Insecure Commitment and Resistance: an Examination of Change Leadership, Self-Efficacy, and Trust on the Relationship Between Jo

Home , Robert Reich

Insecure Commitment and Resistance: An Examination of Change Leadership, Self-Efficacy, and Trust on the Relationship between Job Insecurity, Employee Commitment, and Resistance to Organizational Change

A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY

Robert Elijah Smith

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

Dr. Kenneth R. Bartlett, Advisor

September, 2013

ACKNOWLEDGEMENTS

Throughout the journey culminating in this study, there were a number of individuals who provided support, knowledge, and encouragement without whom this dissertation would not have been possible. I have been fortunate to have been surrounded by encouraging family members, friends, and colleagues along the way. I would first like to thank my advisor, Dr. Kenneth Bartlett, who served not only as an exceptional advisor but also a wonderful mentor before my doctoral journey began and every step along the

way.

I would also like to thank the members of my committee including, Dr. Theresa

Glomb, Dr. Alexandre Ardichvilli, and Dr. James Brown. I had the privilege of being a

student in their classes and gaining from their perspective throughout the dissertation

process. I would also like to thank Dr. Theodore Lewis, Dr. Andrew Van de Ven, Dr.

Timothy McClernon, Dr. Shari Peterson, and Dr. Ernest Davenport whose insights and

courses had a strong influence on the development of many the ideas presented. Thanks

to my study buddy, colleague, and friend Jane, for the recommendations, countless study

sessions, and commiseration. From a data gathering perspective, this study would not

have been possible without the support of Rashad and Scotty—many thanks to you both.

I also wish to thank Vicky whose statistical expertise was invaluable. Also, a special

thanks to the Directors of the Western Golf Association Evans Scholarship Foundation

and the Minnesota Evans Scholars Directors and Faculty Advisor, John, Ede, Jim, and

Rodney for the opportunity.

Above all, I especially want to thank my family. First, to my wife, Luise, whose

feedback, HR expertise, encouragement, and willingness to support home responsibilities

enabled me to finish. My sister, Chrystal, was a constant source of encourage and

support. Finally, I wish to thank my parents, Helen and Jerome Smith, and grandparents,

Ineater and Robert Franklin and Alease and Charles Smith, whose years of struggle, hard

work, prayers, and focus on the value of education, enabled me to reach the highest heights.

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ABSTRACT

This study was designed to examine the mediation role of self-efficacy and the

moderating roles of change leadership strategy and trust on the change attitudes of job

insecure employees. Using job insecurity theory (Greenhalgh, 1983), Chin & Benne’s

(1961) seminal classification of change leadership strategies and the tripartite model of

attitudes (Breckler, 1984; McDougal, 1909) as a theoretical basis, data were collected

from two samples of employees including a manufacturing firm (n=275) and a retail

company (n= 350). The samples and study hypotheses were analyzed using hierarchical

multiple regression analysis.

As predicted, job insecurity was directly positively related to affective,

behavioral, and cognitive resistance to change and self-efficacy partially or fully mediated the relationships. Mixed results were found for the role of trust as well as information and participation-based change leadership strategies in moderating employee resistance to change. In some cases perceived information-based and participation change leadership approaches were associated with increased resistance rather than decreased resistance to change. Power-based change leadership strategies however were found to be consistently associated with more pessimistic employee attitudes.

Results support previous findings showing that individuals who believe they will be negatively impacted by organizational change are particularly sensitive to change leadership approaches. The results also suggest that commonly prescribed change leadership strategies such as increased information, communication, and participation during periods of heightened job insecurity may not always be effective in reducing resistance to change but efforts to increase employee self-efficacy may support the

iv coping mechanism employees use to reduce resistance to change attitudes in organizational change climates with moderate levels of job insecurity.

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... i ABSTRACT ...... iii TABLE OF CONTENTS ...... v LIST OF TABLES ...... vii LIST OF FIGURES ...... xi CHAPTER 1: INTRODUCTION ...... 1 Changing Technology...... 4 Globalization ...... 17 Changing Customer Preferences ...... 22 Job Threatening Organizational Changes ...... 27 Research Purpose and Questions...... 38 Significance of the Study ...... 39 Contributions and Implications for HRD ...... 43 Summary of Chapter 1 ...... 45 CHAPTER 2: LITERATURE REVIEW ...... 47 Organizational Change ...... 48 Job Insecurity ...... 49 Change Related Self-Efficacy ...... 57 Resistance to Organizational Change ...... 62 Commitment to Organizational Change ...... 68 Leading and Managing Organizational Change ...... 77 Trust in Management ...... 89 Insecure Resistance: Toward an Integrated Model of Job Insecurity and ...... 93 Employee Commitment and Resistance ...... 94 Summary of Chapter 2 ...... 99 CHAPTER 3: METHODS ...... 101 Population and Sample 1 (Manufacturing Sample) ...... 102 Population and Sample 2 (Retail Sample)...... 108

Data Analysis ...... 138 Summary of Chapter 3 ...... 141 CHAPTER 4: RESULTS ...... 143 Descriptive Statistics ...... 143 Hypothesis Test Results ...... 145 Manufacturing Sample Results ...... 147 Retail Sample Results ...... 160 Combined Manufacturing and Retail Sample Hypotheses Results ...... 176 Full Hypothesized Relationship Model Results ...... 190 Summary of Chapter 4 ...... 191 CHAPTER 5: SUMMARY, CONCLUSIONS, DISCUSSION, AND ...... 198 RECOMMENDATIONS ...... 198 Summary ...... 198 Conclusions ...... 201 Discussion ...... 205 Job Insecurity, Commitment to Change, and Resistance to Change ...... 205 Job Insecurity, Change Related Self-Efficacy, and Resistance to Change ...... 205 Job Insecurity, Change Leadership, Commitment, and Resistance to Change ...... 207 Job Insecurity, Change Leadership, Trust in Management, and Change Attitudes ...... 212 Discussion Summary of Job Insecurity, Change Self-Efficacy, Change Leadership, Trust in Management, and Change Attitudes ...... 215 Limitations of the Study ...... 216 Recommendations for Future Research ...... 217 Recommendations for Practice...... 221 REFERENCES ...... 229 APPENDIX B: RESEARCH PARTICIPANT COMMUNICATIONS ...... 284 APPENDIX C: CHANGE MANAGEMENT SURVEY ...... 287

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LIST OF TABLES

Table 1.1 Predictions of the Task Model for the Impact of Computerization on Four

Categories of Workplace Tasks ...... 10

Table 1.2 Output, Employment, and Job Destruction between 1992 and 1997...... 37

Table 2.1 Typology of Resistance to Change ...... 65

Table 3.1 Chi-squared Test Results Summary for Paper Compared to Web Surveys

(manufacturing sample) ...... 107

Table 3.2 Gender of Respondents (manufacturing sample)...... 111

Table 3.3 Age Range (manufacturing sample) ...... 113

Table 3.4 Race / Ethnicity (manufacturing sample) ...... 113

Table 3.5Marital Status (manufacturing sample)...... 114

Table 3.6 Children Financially Responsible (manufacturing sample)...... 114

Table 3.7 Education (manufacturing sample) ...... 114

Table 3.8 Organizational Level (manufacturing sample) ...... 115

Table 3.9 Role Function (manufacturing sample) ...... 115

Table 3.10 Organizational Change Type (manufacturing sample) ...... 116

Table 3.11 Significance of Change (manufacturing sample)...... 116

Table 3.12 Likelihood of Job Loss due to Change (manufacturing sample) ...... 117

Table 3.13 Most Likely Cause of Job Loss (manufacturing sample) ...... 117

Table 3.14 Gender of Respondents (retail sample) ...... 119

Table 3.15 Age Range (retail sample) ...... 119

Table 3.16 Race / Ethnicity (retail sample) ...... 120

Table 3.17 Marital Status (retail sample) ...... 120

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Table 3.18 Children Financially Responsible (retail sample) ...... 121

Table 3.19 Education (retail sample) ...... 121

Table 3.20 Organizational Level (retail sample) ...... 122

Table 3.21 Role Function (retail sample) ...... 122

Table 3.22 Organizational Change Type (retail sample) ...... 123

Table 3.23 Significance of Change (retail sample) ...... 123

Table 3.24 Likelihood of Job Loss due to Change (retail sample) ...... 124

Table 3.25 Most Likely Cause of Job Loss (retail sample) ...... 124

Table 3.26 Chi-squared test results summary for Manufacturing compared to Retail

samples ...... 126

Table 3.27 Anderson-Darling Test of Normality Results ...... 128

Table 3.28 Sources, Authors, and Reliability Coefficients of Measuring Instruments of

Study Constructs ...... 138

Table 4.1 Descriptive Statistics of Instrument Scales and Items (manufacturing sample) ...... 144

Table 4.2 Descriptive Statistics of Instrument Scales and Items (Retail Sample) ...... 145

Table 4.4 Regression Results for Hypothesis 1 and 2 Job Insecurity Relationship to

Affective Commitment to Change and Three Resistance to Change Measures and

Hypothesis 3 Change Related Self-Efficacy Mediation Between Job Insecurity,

Affective Commitment to Change and Three Resistance to Change Types

(manufacturing sample) ...... 148

Table 4.5 Regression Results for Hypotheses 4a and 4b Job Insecurity and Rational

Empirical (information-based) Change Leadership Strategy and Moderated

Interactions (manufacturing sample) ...... 150

Table 4.6 Regression Results for Hypotheses 5a and 5b Normative Re-educative

(participation-based) Change Leadership Strategy and their Interactions

(manufacturing sample) ...... 154

Table 4.7 Regression Results for Hypotheses 6a and 6b Power Coercive (top-down)

Change Leadership Strategy (manufacturing sample) ...... 156

Table 4.8 Regression Results for Hypotheses 6a and 6b Trust in Management and their

Interactions (manufacturing sample) ...... 158

Table 4.9 Regression Results for Hypothesis 1 and 2 Job Insecurity Relationship to

Affective Commitment to Change and Three Resistance to Change Measures and

Hypothesis 3 Change Related Self-Efficacy Mediation Between Job Insecurity,

Affective Commitment to Change and Three Resistance to Change Types (retail

sample)...... 162

Table 4.10 Regression Results for Hypotheses 4a and 4b Job Insecurity and Rational

Empirical (information-based) Change Leadership Strategy and Moderated

Interactions (retail sample) ...... 164

Table 4.11 Regression Results for Hypotheses 5a and 5b Normative Re-educative

(participation-based) Change Leadership Strategy and their Interactions (retail

sample)...... 167

Table 4.12 Regression Results for Hypotheses 6a and 6b Power Coercive (top-down)

Change Leadership Strategy and their Interactions (retail sample) ...... 170

Table 4.13 Regression Results for Hypotheses 7a and 7b Trust in Management and their

Interactions (retail sample) ...... 174

Table 4.14 Regression Results for Hypothesis 1 and 2 Job Insecurity Relationship to

Affective Commitment to Change and Three Resistance to Change Measures and

Hypothesis 3 Change Related Self-Efficacy Mediation Between Job Insecurity,

Affective Commitment to Change and Three Resistance to Change Types (combined

manufacturing and retail sample) ...... 178

Table 4.15 Regression Results for Hypotheses 4a and 4b Job Insecurity and Rational

Empirical (information-based) Change Leadership Strategy and Moderated

Interactions (combined manufacturing and retail sample) ...... 180

Table 4.16 Regression Results for Hypotheses 5a and 5b Normative Re-educative

(participation-based) Change Leadership Strategy and their Interactions (combined

manufacturing and retail sample) ...... 183

Table 4.17 Regression Results for Hypotheses 6a and 6b Power Coercive (top-down)

Change Leadership Strategy and their Interactions (combined manufacturing and

retail sample) ...... 185

Table 4.18 Regression Results for Hypotheses 7a & 7b Trust in Management and their

Interactions (combined manufacturing and retail sample) ...... 189

Table 4.19 Summary of Tested Hypotheses (manufacturing, retail and combined results) ...... 191

Table 5.1 Alignment of Change Leadership Strategies and Strategic Process Design ...... 225

LIST OF FIGURES

Figure 1.1. Model of job-threatening macro-level and organizational change and impacts

on employee responses ...... 4

Figure 1.2. Outsourcing moving towards core competencies...... 22

Figure 1.3. The work systems framework...... 25

Figure 1.4. Outsourcing impact model...... 36

Figure 2.1. Causes and consequences of a job insecurity crisis ...... 53

Figure 2.2. Comparison of organizational commitment versus commitment to

organizational change...... 75

Figure 2.3. Job insecurity, affective commitment to change, and resistance to change

hypothesized relationships model ...... 95

Figure 2.4. Job insecurity, change related self-efficacy, affective commitment to change,

and resistance to change mediation hypothesized relationships model...... 95

Figure 2.5. Job insecurity, perceptions of change leadership strategy, trust in

management, affective commitment to change, and resistance to change moderation

hypothesized relationships model ...... 96

Figure 3.1. Normal Q-Q plot of mean score for manufacturing sample...... 129

Figure 3.2. Normal Q-Q plot of mean score for retail sample...... 129

Figure 3.3. Normal Q-Q plot of mean score for combined manufacturing and retail

sample...... 130

Figure 4.1. Influence of rational-empirical change leadership strategy on affective

commitment to change for low and high job insecure individuals (manufacturing

sample)...... 151

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Figure 4.2. Influence of rational-empirical change leadership strategy on affective

resistance to change for low and high job insecure individuals (manufacturing

sample)...... 152

Figure 4.3. Influence of rational-empirical change leadership strategy on affective

resistance to change for low and high job insecure individuals (retail sample)...... 165

Figure 4.4. Influence of normative re-educative (participation-based) change leadership

strategy on affective resistance to change for low and high job insecure individuals

(retail sample)...... 168

Figure 4.5. Influence of power coercive (top-down) change leadership strategy on

affective resistance to change for low and high job insecure individuals (retail

sample)...... 171

Figure 4.6. Influence of power coercive (top-down) change leadership strategy on

behavioral resistance to change for low and high job insecure individuals (retail

sample)...... 172

Figure 4.7. Influence of trust in management on behavioral resistance to change for low

and high job insecure individuals (retail sample)...... 175

Figure 4.8. Influence of rational-empirical change leadership strategy on affective

resistance to change for low and high job insecure individuals (combined

manufacturing and retail sample)...... 181

Figure 4.9. Influence of rational-empirical change leadership strategy on cognitive

resistance to change for low and high job insecure individuals (combined

manufacturing and retail sample)...... 181

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Figure 4.10. Influence of power coercive (top-down) change leadership strategy on

affective resistance to change for low and high job insecure individuals (combined

manufacturing and retail sample)...... 186

Figure 4.11. Influence of power coercive (top-down) change leadership strategy on

behavioral resistance to change for low and high job insecure individuals (combined

manufacturing and retail sample)...... 187

Figure 4.12. Influence of power coercive (top-down) change leadership strategy on

cognitive resistance to change for low and high job insecure individuals (combined

manufacturing and retail sample)...... 187

Figure 5.1. Most commonly reported organizational changes (manufacturing sample)...... 199

Figure 5.2. Most commonly reported organizational changes (retail sample)...... 200

CHAPTER 1: INTRODUCTION

The workforce in the United States has witnessed unprecedented changes over the

past 30 years. These changes have taken the form of drastic, organizational shifts that

range from technological changes, outsourcing, offshoring, downsizing, and

organizational restructuring which all have significant impacts on employees and their

ability to remain in their jobs (Burke & Cooper, 2000; Reisel & Banai, 2002). Though

dramatic, observing these workplace changes in isolation presents a skewed picture

because it ignores the seismic shifts that undergird the macro-economic changes that have

led to decisions to restructure organizations in ways that have resulted in the threat of

significant job losses. Economic recessions, new information technology, industrial

restructuring, and accelerated global competition have all proven to be central factors

influencing the nature of work and employee job security (Hu & Zuo, 2007; Probst,

2005). Increased globalization, changing customer needs and preferences, and new

technology have converged over the past several decades and together constitute the

driving forces behind most contemporary organizational change (Beer, Voelpel, Leibold,

& Tekie, 2005; Freeman, 1999 Fugate, Pressia, & Kinicki, 2012; Jenster & Pedersen,

2000; Ulrich, 1998).

As organizational leaders attempt to restructure their organizations to adapt to

these changes, the result has been increased threat and levels of downsizing activity

resulting in higher levels of job insecurity among employees (Ashford, Lee, & Bobko,

1989; Otto, Hoffmann-Biencourt, & Mohr, 2011; Probst, 2003; Roskies & Louis-Guerin,

1990). Twenty-five years of job insecurity research has overwhelmingly shown the deleterious impact employee concerns over job loss has on a variety of factors ranging

from diminished health, decreased performance, commitment, and creativity (see

Greenhalgh & Rosenblatt, 2010). As macro-economic factors continue to shift the

operating environment of private and government organizations alike, the pace of

organizational change will continue to accelerate employee job insecurity and those

employees will be asked to commit to organizational changes that will increase their job insecurity.

The goal of this chapter is to provide an overview of these changes and their relationship to the ways in which they threaten jobs and lead to increases in workers’ job insecurity. Given the scale of these topics, broad theoretical lenses are required to provide a coherent framework to examine the contemporary forces that have shaped the U.S and global workforces. Work systems theory comes from Information Technology (IT) studies and has been used to describe the link between changing customer needs and preferences and the corresponding changes in workplace production environments.

Transaction cost economic theory has roots in economics and has been used to explain how and why organizations restructure their operations in ways that ultimately result in significant job changes. Using these theoretical perspectives, this research seeks to explore the broad economic and organizational changes that have altered the nature of work over the past 30 years.

Any sound discussion of work-related changes in the U.S. should acknowledge the fact that small, domestic businesses constitute the vast majority of American business.

Small businesses in the U.S. (firms with 19 employees or less) make up approximately 78 percent of all U.S. businesses and account for between 50 and 65 percent of all new job creation while the Fortune 1,000 companies (Microsoft, General Electric, Target, Coca-

Cola, etc.) employ less than 20 percent of the U.S. workforce (O'Toole & Lawler, 2006;

Neumark, Wall, & Zhang, 2011; Ozgulbas, Koyuncugil, & Yilmaz, 2006; United States

Small Business Administration, 2011). In addition, the percentage of workers employed by Fortune 1,000 companies has dropped over the past several decades due to many of the changes that will be outlined throughout this chapter. Nevertheless, an examination of the economic forces that have transformed these organizations is important since the efficacy of smaller firms is often contingent upon the success of larger firms directly and indirectly by way of clientele, supplier relationships, and portfolio investments such as retirement plans.

The central focus of this research is the examination of the impact of perceptions of job insecurity on employee responses to organizational change. To fully understand this impact, researchers and Human Resource Development (HRD) professionals alike must first understand the broader macro-economic and organizational level change context. Garavan (2007) argued that a broader change perspective is an essential element of strategic HRD. Porras and Silvers (1991) suggested that organizational change is usually activated by environmental shifts that generate organizational responses. The central thesis of this chapter is that macro-economic changes specifically, technology changes, globalization, and shifting customer preferences, have led organizational leaders to implement organizational level changes such as automation, downsizings/layoffs, mergers and acquisitions, and restructuring that have increased employees’ levels of job insecurity (see Figure 1.1).

Figure 1.0.1. Model of job-threatening macro-level and organizational change and impacts on employee responses (A figure created by the study author and researcher, R. Smith, using Microsoft PowerPoint). Macro-level changes such as job-eliminating technologies, global competition, labor cost alternatives (e.g. automation and outsourcing), have been identified as sources of job insecurity (Bowman & Singh, 1993; Burchell, 1999; Martin & Freeman, 1998). The following sections outline three macro-level economic changes that have produced the largest impacts on job-threatening organizational change in the U.S. over the past 30 years.

Changing Technology

Technology has had an enormous impact on the U.S. workplace over the past 30 years and volumes of scholarly and popular literature have been devoted to the topic of how technology changes work (see Freidman, 2005; Rifkin, 1995; Tushman &

Rosenkopf, 1992; Zuboff, 1988). Two central questions have arisen in response to the dramatic and rapid impact of technology in the workplace. The first pertains to the impact of technology on how work is performed (e.g. does technology increase the skill-levels required to perform work?) and the second question relates to the extent to which technology eliminates work and jobs. These are questions that find their origins in some of the earliest writings on jobs and economics but the goal here is not to parse out the nuances of scholarly arguments that come down on either side of responses to these

questions, but rather to provide some context for understanding how recent technological

developments influence the nature of work and worker’s perceptions of the continuity of

their jobs in contemporary organizational settings.

According to Kemper (1972), Adam Smith’s utilitarianism is the view of

technology that has been embraced by most sociologists. In this view, managers divide

labor and introduce machinery into the production process with the goal of reducing labor

costs and increasing profits. Despite modifications to this view, mostly centered around

the degree to which technology offers control of work to management rather than

workers, the Smithian view has stood the test of time (Braverman, 1974; Marglin, 1972).

In general, managers have embraced technological advancements that have reduced the

cost of labor by reducing wage differentials and their reliance on skilled workers who are

able to demand higher wages (Form, 1987). Yet, many scholars have argued that

advances in technology have the greatest impact on unskilled workers by eliminating

routine and cognitively simplistic work tasks (Autor, Levy, & Murname, 2003; Freifeld,

1984).

Given rapid technological advancements, some theorists have gone so far as to

predict the ultimate disappearance of unskilled work to be replaced by technical and

professional work (Bell, 1973; Ford, 2009; Rifkin, 1995; Leontief, 1983). Autor et al.

(2003) pointed to four central developments over the past several decades to support their

view that technology has increased demand for skilled and educated workers compared to non-skilled and undereducated. The first, beginning in the 1970s, demand for manual, unskilled labor in the U.S. declined and while demand for interactive, non-routine labor

increased (e.g. service sector employment). Second, as might be expected, demand for

6 non-routine labor was highest in those industries that had the highest rates of computerization. Third, the change in demand toward more skilled labor could not be accounted for by differences in education alone. In other words, the need for skilled, technologically proficient labor stretched across educational levels. Finally, the increased demand for non-routine labor transcended occupations such that any occupation that was being affected by increased computerization saw a shift toward non-routine labor; it was not a phenomenon that was only occurring in industries where one might expect to see increased demand (e.g. IT, software programmers, etc.). Addison, Fox, and Ruhm (2000) uncovered almost identical findings in their empirical analysis of technology and job loss in the U.S. during the late 1980s and early 1990s as did Givord and Maurin (2004) in their analysis of job loss in France between 1982 and 2002. In the French case, it was hypothesized that job losses would have been even greater had it not been for comparatively stringent labor protection laws in that country (Givord & Maurin, 2004).

In each of these studies, job loss at the macro and organizational levels were, in large part, accounted for by changes and advancements in technology and IT. Understandably, these changes lead some theorists to contemplate ‘the end of work’ as we know it and to question whether or not today’s unskilled manual worker will have a place in the twenty first century workplace (Rifkin, 1995).

Computers and robots replace humans in the exercise of mental functions in the

same way as mechanical power replaced them in the performance of physical

tasks. As time goes on, more and more complex mental functions will be

performed by machines. Any worker who now performs his task by following

specific instructions can, in principle, be replaced by a machine. This means that

the role of humans as the most important factor of production is bound to

diminish—in the same way that the role of horses in agricultural production was

first diminished and then eliminated by the introduction of tractors (Leontief,

1983, p. 3-4).

To some extent, each new wave of technological advancement has created

concern over the future of work and employment as the U.S. workforce transitioned

through the Industrial Age, and now, the Information Age. In 1840, nearly two-thirds of

U.S. workers were involved in some form of agricultural labor but since that time, the

U.S. workforce has witnessed the decline of agricultural work and more recently the dramatic decline of manufacturing work. Less than 3% percent of U.S. workers are now employed in agriculture and less than 15 percent of workers are employed in the manufacturing sector (O’Marah, 2008; U.S. Bureau of Labor Statistics, 2010; U.S.

Department of Agriculture, 2009). Nevertheless, the integration of technology and work has, to some extent, always had deleterious effects on employment. For example the

Gutenberg printing press devastated employment in the illuminated manuscript business and the telegraph did the same for Pony Express riders in the U.S. during the period of

Manifest Destiny1 (Collins & Ryan, 2007).

1 Manifest Destiny is the term used to refer to the prevalent 19th century notion in the United States that the U.S. was destined (and even divinely ordained) to expand across the North American continent eventually enveloping Canada, Mexico, Cuba, and Central America (Stephanson, 1996). Pony Express riders were used during this period to transport mail and other correspondence from the developed, eastern U.S. across largely undeveloped areas of the Western U.S. to frontier settlers. Pony Express riders provided the central means of connection and communication between the Atlantic (eastern U.S.) and Pacific (western U.S.) areas before telegraph and telephone services were available or widely used (Dicerto, 2002).

Previous macro-level changes resulting from technological advancements have eliminated pre-existing forms of labor while simultaneously opening up new employment opportunities. The Industrial Revolution was the quintessential macro-level example of this process. As technological advancements in farming increased fewer workers were needed to perform the same work with equal, if not greater, levels of productivity. The same mechanizing technology opened up opportunities for work in mass production in

U.S. factories (Collins & Ryan, 2007; Keynes, 1963; Schumpeter, 1942). A cursory glance at more recent technological advancements including networked IT systems and automation (by definition, the term automation refers to any work process that replaces human labor) might lead one to conclude that the same processes are at work—namely that emerging technologies are supplanting the need for manual, production-based workers but newer, high-tech jobs will be available for those possessing the skills to work in those areas (Drezner, 2004; Merriam-Webster, 2008).

The counter argument is that technology creates improvements in efficiency without the need to replace workers; consequently, jobs lost to advancements in technology are jobs that are simply eliminated from the economy (Collins & Ryan,

2007). This point is borne out by data showing decreases in manufacturing employment worldwide between 1995 and 2002 ranging from 11% in the U.S., 15% in China, and

20% in Brazil (Drezner, 2004). These decreases in employment paralleled a 30% increase in global manufacturing output suggesting that technology is the central driver of decreased manufacturing employment as well as increased productivity. In other words, technological advancements allow organizations to do more with fewer workers. These trends have led some researchers to conclude that technological advancements will lead

to widespread structural unemployment and large numbers of unemployed workers while

simultaneously polarizing the workforce into high-skill and low-skilled workers

(Gottinger, 1990). Autor et al. (2003) argued that technological changes have benefited

high-education occupations by providing additional resources to assist in the completion

of complex tasks (complementary effect), but the same technologies have had a

deleterious effect on jobs requiring moderate levels of education (substitutable effect) and

has minimal effects on occupations requiring low-levels of education.

To understand the impact of technology on the work and employment, it is important to understand generally how technology aids in getting work accomplished.

Technology can serve as a substitute for human skills by carrying out a limited and well-

defined set of cognitive and manual tasks which Autor et al. (2003) referred to as “routine

tasks”. Because IT systems are especially adept at handling these tasks, particularly

within a limited space, this technology frequently substitutes for basic human skills. This

is commonly observed in production settings that have utilized increasingly sophisticated

automation technology and IT systems. This also helps to explain decreased employment

levels and attendant productivity increases in the manufacturing sector noted above as

well as the so-called “jobless recovery” following the rapid decline of the technology

sector market bubble of the early 2000s (Groshen & Potter, 2003).

Not all technology however can substitute for human skills. Such substitutions are

limited to a defined set of programmable parameters. Technology can complement

human skills by managing more routine tasks allowing technology users to focus on

broader and more complex tasks (Autor et al., 2003). This is the domain of non-routine

work and, in general, these types of work tasks do not lend themselves to the technology

substitution effects that place jobs with more routine characteristics at risk. Table 1.1

provides examples of the types of jobs in which current technologies are likely to

substitute or complement human labor. These include record-keeping, calculation,

repetitive customer service (e.g. bank tellers), picking and sorting-oriented jobs, and

repetitive assembly work.

Table 1.1

Predictions of the Task Model for the Impact of Computerization on Four Categories of

Workplace Tasks

Routine tasks Non-routine tasks Analytic and Interactive Tasks Examples • Record-keeping • Forming/testing hypotheses • Calculation • Medical diagnosis • Repetitive customer service • Legal writing (e.g. bank teller) • Persuading/selling • Managing others Computer Impact • Substantial substitution • Strong complementarities

Manual tasks Examples • Picking or sorting • Janitorial services • Repetitive assembly • Truck driving Computer Impact • Substantial substitution • Limited opportunities for substitution or complementarities

Note. From “The Skill Content of Recent Technological Change: An Empirical Exploration,” by D. H. Autor, F. Levy, & R. J. Murname, 2003, The Quarterly Journal of Economics, 118, p. 1286. Reprinted with permission.

In general, the degree to which a job can be transformed from non-routine to

routine is the degree to which technology can substitute for that job. While it is true that

one of the main functions of technology has always been to decrease the amount of

energy devoted to mundane tasks allowing workers to focus on more complex job tasks

and functions, theorists (see Kurzweil, 2005; Rifkin, 1995; Zuboff, 1998) have predicted

that the rate of IT advancements will soon match the level of non-routine work tasks

essentially making technology a perfect substitute for human skills and abilities.

Properly programmed, these new ‘thinking machines’ are increasingly capable of

performing conceptual, managerial, and administrative functions and of

coordinating the flow of production, from extraction of raw materials to the

marketing and distribution of final goods and services (Rifkin, 1995, p. 60).

In the manufacturing and production sectors in particular, researchers have

demonstrated dramatic shifts that have occurred in the nature of work over the past

several decades (Gardner, 1995; Lewis, 1997; Rifkin, 1995; Zuboff, 1988). Studies of

changes in the economic environment and a number of case studies have led researchers

to four theories on the effects of automation (Form, 1987). Blauner’s (1964) U Theory of

Automation purports that mechanization (improvements to work processes that do not replace human operators) causes deskilling whereas, automation leads to upgrades in the skills required to perform work. The Hump Theory of automation suggests that automation upgrades the types of skills required to perform work then it later quickly accelerates deskilling (Braverman, 1974; Bright, 1966). The Hump Theory of automation hypothesizes that in the long-run, automation will result in the diminution of skills. When coupled with more recent research (see Autor et al., 2003; Givord & Maurin, 2004) noting the substitutability effects of technology on human labor, the long view of technology’s impact becomes markedly pessimistic since lower skilled, routine work has been found to be the first domain of technological substitution. In light of this theory, automation represents a self-perpetuating system in which more automation leads to decreased skills and diminished skills, in turn, lead to even more automation. A third

theory of automation is the Polarization Theory in which automation results in skill

increases for some jobs and decreases in skills for other jobs (Danziger, 1985). The

fourth, Central Automation Theory, is similar to the third which is a contingency theory

of automation (Simpson, 1985). According to this theory, the effects of automation are

contingent upon the type of industry, occupation, market, and other factors.

To provide some perspective on contemporary applications of automation and

technology, Hecht (2001) and Autor, Levy, and Murname (2002) provided explorations

of the relationship between technology and work in the insurance and banking industries.

In particular, Hecht identified five generations of technological advancement and

computerization in the insurance industry beginning in the 1960s. The first through fourth

generations were characterized by improvements in computing storage, speed, and

processing complexity but the fifth-generation (current time period) of computing

technology is distinct in terms of its ability to mimic basic human intelligence using

large-scale integration technology (Turner & Gatza, 1990). Hecht noted that the

fundamental processes of the industry have become increasingly dependent on “a strategy

of intensive automation and computerization of many operations including policy

production and procurement, claims management, accounting, underwriting and

investment” (pp. 527-528). More intense reliance on technological systems has resulted in decreased labor and increased productivity predicted by economic models of work and technological displacement. These investments in advanced automation and computer technologies have been taking place in large, highly visible insurance firms including

State Farm, Allstate, USAA, GEICO, and Progressive (Hecht, 2001). Since 1978, the number of production and clerical workers in the insurance industry has significantly

declined. These workers comprised nearly 30% of workers in the insurance industry in

1978 and by 1996, the same category of workers declined to nearly 10%.

The work performed by individuals in these categories is comprised of the same

routine, high-volume, standardized, and transactional work outlined by Autor et al.

(2002). These figures provide evidence for the ‘technologization’ of work. In his multi- decade review of the insurance industry and technology adoption, Hecht (2001) concluded that the direction of technological advancement is both uniform and irreversible. Firms that opted to increase investments in technology and automation never reverted back to non-technical work methodologies or work systems. This is borne out by

Labor Department reports showing that labor costs have increased while technology and capital costs have decreased (Rampell, 2011) suggesting that organizational leaders may have a built-in incentive to invest in technology-based alternatives to human labor since technology investments have lower long-term cost outlays. This also provides some explanation behind why research shows that investments in technology tend to increase over time. In light of previous research on work and technology, evidence for the impact of technology on work in the insurance industry in particular suggests three important conclusions. First, work that lends itself to routinization and standardization can be automated leading to decreased employment levels in impacted occupations and jobs.

Second, investments in technological improvements can and will be made at the organization and industry levels; and third, industries in the manufacturing sector are not the only industries influenced by technological advancements on employment.

Managers as mediators of job-threatening technology change

Organizing production around technology as opposed to human labor does not

always result in decreased employment opportunities. Managers have a significant role to

play in post-technology work re-organizations and the ways in which technology

advancements are implemented can have vastly differing effects in the workplace

(Hunter, 1999; Zuboff, 1988). In their 2002 study of two departments in the same bank

following the introduction of Optical Character Recognition (OCR) software, Autor et al.

(2002) found that department managers were left with two very different alternatives for how they could restructure work once the new technology had been introduced.

Government regulations, processing costs, increased competition, and customer demands placed pressure on the bank leadership to decrease costs associated with their check processing unit and increase the speed at which checks were processed. The OCR technology allowed the bank to meet these demands by ensuring checks were scanned electronically. This resulted in a decreased need for human check scanners. In one unit, company managers decided to make the remaining jobs more specialized (narrow) and in the other department, managers elected to make the remaining jobs broader. In both cases, the new technology resulted in a decreased need for human labor, but in one case the technology resulted in the creation of broader job tasks (e.g. the CSR-effect as noted in the insurance industry—lower level job tasks and some higher-level tasks were combined into a single job), and in the other case the same technology led to more specialized tasks. In both situations, the new technology increased the skills required for employees to complete their work. Even when managers decided to increase the level of specialization among the remaining jobs, the workers received an increase in pay because those jobs were made more complex. These findings led the authors of the study to

conclude that the impact of technology on work is moderated by managerial decision-

making (Autor et al., 2002). Their conclusions about the relationship between work and

technology in this case study also mirrored the macro-level, empirical findings of Bauer

and Bender (2004) who found that organizations that undergo technology-based restructuring and work reorganizations have lower net-employment rates for unskilled and some types of skilled workers.

It would be incorrect however to glibly presume that technology’s primary effects on work are only substitution, elimination, or deskilling. In keeping with Autor et al.’s

(2003) observations concerning the complementary effects of technology, there has been growth in a new type of worker in the insurance industry known as the customer service representative (CSR) (Hecht, 2001). The CSR role combines the skills of a receptionist, telephone operator, underwriter, and a claims representative into a single role that also performs over-the-phone marketing, underwriting, and claims servicing (Hecht, 2001).

CSRs and related service occupations have shown significant increases over the past several decades in a variety of industries including banking, retail, insurance, airlines, telecommunications, and manufacturing service centers (Autor & Dorn, 2007; Lawler et

al., 1995). Some estimates have shown that work in these occupations comprise nearly

80% of the U.S. labor force (US Bureau of Labor Statistics, 2012). The growth of service

sector employment between 1980 and 2005 rose from 11% to 14.9%, a rate higher than

the growth rate among both managerial and professional occupations (Autor & Dorn,

2009). Many economists have concluded that jobs in service sector are the new domain

of workers who are relatively unskilled and have lower levels of educational attainment

(Autor & Dorn, 2009; Leonardi, 2008). These are the same workers who once dominated

16 the production and manufacturing sectors which have both witness significant declines over the last two decades.

According to Hulin and Roznowski (1985), as the level of technological complexity required in organizations rises, so do worker requirements. As a consequence, technological change can increase job insecurity and eventually lead to job loss for those workers whose skills and positions become obsolete. To that end, Givord and Maurin

(2004) noted that new information technologies are a critical factor in understanding the persistence of job insecurity. Taken together, the above research shows that technology has both a beneficial and detrimental influence on jobs, employment, and workers.

Historically, advances in technology have almost always resulted in job loss but those same advances have led to the creation of other types of jobs. IT and automation systems are becoming increasingly complex and this fact has led to relatively moderate views about the impact of technology as temporarily displacing workers to more radical views of technology which see technological advancement as eventually replacing human labor altogether. At present, technological and IT systems do not show immediate signs of overtaking large categories of labor (at least in the U.S.) but the rapid replacement of labor by automation and IT systems in the manufacturing sector over the past several decades coupled with rapid shifts in technological capabilities2 may be a sign of things to come particularly as organizations seek ways to decrease their expenditures to remain competitive in the global market.

2 See also Race Against the Machine which outlines several examples of technology shifts once thought of as being years away or only in the realm of science fiction that were developed and improved relatively rapidly including Google’s driverless car and IBM’s Computer Jeopardy Champion Watson.

Globalization

Prior to the 1980s the degree to which international markets factored into U.S.

production and decision-making was minimal. As late as the 1970s, international trade

accounted for only 15 percent of the country's manufacturing output (O'Toole & Lawler,

2006). Since that time, new entrants into the manufacturing markets, including China and

India, have placed increased competitive pressure on the U.S. to produce and sell

products in ever-more cost-effective ways. During that same time, Japanese and Korean

automobile and electronic manufacturers had been continually increasing the quality of

their product offerings while maintaining low productivity costs that allowed them to

recoup international tariff costs while simultaneously selling products to U.S. customers

at lower prices (O'Toole & Lawler, 2006). This, in large part, accounts for the

commanding position foreign automobile manufacturers like Toyota, Nissan, Honda, and

Hyundai hold in the U.S. automobile market today. The increased competition from non-

U.S. organizations, such as those observed in the automotive industry, is broadly referred to as globalization.

Globalization is defined as “a process fueled by, and resulting in, increasing cross-border flows of goods, services, money, people, information, and culture” (Held,

McGrew, Goldblatt, & Perraton, 1999, p. 16). In a globalized environment, organizations compete in an international marketplace rather than separate national and protected markets (Jenster & Pedersen, 2000). Globalization allows countries to compete against one another in providing products and services to the global marketplace consequently companies, irrespective of geographic location, that are able to provide the products and services consumers want to buy are the companies that will reap the highest profits. The

notion that competition is central to globalization is a concept that runs through some of

the earliest definitions of global industries. Morrison (1990) described global industries

as those characterized by intense levels of international competition, standardized

products that are available worldwide, and industry competitors with a presence in all key

international markets and at high levels of international trade.

According to O'Toole and Lawler (2006), globalization offers U.S. businesses

three main benefits: 1) the ability to speed production and design processes by expanding

the capacity of businesses to operate without ceasing operations, 2) the ability to expand

by selling products and services in more markets, and 3) the ability to source products

globally to get the best, most cost-effective prices for materials, products, and labor.

These organizational benefits are similar to those outlined by earlier researchers who suggested that the benefits of globalization include cost reduction (Kogut, 1985), improved quality of products or programs (Yip, 1989), enhanced customer preferences

(Levitt, 1983), and increased competitive leverage (Hamel & Prahalad, 1985; Hout et al,

1982).

Since workplace, labor, and human resources (HR) considerations are principle focus of this research, it is useful to note that labor costs typically comprise 30-80% of organizational expenditures so opportunities to make even relatively small cuts in those expenses are likely to receive consideration from senior managers (Cascio, 1993).

Organizations that are able to produce on a 24/7 basis are, by definition, more productive than those who are not able to do so. Therefore, the ability to operate continuously puts firms at a distinct advantage when compared to competitors selling the same products and services that are not able to the same (Hout et. al., 1982; Porter, 1985; 1986). The same

principle holds true for other firm-level benefits of globalization. Businesses that are able to take advantage of increased sales to new markets and cost-savings accrued by low-cost

supplies (including labor supplies) will be able to compete more effectively on a global

scale and realize greater profits.

An important subset of globalization is outsourcing. Jenster and Pedersen (2000)

defined outsourcing as the externalization of tasks and services previously performed in-

house to outside vendors. At its core, outsourcing is a make-or-buy decision wherein firm managers make a decision about whether to produce a needed product or service within their own organization or procure that product or service from the larger market

(Semilinger, 1991). Transaction cost economic theory has been used to explain the mechanics of outsourcing by predicting when organizations will opt to perform a particular activity in-house versus when it will outsource that activity or purchase it from the general market (Williamson, 1981; 1985). Outsourcing is a clear example of a firm opting to buy on the general market and, as predicted by transaction cost theory, this is done because the costs of coordinating those activities is within the organization is either too high or too resource intensive. Outsourcing is one of the most popular trends in organizational strategy over the past decade (Logan, Faught, & Ganster, 2004; Willcocks et al., 2012).

IT and other advanced technologies have converged to create the conditions for globalization by establishing an infrastructure to allow business to be conducted in a truly global fashion (Cseh, 1999; Friedman, 2005; Kobrin, 1997). Before recent technological advancements in IT, telecommunications, and computerization, globalization and outsourcing could not have reached the levels or scope seen in today’s organizational

operating environment. To that end, Kobrin (1997) argued that globalization is not driven

so much by foreign trade and investment as much as it is driven by increasing

technological scale and information flows. Kobrin’s argument is in line with the central

thesis of this chapter that changes in technology over the past several decades are the

central impetus behind changes taking place in the workplace.

Technological progress-in particular, improvements in computer hardware,

software, and networks-has been so rapid and so surprising that many present-day

organizations, institutions, policies, and mindsets are not keeping up. Viewed

through this lens, the increase in globalization is not an alternative explanation,

but rather one of the consequences of the increased power and ubiquity of

technology (Brynjolfsson & McAfee, 2011, p. 8).

