, and Electronic Data Systems all
posted non-U.S. revenue gains of at least 24% (Zajac, 1999). Historically,
trade flows have consisted mainly of tangibles. But with the Internet, it be
comes much easier to provide services of all types, including banking, edu
cation, consulting, and retailing, all through a Web site that is globally
accessible. It is much easier than ever before for individuals to buy a piece
of a company through the stock market.
Globalization is influencing what we buy and sell. In 1993 foreigners
bought $231 billion more U.S. assets than they sold. That figure is 57%
above the figure for 1992 (Lombo, 1994). In 1993 aggregate foreign sales
of the 100 largest U.S. multinationals totaled $703 billion (Lombo). In 1998
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
the top 500 international companies had revenues of $7.8 trillion—more
than half of it coming from Japan, the United Kingdom, and Germany
(Zajac, 1999). In that same year, revenues derived from goods and
services sold outside the United States by the 100 top multinational firms
increased 5%, to $958 billion, while overall revenues rose just 3% (Zajac).
In 2000, 24 of the top 25 multinationals saw increased revenues compared
to 1999, both overseas and in total (White, 2001). Total revenue for the
Global 500 was up 10.8% from 1999 and profits were up 20.4% (Kahn,
2001).
The advances in technology are making it easier to break through
national borders and integrate business around the globe. In 1998 foreign
investment in the United States jumped to $201 billion, up from $70 billion
in 1997 (Zajac, 1999). In contrast, the total value of cross-border deals an
nounced during 1991 was $43 billion (Seneker, 1992). American compa
nies are acquiring assets overseas with acquisitions such as the United
Kingdom power generator Eastern Group, Netherlands-based truck manu
facturer DAF, and the United Kingdom’s Leyland Trucks (Zajac). U.S.
firms, such as Chrysler, Amoco, AirTouch, and Bankers Trust, are also be
ing bought at an increased pace (Zajac).
Globalization is affecting governments as well as corporations.
The European Union (EU) was formed in the late 1950s to help protect
European markets. This was the dawn of a new global era that has grown
exponentially over the past 40 years. The EU has grown in recent years
from an initial 6 member nations to over 15 member nations, with more
waiting to join. Even though it had a controversial beginning, the North
5
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
American Free Trade Agreement (NAFTA) other international trade agree
ments like it are becoming crucial to the success of regional economies.
The World Trade Organization (WTO) is increasingly more active in over
seeing new global trade negotiations (Garten, 1999; Green & Himelstein,
1999). Isolationists can no longer compete in this global market place.
Multinationals understand that remaining competitive means being
global. That often requires mergers and acquisitions. Mergers not only
continued in 2000 but surpassed the total number of mergers in 1999, the
previous record-holding year. In 2000 there were over 38,000 merger and
acquisition transactions, totaling $3.5 trillion (Kahn, 2001). Successful
businesses must look at their entire enterprise in a global context. No
longer will location be key to most business decisions. The transnational
enterprise is on the rise (Slater, 1998). As multinational companies push
into new markets, new international business relationships and cross-
border alliances are becoming commonplace (Sparks, 1999). For example,
in September 1999 CMGI, an Internet holding company, announced a $350
million joint venture with Hong Kong-based Pacific Century Cyber-Works
(PCCW) aimed at selling e-commerce services to the exploding Chinese
Internet market (Green & Himelstein, 1999). Cisco Systems, an Internet
technology firm, is spending millions of U.S. dollars to bankroll telecom
companies all over Europe (Baker, 1999). This poses challenges for busi
nesses that, to be successful in a global world, must change at the very
core of their operations.
America’s strength in technology, innovation, education, and adapta
bility place it in a position to succeed globally. But corporations all over the
6
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
world are facing immense challenges in moving to a globally connected
environment in the Information Age. If they are to succeed, changes in
infrastructure and operations will have to be made.
Statement of the Problem
The next 25 years will see the fastest technological changes that the
world has known (Cairncross, 1997). We are on the cusp of an Information
Revolution characterized by technology and globalization, both of which
have consequences that will affect all aspects of our lives. These advances
create a need for companies to change their operating environments, their
work force, and their processes. Companies in today’s environment no
longer have a choice. Learning to function effectively in today’s global
economy will be the difference between success and failure for many com
panies. Functioning effectively in today’s working environment means
recognizing the changes that are occurring in the workplace and readily
adapting to these changes. If businesses are to be successful, they must
have the tools and ability to predict where resistance and resilience to
change are likely to occur and what interventions are most appropriate to
ensure a resilient work force.
Significance of the Study
While there is research on various aspects of commitment, personal
control, job ambiguity, and change, there is a lack of research in the area of
resilience versus resistance to organizational change and the factors that
impact whether an employee will commit to the changes or become resist
ant to them. Executives can plan for mergers and acquisitions, technology
7
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
innovations, globalization, and a changing market place. But if employees
are not committed to adopting these changes, even the best plans may
succumb to failure.
The significance of this study is to identify how job ambiguity and
perceived lack of personal control impact resiliency or resistance to or
ganizational change in the employee population. It was hypothesized that
high levels of job ambiguity and/or low levels of personal control would dif
ferentiate resistant employees from resilient ones. With a growing number
of companies experiencing massive amounts of change, the factors that
influence employee responses to organizational change should be exam
ined. The impact on employees and the success of the company can be
serious if this change process does not factor in the human element. Can
resistance or resilience be predicted? If so, what interventions can be put
in place to help the employee population more readily adapt to change and
therefore build resilience? Management’s approach to the human elements
of organizational change may need to be revisited as a result of the findings
in this study.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
CHAPTER 2
LITERATURE REVIEW
The following questions are addressed in the literature review:
1. What is the nature of change in the 21 st century?
2. How does change impact performance in the work place?
3. What are the reactions to change?
4. How do job ambiguity, personal control, and commitment impact
resistance or resilience to change?
The Nature of Change in the 21st Century
Change today is different from change in the past. One way in which
change is different is that it is no longer predictable. The old rules of the
industrial era no longer apply to today’s businesses. In the industrial era,
change moved much more slowly and could be anticipated. Today, change
happens with such speed that few can accurately predict the next shift in
business. It is no longer predicting the trends that make a company suc
cessful; it is being sufficiently agile to capitalize on the trends once they are
recognized. This requires organizations to be flexible and able to adapt
easily to changes at a moment’s notice.
The model of growth and change, which has long served to illustrate
a company’s life cycle, has itself changed. The growth curve model, or S-
Curve model, established through years of research (Teece, 1987), is
rooted in general systems science. Woodward (1994) described three
stages of organizational growth and change along this curve: forming,
norming, and transforming. According to this model, organizations go
9
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
through each of these stages in an iterative process. In the 1970s and
1980s, most people perceived their companies or organizations to be
evenly distributed across the various stages of growth life cycles. In a
sense, they could predict the ebb and flow of business based on this model.
Today, however, most companies perceive themselves to be in a perpetual
state of transformation (Woodward). Unpredictability is now the way of to
day’s business world.
The second way in which change is different is its increasingly para
doxical nature. Managers are asked to choose between two extremes of a
continuum, such as suffering lower costs while improving quality (Collins &
Porras, 1994), dealing with the demands of the present while focusing on
the long term (Collins & Porras; Mourier, 1998), empowering people versus
being personally accountable (Kets de Vries, 1980), managing simplicity
versus complexity (Bennis, 1993), and managing using influence versus
authority (Cohen & Bradford, 1989; Kets de Vries, 1991). These and other
conflicting options that are at the core of paradox, set up ambiguous situa
tions for managers that result in anxiety. Since people naturally seek out
stability and certainty (Bandura, 1997; Connor, 1992; Siegall & Cummings,
1986; Tetenbaum, 1998), managers attempt to reduce the tension that
paradoxes create. They accomplish this by concentrating efforts at one end
of the spectrum over the other, rather than reconciling both ends in a
“both/and” way of thinking (Collins & Porras; Tetenbaum).
In addition to unpredictability and a sea of paradoxes, another way
in which change is different today from change in the past is that it is non
linear. Organizations are experiencing broad-based organizational change
10
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
that reorients the entire organization vis-a-vis industry discontinuity, shifts in
consumer demand patterns, or interorganizationai dynamics.
Because change is not predictable, is replete with paradox, and is
nonlinear, there has been a shift in change from programmatic to discon
tinuous. In the 1960s through the 1980s, the predominant approach to
change was a programmatic one, characterized by strategic, structural
changes derived through rational processes. Planning was guided by mis
sion and vision statements and directed toward maintaining momentum and
improving the efficiency of the organization. Common examples of this ap
proach to change include total quality management, downsizing, and
benchmarking. With the shift in paradigm to an Information Age and the
advent of the Internet, programmatic change is giving way to another, more
radical type of change—one which is discontinuous (Nadler, 1998).
All entities, whether humans or organizations, strive for a state of
equilibrium (Siegall & Cummings, 1986). While the organization is in equi
librium, the goal is to maintain a balance, or what Nadler (1998) called “fit,”
among all components within the organization. Most of an organization’s
energies are spent in striving for and maintaining this equilibrium (Nadler;
Nohria & Khurana, 1993). However, there are times when it is critical to the
long-term survival of the organization to disrupt this momentum due to ex
ternal or internal factors. This radical disruption, called “discontinuous
change,” is a fundamental departure from where the organization was
heading. During discontinuous change, “fit” is in direct opposition to the
goal of the change, reinforcing resistance to the change (Nadler; Strebel,
1996).
11
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Discontinuous change is much more intense than programmatic
change. Changes of this nature require far more dramatic changes in
strategy and abrupt changes in work, structures, and culture, often leading
to a complete overhaul of the organization (Nadler, 1998; Nohria &
Khurana, 1993). This type of change is driven by three basic factors: in
dustry discontinuity, interorganizational dynamics, and consumer patterns.
An organization facing any of these three factors can do so in an anticipa
tory or proactive manner or in a response or reactive manner. Either way,
the entire organization, including the people, the processes, and the struc
ture, will be affected by this discontinuous change.
The risks associated with discontinuous change are different from
those associated with programmatic change. Whereas programmatic
change carries the risk of behaviors being misaligned with the initiative, dis
continuous change carries with it the more dangerous risk of individual op
position and high levels of resistance. Resistance, or individual opposition,
to change is often rooted in anxiety (Nohria & Khurana, 1993) and loss of
control (Connor, 1992). The very nature of discontinuous change, charac
terized by redefined roles and responsibilities as well as radical changes in
staffing and new processes, makes control difficult to maintain, arousing
anxiety in participants.
The final way in which change is different today is the speed with
which it is occurring. Cairncross (1997) predicted that the next 25 years will
see the fastest technological changes that the world has known. The tech
nological advances seen in the past decade are unsurpassed by any other
time in history. This is seen most clearly with the computer revolution. In
12
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
1960 there were only 5,000 computers in the United States. Today, com
puters are as common as television sets. They are in the home, the office,
and the school, effecting changes in personal and professional lives.
No longer is location a key to many business decisions (Cairncross,
1997). The telecommunications revolution has institutionalized the use of
the fax machine, the digital phone, e-mail, and the Internet, making workers
more mobile and more accessible than ever before. This, too, creates
changes that bring tremendous social implications.
Multinational firms are on the rise. Corporations are merging and
acquiring at an ever-increasing pace (Zajac, 1999), resulting in changes to
work processes, corporate cultures, and staffing. In 1998 worldwide merg
ers and acquisitions were estimated to be worth $2.4 trillion (Handbook of
U.S. Labor Statistics, 1998) growing to $3.5 trillion in 2000 (Kahn, 2001).
This activity has a profound impact on the affected organizations, often in
cluding major changes in corporate cultures. This, in turn, has a major im
pact on the individuals within the organization.
The aggregate of all of the changes experienced today is immense
and has tremendous social and psychological implications for individuals
and organizations. The rapid and abrupt changes stemming from globaliza
tion and technological change breed anxiety in the work place (DeFrank &
Ivancevich, 1999). It is these aspects of change—unpredictability, paradox,
discontinuity, and speed, rooted in ambiguity and perceived loss of personal
control over the situation—that create anxiety in the organization.
13
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Anxiety in the Work Place
The anxiety created by unpredictability, paradoxes, discontinuity, and
speed are a function of the ambiguity inherent in each of these aspects of
change. Anxiety is a feeling of uneasiness and apprehension concerning a
situation, typically one with an uncertain outcome (Argyris, 1993; Ormrod,
1995). Unlike fear, anxiety is vague and relatively unfocused. In psycho
logy, there are two types of anxiety: state anxiety, which is a temporary
condition elicited by a particular situation; and trait anxiety, which is more
chronic in some individuals (Argyris; Ormrod; Snow & Jackson, 1994).
In the context of organizational change, it is state anxiety that becomes
aroused, although it will more probably occur among persons who manifest
trait anxiety.
Early researchers found that anxiety levels affect learning and per
formance in a curvilinear fashion (Fiske & Maddi, 1961; O’Neil & Drillings,
1994). This means that there is an optimal amount of anxiety at which per
formance is maximized. A high level of anxiety enhances human perform
ance on easy and automated tasks, but the same level of anxiety interferes
with performance on difficult or ambiguous tasks (Ormrod, 1995). This is
the difference between threat-where individuals believe that they have little
chance of success-and challenge-where individuals believe that they can
succeed if they try hard enough (Ormrod). In a change situation, individuals
often feel threatened due to the loss of control over the situation and the
ambiguous nature of the situation (Eccles & Midgley, 1989). Regardless, in
a radical change situation common in today’s world, the quantity of anxiety
14
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
can reduce energy, lower motivation, and increase self-absorption (Bridges,
1991).
Individuals experiencing anxiety typically perform more poorly than
their peers (Kets de Vries, 1980; Ormrod, 1995). This often leads to failure
at tasks, which, in turn, leads to anxiety. The result is the development of
aversion for the task and resistance to it (Bandura, 1997; Ormrod). Accord
ing to Kotter (1995), more than half of all change initiatives do not survive
the initial phases. In the context of change, the major consequence of
anxiety is resistance.
Reactions to Change: Resistance
Possibly the most important pitfall to any change process is under
estimating individual resistance to change. Nearly three quarters of com
panies undergoing organizational transformation face resistance to change
embedded in the corporate culture (Romano, 1995). Failure to anticipate
this resistance to change can result in frustration on the part of the change
agent, the management, and the employees. Resistance can lead to dys
functional behavior, such as withdrawal (Abramson, Seligman, & Teasdale,
1978), decrements in performance (Bazerman, 1982), sabotage (Allen &
Greenberger, 1980), and acting out (Galpin, 1996).
Resistance is a response to a real or imagined threat to traditional
norms, power relationships, and ways of conducting business (Senge,
1990). In a sense, resistance may function as a survival mechanism when
change is perceived as a threat (Goldstein, 1988; Maurer, 1996). Since
people are more influenced by their perceptions and interpretations of their
environment than they are by objective reality (Bandura, 1997; Thomas &
15
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Velthouse, 1990), an imagined threat is almost more potent than a real
threat. Through resistance, people attempt to maintain explicit goals, roles,
and behaviors that have become the norm.
Resistance to change is often discussed in terms of Lewin’s (1951)
force field model. This model distinguishes between the driving forces that
lead to movement and the restraining forces associated with resistance.
Resistance is seen as something to be reduced, worked through, or pre
empted (Armenakis, Harris, & Mossholder, 1993; Lewin; Maurer, 1996).
In this light, resistance is seen as a lack of willingness or motivation to
change. Therefore, resistance can be defined as a negative behavioral or
psychological response to change.
It has been argued that resistance to change is inevitable, but how
that resistance is manifested and managed can vary greatly (Connor, 1992,
1998; Maurer, 1996). Connor (1992) argued that change occurs when
people experience significant disruptions in their lives, and that, when ex
pectations regarding that disruption are not met, resistance occurs. This
resistance can manifest itself in overt, public ways, or covert, private ways.
The resistance can be punished or it can be encouraged. Regardless, it will
happen. Given this, it is important to ensure that resistance is brought to
the fore so it can be acknowledged and addressed.
Resistance can be a healthy response to change. Elizabeth Kubler-
Ross (1969) articulated the stages through which a person goes when ex
periencing a negatively perceived change, such as the loss of a loved one
or suffering from a terminal illness. Her model has relevance in the world of
organizational change. While the impact is not nearly as catastrophic as
16
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
death, the stages through which people go during an organizational change
are the same, providing some insight into how to understand and manage
these responses to change. According to this model, everyone goes
through some form of resistance, whether it is manifested in denial, anger,
or learned helplessness (Cox, 1997). The challenge is helping people to
move through this model to acceptance.
In contrast to Kubler-Ross’s model on negatively perceived change,
Connor (1992, 1998) described a model that illustrates the phases experi
ence in a positive response to change. This five-phase model, similar to
Maurer’s Cycles of Change model (1996), can be applied to those who
originally embrace the change but resist later in the change process. The
first phase of the five-phase model is uninformed optimism, characterized
by enthusiasm but often founded on insufficient data. This is often called
the “honeymoon phase.” It is followed by informed pessimism, character
ized by the realization that the decision just undertaken has unexpected
hidden costs and implications. This often results in second thoughts and
doubt about the changes. Although this is a natural part of the process to
ward achieving the goals of the change, it can result in the individual
“checking out” or resisting the changes. If the resistance is open and can
be resolved, the individual moves to the next phase of the model, hopeful
realism. It is here that the individual begins to see hope in the change and
his or her role in it. This helps to move toward informed optimism, reflected
by a stronger confidence in the changes and the ability to be successful.
The final stage, completion, is attained when the changes have been im
plemented successfully.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
In another model, Prochaska, Norcross, and DiClemente (1994)
identified six stages of change: precontemplation, contemplation, prepara
tion, action, maintenance, and termination. They argued that any change,
whether it is self-induced or externally driven, will guide individuals through
each of these well-defined stages. The research showed that those who
are not ready for change will inevitably resist in any of the six stages, pro
ducing failure of the initiative. However, the most common stage for resis
tance is the precontemplation stage, in which people feel that they do not
need to change but things around them should change.
These models, applied in organizational settings, show that resist
ance is a natural response to change, regardless of whether the individual
is initially optimistic or pessimistic regarding the changes (Connor, 1992,
1998; Prochaska et al., 1994). Signs of resistance include denial (Kubler-
Ross, 1969; Maurer, 1996), sabotage (Allen & Greenberger, 1980), con
fusion (Nadler, 1998), and malicious compliance (Maurer). The key is to
redirect the energy of the resistance (Connor, 1992, 1998; Maurer; Pro
chaska et al.).
In sum, it is well documented that individuals and organizations resist
change (Bridges, 1980; Maurer, 1996; Robbins, 1997). Resistance is
worsened by two characteristics, one in the individual-personal control
stemming from the inability to predict what is going to occur-and one in the
work setting-job ambiguity stemming from a lack of clarity concerning roles.
If there is a perception that they are losing control, or if the changes to their
roles are unclear or ambiguous, employees will resist the change in an at
tempt to maintain control and keep their environment stable.
18
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Job Ambiguity
An individual perceiving a lack of understanding of his or her role or
a lack of clarity regarding expectations of management is said to be in a
state of job ambiguity (Kahn, Wolfe, Quinn, Snoek, & Rosenthal, 1964).
The seminal work of Kahn et al. regarding job ambiguity sparked a prolifera
tion of research on the subject. In much of this research, job ambiguity is
seen as a perceived lack of necessary job-related information; the behav
iors, expectations, or performance criteria are unclear or unknown (Breaugh
& Colihan, 1994; Rizzo, House, & Lirtzman, 1970; Spreitzer, 1996) or there
is a lack of clarity regarding what is expected of the participants (Spreitzer).
Job ambiguity is distinct from role conflict, which occurs when an in
dividual is confronted by divergent or inconsistent expectations in his or her
role or the set of behaviors attributed to the role (Robbins, 1993; Rizzo et
al., 1970). Researchers tend to agree that job ambiguity is more highly re
lated to stress and anxiety than is role conflict (Fisher & Gitelson, 1983;
Rizzo et al.). Based on a meta-analysis, it has been determined that job
ambiguity is also more strongly related to commitment and job involvement
than is role conflict; however, job ambiguity is not more strongly related to
satisfaction variables (Fisher & Gitelson). Fisher and Gitelson argued that,
because management practices and leadership are strong determinants of
job ambiguity, job ambiguity should be easier to control than role conflict.
Most researchers agree that people have a need for clarity and cer
tainty and that they find the state of uncertainty to be stressful (Greenberger
& Strasser, 1986; Kagan, 1972; Kahn et al., 1964; Sorrentino & Short,
1986). The lack of clarity resulting from ambiguity creates feelings of threat
19
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
and resentment toward the change and fear of the unknown. Job ambiguity
increases the probability that people will be dissatisfied with their roles, will
experience anxiety, and will perform less effectively (Rizzo et al., 1970).
Research also suggests that job ambiguity threatens personal con
trol, creates stress, and prevents employees from performing at their high
est potential because it affects their perceptions about the likelihood of
being successful in a given task (Bandura, 1997; Breaugh & Colihan, 1994;
Ford, 1992; Gist & Mitchell, 1992). Employees will tend to avoid activities
that they believe exceed their abilities and they will undertake activities that
they judge themselves capable of performing (Bandura). It is not surprising,
then, that job ambiguity is related to low levels of intrinsic motivation (Saw
yer, 1992). Other symptoms of anxiety produced through ambiguity include
avoidance behaviors such as withdrawal and reduced communications
(Abramson et al., 1978; Kets de Vries, 1980). Thus, employees respond by
resisting the change that created the ambiguity in their roles, hoping to re
turn to the roles in which they were confident and which they understood.
Job ambiguity in organizations is directly related to the amount and
adequacy of information regarding job performance (Kets de Vries, 1980).
People exposed to ambiguous situations become confused about standards
of performance and evaluation methods (Kets de Vries, 1980; Robbins
1993). Complexity, unpredictability, and vague communication often lead
to ambiguous situations and roles (Bolman & Deal, 1997). Many times,
events themselves are so complex and apparently disjointed that it is diffi
cult to fully understand and internalize what is going on. Siegall and Cum
mings (1986) found that the source of ambiguity affects the relationship
20
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
between job ambiguity and satisfaction. For example, satisfaction with the
supervisor is related to job clarity or its inverse, job ambiguity, when the
supervisor is the source of unclear expectations (Siegall & Cummings).
Clear role definition is an important element in reducing job ambigu
ity (Kets de Vries, 1980; Robbins, 1993). Every position within a formal or
ganization should have clearly articulated expectations and responsibilities,
whether the roles are new or continuing. This enables employees to take
responsibility for their performance and for management to hold them ac
countable for their performance (Rizzo et al., 1970). Clear role definition
provides for a greater feeling of personal control (Kets de Vries, 1980).
Personal Control
Personal control is defined as a person’s belief, at a given point in
time, in his or her ability to effect a change in the environment (Greenberger
& Strasser, 1986). It is subjective and influenced by the attitudes and be
haviors of others. Perceptions of personal control also change over time
and are influenced by situations and context.
The construct of personal control has often been confused with that
of locus of control. However, these constructs are conceptually different.
Locus of control is the degree to which individuals believe that they have
control over their fate. As such, locus of control involves outcome
contingencies, which are generally viewed as either internally driven or
externally driven (Thomas & Velthouse, 1990). External locus of control
assumes that the individual is a pawn of fate and, as such, is subject to
things such as luck, chance, or the power of other people (Robbins, 1993;
Rotter, 1966). People with this predisposition tend to experience less
21
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
personal effectiveness (Heisler, 1974). On the other hand, internal locus of
control assumes that the individual is the master of his or her fate, and is,
therefore, encouraged to think about problems and to try to solve them
(Landau, 1995). This perception of causality is relatively stable (Robbins,
1993; Trusty & Macan, 1995).
In contrast, personal control can vary from situation to situation and
overtime (Greenberger, Porter, Miceli, & Strasser, 1991; Greenberger &
Strasser, 1986). It has been argued that internal locus of control is a form
of belief in personal control (Posner & Butterfield, 1979). This can be seen
as congruent with the argument that, while personal dispositions such as
having high internal or external locus of control may impact perceptions of
personal control, personal control itself is cognitively based (Greenberger et
al.; Greenberger & Strasser). It is a belief regarding the amount of control
that one has at any given moment to affect change, whether that change is
internal or external.
Greenberger and Strasser (1986) proposed a three-stage model of
personal control in organizations. In the first stage, antecedents of personal
control, situational factors (such as loss of control due to organizational
changes and the resulting job ambiguity) and dispositional factors (such as
locus of control and other personality traits) influence the amount of control
an individual desires and the amount of control that he or she has. In the
next stage, control ratio, the individual compares the amount of control
actually possessed to the amount desired. Greenberger and Strasser ar
gued that people generally desire more control than they possess. If the
ratio is balanced, the individual is in a state of homeostasis and can exit the
22
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
model. However, if the ratio is not balanced, the individual enters the last
stage, reaction. In this stage, the individual reacts in an attempt to restore
balance in the control ratio, using both direct and indirect means. This