Deavers (1997) noted that IT makes it cheaper to get the needed quality of work

done by specialists outside the organization. Sometimes work is outsourced to other on-

shore firms domestically and sometimes it is outsourced to non-U.S. countries

(offshoring). The computer firm, Hewlett-Packard is a prime example of the changes that have been taking place and the impact those changes have had on their workforce. Less than a decade ago, almost all of HP’s workers were domestic, regular, full-time, and permanent career employees but recently, the company has transformed itself into a global, more flexible firm in which half of its employees are either non-domestic, contingent, or contractual (O’Toole & Lawler, 2006).

The Hewlett-Packard example is representative of what has been occurring in a number of organizations that have been repositioning themselves to operate and compete in a global marketplace. Global competitive pressures that have arisen over the past

21 several decades have increased the complexity of operations and have led organizations to recreate themselves to be flatter, more flexible, and responsive (Jenster & Pedersen,

2000). As late as the 1970s, large conglomerate corporations were the quintessential model of the large organization in the U.S. where many of the jobs, functions, and work tasks were done in-house (vertically integrated). Over time, globalization, driven by outsourcing and technology advancements, has led organizations to outsource a number of functions that do not represent the core competencies. Core competence has been defined as “the collective learning in the organization, especially how to co-ordinate diverse production skills and integrate multiple streams of technologies” (Prahalad &

Hamel, 1990, p. 82). Gallon, Stillman, Harold, and Coates (1995, p. 1) defined core competencies as, “the things that some companies know how to do uniquely well and that have the scope to provide them with a better-than-average degree of success over the long-term.”

Each of these definitions implies there are relatively small sets of focused activities and functions that firms are uniquely suited to perform well. Those activities and functions represent the organization’s core competencies. Firms that perform a large number of activities and functions that fall outside their core competencies are at increased risk of operating less efficiently and as a result may elect to have those functions done outside the firm at a lower price while increasing efficiently. As a consequence of mounting competitive pressures, many organizations have elected to contract out more and more activities that were previously done in-house (Bacon &

Spiller, 1996). Figure 1.2 shows the types of outsourcing that can be performed, their relative importance and proximity to the core competencies of the organization.

Figure 1.0.2. Outsourcing moving towards core competencies. Adapted from “Services offshoring and its strategic effects on value chains,” by P. R. Giao, M. D. M. O. Junior, & E. P. G. D. Vasconcellos, 2008, Brazilian Administration Review, 5(3), p. 205. Adapted with permission. In a 2012 study, rates of outsourcing by function were found to be 40% in finance

and accounting, 21% in contact centers, and 16% in human resources (HR), and 11% in

the sales and marketing functions (Willcocks et al., 2012). As organizations increasingly

turn to outsourcing as a strategy for focusing on core competencies, critical employee

considerations including downsizing of employees previously employed in outsourced

roles and the re-training of workers associated with outsourced functions becomes

paramount. 3

Changing Customer Preferences

Changing technologies have allowed U.S. organizations to operate more

effectively and efficiently while broadening their scope into global markets (Karmarker,

3 Although relevant to discussions about globalization, considerations about the North American Free Trade Agreement (NAFTA) have been excluded due to the relatively low impact on the U.S. workforce and jobs. For instance, McLaren and Haboyan (2010) failed to find significant effects of NAFTA on U.S. labor markets though they did find evidence that NAFTA did depress wage growth in some industries.

2004). Amid increased global competition, customers expect organizations to both anticipate and deliver on their expected needs and wants more now than at any point in

recent history. Ironically, the net result of the Industrial Revolution (that brought mass

production) coupled with increased technological specialization is mass customization

which allows organizations to provide more specialized products and services to

customers in much the same way that prior to the Industrial Revolution, products and

services were created individually for each customer through craftwork. This view is

supported by research showing that manufacturing facilities that adopt advanced

technologies tend to also shift their business strategies toward production of more

customized products (Bartell, Ichniowski, & Shaw, 2007). Today, organizations that fail

respond to these changing customer preferences run the risk not surviving. The ability to

operate globally has in turn allowed organizations to capitalize on new organizational

structures, world-wide markets, and, in a growing number of cases, new workforces.

But each of these changes mean little if there are no markets into which U.S.

firms can sell products and services. Changing customer preferences represent the final,

macro-level change that has led to dramatic changes in work and organizations.

Consumer spending accounts for about two-thirds or 67% of all economic activity in the

U.S. and over the past few years that has risen to 72% (Fox-Davos, 2008). Indeed, the

age of mass production with limited variation or selection of products and services is

over. Customers now expect mass customization that allows them create and add their

own unique style to the products the products and services they buy (Cameron & Quinn,

1999; Prahalad & Venkatram, 2000). The focus on customer orientation not only affects

private sector firms as there are numerous examples of public-sector/government

24 organizations that have had to adopt practices from the private sector in order to improve performance and quality levels and maintain public sector funding (Parys, 2003; Pollit &

Bouchkaert, 2000; Solomon, 1986). For instance, when FedEx, Inc. began offering overnight package delivery service, the United States Postal Service (USPS) began offering the same service requiring a significant internal reorganization of their operating and delivery model (Emory, 2010; United States Postal Service, 2010). Similarly, a number of public schools in major cities face growing ‘competition’ from private charter schools like the Knowledge is Power Program (KIPP) that claim to offer superior educational results to their students (or “customers”--the term KIPP uses to refer to their students and parents of students) (Peterson, 2010).

The trend of organizations changing to match shifting customer demands has been explained by several theories including work systems theory. Work systems theory explains organizational change from a customer point of view by outlining the relationship between customers and organizational work arrangements. According to work systems theory, the goal of any work system is to produce products and services that customers want (Alter, 1999; 2002). Similar to Nadler’s (2006) congruence model of organizational change, work systems theory contends that organizations exist to produce output. In the case of firms, the work system, “exists to produce things customers want”

(Alter, 2002, p. 92). In line with the central argument of this chapter and models of effective organizational performance (see Burke & Litwin, 1992), work systems theory considers an organization’s external environment and its internal infrastructure as key determinants of whether an organization can operate as intended to accomplish its goals of meeting customer demands. The organizational operating environment and available

infrastructure are key determinants of whether a work system can accomplish its goals of

meeting customer demands which is why technology changes and globalization have

been drivers of organizational capacity to meet customer demand (see Figure 1.3).

Figure 1.0.3. The work systems framework. From, “The work system method for understanding information systems and information systems research,” by S. Alter, 2002,

Communications of the Association for Information Systems, 9(1), p. 93. Reprinted with permission.

Customer preferences are constantly changing and the competitive landscape of

many organizations require customer responsiveness and tracking (Jaworksi & Kohli,

1993). In the research literature, customer focus and orientation has been referred to as

market orientation. There is substantial evidence to suggest that organizations that adopt

26 a market orientation are successful and viable across a variety of performance measures.

Customers now demand more quality and clearer value for their purchases (Sims, 2002).

Customer satisfaction and value are seen vital to market performance, sales, and profitability (Woodruff, 1997). Research into customer and market oriented firms has uncovered positive relationships to profitability (Cole, Bacdayan, & White, 1993), competitiveness (Deshpande, Farley, & Webster, 1993), organization performance

(Atuahene-Gima, 1996; Balakrishnan, 1996; Deshpande et al., 1993; Jaworksi & Kohli,

1993; Ruekert, 1992; Slater & Narver, 1994), and long-term financial performance

(Ruekert, 1992).

With benefits like these, organizations have begun adopting a more customer- oriented approach which has, in turn, led to a number of organizational changes including shifts in management, communications, employee reward systems, and contractor/outsourcing relationships (Conduit & Mavondo, 2001). Flint, Woodruff, and

Gardial (1997), noted that to be successful in the current economic environment, firms must be aware of three critical customer factors: 1) the current needs of their customers

(what they value), 2) the ability to meet customers’ needs (to create value for them), and

3) the forces that drive customers’ perceptions of value to change over time.

Organizations that are customer or client-focused are committed to their customers, understand their needs, and seek ways to gain and increase customer satisfaction by increasing the value they provide to their customers (Dean, 2007). The organizational changes that have been occurring over the past several decades have enabled organizations to be more responsive to changing customer demands. For employees, increased customer focus often means increased job expectations, responsibilities, stress,

27 longer work hours, and higher standards for customer interactions (Harris, 2002). At the same time, changing to meet changing customer preferences has led to organizational restructuring and other job-threatening organizational changes that increase worker job insecurity.

Job Threatening Organizational Changes

The increasing pace of technological change, increased globalization, and efforts to remain competitive by meeting changing customer preferences have led organizational leaders to take extreme measures to remain competitive (Probst, Stewart, Gruys, &

Tierney, 2007). In response to environmental pressures, organizations have implemented a number of cost-cutting and restructuring measures that have led to a crisis of job insecurity. Job threatening organizational change initiatives in the form of downsizings, mergers and acquisitions, plant closures, and workforce reorganizations affect millions of

U.S. employees each year.

The following section outlines the relationship between job insecurity and seven types of organizational changes that have been characterized as job threatening based on the fact that when these changes are implemented, they often lead to or result in job loss.

Job insecurity. According to Ironson (1992), job insecurity is one of the most significant organizational stressors faced by employees. Sverke, Hellgren, and Naswall’s

(2002) meta-analysis summarizing the effects of job insecurity found that employees experiencing high levels of job insecurity experience decreased job satisfaction (Ashford,

Lee, & Bobko, 1989; Davy, Grunberg, Moore, & Greenberg, 1998; Davy, Kinicki, &

Scheck, 1991), are less involved in their jobs (Kuhnert & Palmer, 1991), are less committed to the organization and are more likely to quit their jobs (Ashford et al., 1989;

Davy et al., 1991), place less trust in management (Ashford et al., 1989), and have lower

levels of performance compared to workers with more job security (Abramis, 1994).

Cheng and Chan’s (2008) more recent meta-analysis which included 133 studies and 172 independent samples on the impacts of job insecurity replicated many of these results showing decreased job performance, job involvement, health effects, and increased turnover intentions. Other studies on job insecurity have shown that it leads to higher levels of job-related stress, more work withdrawal behaviors (absenteeism, tardiness, work task avoidance, less effort, longer break times than permitted, and working more slowly) (Reisel, Probst, Chia, Maloles, & Kong, 2010), lower levels of creativity

(Carnevale & Probst, 1998; Probst & Tierney, 2003), and decreased levels of employee

engagement (Bosman, Rothmann, & Buitendach, 2005; Manuno, Leskinen, & Kinnunen,

2001; Stander & Rothmann, 2010). The research on job insecurity over the past 20-plus

years indicates that job insecurity has multiple detrimental effects on employees, jobs,

and productivity.

Another important finding from the research on job insecurity is the fact that

employees who were highly invested in their jobs are more adversely affected by job

insecurity than their counterparts who are not as invested (Probst, 2000). This finding in

particular has important implications for organizational change initiatives. Organizational

changes that increase employees’ job insecurity have been found to have the most

adverse impact the employees who are most likely to be invested in their jobs and work.

Consequently, organizational changes that increase levels of job insecurity run the risk of

undermining organizational change goals by disrupting the work effort, innovation, and

creativity of the most important organizational members. According to Greenhalgh’s

(1983) job insecurity theory, employees who experience threats to their job security will

have poorer work attitudes, diminished productivity, and adaptability.

The pervasiveness of job insecurity among workers is not surprising given the

competition and pressures to remain competitive (Reisel, Chia, Maloles, & Slocum,

2007). Indeed, competition and government deregulation set in motion by many of the

forces described above have led organizational leaders worldwide to take extreme

measures in order to remain viable and a consequence has been increased job insecurity

(Probst et al., 2007; Sverke, Hellgren, & Naswall, 2006). According to the American

Management Association’s (1994) survey on downsizing, organizational rationales for

downsizing include improved productivity, plant obsolescence, mergers and acquisitions,

location transfers, and new technology. Chapter 2 provides a more detailed review of job

insecurity and its effects but by way of introduction, the following sections briefly outline

several organizational changes that have been characterized as job threatening. These

changes include technology (automation), downsizings and layoffs, restructuring, mergers and acquisitions, outsourcing and offshoring, and plant closures. Each of these changes has been identified in the job insecurity literature as changes that increase job insecurity among employees affected by them (Ashfored et al., 1989; Burke & Cooper,

2000; Burke & Nelson, 1998; Klandermans & Van Vuuren, 1999; Reisel, 2003; Roskies

& Louis-Guerin, 1990; Tetrick & Quick, 2003).

Technology Change (Automation). Automation refers to any technology or system that replaces human labor (Merriam-Weber, 20008). The section above on changing technology details how technology has fundamentally changed how work gets done and the degree to which workers are needed (or not) to complete that work. Beginning on a

30 large scale in the 1980s, advanced technology systems in the manufacturing sector began substituting machines for human workers on jobs that involved largely physical or manual processes (Autor et al., 2003; Gardner, 1995; Kletzer, 2005; Lewis, 1996; 1997).

Since that time, technology has been combined with IT advancements to provide increasingly sophisticated sales and trending analytic data and for what was once only the domain of accountants and managers (Kallinikos, 1998; Pink, 2005; Snell & Dean, 1992).

Several studies have shown that new technology in the workplace raises employee concerns about job loss (Carayon, 1997; Clegg & Frese, 1996; Markus & Robey, 1988;

Marquie, 1994; Vieitez et al., 2001). It remains to be seen how much of an impact advancements in technology will have on jobs and employment going forward but if the manufacturing sector is a leading indicator, automation and other advancement in technology may still prove to be a growing threat to jobs outside of that sector.

Downsizings and Layoffs. Globalization, technological advances, the growing importance of the service sector, and competitive benchmarking are market forces that lead to downsizing (Burke & Nelson, 1998; Datta, Guthrie, Basuil, & Pandey, 2010). One of the most common means of reducing variable costs of organizations is via layoffs

(Nixon, Hitt, Lee, & Jeong, 2004). Each year, millions of U.S. workers are terminated by their employers in an effort to reduce costs (U.S. Bureau of Labor Statistics, 2006). In the

U.S. in 2008, the Bureau of Labor Statistics recorded 21,137 “mass layoff events” that resulted in more than 2 million job losses (Bureau of Labor Statistics, 2009). McLean

(2006) observed that organizations downsize for a number of reasons including improved technology, mergers or acquisitions, downturns in the economy and/or demand for an

organization’s goods or services, improved processes, the replacement of regular

employees with contract employees, and outsourcing.

Downsizing has been defined as the planned elimination of positions or jobs and

is done by eliminating functions, hierarchical levels, or business units (Casico, 1993).

Loss of market share and decreases in profitability lead to downsizing in corporations.

Organizational rationales for downsizing include lower overhead costs, less bureaucracy,

faster decision making, smoother communications, greater entrepreneurship, and

increases in productivity (Heenan, 1989). Casico (1993; 2002a; 2002b) argued that

cutting costs by cutting people can be a natural strategy for companies struggling to stay

alive in an unprecedented, globally competitive market. Compelling arguments for and

against downsizing have been made and the moral appropriateness of downsizing as a

strategy is debatable, often hinging on the ways in which the downsizing is enacted.

Uchitelle (2006) argued that downsizing was once a rare occurrence that organizations

tried to avoid at all costs but over the past several decades, aided by industry-friendly and

flexibility-oriented public policies, downsizing has become a more frequent occurrence

and a strategy of first resort (rather than last). Today, downsizing is a taken-for-granted strategy that has become legitimized (McKinlay, Mone, & Barker, 1998).

Millions of U.S. employees have been impacted by downsizing. Between 1983 and 1993, Fortune 500 companies reduced their total workforces by 2.5 million employees (Simons, 1998). Downsizing researchers have identified three primary approaches organizations take to downsizing (DeRue, Hollenbeck, Johnson, Ilgen, &

Jundt, 2008). The workforce reduction approach represents a simple reduction in the number of lower-level operational employees with little or no consideration of structural

32 differentiation. Usually, early retirement or buyout (severance) packages are offered when organizations adopt this downsizing strategy and the focus is on fast implementation and short-term payoffs. Casio, Young, and Morris (1997) found that this strategy resulted in initial cost savings but ultimately ends with diminished organizational performance. The redesign strategy involves downsizing a unit through the redesign of work and structural change. Finally, the systemic layoff strategy involves a focus on changing the way employees approach their work.

Downsizing has been found to have an undesirable influence on employees that remain because it can result in unwanted job enlargement and increased work demands that overburden remaining employees (Kozlowski et al., 1993). Another outcome of downsizing is the “survivor syndrome” in which employees who remain in their organization following a layoff experience emotions such as anger, job insecurity, a perception of unfairness from management, depression, reduced risk taking and motivation, and low levels of morale (Kets de Vries & Balzas, 1997; Kettley, 1995; Noer,

1993). Repeated mass layoffs over several years create emotional reactions that lead to

“disidentification” of employees’ relationship to the organization (Kreiner & Asforth,

2003). Clearly downsizings and layoffs have the power to increase employee stress levels and fundamentally change the structure of work and workplace relationships. Most organizational downsizings are the result of organizational leaders attempting to keep their costs under control during periods increased competition and decreased profitability and revenues (Kets de Vries & Balzas, 1997; Nixon, Lee, & Jeong, 2004). Repeated downsizing and the threat of layoffs have often been found to result in employee

33 perceptions of job insecurity (Ashford et al., 1989; Brockner, Grover, O’Malley, Reed, &

Glynn, 1993; Probst, 2003; Roskies & Louis-Guerin, 1990).

Restructuring. Over the past several decades, organizations have moved away from large, hierarchical, and rigid structures to smaller, more flexible and agile forms

(Bahrami, 1992; Lewin & Johnston, 2000). Restructuring encompasses a broad range of organizational activities including selling lines of business, making significant acquisitions, changing the capital structure of the organization by infusing high levels of debt, and/or changing the organization’s internal structure (Bowman & Singh, 1993; Lin,

Lee, & Gibbs, 2008). These changes are made with the goal of enabling organizations to adapt quickly to changes in the operating environment by becoming ‘lean and mean’ and

‘able to turn on a dime’ (Landsbergis, Cahill, & Schnall, 1999; Womack, Jones, & Roos,

1990). The results on the success of restructuring initiatives are mixed. Prior studies have shown that restructuring initiatives have resulted in more efficient and competitive firms while other similar efforts have led to uncertain earnings and disruptive change (Brickly

& Van Drunen, 1990; Atiase et al., 2004). Examples of these mixed results include IBM whose restructuring initiatives in the 1990s under then CEO, Louis Gerstner, resulted in a successful return to profitability and business solvency for this computer conglomerate.

Alternatively, the once powerful retail giant, Montgomery Ward, failed in its restructuring initiatives after closing more than half of its stores it finally ceased operations in 2001. After years of restructuring the long-term success of such efforts for companies such as Kmart and Eastman Kodak remains to be seen (Lin, Lee, & Gibbs,

2008). To give some perspective on the rate of restructuring over the past 50 years,

Markides (1990) estimated that between 20 and 50 percent of Fortune 500 companies

34 restructured between 1981 and 1987 compared to around 1 percent of firms during the

1960s and 1970s. To that end, Bowman and Singh (1993) argued that waves of restructuring have taken place in the past but what has made restructuring unique in more recent decades is the focus on achieving greater efficiency through downsizing. In line with the previous sections on globalization and downsizing, the researchers go on to state that international competition has forced firms to downsize their operations to keep costs in line with competitors.

Mergers and Acquisitions (M&As). One sub-set of restructuring is mergers and acquisitions. Many researchers have reported that mergers and acquisitions heighten employees’ expectations that they will be laid-off in addition to actual rates of layoffs

(Callahan, 1986; DeMeuse, 1986; Hayes, 1979). Mergers have been associated with increased job insecurity and uncertainty (Schweiger & Denisi, 1991), feelings of threat and reduced control (Burlew, Pederson, & Bradley, 1994; Schweiger & Denisi, 1991).

Among lower-level employees and senior executives, Marks and Mirvis (1986) found that fears of job loss were the most common consequence of a merger. Astrachan (2004) observed that during a merger or acquisition, employees will know of someone who is impacted even if they are not directly impacted themselves. This means that organizational managers and HRD professionals alike should include plans for helping employees cope with increased job insecurity in the wake of M&A activity. The following four steps were developed by Guknecht and Keys (1993) to provide guidance for establishing an effective acquisition plan:

• Communicate the impending change as soon as negotiations will allow

• Develop a new strategic plan and share it with both acquired and acquiring

company employees as soon as possible

• Become flexible and creative with necessary job changes

• Invest in the development and retraining of employees who are survivors of the

acquisition.

Because announcements of M&As often create apprehension, concerns, and questions among employees such as “Will I keep my job?” managers and HRD professionals need to understand the impact of M&A activity on employee responses and the relationship to job insecurity.

Outsourcing and Offshoring. As noted in the previous section on globalization, outsourcing is defined as the switch toward garnering products and services from the external market that were once done in the organization (Sako, 2005). Offshoring refers to the movement of production overseas. The offshoring of increasingly sophisticated work functions has been on the rise (Crino, 2007). Beginning on a large scale in the

1990s, organizations began identifying their core competencies and jettisoning other functions in mass (Martin & Freeman, 1998; Prahalad & Hamel; 1990; Wernerfelt,

1989). Such activity has stoked worker concerns about job loss and the potential for their jobs to be moved oversees (Amiti & Wei, 2005). The perception of job losses has enraged trade unions, generated controversy in the media, and prompted debate by government officials (Durvasula & Lysonski, 2009; Mankiw & Swagel, 2006). While there is evidence to suggest that the impact of outsourcing and offshoring on overall job loss is minimal (see GAO, 2004), employee fears about outsourcing-related job loss remains high. Outsourcing activities do result in restructuring and downsizing (Kletzer,

2005; McLean, 2006). For instance Continental Bank Corporation outsourced its legal,

audit, cafeteria, and mailroom operations to outside companies (Cooper, 2002). Similarly,

American Airlines has outsourced customer services job at more than 30 airports. Often,

technical workers are asked to train their foreign replacements who return to their home

countries once their training is completed (Noe, 2005). Workers that occupy functions at

the periphery of their organizations are the ones most susceptible to outsourcing and run

the greatest risk of being impacted by outsourcing and offshoring decisions. Figure 1.4

depicts how jobs at the periphery of the organization have a higher risk of being

outsourced.

Figure 1.0.4. Outsourcing impact model (A figure created by the study author and researcher, R. Smith, using Microsoft PowerPoint). Armstrong-Stassen (2002) found that workers whose jobs were designated redundant because another position in the organization matched theirs or the same work could be done elsewhere have higher levels of job insecurity. Outsourcing-related job loss is especially a concern for low-skilled workers therefore job insecurity is a

reasonable reaction for employees who perceive that their jobs may be impacted by

outsourcing and offshoring decisions (Kletzer, 2002; 2005).

Plant Closures. MacLachlan (1992) argued that plant closures are one of the most

visible manifestations of the impact of market dynamics and corporate restructuring on

the economic landscape. Research on plant closures point to loss of market share and

increased competition as the underlying causes behind why plants shut down. Stafford

(1991) found that most plant closures were due to excess capacity and inadequate

profitability. Over a typical 5-year period, more than 30% of U.S. manufacturing plants

shut down (Bernard & Jensen, 2007). Between 1992 and 1997, plant closures accounted Table 2: Output, Employment, and Job Destruction for more than 2.8 million job losses and 58% of gross job destruction in the between 1992 and 1997 (from Bernard & Jensen, 2007) manufacturing sector (see table 1.2).

Table 1.2

Output, Employment, and Job Destruction between 1992 and 1997

Number Employment Output Change in Employment of Plants (1992) (1992) (1992 to 1997) Plants w/ net job creation 156,270 6,858,402 1,286 2,955,873 Plants w/ net job destruction 214,720 10,127,455 1,719 -4,909,186 Survivors 84,995 7,263,745 1,314 -2,045,476 Deaths 129,725 2,863,710 405 -2,863,710

Note. Calculations by the authors. Output is given in billions of 1987 dollars. Source: Longitudinal Research Database, Bureau of the Census. From, “Firm structure, Multinationals, and Manufacturing Plant Deaths,” by A. B. Bernard, & B. Jensen, 2007, The Review of Economics and Statistics, 89, p. 194.

Figures such as these not only raise employee concerns about job loss and opportunities

for future employment, they also prompt questions about how much the U.S. is de-

industrializing and whether manufacturing will continue to be a foundation for the U.S.

economy (Harris, 1984). While these questions are beyond the scope of the current

research, the questions these figures raise about job security in the manufacturing sector

serve as a backdrop to numerous political and employment discussions about job security

in the U.S.

In lieu of the research findings on plant closures several factors that contribute to

likelihood of whether or not a plant will close in the future are relevant. Characteristics

that reduce a plants’ likelihood of closing include 1) production of multiple (instead of

single) goods and products, 2) ownership by a U.S. multinational firm, and 3) having a history of ownership change (Bernard & Jensen, 2007). Interestingly, each of the characteristics of plants that make them somewhat immune to closure, also serve to increase their likelihood of involvement in other types of significant organizational changes. For example, leadership/ownership changes are often viewed as significant organizational changes (see Reisel, 2003) as are technology changes required to enable production of multiple product types at the same facility (see Lippert & Davis, 2006).

Reports or rumors of plant closures are likely to result in higher employee job insecurity and voluntary turnover since plant closures result in large-scale downsizing and/or forced relocations of plant facilities and employees (Bordia, Hunt, Paulsen, Tourish, & DiFonzo,

2004; Schwerdt, 2008).

Research Purpose and Questions

It is evident that several decades of organizational change have resulted in significant, detrimental impacts to long-term employee job prospects. Additionally, these changes show few signs of abating and as organizations reorient themselves to adapt to changing global and marketplace pressures. Employees will frequently find themselves

unexpected recipients of these changes. This underscores the importance of the ongoing

study of job insecurity in a new age of technological advancement and global expansion.

As employees, managers, and HRD professionals seek to best position themselves in the

shifting organizational environment, job security-related issues will need to be assessed to ensure the delicate balance between organizational flexibility and the uncertainty inherent to organizational change is maintained.

The purpose of this research study was to gain a better understanding of individual responses to job threatening organizational change and learn what factors within the organizational change process relate to either beneficial or detrimental employee attitudes. This was done by determining whether change leadership strategies, trust in management, and change related self-efficacy have significant influence on the relationships between job insecurity, commitment to change, and resistance to change.

The specific research questions are:

1. To what extent is job insecurity related to workers’ commitment to change and

change attitudes (e.g. commitment and resistance to change)?

2. How are the inter-relationships among job insecurity, commitment to change, and

resistance to change mediated by workers’ self-efficacy and moderated by

perceptions of change leadership strategies, and trust in management?

Significance of the Study

Job insecurity is often a function of macroeconomic forces that limit the strategic options of organizational leaders forcing them to have to ‘do more with less’ (Romanelli

& Tushman, 1994). The previous sections outlined how macroeconomic changes in technology, customer preferences, and globalization drive strategic, organization-wide

40 decisions about how organizational leaders elect to structure their workforces. In particular, it was argued that technology changes and automation are the root cause behind most of the job threatening changes organizations have experienced over the past several decades. As has been noted by recent researchers (see Garavan, 2007), perspectives on macroeconomic forces are especially useful for strategic HRD since they enable practitioners to tailor solutions to the current and future developmental and change needs of organizations and their members.

In the context of increasing and continuous job-threatening organizational change, understanding the effects of job insecurity on employee attitudes will remain an important area of research for the foreseeable future. This research aims to illuminate the interrelationships between job insecurity, self-efficacy, change leadership strategies, trust, commitment, and resistance. Jimmieson, Terry, and Callan (2004) noted that research on constructs such as change related self-efficacy in applied settings is limited. It is believed that understanding these relationships will shed both practical and theoretical light on constructs of growing interest in dynamic and changing workplaces.

Organizational leaders have to contend with a variety of organizational changes ranging from restructuring to new technology implementations. What each of these changes have in common is the fact that they 1) increase employee concerns about job loss (Probst, 2005; Reisel , 2003), 2) test organizational leaders’ ability to lead and manage difficult organizational change (Gilley & Gilley, 2008), 3) test workers’ internal abilities to cope with organizational change (Jimmieson, et al., 2004; Wanberg & Banas,

2000), and 4) strain workers’ trust relationships with their managers (Mishra & Mishra,

1994; Morgan & Zeffane, 2003). The paradox of many job-threatening organizational

41 changes is that they require creativity and innovation from the same employees whose jobs are threatened by the proposed change initiatives (Probst et al., 2007). Under such circumstances, legitimate questions arise about what are the impacts of job insecurity on employees’ willingness to commit to and/or resist organizational changes and what change leadership strategies have the ability to foster employee commitment to change and reduce resistance to change? The present study addresses each of these factors to some degree and provides insight into how organizational change can be managed for all organizational members particularly when changes threaten employment.

Based on the tripartite theory of attitudes (Breckler, 1984), organizational researchers have recently developed more sophisticated theoretical models and measurement instruments to understand and study employee responses to organizational change and organizational change strategies (Herchovitch & Meyer, 2002; Szabla, 2007).

These models have served to broaden current understandings of change processes. This study advances knowledge in this area by empirically testing linkages among constructs that have been points of interest in a number of previous studies. By demonstrating how job insecurity, self-efficacy, change leadership strategy, trust, commitment, and resistance to change are interrelated, future research can be done to examine how and under what organizational conditions these relationships will hold.

Although it is generally agreed that job insecurity leads to poor employee outcomes, at present, there is a debate in the job insecurity research literature concerning whether or not, omnibus rebus consideratis (all things considered), job insecurity leads to positive or negative organizational outcomes. For instance, it is unclear whether the adverse impact of job insecurity on employee attitudes and commitment sufficiently

42 offsets the productivity gains realized by organizations when employees work harder in the hopes of lowering their chances of being laid-off. By itself, the present research will not be able to resolve this question, but the results will shed additional light on what types of employee responses (e.g. emotional, behavioral, and cognitive) organizational members might be able to expect when organizational changes threaten workers’ employment.

Finally, the field of HRD is broadly concerned with human and organizational development through the use of career development, organization development (OD), and training and development (Swanson & Holton, 2001), but save for a few articles, relatively little research has been done in the field related to issues of job insecurity.

Organizational change is a core HRD concept and is central to the definition of OD (see

Swanson, 2001; Swanson & Holton, 2001). OD researchers are particularly interested in the role of organizational change processes (Beer, 1980; Quirke, 1996). Having a more complete understanding of the role job insecurity plays in organizational change initiatives will assist HRD practitioners with implementing changes to improve organizational performance particularly during periods of job uncertainty. Research has demonstrated that job insecurity is directly and indirectly related to employee attitudes and that employee commitment to change is positively related to employees’ willingness to participate in workplace learning (see Parish, Cadwallader, & Busch, 2008).

Conversely, Farrell and Mondavo (2004) found that downsizing led to decreases in employees’ commitment to learning for employees who remained with their organizations following downsizing. These recent research findings suggest important linkages between job insecurity and workplace learning that deserve further exploration.

Understanding the dynamics of job insecurity and employee commitment to change particularly in lieu of organizational changes that require employees to learn new ways of working is a topic of interest to HRD scholars and practitioners alike.

Contributions and Implications for HRD

HRD has been defined several different ways but, as has been observed by

Chalofsky (2008), the seminal foundations of the HRD discipline are the interrelationships among people, learning, and organizations. Chalofsky noted that HRD is an applied behavioral science that is primarily concerned with people’s performance in the workplace and their ability to reach their human potential.

Among HRD practitioners, one of the most common yet irresolvable issues in employee training, and leadership training programs more specifically, is that the employees for whom these programs are developed frequently do not participate in them.

For instance, Fenton and Pettigrew (2006) found that managers at an engineering firm they studied did not take advantage of leadership or managerial training that was made available to them despite significant structural changes taking place at the firm and widespread acknowledgement of the need for the skills those programs were designed to address. The researchers attributed this lack of participation (or “resistance to training”) to a persistent cultural view among managers that effective leadership is based on personality characteristics rather than behaviors and skills. In other words, leadership is something one either possesses or does not—it cannot be effectively taught. That belief was reinforced by a view that management and leadership skills are non-scientific, soft skills and therefore afforded a lower status. The present research aims to dispel this perception of HRD programs and, more specifically, change management and leadership

44 training by demonstrating the importance of effective change management practices for organizations.

It has been argued that human capital is one of the most under-developed resources in organizations so in addition to investments of time and resources in organizational infrastructure, organizations have room to gain a number of benefits from investing in employee training and education (Satkunasigham, 2002). Investing in human resources is stated as one of the core tenants of HRD (Holton & Naquin, 2002).

Improvements in workforce development result in increased work energy, positive workplace attitudes, reliability, commitment, ability to learn, aptitude, imagination, creativity, and motivation (Fitz-Enz, 2000). Interestingly, these are same qualities employers and managers look for among their staff particularly since the rate and pace of organizational change has been steadily increasing and it shows few signs slowing in the coming years. Workplaces that are made up of employees who are willing to learn will be better equipped to deal with organizational change. In fact, Parish et al. (2004) found that employees who have affective commitment to change show an increased willingness to learn. This suggests that change leadership strategies that lead to increased levels of commitment will also foster an environment of adaptability and flexibility in which employees will be willing to learn new technologies, systems, processes, and strategies to help aid their organizations in accomplishing strategic goals. McClernon and Swanson

(1997) argued that as macro-level changes and the overall speed of change increases,

HRD will becoming increasingly important to both organizational strategy development and implementation. This study contributes to the role of strategic HRD by identifying

45 core people, organization, and learning processes required to facilitate the change process in job-threatening, market-driven organizations.

Summary of Chapter 1

The goal of this introduction has been to outline major shifts occurring in the U.S. workforce over the last 30 years that have led to increased job insecurity and to identify their underlying causes. Because the ability of contemporary organizations to remain flexible and adaptive to dynamic business environments is such a key feature of successful organizational change, understanding the relationship between macro- economic changes (e.g. technological changes, globalization, and customer preferences) and organization-level responses (e.g. downsizing, layoffs, restructuring, outsourcing, etc.) will assist organizational leaders and employees alike in preparing for and managing those organizational changes and their impacts. This study adds to the knowledge base of the field of HRD by linking job insecurity to change leadership strategies and employee commitment and resistance attitudes which have been linked to employees’ willingness to learn and adapt to workplace change in previous studies. A new model for assessing the influencing factors of change leadership, change related self-efficacy, and trust in management on the relationship between job insecurity, commitment to change and resistance to change was developed as a result of this study. Finally, this study provided guidance to organizational leaders and HRD practitioners seeking to effectively manage organizational change in job threatening work contexts.

The following chapter includes a review of the literature related to the key variables of interest for this study including job insecurity, organizational change

46 leadership and management, commitment to change, resistance to change, trust in management, and change related self-efficacy. Study hypotheses are also presented.

CHAPTER 2: LITERATURE REVIEW

In their 2006 study of the effects of a merger and acquisition, van Dick, Ullrich, and Tissington use the metaphor of black cloud to represent the impact a merger announcement had on employee levels of job insecurity. When studying the topic of job insecurity, that metaphor is particularly fitting because the gathering of dark clouds indicates rain or a coming thunderstorm but it is often difficult to tell precisely when the rain will come. Job insecurity functions in much the same way. Declines in organizational performance, organizational restructuring, mergers and acquisitions, and prior downsizing activity are all suggestive of pending job changes or layoffs but an individual employee rarely knows if or when they will be let go. This creates a stressful work climate as employees spend their time worrying about and assessing whether and how the changing organization will impact them. This in turn negatively impacts productivity, creativity, and innovation often during periods when organizations are most in need of higher levels of each in order to successfully adapt to organizational changes

(Gandolfi & Oster, 2009; Probst, 2007). The goal of this research is to examine relevant employee responses to job insecurity in terms of commitment and resistance as well as key moderators of that relationship including change leadership strategies and trust. The literature review in following sections provides an overview of key research in each of these areas including organizational change, job insecurity, change related self-efficacy, resistance to organizational change, commitment to organizational change, change management, and trust in management.

The current study was undergirded by several theoretical frameworks. Job insecurity theory (Greenhalgh, 1983) predicts that employees’ behavior and attitudes will

48 worsen as job insecurity increases. Indeed, a number of studies have demonstrated this to be the case (see Ashford et al., 1989; Sverke, Hellgren, & Naswall, 2002). Because employee behavior and attitudes are central to understanding employee reactions to job insecurity, the tripartite theory of attitudes (Breckler, 1984) is used to operationalize the employee impact of job insecurity. According to this theory, understanding emotional, behavioral, and cognitive responses are relevant for evaluating employee responses to potential threats such as job-threatening organizational changes.

Organizational Change

Chapter One introduced the concepts of technological change, globalization, and changing customer preferences that have accelerated the pace of change virtually forcing organizational leaders to develop and implement job-threatening organizational change initiatives such as automation, outsourcing, and restructuring in order to remain competitive or even survive (Burke & Trahant, 2000; Casico, 1993; Fay & Lurhrmann,

2004; Gordon, Stewart, Swed, & Luker, 2000; Greenwood & Hinings, 1996). Because organizational change is often so varied and multifaceted, multiple research approaches have been used to effectively understand, identify, and manage organizations undergoing change. In their review of organizational change research, Armenakis and Bedeian’s

(1999) observed five themes that are common to organizational change efforts including:

(a) content issues, that address the substance and nature of a particular change; (b) contextual issues, which address the conditions existing in an organization’s internal and external environments (see Work Systems in Chapter One); (c) process issues, that address the actions (see Rational Adaptive theories in Chapter One); (d) criterion issues, that address the outcomes of change; and (e) affective and behavioral reactions to

49 change. Devos, Buelens, and Bouckenooghe (2007) indeed found that content, context, and process factors each had significant and independent impacts or employees’ reactions to organizational change. Given the wide breath and scope of organizational change and the substantial amount of research that has been done on the topic, it is practically impossible to effectively capture all five of the relevant dynamics of organizational change in a single study, however, this research represents an attempt to capture several critical change themes including content (perceived change types), context (job insecurity), process (change leadership strategies), and affective and behavioral reactions to change (commitment and resistance to change).

Job Insecurity

A 1999 U.S.-based study found that 37% of workers were afraid or concerned about job loss (Belton, 1999). Concerns about job loss are no doubt more pervasive now than in the recent past due to the collapse of the financial markets that precipitated some of the largest lay-offs and unemployment figures seen in more than two decades (Crotty,

2009). These figures suggest that a significant portion of the U.S. workforce struggles with job insecurity. By definition, job insecurity is an employee’s perception that his or her job is uncertain and may come to an end sooner than expected (Reisel et al., 2007).

One survey found that the top source of employee stress was job insecurity (USA Today,

1995). Job loss is immediate but job insecurity is the prolonged, everyday experience of uncertainty about the future (Jacobson, 1991). For many workers, the loss of a job often means substantially reduced pay and benefits as well as limited part-time work opportunities when full-time employment is preferred or needed to support one’s self and family (Root, 2006).

Additionally, employment is often viewed as a sign of status and identity that is representative of one’s place in the world and contribution to society consequently individuals’ who feel their jobs are being threatened may also believe their identities are being threatened as well. Job insecurity is not a U.S.-only phenomenon. Job insecurity perceptions have been observed in Great Britain, Israel, Holland, Finland, and China

(Hartley, Jacobson, Klandermas, & Van Vuuren, 1991; Kinnunen, Mauno, Natti, &

Happonen, 2000; Probst & Lawler, 2006; Rosenblatt & Ruvio, 1996). According to a

UK study involving over 300 interviews in 20 organizations, almost one-third of those interviewed felt they were at some risk of losing their job during the coming year and the concern was particularly significant among those with young families and/or large mortgages (Burchell, 1999).

In addition to job insecurity’s global presence, it is projected that increasing use of new technologies such as robotics and automation, and continued merger and acquisition activity will make job insecurity a relevant issue for the foreseeable future

(Brynjolfsson & McAfee, 2012; Ward-Cook, 2002). In a 2006 study of a hospital undergoing several substantial change initiatives, job insecurity and rumors of downsizing and job losses topped the list of employee concerns (Bordia et al., 2004). One employee stated, “…there will be fewer staff required because of new technology”

(Bordia et al., 2004, p. 609).

Job loss has also been shown to have more damaging effects for minorities

(Dwyer & Arbelo, 2012). Longitudinal studies indicate that minorities who are laid off experience longer periods of unemployment, lower earnings, and downward career mobility as compared to their white counterparts (Ong & Mar, 1992; Spalter-Roth &

Deitch, 1999). Individuals who may have had to contend with job loss are likely to have

greater concerns about pending job displacements particularly during periods of job-

threatening organizational change.

Job insecurity has a number of definitions in the research literature and has been

measured in a variety of ways. The current study defines job insecurity as an individual’s perception of threats to their job and concerns that their job may be lost. The definition in of job insecurity in this study most closely aligns to the one used by Greenhalgh and

Rosenblatt (1984). Several definitions of job insecurity are listed below:

• "sense of powerlessness to maintain desired continuity in a threatened job

situation" (Greenhalgh & Rosenblatt, 1984, p. 438).

• "expectations about continuity in a job situation" (Davy, Kinicki, & Scheck, 1997,

p. 323).

• an employee's "concern about the future permanence of the job" (van Vuuren &

Klandermans, 1990, p. 133).

• an individual's "perception of a potential threat to continuity in his or her current

job" (Heaney, Israel, & House, 1994, p. 1431).

• "an overall concern about the continued existence of the job in the future" (De

Witte, 1999, p. 156).

This study is primarily concerned with what is known in the job insecurity literature as global or quantitative job insecurity. Quantitative job insecurity refers to employee concerns about the loss of one’s job (Hellgren, Sverke, & Isaksson, 1999). Job insecurity is a subjective measure and depends upon an individual’s assessment of uncertainties or job threats in the immediate work environment (Hellgren et al., 1999;

Klandermans & van Vuuren, 1999). The level of perceived threat can vary from person to person.