model predicts that individual reactions to change in perceptions of control
can positively or negatively influence behavior.
It is the reaction stage of this model that has the greatest implica
tions for organizations. Greenberger and Strasser (1986) argued that,
when perceptions of control do not match desired levels of control, the indi
vidual will behave in a manner that is designed to increase his or her level
of control. These behaviors can be dysfunctional, including withdrawal
(Abramson et al., 1978), decrements in performance (Bandura, 1997;
Bazerman, 1982; Ford, 1992), and learned helplessness (Martinko & Gard
ner, 1982). When outcomes are not directly controllable, passive com
pliance to the situation may occur; however, negative consequences to this
include anxiety, lowered performance, and depression (Greenberger &
Strasser). Orpen (1994) found that the effects of work motivation moderate
personal control. Highly motivated individuals were more adversely af
fected by low personal control.
Rothbaum, Weisz, and Snyder (1982) argued that the state of per
sonal control is so greatly valued that the quest to achieve the desired level
is rarely abandoned. However, the methods used to attain that level of
control may vary. The individual may use primary methods or secondary
methods. Primary control involves direct methods of regaining personal
control, including such strategies as directly confronting the source of the
problem (Greenberger & Strasser, 1986; Heckhausen & Schulz, 1995). If
23
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
the individual’s role is restructured as a result of an organizational change,
the individual may attempt to regain control of the situation by directly con
fronting the management that made the decision in an attempt to get
it reversed. In contrast, secondary control involves more indirect methods
of attaining control, such as adapting to the changed environment (Green
berger & Strasser; Heckhausen & Schulz). Rather than attempting to
change the job restructure, the individual changes. These changes can be
positive-proactively seeking opportunities within the restructure-or nega
tive-resisting the change by lowering productivity or sabotaging the change.
Both primary and secondary methods of control are proactive means
to attain equilibrium of desired and possessed personal control. Individuals
seeking to regain control will first employ primary control strategies. If
these are not successful, or if the individual perceives a low probability of
success, the individual will employ secondary strategies. These reactions
are not limited to behaviors or actions. Reactions may also be cognitive
in nature, such as reevaluating the situation and adjusting the control ratio
based on the reevaluation (Greenberger & Strasser, 1986). These adjust
ments can be in terms of reevaluating the level of control desired or re
evaluating the level of control possessed.
While dispositional factors, such as locus of control, can influence
perceptions of personal control, situational factors, such as organizational
change, can also influence perceptions of personal control (Greenberger &
Strasser, 1986). These perceived or actual decreases in personal control
can result in increased anxiety (Greenberger et al., 1991). Other situational
factors, such as experiences, can affect long-term patterns of behavior,
24
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
such as the need for control (Ford, 1992). For example, experiences that
undermine a sense of self-determination tend to promote compliance or
defiance. Defiance, or resistance, is an attempt to regain some level of
personal control (Greenberger et al.).
There is compelling literature on the motivational consequences of
controlling experiences that indicate that people lose interest in activities
when they feel coerced or manipulated to engage in those activities (Deci &
Ryan, 1985). They may feel the need to counteract feelings of being con
trolled by an event (Kets de Vries, 1991; Trusty & Macan, 1995). If left un
addressed, the problems of loss of control due to organizational changes
can cripple an organization (Maurer, 1996; Nadler, 1998). If managers are
to successfully manage change, they must account for this strong need for
control.
Research on personal control has only begun to examine what un
derlying factors tend to affect perceptions of individual personal control in
organizations (Trusty & Macan, 1995), but it is clear that personal control is
important in understanding numerous organizational behaviors (Bazerman,
1982; Greenberger et al., 1991; Martinko & Gardner, 1982). Situations that
can be predicted or anticipated are easier to manage and tend to bring
feelings of self-determination and personal control over the situation.
When what is currently happening matches expectations about the situa
tion, people feel a sense of control and predictability. When expectations
diverge from the current situation, a sense of anxiety ensues and personal
control is threatened. The illusion of control is a powerful one (Aronson,
1994). Deci and Ryan (1985) argued that, if an individual perceives a
25
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
sense of being controlled by the situation, anxiety and dysfunctional be
haviors often result. In contrast, perceived control produces greater flexibil
ity and resiliency.
Commitment
Personal control and job ambiguity are two variables that impact
commitment. When personal control is out of balance or the job is ambigu
ous, resistance is likely in the form of low or no commitment to the changes.
Commitment is the choice to accept a task and persist at that task
(Clark, 1997). Commitment can be defined as the persistence at a work
goal over time and in the face of distraction (Clark, 1998). In relation to
change, commitment is the choice to accept the organizational changes
proposed and to continue to support those changes throughout the process.
According to Ford (1992), three variables predict commitment: (a) goal
value (will achieving this goal increase personal control or effectiveness?)
(b) efficacy, and (c) emotions. This commitment is achieved only when the
individual values the change and is efficacious about the change (Clark,
1997).
This is consistent with the research in job ambiguity and personal
control. Job ambiguity is strongly related to commitment (Fisher & Gitelson,
1983). Efficacy can be impacted by job ambiguity (Bandura, 1997; Breaugh
& Colihan, 1994; Ford, 1992). When the individual is unclear about the job
role, efficacy is significantly decreased and commitment is not achieved.
Personal control impacts commitment by affecting the value placed
on either adapting to the changes or resisting those changes. In a survey
conducted by the University of Michigan Survey Research Center (1971),
26
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
having sufficient authority over one’s job is very important. Having control
in the work place is important as well (Lawler, 1985). If the changes pro
posed negatively impact perceived personal control, the individual will place
less value on the change and therefore will not commit to it. The result is
resistance.
Resilience to Change
An opposite response to resistance to change is that of resilience to
change, which is a key element to successful change efforts. Bandura
(1997), referring to personal changes, defined resilience as the ability to
recover quickly from an adverse experience. Connor (1992, 1998), refer
ring to organizational changes, defined resilience as the ability to absorb
high levels of change while displaying minimal dysfunctional behavior. Ac
cording to Connor, organizations can do little to slow the pace of change
today, but they can increase the pace at which their members can absorb it.
These researchers agreed that resilience is both teachable and malleable
(Bandura; Connor, 1992).
To better understand resilience, it is important to understand “future
shock,” a term coined by Alvin Toffler in the 1960s to refer to a psychologi
cal state characterized by disorientation and dysfunctional behavior brought
about by being subjected to too much change in too short a time (Toffler,
1970). The predicted consequences of future shock include reduced
effectiveness, increased stress and anxiety, and an inability to adapt quickly
to change. Connor (1992) defined future shock as “that point when humans
can no longer assimilate change without displaying dysfunctional behavior”
(p. 54). Unfortunately, organizations have become adept at
27
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
accommodating this decreased effectiveness and dysfunctional behavior,
ignoring that they exist.
It is clear that there is more change than ever before, both in per
sonal and professional environments. As a result, there is more future
shock and dysfunctional behavior than ever before (Connor, 1998). Resis
tance is often due to the sheer immensity of what is demanded of the indi
vidual (Maurer, 1996). The dysfunctional behavior can be relatively mild,
such as increased conflict with coworkers (Connor, 1992), or more serious,
such as degraded health (Bandura, 1997) or learned helplessness (Kets de
Vries, 1980; Martinko & Gardner, 1982). By studying individuals and or
ganizations that manage and adapt to change well, Connor has identified
key elements that encourage and facilitate successful organizational
change. Critical among these elements is resilience and the ability that
resilient people have to understand the patterns of change.
Resilient people display some specific characteristics, including self-
assurance, focus, flexibility, structured approach to dealing with ambiguity,
and a proactive nature (Connor, 1998). These characteristics help to in
crease the ability to adapt and to apply personal resources more efficiently.
While resilient people experience the same emotions that nonresilient peo
ple experience (e.g., anxiety, frustration, and fear), they are usually able to
maintain their productivity, as well as their physical health, while achieving
the change goals and not succumbing to dysfunctional behaviors (Bandura,
1997; Connor, 1992, 1998).
To maximize resilience and manage change effectively, organiza
tions must ensure that the processes and interventions used to drive and
28
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
manage the changes are tightly aligned. Longitudinal studies in children
have shown that personal attributes, such as resilience, operate interac
tively with external aids and social influences in helping to achieve major
redirection or change in life (Werner & Smith, 1992). Werner and Smith
reported that a sense of personal control over situations and circumstances
is a key factor in building and maintaining resilience. Whether in children or
adults, resiliency is not created by a few successes but by successes over
time. Connor (1992) argued that, when resilient people face the ambiguity
and loss of control often associated with major change, they tend to grow
stronger and more resilient from their experiences and are more able to
adapt and handle future changes.
Thus, change efforts should be structured in such a way as to build
resilience. One way to do this is by ensuring the accumulation of accom
plishments that can help to build resilience to adverse situations such as
ongoing organizational change. Other ways to build resilience include cre
ating a sense of readiness (Armenakis et al., 1993), coaching (Bandura,
1997), providing skills training (Knowles, 1984) as well as adequate infor
mation regarding roles (Kets de Vries, 1980), feedback (Ford, 1992), and
reducing ambiguity (Bandura 1997; Connor, 1992; Kets de Vries, 1980).
However, even the most resilient find themselves struggling with self-doubts
in the face of setbacks (Bandura). Therefore, it is important for those who
manage organizational changes to ensure that resilience is supported
throughout the change process.
The capacity of adverse events to bring forth dysfunctional behaviors
is dependent upon personal resiliency as well as environmental support
29
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
(Bandura, 1997). Therefore, it is critical that environmental supports be put
in place to help people to develop and maintain resilience in the face of or
ganizational change. By building individual efficacy regarding change,
managers can help employees to maximize their resilience to changes. By
managing the changes in a more effective and proactive manner, managers
can help to minimize the impact of change.
Summary
Technology and globalization have created massive disruption in or
ganizations. This, in turn, has made people anxious, producing high levels
of resistance. This situation is exacerbated when people experience job
ambiguity and a perceived lack of personal control. The antidote to resis
tance may be resilience.
Change causes many emotions, notably anxiety. This comes from
perceived or experienced loss of personal control. Job ambiguity reinforces
this anxiety. In an effort to regain some control, people search for clarity.
When clarity is not forthcoming, the anxiety levels increase. When pushed
to accommodate the organizational change, they resist in an effort to regain
some control of the environment. Therefore, it is critical to understand indi
vidual resistance to organizational change and effectively address it through
efforts to build resilience.
Purpose of the Study
This study attempts to answer the following questions:
1. How do personal control and job ambiguity predict resistance to
organizational change?
30
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
2. How do personal control and job ambiguity predict resilience to
organizational change?
Hypotheses
1. Personal control will predict commitment (as impacted by specific
aspects of change) by itself and over and above what is predicted by job
ambiguity.
2. Job ambiguity will predict commitment (as impacted by specific
aspects of change) by itself and over and above what is predicted by per
sonal control.
3. Personal control will predict commitment (as impacted by the
change process) by itself and over and above what is predicted by job am
biguity.
4. Job ambiguity will predict commitment (as impacted by the
change process) by itself and over and above what is predicted by personal
control.
5. Perceived personal control, job ambiguity, and expectations for
the changes will differentiate persons who are resilient to change from
those who are resistant to change.
31
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
CHAPTER 3
METHODOLOGY
Subjects
The sample consisted of approximately 125 managers in a customer
service and support department of a high-tech company who were attend
ing company-sponsored focus groups. All attendees voluntarily completed
the three instruments used in this study, due in part to an organizational
culture that supports such participation. The company, headquartered in
the Silicon Valley (California), had recently split from the main company.
Thus, all participants had recently undergone a major change and were
continuing to experience change as the new company evolved.
The respondents included 24 female and 111 male managers, a
typical ratio in this field of engineering. They ranged in age from 25 to 50
years. On average, they had worked 15 to 20 years with this company. All
held a bachelor’s or master’s degree, most in the field of engineering.
Within this sample of 125 participants, 70 were outside the United
States. The international participants came from Western Europe (40), in
cluding England, France, Germany and Italy; Asia Pacific (25), including
Japan, Korea, China, Singapore, Taiwan, and Australia; and Canada (5).
Of these 70 international participants, 50 reported English as a second lan
guage.
The participants completed the study’s instruments on a voluntary
basis. However, of the 125 surveys, only 115 were complete and usable for
this study.
32
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
This study used instruments in a manner such that the specific iden
tity of a respondent could not be determined unless so desired by the indi
vidual respondent.
Definition of Terms
Job ambiguity is defined as a lack of understanding of the indi
vidual’s job/role. Highly related to stress and anxiety, job ambiguity is per
ceived lack of clarity regarding necessary job-related expectations,
expected behaviors, or performance criteria. The Job Ambiguity Scale
(JAS; Breaugh & Colihan, 1994) operationalized job ambiguity in this study.
(See the appendix for copies of all three of these instruments.)
Personal control is defined as the belief, at a given point in time, in
the ability to effect a change on the environment. This belief is subjective
and influenced by several factors, including the attitudes and behaviors of
others, as well as situations and context. The Personal Control Scale
(PCS; Greenberger et al., 1991) operationalized personal control in this
study.
Resistance is an individual behavioral or cognitive response to
change designed to protect the individual from the perceived negative ef
fects of change. Often seen as a negative behavioral or psychological re
sponse to change, resistance varies in its manifestations and desired
results but includes efforts at altering the changes and maintaining status
quo.
Resilience is the ability to absorb high levels of change while display
ing minimal dysfunctional behavior. Resilience, a characteristic that is both
33
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
teachable and malleable, is also defined as the ability to recover quickly
from an adverse experience.
Resistance and resilience are not opposite constructs, since, accord
ing to Connor, people can experience a change in similar ways but respond
differently. Both resistors and resilient individuals can experience stress,
anxiety, and discomfort in confronting change. However, whereas resistors
exhibit dysfunctional behaviors, resilient people exhibit a healthy response.
The Individual Response to Change (IRC) scale operationalizes resistance
and resilience in this study. For the first four hypotheses, scores on this
instrument measured resistance and resilience. For the fifth hypothesis,
high scores on perceived cost and high scores on commitment identified
a resilient person. In contrast, scores that are high on cost and low on
commitment identified a resistant person (Table 1). In this study, it was hy
pothesized that personal control and job ambiguity would impact resistance
and resilience.
Table 1
Cost and Commitment Matrix
Perceived “cost” to employees
Commitment to Change High Low
High Resilient Doers
Low Resistant Slackers
34
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Specific change refers to explicit changes that directly impact the
individual’s daily work or job, such as changes in responsibilities, resources,
and reporting relationships. The IRC measured this variable in this study.
General change refers to broad changes and the processes and
activities that accomplish those changes, such as individual involvement in
change, communications regarding change, incentives, and personal
understanding of the changes. The IRC measured this variable in this
study.
Instrumentation
Job Ambiguity
In this study, job ambiguity was hypothesized to be a predictor of
commitment to change as well as resistance and resilience. It was
measured by the JAS (Breaugh & Colihan, 1994), a three-factor, 9-item
Likert-type scale with ratings ranging from 1 = disagree strongly to 7 =
agree strongly.
Cronbach’s alpha reliabilities for the JAS range from .88 to .93
(Breaugh & Colihan, 1994). Although test-retest reliabilities are substanti
ally lower, ranging from .54 to .80, they are sufficiently high for research
purposes.
Breaugh and Colihan (1994) reported strong evidence for the con
struct validity of their instrument. In addition to demonstrating a satisfactory
three-factor factor analytic solution, all hypothesized relationships using the
JAS were confirmed. Job ambiguity was related to work attitudes and
behaviors (e.g., satisfaction with work, satisfaction with supervision, job
35
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
performance), with longevity (e.g., tenure with company, tenure with super
visor), and with environmental conditions (e.g., feedback). Additional con
struct validity was evidenced in the correlation between the JAS and the job
ambiguity scale of Rizzo al. (1970). The three sub-scales of the JAS corre
late between .34 and .49 with the Rizzo et al. scale, indicating concurrent
validity while providing evidence for differences between the two scales.
The alpha reliability of this 9-item scale used with participants in this
study was .89 (N = 113). Each of the three-item subsections had
sufficiently high reliabilities (ranging from .88 to .97) to be used as separate
scales, although they were not used in this way in the study.
Personal Control
Personal control, like job ambiguity, was hypothesized to be a pre
dictor of commitment to change as well as resistance and resilience. It was
measured by the PCS (Greenberger et al., 1991), a two-factor, 22-item
Likert-type scale with ratings ranging from 1 = very little to 5 = very much.
The alpha reliability of the 11-item Perceived Control subscale for
participants in this study was .74 (N =114), while that of the Desired
Control subscale was .83 (N = 114). The correlation between the two sub
scales was .65 (p < .001), which indicates that approximately one third of
the variability in one scale was shared by the other scale. While there is
justification for combining the two scales, only the Perceived Control scale
was used in this study. This was done because perception is believed to be
more closely related to actual behavior.
36
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Resistance
Despite the interest in the field of change, and particularly the aspect
of resistance to change, few instruments exist to assess this construct.
One commonly used instrument, the Change Resistance Scale (Connor,
1992) has little to no psychometric information available. Neither reliability
nor validity data exist for this instrument. Therefore, in the absence of psy-
chometrically sound instrumentation to measure resistance, the researcher
devised the IRC. This 17-item Likert-type scale includes items pertaining to
aspects of the process of change and aspects of change itself, rating them
on a scale ranging from 1 = Low to 7 = High.
A pilot test was conducted to establish reliability and validity of the
IRC as an instrument to measure resistance. Twenty front-line and mid
level managers at the company who were not part of the study served as
the participants. The IRC was administered once, and then administered
again 10 days later. The test-retest reliability coefficient was .79, which is
sufficiently high for research purposes.
The Known Group Technique was used to establish the validity of
the IRC as an instrument to measure resistance. The senior managers, to
whom the front-line and mid-level managers participating in the reliability
phase of the pilot study report, were asked to identify those whom they
perceived to be Resisters and those whom they perceived to be Non-
Resisters. These senior managers were provided with the definition of
resistance provided in this paper. Seventy-one percent of the front-line and
mid-level managers were correctly classified, according to their scores on
the IRC, thereby supporting the validity of the instrument.
37
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Resilience
Resilience, an antidote for resistance, was measured using the IRC
scale. The Known Group Technique was used to establish the validity of
the IRC as an instrument to measure resilience. The senior managers, to
whom the participants in the reliability phase of the pilot study report, were
asked to identify those whom they perceived to be resilient to change and
those whom they perceived to be not resilient to change. These senior
managers were provided with the definition of resilience used in this paper.
Sixty-three percent of the participants were correctly classified, according to
their scores on the IRC, thereby supporting the validity of the instrument.
The 8-item scale that assessed commitment to change in general,
had an alpha reliability of .92. The 8-item scale that assessed commitment
to change with regard to specific aspects had an alpha reliability of .85.
The correlation between the two scales was .27 (p < .003).
The 8-item scale that assessed perception of the cost of change in
general had an alpha reliability of .79. The 8-item scale that assessed
perception of the effectiveness of specific aspects of change had an alpha
reliability of .89. The correlation between the two scales was .27 (p <
.003), indicating that they were measuring two different constructs.
Procedure
All attendees at the company-sponsored focus groups held in the
February-March 2000 time frame were administered the three instruments
used in this study. A cover letter indicated that, while participation was
voluntary, management encouraged cooperation. Participants were
guaranteed anonymity.
38
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
The three instruments were collated randomly to avoid order effects.
Participants were allowed approximately 30 minutes to complete the three
instruments.
Data Analysis
Means and standard deviations were computed for all variables:
(a) job ambiguity (three subscale scores and a total score), (b) personal
control, (c) the perceived “costs” of specific organizational changes and
the ensuing commitment, (d) the perceived effectiveness of the generic
changes and the ensuing commitment; and (e) responses to a single
question pertaining to expectations of the larger organizational split. The
relationships among these variables are presented in a correlation matrix.
The first four hypotheses were tested using a multiple regression
analysis. Multiple regression analyzes the common and separate influ
ences of two or more independent variables on a dependent variable. It is
a method for studying the effects and the magnitudes of the effects of
X2, ..., Xk on Y (Kerlinger, 1986). The method and calculations are done to
give the best prediction possible, given the correlations among all of the
variables. In this study, job ambiguity and personal control were two
independent variables used to predict commitment to change.
The fifth hypothesis, which sought to differentiate resilient people
from resistant people, was tested using discriminant analysis, which is
closely related to multiple regression. It is a regression equation with a
dependent variable that represents group membership. This statistic
maximally discriminates the members of the group assigning each subject
to the “correct” group membership (Kerlinger, 1986). In other words, dis-
39
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
criminant function assigns individuals to groups on the basis of their scores
on two or more measures. As is the case in this study, when dealing with
two groups, a discriminant function is simply a multiple regression equation
in which the dependent variable is a nominal variable that is coded 0 and 1
to represent group membership (Kerlinger).
Calculations and analyses were computed using a standard
computer software statistical analysis application.
40
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
CHAPTER 4
RESULTS
This chapter presents the descriptive statistics computed for the two
independent variables (Job Ambiguity and Perceived Control) and for the
two dependent variables (Commitment to Change [General] and Commit
ment to Change [Specific]) as well as two supplemental variables (Cost of
Change and Effectiveness of Change). The first four hypotheses were
tested with multiple regression and the fifth hypothesis was tested with a
discriminant function analysis.
Descriptive Statistics
Table 2 presents the means and standard deviations for the two
independent variables, two dependent variables, and two supplemental
variables.
Personal Control assessed the respondent’s subjective belief in his
or her ability to effect a change on the environment. The overall mean for
Perceived Control was 3.61 (s = .54), which corresponds to between
“moderate” and “much” influence in each area. This indicates that respond
ents appeared to experience sufficient personal control in the work place.
Job Ambiguity assessed the extent to which the respondents under
stand what their job was during the company transition. The overall mean
for Job Ambiguity was 5.37 (s = .98), indicating that respondents “agreed”
or “agreed slightly” with the questions asked. This indicates that they
experienced only minimal ambiguity with job roles.
41
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table 2
Variable Means and Standard Deviations for All Independent, Dependent,
and Supplemental Variables (N = 115)
Variable Mean SD
Independent
Perceived Control 3.61 0.54
Job Ambiguity 5.37 0.98
Dependent
Commitment to Change (General) 4.30 1.32
Commitment to Change (Specific) 4.14 1.15
Supplemental
Cost of Change 3.90 1.10
Effectiveness of Change 3.94 1.27
Note. All means and standard deviations are based on a 7-point Likert-type
scale for which 7 is high and 1 is low, except for the Personal Control Scale,
which used a 5-point Likert-scale for which 5 is high and 1 is low.
The two dependent variables were Commitment to Change
(General) and Commitment to Change (Specific). On average, all change
responses for the dependent variables of commitment centered around 4,
ranging from 3.90 to 4.30 (s = 1.10 to 1.32). Four is the midpoint on the
Likert-type scale used for this instrument. This response indicates neutral
ity not only on the effectiveness of the changes that respondents experi
enced but also on the personal “cost” of the change. This neutral result
across the two dependent variables and the Personal Control Scale was
not expected, especially given anecdotal data and observations collected
during the study. This finding is elaborated in chapter 5.
42
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table 3 presents the correlation matrix for all variables in the study.
Several of the relationships shown in the matrix were significant. Personal
Control and Job Ambiguity (r = .20) and Commitment to Change (General)
and Cost of Change (r = . 19) were significantly correlated at p < .05. Oth
ers were significant at p < .01: Commitment to Change (General)
and Commitment to Change (Specific; r = .60), Commitment to Change
(General) and Effectiveness of Change (r = .51), Commitment to Change
(Specific) and Cost of Change (r = .29), Commitment to Change (Specific)
and Effectiveness of Change (r = .27), and Perceived Control and Commit
ment to Change (General; r = .24). The meaningfulness of the correlations
may be more important than the significance measure, particularly given the
large sample size (N = 115). All correlations shared less than 7% of their
variability, except for Commitment to Change (General), which shared 36%
of its variance with Commitment to Change (Specific) and 26% with Effec
tiveness of Change. While the former finding is not surprising, the latter has
implications that will be explored in chapter 5.
Resilience
Hypothesis 1 stated, “Personal control will predict commitment (as
impacted by specific aspects of change) by itself and over and above what
is predicted by job ambiguity.” Hypothesis 2 stated, “Job ambiguity will pre
dict commitment (as impacted by specific aspects of change) by itself and
over and above what is predicted by personal control.” As can be seen in
Table 4, neither Hypothesis 1 nor Hypothesis 2 was supported. The two
43
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission.
Table 3
Correlation Matrix for All Variables (N = 115)
Control
Job
Ambiguity
Commitment
to Change
(General)
Commitment
to Change