Job insecurity is a workplace stressor that has been shown to have negative psychological, physical health, and job-related reactions implications (Ashford, Lee, &

Bobko, 1989; Crandall & Perrewe, 1995; Probst, 2000; Quick & Tetrick, 2003; Sverke et al., 2002). Some researchers have found that job insecurity is often more stressful than the actual job loss itself (Kasl, Gore, & Cobb, 1975) in part because job insecurity carries the added stress of uncertainty that job loss does not. Job insecurity is associated with an increased employee tendency to quit (Ashford et al., 1989). According to job adaptation theory, employees will attempt to withdraw themselves from stressors by becoming less committed to the organization, and having greater intentions to leave (Davy, Kinicki, &

Scheck, 1997; Hulin, 1991; Hulin & Roznowski, 1985; Probst, 2000; 2002). Similarly, job insecurity theory predicts that employees will be less productive, resist change, and begin to turn over when they experience high levels of job insecurity (Greenhalgh, 1983).

Figure 2.1. Causes and consequences of a job insecurity crisis. From “Managing the job insecurity crisis,” by L. Greenhalgh, 1983, Human Resource Management, 22, p. 433.

Reprinted with permission.

Job insecurity has been shown to increase employee resistance to change and that has the potential to adversely impact organizational change initiatives and organizational effectiveness (Lawler, 1986; Swanson & Holton, 2001). Several early studies that pre- date job insecurity theory found positive relationships between job insecurity and resistance to change although none of the previous studies used a multi-dimensional construct of resistance to determine precisely what type of resistance can be expected from job insecurity (Fox & Staw, 1979; Greenhalgh, 1979; Rosenblatt & Ruvio, 1996;

Rothman, Schwartzbaum, & McGrath, 1971; Rosenblatt, Talmud, & Ruvio, 1999). Based on these findings it was hypothesized that:

Hypothesis 1: Job insecurity will be negatively related to affective commitment to

change.

Hypothesis 2: Job insecurity will be positively related to cognitive, emotional, and

behavioral resistance to change.

Many of the early studies of job insecurity focused on employee perceptions of job insecurity in the midst of organizational decline (Greenhalgh, 1983). However increasingly, in addition to changes in work structures and organizational downturns, job loss is becoming disassociated from organizational declines and drops in performance

(Blyton & Bacon, 2001; Cappelli, Bassi, Katz, Knoke, Osterman, & Useem, 1997;

Hillier, Marshall, McColgan, & Werema, 2006; Wayhan & Werner, 2000). Profitability and high performance once served as buffers against job loss but increasingly, organizational leaders use layoffs as part of regular business operation strategy to increase workplace efficiency. This has led to a view that job insecurity has simply become another aspect of regular business processes and operations (see Peter Cappelli’s,

1999, book The New Deal at Work and Richard Sennett’s, 2000, book The Corrosion of

Character).

This general increase in job insecurity may prove to be beneficial to organizations as it may prompt employees to more readily embrace organizational change (Heckscher,

1995). Cappelli (1999) observed that employees are less inclined to fight organizational changes that threaten their jobs and they simply go along lest they find themselves next in line for lay-offs. This rationale appears to explain why work changes such as team- working and other ‘high-commitment’ work practices are successful despite the fact that the introduction of those practices are often directly associated with higher rates of

layoffs (Osterman, 1998; 2000). Indeed, there is evidence that organizations perform well

despite lowered employee morale resulting from layoffs (Cappelli et al., 1997; Grunberg,

Moore, Greenberg, & Sikora, 2008). In the same vein, Jalajas and Bommer (1999) found

that among a group of engineers studied, the level of motivation they derived from their

jobs had a more powerful influence on their behaviors as compared to the effects of past

or future downsizing. Reisel et al. (2007) lamented the fact that organizational

researchers are not in a better position to advise managers on why job insecurity should

be minimized other than for humanitarian reasons. Consequently, even though employees

will view threats to their job in negative light, senior managers may view organizational changes that lead to job losses as a difficult but necessary part of their role as decision- makers. The researchers note that:

Managers justify difficult human resource decisions in the name of organizational

survival. The collateral effects of job insecurity on organizational performance are

not clear enough to argue otherwise (Reisel et al., 2007, p. 107).

The more commonly accepted view of job insecurity is that it violates employees’ psychological contract with the organization (Robinson & Rousseau, 1994; Rousseau,

1995). In this view, employees expect a certain level of job security from their employer based on their hard work and commitment to their employer. Employees may feel that as long as they are performing well, there should be little reason for their employer to get rid of them (Morrison & Robinson, 1997). In less secure environments where those expectations are violated, employees may withhold discretionary effort thus preventing the organization from operating at optimal levels of performance (Appelbaum, Bailey,

Berg, & Kalleberg, 2000). Unfortunately, as noted above, while clear individual-level

56 impacts of job insecurity have been demonstrated, only a small number of studies have demonstrated organizational-level impacts of job insecurity.

Irrespective of whether job insecurity leads to increased employee commitment to organizational change or increased passive or overt resistance to change, the issue of job insecurity remains critical because of its potential to impact employees’ work and family lives as well as their behavior within the organizations that employ them (Blyton &

Bacon, 2001). By comparing the outcomes of job insecurity (e.g. commitment versus resistance) this study will help shed light on the debate between organizational researchers about whether job insecurity results primarily in either poor or beneficial employee responses to organizational change.

A brief word about job security. While much attention has been paid to job insecurity in the research literature, comparatively fewer studies have examined the role of job security or the employer practice of protecting workers against loss of employment

(Allen & Loseby, 1993). One notable exception is a 1993 study comparing the financial performance of Fortune 500 companies with job security policies to those that did not

(Allen & Loseby, 1993). The researchers found no difference in financial performance between those companies that practiced job security versus those that did not. These findings along with other research suggest that downsizings rarely lead to significant financial gains (Campbell-Jamison, Worrall, & Cooper, 2001; Cascio, Young, & Morris,

1997; Sahdev, 2003). This also provides an alternative to the widely accepted view that mass layoffs, and by extension, increased levels of job insecurity, are the only available options for organizational leaders aiming to remain viable amidst increased macro-level change. At the individual level, perceptions of job security have been found to be

positively associated with employee health (Kuhnert & Palmer, 1991). These findings

suggest that job security is key to the physical and psychological well-being of

employees.

Change Related Self-Efficacy

The concept of self-efficacy is an important component of employee responses to

organizational change because it refers to an individual’s beliefs and confidence in their

ability to succeed and achieve specific outcomes (Bandura, 1986). Lazarus and

Folkman’s (1984) cognitive phenomenological model of stress and coping demonstrated

that individuals assess outcomes a change will have on their personal well-being then

make a determination about the change itself. Self-efficacy is defined as the, “beliefs in

one’s capabilities to mobilize the motivation, cognitive resources, and courses of action

needed to meet given situational demands” (Wood & Bandura, 1989, p. 408). Self-

efficacy has been further sub-divided among three dimensions: (a) level or magnitude

(the degree of difficulty), (b) strength (certainty of successful performance), and (c)

generality (the extent to which the degree of confidence one has about their abilities

generalize to other situations and tasks). Self-efficacy has been found to be predictive of

job attitudes (Saks, 1995), training proficiency (Martocchio & Judge, 1997), job

performance (Stajkovic & Luthan, 1998).

In terms of the relationship between job insecurity and self-efficacy, researchers

have found that individual differences factor into the experience of job insecurity. Diez-

Roux (1998) stressed the importance of analyzing individual characteristics as well as social context when attempting to gain insight into individual-level outcomes (see Sora,

Caballer, Peiro, & de Witte, 2009). Individual differences have been found to mitigate the

58 negative psychological effects of workplace stressors such as job insecurity (Cooper,

Dewe, & O’Driscoll, 2001; Holmes & Rahe, 1967). For instance, Ashford et al. (1989) found that internal locus of control was associated with lower levels of job insecurity.

Tolerance for ambiguity is another individual difference measure that has been found to be negatively associated with job insecurity (Adkins, Werbel, & Farh, 2001). In other words, individuals with a greater ability to cope with uncertain situations are less likely to experience job insecurity. In the context of job insecurity, an individual’s ability to cope with uncertainty is especially important because there are two main components of job insecurity that make it burdensome and harmful—unpredictability and uncontrollability.

An essential characteristic of job insecurity is the unpredictable nature of what will happen in the future (e.g. will I have a job or not?) (Joelson & Wahlquist, 1987). Such unpredictability makes it difficult for employees to react appropriately. The following is a list of three questions most employee’s coping with job insecurity ask themselves:

• If I work harder, will that prevent my job from being eliminated?

• Do I trust that my manager can or will look out for me?

• Should I start looking for other jobs elsewhere?

Related to unpredictability is uncontrollability which is another core component of job insecurity (De Witte, 1999). Uncontrollability in the context of job insecurity refers to the feeling of powerlessness an employee has toward the factors that threaten their employment. The research examining job insecurity and personality would suggest that individuals experience job insecurity differently based on personality differences. Self- efficacy is important for understanding differences in individuals’ job insecurity perceptions because one’s level of self-efficacy is likely to affect their ability to cope

with the unpredictable and uncontrollable nature of job insecurity in the same way locus

of control and tolerance for ambiguity affect employee job insecurity perceptions. Given

that a higher tolerance for ambiguity is related to lower levels of job insecurity, it is

logical to conclude that the more confidence individuals have in their ability to cope with

change, the less likely they are to experience high levels of job insecurity because they

have more confidence in ability to handle organizational changes that lead to job

insecurity.

In work contexts undergoing organizational change, change related self-efficacy

is especially applicable. Change related self-efficacy refers to an individual’s perceived

ability to handle change in particular situation and to perform well on the job despite the

demands of the change (Wanberg & Banas, 2000). Individuals may not perform well in

change environments when they lack confidence in their abilities (Connor, 1992) thus,

they may avoid activities they believe exceed their abilities (Armenakis, Harris, &

Mossholder, 1993). Alternatively, individuals will more readily perform activities that

they perceive themselves capable of performing well. Employees who view an impending

organizational change as something they have control over and the ability to cope with

are more likely to have better levels of adjustment to the change (Jimmieson et al., 2004).

In fact, Jimmieson et al (2004) found that higher rates of change related self-efficacy

were linked to reduced stress levels, better client engagement, higher rates of job

satisfaction, and a view of organizational change as an opportunity rather than a threat. In

their 2010 study, König et al. found that self-efficacy and communication moderated the self and supervisor-rated job insecurity.

Results from Sora et al.’s (2008) work on job insecurity and employee attitudes indicate that job insecurity climates precipitate employee attitudes and perceptions.

Employees experiencing job insecurity may believe that working harder may lessen their vulnerability to job loss (Furnham, 1990; Heckscher, 1995). Laboratory and field studies have found that job insecurity increases productivity while decreasing negative and counterproductive work behaviors (Probst, 2007). Ostensibly this is because employees who are afraid of losing their jobs try harder to demonstrate their abilities and worth to the organization to avoid being laid-off. According to a New York Times poll, 80 percent of respondents said they would work longer hours if it would help preserve their job

(Uchitelle & Kleinfield, 1996). In a study examining the behavior of 12 software engineers at Xerox, Inc. experiencing heightened levels of job insecurity due to pending layoffs, they were more inclined to demonstrate individual heroics and “show off” for their managers (Perlow, 1994). According to the researcher, “The layoff atmosphere exacerbated this need to ‘show’ performance” (Uchitelle & Kleinfield, 1996, p. 3). The behavior of the Xerox employees and the results of the New York Times poll are consistent with the inverted U hypothesis of job insecurity (Brockner, Grover, Reed, and

Dewitt, 1992). According to the Inverted-U theory, when workers experience high levels of job insecurity resulting from job threatening organizational changes combined with the reduced ability to control those threats, they will be unmotivated and will exert less effort toward their work. However, when job insecurity is moderate and employees perceive they have control over the threats to their jobs, they will exert higher levels of work effort. Their research provides an empirical explanation for Probst’s (2007) study demonstrating that job insecurity increases productivity. Based on these findings and

Brockner et al.’s (1992) findings, it would appear that, in some cases, job insecurity has

the effect of increasing employees’ self-efficacy levels (at least artificially) by heightening their perceptions that they can overcome obstacles to their continued employment by working hard. This corresponds to research demonstrating that when a stressor such as job insecurity is perceived primarily as a challenge, it may lead to higher performance outcomes (LePine, Podsakoff, & LePine, 2005; McGrath, 1976). Therefore:

Hypothesis 3a: Change related self-efficacy will mediate the negative relationship

between job insecurity and affective commitment to change

Hypothesis 3b: Change related self-efficacy will mediate the positive relationship

between job insecurity and resistance to change (affective, behavioral, and

cognitive).

These hypotheses are consistent with research showing that employees who hold an ideology of self-reliance are less likely to perceive layoffs as a breach of the psychological contract (Edwards, Rust, McKinley, & Moon, 2003) and research showing that self-efficacy is associated with higher self and supervisor performance ratings among job insecure employees (König et al., 2010). Bandura (1977) argued that self-efficacy is situational and can be increased through organizational interventions. Approaches to increasing self-efficacy include verbal persuasion (encouragement), logical verification

(linking tasks that have already been mastered with tasks that have yet to be), and modeling (demonstrations) (Gist & Mitchell, 1992). In job insecure contexts, change related self-efficacy is relevant not only because it is likely to increase worker productivity but also because it is has been shown to be positively associated with greater acceptance of change (Wanberg & Banas, 2000). Employees who lack confidence in their

abilities to perform before, during or after an organizational change may not perform well

(Coch & French, 1948) so training has been stressed as an important element of

workplace change (Castrillon & Cantorna, 2004; Gorman and Mullan, 1973; Vieitez,

Garcia, & Rodriguez, 2001, Wanberg & Banas, 2000). Training related change

interventions fall within the purview of HRD as practitioners are often expected to have

sufficient knowledge of HR conditions affecting change implementations and are able to

provide training interventions that can prepare both managers and employees for change

(Hanpachern, Morgan, and Griego, 1998; Fazzari & Mosca, 2009).

Resistance to Organizational Change

As organizational leaders have made changes to their organizational work systems

to cope with complex and turbulent economic, technological, and social environments,

they must increasingly rely on their employees’ willingness and ability to adapt to change

(Armenakis, Harris, & Mossholder, 1993). As previously noted, for employees,

organizational change often translates into increased work expectations, responsibilities,

stress, and work hours. Perhaps because of these impacts of organizational change,

employees often resist changes they believe would lead to these outcomes. Employee

perceptions of organizational change processes and outcomes influence their reactions to

organizational events (Brockner et al., 1992) and one possible reaction is resistance.

Employees’ resistance-oriented reactions to change are frequently the result of feelings of uncertainty, loss of control, and/or fear of failure (Ashford et al., 1989, Coch & French,

1948, Oreg, 2003). Employee resistance to change has been cited as a major obstacle to organizational change initiatives (Kotter & Schlesinger, 1979; Lippert & Davis, 2006;

Strebel, 1996). Wangberg and Banas (2002) found that lower levels of change acceptance

63 were related to less job satisfaction and stronger turnover intentions. In fact, the failure of many organizational change programs is directly attributable to employee resistance

(Martin, 1975; Maurer, 1997; Regar, Mullane, Gustafson, & DeMarie, 1994; Spiker &

Lesser, 1995).

While employee resistance is often cited as an employee-produced obstacle to a number of different change initiatives, relatively few writings on the topic actually provide any detail about what characteristics or perceived outcomes of the change initiative lead to employee resistance or how employee resistance is exhibited in the workplace. Often, employee resistance to change is an assumed by-product of any top- down organizational change with little or no description of what behaviors or attitudes might reasonably be expected from ‘resistant’ employees (see Dent & Goldberg, 1999).

One notable exception to this assumption-laden approach to employee resistance comes from the work of Harris (2002) who outlined five different types of resistance or sabotaging behaviors displayed by employees who were experiencing several organizational changes intended to increase their organization’s market orientation and customer focus. Sabotage is a construct similar to resistance and is generally defined as planned behavior meant to negatively affect efforts to change (Ackroyd & Thompson,

1999; Sprouse, 1992; Sykes, 1997). Although, Harris’ research was focused on employee resistance to market-oriented change initiatives (see chapter 1), these resistance rationales and behaviors are no doubt applicable to a variety of organizational change initiatives.

The first set of justifications for resisting change included the view that the changes were politically motivated and meant primarily to enhance the authority or status of particular leaders or departments. This justification was most prevalent among middle managers

64 and executives. A second reason for resisting organizational change stemmed from employee concerns that the change would reduce the amount of available resources. This view tended to be held by middle and top managers.

Oh yeah, I mean how stupid do they think we are? This is just a smokescreen to

get more of the budget (Harris, 2002, p. 63).

A third category of resistance rationales included priority-based rationales for resistance or the view that the change may be valuable in the long-run, but more urgent needs should be addressed first. This view was common at lower management levels and among front-line employees.

Head Office guidelines all too often just get in my way and stop me from doing

what I know is right, not for them, but for our customers (Harris, 2002, p. 64).

A final justification for resisting change identified by Harris was the perceived exploitation-based justification. According to this rationale, employees resist change because they believe it will subject them to unreasonable and/or overwhelming demands.

This view was particularly prevalent among front-line, customer-contact employees.

Table 2.1 provides a summary of resistance to change behaviors and rationales.

In recent years, there have been calls to among some organizational researchers to retire the phrase ‘resistance to change’ (see Piderit, 2000 and Dent & Goldberg, 1999).

Such calls have some merit particularly in instances where managers misuse the term to deflect attention away from poor [change] management practices or poor decisions, but misuse of the term does not constitute sufficient grounds for its dismissal.

Table 2.1

Typology of Resistance to Change

"the Who" - Category of Workers Most "the Why" - Rationale "the What & How" - Form of Resistance Likely to Use for Resistance Justification and/or Resistance Type Change initiative is • Scarcity Creation - resistance or • Executives politically motivated to sabotage by diverting, over consuming, or • Middle Managers enhance the authority or restricting access to resources required to status of particular make the change successful leaders or departments • Direct Conflict - resistance or sabotage by directly or overtly hampering the change initiative Change initiative will • Hijacking - resistance or sabotage by • Executives reduce the amount of transforming the change into something • Middle Managers needed or valued that was not originally intended resources • Direct Conflict - resistance or sabotage by directly or overtly hampering the change initiative Change initiative is not Prolonged Argument - Resistance or • Lower-level the most urgent or needed sabotage by erosion of enthusiasm, Managers priority support, agreement, and/or momentum for • Front-line Employees the change Change initiative will Lip Service - resistance or sabotage by • Front-line Employees result in too much work overt acknowledgement but covert or unreasonable demands inactivity or not following through of time or effort

Note. Adapted from “Sabotaging Market-Oriented Culture Change: An Exploration of Resistance Justifications and Approaches,” by L. C. Harris, 2003, Journal of Marketing Theory and Practice. Reprinted with permission.

As Harris’ (2002) and Kotter and Schlisinger’s (1979) research show, employees can and do resist organizational change initiatives for a variety of reasons that may have little to do with manager or leader behaviors or intent. That said, research into understanding why such resistance occurs and how it might be mitigated continues to hold relevance for organizations.

McLean (2006) argued that individuals do not resist change, rather, they resist the

negative implications or undesirable consequences of change. This view is consistent

with the views of employee resistance to change espoused by Kotter and Schlesinger

(1979) as well as the underlying hypothesis of this study which suggests that

organizational changes that increase job insecurity lead to reduced employee commitment

to change and increased employee resistance to change primarily because employees are

fearful of job loss. Kotter and Schlesinger (1979) outlined four common reasons for

individuals’ resistance to change including 1) a desire not to lose something of value (e.g.

one’s job or livelihood), 2) a misunderstanding of the change and its implications, 3) a

belief that the change does not make sense for the organization, and 4) a low tolerance for

change (e.g. personality and/or dispositional resistance to change). An astute reading of

these reasons for resistance reveals some of the ‘blame-the-victim’ undertones criticized by Dent and Goldberg (1999) but nevertheless, these reasons bear a fair amount of resemblance to the rationales for resistance uncovered by Harris (2002) as well as a number of real-world workplace causes of employee resistance

There have been calls to retire the term resistance to change due to more critical

views of resistance suggesting employee resistance may be more a function of ineffective

management (i.e. poor communication or deficient change management practices), the

ability of employees to see potential problems associated with change that change leaders

are unable (or unwilling) to see, and/or misuse of the terms “resistance” and “resisters” to

de-legitimize employee concerns (Dent & Goldberg, 1999; Nord & Jermier, 1994). As

noted above, resistance to change may not be resistance to the change at all, rather, it

may be more a function of employee resistance to the impacts of change (e.g. job loss) be

it real or imagined. This view is supported by research showing that shows,

unsurprisingly, employees tend to have lower commitment to changes that will result in

unfavorable outcomes for them personally irrespective of change approaches or

management style (Fedor, Caldwell, & Herold, 2006).

Resistance to change is a complex construct and many employees may respond to change in several different ways including emotionally, cognitively, and behaviorally/intentionally (McDougal, 1908; Ostrom, 1969; Kothandapani, 1971;

Bogozzi, 1978; Breckler, 1983; 1984; Piderit, 2000; Oreg, 2006). According to the tripartite theory of attitudes, individuals respond to events along three central dimensions: cognitively, emotionally (affective), and behaviorally (intentions) (Breckler, 1983; 1984).

The affective component regards how one feels about the change (e.g., angry,

anxious); the cognitive component involves what one thinks about the change

(e.g., Is it necessary? Will it be beneficial?); and the behavioral component

involves actions or intention to act in response to the change (e.g., complaining

about the change, trying to convince others that the change is bad). Of course the

three components are not independent of one another, and what people feel about

a change will often correspond with what they think about it and with their

behavioral intentions in its regard (Oreg, 2006, p. 76).

In the context of organizational change and job insecurity in particular, understanding the three components of employee responses is important because frequently, change leadership approaches fail to account for more than one type of employee resistance response and as a result, run a greater risk of failure. Oreg (2006) found that certain employee responses impact other response types. For instance,

cognitive responses can influence behavioral responses. In such cases, individuals who

do not believe in the value of the change (cognitive resistance) may also behave in ways

that run counter to effective change implementation (e.g. behavioral resistance in the

form of telling colleagues that the change is not valuable). In government settings, such

employee responses can be particularly deleterious to change efforts because employees who are resistant to change can often simply ‘wait-out’ change plans from elected leaders until the next governmental leader or administration comes into office (Fernandez &

Rainey, 2006). Szabla’s (2007) research found that among the three types of employee responses (e.g. emotional, cognitive, and behavioral), emotional responses to change appeared to have the largest impact on cognitive and behavioral responses. In other words, how employees felt about the change and the change management approach impacted what they thought and what they did about it. This view of managing change is

important because a number of change communication strategies fail to include or

address emotional aspects of the change process. By overemphasizing cognitive (e.g.,

What will happen?) and behavioral (e.g., What is expected?) aspects of change

implementations while underemphasizing affective aspects (e.g., What will this mean?),

organizational change managers risk undermining the success of their change initiatives.

Commitment to Organizational Change

Connor (1992) described commitment as, “the glue that provides the vital bond

between people and change goals” (p. 147). Conner and Patterson (1982) stated that “the

most prevalent factor contributing to failed change projects is a lack of commitment by

the people” (p. 18). In the context of job insecurity, it is not sufficient to only examine

those factors that contribute to employee resistance. An examination of the factors that

contribute to employees’ commitment to change is also equally if not more important

because commitment is predictive of employee behaviors that facilitate organizational change. Determining the factors that lead to employee commitment to change is arguably just as important, if not more important than employee resistance to change because employees’ commitment to change implies that employees will be willing to help make change happen in the organization, help advocate for change, and facilitate the change process. Herscovitch and Meyer (2002) defined commitment to change, “as a force

(mind-set) that binds an individual to a course of action deemed necessary for the successful implementation of a change initiative. This definition is comprised of several sub-components that include affective commitment to change, continuance commitment to change, and normative commitment to change.

Affective commitment to change is defined as a desire to provide support for a

change based on a belief in its inherent benefits (e.g. “I believe in the value of this

change”). Individuals with high affective commitment are committed to change because

they believe the change itself has value. Several organizational change researchers have

argued that such positive attitudes among employees are a “necessary, initial condition

for successful planned change” (Miller, Johnson, & Grau, 1994, p. 60). Continuance

commitment to change commitment is defined as commitment to change because the

costs of not being committed are too great or too undesirable (e.g., “I have no choice but

to go along with this change”). Individuals with continuance commitment are generally

committing to the change because they feel they have no other choices or options but to

go along. Normative commitment to change is defined as the obligation an individual

feels to provide support for change (e.g., “I would feel guilty about opposing this

change”). Persons high in normative commitment change are committed to change

because they feel personally obligated to commit to the change. That sense of obligation

may be the result of feelings of reciprocity (e.g. “this organization has given me so much

that I should give back”) or the sense of obligation may be based on trust or commitment

to organizational leaders or colleagues who may be affected by the change. Herscovitch

and Meyer (2002) developed a validated 18-item scale to measure the degree of

continuance, affective, and normative commitment and found that all three are positively

correlated with cooperation and championing behaviors. Taken together, commitment to

change is an important construct because it extends beyond the notion of employees’

having favorable views of a change, it also includes employees’ intent to work toward the

success of the change (Fedor, Caldwell, & Herold, 2006).

Parsing out the various types of commitment is important for understanding what

types of commitment behaviors are likely to result. For example, several researchers have

found that affective commitment is more likely to be related to employees’ willingness to

cooperate with others, willingness to exert extra effort, and serve as champions of change

to achieve change goals (Herscovitch & Meyer, 2002). Parish, Cadwalader and Busch

(2008) found that affective commitment to change was positively related to individual

learning, improved performance, and the successful implementation of change. Like

other multidimensional and one-dimensional models of complex psychological constructs

(see job insecurity definition), employee commitment developed over time.

Organizational commitment has been divided into two types; one focused on attitudes and other focused on behaviors. Research on attitudinal commitment has emphasized the degree to which employees’ believe their values and goals align with that of the

71 organization (Mowday, Porter, & Steers, 1982). In research examining behaviorally- focused commitment, the focus has been on understanding the conditions that cause individuals to commit to particular courses of action (Salancik, 1977).

There is a large body of research examining attitudinal and behavioral organization commitment but less pertaining to employee’s commitment to organizational changes. Foster (2008) argued that Herscovitch and Meyer’s (2002) commitment to change scale represents the most current and relevant measure of commitment to change. A greater focus on commitment to change (rather change organizational commitment) is also supported by research showing that employees’ conceptualize organization commitment and commitment to change in different ways.

Ford, Weissbein, and Plamondon (2003) found that commitment to change is conceptually and empirically distinct from organizational commitment and commitment to change does a better job of predicting specific change related behaviors than does organizational commitment.

Employees’ reactions and commitment to organizational change are based on a complex calculus that is reflective of the different aspects of the change and its consequences. Equity theory for instance posits that employees will adjust their level of work output to match the inputs they perceive they are receiving from their employers

(e.g. fair labor for fair pay) (Adams, 1965). Many employee compensation programs are based on this theory (see Fehr & Falk, 2001). However, when employees perceive that their output is exceeding what they are getting in return, they will reduce their effort. This may be one of the reasons work effort decreases when job insecurity is high (see

Brockner, Greenberg, Brockner, Bortz, Davy, & Carter, 1986; Brockner et al, 1992).

Workers may perceive that their effort will not generate any additional positive results especially when they believe they are about to be laid-off. To that end, job insecurity disrupts the equity calculus between workers and their organizations particularly during periods of organizational change since employees may feel they are being asked to exceed regular work expectations when the organization, and not the employee herself, will reap the rewards of the additional effort. In essence, employees begin to feel they are putting more in than they are getting back and that diminishes commitment.

Because job insecurity and other job-threatening organizational changes threaten employees’ psychological contract, employees are, in general, less committed to organizations (Cappelli, 1999; O’Toole, 2006; Robinson & Rousseau, 1994).

Employment relationships in organizations are becoming increasingly shorter and there is evidence to suggest that worker commitment to organizations is decreasing as a result of the new psychological contract and a greater market orientation. In this context it may make more sense to examine the degree to which employees will be committed to specific organizational changes rather than the organization more broadly. For instance,

Fedor and Herold (2004) found that individuals are more likely to embrace changes being implemented while becoming less committed to the organization more generally.

Employees who will have shorter organizational tenures, less job security, and experience more career changes than their counterparts of yesteryear will likely be less committed to the organizations for which they work but that does not necessarily mean they cannot or will not be productive and committed during their time with a particular organization (de

Ruyter & Burgess, 2000; Rousseau & Parkes, 1993). The question is how will they be committed? In the contemporary workplace, commitment to specific organizational

changes rather than the organization itself arguably represents a more useful way of

understanding commitment and predicting employee behavior. This is especially true

now that there has been a substantial increase in the number of temporary or contract

workers organizations are hiring for short-term work (Cappelli, 1999; Purcell & Purcell,

1998). Such an approach to commitment is not intended to diminish the importance or value of organizational commitment particularly since there is a significant relationship between organizational commitment and a number of beneficial organizational outcomes including job effort, performance, and organizational citizenship (see Leong, Randall, &

Cote, 1994; Meyer, Srinivas, Lal, & Topolnytsky, 2007; Randall, 1990). However, for those firms that have adopted a decidedly market-based orientation to their workforce

(e.g. greater use of non-permanent employees including temporary, part-time, or contract workers) or for those firms that have accepted and, to some degree, expect high rates of turnover in certain employee populations, commitment to organizational change may represent a more relevant indicator of the likelihood of change success.

In a Belgian study of 447 workers representing a wide variety of organizations and employment types, job insecurity was found to be positively associated with organizational commitment among workers who were voluntary temporary/contract workers (De Cuyer & DeWitte, 2007). The researchers found this finding puzzling since it would seem more intuitive that employees who are working on a temporary basis against their wishes might be more committed to their organizations. The researchers speculated that the voluntary temporary workers may have accepted temporary employment in the hopes that doing so might lead to permanent employment. The researchers’ conclusion suggests that even in a market-driven workforce where short-

74 term employment is expected and viewed as “the norm” there seems to be a quest among employees for job security.

Figure 2.2 provides a model, developed by the researcher, of how workplace trends such as organizational moves toward a more market-driven orientation have shifted the focus of commitment away from organizations and towards organizational change (see Cappelli, 1999; Jaworki & Kohli, 1993; Martin & Freeman, 1998; Slater &

Navar, 1995).

Because the focus of the current study is employee commitment to organizational changes, rather than employees’ commitment to the organization per se, Herscovitch and

Meyer’s (2002) scale represents the most useful scale for measuring the construct of employees’ commitment to organizational change. There are other studies that have measured constructs related to commitment to change including openness toward change, readiness for change, readiness (Armenakis et al., 1993; Miller et al., 1994; Wanberg &

Banas, 2000). For instance, Wanberg and Banas (2000) found that employee resilience (a construct similar to need for achievement) was associated with an increased likelihood of accommodating a required change at work but is not necessarily related to agreement

with whether that change is beneficial to the organization and its clients.

Figure 2.2. Comparison of organizational commitment versus commitment to organizational change (A figure created by the study author and researcher, R. Smith, using Microsoft PowerPoint). The researchers also found that higher levels of participation were associated with

more favorable views of the workplace change. Wanberg and Banas' (2000) and Miller et

al.'s (1994) findings are important for research on organizational commitment and

commitment to change because they demonstrate the importance of being able to identify

the type of commitment an employee has when committing to change. For instance

continuance commitment to change may represent a shorter-term commitment to change

as compared to affective or normative commitments so organizations that pursue long-

term, continuous change may have employees with eroding levels of commitment to

successive changes (Herscovitch & Meyer, 2002; Parish et al., 2008). Although the

current study does not examine all three commitment types posed by Herscovitch and

Meyer (2002), previous researchers’ findings on the employee commitment to change

underscore the tenuous nature of employee commitment. In their 2006 study of

employees in the paper industry, Fedor and Herold found that employees’ commitment to

change was high when the change impact to them was low. Conversely, employee

commitment to change was low when the personal impact was high. Their findings

suggest not only that special attention needs to be given to employee groups most directly

impacted by organizational change but their findings also belie a another seemingly

intuitive but frequently overlooked aspect of organizational change research—namely

that successfully leading organizational change is extremely difficult and there are no

simple solutions to garnering employee buy-in especially when employees are aware of the negative impacts organizational changes may have for them (e.g. layoffs). Oreg put it best when he stated:

“Presumably, one of the first determinants of whether employees will accept or

resist change is the extent to which the change is perceived as beneficial versus

detrimental to them” (Oreg, 2006, p. 79).

Conversely, positive perceptions of the outcomes of change by employees tend to

increase their commitment to change (Novelli, Kirkman, & Shapiro, 1995). Therefore,

attempting to lead organizational changes that will have negative outcomes for

employees such as job loss is inherently an uphill battle. In this context, how do those

leading change overcome the lack of commitment and resistance that is endemic to job-

threatening organizational change? It is tempting to believe that properly managed

organizational change (e.g. employee participation and inclusiveness) will necessarily

result in employee commitment to change but the findings of Fedor and Herold (2004),

Fedor et al. (2006) other research showing a general lack of effective change

management practices runs counter to simplistic understandings and approaches to

leading change. The next section addresses several strategies for leading organizational

change and the impact these strategies have for fostering or hindering employees’

commitment to workplace initiatives that have the potential to harm them.

Leading and Managing Organizational Change

It has been said that effective implementation of change is the core of an organization’s ability to remain flexible and adaptive to changing economic realities

(Bossidy & Charan, 2002; Drucker, 1999, Finkelstein, 2003). Leadership generally refers behaviors including establishing direction, aligning people, motivating, and inspiring whereas definitions of management have generally included planning and budgeting, organizing and staffing, controlling and problem-solving (Fugate 2012; Kotter, 1990).

Given the fast pace and rapid flexibility organizational change necessitates, any change approach that can reduce the time between initiation of organizational change and the point at which employees are ready to commit to those changes can result in substantial differences in competitive advantage for private organizations and rapid budget savings for government organizations and non-profit organizations facing funding shortages or shortfalls. The outcomes of effective organizational change capabilities include organizational financial success and long-term organizational viability (Conner, 1992;

Cummings & Worley, 2005; Pfeffer, 2005). This is particularly true in the high-profile, high-tech industry where the lifespan from startup to maturity for firms such Google,

Microsoft, Amazon, and Facebook has decreased dramatically and the competition for leading, innovative products has increased (Eichenwald, 2012; Helft & Hempel, 2011).

The rational adaptive theories of organizational change as well as a research on effective

change management all suggest that effective leadership is required to both enable and

drive organizational change initiatives (Cyert & March, 1963; Gilley, 2005; Gilley,

Quatro, Hoekstra, Whittle, & Maycunich, 2001; Lawrence & Lorsch, 1967; March &

Simon, 1958; Pfeffer, 2005; Thompson, 1967; Williamson, 1981). Conversely, an

organization’s inability to change is also closely linked to its leadership capabilities

(Gilley, Dixon, & Gilley, 2008; Gilley, Gilley, & McMillan, 2009). Quinn (2004)

estimates that as much as 50% of all organizational change efforts fail due to poor change leadership. Lack of management recognition, rewards, inability to motivate and, most importantly for the current study, failure to understand effective change implementation techniques have all been cited as reasons for why organizational change fails (Burke,

1992; Kotter, 1995; Patterson, 1997; Ulrich, 1998). The leadership of an organization has a direct influence on workplace behavior that enables change and can overcome individual resistance to change (Drucker, 1999; Gilley, 2005; Howkins, 2001).

According to Weick and Quinn (1999) organizational change can generally be

classified as either episodic or continuous. Episodic change refers to changes that are

planned, are relatively infrequent, follow a pre-determined sequence, and are intentional.

Continuous change refers more to the dynamic, evolving and unending cycle of change that occurs on an ongoing basis in organizational settings. Organizational responses to macro-level changes such as those described in chapter 1 (e.g. downsizing, restructuring, outsourcing, etc.) are most closely aligned to episodic change because, more often than not, they cannot occur without planning or intentional managerial efforts or actions.

Embedded in the study’s episodic or planned change approach is the implication that

change is happening to the change recipients (employees). This is important because this

research assumes that the organizational changes under review are not employee-led,

rather, employees without significant decision-making authority in their organizations will generally have to deal with the outcomes of organizational decisions that are beyond their ability to control directly. Within the context of planned organizational change, Chin and Benne (1961) outlined three fundamental strategies for leading planned organizational change including rational-empirical, normative re-educative, and power- coercive.

Rational-Empirical Change Leadership Strategy (information-based change approach).

At its core, a rational-empirical change leadership strategy refers to the change

leaders’ use of reasoning and logic to motivate change (Chin & Benne, 1961). The

assumption of this approach is that change respondents or change recipients (employees)

are guided by logic and use reason to make decisions about whether and how they will

change their behavior (Szabla, 2007). In short, the rational-empirical change leadership

strategy presumes that employees will adopt organizational changes that they believe to

be logically justified and have demonstrable benefits.

The notion that employees will more readily accept changes that are justified is

closely related to employees’ ability to evaluate the legitimacy of change. Katz and Kahn

(1966) argued that employees’ perceptions of the legitimacy of and their commitment to

organizational change is gained when information about changes a) provide

organizational members with clarity about the process and b) clarify the goals,

motivation, and legitimacy of the organizational change. Questions about the legitimacy

of change are centered on whether or not the change is useful and/or whether the change

is considered to be a waste of time and energy (Torenvlied & Velner, 1998). When

employees positively evaluate the change, they become more committed to it (Frey,

1990; Zaltman & Duncan, 1977). If however, the decision makers are the only ones with

access to change related information, employees’ ability to make positive assessments of

the change will be low (O’Connor, 1993). In their study of employees representing a

cross-section of federal and local government as well as private sector employees in

Australia, researchers found that employees who possessed higher levels of irrational

ideas were more likely to resist organizational change as compared to those who did not

(Bovey & Hede, 2001). In a case study of a manufacturing firm, researchers found that

letting employees know when incomplete information was being given while at the same

time, providing a timeline for when more information would be available helped

minimize negative rumors and reduced employee anxiety (DiFonzo & Bordia, 1998).

Schweiger and DeNisi (1991) found that providing employees in a company undergoing

a merger with realistic communications by way of hotlines, weekly meetings, and newsletters reduced dysfunctional outcomes associated with the organizational change.

Researchers also found that employees had better reactions to job redundancies

when information was provided to them about why resources were allocated in particular ways (Brockner, DeWitt, Grover, & Reed, 1990). Even as AT&T was undergoing one of the largest divestitures in U.S. history, open communication and information-sharing was

found to increase employee job satisfaction (Shaw, Field, Thacker, & Fisher, 1993). Each of these research findings suggests that an information-based approach may be effective in reducing resistance to change because it will decrease irrational or untrue beliefs about change, enable employees to predict and have higher degrees of certainty about what is

next, and even reduce job insecurity during job-threatening organizational change.

Therefore, the absence of an information-based approach to change may lead to employee resistance because employees have little to no means of accessing the validity

of the changes being made, however, when employees feel that organizational change

leaders are providing sufficient amounts of information about the change and the

rationale behind it, they will be less likely to resist the change and more likely to commit

to it. Despite a myriad of research showing support for greater use of information, Falbe

and Yukl (1992) showed that ‘rational persuasion’ (i.e., citing factual evidence) and

‘legitimating’ (i.e. using rules and policies to legitimate claims) often results in employee

resistance as this approach may be viewed as ‘defensive reasoning’ to justify a change

that has been predetermined rather than change based on openness and unbiased facts

(Werther, 2003). To this end, an information-based approach may be perceived as or used in ways that run counter to the simple function of providing information about a pending change.

Information provided to employees by change leaders during periods of threatening organizational change is critical. Adkins, Werbel, and Farh (2001) found that individuals who perceive that they are receiving accurate and sufficient information are less likely to experience job insecurity. Similarly, König et al. (2010) found that when employees perceived that the flow of information about what was taking place in the organization was viewed as honest, sufficient, and adequate, they demonstrated higher levels of self-rated performance (particularly at lower levels of job insecurity). When significant organizational change is occurring, employees experience uncertainty and threats and go through a process of sense-making in which they need information to help

them establish a sense of continuity and prediction (Jimmieson et al., 2004). Degoey,

(2002) found that uncertainty associated with change results in increased social

information processing and sensemaking attempts by and from fellow participants in

change. Change leaders who provide adequate information about the time frame and the

need for organizational change may give employees the sense of prediction and

understanding they need to help them cope with change (Sutton & Kahn, 1986). In the

same vein, Lazarus and Folkman (1984) suggested that information provided by trusted

sources is likely to reduce ambiguity and uncertainty allowing employees to more easily

cope with stressful events such as job insecurity. Based on these findings it is reasonable

to expect that:

Hypothesis 4a: Rational-empirical (information-based) change leadership strategy

will moderate the negative relationship between job insecurity and affective

commitment to change such that the negative relationship between job insecurity

and affective comment to change will be weaker for individuals who perceive an

information-based change leadership approach.

Hypothesis 4b: Rational-empirical change leadership strategy will moderate the

positive relationship between job insecurity and resistance to change (affective,

behavioral, and cognitive) such that the positive relationship between job

insecurity and resistance to change will be weaker for individuals who perceive an

information-based change leadership approach.

As will be examined later, the rational-empirical change approach aligns closely to cognitive approaches to change in which thoughts and analytical assessments (as opposed to emotions) are viewed as the primary vehicle through which individuals make decisions

83 about behavior (see Oreg, 2006). In the absence of adequate information change leadership, employees are likely to turn to each other and/or the rumor mill to satisfy their need for information about workplace changes they believe will impact them (DiFonzo &

Bordia, 1998; Oreg, 2006).