(Specific)
Cost
of
Change
Effective
ness of
Change
Control 1.00 .20* .24** .14 -.05 .17
Job Ambiguity 1.00 .09 .06 .02 .21*
Commitment to Change (General) 1.00 .60** .19* .51**
Commitment to Change (Specific) 1.00 .29** .27**
Cost of Change 1.00 .12
Effectiveness of Change 1.00
*p < .05. **p < .01.
4^
4^
variables, Personal Control and Job Ambiguity, together explained only 2%
percent of the variability in Change (Specific).
Table 4
Regression Analysis for Personal Control and Job Ambiguity on Change
(Specific)
Variable R R2
Change
in R*
F
change
P
Personal Control .14 .02 .02 2.21 .14 n.s.
Job Ambiguity
(above Personal Control) .14 .02 .00 .14 .71 n.s.
Job Ambiguity .06 .00 .00 .43 .51 n.s.
Personal Control
above Job Ambiguity .14 .02 .02 1.90 .17 n.s.
Hypothesis 3 stated, “Personal control will predict commitment (as
impacted by the change process) by itself and over and above what is pre
dicted by job ambiguity.” Hypothesis 4 stated, “Job ambiguity will predict
commitment (as impacted by the change process) by itself and over and
above what is predicted by personal control.” As can be seen in Table 5,
Hypothesis 3 was confirmed. Personal Control alone predicted commit
ment to Change in General, accounting for 6% (p < .01) of the variance in
the dependent variable. When entered after Job Ambiguity, it accounted for
5% of the change in R2 (p < .02). Hypothesis 4 was not confirmed.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table 5
Regression Analysis for Personal Control and Job Ambiguity on Change
(General)
Variable R R2
Change
in R
F
change
P
Personal Control .24 .06 .06 2.21 .01
Job Ambiguity
(above Personal Control) .24 .06 .00 .14 .67 n.s.
Job Ambiguity .09 .01 .01 .86 .36 n.s.
Personal Control
above Job Ambiguity .24 .06 .05 6.21 .02
A supplementary analysis was conducted to determine whether
there was a relationship between Commitment to Change (General) and
Effectiveness. The 115 respondents with completed questionnaires were
rank ordered on both scales and a median split was conducted. The results
can be seen in Table 6.
Table 6
Commitment to Change (General) and Perceived Effectiveness
High Low
Commitment to Change (General) effectiveness effectiveness
High 71.9% (n = 41) 28.1% (n = 16)
Low 29.3% (n = 17) 70.7% (n = 41)
46
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
A chi-square test with continuity corrections was computed and
found to be 19.22 (df = 1, p < .001). Those with high Commitment to
Change (General) perceived themselves to be more effective, while whose
with low Commitment to Change (General) perceived themselves to be less
effective.
The 115 respondents with completed questionnaires were again rank
ordered, this time on both Commitment to Change (Specific) and Perceived
Cost of Change. A median split was conducted on both variables. The
results can be seen in Table 7.
Table 7
Commitment to Change (Specific) Versus Perceived Cost of Change
Commitment to Change (Specific) High Cost Low Cost
High 60.3% (n = 35) 39.7% (n = 23)
Low 40.4% (n = 23) 59.6% (n = 34)
A chi-square test with continuity corrections was computed to deter
mine the relationship between Commitment to Change (Specific) and Per
ceived Cost of Change. The resultant chi-square of 3.8 (df = 1, p < .05)
was significant; however, the relationship between specific aspects of
change and cost was not as strong as that between Commitment to
Change (General) and Perceived Cost. Where cost was perceived as high,
respondents were more committed than where cost was perceived as low.
47
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Hypothesis 5 predicted that “Perceived personal control, job ambigu
ity, and expectations for the changes will differentiate persons who are re
silient to change from those who are resistant to change.” While Perceived
Control and Job Ambiguity were the primary independent variables for this
study, the variable Expectations was added to this hypothesis. According
to Connor (1998), expectations are part of the change process. People feel
in control when they are prepared for what they are going to experience.
Unexpected events create ambiguity and cause unease and anxiety. There
is ample evidence to suggest that expectations make people more resilient.
Change-related anxiety is caused by shifting or inaccurate expectations.
When expectations are clear and accurate, resilience grows (Connor,
1998). Expectations provide information necessary to determine whether to
begin or continue an activity (resilience) or to discontinue or inhibit an activ
ity (resistance; Ford, 1992). The Expectations variable was measured on a
single 7-point Likert-type scale, with 7 = totally prepared (expectations have
been set and met) and 1 = totally unprepared.
Using a median split, the 114 participants with complete data were
assigned to one of four cells according to whether their scores were high or
low on perceived personal cost of change to them and high or low on their
commitment to the change. The four resultant groups are shown in Table
8. Resilient people were those with perceived high personal cost and high
commitment to change. Resistant people were those with perceived high
personal cost and low commitment to change. Doers were those with per
ceived low personal cost and high commitment to change. Slackers were
those with perceived low personal cost and low commitment to change. Of
48
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
the 114 respondents, 35 were Resilient and 22 were Resistant, with 23 Do
ers and 34 Slackers.
Table 8
Cost and Commitment to Change (General) and Perceived Cost of Change
Perceived “cost” to employees
Commitment to Change (General) High cost Low cost
High Resilient (n = 35) Doers (n = 23)
Low Resistant (n = 22) Slackers (n = 34)
The means and standard deviations for these four groups on the
three variables are shown in Table 9. The means for Personal Control in all
four groups were between a rating of 3 and 4 on a 5-point scale. The
standard deviations were above 0.5 for Resilient and Resistant groups but
below 0.5 for Doers and Slackers. The means for Job Ambiguity for all four
groups were between 5 and 6 on a 7-point scale, with standard deviations
over 1.0 in all groups except the Resistant group, where it was just over 0.5.
The mean for Expectations was highest for the Resistant group, although
the standard deviations were high (ranging from 1.5 to 2.0) in all groups.
Table 10 shows univariate information within and between the
various groupings of respondents for the three variables the means and
standard deviations of which were presented in Table 9.
49
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table 9
Means and Standard Deviations: Personal Control, Job Ambiguity, and
Expectations for Resilient, Resistant, Doer, and Slacker Types
Type Mean SD n
Resilient
Personal Control 3.58 0.58 35
Job Ambiguity 5.14 1.05 35
Expectations 5.03 1.96 35
Resistant
Personal Control 3.56 0.61 22
Job Ambiguity 5.72 0.57 22
Expectations 4.32 1.55 22
Doers
Personal Control 3.85 0.48 23
Job Ambiguity 5.45 1.07 23
Expectations 4.39 1.67 23
Slackers
Personal Control 3.49 0.46 34
Job Ambiguity 5.32 1.04 34
Expectations 4.29 1.95 34
Total
Personal Control 3.60 0.54 114
Job Ambiguity 5.37 0.99 114
Expectations 4.54 1.83 114
Note. All variables were measured on a 7-point Likert-type scale except
Personal Control, which was measured on a 5-point Likert-type scale.
Individually, none of these was significant. A discriminant function
run on the four groups taken together was not significant, producing a
Canonical Correlation of .26 (p < .10). To test Hypothesis 5, a second d
discriminant function analysis was conducted using only the Resilient and
Resistant groups. The findings were not significant (Table 11).
50
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table 10
One-Way Analysis of Variance: Job Ambiguity, Control, and Expectations
Variable and measure SS df
Mean
square F
P
Job Ambiguity
Between groups
Within groups
Total
4.926
105.651
110.576
3
111
114
1.642
.952
1.725 .166
Perceived Control
Between groups
Within groups
Total
1.833
31.562
33.394
3
111
114
.611
.284
2.148 .098
Desired Control
Between groups
Within groups
Total
1.264
28.018
29.282
3
111
114
.421
.252
1.669 .178
Expectations
Between groups
Within groups
Total
11.999
368.281
380.281
3
110
113
4.000
3.348
1.195 .315
Table 11
Discriminant Function Analysis Using Personal Control, Job Ambiguity, and
Expectations to Predict the Membership of Resilient and Resistant Groups
Function Eigenvalue
Canonical
correlation
Wilk’s
Lambda X2
P
Resilient/
Resistant 131 .34 .88 6.6 .09
51
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
It is interesting to note that the variable that appears to have most
strongly differentiated the two groups was Job Ambiguity, which correlated
.88 with the Wilk’s Lambda function.
52
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
CHAPTER 5
DISCUSSION AND CONCLUSIONS
The pace of change in the work place has not slowed with the econ
omy. Globalization, competition, and technological advances have contin
ued to spark major change in the work place, whether it is through mergers
and acquisitions, downsizing, or going out of business. These corporate
activities often leave employees behind. Between October 1998 and Octo
ber 1999, U.S. companies announced over 803,000 layoffs. Since January
2001, layoffs resulting from mergers, acquisitions, or divestitures have con
tinued, with companies such as DaimlerChrysler announcing 26,000 layoffs
and Lucent announcing 16,000 layoffs (Morris, 2001).
This strategy is not always conducive to economic recovery. Com
panies that laid off over 15% of their work force during the 1992 recession
performed significantly below average in the following 3 years (Zimmerman,
2001). The literature suggests that poorly handled downsizing (poor com
munications, lack of employee involvement, etc.) impacts productivity and
morale of the employees retained as well as future recruiting. The literature
suggests that, in these tumultuous times, people desire and need control
over their situation as well as clear expectations. When there is monu
mental ambiguity and a perceived loss of control due to poorly managed
organizational change, how do employees respond to that change? It is
important to have a study that discusses what occurs when these needs are
not met.
53
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
This chapter discusses the study’s findings, reviews the limitations of
the study, and provides suggestions for future research.
Summary
The purpose of the study was to determine whether personal control
and/or job ambiguity would predict commitment to change in an organiza
tional setting. The hypotheses were as follows:
1. Personal control will predict commitment (as impacted by specific
aspects of change) by itself and over and above what is predicted by job
ambiguity.
2. Job ambiguity will predict commitment (as impacted by specific
aspects of change) by itself and over and above what is predicted by per
sonal control.
3. Personal control will predict commitment (as impacted by the
change process) by itself and over and above what is predicted by job am
biguity.
4. Job ambiguity will predict commitment (as impacted by the
change process) by itself and over and above what is predicted by personal
control.
5. Perceived personal control, job ambiguity, and expectations for
the changes will differentiate persons who are resilient to change from
those who are resistant to change.
Respondents
Respondents for this study consisted of 125 managers, mostly male,
ranging in age from 25 to 50 years. Seventy participants were from Asia,
54
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Europe, and the Americas, and 50 of those spoke English as a second lan
guage. These managers worked in a global customer service and support
department of a high-tech company. All had recently participated in a major
organizational change and were continuing to experience change as the
company evolved.
Instruments
Three instruments were used to study the five hypotheses: the JAS
(Breaugh & Colihan, 1994), the PCS (Greenberger et al., 1991), and the
IRC. The last was used to measure resistance as well as resilience by as
sessing commitment to general and specific organizational change.
Procedure
All attendees at company-sponsored focus groups held in the
February-March 2000 time frame were administered the three instruments
used in this study. Participation was voluntary and respondents were guar
anteed anonymity. The three instruments were collated randomly to avoid
any order effects. Participants were allowed approximately 30 minutes to
complete the three instruments.
Results
Overall, of the five hypotheses tested, only one was confirmed.
Hypothesis 1 stated, “Personal control will predict commitment to
specific aspects of change by itself and over and above what is predicted
by job ambiguity.” Hypothesis 2 stated “Job ambiguity will predict commit
ment to specific aspects of change by itself and over and above what is
55
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
predicted by personal control.” Neither hypothesis was supported by this
study.
Hypothesis 3 stated, “Personal control will predict commitment to the
change process by itself and over and above what is predicted by job ambi
guity.” Hypothesis 4 stated, “Job ambiguity will predict commitment to the
change process by itself and over and above what is predicted by personal
control.” Hypothesis 3 was confirmed (p < .01), accounting for 6% of the
variability in the dependent variable. Hypothesis 4 was not confirmed.
While job ambiguity did not predict commitment to the change process, it
did differentiate those who would resist change from those who would be
more resilient.
Hypothesis 5 stated, “Perceived personal control, job ambiguity, and
expectations for the changes will differentiate those who are resilient to
change from those who are resistant to change.” While it was not con
firmed by this study, this hypothesis 5 produced interesting findings. Of the
114 study participants, only 57 were used for analysis on this hypothesis.
Since canonical correlations greater than .30 are typically considered
meaningful, the finding of a canonical equal to .34 (Table 11), despite not
being significant (p < .09), is meaningful. Since significance is a function of
sample size, it should be noted that this analysis was conducted on only 57
people. Thus, while Hypothesis 5 was not confirmed, it may well have been
supported, given a larger sample.
56
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Qualitative Findings
In addition to the quantitative data collected in relation to the hypo
thesis testing, qualitative data were collected through focus groups. The
questions posed during the focus groups mirrored the IRC scale. They
centered on two topics: (a) the personal cost of the changes to the indivi
duals and how that impacted their commitment to the changes, and (b) the
effectiveness of the change process and how that impacted their commit
ment to the changes.
A single researcher collected the qualitative data during focus groups
held globally. The participants of these focus groups were the same per
sons who participated in the research study. All voluntarily participated in
the focus groups. The same researcher also made all observations dis
cussed in this section. The findings, while not conclusive, are interesting.
The qualitative data appear to support a relationship (r = .29) be
tween personal cost and commitment to specific change, which can be
seen in the correlation matrix (Table 3). Here, the primary driver of cost
was job ambiguity. Many respondents commented on the difficulty of ad
justing to the “constantly evolving” changes, especially in the areas of job
responsibilities and group functions. In these groups, there appeared to be
a high level of job ambiguity that carried with it a high degree of personal
cost. This cost manifested itself in increased stress, anxiety, and burnout.