Normative re-educative change leadership strategy (participation-based). At the heart of the normative re-educative change leadership approach is the notion that organizational members ought to participate in the design, development, and implementation of change initiatives (Chin & Benne, 1961; Szabla, 2007). Kotter and Schlisinger (1979) stated that in a participative change effort, change leaders listen to the people the change involves and use their advice. Employee participation in organizational change initiatives has a long history in the field of OD and it has been seen the approach of choice for overcoming resistance to change and increasing psychological commitment among employees toward proposed changes (Coch & French, 1948; Lines, 2004; Pasmore &

Fagans, 1992). Participative approaches to leading change have also received the greatest amount of attention in the change management research literature and it has been proven to be a powerful determinant of individuals’ reactions to major organizational change

(Beer, 1980; Brockner et al., 1994; Lind & Tyler, 1988). Sashkin (1984) argued that greater employee involvement leads to increased job satisfaction, higher levels of motivation, and greater acquisition and development of skills. Probst (2005) found that participative decision making was associated with higher coworker and supervisor satisfaction, higher work satisfaction, lower turnover intentions, and lower levels of work withdrawal for high job insecure employees.

As enticing as the participative approach to leading change is due to its involvement of workers, a number of organizational researchers have argued this approach is not a panacea and it may only be useful in certain situations. In fact, participatory approaches can lead to lower levels of employee satisfaction and productivity (Lock, Schweiger, & Latham, 1986). For instance, Lock and Schweiger

(1979) found that participation had almost no effect on group or organizational level productivity. Locke and Schweiger (1979) also found that employee participation raised productivity in only 10 out of 46 (22%) and decreased production in 10 studies (22%).

One of the reasons employee participation in change initiatives has been proven to be so decisively effective is that there are different types of employee participation that have a number of beneficial employee impacts. There are four central types of participation outlined by Sashkin (1984) that include participation in: setting goals, making decisions, solving problems, and making changes in the organization. In a study of 222 education managers, changes were more readily accepted when change participants had a chance participate in more tactical, rather than strategic, aspects of organizational changes

(Sagie, Elizur, & Koslowsky, 1990). Kanter (1982) argued that for participatory change approaches to be effective, individuals must be ready and willing to be involved in the change process. The fact that the need for employee participation may vary based on the different types of participation may explain the almost remarkable willingness employees have demonstrated toward accepting of organizational changes that have been linked to job insecurity and downsizing (Cappelli, 1999). Cappelli (1999) noted that at the same time organizations began downsizing in the 1980s and 1990s, they instituted employee empowerment and participation programs that gave employees greater autonomy over

85 their work. Such an approach is consistent with research showing that employee perceptions of positive changes can counterbalance negative consequences of change as well as other research that proposes solutions for effectively dealing with employee morale in the aftermath of downsizing (see Mishra & Spreitzer, 1998; Morgan &

Zeffance, 2003). In a similar vein, the culture of an organization appears to have a significant influence on the effectiveness of participative approaches. Bureaucratic organizations with a penchant for rules, regulations, and limited autonomy may not readily embrace organizational change approaches that require employee involvement and input (Conger & Kanugo, 1988). When organizational changes are not compatible with the organizational culture, employee participation has a greater beneficial impact

(Lines, 2004).

Despite a few drawbacks, change strategies that incorporate participative approaches have been cited as being very successful for implementing organizational change (Szabla, 2007). In the classic Hawthorne Plant studies conducted at the Western

Electric Company, employee participation in determining work breaks and working hours increased morale and productivity (Roethlisberger & Dickson, 1939). Similarly, in a study that would later spawn the term “resistance to change”, Coch and French (1948) found that workers in a pajama factory in Virginia would more readily accept and showed greater willingness to learn new work methods when they had a chance to participate in decisions associated with the change. They also found that participative decision making lessened resistance to change. More recently, Wanberg and Banas (2000) found that employee participation in the change process led them to have more favorable views of the change and Vander Elst et al.’s (2010) finding that organizational participation was

negatively related to job insecurity. Employee participation through direct and open

involvement in organizational change has also been found to increase employee’s trust in

management (Littler, Dunford, Bramble, & Hede, 1997; Morgan & Zeffane, 2003).

Therefore:

Hypothesis 5a: Normative re-educative (participation-based) change leadership

strategy will moderate the negative relationship between job insecurity and

affective commitment to change such that the negative relationship between job

insecurity and affective commitment to change will be weaker for individuals

who perceive a participation-based change approach.

Hypothesis 5b: Normative re-educative change leadership strategy will moderate

the positive relationship between job insecurity and resistance to change

(affective, behavioral, and cognitive) such that the positive relationship between

job insecurity and resistance to change will be weaker for individuals who

perceive a participation-based change leadership approach.

Power Coercive Change Leadership Strategy (power-based, top-down). A power-based change strategy is generally considered to be the least effective approach to implementing organizational change. One early study by White and Lippitt (1960) found that power- based leadership styles created tension and hostility resulting in submissive and dependent employee behavior. Rahe and Morales (2005) observed:

From the employees’ perspective, an authoritarian style of change management

includes an unpredictable component. The employees are informed later about

decisions that have already been taken, without really knowing the context which

influenced decision making. This raises uncertainty and generates a lack of

security (Rahe & Morales, p. 5).

Essentially, power-based approaches to leadership provide a faster route to getting

employees to behave in ways leaders may wish but it hurts employee willingness to

commit to change and results in poorer employee perceptions of the change and change

leaders (Nutt, 1992; Szabla; 2007). Nutt (1996) found that power-based approaches were

the least preferred method for change in health care organizations. Based on these

findings, it is hypothesized that:

Hypothesis 6a: Power-coercive (power-based) change leadership strategy will

moderate the negative relationship between job insecurity and affective

commitment to change such that the negative relationship between job insecurity

and affective commitment to change will be stronger for individuals who perceive

a top-down, power-based change approach

Hypothesis 6b: Power-coercive (power-based) change leadership strategy will

moderate the positive relationship between job insecurity and resistance to change

such that the positive relationship between job insecurity and resistance to change

will be stronger for individuals who perceive a top-down, power-based change

leadership approach.

A top-down approach to change is most likely to be utilized when required organizational changes pose an immediate or urgent threat to the organization (Eisenhardt, 1989). In such cases, differences across business units, departments, or other groups of workers may be deemphasized in lieu of an organization-wide focus on overcoming the threat by virtually any means (Armenakis et al., 1993). Another reason for why studying power-

coercive change leadership strategies is important is that many managers may be using

this approach inadvertently (Szabla, 2007). Despite that fact that inclusive and

participative approaches to change may be the order of the day or considered “best-

practice” (Dachler, 1978; Locke & Schweiger, 1979; Maher & Hall, 1998; Walton, 1985)

those leading organizational change may, in their quest for organizational flexibly, adopt

a power-coercive approach without being fully aware they are doing so. This may

account for research demonstrating substantial differences between employee and

managerial perceptions of change management (van Dijk & van Dick, 2009). Change leaders may believe they are providing ample opportunities for employees to participate in and provide input into the change process when in reality, those opportunities may be

(or perceived to be) token or disingenuous attempts to get employees to buy-in to already pre-determined change efforts.

Participation, power, & information change leadership: What do we know?

Lines (2004) argued that the complexity organizational leaders face when considering organizational changes provides them with several different options for communicating and leading organizational change. Chin and Benne (1968) argued that within these options three central approaches can be adopted and used. An information- based approach may be used when change leaders want to ensure that change recipients are fully aware of the need and rationale for the change. A participation-based approach may be used when change leaders want to ensure change recipients are fully involved in the planning, implementation, and decision-making aspects of the change. Finally, a top- down approach may be used when change leaders need to make urgent and quick changes. Typically, change leaders may use some combination of these approaches to

89 implement change but one form is emphasized more than the others. Lines (2004) suggested that in order for employees to accept and become committed to organizational change, change leaders must explain the rationale, content, and personal consequences of change (information-based approach). When a participation-based approach is not a viable option, an information-based approach may be the next best alternative for garnering employee commitment and reducing resistance. Top-down approaches may only be effective if change leaders are able to demonstrate high levels of urgency and immediacy.

It is clear that organizational leaders have options for leading change but the impact of these options for job insecure employees is less than clear. While a number of studies have examined the effects of job insecurity through the lens of the processes used by managers in workforce reductions (Brocker et al, 1992; Grunberg et al., 2000), few studies have examined the role of change leaders on employee responses to job insecurity. As important as having the best possible change leadership approach is,

Worley et al. (1999) suggested that trust is also a critical factor in successfully leading organizational change.

Trust in Management

Former U.S. Labor Secretary, Robert Reich argued that, “Trust is one of the most valuable yet brittle assets in any enterprise” (Reich, 1996 p. 1). In workplaces where the level of job insecurity is high, employees’ trust in management may be one of the only factors that prevents employees from leaving or engaging in behaviors that might disrupt the organizational change initiatives. Employees that are more committed are more likely to collectively support organizational change programs when there is a sense of trust and

attachment to the organization (Huy, 2002). Researchers have found that job insecurity

reduces trust in management (Ashford et al., 1989; Davy et al., 1997). In their 2002 meta- analysis of job insecurity, Sverke et al., found that job insecurity had a greater negative impact on trust than organizational commitment. Their findings correspond to research showing that even though employees demonstrate commitment and positive work behaviors in the midst of job insecurity, they do not always believe organizational leaders have credibility. In other words, employees may be doing what is required of them to maintain their jobs and positions in their organizations but they do not trust organizational leaders to take employee needs into consideration particularly around areas of job security. This sentiment is reflected in employee comments about management and job security:

I don’t trust them a great deal because external forces are external forces! Even if

they wanted to maintain the headcount, the shareholders and economics will

finally decide what happens.

I don’t trust them at all because in the last few years we’ve got this new

atmosphere where managers don’t care and their powerless to do anything about

it even if they do care (Mankelow, 2002, p. 151).

Interestingly, these comments also reflect a level of understanding employees have gained about the nature of the workplace and how managerial decisions are made.

Employee job security is no longer a key part of the downsizing calculus performed by managers. It is therefore increasingly difficult for employees to trust that managers will make decisions that will protect, even modestly, employee job security. Beyond managerial decision-making, employees seem to be more aware of the macro-economic

changes that may result in the eventual loss of their jobs (Klandermans & van Vuuren,

1999; Reisel, 2003). Lines et al. (2005) noted that employees monitor the organizational

environment to assess whether or not to place their trust in management. Despite growing

employee cynicism about management’s willingness to consider their job security, a

number of positive outcomes result when employees trust management. For instance,

when employees trust management, constructive responses such as commitment to

change will be enhanced (Mishra & Spreitzer, 1998).

Trust is defined as a willingness to be vulnerable to others, based on the prior

belief that those others are trustworthy (Mayer, Davis, & Schoorman, 1995; Mishra,

1996; Sitkin & Roth, 1993). The portion of that definition related to vulnerability means

that employees have a choice about whether to or not to trust their employer when a

significant potential for loss exists (Deutsch, 1973; Luhmann, 1979; Zand, 1972). Trust

has also been defined as one party’s willingness to be vulnerable to another party based on the belief that the other part is 1) competent, 2) open, 3) concerned, and 4) reliable

(Mishra, 1996). In the context of job insecurity, employee vulnerability may be manifested in high-performing survivors of downsizings who may still feel insecure about their own jobs but elect to remain with the organization even though they are able to find a good job elsewhere (Mishra & Spreitzer, 1998; Noer, 1993). Evidence from research into employment shifts amid pending plant closures shows that employees who leave prior to the actual plant closure or layoff generally fair better in terms of pay and other long-term employment outcomes (Schwerdt, 2008). Managers should pay special attention to talent shifts in the midst of pending downsizing or layoff situations because often the most talented employees will leave the organization during periods of

organizational transition to avoid being laid-off (Gutknecht & Keys, 1993). Previous

research has found that employees’ trust in management declines when downsizing occurs or is threatened (Mishra & Mishra, 1994; Mishra & Spreitzer, 1998). In their meta-analysis of job insecurity, Sverke et al. (2002) as well as Borg and Elizur (1992) found that job insecurity was significantly associated with lower levels of employee trust suggesting that job insecurity impairs organizational attitudes of employees (e.g. trust in management). Trust however is increased when employees are provided with direct and open involvement in organizational change (Littler et al., 1997; Morgan & Zeffane,

2003). Employee trust is also increased when employees believe that management has evaluated organizational change initiatives in terms of the impact to the welfare and well being of employees (Kotter & Schlesinger, 1979). In particular, employees will increase their level of trust in top management if they believe those managers care about employees’ job security (Kanter, 1983; Kanter, 1989).

Tyler (1994) found that organizational members are more willing to accept and

abide by unpopular decisions if there are higher levels of trust. Trust is also key to

reducing employee resistance to change (Krackhardt, 1992; Krackhardt & Stern, 1988).

Therefore:

Hypothesis 7a: Trust in management will moderate the negative relationship

between job insecurity and affective commitment to change such that the negative

relationship between job insecurity and affective commitment to change will be

weaker for individuals who have high levels of trust in management

Hypothesis 7b: Trust in management will moderate the positive relationship

between job insecurity and resistance to change (affective, behavioral, and

cognitive) such that the positive relationship between job insecurity and resistance

to change will be weaker for individuals who have high levels of trust in

management.

In addition to the impact of trust on individual employee responses, trust has also been proven to have a significant amplifying social effect. Torenvlied and Velner (1998) used social network analysis to examine how trust influenced employees’ resistance to changes related the implementation of ISO quality standards in a small transportation company. They found that resistance to the changes diminished when three mid-level employees (non-managers) demonstrated a willingness to implement the changes. This suggests that certain organizational members can have a strong influence over whether larger groups of employees will resist or accept organizational change. Similar to Tyler’s

(1994) findings, these researchers also found that high trust contributed to employees’ confidence in the future despite significant changes in work flows and procedures.

Conversely, low trust was associated with higher resistance.

The behavior and approach of leaders has a strong influence on employees’ level of trust as well as their attitudes and behaviors particularly in the context of job insecurity. Managers and leaders who are able to garner higher levels of trust from employees are also able to more effectively promote change and innovation in the workplace (Kirkpatrick & Lock, 1991). High trust relationships between leaders and followers have been found to be related to a range of organizationally beneficial outcomes including positive employee attitudes, increased organizational citizenship behavior, and positive social exchanges (Dirks & Ferrin, 2002).

Insecure Resistance: Toward an Integrated Model of Job Insecurity and

Employee Commitment and Resistance

The current research provides a testable, empirical approach to understanding

moderators (and mediator) of employee commitment and resistance to change for

employees experiencing job insecurity. It is hypothesized here that workers derive

meaning from their perceptions of events taking place in their organization. In particular,

their perceptions of job insecurity and their own self-efficacy affect their willingness to commit to or resistance organizational change. This study hypothesized that job insecurity would be negatively related to change related self-efficacy and that change leadership strategies and trust in management moderate the relationship between job insecurity, commitment to change, and resistance to change while change related self- efficacy mediates those relationships. In short, these models predict that workers who are insecure about their job will experience higher commitment to and lower resistance to organizational change initiatives if and when they perceive that change leaders use information-based and participation-based change leadership strategies. Additionally, employees who have higher levels of trust in the organization’s management will also have higher commitment to and lower resistance to organizational change.

Figure 2.3. Job insecurity, affective commitment to change, and resistance to change hypothesized relationships model (A figure created by the study author and researcher, R. Smith, using Microsoft PowerPoint).

Figure 2.4. Job insecurity, change related self-efficacy, affective commitment to change, and resistance to change mediation hypothesized relationships model (A figure created by the study author and researcher, R. Smith, using Microsoft PowerPoint).

Figure 2.5. Job insecurity, perceptions of change leadership strategy, trust in management, affective commitment to change, and resistance to change moderation hypothesized relationships model (A figure created by the study author and researcher, R. Smith, using Microsoft PowerPoint).

Several other studies (see Rosenblatt & Ruvio, 1996; Rosenblatt, Talmud, & Ruvio,

1999) have empirically demonstrated the link between job insecurity and resistance to change even though this link is often simply assumed in writings addressing employee responses to change (see Kotter & Schlesinger, 1979). Other researchers have found positive relationships between job security and openness to change (a construct similar to commitment to change) (Chawla & Kelloway, 2004). To date, no other studies have examined the relationship between job insecurity and affective commitment to change.

None of the previous studies linking job insecurity and resistance to change have examined the moderating effects of change leadership strategies and trust in management on employee responses.

In addition to examining the relationships between job insecurity, employee

commitment and resistance, understanding how those relationships are influenced by

change leadership strategies is a central focus of the present study. Relatively few studies

have simultaneously examined the role of leadership and job insecurity. One exception is

the research work of Hu and Zuo (2007) who found that high-quality relationships

between employees and their managers (leader-member exchange theory) reduced the

deleterious impacts of job insecurity on employees’ organizational commitment.

This study aims to fill the research gaps in these areas while providing academic

and practitioner-oriented approaches to effectively understanding and managing the

relationship between the job insecurity and employee reactions to change present in many

contemporary workplaces. It is this researcher’s belief that understanding the

relationships among these variables will continue to be of increasing importance as

researchers, managers, and HRD practitioners alike contend with organizational changes

that require employees to commitment to ever-more complex and demanding workplace changes without the benefit of knowing their commitment will result in continued employment. Such an approach is consistent with the applied behavioral science foundations of HRD.

Job insecurity and HRD. The issue of job insecurity has significant implications for the field of HRD. Greater job insecurity for instance reduces returns on training investments (Alogoskoufis, Bean, Bertola, Cohen, Dolado, & Saint-Paul, 1995). Human capital theory is closely associated with HRD (Holton & Naquin, 2002) and according to this theory, organizations that invest in training get returns on their investments in the form of improved individual performance and improved organizational productivity and

98 profitability (Nafukho, Hairston, & Brooks, 2004). Ostensibly, this is why organizational managers have training budgets set aside to train and develop employees. As job insecurity increases however, returns from job-specific training decrease because organizational managers question whether it is wise to invest in training for employees whose jobs may be eliminated and employees question whether it is worth it to spend time and effort increasing their knowledge and expertise in a single firm when more general knowledge may better equip them to deal with finding employment in a different organization following a layoff (Alogoskoufis et al., 1995). Cappelli (1999) concludes that the spate of job threatening organizational changes beginning in the 1980s has resulted in organizations that do not develop their employees for long-term employment relationships as well as employees who lack commitment and loyalty to their employers and have a more individualistic, short-term orientation (see Robinson & Rousseau, 1994

& Turnley & Feldman, 1999) . The result has been a loss of employees with organization-specific competencies and ‘survivors’ of layoffs who are less likely to exert extra effort toward their employers (Blyton & Bacon, 2001). Despite the fact that training has been linked to organizational commitment (see Bartlett, 2001; Bartlett & Kang, 2004;

Tansky & Cohen, 2001), job insecurity has the effect of eroding the benefits of the very training, OD, and career development programs HRD practitioners are charged with managing and delivering (Swanson & Holton, 2001).

Beyond the training implications of job insecurity, the field of HRD has been relatively quiet about the issue of job insecurity and related topics. This is somewhat surprising given the fact that job insecurity research ballooned in during the 1990s in the wake of increased downsizing in most western countries (Mohr, 2000). Three notable

exceptions to this stance come from the work of Ronald Jacobs and Michael Jones (1990)

published in the very first volume of Human Resource Development Quarterly (HRDQ),

Abraham Morrall Jr.’s (1999) article on downsizing survivor loyalty, and David Jalajas

and Michael Bommer’s (1999) research article on employee motivation and downsizing

both published in HRDQ. Given HRD’s focus on employee development and

organizational improvement (McLean & McLean, 2001; Swanson, 2001) coupled with

increasing rates organizational change, issues of job insecurity should be paramount to

the field of HRD.

Summary of Chapter 2

The goal of this chapter was to outline the key theory and constructs related to this study. First, models and theories of organizational change were discussed followed by a discussion of job insecurity theories. Next, self-efficacy research findings were discussed in the context of organizational change and job insecurity. Resistance and commitment to change research were presented as attitudinal outcomes of job insecurity and based on Breckler’s (1984) tripartite theory of attitudes (see also McDougal, 1908).

Recent research on the role of employee trust on employee attitudes was also presented.

The chapter concluded with a presentation of organizational change leadership theories and the relationship to individual reactions to organizational change. Based on the theoretical and empirical research from previous studies, seven hypothesized relationships were presented throughout the chapter. Overall, this chapter was intended to provide the framework for empirically testing the relationship between job insecurity and commitment/resistance to change and the influencing roles of self-efficacy, trust in management, and change leadership strategies. The following chapter will detail the

100 research methods that were used to examine the interrelationships among these key variables.

101

CHAPTER 3: METHODS

The purpose of this study was to explore the mediating relationships of change

related self-efficacy and the moderating relationship of perceptions of change leadership

strategies, and trust in management, on the relationship between job insecurity, affective

commitment to change, and resistance to change. A cross-sectional, applied survey research design was used to test the relationships between and among these six constructs to test the stated hypotheses. Individual-level, perception data was gathered using web- based and paper-based surveys. The surveys consisted of two global, quantitative measures of job insecurity developed by Francis and Barling (2005) and Borg (1992), the

Change Related Self-Efficacy scale developed by Ashford (1988), the three-component

Perceptions of Change Leadership Strategy developed by Szabla (2007), the Trust in

Management scale developed by Stanley et al (2005), the Affective Commitment to

Change sub-scale developed by Herscovitch and Meyer (2002), and the Change Attitude

Scale developed by (Oreg, 2006). This chapter outlines the research design used, information on the target population and sample, data collection methods, instruments, and data analysis techniques.

Research Design

This study aimed to address questions pertaining to the empirical relationships among several constructs and present a generalizable set of findings. Given the scope and nature of the research questions of this study, a quantitative survey research design using a correlational research design was determined to be most appropriate. The purpose of correlational research is to determine if relationships exists between independent and dependent (or outcome) variables by using correlational statistics. This is consistent with

102

Gall, Gall, and Borg’s (2003) observation that correlational survey research designs are

the most frequently used method to collect data on phenomenon that are not directly

observable, much like the constructs being examined for this study (e.g. perceptions of

change leadership, job insecurity, etc.). Because the goal of this study was to examine the relationships among several different constructs with the intent of making inferences about predictability in organizational settings, a positivistic, quantitative approach was

adopted. Survey research design has several advantages over other research design

methods including convenience and lower time commitment for study participants.

However, unlike more qualitative research approaches, the survey design does not allow for deep exploration of individual respondent’s beliefs and attitudes (Gall et al., 2003).

Population and Sample 1 (Manufacturing Sample)

One of the main goals of empirical research is to generalize from a sample to a larger population. The constructs of job insecurity, resistance to change, and commitment

to change are relevant to a majority of work settings but in an effort to focus this study,

two specific populations were targeted to include employees working in a private-sector, manufacturing company in the U.S. This population was selected for several reasons.

First, the company’s facilities contain a cross-section of employee groups including managers, professionals (non-union), and union employees which allowed for analysis of organizational change impacts by various employee types. This approach is consistent with research demonstrating that organizational changes that lead to increases in job insecurity have differential impacts depending upon the employee group and level (see

Iverson, 1996; Worrall, Parkes, & Cooper, 2004). Second, a central thesis of Chapter One

was that macro-level changes lead to organizational and, by extension, individual-level

103

impacts. At the macro-level, manufacturing employment decreased by 20% between

2000 and 2007 (Sherk, 2010) with more than 7 million jobs lost since the late 1970s

(Perry, 2011) and continuing declines in the manufacturing sector have been predicted

(Schwartz, 2009). Conducting research on organizational change and job insecurity within an industry undergoing decline is consistent with early theoretical approaches to researching job insecurity and more recent calls for increasing the scope of job insecurity research to account for macro-level effects (see Greenhalgh, 1983; Probst, 2005).

Although this sample was broadly targeted based on research implications, identification

of this particular company was the result of the researcher’s personal contacts in the

HR/HRD community. The selection of this organization is what is known as a

nonprobability sampling technique or convenience sampling (Bartlett, 2005).

In keeping with company policy, the researcher partnered with the regional HR

Manager for the company who was directly responsible for soliciting volunteers and administering the survey. The population consisted of employees at 5 manufacturing locations across the U.S. in the states of Florida, Kentucky, Maryland, North Carolina, and Wisconsin. The survey responses were aggregated across all the locations. In cases where company employees did not have computer/internet access or employees preferred, paper-based surveys were made available. The HR Manager collected the paper results and mailed them to the researcher. For those employees with computer/internet access, a survey link was emailed to all employees within the division.

The questionnaire for the web-based portion of the study was hosted through the

University of Minnesota’s College of Liberal Arts Office of Information Technology survey hosting site. No one at the company, including the regional HR manager had

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access to the web-based survey results and all data analysis was conducted by the researcher (note: paper-based surveys were collected and mailed to the researcher

immediately following completion).

At the time the surveys were distributed, participants were informed their responses were voluntary and anonymous and that any use of survey data by management would be used at an aggregate, non-personally identifiable, level. Informed consent was

sought from each prospective participant at the beginning of the survey, and participant

acknowledgement of consent was a precondition for access to the web-based survey. In

the case of the paper-based survey, participants were asked to mark an X on the first page

of the survey indicating their consent before continuing to the next page. These

procedures were in line with the required research participant protection standards

outlined by the researcher and approved for exemption by the University of Minnesota

Institutional Review Board in September 2010 for this study.

Response Rate Manufacturing Sample

Once the survey was made available to all company employees within the

division, all paper-based and web-based survey data was collected over a period of

approximately 6 weeks between May 17-July 1, 2011. A total of 692 employees within

the division were targeted and 275 usable surveys were obtained for a response rate of

40%. According to a meta-analysis of web-based surveys, the average response rate is

34.6% (Cook, Heath, & Thompson, 2000) so the response rate for the manufacturing

sample was above average.

Web-based and Paper Administration Sample Equivalence (manufacturing sample)

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Because the data were gathered using two different data collection methods in the

manufacturing sample (web-based and paper-based), the possibility for significant

difference between responses from the online compared to the paper-based survey were

examined. The researcher conducted an independent samples t-test that compared the data collected by the two methods across the seven scale-based control variables (change impact, change significance, years worked for the organization, employment dependence, employability, job satisfaction, age, gender, race, marital status, children (financially responsible), education, organizational level, and role). Of the 275 usable surveys in the manufacturing sample a total of 71 paper-based surveys were completed which represented 26% of the manufacturing sample.

Tests of Independence (manufacturing sample)

Because the two groups within the manufacturing sample were unequal in size

(paper-based n = 71 and web-based n = 204), several independence tests were conducted to determine if the paper sample was different from the larger web-based sample in any statistically meaningful ways. The Levene’s Test for the Equality of Differences and t- tests were conducted on the continuous, numeric, and scale-based control variables while chi-squared tests were conducted on discrete, categorical control variables.

The Levene’s test indicated no significant differences between the variances of the paper-based sample and the web-based sample for participants on job satisfaction (p =

0.124), years worked for the organization (p = 0.772), employment dependency (p =

0.435), and employability (p = 0.458) as the significance values (p-values) were larger

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than 0.05 in each of these cases. The Levene’s test did however show differences in

variance for responses to change impact (p = 0.022), and change significance (p = 0.000).

Following the Levene’s tests, t-tests were conducted on the same survey controls

where the equality of variances were found to be significant (p < 0.05) and where the

statistical software package (SPSS 15.0) automatically adjusted the degrees of freedom to

allow for the assumption of equal variances. The t-test results did not reveal a statistical

difference between the means of change impact (t(140.579) = 0.720, p = 0.473), job

satisfaction (t(267) =, p = 0.922), and years worked for the organization (t(257) = 1.118,

p = 0.264). However, the t-test results did reveal there were statistical differences

between the means of the scores for employment dependence (t(272) = 2.441, p = 0.015),

employability (t(271) = 2.453, p = 0.015), and change significance (t(87.618) = 5.744, p

= 0.00).

Finally, chi-squared tests were conducted on the discrete and/or categorical

variables of gender, age, marital status, children (financially responsible), education, and

level in the organization. Associations between how the survey was completed (paper or web) and each of these control variables were found with the exception of gender. Table

3.1 provides a summary.

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Table 3.1

Chi-squared Test Results Summary for Paper Compared to Web Surveys (manufacturing sample)

Association Control Chi-Squared Result found Result Description Variable Result Value Summary (yes / no) χ² (1, N = 262) = Gender No No differences n/a 1.019, p=0.313 Over 50% of employees More age ranges χ² (9, N = 266) = in each age range Age Yes completed web- 18.511, p = 0.018 completed the web-based based survey Over 79% of those who Web-based more χ² (5, N = 263) = completed the web-based Race Yes racially 21.118, p = 0.001 survey selected ‘White’ homogenous or ‘Caucasian’ The majority, 81%, of Web-based Marital χ² (1, N = 260) = those who completed the Yes tended to Status 11.559, p = 0.001 web-based survey were married married The majority, 80%, of those who completed the Children χ² (1, N = 263) = Web-based web-based survey (financially Yes 4.577, p = 0.032 tended to have indicated they had responsible) children children for whom they were financially responsible χ² (7, N = 264) = Web-based more Over 50% of employees Education Yes 71.724, p = 0.000 educated who completed the web Managers and senior Web-based Level in χ² (4, N = 258) = managers accounted for Yes higher in Organization 23.076, p = 0.00 25% of the web-based hierarchy survey completions

The independence of samples tests revealed a number of control variable

differences between those employees who completed the paper survey compared to the

web-based survey. Employees who completed the paper-based survey had significantly higher employment dependence compared to their counterparts and somewhat consistent with that finding, employees who completed the web-based survey showed higher levels

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of employability compared to their counterparts who completed the paper survey.

Similarly, those who completed the paper-based survey tended to be racial minorities,

unmarried, did not have children they needed to support financially, were less educated, and occupied lower level positions and roles in the company. Overall, differences

between the paper and web-based survey responses were found on 11 of the 13 control

variables. Despite these differences, the researcher opted to include the paper survey

responses in the overall manufacturing sample because this sub-sample of survey data

met the standards of completeness and usability and the results are representative of both

the company and industry as all employees were given the option of the completing the

survey online or using paper. The above analysis serves to demonstrate statistical

differences and similarities between the paper and web-based survey data within the

manufacturing sample.

Population and Sample 2 (Retail Sample)

In an effort to increase the study’s external validity (Levin & Cross, 2004), data

was also collected from a second company, a large retail organization. A sample from

this sector of the economy was targeted because, as noted in the first chapter, the service

sector of the U.S. economy has witnessed significant growth over the past decade

compared to the manufacturing sector. Studying job insecurity and employee reactions to

organizational change in this context provides a useful comparison case for evaluating

whether employee perceptions to job insecurity and reactions to organizational change

are similar or different in growing economic sectors (service/retail) versus declining

(production/manufacturing).

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As with the manufacturing sample, the retail sample was broadly targeted based on research implications but identification of this particular company was the result of the researcher’s personal contacts in the HR/HRD community. The researcher partnered with a regional VP of Sales who granted permission and access to research participants on behalf of the company. The population consisted of employees at approximately 200 retail locations across the U.S. The sampling method could not determine precisely how many employees at a single retail location completed the survey but the survey responses were aggregated across all the locations.

The researcher was responsible for administration of the survey. All employees in this company had computer/internet access and a survey link was emailed to all employees within the division requesting their participation. The questionnaire for the web-based portion of the study was hosted through the University of Minnesota’s College of Liberal Arts Office of Information Technology survey hosting site. No one at the company, including the regional VP of Sales had access to the web-based survey results and all data analysis was conducted by the researcher.

All survey data for the retail sample was collected over a period of approximately

4 weeks between May 1-June 1, 2012. Upon opening the invitation to the survey, participants were informed that responses were voluntary and anonymous and that any use of survey data by management would be used at an aggregate, non-personally identifiable, level. Informed consent was sought from each prospective participant at the beginning of the survey, and participant acknowledgement of consent was a precondition for access to the web-based survey. As with the manufacturing sample, these procedures were in line with the required research participant protection standards outlined by the

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researcher and approved for exemption by the Institutional Review Board in September

2010 for this study.

Response Rate Retail Sample

A sample size of between 100 and 200 has been suggested to be the “critical

sample size” (Comrey & Lee, 1992; Hair, Anderson, Tatham, & Black, 1992). The

sample size obtained from the target company was 350. A total of 2,539 individuals were

employed within the division of the retail company and the sample size obtained was 350

for a response rate of 14%. This response rate fell below the 34.6% average target (Cook,

Heath, & Thompson, 2000). Compared to the manufacturing sample, the level of senior

management involvement in, and dedication of resources to the data collection process

was not as high which is perhaps the most likely reason the response rate was not as high.

Treatment of Missing Data

Based on the design of the web-based survey for both the manufacturing and retail samples, the web-based survey system would not accept partially completed surveys so this effectively eliminated the issue of incomplete web-based surveys,

however, approximately 36% of the manufacturing sample population completed paper surveys and of those, 10% (12) of the paper-based surveys needed to be removed due to incomplete or inconsistent responses. The web-based survey design requirement of

having to complete the survey for it to be considered “usable” may have contributed to

the lower than average response rate in the retail sample.

Description of Manufacturing Sample (sample 1)

Data was collected on a number of participant characteristics with the goal of describing the study sample and to serve as control variables which were held constant

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across the data analyses for each research question. Descriptive statistics for each

demographic variable are presented in the following section. The potential respondents

for the manufacturing sample held a variety of production, administrative, and

managerial roles (n = 692). Of the employees who responded to the questionnaire (n =

275), 70% were male and 30% were female with the highest number of respondents averaging between 50-54 years of age. The average number of years worked was 19.

Employee (non-supervisory) was the most common level of employees who responded to the survey (68%) and shop floor/factory worker was the second most reported job role of those in this sample (28%). Most respondents had either a high school diploma (32%) or some college (22%). Given the fact that job security was a central focus of the study, family-related questions were included. The majority of respondents were married (62%)

and just over half of respondents who answered the question had children for whom they

were financially responsible (48%). On the question of, “what is the most significant

organizational change taking place that will have an impact on your job”, outsourcing

was the most common answer (23%) followed closely by new IT/technology/software

(22%) and most respondents felt these would have either an a “moderately major” or

“extremely major” change impact (63%). However, on the question of how likely the

change is to result in job loss, the respondents were fairly evenly split with 30% saying it

was “very unlikely”, 20% saying it was “neither likely or unlikely” and 20% indicating

that it was “very likely”. Tables 3.2 through 3.13 below provide additional information

on the manufacturing sample.

Table 3.2

Gender of Respondents (manufacturing sample)

Gender Frequency Percent

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Male 185 67.3 Female 77 28.0 Missing 13 4.7 Response total 262 95.3 Total 275 100.0

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Table 3.3

Age Range (manufacturing sample)

Age Range Frequency Percent 20-24 4 1.5 25-29 5 1.8 30-34 11 4.0 35-39 21 7.6 40-44 27 9.8 45-49 40 14.5 50-54 62 22.5 55-59 61 22.2

60 or older 35 12.7

Missing 9 3.3 Response total 266 96.7 Total 275 100.0

Table 3.4

Race / Ethnicity (manufacturing sample)

Race / Ethnicity Frequency Percent White or 208 75.6 Caucasian Black or African 37 13.5 America Asian or Pacific 8 2.9 Islander Native American 1 0.4 or Alaskan Native Mixed Racial 8 2.9 Background Other Race 1 0.4

Missing 12 4.4

Response total 263 95.6

Total 275 100.0

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Table 3.5

Marital Status (manufacturing sample)

Marital Status Frequency Percent Married 170 61.8 Not Married 90 32.7 Missing 15 5.5 Response total 266 96.7 Total 275 100.0

Table 3.6

Children Financially Responsible (manufacturing sample)

Children Financially Frequency Percent Responsible Yes 132 48.0 No 131 47.6 Missing 12 4.4 Response total 263 95.6 Total 275 100.0

Table 3.7

Education (manufacturing sample)

Education Frequency Percent High School 89 32.4 Diploma Some College 61 22.2 Two year degree / 28 10.2 certificate Four year college / 48 17.5 degree Masters degree 20 7.3 Doctoral degree 1 0.7 Other 12 4.4 Missing 11 4.0 Response total 264 96.0 Total 275 100.0

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Table 3.8

Organizational Level (manufacturing sample)

Level Frequency Percent Contractor 6 2.2 Employee (non 187 68 supervisor) Supervisor 25 9.1 Manager 33 12 Senior Manager / 7 2.5 Executive Missing 17 6.2 Response total 258 93.8 Total 275 100.0

Table 3.9

Role Function (manufacturing sample)

Role Frequency Percent Shop Floor / Factory Worker 78 28.4 Welder / Assembler 33 12 Material Handler / Forklift Operator 8 2.9 Machine Setup Operator 15 5.5 Machine Operator 9 3.3 Maintenance Technician 10 3.6 Marketing / Sales 21 7.6 IT / Software / Hardware 3 1.1 Administrative (planning, finance, HR, legal) 17 6.2 Research / Development 7 2.5 Engineering 18 6.5 Management / Leadership 32 11.6 Other 12 4.4 Missing 12 4.4 Response total 263 95.6 Total 275 100.0

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Table 3.10

Organizational Change Type (manufacturing sample)

Change Type Frequency Percent New / revised IT, technology, or software 60 21.8 changes New quality or process improvement 23 8.4 Facilities Changes 16 5.8 Culture Changes 34 12.4 New payment, compensation, bonus, or 18 6.5 benefits structure Product or service rebranding 9 3.3 Operational changes to respond to new legislation, changing economic conditions, or 12 4.4 national / international events Merger or Acquisition 1 0.4 Outsourcing 64 23.3 Ownership Change 11 4 Other 23 8.4 Missing 3 1.1 Response total 272 98.9 Total 275 100.0

Table 3.11

Significance of Change (manufacturing sample)

Change Frequency Percent Significance Extremely Minor 7 2.5 Moderately Minor 6 2.2 Slightly Minor 13 4.7 Neither Major or 20 7.3 Minor Slightly Major 54 19.6 Moderately Major 88 32.0 Extremely Major 87 31.6 Response total 275 100.0 Total 275 100.0

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Table 3.12

Likelihood of Job Loss due to Change (manufacturing sample)

Likelihood of Job Frequency Percent Loss Very Unlikely 84 30.5 Slightly Unlikely 34 12.4 Neither Likely or 55 20 Unlikely Slightly Likely 47 17.1 Very Likely 55 20 Response total 275 100 Total 275 100.0

Table 3.13

Most Likely Cause of Job Loss (manufacturing sample)

Likelihood of Job Loss Frequency Percent Site / Company Closure or Relocation 68 24.7 Acquisition or Merger 3 1.1 Offshoring or Outsourcing 54 19.6 Management or Leadership Change 37 13.5 IT, automation, or technology changes 8 2.9 Government Spending / Funding Cuts 3 1.1 Decreased donations / endowments 3 1.1 Decreased Sales of Products or 71 25.8 Services Individual Job Performance 13 4.7 Other 15 5.5 Response total 275 100 Total 275 100.0

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Description of Retail Sample (sample 2)

As with the manufacturing sample, similar data was collected on participant characteristics for the retail sample with the goal of describing the study sample and to serve as control variables which were held constant across the data analyses for each hypothesis. Descriptive statistics for each demographic variable are presented in the following tables. The potential respondents for the retail sample held a variety of cashier, sales floor, and supervisory roles (n = 2,539). Of the employees who responded to the questionnaire (n = 350), 34% were male and 66% were female with the highest number of respondents averaging between 20-24 years of age. The average number of years worked was five. Employee (non-supervisory) was the most common level of employees who responded to the survey (83%) and sales floor associate was the job role of the majority in this sample (32%). Most respondents had either a high school diploma (30%) or some college (42%). Given the fact that job security was a central focus of the study, family-related questions were included as parental levels of job insecurity has been linked to changes in parenting behaviors (Stewart & Barling, 1996) and increased cynicism about work among children (Barling, Dupre, & Hepburn, 1998). Unlike the manufacturing sample, the majority of respondents in the retail sample were not married

(59%) and the majority did not have children for whom they were financially responsible

(69%). On the question of, “what is the most significant organizational change taking place that will have an impact on your job”, ownership change was the most common answer (26%) followed closely by new IT/technology/software (22%) and most

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respondents felt these would have either an a “slightly”, “moderately” or “extremely”

major change impact (80%). On the question of how likely the change is to result in job

loss, the respondents felt confident that these changes would not result in the loss of their

jobs with 49% saying job loss was “very unlikely”. The differences between those in the

manufacturing sample compared to the retail sample on job loss concerns seem to

broadly reflect macro-level trends on job growth in the service sector and decline in the

manufacturing sample. Tables 3.14-3.25 below provide additional information on the

retail sample.