Many respondents commented on the need to rely on “personal heroics”
to get the job done. The new processes, job responsibilities, and organiza
tional structure were not completely understood or committed to. The dis
tinction between headquarters and regional responsibilities was unclear to
57
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
many respondents. This was compounded by the fact that many people
were moved into new positions. This not only impacted the specific group
function but also the cross-functional processes. Before the changes, re
spondents personally knew whom to go to for specific deliverables. During
and after the changes, this was no longer the case. As several respon
dents put it, “I don’t know the person who is responsible for this, so I’ll either
do it myself or go to the person who used to do it and hope they can help.”
This reliance on old networks, instead of the new processes put in place, is
indicative of an attempt to regain some level of control over an ambiguous
situation (Greenberger & Strasser, 1986). According to 80% of the focus
group participants, these personal costs had a direct effect on their com
mitment to the specific organizational changes.
Similarly, regarding the effectiveness of the change process, the
qualitative findings support a relationship (r= .51) between effectiveness
and commitment to change, which can be seen in the correlation matrix.
When asked about the effectiveness of the communication process and the
degree of understanding regarding the change process, 92% of the focus
group participants who rated these activities high also felt a strong commit
ment to the change process. This indicates that, when expectations are
clearly set, commitment is more forthcoming. Also, 87% of the focus group
participants who felt that they had a high degree of involvement and support
from the organization were more likely to be committed to the changes.
Conversely, 86% of the focus group participants who felt very little involve
ment or organizational support were far less likely to commit to the
changes. This was more acute in remote sites, with 94% of the focus group
58
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
participants having low commitment as a result of perceived ineffectiveness
in the change process. This low commitment manifested itself in a variety
of ways, including surreptitious resistance, higher turnover, and vocal dis
sent. All focus group participants with low commitment to the change
process agreed that improved communication and clear expectations that
are met in a timely manner would be enough to gain their support of the
organizational changes in question.
According to the data, the primary driver of commitment to the
change process appears to be expectations, with a secondary driver of per
sonal control. When respondents felt that their expectations were met and
they had some level of control over the situation, they were more likely to
support the change process. Conversely, when expectations were unclear
or not met, there were feelings of lack of control and resistance occurred.
Discussion
Past research has shown that job ambiguity and insufficient personal
control negatively affect employee effectiveness, productivity, and resili
ence, producing such detrimental effects as anxiety, stress, and dysfunc
tional behavior (Bandura, 1997; Kets de Vries, 1980; Martinko & Gardner,
1982; Ormrod, 1995). This study attempted to show links among job ambi
guity, personal control, and individual resistance or resilience to organiza
tional change. The data did not entirely support the predictions of the
study. However, it was found that personal control predicted commitment
to general change. This relationship, supported by past research (Green-
berger & Strasser, 1986; Heckhausen & Schulz, 1995), indicates that those
who feel more in control of their situation are more likely to commit to the
59
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
change process. The individual feels no threat to personal control, and
there is no need to find mechanisms to increase levels of personal control;
therefore, general commitment to the changes is less demanding. As Con
nor suggested (1992, 1998), this relationship between perceived control
and commitment to change helps to build resiliency in individuals as well as
in organizations. This suggests that giving employees opportunities to in
fluence their situation, either directly or indirectly, can increase their com
mitment to change.
While job ambiguity during times of organizational change did not
predict resistance or resilience, the data suggest that it strongly differenti
ated resistant individuals from resilient individuals. This finding indicates
that those who are more confident in their understanding of their job and
the “white space” in which they work are more likely to be resilient to the
changes going on around them. Expectations seem to be key here. The
data support Connor’s (1992) idea that, when individual expectations re
garding changes, organizationally or within the individual’s job or role,
are met, there is a higher degree of resilience. When these expectations
are not met, resistance occurs.
In addition to these instruments, which provided quantitative data,
focus groups were held with the participants. The collection of qualitative
data relies on rigorous data collection and triangulation of the data, which
includes interviews, observations, and documentation. The purpose of tri
angulation is to validate results obtained, particularly when a single re
searcher obtains them. As triangulation was not conducted for this study,
the qualitative findings should be considered, but with discretion.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
The qualitative data collected in these sessions did not necessarily
agree with the quantitative findings of this study. In general, the qualitative
data suggest that those most frustrated by job ambiguity or those who felt
that they were “losing” something, most notably control of their own situa
tion, were most likely to resist the organizational changes that they were
facing, thus impacting commitment to change. They were not resisting
changes clearly stemming from market changes or changes in technology.
New marketing strategies and new technologies were readily adapted and
embraced; however, processes were not readily adapted or embraced.
These employees were resisting things that were much more personal and
impactful on a daily basis, such as changes in how they worked and with
whom they worked. This form of resistance may not become evident
through the use of surveys. This resistance is subtle but very evident, if
ones know where to look.
It was expected that all five hypotheses would be supported by the
data and that the focus groups would corroborate these findings. This did
not occur. There are two primary factors that could have contributed to this
discrepancy.
1. Language skills: Fifty of the 125 participants reported that Eng
lish was their second language. The verbal loading of the instruments was
challenging, even for native speakers, and may have caused confusion in
the participants. The focus groups, on the other hand, allowed for clarifica
tion of questions and intent, as well as follow-up discussion. This factor
alone could explain the differences in the data.
61
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
2. Measure selection: The discrepancy in the qualitative and quanti
tative data could also be due to poor instrumentation selection. There is a
variety of measures in the subject areas researched in this study, and the
ones that were chosen may not have been the best choices, especially
given the language issues discussed above. While validated by previous
research, the JAS and PCS have not been used to measure resistance or
resilience to change. The IRC scale, although tested in a pilot study, was
not as reliable and valid as necessary when applied in the field. Additional
validation of this instrument is necessary if it is to be used in future re
search.
This apparent discrepancy between qualitative and quantitative data
leads to an interesting question: If there is a discrepancy between quali
tative data (focus groups) and quantitative data (survey results), which
should be trusted? Additional study is warranted in this area.
There also appears to be a relationship between expectations and
resistance or resilience. Past research (Aronson, 1994; Bandura, 1997)
has indicated that situations that can be predicted or anticipated are easier
to manage and tend to bring feelings of self-determination. When what is
currently happening matches expectations, people feel a greater sense of
control and resilience (Deci & Ryan, 1985).
Findings in this study indicate that involving employees and ensuring
that expectations regarding their jobs are clear and timely can improve or
ganizational performance through times of change. Setting clear expecta
tions around job roles, company direction, and the change process itself
appear to have a positive impact on employee commitment to change and
62
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
overall resistance to change. Further study in this area is needed to deter
mine appropriate interventions and their implications.
Another area of study involves culture. Does corporate culture play
a part in the validity of qualitative or quantitative data collection? It should
be stated that, for this particular sample population, the culture of the com
pany did, in fact, support and encourage integrity of response during focus
group activities. It was clearly stated in each session that the results would
remain anonymous. The person conducting these groups was well known
to the participants, either personally or through reputation, as having high
integrity and discretion. This, along with a supportive culture, supports the
position that the quantitative data paint not only an accurate picture of the
organization but a compelling one as well, especially in light of the dis
crepancy with the study results. However, this question demands further
study and debate.
Conclusions and Recommendations
From the results of the study, it cannot be concluded that job ambi
guity or perceived loss of control predicts resistance or resilience to organ
izational change. However, the discrepancy between the quantitative data
and qualitative findings obtained through focus groups demands further
study.
Based on past research and supported by the personal observations,
anecdotal data, and the qualitative focus group data, it appears that
ambiguity and/or a perceived loss of control increase individual anxiety and,
therefore, resistance to change. If not addressed in a timely manner with
constant positive reinforcement from a source with unquestioned integrity,
63
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
this anxiety can lead to individual as well as organizational resistance to
change. It is interesting to note that, based on observations during the
period of this study, clusters of anxiety and frustration, when left unchecked,
festered and infected pockets of the organization that might not have felt
these negative emotions individually. This phenomenon requires additional
study.
Based on this study and related peripheral activities, the researcher
has concluded that resistance or resilience to change is a very personal
reaction and that it must be studied as such. Surveys provide background
and trends but, as evidenced in this study, do not necessarily paint an en
tirely accurate picture. Qualitative data collection, even if informal, is a
necessary activity to truly understanding the pulse of the organization in
question.
64
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
REFERENCES
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
REFERENCES
Abramson, L. Y., Seligman, M., & Teasdale, J. D. (1978). Learned help
lessness in humans: Critique and reformulation. Journal of Abnor
mal Psychology, 87(1), 49-74.
Albrecht, K. (1991). The northbound train: Finding purpose, setting direc
tion, shaping the destiny of your organization. New York:
AMACOM.
Allen, V. I., & Greenberger, D. B. (1980). Destruction and perceived con
trol. In A. Baum & J. E. Singer (Eds.), Advances in environmental
psychology (Vol. 2, pp. 85-110). Mahwah, NJ: Erlbaum.
Argyris, C. (1993). Knowledge for action: A guide for overcoming barriers
to organizational change. San Francisco: Jossey-Bass.
Armenakis, A. A., Harris, S. G., & Mossholder, K. W. (1993). Creating
readiness for organizational change. Human Relations, 46, 681-703.
Aronson, E. (1994). The social animal. New York: Freeman.
Baker, S. (1999, October 18). Cisco’s telecom two-step in Europe. Busi
ness Week, 198.
Bandura, A. (1997). Self-efficacy: The exercise of control. New York:
Freeman.
Bazerman, M. H. (1982). Impact of personal control on performance: Is
added control always beneficial? Journal of Applied Psychology, 67,
472-479.
Bennis, W. (1993). Beyond bureaucracy: Essays on the development and
evolution of human organizations. San Francisco: Jossey-Bass.
Bolman, L. G., & Deal, T. E. (1997). Reframing organizations: Artistry,
choice, and leadership. San Francisco: Jossey-Bass.
Breaugh, J. A., & Colihan, J. P. (1994). Measuring facets of job ambiguity:
Construct validity evidence. Journal of Applied Psychology, 79, 191-
202.
Bridges, W. (1980). Making sense of life’s transitions: Strategies for cop
ing with the difficult, painful, and confusing times of your life.
Boulder, CO: Perseus Books.
Bridges, W. (1991). Managing transitions: Making the most of change.
Reading, MA: Addison-Wesley.
66
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Cairncross, F. (1997). The death of distance: How the communications
revolution will change our lives. Boston: Harvard Business School
Press.
Clark, R. E. (1997). Research driven technology model. Unpublished lec
ture, University of Southern California.
Clark, R. E. (1998). The CANE model of motivation to learn and to work:
A two-stage process of goal commitment and effort. International
Journal of Educational Research, 37(8), 39-47.
Cohen, A. R., & Bradford, D. L. (1989). Influence without authority. New
York: Wiley.
Collins, J. C., & Porras, J. I. (1994). Built to last: Successful habits of
visionary companies. New York: Harper Business.
Computer Industry Almanac, Inc. (1996). The 8th annual computer indus
try almanac. Washington, DC: Thompson.
Connor, D. R. (1992). Managing at the speed of change: How resilient
managers succeed and prosper where others fail. New York: Vil-
lard.
Connor, D. R. (1998). Leading at the edge of chaos: How to create the
nimble organization. New York: Wiley.
Cox, J. R. (1997). Manufacturing the past: Loss and absence in organiza
tional change. Organizational Studies, [Berlin] 18, 623-654.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determina
tion in human behavior. New York: Plenum.
DeFrank, R. S., & Ivancevich, J. M. (1999). Stress on the job: An execut
ive update. Engineering Management Review, 27(3), 31 -41.
Eccles, J. S., & Midgley, C. (1989). Stage-environment fit: Development-
ally appropriate classrooms for young adolescents. In C. Ames & R.
Ames (Eds.), Research on motivation in education. Vol. 3: Goals
and cognitions (pp. 139-186). San Diego: Academic Press.
Fisher, C. D., & Gitelson, R. (1983). A meta-analysis of the correlates of
role conflict and ambiguity. Journal of Applied Psychology, 68, 320-
333.
Fiske, D. W., & Maddi, S. R. (1961). Functions of varied experience.
Homewood, IL: Dorsey.
Ford, M. E. (1992). Motivating humans: Goals, emotions, and personal
agency beliefs. Newbury Park, CA: Sage.
67
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Galpin, T. J. (1996). The human side of change: A practical guide to or
ganization redesign. San Francisco: Jossey-Bass.
Garten, J. (1999, October 18). What global capitalism needs now: Less
red tape. Business Week, 26.