Table 3.14

Gender of Respondents (retail sample)

Gender Frequency Percent Male 119 34.0 Female 231 66.0 Response total 350 100 Total 350 100.0

Table 3.15

Age Range (retail sample)

Age Range Frequency Percent Under 20 41 11.7 20-24 93 26.6 25-29 40 11.4 30-34 22 6.3 35-39 17 4.9 40-44 20 5.7 45-49 25 7.1 50-54 30 8.6 55-59 34 9.7

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60 or older 28 8.0 Response total 350 100 Total 350 100.0

Table 3.16

Race / Ethnicity (retail sample)

Race / Ethnicity Frequency Percent White or Caucasian 313 89.4 Black or African 13 3.7 America Asian or Pacific 5 1.4 Islander Native American or 4 1.1 Alaskan Native Mixed Racial 15 4.3 Background Response total 350 100 Total 350 100.0 Table 3.17

Marital Status (retail sample)

Marital Status Frequency Percent Married 138 39.4 Not Married 208 59.4 Missing 4 1.1 Response total 346 98.8 Total 350 100.0

Table 3.18

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Children Financially Responsible (retail sample)

Children Financially Frequency Percent Responsible Yes 107 30.6 No 243 69.4 Missing 0 0 Response total 350 100 Total 350 100.0

Table 3.19

Education (retail sample)

Education Frequency Percent High School 105 30 Diploma Some College 146 41.7 Two year degree / 55 15.7 certificate Four year college / 30 8.6 degree Masters degree 4 1.1 Doctoral degree 3 0.9 Other 7 2 Missing 0 0 Response total 350 100 Total 350 100.0

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Table 3.20

Organizational Level (retail sample)

Role Frequency Percent Contractor 2 0.6 Employee (non 291 83.1 supervisor) Supervisor 42 12 Manager 14 4 Senior Manager / 1 0.3 Executive Missing 0 0 Response total 350 100 Total 350 100.0

Table 3.21

Role Function (retail sample)

Job Function Frequency Percent Sales Floor Associate 111 31.7 Sales Floor Supervisor 17 4.9 Cashier 73 20.9 Maintenance 4 1.1 Operations 5 1.4 Marketing / Sales 1 0.3 IT / Software / Hardware 31 8.9 Administrative (planning, finance, HR, legal) 2 0.6 Research / Development 106 30.3 Missing 0 0 Response total 350 100 Total 350 100.0

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Table 3.22

Organizational Change Type (retail sample)

Change Type Frequency Percent New / revised IT, technology, or software changes 78 22.3 New quality or process improvement 20 5.7 Facilities Changes 32 9.1 Culture Changes 39 11.1 Diversity and/or cross-cultural communication initiatives 3 0.9 New payment, compensation, bonus, or benefits structure 29 8.3 Product or service rebranding 12 3.4 Operational changes to respond to new legislation, changing 13 3.7 economic conditions, or national/international events Merger or Acquisition 1 0.3 Outsourcing 2 0.6 Ownership Change 92 26.3 Other 29 8.3 Missing 0 0 Response total 350 100 Total 350 100.0

Table 3.23

Significance of Change (retail sample)

Change Frequency Percent Significance Extremely Minor 6 1.7 Moderately Minor 6 1.7 Slightly Minor 13 3.7 Neither Major or 44 12.6 Minor Slightly Major 106 30.3 Moderately Major 99 28.3 Extremely Major 76 21.7 Response total 350 100.0

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Table 3.24

Likelihood of Job Loss due to Change (retail sample)

Likelihood of Job Frequency Percent Loss Very Unlikely 174 49.7 Slightly Unlikely 41 11.7 Neither Likely or 56 16 Unlikely Slightly Likely 46 13.1 Very Likely 33 9.4 Response total 350 100 Total 350 100.0

Table 3.25

Most Likely Cause of Job Loss (retail sample)

Likelihood of Job Loss Frequency Percent Site / Company Closure or Relocation 35 10.0 Acquisition or Merger 1 0.3 Offshoring or Outsourcing 9 2.6 Management or Leadership Change 52 14.9 IT, automation, or technology changes 15 4.3 Government Spending / Funding Cuts 9 2.6 Decreased donations / endowments 3 0.9 Decreased Sales of Products or 80 22.9 Services Individual Job Performance 110 31.4 Other 36 10.3 Response total 350 100 Total 350 100.0

Combining the Manufacturing and Retail samples

In order to test the overall efficacy of the full hypothesized relationship model, the researcher decided to combine the data sets across the manufacturing and retail samples.

As observed by Roberts and Binder (2009), combining survey results with similar variables across data frame methods (e.g. cross-industry) may improve the data coverage.

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The following section outlines the tests of independence that were conducted across both

samples as well as the rationale for combining the two samples.

Since the manufacturing (n = 275) and retail (n = 350) samples were unequal,

several independence tests were conducted to determine if the paper sample differed from

the larger web-based sample in any statistically meaningful ways. The Levene’s Test for

the Equality of Differences and t-tests were conducted on the continuous, numeric, and

scale-based control variables while chi-squared tests were conducted on discrete and categorical control variables.

The Levene’s test indicated no significant differences between the variances of the paper sample and the web-based sample for participants on change impact (p =

0.493), job satisfaction (p = 0.666), employability (p = 0.092), and change significance (p

= 0.230) as the significance values (p-values) were larger than 0.05 in each of these cases.

The Levene’s test did however show differences in variance for responses to number of years worked for the organization (p = 0.00), and employment dependence (p = 0.008).

Following the Levene’s tests, t-tests were conducted on the same survey controls

where the equality of variances were found to be significant (p < 0.05) and where the

statistical software package (SPSS 15.0) automatically adjusted the degrees of freedom to

allow for the assumption of equal variances. The t-test results did not reveal a statistical

difference between the means of change impact (t(619) = 0.248, p = 0.804) and job

satisfaction (t(616) = 0.292, p = 0.770). However, the t-test results did reveal there were statistical differences between the means of the scores for number of years worked for the organization (t(353.490) = 18.171, p = 0.00), employment dependence (t(611.012) =

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2.631, p = 0.009), employability (t(621) = 4.404, p = 0.00), and change significance

(t(623) = 2.008, p = 0.045).

Finally, chi-squared tests were conducted on the discrete and/or categorical variables of gender, age, marital status, children (financially responsible), education, and level in the organization. Table 3.26 provides a summary.

Table 3.26

Chi-squared test results summary for Manufacturing compared to Retail samples

Associat ion Control Chi-Squared found Result Summary Result Description Variable Result Value (yes / no) Over 71% of manufacturing χ² (1, N = 612) More women in retail, sample employees were male Gender Yes = 80.336, more men in and 75% retail sample p=0.00 manufacturing employees were female χ² (9, N = 616) Manufacturing sample Over 50% of manufacturing Age Yes = 169.758, p = included more older sample included those in age 0.00 employees range of 35-39 and higher χ² (5, N = 613) Manufacturing sample Nearly 90% of retail sample Race Yes = 26.490, p = included more racial who completed the surveys 0.00 minorities were white or Caucasian χ² (1, N = 606) About 70% of employees who Marital Retail sample tended Yes = 38.623, p = were not married came from the Status to be unmarried 0.00 retail sample Children χ² (1, N = 613) About 65% of employees who Retail sample tended (financially Yes = 24.297, p = did not have children came to have no children responsible) 0.00 from the retail sample About 7% of the employees Manufacturing sample χ² (7, N = 614) with masters degrees came from tended to have higher Education Yes = 54.363, p = the manufacturing sample levels of post- 0.00 compared to 1% from the retail secondary education sample Manufacturing sample About 88% of employees in χ² (4, N = 608) tended to have a Level in manager or leadership position Yes = 27.838, p = higher percentage of Organization respondents came from the 0.00 senior-level manufacturing sample respondents

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Associations between how the survey was completed (paper or web) and each of these control variables were found with the exception of gender. The independence of samples tests revealed a number of control variable differences between those employees in the manufacturing sample compared to those in the retail sample. Somewhat

expectedly, employees from the manufacturing sample had longer tenures, greater

employment dependence, and rated the change as being of greater significance compared

to their retail sample counterparts. In addition, the manufacturing sample tended to be older, more racially homogenous, were married with children they were supporting financially, and had individuals in higher levels of the organization overrepresented in the sample compared to the retail sample. Overall, differences between manufacturing and retail samples survey responses were found on 11 of the 13 control variables. These results indicated there were statistically significant differences in the demographics of the employees in each sample, however, since the purpose of this study was test the generalized variable relationships, rather than relationships for a specific population or sample, the researcher opted to combine survey responses across both samples based on the results. The goodness of fit tests described in the following section provide statistical justification for combining the manufacturing and retail sample data.

Goodness of Fit Tests & Q-Q Plots

Given the increasing importance of combining data to draw conclusions about datasets, researchers have outlined ways to combine data from two or more samples (see

Roberts & Binder, 2009; Schenker & Raghunathan, 2007). In line with these approaches, the researcher completed goodness of fit tests, and Q-Q plots to test whether combining

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the manufacturing and retail samples was justified based on how the data was normally

distributed.

To determine if the samples followed a normal distribution, the researcher

combined a mean score of the 10 main constructs in the sample (Job Insecurity 1&2,

Change Related Self-Efficacy, Rational Empirical, Normative Re-educative, and Power

Coercive Change Leadership Strategies, Trust in Management, Affective Commitment to

Change, and Affective, Behavioral, and Cognitive Resistance to change). As indicated by the results of the Anderson-Darling goodness of fit test, both the manufacturing and retail samples showed that each of the samples were normally distributed. In each case, the P-

values for the manufacturing and retail sample were greater than 0.05, showing support

for the null hypothesis that the dataset were normally distributed.

Table 3.27

Anderson-Darling Test of Normality Results

Mean score N Mean Std. AD P-Value

Manufacturing Set 275 3.54 0.28 0.49 0.22

Retail Set 350 3.40 0.26 0.41 0.34

Combined Set 625 3.48975 0.27 0.91 0.02

Q-Q plots of the manufacturing sample and retail sample were also conducted as

it has been recommended that normality tests should not be exclusively relied upon to

determine normality (Doksum & Sievers, 1973; Kratz & Resnick, 1995). Based on the

Q-Q Plots the data followed a normal distribution (e.g. plots follows the straight line).

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Figure 3.1. Normal Q-Q plot of mean score for manufacturing sample.

Figure 3.2. Normal Q-Q plot of mean score for retail sample.

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Figure 3.3. Normal Q-Q plot of mean score for combined manufacturing and retail sample.

The Q-Q plots for the manufacturing and retail samples, especially the combined samples seemed to fit very well therefore the researcher opted to combine the datasets for the purpose of this study.

Instrumentation

Survey items for this study were drawn from existing multi-item scales that have been used in past research and remain unchanged from their original formats. Table 3.28 summarizes the constructs that were measured on the survey. All items were anchored by either a 7-point or 5-point Likert-type response scale. The following section details the dependent, independent, mediator, and moderator variables used in the study.

Independent Variables

The independent variables for this study were job insecurity and change related self-efficacy. The following section provides background on the scales used to measure these constructs in the study.

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Measuring job insecurity. Job insecurity has been measured several ways based

on different theoretical approaches and definitions (see Greenhalgh & Rosenblatt, 1984;

Hellgren et al., 1999). In line with research showing that global (more quantitatively

oriented) measures of job insecurity are just as effective in capturing employee job

insecurity perceptions (see Reisel & Banai, 2002), the current study adopted the global

Job Insecurity Scale used by Francis and Barling (2005). The job insecurity construct was

measured using a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5

(strongly agree). This 3-item scale includes the following items: “I can be sure of my

present job as long as I do good work”, “I am not really sure how long my present job

will last”, and “I am afraid of losing my present job.” Higher scores indicate higher

degrees of insecurity with the exception of the first item which was reverse scored.

Francis and Barling (2005) reported high reliability (using Cronbach’s alpha coefficients)

for their Job Insecurity scale data at .81. In addition, a second job insecurity measure was

used, Borg and Elizur’s (1992) job insecurity scale. This second, 3-item job insecurity

scale included the following items: “I believe that my job in secure” (reverse scored), “In

my opinion, I will keep my job in the near future”, and “In my opinion I will be employed for a long time in my present job”. Each of these items were reverse-scored such that higher scores indicate lower degrees of job insecurity. Francis and Barling

(2005) reported high reliability (using Cronbach’s alpha coefficients) for their Job

Insecurity scale data at .71. In the current study, the two job insecurity measures were combined to create a composite Job Insecurity 1 and 2 scale (JI1&2). These scales were combined because they were similar in that they measure the same global job insecurity global construct. The Cronbach’s alpha in the current study for the combined job

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insecurity scales was .88. Combining the two scales allowed for a more robust measure of

the same construct.

Measuring change related self-efficacy. Self-efficacy is an individual difference construct and was measured using Ashford’s (1988) scale. As noted above, change related self-efficacy is arguably more closely related to the changes being experienced by employees suggesting that it may better capture employees’ beliefs about their ability to cope with specific organizational change initiatives. The change related self-efficacy

construct was measured using a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Items for this scale include: “I get nervous that I may not be able to do all that is demanded of me by the change” and “I have reason to believe I may not perform well in my job situation following the change”. The reliability estimate for this data was .67 in Ashford’s 1988 study. Although this figure represents the lowest reliability estimate of all the scale data used in this study, it has been argued that

reliability estimates between 0.60 and 0.75 constitute “good” reliability estimate

measures for both clinical and organizational data significance in social science research

(Aron, Aron, & Coups, 2005; Cicchitti ,1994; Garson, 2007).

Dependent Variables

The dependent variables for this study were affective commitment to change as well as affective, behavioral, and cognitive. The following section provides background on the scales used to measure these constructs in the study.

Measuring affective commitment to change. Employee commitment has been operationalized using different measures (e.g. Meyer & Allen, 1991; 1997; Mowday et al., 1982; O’Reilly & Chatman, 1986) but these scales were designed to measure

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organizational commitment which is a slightly different construct. The Commitment to

Organizational Change Scale was specifically designed to capture employee commitment

to organizational change. Since employees’ responses to organizational change in the

midst of job insecurity is a central focus of the current study, Herscovitch and Meyer’s

(2002) scale represents the best scale option for this research given existing studies have

found that commitment to change was a better predictor of behavioral support for change

compared to organizational commitment. The Commitment to Change scale is comprised

of affective, continuance, and normative measures of commitment to change. Since

affective commitment to change has been consistently identified in the research literature

as the most optimal employee response (Herscovitch & Meyer, 2002; Parish et al., 2008),

the affective sub-scale was selected and used for the study. The affective commitment to

change construct was measured using a 7-point Likert-type scale ranging from 1 (strongly

disagree) to 7 (strongly agree). Survey items for affective commitment to change include:

“This change serves an important purpose” and “This change is a good strategy for this

organization”. The Cronbach alpha reliability estimate for the scale data was .94 in the

original study.

Measuring change attitudes (resistance to change). The Change Attitude scale

was developed by Oreg (2006) and first assessed on approximately 800 employees

undergoing an organizational merger. The origins of this scale make it ideally suited for a study measuring employee responses to job insecurity. The change attitudes construct was measured using a 7-point Likert-type scale ranging from 1 (strongly disagree) to 7

(strongly agree). Several of the survey items in this scale include: “The change makes me upset (emotional),” “I looked for ways to prevent the change from happening

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(behavioral)”, and “I believe the change will benefit the organization”. For the affective

(emotional), behavioral, and cognitive components of the Change Attitude scale, the

reliability estimates for the data were .78, .77, and .86 respectively (Oreg, 2006).

Moderator Variables

The moderator variables for this study were perceptions of change leadership

strategy and trust in management. A moderator variable as defined by Baron and Kenny

(1986) is any variable, “that affects the direction and/or strength of the relationship

between an independent or predictor variable and a dependent or criterion variable” (p.

1174). As noted in the hypotheses 4-7, change leadership strategies and trust in management were expected to moderate the relationships among job insecurity, change related self-efficacy, commitment, and resistance to change. The following section

provides background on the scales used to measure these constructs in the study.

Measuring perceptions of change leadership strategy. The scale selected to test

change leadership strategies was selected in part because it is intentionally broad making

no mention of supervisors, managers, or executives. This is relevant because

examinations of change leadership that focus too narrowly on the relationship between

organizational change and employees’ direct managers (see leader-member exchange theory) sometimes fail to capture the broader organizational dynamics over which a direct supervisor or manager may have no control (Larkin & Larkin, 1994). Frequently, ‘global change agents’ (e.g. distant and untouchable senior executives) are viewed as the initiators of change rather than immediate supervisors (Self, Armenakis, & Schraeder,

2007).

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The perceptions of change leadership strategy construct was measured using a 5-

point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree) developed

by Szabla (2007). Survey items in this scale include: “Decisions about this change are

being made by experts who are extremely knowledgeable about this change (rational

empirical)”, “The pressure to change put forth by those leading this change has been high

(power coercive)”, and “To get employees to change those leading this change are

involving employees from many levels of the organization to implement this change.”

The reliabilities for this scale, as reported, are sufficiently high. For the rational-

empirical, normative-reeducative, and power-coercive survey items the alphas are 0.81,

0.77, and 0.73 respectively.

Measuring trust in management. Trust was measured using Stanley et al.’s (2005)

Trust in Management scale. Oreg (2006) developed a similar trust scale but noted that

one of the limitations was that it was it overemphasized employees’ cognitive evaluations

of managers (rather than emotional and behavioral). The Stanley et al.’s scale appears to

overcome that limitation by including items such as: “I trust management to make the

right decisions in situations that affect me personally” and “I am willing to follow

management’s lead even in risky situations”. The trust in management construct was

measured using a 5-item, 6-point Likert-type scale ranging from 1 (strongly disagree) to 6

(strongly agree). The Cronbach alpha reliability estimate for this scale data was .85.

Control Variables

The control variables for this study included change impact, change significance,

job satisfaction, and demographic information. The following section provides

background on the measures used for these constructs in the study.

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Measuring control variables (change impact, change significance, job

satisfaction, and demographics). This study used a number of control variables including

the Change Significance and Change Impact scales developed by Herscovitch and Meyer

(2002). Armenakis and Bedeian’s (1999) and Fedor and Herold’s (2004) studies found

evidence that employee responses to change were influenced by how the change would

impact them personally, their work unit, as well as other broader change impacts. The

researchers concluded that multiple levels of change including how the change was

managed, the impact of the change, and individual differences are all factors of how

employees will respond to change. The current study directly examined change management via the Perceptions of Change Leadership Strategy scale and individual differences via the Change Related Self-Efficacy scale. Although change impact and change significance are not directly examined among the research hypotheses of this study, previous research findings indicate the importance of these factors to employee responses to change therefore they were included as control variables (see Herscovitch &

Meyer, 2002).

The change significance construct was measured using a 7-point Likert-type scale ranging from 1 (extremely minor) to 7 (extremely major). The change impact construct was measured using a 7-point Likert-type scale ranging from 1 (large negative impact to

7 (large positive impact). In addition, a list of workplace-related changes was created for this study based on many of the job threatening organizational changes discussed above in Chapter 1 (e.g. automation, outsourcing, mergers and acquisitions). This list allowed survey participants to select from a range of organizational change and job threat options since employees may have different perceptions of the main changes taking place within

137 their organization (Reisel, 2003). Additionally, job satisfaction has been shown to impact employee resistance to change and organizational commitment (Rush, Schoel, &

Barnard, 1995; Schweiger & Dinis, 1991) so Cammann et al.’s (1983) Job Satisfaction scale was used as a control variable to account for differences in resistance and commitment attributable to job satisfaction. The job satisfaction construct was measured using a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Employability (likelihood of finding another job if the current one is terminated) and employment dependence (inability to easily transfer employment to another organization and/or the economic need to work) were also included as controls as employability has been shown to decrease levels of job insecurity (see Brockner et al.,

1992; Fugate, Kinicki, & Ashford, 2004; Kalyal, Berntson, Baraldi, Naswall, & Sverke,

2010) and employment dependence has been shown to increase job insecurity (Fugate et al., 2004; Ito & Brotheridge, 2007).

Finally, demographic information was collected at the end of the survey specifically; participants were given the option to provide information about their age, gender, tenure with the organization, level of formal education, and current position/role.

Gender is an important control variable since past studies of job insecurity have found mixed results in terms of whether women or men have more adverse impacts resulting from job insecurity (see Cheng & Chan, 2008).

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Table 3.28

Sources, Authors, and Reliability Coefficients of Measuring Instruments of Study Constructs

Question Number Number(s) Construct Measured of Items Alpha Author(s)

9-11 Job Insecurity 1 3 0.81 Francis & Barling (2005)

12-14 Job Insecurity 2 3 0.71 Borg & Elizur (1992)

Perceptions of Change 15-47 33 0.73-0.81 Szabla (2007) Leadership Strategy

Change Attitude 0.77, 0.78, 48-62 15 Oreg (2006) (resistance to change) 0.86

Affective Commitment to Herscovitch & Meyer 63-68 6 0.94 Organizational Change (2002)

Change Related Self- 69-72 4 0.67 Ashford (1988) efficacy

Stanley, Meyer, & 76-81 Trust in Management 5 0.85 Topolnytsky (2005)

Also, an optional race/ethnicity demographic question was included. The race and ethnicity-related question was included in this study of job insecurity because downsizing decisions have been shown to have a particularly deleterious effect on minorities and black workers in particular have been shown to have higher perceptions of job insecurity than do white workers of the same age, education, and gender (Elvira & Zatzick, 2002;

Manski & Straub, 2000).

Data Analysis

The relationships between the main constructs of this study were analyzed using hierarchical multiple regression analysis. This is a commonly used analysis technique for

139 studies of job insecurity and employee outcomes (see De Cuyper & De Witte, 2007; Ito

& Brotheridge, 2007; Jimmieson et al., 2004). This data-analytic technique is used to examine both mediator and moderator effects which was essential to testing the hypotheses of this study (Frazier, Tix, & Barron, 2004). Specifically, hierarchical multiple regression was used to test the moderating effects of perceptions of change leadership strategy and trust in management as well as the mediating effect of change related self-efficacy. The data analysis consisted of three steps.

First, a multiple regression was performed to test the relationship between the independent variable (job insecurity) and the four dependent variables (affective commitment to change and affective, behavioral, and cognitive resistance to change). The relationship between the independent variable, job insecurity and the four dependent variables including affective commitment to change and affective, behavioral, and cognitive resistance to change together comprise the foundational model. Multiple regression analysis can establish that a set of independent variables explains a proportion of the variance in a dependent variable at a significant level and can establish the relative predictive importance of the independent variables (Garson, 2004). The Pearson’s r test provided a means of evaluating the strength of the relationships between the independent and dependent variables (Kurtz, 1999). Following this regression, the control variables

(change impact, change significance, job satisfaction, and demographics) were regressed to ensure that the effects of the controls were taken into account and held constant thereby isolating the relationships of the variables of interest.

Another multiple regression analysis was performed to assess the effect of change leadership strategies and trust in management. At this step (Step 4), the moderating

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effects of rational-empirical, normative re-educative, and power-coercive change leadership strategies along with trust in management were tested (Hypotheses 3-7).

The following interaction terms were included in the regression model (job insecurity × rational empirical change leadership strategies; job insecurity × normative re-educative change leadership strategy; job insecurity × power coercive change leadership strategy; job insecurity × trust in management). Interaction terms allow for testing of moderator effects between independent and dependent variables under study. In cases where the interaction term explains a statistically significant amount of variance in the dependent variable(s), a moderator effect is present (Bennett, 2000). Problems of multicollinearity can arise when testing moderated relationships. To reduce this, the independent and moderator variables were centered by subtracting the sample mean from each variable resulting in a centered deviation score with a mean of zero. Centering beta terms has been shown to reduce the magnitude of correlations between the independent variables, thereby reducing multicollinearity (Aldwin, 1994).

Lastly, by integrating the first and second regression models, the mediating role of change related self-efficacy between the four employee responses was tested (Hypotheses

3a and 3b). The third multiple regression contained the independent variable and the mediator variable simultaneously entered with the dependent variables. The mediator should have a significant relationship and the relationship between the independent variable (job insecurity) and the dependent variables (employee attitudes) should be less than the second regression. The degree to which the effect is reduced will be evaluated by examining any reduction of the regression coefficient in the third multiple regression

(Lindley & Walker, 1993). If the strength of the associated between job insecurity and

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employee responses is reduced with the addition of change related self-efficacy, a mediation effect will have been demonstrated. The effects were examined for alignment

and consistency with the proposed hypothesized relationships. SPSS 15.0 for Windows

was used to conduct the analyses.

Summary of Chapter 3

This chapter detailed how the current study was conducted and provided information about the sample population and instruments used to answer the research questions and test the study hypotheses. Specifically, the target organizations and participants were identified and the approach for administering paper and web-based surveys to participants. Data was collected from two private sector companies, one manufacturing company (n = 275) and one retail company (n = 350). Study participants

at these companies completed surveys consisting of questions related to perceptions of

significant changes taking place within their organization, their ability to cope with those

changes, the level of job insecurity they are experiencing, their perceptions of change

leadership strategies, their level of trust in management, and their affective, behavioral,

and emotional responses to those changes all related to the research questions and hypotheses of this study.

The data in this study was analyzed using hierarchical multiple regression

analysis. The proceeding sections also included discussions of the research design and

data collection consisting of a cross-sectional survey research designed used to collect individual level data using paper and web-based (sample 1 only) questionnaires. The instrumentation section presented the scales used to measure job insecurity, change related self-efficacy, commitment to change, resistance to change, trust in management,

142 and perceptions of change leadership strategy. Each of the scales has been used in past research and previously reported reliabilities from earlier studies were presented.

A hierarchical multiple regression analysis approach was used to analyze the data collected. The three-step process for the data analysis consistent with recommendations from Frazier, Tix, and Barron (2004) for examining moderating and mediating effects in a hypothesized relationships model was outlined. The results of these analyses are presented in the following chapter.

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CHAPTER 4: RESULTS

This chapter presents the findings of the statistical analysis of 625 survey respondents in the two samples (manufacturing and retail samples respectively) as well as results for the combined sample (manufacturing and retail samples combined). The two samples consisted of one manufacturing sample (n = 275) and a retail sample (n = 350).

Analyses of these samples are organized into three sections. Section 1, Descriptive

Statistics, includes the means and standard deviations of all the survey items. Section 2,

Hypothesis Test Results, provides the outcomes of the hypotheses tests related to the core hypothesized relationships among job insecurity, affective commitment to change, and the three resistance types for the manufacturing, retail, and combined manufacturing and retail samples. Section 3, Full Model Test Results, provides the results of the hypothesized relationships among all the key constructs for the full model.

Descriptive Statistics

The means and standard deviations for each of the scales measured are listed below in Tables 4.1 and 4.2. A total of eleven scales were used to measure six constructs.

On average, participants in both samples gave the highest ratings to the change related self-efficacy items. This would seem to indicate that employees who participated in this study felt they were able to effectively cope with the organizational changes taking place in their firms. Affective commitment to change items received the second highest ratings in the manufacturing sample while cognitive resistance to change received second highest ratings in the retail sample.

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The lowest scale scores were reported on the Normative Re-educative Change

Leadership Strategy scale which suggests that study participants perceived relatively low levels of participation-based change leadership strategy.

Table 4.1

Descriptive Statistics of Instrument Scales and Items (manufacturing sample) Scale Construct Number Scale Scale Type Standard Measured of Items Mean Deviation Job Insecurity 1 3 3.06 1.02 Job Insecurity Job Insecurity 2 3 3.11 1.01 Job Insecurity 1&2 6 2.98 0.93 Self-Efficacy Change Related Self-efficacy 4 4.64 1.11 Rational Empirical Change 8 2.97 0.78 Leadership Strategy Change Normative Re-educative Change Leadership 12 2.61 0.75 Leadership Strategy Strategy Power Coercive Change 13 3.37 0.69 Leadership Strategy Commitment to Affective Commitment to 6 4.29 1.68 Change Organizational Change Affective Resistance to Change 5 3.92 1.55 Resistance to Behavioral Resistance to Change 5 3.52 1.49 Change Cognitive Resistance to Change 5 4.01 1.60 Trust Trust in Management 5 3.13 1.19

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Table 4.2 Descriptive Statistics of Instrument Scales and Items (Retail Sample) Scale Construct Number Scale Scale Type Standard Measured of Items Mean Deviation Job Insecurity 1 3 2.37 1.05 Job Insecurity Job Insecurity 2 3 3.53 1.05 Job Insecurity 1&2 6 2.42 0.98 Self-Efficacy Change Related Self-efficacy 4 4.38 1.17 Rational Empirical Change 8 2.93 0.85 Leadership Strategy Change Normative Re-educative Change Leadership 12 2.49 0.79 Leadership Strategy Strategy Power Coercive Change 13 3.43 0.66 Leadership Strategy Commitment to Affective Commitment to 6 3.99 1.81 Change Organizational Change Affective Resistance to Change 5 3.82 1.71 Resistance to Behavioral Resistance to Change 5 3.49 1.45 Change Cognitive Resistance to Change 5 4.04 1.70 Trust Trust in Management 5 3.03 1.27

Hypothesis Test Results

The strength of the relationships between the variables of interest in this study were assessed using bi-variate correlation analysis (i.e., product moment correlation coefficient, or Pearson’s r). Between-variable correlations for the combined manufacturing and retail sample are presented in Table 4.3 below. Coefficient values

between 0.10 and 0.30 are considered small or weak, those between 0.40 and 0.60 are

considered moderate, and those over 0.70 are considered high or strong (McMillian,

2000).

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Table 4.3

Descriptive Statistics, Means, Standard Deviations, and Cronbach Alphas (combined manufacturing and retail sample)

Std. Mean JI12 ED E CI JS CRSE RECLS NRCLS PCLS TIM ACTC ARTC BRTC CRTC Deviation Job Insecurity 2.6648 .99317 0.875

Employment 2.8814 1.20774 .316** 0.766 Dependence Employability 3.0903 1.05618 -.376** -.600** 0.842

Change Impact 3.8854 1.33839 -.361** -.174** .229** 0.809

Job Satisfaction 4.3818 1.42910 -.389** -.034 .061 .312** 0.888

Self-efficacy 4.4925 1.14637 -.349** -.244** .202** .385** .333** 0.409

Rational Empirical 2.9545 .82195 -.390** -.085* .170** .542** .415** .269** 0.857

Strategy Normative Re- educative 2.5477 .77929 -.341** -.091* .211** .472** .373** .226** .687** 0.889

Strategy Power Coercive 3.4119 .66711 .216** .221** -.053 -.214** -.255** -.281** -.218** -.400** 0.837 Strategy Trust in 3.0731 1.14966 -.416** -.115** .121** .388** .525** .248** .543** .525** -.332** 0.843 Management Affective Commitment to 4.1251 1.76287 -.373** -.235** .215** .605** .414** .474** .578** .514** -.423** .489** 0.939

Change Affective Resistance to 3.8651 1.64243 .441** .286** -.263** -.576** -.366** -.562** -.492** -.413** .387** -.421** -.771** 0.882

Change Behavioral Resistance to 3.5065 1.46561 .364** .228** -.201** -.486** -.370** -.515** -.482** -.338** .335** -.396** -.743** .787** 0.806

Change Cognitive Resistance to 4.0272 1.65574 .410** .270** -.221** -.638** -.392** -.525** -.585** -.520** .437** -.466** -.885** .833** .784** 0.878 Change

JI12 = job insecurity scales 1 and 2 combined, ED = employment dependence, E = employability, CI = change impact, JS = job satisfaction, CRSE = change related self-efficacy, RECLS = rational empirical change leadership strategy, NRCLS = normative re-educative change leadership strategy, CLS = power coercive change leadership strategy, TIM = trust in management, ACTC = affective commitment to change, ARTC = affective resistance to change, BRTC = behavioral resistance to change, CRTC = cognitive resistance to change; *p < .05, **p < .01, ***p < .001.

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Manufacturing Sample Results

There were several meaningful relationships among the core independent variables and the outcome variables in the manufacturing sample. All meaningful relationships were significant at p < .01, p < .05, or p < .10. The correlations between the predictor (job insecurity scales 1 & 2 combined = JI12), outcome, mediator, and moderator variables are reported as follows rounded to the nearest hundredth.

Job Insecurity, Affective Commitment to Change, and Resistance to Change Direct Relationship Hypotheses Results (manufacturing sample)

Hypothesis 1 predicted that job insecurity would be negatively related to affective commitment to change and hypothesis 2 predicted job insecurity would be positive related to all three resistance to change measures. In the manufacturing sample, the relationships among job insecurity, affective resistance to change (β = 0.15, p < 0.05), and cognitive resistance to change (β = 0.12, p < 0.05) were significant and in the direction of the hypothesized relationships (positively related). Together, the manufacturing sample results for hypotheses 1 and 2 show no support for hypothesis 1 and partial support for hypothesis 2.

Change Related Self-efficacy Hypotheses Results (manufacturing sample)

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which suggests a partial mediation. The relationship between job insecurity and cognitive

resistance to change (β = 0.12, p < 0.05 to 0.10 p < 0.10) also decreased but stayed

significant which suggests a partial meditation. A Sobel test (Preacher & Hayes, 2004)

was conducted to further test the mediation effects of change related self-efficacy on the

relationships between job insecurity and commitment and resistance. The results showed

significant indirect effects of change related self-efficacy on the relationships between

job insecurity, affective resistance to change (zsobel = 1.69 (σx̅ = 0.03), p < 0.05),

cognitive resistance to change (zsobel = 1.66 (σx̅ = 0.03), p < 0.01), and cognitive resistance to change (zsobel = 1.66 (SE = 0.03), p < 0.05) providing further support for

the mediation effect of change related self-efficacy in the manufacturing sample.

Table 4.4 Regression Results for Hypothesis 1 and 2 Job Insecurity Relationship to Affective Commitment to Change and Three Resistance to Change Measures and Hypothesis 3 Change Related Self-Efficacy Mediation Between Job Insecurity, Affective Commitment to Change and Three Resistance to Change Types (manufacturing sample) CRSE ACTC ARTC BRTC CRTC Variable β t β t β t β t β t Step 0 Change Significance (1st) -0.03 -0.39 -0.09 -1.46 0.16 2.39 * 0.11 1.56 0.20 3.34 ** Change Significance (2nd) 0.12 1.59 0.00 0.03 -0.06 -0.93 -0.03 -0.38 -0.07 -1.23 Change Impact 0.34 5.10 ** 0.47 8.61 ** -0.41 -7.33 ** -0.30 -4.97 ** -0.53 -10.37 ** Job Satisfaction 0.10 1.56 0.17 3.15 ** -0.16 -2.84 ** -0.17 -2.83 ** -0.13 -2.58 * Gender -0.06 -0.91 -0.09 -1.67 0.10 1.78 0.02 0.31 0.05 1.01 Age Range -0.11 -1.48 -0.03 -0.46 0.06 0.92 0.03 0.47 0.06 0.99 Race -0.02 -0.36 -0.09 -1.65 0.07 1.23 0.13 2.22 * 0.11 2.34 * Marital Status 0.04 0.63 0.09 1.57 -0.03 -0.53 -0.06 -1.00 -0.05 -0.94 Children (financially responsible) -0.11 -1.75 0.02 0.41 -0.03 -0.47 -0.06 -0.96 -0.05 -1.01 Education 0.03 0.49 -0.01 -0.15 -0.01 -0.16 0.00 -0.07 0.02 0.36 Year(s) Worked for Organization 0.09 1.17 -0.04 -0.66 0.06 1.01 0.07 0.95 0.03 0.59 Level in Organization 0.01 0.11 0.18 3.17 ** -0.19 -3.24 ** -0.27 -4.23 ** -0.20 -3.82 ** Role / Function in Organization 0.03 0.45 0.07 1.15 -0.02 -0.26 -0.07 -1.00 -0.04 -0.73 Employment Dependence -0.11 -1.37 -0.12 -1.86 0.16 2.37 * 0.07 0.96 0.16 2.53 * Employability 0.06 0.76 0.01 0.11 -0.03 -0.51 -0.05 -0.67 0.04 0.65 R-Square 0.24** 0.48** 0.45** 0.36** 0.55** Step 1 Job Insecurity 1&2 -0.14 -1.86 + -0.12 -1.93 0.15 2.49 * 0.07 1.01 0.12 2.19 * Step 2 CRSE 0.14 2.45 * -0.24 -4.24 ** -0.28 -4.74 ** -0.20 -3.89 ** R-Square 0.25+ 0.49+ 0.46* 0.36 0.57* Step 3 Job Insecurity 1 & 2 ( + CRSE) -0.10 -1.66 + 0.12 2.03 * 0.03 0.49 0.10 1.80 + R-Square 0.50* 0.50** 0.42** 0.59** 1.69 (σx̅ = 0.03) 1.66 (σx̅ = 0.03) Sobel Test P>0.05 P>0.05 CRSE = change related self-efficacy, ACTC = affective commitment to change, ARTC = affective resistance to change, BRTC = behavioral resistance to change, CRTC = cognitive resistance to change; Standardized Coefficient Beta *p < .05, **p < .01, + p<0.10.

148 149

A mediation effect could not be determined between the relationship of job insecurity,

affective commitment to change and behavioral resistance to change because the direct

relationship was found to be non-significant--a condition that must be met in order to

establish a meditation effect (Baron & Kenny, 1986).

Rational Empirical (information-based) Change Leadership Hypotheses Results (manufacturing sample)

Table 4.5

Regression Results for Hypotheses 4a and 4b Job Insecurity and Rational Empirical (information-based) Change Leadership

Strategy and Moderated Interactions (manufacturing sample) ACTC ARTC BRTC CRTC Variable Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Step 0 Change Significance (1st) -0.12 -0.08 -0.07 0.19 * 0.17 * 0.16 * 0.13 0.11 0.10 0.25 ** 0.21 ** 0.20 ** Change Significance (2nd) 0.00 0.01 0.01 -0.06 -0.07 -0.06 -0.03 -0.03 -0.03 -0.08 -0.09 -0.08 Change Impact 0.60 ** 0.45 ** 0.43 ** -0.48 ** -0.44 ** -0.42 ** -0.34 ** -0.27 ** -0.26 ** -0.64 ** -0.51 ** -0.50 ** Job Satisfaction 0.23 ** 0.11 0.11 -0.20 ** -0.13 -0.13 -0.20 ** -0.14 -0.14 -0.17 * -0.06 -0.06 Gender -0.34 -0.23 -0.28 0.34 0.30 0.38 * 0.08 0.01 0.05 0.18 0.07 0.12 Age Range -0.04 -0.02 -0.01 0.06 0.04 0.02 0.04 0.03 0.02 0.06 0.04 0.03 Race -0.18 -0.18 -0.17 0.13 0.15 0.14 0.24 * 0.24 * 0.23 * 0.22 * 0.22 * 0.22 * Marital Status 0.30 0.29 0.28 -0.09 -0.08 -0.07 -0.18 -0.17 -0.17 -0.16 -0.16 -0.16 Children (financially responsible) 0.11 0.00 -0.01 -0.10 -0.09 -0.08 -0.21 -0.16 -0.15 -0.18 -0.10 -0.08

Education 0.00 0.02 0.03 -0.01 -0.02 -0.02 -0.01 -0.02 -0.03 0.01 -0.01 -0.01 Year(s) Worked for Organization 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00

Level in Organization 0.36 ** 0.27 * 0.25 * -0.34 ** -0.31 ** -0.27 * -0.47 ** -0.43 ** -0.41 ** -0.39 ** -0.31 ** -0.29 ** Role / Function in Organization 0.03 0.01 0.01 -0.01 0.00 0.00 -0.02 -0.01 -0.01 -0.01 0.00 0.00

Employment Dependence -0.18 -0.22 * -0.21 * 0.22 * 0.22 * 0.19 * 0.09 0.11 0.10 0.22 * 0.26 ** 0.24 ** Employability 0.03 -0.08 -0.07 -0.06 0.00 0.00 -0.08 -0.03 -0.03 0.05 0.15 0.14 Step 1 Job Insecurity 1&2 -0.16 -0.19 0.24 * 0.29 ** 0.08 0.11 0.17 + 0.19 * Step 2 RECLS 0.62 ** 0.65 ** -0.10 -0.14 -0.31 * -0.33 * -0.51 ** -0.53 ** Step 3 Job Insecurity 1&2 X RECLS -0.21 * 0.29 ** 0.15 0.17 + R-Square 0.49 ** 0.54 ** 0.55 * 0.45 ** 0.47 0.49 * 0.37 ** 0.38 * 0.39 0.55 ** 0.59 ** 0.60 + ∆ R-Square 0.04 ** 0.01 * 0.00 0.02 * 0.01 * 0.01 0.03 ** 0.01 +

Unstandardized regression coefficient * ρ <0.05, **ρ<0.01, + p<0.10

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Hypothesis 4a was supported by the analysis as the interaction term (job

insecurity1&2 x affective commitment to change) was significant and the direction of the

relationship followed the hypothesis (β = -0.21± 0.01 [95% CI], p < .0.05). Affective

commitment to change was stronger for both low and high job insecure individuals who

perceived higher levels of rational-empirical change leadership strategy (though not as

strong for individuals with high levels of job insecurity). Figure 4.1 provides a visual

depiction of the relationships.

Rational Empirical Change Leadership Strategy 4 3.8 3.6 3.4 HIGH 3.2 LOW 3 2.8 2.6 2.4

Affective Change to Commitment Affective LOW HIGH Job Insecurity

Figure 4.1. Influence of rational-empirical change leadership strategy on affective commitment to change for low and high job insecure individuals (manufacturing sample).

Hypothesis 4b predicted that a perceived rational-empirical (information-based) change leadership strategy will negatively moderate the positive relationship between job insecurity and the three resistance to change measures (affective, behavioral, and cognitive). In the manufacturing sample, the positive relationship between job insecurity and affective resistance to change (β = 0.29 ± 0.02 [99% CI], p < .0.01) was weaker for those individuals with low levels of job insecurity who reported a higher rational-

152

empirical change leadership approach however for individuals with high levels of job

insecurity, the rational empirical change leadership approach appeared to significantly

increase affective resistance to change. This finding was counter to the hypothesized

direction of the relationship since it appears that rational empirical change leadership was

associated with increased levels of affective resistance to change for high job insecure

individuals.

Rational Empirical Change Leadership Strategy

5.4 5.2 5 HIGH 4.8 LOW 4.6 4.4 LOW HIGH Affective Resistance to Change Job Insecurity

Figure 4.2. Influence of rational-empirical change leadership strategy on affective resistance to change for low and high job insecure individuals (manufacturing sample).

No significant moderating relationships were found for the interaction terms

between job insecurity, behavioral, or cognitive resistance to change and rational

empirical change leadership strategy. Taken together, the results support hypothesis 4a

since the moderator effect of rational empirical change leadership strategy on the

relationship between job insecurity and affective commitment to change was significant

and in the direction of the hypothesized relationship. The results failed to support for

hypothesis 4b since the moderator affect of rational empirical change leadership on the

153 relationship between job insecurity and affective resistance to change was significant but in the opposite direction of the hypothesized relationship and the other resistance to change measures (behavioral & cognitive) were not significant.