Gist, M. E., & Mitchell, T. R. (1992). Self-efficacy: A theoretical analysis of
its determinants and malleability. Academy of Management Review,
17, 183-211.
Goldstein, J. (1988). A far-from-equilibrium systems approach to resis
tance to change. Organizational Dynamics, 17(2), 16-26.
Green, H., & Himelstein, L. (1999, October 4). Betting that even Beijing
can’t control cyberspace. Business Week, 38-39.
Greenberger, D. B., Porter, G., Miceli, M. P., & Strasser, S. (1991). Re
sponses to inadequate personal control in organizations. Journal of
Social Issues, 47(A), 111-128.
Greenberger, D. B., & Strasser, S. (1986). Development and application of
a model of personal control in organizations. Academy of Manage
ment Review, 11(1)164-177.
Handbook of U.S. labor statistics: Employment, earnings, prices, productiv
ity, and other labor data (2nd ed.). (1998) Lanham, MD: Bernan.
Heckhausen, J., & Schulz, R. (1995). A life-span theory of control. Psy
chological Review, 102, 284-304.
Heisler, W. J. (1974). A performance correlate of personal control beliefs
in an organizational context. Journal of Applied Psychology, 59,
504-506.
Kagan, J. (1972). Motives and development. Journal of Personality and
Social Psychology, 22, 51-66.
Kahn, J. (2001, July 23). The world’s largest corporations. Fortune, 144-
152.
Kahn, R., Wolfe, D., Quinn, R., Snoek, J., & Rosenthal, R. (1964). Or
ganizational stress: Studies in role conflict and ambiguity. New
York: Wiley.
Kerlinger, F. N. (1986). Foundations of behavioral science (3rd ed.). New
York: Holt, Rinehart & Winston.
Kestler, D. (2002). Career information center (Vol. 13, 8th ed.). New York:
Macmillan.
68
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Kets de Vries, M. F. R. (1980). Organizational paradoxes: Clinical ap
proaches to management. New York: Routledge.
Kets de Vries, M. F. R. (1991). Organizations on the couch: Clinical per
spectives on organizational behavior and change. San Francisco:
Jossey-Bass.
Knowles, M. S. (1984). Andragogy in action: Applying modern principles
of adult learning. San Francisco: Jossey-Bass.
Kotter, J. P. (1995, March/April). Leading change: Why transformation
efforts fail. Harvard Business Review, 59-67.
Kubler-Ross, E. (1969). On death and dying: What the dying have to
teach doctors, nurses, clergy and their own families. New York:
Macmillan.
Landau, R. (1995). Locus of control and socioeconomic status: Does in
ternal locus of control reflect real resources and opportunities or per
sonal coping abilities? Social Science Medical, 41, 1499-1505.
Lawler, E. E., III. (1985). Education, management style, and organizational
effectiveness. Personnel Psychology, 38, 1 -26.
Lewin, K. (1951). Field theory in social science. New York: Flarperand
Row.
Lombo, G. (1994, July 18). A year for the record books. Forbes, 266-279.
Maglee, F., & Schiller, F. (1998, August 28). Mergers, acquisitions, divesti
tures: It’s a mad world for IT. GartnerGroup Research Notes.
Martinko, M. J., & Gardner, W. L. (1982). Learned helplessness: An alter
native explanation for performance deficits. Academy of Manage
ment Review, 7, 195-204.
Maurer, R. (1996). Beyond the wall of resistance: Unconventional strate
gies that build support for change. Texas: Bard Books.
Morris, B. (2001, July 23). White collar blues. Fortune, 99-110.
Mourier, P. (1998). How to implement organizational change that produces
results. Performance Improvement, 37(7), 19-28.
Nadler, D. A. (1998). Champions of change: How CEOs and their com
panies are mastering the skills of radical change. San Francisco:
Jossey-Bass.
Nohria, N., & Khurana, R. (1993). Executing change: Three generic
strategies. Boston: Harvard Business School Press.
69
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
O’Neil, H. F., & Drillings, M. (1994). Motivation: Theory and research.
Mahwah, NJ: Erlbaum.
Ormrod, J. E. (1995). Human learning. Upper Saddle River, NJ: Prentice-
Hall.
Orpen, C. (1994). Interactive effects of work motivation and personal con
trol on employee job performance and satisfaction. Journal of Social
Psychology, 134, 855-856.
Plunkett, J. (2001). Plunkett’s infotech industry almanac 2001-02. Gal
veston: Plunkett Research.
Posner, B. Z., & Butterfield, D. A. (1979). Personal correlates of organiza
tional control. Journal of Psychology, 102, 299-306.
Prochaska, J. O., Norcross, J. C., & DiClemente, C. C. (1994). Changing
for good: A revolutionary six-stage program for overcoming bad
habits and moving your life positively forward. New York: Avon
Books.
Rizzo, J. R., House, R. J., & Lirtzman, S. I. (1970). Role conflict and ambi
guity in complex organizations. Administrative Science Quarterly,
15, 150-163.
Robbins, S. R. (1993). Organizational behavior: Concepts, controversies,
and applications. New York: Simon & Schuster.
Robbins, S. R. (1997). Essentials of organizational behavior. Upper Sad
dle River, NJ: Prentice-Hall.
Romano, C. (1995). Managing change, diversity and emotion. The Man
agement Review, 84(7), 6-9.
Rothbaum, F., Weisz, J. R., & Snyder, S. S. (1982). Changing the world
and changing the self: A two-process model of perceived control.
Journal of Personality and Social Psychology, 42, 5-37.
Rotter, J. B. (1966). Generalized expectancies for internal vs. external
control of reinforcement. Psychological Monographs, 80, 1-28.
Sawyer, J. E. (1992). Goal and process clarity: Specification of multiple
constructs of role ambiguity and a structural equation model of their
antecedents and consequences. Journal of Applied Psychology, 71,
130-142.
Seneker, H. (1992, July 20). Fewer deals, less investments. Forbes, 290-
296.
70
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Senge, P. M. (1990). The fifth discipline: The art and practice of the
learning organization. New York: Doubleday.
Siegall, M., & Cummings, L. L. (1986). Task role ambiguity, satisfaction,
and the moderating effect of task instruction source. Human Rela
tions, 39, 1017-1032.
Slater, C. (1998). From the turbulent seventies to the virtuous nineties:
Three decades of economic change. Washington, DC: Bernan.
Snow, R. E., & Jackson, D. N. (1994). Individual differences in conation:
Selected constructs and measures. In H. O’Neil & C. Drillings (Eds.),
Motivation: Theory and research (pp. 71-100). Mahwah, NJ: Erl-
baum.
Sorrentino, R. M., & Short, J. C. (1986). Uncertainty orientation, motiva
tion, and cognition. In R. M. Sorrentino, E. Tory Higgins, & E. Tony
Higgins (Eds.), Handbook of motivation and cognition (pp. 379-403).
New York: Guilford.
Sparks, D. (1999, October 25). Partners: A special report. Business
Week, 106-112.
Spreitzer, G. M. (1996, April). Social structural characteristics of psycho
logical empowerment. Academy of Management Journal, 483-504.
Strebel, P. (1996, May/June). Why do employees resist change? Harvard
Business Review, 86-92.
Teece, D. J. (1987). The competitive challenge: Strategies for industrial
innovation and renewal. Cambridge, MA: Ballinger.
Tetenbaum, T. J. (1998, Spring). Shifting paradigms: From Newton to
chaos. Organizational Dynamics, 21 -32.
Thomas, K. W., & Velthouse, B. A. (1990). Cognitive elements of
empowerment: An “interpretive” model of intrinsic task motivation.
Academy of Management Review, 15, 666-681.
Toffler, A. (1970). Future shock. New York: Random House.
Trusty, M. L., & Macan, T. H. (1995). Personal control: Effects of reward
contingency and locus of control. Journal of Social Behavior and
Personality, 10, 201-214.
University of Michigan, Survey Research Center. (1971). Survey of work
ing conditions. Washington, DC: U.S. Government Printing Office.
U.S. Bureau of the Census. (1998). Statistical abstract of the United
States (118th ed.). Washington, DC: Author.
71
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
U.S. Department of Labor, Bureau of Labor Statistics. (2001). Occupa
tional outlook handbook. Washington, DC: Author.
Weidenbaum, M. (1995). Isolationism in a global economy. The Manage
ment Review, 84(12), 43-47.
Werner, E. E., & Smith, R. S. (1992). Overcoming the odds: High-risk
children from birth to adulthood. New York: Cornell University
Press.
White, V. (2001, July 23). What recession? Forbes, 136.
Woodward, H. (1994). Navigating through change. Chicago: McGraw-
Hill.
Zajac, B. (1999, July 26). Spanning the world. Forbes, 202-218.
Zimmerman, E. (2001, November). Why deep lay-offs hurt long-term
recovery. Workforce, 48-53.
72
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
APPENDIX
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Job Ambiguity Scale (JAS)
Using the following scale, please rate your opinion of the following items.
Please write the appropriate number in the space provided before each
item.
1 = strongly disagree
2 = disagree
3 = disagree slightly
4 = neutral
5 = agree slightly
6 = agree
7 = agree strongly
Work Method
1. ___ I am certain how to go about getting my job done (the methods to
use).
2. I know what is the best way (approach) to go about getting my work
done.
3. I know how to get my work done (what procedures to use).
Scheduling
1. I know when I should be doing a particular aspect (part) of my job.
2. I am certain about the sequencing of my work activities (when to do
what).
3. My job is such that I know when I should be doing a given work
activity.
Performance Criteria
1. I know what my manager considers satisfactory work performance
2. It is clear to me what is considered acceptable performance by my
manager.
3. I know what level of performance is considered acceptable by my
manager.
Source: “Measuring Facets of Job Ambiguity: Construct Validity Evidence,”
by J. A. Breaugh and J. P. Colihan, 1994, Journal of Applied Psychology,
79, p. 195.
74
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Personal Control Scale
The following series of questions asks how much influence you now have in
each of several areas. By influence, we mean the degree to which you
control what is done by others at work and have freedom to determine what
you do yourself at work. In the space provided before each statement, write
the number that indicates how much influence you actually have in each
area.
1 = very little; 2 = little; 3 = a moderate amount; 4 = much; 5 = very much
1. ___ How much influence do you have over the variety of tasks you
perform?
2 . ___ How much influence do you have over the order in which you
perform tasks at work?
3 . ___ How much influence do you have over the amount of work you
do?
4 . ___ How much influence do you have over the quality of the work that
you do?
5 . ___ How much influence do you have over the arrangement and
decoration of your work area?
6 . ___ How much influence do you have over the decisions concerning
which individuals in your work unit do which tasks?
7 . ___ How much influence do you have over the decisions as to when
things will be done in your unit?
8 . ___ How much do you influence the policies, procedures, and
performance standards in your unit?
9 . ___ How much influence do you have over the training of other
workers in your work unit?
10 . ___ How much influence do you have over the arrangement of desks
and work equipment in your unit?
11 . ___ In general, how much influence do you have over work and work-
related factors?
75
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
The next series of questions asks how much influence you would like to
have in each of several areas.
1 = very little; 2 = little; 3 = a moderate amount; 4 = much; 5 = very much
1 . ___ How much influence would you like to have over the variety of
tasks you perform?
2 . ___ How much influence would you like to have over the order in
which you perform tasks at work?
3 . ___ How much influence would you like to have over the amount of
work you do?
4 . ___ How much influence would you like to have over the quality of the
work that you do?
5 . ___ How much influence would you like to have over the arrangement
and decoration of your work area?
6 . ___ How much influence would you like to have over the decisions
concerning which individuals in your work unit do which tasks?
7 . ___ How much influence would you like to have over the decisions as
to when things will be done in your unit?
8 . ___ How much would you like to influence the policies, procedures,
and performance standards in your unit?
9 . ___ How much influence would you like to have over the training of
other workers in your work unit?
10 . ___ How much influence would you like to have over the arrangement
of desks and work equipment in your unit?
11 . ___ In general, how much influence would you like to have over work
and work-related factors?
Source: “Development and Application of a Model of Personal Control in
Organizations,” by D. B. Greenberger and S. Strasser, 1986, Academy of
Management Review, 11{ 1), p. 168.
76
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Individual Response to Change (IRC) Scale
With the split-up of SSV and the integration of SSV and ISSD, the Services
and Support organization (SSU) has undergone major changes. In an effort
to better understand the impact of those changes on the SSU workforce, we
would appreciate your completing this form as honestly as you can. There
are no right or wrong answers.
Your responses on this form are completely anonymous.
For each aspect of change identified below, please indicate in the second
column the degree to which you feel that the particular aspect is “costing”
you physically, intellectually, or emotionally. Then, in the third column,
please indicate the degree to which you feel that the particular aspect is
positively (high) or negatively (low) impacting your commitment to the job.
(Circle the most appropriate rating.)
Aspect of the Change
“Cost” to Me of this
aspect of the change
H igh................. Low
My Commitment
to my job
H igh................. Low
1. Changes in
Management
7 6 5 4 3 2 1 7 6 5 4 3 2 1
2. Turnover among
employees
7 6 5 4 3 2 1 7 6 5 4 3 2 1
3. Changes in job
responsibility
7 6 5 4 3 2 1 7 6 5 4 3 2 1
4. Changes in
customer interfac
ing functions
7 6 5 4 3 2 1 7 6 5 4 3 2 1
5. Changes in
resource allocation
7 6 5 4 3 2 1 7 6 5 4 3 2 1
6. ISSD and SSV
organizational
changes and
integration
7 6 5 4 3 2 1 7 6 5 4 3 2 1
7. Creation of
standardized,
consistent services
7 6 5 4 3 2 1 7 6 5 4 3 2 1
8. Creation of new
functions
7 6 5 4 3 2 1 7 6 5 4 3 2 1
77
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
In the section below, for each aspect of the change process, please indicate
in the second column the degree to which you feel that the particular aspect
is being handled effectively. Then in the third column, please indicate the
degree to which you feel the particular aspect is positively (high) or
negatively (low) impacting your commitment to the job. (Circle the most
appropriate rating.)
Aspects of the Change
Process
Effectiveness
H igh................. Low
My Commitment
to my job
H igh................. Low
1. My understanding of
the purpose of the
change
7 6 5 4 3 2 1 7 6 5 4 3 2 1
2. My involvement in
planning the changes
7 6 5 4 3 2 1 7 6 5 4 3 2 1
3. Management’s com
munications regard
ing the changes
7 6 5 4 3 2 1 7 6 5 4 3 2 1
4. Organizational sup
port provided while
making the changes
(e.g., training)
7 6 5 4 3 2 1 7 6 5 4 3 2 1
5. Incentives provided
to me to engage in
the change
7 6 5 4 3 2 1 7 6 5 4 3 2 1
6. My understanding of
the necessity for the
changes
7 6 5 4 3 2 1 7 6 5 4 3 2 1
7. My understanding of
my role as a result of
the changes
7 6 5 4 3 2 1 7 6 5 4 3 2 1
8. My ability to influence
the course of the
changes
7 6 5 4 3 2 1 7 6 5 4 3 2 1
On the scale below, rate the extent to which you anticipated the
announcement of the HP/Agilent split back in March 1999. (Circle the
most appropriate rating)________
Totally Neither prepared Totally
prepared nor unprepared unprepared
7 6 5 4 3 2 1
By Martha Jensen.
78
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Commitment to deliberate practice in the development of writing expertise: A survey of the motivational patterns of academic writers in Tier One research universities