Normative Re-educative (participation-based) Change Leadership Hypotheses Results (manufacturing sample)

Hypothesis 5a predicated that a perceived normative re-educative (participation- based) change leadership will moderate the negative relationship between job insecurity and affective commitment to change such that the negative relationship would be weaker for those who reported higher levels of perceived normative re-educative (participation- based) change leadership approach. Hypothesis 5a was not supported by the analysis as the interaction term (job insecurity 12 x normative re-educative change leadership strategy) failed to reach significance for affective commitment to change. Hypothesis 5b predicted that a perceived normative re-educative change leadership approach would moderate the positive relationship between job insecurity and the three resistance to change types.

Table 4.6

Regression Results for Hypotheses 5a and 5b Normative Re-educative (participation-based) Change Leadership Strategy and their Interactions (manufacturing sample) ACTC ARTC BRTC CRTC

Variable Model 4 Model 5 Model 4 Model 5 Model 4 Model 5 Model 4 Model 5 Step 0 Change Significance (1st) -0.08 -0.08 0.16 * 0.16 * 0.10 0.10 0.20 ** 0.20 ** Change Significance (2nd) 0.02 0.02 -0.08 -0.07 -0.04 -0.04 -0.10 -0.10 Change Impact 0.45 ** 0.45 ** -0.40 ** -0.39 ** -0.26 ** -0.26 ** -0.49 ** -0.49 ** Job Satisfaction 0.13 0.13 -0.12 -0.13 -0.15 -0.16 -0.06 -0.07 Gender -0.32 -0.32 0.31 0.34 0.05 0.05 0.16 0.17 Age Range -0.02 -0.02 0.03 0.03 0.02 0.02 0.04 0.04 Race -0.18 -0.18 0.14 0.14 0.23 * 0.23 * 0.22 * 0.22 * Marital Status 0.22 0.22 -0.05 -0.02 -0.15 -0.14 -0.08 -0.07 Children (financially responsible) 0.05 0.05 -0.08 -0.09 -0.15 -0.16 -0.13 -0.14 Education 0.01 0.01 -0.01 -0.01 -0.01 -0.01 0.00 0.00

Year(s) Worked for Organization -0.01 -0.01 0.01 0.01 0.01 0.01 0.01 0.01 Level in Organization 0.27 * 0.27 * -0.29 ** -0.27 * -0.43 ** -0.43 ** -0.29 ** -0.29 ** Role / Function in Organization 0.01 0.01 0.00 0.00 -0.02 -0.02 0.00 0.00 Employment Dependence -0.19 -0.19 0.22 * 0.21 * 0.10 0.09 0.23 ** 0.22 * Employability -0.10 -0.10 0.03 0.02 -0.01 -0.01 0.17 0.17 Step 1 Job Insecurity 1&2 -0.15 -0.15 0.23 * 0.25 * 0.08 0.08 0.15 0.16 + Step 2 NRCLS 0.54 ** 0.54 ** -0.24 -0.23 -0.28 + -0.27 + -0.54 ** -0.54 ** Step 3 Job Insecurity 1&2 X NRCLS -0.01 0.14 0.03 0.04 R-Square 0.52 ** 0.52 0.47 0.48 0.37 + 0.37 0.59 ** 0.59 ∆ R-Square 0.03 ** 0.00 0.01 0.00 0.01 0.00 0.03 ** 0.00 Unstandardized regression coefficient * ρ <0.05, **ρ<0.01, + p<0.10

154

155

No significant moderating relationships were found among the interaction terms job insecurity, affective, behavioral, or cognitive resistance to change and power coercive change leadership strategy (Model 5). Overall, the results show no support for hypotheses

5a and 5b in the manufacturing sample.

Power Coercive (top-down) Change Leadership Hypotheses Results (manufacturing sample)

Hypothesis 6a predicated that a perceived power-coercive (top-down/power- based) change leadership approach will moderate the negative relationship between job insecurity and affective commitment to change such that the negative relationship would be stronger. Hypothesis 6a was not supported by the analysis as the interaction term (job insecurity 12 x power coercive change leadership strategy) failed to reach significance for affective commitment to change. Hypothesis 6b, predicted that a perceived power- coercive change leadership approach will moderate the positive relationship between job insecurity and the three resistance to change measures (affective, behavioral, and cognitive).

Table 4.7

Regression Results for Hypotheses 6a and 6b Power Coercive (top-down) Change Leadership Strategy (manufacturing

ACTC ARTC BRTC CRTC Variable Model 6 Model 7 Model 6 Model 7 Model 6 Model 7 Model 6 Model 7 Step 0 Change Significance (1st) -0.13 -0.13 0.19 ** 0.20 ** 0.14 0.13 0.25 ** 0.26 ** Change Significance (2nd) 0.04 0.04 -0.10 -0.10 -0.06 -0.06 -0.12 * -0.11 Change Impact 0.51 ** 0.51 ** -0.39 ** -0.37 ** -0.26 ** -0.27 ** -0.55 ** -0.53 ** Job Satisfaction 0.10 0.10 -0.07 -0.07 -0.10 -0.10 -0.04 -0.04 Gender -0.39 * -0.39 * 0.38 * 0.41 * 0.12 0.11 0.23 0.25 Age Range 0.00 0.00 0.02 0.01 0.01 0.01 0.02 0.02 Race -0.14 -0.14 0.10 0.08 0.18 0.19 0.18 * 0.17 * Marital Status 0.04 0.04 0.15 0.17 0.07 0.07 0.11 0.12 Children (financially responsible) 0.09 0.09 -0.10 -0.11 -0.19 -0.18 -0.18 -0.19 Education 0.01 0.01 -0.02 -0.02 -0.02 -0.03 0.00 0.00 Year(s) Worked for Organization -0.01 -0.01 0.01 0.01 0.01 0.01 0.01 0.01 Level in Organization 0.24 * 0.24 * -0.23 * -0.23 * -0.36 ** -0.36 ** -0.26 ** -0.26 ** Role / Function in Organization 0.01 0.01 0.01 0.01 -0.01 -0.01 0.00 0.00 Employment Dependence -0.02 -0.02 0.07 0.07 -0.07 -0.07 0.06 0.06 Employability 0.08 0.08 -0.09 -0.10 -0.16 -0.15 0.00 -0.01 Step 1 Job Insecurity 1&2 -0.15 -0.15 0.20 * 0.22 * 0.05 0.04 0.15 + 0.16 + Step 2 PCCLS -0.73 ** -0.73 ** 0.65 ** 0.65 ** 0.73 ** 0.73 ** 0.73 ** 0.72 ** Step 3 Job Insecurity 1&2 X PCCLS -0.03 -0.18 + 0.06 -0.11 R-Square 0.55 ** 0.55 0.52 ** 0.53 + 0.44 ** 0.44 0.62 ** 0.63 ∆ R-Square 0.06 ** 0.00 0.06 ** 0.01 + 0.08 ** 0.00 0.07 ** 0.00 sample)

Unstandardized regression coefficient * ρ <0.05, **ρ<0.01, + p<0.10

156 157

No significant moderating relationships were found for the interaction terms among job

insecurity, affective, behavioral, or cognitive resistance to change, and power coercive change leadership strategy (Model 7). Overall, the results show no support for hypotheses

6a and 6b in the manufacturing sample.

Trust in Management Hypotheses Results (manufacturing sample)

Finally, hypothesis 7a predicted that higher reported levels of trust in management

will moderate the negative relationship between job insecurity and affective commitment

to change such that the negative relationship would be weaker. Hypothesis 7a was not

supported by the analysis as the interaction term (job insecurity 1&2 x trust in

management) failed to reach significance for affective commitment to change.

Hypothesis 7b predicted that higher reported levels of trust in management will moderate

the positive relationship between job insecurity and the three resistance to change types.

Table 4.8

Regression Results for Hypotheses 6a and 6b Trust in Management and their Interactions (manufacturing sample) ACTC ARTC BRTC CRTC Variable Model 8 Model 9 Model 8 Model 9 Model 8 Model 9 Model 8 Model 9 Step 0 Change Significance (1st) -0.10 -0.10 0.17 * 0.17 * 0.10 0.10 0.22 ** 0.22 ** Change Significance (2nd) 0.02 0.03 -0.08 -0.07 -0.05 -0.05 -0.10 -0.10 Change Impact 0.53 ** 0.55 ** -0.44 ** -0.44 ** -0.30 ** -0.30 ** -0.60 ** -0.61 ** Job Satisfaction 0.13 0.12 -0.13 -0.13 -0.13 -0.12 -0.10 -0.09 Gender -0.31 -0.29 0.30 0.30 0.03 0.03 0.17 0.16 Age Range -0.02 -0.03 0.03 0.03 0.03 0.03 0.05 0.05 Race -0.19 -0.20 0.14 0.14 0.26 ** 0.27 ** 0.26 ** 0.26 ** Marital Status 0.21 0.21 -0.03 -0.03 -0.14 -0.14 -0.14 -0.13 Children (financially responsible) 0.07 0.07 -0.10 -0.09 -0.16 -0.16 -0.16 -0.17 Education -0.01 -0.01 0.00 0.00 -0.01 -0.01 0.01 0.01 Year(s) Worked for Organization -0.01 -0.01 0.01 0.01 0.01 0.01 0.00 0.00 Level in Organization 0.27 * 0.30 * -0.30 ** -0.29 * -0.40 ** -0.40 ** -0.34 ** -0.36 ** Role / Function in Organization 0.01 0.01 0.00 0.00 -0.01 -0.01 -0.01 -0.01 Employment Dependence -0.20 -0.21 * 0.22 * 0.22 * 0.11 0.11 0.23 0.24 ** Employability -0.05 -0.07 0.01 0.01 -0.02 -0.01 0.12 0.14 Step 1 Job Insecurity 1&2 -0.18 + -0.16 0.24 * 0.24 * 0.09 0.09 0.20 * 0.19 + Step 2 TIM 0.21 * 0.21 * -0.08 -0.08 -0.19 * -0.19 * -0.09 -0.09 Step 3 Job Insecurity 1&2 X TIM 0.08 0.01 -0.01 -0.07 R-Square 0.50 * 0.51 0.47 0.47 0.38 * 0.38 0.56 0.56 ∆ R-Square 0.01 * 0.00 0.00 0.00 0.01 * 0.00 0.00 0.00 Unstandardized regression coefficient * ρ < 0.05, **ρ < 0.01, + p < 0.10

158 159

No significant moderating relationships were found for the interaction terms between job insecurity, affective, behavioral, cognitive resistance to change, or trust in management.

Overall, the results show no support for hypotheses 7a and 7b in the manufacturing sample (Model 9).

160

Retail Sample Results

There were several meaningful relationships among the core independent

variables and the outcome variables in the retail sample. All meaningful relationships

were significant at p < .01, p < .05, or p < .10. The correlations between the predictor and

outcome variables are reported as follows.

Job Insecurity, Affective Commitment to Change, and Resistance to Change Direct Relationship Hypotheses Results (retail sample)

Hypothesis 1 predicted that job insecurity would be negatively related to affective

commitment to change and hypothesis 2 predicted job insecurity would be positively

related to all three resistance to change measures. In the retail sample, the correlations

among job insecurity, affective resistance to change (β = 0.17, p < 0.01), behavioral

resistance to change (β = 0.13, p < .0.05), and cognitive resistance to change (β = 0.12, p

< 0.05) were significant and in the direction of the hypothesized relationships showing no support for hypothesis 1 and partial support for hypothesis 2.

Change Related Self-efficacy Hypotheses Results (manufacturing sample)

Hypothesis 3 predicted that change related self-efficacy would mediate the negative relationship between job insecurity and affective commitment to change (H3a) and the positive relationships among job insecurity and the three resistance to change measures (H3b). Results below indicate that after change related self-efficacy was taken into account, the strength of the relationship between job insecurity and affective resistance to change (β = 0.17, p < 0.01 to 0.07 p < 0.01) decreased but stayed significant which suggests a partial mediation. The relationship between job insecurity and behavioral resistance (β = 0.13, p < 0.05 to 0.10, p < 0.01), which suggests a partial

161

meditation. The relationship between job insecurity and cognitive resistance (β = 0.12, p

< 0.05 to 0.04, p < 0.01), which suggests a partial meditation.

A mediated relationship could not be determined between job insecurity and affective commitment to change because the direct relationship was found to be non-significant--a condition that must be met in order to establish a meditation effect (Baron & Kenny,

1986).

Table 4.9

Regression Results for Hypothesis 1 and 2 Job Insecurity Relationship to Affective Commitment to Change and Three Resistance to Change Measures and Hypothesis 3 Change Related Self-Efficacy Mediation Between Job Insecurity, Affective Commitment to Change and Three Resistance to Change Types (retail sample) CRSE ACTC ARTC BRTC CRTC Variable β t β t β t β t β t Step 0 Change Significance (1st) -0.12 -2.15 * 0.00 -0.02 0.08 1.61 0.05 0.93 0.02 0.40 Change Significance (2nd) 0.04 0.69 -0.03 -0.59 0.07 1.56 0.07 1.45 0.10 2.27 * Change Impact 0.21 3.96 ** 0.49 10.92 ** -0.48 -10.53 ** -0.40 -8.12 ** -0.54 ##### ** Job Satisfaction 0.33 6.37 ** 0.27 6.12 ** -0.26 -5.93 ** -0.30 -6.31 ** -0.26 -6.03 ** Gender 0.01 0.20 -0.02 -0.56 0.00 0.00 0.03 0.72 0.04 0.88 Age Range 0.02 0.30 0.03 0.64 -0.08 -1.47 0.03 0.45 -0.06 -1.20 Race 0.06 1.27 0.01 0.29 -0.02 -0.49 -0.06 -1.41 -0.05 -1.28 Marital Status 0.16 2.81 ** 0.14 2.80 ** -0.16 -3.26 ** -0.09 -1.74 -0.11 -2.35 * Children (financially responsible) -0.09 -1.86 -0.10 -2.36 * 0.09 2.13 * 0.12 2.70 ** 0.09 2.36 * Education 0.07 1.51 0.01 0.30 0.02 0.55 -0.01 -0.13 0.01 0.15 Year(s) Worked for Organization -0.01 -0.16 -0.10 -2.01 * 0.06 1.14 0.07 1.21 0.10 2.14 * Level in Organization -0.02 -0.42 0.04 0.89 0.03 0.63 0.00 -0.09 0.00 0.04 Role / Function in Organization 0.14 2.78 ** -0.01 -0.16 -0.06 -1.40 -0.07 -1.64 -0.05 -1.26 Employment Dependence -0.20 -3.39 ** -0.10 -2.04 * 0.16 3.18 ** 0.14 2.48 * 0.13 2.63 ** Employability 0.02 0.34 0.01 0.13 0.01 0.11 0.06 1.00 0.02 0.45 R-Square 0.305** 0.482** 0.481** 0.399** 0.527** Step 1 Job Insecurity 1 & 2 -0.24 -3.96 ** -0.10 -1.83 + 0.17 3.22 ** 0.13 2.30 * 0.12 2.33 * Step 2 CRSE 0.26 5.79 ** -0.44 -10.68 ** -0.39 -8.37 ** -0.32 -7.73 ** R-Square 0.337** 0.487+ 0.496** 0.408* 0.534* Step 3 Job Insecurity 1 & 2 ( + CRSE) -0.04 -0.69 0.07 1.44 0.04 0.75 0.04 0.89 R-Square 0.531** 0.617** 0.505** 0.6** 3.71 (σx̅ = 3.57 (σx̅ = 3.51 (σx̅ = Sobel Test 0.05) 0.04) 0.04) CRSE = change related self-efficacy, ACTC = affective commitment to change, ARTC = affective resistance to change, BRTC = behavioral resistance to change, CRTC = cognitive resistance to change; Standardized Coefficient Beta *p < 0.05, **p < 0.01, + p<0.10.

162 163

As with the manufacturing sample, the Sobel test (Preacher & Hayes, 2004) was

conducted to further test the mediation effects of change related self-efficacy on the relationships between job insecurity and commitment and resistance. The results showed significant indirect effects of change related self-efficacy on the relationships between job insecurity, affective resistance to change (zsobel = 3.71 (σx̅ = 0.05), p < 0.01),

behavioral resistance to change (zsobel = 1.69 (σx̅ = 0.03), p < 0.01), and cognitive

resistance to change (zsobel = 1.69 (SE = 0.03), p < 0.01) providing further support for

the mediation effect of change related self-efficacy in the retail sample. Overall the

results of the retail sample show no support for hypothesis 3a and full support for

hypothesis 3b.

Rational Empirical (information-based) Change Leadership Hypotheses Results (retail sample)

Hypothesis 4a predicted that a perceived rational-empirical (information-based)

change leadership approach will moderate the negative relationship between job

insecurity and affective commitment to change such that the negative relationship would

be weaker. Hypothesis 4a was not supported by the analysis as the interaction term (job

insecurity 1&2 x affective commitment to change x rational empirical change leadership

strategy) failed to reach significance. Hypothesis 4b predicted that a perceived rational-

empirical change leadership approach will moderate the positive relationship between job

insecurity and the three resistance to change measures (affective, behavioral, and

cognitive).

Table 4.10

Regression Results for Hypotheses 4a and 4b Job Insecurity and Rational Empirical (information-based) Change Leadership

Strategy and Moderated Interactions (retail sample) ACTC ARTC BRTC CRTC Variable Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Step 0 Change Significance (1st) 0.00 0.01 0.00 0.10 0.10 0.10 0.05 0.05 0.05 0.02 0.02 0.02 Change Significance (2nd) -0.03 0.00 -0.01 0.08 0.06 0.06 0.06 0.05 0.05 0.11 * 0.08 0.09 * Change Impact 0.67 ** 0.53 ** 0.53 ** -0.61 ** -0.51 ** -0.51 ** -0.43 ** -0.34 ** -0.34 ** -0.68 ** -0.54 ** -0.56 ** Job Satisfaction 0.33 ** 0.21 ** 0.21 ** -0.30 ** -0.17 ** -0.17 ** -0.29 ** -0.20 ** -0.20 ** -0.29 ** -0.17 ** -0.17 ** Gender -0.09 -0.09 -0.09 0.00 0.01 0.01 0.10 0.11 0.10 0.13 0.13 0.12 Age Range 0.02 0.02 0.02 -0.04 -0.05 -0.04 0.01 0.01 0.01 -0.03 -0.03 -0.03 Race 0.02 0.01 0.01 -0.04 -0.04 -0.05 -0.10 -0.09 -0.10 -0.09 -0.08 -0.09 Marital Status 0.50 ** 0.46 ** 0.44 * -0.55 ** -0.52 ** -0.48 ** -0.27 -0.24 -0.22 -0.38 * -0.34 * -0.29 Children (financially responsible) -0.39 * -0.37 * -0.36 * 0.33 * 0.32 * 0.30 0.39 ** 0.37 ** 0.36 * 0.35 * 0.33 * 0.31 * Education 0.02 0.04 0.04 0.03 0.02 0.02 -0.01 -0.02 -0.02 0.01 -0.02 -0.01 Year(s) Worked for Organization -0.03 * -0.03 -0.03 0.02 0.02 0.02 0.02 0.01 0.02 0.03 * 0.02 * 0.03 * Level in Organization 0.13 0.12 0.11 0.09 0.09 0.11 -0.01 -0.01 0.01 0.01 0.01 0.05 Role / function in Organization 0.00 0.00 0.00 -0.03 -0.02 -0.02 -0.03 -0.03 -0.03 -0.02 -0.03 -0.02 Employment Dependence -0.15 * -0.16 * -0.16 * 0.22 ** 0.21 ** 0.20 ** 0.16 * 0.15 * 0.15 * 0.17 ** 0.18 ** 0.18 ** Employability 0.01 -0.04 -0.03 0.01 0.06 0.05 0.07 0.11 0.11 0.03 0.09 0.08 Step 1 Job Insecurity 1 & 2 -0.09 -0.11 0.25 ** 0.29 ** 0.15 + 0.17 + 0.12 0.16 + Step 2 RECLS 0.49 ** 0.49 ** -0.26 * -0.24 * -0.27 ** -0.26 ** -0.46 ** -0.44 ** Step 3 Job Insecurity 1 & 2 x RECLS -0.08 0.15 + 0.08 0.17 * R-Square 0.48 ** 0.52 ** 0.52 0.48 ** 0.51 * 0.51 + 0.40 ** 0.42 ** 0.43 0.53 ** 0.56 ** 0.57 * ∆ R-Square 0.03 ** 0.00 0.01 * 0.01 + 0.01 ** 0.00 0.03 ** 0.01 *

ACTC = affective commitment to change, ARTC = affective resistance to change, BRTC = behavioral resistance to change, CRTC = cognitive resistance to change; Unstandardized regression coefficient *p < .05, **p < 0.01, + p < 0.10.

164 165

As hypothesized, in the retail sample, the positive relationship between job

insecurity and cognitive resistance to change was weaker for both low and high job

insecure individuals who perceived higher levels of rational-empirical change leadership

(β = 0.17 ± 0.01 [95% CI], p < .0.05).

Rational Empirical Change Leadership Strategy

6.2 6 5.8 5.6

5.4 HIGH 5.2 LOW 5 4.8 LOW HIGH Cognitive Resistance Cognitive Resistance to Change Job Insecurity 1&2

Figure 4.3. Influence of rational-empirical change leadership strategy on affective resistance to change for low and high job insecure individuals (retail sample).

Taken together, the results show no support for hypothesis 4a and partial support for hypothesis 4b on one out of the three resistance to change types.

Normative Re-educative (participation-based) Change Leadership Hypotheses Results (retail sample)

Hypothesis 5a predicated that a perceived normative re-educative (participation- based) change leadership strategy will moderate the negative relationship between job insecurity and affective commitment to change such that the negative relationship would be weaker. Hypothesis 5a was not supported by the analysis as the interaction term (job insecurity 1&2 x rational empirical change leadership strategy) failed to reach significance for affective commitment to change. Hypothesis 5b, predicted that a

166 perceived normative re-educative change leadership approach will moderate the positive relationship between job insecurity and the three resistance to change measures

(affective, behavioral, and cognitive).

Table 4.11

Regression Results for Hypotheses 5a and 5b Normative Re-educative (participation-based) Change Leadership Strategy and their Interactions (retail sample) ACTC ARTC BRTC CRTC Variable Model 4 Model 5 Model 4 Model 5 Model 4 Model 5 Model 4 Model 5 Step 0 Change Significance (1st) 0.00 0.00 0.10 0.11 0.06 0.06 0.02 0.03

Change Significance (2nd) -0.01 -0.01 0.06 0.07 0.06 0.07 0.09 0.09 *

Change Impact 0.59 ** 0.59 ** -0.55 ** -0.56 ** -0.43 ** -0.43 ** -0.60 ** -0.61 **

Job Satisfaction 0.23 ** 0.23 ** -0.19 ** -0.19 ** -0.24 ** -0.25 ** -0.19 ** -0.19 **

Gender -0.09 -0.09 0.02 0.01 0.12 0.11 0.13 0.13 Age Range 0.03 0.03 -0.05 -0.05 0.01 0.01 -0.05 -0.04 Race -0.01 -0.01 -0.04 -0.04 -0.13 -0.13 -0.07 -0.07 Marital Status 0.47 ** 0.47 ** -0.53 ** -0.52 ** -0.27 -0.26 -0.35 * -0.34 * Children (financially responsible) -0.37 * -0.37 * 0.32 * 0.31 * 0.40 ** 0.38 ** 0.33 * 0.31 * Education 0.03 0.03 0.03 0.03 0.00 0.01 -0.01 0.00 Year(s) Worked for Organization -0.03 -0.03 0.02 0.02 0.02 0.02 0.03 0.03 Level in Organization 0.08 0.08 0.10 0.13 -0.05 -0.02 0.06 0.09 Role / Function in Organization 0.00 0.00 -0.02 -0.02 -0.02 -0.02 -0.03 -0.03 Employment Dependence -0.18 * -0.18 * 0.21 ** 0.20 ** 0.12 + 0.12 + 0.20 ** 0.19 * Employability -0.06 -0.06 0.06 0.06 0.08 0.08 0.11 0.10 Step 1 Job Insecurity 1 & 2 -0.12 -0.12 0.28 ** 0.32 ** 0.22 * 0.25 ** 0.15 + 0.19 * Step 2 NRCLS 0.40 ** 0.40 ** -0.12 -0.09 0.17 + 0.19 * -0.38 ** -0.35 ** Step 3 Job Insecurity 1 & 2 X NRCLS 0.00 0.18 * 0.14 + 0.15 + R-Square 0.51 ** 0.51 0.50 0.51 * 0.42 + 0.42 + 0.56 ** 0.56 + ∆ R-Square 0.02 ** 0.00 0.00 0.01 * 0.01 + 0.01 + 0.02 ** 0.01 +

167 168

Of the three resistance to change measures, a moderating relationship was found

between job insecurity and affective resistance to change (β = 0.18 ± 0.01 [95% CI], p <

.0.05). Specifically, a perceived normative re-educative change leadership strategy was associated with lower levels of affective resistance to change for low job insecure individuals but was associated with higher levels of affective resistance to change for high job insecure individuals (unexpected result). The results appear to suggest that higher perceived levels of normative re-educative change leadership at low levels of job insecurity may reduce affective resistance to change, however, increased use of normative re-educative change leadership strategy appears to increase affective resistance to change in high job insecure situations. This result is opposite of the hypothesized relationship.

Normative Reeducative Change Leadership Strategy 5.7 5.6 5.5 5.4 5.3 5.2 5.1 HIGH 5 LOW 4.9 4.8 4.7 LOW HIGH Affective Resistance to Change Job Insecurity 1&2

Figure 4.4. Influence of normative re-educative (participation-based) change leadership strategy on affective resistance to change for low and high job insecure individuals (retail sample).

169

Overall, the results show no support for hypotheses 5a or 5b due to insignificant results or, in the case of affective resistance to change, results that were opposite the hypothesized direction.

Power Coercive (top-down) Change Leadership Hypotheses Results (retail sample)

Table 4.12

Regression Results for Hypotheses 6a and 6b Power Coercive (top-down) Change Leadership Strategy and their Interactions

(retail sample) ACTC ARTC BRTC CRTC Variable Model 6 Model 7 Model 6 Model 7 Model 6 Model 7 Model 6 Model 7 Step 0 Change Significance (1st) 0.00 0.00 0.10 0.10 0.05 0.06 0.02 0.02 Change Significance (2nd) 0.03 0.03 0.02 0.01 0.05 0.04 0.05 0.05 Change Impact 0.62 ** 0.62 ** -0.55 ** -0.54 ** -0.40 ** -0.39 ** -0.64 ** -0.63 ** Job Satisfaction 0.22 ** 0.22 ** -0.16 ** -0.16 ** -0.22 ** -0.21 ** -0.18 ** -0.18 ** Gender -0.18 -0.17 0.09 0.07 0.13 0.12 0.21 0.20 Age Range 0.02 0.02 -0.05 -0.04 0.01 0.02 -0.04 -0.03

Race 0.02 0.02 -0.04 -0.05 -0.11 -0.11 -0.10 -0.10 Marital Status 0.43 * 0.43 * -0.48 ** -0.48 ** -0.25 -0.24 -0.31 * -0.31 * Children (financially responsible) -0.37 * -0.38 * 0.32 * 0.33 * 0.38 ** 0.39 ** 0.34 * 0.34 * Education 0.02 0.02 0.03 0.03 -0.01 -0.01 0.01 0.01 Year(s) Worked for Organization -0.03 * -0.03 * 0.02 0.02 0.02 0.02 0.03 * 0.03 * Level in Organization 0.10 0.09 0.11 0.13 -0.01 0.00 0.03 0.04 Role / Function in Organization -0.01 -0.01 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 Employment Dependence -0.07 -0.08 0.14 * 0.15 * 0.13 + 0.13 * 0.10 0.11 Employability 0.05 0.05 -0.01 0.00 0.09 0.10 0.00 0.01 Step 1 Job Insecurity 1 & 2 -0.17 + -0.17 + 0.29 ** 0.30 ** 0.19 * 0.21 * 0.19 * 0.20 * Step 2 PCCLS -0.58 ** -0.61 ** 0.49 ** 0.54 ** 0.12 0.18 + 0.50 ** 0.53 ** Step 3 Job Insecurity 1 & 2 X PCCLS 0.10 -0.23 ** -0.25 ** -0.15 +

R-Square 0.52 ** 0.53 0.53 ** 0.54 ** 0.41 0.43 ** 0.57 ** 0.57 + ∆ R-Square 0.04 ** 0.00 0.03 ** 0.01 ** 0.00 0.02 ** 0.03 ** 0.00 + ACTC = affective commitment to change, ARTC = affective resistance to change, BRTC = behavioral resistance to change, CRTC = cognitive resistance to change; Unstandardized regression coefficient *p < .05, **p < .01, + p<0.10.

170 171

Of the three resistance to change measures, a moderating relationship was found

for the relationship between job insecurity and affective resistance to change (β = -0.23 ±

0.01 [99% CI], p < .0.01) and behavioral resistance to change (β = -0.25 ± 0.02 [99% CI], p < .0.01). As hypothesized, the positive relationship between job insecurity and affective resistance to change was stronger for job insecure individuals who perceived higher levels of power coercive change leadership strategy.

Power Coercive Change Leadership Strategy 4.9

4.8 4.7 4.6 4.5 4.4 HIGH 4.3 LOW 4.2 4.1 Behavioral Resistance Behavioral Resistance Changeto 4 LOW HIGH Job Insecurity 1&2

Figure 4.5. Influence of power coercive (top-down) change leadership strategy on affective resistance to change for low and high job insecure individuals (retail sample).

Also, the positive relationship between job insecurity and behavioral resistance to change was stronger for job insecure individuals who perceived higher levels of power coercive change leadership strategy however, even at lower perceived levels of power coercive change leadership strategy, individuals who were high in job insecurity appeared to have behavioral resistance levels equal to (if not slightly higher than) individuals with high job

172

insecurity and high perceptions of power coercive change leadership strategy. This result

seems to suggest that high levels of job insecurity have a similar (if not worse) influence

on behavioral resistance to change as does high power coercive change leadership.

Power Coercive Change Leadership Strategy 6.2 6 5.8 5.6 5.4 HIGH 5.2 LOW 5 4.8

Affective Resistance to Change 4.6 LOW HIGH Job Insecurity 1&2

Figure 4.6. Influence of power coercive (top-down) change leadership strategy on behavioral resistance to change for low and high job insecure individuals (retail sample).

Overall, the results show no support for hypothesis 6a and partial support for

hypothesis 6b in the retail sample.

Trust in Management Hypotheses Results (retail sample)

Finally, hypothesis 7a predicted that higher reported levels of trust in management

will moderate the negative relationship between job insecurity and affective commitment

to change such that the negative relationship would be weaker. Hypothesis 7a was not

supported by the analysis as the interaction term (job insecurity 12 x trust in

management) failed to reach significance for affective commitment to change.

173

Hypothesis 7b predicted that higher reported levels of trust in management will moderate the positive relationship between job insecurity and the three resistance to change types.

Table 4.13

Regression Results for Hypotheses 7a and 7b Trust in Management and their Interactions (retail sample)

ACTC ARTC BRTC CRTC Variable Model 8 Model 9 Model 8 Model 9 Model 8 Model 9 Model 8 Model 9 Step 0 Change Significance (1st) -0.01 -0.01 0.10 0.12 0.06 0.06 0.03 0.03 Change Significance (2nd) -0.01 -0.02 0.06 0.06 0.06 0.06 0.09 * 0.09 * Change Impact 0.61 ** 0.61 ** -0.55 ** -0.55 ** -0.40 ** -0.40 ** -0.63 ** -0.63 ** Job Satisfaction 0.16 * 0.16 * -0.14 * -0.14 * -0.22 ** -0.22 ** -0.14 * -0.14 * Gender -0.14 -0.14 0.04 0.04 0.12 0.12 0.17 0.17 Age Range 0.02 0.02 -0.05 -0.05 0.01 0.01 -0.04 -0.04 Race 0.00 0.00 -0.03 -0.03 -0.11 -0.10 -0.08 -0.08 Marital Status 0.43 * 0.43 * -0.50 ** -0.48 ** -0.26 -0.24 -0.33 * -0.32 *

Children (financially responsible) -0.40 * -0.40 * 0.34 * 0.33 * 0.39 ** 0.38 ** 0.36 * 0.36 *

Education 0.02 0.02 0.03 0.02 -0.01 -0.01 0.00 0.00 Year(s) Worked for Organization -0.03 -0.03 0.02 0.02 0.02 0.02 0.03 * 0.03 * Level in Organization 0.10 0.09 0.10 0.15 -0.02 0.02 0.03 0.05 Role / Function in Organization 0.00 0.00 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 Employment Dependence -0.13 -0.13 0.19 ** 0.18 ** 0.14 * 0.13 * 0.15 * 0.15 * Employability 0.00 0.00 0.04 0.03 0.10 0.09 0.05 0.05 Step 1 Job Insecurity 1 & 2 -0.11 -0.11 0.25 ** 0.29 ** 0.19 * 0.22 * 0.15 + 0.17 + Step 2 TIM 0.28 ** 0.28 ** -0.17 * -0.15 * -0.03 -0.01 -0.21 ** -0.20 ** Step 3 Job Insecurity 1 & 2 X TIM -0.02 0.11 * 0.07 0.05 R-Square 0.51 ** 0.51 0.51 * 0.51 * 0.41 0.41 0.55 ** 0.55 ∆ R-Square 0.02 ** 0.00 0.01 * 0.01 * 0.00 0.01 0.01 ** 0.00

174 175

As hypothesized, for the retail sample, the positive relationship between job

insecurity and affective resistance to change (β = 0.11 ± 0.01 [95% CI], p < .0.05) was

weaker for those individuals who reported higher levels of trust in management. This

result seems to suggest that high levels of job insecurity have a similar (if not worse)

influence on affective resistance to change as does low trust in management.

Trust in Management

5.4

5.2

5 HIGH 4.8 LOW

4.6

Affective Resistance to Change 4.4 LOW HIGH Job Insecurity 1&2

Figure 4.7. Influence of trust in management on behavioral resistance to change for low and high job insecure individuals (retail sample).

Overall, no support was found for hypothesis 7a and partial support was found for

hypothesis 7b in the retail sample.

176

Combined Manufacturing and Retail Sample Hypotheses Results

Several meaningful relationships were found among the core independent variables and the outcome variables when the retail and manufacturing samples were combined. All meaningful relationships were significant at p < .01 or p < .05.

Correlations between the predictor and outcome variables are reported as follows.

In the combined samples, the correlations among job insecurity, affective resistance to change (β = 0.16, p < 0.01), behavioral resistance to change (β = 0.10, p <

.0.05), and cognitive resistance to change (β = 0.11, p < 0.01) were significant and in the direction of the hypothesized relationships. No support was found for hypothesis 1 however hypothesis 2 was fully supported.

Change Related Self-efficacy Hypotheses Results (combined manufacturing and retail sample)

Hypothesis 3 predicted that change related self-efficacy would mediate the negative relationship between job insecurity and affective commitment to change (H3a) and the positive relationships among job insecurity and the three resistance to change measures (H3b). Results below indicate that after change related self-efficacy was taken into account, the strength of the relationship between job insecurity and affective resistance to change (β = 0.16, p < 0.01 to 0.10 p < 0.01) decreased but stayed significant which suggests a partial mediation. The relationship between job insecurity and behavioral resistance (β = 0.10, p < 0.05 to 0.04) became non-significant which suggests a complete meditation. The relationship between job insecurity and cognitive resistance

(β = 0.11, p < 0.05 to 0.06, p < 0.10) decreased which suggests a partial meditation.

177

Results indicated that after change related self-efficacy was taken into account, the relationship between job insecurity and each of the three resistance to change constructs either decreased or became non-significant demonstrating partial or full mediation and full support for hypothesis 3b. A mediated relationship could not be determined between job insecurity and affective commitment to change because the direct relationship was found to be non-significant--a condition that must be met in order to establish a meditation effect (Baron & Kenny, 1986).

Table 4.14

Regression Results for Hypothesis 1 and 2 Job Insecurity Relationship to Affective Commitment to Change and Three Resistance to Change Measures and Hypothesis 3 Change Related Self-Efficacy Mediation Between Job Insecurity, Affective Commitment to Change and Three Resistance to Change Types (combined manufacturing and retail sample) CRSE ACTC ARTC BRTC CRTC Variable β t β t β t β t β t Step 0 Change Significance (1st) -0.09 -2.02 * -0.04 -1.13 0.11 2.79 ** 0.08 2.04 * 0.09 2.54 * Change Significance (2nd) 0.06 1.50 -0.02 -0.47 0.02 0.51 0.04 0.92 0.04 1.00 Change Impact 0.26 6.46 ** 0.49 14.45 ** -0.45 -12.98 ** -0.36 -9.57 ** -0.54 -16.39 ** Job Satisfaction 0.25 6.31 ** 0.26 7.64 ** -0.23 -6.72 ** -0.26 -6.93 ** -0.22 -6.74 ** Gender -0.01 -0.36 -0.03 -0.89 0.04 1.31 0.05 1.28 0.05 1.47 Age Range -0.05 -0.89 0.00 -0.02 0.00 0.02 0.06 1.34 0.00 -0.12 Race 0.02 0.50 -0.03 -0.84 0.01 0.45 0.02 0.50 0.01 0.38 Marital Status 0.09 2.11 * 0.12 3.40 ** -0.09 -2.55 * -0.06 -1.59 -0.08 -2.23 * Children (financially responsible) -0.12 -3.15 ** -0.07 -2.13 * 0.05 1.48 0.06 1.57 0.05 1.60 Education 0.06 1.50 0.02 0.60 0.00 -0.08 -0.02 -0.43 0.01 0.23 Year(s) Worked for Organization 0.09 1.82 -0.02 -0.45 0.08 2.04 * 0.05 1.14 0.05 1.34 Level in Organization -0.02 -0.56 0.10 3.00 ** -0.07 -2.08 * -0.13 -3.45 ** -0.09 -2.66 ** Role / Function in Organization 0.09 2.37 * 0.01 0.42 -0.04 -1.19 -0.07 -1.86 -0.04 -1.19 Employment Dependence -0.15 -3.20 ** -0.11 -2.63 ** 0.15 3.68 ** 0.09 2.14 * 0.14 3.54 ** Employability 0.04 0.88 0.01 0.19 -0.03 -0.70 -0.01 -0.23 0.02 0.55 R-Square 0.272** 0.467** 0.451** 0.357** 0.508** Step 1 Job Insecurity 1 & 2 -0.18 -3.82 ** -0.07 -1.89 + 0.16 4.15 ** 0.10 2.22 * 0.11 2.82 ** Step 2 Change Related Self-Efficacy 0.21 5.95 ** -0.36 -10.62 ** -0.33 -8.84 ** -0.27 -8.39 ** R-Square 0.290** 0.470+ 0.468** 0.362* 0.515** Step 3 Job Insecurity 1 & 2 ( + CRSE) -0.04 -1.02 0.10 2.81 ** 0.04 0.95 0.06 1.65 + R-Square 0.499** 0.550** 0.438** 0.566** 3.57 (σx̅ = 0.03) 3.48 (σx̅ = 0.02) 3.44 (σx̅ = 0.02) Sobel Test P>0.001 P>0.001 P>0.001

CRSE = change related self-efficacy, ACTC = affective commitment to change, ARTC = affective resistance to change, BRTC = behavioral resistance to change, CRTC = cognitive resistance to change; Standardized Coefficient Beta *p < 0.05, **p <0 .01, + p<0.10.

178 179

The Sobel test (Preacher & Hayes, 2004) was conducted to further test the mediation effects of change related self-efficacy on the relationships between job insecurity and commitment and resistance. The results showed significant indirect effects of change related self-efficacy on the relationships between job insecurity, affective resistance to change (zsobel = 3.57 (SE = -0.36), p < 0.01), behavioral resistance to change (zsobel = 3.49 (SE = 0.33), p < 0.01), and cognitive resistance to change (zsobel =

3.49 (SE = -0.27), p < 0.01) providing further support for the mediation effect of change related self-efficacy. Overall, the results of the combined manufacturing and retail sample showed no support for hypothesis 3a and full support for hypothesis 3b.

Rational Empirical (information-based) Change Leadership Hypotheses Results (combined manufacturing and retail sample)

Hypothesis 4a predicted that a perceived rational-empirical (information-based) change leadership approach will moderate the negative relationship between job insecurity and affective commitment to change such that the negative relationship would be weaker. Hypothesis 4a was not supported by the analysis as the interaction term (job insecurity 12 x affective commitment to change x rational empirical change leadership strategy) failed to reach significance. Hypothesis 4b predicted that a perceived rational- empirical change leadership approach will moderate the positive relationship between job insecurity and the three resistance to change measures (affective, behavioral, and cognitive).

Table 4.15

Regression Results for Hypotheses 4a and 4b Job Insecurity and Rational Empirical (information-based) Change Leadership

Strategy and Moderated Interactions (combined manufacturing and retail sample)

ACTC ARTC BRTC CRTC Variable Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Step 0 Change Significance (1st) -0.06 -0.04 -0.04 0.13 ** 0.13 ** 0.13 ** 0.09 * 0.08 0.08 0.12 0.10 * 0.10 * Change Significance (2nd) -0.02 0.01 0.00 0.02 0.00 0.01 0.03 0.02 0.02 0.04 0.01 0.02 Change Impact 0.65 ** 0.49 ** 0.49 ** -0.55 ** -0.46 ** -0.46 ** -0.40 ** -0.29 ** -0.29 ** -0.67 ** -0.52 ** -0.52 ** Job Satisfaction 0.32 ** 0.19 ** 0.20 ** -0.27 ** -0.15 ** -0.16 ** -0.26 ** -0.17 ** -0.17 ** -0.25 ** -0.13 ** -0.13 ** Gender -0.11 -0.08 -0.08 0.14 0.10 0.10 0.14 0.11 0.11 0.15 0.12 0.12 Age Range 0.00 0.00 0.00 0.00 -0.01 -0.01 0.03 0.03 0.03 0.00 -0.01 -0.01 Race -0.06 -0.07 -0.06 0.03 0.04 0.03 0.03 0.04 0.04 0.02 0.03 0.03 Marital Status 0.44 ** 0.40 ** 0.40 ** -0.31 * -0.29 * -0.28 * -0.18 -0.16 -0.15 -0.26 * -0.23 * -0.22 * Children (financially responsible) -0.24 * -0.27 * -0.27 * 0.16 0.18 0.17 0.16 0.18 0.17 0.17 0.20 0.18 Education 0.03 0.06 0.06 -0.01 -0.02 -0.03 -0.02 -0.04 -0.04 0.00 -0.02 -0.02 Year(s) Worked for Organization 0.00 0.00 0.00 0.01 * 0.01 0.01 0.01 0.00 0.00 0.01 0.00 0.00 Level in Organization 0.27 ** 0.20 * 0.20 * -0.17 * -0.14 -0.12 -0.28 ** -0.24 ** -0.23 ** -0.21 ** -0.16 * -0.14 Role / Function in Organization 0.01 0.01 0.01 -0.02 -0.01 -0.01 -0.03 -0.02 -0.02 -0.02 -0.01 -0.01 Employment Dependence -0.15 * -0.17 ** -0.17 ** 0.20 ** 0.19 ** 0.19 ** 0.11 * 0.12 * 0.12 * 0.18 ** 0.20 ** 0.20 ** Employability 0.02 -0.04 -0.04 -0.05 0.02 0.02 -0.02 0.03 0.03 0.03 0.09 0.09 Step 1 Job Insecurity 1 & 2 -0.06 -0.07 0.24 ** 0.26 ** 0.10 + 0.11 + 0.12 + 0.14 * Step 2 RECLS 0.62 ** 0.62 ** -0.28 ** -0.27 ** -0.37 ** -0.37 ** -0.54 ** -0.54 ** Step 3 Job Insecurity 1 & 2 X RECLS -0.07 0.17 ** 0.06 0.14 ** R-Square 0.47 ** 0.52 ** 0.52 0.45 ** 0.48 ** 0.49 ** 0.36 ** 0.39 ** 0.39 0.51 ** 0.56 ** 0.56 ** ∆ R-Square 0.05 ** 0.00 0.01 ** 0.01 ** 0.03 ** 0.00 0.04 ** 0.01 **

180 181

As hypothesized, in the combined retail and manufacturing samples, the positive

relationship between job insecurity and affective resistance to change (β = 0.17 ± 0.008

[99% CI], p < .0.01) and cognitive resistance to change (β = 0.14 ± 0.005 [99% CI], p <

.0.01) was weaker for both low and high job insecure individuals who perceived higher levels of rational-empirical change leadership.

Rational Empirical Change Leadership Strategy

5.6

5.4

5.2 HIGH Change 5 LOW

4.8Affective Resistance to

4.6 LOW HIGH Job Insecurity 1&2

Figure 4.8. Influence of rational-empirical change leadership strategy on affective resistance to change for low and high job insecure individuals (combined manufacturing and retail sample).

Rational Empirical Change Leadership Strategy

5.8

5.6

5.4

5.2 HIGH LOW 5

4.8

4.6

Cognitive Resistance Resistance Cognitive Changeto LOW HIGH Job Insecurity 1&2

182

Figure 4.9. Influence of rational-empirical change leadership strategy on cognitive resistance to change for low and high job insecure individuals (combined manufacturing and retail sample).

Taken together, the results show no support for hypothesis 4a and partial support

for hypothesis 4b on two out of the three resistance to change types.

Hypothesis 5 Normative Re-educative (participation-based) Change Leadership Hypotheses Results (combined manufacturing and retail sample)

Hypothesis 5a predicated that a perceived normative re-educative (participation-

based) change leadership strategy will moderate the negative relationship between job

insecurity and affective commitment to change such that the negative relationship would

be weaker. Hypothesis 5a was not supported by the analysis as the interaction term (job

insecurity 12 x rational empirical change leadership strategy) failed to reach significance

for affective commitment to change. Hypothesis 5b, predicted that a perceived normative

re-educative change leadership approach will moderate the positive relationship between

job insecurity and the three resistance to change measures (affective, behavioral, and

cognitive). Hypothesis 5b was not supported by the analysis as the interaction term (job

insecurity 12 x affective, behavioral, and cognitive resistance to change) failed to reach

significance for any of the resistance to change types.

Table 4.16

Regression Results for Hypotheses 5a and 5b Normative Re-educative (participation-based) Change Leadership Strategy and their Interactions (combined manufacturing and retail sample)

ACTC ARTC BRTC CRTC Variable Model 4 Model 5 Model 4 Model 5 Model 4 Model 5 Model 4 Model 5 Step 0 Change Significance (1st) -0.03 -0.03 0.12 ** 0.12 ** 0.09 * 0.09 * 0.09 * 0.09 * Change Significance (2nd) 0.00 0.00 0.01 0.01 0.03 0.03 0.02 0.02 Change Impact 0.54 ** 0.54 ** -0.49 ** -0.49 ** -0.37 ** -0.37 ** -0.56 ** -0.56 ** Job Satisfaction 0.22 ** 0.22 ** -0.17 ** -0.18 ** -0.22 ** -0.22 ** -0.15 ** -0.15 ** Gender -0.11 -0.11 0.11 0.11 0.12 0.12 0.15 0.15 Age Range 0.01 0.01 -0.01 -0.01 0.03 0.03 -0.02 -0.02 Race -0.08 -0.08 0.04 0.04 0.03 0.03 0.05 0.05 Marital Status 0.37 ** 0.37 ** -0.28 * -0.27 * -0.18 -0.18 -0.19 -0.19

Children (financially responsible) -0.24 * -0.25 * 0.17 0.16 0.18 0.18 0.16 0.15 Education 0.04 0.04 -0.01 -0.01 -0.02 -0.02 -0.01 0.00 Year(s) Worked for Organization 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.01 Level in Organization 0.18 * 0.19 * -0.14 -0.12 -0.27 ** -0.27 ** -0.13 -0.12 Role / Function in Organization 0.01 0.01 -0.01 -0.01 -0.02 -0.02 -0.01 -0.02 Employment Dependence -0.18 ** -0.18 ** 0.20 ** 0.20 ** 0.11 * 0.11 * 0.21 ** 0.21 ** Employability -0.06 -0.07 0.03 0.03 0.02 0.02 0.12 0.11 Step 1 Job Insecurity 1 & 2 -0.09 -0.09 0.26 ** 0.27 ** 0.14 * 0.14 * 0.14 * 0.15 * Step 2 NRCLS 0.51 ** 0.52 ** -0.18 * -0.17 * -0.04 -0.04 -0.47 ** -0.46 ** Step 3 Job Insecurity 1 & 2 X NRCLS 0.05 0.11 0.00 0.07 R-Square 0.50 ** 0.50 0.47 * 0.48 0.36 0.36 0.55 ** 0.55

∆ R-Square 0.03 ** 0.00 0.01 * 0.00 0.00 0.00 0.03 ** 0.00 ACTC = affective commitment to change, ARTC = affective resistance to change, BRTC = behavioral resistance to change, CRTC = cognitive resistance to change; Unstandardized regression coefficient *p < .05, **p < .01, + p<0.10.

183 184

Overall, the results show no support for hypotheses 5a or 5b due to insignificant

results.

Power Coercive (top-down) Change Leadership Hypotheses Results (combined manufacturing and retail sample)

Table 4.17

Regression Results for Hypotheses 6a and 6b Power Coercive (top-down) Change Leadership Strategy and their Interactions

(combined manufacturing and retail sample)

ACTC ARTC BRTC CRTC Variable Model 6 Model 7 Model 6 Model 7 Model 6 Model 7 Model 6 Model 7 Step 0 Change Significance (1st) -0.05 -0.05 0.13 ** 0.14 ** 0.09 * 0.10 * 0.11 * 0.11 ** Change Significance (2nd) 0.03 0.03 -0.03 -0.03 0.01 0.00 -0.01 -0.02 Change Impact 0.60 ** 0.59 ** -0.49 ** -0.48 ** -0.36 ** -0.35 ** -0.61 ** -0.60 ** Job Satisfaction 0.23 ** 0.23 ** -0.15 ** -0.15 ** -0.20 ** -0.20 ** -0.15 ** -0.16 **

Gender -0.13 -0.14 0.15 0.16 0.14 0.15 0.17 0.18 Age Range 0.01 0.01 -0.01 -0.01 0.03 0.03 -0.01 -0.01

Race -0.05 -0.04 0.02 0.01 0.03 0.02 0.02 0.01 Marital Status 0.31 * 0.31 * -0.20 -0.21 -0.12 -0.12 -0.14 -0.14 Children (financially responsible) -0.23 * -0.23 * 0.16 0.15 0.16 0.16 0.15 0.15 Education 0.03 0.03 -0.02 -0.01 -0.02 -0.02 0.00 0.00 Year(s) Worked for Organization 0.00 0.00 0.01 0.01 0.01 0.00 0.01 0.01 Level in Organization 0.17 * 0.17 -0.09 -0.09 -0.23 ** -0.23 ** -0.12 -0.12 Role / Function in Organization 0.00 0.00 0.00 0.00 -0.02 -0.02 -0.01 -0.01 Employment Dependence -0.06 -0.06 0.12 * 0.12 * 0.06 0.06 0.10 0.10 Employability 0.06 0.06 -0.05 -0.05 -0.02 -0.02 0.00 0.00

Step 1 Job Insecurity 1 & 2 -0.11 -0.11 0.25 ** 0.27 ** 0.13 * 0.14 * 0.15 * 0.17 ** Step 2 PCCLS -0.62 ** -0.62 ** 0.51 ** 0.52 ** 0.32 ** 0.32 ** 0.58 ** 0.58 **

Step 3 Job Insecurity 1 & 2 X PCCLS 0.05 -0.24 ** -0.16 * -0.14 * R-Square 0.51 ** 0.51 0.50 ** 0.51 ** 0.38 ** 0.39 * 0.56 ** 0.56 * ∆ R-Square 0.04 ** 0.00 0.04 ** 0.01 ** 0.02 ** 0.01 * 0.04 ** 0.00 * PCCLS = power coercive change leadership strategy, ACTC = affective commitment to change, ARTC = affective resistance to change, BRTC = behavioral resistance to change, CRTC = cognitive resistance to change; Unstandardized regression coefficient *p < 0.05, **p < 0.01, + p < 0.10.

185 186

Of the three resistance to change measures, moderating relationships were found

for all three interactions including affective resistance to change (β = -0.24 ± 0.011 [99%

CI], p < .0.01), behavioral resistance to change (β = -0.16 ± 0.007 [95% CI], p < .0.05),

and cognitive resistance to change (β = -0.14 ± 0.004 [95% CI], p < .0.05). As hypothesized, the positive relationship between job insecurity and affective resistance to change was stronger for job insecure individuals who perceived higher levels of power coercive change leadership strategy.

Power Coercive Change Leadership Strategy

6.2

5.8

5.6

5.4 HIGH LOW 5.2

4.8 LOW HIGH Affective Resistance to Change Job Insecurity 1&2

Figure 4.10. Influence of power coercive (top-down) change leadership strategy on affective resistance to change for low and high job insecure individuals (combined manufacturing and retail sample).

Also, the positive relationship between job insecurity and behavioral resistance to

change was stronger for job insecure individuals who perceived higher levels of power

coercive change leadership strategy however, even at lower perceived levels of power

coercive change leadership strategy, individuals who were high in job insecurity showed

higher levels of behavioral resistance to change.

187

Power Coerceive Change Leadership Strategy

5.5 5.4 5.3 5.2 5.1 HIGH 5 LOW 4.9 4.8 4.7 LOW HIGH

Behavioral Resistance Behavioral Resistance Changeto Job Insecurity 1&2

Figure 4.11. Influence of power coercive (top-down) change leadership strategy on behavioral resistance to change for low and high job insecure individuals (combined manufacturing and retail sample). Finally, the positive relationship between job insecurity and cognitive resistance

to change was stronger for job insecure individuals who perceived higher levels of power

coercive change leadership strategy.

Power Coercive Change Leadership Strategy

7 6.8 6.6 6.4

6.2 HIGH 6 LOW 5.8 5.6 LOW HIGH

Cognitive Resistance Resistance Cognitive Changeto Job Insecurity 1&2

Figure 4.12. Influence of power coercive (top-down) change leadership strategy on cognitive resistance to change for low and high job insecure individuals (combined manufacturing and retail sample).

188

Overall, the results show no support for hypothesis 6a and full support for hypothesis 6b in the combined manufacturing and retail sample. These results seem to

suggest that in the context of job insecurity, high levels of perceived power coercive

change leadership strategy shows a consistent association with all three resistance to

change types.

Trust in Management Hypotheses Results

Finally, hypothesis 7a predicted that higher reported levels of trust in management

will moderate the negative relationship between job insecurity and affective commitment

to change such that the negative relationship would be weaker. Hypothesis 7a was not

supported by the analysis as the interaction term (job insecurity 12 x trust in

management) failed to reach significance for affective commitment to change.

Hypothesis 7b, predicted that perceived trust in management will moderate the positive

relationship between job insecurity and the three resistance to change measures

(affective, behavioral, and cognitive). Hypothesis 7b was not supported by the analysis as the interaction term (job insecurity 12 x affective, behavioral, and cognitive resistance to change) failed to reach significance for any of the resistance to change types. Overall, the

results show no support for hypotheses 7a or 7b due to insignificant results in the

combined retail and manufacturing sample.

Table 4.18

Regression Results for Hypotheses 7a & 7b Trust in Management and their Interactions (combined manufacturing and retail sample) ACTC ARTC BRTC CRTC Variable Model 8 Model 9 Model 8 Model 9 Model 8 Model 9 Model 8 Model 9 Step 0 Change Significance (1st) -0.05 -0.05 0.13 ** 0.13 ** 0.08 0.09 0.11 * 0.11 *

Change Significance (2nd) 0.00 0.00 0.00 0.01 0.02 0.02 0.02 0.02 Change Impact 0.59 ** 0.59 ** -0.50 ** -0.50 ** -0.36 ** -0.36 ** -0.62 ** -0.62 ** Job Satisfaction 0.18 ** 0.18 ** -0.14 ** -0.15 ** -0.18 ** -0.18 ** -0.14 ** -0.14 ** Gender -0.11 -0.11 0.11 0.10 0.12 0.12 0.15 0.15 Age Range 0.00 0.00 -0.01 -0.01 0.03 0.03 -0.01 -0.01 Race -0.07 -0.07 0.03 0.03 0.04 0.04 0.04 0.04 Marital Status 0.35 ** 0.35 ** -0.25 * -0.26 * -0.15 -0.15 -0.21 -0.21

Children (financially responsible) -0.28 * -0.28 * 0.19 0.19 0.19 0.19 0.20 0.20 Education 0.03 0.03 -0.01 -0.01 -0.02 -0.02 0.00 0.00 Year(s) Worked for Organization 0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.00 Level in Organization 0.18 * 0.18 * -0.12 -0.10 -0.23 ** -0.23 ** -0.15 -0.14 Role / Function in Organization 0.00 0.00 -0.01 -0.01 -0.02 -0.02 -0.01 -0.01 Employment Dependence -0.15 * -0.15 * 0.18 ** 0.18 ** 0.11 * 0.10 0.18 ** 0.18 ** Employability -0.01 -0.01 0.01 0.00 0.02 0.01 0.07 0.06 Step 1 Job Insecurity 1 & 2 -0.08 -0.07 0.24 ** 0.26 ** 0.12 + 0.12 + 0.14 * 0.15 * Step 2 TIM 0.31 ** 0.31 ** -0.16 ** -0.15 ** -0.14 * -0.14 * -0.20 ** -0.20 ** ] Step 3 Job Insecurity 1 & 2 X TIM 0.02 0.08 + 0.01 0.03 R-Square 0.49 ** 0.50 0.48 ** 0.48 + 0.37 * 0.37 0.53 ** 0.53 ∆ R-Square 0.02 ** 0.00 0.01 ** 0.00 + 0.01 * 0.00 0.01 ** 0.00 TIM = trust in management, ACTC = affective commitment to change, ARTC = affective resistance to change, BRTC = behavioral resistance to change, CRTC = cognitive resistance to change; Unstandardized regression coefficient *p < 0.05, **p < 0.01, + p < 0.10.

189 190

Full Hypothesized Relationship Model Results

Table 4.19 provides a summary of each hypothesized relationship and the results of the analysis in the manufacturing, retail, and combined samples. The retail and combined samples showed the greatest support for the full model with 11 of the 24 hypothesized relationships (46%) being supported by the analysis. The hypothesized relationships between job insecurity and resistance to change and the mediation role of change related self-efficacy on the relationship between job insecurity and resistance to change had the greatest support in the overall model. Similarly, the hypothesized relationships among job insecurity, power coercive change leadership strategy, and resistance to change also showed strong support across the retail and combined samples.

Table 4.19

Summary of Tested Hypotheses (manufacturing, retail and combined results)

Hypothe Hypothesis Statement Manufacturing Retail Sample Combined sis # Sample Results Results Sample Results H1: Job insecurity will be negatively related to affective Not Supported (0 Not Supported Not Supported commitment to change of 1) (0 of 1) (0 of 1)

H2: Job insecurity will be positively related to affective, Partially Fully Supported Fully Supported Supported (1 behavioral, and cognitive, resistance to change (3 of 3) (3 of 3) of 3) H3a: Change related self-efficacy will mediate the negative Not Supported (0 Not Supported Not Supported relationship between job insecurity and affective commitment of 1) (0 of 1) (0 of 1) to change H3b: Change related self-efficacy will mediate the positive Partially Fully Supported Fully Supported relationship between job insecurity and resistance to change Supported (1 (3 of 3) (3 of 3) (affective, behavioral, and cognitive) of 3)

H4a: Rational-empirical (information-based) change leadership strategy will moderate the negative relationship between job insecurity and affective commitment to change such that the Fully Supported Not Supported Not Supported relationship will be less negative when rational-empirical change leadership is perceived to be high; specifically, the (1 of 1) (0 of 1) (0 of 1) negative relationship between job insecurity and affective comment to change will be weaker for individuals who perceive an information-based change leadership approach. H4b: Rational-empirical (information-based) change leadership strategy will moderate the positive relationship between job Partially insecurity and resistance to change (affective, behavioral, and Partially Partially Supported (1 cognitive) such that the relationship will be less positive when Supported Supported of 3; opposite rational-empirical change leadership is perceived to be high; (1 of 3) (2 of 3) specifically, the positive relationship between job insecurity direction) and resistance to change will be weaker for individuals who perceive an information-based change leadership approach.

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H5a: Normative re-educative (participation-based) change leadership strategy will moderate the negative relationship between job insecurity and affective commitment to change such that the relationship will be less negative when Not Supported Not Supported Not Supported normative re-educative change leadership is perceived to be high; specifically, the negative relationship between job (0 of 1) (0 of 1) (0 of 1) insecurity and affective commitment to change will be weaker for individuals who perceive a participation-based change approach.

H5b: Normative re-educative change leadership strategy will moderate the positive relationship between job insecurity and resistance to change (affective, behavioral, and cognitive) such that the relationship between job insecurity and Not Supported Not Supported Not Supported resistance to change will be less positive when normative re- (1 of 3; opposite (0 of 3) (0 of 3) educative change leadership is perceived to be high; direction) specifically, the positive relationship between job insecurity and resistance to change will be weaker for individuals who perceive a participation-based change leadership approach.

H6a: Power-coercive (power-based) change leadership strategy will moderate the negative relationship between job insecurity and affective commitment to change such that the relationship Not Supported Not Supported Not Supported will be more negative when power-coercive change leadership is perceived to be high; specifically, the negative (0 of 1) (0 of 1) (0 of 1) relationship between job insecurity and affective commitment to change will be stronger for individuals who perceive a top- down, power-based change approach

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H6b: Power-coercive (power-based) change leadership strategy will moderate the positive relationship between job insecurity and resistance to change such that the relationship between job insecurity and affective Partially commitment to change will be more positive when power- Not Supported Supported Fully Supported (3 coercive change leadership is perceived to be high; (0 of 3) of 3) specifically, the positive relationship between job (2 of 3) insecurity and resistance to change will be stronger for individuals who perceive a top-down, power-based change leadership approach.

H7a: Trust in management will moderate the negative relationship between job insecurity and affective commitment to change such that the relationship will be less negative when trust in management is high; Not Supported Not Supported Not Supported (0 of specifically, the negative relationship between job (0 of 1) (0 of 1) 1) insecurity and affective commitment to change will be weaker for individuals who have high levels of trust in management

H7b: Trust in management will moderate the positive relationship between job insecurity and resistance to change (affective, behavioral, and cognitive) such that the Not Supported Partial Support Not Supported (0 of relationship will be less positive when trust in management (0 of 3) (1 of 3) 3) is high; specifically, the positive relationship between job insecurity and resistance to change will be weaker for individuals who have high levels of trust in management. All Support for overall model (% supported) 4 of 24 (17%) 11 of 24 (46%) 11 of 24 (46%) hypotheses

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Summary of Chapter 4

This chapter began with a presentation of the descriptive statistics for each of the

scales used to analyze the variables of interest in each sample (manufacturing and retail

respectively). Correlations between the study variables and scales were presented for the

combined retail and manufacturing samples. On average, study participants in both

samples gave the highest ratings to change related self-efficacy, affective commitment to

organizational change, and cognitive resistance to change indicating that although they

may have been cognitively opposed to changes taking place within the organization, they

felt confident that they could handle the changes. The scores on the study construct, job

insecurity, were average across both samples.

The second section of this chapter presented the results of the study analyses by

research hypothesis for the manufacturing, retail, and combined manufacturing and retail

sample. The first set of analyses and hypotheses focused on the relationships between job

insecurity and commitment and resistance to change. Relationships between the scales

were analyzed using regression analysis. The results showed positive relationships

between job insecurity and affective resistance to change (manufacturing, retail, and

combined samples), behavioral resistance to change (retail and combined samples only),

and cognitive resistance to change (manufacturing, retail, and combined samples). These

relationships represented the core hypothesized relationships since the mediator and

moderator relationships were based on these.

The second set of analyses and hypotheses focused on change related self-efficacy as a mediator of the job insecurity-commitment-resistance to change relationship. When change related self-efficacy was included in the second set of analyses, results showed

195 this construct partially mediated the relationship between job insecurity and one or more of the resistance to change relationships in both the manufacturing and retail samples as well as the combined sample. To date, this is the first study empirically showing change related self-efficacy as a mediator of the job insecurity-resistance to change relationship.

The third set of analyses examined the moderator effects of perceptions of change leadership strategy and trust in management on the job insecurity-commitment/resistance to change relationship and were conducted using hierarchical multiple regression analysis. Rational-empirical (information-based) change leadership strategy was found to moderate the job insecurity-affective commitment to change relationship such that higher perceived rational-empirical change leadership was associated with higher levels of affective commitment to change for both high and low job insecure individuals

(manufacturing sample only). Rational-empirical change leadership strategy moderated the job insecurity-affective resistance to change relationship such that individuals low in job insecurity who reported higher rational-empirical change leadership perceptions showed decreased affective resistance to change but those high in job insecurity who also reported higher levels of rational empirical change leadership, showed increased levels of affective resistance to change (manufacturing sample only) (note: in the combined manufacturing and retail sample, rational-empirical change leadership strategy moderated job insecurity-affective resistance to change relationship such that affective resistance to change was lower for both low and high job insecure individuals who perceived a higher rational-empirical change leadership approach). Rational-empirical change leadership moderated the job insecurity-cognitive resistance to change relationship such that

196 individuals both low and high in job insecurity reported lower levels of cognitive resistance to change (retail and combined samples only).

Normative re-educative (participation-based) change leadership moderated the job insecurity-affective resistance to change relationship such that individuals who perceived higher levels of normative re-educative change leadership strategy and were low in job insecurity reported lower levels of affective resistance to change but individuals who perceived higher levels of normative re-educative change leadership but were high in job insecurity reported slightly higher levels of affective resistance to change compared to their lower perceived normative re-educative change leadership counterparts (retail sample only).

Power coercive (top-down) change leadership moderated the job insecurity- affective resistance to change relationship such that higher perceptions of power coercive change leadership strategy were associated with higher levels of affective resistance to change for both low and high job insecure individuals (retail and combined samples).

Power coercive change leadership also moderated the job insecurity-behavioral resistance to change relationship such that individuals who perceived higher levels of normative re- educative change leadership strategy and were low in job insecurity reported higher levels of behavioral resistance to change but individuals high in job insecurity who perceived higher levels of power coercive change leadership reported slightly lower levels of behavioral resistance to change compared to their lower perceived power coercive change leadership counterparts (retail sample only) (note: in the combined manufacturing and retail sample, power coercive change leadership strategy moderated the job insecurity-behavioral resistance to change relationship such that behavioral

197 resistance to change was higher for both low and high job insecure individuals who perceived a higher power coercive change leadership approach). Power coercive change leadership strategy also moderated the job insecurity-cognitive resistance to change relationship such that cognitive resistance to change was higher for both low and high job insecure individuals who perceived a higher power coercive change leadership approach.

Finally trust in management was found to moderate the job insecurity-affective resistance to change relationship where both high and low job insecure individuals reporting higher levels of trust in management showed lower levels of affective resistance to change (retail sample only). Table 4.18 provides a summary of results for all the hypothesized relationships in the core and full hypothesized relationships models presented in Chapter 3. The implications of these results will be further discussed in

Chapter 5.

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CHAPTER 5: SUMMARY, CONCLUSIONS, DISCUSSION, AND RECOMMENDATIONS

This chapter provides a summary of the study as well as general conclusions based on the study results. Potential implications of the study for research and practice are also presented. The results of this study are also discussed in conjunction with the results of previous studies.

Summary

Employee attitudes including commitment and resistance have been cited in the change management literature as one of the key elements of planned change initiatives particularly as the frequency and complexity of organizational change increases

(Armenakis et al., 1999; Caldwell, Herold, & Fedor, 2004; Herscovitch & Meyer, 2002).

To date, few studies have been conducted examining the relationships among job insecurity, employee commitment, and resistance to organizational change (exceptions including Kalyal et al., 2010; Rosenblatt & Ruvio, 1996) and no studies have been done examining perceived change leadership strategies as moderators of the relationship between job insecurity and employee commitment/resistance attitudes. The purpose of this research study was to gain a better understanding of individual responses to job threatening organizational change and learn what factors within the organizational change process relate to either beneficial (commitment to change) or detrimental (resistance to change) employee attitudes. The following research questions were addressed:

1. To what extent is job insecurity related to workers’ commitment to change and

change attitudes (e.g. commitment and resistance to change)?

2. How are the inter-relationships between job insecurity and commitment to change

and job insecurity and resistance to change mediated by workers’ self-efficacy

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and moderated by perceptions of change leadership strategies, and trust in

management?

To explore these research questions, data were gathered from a mid-sized U.S.-based

manufacturing company and a large, U.S.-based retail company using paper and web-

based surveys. A total of 625 usable survey responses were collected with a sample size

of 275 from the manufacturing company and 350 from the retail company with a combined response rate of 19%. A slight majority of respondents were female (51%) and the most commonly reported age range was between 50 and 54 for the manufacturing sample and between 20 and 24 for the retail sample. The most commonly cited organizational changes taking place included ‘outsourcing’ (23%) for the manufacturing

sample and ‘ownership change’ for the retail sample (26%).

Figure 5.1. Most commonly reported organizational changes (manufacturing sample).

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Figure 5.2. Most commonly reported organizational changes (retail sample).

The survey gathered individual-level perception data on organizational change leadership strategies, change related self-efficacy, trust in management, job insecurity, and commitment and resistance to organizational change. The scales used in this study included the Perceptions of Change Leadership scale developed by Szabla (2007), the

Change Related Self-Efficacy scale developed by Ashford (1988), the Trust in

Management scale developed by Stanley, Meyer, and Topolnytsky (2005), the Affective

Commitment to Organizational Change sub-scale developed by Herscovich and Meyer

(2002), and the Change Attitudes scale (resistance to change) developed by Oreg (2006).

Based on the above scales and the 7 hypotheses, a hypothesized relationship model was developed to explain the relationships among the key study variables.

Several data analysis techniques were used to address the research questions and test the hypothesized relationships including descriptive statistics, correlations, and hierarchical multiple regression analysis. Multiple regression analysis was used to analyze the direct relationships between the dependent and independent variables, while hierarchical multiple regression analysis was conducted to analyze the mediator and

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moderator effects of the variables of interest. Key findings and conclusions of this study are summarized in the following section.

Conclusions

Based on the results of the study data analysis, several conclusions were drawn.

Results of the regression analysis revealed positive relationships between job insecurity

and affective resistance to change (β= 0.15, p < 0.05) and cognitive resistance to change

(β = 0.12, p < 12, p < 0.05) in the manufacturing sample and positive relationships

between job insecurity and affective commitment to change (β = 0.17, p < 0.01), behavioral resistance to change (β = 0.13, p < 0.05), and cognitive resistance to change (β

= 0.12, p < 0.05) in the retail sample. In the combined manufacturing and retail samples,

positive relationships were found between job insecurity and each of the three resistance

to change measures including affective resistance to change (β = 0.16, p < 0.01), behavioral resistance to change (β = 0.10, p < 0.05), and cognitive resistance to change (β

= 0.11, p < 0.01). Among each of the three resistance to change measures, the

relationship between job insecurity and affective (emotional) resistance to change

demonstrated the strongest relationship in the manufacturing and retail samples

respectively as well as the combined retail and manufacturing samples. Overall, the

results of the analysis show that increases in job insecurity among employees are

associated with increases in affective (emotional), behavioral (intentional), and cognitive

resistance to change.

Change related self-efficacy results. When change related self-efficacy was added to

the analysis, it partially mediated or fully mediated the job insecurity and affective,

behavioral, and cognitive resistance to change relationship behavioral in the

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manufacturing, retail, and combined samples. Here, the results show that change related

self-efficacy is a factor in reduced levels of affective, behavioral, and cognitive resistance

to change.

Rational empirical (information-based) change leadership strategy results. Despite the fact that no significant, direct relationship between job insecurity and affective commitment to change was found in any of the samples, when rational empirical change leadership strategy was added to the interaction term in the manufacturing sample, affective commitment to change was found to be stronger for individuals high in job insecurity (β = -0.21± 0.01 [95% CI], p < .0.05). These results, including that fact that job insecurity-affective commitment to change relationship was only significant when rational empirical change leadership was added, suggest that information-based leadership may be linked to commitment to change. For example, individuals may be unable to be fully commit to a given organizational change unless some form of information-based leadership strategy is utilized. Somewhat unexpectedly, affective resistance to change appeared to be stronger for individuals high in job insecurity who perceived a rational empirical change leadership strategy in the manufacturing sample (β

= 0.29 ± 0.02 [99% CI], p < .0.01). The somewhat paradoxical finding in the manufacturing sample that both affective commitment and affective resistance change appeared to be higher in the presence of perceived rational empirical change leadership strategy for high job insecure individuals may be explained by differences in interpretation of the information provided by organizational leaders. For example, some employees may see how rationally, the changes may improve the overall performance and competitiveness of the organization (e.g. increased affective commitment to change)

203 but the same information may be perceived as threatening particularly if job loss is seen as a possibility and that may lead to more negative change attitudes (e.g. affective resistance to change) (note: outsourcing and new IT/technology were the most commonly reported changes in the manufacturing sample both of which, at the macro-level, have resulted in job losses in the manufacturing sector overall). In the retail sample, perceived rational empirical change leadership strategy was associated with lower cognitive resistance to change (β = 0.17 ± 0.01 [95% CI], p < .0.05) and in the combined manufacturing and retail samples, rational empirical change leadership strategy was associated with lower affective (β = 0.17 ± 0.008 [99% CI], p < .0.01) and cognitive (β =

0.14 ± 0.005 [99% CI], p < .0.01) resistance to change for individuals high in job insecurity. In general, it can be concluded that an information-based change leadership approach appears to moderate job insecurity lowering affective and cognitive resistance to change.

Normative re-educative (participation-based) change leadership results. In the retail sample, perceived normative re-educative change leadership was associated with higher affective resistance to change for individuals high in job insecurity and lower affective resistance to change for individuals low in job insecurity (β = 0.18 ± 0.01 [95% CI], p <

.0.05). This result was opposite of the hypothesized relationship. No significant relationships were found for normative re-educative change leadership strategy in the manufacturing or combined manufacturing and retail samples. It can be concluded that job insecurity plays a significant role in the efficacy of participation-based change leadership strategies. For instance, normative re-educative change leadership may contribute to lower levels of affective resistance to change for individuals who are

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relatively secure in their jobs but may have the opposite effect for job insecure

employees.

Power coercive (top-down) change leadership results. In the retail sample, perceived

power coercive change leadership strategy was associated with higher affective (β = -

0.23 ± 0.01 [99% CI], p < .0.01), and behavioral (β = -0.25 ± 0.02 [99% CI], p < .0.01) resistance to change. However, compared to those high in job insecurity and high in perceived power coercive change leadership, behavioral resistance to change for individuals high in job insecurity who reported lower levels of power coercive change leadership strategy was slightly higher suggesting that power coercive change leadership strategy may play a role in reducing (or at least limiting) behavioral resistance in job insecure conditions. Low-levels of job insecurity and power coercive change leadership are associated with greater behavioral resistance to change but, job insecurity and higher power coercive change leadership perceptions appear to be associated with slightly less behavioral resistance to change. This seems to support research showing that job insecurity is linked to increased work performance at moderate levels but decreased work performance at low and high levels (see Brockner et al., 1992, Probst, 2002). In the combined manufacturing and retail sample, power coercive change leadership strategy was associated with higher levels of each of the resistance to change measures including affective resistance to change (β = -0.24 ± 0.011 [99% CI], p < .0.01), behavioral resistance to change (β = -0.16 ± 0.007 [95% CI], p < .0.05), and cognitive resistance to change (β = -0.14 ± 0.004 [95% CI], p < .0.05) for both high and low job insecure individuals. Overall, in can be concluded that in most cases, a top-down change

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leadership strategy is associated with increased affective, behavioral, and cognitive

resistance to change.

Trust in management results. In the retail sample, higher reported levels of trust in

management were associated with lower affective resistance to change (β = -0.11 ± 0.01

[95% CI], p < .0.05). Trust in management moderated the job insecurity-affective

resistance to change relationship where both high and low job insecure individuals

reporting higher levels of trust in management showed lower levels of affective resistance

to change. Results suggest that trust in management is associated with decreased affective

resistance to change.

Discussion

In this section, the study findings were interpreted and discussed in terms of their

relationships and implications. The results were compared with findings from previous

research studies and current theory.

Job Insecurity, Commitment to Change, and Resistance to Change

Since the beginning of research on job insecurity, it has been theorized to be

associated with negative employee attitudes and behaviors. These relationships have been

supported by a number of empirical studies including two meta-analyses demonstrating

that increased levels of job insecurity result in reduced employee attitudes including

resistance to change (see Cheng & Chan, 2008; Greenhalgh, 1984; Rosenblatt & Ruvio,

1996; Rosenblatt, Talmud, & Ruvio, 1999; Sverke, Hellgren, & Naswall, 2002). The

results of this study generally confirm previous findings that job insecurity is associated with unfavorable employee attitudes including resistance to change and cynicism.

Job Insecurity, Change Related Self-Efficacy, and Resistance to Change

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From an organizational change perspective, self-efficacy has been defined as a

motivational construct that influences individual choices, goals, emotional reactions,

effort, coping, and persistence (Bandura, 1997; Gist & Mitchell, 1992). In their meta-

analysis of 114 studies on self-efficacy, Stajkovic and Luthans (1998) found that self-

efficacy was significantly associated with enhanced work performance. Dirks et al.

(1996) noted that situations and organizational changes that lower efficacy perceptions

will hinder change management efforts. Organizational changes that reduce job security

are likely to have this affect. The findings from the current study showing change related

self-efficacy as a mediator of the job insecurity-resistance to change relationship suggest that employees use change related self-efficacy as a coping mechanism and efforts to increase employee perceptions of their ability to cope with organizational change may reduce resistance to change attitudes particularly for organizational changes that may result in moderate levels of job insecurity (slight job loss anxiety). These results support

and build upon Herold, Fedor, and Caldwell’s 2007 study showing that increased change

self-efficacy was associated with higher levels of commitment to change even in high

turbulence change contexts as well as Neves’ (2009) study showing that self-efficacy reduced turnover intentions. Judge et al. (1999), Cunningham (2006), and Jimmieson et al. (2004) also found positive relationships between change related self-efficacy-based coping with change and organizationally beneficial outcomes including intention to stay, increased job satisfaction, and higher job performance suggesting that individuals who are better able to cope with organizational change are also likely to adapt to change in the workplace and better recognize potentially beneficial career outcomes associated with organizational changes. The results of the current study however run counter to König et

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al.’s 2010 study which found no significant interactions between occupational self-

efficacy and self/supervisor performance ratings.

Job Insecurity, Change Leadership, Commitment, and Resistance to Change

Effective change leadership has been linked to a variety of organizational success

factors including long-term viability and innovation suggesting that effective change

leadership is a requirement for successful change management for many types of

organizations undergoing a variety of organizational changes (Gilley, 2005; Gilley,

Quatro, Hoekstra, Whittle, & Maycunich, 2001; Pfeffer, 2005). As noted in Chapter 2,

three main change leadership strategies have been outlined in the planned change

literature including rational-empirical (information-based), normative re-educative

(participation-based), and power-coercive (top-down) change leadership strategies (Chin

& Bin, 1961; Zaltman and Duncan, 1977). Because job insecurity has been empirically linked to detrimental employee and organizational outcomes including decreased organizational commitment and job involvement (see Brown, 1996; Mathieu & Zajac,

1990;Sverke, Hellgren, & Naswall, 2002; Cheng & Chen; 2008) this study hypothesized that job insecurity would be negatively linked to employee commitment to change and positively linked to resistance to change.

Given the importance of being able to increase commitment and reduce resistance to change in job insecure organizational contexts that are undergoing planned change initiatives, it was hypothesized that change leadership strategies would play an influencing role. Of the three change leadership types examined, it was hypothesized that information and participation-based change leadership strategies would be associated

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with increased affective commitment to change and reduced affective, behavioral, and

cognitive resistance to change. The results of this study showed mixed results.

Rational empirical change leadership - Individuals who reported higher levels of information-based change leadership tended to report that implementation of the change was justified by experts who were knowledgeable. They also reported that those leading the change focused on factual evidence and logical arguments to promote the benefits of change and to implement the change. Information-based change leadership was

associated with increased affective commitment to change and lower affective and

cognitive resistance to change for both high and low job insecure individuals (exception

in the manufacturing sample). These findings were consistent with research showing that

communication reduces job insecurity (see Adkins et al., 2001; König et al., 2010;

Kramer et al., 2004) and research showing that rational-empirical approaches to change

are frequently used by leaders and demonstrate moderate levels of effectiveness in

influencing employees to adopt organizational change (Chin & Benne, 1961; Kline,

1996; Schweiger & DeNisi, 1991; Nutt, 1986; 1998). These findings also support Neves’

(2009) study which found change appropriateness to be significantly related to affective commitment to change.

It may be the case that when used effectively, an information-based change leadership strategy could heighten employee perceptions of the need for change and change appropriateness (a cognitive process) which then leads to increased affective commitment to change. As Lines (2004) noted, “In order to make change recipients accept, and become committed to such changes, more change agents must put more effort into explaining the rationale, content and personal consequences of the changes” (p. 211). If

209 organizational members are convinced that a decision is well conceived, they are more likely to accept rather than resist (Dean & Sharfman, 1996). This view is consistent with research showing cognition as the primary initial factor in the attitude formation process followed by emotion then finally behavior. However, once the emotional response is formed, all subsequent information is primarily filtered through an emotional filter rather than cognition (Ajzen, 2001; Lines, 2005; Smollan, 2006). In other words, capture the head and the heart and actions will follow.

Normative re-educative change leadership - Individuals who reported higher levels of participation-based change leadership tended to view the change process as more collaborative with higher levels of involvement and decision-making across many levels of the organization. They observed that change leaders were dealing with employee acceptance by spending time focusing on how the change was being accepted by employees. They perceived a change strategy characterized primarily by participation and observed that those leading the change were establishing relationships with key individuals to help carry out the change. Participation-based change leadership was associated with higher affective resistance to change for individuals high in job insecurity and lower affective resistance for individuals low in job insecurity. Despite the overall advantages associated with participation-based change leadership, it is not surprising that individuals who believe they will be negatively impacted by the change may be reluctant to help implement the change particularly if they believe the changes are likely to result in the loss of their jobs. As Herold and Fedor (2008) observed, “…when you ask an IT group to train their outsourcing replacements, when all they have to look forward to is losing their jobs, you should not be surprised if their motivation for carrying out this task

210 is absent (p. 1057). This is consistent with research showing that individuals’ level of work engagement tended to be higher as the level of participation in change-related decision-making increased however, these increases were not as great for individuals with high levels of job insecurity (König et al., 2010).