PDF

Are executives overconfident?

PDF

An evaluation of perceived task value, self-efficacy, and performance in a geography blended distance course

PDF

A formative evaluation of the training effectiveness of a computer game

PDF

Data warehouse: A case study of the factors effecting user satisfaction

PDF

Cost of university -based online, distributed, and traditional learning

PDF

Distance learning as an alternative method for training in the private sector of Saudi Arabia

PDF

Comparing cognitive task analysis to behavior task analysis in training first year interns to place central venous catheters

PDF

Assessing the critical behavioral competencies of information technology (IT) project managers at Southern California Edison

PDF

Effects of group financial incentives and individual evaluation upon group performance

PDF

Cognitive efficiency of animated pedagogical agents for learning English as a second language

PDF

A pilot study to investigate the relationship between student self -regulatory resource management strategies and academic achievement in a Web-based hybrid graduate nursing course

PDF

The Effects Of Feedback On Accounting Achievement In A Community College

PDF

Effects of interactive multimedia for the prevention of obesity on self-efficacy, beliefs about physical activity, and social influence

PDF

Assessing students' and professors' attitudes toward the use of computer -based technology in the classroom: A case study at the University of Jordan

PDF

Academic self regulation, task persistence, and completion of assignments in an e-mail-supported distance learning environment

PDF

Interfacing with a digital archive (ISLA) in enhancing elementary school geography instruction and learning

PDF

Improving English language development via interfacing with a digital archive

PDF

Development of a diagnostic tool for assessing knowledge management

PDF

How has the requirement to implement content standards affected the instructional program in schools and classrooms?

Asset Metadata

Creator Jensen, Martha Marie (author)

Core Title Individual resistance to organizational change: The impact of personal control and job ambiguity

School Rossier School of Education

Degree Doctor of Education

Degree Program Education

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

Tag education, business,OAI-PMH Harvest

Language English

Contributor Digitized by ProQuest (provenance)

Advisor Clark, Richard (committee chair), Kazlauskas, Edward (committee member), Totenbaum, Toby (committee member)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c16-638246

Unique identifier UC11335027

Identifier 3116720.pdf (filename),usctheses-c16-638246 (legacy record id)

Legacy Identifier 3116720.pdf

Dmrecord 638246

Document Type Dissertation

Rights Jensen, Martha Marie

Type texts

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

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

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus, Los Angeles, California 90089, USA