The current study’s results support earlier research findings showing that workers tend to commit to changes they see as beneficial and have low personal adverse impact

(see Fedor & Herold, 2004), as well as research showing individuals who are highly impacted by organizational change will be especially sensitive and responsive to the effects of leadership (Herold et al., 2008). The findings of this study on the moderating role of participation-based change leadership also support Brockner et al’s Inverted-U

Theory of Job Insecurity where moderate levels of job insecurity were found to be associated with the most optimal work performance (see Brockner et al., 1992; Perlow,

1994). In the case of this study, high levels of job insecurity and participation-based change leadership were associated with increased behavioral resistance to change. The results on participation-based leadership show that it may be useful in reducing resistance to change in low job insecurity contexts but not in high job insecurity climates. Several studies have shown that rational empirical change leadership strategies are not as effective compared to participation-based change leadership strategies (see Nutt, 1992;

Zaltman & Duncan, 1977). However the results of the current study suggest this may not always be the case. In high job insecure contexts more information, rather than increased participation, may be the better option for reducing resistance to change.

Power coercive change leadership - Individuals who reported higher levels of power- coercive change leadership strategy observed that the change was justified only by

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members of top management. They perceived those leading the change were playing the role of order giver and that change leaders were not focusing on how employees were accepting the change. These individuals also believed that those leading the change were

using their positions of power and threats to carry out the change, thus, creating a divide

between themselves and those responsible for carrying out the change.

The current study showed that top-down change leadership was associated with increased affective, behavioral, and cognitive resistance to change (exception in the retail sample). This finding supports years of empirical research showing that when a top-down

change strategy is adopted, cognitive, emotional, and intentional responses toward the

change will be lower compared to information-based and/or participation based change

leadership approaches (Chin & Benne, 1961; Harris, 2002; Nutt, 1998; Prasad & Prasad,

2000; Szabla, 2007; White & Lippitt, 1960). Though the findings on top-down change

leadership are not particularly surprising, the fact that employees in both samples

reported this change leadership strategy suggests that leaders’ reliance on this ‘high-risk’

change leadership strategy continues. Indeed, several studies have demonstrated a clear

link between use of power techniques and task, goal, and implementation success (Brass

& Burkhart, 1993; Lines, 2007). In the context of intense environmental pressures and

time constraints, leaders may be tempted to utilize this approach and should be mindful

of the potential attitudinal and behavioral consequences and diminished capacity of

employees’ willingness to assist in facilitating change. When organizational leaders in

job insecure change contexts are perceived to be more directive in their approach to

organizational change, employees may behave in less resistant ways even if they disagree

(emotionally or cognitively) with the organizational change as they may feel there is little

212 choice but to comply (see Parish et al., 2008). Although this compliance (continuance commitment “have to”) may come at the expense of more productive, learning-focused, or innovative employees (see Probst, 2007).

Together, the change leadership-focused results of this study demonstrated a significant moderating role of change leadership strategy on the relationship between job insecurity and employee change attitudes. This supports previous research showing that change process (e.g. change leadership strategy) as well as change outcomes (job loss) influence employee reactions to organizational change (Fedor & Herold, 2004). This is consistent with Armenakis and Bedeian’s (1999) five common change themes including change content, context, process, criterion, and affective and behavioral reactions to change.

Despite the obvious importance of change leadership to employee commitment to change, research on the affects of leadership on organizational change outcomes has been relatively modest. Burke (2002) observed that, “…what has not been as clear from the literature is the impact of leadership on organizational change” (p. 241). Notable empirical research findings on organizational change leadership include Grove’s (2005) research showing a positive relationship between follower’s ratings of leader charisma and general openness to organizational change and Herold et al.’s (2008) study showing a positive relationship between transformational leadership and employee commitment to change and Gilley and Gilley’s (2008) article showing communication skills and ability to motivate were leadership skills most closely linked to perceptions of ability to lead change and innovation.

Job Insecurity, Change Leadership, Trust in Management, and Change Attitudes

213

Organizational change presents a critical opportunity to either create or destroy employees’ trust in management (Morgan & Zeffane, 2003). Because organizational change, particularly job threatening organizational changes create high levels of uncertainty among employees along a variety of dimensions including job security, ongoing social work relationships, and post-change job characteristics, employee trust in management becomes particularly salient under such circumstances (Bordia Lines et al.,

2005). Mayer and Gavin (2005) argued, employees who lack trust in management are likely to spend time and mental energy speculating about their future and the future of the organization. Higher levels of trust in management have been associated with better task performance (Oldham, 1975; Robinson, 1996), openness in communication and information sharing (Boss, 1978; Dirks, 1999; Dirks & Ferrin, 2001), organizational commitment (Song, Kim, & Kolb, 2009), increased organizational citizenship behavior

(Konovsky & Pugh, 1994), and increased acceptance of decisions and goals (Oldham,

1975; Tyler & Degoey, 1996). Several studies have found that increased organizational change is associated with decreased trust in management particularly when the change causes emotional strain for employees (see Lines et al., 2005; Morgan & Zeffane, 2003).

In the current study, trust in management failed to reach significance in the manufacturing and combined manufacturing and retail sample however, it was associated with slightly lower levels of affective resistance to change in the retail sample. These results support researching showing trust may decrease the negative outcomes of organizational change events such as downsizing (Hopkins & Weatherington, 2006).

Nevertheless, the overall lack of significance in the current study is somewhat surprising.

Several possible explanations may account for these findings. First, the change leadership

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strategy scale may have captured the factors that increase employee trust more effectively

than the trust in management scale. In a longitudinal study of interactions that influence

attitudes about technology changes, Burkhardt (1994) found that attitudes about

technological change were more influenced by individuals with similar job roles rather

than individuals who happen to work in close proximity but do not perform the same type

of work.

Additionally Ford and Ford (1995; 2009) observed that the three-fold change

management process which includes: 1) specifying the conditions for achieving

satisfactory change; 2) increasing involvement, participation, and support on the part of those engaged in the change; and, 3) translating events, instilling meanings, and developing shared understanding has the combined effect of producing shared meaning on the rationale and purpose of the change and building trust. Both the rational empirical and normative re-educative change leadership strategies contain elements of Burkhardt’s

findings and Ford and Ford approaches which may better account for increased levels of

trust and/or lack of significance on the trust in management scale.

Second, and perhaps more directly, job insecurity and trust in management may

simply be weakly linked. In their 2009 study examining the role of trust in secure and

insecure employment situations, the researchers failed to find a moderating effect of job insecurity on the relationship between trust and employee attitudes (Jong, Schalk, &

Croon, 2009). They attributed their findings to several factors including, 1) the possibility that in job insecure contexts, employees may rely more on past treatment (e.g. a history of layoffs) rather than trust as the primary basis for making attitudinal assessments, and

2) the educational level of employees. Employees with lower education and hierarchal

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status tend to view unequal allocation of rewards as less unfair compared to higher status

employees (Molm et al., 1994; Simpson, 1976) thus making higher status employees

more sensitive to changes in employment circumstances that may reduce job security.

Studies on trust with greater percentages of higher-status participants tend to show stronger results on trust measures compared to studies with greater variety of both high and low status participants such as the current study (Hopkins & Weatherington, 2006;

Ten Brink et al., 2001).

Despite the difficulty with finding strong links between job insecurity and trust in the current and previous studies, trust undoubtedly plays a strong role in the organizational change process. Overall, trust in management is likely an important factor in the change

management process but additional research is needed to explore its relative contribution

to employee attitudes throughout the organizational change process.

Discussion Summary of Job Insecurity, Change Self-Efficacy, Change Leadership,

Trust in Management, and Change Attitudes

In summary, the current study found that job insecurity was associated employee

resistance to change and change related self-efficacy partially or fully mediated that association. Three change leadership types were examined included information-based,

participation-based, and top-down. Information-based change leadership was found to be associated with higher affective commitment to change and lower affective and cognitive resistance to change. Participation-based change leadership was associated with increased affective resistance to change for individuals high in job insecurity and lower affective resistance to change for individuals low in job insecurity. Top-down change leadership was associated with affective, behavioral, and cognitive resistance to change. Finally,

216

trust in management was associated with slightly lower affective resistance to change.

Taken together, these results were generally supported by or built upon extant research

and theory on job insecurity, change management/leadership, self-efficacy, trust, and

change attitudes. The next section will examine the several limitations of the current study.

Limitations of the Study

A number of limitations are present in this study. First, the generalizability of the results is limited to the degree to which other populations resemble the two samples studied. The populations in the current study consisted of employees in a mid-sized, publicly traded manufacturing firm and a large, publically traded retailer. Similar studies with different populations might yield different results than the ones found in the present study because the characteristics of employees in the manufacturing and retail industries may be different from employees in other industries.

A second limitation of this study is that employee attitudes were assessed at only one point during the change implementation. No pre-data were collected on any of this study’s variables which limits the ability of this study to assess changes (or lack of change) in employee attitudes or outcomes over time. Longitudinal research which examines how change leadership strategies and management activities might impact employee attitudes toward change over the entire change process would be of great value in identifying causal relationships.

Third, the present study used only one source and one data collection method to address the research questions. Therefore, single-source, single-method bias is a potential concern. However, due to resource constraints and limitations set by the participating

217 organizations on access to employee time, the data collection methods used in the present study were deemed the most feasible and appropriate. As in all self-report studies, common method variance is possible. Common method variance refers to the fact that because both measures come from the same source, any defect in the source contaminates both measures (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). The employee respondents were the single-source, and the questionnaire was the single method.

Additional data collection methods could have been used to address this bias. Examples of additional sources of data could have included interview data or direct observation data.

A fourth limitation of this study is that the convenience sampling method utilized may bias the results, as those who volunteered to participate in the study may have been more resistant or possibly more committed to change. For example, it is possible that individuals who were more committed to the organizational changes were willing to make extra effort and participate in the present study. It is also plausible that those employees that participated in the present study may have been more resistant to change and were seeking an outlet to voice their dissatisfaction. The next section will examine suggested recommendation for future research in light of the current study findings.

Recommendations for Future Research

While the current study offers contributions to the change management and job insecurity research literature on the influence of change leadership factors on employee resistance to change attitudes in job insecure contexts, this study also raised questions about the relationships among the variables of interest and related research. The following recommendations are suggested for future research.

218

First, future research is needed to examine the comparative contribution of change

process factors such as change leadership (Szabla, 2007), procedural justice (Colquitt,

2001), and change support (Kaplan et al., 2009) on the three main commitment to change types (affective, continuance, and normative commitment). The current study only used

Herscovich and Meyer’s (2002) affective commitment to change sub-scale construct, however, it is possible that different process approaches to organizational change may influence individuals’ commitment to change in different ways resulting in differing types of commitment. Testing these relationships using attitude theory (Thompson &

Hunt, 1996) and the tripartite theory of attitudes (Breckler, 1984) may further reveal how change attitudes are formed among employees experiencing organizational change in job insecure contexts.

Second, additional research into the lingering effects of past change leadership is

needed particularly in job insecure change contexts. For example, past organizational

changes that resulted in job losses are likely to influence employee perceptions of adverse

change outcomes by heightening job insecurity and lowering commitment to change and

trust (Bordia et al., 2007; Jong et al., 2009; Stensaker et al., 2002; Stensaker & Meyer,

2012). While the current study found that information-based change leadership was

associated with increased affective commitment to change, this strategy was also

associated with increased affective resistance to change as well. Additional research is

needed into whether it is possible to ameliorate the detrimental attitudinal effects of job

threatening organizational change by modifying the change process perhaps by explicitly

communicating expected job loss impacts coupled with job support in the form of short-

term performance incentives and alternative employment search support prior to layoff.

219

Such approaches may allow for continued work performance, job involvement, and

creativity amongst job insecure employees by directly addressing their most pressing

employment-related concerns.

Third, the current study examined change leadership and trust in management as

moderators of employee responses however, future research should focus on the salience

and limits of both for employee responses. During periods of job threatening organizational change, which is more important—the change strategy leaders adopt or the level of trust employees have in the leaders? In other words, should change leaders invest more time and energy in improving their leadership style or attempting to improve employees’ trust? The relatively weak results for trust in the current and previous studies provide some direction on this question but additional research is needed. A related question is which of these constructs comes first? Do effective change leadership strategies produce greater levels of trust in management or does trust in management produce a halo effect (see Thorndike, 1920) that can either heighten employees’ perceptions of more inclusive and participatory change leadership approaches or dampen employee perceptions of ineffective or more autocratic change leadership styles?

Additional research into the relationships between organizational change leadership and trust will be valuable.

Fourth, although the current study failed to demonstrate a direct relationship between job insecurity and commitment to change (affective commitment), the research on commitment to change has shown a consistent, positive relationship on desirable employee behaviors. For example, Neubert and Cady (2001) found associations between commitment to change and employees’ willingness to attend change-implementation

220

meeting and Meyer et al. (2007) found that commitment to change was associated with employee willingness to enthusiastically champion change rather than mere compliance.

Additional research is needed to determine the relationships between commitment to change and organizationally beneficial employee behaviors particularly in job threatening works contexts where commitment to organizational change might be particularly difficult to elicit.

Fifth, much of the current theory and research on change management treat change management and associated processes in a relatively generic way. For example,

By’s (2005) review of the most commonly used change management models showed that many of these models focused on the rate of change (primarily increasing), the means or how change comes about (planned vs. emergent), or the scale of change (transformational or incremental). This view of change management obfuscates many of the micro and individual level factors associated with the rate, means, and scale of organizational change. Change management theories that incorporate individual-level factors will add to the efficacy and usefulness of these models. Future change theoretical models should factor in individual-level factors including employee attitudes such as trust, commitment, and resistance. One example is Herold and Fedor’s (2008) Change Model which includes personal theories, egos, personal agendas, motives, and personality as influencing factors on the interpretation of content factors of organizational change.

Sixth, from a methodological perspective, research on job insecurity, change leadership, and change attitudes are particularly amenable to time considerations. For instance, in their longitudinal study of an organizational change, Meyer et al. (2007) found differences between time 1 and time 2 behavioral support for change. The results

221

suggest that longitudinal analyses and design of organizational change will be useful in

furthering understanding of employee reactions to organizational change. Also, as

suggested by Jaros (2010), Latent Growth Modeling would be useful in future analysis of

organizational change as it allows researchers to specify changes in variables over a

three-wave data collection allowing for the capture of nuances in the change process as it

unfolds over time which is particularly applicable in the study of organizational change

since it is often a dynamic, unfolding, and multi-faceted process (see Bentein,

Vandenberghe, Vandenberg, & Stinglhamber, 2005).

Finally, the current study uncovered promising results on the role change related-

self-efficacy can play in mediating negative attitudes among job insecure employees.

While previous research into self-efficacy has yielded important findings (see Bandura,

1977; Gist & Mitchell, 1992; Rafferty & Simons, 2006) more research into which specific HR/HRD interventions are associated with increased self-efficacy is needed to guide practitioners in creating training and change programs designed to both reduce resistance to potentially threatening organizational changes and increase commitment.

Recommendations for Practice

In the following section, recommendations are made based on findings and implications of this study for HRD practitioners, managers, and organizational change leaders.

The results of this study clearly demonstrate that leaders’ approaches to organizational changes initiatives influence the degree to which employees positively or negatively respond to organizational changes. The process organizational change leaders use to implement change is in important determinant of an organization’s ability to

222 respond to competitive and environmental pressures (Armenakis & Bedeian, 1999;

Herold & Fedor, 2008). In their longitudinal study of employee responses to layoffs and layoff risks over a ten year period, Grunberg et al. (2008) found that despite a culture of relatively high job insecurity, employees were able to adapt to radically different work processes, a variety of new initiatives, and changes in top management while having attitudes return to levels close to those captured at the start of the changes. In particular, they found notable decreases in employee uncertainty and intentions to quit. The researchers hypothesized that this rebound was attributable to employee perceptions that:

• The organization was doing better economically (see Harter, Hayes, & Schmidt,

2002)

• Work was more challenging and satisfying and less hierarchical (job empowering

work design /organization design)

• Workers had more control over their job tasks (Probst, 2005)

• The organization was seen as more supportive of workers (OD interventions /

transformational leadership behaviors; see Bommer et al. 2005)

• Management was seen as more competent and possessing greater integrity (see

Stanley et al, 2005; Bommer et al, 2005)

Jalajas and Bommer (1999) found that the nature of the job itself had a greater influence on employee motivation compared to the effects of past downsizing or possible future downsizing. The above findings regarding the long-term impacts of job insecurity and job threatening organizational change suggest that using HRD interventions such as leadership development programs focused developing behaviors to enhance change leaders’ trust, credibility, and ability to provide support will be beneficial. In addition,

223

employee-focused training designed to create awareness about new roles, and enhance

job skills required for new work tasks and organizational assignments may improve

employees’ self-efficacy and buffer against resistance to change attitudes particularly for

organizational changes that may result in moderate levels of job insecurity (slight job loss

anxiety) (see also Whelan-Berry, Gordon, & Hinings, 2003). Additionally, the

importance of crafting change-focused communications that enhance employees’ agency

or their ability to see possibilities about the future and act on them cannot be

underestimated. This provides options for employees to partner in co-creating the future organization and aligns to research showing that organizational change can be a positive employee experience if and when changes are perceived to offer beneficial outcomes and provide opportunities for growth and development (Kiefer, 2002; Rousseau & Tijoriwala,

1999). This approach is at the core of narrative theory (Tomm, 1989), appreciative inquiry (Cooperrider, 1990, 1995, 1996), and participation-based approaches (Probst,

2005; Schweiger & DeNisi, 1991). Such approaches may be particularly effective in organizational change settings where job insecurity may be seen as likely but are not actually planned. For example, any management communications regarding outsourcing plans are likely to be perceived by employees as job-threatening due to the connotations surrounding the term and particularly if workforce reductions have been planned or have occurred in the past. However, not all outsourcing-based initiatives result in workforce reductions, consequently, organizational leaders must be especially diligent in crafting change communications to address the very rational job loss-related concerns employees may have. As has been noted previously in the change management literature and in

Chapter 2 above, organizational change from the perspective of decision-makers is often

224

perceived quite differently from the perspective of change recipients. In order to better

link strategic organizational change goals with employee inputs, the strategic process

design model has been proposed which is focused on managing change at the work task

level (Vestergaard, 2012). According to this model, unpopular organizational changes that are likely to heighten employees’ job insecurity and decrease trust in management can be mitigated through design of change processes that are fair, inclusive, and informative. Table 5.1 provides an outline of the associated steps and linkages to the change leadership strategies analyzed in the current study. This model is particularly useful in situations in which forthcoming organizational changes may raise employee fears of job loss but no job eliminations are planned or will be minimal.

226

Table 5.1

Alignment of Change Leadership Strategies and Strategic Process Design Associated Change Leadership Strategic Process Brief Description Workplace Change Result Strategy Design Step Rational- Normative Power Empirical Reeducative Coercive (information- (participation- (top-down) based) based) High Low Medium 1) Define the context Define the context for the Clarify expectations to change, its background, purpose, employees and what employee framework and direction can expect from management Medium High Low 2) Engage employees Ask solution-focused questions Create opportunities for in development of regarding the practical employees to develop solutions, solutions experience of participants and see possibilities about the future discuss small, action-oriented and act on them, and solve steps forward important challenges High Low Medium 3) Select solutions for Explain the rationale behind Build trust and enable testing in practice decisions and why the development of future and explain rationale input/ideas of individuals will be suggestions / recommendations behind selection and selected or deselected to meet organizational de-selection challenges Low High Low 4) Engage employees Provide employees the Identify problems associated in testing the opportunity and responsibility of with possible solutions and how solutions in practice testing solutions in practice they might be solved High Medium Medium 5) Select solution for Explain process and criteria for Selection of most optimal implementation and selecting /de-selecting solutions solutions to meet organizational explain rationale for implementation including challenges behind selection and how the solutions will create de-selection value in terms of work processes and organizational strategy Note. Adapted from “Leading Unpopular Changes With Fair Process: Towards a Strategic Process Design,” by B. Vestergaard, 2012, Proceedings of the Academy of Management Conference 2012. Reprinted with permission.

226

If the elimination of jobs is unavoidable, often it will not be possible (or practical) to

involve employees in the decision-making process. In such cases, a strong communication and information-based approach will be required. In addition to making

sure that all affected employees are treated fairly and with honesty and dignity, HRD

professionals and organizational leaders should explore the following communication

strategies:

• Help prepare any widely distributed announcement of the organization’s layoff

decision. The announcement should clearly explain why the

automation/technology, outsourcing, M&A, or other job elimination factor option

was chosen.

• Schedule meetings with individual workers affected by the decision. Information

about the meetings should be provided at the time of the announcement, and the

meetings themselves should take place as soon as possible after the

announcement.

• Be prepared to give employees details (i.e., date that job reductions will occur,

etc.).

• Offer counseling or alternative job placement service to affected workers.

• Make plans to obtain additional resources for HRD/HR, should it become

overburdened temporarily with job elimination-related activities (Hayes, 2003).

Research provides the following guidelines for communication during times of corporate

transformation:

• Be consistent between messages

• Be rapid, honest, and frequent

227

• Use multiple communication channels

• Repeat common themes (Corporate Leadership Council, 2000).

Additionally, communication plans should address four considerations:

1. Audience - the message must fit the audience and be consistent with

organizational culture.

2. Timing - the message must be communicated quickly enough to thwart rumors

(see also Bordia et al., 2006).

3. Mode - the more controversial or complex the message is, the greater the need for

face-to-face communication.

4. Message - the message should be honest and unembellished (Corporate

Leadership Council, 2000).

Finally, communications about pending layoffs should be done in one-on-one meetings rather than in groups and associated job elimination messages, assuming they are true,

should convey the following themes:

• Convey to surviving employees that management does not layoff workers on a

whim.

• Demonstrate respect for the integrity of employees.

• Indicate appreciation for employee contributions.

• Illustrate management’s willingness to tackle tough HR and staff issues

(Workforce, 2001).

Change leadership strategies, trust in management, and job insecurity will be topics of

increasing importance as organizational leaders attempt to navigate organizational

change. HRD and management practices that allow organizations to become more

228 responsive to change while enabling organizational members to be as creative and innovative as possible will facilitate faster realization of organizational value. It has been observed that organizations to do not change, rather it is the people in them that must change in order for the organization to move forward. As HRD professionals and organizational change agents develop further understanding of the change process, they will be able to help their teams and organizations meet their full potential.

229

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APPENDIX B: RESEARCH PARTICIPANT COMMUNICATIONS

Research Study Invitation to Organizations to Participate:

Understanding Resistance and Commitment to Change: A Study of Employee Responses to Job Insecurity and Organizational Change Strategies

A Study for a Doctoral Dissertation Robert E. Smith, M.A. Ph.D. Candidate, Dept. of Work and Human Resource Education, College of Education and Human Development and Carlson School of Management, University of Minnesota XXX-XXX-XXXX [email protected]

Purpose of the Study: This study is intended to develop a better understanding of the factors that increase employee commitment to organizational change.

Resistance to change and lack of commitment are well-documented obstacles to successful change initiatives. There is little research outlining the most effective methods for fostering employee commitment to organizational changes. A better understanding of the process by which employees choose to commit to change can help: 1. Improve an organization’s change management processes, 2. Increase the success rate of change initiatives, and 3. Increase employee engagement around organizational changes.

Need for this study: The paradox of organizational change is that it often requires the creativity and innovation of the same employees who feel threatened by the negative effects of organizational change. Job insecurity may represent the missing link in explaining why so few change initiatives are successful. This research study will determine how effective change leadership strategies can enhance employee commitment to change.

How it will work: • Select who will participate (specific departments or complete organization) • Employees receive an email inviting their participation • Employees complete a 10-15 minute web-based survey

Benefits for Participating: • An executive summary of the results • Insight into your employee’s motivations and tendencies toward organizational change • Assess the current state of employee perceptions of managerial change strategies • Measure the effectiveness of management practices for fostering commitment and/or resistance to organizational change initiatives

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Getting Started: For additional details or to arrange to have the survey deployed in your organization, contact Robert Smith at [email protected] or (XXX) XXX-XXXX.

Confidentiality: All data gathered will be kept strictly confidential. The findings will be aggregated from all the data and will not be attributable to specific individuals or organizations. No information will be published without prior approval of participating individuals and organizations.

About the Researcher: Robert Smith is a fourth-year PhD graduate student in the University of Minnesota’s Department of Work and Human Resource Education and is a graduate of the Carlson School of Management’s Center for Human Resources & Labor Studies masters program. His professional experience includes various human resources and organizational development roles at Anheuser-Busch, Inc., Carlson Companies, and Target, Corp. where he was responsible for company-wide coaching and mentoring program development, onboarding initiatives, and survey research.

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Survey Cover Letter

Information to research participants about the purpose and voluntary nature of the study:

Hello,

My name is Robert Smith, and I am doctoral student in Human Resource Development at the University of Minnesota.

I am writing to invite you to participate in a research study investigating the nature of employee response to organizational change. I am conducting this research in completion of my Ph.D. program, and your participation is appreciated. The confidential survey takes about 10-15 minutes to complete.

Your company has elected to forward this e-mail to employees, and before you begin it is important that you are aware that:

• Participation in this study is voluntary. • There are no known psychological or physical risks to participating in this study. • All information reported in this study will remain confidential and anonymous. • As a benefit for participating, a copy of the research report can be provided, if desired • Your employer will not have access to the data. • In any report that might be published, no information will be included that makes it possible to identify individuals or organizations.

To begin the survey, please click on the following link below:

[http://...... ]

If you have any questions you may contact the researcher, Robert Smith, at [email protected], or at (614) XXX-XXXX.

The survey will remain active until [DATE].

Robert E. Smith University of Minnesota

Thank you for your participation!

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APPENDIX C: CHANGE MANAGEMENT SURVEY

List of Organizational Change Related Survey Items Job Insecurity, Change Impact, Change Related Self-Efficacy, Change Leadership Strategy, Job Satisfaction, Change Attitudes, Commitment to Change

ORGANIZATIONAL CHANGE SURVEY

This survey contains about 90 questions and is designed to capture your perceptions of organizational change in your workplace. It takes about 10 to 15 minutes to fill out this survey. This is not a test, thus there are no right or wrong answers. Please fill out ALL of the questions. There are 55 questions divided into three sections, plus some demographic information to fill out.

In the box below, please briefly describe a single recent or ongoing organizational change that has an impact on the way you perform your job. Changes could include new technology, departmental re-organizations, or a new process of doing something -but it could also be another type of change.

A change that has had an impact on the way I perform my job is…

Type of Organizational Change

Which of the following Please initiatives represents the most select significant organizational one change taking place at your workplace that you believe will have the greatest impact on your job? 1 New/revised IT, technology, or ○ software changes (e.g. automation, operating software, ERP, etc.) New quality or process ○ improvement programs (e.g. Six Sigma, TQM, Lean, etc.) Facilities changes (e.g. ○ relocation of organization’s operations, new security procedures, facility closures, etc.) Culture changes (e.g. change in ○ senior leadership or

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mission/values changes) Diversity and/or cross-cultural ○ communication initiatives New payment, compensation, ○ bonus, or benefits structure Product or service rebranding ○ Operational changes to respond ○ to new legislation, changing economic conditions, or national/international events Merger or acquisition ○ Outsourcing ○ Ownership change ○ Other (fill in the blank) ○

Significance of Change

Extremel Moderatel Slightl Neithe Slightl Moderatel Extremel y minor y minor y r y y major y major minor major Major or minor 2 How 1 2 3 4 5 6 7 significant a change would you consider this to be for your organization ?

Stage of Change

Beginning Middle End (the (the (change has change change been initiative has underway is in its recently for some final begun time but is phase and not at will soon completion) be completed 3 For the most significant ○ ○ ○ organizational change you identified, what stage of the

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change are you in?

Likelihood of Job Loss Due to Organizational change

Very Slightly Neither Slightly Very unlikely unlikely likely likely likely or unlikely 4 How likely is it that the 1 2 3 4 5 most significant organizational change you identified would result in the elimination of your job in the near future?

Type of Organizational Change (2nd)

Which of the following Please initiatives represents the second select most significant organizational one change taking place at your workplace that you believe will have the greatest impact on your job? 5 New/revised IT, technology, or ○ software changes (e.g. automation, operating software, ERP, etc.) New quality or process ○ improvement programs (e.g. Six Sigma, TQM, Lean, etc.) Facilities changes (e.g. ○ relocation of organization’s operations, new security procedures, facility closures, etc.) Culture changes (e.g. change in ○ senior leadership or mission/values changes) Diversity and/or cross-cultural ○ communication initiatives New payment, compensation, ○ bonus, or benefits structure

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Product or service rebranding ○ Operational changes to respond ○ to new legislation, changing economic conditions, or national/international events Merger or acquisition ○ Outsourcing ○ Ownership change ○ Other (fill in the blank) ○

Change Significance

Extremel Moderatel Slightl Neithe Slightl Moderatel Extremel y minor y minor y r y y major y major minor major Major or minor 6 For the 1 2 3 4 5 6 7 second major organization al change you identified, how significant a change would you consider this to be for your organization ?

Stage of Change

Beginning Middle End (the (the (change has change change been initiative is has underway in its final recently for some phase and begun time but is will soon not at be completion) completed) 7 For the most significant ○ ○ ○ organizational change you

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identified, what stage of the change are you in?

Most Likely Cause of Job Loss

If your job were to be eliminated Please in the near future, which of the select following would be the most one likely cause? 8 Site/company closure or ○ relocation, Acquisition or merger, ○ Offshoring or outsourcing, ○ Management or leadership ○ change, IT, automation, or technology ○ change, Government spending/funding ○ cuts, Decreased ○ donations/endowments Decreased sales of products or ○ services, Individual job performance, ○ Other (fill in the blank) ○

Job Insecurity scale 1 (Francis & Barling, 2005)

To what extent do you strongly disagree neither agree strongly agree or disagree with disagree agree or agree the following statements: disagree 9 I can be sure of my 1 2 3 4 5 present job as long as I do good work (R) 10 I am not really sure how 1 2 3 4 5 long my present job will last 11 I am afraid of losing my 1 2 3 4 5 present job

Job Insecurity scale 2 (Borg & Elizur, 1992)

To what extent do you strongly disagree neither agree strongly

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agree or disagree with disagree agree or agree the following statements: disagree 12 My job is secure (R) 1 2 3 4 5 13 In my opinion, I will 1 2 3 4 5 keep my job in the near future (R) 14 In my opinion, I will be 1 2 3 4 5 employed for a long time in my present job (R)

Employment Dependence (Ito & Brotheridge, 2007)

To what extent do you strongly disagree neither agree strongly agree or disagree with disagree agree or agree the following statements: disagree 15 It would be hard to find 1 2 3 4 5 employment outside of my organization 16 I do not have options 1 2 3 4 5 other than to continue to work for my current organization

Employability (Naswall et. al, 2006)

To what extent do you strongly disagree neither agree strongly agree or disagree with the disagree agree or agree following statements: disagree 17 I could get a similar (or 1 2 3 4 5 better) job without having to relocate 18 With my work 1 2 3 4 5 qualifications I can find new work relatively quickly 19 With my experience I can 1 2 3 4 5 find new work relatively quickly

Change Impact (Herscovich & Meyer, 2002)

This section Large Moderately Slightly Neither Slightly Moderately Large focuses on your negative negative negative negative positive positive positive effect effect effect or effect effect effect

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views about the positive impact of the effect most significant organizational change you identified. 20 To what extent 1 2 3 4 5 6 7 will this change affect the performance of your job? To what extent 1 2 3 4 5 6 7 will this change affect the climate in your organization? To what extent 1 2 3 4 5 6 7 will this change affect your non-work life?

Perceptions of Change Leadership Strategy (Szabla, 2007)

This section focuses Strongly Disagree Neither Agree Strongly Notes on your views about disagree somewhat somewhat agree how the most significant organizational change you identified is being managed. 15 Decisions about this 1 2 3 4 5 Rational- change are being Empirical made by experts who Strategy are extremely (using knowledgeable about information & reason to this change. lead change) 16 I have some authority 1 2 3 4 5 Normative to make decisions Re- about this change. educative Strategy (use of participative approaches to leading

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change ) 17 Those leading this 1 2 3 4 5 Power- change are playing the Coercive role of persuader. Strategy (using top- down approach & power to lead change) 18 My role in this change 1 2 3 4 5 Power- has been more passive Coercive than active. Strategy (using top- down approach & power to lead change ) 19 The pressure to 1 2 3 4 5 Rational- change put forth by Empirical those leading this Strategy change has been (using moderate. information & reason to lead change) 20 The relationship 1 2 3 4 5 Rational- between those leading Empirical this change and those Strategy responsible for (using carrying out this information & reason to change is lead professional. change) 21 Those leading this 1 2 3 4 5 Rational- change have been Empirical communicating about Strategy this change primarily (using through information & reason to lectures/presentations. lead change) 22 To get employees to 1 2 3 4 5 Rational- change, those leading Empirical this change are using Strategy logical arguments and (using factual evidence to information & reason to carry out this change. lead change)

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23 Those leading this 1 2 3 4 5 Rational- change are focusing Empirical on the facts and Strategy promoting the benefits (using of this change. information & reason to lead change) 24 The need for this 1 2 3 4 5 Rational- change was justified Empirical by experts who are Strategy knowledgeable about (using this change. information & reason to lead change) 25 Those leading this 1 2 3 4 5 Rational- change are Empirical establishing links Strategy between themselves (using and key individuals information & reason to responsible for lead carrying out this change) change. 26 Decisions about this 1 2 3 4 5 Power- change are being Coercive made by members of Strategy top management. (using top- down approach & power to lead change) 27 I have no authority to 1 2 3 4 5 Power- make decisions about Coercive this change. Strategy (using top- down approach & power to lead change) 28 Those leading this 1 2 3 4 5 Power- change are playing the Coercive role of order giver. Strategy (using top- down approach & power to lead

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change) 29 My role in this change 1 2 3 4 5 Power- has been completely Coercive passive. Strategy (using top- down approach & power to lead change) 30 The pressure to 1 2 3 4 5 Power- change put forth by Coercive those leading this Strategy change has been high. (using top- down approach & power to lead change) 31 The relationship 1 2 3 4 5 Power- between those leading Coercive this change and those Strategy responsible for (using top- carrying out this down approach & change is strained. power to lead change) 32 Those leading this 1 2 3 4 5 Power- change have been Coercive communicating about Strategy this change primarily (using top- through emails and down approach & memos. power to lead change) 33 Those leading this 1 2 3 4 5 Power- change are not Coercive focusing on how Strategy employees are (using top- accepting this change. down approach & power to lead change ) 34 To get employees to 1 2 3 4 5 Power- change, those leading Coercive this change are using Strategy their positions of (using top- down

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power and using approach & threats to implement power to this change. lead change ) 35 The need for this 1 2 3 4 5 Power- change was justified Coercive by members of top Strategy management only. (using top- down approach & power to lead change ) 36 Those leading this 1 2 3 4 5 Power- change are creating a Coercive division between Strategy themselves and those (using top- responsible for down approach & carrying out this power to change lead change ) 37 Decisions about this 1 2 3 4 5 Normative change are being Re- made by employees educative from many different Strategy levels of the (use of participative organization. approaches to leading change ) 38 I have a lot of 1 2 3 4 5 Normative authority to make Re- decisions about this educative change. Strategy (use of participative approaches to leading change ) 39 Those leading this 1 2 3 4 5 Normative change are playing the Re- role of coach. educative Strategy (use of participative approaches to leading change ) 40 My role in this change 1 2 3 4 5 Normative has been more active Re-

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than passive. educative Strategy (use of participative approaches to leading change ) 41 The pressure to 1 2 3 4 5 Normative change put forth by Re- those leading this educative change has been low. Strategy (use of participative approaches to leading change ) 42 The relationship 1 2 3 4 5 Normative between those leading Re- this change and those educative responsible for Strategy carrying out this (use of participative change is approaches collaborative. to leading change ) 43 Those leading this 1 2 3 4 5 Normative change have been Re- communicating about educative this change primarily Strategy through group (use of participative discussions. approaches to leading change ) 44 To get employees to 1 2 3 4 5 Normative change, those leading Re- this change are educative involving employees Strategy from many levels of (use of participative the organization to approaches implement this to leading change. change ) 45 Those leading this 1 2 3 4 5 Normative change are spending a Re- lot of time dealing educative with how the change Strategy is being accepted by (use of participative employees. approaches to leading

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change ) 46 The need for this 1 2 3 4 5 Normative change was justified Re- by employees from educative many levels of the Strategy organization. (use of participative approaches to leading change ) 47 Those leading this 1 2 3 4 5 Normative change are uniting Re- and creating bonds educative among everyone Strategy involved in carrying (use of participative out this change. approaches to leading change )

Change Attitudes (Oreg, 2006)

This section Strongl Moderatel Slightl Neither Slightl Moderatel Strongl focuses on y y disagree y agree y agree y agree y agree your views disagre disagre or about the e e disagre most e significant organization al change you identified. (Please select one response for each statement) 4 I am afraid of 1 2 3 4 5 6 7 8 the change. 4 I had a bad 1 2 3 4 5 6 7 9 feeling about the change. 5 I was quite 1 2 3 4 5 6 7 0 excited about the change (R) 5 The change 1 2 3 4 5 6 7 1 made me

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upset. 5 I was stressed 1 2 3 4 5 6 7 2 by the change 5 I looked for 1 2 3 4 5 6 7 3 ways to prevent the change from taking place 5 I protested 1 2 3 4 5 6 7 4 against the change 5 I complained 1 2 3 4 5 6 7 5 about the change to my colleagues or co-workers 5 I presented 1 2 3 4 5 6 7 6 my objections or concerns regarding the change to management 5 I spoke rather 1 2 3 4 5 6 7 7 highly of the change to others (R) 5 I believed 1 2 3 4 5 6 7 8 that the change would harm the way things are done in the organization. 5 I thought that 1 2 3 4 5 6 7 9 it’s a negative thing that we were going through this change 6 I believe that 1 2 3 4 5 6 7 0 the change would make my job harder 6 I believed 1 2 3 4 5 6 7 1 that the

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change would benefit the organization (R) 6 I believed 1 2 3 4 5 6 7 2 that I could personally benefit from the change (R)

Affective Commitment to Change (Herscovich & Meyer, 2002)

This section Strongl Moderatel Slightl Neither Slightl Moderatel Strongl focuses on y y disagree y agree y agree y agree y agree your views disagre disagre or about the e e disagre most e significant organization al change process you identified. (Please select one response for each statement) 6 I believe in 1 2 3 4 5 6 7 3 the value of this change. 6 This change 1 2 3 4 5 6 7 4 is a good strategy for this organization. 6 I think that 1 2 3 4 5 6 7 5 management is making a mistake by introducing this change. 6 This change 1 2 3 4 5 6 7 6 serves an important purpose.

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6 Things would 1 2 3 4 5 6 7 7 be better without this change. 6 This change 1 2 3 4 5 6 7 8 is not necessary.

Change Related Self-Efficacy (Ashford, 1988)

This section Strongl Moderatel Slightl Neither Slightl Moderatel Strongl focuses on y y disagree y agree y agree y agree y agree your views disagre disagre or about the e e disagre most e significant organization al change you identified. (Please select one response for each statement) 6 Wherever the 1 2 3 4 5 6 7 9 change takes me, I’m sure I can handle it. 7 I get nervous 1 2 3 4 5 6 7 0 that I may not be able to do all that is demanded of me of me by the change (R) 7 I have reason 1 2 3 4 5 6 7 1 to believe I may not perform well in my job situation following the change (R)

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7 Though I 1 2 3 4 5 6 7 2 may need some training, I have little doubt I can perform well following the change (R)

Job Satisfaction (Cammann et al., 1983)

This section focuses on Strongly Disagree Tend to Tend Agree Strongly your views about your disagree disagree to agree job. (Please select one agree response for each statement) 73 All in all, I am satisfied 1 2 3 4 5 6 with my job. 74 In general, I don’t like 1 2 3 4 5 6 my job. (R) 75 In general, I like 1 2 3 4 5 6 working here.

Trust in Management

This section focuses on Strongly Disagree Tend to Tend Agree Strongly your views about disagree disagree to agree management at your agree organization. (Please select one response for each statement) 76 If I was given a choice, 1 2 3 4 5 6 I would not allow management to make decisions concerning employee well-being (R) 77 I am willing to follow 1 2 3 4 5 6 management’s lead even in risky situations 78 I trust management to 1 2 3 4 5 6 make the right decisions in situation

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that affect me personally 79 When it comes to 1 2 3 4 5 6 making decisions that affect me, I have as much or more faith in management’s judgment as I would in my own 80 Even if a bad decision 1 2 3 4 5 6 could have very negative consequences for me, would trust management’s judgment

The following questions include demographic information about you. Please select the item that best describes you.

Demographic Variables (9 items)

Gender

Female Male 81 Gender 1 2

Age

What is your age range? Please select one 82 Under 20 ○ Between 20-24 ○ Between 25-29 ○ Between 30-34 ○ Between 35-39 ○ Between 40-44 ○ Between 45-49 ○ Between 50-54 ○ Between 55-59 ○ Between 60 or older ○

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Race

Racially/ethnically do you Please consider yourself? select one 83 White or Caucasian ○ Black or African American ○ Asian or Pacific Islander ○ Native American or Alaskan ○ Native Mixed racial background ○ Other race ○ Decline to answer ○

Marital Status

Married Not Married 84 What is your marital status 1 2 (optional)

Education

What is the highest level of Please education you have completed? select one 85 high school diploma ○ some college ○ two year degree/certificate ○ four year college degree ○ master's degree ○ doctoral degree ○ other (fill in the blank) ○

Years in Organization

Please fill in the blank 86 How long have you been working in your current

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organization?

Level in Organization

Please select which of the Please following most closely select describes your level in your one organization? 87 contractor ○ employee (non-supervisory) ○ supervisor ○ manager ○ senior manager/executive ○

Role or Function in Organization

Please select which of the Please following most closely describes select your level in your organization? one 88 Marketing/Sales ○ IT/Software/Hardware ○ Manufacturing/Operations ○ Administrative ○ (Planning/Finance/HR/Legal) Research/Development ○ Engineering ○ Management/Leadership ○ Consultant ○ Other (fill in the blank) ○

Additional Comments

If you have any additional Fill in comments or things you would the like to share, please note them blank here: here Thank you!